📅 Data as of 1st September 2024

This project is an Anime Recommendation System that leverages a combination of content-based filtering and collaborative filtering to suggest anime based on user preferences. Built with Flask for the backend API and a sleek front end using Tailwind CSS and JavaScript. 🌟

• main.py: 🧠 Script for training the anime recommendation model and generating required data files.
• app.py: 🚀 Flask API providing endpoints for anime recommendations.
• index.html: 🌐 Front-end interface designed with Tailwind CSS and JavaScript.
• /: 📂 Directory containing the raw anime dataset used for model training.
• anime_dataset: 📊 Explore anime_dataset_extended_final.csv for insights!
• .gitignore: 🗂️ Configuration to ignore generated files from version control.

Ensure you have the following installed:

• 🐍 Python 3.7+
• 📦 pip (Python package manager)

Run the following command to install the required Python libraries:

pip install -r requirements.txt

Start the Flask server with:

python app.py

The server will be live at http://127.0.0.1:5000 🌍

Open index.html in your web browser to interact with the recommendation system. To avoid CORS issues, use a local HTTP server:

python -m http.server 8000

Then navigate to http://localhost:8000/index.html in your browser. 🚀

1. Preferred Genres: Enter genres like "Action, Adventure, Romance".
2. Liked Anime Titles: Provide a list of anime you enjoyed, such as "Attack on Titan, Black Clover, Kimi no Todoke, Horimiya, Mashle, Vinland Saga, Berserk".
3. Click Get Recommendations to see your personalized anime list! 🎉

⚠️ Note: The "Existing User" recommendation feature is currently under maintenance. Please use the "New User" option for now. 🔧

Files generated during the model training process and ignored by version control (.gitignore):

• anime_model_data.csv
• collab_sim.npy
• tfidf_vectorizer.pkl
• content_sim.npy
• simulated_user_data.csv

• The Existing User recommendation feature is under maintenance. Please use the New User option.
• The app relies on the dataset in the / directory; model training is required before use.

• Fix the existing user recommendation functionality.
• Enhance dataset quality and filtering for more accurate results.
• Add features like user authentication and personalized profiles.

Contributions are welcome! Feel free to submit a Pull Request to Model Repo.

This project is licensed under the MIT License.

For any inquiries or support, please contact Harshit Kumar, reach out on Instagram, or visit leoncyriac.me 🌐.