Kaggle项目实战一:Titanic: Machine Learning from Disaster

项目地址

    https://www.kaggle.com/c/titanic

项目介绍:

    除了乘客的编号以外,还包括下表中10个字段,构成了数据的所有特征

Variable

Definition

Key

survival

是否存活

0 = No, 1 = Yes

pclass

票的等级

1 = 1st, 2 = 2nd, 3 = 3rd

sex

性别

 

Age

年龄

 

sibsp

同乘配偶或兄弟姐妹

 

parch

同乘孩子或父母

 

ticket

票号

 

fare

乘客票价

 

cabin

客舱号码

 

embarked

登船港口

C = Cherbourg, Q = Queenstown, S = Southampton

一、导入数据

train_df = pd.read_csv("..\train.csv")
test_df = pd.read_csv("..\test.csv")

 查看数据整体缺失情况

结果如下:存在null值得字段有Age、Fare和Cabin,其中Cabin缺失最为严重,缺失率77.1%

train_df.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

连续型变量分布情况

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

离散变量情况( 包括客舱号码,登船港口,票的等级,性别)

train_df.describe(include=['O'])
                         Name   Sex  Ticket    Cabin Embarked
count                     891   891     891      204      889
unique                    891     2     681      147        3
top     Greenberg, Mr. Samuel  male  347082  B96 B98        S
freq                        1   577       7        4      644

 

  • Total samples are 891 or 40% of the actual number of passengers on board the Titanic (2,224).
  • Survived is a categorical feature with 0 or 1 values.
  • Around 38% samples survived representative of the actual survival rate at 32%.
  • Most passengers (> 75%) did not travel with parents or children.
  • Nearly 30% of the passengers had siblings and/or spouse aboard.
  • Fares varied significantly with few passengers (<1%) paying as high as $512.
  • Few elderly passengers (<1%) within age range 65-80.

 讨论特征增加和删除:

    delete:用户id,用户名称可能需要删掉

    create:Age range feature,fare range feature

    discuss:年龄小的,性别为女的获救的几率应该比较大

train_df[['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
train_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)
      Sex  Survived
0  female  0.742038
1    male  0.188908

二、可视化

 Survival by Age, Class and Gender 

g = sns.FacetGrid(train_df, col='Survived')
g.map(plt.hist, 'Age', bins=20)

Kaggle项目实战一:Titanic: Machine Learning from Disaster_第1张图片

Survival by Age, Class and Gender

grid = sns.FacetGrid(train_df, col = "Pclass", row = "Sex", hue = "Survived", palette = 'seismic')
grid = grid.map(plt.scatter, "PassengerId", "Age")
grid.add_legend()

Kaggle项目实战一:Titanic: Machine Learning from Disaster_第2张图片

Kaggle项目实战一:Titanic: Machine Learning from Disaster_第3张图片

 三、处理数据

3.1 去掉没得用的特征

删除数据中对预测没有实际效果的特征,提高模型速度,减少分析流程。

需要删除的特征有:客舱号码Cabin、票号Tickets

train_df = train_df.drop(['Ticket','Cabin'],axis=1)
test_df = test_df.drop(['Ticket','Cabin'],axis=1)

3.2 建立新的特征 

对人名进行分析发现,带有master的一般都活下来了,于是对人名进行拆分,提取,和.之间的数据

combine = [train_df, test_df]
for dataset in combine:
    dataset['Title'] = dataset.Name.str.extract('([A-Za-z]+)\.',expand=False)

  

转载于:https://www.cnblogs.com/bethansy/p/9037513.html

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