Machine learning code combat-split training set and test set (train_test_split)

1. Experimental Purpose

We have a data set containing the prices of used BMW cars. We will analyze this data set and build a prediction function that can predict the price by taking the mileage and age of the car as inputs. We will use the sklearn train_test_split method to split the training and test data sets

Data link
Password: n3dp

2. Import the necessary modules and read the data

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split   #数据拆分模块
from sklearn.linear_model import LinearRegression
   
clf = LinearRegression()    #实例化（由散点图可知线性回归比较合适） 
clf.fit(X_train, y_train)   #训练
clf.score(X_test, y_test)   #计算得分
clf.coef_      #打印系数
clf.intercept_    #打印截距
clf.predict(X_test)   #预测

If you want the data set to be split every time to be the same, you can add the random_state parameter to train_test_split as follows:

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=10)
X_test

10 is a state, and different numbers represent different states.