编写泰坦尼克号项目代码，并假设已经有了原始的pandas DataFrame titanic_df，其中包含embarked（登船港口）等列。

编写一个基本的泰坦尼克号预测项目的Python代码，通常会涉及到数据预处理、特征工程、模型训练和评估。这里是一个简化的示例，我们将使用`pandas`, `numpy`, 和 `scikit-learn`库：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 加载数据
titanic_df = pd.read_csv('titanic_data.csv')  # 假设数据文件名是'titanic_data.csv'

# 数据预处理
# 将embarked列转换为数值类别
le = LabelEncoder()
titanic_df['Embarked'] = le.fit_transform(titanic_df['Embarked'])

# 创建特征和目标变量
X = titanic_df[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]
y = titanic_df['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)

# 模型训练
model = LogisticRegression()
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

# 评估
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

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