更新时间：2021-07-02 21:07:43
coverpage
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
Getting Started with Predictive Analytics
Predictive analytics are in so many industries
Predictive Analytics in marketing
Predictive Analytics in healthcare
Predictive Analytics in other industries
Skills and roles that are important in Predictive Analytics
Related job skills and terms
Predictive analytics software
Open source software
Closed source software
Peaceful coexistence
Other helpful tools
Past the basics
Data analytics/research
Data engineering
Management
Team data science
Two different ways to look at predictive analytics
R
CRAN
R installation
Alternate ways of exploring R
How is a predictive analytics project organized?
Setting up your project and subfolders
GUIs
Getting started with RStudio
Rearranging the layout to correspond with the examples
Brief description of some important panes
Creating a new project
The R console
The source window
Creating a new script
Our first predictive model
Code description
Saving the script
Your second script
The predict function
Examining the prediction errors
R packages
The stargazer package
Installing stargazer package
Saving your work
References
Summary
The Modeling Process
Advantages of a structured approach
Ways in which structured methodologies can help
Analytic process methodologies
CRISP-DM and SEMMA
CRISP-DM and SEMMA chart
Agile processes
Six sigma and root cause
To sample or not to sample?
Using all of the data
Comparing a sample to the population
An analytics methodology outline – specific steps
Step 1 business understanding
Communicating business goals – the feedback loop
Internal data
External data
Tools of the trade
Process understanding
Data lineage
Data dictionaries
SQL
Example – Using SQL to get sales by region
Charts and plots
Spreadsheets
Simulation
Example – simulating if a customer contact will yield a sale
Example – simulating customer service calls
Step 2 data understanding
Levels of measurement
Nominal data
Ordinal data
Interval data
Ratio data
Converting from the different levels of measurement
