Kaggle之泰坦尼克号（2）

书接上篇Kaggle之泰坦尼克号（1），上面提到的解决方案一经过特征工程、模型直接预测（0.78229）、优化超参数（0.78468），精度提升了0.2个百分点，最终精度排名为1700/14296（11.89%），下面说明基于特征工程的解决方案二。

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解决方案二：

score：0.79425
Leaderboard：1244/14296（8.7%）

一、特征工程

训练集一共提供11个特征包括6个数值型数据，5个文本型数据：

• 数值型：PassengerId（乘客ID）、Pclass（乘客等级）、Age（年龄）、SibSp（堂兄弟妹个数）、Parch（父母与小孩的个数）、Fare（票价）
• 文本型：Name（姓名）、Sex（性别）、Ticket（船票信息）、Cabin（船舱信息）、Embarked（登船港口）

缺失值处理

数据缺失情况
训练集

测试集

#对数据进行简单的预处理
#对fare缺失值使用均值替换
train_Fare_mean=train["Fare"].mean()
test.loc[test["Fare"].isnull()==True,"Fare"]=train_Fare_mean
#检验对test中fare空值是否替换完成
print("test对fare空值替换后为：\n",test.isnull().sum())

#对train中的embarked进行缺失值替换
train_embarked_mode=train["Embarked"].mode()
train.loc[train["Embarked"].isnull()==True,"Embarked"]=train_embarked_mode[0]
#检验成功对train的Embarked缺失值进行替换
print("train替换Embarked缺失值后为:\n",train.isnull().sum())

#对train\test中age数据采取mean替换
train.loc[train["Age"].isnull()==True,"Age"]=train["Age"].mean()
test.loc[test["Age"].isnull()==True,"Age"]=train["Age"].mean()
#验证train\test中Age缺失值补充完毕
print("补充train中Age后：\n",train.isnull().sum())
print("补充test中Age后：\n",test.isnull().sum())

#数据分割,模拟训练集和测试集的关系
label = train['Survived']
train.drop('Survived',axis=1,inplace=True)
X_train,X_test,Y_train,Y_test = train_test_split(train,label,test_size = 0.3,random_state = 1)
X_train['Survived'] = Y_train
X_test['Survived'] = Y_test  

文本型数据处理-Sex

# 对sex进行编码 male==1,female==0
train['Sex'] = train['Sex'].apply(lambda x: 1 if x == 'male' else 0)
test['Sex'] = test['Sex'].apply(lambda x: 1 if x == 'male' else 0)
train = pd.get_dummies(data= train,columns=['Sex'])
test = pd.get_dummies(data= test,columns=['Sex'])

文本型数据处理-Name

• 名称类别对存活的影响（编码）
• 名称长度对存活的影响

# Name 名字开头的数量
def Name_Title_Code(x):
    if x == 'Mr.':
        return 1
    if (x == 'Mrs.') or (x=='Ms.') or (x=='Lady.') or (x == 'Mlle.') or (x =='Mme'):
        return 2
    if x == 'Miss':
        return 3
    if x == 'Rev.':
        return 4
    return 5
X_train['Name_Title'] = X_train['Name'].apply(lambda x: x.split(',')[1]).apply(lambda x: x.split()[0])
X_test['Name_Title'] = X_test['Name'].apply(lambda x: x.split(',')[1]).apply(lambda x: x.split()[0])
# train.groupby(["Name_Call","Survived"])["Survived"].count()
train['Name_Title'] = train['Name'].apply(lambda x: x.split(',')[1]).apply(lambda x: x.split()[0])
test['Name_Title'] = test['Name'].apply(lambda x: x.split(',')[1]).apply(lambda x: x.split()[0])
train['Name_Title'] = train['Name_Title'].apply(Name_Title_Code)
# fig,axis=plt.subplots(1,1,figsize=(15,5))
# sns.barplot("Name_Call","Survived",data=train,ax=axis)
test['Name_Title'] = test['Name_Title'].apply(Name_Title_Code)
train = pd.get_dummies(columns = ['Name_Title'], data = train)
test = pd.get_dummies(columns = ['Name_Title'], data = test)
# 名字长度对存活的影响
X_train['Name_len'] = X_train['Name'].apply(lambda x: len(x))
X_test['Name_len'] = X_test['Name'].apply(lambda x: len(x))
train['Name_len'] = train['Name'].apply(lambda x: len(x))
test['Name_len'] = test['Name'].apply(lambda x: len(x))

文本型数据处理-Ticket

• Ticket首字母对存活的影响（编码）

#Ticket船票
#获取票的第一个字母
def Ticket_First_Let(x):
    return x[0]
X_train['Ticket_First_Letter'] = X_train['Ticket'].apply(Ticket_First_Let)
X_test['Ticket_First_Letter'] = X_test['Ticket'].apply(Ticket_First_Let)
#可视化船票编号与存活的关系
#p1=plt.figure(figsize=(10,8))
#a1=p1.add_subplot(1,1,1)
def Ticket_First_Letter_Code(x):
    if (x == '1'):
        return 1
    if x == '3':
        return 2
    if x == '4':
        return 3
    if x == 'C':
        return 4
    if x == 'S':
        return 5
    if x == 'P':
        return 6
    if x == '6':
        return 7
    if x == '7':
        return 8
    if x == 'A':
        return 9
    if x == 'W':
        return 10
    return 11
#test["Ticket_first"]=test["Ticket"].apply(Ticket_First_Letter)
#sns.barplot("Ticket_first","Survived",data=train_train.sort_values("Ticket_first"),ax=a1,capsize=0.2)
#sns.barplot("Ticket_first","Survived",data=test.sort_values("Ticket_first"),ax=axis[1])
#定义票的编号对存活的影响
train['Ticket_First_Letter'] = train['Ticket'].apply(Ticket_First_Let)
test['Ticket_First_Letter'] = test['Ticket'].apply(Ticket_First_Let)
train['Ticket_First_Letter'] = train['Ticket_First_Letter'].apply(Ticket_First_Letter_Code)
test['Ticket_First_Letter'] = test['Ticket_First_Letter'].apply(Ticket_First_Letter_Code)
train = pd.get_dummies(columns = ['Ticket_First_Letter'], data = train) 
test = pd.get_dummies(columns = ['Ticket_First_Letter'], data = test)
#train.groupby(["Ticket_first","Survived"])["Survived"].count()
#fig,axis=plt.subplots(1,1,figsize=(15,5))
#sns.barplot("Ticket_first","Survived",data=train,ax=axis)

文本型数据处理-Cabin

• 缺失值补充及编码

#Cabin 缺失值,判断条件为是否为空
X_train['Cabin'] = X_train['Cabin'].fillna('Missing')
X_test['Cabin'] = X_test['Cabin'].fillna('Missing')
def Cabin_First_Letter(x):
    if x == 'Missing':
        return 'XX'
    return x[0]
X_train['Cabin_First_Letter'] = X_train['Cabin'].apply(Cabin_First_Letter)
X_test['Cabin_First_Letter'] = X_test['Cabin'].apply(Cabin_First_Letter)
def Cabin_First_Letter_Code(x):
    if x == 'XX':
        return 1
    if x == 'B':
        return 2
    if x == 'C':
        return 3
    if x == 'D':
        return 4     
    return 5
train['Cabin'] = train['Cabin'].fillna('Missing')
test['Cabin'] = test['Cabin'].fillna('Missing')
#fig,axis=plt.subplots(1,2,figsize=(15,5))
#sns.barplot("Cabin_first","Survived",data=train_train.sort_values("Cabin_first"),ax=axis[0])
#sns.barplot("Cabin_first","Survived",data=train_test.sort_values("Cabin_first"),ax=axis[1])
train['Cabin_First_Letter'] = train['Cabin'].apply(Cabin_First_Letter)
test['Cabin_First_Letter'] = test['Cabin'].apply(Cabin_First_Letter) 
train['Cabin_First_Letter'] = train['Cabin_First_Letter'].apply(Cabin_First_Letter_Code)
test['Cabin_First_Letter'] = test['Cabin_First_Letter'].apply(Cabin_First_Letter_Code)
train = pd.get_dummies(columns = ['Cabin_First_Letter'], data = train) 
test = pd.get_dummies(columns = ['Cabin_First_Letter'], data = test) 
#train.groupby(["Cabin_first","Survived"])["Survived"].count()
#fig,axis=plt.subplots(1,1,figsize=(15,5))
#sns.barplot("Cabin_first","Survived",data=train,ax=axis)

文本型数据处理-Embarked

• Embarked to 唯一值01编码字段

#对Embarked的处理,统计特性不强，候补
#p3=plt.figure(figsize=(10,8))
#a3=p3.add_subplot(1,1,1)
#sns.barplot("Embarked","Survived",data=train_train.sort_values("Embarked"),capsize=0.5,ax=a3)
train = pd.get_dummies(train,columns = ['Embarked'])
test = pd.get_dummies(test,columns = ['Embarked'])

特征扩充-SibSp、Parch

• 家庭情况对存活的影响

#Parch+SibSp 家人对存活的影响,0/>4存活较低，1<Family<3存活较高
#fig,axis=plt.subplots(1,2,figsize=(15,5))
#sns.barplot("Family","Survived",data=train_train.sort_values("Family"),ax=axis[0])
#sns.barplot("Family","Survived",data=train_test.sort_values("Family"),ax=axis[1])
# =============================================================================
X_train['Fam_Size'] = X_train['SibSp']  + X_train['Parch'] 
X_test['Fam_Size'] = X_test['SibSp']  + X_test['Parch'] 
def Family_feature(train, test):
    for i in [train, test]:
        i['Fam_Size'] = np.where((i['SibSp']+i['Parch']) == 0 , 'Solo',
                           np.where((i['SibSp']+i['Parch']) <= 3,'Nuclear', 'Big'))
        del i['SibSp']
        del i['Parch']
    return train, test 
train, test  = Family_feature(train, test)
train = pd.get_dummies(train,columns = ['Fam_Size']) 
test =  pd.get_dummies(test,columns = ['Fam_Size']) 

特征扩充-Pclass

# Pclass 票的等级对存活的影响
# PclassNumber=train.groupby(["Pclass","Survived"])["Survived"].count()
# PclassMean=train[["Pclass","Survived"]].groupby(by="Pclass").mean()
# PclassMean.plot(kind="bar",rot=0,figsize=(10,6),fontsize=18)
# =============================================================================
train['Pclass_1']  = np.int32(train['Pclass'] == 1)  
train['Pclass_2']  = np.int32(train['Pclass'] == 2)  
train['Pclass_3']  = np.int32(train['Pclass'] == 3)  
test['Pclass_1']  = np.int32(test['Pclass'] == 1)  
test['Pclass_2']  = np.int32(test['Pclass'] == 2)  
test['Pclass_3']  = np.int32(test['Pclass'] == 3)  

特征扩充-Age

#年龄对存活的影响
#AgeNumber=train.groupby(["Age","Survived"])["Survived"].count()
#SurvivedAge=train[train["Survived"]==1]["Age"]
#DeadAge=train[train["Survived"]==0]["Age"]
#AgeFrame=pd.concat([SurvivedAge,DeadAge],axis=1)
#AgeFrame.columns=["Survived","Dead"]
#AgeFrame.head()
## 为避免颜色覆盖，使用alpha通道属性
#AgeFrame.plot(kind="hist",bins=30,alpha=0.5,figsize=(10,6))
#年龄低于5岁，年龄在15-35岁间，年龄大于75岁

# =============================================================================
# fig,axis=plt.subplots(1,1,figsize=(15,5))
# sns.barplot("Age","Survived",data=train,ax=axis)
# 
# =============================================================================
train['Small_Age'] = np.int32(train['Age'] <= 5)  
train['Old_Age'] = np.int32(train['Age'] >= 65)  
train['Middle_Age'] = np.int32((train['Age'] >= 15) & (train['Age'] <= 25))  
test['Small_Age'] = np.int32(test['Age'] <= 5)  
test['Old_Age'] = np.int32(test['Age'] >= 65)  
test['Middle_Age'] = np.int32((test['Age'] >= 15) & (test['Age'] <= 25)) 

特征扩充-Fare

#对数据进行处理
#Fare，票价越高越容易生存，但价格分布大，选择log(fare+1)<=2时存活几乎为0
X_train['Fare'] = X_train['Fare'] + 1
X_test['Fare'] = X_test['Fare'] + 1
X_train['Fare'] = X_train['Fare'].apply(np.log)
X_test['Fare'] = X_test['Fare'].apply(np.log)
# =============================================================================
# fig,axis=plt.subplots(1,2,figsize=(15,5))
# sns.barplot("Fare","Survived",data=train_train.sort_values["Fare"],ax=axis[0],capsize=0.2)
# sns.barplot("Fare","Survived",data=train_test.sort_values["Fare"],ax=axis[1],capsize=0.2)
# =============================================================================
train['Fare'] = train['Fare'] + 1
test['Fare'] = test['Fare'] + 1
train['Fare'] = train['Fare'].apply(np.log)
test['Fare'] = test['Fare'].apply(np.log) 
train['Fare_0_2'] = np.int32(train['Fare'] <= 2)
train['Fare_2_3'] = np.int32((train['Fare'] > 2) & (train['Fare'] <= 3) )
train['Fare_3_4'] = np.int32((train['Fare'] > 3) & (train['Fare'] <= 4) )
train['Fare_4_5'] = np.int32((train['Fare'] > 4) & (train['Fare'] <= 5)) 
train['Fare_5_'] = np.int32(train['Fare'] > 5)
test['Fare_0_2'] = np.int32(test['Fare'] <= 2)
test['Fare_2_3'] = np.int32((test['Fare'] > 2) & (test['Fare'] <= 3) )
test['Fare_3_4'] = np.int32((test['Fare'] > 3) & (test['Fare'] <= 4) )
test['Fare_4_5'] = np.int32((test['Fare'] > 4) & (test['Fare'] <= 5)) 
test['Fare_5_'] = np.int32(test['Fare'] > 5)

二、特征选择及建模预测

#特征选择,模型训练
#冗余数据及文本数据进行删除，共同列提取与对齐
train.drop(['Ticket','PassengerId','Name','Age','Cabin','Pclass'],axis = 1, inplace=True)
test.drop( ['PassengerId','Ticket','Name','Age','Cabin','Pclass'],axis =1, inplace=True)  
X_train_ = train.loc[X_train.index]
X_test_ = train.loc[X_test.index]
Y_train_ = label.loc[X_train.index]
Y_test_ = label.loc[X_test.index]
X_test_ = X_test_[X_train_.columns]
test = test[train.columns]

#模型训练
#随机森林
rf_ = RandomForestClassifier(criterion='gini', 
                             n_estimators=700,
#                            max_depth=5,
                             min_samples_split=16,
                             min_samples_leaf=1,
                             max_features='auto',  
                             random_state=10,
                             n_jobs=-1) 
                             
rf_.fit(X_train_,Y_train_) 
rf_.score(X_test_,Y_test_)
rf_.fit(train,label) 

# 预测
pd.concat((pd.DataFrame(train.columns, columns = ['variable']), 
           pd.DataFrame(rf_.feature_importances_, columns = ['importance'])), 
          axis = 1).sort_values(by='importance', ascending = False)[:20]


submit = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
submit.set_index('PassengerId',inplace=True)

res_rf = rf_.predict(test)
submit['Survived'] = res_rf
submit['Survived'] = submit['Survived'].apply(int)
submit.to_csv('submission.csv')