book

The Applied Data Science Workshop - Second Edition

Name: The Applied Data Science Workshop - Second Edition
ISBN: 9781800202504

by Alex Galea, Paul Van Branteghem, Guillermina Bea j, Shovon Sengupta, Karen Yang

July 2020

Beginner

352 pages

5h 58m

English

Packt Publishing

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The Applied Data Science Workshop
Second Edition
Preface
About the BookAudienceAbout the ChaptersConventionsCode PresentationSetting up Your EnvironmentInstalling PythonInstalling LibrariesWorking with JupyterLab and Jupyter NotebookAccessing the Code Files
1. Introduction to Jupyter Notebooks
IntroductionBasic Functionality and Features of Jupyter NotebooksWhat Is a Jupyter Notebook and Why Is It Useful?Editing Notebooks with Jupyter Notebooks and JupyterLabExercise 1.01: Introducing Jupyter NotebooksExercise 1.02: Introducing the JupyterLab PlatformJupyter FeaturesExercise 1.03: Demonstrating the Core Jupyter FeaturesConverting a Jupyter Notebook into a Python ScriptPython LibrariesActivity 1.01: Using Jupyter to Learn about pandas DataFramesSummary
2. Data Exploration with Jupyter
IntroductionOur First Analysis – the Boston Housing DatasetExercise 2.01: Importing Data Science Libraries and Setting Up the Notebook Plotting EnvironmentLoading the Data into Jupyter Using a pandas DataFrameExercise 2.02: Loading the Boston Housing DatasetData ExplorationExercise 2.03: Analyzing the Boston Housing DatasetIntroduction to Predictive Analytics with Jupyter NotebooksExercise 2.04: Training Linear Models with Seaborn and scikit-learnUsing Categorical Features for Segmentation AnalysisExercise 2.05: Creating Categorical Fields from Continuous Variables and Making Segmented VisualizationsActivity 2.01: Building a Third-Order Polynomial ModelSummary
3. Preparing Data for Predictive Modeling
IntroductionMachine Learning ProcessApproaching Data Science ProblemsUnderstanding Data from a Modeling PerspectivePreparing Data for ModelingExercise 3.01: Data Cleaning for Machine Learning with pandasExercise 3.02: Preparing Data for Machine Learning with pandasIntroducing the Human Resource Analytics DatasetActivity 3.01: Preparing to Train a Predictive Model for Employee RetentionSummary
4. Training Classification Models
IntroductionUnderstanding Classification Algorithms Exercise 4.01: Training Two-Feature Classification Models with scikit-learnThe plot_decision_regions FunctionExercise 4.02: Training k-Nearest Neighbors Classifiers with scikit-learnExercise 4.03: Training Random Forest Classifiers with scikit-learnActivity 4.01: Training and Visualizing SVM Models with scikit-learnSummary
5. Model Validation and Optimization
IntroductionAssessing Models with k-Fold Cross ValidationTuning Hyperparameters with Validation CurvesExercise 5.01: Using k-Fold Cross Validation and Validation Curves in Python with scikit-learnDimensionality Reduction with PCAExercise 5.02: Dimensionality Reduction with PCAModel Training for ProductionExercise 5.03: Training a Production-Ready Model for Employee TurnoverActivity 5.01: Hyperparameter Tuning and Model SelectionSummary
6. Web Scraping with Jupyter Notebooks
IntroductionInternet Data SourcesIntroduction to HTTP RequestsMaking HTTP Requests with PythonExercise 6.01: Using Python and Jupyter Notebooks to Make HTTP RequestsMaking API Calls with PythonExercise 6.02: Making API calls with Python and Jupyter NotebooksParsing HTML with Jupyter NotebooksExercise 6.03: Parsing HTML with Python and Jupyter NotebooksActivity 6.01: Web Scraping with Jupyter NotebookData Workflow with pandasExercise 6.04: Processing Data for Analysis with pandasExercise 6.05: Merging Data with pandasActivity 6.02: Analyzing Country Populations and Interest RatesSummary
Appendix
1. Introduction to Jupyter NotebooksActivity 1.01: Using Jupyter to Learn about pandas DataFrames2. Data Exploration with JupyterActivity 2.01: Building a Third-Order Polynomial Model3. Preparing Data for Predictive ModelingActivity 3.01: Preparing to Train a Predictive Model for Employee Retention4. Training Classification ModelsActivity 4.01: Training and Visualizing SVM Models with Scikit-learnSolution:5. Model Validation and OptimizationActivity 5.01: Hyperparameter Tuning and Model Selection6. Web Scraping with Jupyter NotebooksActivity 6.01: Web Scraping with Jupyter NotebookActivity 6.02: Analyzing Country Populations and Interest Rates

Content preview from The Applied Data Science Workshop - Second Edition

4. Training Classification Models

Overview

In this chapter, you will learn about algorithms such as Support Vector Machines, Random Forests, and k-Nearest Neighbors classifiers. While training and comparing a variety of models, you'll learn about the concept of overfitting with the help of decision boundary charts. By the end of this chapter, you will be able to use scikit-learn to apply these algorithms in order to train models for a real-world classification problem.

Introduction

In the previous chapters, we walked through the steps that we need to take in a data science project before we can train a machine learning model. This included the planning phase, that is, identifying business problems, assessing data sources for suitability, ...

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The Applied Data Science Workshop - Second Edition

by Alex Galea, Paul Van Branteghem, Guillermina Bea j, Shovon Sengupta, Karen Yang

4. Training Classification Models

Introduction

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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