ml14.py

import numpy as np
from sklearn import preprocessing, cross_validation, neighbors
import pandas as pd


accuracies = []

for i in range(25):

    df = pd.read_csv('breast-cancer-wisconsin.data')
    df.replace('?',-99999, inplace=True)
    df.drop(['id'], 1, inplace=True)

    X = np.array(df.drop(['class'], 1))
    y = np.array(df['class'])

    X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)

    clf = neighbors.KNeighborsClassifier()
    clf.fit(X_train, y_train)
    accuracy = clf.score(X_test, y_test)
    #print(accuracy)

    example_measures = np.array([[4,2,1,1,1,2,3,2,1],[4,2,1,1,1,2,3,2,1]])
    example_measures = example_measures.reshape(len(example_measures), -1)
    prediction = clf.predict(example_measures)
    #print(prediction)

    print('Accuracy: ', accuracy)
    accuracies.append(accuracy)

print(sum(accuracies)/len(accuracies))