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Data Science Projects with Python

You're reading from   Data Science Projects with Python A case study approach to gaining valuable insights from real data with machine learning

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781800564480
Length 432 pages
Edition 2nd Edition
Languages
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Author (1):
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Stephen Klosterman Stephen Klosterman
Author Profile Icon Stephen Klosterman
Stephen Klosterman
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Table of Contents (9) Chapters Close

Preface
1. Data Exploration and Cleaning 2. Introduction to Scikit-Learn and Model Evaluation FREE CHAPTER 3. Details of Logistic Regression and Feature Exploration 4. The Bias-Variance Trade-Off 5. Decision Trees and Random Forests 6. Gradient Boosting, XGBoost, and SHAP Values 7. Test Set Analysis, Financial Insights, and Delivery to the Client Appendix

Examining the Relationships Between Features and the Response Variable

In order to make accurate predictions of the response variable, good features are necessary. We need features that are clearly linked to the response variable in some way. Thus far, we've examined the relationship between a couple of features and the response variable, either by calculating the groupby/mean of a feature and the response variable, or using individual features in a model and examining performance. However, we have not yet done a systematic exploration of how all the features relate to the response variable. We will do that now and begin to capitalize on all the hard work we put in when we were exploring the features and making sure the data quality was good.

A popular way of getting a quick look at how all the features relate to the response variable, as well as how the features are related to each other, is by using a correlation plot. We will first create a correlation plot for the case...

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