A machine learning–powered web application that predicts the market price of a car based on key features such as brand, year of manufacture, fuel type, transmission, and mileage.
Built using Python, Scikit-learn, and Streamlit with a clean and interactive UI.
- Predicts car prices using a trained ML regression model
- Interactive and user-friendly Streamlit interface
- Real-time predictions based on user input
- Easy to run locally and deploy
- Type: Supervised Learning (Regression)
- Algorithms: Linear Regression / Random Forest
- Model File:
model.pkl - Training Data: Historical car pricing dataset
- Language: Python
- Frontend: Streamlit
- ML: Scikit-learn
- Data: Pandas, NumPy
git clone https://github.com/your-username/car-price-prediction.git
cd car-price-prediction
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt
streamlit run app.py