I Built a Stock Price Prediction App While Learning ML — Here's What I Learned

A developer built Stock Price Prediction, an open-source full-stack application that applies seven machine learning algorithms to 1,001 stocks across US and Indian markets, using a Flask, TensorFlow and scikit-learn backend with a React and TypeScript frontend. The project runs fully offline without API keys, requiring credentials only for live prices, and has drawn 27 contributors across 14 releases. The developer said the work began as a way to apply university ML coursework to a real-world problem, noting that "data quality is everything" and that a robust preprocessing pipeline matters more than a fancy model.

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 https://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.