#Bank Marketing Example

Introduction

This page describes how to run Mahout’s SGD classifier on the UCI Bank Marketing dataset. The goal is to predict if the client will subscribe a term deposit offered via a phone call. The features in the dataset consist of information such as age, job, marital status as well as information about the last contacts from the bank.

Code & Data

The bank marketing example code lives under

mahout-examples/src/main/java/org.apache.mahout.classifier.sgd.bankmarketing

The data can be found at

mahout-examples/src/main/resources/bank-full.csv

Code details

This example consists of 3 classes:

• BankMarketingClassificationMain
• TelephoneCall
• TelephoneCallParser

When you run the main method of BankMarketingClassificationMain it parses the dataset using the TelephoneCallParser and trains a logistic regression model with 20 runs and 20 passes. The TelephoneCallParser uses Mahout’s feature vector encoder to encode the features in the dataset into a vector. Afterwards the model is tested and the learning rate and AUC is printed accuracy is printed to standard output.