Prétraitement pour le Machine Learning en Python

In this chapter you'll learn exactly what it means to preprocess data. You'll take the first steps in any preprocessing journey, including exploring data types and dealing with missing data.

This chapter is all about standardizing data. Often a model will make some assumptions about the distribution or scale of your features. Standardization is a way to make your data fit these assumptions and improve the algorithm's performance.

In this section you'll learn about feature engineering. You'll explore different ways to create new, more useful, features from the ones already in your dataset. You'll see how to encode, aggregate, and extract information from both numerical and textual features.

This chapter goes over a few different techniques for selecting the most important features from your dataset. You'll learn how to drop redundant features, work with text vectors, and reduce the number of features in your dataset using principal component analysis (PCA).

Now that you've learned all about preprocessing you'll try these techniques out on a dataset that records information on UFO sightings.