diff --git a/pythonProject2/.idea/.gitignore b/pythonProject2/.idea/.gitignore
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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
diff --git a/pythonProject2/.idea/inspectionProfiles/profiles_settings.xml b/pythonProject2/.idea/inspectionProfiles/profiles_settings.xml
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diff --git a/pythonProject2/.idea/misc.xml b/pythonProject2/.idea/misc.xml
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diff --git a/pythonProject2/.idea/modules.xml b/pythonProject2/.idea/modules.xml
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diff --git a/pythonProject2/.idea/pythonProject2.iml b/pythonProject2/.idea/pythonProject2.iml
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diff --git a/pythonProject2/111 b/pythonProject2/111
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+++ b/pythonProject2/111
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+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+import matplotlib.pyplot as plt
+
+# 假设我们有过去24个月的价格数据（随机生成）
+np.random.seed(42)
+dates = pd.date_range(start='2021-01-01', periods=24, freq='M')
+products = ['洗发水', '沐浴露']
+
+# 生成随机价格数据
+prices_data = pd.DataFrame(index=dates, columns=products)
+for product in products:
+ base_price = np.random.randint(50, 100)
+ monthly_changes = np.random.normal(0, 5, 24) # 假设每月价格变化标准差为5
+ prices = [base_price + sum(monthly_changes[:i+1]) for i in range(24)]
+ prices_data[product] = prices
+
+# 转换日期为索引，并添加月份作为特征（这里简单使用月份数字）
+prices_data['Month'] = prices_data.index.month
+
+# 准备数据：特征和标签
+X = prices_data[['Month']] # 特征：月份
+y_shampoo = prices_data['洗发水'] # 标签：洗发水价格
+y_shower_gel = prices_data['沐浴露'] # 标签：沐浴露价格
+
+# 分割数据集为训练集和测试集
+# 这里我们简单地将前18个月作为训练集，后6个月作为测试集
+X_train, X_test, y_train_shampoo, y_test_shampoo = train_test_split(X[:18], y_shampoo[:18], test_size=0.33, shuffle=False)
+X_train, X_test, y_train_shower_gel, y_test_shower_gel = train_test_split(X[:18], y_shower_gel[:18], test_size=0.33, shuffle=False)
+
+# 训练线性回归模型
+model_shampoo = LinearRegression().fit(X_train, y_train_shampoo)
+model_shower_gel = LinearRegression().fit(X_train, y_train_shower_gel)
+
+# 预测未来6个月的价格（这里我们简单使用训练集的月份范围外推）
+future_months = np.arange(19, 25)
+future_X = pd.DataFrame(future_months, columns=['Month'])
+predicted_shampoo_prices = model_shampoo.predict(future_X)
+predicted_shower_gel_prices = model_shower_gel.predict(future_X)
+
+# 合并预测结果到DataFrame中
+predictions_df = pd.DataFrame({
+ 'Date': pd.date_range(start=dates[-1] + pd.DateOffset(months=1), periods=6, freq='M'),
+ 'Predicted 洗发水 Price': predicted_shampoo_prices,
+ 'Predicted 沐浴露 Price': predicted_shower_gel_prices
+})
+
+# 可视化结果
+plt.figure(figsize=(12, 8))
+
+# 洗发水价格
+plt.subplot(2, 1, 1)
+plt.plot(prices_data.index, prices_data['洗发水'], label='Historical Price')
+plt.plot(predictions_df['Date'], predictions_df['Predicted 洗发水 Price'], label='Predicted Price', linestyle='--')
+plt.title('Historical and Predicted Prices for 洗发水')
+plt.xlabel('Date')
+plt.ylabel('Price')
+plt.legend()
+
+# 沐浴露价格
+plt.subplot(2, 1, 2)
+plt.plot(prices_data.index, prices_data['沐浴露'], label='Historical Price')
+plt.plot(predictions_df['Date'], predictions_df['Predicted 沐浴露 Price'], label='Predicted Price', linestyle='--')
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