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Practical Data Analysis Cookbook

You're reading from   Practical Data Analysis Cookbook Over 60 practical recipes on data exploration and analysis

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781783551668
Length 384 pages
Edition 1st Edition
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Author (1):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Preparing the Data FREE CHAPTER 2. Exploring the Data 3. Classification Techniques 4. Clustering Techniques 5. Reducing Dimensions 6. Regression Methods 7. Time Series Techniques 8. Graphs 9. Natural Language Processing 10. Discrete Choice Models 11. Simulations Index

Chapter 6. Regression Methods

In this chapter, we will cover various techniques to predict the output of a power plant. You will learn the following recipes:

  • Identifying and tackling multicollinearity in your data
  • Building a linear regression model to predict the power plant output
  • Using OLS to forecast how much electricity can be produced
  • Estimating the output of an electric plant using CART
  • Employing the kNN model in a regression problem
  • Applying the Random Forest model to a regression analysis
  • Gauging the amount of electricity a plant can produce using SVMs
  • Training a Neural Network to predict the output of a power plant
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