////////////////////prog_9//////////////////////
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.mixture import GaussianMixture
from sklearn.cluster import KMeans
data=pd.read_csv('prog9.csv')
print("Input data and shape")
print(data.shape)
data.head()
f1 = data['V1'].values
f2 = data['V2'].values
x = np.array(list(zip(f1,f2)))
print("X", x)
print('Graph for whole dataset')
plt.scatter(f1,f2,c='black',s=7)
plt.show()
kmeans = KMeans(20, random_state=0)
labels=kmeans.fit(x).predict(x)
print("labels",labels)
centroids = kmeans.cluster_centers_
print("centroids",centroids)
plt.scatter(x[:,0], x[:,1], c=labels, s=40, cmap='viridis');
print('Graph using Kmeans Algorithm ')
plt.scatter(centroids[:,0],centroids[:,1], marker='*', s=200, c='#050505')
plt.show()
gmm=GaussianMixture(n_components=3).fit(x)
labels = gmm.predict(x)
probs = gmm.predict_proba(x)
size = 10*probs.max(1)**3
print('Graph using EM Agoritm')
plt.scatter(x[:,0], x[:,1], c=labels, s=size, cmap='viridis');
plt.show()
////////////////////data_set//////////////////////
V1,V2
5.1,3.5
4.9,3.0
4.7,3.2
4.6,3.1
5.0,3.6
5.4,3.9
4.6,3.4
5.0,3.4
4.4,2.9
4.9,3.1
5.4,3.7
4.8,3.4
4.8,3.0
4.3,3.0
5.8,4.0
5.7,4.4
5.4,3.9
5.1,3.5
5.7,3.8
5.1,3.8
5.4,3.4
5.1,3.7
4.6,3.6
5.1,3.3
4.8,3.4









5.0,3.0
5.0,3.4
5.2,3.5
5.2,3.4
4.7,3.2
4.8,3.1
5.4,3.4
5.2,4.1
5.5,4.2
4.9,3.1
5.0,3.2
5.5,3.5
4.9,3.1
4.4,3.0
5.1,3.4
5.0,3.5
4.5,2.3
4.4,3.2
5.0,3.5
5.1,3.8
4.8,3.0
5.1,3.8
4.6,3.2
5.3,3.7
5.0,3.3
7.0,3.2
6.4,3.2
6.9,3.1
5.5,2.3
6.5,2.8
5.7,2.8
6.3,3.3
4.9,2.4
6.6,2.9
5.2,2.7
5.0,2.0
5.9,3.0
6.0,2.2
6.1,2.9
5.6,2.9
6.7,3.1
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.1,3.5
4.9,3.0
4.7,3.2
4.6,3.1
5.0,3.6
5.4,3.9
4.6,3.4
5.0,3.4
4.4,2.9
4.9,3.1
5.4,3.7
4.8,3.4
4.8,3.0
4.3,3.0
5.8,4.0
5.7,4.4
5.4,3.9
5.1,3.5
5.7,3.8
5.1,3.8
5.4,3.4
5.1,3.7
4.6,3.6
5.1,3.3
4.8,3.4
5.0,3.0
5.0,3.4
5.2,3.5
5.2,3.4
4.7,3.2
4.8,3.1
5.4,3.4
5.2,4.1
5.5,4.2
4.9,3.1
5.0,3.2
5.5,3.5
4.9,3.1
4.4,3.0
5.1,3.4
5.0,3.5
4.5,2.3
4.4,3.2
5.0,3.5
5.1,3.8
4.8,3.0
5.1,3.8
4.6,3.2
5.3,3.7
5.0,3.3
7.0,3.2
6.4,3.2
6.9,3.1
5.5,2.3
6.5,2.8
5.7,2.8
6.3,3.3
4.9,2.4
6.6,2.9
5.2,2.7
5.0,2.0
5.9,3.0
6.0,2.2
6.1,2.9
5.6,2.9
6.7,3.1
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.1,3.5
4.9,3.0
4.7,3.2
4.6,3.1
5.0,3.6
5.4,3.9
4.6,3.4
5.0,3.4
4.4,2.9
4.9,3.1
5.4,3.7
4.8,3.4
4.8,3.0
4.3,3.0
5.8,4.0
5.7,4.4
5.4,3.9
5.1,3.5
5.7,3.8
5.1,3.8
5.4,3.4
5.1,3.7
4.6,3.6
5.1,3.3
4.8,3.4
5.0,3.0
5.0,3.4
5.2,3.5
5.2,3.4
4.7,3.2
4.8,3.1
5.4,3.4
5.2,4.1
5.5,4.2
4.9,3.1
5.0,3.2
5.5,3.5
4.9,3.1
4.4,3.0
5.1,3.4
5.0,3.5
4.5,2.3
4.4,3.2
5.0,3.5
5.1,3.8
4.8,3.0
5.1,3.8
4.6,3.2
5.3,3.7
5.0,3.3
7.0,3.2
6.4,3.2
6.9,3.1
5.5,2.3
6.5,2.8
5.7,2.8
6.3,3.3
4.9,2.4
6.6,2.9
5.2,2.7
5.0,2.0
5.9,3.0
6.0,2.2
6.1,2.9
5.6,2.9
6.7,3.1
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.1,3.5
4.9,3.0
4.7,3.2
4.6,3.1
5.0,3.6
5.4,3.9
4.6,3.4
5.0,3.4
4.4,2.9
4.9,3.1
5.4,3.7
4.8,3.4
4.8,3.0
4.3,3.0
5.8,4.0
5.7,4.4
5.4,3.9
5.1,3.5
5.7,3.8
5.1,3.8
5.4,3.4
5.1,3.7
4.6,3.6
5.1,3.3
4.8,3.4
5.0,3.0
5.0,3.4
5.2,3.5
5.2,3.4
4.7,3.2
4.8,3.1
5.4,3.4
5.2,4.1
5.5,4.2
4.9,3.1
5.0,3.2
5.5,3.5
4.9,3.1
4.4,3.0
5.1,3.4
5.0,3.5
4.5,2.3
4.4,3.2
5.0,3.5
5.1,3.8
4.8,3.0
5.1,3.8
4.6,3.2
5.3,3.7
5.0,3.3
7.0,3.2
6.4,3.2
6.9,3.1
5.5,2.3
6.5,2.8
5.7,2.8
6.3,3.3
4.9,2.4
6.6,2.9
5.2,2.7
5.0,2.0
5.9,3.0
6.0,2.2
6.1,2.9
5.6,2.9
6.7,3.1
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.1,3.5
4.9,3.0
4.7,3.2
4.6,3.1
5.0,3.6
5.4,3.9
4.6,3.4
5.0,3.4
4.4,2.9
4.9,3.1
5.4,3.7
4.8,3.4
4.8,3.0
4.3,3.0
5.8,4.0
5.7,4.4
5.4,3.9
5.1,3.5
5.7,3.8
5.1,3.8
5.4,3.4
5.1,3.7
4.6,3.6
5.1,3.3
4.8,3.4
5.0,3.0
5.0,3.4
5.2,3.5
5.2,3.4
4.7,3.2
4.8,3.1
5.4,3.4
5.2,4.1
5.5,4.2
4.9,3.1
5.0,3.2
5.5,3.5
4.9,3.1
4.4,3.0
5.1,3.4
5.0,3.5
4.5,2.3
4.4,3.2
5.0,3.5
5.1,3.8
4.8,3.0
5.1,3.8
4.6,3.2
5.3,3.7
5.0,3.3
7.0,3.2
6.4,3.2
6.9,3.1
5.5,2.3
6.5,2.8
5.7,2.8
6.3,3.3
4.9,2.4
6.6,2.9
5.2,2.7
5.0,2.0
5.9,3.0
6.0,2.2
6.1,2.9
5.6,2.9
6.7,3.1
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.1,3.5
4.9,3.0
4.7,3.2
4.6,3.1
5.0,3.6
5.4,3.9
4.6,3.4
5.0,3.4
4.4,2.9
4.9,3.1
5.4,3.7
4.8,3.4
4.8,3.0
4.3,3.0
5.8,4.0
5.7,4.4
5.4,3.9
5.1,3.5
5.7,3.8
5.1,3.8
5.4,3.4
5.1,3.7
4.6,3.6
5.1,3.3
4.8,3.4
5.0,3.0
5.0,3.4
5.2,3.5
5.2,3.4
4.7,3.2
4.8,3.1
5.4,3.4
5.2,4.1
5.5,4.2
4.9,3.1
5.0,3.2
5.5,3.5
4.9,3.1
4.4,3.0
5.1,3.4
5.0,3.5
4.5,2.3
4.4,3.2
5.0,3.5
5.1,3.8
4.8,3.0
5.1,3.8
4.6,3.2
5.3,3.7
5.0,3.3
7.0,3.2
6.4,3.2
6.9,3.1
5.5,2.3
6.5,2.8
5.7,2.8
6.3,3.3
4.9,2.4
6.6,2.9
5.2,2.7
5.0,2.0
5.9,3.0
6.0,2.2
6.1,2.9
5.6,2.9
6.7,3.1
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.1,3.5
4.9,3.0
4.7,3.2
4.6,3.1
5.0,3.6
5.4,3.9
4.6,3.4
5.0,3.4
4.4,2.9
4.9,3.1
5.4,3.7
4.8,3.4
4.8,3.0
4.3,3.0
5.8,4.0
5.7,4.4
5.4,3.9
5.1,3.5
5.7,3.8
5.1,3.8
5.4,3.4
5.1,3.7
4.6,3.6
5.1,3.3
4.8,3.4
5.0,3.0
5.0,3.4
5.2,3.5
5.2,3.4
4.7,3.2
4.8,3.1
5.4,3.4
5.2,4.1
5.5,4.2
4.9,3.1
5.0,3.2
5.5,3.5
4.9,3.1
4.4,3.0
5.1,3.4
5.0,3.5
4.5,2.3
4.4,3.2
5.0,3.5
5.1,3.8
4.8,3.0
5.1,3.8
4.6,3.2
5.3,3.7
5.0,3.3
7.0,3.2
6.4,3.2
6.9,3.1
5.5,2.3
6.5,2.8
5.7,2.8
6.3,3.3
4.9,2.4
6.6,2.9
5.2,2.7
5.0,2.0
5.9,3.0
6.0,2.2
6.1,2.9
5.6,2.9
6.7,3.1
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.1,3.5
4.9,3.0
4.7,3.2
4.6,3.1
5.0,3.6
5.4,3.9
4.6,3.4
5.0,3.4
4.4,2.9
4.9,3.1
5.4,3.7
4.8,3.4
4.8,3.0
4.3,3.0
5.8,4.0
5.7,4.4
5.4,3.9
5.1,3.5
5.7,3.8
5.1,3.8
5.4,3.4
5.1,3.7
4.6,3.6
5.1,3.3
4.8,3.4
5.0,3.0
5.0,3.4
5.2,3.5
5.2,3.4
4.7,3.2
4.8,3.1
5.4,3.4
5.2,4.1
5.5,4.2
4.9,3.1
5.0,3.2
5.5,3.5
4.9,3.1
4.4,3.0
5.1,3.4
5.0,3.5
4.5,2.3
4.4,3.2
5.0,3.5
5.1,3.8
4.8,3.0
5.1,3.8
4.6,3.2
5.3,3.7
5.0,3.3
7.0,3.2
6.4,3.2
6.9,3.1
5.5,2.3
6.5,2.8
5.7,2.8
6.3,3.3
4.9,2.4
6.6,2.9
5.2,2.7
5.0,2.0
5.9,3.0
6.0,2.2
6.1,2.9
5.6,2.9
6.7,3.1
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.1,3.5
4.9,3.0
4.7,3.2
4.6,3.1
5.0,3.6
5.4,3.9
4.6,3.4
5.0,3.4
4.4,2.9
4.9,3.1
5.4,3.7
4.8,3.4
4.8,3.0
4.3,3.0
5.8,4.0
5.7,4.4
5.4,3.9
5.1,3.5
5.7,3.8
5.1,3.8
5.4,3.4
5.1,3.7
4.6,3.6
5.1,3.3
4.8,3.4
5.0,3.0
5.0,3.4
5.2,3.5
5.2,3.4
4.7,3.2
4.8,3.1
5.4,3.4
5.2,4.1
5.5,4.2
4.9,3.1
5.0,3.2
5.5,3.5
4.9,3.1
4.4,3.0
5.1,3.4
5.0,3.5
4.5,2.3
4.4,3.2
5.0,3.5
5.1,3.8
4.8,3.0
5.1,3.8
4.6,3.2
5.3,3.7
5.0,3.3
7.0,3.2
6.4,3.2
6.9,3.1
5.5,2.3
6.5,2.8
5.7,2.8
6.3,3.3
4.9,2.4
6.6,2.9
5.2,2.7
5.0,2.0
5.9,3.0
6.0,2.2
6.1,2.9
5.6,2.9
6.7,3.1
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.1,3.5
4.9,3.0
4.7,3.2
4.6,3.1
5.0,3.6
5.4,3.9
4.6,3.4
5.0,3.4
4.4,2.9
4.9,3.1
5.4,3.7
4.8,3.4
4.8,3.0
4.3,3.0
5.8,4.0
5.7,4.4
5.4,3.9
5.1,3.5
5.7,3.8
5.1,3.8
5.4,3.4
5.1,3.7
4.6,3.6
5.1,3.3
4.8,3.4
5.0,3.0
5.0,3.4
5.2,3.5
5.2,3.4
4.7,3.2
4.8,3.1
5.4,3.4
5.2,4.1
5.5,4.2
4.9,3.1
5.0,3.2
5.5,3.5
4.9,3.1
4.4,3.0
5.1,3.4
5.0,3.5
4.5,2.3
4.4,3.2
5.0,3.5
5.1,3.8
4.8,3.0
5.1,3.8
4.6,3.2
5.3,3.7
5.0,3.3
7.0,3.2
6.4,3.2
6.9,3.1
5.5,2.3
6.5,2.8
5.7,2.8
6.3,3.3
4.9,2.4
6.6,2.9
5.2,2.7
5.0,2.0
5.9,3.0
6.0,2.2
6.1,2.9
5.6,2.9
6.7,3.1
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
5.6,3.0
5.8,2.7
6.2,2.2
5.6,2.5
5.9,3.2
6.1,2.8
6.3,2.5
6.1,2.8
6.4,2.9
6.6,3.0
6.8,2.8
6.7,3.0
6.0,2.9
5.7,2.6
5.5,2.4
5.5,2.4
5.8,2.7
6.0,2.7
5.4,3.0
6.0,3.4
6.7,3.1
6.3,2.3
5.6,3.0
5.5,2.5
5.5,2.6
6.1,3.0
5.8,2.6
5.0,2.3
5.6,2.7
5.7,3.0
5.7,2.9
6.2,2.9
5.1,2.5
//////////output////////////////////////
Input data and shape
(1260, 2)
X [[5.1 3.5]
 [4.9 3. ]
 [4.7 3.2]
 ...
 [5.7 2.9]
 [6.2 2.9]
 [5.1 2.5]]
Graph for whole dataset
FIGURE
C:\Users\BITM\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
labels [ 1 18 12 ... 10  4 15]
centroids [[6.43571429 2.86428571]
 [5.07272727 3.42727273]
 [5.51698113 2.46415094]
 [5.425      3.5       ]
 [6.0703125  2.865625  ]
 [5.11666667 3.73333333]
 [4.375      3.025     ]
 [6.225      2.3125    ]
 [4.875      2.25833333]
 [6.95       3.15      ]
 [5.6        2.972     ]
 [5.75       3.9       ]
 [4.675      3.3125    ]
 [5.44       4.1       ]
 [5.736      2.674     ]
 [5.13571429 2.57142857]
 [5.95       3.3       ]
 [6.68913043 2.98043478]
 [4.88888889 3.06666667]
 [6.35       3.25      ]]
Graph using Kmeans Algorithm 
FIGURE
Graph using EM Agoritm
FIGURE





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

datasets = pd.read_csv('10.csv')
x = datasets.iloc[:,[2,3]].values
y = datasets.iloc[:,4].values


from sklearn.model_selection import train_test_split
X_Train,X_Test, Y_Train, Y_Test = train_test_split(x,y, test_size = 0.25, random_state = 0)

from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_Train = sc_X.fit_transform(X_Train)
X_Test = sc_X.transform(X_Test)

from sklearn.svm import SVC
classifier = SVC(kernel = 'linear' , random_state = 0)
classifier.fit(X_Train, Y_Train)


Y_Pred = classifier.predict(X_Test)

from sklearn import metrics
print("Accuracy score",metrics.accuracy_score(Y_Test, Y_Pred))

plt.scatter(X_Train[:,0], X_Train[:,1],c=Y_Train)
plt.title('support vector machine (training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
W=classifier.coef_[0]
a=-W[0]/W[1]
xx=np.linspace(-2.5,2.5)
yy=a*xx -(classifier.intercept_[0])/W[1]
plt.plot(xx,yy)
plt.show();

plt.scatter(X_Test[:,0], X_Test[:,1],c=Y_Test)
plt.title('support vector machine (test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
W=classifier.coef_[0]
a=-W[0]/W[1]
xx=np.linspace(-2.5,2.5)
yy=a*xx -(classifier.intercept_[0])/W[1]
plt.plot(xx,yy)
plt.show();



















////////////////////////////////dataset/////////////////////
User ID,Gender,Age,EstimatedSalary,Purchased
15624510,Male,19,19000,0
15810944,Male,35,20000,0
15668575,Female,26,43000,0
15603246,Female,27,57000,0
15804002,Male,19,76000,0
15728773,Male,27,58000,0
15598044,Female,27,84000,0
15694829,Female,32,150000,1
15600575,Male,25,33000,0
15727311,Female,35,65000,0
15570769,Female,26,80000,0
15606274,Female,26,52000,0
15746139,Male,20,86000,0
15704987,Male,32,18000,0
15628972,Male,18,82000,0
15697686,Male,29,80000,0
15733883,Male,47,25000,1
15617482,Male,45,26000,1
15704583,Male,46,28000,1
15621083,Female,48,29000,1
15649487,Male,45,22000,1
15736760,Female,47,49000,1
15714658,Male,48,41000,1
15599081,Female,45,22000,1
15705113,Male,46,23000,1
15631159,Male,47,20000,1
15792818,Male,49,28000,1
15633531,Female,47,30000,1
15744529,Male,29,43000,0
15669656,Male,31,18000,0
15581198,Male,31,74000,0
15729054,Female,27,137000,1
15573452,Female,21,16000,0
15776733,Female,28,44000,0
15724858,Male,27,90000,0
15713144,Male,35,27000,0
15690188,Female,33,28000,0
15689425,Male,30,49000,0
15671766,Female,26,72000,0
15782806,Female,27,31000,0
15764419,Female,27,17000,0
15591915,Female,33,51000,0
15772798,Male,35,108000,0
15792008,Male,30,15000,0
15715541,Female,28,84000,0
15639277,Male,23,20000,0
15798850,Male,25,79000,0
15776348,Female,27,54000,0
15727696,Male,30,135000,1
15793813,Female,31,89000,0
15694395,Female,24,32000,0
15764195,Female,18,44000,0
15744919,Female,29,83000,0
15671655,Female,35,23000,0
15654901,Female,27,58000,0
15649136,Female,24,55000,0
15775562,Female,23,48000,0
15807481,Male,28,79000,0
15642885,Male,22,18000,0
15789109,Female,32,117000,0
15814004,Male,27,20000,0
15673619,Male,25,87000,0
15595135,Female,23,66000,0
15583681,Male,32,120000,1
15605000,Female,59,83000,0
15718071,Male,24,58000,0
15679760,Male,24,19000,0
15654574,Female,23,82000,0
15577178,Female,22,63000,0
15595324,Female,31,68000,0
15756932,Male,25,80000,0
15726358,Female,24,27000,0
15595228,Female,20,23000,0
15782530,Female,33,113000,0
15592877,Male,32,18000,0
15651983,Male,34,112000,1
15746737,Male,18,52000,0
15774179,Female,22,27000,0
15667265,Female,28,87000,0
15655123,Female,26,17000,0
15595917,Male,30,80000,0
15668385,Male,39,42000,0
15709476,Male,20,49000,0
15711218,Male,35,88000,0
15798659,Female,30,62000,0
15663939,Female,31,118000,1
15694946,Male,24,55000,0
15631912,Female,28,85000,0
15768816,Male,26,81000,0
15682268,Male,35,50000,0
15684801,Male,22,81000,0
15636428,Female,30,116000,0
15809823,Male,26,15000,0
15699284,Female,29,28000,0
15786993,Female,29,83000,0
15709441,Female,35,44000,0
15710257,Female,35,25000,0
15582492,Male,28,123000,1
15575694,Male,35,73000,0
15756820,Female,28,37000,0
15766289,Male,27,88000,0
15593014,Male,28,59000,0
15584545,Female,32,86000,0
15675949,Female,33,149000,1
15672091,Female,19,21000,0
15801658,Male,21,72000,0
15706185,Female,26,35000,0
15789863,Male,27,89000,0
15720943,Male,26,86000,0
15697997,Female,38,80000,0
15665416,Female,39,71000,0
15660200,Female,37,71000,0
15619653,Male,38,61000,0
15773447,Male,37,55000,0
15739160,Male,42,80000,0
15689237,Male,40,57000,0
15679297,Male,35,75000,0
15591433,Male,36,52000,0
15642725,Male,40,59000,0
15701962,Male,41,59000,0
15811613,Female,36,75000,0
15741049,Male,37,72000,0
15724423,Female,40,75000,0
15574305,Male,35,53000,0
15678168,Female,41,51000,0
15697020,Female,39,61000,0
15610801,Male,42,65000,0
15745232,Male,26,32000,0
15722758,Male,30,17000,0
15792102,Female,26,84000,0
15675185,Male,31,58000,0
15801247,Male,33,31000,0
15725660,Male,30,87000,0
15638963,Female,21,68000,0
15800061,Female,28,55000,0
15578006,Male,23,63000,0
15668504,Female,20,82000,0
15687491,Male,30,107000,1
15610403,Female,28,59000,0
15741094,Male,19,25000,0
15807909,Male,19,85000,0
15666141,Female,18,68000,0
15617134,Male,35,59000,0
15783029,Male,30,89000,0
15622833,Female,34,25000,0
15746422,Female,24,89000,0
15750839,Female,27,96000,1
15749130,Female,41,30000,0
15779862,Male,29,61000,0
15767871,Male,20,74000,0
15679651,Female,26,15000,0
15576219,Male,41,45000,0
15699247,Male,31,76000,0
15619087,Female,36,50000,0
15605327,Male,40,47000,0
15610140,Female,31,15000,0
15791174,Male,46,59000,0
15602373,Male,29,75000,0
15762605,Male,26,30000,0
15598840,Female,32,135000,1
15744279,Male,32,100000,1
15670619,Male,25,90000,0
15599533,Female,37,33000,0
15757837,Male,35,38000,0
15697574,Female,33,69000,0
15578738,Female,18,86000,0
15762228,Female,22,55000,0
15614827,Female,35,71000,0
15789815,Male,29,148000,1
15579781,Female,29,47000,0
15587013,Male,21,88000,0
15570932,Male,34,115000,0
15794661,Female,26,118000,0
15581654,Female,34,43000,0
15644296,Female,34,72000,0
15614420,Female,23,28000,0
15609653,Female,35,47000,0
15594577,Male,25,22000,0
15584114,Male,24,23000,0
15673367,Female,31,34000,0
15685576,Male,26,16000,0
15774727,Female,31,71000,0
15694288,Female,32,117000,1
15603319,Male,33,43000,0
15759066,Female,33,60000,0
15814816,Male,31,66000,0
15724402,Female,20,82000,0
15571059,Female,33,41000,0
15674206,Male,35,72000,0
15715160,Male,28,32000,0
15730448,Male,24,84000,0
15662067,Female,19,26000,0
15779581,Male,29,43000,0
15662901,Male,19,70000,0
15689751,Male,28,89000,0
15667742,Male,34,43000,0
15738448,Female,30,79000,0
15680243,Female,20,36000,0
15745083,Male,26,80000,0
15708228,Male,35,22000,0
15628523,Male,35,39000,0
15708196,Male,49,74000,0
15735549,Female,39,134000,1
15809347,Female,41,71000,0
15660866,Female,58,101000,1
15766609,Female,47,47000,0
15654230,Female,55,130000,1
15794566,Female,52,114000,0
15800890,Female,40,142000,1
15697424,Female,46,22000,0
15724536,Female,48,96000,1
15735878,Male,52,150000,1
15707596,Female,59,42000,0
15657163,Male,35,58000,0
15622478,Male,47,43000,0
15779529,Female,60,108000,1
15636023,Male,49,65000,0
15582066,Male,40,78000,0
15666675,Female,46,96000,0
15732987,Male,59,143000,1
15789432,Female,41,80000,0
15663161,Male,35,91000,1
15694879,Male,37,144000,1
15593715,Male,60,102000,1
15575002,Female,35,60000,0
15622171,Male,37,53000,0
15795224,Female,36,126000,1
15685346,Male,56,133000,1
15691808,Female,40,72000,0
15721007,Female,42,80000,1
15794253,Female,35,147000,1
15694453,Male,39,42000,0
15813113,Male,40,107000,1
15614187,Male,49,86000,1
15619407,Female,38,112000,0
15646227,Male,46,79000,1
15660541,Male,40,57000,0
15753874,Female,37,80000,0
15617877,Female,46,82000,0
15772073,Female,53,143000,1
15701537,Male,42,149000,1
15736228,Male,38,59000,0
15780572,Female,50,88000,1
15769596,Female,56,104000,1
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