Kaggle竞赛：泰坦尼克号灾难数据分

https://www.kaggle.com/c/titanic

目标确定 ：根据已有数据预测未知旅客生死
数据准备 ：
- 数据获取，载入训练集csv、测试集csv
- 数据清洗，补齐或抛弃缺失值，数据类型变换(字符串转数字)
- 数据重构，根据需要重新构造数据(重组数据，构建新特征）
数据分析 ：
- 描述性分析，画图，直观分析
- 探索性分析，机器学习模型
成果输出 ：csv文件上传得到正确率和排名

载入库

    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd
[/code]

#  数据获取

```code
    train = pd.read_csv('train.csv')
    test = pd.read_csv('test.csv')
[/code]

```code
    train.head() # 显示头几行数据
[/code]

|  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

```code
    test.head()# 显示头几行数据
[/code]  
  
|  PassengerId  |  Pclass  |  Name  |  Sex  |  Age  |  SibSp  |  Parch  |
Ticket  |  Fare  |  Cabin  |  Embarked  
---|---|---|---|---|---|---|---|---|---|---|---  
0  |  892  |  3  |  Kelly, Mr. James  |  male  |  34.5  |  0  |  0  |  330911
|  7.8292  |  NaN  |  Q  
1  |  893  |  3  |  Wilkes, Mrs. James (Ellen Needs)  |  female  |  47.0  |  1
|  0  |  363272  |  7.0000  |  NaN  |  S  
2  |  894  |  2  |  Myles, Mr. Thomas Francis  |  male  |  62.0  |  0  |  0  |
240276  |  9.6875  |  NaN  |  Q  
3  |  895  |  3  |  Wirz, Mr. Albert  |  male  |  27.0  |  0  |  0  |  315154
|  8.6625  |  NaN  |  S  
4  |  896  |  3  |  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  |  female  |
22.0  |  1  |  1  |  3101298  |  12.2875  |  NaN  |  S  
  
##  数据概览

```code
    train.shape, test.shape # 查看数据的行数，列数
[/code]

```code
    ((891, 12), (418, 11))

    train.info() # 查看具体信息字段
[/code]

```code
    <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

    test.info()
[/code]

```code
    <class 'pandas.core.frame.DataFrame'>
    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

train.csv 具体数据格式

PassengerId 乘客ID
Survived 是否幸存。0遇难，1幸存
Pclass 船舱等级，1Upper，2Middle，3Lower
Name 姓名，object——————————
Sex 性别，object—————————
Age 年龄缺失177——m————————
SibSp 兄弟姐妹及配偶个数
Parch 父母或子女个数
Ticket 乘客的船票号，object————————
Fare 乘客的船票价
Cabin 乘客所在舱位，object,缺失687———————
Embarked 乘客登船口岸，object,缺失3————————

    train.head() # head()方法查看头部几行信息，如果打train则返回所有数据列表
[/code]

|  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  
  
  
  

##  数据清洗

###  缺失过多或无关值抛弃

```code
    # .loc 通过自定义索引获取数据 , 其中 .loc[:,:]中括号里面逗号前面的表示行，逗号后面的表示列
    train2 = train.loc[:,['PassengerId','Survived','Pclass','Sex','Age','SibSp','Parch','Fare']]
    test2 = test.loc[:, ['PassengerId','Pclass','Sex','Age','SibSp','Parch','Fare']]
[/code]

```code
    train2.head()
[/code]

|  PassengerId  |  Survived  |  Pclass  |  Sex  |  Age  |  SibSp  |  Parch  |
Fare  
---|---|---|---|---|---|---|---|---  
0  |  1  |  0  |  3  |  male  |  22.0  |  1  |  0  |  7.2500  
1  |  2  |  1  |  1  |  female  |  38.0  |  1  |  0  |  71.2833  
2  |  3  |  1  |  3  |  female  |  26.0  |  0  |  0  |  7.9250  
3  |  4  |  1  |  1  |  female  |  35.0  |  1  |  0  |  53.1000  
4  |  5  |  0  |  3  |  male  |  35.0  |  0  |  0  |  8.0500

```code
    test2.head()
[/code]  
  
|  PassengerId  |  Pclass  |  Sex  |  Age  |  SibSp  |  Parch  |  Fare  
---|---|---|---|---|---|---|---  
0  |  892  |  3  |  male  |  34.5  |  0  |  0  |  7.8292  
1  |  893  |  3  |  female  |  47.0  |  1  |  0  |  7.0000  
2  |  894  |  2  |  male  |  62.0  |  0  |  0  |  9.6875  
3  |  895  |  3  |  male  |  27.0  |  0  |  0  |  8.6625  
4  |  896  |  3  |  female  |  22.0  |  1  |  1  |  12.2875

```code
    train2.info(), test2.info()
[/code]

```code
    test2.info()
[/code]

```code
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 418 entries, 0 to 417
    Data columns (total 7 columns):
    PassengerId    418 non-null int64
    Pclass         418 non-null int64
    Sex            418 non-null object
    Age            332 non-null float64
    SibSp          418 non-null int64
    Parch          418 non-null int64
    Fare           417 non-null float64
    dtypes: float64(2), int64(4), object(1)
    memory usage: 22.9+ KB

填充年龄空值

    age = train2['Age'].median() # 年龄中位数
    age
[/code]

```code
    28.0

    train2['Age'].isnull() # 空值转bool值
[/code]

```code
    0      False
    1      False
    2      False
    3      False
    4      False
    5       True
    6      False
    7      False
    8      False
    9      False
    10     False
    11     False
    12     False
    13     False
    14     False
    15     False
    16     False
    17      True
    18     False
    19      True
    20     False
    21     False
    22     False
    23     False
    24     False
    25     False
    26      True
    27     False
    28      True
    29      True
           ...  
    861    False
    862    False
    863     True
    864    False
    865    False
    866    False
    867    False
    868     True
    869    False
    870    False
    871    False
    872    False
    873    False
    874    False
    875    False
    876    False
    877    False
    878     True
    879    False
    880    False
    881    False
    882    False
    883    False
    884    False
    885    False
    886    False
    887    False
    888     True
    889    False
    890    False
    Name: Age, Length: 891, dtype: bool

    train2.loc[train2['Age'].isnull(), 'Age'] = age # 为train2年龄为空值的填充年龄中位数
[/code]

```code
    train2.info()
[/code]

```code
    test2.loc[test2['Age'].isnull(), 'Age'] = age # 为test2中年龄为空值的数据填充年龄中位数
[/code]

```code
    test2.info()
[/code]

```code
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 418 entries, 0 to 417
    Data columns (total 7 columns):
    PassengerId    418 non-null int64
    Pclass         418 non-null int64
    Sex            418 non-null object
    Age            418 non-null float64
    SibSp          418 non-null int64
    Parch          418 non-null int64
    Fare           417 non-null float64
    dtypes: float64(2), int64(4), object(1)
    memory usage: 22.9+ KB

填充船票价格空值

    #取众数填充船票价格 Fare
    
    Fare = test2['Fare'].mode()
    Fare
    
    test2.loc[test['Fare'].isnull(),'Fare'] = Fare[0]
    
    train2.info(),test2.info()
[/code]

```code
    train2.head()
[/code]

|  PassengerId  |  Survived  |  Pclass  |  Sex  |  Age  |  SibSp  |  Parch  |
Fare  
---|---|---|---|---|---|---|---|---  
0  |  1  |  0  |  3  |  male  |  22.0  |  1  |  0  |  7.2500  
1  |  2  |  1  |  1  |  female  |  38.0  |  1  |  0  |  71.2833  
2  |  3  |  1  |  3  |  female  |  26.0  |  0  |  0  |  7.9250  
3  |  4  |  1  |  1  |  female  |  35.0  |  1  |  0  |  53.1000  
4  |  5  |  0  |  3  |  male  |  35.0  |  0  |  0  |  8.0500  
  
  

##  数据类型转换

```code
    train2.dtypes,test2.dtypes # 列数据类型
[/code]

```code
    (PassengerId      int64
     Survived         int64
     Pclass           int64
     Sex             object
     Age            float64
     SibSp            int64
     Parch            int64
     Fare           float64
     dtype: object, PassengerId      int64
     Pclass           int64
     Sex             object
     Age            float64
     SibSp            int64
     Parch            int64
     Fare           float64
     dtype: object)

性别转换成整型数据

    train2['Sex'] = train2['Sex'].map({'female':0, 'male':1}).astype(int)
    test2['Sex'] = test2['Sex'].map({'female': 0, 'male': 1}).astype(int)
    train2.head()
[/code]

|  PassengerId  |  Survived  |  Pclass  |  Sex  |  Age  |  SibSp  |  Parch  |
Fare  
---|---|---|---|---|---|---|---|---  
0  |  1  |  0  |  3  |  1  |  22.0  |  1  |  0  |  7.2500  
1  |  2  |  1  |  1  |  0  |  38.0  |  1  |  0  |  71.2833  
2  |  3  |  1  |  3  |  0  |  26.0  |  0  |  0  |  7.9250  
3  |  4  |  1  |  1  |  0  |  35.0  |  1  |  0  |  53.1000  
4  |  5  |  0  |  3  |  1  |  35.0  |  0  |  0  |  8.0500  
  
  

##  数据重构

将SibSp、Parch特征构建两个新特征

  * 家庭人口总数 familysize 
  * 是否单身 isalone 

```code
    train2.loc[:,'SibSp'] #兄妹个数
    train2.loc[:,'Parch'] #父母子女个数
    
    train2['familysize'] = train2.loc[:,'SibSp'] + train2.loc[:,'Parch'] + 1
    test2['familysize'] = test2.loc[:,'SibSp'] + test2.loc[:,'Parch'] + 1
[/code]

```code
    train2.head()
[/code]

|  PassengerId  |  Survived  |  Pclass  |  Sex  |  Age  |  SibSp  |  Parch  |
Fare  |  familysize  
---|---|---|---|---|---|---|---|---|---  
0  |  1  |  0  |  3  |  1  |  22.0  |  1  |  0  |  7.2500  |  2  
1  |  2  |  1  |  1  |  0  |  38.0  |  1  |  0  |  71.2833  |  2  
2  |  3  |  1  |  3  |  0  |  26.0  |  0  |  0  |  7.9250  |  1  
3  |  4  |  1  |  1  |  0  |  35.0  |  1  |  0  |  53.1000  |  2  
4  |  5  |  0  |  3  |  1  |  35.0  |  0  |  0  |  8.0500  |  1

```code
    train2['isalone'] = 0
    train2.loc[train2['familysize'] == 1,'isalone'] = 1
[/code]

```code
    train2.head()
[/code]  
  
|  PassengerId  |  Survived  |  Pclass  |  Sex  |  Age  |  SibSp  |  Parch  |
Fare  |  familysize  |  isalone  
---|---|---|---|---|---|---|---|---|---|---  
0  |  1  |  0  |  3  |  1  |  22.0  |  1  |  0  |  7.2500  |  2  |  0  
1  |  2  |  1  |  1  |  0  |  38.0  |  1  |  0  |  71.2833  |  2  |  0  
2  |  3  |  1  |  3  |  0  |  26.0  |  0  |  0  |  7.9250  |  1  |  1  
3  |  4  |  1  |  1  |  0  |  35.0  |  1  |  0  |  53.1000  |  2  |  0  
4  |  5  |  0  |  3  |  1  |  35.0  |  0  |  0  |  8.0500  |  1  |  1  
  
  

##  数据重构后的最终数据

```code
    train3 = train2.loc[:,['PassengerId','Survived','Pclass','Sex','Age','Fare','familysize','isalone']]
    train3.head()
    test3 = test2.loc[:,['PassengerId','Pclass','Sex','Age','Fare','familysize','isalone']]
    test3.head()
[/code]

|  PassengerId  |  Pclass  |  Sex  |  Age  |  Fare  |  familysize  |  isalone  
---|---|---|---|---|---|---|---  
0  |  892  |  3  |  1  |  34.5  |  7.8292  |  1  |  NaN  
1  |  893  |  3  |  0  |  47.0  |  7.0000  |  2  |  NaN  
2  |  894  |  2  |  1  |  62.0  |  9.6875  |  1  |  NaN  
3  |  895  |  3  |  1  |  27.0  |  8.6625  |  1  |  NaN  
4  |  896  |  3  |  0  |  22.0  |  12.2875  |  3  |  NaN  
  
  
  
  

#  数据分析

##  描述性分析

```code
    #单身存活率
    d = train3[['isalone', 'Survived']].groupby(['isalone']).mean()
    d
    # d.loc[0,'Survived']
[/code]

|  Survived  
---|---  
isalone  |  
0  |  0.505650  
1  |  0.303538

```code
    #单身与否死亡率
    
    plt.bar(
        [0,1],
        [1-d.loc[0,'Survived'],1-d.loc[1,'Survived']],
        0.5,
        color='r',
        alpha=0.5,
    )
    
    plt.xticks([0,1],['notalone','alone'])
    
    plt.show()
[/code]  
  
![这里写图片描述](https://img-
blog.csdn.net/20170912201103052?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvUWlhbllhbkRhaQ==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

```code
    #男性女性存活率
    n = train3[['Sex', 'Survived']].groupby(['Sex']).mean()
    n
[/code]

|  Survived  
---|---  
Sex  |  
0  |  0.742038  
1  |  0.188908

```code
    # 不同性别死亡率条形图
    
    plt.bar(
        [0,1],
        [1-n.loc[0,'Survived'],1-n.loc[1,'Survived']],
        0.5,
        color='g',
        alpha=0.7
    )
    
    plt.xticks([0,1],['female','male'])
    
    plt.show()
[/code]  
  
![这里写图片描述](https://img-
blog.csdn.net/20170912201159226?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvUWlhbllhbkRhaQ==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

```code
    #仓位存活率
    c = train3[['Pclass', 'Survived']].groupby(['Pclass']).mean()
    c
[/code]

|  Survived  
---|---  
Pclass  |  
1  |  0.629630  
2  |  0.472826  
3  |  0.242363

```code
    #三等仓位死亡率条形图
    
    plt.bar(
        [0,1,2],
        [1-c.loc[1,'Survived'],1-c.loc[2,'Survived'],1-c.loc[3,'Survived']],
        0.5,
        color='b',
        alpha=0.7
    )
    
    plt.xticks([0,1,2],[1,2,3])
    
    plt.show()
[/code]  
  
![这里写图片描述](https://img-
blog.csdn.net/20170912201232597?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvUWlhbllhbkRhaQ==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast)

```code
    #年龄存活率
    age = train3[['Age', 'Survived']].groupby(['Age']).mean()
    age
[/code]

|  Survived  
---|---  
Age  |  
0.42  |  1.000000  
0.67  |  1.000000  
0.75  |  1.000000  
0.83  |  1.000000  
0.92  |  1.000000  
1.00  |  0.714286  
2.00  |  0.300000  
3.00  |  0.833333  
4.00  |  0.700000  
5.00  |  1.000000  
6.00  |  0.666667  
7.00  |  0.333333  
8.00  |  0.500000  
9.00  |  0.250000  
10.00  |  0.000000  
11.00  |  0.250000  
12.00  |  1.000000  
13.00  |  1.000000  
14.00  |  0.500000  
14.50  |  0.000000  
15.00  |  0.800000  
16.00  |  0.352941  
17.00  |  0.461538  
18.00  |  0.346154  
19.00  |  0.360000  
20.00  |  0.200000  
20.50  |  0.000000  
21.00  |  0.208333  
22.00  |  0.407407  
23.00  |  0.333333  
…  |  …  
44.00  |  0.333333  
45.00  |  0.416667  
45.50  |  0.000000  
46.00  |  0.000000  
47.00  |  0.111111  
48.00  |  0.666667  
49.00  |  0.666667  
50.00  |  0.500000  
51.00  |  0.285714  
52.00  |  0.500000  
53.00  |  1.000000  
54.00  |  0.375000  
55.00  |  0.500000  
55.50  |  0.000000  
56.00  |  0.500000  
57.00  |  0.000000  
58.00  |  0.600000  
59.00  |  0.000000  
60.00  |  0.500000  
61.00  |  0.000000  
62.00  |  0.500000  
63.00  |  1.000000  
64.00  |  0.000000  
65.00  |  0.000000  
66.00  |  0.000000  
70.00  |  0.000000  
70.50  |  0.000000  
71.00  |  0.000000  
74.00  |  0.000000  
80.00  |  1.000000  
  
88 rows × 1 columns

```code
    #不同年龄存活率
    
    plt.figure(2, figsize=(20,5))
    plt.bar(
        age.index,
        age.values,
        0.5,
        color='r',
        alpha=0.7
    )
    # plt.axis([0,80,0,20])
    plt.xticks(age.index,rotation=90)
    
    plt.show()

![这里写图片描述](https://img-
blog.csdn.net/20170912201319057?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvUWlhbllhbkRhaQ/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA/dissolve/70/gravity/SouthEast)

    #票价存活率
    fare = train3[['Fare', 'Survived']].groupby(['Fare']).mean()
    fare
[/code]

|  Survived  
---|---  
Fare  |  
0.0000  |  0.066667  
4.0125  |  0.000000  
5.0000  |  0.000000  
6.2375  |  0.000000  
6.4375  |  0.000000  
6.4500  |  0.000000  
6.4958  |  0.000000  
6.7500  |  0.000000  
6.8583  |  0.000000  
6.9500  |  0.000000  
6.9750  |  0.500000  
7.0458  |  0.000000  
7.0500  |  0.000000  
7.0542  |  0.000000  
7.1250  |  0.000000  
7.1417  |  1.000000  
7.2250  |  0.250000  
7.2292  |  0.266667  
7.2500  |  0.076923  
7.3125  |  0.000000  
7.4958  |  0.333333  
7.5208  |  0.000000  
7.5500  |  0.250000  
7.6292  |  0.000000  
7.6500  |  0.250000  
7.7250  |  0.000000  
7.7292  |  0.000000  
7.7333  |  0.500000  
7.7375  |  0.500000  
7.7417  |  0.000000  
…  |  …  
80.0000  |  1.000000  
81.8583  |  1.000000  
82.1708  |  0.500000  
83.1583  |  1.000000  
83.4750  |  0.500000  
86.5000  |  1.000000  
89.1042  |  1.000000  
90.0000  |  0.750000  
91.0792  |  1.000000  
93.5000  |  1.000000  
106.4250  |  0.500000  
108.9000  |  0.500000  
110.8833  |  0.750000  
113.2750  |  0.666667  
120.0000  |  1.000000  
133.6500  |  1.000000  
134.5000  |  1.000000  
135.6333  |  0.666667  
146.5208  |  1.000000  
151.5500  |  0.500000  
153.4625  |  0.666667  
164.8667  |  1.000000  
211.3375  |  1.000000  
211.5000  |  0.000000  
221.7792  |  0.000000  
227.5250  |  0.750000  
247.5208  |  0.500000  
262.3750  |  1.000000  
263.0000  |  0.500000  
512.3292  |  1.000000  
  
248 rows × 1 columns

```code
    plt.figure(2, figsize=(20,5))
    plt.bar(
        fare.index,
        fare.values,
        0.5,
        color='r',
        alpha=0.7
    )
    # plt.axis([0,80,0,20])
    plt.xticks(fare.index,rotation=90)
    
    plt.show()

![这里写图片描述](https://img-
blog.csdn.net/20170912201406381?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvUWlhbllhbkRhaQ/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA/dissolve/70/gravity/SouthEast)

得出结论

    # 单身死亡率70%
    jieguo = pd.DataFrame(np.arange(0,418),index=test3.loc[:,'PassengerId'])
    jieguo.loc[:,0] = 1
[/code]

```code
    jieguo.head()
[/code]

|  0  
---|---  
PassengerId  |  
892  |  1  
893  |  1  
894  |  1  
895  |  1  
896  |  1

```code
    jieguo.loc[test3[test3.loc[:,'isalone'] == 1].loc[:,'PassengerId'].values] = 0 #单身死
[/code]

```code
    jieguo.head()
[/code]  
  
|  0  
---|---  
PassengerId  |  
892  |  1  
893  |  1  
894  |  1  
895  |  1  
896  |  1  
  
  
  

##  输出结论

```code
    jieguo.to_csv('isalone.csv')
[/code]

```code
    #判断：男性全死，女性全活，三等仓全死
    new3 = pd.DataFrame(np.arange(0,418),index=test3.loc[:,'PassengerId'].values)
    new3[0] = 0 #默认全死
    new3.head()
[/code]

|  0  
---|---  
892  |  0  
893  |  0  
894  |  0  
895  |  0  
896  |  0

```code
    new3.loc[test3[test3.loc[:,'Sex'] == 0].loc[:,'PassengerId'].values] = 1 #女性活
    new3.head()
[/code]  
  
|  0  
---|---  
892  |  0  
893  |  1  
894  |  0  
895  |  0  
896  |  1

```code
    new3.loc[test2[test2.loc[:,'Pclass'] == 3].loc[:,'PassengerId'].values] = 0 #三等仓死
    new3.head()
[/code]  
  
|  0  
---|---  
892  |  0  
893  |  0  
894  |  0  
895  |  0  
896  |  0

```code
    #写入csv上传
    new3.to_csv('cangwei-xingbie.csv')#判断：男性全死，女性全活，三等仓全死
[/code]  
  
  
  

##  机器学习建模

```code
    train3.head()
[/code]

|  PassengerId  |  Survived  |  Pclass  |  Sex  |  Age  |  Fare  |  familysize
|  isalone  
---|---|---|---|---|---|---|---|---  
0  |  1  |  0  |  3  |  1  |  22.0  |  7.2500  |  2  |  0  
1  |  2  |  1  |  1  |  0  |  38.0  |  71.2833  |  2  |  0  
2  |  3  |  1  |  3  |  0  |  26.0  |  7.9250  |  1  |  1  
3  |  4  |  1  |  1  |  0  |  35.0  |  53.1000  |  2  |  0  
4  |  5  |  0  |  3  |  1  |  35.0  |  8.0500  |  1  |  1

```code
    from sklearn import neighbors,datasets
[/code]

```code
    x = train3.loc[:,['Pclass','Sex','familysize']]
    y = train3.loc[:,'Survived'] #生死
    
    clf = neighbors.KNeighborsClassifier(n_neighbors = 20)
    clf.fit(x,y) #knn训练
    clf
[/code]

```code
    KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
               metric_params=None, n_jobs=1, n_neighbors=20, p=2,
               weights='uniform')

    #knn预测
    z = clf.predict(test3.loc[:,['Pclass','Sex','familysize']])
    z
[/code]

```code
    array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,
           0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,
           0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0,
           0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
           1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
           0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1,
           0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0,
           1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1,
           0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0,
           0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,
           0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,
           0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,
           0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
           0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
           1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1,
           1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
           1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0,
           1, 0, 0, 0], dtype=int64)

    # 构造表
    s = np.arange(892, 1310)
    s
    results = pd.DataFrame(z, index=s)
    results.head()
[/code]  
  
|  0  
---|---  
892  |  0  
893  |  0  
894  |  0  
895  |  0  
896  |  1

```code
    # 写入csv上传
    results.to_csv('Titanic_knn.csv')
[/code]


![在这里插入图片描述](https://img-blog.csdnimg.cn/20210608151750993.gif)

posted @ 2021-07-09 17:15 老酱阅读(389) 评论(0) 收藏举报

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老酱

Kaggle竞赛：泰坦尼克号灾难数据分

Kaggle竞赛：泰坦尼克号灾难数据分

载入库

train.csv 具体数据格式

填充年龄空值

填充船票价格空值

性别转换成整型数据

得出结论

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