| ---
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| license: mit
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| tags:
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| - cbc-reference-model
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| - mlops-100-day
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| - fraud-detection
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| - tabular-classification
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| ---
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|
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| # CBC Reference Model: Credit-Card Fraud Detection
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|
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| > Pre-trained reference model for the **CBC [MLOps 100-Day Track](https://github.com/careerbytecode/cbc-learning-hub/tree/main/100-days/mlops)** (Capstone 2). Published twin of ML Development Capstone 2.
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|
|
| ## Model details
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| - **Type:** XGBoost classifier (Optuna-tuned, `scale_pos_weight=498`), trained on a **time-aware** split (earlier 80% of transactions by Time; tested on the latest 20%).
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| - **Framework:** xgboost 3.2.0 · **Serialization:** joblib · **Seed:** 42
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| - A **pre-filter** that ranks transactions by fraud risk for review — NOT an automated block. `scale_pos_weight` de-calibrates probabilities upward, so the operating threshold is high (not 0.5) and is a serving config, recorded in `metrics.json`.
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|
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| ## Intended use
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| Flag transactions for human/downstream review. Teaching/reference artifact — not a production authorization system.
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|
|
| ## Training data
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| ULB mlg-ulb credit-card fraud (284,807 transactions over two days, 492 frauds, 0.17%). 28 PCA features V1..V28 + Amount; Time is the split variable. ODbL/DbCL.
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|
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| ## Metrics (latest-20%-by-Time test split, evaluated once)
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| | Metric | Value |
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| |---|---|
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| | PR-AUC | 0.8050 (no-skill floor 0.0013) |
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| | ROC-AUC | 0.9656 |
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| | Recall @ threshold 0.7017 | 0.7467 |
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| | Precision @ threshold | 0.8750 |
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| | F1 @ threshold | 0.8058 |
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| Threshold 0.7017 was tuned on a validation slice for precision >= 90%. Top SHAP drivers: V4 (2.61), V14 (2.29), V12 (1.76), V10 (1.34), V11 (1.19).
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|
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| ## How to load and predict
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| ```python
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| import joblib, json, pandas as pd
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| from huggingface_hub import hf_hub_download
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|
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| model = joblib.load(hf_hub_download("careerbytecode/mlops-ref-finance-fraud", "model/pipeline.joblib"))
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| sample = json.load(open(hf_hub_download("careerbytecode/mlops-ref-finance-fraud", "sample_input.json")))
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| proba = float(model.predict_proba(pd.DataFrame([sample]))[0, 1])
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| is_fraud = proba >= 0.7017 # threshold from metrics.json
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| print(proba, is_fraud)
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| ```
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| Input schema: 29 numeric columns — V1..V28 (PCA) + Amount.
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|
|
| ## Limitations
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| - Severely imbalanced (0.17%): accuracy is meaningless; judge by PR-AUC vs floor and recall/precision at threshold.
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| - Features are PCA components — not human-readable; SHAP names components, not transaction attributes.
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| - Temporal drift expected; retrain on a rolling window. Reference/teaching artifact only.
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|
|
| ---
|
| © 2015-2026 CareerByteCode. All rights reserved. | CC BY-NC-SA 4.0 (docs), MIT (code) | Authored by Raghavendra R, Platform Owner CareerByteCode, Solution Architect
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