Book Image

Practical Machine Learning Cookbook

By : Atul Tripathi
Book Image

Practical Machine Learning Cookbook

By: Atul Tripathi

Overview of this book

Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.
Table of Contents (21 chapters)
Practical Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
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

The...