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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, we learned how to evaluate regression models. We used residuals to calculate the MAE and RMSE, and then used those metrics to compare models. We also learned about RFE and how it can be used for feature selection. We were able to see the effect of feature elimination on the MAE and RMSE metrics and relate it to the robustness of the model. We used these concepts to verify that the intuitions about the importance of the "number of competitors" feature were wrong in our case study. Finally, we learned about tree-based regression models and looked at how they can fit some of the non-linear relationships that linear regression is unable to handle. We saw how random forest models were able to perform better than regression tree models and the effect of increasing the maximum tree depth on model performance. We used these concepts to model the spending behavior of people with respect to their age.

In the next chapter, we will learn about classification...

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