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

Regression models on big data

We may find ourselves working with data that's too big to fit on our single machine, in which case we need to use big data solutions. These solutions are the same as for logistic regression on the classification side. In fact, many of these use a GLM, or generalized linear model, that can be configured to be logistic or linear regression. Some of our options are:

Vowpal Wabbit
H2O
TensorFlow
Spark (pyspark)
Dask
AWS SageMaker's or Google Cloud's Linear Learner

Of course, the solution we choose depends on what we have available and is easiest to use. The cloud solutions like AWS and GCP generally provide easy-to-use services, and we may have to use those for other solutions like Spark or Dask anyway. However, if we have an on-premises cluster with certain software installed and/or specific data privacy concerns, we may not want to use cloud solutions.