Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

End-to-end Credit Card Fraud Detection using Machine Learning with SMOTE, Random Forest, and ROC-AUC evaluation.

No description available

Real-time Credit card Fraud detection using Spark Streaming, Spark ML, Spark SQL, Kafka, Cassandra and Airflow

Here is the project of credit card fraud detection using ML

Practical Machine Learning : Machine Learning in Nut shell, Supervised Learning, Unsupervised Learning, ML applications in the real world. Introduction to Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation, Data Validation & Modelling, Feature selection Techniques, Dimensionality reduction, Recommendation Systems and anomaly detection, PCA ML Algorithms: Decision Trees, Oblique trees, Random forest, Bayesian analysis and Naïve bayes classifier, Support vector Machines, KNN, Gradient boosting, Ensemble methods, Bagging & Boosting, Association rules learning, Apriori and FP growth algorithms, Linear and Nonlinear classification, Regression Techniques, Clustering, K-means, Overview of Factor Analysis, ARIMA, ML in real time, Algorithm performance metrics, ROC, AOC, Confusion matrix, F1score, MSE, MAE, DBSCAN Clustering in ML, Anomaly Detection, Recommender System Self-Study: • Usage of ML algorithms, Algorithm performance metrics (confusion matrix sensitivity, Specificity, ROC, AOC, F1score, Precision, Recall, MSE, MAE) • Credit Card Fraud Analysis, Intrusion Detection system

Credit Card Fraud Detection using Logistic Regression

Deteccion de fraudes de tarjetas de credito usando Machile Learning implementando distintos algoritmos y haciendo comparaciones de rendimiento con respecto a la clasificacion de transacciones.

End to End Machine Learning MLOps Project for Credit Card Fraud Detection using Ensemble Models, Data and Model Versioning through DVC, Github Actions, and Deployment

IS525E Data Science for Business — Credit Card Fraud Detection using ML (Finance domain)

This project is a machine learning-based web application to detect fraudulent credit card transactions. It uses a Random Forest Classifier trained on a dataset of historical transactions to classify new transactions as either fraudulent or normal.

No description available

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.

it's a ML based Credit-Card-Fraud-detection app

Hybrid ANN-PCA-MLP Model for Credit Card Fraud Detection combines PCA for dimensionality reduction and an MLP neural network for accurate and efficient fraud detection in credit card transactions.

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

Hybrid ANN-PCA-MLP Model for Credit Card Fraud Detection combines PCA for dimensionality reduction and an MLP neural network for accurate and efficient fraud detection in credit card transactions.

credit card fraud detection by ml

Credit card fraud detection project using ml

Catching Criminals With Math

No description available

Utilizing the power of Random Forest, this project provides a comprehensive solution for Credit Card Fraud Detection. From data preprocessing to model deployment(Using MLFLOW, AWS, Docker), explore the complete MLOps workflow. Dive into the intricacies of feature engineering, model training, and real-time fraud predictions.

No description available

End-to-end ML pipeline for credit card fraud detection with debugging and optimization.

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 ML project

No description available

Credit card fraud detection using Kaggle Data / ML

Credit Card Fraud Detection to detect whether a transaction is legit or fraudulent.

No description available