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
- Box Zoom tool
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
- 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
- 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
- dimensionality reduction techniques
G
- graphviz dependency / Training a Random Forest
H
- Hover Tool
- HTML
- parsing, in Jupyter Notebook / Subtopic C: Parsing HTML in the Jupyter Notebook, Parsing HTML with Python in a Jupyter Notebook
- HTTP methods
- 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
- k-fold cross validation algorithm
- k-Nearest Neighbors
L
- lab-style Notebooks
- Linear Discriminant Analysis (LDA)
- 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)
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
- platform, Jupyter Notebooks
- 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)
- 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
- requests
- about / Subtopic D: Python Libraries
- Requests library
- 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
T
- tab-separated variable (TSV) / Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame
- third-order polynomial model
U
V
- 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
X
- XML (eXtensible Markup Language) / Subtopic C: Parsing HTML in the Jupyter Notebook