The Applied Data Science Workshop - Second Edition

Spis treści książki

• The Applied Data Science Workshop
• Second Edition
• Preface
  • About the Book
    • Audience
    • About the Chapters
    • Conventions
    • Code Presentation
    • Setting up Your Environment
    • Installing Python
  • Installing Libraries
    • Working with JupyterLab and Jupyter Notebook
    • Accessing the Code Files
• 1. Introduction to Jupyter Notebooks
  • Introduction
  • Basic Functionality and Features of Jupyter Notebooks
    • What Is a Jupyter Notebook and Why Is It Useful?
    • Editing Notebooks with Jupyter Notebooks and JupyterLab
    • Exercise 1.01: Introducing Jupyter Notebooks
    • Exercise 1.02: Introducing the JupyterLab Platform
  • Jupyter Features
    • Exercise 1.03: Demonstrating the Core Jupyter Features
    • Converting a Jupyter Notebook into a Python Script
    • Python Libraries
    • Activity 1.01: Using Jupyter to Learn about pandas DataFrames
  • Summary
• 2. Data Exploration with Jupyter
  • Introduction
  • Our First Analysis the Boston Housing Dataset
    • Exercise 2.01: Importing Data Science Libraries and Setting Up the Notebook Plotting Environment
    • Loading the Data into Jupyter Using a pandas DataFrame
    • Exercise 2.02: Loading the Boston Housing Dataset
    • Data Exploration
    • Exercise 2.03: Analyzing the Boston Housing Dataset
    • Introduction to Predictive Analytics with Jupyter Notebooks
    • Exercise 2.04: Training Linear Models with Seaborn and scikit-learn
    • Using Categorical Features for Segmentation Analysis
    • Exercise 2.05: Creating Categorical Fields from Continuous Variables and Making Segmented Visualizations
    • Activity 2.01: Building a Third-Order Polynomial Model
  • Summary
• 3. Preparing Data for Predictive Modeling
  • Introduction
  • Machine Learning Process
  • Approaching Data Science Problems
  • Understanding Data from a Modeling Perspective
    • Preparing Data for Modeling
    • Exercise 3.01: Data Cleaning for Machine Learning with pandas
    • Exercise 3.02: Preparing Data for Machine Learning with pandas
  • Introducing the Human Resource Analytics Dataset
    • Activity 3.01: Preparing to Train a Predictive Model for Employee Retention
  • Summary
• 4. Training Classification Models
  • Introduction
  • Understanding Classification Algorithms
    • Exercise 4.01: Training Two-Feature Classification Models with scikit-learn
    • The plot_decision_regions Function
    • Exercise 4.02: Training k-Nearest Neighbors Classifiers with scikit-learn
    • Exercise 4.03: Training Random Forest Classifiers with scikit-learn
    • Activity 4.01: Training and Visualizing SVM Models with scikit-learn
  • Summary
• 5. Model Validation and Optimization
  • Introduction
  • Assessing Models with k-Fold Cross Validation
    • Tuning Hyperparameters with Validation Curves
    • Exercise 5.01: Using k-Fold Cross Validation and Validation Curves in Python with scikit-learn
  • Dimensionality Reduction with PCA
    • Exercise 5.02: Dimensionality Reduction with PCA
    • Model Training for Production
    • Exercise 5.03: Training a Production-Ready Model for Employee Turnover
    • Activity 5.01: Hyperparameter Tuning and Model Selection
  • Summary
• 6. Web Scraping with Jupyter Notebooks
  • Introduction
  • Internet Data Sources
  • Introduction to HTTP Requests
    • Making HTTP Requests with Python
    • Exercise 6.01: Using Python and Jupyter Notebooks to Make HTTP Requests
    • Making API Calls with Python
    • Exercise 6.02: Making API calls with Python and Jupyter Notebooks
    • Parsing HTML with Jupyter Notebooks
    • Exercise 6.03: Parsing HTML with Python and Jupyter Notebooks
    • Activity 6.01: Web Scraping with Jupyter Notebook
  • Data Workflow with pandas
    • Exercise 6.04: Processing Data for Analysis with pandas
    • Exercise 6.05: Merging Data with pandas
    • Activity 6.02: Analyzing Country Populations and Interest Rates
  • Summary
• Appendix
  • 1. Introduction to Jupyter Notebooks
    • Activity 1.01: Using Jupyter to Learn about pandas DataFrames
  • 2. Data Exploration with Jupyter
    • Activity 2.01: Building a Third-Order Polynomial Model
  • 3. Preparing Data for Predictive Modeling
    • Activity 3.01: Preparing to Train a Predictive Model for Employee Retention
  • 4. Training Classification Models
    • Activity 4.01: Training and Visualizing SVM Models with Scikit-learn
    • Solution:
  • 5. Model Validation and Optimization
    • Activity 5.01: Hyperparameter Tuning and Model Selection
  • 6. Web Scraping with Jupyter Notebooks
    • Activity 6.01: Web Scraping with Jupyter Notebook
    • Activity 6.02: Analyzing Country Populations and Interest Rates

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