Chapter 4. Building Good Training Sets – Data Preprocessing

The quality of the data and the amount of useful information that it contains are key factors that determine how well a machine learning algorithm can learn. Therefore, it is absolutely critical that we make sure to examine and preprocess a dataset before we feed it to a learning algorithm. In this chapter, we will discuss the essential data preprocessing techniques that will help us build good machine learning models.

The topics that we will cover in this chapter are as follows:

  • Removing and imputing missing values from the dataset
  • Getting categorical data into shape for machine learning algorithms
  • Selecting relevant features for the model construction

Dealing with missing data

It is not

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