Gregorio Ambrosio Cestero
July 2016
A text prediction app that takes as input a phrase (multiple words) in a text box input and outputs a prediction of the next word.
Word Predictor, yet another Data Science Capstone Project by Gregorio Ambrosio
Word Predictor is a shiny application that runs on shinyapps.io and takes as input a phrase (multiple words) in a text box input and outputs a prediction of the next word. It predicts the most likely next word, based on frequently occurring phrases (n-grams).
For prediction model a model based on Naive Bayes classifier was built.
The apps is based in Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction. The Naive Bayes model involves a simplifying conditional independence assumption. That is given a class (positive or negative), the words are conditionally independent of each other. This assumption does not affect the accuracy in text classification by much but makes really fast classification algorithms applicable for the problem.