Andere Kunden interessierten sich auch für
![Classificazione accurata del credit scoring mediante Ensemble GradientBoost]( "Classificazione accurata del credit scoring mediante Ensemble GradientBoost")
Classificazione accurata del credit scoring mediante Ensemble GradientBoost26,99 €![Classificação exacta da pontuação de crédito usando o Ensemble GradientBoost]( "Classificação exacta da pontuação de crédito usando o Ensemble GradientBoost")
Classificação exacta da pontuação de crédito usando o Ensemble GradientBoost26,99 €![Genaue Klassifizierung der Kreditwürdigkeitsprüfung mit Ensemble GradientBoost]( "Genaue Klassifizierung der Kreditwürdigkeitsprüfung mit Ensemble GradientBoost")
Genaue Klassifizierung der Kreditwürdigkeitsprüfung mit Ensemble GradientBoost39,90 €![Credit Scoring Model Using Data Mining Techniques a pragmatic approach]( "Credit Scoring Model Using Data Mining Techniques a pragmatic approach")
Credit Scoring Model Using Data Mining Techniques a pragmatic approach32,99 €![Risk Assessment Management In Hybrid Model Of Credit Scoring]( "Risk Assessment Management In Hybrid Model Of Credit Scoring")
Risk Assessment Management In Hybrid Model Of Credit Scoring32,99 €![Novel Ensemble based Approach for Predicting Internet Usage]( "Novel Ensemble based Approach for Predicting Internet Usage")
Novel Ensemble based Approach for Predicting Internet Usage29,99 €![Credit Card Fraud Detection Using Cortical Learning Algorithm]( "Credit Card Fraud Detection Using Cortical Learning Algorithm")
Credit Card Fraud Detection Using Cortical Learning Algorithm26,99 €
The credit industry has grown very rapidly almost all over the world. Giving credit is regarded as a business activity which generates profits for banks and other financial institutions. However, loans can also be a source of risk of loss. The recent financial crisis has raised the attention of banks and other financial institutions about credit risk. Therefore, the Basel Committee asked all banks and financial institutions to implement a credit scoring system to assist in estimating the level of credit risk. The method for Credit Scoring has been developed to select a better model in predicting credit risk. Data mining methods are superior to the statistical methods of dealing with Credit Scoring issues, especially for nonlinear relationships between variables. By flashing the ensemble method with statistical methods, proven to achieve a higher level of accuracy than the method of data mining. the classification process will be carried out to classify prospective borrowers into two classes: good prospective borrowers and bad prospective borrowers based on the characteristics of prospective borrowers using the GradientBoost ensemble method.