Classifying Mushroom edibility with Machine Learning via supervised models.
This is a final project from the subject 'Apredizaje Automático' in the University of Granada, a subject from the Computing Intelligent Systems speciality.
The idea is to analyze and process the Secondary Mushrorom dataset from the Machine Learning Repository: https://archive.ics.uci.edu/dataset/848/secondary+mushroom+dataset
This dataset includes 61069 hypothetical mushrooms with caps based on 173 species (353 mushrooms per species). Each mushroom is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended (the latter class was combined with the poisonous class).
The objetctive is to correctly classify if a mushroom is edible or poisinous