Car Recommendation and Car Price Prediction System using Machine Learning

The automotive industry has witnessed a significant surge in the availability of car options, making it increasingly challenging for buyers and sellers to navigate the market. To address this issue, we present the implementation of a Car Recommendation and Price Prediction System. This paper showcases the development of two intelligent applications: a Car Recommendation System and a Car Price Prediction System. The Car Recommendation System is an intelligent application designed to assist users in finding their ideal cars based on their preferences. The system utilizes a dataset of car details and employs content-based filtering techniques to generate personalized recommendations. Users can specify their desired choices to filter the available car options. The system then analyzes the features of the cars and recommends the most similar and suitable options based on the selected choices. The recommendations are ranked according to various factors such as price and can help users make informed decisions when searching for their desired cars. The Car Price Prediction System is a predictive tool designed to estimate the selling price of used cars. The system leverages a trained regression model to predict the price based on various features. The system employs a random forest regression algorithm that has been trained on a dataset of historical car prices. Users can input the relevant details of the car they intend to sell, and the system generates an estimated selling price. The predicted price provides users with valuable insights into the market value of their cars, enabling them to make informed decisions about pricing and selling their vehicles.