数据集下载
链接：/s/1f6x0ZHlAdwch52rHKDYBgA
提取码：9hgz
数据集简介
PassengerId: 乘客IDSurvived: 是否生存，0代表遇难，1代表还活着Pclass: 船舱等级：1Upper，2Middle，3LowerName: 姓名Sex:性别Age: 年龄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Fare: 乘客的船票价Cabin: 乘客所在的仓位（位置）Embarked:乘客登船口岸
代码演示
导入所需要的库
import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns%matplotlib inlineimport warningswarnings.filterwarnings('ignore')
导入数据集
train = pd.read_csv('E:/persional/数据挖掘/train.csv',engine='python')test = pd.read_csv('E:/persional/数据挖掘/test.csv',engine='python')print(np.array(train).shape)#(891, 12)print(np.array(test).shape)#(418, 11)train.head()
检测缺失值
print(pd.isnull(train).sum())
数据分析
性别分析
#对女性人数进行统计与归一化train["Survived"][train["Sex"]=='female'].value_counts(normalize=True)#1 0.742038#0 0.257962#对男性人数进行统计与归一化train["Survived"][train["Sex"]=='male'].value_counts(normalize=True)#0 0.811092#1 0.188908#女性存活率print(train["Survived"][train["Sex"]=='female'].value_counts(normalize=True)[1]*100)#74.20382165605095#男性存活率print(train["Survived"][train["Sex"]=='male'].value_counts(normalize=True)[1]*100)#18.890814558058924sns.barplot(x="Sex",y="Survived",data=train)
父母或子女个数分析
print(train["Survived"][train["Parch"]==0].value_counts(normalize=True)[1]*100)#34.365781710914455print(train["Survived"][train["Parch"]==1].value_counts(normalize=True)[1]*100)#55.08474576271186print(train["Survived"][train["Parch"]==2].value_counts(normalize=True)[1]*100)#50.0print(train["Survived"][train["Parch"]==3].value_counts(normalize=True)[1]*100)#60.0sns.barplot(x="Parch",y="Survived",data=train)
年龄分析
pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates=‘raise’)
plt.figure(figsize=(10,10))train["Age"] = train["Age"].fillna(-0.5)#填缺失值test["Age"] = test["Age"].fillna(-0.5)bins = [-1,0,5,12,18,24,35,60,np.inf]labels = ['Unknown','Baby','Child','Teenager','Student','Young Adult','Adult','Senior']#print(pd.cut(train["Age"],bins=bins,labels=labels))train["AgeGroup"] = pd.cut(train["Age"],bins,labels=labels)test["AgeGroup"] = pd.cut(train["Age"],bins,labels=labels)sns.barplot(x="AgeGroup",y="Survived",data=train)
乘客所在的仓位（位置） 分析
# 检查是否不为空值或缺失值#有空值或缺失值时返回False，否则返回的是Truetrain["CabinBool"] = (train["Cabin"].notnull().astype('int'))test["CabinBoll"] = (test["Cabin"].notnull().astype('int'))#print(train["Cabin"].notnull().astype('int'))print(train["Survived"][train["CabinBool"]==1].value_counts(normalize=True)[1]*100)#66.66666666666666print(train["Survived"][train["CabinBool"]==0].value_counts(normalize=True)[1]*100)#29.985443959243085sns.barplot(x="CabinBool",y="Survived",data=train)
train.head()
#删掉Cabin与TIcket这两列train = train.drop(["Cabin"],axis=1)test = test.drop(["Cabin"],axis=1)train = train.drop(["Ticket"],axis=1)test = test.drop(["Ticket"],axis=1)
数据预处理
southampton = train[train["Embarked"]=='S'].shape[0]print(southampton)#644cherbourg = train[train["Embarked"]=='C'].shape[0]print(cherbourg)#168queenstown = train[train["Embarked"]=='Q'].shape[0]print(queenstown)#77#添加缺失值train = train.fillna({"Embarked":"S"})print(pd.isnull(train["Embarked"]).sum())#0
combine = [train,test]for dataset in combine:dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\.',expand=False)#对姓名正则化添加到新的列Title#交叉表 pd.crosstab(train['Title'],train['Sex'])
display(train)
#依据Title对姓名进行等级划分for dataset in combine:dataset['Title'] = dataset['Title'].replace(['Lady','Capt','Col','Don','Dr','Myjor','Rev','Jonkheer','Dona'],'Rare')dataset['Title'] = dataset['Title'].replace(['Countess','Lady','Sir'],'Roayal')dataset['Title'] = dataset['Title'].replace('Mlle','Miss')dataset['Title'] = dataset['Title'].replace('Ms','Miss')dataset['Title'] = dataset['Title'].replace('Mme','Mrs')#统计划分好的等级print(train[['Title','Survived']].groupby(['Title'],as_index=False).count())#求取平均值print(train[['Title','Survived']].groupby(['Title'],as_index=False).mean())
display(train)
#对划分好的等级进行映射title_mapping = {"Mr":1,"Miss":2,"Mrs":3,"Master":4,"Royal":5,"Rare":6}for dataset in combine:dataset['Title'] = dataset['Title'].map(title_mapping)dataset['Title'] = dataset['Title'].fillna(0)display(train['Title'])
#根据每一个划分好的等级求最多的年龄段即为众数mr_age = train[train["Title"]==1]["AgeGroup"].mode()#Young Adultmiss_age = train[train["Title"]==2]["AgeGroup"].mode()#Studentmrs_age = train[train["Title"]==3]["AgeGroup"].mode()#Adultmaster_age = train[train["Title"]==4]["AgeGroup"].mode()#Babyroyal_age = train[train["Title"]==5]["AgeGroup"].mode()#Adultrare_age = train[train["Title"]==6]["AgeGroup"].mode()#Adult# display(train["Title"])age_title_mapping={1:"Young Adult",2:"Student",3:"Adult",4:"Baby",5:"Adult",6:"Adult"}#填补Unknown缺失值for x in range(len(train["AgeGroup"])):if train["AgeGroup"][x]=="Unknown":train["AgeGroup"][x]=age_title_mapping[train["Title"][x]]for x in range(len(test["AgeGroup"])):if test["AgeGroup"][x]=="Unknown":test["AgeGroup"][x]=age_title_mapping[test["Title"][x]]display(train)
#每个年龄段进行映射age_mapping = {'Baby':1,'Child':2,'Teenager':3,'Student':4,'Young Adult':5,'Adult':6,'Senior':7}train['AgeGroup'] = train['AgeGroup'].map(age_mapping)test['AgeGroup'] = test['AgeGroup'].map(age_mapping)display(train)
#删除名字这一特征train = train.drop(['Name'],axis=1)test = test.drop(['Name'],axis=1)display(train)
#对性别进行映射sex_mapping = {'male':0,'female':1}train['Sex'] = train['Sex'].map(sex_mapping)test['Sex'] = test['Sex'].map(sex_mapping)display(train)
#对每个港口进行映射embarked_mapping = {'S':1,'C':2,'Q':3}train['Embarked'] = train['Embarked'].map(embarked_mapping)test['Embarked'] = test['Embarked'].map(embarked_mapping)display(train)
#填补测试集的船票价格，依据训练集的船舱等级for x in range(len(test['Fare'])):if pd.isnull(test['Fare'][x]):pclass = test['Pclass'][x]test['Fare'][x] = round(train[train['Pclass']==pclass]['Fare'].mean(),4)
#依据船票划分为四个等级train['FareBand'] = pd.qcut(train['Fare'],4,labels=[1,2,3,4])test['FareBand'] = pd.qcut(test['Fare'],4,labels=[1,2,3,4])display(train['FareBand'])display(test['FareBand'])
#检查数据train.head()
test.head()
display(train.drop(['Survived','PassengerId'],axis=1))
训练数据
from sklearn.model_selection import train_test_splitpredictors = train.drop(['Survived','PassengerId'],axis=1)target = train['Survived']X_train,X_val,y_train,y_val = train_test_split(predictors,target,test_size=0.22,random_state=0)print(X_train.shape)#(694, 11)print(X_val.shape)#(197, 11)print(y_train.shape)#(694,)print(y_val.shape)#(197,)
分别用不同的模型训练数据，并计算其准确率
GNB
from sklearn.naive_bayes import GaussianNBfrom sklearn.metrics import accuracy_scoregaussian = GaussianNB()gaussian.fit(X_train,y_train)y_pred = gaussian.predict(X_val)acc_gaussian = round(accuracy_score(y_pred,y_val)*100,2)print(acc_gaussian)#79.7
LR
from sklearn.linear_model import LogisticRegressionlogreg = LogisticRegression()logreg.fit(X_train,y_train)y_pred = logreg.predict(X_val)acc_logreg = round(accuracy_score(y_pred,y_val)*100,2)print(acc_logreg)#79.19
SVC
from sklearn.svm import SVCsvc = SVC()svc.fit(X_train,y_train)y_pred = svc.predict(X_val)acc_svc = round(accuracy_score(y_pred,y_val)*100,2)print(acc_svc)#73.1
Perceptron
from sklearn.linear_model import Perceptronperceptron = Perceptron()perceptron.fit(X_train,y_train)y_pred = perceptron.predict(X_val)perceptron_logreg = round(accuracy_score(y_pred,y_val)*100,2)print(perceptron_logreg)#62.44
DT
from sklearn.tree import DecisionTreeClassifierdecisiontree = DecisionTreeClassifier()decisiontree.fit(X_train,y_train)y_pred = decisiontree.predict(X_val)acc_decisiontree = round(accuracy_score(y_pred,y_val)*100,2)print(acc_decisiontree)#77.16
RF
from sklearn.ensemble import RandomForestClassifierrandomforset = RandomForestClassifier()randomforset.fit(X_train,y_train)y_pred = randomforset.predict(X_val)acc_randomforset = round(accuracy_score(y_pred,y_val)*100,2)print(acc_randomforset)#86.8
KNN
from sklearn.neighbors import KNeighborsClassifierknn = KNeighborsClassifier()knn.fit(X_train,y_train)y_pred = knn.predict(X_val)acc_knn = round(accuracy_score(y_pred,y_val)*100,2)print(acc_knn)#76.65
GBC
from sklearn.ensemble import GradientBoostingClassifiergbk = GradientBoostingClassifier()gbk.fit(X_train,y_train)y_pred = gbk.predict(X_val)acc_gbk = round(accuracy_score(y_pred,y_val)*100,2)print(acc_gbk)#86.8
SGD
from sklearn.linear_model import SGDClassifiersgd = SGDClassifier()sgd.fit(X_train,y_train)y_pred = sgd.predict(X_val)acc_sgd = round(accuracy_score(y_pred,y_val)*100,2)print(acc_sgd)#77.61
预测结果总结
models = pd.DataFrame({'Model':['Support Vector Machines', 'KNN', 'Logistic Regression','Random Forest', 'Naive Bayes', 'Perceptron','Decision Tree', 'Stochastic Gradient Descent', 'Gradient Boosting Classifier'],'Score': [acc_svc, acc_knn, acc_logreg,acc_randomforset, acc_gaussian, perceptron_logreg, acc_decisiontree,acc_sgd, acc_gbk]})models.sort_values(by='Score',ascending=False)
准确率结果如下：
