{"repo":"AmirhosseinHonardoust/Underwriting-Decision-Safety-Lab","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Underwriting-Decision-Safety-Lab","clone":"git clone https://github.com/AmirhosseinHonardoust/Underwriting-Decision-Safety-Lab.git","description":"A decision-safety lab for loan approval: trains a baseline classifier, calibrates probabilities (ECE/Brier), sweeps confidence thresholds to build a coverage, quality frontier and outputs a defensible abstention policy (auto-decide vs review). Includes a Streamlit dashboard for report cards, triage UI, and data quality checks.","language":"Python","stars":12,"topics":["abstention","calibration","classification","credit-risk","data-quality","data-science","decision-policy","loan-approval","machine-learning","mlops"],"license":"MIT","category":"machine-learning","readme_excerpt":"Underwriting Decision Safety Lab A production-minded underwriting decision-safety workflow for turning loan-approval model scores into calibrated probabilities , abstention policies , coverage-quality tradeoffs , slice safety diagnostics , and human-review decisions . Important: This project is a portfolio and research demo , not a production underwriting or credit-decision system. The model, thresholds, policy variants, and slice reports are designed to demonstrate a professional decision-safety workflow. They should not be used for real lending, credit approval, or automated financial decisions without legal, compliance, fairness, security, and domain review. --- Table of Contents - Project Overview - What This Project Does - What This Project Does Not Do - Key Features - System Workflow - Project Structure - Installation - Quick Start - Training and Evaluation - Abstention and Policy Variants - Slice Safety Reporting - Streamlit Dashboard - Evaluation Metrics - Visual Reports - Testing and CI - Code Quality - Limitations - Responsible Use - Future Improvements - Tech Stack - Author - License --- Project Overview Underwriting is not only a classification problem. In a real decision workflow, a model score is useful only if it can support a defensible action: - approve automatically - reject automatically - route uncertain cases to human review - monitor decision quality across applicant slices - communicate uncertainty and limitations clearly This project demonstrates an en","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Underwriting-Decision-Safety-Lab/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}