Machine Learning with scikit-learn Quick Start Guide

Implementing the linear support vector machine algorithm in scikit-learn

In this section, you will learn how to implement the linear support vector machines in scikit-learn. The first step is to import the data and split it into training and testing sets. We can do this by using the following code:

import pandas as pdfrom sklearn.model_selection import train_test_splitdf = pd.read_csv('fraud_prediction.csv')df = df.drop(['Unnamed: 0'], axis = 1)#Creating the features features = df.drop('isFraud', axis = 1).valuestarget = df['isFraud'].valuesX_train, X_test, y_train, y_test = train_test_split(features, target, test_size = 0.3, random_state = 42, stratify = target)

The next step is to build the linear support vector machine classifier. We can ...