Air Quality Index Prediction Based on Different Technique in Machine Learning in New Delhi

Abstract

Among the many factors contributing to the significant problem of air pollution are the combustion of fossil fuels, industrial activity, and economic growth. This study's main concern is the use of data collection methods for machine learning-based air quality forecasting. The study highlights how pollutants can affect a person's health, including cardiovascular and respiratory conditions, bronchial asthma episodes, strokes, and even death. To solve the problem, we propose to use artificial neural network and data gathering techniques. When applied to classification and regression issues, a variety of machine learning techniques, including decision trees, offer a tree-like structure of findings and alternative predictions. Until a stopping condition is satisfied, the dataset is iteratively divided using the feature that produces the most information gain or impurity reduction.By examining data from the previous year, this study provides a useful method for forecasting the air quality in New Delhi for the subsequent month. This explains how different machine learning algorithms and data acquisition are used to address difficult problems.