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Hands-On Data Analysis with Pandas - Second Edition

You're reading from  Hands-On Data Analysis with Pandas - Second Edition

Product type Book
Published in Apr 2021
Publisher Packt
ISBN-13 9781800563452
Pages 788 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
Stefanie Molin Stefanie Molin
Profile icon Stefanie Molin

Table of Contents (21) Chapters

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

Modeling performance using historical data

The goal of this section is to give us a taste of how to build some models; as such, the following examples are not meant to be the best possible model, but rather a simple and relatively quick implementation for learning purposes. Once again, the stock_analysis package has a class for this section's task: StockModeler.

Important note

To fully understand the statistical elements of this section and modeling in general, we need a solid understanding of statistics; however, the purpose of this discussion is to show how modeling techniques can be applied to financial data without dwelling on the underlying mathematics.

The StockModeler class

The StockModeler class will make it easier for us to build and evaluate some simple financial models without needing to interact directly with the statsmodels package. In addition, we will reduce the number of steps that are needed to generate a model with the methods we create. The following...

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