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 10: Making Better Predictions – Optimizing Models

In the previous chapter, we learned how to build and evaluate our machine learning models. However, we didn't touch upon what we can do if we want to improve their performance. Of course, we could try out a different model and see if it performs better—unless there are requirements that we use a specific method for legal reasons or in order to be able to explain how it works. We want to make sure we use the best version of the model that we can, and for that, we need to discuss how to tune our models.

This chapter will introduce techniques for the optimization of machine learning model performance using scikit-learn, as a continuation of the content in Chapter 9, Getting Started with Machine Learning in Python. Nonetheless, it should be noted that there is no panacea. It is entirely possible to try everything we can think of and still have a model with little predictive value; such is the nature of modeling...