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

DBSCAN

Another clustering method that can work well for strange cluster shapes is DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise. The algorithm is completely different from k-means or hierarchical clustering. With DBSCAN, our clusters are composed of core points and non-core points. Core points are all within a distance, epsilon (eps in the sklearn parameters), of at least n points in the same cluster (n is the min_samples parameter in the sklearn function). Then, any other points within the distance epsilon of the core points are also in the cluster. If any points are not within the epsilon distance of any core points, these are outliers. This algorithm assumes we have some dead space between samples, so our clusters must have at least some separation. We can also tune the eps and min_samples hyperparameters to optimize clustering metrics.

The min_samples hyperparameter should generally be between the number of features and two times the...