{"id":18684830,"url":"https://github.com/j-sephb-lt-n/unsupervised-fraud-detection","last_synced_at":"2026-05-07T07:42:05.247Z","repository":{"id":234414615,"uuid":"788849896","full_name":"J-sephB-lt-n/unsupervised-fraud-detection","owner":"J-sephB-lt-n","description":"Exploring anomaly detection using unsupervised methods in scikit-learn","archived":false,"fork":false,"pushed_at":"2024-04-29T08:20:28.000Z","size":75340,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-28T00:21:17.942Z","etag":null,"topics":["anomaly","anomaly-detection","fraud-detection","isolation-forest","local-outlier-factor","outlier","outlier-det","scikit-learn","sklearn","unsupervised"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/J-sephB-lt-n.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-04-19T07:54:44.000Z","updated_at":"2024-10-12T18:23:35.000Z","dependencies_parsed_at":"2024-11-07T10:29:53.969Z","dependency_job_id":null,"html_url":"https://github.com/J-sephB-lt-n/unsupervised-fraud-detection","commit_stats":null,"previous_names":["j-sephb-lt-n/unsupervised-fraud-detection"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/J-sephB-lt-n%2Funsupervised-fraud-detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/J-sephB-lt-n%2Funsupervised-fraud-detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/J-sephB-lt-n%2Funsupervised-fraud-detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/J-sephB-lt-n%2Funsupervised-fraud-detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/J-sephB-lt-n","download_url":"https://codeload.github.com/J-sephB-lt-n/unsupervised-fraud-detection/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239539799,"owners_count":19655881,"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":["anomaly","anomaly-detection","fraud-detection","isolation-forest","local-outlier-factor","outlier","outlier-det","scikit-learn","sklearn","unsupervised"],"created_at":"2024-11-07T10:19:29.582Z","updated_at":"2026-05-07T07:42:00.207Z","avatar_url":"https://github.com/J-sephB-lt-n.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# unsupervised-fraud-detection\nExploring anomaly detection using unsupervised methods\n\n![](./models/evaluation_output/roc_auc.png)\n\nThis repo is still massively under construction\n\nSimulate transaction data:\n```bash\n$ poetry run python -m data.simdata\nSimulated dataset written to 'data/input/simdata.csv'\n```\n\nCreate model training dataset:\n```bash\n$ poetry run python -m feature_eng.create_train_data\nreading input data from 'data/input/simdata.csv'\nFinished exporting data to 'feature_eng/output/train_data.csv'\n```\n\nRun unsupervised anomaly detection models:\n```bash\n$ poetry run python -m models.train_predict.dist_to_dst_clust_median\n$ poetry run python -m models.train_predict.dist_to_src_clust_median\n$ poetry run python -m models.train_predict.local_outlier_factor\n$ poetry run python -m models.train_predict.isolation_forest\n```\n\nEvaluate models:\n```bash\n$ poetry run python -m models.evaluate\nExported results to 'models/evaluation_output/'\n```\n\nExplain predictions for a specific transaction:\n```bash\n$ poetry run python -m models.explain_prediction --tid 100420\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fj-sephb-lt-n%2Funsupervised-fraud-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fj-sephb-lt-n%2Funsupervised-fraud-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fj-sephb-lt-n%2Funsupervised-fraud-detection/lists"}