Peer-graded Assignment Final Project Submission

The goal of this exercise is to create a product to highlight the prediction algorithm that we 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. To see the shiny app, go here: https://ahmed-zard.shinyapps.io/prediction_algorithm/
2. A presentation consisting of no more than 5 slides created with R Studio Presenter pitching my algorithm and app as if i'm 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 can be found https://github.com/piotrpio2017/Peer-graded-Assignment-Final-Project-Submission
• 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