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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

Class-Imbalanced Data

Consider the scenario we discussed at the beginning of the chapter about the online shopping company. Imagine that out of the four shortlisted sellers, one is a very well-known company. In such a situation, there is a high chance of this company getting most of the orders as compared to the rest of the three sellers. If the online shopping company decided to divert all the customers to this seller, for a large number of customers, it would actually end up matching their preference. This is a classic scenario of class imbalance since one class is dominating the rest of the classes in terms of data points. Class imbalance is also seen in fraud detection, anti-money laundering, spam detection, cancer detection, and many other situations.

Before you go into the details about how to deal with class imbalance, let's first see how it can pose a big problem in a marketing analyst's work in the following exercise.

Exercise 9.03: Performing Classification...

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