Movie Recommendation System

A content-based movie recommendation system that provides personalized suggestions based on your favorite films. This project uses TF-IDF vectorization and cosine similarity to analyze movie features such as genres, keywords, cast, crew, and director, delivering intelligent recommendations.

Key Highlights:

- Content-Based Filtering: Suggests movies similar to your selected favorites based on multiple features.

- Interactive Web App: Built with Streamlit, featuring a modern dark theme and responsive design.

- Adjustable Recommendations: Allows users to select the number of suggestions (1-10).

- Comprehensive Movie Data: Provides detailed information for 4,803 movies including title, genres, language, ratings, director, and cast.

- Real-time Search: Enables case-insensitive movie search for quick results.

Technologies Used: Python, Streamlit, Pandas, Scikit-learn, Pickle, NumPy.

Usage: Enter a movie name, select the number of recommendations, and get personalized movie suggestions with detailed information.