python预测

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_csv('./data/data.csv')
data
x=data.iloc[:,0:-1]
y=data.iloc[:,-1]

#使用Lasso预测
from sklearn import linear_model
from sklearn import model_selection
reg=linear_model.Lasso(alpha=0.1)
x1=np.linspace(0,1,20)
reg.fit(x,y) #训练
y_pre=reg.predict(x) #预测
#对比图
plt.figure()
p1=plt.plot(x1,y,color='green',linewidth='1.0',linestyle='--',label='true')
p2=plt.plot(x1,y_pre,color='magenta',linewidth='1.6',linestyle='dotted',label='predict')
plt.legend()
plt.rcParams['font.sans-serif']=['SimHei'] 
plt.title("2019320143321魏沛然")
plt.show()

 

'''
使用支持向量机回归i预测
'''
from sklearn.svm import SVC
from sklearn.svm import LinearSVR
clf=LinearSVR(C=3)
#训练
clf.fit(x,y)
#预测
y_pre1=clf.predict(x)
#对比
plt.figure()
p1=plt.plot(x1,y,color='green',linewidth='1.0',linestyle='--',label='true')
p2=plt.plot(x1,y_pre1,color='magenta',linewidth='1.6',linestyle='dotted',label='predict')
plt.rcParams['font.sans-serif']=['SimHei'] 
plt.title("2019320143321魏沛然")
plt.legend()
plt.show()

 

 

#线性回归
from sklearn.linear_model import LinearRegression
model=LinearRegression()
model.fit(x,y)
y_pre2=model.predict(x)

#对比图
plt.figure()
p1=plt.plot(x1,y,color='green',linewidth='1.0',linestyle='--',label='true')
p2=plt.plot(x1,y_pre2,color='magenta',linewidth='1.6',linestyle='dotted',label='predict')
plt.rcParams['font.sans-serif']=['SimHei'] 
plt.title("2019320143321魏沛然")
plt.legend()
plt.show()