Air Quality Prediction Based on Decision Tree Using Machine Learning
Soumyalatha Naveen, M S Upamanyu, Karun Chakki, Madhavarapu Chandan, Peru Purayil Hariprasad
Abstract
Air pollution has become a severe problem due to urbanization, industrialization, and the burning of fossil fuels, among other factors. This paper focuses on the use of data mining techniques for predicting air quality using machine learning. The paper highlights the impact of pollutants such as PM2.5 (particulate matter 2.5), PM10 (particulate matter 10), CO (carbon monoxide), NOx (oxides of nitrogen), SO2 (Sulphur dioxide), and O3 (ozone) on human health, which include respiratory and cardiovascular diseases, asthma attacks, strokes, and even death. We propose using data mining and artificial intelligence techniques to solve the problem. Decision trees are used for classification and regression tasks and work by building a tree-like structure of decisions and their possible outcomes. The tree is constructed by recursively splitting the dataset based on the feature that provides the highest information gain or reduction in impurity until a stopping criterion is met. Decision trees are easy to understand and can handle both continuous and categorical features, making them a popular algorithm in machine learning. The paper also discusses the importance of data mining in machine learning and its ability to identify patterns and relationships that would have otherwise gone unnoticed. This paper offers a practical solution to predict air quality of Bengaluru for the next coming month by analyzing the data from the previous 1 year. This provides insights into the use of decision trees and data mining for solving complex problems.