Perceptron classifier

With scikit-learn, you can explore the perceptron classifier and relate it to other classification procedures within scikit-learn. Additionally, perceptrons are the building blocks of neural networks, which are a very prominent part of machine learning, particularly computer vision.

Getting ready

Let's get started. The process is as follows:

1. Load the UCI diabetes classification dataset.
2. Split the dataset into training and test sets.
3. Import a perceptron.
4. Instantiate the perceptron.
5. Then train the perceptron.
6. Try the perceptron on the testing set or preferably compute cross_val_score.

Load the UCI diabetes dataset:

import numpy as np
import pandas as pd

data_web_address = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"

column_names = ['pregnancy_x',
'plasma_con',
'blood_pressure',
'skin_mm',
'insulin',
'bmi',
'pedigree_func',
'age',
'target']

feature_names = column_names[:-1]

all_data = pd.read_csv(data_web_address...