KMeans.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

#Create Dataset
#Class 1
x1 = np.random.normal(25,5,1000)
y1 = np.random.normal(25,5,1000)

#Class 2 
x2 = np.random.normal(55,5,1000)
y2 = np.random.normal(60,5,1000)

#Class 3
x3 = np.random.normal(55,5,1000)
y3 = np.random.normal(15,5,1000)

x = np.concatenate((x1, x2,x3),axis=0)
y = np.concatenate((y1, y2,y3),axis=0)

dictionary = {"x":x,"y":y}

data =pd.DataFrame(dictionary)


#plt.scatter(x1,y1,color="black")
#plt.scatter(x2,y2,color = "black")
#plt.scatter(x3,y3,color="black")
#plt.show()


from sklearn.cluster import KMeans
wcss = []



for k in range(1,15):
    kmeans  = KMeans(n_clusters = k)
    kmeans.fit(data)
    wcss.append(kmeans.inertia_)
    
plt.plot(range(1,15),wcss)
plt.xlabel("Number of k values")
plt.ylabel("wcss")
plt.show()

kmeans2 = KMeans(n_clusters = 3)
clusters = kmeans2.fit_predict(data)
data["label" ] = clusters


plt.scatter(data.x[data.label == 0], data.y[data.label == 0], color = "red")
plt.scatter(data.x[data.label == 1], data.y[data.label == 1], color = "green")
plt.scatter(data.x[data.label == 2], data.y[data.label == 2], color = "blue")