An Improved Bayes Classification Approach to Reduce Affliction of Juvenile

Machine learning plays a major role in computer technology and it is either an evident or non-evident based analyzing problem in many aspects. Machine learning is mainly achieved through classification algorithms. The classification is depends on the availability and nature of data. The classification algorithm is easily scalable when the data set is large. In this research paper, Naive Bayes probabilistic approach is used with assumption and the attributed are conditional independent. The research paper is to develop a new model to improve the efficiency of Bayes classification algorithm applied over the juvenile affliction depends on paternity behavior and usage of digital gadgets. The findings of the research paper has developed a new model of three phased with two different data set. The first phase is ranking prototype, second phase is PEH Model and the third phase is CAPM Model. All the three phases are applied with the data set Paternity behavior & Usage of digital Gadgets. In this paper, Comparison of different classification algorithm with new model has also analyzed in this paper.