

# Importing the libraries 
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

import warnings
warnings.filterwarnings('ignore')
warnings.filterwarnings("ignore", category=DeprecationWarning)





# Importing the datasetm
dataset = pd.read_csv(r'C:\Users\IDEAPAD 320\Desktop\datasets\Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values




from sklearn.preprocessing import LabelEncoder, OneHotEncoder

from sklearn.compose import ColumnTransformer



labelencoder_X_2 = LabelEncoder()

X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])



columnTransformer = ColumnTransformer([('encoder', OneHotEncoder(), [1])], remainder='passthrough')

X = np.array(columnTransformer.fit_transform(X), dtype = np.str)

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) 





# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout





# Initialising 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 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)





# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)







correct_prediction = cm[0][0]+cm[1][1]
wrong_prediction = cm[0][1]+cm[1][0]
total = correct_prediction + wrong_prediction





correct_prediction




wrong_prediction





accuracy = (correct_prediction/total)*100





accuracy






