#Modulo 10 credit card validator matlab code

For predictors that have missing data, there is an explicit bin, with a corresponding WOE value computed from the data. fitmodel then fits a logistic regression model using a stepwise method (by default). fitmodel internally transforms all the predictor variables into WOE values by using the bins found in the automatic binning process. Use fitmodel to fit a logistic regression model using Weight of Evidence (WOE) data. To create a compactCreditScorecard object, you must first develop a credit scorecard model using a creditscorecard object.Ĭreate a creditscorecard object using the CreditCardData.mat file to load dataMissing, a table that contains missing values. After converting the creditscorecard object to a compactCreditScorecard object, you can use the final score to compute model validation statistics with validatemodel. For predictors in a creditscorecard object that have no explicit bin, use the name-value argument 'Missing' in formatpoints to specify how the function treats missing data for scoring purposes. By default, the points for missing and out-of-range values are set to NaN resulting in a score of NaN when running score. Predictors in a creditscorecard object with no missing data in the training set have no bin, so no WOE can be estimated from the training data. For scoring purposes, these points are assigned to missing values and to out-of-range values, and after you convert the creditscorecard object to a compactCreditScorecard object, you can use the final score to compute model validation statistics with validatemodel. These points are computed from the Weight-of-Evidence (WOE) value for the bin and the logistic model coefficients. Predictors in a creditscorecard object that have missing data in the training set have an explicit bin for with corresponding points in the final scorecard. The addition to the next multiple of 10.1. (220)Ī calculation example for Modulo 10 using an EAN-14 / ITF-14 / SCC-14 / DUN-14 code: However, the weighting is different here: Weighting of 4 for odd positions and 9 for straight positions. With the Leitcode and the Identcode the check digit is also calculated according to the modulo 10 calculation method.

#Modulo 10 credit card validator matlab code

In the following you can see a calculation example for Modulo 10 using Code 25: The result of this subtraction is the checksum/check digit. The remainder of the division is subtracted from 10 (calculate the difference to 10). The modulo 10 is calculated from this sum. These resulting results are added to a sum. Then the individual useful digits are multiplied by the weighting. However, weighting 3 starts at the first useful digit from the left. With the Code 25 Codes, the check digit is also calculated with weighting 3 according to the Modulo 10 calculation method. The following is a calculation example for Modulo 10 using an EAN-13 code: The remainder of the division is subtracted from 10. Then the individual digits are multiplied by the weighting. Weighting 3 starts at the first digit from the right.

With the EAN codes the check digit is calculated according to the modulo 10 calculation method with the weighting 3. This documentation is only for the sake of completeness.Ĭalculation of a checksum according to Modulo 10:Ī check digit according to Modulo 10 is used by EAN codes, e.g. ActiveBarcode calculates this check digits for most codes automatically.