Codecademy export

script.py

import codecademylib3_seaborn
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt

breast_cancer_data = load_breast_cancer()
#print(breast_cancer_data.data[0])
#print(breast_cancer_data.feature_names)
#print(breast_cancer_data.target)
#print(breast_cancer_data.target_names)

training_data, validation_data, training_labels, validation_labels = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=80)

#print(len(training_data))
#print(len(training_labels))
accuracies = []
for k in range(1,101):
  classifier = KNeighborsClassifier(n_neighbors = k)
  classifier.fit(training_data, training_labels)
  accuracies.append(classifier.score(validation_data,validation_labels))
   

#plot k
k_list=range(1,101)

plt.plot(k_list,accuracies)
plt.xlabel('k')
plt.ylabel('Validation Accuracy')
plt.title('Breast Cancer Classifier Accuracy')
plt.show()