Book Image

Data Science Projects with Python - Second Edition

By : Stephen Klosterman
Book Image

Data Science Projects with Python - Second Edition

By: Stephen Klosterman

Overview of this book

If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable. In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects. You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest. Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.
Table of Contents (9 chapters)
Preface

Introduction

In the last two chapters, we have gained a thorough understanding of the workings of logistic regression. We have also gotten a lot of experience with using the scikit-learn package in Python to create logistic regression models.

In this chapter, we will introduce a powerful type of predictive model that takes a completely different approach from the logistic regression model: decision trees. Decision trees and the models based on them are some of the most performant models available today for general machine learning applications. The concept of using a tree process to make decisions is simple, and therefore, decision tree models are easy to interpret. However, a common criticism of decision trees is that they overfit to the training data. In order to remedy this issue, researchers have developed ensemble methods, such as random forests, that combine many decision trees to work together and make better predictions than any individual tree could.

We will see that...