Week 13 Practice content

Because this chapter is more difficult to learn, practice this week, the contents of other small partners reference code

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error,r2_score
data = pd.read_csv('/Users/Documents/car_test.csv')
data.head()

# 查看相关性
cormatrix = data.corr()
print(cormatrix)

x_train,x_test ,y_train,y_test = train_test_split(x,y,test_size = 0.25,random_state = 1)

ss = StandardScaler()
ss.fit(x_train)
x_train_ss = ss.transform(x_train)
x_test_ss = ss.transform(x_test)

lr = LinearRegression()
lr.fit(x_train_ss,y_train)

mean_squared_error(y_test,y_pred)
r2_score(y_test,y_pred)