Credit Scoring Is A Way Of Analyzing Statistical Data Used In Financial Organizations And Banks To Acquire A Persons Creditworthiness. The Best Owers Generally Manipulate It To Decide To Widen Or Retract Credit. The Score Plays A Significant Role In Determining The Creditworthiness Of A Person And If He/she Can Be Sanctioned A Loan Or Not. Machine Learning Techniques Help Us To Predict The Credit Score More Accurately Using Classification Algorithms. Few Base And Ensemble Classification Algorithms Were Used In This Research To Perform A Comparative Analysis. To Achieve Better Results. The Objective Of This Paper Is To Predict The Credit Score Based On Different Classifier Models And Evaluate The Performance Of Each Model Based On The Metrics. A Comparative Analysis Is Done To Identify The Best Classifier To Predict The Credit Score. The Evaluation Metrics Used For Evaluating The Model Are Recall, Precision, Fmeasure, And Accuracy. This Helps Us To Improve The Decision In Identifying The More Accurate Classifier Model. The Dataset Used For This Analysis Is The Credit Dataset From The Machine Learning Repository. Experimental Results Prove That The K-nearest Neighbor And Extratree Classifier Model Produces Better Accuracy In Ensemble SMOTE Classifiers And The 95% Better Accuracy In The Base Classifier.
