Machine learning 2: KNN decision tree explores the issue of Titanic survivors

KNN decision tree explores the issue of Titanic survivors

import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.metrics import classification_report
import graphviz   #决策树可视化

data = pd.read_csv(r"titanic_data.csv")
data.drop("PassengerId",axis = 1,inplace = True)  #删除id这一列

data

	Survived	Pclass	Sex	Age
0	0	3	male	22.0
1	1	1	female	38.0
2	1	3	female	26.0
3	1	1	female	35.0
4	0	3	male	35.0
...	...	...	...	...
886	0	2	male	27.0
887	1	1	female	19.0
888	0	3	female	NaN
889	1	1	male	26.0
890	0	3	male	32.0

891 rows × 4 columns

data.loc[data["Sex"] == "male","Sex"] = 1
data.loc[data["Sex"] == "female","Sex"] = 0

data

	Survived	Pclass	Sex	Age
0	0	3	1	22.0
1	1	1	0	38.0
2	1	3	0	26.0
3	1	1	0	35.0
4	0	3	1	35.0
...	...	...	...	...
886	0	2	1	27.0
887	1	1	0	19.0
888	0	3	0	NaN
889	1	1	1	26.0
890	0	3	1	32.0

891 rows × 4 columns

data.fillna(data["Age"].mean(),inplace = True)  #用均值来填充缺失值

data

	Survived	Pclass	Sex	Age
0	0	3	1	22.000000
1	1	1	0	38.000000
2	1	3	0	26.000000
3	1	1	0	35.000000
4	0	3	1	35.000000
...	...	...	...	...
886	0	2	1	27.000000
887	1	1	0	19.000000
888	0	3	0	29.699118
889	1	1	1	26.000000
890	0	3	1	32.000000

891 rows × 4 columns

Dtc = DecisionTreeClassifier(max_depth = 5,random_state =8)  #构建决策树
Dtc.fit(data.iloc[:,1:],data["Survived"])    #模型训练
pre = Dtc.predict(data.iloc[:,1:])  #模型预测

print(classification_report(pre,data["Survived"]))   #混淆矩阵

              precision    recall  f1-score   support

           0       0.88      0.84      0.86       573
           1       0.73      0.79      0.76       318

    accuracy                           0.82       891
   macro avg       0.81      0.82      0.81       891
weighted avg       0.83      0.82      0.82       891

pre == data["Survived"]   #比较模型预测值与实际值是否一致

0       True
1       True
2       True
3       True
4       True
       ...  
886     True
887     True
888    False
889    False
890     True
Name: Survived, Length: 891, dtype: bool

Visualization

dot_data = export_graphviz(Dtc,feature_names = ["Pclass","Sex","Age"],class_names="Survive")

graph  = graphviz.Source(dot_data)
graph