Google Appstore Data Classification Using ML Based Naïve’s Bayes Algorithm: A Review

Knowledge accuracy and precision are presumptions made by traditional machine learning methods. This presumption, nevertheless could not always be true due to data uncertainty brought on by assessment oversights, outdated information, measurements being repeated, etc. A probability distribution function (PDF) is used to express the probability of each data point when there is ambiguity. This paper provides a unique naive Bayes categorization strategy for ambiguous information in pdf in this study. Our primary answer is to expand the Bayes model's class conditional probability estimate. The accuracy of the Naive Bayes model can be increased by taking into consideration unpredictable data, according to substantial research using UCI datasheets.