Projects

Selected Work

A collection of data science and machine learning projects — from neural networks and applied ML to optimization and statistical modeling. Most recent professional work is proprietary; these represent foundational and academic highlights.

Feedforward Neural Networks — Artificial Intelligence

An introductory exploration of feedforward neural networks, examining architecture, training, and feature importance through a classification lens.

Neural NetworksAIClassificationPython

Analytics

Regression Splines — Explanations & Examples

A practical walkthrough of regression splines and their use cases — natural splines, smoothing splines, and when to apply each, with R code examples.

RegressionSplinesRStatistical Modeling

Optimization

Pluvio Carry Route Optimization

An optimization project minimizing truck carrier shipping route costs. Demonstrates applied operations research for business logistics.

OptimizationOperations ResearchCost Modeling

Machine Learning

Machine Learning — Polynomial & Step Functions

A presentation-style walkthrough of polynomial and step function approaches in machine learning, designed for a technical audience following along with the lecture.

Machine LearningPolynomial RegressionPresentation

Machine Learning

Zillow Home Prices — Applied ML

An applied machine learning project using live data to understand what drives home prices. Feature engineering, model selection, and business interpretation.

Machine LearningReal EstateFeature EngineeringPrediction

Machine Learning

Ridge & Lasso Regression

Clear explanations and R code examples of Ridge and Lasso regularization — when to use each, how they differ, and practical implementation.

RegularizationRidgeLassoR

Machine Learning

Best Subset Selection

A walkthrough of best subset selection methods for model building, with examples demonstrating how to identify the most informative feature combinations.

Feature SelectionModel SelectionR

Machine Learning

Forward Stepwise Selection

An explanation of forward stepwise selection as a computationally efficient alternative to best subset methods, with practical R examples.

Feature SelectionStepwiseR

Analytics

Model Selection Methods

A comparative study of model selection techniques — cross-validation, AIC, BIC, and adjusted R-squared — applied to real datasets.

Model SelectionCross-ValidationRStatistics