comprehensive Movie Recommendation System project

** Project Overview**

I've developed a comprehensive movie recommendation system using collaborative filtering techniques and matrix factorization. The system analyzes user-movie ratings data to provide personalized movie recommendations based on user preferences and behavioral patterns.

**️ Technical Implementation**

**Data Processing**

- Loaded and merged user ratings data with movie metadata

- Created user-item matrix with 943 users and 1664 movies

- Handled missing values by filling with zeros

**Recommendation Algorithms Implemented:**

*User-Based Collaborative Filtering*

- Computed cosine similarity between users

- Generated recommendations based on similar users' preferences

- Achieved Precision@10: 0.0506

*Item-Based Collaborative Filtering*

- Calculated item similarity matrix

- Recommended movies similar to those users have liked

- Achieved Precision@10: 0.0506

*Matrix Factorization (SVD)*

- Implemented Singular Value Decomposition for latent factor modeling

- Achieved significantly better performance with Precision@10: 0.5786

- RMSE: 0.9621

**Evaluation Metrics**

- Precision@K to measure recommendation quality

- RMSE for rating prediction accuracy

** Key Results**

The SVD-based approach outperformed both collaborative filtering methods, demonstrating the power of matrix factorization in capturing latent patterns in user-item interactions.