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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Table of Contents
<p><div class="lev1 toc-item"><a href="#Building-an-ANN" data-toc-modified-id="Building-an-ANN-1"><span class="toc-item-num">1&nbsp;&nbsp;</span>Building an ANN</a></div><div class="lev2 toc-item"><a href="#Installing-packages" data-toc-modified-id="Installing-packages-11"><span class="toc-item-num">1.1&nbsp;&nbsp;</span>Installing packages</a></div><div class="lev2 toc-item"><a href="#Data-Preprocessing" data-toc-modified-id="Data-Preprocessing-12"><span class="toc-item-num">1.2&nbsp;&nbsp;</span>Data Preprocessing</a></div><div class="lev2 toc-item"><a href="#Building-an-ANN" data-toc-modified-id="Building-an-ANN-13"><span class="toc-item-num">1.3&nbsp;&nbsp;</span>Building an ANN</a></div><div class="lev2 toc-item"><a href="#Making-predictions-and-evaluating-the-model" data-toc-modified-id="Making-predictions-and-evaluating-the-model-14"><span class="toc-item-num">1.4&nbsp;&nbsp;</span>Making predictions and evaluating the model</a></div><div class="lev2 toc-item"><a href="#Evaluating,-Improving-and-Tuning-the-ANN" data-toc-modified-id="Evaluating,-Improving-and-Tuning-the-ANN-15"><span class="toc-item-num">1.5&nbsp;&nbsp;</span>Evaluating, Improving and Tuning the ANN</a></div>
# Building an ANN
Credit
Step1:  Data Preprocessing
Step2:  y (actual value)
Step3:  Building an ANN
Step4:  Making predictions and evaluating the model
Step5:  Evaluating, Improving and Tuning the ANN
Using K-Fold Cross validation with Keras
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Python Code:
# Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
# Installing Tensorflow
# pip install tensorflow
# Installing Keras
# pip install --upgrade keras
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Building-an-ANN" data-toc-modified-id="Building-an-ANN-1"><span class="toc-item-num">1&nbsp;&nbsp;</span>Building an ANN</a></div><div class="lev2 toc-item"><a href="#Installing-packages" data-toc-modified-id="Installing-packages-11"><span class="toc-item-num">1.1&nbsp;&nbsp;</span>Installing packages</a></div><div class="lev2 toc-item"><a href="#Data-Preprocessing" data-toc-modified-id="Data-Preprocessing-12"><span class="toc-item-num">1.2&nbsp;&nbsp;</span>Data Preprocessing</a></div><div class="lev2 toc-item"><a href="#Building-an-ANN" data-toc-modified-id="Building-an-ANN-13"><span class="toc-item-num">1.3&nbsp;&nbsp;</span>Building an ANN</a></div><div class="lev2 toc-item"><a href="#Making-predictions-and-evaluating-the-model" data-toc-modified-id="Making-predictions-and-evaluating-the-model-14"><span class="toc-item-num">1.4&nbsp;&nbsp;</span>Making predictions and evaluating the model</a></div><div class="lev2 toc-item"><a href="#Evaluating,-Improving-and-Tuning-the-ANN" data-toc-modified-id="Evaluating,-Improving-and-Tuning-the-ANN-15"><span class="toc-item-num">1.5&nbsp;&nbsp;</span>Evaluating, Improving and Tuning the ANN</a></div>
# Building an ANN
Credit: [Deep Learning A-Z™: Hands-On Artificial Neural Networks](https://www.udemy.com/deeplearning/learn/v4/content)
- [Getting the dataset](https://www.superdatascience.com/deep-learning/)
## Installing packages
End of explanation
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('./Artificial_Neural_Networks/Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values
Explanation: Data Preprocessing
End of explanation
print (X.shape)
X
print (y.shape)
y
# 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:]
print (X.shape)
X
print (y.shape)
y
# 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)
Explanation: y (actual value): exited, this is the value we are trying to predict, which means if the customer stays or exit the bank.
End of explanation
# 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)
Explanation: Building an ANN
End of explanation
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)
cm
Explanation: Making predictions and evaluating the model
End of explanation
# 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
from keras.layers import Dropout
def build_classifier(optimizer):
    classifier = Sequential()
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
    # classifier.add(Dropout(p = 0.1))
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
    # classifier.add(Dropout(p = 0.1))
    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_
Explanation: Evaluating, Improving and Tuning the ANN
Using K-Fold Cross validation with Keras
End of explanation
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