Edible Mushroom Classification with Machine Learning Techniques

Mushrooms are readily available, and many rural residents rely on them for their nutritional value. However, many mushrooms have the potential to be poisonous, which cannot always be determined by the naked eye. So, we need scientific methods along with machine learning to determine if a mushroom is toxic or not and also to divide them into edible and non-edible groups. It is a summary of a study that classified mushrooms as either safe to consume or not by applying the methods from machine learning. In order to identify mushroom edibility based on physical characteristics, the paper examines a number of classification methods like KNN, Naive Bayes, Random Forest, Support Vector Machine, K-Means, and Decision Tree. The models' accuracy is assessed, and findings from Random Forest and KNN stand out. The article exhibits the potential of machine learning in improving classification accuracy and emphasizes the significance of precise mushroom identification.