metadata
license: mit
tags:
- machine-learning
- air-quality
- aqi
- random-forest
- regression
- scikit-learn
library_name: scikit-learn
🌍 AQI Predictor
A machine learning model that predicts the Air Quality Index (AQI) using pollutant measurements.
The model was trained using air-quality data and uses four pollutant features:
- PM2.5
- PM10
- Carbon Monoxide (CO)
- Nitrogen Oxides (NO)
The trained model is saved as a Python .pkl file using scikit-learn.
🤖 Model
Algorithm: Random Forest Regressor
Task: AQI Regression
Input Features:
| Feature | Description |
|---|---|
| PM2.5 | Fine particulate matter |
| PM10 | Particulate matter |
| CO | Carbon monoxide |
| NO | Nitrogen oxide |
Output:
Predicted AQI value.
📊 Model Performance
The model achieved approximately:
- R²: 0.89
- RMSE: ~22
Performance was evaluated on the project dataset. Results may vary depending on the data split and preprocessing procedure.
🔄 Prediction Pipeline
Pollutant Measurements
↓
PM2.5 | PM10 | CO | NO
↓
Trained Random Forest Model
↓
Predicted AQI