This Python script implements a simple anime recommendation system that suggests similar animes based on user input. Leveraging TF-IDF Vectorization and Cosine Similarity, the system analyzes a dataset containing anime names, genres, and types. Users input their favorite anime, and the script identifies close matches using the difflib library. The recommendation algorithm calculates similarity scores and presents a list of animes ranked by their similarity to the user's input. The project employs pandas for data manipulation, scikit-learn for TF-IDF Vectorization, and difflib for finding close matches. Whether you're an anime enthusiast or a Python developer looking to explore recommendation systems, this project provides a simple yet effective example.
- Python 3.12
- Required Python packages can be installed using the following command:
pip install pandas scikit-learn- Clone the repoaitory
git clone https://github.com/Mrinal-exe/Anime-Recommendation.git- Navigate to the project directory
cd Anime-Recommendation- Run the script
python main.py- Enter your favorite anime when prompted.
- The system will find close matches and suggest similar animes based on TF-IDF Vectorization and Cosine Similarity.
The recommendation system uses anime data from the "anime.csv" file. The dataset includes information about anime names, genres, and types. The dataset was found on Kaggle.
You can customize the recommendation system by modifying the script or using a different dataset.
The recommendation system is built using pandas, scikit-learn, and difflib.