Abstract
Movie Recommendation System is an open-source AI & Machine Learning project. Contains code which covers various methods for recommending movies, some of the methods include matrix factorisation , deep learning based recommendation systems. In addition a data set of the movies includes the movie name and genres. It is built using Jupyter Notebook, Deep Learning. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
In addition a data set of the movies includes the movie name and genres.
2. Objective
Contains code which covers various methods for recommending movies, some of the methods include matrix factorisation , deep learning based recommendation systems
This project demonstrates how Jupyter Notebook, Deep Learning can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
- python 3
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
- pip for dependencies
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/Gurupradeep/Movie-Recommendation-System.git
cd Movie-Recommendation-SystemFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by Gurupradeep and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
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