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Machine Learning with R Cookbook, Second Edition

You're reading from   Machine Learning with R Cookbook, Second Edition Analyze data and build predictive models

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Length 572 pages
Edition 2nd Edition
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Table of Contents (15) Chapters Close

Preface 1. Practical Machine Learning with R FREE CHAPTER 2. Data Exploration with Air Quality Datasets 3. Analyzing Time Series Data 4. R and Statistics 5. Understanding Regression Analysis 6. Survival Analysis 7. Classification 1 - Tree, Lazy, and Probabilistic 8. Classification 2 - Neural Network and SVM 9. Model Evaluation 10. Ensemble Learning 11. Clustering 12. Association Analysis and Sequence Mining 13. Dimension Reduction 14. Big Data Analysis (R and Hadoop)

Introduction

When we think about time, we think about years, days, months, hours, minutes, and seconds. Think of any datasets and you will find some attributes which will be in the form of time, especially data related to stock, sales, purchase, profit, and loss. All these have time associated with them. For example, the price of stock in the stock exchange at different points on a given day or month or year. Think of any industry domain, and sales are an important factor; you can see time series in sales, discounts, customers, and so on. Other domains include but are not limited to statistics, economics and budgets, processes and quality control, finance, weather forecasting, or any kind of forecasting, transport, logistics, astronomy, patient study, census analysis, and the list goes on. In simple words, it contains data or observations in time order, spaced at equal intervals...

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