机器学习基础100天---day03 多元线性回归_机器学习一百天-day3多元线性回归代码解释-优快云博客

本文链接：https://blog.youkuaiyun.com/yangyang_yangqi/article/details/82700107

本文介绍了一种使用线性回归模型预测初创企业盈利能力的方法，通过分析研发支出、行政费用、市场营销支出等关键因素，结合不同州的市场环境，构建了一个有效的预测模型。该模型能够帮助投资者和企业家更好地理解哪些因素对初创企业的利润产生最大影响。

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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