pythonstudy Day24

宁大小白2025-12-02 8:50
复习日

c 复制代码
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")