From f7e112bb6ee55af21130016a1f7ff2aa964fdb8e Mon Sep 17 00:00:00 2001
From: The Code Gnome <thecodegnome@outlook.com>
Date: Tue, 12 May 2026 19:44:21 -0400
Subject: [PATCH 1/3] session 01 live code

---
 .../notebooks/live-code-5-12-2026.ipynb       | 3854 +++++++++++++++++
 1 file changed, 3854 insertions(+)
 create mode 100644 01_materials/notebooks/live-code-5-12-2026.ipynb

diff --git a/01_materials/notebooks/live-code-5-12-2026.ipynb b/01_materials/notebooks/live-code-5-12-2026.ipynb
new file mode 100644
index 000000000..4714d66e4
--- /dev/null
+++ b/01_materials/notebooks/live-code-5-12-2026.ipynb
@@ -0,0 +1,3854 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a45182a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "from mpl_toolkits import mplot3d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0a59dd01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer = pd.read_csv('dataset/wdbc.csv')\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7aac4135",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<StringArray>\n",
+       "['M', 'B']\n",
+       "Length: 2, dtype: str"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9484a183",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant        17.99         10.38          122.80   \n",
+       "1      842517  Malignant        20.57         17.77          132.90   \n",
+       "2    84300903  Malignant        19.69         21.25          130.00   \n",
+       "3    84348301  Malignant        11.42         20.38           77.58   \n",
+       "4    84358402  Malignant        20.29         14.34          135.10   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant        21.56         22.39          142.00   \n",
+       "565    926682  Malignant        20.13         28.25          131.20   \n",
+       "566    926954  Malignant        16.60         28.08          108.30   \n",
+       "567    927241  Malignant        20.60         29.33          140.10   \n",
+       "568     92751     Benign         7.76         24.54           47.92   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0       1001.0          0.11840           0.27760         0.30010   \n",
+       "1       1326.0          0.08474           0.07864         0.08690   \n",
+       "2       1203.0          0.10960           0.15990         0.19740   \n",
+       "3        386.1          0.14250           0.28390         0.24140   \n",
+       "4       1297.0          0.10030           0.13280         0.19800   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564     1479.0          0.11100           0.11590         0.24390   \n",
+       "565     1261.0          0.09780           0.10340         0.14400   \n",
+       "566      858.1          0.08455           0.10230         0.09251   \n",
+       "567     1265.0          0.11780           0.27700         0.35140   \n",
+       "568      181.0          0.05263           0.04362         0.00000   \n",
+       "\n",
+       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
+       "0                0.14710  ...        25.380          17.33           184.60   \n",
+       "1                0.07017  ...        24.990          23.41           158.80   \n",
+       "2                0.12790  ...        23.570          25.53           152.50   \n",
+       "3                0.10520  ...        14.910          26.50            98.87   \n",
+       "4                0.10430  ...        22.540          16.67           152.20   \n",
+       "..                   ...  ...           ...            ...              ...   \n",
+       "564              0.13890  ...        25.450          26.40           166.10   \n",
+       "565              0.09791  ...        23.690          38.25           155.00   \n",
+       "566              0.05302  ...        18.980          34.12           126.70   \n",
+       "567              0.15200  ...        25.740          39.42           184.60   \n",
+       "568              0.00000  ...         9.456          30.37            59.16   \n",
+       "\n",
+       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0        2019.0           0.16220            0.66560           0.7119   \n",
+       "1        1956.0           0.12380            0.18660           0.2416   \n",
+       "2        1709.0           0.14440            0.42450           0.4504   \n",
+       "3         567.7           0.20980            0.86630           0.6869   \n",
+       "4        1575.0           0.13740            0.20500           0.4000   \n",
+       "..          ...               ...                ...              ...   \n",
+       "564      2027.0           0.14100            0.21130           0.4107   \n",
+       "565      1731.0           0.11660            0.19220           0.3215   \n",
+       "566      1124.0           0.11390            0.30940           0.3403   \n",
+       "567      1821.0           0.16500            0.86810           0.9387   \n",
+       "568       268.6           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer['diagnosis'] = cancer['diagnosis'].replace(\n",
+    "    {\n",
+    "        'M':\"Malignant\",\n",
+    "        'B':\"Benign\"\n",
+    "    }\n",
+    ")\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a0fc1cd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diagnosis\n",
+       "Benign       0.627417\n",
+       "Malignant    0.372583\n",
+       "Name: proportion, dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer['diagnosis'].value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "194f8a4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n",
+    "\n",
+    "# Plot\n",
+    "plt.scatter(cancer[\"perimeter_mean\"], cancer['concavity_mean'], \n",
+    "            color=cancer[\"diagnosis\"].map(color_map))\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Perimeter Mean')\n",
+    "plt.ylabel('Concavity Mean')\n",
+    "plt.title('Scatter Plot of Perimeter Mean vs Concavity Mean')\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1cdfd1ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot existing data\n",
+    "plt.scatter(cancer[\"perimeter_mean\"], cancer['concavity_mean'], \n",
+    "            color=cancer[\"diagnosis\"].map(color_map))\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add new observation\n",
+    "new_observation = {'perimeter_mean': 97, 'concavity_mean': 0.20}\n",
+    "plt.scatter(new_observation['perimeter_mean'], new_observation['concavity_mean'],\n",
+    "            color='red', edgecolor='black', s=100, label='New Observation')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Perimeter Mean')\n",
+    "plt.ylabel('Concavity Mean')\n",
+    "plt.title('Scatter Plot of Perimeter Mean vs Concavity Mean')\n",
+    "plt.legend(handles=handles + [plt.Line2D([0], [0], marker='o', color='w', \n",
+    "                                          markerfacecolor='red', markeredgecolor='black', \n",
+    "                                          markersize=10, label='New Observation')], \n",
+    "           title='Diagnosis')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4cdf86a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_obs_Concavity = 0.2\n",
+    "new_obs_Perimeter = 97"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "47a8aab8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "      <th>distance_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>25.800194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "      <td>35.900178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "      <td>33.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "      <td>19.420044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "      <td>38.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "      <td>45.000021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "      <td>34.200046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "      <td>11.300511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "      <td>43.100266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "      <td>49.080407</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant        17.99         10.38          122.80   \n",
+       "1      842517  Malignant        20.57         17.77          132.90   \n",
+       "2    84300903  Malignant        19.69         21.25          130.00   \n",
+       "3    84348301  Malignant        11.42         20.38           77.58   \n",
+       "4    84358402  Malignant        20.29         14.34          135.10   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant        21.56         22.39          142.00   \n",
+       "565    926682  Malignant        20.13         28.25          131.20   \n",
+       "566    926954  Malignant        16.60         28.08          108.30   \n",
+       "567    927241  Malignant        20.60         29.33          140.10   \n",
+       "568     92751     Benign         7.76         24.54           47.92   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0       1001.0          0.11840           0.27760         0.30010   \n",
+       "1       1326.0          0.08474           0.07864         0.08690   \n",
+       "2       1203.0          0.10960           0.15990         0.19740   \n",
+       "3        386.1          0.14250           0.28390         0.24140   \n",
+       "4       1297.0          0.10030           0.13280         0.19800   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564     1479.0          0.11100           0.11590         0.24390   \n",
+       "565     1261.0          0.09780           0.10340         0.14400   \n",
+       "566      858.1          0.08455           0.10230         0.09251   \n",
+       "567     1265.0          0.11780           0.27700         0.35140   \n",
+       "568      181.0          0.05263           0.04362         0.00000   \n",
+       "\n",
+       "     concave points_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0                0.14710  ...          17.33           184.60      2019.0   \n",
+       "1                0.07017  ...          23.41           158.80      1956.0   \n",
+       "2                0.12790  ...          25.53           152.50      1709.0   \n",
+       "3                0.10520  ...          26.50            98.87       567.7   \n",
+       "4                0.10430  ...          16.67           152.20      1575.0   \n",
+       "..                   ...  ...            ...              ...         ...   \n",
+       "564              0.13890  ...          26.40           166.10      2027.0   \n",
+       "565              0.09791  ...          38.25           155.00      1731.0   \n",
+       "566              0.05302  ...          34.12           126.70      1124.0   \n",
+       "567              0.15200  ...          39.42           184.60      1821.0   \n",
+       "568              0.00000  ...          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
+       "0                  0.2654          0.4601                  0.11890   \n",
+       "1                  0.1860          0.2750                  0.08902   \n",
+       "2                  0.2430          0.3613                  0.08758   \n",
+       "3                  0.2575          0.6638                  0.17300   \n",
+       "4                  0.1625          0.2364                  0.07678   \n",
+       "..                    ...             ...                      ...   \n",
+       "564                0.2216          0.2060                  0.07115   \n",
+       "565                0.1628          0.2572                  0.06637   \n",
+       "566                0.1418          0.2218                  0.07820   \n",
+       "567                0.2650          0.4087                  0.12400   \n",
+       "568                0.0000          0.2871                  0.07039   \n",
+       "\n",
+       "     distance_from_new  \n",
+       "0            25.800194  \n",
+       "1            35.900178  \n",
+       "2            33.000000  \n",
+       "3            19.420044  \n",
+       "4            38.100000  \n",
+       "..                 ...  \n",
+       "564          45.000021  \n",
+       "565          34.200046  \n",
+       "566          11.300511  \n",
+       "567          43.100266  \n",
+       "568          49.080407  \n",
+       "\n",
+       "[569 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer['distance_from_new'] = (\n",
+    "    (cancer['perimeter_mean'] - new_obs_Perimeter)**2 +\n",
+    "    (cancer['concavity_mean'] - new_obs_Concavity)**2\n",
+    ")**(1/2)\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a463d8c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "      <th>distance_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>8915</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>14.96</td>\n",
+       "      <td>19.10</td>\n",
+       "      <td>97.03</td>\n",
+       "      <td>687.3</td>\n",
+       "      <td>0.08992</td>\n",
+       "      <td>0.09823</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>0.04819</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.19</td>\n",
+       "      <td>109.1</td>\n",
+       "      <td>809.8</td>\n",
+       "      <td>0.1313</td>\n",
+       "      <td>0.3030</td>\n",
+       "      <td>0.1804</td>\n",
+       "      <td>0.1489</td>\n",
+       "      <td>0.2962</td>\n",
+       "      <td>0.08472</td>\n",
+       "      <td>0.143765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>868826</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.95</td>\n",
+       "      <td>17.57</td>\n",
+       "      <td>96.85</td>\n",
+       "      <td>678.1</td>\n",
+       "      <td>0.11670</td>\n",
+       "      <td>0.13050</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>0.08624</td>\n",
+       "      <td>...</td>\n",
+       "      <td>21.43</td>\n",
+       "      <td>121.4</td>\n",
+       "      <td>971.4</td>\n",
+       "      <td>0.1411</td>\n",
+       "      <td>0.2164</td>\n",
+       "      <td>0.3355</td>\n",
+       "      <td>0.1667</td>\n",
+       "      <td>0.3414</td>\n",
+       "      <td>0.07147</td>\n",
+       "      <td>0.156924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>84799002</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.54</td>\n",
+       "      <td>27.54</td>\n",
+       "      <td>96.73</td>\n",
+       "      <td>658.8</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.15950</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>0.07364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>37.13</td>\n",
+       "      <td>124.1</td>\n",
+       "      <td>943.2</td>\n",
+       "      <td>0.1678</td>\n",
+       "      <td>0.6577</td>\n",
+       "      <td>0.7026</td>\n",
+       "      <td>0.1712</td>\n",
+       "      <td>0.4218</td>\n",
+       "      <td>0.13410</td>\n",
+       "      <td>0.272403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>91594602</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.05</td>\n",
+       "      <td>19.07</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>701.9</td>\n",
+       "      <td>0.09215</td>\n",
+       "      <td>0.08597</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>0.04335</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28.06</td>\n",
+       "      <td>113.8</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>0.1246</td>\n",
+       "      <td>0.2101</td>\n",
+       "      <td>0.2866</td>\n",
+       "      <td>0.1120</td>\n",
+       "      <td>0.2282</td>\n",
+       "      <td>0.06954</td>\n",
+       "      <td>0.288548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>857438</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.10</td>\n",
+       "      <td>22.02</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>712.8</td>\n",
+       "      <td>0.09056</td>\n",
+       "      <td>0.07081</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>0.03334</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31.69</td>\n",
+       "      <td>117.7</td>\n",
+       "      <td>1030.0</td>\n",
+       "      <td>0.1389</td>\n",
+       "      <td>0.2057</td>\n",
+       "      <td>0.2712</td>\n",
+       "      <td>0.1530</td>\n",
+       "      <td>0.2675</td>\n",
+       "      <td>0.07873</td>\n",
+       "      <td>0.298910</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "291      8915     Benign        14.96         19.10           97.03   \n",
+       "138    868826  Malignant        14.95         17.57           96.85   \n",
+       "15   84799002  Malignant        14.54         27.54           96.73   \n",
+       "514  91594602  Malignant        15.05         19.07           97.26   \n",
+       "54     857438  Malignant        15.10         22.02           97.26   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "291      687.3          0.08992           0.09823         0.05940   \n",
+       "138      678.1          0.11670           0.13050         0.15390   \n",
+       "15       658.8          0.11390           0.15950         0.16390   \n",
+       "514      701.9          0.09215           0.08597         0.07486   \n",
+       "54       712.8          0.09056           0.07081         0.05253   \n",
+       "\n",
+       "     concave points_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+       "291              0.04819  ...          26.19            109.1       809.8   \n",
+       "138              0.08624  ...          21.43            121.4       971.4   \n",
+       "15               0.07364  ...          37.13            124.1       943.2   \n",
+       "514              0.04335  ...          28.06            113.8       967.0   \n",
+       "54               0.03334  ...          31.69            117.7      1030.0   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "291            0.1313             0.3030           0.1804   \n",
+       "138            0.1411             0.2164           0.3355   \n",
+       "15             0.1678             0.6577           0.7026   \n",
+       "514            0.1246             0.2101           0.2866   \n",
+       "54             0.1389             0.2057           0.2712   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
+       "291                0.1489          0.2962                  0.08472   \n",
+       "138                0.1667          0.3414                  0.07147   \n",
+       "15                 0.1712          0.4218                  0.13410   \n",
+       "514                0.1120          0.2282                  0.06954   \n",
+       "54                 0.1530          0.2675                  0.07873   \n",
+       "\n",
+       "     distance_from_new  \n",
+       "291           0.143765  \n",
+       "138           0.156924  \n",
+       "15            0.272403  \n",
+       "514           0.288548  \n",
+       "54            0.298910  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# k=5\n",
+    "cancer.nsmallest(5, 'distance_from_new')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0b719e80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'matplotlib.pyplot' from 'c:\\\\Users\\\\Mark\\\\src\\\\gh\\\\lcr\\\\lcr-env\\\\Lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n",
+    "\n",
+    "# Create a 3D plot\n",
+    "ax = plt.axes(projection=\"3d\")\n",
+    "\n",
+    "# Plot data points with color corresponding to diagnosis\n",
+    "sc = ax.scatter3D(cancer['perimeter_mean'], cancer['concavity_mean'], cancer['symmetry_mean'],\n",
+    "                  c=cancer['diagnosis'].map(color_map), marker='o')\n",
+    "\n",
+    "# Define the new observation\n",
+    "new_observation = {'perimeter_mean': 97, 'concavity_mean': 0.20, 'symmetry_mean': 0.22}\n",
+    "\n",
+    "# Plot the new observation\n",
+    "ax.scatter3D(new_observation['perimeter_mean'], new_observation['concavity_mean'],\n",
+    "             new_observation['symmetry_mean'], color='red', edgecolor='black',\n",
+    "             s=100, marker='o', label='New Observation')\n",
+    "\n",
+    "# Add axis labels\n",
+    "ax.set_xlabel('Perimeter Mean')\n",
+    "ax.set_ylabel('Concavity Mean')\n",
+    "ax.set_zlabel('Symmetry Mean')\n",
+    "ax.set_title('3D Scatter Plot of Perimeter Mean, Concavity Mean, and Symmetry Mean')\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "                      for label in labels]\n",
+    "\n",
+    "# Add custom legend for new observation\n",
+    "handles.append(plt.Line2D([0], [0], marker='o', color='red', label='New Observation',\n",
+    "                          markersize=10, markeredgecolor='black'))\n",
+    "\n",
+    "# Add legend\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "\n",
+    "# Show plot\n",
+    "plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d9e998a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_obs_Perimeter = 97\n",
+    "new_obs_Concavity = 0.2\n",
+    "new_obs_Symmetry = 0.22"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9993eef2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer['dist_from_new'] = cancer['dist_from_new'] = (\n",
+    "    (\n",
+    "        (cancer['perimeter_mean'] - new_obs_Perimeter)**2 -\n",
+    "        (cancer['concavity_mean'] - new_obs_Concavity)**2 -\n",
+    "        (cancer['symmetry_mean'] - new_obs_Symmetry)**2\n",
+    "    )\n",
+    ")**(1/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0cab03a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "      <th>distance_from_new</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>868826</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.95</td>\n",
+       "      <td>17.57</td>\n",
+       "      <td>96.85</td>\n",
+       "      <td>678.1</td>\n",
+       "      <td>0.11670</td>\n",
+       "      <td>0.13050</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>0.08624</td>\n",
+       "      <td>...</td>\n",
+       "      <td>121.4</td>\n",
+       "      <td>971.4</td>\n",
+       "      <td>0.1411</td>\n",
+       "      <td>0.21640</td>\n",
+       "      <td>0.3355</td>\n",
+       "      <td>0.16670</td>\n",
+       "      <td>0.3414</td>\n",
+       "      <td>0.07147</td>\n",
+       "      <td>0.156924</td>\n",
+       "      <td>0.140657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>857438</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.10</td>\n",
+       "      <td>22.02</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>712.8</td>\n",
+       "      <td>0.09056</td>\n",
+       "      <td>0.07081</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>0.03334</td>\n",
+       "      <td>...</td>\n",
+       "      <td>117.7</td>\n",
+       "      <td>1030.0</td>\n",
+       "      <td>0.1389</td>\n",
+       "      <td>0.20570</td>\n",
+       "      <td>0.2712</td>\n",
+       "      <td>0.15300</td>\n",
+       "      <td>0.2675</td>\n",
+       "      <td>0.07873</td>\n",
+       "      <td>0.298910</td>\n",
+       "      <td>0.206015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>91594602</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.05</td>\n",
+       "      <td>19.07</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>701.9</td>\n",
+       "      <td>0.09215</td>\n",
+       "      <td>0.08597</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>0.04335</td>\n",
+       "      <td>...</td>\n",
+       "      <td>113.8</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>0.1246</td>\n",
+       "      <td>0.21010</td>\n",
+       "      <td>0.2866</td>\n",
+       "      <td>0.11200</td>\n",
+       "      <td>0.2282</td>\n",
+       "      <td>0.06954</td>\n",
+       "      <td>0.288548</td>\n",
+       "      <td>0.218762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>905680</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.13</td>\n",
+       "      <td>29.81</td>\n",
+       "      <td>96.71</td>\n",
+       "      <td>719.5</td>\n",
+       "      <td>0.08320</td>\n",
+       "      <td>0.04605</td>\n",
+       "      <td>0.04686</td>\n",
+       "      <td>0.02739</td>\n",
+       "      <td>...</td>\n",
+       "      <td>110.1</td>\n",
+       "      <td>931.4</td>\n",
+       "      <td>0.1148</td>\n",
+       "      <td>0.09866</td>\n",
+       "      <td>0.1547</td>\n",
+       "      <td>0.06575</td>\n",
+       "      <td>0.3233</td>\n",
+       "      <td>0.06165</td>\n",
+       "      <td>0.327951</td>\n",
+       "      <td>0.243797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>84799002</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.54</td>\n",
+       "      <td>27.54</td>\n",
+       "      <td>96.73</td>\n",
+       "      <td>658.8</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.15950</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>0.07364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>124.1</td>\n",
+       "      <td>943.2</td>\n",
+       "      <td>0.1678</td>\n",
+       "      <td>0.65770</td>\n",
+       "      <td>0.7026</td>\n",
+       "      <td>0.17120</td>\n",
+       "      <td>0.4218</td>\n",
+       "      <td>0.13410</td>\n",
+       "      <td>0.272403</td>\n",
+       "      <td>0.267377</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 34 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "138    868826  Malignant        14.95         17.57           96.85   \n",
+       "54     857438  Malignant        15.10         22.02           97.26   \n",
+       "514  91594602  Malignant        15.05         19.07           97.26   \n",
+       "414    905680  Malignant        15.13         29.81           96.71   \n",
+       "15   84799002  Malignant        14.54         27.54           96.73   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "138      678.1          0.11670           0.13050         0.15390   \n",
+       "54       712.8          0.09056           0.07081         0.05253   \n",
+       "514      701.9          0.09215           0.08597         0.07486   \n",
+       "414      719.5          0.08320           0.04605         0.04686   \n",
+       "15       658.8          0.11390           0.15950         0.16390   \n",
+       "\n",
+       "     concave points_mean  ...  perimeter_worst  area_worst  smoothness_worst  \\\n",
+       "138              0.08624  ...            121.4       971.4            0.1411   \n",
+       "54               0.03334  ...            117.7      1030.0            0.1389   \n",
+       "514              0.04335  ...            113.8       967.0            0.1246   \n",
+       "414              0.02739  ...            110.1       931.4            0.1148   \n",
+       "15               0.07364  ...            124.1       943.2            0.1678   \n",
+       "\n",
+       "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
+       "138            0.21640           0.3355               0.16670          0.3414   \n",
+       "54             0.20570           0.2712               0.15300          0.2675   \n",
+       "514            0.21010           0.2866               0.11200          0.2282   \n",
+       "414            0.09866           0.1547               0.06575          0.3233   \n",
+       "15             0.65770           0.7026               0.17120          0.4218   \n",
+       "\n",
+       "     fractal_dimension_worst  distance_from_new  dist_from_new  \n",
+       "138                  0.07147           0.156924       0.140657  \n",
+       "54                   0.07873           0.298910       0.206015  \n",
+       "514                  0.06954           0.288548       0.218762  \n",
+       "414                  0.06165           0.327951       0.243797  \n",
+       "15                   0.13410           0.272403       0.267377  \n",
+       "\n",
+       "[5 rows x 34 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer.nsmallest(5,'dist_from_new')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "bfbf6c5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import set_config\n",
+    "# output are dataframes instead of arrays\n",
+    "set_config(transform_output=\"pandas\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "88a4f9ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "39a19e1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>0.30010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>0.08690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>0.19740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>0.24140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>0.19800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>0.24390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>0.14400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>0.09251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>0.35140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     diagnosis  perimeter_mean  concavity_mean\n",
+       "0    Malignant          122.80         0.30010\n",
+       "1    Malignant          132.90         0.08690\n",
+       "2    Malignant          130.00         0.19740\n",
+       "3    Malignant           77.58         0.24140\n",
+       "4    Malignant          135.10         0.19800\n",
+       "..         ...             ...             ...\n",
+       "564  Malignant          142.00         0.24390\n",
+       "565  Malignant          131.20         0.14400\n",
+       "566  Malignant          108.30         0.09251\n",
+       "567  Malignant          140.10         0.35140\n",
+       "568     Benign           47.92         0.00000\n",
+       "\n",
+       "[569 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_train = cancer[[\"diagnosis\", \"perimeter_mean\", \"concavity_mean\"]]\n",
+    "cancer_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c22a3981",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
+       "            n_neighbors\n",
+       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
+       "            algorithm\n",
+       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
+       "            leaf_size\n",
+       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
+       "            p\n",
+       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
+       "            metric\n",
+       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
+       "            metric_params\n",
+       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-1');</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Step 1. define model\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "dcb16511",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 2. define predictor and response vars\n",
+    "x = cancer_train[['perimeter_mean','concavity_mean']]\n",
+    "y = cancer_train['diagnosis']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "907a658f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
+       "            n_neighbors\n",
+       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
+       "            algorithm\n",
+       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
+       "            leaf_size\n",
+       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
+       "            p\n",
+       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
+       "            metric\n",
+       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
+       "            metric_params\n",
+       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-2');</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Step 3. fit model to data\n",
+    "knn.fit(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d7bde363",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>97</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   perimeter_mean  concavity_mean\n",
+       "0              97             0.2"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# step 4. make predictions\n",
+    "new_obs = pd.DataFrame({\n",
+    "    'perimeter_mean':[97],\n",
+    "    'concavity_mean':[0.2]\n",
+    "})\n",
+    "new_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "58e57ca2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant'], dtype=object)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.predict(new_obs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 021db264a2a737a64897a4e44cc40c8a4c886023 Mon Sep 17 00:00:00 2001
From: The Code Gnome <thecodegnome@outlook.com>
Date: Wed, 13 May 2026 19:38:37 -0400
Subject: [PATCH 2/3] session 02 python code

---
 .../notebooks/live-code-05-13-2026.ipynb      | 4786 +++++++++++++++++
 1 file changed, 4786 insertions(+)
 create mode 100644 01_materials/notebooks/live-code-05-13-2026.ipynb

diff --git a/01_materials/notebooks/live-code-05-13-2026.ipynb b/01_materials/notebooks/live-code-05-13-2026.ipynb
new file mode 100644
index 000000000..5e4fa9154
--- /dev/null
+++ b/01_materials/notebooks/live-code-05-13-2026.ipynb
@@ -0,0 +1,4786 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "951637cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import recall_score, precision_score\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "211260a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer = pd.read_csv('dataset/wdbc.csv')\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d9875a1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<StringArray>\n",
+       "['Malignant', 'Benign']\n",
+       "Length: 2, dtype: str"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"] = cancer[\"diagnosis\"].replace({\n",
+    "    \"M\" : \"Malignant\",\n",
+    "    \"B\" : \"Benign\"\n",
+    "})\n",
+    "\n",
+    "cancer[\"diagnosis\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "468e162a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.097064</td>\n",
+       "      <td>-2.073335</td>\n",
+       "      <td>1.269934</td>\n",
+       "      <td>0.984375</td>\n",
+       "      <td>1.568466</td>\n",
+       "      <td>3.283515</td>\n",
+       "      <td>2.652874</td>\n",
+       "      <td>2.532475</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.886690</td>\n",
+       "      <td>-1.359293</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>2.001237</td>\n",
+       "      <td>1.307686</td>\n",
+       "      <td>2.616665</td>\n",
+       "      <td>2.109526</td>\n",
+       "      <td>2.296076</td>\n",
+       "      <td>2.750622</td>\n",
+       "      <td>1.937015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.829821</td>\n",
+       "      <td>-0.353632</td>\n",
+       "      <td>1.685955</td>\n",
+       "      <td>1.908708</td>\n",
+       "      <td>-0.826962</td>\n",
+       "      <td>-0.487072</td>\n",
+       "      <td>-0.023846</td>\n",
+       "      <td>0.548144</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.805927</td>\n",
+       "      <td>-0.369203</td>\n",
+       "      <td>1.535126</td>\n",
+       "      <td>1.890489</td>\n",
+       "      <td>-0.375612</td>\n",
+       "      <td>-0.430444</td>\n",
+       "      <td>-0.146749</td>\n",
+       "      <td>1.087084</td>\n",
+       "      <td>-0.243890</td>\n",
+       "      <td>0.281190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.579888</td>\n",
+       "      <td>0.456187</td>\n",
+       "      <td>1.566503</td>\n",
+       "      <td>1.558884</td>\n",
+       "      <td>0.942210</td>\n",
+       "      <td>1.052926</td>\n",
+       "      <td>1.363478</td>\n",
+       "      <td>2.037231</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.511870</td>\n",
+       "      <td>-0.023974</td>\n",
+       "      <td>1.347475</td>\n",
+       "      <td>1.456285</td>\n",
+       "      <td>0.527407</td>\n",
+       "      <td>1.082932</td>\n",
+       "      <td>0.854974</td>\n",
+       "      <td>1.955000</td>\n",
+       "      <td>1.152255</td>\n",
+       "      <td>0.201391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>-0.768909</td>\n",
+       "      <td>0.253732</td>\n",
+       "      <td>-0.592687</td>\n",
+       "      <td>-0.764464</td>\n",
+       "      <td>3.283553</td>\n",
+       "      <td>3.402909</td>\n",
+       "      <td>1.915897</td>\n",
+       "      <td>1.451707</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.281464</td>\n",
+       "      <td>0.133984</td>\n",
+       "      <td>-0.249939</td>\n",
+       "      <td>-0.550021</td>\n",
+       "      <td>3.394275</td>\n",
+       "      <td>3.893397</td>\n",
+       "      <td>1.989588</td>\n",
+       "      <td>2.175786</td>\n",
+       "      <td>6.046041</td>\n",
+       "      <td>4.935010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.750297</td>\n",
+       "      <td>-1.151816</td>\n",
+       "      <td>1.776573</td>\n",
+       "      <td>1.826229</td>\n",
+       "      <td>0.280372</td>\n",
+       "      <td>0.539340</td>\n",
+       "      <td>1.371011</td>\n",
+       "      <td>1.428493</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.298575</td>\n",
+       "      <td>-1.466770</td>\n",
+       "      <td>1.338539</td>\n",
+       "      <td>1.220724</td>\n",
+       "      <td>0.220556</td>\n",
+       "      <td>-0.313395</td>\n",
+       "      <td>0.613179</td>\n",
+       "      <td>0.729259</td>\n",
+       "      <td>-0.868353</td>\n",
+       "      <td>-0.397100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>2.110995</td>\n",
+       "      <td>0.721473</td>\n",
+       "      <td>2.060786</td>\n",
+       "      <td>2.343856</td>\n",
+       "      <td>1.041842</td>\n",
+       "      <td>0.219060</td>\n",
+       "      <td>1.947285</td>\n",
+       "      <td>2.320965</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.901185</td>\n",
+       "      <td>0.117700</td>\n",
+       "      <td>1.752563</td>\n",
+       "      <td>2.015301</td>\n",
+       "      <td>0.378365</td>\n",
+       "      <td>-0.273318</td>\n",
+       "      <td>0.664512</td>\n",
+       "      <td>1.629151</td>\n",
+       "      <td>-1.360158</td>\n",
+       "      <td>-0.709091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.704854</td>\n",
+       "      <td>2.085134</td>\n",
+       "      <td>1.615931</td>\n",
+       "      <td>1.723842</td>\n",
+       "      <td>0.102458</td>\n",
+       "      <td>-0.017833</td>\n",
+       "      <td>0.693043</td>\n",
+       "      <td>1.263669</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.536720</td>\n",
+       "      <td>2.047399</td>\n",
+       "      <td>1.421940</td>\n",
+       "      <td>1.494959</td>\n",
+       "      <td>-0.691230</td>\n",
+       "      <td>-0.394820</td>\n",
+       "      <td>0.236573</td>\n",
+       "      <td>0.733827</td>\n",
+       "      <td>-0.531855</td>\n",
+       "      <td>-0.973978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.702284</td>\n",
+       "      <td>2.045574</td>\n",
+       "      <td>0.672676</td>\n",
+       "      <td>0.577953</td>\n",
+       "      <td>-0.840484</td>\n",
+       "      <td>-0.038680</td>\n",
+       "      <td>0.046588</td>\n",
+       "      <td>0.105777</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.561361</td>\n",
+       "      <td>1.374854</td>\n",
+       "      <td>0.579001</td>\n",
+       "      <td>0.427906</td>\n",
+       "      <td>-0.809587</td>\n",
+       "      <td>0.350735</td>\n",
+       "      <td>0.326767</td>\n",
+       "      <td>0.414069</td>\n",
+       "      <td>-1.104549</td>\n",
+       "      <td>-0.318409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.838341</td>\n",
+       "      <td>2.336457</td>\n",
+       "      <td>1.982524</td>\n",
+       "      <td>1.735218</td>\n",
+       "      <td>1.525767</td>\n",
+       "      <td>3.272144</td>\n",
+       "      <td>3.296944</td>\n",
+       "      <td>2.658866</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.961239</td>\n",
+       "      <td>2.237926</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>1.653171</td>\n",
+       "      <td>1.430427</td>\n",
+       "      <td>3.904848</td>\n",
+       "      <td>3.197605</td>\n",
+       "      <td>2.289985</td>\n",
+       "      <td>1.919083</td>\n",
+       "      <td>2.219635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-1.808401</td>\n",
+       "      <td>1.221792</td>\n",
+       "      <td>-1.814389</td>\n",
+       "      <td>-1.347789</td>\n",
+       "      <td>-3.112085</td>\n",
+       "      <td>-1.150752</td>\n",
+       "      <td>-1.114873</td>\n",
+       "      <td>-1.261820</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.410893</td>\n",
+       "      <td>0.764190</td>\n",
+       "      <td>-1.432735</td>\n",
+       "      <td>-1.075813</td>\n",
+       "      <td>-1.859019</td>\n",
+       "      <td>-1.207552</td>\n",
+       "      <td>-1.305831</td>\n",
+       "      <td>-1.745063</td>\n",
+       "      <td>-0.048138</td>\n",
+       "      <td>-0.751207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant     1.097064     -2.073335        1.269934   \n",
+       "1      842517  Malignant     1.829821     -0.353632        1.685955   \n",
+       "2    84300903  Malignant     1.579888      0.456187        1.566503   \n",
+       "3    84348301  Malignant    -0.768909      0.253732       -0.592687   \n",
+       "4    84358402  Malignant     1.750297     -1.151816        1.776573   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant     2.110995      0.721473        2.060786   \n",
+       "565    926682  Malignant     1.704854      2.085134        1.615931   \n",
+       "566    926954  Malignant     0.702284      2.045574        0.672676   \n",
+       "567    927241  Malignant     1.838341      2.336457        1.982524   \n",
+       "568     92751     Benign    -1.808401      1.221792       -1.814389   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0     0.984375         1.568466          3.283515        2.652874   \n",
+       "1     1.908708        -0.826962         -0.487072       -0.023846   \n",
+       "2     1.558884         0.942210          1.052926        1.363478   \n",
+       "3    -0.764464         3.283553          3.402909        1.915897   \n",
+       "4     1.826229         0.280372          0.539340        1.371011   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564   2.343856         1.041842          0.219060        1.947285   \n",
+       "565   1.723842         0.102458         -0.017833        0.693043   \n",
+       "566   0.577953        -0.840484         -0.038680        0.046588   \n",
+       "567   1.735218         1.525767          3.272144        3.296944   \n",
+       "568  -1.347789        -3.112085         -1.150752       -1.114873   \n",
+       "\n",
+       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
+       "0               2.532475  ...      1.886690      -1.359293         2.303601   \n",
+       "1               0.548144  ...      1.805927      -0.369203         1.535126   \n",
+       "2               2.037231  ...      1.511870      -0.023974         1.347475   \n",
+       "3               1.451707  ...     -0.281464       0.133984        -0.249939   \n",
+       "4               1.428493  ...      1.298575      -1.466770         1.338539   \n",
+       "..                   ...  ...           ...            ...              ...   \n",
+       "564             2.320965  ...      1.901185       0.117700         1.752563   \n",
+       "565             1.263669  ...      1.536720       2.047399         1.421940   \n",
+       "566             0.105777  ...      0.561361       1.374854         0.579001   \n",
+       "567             2.658866  ...      1.961239       2.237926         2.303601   \n",
+       "568            -1.261820  ...     -1.410893       0.764190        -1.432735   \n",
+       "\n",
+       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0      2.001237          1.307686           2.616665         2.109526   \n",
+       "1      1.890489         -0.375612          -0.430444        -0.146749   \n",
+       "2      1.456285          0.527407           1.082932         0.854974   \n",
+       "3     -0.550021          3.394275           3.893397         1.989588   \n",
+       "4      1.220724          0.220556          -0.313395         0.613179   \n",
+       "..          ...               ...                ...              ...   \n",
+       "564    2.015301          0.378365          -0.273318         0.664512   \n",
+       "565    1.494959         -0.691230          -0.394820         0.236573   \n",
+       "566    0.427906         -0.809587           0.350735         0.326767   \n",
+       "567    1.653171          1.430427           3.904848         3.197605   \n",
+       "568   -1.075813         -1.859019          -1.207552        -1.305831   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                2.296076        2.750622                 1.937015  \n",
+       "1                1.087084       -0.243890                 0.281190  \n",
+       "2                1.955000        1.152255                 0.201391  \n",
+       "3                2.175786        6.046041                 4.935010  \n",
+       "4                0.729259       -0.868353                -0.397100  \n",
+       "..                    ...             ...                      ...  \n",
+       "564              1.629151       -1.360158                -0.709091  \n",
+       "565              0.733827       -0.531855                -0.973978  \n",
+       "566              0.414069       -1.104549                -0.318409  \n",
+       "567              2.289985        1.919083                 2.219635  \n",
+       "568             -1.745063       -0.048138                -0.751207  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a copy of the original 'cancer' dataframe to ensure we're not modifying the original data\n",
+    "standardized_cancer = cancer.copy()\n",
+    "\n",
+    "# Specify the columns that we do NOT want to scale (ID and diagnosis columns)\n",
+    "columns_to_exclude = ['id', 'diagnosis']\n",
+    "\n",
+    "# Select the columns that we want to scale by excluding the 'id' and 'diagnosis' columns\n",
+    "# This will return a list of the numeric columns we need to scale\n",
+    "columns_to_scale = standardized_cancer.columns.difference(columns_to_exclude)\n",
+    "\n",
+    "# Initialize the StandardScaler to standardize the selected numeric columns\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# Apply the scaler to the selected columns. This transforms the data so that each feature\n",
+    "# has a mean of 0 and a standard deviation of 1, which is essential to prevent larger\n",
+    "# scale features from dominating the analysis, especially for distance-based algorithms like KNN.\n",
+    "standardized_cancer[columns_to_scale] = scaler.fit_transform(cancer[columns_to_scale])\n",
+    "\n",
+    "# Output the standardized dataframe with the scaled numeric columns\n",
+    "standardized_cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6038b428",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the seed\n",
+    "np.random.seed(1)\n",
+    "\n",
+    "#split the data\n",
+    "cancer_train, cancer_test = train_test_split(\n",
+    "    standardized_cancer, train_size=0.75, stratify=standardized_cancer[\"diagnosis\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a178033",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c7408b7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = cancer_train[['perimeter_mean','concavity_mean']]\n",
+    "y = cancer_train['diagnosis']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2baf91e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
+       "            n_neighbors\n",
+       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
+       "            algorithm\n",
+       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
+       "            leaf_size\n",
+       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
+       "            p\n",
+       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
+       "            metric\n",
+       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
+       "            metric_params\n",
+       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-1');</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.fit(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f82671b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer_test['predicted'] = knn.predict(cancer_test[['perimeter_mean','concavity_mean']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ee258d39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>predicted</th>\n",
+       "      <th>diagnosis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>Malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>143 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     predicted  diagnosis\n",
+       "357     Benign     Benign\n",
+       "361     Benign     Benign\n",
+       "212  Malignant  Malignant\n",
+       "527     Benign     Benign\n",
+       "21      Benign     Benign\n",
+       "..         ...        ...\n",
+       "364     Benign     Benign\n",
+       "434     Benign     Benign\n",
+       "299     Benign     Benign\n",
+       "488     Benign     Benign\n",
+       "332     Benign     Benign\n",
+       "\n",
+       "[143 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_test[['predicted','diagnosis']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e60edc7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9230769230769231"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calc accuracy\n",
+    "knn.score(cancer_test[['perimeter_mean','concavity_mean']], cancer_test['diagnosis'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "35604ae6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>predicted</th>\n",
+       "      <th>Benign</th>\n",
+       "      <th>Malignant</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Benign</th>\n",
+       "      <td>88</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Malignant</th>\n",
+       "      <td>9</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "predicted  Benign  Malignant\n",
+       "diagnosis                   \n",
+       "Benign         88          2\n",
+       "Malignant       9         44"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab(\n",
+    "    cancer_test['diagnosis'],\n",
+    "    cancer_test['predicted']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "45fcdcd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9565217391304348"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(\n",
+    "    y_true = cancer_test['diagnosis'],\n",
+    "    y_pred = cancer_test['predicted'],\n",
+    "    pos_label = \"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "703fcb5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8301886792452831"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(\n",
+    "    y_true = cancer_test['diagnosis'],\n",
+    "    y_pred = cancer_test['predicted'],\n",
+    "    pos_label = \"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "96dad7ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer_subtrain, cancer_validation = train_test_split(\n",
+    "    cancer_train, train_size = 0.75, stratify=cancer_train['diagnosis']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6a17f5f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
+       "            n_neighbors\n",
+       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
+       "            algorithm\n",
+       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
+       "            leaf_size\n",
+       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
+       "            p\n",
+       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
+       "            metric\n",
+       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
+       "            metric_params\n",
+       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-2');</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(3)\n",
+    "x = cancer_subtrain[['perimeter_mean','concavity_mean']]\n",
+    "y = cancer_subtrain['diagnosis']\n",
+    "knn.fit(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e0e76ad5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8785046728971962"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.score(\n",
+    "    cancer_validation[['perimeter_mean','concavity_mean']],\n",
+    "    cancer_validation['diagnosis']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d61b4cb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "knn = KNeighborsClassifier(3)\n",
+    "x = cancer_subtrain[['perimeter_mean','concavity_mean']]\n",
+    "y = cancer_subtrain['diagnosis']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0434088f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "returned_dictionary = cross_validate(\n",
+    "    estimator=knn,\n",
+    "    cv=5,\n",
+    "    X=x,\n",
+    "    y=y   \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ba1b9110",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.004107</td>\n",
+       "      <td>0.002731</td>\n",
+       "      <td>0.906250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.002582</td>\n",
+       "      <td>0.003511</td>\n",
+       "      <td>0.937500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.001506</td>\n",
+       "      <td>0.003009</td>\n",
+       "      <td>0.859375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.002132</td>\n",
+       "      <td>0.003544</td>\n",
+       "      <td>0.843750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.001606</td>\n",
+       "      <td>0.002042</td>\n",
+       "      <td>0.936508</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fit_time  score_time  test_score\n",
+       "0  0.004107    0.002731    0.906250\n",
+       "1  0.002582    0.003511    0.937500\n",
+       "2  0.001506    0.003009    0.859375\n",
+       "3  0.002132    0.003544    0.843750\n",
+       "4  0.001606    0.002042    0.936508"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df = pd.DataFrame(returned_dictionary)\n",
+    "cv_5_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "eb465e62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.002387</td>\n",
+       "      <td>0.002967</td>\n",
+       "      <td>0.896677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sem</th>\n",
+       "      <td>0.000472</td>\n",
+       "      <td>0.000278</td>\n",
+       "      <td>0.019413</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fit_time  score_time  test_score\n",
+       "mean  0.002387    0.002967    0.896677\n",
+       "sem   0.000472    0.000278    0.019413"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df.agg(['mean','sem'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7fcf05ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# best value for k\n",
+    "# grid search\n",
+    "parameter_grid={\n",
+    "    \"n_neighbors\": range(1,100,5)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "254ee881",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# init gridsearch obj\n",
+    "cancer_tuned_grid = GridSearchCV(\n",
+    "    estimator = knn,\n",
+    "    param_grid=parameter_grid,\n",
+    "    cv=10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "05d097b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=3),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 100, 5)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>This is assumed to implement the scikit-learn estimator interface.<br>Either estimator needs to provide a ``score`` function,<br>or ``scoring`` must be passed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">KNeighborsCla...n_neighbors=3)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('param_grid',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=param_grid,-dict%20or%20list%20of%20dictionaries\">\n",
+       "            param_grid\n",
+       "            <span class=\"param-doc-description\">param_grid: dict or list of dictionaries<br><br>Dictionary with parameters names (`str`) as keys and lists of<br>parameter settings to try as values, or a list of such<br>dictionaries, in which case the grids spanned by each dictionary<br>in the list are explored. This enables searching over any sequence<br>of parameter settings.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">{&#x27;n_neighbors&#x27;: range(1, 100, 5)}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scoring',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scoring,-str%2C%20callable%2C%20list%2C%20tuple%20or%20dict%2C%20default%3DNone\">\n",
+       "            scoring\n",
+       "            <span class=\"param-doc-description\">scoring: str, callable, list, tuple or dict, default=None<br><br>Strategy to evaluate the performance of the cross-validated model on<br>the test set.<br><br>If `scoring` represents a single score, one can use:<br><br>- a single string (see :ref:`scoring_string_names`);<br>- a callable (see :ref:`scoring_callable`) that returns a single value;<br>- `None`, the `estimator`'s<br>  :ref:`default evaluation criterion <scoring_api_overview>` is used.<br><br>If `scoring` represents multiple scores, one can use:<br><br>- a list or tuple of unique strings;<br>- a callable returning a dictionary where the keys are the metric<br>  names and the values are the metric scores;<br>- a dictionary with metric names as keys and callables as values.<br><br>See :ref:`multimetric_grid_search` for an example.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: v0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('refit',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit,-bool%2C%20str%2C%20or%20callable%2C%20default%3DTrue\">\n",
+       "            refit\n",
+       "            <span class=\"param-doc-description\">refit: bool, str, or callable, default=True<br><br>Refit an estimator using the best found parameters on the whole<br>dataset.<br><br>For multiple metric evaluation, this needs to be a `str` denoting the<br>scorer that would be used to find the best parameters for refitting<br>the estimator at the end.<br><br>Where there are considerations other than maximum score in<br>choosing a best estimator, ``refit`` can be set to a function which<br>returns the selected ``best_index_`` given ``cv_results_``. In that<br>case, the ``best_estimator_`` and ``best_params_`` will be set<br>according to the returned ``best_index_`` while the ``best_score_``<br>attribute will not be available.<br><br>The refitted estimator is made available at the ``best_estimator_``<br>attribute and permits using ``predict`` directly on this<br>``GridSearchCV`` instance.<br><br>Also for multiple metric evaluation, the attributes ``best_index_``,<br>``best_score_`` and ``best_params_`` will only be available if<br>``refit`` is set and all of them will be determined w.r.t this specific<br>scorer.<br><br>See ``scoring`` parameter to know more about multiple metric<br>evaluation.<br><br>See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`<br>to see how to design a custom selection strategy using a callable<br>via `refit`.<br><br>See :ref:`this example<br><sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py>`<br>for an example of how to use ``refit=callable`` to balance model<br>complexity and cross-validated score.<br><br>.. versionchanged:: 0.20<br>    Support for callable added.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20an%20iterable%2C%20default%3DNone\">\n",
+       "            cv\n",
+       "            <span class=\"param-doc-description\">cv: int, cross-validation generator or an iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the default 5-fold cross validation,<br>- integer, to specify the number of folds in a `(Stratified)KFold`,<br>- :term:`CV splitter`,<br>- An iterable yielding (train, test) splits as arrays of indices.<br><br>For integer/None inputs, if the estimator is a classifier and ``y`` is<br>either binary or multiclass, :class:`StratifiedKFold` is used. In all<br>other cases, :class:`KFold` is used. These splitters are instantiated<br>with `shuffle=False` so the splits will be the same across calls.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>.. versionchanged:: 0.22<br>    ``cv`` default value if None changed from 3-fold to 5-fold.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=verbose,-int\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int<br><br>Controls the verbosity: the higher, the more messages.<br><br>- >1 : the computation time for each fold and parameter candidate is<br>  displayed;<br>- >2 : the score is also displayed;<br>- >3 : the fold and candidate parameter indexes are also displayed<br>  together with the starting time of the computation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=pre_dispatch,-int%2C%20or%20str%2C%20default%3D%272%2An_jobs%27\">\n",
+       "            pre_dispatch\n",
+       "            <span class=\"param-doc-description\">pre_dispatch: int, or str, default='2*n_jobs'<br><br>Controls the number of jobs that get dispatched during parallel<br>execution. Reducing this number can be useful to avoid an<br>explosion of memory consumption when more jobs get dispatched<br>than CPUs can process. This parameter can be:<br><br>- None, in which case all the jobs are immediately created and spawned. Use<br>  this for lightweight and fast-running jobs, to avoid delays due to on-demand<br>  spawning of the jobs<br>- An int, giving the exact number of total jobs that are spawned<br>- A str, giving an expression as a function of n_jobs, as in '2*n_jobs'</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('error_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=error_score,-%27raise%27%20or%20numeric%2C%20default%3Dnp.nan\">\n",
+       "            error_score\n",
+       "            <span class=\"param-doc-description\">error_score: 'raise' or numeric, default=np.nan<br><br>Value to assign to the score if an error occurs in estimator fitting.<br>If set to 'raise', the error is raised. If a numeric value is given,<br>FitFailedWarning is raised. This parameter does not affect the refit<br>step, which will always raise the error.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('return_train_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=return_train_score,-bool%2C%20default%3DFalse\">\n",
+       "            return_train_score\n",
+       "            <span class=\"param-doc-description\">return_train_score: bool, default=False<br><br>If ``False``, the ``cv_results_`` attribute will not include training<br>scores.<br>Computing training scores is used to get insights on how different<br>parameter settings impact the overfitting/underfitting trade-off.<br>However computing the scores on the training set can be computationally<br>expensive and is not strictly required to select the parameters that<br>yield the best generalization performance.<br><br>.. versionadded:: 0.19<br><br>.. versionchanged:: 0.21<br>    Default value was changed from ``True`` to ``False``</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: KNeighborsClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>KNeighborsClassifier(n_neighbors=16)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
+       "            n_neighbors\n",
+       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">16</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
+       "            algorithm\n",
+       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
+       "            leaf_size\n",
+       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
+       "            p\n",
+       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
+       "            metric\n",
+       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
+       "            metric_params\n",
+       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-3');</script></body>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=3),\n",
+       "             param_grid={'n_neighbors': range(1, 100, 5)})"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fit to training data\n",
+    "cancer_tuned_grid.fit(\n",
+    "    cancer_train[['perimeter_mean','concavity_mean']],\n",
+    "    cancer_train['diagnosis']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8b88773b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>split5_test_score</th>\n",
+       "      <th>split6_test_score</th>\n",
+       "      <th>split7_test_score</th>\n",
+       "      <th>split8_test_score</th>\n",
+       "      <th>split9_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.002153</td>\n",
+       "      <td>0.000503</td>\n",
+       "      <td>0.002549</td>\n",
+       "      <td>0.001008</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.875692</td>\n",
+       "      <td>0.038684</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.001673</td>\n",
+       "      <td>0.000710</td>\n",
+       "      <td>0.002164</td>\n",
+       "      <td>0.000713</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{'n_neighbors': 6}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.908638</td>\n",
+       "      <td>0.043373</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.001304</td>\n",
+       "      <td>0.000460</td>\n",
+       "      <td>0.001917</td>\n",
+       "      <td>0.000317</td>\n",
+       "      <td>11</td>\n",
+       "      <td>{'n_neighbors': 11}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904042</td>\n",
+       "      <td>0.039147</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.001597</td>\n",
+       "      <td>0.000690</td>\n",
+       "      <td>0.001989</td>\n",
+       "      <td>0.000358</td>\n",
+       "      <td>16</td>\n",
+       "      <td>{'n_neighbors': 16}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920487</td>\n",
+       "      <td>0.045315</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.001627</td>\n",
+       "      <td>0.000740</td>\n",
+       "      <td>0.001784</td>\n",
+       "      <td>0.000418</td>\n",
+       "      <td>21</td>\n",
+       "      <td>{'n_neighbors': 21}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913400</td>\n",
+       "      <td>0.046495</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.001778</td>\n",
+       "      <td>0.000576</td>\n",
+       "      <td>0.002218</td>\n",
+       "      <td>0.000640</td>\n",
+       "      <td>26</td>\n",
+       "      <td>{'n_neighbors': 26}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913400</td>\n",
+       "      <td>0.043989</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.001292</td>\n",
+       "      <td>0.000507</td>\n",
+       "      <td>0.001966</td>\n",
+       "      <td>0.000580</td>\n",
+       "      <td>31</td>\n",
+       "      <td>{'n_neighbors': 31}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913400</td>\n",
+       "      <td>0.043989</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.001419</td>\n",
+       "      <td>0.000497</td>\n",
+       "      <td>0.002183</td>\n",
+       "      <td>0.000407</td>\n",
+       "      <td>36</td>\n",
+       "      <td>{'n_neighbors': 36}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.911074</td>\n",
+       "      <td>0.044873</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.001671</td>\n",
+       "      <td>0.000826</td>\n",
+       "      <td>0.002989</td>\n",
+       "      <td>0.000539</td>\n",
+       "      <td>41</td>\n",
+       "      <td>{'n_neighbors': 41}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.911074</td>\n",
+       "      <td>0.044873</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.001309</td>\n",
+       "      <td>0.000521</td>\n",
+       "      <td>0.002358</td>\n",
+       "      <td>0.000471</td>\n",
+       "      <td>46</td>\n",
+       "      <td>{'n_neighbors': 46}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.915781</td>\n",
+       "      <td>0.045368</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.001665</td>\n",
+       "      <td>0.000610</td>\n",
+       "      <td>0.002294</td>\n",
+       "      <td>0.000654</td>\n",
+       "      <td>51</td>\n",
+       "      <td>{'n_neighbors': 51}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913455</td>\n",
+       "      <td>0.046345</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.001620</td>\n",
+       "      <td>0.000615</td>\n",
+       "      <td>0.002219</td>\n",
+       "      <td>0.000599</td>\n",
+       "      <td>56</td>\n",
+       "      <td>{'n_neighbors': 56}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.044212</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.001591</td>\n",
+       "      <td>0.000512</td>\n",
+       "      <td>0.002827</td>\n",
+       "      <td>0.000470</td>\n",
+       "      <td>61</td>\n",
+       "      <td>{'n_neighbors': 61}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.918106</td>\n",
+       "      <td>0.041731</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.001367</td>\n",
+       "      <td>0.000677</td>\n",
+       "      <td>0.002578</td>\n",
+       "      <td>0.000801</td>\n",
+       "      <td>66</td>\n",
+       "      <td>{'n_neighbors': 66}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.041694</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.001512</td>\n",
+       "      <td>0.000768</td>\n",
+       "      <td>0.002073</td>\n",
+       "      <td>0.000634</td>\n",
+       "      <td>71</td>\n",
+       "      <td>{'n_neighbors': 71}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.041694</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.001505</td>\n",
+       "      <td>0.000515</td>\n",
+       "      <td>0.002063</td>\n",
+       "      <td>0.000410</td>\n",
+       "      <td>76</td>\n",
+       "      <td>{'n_neighbors': 76}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.048861</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.001095</td>\n",
+       "      <td>0.000625</td>\n",
+       "      <td>0.002623</td>\n",
+       "      <td>0.000542</td>\n",
+       "      <td>81</td>\n",
+       "      <td>{'n_neighbors': 81}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.048861</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.001428</td>\n",
+       "      <td>0.000509</td>\n",
+       "      <td>0.002372</td>\n",
+       "      <td>0.000627</td>\n",
+       "      <td>86</td>\n",
+       "      <td>{'n_neighbors': 86}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.915725</td>\n",
+       "      <td>0.047731</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.001292</td>\n",
+       "      <td>0.000353</td>\n",
+       "      <td>0.002708</td>\n",
+       "      <td>0.000479</td>\n",
+       "      <td>91</td>\n",
+       "      <td>{'n_neighbors': 91}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913344</td>\n",
+       "      <td>0.047625</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.001391</td>\n",
+       "      <td>0.000490</td>\n",
+       "      <td>0.002337</td>\n",
+       "      <td>0.000825</td>\n",
+       "      <td>96</td>\n",
+       "      <td>{'n_neighbors': 96}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913344</td>\n",
+       "      <td>0.047625</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0        0.002153      0.000503         0.002549        0.001008   \n",
+       "1        0.001673      0.000710         0.002164        0.000713   \n",
+       "2        0.001304      0.000460         0.001917        0.000317   \n",
+       "3        0.001597      0.000690         0.001989        0.000358   \n",
+       "4        0.001627      0.000740         0.001784        0.000418   \n",
+       "5        0.001778      0.000576         0.002218        0.000640   \n",
+       "6        0.001292      0.000507         0.001966        0.000580   \n",
+       "7        0.001419      0.000497         0.002183        0.000407   \n",
+       "8        0.001671      0.000826         0.002989        0.000539   \n",
+       "9        0.001309      0.000521         0.002358        0.000471   \n",
+       "10       0.001665      0.000610         0.002294        0.000654   \n",
+       "11       0.001620      0.000615         0.002219        0.000599   \n",
+       "12       0.001591      0.000512         0.002827        0.000470   \n",
+       "13       0.001367      0.000677         0.002578        0.000801   \n",
+       "14       0.001512      0.000768         0.002073        0.000634   \n",
+       "15       0.001505      0.000515         0.002063        0.000410   \n",
+       "16       0.001095      0.000625         0.002623        0.000542   \n",
+       "17       0.001428      0.000509         0.002372        0.000627   \n",
+       "18       0.001292      0.000353         0.002708        0.000479   \n",
+       "19       0.001391      0.000490         0.002337        0.000825   \n",
+       "\n",
+       "    param_n_neighbors               params  split0_test_score  \\\n",
+       "0                   1   {'n_neighbors': 1}           0.953488   \n",
+       "1                   6   {'n_neighbors': 6}           0.930233   \n",
+       "2                  11  {'n_neighbors': 11}           0.906977   \n",
+       "3                  16  {'n_neighbors': 16}           0.906977   \n",
+       "4                  21  {'n_neighbors': 21}           0.906977   \n",
+       "5                  26  {'n_neighbors': 26}           0.906977   \n",
+       "6                  31  {'n_neighbors': 31}           0.906977   \n",
+       "7                  36  {'n_neighbors': 36}           0.906977   \n",
+       "8                  41  {'n_neighbors': 41}           0.906977   \n",
+       "9                  46  {'n_neighbors': 46}           0.906977   \n",
+       "10                 51  {'n_neighbors': 51}           0.906977   \n",
+       "11                 56  {'n_neighbors': 56}           0.906977   \n",
+       "12                 61  {'n_neighbors': 61}           0.906977   \n",
+       "13                 66  {'n_neighbors': 66}           0.930233   \n",
+       "14                 71  {'n_neighbors': 71}           0.930233   \n",
+       "15                 76  {'n_neighbors': 76}           0.930233   \n",
+       "16                 81  {'n_neighbors': 81}           0.930233   \n",
+       "17                 86  {'n_neighbors': 86}           0.906977   \n",
+       "18                 91  {'n_neighbors': 91}           0.906977   \n",
+       "19                 96  {'n_neighbors': 96}           0.906977   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0            0.837209           0.906977           0.860465   \n",
+       "1            0.953488           0.906977           0.837209   \n",
+       "2            0.930233           0.906977           0.837209   \n",
+       "3            0.953488           0.930233           0.837209   \n",
+       "4            0.953488           0.930233           0.837209   \n",
+       "5            0.953488           0.930233           0.837209   \n",
+       "6            0.953488           0.930233           0.837209   \n",
+       "7            0.953488           0.930233           0.837209   \n",
+       "8            0.953488           0.930233           0.837209   \n",
+       "9            0.953488           0.930233           0.837209   \n",
+       "10           0.953488           0.930233           0.837209   \n",
+       "11           0.976744           0.930233           0.860465   \n",
+       "12           0.953488           0.930233           0.860465   \n",
+       "13           0.953488           0.930233           0.860465   \n",
+       "14           0.953488           0.930233           0.860465   \n",
+       "15           0.976744           0.930233           0.860465   \n",
+       "16           0.976744           0.930233           0.860465   \n",
+       "17           0.976744           0.930233           0.860465   \n",
+       "18           0.976744           0.930233           0.860465   \n",
+       "19           0.976744           0.930233           0.860465   \n",
+       "\n",
+       "    split4_test_score  split5_test_score  split6_test_score  \\\n",
+       "0            0.813953           0.837209           0.880952   \n",
+       "1            0.837209           0.906977           0.928571   \n",
+       "2            0.837209           0.883721           0.952381   \n",
+       "3            0.837209           0.930233           0.952381   \n",
+       "4            0.837209           0.906977           0.952381   \n",
+       "5            0.837209           0.906977           0.952381   \n",
+       "6            0.837209           0.906977           0.952381   \n",
+       "7            0.837209           0.883721           0.928571   \n",
+       "8            0.837209           0.883721           0.952381   \n",
+       "9            0.837209           0.906977           0.952381   \n",
+       "10           0.837209           0.883721           0.952381   \n",
+       "11           0.837209           0.906977           0.952381   \n",
+       "12           0.837209           0.906977           0.952381   \n",
+       "13           0.837209           0.906977           0.952381   \n",
+       "14           0.837209           0.906977           0.952381   \n",
+       "15           0.813953           0.906977           0.952381   \n",
+       "16           0.813953           0.906977           0.952381   \n",
+       "17           0.813953           0.906977           0.928571   \n",
+       "18           0.813953           0.906977           0.904762   \n",
+       "19           0.813953           0.906977           0.904762   \n",
+       "\n",
+       "    split7_test_score  split8_test_score  split9_test_score  mean_test_score  \\\n",
+       "0            0.904762           0.880952           0.880952         0.875692   \n",
+       "1            0.928571           0.880952           0.976190         0.908638   \n",
+       "2            0.904762           0.928571           0.952381         0.904042   \n",
+       "3            0.952381           0.928571           0.976190         0.920487   \n",
+       "4            0.952381           0.880952           0.976190         0.913400   \n",
+       "5            0.928571           0.904762           0.976190         0.913400   \n",
+       "6            0.928571           0.904762           0.976190         0.913400   \n",
+       "7            0.952381           0.904762           0.976190         0.911074   \n",
+       "8            0.928571           0.904762           0.976190         0.911074   \n",
+       "9            0.952381           0.904762           0.976190         0.915781   \n",
+       "10           0.952381           0.904762           0.976190         0.913455   \n",
+       "11           0.952381           0.904762           0.976190         0.920432   \n",
+       "12           0.952381           0.904762           0.976190         0.918106   \n",
+       "13           0.952381           0.904762           0.976190         0.920432   \n",
+       "14           0.952381           0.904762           0.976190         0.920432   \n",
+       "15           0.952381           0.904762           0.976190         0.920432   \n",
+       "16           0.952381           0.904762           0.976190         0.920432   \n",
+       "17           0.952381           0.904762           0.976190         0.915725   \n",
+       "18           0.952381           0.904762           0.976190         0.913344   \n",
+       "19           0.952381           0.904762           0.976190         0.913344   \n",
+       "\n",
+       "    std_test_score  rank_test_score  \n",
+       "0         0.038684               20  \n",
+       "1         0.043373               18  \n",
+       "2         0.039147               19  \n",
+       "3         0.045315                1  \n",
+       "4         0.046495               11  \n",
+       "5         0.043989               11  \n",
+       "6         0.043989               11  \n",
+       "7         0.044873               16  \n",
+       "8         0.044873               16  \n",
+       "9         0.045368                8  \n",
+       "10        0.046345               10  \n",
+       "11        0.044212                2  \n",
+       "12        0.041731                7  \n",
+       "13        0.041694                2  \n",
+       "14        0.041694                2  \n",
+       "15        0.048861                2  \n",
+       "16        0.048861                2  \n",
+       "17        0.047731                9  \n",
+       "18        0.047625               14  \n",
+       "19        0.047625               14  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_grid = pd.DataFrame(cancer_tuned_grid.cv_results_)\n",
+    "accuracy_grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "75b37c82",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot mean test scores with error bars\n",
+    "plt.plot(accuracy_grid['param_n_neighbors'], accuracy_grid['mean_test_score'], '-o', color='blue')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('Accuracy estimate')\n",
+    "plt.title('K-Nearest Neighbors Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2c07093e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 16}"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_tuned_grid.best_params_"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 8a1c12f93d4272a89158d19b7af1ea06c49dc8b9 Mon Sep 17 00:00:00 2001
From: The Code Gnome <thecodegnome@outlook.com>
Date: Thu, 14 May 2026 19:32:39 -0400
Subject: [PATCH 3/3] session 03 live code

---
 .../notebooks/live-code-05-14-2026.ipynb      | 2845 +++++++++++++++++
 1 file changed, 2845 insertions(+)
 create mode 100644 01_materials/notebooks/live-code-05-14-2026.ipynb

diff --git a/01_materials/notebooks/live-code-05-14-2026.ipynb b/01_materials/notebooks/live-code-05-14-2026.ipynb
new file mode 100644
index 000000000..baa1d19fd
--- /dev/null
+++ b/01_materials/notebooks/live-code-05-14-2026.ipynb
@@ -0,0 +1,2845 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "89191315",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
+    "from sklearn.compose import make_column_transformer\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn import set_config\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.metrics import mean_squared_error, r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "038782c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>type</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1005 MORENO WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95838</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>180000</td>\n",
+       "      <td>38.646206</td>\n",
+       "      <td>-121.442767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10105 MONTE VALLO CT</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1578</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>190000</td>\n",
+       "      <td>38.573917</td>\n",
+       "      <td>-121.316916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10133 NEBBIOLO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>289000</td>\n",
+       "      <td>38.391085</td>\n",
+       "      <td>-121.347231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10165 LOFTON WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>266510</td>\n",
+       "      <td>38.387708</td>\n",
+       "      <td>-121.436522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10254 JULIANA WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2484</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>331200</td>\n",
+       "      <td>38.568030</td>\n",
+       "      <td>-121.309966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>808</th>\n",
+       "      <td>9507 SEA CLIFF WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95758</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2056</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>285000</td>\n",
+       "      <td>38.410992</td>\n",
+       "      <td>-121.479043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>809</th>\n",
+       "      <td>9570 HARVEST ROSE WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2367</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>315537</td>\n",
+       "      <td>38.555993</td>\n",
+       "      <td>-121.340352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>9723 TERRAPIN CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2354</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>335750</td>\n",
+       "      <td>38.403492</td>\n",
+       "      <td>-121.430224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>9837 CORTE DORADO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1616</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>227887</td>\n",
+       "      <td>38.400676</td>\n",
+       "      <td>-121.381010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>9861 CULP WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>131200</td>\n",
+       "      <td>38.558423</td>\n",
+       "      <td>-121.327948</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>813 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    street        city    zip state  beds  baths  sq__ft  \\\n",
+       "0          1005 MORENO WAY  SACRAMENTO  95838    CA     3      2    1410   \n",
+       "1     10105 MONTE VALLO CT  SACRAMENTO  95827    CA     4      2    1578   \n",
+       "2        10133 NEBBIOLO CT   ELK GROVE  95624    CA     4      3    2096   \n",
+       "3         10165 LOFTON WAY   ELK GROVE  95757    CA     3      2    1540   \n",
+       "4        10254 JULIANA WAY  SACRAMENTO  95827    CA     4      2    2484   \n",
+       "..                     ...         ...    ...   ...   ...    ...     ...   \n",
+       "808     9507 SEA CLIFF WAY   ELK GROVE  95758    CA     4      2    2056   \n",
+       "809  9570 HARVEST ROSE WAY  SACRAMENTO  95827    CA     5      3    2367   \n",
+       "810       9723 TERRAPIN CT   ELK GROVE  95757    CA     4      3    2354   \n",
+       "811   9837 CORTE DORADO CT   ELK GROVE  95624    CA     4      2    1616   \n",
+       "812          9861 CULP WAY  SACRAMENTO  95827    CA     4      2    1380   \n",
+       "\n",
+       "            type                     sale_date   price   latitude   longitude  \n",
+       "0    Residential  Fri May 16 00:00:00 EDT 2008  180000  38.646206 -121.442767  \n",
+       "1    Residential  Fri May 16 00:00:00 EDT 2008  190000  38.573917 -121.316916  \n",
+       "2    Residential  Fri May 16 00:00:00 EDT 2008  289000  38.391085 -121.347231  \n",
+       "3    Residential  Fri May 16 00:00:00 EDT 2008  266510  38.387708 -121.436522  \n",
+       "4    Residential  Fri May 16 00:00:00 EDT 2008  331200  38.568030 -121.309966  \n",
+       "..           ...                           ...     ...        ...         ...  \n",
+       "808  Residential  Wed May 21 00:00:00 EDT 2008  285000  38.410992 -121.479043  \n",
+       "809  Residential  Wed May 21 00:00:00 EDT 2008  315537  38.555993 -121.340352  \n",
+       "810  Residential  Wed May 21 00:00:00 EDT 2008  335750  38.403492 -121.430224  \n",
+       "811  Residential  Wed May 21 00:00:00 EDT 2008  227887  38.400676 -121.381010  \n",
+       "812  Residential  Wed May 21 00:00:00 EDT 2008  131200  38.558423 -121.327948  \n",
+       "\n",
+       "[813 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Output dataframes instead of arrays\n",
+    "set_config(transform_output=\"pandas\")\n",
+    "\n",
+    "sacramento = pd.read_csv(\"dataset/sacramento.csv\")\n",
+    "sacramento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "852a7e24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "plt.scatter(sacramento[\"sq__ft\"], sacramento['price'])\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.title('Scatter Plot of House size vs Price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d4a9f436",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>type</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>7540 HICKORY AVE</td>\n",
+       "      <td>ORANGEVALE</td>\n",
+       "      <td>95662</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1456</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Thu May 15 00:00:00 EDT 2008</td>\n",
+       "      <td>225000</td>\n",
+       "      <td>38.703056</td>\n",
+       "      <td>-121.235221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>9013 CASALS ST</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95826</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>795</td>\n",
+       "      <td>Condo</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>126960</td>\n",
+       "      <td>38.557045</td>\n",
+       "      <td>-121.371670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>12901 FURLONG DR</td>\n",
+       "      <td>WILTON</td>\n",
+       "      <td>95693</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3788</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>691659</td>\n",
+       "      <td>38.413535</td>\n",
+       "      <td>-121.188211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>9474 VILLAGE TREE DR</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95758</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>210000</td>\n",
+       "      <td>38.413947</td>\n",
+       "      <td>-121.408276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>2901 PINTAIL WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3070</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>495000</td>\n",
+       "      <td>38.398488</td>\n",
+       "      <td>-121.473424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>191 BARNHART CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2605</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>257200</td>\n",
+       "      <td>38.675594</td>\n",
+       "      <td>-121.515878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>8316 NORTHAM DR</td>\n",
+       "      <td>ANTELOPE</td>\n",
+       "      <td>95843</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1235</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>246544</td>\n",
+       "      <td>38.720767</td>\n",
+       "      <td>-121.376678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>2130 CATHERWOOD WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1424</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>251000</td>\n",
+       "      <td>38.675506</td>\n",
+       "      <td>-121.510987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>620 KESWICK CT</td>\n",
+       "      <td>GRANITE BAY</td>\n",
+       "      <td>95746</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2356</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>600000</td>\n",
+       "      <td>38.732096</td>\n",
+       "      <td>-121.219142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>8593 DERLIN WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95823</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1436</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>180000</td>\n",
+       "      <td>38.447585</td>\n",
+       "      <td>-121.426627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>7223 KALLIE KAY LN</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95823</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1463</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>174250</td>\n",
+       "      <td>38.477553</td>\n",
+       "      <td>-121.419463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>3228 I ST</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95816</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1939</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>215000</td>\n",
+       "      <td>38.573844</td>\n",
+       "      <td>-121.462839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>5136 CABOT CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95820</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1462</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Thu May 15 00:00:00 EDT 2008</td>\n",
+       "      <td>121500</td>\n",
+       "      <td>38.528479</td>\n",
+       "      <td>-121.411806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>241 LANFRANCO CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3397</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>465000</td>\n",
+       "      <td>38.665696</td>\n",
+       "      <td>-121.549437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1455 64TH AVE</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95822</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>122000</td>\n",
+       "      <td>38.492177</td>\n",
+       "      <td>-121.503392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>2981 WRINGER DR</td>\n",
+       "      <td>ROSEVILLE</td>\n",
+       "      <td>95661</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3838</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>613401</td>\n",
+       "      <td>38.735373</td>\n",
+       "      <td>-121.227072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>7140 BLUE SPRINGS WAY</td>\n",
+       "      <td>CITRUS HEIGHTS</td>\n",
+       "      <td>95621</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1156</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>161653</td>\n",
+       "      <td>38.720653</td>\n",
+       "      <td>-121.302241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>8986 HAFLINGER WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1857</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>285000</td>\n",
+       "      <td>38.397923</td>\n",
+       "      <td>-121.450219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>7809 VALLECITOS WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95828</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>888</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Thu May 15 00:00:00 EDT 2008</td>\n",
+       "      <td>216021</td>\n",
+       "      <td>38.508217</td>\n",
+       "      <td>-121.411207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>786</th>\n",
+       "      <td>8025 PEERLESS AVE</td>\n",
+       "      <td>ORANGEVALE</td>\n",
+       "      <td>95662</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>334150</td>\n",
+       "      <td>38.711470</td>\n",
+       "      <td>-121.216214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>5579 JERRY LITELL WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3599</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>381300</td>\n",
+       "      <td>38.677126</td>\n",
+       "      <td>-121.500519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>87 LACAM CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95820</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1188</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Thu May 15 00:00:00 EDT 2008</td>\n",
+       "      <td>123675</td>\n",
+       "      <td>38.532359</td>\n",
+       "      <td>-121.411670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>7577 EDDYLEE WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95822</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1436</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>185074</td>\n",
+       "      <td>38.482910</td>\n",
+       "      <td>-121.491509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>5201 BLOSSOM RANCH DR</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4303</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>450000</td>\n",
+       "      <td>38.399436</td>\n",
+       "      <td>-121.444041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>1140 EDMONTON DR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95833</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1204</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Thu May 15 00:00:00 EDT 2008</td>\n",
+       "      <td>174250</td>\n",
+       "      <td>38.624570</td>\n",
+       "      <td>-121.486913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>5340 BIRK WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>234000</td>\n",
+       "      <td>38.672495</td>\n",
+       "      <td>-121.515251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>5805 DOTMAR WAY</td>\n",
+       "      <td>NORTH HIGHLANDS</td>\n",
+       "      <td>95660</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>904</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>63000</td>\n",
+       "      <td>38.672642</td>\n",
+       "      <td>-121.380343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>3662 RIVER DR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95833</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2447</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>246000</td>\n",
+       "      <td>38.604969</td>\n",
+       "      <td>-121.542550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>2327 32ND ST</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95817</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1115</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>220000</td>\n",
+       "      <td>38.557433</td>\n",
+       "      <td>-121.470340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>9629 CEDAR OAK WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3260</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>385000</td>\n",
+       "      <td>38.405527</td>\n",
+       "      <td>-121.431746</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    street             city    zip state  beds  baths  sq__ft  \\\n",
+       "486       7540 HICKORY AVE       ORANGEVALE  95662    CA     3      1    1456   \n",
+       "399         9013 CASALS ST       SACRAMENTO  95826    CA     2      1     795   \n",
+       "233       12901 FURLONG DR           WILTON  95693    CA     5      3    3788   \n",
+       "408   9474 VILLAGE TREE DR        ELK GROVE  95758    CA     4      2    1776   \n",
+       "549       2901 PINTAIL WAY        ELK GROVE  95757    CA     4      3    3070   \n",
+       "43        191 BARNHART CIR       SACRAMENTO  95835    CA     4      2    2605   \n",
+       "181        8316 NORTHAM DR         ANTELOPE  95843    CA     3      2    1235   \n",
+       "250    2130 CATHERWOOD WAY       SACRAMENTO  95835    CA     3      2    1424   \n",
+       "140         620 KESWICK CT      GRANITE BAY  95746    CA     4      3    2356   \n",
+       "392        8593 DERLIN WAY       SACRAMENTO  95823    CA     3      2    1436   \n",
+       "770     7223 KALLIE KAY LN       SACRAMENTO  95823    CA     3      2    1463   \n",
+       "280              3228 I ST       SACRAMENTO  95816    CA     4      3    1939   \n",
+       "448         5136 CABOT CIR       SACRAMENTO  95820    CA     4      2    1462   \n",
+       "546      241 LANFRANCO CIR       SACRAMENTO  95835    CA     4      4    3397   \n",
+       "27           1455 64TH AVE       SACRAMENTO  95822    CA     3      2    1169   \n",
+       "550        2981 WRINGER DR        ROSEVILLE  95661    CA     4      3    3838   \n",
+       "628  7140 BLUE SPRINGS WAY   CITRUS HEIGHTS  95621    CA     3      2    1156   \n",
+       "197     8986 HAFLINGER WAY        ELK GROVE  95757    CA     3      2    1857   \n",
+       "491    7809 VALLECITOS WAY       SACRAMENTO  95828    CA     3      1     888   \n",
+       "786      8025 PEERLESS AVE       ORANGEVALE  95662    CA     2      1    1690   \n",
+       "743  5579 JERRY LITELL WAY       SACRAMENTO  95835    CA     5      3    3599   \n",
+       "511           87 LACAM CIR       SACRAMENTO  95820    CA     2      2    1188   \n",
+       "163       7577 EDDYLEE WAY       SACRAMENTO  95822    CA     4      2    1436   \n",
+       "325  5201 BLOSSOM RANCH DR        ELK GROVE  95757    CA     4      4    4303   \n",
+       "415       1140 EDMONTON DR       SACRAMENTO  95833    CA     3      2    1204   \n",
+       "599          5340 BIRK WAY       SACRAMENTO  95835    CA     3      2    1776   \n",
+       "131        5805 DOTMAR WAY  NORTH HIGHLANDS  95660    CA     2      1     904   \n",
+       "293          3662 RIVER DR       SACRAMENTO  95833    CA     4      3    2447   \n",
+       "52            2327 32ND ST       SACRAMENTO  95817    CA     2      1    1115   \n",
+       "661     9629 CEDAR OAK WAY        ELK GROVE  95757    CA     5      4    3260   \n",
+       "\n",
+       "            type                     sale_date   price   latitude   longitude  \n",
+       "486  Residential  Thu May 15 00:00:00 EDT 2008  225000  38.703056 -121.235221  \n",
+       "399        Condo  Mon May 19 00:00:00 EDT 2008  126960  38.557045 -121.371670  \n",
+       "233  Residential  Mon May 19 00:00:00 EDT 2008  691659  38.413535 -121.188211  \n",
+       "408  Residential  Mon May 19 00:00:00 EDT 2008  210000  38.413947 -121.408276  \n",
+       "549  Residential  Tue May 20 00:00:00 EDT 2008  495000  38.398488 -121.473424  \n",
+       "43   Residential  Fri May 16 00:00:00 EDT 2008  257200  38.675594 -121.515878  \n",
+       "181  Residential  Fri May 16 00:00:00 EDT 2008  246544  38.720767 -121.376678  \n",
+       "250  Residential  Mon May 19 00:00:00 EDT 2008  251000  38.675506 -121.510987  \n",
+       "140  Residential  Fri May 16 00:00:00 EDT 2008  600000  38.732096 -121.219142  \n",
+       "392  Residential  Mon May 19 00:00:00 EDT 2008  180000  38.447585 -121.426627  \n",
+       "770  Residential  Wed May 21 00:00:00 EDT 2008  174250  38.477553 -121.419463  \n",
+       "280  Residential  Mon May 19 00:00:00 EDT 2008  215000  38.573844 -121.462839  \n",
+       "448  Residential  Thu May 15 00:00:00 EDT 2008  121500  38.528479 -121.411806  \n",
+       "546  Residential  Tue May 20 00:00:00 EDT 2008  465000  38.665696 -121.549437  \n",
+       "27   Residential  Fri May 16 00:00:00 EDT 2008  122000  38.492177 -121.503392  \n",
+       "550  Residential  Tue May 20 00:00:00 EDT 2008  613401  38.735373 -121.227072  \n",
+       "628  Residential  Tue May 20 00:00:00 EDT 2008  161653  38.720653 -121.302241  \n",
+       "197  Residential  Fri May 16 00:00:00 EDT 2008  285000  38.397923 -121.450219  \n",
+       "491  Residential  Thu May 15 00:00:00 EDT 2008  216021  38.508217 -121.411207  \n",
+       "786  Residential  Wed May 21 00:00:00 EDT 2008  334150  38.711470 -121.216214  \n",
+       "743  Residential  Wed May 21 00:00:00 EDT 2008  381300  38.677126 -121.500519  \n",
+       "511  Residential  Thu May 15 00:00:00 EDT 2008  123675  38.532359 -121.411670  \n",
+       "163  Residential  Fri May 16 00:00:00 EDT 2008  185074  38.482910 -121.491509  \n",
+       "325  Residential  Mon May 19 00:00:00 EDT 2008  450000  38.399436 -121.444041  \n",
+       "415  Residential  Thu May 15 00:00:00 EDT 2008  174250  38.624570 -121.486913  \n",
+       "599  Residential  Tue May 20 00:00:00 EDT 2008  234000  38.672495 -121.515251  \n",
+       "131  Residential  Fri May 16 00:00:00 EDT 2008   63000  38.672642 -121.380343  \n",
+       "293  Residential  Mon May 19 00:00:00 EDT 2008  246000  38.604969 -121.542550  \n",
+       "52   Residential  Fri May 16 00:00:00 EDT 2008  220000  38.557433 -121.470340  \n",
+       "661  Residential  Tue May 20 00:00:00 EDT 2008  385000  38.405527 -121.431746  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.seed(10)\n",
+    "\n",
+    "small_sacramento = sacramento.sample(n=30)\n",
+    "small_sacramento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4db5b559",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fKIVFlR6qC1FlWRicV/y/wThaaGuHHwjWMOwG2lshUMFxQlsm7BvfHddMaUOV6b/+9S/9fxTnHf/vkW+UTKH0D23jcE2gQTz+/6PaFW3Y8D1RwoamA7hmjQF1yUe4u/shkSdbunSpeuKJJ1Tjxo1VZGSkCgkJUQ0aNFBDhw5VR48eLZAe3axbtmypu2pXqFBBd8FesWKFZfv69etV+/btVXh4uO6SbgzRYN+F/dy5c+qRRx5R5cuX19ush1dYsGCBiouL013V7Ycw2LZtm+rZs6eqVKmSzgPe99BDD6mVK1cW6F6OruWuMLrYJyYmFprO0ZAK8P3336ubbrpJf2d05+/evbvatWtXgfe/8cYbqmbNmrobvSvDK8ydO9dyrCtWrKj69Omj/vrrL5s0VzKkgrO0OJfWQyrAxYsX1bhx4/SwFcHBwap27dpq9OjRNsMdAIbZGDVqlKpcubIePgNDX+zbt6/AkAp///vfVdu2bfV5x/HCdYfhCOyH8Ni/f78ekiImJkZ/Lo7b3Xffrb766qtCvyO2d+7cWQ9fgGu5Tp06ekgPY2gER0MqGNeLo8l6iAH473//qzp06KDKli2rJ+R/yJAheigDV+D/CvaLoToOHTpks+3EiRN6X9gn9h0dHa3atWun5s2bV+R+Xb3mHZ1j62323xdDlrzyyiuW84/z8cADD+jzY+2TTz7Rw1DgnJYrV04PiYH/+xiehHxLAP5xd2BHRERE5O3YpoqIiIjIBAyqiIiIiEzAoIqIiIjIBAyqiIiIiEzAoIqIiIjIBAyqiIiIiEzAwT9LEUbaPnLkiB4U72qeF0ZERESlB6NPnT17Vg/mjCcROMOgqhQhoKpdu3ZpfiQRERGZ5NChQ/ppBM4wqCpFKKEyTgoeG0LkVTIzRWrUyJ8/cgQP7HN3joiISsWZM2d0oYhxH3eGQVUpMqr8EFAxqCKv878HQmv4UcCgioj8TEARTXfYUJ2IiIjIBAyqiIiIiEzA6j8icvGvRRmRfv0uzxMRkQ3+ZSQi14SGisyaxaNFROQEq/+IiIiIvD2oqlevnm5Jbz8NGTJEb8/OztbzlSpVksjISLn//vvl6NGjNvs4ePCgdOvWTSIiIqRq1aoyYsQIuXTpkk2aNWvWSKtWrSQ0NFQaNGggsxz82p46darOT1hYmLRr1042btxos92VvBD5NKXyh1XAhHkiIvKcoGrTpk2SmppqmVasWKHXP/jgg/r1ueeek4ULF0piYqKsXbtWD57Zs2dPy/tzc3N1QHXhwgXZsGGDzJ49WwdMY8aMsaRJSUnRaW6//XZJTk6W4cOHy8CBA2X58uWWNHPnzpXnn39exo4dK1u3bpXmzZtLQkKCHDt2zJKmqLwQ+bzz50UiI/MnzBMRkS3lQYYNG6bq16+v8vLyVHp6ugoODlaJiYmW7bt378bPY5WUlKSXlyxZogIDA1VaWpolzbRp01RUVJTKycnRyyNHjlRNmjSx+ZxevXqphIQEy3Lbtm3VkCFDLMu5ubmqRo0aauLEiXrZlby4IiMjQ78Hr0Re59w5lE/lT5gnIvITGS7evz2mTRVKm7744gt54okndBXgli1b5OLFi9KpUydLmsaNG0udOnUkKSlJL+O1WbNmUq1aNUsalDBh5NOdO3da0ljvw0hj7AOfi8+yToPn+mDZSONKXhzJycnRebGeiIiIyDd5TFA1f/58SU9Pl8cff1wvp6WlSUhIiJQvX94mHQIobDPSWAdUxnZjW2FpEOBkZWXJiRMndDWiozTW+ygqL45MnDhRoqOjLROf+0dERGbKzVOStP+kLEg+rF+xTO7jMUMq/N///Z907dpVPwHaV4wePVq31bJ/dhAREdHVWrYjVcYt3CWpGdmWddWjw2Rs9zjp0rQ6D7C/llQdOHBAvv/+e92A3BATE6Or5lB6ZQ097rDNSGPfA89YLioNnr0XHh4ulStXlqCgIIdprPdRVF4cQW9D4zl/fN4fERGZGVAN/mKrTUAFaRnZej22k58GVTNnztTDIaCXnqF169YSHBwsK1eutKzbs2ePHkIhPj5eL+N1+/btNr300IMQAUxcXJwljfU+jDTGPlCth8+yTpOXl6eXjTSu5IWIiKg0oIoPJVSOKvqMddjOqkA/rP5DAIOgql+/flLG6tEXaIM0YMAAXX1WsWJFHSgNHTpUBzHt27fXaTp37qyDp759+8qkSZN0+6ZXX31VjyeFUiIYNGiQfPTRRzJy5EjdCH7VqlUyb948Wbx4seWz8Bn4/DZt2kjbtm3l/fffl8zMTOnfv7/LeSHyeUFBIg88cHmeiNxiY8qpAiVU9oEVtiNdfP1KpZo3f+f2oArVfijxQcBj77333tM98TDQJnrSodfexx9/bNmOartFixbJ4MGDdYBTtmxZHRyNHz/ekiY2NlYHUBhnasqUKVKrVi357LPP9L4MvXr1kuPHj+vxrRCYtWjRQpYtW2bTeL2ovBD5vLAwkcREd+eCyO8dO+s8oLqSdGSeAIyrYOL+qBBoqI5Sr4yMDF3aRUREVFzo5df705+KTPefJ9uzpKqU798e0aaKiIiIXNM2tqLu5RfgZDvWYzvSUeliUEVErsEz/wIC8ifME5FbBAUG6GETwD6wMpaxHemodDGoIiIi8jIYh2rao60kJjrMZj2WsZ7jVPlpQ3UiIiIqPgROd8bF6F5+aJRetVx+lR9LqNyHQRUREZGXQgDFYRM8B6v/iIiIiEzAoIqIiIjIBAyqiIiIiEzANlVE5Bo8muauuy7PExGRDQZVROT6Y2qsnplJRES2WP1HREREZAIGVUREREQmYFBFRK7Bo2nKls2f+JgaIqIC2KaKiFx3/jyPFhGREyypIiIiIjIBgyoiIiIiEzCoIiIiIjIBgyoiIiIiEzCoIiIiIjIBe/8RkWsCA0VuvfXyPBER2WBQRUSuCQ8XWbOGR4uIyAn+3CQiIiIyAYMqIiIiIhMwqCIi1+DRNFWq5E98TA0RUQFsU0VErjtxgkeLiMgJllQRERERmYBBFREREZEJGFQRERERmYBBFREREZEJGFQRERERmYC9/4jINXg0TZs2l+eJiMgGgyoicv0xNZs28WgRETnBn5tEREREJmBQRURERGQCBlVE5Jrz50Xq1cufME9ERDbYpoqIXKOUyIEDl+eJyKPl5inZmHJKjp3NlqrlwqRtbEUJCgxwd7Z8GoMqIiIiH7NsR6qMW7hLUjOyLeuqR4fJ2O5x0qVpdbfmzZex+o+IiMjHAqrBX2y1CaggLSNbr8d2KhkMqoiIiHyoyg8lVI4q6I112I50ZD4GVURERD4CbajsS6isIZTCdqQj8zGoIiIi8hFolG5mOioeNlQnItcEBIjExV2eJyKPg15+Zqaj4mFQRUSuiYgQ2bnT644Wu5WTP8GwCejlh0bpjlpN4edQTHT+8Arkg9V/hw8flkcffVQqVaok4eHh0qxZM9m8ebNlu1JKxowZI9WrV9fbO3XqJHv37rXZx6lTp6RPnz4SFRUl5cuXlwEDBsi5c+ds0vz6669y8803S1hYmNSuXVsmTZpUIC+JiYnSuHFjnQb5WLJkic12V/JCRJ4DvZw6vL1Ken/6kwz7Mlm/Ypm9n8hXYRwqDJsA9uXJxjK2c7wqHwyqTp8+LTfddJMEBwfL0qVLZdeuXfKPf/xDKlSoYEmD4OeDDz6Q6dOny88//yxly5aVhIQEyc6+XB+MgGrnzp2yYsUKWbRokfzwww/y1FNPWbafOXNGOnfuLHXr1pUtW7bI5MmT5fXXX5dPPvnEkmbDhg3Su3dvHZBt27ZN7r33Xj3t2LGjWHkhIs/AbuXkrzAO1bRHW+kSKWtYxnqOU1VyAhSKX9zkpZdekvXr18uPP/7ocDuyVqNGDXnhhRfkxRdf1OsyMjKkWrVqMmvWLHn44Ydl9+7dEhcXJ5s2bZI2bdroNMuWLZO77rpL/vrrL/3+adOmySuvvCJpaWkSEhJi+ez58+fLb7/9ppd79eolmZmZOigztG/fXlq0aKGDKFfyUhQEd9HR0fp9KFUj8ip4NM0NN+TPb9qUXx3owVV+KJFy1gvKqAJZN+oO/mInn8Wqb/O4ev92a0nVt99+qwOhBx98UKpWrSotW7aUTz/91LI9JSVFB0KoZjPgS7Vr106SkpL0Ml5R5WcEVID0gYGBujTJSHPLLbdYAipACdOePXt0aZmRxvpzjDTG57iSF3s5OTn6RFhPRF4Lv7927cqfPPwxNexWTpRfFRhfv5L0aFFTv7LKr+S5Naj6448/dClSw4YNZfny5TJ48GB59tlnZfbs2Xo7ghhAaZA1LBvb8IqAzFqZMmWkYsWKNmkc7cP6M5ylsd5eVF7sTZw4UQdexoS2XERU8titnIj8LqjKy8uTVq1ayYQJE3QpFdpBPfnkk7q6zReMHj1aFxUa06FDh9ydJSK/wG7lROR3QRV60aE9lLXrrrtODh48qOdjYmL069GjR23SYNnYhtdjx47ZbL906ZLuEWidxtE+rD/DWRrr7UXlxV5oaKiue7WeiKj0upU7G00L67Gd3cqJyGeCKvT8Q7sma7///rvupQexsbE6YFm5cqVlO9oloa1UfHy8XsZrenq67tVnWLVqlS4FQ3snIw16BF68eNGSBj0FGzVqZOlpiDTWn2OkMT7HlbwQkWdgt3IicgvlRhs3blRlypRRb775ptq7d6+aM2eOioiIUF988YUlzVtvvaXKly+vFixYoH799VfVo0cPFRsbq7KysixpunTpolq2bKl+/vlntW7dOtWwYUPVu3dvy/b09HRVrVo11bdvX7Vjxw715Zdf6s+ZMWOGJc369et1Xt555x21e/duNXbsWBUcHKy2b99erLwUJiMjA6179SuR1zl3Ds3T8yfMe4Gl24+o9hO+V3VHLbJMWMZ6IiJXuXr/dmtQBQsXLlRNmzZVoaGhqnHjxuqTTz6x2Z6Xl6dee+01HRQhTceOHdWePXts0pw8eVIHUZGRkSoqKkr1799fnT171ibNL7/8ojp06KD3UbNmTR0g2Zs3b5669tprVUhIiGrSpIlavHhxsfNSGAZV5NUyM5WqWzd/wryXuJSbpzbsO6Hmb/tLv2KZiKg4XL1/u3WcKn/DcaqIiIi8j1eMU0VERETkKxhUEREREZmAQRURuSYrK/8xNZgwT0RENsrYLhIROZGXJ7J58+V5IiKywZIqIiIiIhMwqCIiIiIyAYMqIiIiIhMwqCIiIiIyAYMqIiIiIhOw9x8Rua5yZR4tIiInGFQRkWvKlhU5fpxHi4jICVb/EREREZmAQRURERGRCRhUEZFr8Gia227Ln/iYGiKiAtimiohcg0fTrF17eZ6IiGywpIqIiIjIBAyqiIiIiEzAoIqIiIjIBAyqiIiIiEzAoIqIiIjIBOz9R0Sui4jg0SIicoJBFRG5/piazEweLSIiJ1j9R0RERGQCBlVEREREJmBQRUSuyc4W6dYtf8I8ERHZYJsqInJNbq7IkiWX54mIyAZLqoiIiIhMwKCKiIiIyAQMqoiIiIhMwKCKiIiIyAQMqoiIiIhMwKCKiIiIyAQcUoGIXH9MjVI8WkRETrCkioiIiMgEDKqIiIiITMCgiohcg0fTPPhg/sTH1BARFcCgiohcg0fTfPVV/sTH1BARFcCgioiIiMgEDKqIiIiITMCgioiIiMgEDKqIiIiITMCgioiIiMjbg6rXX39dAgICbKbGjRtbtmdnZ8uQIUOkUqVKEhkZKffff78cPXrUZh8HDx6Ubt26SUREhFStWlVGjBghly5dskmzZs0aadWqlYSGhkqDBg1k1qxZBfIydepUqVevnoSFhUm7du1k48aNNttdyQsRERH5L7eXVDVp0kRSU1Mt07p16yzbnnvuOVm4cKEkJibK2rVr5ciRI9KzZ0/L9tzcXB1QXbhwQTZs2CCzZ8/WAdOYMWMsaVJSUnSa22+/XZKTk2X48OEycOBAWb58uSXN3Llz5fnnn5exY8fK1q1bpXnz5pKQkCDHjh1zOS9EPi8iQuTcufwJ80REZEu50dixY1Xz5s0dbktPT1fBwcEqMTHRsm737t148JhKSkrSy0uWLFGBgYEqLS3NkmbatGkqKipK5eTk6OWRI0eqJk2a2Oy7V69eKiEhwbLctm1bNWTIEMtybm6uqlGjhpo4caLLeXFFRkaGfg9eiYh80aXcPLVh3wk1f9tf+hXLRN7O1fu320uq9u7dKzVq1JBrrrlG+vTpo6vzYMuWLXLx4kXp1KmTJS2qBuvUqSNJSUl6Ga/NmjWTatWqWdKghOnMmTOyc+dOSxrrfRhpjH2glAufZZ0mMDBQLxtpXMmLIzk5OTov1hMRka9atiNVOry9Snp/+pMM+zJZv2IZ64n8gVuDKrRdQnXdsmXLZNq0abqq7uabb5azZ89KWlqahISESPny5W3egwAK2wCv1gGVsd3YVlgaBDhZWVly4sQJXY3oKI31PorKiyMTJ06U6Ohoy1S7du0rOEpEHiInR+Txx/MnzBNZQeA0+IutkpqRbXNc0jKy9XoGVuQPyrjzw7t27WqZv/7663WQVbduXZk3b56Eh4eLtxs9erRuq2VAIMfAirwWOoDMnp0/P3WqSGiou3NEHiI3T8m4hbsE9SP2sC5ARG+/My5GggKxROSb3F79Zw0lQddee63s27dPYmJidNVcenq6TRr0uMM2wKt9Dzxjuag0UVFROnCrXLmyBAUFOUxjvY+i8uIIehvic6wnIiJfszHlVIESKvvACtuRjsiXeVRQde7cOdm/f79Ur15dWrduLcHBwbJy5UrL9j179ug2V/Hx8XoZr9u3b7fppbdixQodvMTFxVnSWO/DSGPsA9V6+CzrNHl5eXrZSONKXoiI/NWxs9mmpiPyVm6t/nvxxRele/fuusoPQxRgSAOUGvXu3Vu3QRowYICuPqtYsaIOlIYOHaqDmPbt2+v3d+7cWQdPffv2lUmTJun2Ta+++qoeTwqlRDBo0CD56KOPZOTIkfLEE0/IqlWrdPXi4sWLLfnAZ/Tr10/atGkjbdu2lffff18yMzOlf//+ersreSEi8ldVy4WZmo7IW7k1qPrrr790AHXy5EmpUqWKdOjQQX766Sc9D++9957uiYeBNtGTDr32Pv74Y8v7EYAtWrRIBg8erAOcsmXL6uBo/PjxljSxsbE6gMI4U1OmTJFatWrJZ599pvdl6NWrlxw/flyPb4XArEWLFrrxvHXj9aLyQkTkr9rGVpTq0WG6UbqjdlVoRRUTHabTEfmyAIyr4O5M+As0VEepV0ZGBttXkffJzBSJjMyfxwCgZcu6O0fkgb3/wPqmYjRLn/ZoK+nStLpb8kZUWvdvj2pTRURE3gkBEwInlEhZwzIDKvIXbq3+IyIvgkfTGJ1C+JgachJYYdgE9PJDo3S0oUKVH4dRIH/BoIqIXBMQIPK/9o5EziCAiq9fiQeI/BKr/4iIiIhMwKCKiFyDR9MMGZI/8TE1REQFMKgiItcfU4NhRDBhnoiIbDCoIiIiIjIBG6oTEZFfPgSavRTJbAyqiIjI7wYqHbdwl81DoDEi/NjucRyglK4Kq/+IiMjvRn63DqgAj9jBemwnulIMqoiIyG+q/FBC5ejZbMY6bEc6oivBoIqIiPwC2lDZl1BZQyiF7UhHdCXYpoqIXBMeLpKScnmeyMvg0TlmpiOyx6CKiFwTGChSrx6PFnktPIvQzHRE9lj9R0REfgEPd0YvvwAn27Ee25GO6EowqCIi11y4IDJiRP6EeSIvfNgzhk0A+8DKWMZ2pCPvkpunJGn/SVmQfFi/uquzQYBSit0cSsmZM2ckOjpaMjIyJCoqqrQ+lsgcmZkikZH58+fOiZQtyyNLXonjVPmWZaUw7pir928GVaWIQRV5NQZV5EM4orpvjTum7NYbZY3THm1lSmDl6v2bDdWJiMjvoIovvn4ld2eDSnDcMQRW2H5nXEypVemyTRURERF5nY0eOO4YgyoiIiLyOsc8cNwxBlVERETkdap64LhjxW5TlZKSIj/++KMcOHBAzp8/L1WqVJGWLVtKfHy8hIVxwDQiIiIqvXHH8DBsR+2q0IoqppTHHXM5qJozZ45MmTJFNm/eLNWqVZMaNWpIeHi4nDp1Svbv368Dqj59+sioUaOkbt26JZtrIip9eDTNjh2X54mIPGDcscFfbNUBlPKAccdcqv5DSdQHH3wgjz/+uC6hSk1NlS1btsi6detk165duqvhggULJC8vT9q0aSOJiYkln3MiKv3H1DRpkj9hnojIzbo0ra6HTUCJlDUsmzWcQnG4NE7V8uXLJSEhwaUdnjx5Uv78809p3bq1GfnzKRynioiIyPvGHePgnx6IQRV5NTyaZsKE/PmXXxYJCXF3joiISkWJBVXY4YoVK3RpVEBAgMTGxkqnTp342BUTTwqRR+KI6kTkp86UxIjqX3zxhTzzzDN659bwQdOnT5devXpdeY6JiIiIvJjLrU23bt0q/fv3l3vvvVe2bdsmWVlZekgF9Abs3r279O3bV3755ZeSzS0RERGRh3K5+g8B1blz55z27HvggQd0kdg///lPs/PoM1j9R16N1X9E5KfOuFj953JJ1fr16+Xpp592un3QoEF6iAUiIiIif+RyUHXkyBG59tprnW7HtsOHD5uVLyIiIiLfDKrQfqqwx9CEhoZKdnbpPbSQiIiIyJMUq/cfBgFFnaIj6enpZuWJiDwRflRt3Hh5noiIrqyheqALj6XAuFW5ubmu7M4vsaE6ERGR9zF9nCo814+IiIiITKj+IyI/f0zNlCn588OG8TE1RERX2lD9999/l41Ge4r/Wblypdx+++3Stm1bmWA8E4yIfNPFiyIjR+ZPmCcioisLqkaNGiWLFi2yLKekpOiR1ENCQiQ+Pl4mTpwo77//vqu7IyIiIvLP6j88jmYkfqH+z5w5c/TYVOgRCNdff718+OGHMnz48JLJKREREZEvlFSdOHFCatWqZVlevXq1Lqky3HbbbfLnn3+an0MiIiIiXwqqKlasKKmpqZaegCi5at++vWX7hQsXxMXRGYiIiIj8N6hCSdQbb7whhw4d0m2nEFhhnWHXrl1Sr169K87IW2+9pce5sq4+xAjtQ4YMkUqVKklkZKTcf//9cvToUZv3HTx4ULp16yYRERFStWpVGTFihFy6dMkmzZo1a6RVq1Z61PcGDRrIrFmzCnz+1KlTdf4xany7du0KNMp3JS9ERETkv1wOqt5880357bffpG7durrR+qRJk6Rs2bKW7Z9//rnccccdV5SJTZs2yYwZM3S7LGvPPfecLFy4UBITE2Xt2rX6+YM9e/a0bMdAowioUEq2YcMGmT17tg6YxowZY9OgHmnQSzE5OVkHbQMHDrS0BYO5c+fK888/L2PHjpWtW7dK8+bNJSEhQY4dO+ZyXoiIiMjPqWK4ePGiSk5OVocPHy6wDetPnDihiuvs2bOqYcOGasWKFerWW29Vw4YN0+vT09NVcHCwSkxMtKTdvXs36hdVUlKSXl6yZIkKDAxUaWlpljTTpk1TUVFRKicnRy+PHDlSNWnSxOYze/XqpRISEizLbdu2VUOGDLEs5+bmqho1aqiJEye6nBdXZGRk6PfglcjrXLqk1OrV+RPmiYj8RIaL92+XS6qgTJkyuhSnRo0aBbZhParGigtVaihJ6tSpk836LVu2yMWLF23WN27cWOrUqSNJSUl6Ga/NmjWTatWqWdKghAnDye/cudOSxn7fSGPsA6Vc+CzrNHgkD5aNNK7kxZGcnBydF+uJyGsFBaEdQP6EeSIiurIhFZxVdeFZOBhaAVVqVapUkeL48ssvdXUbqv/spaWl6TGwypcvb7MeARS2GWmsAypju7GtsDQIcLKysuT06dO6GtFRGlR3upoXRzB217hx41w6FkREROTdXC6pQvDkaEpPT5dPP/1UGjVqJDt27HD5g9HgfdiwYXq8KzQO90WjR4/WD180JnxnIq+FUdSnTs2fOKI6EdGVl1TNnDnT6Tb0BHzyySd1EIHG3K5AlRoagqNXngElRj/88IN89NFHuiE5quYQtFmXEKHHXUxMjJ7Hq30vPaNHnnUa+156WMZTpsPDwyUoKEhPjtJY76OovDiC3oaYiHzm2X/PPJM///jjIsHB7s4REZFHCTRlJ4GB8uyzz+pAyVUdO3aU7du36x55xtSmTRvp06ePZT44OFg/X9CwZ88ePYQCHosDeMU+rHvprVixQgdMcXFxljTW+zDSGPtAtV7r1q1t0iBIxLKRBtuLygsREZW83DwlSftPyoLkw/oVy0ReV1JVFAyvcP78eZfTlytXTpo2bVpgH2jsbqwfMGCAHuoAA48iUBo6dKgOYoxBRzt37qyDp759++ohHtC+6dVXX9WN340SokGDBumSLzxi54knnpBVq1bJvHnzZPHixZbPxWf069dPB3J4ODTG4crMzJT+/fvr7ajmLCovRERUspbtSJVxC3dJaka2ZV316DAZ2z1OujStzsNPvhNUofQHDdbN9N577+lSMAy0iZ506LX38ccfW7aj2g4PeR48eLAOcBCUITgaP368JU1sbKwOoDDO1JQpU/Sjdj777DO9L0OvXr3k+PHjenwrBGYtWrSQZcuW2TReLyovRERUsgHV4C+2in25VFpGtl4/7dFWHhlYoSRtY8opOXY2W6qWC5O2sRUlKDDA3dmiEhKAcRVcSfjtt986XI8G2Kj2Q6CC6eGHHzY7jz4DPQ5R6oVjhtIuIq+SmSkSGZk/f+4cipbdnSPyEwhMOry9yqaEyhpClJjoMFk36g6PClhYsuZ/92+XS6ruvfdep9V46PnHgIqIiEoCSnqcBVSAkgFsR7r4+sUfL7EkeGvJGl0dl4MqNN4mIiIqbag6MzNdaZSsoe2Xo2ogrENZGrbfGRfjUSVr5EFtqojIx6Hzx6JFl+eJSgnaIpmZrqR5Y8kaleKQChj53FUY4HL9+vVXkyci8kRlyoh065Y/YZ6olKBxN3r5OSvTwXpsRzpP4G0la1TKQdW0adPkuuuu08MW7N69u8B2NNxasmSJPPLII3owz5MnT5qYRSIi8meoIsOwCWAfWBnL2O4pVWneVrJGpRxUrV27Vt5++209bALGkELL94YNG+qHGWOIAowthTGg8IBhPKrmnnvuMTGLROQR8GiaWbPyJz6mhkoZGnWjcTd6+VnDsqc1+va2kjVyw5AKhhMnTsi6devkwIED+oHElStXlpYtW+oJ4ziRcxxSgbwah1QgD+At4z4Zvf/A+iZr5NTTAkEy5/5d7KCKrhyDKvJqDKqIioXjVPkO08epIiIiItehJArDJnhDyRqZg0EVERFRCUEAxWET/AcbQRERERGZgEEVERERkTuDqgsXLsiePXvk0qVLZuSDiIiIyL+CqvPnz8uAAQMkIiJCmjRpIgcPHtTrhw4dKm+99VZJ5JGIPAEeTTNvXv7Ex9QQEV19UDV69Gj55ZdfZM2aNRIWdnkQtk6dOsncuXOLuzsi8hZ4NM2DD+ZPfEwNEdHV9/6bP3++Dp7at28vAQGXu4Wi1Gr//v3F3R0RERGRfwZVx48fl6pVqxZYn5mZaRNkEZGPQfvJb77Jn7/vPpZWERFdbfVfmzZtZPHixZZlI5D67LPPJD4+vri7IyJvkZMj8tBD+RPmiYjo6kqqJkyYIF27dpVdu3bpnn9TpkzR8xs2bNAPXiYiIiLyR8UuqerQoYMkJyfrgKpZs2by3Xff6erApKQkad26dcnkkoiIiMgXH1NTv359+fTTT83PDREREZG/lFQtWbJEli9fXmA91i1dutSsfBERERH5dlD10ksvSW5uboH1Sim9jYiIiMgfFbv6b+/evRIXF1dgfePGjWXfvn1m5YvI7+TmKdmYckqOnc2WquXCpG1sRf2EeyIi8tGgKjo6Wv744w+pV6+ezXoEVGXLljUzb0R+Y9mOVBm3cJekZmRb1lWPDpOx3eOkS9Pq4hFCQkRmzrw8T0REV1f916NHDxk+fLjN6OkIqF544QW55557irs7Ir+HgGrwF1ttAipIy8jW67HdIwQHizz+eP6EeSIiurqgatKkSbpECtV9sbGxerruuuukUqVK8s477xR3d0Ti71V+KKFSDrYZ67Ad6YiIyAer/zDQ54oVK/SDlcPDw+X666+XW265pWRySOTD0IbKvoTKGkIpbEe6+PqVxO2PqTF6/iYk8DE1RERmjFOFR9N07txZT0R05dAo3cx0JQqPprn77vz5c+cYVBERXUlQ9cEHH8hTTz0lYWFher4wzz77rCu7JCIR3cvPzHREROQ+AQoDTBUB7aY2b96s201h3unOAgJ0z0By7MyZM7r6NCMjQ6KioniYSLeV6vD2Kt0o3dF/RAyoEBMdJutG3eH+4RUyM0UiIy+XVLG3LxH5iTMu3r9dKqlKSUlxOE9EVweBEoZNQC8/hEzWgZURQmG72wMqIiIyt/ffxYsX9XP/du/eXZy3EVEhMA7VtEdb6RIpa1jGeo8Zp4qIiMxrqB4cHCzZ2R7QYJbIxyBwujMuhiOqExH50zhVQ4YMkbffflsuoXs1EZkGVXwYNqFHi5r6lVV+REQ+PqTCpk2bZOXKlfLdd99Js2bNCjya5uuvvzYzf0TkKfBomo8+ujxPRERXF1SVL19e7r///uK+jYi8HR5NM2SIu3NBROQ7QdVM44GqRERERFT8NlV5eXm6LdVNN90kN9xwg7z00kuSlZXl6tuJyNvl5oqsWZM/YZ6IiK4sqHrzzTfl5ZdflsjISKlZs6ZMmTJFN1onIj+Bnr+3354/sRcwEdGVB1X/+te/5OOPP5bly5fL/PnzZeHChTJnzhxdgkVERETk71wOqg4ePCh33XWXZblTp076sTRHjhwpqbwRERER+V5QhXGp8EBl+8FAMcr6lZo2bZpcf/31+jk6mOLj42Xp0qWW7RhoFFWMeOYgqh3R6/Do0aMFgr1u3bpJRESEVK1aVUaMGFFgDK01a9ZIq1atJDQ0VBo0aCCzZs0qkJepU6dKvXr19Hds166dbNy40Wa7K3khIiIi/+Vy7z88d/nxxx/XgYl1oDFo0CCbsaqKM05VrVq15K233pKGDRvq/c+ePVt69Ogh27ZtkyZNmshzzz0nixcvlsTERP0gw2eeeUZ69uwp69ev1+/Pzc3VAVVMTIxs2LBBUlNT5bHHHtPB3oQJEyzPKkQa5BPVlRhja+DAgVK9enVJSEjQaebOnSvPP/+8TJ8+XQdU77//vt62Z88eHahBUXkhIiIi/xagEM24oH///qUy5ELFihVl8uTJ8sADD0iVKlXk3//+t56H3377Ta677jpJSkqS9u3b61Ktu+++W1dBVqtWTadBYDRq1Cg5fvy4hISE6HkEQzt27LB8xsMPPyzp6emybNkyvYxACj0aP/rfwIZoJ1a7dm0ZOnSo7uWIp1IXlRczn3JN5JEyM0UiI/Pnz50TsRv4l4jIV7l6/y7jKeNTodQJpUCZmZm6GnDLli26ahFttwyNGzeWOnXqWAIZvGJUdyOgApQwDR48WHbu3CktW7bUaaz3YaQZPny4nr9w4YL+rNGjR1u2BwYG6vfgveBKXhzJycnRk/VJISIiIt9U7ME/zbZ9+3YdRKEqEW2VvvnmG4mLi5Pk5GRd0oQR3K0hgEpLS9PzeLUOqIztxrbC0iDAwThbp0+f1gGdozQojTL2UVReHJk4caKMGzfuCo4KkYeOqD5p0uV5IiLyrKCqUaNGOoBCkdpXX30l/fr1k7Vr14ovQOkX2moZEMihWpHIK+F5fyNGuDsXREQey+1BFUqA0CMPWrdurR/YjIFFe/Xqpavm0PbJuoQIPe7QMB3wat9Lz+iRZ53GvpcellEnGh4eLkFBQXpylMZ6H0XlxRE06rdu2E9ERES+y+UhFUoLGomjHRICLPTiQ289A3rjYQgFVBcCXlF9eOzYMUuaFStW6IAJVYhGGut9GGmMfSCow2dZp0EesGykcSUvRJ4qN09J0v6TsiD5sH7F8pXtKFdk06b8iY+pISLyrJIqVI917dpVN/g+e/as7l2HMaUwajta2Q8YMEBXn6FHIAIl9MZDEGM0DO/cubMOnvr27SuTJk3S7ZteffVVPZ6UUUKEoRTQq2/kyJHyxBNPyKpVq2TevHm6R6ABn4FqxzZt2kjbtm31kApoMG/0eHQlL0SeaNmOVBm3cJekZmRb1lWPDpOx3eOkS9PqxdsZHk3Ttm3+PHv/ERF5VlCFEiaMK4XxpRC4YCBQBFR33nmn3v7ee+/pnngYaBOlV+i1h0flGFBtt2jRIt3bDwEOxstCcDR+/HhLmtjYWB1AYZwpVCtibKzPPvvMMkYVoKoRQzCMGTNGB2YtWrTQwy1YN14vKi9EnhhQDf5iq9iXS6VlZOv10x5tVfzAioiIrn6cKrp6HKeKSguq+Dq8vcqmhMpaANoKRofJulF3SFAgllzAcaqIyE+dcXGcKo9rU0VEV29jyimnARXglxS2Ix0REZmDQRWRDzp2NtvUdEREVDQGVUQ+qGq5MFPTERFR0RhUEfmgtrEVdS8/Z62lsB7bkY6IiHxk8E8iMh8an2PYBPTyQwBl3RvFCLSw3eVG6sajacaOvTxPREQ22PuvFLH3H3n1OFVERH7qjIu9/1hSReTDEDjdGReje/mhUTraUKHKr1glVERE5BIGVUQ+DgFUfP1KV7+jvDyR3bvz56+7TiSQTTKJiKwxqCIi12RliTRtmj/Px9QQERXAn5pEREREJmBQRURERGQCVv8REZXwcxjZUYDIPzCoIiIqIRzSgsi/sPqPiKiEAioMvmr/YOu0jGy9HtuJyLcwqCK/h+qZpP0nZUHyYf2KZaKrgWsIg646upKMddjOa43It7D6j/waq2eKAY+mefHFy/PkFNpQ2ZdQ2QdW2I50powhRkQegUEVib9Xz9iXJhjVM9MebcVHuVgLCRGZPLlUz5G3wuj1ZqYjIu/AoIr8svdUUdUz2Du24xEvfKSLfzHjesP7zExHRN6BQRX5ZfUcq2eu8DE1Bw/mz9ep45OPqTHrekMghveh1NNR4I4QLSY6P2AjIt/he38VyWeUZO8pVs9c4WNqYmPzJ8z7GDOvN5RsIRAD+zIuYxnbWQpK5FsYVJFf9p5i9QyV9PWGki20y0OJlDUss70ekW9i9R95pJKunmP1DJXG9YbACu3yOKI6kX9gUEUeqaSr54zqGVTroDrGuvyB1TP+pySvN1xrHDaByD+w+o88UmlUz7F6hkrzeiMi38eSKvJIpVU9583VM3xQr3lYHUxEZmBQRR6pNKvnvLF6hiPBm4vVwURkBlb/kcdi9ZyHPai3TBmRv/0tf8K8j+H1RkRXK0ApxafHlpIzZ85IdHS0ZGRkSFRUVGl9rNdjNZftsejw9iqnPdWMatF1o+7wiipMT8TrjYiu9P7tez83yeeUdvWcJ99UORJ8yfPG6mAi8gwMqoi8qK2SW0eCR6H2iRP585UriwR4RqBJROQp2KaKyN1tlbyl6//58yJVq+ZPmCciIhsMqqjUqtSS9p+UBcmH9euVPl7GWx+LY3bXf2dlRFiP7XxQLxFR6WP1H4m/V6l5U1sldv0nIvJcLKnyM6VdYuQNVWpub6tUTOz6T0TkmVhS5UdKu8SoqCo1VFVhO0Y0d3fvOm97TElJjATvyb0eiYi8AYMqP2GUGNkHOEaJ0bRHW5keWHlLlZq3PqbEzK7/3lBFS0Tk6Vj95wfc1Qjbm6rUjLZKYF82Y/ZjcTytKtdbqmiJiDwdS6r8gLtKjLyxSg0ldvYlNjEeWGJjVslSsapo8Wiafv3yN/rgY2qIiK4W/zL6AXeVGHljlVpJtFXy5KrcYgfcs2ZdZe6JiHwXq//8gLtKjLyxSs26rVKPFjX1qyflz+yqXG+qoiUi8nQMqvyAOweMZPd/cxWnZMn0gBuPqcnMzJ/4HHYiogJY/ecH3D1gpDdUqXkLs0uWilVFi0fTREbmbzh3TqRs2eJknYjI57m1pGrixIlyww03SLly5aRq1apy7733yp49e2zSZGdny5AhQ6RSpUoSGRkp999/vxw9etQmzcGDB6Vbt24SERGh9zNixAi5dOmSTZo1a9ZIq1atJDQ0VBo0aCCzHLQNmTp1qtSrV0/CwsKkXbt2snHjxmLnxVO5u8TIk6vU/Lkq11uraImIPJFbg6q1a9fqIOWnn36SFStWyMWLF6Vz586SieqF/3nuuedk4cKFkpiYqNMfOXJEevbsadmem5urA6oLFy7Ihg0bZPbs2TpgGjNmjCVNSkqKTnP77bdLcnKyDB8+XAYOHCjLly+3pJk7d648//zzMnbsWNm6das0b95cEhIS5NixYy7nxdMhcFo36g75z5PtZcrDLfQrlj2pVxuVflWuuwNuIiJfEaCU5zSOOH78uC5pQsByyy23SEZGhlSpUkX+/e9/ywMPPKDT/Pbbb3LddddJUlKStG/fXpYuXSp33323DnCqVaum00yfPl1GjRql9xcSEqLnFy9eLDt27LB81sMPPyzp6emybNkyvYySKZSaffTRR3o5Ly9PateuLUOHDpWXXnrJpbwU5cyZMxIdHa33FRUVVSLHkHyf0ftPnFTlXmkgVOSI6vixw+o/IvJDZ1y8f3tUQ3VkFipWzP+VvWXLFl161alTJ0uaxo0bS506dXQgA3ht1qyZJaAClDDhAOzcudOSxnofRhpjHyjlwmdZpwkMDNTLRhpX8kJUGkqqZIlVtEREPtJQHSVDqJa76aabpGnTpnpdWlqaLmkqX768TVoEUNhmpLEOqIztxrbC0iDwysrKktOnT+tqREdpUBrlal7s5eTk6MmAzyMyAxv/ExF5Ho8JqtC2CtVz69atE1+Bhvjjxo1zdzbIR5n57D8iIrp6HlH998wzz8iiRYtk9erVUqtWLcv6mJgYXTWHtk/W0OMO24w09j3wjOWi0qBeNDw8XCpXrixBQUEO01jvo6i82Bs9erSu0jSmQ4cOFfvYEHmMoCARtCfEhHkiIvKcoApt5BFQffPNN7Jq1SqJjY212d66dWsJDg6WlStXWtZhyAUMoRAfH6+X8bp9+3abXnroSYiAKS4uzpLGeh9GGmMfqNbDZ1mnQXUklo00ruTFHoZvQD6sJyKvFRYmkpiYP2GeiIhsKTcaPHiwio6OVmvWrFGpqamW6fz585Y0gwYNUnXq1FGrVq1SmzdvVvHx8XoyXLp0STVt2lR17txZJScnq2XLlqkqVaqo0aNHW9L88ccfKiIiQo0YMULt3r1bTZ06VQUFBem0hi+//FKFhoaqWbNmqV27dqmnnnpKlS9fXqWlpbmcl6JkZGSgs5Z+Jc9zKTdPbdh3Qs3f9pd+xTIREVGGi/dvtwZV/+sRXmCaOXOmJU1WVpb629/+pipUqKADo/vuu08HXtb+/PNP1bVrVxUeHq4qV66sXnjhBXXx4kWbNKtXr1YtWrRQISEh6pprrrH5DMOHH36ogyakadu2rfrpp59struSl8IwqPJcS7cfUe0nfK/qjlpkmbCM9URE5N8yXAyqPGqcKl/Hcao8e9wn+/8IVzvuk8/hOFVE5KfOeOM4VUSlDQNejlu4y+Fz74yiU2xHOiIiosIwqCK/hhHEUzMKf/gwtiMdERGRV4xTReTy41JMlHam8IDKkJqexRNERESFYlBFHte+CdVt1qVHeEDw2O5xJdKu6dS5yyPeF2bswp0SERrEtlVEROQUq//I4xqM21fHpWVk6/XYbraKZUNcSnc2+1KJ5YGIiHwDgyryigbjUkINxmOiw4uVno3WiYjIGQZV5BUNxlUJNRhHey1UL7qipPLgNfBomrvuyp/4mBoiogIYVJFHQKN0M9O5Cg3g0V6rOM3gzc6D18CjaRYvzp/4mBoiogIYVJFHQC8/M9MVBxrAY4DPimWD3ZYHIiLyfgyqyG3QPipp/0lZkHxY8vKUxESFOS0xwnpU06G6riQgsPppdKdCG66XdB6IiMi7cUgF8pihE8pHBOt2SwherJujG4EWqulKarwqCCkTKBPua6p7+Ymb8uDxj6mpWjV//tgxkbJl3Z0jIiKPwpIq8pihEzLOX9Sv0RG21XAx0WGl9vw9oyoQn+muPHi08+fzJyIiKoAlVeRRQyegDCisTKDMGdhOTpzLKfER1R1B4HRnXEypjepORES+gUEVedzQCWlnciQwIEB6tKgp7oIAKr5+Jbd9PhEReR9W/5FfDJ1ARERU0hhUkd8MnUBERFSSGFRRqTJGMHfX0AlEREQlhUEVlSpjBHOwD6w4bIGHCwwUufXW/AnzRERkg38ZqdRx2AIvFR4usmZN/oR5IiKywd5/5BYctoCIiHwNgypyGw5bQEREvoTVf0Tk+mNqqlTJnzBPREQ2WFJFRK47cYJHi4jICZZUEREREZmAQRURERGRCRhUEREREZmAQRURERGRCRhUEREREZmAvf+8XG6eko0pp+TY2Wz9EGI8Mw/jPxGZDo+madPm8jwREdlgUOXFlu1IlXELd0lqRrZlHR5GjGfrYcRyb8MA0cPh0TSbNrk7F0REHotBlRcHVIO/2CrKbn1aRrZeP+3RVl4TWCGY+mjVPpm5PkXSsy76RIBIRET+h2X4XghBCEqo7AMqMNZhO9J5Q3DY+u8r5L3vf7cJqKwDRKQpbTh2SftPyoLkw/rVG44lERG5F0uqvBDaUFlX+dnD7R/bkS6+fiXxVAiWBn2xtdDvEfC/APHOuJhSayvma9Wqpjl/XiQuLn9+1y6RiAh354iIyKOwpMoLoVG6mencWdpWFOsAsTSrVe2DVneWmnkMpUQOHMifME9ERDYYVHkh9PIzM50nlra5I0D0pWpVIiIqfQyqvBCGTUB1lLPKMKzHdqTzVMUNkkojQCxOtSoREZE9BlVeCG2L0L4H7AMrYxnbPXm8quIESaUVIPpCtSoREbkPgyovhQbTGDYhJto2OMGyNwynUFRpmyGgFANEX6hWJSIi92HvPy+GwAm94rxxRHWjtA2Nv5FbR62UKkQEy8SezUotQDQCPTRKd5SfgP8FrZ5crUpERO7DoMrLITjx5GETXCltsx++oHx4sPS/qZ48c0fDUg0QCwv0vKVatUQFBFweUgHzRERkI0Ap9o0uLWfOnJHo6GjJyMiQqKioUvtcT+dpj6fhOFVERHQl928GVaWIQZX38LRAj4iIPP/+zeo/Ih+rViUiIj/s/ffDDz9I9+7dpUaNGhIQECDz58+32Y6ayTFjxkj16tUlPDxcOnXqJHv37rVJc+rUKenTp4+OHMuXLy8DBgyQc+fO2aT59ddf5eabb5awsDCpXbu2TJo0qUBeEhMTpXHjxjpNs2bNZMmSJcXOC5HPP6amSZP8CfNEROQ5QVVmZqY0b95cpk6d6nA7gp8PPvhApk+fLj///LOULVtWEhISJDv7cqNmBFQ7d+6UFStWyKJFi3Sg9tRTT9kU2XXu3Fnq1q0rW7ZskcmTJ8vrr78un3zyiSXNhg0bpHfv3jog27Ztm9x777162rFjR7HyQuTT0PwSz/zDxKaYREQFKQ+BrHzzzTeW5by8PBUTE6MmT55sWZeenq5CQ0PVf/7zH728a9cu/b5NmzZZ0ixdulQFBASow4cP6+WPP/5YVahQQeXk5FjSjBo1SjVq1Miy/NBDD6lu3brZ5Kddu3bq6aefdjkvrsjIyND5xSuR1zl3Dv9R8yfMExH5iQwX798eO/hnSkqKpKWl6Wo2AxqJtWvXTpKSkvQyXlHl16ZNG0sapA8MDNSlSUaaW265RUJCQixpUMK0Z88eOX36tCWN9ecYaYzPcSUvjuTk5OiSMuuJiIiIfJPHBlUIYqBatWo267FsbMNr1apVbbaXKVNGKlasaJPG0T6sP8NZGuvtReXFkYkTJ+rgy5jQnouIiIh8k8cGVb5g9OjRuvulMR06dMjdWSIiIiJ/C6piYmL069GjR23WY9nYhtdjx47ZbL906ZLuEWidxtE+rD/DWRrr7UXlxZHQ0FDdK9F6IiIiIt/ksUFVbGysDlhWrlxpWYc2SWgrFR8fr5fxmp6ernv1GVatWiV5eXm6vZORBj0CL168aEmDnoKNGjWSChUqWNJYf46RxvgcV/JC5PPwaJq6dfMnPqaGiKgg5UZnz55V27Zt0xOy8u677+r5AwcO6O1vvfWWKl++vFqwYIH69ddfVY8ePVRsbKzKysqy7KNLly6qZcuW6ueff1br1q1TDRs2VL1797bppVetWjXVt29ftWPHDvXll1+qiIgINWPGDEua9evXqzJlyqh33nlH7d69W40dO1YFBwer7du3W9K4kpeisPcfERGR93H1/u3WoGr16tU6k/ZTv379LEMZvPbaazoowvAFHTt2VHv27LHZx8mTJ3UQFRkZqaKiolT//v11sGbtl19+UR06dND7qFmzpg6Q7M2bN09de+21KiQkRDVp0kQtXrzYZrsreSkKgyoiIiLv4+r9m8/+K0V89h8REZHv3r89tk0VEXmYrCyRG27InzBPREQ2+EBlInJNXp7I5s2X54mIyAZLqoiIiIhMwKCKiIiIyAQMqoiIiIhMwKCKiIiIyAQMqoiIiIhMwN5/ROS6ypV5tIiInGBQ5ady85RsTDklx85mS9VyYdI2tqIEBQa4O1vkycqWFTl+3N25ICLyWAyq/NCyHakybuEuSc3ItqyrHh0mY7vHSZem1d2aNyIiIm/FNlV+GFAN/mKrTUAFaRnZej22ExERUfExqPKzKj+UUOGpkPaMddiOdEQF4NE0t92WP/ExNUREBbD6z4+gDZV9CZU1hFLYjnTx9SuVat7IC+DRNGvXXp4nIiIbLKnyI2iUbmY6IiIiuoxBlR9BLz8z0xEREdFlDKr8CIZNQC8/ZwMnYD22Ix0REREVD4MqP4JxqDBsAtgHVsYytnO8KiIiouJjUOVnMA7VtEdbSUy0bRUflrGe41QRERFdGfb+80MInO6Mi+GI6lR8ERE8akRETjCo8lOo4uOwCVTsx9RkZvKgERE5weo/IiIiIhMwqCIiIiIyAYMqInJNdrZIt275E+aJiMgG21QRkWtyc0WWLLk8T0RENlhSRURERGQCBlVEREREJmBQRURERGQCBlVEREREJmBQRURERGQC9v4rRUop/XrmzJnS/Fgic1iPpo5rmD0AichPnPnffdu4jzvDoKoUnT17Vr/Wrl27ND+WyHw1avCoEpFf3sejo6Odbg9QRYVdZJq8vDw5cuSIlCtXTgICArw2WkdQeOjQIYmKihJ/w+/v3+cfeA349zXA8++f518ppQOqGjVqSGCg85ZTLKkqRTgRtWrVEl+A/0z+9B/KHr+/f59/4DXg39cAz7//nf/oQkqoDGyoTkRERGQCBlVEREREJmBQRcUSGhoqY8eO1a/+iN/fv88/8Brw72uA59+/z39R2FCdiIiIyAQsqSIiIiIyAYMqIiIiIhMwqCIiIiIyAYMqIiIiIhMwqPJDP/zwg3Tv3l2PDIuR3efPn19g5NgxY8ZI9erVJTw8XDp16iR79+61SXPq1Cnp06ePHvytfPnyMmDAADl37pxNml9//VVuvvlmCQsL0yPwTpo0Sbzh+z/++ON6vfXUpUsXn/n+EydOlBtuuEGP7F+1alW59957Zc+ePTZpsrOzZciQIVKpUiWJjIyU+++/X44ePWqT5uDBg9KtWzeJiIjQ+xkxYoRcunTJJs2aNWukVatWuqdQgwYNZNasWeIN3/+2224rcA0MGjTIJ77/tGnT5Prrr7cMXhkfHy9Lly71i3Pv6jHw5fPvyFtvvaW/4/Dhw/3qOigReEwN+ZclS5aoV155RX399dd4RJH65ptvbLa/9dZbKjo6Ws2fP1/98ssv6p577lGxsbEqKyvLkqZLly6qefPm6qefflI//vijatCggerdu7dle0ZGhqpWrZrq06eP2rFjh/rPf/6jwsPD1YwZM5Snf/9+/frp75eammqZTp06ZZPGm79/QkKCmjlzps5XcnKyuuuuu1SdOnXUuXPnLGkGDRqkateurVauXKk2b96s2rdvr2688UbL9kuXLqmmTZuqTp06qW3btuljWrlyZTV69GhLmj/++ENFRESo559/Xu3atUt9+OGHKigoSC1btkx5+ve/9dZb1ZNPPmlzDeCc+sL3//bbb9XixYvV77//rvbs2aNefvllFRwcrI+Hr597V4+BL59/exs3blT16tVT119/vRo2bJhlvT9cByWBQZWfsw8q8vLyVExMjJo8ebJlXXp6ugoNDdWBAeA/B963adMmS5qlS5eqgIAAdfjwYb388ccfqwoVKqicnBxLmlGjRqlGjRopT+IsqOrRo4fT9/jS94djx47p77N27VrL+cYNJjEx0ZJm9+7dOk1SUpJexh/QwMBAlZaWZkkzbdo0FRUVZfnOI0eOVE2aNLH5rF69eumgxpO/v3FTtb7B2POl7w+4Vj/77DO/O/eOjoE/nf+zZ8+qhg0bqhUrVth8Z3++Dq4Wq//IRkpKiqSlpekqP+vnHbVr106SkpL0Ml5R5dWmTRtLGqTHsw1//vlnS5pbbrlFQkJCLGkSEhJ0Ncvp06c9/qijyBrF2Y0aNZLBgwfLyZMnLdt87ftnZGTo14oVK+rXLVu2yMWLF22ugcaNG0udOnVsroFmzZpJtWrVbL4fHja7c+dOSxrrfRhpjH146vc3zJkzRypXrixNmzaV0aNHy/nz5y3bfOX75+bmypdffimZmZm6Cszfzr2jY+BP5x/Ve6i+s8+nP14HZuEDlckGAiqw/o9iLBvb8IqAw+ZCKlNG35Ss08TGxhbYh7GtQoUKHnvk0X6qZ8+eOv/79++Xl19+Wbp27ar/EAQFBfnU98/Ly9PtKG666SZ98zDyh2AQgWNh14Cja8TYVlga/NHNysrS7fU88fvDI488InXr1tXt7tA2btSoUTog/vrrr33i+2/fvl0HEGg3g/Yy33zzjcTFxUlycrLfnHtnx8Afzj8gkNy6dats2rSpwDZ/+htgNgZVRHYefvhhyzx+iaFBa/369XXpVceOHX3qeOGX6o4dO2TdunXij5x9/6eeesrmGkCnDZx7BNm4FrwdSmARQKGU7quvvpJ+/frJ2rVrxZ84OwYIrHz9/B86dEiGDRsmK1as0B1pyDys/iMbMTEx+tW+lweWjW14PXbsmM129PhAjzjrNI72Yf0Z3uKaa67R1QD79u3zqe//zDPPyKJFi2T16tVSq1Yty3rk78KFC5Kenl7oNVDU93OWBr2tPOEXqrPv7wiqv8H6GvDm749SCPTEat26te4N2bx5c5kyZYrfnPvCjoE/nH9U7+FvGHrloZQdEwLKDz74QM+jNMlfrgOzMagiG6iywn+ElStXWtahqBZthYz2BnjFfzb8xzSsWrVKV6UYf3yQBkMXoF7egF9F+HXoKVVfrvrrr790myr8WvWF74/2+QgoUN2BfNtXU+ImExwcbHMNoOoD3aetrwFUn1gHl/h++GNpVKEgjfU+jDTW7VY88fs7ghINsL4GvPX7O4JrNycnx+fPvSvHwB/OP0rdkH98L2NCG1EME2PM++t1cNWuuqk7eR30+EAXWEy4BN599109f+DAAcuQCuXLl1cLFixQv/76q+4J52hIhZYtW6qff/5ZrVu3TvcgsR5SAL1HMKRA3759dTflL7/8Unet9YQhBQr7/tj24osv6h4uKSkp6vvvv1etWrXS3y87O9snvv/gwYP1kBlr1qyx6TJ+/vx5m+7UGGZg1apVujt1fHy8nuy7U3fu3FkPS4Au0lWqVHHYnXrEiBG659DUqVM9ojt1Ud9/3759avz48fp74xrA/4NrrrlG3XLLLT7x/V966SXd0xHfDf+/sYyeq999953Pn3tXjoGvn39n7Hs8+sN1UBIYVPmh1atX62DCfsJQAsawCq+99poOCjCUQseOHfVYLtZOnjypg4jIyEjdhbZ///46ILGGMa46dOig91GzZk0drHn698eNFX8k8McBXYrr1q2rx6ux7jbs7d/f0XfHhLGbDAig//a3v+lu5vijeN999+nAw9qff/6punbtqsffwvg0L7zwgrp48WKBY92iRQsVEhKib0zWn+Gp3//gwYP6BlqxYkV97jAGGW4K1uMUefP3f+KJJ/R1jTzhOsf/byOg8vVz78ox8PXz72pQ5Q/XQUkIwD9XX95FRERE5N/YpoqIiIjIBAyqiIiIiEzAoIqIiIjIBAyqiIiIiEzAoIqIiIjIBAyqiIiIiEzAoIqIiIjIBAyqiMgnBQQEyPz580v0M/DoDjzW6ezZs+KrXn/9df0suJI+nidOnJCqVavqx0IReSsO/knkxx5//HH9HEP7m+WaNWvk9ttvl9OnT0v58uXFG6WlpennLIaGhpbYZ/Ts2VM/L++VV14RX7R79279HDc8J7F9+/amHU9n192LL76or7n/+7//u+rPIHIHllQRkU9CCVJJBlR4uOyiRYt0gODprB/sXRz79+/Xrz169Cjx4wn9+/eXOXPmyKlTp0r0c4hKCoMqInLJf//7X2nSpIm+sdarV0/+8Y9/2Gx3VD2EUq5Zs2bp+QsXLsgzzzwj1atXl7CwMKlbt65MnDjRkhYlFwMHDpQqVaroJ93fcccd8ssvvzjNT1H7s84PqrCwbD8ZecvLy9PvjY2NlfDwcGnevLl89dVXhR6PefPm6XQ1a9a0rDtw4IB0795dl+iULVtWH68lS5ZYtmP+2muv1Z+BkkB8PvKB727ks0WLFjaf8/777+vjbdi0aZPceeedUrlyZYmOjpZbb71Vtm7dWuBcTJs2Te655x6djzfffFOvX7BggbRq1Uofr2uuuUbGjRsnly5dcvj9kBd8FwgMDNT7NHz22Wdy3XXX6f00btxYPv74Y5v3Hjp0SB566CF9/itWrKiDsj///NOy39mzZ+u8GOcBJaOA41WjRg1dMkbkjRhUEVGRtmzZom+SDz/8sGzfvl3fGF977TVLUOKKDz74QL799lsdjKAtEkokrIOFBx98UI4dOyZLly7Vn4ebf8eOHZ2WWhS1P/tqpdTUVMv0zjvvSEREhLRp00ZvR0D1r3/9S6ZPny47d+6U5557Th599FFZu3at0+/z448/Wt5vGDJkiOTk5MgPP/ygj9Pbb78tkZGRlkAD1YUIVJKTk3UA+dJLL0lxof1Wv379ZN26dfLTTz9Jw4YN5a677irQrgvn6L777tP5eOKJJ3R+H3vsMRk2bJjs2rVLZsyYoc+fEXA5OmYzZ87U88ZxAxznMWPG6PehenDChAn6WkCgZJSKJSQkSLly5fRnrl+/Xh+DLl266EAY+8W1hGVjvzfeeKPlc9u2bavfR+SVSuQxzUTkFfr166eCgoJU2bJlbaawsDA8aF2dPn1ap3vkkUfUnXfeafPeESNGqLi4OMsy0n/zzTc2aaKjoy1PpR86dKi64447VF5eXoF8/PjjjyoqKkplZ2fbrK9fv76aMWOGw7wXtj9n+YGkpCT9/ebOnauX8ZkRERFqw4YNNukGDBigevfurZxp3ry5Gj9+vM26Zs2aqddff91h+tGjR9scLxg1apTNcR47dqzer7X33ntP1a1b12k+cnNzVbly5dTChQttvvvw4cNt0nXs2FFNmDDBZt3nn3+uqlev7nTfOH72twmck3//+98269544w0VHx9v2WejRo1szktOTo4KDw9Xy5cvt1x3PXr0cPiZzz33nLrtttuc5onIk5Vxd1BHRO6FaihUFVn7+eefdUmNASUSqMKxdtNNN+mqqdzcXAkKCiryc9D2CNVWjRo10qUUd999t3Tu3FlvQzXfuXPnpFKlSjbvycrKsrTrKc7+CmsHde+991pKS2Dfvn1y/vx5vS9rKFVp2bKl030hb6j+svbss8/K4MGD5bvvvpNOnTrJ/fffL9dff73lGLZr184mfXx8vBTX0aNH5dVXX9VVZijZw/FH/vHdrNmXouEYo9TIumQK783OztbvR8ldUTIzM/X5GDBggDz55JOW9ahCRFWk8Tk4piipsobPcXYuraFqFPkh8kYMqoj8HNrcNGjQwGbdlXRrR9uY/EISxw2kUZ2XkpKiq/e+//57HdQg8EDbJQRUaBtltK2x5qz3YWH7cxYQoI0RApnx48db1uOzYfHixTbto6Cwhtlo04SeatZQpYeqL+wLgRWqFdH2bOjQoeIKtF0q7BgCqv5OnjwpU6ZM0e3IkEd8JwSB9ufVGr4n2lChCtKefXDojHGsPv300wIBohFYIw16RKKa0B7ayxUF1b2upCPyRAyqiKhIaJSMUg5rWEaja+Nmihuh0e4G9u7dW6DEAQ3Qe/XqpacHHnhAlzDhJooACUMglClTxmm7KEec7Q+No60hUEHJGxqkf/755zaNrjFkAAITlPSg0berUIqFtkn2ateuLYMGDdLT6NGjdQCCoArHEG3ArKFNlDUcQxwH5NfII9pf2R93NAxHOyqjrRbGeCoKjjHantkH0MWB8arQkPyPP/6QPn36OP2cuXPn6jGncH4cCQkJ0aVkjuzYsUNuu+22K84jkTsxqCKiIr3wwgtyww03yBtvvKEDmKSkJPnoo49sen2htx7WodQEN8xRo0ZJcHCwZfu7776rS6MQjKBEJjExUXfTR0kUSpjwPlTNTZo0SQdrR44c0SU+aGxtX5VV1P7sodE2SrNQeoSSFKPEBVVWqKZCdSAapyPo6tChg2RkZOjgBUEBSoYcQYkUSqasqz+HDx8uXbt21flHKdbq1at1MAUIslBqNWLECP0+NMa3b+iPYOL48eP6GCBIXLZsmS6Jsw5O0DAdgSGOyZkzZ/T+UGVWFDQuRxVpnTp19L5xzFBVhyDm73//u7gKpV2o5sSxQxCLhvmbN2/W3/f555/XwdbkyZN1dTFKBGvVqqV7RX799dcycuRIvYzAefny5TrIQ5Uv9oVrBUE4jgsavxN5JXc36iIi93HWYHj16tU2Dajhq6++0g2tg4ODVZ06ddTkyZNt3nP48GHVuXNn3dC9YcOGasmSJTYN1T/55BPVokULvR2N0tFweuvWrZb3nzlzRjc+r1Gjhv6M2rVrqz59+qiDBw86zHtR+7NuqH7rrbfqZfvJyBsaVb///vu6gTU+u0qVKiohIUGtXbvW6bG7ePGizuuyZcss65555hndkDs0NFTvo2/fvurEiROW7WhM3qBBA7395ptvVv/85z8LHOdp06bp747v9dhjj6k333zTpqE6vmObNm10Y3sc58TERL0dDdodfXdryOuNN96oG43jmLVt21Yfx+I0VIc5c+boYx8SEqIqVKigbrnlFvX1119btqempuq8V65cWX/Xa665Rj355JMqIyNDbz927Jju+BAZGan3j+sN0AAe54DIW3FEdSKiKzR16lRdpYdSlyvhCyPXmwmjtqMU7JFHHnF3VoiuCKv/iIiu0NNPP60H7sQYUfa93ah40C4Mjeh79+7NQ0deiyVVRERuwpIqIt/CoIqIiIjIBHxMDREREZEJGFQRERERmYBBFREREZEJGFQRERERmYBBFREREZEJGFQRERERmYBBFREREZEJGFQRERERmYBBFREREZFcvf8HQg6UmCxIp0EAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# house size = 2000, sale price?\n",
+    "\n",
+    "# Plot\n",
+    "plt.scatter(small_sacramento[\"sq__ft\"], small_sacramento['price'])\n",
+    "\n",
+    "# Add a vertical line at 2,000 square feet\n",
+    "plt.axvline(x=2000, color='red', linestyle='--', label='2000 sqft')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.title('Scatter Plot of House size vs Price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "523b78f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#calc abs diff between 2000 and each house size in set\n",
+    "small_sacramento['dist'] = (2000 - small_sacramento['sq__ft']).abs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "448cd281",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nearest_neighbors = small_sacramento.nsmallest(5,'dist')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e47c1ba5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x280d99c61d0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatter plot\n",
+    "plt.scatter(small_sacramento[\"sq__ft\"], small_sacramento['price'], label='All houses')\n",
+    "\n",
+    "# Plot nearest neighbors in orange\n",
+    "plt.scatter(nearest_neighbors[\"sq__ft\"], nearest_neighbors['price'], color='orange', label='Nearest neighbors', edgecolor='black')\n",
+    "\n",
+    "# Add a vertical line at 2,000 square feet\n",
+    "plt.axvline(x=2000, color='red', linestyle='--', label='2000 sqft')\n",
+    "\n",
+    "# Add labels, title, and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.title('Scatter Plot of House Size vs Price')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f8aa93e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(255630.0)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# prediction is avg price of nearest_neighbors\n",
+    "nearest_neighbors['price'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "590cc4f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1 - split data\n",
+    "sacramento_train, sacramento_test = train_test_split(\n",
+    "    sacramento, train_size=0.75, random_state = 42\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "cb361d52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# step 2 - define predictors and response variables\n",
+    "X_train = sacramento_train[['sq__ft']]\n",
+    "y_train = sacramento_train['price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9807f747",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# step 3 - init model\n",
+    "knn_regressor = KNeighborsRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "f83bdb06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# step 4 - define param grid\n",
+    "param_grid = {\n",
+    "    'n_neighbors': range(1,201,3)\n",
+    "} "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8ed288f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 201, 3)},\n",
+       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>This is assumed to implement the scikit-learn estimator interface.<br>Either estimator needs to provide a ``score`` function,<br>or ``scoring`` must be passed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">KNeighborsRegressor()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('param_grid',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=param_grid,-dict%20or%20list%20of%20dictionaries\">\n",
+       "            param_grid\n",
+       "            <span class=\"param-doc-description\">param_grid: dict or list of dictionaries<br><br>Dictionary with parameters names (`str`) as keys and lists of<br>parameter settings to try as values, or a list of such<br>dictionaries, in which case the grids spanned by each dictionary<br>in the list are explored. This enables searching over any sequence<br>of parameter settings.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">{&#x27;n_neighbors&#x27;: range(1, 201, 3)}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scoring',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scoring,-str%2C%20callable%2C%20list%2C%20tuple%20or%20dict%2C%20default%3DNone\">\n",
+       "            scoring\n",
+       "            <span class=\"param-doc-description\">scoring: str, callable, list, tuple or dict, default=None<br><br>Strategy to evaluate the performance of the cross-validated model on<br>the test set.<br><br>If `scoring` represents a single score, one can use:<br><br>- a single string (see :ref:`scoring_string_names`);<br>- a callable (see :ref:`scoring_callable`) that returns a single value;<br>- `None`, the `estimator`'s<br>  :ref:`default evaluation criterion <scoring_api_overview>` is used.<br><br>If `scoring` represents multiple scores, one can use:<br><br>- a list or tuple of unique strings;<br>- a callable returning a dictionary where the keys are the metric<br>  names and the values are the metric scores;<br>- a dictionary with metric names as keys and callables as values.<br><br>See :ref:`multimetric_grid_search` for an example.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;neg_root_mean_squared_error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: v0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('refit',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit,-bool%2C%20str%2C%20or%20callable%2C%20default%3DTrue\">\n",
+       "            refit\n",
+       "            <span class=\"param-doc-description\">refit: bool, str, or callable, default=True<br><br>Refit an estimator using the best found parameters on the whole<br>dataset.<br><br>For multiple metric evaluation, this needs to be a `str` denoting the<br>scorer that would be used to find the best parameters for refitting<br>the estimator at the end.<br><br>Where there are considerations other than maximum score in<br>choosing a best estimator, ``refit`` can be set to a function which<br>returns the selected ``best_index_`` given ``cv_results_``. In that<br>case, the ``best_estimator_`` and ``best_params_`` will be set<br>according to the returned ``best_index_`` while the ``best_score_``<br>attribute will not be available.<br><br>The refitted estimator is made available at the ``best_estimator_``<br>attribute and permits using ``predict`` directly on this<br>``GridSearchCV`` instance.<br><br>Also for multiple metric evaluation, the attributes ``best_index_``,<br>``best_score_`` and ``best_params_`` will only be available if<br>``refit`` is set and all of them will be determined w.r.t this specific<br>scorer.<br><br>See ``scoring`` parameter to know more about multiple metric<br>evaluation.<br><br>See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`<br>to see how to design a custom selection strategy using a callable<br>via `refit`.<br><br>See :ref:`this example<br><sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py>`<br>for an example of how to use ``refit=callable`` to balance model<br>complexity and cross-validated score.<br><br>.. versionchanged:: 0.20<br>    Support for callable added.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20an%20iterable%2C%20default%3DNone\">\n",
+       "            cv\n",
+       "            <span class=\"param-doc-description\">cv: int, cross-validation generator or an iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the default 5-fold cross validation,<br>- integer, to specify the number of folds in a `(Stratified)KFold`,<br>- :term:`CV splitter`,<br>- An iterable yielding (train, test) splits as arrays of indices.<br><br>For integer/None inputs, if the estimator is a classifier and ``y`` is<br>either binary or multiclass, :class:`StratifiedKFold` is used. In all<br>other cases, :class:`KFold` is used. These splitters are instantiated<br>with `shuffle=False` so the splits will be the same across calls.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>.. versionchanged:: 0.22<br>    ``cv`` default value if None changed from 3-fold to 5-fold.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=verbose,-int\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int<br><br>Controls the verbosity: the higher, the more messages.<br><br>- >1 : the computation time for each fold and parameter candidate is<br>  displayed;<br>- >2 : the score is also displayed;<br>- >3 : the fold and candidate parameter indexes are also displayed<br>  together with the starting time of the computation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=pre_dispatch,-int%2C%20or%20str%2C%20default%3D%272%2An_jobs%27\">\n",
+       "            pre_dispatch\n",
+       "            <span class=\"param-doc-description\">pre_dispatch: int, or str, default='2*n_jobs'<br><br>Controls the number of jobs that get dispatched during parallel<br>execution. Reducing this number can be useful to avoid an<br>explosion of memory consumption when more jobs get dispatched<br>than CPUs can process. This parameter can be:<br><br>- None, in which case all the jobs are immediately created and spawned. Use<br>  this for lightweight and fast-running jobs, to avoid delays due to on-demand<br>  spawning of the jobs<br>- An int, giving the exact number of total jobs that are spawned<br>- A str, giving an expression as a function of n_jobs, as in '2*n_jobs'</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('error_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=error_score,-%27raise%27%20or%20numeric%2C%20default%3Dnp.nan\">\n",
+       "            error_score\n",
+       "            <span class=\"param-doc-description\">error_score: 'raise' or numeric, default=np.nan<br><br>Value to assign to the score if an error occurs in estimator fitting.<br>If set to 'raise', the error is raised. If a numeric value is given,<br>FitFailedWarning is raised. This parameter does not affect the refit<br>step, which will always raise the error.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('return_train_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=return_train_score,-bool%2C%20default%3DFalse\">\n",
+       "            return_train_score\n",
+       "            <span class=\"param-doc-description\">return_train_score: bool, default=False<br><br>If ``False``, the ``cv_results_`` attribute will not include training<br>scores.<br>Computing training scores is used to get insights on how different<br>parameter settings impact the overfitting/underfitting trade-off.<br>However computing the scores on the training set can be computationally<br>expensive and is not strictly required to select the parameters that<br>yield the best generalization performance.<br><br>.. versionadded:: 0.19<br><br>.. versionchanged:: 0.21<br>    Default value was changed from ``True`` to ``False``</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: KNeighborsRegressor</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>KNeighborsRegressor(n_neighbors=19)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
+       "            n_neighbors\n",
+       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">19</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Uniform weights are used by default.<br><br>See the following example for a demonstration of the impact of<br>different weighting schemes on predictions:<br>:ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
+       "            algorithm\n",
+       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
+       "            leaf_size\n",
+       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#:~:text=p,-float%2C%20default%3D2\">\n",
+       "            p\n",
+       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is<br>equivalent to using manhattan_distance (l1), and euclidean_distance<br>(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#:~:text=metric,-str%2C%20DistanceMetric%20object%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
+       "            metric\n",
+       "            <span class=\"param-doc-description\">metric: str, DistanceMetric object or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.<br><br>If metric is a DistanceMetric object, it will be passed directly to<br>the underlying computation routines.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
+       "            metric_params\n",
+       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "forceTheme('sk-container-id-1');</script></body>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
+       "             param_grid={'n_neighbors': range(1, 201, 3)},\n",
+       "             scoring='neg_root_mean_squared_error')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# step 5 - init and fit grid search\n",
+    "sacr_gridsearch = GridSearchCV(\n",
+    "    estimator = knn_regressor,\n",
+    "    param_grid = param_grid,\n",
+    "    cv = 5,\n",
+    "    scoring = \"neg_root_mean_squared_error\"\n",
+    ")\n",
+    "\n",
+    "sacr_gridsearch.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d1f0c71c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.001482</td>\n",
+       "      <td>0.000646</td>\n",
+       "      <td>0.001924</td>\n",
+       "      <td>0.000381</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>-111432.646994</td>\n",
+       "      <td>-124930.376771</td>\n",
+       "      <td>-111553.640165</td>\n",
+       "      <td>-121189.571040</td>\n",
+       "      <td>-110534.126317</td>\n",
+       "      <td>-115928.072257</td>\n",
+       "      <td>5952.545896</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.001474</td>\n",
+       "      <td>0.000679</td>\n",
+       "      <td>0.001681</td>\n",
+       "      <td>0.000335</td>\n",
+       "      <td>4</td>\n",
+       "      <td>{'n_neighbors': 4}</td>\n",
+       "      <td>-84191.452465</td>\n",
+       "      <td>-96768.557437</td>\n",
+       "      <td>-99395.447194</td>\n",
+       "      <td>-91776.634858</td>\n",
+       "      <td>-96991.842965</td>\n",
+       "      <td>-93824.786984</td>\n",
+       "      <td>5417.090443</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.001253</td>\n",
+       "      <td>0.000282</td>\n",
+       "      <td>0.001027</td>\n",
+       "      <td>0.000043</td>\n",
+       "      <td>7</td>\n",
+       "      <td>{'n_neighbors': 7}</td>\n",
+       "      <td>-82055.054082</td>\n",
+       "      <td>-94969.980001</td>\n",
+       "      <td>-97268.515757</td>\n",
+       "      <td>-88221.977923</td>\n",
+       "      <td>-90245.964227</td>\n",
+       "      <td>-90552.298398</td>\n",
+       "      <td>5335.745149</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000815</td>\n",
+       "      <td>0.000409</td>\n",
+       "      <td>0.001257</td>\n",
+       "      <td>0.000281</td>\n",
+       "      <td>10</td>\n",
+       "      <td>{'n_neighbors': 10}</td>\n",
+       "      <td>-79700.428498</td>\n",
+       "      <td>-94364.712988</td>\n",
+       "      <td>-95672.079987</td>\n",
+       "      <td>-84119.947681</td>\n",
+       "      <td>-88172.348589</td>\n",
+       "      <td>-88405.903549</td>\n",
+       "      <td>6041.754570</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.001488</td>\n",
+       "      <td>0.000428</td>\n",
+       "      <td>0.001764</td>\n",
+       "      <td>0.000600</td>\n",
+       "      <td>13</td>\n",
+       "      <td>{'n_neighbors': 13}</td>\n",
+       "      <td>-77034.701093</td>\n",
+       "      <td>-92991.526184</td>\n",
+       "      <td>-96360.545374</td>\n",
+       "      <td>-84692.931752</td>\n",
+       "      <td>-84919.254540</td>\n",
+       "      <td>-87199.791789</td>\n",
+       "      <td>6815.832448</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.001319</td>\n",
+       "      <td>0.000283</td>\n",
+       "      <td>0.003062</td>\n",
+       "      <td>0.000330</td>\n",
+       "      <td>187</td>\n",
+       "      <td>{'n_neighbors': 187}</td>\n",
+       "      <td>-89404.692337</td>\n",
+       "      <td>-103880.184049</td>\n",
+       "      <td>-110873.710254</td>\n",
+       "      <td>-98278.819705</td>\n",
+       "      <td>-78553.282689</td>\n",
+       "      <td>-96198.137807</td>\n",
+       "      <td>11280.135742</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>0.001535</td>\n",
+       "      <td>0.000708</td>\n",
+       "      <td>0.002641</td>\n",
+       "      <td>0.000495</td>\n",
+       "      <td>190</td>\n",
+       "      <td>{'n_neighbors': 190}</td>\n",
+       "      <td>-89807.967415</td>\n",
+       "      <td>-104221.691818</td>\n",
+       "      <td>-111088.875603</td>\n",
+       "      <td>-98698.022523</td>\n",
+       "      <td>-78654.732762</td>\n",
+       "      <td>-96494.258024</td>\n",
+       "      <td>11318.372825</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>0.001178</td>\n",
+       "      <td>0.000261</td>\n",
+       "      <td>0.002679</td>\n",
+       "      <td>0.000318</td>\n",
+       "      <td>193</td>\n",
+       "      <td>{'n_neighbors': 193}</td>\n",
+       "      <td>-90255.812044</td>\n",
+       "      <td>-104469.469062</td>\n",
+       "      <td>-111371.637819</td>\n",
+       "      <td>-99100.345423</td>\n",
+       "      <td>-78654.579110</td>\n",
+       "      <td>-96770.368691</td>\n",
+       "      <td>11388.795184</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>0.001519</td>\n",
+       "      <td>0.000522</td>\n",
+       "      <td>0.003041</td>\n",
+       "      <td>0.000389</td>\n",
+       "      <td>196</td>\n",
+       "      <td>{'n_neighbors': 196}</td>\n",
+       "      <td>-90612.174931</td>\n",
+       "      <td>-104531.594900</td>\n",
+       "      <td>-111725.067577</td>\n",
+       "      <td>-99524.332322</td>\n",
+       "      <td>-78755.965603</td>\n",
+       "      <td>-97029.827067</td>\n",
+       "      <td>11433.017471</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>0.001204</td>\n",
+       "      <td>0.000669</td>\n",
+       "      <td>0.002283</td>\n",
+       "      <td>0.000511</td>\n",
+       "      <td>199</td>\n",
+       "      <td>{'n_neighbors': 199}</td>\n",
+       "      <td>-90882.885063</td>\n",
+       "      <td>-104723.464423</td>\n",
+       "      <td>-111923.255417</td>\n",
+       "      <td>-99830.027818</td>\n",
+       "      <td>-78899.733892</td>\n",
+       "      <td>-97251.873323</td>\n",
+       "      <td>11446.274851</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>67 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0        0.001482      0.000646         0.001924        0.000381   \n",
+       "1        0.001474      0.000679         0.001681        0.000335   \n",
+       "2        0.001253      0.000282         0.001027        0.000043   \n",
+       "3        0.000815      0.000409         0.001257        0.000281   \n",
+       "4        0.001488      0.000428         0.001764        0.000600   \n",
+       "..            ...           ...              ...             ...   \n",
+       "62       0.001319      0.000283         0.003062        0.000330   \n",
+       "63       0.001535      0.000708         0.002641        0.000495   \n",
+       "64       0.001178      0.000261         0.002679        0.000318   \n",
+       "65       0.001519      0.000522         0.003041        0.000389   \n",
+       "66       0.001204      0.000669         0.002283        0.000511   \n",
+       "\n",
+       "    param_n_neighbors                params  split0_test_score  \\\n",
+       "0                   1    {'n_neighbors': 1}     -111432.646994   \n",
+       "1                   4    {'n_neighbors': 4}      -84191.452465   \n",
+       "2                   7    {'n_neighbors': 7}      -82055.054082   \n",
+       "3                  10   {'n_neighbors': 10}      -79700.428498   \n",
+       "4                  13   {'n_neighbors': 13}      -77034.701093   \n",
+       "..                ...                   ...                ...   \n",
+       "62                187  {'n_neighbors': 187}      -89404.692337   \n",
+       "63                190  {'n_neighbors': 190}      -89807.967415   \n",
+       "64                193  {'n_neighbors': 193}      -90255.812044   \n",
+       "65                196  {'n_neighbors': 196}      -90612.174931   \n",
+       "66                199  {'n_neighbors': 199}      -90882.885063   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0      -124930.376771     -111553.640165     -121189.571040   \n",
+       "1       -96768.557437      -99395.447194      -91776.634858   \n",
+       "2       -94969.980001      -97268.515757      -88221.977923   \n",
+       "3       -94364.712988      -95672.079987      -84119.947681   \n",
+       "4       -92991.526184      -96360.545374      -84692.931752   \n",
+       "..                ...                ...                ...   \n",
+       "62     -103880.184049     -110873.710254      -98278.819705   \n",
+       "63     -104221.691818     -111088.875603      -98698.022523   \n",
+       "64     -104469.469062     -111371.637819      -99100.345423   \n",
+       "65     -104531.594900     -111725.067577      -99524.332322   \n",
+       "66     -104723.464423     -111923.255417      -99830.027818   \n",
+       "\n",
+       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
+       "0      -110534.126317   -115928.072257     5952.545896               67  \n",
+       "1       -96991.842965    -93824.786984     5417.090443               50  \n",
+       "2       -90245.964227    -90552.298398     5335.745149               32  \n",
+       "3       -88172.348589    -88405.903549     6041.754570               19  \n",
+       "4       -84919.254540    -87199.791789     6815.832448                6  \n",
+       "..                ...              ...             ...              ...  \n",
+       "62      -78553.282689    -96198.137807    11280.135742               62  \n",
+       "63      -78654.732762    -96494.258024    11318.372825               63  \n",
+       "64      -78654.579110    -96770.368691    11388.795184               64  \n",
+       "65      -78755.965603    -97029.827067    11433.017471               65  \n",
+       "66      -78899.733892    -97251.873323    11446.274851               66  \n",
+       "\n",
+       "[67 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = pd.DataFrame(sacr_gridsearch.cv_results_)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "fc441219",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.001482</td>\n",
+       "      <td>0.000646</td>\n",
+       "      <td>0.001924</td>\n",
+       "      <td>0.000381</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>-111432.646994</td>\n",
+       "      <td>-124930.376771</td>\n",
+       "      <td>-111553.640165</td>\n",
+       "      <td>-121189.571040</td>\n",
+       "      <td>-110534.126317</td>\n",
+       "      <td>115928.072257</td>\n",
+       "      <td>5952.545896</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.001474</td>\n",
+       "      <td>0.000679</td>\n",
+       "      <td>0.001681</td>\n",
+       "      <td>0.000335</td>\n",
+       "      <td>4</td>\n",
+       "      <td>{'n_neighbors': 4}</td>\n",
+       "      <td>-84191.452465</td>\n",
+       "      <td>-96768.557437</td>\n",
+       "      <td>-99395.447194</td>\n",
+       "      <td>-91776.634858</td>\n",
+       "      <td>-96991.842965</td>\n",
+       "      <td>93824.786984</td>\n",
+       "      <td>5417.090443</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.001253</td>\n",
+       "      <td>0.000282</td>\n",
+       "      <td>0.001027</td>\n",
+       "      <td>0.000043</td>\n",
+       "      <td>7</td>\n",
+       "      <td>{'n_neighbors': 7}</td>\n",
+       "      <td>-82055.054082</td>\n",
+       "      <td>-94969.980001</td>\n",
+       "      <td>-97268.515757</td>\n",
+       "      <td>-88221.977923</td>\n",
+       "      <td>-90245.964227</td>\n",
+       "      <td>90552.298398</td>\n",
+       "      <td>5335.745149</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000815</td>\n",
+       "      <td>0.000409</td>\n",
+       "      <td>0.001257</td>\n",
+       "      <td>0.000281</td>\n",
+       "      <td>10</td>\n",
+       "      <td>{'n_neighbors': 10}</td>\n",
+       "      <td>-79700.428498</td>\n",
+       "      <td>-94364.712988</td>\n",
+       "      <td>-95672.079987</td>\n",
+       "      <td>-84119.947681</td>\n",
+       "      <td>-88172.348589</td>\n",
+       "      <td>88405.903549</td>\n",
+       "      <td>6041.754570</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.001488</td>\n",
+       "      <td>0.000428</td>\n",
+       "      <td>0.001764</td>\n",
+       "      <td>0.000600</td>\n",
+       "      <td>13</td>\n",
+       "      <td>{'n_neighbors': 13}</td>\n",
+       "      <td>-77034.701093</td>\n",
+       "      <td>-92991.526184</td>\n",
+       "      <td>-96360.545374</td>\n",
+       "      <td>-84692.931752</td>\n",
+       "      <td>-84919.254540</td>\n",
+       "      <td>87199.791789</td>\n",
+       "      <td>6815.832448</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.001319</td>\n",
+       "      <td>0.000283</td>\n",
+       "      <td>0.003062</td>\n",
+       "      <td>0.000330</td>\n",
+       "      <td>187</td>\n",
+       "      <td>{'n_neighbors': 187}</td>\n",
+       "      <td>-89404.692337</td>\n",
+       "      <td>-103880.184049</td>\n",
+       "      <td>-110873.710254</td>\n",
+       "      <td>-98278.819705</td>\n",
+       "      <td>-78553.282689</td>\n",
+       "      <td>96198.137807</td>\n",
+       "      <td>11280.135742</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>0.001535</td>\n",
+       "      <td>0.000708</td>\n",
+       "      <td>0.002641</td>\n",
+       "      <td>0.000495</td>\n",
+       "      <td>190</td>\n",
+       "      <td>{'n_neighbors': 190}</td>\n",
+       "      <td>-89807.967415</td>\n",
+       "      <td>-104221.691818</td>\n",
+       "      <td>-111088.875603</td>\n",
+       "      <td>-98698.022523</td>\n",
+       "      <td>-78654.732762</td>\n",
+       "      <td>96494.258024</td>\n",
+       "      <td>11318.372825</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>0.001178</td>\n",
+       "      <td>0.000261</td>\n",
+       "      <td>0.002679</td>\n",
+       "      <td>0.000318</td>\n",
+       "      <td>193</td>\n",
+       "      <td>{'n_neighbors': 193}</td>\n",
+       "      <td>-90255.812044</td>\n",
+       "      <td>-104469.469062</td>\n",
+       "      <td>-111371.637819</td>\n",
+       "      <td>-99100.345423</td>\n",
+       "      <td>-78654.579110</td>\n",
+       "      <td>96770.368691</td>\n",
+       "      <td>11388.795184</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>0.001519</td>\n",
+       "      <td>0.000522</td>\n",
+       "      <td>0.003041</td>\n",
+       "      <td>0.000389</td>\n",
+       "      <td>196</td>\n",
+       "      <td>{'n_neighbors': 196}</td>\n",
+       "      <td>-90612.174931</td>\n",
+       "      <td>-104531.594900</td>\n",
+       "      <td>-111725.067577</td>\n",
+       "      <td>-99524.332322</td>\n",
+       "      <td>-78755.965603</td>\n",
+       "      <td>97029.827067</td>\n",
+       "      <td>11433.017471</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>0.001204</td>\n",
+       "      <td>0.000669</td>\n",
+       "      <td>0.002283</td>\n",
+       "      <td>0.000511</td>\n",
+       "      <td>199</td>\n",
+       "      <td>{'n_neighbors': 199}</td>\n",
+       "      <td>-90882.885063</td>\n",
+       "      <td>-104723.464423</td>\n",
+       "      <td>-111923.255417</td>\n",
+       "      <td>-99830.027818</td>\n",
+       "      <td>-78899.733892</td>\n",
+       "      <td>97251.873323</td>\n",
+       "      <td>11446.274851</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>67 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0        0.001482      0.000646         0.001924        0.000381   \n",
+       "1        0.001474      0.000679         0.001681        0.000335   \n",
+       "2        0.001253      0.000282         0.001027        0.000043   \n",
+       "3        0.000815      0.000409         0.001257        0.000281   \n",
+       "4        0.001488      0.000428         0.001764        0.000600   \n",
+       "..            ...           ...              ...             ...   \n",
+       "62       0.001319      0.000283         0.003062        0.000330   \n",
+       "63       0.001535      0.000708         0.002641        0.000495   \n",
+       "64       0.001178      0.000261         0.002679        0.000318   \n",
+       "65       0.001519      0.000522         0.003041        0.000389   \n",
+       "66       0.001204      0.000669         0.002283        0.000511   \n",
+       "\n",
+       "    param_n_neighbors                params  split0_test_score  \\\n",
+       "0                   1    {'n_neighbors': 1}     -111432.646994   \n",
+       "1                   4    {'n_neighbors': 4}      -84191.452465   \n",
+       "2                   7    {'n_neighbors': 7}      -82055.054082   \n",
+       "3                  10   {'n_neighbors': 10}      -79700.428498   \n",
+       "4                  13   {'n_neighbors': 13}      -77034.701093   \n",
+       "..                ...                   ...                ...   \n",
+       "62                187  {'n_neighbors': 187}      -89404.692337   \n",
+       "63                190  {'n_neighbors': 190}      -89807.967415   \n",
+       "64                193  {'n_neighbors': 193}      -90255.812044   \n",
+       "65                196  {'n_neighbors': 196}      -90612.174931   \n",
+       "66                199  {'n_neighbors': 199}      -90882.885063   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0      -124930.376771     -111553.640165     -121189.571040   \n",
+       "1       -96768.557437      -99395.447194      -91776.634858   \n",
+       "2       -94969.980001      -97268.515757      -88221.977923   \n",
+       "3       -94364.712988      -95672.079987      -84119.947681   \n",
+       "4       -92991.526184      -96360.545374      -84692.931752   \n",
+       "..                ...                ...                ...   \n",
+       "62     -103880.184049     -110873.710254      -98278.819705   \n",
+       "63     -104221.691818     -111088.875603      -98698.022523   \n",
+       "64     -104469.469062     -111371.637819      -99100.345423   \n",
+       "65     -104531.594900     -111725.067577      -99524.332322   \n",
+       "66     -104723.464423     -111923.255417      -99830.027818   \n",
+       "\n",
+       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
+       "0      -110534.126317    115928.072257     5952.545896               67  \n",
+       "1       -96991.842965     93824.786984     5417.090443               50  \n",
+       "2       -90245.964227     90552.298398     5335.745149               32  \n",
+       "3       -88172.348589     88405.903549     6041.754570               19  \n",
+       "4       -84919.254540     87199.791789     6815.832448                6  \n",
+       "..                ...              ...             ...              ...  \n",
+       "62      -78553.282689     96198.137807    11280.135742               62  \n",
+       "63      -78654.732762     96494.258024    11318.372825               63  \n",
+       "64      -78654.579110     96770.368691    11388.795184               64  \n",
+       "65      -78755.965603     97029.827067    11433.017471               65  \n",
+       "66      -78899.733892     97251.873323    11446.274851               66  \n",
+       "\n",
+       "[67 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results['mean_test_score'] = results['mean_test_score'].abs()\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "43a649df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot mean test scores with error bars\n",
+    "plt.plot(results['param_n_neighbors'], results['mean_test_score'], '-o', color='blue')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('RMSPE')\n",
+    "plt.title('K-Nearest Neighbors Regression Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d04ced11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 19}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# best value of K\n",
+    "sacr_gridsearch.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "e2b8bd29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# generate predictions on test set\n",
+    "sacramento_test['predicted'] = sacr_gridsearch.predict(sacramento_test[['sq__ft']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "7b8d79a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "74244.70520282978"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calc rmspe\n",
+    "mean_squared_error(\n",
+    "    y_true = sacramento_test['price'],\n",
+    "    y_pred = sacramento_test['predicted']\n",
+    ")**(1/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "c12f7455",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.49520697046011675"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calc r2\n",
+    "r2_score(\n",
+    "    y_true = sacramento_test['price'],\n",
+    "    y_pred = sacramento_test['predicted']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "16830ab4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Mark\\src\\gh\\lcr\\lcr-env\\Lib\\site-packages\\sklearn\\utils\\validation.py:2691: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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sTiCscH7iBWGDz4ixwb3JOBHCVuB+UyLfL2zpINEpQzjDxYRrCVnd5tql9hqS33GslGB/Ku9pztjX5oCbDtuF9qJtcmyftfNGvjfLDx3GyHGT5rZRvtfBheroeWgLaA/aYmxAkF2Qxvc3a+cO9hUEKsQ0zlfETTZr1kyUF8LxdhcsuLwEqHOIL7zQIcK3jqcpPPmoAU856CxwgaFjRTwRbvi4kAYOHOhQQKf8W8SBmUtRRhyYEqW1TQ2lSpXi0gomcMSioEyWAIizgJBBcgXiORD3AlHx448/ilgX+Th/8sknwqJlCuPOyxKI6YLlEtYqxNkhHsua4EKsHKxaSBAxPtfQPpwn3377rcnfIojZErgJQxAhdgqdNGK3EEsCIabc34hLQTo+pkO4Yxu++eYb8Z3xeW4MBL6aEiG2Xh+WwH6BpQLJA6bA/cQSsFxBJMJaAIEFAYb9gO/xGVZnrEO2KtiLqaB2YGuMJ44dzlnEJCLQ2tqDqDML7Dq6r80B8WHPeSNfs4jjMiUsfCUTOEjFuQOLJKxwsMbhusVDI2LVYIAoV66cW9rpG3vbz5AtIDADA4gnXFjI/DMneGBKhesH1jJlYKupLD9zNyBz38vB4BBzWqk3JQdonzp1yqRbB1Yy2bplyw1XuVzjp0Z8J093NVgPjjmeYJWBp3DzQFwr24GnPeOirrCYyuePElgtIObxgoUH5wo6LgguiCLZiuDM44z24RxCcogl0LlD7OHG2b59+xTTsQxYWuDGtrcThbsUog5P7HAnQoBBiBmD7/BC6RZYU9AeuK+wn9wFrCi45qztN+wXHEt7j5kspOCeQuaXXB8N5waCryG4cC0hYcYS7ho5AEIQFg/c82DVcPQakt8xn3wNyJYdY+ubo/va2cj3ZlhRLbVJuY3GmLqH2nMeBth4n4WlEMdHaeWSQ1/svc9if8DKhRe2Ff0lHpbMFcx1NhzDpWHgXzf1dCeb92W3FlwaOClhuTK2VMm/l58AlMtDp/vDDz+kWD5unqZcjLJAMe68caPFiQzXkDL+RUZOd3cniAvCxYQ4L2V7cVOA9QaZRta2y5zYxc0LT9DKmCOkXcOdhXgcdyC33zjrSbbuKNuBY2McO4LsSWMLFwS5ElhrYLGStxPbDRca6v6YEmvWjjPEkKkMTLgH8LBgyk0rg/VBDKEzlTNqjcF0xJDBGmMMLFVy1pYlYM3C9uK8QRYklqkEHazxNSk/5KiNQXMWuOZx7cPSZmqEArmd2AbEguKp3hic8yiNYAm4qeAmhrUTcWtyWQ8IMVhEYfGD+LRmLbHlOnMEuWQFrP9wiTt6DUGo4CED8XbKY28q49DRfe1sYImGGEI2uKmYQ/maVd4vlfd+iGxcm844DyNsOP44NijjgIceGew7HAPcl+CtsQXEKaPWnxLcF1Eiw53XLVu4NAyCNHGiwOWC+CAIJMStyE/esEIAdIooQ4B6MrgJIpASPms8jeLpE2ZTlG+AJQFxVgg8xE0JZmZTgg4CCutAMClcmDjBYYrFCYr4AYgNnKi4gBB4iRsy3E8owYDYArQLN2h0fhCNuOBxMbobdMxoU0xMjAjOlstCIFZIWUNGfjLHPmzbtq24uWJ7TcV3YRqemrGNuOgR1CmXhcAx6du3r1u2Da4SHEsIJ9ldvHv3bnHDxM0PtW5kYHVBkgJij+DugPBBh6AsDgngeoGgwv6ApQs3T3SmSMtWBvVD9MB1h1gqPPFj+9HJoJQIlm0O3LzRCSIJBB00ziu4qRAwjpuepQrkOGfROcBVA0uSEsSY4YXOFe5AbCvOOwgDiEo8FeN7bLO5JAtlgUn5ekKblO5EgP2LhxRck7gekDwAgYdzXCni3QU6UjxA4PjLpTAgThFugHhCXK8IcEdcGmJWkESC4wvxiZIZOL4oa2F8LhiD+wr2O467HB+DfYVrBCU31MRvydcZjiWEAB4Ccb25AsQi4uWMawgWHFhWcR/FPsRxRjkFPGQZ7zdn7GtngvMSVkhcGzhe2N/YHpRCQFIArhHETwJsH0Qmrm+4yZEkg/sl7ummHqRtPQ/Lli0rjjnunxB16KPgJTAVw4hl4MEO+3Dfvn3i3or9h6QNCF30P7aAcxSWbwhi3OfwcIB4aNy7XHUOmsSjOZKMRVasWCF16dJFpJIjFRapyQULFpQ++OADk6n0KJ2AdOHQ0FApQ4YMIs12zZo1+ulIi69ataoUHh4uZc+eXV9mwjhV9/Hjx9Jbb70l0omNU4KRblu8eHEpVapUKdK+Dxw4ILVq1Uqk6KIN+N2bb74prVu3LkW6P1Kc1aBM+baEqTR0sHbtWql69epim9OlSyfS748fP57i90jnRlkApDarKRHx559/6vc10uHbt28vXb161WAee8pCmJvXOKUdPH/+XKSLo/RGcHCwlCtXLmnw4MEGJRsASoUMHDhQioqKEiVAUL7j7NmzKcpCfPnll1LlypXFccf+wnk3atSoFGVIzp07J9LAo6OjxXqx35o1ayb99ddfFrfx/PnzomwGzkGU1sA5lDlzZpFuryxjYaosBLbfXLkMZXo62jp27Fixr+TroEKFCmI/xcXFSWr47LPPxHJxrRmzf/9+UdIkd+7cYvnYDmz73r17rS7X1DE0BdaNEgrmphmXErh06ZI4HtiXaBPKIOD3iYmJBuUBcG5gm3AfwbmAUh5ff/11iuNriilTpoh19+zZ0+D7+vXri++V17i56xHlCnDvQjtRukE+vvK8plL21ZROUHuPcPQawnwom4Bro06dOtLRo0dTXEO27GtbykJYK2dg7f6BfYTrHqUgwsLCpAIFCoiyLMbnLcpMFCtWTJxHuM8vXLhQbJ+1shBqz8Pp06eL71EiQ9nvGJeFAOjjOnfuLPYf9iPK0Rjf39WeO3fu3BFtwT0NZTuwH6pUqSLKvriTgJcNYxiGYRiGYVwEx3AxDMMwDMO4GBZcDMMwDMMwLoYFF8MwDMMwjIthwcUwDMMwDONiWHAxDMMwDMO4GBZcDMMwDMMwLoYLn7oRVIG/fv26KNrmrmEuGIZhGIZxDFTQQqFjFBM3HlRbLSy43AjElrUBdBmGYRiG0SZXrlyhnDlz2vVbFlxuRB6OAAcMQy4wDMMwDKN9MKA9DCa2DiukhAWXG5HdiBBbLLgYhmEYxrtwJByIg+YZhmEYhmFcDAsuhmEYhmEYF8OCi2EYhmEYxsWw4GIYhmEYhnExLLgYhmEYhmFcDAsuhmEYhmEYF8OCi2EYhmEYxsWw4GIYhmEYhnExLLgYhmEYhmFcDFeaZxiGYRjGq3mRLNHuC/fo1qMEypI2jCrny0hBgfZXhXcFLLgYhmEYhvFaVh69QSOWHKcbcQn677JFhtGw5sWpUclspBXYpcgwDMMwjNeKrZ5/7DcQWyA2LkF8j+lagQUXwzAMwzBe6UYcseQ4SSamyd9hOubTAiy4GIZhGIbxOnZfuJfCsqUEMgvTMZ8WYMHFMAzDMIzXcetRglPnczUsuBiGYRiG8TqypA1z6nyuhgUXwzAMwzBeR+V8GUU2orniD/ge0zGfFmDBxTAMwzCM1xEUGCBKPwBj0SV/xnSt1ONiwcUwDMMwjFfSqGQ2mtqhPEVHGroN8Rnfa6kOFxc+ZRiGYRjGa2lUMhu9WjyaK80zDMMwDMO4ErgNYwpkIi3DLkWGYRiGYRgXw4KLYRiGYRjGxbDgYhiGYRiGcTEsuBiGYRiGYVwMCy6GYRiGYRgXw4KLYRiGYRjGxXAdLobxIV4kS5qvRcMwDOOPsOBiGB9h5dEbNGLJcboRl6D/DuOIYWgLLVVbZhiG8UfYpcgwPiK2ev6x30Bsgdi4BPE9pjMMwzCegwUXw/iAGxGWLcnENPk7TMd8DMMwjGdgwcUwXg5itowtW0ogszAd8zEMwzCegQUXw3g5CJB35nwMwzCM82HBxTBeDrIRnTkfwzAM43xYcDGMl4PSD8hGNFf8Ad9jOuZjGIZhPAMLLobxclBnC6UfgLHokj9jOtfjYhiG8RwsuBjGB0CdrakdylN0pKHbEJ/xPdfhYhiG8Sxc+JRhfASIqleLR3OleYZhGA3CgothfAi4DWMKZPJ0MxiGYRgj2KXIMAzDMAzjYlhwMQzDMAzDuBgWXAzDMAzDMC6GBRfDMAzDMIwvC64XL17QkCFDKF++fBQeHk4FChSgkSNHkiT9N8gu/h86dChly5ZNzFO/fn06c+aMwXLu3btH7du3p3Tp0lH69Ompa9eu9PjxY4N5Dh8+TDVr1qSwsDDKlSsXjRs3LkV7FixYQEWLFhXzlCpVipYvX24wXU1bGIZhGIZhNCW4xo4dS1OnTqXJkyfTiRMnxGcIoe+//14/Dz5PmjSJfvzxR9q1axdFRERQw4YNKSHhv3HhILaOHTtGa9asoaVLl9LmzZvpvffe009/+PAhNWjQgPLkyUP79u2j8ePH0/Dhw+mnn37Sz7N9+3Zq166dEGsHDhygli1bitfRo0dtagvDMAzDMEwKJA/StGlTqUuXLgbftWrVSmrfvr34Pzk5WYqOjpbGjx+vn/7gwQMpNDRUmjt3rvh8/PhxmMOkPXv26OdZsWKFFBAQIF27dk18/uGHH6QMGTJIiYmJ+nkGDhwoFSlSRP/5zTffFO1RUqVKFal79+6q22KNuLg40Va8MwzDMAzjHTij//aohatatWq0bt06On36tPh86NAh2rp1KzVu3Fh8vnDhAsXGxgrXnUxkZCRVqVKFduzYIT7jHW7EihUr6ufB/IGBgcIKJc9Tq1YtCgkJ0c8Dy9SpU6fo/v37+nmU65Hnkdejpi3GJCYmCuua8sUwDMMwjP/h0cKngwYNEiIEcVNBQUEipmvUqFHCRQggcEDWrFkNfofP8jS8Z8mSxWB6qlSpKGPGjAbzIE7MeBnytAwZMoh3a+ux1hZjRo8eTSNGjLB5vzAMwzAM41t41MI1f/58mj17Ns2ZM4f2799Pv/32G3399dfi3RcYPHgwxcXF6V9XrlzxdJMYhmEYhvE3C1f//v2Flatt27biMzIDL126JCxDHTt2pOjoaPH9zZs3RWagDD6XLVtW/I95bt26ZbDcpKQkkbko/x7v+I0S+bO1eZTTrbXFmNDQUPFiGIZhGMa/8aiF68mTJyLWSglci8nJyeJ/uAEhdBDnJQMXJGKzYmJixGe8P3jwQGQfyqxfv14sA/FV8jzIXHz+/Ll+HmQ0FilSRLgT5XmU65Hnkdejpi0MwzAMwzAmkTxIx44dpRw5ckhLly6VLly4IC1cuFCKioqSBgwYoJ9nzJgxUvr06aVFixZJhw8fllq0aCHly5dPevr0qX6eRo0aSeXKlZN27dolbd26VSpUqJDUrl07g2zCrFmzSm+//bZ09OhRad68eVLq1KmladOm6efZtm2blCpVKunrr7+WTpw4IQ0bNkwKDg6Wjhw5YlNbLMFZigzDMAzjfTij//ao4Hr48KHUp08fKXfu3FJYWJiUP39+6bPPPjMo34ByDEOGDBGCCSUY6tWrJ506dcpgOXfv3hUCK02aNFK6dOmkzp07S48ePTKY59ChQ1KNGjXEMiDyIJ6MmT9/vlS4cGEpJCREKlGihLRs2TKD6WraYgkWXAzDMAzjfTij/w7AH9O2L8bZwAWJUhIIoEdVfIZhGIZh/KP/5rEUGYZhGIZhXAwLLoZhGIZhGBfDgothGIZhGMbFsOBiGIZhGIbx5cKnDMMwDOOPvEiWaPeFe3TrUQJlSRtGlfNlpKDAAE83i3EhLLgYhmEYxo2sPHqDRiw5TjfiEvTfZYsMo2HNi1Ojkv+NZML4FuxSZBiGYRg3iq2ef+w3EFsgNi5BfI/pjG/CgothGIZh3ORGhGXLVPFL+TtMx3yM78GCi2EYhmHcAGK2jC1bSiCzMB3zMb4HCy6GYRiGcQMIkHfmfIx3wYKLYRiGYdwAshGdOR/jXbDgYhiGYRg3gNIPyEY0V/wB32M65mN8DxZcDMMwDOMGUGcLpR+AseiSP2M61+PyTVhwMQzDMIybQJ2tqR3KU3SkodsQn/E91+HyXbjwKcMwDMO4EYiqV4tHc6V5P4MFF8MwDMO4GbgNYwpk4v3uR7BLkWEYhmEYxsWw4GIYhmEYhnExLLgYhmEYhmFcDAsuhmEYhmEYF8OCi2EYhmEYxsWw4GIYhmEYhnExLLgYhmEYhmFcDAsuhmEYhmEYF8OCi2EYhmEYxsWw4GIYhmEYhnExLLgYhmEYhmFcDAsuhmEYhmEYF8ODVzMMw7iAF8kS7b5wj249SqAsacOocr6MYsBihmH8ExZcDMMwTmbl0Rs0YslxuhGXoP8uW2QYDWtenBqVzMb7m2H8EHYpMgzDOFls9fxjv4HYArFxCeJ7TGcYxv9gwcUwDONENyIsW5KJafJ3mI75GMbec2zHubu06OA18c7nkvfALkWGYRgngZgtY8uWEsgsTMd8MQUy8X5nbIJd1d4NW7gYhmGcBALknTkfw8iwq9r7YcHFMAzjJJCN6Mz5GAawq9o3YMHFMAzjJFD6AdmI5oo/4HtMx3xagOOBfM9VzWgXjuFiGIZxEqizhdIPyEaEuFKGxssiDNO1UI+L44G8B3ZV+wZs4WIYxuP4kqUFdbamdihP0ZGGbkN8xvdaqMNlLR5o+eEbPnM8fAF2VfsGbOFiGMaj+KKlBe1+tXi0JivNq4kH6j13Pyk1lrcfD19xVUMQmzpuAS8FvVZc1Yxp2MLFMIzH8OXMK4grlH5oUTaHeNeC2FITDwSMDVq+cDx8wVUNjM8irbmqGfOw4GIYxiNw5pVnsKckhS8UbfV2t7U3uKoZy7BLkWEYj8BFQj2DvSUpvLloq6+4rbXsqmasw4KLYRiPwJlX2owH8rWirbLb2nhbZTept1mHZFc1432wS5FhGI+gxcwrb3c7ORoP5GtFW9ltzWgJtnAxDOMRtJZ55StuJ1vigYy3F54pcxrTGzPh2G3NaAkWXAzDkL8XCfU1t5O98UD34xOp15wDYrqWi7aqhd3WjJZglyLDMB5DC5lX/ux2Mi5d0aR0do8fD193WzP+C1u4GIbx68wrdjtp63j4stua8W9YcDEM49eZV+x28t1MOC25rRmGXYoMw/g17HbybbTgtmYYwBYuhmH8GnY7+T6+5CZlvBcWXAzD+DXsdvIPfMVNyngv7FJkGMbvYbcTwzCuhi1cDMMwXuB2QlkKrbaNYRjrsOBiGIbRuNvJn6rgM4yvwi5FhmEYDSNXwVeKLWUVfExnGEb7sOBiGIbRKP5cBZ9hfA0WXAzDMBrFlir4DMNoGxZcDMMwGoWr4DOM78CCi2EYRqNwFXyG8R1YcDEMw2i8Cr654g/4HtN58GWG0T4suBiGYTReBR8Yiy4efJlhvAsWXAzDMBqGq+AzakCm6o5zd2nRwWvinTNXtQcXPmUYhtE4Wq+Cz3h2RAAujOsdBEiSxAVc3MTDhw8pMjKS4uLiKF26dO5aLcMwDOMGPCF85MK4xh25LPGmdijPoxFopP9mlyLDMAzDeOGIAFwY17tgwcUwDMMwXih8uDCud8GCi2EYxgwciMxoWfioLYwb+1DdfIyPC65r165Rhw4dKFOmTBQeHk6lSpWivXv36qcjxGzo0KGULVs2Mb1+/fp05swZg2Xcu3eP2rdvL/yq6dOnp65du9Ljx48N5jl8+DDVrFmTwsLCKFeuXDRu3LgUbVmwYAEVLVpUzIN2LF++3GC6mrYwDOMbwAVUY+x6ajd9J/WZd1C84zMPFs1oZUQAtYVxRy49xuetvwuu+/fvU/Xq1Sk4OJhWrFhBx48fp2+++YYyZMignwfCaNKkSfTjjz/Srl27KCIigho2bEgJCf+duBBbx44dozVr1tDSpUtp8+bN9N577xkEuzVo0IDy5MlD+/bto/Hjx9Pw4cPpp59+0s+zfft2ateunRBrBw4coJYtW4rX0aNHbWoLwzDejyficRjvxVMjAlgrjCtzL/45n7f+nqU4aNAg2rZtG23ZssXkdDQte/bs1K9fP/rkk0/Ed8gQyJo1K/3666/Utm1bOnHiBBUvXpz27NlDFStWFPOsXLmSmjRpQlevXhW/nzp1Kn322WcUGxtLISEh+nX/+++/dPLkSfG5TZs2FB8fLwSbTNWqVals2bJCYKlpizU4S5FhvMONCEuWORcROrfoyDDaOrAul2VgDM4ZCHLJzeeM/HAALHXmfN76eZbi4sWLhUj63//+R1myZKFy5crR9OnT9dMvXLggRBJcdzLY4CpVqtCOHTvEZ7zDjSiLLYD5AwMDhRVKnqdWrVp6sQVgmTp16pSwssnzKNcjzyOvR01bjElMTBQHSfliGEbbcCAy400jAsiFcTNE/Ne/uTOOjPESwXX+/HlhfSpUqBCtWrWKevbsSR9++CH99ttvYjoEDoAVSQk+y9PwDrGmJFWqVJQxY0aDeUwtQ7kOc/Mop1trizGjR48Wokx+IXaMYRht46l4HMa78eSIAFj2kKbFVM3L562fVppPTk4WlqmvvvpKfIaFCzFTcOF17NiRvJ3BgwfTxx9/rP8MCxeLLobRNp6Kx2G8H0+OCBAdGa5qPj5v/dTChWw/xF8pKVasGF2+fFn8Hx0dLd5v3rxpMA8+y9PwfuvWLYPpSUlJInNROY+pZSjXYW4e5XRrbTEmNDRU+HqVL4ZhtF3awVogMr7HdMzHMMZAXMUUyEQtyuYQ7+4afonPW+3jUcGFDEXEUSk5ffq0yCYE+fLlE2Jm3bp1BlYixGbFxMSIz3h/8OCByD6UWb9+vbCeIb5KngeZi8+fP9fPg4zGIkWK6DMiMY9yPfI88nrUtIVhGO8v7eDJeByGsfdBgc9b7ePRLEVkFlarVo1GjBhBb775Ju3evZu6desmyjWg1AMYO3YsjRkzRsR1QfQMGTJE1NRCCQnUywKNGzcWlia4IiGqOnfuLFyVc+bMEdORVQBxhdIQAwcOFG7LLl260HfffacvH4GyELVr1xbratq0Kc2bN0+4Ovfv308lS5ZU3RZLcJYiw7gPR8eY4wGBGU/hyLnH561rcEb/7fHBq1GGAbFOKCAKEYOYJ4guGTRv2LBhQoTBklWjRg364YcfqHDhwvp54D7s3bs3LVmyRGQntm7dWtTLSpMmjX4eCKNevXoJkRcVFUUffPCBEF/GhU8///xzunjxogjkR90tlJewpS2WYMHlneDJ0hMxGYznSzvwsdcevn5MnDEYta/vI0/gE4LLn2DB5X3w06J3AhcM3IfWmNutqoizYbwDX78ePVkDjkWa6/tvj2YpMow3PmnK1cZdnerN2A+XdvA9/OF6tKUGnDMfFHxdyGoFj4+lyDBaBE97uAGZMv/K32G6tUBWxjNwaQffwl+uR088KPAwVu6DBRfDmICrjXs3nCLvW/jL9ejuBwV/EbJagQUXw5iAXVLeDafI+xb+cj26+0HBX4SsVmDBxTAmYJeU9+PJoVYY5+Iv16O7HxT8RchqBQ6aZxgLT5oIyJUsZAtxtXFt48mhVhjn4U/Xo/ygYBzEHu2CIHZ/EbJagQUXw1h40kT2E27myps8Vxv3zqFWGO/F365Hdz0o+JOQ1QLsUmQYM7BLimG0g79dj+4Yk5FjHd0LFz51I1z41DvhgoAMox34enQ+XIfLOlxp3stgweX7cGfAMIw3wvcuy3CleYbREPyUyDCMt8Kxjq6HY7gYxglwtWaGYRjGqVmKFy5coC1bttClS5foyZMnlDlzZipXrhzFxMRQWBinjjL+h7VqzQh1xXRkHflKFhVjGnbLMAzjsOCaPXs2TZw4kfbu3UtZs2al7NmzU3h4ON27d4/OnTsnxFb79u1p4MCBlCdPHrWLZRivx1MDzjLagl3KDMM47FKEBWvSpEnUqVMnYdm6ceMG7du3j7Zu3UrHjx8XwWSLFi2i5ORkqlixIi1YsEDNYhnGJ+BqzQy7lBmGcYqFa8yYMdSwYUOz00NDQ6lOnTriNWrUKLp48aKaxTKMT8DVmv0bdikzDOM0wWVJbBmTKVMm8WIYf4GrNfs37FJOCceyMYwTgubj4uJozZo1wooVEBBA+fLlo/r161O6dOlsXRTD+AT+NuwIYwi7lJ0fy8aCjSF/F1x//PEH9e7dW8RsKYmMjKQff/yR2rRp4+z2MYxX4M4BZxltwS7llLFsxhm7GKsP36sZgoeTDxjy96F99u/fT1WqVBGZiH379qWiRYsSfoqg+QkTJtC8efNoz549VKZMGde32kvhSvO+Dz+Z++cxrzF2vdUBgLcOrOvTVk55P5jL2FWzH8wJNnluXxwzkfEO3Dq0T+fOnenx48dmMxDfeOMN0YgZM2bY1RB/gAUXw/gmslAgMy5lfxAKO87dpXbTd1qdb263qibLozhDsDGMlvtv1ZXmt23bRt27dzc7vUePHqJMBMMwjL+6lCEIlOCzP4gtZ8Sy2ZJ8wDA+HcN1/fp1Kly4sNnpmHbt2jVntYthGMargKjCaAIQBBAViO1CBqu/WGMcjWXj5AMn8OwZ0bx5RI8ekV9Tpw5RiRLktYILw/hYGroHtbgSEtQ94TAMw/gi/jwAsKPlUbwl+UDTcZpjxxINHerpVnieadO8W3CBVatWCR+mKR48eOCsNjEMwzB+Vh7FG+rZaTqD8ulTokmTdP/Xq0eUIQP5LfnykRZRHTQfGGg93At1uV68eOGMdvkkHDTvm2j6iZdhvEiUaDn5QPMZlD/9RIQ4a4xlfPYsUSqby2wyWslSZByHBZfvoeknXobxwocQLV5Tms+gTE4mKl6c6NQpom+/Jerb1/1t8HEesuDyLlhw+Raaf+L1AGztY3zxPHK05IXLWbqUqHlzIlherlzRvTOa679V2xxPnz4t4rQqV66s/27dunX05ZdfUnx8PLVs2ZI+/fRTuxrBMN4GD1jsHZYJxjvRWvKB5jMov/lG9/7eeyy2NIzqOlwDBw6kpVDRL7lw4QI1b96cQkJCKCYmhkaPHi0qzjOMP8A1g0xb+4xdLvKQLpjO+PcDCqxEiw5eE+/47E1oOoNy/36ijRt1MVsffuj+9TPOt3Dt3buXBgwYoP88e/ZsUXsLmYugdOnS9P3339NHH32kfu0M46Vo/onXjbC1j/F1y6emMyhl69abbxLlyuX+9TPOt3DduXOHcubMqf+8YcMGYeGSqVOnDl28eFH9mhnGi9H0E6+bYWsf40uWT1PWOLnkBTCOJFNT8sJlIF7rzz91//fr5951M66zcGXMmJFu3LhBuXLlouTkZGHx+vjjj/XTnz17JgazZhh/QNNPvG6GrX2Mr1g+rVnjkAhjPD3aHmtdbCzR1au2NxB97PPnRElJuvc//iBCKSZUVi9f3vblMdoUXLBgjRw5kn744QcxgDVEF76TOX78OOXNm9dV7WQYnyry6EsZXWqteFERoeSPePr4eIPlUwsB8uayjmVrnJx17PDwTVOnEn3wgU4oOQu2bvmW4Bo1ahS9+uqrlCdPHgoKCqJJkyZRRESEfvqsWbOobt26rmonw2gOpz7xenF8jDVrn0y/BYdo+GveE7fjK8fHU3iT5dNWa5xeIO7bRzR3JVGrVkTh4VZW8oLok0+I5OSybNmIgoNtbyx+gwB5vONVqRJRkya2L4dxOzYVPk1KSqJjx45R5syZKXv27AbTDh06JGK8MmXy/JOKVuE6XL6JpywYWqoDZq5CuKfb5Um0dHw8geZrVzna1hkzdJXd4d7LkoUICWM9exKlT5/yh48fE731FtGSJbrPo0YRDR6M4VmcvSmMi+DCp14GCy7GlytfQ2AMX3yMYh8mmp3H4xW5/fj4eGofWItz1MI+QIB8n3kHrc43sW1ZalE6G9HnnxONHq37EkUwHz787/8ePYgU9SpFFfivviI6eJAoLIzot990GYWMV+HWwqetYDI1ARqA8hDvvvuusHwxDOOf8TGw1qQNC6b2P+/SVLs8gRaPjyfjHElLmX0OxCFGB0tEbdsSLVig+2LIEJ34mj+faMwYomPHiMaNM7OSLESLFhFVrerEljPehGrBBWFlClSfnz59Oo0fP542b95MJUuWdGb7GIbxoviYO4/NW7e0Frfjj8fHE0SmDqYHT54bfJc+dTCNblVKMy5VNVnHhYOfUeV3/0e0c6cudmr6dKKOHXUzdOigcxkuX64bRPrBA8MFIF5r7FgiNyeW+WvChtcLrpkzZ5qdhozFbt260eDBg2mJ7KNmGMbv6oBptV3u7oy8aT+4O4YN3DcSYN6QdTzj8DwKgNjKkIFo4UJdKQYlgYFEzZrpXhrAnxM2vL7wqcWFBAbShx9+SPuQscEwjNueyM3JA3yfzQN1wLTaLlOdEeKLECiN2B2847OzCnF6y37wRNYfKbL+tDTEj5x1jLgyJfg8t+QLyrF4vu6LZctSii2N4Y0FZ/0BpwgugBIRT548cdbiGIaxgFYrX2u1Xe7ujLxhP7gSbxl9wLiqPMo+IIgf2YgIkMf71k9qU9Vvh+t+0KULUUwMaRlrJS5Ig2LXX1DtUrTGmjVrRPA8wzD+UwfMm9rl7urnWt4PrsYbYthUu9x+/JHowAFduQc5M1HDcMKGDwiuxYsXm/weKZJwJf7888/ixTCM+3BK5WuNtssVMVbu7oxcdXyM902FPBlo36X7mjkHtB7DpraqPN29S/TZZ7qJI0fqMg19WOxykL1GBFfLli1Nfp82bVoqUqSIEFttkS7LMIxbMah87SPtclXArycsL84+Pqb2DbSV0kPk6eBoLY81apOVE2Lr3j2i0qV19bW8AHvFLgfZayiGC5mIpl6wcO3evZvFFsMwmo+xcsTyYhzv44kYGHP7xrgpng6O1nIMm1or57El63UlHsDkybrhdLwAexI2OMjePXjHGcQwjF/g6hgrey0vWnj6t5b558p4NF+KYTNlvax28SCNWj2Fwp4/03+X+fsEIox81749Uc2a5C2oKXGhFLvujGs0h7+4MlUJrnnz5qm2YF25coUuX75M1atXd7RtDMP4Ga6OsbK1M7Ip3sfFHY61faPFavZajDE0Zb0csPl3ynffhDUQNbfMVY7XMLaIXU8H2a+08DCjtXPHLYJr6tSpNGLECOrcuTM1b96cihUrZjAdbsVt27bRH3/8IbIVf/nlF1e1l2EYH2bt8ViXx1jZ0hm54+lfrfXM3m32dDV7V8YY2mMZMbZylrpxhsreOE2JQamofdtR9DQ4jKLShNCMTpUpKG8eokzai490ptj1ZEbpSgsPMz3+2C9GJFCOUuDp2ES3CK5NmzaJLMXvv/9eVJNHza2sWbNSWFgY3b9/n2JjYykqKoo6depER48eFdMYxhfwF1O3Fvbt/fhE+mXbRbdkt6ntjFz99G+L9czebTYXj+bt57W9bl5jK+c7+5eJ75cVrUn7cpYQ/2O/B3lpp26r2PVURukLFfXCjIeEcqZVWdMxXK+99pp43blzh7Zu3UqXLl2ip0+fCqFVrlw58ULFeYbxFbQQt+OrmMu0s4Yzs9vUdEaufPq31XpmLf7M0r5SCqyLd+Jp7u7LFPsw0WvPa0fdvLKV87s/d1Dzk5vFd7PKNfV4fJk/ZZTuttFFrpXYRLcGzUNgmSsRwTC+gjvidvwVc/tWTdKf5ObsNlc+/dtqPbMUf2aMMh5tzfHYFOKWvPi8dpabF9vZYPlZCkx6Rg+KlaIBwzpS5fy6/exP2BPX6Axu2emi1EJsor2wSYphjOChMbSRaWeKLtXzulUQuHJMRHusZ+bG+zPuCzEd8wFTZSS8ecgXpw0b9OIFBaKKPBGl79eHYgpG+Z3YUjOOpKtEeBYHXZSejk20By4LwTBGeDprx5exx42gBFYLX3n6t9d6Zir+zFSleYABudXKJ285r53m5l25kujCBV0mYrt25O+4O6O0so0ucq2MUuAILLgYxgvHgbMHLQRK27vPPFmZ3FX1pByJnTEVf2b8GcVZ7RG3Wj+vnebmnTJF9965M1Hq1E5omffjzlErgmxwkWvlXuAoLLgYxsvGgfPmBAB79pmnK5O76unf1bEz9gonrZ/XTgnyPndOZ+ECPXuSrz/oaJVGZh5m5HIQ7owp07TgevbsGV24cIEKFChAqbxkyAOG8fZx4NyZAOCKjkKNG8F4XECtZI654unfldXYbRVO3nJeK4VqxLOnlPfeNTqWtQBRQID6DhmxW6gi36gRUcGCPv+go2UamXmYMZXsoZV7gb0ESBLOOvU8efKEPvjgA/rtt9/E59OnT1P+/PnFdzly5KBBgwa5qq1ez8OHDykyMlIUik2XLp2nm8OoEClk5gnLldlczhQ6WBbieMy5luROduvAuimqqzujozC1LbiRWtq3U94qTxkiQvzKIuAKcSsfezUxMu44r529X9ZvPEwF/teU8ty5Sn+VrEefNXifMkZFpjxH0cWdOUO0fz/RgQO69y1biBITiRYvJmreXNX6nPWgo9V9rUVeaMg66Iz+22bB1adPH1FVfsKECdSoUSM6fPiwEFyLFi2i4cOH0wGc0IzLDhjjPjzxdOrsdSKOp930nVbnm9utqt5646yOwtK2AE8/+WvpZu7uBwdjtGh1sXgt5AgjqlOH6PBh/bTHxUtR+OJ/KahA/v8Wsn490aefEu3alXIFZcoQ7dtHFBRkfX027hd7H3QY7eIRwZUnTx76888/qWrVqpQ2bVo6dOiQEFxnz56l8uXLi0YxrjtgjHtxZ6fsiifiRQevUZ95B63ON7FtWWpRNofTOgo12+LJcdL8ydVjblvbVspNeaNSu37fnz6tsyS98w5Rliym50E3dPQoUfHiQgBZOn8iEp/Q1vVjKP3h/UTR0USjRxP170905w5RxoxEc+YQpU9P9NlnROvW6X4YEkJUrpzuVb687r10ad33Lrj27HnQ8deHAn/qv20Ovrp9+zZlMXHRxMfHU0AAnwiMb+GurB1XjdmnNo4nKk2o6CS2nb3jcEkMW7bFE+UH/K2orccGkMbD98iRRBMnEj1/TvTnn0RbtxKFhqacF6EoGCS6aVN68e8is+dPSNIzmvbPl5T+0mGSMmakgDVriEqWJKpXj6h1a6I9e4gaN9YJOBAcTNSjh87KBXHmpmvPGZnO/vRQ4C/YXPi0YsWKtGyZbuwpIIusn3/+mWJiYpzbOobRMLhRQ6TAioR3RwpGOq2Yox2FOzOkDqZ+8w+KJ/LJG8463FE4ui3O3K/2jN82fPExITxdsX5PPzjAiilXrXcZyclEM2YQFSpE9PXXOrEF4bN3L1HfvinnnztXJ7bAsmV0ecRYk+dPqhdJNHnRGKp+6TA9DgmnIz/P04ktkCsX0ebNRN266cQWhpnr0kUXuzVpklmx5aprz9FMZ/mhwLhd8kMBpjPeh80Wrq+++ooaN25Mx48fp6SkJJo4caL4f/v27WKQa4bxB5z99Omq2l/WSg/g832jAWId7VAc2RZXP9Wr6VwxxmD7n/+L+WGrgg3cukWEod927NB9LlyYaMIEkUFITZoQTZ1KhAfzt9/WTUfMb9euuv+rViXauZNyjx1BxTp8TSey/BeLFfziOX2/eBy9enY3JaQKoXdbD6F2+YpTaeW6w8KIfvpJ57rMlo2oQAGPXXuOZDq7ytrNeKGFq0aNGnTw4EEhtkqVKkWrV68WLsYdO3ZQhQoVXNNKhtEQrnj6dGXtL0vDdqDejS2oGcrG3m2xtF97/LGfvlhyzGGLkz21qdiqoJKrV4lq1dKJrbRpddatI0d0Lj6UXxg6VDdf9+6672/f1omzp09188Dd+NprFPT8GX2/aByFPU/Qi60pi8ZSo9M7KDEomHq0/JR25i5t/jyrUUO12HLVtSc/6ABjSWStdIWrrN2M57GrgBZqb02fPt35rWEYjeOqp09X1/4yFceTnCxR+19MZG+ZQW2NI3u2RY2rb8a2i+LliMXJHsHqDVYFh4OrHzwgGjGCqFIl3TA3tsbjopAo4qguXdK599au1Vm3lAwZIixYtGqVLt4qe3aiy5d1rkcEuiNb8JdfSCpdmgreuEpD1v9MI+p1pymLRgvLFsRWt1af05b8Fewev9Kd1569NdZ8daQLxg7BtXz5cgoKCqKGDRsafL9q1SpKTk4W7kaG8VVcNc6iq6uOy+tQtgkxSragtuigPdtiyxiLjgS32zt+m5bHGXTYDYuYK7jhlizRfV6wgGjaNPMZhcYcO0b06qtEN27oiogiMzB37pTzQVD98QcRPCGIrcIrTRqif//VZRWCqCgKmDWLpFdfpfYHV1LZ66epxK3zOjdiqyG0LV85p1cad+W1Z0/Cgi+OdMHY6VJEYdMXL16k+B7VJbjoKePruPLp05Lrz1mZc8qA9DuPElX9pvcrBUT6OkpBqG2Drdtiy/6SO0SIDHPuRXOB95ZcPWqIjXtKrsaWpAGnuLfh+oPYQokEBLdDAJUqpSvlYA3UuapdWye2EMCOgqKmxJZMVJRO0GE9YNYsXSkIJfXqUQDKPBAJsfU0VSh1aT2UtuYr59RrwV3Xnq0JC2oSXZxp4WPch811uMLDw+nEiROUN29eg+8vXrxIJUqUEOUh7GHMmDE0ePBgUVgVRVVBQkIC9evXj+bNm0eJiYnCqvbDDz9Q1qxZ9b+7fPky9ezZkzZs2EBp0qShjh070ujRow2GG9q4cSN9/PHHdOzYMcqVKxd9/vnn1KlTJ4P1T5kyhcaPH0+xsbFUpkwZ+v7776ly5cr66WraYg2uw+X9OKu+jiVcVXvHlCXEeBgdZxdnVLMtmOfXbRdo5LITNi/f1H5WY/ExNY8aMkaE0Fevl9RE4Vun1ExDZl/dukR4iIZVq0oVXUA7YqxAx45EH36oq1uldDPCOjVggE6cAbgiMTYh6mCpATW3Hj/WBcqb4tkzEfclHTxIxyf9QmdLVnZLOQut1L3y5EgXjIYKn0ZHR9OcOXOoLi5SBWvXrqW33nqLbiFLxUb27NlDb775ptiIV155RS+4IKRQguLXX38VG9q7d28KDAwUle4BLG1ly5YVbYJYunHjBr3zzjvUrVs3kU0JMN5jyZIlqUePHvTuu+/SunXr6KOPPhLLld2iKOSK3/34449UpUoVsf4FCxbQqVOn9DXHrLVFDSy4vB9rw6VotYK0udpT5nDXjd1e4WNcsFW5PLUFLJWda1REKPVbcIhuPrTsanTlfrG1+KZJ8S9JNHnRWKp9YZ/+q/DgIEoVEkzUrJmuHlXRoroJN2+SVK4cBdy4QVeatqKrE6ZRhbwZaf/pWMo0biQV/H0aOgjdvPnzE73xhm4YnL/+whMqUVKSzk2IIPgxY3SB8s4Erk6UlDBVt0vDYslZcB0ubeERwdW9e3eRkfjPP/+I4HmAKvOtW7emSpUqiXpctvD48WNRoR7Woi+//FIIKAgebFTmzJmFuHsDFzoRnTx5kooVKybWj0r3K1asoGbNmtH169f1liaIpoEDB4oCrSEhIeJ/CKWjeKJ6Sdu2benBgwe08uVo8RBZaPvkyZPFZ8SiwRKG8SHhJlXTFjWw4PINvO3p05olxJSlyx2lEGwVgdYsXI5afNS2xxWi2p62mxpFoMLV4/T37AEWGh9A9OabRIMH093uvSnTrq10Kio3tXz7W3oaEmZwHmBZvQ4tpdpnd1NQgol2NW1KNH48UbFi5GmcPSyPVoSbltri7zx0guCyOYZr3LhxFBERQUWLFqV8+fKJF4RHpkyZ6GvEAthIr169qGnTplS/fn2D7/ft20fPnz83+B7rzJ07txA5AO8oTaF068FqhR0D96E8j/GyMY+8jGfPnol1KeeB5Qqf5XnUtMUUcD2iLcoX4/24I9bKmfE/agLS0ckOaVpMWIxsjdeyt53mshLVYCqOxdF0evm4ZoywXCrDFWn59rTdVND02wd0RakXlniFar03Xbz2r9uji61q0UJXFBQV38uWFWIrPjiM3m8xWIgtoBTd+3IWp65NB1Cpnn/Qga+nEf3vf0ToaDA0Diq8L12qGbHlrDItmBfCF5ZDiFm847OnCo26tWAto70sRSg8FDlds2aNGEcRMV2lS5emWqi/YiOIh9q/f79wKRqDWCpYqNLL2SsvgbjCNHke4xgq+bO1eSB+nj59Svfv3xeuSVPzwIqlti2mQCzZCKRaMz6Hx4ZLsfEpHzFHLcqoE05RaUMN3HOufNq2JSvRGHOZY85IaMBxffo8mfr+edCtafn2tN044zJT/ANqclIX4vBLxRZ0JUM28RBQpk4FnQkT9akOH6bkkV8S/f0XBUoSDW70AZ2LymV2fVguxNj7zwrQ1nndNNfhO7NMi78N+cR4SR0uDOfToEED8bKXK1euiAB5CLcwVAj2QZAEgGB9GYg8uCoZ38Bd4yyqwVxncS/+Gc3cfsnhNHNPVda3pTyFs9Lpo9O5Py3fnrYblzN488gaCklOogPZitDx6IKmyxmULk27xkylTzPUp8iEx3QwexGvLomx89xdp5Rp4erujGYE16RJk+i9994Twgj/W+JDZLSoAG46BNgjfksGlqbNmzeLWCrU9YK7D7FWSsvSzZs3RZA8wPvu3bsNlovp8jT5Xf5OOQ98sLDOoaYYXqbmUS7DWltMERoaKl4M40qc4Z6zVNjRFU/+9ooVlKjo+2oRk9YKZxWwdHURWmeuU3aDjlx0hNofWCG+m1W+icWaaRC7FzKat2R6S6FNnJeD/n6ZTelg211VX49hbBZc3333HbVv314ILvxvyfKlVnDVq1ePjsipxy/p3LmziI1CoDssQcHBwSKrEAH5AFmDKAMhD5KN91GjRgnhJmcTwmIGMVX8ZW0XzINirUowj7wMuAoxJBHW0xLDTLwMmsdnZCICTLfWFoZxBva47Rx1z6EzaVxS5x41Xp+nKuubo3rBzGbX46wClu4oQuvMdQr39vm9FPTwFj2LTE9vjutH44tmN9s+e8Wulgpt2ppwYa3trqivxwHvjF2CC6UVTP3vCGnTphXlGpQgGB/B9/L3Xbt2FS65jBkzChGFrEEIHDkrEC5NCKu3335bBPMjngo1thCIL1uWUA4CFrMBAwZQly5daP369TR//nyRuSiDdaB+V8WKFUXtLWRJop4YBKAct2atLYx3oqWbor1uO0csD0haQxy1uWFzPFFZ3xxqij3aO5yKq5ZjC46sM2jaj+I9pNu7VLV4DqeKXVdY9Nxl0VXbdmdXd+eSDozDMVzI1IMFaunSpSIz0dXAmoaMQViVlMVGZeAKRFtQIwviB4INwumLL77Qz4MsSoirvn370sSJEylnzpyidIVyaKI2bdqIMhJDhw4Vog2lKVAyQhlIb60tjPehpZuiI247eywPsGitOBqbouip8frcUVlfbR2u18pkUyWGnZXQ4InECLvWef480QqdO1HUxHKi2HWVRc+RhyBbLbpq2u5MNzIH3zNOq8OVI0cOUeTUHYLL1+A6XNrB1iKTrsTR+lH4faVRa0WAvFrShwfTg6fPra4PnZs7Kusj+LnXnP1m2wTQIW7q/wrtu3RfExZJzTBwIOr1oN6Nrtq7E0cecPcDiJqHIFP1x8yd42Nal1LddmfU13NK9X/GZ/tvm7MU4a4bO3assBIph89hGG9BaxlJjrjtZGsASj+ozUYEloSNcn3uCCDHPg4MDLDYJnrZpqqj1xkIS09ZJDUDCpLOmKH7//33bXKRm7KmVciTwWOCVq1lSK1Fd0r78lS9YJTZ6cb7CvvCUTcyB98zlrBZMaFmFoLHV69eLYqOwo2nZOHChbYukmHcitZuiva67ewZFgddZ2TqYHrw5Lmq9bkrgFztPjC24vlCjSSH4ggxzM6dO0S5ctHKPOVphJF1xZogNVXaxBNZeLY8BKl9CKiaP5NdljTZsmvP8XClC57xQ8GFsghyph7DeCNauynaE7Br77A4mD8mfyYRv6V2fe4IILc3A84TFklNxRFiwGkiOt2iHfWce8hri3ba+hDkyEOAK2OsnB18z/i54Jo5c6ZrWsIwbkJrN0Vb3XZqsrTShAaJDifuaVKK+BxrYku5Ptn6kpiUTF+/UUZMvPM40enuJnvLRHhzjSSHO34MX7Z1K0lBQdQvTXmRbeqtgtTWhyB7HwJcHU7giRpujA8KLtSmGj9+PC1evFgUAUUdrWHDhonioQzjTWjtpmir205NltbjxBc0+90qFBgQQGuOx4qSD8YZiZbA+vA7c9YX5YDRsvslKiLUbkFmT5kIZ1kkPVEaxCkd//Tp4u1evUZ0RErj1YLUnocgezI6XR1O4IkabowPCi4UGB0+fLgYwBkiCyUWUHB0hhywyTBeghZvirY8sasVFhA+zUpnp4/nW8/oUvJerXzi3Zr1BViKIbM1oN3cPsBg0vfin7vEIump0iAOd/xPnxL99pv49/RrbYmueHfckL0PQbYOr+WOcAJP1HBjfExw/f7776LuVPeXdV5QGqJp06YiWxH1qRjGm9DiTVHtE7st1gBbaxZhTYsP3aBFB6+btb6Aj+cfoifPXlhclj0xMeYy52qP32C1M05OlkTJALVWKk/WS3K440ew/IMHRHnyEDVoSPSL4RBn3hY35K6HIHeFE2hxcHvGiwQXhrFp0qSJ/jMsXRjK5/r166KYKMN4G1q8Kap5YlcT7yRXZV96+LpN65ctK9awJrYciYkxtQ8sdcb4/PT5C2r/yy7VVipPlwZxuON/GSxP3bpR5QJRmnKRa/khyJ3hBFoa3J7RBqpNU0lJSWIsRSUYXxDV5xnGW5Fvii3K5hDv3vAEKlsDgLnWQoAgBsvTVg2la8wR0NlOeas8ZYgIMfg+fepg8W5c5gLrhECDFctRl54rkDt+c8cvwNJQRgiW37YNQ21gAFqL54P8eUjT4mJbYAHcce6uEJyOgN9jOc5anvI4oywDCulObFtWvOOzsyyNavYVx1gxHrdwoSB9p06d9GMUgoSEBDFWobIWF9fhYhj3WQMGLTxisqZW3JPnQnBMeauc3dl/zgTiD+LBXmsihNPIZccN6nBlSB1MyRYGysCUwQuPmLRSebo0iEMutJ9+0r2/9hpR9uxWrUMYEgn7Tk2cmpoEAlfEvbkzcUGL4QSMf6B6aB95IGdrcNkI8/DQPowzQSdVfcw6in2YaHK67B6BdQPD5gBrF3vAS6vRfRWFUW0Fy1WKQ7WdtL01x2T61i9EfeoXNvjuzNc/UOTwz2l5keo0teobdDOtYUXyoOQX1OD0Dhoedo2yfvIhUcWK5ArMiZe2lXJT3qjUKcUHguUhshC/hfETGzWyKFzuxydSrzkHVA1hpUZIuWJILE8lLmhp4HrGP/pvm8dSZOyHBRfjTODKUTvOYdzTZ1ar0stdjdpK9I6ippO2NjadGlKHBNGR4Q3/60yTk0nKn58CLumGQkoMSkVzyzQSwutZUDC1O7SK2h9YQTke3dbND6s+rErvvEOu7vgv3omnubsvG4hoA/Hx++9EHTvqguUxaLWFhCVbxvWDBdKakIKl0NnjBGppTFOG0dxYigzjTfjyU6wtbjHEqCkTBC7eefKyYzd0qcCy8t3a0+QO1ASn25plaS7Af/L6s9SnfiHdF5s2CbH1MCQ1nciSj6pcPUad9i8VQguEvtCJzTupIymkaBFKt3+3EDnXN2ynvb0GU+YMaZx6HslxhBAfE9aeMRAfaRKfUJpzl2nOyO2Uo2QElZr/svB0t24WxZYtcWo7z99VlUCQNjTYqTWsPJ24wDDuhgUX47N4ylXhLiCa1CAHzhtnTfWuWzCFGLU1q9FRrHXSzoqhmrn9gthe0XH/+qv4bmmxWvRpw14Uc/kw9d06mypfPS6+PxxdkH6t0JyWFa1J496qSCWmfUsFf5pA2X+dRhk37KSeLQZSeHQWp55HKcSHJFH/zb9Tj11/U5CUrPtu/stpqVKJYHlrqN13sJSqEVI7zt9RtTy169XamKYM42pYcPkwvmzdsYYnayy5A501xLIlytSQQMbng3FHZm9WY+OSWWnF0ZtkL+Y6aWdlWcJFuv3sHaqZLUxXwwqlrErVIwoIoB15ytCO3KWp5M1z4nw5lrWA+B6cv/uE+mSoT41ahtM3y76jGpcO0d9/DKCmnSY69TwyFh99ts2lXjsXiP/jQiPoRtooik0bRcUqFaOs7Vrrg+VlTB1b9ftObVSJunuH2vV6OnGBYdwNCy4fxdetO/7sqlAzliLAdGSoYRvVng/oqNOHB9ODp89VB8KPaVVKH99jbzakuU7akTEWjXlnxm76PvEgNXvyhJ7kK0jnC5QiSng51mRAAB2NLqifN+BlLNv368+KzyuLVKcLGXPQH/M+pwL3rtIr5/bQyqI1nHYeKUVFlz2LqO+2OeL/YfW7028VmuunoVQC3MNKzB1bJEuoqTkVkz+KJm84Z7WNEOd/77/qtBpWWhvTlGFcDZeI92HrjrG5PtZKbSJfwRU1llxVd8ie5doS1/TT5gs0evlx1ecDhEPn6nlVt39KO52FR01tMJvrTamsOaYW7NnMf80V/3+fuzo9kMWWiTZJL61iysNxKnNe+htWMQjZE5sNziNTx1HtscX3dx7pguT/d3gNDV2vGyPx65odDMSWKfFh6VpHZioEt7xNxtsIsG+rFsikqiZY1fyZnFrDyqFaZAzjhbCFy8fwdeuOJ1wVrrIW2rpc2W20wgbBjGP+05YLNp0PvesWopnbL1rNVIxOFyo6a+P6RsMXHzPIsosMT0VxT5PsHrLFWs0xteS5f10EyL8ICKSFJV4xO1/WdKGUkJRscl2Li9UWcVV1z+2htInx9Cg0QmT4YbxK5XE0VZDV1LFVngONT26lMSu/F9//VOl1mhzTxqL1SM21jmGaUItt5LITFmtOqa0J5swaVloc05RhXAkLLh+DA1Gd66pwVSyYrcs1Jc7UYqnwi6nAZHRwcBP2+ENXu8scw18roe8MZTGI17MXhisMD05FbSvlEp2/I500irk6Qquj68X71rxlU9TdAnClTmmvG5S7/c//DROk5HiWfHQmUy4qdPcKNTi9U1i8Zmy7mGI+U2LN+Ngqz4Fy107SxCVfiwD5uaUb0FevdNHHkZkTH2qv9QwRoaJUg6V4TluElDOHxOIipIw/wYLLx+BAVOeNl+Yqa6Gty3W08Kc95w06wh/NWJXkuC1LBTOV3HyYIFyb8tA8qjvpuXOJvv6akvt9QiMuZXFo+wOkZGp9dJ34/6+SOregMYhbCwwIsHwNBQTQ4mK1qN/W2dTi+Eb6p3Q9A7ejJZTHtm7RrPpzIP3ThzR50VgKSU6ilYVj6LOGvfRiy5IwteVaVzOun6fGFtXimKYM4wpYcPkYHIjqPFeFq6yFtiwXHY+aAHlXnDdyR4g6TYhBQssQYA03orzv1IhBWWhgeBlVRTHj4oh69SKaPVt8fNG7N8V1mkYUEm739lW9fIRyPrxND0MjaHWhqmbnk4uPWmJx8dpCcFW7dIgyPH5AdyPSq26HfGxn7bgo3iEEv136rSiyeiFDNvqkSV9KDgzSzz+kaTHqVD2fyX3mimtdjTBzhYtdqwM9m8v09ucMcMZ+WHD5GM6y7ng7znBVqLUgrD0eq7rQoy0xWFi/Mwp/WsLa+YBOpHrBKPGyN1vSJnG6ZQvR228ToQo8CnumT0/B9+6Jyu/Tq7Qie3njyFrxvqRYTUoM/m88WGOiIkJp9PITFpd1KUN2OhxdiErHnqEmp7bSrPLNbG7PpXu6GmoiHuz8XkoMCqb3Ww6mx6GpDduTNtRsR+6Ja93Xy62oEZZIRDB2j/tLBjjjGJyl6GNYyuryt0BU3PxgUcHQNkinxzs+q70pqrUM/LLtokGmn6nsNExH2QQMxfP7jkuq1++OGkT2ng/2iEHl9hjvp+SvvyaqU0cntvLnJ9q6VbgUwXt7FlLoc9NjRsr0fqWgOMb1i2Ux+D7rozvU5NR28f9fJetbzIjDP+bGplRy/7XW4v2145vJHvJkTE2VrxylTzbPEp+HvtqDTmTJb9M56O5r3ZorHGC6PRm8rsoCthdz2Z/4PG3zBb/NAGccgy1cPggHojrHVWFLDSg55goZa8ZPxenDU9GDp6ZLEJAVy4Ta0hV96xemeXsu2ySAMkWE0KjXS9r9VG6PGJQFhLH1IPLpI9o3eSAFJicTdepENGkSUdq0YtBoafhwynz5MrU9vDpFmQR5fyGzEMc5Nu4p7b/8QD8t+8NbNGfuZxSelCgC3g9kL2Ly97L7bvs5ddXU98W8SrWmj6VK145TzrhbdDXSUORZO7ZvF0hNry0ZL4Lk/y7xCv1ZuoFd1il3XuuucrFrrWagLZZbf8sAZxyDBZePwoGojiNbEKxl6wF0FgP/Okx/7b+aYpqtYgugaCU6Lox1mDEimO7Fm87QkztmDFsjD9UD0YEyAPfjn5ntNLDMHYPrUUgqy0ZuS7EqtsQGKQWEKbdU7Qv7KVVyMp2MykMX+31FjSC2QHAwBQwaRPT++9R91980t2wjMcC0crlYDso4GGcW5noQS3Pnfko5H96iS+mjqVurIQbB6DLRL91ExqUTLDHp5FPqWDGGMu3ZTs1ObKZpVd+w2kHLa/6qaiYKea0ZZX50l05nyk1DGhgGydtqnXLXte6KhBwtuijtdePzUESMNVhw+TBaDUT1JnCz71I9r8nUf2NMiS1b+a/zt14CwlTHLB/v8JAgi0kDX71eyqrYsmZ5UGsBVLYTmLIe1D23W7yvL1iJZhlbCTBu4MiRlP3GDep8bjNNK/xfliGqwSOL0jiTMu+9azRn3meU/dEdOpcxB7VvM4pi0/0Xh9aoRDQ1LhUtxAmEKYqE2urEmp6jMg3as53ej91N81K3s163LDKMvi4sUfV3mhNdvUqUKRPd/OV3ijyeRE+cUNPK1de6s4P0tVoz0FE3Pg9FxJiDBRfDWAE3fDWCyxHeiclDjUtmo/vxidRrzgFVnb+ljtlRV5M1ywOKaaK+U5OS0SKGTW07TQ2UHJT8guqc3yf+X1egckq3VFgY0YABRH370qCD/1Kd0QPp1tMkEeDeb8EhIjIUOgXuXhFiK+vje6Jm1lttR9HtNIauObgO5ZpbiK2zJ2JoXu7KNCBVKkp36jgVvXeFdoZFW3Tfbi72mII7tCeKjycqWpRo6VKqWaAAbW3unow3RzPrnB2kr9WagY4OJcRDETHmYMHFMFZw5nh+5oDYwnosdf7o0DJGhNDnTYtRdGS41Q7TXleTmuDo3nMPGNSfwiKVn+GyfL1sDqpfPNpgnYhxM6b8tROUPuEx3Q9Lq4+xSmEl6NaN6KuvKODCBYrZvVpkMkK8weVqzFcrJwuxdSJzXurQ5kuTZRseJiTp4+PszQJ9EJ6ObsXUpugt6+inqR9SYnCIflpCqlC6li4zXU4fTVcis4oYslTD/tZVoa1XTzeAdvr0BtYpWRAtPXzd6cLLGXFSzq4Mr9WagfZe7/6SAc7YDwsuxq+w5ynfUkfjDGD9QNzVr9tSZj8pwXrvxj/Tiy219YFstQ6oiWExTiKTPzcumZXeqpSHAoMC6M7jxBSdvilLYb1ze8T7xvwV9DWoUlgJIiKIPv6YaPBgIbzorbdMdsQ5H8SK4XuSKYC6vDHMYo0sZ3TkDzp0FoIr3bMnRHgpyBV3k6peOWr4g+7dib7/XsSmKY8Van/N3X3ZIEPSWYHjzoyTcmaQvlZrBtpzvftbBjhjHyy4GL/Bnqd8uVNMTEqmj+oXopnbLtgUBK8GiKi+8+EaU8f0LedSjN3nzPpAjgiRFUdv0qpjNw0EGdqAJADEpZmi7lmd4FpfoJJ+fpNWgvffJ2nsWAo4eZJ2TfqN7lSsk2IWDCoNtucpTTfSZXZZRy5bMwq9+xbtK1aSPv11q8H0iMSnlCsuVoiuXA9uUpb4exTcuhXFjB5EQUGBqoZqckbguCvipJwVpK/lmoHmhKW568wVWaGM78GCi/EL7HnKd2T8Qley/uTtFN/J9YHIhu0zZ+1z1KJgbP1CG96fYzrTExapwncvU1JAIG3KX8GilWDl5Xi6U6oBddgynxK+n0Ij38yawpX52vFN4n1R8doW2yiLulV21E0ytmaUrV6aHm69k0I47M9ZLOV6x20QHTaGOrJmOXFG4Lir4qSUQfr2xoZpffBqS8JyQKNiXGmesRkWXIzPgw5h+OJjNj3lu2P8QndgafvMWfswnzNj1iwto+5Ld+LenMXpYVga6lo9r0krgXw8chV7ld7askCUkch9/wZdzvDfvEVuX6Sidy5RYlAqWlW4msU2QfQAlIKwBsaOVGYgGlszbHFBxZoRxq4KHHd1nJSjsWFarxloLvuTM8AZe2DBxfg8k9eftVg93LhTs6fwoZYx3j411j5XxqyZit9CdiKoWyyrGcGsOx4QWJvyl6dXzu+j9gdX0OhXuujnw2DSYEOBSkK8WQIuoVqFsqiyXk5pV54CAwMsWnDMCQdj7N2X9goiV8ZJOSs2jGsGMv4CD+3D+DToFL5be9qmTs3V4xd6CmyftZgeSWENQ4cJS4OrSP3sKVW9fFhff0vfCCMmrz9jkI04q1xT8f7m4TX64X4wCLTenVjMsjsR4PjuOK+uqvyd+EQhVFuUzSHezbm45KGkULHe2djr5pXjpMw55eQhjWyNk3L2MD+yxcjaPmYYb4YFF6NZHB1fTe4UbO3UfLVwIbZPjZiUrWHKsShRJ8zZ1Lh4kEJfJNHF9NnoXMacenGjPO4T156m79aeMfgdshmvpstCGRIeUdNTumD1CtdOUM6Ht+lRSLg++N46AU4XOxAKGHDaWdgriFw93qItsWEMw+hglyKjSZxRN8gWS5WyU/PFwoXoTyvkyUDLjqgLEl99LFZvaZBjWNQOuq0WOX5rQ4GK+qFt1hy/SWNWnLR43FA6Yk7ZRjRg8+/09v7ltLBkPWrx0rqF2K3EYHWCB9v19/6rTs+Sc9b546zAcVfESWm1hpYWcbTgLOM7sOBiNIezYkNsudkrOzV3FDp1NzAO7rt0n+4Z1cYyB4TI581S7hNnuVrhAqxrFL8Flh5WJwgx2PNHW+dQuRunqOz1U9T05FZV2Yli3S/FRqW8GaltpdwmXc6OiB1nnT/ODBx3dpyUWlF58Y5hbTJ/Q2sDczOehV2KjKZwZmyI2k6hb/1CBjc/S24Yb0Y3EPZ/1dAtoazEbrxPnEHJ2HOUJf4+PQ4Jp925Str8exQ0XV60uvj/m2XfUsanD+l26vS0PU8Zq7+VXmYp1h6/wWx8H8SOvfWv5H1l6QxNHaIr8GqcDdm3fmGa2LascOPCnevMTtmZcVLWYsNkJqw9LUSHPz84Gj+kyA+O/rpf/BkWXIymcGZsiJpOAdN71y1k1g3jyqBxd/PFkqN0+d5T1fMbWwixT/rUS7mvjMl/9yr12zyL8ty/bnJ65sf3afjaaeL/LXnL0bNUwWQPf5RrIt4L3Lsm3pcWq0kvXlaqtwZKM5g7zyDAjcWOo/GExjx59iLFd3FPnlOR6DR2CSJnt88ZopLsCJ73FZydVMD4BuxSZDSFM2NDHC2sqHTDbDt7myZvOEfezP0nScKiExEaRPGJKTt8NRbC/JkjLP/m0V2aM+9Tin58j97d8y99U7M9zajYQj9kT+kbp2nawlGU7fFdehgaQVOrvmH39uzNUVyMl1jstm64oMUqshOtgTNh3p4rBiLcVrcQOtFBC4/YtX57ipx6ym2FZUOcGic1aGEAak+j1YG5Gc/CFi5GUzg7NsScpUqty0h2wxTKmpYc4f06+UW5gF6vFCBPI2EAZSuYy4yzdHxQouGnf0YJsZWQKkQM2Pz5hhm08I/+VPj2RWp9ZB0tmD1QiK0zmXLRa+98S4ezFbZrG4QcCQjQW7kupY/WD3ztCMYWVHvcQjvP3zUolGrvur3BbZU3yrIA99fgeU4qYEzBFi5GU6gNOEZsCNwv7iqsaG/mmRyg3a9BUbG+f/ZfJVeT+fE96rNtLk2r0pqupI9OMf3Js2RqUjIrLT9602ybzVn+zB4fSaIxK7+nsjdO0/2wtNTynW+o6uUj9Nn6X8R3y2d+SKmkZDHrmoJVqG+zfvQ4NLXDAeUBbUvT90FJtDpzMX2mo1oLnqM1y8wNuwOXnqPr9tQ4ibai1QGoPQ3vF8YULLgYTSG7AXv8YXrsPSW2dCaODMWBji05WaL04cH04Kl6y4XsxmxbKRctPXxd3ITvPH5Grqbrnn+pw8EVlDbxCfV5rb/JeTadNl30MzpdKLWrnFsM1g3hIFu5ZLFactl8WjH3d/olY0n6q2R9upEuSkzvuesvev34RnoeGETvtxxMlzJkFy/UzPpy9VR69ewuMd/Eau1oQo12lCFNGFG87fui9ysFqHrBzAaCuf7SH6miQkzjWLX/Rbc+V9YsM+8WktzSWWvBbaXlAag9Ce8XxhQsuBjNoaXYEDUDWMvCynjMvcjUumBw5XZEhtsXIG4Lhe9cFu/VLh8SlifZ8qMk3kTQNkhISjZoL7YJy3jwNIny3rtGq2YMEMVK+9FBUZYBgmp/9qLUb8sfYv6TA0fS4eByRC8tTDfTRlG3Vp9TzYsHhJtxb66SogPe1P8V2nPxHvWavd8mEQvXrvHxlsW0vt7R40TKGBFM9+Jtd+spBQJEsj0WqZj8UXbF+9kqTtYcj7Wrfc5E6wNQewreL4wpWHAxmkRtbMiKlzEqjhYTNFWcEB2amgGsZfeW0m2JGDO4PY1/G2eDuLCXAvd0bsvM8Q+o0J3LdCaz+irxxrFH+s+SRF+s+VGIrYPZClFiqlCqcuWoGAtRHg/xUpuO9F7ayhRvLE4DAmhLvvIGHXBIqkAKDAiwSWxZsv5AGGOAcktjZoqmKESBNYFgr1uoaoFMKcS3NWwVJzhf/z14XXX7XFl8U+sDUHsK3i+MMSy4GE2itrND9XO8HMnKMmXFgmsN1h5LYgsuxinty1PV/P+l8MuWlhpj13ukaCoC13M9+C82q9rlwzYJLnM0PrWNal08QIlBqeijZp/QxYw5qMyTWPon7BQFzp1LNwuXpPq5XqfnFiyBxh2wrZYXiBi4C7F/lWIBx0+NC1rZBmBNINjrFkLbxrQqZbFNxoJMuW414gjT76lwycLSdz/+mTgfXZnFyANQ835hrMOCi9EktlbrtrUKvdWq9lYsJQDWGVhpTHWGnhr8Ot/96xSo2Jpqlw7RbxWam632LgUEqhpkeui66eL/H6u8IcQWOJQ6mn5p8gp1/GoMtRy/waLYypg6WLgRYdmyN5AaAgWxWUqxoKYEQ5rQVDSyRQmKjgw3EC/WEikccQuhbT92KJ/C6gYhP/y1EmbXrbbEg1qxWi5Xeuo1x/FRG9TgSJykL8P7hZFhwcVoEkudHTkhKwsdNdL3B/512CFL1Nazt0UFdwyZgyru6NTx2VMUvHtFvD8MSU3pnj0RmYKByS/0dbBkyl07SbPmD6GVhavRZw3etzj+4Ifb5opSDpcjs9IPVf9nMG3U8hM0ddM5q9aWe0+e09SN56hP/UIOD4GjFAtpQ6277h4nJglRYy72y1VuIWtWH+N12zKklVqxeuBKnEezGBmG+Q8WXIxmMdfZORpIryYQXi1TTARHw43jKQrc1cVvrSlclV49vZMiE+OpxM3zdCRboRSZjGmePaU3jq4TcV7dX/+MYl9mHCopdPsSdd27SPw/vH53k8JMjWsLfGdUysNWUW1KLLQqp7O2WWPH+TtUvVDK7XO1u0ytdcPWEg9q3J0ZROKA+WPDxTcZxr1w4VNGs6ATigwPoQGNioqioR2q5lb1O0vuFnOFIp2JPdlxzg6YPxWVh3blLvVftqKCDE/iqMGZnXpLWJnYM7T4977C6mVcz+vLNVMpOPkFrS5UldYX/G+QaXsxHs7EXGFaa6JVFgvXH6gdqijApeMQmhtaB+/bzt6hcStP0kfzDtD4lSfFZ+MhXWwd0srSeJ/y59fLqhOj/laUlGE8BVu4GE1iygqlduBlc+4WS1YEX0F2KZ7LlJOeB6US9a+qXTpM06r8N4ROi+ObKCQ5iY5mLUA9Wg6m6Qu/FMPjzJs7iH6p1JKyP7xNFa6dpFxxuuD7p6lCaUS995zSPlMWSFMWJLhl+/550OrysmcIV7Ved5cOgfUJA2T/ufdqCpfnlI3nRNA8AuttTSBQzmfN3YmHlV+26YY9soS/FSVlGE/BgovRHOZiWZBt5UgdI08Gs7sDBMHnfzmQ89lMuehqZFbxf6Wrxyj4xXN6HqSzGv3vyFrxPr9UfbqaPppadxhP3y77lhqd3kHv7/xLv7wXAYF0Oio3fVejPV2LzOK0dpoq5WHselNbrb1a/iiaue2iycGgZTKkDhaZpPZiKWvQ3LmK8wwDZJsDIgxZjAish3BSK3rO3HysL0iLNlhyd6LdWipK6srSFAzjDbDgYjSFtVgWR+oY+brrJEfcLQpLeiZKN0BsJQcE0J3UkRT1JI7K3DhNe3OWoBI3z1GJW+fFPIuK1xG/exISTj1bDqbOe5dQ5atH6UWp0jQnVS46lK2wQ8PvOFLKQ01APaajrpklsQVGtypld8duKWsQQsdRi6kcl6U2gWDyhrPipdx35uLEtFR801MDbDOMluAYLkZTqLVCGcf4qBmM2tddJwVfBsxfzJCdXgQGiZIPO3KXFt9Vv6iL4/rf4TXifU2hGIoL/29Absy7on4bSl7wN31Z4X+0LW9Zl4gttQMsK2OUzNGsdDSNXHbcakmIukWzmo2xsoS5eD98hnXq+3WnHbaYyi5WSzFZplA7OLWjg7c7A08PsM0wWoEtXIymUGuFGtKsBEWnCzNwTwAEJOvcUZIYYgVVv+UneNmK4E1uxbLXTwkrFVx/agPm4U6U2Z6nDDU/uUUUQP0x6Q1qeXyj3p0oU6dwFHWvXVDsH3e6Xa2VJoAYeK9WPrOuuelbrMcnoSRE+ZGrKVVQoEEsFeIBW5TJRjkzpKaMaULFuaR0camJ95u47iw585y3JSvXlrIOnixKqoUBthlGK7DgYjQVl6HWCoUOUulGwVMyCmAqO1WMZ6cMTrZlYGwtgKzBv//oT3FhaahRl+/FuISWKCAHzGfMqf9ue57S+mU1P7GZ0ic8putpo2hr3rL6eVCP7Kd3Kon9o3Z8PmchZ9/tPHeXAgMDUgjoxYcct348FuM6GrodUS5h5vZLBt8pXVxqhKezki+U57xSHG07e9vimIy2lHXwVPFNLQywzTBagQUXo6m4DHuGU7E0tIscnIzBsHvXLSTa9cNb5aj33AOkwqvkUfptmSWqxmdIeETfLPuO3mkzkiIjQilZkijuaZLZGlyXsvxXPuNS+mx0LW1myvHoNg3aOFN893fJegaFUBOSJKry1Vp6s2JOmqEiq80VoBq6clxFnANtK+VyqzXyhqLAaGJSslvWmc3M0EAQH/ZkLmoNX9gGhnEWHMPFaCouQ019IWWgLyxswxdbjuMB3609Q9XHrBftalI6O01qU460TMylw1Tj0iF6FpiKngSHiv+77v6X7j95blJsKUtCnEyvq78khtEJCKAdL61cCJ4Hf5Wql+K3WK6lrDpXYzyINc4jHDNPgIeIqAjzlfediaWgdXsHz9YSvrANDOMsWHAxLskkNC5waQtyLEvWdIY34QwRIdS5el5RX0heNlxRaofSwXyyGMyU1j0dql1IkrBugbllG9IXdbuJ//tv/p1KxJqOG0Ix00xPH4r/z790KT57aaXZlqeMfr5duUrSpQzZSet4yvgou7ig7mF9UgsGMleC33avlU+4tM2VqpBLQliz9ppz0AeYsZBpCV/YBoZxFuxSZDQZl5GcTJSYlDLuBi4vvOTCkvP26NxoapFeikFUr9cqdc7vo4rXTlBCqhCaUvVNupUmI9W5sE/UyZq05Gtq1nECPQ0xFANy/a2r6TKnmCZnKoL5pV5101Z4N3ceJ9oU7zflrfIpYtBguRrQqJiIkUMyB6ri50gfTtUKRom6YNZiHbVU1sFefGEbGMZZsOBiNBeXMXr5cavuLWuFJa39dtuZ26R169Zv5ZvRrbQ6wTqo0QdU5vppkYk4ZP3P9Gmj3ibdibJ1SwnGSPy7xCuU+8FNWl6kuls2w9uRB7u2Fu8nxxQqs2GV4LvqBaPEyx4cGTxbK/jCNjCMM2DBxXgsLsNUhuOqozfcEkv01/5rwt0T9+S5pob6aXhmB5W6eY4eh4TTtCqt9d8/CE9HHzf7mGbP+5zeOrSSVheqQhsLVEoRMK8sCaGkX7N+TmlfZFgqql88K/29X2dRcwcf1StEk9afcUuSg3FSBuL9JlMAvT8npaXLXRYaT5Z1cBa+sA0M4ygsuBi3ZBKqyXBEqYfHiZ4b+NnTBCa/oI+3/CH+n1HhNbqXOtJg+o48ZWhGxdfo3b2LqP/mWbQxf0URFG88hqI5jF069rDz0/q04eRN+ufANasCqH6xLLT+5C27hZJ8Hn1QrxAVzpqG3p9zwK7loPgp6nGpZUjT4gbCoGHJaBFv5UkLjafKOjgTX9gGhnEEFlyM2+MyzI0/pzb43VmgZETf+oVp7u7LDq17yLrplPvBDer92kBKDA61W2x12reUity5THGhEfRz5ddNzje5Whtqe3i1GJ4HA1OvKVTVoOipJcGF/f1G+Zz0137b4t6UDPn3KP29/6oq4bb2xC2712N8HsHS9IMZS5Mper1SgApnTasbCDvuKfWdr6u0bwlYPNtUzCmq15sqdbJ1YF220DAMYzecpcjYhDxECuoUfVS/EGVNZygw8BnfY7qpIVTUVPB2J3mjUtO2QXWpT72Cdv0+9/0b1HXvInr17G7h6rOV9E8fUvddf9Gmn96joeuni+9+qtyKHoalMTk/XIuI7QJ9ts0VMV+hzxMp14ObFl2KMjULRwlrjXFWnVqWHbnhlmNnauiZJqWzUevy6jIsM6YOoRZlcwiLSnRkuKrfdIzJQz9tvmC21AmKwmJ58nLZHcYwjC2whYtRjTk3IKxEEC4X7zwR1iJl/STjQqjuHDpGDai3hI50/l77rD6vndik/7/Hrr9pTtnGlJgqxOrvQpOeCcvYG0fXiQGnwYOwNDS7bGOD2C1T/FypJXXat4RK3jxH9c7tpmvpsogCqbCM3UmdXlUweNrQYGr/yy6ylafPLQ8U7Shdq+el+i8HczYlaGoUzEx/779udTkYusdWFzgG1OYhaBiGcRVs4WIcKnR682ECTVh7mo5fjxPvxq4540KoWqsoDYFoartUIUnU8phubMIXAYGU9fE9antolaqfvrfrb+pwcIUQW0ezFqD+jT+kqu//SuNrd6SkIMvPQfdTR9JvFXRWro+2ztHHbwnr1suYLlMo6x1VypeRtBSvjLbB8jakeQmL1iO11irlfNaK6UJkPUp4Loq/qil1wjAMYw8suBinFDqdvuWCqkKoWqsovdQBF1nxWxeo4L2rlBgUTONqvyO+67lzgbBeWSLz4/vCGgYGNvpA1NVaULoBJQSr3zfTK71O8cFhIqOx655FqgLmlXF1ey7c08zQRshCRHyUmuBz2VplCVOFNOXSBLBkKZELk+rGW7SO1h4YGIbxHjwquEaPHk2VKlWitGnTUpYsWahly5Z06tQpg3kSEhKoV69elClTJkqTJg21bt2abt7UxavIXL58mZo2bUqpU6cWy+nfvz8lJRlmJW3cuJHKly9PoaGhVLBgQfr1119TtGfKlCmUN29eCgsLoypVqtDu3bttbou/Fjq11HkrrQPWKk97E68d11m31hasTDMrtBCDQkc/vkdtrFi5+m6dTRHPE+hgtsL0Z+kGFq1Slqxcs8o3Ff+Xu3HKouCKThdqEA8FayPGLtQKv+9QP36jbK2yVLncXMIGth/Cbm63qjSxbVma/W4VCsXwRzagtQcGhmG8B48Krk2bNgkBs3PnTlqzZg09f/6cGjRoQPHx8fp5+vbtS0uWLKEFCxaI+a9fv06tWrXST3/x4oUQW8+ePaPt27fTb7/9JsTU0KFD9fNcuHBBzPPKK6/QwYMH6aOPPqJ3332XVq36r2P8888/6eOPP6Zhw4bR/v37qUyZMtSwYUO6deuW6rb4Ks56qsdyLLl3vIkAKZleO7FZ/L+4eG16liqYfoh5U3zuufMvCkky7Z4qdPsStTm8Wvz/Zd2udoktGQTXY5xFGXMB89+8WVbUQEISwxdLjonq6cZjF3qSe0+e2+Sqk61VxpaujBHB1MVo6CdzpQkQ+B4YEECxDxNVrZOHoGEYxlECJEnSiGOB6Pbt28JCBTFTq1YtiouLo8yZM9OcOXPojTfeEPOcPHmSihUrRjt27KCqVavSihUrqFmzZkL8ZM2aVczz448/0sCBA8XyQkJCxP/Lli2jo0eP6tfVtm1bevDgAa1cqcssg0UL1rbJkyeLz8nJyZQrVy764IMPaNCgQaraYo2HDx9SZGSkWFa6dOnIW0BH3W76ToeXA8uCXIfHVAC+N1H5ylGaP2cQPQyNoEq9Z4lAeYisjT91o+yP7tDnr/akP15aoJTMWDCc6p7fSysKV6Oer3/qcDsGbZhBPXYvFP/X6TaNLmbUDVytpHO1vLTyWKym9zUsThBBtiAXzkXSw78Hr4uhn8wla5hi0cFr1GfeQdWCyzhrkmEY/+GhE/pvTcVwYUNAxoy6+It9+/YJq1f9+vX18xQtWpRy584tRA7Ae6lSpfRiC8AyhZ1z7Ngx/TzKZcjzyMuAdQzrUs4TGBgoPsvzqGmLMYmJiaIdypc3omYAWksB2KasA0r3ToPi/x07TSFJIiar1/Y/hUVLSYuX7kQIJzkrEVauqVV1Yvz9nQtSWLmqXzwoxNbzwCAaW7ujU5o4vXIruhuejq6my0JX0kebnGfm9otuE1sRIUHUqES0GJzZ1a46WKvinj6jmdsuGogtU8kajqwzU0QIiy2GYRxGM4ILFiW4+qpXr04lS5YU38XGxgoLVfr0hqnuEFeYJs+jFFvydHmapXkggJ4+fUp37twRrklT8yiXYa0tpmLUoIjlFyxm3oi1LC/QrWY+s7+XzMTVyO4dFKjUIuWun6KBm36j/ltm0fC104QAA8EvnlOTk9vE/4uK1zb4zfzSDSg2TUZh5fpq1WRhCUv1IkkUNv18/c9inj/KNTFpibKHuxHpqUHXH6hpp4n0IjCIPEHjktGUOkS37vhnL4Q1zVLGn6kYM0ujEjiSzCEna5hCTTwh3JQ7BtdjyxbDML4juBDLBZffvHnzyFcYPHiwsNrJrytXdOn73oi5LC+5QGW53BnsXrZWh/vouH+J4v9lNGjTr0J01bxwgDIkPKJbERloZ+5SBr+BtWtytbbif9TYgtvxwKR24r3Y7YvCBTmxejunthOiKy7cc6J1xdFYevLM/vpcw18rYVcRUTXJHJZKOVh7kMDrq9dLUYiNgfUMwzCaLXzau3dvWrp0KW3evJly5vwv0yo6Olq4+xBrpbQsITMQ0+R5jLMJ5cxB5TzG2YT4DD9seHg4BQUFiZepeZTLsNYWY5ARiZevYG4AWlBj7Hqzvwt4aWnAb011rFXzZxLp+RhqRyugdINsxZpZoTl13rdElHJAKYaCLweKXlKsFiWbsCr9UbYx3UkdSQ1P76BaF/ZTpqcPqeK1E2La9zFtRLV4b8EZ4y9iLMNkSUohynDMx7QqZbf1SG0yh6X55AcJT46TyDCMf+BRwYV4fQSl//PPP6JsQ758hm6pChUqUHBwMK1bt06UYAAoG4EyEDExMeIz3keNGiWyCRFwD5DxCDFVvHhx/TzLly83WDbmkZcBVyHWhfWgNIXs4sRniEG1bfEHTA1Ai6B6tZYGU9YsLBMdL7LntMJbB1dQSHIS7ctelEbU705XIqPF0Dv9ts6mpIBAk+5EPQEBtLJIdfFC7FfJ2HNU5/xeSpX8gn6t2Jy8CWdk1GDg6F51ClD61CF0/8kzEe8Xkz+Kqjo4PI7aGCxr85l7kOChexiG8RnBBTcisv4WLVokanHJsVCId4LlCe9du3YV5RoQSA8RBYEGgSNnBaKMBITV22+/TePGjRPL+Pzzz8WyZetSjx49RPbhgAEDqEuXLrR+/XqaP3++yFyUwTo6duxIFStWpMqVK9OECRNEeYrOnTvr22StLf6KsywNqDQ+fPFxtw9ibQxitNofXCH+/62CTiDNqNSCwp8niHiuVFIyXciQjQ5HF7K6LCkgkI5kKyRe/syUjecMsgerF4pyeJlqh+xREx9m6kGCYRjGmXg0OGHq1KkitqlOnTqULVs2/Qs1sWS+++47UfYBViWUioD7buFCXRo8gCsQ7ki8Q/x06NCB3nnnHfriiy/088ByBnEFqxbqa33zzTf0888/i0xFmTZt2tDXX38t6neVLVtW1OtCyQhlIL21tvgrzrQ0fPO/MuRpGp/aTlni74sYrRVFqum/n1KtDU2KaSP+n1WumUM1tPwVNdmDzkzmMFcElWEYxq/rcPk63lqHyxrIAkMMlzVLA8pAmOr85HpKsICdufmYJm84S57k71mfUIXrJ+nbGu1pkokA93QJj0XwOwsu+7B2PtiKqZpuaupwMQzDuLP/1kTQPOPdyJYGWC6Mg6ytWRq0VgC11I0zQmw9C0xFc8s0MjnPw7A0bm+XL2Etps9WIKrqFs1Ks3ZcpEv3nlCejKnp7Zi8nF3IMIymYMHFOAV7sr0gtiDSnGliDQ4KoOcv7F9ip5elIJYVrUG309hf6oJx35BRpkT7z1svsIWLYRhNwYKLcRq2ZHs9S0qmT/856lSxBSC2UITTnrpQmeIfULOX4yPKwfKM67h454nDyzAn2uVYMR6Oh2EYrcCCi3EqarK90El++s8Ruhfvmrpb9hbhxKDSoS+S6GC2QnQwexGnt8tbkd3Ezq6VNmHtaSoSncbuOCtrleat1X9jGIZxJ1xCmXErskVCrdiaknyMVjzeTHO7VqZ3q5sfPshRMPQOam+B38s3c9l6vBG4hVGyY9/nr9KQpsWcumxLQ++4utK8K8E2oT4dBsjGu73byDCM78AWLsYlKDMPlRXpzVkkTIFswMY/fE6Bz5/T3hKladkD0xX9nUGd8/so58PbdD8sLS0rUsNl6/EmulTPK6xDSrdwVNpQzQTPO6P+myvgrEmGYUzBgotxW4fTtlJum7IRW10/KMQWuP/tJLrReqjLjpZc6PSvUvUoMdh3hmOyh/ThwTSmtekhd9TWXLMFewWRs+q/OROOKWMYxhzsUmRc0uEYCysEMX+39rRNy6p2ZIv+/7rn9lL2h7fIFeSIu0WvnNsr/p9TtjH5O71eKWA2rkqu7u7MiCh7BZG1tuD7bCorzTsDazFljrpQGYbxblhwMU6LVdl29g4NX3zMYoejlrDnCVTzrE4EXU2XhYKkZGp3cJVLjlbbQ6sokCTamqcMXciYg/ydqDShdlV3txVHBZHWKs1rOaaMYRjPw4KLcciahQrz7abvpD7zDlL7n3dR7MNEp+zRWhcOUHhSohBbX73SRXzX9vAqMc6hM8HysFwwm61bgujIcFU11xBMrwTiqXutfOLdGs4SRObags/uLgmh1ZgyhmG0AcdwMXYFw685Hksztl102d5reHq7eF9VOIZWF6oqxjXE+IavntlFy4s6L6gdy8sc/0Asf00h/x6EnGywOFmquTagUTGD7+/HP6ORy9QXxHVl/TdXosWYMoZhtAMLLkY1zhqGp3npaFpyONb8Sfkiieqf3a0XXElBqWhe6Qb04Y4/qcOB5XYJrpoX9lPD0ztoZsXX6FymXPrv2x9cLt7/LN1ArMefCbDR4mSu5pqp7xuWdK0gUlP/zdXIMWXWxhR1V0wZwzDagl2KjEPB8PbE7Oy5eN/ifFWuHKXIxHi6kzqS9ubQ1X2aW7YRvQgIpGqXD1OBu1dsWm/z45to5oLh1OHgClox4wPqu2U2hSY9o/x3r1L1S4fFcueWbUj+TDYXu+BkQdSibA7x7ouFSLUWU8YwjLZgwcU4lH2lFrmLQWkIa3FejV66E9cUrELJgUHi/xvpMtP6ApXE/+0P6Eo4qKH1kXU0Yek3lEpKpsuRWSkkOYn6bJ9LK2b0pk83/CLm2ZC/Al1Pl4X8EVSPn921Cm0dWNchscWFPrUXU8YwjLbwbx8K45TsKzXIMTuJSckW5wuQkqnBmZ3i/1WFqxlMQ1D7q2d3Ueuj62h8rXeo8J1L1Pj0dmp0ajtlfBJHK4tUE67BfbCKBQSIyvFfrZoifjunTEP6vMH7wq04fN1PlP/+dfESyy3XhPyVNhVzUvVCUQ4tgwt9ajOmjGEYbcGCi3F6VpUcq/L1G2XoTnyiQYeDEhKWKHf9FGV9fI8ehYTT9jxlDKZtyl9eWKlyx92knT90FG5HJW8eWSte5zLmFK7INkfWiO9nVmhOI+q9J0TYiqI1aGu+ctR/0+8iHux0VG7alK+8354FP22+QOVyZ7Db8sKFPrUbU8YwjLZgwcU4NatKGatiynJiLbC4wekd4n19gcr0LFWwwTQpIJBml2tMgzf+KsRWfHCYcDOuKFKd7qaOFJavZie3UIF7V8UL/FilNY2p3UmILZlHoRE0tEFPmhzzJiUEh+rdlv6KvQM88+DRDMMw6mHBxVjFmkhSkiEihKrky0BHrz2ktGHBVDW/YYC0HFiMAHx8a7A8SaJGLwUXshNNMaNiC0oMCqFrkVloc95yBsPw7MpdSliyILqantxKm/OVo58rvW4gtpTcSusaC0RQIMSIY8tAi8NDgujJsxc2/w77NLXK3zoynqEthT7Z2sMwjL/DgouxikWR9JJ6RTPTzgv36F78M1px9CYR3aTJG86KoOwxrQzH5ZMDi1GVXhlAXy7uCuV9cIMSg4JpY/4KJtvyPCiYfq34mtm2xoempj/LNBQvT+Go2HqjfE76qlUpsd/lmmf/Hrwu9q01IkKDaFzr0tSwZDbx2xVHb9DvOy65pBgnF/pkGIZRD2cpMqpcR5HhIdS5el5hwTJVXXzdydsUn5jSovLgyXPq8cd+EetjTMJzQ2VS5+XYibBMPQmxXO3cVweN7lu/MI19ozSFpArUxwENbV6C9nxWn/rWL2R1GY8TX9Cn/x4VIg2/bawyNsueYpxc6JNhGEY9bOHyBa5eJbp3T7V4Onotju7FJ1LGiFAqGZZEQadPEZ06RXTyJNHZs0TP/rOkPH3+gu4lJlNEaBqqE5aWyoWloadpIylPdHrKnSmCssSH0F9LrtBHgeEiXiohOMxqnBDEF0SYAZJELY5vFP8uLVqT/JEHT5+LAb7n7blssgr7vD3q6o9B5MIaCSsi9rmrinE6UuhTHrGAs/gYhvEXWHD5Al9+STRtmqpZER5umPtnGdiZMJyzpSGd27x8D0xOpm9rvW1yHjmWB50vXInGlL92UrgTEQi/upDp+C1/AQJGFkyy6LK1NIekELnm3MGOFuO05Gq2tGwuI8EwjD/CgssXSJeOKDra4iwJScn08GnKgZ/jQ8LpfMYclL9GecpXoyJR4cJEERHCAtH1tz105/EzCnnxnCITHlP6p48ofcIjSv/0MaWSXlBEaCqKyZ+RDuw9TW0Pr6Yu+xbTjEot6EF4OpNt+PyfI5QhIthk4dPXj28Q7ysLx9DTEP8eaw7CJcDIKmhPjJUscuWYOeNhmZwxnqGty+YyEgzD+CssuHyBceN0LzNAPL0ydr1ZC4ns+tnapa7eGrH73F3amOYmURrLqx7StBiNjDpOJW+eE6/uuxbS2DqdTM577k480Z2U3we/eE7NTujit/4pUdfyCv0E4ww/ewc8loWaK4txql02l5HwX5KTk+mZIlSBYbRGcHAwBQW5tkQQCy4/wJ70fbUWlasPnoqyC9/WaE8z/v6COu5fQr9UakF3IjKobl+d8/soQ8IjupkmI23PU1r17/wB+TjYUppDiVKoubIYp5plcxkJ/wRC68KFC0J0MYyWSZ8+PUVHR1OAmVJCjsKCyw+wJ33/4p0nqn6z6KBueBwUID2YrTCVvXGauu/6m0bVfVd1+14/ul63rGK1/b4IqTnBpIyXUoMjwfCugstI+B+SJNGNGzeE5SBXrlwUGMiJ8Yw2z9MnT57QrVu3xOds2Vwz5ikLLj/A1vR9xNlMWHvaaoeOeCx9baiXVq7fFwyjtw8sp+mVXldVWDRdwmOqd263+P+fkq+oaqe/kM1IMJmLlzLG0WB4V8FlJPyPpKQk0ZFlz56dUqdO7enmMIxZwsN1pYggurJkyeIS9yI/bvgBsjvKXNeL7zNFhFBs3FPaduYODV983KrbCtOrGllPNucrT3tyFKewpGf0/s4FqtrW5ORWCn2RRCcy56UTWfKr3CL/wJRggujaOrAuze1Wlb5rU5beKJ+DIsMNh0CCZUuZ4ehN56GxyGS8mxcvdLX5QkIM6/cxjBaRHwqeP0+ZYOYM2MLlB1irFI/Pd+OfUd/5h2xa7nJRUV4BrFw129PceZ9Ru0MraVqV1nQjXWaLy3j9mC478d8SdWxat6+DAqjmBJMyXur1cjlM1rQCGChcS3Wu7C0jwXg/roqJYRhvOk/ZwuUnyO4oWD9cyY48ZWhH7lLCavXhtrkW580Zd5OqXD1GyRRAi4qx4JKJThdKvesWVL3PZQHWomwO8Y4q8zXGrqd203dSn3kHxTs+m6r2r5XzUKtWOYbRqjD4999/nb7cvHnz0oQJE5y+XEYHCy4/wtgdlSG1oSvKWXxTs4N4b3d4NbU/sNzsfC2O6SrLIzMxNl0U+TsBL1/DXytht5VHrnNlHOMlF1PViuiSz8OJbcuKd3xmscVojR07dohYnqZNm3qVeOnUqZMQZXjBnVuwYEH64osvREydJfbs2UPvvfee29rpb7Dg8jNka8jlu0/o/hPX+Kn35ixBk2J09edHrp5KzY9vSjFPpvgH9L8ja8X//5bgYHlnWHms1bkCmI75PI2xVY7diIwW+eWXX+iDDz6gzZs30/Xruoxsb6FRo0YiQ/TMmTPUr18/Gj58OI0fP97kvHKNtMyZM3NygwthweWHwMqBMftcybc1O9Bv5ZtSIEn07bJvqc65PboJkkTNTmymNb+8L4byeRCWhlYWrkb+zDsxeZxi5bGlzhXDeBN4SEBM4qKD18S7Ox4aHj9+TH/++Sf17NlTWLh+/fXXFPMsWbKEKlWqRGFhYRQVFUWvv/66+L5OnTp06dIl6tu3r97SBCB6ypYta7AMWMFgDVNamV599VWxvMjISKpduzbt36+uHIyS0NBQUVMqT548Yhvq169Pixcv1lvAWrZsSaNGjRIZpEWKFDFplXvw4AF1796dsmbNKraxZMmStHTpUv30rVu3Us2aNUWGH8p+fPjhhxQfH6+f/sMPP1ChQoXEb7NmzUpvvPEG+TMcNO9nyFYQlxMQQMPrdxdDArU8vol+/Hc09Wn2CbU8vpEan94uZjmeJR/1a9qXHof6d7p445LZnFKQlOtcMb6Ip8benD9/PhUtWlSIkQ4dOtBHH31EgwcP1ounZcuWCYH12Wef0e+//y6sRMuX60IoFi5cSGXKlBHuuW7dutm03kePHlHHjh3p+++/F/WhvvnmG2rSpImwVKVNm9bu7YEounv3rv7zunXrKF26dLRmzRqT86NQbePGjUV7/vjjDypQoAAdP35cXy7h3Llzwor25Zdf0owZM+j27dvUu3dv8Zo5cybt3btXCLBZs2ZRtWrV6N69e7Rli25EEX+FBZefYcsgyAje/ubNsrT97B2asvGczeuSAgLpkyZ9KU3iE6p/bg9N+/cr8f3zwCCaHNOGfoj5Hz0Pck0cmauIyZeRdl28R854wHZ2cVKuc8X4Gp4cexPuRAgtAGERFxdHmzZtEtYrAOtQ27ZtacSIEfrfQGSBjBkzCmECgQQrky3UrWs4vNlPP/0kKqBj3c2aNbN5OyDaIK5WrVol3KMyERER9PPPP5st2bF27VravXs3nThxggpjjF0iyp//v9I9o0ePpvbt2wshCmDJmjRpkrDITZ06lS5fvizWgTZjP+TJk4fKlStH/gy7FP0MWwZBRvB29YJR9HGDIpTezgD7pKBU1KvFINqZq6TeqtXine9oYo23vE5sgR0X7lFURAiFBTt26biiDALXuWJ8CU/GJJ46dUqIjXbt2onPqVKlojZt2ggRJnPw4EGqV6+e09d98+ZNYRWDgIFLEVYouDchYGwBrr80adIIdx4sVWg/XJoypUqVslgfDduXM2dOvdgy5tChQ8LNinXIr4YNGwrLGIZyglsUIgsi7e2336bZs2eLIrj+DFu4/Ay1VpC+9QvpnxwhCMa0KkU9VA4rY0xicCi98+ZIKnPjFB3MXsQrhZaSW49tH4Q3fXgwPXj6X5JCtAtcIlznivElPDn2JoQVMvoQ36RfnySJuKjJkycLISRXJrcFDG2E5SgxLrIJdyJcfxMnThSCBeuMiYmxefDvV155RViaIKqwHRCNSmB9soS17YMIRHwX3IbG5M6dW6wXsWcbN26k1atX09ChQ4XgQ4waLHb+CAsuP0PNIMiY3rtuIYPvIAx+7FBeVKGPfajeSibzLFUw7Xlp5fJHprQvT4EBAS4vRGpu+B9XCDyGcSWeikmE0EJMFmKnGjRoYDANgeZz586lHj16UOnSpYWrrnPnziaXA8EhV9qXQRZgbGysEF1yLBgsSUq2bdsmgs0RtwWuXLlCd+7csXk7IKhQDsJesH1Xr16l06dPm7RylS9fXsR0WVoHRB6C9fEaNmyYEFrr16+nVq1akT/CgsvPcMQKgs761eLRNHn9WZdnOXoSbHnasFT0MCHJKcuC2Kma332lD+TjZFx9nksvMN6Ep2IS4Yq7f/8+de3aVViylLRu3VpYvyC4ICDgUkQwOWK5INQQND9w4EB9xh/KSWAarFTIOkT8F4LLx40bJzL2Vq5cSStWrBBuQxm4EhFoXrFiRXr48CH179/fLmuaoyAWq1atWmKbv/32WyGsTp48KYQiYtqwnVWrVhVB8u+++64QeBBgCMKHFRD78fz582IZGTJkEPsG7kY5I9If4RguP8TRat/z9tgWS+BtQIS+USGnw8vx5HA1XOeK8XY8FZMIQQWLjLHYAhAfyL47fPiwEE8LFiwQpRZQ6gHB7oj7kkGh0YsXLwpBBssWKFasmLBeTZkyRQTYY/5PPvkkxfoh+GBBQuwTXHYYTNkT/P3336LsBWLZihcvTgMGDNBb7WABQyA/LGAoDYGAeLgNZTcsrFnI1sR+wXb/+OOPwjpYokQJ8lcCJGOHMuMy8LSCixjZLsonGk9hagw+a8IANXAwVIwv06V6XmEhcnQ73ZG6zjBaJiEhQQRQ58uXTwRv25ulSGas8TwcFONMLJ2vzui/2aXoxygHQVaLs+MltAjElrVYN9zwI1MHU1iqIIOYtowRwfR62RxU/+Uy2I3HMPbDMYmML8GCy8+x1crl7HgJtaQOCaQnz5Ldsq778c9Uxbohc5NjpRjGtXBMIuMrsODyY5Yfvk6fLzpK9+Kfq3aDqclydAXuEltg5LLj1LBktOqna2enpDMM47g1nmG0BgsuP2X08uM0bfOFFN/fsFLB2ZLlx1dQ1vbx5NO1PTF2DMMwjDZhweWHLD98w6TYkoGIglUHQsNceQhTlh9fQhmr5omna0+NH8cwDMO4Bi4L4WfAagI3olorjznQ6W8dWJfmdqtKFfNkIF/DU7FqyswsYzErjx+H6QzDMIx3wYLLz4CIuhf/zCkZibLlp49RVXqtg3Eh3V3bxxvGj2MYhmFcBwsuLwcdL2pjLTp4Tbxb64htKeug1spTrVAUpQ4JIq0D7+gPb5UX2YUgQEOFSu0ZP45hGIbxHjiGy4uxJ85HrYhCPSm1Vh6Ik2/fLGP34NbuYnK7ctSktG6/aHW8QU+NH8cwDMO4FrZweSn2xvnIZR2s8WWLkjZZeeTBrbOmDSF3ERmeSrgHleCz8XfYXrStSensJmPQJrYtK97x2dMB6Z4aP45hGPeyceNGMS7hgwcPVP8G4zNOmDCBtMDw4cPFkEYynTp1EoN7O0InJyxDy7CFywuxFucTYCHLUFnWwZzzsXutfAbiRC3KEgrbzt6hyRvOkqvoW78w9a6rG6XeuHSCqe9MiUct1vZRU+E+2oMxZgzjD6Dj/+2336h79+5iDEAlvXr1EuMhduzYkX799VfSEhBBI0aMEP8HBQVRzpw56fXXX6eRI0dSmjRpXLruiRMnktqRAi9evCiGzzlw4ICBaLNlGd4ICy4vxJY4H1OCwlxZh0wRITSyRUm9280eZBEDQfD3/quqy0Zg3S3KZqd6RbMKVXHncSJdvBNPc3dfptiHiRZdpqa2UWtCSi1qKtx7MsaMYfyFXLly0bx58+i7776j8PBw/Vh7c+bMody5c5NWweDQa9eupaSkJNq2bRt16dKFnjx5QtOmTUsx77NnzygkxDleCVODfXtiGVqGXYpeiDPifEy51HZ/Vt8hsaUEguC1MpaX1bx0tMG6hzYvQdULRVH1glHUomwO6lO/MG0bVE9zbj9XIwtiWLKU4DMP1ssw7qF8+fJCdC1cuFD/Hf6H2CpXrpzBvImJifThhx9SlixZxKDHNWrUoD179hjMs3z5cipcuLAQb6+88oqw8hizdetWqlmzppgH68Yy4+PjbWp3qlSpKDo6Wli32rRpQ+3bt6fFixcbuAF//vlngwGa4dZ89913KXPmzGJg5rp169KhQ4cMljtmzBjKmjUrpU2blrp27SrEpyV3YHJyMo0bN44KFixIoaGhYr+NGjVKTMO6AfYj3Kp16tQxuQxr+1V2y65bt44qVqxIqVOnpmrVqtGpU6f082A7sL/RbmxbhQoVaO/eveQJ2MLlhTgrzseVLjW4PRcfslwvau+lBzShbXmL1hotuv3cAY8fx/gkcBc9eeKZdadOTRRgm2UY1qGZM2cK0QJmzJhBnTt3Fh29kgEDBtDff/8t3JB58uQRQqNhw4Z09uxZypgxI125coVatWol3JHvvfee6PD79etnsIxz585Ro0aN6MsvvxTruX37NvXu3Vu80AZ7gXiDJUsGbUJbIR7hdgT/+9//xHwrVqwQViZYw+rVq0enT58W7Z8/f74Qa1OmTBGiZ9asWTRp0iTKnz+/2fUOHjyYpk+fLiyE+M2NGzfo5MmTYtru3bupcuXKwhIHi5w5K5u1/Srz2Wef0TfffCMEY48ePcRxg3UP4NhB2E2dOlVs78GDByk42DDO112w4PJCvCHOx5rbk6y4Pd2B1ofO8VexyfgwEFsujiUyy+PHRBERNv2kQ4cOQjhcunRJfEYnDjejUnDBAoXOHPFcjRs3Ft9BaKxZs4Z++eUX6t+/v5heoEABIQpAkSJF6MiRIzR27Fj9ckaPHi3EwUcffSQ+FypUSIia2rVri9/L1ihb2Ldvn3CBwmIlA/H1+++/C3EiW9UggG7duiUsUeDrr7+mf//9l/766y8hEBGoD6sWXgCiEGLJ2Mol8+jRIxGPNXnyZBHrBgoUKCCEF5DXnSlTJmGNM4Wa/SoDyxn2Exg0aBA1bdpUtA377PLly2LeokWL6verp2DB5YV4Q5yP1ssb8NA5DMNYA8IAnTc6fQRz4/+oqKgUlqnnz59T9erV9d/BggILzokTJ8RnvFepUsXgdzExMQaf4fo6fPgwzZ49W/8d1gnX3IULF6hYsWKqDhiEHALkX7x4IcQV2gzhIwNLkSx45PU+fvxYiB8lT58+Fdsmtx+WI+P2b9iwwWQbMD/cgbCS2cs5FftVpnTp0vr/s2XThZxAQMKN+fHHHwt3Kaxy9evXF9Y8iD9PwILLSzEX+K6FWlJaL28gl9Qwtg7KJTU4TophXOjWg6XJU+u2A7in4NYDcKm5CogeZEUiZskYW4L0YT1DzBZiubJnz57CXRdhZOXDeiFSjN2kIH369GQPcpKBuwhWuAgR0wUgVAFcoW+99RYtW7ZMuEyHDRsmrJTI3nQ3LLi8GC3H+WjV7elISQ2GYRwEnaGNbj1Pg7gqWIrQkSN+yBhYSyBq4G6E9QjAMoPgbtk9COuUHLgus3PnzhRB+sePHxdB5o6AttiyDKw3NjZWCDTU+TIF2r9r1y565513zLZfCdx2EF0IZod1yVQbAaxw5lCzX9WCZAW8+vbtS+3atRMxcZ4QXJyl6OXIcT7I6sO7VkSC7PbU2hA6PHQOwzC2gEBruLAghuQgc2OLUc+ePUWc0MqVK8V83bp1E6UY5JgnuOPOnDkj5kEGHeKqjGt4DRw4kLZv3y6saQjsxvyLFi3SW9dcBdxscA8iO3D16tUiexLtQCC6nM3Xp08fEcgPoYJAeliJjh07ZnaZiJ3C9iDoHfFicA/u3LlTxF4BZB1CkGF/3bx5k+Li4uzar9aAWxT7D9Y7xOFBvEGwqXXPOhsWXIxflTfQemwZwzDaA+UE8DIHSia0bt2a3n77bWExQhbdqlWrKEOGDHqXILLtEIhepkwZUUz1q6++ShGHtGnTJiFoUBoCmXVDhw4VbkFXAssdSlbUqlVLZGDCEtS2bVshUFAGAqC8xJAhQ4SAQlkFTIMYsgTmRyYmtgECp02bNiKuCsCahoQAZENi+1q0aGHXfrUGBPLdu3eFZQ7b9eabb4oAfLk4rLsJkHy5rKvGePjwoUi5hZq3dPH6GlrKBsQA3+2mmzeFy6DmF2cIMoxjIFMMAd/Kmk8M443nqzP6b47hYvyqvIFWY8sYhmEY34ZdioxfodXYMoZhGMa3YcHF+B1ajC1jGIZhfBt2KTJ+iZZLajAMwzC+Bwsuxm/RUmwZwzAM49uwS9FGUGkYxeGQwYChGjAGFcMwDGMeToZnvAFXn6csuGzgzz//FOMyoejb/v37RT0VVB6Wa4swDMMw/yEXCkWldobROiiqajxUkDPhOlw2AItWpUqV9AOBYqymXLly0QcffCBGKLeGv9bhYhjGfy0Gly9fFkOyoMBlYCA/4zPaPE8htmA8wfiR8gDYSrgOlxvBE9q+ffto8ODB+u9w88CwCDt27HBnUxiGYbwCVDFH54VikqhOzjBaBmIrOjraZcvnoHmV3LlzRwy0KQ91IIPPJ0+eNPmbxMRE8VIqZIZhGH8CAxBjMGN2KzJaBm5EU2NlOhMWXC5k9OjRHhuziWEYRivAG8BD+zD+DjvUVRIVFSXUL0Y2V4LP5kyQcD8iXkt+XblyxfEjxjAMwzCM18GCywazOEZJX7dunf47BM3jc0xMjMnfhIaG6keZtzbaPMMwDMMwvgu7FG0AJSE6duxIFStWpMqVK9OECRMoPj6eOnfu7LojxDAMwzCM18OCywbatGlDt2/fpqFDh1JsbCyVLVuWVq5cmSKQ3lpRNQ6eZxiGYRjvQe63HSmOynW43MjVq1dF3S6GYRiGYbwPxGLnzJnTrt+y4HIjiPm6fv06pU2bVtSncYUCh6DDCeHL8WL+sJ3+sI2At9N34GPpO/CxTAksW48ePXKogC+7FN0IDpK9ytgW/CVA3x+20x+2EfB2+g58LH0HPpaGYKQYR+AsRYZhGIZhGBfDgothGIZhGMbFsODyIVD3a9iwYeLdl/GH7fSHbQS8nb4DH0vfgY+la+CgeYZhGIZhGBfDFi6GYRiGYRgXw4KLYRiGYRjGxbDgYhiGYRiGcTEsuBiGYRiGYVwMCy6NM3z4cFGVXvkqWrSofnpCQgL16tWLMmXKRGnSpKHWrVvTzZs3DZZx+fJlatq0KaVOnZqyZMlC/fv3p6SkJPIkmzdvpubNm4uqvdimf//9N0VVX4xZmS1bNgoPD6f69evTmTNnDOa5d+8etW/fXhTnS58+PXXt2pUeP35sMM/hw4epZs2aFBYWJiq3jxs3jrSyjZ06dUpxbBs1auRV2whGjx5NlSpVEiMo4Pxq2bIlnTp1ymAeZ52nGzdupPLly4ssqoIFC9Kvv/6qmW2sU6dOiuPZo0cPr9lGMHXqVCpdurS+4GVMTAytWLHCZ46jmm30heNozJgxY8R2fPTRRz51LNVsp6aOp8RommHDhkklSpSQbty4oX/dvn1bP71Hjx5Srly5pHXr1kl79+6VqlatKlWrVk0/PSkpSSpZsqRUv3596cCBA9Ly5culqKgoafDgwZInQTs+++wzaeHChRgJVPrnn38Mpo8ZM0aKjIyU/v33X+nQoUPSa6+9JuXLl096+vSpfp5GjRpJZcqUkXbu3Clt2bJFKliwoNSuXTv99Li4OClr1qxS+/btpaNHj0pz586VwsPDpWnTpmliGzt27Ci2QXls7927ZzCP1rcRNGzYUJo5c6ZY/8GDB6UmTZpIuXPnlh4/fuzU8/T8+fNS6tSppY8//lg6fvy49P3330tBQUHSypUrNbGNtWvXlrp162ZwPHF8vGUbweLFi6Vly5ZJp0+flk6dOiV9+umnUnBwsNhuXziOarbRF46jkt27d0t58+aVSpcuLfXp00f/vS8cSzXbqaXjyYLLCwQXOlxTPHjwQNwoFixYoP/uxIkTonPfsWOH+IyTJzAwUIqNjdXPM3XqVCldunRSYmKipAWMxUhycrIUHR0tjR8/3mBbQ0NDhaAAOOnxuz179ujnWbFihRQQECBdu3ZNfP7hhx+kDBkyGGznwIEDpSJFikjuxpzgatGihdnfeNs2yty6dUu0e9OmTU49TwcMGCAePpS0adNGiCFPb6N8Y1fe6I3xtm2Uwfn1888/++RxNN5GXzuOjx49kgoVKiStWbPGYLt87Vg+MrOdWjue7FL0AuBKg1sqf/78wr0E8yfYt28fPX/+XLjbZOBuzJ07N+3YsUN8xnupUqUoa9as+nkaNmwoBic9duwYaZELFy5QbGyswXZhDKsqVaoYbBdcbBUrVtTPg/kxXuWuXbv089SqVYtCQkIMth2uoPv375MWgJkaJuwiRYpQz5496e7du/pp3rqNcXFx4j1jxoxOPU8xj3IZ8jzyMjy5jTKzZ8+mqKgoKlmyJA0ePJiePHmin+Zt2/jixQuaN28excfHC7ebLx5H4230teMIlyFcZcZt8bVj2cvMdmrtePLg1RoHIgO+YnTIN27coBEjRoh4naNHjwpRgo4WnbISnDiYBvCuPJHk6fI0LSK3y1S7ldsFoaIkVapUogNUzpMvX74Uy5CnZciQgTwJ4rVatWol2nju3Dn69NNPqXHjxuIiDgoK8sptTE5OFvET1atXFzc3uR3OOE/NzYMb49OnT0Wsn6e2Ebz11luUJ08e8XCEuLqBAwcK4btw4UKL7ZenaWUbjxw5IsQHYnwQ2/PPP/9Q8eLF6eDBgz5zHM1toy8dRwjJ/fv30549e1JM86Vrcp6F7dTa8WTBpXHQAcsg0BMCDCfP/Pnz3dbBMK6hbdu2+v/xhIXjW6BAAWH1qlevnlfudjxp4mFg69at5KuY28b33nvP4Hgi4QPHEWIax9VbwMMdxBWseH/99Rd17NiRNm3aRL6EuW2E6PKF43jlyhXq06cPrVmzRiTT+CpXVGynlo4nuxS9DDyRFC5cmM6ePUvR0dH07NkzevDggcE8yDTBNIB348wT+bM8j9aQ22Wq3crtunXrlsF0ZJUgq89btx0uY5i9cWy9cRt79+5NS5cupQ0bNlDOnDn13zvrPDU3DzLN3PXwYW4bTYGHI6A8nt6wjbB8IAurQoUKIjuzTJkyNHHiRJ86jua20VeOI1yGuHcgqw5WcbwgKCdNmiT+h3XGF47lPivbCZexlo4nCy4vAyUBoMyh0nGzCA4OpnXr1umnw1SKGC85HgHvMJ8rO248DeBEkU3oWgMuMpzgyu2C6RZxS8rtws0CF5zM+vXrhbtHvqAwD0ozIFZBue14uvW0O9EUV69eFTFcOLbetI3ICYAQgVsG7TN2cTrrPMU8ymXI8yhjbzy1jaaABQUoj6eWt9EcON8SExN94jha20ZfOY6w4KCNaLv8QiwoYoDl/33hWNazsp0IzdDU8bQpxJ5xO/369ZM2btwoXbhwQdq2bZtIXUXKKrKk5NRepKevX79epPbGxMSIl3HKa4MGDUQ6O9JYM2fO7PGyEMgqQQouXjgNv/32W/H/pUuX9GUh0qdPLy1atEg6fPiwyOYzVRaiXLly0q5du6StW7eKLBVlyQRk4qBkwttvvy1SvufNmydSe91VMsHSNmLaJ598IjKCcGzXrl0rlS9fXmxDQkKC12wj6NmzpyjhgfNUmXr95MkT/TzOOE/l1Oz+/fuLjKopU6a4LQXd2jaePXtW+uKLL8S24XjivM2fP79Uq1Ytr9lGMGjQIJF5iW3AdYfPyIpdvXq1TxxHa9voK8fRFMbZer5wLK1tp9aOJwsujYPU02zZskkhISFSjhw5xGecRDIQIO+//75Ia8YJ8frrr4uOQMnFixelxo0bi/pMEGsQcc+fP5c8yYYNG4QIMX6hVIJcGmLIkCFCTKAcRL169UTNHCV3794V4iNNmjQihbdz585CyChBDa8aNWqIZWD/QchpYRvRUeMCx4WN9Ow8efKIWjHK1GRv2EZgahvxQt0qZ5+n2Kdly5YV1wNunMp1eHIbL1++LG7iGTNmFMcB9dJwc1bW+9H6NoIuXbqIcxHrxrmJ604WW75wHK1to68cRzWCyxeOpbXt1NrxDMAfe0x5DMMwDMMwjDo4hothGIZhGMbFsOBiGIZhGIZxMSy4GIZhGIZhXAwLLoZhGIZhGBfDgothGIZhGMbFsOBiGIZhGIZxMSy4GIZhGIZhXAwLLoZhfJKAgAD6999/XboODIeCYagePXpEvsrw4cPF2Huu3p937tyhLFmyiCGuGMYX4cKnDOPHdOrUSYzXaNyRbty4kV555RW6f/++GDDdG4mNjRXjSYaGhrpsHa1atRJjDH722Wfki5w4cUKMJ4cxJKtWreq0/WnuvPvkk0/EOffLL784vA6G0Rps4WIYxieB5cmVYgsD/S5dulSIB62jHNzcFs6dOyfeW7Ro4fL9CTp37kyzZ8+me/fuuXQ9DOMJWHAxDKOKv//+m0qUKCE63bx589I333xjMN2UywnWsV9//VX8/+zZM+rduzdly5aNwsLCKE+ePDR69Gj9vLB4vPvuu5Q5c2ZKly4d1a1blw4dOmS2PdaWp2wP3GL4bPyS25acnCx+my9fPgoPD6cyZcrQX3/9ZXF/zJ8/X8yXI0cO/XeXLl2i5s2bC0tQRESE2F/Lly/XT8f/hQsXFuuABRHrRzuw7XI7y5Yta7CeCRMmiP0ts2fPHnr11VcpKiqKIiMjqXbt2rR///4Ux2Lq1Kn02muviXaMGjVKfL9o0SIqX7682F/58+enESNGUFJSksntQ1uwLSAwMFAsU+bnn3+mYsWKieUULVqUfvjhB4PfXrlyhd58801x/DNmzCgE28WLF/XL/e2330Rb5OMAiyrA/sqePbuwqDGMr8GCi2EYq+zbt090oG3btqUjR46ITnPIkCF6waKGSZMm0eLFi4VQQewTLBlKIfG///2Pbt26RStWrBDrgzCoV6+eWWuHteUZu6pu3Lihf3399deUOnVqqlixopgOsfX777/Tjz/+SMeOHaO+fftShw4daNOmTWa3Z8uWLfrfy/Tq1YsSExNp8+bNYj+NHTuW0qRJoxchcEFCxBw8eFCIy0GDBpGtIF6sY8eOtHXrVtq5cycVKlSImjRpkiKODMfo9ddfF+3o0qWLaO8777xDffr0oePHj9O0adPE8ZPFmKl9NnPmTPG/vN8A9vPQoUPF7+By/Oqrr8S5ABElW9MaNmxIadOmFevctm2b2AeNGjUSIhnLxbmEz/Jyq1Wrpl9v5cqVxe8YxuewebhrhmF8ho4dO0pBQUFSRESEwSssLAyD2kv3798X87311lvSq6++avDb/v37S8WLF9d/xvz//POPwTyRkZHSzJkzxf8ffPCBVLduXSk5OTlFO7Zs2SKlS5dOSkhIMPi+QIEC0rRp00y23dLyzLUH7NixQ2zfn3/+KT5jnalTp5a2b99uMF/Xrl2ldu3aSeYoU6aM9MUXXxh8V6pUKWn48OEm5x88eLDB/gIDBw402M/Dhg0Ty1Xy3XffSXny5DHbjhcvXkhp06aVlixZYrDtH330kcF89erVk7766iuD72bNmiVly5bN7LKx/4y7CRyTOXPmGHw3cuRIKSYmRr/MIkWKGByXxMREKTw8XFq1apX+vGvRooXJdfbt21eqU6eO2TYxjLeSytOCj2EYzwLXFtxPSnbt2iUsPDKwZMAtpKR69erC3fXixQsKCgqyuh7EOsEVVqRIEWHdaNasGTVo0EBMg+vw8ePHlClTJoPfPH36VB9HZMvyLMVdtWzZUm9lAWfPnqUnT56IZSmBNaZcuXJml4W2waWm5MMPP6SePXvS6tWrqX79+tS6dWsqXbq0fh9WqVLFYP6YmBiylZs3b9Lnn38u3HCwCGL/o/3YNiXG1jfsY1iblBYt/DYhIUH8HhY/a8THx4vj0bVrV+rWrZv+e7gl4d6U14N9CguXEqzH3LFUAncr2sMwvgYLLobxcxDjU7BgQYPv7EnNRyyOzrhiOlgbLsILFy4Il+HatWuF4IEoQawUxBZiseRYHiXmsiQtLc+cWEBME0TOF198of8e6wbLli0ziMcCloLEEUOFjDolcBPCnYZlQXTBVYlYtw8++IDUgFgpS/sQwJ149+5dmjhxoohbQxuxTRCIxsdVCbYTMVtwaxpjLBzNIe+r6dOnpxCPsujGPMjchOvRGMTnWQMuZDXzMYy3wYKLYRirIEAa1hEl+IwAcLmjRScpx/mAM2fOpLBUIBi+TZs24vXGG28IyxQ6WIgnlHFIlSqV2TgsU5hbHgK1lUDEwGKH4PhZs2YZBICj7AFECyxECEBXC6xfiIUyJleuXNSjRw/xGjx4sBAnEFzYh4g5U4IYLCXYh9gPaK/cRsR7Ge93BKkjbkuODUMNK2tgHyPWzVhc2wLqcSGo/fz589S+fXuz6/nzzz9FTS0cH1OEhIQI65opjh49SnXq1LG7jQyjVVhwMQxjlX79+lGlSpVo5MiRQtzs2LGDJk+ebJCdhqxCfAdrCzrTgQMHUnBwsH76t99+K6xYECqw5CxYsECUGoAFC5Yp/A7uvnHjxgkhd/36dWEpQuC3sXvM2vKMQQA5rGCwOsECI1tq4AaD6wsuRgTKQ5DVqFGD4uLihLCBYIBFyRSwZMGipXSpfvTRR9S4cWPRfli/NmzYIIQWgACDtat///7id0gMME46gNC4ffu22AcQkCtXrhQWPKVwQZA8RCP2ycOHD8Xy4IazBgLd4XbNnTu3WDb2Gdx/EDhffvklqQVWMrhOse8gcJEksHfvXrG9H3/8sRBi48ePFy5oWBJz5swpsjcXLlxIAwYMEJ8hqletWiUEINzIWBbOFQh07BcE4jOMz+HpIDKGYTyHueDlDRs2GARzg7/++ksEfQcHB0u5c+eWxo8fb/Cba9euSQ0aNBBB94UKFZKWL19uEDT/008/SWXLlhXTESCPIO79+/frf//w4UMRCJ89e3axjly5cknt27eXLl++bLLt1panDJqvXbu2+Gz8ktuGAO8JEyaIYG+sO3PmzFLDhg2lTZs2md13z58/F21duXKl/rvevXuLoPLQ0FCxjLffflu6c+eOfjoC2wsWLCim16xZU5oxY0aK/Tx16lSx7diud955Rxo1apRB0Dy2sWLFiiLwH/t5wYIFYjqC601tuxK0tVq1aiKAHfuscuXKYj/aEjQPZs+eLfZ9SEiIlCFDBqlWrVrSwoUL9dNv3Lgh2h4VFSW2NX/+/FK3bt2kuLg4Mf3WrVsiCSNNmjRi+TjfAILxcQwYxhfhSvMMwzB2MmXKFOEmhLXGHnyhor8zQTV7WM/eeustTzeFYZwOuxQZhmHspHv37qJoKWpgGWflMbaBODQE9Ldr1453HeOTsIWLYRjGQ7CFi2H8BxZcDMMwDMMwLoaH9mEYhmEYhnExLLgYhmEYhmFcDAsuhmEYhmEYF8OCi2EYhmEYxsWw4GIYhmEYhnExLLgYhmEYhmFcDAsuhmEYhmEYF8OCi2EYhmEYxsWw4GIYhmEYhiHX8n+eyJQttj+WawAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate a range of house sizes for prediction\n",
+    "sizes = np.linspace(sacramento[\"sq__ft\"].min(), sacramento[\"sq__ft\"].max(), 100).reshape(-1, 1)\n",
+    "\n",
+    "# Predict house prices for these sizes using the best model from GridSearchCV\n",
+    "predicted_prices = sacr_gridsearch.predict(sizes)\n",
+    "\n",
+    "# Plot the original data\n",
+    "plt.scatter(sacramento[\"sq__ft\"], sacramento[\"price\"], label=\"Actual Prices\")\n",
+    "\n",
+    "# Plot the model predictions as a line\n",
+    "plt.plot(sizes, predicted_prices, color='red', label=\"Model Predictions\")\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel(\"Price (USD)\")\n",
+    "plt.title(\"Scatter Plot of House Size vs Price with Model Predictions\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}