Beginning Data Science with Python and Jupyter电子书

Table of Contents

Beginning Data Science with Python and Jupyter

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PacktPub.com

Contributors

About the author

About the reviewer

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Preface

What This Book Covers

What You Need for This Book

Installation and Setup

Installing Anaconda

Updating Jupyter and Installing Dependencies

Who This Book is for

Conventions

Note

Tip

Reader Feedback

Customer Support

Downloading the Example Code

Errata

Piracy

Questions

Chapter 1. Jupyter Fundamentals

Lesson Objectives

Note

Basic Functionality and Features

Subtopic A: What is a Jupyter Notebook and Why is it Useful?

Subtopic B: Navigating the Platform

Introducing Jupyter Notebooks

Note

Note

Note

Subtopic C: Jupyter Features

Note

Explore some of Jupyter's most useful features

Converting a Jupyter Notebook to a Python Script

Subtopic D: Python Libraries

Note

Import the external libraries and set up the plotting environment

Our First Analysis - The Boston Housing Dataset

Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame

Note

Load the Boston housing dataset

Note

Note

Subtopic B: Data Exploration

Explore the Boston housing dataset

Note

Subtopic C: Introduction to Predictive Analytics with Jupyter Notebooks

Linear models with Seaborn and scikit-learn

Note

Note

Activity B: Building a Third-Order Polynomial Model

Note

Subtopic D: Using Categorical Features for Segmentation Analysis

Create categorical fields from continuous variables and make segmented visualizations

Note

Note

Summary

Chapter 2. Data Cleaning and Advanced Machine Learning

Preparing to Train a Predictive Model

Note

Subtopic A: Determining a Plan for Predictive Analytics

Subtopic B: Preprocessing Data for Machine Learning

Note

Explore data preprocessing tools and methods

Activity A: Preparing to Train a Predictive Model for the Employee-Retention Problem

Note

Note

Training Classification Models

Subtopic A: Introduction to Classification Algorithms

Note

Training two-feature classification models with scikit-learn

Note

The plot_decision_regions Function

Training k-nearest neighbors for our model

Note

Training a Random Forest

Note

Subtopic B: Assessing Models with k-Fold Cross-Validation and Validation Curves

Note

Note

Using k-fold cross validation and validation curves in Python with scikit-learn

Note

Note

Subtopic C: Dimensionality Reduction Techniques

Training a predictive model for the employee retention problem

Note

Note

Note

Note

Summary

Chapter 3. Web Scraping and Interactive Visualizations

Lesson Objectives

Scraping Web Page Data

Subtopic A: Introduction to HTTP Requests

Subtopic B: Making HTTP Requests in the Jupyter Notebook

Note

Handling HTTP requests with Python in a Jupyter Notebook

Subtopic C: Parsing HTML in the Jupyter Notebook

Parsing HTML with Python in a Jupyter Notebook

Note

Activity A: Web Scraping with Jupyter Notebooks

Note

Interactive Visualizations

Subtopic A: Building a DataFrame to Store and Organize Data

Building and merging Pandas DataFrames

Note

Note

Subtopic B: Introduction to Bokeh

Note

Introduction to interactive visualizations with Bokeh

Note

Activity B: Exploring Data with Interactive Visualizations

Note

Summary

Index

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