Data Preparation for Machine Learning: Cleansing, Transformation & Feature Engineering

The purpose of the Data Preparation stage is to get the data into the best format for machine learning, this includes three stages: Data Cleansing, Data Transformation, and Feature Engineering. Quality data is more important than using complicated algorithms so this is an incredibly important step and should not be skipped. During the Data Understanding activities, you explored your data and detected incomplete or incorrect values. Most machine learning models require all features to be complete, therefore, missing values must be dealt with. The simplest solution is to remove all rows that have a missing value but important information could be lost or bias introduced.