📍 Data Science Projects

• This repository contains all unique projects I worked on during my masters of business analytics and data science degree at the University of Cincinnati
• Over the course of my studies / career you will be able to see the progression of where I started and how much I've grown since
• Enjoy 🚀

🏡 Credit Risk Classification (May 2024)

• This project leverages logistic regression & XGBoost for predicting whether a user will default on credit payments
• There is a separate repository for this project with multiple files (links soon)

🍎 CNN Apple Image Classifier (March 2024)

• Final project for Machine Learning Systems Design from Chip Huyen. This project was completed with the help of others.
• Full code files are not posted
• Credit: Laura Neltner, Sam Hinnenkamp, Sarah Solt, Christian Wall, Brett Karsten
• CNN apple classifier

🪼 Mood Disorder Classification 🦑 (Feb 2024)

• Classification algorithms applied to a data set to predict 4 different mood disorders based on various predictors
• Decision trees and random forests used
• sklearn, pandas, numpy, seaborn, matplotlib
• view

🍊🍉🫐 Data Wrangling Final Project Link 🍒🥝🍏 (Sept 2023)

• This project was uploaded to my Rpubs page
• Click the link to check it out!
• Description of project, full analysis and findings are included in the HTML
• Seasonality of Fruit Analysis

🍷🍇 Wine Quality Exploratory Data Analysis (Sept 2023)

• This project contains four separate parts analyzing the classic wine quality data set
• This was one of the first projects I worked on demonstrating statistical methods for EDA
• Descriptive statistics, visualization, sampling methods and other common techniques were used
• Half of this project was done in python, the other half was completed in R

👾 Tech Company Revenue (April 2023)

• Another one of my very first projects done in python, which analyzed a small data set from kaggle. This was a final assignment in my introduction to python course
• This was an introduction to data science, but the main focus was to demonstrate proficiency in python scripting
• We were to manually extract the data and put the columns / rows into lists & dictionaries using key-value pairs and compute statistics without using powerful libraries
• After manually wrangling the data, we then used common libraries (Pandas, Numpy, Matplotlib) to quickly compute the same stats