Python股票预测：基于线性回归的机器学习模型

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

# 读取数据集
data = pd.read_excel('E:\pythonProject5\深度学习\新建 XLS 工作表.xls')

# 选择特征和目标变量
features = data[['开盘', '涨跌额', '涨跌幅', '最低', '最高', '成交量', '成交金额']]
target = data['收盘']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)

# 训练模型
model = LinearRegression()
model.fit(X_train, y_train)

# 预测股票价格
predictions = model.predict(X_test)

# 对比实际价格进行验证
mse = mean_squared_error(y_test, predictions)
print('均方误差(MSE):', mse)

# 输出预测结果和实际价格
result = pd.DataFrame({'预测价格': predictions, '实际价格': y_test})
print(result)

# 绘制预测价格和实际价格的图像
plt.plot(result.index, result['预测价格'], label='预测价格')
plt.plot(result.index, result['实际价格'], label='实际价格')
plt.xlabel('样本编号')
plt.ylabel('股票价格')
plt.title('预测价格 vs 实际价格')
plt.legend()
plt.show()