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)

Final Thoughts on Delivering a Predictive Model to the Client

We have now completed the modeling activities and also created a financial analysis to indicate to the client how they can use the model. While we have completed the essential intellectual contributions that are the data scientist's responsibility, it is necessary to agree with the client on the form in which all these contributions will be delivered.

A key contribution is the predictive capability embodied in the trained model. Assuming the client can work with the trained model object we created with XGBoost, this model could be saved to disk as we've done and sent to the client. Then, the client would be able to use it within their workflow. This pathway to model delivery may require the data scientist to work with engineers in the client's organization, to deploy the model within the client's infrastructure.

Alternatively, it may be necessary to express the model as a mathematical equation...