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

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) import matplotlib.pyplot as plt

解决中文显示问题

plt.rcParams['font.family'] = 'SimHei' # 设置字体为SimHei

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()