Compare 5 ML Models for Fraud Detection in Imbalanced Data

Compare 5 ML Models for Fraud Detection in Imbalanced DataCompare 5 ML Models for Fraud Detection in Imbalanced Data

The network for creativity

Join 1.25M professional creatives like you

Connect with clients, get discovered, and run your business 100% commission-free

Creatives on Contra have earned over $150M and we are just getting started

Back to feedPost

Sadek Harite

• 2d

Side-by-side confusion matrix comparison across 5 ML models for fraud detection on highly imbalanced data. Models include Logistic Regression, LOF, Isolation Forest, Random Forest and XGBoost. XGBoost achieved 85 true fraud detections with only 13 false negatives.

XGBoost Data Analysis Data Science Python scikit-learn

Swetha Raj

• Apr 16

Developed a live ingredient safety analysis tool using FDA adverse event data, with interactive visualizations to identify high-risk ingredients and common reactions.