K Nearest Neighbours Practice: Breast Cancer Classifier

Practice of Logistic Regression to predict breast cancer from a set of patient data

Import Libraries

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np

The breast cancer dataset is inbuilt in the sklearn library

from sklearn.datasets import load_breast_cancer

breast_cancer_data = load_breast_cancer()

Inspect the Data

print(breast_cancer_data.feature_names)
print(breast_cancer_data.data[0])
print('')
print(breast_cancer_data.target_names)
print(breast_cancer_data.target[0])

['mean radius' 'mean texture' 'mean perimeter' 'mean area'
 'mean smoothness' 'mean compactness' 'mean concavity'
 'mean concave points' 'mean symmetry' 'mean fractal dimension'
 'radius error' 'texture error' 'perimeter error' 'area error'
 'smoothness error' 'compactness error' 'concavity error'
 'concave points error' 'symmetry error' 'fractal dimension error'
 'worst radius' 'worst texture' 'worst perimeter' 'worst area'
 'worst smoothness' 'worst compactness' 'worst concavity'
 'worst concave points' 'worst symmetry' 'worst fractal dimension']
[1.799e+01 1.038e+01 1.228e+02 1.001e+03 1.184e-01 2.776e-01 3.001e-01
 1.471e-01 2.419e-01 7.871e-02 1.095e+00 9.053e-01 8.589e+00 1.534e+02
 6.399e-03 4.904e-02 5.373e-02 1.587e-02 3.003e-02 6.193e-03 2.538e+01
 1.733e+01 1.846e+02 2.019e+03 1.622e-01 6.656e-01 7.119e-01 2.654e-01
 4.601e-01 1.189e-01]

['malignant' 'benign']
0

Split the data into training and test data

training_data, test_data, training_labels, test_labels = train_test_split(breast_cancer_data.data,breast_cancer_data.target,test_size = 0.2, random_state = 100)

Compare the accuracy of the training/test data using a value of K between 1 and 100

This model is giving most accuracy with a K value of 24

accuracies = []
for k in range(1,101):
  classifier = KNeighborsClassifier(n_neighbors = k)
  classifier.fit(training_data,training_labels)
  accuracies.append(classifier.score(test_data, test_labels))

k_list = range(1,101)

plt.plot(k_list, accuracies)
plt.axvline(x=24, color='r', linestyle='--')
plt.xlabel('k (Number of Neighbours)')
plt.ylabel('Accuracy')
plt.title('Classifier Accuracy using K Nearest Neighbour Algorithm')
plt.show()

classifier = KNeighborsClassifier(n_neighbors = 24)
classifier.fit(training_data,training_labels)

KNeighborsClassifier(n_neighbors=24)

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Collect Data From a New Patient

NewPatientData = [[0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.20,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,30.0]]

Run the Model on this New Patient Data

predicted_label = classifier.predict(NewPatientData)

if predicted_label[0] == 0:
    print("You may not have breast cancer.")
else:
    print("You may have breast cancer.")

You may have breast cancer.