Importing required libraries and data to start building the perceptron model

Perceptron_Preprocessing.py

#import packages
import sklearn.datasets
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

#load the breast cancer data
breast_cancer = sklearn.datasets.load_breast_cancer()

#convert the data to pandas dataframe.
data = pd.DataFrame(breast_cancer.data, columns = breast_cancer.feature_names)
data["class"] = breast_cancer.target
data.head()
data.describe()

#plotting a graph to see class imbalance
data['class'].value_counts().plot(kind = "barh")
plt.xlabel("Count")
plt.ylabel("Classes")
plt.show()

from sklearn.preprocessing import MinMaxScaler
#perform scaling on the data.
X = data.drop("class", axis = 1)
Y = data["class"]
mnscaler = MinMaxScaler()
X = mnscaler.fit_transform(X)
X = pd.DataFrame(X, columns=data.drop("class",axis = 1).columns)

#train test split.
X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.1, stratify = Y, random_state = 1)