From 012f29e6bc599fe1f2bcc2bdb0bb31df0bcca4a3 Mon Sep 17 00:00:00 2001 From: waqarahmed1995 Date: Tue, 12 May 2026 19:51:59 -0400 Subject: [PATCH 1/4] update --- 01_materials/notebooks/Classification-1.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/01_materials/notebooks/Classification-1.ipynb b/01_materials/notebooks/Classification-1.ipynb index 7b6959a7a..93ed13871 100644 --- a/01_materials/notebooks/Classification-1.ipynb +++ b/01_materials/notebooks/Classification-1.ipynb @@ -2326,7 +2326,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "lcr-env", "language": "python", "name": "python3" }, @@ -2340,7 +2340,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.15" } }, "nbformat": 4, From 1bf570589880d6bfd9d23b40b01cfef86574eba2 Mon Sep 17 00:00:00 2001 From: waqarahmed1995 Date: Tue, 12 May 2026 19:52:16 -0400 Subject: [PATCH 2/4] update 02_activities/assignments/assignment_1.ipynb --- 02_activities/assignments/assignment_1.ipynb | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index b0a47da71..2b1bf93d7 100644 --- a/02_activities/assignments/assignment_1.ipynb +++ b/02_activities/assignments/assignment_1.ipynb @@ -365,7 +365,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.4", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -379,12 +379,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" - }, - "vscode": { - "interpreter": { - "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e" - } + "version": "3.13.1" } }, "nbformat": 4, From 7c6ebe04c4afbd6b4ef79ff71938757afb8b92ab Mon Sep 17 00:00:00 2001 From: waqarahmed1995 Date: Mon, 18 May 2026 20:52:00 -0400 Subject: [PATCH 3/4] update my assignment_1 --- 02_activities/assignments/assignment_1.ipynb | 459 +++++++++++++++++-- 1 file changed, 419 insertions(+), 40 deletions(-) diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index 2b1bf93d7..012c4072a 100644 --- a/02_activities/assignments/assignment_1.ipynb +++ b/02_activities/assignments/assignment_1.ipynb @@ -34,10 +34,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "4a3485d6-ba58-4660-a983-5680821c5719", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib is building the font cache; this may take a moment.\n" + ] + } + ], "source": [ "# Import standard libraries\n", "import pandas as pd\n", @@ -51,18 +59,295 @@ "from sklearn.metrics import recall_score, precision_score\n", "from sklearn.model_selection import cross_validate\n", "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.metrics import accuracy_score" + "from sklearn.metrics import accuracy_score\n", + "from sklearn.datasets import load_wine\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", + "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", + "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", + "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", + "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", + "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", + ".. ... ... ... ... ... ... \n", + "173 13.71 5.65 2.45 20.5 95.0 1.68 \n", + "174 13.40 3.91 2.48 23.0 102.0 1.80 \n", + "175 13.27 4.28 2.26 20.0 120.0 1.59 \n", + "176 13.17 2.59 2.37 20.0 120.0 1.65 \n", + "177 14.13 4.10 2.74 24.5 96.0 2.05 \n", + "\n", + " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", + "0 3.06 0.28 2.29 5.64 1.04 \n", + "1 2.76 0.26 1.28 4.38 1.05 \n", + "2 3.24 0.30 2.81 5.68 1.03 \n", + "3 3.49 0.24 2.18 7.80 0.86 \n", + "4 2.69 0.39 1.82 4.32 1.04 \n", + ".. ... ... ... ... ... \n", + "173 0.61 0.52 1.06 7.70 0.64 \n", + "174 0.75 0.43 1.41 7.30 0.70 \n", + "175 0.69 0.43 1.35 10.20 0.59 \n", + "176 0.68 0.53 1.46 9.30 0.60 \n", + "177 0.76 0.56 1.35 9.20 0.61 \n", + "\n", + " od280/od315_of_diluted_wines proline class \n", + "0 3.92 1065.0 0 \n", + "1 3.40 1050.0 0 \n", + "2 3.17 1185.0 0 \n", + "3 3.45 1480.0 0 \n", + "4 2.93 735.0 0 \n", + ".. ... ... ... \n", + "173 1.74 740.0 2 \n", + "174 1.56 750.0 2 \n", + "175 1.56 835.0 2 \n", + "176 1.62 840.0 2 \n", + "177 1.60 560.0 2 \n", + "\n", + "[178 rows x 14 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from sklearn.datasets import load_wine\n", - "\n", "# Load the Wine dataset\n", "wine_data = load_wine()\n", "\n", @@ -73,7 +358,7 @@ "wine_df['class'] = wine_data.target\n", "\n", "# Display the DataFrame\n", - "wine_df\n" + "wine_df" ] }, { @@ -91,12 +376,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "56916892", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "178" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "wine_df.shape[0]" ] }, { @@ -109,12 +405,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "df0ef103", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "14" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "wine_df.shape[1]" ] }, { @@ -127,12 +434,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "47989426", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(dtype('int64'), array([0, 1, 2]))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "wine_df['class'].dtype, wine_df['class'].unique()" ] }, { @@ -146,12 +464,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "bd7b0910", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "13" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "wine_df.shape[1] - 1" ] }, { @@ -175,10 +504,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "cc899b59", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium \\\n", + "0 1.518613 -0.562250 0.232053 -1.169593 1.913905 \n", + "1 0.246290 -0.499413 -0.827996 -2.490847 0.018145 \n", + "2 0.196879 0.021231 1.109334 -0.268738 0.088358 \n", + "3 1.691550 -0.346811 0.487926 -0.809251 0.930918 \n", + "4 0.295700 0.227694 1.840403 0.451946 1.281985 \n", + "\n", + " total_phenols flavanoids nonflavanoid_phenols proanthocyanins \\\n", + "0 0.808997 1.034819 -0.659563 1.224884 \n", + "1 0.568648 0.733629 -0.820719 -0.544721 \n", + "2 0.808997 1.215533 -0.498407 2.135968 \n", + "3 2.491446 1.466525 -0.981875 1.032155 \n", + "4 0.808997 0.663351 0.226796 0.401404 \n", + "\n", + " color_intensity hue od280/od315_of_diluted_wines proline \n", + "0 0.251717 0.362177 1.847920 1.013009 \n", + "1 -0.293321 0.406051 1.113449 0.965242 \n", + "2 0.269020 0.318304 0.788587 1.395148 \n", + "3 1.186068 -0.427544 1.184071 2.334574 \n", + "4 -0.319276 0.362177 0.449601 -0.037874 \n" + ] + } + ], "source": [ "# Select predictors (excluding the last column)\n", "predictors = wine_df.iloc[:, :-1]\n", @@ -204,7 +560,7 @@ "id": "403ef0bb", "metadata": {}, "source": [ - "> Your answer here..." + "> KNN uses distance calculations to identify nearest neighbors. If variables are measured on different scales, variables with larger values will dominate the distance calculation. Standardization ensures that all predictors contribute equally. here..." ] }, { @@ -220,7 +576,7 @@ "id": "fdee5a15", "metadata": {}, "source": [ - "> Your answer here..." + "> The response variable contains categorical labels (0, 1, and 2). These labels are identifiers rather than measurements, so scaling them would have no meaning." ] }, { @@ -236,7 +592,10 @@ "id": "f0676c21", "metadata": {}, "source": [ - "> Your answer here..." + "> random.seed(123)\n", + "\n", + "Setting a seed ensures reproducibility, meaning the same train/test split and results will be obtained each time the code is run.\n", + "The specific value (123) is arbitrary; any fixed number would work as long as it is used consistently." ] }, { @@ -251,17 +610,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "72c101f2", "metadata": {}, "outputs": [], "source": [ - "# set a seed for reproducibility\n", - "np.random.seed(123)\n", - "\n", - "# split the data into a training and testing set. hint: use train_test_split !\n", - "\n", - "# Your code here ..." + "X_train, X_test, y_train, y_test = train_test_split(predictors_standardized, wine_df['class'], test_size=0.25, random_state=123)\n" ] }, { @@ -284,12 +638,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "08818c64", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here..." + "grid_search = GridSearchCV(KNeighborsClassifier(), {'n_neighbors': range(1, 51)}, cv=10)\n", + "grid_search.fit(X_train, y_train)\n", + "grid_search.best_params_['n_neighbors']" ] }, { @@ -305,12 +672,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "ffefa9f2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test set accuracy: 0.9333\n" + ] + } + ], "source": [ - "# Your code here..." + "knn_model = KNeighborsClassifier(n_neighbors=grid_search.best_params_['n_neighbors'])\n", + "knn_model.fit(X_train, y_train)\n", + "y_pred = knn_model.predict(X_test)\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(f\"Test set accuracy: {accuracy:.4f}\")" ] }, { @@ -365,7 +744,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "lcr-env", "language": "python", "name": "python3" }, @@ -379,7 +758,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.1" + "version": "3.11.15" } }, "nbformat": 4, From 292110f1f48c41c64dda6d62cfb79255c6141174 Mon Sep 17 00:00:00 2001 From: waqarahmed1995 Date: Sat, 23 May 2026 16:27:50 -0400 Subject: [PATCH 4/4] little change in some codes --- 02_activities/assignments/assignment_1.ipynb | 46 ++++++++------------ 1 file changed, 19 insertions(+), 27 deletions(-) diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index 012c4072a..d07cba25d 100644 --- a/02_activities/assignments/assignment_1.ipynb +++ b/02_activities/assignments/assignment_1.ipynb @@ -34,18 +34,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "id": "4a3485d6-ba58-4660-a983-5680821c5719", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Matplotlib is building the font cache; this may take a moment.\n" - ] - } - ], + "outputs": [], "source": [ "# Import standard libraries\n", "import pandas as pd\n", @@ -65,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19", "metadata": {}, "outputs": [ @@ -342,7 +334,7 @@ "[178 rows x 14 columns]" ] }, - "execution_count": 2, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -376,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 21, "id": "56916892", "metadata": {}, "outputs": [ @@ -386,7 +378,7 @@ "178" ] }, - "execution_count": 3, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -405,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 24, "id": "df0ef103", "metadata": {}, "outputs": [ @@ -415,7 +407,7 @@ "14" ] }, - "execution_count": 4, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -434,7 +426,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 28, "id": "47989426", "metadata": {}, "outputs": [ @@ -444,7 +436,7 @@ "(dtype('int64'), array([0, 1, 2]))" ] }, - "execution_count": 7, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -464,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 31, "id": "bd7b0910", "metadata": {}, "outputs": [ @@ -474,7 +466,7 @@ "13" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -504,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 34, "id": "cc899b59", "metadata": {}, "outputs": [ @@ -610,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 37, "id": "72c101f2", "metadata": {}, "outputs": [], @@ -638,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 40, "id": "08818c64", "metadata": {}, "outputs": [ @@ -648,7 +640,7 @@ "15" ] }, - "execution_count": 18, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -672,7 +664,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 43, "id": "ffefa9f2", "metadata": {}, "outputs": [ @@ -744,7 +736,7 @@ ], "metadata": { "kernelspec": { - "display_name": "lcr-env", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -758,7 +750,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.15" + "version": "3.11.9" } }, "nbformat": 4,