把每一步的代码都实现

当然，以下是逐步实现每个关键步骤的代码示例，假设我们已经将数据集分割成了`X_train`, `y_train`, `X_test`和`y_test`：

### 1. 加载数据

import pandas as pd
data = pd.read_csv('kc_house_data.csv')

# 提取特征和目标变量
X = data.drop('price', axis=1) # 特征
y = data['price'] # 目标变量

### 2. 数据预处理

from sklearn.impute import SimpleImputer
# 缺失值处理
imputer = SimpleImputer(strategy='mean')  
X.fillna(imputer.fit_transform(X), inplace=True)

# 可能还需要其他数据清理步骤，比如转换类别变量

### 3. 特征工程

# 示例，这里假设有些特征需要转换成合适的数据类型
if 'date' in X.columns:
    X['date'] = pd.to_datetime(X['date'])

### 4. 划分数据集

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

### 5. 决策树模型

from sklearn.tree import DecisionTreeRegressor
dtree = DecisionTreeRegressor()
dtree.fit(X_train, y_train)

### 6. 随机森林模型

from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_estimators=100)
rf.fit(X_train, y_train)

### 7. GBDT模型

from sklearn.ensemble import GradientBoostingRegressor
gbr = GradientBoostingRegressor(random_state=42)
gbr.fit(X_train, y_train)

### 8. 模型评估

from sklearn.metrics import mean_squared_error, r2_score
y_pred_dt = dtree.predict(X_test)
print("Decision Tree R^2:", r2_score(y_test, y_pred_dt))
y_pred_rf = rf.predict(X_test)
print("Random Forest R^2:", r2_score(y_test, y_pred_rf))
y_pred_gbr = gbr.predict(X_test)
print("GBDT R^2:", r2_score(y_test, y_pred_gbr))

### 9. 超参数调优（此处仅作示例，可能用GridSearchCV）

from sklearn.model_selection import GridSearchCV
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20]
}
grid_search_rf = GridSearchCV(rf, param_grid, cv=5)
grid_search_rf.fit(X_train, y_train)
best_rf = grid_search_rf.best_estimator_

### 10. 最终预测

final_pred = best_rf.predict(X_test)