A 5-slide reproducible pitch for a Shiny application that predicts car mileage using horsepower and weight.

A comprehensive R tutorial on applying Survival Analysis to sports data, moving beyond simple binary classification for injury prediction. This analysis simulates a 25-man football squad over 4 seasons to model the "time-to-event" for non-contact soft tissue injuries. It covers the creation of Kaplan-Meier survival curves, multivariate Cox Proportional Hazards regression, and the interpretation of Hazard Ratios to quantify the cost of workload and squad rotation.

This presentation pitches a data product designed for the Coursera Data Science Capstone. It outlines the development of a next-word prediction algorithm using N-gram modeling and a "Stupid Backoff" strategy. The project demonstrates the full data science pipeline: from exploratory analysis of a 4-million-line corpus (Twitter, News, Blogs) to the deployment of a reactive Shiny application. The focus is on balancing computational speed with predictive accuracy for mobile-first environments.

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An interactive Plotly scatter plot created using R Markdown, visualizing the Iris dataset.

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MPG Prediction Shiny Application

This presentation explains an interactive Shiny application that predicts vehicle fuel efficiency (MPG) based on car weight and horsepower using linear regression. It includes problem motivation, application overview, example prediction, and visualization.