Sportpark Visitor Analytics

A hobby project exploring how far you can get building a full-stack application from a single specification document — with AI assistance and a few manual tweaks.

The app scrapes live visitor counts from a fitness club, stores historical data, and provides an analytics dashboard with occupancy trends, personalized recommendations, and ML-based visitor predictions.

What's in here

Component	Tech	Purpose
Scraper	Python + APScheduler	Polls visitor count + weather data on a schedule
Backend	FastAPI	REST API for analytics, recommendations, ML training & prediction
Frontend	React + Recharts	Dashboard with live status, charts, heatmap, predictions
ML Pipeline	scikit-learn, XGBoost, LightGBM, Optuna	Visitor count forecasting + realtime ghost correction
Database	PostgreSQL	Time-series storage for visitor counts and weather
Deployment	Docker Compose	Single-command local deploy

How it was built

The entire application — scraper, backend API, ML pipeline, frontend dashboard, database schema, and Docker setup — was generated from SPECIFICATION.md using AI coding tools. Manual additions were limited to tweaks and iterative refinements.

Quick start

cp .env.example .env
# Edit .env with your API URL and a strong database password
docker compose up -d

The dashboard is available at http://localhost:18080 and the API at http://localhost:18432.

Configuration

See .env.example for all available environment variables.

Project specification

The full specification that this project was built from is in SPECIFICATION.md.