泰坦尼克号 决策树分类python代码

泰坦尼克号的数据集是一个经典的机器学习入门案例，通常用于训练决策树模型。在这个例子中，我们将使用Python的scikit-learn库来构建一个决策树，预测乘客是否在泰坦尼克号沉船事故中存活。首先，确保已安装了必要的库，如pandas、numpy和sklearn。

# 导入所需的库
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# 加载数据集
url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
titanic_data = pd.read_csv(url)

# 数据预处理（清洗、缺失值处理等）
titanic_data = titanic_data.dropna(subset=['Survived', 'Age', 'Sex', 'Pclass']) # 示例，假设这些特征对生存有影响
titanic_data['Sex'] = titanic_data['Sex'].map({'male': 0, 'female': 1}) # 将性别转换为数值

# 特征选择和编码
features = ['Pclass', 'Sex', 'Age']
X = titanic_data[features]
y = titanic_data['Survived']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建并训练决策树模型
clf = DecisionTreeClassifier(random_state=42)
clf.fit(X_train, y_train)

# 预测测试集结果
y_pred = clf.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

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