text: import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  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:]
  
  # 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)
  
  import keras
  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_
  
  #Confusion Matrix
  from sklearn.metrics import confusion_matrix
  cm = confusion_matrix(y_test, y_pred)