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

Ensemble methods

Ensemble methods combine many models (often weak ones) to create a stronger one that will either minimize the average error between observed and predicted values (the bias) or improve how well it generalizes to unseen data (minimize the variance). We have to strike a balance between complex models that may increase variance, as they tend to overfit, and simple models that may have high bias, as these tend to underfit. This is called the bias-variance trade-off, which is illustrated in the following subplots:

Figure 10.11 – The bias-variance trade-off

Ensemble methods can be broken down into three categories: boosting, bagging, and stacking. Boosting trains many weak learners, which learn from each other's mistakes to reduce bias, making a stronger learner. Bagging, on the other hand, uses bootstrap aggregation to train many models on bootstrap samples of the data and aggregate the results together (using voting for classification...