Financial inclusion dataset classification in Eswatini using support vector machine and logistic regression

Small scale enterprises grow with provision of financial inclusion (FI) schemes for entrepreneurs. This widens their capital base; hence invest more and increase employment rate. We focus on Eswatini FI scheme from 2018; applied SVM and LR to classify FI dataset; discovered degree to which small, micro and medium enterprises (SMMEs) within Eswatini access funds. FI dataset was extracted from Finscope database. We selected parameters; classified FI for Manzini, Hhohho, Lubombo, and Shiselweni in Eswatini using LR with 80% split for training; ten-fold cross-validation. Manzini has ten-fold cross-validation recall rate of 69.4% using SVM and 63.4% using LR; optimal performance of the 80% percentage split recall rate of 73% was for Manzini using SVM and 77.8% using LR. The 80% split outperforms ten-fold cross-validation. Findings reflect that Eswatini Government should pay more attention to enhance FI in Hhohho, Shiselweni and Lubombo and consider mobile money as key indicator for FI.