TL;DR: I built a full-stack stock prediction app with 7 ML algorithms, 1,001 stocks, and offline-first support. It's open source, and I'd love your feedback.
🔗 github.com/ankit02327/stock-price This project began from something I was personally learning: machine learning.
While taking ML courses at university, I wanted to understand how the algorithms I was studying could be combined into a complete, practical application. That led me to build Stock Price Prediction around a real-world problem instead of keeping the work inside individual coursework exercises. The educational purpose has remained central. I want someone learning machine learning to be able to look at a real application, understand how different models are used, work with real financial data, experiment with the code, and eventually build projects of their own.
| Layer | Technology |
|---|---|
| Backend | Flask 2.3.3, Python 3.8+, TensorFlow 2.20, scikit-learn, statsmodels |
| Frontend | React 18, TypeScript, Vite, Tailwind CSS, Recharts |
| APIs | Finnhub (US), Upstox (India), yfinance (historical) | The system works completely offline without any API keys. This was a deliberate design choice — I wanted anyone to be able to clone the repo and start experimenting immediately, without signing up for API keys or worrying about rate limits.
What works offline:
What needs API keys: Live prices only.
Machine learning is not magic. It finds patterns, but you need domain knowledge to make sense of them.
Data quality is everything. Stock data is messy — splits, dividends, missing values. A robust preprocessing pipeline matters more than a fancy model.
Start simple, then iterate. My first model was basic linear regression. It wasn't great, but it worked, and it gave me a foundation to build on.
Open source is a two-way street. The project now has 27 contributors and 14 releases. I've learned as much from the community as from building it.
I'm planning substantial improvements: FastAPI migration, Redis caching, PostgreSQL, Docker Compose, MLflow experiment tracking, news sentiment analysis, and a backtesting system.
The project is still growing. I see its current US and Indian market coverage as a starting point rather than the final scope.
If this sounds interesting: Thanks for reading. If you're learning ML too, I'd love to hear what you're building.