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

Data Quality Assurance and Exploration

So far, we remedied two data quality issues just by asking basic questions or by looking at the .info() summary. Let's now take a look at the first few columns of data. Before we get to the historical bill payments, we have the credit limits of the LIMIT_BAL accounts, and the SEX, EDUCATION, MARRIAGE, and AGE demographic features. Our business partner has reached out to us, to let us know that gender should not be used to predict credit-worthiness, as this is unethical by their standards. So we keep this in mind for future reference. Now we'll explore the rest of these columns, making any corrections that are necessary.

In order to further explore the data, we will use histograms. Histograms are a good way to visualize data that is on a continuous scale, such as currency amounts and ages. A histogram groups similar values into bins and shows the number of data points in these bins as a bar graph.

To plot histograms, we will start...