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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 2. Exploring the Data FREE CHAPTER 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

Introduction

One of the most common problems in the real world is to predict certain quantities or, in more general terms, find a relationship between a set of independent variables and the dependent one. In this chapter, we will focus on predicting the output of a power plant.

The dataset that we will use in this chapter comes from the U.S. Energy Information Administration. We procured the 2014 data from their website, http://www.eia.gov/electricity/data/eia923/xls/f923_2014.zip.

We will use the data from the EIA923_Schedules_2_3_4_5_M_12_2014_Final_Revision.xlsx file only, sheet Generation and Fuel Data. We will be predicting Net Generation (Megawatt hours). As most of the data is categorical (state or fuel type), we decided to dummy code them.

Ultimately, our dataset holds only a subset of 4,494 records of the whole dataset. We selected only the power plants with an output greater than 100 MWh in 2014 that were located in a handful of selected states. We also only selected plants that...

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