5. Breast cancer classifier.

import seaborn as sns #1 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#13

#2 breast_cancer_data = load_breast_cancer() print(breast_cancer_data.data[0])

#3 print(breast_cancer_data.target) print(breast_cancer_data.target_names) print(breast_cancer_data.feature_names)

#4 train_test_split( breast_cancer_data.data, breast_cancer_data.target, test_size = 0.2, random_state = 100 )

#5 print(train_test_split( breast_cancer_data.data, breast_cancer_data.target, test_size = 0.2, random_state = 100 ))

#6 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)

#7 print(len(training_data)) print(len(training_labels))

#8-#9 classifier = KNeighborsClassifier(n_neighbors = 3)

#10 classifier.fit(training_data, training_labels)

#11 print(classifier.score(validation_data, validation_labels))

#12 accuracies = [] #<<<<<<<15 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)) print(accuracies) print(classifier.score(validation_data, validation_labels))

#13 #import matplotlib.pyplot as plt#13^^^^

#14 k_list = range(1, 101)

#16 plt.plot(k_list, accuracies) plt.show()

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

# Final..