README.md

metadata

title: Credit Scoring - Home Credit Default Risk
emoji: 📊
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.44.1
python_version: '3.12'
app_file: app.py
pinned: false

OC_P6 - API Scoring Credit (MLOps)

🚀 Demo live

https://huggingface.co/spaces/ASI-Engineer/OC_P8_prod https://huggingface.co/spaces/ASI-Engineer/OC_P8_test

Resultats optimisation etape 4

• Gain latence : 15.7x (0.64 ms -> 0.04 ms par requete)
• Precision : 100 % identique
• Voir reports/rapport_optimisation.md complet

Architecture finale

• FastAPI/Gradio + Docker (entrypoint : app.py)
• Monitoring logs + Evidently (drift)
• Optimisation : VectorizedPreprocessor (15.7x)

Etapes realisees

• Etape 2 : API + Docker + CI/CD
• Etape 3 : Stockage + analyse prod
• Etape 4 : Optimisation perfs (terminee)

Apercu du projet (audit rapide)

• Donnees brutes et features : data/raw, data/processed
• Pipeline data/model : src/load_data.py, src/preprocessing.py
• Experiments et artefacts : mlruns, models
• Notebooks MLOps : notebooks
• Monitoring prod : logs/predictions.jsonl, reports
• Tests : tests
• Conteneurisation : Dockerfile

Structure du projet

OC_P6/
├── app.py
├── Dockerfile
├── pyproject.toml
├── requirements.txt
├── requirements-inference.txt
├── data/
│   ├── raw/
│   └── processed/
├── logs/
│   └── predictions.jsonl
├── mlruns/
├── models/
│   ├── export_model.py
│   ├── export_preprocessor.py
│   ├── lightgbm.txt
│   └── preprocessor.joblib
├── notebooks/
│   ├── 01_exploration.ipynb
│   ├── 02_preparation_features.ipynb
│   ├── 03_LGBM.ipynb
│   ├── 04_regression.ipynb
│   ├── 05_model_interpretation.ipynb
│   ├── 06_analyse_logs.ipynb
│   ├── 07_detect_data_drift.ipynb
│   ├── 08_analyze_logs_2.ipynb
│   ├── 09_profiling.ipynb
│   └── 10_optimisation.ipynb
├── reference/
│   ├── reference.csv
│   └── simulate_production_calls.py
├── reports/
│   ├── data_drift_report.html
│   ├── monitoring_study.md
│   └── plots/
├── src/
│   ├── __init__.py
│   ├── load_data.py
│   ├── mlflow_config.py
│   └── preprocessing.py
└── tests/
  ├── conftest.py
  ├── test_predict.py
  └── test_preprocessing.py

Installation (UV recommande)

curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync

Donnees

Source : Kaggle Home Credit Default Risk. Placer les fichiers dans data/raw :

• application_train.csv
• application_test.csv
• bureau.csv
• bureau_balance.csv
• credit_card_balance.csv
• installments_payments.csv
• POS_CASH_balance.csv
• previous_application.csv

Notebooks (resume)

• Exploration : notebooks/01_exploration.ipynb
• Feature engineering : notebooks/02_preparation_features.ipynb
• Modelling LGBM + MLflow : notebooks/03_LGBM.ipynb
• Baseline regression : notebooks/04_regression.ipynb
• Interpretation : notebooks/05_model_interpretation.ipynb
• Monitoring et drift : notebooks/06_analyse_logs.ipynb, notebooks/07_detect_data_drift.ipynb
• Profiling et optimisation : notebooks/09_profiling.ipynb, notebooks/10_optimisation.ipynb

Comment tester localement

uv sync
uv run python app.py

Option Docker :

docker build -t oc_p6:latest .
docker run --rm -it -p 7860:7860 oc_p6:latest

Usage API (local ou HF Space)

Exemple JSON minimal :

{"SK_ID_CURR": 100001, "AMT_INCOME_TOTAL": 202500.0, "AMT_CREDIT": 80000.0, "CODE_GENDER": "M", "DAYS_BIRTH": -12000}

Requete vers la Space de production :

curl -s -X POST "https://huggingface.co/spaces/ASI-Engineer/OC_P8_prod/api/predict" \
  -H "Content-Type: application/json" \
  -d '{"data":["{\"SK_ID_CURR\":100001,\"AMT_INCOME_TOTAL\":202500.0,\"AMT_CREDIT\":80000.0,\"CODE_GENDER\":\"M\",\"DAYS_BIRTH\":-12000}"]}'

Monitoring et data drift

• Rapport monitoring : reports/monitoring_study.md
• Rapport drift Evidently : reports/data_drift_report.html
• Plots latence et scores : reports/plots
• Simulation d'appels prod : reference/simulate_production_calls.py

Tests

uv run pytest

Date : 25 fevrier 2026
Statut : Projet termine OK, pret pour soutenance