Support Vector Machines in python

Support Vector Machines is considered to be a classification approach, it but can be employed in both types of classification and regression problems. It can easily handle multiple continuous and categorical variables.

It is a combination of three parts:

Support Vectors
Hyperplane
Margin

SVM divided into three parts:

Linear SVM Classification
Non-linear SVM Classification

Linear SVM Classification

In this classes of datapoints are divided using the simple line

Non-linear SVM Classification

How to implement it using the python

Load datasets

#Import scikit-learn dataset library
from sklearn import datasets

#Load dataset
cancer = datasets.load_breast_cancer()

Features and target columns


# print the names of the 13 features
print("Features: ", cancer.feature_names)

# print the label type of cancer('malignant' 'benign')
print("Labels: ", cancer.target_names)

Split datasets

# Import train_test_split function
from sklearn.model_selection import train_test_split

# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.3,random_state=109) # 70% training and 30% test

Fit it into the model


#Import svm model
from sklearn import svm

#Create a svm Classifier
clf = svm.SVC(kernel='linear') # Linear Kernel

#Train the model using the training sets
clf.fit(X_train, y_train)

#Predict the response for test dataset
y_pred = clf.predict(X_test)

Finding the accuracy of the model


#Import scikit-learn metrics module for accuracy calculation
from sklearn import metrics

# Model Accuracy: how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

Output:

Accuracy: 0.9649122807017544

#machinelearning #python #datascience

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