This work consists of designing and implementing an AI-powered car recommendation system called My Future Drive.
The system analyzes user preferences such as budget, city, vehicle type, and usage through a conversational interface.
Based on the user input, the application retrieves relevant vehicle data, applies scoring logic, and recommends the most suitable cars.
The solution uses a local LLM with Retrieval-Augmented Generation (RAG) and embeddings to ensure accurate and contextual responses.
Vehicle data is structured and managed using a Neo4j graph database, enabling efficient relationships between cars and user needs.
The project includes a complete front-end built with HTML, CSS, and JavaScript, offering a clean and interactive user experience.
This work demonstrates full-stack development, AI integration, and decision-support logic for educational and demonstration purposes.