Predicting Anime Ratings using Matrix Factorization in PyTorch

#library imports
import numpy as np
import pandas as pd
from collections import Counter
from sklearn.model_selection import train_test_split
from scipy import sparse
import jovian

Problem Statement :

Given a set of user ratings for anime, predict the rating for each user-anime pair

Dataset

Anime Recommendations Database from Kaggle:
https://www.kaggle.com/CooperUnion/anime-recommendations-database

We have a total of 69600 users and 9927 anime. There are 6337241 ratings provided (out of 690,919,200 possible ratings).

anime_ratings_df = pd.read_csv("rating.csv")
anime_ratings_df.shape
print(anime_ratings_df.head())

   user_id  anime_id  rating
0        1        20      -1
1        1        24      -1
2        1        79      -1
3        1       226      -1
4        1       241      -1

Rating of "-1" indicates a missing ratings, we can get rid of these rows