Paper Title
AN ENHANCED GENDER GAP PREDICTION ALGORITHM USING MACHINE LEARNING AND SMALL DATA

Abstract
Abstract - The importance of diversity and minimising gaps between minorities and majorities is much discussed. In order to measure a community’s progress in minimising these gaps, there is an interest in being able to predict the future diversity of communities. There are well-designed data-forecasting algo- rithms in data science using large data sets. However, diversity data has only been collected over the last few decades. This paper adopts algorithms from the grey model (GM) and ARIMA (Auto-Regressive Integrated Moving Average), using small data to predict the likely diversity of a cohort for a specified time in the near future. Our results demonstrate there is more reliable forecasting for “country of birth” diversity, but predicting linguistic and religious diversity requires greater data due to the changeable nature of these factors throughout an individual’s life. Keywords - Diversity, Data Forecasting, Small Data, Mutu- Ality, Diversity Atlas