Predicting the Air Quality Index (AQI) using Python is a common and highly practical machine learning project. It involves analyzing various air pollutant concentrations and meteorological factors to forecast the AQI, which is crucial for public health and environmental monitoring.
1. 📊 Data Preparation & Feature Engineering
You’ll typically need:
Air pollutant concentrations (PM2.5, PM10, NO₂, SO₂, CO, O₃, etc.)
Meteorological data (temperature, humidity, pressure, wind speed, etc.)
Time component (timestamps, lag features)
Common steps:
Load data (e.g. CSV from Kaggle, CPCB, IQAir).
Clean missing values and outliers.
Engineer time-based features or lag variables: e.g. rolling averages over 6h, 12h, 24h
Optionally encode wind direction, station ID, etc.
2. 📈 Modeling Approaches
a) Ensemble Methods (Random Forest, LightGBM)
Random Forest Regressor: Quick to implement, robust against noise. Example predicts AQI using features like meteorology and pollutant levels
LightGBM: Handles large datasets efficiently and often outperforms other models for AQI forecasting.Python Course Training in Bangalore
b) Support Vector Regression (SVR)
Works well with smaller, high-dimensional datasets; robust to outliers
c) Time Series Models (ARIMA, SARIMA)
Ideal for pure time-based forecasting (using past AQI values). Fit ARIMA/SARIMA using statsmodels, determine order with ACF/PACF and AIC
d) Deep Learning (LSTM, Bi-LSTM, Hybrid)
Manage temporal dependencies well. Simple LSTM example:
3. 🔧 Workflow Summary
Gather & clean data – pollutant and meteorological variables.
Engineer features – time lags, rolling averages.
Split train/test set.
Choose a model depending on your dataset and project needs:
Ensemble (RF, LightGBM) for baseline performance,
SVR for simpler datasets,
ARIMA/SARIMA for pure time-series,
LSTM or hybrids for deep learning.
Evaluate (MAE, RMSE, R²).Best Python Course in Bangalore
Iterate – hyperparameter tuning, feature additions.
Deploy – save models (e.g., joblib or HDF5), serve via API or dashboard.
4. 🧩 Helpful Python Libraries
pandas, numpy, scikit-learn
statsmodels for ARIMA
lightgbm, xgboost for boosting models
keras or tensorflow for deep networks
python-aqi to compute AQI from pollutant levels
Final Checklist
✅ Clean & preprocess data, include lag features.
✅ Baseline with Random Forest or LightGBM.
✅ Model time-series trends with ARIMA or LSTM.
✅ Evaluate, tune, and deploy (save models).
✅ Optionally integrate AQI calculators for real-time inference.
Conclusion
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