Anime Recommendation System

Overview

The Anime Recommendation System is an advanced application designed to recommend anime shows and movies based on user preferences. By utilizing sophisticated recommendation algorithms, the system aims to deliver a tailored experience for anime enthusiasts, enhancing their journey of discovering new content.

Features

• Personalized Recommendations: Suggests anime titles based on user history and preferences.
• Advanced Search: Enables users to search for anime by title, genre, or keywords.
• Dynamic Filtering: Employs collaborative filtering, content-based filtering, and hybrid methods for precise recommendations.
• Intuitive Interface: Offers a clean, user-friendly interface for seamless navigation.

Tech Stack

• Programming Language: Python
• Key Libraries and Tools:
  • Pandas: Data manipulation and analysis
  • NumPy: Numerical computations
  • Scikit-learn: Machine learning algorithms
  • Flask: Backend web framework for deployment
  • Jupyter Notebook: Prototyping and testing
• Database: CSV/SQL (configurable to user requirements)
• Visualization Tools: Matplotlib, Seaborn

Installation

Follow these steps to set up and run the project locally:

1. Clone the Repository:

   git clone https://github.com/kunal-mallick/Anime_Recommendations_System.git
   cd Anime_Recommendations_System

2. Set Up 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. Launch the Application:

   python app.py

Usage

1. Start the application and open your browser at http://localhost:5000.
2. Input your preferences, such as genres, ratings, or specific titles.
3. Receive personalized anime recommendations tailored to your choices.

Data

The recommendation engine uses a dataset containing detailed information about various anime, such as:

• Titles
• Genres
• User ratings
• Popularity metrics

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Acknowledgments

• Data Sources: MyAnimeList and other public anime datasets.
• Inspiration: Contributions from anime enthusiasts and research on recommendation systems.
• Contributors: Heartfelt thanks to everyone who has contributed to this project.

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Feel free to explore, use, and enhance this project. Your feedback and contributions are highly valued. Happy coding!