Introduction

The goal of this exercise is to create a product to highlight the prediction algorithm that you have built and to provide an interface that can be accessed by others. For this project you must submit:

• A Shiny app that takes as input a phrase (multiple words) in a text box input and outputs a prediction of the next word.

• A slide deck consisting of no more than 5 slides created with R Studio Presenter (https://support.rstudio.com/hc/en-us/articles/200486468-Authoring-R-Presentations) pitching your algorithm and app as if you were presenting to your boss or an investor.

• Loading Libraries - First step of the project is to load all the libraries necessary to complete all the tasks outlined in the introduction.

• Loading Data - The data used in this project downloaded from Coursera-SwiftKey dataset including News and Twitter examples to feed the model

• Creating a Data Sample - The sample datasets consists 0f 411197 wordcount in total.

• Cleaning Data - Convert all text to lowercase, remove all punctuation, numbers, whitespace and “english” stop words

• Creating the corresponding n-gram frequencies

• Saving n-grams as .rds files

• N-gram model used ( from 1 to 4 n-gram )

• If no match is found in any of the 4 n-grams, the algorimth indicates that the sample is too small

• Stupid back-off strategy implemended

• Create ngram models feeded with the generated Corpus

• Use ngram models to predict the next word

• User inputs a word into the app interface

• The app then checks the word against the prediction algorithm

• The next word is proactively predicted

• This prediction is based from longest to shortest N-gram frequency

• Prediction is displayed

• The Shiny app provided: https://sola1991.shinyapps.io/SingleWordPrediction/