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Practical Data Science with Python
book

Practical Data Science with Python

by Nathan George
September 2021
Beginner to intermediate
620 pages
15h 30m
English
Packt Publishing
Content preview from Practical Data Science with Python

11

Machine Learning for Classification

Once our data has been prepared with some cleaning, feature selection, and feature engineering, we can begin using machine learning algorithms. As we saw in the previous chapter, machine learning falls into three broad categories: supervised, unsupervised, and reinforcement learning. Classification falls under supervised learning, since we have targets or labels in our data. For example, we will look at a credit card loan default dataset here first. This dataset has labels for each data point, indicating whether someone defaulted on a credit card payment.

We will learn the basics of classification with machine learning in this chapter using the sklearn and statsmodels packages. In this chapter, we'll cover ...

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Publisher Resources

ISBN: 9781801071970Supplemental Content