This project is a full-stack Movie Recommendation Platform that allows users to search for movies and receive personalized suggestions based on content similarity. The platform integrates a Python-based backend using Flask for handling API requests and machine learning logic, and a ReactJS frontend for a dynamic, responsive user interface. The core recommendation engine is powered by content-based filtering, utilizing TF-IDF vectorization and cosine similarity to analyze and compare movie metadata such as genres, keywords, and descriptions. The system is optimized to handle over 5,000+ movie records and supports real-time search suggestions, intelligent fallback handling, and a polished UI/UX for an intuitive user experience.

Technologies & Stack

• Frontend: ReactJS, HTML, CSS, Axios
• Backend: Python, Flask, pandas, scikit-learn, NumPy
• Machine Learning: TF-IDF, Cosine Similarity, Content-Based Filtering
• Version Control: Git, GitHub

My goal is to keep updating and adding new features to this project. I'm considering adding a userbase, and watch lists in the future which would then allow me to add collaborative filtering to the recommendations as well to ensure higher accuracy and improve the application's usabillity.