{"id":19692302,"url":"https://github.com/shubhamdeepkeshav/fraud-detection-project","last_synced_at":"2026-05-11T01:45:10.571Z","repository":{"id":248552341,"uuid":"829012956","full_name":"shubhamdeepkeshav/FRAUD-DETECTION-PROJECT","owner":"shubhamdeepkeshav","description":"Welcome to the Fraud Detection Project! This repository uses machine learning 🧠 to detect fraudulent transactions 💳. It includes data preprocessing 🛠️, model training 📚, evaluation 📊, and visualization 📈. Explore, experiment, and contribute 🤝 to improve fraud detection accuracy. Check the README for setup and usage instructions. ","archived":false,"fork":false,"pushed_at":"2024-07-15T15:33:25.000Z","size":250,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-10T08:58:32.427Z","etag":null,"topics":["accuracy","datascience","frauddectection","jupyter-notebook","mechinelearning","modeltraining","python","vizualisation"],"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/shubhamdeepkeshav.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":"2024-07-15T15:20:35.000Z","updated_at":"2024-07-21T11:58:29.000Z","dependencies_parsed_at":"2024-07-15T18:51:03.447Z","dependency_job_id":"bc37d074-fb76-4c8d-a666-600e48dba2dc","html_url":"https://github.com/shubhamdeepkeshav/FRAUD-DETECTION-PROJECT","commit_stats":null,"previous_names":["shubhamdeepkeshav/fraud-detection-project"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shubhamdeepkeshav%2FFRAUD-DETECTION-PROJECT","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shubhamdeepkeshav%2FFRAUD-DETECTION-PROJECT/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shubhamdeepkeshav%2FFRAUD-DETECTION-PROJECT/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shubhamdeepkeshav%2FFRAUD-DETECTION-PROJECT/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shubhamdeepkeshav","download_url":"https://codeload.github.com/shubhamdeepkeshav/FRAUD-DETECTION-PROJECT/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241001121,"owners_count":19891933,"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":["accuracy","datascience","frauddectection","jupyter-notebook","mechinelearning","modeltraining","python","vizualisation"],"created_at":"2024-11-11T19:12:54.985Z","updated_at":"2026-05-11T01:45:05.532Z","avatar_url":"https://github.com/shubhamdeepkeshav.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Fraud Detection Project 🕵️\nThis project focuses on using machine learning algorithms to detect fraud in financial transactions.\n# Overview  📋\n The repository contains code for training and evaluating different models to detect fraudulent activities in transaction data. It aims to provide accurate fraud detection capabilities using supervised learning techniques.\n# Features 🛠️\n    •\tData Preprocessing🛠️: Cleaning, transforming, and preparing the dataset for analysis.\n    \n    •\tModel Training📚: Training classifiers including Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbors.\n    \n    •\tModel Evaluation📊: Assessing model performance using accuracy score, ROC curve, Precision-Recall curve, and confusion matrix.\n    \n    •\tVisualization 📊: Visualizing model comparisons, feature importance, and evaluation metrics using matplotlib and seaborn.\n    \n    •\tDeployment🚀: Options for deploying trained models in production environments for real-time fraud detection.\n# Files 📁\n    •\tfraud_detection.ipynb: Jupyter Notebook with code for data preprocessing, model training, evaluation, and visualization.\n    \n    •\tdata.csv: Sample dataset used for training and testing.\n    \n    •\tREADME.md: This file providing an overview of the project.\n# Results 📈\n    \n    •\tRandomForest 🌲: Accuracy - 98%\n    \n    •\tDecisionTree 🌳: Accuracy - 97%\n    \n    •\tLogistic Regression 📈: Accuracy - 98%\n    \n    •\tKNN 📏: Accuracy - 98%\n   ![Screenshot 2024-07-13 143847](https://github.com/user-attachments/assets/e6ad65a8-53f7-4df8-b934-866f51a40367)\n      \n# Additional Notes 📝\n    \n    •\tThe dataset used in this project contains simulated transaction data.\n    \n    •\tThe models are trained and evaluated on a balanced dataset with synthetic fraud cases.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamdeepkeshav%2Ffraud-detection-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshubhamdeepkeshav%2Ffraud-detection-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamdeepkeshav%2Ffraud-detection-project/lists"}