# Titanic : Machine Learning from DisasterQuestion要求你建立一个预测模型来回答这个问题：“什么样的人更有可能生存？”使用乘客数据(如姓名、年龄、性别、社会经济阶层等)。

一、导入数据包和数据集

import pandas as pd

from pandas import Series, DataFrame

import numpy as np

from matplotlib import pyplot as plt

import seaborn as sns重点：在kaggle notebook上时，应该把pd.read_csv("./kaggle/input/titanic/train.csv")引号中第一个'.'去掉

读入训练集和测试及都需要

train = pd.read_csv("./kaggle/input/titanic/train.csv")

test = pd.read_csv("./kaggle/input/titanic/test.csv")

allData = pd.concat([train, test], ignore_index=True)

# dataNum = train.shape[0]

# featureNum = train.shape[1]

train.info()

二、数据总览

概况输入train.info()回车可以查看数据集整体信息

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输入train.head()可以查看数据样例

特征

Variable | Definition | Key :-:|:-:|:-: survival | Survival | 0 = No, 1 = Yes pclass | Ticket class(客舱等级) | 1 = 1st, 2 = 2nd, 3 = 3rd sex | Sex Age | Age in years sibsp | # of siblings / spouses aboard the Titanic(旁系亲属) parch | # of parents / children aboard the Titanic(直系亲属) ticket | Ticket number fare | Passenger fare cabin | Cabin number(客舱编号) embarked | Port of Embarkation(上船港口编号) | C = Cherbourg, Q = Queenstown, S = Southampton

三、可视化数据分析

性别特征Sex女性生存率远高于男性

# Sex

sns.countplot('Sex', hue='Survived', data=train)

plt.show()

等级特征Pclass乘客等级越高，生存率越高

# Pclass

sns.barplot(x='Pclass', y="Survived", data=train)

plt.show()

家庭成员数量特征FamilySize=Parch+SibSp

家庭成员数量适中，生存率高

# FamilySize = SibSp + Parch + 1

allData['FamilySize'] = allData['SibSp'] + allData['Parch'] + 1

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

plt.show()

上船港口特征Embarked上船港口不同，生存率不同

# Embarked

sns.countplot('Embarked', hue='Survived', data=train)

plt.show()

年龄特征Age年龄小或者正值壮年生存率高

# Age

sns.stripplot(x="Survived", y="Age", data=train, jitter=True)

plt.show()

- 年龄生存密度

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')

plt.show()

儿童相对于全年龄段有特殊的生存率

作者将10及以下视为儿童，设置单独标签

费用特征Fare费用越高，生存率越高

# Fare

sns.stripplot(x="Survived", y="Fare", data=train, jitter=True)

plt.show()

姓名特征Name

头衔特征Title头衔由姓名的前置称谓进行分类

# Name

allData['Title'] = allData['Name'].apply(lambda x:x.split(',')[1].split('.')[0].strip())

pd.crosstab(allData['Title'], allData['Sex'])统计分析

TitleClassification = {'Officer':['Capt', 'Col', 'Major', 'Dr', 'Rev'],

'Royalty':['Don', 'Sir', 'the Countess', 'Dona', 'Lady'],

'Mrs':['Mme', 'Ms', 'Mrs'],

'Miss':['Mlle', 'Miss'],

'Mr':['Mr'],

'Master':['Master','Jonkheer']}

for title in TitleClassification.keys():

cnt = 0

for name in TitleClassification[title]:

cnt += allData.groupby(['Title']).size()[name]

print (title,':',cnt)设置标签

TitleClassification = {'Officer':['Capt', 'Col', 'Major', 'Dr', 'Rev'],

'Royalty':['Don', 'Sir', 'the Countess', 'Dona', 'Lady'],

'Mrs':['Mme', 'Ms', 'Mrs'],

'Miss':['Mlle', 'Miss'],

'Mr':['Mr'],

'Master':['Master','Jonkheer']}

TitleMap = {}

for title in TitleClassification.keys():

TitleMap.update(dict.fromkeys(TitleClassification[title], title))

allData['Title'] = allData['Title'].map(TitleMap)头衔不同，生存率不同

sns.barplot(x="Title", y="Survived", data=allData)

plt.show()

票号特征Ticket有一定连续座位(存在票号相同的乘客)生存率高

#Ticket

TicketCnt = allData.groupby(['Ticket']).size()

allData['SameTicketNum'] = allData['Ticket'].apply(lambda x:TicketCnt[x])

sns.barplot(x='SameTicketNum', y='Survived', data=allData)

plt.show()

# allData['SameTicketNum']

二维/多维分析可以将任意两个/多个数据进行分析

二维分析之Pclass & Age

# Pclass & Age

sns.violinplot("Pclass", "Age", hue="Survived", data=train, split=True)

plt.show()

二维分析之Age & Sex

# Age & Sex

sns.swarmplot(x='Age', y="Sex", data=train, hue='Survived')

plt.show()

四、数据清洗 & 异常处理

离散型数据

有可用标签 --> One-Hot编码Sex & Pclass & Embarked 都有已经设置好的标签(int或float或string等)，可以直接进行get_dummies，拆分成多维向量，增加特征维度

其中，Embarked存在一定缺失值，通过对整体的分析，填充上估计值

# Sex

allData = allData.join(pd.get_dummies(allData['Sex'], prefix="Sex"))

# Pclass

allData = allData.join(pd.get_dummies(allData['Pclass'], prefix="Pclass"))

# Embarked

allData[allData['Embarked'].isnull()] # 查看缺失值

allData.groupby(by=['Pclass','Embarked']).Fare.mean() # Pclass=1, Embark=C, 中位数=76

allData['Embarked'] = allData['Embarked'].fillna('C')

allData = allData.join(pd.get_dummies(allData['Embarked'], prefix="Embarked"))

无可用标签 --> 设计标签 --> One-HotFamilySize & Name & Ticket需要对整体数据统一处理，再进行标记

# FamilySize

def FamilyLabel(s):

if (s == 4):

return 4

elif (s == 2 or s == 3):

return 3

elif (s == 1 or s == 7):

return 2

elif (s == 5 or s == 6):

return 1

elif (s < 1 or s > 7):

return 0

allData['FamilyLabel'] = allData['FamilySize'].apply(FamilyLabel)

allData = allData.join(pd.get_dummies(allData['FamilyLabel'], prefix="Fam"))

# Name

TitleLabelMap = {'Mr':1.0,

'Mrs':5.0,

'Miss':4.5,

'Master':2.5,

'Royalty':3.5,

'Officer':2.0}

def TitleLabel(s):

return TitleLabelMap[s]

# allData['TitleLabel'] = allData['Title'].apply(TitleLabel)

allData = allData.join(pd.get_dummies(allData['Title'], prefix="Title"))

# Ticket

def TicketLabel(s):

if (s == 3 or s == 4):

return 3

elif (s == 2 or s == 8):

return 2

elif (s == 1 or s == 5 or s == 6 or s ==7):

return 1

elif (s < 1 or s > 8):

return 0

allData['TicketLabel'] = allData['SameTicketNum'].apply(TicketLabel)

allData = allData.join(pd.get_dummies(allData['TicketLabel'], prefix="TicNum"))

连续型数据

Age & Fare进行标准化，缩小数据范围，加速梯度下降

# Age

allData['Child'] = allData['Age'].apply(lambda x:1 if x <= 10 else 0) # 儿童标签

allData['Age'] = (allData['Age']-allData['Age'].mean())/allData['Age'].std() # 标准化

allData['Age'].fillna(value=0, inplace=True) # 填充缺失值

# Fare

allData['Fare'] = allData['Fare'].fillna(25) # 填充缺失值

allData[allData['Survived'].notnull()]['Fare'] = allData[allData['Survived'].notnull()]['Fare'].apply(lambda x:300.0 if x>500 else x)

allData['Fare'] = allData['Fare'].apply(lambda x:(x-allData['Fare'].mean())/allData['Fare'].std())

清除无用特征清除无用特征，降低算法复杂度

# 清除无用特征

allData.drop(['Cabin', 'PassengerId', 'Ticket', 'Name', 'Title', 'Sex', 'SibSp', 'Parch', 'FamilySize', 'Embarked', 'Pclass', 'Title', 'FamilyLabel', 'SameTicketNum', 'TicketLabel'], axis=1, inplace=True)

重新分割训练集/测试集一开始，为了处理方便，作者将训练集和测试集合并，现在根据Survived是否缺失来讲训练集和测试集分开

# 重新分割数据集

train_data = allData[allData['Survived'].notnull()]

test_data = allData[allData['Survived'].isnull()]

test_data = test_data.reset_index(drop=True)

xTrain = train_data.drop(['Survived'], axis=1)

yTrain = train_data['Survived']

xTest = test_data.drop( ['Survived'], axis=1)

特征相关性分析该步骤用于筛选特征后向程序员反馈，特征是否有效、是否重叠

若有问题，可以修改之前的特征方案

# 特征间相关性分析

Correlation = pd.DataFrame(allData[allData.columns.to_list()])

colormap = plt.cm.viridis

plt.figure(figsize=(24,22))

sns.heatmap(Correlation.astype(float).corr(), linewidths=0.1, vmax=1.0, cmap=colormap, linecolor='white', annot=True, square=True)

plt.show()

五、模型建立 & 参数优化

导入模型包

from sklearn.pipeline import Pipeline

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import GridSearchCV

from sklearn.feature_selection import SelectKBest作者选择随机森林分类器

网格搜索调试参数

pipe = Pipeline([('select', SelectKBest(k=10)),

('classify', RandomForestClassifier(random_state = 10, max_features = 'sqrt'))])

param_test = {'classify__n_estimators':list(range(20,100,5)),

'classify__max_depth' :list(range(3,10,1))}

gsearch = GridSearchCV(estimator=pipe, param_grid=param_test, scoring='roc_auc', cv=10)

gsearch.fit(xTrain, yTrain)

print (gsearch.best_params_, gsearch.best_score_)运行时间较长，结束后出现结果：

{'classify__max_depth': 6, 'classify__n_estimators': 70} 0.8790924679681529

建立模型用以上参数进行输入模型

训练

rfc = RandomForestClassifier(n_estimators=70, max_depth=6, random_state=10, max_features='sqrt')

rfc.fit(xTrain, yTrain)

导出结果

predictions = rfc.predict(xTest)

output = pd.DataFrame({'PassengerId':test['PassengerId'], 'Survived':predictions.astype('int64')})

output.to_csv('my_submission.csv', index=False)

六、提交评分

附：完整代码Jupiter Notebook导出为Python Script格式，需要ipynb格式请点击

# To add a new cell, type '# %%'

# To add a new markdown cell, type '# %% [markdown]'

# %%

import pandas as pd

from pandas import Series, DataFrame

import numpy as np

from matplotlib import pyplot as plt

import seaborn as sns

# %% [markdown]

# # Features

# Variable | Definition | Key

# :-:|:-:|:-:

# survival | Survival | 0 = No, 1 = Yes

# pclass | Ticket class(客舱等级) | 1 = 1st, 2 = 2nd, 3 = 3rd

# sex | Sex

# Age | Age in years

# sibsp | # of siblings / spouses aboard the Titanic(旁系亲属)

# parch | # of parents / children aboard the Titanic(直系亲属)

# ticket | Ticket number

# fare | Passenger fare

# cabin | Cabin number(客舱编号)

# embarked | Port of Embarkation(上船的港口编号) | C = Cherbourg, Q = Queenstown, S = Southampton

# %%

train = pd.read_csv("./kaggle/input/titanic/train.csv")

test = pd.read_csv("./kaggle/input/titanic/test.csv")

allData = pd.concat([train, test], ignore_index=True)

# dataNum = train.shape[0]

# featureNum = train.shape[1]

train.head()

# %%

# Sex

sns.countplot("Sex", hue="Survived", data=train)

plt.show()

# %%

# Pclass

sns.barplot(x="Pclass", y="Survived", data=train)

plt.show()

# Pclass & Age

sns.violinplot("Pclass", "Age", hue="Survived", data=train, split=True)

plt.show()

# %%

# FamilySize = SibSp + Parch + 1

allData["FamilySize"] = allData["SibSp"] + allData["Parch"] + 1

sns.barplot(x="FamilySize", y="Survived", data=allData)

plt.show()

# %%

# Embarked

sns.countplot("Embarked", hue="Survived", data=train)

plt.show()

# %%

# Age

sns.stripplot(x="Survived", y="Age", data=train, jitter=True)

plt.show()

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")

plt.show()

# Age & Sex

sns.swarmplot(x="Age", y="Sex", data=train, hue="Survived")

plt.show()

# %%

# Fare

sns.stripplot(x="Survived", y="Fare", data=train, jitter=True)

plt.show()

# %%

# Name

# allData['Title'] = allData['Name'].str.extract('([A-Za-z]+)\.', expand=False) # str.extract不知道在干嘛

allData["Title"] = allData["Name"].apply(

lambda x: x.split(",")[1].split(".")[0].strip()

)

# pd.crosstab(allData['Title'], allData['Sex'])

TitleClassification = {

"Officer": ["Capt", "Col", "Major", "Dr", "Rev"],

"Royalty": ["Don", "Sir", "the Countess", "Dona", "Lady"],

"Mrs": ["Mme", "Ms", "Mrs"],

"Miss": ["Mlle", "Miss"],

"Mr": ["Mr"],

"Master": ["Master", "Jonkheer"],

}

TitleMap = {}

for title in TitleClassification.keys():

TitleMap.update(dict.fromkeys(TitleClassification[title], title))

"""# cnt = 0for name in TitleClassification[title]:cnt += allData.groupby(['Title']).size()[name]# print (title,':',cnt)"""

allData["Title"] = allData["Title"].map(TitleMap)

sns.barplot(x="Title", y="Survived", data=allData)

plt.show()

# %%

# Ticket

TicketCnt = allData.groupby(["Ticket"]).size()

allData["SameTicketNum"] = allData["Ticket"].apply(lambda x: TicketCnt[x])

sns.barplot(x="SameTicketNum", y="Survived", data=allData)

plt.show()

# allData['SameTicketNum']

# %% [markdown]

# # 数据清洗

# - Sex & Pclass & Embarked --> Ont-Hot

# - Age & Fare --> Standardize

# - FamilySize & Name & Ticket --> ints --> One-Hot

# %%

# Sex

allData = allData.join(pd.get_dummies(allData["Sex"], prefix="Sex"))

# Pclass

allData = allData.join(pd.get_dummies(allData["Pclass"], prefix="Pclass"))

# Embarked

allData[allData["Embarked"].isnull()] # 查看缺失值

allData.groupby(by=["Pclass", "Embarked"]).Fare.mean() # Pclass=1, Embark=C, 中位数=76

allData["Embarked"] = allData["Embarked"].fillna("C")

allData = allData.join(pd.get_dummies(allData["Embarked"], prefix="Embarked"))

# %%

# Age

allData["Child"] = allData["Age"].apply(lambda x: 1 if x <= 10 else 0) # 儿童标签

allData["Age"] = (allData["Age"] - allData["Age"].mean()) / allData["Age"].std() # 标准化

allData["Age"].fillna(value=0, inplace=True) # 填充缺失值

# Fare

allData["Fare"] = allData["Fare"].fillna(25) # 填充缺失值

allData[allData["Survived"].notnull()]["Fare"] = allData[allData["Survived"].notnull()][

"Fare"

].apply(lambda x: 300.0 if x > 500 else x)

allData["Fare"] = allData["Fare"].apply(

lambda x: (x - allData["Fare"].mean()) / allData["Fare"].std()

)

# %%

# FamilySize

def FamilyLabel(s):

if s == 4:

return 4

elif s == 2 or s == 3:

return 3

elif s == 1 or s == 7:

return 2

elif s == 5 or s == 6:

return 1

elif s < 1 or s > 7:

return 0

allData["FamilyLabel"] = allData["FamilySize"].apply(FamilyLabel)

allData = allData.join(pd.get_dummies(allData["FamilyLabel"], prefix="Fam"))

# Name

TitleLabelMap = {

"Mr": 1.0,

"Mrs": 5.0,

"Miss": 4.5,

"Master": 2.5,

"Royalty": 3.5,

"Officer": 2.0,

}

def TitleLabel(s):

return TitleLabelMap[s]

# allData['TitleLabel'] = allData['Title'].apply(TitleLabel)

allData = allData.join(pd.get_dummies(allData["Title"], prefix="Title"))

# Ticket

def TicketLabel(s):

if s == 3 or s == 4:

return 3

elif s == 2 or s == 8:

return 2

elif s == 1 or s == 5 or s == 6 or s == 7:

return 1

elif s < 1 or s > 8:

return 0

allData["TicketLabel"] = allData["SameTicketNum"].apply(TicketLabel)

allData = allData.join(pd.get_dummies(allData["TicketLabel"], prefix="TicNum"))

# %%

# 清除无用特征

allData.drop(

[

"Cabin",

"PassengerId",

"Ticket",

"Name",

"Title",

"Sex",

"SibSp",

"Parch",

"FamilySize",

"Embarked",

"Pclass",

"Title",

"FamilyLabel",

"SameTicketNum",

"TicketLabel",

],

axis=1,

inplace=True,

)

# 重新分割数据集

train_data = allData[allData["Survived"].notnull()]

test_data = allData[allData["Survived"].isnull()]

test_data = test_data.reset_index(drop=True)

xTrain = train_data.drop(["Survived"], axis=1)

yTrain = train_data["Survived"]

xTest = test_data.drop(["Survived"], axis=1)

# allData.columns.to_list()

# %%

# 特征间相关性分析

Correlation = pd.DataFrame(allData[allData.columns.to_list()])

colormap = plt.cm.viridis

plt.figure(figsize=(24, 22))

sns.heatmap(

Correlation.astype(float).corr(),

linewidths=0.1,

vmax=1.0,

cmap=colormap,

linecolor="white",

annot=True,

square=True,

)

plt.show()

# %% [markdown]

# # 网格筛选随机森林参数

# - n_estimator

# - max_depth

# %%

from sklearn.pipeline import Pipeline

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import GridSearchCV

from sklearn.feature_selection import SelectKBest

# %%

pipe = Pipeline(

[

("select", SelectKBest(k=10)),

("classify", RandomForestClassifier(random_state=10, max_features="sqrt")),

]

)

param_test = {

"classify__n_estimators": list(range(20, 100, 5)),

"classify__max_depth": list(range(3, 10, 1)),

}

gsearch = GridSearchCV(estimator=pipe, param_grid=param_test, scoring="roc_auc", cv=10)

gsearch.fit(xTrain, yTrain)

print(gsearch.best_params_, gsearch.best_score_)

# %%

rfc = RandomForestClassifier(

n_estimators=70, max_depth=6, random_state=10, max_features="sqrt"

)

rfc.fit(xTrain, yTrain)

predictions = rfc.predict(xTest)

output = pd.DataFrame(

{"PassengerId": test["PassengerId"], "Survived": predictions.astype("int64")}

)

output.to_csv("my_submission.csv", index=False)

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