Increasing the accuracy of this study’s main forecast, which is housing prices. Materials and Methods: The primary source of data for this study was the Kaggle dataset. In this investigation, two separate groups, Group I and Group 2, each with 20 samples, were used. Group 2 used the Random Forest Regression, while Group I used the Linear Regression. Forty people made up the study’s entire sample size. Python was used for implementation and computations of sample size for statistical analysis and subsequent performance evaluation. Using clincalc.com, the statistical evaluation was performed with alpha (a) set at 0.05, beta (13) at 0.2, and statistical power (G-power) at 85%. The main goal of the analysis was to compare how well the Linear Regression and Algorithm using accuracy value as the primary assessment parameter. Result: Linear Regression (94.189%) outperforms Random Forest Regression (79.901%) in terms of accuracy, with a < .001 two-tailed significance value for p > 0.05.In conclusion, Linear Regression has superior accuracy compared to Random Forest Regression.