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

Summary

In this chapter, we've learned some of the most cutting-edge techniques for building machine learning models with tabular data. While other types of data, such as image or text data, warrant exploration with different types of models such as neural networks, many standard business applications leverage tabular data. XGBoost and SHAP are some of the most advanced and popular tools you can use to build and understand models with this kind of data. Having gained familiarity and practical experience using these tools with synthetic data, in the following activity, we return to the dataset for the case study and see how we can use XGBoost to model it, including the samples with missing feature values, and use SHAP values to understand the model.