Machine Learning with scikit-learn Quick Start Guide

Implementing the random forest regressor in scikit-learn

In this section, you will learn how you can implement the random forest regressor in scikit-learn. The first step is to import the data and split it into training and testing sets. This can be done using the following code:

import pandas as pdfrom sklearn.model_selection import train_test_split#Reading in the datasetdf = pd.read_csv('fraud_prediction.csv')#Dropping the indexdf = df.drop(['Unnamed: 0'], axis = 1)#Creating the features and target arraysfeatures = df.drop('amount', axis = 1).valuestarget = df['amount'].values#Splitting the data into training and test setsX_train, X_test, y_train, y_test = train_test_split(features, target, test_size = 0.3, random_state = 42)

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