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Data Science for Marketing Analytics

You're reading from Data Science for Marketing Analytics A practical guide to forming a killer marketing strategy through data analysis with Python

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781800560475

Length 636 pages

Edition 2nd Edition

Languages

Python

Tools

GDI

Concepts

Data Analysis

Authors (3):

Vishwesh Ravi Shrimali

Mirza Rahim Baig

Gururajan Govindan

View More author details

Table of Contents (11) Chapters

Preface

1. Data Preparation and Cleaning

2. Data Exploration and Visualization FREE CHAPTER

3. Unsupervised Learning and Customer Segmentation

4. Evaluating and Choosing the Best Segmentation Approach

5. Predicting Customer Revenue Using Linear Regression

6. More Tools and Techniques for Evaluating Regression Models

7. Supervised Learning: Predicting Customer Churn

8. Fine-Tuning Classification Algorithms

9. Multiclass Classification Algorithms

Appendix

Classification Problems

Consider a situation where you have been tasked to build a model to predict whether a product bought by a customer will be returned or not. Since we have focused on regression models so far, let's try and imagine whether these will be the right fit here. A regression model will give continuous values as output (for example, 0.1, 100, 100.25, and so on), but in our case study we just have two values as output – a product will be returned, or it won't be returned. In such a case, except for these two values, all other values will be incorrect/invalid. While we can say that product returned can be considered as the value 0, and product not returned can be considered as the value 1, we still can't define what a value of 1.5 means.

In scenarios like these, classification models come into the picture. Classification problems are the most common type of machine learning problem. Classification tasks are different from regression tasks in the...

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Authors (3)

Mirza Rahim Baig

Mirza Rahim Baig

Mirza Rahim Baig is a Data Science and Artificial Intelligence leader with over 13 years of experience across e-commerce, healthcare, and marketing. He currently holds the position of leading Product Analytics at Marketing Services for Zalando, Europe's largest online fashion platform. In addition, he serves as a Subject Matter Expert and faculty member for MS level programs at prominent Ed-Tech platforms and institutes in India. He is also the lead author of two books, 'Data Science for Marketing Analytics' and 'The Deep Learning Workshop,' both published by Packt. He is recognized as a thought leader in my field and frequently participates as a guest speaker at various forums.

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Vishwesh Ravi Shrimali

Vishwesh Ravi Shrimali

Vishwesh Ravi Shrimali graduated from BITS Pilani, where he studied mechanical engineering, in 2018. He also completed his Masters in Machine Learning and AI from LJMU in 2021. He has authored - Machine learning for OpenCV (2nd edition), Computer Vision Workshop and Data Science for Marketing Analytics (2nd edition) by Packt. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar.

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Gururajan Govindan

Gururajan Govindan

Gururajan Govindan is a data scientist, intrapreneur, and trainer with more than seven years of experience working across domains such as finance and insurance. He is also an author of The Data Analysis Workshop, a book focusing on data analytics. He is well known for his expertise in data-driven decision-making and machine learning with Python.

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