elbow_plot.py

from sklearn.cluster import KMeans
from sklearn import preprocessing
from scipy.spatial.distance import cdist
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

data = pd.read_csv('result.csv')
print(data.columns)
X = preprocessing.normalize(data.drop(columns=['Category']))

distortions = []
inertias = []
mapping1 = {}
mapping2 = {}

K = range(1, 20)

for k in K:
    # Building and fitting the model
    kmeanModel = KMeans(n_clusters=k).fit(X)
    kmeanModel.fit(X)
 
    distortions.append(sum(np.min(cdist(X, kmeanModel.cluster_centers_,
                                        'euclidean'), axis=1)) / X.shape[0])
    inertias.append(kmeanModel.inertia_)
 
    mapping1[k] = sum(np.min(cdist(X, kmeanModel.cluster_centers_,
                                   'euclidean'), axis=1)) / X.shape[0]
    mapping2[k] = kmeanModel.inertia_


plt.plot(K, distortions, 'bx-')
plt.xlabel('Values of K')
plt.ylabel('Distortion')
plt.title('The Elbow Method using Distortion')
plt.savefig('elbow_plot.png')