A Study of gainful employment of learners receiving skills training in the informal sector using Machine Learning

Abstract

Analyzing data on the populace involved in BRAC’s Skills Development Programme (SDP), receiving training for jobs and businesses in the informal sector of the economy, research aims to predict and/or classify whether or not learners had a gainful employment, based on background data inferred from past learners and to see how efficiently different machine learning algorithms can achieve this. As this work involves indirectly helping people who work in the informal sector, it is safe to assume that most of the learners will ask to be enrolled in trades that they see the majority pursuing, instead of making an informed decision. The observations from the research aims to find how effectively different machine learning algorithms discover correlation between a learner’s background data and their chances of success in securing lucrative employment compared to their peers, and grouping learners into groups of successful and unsuccessful categories to determine how the performance of learners are in the job sector after receiving training. Some of the investigating criteria are their backgrounds, post training information and current salary, to name a few.