sk-learn,Boston房价预测

基于sk-learn_多种模型对Boston房价预测对比

使用sk-learn进行房价预测的完整过程。

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1.导入需要的模块

import numpy as np
import pandas as pd
from pandas import Series,DataFrame

import matplotlib.pyplot as plt
%matplotlib inline

import sklearn.datasets as datasets

#机器算法模型
from sklearn.neighbors import KNeighborsRegressor#KNN
from sklearn.linear_model import LinearRegression#
from sklearn.linear_model import SGDRegressor
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR

#切割训练数据和样本数据
from sklearn.model_selection import train_test_split

#用于模型评分
from sklearn.metrics import r2_score

2.生成训练数据和测试数据

boston = datasets.load_boston()
train = boston.data
target = boston.target

#切割数据样本集合测试集
X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2)

3.创建学习模型

knn = KNeighborsRegressor()
linear = LinearRegression()
ridge = Ridge()
lasso = Lasso()
decision = DecisionTreeRegressor()
svr = SVR()

4.训练模型

knn.fit(X_train,y_train)
linear.fit(X_train,y_train)
ridge.fit(X_train,y_train)
lasso.fit(X_train,y_train)
decision.fit(X_train,y_train)
svr.fit(X_train,y_train)

5.预测模型

y_pre_knn = knn.predict(x_test)
y_pre_linear = linear.predict(x_test)
y_pre_ridge = ridge.predict(x_test)
y_pre_lasso = lasso.predict(x_test)
y_pre_decision = decision.predict(x_test)
y_pre_svr = svr.predict(x_test)

6.评分

knn_score = r2_score(y_true,y_pre_knn)
linear_score=r2_score(y_true,y_pre_linear)
ridge_score=r2_score(y_true,y_pre_ridge)
lasso_score=r2_score(y_true,y_pre_lasso)
decision_score=r2_score(y_true,y_pre_decision)
svr_score=r2_score(y_true,y_pre_svr)
display(knn_score,linear_score,ridge_score,lasso_score,decision_score,svr_score)

7.绘图

#KNN
plt.plot(y_true,label='true')
plt.plot(y_pre_knn,label='knn')
plt.legend()

#Linear
plt.plot(y_true,label='true')
plt.plot(y_pre_linear,label='linear')
plt.legend()

#Ridge
plt.plot(y_true,label='true')
plt.plot(y_pre_ridge,label='ridge')
plt.legend()

#lasso
plt.plot(y_true,label='true')
plt.plot(y_pre_lasso,label='lasso')
plt.legend()

#decision
plt.plot(y_true,label='true')
plt.plot(y_pre_decision,label='decision')
plt.legend()

#SVR
plt.plot(y_true,label='true')
plt.plot(y_pre_svr,label='svr')
plt.legend()

• 作者： Chris Yan
• 链接： https:/Yansz.github.io/2019/03/15/sk-learn,Boston房价预测/
• 版权声明： 本博客所有文章除特别声明外，均采用 MIT 许可协议。转载请注明出处！