Movie Recommendation Engine

An interactive recommendation engine built to deliver personalized movie suggestions based on user preferences, genre alignment, and similarity metrics.

Key Contributions & Architecture:

Recommendation Logic: Implemented Content-Based Filtering using TF-IDF Vectorization and Cosine Similarity to recommend movies based on metadata (plot keywords, genres, cast, and directors).

Pipeline Efficiency: Optimized matrix calculations for fast, low-latency recommendations across large datasets.

Interactive Interface: Designed a clean user interface to test recommendations and display movie details with similarity scores.

Technologies Used: Python, Scikit-Learn, Pandas, NLP (TF-IDF), Cosine Similarity, Git.