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Data Science Projects with Python

You're reading from   Data Science Projects with Python A case study approach to successful data science projects using Python, pandas, and scikit-learn

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781838551025
Length 374 pages
Edition 1st Edition
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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

Data Science Projects with Python
Preface
1. Data Exploration and Cleaning FREE CHAPTER 2. Introduction toScikit-Learn and Model Evaluation 3. Details of Logistic Regression and Feature Exploration 4. The Bias-Variance Trade-off 5. Decision Trees and Random Forests 6. Imputation of Missing Data, Financial Analysis, and Delivery to Client Appendix

Examining the Relationships between Features and the Response


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 features and the response variable, either by calculating a groupby/mean of the response variable, or by trying models directly, which is another way to make this kind of exploration. However, we have not yet made a systematic exploration of how all the features relate to the response variable. We will do that now and 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 the correlation plot for the case study data, then discuss how to...

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