(untitled) (Journal of Pharmaceutical Negative Results, 2022)
Aim:Prediction of human activity data using Random forest algorithm against Decision tree algorithm for better accuracyusing machine learning. Materials and Methods: Random forest algorithm (N=20)and Decision Tree algorithm (N=20) thesetwo algorithms are calculated by using 2 Groups and I have taken 20 samples for both algorithm and accuracy in this work.Results: Based on the Results Accuracy obtained in terms of accuracy is identified by Random forest algorithm (90.6%) overDecision Tree algorithm algorithm(86.66%).Statistical significance difference between Random forest algorithm and DecisionTree algorithm was found to be 0.001 (p<0.05), using α=0.05 and power=0.80.conclusion: From the analysis it was observedthat the human activity data using Random forest algorithm is better than Decision Tree algorithm for better accuracy usingmachine learning.