Movie-Recommendation-System-CF-CBF

A web-based movie recommendation engine built using Streamlit, implementing both Content-Based Filtering and Collaborative Filtering (KNN).

• Content-Based Filtering: Computes cosine similarity between movie metadata to suggest similar titles.
• Collaborative Filtering: Utilizes user-item matrix and K-Nearest Neighbors (KNN) for personalized recommendations.

Datasets

• https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata?select=tmdb_5000_movies ( credits, movies )
• https://www.kaggle.com/datasets/aayushsoni4/tmdb-6000-movie-dataset-with-ratings ( ratings )

⚙️ Setup Instructions

1. Clone the Repository

git clone https://github.com/Sneha-mav/Movie-Recommendation-System-CF-CBF.git
cd Movie-Recommendation-System-CF-CBF

2. Install Dependencies

pip install -r requirements.txt

3. Configure Environment Variables

Create a .env file and add your TMDB Bearer Token:

TMDB_API_KEY=your_token

4. Run the Application

streamlit run app.py