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 = 100
  )

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)
  validation_accuracy = classifier.score(validation_data,validation_labels)
  accuracies.append(validation_accuracy)
print(validation_accuracy)

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()











