Human Behaviour Impact to Use of Smartphones with the Python Implementation Using Naive Bayesian

Abstract

A change of behavior in special groups and many sustainable smart populations increasing day by day for excessive uses of smartphones. In recent years, the use of smartphones and mental imbalances have become a major problem with increasing negative effects. In our study, we find out the major problem of the negative side and its different sources like mental imbalance, stress, depression, loneliness, etc. Bayes' theorem and classifier, support vector machine, special data set of human behavior, and probability are used to calculate accuracy. For collecting data from three major sections, we use the physical methods, virtual methods, and medical reports. So, a vast data set is trained by data to compare method, and also probability is used for predicting the validity of the data model. Naive Bayes' theorem accurate 71% positive which is indicated the negative impact of human behavior. Based on the SVM classifier, we separate the barrier between the impact of positive and negative data. In SVM, we set up a parameter to measure negative and positive values. Python library function is a major component to calculate all instructions and also use for data training. Finally, we compare the results obtained by our proposed specialization with the results obtained from the three baseline landmarks.