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Churn_.py
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Churn_.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Mar 2 11:37:11 2019
@author: manju
"""
import pandas as pd
churn=pd.read_csv("Churn_Modelling.csv")
churn.head()
churn=churn.drop(['RowNumber','CustomerId','Surname'],axis=1)
Y=churn['Exited']
churn=churn.drop(['Exited'],axis=1)
import keras
from keras.models import Sequential
from keras.layers import Dense
my_ann=Sequential()
my_ann.add(Dense(units=32,kernel_initializer='uniform',activation='relu',input_dim=10))
churn=churn.get_dummies(churn)
X=churn.drop(['Geography_France','Gender_Female'],axis=1)
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
X=sc.fit_transform(X)
my_ann.add(Dense(units=32,kernel_initializer='uniform',activation='relu',input_dim=11))
----------------
##add hidden layer
my_ann.add(Dense(units=32,kernel_initializer='uniform',activation='relu'))
##output layer
my_ann.add(Dense(units=1,kernel_initializer='uniform',activation='sigmoid'))
print(my_ann.summary())
my_ann.complile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
from sklearn.model_selection import train_test_split
[xtrain,xtest,ytrain,ytest]=train_test_split(X,Y,test_size=0.3,random_state=42)
my_ann.fit(xtrain,ytrain,batch_size=10,epochs=100)
ypred=my_ann.predict(xtest)
ypred=(ypred>0.5)
from sklearn.metrics import accuracy_score
acc=accuracy_score(ytest,ypred)
print(acc)