Abstract:
Mushrooms are the most recognizable scrumptious food which is cholesterol
free as well as plentiful in nutrients and minerals. Numerous types of mushrooms
have been figured out all through the earth. Distinguishing palatable or harmful
mushrooms through the unaided eye is very difficult, so mushroom species should
have to arrange eatable and noxious. This framework will be arranged the sort of
mushroom by utilizing Naive Bayesian classifier and K-Nearest Neighbor Method to
foster helpful subset of mushroom highlights for characterization task. This system
can classify the edible and poisonous mushrooms from mushroom dataset by using
Naive Bayes Classifier. In this system, performance comparison of the two algorithms
are used Naïve Bayesian classifiers and K-Nearest neighbor (KNN) by using
confusion matrix. The Naive Bayesian classifiers have been perhaps the most loved
approaches as premise of numerous grouping technique both hypothetically and
basically. K-closest neighbor (KNN) is a regulated learning calculation where the
consequence of new case inquiry is ordered in light of greater part of K-closest
neighbor class.
This system is implemented by using C# programming language with
Microsoft Visual 2013 and Microsoft SQL Server as the system database engine.
