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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

Training a Random Forest Classifier

In this chapter, we will use the Random Forest algorithm for multiclass classification. There are other algorithms on the market, but Random Forest is probably one of the most popular for such types of projects.

The Random Forest methodology was first proposed in 1995 by Tin Kam Ho but it was first developed by Leo Breiman in 2001.

So Random Forest is not really a recent algorithm per se. It has been in use for almost two decades already. But its popularity hasn't faded, thanks to its performance and simplicity.

In this chapter, we will be using a dataset called "Activity Recognition system based on Multisensor data." It was originally shared by F. Palumbo, C. Gallicchio, R. Pucci, and A. Micheli, Human activity recognition using multisensor data fusion based on Reservoir Computing, Journal of Ambient Intelligence and Smart Environments, 2016, 8 (2), pp. 87-107.

Note

The complete dataset can be found here: https://packt...

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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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