Titanic

Halima ·

更新时间:2024-11-11

· 535 次阅读

导言：

这算是第一个真正意义上独立完成的Kaggle项目。期间参考了许多大神的做法，受益匪浅。本人技术不精，况且又是第一次做Kaggle的项目，请各位读者谅解，如有问题还请各位提出。谢谢大家的支持。

数据包与数据集导入

%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

train = pd.read_csv('~/Coding/Kaggle/Titanic/train.csv')
test = pd.read_csv('~/Coding/Kaggle/Titanic/test.csv')
combine = pd.concat([train, test], sort = False, ignore_index=True)
PassengerId = test['PassengerId']

数据分析 概览

train.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

我们不难看出，原始数据包括
PassengerId, Survived, Pclass, Name, Sex, Age, Sibsp, Parch, Ticket, Fare, Cabin, Embarked
这几个类目。

print(train.columns.values)

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

各类目数据类型和缺省值

接下来我们分析训练数据类目的类型以及其缺省值。

train.info()


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

test.info()


RangeIndex: 418 entries, 0 to 417
Data columns (total 11 columns):
PassengerId    418 non-null int64
Pclass         418 non-null int64
Name           418 non-null object
Sex            418 non-null object
Age            332 non-null float64
SibSp          418 non-null int64
Parch          418 non-null int64
Ticket         418 non-null object
Fare           417 non-null float64
Cabin          91 non-null object
Embarked       418 non-null object
dtypes: float64(2), int64(4), object(5)
memory usage: 36.0+ KB

从上可以看出：

train中，Age和Cabin有缺少 test中，Age, Fare and Cabin有缺少 数据初步分析

继续分析

train.describe()

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

通过上述结果，发现：

PassengerId是unique的，与目标无关，在训练集中可以舍去最终结果Survived是0，1表示的很多人都没有直系亲属或旁系亲属只有很少的人支付512美元的船票登船人员的年龄普遍不是很大

我们首先将PassengerId条目在训练集中删去

紧接着，我们来观察非numeric的值

train.describe(include=['O'])# 注意，这个‘O’是 大写的‘o’，而不是数字0

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

发现：

乘客大部分为男士 Ticket中有210张同样的票大部分人从S港口登船 下面分析各类目与Survived之间的相互关系。 Sex feature

观察男女的生存率差异

train[['Sex', 'Survived']].groupby('Sex', as_index=False).mean()\
.sort_values(by='Survived', ascending=False)

	Sex	Survived
0	female	0.742038
1	male	0.188908

女性的生存率普遍高于男性，Sex是较为重要的一个条目，需要保留。

Pclass

train[['Pclass', 'Survived']].groupby('Pclass', as_index=False).mean()\
.sort_values(by='Survived', ascending=False)

	Pclass	Survived
0	1	0.629630
1	2	0.472826
2	3	0.242363

Pclass数值越小，生存几率越大。

SibSp和Parch

train[['SibSp', 'Survived']].groupby('SibSp', as_index=False).mean()\
.sort_values(by='Survived', ascending=False)

	SibSp	Survived
1	1	0.535885
2	2	0.464286
0	0	0.345395
3	3	0.250000
4	4	0.166667
5	5	0.000000
6	8	0.000000

train[['Parch', 'Survived']].groupby('Parch', as_index=False).mean()\
.sort_values(by='Survived', ascending=False)

	Parch	Survived
3	3	0.600000
1	1	0.550847
2	2	0.500000
0	0	0.343658
5	5	0.200000
4	4	0.000000
6	6	0.000000

在这边其实可以考虑将Parch和SibSp合并成FamilySize

Embarked

train[['Embarked', 'Survived']].groupby('Embarked', as_index=False).mean()\
.sort_values(by='Survived', ascending=False)

	Embarked	Survived
0	C	0.553571
1	Q	0.389610
2	S	0.336957

C港口登船的船员生存率最高

Ticket

train[['Ticket', 'Survived']].groupby('Ticket', as_index=False).mean()\
.sort_values(by='Survived', ascending=False).head()

	Ticket	Survived
0	110152	1.0
180	26360	1.0
483	386525	1.0
479	382651	1.0
151	244373	1.0

关于登船人员的票证，因为特异值太多，可以考虑数据清洗时将其根据相同票证进行分组

Age
由于Age是连续值，所以我们的分析通过图像来进行。

facet = sns.FacetGrid(train, hue='Survived', aspect=2)
facet.map(sns.kdeplot, 'Age', shade=True)
facet.set(xlim=(0, train['Age'].max()))
facet.add_legend()
plt.xlabel('Age')
plt.ylabel('density')

Text(12.359751157407416, 0.5, 'density')

在这里插入图片描述

通过年龄-生存密度图可以看出，两曲线形状大部分重叠，只有在15岁以下生存率有差别。可以将年龄较低的部分单独分组。

Fare

far = sns.FacetGrid(train, hue='Survived', aspect=2)
far.map(sns.kdeplot, 'Fare', shade=True)
far.set(xlim=(0, train['Fare'].max()))
far.add_legend()
plt.xlabel('Fare')
plt.ylabel('density')

Text(11.594234664351859, 0.5, 'density')

在这里插入图片描述

150之后的fare可以分为一组。0-25分组，25-100分组，100-150分组。

Cabin

train['Cabin'].value_counts(dropna=True).head()

C23 C25 C27    4
B96 B98        4
G6             4
F2             3
D              3
Name: Cabin, dtype: int64

可以看出Cabin的数量非常多，在后面处理时会考虑只提取第一个字母来进行训练。

Name

有关Name的这一部分将会在下面特征提取部分涉及。

特征提取

需要注意的是，接下来的特征提取以及数据清洗的阶段需要对整个数据集进行。

FamilySize

将Parch和SibSp相加并加上1.

combine['FamilySize'] = combine['Parch'] + combine['SibSp'] + 1
combine = combine.drop(['Parch', 'SibSp'], axis=1)

处理完成之后我们再观察新创立的FamilySize。

sns.barplot(x='FamilySize', y='Survived', data=combine)

在这里插入图片描述

我们将FamilySize分成三组。

def fam(d):
    if (d>=2) & (d4) & (d7):
        return 0
combine['FamilyLabel'] = combine['FamilySize'].apply(fam)
sns.barplot(x='FamilyLabel', y='Survived', data=combine)

在这里插入图片描述

对FamilySize进行丢弃。

combine = combine.drop('FamilySize', axis=1)

对Cabin进行提取

首先将Cabin数据丢失的部分进行填充

combine['Cabin'] = combine['Cabin'].fillna('Unknown')

紧接着我们创建Deck属性，即对Cabin第一字母的提取。

combine['Deck'] = combine['Cabin'].str.get(0)
sns.barplot(x='Deck', y='Survived', data=combine)

在这里插入图片描述

对Cabin进行丢弃。

combine = combine.drop('Cabin', axis=1)

将Deck进行分组。

deck_dict = {}
deck_dict.update(dict.fromkeys(['C', 'E', 'D', 'B', 'F'], 1))
deck_dict.update(dict.fromkeys(['U', 'G', 'A', 'T'], 0))
#combine['Deck'] = combine['Deck'].map(deck_dict)
#combine['Deck'] = combine['Deck'].astype(int)

Ticket

主要是对Ticket进行分组。

Tik_num = dict(combine['Ticket'].value_counts())
combine['TickGrp'] = combine['Ticket'].apply(lambda x: Tik_num[x])
sns.barplot(x='TickGrp', y='Survived', data=combine)

在这里插入图片描述

combine = combine.drop('Ticket', axis=1)

将TickGrp分成三组。

def tic(s):
    if (s>=2) & (s4) & (s8):
        return 0
combine['TickGrp'] = combine['TickGrp'].apply(tic)
sns.barplot(x='TickGrp', y='Survived', data=combine)

在这里插入图片描述

Name

具体某一位乘客的姓名肯定是和生存率无关。Name中有作用的成分应该是其中的Title。

下面提取出每个人的Title并创建新的Title条目。

combine['Title'] = combine['Name'].apply(lambda x: x.split(',')[1].split('.')[0].strip())
pd.crosstab(combine['Title'], combine['Survived'])

Survived	0.0	1.0
Title
Capt	1	0
Col	1	1
Don	1	0
Dr	4	3
Jonkheer	1	0
Lady	0	1
Major	1	1
Master	17	23
Miss	55	127
Mlle	0	2
Mme	0	1
Mr	436	81
Mrs	26	99
Ms	0	1
Rev	6	0
Sir	0	1
the Countess	0	1

combine = combine.drop('Name', axis=1)

我们需要将Title分类。

title_dict = {}
title_dict.update(dict.fromkeys(['Lady', 'the Countess','Capt', 'Col',\
 	'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare'))
title_dict.update(dict.fromkeys(['Mlle'], 'Miss'))
title_dict.update(dict.fromkeys(['Ms'], 'Miss'))
title_dict.update(dict.fromkeys(['Mme'], 'Mrs'))
title_dict.update(dict.fromkeys(['Mr'], 'Mr'))
title_dict.update(dict.fromkeys(['Mrs'], 'Mrs'))
title_dict.update(dict.fromkeys(['Master'], 'Master'))
combine['Title'] = combine['Title'].map(title_dict)
combine[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()\
.sort_values(by='Survived', ascending=False)

	Title	Survived
1	Miss	1.000000
3	Mrs	0.793651
0	Master	0.575000
4	Rare	0.347826
2	Mr	0.156673

title_mapping = {"Mr": 1, "Miss": 5, "Mrs": 4, "Master": 3, "Rare": 2}
combine['Title'] = combine['Title'].map(title_mapping)
combine['Title'] = combine['Title'].fillna(0)

把Sex转换为数字

combine['Sex'] = combine['Sex'].map( {'female': 1, 'male': 0} )

#combine.head()

数据清洗 将Embarked缺省值用众数填充

freq_port = combine.Embarked.dropna().mode()[0]
freq_port

'S'

combine['Embarked'] = combine['Embarked'].fillna(freq_port)
combine[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean()\
.sort_values(by='Survived', ascending=False)

	Embarked	Survived
0	C	0.553571
1	Q	0.389610
2	S	0.339009

#combine['Embarked'] = combine['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} )
combine.head()

	PassengerId	Survived	Pclass	Sex	Age	Fare	Embarked	FamilyLabel	Deck	TickGrp	Title
0	1	0.0	3	male	22.0	7.2500	S	2	U	1	Mr
1	2	1.0	1	female	38.0	71.2833	C	2	C	2	Mrs
2	3	1.0	3	female	26.0	7.9250	S	1	U	1	NaN
3	4	1.0	1	female	35.0	53.1000	S	2	C	2	Mrs
4	5	0.0	3	male	35.0	8.0500	S	1	U	1	Mr

Fare

我们用Fare的平均数来填充Nan

combine['Fare'].fillna(combine['Fare'].dropna().median(), inplace=True)
combine.head()

	PassengerId	Survived	Pclass	Sex	Age	Fare	Embarked	FamilyLabel	Deck	TickGrp	Title
0	1	0.0	3	male	22.0	7.2500	S	2	U	1	Mr
1	2	1.0	1	female	38.0	71.2833	C	2	C	2	Mrs
2	3	1.0	3	female	26.0	7.9250	S	1	U	1	NaN
3	4	1.0	1	female	35.0	53.1000	S	2	C	2	Mrs
4	5	0.0	3	male	35.0	8.0500	S	1	U	1	Mr

创建FareBand

combine['FareBand'] = pd.qcut(combine['Fare'], 4)
combine[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean()\
.sort_values(by='FareBand', ascending=True)

	FareBand	Survived
0	(-0.001, 7.896]	0.197309
1	(7.896, 14.454]	0.303571
2	(14.454, 31.275]	0.441048
3	(31.275, 512.329]	0.600000

combine.loc[ combine['Fare']  7.91) & (combine['Fare']  14.454) & (combine['Fare']  31, 'Fare'] = 3
combine['Fare'] = combine['Fare'].astype(int)

combine = combine.drop('FareBand', axis=1)

combine.head()

	PassengerId	Survived	Pclass	Sex	Age	Fare	Embarked	FamilyLabel	Deck	TickGrp	Title
0	1	0.0	3	male	22.0	0	S	2	U	1	Mr
1	2	1.0	1	female	38.0	3	C	2	C	2	Mrs
2	3	1.0	3	female	26.0	1	S	1	U	1	NaN
3	4	1.0	1	female	35.0	3	S	2	C	2	Mrs
4	5	0.0	3	male	35.0	1	S	1	U	1	Mr

Age

因为Age的缺省值比较多，所以这边利用Sex, Title and Pclass三个特征值来构建随机森林模型，填充Age的缺失值。

from sklearn.ensemble import RandomForestRegressor

age = combine[['Age', 'Pclass', 'Sex', 'Title']]
age = pd.get_dummies(age)
kage = age[age.Age.notnull()].as_matrix()
ukage = age[age.Age.isnull()].as_matrix()
y = kage[:, 0]
X = kage[:, 1:]
rf = RandomForestRegressor(random_state=0, n_estimators=100, n_jobs=-1)
rf.fit(X, y)
P_age = rf.predict(ukage[:, 1::])
combine.loc[(combine.Age.isnull()), 'Age'] = P_age

/Users/helix/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  This is separate from the ipykernel package so we can avoid doing imports until
/Users/helix/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  after removing the cwd from sys.path.

combine.head()

	PassengerId	Survived	Pclass	Sex	Age	Fare	Embarked	FamilyLabel	Deck	TickGrp	Title
0	1	0.0	3	male	22.0	0	S	2	U	1	Mr
1	2	1.0	1	female	38.0	3	C	2	C	2	Mrs
2	3	1.0	3	female	26.0	1	S	1	U	1	NaN
3	4	1.0	1	female	35.0	3	S	2	C	2	Mrs
4	5	0.0	3	male	35.0	1	S	1	U	1	Mr

训练 特征转换

选取特征转换为数值变量。并且划分训练和测试集。

combine = combine[['Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked', 'FamilyLabel',\
                   'Deck', 'TickGrp', 'Title']]
combine = pd.get_dummies(combine)
train = combine[combine['Survived'].notnull()]
test = combine[combine['Survived'].isnull()].drop('Survived', axis=1)
X = train.as_matrix()[:, 1:]
y = train.as_matrix()[:, 0]

/Users/helix/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
/Users/helix/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  import sys

模型选择和参数优化

在这边偷懒，参考了他人的解答，直接选择随机森林模型。参数选择也没有使用GridSearchCV来选择。

from sklearn.pipeline import make_pipeline
from sklearn.feature_selection import SelectKBest
select = SelectKBest(k=20)
clf = RandomForestRegressor(random_state=10, warm_start=True,
                                   n_estimators=26,
                                   max_depth=6,
                                   max_features='sqrt')
pipeline = make_pipeline(select, clf)
pipeline.fit(X, y)

Pipeline(memory=None,
         steps=[('selectkbest',
                 SelectKBest(k=20,
                             score_func=)),
                ('randomforestregressor',
                 RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,
                                       criterion='mse', max_depth=6,
                                       max_features='sqrt', max_leaf_nodes=None,
                                       max_samples=None,
                                       min_impurity_decrease=0.0,
                                       min_impurity_split=None,
                                       min_samples_leaf=1, min_samples_split=2,
                                       min_weight_fraction_leaf=0.0,
                                       n_estimators=26, n_jobs=None,
                                       oob_score=False, random_state=10,
                                       verbose=0, warm_start=True))],
         verbose=False)

获得结果并输出

pre=pipeline.predict(test)
def sss(d):
    if (d>=0.5):
        return 1
    elif (d<0.5):
        return 0
pre = pd.DataFrame(pre)
pre = pre[0].apply(sss)


sub=pd.DataFrame({"PassengerId": PassengerId, "Survived": pre.astype(np.int32)})
sub.to_csv('~/Coding/Kaggle/Titanic/res.csv', index=False)

参考：

Titanic Data Science Solutions kaggle入门–泰坦尼克号之灾(手把手教你)
作者：Alex Hwang

titanic

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