Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
5 (1)
Book Image

Hands-On Data Analysis with Pandas - Second Edition

5 (1)
By: Stefanie Molin

Overview of this book

Extracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.
Table of Contents (21 chapters)
Section 1: Getting Started with Pandas
Section 2: Using Pandas for Data Analysis
Section 3: Applications – Real-World Analyses Using Pandas
Section 4: Introduction to Machine Learning with Scikit-Learn
Section 5: Additional Resources

Chapter 11: Machine Learning Anomaly Detection

For our final application chapter, we will be revisiting anomaly detection on login attempts. Let's imagine we work for a company that launched its web application at the beginning of 2018. This web application has been collecting log events for all login attempts since it launched. We know the IP address that the attempt was made from, the result of the attempt, when it was made, and which username was entered. What we don't know is whether the attempt was made by one of our valid users or a nefarious party.

Our company has been expanding and, since data breaches seem to be in the news every day, has created an information security department to monitor the traffic. The CEO saw our rule-based approach to identifying hackers from Chapter 8, Rule-Based Anomaly Detection, and was intrigued by our initiative, but wants us to move beyond using rules and thresholds for such a vital task. We have been tasked with developing a machine...