A Proposed Model for Multi-Dimensional Data Quality | ||||
| المجلة العلمية للبحوث والدراسات التجارية | ||||
| Volume 39, Issue 2, June 2025, Page 1719-1743 PDF (709.16 K) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/sjrbs.2025.367570.1924 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Rabab Yehia Oraby  1; Ahmed Mohamed Abd-Elwahab 2
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| 1Data Analyst and Officer Administrator, International Ranking Unit, Helwan University, Cairo, Egypt. | ||||
| 2Information Systems Department, Faculty of Commerce and Business Administration, Helwan University, Cairo, Egypt. | ||||
| Abstract | ||||
| In recent years, business intelligence has emerged as a critical field leveraging data analysis to generate actionable insights for informed decision-making. This paper emphasizes the significance of data quality and the choice of the most appropriate model to enhance the accuracy of predictions, which contributes to improving marketing strategies and banking decision-making. The aim of this paper is to evaluate the ability of machine learning models to predict the outcomes of direct marketing campaigns for banks using accurate and unbiased data. The five models tested were K-Nearest Neighbour's (KNN), Random Forest, Decision Tree, Gradient Boosting, and Multi-Layer Perceptron (MLP). The data showed that the Gradient Boosting model performed better than others in marketing applications, with an accuracy of 91.45%. Random Forest showed similar performance with an accuracy of 91.24% despite the longer prediction time, (MLP) achieved 90.24%, (KNN) achieved 89.55%, and Decision Tree was the fastest, but its accuracy was somewhat lower at 88.58%. | ||||
| Keywords | ||||
| .Data Quality; Data Quality Dimensions; Quality Metrics; Model Evaluation; Machine Learning | ||||
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