Fraud Detection Model using Ensemble Models with a Focus on Explainability

This project aims to develop a Fraud Detection Model using Ensemble Models, with a particular emphasis on Explainability. The goal is to build a robust model that effectively detects fraudulent activities while providing clear insights into the decision-making process for better interpretability.

For this Fraud Detection project, I opted to use Ensemble Models, specifically a Random Forest Classifier. The decision to choose this model is based on its proven effectiveness in handling complex datasets and providing robust results. Ensemble models combine multiple base models to enhance predictive performance and mitigate overfitting.

You can find the project at : https://github.com/roshni-1/Fraud-Detection-Model-using-Ensemble-Models-with-a-focus-on-Explainability