Titanic数据分析报告
Titanic数据分析报告
1.1 数据加载与描述性统计
加载所需数据与所需的python库。
import statsmodels.api as sm
import statsmodels.formula.api as smf
import statsmodels.graphics.api as smg
import patsy
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
from scipy import stats
import seaborn as snstrain = pd.read_csv("D:/学习/数据挖掘与机器学习/Titanic/train.csv")train.head()| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th… | female | 38 | 1 | 0 | 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 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.0500 | NaN | S |
train.info()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 |
1.2单变量探索
1.2.1 年龄与费用
画出训练集中乘客年龄和费用的分布直方图，如下所示。可以发现，大部分乘客的年龄位于20-40岁之间，总体上呈正态分布。大部分乘客的票价很低，位于0-100之间，其他少部分乘客的票价较高。
fig,ax = plt.subplots(nrows=1,ncols=2,figsize=(15,5))
train["Age"].hist(ax=ax[0])
ax[0].set_title("Hist plot of Age")
train["Fare"].hist(ax=ax[1])
ax[1].set_title("Hist plot of Fare")<matplotlib.text.Text at 0x7672e4e588>
1.2.2 乘客是否获救
画出乘客获救与没有获救的条形图，如下所示。可以发现，大部分乘客没有获救。
fig,ax = plt.subplots(figsize=(7,5))
train["Survived"].value_counts().plot(kind="bar")
ax.set_xticklabels(("Not Survived","Survived"), rotation= "horizontal" )
ax.set_title("Bar plot of Survived ")<matplotlib.text.Text at 0x7673102940>
1.2.3 性别
画出乘客性别条形分布图，如下所示。可以发现，大部分乘客为男性。
fig,ax = plt.subplots(figsize=(7,5))
train["Sex"].value_counts().plot(kind="bar")
ax.set_xticklabels(("male","female"),rotation= "horizontal" )
ax.set_title("Bar plot of Sex ")<matplotlib.text.Text at 0x767307af60>
1.2.4 乘客所在的船舱等级
画出乘客的Pclass条形分布图，如下所示。可以发现，大部分乘客位于第三等级，第一等级和第二等级的乘客各有200个左右。
fig,ax = plt.subplots(figsize=(7,5))
train["Pclass"].value_counts().plot(kind="bar")
ax.set_xticklabels(("Class3","Class1","Class2"),rotation= "horizontal" )
ax.set_title("Bar plot of Pclass ")<matplotlib.text.Text at 0x76731587b8>
1.2.5 乘客座位号
对乘客座位号数据进行处理，将缺失值赋值为Unknown。从乘客座位号数据可以发现，第一个字母可能代表了船舱号码，将该字符提取出来，赋值给Cabin，视为船舱号。
train.Cabin.fillna("Unknown",inplace=True)
for i in range(0, 891):
train.Cabin[i]= train.Cabin[i][0]fig,ax = plt.subplots(figsize=(7,5))
train.Cabin.value_counts().plot(kind="bar")
ax.set_title("Bar plot of Cabin ")<matplotlib.text.Text at 0x76731bd908>
1.2.6 兄弟姐妹与配偶数目
画出乘客兄弟姐妹与配偶数目的条形分布图，如下所示。可以发现，大部分乘客在船上没有兄弟姐妹或配偶，大约200位乘客在船上有1个兄弟姐妹或配偶。
fig,ax = plt.subplots(figsize=(7,5))
train["SibSp"].value_counts().plot(kind="bar")
ax.set_title("Bar plot of SibSp ")<matplotlib.text.Text at 0x76713d7438>
1.2.7 父母与子女数目
画出乘客父母与子女数目的条形分布图，如下所示。可以发现，大部分乘客在船上没有父母或子女，100多位乘客在船上有1个兄弟姐妹或配偶，大约90位乘客在船上有2个兄弟姐妹或配偶。
fig,ax = plt.subplots(figsize=(7,5))
train["Parch"].value_counts().plot(kind="bar")
ax.set_title("Bar plot of Parch ")<matplotlib.text.Text at 0x7671457ef0>
1.2.8 乘客出发港口
画出乘客出发港口的分布条形图，如下所示。可以发现，大部分乘客从Southampton港口出发，不到200位乘客从Cherburge出发，不到100位乘客从Queentown出发。
fig,ax = plt.subplots(figsize=(7,5))
train["Embarked"].value_counts().plot(kind="bar")
ax.set_xticklabels(("Southampton","Cherbourg","Queenstown"),rotation= "horizontal" )
ax.set_title("Bar plot of Embarked ")<matplotlib.text.Text at 0x76714d9ac8>
1.3 多变量探索
1.3.1 性别与是否获救
画出性别与是否获救的交叉表和条形图，如下所示。可以发现，女性获救的可能性更高，而男性获救的比例很低。
pd.crosstab(train["Sex"],train["Survived"])| Survived | 0 | 1 |
|---|---|---|
| Sex | ||
| female | 81 | 233 |
| male | 468 | 109 |
pd.crosstab(train["Sex"],train["Survived"]).plot(kind="bar")<matplotlib.axes._subplots.AxesSubplot at 0x76714d0d68>
1.3.2 船舱等级与是否获救
画出船舱等级与是否获救的交叉表与条形图，如下所示。可以发现，第一等级的乘客获救的可能性更高，超过50%，第二等级的乘客获救可能性在50%左右，而第三等级的乘客获救可能性很低。
pd.crosstab(train["Pclass"],train["Survived"])| Survived | 0 | 1 |
|---|---|---|
| Pclass | ||
| 1 | 80 | 136 |
| 2 | 97 | 87 |
| 3 | 372 | 119 |
pd.crosstab(train["Pclass"],train["Survived"]).plot(kind="bar")<matplotlib.axes._subplots.AxesSubplot at 0x76741cbf98>
1.3.3 兄弟姐妹或配偶数量与是否获救
画出兄弟姐妹与配偶数目与是否获救的交叉表与条形图，如下所示。可以发现，有数量为1或2的乘客获救的可能性更高。
pd.crosstab(train["SibSp"],train["Survived"])| Survived | 0 | 1 |
|---|---|---|
| SibSp | ||
| 0 | 398 | 210 |
| 1 | 97 | 112 |
| 2 | 15 | 13 |
| 3 | 12 | 4 |
| 4 | 15 | 3 |
| 5 | 5 | 0 |
| 8 | 7 | 0 |
pd.crosstab(train["SibSp"],train["Survived"]).plot(kind="bar")<matplotlib.axes._subplots.AxesSubplot at 0x76714a9d68>
1.3.4 父母或子女数目和是否获救
画出父母或子女数目与是否获救的交叉表与条形图，如下所示。可以发现，有数量为1或2的乘客获救的可能性更高。
pd.crosstab(train["Parch"],train["Survived"])| Survived | 0 | 1 |
|---|---|---|
| Parch | ||
| 0 | 445 | 233 |
| 1 | 53 | 65 |
| 2 | 40 | 40 |
| 3 | 2 | 3 |
| 4 | 4 | 0 |
| 5 | 4 | 1 |
| 6 | 1 | 0 |
pd.crosstab(train["Parch"],train["Survived"]).plot(kind="bar")<matplotlib.axes._subplots.AxesSubplot at 0x767435b7b8>
1.3.5 登船港口与是否获救
画出登船港口与是否获救的交叉表与条形图，如下所示。可以发现，从Cherburge出发的乘客获救的人数比例更高。
pd.crosstab(train["Embarked"],train["Survived"])| Survived | 0 | 1 |
|---|---|---|
| Embarked | ||
| C | 75 | 93 |
| Q | 47 | 30 |
| S | 427 | 217 |
pd.crosstab(train["Embarked"],train["Survived"]).plot(kind="bar")<matplotlib.axes._subplots.AxesSubplot at 0x767436f4e0>
1.3.6 乘客船舱与是否获救
画出乘客所在船舱与是否获救的交叉表与条形图，如下所示。可以发现，船舱后没有缺失的乘客获救的人数比例更高。
pd.crosstab(train["Cabin"],train["Survived"])| Survived | 0 | 1 |
|---|---|---|
| Cabin | ||
| A | 8 | 7 |
| B | 12 | 35 |
| C | 24 | 35 |
| D | 8 | 25 |
| E | 8 | 24 |
| F | 5 | 8 |
| G | 2 | 2 |
| T | 1 | 0 |
| U | 481 | 206 |
pd.crosstab(train["Cabin"],train["Survived"]).plot(kind="bar")<matplotlib.axes._subplots.AxesSubplot at 0x767441f6d8>
1.3.7 乘客年龄与是否获救
画出乘客是否获救与年龄的箱线图，如下所示。从箱线图上来看，两者关系并不明显。
fig,ay = plt.subplots()
Age1 = train.Age[train.Survived == 1].dropna()
Age0 = train.Age[train.Survived == 0].dropna()
plt.boxplot((Age1,Age0),labels=('Survived','Not Survived'))
ay.set_ylim([-5,70])
ay.set_title("Boxplot of Age")<matplotlib.text.Text at 0x7674557a20>
1.3.8 票价与是否获救
画出乘客是否获救与票价的箱线图，如下所示。可以发现，总体而言，获救的乘客票价更高。
fig,ay = plt.subplots()
Fare1 = train.Fare[train.Survived == 1]
Fare0 = train.Fare[train.Survived == 0]
plt.boxplot((Fare1,Fare0),labels=('Survived','Not Survived'))
ay.set_ylim([-10,150])
ay.set_title("Boxplot of Fare")<matplotlib.text.Text at 0x76745e5470>
1.3.9 票价与乘客舱位等级
画出乘客票价与舱位等级的箱线图，如下所示。可以明显的发现，舱位等级越高的乘客，票价越高。这两个变量之间存在非常明显的线性相关关系。
fig,ay = plt.subplots()
Farec1 = train.Fare[train.Pclass == 1]
Farec2 = train.Fare[train.Pclass == 2]
Farec3 = train.Fare[train.Pclass == 3]
plt.boxplot((Farec1,Farec2,Farec3),labels=("Pclass1","Pclass2","Pclass3"))
ay.set_ylim([-10,180])
ay.set_title("Boxplot of Fare and Pclass")<matplotlib.text.Text at 0x767466e8d0>
1.4 数据处理
1.4.1 缺失值处理
用年龄的均值填充年龄的缺失值，用出发港口的众数填补出发港口的缺失值。
train.Age.mean()
train.Age.fillna(29.7,inplace=True)
train.Embarked.fillna("S",inplace=True)1.4.2 数据分箱
根据以上分析结果和变量间的关系，将年龄数据分段为0-5岁、5-15岁、15-20岁、20-35岁、35-50岁、50-60岁、60-100岁7段。将Parch变量分成数目为0、数目为1或2、数目为大于2三段。将SibSp变量分成数目为0、数目为1或2、数目为大于2三段。将Cabin变量分为缺失和没有缺失两段。
train.age=pd.cut(train.Age,[0,5,15,20,35,50,60,100])
pd.crosstab(train.age,train.Survived).plot(kind="bar")<matplotlib.axes._subplots.AxesSubplot at 0x7674657e48>
train.Parch[(train.Parch>0) & (train.Parch<=2)]=1
train.Parch[train.Parch>2]=2
train.SibSp[(train.SibSp>0) & (train.SibSp<=2)]=1
train.SibSp[train.SibSp>2]=2
train.Cabin[train.Cabin!="U"]="K"1.4.3 创建虚拟变量
为Pclass、Sex、Embarked、Parch、SibSp、Cabin变量创建虚拟变量
dummy_Pclass = pd.get_dummies(train.Pclass, prefix='Pclass')
dummy_Sex = pd.get_dummies(train.Sex, prefix='Sex')
dummy_Embarked = pd.get_dummies(train.Embarked, prefix='Embarked')
dummy_Parch = pd.get_dummies(train.Parch, prefix='Parch')
dummy_SibSp = pd.get_dummies(train.SibSp, prefix='SibSp')
dummy_Age = pd.get_dummies(train.age, prefix='Age')
dummy_Cabin = pd.get_dummies(train.Cabin, prefix='Cabin')1.5 模型建立
1.5.1 创建训练集
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, roc_curve,roc_auc_score,classification_report train_y = train[:623]["Survived"]
cols_to_keep = ["Fare"]
train_x = train[:623][cols_to_keep].join(dummy_Sex.ix[:, "Sex_male":]).join(dummy_Embarked.ix[:,"Embarked_Q":]).join(dummy_Parch.ix[:,"Parch_1":]).join(dummy_SibSp.ix[:,"SibSp_1":]).join(dummy_Age.ix[:,"Age_(5, 15]":]).join(dummy_Cabin.ix[:,"Cabin_U" :])
train_x['intercept'] = 1.0
train_x.tail()| Fare | Sex_male | Embarked_Q | Embarked_S | Parch_1 | Parch_2 | SibSp_1 | SibSp_2 | Age_(5, 15] | Age_(15, 20] | Age_(20, 35] | Age_(35, 50] | Age_(50, 60] | Age_(60, 100] | Cabin_U | intercept | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 618 | 39.0000 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 619 | 10.5000 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
| 620 | 14.4542 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
| 621 | 52.5542 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 622 | 15.7417 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 |
1.5.2 模型构建
对训练集构建逻辑斯蒂模型。
clf = LogisticRegression()
clf.fit(train_x,train_y)1.5.3 模型检验
划分测试集，将编号为624-890的乘客作为测试集。
test_y = train[623:]["Survived"]
cols_to_keep = ["Fare"]
test_x = train[623:][cols_to_keep].join(dummy_Sex.ix[:, "Sex_male":]).join(dummy_Embarked.ix[:,"Embarked_Q":]).join(dummy_Parch.ix[:,"Parch_1":]).join(dummy_SibSp.ix[:,"SibSp_1":]).join(dummy_Age.ix[:,"Age_(5, 15]":]).join(dummy_Cabin.ix[:,"Cabin_U" :])
test_x['intercept'] = 1.0
test_x.head()| Fare | Sex_male | Embarked_Q | Embarked_S | Parch_1 | Parch_2 | SibSp_1 | SibSp_2 | Age_(5, 15] | Age_(15, 20] | Age_(20, 35] | Age_(35, 50] | Age_(50, 60] | Age_(60, 100] | Cabin_U | intercept | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 623 | 7.8542 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
| 624 | 16.1000 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
| 625 | 32.3208 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 626 | 12.3500 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
| 627 | 77.9583 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
利用测试集对模型进行测试
clf.predict(test_x)array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,
0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1,
1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1,
0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0], dtype=int64)
clf.predict_proba(test_x)array([[ 0.86039834, 0.13960166],
[ 0.85711962, 0.14288038],
[ 0.74830885, 0.25169115],
[ 0.84502153, 0.15497847],
[ 0.12886629, 0.87113371],
[ 0.86038196, 0.13961804],
[ 0.81492416, 0.18507584],
[ 0.74973913, 0.25026087],
[ 0.88595841, 0.11404159],
[ 0.60609598, 0.39390402],
[ 0.86346251, 0.13653749],
[ 0.61197924, 0.38802076],
[ 0.35569986, 0.64430014],
[ 0.86037046, 0.13962954],
[ 0.79463014, 0.20536986],
[ 0.57570079, 0.42429921],
[ 0.83467975, 0.16532025],
[ 0.85798051, 0.14201949],
[ 0.10986907, 0.89013093],
[ 0.56445448, 0.43554552],
[ 0.84012224, 0.15987776],
[ 0.15820833, 0.84179167],
[ 0.56790934, 0.43209066],
[ 0.85796389, 0.14203611],
[ 0.65552637, 0.34447363],
[ 0.86051808, 0.13948192],
[ 0.35980506, 0.64019494],
[ 0.86038196, 0.13961804],
[ 0.29324608, 0.70675392],
[ 0.86017015, 0.13982985],
[ 0.28622388, 0.71377612],
[ 0.28287555, 0.71712445],
[ 0.8070549 , 0.1929451 ],
[ 0.86038196, 0.13961804],
[ 0.20682467, 0.79317533],
[ 0.85835972, 0.14164028],
[ 0.53882734, 0.46117266],
[ 0.80220769, 0.19779231],
[ 0.85539757, 0.14460243],
[ 0.69483827, 0.30516173],
[ 0.87933359, 0.12066641],
[ 0.83561804, 0.16438196],
[ 0.8070549 , 0.1929451 ],
[ 0.85835972, 0.14164028],
[ 0.86042952, 0.13957048],
[ 0.87914067, 0.12085933],
[ 0.11937883, 0.88062117],
[ 0.2853273 , 0.7146727 ],
[ 0.59808311, 0.40191689],
[ 0.90594814, 0.09405186],
[ 0.85835972, 0.14164028],
[ 0.86346251, 0.13653749],
[ 0.85801214, 0.14198786],
[ 0.86032122, 0.13967878],
[ 0.35349566, 0.64650434],
[ 0.52724747, 0.47275253],
[ 0.22879217, 0.77120783],
[ 0.28601744, 0.71398256],
[ 0.56939306, 0.43060694],
[ 0.85743204, 0.14256796],
[ 0.94208727, 0.05791273],
[ 0.82348607, 0.17651393],
[ 0.74901495, 0.25098505],
[ 0.94336392, 0.05663608],
[ 0.85705258, 0.14294742],
[ 0.85800384, 0.14199616],
[ 0.05587819, 0.94412181],
[ 0.5941366 , 0.4058634 ],
[ 0.18541791, 0.81458209],
[ 0.84012224, 0.15987776],
[ 0.83358085, 0.16641915],
[ 0.87933975, 0.12066025],
[ 0.88380587, 0.11619413],
[ 0.87914067, 0.12085933],
[ 0.28628812, 0.71371188],
[ 0.48214083, 0.51785917],
[ 0.70716252, 0.29283748],
[ 0.05715212, 0.94284788],
[ 0.65795297, 0.34204703],
[ 0.25709963, 0.74290037],
[ 0.81492001, 0.18507999],
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[ 0.8727473 , 0.1272527 ],
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[ 0.60648726, 0.39351274],
[ 0.8149159 , 0.1850841 ]])
preds = clf.predict(test_x)计算模型的混淆矩阵如下所示。
confusion_matrix(test_y,preds)array([[157, 15],
[ 35, 61]])
计算模型的ROC/AUC得分，并画出ROC曲线。模型的ROC/AUC得分为0.88，表明预测准确的概率为88%左右。模型预测结果较好。
pre = clf.predict_proba(test_x)
roc_auc_score(test_y,pre[:,1])0.88114704457364346
fpr,tpr,thresholds = roc_curve(test_y,pre[:,1])
fig,ax = plt.subplots(figsize=(8,5))
plt.plot(fpr,tpr)
ax.set_title("Roc of Logistic Regression")<matplotlib.text.Text at 0x7674a1f588>
模型预测结果分类报告如下所示。
print(classification_report(test_y,preds)) precision recall f1-score support
0 0.82 0.91 0.86 172
1 0.80 0.64 0.71 96
avg / total 0.81 0.81 0.81 268
总体而言，模型的拟合结果较好。