Predicting Nigeria Budget Allocation Using Regression Analysis: A Data Mining Approach

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dc.contributor.authorAdewole, K. S.
dc.contributor.authorMabayoje, M. A.
dc.contributor.authorAbdulsalam, S. O.
dc.contributor.authorAjao, J. F.
dc.date.accessioned2017-11-23T14:12:34Z
dc.date.available2017-11-23T14:12:34Z
dc.date.issued2014
dc.description.abstractBudget is used by the Government as a guiding tool for planning and management of its resources to aid in effective decision-making. Data mining is one of the most vital areas of research with the objective of finding meaningful information from large datasets. The delay in the preparation of budget of the Federation by the Government has become incessant issue in the running of affairs of the country. This is evident in the delay in implementation of the previous budgets in the country; hence, the need for automated system to tackle the setback. In this paper, regression analysis which is one of the data mining techniques is employed to predict budget allocation from Nigeria budget dataset. 200 records consisting of the budget allocation summary for the year 2008, 2009, 2010, 2011, and 2012 across 40 data points containing Ministries, Departments, Commissions and Agencies (MDCAs) were used. A web-based data mining tool that employed linear regression to predict both Nigeria budget allocation across the 40 data points and the overall budget summary allocation of the Federation is proposed. The proposed data mining software predicted N1,803,196,024,657.40, N1,871,754,338,112.68 and N2,007,780,403,902.98 for the year 2013, 2014 and 2015 respectively. The tool is found capable of discovering interesting patterns in the data and for predicting budget allocation.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/21
dc.language.isoenen_US
dc.publisherThe Journal of Computer Science and Its Applicationsen_US
dc.subjectBudget; Data Mining; Dataset; Linear Regression; Predictionen_US
dc.titlePredicting Nigeria Budget Allocation Using Regression Analysis: A Data Mining Approachen_US
dc.typeArticleen_US

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