Using Python and sklearn to create a Breast Cancer Classifier and predict malignant or benign tumours, based on features list.

brast_cancer_classifier.py

# Importing breast cancer data and features.
# Importing training model, classifier and matplotlib

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

# Split data
breast_cancer_data = load_breast_cancer()
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 = 500)

# Create the classifier and test the best accuracy.
best_score = 0
best_k = 0
k_list = range(1,100)
accuracies = []
for k in k_list:
  classifier = KNeighborsClassifier(n_neighbors = k)
  classifier.fit(training_data, training_labels)
  the_score = classifier.score(validation_data, validation_labels)
  accuracies.append(the_score)
  if the_score > best_score:
    best_score = the_score
    best_k = k
    
# Plotting the Classifier
plt.plot(k_list, accuracies)
plt.xlabel("k")
plt.ylabel("Validation Accuracy")
plt.title("Breast Cancer Classifier Accuracy")
plt.show()