Examining the Impact of Income Inequality and Gender on School Completion in Malaysia: A Machine Learning Approach Utilizing Malaysia's Public Sector Open Data
Muhammad Sukri Bin Ramli

TL;DR
This study uses machine learning techniques on Malaysian public data to analyze how income inequality and gender affect school completion rates, revealing disparities and forecasting future trends to inform policy interventions.
Contribution
It introduces a comprehensive machine learning approach to analyze and predict school completion disparities in Malaysia using open data from 2016-2022.
Findings
Significant disparities in completion rates across states, genders, and income levels.
Identification of regional clusters with similar educational outcomes.
Accurate forecasting of future school completion rates.
Abstract
This study examines the relationship between income inequality, gender, and school completion rates in Malaysia using machine learning techniques. The dataset utilized is from the Malaysia's Public Sector Open Data Portal, covering the period 2016-2022. The analysis employs various machine learning techniques, including K-means clustering, ARIMA modeling, Random Forest regression, and Prophet for time series forecasting. These models are used to identify patterns, trends, and anomalies in the data, and to predict future school completion rates. Key findings reveal significant disparities in school completion rates across states, genders, and income levels. The analysis also identifies clusters of states with similar completion rates, suggesting potential regional factors influencing educational outcomes. Furthermore, time series forecasting models accurately predict future completion…
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Taxonomy
TopicsPoverty, Education, and Child Welfare
