Mushroom classification using machine-learning techniques

Mushroom is one of the important ingredient in our food that has good nutrients. Most types of mushroom are poisonous (inedible), and because of its importance, we need to identify poisonous from eatable mushrooms. Machine learning (ML) techniques such as naïve Bayes, decision tree, SVM, and more applied on mushroom features to classify it into edible or not. There is a limited research on mushroom classification, existed research focus on applying ML techniques individually, where some algorithm perform better in term of accuracy. This research proposed an integrated model that combine most accurate technique’s decisions into one decision instead off treating them individually. Mushroom dataset downloaded from UCI repository. Results shows that the performance of the integrated model outperform other techniques by 94% accuracy.