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Practical Machine Learning Cookbook

You're reading from   Practical Machine Learning Cookbook Supervised and unsupervised machine learning simplified

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781785280511
Length 570 pages
Edition 1st Edition
Languages
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Author (1):
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Atul Tripathi Atul Tripathi
Author Profile Icon Atul Tripathi
Atul Tripathi
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Toc

Table of Contents (15) Chapters Close

Preface 1. Introduction to Machine Learning FREE CHAPTER 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

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...
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