Machine Learning

Used Car Price Prediction Using Machine Learning

The Used Car Price Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts used car prices using vehicle specifications including year, kilometers driven, fuel type, transmission, engine capacity, and power output. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced regression algorithms like Random Forest, Ridge, and Lasso Regression to achieve 91.5% R² accuracy.

The system leverages features including vehicle age, kilometers driven, engine capacity, power output, and derived features like km per year and power-to-weight ratio. It provides interactive visualizations, feature importance analysis, model comparison, and real-time price prediction to help buyers, sellers, and dealerships make data-driven pricing decisions.

• Regression-Based Price Prediction
• Random Forest (91.5% R²)
• Ridge Regression (86.2% R²)
• Lasso Regression (85.5% R²)
• Feature Importance Analysis
• Real-time Price Prediction
• Model Performance Comparison
• Interactive Visualizations
• 5-Fold Cross-Validation
• Flask Web Application