diff --git a/machine_learning/svm.py b/machine_learning/svm.py
new file mode 100644
index 0000000..6789dc5
--- /dev/null
+++ b/machine_learning/svm.py
@@ -0,0 +1,46 @@
+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) 
+  
+# 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() 
+# importing required libraries 
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+
+# reading csv file and extracting class column to y. 
+x = pd.read_csv("C:\...\cancer.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) 
+# 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)