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# Lasso Regression

import pandas

from sklearn import model_selection

from sklearn.linear_model import Lasso

url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data"

names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']

dataframe = pandas.read_csv(url, delim_whitespace=True, names=names)

array = dataframe.values

X = array[:,0:13]

Y = array[:,13]

seed = 7

kfold = model_selection.KFold(n_splits=10, random_state=seed)

model = Lasso()

scoring = 'neg_mean_squared_error'

results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)

print(results.mean())