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

Introduction


Loans! A liability for the borrower and an asset for the bank! Banks would certainly like to give only loans and not any of the savings schemes, such as savings accounts, fixed deposits, recurring deposits, and so on. The simple reason is that banks must pay the customer after some period and if they don't earn enough, they can't give away the interest. Though the banks would like to give away as many loans possibly can, there are plenty of reason that loans would never be given on a first-come-first-serve basis. The apparently simple reason being that if the customer defaults, the bank stands out as well as an opportunity to serve a better customer. The obvious question is how does one define a better customer and will analytical methods help here. A practical data set is the German data set, which consists of the final status of whether or not the customer fully paid back their loan and a host of other important variables.

A lot of analyses has been performed and it has now...