  This paper uses the dataset of Amazon to predict the books ratings listed on
Amazon website. As part of this project, we predicted the ratings of the books,
and also built a recommendation cluster. This recommendation cluster provides
the recommended books based on the column's values from dataset, for instance,
category, description, author, price, reviews etc. This paper provides a flow
of handling big data files, data engineering, building models and providing
predictions. The models predict book ratings column using various PySpark
Machine Learning APIs. Additionally, we used hyper-parameters and parameters
tuning. Also, Cross Validation and TrainValidationSplit were used for
generalization. Finally, we performed a comparison between Binary
Classification and Multiclass Classification in their accuracies. We converted
our label from multiclass to binary to see if we could find any difference
between the two classifications. As a result, we found out that we get higher
accuracy in binary classification than in multiclass classification.
