A paired-algorithm clustering model for describing field staff Deployment in non-governmental organizations (ngos).

Abstract

This research addresses the inefficiencies and challenges faced by non-governmental organizations (NGOs) in deploying field staff, focusing on the manual processes prevalent in the current systems and leveraging on the possibilities offered by predictive machine learning algorithms. The problem stems from time-consuming and error-prone manual data entry methods, hindering optimal resource allocation. Our objective is to develop and implement a machine learning clustering algorithm to automate the field staff deployment process. By leveraging data analytics – hierarchical and k-means machine learning algorithms – we aim to enhance the efficiency and accuracy of deployment, leading to improved allocation of personnel and resources. The expected outcome is a streamlined deployment system that significantly reduces errors, minimizes time consumption, and maximizes overall operational efficiency in NGO field operations. The project outcomes will also inform advances in the use of combined methods in clustering machine learning algorithms and data analytics.