AN ANALYSIS OF CAR PRICE PREDICTION USING MACHINE LEARNING

Abstract

Car price prediction is a critical task in the automotive industry, enabling buyers, sellers, and financial institutions to make informed and objective decisions. This research focuses on applying machine learning techniques, specifically Linear Regression and Lasso Regression to predict used car prices based on multiple factors including fuel type, transmission, seller type, vehicle age, and kilometers driven. The dataset was carefully preprocessed to handle missing values and encode categorical variables, ensuring the data was suitable for model training. Both models were evaluated using R² scores, with Linear Regression achieving high accuracy and Lasso Regression providing a more simplified model by reducing overfitting. The findings demonstrate that even basic regression models can deliver reliable predictions, highlighting the potential of machine learning to improve transparency and efficiency in car pricing within real-world applications.