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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
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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

Performing and Interpreting Linear Regression

In Exercise 5.01, Predicting Sales from Advertising Spend Using Linear Regression, we implemented and saw the output of a linear regression model without discussing the inner workings. Let us understand the technique of linear regression better now. Linear regression is a type of regression model that predicts the outcome using linear relationships between predictors and the outcome. Linear regression models can be thought of as a line running through the feature space that minimizes the distance between the line and the data points.

The model that a linear regression learns is the equation of this line. It is an equation that expresses the dependent variable as a linear function of the independent variables. This is best visualized when there is a single predictor (see Figure 5.28). In such a case, you can draw a line that best fits the data on a scatter plot between the two variables.

Figure 5.28: A visualization of a linear regression...

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