a web-based credit card rating model that predicts the creditworthiness of applicants based on their submitted information. This project aims to provide a user-friendly interface for users to input their data, receive a credit rating prediction, and view the result conveniently.
A Flask-based web application that predicts credit risk using a trained Logistic Regression model. Users can upload a CSV file with customer data, and the app returns predictions along with visualizations like confusion matrix and ROC curves.
Real-Time Explainable Credit Intelligence Platform - ML-powered credit scoring with multi-source data fusion
Predição de pontuação de crédito com Random Forest Classifier. Pipeline completo: EDA → pré-processamento → treinamento → avaliação com validação cruzada estratificada.
Developed a multiclass classification system using supervised learning to predict credit score tiers from financial data. Applied EDA, feature engineering, hyperparameter tuning, and evaluated models using ROC-AUC, confusion matrices, and feature importance.
Machine Learning-Based Credit Scoring System (MLCSS) is a machine learning algorithm designed to evaluate and score the creditworthiness of individuals.
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