Data Science Capstone
Gredy Garrido
3 August 2015
Executive Summary
For the Data Science Capstone project offered by Johns Hopkins University through Coursera, JHU partnered with SwiftKey a popular keyboard replacement app for mobile devices that uses predictive analytics to predict the next word a user types.
This presentation is part of the Data Science Capstone project. The objective of this project is to build a predictive text app such as SwiftKey. To build this engine, we use the data from HC Corpora which contains a large number of sentences.
For the Capstone project, the main goal is to build a Shiny application that is able to predict the next word.
Techniques Used
To create the word prediction app, we took the following steps:
- From the HC Corpora dataset, we cleaned the data and then tokenize the data into n-grams. We created bigrams, trigrams and quadgrams.
- These n-grams are then stored in term freqency matrices.
- The matrices are then used to do word prediction.
How to Use the App
When the app is launched, simply enter the text in the Enter your text here:.
The predicted next word will be shown immediately together with the tokenized and “cleaned” text and the number of words you entered.
Additional Information
The app is hosted on ShinyApps.io
The R Presentation is available at RPubs.
Source code for ui.R and server.R files and the word prediction app are available in my [GitHub repository](https://github.com/gredoy/.
The interim milestone report is available [here](http://rpubs.com/gredoy/.