Predict Tax Refund Claim Adjustment

Your model is deployed in a secure sandbox. What now?

Choose the Model
The ADAPA Scoring engine supports a number of data mining algorithms. Here you can choose among models using different algorithms including: Random Forest, Support Vector Machines, Decision Trees and Neural Networks. Each of the models is implemented in PMML; the code is listed below. Though all the models have been trained on the same data set, they will have different performance characteristics.
Score Your Data
Submit your data to be scored to the chosen model. The model will be executed by the ADAPA scoring engine. Data can be submitting via a data file or interactively.
Select Model Type to use for Scoring
Score Data
Interactive Scoring Form
Add / Delete Rows Claim Adjusted? Age Employment Education Marital Occupation Income Gender Deductions Hours
Add / Delete 38PrivateCollegeUnmarriedService81838Female072
Add / Delete 35PrivateAssociateAbsentTransport72099Male030
Add / Delete 32PrivateHSgradDivorcedClerical154676.74Male040
Add / Delete 45PrivateBachelorMarriedRepair27743.82Male055
Add / Delete 60PrivateCollegeMarriedExecutive7568.23Male040
Add / Delete 74PrivateHSgradMarriedService33144.4Male030
Add / Delete 43PrivateBachelorMarriedExecutive43391.17Male050
Add / Delete 35PrivateYr12MarriedMachinist59906.65Male040
Add / Delete 25PrivateAssociateDivorcedClerical126888.91Female040
Add / Delete 22PrivateHSgradAbsentSales52466.49Female037
Add / Delete 48PrivateCollegeDivorcedService291416.11Female035
Add / Delete 60PrivateVocationalWidowedClerical24155.31Male040
Add / Delete 21PrivateCollegeAbsentService143254.86Female035
Add / Delete 21PrivateCollegeAbsentMachinist120554.81Male040
Add / Delete 50PrivateMasterMarriedExecutive34919.16Male040
Add / Delete 37PrivateHSgradDivorcedExecutive67176.79Male035
Add / Delete 30ConsultantHSgradDivorcedRepair9608.48Male040
Add / Delete 32PrivateHSgradMarriedMachinist12475.84Male040
Add / Delete 65SelfEmpCollegeMarriedSales32963.39Male040
Add / Delete 28PrivateCollegeMarriedExecutive31534.97Male055
Add / Delete 40PSLocalVocationalDivorcedExecutive182165.08Female040
Add / Delete 41PSStateBachelorDivorcedExecutive70603.7Male040
Add / Delete 30PrivateHSgradAbsentService88125.97Male030
Add / Delete 38PrivateHSgradMarriedRepair8670.9Male040
Add / Delete 23PrivateYr11UnmarriedProfessional260405.44Male035
Add / Delete 42PSStateCollegeAbsentExecutive66139.36Female040
Add / Delete 26PrivateBachelorAbsentSales73751.48Female040
Add / Delete 32ConsultantHSgradMarriedSales1428.27Male060
Add / Delete 49PSFederalCollegeMarriedSupport15345.33Male040
Add / Delete 26PrivateHSgradMarriedRepair48114.39Male040
Add / Delete 28PrivateYr10MarriedMachinist33493.89Male040
Add / Delete 41PSFederalBachelorMarriedSupport54653.36Male024
Add / Delete 46PrivateHSgradAbsentService229077.27Female024
Add / Delete 42PrivateCollegeAbsentMachinist59201.06Female040
Add / Delete 39PrivateCollegeDivorcedClerical31036.73Female040
Add / Delete 50PrivateYr11AbsentMachinist187250.07Female040
Add / Delete 47PSLocalDoctorateAbsentProfessional161837.75Female040
Add / Delete 24PrivateAssociateUnmarriedRepair193135.59Male040
Add / Delete 45PrivateVocationalMarriedRepair26717.49Male040
Add / Delete 40PSFederalAssociateAbsentClerical99748.58Female040
Add / Delete 51SelfEmpDoctorateMarriedProfessional13612.07Male040
Add / Delete 77PrivateHSgradMarriedService39950.92Male025
Add / Delete 35PrivateCollegeMarriedSupport44130.45Male045
Add / Delete 39PrivateYr9DivorcedCleaner78516.3Male050
Add / Delete 39PSStateBachelorAbsentExecutive92268.68Female040
Add / Delete 63SelfEmpBachelorMarriedFarming9092.19Male040
Add / Delete 64PrivateHSgradWidowedService148865.82Female038
Add / Delete 39PrivateBachelorMarriedProfessional21190.02Male040
Add / Delete 66PrivateYr5t6Married-spouse-absentCleaner139087.01Female040
Add / Delete 18UnemployedYr11AbsentNA148836.93Female010
The easiest and quickiest way to score some data is to submit this sample input dataset. The data conforms to the data dictionary for the model type selected above.
After the uploaded file is scored, it will be returned with an additional column:
Claim Adjusted (the last column in the file). The column will have a 1 or 0. '1'
means the claim has to be adjusted and '0' means there is no need to adjust it.
Submit your own csv file with the data to be scored.
The csv file must conform to the data specification used for training the model. Therefore, we suggest that you follow these three simple steps:
Step 1:   Start with this Sample File as template.
Step 2:   Customize with your own data.
Step 3:   Upload the file.