{"id":26054787,"url":"https://github.com/jaypanchal9/fraud-detection-case-study","last_synced_at":"2026-04-15T10:31:40.664Z","repository":{"id":281225898,"uuid":"944613906","full_name":"jaypanchal9/Fraud-Detection-Case-Study","owner":"jaypanchal9","description":"A comprehensive case study applying machine learning techniques to detect fraudulent transactions effectively.","archived":false,"fork":false,"pushed_at":"2025-03-07T17:03:19.000Z","size":265,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-07T18:19:36.462Z","etag":null,"topics":["machine-learning","matplotlib","numpy","pandas","python3","scikit-learn","seaborn","xgboost"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/jaypanchal9.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":"2025-03-07T16:55:04.000Z","updated_at":"2025-03-07T17:05:25.000Z","dependencies_parsed_at":"2025-03-07T18:19:40.114Z","dependency_job_id":"5ace3f5d-517f-43bc-87dd-617ed4f11fc3","html_url":"https://github.com/jaypanchal9/Fraud-Detection-Case-Study","commit_stats":null,"previous_names":["jaypanchal9/fraud-detection-case-study"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaypanchal9%2FFraud-Detection-Case-Study","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaypanchal9%2FFraud-Detection-Case-Study/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaypanchal9%2FFraud-Detection-Case-Study/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaypanchal9%2FFraud-Detection-Case-Study/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jaypanchal9","download_url":"https://codeload.github.com/jaypanchal9/Fraud-Detection-Case-Study/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242532349,"owners_count":20144726,"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":["machine-learning","matplotlib","numpy","pandas","python3","scikit-learn","seaborn","xgboost"],"created_at":"2025-03-08T09:59:54.918Z","updated_at":"2026-04-15T10:31:40.623Z","avatar_url":"https://github.com/jaypanchal9.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Fraud Detection Case Study\n\n## Overview\n\nThis project focuses on building a robust model for detecting fraudulent transactions using machine learning techniques. The implementation leverages exploratory data analysis (EDA), feature engineering, and various predictive modeling techniques to identify and mitigate financial fraud.\n\n## Objectives\n\n- Perform exploratory data analysis to understand the characteristics of fraudulent transactions.\n- Apply data preprocessing techniques to clean and prepare the dataset for modeling.\n- Engineer meaningful features to improve predictive performance.\n- Train and evaluate multiple machine learning models to detect fraud effectively.\n- Select the best-performing model based on evaluation metrics.\n\n## Dataset\n\nThe dataset used contains historical transaction data labeled as fraudulent or legitimate. Key attributes include transaction amount, time, user details, and transaction type.\n\n## Methodology\n\n### 1. Data Exploration\n\n- Understanding data distribution.\n- Identifying patterns and anomalies indicative of fraud.\n\n### 2. Data Preprocessing\n\n- Handling missing values.\n- Dealing with class imbalance using techniques such as undersampling or oversampling.\n\n### 3. Feature Engineering\n\n- Creating derived features that enhance the ability to detect fraud.\n- Scaling and normalizing data for improved model performance.\n\n### 4. Modeling\n\n- Implementing machine learning models such as Logistic Regression, Random Forest, and XGBoost.\n- Evaluating models using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.\n\n### 5. Model Selection and Evaluation\n\n- Comparing performance across different models.\n- Selecting the best-performing model based on metrics suited for imbalanced datasets.\n\n## Usage\n\n- Clone this repository.\n- Install necessary libraries using `pip install -r requirements.txt`.\n- Run the Jupyter notebook `Fraud_Detection_Case_Study_Code.ipynb` to reproduce the analysis.\n\n## Dependencies\n\n- Python 3.x\n- Pandas\n- NumPy\n- Scikit-learn\n- XGBoost\n- Matplotlib\n- Seaborn\n\n## Results\n\nThe best-performing model achieves a high level of accuracy and recall, significantly reducing false negatives and effectively identifying fraudulent transactions.\n\n## Future Work\n\n- Explore additional advanced modeling techniques like neural networks.\n- Implement real-time fraud detection solutions.\n- Continuously update models with new transaction data to maintain accuracy.\n\n## License\n\nThis project is licensed under the GNU General Public License v3.0. See the [LICENSE](LICENSE) file for details.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjaypanchal9%2Ffraud-detection-case-study","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjaypanchal9%2Ffraud-detection-case-study","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjaypanchal9%2Ffraud-detection-case-study/lists"}