python-线性回归预测

导入包

# Required Packages
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn import datasets, linear_model

回归拟合的建立

创建一个线性模型，用我们的X_parameters和Y_parameter训练它。

# Function for Fitting our data to Linear model
def linear_model_main(X_parameters,Y_parameters,predict_value):
 
 # Create linear regression object
 regr = linear_model.LinearRegression()
 regr.fit(X_parameters, Y_parameters)
 predict_outcome = regr.predict(predict_value)
 predictions = {}
 predictions['intercept'] = regr.intercept_
 predictions['coefficient'] = regr.coef_
 predictions['predicted_value'] = predict_outcome
 return predictions

预测

X,Y = get_data('input_data.csv')
predictvalue = 700
result = linear_model_main(X,Y,predictvalue)
print "Intercept value " , result['intercept']
print "coefficient" , result['coefficient']
print "Predicted value: ",result['predicted_value']
#脚本输出：
Intercept value 1771.80851064 
coefficient [ 28.77659574] 
Predicted value: [ 21915.42553191] 
[Finished in 0.7s]
#这里，Intercept value（截距值）就是θ0的值，coefficient value（系数）就是θ1的值。 我们得到预测的价格值为21915.4255