ANN

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""

# 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
#initializin 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 second hidden layer
classifier.add(Dense(output_dim= 6, init='uniform', activation= 'relu'))

#adding output layer
classifier.add(Dense(output_dim= 1, init='uniform', activation= 'sigmoid'))

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

#fitting ann to the training set
classifier.fit(X_train, y_train, batch_size =10, nb_epoch= 100 )

# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred= (y_pred> 0.5)

#homework
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)