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Fraud Fraud detection Credit card Cybersecurity Deployment Keras Anomaly detection Autoencoder Credit card data
  1. Workflow
    Fraud Detection: Model Training
    Fraud Fraud detection Random forest
    +5
    This workflow reads in the creditcard.csv file and trains and evaluates a Random Forest model to classify transactions as either …
    knime > Examples > 50_Applications > 39_Fraud_Detection > 01_Fraud_Detection_Model_Training
    0
    knime
  2. Workflow
    Fraud Detection: Model Deployment
    Fraud Fraud detection Random forest
    +5
    This workflow, the deployment workflow, reads the trained model, as well as the new transaction and applies the model to classify…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 02_Fraud_Detection_Deployment
    1
    knime
  3. Workflow
    Keras Autoencoder for Fraud Detection Deployment
    Autoencoder Keras Neural network
    +16
    Read Keras model. Read deployment data, which are normalized into range [0,1]. Apply the Keras model to the deployment data, calc…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 04_Keras_Autoencoder_for_Fraud_Detection_Deployment
    1
    knime
  4. Workflow
    Fraud Detection by Supervised Learning
    Fraud Fraud detection Random forest
    +9
    This workflow reads in the creditcard.csv file and trains and evaluates a Logistic Regression and a Random Forest model to classi…
    knime > Education > Learnathons > Fraud_Detection_Tutorial > Solutions > 01_Fraud_Detection_by_Supervised_Learning
    0
    knime
  5. Workflow
    Fraud Detection by Unsupervised Learning
    Fraud Fraud detection Banking
    +8
    This workflow reads in the creditcard.csv file and trains and evaluates an Isolation Forest model that detects fraudulent transac…
    knime > Education > Learnathons > Fraud_Detection_Tutorial > Exercises > 02_Fraud_Detection_by_Unsupervised_Learning
    0
    knime
  6. Workflow
    Fraud Detection by Supervised Learning
    Fraud Fraud detection Random forest
    +9
    This workflow reads in the creditcard.csv file and trains and evaluates a Logistic Regression and a Random Forest model to classi…
    knime > Education > Learnathons > Fraud_Detection_Tutorial > Exercises > 01_Fraud_Detection_by_Supervised_Learning
    0
    knime
  7. Workflow
    Fraud Detection by Unsupervised Learning
    Fraud Fraud detection Banking
    +8
    This workflow reads in the creditcard.csv file and trains and evaluates an Isolation Forest model that detects fraudulent transac…
    knime > Education > Learnathons > Fraud_Detection_Tutorial > Solutions > 02_Fraud_Detection_by_Unsupervised_Learning
    0
    knime
  8. Workflow
    Keras Autoencoder for Fraud Detection Training
    Autoencoder Keras Neural network
    +16
    Partition numeric input data into a training, test, and validation set. Normalize the data into range [0,1]. Build a Keras autoen…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 03_Keras_Autoencoder_for_Fraud_Detection_Training
    2
    knime
  9. Workflow
    Isolation Forest for Fraud Detection: Model Deployment
    Fraud detection Deployment Isolation forest
    +5
    This workflow reads the trained isolation forest model, as well as the new transaction and applies the model to it. Based on the …
    knime > Examples > 50_Applications > 39_Fraud_Detection > 06_Isolation_Forest_for_Fraud_Detection_Deployment
    0
    knime
  10. Workflow
    Isolation Forest for Fraud Detection: Model Training
    Fraud H2o Isolation forest
    +3
    This workflow uses the KNIME H2O Machine Learning Integration to train an isolation forest model for fraud detection.
    knime > Examples > 50_Applications > 39_Fraud_Detection > 05_Isolation_Forest_for_Fraud_Detection_Training
    0
    knime
  11. Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +16
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Solutions > 02_Fraud_Detection_Deployment_Solution
    0
    knime
  12. Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +16
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Exercises > 02_Fraud_Detection_Deployment_Exercise
    0
    knime
  13. Workflow
    Fraud Detection: JSON Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (JSON) and Container Output (JSON) nodes can be used to create a REST API for a w…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 04_Fraud_Detection_JSON_Input
    0
    jtyler
  14. Workflow
    Fraud Detection: Table Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (Table) and Container Output (Table) nodes can be used to create a REST API for a…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 02_Fraud_Detection_Table_Input
    0
    jtyler
  15. Workflow
    Fraud Detection: Row Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (Row) and Container Output (Row) nodes can be used to create a REST API for a wor…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 01_Fraud_Detection_Row_Input
    0
    jtyler
  16. Workflow
    Keras Autoencoder for Fraud Detection - Training
    Autoencoder Keras Neural network
    +13
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to train an autoencoder model to detect …
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Solutions > 01_Fraud_Detection_Training_Solution
    0
    knime
  17. Workflow
    Keras Autoencoder for Fraud Detection - Training
    Autoencoder Keras Neural network
    +13
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to train an autoencoder model to detect …
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Exercises > 01_Fraud_Detection_Training_Exercise
    0
    knime
  18. Workflow
    Keras Autoencoder for Fraud Detection - Integrated Deployment
    Autoencoder Keras Neural network
    +13
    This workflow replicates the exercises of session 2, combining training and deployment using Integrated deployment. The purple bo…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Supplementary workflows > Autoencoder > 01_Fraud_Detection_Integrated_Deployment
    0
    knime
  19. Workflow
    Fraud Detection: Row and Variable Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (Row), Container Input (Variable) and Container Output (JSON) nodes can be used t…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 03_Fraud_Detection_Row_And_Variable_Input
    0
    jtyler
  20. Workflow
    Keras Autoencoder for Fraud Detection - Integrated Deployment Call
    Autoencoder Keras Neural network
    +16
    This workflow executes the model generated by the Integrated deployment to get a prediction of fraudolent transaction.
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Supplementary workflows > Autoencoder > 02_Fraud_Detection_Call
    0
    knime