// Portfolio

Featured Projects

A collection of full-stack data platforms, hackathon winners, and Linux developer environments.

★ FEATURED PROJECT Full-Stack Data Analytics & ETL

US County Health Analysis

An end-to-end data analytics platform analyzing county-level public health outcomes across the United States. Ingests and cleans over 293,000 observations across ~3,000 counties and 12 health metrics, backed by a PostgreSQL database, Flask backend, and interactive Plotly Dash visualization suite.

▸ Automated ETL data ingestion & normalization pipeline
▸ Relational PostgreSQL schema with optimized SQL queries
▸ Interactive Plotly Dash visualizations with geographic maps & trend filters
▸ Deployed on Render with production-ready Flask server

PythonPostgreSQLFlaskPlotly DashpandasSQLAlchemy

293K+

Observations

~3,000

US Counties

Health Metrics

Live Interactive Dashboard View Source Repository

AI / EdTech • 1st Place Winner 🥇 1st Place @ CodeRed:Astra

Clarity AI

A multi-tier educational platform designed to bridge the feedback gap between teachers and students by providing an anonymous, real-time channel for students to express confusion points and receive targeted assistance during or after lectures.

▸ Won 1st Place at the CodeRed:Astra Hackathon
▸ Anonymous student feedback and comprehension tracking workflow
▸ Multi-tier educator dashboard for live lecture pulse checks

PythonTypeScriptReactTailwind CSSFlask

Code

Linux & Systems Configuration

Personal Dotfiles & Arch Rice

Reproducible, modular Arch Linux workstation environment orchestrated with GNU Stow. Built around the scrollable-tiling Niri Wayland compositor, featuring custom workflows for Fish shell, Neovim, WezTerm, and Starship prompt.

▸ Modular configuration symlinking via GNU Stow
▸ Scrollable-tiling workflow configured for Niri Wayland compositor
▸ Custom Neovim IDE setup with LSP, treesitter, and Gruvbox theme

Arch LinuxNiri (Wayland)GNU StowFish ShellNeovimLua

Code

Data Science & Machine Learning

Applied Machine Learning (ITAI 1371)

Comprehensive machine learning coursework and implementations from Houston City College. Covers the end-to-end predictive modeling lifecycle: exploratory data analysis, feature engineering, regression, classification, clustering, and ensemble models.

▸ Data preparation & feature selection pipelines in scikit-learn
▸ Comparative evaluation of regression and classification models
▸ Model validation with ROC-AUC, confusion matrices, and cross-validation

PythonJupyterscikit-learnpandasNumPyMatplotlib

Code

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