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

Summary

In this chapter, we introduced the final details of logistic regression and continued to understand how to use scikit-learn to fit logistic regression models. We gained more visibility into how the model fitting process works by learning about the concept of a cost function, which is minimized by the gradient descent procedure to estimate parameters during model fitting.

We also learned of the need for regularization by introducing the concepts of underfitting and overfitting. In order to reduce overfitting, we saw how to adjust the cost function to regularize the coefficients of a logistic regression model using an L1 or L2 penalty. We used cross-validation to select the amount of regularization by tuning the regularization hyperparameter. To reduce underfitting, we saw how to do some simple feature engineering with interaction features for the case study data.

We are now familiar with some of the most important concepts in machine learning. We have, so far, only used...

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