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 previous chapter, you became familiar with basic Python and then learned about the pandas tool for data exploration. Using Python and pandas, you performed operations such as loading a dataset, verifying data integrity, and performing exploratory analysis of the features, or independent variables, in the data.

In this chapter, we will finish our exploration of the data by examining the response variable. After we've concluded that the data is of high quality and makes sense, we will be ready to move forward with developing machine learning models. We will take our first steps with scikit-learn, one of the most popular machine learning packages available in the Python language. Before learning the details of how mathematical models work in the next chapter, here we'll start to get comfortable with the syntax for using them in scikit-learn.

We will also learn some common techniques for answering the question, "Is this model good or not?" There are many possible ways to approach model evaluation. For business applications, a financial analysis to determine the value that could be created by a model is an important way to understand the potential impact of your work. Usually, it's best to scope the business opportunity of a project at the very beginning. However, as the emphasis of this book is on machine learning and predictive modeling, we will demonstrate a financial analysis in the final chapter.

There are several important model evaluation criteria that are considered standard knowledge in data science and machine learning. We will cover a few of the most widely used classification model performance metrics here.