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.

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Table of Contents (9 chapters)
Preface
About the Book
1
1. Data Exploration and Cleaning
1. Data Exploration and Cleaning
Introduction
Python and the Anaconda Package Management System
Different Types of Data Science Problems
Loading the Case Study Data with Jupyter and pandas
Data Quality Assurance and Exploration
Exploring the Financial History Features in the Dataset
Summary
Free Chapter
2
2. Introduction to Scikit-Learn and Model Evaluation
2. Introduction to Scikit-Learn and Model Evaluation
Introduction
Exploring the Response Variable and Concluding the Initial Exploration
Introduction to Scikit-Learn
Model Performance Metrics for Binary Classification
Summary
3
3. Details of Logistic Regression and Feature Exploration
3. Details of Logistic Regression and Feature Exploration
Introduction
Examining the Relationships Between Features and the Response Variable
Univariate Feature Selection: What it Does and Doesn't Do
Summary
4
4. The Bias-Variance Trade-Off
4. The Bias-Variance Trade-Off
Introduction
Estimating the Coefficients and Intercepts of Logistic Regression
Cross-Validation: Choosing the Regularization Parameter
Summary
5
5. Decision Trees and Random Forests
5. Decision Trees and Random Forests
Introduction
Decision Trees
Random Forests: Ensembles of Decision Trees
Summary
6
6. Gradient Boosting, XGBoost, and SHAP Values
6. Gradient Boosting, XGBoost, and SHAP Values
Introduction
Gradient Boosting and XGBoost
XGBoost Hyperparameters
Another Way of Growing Trees: XGBoost's grow_policy
Explaining Model Predictions with SHAP Values
Missing Data
Summary
7
7. Test Set Analysis, Financial Insights, and Delivery to the Client
7. Test Set Analysis, Financial Insights, and Delivery to the Client
Introduction
Review of Modeling Results
Model Performance on the Test Set
Financial Analysis
Final Thoughts on Delivering a Predictive Model to the Client
Summary
Appendix
1. Data Exploration and Cleaning
2. Introduction to Scikit-Learn and Model Evaluation
3. Details of Logistic Regression and Feature Exploration
4. The Bias-Variance Trade-Off
5. Decision Trees and Random Forests
6. Gradient Boosting, XGBoost, and SHAP Values
7. Test Set Analysis, Financial Insights, and Delivery to the Client

Different Types of Data Science Problems

Much of your time as a data scientist is likely to be spent wrangling data: figuring out how to get it, getting it, examining it, making sure it's correct and complete, and joining it with other types of data. pandas is a widely used tool for data analysis in Python, and it can facilitate the data exploration process for you, as we will see in this chapter. However, one of the key goals of this book is to start you on your journey to becoming a machine learning data scientist, for which you will need to master the art and science of predictive modeling. This means using a mathematical model, or idealized mathematical formulation, to learn relationships within the data, in the hope of making accurate and useful predictions when new data comes in.

For predictive modeling use cases, data is typically organized in a tabular structure, with features and a response variable. For example, if you want to predict the price of a house based on...

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