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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

Activity 2.01: Analyzing Advertisements

Solution:

Perform the following steps to complete this activity:

  1. Import pandas and seaborn using the following code:

    import pandas as pd

    import seaborn as sns

    import matplotlib.pyplot as plt

    sns.set()

  2. Load the Advertising.csv file into a DataFrame called ads and examine if your data is properly loaded by checking the first few values in the DataFrame by using the head() command:

    ads = pd.read_csv("Advertising.csv", index_col = 'Date')

    ads.head()

    The output should be as follows:

    Figure 2.65: First five rows of the DataFrame ads

  3. Look at the memory usage and other internal information about the DataFrame using the following command:

    ads.info

    This gives the following output:

    Figure 2.66: The result of ads.info()

    From the preceding figure, you can see that you have five columns with 200 data points in each and no missing values.

  4. Use describe() function to view basic statistical details...
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