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Predicting Off-Campus Hostel Rent Prices at the University of Cape Coast Using Machine Learning: A Deployed Web Application

Domain:

socioeconomic

Record type:

papermodelsoftware
Creator:
Kam
Publisher:
Zenodo
Host:avatar

This study presents a machine learning framework for predicting hostel rent prices at the University of Cape Coast, Ghana. The research explores key factors influencing student accommodation costs and applies multiple regression algorithms, including Linear Regression, Decision Tree, Random Forest, and Gradient Boosting. Among these, the Gradient Boosting model achieved the highest predictive performance with an R² score of 0.923.

A key contribution of this work is the deployment of the predictive model as a Streamlit web application, providing students and administrators with an interactive tool for real-time rent estimation. The study demonstrates how data-driven methods can improve decision-making in student housing and urban planning within developing country contexts.

This is a preprint version of a manuscript currently under review at Heliyon (Elsevier). The content may differ from the final published version following peer review.

All datasets and model source code are available openly on GitHub to ensure transparency and reproducibility.

  • GitHub Repository: https://github.com/Mo-Kam/U…

  • Deployed Web App: ucc-rent-predict.streamlit.…

Keywords: machine learning, rent prediction, Ghana, Streamlit, data science, student housing

Visit

doi.org

Languages

Kam

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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