Mohamed R. Ezzeldin

This study investigates the performance of hedonic regression against artificial neural networks and random forest for real estate price prediction in Riyadh, Saudi Arabia. Using data from a major property platform collected in Q3 2024, the analysis spans 2,997 residential units across five major districts. The study is situated within the context of Vision 2030 and addresses the challenges of real estate valuations in a rapidly evolving urban environment. Twelve locational and structural features were analyzed using a 70/30 training-testing split. Random Forest led with test R²=0.9065, RMSE=0.1911, MAE=0.1353, outperforming Artificial Neural Network (R²=0.8968, RMSE=0.2008, MAE=0.1450) and Hedonic Regression (R²=0.8034, RMSE=0.2673, MAE=0.1970). Random Forest feature importance: North of Riyadh (0.768659), West of Riyadh (0.612986), building type (0.180799), entrances (0.171134), size (0.127103). Random Forest and Artificial Neural Network excelled in capturing non-linear and spatial price trends in Riyadh’s housing market. This study supports the incorporation of machine learning models into workflows for predicting property prices, especially in cities like Riyadh that are quickly urbanizing. The research provides a useful foundation for improving real estate valuation procedures in accordance with Saudi Arabia's Vision 2030 goals by demonstrating the predictive benefits of RF over conventional methods.

This study applies machine learning to investigate gendered power dynamics and women's socio-economic and political engagement in Nigeria (1991-2023, World Bank, UNDP, INEC data). We trained Random Forest, Support Vector Machine (SVM), and Neural Network models with k-fold cross-validation, evaluating performance with R2, RMSE, and MSE. The SVM model demonstrated superior performance (R2 = 0.998). Feature analysis revealed that women's industry participation positively correlates with population share and education, while rural residence diminishes their likelihood of being employers. Additionally, K-means clustering of 2023 voting data uncovered regional variations in women's political representation. This research highlights enduring socio-economic and spatial barriers, demonstrating how AI-based evidence can inform gender-sensitive policies for inclusive representation in Nigeria.