{"id":25256781,"url":"https://github.com/onome-joseph/ml-fraud-dectection","last_synced_at":"2025-04-06T00:28:18.225Z","repository":{"id":269048249,"uuid":"906225791","full_name":"Onome-Joseph/ML-Fraud-Dectection","owner":"Onome-Joseph","description":"This project is designed to identify fraudulent transactions with high accuracy.","archived":false,"fork":false,"pushed_at":"2024-12-20T14:16:31.000Z","size":139,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-12T06:39:09.942Z","etag":null,"topics":["classfication-model","data-analysis","data-science","machine-learning","problem-solving"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Onome-Joseph.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":"2024-12-20T12:30:53.000Z","updated_at":"2024-12-20T14:52:09.000Z","dependencies_parsed_at":"2024-12-20T15:26:10.136Z","dependency_job_id":"683eb70e-63cf-432f-bbab-04dd99b7dbb5","html_url":"https://github.com/Onome-Joseph/ML-Fraud-Dectection","commit_stats":null,"previous_names":["onome-joseph/ml-fraud-dectection"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Onome-Joseph%2FML-Fraud-Dectection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Onome-Joseph%2FML-Fraud-Dectection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Onome-Joseph%2FML-Fraud-Dectection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Onome-Joseph%2FML-Fraud-Dectection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Onome-Joseph","download_url":"https://codeload.github.com/Onome-Joseph/ML-Fraud-Dectection/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247419637,"owners_count":20936009,"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":["classfication-model","data-analysis","data-science","machine-learning","problem-solving"],"created_at":"2025-02-12T06:27:33.321Z","updated_at":"2025-04-06T00:28:18.200Z","avatar_url":"https://github.com/Onome-Joseph.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Fraud Detection Machine Learning Model  \n \nThis project implements a **Fraud Detection Machine Learning Model** designed to identify fraudulent transactions with high accuracy. By leveraging multiple algorithms and optimizing for accuracy, the model ensures reliable predictions, making it a valuable tool for financial institutions and businesses.  \n\n## Aim  \nThe aim of this project is to enhance fraud prevention by providing a robust system that can detect anomalies and flag potential fraudulent activities in real-time.  \n\n## Key Features  \n- **Multiple Algorithms**: The model combines the strengths of various algorithms to achieve the highest accuracy.  \n- **High Accuracy**: Reliable and efficient in detecting fraudulent activities.  \n- **Scalable**: Designed to handle large transaction datasets.  \n\n## Applications  \n1. **Banking and Finance**: Detect unauthorized transactions, credit card fraud, and other financial anomalies.  \n2. **E-Commerce**: Prevent fraudulent orders and transactions on online platforms.  \n3. **Insurance**: Identify fraudulent claims to reduce losses.  \n4. **Cybersecurity**: Protect user accounts from suspicious activities.  \n\n## How It Benefits Businesses  \n- **Reduced Losses**: Minimizes financial losses by detecting fraud early.  \n- **Improved Customer Trust**: Protects users, building trust and loyalty.  \n- **Operational Efficiency**: Reduces manual efforts in fraud detection by automating processes.\n\nContributions are welcome! Feel free to fork the repository, suggest improvements, or report issues.  \n \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fonome-joseph%2Fml-fraud-dectection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fonome-joseph%2Fml-fraud-dectection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fonome-joseph%2Fml-fraud-dectection/lists"}