Movie Recommender System

Overview

This is a Content-Based Movie Recommendation System that suggests similar movies based on user input. It uses vectorization and distance comparison to find and recommend movies with similar characteristics. Users can search or select a movie from the TMDB database and receive the top 5 most similar movies as recommendations.

Features

• Content-Based Filtering: Uses movie metadata for recommendations.
• Vectorization & Similarity Comparison: Converts movie data into vectors and computes distances to find similar movies.
• TMDB Integration: Users can search or select movies from the TMDB database.
• Top 5 Recommendations: Returns the most relevant movies based on content similarity.

Technologies Used

• Python (for backend processing)
• Scikit-learn (for vectorization and similarity computation)
• Pandas & NumPy (for data processing)
• Streamlit (for web application)
• TMDB API (for movie search and details)

Installation

1. Clone the Repository

   git clone https://github.com/aakashbisht0704/movie-recommender-system.git
   cd movie-recommender-system

2. Install Dependencies

   pip install -r requirements.txt

3. Run the Application

   streamlit run app.py

Usage

1. Search or Select a Movie from the TMDB database.
2. Receive Recommendations: The system will return the top 5 most similar movies.

Contributing

Feel free to contribute! Fork the repository and submit a pull request with your improvements.