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

> Source: <https://dev.to/ankit02327/i-built-a-stock-price-prediction-app-while-learning-ml-heres-what-i-learned-19ho>
> Published: 2026-09-25 21:38:36+00:00

**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.*