下载地址：https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data

打开方式： 直接用excel可以打开，转存为csv等

模型训练

from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_boston
 
boston = load_boston()
scaler = StandardScaler()

X = scaler.fit_transform(boston["data"])
print('X',X)
Y = boston["target"]
print('Y', Y)
names = boston["feature_names"]
print('names',names)

LASSO惩罚回归

from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_boston
import numpy as np

#输出线性方程的辅助函数
def pretty_print_linear(coefs, names = None, sort = False):
	if names is None:
		names = ["X%s" % x for x in range(len(coefs))]
	lst = zip(coefs, names)
	if sort:
		lst = sorted(lst,  key = lambda x:-np.abs(x[0]))
	return " + ".join("%s * %s" % (np.round(coef, 3), name) for coef, name in lst)
boston = load_boston()
scaler = StandardScaler()
X = scaler.fit_transform(boston["data"])
Y = boston["target"]
names = boston["feature_names"]

lasso = Lasso(alpha=.3)
lasso.fit(X, Y)

print("lasso model:", pretty_print_linear(lasso.coef_, names, sort = True))