Research on the Link Between Economic Development Variables and the Rate of Access to Drinking Water in Rural Senegal Using Machine Learning

Abstract

After pre-processing a set of data collected from Senegalese state structures, official United Nations (UN), and world bank sites and after replacing missing values by interpolation using the mean between two values, we study the effect of macroeconomic development variables on the rate of access to drinking water in rural Senegal. To do this, we use Machine Learning (ML) techniques such as Linear Regression (LR), Decision Tree (DT), and Random Forest (RF) to identify a hidden correlation between the rate of access to drinking water and other variables. Based on the collected data, LR provides the best predictive accuracy, best RMSE (0.001), best MAE (0.011), and best R2 (96.9%) and help public development net received (ODA_NR_FBC) appears to be the most influential variable, predicting the rate of access to drinking water with greater precision than the other variables. This innovative approach can help us to better understand the factors influencing access to drinking water and to propose effective solutions.