ashokasr

impute=titanic.pivot_table(values=['Age'],index=['Sex','Survived'],aggfunc=np.mean)
impute
for i in range(len(titanic)):
    if np.isnan(titanic.Age[i]):
        if (titanic.Sex[i]=='female') & (titanic.Survived[i]==0):
            titanic.Age[i]=25.04
for i in range(len(titanic)):
    if np.isnan(titanic.Age[i]):
        if (titanic.Sex[i]=='female') & (titanic.Survived[i]==1):
            titanic.Age[i]=28.84

ashokasr

# -*- coding: utf-8 -*-
"""
Created on Sun Dec 10 22:55:00 2017

@author: Ashok
"""

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Convolution2D

ashokasr

# 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')
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()