In [34]:

    

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier


    

In [35]:

    

train_data = pd.read_csv('train.csv')


    

In [36]:

    

train_data.head()


    

    
Out[36]:







  

    

      

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

    

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

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

      
2
      
3
      
1
      
3
      
Heikkinen, Miss. Laina
      
female
      
26.0
      
0
      
0
      
STON/O2. 3101282
      
7.9250
      
NaN
      
S
    
    

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

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

In [37]:

    

mean_male_age = train_data[(train_data['Age'].notnull()) & (train_data['Sex'] == 'male')]['Age'].mean()
mean_female_age = train_data[(train_data['Age'].notnull()) & (train_data['Sex'] == 'female')]['Age'].mean()


    

In [38]:

    

train_data['Age'].loc[(train_data['Age'].isnull()) & (train_data['Sex'] == 'male')] = mean_male_age
train_data['Age'].loc[(train_data['Age'].isnull()) & (train_data['Sex'] == 'female')] = mean_female_age


    

In [39]:

    

max_pass_embarked = train_data.groupby('Embarked').count()['PassengerId']
train_data['Embarked'].loc[train_data['Embarked'].isnull()] = 'S'


    

In [40]:

    

train_target = train_data['Survived']
train_features = train_data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]


    

In [41]:

    

train_features.head()


    

    
Out[41]:







  

    

      

      
Pclass
      
Sex
      
Age
      
SibSp
      
Parch
      
Fare
      
Embarked
    
  
  

    

      
0
      
3
      
male
      
22.0
      
1
      
0
      
7.2500
      
S
    
    

      
1
      
1
      
female
      
38.0
      
1
      
0
      
71.2833
      
C
    
    

      
2
      
3
      
female
      
26.0
      
0
      
0
      
7.9250
      
S
    
    

      
3
      
1
      
female
      
35.0
      
1
      
0
      
53.1000
      
S
    
    

      
4
      
3
      
male
      
35.0
      
0
      
0
      
8.0500
      
S
    
  

In [42]:

    

label = LabelEncoder()

label.fit(train_features['Sex'].drop_duplicates())
train_features['Sex'] = label.transform(train_features['Sex'])

label.fit(train_features['Embarked'].drop_duplicates())
train_features['Embarked'] = label.transform(train_features['Embarked'])

train_features.head()


    

    




/usr/local/lib/python2.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  after removing the cwd from sys.path.
/usr/local/lib/python2.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  import sys






    
Out[42]:







  

    

      

      
Pclass
      
Sex
      
Age
      
SibSp
      
Parch
      
Fare
      
Embarked
    
  
  

    

      
0
      
3
      
1
      
22.0
      
1
      
0
      
7.2500
      
2
    
    

      
1
      
1
      
0
      
38.0
      
1
      
0
      
71.2833
      
0
    
    

      
2
      
3
      
0
      
26.0
      
0
      
0
      
7.9250
      
2
    
    

      
3
      
1
      
0
      
35.0
      
1
      
0
      
53.1000
      
2
    
    

      
4
      
3
      
1
      
35.0
      
0
      
0
      
8.0500
      
2
    
  

In [43]:

    

dtc = DecisionTreeClassifier()
dtc.fit(train_features, train_target)


    

    
Out[43]:





DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')

In [72]:

    

test_data = pd.read_csv('test.csv')


    

In [73]:

    

test_mean_male_age = test_data[(test_data['Age'].notnull()) & (test_data['Sex'] == 'male')]['Age'].mean()
test_mean_female_age = test_data[(test_data['Age'].notnull()) & (test_data['Sex'] == 'female')]['Age'].mean()


    

In [74]:

    

test_data['Age'].loc[(test_data['Age'].isnull()) & (test_data['Sex'] == 'male')] = test_mean_male_age
test_data['Age'].loc[(test_data['Age'].isnull()) & (test_data['Sex'] == 'female')] = test_mean_female_age


    

In [75]:

    

test_data['Embarked'].loc[test_data['Embarked'].isnull()] = 'S'


    

In [77]:

    

test_features = test_data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]


    

In [78]:

    

label.fit(test_features['Sex'].drop_duplicates())
test_features['Sex'] = label.transform(test_features['Sex'])

label.fit(test_features['Embarked'].drop_duplicates())
test_features['Embarked'] = label.transform(test_features['Embarked'])


    

    




/usr/local/lib/python2.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  
/usr/local/lib/python2.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """

In [89]:

    

test_features['Fare'][test_features['Fare'].isnull()] = 35.627188489208635

prediction = dtc.predict(test_features)


    

    




/usr/local/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.

In [92]:

    

result = pd.DataFrame(test_data['PassengerId'])
result['Survived'] = prediction

result.to_csv('RESULT.csv', index=False)