create a model that predicts book ratings

you're supposed to solve yet another machine learning problem. This time you're going to create a model that predicts book ratings.

You can use any algorithms, any feature processing, done in any programming language or machine learning platform. The only thing that matters now is the result you get.

For the best score, your solution should be reproducible.

Само задание расположено в Kaggle (https://www.kaggle.com/t/3da9d01d77e3415ca577651e1ef8ea58), продублировано ниже:

In this competition we're going to use data from goodreads dataset. Your goal is to predict ratings of different books based on authors, title, and other attributes.

As a start you're free to submit the sample submission.

• books_train.csv - the training set
• books_test.csv - the test set
• books_sample_submission.csv - a sample submission file in the correct format

The evaluation metric for this competition is MSE.

For every book in the test set, submission files should contain two columns: `bookID` and `average_rating`. The `average_rating` field should be a number.

The file should contain a header and have the following format:

bookID,average_rating

In this homework you're supposed to learn how the process of data competitions looks like. You're not supposed to achieve any specific result with your model so please do not cheat: do not use the available data for perfect submission.

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