Improving the Regression of Air Quality Using Ensemble of Machine Learning Models

Abstract

Air pollution is a particularly important problem in most countries right now because of its terrible effects on both the environment and human health. Big cities are most impacted because of the country’s quick industrial and economic development. In this paper, the authors proposed various regression model for the prediction of air quality including decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The air quality dataset, in Itally cities, is used for training and evaluation the proposed model. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods.

Classification of Cocoa Beans Based on the Grade Level of Fermentation using KNN Algorithm

“…Alhasani (2) elevated the accuracy of breast cancer classification through the application of information gain feature selection and machine learning techniques on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The presented model attains a remarkable maximum classification accuracy of 100%, accompanied by a weighted average precision and recall of 100%, using a C4.5 decision tree.…”