Keras inverse scaling prediction from model causes problems with broadcasting with shapes

I
have built multi classification model with Keras and after model is finished I would like to predict value for one of my test input.

This is the part where I scaled features:

x = dataframe.drop("workTime", axis = 1)
x = dataframe.drop("creation", axis = 1)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x = pd.DataFrame(sc.fit_transform(x))
y = dataframe["workTime"]

import seaborn as sb
corr = dataframe.corr()
sb.heatmap(corr, cmap="Blues", annot=True)

print("Scaled features:", x.head(3))

Then I did:

y_cat = to_categorical(y)
x_train, x_test, y_train, y_test = train_test_split(x.values, y_cat, test_size=0.2)

And built model:

model = Sequential()

model.add(Dense(16, input_shape = (9,), activation = "relu"))
model.add(Dense(8, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(6, activation = "softmax"))
model.compile(Adam(lr = 0.0001), "categorical_crossentropy", metrics = ["categorical_accuracy"])
model.summary()

model.fit(x_train, y_train, verbose=1, batch_size = 8, epochs=100, shuffle=True)

After my calculation finished, I wanted to take first element from test data and predict
value/classify it.

print(x_test.shape, x_train.shape) // (1550, 9) (6196, 9)
firstTest = x_test[:1]; // [[ 2.76473141  1.21064165  0.18816548 -0.94077449 -0.30981017 -0.37723917
  -0.44471711 -1.44141792  0.20222467]]

prediction = model.predict(firstTest)


print(prediction) // [[7.5265622e-01 2.4710520e-01 2.3643016e-04 2.1405797e-06 3.8411264e-19
  9.4137732e-23]]
print(prediction[0]) // [7.5265622e-01 2.4710520e-01 2.3643016e-04 2.1405797e-06 3.8411264e-19
 9.4137732e-23]

unscaled = sc.inverse_transform(prediction)

print("prediction", unscaled)

During this I retrieve:

ValueError: operands could not be broadcast together with shapes (1,6) (9,) (1,6) 

I think it may be related to my scalers.
And please correct me if I wrong, but what I want to achieve here is to either have one output value which points me how this entry was classified or array of possibilities for each classification label.

Thank you for hints