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The Data Science Workshop

You're reading from The Data Science Workshop A New, Interactive Approach to Learning Data Science

Product type Paperback

Published in Jan 2020

Publisher

ISBN-13 9781838981266

Length 818 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Data Science

Authors (5):

Thomas Joseph

Andrew Worsley

Robert Thas John

Anthony So

Dr. Samuel Asare

+1 more

View More author details

Table of Contents (18) Chapters

Preface

About the Book

1. Introduction to Data Science in Python

2. Regression FREE CHAPTER

3. Binary Classification

4. Multiclass Classification with RandomForest

5. Performing Your First Cluster Analysis

6. How to Assess Performance

7. The Generalization of Machine Learning Models

8. Hyperparameter Tuning

9. Interpreting a Machine Learning Model

10. Analyzing a Dataset

11. Data Preparation

12. Feature Engineering

13. Imbalanced Datasets

14. Dimensionality Reduction

15. Ensemble Learning

16. Machine Learning Pipelines

17. Automated Feature Engineering

Understanding the Business Context

The business head of the bank for which you are working as a data scientist recently raised the alarm about the results of the term deposit propensity model that you built in Chapter 3, Binary Classification. It has been observed that a large proportion of customers who were identified as potential cases for targeted marketing for term deposits have turned down the offer. This has made a big dent in the sales team's metrics on upselling and cross-selling. The business team urgently requires your help in fixing the issue to meet the required sales targets for the quarter. Don't worry, though – this is the problem that we will be solving later in this chapter.

First, we begin with an analysis of the issue.

Exercise 13.01: Benchmarking the Logistic Regression Model on the Dataset

In this exercise, we will be analyzing the problem of predicting whether a customer will buy a term deposit. For this, you will be fitting a logistic...

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Authors (5)

Anthony So

Anthony So

Anthony So is a renowned leader in data science. He has extensive experience in solving complex business problems using advanced analytics and AI in different industries including financial services, media, and telecommunications. He is currently the chief data officer of one of the most innovative fintech start-ups. He is also the author of several best-selling books on data science, machine learning, and deep learning. He has won multiple prizes at several hackathon competitions, such as Unearthed, GovHack, and Pepper Money. Anthony holds two master's degrees, one in computer science and the other in data science and innovation.

See other products by Anthony So

Thomas Joseph

Thomas Joseph

Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning toolsets across multiple industry segments.

See other products by Thomas Joseph

Dr. Samuel Asare

Dr. Samuel Asare

Dr. Samuel Asare is a professional engineer with enthusiasm for Python programming, research, and writing. He is highly skilled in applying data science methods to the extraction of useful insights from large data sets. He possesses solid skills in project management processes. Samuel has previously held positions, in industry and academia, as a process engineer and a lecturer of materials science and engineering respectively. Presently, he is pursuing his passion for solving industry problems, using data science methods, and writing.

See other products by Dr. Samuel Asare

Andrew Worsley

Andrew Worsley

Andrew David Worsley is an independent consultant and educator with expertise in the areas of machine learning, statistics, cloud computing, and artificial intelligence. He has practiced data science in several countries across a multitude of industries including retail, financial services, marketing, resources, and healthcare.

See other products by Andrew Worsley

Robert Thas John

Robert Thas John

Robert Thas John is a data engineer with a career that spans two decades. He manages a team of data engineers, analysts, and machine learning engineers – roles that he has held in the past. He leads a number of efforts aimed at increasing the adoption of machine learning on embedded devices through various programs from Google Developers and ARM Ltd, which licenses the chips found in Arduinos and other microcontrollers. He started his career as a software engineer with work that has spanned various industries. His first experience with embedded systems was in programming payment terminals.

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