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diff --git a/代码/tset.py b/代码/tset.py
new file mode 100644
index 0000000..506694d
--- /dev/null
+++ b/代码/tset.py
@@ -0,0 +1,10 @@
+import time
+import keyboard
+
+time.sleep(3) # 等待3秒
+with open(r'C:\Users\唐志超\Desktop\pp.txt', encoding='utf-8') as file:
+ for line in file:
+ keyboard.write(line)
+
+
+
diff --git a/代码/任务8.2 分析财政收入数据特征的相关性.py b/代码/任务8.2 分析财政收入数据特征的相关性.py
new file mode 100644
index 0000000..8d7e4ec
--- /dev/null
+++ b/代码/任务8.2 分析财政收入数据特征的相关性.py
@@ -0,0 +1,15 @@
+# -*- coding: utf-8 -*-
+
+###############################################################################
+####################### 任务实现 #######################
+###############################################################################
+
+# 代码 8-1
+import numpy as np
+import pandas as pd
+# inputfile = '../data/data.csv' ## 输入的数据文件
+inputfile = './data.csv' ## 输入的数据文件
+data = pd.read_csv(inputfile) ## 读取数据
+## 保留两位小数
+print('相关系数矩阵为:',np.round(data.corr(method = 'pearson'), 2))
+
diff --git a/代码/任务8.3 使用Lasso回归选取财政收入预测的关键特征.py b/代码/任务8.3 使用Lasso回归选取财政收入预测的关键特征.py
new file mode 100644
index 0000000..9490dfe
--- /dev/null
+++ b/代码/任务8.3 使用Lasso回归选取财政收入预测的关键特征.py
@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+
+###############################################################################
+####################### 任务实现 #######################
+###############################################################################
+
+# 代码 8-2
+# 使用了 Lasso 回归模型来分析数据集的特征,
+# 从中筛选出与目标变量 y 相关的重要特征,并将结果保存到一个新的 CSV 文件中。以下是对代码的逐步分析:
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import Lasso
+# inputfile = '../data/data.csv' #输入的数据文件
+inputfile = './data.csv' #输入的数据文件
+data = pd.read_csv(inputfile) #读取数据
+# lasso = Lasso(1000) #调用Lasso()函数,设置λ的值为1000
+lasso = Lasso(alpha=1000, max_iter=5000) # 增加迭代次数
+lasso.fit(data.iloc[:,0:13],data['y'])
+print('相关系数为:',np.round(lasso.coef_,5)) #输出结果,保留五位小数
+
+## 计算相关系数非零的个数
+print('相关系数非零个数为:',np.sum(lasso.coef_ != 0))
+
+
+mask = lasso.coef_ != 0 #返回一个相关系数是否为零的布尔数组
+print('相关系数是否为零:',mask)
+
+# outputfile = '../tmp/new_reg_data.csv' #输出的数据文件
+outputfile = './new_reg_data.csv' #输出的数据文件
+# new_reg_data = data.iloc[:, mask] #返回相关系数非零的数据
+# 返回相关系数非零的数据
+new_reg_data = data.iloc[:, :13].loc[:, mask] # 只选择0到12列中非零的列
+new_reg_data.to_csv(outputfile) #存储数据
+print('输出数据的维度为:',new_reg_data.shape) #查看输出数据的维度
diff --git a/代码/任务8.4 使用灰色预测和SVR构建财政收入预测模型.py b/代码/任务8.4 使用灰色预测和SVR构建财政收入预测模型.py
new file mode 100644
index 0000000..919e3fb
--- /dev/null
+++ b/代码/任务8.4 使用灰色预测和SVR构建财政收入预测模型.py
@@ -0,0 +1,92 @@
+# -*- coding: utf-8 -*-
+
+###############################################################################
+####################### 任务实现 ########################
+###############################################################################
+# 代码 8-3这段代码的主要功能是使用灰色预测模型对时间序列数据进行预测,并将结果保存到 Excel 文件中。以下是代码的逐步解析:
+import numpy as np
+import pandas as pd
+from GM11 import GM11 # 引入自编的灰色预测函数
+
+# 输入的数据文件
+inputfile = './new_reg_data.csv'
+inputfile1 = './data.csv'
+new_reg_data = pd.read_csv(inputfile) # 读取经过特征选择后的数据
+data = pd.read_csv(inputfile1) # 读取总的数据
+
+# 设置索引
+new_reg_data.index = range(1994, 2014)
+new_reg_data.loc[2014] = None
+new_reg_data.loc[2015] = None
+
+# 需要预测的列
+l = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']
+outputfile = './new_reg_data_GM11.xlsx'
+y = list(data['y'].values) # 提取财政收入列,合并至新数据框中
+y.extend([np.nan, np.nan]) # 添加2014和2015年对应的NaN值
+
+for i in l:
+ f = GM11(new_reg_data.loc[range(1994, 2014), i].values)[0]
+ new_reg_data.loc[2014, i] = f(len(new_reg_data) - 1) # 2014年预测结果
+ new_reg_data.loc[2015, i] = f(len(new_reg_data)) # 2015年预测结果
+ new_reg_data[i] = new_reg_data[i].round(2) # 保留两位小数
+
+# 使用 .xls 扩展名
+new_reg_data['y'] = y # 添加财政收入列到new_reg_data
+new_reg_data.to_excel(outputfile, index=True) # 结果输出,索引列也写入
+# 显示预测结果
+print('预测结果为:', new_reg_data.loc[2014:2015, :]) # 预测结果展示
+
+
+
+# 代码 8-4
+# 利用灰色预测的输出,进一步应用 SVR 模型进行建模与预测,并将结果进行保存
+import pandas as pd
+import numpy as np
+from sklearn.svm import LinearSVR
+import matplotlib.pyplot as plt
+from sklearn.metrics import explained_variance_score,\
+mean_absolute_error,mean_squared_error,\
+median_absolute_error,r2_score
+# inputfile = '../tmp/new_reg_data_GM11.xls' #灰色预测后保存的路径
+inputfile = './new_reg_data_GM11.xlsx' #灰色预测后保存的路径
+data = pd.read_excel(inputfile,index_col=0) #读取数据
+feature = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']
+data_train = data.loc[range(1994,2014)].copy()#取2014年前的数据建模
+data_mean = data_train.mean()
+data_std = data_train.std()
+data_train = (data_train - data_mean)/data_std #数据标准化
+# 计算数据的均值和标准差,然后对数据进行标准化处理,使其均值为0,标准差为1。
+# 特征数据和标签数据
+x_train = data_train[feature].values # 使用 .values 替代 .as_matrix()
+y_train = data_train['y'].values # 使用 .values 替代 .as_matrix()
+# 创建 SVR 模型实例,并使用训练数据进行拟合。
+linearsvr = LinearSVR() #调用LinearSVR()函数
+linearsvr.fit(x_train,y_train)
+# 将数据再次进行标准化,然后用模型进行预测,并将预测值还原到原始尺度上(解标准化)。
+x = ((data[feature] - data_mean[feature])/ \
+# data_std[feature]).as_matrix() #预测,并还原结果。
+data_std[feature]).values #预测,并还原结果。
+data[u'y_pred'] = linearsvr.predict(x) * \
+data_std['y'] + data_mean['y']
+## SVR预测后保存的结果
+# outputfile = '../tmp/new_reg_data_GM11_revenue.xls'
+outputfile = './new_reg_data_GM11_revenue.xlsx'
+data.to_excel(outputfile)
+print('真实值与预测值分别为:',data[['y','y_pred']])
+
+print('预测图为:',data[['y','y_pred']].plot(subplots = True,
+ style=['b-o','r-*']))
+# 绘制真实值与预测值在同一个图形中
+# 为确保中文标题能正常显示
+plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] # 替换为可以显示中文的字体
+plt.rcParams['axes.unicode_minus'] = False # 确保负号显示正常
+ax = data[['y', 'y_pred']].plot(style=['r-o', 'b-*'], figsize=(10, 5))
+plt.title('真实值与预测值对比') # 添加标题
+plt.xlabel('时间') # 添加x轴标签
+plt.ylabel('值') # 添加y轴标签
+
+# 添加图例,明确哪条线是哪个
+plt.legend(['真实值', '预测值']) # 添加图例
+plt.show() # 显示图形
+
diff --git a/数据/data.csv b/数据/data.csv
new file mode 100644
index 0000000..6a7d20d
--- /dev/null
+++ b/数据/data.csv
@@ -0,0 +1,21 @@
+x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,y
+3831732,181.54,448.19,7571,6212.7,6370241,525.71,985.31,60.62,65.66,120,1.029,5321,64.87
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diff --git a/财政收入预测分析报告.docx b/财政收入预测分析报告.docx
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