🚗 End-to-end ML project for predicting car prices based on various features. Includes data preprocessing, model training, and a Flask web for predictions.
MIT License
Welcome to the Car Price Prediction repository! This project utilizes machine learning techniques to predict car prices based on various features such as make, model, year, and more.
This repository features a machine learning project aimed at predicting car prices. It involves data preprocessing, model training, and evaluation to provide accurate pricing predictions based on various input features.
To get started with this project, follow these steps:
Clone the repository:
git clone https://github.com/Md-Emon-Hasan/ML-Project-Car-Price-Prediction.git
Navigate to the project directory:
cd ML-Project-Car-Price-Prediction
Create a virtual environment and activate it:
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
Install the dependencies:
pip install -r requirements.txt
Run the application:
python app.py
Open your browser and visit:
http://127.0.0.1:5000/
Check out the live version of the Car Price Predictor app here.
Recommendations for maintaining and improving this project:
Q: What is the purpose of this project? A: This project predicts car prices using machine learning, providing insights for buyers and sellers.
Q: How can I contribute to this repository? A: Refer to the Contributing section for details on how to contribute.
Q: Where can I learn more about machine learning? A: Check out Scikit-learn Documentation and Kaggle for more information.
Q: Can I deploy this app on cloud platforms? A: Yes, you can deploy the Flask app on platforms such as Heroku, Render, or AWS.
Common issues and solutions:
Issue: Flask App Not Starting Solution: Ensure all dependencies are installed and the virtual environment is activated properly.
Issue: Model Not Loading Solution: Check the path to the model file and verify it's not corrupted.
Issue: Inaccurate Predictions Solution: Verify the input features are correctly formatted and ensure the model is well-trained.
Contributions are welcome! Here's how you can contribute:
Fork the repository.
Create a new branch:
git checkout -b feature/new-feature
Make your changes:
Commit your changes:
git commit -am 'Add a new feature or update'
Push to the branch:
git push origin feature/new-feature
Submit a pull request.
Explore these resources for more insights into machine learning and Flask development:
Some challenges during development:
Key takeaways from this project:
This repository was created to showcase the use of machine learning for predicting car prices, demonstrating the end-to-end process from data preparation to deployment.
This repository is licensed under the MIT License. See the LICENSE file for more details.
Feel free to adjust and expand this template based on the specifics of your project and requirements.