Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

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

Domaine:

socioeconomic

Type de record:

papermodelsoftware
Créateur:
Kam
Éditeur:
Zenodo
Hôte: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

Similaires

Machine Learning Web Application to Estimate Listing Prices of South African HomesPredicting House Rental Prices in Ghana Using Machine LearningPredicting students’ continuance use of learning management system at a technical university using machine learning algorithmsPredicting Food Prices in Nigeria Using Machine Learning: Symbolic RegressionPredicting Property Prices With Machine LearningPredicting Television Prices in Ghana: A Machine Learning Approach Using Weekly Market Survey Data

Machine Learning Web Application to Estimate Listing Prices of South African Homes

Due to the heterogeneous nature of residential properties, determining selling prices which will rec

Predicting House Rental Prices in Ghana Using Machine Learning

This study investigates the efficacy of machine learning models for predicting house rental prices i

Predicting students’ continuance use of learning management system at a technical university using machine learning algorithms

Purpose This study aims to investigate factors that could predict the continued usage of e-learnin

Predicting Food Prices in Nigeria Using Machine Learning: Symbolic Regression

The aim of this study is to predict the prices of local rice, beans, and Garri in the South West (SW

Predicting Property Prices With Machine Learning

In this study, the authors aim to explore the potential of machine learning (ML) in real estate valu

Predicting Television Prices in Ghana: A Machine Learning Approach Using Weekly Market Survey Data

This paper presents the first machine learning price prediction model for the Ghanaian television ma