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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

Summary

In practice, this isn't easy. Real-life hackers are much more savvy than the ones in this simulation. Attacks are also much less frequent, creating a huge class imbalance. Building machine learning models that will catch everything just isn't possible. That is why it is so vital we work with those who have subject area knowledge; they can help us squeeze some extra performance out of our models by really understanding the data and its peculiarities. No matter how experienced we become with machine learning, we should never turn down help from someone who often works with the data in question.

Our initial attempts at anomaly detection were unsupervised while we waited for the labeled data from our subject matter experts. We tried LOF and isolation forest using scikit-learn. Once we received the labeled data, we determined that LOF was better for our data.

However...

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