Movie Recommender

Website

Movie Recommender Web App

Overview

This project is a movie recommender system based on content-based recommendation using vectorization. It utilizes Jupyter Notebook for preprocessing and creating .pkl files, and Python with Streamlit for launching a web application.

Requirements

• Python 3.x
• Pandas
• Streamlit
• Scikit-learn

Usage

1. Clone the repository:

   git clone <repository_url>

2. Navigate to the project directory:

   cd movie-recommender

3. Install dependencies:

   pip install -r requirements.txt

4. Launch the web application:

   streamlit run app.py

5. Visit the following link in your web browser: Movie Recommender Web App

How it works

• The project preprocesses movie data and uses vectorization techniques for content-based recommendation.
• Jupyter Notebook files (preprocessing.ipynb, vectorization.ipynb) are used to create .pkl files for the model.
• The web application is launched using Streamlit, where users can input their GitHub link to access the movie recommender.

Inspiration

This project is inspired by this YouTube video.

License

MIT License