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This notebook is ispired by the AIX360 HELOC Credit Approval Tutorial, which shows different explainability methods for a credit approval process. Here XGBoost is used for classification, achieving better accuracy than most of the models used in that notebook. Then, feature importance methods are shown, to be compared with the Data Scientist explanations methods provided in the above notebook. The first ones come directly with XGBoost and the other is based on SHAP.
In this prototype, credit card approval data was analysed and a machine learning model was created to forecast the approval of credit card requests.
A Django-based Credit Approval System that intelligently determines loan eligibility and offers real-time insights based on past loan data and customer profiles using PostgreSQL.
Classified and clustered bank clients with respect to their user profile and decided if they should get credit. KNN & KMeans algorithms, K-Fold developed in C without libraries.
From Philosophy to Interfaces: an Explanatory Method and a Tool Inspired by Achinstein’s Theory of Explanation
Classifying credit applicants with 9 different ML models
Credit Approval System: SVM Model (Built from scratch and compared against python sklearn fn)
Exploratory Data Analysis about Credit Approval dataset