This project was created for the Developing Data Products course as part of the Data Science Specialization offered through Coursera from Johns Hopkins University.
The source code files for this project can be found on GitHub:
2024-08-28
This project was created for the Developing Data Products course as part of the Data Science Specialization offered through Coursera from Johns Hopkins University.
The source code files for this project can be found on GitHub:
The course project is a two part peer-graded assignment:
Create a Shiny application and deploy it on RStudio’s servers
Use Slidify or RStudio Presenter to prepare a reproducible pitch presentation about your application.
The name of the Shiny application developed for this project is the **Next Word Prediction App* and is hosted on RStudio’s shinyapps.io hosted service:
Create an algorithm for predicting the next word given one or more words as input using NLP
A large corpus of blog, news and twitter data was loaded and analyzed
N-grams were extracted from the corpus and then used for building the predictive model
Various methods of improving the prediction accuracy and speed were explored
N-gram model with stupid back-off strategy was used
Dataset was cleaned, lower-cased, removing links, twitter handles, punctuations, numbers and extra whitespaces, etc
Matrices from 6-gram to uni-gram were extracted using RWeka
Reduced size of model by dropping least frequent N-grams