Certainly! Below is a sample README for your Movie Recommendation ML Project:

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Movie Recommendation System with Streamlit

This project implements a Movie Recommendation System using natural language processing techniques and the TMDb (The Movie Database) dataset API. The recommendation system is exposed through a user-friendly web interface created using Streamlit.

Table of Contents

• Introduction
• Setup
• Execution
• Project Structure
• Technologies Used
• References

Introduction

This Movie Recommendation System utilizes a dataset from TMDb containing movie information, including titles, overviews, genres, and more. The system uses TF-IDF vectorization and cosine similarity to recommend movies similar to a user-selected movie.

Setup

1. Clone the repository:

   git clone https://github.com/your-username/movie-recommendation-system.git
   cd movie-recommendation-system

2. Install dependencies:

   pip install -r requirements.txt

3. Obtain API key:

   • Register on the TMDb website to get an API key.

4. Create a .env file in the project root and add your TMDb API key:

   TMDB_API_KEY=your-api-key-here
   

Execution

1. Run the Jupyter Notebook to preprocess the dataset and train the recommendation model:

   jupyter notebook movie.ipynb

   Execute all the cells in the notebook.

2. Run the Streamlit app:

   streamlit run app.py

   This will start a local server, and you can access the Movie Recommendation System by visiting http://localhost:8501 in your web browser.

Project Structure

• movie.ipynb: Jupyter Notebook for data preprocessing and model training.
• app.py: Streamlit app for the web interface.
• requirements.txt: List of dependencies.

Technologies Used

• Python
• Streamlit
• TMDb API
• Jupyter Notebook

References

• Streamlit Documentation: https://docs.streamlit.io/
• TMDb API Documentation: https://developers.themoviedb.org/3/getting-started/introduction