{"id":18332883,"url":"https://github.com/samkazan/fraud-detection-ml","last_synced_at":"2026-05-08T17:39:35.093Z","repository":{"id":246315689,"uuid":"820687905","full_name":"SamKazan/fraud-detection-ml","owner":"SamKazan","description":"Machine learning models for enhanced fraud detection in e-commerce transactions, exploring feature engineering, distance prediction, and clustering analysis.","archived":false,"fork":false,"pushed_at":"2024-06-27T04:19:43.000Z","size":11983,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-09T18:55:00.788Z","etag":null,"topics":["clustering","data-science","data-visualization","dataanalytics","dbscan","eda","hierarchical-clustering","kmeans-clustering","knn-imputer","matplotlib","mlxtend","python","scikit-learn","seaborn","xgboost"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SamKazan.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-06-27T01:35:17.000Z","updated_at":"2024-06-27T04:19:46.000Z","dependencies_parsed_at":"2024-06-27T06:09:48.699Z","dependency_job_id":null,"html_url":"https://github.com/SamKazan/fraud-detection-ml","commit_stats":null,"previous_names":["samkazan/fraud-detection-ml"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/SamKazan/fraud-detection-ml","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamKazan%2Ffraud-detection-ml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamKazan%2Ffraud-detection-ml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamKazan%2Ffraud-detection-ml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamKazan%2Ffraud-detection-ml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SamKazan","download_url":"https://codeload.github.com/SamKazan/fraud-detection-ml/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamKazan%2Ffraud-detection-ml/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32791089,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-08T08:22:46.396Z","status":"ssl_error","status_checked_at":"2026-05-08T08:22:45.650Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["clustering","data-science","data-visualization","dataanalytics","dbscan","eda","hierarchical-clustering","kmeans-clustering","knn-imputer","matplotlib","mlxtend","python","scikit-learn","seaborn","xgboost"],"created_at":"2024-11-05T19:40:29.242Z","updated_at":"2026-05-08T17:39:35.071Z","avatar_url":"https://github.com/SamKazan.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning for Fraud Detection in E-commerce Transactions\n\n## Overview\nThis project investigates the application of machine learning techniques to enhance fraud detection in e-commerce transactions. By leveraging a comprehensive dataset from Vesta, we explore feature engineering, distance prediction, and clustering analysis to identify fraudulent activities.\n\n## Problem Statement\nThe increasing sophistication of financial fraud poses significant challenges to businesses and consumers. Traditional rule-based fraud detection systems often struggle to keep pace with evolving fraudulent tactics. This project aims to develop more robust and accurate fraud detection models using machine learning.\n\n## Methodology\nThis project addresses three key research questions:\n\n**RQ1: Feature Engineering and Selection**\n* **Objective:** Improve fraud detection accuracy by identifying and engineering the most predictive features.\n* **Techniques:** Recursive Feature Elimination (RFE), Feature Importance from Gradient Boosting, Principal Component Analysis (PCA).\n\n**RQ2: Predicting Transaction Distances**\n* **Objective:** Develop models to predict transaction distances and identify geographic anomalies indicative of fraud.\n* **Techniques:** Linear Regression, XGBoost.\n\n**RQ3: Clustering for Coordinated Fraud Detection**\n* **Objective:** Utilize clustering techniques to uncover groups of transactions potentially associated with coordinated fraud.\n* **Techniques:** K-Means Clustering, HDBSCAN, Hierarchical Clustering.\n\n## Results\n* **Feature Engineering:** PCA significantly enhanced model accuracy, highlighting its effectiveness in capturing relevant data structures.\n* **Distance Prediction:** XGBoost models demonstrated promising results in predicting transaction distances, aiding in the identification of high-risk transactions.\n* **Clustering Analysis:** K-Means Clustering provided the most interpretable and well-separated clusters, potentially revealing patterns of coordinated fraud.\n\n## Data\n* **Source:** \"IEEE-CIS Fraud Detection\" dataset from Kaggle, provided by Vesta.\n* **Size:** Over 140,000 transactions with 434 features (transaction details, card information, addresses, Vesta-engineered features).\n\n## Contact\nCem Kazan - kzncem@gmail.com# fraud-detection-ml\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamkazan%2Ffraud-detection-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsamkazan%2Ffraud-detection-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamkazan%2Ffraud-detection-ml/lists"}