Car Price Prediction 🚗💰 Overview This project predicts used car prices using machine learning based on features like brand, year, mileage, fuel type, and transmission. It helps buyers and sellers estimate fair market values.

Technologies Used Python (Pandas, NumPy, Matplotlib, Seaborn) Machine Learning (Linear Regression, Random Forest, XGBoost) Web Deployment (Flask/Django - optional) Approach Data Preprocessing: Handling missing values, encoding, and scaling. EDA: Analyzing price trends and feature correlations. Model Training: Testing different algorithms & tuning hyperparameters. Evaluation: RMSE, MAE, and R² Score to measure accuracy. Deployment: Web app integration (if applicable). Results Key factors: brand, year, mileage, and fuel type. 10-15% error margin in price estimation. Future Scope Real-time price updates Deep learning for better accuracy Mobile app integration