You're reading from Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801071970

Length 620 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Data Science

Author (1):

Nathan George

View More author details

Table of Contents (30) Chapters

Preface

1. Part I - An Introduction and the Basics

2. Introduction to Data Science FREE CHAPTER

3. Getting Started with Python

4. Part II - Dealing with Data

5. SQL and Built-in File Handling Modules in Python

6. Loading and Wrangling Data with Pandas and NumPy

7. Exploratory Data Analysis and Visualization

8. Data Wrangling Documents and Spreadsheets

9. Web Scraping

10. Part III - Statistics for Data Science

11. Probability, Distributions, and Sampling

12. Statistical Testing for Data Science

13. Part IV - Machine Learning

14. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction

15. Machine Learning for Classification

16. Evaluating Machine Learning Classification Models and Sampling for Classification

17. Machine Learning with Regression

18. Optimizing Models and Using AutoML

19. Tree-Based Machine Learning Models

20. Support Vector Machine (SVM) Machine Learning Models

21. Part V - Text Analysis and Reporting

22. Clustering with Machine Learning

23. Working with Text

24. Part VI - Wrapping Up

25. Data Storytelling and Automated Reporting/Dashboarding

26. Ethics and Privacy

27. Staying Up to Date and the Future of Data Science

28. Other Books You May Enjoy

29. Index

Test your knowledge

We learned a lot of feature selection and engineering techniques here. To practice some of these, use the same loans dataset with the LOAN_DEFAULT column as the target variable, and perform the following:

Plot the ANOVA F-scores and p-values for the numeric features.
Evaluate the STATE_ID feature and decide on the number of top state IDs to keep. Put all other state IDs in an other column, and then one-hot encode this feature. Finally, join it together with your original loan DataFrame.
Extract the day of week from the DISBURSAL_DATE feature and check the phi-k correlation. Comment on the strength of the correlation and if this new feature should be used.

As always, write some analysis explaining and interpreting the results.

Using another loan default dataset (the "default of credit card clients.xls" file in the GitHub repository for the book under Chapter10/test_your_knowledge/data), perform PCA and examine the explained...