Car Price Prediction Using Machine Learning: A Data-Driven Approach

Abstract

Predicting car prices is a significant challenge in the automotive market, affecting buyers, sellers, and manufacturers. This study explores various machine learning models to predict car prices based on features such as brand, model, year, engine size, mileage, fuel type, and transmission. We analyze a dataset of 10,000 cars and apply exploratory data analysis (EDA) to understand key trends. Several Machine learning models, including Gradient Boosting, Light GBM, and Neural Networks, are trained and evaluated. Our results demonstrate that Gradient Boosting achieves the highest accuracy, making it a promising approach for car price estimation.