🎮 JCLimix | GitHub Readme (I'm too lazy to write these so here's what AI made with my github and resume)
Yo, I'm James Ezeilo — creator, coder, and strategist who loves transforming ideas into data-driven experiences.
- 🔭 Passion Projects: Basketball analytics, Healthcare innovation
- 🛠️ Favorite Tools: Python, Flask, SQL, Pandas, AWS S3, Airflow
- 💬 Open for collabs, feedback, and conversations about tech, sports, and everything in between
- 📫 Reach me anytime: jmge.work@gmail.com
- ⚡ Fun Fact: I once used data analysis to win a fantasy sports league. (Also... my Smash Bros main is Kirby.)
🏀 Where basketball meets automation and analytics.
HooperLabs is my end-to-end basketball data platform, combining scraping, automation, database engineering, and web apps — helping fans, analysts, and future GMs find deeper insights in the game.
- Custom Linux Server: Hosts all apps with Flask + NGINX reverse proxy
- PostgreSQL Database: Central hub for all basketball datasets (HooperData)
- Automated ETL Pipelines: Python scripts + Cron + Airflow manage scraping, cleaning, and updating
- Fully Custom Web Apps: Basketball tools built for analysis, prediction, and discovery
- 🧬 HooperDNA — Compare college players to NBA pros by statistical DNA
- 🐐 NBA GOAT Calculator — Create your own Top 100 NBA list based on your favorite criteria
To revolutionize basketball analysis by combining automated pipelines with interactive, accessible tools — bringing deep basketball insights to every fan.
🏥 Simulating smarter healthcare, one (fake) patient at a time.
PediaMetrics is an automated pediatric health tracking platform — simulating patient visits, diagnoses, lab results, and clinical reporting using fully randomized, realistic data.
Built to model how data can transform pediatric care workflows through automation and real-time dashboards.
- Simulate patient profiles, vitals, and symptoms dynamically
- Run lab test simulations tailored to pediatric ranges
- Predict disease likelihoods using weighted algorithms
- Auto-generate visit summaries in PDF format
- Connect to live Tableau dashboards via AWS S3 and Google Drive APIs
- Schedule new patient visits automatically with cron jobs
- Python (Flask, Pandas, NumPy)
- AWS S3 (Boto3), Google Drive API
- Apache Airflow (automation)
- Loguru (logging)
PediaMetrics uses entirely fake, randomly generated data for simulation purposes. No real patient data is stored or shared.
| Area | Skills |
|---|---|
| Data Engineering | ETL Pipelines (Python + Airflow), Cron Jobs, PostgreSQL Database Design, AWS S3 Integration, Data Automation, Linux Server Admin |
| Data Science | Predictive Modeling, Statistical Analysis (Pandas, NumPy, SciPy), Disease Prediction Algorithms, Basketball Player Comparison Models |
| Data Analytics | Data Visualization (Matplotlib, Plotly, Tableau), KPI Development, Metrics Building, Web Scraping (BeautifulSoup), Data Cleaning (Pandas) |