k means++ Cluster algorithm for Heart Disease prediction

Implementation Details:

Heart Disease Prediction using K-Means and K-means++ clustering and Logistics Regression

1. We are taken dataset data.csv

2. Input data.csv is split into three cluster by K-means algorithm taking centroid automatically. Whereas k-means++ arrives centroid with distance

Cluster 0, Cluster1, Cluster2

3. Every cluster data is taken for getting trainset and test set

Trainset contains 14 columns, whereas testset contains 13 column

4. Every cluster testset is predicted for heart disease

Accuracy is arrived
5. Logistics regression is performed and accuracy arrived

Python demo