Kaggle competition: Predicting Molecular Properties
def prepare_labels(y):
values = np.array(y)
 label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(values)
 onehot_encoder = OneHotEncoder(sparse=False)
 integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
 onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
 y = onehot_encoded
return y, label_encoder<categorify>
sub1['scalar_coupling_constant'] = 0.6*sub1['scalar_coupling_constant'] + 0.4*sub2['scalar_coupling_constant']
sub1.to_csv('weighted-avg-blend-lgb-keras-1.csv', index=False)
 sub1['scalar_coupling_constant'].describe()