Data Science Projects with Python

封面

版权页

Preface

About

Chapter 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

Chapter 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

Chapter 3: Details of Logistic Regression and Feature Exploration

Introduction

Examining the Relationships between Features and the Response

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

Summary

Chapter 4: The Bias-Variance Trade-off

Introduction

Estimating the Coefficients and Intercepts of Logistic Regression

Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters

Summary

Chapter 5: Decision Trees and Random Forests

Introduction

Decision trees

Random Forests: Ensembles of Decision Trees

Summary

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

Introduction

Review of Modeling Results

Dealing with Missing Data: Imputation Strategies

Final Thoughts on Delivering the Predictive Model to the Client

Summary

Appendix

About

Chapter 1: Data Exploration and Cleaning

Chapter 2: Introduction to Scikit-Learn and Model Evaluation

Chapter 3: Details of Logistic Regression and Feature Exploration

Chapter 4: The Bias-Variance Trade-off

Chapter 5: Decision Trees and Random Forests