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This is the R Markdown file compiled to meet the specifications of IE2341 Assignment 2 Question 3.

Composite z-score based on diabetes-correlated health indicators.

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This project delivers an interactive text-prediction web application built using R and Shiny, satisfying the final capstone requirement for the Johns Hopkins Data Science Specialization. The application utilizes a highly compressed N-gram natural language model (built from blogs, news, and Twitter text data) and implements an efficient "Stupid Back-off" algorithm to predict the next word dynamically as a user types. The entire system is engineered for low latency and a minimal memory footprint, making it ideal for deployment on standard web environments or mobile devices. Included are the links to the live, operational interactive Shiny application hosted on shinyapps.io, alongside a compiled 5-slide markdown presentation pitching the data pipeline, algorithm structure, and performance metrics of the tool.

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This is the analysis of the economic and environmental impact of flood in U. states

yay project!

Predicting corporate bankruptcy using logistic regression, random forest, and classification trees on five years of Polish financial ratio data (64 predictors). Models evaluated using ROC curves, confusion matrices, 5-fold cross-validation, and asymmetric cost functions. Built in R — Group 13: Tasnim Al-Agtash, Vanessa Reyes, Lilian Fernandez.