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Hands-On Data Analysis with Pandas

You're reading from   Hands-On Data Analysis with Pandas Efficiently perform data collection, wrangling, analysis, and visualization using Python

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Product type Paperback
Published in Jul 2019
Publisher
ISBN-13 9781789615326
Length 740 pages
Edition 1st Edition
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Author (1):
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Stefanie Molin Stefanie Molin
Author Profile Icon Stefanie Molin
Stefanie Molin
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Getting Started with Pandas FREE CHAPTER
2. Introduction to Data Analysis 3. Working with Pandas DataFrames 4. Section 2: Using Pandas for Data Analysis
5. Data Wrangling with Pandas 6. Aggregating Pandas DataFrames 7. Visualizing Data with Pandas and Matplotlib 8. Plotting with Seaborn and Customization Techniques 9. Section 3: Applications - Real-World Analyses Using Pandas
10. Financial Analysis - Bitcoin and the Stock Market 11. Rule-Based Anomaly Detection 12. Section 4: Introduction to Machine Learning with Scikit-Learn
13. Getting Started with Machine Learning in Python 14. Making Better Predictions - Optimizing Models 15. Machine Learning Anomaly Detection 16. Section 5: Additional Resources
17. The Road Ahead 18. Solutions
19. Other Books You May Enjoy Appendix

Regression

With our planet data, we want to predict the length of the year, which is a numeric value, so we turn to regression. As mentioned at the beginning of this chapter, regression is a technique for modeling the strength and magnitude of the relationship between independent variables (our X data)—often called regressors—and the dependent variable (our y) that we want to predict.

Linear regression

Scikit-learn provides many algorithms that can handle regression tasks, ranging from decision trees to linear regression, spread across modules according to the various algorithm classes. However, typically, the best starting point is a linear regression, which can be found in the linear_model module. In simple...

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