Day_3 多元线性回归

第1步: 数据预处理

导入库

import pandas as pd
import numpy as np

导入数据集

dataset = pd.read_csv('50_Startups.csv')
X = dataset.iloc[: , :-1].values
Y = dataset.iloc[: , 4].values

将类别数据数字化

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

X = X[: , 1 :]

拆分数据集为训练集和测试集

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)

第2步： 在训练集上训练多元线性回归模型

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

第3步: 在测试集上预测结果

In [8]: y_pred = regressor.predict(X_test)
In [9]: print(y_pred)

[103015.20159796 132582.27760815 132447.73845175  71976.09851258
 178537.48221056 116161.24230166  67851.69209676  98791.73374687
 113969.43533013 167921.06569551]