波士顿房价预测 基础用法

1.导入模块

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

from sklearn.linear_model import LinearRegression

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.预测数据

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()