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)


As we saw in the previous chapter, decision trees and ensemble models based on them provide powerful methods for creating machine learning models. While random forests have been around for decades, recent work on a different kind of tree ensemble, gradient boosted trees, has resulted in state-of-the-art models that have come to dominate the landscape of predictive modeling with tabular data, or data that is organized into a structured table, similar to the case study data. The two main packages used by machine learning data scientists today to create the most accurate predictive models with tabular data are XGBoost and LightGBM. In this chapter, we'll become familiar with XGBoost using a synthetic dataset, and then apply it to the case study data in the activity.


Perhaps some of the best motivation for using XGBoost comes from the paper describing this machine learning system, in the context of Kaggle, a popular online forum for machine learning competitions...