Recap: Diabetes Data

Ten measurements of 442 diabetes patients, already scaled to small numbers around 0, and a label that is a number: how far the disease progressed a year later.

Load load_diabetes with as_frame=True. The program reads the feature a doctor asks about. Print:

1. First look: patients and missing cells.
2. The label's mean and median, to 1 decimal.
3. The strongest feature and its signed r, to 2 decimals.
4. A line from the asked feature: slope and intercept to 1 decimal, R squared to 2.

For a made-up clinic and the input age:

Patients: 300
Missing cells: 0
Progression: mean 118.6, median 118.0
Strongest feature: s2 (r = -0.75)
Line for age: slope 127.6, intercept 118.6
R squared: 0.01

One feature explains a part. In section 3, a model will use all ten at once.