AutoFinder : Car Recommendation System using Machine Learning

Abstract

This project introduces a comprehensive web-based Car Recommendation System that integrates advanced machine learning techniques. Within this web application, users are not only offered personalized car recommendations but also provided with tools for detailed vehicle comparisons, reviewing their historical interactions, and managing their profiles, including personal information editing. Leveraging machine learning algorithms such as Content-based filtering and K-Nearest Neighbors algorithm, the system accumulates and processes extensive datasets encompassing various car models, features, pricing data, and user experiences. By focusing on feature engineering and training a robust machine learning model, the web-based Car Recommendation System ensures the delivery of accurate and individualized car recommendations tailored to user preferences. Users of this web application have the added advantage of engaging in in-depth car comparisons, accessing their recommendation and comparison histories, and managing their profiles efficiently. Continuous user feedback further refines the system’s accuracy and relevance. This project involves various processes contributing to a comprehensive and user-centric online experience. By offering a feature-rich car recommendation solution in a web-based environment, this work aims to empower consumers to make informed vehicle purchasing decisions, ultimately creating value for car buyers and industry stakeholders