Comparative Analysis Among Decision Tree vs. Naive Bayes for Prediction of Weather Prognostication

Abstract

In the previous era, a computer is programmed for some specific task. An electronic device is programmed to do its function electronically. It was done with a target device, the programming environment and the system. We get the necessary intermediate code by running the program with the above said environment and committed into the target device. Thus the device performs the task it was intended to do. In case if we need to change the functionality of the device by the learning experience of the vendor and users, the vendor will upgrade the product. Nowadays in this machine learning era, the devices are programmed in such a way it can learn by its own experience and with the available data it collected it can even manipulate the algorithm by itself with the provided data set. Thus machine learning is ruling this era. We are going to discuss the machine learning algorithms here which was used to predict by itself with the data set collected. Therefore, machine learning is all about learning about computer algorithms that progress its potential through the experience. Thus, Machine learning is presently highly regarded analysis topic and applied to all told application in day to day life. In this paper we have a tendency to extract the knowledge of machine learning algorithms like decision tree, Naive Bayes and enforce the algorithms with sample dataset of weather prognostication.

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