Predicting House Price Model : A Comprehensive Analysis with Random Forest and Decision Tree Method

This paper explores the field of predicting property prices through a thorough comparison of several regression techniques. Linear regression, decision tree regression, random forest regression, K-neighbors regression, gradient boosting regression, and XGBoost regression are the regression models that are being examined. Finding the best model to reliably estimate house prices based on a set of features is the aim.The dataset used in this study includes location, bedroom count, square footage, and other relevant parameters that are known to affect property values. Each regression model is trained and tested on this dataset using a methodical evaluation procedure, and its performance is evaluated using important metrics including R-squared, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and overall correctness. predicting house prices mainly use harlfoxem (Owner) repository of housing dataset will contain the homes sold between May 2014 and May 2015 with 21613 entries and 21 attribute were used to evaluate the performance of machine learning prediction model. For practitioners and scholars working in real estate, housing market analysis, and related sectors, this study offers insightful information. It emphasizes how crucial it is to properly choose regression techniques depending on the particulars of the prediction task and the distinct qualities of the dataset.