I built an ML classification model using the decision tree framework to classify a mushroom species as edible or poisonous. The model is a simplified decision tree classifier that begins at the root node and considers three features (cap color, stalk shape, and solidarity. The model calculates information gain for splitting by each feature at each node, then selects the feature with the highest information gain (by calculating the entropy), and lastly splits the data into left and right branches according to the selected feature. This process is repeated again and again until the stopping criteria is met (maximum depth of 2).