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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Pages 570 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Atul Tripathi

Table of Contents (21) Chapters

Practical Machine Learning Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Introduction to Machine Learning

2. Classification

3. Clustering

4. Model Selection and Regularization

5. Nonlinearity

6. Supervised Learning

7. Unsupervised Learning

8. Reinforcement Learning

9. Structured Prediction

10. Neural Networks

11. Deep Learning

12. Case Study - Exploring World Bank Data

13. Case Study - Pricing Reinsurance Contracts

14. Case Study - Forecast of Electricity Consumption

Introduction

Shrinkage methods - calories burned per day

In order to compare the metabolic rate of humans, the concept of basal metabolic rate (BMR) is critical, in a clinical context, as a means of determining thyroid status in humans. The BMR of mammals varies with body mass, with the same allometric exponent as field metabolic rate, and with many physiological and biochemical rates. Fitbit, as a device, uses BMR and activities performed during the day to estimate calories burned throughout the day.

Getting ready

In order to perform shrinkage methods, we shall be using a dataset collected from Fitbit and a calories-burned dataset.

Step 1 - collecting and describing data

The dataset titled fitbit_export_20160806.csv which is in CSV format shall be used. The dataset is in standard format. There are 30 rows of data and 10 variables. The numeric variables are as follows:

Calories Burned
Steps
Distance
Floors
Minutes Sedentary
Minutes Lightly Active
Minutes Fairly Active
ExAng
Minutes Very Active
Activity Calories

The...

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Authors (1)

Atul Tripathi

Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.

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