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.