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

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

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:

  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