Correlation Analysis of Voting Regression and Decision Tree Algorithm to Predict House Price with Improved Accuracy Rate

The primary goal of this study is to use efficient machine learning algorithms anticipate better house prices, typically inflated. Materials and Methods: : This will the differences between near-accurate price prediction utilizing Novel Voting Regression (Group 2) Decision Tree methods 1). sample size used carry out research was N=10 for each group studied. Clincle calculate size. pre-test analysis maintained at 80%. G-power Statistical yielded a significance value 0.001. Results: accuracy Algorithm 82.94%, which greater than Algorithm’s 72.54%. Independent Sample T-test has statistical 0.584. Conclusion: As result, it can be stated that technique produce results are almost as accurate technique.