$ cat about.txtName : Aditya Kumar
Role : Full-Stack Developer + ML Learner
University: VIT Bhopal University — B.Tech CS
Location : Bhopal, India 🇮🇳
Status : Open to internships & collaborations
I'm not chasing titles — I'm chasing understanding.
Whether it's wiring up a MERN app from scratch, training a regression model on messy data, or debugging why a useEffect fires twice, I care about knowing the why, not just the what.
Currently spending most of my time building full-stack projects, exploring data science fundamentals, and studying how large systems are designed. I think the best developers are the ones who are still curious after two years — I'm working on being one of them.
🏏 When I'm not at a keyboard, you'll find me watching cricket or arguing about it.
Tools are just tools — but the right ones, used well, make all the difference.
Languages
Frameworks & Libraries
Databases & Tools
Projects I built because I wanted the thing to exist — not just for my portfolio.
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The problem: Most to-do apps are either too simple or bloated with features nobody asked for. Built a full-stack task management app with user authentication, priority tagging, and deadline tracking. The UI is clean and fast — no tutorial clutter.
What I learned: REST API design, JWT authentication flow, and how to structure a React app that doesn't become a mess after 500 lines. |
The problem: EDA involves a lot of repetitive, copy-pasted code that slows down actual thinking. Built a Python tool that auto-generates summary statistics, correlation heatmaps, and missing value reports from any CSV — so analysis can start sooner.
What I learned: How to write reusable data pipelines and why data cleaning takes longer than the model ever does. |
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The problem: Browser bookmarks are a graveyard nobody visits. Built a minimal bookmark manager where users can save, tag, and search links — with a clean card-based UI and instant search. No login friction.
What I learned: State management without Redux, and how much you can do with browser-native APIs before reaching for a library. |
The problem: Real estate pricing lacks transparency for buyers. Trained a regression model on housing data to predict prices based on location, size, and age. Compared Linear Regression vs Random Forest — documented results with visualizations.
What I learned: Feature engineering matters more than algorithm selection. Outliers will ruin your R² score every single time. |
Current Focus ──────────────────────────────────────────────────────────► Future
✅ MERN Stack ✅ ML Fundamentals 🔄 DSA & Patterns ⏳ System Design
✅ REST APIs ✅ EDA + Regression 🔄 TypeScript ⏳ DevOps & CI/CD
✅ Git & GitHub ✅ Data Visualization 🔄 Next.js ⏳ Cloud (AWS/GCP)
I move linearly — one solid foundation before the next floor. No skipping.
I didn't get into programming because it was trending. I got into it because I realized you could build something from nothing — no materials, no factory, no budget — just a machine and an idea.
That still gets me.
The feeling of watching a UI come together, or seeing a model's accuracy climb after tuning, or finding the one bug that's been breaking everything for three hours — it doesn't get old.
I'm not looking for easy wins. I want to work on hard problems with people who care about getting things right. I want to write code that someone else can read a year later and actually understand.
That's the standard I hold myself to. Still getting there.
Open to internship opportunities, project collaborations, cold emails about interesting ideas, and good conversations about tech — or cricket.
