Movie-Recommender-System

Movie Recommender System

🎬 A Movie Recommender System that suggests movies based on user preferences using the TMDB dataset and Streamlit for the web interface.

Table of Contents

• Introduction
• Technologies Used
• Dataset
• Installation
• Usage
• How It Works
• Contributing
• License

Introduction

This project is designed to recommend movies to users based on their selected movie. It utilizes a content-based filtering approach, leveraging the TMDB dataset to analyze similarities between movies and provide personalized suggestions.

Technologies Used

• Python
• Streamlit
• Pandas
• NumPy
• Scikit-learn
• TMDB API

Dataset

The project uses the TMDB dataset, which includes two main files:

• tmdb_5000_movies.csv: Contains information about 5000 movies.
• tmdb_5000_credits.csv: Contains credits data for the movies. You can download the dataset from here.

Installation

To run this project, you'll need to have Python installed on your machine. Follow these steps to set up the project:

1. Clone the repository:

   git clone https://github.com/yourusername/movie-recommender-system-tmdb-dataset.git
   cd movie-recommender-system-tmdb-dataset

2. Install the required packages:

   pip install -r requirements.txt

3. Create a model directory and place your movie_list.pkl and similarity.pkl files inside it.
4. Set up your TMDB API key in the code (replace the API key in the fetch_poster function).

Usage

1. To run the application, execute the following command:

   streamlit run app.py
   

How It Works

1. Data Loading: The application loads movie data and similarity data from pickle files.
2. User Input: Users can select a movie from the dropdown menu.
3. Recommendation: The app computes movie recommendations using a content-based filtering approach, fetching movie posters from the TMDB API.
4. Display: Recommended movies and their posters are displayed in a user-friendly layout.

License

This project is licensed under the MIT License