{"id":19940723,"url":"https://github.com/kurtispykes/fraud-detection-project","last_synced_at":"2025-05-03T15:31:30.381Z","repository":{"id":45309690,"uuid":"421347301","full_name":"kurtispykes/fraud-detection-project","owner":"kurtispykes","description":"A mono-repository containing a packaged machine learning model and simple REST API. ","archived":false,"fork":false,"pushed_at":"2021-12-22T11:13:49.000Z","size":133,"stargazers_count":12,"open_issues_count":0,"forks_count":6,"subscribers_count":3,"default_branch":"main","last_synced_at":"2024-10-12T18:16:25.291Z","etag":null,"topics":["feature-engineering","gemfury","machine-learning","portfolio","python","random-forest","rest-api"],"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/kurtispykes.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}},"created_at":"2021-10-26T08:42:36.000Z","updated_at":"2024-07-01T06:54:55.000Z","dependencies_parsed_at":"2022-07-17T02:16:26.257Z","dependency_job_id":null,"html_url":"https://github.com/kurtispykes/fraud-detection-project","commit_stats":null,"previous_names":["kurtispykes/portfolio-projects"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kurtispykes%2Ffraud-detection-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kurtispykes%2Ffraud-detection-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kurtispykes%2Ffraud-detection-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kurtispykes%2Ffraud-detection-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kurtispykes","download_url":"https://codeload.github.com/kurtispykes/fraud-detection-project/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224365490,"owners_count":17299156,"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":["feature-engineering","gemfury","machine-learning","portfolio","python","random-forest","rest-api"],"created_at":"2024-11-13T00:06:40.595Z","updated_at":"2024-11-13T00:06:41.075Z","avatar_url":"https://github.com/kurtispykes.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Fraud Detection Project\nIEEE-CIS Fraud Detection challenge was first hosted by Kaggle in 2019. The idea was for competitors to develop a model \nto detect fraud from customer transactions. While IEEE-CIS already have a fraud prevention system in place, researchers\nwere looking for ways to improve the current figure being saved by the system, and improve the customer experience.\n\n## Usage\nClone this repository to your computer. \nTo view explorations navigate to the project directory cd IEEE-CIS Fraud Detection from \nyour terminal then cd into the `notebooks` directory. This directory contains data analysis\nand the pipeline we converted into a package. To run the notebooks, you'll have\nto install the [data](https://www.kaggle.com/c/ieee-fraud-detection/data) into a directory\ncalled data. The directory must live at the same level as the `notebooks` and `packages`\ndirectory. \n\nTo use the sample the deployed model locally through the API, navigate to the project \ndirectory from your terminal then cd into `packages/fraud_detection_api`. From here, \nrun the following command: \n`py -m tox -e run`\nThis will create a localhost link, simply click it or copy and paste it into your \nbrowser. Then select the docs option and go to the `predict` heading. There is already\nan example instance there, but you may play around with the values.\n\n## Extending This Work\nSome ideas to extend this work:\n- Replace the model \n- Add monitoring \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkurtispykes%2Ffraud-detection-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkurtispykes%2Ffraud-detection-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkurtispykes%2Ffraud-detection-project/lists"}