Anime Recommendation Engine

A machine learning-based Anime Recommendation Engine, implemented in Python, designed to suggest anime titles based on content similarity. This project showcases data exploration, preprocessing, feature engineering, and basic recommendation algorithms using the included anime dataset.

📚 Table of Contents

• Features
• MLOps Architecture
• Getting Started
• API Deployment
• Project Structure
• Technologies Used
• Results
• License

🚀 MLOps Architecture

This project now includes a production-ready MLOps pipeline with:

FastAPI REST API

• Endpoint: /recommend/{anime_name} - Get personalized anime recommendations
• Features:
  • Fast, async API built with FastAPI
  • Automatic API documentation (Swagger UI at /docs)
  • Input validation and error handling
  • Pre-loaded similarity matrix for instant recommendations

Docker Containerization

• Dockerfile included for reproducible deployments
• Port: Exposed on 8000
• Benefits:
  • Consistent environment across development and production
  • Easy deployment to cloud platforms (AWS, GCP, Azure)
  • Isolated dependencies

Key Components

• anime-app/main.py - FastAPI application with recommendation logic
• anime-app/anime_list.pkl - Serialized dataframe with similarity scores
• anime-app/Dockerfile - Container configuration
• anime-app/requirements.txt - API dependencies

Features

• Data source: Uses anime.csv (Kaggle anime dataset).
• Recommendation Algorithms:
  • Content-based filtering (genre, synopsis, etc.)
  • (Extendable for collaborative filtering)
• Jupyter Notebook: All code and explanations in Anime_Recommendation_Engine.ipynb.

Getting Started

1. Clone the repository:

   git clone https://github.com/<your-username>/Anime-Recommendation-Engine.git

2. Install requirements:

   pip install requirements.txt

3. Run the notebook:

• Open Anime_Recommendation_Engine.ipynb in Jupyter Notebook, JupyterLab, or Colab.

4. Dataset:

• The project includes anime.csv. No separate download needed.

• 📡 API Deployment

Local Development

1. Navigate to the API directory

cd anime-app

2. Install dependencies

pip install -r requirements.txt

3. Run the FastAPI server

uvicorn main:app --reload --host 0.0.0.0 --port 8000

4. Access the API

• API: http://localhost:8000
• Interactive Docs (Swagger UI): http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

5. Test the endpoint

curl http://localhost:8000/recommend/Naruto

Docker Deployment

1. Build the Docker image

cd anime-app
docker build -t anime-recommendation-api .

2. Run the container

docker run -d -p 8000:8000 anime-recommendation-api

3. Verify the deployment

curl http://localhost:8000/

API Usage Example

Request:

GET /recommend/Death%20Note

Response:

{
  "anime": "Death Note",
  "recommendations": [
    "Code Geass",
    "Steins;Gate",
    "Monster",
    "Psycho-Pass",
    "Ergo Proxy"
  ]
}

Project Structure

anime-recommendation-engine/
│
├── Anime_Recommendation_Engine.ipynb   # Main notebook with code 
├── /data/anime.csv                          # Anime dataset
├── requirements.txt                    # required libraries
└── README.md                          # Project documentation
├── /anime-app/                       # FastAPI deployment
│   ├── main.py                      # FastAPI application
│   ├── anime_list.pkl               # Serialized data & similarity matrix
│   ├── Dockerfile                   # Container configuration
│   ├── requirements.txt             # API dependencies
│   └── .gitignore                   # API-specific ignores

Results

• Sample recommendations:
  The notebook demonstrates how the system recommends similar anime titles.

  🛠️ Technologies Used

Category	Technologies
Language	Python 3.9+
ML/Data Science	pandas, NumPy, scikit-learn
NLP	CountVectorizer, TF-IDF
API Framework	FastAPI, Uvicorn
Deployment	Docker
Development	Jupyter Notebook
Version Control	Git, GitHub

License

Distributed under the MIT License. See LICENSE for details.