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完整代码如下#xff1a;
import pandas as pd
import numpy as npdf pd.read_csv(train.csv1 . 泰坦尼克号 使用一个简单的决策树进行模型构建达到75.8%的准确率有点低但是刚开始
完整代码如下
import pandas as pd
import numpy as npdf pd.read_csv(train.csv)df.infolabel [Pclass,Sex,Age,SibSp,Fare,Embarked]x df[label]
y df[Survived]
print(x.loc[0])x[Embarked] x[Embarked].map({C: 1, Q: 2, S: 3})x[Sex] x[Sex].map({male: 1,female : 2})
print(x.loc[0])x x.fillna(x.mean())import sklearn
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_scoretrain_x,test_x,train_y,test_y train_test_split(x,y,test_size0.2,random_state42,shuffleTrue)clf DecisionTreeClassifier()
clf.fit(train_x,train_y)y_pred clf.predict(test_x)accuracy accuracy_score(y_pred,test_y)
print(fAccuracy: {accuracy * 100:.2f}%)res pd.read_csv(test.csv)
print(res.loc[0])res_x res[label]
res_x[Embarked] res_x[Embarked].map({C: 1, Q: 2, S: 3})
res_x[Sex] res_x[Sex].map({male: 1,female : 2})
print(res_x.loc[0])res_x res_x.fillna(res_x.mean())pred clf.predict(res_x)
print(pred[0])ans res[[PassengerId]].copy()
ans[Survived] predprint(ans.loc[0])ans.to_csv(ans.csv)
