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Data Science for Marketing Analytics

You're reading from Data Science for Marketing Analytics Achieve your marketing goals with the data analytics power of Python

Product type Paperback

Published in Mar 2019

Publisher

ISBN-13 9781789959413

Length 420 pages

Edition 1st Edition

Languages

Python

Tools

Pandas

Concepts

Data Science

Authors (3):

Tommy Blanchard

Debasish Behera

Pranshu Bhatnagar

View More author details

Table of Contents (12) Chapters

Data Science for Marketing Analytics

Preface

1. Data Preparation and Cleaning FREE CHAPTER

2. Data Exploration and Visualization

3. Unsupervised Learning: Customer Segmentation

4. Choosing the Best Segmentation Approach

5. Predicting Customer Revenue Using Linear Regression

6. Other Regression Techniques and Tools for Evaluation

7. Supervised Learning: Predicting Customer Churn

8. Fine-Tuning Classification Algorithms

9. Modeling Customer Choice

Appendix

Performance Metrics

In the case of classification algorithms, we use a confusion matrix, which gives us the performance of the learning algorithm. It is a square matrix that counts the number of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) outcomes.

Figure 8.48: Confusion matrix

True positive: The number of cases that were observed and predicted as 1.

False negative: The number of cases that were observed as 1 but predicted as 0.

False positive: The number of cases that were observed as 0 but predicted as 1.

True negative: The number of cases that were observed as 1 but predicted as 0.

Precision

It is the ability of a classifier to not label a sample that is negative as positive. The precision for an algorithm is calculated using the following formula:

Figure 8.49: Precision

This is useful in the case of email spam detection. In this scenario, we do not want any important emails to be detected as spam.

Recall

It refers to the ability of a classifier to...

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Authors (3)

Blanchard

Blanchard

Tommy Blanchard earned his PhD from the University of Rochester and did his postdoctoral training at Harvard. Now, he leads the data science team at Fresenius Medical Care North America. His team performs advanced analytics and creates predictive models to solve a wide variety of problems across the company.

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Behera

Behera

Debasish Behera works as a data scientist for a large Japanese corporate bank, where he applies machine learning/AI to solve complex problems. He has worked on multiple use cases involving AML, predictive analytics, customer segmentation, chat bots, and natural language processing. He currently lives in Singapore and holds a Master's in Business Analytics (MITB) from the Singapore Management University.

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Pranshu Bhatnagar

Pranshu Bhatnagar

Pranshu Bhatnagar works as a data scientist in the telematics, insurance, and mobile software space. He has previously worked as a quantitative analyst in the FinTech industry and often writes about algorithms, time series analysis in Python, and similar topics. He graduated with honors from the Chennai Mathematical Institute with a degree in Mathematics and Computer Science and has completed certification courses in Machine Learning and Artificial Intelligence from the International Institute of Information Technology, Hyderabad. He is based in Bangalore, India.

See other products by Pranshu Bhatnagar

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