kmean_model = KMeans(init='k-means++',n_clusters = 8,max_iter = 200,n_init=10) #here we are using the KMEANS class and configuring it's parameters such as
# initializor , total number of clusters to apply, maximum iterations and no of times to run the algorithm with different centroid seeds
kmean_model.fit(df)
print(kmean_model.cluster_centers_)
predict = kmean_model.predict(df)
plt.scatter(df.iloc[:,2],df.iloc[:,3],c = predict,cmap = 'viridis')