scale and prepare X and y data for keras.py

Xscaler = MinMaxScaler(feature_range=(0, 1)) # scale so that all the X data will range from 0 to 1
Xscaler.fit(X_train)
scaled_X_train = Xscaler.transform(X_train)
print(X_train.shape)
Yscaler = MinMaxScaler(feature_range=(0, 1))
Yscaler.fit(y_train)
scaled_y_train = Yscaler.transform(y_train)
print(scaled_y_train.shape)
scaled_y_train = scaled_y_train.reshape(-1) # remove the second dimention from y so the shape changes from (n,1) to (n,)
print(scaled_y_train.shape)

scaled_y_train = np.insert(scaled_y_train, 0, 0)
scaled_y_train = np.delete(scaled_y_train, -1)


>>>(28916, 3)
>>>(28916, 1)
>>>(28916,)