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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

Classifiers in Multiclass Classification

Let's consider two problem statements:

An online trading company wants to provide additional benefits to its customers. The marketing analytics team has divided the customers into five categories based on when the last time they logged in to the platform was.
The same trading company wants to build a recommendation system for mutual funds. This will recommend their users a mutual fund based on the risk they are willing to take, the amount they are planning to invest, and some other features. The number of mutual funds is well above 100.

Before you jump into more detail about the differences between these two problem statements, let's first understand the two common ways of approaching multiclass classification.

Multiclass classification can be implemented by scikit-learn in the following two ways:

One-versus-all (one-versus-rest) classifier: Here, one classifier is fit against one class. For each of the classifiers...

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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.

See other products by Vishwesh Ravi Shrimali

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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