Air Quality Prediction Using Adaboost

The survival of human beings is contingent upon the presence of air. The detrimental impact of contemporary culture on air quality is seen in nearly all regions. Pollution of the environment is caused by activities in the industrial, transit, and residential sectors. The monitoring and prediction of air quality have become imperative in contemporary times, particularly in developing nations such as India. The most efficient techniques for assessing contemporary threats are thought to be prediction technologies based on machine learning. This study looks at a six-year-old dataset with a particular emphasis on air pollution data gathered from 23 Indian cities. The purpose of this investigation is to gather data and create forecasting models for air pollution. The Gaussian Naive Bayes Algorithm was used to perform substantial pre-processing on the dataset, and correlation analysis was used to identify standout features. Exploratory data analysis is used to identify specific contaminants whose presence has an immediate effect on the air quality index and uncover hidden patterns in datasets. Resampling is used to remedy the imbalance in the data problem. The AdaBoost algorithm exhibits the highest possible degree of precision.