This repository hosts a movie recommendation system implemented in Python, utilizing pandas for data handling and scikit-learn for machine learning functionalities such as CountVectorizer and cosine similarity computation.
The project aims to develop a user-friendly web application for effortless movie selection and personalized recommendation display, focusing on enhancing the overall user experience for movie enthusiasts.
Ensure Docker is installed to utilize the devcontainer.json file, configuring a consistent development environment with Python 3.9, Visual Studio Code extensions, and essential libraries like pandas, scikit-learn, and flask.
- Clone the repository to your local machine.
- Access the project directory.
- Utilize Visual Studio Code with the Remote - Containers extension for an integrated development environment.
- Install necessary Python libraries by executing
pip install -r requirements.txt. - Customize your movie recommendation system using the provided Python scripts and web application templates.
Contributions are welcome through forking the repository and submitting pull requests for enhancements, bug fixes, or new features to collectively enhance the movie recommendation system for all users.
Inspired by the article Build a Movie Recommendation System with Python, this project expands upon the discussed concepts to create a practical and user-centric movie recommendation solution.
Explore the code, experiment with diverse algorithms, and contribute to refining this movie recommendation system to be more potent and user-friendly. Delight in advancing your movie recommendation skills with Python!