This study explores the application of machine learning models for predicting house prices, comparing the performance of traditional regression techniques (Linear, Ridge, and Lasso) with advanced ensemble methods (Random Forest and XGBoost). The findings reveal that ensemble models, particularly XGBoost, outperform traditional methods, achieving the lowest RMSE of 0.4069. Key predictors identified include **distance to MRT**, **house age**, and **latitude**, with proximity to transportation being the most significant factor influencing house prices. The study underscores the efficacy of ensemble techniques in capturing complex relationships, offering valuable insights for real estate forecasting and decision-making.

This study explores the application of machine learning models for predicting house prices, comparing the performance of traditional regression techniques (Linear, Ridge, and Lasso) with advanced ensemble methods (Random Forest and XGBoost). The findings reveal that ensemble models, particularly XGBoost, outperform traditional methods, achieving the lowest RMSE of 0.4069. Key predictors identified include **distance to MRT**, **house age**, and **latitude**, with proximity to transportation being the most significant factor influencing house prices. The study validates the efficacy of ensemble techniques in capturing complex relationships, offering valuable insights for real estate forecasting and decision-making.

This study examines the impact of government capital expenditures on economic growth in Nigeria, focusing on both the short-term and long-term effects. It analyzes the relationship between various expenditure categories, including administration, economic services, and social/community services, and GDP using advanced econometric techniques in R. The findings aim to provide actionable insights for optimizing capital spending to foster sustainable economic development.

This study investigates the sustainability of the Nigerian government’s administrative recurrent expenditures in three critical classifications —general administration, defense, and internal security—and their impact on the country’s GDP from 1981 to 2022. Understanding how these expenses influence economic performance is crucial for formulating policies aimed at maintaining a balance between necessary public expenditure and sustainable economic growth.

Recurrent government expenditures on education show a potential positive relationship with Nigeria’s GDP, although the effect is not strongly significant. Expenditures on health and other social services, however, do not appear to have a significant impact on GDP based on the current dataset. While government spending is essential for providing public goods and services, inefficiencies in resource allocation or delayed economic returns may explain the weak effects seen here.

Nigeria, an economic powerhouse in Africa, has experienced fluctuations in its Gross Domestic Product (GDP) over the years. Understanding the factors that influence this vital economic indicator is crucial for sustainable growth and development. This publication analyzes the impact of recurrent expenditure on Nigeria’s GDP, drawing upon data from the Nigeria National Bureau of Statistics (NBS) spanning from 1981 to 2022.