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CreditScoreByAryan

Credit ScoringCredit scoring is a technique used to determine whether or not to extend credit (and if so, how much) to a borrower. This workflow illustrates how to create and choose a credit scoring modelbased on both historical data and on the application of different machine learning algorithms.Task Create a credit scoring model based on historical data. Select the best machine learning algorithm to be applied. Use cross-validation to evaluate model performance.A use case is described at URL: https://www.knime.org/knime-applications/credit-scoring Data ReadingThe data are GermanCredit data, includingcredit status,demographic data, andcustomer history. Thefile is located inTheData/Credit Pre-processingLearners such as neuralnetwork or SVM can onlyhandle numeric attributes.Nominal columns areconverted into numericalcolumns. Model Training and Evaluation1)The following algorithms are trained andevaluated with cross-validation: - Neural Network - SVM - Decision Tree2) Double-click on the metanode to see thesubworkflow Model SelectionAll results, i.e. accuracies and respectivemodels, are combined in one single table.Rows are then sorted by descendingaccuracy and only first row (best performingmodel) is kept. Bar Chart Save the Model - Convert the model cell back to PMML - Save the model. KNIME Analytics Platform writes out themodel in the official PMML format, so thatother applications can use the model. Try this:1) Choose your own algorithm and concatenate itwith the other algorithms. Check if your algorithmperforms better than the others.2) Change the aggregation method to "Sum" or"Average" in the view, to see the accuracies. Sort by AccuracyPick up the best modelReadingcredit scoring dataset Train and Cross Validatea Decision Tree Train and Cross Validatea Neural Network Train and CrossValidate a SVM Sorter Row Filter Cell To PMML Category To Number Bar Chart Concatenate PMML Writer CSV Reader Credit ScoringCredit scoring is a technique used to determine whether or not to extend credit (and if so, how much) to a borrower. This workflow illustrates how to create and choose a credit scoring modelbased on both historical data and on the application of different machine learning algorithms.Task Create a credit scoring model based on historical data. Select the best machine learning algorithm to be applied. Use cross-validation to evaluate model performance.A use case is described at URL: https://www.knime.org/knime-applications/credit-scoring Data ReadingThe data are GermanCredit data, includingcredit status,demographic data, andcustomer history. Thefile is located inTheData/Credit Pre-processingLearners such as neuralnetwork or SVM can onlyhandle numeric attributes.Nominal columns areconverted into numericalcolumns. Model Training and Evaluation1)The following algorithms are trained andevaluated with cross-validation: - Neural Network - SVM - Decision Tree2) Double-click on the metanode to see thesubworkflow Model SelectionAll results, i.e. accuracies and respectivemodels, are combined in one single table.Rows are then sorted by descendingaccuracy and only first row (best performingmodel) is kept. Bar Chart Save the Model - Convert the model cell back to PMML - Save the model. KNIME Analytics Platform writes out themodel in the official PMML format, so thatother applications can use the model. Try this:1) Choose your own algorithm and concatenate itwith the other algorithms. Check if your algorithmperforms better than the others.2) Change the aggregation method to "Sum" or"Average" in the view, to see the accuracies. Sort by AccuracyPick up the best modelReadingcredit scoring dataset Train and Cross Validatea Decision Tree Train and Cross Validatea Neural Network Train and CrossValidate a SVM Sorter Row Filter Cell To PMML Category To Number Bar Chart Concatenate PMML Writer CSV Reader

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Created by: hp
Created at: 2023-02-17
On NodePit since: 2023-02-18
Last update: 2024-11-21
Created with KNIME version: v4.7.1

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