Introduction
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In this workshop we'll be using the Cloud Pak for Data platform to Analyze Data and Infuse AI into our applications. The goals of this workshop are:
Create and a machine learning model using AutoAI
Test the deployed model
(Optional) Create a Python app to use the model
In this workshop we will be using a credit risk / lending scenario. In this scenario, lenders respond to an increased pressure to expand lending to larger and more diverse audiences, by using different approaches to risk modeling. This means going beyond traditional credit data sources to alternative credit sources (i.e. mobile phone plan payment histories, education, etc), which may introduce risk of bias or other unexpected correlations.
This file has the following attributes:
CUSTOMERID (hex number, used as Primary Key)
CHECKINGSTATUS
CREDITHISTORY
EXISTINGSAVINGS
INSTALLMENTPLANS
EXISTINGCREDITSCOUNT
This file has the following attributes:
CUSTOMERID
LOANDURATION
LOANPURPOSE
LOANAMOUNT
INSTALLMENTPERCENT
OTHERSONLOAN
RISK
This file has the following attributes:
CUSTOMERID
EMPLOYMENTDURATION
SEX
CURRENTRESIDENCEDURATION
OWNSPROPERTY
AGE
HOUSING
JOB
DEPENDENTS
TELEPHONE
FOREIGNWORKER
FIRSTNAME
LASTNAME
STREETADDRESS
CITY
STATE
POSTALCODE
Creating a project, downloading the data set, seeding a database
Use AutoAi to quickly generate a Machine Learning pipeline and model
Deploy and machine learning models using several approaches
This workshop has been tested on the following platforms:
macOS: Mojave (10.14), Catalina (10.15)
Google Chrome version 81
Microsoft: Windows 10
Google Chrome, Microsoft Edge
The credit risk model that we are exploring in this workshop uses a training data set that contains 20 attributes about each loan applicant. The scenario and model use synthetic data based on the [UCI German Credit dataset]()). The data is split into three CSV files and are located in the directory of the GitHub repository you will download in the pre-work section.
