7.4 多元回归分析（multiple Regression）应用

来源：互联网发布：python在线编译器编辑：程序博客网时间：2024/05/17 21:38

1、例子

一家快递公司送货：X1：运输里程 X2：运输次数 Y：总运输时间

from numpy import genfromtxtimport numpy as npfrom sklearn import datasets,linear_modeldataPath = r"E:\data\Delivery.csv"deliveryData = genfromtxt(dataPath,delimiter=',')print "data"print deliveryDataX = deliveryData[:,:-1]Y = deliveryData[:,-1]print "X:"print Xprint "Y:"print Yregr = linear_model.LinearRegression()regr.fit(X,Y)print "coefficients"print regr.coef_  #输出b0，b1print "intercept: "print regr.intercept_#输出截距xPred = [102,6]yPred = regr.predict(xPred)print "predicted y:"print yPred

车型用001,010等表示：

第一行删除了：

车型已经用另外一种表达方式：

from numpy import genfromtxtimport numpy as npfrom sklearn import datasets,linear_modeldataPath = r"E:\data\Delivery.csv"deliveryData = genfromtxt(dataPath,delimiter=',')print "data"print deliveryDataX = deliveryData[:,:-1]Y = deliveryData[:,-1]print "X:"print Xprint "Y:"print Yregr = linear_model.LinearRegression()regr.fit(X,Y)print "coefficients"print regr.coef_  #输出b0，b1print "intercept: "print regr.intercept_#输出截距

输出结果为：

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