In this project we use a dataset of Google Play store reviews of the 23 most popular mobile apps.
In this dataset reviews are classified as good or bad. The aim of this project is to train a Naive Bayes model to classify new reviews.
We try with differente preprocessing steps of the text data and evaluate models with default hyperparameters. Then, we select the best hyperparameters using randomized grid search.