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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

6. More Tools and Techniques for Evaluating Regression Models

Activity 6.01: Finding Important Variables for Predicting Responses to a Marketing Offer

Solution:

Perform the following steps to achieve the aim of this activity:

  1. Import pandas, read in the data from offer_responses.csv, and use the head function to view the first five rows of the data:

    import pandas as pd

    df = pd.read_csv('offer_responses.csv')

    df.head()

    Note

    Make sure you change the path (emboldened) to the CSV file based on its location on your system. If you're running the Jupyter notebook from the same directory where the CSV file is stored, you can run the preceding code without any modifications.

    You should get the following output:

    Figure 6.22: The first five rows of the offer_responses data

  2. Extract the target variable (y) and the predictor variable (X) from the data:

    X = df[['offer_quality',\

            'offer_discount',\

      &...

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