import numpy as np
max_element_column = np.max(X, 0)
min_element_column = np.amin(X, 0)

# @title Testing the Model

pH = input('pH: ')
temp = input('Temp: ')
taste = input('Taste: ')
odor = input('Odor: ')
fat = input('Fat: ')
turb = input('Turbidity: ')
colour = input('Colour: ')

test_data = np.array([float(pH),float(temp),float(taste),float(odor),
                      float(fat),float(turb),float(colour)])
test_data = np.reshape(test_data,(1,-1))
print(test_data)

# Convert max_element_column and min_element_column to NumPy arrays
max_element_column = max_element_column.to_numpy()
min_element_column = min_element_column.to_numpy()

#karena newmin=0 dan newmax=1 maka bisa dipke rumus dibawah
test_data = (test_data - min_element_column) / \
           (max_element_column - min_element_column)
print(test_data)

hasil=rfc.predict(test_data)
print('Hasil dari random forest: ', hasil)