{"id":28215264,"url":"https://github.com/jlee9503/defense-risk-prediction","last_synced_at":"2026-01-25T12:01:11.099Z","repository":{"id":293739132,"uuid":"984984772","full_name":"jlee9503/defense-risk-prediction","owner":"jlee9503","description":"Build a machine learning pipeline that ingests defense procurement data, identifies high-risk contracts, and visualizes the results in an interactive dashboard.","archived":false,"fork":false,"pushed_at":"2025-06-03T18:49:36.000Z","size":1053,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-12T02:47:21.546Z","etag":null,"topics":["data-analysis","data-visualization","exploratory-data-analysis","python"],"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/jlee9503.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,"zenodo":null}},"created_at":"2025-05-16T21:16:17.000Z","updated_at":"2025-06-03T18:49:37.000Z","dependencies_parsed_at":"2025-05-16T22:25:28.096Z","dependency_job_id":"509d2d2d-8439-43d6-b36a-16295c669c3e","html_url":"https://github.com/jlee9503/defense-risk-prediction","commit_stats":null,"previous_names":["jlee9503/defense-risk-prediction"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jlee9503/defense-risk-prediction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jlee9503%2Fdefense-risk-prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jlee9503%2Fdefense-risk-prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jlee9503%2Fdefense-risk-prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jlee9503%2Fdefense-risk-prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jlee9503","download_url":"https://codeload.github.com/jlee9503/defense-risk-prediction/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jlee9503%2Fdefense-risk-prediction/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28752671,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-25T10:25:12.305Z","status":"ssl_error","status_checked_at":"2026-01-25T10:25:11.933Z","response_time":113,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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-analysis","data-visualization","exploratory-data-analysis","python"],"created_at":"2025-05-17T22:10:47.795Z","updated_at":"2026-01-25T12:01:11.092Z","avatar_url":"https://github.com/jlee9503.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Defense Procurement Risk Forecasting\n\nThis project simulates a data science pipeline for identifying risk in defense acquisition contracts. Inspired by real-world use cases in government and defense analytics, it demonstrates skills in data engineering, statistical modeling, automation, and dashboard design.\n\n---\n\n## 🔍 Problem Statement\n\nDefense procurement often suffers from budget overruns, delays, and vendor risks. This project aims to use simulated acquisition data to build a machine learning pipeline that flags high-risk contracts based on past performance, supplier patterns, and contract size/timing.\n\n---\n\n## 🧰 Tools Used\n\n- Python (Pandas, sqlalchemy, Pathlib)\n- SQL (MS SQL Server)\n- Matplotlib / Seaborn (for internal visualizations)\n- Git + VS Code\n- Docker (SQL Servcer Container)\n\n---\n\n## 📈 Pipeline Overview\n\n1. **Data Ingestion:**  \n   - Loads multiple data sources (CSV, JSON) simulating contract records, supplier history, and known delays.\n\n2. **Data Cleaning + Feature Engineering:**  \n   - Identify missing values and duplicates\n   - Merge datasets using `supplier_id` and `contract_id`\n   - Risk feature encoding (contract age, contract value per month, risk score)\n\n3. **Exploratory Data Analysis:**  \n   - Univariate\n      - Categorical: `contract_type`, `compliance_issues`\n      - Numeric: `contract_value_million`, `expected_duration_months`, `average_delay_days`, `delay_days`, `risk_score`, `value_per_month`, `contract_age_days`\n   - Bivariate\n      - Relationship between target variable (`risk_score`) and each feature\n\n---\n\n## 📁 Project Status\n\n✅ Completed: Setup database, Explore \u0026 Clean dataset, Exploratory Data Analysis (EDA)\n\n🚧 In Progress: Model Training and Evaluation\n\n---\n## 🧾 License\n\nMIT License\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjlee9503%2Fdefense-risk-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjlee9503%2Fdefense-risk-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjlee9503%2Fdefense-risk-prediction/lists"}