Publication

Abstract : This study investigates the usage of water in urban areas, with particular attention to location, age, water quality, and bathing habits. We examined the data using machine learning, more especially a RandomForestClassifier. After accounting for several variables, the study’s accuracy rate in predicting the types of baths was 83.63% without SMOTE and reached 90.51% after SMOTE analysis. Our knowledge of urban water consumption is improved by this research, which also shows how flexible machine learning is in identifying intricate links within datasets. The results of this study can help with resource management, urban planning, and environmental initiatives.