Book Image

Data Science for Marketing Analytics

By : Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar
Book Image

Data Science for Marketing Analytics

By: Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar

Overview of this book

Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions.
Table of Contents (12 chapters)
Data Science for Marketing Analytics
Preface

Summary


In this chapter, you learnt how to perform classification using some of the most commonly used algorithms. You also understood the advantage and disadvantage of each algorithm. You also learned in depth how a tree-based model works.

You got to grips with why the pre-processing of data using techniques such as standardization is necessary, and implemented various fine-tuning techniques for optimizing a machine learning model. You were able to choose the right performance metrics for your classification problems and explored the concept behind the confusion matrix. You also learned how to compare different models and choose the best performing models.

In the next chapter, you will learn about multi-classification problems and how to tackle imbalanced data.