diff --git a/泰坦尼克号生存情况分析大作业.py b/泰坦尼克号生存情况分析大作业.py
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+++ b/泰坦尼克号生存情况分析大作业.py
@@ -0,0 +1,260 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue May 31 15:44:45 2022
+
+@author: FADER
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import cross_val_score
+from time import time
+import datetime
+plt.rcParams['font.sans-serif'] = ['SimHei'] 
+# 步骤一（替换sans-serif字体）
+plt.rcParams['axes.unicode_minus'] = False  
+train = pd.read_csv('train.csv')
+test = pd.read_csv('test.csv')
+#print('训练数据集:',train.shape,'测试数据集：',test.shape)
+full = train.append( test , ignore_index = True )
+#print(full.info)
+#print(full.head(10))
+#print ('合并后的数据集:',full.shape)
+#print(full.info())
+#填补缺失的数据
+full['Age']=full['Age'].fillna(full['Age'].mean())
+full['Fare']=full['Fare'].fillna(full['Fare'].mean())
+full['Embarked']=full['Embarked'].fillna('S')
+full['Cabin'] = full['Cabin'].fillna( 'U' )
+full['Embarked']=full['Embarked'].fillna('S')
+#print(full['Sex'].head())
+dict1 = {'male':1,'female':0}
+full['Sex']=full['Sex'].map(dict1)
+#print(full['Sex'].head())
+#print(full['Embarked'].head())
+#将Embarked的数据分类后并提取为新的列
+def embarkeddefyC(x):
+    return 1 if x == 'C' else 0
+def embarkeddefyQ(x):
+    return 1 if x == 'Q' else 0
+def embarkeddefyS(x):
+    return 1 if x == 'S' else 0
+full['Embarked_C']=full['Embarked'].map(embarkeddefyC)
+full['Embarked_Q']=full['Embarked'].map(embarkeddefyQ)
+full['Embarked_S']=full['Embarked'].map(embarkeddefyS)
+full.drop('Embarked',axis=1,inplace=True)
+#print(full.head())
+#将Pclass的数据分类后并提取为新的列
+pclassDf = pd.DataFrame()
+#使用get_dummies进行one-hot编码，列名前缀是Pclass
+pclassDf = pd.get_dummies(full['Pclass'],prefix='Pclass')
+#print(pclassDf.head())
+full = pd.concat([full,pclassDf],axis=1)
+full.drop('Pclass',axis=1,inplace=True)
+#print(full.head())
+#提取名字的信息
+def getTitle(name):
+    str1=name.split(',')[1]
+    str2=str1.split('.')[0]
+    str3=str2.strip()
+    return str3
+Name = pd.DataFrame()
+Name['Title']=full['Name'].map(getTitle)
+title_mapDict={
+                'Capt':        'Officer',
+                'Col':         'Officer',
+                'Major':       'Officer',
+                'Jonkheer':    'Royalty',
+                'Don':         'Royalty',
+                'Sir':         'Royalty',
+                'Dr':          'Officer',
+                'Rev':         'Officer',
+                'the Countess': 'Royalty',
+                'Dona':         'Royalty',
+                'Mme':          'Mrs',
+                'Mlle':         'Miss',
+                'Ms':           'Mrs',
+                'Mr':           'Mr',
+                'Mrs':          'Mrs',
+                'Miss':         'Miss',
+                'Master':       'Master',
+                'Lady':         'Royalty'}
+Name['Title']=Name['Title'].map(title_mapDict)
+Name=pd.get_dummies(Name['Title'])
+full = pd.concat([full,Name],axis = 1 )
+full.drop('Name',axis = 1,inplace = True)
+name=['Officer','Royalty','Miss','Mrs','Mr''Master']
+#print(full.head())
+
+#提取Cabin的信息
+#print(full['Cabin'].value_counts())
+def Cabinchange(x):
+    return x[0]
+full['Cabin']=full['Cabin'].map(Cabinchange)
+#print(full['Cabin'].head())
+Cabinin = pd.DataFrame()
+Cabinin = pd.get_dummies(full['Cabin'],prefix='Cabin')
+#print(Cabinin.head())
+full = pd.concat([full,Cabinin],axis = 1)
+#提取家庭成员人数信息
+familyDf = pd.DataFrame()
+familyDf['FamilySize']=full['Parch']+full['SibSp']+1
+familyDf['Family_Single']=familyDf['FamilySize'].map(lambda s : 1 if s==1 else 0)
+familyDf['Family_Small']=familyDf['FamilySize'].map(lambda s :1 if 2<= s <= 4 else 0)
+familyDf['Family_Large']=familyDf['FamilySize'].map(lambda s :1 if 5<= s else 0)
+full = pd.concat([full,familyDf],axis=1)
+#计算各组数据与Surrvived的相关系数
+corrDf = abs(full.corr())
+sort=corrDf['Survived'].sort_values(ascending =False)
+plt.figure('fig')
+plt.xlim(0,1)
+plt.barh(sort.index,sort.values)
+
+#(corrDf['Survived'])
+#构建模型
+full_X = pd.concat( [Name,#头衔
+                     pclassDf,
+                     familyDf,
+                     full['Fare'],
+                     Cabinin,
+                     full['Embarked_C'],
+                     full['Embarked_Q'],
+                     full['Embarked_S'],
+                     full['Sex'],
+                    ] , axis=1 )
+#print(full_X.head())
+sourceRow = 891
+source_X = full_X.loc[0:sourceRow-1,:]
+source_y = full.loc[0:sourceRow-1,'Survived']
+pred_X = full_X.loc[sourceRow:,:]
+print('原始数据集有多少行：',source_X.shape[0])
+print('预测数据集有多少行：',pred_X.shape[0])
+from sklearn.model_selection import train_test_split
+train_X,test_X,train_y,test_y = train_test_split(source_X,source_y,train_size=0.8,random_state=33)
+print('原始数据集的特征：',source_X.shape,
+     '训练数据集特征：',train_X.shape,
+     '测试数据集特征：',test_X.shape)
+print('原始数据集的标签：',source_y.shape,
+     '训练数据集的标签：',train_y.shape,
+     '测试数据集的标签：',test_y.shape)
+#取不同的n_neighbors值并观察取何值时拟合程度最高
+k_range = range(1,21,2)
+cv_scores = []
+time0 = time()
+for n in k_range:
+    print(n)
+    knn = KNeighborsClassifier(n_neighbors=n)
+    scores = cross_val_score(knn,train_X,train_y,cv=10,scoring='accuracy')
+    cv_scores.append(scores.mean())
+print('计算所用时长：%s' % (datetime.datetime.fromtimestamp(time()-time0).strftime("%M:%S:%f")))
+print('最高准确率:',max(cv_scores),',对应的k值为:',k_range[cv_scores.index(max(cv_scores))])
+plt.figure()
+plt.plot(k_range,cv_scores)
+plt.xlabel('K')
+plt.ylabel('Accuracy')
+plt.show()
+model = KNeighborsClassifier(n_neighbors = k_range[cv_scores.index(max(cv_scores))])
+model.fit( train_X , train_y )
+#分类问题，score得到的是模型的正确率
+print('模型得拟合程度为：',model.score(test_X , test_y ))
+from sklearn.metrics import roc_curve, auc, roc_auc_score
+model_y_score = model.predict_proba(test_X)
+model_fpr, model_tpr, _ = roc_curve(test_y,model_y_score[:,1], pos_label=1)
+model_auc = auc(model_fpr, model_tpr)
+plt.plot(model_fpr, model_tpr,
+         label='micro-average ROC curve',
+         color='b', linewidth=4)
+plt.plot([0, 1], [0, 1], 'k--', lw=2,c='r')
+plt.stackplot(model_fpr, model_tpr, color=['#ff0000'])
+plt.text(0.5, 0.3, 'ROC', ha='center', fontsize=50, c='black', alpha=0.4)
+plt.title('model roc')
+plt.show()
+#使用机器学习模型，对预测数据集中的生存情况进行预测
+pred_Y=model.predict(pred_X)
+#生成的预测值是浮点数（0.0,1,0），转换成整数
+pred_Y=pred_Y.astype(int) 
+#3.显示男性与女性乘客生存比例并进行柱状图可视化
+pred_X['predict'] = pred_Y
+#print(pred_X.head())
+x3 = [0,1,2,3]
+index = ['男性','男性存活人数','女性','女性存活人数']
+df = pred_X.groupby(by = ['Sex','predict']).count()
+#print(df)
+plt.figure('fig1')
+plt.title('男性与女性乘客生存比例')
+plt.ylabel('人数')
+height = [266,45,152,91]
+plt.bar(x3,height)
+for x,y in zip(x3,height):
+    plt.text(x,y,'%.f'%(y),ha = 'center',va = 'bottom')
+plt.xticks(x3,index)
+fig=plt.figure()
+plt.title('男女生存乘客生存比例')
+
+ax = fig.gca()
+explode = [0,0.1]
+labels1 = ['女性死亡','女性存活']
+labels2 = ['男性死亡','男性存活']
+colors = ['yellow','green']
+ax.pie([(152-91)/152,91/152],explode = explode,labels = labels1,colors = colors,autopct = '%1.1f%%',shadow = True,startangle = 90,radius = 0.35,center = (0,0),frame = True)
+ax.pie([(266-45)/266,45/266],explode = explode,labels = labels2,colors = colors,autopct = '%1.1f%%',shadow = True,startangle = 90,radius = 0.35,center = (1,0),frame = True)
+ax.set_xticks([0,1])
+ax.set_yticks([0])
+ax.set_xticklabels(["女","男"])
+ax.set_xlim((-0.5,1.5))
+ax.set_ylim((-0.5,0.5))
+#4.显示不同客舱乘客生存比例并进行柱状图可视化
+plt.figure('fig2')
+plt.title('不同客舱乘客生存比例')
+plt.ylabel('人数')
+x = [0,1,2]
+x = np.array(x)
+width = 0.1
+index = ['一等舱','二等舱','三等舱']
+height1 = [107,93,218]
+height2 = [48,36,72]
+plt.bar(x-width,height1,width)
+plt.bar(x+width,height2,width)
+for x,y,i,j in zip(x-width,height1,x+width,height2):
+    plt.text(x,y,'%.f'%int(y),ha = 'center',va = 'bottom')
+    plt.text(i,j,'%.f'%int(j),ha = 'center',va = 'bottom')
+x = [0,1,2]
+plt.xticks(x,index)
+fig=plt.figure('fig3')
+plt.title('不同货舱乘客生存比例')
+ax = fig.gca()
+explode = [0,0.1]
+labels = ['死亡','存活']
+colors = ['yellow','green']
+ax.pie([(107-48)/107,48/107],explode = explode,labels = labels,colors = colors,autopct = '%1.1f%%',shadow = True,startangle = 90,radius = 0.35,center = (0,0),frame = True)
+ax.pie([(93-36)/93,36/93],explode = explode,labels = labels,colors = colors,autopct = '%1.1f%%',shadow = True,startangle = 90,radius = 0.35,center = (1,0),frame = True)
+ax.pie([(218-72)/218,72/218],explode = explode,labels = labels,colors = colors,autopct = '%1.1f%%',shadow = True,startangle = 90,radius = 0.35,center = (2,0),frame = True)
+ax.set_xticks([0,1,2])
+ax.set_yticks([0])
+ax.set_xticklabels(['一等舱','二等舱','三等舱'])
+ax.set_xlim((-0.5,2.5))
+ax.set_ylim((-0.5,0.5))
+plt.show()
+df = pred_X.groupby(by = ['Officer','Royalty','Miss','Mrs','Mr','Master']).count()
+df = pred_X.groupby(by = ['Officer','Royalty','Miss','Mrs','Mr','Master','predict']).count()
+cunhuolv = [2/5,0,48/78,51/73,29/240,7/21]
+name=['Officer','Royalty','Miss','Mrs','Mr','Master']
+dict1 = dict(zip(name,cunhuolv))
+list1 = sorted(dict1.items(),key = lambda x:x[-1],reverse = True)
+dict1 = dict(list1)
+plt.figure('fig4')
+plt.title('不同身份的乘客的生存比例')
+x1 = [0,1,2,3,4,5]
+plt.bar(x1,list(dict1.values()))
+for x,y in zip(x1,list(dict1.values())):
+    plt.text(x,y,'%.2f%%'%(y*100),ha = 'center',va = 'bottom')
+na = list(dict1.keys())
+plt.xlabel('身份')
+plt.ylabel('存活率')
+plt.xticks(x1,na)
+
+
+