Main Reference PaperAn experimental comparison of classification techniques in debt recoveries scoring: Evidence from South Africa’s unsecured lending market, Expert Systems With Applications, 2018 [R]
  • This work investigates the predictive model using popular classification techniques. The classification techniques is to predict the propensity of a borrower who is 90 days or more in arrears on an unsecured loan to pay over a fixed window period.

Description
  • This work investigates the predictive model using popular classification techniques. The classification techniques is to predict the propensity of a borrower who is 90 days or more in arrears on an unsecured loan to pay over a fixed window period.

  • To improve the prediction for imbalanced datasets.

  • The prediction accuracy performance is evaluated using classifier results.

Aim & Objectives
  • To improve the prediction for imbalanced datasets.

  • The prediction accuracy performance is evaluated using classifier results.

  • Other context information are incorporated to improve the accuracy further.

Contribution
  • Other context information are incorporated to improve the accuracy further.

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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