An accuracy analysis and prediction of daily workout using smart phone dataset using novel random forest algorithm over linear regression.

The proposed research work anticipates the regular workout patterns of users by utilising the data collected from their smartphones. The study indicates that the Random Forest algorithm is more effective in predicting human exercise routines with higher precision than the Linear regression algorithm. A dataset of 3000 entry with the headings 'user','activity','Timestamp', and so on are used to compare, with two groups of 10 sample size for each being studied. Linear Regression recorded an accuracy of 73.67%, while the innovative Random Forest Algorithm gives an precision of 76.51. The difference between these groups is statistically significant at 0.033, which is smaller than 0.05 (p<0.05). It is obvious that these groups differ statistically significantly from one another. Compared to the results of the linear regression, the predictions made by the Random Forest algorithm model are more accurate. [ABSTRACT FROM AUTHOR]