Python - /MACHINE LEARNING/ problema al adaptar el código de la predicción del Churn Modelling.

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

# Importar la base de datos

dataset = pd.read_csv('https://raw.githubusercontent.com/AaronWard/Churn-Modelling-Artificial-Neural-Network/master/Churn_Modelling.csv');

X = dataset.iloc[:, 3:13].values

Y = dataset.iloc[:, 13].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

labelencoder_X_1 = LabelEncoder()

X[:,1] = labelencoder_X_1.fit_transform(X[:,1])

labelencoder_X_2 = LabelEncoder()

X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])

onehotencoder = OneHotEncoder()

X = onehotencoder.fit_transform(X).toarray()

X = X[:, 1:]

#Dividir el dataset en Training set y Test Set

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2)

# Feature Scaling

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)

# Importar la libreria de Keras y sus paquetes

import keras

from keras.models import Sequential

from keras.layers import Dense

#Inicializar la red neuronal

classifier = Sequential()

classifier.add(Dense(units = 6, kernel_initializer = 'uniform',

activation = 'relu', input_dim = 11))

classifier.add(Dense(units = 6, kernel_initializer = 'uniform',

activation = 'relu'))

classifier.add(Dense(units = 1, kernel_initializer = 'uniform',

activation = 'sigmoid'))

# Compilar red neuronal

classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

classifier.fit(X_train, y_train, batch_size = 10, epochs = 50)

# Predecir los resultados del Test set

y_pred = classifier.predict(X_test)

y_pred = (y_pred > 0.5)

new_prediction = classifier.predict(sc.transform

(np.array([[0.0, 0, 500, 1, 40, 3, 50000, 2, 1, 1, 40000]])))

new_prediction = (new_prediction > 0.5)

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_test, y_pred)

print('cm: ', cm)

print('New prediction: ', new_prediction)