Recommendation System with TensorFlow (Zero to Deployment)

Overview

This project is a content-based recommendation system built using TensorFlow. It takes the MovieLens Small dataset and trains a recommendation model from scratch, then deploys it as a web service.

Features

• Data Preprocessing: Cleaning and preparing MovieLens Small dataset.
• Model Building: Using TensorFlow to create a recommendation model.
• Training & Evaluation: Optimizing performance with different hyperparameters.
• Deployment: Serving the model via FastAPI or Flask.

Dataset

We use the MovieLens Small (ml-latest-small) dataset, which contains 100,000 ratings across 9,000 movies from 600 users. It includes:

• ratings.csv – user ratings for movies.
• movies.csv – metadata of movies (title, genre, etc.).

Tech Stack

• Python
• TensorFlow & Keras
• Pandas & NumPy
• Scikit-learn
• FastAPI/Flask (for API deployment)
• Docker (optional for containerization)

Installation

1. Clone this repository: bash git clone https://github.com/yourusername/recommendation-system.git cd recommendation-system
2. Install dependencies: bash pip install -r requirements.txt

Model Training

Run the training script:

python train.py

This will:

• Load and preprocess the dataset
• Train the TensorFlow model
• Save the trained model to disk

Deployment

1. Start the API server: bash uvicorn app:app --reload
2. Access the API at http://127.0.0.1:8000
3. Example API call: bash curl -X GET "http://127.0.0.1:8000/recommend?user_id=1" This returns recommended movies for user_id=1.

Future Improvements

• Integrate Collaborative Filtering for better recommendations.
• Implement Hybrid Recommendation combining multiple methods.
• Deploy on Cloud (AWS/GCP/Azure) for scalability.

Contributing

Feel free to fork this repository and submit a pull request if you have any improvements!

License

This project is licensed under the MIT License.

Built With

• css
• html
• javascript
• python
• tensorflow