This project demonstrates a data product focused on Natural Language Processing. The application predicts the most likely next word in an English phrase based on a simple statistical language model. The model uses cleaned text data to create bigram frequency tables and returns the most probable next word for user input. The goal is to provide a fast, interactive prototype that showcases how predictive text systems are designed.

This is the reproducible pitch presentation for my BMI Calculator Shiny App created for the Course Project of Developing Data Products. It explains the motivation, features, server logic, and usage of the application.

This report analyzes data collected from accelerometer sensors on the belt, arm, forearm, and dumbbell while subjects performed barbell lifting exercises. A machine learning model is trained to predict the manner in which the exercise was performed (classe). Data preprocessing, cross-validation, model selection, and expected out-of-sample error are discussed. The final random forest model is applied to 20 test cases for submission.

Milestone report for the SwiftKey Capstone Project. Includes exploratory data analysis, dataset summaries, and plans for building the next-word prediction model and Shiny application.

developing data products