目录

复杂的线性回归

vllbc 收录于 Sklearn

2022-03-04 约 112 字预计阅读 1 分钟次阅读

目录

import pandas as pd
import numpy as np

读入数据

data = pd.read_csv("./datasets/50_Startups.csv")
data.head()

	R&D Spend	Administration	Marketing Spend	State	Profit
0	165349.20	136897.80	471784.10	New York	192261.83
1	162597.70	151377.59	443898.53	California	191792.06
2	153441.51	101145.55	407934.54	Florida	191050.39
3	144372.41	118671.85	383199.62	New York	182901.99
4	142107.34	91391.77	366168.42	Florida	166187.94

分开xy

X = data.iloc[:,:-1].values
Y = data.iloc[:,-1].values

编码

from sklearn.preprocessing import LabelEncoder,OneHotEncoder
labelEncoder = LabelEncoder()
X[:,3] = labelEncoder.fit_transform(X[:,3])
onehotencoder = OneHotEncoder()
X = onehotencoder.fit_transform(X).toarray()

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 = 0.2, random_state = 0)

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,Y_train)

LinearRegression()