Welcome to my R programming portfolio showcasing data analysis, machine learning, and interactive visualization capabilities. This collection demonstrates my expertise in transforming complex data into actionable insights using R's powerful ecosystem.
Predictive Modeling: Classification Engine
- Built and evaluated classification models (Logistic Regression, Random Forest, XGBoost)
- Achieved 92% accuracy in predicting customer churn
- Key Techniques:
- Feature engineering with
recipes - Hyperparameter tuning using
tidymodels - Model interpretation with
DALEX
- Feature engineering with
COVID-19 Tracking Dashboard | Shiny Tutorial Series
- Developed real-time pandemic monitoring tool with:
- Interactive leaflet maps of case clusters
- Time-series forecasting visualizations
- Hospital capacity risk indicators
- Tech Stack:
Shiny,flexdashboard,plotly
- Processed 500K+ records from Johns Hopkins dataset
- Key Deliverables:
- Reproduction number (R₀) estimation
- Mobility vs. infection rate correlation analysis
- Automated PDF report generation with
rmarkdown
# Sample Code Snippet (Machine Learning)
library(tidymodels)
model <- logistic_reg() %>%
set_engine("glmnet") %>%
fit(churn ~ ., data = training_set)