Python Bytes - Machine Learning K Means Part 7 Plot New Cluster Data Matplotlib Code in Description

Published: 23 May 2023
on channel: AC

#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

kmeans = KMeans(n_clusters=3)

kmeans.fit(X_scaled)

y_pred = kmeans.predict(X_scaled)

X_new = numpy.array([[13, 2.5]])

X_new_scaled = scale.transform(X_new)

n = ['0','1','2']
fig, ax = plt.subplots()

plt.scatter(X_new_scaled[:,0],
X_new_scaled[:,1], marker="*",
c= 'red')

plt.scatter(X_scaled[:,0],
X_scaled[:,1],
c= y_pred)

plt.scatter(kmeans.cluster_centers_[:, 0],
kmeans.cluster_centers_[:, 1],
marker="o",
s = 250,
c = [0,1,2],
edgecolors='k')

for i, txt in enumerate(n):
plt.annotate(txt,(kmeans.cluster_centers_[:, 0][i],kmeans.cluster_centers_[:, 1][i]))
plt.xlabel('Alcohol'); plt.ylabel('Total_Phenols')
plt.title('k-means (k=3)')
plt.savefig("plot2.png")
plt.show()

#datascience #python #coding

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