Car Price Prediction

This project is a car price prediction tool built with Python and Streamlit. The model leverages machine learning techniques to predict the price of a car based on various input features like model year, mileage, engine type, and more.

This project is a car price prediction tool built with Python and Streamlit. The model leverages machine learning techniques to predict the price of a car based on various input features like model year, mileage, engine type, and more.

Link streamlit app:

Features

• Predicts car prices based on user-provided details
• Interactive UI built with Streamlit for easy usability
• Data preprocessing, transformation, and model loading handled in the backend

Project Structure

   project-folder/
   │
   ├── main.py                  # Main Streamlit application
   ├── car_price_model.joblib   # Trained machine learning model
   ├── requirements.txt         # Python dependencies
   └── README.md                # Project documentation

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/car-price-prediction.git
   cd car-price-prediction

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run main.py

4. Open the app in your browser by going to http://localhost:8501.

Usage

• Enter details like car model, year, mileage, transmission type, and other relevant information.
• Click the "Predict Price" button to get an estimated price.

Model Training

The model was trained using various features, including:

• Model year
• Mileage
• Transmission type
• Fuel type
• Engine capacity and more...

The model was saved using joblib and can be reloaded for predictions.

Dependencies

This project uses the following libraries:

• Streamlit - For building the interactive web application
• Scikit-learn - For machine learning model training and pipeline management
• XGBoost - For the prediction model
• Pandas - For data manipulation and preprocessing

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Feel free to reach out if you have any questions or suggestions for improvement.

### Notes:
- Replace **username** in `git clone' with your GitHub username.
- If you don't have the `requirements.txt' file, you can create it by running the following command:
  ```bash
  pip freeze > requirements.txt