This presentation describes the features and structural choices made in the development of the Text Prediction application. This application was developed as a part of the Capstone Project for the Johns Hopkins-Coursera series of courses.

Milestone report for Johns Hopkins-Coursera capstone project. Exploratory analysis of text mining data set, and plan for project.

Exploration of multi-variable regression with mtcars data set.

A brief presentation on the Iris flower data set.

A "leaflet" map of Joplin restaurants favored by this food critic.

The Random Forest algorithm was used to predict the ranking of 20 different weight lifting test cases. The authors of the study are referenced below. Velloso, E.; Bulling, A.; Gellersen, H.; Ugulino, W.; Fuks, H. Qualitative Activity Recognition of Weight Lifting Exercises. Proceedings of 4th International Conference in Cooperation with SIGCHI (Augmented Human '13). Stuttgart, Germany: ACM SIGCHI, 2013.

This report examines Motor Trend car road tests extracted from the 1974 Motor Trend US magazine. We focused here on the miles per gallon (MPG) performance of automatic versus manual transmissions.

Analysis of NOAA's storm threat database to identify the greatest health and economic threats posed by severe weather.