Comparison-of-ML-models-for-predicting-AQI

Goal

In this project we are comparing various machine learning models to find which model works better for predicting the AQI (Air Quality Index).

Machine learning models used

In this project we are using regression models such as:

• Multiple Linear Regression
• Polynomial Regression
• Decision Tree Regression
• Random Forest Regression
• Support Vector regression (SVR)

Libraries Used: numpy, pandas, sklearn
IDE used: spyder (Anaconda 3)

Error Metrics Used

In this project we have used the following error metrics to evaluate and compare our models:

• Coefficient of determination (R^2)
• Root Mean Square Error (RMSE)
• Mean absolute error (MAE)
• Root Mean Squared Logarithmic Error (RMSLE)

AQI table

Data Source

The data set is taken from Open Government Data (OGD) Platform India. The site provides Real time National Air Quality Index values from different monitoring stations across India. The pollutants monitored are Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Particulate Matter (PM10 and PM2.5) , Carbon Monoxide (CO), Ozone(O3) etc. The site provides data on hourly basis thus the site's data is refreshed every hour.

Result

• Results on training set:

models	R^2	RMSE	MAE	RMSLE
MLR	0.9965	5.9334	3.2952	0.0595
Decision Tree	1.0000	0.0000	0.0000	0.0000
Random Forest	0.9996	2.0237	0.7106	0.0195
SVR	0.9494	22.628	16.076	0.1423
Poly R	1.00	0.09	0.018	0.0012

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• Results on testing set:

Models	R^2	RMSE	MAE	RMSLE
MLR	0.9965	5.4973	3.4796	0.0517
Decision Tree	0.9955	6.2370	2.354	0.0563
Random Forest	0.9982	3.8577	1.7016	0.0422
SVR	0.9164	27.0025	19.0722	0.1686
Poly R	-4.1417	211.8759	81.5855	0.4638

Prediction results

Conclusion

From the above table it is evident that the Random Forest Regressor performed the best out of all other regression models.