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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TO PREDICT GROUND RENT FEES IN ZAMBIA |
| Authors Name: | DEAN J. NALUMPA , DR K. M. ABUBAKKAR SITHIK , DR TAWARISH |
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IJNRD_214025
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| Published Paper Id: | IJNRD2402199 |
| Published In: | Volume 9 Issue 2, February-2024 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | AI,ML,Ground Rent Fees, Supervised Machine Learning, Linear Regression , data importation, data splitting, training and testing ,evaluating machine model |
| Cite Article: | "APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TO PREDICT GROUND RENT FEES IN ZAMBIA", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 2, page no.b826-b839, February-2024, Available :http://www.ijnrd.org/papers/IJNRD2402199.pdf |
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2402199 Registration ID: 214025 Published In: Volume 9 Issue 2, February-2024 DOI (Digital Object Identifier): Page No: b826-b839 Country: LUSAKA, LUSAKA PROVINCE, Zambia Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2402199 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2402199 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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