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 9: Getting Started with Machine Learning in Python

This chapter will expose us to the vernacular of machine learning and the common tasks that machine learning can be used to solve. Afterward, we will learn how we can prepare our data for use in machine learning models. We have discussed data cleaning already, but only for human consumption—machine learning models require different preprocessing (cleaning) techniques. There are quite a few nuances here, so we will take our time with this topic and discuss how we can use scikit-learn to build preprocessing pipelines that streamline this procedure, since our models will only be as good as the data they are trained on.

Next, we will walk through how we can use scikit-learn to build a model and evaluate its performance. Scikit-learn has a very user-friendly API, so once we know how to build one model, we can build any number of them. We won't be going into any of the mathematics behind the models; there are entire...