Movie Recommendation System

A Movie Recommendation System that leverages collaborative and content-based filtering techniques to suggest movies based on user preferences. Built with Python, this system uses Streamlit to provide an interactive user interface and TMDb API to fetch movie posters.

Features

• Personalized movie recommendations based on the selected movie.
• Displays movie posters for each recommendation.
• User-friendly web interface powered by Streamlit.
• Recommendation based on collaborative filtering and content-based filtering.

Table of Contents

1. Project Description
2. Features
3. Requirements
4. Installation
5. Usage
6. How it Works
7. License
8. Acknowledgements

Project Description

This project implements a Movie Recommendation System using Machine Learning. It uses a combination of collaborative filtering (based on user preferences) and content-based filtering (based on movie metadata like genres, cast, etc.) to generate personalized movie recommendations.

The recommendation model is integrated into an interactive web application using Streamlit, which makes it easy for users to interact with and get movie recommendations.

Requirements

Before running the application, ensure that you have the following dependencies installed:

• streamlit - For building the web application.
• pandas - For handling movie data.
• pickle - For loading pre-trained models and similarity matrices.
• requests - For fetching movie posters from the TMDb API.

You can install the required libraries using pip:

pip install streamlit pandas requests

Installation

Follow these steps to set up the project locally:

1. Clone the repository to your local machine:

   git clone https://github.com/yourusername/movie-recommendation-system.git

2. Navigate into the project directory:

   cd movie-recommendation-system

3. Install the necessary dependencies:

   pip install -r requirements.txt

###Usage

1. To run the app, use the following command in the terminal:

   streamlit run app.py

2. This will launch the Streamlit app locally.
   You can access it in your browser at http://localhost:8501.

3. Once the app is up and running, you can select a movie from the dropdown list, and the system
   will suggest similar movies along with their posters.

How it Works

1. Data: The movie_list.pkl file contains movie metadata (e.g., titles, genres, etc.), and the similarity.pkl file stores a matrix of movie similarity scores based on content or user interactions.
2. Recommendation Logic: When a user selects a movie, the system uses the similarity matrix to recommend the most similar movies.
3. Movie Poster Fetching: The TMDb API is used to fetch movie posters using the movie's unique identifier (movie ID) for better visualization.

License

This project is open source and available under the MIT License.

Acknowledgements Streamlit - for making it easy to build interactive web applications.
TMDb API - for providing movie data and posters.
pandas, pickle, and requests - for supporting the core functionality of the application.
Special thanks to all contributors and the open-source community.