Peer-graded Assignment: Final Project Submission

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:

1. A Shiny app that takes as input a phrase (multiple words) in a text box input and outputs a prediction of the next word.
2. 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.

To see the shiny app, go here: https://ninja555.shinyapps.io/FINAL/

• 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 can be found here
• Summarizing Data Files - create a very basic overview of the data file statistics in terms of File Names, File Sizes, Number of Rows in each file as well as word count.
• Creating a Data Sample - 1000 lines from each file were sampled. Total sample size will be 2000.
• 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
• Important: Sample size was limited to 2000 due to relatively small processing power of the pc that was used

• 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 longerst to shortest N-gram frequency
• Prediction is displayed