---
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