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

6. Gradient Boosting, XGBoost, and SHAP Values

Activity 6.01: Modeling the Case Study Data with XGBoost and Explaining the Model with SHAP 

Solution:

In this activity, we'll take what we've learned in this chapter with a synthetic dataset and apply it to the case study data. We'll see how an XGBoost model performs on a validation set and explain the model predictions using SHAP values. We have prepared the dataset for this activity by replacing the samples that had missing values for the PAY_1 feature, that we had previously ignored, while maintaining the same train/test split for the samples with no missing values. You can see how the data was prepared in the Appendix to the notebook for this activity.

  1. Load the case study data that has been prepared for this exercise. The file path is ../../Data/Activity_6_01_data.pkl and the variables are: features_response, X_train_all, y_train_all, X_test_all, y_test_all...