Data Science Projects with Python电子书

Preface

About the Book

About the Author

Objectives

Audience

Approach

Hardware Requirements

Software Requirements

Installation and Setup

Conventions

Chapter 1:

Data Exploration and Cleaning

Introduction

Python and the Anaconda Package Management System

Indexing and the Slice Operator

Exercise 1: Examining Anaconda and Getting Familiar with Python

Different Types of Data Science Problems

Loading the Case Study Data with Jupyter and pandas

Exercise 2: Loading the Case Study Data in a Jupyter Notebook

Getting Familiar with Data and Performing Data Cleaning

The Business Problem

Data Exploration Steps

Exercise 3: Verifying Basic Data Integrity

Boolean Masks

Exercise 4: Continuing Verification of Data Integrity

Exercise 5: Exploring and Cleaning the Data

Data Quality Assurance and Exploration

Exercise 6: Exploring the Credit Limit and Demographic Features

Deep Dive: Categorical Features

Exercise 7: Implementing OHE for a Categorical Feature

Exploring the Financial History Features in the Dataset

Activity 1: Exploring Remaining Financial Features in the Dataset

Summary

Chapter 2:

Introduction to Scikit-Learn and Model Evaluation

Introduction

Exploring the Response Variable and Concluding the Initial Exploration

Introduction to Scikit-Learn

Generating Synthetic Data

Data for a Linear Regression

Exercise 8: Linear Regression in Scikit-Learn

Model Performance Metrics for Binary Classification

Splitting the Data: Training and Testing sets

Classification Accuracy

True Positive Rate, False Positive Rate, and Confusion Matrix

Exercise 9: Calculating the True and False Positive and Negative Rates and Confusion Matrix in Python

Discovering Predicted Probabilities: How Does Logistic Regression Make Predictions?

Exercise 10: Obtaining Predicted Probabilities from a Trained Logistic Regression Model

The Receiver Operating Characteristic (ROC) Curve

Precision

Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve

Summary

Chapter 3:

Details of Logistic Regression and Feature Exploration

Introduction

Examining the Relationships between Features and the Response

Pearson Correlation

F-test

Exercise 11: F-test and Univariate Feature Selection

Finer Points of the F-test: Equivalence to t-test for Two Classes and Cautions

Hypotheses and Next Steps

Exercise 12: Visualizing the Relationship between Features and Response

Univariate Feature Selection: What It Does and Doesn't Do

Understanding Logistic Regression with function Syntax in Python and the Sigmoid Function

Exercise 13: Plotting the Sigmoid Function

Scope of Functions

Why is Logistic Regression Considered a Linear Model?

Exercise 14: Examining the Appropriateness of Features for Logistic Regression

From Logistic Regression Coefficients to Predictions Using the Sigmoid

Exercise 15: Linear Decision Boundary of Logistic Regression

Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients

Summary

Chapter 4:

The Bias-Variance Trade-off

Introduction

Estimating the Coefficients and Intercepts of Logistic Regression

Gradient Descent to Find Optimal Parameter Values

Exercise 16: Using Gradient Descent to Minimize a Cost Function

Assumptions of Logistic Regression

The Motivation for Regularization: The Bias-Variance Trade-off

Exercise 17: Generating and Modeling Synthetic Classification Data

Lasso (L1) and Ridge (L2) Regularization

Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters

Exercise 18: Reducing Overfitting on the Synthetic Data Classification Problem

Options for Logistic Regression in Scikit-Learn

Scaling Data, Pipelines, and Interaction Features in Scikit-Learn

Activity 4: Cross-Validation and Feature Engineering with the Case Study Data

Summary

Chapter 5:

Decision Trees and Random Forests

Introduction

Decision trees

The Terminology of Decision Trees and Connections to Machine Learning

Exercise 19: A Decision Tree in scikit-learn

Training Decision Trees: Node Impurity

Features Used for the First splits: Connections to Univariate Feature Selection and Interactions

Training Decision Trees: A Greedy Algorithm

Training Decision Trees: Different Stopping Criteria

Using Decision Trees: Advantages and Predicted Probabilities

A More Convenient Approach to Cross-Validation

Exercise 20: Finding Optimal Hyperparameters for a Decision Tree

Random Forests: Ensembles of Decision Trees

Random Forest: Predictions and Interpretability

Exercise 21: Fitting a Random Forest

Checkerboard Graph

Activity 5: Cross-Validation Grid Search with Random Forest

Summary

Chapter 6:

Imputation of Missing Data, Financial Analysis, and Delivery to Client

Introduction

Review of Modeling Results

Dealing with Missing Data: Imputation Strategies

Preparing Samples with Missing Data

Exercise 22: Cleaning the Dataset

Exercise 23: Mode and Random Imputation of PAY_1

A Predictive Model for PAY_1

Exercise 24: Building a Multiclass Classification Model for Imputation

Using the Imputation Model and Comparing it to Other Methods

Confirming Model Performance on the Unseen Test Set

Financial Analysis

Financial Conversation with the Client

Exercise 25: Characterizing Costs and Savings

Activity 6: Deriving Financial Insights

Final Thoughts on Delivering the Predictive Model to the Client

Summary

Appendix

Chapter 1: Data Exploration and Cleaning

Activity 1: Exploring Remaining Financial Features in the Dataset

Chapter 2: Introduction to Scikit-Learn and Model Evaluation

Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve

Chapter 3: Details of Logistic Regression and Feature Exploration

Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients

Chapter 4: The Bias-Variance Trade-off

Activity 4: Cross-Validation and Feature Engineering with the Case Study Data

Chapter 5: Decision Trees and Random Forests

Activity 5: Cross-Validation Grid Search with Random Forest

Chapter 6: Imputation of Missing Data, Financial Analysis, and Delivery to Client

Activity 6: Deriving Financial Insights