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CS Student • Aviation-focused

Hi, I'm

Rinku

I'm a CS student who learns by building and improving things step by step. I'm especially interested in aviation, backend systems, cloud infrastructure, and machine learning, and I use projects to turn that curiosity into real engineering experience.

Profile Feed
Focus
Stack
Style
Next
Python
Streamlit
AWS EC2
Azure
Linux
ML Pipeline
REST APIs
SQLite / DB
AI6E · DEL
6E241 · BOM
SG103 · HYD
247
Active Flights
Live
AirLabs Feed
📚
6
Active Learning Areas
🧑‍💻
4
Core Skill Areas
1
Featured Aviation Build
🤖
ML
Current Growth Focus
R
Rinku
@rinkuuu · CS Student
Open to Learning
Core Stack
Python 3.14
Streamlit
Pandas
XGBoost
SQLite
Mapbox
AWS EC2
Azure
Linux
REST APIs
About me

Still learning — but
becoming sharper through building every month.

I'm a CS student who likes understanding how systems work from end to end. I started with small Python programs, then gradually moved into APIs, databases, cloud deployment, and machine learning by actually building things. My aviation project is the strongest example of that approach, but the main story here is how I learn: pick something real, go deep, and turn it into working software.

🔧
Backend Engineering
Python · SQLite · APIs · Threading
☁️
Cloud Infrastructure
AWS EC2 · Azure · Linux · Deployment
🤖
Machine Learning
XGBoost · scikit-learn · Feature Eng.
🌐
Web Development
Streamlit · Mapbox GL JS · HTML/CSS
Proficiency
Python
94%
Data Engineering
85%
Cloud / DevOps
80%
Machine Learning
78%
Web / UI
72%
Always Growing

What I'm Learning Right Now

I learn best by building things. Here's what I'm actively studying, exploring, and planning to pick up next.

🐳
Docker & Containers
Containerizing the Aviation Platform with Docker and docker-compose — learning how apps run consistently across any environment.
In Progress
✈️
ADS-B & Aviation Protocols
Going deep on Mode S, squawk codes, and how ADS-B transponders work — understanding the aviation tech stack I'm building on top of.
In Progress
📊
Data Pipelines & ETL
Building proper ingestion pipelines and making the aviation data collection more robust — exploring Apache Airflow basics.
In Progress
⚛️
React / Next.js
Streamlit is great for prototyping, but I want to build more custom UIs. React is next on the list.
Up Next
🧠
Deep Learning
Done with Andrew Ng's ML course, now diving into neural nets — planning to apply it to aviation turbulence prediction.
Up Next
🗄️
PostgreSQL & Databases
Outgrowing SQLite for larger datasets. Learning PostgreSQL, indexing strategies, and how to design schemas that actually scale.
Planned
Featured Aviation Project

Aviation Intelligence
Platform

This is the main project that represents how I like to learn: pick a real problem, build the full system, and ship it end-to-end.

INDIA AIRSPACE · LIVE
Live Metrics
✈️
Active Flights
247
⏱️
Avg Delay Risk
34%
🛫️
Congestion · DEL
High
🌦️
Weather Alerts
3 METAR
Aviation Intelligence Platform

A self-learning project built end-to-end — live AirLabs API feed, SQLite snapshot engine, CheckWX METAR weather, Mapbox GL JS satellite map with live flight overlays, and a hybrid XGBoost + rule-engine delay predictor. Built this to learn real-world Python: threading, API design, ML pipelines, and cloud deployment.

Python 3.14 Streamlit Mapbox GL JS XGBoost AirLabs API CheckWX SQLite Threading
Launch App
Live
✈️

Live Flight Ops

Real-time positions for all active Indian flights on a Mapbox satellite map. Auto-refreshes every 10 seconds via a zero-reload JS bridge.

ML

Delay Prediction

Hybrid XGBoost + rule engine scores every flight for delay risk. Factors: congestion, METAR severity, schedule pressure, route OTP, aircraft type.

Live
🛫️

Airport Analytics

Congestion score, inbound density, low-altitude pressure, and 1-hour schedule demand per airport — bottleneck detection before cascades start.

🚨

Anomaly Alerts

Auto-detects holding patterns, go-arounds, diversions, and stale contacts. Surfaces anomalies before ATC broadcasts them.

🧳

Passenger Tracker

Search any active flight by call sign — see live position, estimated arrival, distance to destination, and predicted delay reason.

Live
🌦️

Weather Integration

CheckWX METAR ingestion with deduplication and severity scoring. Layers directly into the delay prediction model and map overlays.

Upcoming Build

What I'm Planning Next

The next aviation-focused idea I want to turn into a real project.

🛫
Planned
Indian Airport NOTAM Tracker
Parsing and visualizing NOTAMs for Indian airports — surfacing active runway closures, airspace restrictions, and temporary flight rules.
Python NLP Aviation Parsing
Technologies used in this project
🐍 Python 3.14
📊 Streamlit
🗺️ Mapbox GL JS
🐼 Pandas
🤖 XGBoost
⚙️ scikit-learn
🗄️ SQLite
✈️ AirLabs API
🌦️ CheckWX API
🔒 python-dotenv
🧵 Threading
☁️ AWS EC2
🔵 Azure
🐧 Linux
✈ Let's Connect

Open to learning,
collaboration & feedback.

I'm a CS student always happy to talk aviation, tech, or projects. Reach out on any of the links below — or connect with me on LinkedIn.

LinkedIn