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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

2. Data Exploration and Visualization

Objectives

In this chapter, you will learn to explore, analyze, and reshape your data so that you can shed light on the attributes of your data that are important to the business – a key skill in a marketing analyst's repertoire. You will discover functions that will help you derive summary and descriptive statistics from your data. You will build pivot tables and perform comparative tests and analyses to discover hidden relationships between various data points. Later, you will create impactful visualizations by using two of the most popular Python packages, Matplotlib and seaborn.

You have been reading a chapter from
Data Science for Marketing Analytics - Second Edition
Published in: Sep 2021
Publisher: Packt
ISBN-13: 9781800560475
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