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Anime Shrine

A website made using HTML, CSS, Bootstrap and Flask to recommend anime. We used KNN and K-Means algorithms to give three kinds of recommendations - based on genre, based on user ratings and finally the top recommendations.

Contributors

  • Dhruv Garg
  • Aditya Upadhyay

Installation and Setup

  • Fork the repo and clone it.
https://github.com/EnigmAI/Anime-Shrine.git
  • Install the necessary packages required to run the code
  • Run the following command
python app.py
  • Next go to your web browser and open the url below
http://127.0.0.1:5000/
  • Now you can use our search option to see which anime are present in our database
  • Then just copy name of the anime you like from the search results and press the recommend button
  • If you want to learn more about how the ML algorithms have been applied refer the jupyter notebooks inside 'ML models' folder

Dataset

We used the Anime Recommendations Database available on Kaggle here - https://www.kaggle.com/CooperUnion/anime-recommendations-database. This data set contains information on user preference data from 73,516 users on 12,294 anime. Each user is able to add anime to their completed list and give it a rating and this data set is a compilation of those ratings.

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About

A website made using HTML, CSS, Bootstrap and Flask to recommend anime. We used KNN and K-Means algorithms to generate the recommendations.

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  • Jupyter Notebook 99.3%
  • Other 0.7%