Movie Time

Abstract

1. Introduction

Movie Time uses these tagged movies to relate them to each other, and presents random recommendations from the other 29,000 unrelatable movies.

A movie recommendation system based on the GroupLens dataset of MovieLens data. The dataset contains about 40,000 movies, and around 11,000 of those have tags associated with them and could be related to one another.

Reset your terminal and the commands below should be available to use

2. Objective

A movie recommendation system based on the GroupLens dataset of MovieLens data

This project demonstrates how Jupyter Notebook can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

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/osama-haggag/movie-time.git
cd movie-time

Full 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.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by osama-haggag 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.