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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
```text
Pollutant Measurements
↓
PM2.5 | PM10 | CO | NO
↓
Trained Random Forest Model
↓
Predicted AQI |