{"id":23126079,"url":"https://github.com/daniel-elston/data-mining-fraud-outlier-detection","last_synced_at":"2025-04-04T05:12:28.183Z","repository":{"id":156363669,"uuid":"467537696","full_name":"Daniel-Elston/Data-Mining-Fraud-Outlier-Detection","owner":"Daniel-Elston","description":"Data mining techniques used to identify outliers and potential fraudulent activity in a stocks dataset.","archived":false,"fork":false,"pushed_at":"2022-06-11T21:30:59.000Z","size":242,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-09T16:36:47.146Z","etag":null,"topics":["data-mining","data-science","fraud-detection","machine-learning"],"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/Daniel-Elston.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":"2022-03-08T14:07:30.000Z","updated_at":"2022-06-03T14:00:34.000Z","dependencies_parsed_at":null,"dependency_job_id":"c6741e41-e0d2-417a-9178-1274c54f92cc","html_url":"https://github.com/Daniel-Elston/Data-Mining-Fraud-Outlier-Detection","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Daniel-Elston%2FData-Mining-Fraud-Outlier-Detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Daniel-Elston%2FData-Mining-Fraud-Outlier-Detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Daniel-Elston%2FData-Mining-Fraud-Outlier-Detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Daniel-Elston%2FData-Mining-Fraud-Outlier-Detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Daniel-Elston","download_url":"https://codeload.github.com/Daniel-Elston/Data-Mining-Fraud-Outlier-Detection/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247123089,"owners_count":20887261,"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":["data-mining","data-science","fraud-detection","machine-learning"],"created_at":"2024-12-17T08:18:18.275Z","updated_at":"2025-04-04T05:12:28.169Z","avatar_url":"https://github.com/Daniel-Elston.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \n![](https://github.com/Daniel-Elston/Daniel-Elston/blob/main/GitBanner3.png)\n\n\u003c/p\u003e\n\u003chr\u003e\n\n\n\u003ch1 align='center'\u003e Data Mining - Fraud Outlier Detection \u003c/h1\u003e\n\n\u003chr\u003e\n\n\n## Table of contents\n- [Status and Details](#status-and-details)\n- [Technology](#technology)\n- [Introduction](#introduction)\n    - [Project Description](#project-description)\n    - [Objectives](#objectives)\n- [Data Science Methodology](#data-science-methodology)\n    - [Problem Formulation](#problem-formulation)\n- [Conclusions](#conclusions)\n- [Contributing Members and Contacts](#contributing-members-and-contacts)\n\n\n## Status and Details\n- **Project Status**: [Completed]\n- **Date Coded**: 07/10/21\n\n\n## Technology\n- **Language**: Python 3.6.9\n- **Libraries**: numpy, matplotlib, pandas, sklearn\n- **Set up File**: N/A\n\n\n## Introduction\nThe purpose of this project is to identify potential fraudulent activity. Such analysis is important in the financial sector.\n\n\n### Project Description\nThe dataset used for this analysis is a stock dataset with over 2500 observations. The data denotes the percentage of changes in the daily closing price of stocks for Microsoft, Ford and Bank of America. \n\nA one-class SVM is trained on the dataset to identify outliers, potentially preventing fraudulent activity. The results are plotted on a 3D plot with a colour coded system to denote outlier score.\n\n\n### Objectives\n- Train one-class SVM to return outlier labels\n- Plot results to visualise potential outliers\n\n\n## Data Science Methodology\nThe below subsections outline the standard methodology of data scientists.\n\n\n### Problem Formulation\nMarket manipulation can causes harm to investors in such companies. By reducing fraudulent stock activity, investors can be protected.\n\n\n## Conclusions\nThe resultant plots for this project clearly show outliers in a dark red. The closer to thje center of the plot, the closer to blue the colour and also the less likely the observation is to be fraudulent.\n\n\n## Contributing Members and Contacts\n**Team Lead: [Daniel Elston](https://github.com/Daniel-Elston)**\n\n|Name     |  GitHub Handles   |  \n|---------|-----------------|\n| Daniel Elston | [Git DE](https://github.com/Daniel-Elston)   |\n\nPlease feel free to contact me if you have any questions, require any further information or wish to contribute.\u003cbr/\u003e\nEmail 1: delstonds@outlook.com\u003cbr/\u003e\nEmail 2: ec21024@qmul.ac.uk\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaniel-elston%2Fdata-mining-fraud-outlier-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdaniel-elston%2Fdata-mining-fraud-outlier-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaniel-elston%2Fdata-mining-fraud-outlier-detection/lists"}