Flight Price Prediction

Project Overview

This project focuses on predicting the price of flight tickets based on various features such as airline, departure time, arrival time, duration, number of stops, and other relevant factors. By utilizing machine learning models, this project aims to provide an accurate price prediction, which can help users make informed decisions when booking flights.

Features

• Data Preprocessing: Handles missing values, encodes categorical variables and scales numerical features.
• Exploratory Data Analysis (EDA): Visualizes and understands the data distribution and relationships between features.
• Feature Engineering: Extracts meaningful insights such as time-based features from date and time columns.
• Model Development: Trains and evaluates multiple machine learning models such as Random Forest, Gradient Boosting, and Linear Regression.
• Model Evaluation: Assesses model performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.

Technologies Used

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• IDE/Tools: Jupyter Notebook, Visual Studio Code

Dataset

The dataset used in this project contains information about flights, including:

• Airline
• Date of Journey
• Source and Destination
• Route
• Duration
• Total Stops
• Additional Information

The dataset can be obtained from Kaggle or any other similar platform.

Installation and Setup

1. Clone the repository:

   git clone https://github.com/your-username/flight-price-prediction.git

2. Navigate to the project directory:

   cd flight-price-prediction

3. Install the required libraries:

   pip install -r requirements.txt

4. Run the project:

   python main.py