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

Summary

In this chapter we learned the basics of regression. We mainly covered linear regression, which is one of the easier-to-use models and is easier to interpret. The assumptions for linear regression were discussed, including linear relationships between the features and target, normal distributions of data, no multicollinearity, no autocorrelation of the target, and homoscedasticity (a uniform spread of residuals among target values). We saw how we can use regularization with linear regression to select features with L1 or Lasso regularization, since it will move some coefficients to 0. We also saw how we can try L2 or Ridge regression as well as using a combination of L1 and L2 with ElasticNet.

We saw how other sklearn models can be used for regression as well and demonstrated the KNN model for this. Additionally, the statsmodels package was used for linear regression to be able to get p-values for the statistical significance of our coefficients. Metrics for evaluating...