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)


In the previous chapter, we developed a few example machine learning models using scikit-learn, to get familiar with how it works. However, the features we used, EDUCATION and LIMIT_BAL, were not chosen in a systematic way.

In this chapter, we will start to develop techniques that can be used to assess features for their usefulness in modeling. This will enable you to make a quick pass over all candidate features, to have an idea of which will be the most important. For the most promising features, we will see how to create visual summaries that serve as useful communication tools.

Next, we will begin our detailed examination of logistic regression. We'll learn why logistic regression is considered to be a linear model, even if the formulation involves some non-linear functions. We'll learn what a decision boundary is and see that as a key consequence of its linearity, the decision boundary of logistic regression could make it difficult to accurately classify...