deepLearning.py

import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import confusion_matrix


dataSet = pd.read_csv('Churn_Modelling.csv')
X = dataSet.iloc[:, 3:13].values
y = dataSet.iloc[:, 13].values
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])
transformer = ColumnTransformer([('one_hot_encoder', OneHotEncoder(), [1])], remainder='passthrough')

X = np.array(transformer.fit_transform(X),dtype=float)
X=X[:,1:]
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state = 0 )

sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Part 2 - Now let's make the ANN!

# Initialising the ANN
classifier = Sequential()

# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))

# Adding the second hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))

# Adding the output layer
classifier.add(Dense(units = 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, 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)
cm=confusion_matrix(y_test,y_pred)