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

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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

k-anonymity, l-diversity, and t-closeness

There are a few methodologies for protecting privacy with data, especially if we are going to be publishing data or sharing it with others. For example, we may need to send data to a service like Amazon Mechanical Turk for data labeling, and we don't want to have a data breach as a result of sending the data there. The first methodology for protecting privacy is k-anonymity, which was first introduced in 1998. This says that if we have at least k records with identical tuples of quasi-identifiers (QIs) then we have k-anonymity (where k is a positive integer). QIs are PII that has been semi-anonymized. For example, age and zip code could make a tuple of quasi-identifiers by converting ages to ranges and removing the last few digits of zip codes.

As an example, let's look at the simple dataset in the GitHub repository for this chapter of the book. This is a mock dataset that has HIV test results of individuals. Let's first...