Book Image

The Data Analysis Workshop

By : Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev
Book Image

The Data Analysis Workshop

By: Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev

Overview of this book

Businesses today operate online and generate data almost continuously. While not all data in its raw form may seem useful, if processed and analyzed correctly, it can provide you with valuable hidden insights. The Data Analysis Workshop will help you learn how to discover these hidden patterns in your data, to analyze them, and leverage the results to help transform your business. The book begins by taking you through the use case of a bike rental shop. You'll be shown how to correlate data, plot histograms, and analyze temporal features. As you progress, you’ll learn how to plot data for a hydraulic system using the Seaborn and Matplotlib libraries, and explore a variety of use cases that show you how to join and merge databases, prepare data for analysis, and handle imbalanced data. By the end of the book, you'll have learned different data analysis techniques, including hypothesis testing, correlation, and null-value imputation, and will have become a confident data analyst.
Table of Contents (12 chapters)
Preface
7
7. Analyzing the Heart Disease Dataset
9
9. Analysis of the Energy Consumed by Appliances

Building a Profile of a High-Risk Customer

Based on the analysis performed in the previous sections, we can now build a profile of the customer who is most likely to default. With this predicted customer profile, credit card companies can take preventive steps (such as reducing credit limits or increasing the rate of interest) and can demand additional collateral from customers who are deemed to be high risk.

The customer who satisfies the majority of the following conditions can be classified as a high-risk customer. A high-risk customer is one who has a higher probability of default:

  • A male customer is more likely to default than a female customer.
  • People with a relationship status of other are more likely to default than married or single people.
  • A customer whose highest educational qualification is a high-school diploma is more likely to default than a customer who has gone to graduate school or university.
  • A customer who has delayed payment for 2 consecutive...