机器学习波士顿房价

流程

1. 数据获取
2. 导入需要的包
3. 引入文件,查看内容
4. 划分训练集和测试集
5. 调用模型
6. 查看准确率

数据获取

链接：https://pan.baidu.com/s/1deECYRPQFx8h28BvoZcbWw?pwd=ft5a 
提取码：ft5a 
--来自百度网盘超级会员V1的分享

导入需要的包

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

引入文件查看内容

data= pd.read_csv("boston.csv")
data.head()

波士顿房价

划分训练集和测试集

x ,r = data[data.columns.delete(-1)], data['MEDV']
x_train, x_test, r_train, r_test = train_test_split(x, r, test_size=0.2, random_state=888)

查看训练集和测试集合大小

print(x_train.shape,r_train.shape)
print(x_test.shape,r_test.shape)
#(404, 13) (404,)
#(102, 13) (102,)

调用模型训练数据

linear_model = LinearRegression()
linear_model.fit(x_train, r_train)

查看准确率

line_pre = linear_model.predict(x_test)
print('SCORE:{:.4f}'.format(linear_model.score(x_test, r_test)))
#SCORE:0.7559