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

7. Test Set Analysis, Financial Insights, and Delivery to the Client

Overview

This chapter presents several techniques for analyzing a model test set for deriving insights into likely model performance in the future. These techniques include the same model performance metrics we've already calculated, such as the ROC AUC, as well as new kinds of visualizations, such as the sloping of default risk by bins of predicted probability and the calibration of predicted probability. After reading this chapter, you will be able to bridge the gap between the theoretical metrics of machine learning and the financial metrics of the business world. You will be able to identify key insights while estimating the financial impact of a model and provide guidance to the client on how to realize this impact. We close with a discussion of the key elements to consider when delivering and deploying a model, such as the format of delivery and ways to monitor the model as it is being used.

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