Classification of Fish Based on Naïve Bayesian Classifier

Abstract

Classification is the form of data analysis that can be used to extract models describing import data class or to predict future data trends. This system uses the Naive Bayesian Classifier. This system is useful in predicting the probability that a sample belongs to a particular class. It makes the assumption of class conditional independence that is, given the class of a sample the values of attributes are conditionally independent of one another. When the assumption holds true, then the naïve Bayesian Classifier is the Classifier is more Bayesian Classifier and more accurate in comparison with all other classifiers. This system is used to training dataset of fish. And then receiving the user's input testing data of fish. By using these testing data, the system will decide what kinds of edible or poisonous of fish, based on a probabilistic model of the observed data and prior knowledge. This system calculates the accuracy of testing data using holdout method.