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Engineered a Python-based data analytics solution designed to parse, process, and visualize environmental sensor telemetry data for urban air quality monitoring.
Key Technical Achievements:
Built a robust data pipeline using Python's native JSON library to ingest complex multi-point time-series sensor logs seamlessly.
Implemented a programmatic classification engine based on international air quality metrics to track PM2.5 concentrations in real-time.
Automated the generation of visual intelligence assets using Matplotlib, outputting professional data trend charts for corporate environmental compliance reporting.
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
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I've been watching Formula 1 since 2014. Through the Mercedes era, the 2021 title fight that broke my heart and rebuilt it, and the Sunday-afternoon alarms for flyaway races, F1 has been a constant for me for over a decade.
So I finally built the thing I always wanted to exist: a real-time F1 race dashboard that doesn't just show what's happening but predicts what's about to.
🏎️ What it does:
A live track map with every car moving in real time, built from real GPS data.
Live timing, tires, gaps, and pit stops.
And the part I'm most proud of: AI predictions for tire life, pit windows, safety-car risk, and where each car will actually finish
The honest results (because numbers should mean something):
Its finishing-position guess is on average within ~2 places of where a car really ends up. So if it says "P5," reality is usually P3–P7.
It gets about 3 in 4 of its podium calls right while the race is still running.
For safety cars: when it says there's roughly a 1-in-8 chance of one soon, that's about how often one actually appears, i.e., the percentages are honest, not vibes.
What I learned the hard way: in a sport this strategic, the live running order is already a brutally good predictor. Beating it isn't about more data; it's about modeling the chaos (safety cars, rain, and DNFs). That reframing was the whole project.
Built with Python, FastAPI, React, and a Monte Carlo simulation engine. Every predicted number is labelled so it's never confused with real telemetry.
From fan to builder, easily the most fun I've had with a side project. 🏁
Repo + write-up in the comments. Would love feedback from other F1 + data folks.
Title : Customer Sales Analytics Dashboard
Description : Performed sales analysis using SQL and Excel to identify revenue trends, customer behavior, and business insights.
Description:
ulTRACKare is a responsive healthcare operations dashboard tailored for rural health workers managing high-risk patient volumes, chronic condition tracking, and upcoming care visits. The core architecture uses a strict visual hierarchy that allows medical staff to check high-level operational counts (like active patients and open appointments) instantly at a single glance. Below the main metrics, the platform breaks down priority cases, using specialized, high-visibility alert cards to surface critical cases—such as severe hypertension alerts—ensuring urgent follow-up tasks are never overlooked.
AI Utilization & Execution:
I utilized the Bubble AI app builder as the core foundation for this build by writing a highly structured, tokenized design prompt. The prompt directed the AI to establish a dark-mode user interface complete with frosted container cards, background blurs, and crisp 1px borders. After the AI built the framework, I took the wheel to clear data-type mismatches, clean container content states, and optimize the responsive layout to ensure a good user experience.
https://montenegrokarlo.bubbleapps.io/version-test/patients?debug_mode=true
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