使用 Python 进行泰坦尼克号生还预测，通常可以按照以下步骤进行： ### 1. 导入必要的库 ```python import pandas as pd from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score ``` ### 2. 读取数据 ```python # 读取训练集和测试集数据 train_data = pd.read_csv('train.csv') test_data = pd.read_csv('test.csv') ``` ### 3. 数据预处理 ```python # 处理残缺数据 # 用平均年龄填充残缺的年龄 train_data['Age'].fillna(train_data['Age'].mean(), inplace=True) test_data['Age'].fillna(test_data['Age'].mean(), inplace=True) # 用频繁项填充残缺的登船港口 train_data['Embarked'].fillna(train_data['Embarked'].mode()[0], inplace=True) test_data['Embarked'].fillna(test_data['Embarked'].mode()[0], inplace=True) # 用中位数填充测试集中残缺的票价 test_data['Fare'].fillna(test_data['Fare'].median(), inplace=True) # 特征向量化 # 对性别和登船港口进行编码 label_encoder_sex = LabelEncoder() train_data['Sex'] = label_encoder_sex.fit_transform(train_data['Sex']) test_data['Sex'] = label_encoder_sex.transform(test_data['Sex']) label_encoder_embarked = LabelEncoder() train_data['Embarked'] = label_encoder_embarked.fit_transform(train_data['Embarked']) test_data['Embarked'] = label_encoder_embarked.transform(test_data['Embarked']) # 扔掉多余项 features_to_drop = ['Name', 'PassengerId', 'Cabin', 'Ticket'] train_data = train_data.drop(features_to_drop, axis=1) test_passenger_ids = test_data['PassengerId'] test_data = test_data.drop(features_to_drop, axis=1) ``` ### 4. 划分训练集和验证集 ```python # 划分特征和目标变量 X = train_data.drop('Survived', axis=1) y = train_data['Survived'] # 划分训练集和验证集 X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) ``` ### 5. 模型训练 ```python # 使用随机森林分类器 model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train) ``` ### 6. 模型评估 ```python # 在验证集上进行预测 y_pred = model.predict(X_val) # 计算准确率 accuracy = accuracy_score(y_val, y_pred) print(f"验证集准确率: {accuracy}") ``` ### 7. 对测试集进行预测 ```python # 对测试集进行预测 test_predictions = model.predict(test_data) # 保存预测结果 submission = pd.DataFrame({ 'PassengerId': test_passenger_ids, 'Survived': test_predictions }) submission.to_csv('submission.csv', index=False) ```