Analysis of Landlord’s Land Price Prediction using Machine Learning

This Paper presents an analysis of landlord land price prediction utilizing machine learning techniques. The objective is to develop a predictive model that can accurately forecast land prices, aiding landlords in making informed decisions regarding property investments. The research employs various machine learning algorithms, including but not limited to regression and ensemble methods, to analyze a dataset comprising relevant features such as location, land size, demographics, and economic indicators. The accuracy of the predictive models is evaluated through rigorous testing and validation processes, with performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) utilized to gauge predictive accuracy, the accuracy of the deployed model is 94 percent. The findings demonstrate the efficacy of machine learning in predicting landlord land prices, with the proposed models achieving a high level of accuracy, thus offering valuable insights for landlords and real estate stakeholders in strategic decision-making processes.