import huble
model = huble.sklearn.st_gradient_descent(parameters={'loss': 'hinge', 'penalty': 'l2', 'fit_intercept': True, 'alpha': 0.0001, 'max_iter': 1000, 'tol': 0.001, 'random_state': 'None', 'shuffle': True, 'learning_rate': 'optimal', 'initial_learning_rate': 0, 'early_stopping': False, 'validation_fraction': 1})
data=huble.util.load_dataset('https://ipfs.filebase.io/ipfs/QmRspeqXi9J2PVTmXYwMaBif9dYWVkNhM8EFomUAfajnT1')
data = huble.sklearn.drop_duplicates(data=data,parameters={'subset': [], 'keep': 'first', 'inplace': False, 'ignore_index': False})
data = huble.sklearn.remove_mismatch_data(data=data, parameters={'exceptions': []})
data = huble.sklearn.remove_outliers(data=data,columns=['Ticket'])
data = huble.sklearn.drop_rows_columns(data=data,parameters={'labels': ['PassengerId', 'Pclass', 'Name'], 'axis': 0, 'inplace': False, 'errors': 'raise'})
