Logistic 回归分析_泰坦尼克号生存预测

分析目的：

通过分析训练数据集分析出什么类型的人更可能生存，并预测出数据集中乘客的生还概率

#字段说明

# PassengerId： 乘客编号
# Survived：是否生存，也是我们分析目标。1表示生存，0表示死亡
# Pclass：船舱等级 分1、2、3等级，1等级最高
# Name：乘客姓名
# Sex：性别
# Age：年龄
# SibSp：该乘客一起旅行的兄弟姐妹和配偶的数量(同代直系亲属人数)
# Parch：该和乘客一起旅行的父母和孩子的数量(不同代直系亲属人数)
# Ticket：船票号
# Fare：船票价格
# Cabin：船舱号
# Embarked：登船港口
# S=英国南安普顿Southampton(起航点)
# C=法国 瑟堡市Cherbourg(途经点)
# Q=爱尔兰 昆士敦Queenstown(途经点)

1.获取数据

import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
#获取数据
titanic_train=pd.read_csv("./titanic_train.csv")
titanic_test=pd.read_csv("./titanic_test.csv")

2.数据探索

titanic_train.head()#前5个样本

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

titanic_test.head()

	PassengerId	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	892	3	Kelly, Mr. James	male	34.5	0	0	330911	7.8292	NaN	Q
1	893	3	Wilkes, Mrs. James (Ellen Needs)	female	47.0	1	0	363272	7.0000	NaN	S
2	894	2	Myles, Mr. Thomas Francis	male	62.0	0	0	240276	9.6875	NaN	Q
3	895	3	Wirz, Mr. Albert	male	27.0	0	0	315154	8.6625	NaN	S
4	896	3	Hirvonen, Mrs. Alexander (Helga E Lindqvist)	female	22.0	1	1	3101298	12.2875	NaN	S

titanic_train.info()
titanic_test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 11 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  418 non-null    int64  
 1   Pclass       418 non-null    int64  
 2   Name         418 non-null    object 
 3   Sex          418 non-null    object 
 4   Age          332 non-null    float64
 5   SibSp        418 non-null    int64  
 6   Parch        418 non-null    int64  
 7   Ticket       418 non-null    object 
 8   Fare         417 non-null    float64
 9   Cabin        91 non-null     object 
 10  Embarked     418 non-null    object 
dtypes: float64(2), int64(4), object(5)
memory usage: 36.0+ KB

titanic_train.describe().T

	count	mean	std	min	25%	50%	75%	max
PassengerId	891.0	446.000000	257.353842	1.00	223.5000	446.0000	668.5	891.0000
Survived	891.0	0.383838	0.486592	0.00	0.0000	0.0000	1.0	1.0000
Pclass	891.0	2.308642	0.836071	1.00	2.0000	3.0000	3.0	3.0000
Age	714.0	29.699118	14.526497	0.42	20.1250	28.0000	38.0	80.0000
SibSp	891.0	0.523008	1.102743	0.00	0.0000	0.0000	1.0	8.0000
Parch	891.0	0.381594	0.806057	0.00	0.0000	0.0000	0.0	6.0000
Fare	891.0	32.204208	49.693429	0.00	7.9104	14.4542	31.0	512.3292

titanic_train.describe(include=['O']).T

	count	unique	top	freq
Name	891	891	Ilett, Miss. Bertha	1
Sex	891	2	male	577
Ticket	891	681	347082	7
Cabin	204	147	B96 B98	4
Embarked	889	3	S	644

t_t_pclass=titanic_train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False)
t_t_pclass.mean().sort_values(by='Survived', ascending=False)

	Pclass	Survived
0	1	0.629630
1	2	0.472826
2	3	0.242363

titanic_train[["Sex", "Survived"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)

	Sex	Survived
0	female	0.742038
1	male	0.188908

import seaborn as sns
import matplotlib.pyplot as plt
age_map = sns.FacetGrid(titanic_train, col='Survived')
age_map.map(plt.hist, 'Age', bins=20)

<seaborn.axisgrid.FacetGrid at 0x1f9ebb158c8>

output_13_1.png

grid = sns.FacetGrid(titanic_train, col='Survived', row='Pclass', height=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=.5, bins=20)
grid.add_legend()

<seaborn.axisgrid.FacetGrid at 0x1f9ee46c708>

output_14_1.png

3.特征工程

combine = [titanic_train, titanic_test]
for dataset in combine:
    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)
titanic_train.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	0	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	1	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	1	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	1	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	0	35.0	0	0	373450	8.0500	NaN	S

for dataset in combine:    
    dataset['Age'].fillna(dataset['Age'].median(), inplace = True)
    dataset['Embarked'].fillna(dataset['Embarked'].mode()[0],    inplace = True)
    dataset['Fare'].fillna(dataset['Fare'].median(), inplace = True)
titanic_train.drop(['PassengerId','Cabin', 'Ticket'], axis=1, inplace = True)
titanic_train.info()
titanic_test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    int64  
 1   Pclass    891 non-null    int64  
 2   Name      891 non-null    object 
 3   Sex       891 non-null    int32  
 4   Age       891 non-null    float64
 5   SibSp     891 non-null    int64  
 6   Parch     891 non-null    int64  
 7   Fare      891 non-null    float64
 8   Embarked  891 non-null    object 
dtypes: float64(2), int32(1), int64(4), object(2)
memory usage: 59.3+ KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 11 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  418 non-null    int64  
 1   Pclass       418 non-null    int64  
 2   Name         418 non-null    object 
 3   Sex          418 non-null    int32  
 4   Age          418 non-null    float64
 5   SibSp        418 non-null    int64  
 6   Parch        418 non-null    int64  
 7   Ticket       418 non-null    object 
 8   Fare         418 non-null    float64
 9   Cabin        91 non-null     object 
 10  Embarked     418 non-null    object 
dtypes: float64(2), int32(1), int64(4), object(4)
memory usage: 34.4+ KB

for dataset in combine:    
    dataset['FamilySize'] = dataset ['SibSp'] + dataset['Parch'] + 1
    dataset['IsAlone'] = 1 
    dataset['IsAlone'].loc[dataset['FamilySize'] > 1] = 0
    dataset['FareBin'] = pd.qcut(dataset['Fare'], 4)
    dataset['AgeBin'] = pd.cut(dataset['Age'].astype(int), 5)

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexing.py:670: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_with_indexer(indexer, value)

from sklearn.preprocessing import OneHotEncoder, LabelEncoder
label = LabelEncoder()
for dataset in combine:    
    dataset['Sex_Code'] = label.fit_transform(dataset['Sex'])
    dataset['Embarked_Code'] = label.fit_transform(dataset['Embarked'])
    dataset['AgeBin_Code'] = label.fit_transform(dataset['AgeBin'])
    dataset['FareBin_Code'] = label.fit_transform(dataset['FareBin'])
titanic_train.head()

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Fare	Embarked	FamilySize	IsAlone	FareBin	AgeBin	Sex_Code	Embarked_Code	AgeBin_Code	FareBin_Code
0	0	3	Braund, Mr. Owen Harris	0	22.0	1	0	7.2500	S	2	0	(-0.001, 7.91]	(16.0, 32.0]	0	2	1	0
1	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	1	38.0	1	0	71.2833	C	2	0	(31.0, 512.329]	(32.0, 48.0]	1	0	2	3
2	1	3	Heikkinen, Miss. Laina	1	26.0	0	0	7.9250	S	1	1	(7.91, 14.454]	(16.0, 32.0]	1	2	1	1
3	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	1	35.0	1	0	53.1000	S	2	0	(31.0, 512.329]	(32.0, 48.0]	1	2	2	3
4	0	3	Allen, Mr. William Henry	0	35.0	0	0	8.0500	S	1	1	(7.91, 14.454]	(32.0, 48.0]	0	2	2	1

4.模型开发

Target = ['Survived']
titanic_train_x_bin = ['Sex','Pclass', 'Embarked_Code', 'FamilySize', 'AgeBin_Code', 'FareBin_Code']
titanic_train_xy_bin = Target + titanic_train_x_bin
print('Bin X Y: ', titanic_train_xy_bin, '\n')
#Model Algorithms
from sklearn import linear_model
#Common Model Helpers
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn import feature_selection
from sklearn import model_selection
from sklearn import metrics
#Visualization
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import seaborn as sns
train1_x_bin,test1_x_bin,train1_y_bin,test1_y_bin =model_selection.train_test_split(titanic_train[titanic_train_x_bin],titanic_train[Target],test_size=0.3,random_state = 0)
print("Data1 Shape: {}".format(titanic_train.shape))
print("Train1 Shape: {}".format(train1_x_bin.shape))
print("Test1 Shape: {}".format(test1_x_bin.shape))
train1_x_bin.head()

Bin X Y:  ['Survived', 'Sex', 'Pclass', 'Embarked_Code', 'FamilySize', 'AgeBin_Code', 'FareBin_Code'] 

Data1 Shape: (891, 17)
Train1 Shape: (623, 6)
Test1 Shape: (268, 6)

	Sex	Pclass	Embarked_Code	FamilySize	AgeBin_Code	FareBin_Code
857	0	1	2	1	3	2
52	1	1	0	2	3	3
386	0	3	2	8	0	3
124	0	1	2	2	3	3
578	1	3	0	2	1	2

from sklearn import linear_model
logreg = linear_model.LogisticRegression()
logreg.fit(train1_x_bin, train1_y_bin)
Y_pred = logreg.predict(test1_x_bin)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)

print(logreg.intercept_) #截距
coef=pd.DataFrame(logreg.coef_).T  #参数
columns=pd.DataFrame(train1_x_bin.columns,columns=['A'])
result = pd.concat([columns,coef], axis=1)
result = result.rename(columns={'A': 'Attribute', 0: 'Coefficients'})
result

[1.82313942]

	Attribute	Coefficients
0	Sex	2.605530
1	Pclass	-0.859693
2	Embarked_Code	-0.254773
3	FamilySize	-0.290905
4	AgeBin_Code	-0.568369
5	FareBin_Code	0.219886

5.模型评估

acc_train = round(logreg.score(train1_x_bin, train1_y_bin) * 100, 2)
acc_train

78.81

# View summary of common classification metrics
print("------------------------Metrices---------------------")
print(metrics.classification_report(y_true = test1_y_bin,y_pred = Y_pred))

------------------------Metrices---------------------
              precision    recall  f1-score   support

           0       0.85      0.84      0.84       168
           1       0.74      0.75      0.74       100

    accuracy                           0.81       268
   macro avg       0.79      0.79      0.79       268
weighted avg       0.81      0.81      0.81       268

from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
probs = logreg.predict_proba(test1_x_bin)
preds = probs[:,1]
#---find the FPR, TPR, and threshold---
fpr, tpr, threshold = roc_curve(test1_y_bin, preds)
#---find the area under the curve---
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate (TPR)')
plt.xlabel('False Positive Rate (FPR)')
plt.title('Receiver Operating Characteristic (ROC)')
plt.legend(loc = 'lower right')
plt.show()

output_27_0.png

通过分析得出以下结论：
性别方面：女性的生还率较高，达到74.2%；
年龄方面：4岁以下的婴儿存活率较高，15-25岁的人大部分没有生还。
船层方面：第一船层的乘客大部分生还，而第三船层的乘客大部分没有生还。

模型的准确率为78.49%，模型的AUC等于0.86，这个模型效果良好，能够很好预测
乘客的生存概率。