APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TO PREDICT GROUND RENT FEES IN ZAMBIA

Registration ID: IJNRD_214025

Published ID: IJNRD2402199

DOI: Click Here to Get

DEAN J. NALUMPA , DR K. M. ABUBAKKAR SITHIK , DR TAWARISH

AI,ML,Ground Rent Fees, Supervised Machine Learning, Linear Regression , data importation, data splitting, training and testing ,evaluating machine model

The administration of land in Zambia largely refers to the process of finding available land, surveying it and allocating to citizens or investors that will develop the land. Citizen’s allocated land are expected to develop the land in eighteen months (18) and are required to pay annual statutory fees. Among them ground rent fees and this is according to the Ministry of Justice, Land Act of 1975. The ground rent fees are generated by the Land’s Information System annually. Hardcopy bills are printed on demand and in some cases sent as bulk SMS’s to property owners reminding them of outstanding ground rent fees. In a country like Zambia were resources are low and with properties well above one million and thirty-five thousand (1,035,000), it is very difficult to prioritize as it is not easy to identify which property categories should be targeted. The purpose of this study is to examine the possibility of applying Artificial Intelligence (AI) Supervised Machine Learning (ML) in the prediction of ground rent fees in a particular category or categories (i.e Commercial, Industrial, Residential, Agriculture..etc) for future planning purposes. Land properties from Lusaka, Southern, Copperbelt, Northern and North-Western Provinces will be used in the study as sample data. The predicted results will help identify applicable ground rent fees from the various properties in different categories and this will help prioritize resources and concentrate on categories of properties with future ground rent fees that are expected to be high. This will also help minimize resource wastage as only selected properties with high ground rent fees are targeted.

Published Paper   E-Certificate

DEAN J. NALUMPA, DR K. M. ABUBAKKAR SITHIK, & DR TAWARISH (February-2024). APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TO PREDICT GROUND RENT FEES IN ZAMBIA. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(2), b826-b839. https://ijnrd.org/papers/IJNRD2402199.pdf

Volume 9 Issue 2, February-2024

Pages : b826-b839

Paper Reg. ID: IJNRD_214025

Published Paper Id: IJNRD2402199

Downloads: 000121994

Research Area: Computer Science & Technology 

Country: LUSAKA, LUSAKA PROVINCE, Zambia

Published Paper PDF: https://ijnrd.org/papers/IJNRD2402199.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2402199

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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