GitHub / KrishnenduMR / CredSecure
This project implements a fraud detection application using a Random Forest model, built with Flask and Python to predict fraudulent credit card transactions. The model outperforms others like XGBoost and Decision Trees in accuracy, recall, and ROC-AUC, making it ideal for handling imbalanced datasets in real-time fraud detection.
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KrishnenduMR%2FCredSecure
PURL: pkg:github/KrishnenduMR/CredSecure
Stars: 0
Forks: 0
Open issues: 0
License: mit
Language: Jupyter Notebook
Size: 11.3 MB
Dependencies parsed at: Pending
Created at: 10 months ago
Updated at: 8 months ago
Pushed at: 8 months ago
Last synced at: 8 months ago
Topics: credit-card-fraud, decision-trees, detection-model, flask, linear-regression, random-forest, xgboost