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Practical Data Analysis Cookbook
book

Practical Data Analysis Cookbook

by Tomasz Drabas
April 2016
Beginner to intermediate
384 pages
8h 36m
English
Packt Publishing
Content preview from Practical Data Analysis Cookbook

Chapter 5. Reducing Dimensions

In this chapter, we will cover various techniques to reduce dimensions of your data. You will learn the following recipes:

  • Creating three-dimensional scatter plots to present principal components
  • Reducing the dimensions using the kernel version of PCA
  • Using Principal Component Analysis to find things that matter
  • Finding the principal components in your data using randomized PCA
  • Extracting the useful dimensions using Linear Discriminant Analysis
  • Using various dimension reduction techniques to classify calls using the k-Nearest Neighbors classification model

Introduction

The abundance of data available nowadays can be mind-boggling; the datasets grow not only in terms of the number of observations, but also get richer in terms ...

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Publisher Resources

ISBN: 9781783551668