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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

As a data consumer, the focus is always on searching for right information to form a true analysis from the information gathered over time. Data Exploration is a term used for finding insightful information from data. To find insights from data various steps such as data munging, data analysis, data modeling, and model evaluation are taken.

In any real data exploration project, commonly six steps are involved in the exploration process. They are as follows:

  • Asking the right questions: Asking the right questions will help in understanding the objective and target information sought from the data. Questions can be asked such as What are my expected findings after the exploration is finished?, or What kind of information can I extract through the exploration?.
  • Data collection: Once the right questions have been asked the target of exploration is cleared. Data collected...
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