💪 🤔 Modern Super Learning with Machine Learning Pipelines

Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign

Regression model building and forecasting in R

Exercises From Book "Applied Predictive Modeling" by "Kuhn and Johnson (2013)"

R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model

A rolling version of the Latent Dirichlet Allocation.

An R package for doing repeated k-fold cross validation

Monte Carlo Penalty Selection for graphical lasso

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

Determine a Prototype from a number of runs of Latent Dirichlet Allocation.

R package for focused information criteria for model comparison

D-probabilities of parametric models using nonparametric model reference

Eficient Stepwise Selection in Decomposable Models

# kaefa kwangwoon automated exploratory factor analysis for improving research capability to identify unexplained factor structure with complexly cross-classified multilevel structured data in R environment

This project was in collaboration with University Hospital Birmingham

R programming scripts for statistical data analysis and visualization. Includes data cleaning, descriptive statistics, hypothesis testing (t-tests, ANOVA), correlation, regression modeling, and visualizations using ggplot2 and tidyverse.

Mind Foundry OPTaaS R Client

This GitHub repository features R code for a movie rating prediction project. It utilizes tidyverse in R to clean data, perform linear regression modeling, and evaluate prediction accuracy using RMSE and MAE metrics. The project aims to predict IMDB scores based on movie features such as gross revenue, budget, release year, and content rating.