Main Reference PaperApplication of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions, Applied Soft Computing Journal, 2018 [Python]
  • The credit risk assessment model intends to exploit the eXtreme gradient boosting tree (XGBoost) for classifying the credit risk. Also, it averts the imbalance issue of modeling by using the random sampling technique.

Description
  • The credit risk assessment model intends to exploit the eXtreme gradient boosting tree (XGBoost) for classifying the credit risk. Also, it averts the imbalance issue of modeling by using the random sampling technique.

  • To build a credit risk assessment model

  • To improve the predictive ability

Aim & Objectives
  • To build a credit risk assessment model

  • To improve the predictive ability

  • The system is enhanced by considering the additional information for the reliable system.

Contribution
  • The system is enhanced by considering the additional information for the reliable system.

  • 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.

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.

Leave Comment

Your email address will not be published. Required fields are marked *

clear formSubmit