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The Data Analysis Workshop

You're reading from   The Data Analysis Workshop Solve business problems with state-of-the-art data analysis models, developing expert data analysis skills along the way

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839211386
Length 626 pages
Edition 1st Edition
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Authors (3):
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Konstantin Palagachev Konstantin Palagachev
Author Profile Icon Konstantin Palagachev
Konstantin Palagachev
Gururajan Govindan Gururajan Govindan
Author Profile Icon Gururajan Govindan
Gururajan Govindan
Shubhangi Hora Shubhangi Hora
Author Profile Icon Shubhangi Hora
Shubhangi Hora
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Toc

Table of Contents (12) Chapters Close

Preface
1. Bike Sharing Analysis 2. Absenteeism at Work FREE CHAPTER 3. Analyzing Bank Marketing Campaign Data 4. Tackling Company Bankruptcy 5. Analyzing the Online Shopper's Purchasing Intention 6. Analysis of Credit Card Defaulters 7. Analyzing the Heart Disease Dataset 8. Analyzing Online Retail II Dataset 9. Analysis of the Energy Consumed by Appliances 10. Analyzing Air Quality Appendix

Summary

In this chapter, we learned how to import an ARFF file into a pandas DataFrame. Pandas profiling was performed on the DataFrame to get the correlated features. We detected the missing values using the missingno package and performed imputation using the mean and iterative imputation methods.

In order to find the important features that contribute to bankruptcy, we performed lasso regularization. With lasso regularization, we found which features are responsible for bankruptcy. Even though we get the different important features across all five DataFrames, one of the features occurs across all five DataFrames, which is nothing but the ratio of total liabilities to total assets. This particular ratio has a very high significance in leading to bankruptcy.

However, our analysis is not fully complete since we only found the factors that affect bankruptcy, but not the direction (whether bankruptcy may occur when a particular ratio increases or decreases).

To get a complete...

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