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ANN Python.py
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ANN Python.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jun 19 12:30:34 2018
Artificial Neural Network
@author: Hoang Le
"""
#Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
#Installing Tensorflow
# Install Tensorflow from website
# Install Keras
# pip install --upgrade 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 - Make the ANN
#Import 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(6, input_dim = 11, kernel_initializer = 'uniform', activation = 'relu'))
#Adding the second hidden layer
classifier.add(Dense(6, kernel_initializer = 'uniform', activation = 'relu'))
#Adding the output layer
classifier.add(Dense(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, nb_epoch=200)
# Part 3 - Making Prediction
# Predicting 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)