Breast Cancer Detection

Uses the python sklearn library to develop a correlation heat_map for prediction of breast cancer depending on various features.

Introduction

Imports all the necessary functions from the sklearn library

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

Importing all the of the requires libraries

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

Preparation

Loading the dataset and setup axes

data = load_breast_cancer()
X = data['data']
Y = data['target']

Split the dataset amongst the training and testing groups

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2)

Modelling

Utilise the K-Nearest-Neighbors (KNN) Classifier ALgorithm to model the data

clf = KNeighborsClassifier()
clf.fit(X_train, Y_train)

Testing

Gives the accuracy of our model

print(clf.score(X_test,Y_test))

0.9035087719298246

Visualization

Prepare data to visualize

column_data = np.concatenate([data['data'], data['target'][:, None]], axis=1)
column_names = np.concatenate([data['feature_names'], ["class"] ])

df =  pd.DataFrame(column_data,columns = column_names)

Creates a heatmap to visualise the correlations between the features of the dataset

sns.heatmap(df.corr(), cmap = "coolwarm", annot= True, annot_kws = {"fontsize":8})

<AxesSubplot:>