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Credit Scoring Model


Implementation of a probability evaluation system of default credits on the base of bank own data and the banking credit history over a given time.

Several types of classifiers have been implemented, including SVM and kNN, neural network, logistic regression and some others, as well as algorithms to select optimal parameters for the evaluation. The method of cross-validation was chosen to find the optimal parameters for each of them by taking risk assessment and expected profit into account.

The results were visualized.

The final solution was designed in the form of a program for a computer-aided decision-making.

Key functionality:

  • Data Classification
  • Prediction Modeling
  • Feature Selection