Case Study ML Netflix Movie Recommendation System

Abstract

1. Introduction

Netflix is all about connecting people to the movies they love. To help customers find those movies, they developed world-class movie recommendation system: CinematchSM. Its job is to predict whether someone will enjoy a movie based on how much they liked or disliked other movies. Netflix use those predictions to make personal movie recommendations based on each customer’s unique tastes. And while Cinematch is doing pretty well, it can always be made better. Now there are a lot of interesting alternative approaches to how Cinematch works that netflix haven’t tried. Some are described in the literature, some aren’t. We’re curious whether any of these can beat Cinematch by making better predictions. Because, frankly, if there is a much better approach it could make a big difference to our customers and our business. Credits: https://www.netflixprize.com/rules.html

Netflix provided a lot of anonymous rating data, and a prediction accuracy bar that is 10% better than what Cinematch can do on the same training data set. (Accuracy is a measurement of how closely predicted ratings of movies match subsequent actual ratings.)

A Machine Learning Case Study for Recommendation System of movies based on collaborative filtering and content based filtering.

2. Objective

A Machine Learning Case Study for Recommendation System of movies based on collaborative filtering and content based filtering.

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

4. Technology Stack

• ipython-notebook - Python Text Editor
• sklearn - Machine learning library
• seaborn, matplotlib.pyplot, - Visualization libraries
• numpy, scipy- number python library
• pandas - data handling library
• XGBoost - Used for making regression models
• Surprise - used for making recommendation system models

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/veeralakrishna/Case-Study-ML-Netflix-Movie-Recommendation-System.git
cd Case-Study-ML-Netflix-Movie-Recommendation-System

1. Python 3: https://www.python.org/downloads/
2. Anaconda: https://www.anaconda.com/download/
3. XGBoost: conda install -c conda-forge xgboost
4. Surprise: pip install surprise

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 veeralakrishna 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.