Air is the most essential natural resource for the
survival of humans, animals, and plants on the planet. Air is
polluted due to the burning of fuels, exhaust gases from factories
and industries, and mining operations. Now, air pollution
becomes the most dangerous pollution that humanity ever faced.
This causes many health effects on humans like respiratory, lung,
and skin diseases, which also causes effects on plants, and
animals to survive. Hence, air quality prediction and evaluation as
becoming an important research area. In this paper, a machine
learning-based prediction model is constructed for air quality
forecasting. This model will help us to find the major pollutant
present in the location along with the causes and sources of that
particular pollutant. Air Quality Index value for India is used to
predict air quality. The data is collected from various places
throughout India so that the collected data is preprocessed to
recover from null values, missing values, and duplicate values.
The dataset is trained and tested with various machine learning
algorithms like Logistic Regression, Naïve Bayes Classification,
Random Forest, Support Vector Machine, K Nearest Neighbor,
and Decision Tree algorithm in order to find the performance
measurement of the above-mentioned algorithms. From this, the
prediction model is constructed using the Decision Tree algorithm
to predict the air quality, because it provides the best and highest
accuracy of 100%. The machine learning-based air quality
prediction model helps India meteorological department in
predicting the future of air quality, and its status and depends on
that they can take action.
Classification model of air quality in Jakarta using decision tree algorithm based on air pollutant standard index
