text: # Artificial Neural Network
  
  # Installing Theano
  # pip install --ignore-installed --upgrade scipy
  # pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
  
  # Installing Tensorflow
  # Install Tensorflow from the website: https://www.tensorflow.org/versions/r0.12/get_started/os_setup.html
  # conda create -n tensorflow pip python=3.6
  # activate tensorflow
  # pip install --ignore-installed --upgrade tensorflow
  
  # Installing Keras
  # pip install --upgrade keras
  
  # Part 1 - Data Preprocessing
  
  # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv('Churn_Modelling.csv')
  X = dataset.iloc[:, 3:13].values
  y = dataset.iloc[:, 13].values
  
  # Encoding categorical data
  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(categorical_features = [1])
  X = onehotencoder.fit_transform(X).toarray()
  X = X[:, 1:]
  
  # Splitting the dataset into the Training set and 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, random_state = 0)
  
  # Feature Scaling
  from sklearn.preprocessing import StandardScaler
  sc = StandardScaler()
  X_train = sc.fit_transform(X_train)
  X_test = sc.transform(X_test)
  
  # Part 2 - Now let's make the ANN!
  
  # Importing the Keras libraries and packages
  import keras
  from keras.models import Sequential
  from keras.layers import Dense
  
  # Initialising the ANN
  classifier = Sequential()
  
  # Adding the input layer and the first hidden layer
  classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
  
  # Adding the second hidden layer
  classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
  
  # Adding the output layer
  classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
  
  # Compiling the ANN
  classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
  
  # Fitting the ANN to the Training set
  classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
  
  # Part 3 - Making the predictions and evaluating the model
  
  # Predicting the Test set results
  y_pred = classifier.predict(X_test)
  y_pred = (y_pred > 0.5)
  
  # Making the Confusion Matrix
  from sklearn.metrics import confusion_matrix
  cm = confusion_matrix(y_test, y_pred)