The Data Science Workshop

封面

版权信息

Preface

About the Book

1. Introduction to Data Science in Python

Introduction

Application of Data Science

Overview of Python

Python for Data Science

Scikit-Learn

Summary

2. Regression

Introduction

Simple Linear Regression

Multiple Linear Regression

Conducting Regression Analysis Using Python

Multiple Regression Analysis

Assumptions of Regression Analysis

Explaining the Results of Regression Analysis

Summary

3. Binary Classification

Introduction

Understanding the Business Context

Feature Engineering

Data-Driven Feature Engineering

Correlation Matrix and Visualization

Summary

4. Multiclass Classification with RandomForest

Introduction

Training a Random Forest Classifier

Evaluating the Model's Performance

Maximum Depth

Minimum Sample in Leaf

Maximum Features

Summary

5. Performing Your First Cluster Analysis

Introduction

Clustering with k-means

Interpreting k-means Results

Choosing the Number of Clusters

Initializing Clusters

Calculating the Distance to the Centroid

Standardizing Data

Summary

6. How to Assess Performance

Introduction

Splitting Data

Assessing Model Performance for Regression Models

Assessing Model Performance for Classification Models

The Confusion Matrix

Receiver Operating Characteristic Curve

Area Under the ROC Curve

Saving and Loading Models

Summary

7. The Generalization of Machine Learning Models

Introduction

Overfitting

Underfitting

Data

Random State

Cross-Validation

cross_val_score

LogisticRegressionCV

Hyperparameter Tuning with GridSearchCV

Hyperparameter Tuning with RandomizedSearchCV

Model Regularization with Lasso Regression

Ridge Regression

Summary

8. Hyperparameter Tuning

Introduction

What Are Hyperparameters?

Finding the Best Hyperparameterization

Tuning Using Grid Search

GridSearchCV

Random Search

Summary

9. Interpreting a Machine Learning Model

Introduction

Linear Model Coefficients

RandomForest Variable Importance

Variable Importance via Permutation

Partial Dependence Plots

Local Interpretation with LIME

Summary

10. Analyzing a Dataset

Introduction

Exploring Your Data

Analyzing Your Dataset

Analyzing the Content of a Categorical Variable

Summarizing Numerical Variables

Visualizing Your Data

Boxplots

Summary

11. Data Preparation

Introduction

Handling Row Duplication

Converting Data Types

Handling Incorrect Values

Handling Missing Values

Summary