This is an anime recommender system that suggests new anime based on the user's past preferences and ratings. The system uses a collaborative filtering algorithm to make recommendations.

To install the necessary packages, run the following command:

pip install -r requirements.txt

To use the anime recommender system, follow these steps:

1. Download the dataset from Kaggle.
2. Run the preprocess section to preprocess the dataset.
3. Run the Fit Model section to train the collaborative filtering model.
4. Run the recommendMe function to get anime recommendations for a specific user with given preferences.

The dataset used in this project is the Anime Recommendations Database from Kaggle, which contains information on anime titles, ratings, and user preferences.

Collaborative filtering is a technique used in recommendation systems to predict the preferences of a user by analyzing their past ratings and the ratings of similar users.

In this project, the collaborative filtering algorithm is implemented using the Surprise library, which provides a range of algorithms for recommendation systems.

This project was created by tr1ten. The dataset used in this project was obtained from Kaggle. The Hybrid collaborative filtering algorithm was implemented using the lightfm library.