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.