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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

Summary

In this chapter, you started off by understanding the importance of multiclass classification problems and the different categories of these problems. You learned about one-versus-one and one-versus-all classifiers and how to implement them using the scikit-learn module in Python. Next, you went through various micro- and macro-averages of performance metrics and used them to understand the impact of class imbalance on the model performance. You also learned about various sampling techniques, especially SMOTE, and implemented them using the imblearn library in Python. At the end of the chapter, you used an imbalanced marketing campaign dataset to perform dataset exploration, data transformation, model training, performance evaluation, and dataset balancing using SMOTE.

This book started with the basics of data science and slowly covered the entire end-to-end data science pipeline for a marketing analyst. While working on a problem statement, depending on the need, you will...

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