Python Bytes - Machine Learning K Means Part 8 Calculate Plot Cluster Distortion Code in Description
#Coded by Andrew C
import pandas as pd
from sklearn import datasets
wine_dataset = datasets.load_wine()
wine = pd.DataFrame(wine_dataset.data, columns=wine_dataset.feature_names)
X = wine[['alcohol', 'total_phenols']]
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
scale.fit(X)
X_scaled = scale.transform(X)
from sklearn.cluster import KMeans
import numpy as numpy
import matplotlib.pyplot as plt
inertia = []
differ=[]
pre_inertia=0
for i in numpy.arange(1, 11):
km = KMeans(
n_clusters=i
)
km.fit(X_scaled)
inertia.append(km.inertia_)
differ.append(km.inertia_-pre_inertia)
pre_inertia=km.inertia_
plt.plot(numpy.arange(1, 11), inertia, marker='o')
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
plt.savefig("plot3.png")
plt.show()
distortion = { i + 1 : differ[i] for i in range(0, len(differ) ) }
print(distortion)
print()
#datascience #coding #python
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