Ang, Seng Chun (2024) A new perspective on income earnings using AI. Final Year Project, UTAR.
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Abstract
Income stands to be an important contribution to a country’s economy, happiness index, and development growth. Understanding income dynamics enables policymakers to address the needs of different socioeconomic groups more effectively, improving financial freedom and overall quality of life. This project, titled "A New Perspective on Income Earnings Using AI," addresses the problem of inadequacies in predictive tools for Malaysian income dynamics. Existing predictive tools often fail to incorporate advanced AI techniques, which limits their effectiveness in providing accurate income predictions and insights. The primary objective of this project is to enhance income level prediction by leveraging machine learning and data mining techniques. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, we analyzed a dataset comprising 423 valid responses from residents of Ipoh, Malaysia, an area previously lacking such comprehensive income data. The results reveal significant economic insights and trends that were previously obscured, offering a nuanced understanding of income distribution. This project contributes to the advancement of AI-driven income analysis by creating an interactive dashboard that visualizes complex income data, thereby bridging the gap between high and low-income groups. The implications of this project are substantial for both policymakers and society. By providing a robust analytical tool, the dashboard supports more informed decision-making and enhances the ability to address income inequalities and economic disparities effectively.
Item Type: | Final Year Project / Dissertation / Thesis (Final Year Project) |
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Subjects: | L Education > L Education (General) T Technology > T Technology (General) T Technology > TD Environmental technology. Sanitary engineering |
Divisions: | Faculty of Information and Communication Technology > Bachelor of Information Systems (Honours) Information Systems Engineering |
Depositing User: | ML Main Library |
Date Deposited: | 14 Feb 2025 15:23 |
Last Modified: | 14 Feb 2025 15:23 |
URI: | http://eprints.utar.edu.my/id/eprint/6888 |
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