Classification of Paddy Types using Naïve Bayesian Classifiers

Classification is a form of data analysis that can be used extract models describing important data classes or to predict future data trends. Classification is the process of finding a set of models that describe and distinguish data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. In classification techniques, Naïve Bayesian Classifier is one of the simplest probabilistic classifiers. This paper is to study the Naïve Bayesian Classifier and to classify class label of paddy type data using Naïve Bayesian Classifier. This paper predicts four class labels and displays the selected impacts attribute of each class label by using Naïve Bayesian classifier. Moreover, this paper can predict the types of paddy for paddy dataset by using other classification methods such as Decision Tree and Artificial Neural Network. Furthermore, this system can be used to predict production rate and display the selected impacts attribute of other crops such as soybeans, corns, cottons. This paper focuses on paddy dataset and decides paddy types are Lasbar or Yar Sabar or Yenat Khan Sabar or Sar Ngan Khan Sabar. 

by Mie Mie Aung | Su Mon Ko | Win Myat Thuzar | Su Pan Thaw ""Classification of Paddy Types using Naïve Bayesian Classifiers""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd26585.pdf

Paper URL: https://www.ijtsrd.com/computer-science/other/26585/classification-of-paddy-types-using-na%C3%AFve-bayesian-classifiers/mie-mie-aung

peer reviewed international journal, call for paper economics, ugc approved journals for chemistry