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Data Science for Marketing Analytics

You're reading from   Data Science for Marketing Analytics A practical guide to forming a killer marketing strategy through data analysis with Python

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781800560475
Length 636 pages
Edition 2nd Edition
Languages
Tools
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Authors (3):
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Vishwesh Ravi Shrimali Vishwesh Ravi Shrimali
Author Profile Icon Vishwesh Ravi Shrimali
Vishwesh Ravi Shrimali
Mirza Rahim Baig Mirza Rahim Baig
Author Profile Icon Mirza Rahim Baig
Mirza Rahim Baig
Gururajan Govindan Gururajan Govindan
Author Profile Icon Gururajan Govindan
Gururajan Govindan
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Toc

Table of Contents (11) Chapters Close

Preface
1. Data Preparation and Cleaning 2. Data Exploration and Visualization FREE CHAPTER 3. Unsupervised Learning and Customer Segmentation 4. Evaluating and Choosing the Best Segmentation Approach 5. Predicting Customer Revenue Using Linear Regression 6. More Tools and Techniques for Evaluating Regression Models 7. Supervised Learning: Predicting Customer Churn 8. Fine-Tuning Classification Algorithms 9. Multiclass Classification Algorithms Appendix

5. Predicting Customer Revenue Using Linear Regression

Overview

In this chapter, you will learn how to solve business problems that require the prediction of quantities. You will learn about regression, a supervised learning approach to predict continuous outcomes. You will explore linear regression, a simple yet powerful technique that is the workhorse for predictive modeling in the industry. Then you will learn how to implement some key steps in the modeling process – feature engineering and data cleaning. Later, you will implement your linear regression models and finally interpret the results to derive business insights.

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