Classification of Addiction Behavior based on Regular and Rare Model

Abstract

Realization is the comprehension of existence in its widest terms. Many of us, both physically and virtually, are unconscious of our level of addictive concern. Predicting virtual and emotional-based activity poses certain difficulties in determining an addiction level. Specifically, how to compute the addictive and what types of controls can help us monitor the addiction and get a good estimate of the individual's addicted stage. The threshold levels vary depending on a variety of factors such as age, gender, society, and so on. The addiction mentality system's prediction plays a vital role. In this regard, our research develops a Regular and Rare (RAR) based classification model for finding effective addiction predictors. This RAR classification and prediction technique is based on an examination of addiction patterns' consistency. This strategy focuses on the length of time spent doing the same activity rather than the amount of quantity consumed. The concept behind it if an individual consumes a low density of nicotine but persists for a decade, this is considered as a habitual and addictive activity. In such a way that if an individual doesn't really engage in the very same type of activity for an extended period of time, the action may be considered an uncommon occurrences rather than an addictive class.