Index

B

• Bokeh
  • about / Subtopic D: Python Libraries, Subtopic B: Introduction to Bokeh
  • example / Subtopic B: Introduction to Bokeh
  • interactive visualizations, with / Introduction to interactive visualizations with Bokeh
• Boston housing dataset
  • about / Our First Analysis - The Boston Housing Dataset
  • loading, Pandas DataFrame / Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame, Load the Boston housing dataset
  • exploring / Explore Boston housing datasetexploringthe Boston housing dataset
• Box Zoom tool
  • about / Activity B: Exploring Data with Interactive Visualizations

C

• categorical fields
  • using, for segmentation analysis / Subtopic D: Using Categorical Features for Segmentation Analysis
  • creating / Create categorical fieldscreatingcategorical fields from continuous variables and make segmented visualizations
• classification algorithms
  • about / Subtopic A: Introduction to Classification Algorithms, Training two-feature classification models with scikit-learn
• comma-separated variable (CSV) / Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame

D

• data
  • exploring, with interactive visualizations / Activity B: Exploring Data with Interactive Visualizations
• data analysis, Jupyter
  • about / Our First Analysis - The Boston Housing Dataset
  • data loading, with Pandas DataFrame / Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame
• data exploration
  • about / Subtopic B: Data Exploration
  • performing / Explore Boston housing datasetexploringthe Boston housing dataset
• DataFrame
  • building, for storing and organizing data / Subtopic A: Building a DataFrame to Store and Organize Data
  • building / Building and merging Pandas DataFrames
  • merging / Building and merging Pandas DataFrames
• deliverable Notebooks
  • about / Subtopic A: What is a Jupyter Notebook and Why is it Useful?
• dimensionality reduction techniques
  • about / Subtopic C: Dimensionality Reduction Techniques, Training a predictive model for the employee retention problem

G

• graphviz dependency / Training a Random Forest

H

• Hover Tool
  • about / Introduction to interactive visualizations with Bokeh, Activity B: Exploring Data with Interactive Visualizations
• HTML
  • parsing, in Jupyter Notebook / Subtopic C: Parsing HTML in the Jupyter Notebook, Parsing HTML with Python in a Jupyter Notebook
• HTTP methods
  • GET / Subtopic A: Introduction to HTTP Requests
  • HEAD / Subtopic A: Introduction to HTTP Requests
  • POST / Subtopic A: Introduction to HTTP Requests
  • PUT / Subtopic A: Introduction to HTTP Requests
• HTTP requests
  • about / Subtopic A: Introduction to HTTP Requests
  • request header / Subtopic A: Introduction to HTTP Requests
  • HTTP methods / Subtopic A: Introduction to HTTP Requests
  • GET request / Subtopic A: Introduction to HTTP Requests
  • response types / Subtopic A: Introduction to HTTP Requests
  • making, in Jupyter Notebook / Subtopic B: Making HTTP Requests in the Jupyter Notebook
  • handling with Python, in Jupyter Notebook / Handling HTTP requests Jupyter NotebooksHTTP requests, handling with Pythonwith Python in a Jupyter Notebook

I

• interactive visualizations
  • benefits / Interactive Visualizations
  • with Bokeh / Introduction to interactive visualizations with Bokeh
• interactive visualizations, of scraped data
  • creating, Bokeh used / Activity B: Exploring Data with Interactive Visualizations

J

• Jupyter
  • about / Subtopic C: Jupyter Features
  • features / Subtopic C: Jupyter Features, Explore some of Jupyter's most useful features
  • magic commands / Explore some of Jupyter's most useful features
  • data analysis / Our First Analysis - The Boston Housing Dataset
• Jupyter Notebooks
  • fundamentals / Lesson Objectives, Lesson Objectives
  • features / Basic Functionality and Features, Subtopic A: What is a Jupyter Notebook and Why is it Useful?
  • about / Subtopic A: What is a Jupyter Notebook and Why is it Useful?
  • functionalities / Subtopic A: What is a Jupyter Notebook and Why is it Useful?
  • lab-style / Subtopic A: What is a Jupyter Notebook and Why is it Useful?
  • deliverable / Subtopic A: What is a Jupyter Notebook and Why is it Useful?
  • platform, navigating / Subtopic B: Navigating the Platform, Introducing Jupyter Notebooks
  • converting, to Python Script / Converting a Jupyter Notebook to a Python Script
  • plotting environment, setting up / Import Jupyter Notebooksplotting environment, setting upthe external libraries and set up the plotting environment
  • HTTP requests, making in / Subtopic B: Making HTTP Requests in the Jupyter Notebook
  • HTTP requests, handling with Python / Handling HTTP requests Jupyter NotebooksHTTP requests, handling with Pythonwith Python in a Jupyter Notebook
  • HTML, parsing / Subtopic C: Parsing HTML in the Jupyter Notebook, Parsing HTML with Python in a Jupyter Notebook
  • web scraping with / Activity A: Web Scraping with Jupyter Notebooks

K

• k-fold cross validation
  • about / Subtopic B: Assessing Models with k-Fold Cross-Validation and Validation Curves
• k-fold cross validation algorithm
  • about / Subtopic B: Assessing Models with k-Fold Cross-Validation and Validation Curves
• k-Nearest Neighbors
  • training / Training k-nearest neighbors fork-Nearest Neighborstraining our model, Training a Random Forest

L

• lab-style Notebooks
  • about / Subtopic A: What is a Jupyter Notebook and Why is it Useful?
• Linear Discriminant Analysis (LDA)
  • about / Subtopic C: Dimensionality Reduction Techniques
• LSTAT feature / Activity B: Building a Third-Order Polynomial Model
• LSTAT values / Activity B: Building a Third-Order Polynomial Model

M

• Matplotlib
  • about / Subtopic D: Python Libraries
• mean-squared error (MSE) / Activity B: Building a Third-Order Polynomial Model
• median house value (MEDV)
  • about / Subtopic C: Introduction to Predictive Analytics with Jupyter Notebooks

N

• NumPy
  • about / Subtopic D: Python Libraries

P

• Pandas
  • about / Subtopic D: Python Libraries
• Pandas DataFrame
  • used, for loading data in Jupyter / Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame, Load the Boston housing dataset
• pandas DataFrames
  • merging / Building and merging Pandas DataFrames
• Pan tool
  • about / Activity B: Exploring Data with Interactive Visualizations
• platform, Jupyter Notebooks
  • navigating / Subtopic B: Navigating the Platform, Introducing Jupyter Notebooks
• plot_decision_regions function
  • about / The plot_decision_regions Function
• predictive analytics, with Jupyter Notebooks
  • about / Linear models with Seaborn and scikit-learn
  • plan, determining / Subtopic A: Determining a Plan for Predictive Analytics
  • data, preparing for machine learning / Subtopic B: Preprocessing Data for Machine Learning, Explore data preprocessing tools and methods
• predictive model
  • preparing, for training / Preparing to Train a Predictive Model
  • preparing, to train Employee-Retention Problem / Activity A: Preparing to Train a Predictive Model for the Employee-Retention Problem
• predictive models
  • assessing, with k-Fold testing and validation curves / Subtopic B: Assessing Models with k-Fold Cross-Validation and Validation Curves, Using k-fold cross validation and validation curves in Python with scikit-learn
• Principal Component Analysis (PCA)
  • about / Subtopic C: Dimensionality Reduction Techniques
• Python Libraries
  • about / Subtopic D: Python Libraries
  • NumPy / Subtopic D: Python Libraries
  • Pandas / Subtopic D: Python Libraries
  • Matplotlib / Subtopic D: Python Libraries
  • Seaborn / Subtopic D: Python Libraries
  • Scikit-learn / Subtopic D: Python Libraries
  • requests / Subtopic D: Python Libraries
  • Bokeh / Subtopic D: Python Libraries

R

• random forest
  • training / Training k-nearest neighbors fork-Nearest Neighborstraining our model
• requests
  • about / Subtopic D: Python Libraries
• Requests library
  • about / Subtopic B: Making HTTP Requests in the Jupyter Notebook
• return on investment (ROI) metric / Subtopic D: Using Categorical Features for Segmentation Analysis

S

• Scikit-learn
  • about / Subtopic D: Python Libraries
• Seaborn
  • about / Subtopic D: Python Libraries
• segmentation analysis
  • categorical fields, using / Subtopic D: Using Categorical Features for Segmentation Analysis
• stratified k-fold
  • about / Subtopic B: Assessing Models with k-Fold Cross-Validation and Validation Curves

T

• tab-separated variable (TSV) / Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame
• third-order polynomial model
  • building / Activity B: Building a Third-Order Polynomial Model

U

• urllib / Subtopic B: Making HTTP Requests in the Jupyter Notebook

V

• validation curves
  • about / Subtopic B: Assessing Models with k-Fold Cross-Validation and Validation Curves

W

• web page data
  • scraping / Scraping Web Page Data
• web scraping
  • about / Scraping Web Page Data
  • with Jupyter Notebooks / Activity A: Web Scraping with Jupyter Notebooks
• Wheel Zoom tool
  • about / Activity B: Exploring Data with Interactive Visualizations

X

• XML (eXtensible Markup Language) / Subtopic C: Parsing HTML in the Jupyter Notebook