Comparative Machine Learning Modeling of Self-help Groups’ Impact on Livelihoods in Murang’a East Sub-county

Abstract

Self-help groups are widely used within communities to mobilise savings, access credit and support income-generating activities. This study compared three machine learning techniques: Logistic Regression, Naïve Bayes and Support Vector Machine, to model wealth status among self-help group members in Murang’a East Sub-County, Kenya. Primary data were collected through structured questionnaires from 969 self-help group members included in the final analysis, drawn from a target population of 2,250 members. Principal Component Analysis identified the factors most strongly associated with self-help groups’ performance. The PCA results ranked frequency of meetings and quality of discussions as the most important factors, followed by access to financial resources and credit facilities, collaboration with other organisations and stakeholders, member participation and engagement, community support, supportive policies, training and capacity building, and leadership and management. Logistic Regression, Naïve Bayes and Support Vector Machine were then fitted to classify members according to whether their wealth status had improved since joining a self-help group. Logistic Regression achieved an accuracy of 88.04%, Naïve Bayes 92.34% and Support Vector Machine 84.62%. Naïve Bayes also recorded the highest precision (94.92%), recall (96.89%) and F1-score (95.89%). The results indicated that the choice of classification method affected predictive performance with Naïve Bayes providing the strongest overall performance. The study contributes to knowledge of self-help groups’ role in improving rural livelihoods and reducing poverty in Kenya and makes a methodological contribution by comparing classificationmodels for predicting members’ wealth status and demonstrating the applicability of Naïve Bayes for analysing self-help group livelihood outcomes.