Sync to HF Spaces [no-ci]

.dockerignore

@@ -0,0 +1,17 @@
+# Fichiers lourds et inutiles en déploiement
+data/
+datasets/
+mlruns/
+models/*.pkl
+models/export_*.py
+*.csv
+*.parquet
+notebooks/
+.ipynb_checkpoints/
+__pycache__/
+*.pyc
+.git
+.gitignore
+venv/
+.env
+uv.lock  # optionnel, si tu gardes uv localement

.github/workflows/ci-cd.yml

@@ -0,0 +1,60 @@
+name: CI/CD Pipeline
+
+# Run CI on changes to main and dev branches and PRs targeting them.
+on:
+  push:
+    branches: [main, dev]
+  pull_request:
+    branches: [main, dev]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      # Checkout repository code.
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      # Set up Python 3.11 for tests.
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      # Install dependencies and test tooling.
+      - name: Install dependencies
+        run: |
+          pip install -r requirements-inference.txt
+          # Install the package in editable mode so `src` is importable in CI
+          pip install -e .
+          pip install pytest pytest-cov
+
+      - name: Export preprocessor
+        run: |
+          python3 models/export_preprocessor.py
+
+      # Run test suite with coverage.
+      - name: Run tests
+        run: pytest --cov=app --cov-report=term-missing -v
+
+  build-docker:
+    # Only build if tests succeed.
+    needs: test
+    if: success()
+    runs-on: ubuntu-latest
+    steps:
+      # Checkout repository code.
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      # Optional: set up Python (not required for Docker build).
+      - name: Setup Python (optional)
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      # Build Docker image locally (no registry push).
+      - name: Build Docker image
+        run: docker build -t api .
+
+

.github/workflows/sync-hf-spaces.yml

@@ -0,0 +1,37 @@
+name: Sync to HF Spaces
+
+on:
+  push:
+    branches:
+      - dev
+      - main
+
+jobs:
+  sync:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+
+      - name: Push dev to HF test space
+        if: github.ref == 'refs/heads/dev'
+        run: |
+          git config user.email "github-actions@github.com"
+          git config user.name "GitHub Actions"
+          git remote add hf-test https://ASI-Engineer:${{ secrets.HF_TOKEN }}@huggingface.co/spaces/ASI-Engineer/OC_P8_test 2>/dev/null || true
+          git checkout --orphan hf-sync-temp
+          git rm -rf reports/ 2>/dev/null || true
+          git commit -m "Sync to HF Spaces [no-ci]"
+          git push hf-test hf-sync-temp:main --force
+
+      - name: Push main to HF prod space
+        if: github.ref == 'refs/heads/main'
+        run: |
+          git config user.email "github-actions@github.com"
+          git config user.name "GitHub Actions"
+          git remote add hf-prod https://ASI-Engineer:${{ secrets.HF_TOKEN }}@huggingface.co/spaces/ASI-Engineer/OC_P8_prod 2>/dev/null || true
+          git checkout --orphan hf-sync-temp
+          git rm -rf reports/ 2>/dev/null || true
+          git commit -m "Sync to HF Spaces [no-ci]"
+          git push hf-prod hf-sync-temp:main --force

.gitignore

@@ -0,0 +1,120 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+.venv/
+venv/
+ENV/
+env/
+
+# UV
+uv.lock
+.python-version.bak
+
+# Jupyter Notebook
+.ipynb_checkpoints/
+*.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# MLflow
+mlruns/
+mlartifacts/
+mlflow.db
+notebooks/mlflow.db
+
+# Data files (trop volumineux pour Git)
+data/
+
+# Reports generated by notebooks or evaluation
+reports/
+
+# Models
+models/*.pkl
+models/*.joblib
+models/*.h5
+*.pkl
+*.joblib
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Pytest
+.pytest_cache/
+.coverage
+htmlcov/
+
+# Logs
+*.log
+
+# Experiments artifacts
+experiments/
+
+# Temporary files
+*.tmp
+*.bak
+
+# Env and credentials
+.env
+.env.*
+*.env
+credentials/
+# === Fichiers exclus pour HF Spaces (binaires lourds) ===
+*.db
+mlflow.db
+notebooks/mlflow.db
+*.png
+*.jpg
+*.jpeg
+*.pdf
+mlruns/
+# notebooks/         # previously ignored entire directory, now allow individual .ipynb files
+# keep data out of repo
+data/
+datasets/
+*.parquet
+*.csv
+uv.lock
+# but we still want to track notebooks themselves
+!notebooks/*.ipynb
+!notebooks/**/*.ipynb
+
+# EXPLICATION : Logs de production - jamais commiter (contiennent des données sensibles)
+logs/
+*.jsonl
+
+# Reports et artefacts de rapports générés
+*.html
+*.png
+!reports/**/*.html
+!reports/**/*.png

Dockerfile

@@ -0,0 +1,42 @@
+# syntax=docker/dockerfile:1
+
+# Base image (lightweight Python 3.11)
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# EXPLICATION : Sous-étape 3 - création dossier logs pour persistance (évite erreurs permissions)
+RUN mkdir -p /app/logs
+
+# Install system dependencies required by LightGBM (OpenMP)
+RUN apt-get update && apt-get install -y --no-install-recommends libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy dependency manifests first for better caching
+COPY pyproject.toml uv.lock ./
+
+# Install uv and sync dependencies (without installing the project)
+RUN pip install --no-cache-dir uv \
+    && uv sync --frozen --no-install-project
+
+# Copy application code
+COPY . ./
+
+# Install project (and any remaining dependencies)
+RUN uv sync --frozen
+
+# Expose Gradio default port
+EXPOSE 7860
+
+# Set PORT for compatibility
+ENV PORT=7860
+
+# Ensure Python output is not buffered (logs visible immediately)
+ENV PYTHONUNBUFFERED=1
+
+# EXPLICATION : Volume pour logs (bonne pratique Docker - permet docker cp ou mount externe)
+VOLUME ["/app/logs"]
+
+# Launch the Gradio app
+CMD ["uv", "run", "app.py"]

README.md

@@ -0,0 +1,155 @@
+---
+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](reports/rapport_optimisation.md) complet
+
+## Architecture finale
+- FastAPI/Gradio + Docker (entrypoint : [app.py](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/raw), [data/processed](data/processed)
+- Pipeline data/model : [src/load_data.py](src/load_data.py), [src/preprocessing.py](src/preprocessing.py)
+- Experiments et artefacts : [mlruns](mlruns), [models](models)
+- Notebooks MLOps : [notebooks](notebooks)
+- Monitoring prod : [logs/predictions.jsonl](logs/predictions.jsonl), [reports](reports)
+- Tests : [tests](tests)
+- Conteneurisation : [Dockerfile](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)
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+uv sync
+```
+
+## Donnees
+Source : Kaggle Home Credit Default Risk.
+Placer les fichiers dans [data/raw](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](notebooks/01_exploration.ipynb)
+- Feature engineering : [notebooks/02_preparation_features.ipynb](notebooks/02_preparation_features.ipynb)
+- Modelling LGBM + MLflow : [notebooks/03_LGBM.ipynb](notebooks/03_LGBM.ipynb)
+- Baseline regression : [notebooks/04_regression.ipynb](notebooks/04_regression.ipynb)
+- Interpretation : [notebooks/05_model_interpretation.ipynb](notebooks/05_model_interpretation.ipynb)
+- Monitoring et drift : [notebooks/06_analyse_logs.ipynb](notebooks/06_analyse_logs.ipynb), [notebooks/07_detect_data_drift.ipynb](notebooks/07_detect_data_drift.ipynb)
+- Profiling et optimisation : [notebooks/09_profiling.ipynb](notebooks/09_profiling.ipynb), [notebooks/10_optimisation.ipynb](notebooks/10_optimisation.ipynb)
+
+## Comment tester localement
+```bash
+uv sync
+uv run python app.py
+```
+
+Option Docker :
+```bash
+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 :
+```json
+{"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 :
+```bash
+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](reports/monitoring_study.md)
+- Rapport drift Evidently : [reports/data_drift_report.html](reports/data_drift_report.html)
+- Plots latence et scores : [reports/plots](reports/plots)
+- Simulation d'appels prod : [reference/simulate_production_calls.py](reference/simulate_production_calls.py)
+
+## Tests
+```bash
+uv run pytest
+```
+
+**Date** : 25 fevrier 2026  
+**Statut** : Projet termine OK, pret pour soutenance

app.py

@@ -0,0 +1,509 @@
+"""Gradio app for Credit Scoring using an MLflow LightGBM model."""
+
+import json
+from typing import Any, Dict
+# EXPLICATION : Imports nécessaires pour le logging structuré JSON
+import logging
+import time
+from datetime import datetime
+# EXPLICATION : Path pour gestion robuste des chemins de logs (multi-plateforme)
+from pathlib import Path
+
+# Compatibility shim: HF Spaces may install a `huggingface_hub` that no longer
+# exports `HfFolder` (used by older Gradio 4.x oauth). Try to import and patch
+# the real `huggingface_hub` when available; only create a minimal shim if the
+# package is absent so we don't shadow the real implementation.
+import os
+try:
+    import huggingface_hub as _hf  # prefer the real package when available
+except Exception:
+    _hf = None
+
+if _hf is not None:
+    # Patch only missing symbols to preserve real package behaviour
+    if not hasattr(_hf, 'HfFolder'):
+        class HfFolder:
+            @staticmethod
+            def get_token():
+                return os.environ.get('HF_TOKEN') or os.environ.get('HUGGINGFACE_HUB_TOKEN')
+        _hf.HfFolder = HfFolder
+    if not hasattr(_hf, 'whoami'):
+        def whoami(token=None):
+            return {}
+        _hf.whoami = whoami
+else:
+    import sys, types
+    _mod = types.ModuleType('huggingface_hub')
+    class HfFolder:
+        @staticmethod
+        def get_token():
+            return os.environ.get('HF_TOKEN') or os.environ.get('HUGGINGFACE_HUB_TOKEN')
+    def whoami(token=None):
+        return {}
+    _mod.HfFolder = HfFolder
+    _mod.whoami = whoami
+    sys.modules['huggingface_hub'] = _mod
+
+import re as _re
+
+import gradio as gr
+import mlflow
+import mlflow.lightgbm
+import pandas as pd
+import numpy as np
+from pathlib import Path
+
+# joblib est requis pour charger le preprocessor vectorisé (etape 4 optimisée)
+import joblib
+
+# Lightweight transformer to accept "raw" payloads (categorical strings, booleans)
+# === VERSION OPTIMISÉE 4.4 - Gain 15.7x : import VectorizedPreprocessor ===
+from src.preprocessing import RawToModelTransformer, VectorizedPreprocessor
+
+
+# Load the model once at startup for efficiency (lazy loading for tests).
+MODEL = None
+
+def _load_model():
+	"""Lazy-load the model on first use.
+
+	Behavior:
+	- Try local LightGBM model file `models/lightgbm.txt` first (fastest, works in Docker/HF).
+	- If that fails, try the MLflow Model Registry as fallback (for local dev with MLflow server).
+	"""
+	global MODEL
+	if MODEL is None:
+		import lightgbm as lgb
+
+		# 1) Local model file (primary — portable for Docker / HF Spaces)
+		candidate_paths = [
+			Path(__file__).resolve().parent / "models" / "lightgbm.txt",
+			Path.cwd() / "models" / "lightgbm.txt",
+		]
+		env_path = os.environ.get("LOCAL_MODEL_PATH")
+		if env_path:
+			candidate_paths.insert(0, Path(env_path))
+
+		for p in candidate_paths:
+			if p.exists():
+				try:
+					MODEL = lgb.Booster(model_file=str(p))
+					print(f"Loaded local LightGBM model from {p}")
+					return MODEL
+				except Exception as err:
+					print(f"Warning: failed to load {p}: {err}")
+
+		# 2) Fallback: MLflow Model Registry (for local dev)
+		try:
+			MODEL = mlflow.lightgbm.load_model("models:/LightGBM/Production")
+			print("Loaded model from MLflow registry")
+			return MODEL
+		except Exception as mlflow_err:
+			raise RuntimeError(
+				f"No local model found at {[str(p) for p in candidate_paths]} "
+				f"and MLflow registry failed: {mlflow_err}. "
+				"Place the model at `models/lightgbm.txt` or set LOCAL_MODEL_PATH."
+			) from mlflow_err
+
+	return MODEL
+
+
+# Preprocessor (accept "raw" input and map to model features)
+PREPROCESSOR = None
+
+def _load_preprocessor():
+	"""Charge le VectorizedPreprocessor (version optimisée étape 4).
+
+	Priorité de chargement :
+	1. models/preprocessor_vectorized.joblib  (VectorizedPreprocessor, 15.7x plus rapide)
+	2. Auto-création depuis models/preprocessor.joblib  (wrap RawToModelTransformer)
+	3. Création d'un RawToModelTransformer de base (fallback)
+	"""
+	global PREPROCESSOR
+	if PREPROCESSOR is not None:
+		return PREPROCESSOR
+
+	# === VERSION OPTIMISÉE 4.4 - Gain 15.7x ===
+	# Essayer d'abord le preprocessor vectorisé sauvegardé
+	vectorized_path = Path("models") / "preprocessor_vectorized.joblib"
+	if vectorized_path.exists():
+		try:
+			PREPROCESSOR = joblib.load(vectorized_path)
+			print("✅ VectorizedPreprocessor chargé (étape 4 optimisée)")
+			return PREPROCESSOR
+		except Exception as e:
+			print(f"⚠️  Chargement vectorized échoué, fallback : {e}")
+			PREPROCESSOR = None
+
+	# Auto-création : wrapper VectorizedPreprocessor autour de l'ancien preprocessor
+	base_path = Path("models") / "preprocessor.joblib"
+	base_transformer = None
+	if base_path.exists():
+		try:
+			base_transformer = joblib.load(base_path)
+			# Vérifier que c'est bien un RawToModelTransformer (pas déjà un VectorizedPreprocessor)
+			if isinstance(base_transformer, VectorizedPreprocessor):
+				PREPROCESSOR = base_transformer
+				print("✅ VectorizedPreprocessor chargé depuis preprocessor.joblib")
+				return PREPROCESSOR
+			if not isinstance(base_transformer, RawToModelTransformer):
+				base_transformer = None
+		except Exception:
+			base_transformer = None
+
+	if base_transformer is None:
+		# Aucun fichier disponible : créer un RawToModelTransformer de base
+		base_transformer = RawToModelTransformer()
+
+	# Wrappeur VectorizedPreprocessor (cœur du gain 15.7x)
+	PREPROCESSOR = VectorizedPreprocessor(base_transformer)
+	print("✅ VectorizedPreprocessor créé (étape 4 optimisée)")
+
+	# Sauvegarder pour les prochains démarrages
+	try:
+		vectorized_path.parent.mkdir(parents=True, exist_ok=True)
+		joblib.dump(PREPROCESSOR, vectorized_path)
+		print(f"✅ VectorizedPreprocessor sauvegardé → {vectorized_path}")
+	except Exception as e:
+		print(f"⚠️  Sauvegarde échouée (non bloquant) : {e}")
+
+	return PREPROCESSOR
+
+def _validate_payload(payload: Dict[str, Any]) -> Dict[str, Any]:
+	"""Basic validation on input payload.
+
+	Raises:
+		ValueError: If the payload is invalid.
+	"""
+	if not isinstance(payload, dict):
+		raise ValueError("Le JSON doit être un objet (clé/valeur).")
+
+	if not payload:
+		raise ValueError("Le JSON est vide.")
+
+	for key, value in payload.items():
+		# EXPLICATION : None est accepté (LightGBM gère nativement les NaN)
+		if value is not None and isinstance(value, (list, dict)):
+			raise ValueError(f"La valeur de '{key}' doit être scalaire.")
+
+	return payload
+
+
+def _parse_json_line(json_line: str) -> pd.DataFrame:
+	"""Parse a single JSON line into a one-row DataFrame."""
+	try:
+		raw = json.loads(json_line)
+	except json.JSONDecodeError as exc:
+		raise ValueError("JSON invalide. Vérifie la syntaxe.") from exc
+
+	payload = _validate_payload(raw)
+
+	# Build a single-row DataFrame and sanitize common problematic inputs:
+	# - convert empty strings to NaN so numeric coercion / imputation works
+	# - convert string booleans to actual booleans ("True"/"False")
+	df = pd.DataFrame([payload])
+	df = df.replace({"": np.nan, "True": True, "False": False})
+
+	# EXPLICATION : Sanitiser les noms de colonnes pour matcher ceux attendus par le modèle.
+	# Le modèle a été entraîné avec des noms sanitisés (espaces → _, caractères spéciaux → _).
+	# Sans cette étape, des colonnes comme "BURO_CREDIT_ACTIVE_Bad debt_MEAN" ne matchent pas
+	# "BURO_CREDIT_ACTIVE_Bad_debt_MEAN" → fill_value=0 → prédictions faussées (tout Accordé).
+	df.columns = [_re.sub(r'[^a-zA-Z0-9_]', '_', c.replace(' ', '_')) for c in df.columns]
+
+	# Force all columns to numeric dtypes (LightGBM rejects object/str columns).
+	# Booleans become 1/0, strings that are still present become NaN.
+	for col in df.columns:
+		df[col] = pd.to_numeric(df[col], errors='coerce')
+
+	# Try to apply a lightweight preprocessor to accept "raw" payloads
+	# The transformer maps categorical strings (ex. NAME_CONTRACT_TYPE) to the
+	# one-hot columns expected by the trained model. On any failure we keep the
+	# original dataframe and rely on column reindexing later.
+	#
+	# IMPORTANT: Skip preprocessor if input is already processed data (e.g. from
+	# features_train.csv / reference.csv). Detect this by checking how many input
+	# columns match expected model features. If >50% match, data is already
+	# processed — running the preprocessor would replace NaN with median values,
+	# destroying the signal that LightGBM uses for missing-value splits.
+	try:
+		pre = _load_preprocessor()
+		if pre is not None:
+			expected_feats = set(pre.get_feature_names_out()) if hasattr(pre, 'get_feature_names_out') else set()
+			overlap = len(set(df.columns) & expected_feats)
+			if expected_feats and overlap / len(expected_feats) > 0.5:
+				# Data is already processed — skip preprocessor to avoid double processing
+				pass
+			else:
+				df = pre.transform(df)
+	except Exception:
+		# Non-fatal: continue with the original df (alignment step will fill missing)
+		pass
+
+	return df
+
+
+def _get_model_feature_names(model) -> list | None:
+	"""Try to obtain the model's expected feature names.
+
+	Tries common LightGBM / sklearn attributes first, then falls back to
+	reading the header of `data/processed/features_train.csv`.
+	Returns a list of column names or None if not found.
+	"""
+	# 1) common LightGBM / sklearn attributes
+	try:
+		fn = getattr(model, "feature_name", None)
+		if callable(fn):
+			names = list(fn())
+			if names:
+				return names
+	except Exception:
+		pass
+
+	names = getattr(model, "feature_name_", None)
+	if isinstance(names, (list, tuple)):
+		return list(names)
+
+	# LightGBM scikit-learn wrapper exposes `booster_`
+	try:
+		if hasattr(model, "booster_") and getattr(model.booster_, "feature_name", None):
+			return list(model.booster_.feature_name())
+	except Exception:
+		pass
+
+	# 2) Fallback to header from the preprocessed training CSV
+	try:
+		header_path = Path("data/processed/features_train.csv")
+		if header_path.exists():
+			df_header = pd.read_csv(header_path, nrows=0)
+			cols = [c for c in df_header.columns if c not in ("SK_ID_CURR", "TARGET")]
+			# Apply same sanitization as training notebook (spaces → _, non-alnum → _)
+			cols = [_re.sub(r'[^a-zA-Z0-9_]', '_', c.replace(' ', '_')) for c in cols]
+			if cols:
+				return cols
+	except Exception:
+		pass
+
+	return None
+
+
+# EXPLICATION : Fonction helper pour logger chaque prédiction avec tous les champs requis
+# IMPORTANT : Écrit DIRECTEMENT dans le fichier (pas de FileHandler)
+# pour éviter les problèmes d'interférence avec Gradio/autres loggers
+def log_prediction(input_raw: str, input_features: dict, output_proba: float, 
+                   output_decision: str, execution_time_ms: float, error: str = None):
+	"""Log une prédiction au format JSON structuré dans logs/predictions.jsonl."""
+	try:
+		# Chemin absolu ancré sur app.py → fonctionne quel que soit le cwd de lancement
+		_log_dir = Path(__file__).resolve().parent / "logs"
+		_log_dir.mkdir(parents=True, exist_ok=True)
+		
+		# EXPLICATION : Construit l'entrée JSON
+		log_entry = {
+			"timestamp": datetime.utcnow().isoformat() + "Z",
+			"input_raw": input_raw,
+			"input_features": input_features,
+			"output_proba": round(output_proba, 4) if output_proba is not None else None,
+			"output_decision": output_decision,
+			"execution_time_ms": round(execution_time_ms, 1),
+			"error": error,
+			"model_version": "models:/LightGBM/Production",
+			"threshold": 0.4
+		}
+		
+		# EXPLICATION : Écrit DIRECTEMENT dans le fichier (robuste à Gradio)
+		# Mode "a" = append, newline assuré après chaque log
+		log_line = json.dumps(log_entry, ensure_ascii=False) + "\n"
+		log_file = _log_dir / "predictions.jsonl"
+		
+		with open(log_file, "a", encoding="utf-8") as f:
+			f.write(log_line)
+			f.flush()  # Force l'écriture immédiate (important pour le suivi en temps réel)
+		
+		# EXPLICATION : Aussi afficher dans la console pour Docker/HF Spaces
+		print(f"[LOG] {log_line.strip()}")
+		
+	except Exception as exc:
+		# EXPLICATION : N'échoue pas silencieusement si le logging échoue
+		print(f"[ERROR] Logging échoué : {exc}", flush=True)
+
+
+# === VERSION OPTIMISÉE 4.4 - Gain 15.7x ===
+# Remplace l'ancienne _predict (boucle ligne par ligne)
+# par une version vectorisée pandas : prétraitement en une seule opération.
+def _predict(json_line: str, threshold: float = 0.4) -> str:
+	"""Predict default probability and return a formatted response.
+
+	Version optimisée étape 4 (15.7x plus rapide - vectorisation pandas)
+	"""
+	# Capture du temps de début pour calculer execution_time_ms
+	start_time = time.perf_counter()
+
+	try:
+		# === ÉTAPE 1 : Validation JSON (fail-fast avant tout traitement) ===
+		try:
+			raw = json.loads(json_line)
+		except json.JSONDecodeError as exc:
+			raise ValueError("JSON invalide. Vérifie la syntaxe.") from exc
+		_validate_payload(raw)
+
+		# === ÉTAPE 2 : Preprocessing vectorisé (cœur du gain 15.7x) ===
+		# VectorizedPreprocessor.transform_one_sample construit le DataFrame
+		# depuis le dict en UNE seule opération pandas (pas de boucle).
+		prep = _load_preprocessor()
+		if prep is not None and isinstance(prep, VectorizedPreprocessor):
+			# Chemin optimisé : VectorizedPreprocessor (vectorisation pandas)
+			df = prep.transform_one_sample(json_line)
+		else:
+			# Fallback : ancien chemin (RawToModelTransformer ligne par ligne)
+			df = _parse_json_line(json_line)
+
+		# === ÉTAPE 3 : Alignement colonnes sur les features attendues du modèle ===
+		# fill_value=np.nan (pas 0) : LightGBM utilise ses splits natifs manquants
+		model = _load_model()
+		expected = _get_model_feature_names(model)
+		if expected:
+			df = df.reindex(columns=expected, fill_value=np.nan)
+
+		# Garantie finale : toutes les colonnes numériques (LightGBM requirement)
+		# NaN préservés — LightGBM les gère nativement.
+		for col in df.columns:
+			df[col] = pd.to_numeric(df[col], errors='coerce')
+
+		# === ÉTAPE 4 : Inférence LightGBM (predict_proba vectorisé) ===
+		try:
+			proba = float(model.predict_proba(df)[:, 1][0])
+		except AttributeError:
+			# Fallback pour les modèles exposant predict() retournant des probabilités
+			proba = float(model.predict(df)[0])
+
+		if not 0.0 <= proba <= 1.0:
+			raise ValueError("La probabilité prédite est hors de l'intervalle [0, 1].")
+
+		score = int(proba * 1000)
+		# Seuil de décision : < threshold = Accordé (risque faible)
+		decision = "Accordé" if proba < threshold else "Refusé"
+
+		# === ÉTAPE 5 : Log structuré de la prédiction réussie ===
+		execution_time_ms = (time.perf_counter() - start_time) * 1000
+		log_prediction(
+			input_raw=json_line,
+			input_features=raw,
+			output_proba=proba,
+			output_decision=decision,
+			execution_time_ms=execution_time_ms,
+			error=None
+		)
+
+		return (
+			f"Score: {score}\n"
+			f"Probabilité de défaut: {proba:.4f}\n"
+			f"Décision: {decision}"
+		)
+
+	except ValueError as exc:
+		# Log de l'erreur avec temps d'exécution et message d'erreur
+		execution_time_ms = (time.perf_counter() - start_time) * 1000
+		try:
+			input_features = json.loads(json_line)
+		except Exception:
+			input_features = {}
+		log_prediction(
+			input_raw=json_line,
+			input_features=input_features,
+			output_proba=None,
+			output_decision="Erreur",
+			execution_time_ms=execution_time_ms,
+			error=f"ValueError: {exc}"
+		)
+		return f"Erreur: {exc}"
+	except KeyError as exc:
+		execution_time_ms = (time.perf_counter() - start_time) * 1000
+		try:
+			input_features = json.loads(json_line)
+		except Exception:
+			input_features = {}
+		log_prediction(
+			input_raw=json_line,
+			input_features=input_features,
+			output_proba=None,
+			output_decision="Erreur",
+			execution_time_ms=execution_time_ms,
+			error=f"KeyError: {exc}"
+		)
+		return f"Erreur: colonne manquante ({exc})."
+	except TypeError as exc:
+		execution_time_ms = (time.perf_counter() - start_time) * 1000
+		try:
+			input_features = json.loads(json_line)
+		except Exception:
+			input_features = {}
+		log_prediction(
+			input_raw=json_line,
+			input_features=input_features,
+			output_proba=None,
+			output_decision="Erreur",
+			execution_time_ms=execution_time_ms,
+			error=f"TypeError: {exc}"
+		)
+		return f"Erreur: type invalide ({exc})."
+	except Exception as exc:  # noqa: BLE001
+		execution_time_ms = (time.perf_counter() - start_time) * 1000
+		try:
+			input_features = json.loads(json_line)
+		except Exception:
+			input_features = {}
+		log_prediction(
+			input_raw=json_line,
+			input_features=input_features,
+			output_proba=None,
+			output_decision="Erreur",
+			execution_time_ms=execution_time_ms,
+			error=f"Exception: {exc}"
+		)
+		return f"Erreur inattendue: {exc}"
+
+
+def build_demo() -> gr.Blocks:
+	"""Build and return the Gradio Blocks demo."""
+	with gr.Blocks(title="Credit Scoring API") as demo:
+		gr.Markdown(
+			"# Credit Scoring API\n"
+			"Saisis une seule ligne JSON avec les variables d'entrée.\n"
+			"Le modèle LightGBM retourne une probabilité de défaut, un score, et une décision.\n"
+			"*Version optimisée étape 4 (15.7x plus rapide - vectorisation pandas)*"
+		)
+
+		with gr.Row():
+			input_json = gr.Textbox(
+				label="JSON (ligne unique)",
+				lines=12,
+				max_lines=30,
+				placeholder='{"feature1": value1, "feature2": value2, ...}'
+			)
+
+		output_text = gr.Textbox(
+			label="Résultat",
+			lines=5,
+		)
+
+		predict_btn = gr.Button("Prédire")
+		predict_btn.click(
+			fn=_predict,
+			inputs=[input_json],
+			outputs=[output_text],
+		)
+
+		gr.Markdown(
+			"**Note:** Le seuil de décision est fixé à 0.4 par défaut."
+		)
+
+	return demo
+
+
+demo = build_demo()
+
+if __name__ == "__main__":
+	demo.launch(
+		server_name="0.0.0.0",
+		server_port=int(os.environ.get("PORT", 7860)),
+	)

models/export_model.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import mlflow
+import mlflow.lightgbm
+from mlflow.tracking import MlflowClient
+
+try:
+	from src.mlflow_config import DEFAULT_EXPERIMENT_NAME
+except Exception:  # pragma: no cover - fallback si import impossible
+	DEFAULT_EXPERIMENT_NAME = "OC_P6_Credit_Scoring"
+
+# Nom du modèle enregistré et stage cible
+MODEL_NAME = "LightGBM"
+MODEL_STAGE = "Production"
+
+
+def resolve_tracking_uri() -> str:
+	env_uri = os.getenv("MLFLOW_TRACKING_URI")
+	if env_uri:
+		return env_uri
+	local_store = Path("mlruns")
+	if local_store.exists():
+		return local_store.resolve().as_uri()
+	return mlflow.get_tracking_uri()
+
+
+tracking_uri = resolve_tracking_uri()
+mlflow.set_tracking_uri(tracking_uri)
+
+client = MlflowClient()
+model_uri = None
+
+# 1) Essaye le Model Registry avec stage (si présent)
+try:
+	latest_versions = client.get_latest_versions(MODEL_NAME, stages=[MODEL_STAGE])
+	if latest_versions:
+		model_version = latest_versions[0].version
+		model_uri = f"models:/{MODEL_NAME}/{model_version}"
+except Exception:
+	model_uri = None
+
+# 2) Sinon, prend la dernière version enregistrée (tous stages)
+if model_uri is None:
+	try:
+		versions = client.search_model_versions(f"name='{MODEL_NAME}'")
+		if versions:
+			latest = max(versions, key=lambda v: int(v.version))
+			model_uri = f"models:/{MODEL_NAME}/{latest.version}"
+	except Exception:
+		model_uri = None
+
+# 3) Sinon, fallback sur le dernier run de l'expérience
+if model_uri is None:
+	experiment_name = os.getenv("MLFLOW_EXPERIMENT_NAME", DEFAULT_EXPERIMENT_NAME)
+	experiment = mlflow.get_experiment_by_name(experiment_name)
+	if experiment:
+		runs = mlflow.search_runs(
+			[experiment.experiment_id],
+			order_by=["start_time DESC"],
+			max_results=1,
+		)
+		if not runs.empty:
+			run_id = runs.loc[0, "run_id"]
+			model_uri = f"runs:/{run_id}/model"
+
+if model_uri is None:
+	raise RuntimeError(
+		"Aucun modèle trouvé. Vérifie MLFLOW_TRACKING_URI, le Model Registry, "
+		"ou l'expérience MLflow."
+	)
+
+# Charge et sauvegarde en fichier simple
+model = mlflow.lightgbm.load_model(model_uri)
+output_path = Path("models") / "lightgbm.txt"
+output_path.parent.mkdir(parents=True, exist_ok=True)
+model.save_model(str(output_path))
+
+print(f"Modèle exporté depuis {model_uri} vers {output_path}")

models/export_preprocessor.py

@@ -0,0 +1,19 @@
+"""Create and persist the preprocessing transformer used by the API.
+
+Run this script after you change `data/processed/features_train.csv` to refresh the
+serialized preprocessor at `models/preprocessor.joblib`.
+"""
+from pathlib import Path
+import joblib
+
+from src.preprocessing import RawToModelTransformer
+
+MODEL_DIR = Path("models")
+MODEL_DIR.mkdir(parents=True, exist_ok=True)
+PREPROC_PATH = MODEL_DIR / "preprocessor.joblib"
+
+pre = RawToModelTransformer()
+print(f"Inferred {len(pre.get_feature_names_out())} expected features")
+
+joblib.dump(pre, PREPROC_PATH)
+print(f"Preprocessor saved to {PREPROC_PATH.resolve()}")

notebooks/02_preparation_features.ipynb

@@ -0,0 +1,1114 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6083b4ff",
+   "metadata": {},
+   "source": [
+    "# 02 - Préparation des features (Feature Engineering)\n",
+    "\n",
+    "Ce notebook implémente le processus complet de préparation des données pour le projet Home Credit Default Risk.\n",
+    "\n",
+    "**Objectifs principaux :**\n",
+    "- Charger et fusionner toutes les tables de données\n",
+    "- Créer des features (caractéristiques) pertinentes par agrégation\n",
+    "- Encoder les variables catégorielles\n",
+    "- Préparer le jeu de données final pour la modélisation\n",
+    "\n",
+    "**Approche utilisée :**\n",
+    "- Fonction modulaire pour chaque table de données\n",
+    "- Agrégations statistiques (min, max, mean, sum, var) sur les données groupées\n",
+    "- Création de ratios et pourcentages entre variables importantes\n",
+    "- Features spécifiques pour les crédits actifs/fermés et les demandes approuvées/refusées"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ec6ca912",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Bibliothèques importées avec succès\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import des bibliothèques nécessaires\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import gc  # Garbage collector pour libérer la mémoire\n",
+    "import time\n",
+    "from contextlib import contextmanager\n",
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+    "\n",
+    "print(\"✓ Bibliothèques importées avec succès\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2eafa35c",
+   "metadata": {},
+   "source": [
+    "## 1. Fonctions utilitaires\n",
+    "\n",
+    "Nous commençons par définir des fonctions helper qui seront utilisées tout au long du notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "790000b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fonction pour mesurer le temps d'exécution\n",
+    "@contextmanager\n",
+    "def timer(title):\n",
+    "    \"\"\"\n",
+    "    Context manager pour mesurer le temps d'exécution d'un bloc de code.\n",
+    "    Usage: with timer(\"Mon processus\"):\n",
+    "               # code à mesurer\n",
+    "    \"\"\"\n",
+    "    t0 = time.time()\n",
+    "    yield\n",
+    "    print(\"{} - terminé en {:.0f}s\".format(title, time.time() - t0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f36b5c19",
+   "metadata": {},
+   "source": [
+    "### Encodage One-Hot des variables catégorielles\n",
+    "\n",
+    "Le One-Hot encoding transforme les variables catégorielles en colonnes binaires (0 ou 1).\n",
+    "Par exemple, si une colonne \"Couleur\" contient [\"Rouge\", \"Bleu\"], elle sera transformée en deux colonnes : \"Couleur_Rouge\" et \"Couleur_Bleu\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b02ee9c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def one_hot_encoder(df, nan_as_category=True):\n",
+    "    \"\"\"\n",
+    "    Applique le One-Hot encoding aux colonnes catégorielles.\n",
+    "    \n",
+    "    Paramètres:\n",
+    "    -----------\n",
+    "    df : DataFrame\n",
+    "        Le DataFrame à encoder\n",
+    "    nan_as_category : bool\n",
+    "        Si True, les valeurs manquantes (NaN) sont traitées comme une catégorie à part\n",
+    "    \n",
+    "    Retourne:\n",
+    "    ---------\n",
+    "    df : DataFrame encodé\n",
+    "    new_columns : liste des nouvelles colonnes créées\n",
+    "    \"\"\"\n",
+    "    original_columns = list(df.columns)\n",
+    "    # Identifier les colonnes avec type 'object' (chaînes de caractères = catégorielles)\n",
+    "    categorical_columns = [col for col in df.columns if df[col].dtype == 'object']\n",
+    "    # Appliquer pd.get_dummies pour créer les colonnes binaires\n",
+    "    df = pd.get_dummies(df, columns=categorical_columns, dummy_na=nan_as_category)\n",
+    "    # Retourner aussi la liste des nouvelles colonnes créées\n",
+    "    new_columns = [c for c in df.columns if c not in original_columns]\n",
+    "    return df, new_columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9acd8b9",
+   "metadata": {},
+   "source": [
+    "## 2. Traitement de application_train.csv et application_test.csv\n",
+    "\n",
+    "Ces fichiers contiennent les informations principales sur chaque demande de crédit (données du client, montants, etc.)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3945bb46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def application_train_test(num_rows=None, nan_as_category=False):\n",
+    "    \"\"\"\n",
+    "    Charge et prétraite les données d'application (train + test).\n",
+    "    \n",
+    "    Étapes :\n",
+    "    1. Charge les fichiers train et test\n",
+    "    2. Fusionne les deux datasets\n",
+    "    3. Nettoie les données (suppression de valeurs aberrantes)\n",
+    "    4. Encode les variables catégorielles\n",
+    "    5. Crée de nouvelles features (ratios, pourcentages)\n",
+    "    \"\"\"\n",
+    "    # Chargement des données\n",
+    "    df = pd.read_csv('../data/raw/application_train.csv', nrows=num_rows)\n",
+    "    test_df = pd.read_csv('../data/raw/application_test.csv', nrows=num_rows)\n",
+    "    print(\"Échantillons train: {}, test: {}\".format(len(df), len(test_df)))\n",
+    "    \n",
+    "    # Fusionner train et test pour appliquer les mêmes transformations\n",
+    "    # Note: Utiliser pd.concat() au lieu de .append() (deprecated dans pandas 2.0+)\n",
+    "    df = pd.concat([df, test_df], ignore_index=True)\n",
+    "    \n",
+    "    # Nettoyage : Supprimer les 4 applications avec CODE_GENDER = 'XNA' (valeur aberrante)\n",
+    "    df = df[df['CODE_GENDER'] != 'XNA']\n",
+    "    \n",
+    "    # Encodage binaire (0 ou 1) pour les features avec seulement 2 catégories\n",
+    "    for bin_feature in ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']:\n",
+    "        df[bin_feature], uniques = pd.factorize(df[bin_feature])\n",
+    "    \n",
+    "    # One-Hot encoding pour les autres features catégorielles\n",
+    "    df, cat_cols = one_hot_encoder(df, nan_as_category)\n",
+    "    \n",
+    "    # Nettoyage : La valeur 365243 pour DAYS_EMPLOYED est une valeur sentinel (code pour \"inconnu\")\n",
+    "    # On la remplace par NaN\n",
+    "    df['DAYS_EMPLOYED'].replace(365243, np.nan, inplace=True)\n",
+    "    \n",
+    "    # Création de nouvelles features (ratios et pourcentages)\n",
+    "    # Ces ratios sont souvent plus informatifs que les valeurs absolues\n",
+    "    \n",
+    "    # Pourcentage d'emploi par rapport à l'âge\n",
+    "    df['DAYS_EMPLOYED_PERC'] = df['DAYS_EMPLOYED'] / df['DAYS_BIRTH']\n",
+    "    \n",
+    "    # Pourcentage du crédit par rapport au revenu\n",
+    "    df['INCOME_CREDIT_PERC'] = df['AMT_INCOME_TOTAL'] / df['AMT_CREDIT']\n",
+    "    \n",
+    "    # Revenu par personne dans le foyer\n",
+    "    df['INCOME_PER_PERSON'] = df['AMT_INCOME_TOTAL'] / df['CNT_FAM_MEMBERS']\n",
+    "    \n",
+    "    # Pourcentage de l'annuité par rapport au revenu (capacité de remboursement)\n",
+    "    df['ANNUITY_INCOME_PERC'] = df['AMT_ANNUITY'] / df['AMT_INCOME_TOTAL']\n",
+    "    \n",
+    "    # Taux de paiement : annuité / montant du crédit\n",
+    "    df['PAYMENT_RATE'] = df['AMT_ANNUITY'] / df['AMT_CREDIT']\n",
+    "    \n",
+    "    # Libération de la mémoire\n",
+    "    del test_df\n",
+    "    gc.collect()\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c64f7dff",
+   "metadata": {},
+   "source": [
+    "## 3. Traitement de bureau.csv et bureau_balance.csv\n",
+    "\n",
+    "**bureau.csv** : Historique des crédits antérieurs du client auprès d'autres institutions financières  \n",
+    "**bureau_balance.csv** : Historique mensuel des soldes pour ces crédits bureau\n",
+    "\n",
+    "**Stratégie :**\n",
+    "- Agréger bureau_balance au niveau bureau (une ligne par crédit)\n",
+    "- Créer des features distinctes pour les crédits ACTIFS vs FERMÉS\n",
+    "- Agréger au niveau client (SK_ID_CURR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0f2323dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bureau_and_balance(num_rows=None, nan_as_category=True):\n",
+    "    \"\"\"\n",
+    "    Traite les données bureau (crédits externes du client).\n",
+    "    \n",
+    "    Étapes :\n",
+    "    1. Charge bureau et bureau_balance\n",
+    "    2. Agrège bureau_balance par crédit (SK_ID_BUREAU)\n",
+    "    3. Fusionne avec bureau\n",
+    "    4. Crée des agrégations générales par client\n",
+    "    5. Crée des features spécifiques pour crédits actifs\n",
+    "    6. Crée des features spécifiques pour crédits fermés\n",
+    "    \"\"\"\n",
+    "    # Chargement des données\n",
+    "    bureau = pd.read_csv('../data/raw/bureau.csv', nrows=num_rows)\n",
+    "    bb = pd.read_csv('../data/raw/bureau_balance.csv', nrows=num_rows)\n",
+    "    \n",
+    "    # Encodage des variables catégorielles\n",
+    "    bb, bb_cat = one_hot_encoder(bb, nan_as_category)\n",
+    "    bureau, bureau_cat = one_hot_encoder(bureau, nan_as_category)\n",
+    "    \n",
+    "    # === BUREAU BALANCE : Agrégation au niveau crédit ===\n",
+    "    # Pour chaque crédit (SK_ID_BUREAU), on calcule des statistiques sur les mois\n",
+    "    bb_aggregations = {'MONTHS_BALANCE': ['min', 'max', 'size']}\n",
+    "    # Pour chaque colonne catégorielle encodée, on calcule la moyenne\n",
+    "    for col in bb_cat:\n",
+    "        bb_aggregations[col] = ['mean']\n",
+    "    \n",
+    "    bb_agg = bb.groupby('SK_ID_BUREAU').agg(bb_aggregations)\n",
+    "    # Renommer les colonnes pour indiquer la provenance\n",
+    "    bb_agg.columns = pd.Index([e[0] + \"_\" + e[1].upper() for e in bb_agg.columns.tolist()])\n",
+    "    \n",
+    "    # Joindre les agrégations de bureau_balance à bureau\n",
+    "    bureau = bureau.join(bb_agg, how='left', on='SK_ID_BUREAU')\n",
+    "    bureau.drop(['SK_ID_BUREAU'], axis=1, inplace=True)\n",
+    "    \n",
+    "    del bb, bb_agg\n",
+    "    gc.collect()\n",
+    "    \n",
+    "    # === BUREAU : Agrégations numériques ===\n",
+    "    # Définir les agrégations à calculer pour chaque feature numérique\n",
+    "    num_aggregations = {\n",
+    "        'DAYS_CREDIT': ['min', 'max', 'mean', 'var'],\n",
+    "        'DAYS_CREDIT_ENDDATE': ['min', 'max', 'mean'],\n",
+    "        'DAYS_CREDIT_UPDATE': ['mean'],\n",
+    "        'CREDIT_DAY_OVERDUE': ['max', 'mean'],\n",
+    "        'AMT_CREDIT_MAX_OVERDUE': ['mean'],\n",
+    "        'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],\n",
+    "        'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],\n",
+    "        'AMT_CREDIT_SUM_OVERDUE': ['mean'],\n",
+    "        'AMT_CREDIT_SUM_LIMIT': ['mean', 'sum'],\n",
+    "        'AMT_ANNUITY': ['max', 'mean'],\n",
+    "        'CNT_CREDIT_PROLONG': ['sum'],\n",
+    "        'MONTHS_BALANCE_MIN': ['min'],\n",
+    "        'MONTHS_BALANCE_MAX': ['max'],\n",
+    "        'MONTHS_BALANCE_SIZE': ['mean', 'sum']\n",
+    "    }\n",
+    "    \n",
+    "    # === BUREAU : Agrégations catégorielles ===\n",
+    "    cat_aggregations = {}\n",
+    "    for cat in bureau_cat:\n",
+    "        cat_aggregations[cat] = ['mean']\n",
+    "    for cat in bb_cat:\n",
+    "        cat_aggregations[cat + \"_MEAN\"] = ['mean']\n",
+    "    \n",
+    "    # Agrégation générale par client (SK_ID_CURR)\n",
+    "    bureau_agg = bureau.groupby('SK_ID_CURR').agg({**num_aggregations, **cat_aggregations})\n",
+    "    bureau_agg.columns = pd.Index(['BURO_' + e[0] + \"_\" + e[1].upper() for e in bureau_agg.columns.tolist()])\n",
+    "    \n",
+    "    # === CRÉDITS ACTIFS : Features spécifiques ===\n",
+    "    # Filtrer uniquement les crédits actifs et créer des agrégations spécifiques\n",
+    "    active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1]\n",
+    "    active_agg = active.groupby('SK_ID_CURR').agg(num_aggregations)\n",
+    "    active_agg.columns = pd.Index(['ACTIVE_' + e[0] + \"_\" + e[1].upper() for e in active_agg.columns.tolist()])\n",
+    "    bureau_agg = bureau_agg.join(active_agg, how='left', on='SK_ID_CURR')\n",
+    "    \n",
+    "    del active, active_agg\n",
+    "    gc.collect()\n",
+    "    \n",
+    "    # === CRÉDITS FERMÉS : Features spécifiques ===\n",
+    "    # Même logique pour les crédits fermés\n",
+    "    closed = bureau[bureau['CREDIT_ACTIVE_Closed'] == 1]\n",
+    "    closed_agg = closed.groupby('SK_ID_CURR').agg(num_aggregations)\n",
+    "    closed_agg.columns = pd.Index(['CLOSED_' + e[0] + \"_\" + e[1].upper() for e in closed_agg.columns.tolist()])\n",
+    "    bureau_agg = bureau_agg.join(closed_agg, how='left', on='SK_ID_CURR')\n",
+    "    \n",
+    "    del closed, closed_agg, bureau\n",
+    "    gc.collect()\n",
+    "    \n",
+    "    return bureau_agg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "614f1115",
+   "metadata": {},
+   "source": [
+    "## 4. Traitement de previous_application.csv\n",
+    "\n",
+    "Ce fichier contient toutes les demandes de crédit précédentes du client chez Home Credit.\n",
+    "\n",
+    "**Stratégie :**\n",
+    "- Créer des agrégations générales\n",
+    "- Features spécifiques pour demandes APPROUVÉES\n",
+    "- Features spécifiques pour demandes REFUSÉES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "26379308",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def previous_applications(num_rows=None, nan_as_category=True):\n",
+    "    \"\"\"\n",
+    "    Traite les demandes de crédit précédentes.\n",
+    "    \n",
+    "    Étapes :\n",
+    "    1. Charge previous_application\n",
+    "    2. Nettoie les valeurs sentinelles (365243 = inconnu)\n",
+    "    3. Crée de nouvelles features (ratios)\n",
+    "    4. Agrégations générales par client\n",
+    "    5. Features spécifiques pour demandes approuvées\n",
+    "    6. Features spécifiques pour demandes refusées\n",
+    "    \"\"\"\n",
+    "    # Chargement des données\n",
+    "    prev = pd.read_csv('../data/raw/previous_application.csv', nrows=num_rows)\n",
+    "    prev, cat_cols = one_hot_encoder(prev, nan_as_category=True)\n",
+    "    \n",
+    "    # Nettoyage : Remplacer les valeurs sentinel 365243 par NaN\n",
+    "    prev['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace=True)\n",
+    "    prev['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace=True)\n",
+    "    prev['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace=True)\n",
+    "    prev['DAYS_LAST_DUE'].replace(365243, np.nan, inplace=True)\n",
+    "    prev['DAYS_TERMINATION'].replace(365243, np.nan, inplace=True)\n",
+    "    \n",
+    "    # Nouvelle feature : Pourcentage entre montant demandé et montant reçu\n",
+    "    # Indique si le client a obtenu ce qu'il demandait\n",
+    "    prev['APP_CREDIT_PERC'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT']\n",
+    "    \n",
+    "    # === Agrégations numériques ===\n",
+    "    num_aggregations = {\n",
+    "        'AMT_ANNUITY': ['min', 'max', 'mean'],\n",
+    "        'AMT_APPLICATION': ['min', 'max', 'mean'],\n",
+    "        'AMT_CREDIT': ['min', 'max', 'mean'],\n",
+    "        'APP_CREDIT_PERC': ['min', 'max', 'mean', 'var'],\n",
+    "        'AMT_DOWN_PAYMENT': ['min', 'max', 'mean'],\n",
+    "        'AMT_GOODS_PRICE': ['min', 'max', 'mean'],\n",
+    "        'HOUR_APPR_PROCESS_START': ['min', 'max', 'mean'],\n",
+    "        'RATE_DOWN_PAYMENT': ['min', 'max', 'mean'],\n",
+    "        'DAYS_DECISION': ['min', 'max', 'mean'],\n",
+    "        'CNT_PAYMENT': ['mean', 'sum'],\n",
+    "    }\n",
+    "    \n",
+    "    # === Agrégations catégorielles ===\n",
+    "    cat_aggregations = {}\n",
+    "    for cat in cat_cols:\n",
+    "        cat_aggregations[cat] = ['mean']\n",
+    "    \n",
+    "    # Agrégation générale par client\n",
+    "    prev_agg = prev.groupby('SK_ID_CURR').agg({**num_aggregations, **cat_aggregations})\n",
+    "    prev_agg.columns = pd.Index(['PREV_' + e[0] + \"_\" + e[1].upper() for e in prev_agg.columns.tolist()])\n",
+    "    \n",
+    "    # === DEMANDES APPROUVÉES : Features spécifiques ===\n",
+    "    approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]\n",
+    "    approved_agg = approved.groupby('SK_ID_CURR').agg(num_aggregations)\n",
+    "    approved_agg.columns = pd.Index(['APPROVED_' + e[0] + \"_\" + e[1].upper() for e in approved_agg.columns.tolist()])\n",
+    "    prev_agg = prev_agg.join(approved_agg, how='left', on='SK_ID_CURR')\n",
+    "    \n",
+    "    # === DEMANDES REFUSÉES : Features spécifiques ===\n",
+    "    refused = prev[prev['NAME_CONTRACT_STATUS_Refused'] == 1]\n",
+    "    refused_agg = refused.groupby('SK_ID_CURR').agg(num_aggregations)\n",
+    "    refused_agg.columns = pd.Index(['REFUSED_' + e[0] + \"_\" + e[1].upper() for e in refused_agg.columns.tolist()])\n",
+    "    prev_agg = prev_agg.join(refused_agg, how='left', on='SK_ID_CURR')\n",
+    "    \n",
+    "    del refused, refused_agg, approved, approved_agg, prev\n",
+    "    gc.collect()\n",
+    "    \n",
+    "    return prev_agg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b440c44",
+   "metadata": {},
+   "source": [
+    "## 5. Traitement de POS_CASH_balance.csv\n",
+    "\n",
+    "Ce fichier contient les historiques mensuels des soldes pour les crédits POS (Point of Sale) et CASH."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d6cbe990",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pos_cash(num_rows=None, nan_as_category=True):\n",
+    "    \"\"\"\n",
+    "    Traite les données de soldes POS et CASH.\n",
+    "    \n",
+    "    Agrège les informations mensuelles au niveau client :\n",
+    "    - Nombre de mois d'historique\n",
+    "    - Retards de paiement (DPD = Days Past Due)\n",
+    "    - Distribution des statuts de paiement\n",
+    "    \"\"\"\n",
+    "    # Chargement des données\n",
+    "    pos = pd.read_csv('../data/raw/POS_CASH_balance.csv', nrows=num_rows)\n",
+    "    pos, cat_cols = one_hot_encoder(pos, nan_as_category=True)\n",
+    "    \n",
+    "    # === Agrégations ===\n",
+    "    aggregations = {\n",
+    "        'MONTHS_BALANCE': ['max', 'mean', 'size'],  # size = nombre de mois\n",
+    "        'SK_DPD': ['max', 'mean'],                   # Jours de retard\n",
+    "        'SK_DPD_DEF': ['max', 'mean']                # Jours de retard (définition alternative)\n",
+    "    }\n",
+    "    \n",
+    "    # Agrégations pour les colonnes catégorielles\n",
+    "    for cat in cat_cols:\n",
+    "        aggregations[cat] = ['mean']\n",
+    "    \n",
+    "    pos_agg = pos.groupby('SK_ID_CURR').agg(aggregations)\n",
+    "    pos_agg.columns = pd.Index(['POS_' + e[0] + \"_\" + e[1].upper() for e in pos_agg.columns.tolist()])\n",
+    "    \n",
+    "    # Compter le nombre de comptes POS CASH pour chaque client\n",
+    "    pos_agg['POS_COUNT'] = pos.groupby('SK_ID_CURR').size()\n",
+    "    \n",
+    "    del pos\n",
+    "    gc.collect()\n",
+    "    \n",
+    "    return pos_agg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa798b95",
+   "metadata": {},
+   "source": [
+    "## 6. Traitement de installments_payments.csv\n",
+    "\n",
+    "Ce fichier contient l'historique de remboursement des versements précédents (installments).  \n",
+    "**Idée clé :** Comparer ce qui devait être payé (AMT_INSTALMENT) avec ce qui a réellement été payé (AMT_PAYMENT)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "afce104d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def installments_payments(num_rows=None, nan_as_category=True):\n",
+    "    \"\"\"\n",
+    "    Traite l'historique des paiements par versements.\n",
+    "    \n",
+    "    Crée des features pour mesurer le comportement de paiement :\n",
+    "    - DPD : Days Past Due (jours de retard)\n",
+    "    - DBD : Days Before Due (jours d'avance)\n",
+    "    - PAYMENT_PERC : Pourcentage payé vs attendu\n",
+    "    - PAYMENT_DIFF : Différence entre attendu et payé\n",
+    "    \"\"\"\n",
+    "    # Chargement des données\n",
+    "    ins = pd.read_csv('../data/raw/installments_payments.csv', nrows=num_rows)\n",
+    "    ins, cat_cols = one_hot_encoder(ins, nan_as_category=True)\n",
+    "    \n",
+    "    # === Nouvelles features de comportement de paiement ===\n",
+    "    \n",
+    "    # Pourcentage payé par rapport au montant prévu\n",
+    "    ins['PAYMENT_PERC'] = ins['AMT_PAYMENT'] / ins['AMT_INSTALMENT']\n",
+    "    \n",
+    "    # Différence entre montant prévu et montant payé (positif = sous-paiement)\n",
+    "    ins['PAYMENT_DIFF'] = ins['AMT_INSTALMENT'] - ins['AMT_PAYMENT']\n",
+    "    \n",
+    "    # DPD : Days Past Due = nombre de jours de retard (seulement valeurs positives)\n",
+    "    ins['DPD'] = ins['DAYS_ENTRY_PAYMENT'] - ins['DAYS_INSTALMENT']\n",
+    "    ins['DPD'] = ins['DPD'].apply(lambda x: x if x > 0 else 0)\n",
+    "    \n",
+    "    # DBD : Days Before Due = nombre de jours d'avance (seulement valeurs positives)\n",
+    "    ins['DBD'] = ins['DAYS_INSTALMENT'] - ins['DAYS_ENTRY_PAYMENT']\n",
+    "    ins['DBD'] = ins['DBD'].apply(lambda x: x if x > 0 else 0)\n",
+    "    \n",
+    "    # === Agrégations ===\n",
+    "    aggregations = {\n",
+    "        'NUM_INSTALMENT_VERSION': ['nunique'],      # Nombre de versions différentes\n",
+    "        'DPD': ['max', 'mean', 'sum'],              # Statistiques sur les retards\n",
+    "        'DBD': ['max', 'mean', 'sum'],              # Statistiques sur les avances\n",
+    "        'PAYMENT_PERC': ['max', 'mean', 'sum', 'var'],  # Comportement de paiement\n",
+    "        'PAYMENT_DIFF': ['max', 'mean', 'sum', 'var'],\n",
+    "        'AMT_INSTALMENT': ['max', 'mean', 'sum'],\n",
+    "        'AMT_PAYMENT': ['min', 'max', 'mean', 'sum'],\n",
+    "        'DAYS_ENTRY_PAYMENT': ['max', 'mean', 'sum']\n",
+    "    }\n",
+    "    \n",
+    "    for cat in cat_cols:\n",
+    "        aggregations[cat] = ['mean']\n",
+    "    \n",
+    "    ins_agg = ins.groupby('SK_ID_CURR').agg(aggregations)\n",
+    "    ins_agg.columns = pd.Index(['INSTAL_' + e[0] + \"_\" + e[1].upper() for e in ins_agg.columns.tolist()])\n",
+    "    \n",
+    "    # Compter le nombre de versements pour chaque client\n",
+    "    ins_agg['INSTAL_COUNT'] = ins.groupby('SK_ID_CURR').size()\n",
+    "    \n",
+    "    del ins\n",
+    "    gc.collect()\n",
+    "    \n",
+    "    return ins_agg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb6d85ea",
+   "metadata": {},
+   "source": [
+    "## 7. Traitement de credit_card_balance.csv\n",
+    "\n",
+    "Ce fichier contient les historiques mensuels des soldes de cartes de crédit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9461c88a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def credit_card_balance(num_rows=None, nan_as_category=True):\n",
+    "    \"\"\"\n",
+    "    Traite les données de soldes de cartes de crédit.\n",
+    "    \n",
+    "    Stratégie :\n",
+    "    - Agrégations numériques classiques sur les colonnes numériques\n",
+    "    - Agrégations catégorielles adaptées (proportions par statut)\n",
+    "    \"\"\"\n",
+    "    # Chargement des données\n",
+    "    cc = pd.read_csv('../data/raw/credit_card_balance.csv', nrows=num_rows)\n",
+    "    \n",
+    "    # On n'a pas besoin de SK_ID_PREV pour les agrégations finales\n",
+    "    cc.drop(['SK_ID_PREV'], axis=1, inplace=True)\n",
+    "    \n",
+    "    # === Agrégations numériques ===\n",
+    "    numeric_cols = cc.select_dtypes(exclude=['object']).columns.tolist()\n",
+    "    numeric_cols = [c for c in numeric_cols if c != 'SK_ID_CURR']\n",
+    "    num_agg = {col: ['min', 'max', 'mean', 'sum', 'var'] for col in numeric_cols}\n",
+    "    cc_num_agg = cc.groupby('SK_ID_CURR').agg(num_agg)\n",
+    "    cc_num_agg.columns = pd.Index(['CC_' + e[0] + \"_\" + e[1].upper() for e in cc_num_agg.columns.tolist()])\n",
+    "    \n",
+    "    # === Agrégations catégorielles ===\n",
+    "    if 'NAME_CONTRACT_STATUS' in cc.columns:\n",
+    "        if nan_as_category:\n",
+    "            cc['NAME_CONTRACT_STATUS'] = cc['NAME_CONTRACT_STATUS'].fillna('Unknown')\n",
+    "        \n",
+    "        # Créer un crosstab avec proportions\n",
+    "        cc_cat_agg = pd.crosstab(\n",
+    "            cc['SK_ID_CURR'], \n",
+    "            cc['NAME_CONTRACT_STATUS'], \n",
+    "            normalize='index'\n",
+    "        ).fillna(0)\n",
+    "        cc_cat_agg.columns = ['CC_STATUS_' + str(col) for col in cc_cat_agg.columns]\n",
+    "        cc_agg = cc_num_agg.join(cc_cat_agg, how='left')\n",
+    "    else:\n",
+    "        cc_agg = cc_num_agg\n",
+    "    \n",
+    "    # Compter le nombre de lignes (mois) de carte de crédit par client\n",
+    "    cc_agg['CC_COUNT'] = cc.groupby('SK_ID_CURR').size()\n",
+    "    \n",
+    "    del cc\n",
+    "    gc.collect()\n",
+    "    \n",
+    "    return cc_agg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "440e0782",
+   "metadata": {},
+   "source": [
+    "## 8. Fonction principale : Fusion de toutes les données\n",
+    "\n",
+    "Cette fonction orchestre tout le processus :\n",
+    "1. Charge et traite les données principales (application)\n",
+    "2. Charge et fusionne chaque table secondaire\n",
+    "3. Retourne le DataFrame final prêt pour la modélisation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1b341561",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prepare_full_dataset(debug=False):\n",
+    "    \"\"\"\n",
+    "    Fonction principale qui orchestre toute la préparation des données.\n",
+    "    \n",
+    "    Paramètres:\n",
+    "    -----------\n",
+    "    debug : bool\n",
+    "        Si True, charge seulement 10000 lignes de chaque fichier (pour tests rapides)\n",
+    "    \n",
+    "    Retourne:\n",
+    "    ---------\n",
+    "    df : DataFrame complet avec toutes les features\n",
+    "    \"\"\"\n",
+    "    # En mode debug, on limite le nombre de lignes pour aller plus vite\n",
+    "    num_rows = 10000 if debug else None\n",
+    "    \n",
+    "    # === 1. Charger les données principales ===\n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"ÉTAPE 1 : Chargement des données application (train + test)\")\n",
+    "    print(\"=\"*80)\n",
+    "    df = application_train_test(num_rows)\n",
+    "    print(f\"✓ Shape après application : {df.shape}\")\n",
+    "    \n",
+    "    # === 2. Bureau et bureau_balance ===\n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"ÉTAPE 2 : Traitement des données Bureau (crédits externes)\")\n",
+    "    print(\"=\"*80)\n",
+    "    with timer(\"Traitement bureau et bureau_balance\"):\n",
+    "        bureau = bureau_and_balance(num_rows)\n",
+    "        print(f\"   Bureau shape: {bureau.shape}\")\n",
+    "        df = df.join(bureau, how='left', on='SK_ID_CURR')\n",
+    "        del bureau\n",
+    "        gc.collect()\n",
+    "    print(f\"✓ Shape après fusion bureau : {df.shape}\")\n",
+    "    \n",
+    "    # === 3. Previous applications ===\n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"ÉTAPE 3 : Traitement des demandes précédentes\")\n",
+    "    print(\"=\"*80)\n",
+    "    with timer(\"Traitement previous_applications\"):\n",
+    "        prev = previous_applications(num_rows)\n",
+    "        print(f\"   Previous applications shape: {prev.shape}\")\n",
+    "        df = df.join(prev, how='left', on='SK_ID_CURR')\n",
+    "        del prev\n",
+    "        gc.collect()\n",
+    "    print(f\"✓ Shape après fusion previous : {df.shape}\")\n",
+    "    \n",
+    "    # === 4. POS-CASH balance ===\n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"ÉTAPE 4 : Traitement des soldes POS-CASH\")\n",
+    "    print(\"=\"*80)\n",
+    "    with timer(\"Traitement POS-CASH balance\"):\n",
+    "        pos = pos_cash(num_rows)\n",
+    "        print(f\"   Pos-cash balance shape: {pos.shape}\")\n",
+    "        df = df.join(pos, how='left', on='SK_ID_CURR')\n",
+    "        del pos\n",
+    "        gc.collect()\n",
+    "    print(f\"✓ Shape après fusion POS : {df.shape}\")\n",
+    "    \n",
+    "    # === 5. Installments payments ===\n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"ÉTAPE 5 : Traitement des paiements par versements\")\n",
+    "    print(\"=\"*80)\n",
+    "    with timer(\"Traitement installments payments\"):\n",
+    "        ins = installments_payments(num_rows)\n",
+    "        print(f\"   Installments payments shape: {ins.shape}\")\n",
+    "        df = df.join(ins, how='left', on='SK_ID_CURR')\n",
+    "        del ins\n",
+    "        gc.collect()\n",
+    "    print(f\"✓ Shape après fusion installments : {df.shape}\")\n",
+    "    \n",
+    "    # === 6. Credit card balance ===\n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"ÉTAPE 6 : Traitement des soldes de cartes de crédit\")\n",
+    "    print(\"=\"*80)\n",
+    "    with timer(\"Traitement credit card balance\"):\n",
+    "        cc = credit_card_balance(num_rows)\n",
+    "        print(f\"   Credit card balance shape: {cc.shape}\")\n",
+    "        df = df.join(cc, how='left', on='SK_ID_CURR')\n",
+    "        del cc\n",
+    "        gc.collect()\n",
+    "    print(f\"✓ Shape après fusion credit card : {df.shape}\")\n",
+    "    \n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"PRÉPARATION TERMINÉE !\")\n",
+    "    print(\"=\"*80)\n",
+    "    print(f\"Dataset final : {df.shape[0]} lignes, {df.shape[1]} colonnes\")\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7862dde1",
+   "metadata": {},
+   "source": [
+    "## 9. Exécution du pipeline de préparation\n",
+    "\n",
+    "Maintenant, exécutons le pipeline complet pour préparer nos données."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c3fef44a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🚀 Début de la préparation des données...\n",
+      "Mode: DEBUG (10000 lignes)\n",
+      "\n",
+      "\n",
+      "================================================================================\n",
+      "ÉTAPE 1 : Chargement des données application (train + test)\n",
+      "================================================================================\n",
+      "Échantillons train: 10000, test: 10000\n",
+      "✓ Shape après application : (20000, 245)\n",
+      "\n",
+      "================================================================================\n",
+      "ÉTAPE 2 : Traitement des données Bureau (crédits externes)\n",
+      "================================================================================\n",
+      "   Bureau shape: (2011, 108)\n",
+      "Traitement bureau et bureau_balance - terminé en 0s\n",
+      "✓ Shape après fusion bureau : (20000, 353)\n",
+      "\n",
+      "================================================================================\n",
+      "ÉTAPE 3 : Traitement des demandes précédentes\n",
+      "================================================================================\n",
+      "   Previous applications shape: (9734, 242)\n",
+      "Traitement previous_applications - terminé en 0s\n",
+      "✓ Shape après fusion previous : (20000, 595)\n",
+      "\n",
+      "================================================================================\n",
+      "ÉTAPE 4 : Traitement des soldes POS-CASH\n",
+      "================================================================================\n",
+      "   Pos-cash balance shape: (9494, 15)\n",
+      "Traitement POS-CASH balance - terminé en 0s\n",
+      "✓ Shape après fusion POS : (20000, 610)\n",
+      "\n",
+      "================================================================================\n",
+      "ÉTAPE 5 : Traitement des paiements par versements\n",
+      "================================================================================\n",
+      "   Installments payments shape: (8893, 26)\n",
+      "Traitement installments payments - terminé en 0s\n",
+      "✓ Shape après fusion installments : (20000, 636)\n",
+      "\n",
+      "================================================================================\n",
+      "ÉTAPE 6 : Traitement des soldes de cartes de crédit\n",
+      "================================================================================\n",
+      "   Credit card balance shape: (9520, 106)\n",
+      "Traitement credit card balance - terminé en 0s\n",
+      "✓ Shape après fusion credit card : (20000, 742)\n",
+      "\n",
+      "================================================================================\n",
+      "PRÉPARATION TERMINÉE !\n",
+      "================================================================================\n",
+      "Dataset final : 20000 lignes, 742 colonnes\n",
+      "Pipeline complet de préparation - terminé en 1s\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exécuter en mode DEBUG (10000 lignes) pour un test rapide\n",
+    "# Pour la version complète, mettre debug=False\n",
+    "DEBUG_MODE = True\n",
+    "\n",
+    "print(\"🚀 Début de la préparation des données...\")\n",
+    "print(f\"Mode: {'DEBUG (10000 lignes)' if DEBUG_MODE else 'COMPLET'}\\n\")\n",
+    "\n",
+    "with timer(\"Pipeline complet de préparation\"):\n",
+    "    df_final = prepare_full_dataset(debug=DEBUG_MODE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bae04f2",
+   "metadata": {},
+   "source": [
+    "## 10. Exploration du dataset final\n",
+    "\n",
+    "Examinons le résultat de notre préparation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "97cf2f45",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 APERÇU DU DATASET FINAL\n",
+      "================================================================================\n",
+      "Nombre de lignes : 20,000\n",
+      "Nombre de colonnes (features) : 742\n",
+      "\n",
+      "Mémoire utilisée : 95.73 MB\n",
+      "\n",
+      "Premières colonnes :\n",
+      "['SK_ID_CURR', 'TARGET', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE', 'REGION_POPULATION_RELATIVE', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'OWN_CAR_AGE', 'FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE', 'FLAG_CONT_MOBILE']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Aperçu général du dataset\n",
+    "print(\"📊 APERÇU DU DATASET FINAL\")\n",
+    "print(\"=\"*80)\n",
+    "print(f\"Nombre de lignes : {df_final.shape[0]:,}\")\n",
+    "print(f\"Nombre de colonnes (features) : {df_final.shape[1]:,}\")\n",
+    "print(f\"\\nMémoire utilisée : {df_final.memory_usage(deep=True).sum() / 1024**2:.2f} MB\")\n",
+    "print(\"\\nPremières colonnes :\")\n",
+    "print(df_final.columns.tolist()[:20])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6f4abb4",
+   "metadata": {},
+   "source": [
+    "### Séparation train/test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "baddbf20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 SÉPARATION TRAIN / TEST\n",
+      "================================================================================\n",
+      "Train shape : (10000, 742)\n",
+      "Test shape  : (10000, 742)\n",
+      "\n",
+      "Distribution de la variable cible (TARGET) dans train :\n",
+      "TARGET\n",
+      "0.0    9225\n",
+      "1.0     775\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Pourcentage de défaut : 7.75%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Séparer les données train (avec TARGET) et test (sans TARGET)\n",
+    "train_df = df_final[df_final['TARGET'].notnull()].copy()\n",
+    "test_df = df_final[df_final['TARGET'].isnull()].copy()\n",
+    "\n",
+    "print(\"📊 SÉPARATION TRAIN / TEST\")\n",
+    "print(\"=\"*80)\n",
+    "print(f\"Train shape : {train_df.shape}\")\n",
+    "print(f\"Test shape  : {test_df.shape}\")\n",
+    "print(f\"\\nDistribution de la variable cible (TARGET) dans train :\")\n",
+    "print(train_df['TARGET'].value_counts())\n",
+    "print(f\"\\nPourcentage de défaut : {train_df['TARGET'].mean()*100:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44a3c899",
+   "metadata": {},
+   "source": [
+    "### Analyse des valeurs manquantes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8d195eec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 COLONNES AVEC VALEURS MANQUANTES (>1%)\n",
+      "================================================================================\n",
+      "Nombre de colonnes concernées : 551\n",
+      "\n",
+      "Top 10 colonnes avec le plus de valeurs manquantes :\n",
+      "                      Colonne  Valeurs_manquantes  Pourcentage\n",
+      "  BURO_MONTHS_BALANCE_MIN_MIN               10000        100.0\n",
+      "      BURO_STATUS_C_MEAN_MEAN               10000        100.0\n",
+      "      BURO_STATUS_1_MEAN_MEAN               10000        100.0\n",
+      "    BURO_STATUS_nan_MEAN_MEAN               10000        100.0\n",
+      "      BURO_STATUS_0_MEAN_MEAN               10000        100.0\n",
+      "      BURO_STATUS_2_MEAN_MEAN               10000        100.0\n",
+      "      BURO_STATUS_X_MEAN_MEAN               10000        100.0\n",
+      "  BURO_MONTHS_BALANCE_MAX_MAX               10000        100.0\n",
+      "BURO_MONTHS_BALANCE_SIZE_MEAN               10000        100.0\n",
+      "      BURO_STATUS_3_MEAN_MEAN               10000        100.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculer le pourcentage de valeurs manquantes par colonne\n",
+    "missing_values = train_df.isnull().sum()\n",
+    "missing_percent = (missing_values / len(train_df)) * 100\n",
+    "missing_df = pd.DataFrame({\n",
+    "    'Colonne': missing_values.index,\n",
+    "    'Valeurs_manquantes': missing_values.values,\n",
+    "    'Pourcentage': missing_percent.values\n",
+    "})\n",
+    "\n",
+    "# Filtrer les colonnes avec au moins 1% de valeurs manquantes\n",
+    "missing_df = missing_df[missing_df['Pourcentage'] > 1].sort_values('Pourcentage', ascending=False)\n",
+    "\n",
+    "print(\"📊 COLONNES AVEC VALEURS MANQUANTES (>1%)\")\n",
+    "print(\"=\"*80)\n",
+    "print(f\"Nombre de colonnes concernées : {len(missing_df)}\")\n",
+    "print(\"\\nTop 10 colonnes avec le plus de valeurs manquantes :\")\n",
+    "print(missing_df.head(10).to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64a9febd",
+   "metadata": {},
+   "source": [
+    "### Aperçu des features créées par catégorie"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "810668eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 RÉPARTITION DES FEATURES PAR ORIGINE\n",
+      "================================================================================\n",
+      "Application          :  245 features\n",
+      "Bureau (BURO)        :   54 features\n",
+      "Bureau Active        :   27 features\n",
+      "Bureau Closed        :   27 features\n",
+      "Previous (PREV)      :  182 features\n",
+      "Approved             :   30 features\n",
+      "Refused              :   30 features\n",
+      "POS Cash             :   15 features\n",
+      "Installments         :   26 features\n",
+      "Credit Card          :  106 features\n",
+      "\n",
+      "TOTAL                :  742 features\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compter les features par préfixe (provenance)\n",
+    "prefixes = {\n",
+    "    'Application': [col for col in df_final.columns if not any(col.startswith(p) for p in ['BURO_', 'ACTIVE_', 'CLOSED_', 'PREV_', 'APPROVED_', 'REFUSED_', 'POS_', 'INSTAL_', 'CC_'])],\n",
+    "    'Bureau (BURO)': [col for col in df_final.columns if col.startswith('BURO_')],\n",
+    "    'Bureau Active': [col for col in df_final.columns if col.startswith('ACTIVE_')],\n",
+    "    'Bureau Closed': [col for col in df_final.columns if col.startswith('CLOSED_')],\n",
+    "    'Previous (PREV)': [col for col in df_final.columns if col.startswith('PREV_')],\n",
+    "    'Approved': [col for col in df_final.columns if col.startswith('APPROVED_')],\n",
+    "    'Refused': [col for col in df_final.columns if col.startswith('REFUSED_')],\n",
+    "    'POS Cash': [col for col in df_final.columns if col.startswith('POS_')],\n",
+    "    'Installments': [col for col in df_final.columns if col.startswith('INSTAL_')],\n",
+    "    'Credit Card': [col for col in df_final.columns if col.startswith('CC_')]\n",
+    "}\n",
+    "\n",
+    "print(\"📊 RÉPARTITION DES FEATURES PAR ORIGINE\")\n",
+    "print(\"=\"*80)\n",
+    "for name, cols in prefixes.items():\n",
+    "    print(f\"{name:20s} : {len(cols):4d} features\")\n",
+    "    \n",
+    "print(f\"\\n{'TOTAL':20s} : {df_final.shape[1]:4d} features\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c484431",
+   "metadata": {},
+   "source": [
+    "## 11. Sauvegarde des données préparées\n",
+    "\n",
+    "Sauvegardons nos datasets préparés pour une utilisation ultérieure dans la modélisation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "458e51ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Dataset complet sauvegardé : ../data/processed/features_full.csv\n",
+      "  Taille du fichier : 34.23 MB\n",
+      "\n",
+      "✓ Train sauvegardé : ../data/processed/features_train.csv\n",
+      "  Taille du fichier : 17.04 MB\n",
+      "\n",
+      "✓ Test sauvegardé : ../data/processed/features_test.csv\n",
+      "  Taille du fichier : 17.21 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Créer le répertoire de sortie s'il n'existe pas\n",
+    "import os\n",
+    "os.makedirs('../data/processed', exist_ok=True)\n",
+    "\n",
+    "# Sauvegarder le dataset complet\n",
+    "output_path_full = '../data/processed/features_full.csv'\n",
+    "df_final.to_csv(output_path_full, index=False)\n",
+    "print(f\"✓ Dataset complet sauvegardé : {output_path_full}\")\n",
+    "print(f\"  Taille du fichier : {os.path.getsize(output_path_full) / 1024**2:.2f} MB\")\n",
+    "\n",
+    "# Sauvegarder séparément train et test\n",
+    "output_path_train = '../data/processed/features_train.csv'\n",
+    "output_path_test = '../data/processed/features_test.csv'\n",
+    "\n",
+    "train_df.to_csv(output_path_train, index=False)\n",
+    "test_df.to_csv(output_path_test, index=False)\n",
+    "\n",
+    "print(f\"\\n✓ Train sauvegardé : {output_path_train}\")\n",
+    "print(f\"  Taille du fichier : {os.path.getsize(output_path_train) / 1024**2:.2f} MB\")\n",
+    "print(f\"\\n✓ Test sauvegardé : {output_path_test}\")\n",
+    "print(f\"  Taille du fichier : {os.path.getsize(output_path_test) / 1024**2:.2f} MB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "886c3450",
+   "metadata": {},
+   "source": [
+    "## 12. Résumé et prochaines étapes\n",
+    "\n",
+    "### ✅ Ce qui a été fait dans ce notebook :\n",
+    "\n",
+    "1. **Chargement et fusion** de 7 tables de données différentes\n",
+    "2. **Nettoyage** des valeurs aberrantes et sentinelles (365243 → NaN)\n",
+    "3. **Encodage** des variables catégorielles (One-Hot encoding)\n",
+    "4. **Création de features** par agrégation (min, max, mean, sum, var)\n",
+    "5. **Features spécifiques** :\n",
+    "   - Ratios et pourcentages (ex: INCOME_CREDIT_PERC, PAYMENT_RATE)\n",
+    "   - Comportement de paiement (DPD, DBD, PAYMENT_PERC)\n",
+    "   - Distinction crédits actifs/fermés\n",
+    "   - Distinction demandes approuvées/refusées\n",
+    "6. **Séparation** train/test\n",
+    "7. **Sauvegarde** des données préparées\n",
+    "\n",
+    "### 📊 Résultat :\n",
+    "\n",
+    "- **Dataset final** : ~{df_final.shape[1]} features créées\n",
+    "- **Prêt pour la modélisation** avec LightGBM ou autre algorithme\n",
+    "\n",
+    "### 🔜 Prochaines étapes :\n",
+    "\n",
+    "1. **Feature Selection** : Identifier les features les plus importantes\n",
+    "2. **Modélisation** : Entraîner un modèle LightGBM avec validation croisée\n",
+    "3. **Optimisation** : Tuning des hyperparamètres\n",
+    "4. **Évaluation** : Analyser les performances (ROC-AUC)\n",
+    "5. **Prédictions** : Générer les prédictions pour le test set\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Note importante** : Ce notebook utilise l'approche du kernel Kaggle \"LightGBM with Simple Features\" de jsaguiar, qui a obtenu d'excellents résultats sur cette compétition. L'approche privilégie la création de nombreuses features par agrégation, ce qui peut entraîner de l'overfitting. Une sélection de features sera donc importante dans les étapes suivantes."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "OC_P6",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/04_regression.ipynb

@@ -0,0 +1,1914 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "425434fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Configuration chargée avec succès !\n",
+      "MLflow Experiment: OC_P6_Credit_Scoring\n",
+      "Model: LogisticRegression\n",
+      "Fixed threshold: 0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# VERSION 1: Baseline LogisticRegression - paramètres par défaut\n",
+    "# ============================================================================\n",
+    "# Objectif: modèle le plus simple possible, sans aucune gestion du déséquilibre\n",
+    "#           ni ajustement de seuil\n",
+    "# Validation: StratifiedKFold (5 folds) pour conserver la proportion de classes\n",
+    "# Modèle: LogisticRegression() avec max_iter=1000, random_state=42\n",
+    "# Features: X_train, y_train, X_test, y_test (seront scalés avec StandardScaler)\n",
+    "# Seuil fixe: 0.5\n",
+    "# Métriques par fold: AUC-ROC, Accuracy, F1-score, Recall classe 1\n",
+    "# Coût métier: 10 * FN + 1 * FP (avec seuil=0.5)\n",
+    "# MLflow: run_name=\"V1_LogisticRegression_Baseline\"\n",
+    "# Tags: version=\"1\", model=\"LogisticRegression\"\n",
+    "\n",
+    "import datetime\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import roc_auc_score, accuracy_score, f1_score, recall_score, confusion_matrix\n",
+    "from sklearn.exceptions import ConvergenceWarning\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore', message='.*Failed to resolve installed pip version.*')\n",
+    "warnings.filterwarnings('ignore', category=FutureWarning, message='.*penalty.*deprecated.*')\n",
+    "warnings.filterwarnings('ignore', category=ConvergenceWarning)\n",
+    "\n",
+    "# ============================================================================\n",
+    "# CONFIGURATION\n",
+    "# ============================================================================\n",
+    "MLFLOW_TRACKING_URI = \"http://127.0.0.1:5000\"\n",
+    "MLFLOW_EXPERIMENT_NAME = \"OC_P6_Credit_Scoring\"\n",
+    "\n",
+    "PROJECT_VERSION = \"1.0\"\n",
+    "MODEL_NAME = \"LogisticRegression\"\n",
+    "NOTEBOOK_NAME = \"04_regression\"\n",
+    "RUN_DATE = datetime.datetime.now()\n",
+    "\n",
+    "DATA_PATH = \"../data/processed/\"\n",
+    "TRAIN_FILE = \"features_train.csv\"\n",
+    "TEST_FILE = \"features_test.csv\"\n",
+    "\n",
+    "# Configuration du modèle baseline (paramètres par défaut)\n",
+    "MODEL_CONFIG_V1 = {\n",
+    "    \"max_iter\": 1000,\n",
+    "    \"random_state\": 42\n",
+    "}\n",
+    "\n",
+    "RANDOM_STATE = 42\n",
+    "THRESHOLD_FIXED = 0.5  # Seuil fixe pour les prédictions\n",
+    "\n",
+    "print(\"Configuration chargée avec succès !\")\n",
+    "print(f\"MLflow Experiment: {MLFLOW_EXPERIMENT_NAME}\")\n",
+    "print(f\"Model: {MODEL_NAME}\")\n",
+    "print(f\"Fixed threshold: {THRESHOLD_FIXED}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a076e751",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configuration MLflow\n",
+    "from src.mlflow_config import configure_mlflow\n",
+    "\n",
+    "mlflow = configure_mlflow(autolog=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fc246658",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Données chargées:\n",
+      "  X_train: (10000, 741)\n",
+      "  y_train: (10000,)\n",
+      "  X_test: (10000, 741)\n",
+      "  y_test: (10000,)\n",
+      "  Classe 0: 9225, Classe 1: 775\n",
+      "\n",
+      "✓ Vérification initiale des NaN:\n",
+      "  Total NaN in X_train: 5146964\n",
+      "  Total NaN in X_test: 5106144\n",
+      "\n",
+      "✓ Identification des colonnes vides (100% NaN):\n",
+      "  Colonnes vides dans X_train: 17\n",
+      "  Colonnes vides dans X_test: 1\n",
+      "  Suppression de 17 colonnes vides...\n",
+      "  X_train après suppression: (10000, 724)\n",
+      "  X_test après suppression: (10000, 724)\n",
+      "\n",
+      "✓ Imputation des NaN restants:\n",
+      "  NaN restants in X_train: 4976964\n",
+      "  NaN restants in X_test: 4936311\n",
+      "  Imputation avec la médiane...\n",
+      "  X_train après imputation: (10000, 724)\n",
+      "  X_test après imputation: (10000, 724)\n",
+      "  Vérification post-imputation:\n",
+      "    NaN in X_train: 0\n",
+      "    NaN in X_test: 0\n",
+      "\n",
+      "✓ Données finales après nettoyage:\n",
+      "  X_train: (10000, 724)\n",
+      "  y_train: (10000,)\n",
+      "  X_test: (10000, 724)\n",
+      "  y_test: (10000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# CHARGEMENT ET PRÉPARATION DES DONNÉES\n",
+    "# ============================================================================\n",
+    "\n",
+    "# Chargement des données d'entraînement\n",
+    "X_train = pd.read_csv(DATA_PATH + TRAIN_FILE)\n",
+    "y_train = X_train.pop(\"TARGET\")\n",
+    "\n",
+    "# Chargement des données de test\n",
+    "X_test = pd.read_csv(DATA_PATH + TEST_FILE)\n",
+    "y_test = X_test.pop(\"TARGET\")\n",
+    "\n",
+    "print(f\"✓ Données chargées:\")\n",
+    "print(f\"  X_train: {X_train.shape}\")\n",
+    "print(f\"  y_train: {y_train.shape}\")\n",
+    "print(f\"  X_test: {X_test.shape}\")\n",
+    "print(f\"  y_test: {y_test.shape}\")\n",
+    "print(f\"  Classe 0: {(y_train==0).sum()}, Classe 1: {(y_train==1).sum()}\")\n",
+    "\n",
+    "# ============================================================================\n",
+    "# NETTOYAGE DES DONNÉES: Suppression des colonnes avec 100% NaN\n",
+    "# ============================================================================\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "\n",
+    "# Vérifier les NaN\n",
+    "nan_train = X_train.isna().sum().sum()\n",
+    "nan_test = X_test.isna().sum().sum()\n",
+    "\n",
+    "print(f\"\\n✓ Vérification initiale des NaN:\")\n",
+    "print(f\"  Total NaN in X_train: {nan_train}\")\n",
+    "print(f\"  Total NaN in X_test: {nan_test}\")\n",
+    "\n",
+    "# Identifier et supprimer les colonnes entièrement NaN dans X_train\n",
+    "empty_cols_train = X_train.columns[X_train.isna().all()].tolist()\n",
+    "empty_cols_test = X_test.columns[X_test.isna().all()].tolist()\n",
+    "\n",
+    "print(f\"\\n✓ Identification des colonnes vides (100% NaN):\")\n",
+    "print(f\"  Colonnes vides dans X_train: {len(empty_cols_train)}\")\n",
+    "print(f\"  Colonnes vides dans X_test: {len(empty_cols_test)}\")\n",
+    "\n",
+    "# Supprimer les colonnes vides (union des deux ensembles)\n",
+    "cols_to_drop = set(empty_cols_train) | set(empty_cols_test)\n",
+    "if cols_to_drop:\n",
+    "    print(f\"  Suppression de {len(cols_to_drop)} colonnes vides...\")\n",
+    "    X_train = X_train.drop(columns=list(cols_to_drop))\n",
+    "    X_test = X_test.drop(columns=list(cols_to_drop))\n",
+    "    print(f\"  X_train après suppression: {X_train.shape}\")\n",
+    "    print(f\"  X_test après suppression: {X_test.shape}\")\n",
+    "\n",
+    "# ============================================================================\n",
+    "# IMPUTATION DES VALEURS NaN RESTANTES AVEC LA MÉDIANE\n",
+    "# ============================================================================\n",
+    "\n",
+    "nan_train_remaining = X_train.isna().sum().sum()\n",
+    "nan_test_remaining = X_test.isna().sum().sum()\n",
+    "\n",
+    "print(f\"\\n✓ Imputation des NaN restants:\")\n",
+    "print(f\"  NaN restants in X_train: {nan_train_remaining}\")\n",
+    "print(f\"  NaN restants in X_test: {nan_test_remaining}\")\n",
+    "\n",
+    "if nan_train_remaining > 0 or nan_test_remaining > 0:\n",
+    "    print(f\"  Imputation avec la médiane...\")\n",
+    "    \n",
+    "    # Créer un imputer avec stratégie médiane\n",
+    "    imputer = SimpleImputer(strategy='median')\n",
+    "    \n",
+    "    # Fit sur X_train et transformer X_train et X_test\n",
+    "    X_train_imputed = imputer.fit_transform(X_train)\n",
+    "    X_test_imputed = imputer.transform(X_test)\n",
+    "    \n",
+    "    # Reconvertir en DataFrame\n",
+    "    X_train = pd.DataFrame(X_train_imputed, columns=X_train.columns)\n",
+    "    X_test = pd.DataFrame(X_test_imputed, columns=X_test.columns)\n",
+    "    \n",
+    "    print(f\"  X_train après imputation: {X_train.shape}\")\n",
+    "    print(f\"  X_test après imputation: {X_test.shape}\")\n",
+    "    print(f\"  Vérification post-imputation:\")\n",
+    "    print(f\"    NaN in X_train: {X_train.isna().sum().sum()}\")\n",
+    "    print(f\"    NaN in X_test: {X_test.isna().sum().sum()}\")\n",
+    "else:\n",
+    "    print(f\"  Aucun NaN à imputer !\")\n",
+    "\n",
+    "print(f\"\\n✓ Données finales après nettoyage:\")\n",
+    "print(f\"  X_train: {X_train.shape}\")\n",
+    "print(f\"  y_train: {y_train.shape}\")\n",
+    "print(f\"  X_test: {X_test.shape}\")\n",
+    "print(f\"  y_test: {y_test.shape}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "44d75270",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✓ Features standardisées (StandardScaler):\n",
+      "  Shape train: (10000, 724)\n",
+      "  Mean: -0.00000000 (≈ 0)\n",
+      "  Std:  0.874353 (≈ 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# STANDARDISATION DES FEATURES\n",
+    "# ============================================================================\n",
+    "# La régression logistique est sensible à l'échelle des features\n",
+    "# Utiliser StandardScaler (fit sur train, transform sur test)\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "# Reconvertir en DataFrame pour conserver les noms de colonnes\n",
+    "X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns)\n",
+    "X_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns)\n",
+    "\n",
+    "print(f\"\\n✓ Features standardisées (StandardScaler):\")\n",
+    "print(f\"  Shape train: {X_train_scaled.shape}\")\n",
+    "print(f\"  Mean: {X_train_scaled.mean().mean():.8f} (≈ 0)\")\n",
+    "print(f\"  Std:  {X_train_scaled.std().mean():.6f} (≈ 1)\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "59eabb43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fold 1/5 | AUC=0.6995 | Acc=0.9170 | F1=0.0568 | Recall=0.0323 | Cost=1516\n",
+      "Fold 2/5 | AUC=0.6972 | Acc=0.9225 | F1=0.1243 | Recall=0.0710 | Cost=1451\n",
+      "Fold 3/5 | AUC=0.7036 | Acc=0.9160 | F1=0.0562 | Recall=0.0323 | Cost=1518\n",
+      "Fold 4/5 | AUC=0.7061 | Acc=0.9140 | F1=0.0444 | Recall=0.0258 | Cost=1531\n",
+      "Fold 5/5 | AUC=0.6984 | Acc=0.9145 | F1=0.0339 | Recall=0.0194 | Cost=1539\n",
+      "\n",
+      "✓ Cross-Validation LogisticRegression V1 terminée\n",
+      "  AUC moyen: 0.7010 ± 0.0038\n",
+      "  F1 moyen: 0.0631 ± 0.0355\n",
+      "  Recall moyen: 0.0361 ± 0.0202\n",
+      "  Coût métier moyen: 1511.00 ± 34.85\n",
+      "  Seuil optimal: 0.50\n",
+      "🏃 View run V1_LogisticRegression_Baseline at: http://127.0.0.1:5000/#/experiments/1/runs/00e6a5708f0340678afb3fe611ba11c8\n",
+      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# CROSS-VALIDATION: LogisticRegression V1 Baseline\n",
+    "# ============================================================================\n",
+    "# StratifiedKFold (5 folds) pour conserver la proportion de classes\n",
+    "# Seuil fixe = 0.5 pour les prédictions (pas d'optimisation)\n",
+    "\n",
+    "from src.mlflow_config import configure_mlflow\n",
+    "\n",
+    "mlflow = configure_mlflow(autolog=False)\n",
+    "\n",
+    "# Terminer tout run actif avant de commencer\n",
+    "mlflow.end_run()\n",
+    "\n",
+    "RUN_NAME_V1 = \"V1_LogisticRegression_Baseline\"\n",
+    "\n",
+    "fold_results = []\n",
+    "\n",
+    "with mlflow.start_run(run_name=RUN_NAME_V1):\n",
+    "    # ========== Logging des paramètres et tags ==========\n",
+    "    mlflow.log_params(MODEL_CONFIG_V1)\n",
+    "    mlflow.set_tag(\"version\", \"1\")\n",
+    "    mlflow.set_tag(\"model\", \"LogisticRegression\")\n",
+    "    mlflow.set_tag(\"notebook\", NOTEBOOK_NAME)\n",
+    "    mlflow.set_tag(\"phase\", \"baseline_cv\")\n",
+    "    mlflow.set_tag(\"threshold\", str(THRESHOLD_FIXED))\n",
+    "    mlflow.set_tag(\"scaling\", \"StandardScaler\")\n",
+    "    mlflow.set_tag(\"model_type\", \"LogisticRegression\")\n",
+    "    \n",
+    "    # ========== StratifiedKFold (5 folds) ==========\n",
+    "    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n",
+    "    \n",
+    "    for fold_idx, (train_idx, val_idx) in enumerate(skf.split(X_train_scaled, y_train), start=1):\n",
+    "        X_tr, X_val = X_train_scaled.iloc[train_idx], X_train_scaled.iloc[val_idx]\n",
+    "        y_tr, y_val = y_train.iloc[train_idx], y_train.iloc[val_idx]\n",
+    "        \n",
+    "        # ========== Entraînement ==========\n",
+    "        model = LogisticRegression(**MODEL_CONFIG_V1)\n",
+    "        model.fit(X_tr, y_tr)\n",
+    "        \n",
+    "        # ========== Prédictions ==========\n",
+    "        y_val_proba = model.predict_proba(X_val)[:, 1]  # Probabilités classe 1\n",
+    "        y_val_pred = (y_val_proba >= THRESHOLD_FIXED).astype(int)  # Seuil fixe 0.5\n",
+    "        \n",
+    "        # ========== Métriques ==========\n",
+    "        auc = roc_auc_score(y_val, y_val_proba)\n",
+    "        accuracy = accuracy_score(y_val, y_val_pred)\n",
+    "        f1 = f1_score(y_val, y_val_pred)\n",
+    "        recall = recall_score(y_val, y_val_pred)\n",
+    "        \n",
+    "        # ========== Coût métier (seuil=0.5) ==========\n",
+    "        tn, fp, fn, tp = confusion_matrix(y_val, y_val_pred).ravel()\n",
+    "        cost = 10 * fn + 1 * fp\n",
+    "        \n",
+    "        fold_results.append({\n",
+    "            \"fold\": fold_idx,\n",
+    "            \"auc\": auc,\n",
+    "            \"accuracy\": accuracy,\n",
+    "            \"f1_score\": f1,\n",
+    "            \"recall_class1\": recall,\n",
+    "            \"business_cost_min\": cost,\n",
+    "            \"optimal_threshold\": THRESHOLD_FIXED,\n",
+    "            \"tp\": tp,\n",
+    "            \"fp\": fp,\n",
+    "            \"fn\": fn,\n",
+    "            \"tn\": tn\n",
+    "        })\n",
+    "        \n",
+    "        print(f\"Fold {fold_idx}/5 | AUC={auc:.4f} | Acc={accuracy:.4f} | \"\n",
+    "              f\"F1={f1:.4f} | Recall={recall:.4f} | Cost={cost:.0f}\")\n",
+    "    \n",
+    "    # ========== Agrégation des résultats ==========\n",
+    "    cv_results_df = pd.DataFrame(fold_results)\n",
+    "    \n",
+    "    metrics_mean = {\n",
+    "        \"auc\": cv_results_df[\"auc\"].mean(),\n",
+    "        \"f1_score\": cv_results_df[\"f1_score\"].mean(),\n",
+    "        \"recall_class1\": cv_results_df[\"recall_class1\"].mean(),\n",
+    "        \"business_cost_min\": cv_results_df[\"business_cost_min\"].mean(),\n",
+    "        \"optimal_threshold\": THRESHOLD_FIXED,\n",
+    "    }\n",
+    "    \n",
+    "    metrics_std = {\n",
+    "        \"auc\": cv_results_df[\"auc\"].std(),\n",
+    "        \"f1_score\": cv_results_df[\"f1_score\"].std(),\n",
+    "        \"recall_class1\": cv_results_df[\"recall_class1\"].std(),\n",
+    "        \"business_cost_min\": cv_results_df[\"business_cost_min\"].std(),\n",
+    "    }\n",
+    "    \n",
+    "    # ========== Logging dans MLFlow ==========\n",
+    "    # Utiliser les MÊMES noms que le schéma standard MLflow (sans préfixe)\n",
+    "    mlflow.log_metric(\"auc\", metrics_mean[\"auc\"])\n",
+    "    mlflow.log_metric(\"f1_score\", metrics_mean[\"f1_score\"])\n",
+    "    mlflow.log_metric(\"recall_class1\", metrics_mean[\"recall_class1\"])\n",
+    "    mlflow.log_metric(\"business_cost_min\", metrics_mean[\"business_cost_min\"])\n",
+    "    mlflow.log_metric(\"optimal_threshold\", metrics_mean[\"optimal_threshold\"])\n",
+    "    \n",
+    "    # Log artefact JSON avec détails par fold\n",
+    "    mlflow.log_dict(cv_results_df.to_dict(orient=\"records\"), \"cv_results_per_fold.json\")\n",
+    "    \n",
+    "    print(\"\\n✓ Cross-Validation LogisticRegression V1 terminée\")\n",
+    "    print(f\"  AUC moyen: {metrics_mean['auc']:.4f} ± {metrics_std['auc']:.4f}\")\n",
+    "    print(f\"  F1 moyen: {metrics_mean['f1_score']:.4f} ± {metrics_std['f1_score']:.4f}\")\n",
+    "    print(f\"  Recall moyen: {metrics_mean['recall_class1']:.4f} ± {metrics_std['recall_class1']:.4f}\")\n",
+    "    print(f\"  Coût métier moyen: {metrics_mean['business_cost_min']:.2f} ± {metrics_std['business_cost_min']:.2f}\")\n",
+    "    print(f\"  Seuil optimal: {metrics_mean['optimal_threshold']:.2f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9269e02",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==============================================================================================================\n",
+      "TABLEAU RÉCAPITULATIF: Métriques par fold\n",
+      "==============================================================================================================\n",
+      " fold      auc  f1_score  recall_class1  business_cost_min  optimal_threshold\n",
+      "    1 0.699467  0.056818       0.032258               1516                0.5\n",
+      "    2 0.697180  0.124294       0.070968               1451                0.5\n",
+      "    3 0.703589  0.056180       0.032258               1518                0.5\n",
+      "    4 0.706146  0.044444       0.025806               1531                0.5\n",
+      "    5 0.698425  0.033898       0.019355               1539                0.5\n",
+      "\n",
+      "==============================================================================================================\n",
+      "RÉSUMÉ GLOBAL: Moyennes et Écart-types sur 5 folds\n",
+      "==============================================================================================================\n",
+      "       Métrique Moyenne Écart-type\n",
+      "        AUC-ROC  0.7010     0.0038\n",
+      "       F1-Score  0.0631     0.0355\n",
+      "Recall Classe 1  0.0361     0.0202\n",
+      "Coût Métier Min 1511.00      34.85\n",
+      "  Seuil Optimal    0.50          -\n",
+      "==============================================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# TABLEAU RÉCAPITULATIF: Métriques par fold\n",
+    "# ============================================================================\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*110)\n",
+    "print(\"TABLEAU RÉCAPITULATIF: Métriques par fold\")\n",
+    "print(\"=\"*110)\n",
+    "\n",
+    "display_df = cv_results_df[[\"fold\", \"auc\", \"f1_score\", \"recall_class1\", \"business_cost_min\", \"optimal_threshold\"]].copy()\n",
+    "print(display_df.to_string(index=False))\n",
+    "\n",
+    "# Afficher les moyennes et écart-types\n",
+    "print(\"\\n\" + \"=\"*110)\n",
+    "print(\"RÉSUMÉ GLOBAL: Moyennes et Écart-types sur 5 folds\")\n",
+    "print(\"=\"*110)\n",
+    "\n",
+    "summary_data = {\n",
+    "    \"Métrique\": [\"AUC-ROC\", \"F1-Score\", \"Recall Classe 1\", \"Coût Métier Min\", \"Seuil Optimal\"],\n",
+    "    \"Moyenne\": [\n",
+    "        f\"{metrics_mean['auc']:.4f}\",\n",
+    "        f\"{metrics_mean['f1_score']:.4f}\",\n",
+    "        f\"{metrics_mean['recall_class1']:.4f}\",\n",
+    "        f\"{metrics_mean['business_cost_min']:.2f}\",\n",
+    "        f\"{metrics_mean['optimal_threshold']:.2f}\",\n",
+    "    ],\n",
+    "    \"Écart-type\": [\n",
+    "        f\"{metrics_std['auc']:.4f}\",\n",
+    "        f\"{metrics_std['f1_score']:.4f}\",\n",
+    "        f\"{metrics_std['recall_class1']:.4f}\",\n",
+    "        f\"{metrics_std['business_cost_min']:.2f}\",\n",
+    "        \"-\",\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "summary_df = pd.DataFrame(summary_data)\n",
+    "print(summary_df.to_string(index=False))\n",
+    "print(\"=\"*110)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0dcbf61a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✓ Modèle final LogisticRegression V1 entraîné sur l'ensemble train complet\n",
+      "  Nombre de features: 724\n",
+      "  Intercept: -3.504665\n",
+      "  Norme des coefficients: 3.948967\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# ENTRAÎNEMENT FINAL: LogisticRegression sur l'ensemble train complet\n",
+    "# ============================================================================\n",
+    "\n",
+    "final_model_v1 = LogisticRegression(**MODEL_CONFIG_V1)\n",
+    "final_model_v1.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "print(\"\\n✓ Modèle final LogisticRegression V1 entraîné sur l'ensemble train complet\")\n",
+    "print(f\"  Nombre de features: {X_train_scaled.shape[1]}\")\n",
+    "print(f\"  Intercept: {final_model_v1.intercept_[0]:.6f}\")\n",
+    "print(f\"  Norme des coefficients: {np.linalg.norm(final_model_v1.coef_):.6f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0bdf34fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Vérification initiale y_test:\n",
+      "  y_test shape: (10000,)\n",
+      "  NaN in y_test: 10000\n",
+      "\n",
+      "⚠️  ATTENTION: y_test est entièrement NaN - Évaluation test IGNORÉE\n",
+      "   Les données de test n'ont pas de cible valide.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# ÉVALUATION SUR L'ENSEMBLE TEST\n",
+    "# ============================================================================\n",
+    "# Utiliser le même seuil fixe de 0.5\n",
+    "\n",
+    "# Vérifier et nettoyer les NaN dans y_test\n",
+    "print(f\"✓ Vérification initiale y_test:\")\n",
+    "print(f\"  y_test shape: {y_test.shape}\")\n",
+    "print(f\"  NaN in y_test: {y_test.isna().sum()}\")\n",
+    "\n",
+    "# Vérifier si y_test est entièrement NaN\n",
+    "if y_test.isna().sum() == len(y_test):\n",
+    "    print(f\"\\n⚠️  ATTENTION: y_test est entièrement NaN - Évaluation test IGNORÉE\")\n",
+    "    print(f\"   Les données de test n'ont pas de cible valide.\")\n",
+    "    test_auc = None\n",
+    "    test_accuracy = None\n",
+    "    test_f1 = None\n",
+    "    test_recall = None\n",
+    "    test_cost = None\n",
+    "    tp_test = None\n",
+    "    fp_test = None\n",
+    "    fn_test = None\n",
+    "    tn_test = None\n",
+    "    \n",
+    "else:\n",
+    "    # Supprimer les lignes avec NaN dans y_test\n",
+    "    if y_test.isna().sum() > 0:\n",
+    "        print(f\"  Suppression de {y_test.isna().sum()} lignes avec NaN dans y_test...\")\n",
+    "        mask_test_clean = ~y_test.isna()\n",
+    "        y_test = y_test[mask_test_clean]\n",
+    "        X_test_scaled = X_test_scaled[mask_test_clean]\n",
+    "        print(f\"  y_test après suppression: {y_test.shape}\")\n",
+    "        print(f\"  X_test_scaled après suppression: {X_test_scaled.shape}\")\n",
+    "\n",
+    "    # Réinitialiser les indices\n",
+    "    y_test.reset_index(drop=True, inplace=True)\n",
+    "    X_test_scaled.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "    # Prédictions sur le test\n",
+    "    y_test_proba = final_model_v1.predict_proba(X_test_scaled)[:, 1]\n",
+    "    y_test_pred = (y_test_proba >= THRESHOLD_FIXED).astype(int)\n",
+    "\n",
+    "    # Métriques sur le test\n",
+    "    test_auc = roc_auc_score(y_test, y_test_proba)\n",
+    "    test_accuracy = accuracy_score(y_test, y_test_pred)\n",
+    "    test_f1 = f1_score(y_test, y_test_pred)\n",
+    "    test_recall = recall_score(y_test, y_test_pred)\n",
+    "\n",
+    "    # Coût métier\n",
+    "    tn_test, fp_test, fn_test, tp_test = confusion_matrix(y_test, y_test_pred).ravel()\n",
+    "    test_cost = 10 * fn_test + 1 * fp_test\n",
+    "\n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"ÉVALUATION SUR ENSEMBLE TEST (seuil=0.5)\")\n",
+    "    print(\"=\"*80)\n",
+    "    print(f\"AUC-ROC:        {test_auc:.4f}\")\n",
+    "    print(f\"Accuracy:       {test_accuracy:.4f}\")\n",
+    "    print(f\"F1-Score:       {test_f1:.4f}\")\n",
+    "    print(f\"Recall Classe 1: {test_recall:.4f}\")\n",
+    "    print(f\"Coût Métier:    {test_cost:.0f}\")\n",
+    "    print(f\"\\nConfusion Matrix:\")\n",
+    "    print(f\"  TP: {int(tp_test):6d}  |  FP: {int(fp_test):6d}\")\n",
+    "    print(f\"  FN: {int(fn_test):6d}  |  TN: {int(tn_test):6d}\")\n",
+    "    print(\"=\"*80)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "623e9bd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/02/06 01:40:36 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "⚠️  ATTENTION: Métriques test non disponibles (y_test était entièrement NaN)\n",
+      "   Les métriques CV sont utilisées.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/02/06 01:40:39 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✓ Métriques et artefacts du modèle loggés dans MLflow\n",
+      "  AUC (CV): 0.7010\n",
+      "  F1 (CV): 0.0631\n",
+      "  Recall (CV): 0.0361\n",
+      "  Business Cost Min (CV): 1511.00\n",
+      "\n",
+      "  ℹ️  Pour enregistrer le modèle dans la Model Registry :\n",
+      "     - Allez à http://127.0.0.1:5000/#/experiments/1\n",
+      "     - Trouvez le run 'V1_LogisticRegression_Test_Evaluation'\n",
+      "     - Dans l'onglet 'Artifacts', cliquez 'Register Model'\n",
+      "     - Sélectionnez ou créez le nom 'LogisticRegression_V1'\n",
+      "🏃 View run V1_LogisticRegression_Test_Evaluation at: http://127.0.0.1:5000/#/experiments/1/runs/b98cbeb8fddc435f998b929565c06021\n",
+      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# LOGGING MLFLOW: Sauvegarde des métriques et artefacts du modèle\n",
+    "# ============================================================================\n",
+    "# Logger les métriques CV et le modèle comme artefact\n",
+    "# IMPORTANT: Le modèle n'est PAS enregistré dans la Model Registry automatiquement\n",
+    "\n",
+    "# Terminer le run CV précédent\n",
+    "mlflow.end_run()\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"V1_LogisticRegression_Test_Evaluation\"):\n",
+    "    # Logging des paramètres\n",
+    "    mlflow.log_params(MODEL_CONFIG_V1)\n",
+    "    \n",
+    "    # Tags\n",
+    "    mlflow.set_tag(\"version\", \"1\")\n",
+    "    mlflow.set_tag(\"model\", \"LogisticRegression\")\n",
+    "    mlflow.set_tag(\"phase\", \"test_evaluation\")\n",
+    "    mlflow.set_tag(\"threshold\", str(THRESHOLD_FIXED))\n",
+    "    mlflow.set_tag(\"model_type\", \"LogisticRegression\")\n",
+    "    \n",
+    "    # TOUJOURS logger les métriques CV (pour que le modèle ait des métriques)\n",
+    "    mlflow.log_metric(\"auc\", metrics_mean[\"auc\"])\n",
+    "    mlflow.log_metric(\"f1_score\", metrics_mean[\"f1_score\"])\n",
+    "    mlflow.log_metric(\"recall_class1\", metrics_mean[\"recall_class1\"])\n",
+    "    mlflow.log_metric(\"business_cost_min\", metrics_mean[\"business_cost_min\"])\n",
+    "    mlflow.log_metric(\"optimal_threshold\", metrics_mean[\"optimal_threshold\"])\n",
+    "    \n",
+    "    # Si métriques test disponibles, les logger aussi (avec suffixe pour différencier)\n",
+    "    if test_auc is not None:\n",
+    "        mlflow.log_metric(\"test_auc\", test_auc)\n",
+    "        mlflow.log_metric(\"test_f1_score\", test_f1)\n",
+    "        mlflow.log_metric(\"test_recall_class1\", test_recall)\n",
+    "        mlflow.log_metric(\"test_business_cost_min\", test_cost)\n",
+    "        \n",
+    "        # Résultats test en artefact\n",
+    "        test_results = {\n",
+    "            \"auc\": float(test_auc),\n",
+    "            \"f1_score\": float(test_f1),\n",
+    "            \"recall_class1\": float(test_recall),\n",
+    "            \"business_cost_min\": float(test_cost),\n",
+    "            \"optimal_threshold\": float(THRESHOLD_FIXED),\n",
+    "            \"confusion_matrix\": {\n",
+    "                \"tp\": int(tp_test),\n",
+    "                \"fp\": int(fp_test),\n",
+    "                \"fn\": int(fn_test),\n",
+    "                \"tn\": int(tn_test),\n",
+    "            }\n",
+    "        }\n",
+    "        mlflow.log_dict(test_results, \"test_evaluation.json\")\n",
+    "        \n",
+    "        print(f\"\\n✓ Métriques test loggées\")\n",
+    "        print(f\"  Test AUC: {test_auc:.4f}\")\n",
+    "        print(f\"  Test F1: {test_f1:.4f}\")\n",
+    "        print(f\"  Test Recall: {test_recall:.4f}\")\n",
+    "        print(f\"  Test Business Cost Min: {test_cost:.0f}\")\n",
+    "    else:\n",
+    "        print(f\"\\n⚠️  ATTENTION: Métriques test non disponibles (y_test était entièrement NaN)\")\n",
+    "        print(f\"   Les métriques CV sont utilisées.\")\n",
+    "        mlflow.set_tag(\"test_metrics_available\", \"false\")\n",
+    "    \n",
+    "    # LOG: Sauvegarder le modèle comme artefact (accessible via MLflow)\n",
+    "    # IMPORTANT: Le modèle n'est PAS enregistré dans la Model Registry automatiquement\n",
+    "    # Cela doit être fait manuellement via l'interface MLflow\n",
+    "    mlflow.sklearn.log_model(\n",
+    "        final_model_v1,\n",
+    "        artifact_path=\"logistic_regression_v1\"\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"\\n✓ Métriques et artefacts du modèle loggés dans MLflow\")\n",
+    "    print(f\"  AUC (CV): {metrics_mean['auc']:.4f}\")\n",
+    "    print(f\"  F1 (CV): {metrics_mean['f1_score']:.4f}\")\n",
+    "    print(f\"  Recall (CV): {metrics_mean['recall_class1']:.4f}\")\n",
+    "    print(f\"  Business Cost Min (CV): {metrics_mean['business_cost_min']:.2f}\")\n",
+    "    print(f\"\\n  ℹ️  Pour enregistrer le modèle dans la Model Registry :\")\n",
+    "    print(f\"     - Allez à http://127.0.0.1:5000/#/experiments/1\")\n",
+    "    print(f\"     - Trouvez le run 'V1_LogisticRegression_Test_Evaluation'\")\n",
+    "    print(f\"     - Dans l'onglet 'Artifacts', cliquez 'Register Model'\")\n",
+    "    print(f\"     - Sélectionnez ou créez le nom 'LogisticRegression_V1'\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "49e25787",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "====================================================================================================\n",
+      "COMPARAISON: Cross-Validation vs Test Set\n",
+      "====================================================================================================\n",
+      "\n",
+      "⚠️  ATTENTION: Métriques test non disponibles\n",
+      "   (y_test était entièrement NaN - Évaluation test ignorée)\n",
+      "\n",
+      "   Affichage des métriques de Cross-Validation uniquement:\n",
+      "   AUC moyen: 0.7010 ± 0.0038\n",
+      "   F1 moyen: 0.0631 ± 0.0355\n",
+      "   Recall moyen: 0.0361 ± 0.0202\n",
+      "   Coût métier moyen: 1511.00 ± 34.85\n",
+      "   Seuil optimal: 0.50\n",
+      "====================================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# COMPARAISON: Cross-Validation vs Test\n",
+    "# ============================================================================\n",
+    "# Vérifier la stabilité du modèle (généralisation)\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*100)\n",
+    "print(\"COMPARAISON: Cross-Validation vs Test Set\")\n",
+    "print(\"=\"*100)\n",
+    "\n",
+    "if test_auc is None:\n",
+    "    print(\"\\n⚠️  ATTENTION: Métriques test non disponibles\")\n",
+    "    print(\"   (y_test était entièrement NaN - Évaluation test ignorée)\")\n",
+    "    print(\"\\n   Affichage des métriques de Cross-Validation uniquement:\")\n",
+    "    print(f\"   AUC moyen: {metrics_mean['auc']:.4f} ± {metrics_std['auc']:.4f}\")\n",
+    "    print(f\"   F1 moyen: {metrics_mean['f1_score']:.4f} ± {metrics_std['f1_score']:.4f}\")\n",
+    "    print(f\"   Recall moyen: {metrics_mean['recall_class1']:.4f} ± {metrics_std['recall_class1']:.4f}\")\n",
+    "    print(f\"   Coût métier moyen: {metrics_mean['business_cost_min']:.2f} ± {metrics_std['business_cost_min']:.2f}\")\n",
+    "    print(f\"   Seuil optimal: {metrics_mean['optimal_threshold']:.2f}\")\n",
+    "else:\n",
+    "    comparison_data = {\n",
+    "        \"Métrique\": [\"AUC-ROC\", \"F1-Score\", \"Recall Classe 1\", \"Coût Métier Min\", \"Seuil Optimal\"],\n",
+    "        \"CV Mean\": [\n",
+    "            f\"{metrics_mean['auc']:.4f}\",\n",
+    "            f\"{metrics_mean['f1_score']:.4f}\",\n",
+    "            f\"{metrics_mean['recall_class1']:.4f}\",\n",
+    "            f\"{metrics_mean['business_cost_min']:.2f}\",\n",
+    "            f\"{metrics_mean['optimal_threshold']:.2f}\",\n",
+    "        ],\n",
+    "        \"Test\": [\n",
+    "            f\"{test_auc:.4f}\",\n",
+    "            f\"{test_f1:.4f}\",\n",
+    "            f\"{test_recall:.4f}\",\n",
+    "            f\"{test_cost:.2f}\",\n",
+    "            f\"{THRESHOLD_FIXED:.2f}\",\n",
+    "        ],\n",
+    "        \"Diff (Test-CV)\": [\n",
+    "            f\"{test_auc - metrics_mean['auc']:+.4f}\",\n",
+    "            f\"{test_f1 - metrics_mean['f1_score']:+.4f}\",\n",
+    "            f\"{test_recall - metrics_mean['recall_class1']:+.4f}\",\n",
+    "            f\"{test_cost - metrics_mean['business_cost_min']:+.2f}\",\n",
+    "            \"0.00\",\n",
+    "        ]\n",
+    "    }\n",
+    "    \n",
+    "    comparison_df = pd.DataFrame(comparison_data)\n",
+    "    print(comparison_df.to_string(index=False))\n",
+    "\n",
+    "print(\"=\"*100)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92864e1d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "267e8211",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fold 1/5 | AUC=0.6886 | Acc=0.6995 | F1=0.2324 | Recall=0.5871 | Cost=1177\n",
+      "Fold 2/5 | AUC=0.6828 | Acc=0.6980 | F1=0.2412 | Recall=0.6194 | Cost=1135\n",
+      "Fold 3/5 | AUC=0.7118 | Acc=0.7265 | F1=0.2476 | Recall=0.5806 | Cost=1132\n",
+      "Fold 4/5 | AUC=0.7035 | Acc=0.7000 | F1=0.2347 | Recall=0.5935 | Cost=1167\n",
+      "Fold 5/5 | AUC=0.6920 | Acc=0.7185 | F1=0.2277 | Recall=0.5355 | Cost=1211\n",
+      "\n",
+      "✓ Cross-Validation LogisticRegression V2.1 (class_weight='balanced') terminée\n",
+      "  AUC moyen: 0.6957 ± 0.0117\n",
+      "  F1 moyen: 0.2367 ± 0.0078\n",
+      "  Recall moyen: 0.5832 ± 0.0305\n",
+      "  Coût métier moyen: 1164.40 ± 32.60\n",
+      "  Seuil optimal: 0.50\n",
+      "🏃 View run V2_LogisticRegression_ClassWeightBalanced at: http://127.0.0.1:5000/#/experiments/1/runs/d8b12c8475984c75b995472e30f56f69\n",
+      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# VERSION 2.1: LogisticRegression avec class_weight='balanced'\n",
+    "# ============================================================================\n",
+    "# Objectif: Gérer le déséquilibre des classes avec class_weight='balanced'\n",
+    "# Validation: StratifiedKFold (5 folds)\n",
+    "# Modèle: LogisticRegression(max_iter=1000, random_state=42, solver='saga', class_weight='balanced', penalty='l2')\n",
+    "# Features: X_train_scaled, y_train (déjà scalées)\n",
+    "# Seuil fixe: 0.5\n",
+    "# Métriques par fold: AUC-ROC, Accuracy, F1-score, Recall classe 1\n",
+    "# Coût métier: 10 * FN + 1 * FP (avec seuil=0.5)\n",
+    "# MLflow: run_name=\"V2_LogisticRegression_ClassWeightBalanced\"\n",
+    "# Tags: version=\"2\", imbalance_handling=\"class_weight\"\n",
+    "\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "\n",
+    "# Configuration du modèle V2.1 (class_weight balanced)\n",
+    "MODEL_CONFIG_V2_1 = {\n",
+    "    \"max_iter\": 3000,\n",
+    "    \"random_state\": 42,\n",
+    "    \"solver\": \"saga\",\n",
+    "    \"class_weight\": \"balanced\"\n",
+    "}\n",
+    "\n",
+    "RUN_NAME_V2_1 = \"V2_LogisticRegression_ClassWeightBalanced\"\n",
+    "\n",
+    "fold_results_v2_1 = []\n",
+    "\n",
+    "# Terminer tout run actif\n",
+    "mlflow.end_run()\n",
+    "\n",
+    "with mlflow.start_run(run_name=RUN_NAME_V2_1):\n",
+    "    # ========== Logging des paramètres et tags ==========\n",
+    "    mlflow.log_params(MODEL_CONFIG_V2_1)\n",
+    "    mlflow.set_tag(\"version\", \"2\")\n",
+    "    mlflow.set_tag(\"model\", \"LogisticRegression\")\n",
+    "    mlflow.set_tag(\"notebook\", NOTEBOOK_NAME)\n",
+    "    mlflow.set_tag(\"phase\", \"imbalance_handling_cv\")\n",
+    "    mlflow.set_tag(\"threshold\", str(THRESHOLD_FIXED))\n",
+    "    mlflow.set_tag(\"scaling\", \"StandardScaler\")\n",
+    "    mlflow.set_tag(\"imbalance_handling\", \"class_weight\")\n",
+    "    mlflow.set_tag(\"model_type\", \"LogisticRegression\")\n",
+    "    \n",
+    "    # ========== StratifiedKFold (5 folds) ==========\n",
+    "    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n",
+    "    \n",
+    "    for fold_idx, (train_idx, val_idx) in enumerate(skf.split(X_train_scaled, y_train), start=1):\n",
+    "        X_tr, X_val = X_train_scaled.iloc[train_idx], X_train_scaled.iloc[val_idx]\n",
+    "        y_tr, y_val = y_train.iloc[train_idx], y_train.iloc[val_idx]\n",
+    "        \n",
+    "        # ========== Entraînement ==========\n",
+    "        model = LogisticRegression(**MODEL_CONFIG_V2_1)\n",
+    "        model.fit(X_tr, y_tr)\n",
+    "        \n",
+    "        # ========== Prédictions ==========\n",
+    "        y_val_proba = model.predict_proba(X_val)[:, 1]\n",
+    "        y_val_pred = (y_val_proba >= THRESHOLD_FIXED).astype(int)\n",
+    "        \n",
+    "        # ========== Métriques ==========\n",
+    "        auc = roc_auc_score(y_val, y_val_proba)\n",
+    "        accuracy = accuracy_score(y_val, y_val_pred)\n",
+    "        f1 = f1_score(y_val, y_val_pred)\n",
+    "        recall = recall_score(y_val, y_val_pred)\n",
+    "        \n",
+    "        # ========== Coût métier (seuil=0.5) ==========\n",
+    "        tn, fp, fn, tp = confusion_matrix(y_val, y_val_pred).ravel()\n",
+    "        cost = 10 * fn + 1 * fp\n",
+    "        \n",
+    "        fold_results_v2_1.append({\n",
+    "            \"fold\": fold_idx,\n",
+    "            \"auc\": auc,\n",
+    "            \"accuracy\": accuracy,\n",
+    "            \"f1_score\": f1,\n",
+    "            \"recall_class1\": recall,\n",
+    "            \"business_cost_min\": cost,\n",
+    "            \"optimal_threshold\": THRESHOLD_FIXED,\n",
+    "            \"tp\": tp,\n",
+    "            \"fp\": fp,\n",
+    "            \"fn\": fn,\n",
+    "            \"tn\": tn\n",
+    "        })\n",
+    "        \n",
+    "        print(f\"Fold {fold_idx}/5 | AUC={auc:.4f} | Acc={accuracy:.4f} | \"\n",
+    "              f\"F1={f1:.4f} | Recall={recall:.4f} | Cost={cost:.0f}\")\n",
+    "    \n",
+    "    # ========== Agrégation des résultats ==========\n",
+    "    cv_results_v2_1_df = pd.DataFrame(fold_results_v2_1)\n",
+    "    \n",
+    "    metrics_mean_v2_1 = {\n",
+    "        \"auc\": cv_results_v2_1_df[\"auc\"].mean(),\n",
+    "        \"f1_score\": cv_results_v2_1_df[\"f1_score\"].mean(),\n",
+    "        \"recall_class1\": cv_results_v2_1_df[\"recall_class1\"].mean(),\n",
+    "        \"business_cost_min\": cv_results_v2_1_df[\"business_cost_min\"].mean(),\n",
+    "        \"optimal_threshold\": THRESHOLD_FIXED,\n",
+    "    }\n",
+    "    \n",
+    "    metrics_std_v2_1 = {\n",
+    "        \"auc\": cv_results_v2_1_df[\"auc\"].std(),\n",
+    "        \"f1_score\": cv_results_v2_1_df[\"f1_score\"].std(),\n",
+    "        \"recall_class1\": cv_results_v2_1_df[\"recall_class1\"].std(),\n",
+    "        \"business_cost_min\": cv_results_v2_1_df[\"business_cost_min\"].std(),\n",
+    "    }\n",
+    "    \n",
+    "    # ========== Logging dans MLFlow ==========\n",
+    "    mlflow.log_metric(\"auc\", metrics_mean_v2_1[\"auc\"])\n",
+    "    mlflow.log_metric(\"f1_score\", metrics_mean_v2_1[\"f1_score\"])\n",
+    "    mlflow.log_metric(\"recall_class1\", metrics_mean_v2_1[\"recall_class1\"])\n",
+    "    mlflow.log_metric(\"business_cost_min\", metrics_mean_v2_1[\"business_cost_min\"])\n",
+    "    mlflow.log_metric(\"optimal_threshold\", metrics_mean_v2_1[\"optimal_threshold\"])\n",
+    "    \n",
+    "    # Log artefact JSON avec détails par fold\n",
+    "    mlflow.log_dict(cv_results_v2_1_df.to_dict(orient=\"records\"), \"cv_results_per_fold.json\")\n",
+    "    \n",
+    "    print(\"\\n✓ Cross-Validation LogisticRegression V2.1 (class_weight='balanced') terminée\")\n",
+    "    print(f\"  AUC moyen: {metrics_mean_v2_1['auc']:.4f} ± {metrics_std_v2_1['auc']:.4f}\")\n",
+    "    print(f\"  F1 moyen: {metrics_mean_v2_1['f1_score']:.4f} ± {metrics_std_v2_1['f1_score']:.4f}\")\n",
+    "    print(f\"  Recall moyen: {metrics_mean_v2_1['recall_class1']:.4f} ± {metrics_std_v2_1['recall_class1']:.4f}\")\n",
+    "    print(f\"  Coût métier moyen: {metrics_mean_v2_1['business_cost_min']:.2f} ± {metrics_std_v2_1['business_cost_min']:.2f}\")\n",
+    "    print(f\"  Seuil optimal: {metrics_mean_v2_1['optimal_threshold']:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "06214200",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✓ Modèle final LogisticRegression V2.1 entraîné sur l'ensemble train complet\n",
+      "  Nombre de features: 724\n",
+      "  Intercept: -0.917467\n",
+      "  Norme des coefficients: 2.441323\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/02/06 01:48:02 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n",
+      "2026/02/06 01:48:04 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✓ Modèle V2.1 enregistré dans MLflow\n",
+      "  AUC (CV): 0.6957\n",
+      "  F1 (CV): 0.2367\n",
+      "  Recall (CV): 0.5832\n",
+      "  Business Cost Min (CV): 1164.40\n",
+      "🏃 View run V2.1_LogisticRegression_ClassWeight_Final at: http://127.0.0.1:5000/#/experiments/1/runs/0bc8f5f187c94a349c72011de4524c77\n",
+      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# ENTRAÎNEMENT FINAL V2.1: LogisticRegression avec class_weight='balanced'\n",
+    "# ============================================================================\n",
+    "\n",
+    "final_model_v2_1 = LogisticRegression(**MODEL_CONFIG_V2_1)\n",
+    "final_model_v2_1.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "print(\"\\n✓ Modèle final LogisticRegression V2.1 entraîné sur l'ensemble train complet\")\n",
+    "print(f\"  Nombre de features: {X_train_scaled.shape[1]}\")\n",
+    "print(f\"  Intercept: {final_model_v2_1.intercept_[0]:.6f}\")\n",
+    "print(f\"  Norme des coefficients: {np.linalg.norm(final_model_v2_1.coef_):.6f}\")\n",
+    "\n",
+    "# ============================================================================\n",
+    "# LOGGING MLFLOW V2.1: Sauvegarde du modèle\n",
+    "# ============================================================================\n",
+    "\n",
+    "mlflow.end_run()\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"V2.1_LogisticRegression_ClassWeight_Final\"):\n",
+    "    # Logging des paramètres\n",
+    "    mlflow.log_params(MODEL_CONFIG_V2_1)\n",
+    "    \n",
+    "    # Tags\n",
+    "    mlflow.set_tag(\"version\", \"2.1\")\n",
+    "    mlflow.set_tag(\"model\", \"LogisticRegression\")\n",
+    "    mlflow.set_tag(\"phase\", \"final_model\")\n",
+    "    mlflow.set_tag(\"threshold\", str(THRESHOLD_FIXED))\n",
+    "    mlflow.set_tag(\"imbalance_handling\", \"class_weight\")\n",
+    "    mlflow.set_tag(\"model_type\", \"LogisticRegression\")\n",
+    "    \n",
+    "    # Logger les métriques CV\n",
+    "    mlflow.log_metric(\"auc\", metrics_mean_v2_1[\"auc\"])\n",
+    "    mlflow.log_metric(\"f1_score\", metrics_mean_v2_1[\"f1_score\"])\n",
+    "    mlflow.log_metric(\"recall_class1\", metrics_mean_v2_1[\"recall_class1\"])\n",
+    "    mlflow.log_metric(\"business_cost_min\", metrics_mean_v2_1[\"business_cost_min\"])\n",
+    "    mlflow.log_metric(\"optimal_threshold\", metrics_mean_v2_1[\"optimal_threshold\"])\n",
+    "    \n",
+    "    # Sauvegarder le modèle comme artefact\n",
+    "    mlflow.sklearn.log_model(\n",
+    "        final_model_v2_1,\n",
+    "        artifact_path=\"logistic_regression_v2_1_class_weight\"\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"\\n✓ Modèle V2.1 enregistré dans MLflow\")\n",
+    "    print(f\"  AUC (CV): {metrics_mean_v2_1['auc']:.4f}\")\n",
+    "    print(f\"  F1 (CV): {metrics_mean_v2_1['f1_score']:.4f}\")\n",
+    "    print(f\"  Recall (CV): {metrics_mean_v2_1['recall_class1']:.4f}\")\n",
+    "    print(f\"  Business Cost Min (CV): {metrics_mean_v2_1['business_cost_min']:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bf6d4baa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fold 1/5 | AUC=0.6801 | Acc=0.7145 | F1=0.2397 | Recall=0.5806 | Cost=1156\n",
+      "Fold 2/5 | AUC=0.6807 | Acc=0.6985 | F1=0.2299 | Recall=0.5806 | Cost=1188\n",
+      "Fold 3/5 | AUC=0.7055 | Acc=0.7375 | F1=0.2553 | Recall=0.5806 | Cost=1110\n",
+      "Fold 4/5 | AUC=0.6872 | Acc=0.7190 | F1=0.2301 | Recall=0.5419 | Cost=1201\n",
+      "Fold 5/5 | AUC=0.6914 | Acc=0.7435 | F1=0.2377 | Recall=0.5161 | Cost=1188\n",
+      "\n",
+      "✓ Cross-Validation LogisticRegression V2.2 (SMOTE) terminée\n",
+      "  AUC moyen: 0.6890 ± 0.0104\n",
+      "  F1 moyen: 0.2386 ± 0.0104\n",
+      "  Recall moyen: 0.5600 ± 0.0297\n",
+      "  Coût métier moyen: 1168.60 ± 36.73\n",
+      "  Seuil optimal: 0.50\n",
+      "🏃 View run V2_LogisticRegression_SMOTE at: http://127.0.0.1:5000/#/experiments/1/runs/dab29ff5c5a14880bb75287b1c5bcd5c\n",
+      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# VERSION 2.2: LogisticRegression avec SMOTE\n",
+    "# ============================================================================\n",
+    "# Objectif: Gérer le déséquilibre des classes avec SMOTE\n",
+    "# Validation: StratifiedKFold (5 folds)\n",
+    "# Modèle: LogisticRegression(max_iter=1000, random_state=42, solver='saga', penalty='l2')\n",
+    "# Pipeline: StandardScaler -> SMOTE -> LogisticRegression (pour éviter le data leakage)\n",
+    "# Features: X_train, y_train (seront scalées dans le pipeline)\n",
+    "# Seuil fixe: 0.5\n",
+    "# Métriques par fold: AUC-ROC, Accuracy, F1-score, Recall classe 1\n",
+    "# Coût métier: 10 * FN + 1 * FP (avec seuil=0.5)\n",
+    "# MLflow: run_name=\"V2_LogisticRegression_SMOTE\"\n",
+    "# Tags: version=\"2\", imbalance_handling=\"smote\"\n",
+    "\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from imblearn.pipeline import Pipeline as ImbPipeline\n",
+    "\n",
+    "# Configuration du modèle V2.2 (SMOTE)\n",
+    "MODEL_CONFIG_V2_2 = {\n",
+    "    \"max_iter\": 3000,\n",
+    "    \"random_state\": 42,\n",
+    "    \"solver\": \"saga\"\n",
+    "}\n",
+    "\n",
+    "RUN_NAME_V2_2 = \"V2_LogisticRegression_SMOTE\"\n",
+    "\n",
+    "fold_results_v2_2 = []\n",
+    "\n",
+    "# Terminer tout run actif\n",
+    "mlflow.end_run()\n",
+    "\n",
+    "with mlflow.start_run(run_name=RUN_NAME_V2_2):\n",
+    "    # ========== Logging des paramètres et tags ==========\n",
+    "    mlflow.log_params(MODEL_CONFIG_V2_2)\n",
+    "    mlflow.set_tag(\"version\", \"2\")\n",
+    "    mlflow.set_tag(\"model\", \"LogisticRegression\")\n",
+    "    mlflow.set_tag(\"notebook\", NOTEBOOK_NAME)\n",
+    "    mlflow.set_tag(\"phase\", \"imbalance_handling_cv\")\n",
+    "    mlflow.set_tag(\"threshold\", str(THRESHOLD_FIXED))\n",
+    "    mlflow.set_tag(\"scaling\", \"StandardScaler\")\n",
+    "    mlflow.set_tag(\"imbalance_handling\", \"smote\")\n",
+    "    mlflow.set_tag(\"model_type\", \"LogisticRegression\")\n",
+    "    \n",
+    "    # ========== StratifiedKFold (5 folds) ==========\n",
+    "    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n",
+    "    \n",
+    "    for fold_idx, (train_idx, val_idx) in enumerate(skf.split(X_train, y_train), start=1):\n",
+    "        X_tr, X_val = X_train.iloc[train_idx], X_train.iloc[val_idx]\n",
+    "        y_tr, y_val = y_train.iloc[train_idx], y_train.iloc[val_idx]\n",
+    "        \n",
+    "        # ========== Pipeline: Scaler -> SMOTE -> Model ==========\n",
+    "        # SMOTE est appliqué uniquement sur le train de chaque fold\n",
+    "        pipeline = ImbPipeline([\n",
+    "            ('scaler', StandardScaler()),\n",
+    "            ('smote', SMOTE(random_state=RANDOM_STATE)),\n",
+    "            ('model', LogisticRegression(**MODEL_CONFIG_V2_2))\n",
+    "        ])\n",
+    "        \n",
+    "        # ========== Entraînement ==========\n",
+    "        pipeline.fit(X_tr, y_tr)\n",
+    "        \n",
+    "        # ========== Prédictions ==========\n",
+    "        y_val_proba = pipeline.predict_proba(X_val)[:, 1]\n",
+    "        y_val_pred = (y_val_proba >= THRESHOLD_FIXED).astype(int)\n",
+    "        \n",
+    "        # ========== Métriques ==========\n",
+    "        auc = roc_auc_score(y_val, y_val_proba)\n",
+    "        accuracy = accuracy_score(y_val, y_val_pred)\n",
+    "        f1 = f1_score(y_val, y_val_pred)\n",
+    "        recall = recall_score(y_val, y_val_pred)\n",
+    "        \n",
+    "        # ========== Coût métier (seuil=0.5) ==========\n",
+    "        tn, fp, fn, tp = confusion_matrix(y_val, y_val_pred).ravel()\n",
+    "        cost = 10 * fn + 1 * fp\n",
+    "        \n",
+    "        fold_results_v2_2.append({\n",
+    "            \"fold\": fold_idx,\n",
+    "            \"auc\": auc,\n",
+    "            \"accuracy\": accuracy,\n",
+    "            \"f1_score\": f1,\n",
+    "            \"recall_class1\": recall,\n",
+    "            \"business_cost_min\": cost,\n",
+    "            \"optimal_threshold\": THRESHOLD_FIXED,\n",
+    "            \"tp\": tp,\n",
+    "            \"fp\": fp,\n",
+    "            \"fn\": fn,\n",
+    "            \"tn\": tn\n",
+    "        })\n",
+    "        \n",
+    "        print(f\"Fold {fold_idx}/5 | AUC={auc:.4f} | Acc={accuracy:.4f} | \"\n",
+    "              f\"F1={f1:.4f} | Recall={recall:.4f} | Cost={cost:.0f}\")\n",
+    "    \n",
+    "    # ========== Agrégation des résultats ==========\n",
+    "    cv_results_v2_2_df = pd.DataFrame(fold_results_v2_2)\n",
+    "    \n",
+    "    metrics_mean_v2_2 = {\n",
+    "        \"auc\": cv_results_v2_2_df[\"auc\"].mean(),\n",
+    "        \"f1_score\": cv_results_v2_2_df[\"f1_score\"].mean(),\n",
+    "        \"recall_class1\": cv_results_v2_2_df[\"recall_class1\"].mean(),\n",
+    "        \"business_cost_min\": cv_results_v2_2_df[\"business_cost_min\"].mean(),\n",
+    "        \"optimal_threshold\": THRESHOLD_FIXED,\n",
+    "    }\n",
+    "    \n",
+    "    metrics_std_v2_2 = {\n",
+    "        \"auc\": cv_results_v2_2_df[\"auc\"].std(),\n",
+    "        \"f1_score\": cv_results_v2_2_df[\"f1_score\"].std(),\n",
+    "        \"recall_class1\": cv_results_v2_2_df[\"recall_class1\"].std(),\n",
+    "        \"business_cost_min\": cv_results_v2_2_df[\"business_cost_min\"].std(),\n",
+    "    }\n",
+    "    \n",
+    "    # ========== Logging dans MLFlow ==========\n",
+    "    mlflow.log_metric(\"auc\", metrics_mean_v2_2[\"auc\"])\n",
+    "    mlflow.log_metric(\"f1_score\", metrics_mean_v2_2[\"f1_score\"])\n",
+    "    mlflow.log_metric(\"recall_class1\", metrics_mean_v2_2[\"recall_class1\"])\n",
+    "    mlflow.log_metric(\"business_cost_min\", metrics_mean_v2_2[\"business_cost_min\"])\n",
+    "    mlflow.log_metric(\"optimal_threshold\", metrics_mean_v2_2[\"optimal_threshold\"])\n",
+    "    \n",
+    "    # Log artefact JSON avec détails par fold\n",
+    "    mlflow.log_dict(cv_results_v2_2_df.to_dict(orient=\"records\"), \"cv_results_per_fold.json\")\n",
+    "    \n",
+    "    print(\"\\n✓ Cross-Validation LogisticRegression V2.2 (SMOTE) terminée\")\n",
+    "    print(f\"  AUC moyen: {metrics_mean_v2_2['auc']:.4f} ± {metrics_std_v2_2['auc']:.4f}\")\n",
+    "    print(f\"  F1 moyen: {metrics_mean_v2_2['f1_score']:.4f} ± {metrics_std_v2_2['f1_score']:.4f}\")\n",
+    "    print(f\"  Recall moyen: {metrics_mean_v2_2['recall_class1']:.4f} ± {metrics_std_v2_2['recall_class1']:.4f}\")\n",
+    "    print(f\"  Coût métier moyen: {metrics_mean_v2_2['business_cost_min']:.2f} ± {metrics_std_v2_2['business_cost_min']:.2f}\")\n",
+    "    print(f\"  Seuil optimal: {metrics_mean_v2_2['optimal_threshold']:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2d115187",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✓ Pipeline final LogisticRegression V2.2 (SMOTE) entraîné sur l'ensemble train complet\n",
+      "  Nombre de features: 724\n",
+      "  Intercept: -1.226644\n",
+      "  Norme des coefficients: 3.213375\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/02/06 02:01:57 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n",
+      "2026/02/06 02:01:59 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✓ Pipeline V2.2 enregistré dans MLflow\n",
+      "  AUC (CV): 0.6890\n",
+      "  F1 (CV): 0.2386\n",
+      "  Recall (CV): 0.5600\n",
+      "  Business Cost Min (CV): 1168.60\n",
+      "🏃 View run V2.2_LogisticRegression_SMOTE_Final at: http://127.0.0.1:5000/#/experiments/1/runs/9a1cd90834c84f43a6b660e9dcc0a408\n",
+      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# ENTRAÎNEMENT FINAL V2.2: LogisticRegression avec SMOTE\n",
+    "# ============================================================================\n",
+    "\n",
+    "# Pipeline complet avec SMOTE sur l'ensemble train\n",
+    "final_pipeline_v2_2 = ImbPipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('smote', SMOTE(random_state=RANDOM_STATE)),\n",
+    "    ('model', LogisticRegression(**MODEL_CONFIG_V2_2))\n",
+    "])\n",
+    "\n",
+    "final_pipeline_v2_2.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"\\n✓ Pipeline final LogisticRegression V2.2 (SMOTE) entraîné sur l'ensemble train complet\")\n",
+    "print(f\"  Nombre de features: {X_train.shape[1]}\")\n",
+    "print(f\"  Intercept: {final_pipeline_v2_2.named_steps['model'].intercept_[0]:.6f}\")\n",
+    "print(f\"  Norme des coefficients: {np.linalg.norm(final_pipeline_v2_2.named_steps['model'].coef_):.6f}\")\n",
+    "\n",
+    "# ============================================================================\n",
+    "# LOGGING MLFLOW V2.2: Sauvegarde du modèle\n",
+    "# ============================================================================\n",
+    "\n",
+    "mlflow.end_run()\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"V2.2_LogisticRegression_SMOTE_Final\"):\n",
+    "    # Logging des paramètres\n",
+    "    mlflow.log_params(MODEL_CONFIG_V2_2)\n",
+    "    \n",
+    "    # Tags\n",
+    "    mlflow.set_tag(\"version\", \"2.2\")\n",
+    "    mlflow.set_tag(\"model\", \"LogisticRegression\")\n",
+    "    mlflow.set_tag(\"phase\", \"final_model\")\n",
+    "    mlflow.set_tag(\"threshold\", str(THRESHOLD_FIXED))\n",
+    "    mlflow.set_tag(\"imbalance_handling\", \"smote\")\n",
+    "    mlflow.set_tag(\"model_type\", \"LogisticRegression\")\n",
+    "    \n",
+    "    # Logger les métriques CV\n",
+    "    mlflow.log_metric(\"auc\", metrics_mean_v2_2[\"auc\"])\n",
+    "    mlflow.log_metric(\"f1_score\", metrics_mean_v2_2[\"f1_score\"])\n",
+    "    mlflow.log_metric(\"recall_class1\", metrics_mean_v2_2[\"recall_class1\"])\n",
+    "    mlflow.log_metric(\"business_cost_min\", metrics_mean_v2_2[\"business_cost_min\"])\n",
+    "    mlflow.log_metric(\"optimal_threshold\", metrics_mean_v2_2[\"optimal_threshold\"])\n",
+    "    \n",
+    "    # Sauvegarder le pipeline complet comme artefact\n",
+    "    mlflow.sklearn.log_model(\n",
+    "        final_pipeline_v2_2,\n",
+    "        artifact_path=\"logistic_regression_v2_2_smote\"\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"\\n✓ Pipeline V2.2 enregistré dans MLflow\")\n",
+    "    print(f\"  AUC (CV): {metrics_mean_v2_2['auc']:.4f}\")\n",
+    "    print(f\"  F1 (CV): {metrics_mean_v2_2['f1_score']:.4f}\")\n",
+    "    print(f\"  Recall (CV): {metrics_mean_v2_2['recall_class1']:.4f}\")\n",
+    "    print(f\"  Business Cost Min (CV): {metrics_mean_v2_2['business_cost_min']:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9693605b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "========================================================================================================================\n",
+      "TABLEAU COMPARATIF: V1 Baseline vs V2.1 Class Weight vs V2.2 SMOTE\n",
+      "========================================================================================================================\n",
+      "          Version             AUC        F1-Score Recall Classe 1 Coût Métier Min      Imbalance Handling\n",
+      "      V1 Baseline 0.7010 ± 0.0038 0.0631 ± 0.0355 0.0361 ± 0.0202 1511.00 ± 34.85                    None\n",
+      "V2.1 Class Weight 0.6957 ± 0.0117 0.2367 ± 0.0078 0.5832 ± 0.0305 1164.40 ± 32.60 class_weight='balanced'\n",
+      "       V2.2 SMOTE 0.6890 ± 0.0104 0.2386 ± 0.0104 0.5600 ± 0.0297 1168.60 ± 36.73                   SMOTE\n",
+      "========================================================================================================================\n",
+      "\n",
+      "========================================================================================================================\n",
+      "ANALYSE DES AMÉLIORATIONS (vs V1 Baseline)\n",
+      "========================================================================================================================\n",
+      "          Version   Δ AUC Δ F1-Score Δ Recall Classe 1 Δ Coût Métier\n",
+      "V2.1 Class Weight -0.0052    +0.1736           +0.5471       -346.60\n",
+      "       V2.2 SMOTE -0.0120    +0.1754           +0.5239       -342.40\n",
+      "========================================================================================================================\n",
+      "\n",
+      "✓ Meilleure version par métrique:\n",
+      "  AUC:              V1\n",
+      "  F1-Score:         V2.2\n",
+      "  Recall Classe 1:  V2.1\n",
+      "  Coût Métier Min:  V2.1\n",
+      "========================================================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# TABLEAU COMPARATIF: V1 Baseline vs V2 Class Weight vs V2 SMOTE\n",
+    "# ============================================================================\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*120)\n",
+    "print(\"TABLEAU COMPARATIF: V1 Baseline vs V2.1 Class Weight vs V2.2 SMOTE\")\n",
+    "print(\"=\"*120)\n",
+    "\n",
+    "comparison_data = {\n",
+    "    \"Version\": [\"V1 Baseline\", \"V2.1 Class Weight\", \"V2.2 SMOTE\"],\n",
+    "    \"AUC\": [\n",
+    "        f\"{metrics_mean['auc']:.4f} ± {metrics_std['auc']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_1['auc']:.4f} ± {metrics_std_v2_1['auc']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_2['auc']:.4f} ± {metrics_std_v2_2['auc']:.4f}\",\n",
+    "    ],\n",
+    "    \"F1-Score\": [\n",
+    "        f\"{metrics_mean['f1_score']:.4f} ± {metrics_std['f1_score']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_1['f1_score']:.4f} ± {metrics_std_v2_1['f1_score']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_2['f1_score']:.4f} ± {metrics_std_v2_2['f1_score']:.4f}\",\n",
+    "    ],\n",
+    "    \"Recall Classe 1\": [\n",
+    "        f\"{metrics_mean['recall_class1']:.4f} ± {metrics_std['recall_class1']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_1['recall_class1']:.4f} ± {metrics_std_v2_1['recall_class1']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_2['recall_class1']:.4f} ± {metrics_std_v2_2['recall_class1']:.4f}\",\n",
+    "    ],\n",
+    "    \"Coût Métier Min\": [\n",
+    "        f\"{metrics_mean['business_cost_min']:.2f} ± {metrics_std['business_cost_min']:.2f}\",\n",
+    "        f\"{metrics_mean_v2_1['business_cost_min']:.2f} ± {metrics_std_v2_1['business_cost_min']:.2f}\",\n",
+    "        f\"{metrics_mean_v2_2['business_cost_min']:.2f} ± {metrics_std_v2_2['business_cost_min']:.2f}\",\n",
+    "    ],\n",
+    "    \"Imbalance Handling\": [\n",
+    "        \"None\",\n",
+    "        \"class_weight='balanced'\",\n",
+    "        \"SMOTE\",\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "comparison_df = pd.DataFrame(comparison_data)\n",
+    "print(comparison_df.to_string(index=False))\n",
+    "print(\"=\"*120)\n",
+    "\n",
+    "# Analyse des améliorations\n",
+    "print(\"\\n\" + \"=\"*120)\n",
+    "print(\"ANALYSE DES AMÉLIORATIONS (vs V1 Baseline)\")\n",
+    "print(\"=\"*120)\n",
+    "\n",
+    "improvement_data = {\n",
+    "    \"Version\": [\"V2.1 Class Weight\", \"V2.2 SMOTE\"],\n",
+    "    \"Δ AUC\": [\n",
+    "        f\"{metrics_mean_v2_1['auc'] - metrics_mean['auc']:+.4f}\",\n",
+    "        f\"{metrics_mean_v2_2['auc'] - metrics_mean['auc']:+.4f}\",\n",
+    "    ],\n",
+    "    \"Δ F1-Score\": [\n",
+    "        f\"{metrics_mean_v2_1['f1_score'] - metrics_mean['f1_score']:+.4f}\",\n",
+    "        f\"{metrics_mean_v2_2['f1_score'] - metrics_mean['f1_score']:+.4f}\",\n",
+    "    ],\n",
+    "    \"Δ Recall Classe 1\": [\n",
+    "        f\"{metrics_mean_v2_1['recall_class1'] - metrics_mean['recall_class1']:+.4f}\",\n",
+    "        f\"{metrics_mean_v2_2['recall_class1'] - metrics_mean['recall_class1']:+.4f}\",\n",
+    "    ],\n",
+    "    \"Δ Coût Métier\": [\n",
+    "        f\"{metrics_mean_v2_1['business_cost_min'] - metrics_mean['business_cost_min']:+.2f}\",\n",
+    "        f\"{metrics_mean_v2_2['business_cost_min'] - metrics_mean['business_cost_min']:+.2f}\",\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "improvement_df = pd.DataFrame(improvement_data)\n",
+    "print(improvement_df.to_string(index=False))\n",
+    "print(\"=\"*120)\n",
+    "\n",
+    "# Déterminer la meilleure version\n",
+    "best_auc_version = [\"V1\", \"V2.1\", \"V2.2\"][\n",
+    "    np.argmax([metrics_mean['auc'], metrics_mean_v2_1['auc'], metrics_mean_v2_2['auc']])\n",
+    "]\n",
+    "best_f1_version = [\"V1\", \"V2.1\", \"V2.2\"][\n",
+    "    np.argmax([metrics_mean['f1_score'], metrics_mean_v2_1['f1_score'], metrics_mean_v2_2['f1_score']])\n",
+    "]\n",
+    "best_recall_version = [\"V1\", \"V2.1\", \"V2.2\"][\n",
+    "    np.argmax([metrics_mean['recall_class1'], metrics_mean_v2_1['recall_class1'], metrics_mean_v2_2['recall_class1']])\n",
+    "]\n",
+    "best_cost_version = [\"V1\", \"V2.1\", \"V2.2\"][\n",
+    "    np.argmin([metrics_mean['business_cost_min'], metrics_mean_v2_1['business_cost_min'], metrics_mean_v2_2['business_cost_min']])\n",
+    "]\n",
+    "\n",
+    "print(\"\\n✓ Meilleure version par métrique:\")\n",
+    "print(f\"  AUC:              {best_auc_version}\")\n",
+    "print(f\"  F1-Score:         {best_f1_version}\")\n",
+    "print(f\"  Recall Classe 1:  {best_recall_version}\")\n",
+    "print(f\"  Coût Métier Min:  {best_cost_version}\")\n",
+    "print(\"=\"*120)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9d91d18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b014974",
+   "metadata": {},
+   "source": [
+    "# VERSION 3: Meilleur modèle avec scaling robuste optimisé\n",
+    "\n",
+    "Objectif: Réentraîner le meilleur modèle (V2.1 class_weight='balanced') avec un scaling plus adapté\n",
+    "- **RobustScaler**: Utilise la médiane et l'IQR (moins sensible aux outliers que StandardScaler)\n",
+    "- Validation: StratifiedKFold (5 folds)\n",
+    "- Modèle: LogisticRegression avec class_weight='balanced'\n",
+    "- Enregistrement dans MLflow Model Registry sous le nom \"regression\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b365be7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3fb11f15",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✓ Features scalées avec RobustScaler (médiane + IQR):\n",
+      "  Shape train: (10000, 724)\n",
+      "  Median: 0.00000000 (≈ 0)\n",
+      "  IQR (Interquartile Range): 0.052486\n",
+      "\n",
+      "  Comparaison vs StandardScaler:\n",
+      "  StandardScaler - Mean: -0.00000000, Std: 0.874353\n",
+      "  RobustScaler   - Median: 0.00000000, IQR: 0.052486\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# VERSION 3: Meilleur modèle avec RobustScaler (adapté aux outliers)\n",
+    "# ============================================================================\n",
+    "# Objectif: Améliorer le scaling pour des features avec outliers\n",
+    "# RobustScaler utilise la médiane et l'IQR au lieu de la moyenne et l'écart-type\n",
+    "# Plus robuste face aux valeurs extrêmes dans les données de crédit\n",
+    "\n",
+    "from sklearn.preprocessing import RobustScaler\n",
+    "\n",
+    "# Créer le RobustScaler\n",
+    "robust_scaler = RobustScaler()\n",
+    "X_train_robust = robust_scaler.fit_transform(X_train)\n",
+    "X_test_robust = robust_scaler.transform(X_test)\n",
+    "\n",
+    "# Reconvertir en DataFrame\n",
+    "X_train_robust = pd.DataFrame(X_train_robust, columns=X_train.columns)\n",
+    "X_test_robust = pd.DataFrame(X_test_robust, columns=X_test.columns)\n",
+    "\n",
+    "print(f\"\\n✓ Features scalées avec RobustScaler (médiane + IQR):\")\n",
+    "print(f\"  Shape train: {X_train_robust.shape}\")\n",
+    "print(f\"  Median: {X_train_robust.median().mean():.8f} (≈ 0)\")\n",
+    "print(f\"  IQR (Interquartile Range): {(X_train_robust.quantile(0.75) - X_train_robust.quantile(0.25)).mean():.6f}\")\n",
+    "print(f\"\\n  Comparaison vs StandardScaler:\")\n",
+    "print(f\"  StandardScaler - Mean: {X_train_scaled.mean().mean():.8f}, Std: {X_train_scaled.std().mean():.6f}\")\n",
+    "print(f\"  RobustScaler   - Median: {X_train_robust.median().mean():.8f}, IQR: {(X_train_robust.quantile(0.75) - X_train_robust.quantile(0.25)).mean():.6f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecfe6509",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "afbc053c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fold 1/5 | AUC=0.5488 | Acc=0.3675 | F1=0.1516 | Recall=0.7290 | Cost=1643\n",
+      "Fold 2/5 | AUC=0.5648 | Acc=0.4400 | F1=0.1592 | Recall=0.6839 | Cost=1561\n",
+      "Fold 3/5 | AUC=0.5284 | Acc=0.3270 | F1=0.1492 | Recall=0.7613 | Cost=1679\n",
+      "Fold 4/5 | AUC=0.5628 | Acc=0.3750 | F1=0.1554 | Recall=0.7419 | Cost=1610\n",
+      "Fold 5/5 | AUC=0.5070 | Acc=0.3575 | F1=0.1462 | Recall=0.7097 | Cost=1690\n",
+      "\n",
+      "✓ Cross-Validation LogisticRegression V3 (RobustScaler + class_weight) terminée\n",
+      "  AUC moyen: 0.5424 ± 0.0245\n",
+      "  F1 moyen: 0.1523 ± 0.0051\n",
+      "  Recall moyen: 0.7252 ± 0.0298\n",
+      "  Coût métier moyen: 1636.60 ± 52.71\n",
+      "  Seuil optimal: 0.50\n",
+      "🏃 View run V3_LogisticRegression_RobustScaler_ClassWeight at: http://127.0.0.1:5000/#/experiments/1/runs/f3c1d8a8220a4e5193cba3eb73b30df6\n",
+      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# CROSS-VALIDATION V3: Meilleur modèle (V2.1) avec RobustScaler\n",
+    "# ============================================================================\n",
+    "\n",
+    "MODEL_CONFIG_V3 = {\n",
+    "    \"max_iter\": 3000,\n",
+    "    \"random_state\": 42,\n",
+    "    \"solver\": \"saga\",\n",
+    "    \"class_weight\": \"balanced\"\n",
+    "}\n",
+    "\n",
+    "RUN_NAME_V3 = \"V3_LogisticRegression_RobustScaler_ClassWeight\"\n",
+    "\n",
+    "fold_results_v3 = []\n",
+    "\n",
+    "# Terminer tout run actif\n",
+    "mlflow.end_run()\n",
+    "\n",
+    "with mlflow.start_run(run_name=RUN_NAME_V3):\n",
+    "    # ========== Logging des paramètres et tags ==========\n",
+    "    mlflow.log_params(MODEL_CONFIG_V3)\n",
+    "    mlflow.set_tag(\"version\", \"3\")\n",
+    "    mlflow.set_tag(\"model\", \"LogisticRegression\")\n",
+    "    mlflow.set_tag(\"notebook\", NOTEBOOK_NAME)\n",
+    "    mlflow.set_tag(\"phase\", \"robust_scaling_cv\")\n",
+    "    mlflow.set_tag(\"threshold\", str(THRESHOLD_FIXED))\n",
+    "    mlflow.set_tag(\"scaling\", \"RobustScaler\")\n",
+    "    mlflow.set_tag(\"imbalance_handling\", \"class_weight\")\n",
+    "    mlflow.set_tag(\"model_type\", \"LogisticRegression\")\n",
+    "    \n",
+    "    # ========== StratifiedKFold (5 folds) ==========\n",
+    "    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n",
+    "    \n",
+    "    for fold_idx, (train_idx, val_idx) in enumerate(skf.split(X_train_robust, y_train), start=1):\n",
+    "        X_tr, X_val = X_train_robust.iloc[train_idx], X_train_robust.iloc[val_idx]\n",
+    "        y_tr, y_val = y_train.iloc[train_idx], y_train.iloc[val_idx]\n",
+    "        \n",
+    "        # ========== Entraînement ==========\n",
+    "        model = LogisticRegression(**MODEL_CONFIG_V3)\n",
+    "        model.fit(X_tr, y_tr)\n",
+    "        \n",
+    "        # ========== Prédictions ==========\n",
+    "        y_val_proba = model.predict_proba(X_val)[:, 1]\n",
+    "        y_val_pred = (y_val_proba >= THRESHOLD_FIXED).astype(int)\n",
+    "        \n",
+    "        # ========== Métriques ==========\n",
+    "        auc = roc_auc_score(y_val, y_val_proba)\n",
+    "        accuracy = accuracy_score(y_val, y_val_pred)\n",
+    "        f1 = f1_score(y_val, y_val_pred)\n",
+    "        recall = recall_score(y_val, y_val_pred)\n",
+    "        \n",
+    "        # ========== Coût métier (seuil=0.5) ==========\n",
+    "        tn, fp, fn, tp = confusion_matrix(y_val, y_val_pred).ravel()\n",
+    "        cost = 10 * fn + 1 * fp\n",
+    "        \n",
+    "        fold_results_v3.append({\n",
+    "            \"fold\": fold_idx,\n",
+    "            \"auc\": auc,\n",
+    "            \"accuracy\": accuracy,\n",
+    "            \"f1_score\": f1,\n",
+    "            \"recall_class1\": recall,\n",
+    "            \"business_cost_min\": cost,\n",
+    "            \"optimal_threshold\": THRESHOLD_FIXED,\n",
+    "            \"tp\": tp,\n",
+    "            \"fp\": fp,\n",
+    "            \"fn\": fn,\n",
+    "            \"tn\": tn\n",
+    "        })\n",
+    "        \n",
+    "        print(f\"Fold {fold_idx}/5 | AUC={auc:.4f} | Acc={accuracy:.4f} | \"\n",
+    "              f\"F1={f1:.4f} | Recall={recall:.4f} | Cost={cost:.0f}\")\n",
+    "    \n",
+    "    # ========== Agrégation des résultats ==========\n",
+    "    cv_results_v3_df = pd.DataFrame(fold_results_v3)\n",
+    "    \n",
+    "    metrics_mean_v3 = {\n",
+    "        \"auc\": cv_results_v3_df[\"auc\"].mean(),\n",
+    "        \"f1_score\": cv_results_v3_df[\"f1_score\"].mean(),\n",
+    "        \"recall_class1\": cv_results_v3_df[\"recall_class1\"].mean(),\n",
+    "        \"business_cost_min\": cv_results_v3_df[\"business_cost_min\"].mean(),\n",
+    "        \"optimal_threshold\": THRESHOLD_FIXED,\n",
+    "    }\n",
+    "    \n",
+    "    metrics_std_v3 = {\n",
+    "        \"auc\": cv_results_v3_df[\"auc\"].std(),\n",
+    "        \"f1_score\": cv_results_v3_df[\"f1_score\"].std(),\n",
+    "        \"recall_class1\": cv_results_v3_df[\"recall_class1\"].std(),\n",
+    "        \"business_cost_min\": cv_results_v3_df[\"business_cost_min\"].std(),\n",
+    "    }\n",
+    "    \n",
+    "    # ========== Logging dans MLFlow ==========\n",
+    "    mlflow.log_metric(\"auc\", metrics_mean_v3[\"auc\"])\n",
+    "    mlflow.log_metric(\"f1_score\", metrics_mean_v3[\"f1_score\"])\n",
+    "    mlflow.log_metric(\"recall_class1\", metrics_mean_v3[\"recall_class1\"])\n",
+    "    mlflow.log_metric(\"business_cost_min\", metrics_mean_v3[\"business_cost_min\"])\n",
+    "    mlflow.log_metric(\"optimal_threshold\", metrics_mean_v3[\"optimal_threshold\"])\n",
+    "    \n",
+    "    # Log artefact JSON avec détails par fold\n",
+    "    mlflow.log_dict(cv_results_v3_df.to_dict(orient=\"records\"), \"cv_results_per_fold.json\")\n",
+    "    \n",
+    "    print(\"\\n✓ Cross-Validation LogisticRegression V3 (RobustScaler + class_weight) terminée\")\n",
+    "    print(f\"  AUC moyen: {metrics_mean_v3['auc']:.4f} ± {metrics_std_v3['auc']:.4f}\")\n",
+    "    print(f\"  F1 moyen: {metrics_mean_v3['f1_score']:.4f} ± {metrics_std_v3['f1_score']:.4f}\")\n",
+    "    print(f\"  Recall moyen: {metrics_mean_v3['recall_class1']:.4f} ± {metrics_std_v3['recall_class1']:.4f}\")\n",
+    "    print(f\"  Coût métier moyen: {metrics_mean_v3['business_cost_min']:.2f} ± {metrics_std_v3['business_cost_min']:.2f}\")\n",
+    "    print(f\"  Seuil optimal: {metrics_mean_v3['optimal_threshold']:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "140396bb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e7ef636c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✓ Modèle final LogisticRegression V3 entraîné sur l'ensemble train complet\n",
+      "  Nombre de features: 724\n",
+      "  Intercept: -0.000000\n",
+      "  Norme des coefficients: 0.000000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026/02/06 02:11:47 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n",
+      "2026/02/06 02:11:49 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.\n",
+      "Registered model 'regression' already exists. Creating a new version of this model...\n",
+      "2026/02/06 02:11:49 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: regression, version 3\n",
+      "Created version '3' of model 'regression'.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✓ Modèle V3 enregistré dans MLflow Model Registry sous le nom 'regression'\n",
+      "  AUC (CV): 0.5424\n",
+      "  F1 (CV): 0.1523\n",
+      "  Recall (CV): 0.7252\n",
+      "  Business Cost Min (CV): 1636.60\n",
+      "\n",
+      "  Model URI: models:/m-6f0e559865f84c4a9bae981ffb44747e\n",
+      "  ℹ️  Ce modèle est maintenant disponible dans le Model Registry\n",
+      "     Accessible via: mlflow.sklearn.load_model('models:/regression/latest')\n",
+      "🏃 View run V3_LogisticRegression_RobustScaler_Final at: http://127.0.0.1:5000/#/experiments/1/runs/6d7ce4bf0fa94725a3b69b3f85e5bdc8\n",
+      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# ENTRAÎNEMENT FINAL V3 + ENREGISTREMENT DANS MODEL REGISTRY\n",
+    "# ============================================================================\n",
+    "\n",
+    "# Entraîner le modèle final sur tout le train set avec RobustScaler\n",
+    "final_model_v3 = LogisticRegression(**MODEL_CONFIG_V3)\n",
+    "final_model_v3.fit(X_train_robust, y_train)\n",
+    "\n",
+    "print(\"\\n✓ Modèle final LogisticRegression V3 entraîné sur l'ensemble train complet\")\n",
+    "print(f\"  Nombre de features: {X_train_robust.shape[1]}\")\n",
+    "print(f\"  Intercept: {final_model_v3.intercept_[0]:.6f}\")\n",
+    "print(f\"  Norme des coefficients: {np.linalg.norm(final_model_v3.coef_):.6f}\")\n",
+    "\n",
+    "# ============================================================================\n",
+    "# LOGGING MLFLOW V3 + ENREGISTREMENT DANS MODEL REGISTRY\n",
+    "# ============================================================================\n",
+    "\n",
+    "mlflow.end_run()\n",
+    "\n",
+    "with mlflow.start_run(run_name=\"V3_LogisticRegression_RobustScaler_Final\"):\n",
+    "    # Logging des paramètres\n",
+    "    mlflow.log_params(MODEL_CONFIG_V3)\n",
+    "    \n",
+    "    # Tags\n",
+    "    mlflow.set_tag(\"version\", \"3\")\n",
+    "    mlflow.set_tag(\"model\", \"LogisticRegression\")\n",
+    "    mlflow.set_tag(\"phase\", \"final_model\")\n",
+    "    mlflow.set_tag(\"threshold\", str(THRESHOLD_FIXED))\n",
+    "    mlflow.set_tag(\"scaling\", \"RobustScaler\")\n",
+    "    mlflow.set_tag(\"imbalance_handling\", \"class_weight\")\n",
+    "    mlflow.set_tag(\"model_type\", \"LogisticRegression\")\n",
+    "    mlflow.set_tag(\"best_model\", \"true\")\n",
+    "    \n",
+    "    # Logger les métriques CV\n",
+    "    mlflow.log_metric(\"auc\", metrics_mean_v3[\"auc\"])\n",
+    "    mlflow.log_metric(\"f1_score\", metrics_mean_v3[\"f1_score\"])\n",
+    "    mlflow.log_metric(\"recall_class1\", metrics_mean_v3[\"recall_class1\"])\n",
+    "    mlflow.log_metric(\"business_cost_min\", metrics_mean_v3[\"business_cost_min\"])\n",
+    "    mlflow.log_metric(\"optimal_threshold\", metrics_mean_v3[\"optimal_threshold\"])\n",
+    "    \n",
+    "    # Sauvegarder le modèle ET l'enregistrer dans le Model Registry\n",
+    "    model_info = mlflow.sklearn.log_model(\n",
+    "        final_model_v3,\n",
+    "        artifact_path=\"logistic_regression_v3_robust_scaler\",\n",
+    "        registered_model_name=\"regression\"  # Enregistrement automatique dans Model Registry\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"\\n✓ Modèle V3 enregistré dans MLflow Model Registry sous le nom 'regression'\")\n",
+    "    print(f\"  AUC (CV): {metrics_mean_v3['auc']:.4f}\")\n",
+    "    print(f\"  F1 (CV): {metrics_mean_v3['f1_score']:.4f}\")\n",
+    "    print(f\"  Recall (CV): {metrics_mean_v3['recall_class1']:.4f}\")\n",
+    "    print(f\"  Business Cost Min (CV): {metrics_mean_v3['business_cost_min']:.2f}\")\n",
+    "    print(f\"\\n  Model URI: {model_info.model_uri}\")\n",
+    "    print(f\"  ℹ️  Ce modèle est maintenant disponible dans le Model Registry\")\n",
+    "    print(f\"     Accessible via: mlflow.sklearn.load_model('models:/regression/latest')\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7af82263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================================================================================================================================\n",
+      "TABLEAU COMPARATIF FINAL: Toutes les versions\n",
+      "==================================================================================================================================\n",
+      "             Version        Scaling    Imbalance             AUC        F1-Score          Recall     Coût Métier\n",
+      "         V1 Baseline StandardScaler         None 0.7010 ± 0.0038 0.0631 ± 0.0355 0.0361 ± 0.0202 1511.00 ± 34.85\n",
+      "   V2.1 Class Weight StandardScaler class_weight 0.6957 ± 0.0117 0.2367 ± 0.0078 0.5832 ± 0.0305 1164.40 ± 32.60\n",
+      "          V2.2 SMOTE StandardScaler        SMOTE 0.6890 ± 0.0104 0.2386 ± 0.0104 0.5600 ± 0.0297 1168.60 ± 36.73\n",
+      "V3 RobustScaler + CW   RobustScaler class_weight 0.5424 ± 0.0245 0.1523 ± 0.0051 0.7252 ± 0.0298 1636.60 ± 52.71\n",
+      "==================================================================================================================================\n",
+      "\n",
+      "✓ Meilleure version par métrique:\n",
+      "  AUC:              V1 (0.7010)\n",
+      "  F1-Score:         V2.2 (0.2386)\n",
+      "  Recall Classe 1:  V3 (0.7252)\n",
+      "  Coût Métier Min:  V2.1 (1164.40)\n",
+      "\n",
+      "==================================================================================================================================\n",
+      "✓ MODÈLE FINAL SÉLECTIONNÉ: V2.1 StandardScaler + class_weight='balanced'\n",
+      "  Basé sur le Coût Métier (métrique métier principale): 1164.40\n",
+      "  Note: V3 a été enregistré dans Model Registry pour démonstration,\n",
+      "        mais V2.1 StandardScaler + class_weight='balanced' a de meilleures performances\n",
+      "==================================================================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================================\n",
+    "# TABLEAU COMPARATIF FINAL: Toutes les versions (V1, V2.1, V2.2, V3)\n",
+    "# ============================================================================\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*130)\n",
+    "print(\"TABLEAU COMPARATIF FINAL: Toutes les versions\")\n",
+    "print(\"=\"*130)\n",
+    "\n",
+    "comparison_data_final = {\n",
+    "    \"Version\": [\"V1 Baseline\", \"V2.1 Class Weight\", \"V2.2 SMOTE\", \"V3 RobustScaler + CW\"],\n",
+    "    \"Scaling\": [\"StandardScaler\", \"StandardScaler\", \"StandardScaler\", \"RobustScaler\"],\n",
+    "    \"Imbalance\": [\"None\", \"class_weight\", \"SMOTE\", \"class_weight\"],\n",
+    "    \"AUC\": [\n",
+    "        f\"{metrics_mean['auc']:.4f} ± {metrics_std['auc']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_1['auc']:.4f} ± {metrics_std_v2_1['auc']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_2['auc']:.4f} ± {metrics_std_v2_2['auc']:.4f}\",\n",
+    "        f\"{metrics_mean_v3['auc']:.4f} ± {metrics_std_v3['auc']:.4f}\",\n",
+    "    ],\n",
+    "    \"F1-Score\": [\n",
+    "        f\"{metrics_mean['f1_score']:.4f} ± {metrics_std['f1_score']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_1['f1_score']:.4f} ± {metrics_std_v2_1['f1_score']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_2['f1_score']:.4f} ± {metrics_std_v2_2['f1_score']:.4f}\",\n",
+    "        f\"{metrics_mean_v3['f1_score']:.4f} ± {metrics_std_v3['f1_score']:.4f}\",\n",
+    "    ],\n",
+    "    \"Recall\": [\n",
+    "        f\"{metrics_mean['recall_class1']:.4f} ± {metrics_std['recall_class1']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_1['recall_class1']:.4f} ± {metrics_std_v2_1['recall_class1']:.4f}\",\n",
+    "        f\"{metrics_mean_v2_2['recall_class1']:.4f} ± {metrics_std_v2_2['recall_class1']:.4f}\",\n",
+    "        f\"{metrics_mean_v3['recall_class1']:.4f} ± {metrics_std_v3['recall_class1']:.4f}\",\n",
+    "    ],\n",
+    "    \"Coût Métier\": [\n",
+    "        f\"{metrics_mean['business_cost_min']:.2f} ± {metrics_std['business_cost_min']:.2f}\",\n",
+    "        f\"{metrics_mean_v2_1['business_cost_min']:.2f} ± {metrics_std_v2_1['business_cost_min']:.2f}\",\n",
+    "        f\"{metrics_mean_v2_2['business_cost_min']:.2f} ± {metrics_std_v2_2['business_cost_min']:.2f}\",\n",
+    "        f\"{metrics_mean_v3['business_cost_min']:.2f} ± {metrics_std_v3['business_cost_min']:.2f}\",\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "comparison_df_final = pd.DataFrame(comparison_data_final)\n",
+    "print(comparison_df_final.to_string(index=False))\n",
+    "print(\"=\"*130)\n",
+    "\n",
+    "# Déterminer la meilleure version finale\n",
+    "all_metrics = {\n",
+    "    \"V1\": metrics_mean,\n",
+    "    \"V2.1\": metrics_mean_v2_1,\n",
+    "    \"V2.2\": metrics_mean_v2_2,\n",
+    "    \"V3\": metrics_mean_v3\n",
+    "}\n",
+    "\n",
+    "best_auc_v = max(all_metrics.items(), key=lambda x: x[1]['auc'])\n",
+    "best_f1_v = max(all_metrics.items(), key=lambda x: x[1]['f1_score'])\n",
+    "best_recall_v = max(all_metrics.items(), key=lambda x: x[1]['recall_class1'])\n",
+    "best_cost_v = min(all_metrics.items(), key=lambda x: x[1]['business_cost_min'])\n",
+    "\n",
+    "print(\"\\n✓ Meilleure version par métrique:\")\n",
+    "print(f\"  AUC:              {best_auc_v[0]} ({best_auc_v[1]['auc']:.4f})\")\n",
+    "print(f\"  F1-Score:         {best_f1_v[0]} ({best_f1_v[1]['f1_score']:.4f})\")\n",
+    "print(f\"  Recall Classe 1:  {best_recall_v[0]} ({best_recall_v[1]['recall_class1']:.4f})\")\n",
+    "print(f\"  Coût Métier Min:  {best_cost_v[0]} ({best_cost_v[1]['business_cost_min']:.2f})\")\n",
+    "\n",
+    "# Sélection dynamique basée sur le Coût Métier (métrique métier principale)\n",
+    "best_overall_version = best_cost_v[0]\n",
+    "\n",
+    "version_names = {\n",
+    "    \"V1\": \"V1 Baseline\",\n",
+    "    \"V2.1\": \"V2.1 StandardScaler + class_weight='balanced'\",\n",
+    "    \"V2.2\": \"V2.2 StandardScaler + SMOTE\",\n",
+    "    \"V3\": \"V3 RobustScaler + class_weight='balanced'\"\n",
+    "}\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*130)\n",
+    "print(f\"✓ MODÈLE FINAL SÉLECTIONNÉ: {version_names[best_overall_version]}\")\n",
+    "print(f\"  Basé sur le Coût Métier (métrique métier principale): {best_cost_v[1]['business_cost_min']:.2f}\")\n",
+    "\n",
+    "if best_overall_version == \"V3\":\n",
+    "    print(\"  Enregistré dans MLflow Model Registry sous le nom: 'regression'\")\n",
+    "else:\n",
+    "    print(f\"  Note: V3 a été enregistré dans Model Registry pour démonstration,\")\n",
+    "    print(f\"        mais {version_names[best_overall_version]} a de meilleures performances\")\n",
+    "\n",
+    "print(\"=\"*130)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "OC_P6",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/06_analyse_logs.ipynb

@@ -0,0 +1,231 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c826ce47",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ 500 appels chargés\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "import json\n",
+    "\n",
+    "# EXPLICATION : Chargement des logs depuis le fichier JSONL\n",
+    "# (1 JSON par ligne) - format pratique pour l'ajout incrémental d'entrées\n",
+    "# Le chemin relatif \"../logs/predictions.jsonl\" remonte d'un niveau depuis notebooks/\n",
+    "LOG_FILE = Path(\"../logs/predictions.jsonl\")\n",
+    "df = pd.read_json(LOG_FILE, lines=True, convert_dates=['timestamp'])\n",
+    "\n",
+    "# EXPLICATION : Nettoyage des \"\" en NaN - critique car certaines features\n",
+    "# peuvent manquer (représentées comme strings vides). Facilite la détection\n",
+    "# des anomalies plus tard.\n",
+    "df['input_features'] = df['input_features'].apply(lambda x: {k: None if v == \"\" else v for k, v in x.items()} if isinstance(x, dict) else x)\n",
+    "print(f\"✅ {len(df)} appels chargés\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86dea636",
+   "metadata": {},
+   "source": [
+    "## 1. Statistiques opérationnelles de base"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3a15f163",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 Total appels : 500\n",
+      "❌ Taux d'erreur : 0.00%\n",
+      "⏱️  Latence moyenne : 83.86 ms\n",
+      "⏱️  P95 latence : 188.85 ms\n",
+      "🚫 Taux de refus : 8.60%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EXPLICATION : Calcul des métriques clés pour surveiller la santé du service\n",
+    "\n",
+    "# Nombre total d'appels\n",
+    "total_calls = len(df)\n",
+    "print(f\"📊 Total appels : {total_calls}\")\n",
+    "\n",
+    "# EXPLICATION : Taux d'erreur = proportion d'appels avec une erreur enregistrée\n",
+    "# (error != NaN)\n",
+    "error_rate_pct = (df['error'].notna().sum() / total_calls) * 100\n",
+    "print(f\"❌ Taux d'erreur : {error_rate_pct:.2f}%\")\n",
+    "\n",
+    "# EXPLICATION : Latence moyenne et P95 (95e percentile) pour déterminer\n",
+    "# si le service répond assez vite\n",
+    "avg_latence = df['execution_time_ms'].mean()\n",
+    "p95_latence = df['execution_time_ms'].quantile(0.95)\n",
+    "print(f\"⏱️  Latence moyenne : {avg_latence:.2f} ms\")\n",
+    "print(f\"⏱️  P95 latence : {p95_latence:.2f} ms\")\n",
+    "\n",
+    "# EXPLICATION : Taux de refus = proportion d'appels avec prédiction = \"Refusé\"\n",
+    "# Utile pour le monitoring du pattern de décisions du modèle\n",
+    "refused_rate_pct = (df['output_decision'] == 'Refusé').sum() / total_calls * 100\n",
+    "print(f\"🚫 Taux de refus : {refused_rate_pct:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58a61de5",
+   "metadata": {},
+   "source": [
+    "## 2. Visualisations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "164d30e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Visualisations générées\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EXPLICATION : Créer 3 sous-graphiques pour analyser rapidement\n",
+    "# la distribution des latences, des probabilités et la stabilité quotidienne\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
+    "\n",
+    "# Graphique 1 : Distribution des latences (execution_time_ms)\n",
+    "# EXPLICATION : Montre si les appels sont rapides et constants\n",
+    "# ou s'il y a des outliers (appels très lents)\n",
+    "axes[0].hist(df['execution_time_ms'], bins=30, edgecolor='black', color='skyblue')\n",
+    "axes[0].set_xlabel('Temps d\\'exécution (ms)')\n",
+    "axes[0].set_ylabel('Nombre d\\'appels')\n",
+    "axes[0].set_title('Histogramme des latences')\n",
+    "axes[0].axvline(avg_latence, color='red', linestyle='--', label=f'Moyenne: {avg_latence:.1f} ms')\n",
+    "axes[0].legend()\n",
+    "\n",
+    "# Graphique 2 : Distribution des probabilités prédites\n",
+    "# EXPLICATION : Montre si le modèle est confiant (pics aux extrêmes)\n",
+    "# ou hésitant (pics au centre)\n",
+    "axes[1].hist(df['output_proba'], bins=30, edgecolor='black', color='lightgreen')\n",
+    "axes[1].set_xlabel('Probabilité prédite')\n",
+    "axes[1].set_ylabel('Nombre d\\'appels')\n",
+    "axes[1].set_title('Histogramme des probabilités')\n",
+    "\n",
+    "# Graphique 3 : Taux d'erreur par jour\n",
+    "# EXPLICATION : Détecte si des erreurs surviennent de manière récurrente\n",
+    "# ou sporadique (aide à identifier une dégradation du service)\n",
+    "daily_error_rate = df.groupby(df['timestamp'].dt.date).apply(\n",
+    "    lambda x: (x['error'].notna().sum() / len(x)) * 100\n",
+    ")\n",
+    "daily_error_rate.plot(kind='bar', ax=axes[2], color='coral', edgecolor='black')\n",
+    "axes[2].set_xlabel('Date')\n",
+    "axes[2].set_ylabel('Taux d\\'erreur (%)')\n",
+    "axes[2].set_title('Taux d\\'erreur par jour')\n",
+    "axes[2].tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"✅ Visualisations générées\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8199233",
+   "metadata": {},
+   "source": [
+    "## 3. Alertes automatiques simples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "72320a61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Taux d'erreur OK (0.00%)\n",
+      "✅ P95 latence OK (188.85 ms)\n",
+      "✅ Analyse terminée\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EXPLICATION : Définition de seuils d'alerte simples mais efficaces\n",
+    "# pour détecter rapidement les problèmes opérationnels\n",
+    "\n",
+    "error_rate = (df['error'].notna().mean() * 100)\n",
+    "p95_latence = df['execution_time_ms'].quantile(0.95)\n",
+    "\n",
+    "# EXPLICATION : Seuil 5% pour taux d'erreur = niveau d'alerte modéré\n",
+    "# Permet de détecter les dégradations progressives\n",
+    "if error_rate > 5:\n",
+    "    print(\"🔴 ALERTE : Taux d'erreur > 5% !\")\n",
+    "else:\n",
+    "    print(f\"✅ Taux d'erreur OK ({error_rate:.2f}%)\")\n",
+    "\n",
+    "# EXPLICATION : Seuil 500 ms pour P95 latence = limite acceptable pour l'UX\n",
+    "# Les utilisateurs remarquent les délais > 500ms\n",
+    "if p95_latence > 500:\n",
+    "    print(\"🔴 ALERTE : P95 latence > 500 ms !\")\n",
+    "else:\n",
+    "    print(f\"✅ P95 latence OK ({p95_latence:.2f} ms)\")\n",
+    "\n",
+    "print(\"✅ Analyse terminée\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "OC_P6",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/07_detect_data_drift.ipynb

@@ -0,0 +1,241 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "72d11d95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Evidently importe\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EXPLICATION : Imports Evidently pour comparaison de distributions\n",
+    "# - Report : genere les rapports automatiques\n",
+    "# - DataDriftPreset : ensemble de metriques pour detecter le drift (Distribution, KS Test, etc.)\n",
+    "# - ColumnMapping : informe Evidently du type de chaque colonne (numerique/categorique)\n",
+    "\n",
+    "import pandas as pd\n",
+    "import json\n",
+    "from pathlib import Path\n",
+    "\n",
+    "try:\n",
+    "    from evidently.legacy.report import Report\n",
+    "    from evidently.legacy.metric_preset import DataDriftPreset\n",
+    "    from evidently.legacy.pipeline.column_mapping import ColumnMapping\n",
+    "except ImportError:\n",
+    "    # Fallback for older/newer Evidently layouts\n",
+    "    from evidently.report import Report\n",
+    "    from evidently.metric_preset import DataDriftPreset\n",
+    "    from evidently.pipeline.column_mapping import ColumnMapping\n",
+    "\n",
+    "print(\"✅ Evidently importe\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b33c429",
+   "metadata": {},
+   "source": [
+    "## Chargement référence et données production"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "61a259c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "⚠️ Colonnes vides supprimées : 31\n",
+      "✅ Référence : 10000 lignes | Production : 500 lignes\n",
+      "   Colonnes analysées : 711\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EXPLICATION : \n",
+    "# 1. Référence = distribution d'entraînement (dataset pristine)\n",
+    "# 2. Production = features réelles extraites des logs d'inférence\n",
+    "# 3. Nettoyage : convertir \"\" en NaN (valeurs vides)\n",
+    "# 4. Aligner : garder seulement colonnes communes (peut y avoir des différences en production)\n",
+    "\n",
+    "# Référence (entraînement)\n",
+    "reference = pd.read_csv(\"../reference/reference.csv\")\n",
+    "\n",
+    "# Production : extraire input_features des logs\n",
+    "LOG_FILE = Path(\"../logs/predictions.jsonl\")\n",
+    "logs = pd.read_json(LOG_FILE, lines=True)\n",
+    "production = pd.json_normalize(logs['input_features'])\n",
+    "\n",
+    "# Nettoyage (\"\" → NaN, aligner colonnes)\n",
+    "production = production.replace(\"\", pd.NA).infer_objects()\n",
+    "# EXPLICATION : infer_objects() détecte automatiquement les vrais types (ex: strings → objects)\n",
+    "\n",
+    "# Garder seulement les colonnes communes avec la référence\n",
+    "# (en production, certaines colonnes peuvent être absentes ou ajoutées)\n",
+    "common_cols = list(set(reference.columns) & set(production.columns))\n",
+    "reference = reference[common_cols]\n",
+    "production = production[common_cols]\n",
+    "\n",
+    "# Supprimer les colonnes vides (100% NaN) pour éviter les erreurs Evidently\n",
+    "empty_ref = reference.columns[reference.isna().all()].tolist()\n",
+    "empty_prod = production.columns[production.isna().all()].tolist()\n",
+    "empty_cols = sorted(set(empty_ref) | set(empty_prod))\n",
+    "if empty_cols:\n",
+    "    reference = reference.drop(columns=empty_cols)\n",
+    "    production = production.drop(columns=empty_cols)\n",
+    "    print(f\"⚠️ Colonnes vides supprimées : {len(empty_cols)}\")\n",
+    "\n",
+    "print(f\"✅ Référence : {len(reference)} lignes | Production : {len(production)} lignes\")\n",
+    "print(f\"   Colonnes analysées : {len(reference.columns)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a5feb72",
+   "metadata": {},
+   "source": [
+    "## Calcul du data drift + génération du rapport"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8e4c48a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Numériques : 580 | Catégorique : 131\n",
+      "✅ Rapport généré : reports/data_drift_report.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EXPLICATION : ColumnMapping aide Evidently à utiliser les bonnes métriques\n",
+    "# - Features numériques : test KS (Kolmogorov-Smirnov) pour comparaison de distributions\n",
+    "# - Features catégorique : test Chi-Squared pour comparer les fréquences\n",
+    "\n",
+    "column_mapping = ColumnMapping()\n",
+    "column_mapping.numerical_features = reference.select_dtypes(include=['number']).columns.tolist()\n",
+    "column_mapping.categorical_features = reference.select_dtypes(include=['object', 'bool']).columns.tolist()\n",
+    "\n",
+    "print(f\"   Numériques : {len(column_mapping.numerical_features)} | Catégorique : {len(column_mapping.categorical_features)}\")\n",
+    "\n",
+    "# EXPLICATION : DataDriftPreset inclut :\n",
+    "# - Drift per column (KS test pour numériques, Chi2 pour catégories)\n",
+    "# - Dataset drift ratio\n",
+    "# - Détection automatique pour seuil default (0.95 confiance)\n",
+    "data_drift_report = Report(metrics=[DataDriftPreset()])\n",
+    "data_drift_report.run(reference_data=reference, current_data=production, column_mapping=column_mapping)\n",
+    "\n",
+    "# Sauvegarde HTML (dashboard interactif)\n",
+    "REPORT_DIR = Path(\"../reports\")\n",
+    "REPORT_DIR.mkdir(exist_ok=True)\n",
+    "report_path = REPORT_DIR / \"data_drift_report.html\"\n",
+    "data_drift_report.save_html(str(report_path))\n",
+    "print(\"✅ Rapport généré : reports/data_drift_report.html\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6e9f4c5",
+   "metadata": {},
+   "source": [
+    "## Alertes automatiques"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c5497ce9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔴 ALERTE : Drift détecté sur 1 features !\n",
+      "   Exemples : ['AMT_INCOME_TOTAL']\n",
+      "\n",
+      "   📋 Recommandations : \n",
+      "   - Vérifier source des données (anomalie/changement)\n",
+      "   - Envisager réentraînement du modèle\n",
+      "   - Ajouter monitoring continu sur ces features\n",
+      "\n",
+      "📊 Ouvre le fichier reports/data_drift_report.html dans ton navigateur pour le dashboard complet\n"
+     ]
+    }
+   ],
+   "source": [
+    "# EXPLICATION : \n",
+    "# - Extraire les résultats du rapport (dictionnaire structuré)\n",
+    "# - Seuil 0.3 : drift_score > 0.3 = **drift modéré à fort** (sensibilité équilibrée)\n",
+    "#   * 0.1-0.3 = léger (toléré)\n",
+    "#   * > 0.3 = alerte (intervention recommandée)\n",
+    "# - Ce seuil est a :  selon besoin métier (plus strict = plus d'alertes)\n",
+    "\n",
+    "# Exemple d'alerte sur features qui driftent fortement\n",
+    "report_dict = data_drift_report.as_dict()\n",
+    "drift_summary = None\n",
+    "for metric in report_dict.get(\"metrics\", []):\n",
+    "    result = metric.get(\"result\", {})\n",
+    "    if \"drift_by_columns\" in result:\n",
+    "        drift_summary = result[\"drift_by_columns\"]\n",
+    "        break\n",
+    "\n",
+    "if drift_summary is None:\n",
+    "    sample_keys = [list(m.get(\"result\", {}).keys()) for m in report_dict.get(\"metrics\", [])[:3]]\n",
+    "    print(\"⚠️ Impossible de trouver 'drift_by_columns' dans le rapport Evidently\")\n",
+    "    print(f\"   Exemples de clés disponibles : {sample_keys}\")\n",
+    "else:\n",
+    "    drifted_features = [col for col, info in drift_summary.items()\n",
+    "                        if info.get(\"drift_detected\") and info.get(\"drift_score\", 0) > 0.3]\n",
+    "\n",
+    "    if len(drifted_features) > 0:\n",
+    "        print(f\"🔴 ALERTE : Drift détecté sur {len(drifted_features)} features !\")\n",
+    "        print(f\"   Exemples : {drifted_features[:5]}\")\n",
+    "        print(\"\\n   📋 Recommandations : \")\n",
+    "        print(\"   - Vérifier source des données (anomalie/changement)\") \n",
+    "        print(\"   - Envisager réentraînement du modèle\")\n",
+    "        print(\"   - Ajouter monitoring continu sur ces features\")\n",
+    "    else:\n",
+    "        print(\"✅ Aucun drift majeur détecté\")\n",
+    "\n",
+    "print(\"\\n📊 Ouvre le fichier reports/data_drift_report.html dans ton navigateur pour le dashboard complet\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "OC_P6",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/10_optimisation.ipynb

@@ -0,0 +1,540 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e3bb2742",
+   "metadata": {},
+   "source": [
+    "# Optimisation des performances\n",
+    "\n",
+    "**Objectif** : Réduire la latence en vectorisant le preprocessing pandas + passant en ONNX (étape 4 du projet OC_P6)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "aff68336",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Imports réussis\n",
+      "✅ Modèle LightGBM chargé depuis ../models/lightgbm.txt\n",
+      "✅ Preprocessor chargé depuis ../models/preprocessor.joblib\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ─────────────────────────────────────────────────────────────────────────────\n",
+    "# CELLULE 2 : Imports + Chargement du modèle original\n",
+    "# ─────────────────────────────────────────────────────────────────────────────\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import onnxruntime as ort\n",
+    "import lightgbm as lgb\n",
+    "import joblib\n",
+    "import time\n",
+    "import statistics\n",
+    "from pathlib import Path\n",
+    "from typing import Dict, List, Tuple\n",
+    "\n",
+    "# Importer le transformer et la fonction pred de app.py\n",
+    "import sys\n",
+    "sys.path.insert(0, '..')\n",
+    "from src.preprocessing import RawToModelTransformer\n",
+    "\n",
+    "print(\"✅ Imports réussis\")\n",
+    "\n",
+    "# ─ Charger le modèle LightGBM original ─\n",
+    "MODEL = lgb.Booster(model_file=\"../models/lightgbm.txt\")\n",
+    "print(\"✅ Modèle LightGBM chargé depuis ../models/lightgbm.txt\")\n",
+    "\n",
+    "# ─ Charger le preprocessor existant ─\n",
+    "preprocessor = joblib.load(\"../models/preprocessor.joblib\")\n",
+    "print(\"✅ Preprocessor chargé depuis ../models/preprocessor.joblib\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "4b4fe6d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ VectorizedPreprocessor créé\n",
+      "   📊 Nombre de features attendues : 740\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ─────────────────────────────────────────────────────────────────────────────\n",
+    "# CELLULE 3 : Version vectorisée du RawToModelTransformer (ultra-rapide)\n",
+    "# ─────────────────────────────────────────────────────────────────────────────\n",
+    "\n",
+    "class VectorizedPreprocessor:\n",
+    "    \"\"\"Preprocessor vectorisé pour traiter PLUSIEURS lignes en UNE seule opération.\"\"\"\n",
+    "    \n",
+    "    def __init__(self, base_transformer: RawToModelTransformer):\n",
+    "        \"\"\"Initialise avec un transformer de base (récupère expected_features + impute).\"\"\"\n",
+    "        self.base_transformer = base_transformer\n",
+    "        self.expected_features = base_transformer.expected_features\n",
+    "        self._impute_values = base_transformer._impute_values\n",
+    "    \n",
+    "    def transform_batch(self, payloads: List[Dict]) -> pd.DataFrame:\n",
+    "        \"\"\"Transforme une liste de dicts (payloads JSON) → DataFrame features.\n",
+    "        \n",
+    "        Étapes :\n",
+    "        1. Convertir liste de dicts → DataFrame en UNE opération (pandas vectorisé)\n",
+    "        2. Sanitiser les noms de colonnes\n",
+    "        3. Remplir les colonnes manquantes avec fill_value ou impute\n",
+    "        4. Retourner DataFrame prêt pour le modèle\n",
+    "        \"\"\"\n",
+    "        # 🚀 Étape 1 : Créer DataFrame depuis dictlist d'un coup\n",
+    "        df = pd.DataFrame(payloads)\n",
+    "        \n",
+    "        # 🧹 Étape 2 : Nettoyage standard\n",
+    "        df = df.replace({\"\": np.nan, \"True\": True, \"False\": False})\n",
+    "        \n",
+    "        # 🔤 Étape 3 : Convertion à numérique (LightGBM exige numeric)\n",
+    "        for col in df.columns:\n",
+    "            try:\n",
+    "                df[col] = pd.to_numeric(df[col], errors='coerce')\n",
+    "            except Exception:\n",
+    "                pass\n",
+    "        \n",
+    "        # ✂️ Étape 4 : Appliquer le transformer de base\n",
+    "        df = self.base_transformer.transform(df)\n",
+    "        \n",
+    "        return df\n",
+    "    \n",
+    "    def transform_single(self, payload: Dict) -> pd.DataFrame:\n",
+    "        \"\"\"Transforme UN SEUL dict → DataFrame (1 ligne).\"\"\"\n",
+    "        return self.transform_batch([payload])\n",
+    "\n",
+    "# 🏗️ Créer le preprocessor vectorisé\n",
+    "vectorized_prep = VectorizedPreprocessor(preprocessor)\n",
+    "print(\"✅ VectorizedPreprocessor créé\")\n",
+    "print(f\"   📊 Nombre de features attendues : {len(vectorized_prep.expected_features)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "79859df3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📐 Modèle LightGBM : 766 features\n",
+      "✅ Structure LGBMClassifier initialisée\n",
+      "⚠️  Conversion ONNX échouée (fallback LightGBM) : AttributeError: 'Booster' object has no attribute '_Booster'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ─────────────────────────────────────────────────────────────────────────────\n",
+    "# CELLULE 4 : Conversion LightGBM → ONNX + Sauvegarde\n",
+    "# ─────────────────────────────────────────────────────────────────────────────\n",
+    "\n",
+    "import skl2onnx\n",
+    "from skl2onnx import convert_sklearn\n",
+    "from skl2onnx.common.data_types import FloatTensorType\n",
+    "\n",
+    "try:\n",
+    "    import onnxruntime as ort\n",
+    "except ImportError:\n",
+    "    print(\"⚠️  onnxruntime non détecté. Installation non nécessaire (déjà dans requirements.txt)\")\n",
+    "    raise\n",
+    "\n",
+    "# ⚙️ Étape 1 : Récupérer les informations du modèle LightGBM\n",
+    "num_features = MODEL.num_feature()\n",
+    "feature_names = MODEL.feature_name()\n",
+    "print(f\"📐 Modèle LightGBM : {num_features} features\")\n",
+    "\n",
+    "# ⚙️ Étape 2 : Conversion en ONNX\n",
+    "# Approche : Créer un LGBMClassifier vierge et l'entraîner sur un mini-batch,\n",
+    "# puis le remplacer par notre modèle chargé (compatible avec les versions récentes)\n",
+    "\n",
+    "try:\n",
+    "    from lightgbm import LGBMClassifier\n",
+    "    import warnings\n",
+    "    warnings.filterwarnings('ignore')\n",
+    "    \n",
+    "    # 🔧 Créer un LGBMClassifier depuis zéro (structure compatible)\n",
+    "    lgbm_clf = LGBMClassifier(n_estimators=1, random_state=42, verbose=-1)\n",
+    "    \n",
+    "    # Créer un mini-dataset d'entraînement (juste pour initialiser la structure)\n",
+    "    X_train = pd.DataFrame(\n",
+    "        np.random.randn(10, num_features),\n",
+    "        columns=[f\"feature_{i}\" for i in range(num_features)]\n",
+    "    )\n",
+    "    y_train = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])\n",
+    "    \n",
+    "    # Entraîner (rapide : juste 1 arbre)\n",
+    "    lgbm_clf.fit(X_train, y_train)\n",
+    "    print(\"✅ Structure LGBMClassifier initialisée\")\n",
+    "    \n",
+    "    # Récupérer le booster et le remplacer par notre modèle entraîné\n",
+    "    lgbm_clf._Booster = MODEL._Booster  # Remplacer avec notre modèle\n",
+    "    print(\"✅ Modèle chargé injecté\")\n",
+    "    \n",
+    "    # Convertir en ONNX\n",
+    "    initial_type = [('float_input', FloatTensorType([None, num_features]))]\n",
+    "    onnx_model = convert_sklearn(lgbm_clf, initial_types=initial_type)\n",
+    "    \n",
+    "    # Sauvegarder le modèle ONNX\n",
+    "    from pathlib import Path\n",
+    "    onnx_path = Path(\"../models/model_optimized.onnx\")\n",
+    "    onnx_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    \n",
+    "    with open(onnx_path, \"wb\") as f:\n",
+    "        f.write(onnx_model.SerializeToString())\n",
+    "    \n",
+    "    print(f\"✅ Modèle ONNX sauvegardé : {onnx_path}\")\n",
+    "    print(f\"   📦 Taille du fichier : {onnx_path.stat().st_size / 1024:.1f} KB\")\n",
+    "    \n",
+    "except Exception as e:\n",
+    "    print(f\"⚠️  Conversion ONNX échouée (fallback LightGBM) : {type(e).__name__}: {e}\")\n",
+    "    onnx_model = None\n",
+    "    onnx_path = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "737b2248",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Classes et fonctions optimisées définies\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ─────────────────────────────────────────────────────────────────────────────\n",
+    "# CELLULE 5 : Classe OnnxPredictor + _predict_optimized\n",
+    "# ───────────────���─────────────────────────────────────────────────────────────\n",
+    "\n",
+    "class OnnxPredictor:\n",
+    "    \"\"\"Wrapper pour inférence ONNX ultra-rapide.\"\"\"\n",
+    "    \n",
+    "    def __init__(self, onnx_path: Path):\n",
+    "        \"\"\"Charge la session ONNX Runtime.\"\"\"\n",
+    "        self.session = ort.InferenceSession(str(onnx_path))\n",
+    "        self.input_name = self.session.get_inputs()[0].name\n",
+    "        self.output_name = self.session.get_outputs()[0].name\n",
+    "        print(f\"✅ OnnxPredictor initialisé\")\n",
+    "        print(f\"   Input: {self.input_name}, Output: {self.output_name}\")\n",
+    "    \n",
+    "    def predict_proba(self, X: np.ndarray) -> np.ndarray:\n",
+    "        \"\"\"Inférence ONNX : retourne probabilités P(y=1).\"\"\"\n",
+    "        # ONNX Runtime attend float32\n",
+    "        X_float = X.astype(np.float32)\n",
+    "        outputs = self.session.run([self.output_name], {self.input_name: X_float})\n",
+    "        return outputs[0]\n",
+    "\n",
+    "def _predict_optimized(payload_json: Dict, \n",
+    "                     vectorizer: VectorizedPreprocessor,\n",
+    "                     model_onnx: OnnxPredictor = None,\n",
+    "                     model_gbm: lgb.Booster = None,\n",
+    "                     threshold: float = 0.4) -> Tuple[float, str]:\n",
+    "    \"\"\"Fonction prédiction optimisée : ONNX + preprocessing vectorisé.\n",
+    "    \n",
+    "    Retourne :\n",
+    "    - proba : float ∈ [0, 1]\n",
+    "    - decision : str \"Accordé\" ou \"Refusé\"\n",
+    "    \"\"\"\n",
+    "    # 🚀 Étape 1 : Preprocessing vectorisé (UNE seule opération pandas)\n",
+    "    df_features = vectorizer.transform_single(payload_json)\n",
+    "    X = df_features.values.astype(np.float32)\n",
+    "    \n",
+    "    # 🧠 Étape 2 : Inférence (ONNX ou LightGBM natif)\n",
+    "    if model_onnx is not None:\n",
+    "        # Utiliser ONNX Runtime (plus rapide)\n",
+    "        proba_onnx = model_onnx.predict_proba(X)\n",
+    "        proba = float(proba_onnx[0][1])  # P(y=1)\n",
+    "    else:\n",
+    "        # Fallback sur LightGBM natif\n",
+    "        proba = float(model_gbm.predict(X, num_iteration=model_gbm.best_iteration)[0])\n",
+    "    \n",
+    "    # 📊 Étape 3 : Décision basée sur seuil\n",
+    "    decision = \"Accordé\" if proba >= threshold else \"Refusé\"\n",
+    "    \n",
+    "    return proba, decision\n",
+    "\n",
+    "print(\"✅ Classes et fonctions optimisées définies\")\n",
+    "\n",
+    "# Créer une instance du prédicteur ONNX (si possible)\n",
+    "model_onnx_pred = None\n",
+    "if onnx_path is not None:\n",
+    "    try:\n",
+    "        model_onnx_pred = OnnxPredictor(onnx_path)\n",
+    "    except Exception as e:\n",
+    "        print(f\"⚠️  OnnxPredictor échoué, fallback sur LightGBM : {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "3ae54b92",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "⏳ Chargement des données pré-traitées...\n",
+      "✅ 200 lignes pré-traitées chargées (740 colonnes)\n",
+      "   📊 Colonnes manquantes : 175\n",
+      "   📊 Colonnes supplémentaires : 149\n",
+      "   ✅ Reindexé pour le modèle : 766 colonnes attendues\n",
+      "\n",
+      "🔬 Résultats comparatifs (200 prédictions)...\n",
+      "\n",
+      "📊 BASELINE (boucle ligne par ligne)\n",
+      "   ⏱️  Temps TOTAL     : 127.50 ms\n",
+      "   ⏱️  Par requête    : 0.64 ms\n",
+      "   📊 Proba moyenne   : 0.0346\n",
+      "   ✅ Accord (%)      : 1.0%\n",
+      "\n",
+      "🚀 OPTIMISÉE (vectorisée)\n",
+      "   ⏱️  Temps TOTAL     : 8.10 ms\n",
+      "   ⏱️  Par requête    : 0.04 ms\n",
+      "   📊 Proba moyenne   : 0.0346\n",
+      "   ✅ Accord (%)      : 1.0%\n",
+      "\n",
+      "📈 GAINS OBTENUS\n",
+      "   ⏱️  Réduction par requête : +93.6%\n",
+      "   ⚡ Speedup              : 15.7x plus rapide\n",
+      "   📊 Variance probas      : 0.000000 (identiques ✓)\n",
+      "\n",
+      "💡 CONCLUSION\n",
+      "   ✅ Les deux versions donnent EXACTEMENT les mêmes prédictions.\n",
+      "   ✅ Vectorisation obtient 94% de gain par requête.\n",
+      "   ✅ Pour 1000 requêtes/jour : 1s économisées.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ─────────────────────────────────────────────────────────────────────────────\n",
+    "# CELLULE 6 : Benchmark avant/après avec VRAIES DONNÉES (200 samples aléatoires)\n",
+    "# ────────────────────────────────────────��────────────────────────────────────\n",
+    "\n",
+    "# 📋 ÉTAPE 1 : Charger 200 lignes PRÉ-TRAITÉES depuis data/processed/features_test.csv\n",
+    "# Ces données sont DÉJÀ les 766 features finales prêtes pour le modèle\n",
+    "print(\"⏳ Chargement des données pré-traitées...\")\n",
+    "df_features = pd.read_csv(\"../data/processed/features_test.csv\", nrows=200)\n",
+    "\n",
+    "# Exclure les colonnes non-features (SK_ID_CURR, TARGET si présentes)\n",
+    "cols_to_keep = [c for c in df_features.columns if c not in (\"SK_ID_CURR\", \"TARGET\")]\n",
+    "df_features = df_features[cols_to_keep]\n",
+    "\n",
+    "print(f\"✅ {len(df_features)} lignes pré-traitées chargées ({df_features.shape[1]} colonnes)\")\n",
+    "\n",
+    "# Vérifier qu'on a les 766 features attendues par le modèle\n",
+    "expected = list(MODEL.feature_name())\n",
+    "missing = [f for f in expected if f not in df_features.columns]\n",
+    "extra = [f for f in df_features.columns if f not in expected]\n",
+    "\n",
+    "print(f\"   📊 Colonnes manquantes : {len(missing)}\")\n",
+    "print(f\"   📊 Colonnes supplémentaires : {len(extra)}\")\n",
+    "\n",
+    "# Reindexer pour garantir l'ordre exact du modèle\n",
+    "# .reindex() crée automatiquement les colonnes manquantes avec fill_value=0\n",
+    "df_features = df_features.reindex(columns=expected, fill_value=0)\n",
+    "print(f\"   ✅ Reindexé pour le modèle : {df_features.shape[1]} colonnes attendues\")\n",
+    "\n",
+    "# ┌─────────────────────────────────────────────────────────────────────────┐\n",
+    "# │ BASELINE : Prédiction ligne par ligne (boucle = LENT)                    │\n",
+    "# └─────────────────────────────────────────────────────────────────────────┘\n",
+    "def _predict_baseline_loop(df_features: pd.DataFrame) -> Tuple[list, list]:\n",
+    "    \"\"\"Prédiction ligne par ligne (non-vectorisée).\"\"\"\n",
+    "    probas = []\n",
+    "    decisions = []\n",
+    "    \n",
+    "    for idx, row in df_features.iterrows():\n",
+    "        X = row.values.reshape(1, -1).astype(np.float32)\n",
+    "        proba = float(MODEL.predict(X, num_iteration=MODEL.best_iteration)[0])\n",
+    "        decision = \"Accordé\" if proba >= 0.4 else \"Refusé\"\n",
+    "        probas.append(proba)\n",
+    "        decisions.append(decision)\n",
+    "    \n",
+    "    return probas, decisions\n",
+    "\n",
+    "print(\"\\n🔬 Résultats comparatifs (200 prédictions)...\\n\")\n",
+    "\n",
+    "# ┌──────────────────────────────────────────────────────────────────────────┐\n",
+    "# │ RUN 1 : Baseline (boucle, non-vectorisée)                               │\n",
+    "# └──────────────────────────────────────────────────────────────────────────┘\n",
+    "t0_baseline = time.perf_counter()\n",
+    "probas_b, decisions_b = _predict_baseline_loop(df_features)\n",
+    "dt_baseline = (time.perf_counter() - t0_baseline) * 1000  # en ms\n",
+    "\n",
+    "baseline_per_request = dt_baseline / len(df_features)\n",
+    "\n",
+    "print(f\"📊 BASELINE (boucle ligne par ligne)\")\n",
+    "print(f\"   ⏱️  Temps TOTAL     : {dt_baseline:.2f} ms\")\n",
+    "print(f\"   ⏱️  Par requête    : {baseline_per_request:.2f} ms\")\n",
+    "print(f\"   📊 Proba moyenne   : {np.mean(probas_b):.4f}\")\n",
+    "print(f\"   ✅ Accord (%)      : {(decisions_b.count('Accordé') / len(decisions_b) * 100):.1f}%\")\n",
+    "\n",
+    "# ┌──────────────────────────────────────────────────────────────────────────┐\n",
+    "# │ RUN 2 : Optimisée (vectorisée - UNE seule inférence)                     │\n",
+    "# └──────────────────────────────────────────────────────────────────────────┘\n",
+    "def _predict_optimized_vectorized(df_features: pd.DataFrame) -> Tuple[list, list]:\n",
+    "    \"\"\"Prédiction vectorisée (TOUT D'UN COUP = RAPIDE).\"\"\"\n",
+    "    X = df_features.values.astype(np.float32)\n",
+    "    probas = list(MODEL.predict(X, num_iteration=MODEL.best_iteration))\n",
+    "    decisions = [\"Accordé\" if p >= 0.4 else \"Refusé\" for p in probas]\n",
+    "    \n",
+    "    return probas, decisions\n",
+    "\n",
+    "t0_optimized = time.perf_counter()\n",
+    "probas_o, decisions_o = _predict_optimized_vectorized(df_features)\n",
+    "dt_optimized = (time.perf_counter() - t0_optimized) * 1000  # en ms\n",
+    "\n",
+    "optimized_per_request = dt_optimized / len(df_features)\n",
+    "\n",
+    "print(f\"\\n🚀 OPTIMISÉE (vectorisée)\")\n",
+    "print(f\"   ⏱️  Temps TOTAL     : {dt_optimized:.2f} ms\")\n",
+    "print(f\"   ⏱️  Par requête    : {optimized_per_request:.2f} ms\")\n",
+    "print(f\"   📊 Proba moyenne   : {np.mean(probas_o):.4f}\")\n",
+    "print(f\"   ✅ Accord (%)      : {(decisions_o.count('Accordé') / len(decisions_o) * 100):.1f}%\")\n",
+    "\n",
+    "# ┌──────────────────────────────────────────────────────────────────────────┐\n",
+    "# │ GAINS                                                                    │\n",
+    "# └──────────────────────────────────────────────────────────────────────────┘\n",
+    "gain_per_request = ((baseline_per_request - optimized_per_request) / baseline_per_request) * 100\n",
+    "speedup = baseline_per_request / optimized_per_request\n",
+    "\n",
+    "print(f\"\\n📈 GAINS OBTENUS\")\n",
+    "print(f\"   ⏱️  Réduction par requête : {gain_per_request:+.1f}%\")\n",
+    "print(f\"   ⚡ Speedup              : {speedup:.1f}x plus rapide\")\n",
+    "print(f\"   📊 Variance probas      : {abs(np.mean(probas_b) - np.mean(probas_o)):.6f} (identiques ✓)\")\n",
+    "\n",
+    "print(f\"\\n💡 CONCLUSION\")\n",
+    "print(f\"   ✅ Les deux versions donnent EXACTEMENT les mêmes prédictions.\")\n",
+    "print(f\"   ✅ Vectorisation obtient {abs(gain_per_request):.0f}% de gain par requête.\")\n",
+    "print(f\"   ✅ Pour 1000 requêtes/jour : {(baseline_per_request - optimized_per_request) * 1000 / 1000:.0f}s économisées.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "188f6d71",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✅ VÉRIFICATION DE COHÉRENCE\n",
+      "\n",
+      "Comparaison des 200 prédictions :\n",
+      "   Différence MAX entre probas     : 0.00000000\n",
+      "   Différence MOYENNE entre probas : 0.00000000\n",
+      "   Décisions identiques            : 200/200 (100.0%)\n",
+      "\n",
+      "✅ SUCCÈS : Baseline et Optimisée sont PARFAITEMENT identiques.\n",
+      "   → Pas de perte de précision observée.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ─────────────────────────────────────────────────────────────────────────────\n",
+    "# CELLULE 7 : Vérification précision (Baseline vs Optimisée donnent mêmes résultats)\n",
+    "# ─────────────────────────────────────────────────────────────────────────────\n",
+    "\n",
+    "print(\"\\n✅ VÉRIFICATION DE COHÉRENCE\\n\")\n",
+    "\n",
+    "# Vérifier que les probas sont EXACTEMENT identiques (même ordre)\n",
+    "proba_diff = np.abs(np.array(probas_b) - np.array(probas_o))\n",
+    "max_diff = np.max(proba_diff)\n",
+    "mean_diff = np.mean(proba_diff)\n",
+    "\n",
+    "print(f\"Comparaison des 200 prédictions :\")\n",
+    "print(f\"   Différence MAX entre probas     : {max_diff:.8f}\")\n",
+    "print(f\"   Différence MOYENNE entre probas : {mean_diff:.8f}\")\n",
+    "\n",
+    "# Vérifier les décisions\n",
+    "decisions_match = (np.array(decisions_b) == np.array(decisions_o)).sum()\n",
+    "print(f\"   Décisions identiques            : {decisions_match}/200 ({decisions_match/200*100:.1f}%)\")\n",
+    "\n",
+    "if max_diff < 1e-6 and decisions_match == 200:\n",
+    "    print(\"\\n✅ SUCCÈS : Baseline et Optimisée sont PARFAITEMENT identiques.\")\n",
+    "    print(\"   → Pas de perte de précision observée.\")\n",
+    "else:\n",
+    "    print(f\"\\n⚠️  Légères divergences détectées (max delta = {max_diff:.8f}).\")\n",
+    "    print(\"   → Acceptable (dues à la précision numérique).\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20938cf8",
+   "metadata": {},
+   "source": [
+    "# 📊 Résultats obtenus\n",
+    "\n",
+    "## Latence baseline\n",
+    "- **Moyenne par requête** : **0.64 ms** (LightGBM natif + preprocessing ligne par ligne)\n",
+    "- **p95** : ~0.7-0.8 ms (estimé sur 200 appels)\n",
+    "- **p99** : ~0.9-1.0 ms\n",
+    "\n",
+    "## Latence optimisée\n",
+    "- **Moyenne par requête** : **0.04 ms** (LightGBM natif + preprocessing **vectorisé**)\n",
+    "- **p95** : ~0.05 ms\n",
+    "- **p99** : ~0.06 ms\n",
+    "\n",
+    "## Gain obtenu\n",
+    "- **Réduction par requête** : **+93.6 %**\n",
+    "- **Speedup** : **15.7x plus rapide**\n",
+    "- **Précision** : **100 % identique** (différence de probabilité = 0.00000000, décisions identiques sur 200/200)\n",
+    "\n",
+    "## Justification des choix (pédagogique)\n",
+    "1. **Vectorisation pandas** → On passe de 39 950 `__setitem__` (colonne par colonne) à **un seul DataFrame** en une opération. C’est la solution la plus simple et la plus efficace identifiée dans le profiling.\n",
+    "2. **Pas d’ONNX** → La conversion a échoué (`'Booster' object has no attribute '_Booster'`). On garde LightGBM natif (déjà très rapide à ~15 ms dans le profiling).\n",
+    "3. **Aucune perte de précision** → Les probas et décisions sont **strictement identiques**.\n",
+    "\n",
+    "\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Date** : 25 février 2026  \n",
+    "**Gain réel mesuré** : **15.7x**"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "OC_P6",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

projet/etapes.txt

@@ -0,0 +1,115 @@
+Étape 2 - Déployez le modèle via une API et automatisez avec CI/CD
+
+Description: Développez une API (Gradio, FastAPI) pour exposer votre modèle. L'API doit recevoir des données d'entrée et retourner une prédiction. Conteneurisez cette API avec Docker. Ensuite, créez un pipeline d'Intégration Continue et de Déploiement Continu (CI/CD) (ex: GitHub Actions). Ce pipeline devra automatiquement :
+	1.Exécuter des tests (unitaires, intégration) sur votre code API et modèle.
+	2.Construire l'image Docker de l'API si les tests sont concluants.
+	3.Déployer l'image conteneurisée sur un environnement cible (simulé ou réel).
+
+Prérequis:
+	Avoir le code versionné sur une plateforme supportant la CI/CD.
+	Avoir choisi un framework d'API.
+	Avoir installé Docker.
+	
+Résultats attendus :
+	Un code source fonctionnel pour l'API.
+	UnDockerfilepour créer une image Docker de l'API.
+	Un pipeline CI/CD fonctionnel et automatisé visible sur la plateforme, qui déploie l'API.
+	Des tests automatisés intégrés au pipeline.
+
+Recommandations:
+	Commencez par une API simple et un pipeline basique, puis itérez.
+	Incluez une gestion des erreurs dans l'API et documentez-la (ex: Swagger).
+	Séparez les étapes de build, test et déploiement dans le pipeline CI/CD.
+	Utilisez des secrets pour gérer les credentials.
+	Utilisez Hugging Face Spaces qui est particulièrement simple d’utilisation pour ce genre de déploiement.
+
+Points de vigilance:
+	Assurez-vous que les tests sont fiables et couvrent les cas critiques, par exemple :
+		des entrées avec des données manquantes pour des champs obligatoires,
+		des valeurs hors des plages attendues (ex: un âge de -5 ans ou un revenu de 0 si ce n'est pas censé être possible),
+		ou des types de données incorrects (ex: du texte là où un chiffre est attendu).
+
+	Sécurisez l'API et le pipeline (gestion des secrets, validation d'entrée).
+	Gérez correctement le chargement du modèle dans l'API.
+		Lorsque vous intégrez un modèle de machine learning dans une API, il est crucial de ne pas charger le modèle à chaque requête. 
+		Cela entraînerait des lenteurs importantes voire un échec sous charge. 
+		Chargez le modèle une seule fois, au moment du démarrage de l’API, puis réutilisez le dans toutes les requêtes.
+
+	Cela permet de :
+		Réduire le temps de réponse de l’API.
+		Éviter une surcharge mémoire.
+		Améliorer la scalabilité.
+		Vérifiez que l'environnement de déploiement dispose des ressources nécessaires.
+
+Outils: 
+Gradio/FastAPI
+Docker
+Postman/curl
+GitHub Actions/GitLab CI/Jenkins
+Pytest
+Plateformes de déploiement (Hugging Face, Heroku, Google Cloud Run...).
+
+
+
+Étape 3 - Implémentez le stockage et l'analyse des données de production
+
+Description: Concevez et mettez en place une solution pour stocker les données pertinentes générées par votre API en production : logs d'appels, inputs, outputs, et temps d'exécution (à minima). Mettez en œuvre une analyse automatique de ces données pour détecter des anomalies, notamment la dérive des données (data drift), et des problèmes opérationnels (taux d'erreur, latence anormale).
+
+Un prototype (PoC) de cette solution peut être réalisé entièrement en local si vous n’avez pas de service cloud à votre disposition. Tant que tous les aspects requis pour cette partie sont correctement adressés, cela est suffisant. Exemple: les logs peuvent être générés par l’API (cloud) puis téléchargés, stockés et analysés localement. Les données collectées par l’API doivent permettre une analyse ultérieure du drift : assurez-vous de stocker les inputs/outputs du modèle et les métriques clés.
+
+Prérequis:
+Avoir déployé l'API via le pipeline CI/CD.
+Avoir identifié les données clés à logger depuis l'API et l'infrastructure.
+
+Résultat attendu:
+Une solution de stockage des données de production décrite et/ou implémentée.
+Un script ou notebook réalisant l'analyse automatique des données stockées (détection de drift, anomalies).
+Une présentation de l'étude sur la dérive des données et les points de vigilances résultants.
+
+Recommandations:
+Configurez le logging structuré (ex: JSON) dans votre API.
+Utilisez des bibliothèques dédiées à la détection de drift (ex: Evidently AI, NannyML).
+Pensez à visualiser les résultats de l'analyse (ex: dashboard).
+
+Points de vigilance:
+Soyez conscient des contraintes de stockage et de coût.
+Assurez la conformité RGPD si nécessaire.
+La détection de drift nécessite une référence (données d'entraînement ou fenêtre stable (vous pouvez reprendre votre travail réalisé lors du projet Initiez-vous au MLOps partie 1).
+
+Outils: 
+Bibliothèques de logging Python, 
+Analyse des logs: Fluentd, Logstash
+Bases de données:  Elasticsearch, PostgreSQL
+Bibliothèques de détection de drift: Evidently AI, NannyML
+Outils de visualisation : Grafana, Kibana, Dash/Streamlit.
+
+
+
+Étape 4 - Analysez et optimisez les performances du modèle
+
+Description: Maintenant que le modèle est déployé et monitoré, analysez ses performances réelles ou simulées en production. 
+Utilisez les données de monitoring (temps d’inférence, latence, utilisation CPU / GPU) et des outils de profiling pour identifier les goulots d’étranglement. 
+Testez des stratégies d'optimisation (quantification, optimisation de code, hardware) pour améliorer le temps d'inférence/réponse. 
+Intégrez la version optimisée dans votre dépôt et laissez le pipeline CI/CD la déployer. 
+Documentez les optimisations et leurs résultats.
+
+Prérequis:
+Avoir l'API déployée et un système de monitoring/logging en place (même basique)
+
+Résultats attendus :
+Un rapport détaillant les tests d'optimisation effectués post-déploiement, les résultats et les goulots d'étranglement identifiés.
+Une version optimisée du modèle déployée via le pipeline CI/CD.
+Une justification de la configuration finale (librairies, software, hardware).
+L'amélioration du temps d'inférence et de réponse est démontrée.
+
+Recommandations:
+Baser vos hypothèses d'optimisation sur les données de monitoring réelles.
+Documenter rigoureusement l'impact des optimisations sur la performance et la précision.
+
+Points de vigilance:
+Assurez-vous que les optimisations n'introduisent pas de régressions (précision, biais).
+Validez la compatibilité des optimisations avec l'environnement de production.
+
+Outils: 
+Outils de profiling (ex: cProfile). 
+Bibliothèques d'optimisation (ex: ONNX Runtime).

projet/mission.txt

@@ -0,0 +1,32 @@
+Comment allez-vous procéder ? 
+
+Cette mission simule la mise en production d'un modèle de scoring. Suivez les étapes pour réaliser vos livrables. Avant de démarrer, lisez attentivement la mission, consultez les étapes, et préparez vos questions pour la session de mentorat.
+
+ 
+
+Prêt à mener la mission ? 
+
+Vous êtes Data Scientist dans l'entreprise "Prêt à Dépenser". Après avoir développé et versionné un modèle de scoring (Projet Initiez-vous au MLops), vous recevez un message Slack de Chloé Dubois, la Lead Data Scientist :
+
+" Salut ! Excellents résultats sur la dernière version du modèle de scoring ! Le département 'Crédit Express' est très impatient de l'utiliser pour traiter les nouvelles demandes en quasi temps réel. Il nous faut absolument une API fonctionnelle et déployable (Docker Ready!) d'ici la fin de la semaine prochaine. Peux-tu prioriser ça ?  On a aussi besoin d'un dashboard ou rapport de suivi pour vérifier que tout se passe bien une fois en prod (distribution des scores, temps de réponse, ce genre de choses). Tiens-moi au courant de ton plan d'action ! Merci ! "
+
+Vous voila donc chargé de piloter la mise en production effective du modèle de scoring. Cela inclut la création d'une API robuste, la conteneurisation pour un déploiement fluide, et la mise en place d'un monitoring proactif pour garantir la performance et la fiabilité du modèle dans le temps.
+
+
+En structurant vos pensées et en préparant votre to do list, vous rédigez la liste des livrables que vous allez concevoir et présenter à Chloé : 
+
+1. Un historique des versions retraçant la construction du projet que vous rendrez disponible dans votre github en consultant la liste des commits. 
+2. Des scripts : 
+    - Une API fonctionnelle (vous travaillerez probablement avec Gradio ou FastAPI) qui prend les données d'un client en entrée et retourne un score de prédiction. 
+    - Des tests unitaires automatisés.
+3. Un dockerfile pour la conteneurisation du code.
+4. Une analyse du Data Drift: 
+    - Un tableau de bord ou un rapport de monitoring (vous savez que vous pourrez le simuler dans un notebook ou via un outil comme Streamlit voire Dash) montrant des métriques clés (ex.: distribution des scores prédits, latence de l'API, temps d’inférence, etc.)
+    - Des screenshots de la solution de stockage des données de production.
+5. Un pipeline CI/CD: un fichier YAML (ou équivalent) démontrant l’automatisation de la mise en production et des tests lors d’un push sur la branche principale (à minima) du projet.
+6. Une documentation README expliquant comment lancer l'API et interpréter le monitoring.
+
+Dans ce projet, vous vous appuierez sur les livrables que vous avez réalisés lors du projet précédent intitulé Initiez-vous au MLOps (partie 1/2).Plus précisément, il s’agit de reprendre le modèle de scoring que vous avez développé, versionné et évalué précédemment avec MLflow. Ce modèle constitue désormais la base sur laquelle vous allez travailler pour le déployer en production.Vous devrez donc réutiliser les artefacts produits, les adapter si nécessaire, et construire autour un environnement complet de déploiement. 
+
+ 
+De plus, nous vous suggérons de travailler avec les deux outils présentés dans les  ressources pédagogiques de ce projet actuel : Streamlit et Gradio. Vous êtes néanmoins libre de travailler avec d’autres outils si vous le souhaitez mais vous penserez à expliquer vos choix techniques pendant votre soutenance avec l’évaluateur.

pyproject.toml

@@ -0,0 +1,82 @@
+[project]
+name = "oc-p6"
+version = "1.1.0"
+description = "Projet Credit Scoring - Home Credit Default Risk"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    # Core data science libraries
+    "pandas>=2.0.0",
+    "numpy>=2",
+    "scikit-learn>=1.3.0",
+    # Visualization
+    "matplotlib>=3.7.0",
+    "seaborn>=0.12.0",
+    "plotly>=5.14.0",
+    # Machine Learning
+    "lightgbm>=4.0.0",
+    # MLflow for tracking
+    "mlflow>=2.10.0",
+    # Hyperparameter optimization
+    "hyperopt>=0.2.7",
+    "optuna>=3.5.0",
+    "lime>=0.2.0",
+    "numba>=0.59.0",
+    # Data quality
+    "imbalanced-learn>=0.11.0",
+    # Utilities
+    "tqdm>=4.65.0",
+    "joblib>=1.3.0",
+    # Jupyter
+    "jupyter>=1.0.0",
+    "ipykernel>=6.25.0",
+    "ipywidgets>=8.1.0",
+    # Flask for serving
+    "flask>=3.0.0",
+    "gradio==6.6.0",
+    "evidently>=0.7.20",
+]
+
+[project.optional-dependencies]
+dev = [
+    # Testing
+    "pytest>=7.4.0",
+    "pytest-cov>=4.1.0",
+    
+    # Machine Learning (for notebooks only)
+    "xgboost>=2.0.0",
+    "catboost>=1.2.0",
+    
+    # Code quality
+    "black>=23.0.0",
+    "flake8>=6.0.0",
+    "mypy>=1.5.0",
+    "ruff>=0.1.0",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src"]
+
+[tool.black]
+line-length = 100
+target-version = ['py312']
+
+[tool.ruff]
+line-length = 100
+target-version = "py312"
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = "test_*.py"
+python_functions = "test_*"
+addopts = "-v"
+
+[dependency-groups]
+dev = [
+    "pytest>=9.0.2",
+    "pytest-cov>=7.0.0",
+]

reference/simulate_production_calls.py

@@ -0,0 +1,71 @@
+"""Simulateur d'appels de production pour remplir logs/predictions.jsonl."""
+
+# EXPLICATION : Imports standards uniquement (aucune dépendance nouvelle)
+import requests
+import json
+import time
+import pandas as pd
+import numpy as np
+from pathlib import Path
+
+# EXPLICATION : Chargement du dataset de référence (500 lignes échantillonnées de features_train)
+# Path(__file__).parent rend le chemin robuste quel que soit le répertoire courant
+reference = pd.read_csv(Path(__file__).parent / "reference.csv")
+
+# EXPLICATION : Gradio 5.x utilise une API SSE en 2 étapes :
+#   1) POST /gradio_api/call/<fn_name> → retourne un event_id
+#   2) GET  /gradio_api/call/<fn_name>/<event_id> → stream SSE avec le résultat
+BASE_URL = "http://127.0.0.1:7860"
+CALL_URL = f"{BASE_URL}/gradio_api/call/_predict"
+
+# EXPLICATION : Tirage aléatoire de 500 lignes (avec remise si dataset < 500)
+# random_state=42 pour reproductibilité, replace=True pour éviter l'erreur si reference < 500
+sampled = reference.sample(n=500, replace=True, random_state=42).reset_index(drop=True)
+
+# EXPLICATION : Boucle de 500 appels simulés (375 normaux + 125 avec drift)
+for i in range(500):
+    # EXPLICATION : Sélection de la ligne aléatoire pré-tirée
+    row = sampled.iloc[i].to_dict()
+
+    # EXPLICATION : Nettoyage — convertir "" et NaN en None pour JSON propre
+    for k, v in row.items():
+        if v == "" or pd.isna(v):
+            row[k] = None
+
+    # EXPLICATION : 25% des appels avec drift simulé (AMT_INCOME_TOTAL * 1.5)
+    if i % 4 == 0:
+        row["AMT_INCOME_TOTAL"] = row["AMT_INCOME_TOTAL"] * 1.5 if row["AMT_INCOME_TOTAL"] else 100000
+
+    # EXPLICATION : Format payload attendu par l'interface Gradio (app.py)
+    payload = {"data": [json.dumps(row)]}
+
+    start = time.perf_counter()
+    drift_tag = " [DRIFT]" if i % 4 == 0 else ""
+    try:
+        # EXPLICATION : Étape 1 — POST pour obtenir un event_id
+        resp = requests.post(CALL_URL, json=payload, timeout=10)
+        resp.raise_for_status()
+        event_id = resp.json().get("event_id")
+
+        # EXPLICATION : Étape 2 — GET SSE pour récupérer le résultat
+        result_url = f"{CALL_URL}/{event_id}"
+        sse_resp = requests.get(result_url, timeout=30, stream=True)
+        sse_resp.raise_for_status()
+
+        # EXPLICATION : Parse la réponse SSE (format "event: ...\ndata: ...\n")
+        result_text = ""
+        for line in sse_resp.iter_lines(decode_unicode=True):
+            if line and line.startswith("data:"):
+                result_text = line[len("data:"):].strip()
+
+        duration = (time.perf_counter() - start) * 1000
+        print(f"Appel {i+1}/500 - OK - Temps: {duration:.1f}ms{drift_tag}")
+    except Exception as e:
+        duration = (time.perf_counter() - start) * 1000
+        print(f"Erreur appel {i+1}: {e} ({duration:.1f}ms){drift_tag}")
+
+    # EXPLICATION : Pause entre chaque appel pour ne pas surcharger Docker
+    time.sleep(0.3)
+
+# Sous-étape 4 terminée - 500 appels simulés (375 normal + 125 avec drift)
+# Lancer avec : uv run python simulate_production_calls.py (API doit tourner sur 7860)

requirements-inference.txt

@@ -0,0 +1,7 @@
+gradio==6.6.0
+huggingface-hub>=0.33.5,<2.0
+lightgbm>=4.0.0
+pandas>=2.0.0
+scikit-learn>=1.3.0
+numpy>=2
+mlflow>=2.10.0

requirements.txt

@@ -0,0 +1,7 @@
+lightgbm>=4.0.0
+pandas>=2.0.0
+scikit-learn>=1.3.0
+numpy>=2
+mlflow>=2.10.0
+onnxruntime>=1.16.0
+skl2onnx>=1.14.0

src/__init__.py

@@ -0,0 +1,5 @@
+"""
+Package principal pour le projet de credit scoring.
+"""
+
+__version__ = "0.1.0"

src/load_data.py

@@ -0,0 +1,147 @@
+"""
+Module pour charger les données brutes du projet Home Credit.
+"""
+
+import pandas as pd
+from pathlib import Path
+from typing import Dict
+import os
+
+
+class DataContainer(dict):
+    """
+    Conteneur de données permettant l'accès par clé (dict-like) et par attribut.
+    
+    Usage:
+        data = DataContainer({'df1': pd.DataFrame(), 'df2': pd.DataFrame()})
+        data.df1  # Accès par attribut
+        data['df1']  # Accès par clé
+    """
+    def __getattr__(self, name: str):
+        try:
+            return self[name]
+        except KeyError:
+            raise AttributeError(f"'DataContainer' object has no attribute '{name}'")
+    
+    def __setattr__(self, name: str, value):
+        self[name] = value
+
+
+def _find_project_root() -> Path:
+    """
+    Trouve la racine du projet de manière robuste.
+    Stratégie :
+    1. Si __file__ existe (script .py) → on remonte comme avant.
+    2. Sinon (notebook), on part du répertoire courant et on cherche un marqueur
+       classique de projet : le dossier 'data/raw' contenant 'application_train.csv'.
+    Cela évite les erreurs de contexte d'exécution.
+    """
+    try:
+        # Cas classique : exécuté comme module .py
+        return Path(__file__).resolve().parent.parent.parent
+    except (NameError, RuntimeError):
+        # Cas notebook / interactive
+        current = Path.cwd()
+        # On remonte jusqu'à trouver le dossier contenant data/raw/application_train.csv
+        for p in [current] + list(current.parents):
+            candidate = p / "data" / "raw" / "application_train.csv"
+            if candidate.exists():
+                return p
+        
+        # Fallback: cherche un dossier nommé OC_P6 avec data/raw dedans
+        for p in [current] + list(current.parents):
+            candidate = p / "data" / "raw" / "application_train.csv"
+            if candidate.exists():
+                return p
+            # Cherche aussi dans OC_P6 s'il est un sous-dossier
+            oc_p6 = p / "OC_P6"
+            if oc_p6.exists():
+                candidate = oc_p6 / "data" / "raw" / "application_train.csv"
+                if candidate.exists():
+                    return oc_p6
+        
+        raise FileNotFoundError("Impossible de trouver la racine du projet. Vérifie la structure des dossiers.")
+
+
+BASE_DIR = _find_project_root()
+
+
+def load_raw_data(data_dir: str | None = None) -> DataContainer:
+    """
+    Charge toutes les données brutes.
+    
+    Retourne un conteneur permettant l'accès par attribut et par clé :
+        raw_data = load_raw_data()
+        raw_data.application_train  # Accès par attribut
+        raw_data['application_train']  # Accès par clé
+    """
+    if data_dir is None:
+        # First try to use provided BASE_DIR
+        if not (BASE_DIR / "data" / "raw" / "application_train.csv").exists():
+            # If BASE_DIR doesn't have data, search from current working directory
+            current = Path.cwd()
+            found = False
+            for p in [current] + list(current.parents):
+                candidate_file = p / "data" / "raw" / "application_train.csv"
+                if candidate_file.exists():
+                    data_path = p / "data" / "raw"
+                    found = True
+                    break
+            
+            if not found:
+                raise FileNotFoundError(
+                    f"Data files not found. Searched in {BASE_DIR / 'data' / 'raw'} "
+                    f"and from {current} upwards."
+                )
+        else:
+            data_path = BASE_DIR / "data" / "raw"
+    else:
+        data_path = Path(data_dir)
+    
+    print(f"Chargement depuis : {data_path.resolve()}")  # Utile pour debug
+    
+    datasets = {
+        'application_train': 'application_train.csv',
+        'application_test': 'application_test.csv',
+        'bureau': 'bureau.csv',
+        'bureau_balance': 'bureau_balance.csv',
+        'credit_card_balance': 'credit_card_balance.csv',
+        'installments_payments': 'installments_payments.csv',
+        'POS_CASH_balance': 'POS_CASH_balance.csv',
+        'previous_application': 'previous_application.csv'
+    }
+    
+    data = {}
+    for name, filename in datasets.items():
+        filepath = data_path / filename
+        if filepath.exists():
+            print(f"✓ Chargement de {filename}")
+            data[name] = pd.read_csv(filepath)
+        else:
+            print(f"✗ Fichier manquant : {filename} (chemin : {filepath.resolve()})")
+    
+    return DataContainer(data)
+
+
+def load_processed_data(data_dir: str = "data/processed") -> Dict[str, pd.DataFrame]:
+    """
+    Charge les données prétraitées.
+    
+    Args:
+        data_dir: Chemin vers le dossier contenant les données traitées
+        
+    Returns:
+        Dictionnaire contenant les DataFrames train et test
+    """
+    data_path = Path(data_dir)
+    
+    data = {}
+    train_path = data_path / "train_processed.pkl"
+    test_path = data_path / "test_processed.pkl"
+    
+    if train_path.exists():
+        data['train'] = pd.read_pickle(train_path)
+    if test_path.exists():
+        data['test'] = pd.read_pickle(test_path)
+    
+    return data

src/mlflow_config.py

@@ -0,0 +1,41 @@
+"""MLflow configuration helpers for the project."""
+
+from __future__ import annotations
+
+from typing import Mapping, Optional
+
+import mlflow
+import mlflow.lightgbm
+import mlflow.sklearn
+import mlflow.xgboost
+
+DEFAULT_TRACKING_URI = "http://127.0.0.1:5000"
+DEFAULT_EXPERIMENT_NAME = "OC_P6_Credit_Scoring"
+
+
+def configure_mlflow(
+    tracking_uri: str = DEFAULT_TRACKING_URI,
+    experiment_name: str = DEFAULT_EXPERIMENT_NAME,
+    *,
+    autolog: bool = True,
+    log_models: bool = False,
+    extra_tags: Optional[Mapping[str, str]] = None,
+) -> mlflow:
+    """Configure MLflow tracking for this project.
+
+    Returns the mlflow module to allow `mlflow = configure_mlflow()` usage.
+    """
+    if autolog:
+        mlflow.autolog(log_models=log_models)
+    else:
+        # Désactiver tous les autologs explicitement
+        mlflow.autolog(disable=True)
+
+    mlflow.set_tracking_uri(tracking_uri)
+    mlflow.set_experiment(experiment_name)
+
+    if extra_tags:
+        for key, value in extra_tags.items():
+            mlflow.set_tag(key, value)
+
+    return mlflow

src/preprocessing.py

@@ -0,0 +1,306 @@
+"""Preprocessor to convert "raw" input JSON into the model feature vector.
+
+This transformer is purposely lightweight and deterministic:
+- Reads the expected feature names from `data/processed/features_train.csv` when not
+  provided explicitly.
+- If an expected feature is present verbatim in the input it is used.
+- If an expected feature looks like a one-hot column (e.g. "NAME_CONTRACT_TYPE_Cash loans")
+  and the input contains the base column "NAME_CONTRACT_TYPE": "Cash loans", the
+  corresponding one-hot column is set to 1, others to 0.
+- Missing features are filled with `0`.
+
+The goal is to allow the API to accept "raw" payloads (categorical strings, booleans)
+and map them to the exact column names used at training time.
+
+This transformer implements a minimal sklearn-like API (fit/transform) so it can be
+pickled/joblib-dumped if desired.
+"""
+from __future__ import annotations
+
+import re
+from pathlib import Path
+from typing import Iterable, List, Optional
+
+import numpy as np
+import pandas as pd
+
+
+class RawToModelTransformer:
+	"""Transformer that maps raw inputs to model feature vector expected names.
+
+	This improved transformer:
+	- infers expected feature names from the training CSV if not provided
+	- computes a few derived features commonly used in the notebook (PAYMENT_RATE,
+	  INCOME_CREDIT_PERC, INCOME_PER_PERSON, ANNUITY_INCOME_PERC, DAYS_EMPLOYED_PERC)
+	- fills non-computable/unknown features with the column median from
+	  `data/processed/features_train.csv` when available (better than 0)
+	- maps raw categorical columns to one-hot expected columns by prefix match + sanitized
+	  category names (robust to spaces/special chars)
+	
+	The transformer is intentionally conservative — it does not attempt to
+	recreate complex aggregations (BURO_*, PREV_*, POS_*, CC_*, INSTAL_* etc.).
+	"""
+	@staticmethod
+	def _sanitize_column_name(name: str) -> str:
+		"""Sanitize a column name to match the model's feature naming convention.
+
+		Replicates the notebook cleaning (03_LGBM.ipynb cell 6):
+		  1. Replace spaces with '_'
+		  2. Replace all non-alphanumeric/non-underscore chars with '_'
+		Note: double underscores are NOT collapsed — the exported model
+		feature names retain them.
+		"""
+		s = name.replace(' ', '_')
+		s = re.sub(r'[^a-zA-Z0-9_]', '_', s)
+		return s
+
+	def __init__(self, expected_features: Optional[Iterable[str]] = None, fill_value: float = 0.0) -> None:
+		self.fill_value = fill_value
+		self.expected_features = list(expected_features) if expected_features is not None else self._read_features_from_csv()
+
+		# Precompute imputation (median) for expected numeric features from train CSV
+		self._impute_values: dict = {}
+		train_path = Path("data/processed/features_train.csv")
+		if train_path.exists():
+			try:
+				df_train = pd.read_csv(train_path, nrows=10000)
+				# remove identifier/target if present
+				for c in ("SK_ID_CURR", "TARGET"):
+					if c in df_train.columns:
+						df_train = df_train.drop(columns=[c])
+				# Sanitize column names to match expected features
+				df_train.columns = [self._sanitize_column_name(c) for c in df_train.columns]
+				medians = df_train.median(numeric_only=True)
+				for col in self.expected_features:
+					if col in medians.index:
+						self._impute_values[col] = float(medians.loc[col])
+			except Exception:
+				# ignore and keep empty imputation map
+				self._impute_values = {}
+
+	def _read_features_from_csv(self) -> List[str]:
+		"""Read expected feature names from the training CSV header.
+
+		Uses ``pd.read_csv(nrows=0)`` to correctly handle quoted column
+		names that contain commas (e.g. 'Spouse, partner').
+		Applies the same sanitization as the training notebook.
+		"""
+		p = Path("data/processed/features_train.csv")
+		if not p.exists():
+			return []
+		try:
+			df_header = pd.read_csv(p, nrows=0)
+			cols = [c for c in df_header.columns if c not in ("SK_ID_CURR", "TARGET")]
+			return [self._sanitize_column_name(c) for c in cols]
+		except Exception:
+			return []
+
+	def fit(self, X=None, y=None):
+		# Stateless transformer
+		return self
+
+	def _is_nan(self, x) -> bool:
+		return pd.isna(x)
+
+	def _sanitize_category(self, val: str) -> str:
+		"""Normalize a category value to match the one-hot column suffix convention.
+
+		Uses the same logic as ``_sanitize_column_name`` (no collapse of
+		double underscores) so that e.g. 'Spouse, partner' → 'Spouse__partner'
+		matches the model feature name ``NAME_TYPE_SUITE_Spouse__partner``.
+		"""
+		if pd.isna(val):
+			return ""
+		return self._sanitize_column_name(str(val).strip())
+
+	def _compute_derived(self, row: pd.Series) -> dict:
+		# Compute a few numeric derived features when base columns are available
+		out = {}
+		# PAYMENT_RATE = AMT_ANNUITY / AMT_CREDIT
+		if 'AMT_ANNUITY' in row.index and 'AMT_CREDIT' in row.index:
+			try:
+				out['PAYMENT_RATE'] = float(row['AMT_ANNUITY']) / float(row['AMT_CREDIT']) if float(row['AMT_CREDIT']) != 0 else self.fill_value
+			except Exception:
+				out['PAYMENT_RATE'] = self.fill_value
+
+		# INCOME_CREDIT_PERC = AMT_INCOME_TOTAL / AMT_CREDIT
+		if 'AMT_INCOME_TOTAL' in row.index and 'AMT_CREDIT' in row.index:
+			try:
+				out['INCOME_CREDIT_PERC'] = float(row['AMT_INCOME_TOTAL']) / float(row['AMT_CREDIT']) if float(row['AMT_CREDIT']) != 0 else self.fill_value
+			except Exception:
+				out['INCOME_CREDIT_PERC'] = self.fill_value
+
+		# INCOME_PER_PERSON = AMT_INCOME_TOTAL / CNT_FAM_MEMBERS
+		if 'AMT_INCOME_TOTAL' in row.index and 'CNT_FAM_MEMBERS' in row.index:
+			try:
+				cnt = float(row['CNT_FAM_MEMBERS']) if float(row['CNT_FAM_MEMBERS']) not in (0, None) else 1.0
+				out['INCOME_PER_PERSON'] = float(row['AMT_INCOME_TOTAL']) / cnt
+			except Exception:
+				out['INCOME_PER_PERSON'] = self.fill_value
+
+		# ANNUITY_INCOME_PERC = AMT_ANNUITY / AMT_INCOME_TOTAL
+		if 'AMT_ANNUITY' in row.index and 'AMT_INCOME_TOTAL' in row.index:
+			try:
+				out['ANNUITY_INCOME_PERC'] = float(row['AMT_ANNUITY']) / float(row['AMT_INCOME_TOTAL']) if float(row['AMT_INCOME_TOTAL']) != 0 else self.fill_value
+			except Exception:
+				out['ANNUITY_INCOME_PERC'] = self.fill_value
+
+		# DAYS_EMPLOYED_PERC = DAYS_EMPLOYED / DAYS_BIRTH (both negative; ratio meaningful)
+		if 'DAYS_EMPLOYED' in row.index and 'DAYS_BIRTH' in row.index:
+			try:
+				out['DAYS_EMPLOYED_PERC'] = float(row['DAYS_EMPLOYED']) / float(row['DAYS_BIRTH']) if float(row['DAYS_BIRTH']) != 0 else self.fill_value
+			except Exception:
+				out['DAYS_EMPLOYED_PERC'] = self.fill_value
+
+		return out
+
+	def transform(self, df_raw: pd.DataFrame) -> pd.DataFrame:
+		"""Transform a single-row (or multi-row) raw DataFrame into model features.
+
+		Behaviour:
+		- If an expected column exists in df_raw it is copied.
+		- Try to compute derived numeric features from base columns.
+		- Map raw categorical columns to one-hot expected columns by prefix match + sanitized value.
+		- Fill any remaining expected columns with the per-column median (if known) or `fill_value`.
+		"""
+		if not isinstance(df_raw, pd.DataFrame):
+			raise TypeError("df_raw doit être un pandas.DataFrame")
+
+		if not self.expected_features:
+			# Nothing to map to — return copy of input
+			return df_raw.copy()
+
+		# Sanitize input column names so they match model feature names
+		df_raw = df_raw.copy()
+		df_raw.columns = [self._sanitize_column_name(c) for c in df_raw.columns]
+
+		out_rows = []
+		for _, row in df_raw.iterrows():
+			# start from an empty output dict for the expected features
+			out = {feat: None for feat in self.expected_features}
+
+			# 1) copy direct matches
+			for feat in list(out.keys()):
+				if feat in row.index:
+					val = row[feat]
+					out[feat] = int(val) if isinstance(val, (bool, np.bool_)) else (val if not self._is_nan(val) else None)
+
+			# 2) compute derived numeric features and set if present in expected_features
+			derived = self._compute_derived(row)
+			for k, v in derived.items():
+				if k in out:
+					out[k] = v
+
+			# 3) categorical -> one-hot mapping using base column names from raw row
+			for base_col in row.index:
+				if pd.isna(row[base_col]):
+					continue
+				# sanitize raw value once
+				raw_s = self._sanitize_category(row[base_col])
+				for feat in self.expected_features:
+					prefix = feat.split('_')[0]
+					# better check: if feature name starts with base_col + '_'
+					if feat.startswith(f"{base_col}_"):
+						suffix = feat[len(base_col) + 1 :]
+						# compare sanitized forms
+						if suffix == raw_s:
+							out[feat] = 1
+						elif out[feat] is None:
+							# set 0 only if not already set to 1
+							out[feat] = 0
+
+			# 4) final pass: fill remaining None values with impute median or fill_value
+			for feat in out:
+				if out[feat] is None:
+					if feat in self._impute_values:
+						out[feat] = self._impute_values[feat]
+					else:
+						out[feat] = self.fill_value
+
+			out_rows.append(out)
+
+		result = pd.DataFrame(out_rows, columns=self.expected_features)
+
+		# cast numeric-like columns to numeric
+		for col in result.columns:
+			try:
+				result[col] = pd.to_numeric(result[col], errors='coerce').fillna(self.fill_value)
+			except Exception:
+				pass
+
+		return result
+
+	def get_feature_names_out(self) -> List[str]:
+		return list(self.expected_features)
+
+
+# =============================================================================
+# VectorizedPreprocessor — VERSION OPTIMISÉE 4.4 (Gain 15.7x)
+# Wrappeur vectorisé de RawToModelTransformer pour batch et requêtes unitaires.
+# Source : notebooks/10_optimisation.ipynb — Cellule 3
+# =============================================================================
+
+class VectorizedPreprocessor:
+	"""Preprocessor vectorisé pour traiter PLUSIEURS lignes en UNE seule opération.
+
+	Gain de performance : 15.7x plus rapide que la boucle ligne par ligne
+	grâce à la construction du DataFrame depuis une liste de dicts en une
+	seule opération pandas (pd.DataFrame(payloads)).
+
+	Usage dans app.py :
+		prep = VectorizedPreprocessor(base_transformer)
+		df = prep.transform_single(payload_dict)        # requête API unique
+		df = prep.transform_batch([dict1, dict2, ...])  # batch
+		df = prep.transform_one_sample(json_string)     # depuis JSON brut
+	"""
+
+	def __init__(self, base_transformer: "RawToModelTransformer") -> None:
+		"""Initialise avec un transformer de base (récupère expected_features + impute)."""
+		self.base_transformer = base_transformer
+		# Accès direct aux attributs clés pour éviter les appels répétés
+		self.expected_features = base_transformer.expected_features
+		self._impute_values = base_transformer._impute_values
+
+	def transform_batch(self, payloads: list) -> pd.DataFrame:
+		"""Transforme une liste de dicts (payloads JSON) → DataFrame features.
+
+		Étapes :
+		1. Convertir liste de dicts → DataFrame en UNE opération pandas vectorisée
+		2. Nettoyage standard (empty string, boolean string, numeric coercion)
+		3. Appliquer le transformer de base (one-hot, médiane, derived features)
+		4. Retourner DataFrame prêt pour le modèle LightGBM
+		"""
+		# === ÉTAPE 1 : Construction vectorisée du DataFrame (cœur du gain 15.7x) ===
+		df = pd.DataFrame(payloads)
+
+		# === ÉTAPE 2 : Nettoyage standard (same as _parse_json_line) ===
+		df = df.replace({"": np.nan, "True": True, "False": False})
+
+		# Conversion numérique (LightGBM exige des colonnes numériques)
+		for col in df.columns:
+			try:
+				df[col] = pd.to_numeric(df[col], errors='coerce')
+			except Exception:
+				pass
+
+		# === ÉTAPE 3 : Transformer de base (one-hot, dérivées, imputations) ===
+		df = self.base_transformer.transform(df)
+
+		return df
+
+	def transform_single(self, payload: dict) -> pd.DataFrame:
+		"""Transforme UN SEUL dict (payload JSON parsé) → DataFrame (1 ligne)."""
+		return self.transform_batch([payload])
+
+	def transform_one_sample(self, json_line: str) -> pd.DataFrame:
+		"""Parse un JSON string et transforme → DataFrame (1 ligne).
+
+		Point d'entrée principal dans app.py :
+			df = PREPROCESSOR.transform_one_sample(json_line)
+		"""
+		import json as _json
+		payload = _json.loads(json_line)
+		return self.transform_single(payload)
+
+	def get_feature_names_out(self) -> List[str]:
+		return list(self.expected_features)

tests/conftest.py

@@ -0,0 +1,70 @@
+"""Pytest configuration for tests."""
+
+import sys
+from pathlib import Path
+import tempfile
+import pandas as pd
+import pytest
+
+# Add parent directory (project root) to sys.path so that imports work
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+
+@pytest.fixture(scope="session", autouse=True)
+def setup_features_csv():
+    """Create a temporary features_train.csv for tests if it doesn't exist.
+    
+    This ensures tests can run in CI environments without the data files.
+    """
+    features_path = Path("data/processed/features_train.csv")
+    
+    # Skip if file already exists
+    if features_path.exists():
+        return
+    
+    # Create minimal feature set with required columns for tests
+    features = [
+        "CODE_GENDER",
+        "FLAG_OWN_CAR",
+        "FLAG_OWN_REALTY",
+        "CNT_CHILDREN",
+        "AMT_INCOME_TOTAL",
+        "AMT_CREDIT",
+        "AMT_ANNUITY",
+        "AMT_GOODS_PRICE",
+        "REGION_POPULATION_RELATIVE",
+        "DAYS_BIRTH",
+        "DAYS_EMPLOYED",
+        "DAYS_REGISTRATION",
+        "DAYS_ID_PUBLISH",
+        "OWN_CAR_AGE",
+        "FLAG_MOBIL",
+        "FLAG_EMP_PHONE",
+        "FLAG_WORK_PHONE",
+        "FLAG_CONT_MOBILE",
+        "FLAG_PHONE",
+        "FLAG_EMAIL",
+        "CNT_FAM_MEMBERS",
+        "REGION_RATING_CLIENT",
+        "REGION_RATING_CLIENT_W_CITY",
+        "HOUR_APPR_PROCESS_START",
+        "REG_REGION_NOT_LIVE_REGION",
+        "REG_REGION_NOT_WORK_REGION",
+        "LIVE_REGION_NOT_WORK_REGION",
+        "PAYMENT_RATE",
+        "INCOME_CREDIT_PERC",
+        "INCOME_PER_PERSON",
+        "ANNUITY_INCOME_PERC",
+        "DAYS_EMPLOYED_PERC",
+        "NAME_CONTRACT_TYPE_Cash_loans",
+        "NAME_CONTRACT_TYPE_Revolving_loans",
+    ]
+    
+    # Create directory if it doesn't exist
+    features_path.parent.mkdir(parents=True, exist_ok=True)
+    
+    # Create minimal dataframe and save
+    df = pd.DataFrame({col: [0.0] for col in features})
+    df.insert(0, "SK_ID_CURR", [1])
+    df.insert(1, "TARGET", [0])
+    df.to_csv(features_path, index=False)

tests/test_predict.py

@@ -0,0 +1,109 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+import app as app_module
+
+try:
+	from app import predict_score, model
+except ImportError:
+	from app import _predict as predict_score
+	model = app_module.MODEL
+
+
+class DummyModel:
+	def __init__(self, proba: float = 0.2) -> None:
+		self.proba = proba
+
+	def predict_proba(self, df: pd.DataFrame) -> np.ndarray:
+		return np.array([[1.0 - self.proba, self.proba]])
+
+	def predict(self, df: pd.DataFrame) -> np.ndarray:
+		return np.array([self.proba])
+
+
+def _series_json(payload: dict) -> str:
+	# Convert a single-record payload using Series.to_json(orient="records").
+	# Pandas returns a one-item list; trim brackets to get the JSON object.
+	return pd.Series([payload]).to_json(orient="records")[1:-1]
+
+
+def _extract_proba(response: str) -> float:
+	for line in response.splitlines():
+		if line.startswith("Probabilit"):
+			return float(line.split(":", 1)[1].strip())
+	raise AssertionError("Probability line not found in response")
+
+
+@pytest.fixture()
+def dummy_model(monkeypatch: pytest.MonkeyPatch) -> DummyModel:
+	# Patch the global model so tests are fast and independent of disk artifacts.
+	dummy = DummyModel(proba=0.23)
+	monkeypatch.setattr(app_module, "MODEL", dummy, raising=False)
+	monkeypatch.setattr(app_module, "model", dummy, raising=False)
+	return dummy
+
+
+def test_predict_valid_minimal_json(dummy_model: DummyModel) -> None:
+	# Valid minimal JSON should yield a probability between 0 and 1.
+	payload = {
+		"EXT_SOURCE_1": 0.5,
+		"AMT_INCOME_TOTAL": 50000.0,
+	}
+	json_line = _series_json(payload)
+	response = predict_score(json_line)
+
+	assert "Erreur" not in response
+	proba = _extract_proba(response)
+	assert 0.0 <= proba <= 1.0
+
+
+def test_predict_partial_json_missing_columns(dummy_model: DummyModel) -> None:
+	# Missing columns should be handled (reindex + NaN) and still predict.
+	payload = {
+		"EXT_SOURCE_2": 0.1,
+	}
+	json_line = _series_json(payload)
+	response = predict_score(json_line)
+
+	assert "Erreur" not in response
+	proba = _extract_proba(response)
+	assert 0.0 <= proba <= 1.0
+
+
+def test_predict_invalid_json_returns_error() -> None:
+	# Bad JSON format should return an explicit error message.
+	json_line = "{this is not valid json"
+	response = predict_score(json_line)
+
+	assert "Erreur" in response
+
+
+def test_predict_out_of_range_value(dummy_model: DummyModel) -> None:
+	# Aberrant values (e.g., negative income) should still predict for now.
+	payload = {
+		"AMT_INCOME_TOTAL": -1000.0,
+		"EXT_SOURCE_3": 0.2,
+	}
+	json_line = _series_json(payload)
+	response = predict_score(json_line)
+
+	assert "Erreur" not in response
+	proba = _extract_proba(response)
+	assert 0.0 <= proba <= 1.0
+
+
+def test_predict_accepts_raw_categorical(dummy_model: DummyModel) -> None:
+	# The API should accept raw categorical fields and map them to the model's
+	# one-hot columns (e.g. NAME_CONTRACT_TYPE -> NAME_CONTRACT_TYPE_Cash loans).
+	payload = {
+		"NAME_CONTRACT_TYPE": "Cash loans",
+		"AMT_INCOME_TOTAL": 75000.0,
+		"EXT_SOURCE_1": 0.3,
+	}
+	json_line = _series_json(payload)
+	response = predict_score(json_line)
+
+	assert "Erreur" not in response
+	proba = _extract_proba(response)
+	assert 0.0 <= proba <= 1.0

tests/test_preprocessing.py

@@ -0,0 +1,47 @@
+import pandas as pd
+
+from src.preprocessing import RawToModelTransformer
+
+
+def test_transform_computes_derived_features():
+    row = pd.DataFrame([
+        {
+            "AMT_ANNUITY": 1000.0,
+            "AMT_CREDIT": 20000.0,
+            "AMT_INCOME_TOTAL": 60000.0,
+            "CNT_FAM_MEMBERS": 3,
+            "DAYS_EMPLOYED": -1000,
+            "DAYS_BIRTH": -10000,
+            "NAME_CONTRACT_TYPE": "Cash loans",
+        }
+    ])
+
+    pre = RawToModelTransformer()
+    out = pre.transform(row)
+
+    # Derived numeric
+    assert "PAYMENT_RATE" in out.columns
+    assert abs(out["PAYMENT_RATE"].iloc[0] - (1000.0 / 20000.0)) < 1e-8
+    assert "INCOME_CREDIT_PERC" in out.columns
+    assert abs(out["INCOME_CREDIT_PERC"].iloc[0] - (60000.0 / 20000.0)) < 1e-8
+    assert "INCOME_PER_PERSON" in out.columns
+    assert abs(out["INCOME_PER_PERSON"].iloc[0] - (60000.0 / 3.0)) < 1e-8
+    assert "ANNUITY_INCOME_PERC" in out.columns
+    assert abs(out["ANNUITY_INCOME_PERC"].iloc[0] - (1000.0 / 60000.0)) < 1e-8
+    assert "DAYS_EMPLOYED_PERC" in out.columns
+    assert abs(out["DAYS_EMPLOYED_PERC"].iloc[0] - (-1000.0 / -10000.0)) < 1e-8
+
+
+def test_transform_maps_categorical_to_one_hot():
+    row = pd.DataFrame([
+        {"NAME_CONTRACT_TYPE": "Cash loans", "AMT_INCOME_TOTAL": 1000.0}
+    ])
+    pre = RawToModelTransformer()
+    out = pre.transform(row)
+
+    # Expect a one-hot column for the contract type (sanitized name)
+    # We look for any column that starts with NAME_CONTRACT_TYPE_ and contains 'Cash'
+    matching = [c for c in out.columns if c.startswith("NAME_CONTRACT_TYPE_") and "Cash" in c]
+    assert matching, "No one-hot column found for NAME_CONTRACT_TYPE"
+    # the matching column should be 1 for our input
+    assert out[matching[0]].iloc[0] == 1