The process of data preparation and feature improvement in machine learning ranges from discretization to feature engineering and selection, making use of tools such as mutual information and the population stability index. These practices, supported by Python implementations, play a crucial role in creating robust and effective machine learning models. Now, data discretization in machine learning is a crucial technique that addresses the need to transform continuous variables into discrete ones. Discretization not only makes information processing easier, but can also reveal patterns and relationships that might not be evident in continuous data. In the field of feature engineering for machine learning, designing effective features is essential to maximize model performance.