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Text prediction shiny application created for
Coursera-Johns Hopkins University Data Science Specialization Switftkey Capstone Project
Author: Lisa Rodgers
Date: March 16th, 2017
Woracle Predictions ======================================================== transition: rotate css:style.css
The aim of this application is to meet the need of a text predictive Shiny application which can predict the users next word with speed and accuracy with the emphasise on the latter.
“It will not matter how fast the app is, if it is inaccurate.”
The algorithm is based on a N-Gram model that was built from
a large corpora supplied by SwiftKey. The material was sourced from Twitter, News and Blogs.
#### Corpora Stats
| Total Size | Total Words |
|---|---|
| 28MB | 4,500,000 |
Shiny App Interface ======================================================== transition: rotate
Woracle uses a clean minimal user interface (UI) which access the data compression and predict algorithems working in the background.
The user enter text into the input box and waits for the next word to be
predicted.
Algorithm behind the app ========================================================
Fast and simple, the algorithm is build for accurancy first,
then speed. The average typist types 36WPM, the algorithm will work to meet that critiria.
##### Trigam model was utilized.