ann.py

# Artificial Neural Network

# 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_X1 = LabelEncoder()
X[:, 1] = labelencoder_X1.fit_transform(X[:, 1])
labelencoder_X2 = LabelEncoder()
X[:, 2] = labelencoder_X2.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_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

# PART 2 -- MAKING ARTIFICIAL NEURAL NETWORK
# importing the keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense

# intialising the ANN
classifier = Sequential()

# Adding the Input layer and the First Hidden Layer
classifier.add(Dense(output_dim = 6,  init = 'uniform', activation = 'relu', input_dim = 11))

# Adding the Second Hidden Layer
classifier.add(Dense(output_dim = 6,  init = 'uniform', activation = 'relu'))

# Adding the Output Layer
classifier.add(Dense(output_dim = 1,  init = 'uniform', activation = 'sigmoid'))

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

# Fitting the ANN to Training set
classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100) 

# PART 3 -- MAKING PREDICTIONS AND EVALUATING THE MODEL
#predict the test set results
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)