🚗 Car Price Prediction using Machine Learning

This project is a machine learning model that predicts the price of a used car based on various features like year, fuel type, transmission, kilometers driven, and engine specifications. The goal is to help buyers and sellers estimate a fair market price for used cars.

🧠 Tech Stack Python Pandas & NumPy for data processing Scikit-learn for model building Matplotlib & Seaborn for data visualization Linear Regression (can be upgraded to RandomForest/XGBoost) 📁 Dataset Features name (car model) year (manufacture year) km_driven (kilometers driven) fuel (Petrol/Diesel/CNG/LPG/Electric) transmission (Manual/Automatic) owner (first/second owner, etc.) mileage, engine, max_power, seats price (target variable) 📈 Workflow Data cleaning & preprocessing Encoding categorical features Train-test split Model training using Linear Regression Model evaluation (R² score) Predicting price for new input data 💡 Future Improvements Replace Linear Regression with RandomForest or XGBoost Deploy as a web app using Streamlit or Flask Add interactive data visualization Connect to a live car listing API for real-time price comparisons 📊 Sample Output