![]() 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. The credit risk model we are exploring in this workshop uses a training data set that contains 20 attributes about each loan applicant. Launch a browser and navigate to your IBM Cloud Pak for Data deployment.Ĭreate a new IBM Cloud Pak for Data project Set up the project and provision data virtualization on IBM Cloud Pak for Data Log in to IBM Cloud Pak for Data CUSTOMERID (hex number, used as Primary Key).The scenario and model use synthetic data based on the UCI German Credit dataset. In IBM Cloud Pak for Data, we use the concept of a project to collect/organize the resources used to achieve a particular goal (resources to build a solution to a problem). Your project resources can include data, collaborators, and analytic assets like notebooks and models, etc. Go the (☰) navigation menu and under the Projects section, click on All Projects. Select the Analytics project radio button and click the Next button. Provide a name and optional description for the project and click Create. Provision data virtualization on IBM Cloud Pak for Dataįrom the upper-left (☰) hamburger menu, click Services > Instances option.įrom the list of instances, locate the Data Virtualization service, click the action menu (three vertical dots) and select Provision instance. In the Configure service > Start page, enable the checkbox for automatic semaphore configuration and click the Next button. In the Configure service > Nodes page, leave the default single node and resource allocation and click the Next button. Note: Attempting to configure a DV instance with greater than 64GB RAM has previously resulted in configuration errors. In the Configure service > Storage page, you must choose ibmc-file-gold-gid as the storage class for both persistent and cache storage. In the Configure service > Summary page, click the Configure button. The configuration process may take a while to complete. IBM Cloud Pak for Data can work with any database with a JDBC connector. For this tutorial, we demonstrate using IBM Db2 Warehouse on IBM Cloud, IBM Db2 local on IBM Cloud Pak for Data, and Netezza Performance Server. You can use any one or two of these, all three, or any combination of other databases. Set up Netezza Performance Server Use these instructions if you want to test with Netezzaīefore you create connection to IBM Netezza Performance Server, you should create required tables and load the csv data into IBM Netezza Performance Server server using nzload cli. ![]() To install nzload cli, follow the instructions. #Devonthink to go add icloud database install Log in to your IBM Netezza Performance Server console and create three tables for Applicant Loan Data, Applicant Financial Data, and Applicant Personal Data. Note that the tables should exist before you load the data using nzload. Then, you can use the nzload CLI command to load the CSV data to your Netezza Performance Server database. If the nzload CLI is not supported - for example in Mac OSX - you will have to create insert statments for the CSV data provided and run it from the Netezza console. #Devonthink to go add icloud database mac osx This might take little longer than the nzload command.
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