Delete '泰坦尼克号生存情况分析大作业.py'

泰坦尼克号生存情况分析大作业.py

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-# -*- 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(r'C:\Users\FADER\Desktop\python课件\train.csv')
-test = pd.read_csv(r'C:\Users\FADER\Desktop\python课件\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)
-#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 = full.corr()
-corrDf['Survived'].sort_values(ascending =False)
-#(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.plot(k_range,cv_scores)
-plt.xlabel('K')
-plt.ylabel('Accuracy')
-plt.show()
-model = LogisticRegression()
-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 ))
-
-#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())
-index = ['男性','男性存活人数','女性','女性存活人数']
-def get_counts(sequence):
-    counts = {}
-    for x in sequence:
-        if x in counts:
-            counts[x] += 1
-        else:
-            counts[x] =1
-    return counts
-ls = pred_X
-counts = get_counts(pred_X['Sex'])
-
-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(index,height)
-#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 = ['Pclass_1','Pclass_2','Pclass_3']
-height1 = [107,93,218]
-height2 = [48,36,72]
-plt.bar(x-width,height1,width)
-plt.bar(x+width,height2,width)
-plt.xticks(x,index)
-
-plt.show()
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