ann-1.py

#Artificial Neural Network

#Installing Theano

#Installing Tensorflow

#Installing 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
from keras.layers import Dropout

# Initializing the ANN
classifier = Sequential()

#Adding the input layer and the first hidden layer with dropout
classifier.add(Dense(units = 6,kernel_initializer = 'glorot_uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(rate=0.1))
#Adding the second hidden layer
classifier.add(Dense(units = 6,kernel_initializer = 'glorot_uniform', activation = 'relu'))
classifier.add(Dropout(rate=0.1))

#Adding the output layer
classifier.add(Dense(units = 1,kernel_initializer = 'glorot_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)

#Predicting a single new observation
"""Predict if the customer with the following information 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 = 'glorot_uniform', activation = 'relu', input_dim = 11))
    classifier.add(Dense(units = 6,kernel_initializer = 'glorot_uniform', activation = 'relu'))
    classifier.add(Dense(units = 1,kernel_initializer = 'glorot_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 = 'glorot_uniform', activation = 'relu', input_dim = 11))
    classifier.add(Dense(units = 6,kernel_initializer = 'glorot_uniform', activation = 'relu'))
    classifier.add(Dense(units = 1,kernel_initializer = 'glorot_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_