text: # Artificial Neural Network
  
  # 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:]
  
  
  from sklearn.compose import ColumnTransformer
  
  
  
  labelencoder_X_2 = LabelEncoder()
  
  X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
  
  #onehotencoder = OneHotEncoder(categorical_features = [1])
  
  #X = onehotencoder.fit_transform(X).toarray()
  
  columnTransformer = ColumnTransformer([('encoder', OneHotEncoder(), [1])], remainder='passthrough')
  
  X = np.array(columnTransformer.fit_transform(X), dtype = np.str)
  
  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
  from keras.layers import Dropout
  
  # 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))
  # classifier.add(Dropout(p = 0.1))
  
  # Adding the second hidden layer
  classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
  # classifier.add(Dropout(p = 0.1))
  
  # 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 predictions and evaluating the model
  
  # Predicting the Test set results
  y_pred = classifier.predict(X_test)
  y_pred = (y_pred > 0.5)
  
  # Predicting a single new observation
  """Predict if the customer with the following informations will leave the bank:
  Geography: France
  Credit Score: 600
  Gender: Male
  Age: 40
  Tenure: 3
  Balance: 60000
  Number of Products: 2
  Has Credit Card: Yes
  Is Active Member: Yes
  Estimated Salary: 50000"""
  new_prediction = classifier.predict(sc.transform(np.array([[0.0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])))
  new_prediction = (new_prediction > 0.5)
  
  # Making the Confusion Matrix
  from sklearn.metrics import confusion_matrix
  cm = confusion_matrix(y_test, y_pred)
  
  # Part 4 - Evaluating, Improving and Tuning the ANN
  
  # Evaluating the ANN
  from keras.wrappers.scikit_learn import KerasClassifier
  from sklearn.model_selection import cross_val_score
  from keras.models import Sequential
  from keras.layers import Dense
  def build_classifier():
      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'))
      classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
      return classifier
  classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 100)
  accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
  mean = accuracies.mean()
  variance = accuracies.std()
  
  # Improving the ANN
  # Dropout Regularization to reduce overfitting if needed
  
  # Tuning the ANN
  from keras.wrappers.scikit_learn import KerasClassifier
  from sklearn.model_selection import GridSearchCV
  from keras.models import Sequential
  from keras.layers import Dense
  def build_classifier(optimizer):
      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'))
      classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy'])
      return classifier
  classifier = KerasClassifier(build_fn = build_classifier)
  parameters = {'batch_size': [25, 32],
                'epochs': [100, 500],
                'optimizer': ['adam', 'rmsprop']}
  grid_search = GridSearchCV(estimator = classifier,
                             param_grid = parameters,
                             scoring = 'accuracy',
                             cv = 10)
  grid_search = grid_search.fit(X_train, y_train)
  best_parameters = grid_search.best_params_
  best_accuracy = grid_search.best_score_
  