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Hands-On Data Analysis with Pandas

You're reading from   Hands-On Data Analysis with Pandas A Python data science handbook for data collection, wrangling, analysis, and visualization

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800563452
Length 788 pages
Edition 2nd 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
2. Chapter 1: Introduction to Data Analysis FREE CHAPTER 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

Chapter 8: Rule-Based Anomaly Detection

It's time to catch some hackers trying to gain access to a website using a brute-force attack—trying to log in with a bunch of username-password combinations until they gain access. This type of attack is very noisy, so it gives us plenty of data points for anomaly detection, which is the process of looking for data generated from a process other than the one we deem to be typical activity. The hackers will be simulated and won't be as crafty as they can be in real life, but it will give us great exposure to anomaly detection.

We will be creating a package that will handle the simulation of the login attempts in order to generate the data for this chapter. Knowing how to simulate is an essential skill to have in our toolbox. Sometimes, it's difficult to solve a problem with an exact mathematical solution; however, it might be easy to define how small components of the system work. In these cases, we can model the small...

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