Sleep Calibre Assess Model of IT Workers by Machine Learning Algorithms

Abstract

In this research work, an intelligible sleep calibre was judged using the Questionnaire process of IT workers from age 21 to 35years. In this recommended model, overall offset directive ordination of QBUD (Questionnaire Based on Uneven Data sets) is used to bring about an act equate of sleep aspect quality includes “Low sleep time” and “Good sleep time”. These two regulations are used to elucidate the sleep rank. To manifest the appropriateness of the present project, we establish a sleep grade representation based upon the sleep time data sets collected from 120 IT workers. In this work, we are going to classify the quality of sleep using Support Vector Machine and K-Nearest Neighbour classifier to cope up with their proper duration of sleep and furnishing the probabilistically cross reasonable experiments.