Kaggle Titanic

Titanic dataset from Kaggle

VARIABLE DESCRIPTIONS: survival Survival (0 = No; 1 = Yes) pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd) name Name sex Sex age Age sibsp Number of Siblings/Spouses Aboard parch Number of Parents/Children Aboard ticket Ticket Number fare Passenger Fare cabin Cabin embarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton) SPECIAL NOTES: Pclass is a proxy for socio-economic status (SES) 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower Age is in Years; Fractional if Age less than One (1) If the Age is Estimated, it is in the form xx.5 With respect to the family relation variables (i.e. sibsp and parch) some relations were ignored. The following are the definitions used for sibsp and parch. Sibling: Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic Spouse: Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored) Parent: Mother or Father of Passenger Aboard Titanic Child: Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic Other family relatives excluded from this study include cousins, nephews/nieces, aunts/uncles, and in-laws. Some children travelled only with a nanny, therefore parch=0 for them. As well, some travelled with very close friends or neighbors in a village, however, the definitions do not support such relations.



In [218]:

    
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier

from pylab import rcParams
rcParams['figure.figsize'] = 5, 10

# use prettier ggplot style
matplotlib.style.use('ggplot')

% matplotlib inline



In [219]:

    
#print(os.getcwd() + "\n")

# load data
train = pd.read_csv("../data/train.csv")
test = pd.read_csv("../data/test.csv")

Descriptive Statistics



In [220]:

    
uniq = pd.Series([len(pd.unique(train[x].values.ravel())) for x in train.columns.values], train.columns)
print len(train.index), "observations,", len(train.columns), "dimensions\n", \
      pd.DataFrame([train.dtypes, train.count(), uniq, train.isnull().sum()], index=['type', 'count', 'UNIQUE', 'NA']).transpose()









    



891 observations, 12 dimensions
                type count UNIQUE   NA
PassengerId    int64   891    891    0
Survived       int64   891      2    0
Pclass         int64   891      3    0
Name          object   891    891    0
Sex           object   891      2    0
Age          float64   714     89  177
SibSp          int64   891      7    0
Parch          int64   891      7    0
Ticket        object   891    681    0
Fare         float64   891    248    0
Cabin         object   204    148  687
Embarked      object   889      4    2



In [221]:

    
print train.select_dtypes(['object']).describe().transpose(), '\n'
print train.select_dtypes(['int64']).describe().transpose(), '\n'
print train.select_dtypes(['float64']).describe().transpose()









    



         count unique                        top freq
Name       891    891  Graham, Mr. George Edward    1
Sex        891      2                       male  577
Ticket     891    681                   CA. 2343    7
Cabin      204    147                C23 C25 C27    4
Embarked   889      3                          S  644 

             count        mean         std  min    25%  50%    75%  max
PassengerId    891  446.000000  257.353842    1  223.5  446  668.5  891
Survived       891    0.383838    0.486592    0    0.0    0    1.0    1
Pclass         891    2.308642    0.836071    1    2.0    3    3.0    3
SibSp          891    0.523008    1.102743    0    0.0    0    1.0    8
Parch          891    0.381594    0.806057    0    0.0    0    0.0    6 

      count       mean        std   min      25%      50%  75%       max
Age     714  29.699118  14.526497  0.42  20.1250  28.0000   38   80.0000
Fare    891  32.204208  49.693429  0.00   7.9104  14.4542   31  512.3292



In [222]:

    
# print counts per category
#for col in ['Survived', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked']:
#    print pd.DataFrame(train[col].value_counts(dropna=False)), '\n' # automatically sorts by count (desc)



In [223]:

    
# descriptive statistics
train.groupby('Pclass').mean()









    Out[223]:






  
    
      
      PassengerId
      Survived
      Age
      SibSp
      Parch
      Fare
    
    
      Pclass
      
      
      
      
      
      
    
  
  
    
      1
      461.597222
      0.629630
      38.233441
      0.416667
      0.356481
      84.154687
    
    
      2
      445.956522
      0.472826
      29.877630
      0.402174
      0.380435
      20.662183
    
    
      3
      439.154786
      0.242363
      25.140620
      0.615071
      0.393075
      13.675550



In [224]:

    
# clean data (fill missing Embark and Age values)
train.Embarked.fillna('S', inplace=True) # assume 'S'
meanAge = train.Age.mean()
train.Age = train.Age.fillna(meanAge)



In [225]:

    
# convert to categories (requires pandas 0.15+)
for col in ['Survived', 'Pclass', 'Sex', 'Embarked']:
    train[col] = train[col].astype('category')

Feature Correlation



In [226]:

    
# does not work on category or object
corr = train.corr()
sns.heatmap(corr, square=True, annot=True, linewidths='1', cmap="RdBu")
pass



In [227]:

    
matplotlib.style.use('ggplot')

# visualization with matplotlib
plt.scatter(y=train.Fare, x=train.Age, alpha=0.2, color="blue")
plt.gcf().set_size_inches(12,8)

#plt.minorticks_on()
#plt.grid(b=True, which='major', c='gray', linestyle='-')
#plt.grid(b=True, which='minor', c='lightgray', linestyle='--')
plt.xlim([-2,85])
plt.ylim([-10,550])
plt.xlabel('Age')
plt.ylabel('Fare')
pass



In [228]:

    
# Random Forest

# must encode categories as integers
sexEncoder = preprocessing.LabelEncoder()
train['Sex'] = sexEncoder.fit_transform(train.Sex)
embEncoder = preprocessing.LabelEncoder()
train['Embarked'] = embEncoder.fit_transform(train.Embarked)

columns = np.array(['Sex', 'Pclass', 'Age', 'Fare', 'SibSp', 'Parch', 'Embarked'])

forest = RandomForestClassifier(n_estimators=1000, max_depth=5)
fit = forest.fit(train[columns], train.Survived)



In [229]:

    
train.isnull().any()









    Out[229]:





PassengerId    False
Survived       False
Pclass         False
Name           False
Sex            False
Age            False
SibSp          False
Parch          False
Ticket         False
Fare           False
Cabin           True
Embarked       False
dtype: bool



In [230]:

    
# analyze variable importance
# (inspired by http://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html)
varImportance = pd.Series(fit.feature_importances_, index=columns).sort_values()
print varImportance









    



Embarked    0.034499
Parch       0.036366
SibSp       0.049240
Age         0.108600
Pclass      0.145593
Fare        0.170951
Sex         0.454751
dtype: float64



In [231]:

    
varImportance.plot.barh() # color='skyblue'
ax = plt.gca()
ax.yaxis.grid(False)
#ax.set_axisbelow(True)
#ax.set_axis_bgcolor('darkgrey')
#ax.xaxis.grid(color='white', linestyle='solid', linewidth='1')
#ax.set_frame_on(False)
plt.tick_params(axis='y', left='off', right='off')
plt.tick_params(axis='x', top='off', direction='out')
plt.title('Variable Importance')
plt.xlim(-0.015, 0.515)
pass



In [ ]:

	PassengerId	Survived	Age	SibSp	Parch	Fare
Pclass
1	461.597222	0.629630	38.233441	0.416667	0.356481	84.154687
2	445.956522	0.472826	29.877630	0.402174	0.380435	20.662183
3	439.154786	0.242363	25.140620	0.615071	0.393075	13.675550