This project demonstrates hands-on work with bureau-style sandbox data, a credit reporting dashboard hook, and an AWS-based governance gate for model productionization. The repo demonstrates them in a lightweight, auditable way, without committing secrets or spinning heavy compute. It gives concrete artifacts, one-liners to reproduce behavior, and clear pointers to files.

What’s included (at a glance)

• Data contract & ETL scaffold: Bureau-shaped schema and feature derivations for a sandbox context.
• Governance gate (SageMaker, no-compute): Enforces thresholds from a metrics JSON in S3 (AUC ≥ 0.65, KS ≥ 0.20, PSI < 0.10).
• Audit evidence: A captured failing run (PSI=0.20) checked into the repo under CB_Sandbox/evidence/.
• Dashboard hook: README-wired PNG plus a small CSV you can open in Tableau and export a real PNG within minutes.
• One-liners: Windows-friendly commands to reproduce the gate and evidence.

Repository structure (key paths)

CB_Sandbox/
  evidence/                     # audit logs (includes failing run at PSI=0.20)
  pipelines/sagemaker/          # pipeline gate entrypoint (no-compute)
assets/
  bureau_dashboard.png          # placeholder image (replace with Tableau export)
  dashboard_sample.csv          # tiny aggregate for quick Tableau build
data_contracts/
  bureau_schema.yaml            # schema for bureau-like sandbox data
docs/
  model_card_template.md        # template for documenting variables/risks
  audit_log_schema.json         # sample structure for audit logs
governance/
  policy_map.yaml               # threshold/policy configuration
pyspark_etl_modeling/           # feature derivations & ETL scaffold
README.md                       # thresholds, S3 metrics path, execution ARN

How to run (quick start one-liners, Windows)

Prereqs: Python 3.10+, AWS CLI configured, S3 bucket writable, and a role to pass to SageMaker.

1) Recreate a failing evaluation (PSI=0.20), upload to S3, and run the gate (logs saved as evidence):

python -c "import json,os; os.makedirs(r'outputs\metrics',exist_ok=True); json.dump({'auc':0.66,'ks':0.25,'psi':0.20}, open(r'outputs\metrics\evaluation.json','w'))" && aws s3 cp "%CD%\outputs\metrics\evaluation.json" "s3://aws-flagship-project/outputs/metrics/evaluation.json" && python CB_Sandbox\pipelines\sagemaker\pipeline_gate.py --role <YOUR_ROLE_ARN> --bucket s3://aws-flagship-project --prefix cbsandbox --metrics_s3 s3://aws-flagship-project/outputs/metrics/evaluation.json > CB_Sandbox\evidence\gate_failed.txt 2>&1

2) Replace placeholder with a real Tableau PNG:

# In Tableau: open assets\dashboard_sample.csv, build a couple of views (Delinquency Trend, Segment Mix, Score vs Default), export PNG to assets\bureau_dashboard.png, then:
git add assets/bureau_dashboard.png && git commit -m "Dashboard: Tableau export" && git push

Deep dive:

1) What is a “bureau sandbox” and why it matters

A bureau sandbox is a safe, non-production environment that mimics credit-bureau data—using de-identified or synthetic records aligned to real bureau schemas and distributions. It lets teams prototype ETL, features, dashboards, and governance without handling PII or live customer data.

What it’s used for

• Feature & ETL rehearsal: derive variables like delinquency counts, utilization, inquiries, thin-file flags, and cohort segments before touching production feeds.
• Policy & monitoring dry-runs: test release thresholds (AUC/KS/PSI), drift and stability checks, and adverse-action reason logic.
• Dashboard development: build and iterate on credit reporting views (delinquency trend, segment mix, score vs default) without compliance risk.
• Operational rehearsal: validate schemas, data quality rules, and pipeline orchestration so the production cutover is low-risk and audit-ready.

Common fields it simulates

• Identity keys (tokenized) and trade/collection aggregates (balances, limits, utilization).
• Delinquency/charge-off indicators and roll-rate histories.
• Inquiry counts, new-account flags, file age/length.
• Thin-file indicators (e.g., ≤2 tradelines and/or short history), which are important for inclusive-credit analysis.

Limitations & guardrails

• Distributions are approximate; sandbox data should not be used for final model calibration or policy cut decisions without production validation.
• Rare events may be under-represented; treat performance metrics as directional.
• Always document mapping assumptions from sandbox → vendor feeds to avoid drift on go-live.

How this repo applies the concept

• We define a bureau-shaped schema in data_contracts/bureau_schema.yaml, derive example features in pyspark_etl_modeling/, and evaluate stability via PSI/KS/AUC thresholds enforced by an AWS pipeline gate. The dashboard hook (assets/bureau_dashboard.png + assets/dashboard_sample.csv) enables rapid visualization without exposing sensitive data.

What’s implemented

• A reproducible schema (data_contracts/bureau_schema.yaml) that mirrors bureau-style fields (IDs, tradeline aggregates, delinquency/charge-off flags, thin-file markers).
• An ETL/feature scaffold (pyspark_etl_modeling/) demonstrating safe, sandboxed transformations.
• Drift & stability checks are part of the model evaluation story (PSI, KS, AUC thresholds).

Quick Verify (no secrets)

python - << "PY"
print("Schema (first 40 lines):")
print("\n".join(open("data_contracts/bureau_schema.yaml","r",encoding="utf-8").read().splitlines()[:40]))
PY

2) credit reporting dashboard

What’s implemented

• The README image embed is pre-wired to assets/bureau_dashboard.png so docs never break.
• A tiny sample aggregate CSV (assets/dashboard_sample.csv) is provided to drag into Tableau Public and export a real PNG within minutes.

Suggested first views (Tableau)

• Delinquency Trend (line): monthly delinquency/charge-off rate.
• Segment Mix (stacked bar): Prime vs Near-Prime vs Subprime volumes.
• Score vs Default (overlay): binned score distribution against default rate.

Workflow

1. Open assets/dashboard_sample.csv in Tableau Public.
2. Create 2–3 worksheets as above.
3. Export as PNG → overwrite assets/bureau_dashboard.png.
4. Commit and push.

3) productionizing models in AWS

What’s implemented

• A no-compute SageMaker pipeline gate that enforces simple release criteria directly from an S3-hosted metrics JSON: AUC ≥ 0.65, KS ≥ 0.20, PSI < 0.10.
• Audit evidence: a failing run at PSI=0.20 is checked into CB_Sandbox/evidence/gate_failed.txt.
• The README records the S3 metrics path and an execution ARN for a prior gate run.

Why this matters

• Shows how to wire AWS services safely (no secrets committed) and express release governance as code.
• Demonstrates traceability: metrics → policy thresholds → pass/fail + stored evidence.

Reproduce locally (Windows one-liner)

python -c "import json,os; os.makedirs(r'outputs\metrics',exist_ok=True); json.dump({'auc':0.66,'ks':0.25,'psi':0.20}, open(r'outputs\metrics\evaluation.json','w'))" && aws s3 cp "%CD%\outputs\metrics\evaluation.json" "s3://aws-flagship-project/outputs/metrics/evaluation.json" && python CB_Sandbox\pipelines\sagemaker\pipeline_gate.py --role <YOUR_ROLE_ARN> --bucket s3://aws-flagship-project --prefix cbsandbox --metrics_s3 s3://aws-flagship-project/outputs/metrics/evaluation.json > CB_Sandbox\evidence\gate_failed.txt 2>&1

Outcomes

• Sandbox data familiarity: I worked with bureau-shaped schemas and built ETL/feature steps suitable for a sandbox environment, with drift and stability checks aligned to credit risk practice.
• Dashboard-ready: I wired the doc to accept a Tableau export and provided a sample aggregate so a visual can be produced immediately—no repo changes required.
• AWS productionization: I encoded release thresholds as a SageMaker pipeline gate, captured failing-run evidence, and documented the S3-driven evaluation path. This is how I keep “audit-ready” while moving fast.

What I would add next (if time permits)

• CI smoke test: a tiny GitHub Action that imports pipeline_gate.py to catch breakage early.
• Model card + artifacts: fill in docs/model_card_template.md with selections & risks.
• Real Tableau PNG: replace the placeholder with an exported assets/bureau_dashboard.png.
• Optional: Extend ETL to include segmentation and cohort drift monitoring as scheduled jobs.

Output

• Sandbox data: schema + ETL + drift checks present and documented.
• Dashboard: image embed is wired; sample CSV enables a real export in minutes.
• AWS: pipeline gate with thresholds, S3-driven metrics, and audit evidence.