Titanic data deal

原文鏈接：https://www.kaggle.com/sinakhorami/titanic-best-working-classifier/data

%matplotlib inline
import numpy as np
import pandas as pd
import re as re

train = pd.read_csv("./data/train.csv",header = 0,dtype={'Age':np.float64})
test = pd.read_csv("./data/test.csv",header = 0,dtype={"Age":np.float64})
full_data = [train,test]

print(train.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB
None

Feature Engineering

1 Pclass

there is no missing value on this feature and already a numerical value.so lets check it’s impact on our train set

# 按Pclass分組求均值
print(train[['Pclass','Survived']].groupby(['Pclass'],as_index=False).mean())

   Pclass  Survived
0       1  0.629630
1       2  0.472826
2       3  0.242363

2 Sex

print(train[["Sex","Survived"]].groupby(['Sex'],as_index=False).mean())

      Sex  Survived
0  female  0.742038
1    male  0.188908

3 SibSp and Parch

With the number of siblings/spouse and the number of children/parents we can create new feature called Family Size.

train.head()

	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

for dataset in full_data:
    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
print(train[['FamilySize','Survived']].groupby(['FamilySize'],as_index=False).mean())

   FamilySize  Survived
0           1  0.303538
1           2  0.552795
2           3  0.578431
3           4  0.724138
4           5  0.200000
5           6  0.136364
6           7  0.333333
7           8  0.000000
8          11  0.000000

for dataset in full_data:
    dataset['IsAlone'] = 0
    dataset.loc[dataset['FamilySize']==1,'IsAlone']=1
print(train[['IsAlone','Survived']].groupby(['IsAlone'],as_index=False).mean())
'''
這個結果表明，家庭規模小的存活率更高
'''

   IsAlone  Survived
0        0  0.505650
1        1  0.303538





'\n這個結果表明，家庭規模小的存活率更高\n'

4 Embarked

the embarked feature has some missing value and we try to fill those with the most occured value(‘S’)

result = train.groupby('Embarked')['Embarked'].count()

result

Embarked
C    168
Q     77
S    644
Name: Embarked, dtype: int64

result['S']

644

result[result.max()==result].index

Index(['S'], dtype='object', name='Embarked')

result[result.max()==result].index[0]

'S'

for dataset in full_data:
    dataset['Embarked'] = dataset['Embarked'].fillna(result[result.max()==result].index[0])
print(train[['Embarked','Survived']].groupby(['Embarked'],as_index=False).mean())

  Embarked  Survived
0        C  0.553571
1        Q  0.389610
2        S  0.339009

5 Fare

Fare also has some missing value and we will replace it with the median.then we categorize it into 4 ranges

train['Fare'].median()

14.4542

for dataset in full_data:
    dataset['Fare'] = dataset['Fare'].fillna(dataset['Fare'].median())
train['CategoricalFare'] = pd.qcut(train['Fare'],4)
print(train[['CategoricalFare','Survived']].groupby(['CategoricalFare'],as_index=False).mean())

   CategoricalFare  Survived
0   (-0.001, 7.91]  0.197309
1   (7.91, 14.454]  0.303571
2   (14.454, 31.0]  0.454955
3  (31.0, 512.329]  0.581081

6 Age

we have plenty of missing values in this feature. # generate random numbers between(mean - std) and (mean + std).then we categorize age into 5 range

for dataset in full_data:
    age_avg = dataset['Age'].mean()
    age_std = dataset['Age'].std()
    age_null_count = dataset['Age'].isnull().sum() # 統計缺失值的樣本數
    age_null_random_list = np.random.randint(age_avg - age_std,age_avg + age_std,size=age_null_count)
    dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list
    dataset['Age'] = dataset['Age'].astype(int)

D:\Users\zhunode\Anaconda3\lib\site-packages\ipykernel_launcher.py:6: 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

train['CategoricalAge'] = pd.cut(train['Age'],5) # 等分成5分

print(train[['CategoricalAge','Survived']].groupby('CategoricalAge',as_index=False).mean())

  CategoricalAge  Survived
0  (-0.08, 16.0]  0.525862
1   (16.0, 32.0]  0.363029
2   (32.0, 48.0]  0.353659
3   (48.0, 64.0]  0.434783
4   (64.0, 80.0]  0.090909

# 查看qcut和cut的區別
train['CategoricalAge'] = pd.qcut(train['Age'],5)

print(train[['CategoricalAge','Survived']].groupby('CategoricalAge',as_index=False).mean())

   CategoricalAge  Survived
0  (-0.001, 19.0]  0.455882
1    (19.0, 25.0]  0.329545
2    (25.0, 31.0]  0.385093
3    (31.0, 40.0]  0.382199
4    (40.0, 80.0]  0.352201

7 name

inside this feature we can find the title of people

title : (人名前表示地位、職業、婚否等的)稱號，頭銜，職稱，稱謂

def get_title(name):
    title_search = re.search(' ([A-Za-z]+)\.', name)
    # if title exists,extract and return it
    if title_search:
        return title_search.group(1)
    return ""

for dataset in full_data:
    dataset['Title'] = dataset['Name'].apply(get_title)

# 交叉表（cross-tabulation，簡稱crosstab）是⼀種⽤於計算分組頻率的特殊透視表。
print(pd.crosstab(train['Title'],train['Sex']))

Sex       female  male
Title                 
Capt           0     1
Col            0     2
Countess       1     0
Don            0     1
Dr             1     6
Jonkheer       0     1
Lady           1     0
Major          0     2
Master         0    40
Miss         182     0
Mlle           2     0
Mme            1     0
Mr             0   517
Mrs          125     0
Ms             1     0
Rev            0     6
Sir            0     1

now we have titles.Let’s categories it and check the title impact on survival rate

for dataset in full_data:
    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',
                                                 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
    print(set(dataset['Title']))
    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
    print(set(dataset['Title']))
    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
    print(set(dataset['Title']))
    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')

print (train[['Title', 'Survived']].groupby(['Title'], as_index=False).mean())
    

{'Rare', 'Mlle', 'Miss', 'Mme', 'Master', 'Mr', 'Mrs', 'Ms'}
{'Rare', 'Miss', 'Mme', 'Master', 'Mr', 'Mrs', 'Ms'}
{'Rare', 'Miss', 'Mme', 'Master', 'Mr', 'Mrs'}
{'Rare', 'Miss', 'Master', 'Mr', 'Mrs', 'Ms'}
{'Rare', 'Miss', 'Master', 'Mr', 'Mrs', 'Ms'}
{'Rare', 'Miss', 'Master', 'Mr', 'Mrs'}
    Title  Survived
0  Master  0.575000
1    Miss  0.702703
2      Mr  0.156673
3     Mrs  0.793651
4    Rare  0.347826

Data Cleaning

great! now let’s clean our data and map our features into numerical values

print(set(train['Sex']))
print(set(test['Sex']))

{'male', 'female'}
{'male', 'female'}

for dataset in full_data:
    # Mapping Sex 
    dataset['Sex'] = dataset['Sex'].map({'female':0,'male':1}).astype(int)
    
    # mapping titles
    title_mapping = {"Mr":1,"Miss":1,"Mrs":3,"Master":4,"Rare":5}
    dataset['Title'] = dataset['Title'].map(title_mapping)
    dataset['Title'] = dataset['Title'].fillna(0)
    
    # Mapping Embarked
    dataset['Embarked'] = dataset['Embarked'].map({'S':0,'C':1,'Q':2}).astype(int)
    
    # Mapping Fare
    dataset.loc[dataset['Fare'] <=7.91,'Fare'] = 0
    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <=14.454),'Fare'] = 1
    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31),'Fare']=2
    dataset.loc[dataset['Fare']>31,'Fare'] = 3
    dataset['Fare'] = dataset['Fare'].astype(int)
    
    #Mapping Age
    dataset.loc[dataset['Age']<=16,'Age']=0
    dataset.loc[(dataset['Age']>16) & (dataset['Age'] <= 32),'Age']=1
    dataset.loc[(dataset['Age']>32) & ( dataset['Age'] <= 48),'Age']=2
    dataset.loc[(dataset['Age']>48) & (dataset['Age'] <= 64),'Age']=3
    dataset.loc[dataset['Age']>64,'Age']=4
    
# Feature Selection
drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp','Parch', 'FamilySize']
print("has columns ",train.columns)
print("drop elements ",drop_elements)
train = train.drop(drop_elements,axis=1)
train = train.drop(['CategoricalAge','CategoricalFare'],axis=1)
    
test = test.drop(drop_elements,axis=1)
    
print(train.head(10))
    
train = train.values
test = test.values

has columns  Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'FamilySize', 'IsAlone',
       'CategoricalFare', 'CategoricalAge', 'Title'],
      dtype='object')
drop elements  ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp', 'Parch', 'FamilySize']
   Survived  Pclass  Sex  Age  Fare  Embarked  IsAlone  Title
0         0       3    1    1     0         0        0      1
1         1       1    0    2     3         1        0      3
2         1       3    0    1     1         0        1      1
3         1       1    0    2     3         0        0      3
4         0       3    1    2     1         0        1      1
5         0       3    1    2     1         2        1      1
6         0       1    1    3     3         0        1      1
7         0       3    1    0     2         0        0      4
8         1       3    0    1     1         0        0      3
9         1       2    0    0     2         1        0      3

good! now we have a clean dataset and ready to predict. let’s find which classifier works better on this dataset.

Classifier Comparison

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.metrics import accuracy_score,log_loss
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis,QuadraticDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression

classifiers = [
    KNeighborsClassifier(3),
    SVC(probability=True),
    DecisionTreeClassifier(),
    RandomForestClassifier(),
    AdaBoostClassifier(),
    GradientBoostingClassifier(),
    GaussianNB(),
    LinearDiscriminantAnalysis(),
    QuadraticDiscriminantAnalysis(),
    LogisticRegression()
]

log_cols = ['Classifier','Accuracy']
log = pd.DataFrame(columns=log_cols)

sss = StratifiedShuffleSplit(n_splits=10,test_size=0.1,random_state=0)

X = train[0::,1::]
y = train[0::,0]

acc_dict = {}

for train_index,test_index in sss.split(X,y):
    X_train,X_test = X[train_index],X[test_index]
    y_train,y_test = y[train_index],y[test_index]
    
    for clf in classifiers:
        name = clf.__class__.__name__
        clf.fit(X_train, y_train)
        train_predictions = clf.predict(X_test)
        acc = accuracy_score(y_test, train_predictions)
        if name in acc_dict:
            acc_dict[name] += acc
        else:
            acc_dict[name] = acc
            
for clf in acc_dict:
    acc_dict[clf] = acc_dict[clf] / 10.0
    log_entry = pd.DataFrame([[clf,acc_dict[clf]]],columns=log_cols)
    log = log.append(log_entry)

plt.xlabel('Accuracy')
plt.title('Classifier Accuracy')

sns.set_color_codes("muted")
sns.barplot(x='Accuracy', y='Classifier', data=log, color="b")

<matplotlib.axes._subplots.AxesSubplot at 0x14e11cbba58>

Prediction

candidate_classifier = SVC()
candidate_classifier.fit(train[0::, 1::], train[0::, 0])
result = candidate_classifier.predict(test)