Car Price Prediction System | Machine Learning & Flask

Developed an end-to-end Machine Learning web application to predict used car prices based on vehicle features.

• Performed data preprocessing, feature engineering, and categorical variable encoding using Pandas and Scikit-learn.

• Trained and evaluated a regression model using performance metrics such as MAE, RMSE, and R² Score.

• Built a responsive web interface using Flask, HTML, CSS, and JavaScript.

• Integrated the trained model into a REST API for real-time predictions.

• Deployed the application with a complete backend and frontend architecture.

• Version control and source code managed using Git & GitHub.

Technologies: Python, Pandas, NumPy, Scikit-learn, Flask, HTML, CSS, JavaScript, Git, GitHub.