Write a Python program to implement Support Vector Machine (SVM)

# importing required libraries

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

# importing scikit learn with make_blobs

from sklearn.datasets.samples_generator import make_blobs

# creating datasets X containing n_samples

# Y containing two classes

X, Y = make_blobs(n_samples=500, centers=2,

                random_state=0, cluster_std=0.40)

import matplotlib.pyplot as plt

# plotting scatters

plt.scatter(X[:, 0], X[:, 1], c=Y, s=50, cmap='spring');

plt.show()

# creating line space between -1 to 3.5  

xfit = np.linspace(-1, 3.5)

 

# plotting scatter

plt.scatter(X[:, 0], X[:, 1], c=Y, s=50, cmap='spring')

 

# plot a line between the different sets of data

for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:

    yfit = m * xfit + b

    plt.plot(xfit, yfit, '-k')

    plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none',  

    color='#AAAAAA', alpha=0.4)

 

plt.xlim(-1, 3.5);

plt.show()

 

# reading csv file and extracting class column to y.

x = pd.read_csv("iris.csv")

a = np.array(x)

y  = a[:,30] # classes having 0 and 1

 

# extracting two features

x = np.column_stack((x.malignant,x.benign))

x.shape # 569 samples and 2 features

 

print (x),(y)

#Fitting SVM

# import support vector classifier

from sklearn.svm import SVC # "Support Vector Classifier"

clf =SVC(kernel='linear')

# fitting x samples and y classes

clf.fit(x, y)

#predict new values:

clf.predict([[120, 990]])

 

clf.predict([[85, 550]])