Welcome to the Movie Recommendation System project! This project demonstrates my skills in building a movie recommendation system using data science techniques.
The project repository is organized as follows:
- Movie recommendation system/
tmdb_5000_credits.csv: Dataset containing credit informationtmdb_5000_movies.csv: Dataset containing movie detailsmovie.py: Python script implementing the recommendation systemWebsite.html: Home page of the web interface (not connected due to Flask issues)thank_you.html: Thank you page of the web interface (not connected due to Flask issues)
Follow these steps to run the movie recommendation system:
- Clone this repository by running:
git clone https://github.com/Ayushvishwakarma04/Movie-recommendation-system.git - Navigate into the project directory:
cd Movie-recommendation-system - Make sure you have Python installed.
- Run the
movie.pyscript using:python movie.py
The recommendation system will analyze the data and provide movie recommendations based on user preferences.
- Pandas
- NumPy
- scikit-learn
The movie.py script uses data from tmdb_5000_credits.csv and tmdb_5000_movies.csv to create a movie recommendation system. It employs text-based similarity techniques to suggest movies that are similar to the ones users have shown interest in.
I have also created a home page (Website.html) and a thank you page (thank_you.html) for this project. However, I encountered Flask-related issues while trying to connect these pages. I'm actively working to resolve this and will update the project as soon as the issues are resolved.
- Explore more advanced recommendation algorithms.
- Enhance the user interface for a more interactive experience.
- Implement user personalization for more accurate recommendations.
Feel free to contact me if you have any questions or feedback:
- Ayush Vishwakarma
- Email: [email protected]
- LinkedIn: Your LinkedIn Profile