Optimized credit score prediction via an ensemble model and smoteenn integration

HIGHLIGHTS

• What: Drawing inspiration from the successes of these previous research results, this research aims to combine the Neural_Network, LightGBM, and Radom Froest`s strengths, thereby advancing the field of credit score prediction. To enhancing the reliability of the dataset, this approach provided deeper insights into its underlying characteristics, supporting more informed and reliable decisionmaking , . In contrast to the traditional level-wise growth used in conventional Gradient Boosting Machines (GBMs), this research has utilized LightGBM as a strong boosting framework, which improved tradition GBMs by leafwise strategy. 4) Ensemble Model: This study explored various modeling techniques to achieve . . .
  

   

  

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