{"id":20303337,"url":"https://github.com/vbhvsingh0/fraudulent_transactions","last_synced_at":"2026-05-09T14:14:21.211Z","repository":{"id":247705557,"uuid":"826595591","full_name":"vbhvsingh0/fraudulent_transactions","owner":"vbhvsingh0","description":"A few models were developed based on Decision trees and Logistic Regression to categorize fraudulent transactions","archived":false,"fork":false,"pushed_at":"2024-07-10T04:17:45.000Z","size":19,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-14T11:04:31.469Z","etag":null,"topics":["credit-card-fraud","data-science","logistic-regression","machine-learning","numpy","numpy-python","pandas","pandas-python","python3","random-forest-classifier"],"latest_commit_sha":null,"homepage":"","language":"Python","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/vbhvsingh0.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":"2024-07-10T02:43:34.000Z","updated_at":"2024-07-10T15:24:02.000Z","dependencies_parsed_at":"2024-07-10T06:55:32.743Z","dependency_job_id":null,"html_url":"https://github.com/vbhvsingh0/fraudulent_transactions","commit_stats":null,"previous_names":["vbhvsingh0/fraudulent_transactions"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vbhvsingh0%2Ffraudulent_transactions","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vbhvsingh0%2Ffraudulent_transactions/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vbhvsingh0%2Ffraudulent_transactions/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vbhvsingh0%2Ffraudulent_transactions/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vbhvsingh0","download_url":"https://codeload.github.com/vbhvsingh0/fraudulent_transactions/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241801193,"owners_count":20022383,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["credit-card-fraud","data-science","logistic-regression","machine-learning","numpy","numpy-python","pandas","pandas-python","python3","random-forest-classifier"],"created_at":"2024-11-14T16:36:49.049Z","updated_at":"2026-05-09T14:14:16.191Z","avatar_url":"https://github.com/vbhvsingh0.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# fraudulent_transactions\n\nA few models were developed based on Decision trees, Logistic Regression, and clustering models to categorize fraudulent transactions\n\nA. Supervised machine learning models\n\nThe code inside 'supervised' named folder uses the 'fraud_sampledata.csv' to model the supervised machine learning model to categorize fraud transactions. It has used 3 models:\n    \n\ta. Logistic regression \n\tb. random Forest classifier\n \tc. Ensemble model combining above two.\n\nIn terms of performance, the model 'c' was the best having 94 % recall value.\n\nB. Unsupervised machine learning models\n\nThe script is present inside the 'unsupervised' named folder. Here, a types of clustering models were used as given below:\n\t\n\ta. Kmeans clustering method\n\tb. DBSCAN clustering method\n\nIn terms of performance, b worked better suggesting , the shape of the features might be convex shaped.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvbhvsingh0%2Ffraudulent_transactions","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvbhvsingh0%2Ffraudulent_transactions","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvbhvsingh0%2Ffraudulent_transactions/lists"}