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

Summary

In this chapter, you learned several analysis techniques to provide insight into model performance, such as decile and equal-interval charts of default rate by model prediction bin, as well as how to investigate the quality of model calibration. It's good to derive these insights, as well as calculate metrics such as the ROC AUC, using the model test set, since this is intended to represent how the model might perform in the real world on new data.

We also saw how to go about conducting a financial analysis of model performance. While we left this to the end of the book, an understanding of the costs and savings going along with the decisions to be guided by the model should be understood from the beginning of a typical project. These allow the data scientist to work toward a tangible goal in terms of increased profit or savings. A key step in this process, for binary classification models, is to choose a threshold of predicted probability at which to declare a positive...