I’m a computer and data science student who builds data-driven systems using Python, statistics, and machine learning. I enjoy turning messy, real-world data into clear, actionable insight through feature engineering, modeling, and thoughtful software design. My interests live at the intersection of data science and backend engineering, where I focus on building tools that make complex information easier to understand and use.
Python
scikit-learn
PyTorch
R
postgreSQL
pandas
NumPy/SciPy
matplotlib + Seaborn
DockerProjects
Human Activity Recognition (HAR)
End-to-end HAR on the UCI dataset: z-score scaling, label encoding, Random Forest tuned via GridSearchCV, and test evaluation with accuracy, per-class precision/recall/F1, feature-importance, class balance, and confusion matrix visuals saved for the portfolio.
Neural Network Digit Classifier
A machine learning model that uses deep learning to classify handwritten digits in real-time. Built with PyTorch neural network, Flask backend, and interactive Canvas frontend. Users can draw digits and receive instant AI predictions with ~95% accuracy.
Salt Programming Language
A statically typed educational programming language with a custom lexer, recursive descent parser (AST), and interpreter—built in Python with robust error handling and a comprehensive test suite.
Contact
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