DMARC Data Analysis Capstone
(February 2025-May 2025)
This was my Senior Capstone Project. Key Strategic Issue: DMARC is receiving a large increase of new visitors to their food banks. They are looking to gain insights on why and what differentitates new visitors from returning visitors.
Using machine learning models, we were able to predict whether a visitor was a new visitor or a returning visitor. in a given year. Using this, we found the important variables that were most likely to predict a new visitor. We then used this to create visualizations to help DMARC understand their new visitors.
Skills Used: R, data cleansing/prep, API pulls, machine learning models, model evaluation, data visualizations (ggplot, choropleth maps)
Wesley Life
(November-December 2024)
This project took place in my Data Mining and General Linear Models (STAT172) course at Drake University. WesleyLife was looking to expand their Meals on Wheels program to other areas in Iowa. They tasked us to make reccomendations on different Public Use Microdata Areas (PUMA's) in Iowa based on verious food insecurity measures.
Skills Used: R programming, Data cleaning/preparation, Machine learning based modeling (ridge, lasso, random-forest), Data visualization (ggplot2), Github Repositories, Model Comparison, Presentations, Chloropleth Maps
Bird CNN
(November-December 2024)
This project took place in my Machine Learning courese at Drake University. The goal was to create a convolutional neural network that could classify different species of birds from images. The other goal was to compare different network and algorithm combinations to see which one was the most accurate. The networks used were AlexNet, VGGNet, and ResNet. The algorithms used were SGD and Adam.
Skills Used: Python programming, Pytorch, sklearn, pandas, Convolutional Neural Networks, Data cleaning/preparation, Model Comparison,
University Admission Predictions
(September 2024)
This project took place in my Machine Learning courese at Drake University. The goal was to create a machine learning Model that could predict if a student would be admitted to a university based on a variety of different factors. We ran kNN, weighted-kNN, decision-tree, and random forest models on normalized and non-normalized data. We then compared the models to see which one was the most accurate.
Skills Used: Python programming, sklearn, pandas, Machine Learning Models (kNN, weighted-knn, random-forest, decision tree), Data cleaning/preparation, Model Comparison/Evaluation, Parameter tuning
Database Website
(April-May 2024)
This project took place in my Cloud and Database Systems course at Drake University. The goal was to create a website that could interact with a relational and a non-relational database.
Skills Used: AWS (RDS, DynamoDB, EC2, S3), Flask, Python programming, HTML/CSS, Database Design, Website Design