I'm a data enthusiast, finding joy, fulfillment, and personal growth in oscillating between the roles of a data engineer, analyst, scientist, and visualizer. My unique background has enriched my ability to provide value across business functions. I'm honored that you've taken an interest in my work!
This portfolio showcases my data science projects, highlighting my skills and expertise in extracting insights, solving complex problems, and creating impactful solutions. Through a combination of raw code, rendered notebooks, and documentation, it demonstrates my ability to work across diverse tools and technologies in R, Python, and Julia to ultimately deliver business value.
Each project in this portfolio leverages a unique modeling approach and showcases my ability to adapt to diverse challenges using R, Python, and Julia. For each, you'll find links to the raw code and rendered notebooks, providing insight into the methods, results, and takeaways.
Projects spanning multiple languages, modeling approaches, etc., for solving complex business problems
| Name | Description | Language(s) | Modeling Approach(es) | Links |
|---|---|---|---|---|
| Delivery Model Standardization for Swire Coca-Cola | Identifying an improved segmentation strategy | R, Python | Kmeans, Hierarchical, Elastic Net, PCA | Notebooks, Code |
| Home Credit Default Risk | Estimating likelihood of default | R, Python | Random Forest, SVM, Elastic Net, PCA | Notebooks, Code |
| NBA Player Position Roles | Redefining NBA positions and classifing those for incoming prospects | R | KMeans, PCA, Nueral Network | Notebooks, Code |
Supervised learning projects where the target variable is continuous
| Name | Description | Language(s) | Modeling Approach | Links |
|---|---|---|---|---|
| Sacramento Housing | Predicting house prices for value | R | Penalized Regression | Notebooks, Code |
| Food Delivery Times | Predicting time from order to delivery for restaurant | R | Boosted Trees | Notebooks, Code |
| Hotel Rates | Predicting average nightly hotel rates | R | Support Vector Regression | Notebooks, Code |
| Car Prices | Predicting car prices | R, Python, Julia | Linear Regression | Code |
| Laptop Prices | Predicting laptop pricing | R | Decision Tree | Code |
Supervised learning projects where the target variable is categorical
| Name | Description | Language(s) | Modeling Approach | Links |
|---|---|---|---|---|
| Palmer Penguins | Classifying penguin species | R | Neural Network | Notebooks, Code |
| Zoo Animal Classes | Predicting classes of zoo animals | R | Decision Tree | Code |
| Employee Attrition | Classifying employees to leave and stay at the company | R | Decision Tree | - |
| Breast Cancer | Predicting existance of breast cancer among patients | Python | Decision Tree | - |
Unsupervised learning projects for deriving insights from unknown groupings
| Name | Description | Language(s) | Modeling Approach | Links |
|---|
Supervised learning projects where main predictor is time
| Name | Description | Language(s) | Modeling Approach | Links |
|---|---|---|---|---|
| Medicare Perscriptions | Predicting volumes of medicare perscriptions | R | ARIMA | Notebooks, Code |
Unsupervised learning projects synthesizing insights from text
| Name | Description | Language(s) | Modeling Approach | Links |
|---|
| Name | Description | Language(s) | Modeling Approach | Links |
|---|---|---|---|---|
| Alzheimer's Disease | Reducing dimensionality of alzheimer's predictors | R | PCA | Notebooks, Code |
The following resources were constant companions throughout my data science journey. I owe a thanks to all authors and maintainers.