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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

3. Details of Logistic Regression and Feature Exploration

Overview

This chapter teaches you how to evaluate features quickly and efficiently, in order to know which ones will probably be most important for a machine learning model. Once we get a taste for this, we'll explore the inner workings of logistic regression so you can continue your journey to mastery of this fundamental technique. After reading this chapter, you will be able to make a correlation plot of many features and a response variable and interpret logistic regression as a linear model.

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