Applied Data Analysis in Python

from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression

X, y = load_diabetes(as_frame=True, return_X_y=True)

X.head()

	age	sex	bmi	bp	s1	s2	s3	s4	s5	s6
0	0.038076	0.050680	0.061696	0.021872	-0.044223	-0.034821	-0.043401	-0.002592	0.019907	-0.017646
1	-0.001882	-0.044642	-0.051474	-0.026328	-0.008449	-0.019163	0.074412	-0.039493	-0.068332	-0.092204
2	0.085299	0.050680	0.044451	-0.005670	-0.045599	-0.034194	-0.032356	-0.002592	0.002861	-0.025930
3	-0.089063	-0.044642	-0.011595	-0.036656	0.012191	0.024991	-0.036038	0.034309	0.022688	-0.009362
4	0.005383	-0.044642	-0.036385	0.021872	0.003935	0.015596	0.008142	-0.002592	-0.031988	-0.046641

from sklearn.model_selection import train_test_split

train_X, test_X, train_y, test_y = train_test_split(X, y, random_state=42)

model = LinearRegression(fit_intercept=True)
model.fit(train_X[["bmi"]], train_y)

LinearRegression()

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model.score(test_X[["bmi"]], test_y)

0.3172099449537781

import pandas as pd

pred = pd.DataFrame({"bmi": [X["bmi"].min(), X["bmi"].max()]})
pred["y"] = model.predict(pred)

import seaborn as sns

sns.relplot(data=X, x="bmi", y=y)
sns.lineplot(data=pred, x="bmi", y="y", c="red", linestyle=":")

<Axes: xlabel='bmi', ylabel='target'>