Machine Learning with scikit-learn Quick Start Guide

Implementing the random forest classifier in scikit-learn

In this section, we will implement the random forest classifier in scikit-learn. The first step is to read in the data, and split it into training and test sets. This can be done by using the following code:

import pandas as pd#Reading in the datasetdf = pd.read_csv('fraud_prediction.csv')#Dropping the indexdf = 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 random forest classifier. We can do that using the following code:

from sklearn.ensemble import ...