Machine-Learning-Models-/exampleKmeans.py at main · Deeplearningtech/Machine-Learning-Models- · GitHub

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#Import Libaries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
#Import the data with pandas

df = pd.read_csv('mango2.csv')
#print(df.head(3))
#Define x values
X = df.iloc[:, [0,1,2,3]].values

# Lets find out optimum values of the clusters using elobow method

#sq = []
#for i in range(1, 11):
#    kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter =300,
#                    n_init =10, random_state =0)
#   kmeans.fit(X)
#    sq.append(kmeans.inertia_)
#plot the elbow graph
#plt.plot(range(1,11), sq)
#plt.show()

kmeans = KMeans(n_clusters=3, init = 'k-means++', max_iter=300, n_init=10, random_state=0)
y_kmeans = kmeans.fit_predict(X)

plt.scatter(X[y_kmeans==0, 0], X[y_kmeans==0,1], s=100, c='red', label ='Cluster 0')
plt.scatter(X[y_kmeans==1, 0], X[y_kmeans==1,1], s=100, c='blue', label ='Cluster 1')
plt.scatter(X[y_kmeans==2, 0], X[y_kmeans==2,1], s=100, c='Green', label ='Cluster 2')

##################################

plt.scatter(kmeans.cluster_centers_[:,0],
            kmeans.cluster_centers_[:,1], s=300, c='yellow', label ='Centroids')
plt.title('Cluster of Mangoes')
plt.xlabel('Clusters')
plt.ylabel('Cluster Center')
plt.show()