Tinanic Survival

In [1]:

    

import numpy as np
import pandas as pd

#load the files
train = pd.read_csv('input/train.csv')
test = pd.read_csv('input/test.csv')
data = pd.concat([train, test])

#size of training dataset
train_samples = train.shape[0]

#print some of them
data.head()


    

    
Out[1]:







  

    

      

      
Age
      
Cabin
      
Embarked
      
Fare
      
Name
      
Parch
      
PassengerId
      
Pclass
      
Sex
      
SibSp
      
Survived
      
Ticket
    
  
  

    

      
0
      
22.0
      
NaN
      
S
      
7.2500
      
Braund, Mr. Owen Harris
      
0
      
1
      
3
      
male
      
1
      
0.0
      
A/5 21171
    
    

      
1
      
38.0
      
C85
      
C
      
71.2833
      
Cumings, Mrs. John Bradley (Florence Briggs Th...
      
0
      
2
      
1
      
female
      
1
      
1.0
      
PC 17599
    
    

      
2
      
26.0
      
NaN
      
S
      
7.9250
      
Heikkinen, Miss. Laina
      
0
      
3
      
3
      
female
      
0
      
1.0
      
STON/O2. 3101282
    
    

      
3
      
35.0
      
C123
      
S
      
53.1000
      
Futrelle, Mrs. Jacques Heath (Lily May Peel)
      
0
      
4
      
1
      
female
      
1
      
1.0
      
113803
    
    

      
4
      
35.0
      
NaN
      
S
      
8.0500
      
Allen, Mr. William Henry
      
0
      
5
      
3
      
male
      
0
      
0.0
      
373450
    
  

In [2]:

    

#show the data types
data.info()


    

    




<class 'pandas.core.frame.DataFrame'>
Int64Index: 1309 entries, 0 to 417
Data columns (total 12 columns):
Age            1046 non-null float64
Cabin          295 non-null object
Embarked       1307 non-null object
Fare           1308 non-null float64
Name           1309 non-null object
Parch          1309 non-null int64
PassengerId    1309 non-null int64
Pclass         1309 non-null int64
Sex            1309 non-null object
SibSp          1309 non-null int64
Survived       891 non-null float64
Ticket         1309 non-null object
dtypes: float64(3), int64(4), object(5)
memory usage: 132.9+ KB

In [3]:

    

#show the distributions
data.describe()


    

    
Out[3]:







  

    

      

      
Age
      
Fare
      
Parch
      
PassengerId
      
Pclass
      
SibSp
      
Survived
    
  
  

    

      
count
      
1046.000000
      
1308.000000
      
1309.000000
      
1309.000000
      
1309.000000
      
1309.000000
      
891.000000
    
    

      
mean
      
29.881138
      
33.295479
      
0.385027
      
655.000000
      
2.294882
      
0.498854
      
0.383838
    
    

      
std
      
14.413493
      
51.758668
      
0.865560
      
378.020061
      
0.837836
      
1.041658
      
0.486592
    
    

      
min
      
0.170000
      
0.000000
      
0.000000
      
1.000000
      
1.000000
      
0.000000
      
0.000000
    
    

      
25%
      
21.000000
      
7.895800
      
0.000000
      
328.000000
      
2.000000
      
0.000000
      
0.000000
    
    

      
50%
      
28.000000
      
14.454200
      
0.000000
      
655.000000
      
3.000000
      
0.000000
      
0.000000
    
    

      
75%
      
39.000000
      
31.275000
      
0.000000
      
982.000000
      
3.000000
      
1.000000
      
1.000000
    
    

      
max
      
80.000000
      
512.329200
      
9.000000
      
1309.000000
      
3.000000
      
8.000000
      
1.000000
    
  

In [4]:

    

#show categorical
data.describe(include=['O'])


    

    
Out[4]:







  

    

      

      
Cabin
      
Embarked
      
Name
      
Sex
      
Ticket
    
  
  

    

      
count
      
295
      
1307
      
1309
      
1309
      
1309
    
    

      
unique
      
186
      
3
      
1307
      
2
      
929
    
    

      
top
      
C23 C25 C27
      
S
      
Connolly, Miss. Kate
      
male
      
CA. 2343
    
    

      
freq
      
6
      
914
      
2
      
843
      
11
    
  

In [5]:

    

#showing survival percentiles
train['Survived'].quantile([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])


    

    
Out[5]:





0.0    0.0
0.1    0.0
0.2    0.0
0.3    0.0
0.4    0.0
0.5    0.0
0.6    0.0
0.7    1.0
0.8    1.0
0.9    1.0
1.0    1.0
Name: Survived, dtype: float64

In [6]:

    

#showing fare percentiles
data['Fare'].quantile([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])


    

    
Out[6]:





0.0      0.00000
0.1      7.56750
0.2      7.85420
0.3      8.05000
0.4     10.50000
0.5     14.45420
0.6     21.67920
0.7     26.95500
0.8     41.57920
0.9     78.05082
1.0    512.32920
Name: Fare, dtype: float64

In [7]:

    

#Dropping useless features
data = data.drop(['PassengerId', 'Name', 'Embarked', 'Ticket'], axis=1)
data.head(10)


    

    
Out[7]:







  

    

      

      
Age
      
Cabin
      
Fare
      
Parch
      
Pclass
      
Sex
      
SibSp
      
Survived
    
  
  

    

      
0
      
22.0
      
NaN
      
7.2500
      
0
      
3
      
male
      
1
      
0.0
    
    

      
1
      
38.0
      
C85
      
71.2833
      
0
      
1
      
female
      
1
      
1.0
    
    

      
2
      
26.0
      
NaN
      
7.9250
      
0
      
3
      
female
      
0
      
1.0
    
    

      
3
      
35.0
      
C123
      
53.1000
      
0
      
1
      
female
      
1
      
1.0
    
    

      
4
      
35.0
      
NaN
      
8.0500
      
0
      
3
      
male
      
0
      
0.0
    
    

      
5
      
NaN
      
NaN
      
8.4583
      
0
      
3
      
male
      
0
      
0.0
    
    

      
6
      
54.0
      
E46
      
51.8625
      
0
      
1
      
male
      
0
      
0.0
    
    

      
7
      
2.0
      
NaN
      
21.0750
      
1
      
3
      
male
      
3
      
0.0
    
    

      
8
      
27.0
      
NaN
      
11.1333
      
2
      
3
      
female
      
0
      
1.0
    
    

      
9
      
14.0
      
NaN
      
30.0708
      
0
      
2
      
female
      
1
      
1.0
    
  

In [8]:

    

#New column to know if the passenger has family on board
def family(size):
    size +=1
    if size == 1:
        return "alone"
    elif size < 5:
        return "medium"
    else:
        return "large"

data['FamilySize'] = (data['SibSp']+data['Parch']).apply(family)
data = data.drop(['SibSp', 'Parch'], axis=1)

data.head()


    

    
Out[8]:







  

    

      

      
Age
      
Cabin
      
Fare
      
Pclass
      
Sex
      
Survived
      
FamilySize
    
  
  

    

      
0
      
22.0
      
NaN
      
7.2500
      
3
      
male
      
0.0
      
medium
    
    

      
1
      
38.0
      
C85
      
71.2833
      
1
      
female
      
1.0
      
medium
    
    

      
2
      
26.0
      
NaN
      
7.9250
      
3
      
female
      
1.0
      
alone
    
    

      
3
      
35.0
      
C123
      
53.1000
      
1
      
female
      
1.0
      
medium
    
    

      
4
      
35.0
      
NaN
      
8.0500
      
3
      
male
      
0.0
      
alone
    
  

In [9]:

    

#cond = (data['Survived']==1) & (data['Sex']=='female') & (data['Pclass']==1)
cond = (data['Sex']=='female') & (data['Pclass']==3)
#datamean = data[cond].groupby(['Survived','Sex','Pclass'])['Age'].mean()
#print(datamean.mean())
#data.groupby(['Survived','Sex','Pclass'])['Age'].mean()
data.groupby(['Survived','Sex','Pclass'])['Age'].mean()
data[cond].groupby(['Sex','Pclass'])['Age'].mean()


    

    
Out[9]:





Sex     Pclass
female  3         22.185329
Name: Age, dtype: float64

In [10]:

    

def getAge(row):
    surv = row.Survived
    sex = row.Sex
    pclass = row.Pclass
    
    if surv==0 or surv==1:
        condition = (data['Survived']==surv) & (data['Sex']==sex) & (data['Pclass']==pclass)
        df_mean = data[condition].groupby(['Survived','Sex','Pclass'])['Age'].mean()
    else:
        condition = (data['Sex']==sex) & (data['Pclass']==pclass)
        df_mean = data[condition].groupby(['Sex','Pclass'])['Age'].mean()
    
    print("srv: {}, sex: {}, class: {} -> mean: {}".format(surv, sex, pclass, df_mean.mean()))
    return df_mean.mean()
    
data['Age'] = data['Age'].fillna(data.apply(getAge, axis=1))


    

    




srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 2 -> mean: 16.022
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: male, class: 2 -> mean: 16.022
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: female, class: 2 -> mean: 36.0
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 2 -> mean: 16.022
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
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srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
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srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 1 -> mean: 25.666666666666668
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srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 2 -> mean: 16.022
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: male, class: 2 -> mean: 16.022
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: female, class: 2 -> mean: 36.0
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srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
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srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
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srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
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srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
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srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
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srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 2 -> mean: 16.022
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 1 -> mean: 25.666666666666668
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: female, class: 2 -> mean: 36.0
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: male, class: 2 -> mean: 16.022
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: female, class: 2 -> mean: 36.0
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 2 -> mean: 16.022
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srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 0.0, sex: female, class: 3 -> mean: 23.818181818181817
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 3 -> mean: 22.274210526315787
srv: 0.0, sex: male, class: 1 -> mean: 44.58196721311475
srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
srv: 0.0, sex: male, class: 3 -> mean: 27.25581395348837
srv: 1.0, sex: male, class: 1 -> mean: 36.248000000000005
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srv: 1.0, sex: female, class: 3 -> mean: 19.329787234042552
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srv: 1.0, sex: female, class: 1 -> mean: 34.9390243902439
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srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
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srv: 0.0, sex: male, class: 2 -> mean: 33.36904761904762
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srv: 1.0, sex: female, class: 2 -> mean: 28.080882352941178
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srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 2 -> mean: 27.499223300970876
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 2 -> mean: 30.815379746835443
srv: nan, sex: male, class: 1 -> mean: 41.02927152317881
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: female, class: 3 -> mean: 22.185328947368422
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: female, class: 1 -> mean: 37.037593984962406
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187
srv: nan, sex: male, class: 3 -> mean: 25.962263610315187

In [11]:

    

len(data['Age']) - data['Age'].count()


    

    
Out[11]:





0

In [12]:

    

#Get information from Cabin
def getCabinLetter(cabin):
    letter = "U"
    if isinstance(cabin,str) and cabin != "":        
        letter = cabin.split()[0][0]
    return letter

def getCabinPos(cabin):
    num = 0
    if isinstance(cabin,str) and cabin != "":        
        snum = cabin.split()[0][1:]
        num = int(snum) if snum else 0
    return num

def processCabin(df):
    df['CabinLetter'] = df['Cabin'].apply(getCabinLetter)
    #df['CabinPos'] = df['Cabin'].apply(getCabinPos)

processCabin(data)
#print(data[['Cabin','CabinLetter','CabinPos']].head(10))
print(data[['Cabin','CabinLetter']].head(10))
data = data.drop('Cabin', axis=1)


    

    




  Cabin CabinLetter
0   NaN           U
1   C85           C
2   NaN           U
3  C123           C
4   NaN           U
5   NaN           U
6   E46           E
7   NaN           U
8   NaN           U
9   NaN           U

In [13]:

    

# Preprocess CabinLetter 'U'
l='A'
print(data[data['CabinLetter'] == l]['Fare'].min())
print(data[data['CabinLetter'] == l]['Fare'].max())
print(data[data['CabinLetter'] == l]['Fare'].mean())
data.groupby(['CabinLetter'])['Fare'].describe()


    

    




0.0
81.8583
41.2443136364






    
Out[13]:







  

    

      

      
count
      
mean
      
std
      
min
      
25%
      
50%
      
75%
      
max
    
    

      
CabinLetter
      

      

      

      

      

      

      

      

    
  
  

    

      
A
      
22.0
      
41.244314
      
20.140358
      
0.0000
      
30.1250
      
35.0771
      
50.37185
      
81.8583
    
    

      
B
      
65.0
      
122.383078
      
115.312993
      
0.0000
      
57.0000
      
82.2667
      
146.52080
      
512.3292
    
    

      
C
      
94.0
      
107.926598
      
72.912034
      
25.7000
      
52.0000
      
86.2896
      
151.55000
      
263.0000
    
    

      
D
      
46.0
      
53.007339
      
28.126283
      
12.8750
      
27.7208
      
52.5542
      
76.72920
      
113.2750
    
    

      
E
      
41.0
      
54.564634
      
37.738225
      
8.0500
      
26.2875
      
53.1000
      
79.65000
      
134.5000
    
    

      
F
      
21.0
      
18.079367
      
12.215124
      
7.2292
      
7.6500
      
13.0000
      
26.00000
      
39.0000
    
    

      
G
      
5.0
      
14.205000
      
3.416419
      
10.4625
      
10.4625
      
16.7000
      
16.70000
      
16.7000
    
    

      
T
      
1.0
      
35.500000
      
NaN
      
35.5000
      
35.5000
      
35.5000
      
35.50000
      
35.5000
    
    

      
U
      
1013.0
      
19.132707
      
27.489412
      
0.0000
      
7.8542
      
10.5000
      
23.00000
      
512.3292
    
  

In [14]:

    

#Transform categorical to dummies
data = pd.get_dummies(data)


    

In [15]:

    

#finding NaN
data.columns[data.isnull().any()].tolist()


    

    
Out[15]:





['Fare', 'Survived']

In [16]:

    

#Replace the NaN value with the mean (careful at Survided from test -> data[891:])
#data['Age'].fillna(data['Age'].mean(), inplace=True)
data['Fare'].fillna(data['Fare'].mean(), inplace=True)

data.columns[data.isnull().any()].tolist()


    

    
Out[16]:





['Survived']

In [17]:

    

data.describe().T


    

    
Out[17]:







  

    

      

      
count
      
mean
      
std
      
min
      
25%
      
50%
      
75%
      
max
    
  
  

    

      
Age
      
1309.0
      
29.404170
      
13.223943
      
0.17
      
22.0000
      
27.255814
      
36.000
      
80.0000
    
    

      
Fare
      
1309.0
      
33.295479
      
51.738879
      
0.00
      
7.8958
      
14.454200
      
31.275
      
512.3292
    
    

      
Pclass
      
1309.0
      
2.294882
      
0.837836
      
1.00
      
2.0000
      
3.000000
      
3.000
      
3.0000
    
    

      
Survived
      
891.0
      
0.383838
      
0.486592
      
0.00
      
0.0000
      
0.000000
      
1.000
      
1.0000
    
    

      
Sex_female
      
1309.0
      
0.355997
      
0.478997
      
0.00
      
0.0000
      
0.000000
      
1.000
      
1.0000
    
    

      
Sex_male
      
1309.0
      
0.644003
      
0.478997
      
0.00
      
0.0000
      
1.000000
      
1.000
      
1.0000
    
    

      
FamilySize_alone
      
1309.0
      
0.603514
      
0.489354
      
0.00
      
0.0000
      
1.000000
      
1.000
      
1.0000
    
    

      
FamilySize_large
      
1309.0
      
0.062643
      
0.242413
      
0.00
      
0.0000
      
0.000000
      
0.000
      
1.0000
    
    

      
FamilySize_medium
      
1309.0
      
0.333843
      
0.471765
      
0.00
      
0.0000
      
0.000000
      
1.000
      
1.0000
    
    

      
CabinLetter_A
      
1309.0
      
0.016807
      
0.128596
      
0.00
      
0.0000
      
0.000000
      
0.000
      
1.0000
    
    

      
CabinLetter_B
      
1309.0
      
0.049656
      
0.217317
      
0.00
      
0.0000
      
0.000000
      
0.000
      
1.0000
    
    

      
CabinLetter_C
      
1309.0
      
0.071811
      
0.258273
      
0.00
      
0.0000
      
0.000000
      
0.000
      
1.0000
    
    

      
CabinLetter_D
      
1309.0
      
0.035141
      
0.184207
      
0.00
      
0.0000
      
0.000000
      
0.000
      
1.0000
    
    

      
CabinLetter_E
      
1309.0
      
0.031322
      
0.174252
      
0.00
      
0.0000
      
0.000000
      
0.000
      
1.0000
    
    

      
CabinLetter_F
      
1309.0
      
0.016043
      
0.125688
      
0.00
      
0.0000
      
0.000000
      
0.000
      
1.0000
    
    

      
CabinLetter_G
      
1309.0
      
0.003820
      
0.061709
      
0.00
      
0.0000
      
0.000000
      
0.000
      
1.0000
    
    

      
CabinLetter_T
      
1309.0
      
0.000764
      
0.027639
      
0.00
      
0.0000
      
0.000000
      
0.000
      
1.0000
    
    

      
CabinLetter_U
      
1309.0
      
0.774637
      
0.417981
      
0.00
      
1.0000
      
1.000000
      
1.000
      
1.0000
    
  

In [18]:

    

import matplotlib.pyplot as plt

#data[['Pclass','Age','Fare','CabinPos']].head(15).plot()
data[['Pclass','Age','Fare']].head(15).plot()
plt.show()


    

In [19]:

    

#Squeeze the data to [0,1]
from sklearn import preprocessing

scaler = preprocessing.MinMaxScaler()
#data[['Age','Fare','Pclass','CabinPos']] = scaler.fit_transform(data[['Age','Fare','Pclass','CabinPos']])
data[['Age','Fare','Pclass']] = scaler.fit_transform(data[['Age','Fare','Pclass']])
print("Train shape: {}".format(data.shape))

#data[['Pclass','Age','Fare','CabinPos']].head(15).plot()
data[['Pclass','Age','Fare']].head(15).plot()
plt.show()
data.describe().T


    

    




Train shape: (1309, 18)






    













    
Out[19]:







  

    

      

      
count
      
mean
      
std
      
min
      
25%
      
50%
      
75%
      
max
    
  
  

    

      
Age
      
1309.0
      
0.366205
      
0.165651
      
0.0
      
0.273456
      
0.339294
      
0.448829
      
1.0
    
    

      
Fare
      
1309.0
      
0.064988
      
0.100988
      
0.0
      
0.015412
      
0.028213
      
0.061045
      
1.0
    
    

      
Pclass
      
1309.0
      
0.647441
      
0.418918
      
0.0
      
0.500000
      
1.000000
      
1.000000
      
1.0
    
    

      
Survived
      
891.0
      
0.383838
      
0.486592
      
0.0
      
0.000000
      
0.000000
      
1.000000
      
1.0
    
    

      
Sex_female
      
1309.0
      
0.355997
      
0.478997
      
0.0
      
0.000000
      
0.000000
      
1.000000
      
1.0
    
    

      
Sex_male
      
1309.0
      
0.644003
      
0.478997
      
0.0
      
0.000000
      
1.000000
      
1.000000
      
1.0
    
    

      
FamilySize_alone
      
1309.0
      
0.603514
      
0.489354
      
0.0
      
0.000000
      
1.000000
      
1.000000
      
1.0
    
    

      
FamilySize_large
      
1309.0
      
0.062643
      
0.242413
      
0.0
      
0.000000
      
0.000000
      
0.000000
      
1.0
    
    

      
FamilySize_medium
      
1309.0
      
0.333843
      
0.471765
      
0.0
      
0.000000
      
0.000000
      
1.000000
      
1.0
    
    

      
CabinLetter_A
      
1309.0
      
0.016807
      
0.128596
      
0.0
      
0.000000
      
0.000000
      
0.000000
      
1.0
    
    

      
CabinLetter_B
      
1309.0
      
0.049656
      
0.217317
      
0.0
      
0.000000
      
0.000000
      
0.000000
      
1.0
    
    

      
CabinLetter_C
      
1309.0
      
0.071811
      
0.258273
      
0.0
      
0.000000
      
0.000000
      
0.000000
      
1.0
    
    

      
CabinLetter_D
      
1309.0
      
0.035141
      
0.184207
      
0.0
      
0.000000
      
0.000000
      
0.000000
      
1.0
    
    

      
CabinLetter_E
      
1309.0
      
0.031322
      
0.174252
      
0.0
      
0.000000
      
0.000000
      
0.000000
      
1.0
    
    

      
CabinLetter_F
      
1309.0
      
0.016043
      
0.125688
      
0.0
      
0.000000
      
0.000000
      
0.000000
      
1.0
    
    

      
CabinLetter_G
      
1309.0
      
0.003820
      
0.061709
      
0.0
      
0.000000
      
0.000000
      
0.000000
      
1.0
    
    

      
CabinLetter_T
      
1309.0
      
0.000764
      
0.027639
      
0.0
      
0.000000
      
0.000000
      
0.000000
      
1.0
    
    

      
CabinLetter_U
      
1309.0
      
0.774637
      
0.417981
      
0.0
      
1.000000
      
1.000000
      
1.000000
      
1.0
    
  

In [20]:

    

y = np.array(data['Survived'])
X = np.array(data.drop('Survived', axis=1))

#split by idx
idx = train_samples
X_train, X_test = X[:idx], X[idx:]
y_train, y_test = y[:idx], y[idx:]

print("Shape train: {}".format(X_train.shape))
print("Shape test: {}".format(X_test.shape))
print(y_train[0:1])
print(X_train[0:1].tolist())


    

    




Shape train: (891, 17)
Shape test: (418, 17)
[ 0.]
[[0.2734560942001754, 0.014151057562208049, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]]

In [21]:

    

from sklearn.model_selection import train_test_split

X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)
y_val = np.reshape(y_val, [-1,1])
y_train = np.reshape(y_train, [-1,1])

print("Shape X train: {}".format(X_train.shape))
print("Shape y train: {}".format(y_train.shape))
print("Shape X validation: {}".format(X_val.shape))
print("Shape y validation: {}".format(y_val.shape))


    

    




Shape X train: (712, 17)
Shape y train: (712, 1)
Shape X validation: (179, 17)
Shape y validation: (179, 1)

In [22]:

    

import tensorflow as tf

inputs_len = X_train.shape[1]
outputs_len = y_train.shape[1]

inputs = tf.placeholder(tf.float32, [None, inputs_len])
outputs = tf.placeholder(tf.int32, [None, outputs_len])


def nn_layer(inputs_, num_outputs):
    weights_init = tf.truncated_normal_initializer(stddev=1/np.sqrt(inputs_len), seed=42)
    bias_init = tf.truncated_normal_initializer(stddev=1/np.sqrt(num_outputs), seed=42)

    return tf.layers.dense(inputs_,
                             num_outputs, 
                             activation=tf.nn.sigmoid, 
                             kernel_initializer=weights_init,
                             bias_initializer=bias_init)

    
logits = nn_layer(inputs, outputs_len)

cost = tf.reduce_mean(tf.square(tf.to_float(outputs) - logits))
optimizer = tf.train.AdamOptimizer(0.01).minimize(cost)

correct_predition = tf.equal(outputs, tf.cast(logits, tf.int32))
accuracy = tf.reduce_mean(tf.cast(correct_predition, tf.float32))

##
epochs = 500
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    
    for epoch in range(epochs):
        t_acc, _, loss = sess.run([accuracy, optimizer, cost], feed_dict={inputs: X_train, outputs: y_train})
        
        if epoch % 100 == 0:
            print("Epoch({:2}) Training Loss: {:.5f}".format(epoch, loss))   
        
    v_acc = sess.run(accuracy, feed_dict={inputs: X_val, outputs: y_val})    
    print("Training accuracy: {:.5f}  Validation Accuracy: {}".format(t_acc, v_acc))


    

    




Epoch( 0) Training Loss: 0.21369
Epoch(100) Training Loss: 0.13642
Epoch(200) Training Loss: 0.12724
Epoch(300) Training Loss: 0.12499
Epoch(400) Training Loss: 0.12402
Training accuracy: 0.61798  Validation Accuracy: 0.6089385747909546

In [23]:

    

from sklearn.neural_network import MLPClassifier

clf = MLPClassifier(activation='logistic',
                    solver='lbfgs',
                    learning_rate_init=1e-3, 
                    learning_rate='adaptive',
                    shuffle=True,
                    #batch_size=100,
                    max_iter=5000,
                    tol=1e-5,
                    hidden_layer_sizes=(8,8),
                    #warm_start=True,
                    random_state=42,
                    verbose=True)

clf.fit(X_train, y_train.reshape(-1))


#Scores
print("Training score: {}".format(clf.score(X_train, y_train)))
print("Validation score: {}".format(clf.score(X_val, y_val)))


    

    




Training score: 0.9101123595505618
Validation score: 0.776536312849162

In [24]:

    

from sklearn.model_selection import StratifiedKFold

y = np.array(data['Survived'])
X = np.array(data.drop('Survived', axis=1))

#split by idx
#idx = firstNan
idx = train_samples
X_train, X_test = X[:idx], X[idx:]
y_train, y_test = y[:idx], y[idx:]

print("Shape train: {}".format(X_train.shape))
print("Shape test: {}".format(X_test.shape))
print(y_train[0:1])
print(X_train[0:1].tolist())

kf = StratifiedKFold(n_splits=3, random_state=42, shuffle=True)
print(kf)


    

    




Shape train: (891, 17)
Shape test: (418, 17)
[ 0.]
[[0.2734560942001754, 0.014151057562208049, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]]
StratifiedKFold(n_splits=3, random_state=42, shuffle=True)

In [25]:

    

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score

rf = RandomForestClassifier(warm_start=True,
                            random_state = 42)

estimators = 300
epoch = 1
for train_idx, val_idx in kf.split(X_train, y_train):
    X_t, X_v = X_train[train_idx], X_train[val_idx]
    y_t, y_v = y_train[train_idx], y_train[val_idx]
    
    rf.set_params(n_estimators=epoch*estimators)
    rf.fit(X_t, y_t.reshape(-1))
    epoch +=1

    scores = cross_val_score(rf, X_v, y_v.reshape(-1))
    print("{}".format(scores))

#Scores
print("Training score: {}".format(rf.score(X_train, y_train)))
print("Validation score: {}".format(rf.score(X_val, y_val)))


    

    




[ 0.78787879  0.80808081  0.81818182]
[ 0.82828283  0.87878788  0.80808081]
[ 0.74747475  0.75757576  0.87878788]
Training score: 0.9539842873176206
Validation score: 0.9329608938547486

In [26]:

    

from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier

clf1 = LogisticRegression(random_state=42, warm_start=True)
clf2 = RandomForestClassifier(random_state=42, warm_start=True)
clf3 = GaussianNB()

eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2)],
                        voting='soft')

estimators = 10
epoch = 1
for train_idx, val_idx in kf.split(X_train, y_train):
    X_t, X_v = X_train[train_idx], X_train[val_idx]
    y_t, y_v = y_train[train_idx], y_train[val_idx]
    
    clf2.set_params(n_estimators=estimators*epoch)
    epoch += 1
    # predict class probabilities for all classifiers
    probas = [c.fit(X_train, y_train.reshape(-1)).predict_proba(X_test) for c in (clf1, clf2, eclf)]

sample = 13
class1_1 = [pr[sample, 0] for pr in probas]
class2_1 = [pr[sample, 1] for pr in probas]
print("Probabilities not survived (0): {}".format(class1_1))
print("Probabilities survived (1): {}".format(class2_1))
print("Predition: {}".format(eclf.predict(X_test)[sample]))


    

    




Probabilities not survived (0): [0.91104658168498021, 0.96666666666666667, 0.93885662417582338]
Probabilities survived (1): [0.088953418315019767, 0.033333333333333333, 0.061143375824176546]
Predition: 0.0

In [27]:

    

from sklearn.metrics import classification_report,confusion_matrix

X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.3)
y_val = np.reshape(y_val, [-1,1])

predictions = rf.predict(X_val)
cm = confusion_matrix(y_val, predictions)
print(cm)
plt.matshow(cm)
plt.colorbar()
ax = plt.gca()
ax.set_xlabel('Predicted')
ax.set_ylabel('True')

plt.show()


    

    




[[162   4]
 [  8  94]]






    

In [28]:

    

print(classification_report(y_val, predictions))


    

    




             precision    recall  f1-score   support

        0.0       0.95      0.98      0.96       166
        1.0       0.96      0.92      0.94       102

avg / total       0.96      0.96      0.96       268

In [35]:

    

X = pd.read_csv('input/train.csv')
X.fillna(X.mean(), inplace=True)
X= X.drop(['Name','Ticket','Cabin'], axis=1)
X = pd.get_dummies(X)
#
y = np.array(X['Survived'])
X = np.array(X.drop('Survived', axis=1))
#
XtX = X.T.dot(X)
XtX_inv = np.linalg.inv(XtX)
XtY = X.T.dot(y)
theta = XtX_inv.dot(XtY)

#validation predictions
predictions = X.dot(theta)
print(classification_report(y, predictions.astype(int)))


    

    




             precision    recall  f1-score   support

          0       0.64      1.00      0.78       549
          1       0.97      0.08      0.16       342

avg / total       0.76      0.65      0.54       891

In [34]:

    

#test predictions
test = pd.read_csv('input/test.csv')
test.fillna(X.mean(), inplace=True)
test = test.drop(['Name','Ticket','Cabin'], axis=1)
test = pd.get_dummies(test)

predictions = test.dot(theta)
round(abs(predictions));

#print
passengerId = 892
print("PassengerId,Survived")
for i in range(len(predictions)):
    print("{},{}".format(passengerId, (int)(predictions[i])))
    passengerId += 1


    

    




PassengerId,Survived
892,0
893,0
894,0
895,0
896,0
897,0
898,0
899,0
900,0
901,0
902,0
903,0
904,0
905,0
906,0
907,0
908,0
909,0
910,0
911,0
912,0
913,0
914,0
915,0
916,0
917,0
918,1
919,0
920,0
921,0
922,0
923,0
924,0
925,0
926,0
927,0
928,0
929,0
930,0
931,0
932,0
933,0
934,0
935,0
936,0
937,0
938,0
939,0
940,0
941,0
942,0
943,0
944,0
945,0
946,0
947,0
948,0
949,0
950,0
951,1
952,0
953,0
954,0
955,0
956,0
957,0
958,0
959,0
960,0
961,0
962,0
963,0
964,0
965,0
966,1
967,0
968,0
969,0
970,0
971,0
972,0
973,0
974,0
975,0
976,0
977,0
978,0
979,0
980,0
981,0
982,0
983,0
984,0
985,0
986,0
987,0
988,0
989,0
990,0
991,0
992,0
993,0
994,0
995,0
996,0
997,0
998,0
999,0
1000,0
1001,0
1002,0
1003,0
1004,0
1005,0
1006,0
1007,0
1008,0
1009,0
1010,0
1011,0
1012,0
1013,0
1014,0
1015,0
1016,0
1017,0
1018,0
1019,0
1020,0
1021,0
1022,0
1023,0
1024,0
1025,0
1026,0
1027,0
1028,0
1029,0
1030,0
1031,0
1032,0
1033,0
1034,0
1035,0
1036,0
1037,0
1038,0
1039,0
1040,0
1041,0
1042,1
1043,0
1044,0
1045,0
1046,0
1047,0
1048,1
1049,0
1050,0
1051,0
1052,0
1053,0
1054,0
1055,0
1056,0
1057,0
1058,0
1059,0
1060,0
1061,0
1062,0
1063,0
1064,0
1065,0
1066,0
1067,0
1068,0
1069,0
1070,0
1071,0
1072,0
1073,0
1074,0
1075,0
1076,1
1077,0
1078,0
1079,0
1080,0
1081,0
1082,0
1083,0
1084,0
1085,0
1086,0
1087,0
1088,0
1089,0
1090,0
1091,0
1092,0
1093,0
1094,0
1095,0
1096,0
1097,0
1098,0
1099,0
1100,0
1101,0
1102,0
1103,0
1104,0
1105,0
1106,0
1107,0
1108,0
1109,0
1110,0
1111,0
1112,0
1113,0
1114,0
1115,0
1116,0
1117,0
1118,0
1119,0
1120,0
1121,0
1122,0
1123,1
1124,0
1125,0
1126,0
1127,0
1128,0
1129,0
1130,0
1131,0
1132,0
1133,0
1134,0
1135,0
1136,0
1137,0
1138,0
1139,0
1140,0
1141,0
1142,0
1143,0
1144,0
1145,0
1146,0
1147,0
1148,0
1149,0
1150,0
1151,0
1152,0
1153,0
1154,0
1155,0
1156,0
1157,0
1158,0
1159,0
1160,0
1161,0
1162,0
1163,0
1164,1
1165,0
1166,0
1167,0
1168,0
1169,0
1170,0
1171,0
1172,0
1173,0
1174,0
1175,0
1176,0
1177,0
1178,0
1179,0
1180,0
1181,0
1182,0
1183,0
1184,0
1185,0
1186,0
1187,0
1188,0
1189,0
1190,0
1191,0
1192,0
1193,0
1194,0
1195,0
1196,0
1197,0
1198,0
1199,0
1200,0
1201,0
1202,0
1203,0
1204,0
1205,0
1206,0
1207,0
1208,0
1209,0
1210,0
1211,0
1212,0
1213,0
1214,0
1215,0
1216,0
1217,0
1218,0
1219,0
1220,0
1221,0
1222,0
1223,0
1224,0
1225,0
1226,0
1227,0
1228,0
1229,0
1230,0
1231,0
1232,0
1233,0
1234,0
1235,0
1236,0
1237,0
1238,0
1239,0
1240,0
1241,0
1242,0
1243,0
1244,0
1245,0
1246,0
1247,0
1248,0
1249,0
1250,0
1251,0
1252,0
1253,0
1254,0
1255,0
1256,1
1257,0
1258,0
1259,0
1260,0
1261,0
1262,0
1263,1
1264,0
1265,0
1266,0
1267,0
1268,0
1269,0
1270,0
1271,0
1272,0
1273,0
1274,0
1275,0
1276,0
1277,0
1278,0
1279,0
1280,0
1281,0
1282,0
1283,0
1284,0
1285,0
1286,0
1287,0
1288,0
1289,0
1290,0
1291,0
1292,0
1293,0
1294,1
1295,0
1296,0
1297,0
1298,0
1299,0
1300,0
1301,0
1302,0
1303,0
1304,0
1305,0
1306,0
1307,0
1308,0
1309,0

In [166]:

    

predictions = eclf.predict(X_test)

passengerId = 892
print("PassengerId,Survived")
for i in range(len(X_test)):
    print("{},{}".format(passengerId, (int)(predictions[i])))
    passengerId += 1


    

    




PassengerId,Survived
892,0
893,0
894,0
895,0
896,1
897,0
898,0
899,0
900,1
901,0
902,0
903,0
904,1
905,0
906,1
907,1
908,0
909,0
910,0
911,0
912,0
913,0
914,1
915,0
916,1
917,0
918,1
919,0
920,0
921,0
922,0
923,0
924,1
925,1
926,1
927,0
928,1
929,0
930,0
931,0
932,0
933,0
934,0
935,1
936,1
937,0
938,0
939,0
940,1
941,1
942,1
943,0
944,1
945,1
946,0
947,0
948,0
949,0
950,0
951,1
952,0
953,0
954,0
955,1
956,0
957,1
958,1
959,0
960,0
961,1
962,0
963,0
964,1
965,1
966,1
967,0
968,0
969,1
970,0
971,0
972,1
973,0
974,0
975,0
976,0
977,0
978,1
979,1
980,1
981,1
982,0
983,0
984,1
985,0
986,0
987,0
988,1
989,0
990,1
991,0
992,1
993,0
994,0
995,0
996,1
997,0
998,0
999,0
1000,0
1001,0
1002,0
1003,1
1004,1
1005,1
1006,1
1007,0
1008,0
1009,1
1010,0
1011,1
1012,1
1013,0
1014,1
1015,0
1016,0
1017,1
1018,0
1019,1
1020,0
1021,0
1022,0
1023,0
1024,0
1025,0
1026,0
1027,0
1028,0
1029,0
1030,0
1031,0
1032,0
1033,1
1034,0
1035,0
1036,0
1037,0
1038,0
1039,0
1040,0
1041,0
1042,1
1043,0
1044,0
1045,1
1046,0
1047,0
1048,1
1049,0
1050,0
1051,0
1052,1
1053,1
1054,1
1055,0
1056,0
1057,1
1058,0
1059,0
1060,1
1061,0
1062,0
1063,0
1064,0
1065,0
1066,0
1067,1
1068,1
1069,0
1070,1
1071,1
1072,0
1073,0
1074,1
1075,0
1076,1
1077,0
1078,1
1079,0
1080,0
1081,0
1082,0
1083,0
1084,1
1085,0
1086,1
1087,0
1088,1
1089,0
1090,0
1091,1
1092,0
1093,1
1094,0
1095,1
1096,0
1097,0
1098,0
1099,0
1100,1
1101,0
1102,0
1103,0
1104,0
1105,1
1106,0
1107,0
1108,1
1109,0
1110,1
1111,0
1112,1
1113,0
1114,1
1115,0
1116,1
1117,0
1118,0
1119,1
1120,0
1121,0
1122,0
1123,1
1124,0
1125,0
1126,0
1127,0
1128,0
1129,0
1130,1
1131,1
1132,1
1133,1
1134,0
1135,0
1136,0
1137,0
1138,1
1139,0
1140,1
1141,0
1142,1
1143,0
1144,1
1145,0
1146,0
1147,0
1148,0
1149,0
1150,1
1151,0
1152,0
1153,0
1154,1
1155,1
1156,0
1157,0
1158,0
1159,0
1160,1
1161,0
1162,0
1163,0
1164,1
1165,1
1166,0
1167,1
1168,0
1169,0
1170,0
1171,0
1172,0
1173,1
1174,1
1175,0
1176,1
1177,0
1178,0
1179,0
1180,0
1181,0
1182,0
1183,0
1184,0
1185,0
1186,0
1187,0
1188,1
1189,0
1190,0
1191,0
1192,0
1193,0
1194,0
1195,0
1196,1
1197,1
1198,1
1199,1
1200,0
1201,0
1202,0
1203,0
1204,0
1205,0
1206,1
1207,1
1208,0
1209,0
1210,0
1211,0
1212,0
1213,0
1214,0
1215,1
1216,1
1217,0
1218,1
1219,0
1220,0
1221,0
1222,1
1223,0
1224,0
1225,1
1226,0
1227,0
1228,0
1229,0
1230,0
1231,0
1232,0
1233,0
1234,0
1235,1
1236,0
1237,1
1238,0
1239,0
1240,0
1241,1
1242,1
1243,0
1244,0
1245,0
1246,1
1247,0
1248,1
1249,0
1250,0
1251,0
1252,0
1253,1
1254,1
1255,0
1256,1
1257,0
1258,0
1259,0
1260,1
1261,0
1262,0
1263,1
1264,0
1265,0
1266,1
1267,1
1268,0
1269,0
1270,0
1271,0
1272,0
1273,0
1274,0
1275,1
1276,0
1277,1
1278,0
1279,0
1280,0
1281,0
1282,0
1283,1
1284,0
1285,0
1286,0
1287,1
1288,0
1289,1
1290,0
1291,0
1292,1
1293,0
1294,1
1295,0
1296,0
1297,0
1298,0
1299,0
1300,1
1301,1
1302,1
1303,1
1304,0
1305,0
1306,1
1307,0
1308,0
1309,0

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