请根据以上步骤生成代码
时间: 2023-10-13 08:04:27 浏览: 40
好的,下面是根据您的步骤生成的代码:
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
# 1. 加载数据
train_df = pd.read_csv('train_price.csv')
test_df = pd.read_csv('test_price.csv')
# 2. 特征选择
train_mini_df = train_df[train_df['bodyType'] == '微型车']
train_mini_X = train_mini_df[['power', 'kilometer', 'v_0', 'v_3', 'v_8']]
train_mini_y = train_mini_df['price']
# 3. 训练模型
X_train, X_test, y_train, y_test = train_test_split(train_mini_X, train_mini_y, test_size=0.2, random_state=1234)
model = LinearRegression()
model.fit(X_train, y_train)
# 4. 模型评估
y_pred = model.predict(X_test)
r2score = r2_score(y_test, y_pred)
print('R2 Score:', r2score)
# 5. 预测并提交
test_mini_df = test_df[test_df['bodyType'] == '微型车']
test_mini_X = test_mini_df[['power', 'kilometer', 'v_0', 'v_3', 'v_8']]
test_mini_y_pred = model.predict(test_mini_X)
test_mini_df['price'] = test_mini_y_pred
submission_df = test_mini_df[['SaleID', 'price']]
submission_df.to_csv('submission.csv', index=False)
```
请注意,这只是一个示例代码,您可以根据您的具体要求和数据集进行修改。