author: Zvonko Kosic
Next Word Prediction Application
Comprehensive and brief presentation of the Coursera Data Science Capstone Project in cooperation with the John Hopkins University and SwiftKey.
The objective of this project is to build a predictive text model and demonstrate the build algorithm in form of a shiny application.
The data that was used for the model comes from a corpus called HC Corpora.
A data science approach (data cleaning, exploratory analysis, ...) was used to create the application.
Last but not least it should be mentioned that the application/algorithm/model was entirely developed in the R language ecosystem.
The developed application for word prediction is based on a classic N-Gram model.
The input for the N-Gram model is a cleaned (lowercase, remmoving punctuation, numbers, ....) subset of the supplied data (blogs, twitter and news)
The sample was tokenized into N-Grams, namely unigram, bigram, trigram and quadgrams sorted by frequency.
These new data structures were then used to predict the next word depending on the input.
The application is a simple chat were the user can enter his message and the app suggests, predicts the next three words beginning with the best match (1).
Additionally to the predicted words, the application uses autocompletion to
reduce typing as seen in (2).
The chat app is hosted on shinyapps.io: https://zvonkok.shinyapps.io/CourseraDataScienceCapstone/
On the shinapps.io website is a brief description how the app works and how to use the features.
The Rpubs presentation is located here: http://rpubs.com/zvonkok/CourseraDataScienceCapstone