Movie Recommendation System Web Application

Building a Movie Recommendation System web application using Django framework and Collaborative Filtering technique

AI & Machine LearningHTMLApache-2.0

Abstract

Movie Recommendation System Web Application is an open-source AI & Machine Learning project. Building a Movie Recommendation System web application using Django framework and Collaborative Filtering technique. It is built using HTML, Python. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

Building a Movie Recommendation System web application using Django framework and Recommendation technique called Collaborative Filtering

2. Objective

Building a Movie Recommendation System web application using Django framework and Collaborative Filtering technique

This project demonstrates how HTML, Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

HTMLPython
  • JavaScript
  • Bootstrap

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • A modern web browser
  • VS Code or any code editor
  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/abd1007/Movie-Recommendation-System-Web-Application.git
cd Movie-Recommendation-System-Web-Application
  1. Download zip file to your local machine
  2. Extract the zip file
  3. Open terminal/cmd promt
  4. Goto that Path
cd ~/Destop/Movie-Recommender-System-Web-Application-master
python3.6 -m pip install virtualenv
virtualenv venv -p python3.6
source venv/bin/activate
pip install -r requirements.txt

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 abd1007 and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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