机器学习基础100天---day03 多元线性回归

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/yangyang_yangqi/article/details/82700107

R&D Spend,Administration,Marketing Spend,State,Profit
165349.2,136897.8,471784.1,New York,192261.83
162597.7,151377.59,443898.53,California,191792.06
153441.51,101145.55,407934.54,Florida,191050.39
144372.41,118671.85,383199.62,New York,182901.99
142107.34,91391.77,366168.42,Florida,166187.94
131876.9,99814.71,362861.36,New York,156991.12
134615.46,147198.87,127716.82,California,156122.51
130298.13,145530.06,323876.68,Florida,155752.6
120542.52,148718.95,311613.29,New York,152211.77
123334.88,108679.17,304981.62,California,149759.96
101913.08,110594.11,229160.95,Florida,146121.95
100671.96,91790.61,249744.55,California,144259.4
93863.75,127320.38,249839.44,Florida,141585.52
91992.39,135495.07,252664.93,California,134307.35
119943.24,156547.42,256512.92,Florida,132602.65
114523.61,122616.84,261776.23,New York,129917.04
78013.11,121597.55,264346.06,California,126992.93
94657.16,145077.58,282574.31,New York,125370.37
91749.16,114175.79,294919.57,Florida,124266.9
86419.7,153514.11,0,New York,122776.86
76253.86,113867.3,298664.47,California,118474.03
78389.47,153773.43,299737.29,New York,111313.02
73994.56,122782.75,303319.26,Florida,110352.25
67532.53,105751.03,304768.73,Florida,108733.99
77044.01,99281.34,140574.81,New York,108552.04

#_*_coding:utf-8_*_

import pandas as pd
import numpy as np

dataset = pd.read_csv('../data/50_Startups.csv')

X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 4].values
#抽取类别数据，转换成虚拟变量
city = dataset.iloc[ : , 3].values
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
labelencoder = LabelEncoder()
X[: , 3] = labelencoder.fit_transform(X[ : , 3])
onehotencoder = OneHotEncoder(categorical_features = [3])
X = onehotencoder.fit_transform(X).toarray()

#躲避虚拟变量陷阱       排除多个变量之间相互关联

X = X[:,1:]

from sklearn.model_selection import train_test_split

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size= 0.25 ,random_state=0)

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train,Y_train)

y_pred = model.predict(X_test)
print(y_pred)
print('train score: {}'.format(model.score(X_train,Y_train)))
print('test score: {}'.format(model.score(X_test,Y_test)))

更多请参考大神GitHub