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Applied Data Science with Python and Jupyter

Name: Applied Data Science with Python and Jupyter
Author: Alex Galea
ISBN: 9781789958171

by Alex Galea

October 2018

Beginner

192 pages

3h 39m

English

Packt Publishing

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Preface
About the BookAbout the AuthorObjectivesAudienceApproachMinimum Hardware RequirementsSoftware RequirementsInstallation and SetupInstalling AnacondaUpdating Jupyter and Installing DependenciesAdditional ResourcesConventions
Jupyter Fundamentals
IntroductionBasic Functionality and FeaturesWhat is a Jupyter Notebook and Why is it Useful?Navigating the PlatformExercise 1: Introducing Jupyter NotebooksJupyter FeaturesExercise 2: Implementing Jupyter's Most Useful FeaturesConverting a Jupyter Notebook to a Python ScriptPython LibrariesExercise 3: Importing the External Libraries and Setting Up the Plotting EnvironmentOur First Analysis - The Boston Housing DatasetLoading the Data into Jupyter Using a Pandas DataFrameExercise 4: Loading the Boston Housing DatasetData ExplorationExercise 5: Analyzing the Boston Housing DatasetIntroduction to Predictive Analytics with Jupyter NotebooksExercise 6: Applying Linear Models With Seaborn and Scikit-learnActivity 1: Building a Third-Order Polynomial ModelUsing Categorical Features for Segmentation AnalysisExercise 7: Creating Categorical Fields From Continuous Variables and Make Segmented VisualizationsSummary
Data Cleaning and Advanced Machine Learning
IntroductionPreparing to Train a Predictive ModelDetermining a Plan for Predictive AnalyticsExercise 8: Explore Data Preprocessing Tools and MethodsActivity 2: Preparing to Train a Predictive Model for the Employee-Retention ProblemTraining Classification ModelsIntroduction to Classification AlgorithmsExercise 9: Training Two-Feature Classification Models With Scikit-learnThe plot_decision_regions FunctionExercise 10: Training K-nearest Neighbors for Our ModelExercise 11: Training a Random ForestAssessing Models With K-fold Cross-Validation and Validation CurvesExercise 12: Using K-fold Cross Validation and Validation Curves in Python With Scikit-learnDimensionality Reduction TechniquesExercise 13: Training a Predictive Model for the Employee Retention ProblemSummary
Web Scraping and Interactive Visualizations
IntroductionScraping Web Page DataIntroduction to HTTP RequestsMaking HTTP Requests in the Jupyter NotebookExercise 14: Handling HTTP Requests With Python in a Jupyter NotebookParsing HTML in the Jupyter NotebookExercise 15: Parsing HTML With Python in a Jupyter NotebookActivity 3: Web Scraping With Jupyter NotebooksInteractive VisualizationsBuilding a DataFrame to Store and Organize DataExercise 16: Building and Merging Pandas DataFramesIntroduction to BokehExercise 17: Introduction to Interactive Visualization With BokehActivity 4: Exploring Data with Interactive VisualizationsSummary
Appendix A
Chapter 1: Jupyter FundamentalsActivity 1: Building a Third-Order Polynomial ModelChapter 2: Data Cleaning and Advanced MachineActivity 2: Preparing to Train a Predictive Model for the Employee-Retention ProblemChapter 3: Web Scraping and Interactive VisualizationsActivity 3: Web Scraping with Jupyter NotebooksActivity 4: Exploring Data with Interactive Visualizations

Content preview from Applied Data Science with Python and Jupyter

Data Cleaning and Advanced Machine Learning

Learning Objectives

By the end of this chapter, you will be able to:

Plan a machine learning classification strategy
Preprocess data to prepare it for machine learning
Train classification models
Use validation curves to tune model parameters
Use dimensionality reduction to enhance model performance

In this chapter, you will learn data preprocessing and machine learning by completing several practical exercises.

Introduction

Consider a small food-delivery business that is looking to optimize their product. An analyst might look at the appropriate data and determine what type of food people are enjoying most. Perhaps they find a large amount of people are ordering the spiciest food options, ...

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Beginning Data Science with Python and Jupyter

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ISBN: 9781789958171

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