Movie Recommendation System

Abstract

1. Introduction

The Movie Recommendation System provides intelligent movie suggestions using content-based filtering with TF-IDF and SVD dimensionality reduction. It features a modern web interface, RESTful API, and supports datasets from 2K to 1M+ movies.

2. Objective

Movie Recommendation System with Complete End-to-End Pipeline, Model Integration & Web Application Hosted.

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

3. Key Features / Modules

• Smart Search - Real-time autocomplete with fuzzy matching
• AI Recommendations - Content-based filtering with 15+ suggestions
• ⭐ Rich Metadata - Ratings, votes, genres, production companies
• External Links - Google Search and IMDb integration
• Responsive Design - Works seamlessly on all devices
• Fast Performance - Sub-50ms recommendation generation
• Advanced ML - TF-IDF + SVD dimensionality reduction
• Scalable - Handles 2K to 1M+ movies
• Efficient Storage - Parquet format with compression
• Configurable - Easy model switching via MODEL_DIR

4. Technology Stack

• Backend: Django 6.0, Python 3.11+
• ML/Data: scikit-learn, pandas, numpy, scipy
• Storage: Parquet (efficient data format)
• Deployment: Render, Heroku, Docker compatible
• Advanced ML - TF-IDF + SVD dimensionality reduction
• Scalable - Handles 2K to 1M+ movies
• Efficient Storage - Parquet format with compression
• Configurable - Easy model switching via MODEL_DIR

5. System Requirements

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

• 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/inboxpraveen/Movie-Recommendation-System.git
cd Movie-Recommendation-System

1. SECRET_KEY set (the app refuses to start with the dev key when DEBUG=False)
2. DEBUG=False
3. ALLOWED_HOSTS includes your domain
4. A model is reachable — curl https://your-app/api/health/ returns "status": "healthy"

# 1. Clone the repository
git clone https://github.com/yourusername/movie-recommendation-system.git
cd movie-recommendation-system

# 2. Create virtual environment
python -m venv venv

# 3. Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

# 4. Install dependencies
pip install -r requirements.txt

# 5. Run database migrations
python manage.py migrate

# 6. Start the development server
python manage.py runserver

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 inboxpraveen 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.