import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
# 读取股票数据
data = pd.read_csv('stock_data.csv')
# 特征选择
features = ['Open', 'High', 'Low', 'Volume'] # 假设选择这些特征
# 划分数据集
train_data, test_data = train_test_split(data, test_size=0.2, shuffle=False)
# 准备训练数据和标签
X_train = train_data[features]
y_train = train_data['Close']
# 初始化随机森林模型
model = RandomForestRegressor(n_estimators=100, random_state=0)
# 训练模型
model.fit(X_train, y_train)
# 预测股票价格
X_test = test_data[features]
y_pred = model.predict(X_test)
# 评估模型性能
mse = mean_squared_error(test_data['Close'], y_pred)
print(f"Mean Squared Error: {mse}")
