Mushroom binary classifier is a basic binary classification project that trains different models on recognizing if a given mushroom is poisonous or edible based on its characteristics.
Some of these features are:
- cap-shape
- cap-surface
- habitat
- has-ring
- stem-color
- etc...
Models such as Perceptron, Random Forest and Gradient Boosting are used in this project. Each of them is associated with its Confusion Matrix.
Eventually, a ROC Curve is displayed, in order to better visualize the performance of each model.
Then, after applying RFECV to the dataset, one of the best models (Gradient Boosting) is trained again to boost its performance.
Eventually, the final model is saved in a .sav file using the pickle library
so it can be reused to classify future given mushrooms.