A logistic regression analysis examining associations between cardiovascular disease and modifiable health and lifestyle factors (physical activity, smoking history, body weight) using 2024 BRFSS survey data. The project emphasizes model comparison, diagnostics, and interpretation through predicted probabilities and visualizations.

This presentation describes a Shiny application that predicts the next word in a phrase using an N-gram language model.

This is the introduction to Quantitative trading and investment with R

Creditors invest significant efforts in creating algorithms to predict the likelihood of a customer defaulting on a loan (PD). Defaults can result in substantial financial losses, impacting both the profitability and stability of financial institutions. To mitigate these risk, it is essential to develop robust predictive models that help to identify potential defaulters before credit is granted. Our aim here is to develop such models.

This report presents a brief exploratory analysis of the SwiftKey text data, including blogs, news, and Twitter sources. It summarizes basic statistics and outlines plans for building a next-word prediction model.