8_ANN.py

import os
os.chdir('/Users/narsingrao/Documents/Satish_ML/Machine Learning A-Z (Codes and Datasets)/Part 8 - Deep Learning/Section 39 - Artificial Neural Networks (ANN)/Python')

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

labelencoder_X_1 = LabelEncoder()
labelencoder_X_2 = LabelEncoder()

X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
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.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)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)

#Part-2

import keras
from keras.models import Sequential
from keras.layers import Dense

classifier = Sequential()

classifier.add(Dense(output_dim = 6,
                     init = 'uniform',
                     activation = 'relu',
                     input_dim = 11))


classifier.add(Dense(output_dim = 6,
                     init = 'uniform',
                     activation = 'relu'))


classifier.add(Dense(output_dim = 1,
                     init = 'uniform',
                     activation = 'sigmoid'))


classifier.compile(optimizer = 'adam',
                   loss = 'binary_crossentropy',
                   metrics  = ['accuracy'])

classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100)


y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

accuracy = (cm[0][0]+cm[0][1])/(cm[1][0]+cm[1][1])