Book Image

Hands-On Predictive Analytics with Python

By : Alvaro Fuentes
Book Image

Hands-On Predictive Analytics with Python

By: Alvaro Fuentes

Overview of this book

Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.
Table of Contents (11 chapters)

Evaluation for classification models

So far, we have been using accuracy as the default metric for evaluating classification models. We used it because it is the most intuitive metric—it is just the proportion of cases correctly predicted by the classifier. So, an accuracy of 0.75 (or 75%) means that, on average, we should expect the classifier to make an accurate prediction 75% of the time. Although sometimes useful, this metric is very limited. Evaluating a classifier, even a binary classifier such as the one we are working with in the credit card default problem, is tricky.
In this section, we examine how we can make a more detailed evaluation of a binary classification model.
We will use the credit card default dataset again, so we need to load and prepare everything again for this chapter. Let's run the code we have in one of the notebooks for this chapter. As...