Automated collection of weather and pollutant data.
Pearls AQI Predictor
Air quality forecasting, explained like an intelligent control room.
An advanced air quality forecasting platform for Islamabad that combines automated feature ingestion, daily model retraining, and a cloud model registry to deliver live, 3-day AQI predictions.
Daily retraining pipeline to find the most accurate models.
Serving Day +1, Day +2, and Day +3 AQI forecasts.
MongoDB Atlas stores features, metrics, and registered models.
Real-Time Forecast
A fully operational machine learning product.
Observe the entire pipeline: fresh data enters the cloud store, models train and update automatically, predictions are served via an API, and the dashboard presents the results with model explanation.
Live preview
3-day Islamabad AQI forecast
Loading live forecast from the deployed API...
Day +1
--Day +2
--Day +3
--System Map
Four integrated layers powering the platform.
Feature Store
Clean hourly weather and pollutant features are validated and stored securely in MongoDB Atlas.
Deduplication & boundary checks run 24/7.
Model Registry
Ridge, Random Forest, Gradient Boosting, and MLP models compete daily. The best performers are registered.
Models validated with honest forward-chaining split.
Prediction API
A robust FastAPI backend retrieves the latest registered model artifact and returns 72-hour predictions.
Serves live coefficients and predicted values instantly.
Visual Analytics
A modern analytics interface turns complex model outputs into decision-ready predictions.
Interactive 72-hour trend and model ranking telemetry.
Ready for review