Netflix-Style Movie Recommendation System

This project implements a personalized movie recommendation system inspired by Netflix. It combines collaborative filtering (CF) using K-Nearest Neighbors (KNN) with latent features and content-based filtering (CBF) using Singular Value Decomposition (SVD). The system is deployed using Docker and Google Cloud Run for scalable and efficient serving.

🔍 Project Overview

• Collaborative Filtering (CF): Utilizes KNN with latent features to recommend movies based on user-item interactions.
• Content-Based Filtering (CBF): Employs SVD to suggest movies similar to those a user has previously liked, based on movie metadata.
• Hybrid Approach: Integrates CF and CBF to provide more accurate and diverse recommendations.
• Deployment: The model is containerized using Docker and deployed on Google Cloud Run for seamless scaling and accessibility.

📦 Technologies Used

• Python: Programming language for data processing and model development.
• Pandas: Data manipulation and analysis.
• Scikit-learn: Machine learning algorithms and utilities.
• Flask: Web framework for serving the recommendation model.
• Streamlit: Interactive web interface for user interaction.
• Docker: Containerization of the application.
• Google Cloud Run: Serverless platform for deploying the containerized application.

📁 Repository Structure

├── deployment/             # Docker and deployment scripts
├── training/               # Model training scripts
├── main.ipynb              # Jupyter notebook for EDA and model training
├── dataset.txt             # Sample dataset
├── dataset_summary.txt     # Summary of the dataset
├── live-demo.txt           # Instructions for accessing the live demo
└── README.md               # Project documentation

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⚙️ Setup Instructions

1. Clone the Repository:

git clone https://github.com/aydiegithub/netflix-movie-recommendation-system-eda-ml-training.git
cd netflix-movie-recommendation-system-eda-ml-training

2. Create a Virtual Environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Set TMDB API Key:

   Obtain an API key from TMDB and set it as an environment variable:

   export TMDB_API_KEY='your_api_key'  # On Windows: set TMDB_API_KEY=your_api_key

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🚀 Running the Application

Option 1: Streamlit Interface

^Provides an interactive web interface for movie recommendations.

streamlit run app/streamlit_app.py

• ^Access the app at http://localhost:8501/.

Option 2: Flask Interface

^Offers a web application using Flask framework.

python app/main.py

• ^Access the app at http://localhost:5000/.

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📦 Deployment on Google Cloud Run

1. Build Docker Image:

   docker build -t netflix-recommender .

2. Deploy to Google Cloud Run:

   Follow Google Cloud Run deployment guide to deploy the Docker image.

3. Set Environment Variables:

   Ensure TMDB_API_KEY is set in the Cloud Run service settings.

4. Access the Application:

   Once deployed, access the application via the URL provided by Google Cloud Run.

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📝 Acknowledgments

• ^[TMDB API for movie data and images.]({"attribution":{"attributableIndex":"2566-9"}})
• ^[Surprise Library for building the recommendation engine.]({"attribution":{"attributableIndex":"2566-10"}})
• ^[Streamlit and Flask for web interfaces.]({"attribution":{"attributableIndex":"2566-11"}})

📞 Contact

Aditya Dinesh K
Founder of Aydie’s Avenue
📧 business@aydie.in / aditya@aydie.in
📞 +91 9036 4694 92
🌐 aydie.in