复习日
c
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
# 1. 读入数据（注意路径：Kaggle Notebook 里直接就是 /kaggle/input/...）
train = pd.read_csv("/kaggle/input/titanic/train.csv")
test = pd.read_csv("/kaggle/input/titanic/test.csv")
# 2. 简单特征工程
# 选择一些比较有用的特征
features = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]
train = train[features + ["Survived"]]
test_features = test[features]
# 处理缺失值
# Age 和 Fare 用中位数填充，Embarked 用众数填充
for df in [train, test_features]:
df["Age"].fillna(df["Age"].median(), inplace=True)
df["Fare"].fillna(df["Fare"].median(), inplace=True)
df["Embarked"].fillna(df["Embarked"].mode()[0], inplace=True)
# 把 Sex 和 Embarked 变成数字（one-hot 编码）
train = pd.get_dummies(train, columns=["Sex", "Embarked"])
test_features = pd.get_dummies(test_features, columns=["Sex", "Embarked"])
# 对齐列（避免测试集缺某些 dummy 列）
test_features = test_features.reindex(columns=train.drop("Survived", axis=1).columns, fill_value=0)
X = train.drop("Survived", axis=1)
y = train["Survived"]
# 3. 划分一部分训练集做本地验证（可选）
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)
# 4. 训练模型（随机森林只是示例，其他模型也可以）
model = RandomForestClassifier(
n_estimators=200,
max_depth=5,
random_state=42
)
model.fit(X_train, y_train)
# 在验证集上看一下效果（仅自我检查）
y_pred_valid = model.predict(X_valid)
print("Validation accuracy:", accuracy_score(y_valid, y_pred_valid))
# 5. 用全部训练数据重新训练，然后在测试集上预测
model.fit(X, y)
test_pred = model.predict(test_features)
# 6. 生成提交文件
submission = pd.DataFrame({
"PassengerId": test["PassengerId"],
"Survived": test_pred
})
submission.to_csv("submission.csv", index=False)
print("submission.csv 已保存")