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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

Predicting customer churn is one of the most common use cases in marketing analytics. Churn prediction not only helps marketing teams to better strategize their marketing campaigns but also helps organizations to focus their resources wisely.

In this chapter, we explored how to use the data science pipeline for any machine learning problem. We also learned the intuition behind using logistic regression and saw how it is different from linear regression. We looked at the structure of the data by reading it using a pandas DataFrame. We then used data scrubbing techniques such as missing value imputation, renaming columns, and data type manipulation to prepare our data for data exploration. We implemented various data visualization techniques, such as univariate and bivariate analysis and a correlation plot, which enabled us to find useful insights from the data. Feature selection is another important part of data modeling. We used a tree-based classifier to select important...

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