财政收入影响因素分析及预测（2014-2016）

import numpy as np
import pandas as pd
inputfile = 'D:/xuexi/Anaconda/data/data.csv'
data = pd.read_csv(inputfile)

description = [data.min(), data.max(), data.mean(), data.std()]

description = pd.DataFrame(description, index = ['Min', 'Max', 'Mean', 'STD']).T
print('描述性统计结果:\n', np.round(description, 2))
corr = data.corr(method='pearson')
print('相关系数矩阵为: \n', np.round(corr, 2))
import matplotlib.pyplot as plt
import seaborn as sns
plt.subplots(figsize=(10, 10))
sns.heatmap(corr, annot=True, vmax=1, square=True, cmap='Reds')
plt.rcParams['font.sans-serif'] = ['SimHei'] #中文
plt.title('2020310143032')
plt.close

 import numpy as np
 import pandas as pd
 from sklearn.linear_model import Lasso

 inputfile = 'D:/xuexi/Anaconda/data/data.csv'
 data = pd.read_csv(inputfile)
 lasso = Lasso(1000)
 lasso.fit(data.iloc[:,0:13],data['y'])
 print('相关系数为：',np.round(lasso.coef_,5))
 print('相关系数非零个数为：',np.sum(lasso.coef_ != 0))
 mask = lasso.coef_ != 0
 mask = np.append(mask,True)
 print('相关系数是否为零：',mask)
 outputfile = 'D:/xuexi/Anaconda/data/new_reg_data.csv'
 new_reg_data = data.iloc[:,mask]
 new_reg_data.to_csv(outputfile)
 print('输出数据的维度为：',new_reg_data.shape

 

 

 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.svm import LinearSVR

inputfile = 'D:/xuexi/Anaconda/data/new_reg_data_GM11.xls' # 灰色预测后保存的路径
data = pd.read_excel((inputfile),index_col = 0,header = 0) # 读取数据
feature = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13'] # 属性所在列
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
x_train = data_train[feature].values # 属性数据
y_train = data_train['y'].values # 标签数据

linearsvr = LinearSVR() # 调用LinearSVR()函数
linearsvr.fit(x_train,y_train)
x = ((data[feature] - data_mean[feature])/data_std[feature]).values # 预测，并还原结果。
data['y_pred'] = linearsvr.predict(x) * data_std['y'] + data_mean['y']
outputfile = 'D:/xuexi/Anaconda/data/new_reg_data_GM11_revenue.xls' # SVR预测后保存的结果
data.to_excel(outputfile)

print('真实值与预测值分别为：\n',data[['y','y_pred']])
plt.rcParams['font.sans-serif'] = 'SimHei'
fig = data[['y','y_pred']].plot(subplots = True, style=['b-o','r-*']) # 画出预测结果图
plt.title('2020310143032')
plt.show()