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)
1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications – Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Regularization

When working with regressions, we may look to add a penalty term to our regression equation to reduce overfitting by punishing certain decisions for coefficients made by the model; this is called regularization. We are looking for the coefficients that will minimize this penalty term. The idea is to shrink the coefficients toward zero for features that don't contribute much to reducing the error of the model. Some common techniques are ridge regression, LASSO (short for Least Absolute Shrinkage and Selection Operator) regression, and elastic net regression, which combines the LASSO and ridge penalty terms. Note that since these techniques rely on the magnitude of the coefficients, the data should be scaled beforehand.

Ridge regression, also called L2 regularization, punishes high coefficients () by adding the sum of the squares of the coefficients to the cost function (which regression looks to minimize when fitting), as per the following penalty term:

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