Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By : Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda
Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By: Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

Overview of this book

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Simple data transformations


The German credit data available from the RSADBE 1.0 version has certain limitations. The data file in the package is named GC. Many of the categorical variables are stored as integer classes, which affects the overall analysis. Also, some variables are not important here and after conversion from the integer class to the factor class, re-labeling is needed. For instance, detailed information about the variables can be obtained at https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data). In this section, we'll use the data set and carry out the necessary transformation.

Getting ready

The reader will need to install the RSADBE package, which consists of the GC dataset. As earlier, we first load all the pre-requisite libraries:

library (data.table)
library (dplyr)
library (RSADBE)
library (rpart)
library (randomForestSRC)
library (ROCR)
library (plyr)

How to do it...

The GC dataset is available in the RSADBE R package. As mentioned earlier, datasets consist...