Predict fraudulent transactions using Findability Platform Predict Plus

Summary

This code pattern walks you through how to predict fraudulent transactions using historical data and demonstrates the automated process of building models using the Findability Platform Predict Plus operator from Red Hat Marketplace.

Components

• Red Hat Marketplace: A simpler way to buy and manage enterprise software, with automated deployment to any cloud
• Findability Platform Predict Plus: An automated, self learning, and multi-modeling AI tool that handles discrete target variables, continuous target variables, and time series data with no need for coding
• Red Hat OpenShift Container Platform: A hybrid cloud, enterprise container platform that empowers developers to innovate and ship faster

Description

After completing this code pattern, you will understand how to:

• Quickly set up the instance on an OpenShift cluster for model building.
• Ingest the data and initiate the FP Predict Plus process.
• Build different models using FP Predict Plus and evaluate the performance of those models.
• Choose the best model and complete the deployment.
• Generate new predictions using the deployed model.

Flow

1. User logs into the FP Predict Plus platform using an instance of the FP Predict Plus Operator.
2. User uploads the data file in the CSV format to the Kubernetes storage on the Red Hat OpenShift platform.
3. User initiates the model-building process using the FP Predict Plus operator on an OpenShift cluster and creates pipelines.
4. User evaluates different pipelines from FP Predict Plus and selects the best model for deployment.
5. User generates accurate predictions by using the deployed model.

Instructions

Find the detailed steps for this pattern in the README file. The steps will show you how to:

1. Add the data
2. Create a job
3. Review the job details
4. Analyze results
5. Download the model file
6. Make predictions using new data
7. Create a predict job
8. Check job summary
9. Analyze results of predict job
10. Download the predicted results

View the detailed steps