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Data Science for Marketing Analytics

Data Science for Marketing Analytics

By : Mirza Rahim Baig , Gururajan Govindan , Vishwesh Ravi Shrimali
4.3 (203)
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Data Science for Marketing Analytics

Data Science for Marketing Analytics

4.3 (203)
By: Mirza Rahim Baig , Gururajan Govindan , Vishwesh Ravi Shrimali

Overview of this book

Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.
Table of Contents (11 chapters)
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Preface

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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