This dataset is used to detect the credit card fraud detection. This is a classification problem. This is an imbalanced dataset based on target variable. So In this Project, I will use encoding and decoding techniques to balanced dataset.

Business Problem: Credit card fraud detection using supervise ML Model/Algorithm in R language

An intelligent machine learning system for detecting fraudulent credit card transactions using multiple ML algorithms and ensemble methods. The system provides a web-based dashboard for real-time fraud detection, data visualization, and model performance analysis.

Credit card fraud detection using 4 sampling methods (Random, Systematic, Stratified, Clustered) to predict the result with the help of 5 ML models (Logistic Regression, Decision Trees, Random Forest, Naive Bayes & K-Nearest Neighbor)

💳 Credit Card Fraud Detection – Build a robust ML model using XGBoost and SMOTE to detect and predict fraudulent transactions on a highly imbalanced dataset. Evaluate performance with ROC-AUC, confusion matrix, and feature importance for real-world financial insights.

A fraud detection system for e-commerce and banking transactions using advanced machine learning techniques and real-time monitoring. This project implements multiple ML models to detect fraudulent activities in both e-commerce and credit card transactions, featuring geolocation analysis, transaction pattern recognition, and an interactive dashboar

A fraud detection system for e-commerce and banking transactions using advanced machine learning techniques and real-time monitoring. This project implements multiple ML models to detect fraudulent activities in both e-commerce and credit card transactions, featuring geolocation analysis, transaction pattern recognition, and an interactive dashboar

Credit Card Fraud Detection using ML models

Credit card fraud detection using XG Boost ensemble model in python and deployed to cloud using flask, heroku which is integrated with dialogue flow chatbot to assist on credit card and ML keywords.

This repository implements credit card fraud detection using ML models.

A Credit Card Fraud Detection model using Various ML algorithms

Credit Card Fraud Detection using ML models and a Customized Voting Classifier

💳 Credit Card Fraud Detection - ML-powered web dashboard for real-time fraud detection using various models.

Credit Card Fraud Detection Using Multiple Models, Deployed using Azure and monitored in ML flow

Creating an ML model for credit card fraud detection using various ml algorithms and techniques.

this repo contains contents regarding model of credit card fraud detection using ML.

Credit card fraud detection models using both ML(Decission Trees,Random forest) and DL(Deep neural network) classification techniques.

Credit card fraud detection using multiple ML models (XGBoost, Random Forest, Logistic Regression) on imbalanced data with SMOTE and Class Weights

Codesoft Internship Project: Credit Card Fraud Detection using Machine Learning. Detect fraudulent transactions using ML models like Logistic Regression, Random Forest, and XGBoost.

This is my portfolio project of fraud detection and analysis in credit card. I've deep learning using tensorflow library and classification ML and ensemble models to find the best suited model for fraud detection.

Wh Fraud Detection Using Machine Learning | Implementations ... Fraud Detection Using Machine Learning deploys a machine learning (ML) model and an example dataset of credit card transactions to train the model to recognize fraud patterns. The model is self-learning which enables it to adapt to new, unknown fraud patterns.

Implemented an end-to-end ML-based credit card fraud detection system using multiple models (DT, RF, XGBoost, LightGBM) along with efficient data preprocessing and feature engineering.

Here I deployment of a credit card fraud detection system using ML & DL models- LR, ANN, GMB & Decision Tree to predict fraudulent transactions with high precision & recall using R-Programming.

FinRisk-ML is designed as a supervised machine learning model powered credit card fraud detection system. Useful for use in Fintech and e-commerce environment

AI/ML project on Credit Card Fraud Detection using TVAE for synthetic data generation. Includes EDA, baseline vs augmented model comparison, ROC curves, t-SNE visualization, and final trained model for deployment.

This project tackles credit card fraud detection using ML with Scikit-Learn and Snap ML on a highly imbalanced dataset (0.172% fraud). It compares Decision Tree and SVM models after preprocessing and standardization. Snap ML shows faster training than Scikit-Learn with similar ROC-AUC performance, detecting fraud while handling class imbalance.

This repository focuses on Credit Card Fraud Detection using ML models such as Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) built from scratch, without utilizing any pre-built commands.

Credit Card Fraud Detection using Machine Learning End-to-end ML workflow for detecting fraudulent credit card transactions. Includes data preprocessing, feature engineering, model training (Logistic Regression & Decision Tree), evaluation, hyperparameter tuning, and cross-validation to ensure robustness and generalization.

Credit card fraud detection system using ML models (RandomForest, KNN, XGBoost). Features preprocessing pipelines, SMOTE for class imbalance, and a Flask API for real-time alerts. Achieved 85%+ accuracy and reduced fraud by 25% across 1M+ transactions with AUC/F1-optimized metrics.

Credit card fraud detection uses Machine Learning (ML) models to analyze transaction data for unusual patterns. These models compare new transactions against historical, legitimate, and fraudulent data, flagging suspicious activity in real-time to prevent financial loss. Key techniques include anomaly detection and predictive modeling.