Python中如何进行机器学习？

import pandas as pd
import numpy as np

# 读取数据
data = pd.read_csv('data.csv')

# 处理缺失值
data = data.dropna()

# 特征工程
data['new_feature'] = data['feature1'] * data['feature2']

# 标准化数据
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data[['feature1', 'feature2']] = scaler.fit_transform(data[['feature1', 'feature2']])

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor

# 分割数据集
X = data.drop('target', axis=1)
y = data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 线性回归模型
lr_model = LinearRegression()
lr_model.fit(X_train, y_train)

# 随机森林模型
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)

from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_error, r2_score

# 交叉验证
cv_scores = cross_val_score(lr_model, X, y, cv=5)
print(f'交叉验证得分: {cv_scores.mean()}')

# 模型评估
y_pred = lr_model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f'均方误差: {mse}')
print(f'R²得分: {r2}')

from sklearn.model_selection import GridSearchCV

# 调参
param_grid = {
    'n_estimators': [100, 200, 300],
    'max_depth': [None, 10, 20, 30]
}
grid_search = GridSearchCV(RandomForestRegressor(random_state=42), param_grid, cv=5)
grid_search.fit(X_train, y_train)

print(f'最佳参数: {grid_search.best_params_}')
print(f'最佳得分: {grid_search.best_score_}')