Used Car Price Prediction using Machine Learning

This project builds a machine learning model to estimate the selling price of used cars using real-world vehicle data. The dataset includes features such as manufacturing year, present price, kilometers driven, fuel type, transmission, and owner history. Data preprocessing and feature engineering are performed to prepare the data, followed by training regression models like Linear Regression and Random Forest. The models are evaluated and compared to identify the most accurate predictor, and visualizations are used to understand the key factors affecting car resale value.