The main goal is to predict which mushroom is poisonous & which is edible by Using Machine Learning.
The Audubon Society Field Guide to North American Mushrooms contains descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom (1981). Each species is labelled as either definitely edible, definitely poisonous, or maybe edible but not recommended. This last category was merged with the toxic category. The Guide asserts unequivocally that there is no simple rule for judging a mushroom's edibility, such as "leaflets three, leave it be" for Poisonous Oak and Ivy. The main goal is to predict which mushroom is poisonous & which is edible.
https://www.kaggle.com/datasets/uciml/mushroom-classification
Machine Learning Technology
Agriculture
Intermediate
Python
Google Collbs , Ms, Python Excel, SciKit Learn, Numy , Panda , SeaBoarn
From the confusion matrix, we saw that our train and test data is balanced. Most of classfication methods hit 100% accuracy with this dataset.
In conclusion, the application of machine learning in mushroom classification has demonstrated its remarkable potential in automating and enhancing the accuracy of identifying mushroom species. Through the utilization of advanced algorithms and vast datasets, we have witnessed the development of robust models capable of distinguishing between edible and toxic mushrooms with a high degree of confidence. As technology continues to advance and more research is conducted, we can anticipate even greater strides in the accuracy and efficiency of mushroom classification using machine learning. This, in turn, will contribute to safer mushroom foraging practices, greater understanding of fungal biodiversity, and the preservation of ecosystems.