数据挖掘作业2

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

inputfile = 'D:/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:/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('学号20')
plt.show()