An in-depth analysis of career data, pitted against the demands of academia. This Exploratory Data Analysis project uses Python's requests module, BeautifulSoup, PANDAS, and Seaborn to explore skills gained through my major and the skills most sought-after in the job market.

Analysis of Greenhouse gas emissions and other pollutants. This project focused on cleaning data and using the Altair module to visualize information, revealing patterns and trends in environmental data over time.

An image classification machine learning project comparing two approaches: Convolutional Neural Networks and Support Vector Machines. Using images of traffic lights (red, green, yellow), I regularized and normalized the data to evaluate performance differences between a neural network and a white-box SVM model from scikit-learn.

This group project analyzed Spotify data to identify factors that contribute to song popularity. Our findings revealed that energy levels in songs have decreased over time, along with other interesting patterns in musical attributes and listener preferences.

A machine learning project analyzing over 100,000 credit card transactions to detect fraudulent activity. I implemented and compared K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) approaches, achieving 96% accuracy and F1 score in fraud detection.