from sklearn.datasets import load_diabetes
dataset = load_diabetes()
X, y = dataset.data, dataset.target
features = dataset.feature_names
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X, y);
model.intercept_
152.1334841628965
model.coef_
array([ -10.01219782, -239.81908937, 519.83978679, 324.39042769, -792.18416163, 476.74583782, 101.04457032, 177.06417623, 751.27932109, 67.62538639])
# display the feature names with the coefficients
list(zip(features, model.coef_))
[('age', -10.012197817470962), ('sex', -239.81908936565566), ('bmi', 519.8397867901349), ('bp', 324.39042768937657), ('s1', -792.1841616283053), ('s2', 476.7458378236622), ('s3', 101.04457032134493), ('s4', 177.06417623225025), ('s5', 751.2793210873947), ('s6', 67.62538639104369)]
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