如何在Python中根据列值预测列值

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# 1. 数据准备
data = pd.read_csv('data.csv')
X = data[['feature1', 'feature2', ...]]  # 输入特征列
y = data['target']  # 目标列

# 2. 特征工程（可根据需要进行处理）

# 3. 模型选择和训练
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
model = LinearRegression()
model.fit(X_train, y_train)

# 4. 模型评估
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print('Mean Squared Error:', mse)

# 5. 模型预测
new_data = pd.DataFrame({'feature1': [value1], 'feature2': [value2], ...})  # 新数据
prediction = model.predict(new_data)
print('Prediction:', prediction)