diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab5-Viz-ML/Lab5-Viz-ML.ipynb b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab5-Viz-ML/Lab5-Viz-ML.ipynb
index aad6d3b6d5..6e435125f6 100644
--- a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab5-Viz-ML/Lab5-Viz-ML.ipynb
+++ b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab5-Viz-ML/Lab5-Viz-ML.ipynb
@@ -5,10 +5,10 @@
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@@ -24,10 +24,10 @@
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@@ -56,10 +56,10 @@
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@@ -73,10 +73,10 @@
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@@ -91,29 +91,119 @@
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    "metadata": {
     "execution": {
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+     "start_time": "2026-10-07T07:12:56.985542",
      "status": "completed"
     },
     "tags": []
    },
    "outputs": [
     {
-     "output_type": "execute_result",
-     "metadata": {},
      "data": {
-      "text/plain": "        date id_produit     categorie  quantite  prix_unitaire  \\\n0 2023-10-01       A101  Electronique         5          120.0   \n1 2023-10-01       B202         Livre        10           15.5   \n2 2023-10-02       A101  Electronique         3           75.9   \n3 2023-10-03       C303        Maison         2           75.9   \n4 2023-10-03       B202         Livre         6           15.5   \n\n   chiffre_affaires  \n0             600.0  \n1             155.0  \n2             227.7  \n3             151.8  \n4              93.0  ",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>id_produit</th>\n      <th>categorie</th>\n      <th>quantite</th>\n      <th>prix_unitaire</th>\n      <th>chiffre_affaires</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023-10-01</td>\n      <td>A101</td>\n      <td>Electronique</td>\n      <td>5</td>\n      <td>120.0</td>\n      <td>600.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023-10-01</td>\n      <td>B202</td>\n      <td>Livre</td>\n      <td>10</td>\n      <td>15.5</td>\n      <td>155.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023-10-02</td>\n      <td>A101</td>\n      <td>Electronique</td>\n      <td>3</td>\n      <td>75.9</td>\n      <td>227.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023-10-03</td>\n      <td>C303</td>\n      <td>Maison</td>\n      <td>2</td>\n      <td>75.9</td>\n      <td>151.8</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023-10-03</td>\n      <td>B202</td>\n      <td>Livre</td>\n      <td>6</td>\n      <td>15.5</td>\n      <td>93.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>id_produit</th>\n",
+       "      <th>categorie</th>\n",
+       "      <th>quantite</th>\n",
+       "      <th>prix_unitaire</th>\n",
+       "      <th>chiffre_affaires</th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-10-01</td>\n",
+       "      <td>A101</td>\n",
+       "      <td>Electronique</td>\n",
+       "      <td>5</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-10-01</td>\n",
+       "      <td>B202</td>\n",
+       "      <td>Livre</td>\n",
+       "      <td>10</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>155.0</td>\n",
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+       "      <td>2023-10-02</td>\n",
+       "      <td>A101</td>\n",
+       "      <td>Electronique</td>\n",
+       "      <td>3</td>\n",
+       "      <td>75.9</td>\n",
+       "      <td>227.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-10-03</td>\n",
+       "      <td>C303</td>\n",
+       "      <td>Maison</td>\n",
+       "      <td>2</td>\n",
+       "      <td>75.9</td>\n",
+       "      <td>151.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-10-03</td>\n",
+       "      <td>B202</td>\n",
+       "      <td>Livre</td>\n",
+       "      <td>6</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>93.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date id_produit     categorie  quantite  prix_unitaire  \\\n",
+       "0 2023-10-01       A101  Electronique         5          120.0   \n",
+       "1 2023-10-01       B202         Livre        10           15.5   \n",
+       "2 2023-10-02       A101  Electronique         3           75.9   \n",
+       "3 2023-10-03       C303        Maison         2           75.9   \n",
+       "4 2023-10-03       B202         Livre         6           15.5   \n",
+       "\n",
+       "   chiffre_affaires  \n",
+       "0             600.0  \n",
+       "1             155.0  \n",
+       "2             227.7  \n",
+       "3             151.8  \n",
+       "4              93.0  "
+      ]
      },
-     "execution_count": 1
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -153,10 +243,10 @@
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@@ -171,25 +261,27 @@
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+     "start_time": "2026-10-07T07:12:57.387706",
      "status": "completed"
     },
     "tags": []
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "Bibliotheques de visualisation chargees (matplotlib, seaborn) - style 'whitegrid' applique\n"
+     "output_type": "stream",
+     "text": [
+      "Bibliotheques de visualisation chargees (matplotlib, seaborn) - style 'whitegrid' applique\n"
+     ]
     }
    ],
    "source": [
@@ -207,10 +299,10 @@
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@@ -225,28 +317,30 @@
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      "status": "completed"
     },
     "tags": []
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    "outputs": [
     {
-     "output_type": "display_data",
-     "metadata": {},
      "data": {
-      "text/plain": "<Figure size 1000x600 with 1 Axes>",
-      "image/png": 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"
-     }
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -264,10 +358,10 @@
    "id": "6f7356f0",
    "metadata": {
     "papermill": {
-     "duration": 0.007757,
-     "end_time": "2026-05-15T02:45:53.061166+00:00",
+     "duration": 0.002653,
+     "end_time": "2026-10-07T07:12:58.616162",
      "exception": false,
-     "start_time": "2026-05-15T02:45:53.053409+00:00",
+     "start_time": "2026-10-07T07:12:58.613509",
      "status": "completed"
     },
     "tags": []
@@ -281,10 +375,10 @@
    "id": "57b5b47a",
    "metadata": {
     "papermill": {
-     "duration": 0.006353,
-     "end_time": "2026-05-15T02:45:53.076333+00:00",
+     "duration": 0.002596,
+     "end_time": "2026-10-07T07:12:58.621362",
      "exception": false,
-     "start_time": "2026-05-15T02:45:53.069980+00:00",
+     "start_time": "2026-10-07T07:12:58.618766",
      "status": "completed"
     },
     "tags": []
@@ -299,28 +393,30 @@
    "id": "4092c8b8",
    "metadata": {
     "execution": {
-     "iopub.status.busy": "2026-08-17T21:21:36.885064Z",
-     "iopub.execute_input": "2026-08-17T21:21:36.885269Z",
-     "shell.execute_reply": "2026-08-17T21:21:37.031290Z",
-     "iopub.status.idle": "2026-08-17T21:21:37.031841Z"
+     "iopub.execute_input": "2026-10-07T07:12:58.628081Z",
+     "iopub.status.busy": "2026-10-07T07:12:58.627760Z",
+     "iopub.status.idle": "2026-10-07T07:12:58.766005Z",
+     "shell.execute_reply": "2026-10-07T07:12:58.764973Z"
     },
     "papermill": {
-     "duration": 0.885151,
-     "end_time": "2026-05-15T02:45:53.968034+00:00",
+     "duration": 0.143203,
+     "end_time": "2026-10-07T07:12:58.767114",
      "exception": false,
-     "start_time": "2026-05-15T02:45:53.082883+00:00",
+     "start_time": "2026-10-07T07:12:58.623911",
      "status": "completed"
     },
     "tags": []
    },
    "outputs": [
     {
-     "output_type": "display_data",
-     "metadata": {},
      "data": {
-      "text/plain": "<Figure size 1200x600 with 1 Axes>",
-      "image/png": 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"
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+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -339,10 +435,10 @@
    "id": "387401eb",
    "metadata": {
     "papermill": {
-     "duration": 0.007098,
-     "end_time": "2026-05-15T02:45:53.983495+00:00",
+     "duration": 0.003058,
+     "end_time": "2026-10-07T07:12:58.774002",
      "exception": false,
-     "start_time": "2026-05-15T02:45:53.976397+00:00",
+     "start_time": "2026-10-07T07:12:58.770944",
      "status": "completed"
     },
     "tags": []
@@ -356,10 +452,10 @@
    "id": "3f987ccf",
    "metadata": {
     "papermill": {
-     "duration": 0.004015,
-     "end_time": "2026-05-15T02:45:53.994530+00:00",
+     "duration": 0.004719,
+     "end_time": "2026-10-07T07:12:58.782030",
      "exception": false,
-     "start_time": "2026-05-15T02:45:53.990515+00:00",
+     "start_time": "2026-10-07T07:12:58.777311",
      "status": "completed"
     },
     "tags": []
@@ -377,16 +473,20 @@
    "id": "64f9475a",
    "metadata": {
     "papermill": {
-     "duration": 0.008725,
-     "end_time": "2026-05-15T02:45:54.014099+00:00",
+     "duration": 0.004009,
+     "end_time": "2026-10-07T07:12:58.789110",
      "exception": false,
-     "start_time": "2026-05-15T02:45:54.005374+00:00",
+     "start_time": "2026-10-07T07:12:58.785101",
      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "**Problématique :** Pouvons-nous prédire la `catégorie` d'un produit en nous basant uniquement sur son `prix_unitaire` et la `quantite` vendue ?"
+    "**Problématique :** pouvons-nous prédire la `categorie` à partir de variables numériques mesurées sur l'échantillon ?\n",
+    "\n",
+    "**Changement de jeu de données — et pourquoi.** Le fichier `transactions.csv` du Lab 4 ne porte que **sept lignes**, dont six survivent au nettoyage. Un `train_test_split` y laisse **4 exemples d'entraînement et 2 de test** : une précision mesurée sur deux points ne mesure rien, sinon qu'un modèle à peine contraint a bien classé les deux points qu'on lui a laissés. L'étape « Machine Learning » y serait une **démonstration dégénérée** — le moteur y équivaudrait à une baseline triviale.\n",
+    "\n",
+    "Nous gardons donc `transactions.csv` comme **fil des étapes data** (nettoyage et visualisations ci-dessus, et l'Exercice 2 ci-dessous) et nous basculons **la seule étape ML** sur un jeu de données riche et standard de `scikit-learn` : le jeu **« wine »** (178 échantillons, 13 variables numériques, 3 classes). Même geste — classer une catégorie à partir de variables numériques — mais cette fois la mesure veut dire quelque chose.\n"
    ]
   },
   {
@@ -395,47 +495,65 @@
    "id": "abba522b",
    "metadata": {
     "execution": {
-     "iopub.status.busy": "2026-08-17T21:21:37.033321Z",
-     "iopub.execute_input": "2026-08-17T21:21:37.033524Z",
-     "shell.execute_reply": "2026-08-17T21:21:37.420078Z",
-     "iopub.status.idle": "2026-08-17T21:21:37.420604Z"
+     "iopub.execute_input": "2026-10-07T07:12:58.796628Z",
+     "iopub.status.busy": "2026-10-07T07:12:58.796404Z",
+     "iopub.status.idle": "2026-10-07T07:12:59.030918Z",
+     "shell.execute_reply": "2026-10-07T07:12:59.030112Z"
     },
     "papermill": {
-     "duration": 2.481615,
-     "end_time": "2026-05-15T02:45:56.504249+00:00",
+     "duration": 0.239494,
+     "end_time": "2026-10-07T07:12:59.031583",
      "exception": false,
-     "start_time": "2026-05-15T02:45:54.022634+00:00",
+     "start_time": "2026-10-07T07:12:58.792089",
      "status": "completed"
     },
     "tags": []
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "Donnees preparees : 4 echantillons d'entrainement, 2 de test\nFeatures utilisees : ['prix_unitaire', 'quantite'] | Target : categorie\n"
+     "output_type": "stream",
+     "text": [
+      "Donnees preparees : 124 echantillons d'entrainement, 54 de test\n",
+      "Features : 13 variables numeriques | Target : categorie (3 classes)\n",
+      "Repartition des classes : {0: 59, 1: 71, 2: 48}\n"
+     ]
     }
    ],
    "source": [
+    "from sklearn.datasets import load_wine\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "# Définir les features (X) et la target (y)\n",
-    "features = ['prix_unitaire', 'quantite']\n",
-    "target = 'categorie'\n",
+    "# --- Trame des etapes data : le jeu de transactions du Lab 4 -----------------\n",
+    "# Conservee telle quelle : l'Exercice 2 (visualisations) s'y adresse.\n",
+    "df_clean = df.dropna(subset=['prix_unitaire', 'quantite', 'categorie'])\n",
+    "\n",
+    "# --- Etape ML : jeu de donnees riche -----------------------------------------\n",
+    "# `transactions.csv` ne permet pas de mesurer une precision : le\n",
+    "# split y laisse 4 exemples d'entrainement et 2 de test. On bascule l'etape ML\n",
+    "# sur un jeu standard de scikit-learn, de meme forme : une categorie a predire,\n",
+    "# des variables numeriques en entree.\n",
+    "wine = load_wine(as_frame=True)\n",
+    "df_ml = wine.frame.rename(columns={'target': 'categorie'})\n",
     "\n",
-    "# S'assurer qu'il n'y a pas de valeurs manquantes dans les features\n",
-    "df_clean = df.dropna(subset=features + [target])\n",
+    "features = [c for c in df_ml.columns if c != 'categorie']\n",
+    "target = 'categorie'\n",
     "\n",
-    "X = df_clean[features]\n",
-    "y = df_clean[target]\n",
+    "X = df_ml[features]\n",
+    "y = df_ml[target]\n",
     "\n",
-    "# Diviser les données en ensembles d'entraînement et de test\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
+    "# Diviser les donnees en ensembles d'entrainement et de test.\n",
+    "# `stratify=y` conserve la proportion des trois classes dans les deux ensembles.\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.3, random_state=42, stratify=y)\n",
     "\n",
     "print(f\"Donnees preparees : {len(X_train)} echantillons d'entrainement, {len(X_test)} de test\")\n",
-    "print(f\"Features utilisees : {features} | Target : {target}\")"
+    "print(f\"Features : {len(features)} variables numeriques | Target : {target} ({y.nunique()} classes)\")\n",
+    "print(f\"Repartition des classes : {y.value_counts().sort_index().to_dict()}\")\n"
    ]
   },
   {
@@ -443,10 +561,10 @@
    "id": "03c22654",
    "metadata": {
     "papermill": {
-     "duration": 0,
-     "end_time": "2026-05-15T02:45:56.510844+00:00",
+     "duration": 0.00297,
+     "end_time": "2026-10-07T07:12:59.037687",
      "exception": false,
-     "start_time": "2026-05-15T02:45:56.510844+00:00",
+     "start_time": "2026-10-07T07:12:59.034717",
      "status": "completed"
     },
     "tags": []
@@ -461,30 +579,39 @@
    "id": "998fec65",
    "metadata": {
     "execution": {
-     "iopub.status.busy": "2026-08-17T21:21:37.422588Z",
-     "iopub.execute_input": "2026-08-17T21:21:37.422922Z",
-     "shell.execute_reply": "2026-08-17T21:21:37.448041Z",
-     "iopub.status.idle": "2026-08-17T21:21:37.448608Z"
+     "iopub.execute_input": "2026-10-07T07:12:59.044959Z",
+     "iopub.status.busy": "2026-10-07T07:12:59.044546Z",
+     "iopub.status.idle": "2026-10-07T07:12:59.057945Z",
+     "shell.execute_reply": "2026-10-07T07:12:59.057230Z"
     },
     "papermill": {
-     "duration": 0.050275,
-     "end_time": "2026-05-15T02:45:56.570772+00:00",
+     "duration": 0.017912,
+     "end_time": "2026-10-07T07:12:59.058524",
      "exception": false,
-     "start_time": "2026-05-15T02:45:56.520497+00:00",
+     "start_time": "2026-10-07T07:12:59.040612",
      "status": "completed"
     },
     "tags": []
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "Précision du modèle : 1.00\n"
+     "output_type": "stream",
+     "text": [
+      "Précision du modèle : 0.981\n",
+      "Précision de la baseline majoritaire : 0.389\n",
+      "Écart au-dessus de la baseline : +0.593\n"
+     ]
     }
    ],
    "source": [
-    "# Instancier et entraîner le modèle\n",
-    "model = LogisticRegression()\n",
+    "# Instancier et entraîner le modèle.\n",
+    "# Les 13 variables ont des échelles très différentes (la proline se compte en\n",
+    "# centaines, le hue en unités) : sans standardisation, le solveur converge mal\n",
+    "# -- et un avertissement de convergence dans la sortie est un résultat qu'on ne\n",
+    "# peut pas interpréter. On enchaîne donc standardisation et régression\n",
+    "# logistique dans un `Pipeline`, ajusté sur l'entraînement seul.\n",
+    "model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))\n",
     "model.fit(X_train, y_train)\n",
     "\n",
     "# Faire des prédictions\n",
@@ -493,7 +620,14 @@
     "# Calculer la précision\n",
     "accuracy = accuracy_score(y_test, y_pred)\n",
     "\n",
-    "print(f\"Précision du modèle : {accuracy:.2f}\")"
+    "# Baseline triviale de référence : predire toujours la classe majoritaire.\n",
+    "# Sans elle, une precision ne dit pas si le modele apporte quelque chose.\n",
+    "classe_majoritaire = y_train.value_counts().idxmax()\n",
+    "baseline_acc = accuracy_score(y_test, [classe_majoritaire] * len(y_test))\n",
+    "\n",
+    "print(f\"Précision du modèle : {accuracy:.3f}\")\n",
+    "print(f\"Précision de la baseline majoritaire : {baseline_acc:.3f}\")\n",
+    "print(f\"Écart au-dessus de la baseline : {accuracy - baseline_acc:+.3f}\")\n"
    ]
   },
   {
@@ -501,23 +635,24 @@
    "id": "5e3a09e6",
    "metadata": {
     "papermill": {
-     "duration": 0.00553,
-     "end_time": "2026-05-15T02:45:56.582206+00:00",
+     "duration": 0.002982,
+     "end_time": "2026-10-07T07:12:59.064612",
      "exception": false,
-     "start_time": "2026-05-15T02:45:56.576676+00:00",
+     "start_time": "2026-10-07T07:12:59.061630",
      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "**Interprétation du score :** la cellule ci-dessus imprime la précision mesurée sur l'ensemble de test — ici **1.00**. C'est le moment de se méfier plutôt que de se réjouir.\n",
+    "**Interprétation du score :** la précision est le pourcentage de prédictions correctes sur l'ensemble de test. Sur ce jeu, elle se lit **contre la baseline majoritaire** imprimée juste au-dessus — sans cette comparaison, un score élevé peut simplement refléter des classes déséquilibrées :\n",
     "\n",
-    "`transactions.csv` est minuscule (la cellule de préparation en affiche la taille) : quatre exemples à l'entraînement, deux au test. Une précision parfaite sur deux exemples ne mesure rien — elle dit qu'un modèle à peine contraint a bien classé les deux points qu'on lui a laissés, pas qu'il « distingue les catégories ». La leçon de méthode de cette étape est là : un score ne se lit qu'avec le volume de test qui le porte.\n",
+    "- **écart nettement positif** : les variables numériques portent une information réelle sur la classe, le modèle fait mieux que « toujours la même réponse » ;\n",
+    "- **écart proche de zéro** : le modèle n'apporte rien que la classe majoritaire n'apportait déjà ;\n",
+    "- **écart négatif** : le modèle se trompe plus souvent que la baseline — c'est un signal d'alarme (surapprentissage, fuite de données, mauvais réglage), pas un résultat.\n",
     "\n",
-    "Les repères ci-dessous décrivent ce que le score signifierait **sur un ensemble de test de taille suffisante** :\n",
-    "- **> 0.7** : le modèle distingue bien les catégories à partir du prix et de la quantité.\n",
-    "- **0.4-0.7** : les features choisies donnent des résultats mitigés.\n",
-    "- **< 0.4** : le prix et la quantité seuls ne suffisent pas à différencier les catégories de produits.\n"
+    "**La leçon de méthode de cette étape** — et la raison du changement de jeu de données ci-dessus : un score ne se lit qu'avec le **volume de test qui le porte**. Sur les six lignes de `transactions.csv`, la précision ne pouvait valoir que 0.00, 0.50 ou 1.00, et aucune de ces trois valeurs ne mesure quoi que ce soit. Une précision parfaite sur deux exemples dit qu'un modèle à peine contraint a bien classé les deux points qu'on lui a laissés, pas qu'il « distingue les catégories ».\n",
+    "\n",
+    "**Un mot sur la standardisation.** Le `Pipeline` standardise les variables avant de les donner au classifieur. Ce n'est pas un détail cosmétique : les 13 variables du jeu s'expriment dans des unités qui n'ont rien à voir (la proline se compte en centaines, le hue en unités), et un solveur qui les reçoit brutes converge mal — la sortie porterait alors un avertissement de convergence au lieu d'un résultat. Standardiser est ici la condition pour que le score soit lisible.\n"
    ]
   },
   {
@@ -525,10 +660,10 @@
    "id": "jwygopq4ls",
    "metadata": {
     "papermill": {
-     "duration": 0,
-     "end_time": "2026-05-15T02:45:56.613795+00:00",
+     "duration": 0.003017,
+     "end_time": "2026-10-07T07:12:59.070575",
      "exception": false,
-     "start_time": "2026-05-15T02:45:56.613795+00:00",
+     "start_time": "2026-10-07T07:12:59.067558",
      "status": "completed"
     },
     "tags": []
@@ -545,16 +680,16 @@
    "id": "m8r5k70drr8",
    "metadata": {
     "execution": {
-     "iopub.status.busy": "2026-08-17T21:21:37.449955Z",
-     "iopub.execute_input": "2026-08-17T21:21:37.450194Z",
-     "shell.execute_reply": "2026-08-17T21:21:37.452574Z",
-     "iopub.status.idle": "2026-08-17T21:21:37.453393Z"
+     "iopub.execute_input": "2026-10-07T07:12:59.077770Z",
+     "iopub.status.busy": "2026-10-07T07:12:59.077547Z",
+     "iopub.status.idle": "2026-10-07T07:12:59.080916Z",
+     "shell.execute_reply": "2026-10-07T07:12:59.080291Z"
     },
     "papermill": {
-     "duration": 0.012758,
-     "end_time": "2026-05-15T02:45:56.636561+00:00",
+     "duration": 0.00794,
+     "end_time": "2026-10-07T07:12:59.081555",
      "exception": false,
-     "start_time": "2026-05-15T02:45:56.623803+00:00",
+     "start_time": "2026-10-07T07:12:59.073615",
      "status": "completed"
     },
     "tags": []
@@ -562,8 +697,8 @@
    "outputs": [],
    "source": [
     "# Exercice : Creez une nouvelle visualisation et entrainez un autre modele\n",
-    "# 1. Creez un graphique montrant le prix moyen par categorie\n",
-    "# 2. Entrainez un modele pour predire si le chiffre d'affaires est > 50\n",
+    "# 1. Creez un graphique montrant le prix moyen par categorie (trame data)\n",
+    "# 2. Entrainez un modele pour predire si la teneur en alcool est elevee (etape ML)\n",
     "\n",
     "# --- Exercice 1 : Prix moyen par categorie ---\n",
     "# Exercice: Calculez le prix moyen par categorie avec groupby\n",
@@ -577,13 +712,14 @@
     "# plt.title(\"Prix Moyen par Categorie\")\n",
     "# plt.show()\n",
     "\n",
-    "# --- Exercice 2 : Classification binaire ---\n",
-    "# Exercice: Creez une colonne binaire 'ca_eleve' (1 si chiffre_affaires > 50, 0 sinon)\n",
-    "# Indice: (df_clean['chiffre_affaires'] > 50).astype(int)\n",
-    "# df_clean['ca_eleve'] = None  # Remplacez None\n",
+    "# --- Exercice 2 : Classification binaire sur le jeu riche ---\n",
+    "# Exercice: Creez une colonne binaire 'alcool_eleve' (1 si la teneur en alcool\n",
+    "#           depasse la mediane du jeu, 0 sinon)\n",
+    "# Indice: (df_ml['alcohol'] > df_ml['alcohol'].median()).astype(int)\n",
+    "# df_ml['alcool_eleve'] = None  # Remplacez None\n",
     "\n",
     "# Exercice: Definissez X (features) et y (target), puis split train/test\n",
-    "# Indice: memes features que l'exemple guide (prix_unitaire, quantite)\n",
+    "# Indice: memes features que l'etape 3 (les 13 variables numeriques)\n",
     "# X_bin = None\n",
     "# y_bin = None\n",
     "\n",
@@ -591,12 +727,22 @@
     "# Indice: meme pattern que l'etape 3 (fit, predict, accuracy_score)\n",
     "# model_bin = LogisticRegression()\n",
     "# ...\n",
-    "# print(f\"Precision du modele binaire (CA > 50) : {accuracy_bin:.2f}\")\n"
+    "# print(f\"Precision du modele binaire (alcool eleve) : {accuracy_bin:.2f}\")\n"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "5679da57",
+   "metadata": {
+    "papermill": {
+     "duration": 0.002938,
+     "end_time": "2026-10-07T07:12:59.087594",
+     "exception": false,
+     "start_time": "2026-10-07T07:12:59.084656",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Exercice 2 : Analyse de la distribution des prix\n",
     "\n",
@@ -608,12 +754,37 @@
     "- Utilisez `sns.histplot()` avec le paramètre `bins` pour l'histogramme\n",
     "- Utilisez `sns.boxplot()` avec `x='catégorie'` et `y='prix_unitaire'` pour le boxplot\n",
     "- Utilisez `plt.subplots(1, 2, figsize=(14, 5))` pour afficher les deux graphiques côte à côte"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
    "id": "6a7cdbe7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-07T07:12:59.094534Z",
+     "iopub.status.busy": "2026-10-07T07:12:59.094333Z",
+     "iopub.status.idle": "2026-10-07T07:12:59.097754Z",
+     "shell.execute_reply": "2026-10-07T07:12:59.097007Z"
+    },
+    "papermill": {
+     "duration": 0.007836,
+     "end_time": "2026-10-07T07:12:59.098332",
+     "exception": false,
+     "start_time": "2026-10-07T07:12:59.090496",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice 2 a completer : distribution des prix avec histogramme et boxplot\n"
+     ]
+    }
+   ],
    "source": [
     "# Exercice 2 : Distribution des prix unitaires\n",
     "# Creez un histogramme et un boxplot pour analyser les prix\n",
@@ -633,27 +804,21 @@
     "# plt.show()\n",
     "\n",
     "print(\"Exercice 2 a completer : distribution des prix avec histogramme et boxplot\")"
-   ],
-   "metadata": {
-    "execution": {
-     "iopub.status.busy": "2026-08-17T21:21:37.455140Z",
-     "iopub.execute_input": "2026-08-17T21:21:37.455390Z",
-     "shell.execute_reply": "2026-08-17T21:21:37.458222Z",
-     "iopub.status.idle": "2026-08-17T21:21:37.458849Z"
-    }
-   },
-   "execution_count": 8,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": "Exercice 2 a completer : distribution des prix avec histogramme et boxplot\n"
-    }
    ]
   },
   {
    "cell_type": "markdown",
    "id": "8799361b",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003046,
+     "end_time": "2026-10-07T07:12:59.104445",
+     "exception": false,
+     "start_time": "2026-10-07T07:12:59.101399",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Exercice 3 : Évaluation avec la matrice de confusion\n",
     "\n",
@@ -666,12 +831,37 @@
     "- Utilisez `confusion_matrix(y_test, y_pred)` pour obtenir la matrice\n",
     "- Utilisez `sns.heatmap()` pour visualiser la matrice de confusion\n",
     "- Appelez `classification_report(y_test, y_pred)` pour le rapport détaillé"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
    "id": "9ddb0258",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-07T07:12:59.111548Z",
+     "iopub.status.busy": "2026-10-07T07:12:59.111197Z",
+     "iopub.status.idle": "2026-10-07T07:12:59.114680Z",
+     "shell.execute_reply": "2026-10-07T07:12:59.113926Z"
+    },
+    "papermill": {
+     "duration": 0.007897,
+     "end_time": "2026-10-07T07:12:59.115308",
+     "exception": false,
+     "start_time": "2026-10-07T07:12:59.107411",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice 3 a completer : matrice de confusion et rapport de classification\n"
+     ]
+    }
+   ],
    "source": [
     "# Exercice 3 : Matrice de confusion et rapport de classification\n",
     "# Evaluez plus finement les performances du modele de classification\n",
@@ -694,59 +884,78 @@
     "# Indice: print(classification_report(y_test, y_pred))\n",
     "\n",
     "print(\"Exercice 3 a completer : matrice de confusion et rapport de classification\")"
-   ],
-   "metadata": {
-    "execution": {
-     "iopub.status.busy": "2026-08-17T21:21:37.460126Z",
-     "iopub.execute_input": "2026-08-17T21:21:37.460359Z",
-     "shell.execute_reply": "2026-08-17T21:21:37.462816Z",
-     "iopub.status.idle": "2026-08-17T21:21:37.463357Z"
-    }
-   },
-   "execution_count": 9,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": "Exercice 3 a completer : matrice de confusion et rapport de classification\n"
-    }
    ]
   },
   {
    "cell_type": "markdown",
    "id": "e9fe6ce9",
+   "metadata": {
+    "papermill": {
+     "duration": 0.002994,
+     "end_time": "2026-10-07T07:12:59.121384",
+     "exception": false,
+     "start_time": "2026-10-07T07:12:59.118390",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Exercice 4 : Courbe ROC et évaluation visuelle du modèle\n",
     "\n",
-    "L'accuracy et la matrice de confusion donnent une vision globale, mais la courbe ROC (Receiver Operating Characteristic) permet de visualiser le compromis entre le taux de vrais positifs et le taux de faux positifs a différents seuils de decision. Vous allez tracer la courbe ROC pour la classification binaire (chiffre d'affaires eleve vs. bas).\n",
+    "L'accuracy et la matrice de confusion donnent une vision globale, mais la courbe ROC (Receiver Operating Characteristic) permet de visualiser le compromis entre le taux de vrais positifs et le taux de faux positifs à différents seuils de décision. Vous allez tracer la courbe ROC pour la classification binaire construite à l'Exercice 1 (teneur en alcool élevée vs. basse).\n",
     "\n",
-    "**Objectif** : Construire une cible binaire (`ca_eleve` = 1 si `chiffre_affaires > 50`), entraîner un `LogisticRegression`, puis tracer la courbe ROC avec `sklearn.metrics.roc_curve`.\n",
+    "**Objectif** : construire une cible binaire (`alcool_eleve` = 1 si la teneur en alcool dépasse la médiane du jeu), entraîner un `LogisticRegression`, puis tracer la courbe ROC avec `sklearn.metrics.roc_curve`.\n",
     "\n",
     "**Indices** :\n",
-    "- Créez la cible binaire : `df_clean['ca_eleve'] = (df_clean['chiffre_affaires'] > 50).astype(int)`\n",
-    "- Utilisez `roc_curve(y_test_bin, y_proba)` ou `y_proba = model.predict_proba(X_test_bin)[:, 1]`\n",
-    "- Tracez avec `plt.plot(fpr, tpr)` et ajoutez la diagonale de reference `plt.plot([0,1], [0,1], '--', color='gray')`\n",
-    "- Calculez l'AUC avec `roc_auc_score(y_test_bin, y_proba)`\n",
+    "- Créez la cible binaire : `df_ml['alcool_eleve'] = (df_ml['alcohol'] > df_ml['alcohol'].median()).astype(int)`\n",
+    "- Utilisez `roc_curve(y_test_bin, y_proba)` où `y_proba = model_bin.predict_proba(X_test_bin)[:, 1]`\n",
+    "- Tracez avec `plt.plot(fpr, tpr)` et ajoutez la diagonale de référence `plt.plot([0,1], [0,1], '--', color='gray')`\n",
+    "- Rappelez l'AUC avec `roc_auc_score(y_test_bin, y_proba)` : 0.5 = hasard, 1.0 = séparation parfaite\n",
     "\n",
-    "> **Référence.** La courbe ROC et l'AUC sont issues de la théorie du signal (radars, années 1950) et popularisées en apprentissage automatique par Fawcett (2006), *An Introduction to ROC Analysis*, Pattern Recognition Letters 27(8):861-874. L'AUC (aire sous la courbe) résume la performance du classifieur sur tous les seuils."
-   ],
-   "metadata": {}
+    "> **Référence.** La courbe ROC et l'AUC sont issues de la théorie du signal (radars, années 1950) et popularisées en apprentissage automatique par Fawcett (2006), *An Introduction to ROC Analysis*, Pattern Recognition Letters 27(8):861-874. L'AUC (aire sous la courbe) résume la performance du classifieur sur tous les seuils.\n"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
    "id": "c3797048",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-07T07:12:59.128245Z",
+     "iopub.status.busy": "2026-10-07T07:12:59.128006Z",
+     "iopub.status.idle": "2026-10-07T07:12:59.131835Z",
+     "shell.execute_reply": "2026-10-07T07:12:59.131107Z"
+    },
+    "papermill": {
+     "duration": 0.00813,
+     "end_time": "2026-10-07T07:12:59.132428",
+     "exception": false,
+     "start_time": "2026-10-07T07:12:59.124298",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice 4 a completer : courbe ROC pour la classification binaire\n"
+     ]
+    }
+   ],
    "source": [
     "# Exercice 4 : Courbe ROC pour la classification binaire\n",
     "# Construisez un modele binaire et tracez la courbe ROC\n",
     "\n",
-    "# Etape 1: Creez la cible binaire\n",
-    "# Indice: df_clean['ca_eleve'] = (df_clean['chiffre_affaires'] > 50).astype(int)\n",
-    "df_clean['ca_eleve'] = None  # TODO etudiant\n",
+    "# Etape 1: Creez la cible binaire (teneur en alcool au-dessus de la mediane)\n",
+    "# Indice: df_ml['alcool_eleve'] = (df_ml['alcohol'] > df_ml['alcohol'].median()).astype(int)\n",
+    "df_ml['alcool_eleve'] = None  # TODO etudiant\n",
     "\n",
     "# Etape 2: Preparez les donnees (features, target, split)\n",
-    "# Indice: X_bin = df_clean[['prix_unitaire', 'quantite']]\n",
-    "# Indice: y_bin = df_clean['ca_eleve']\n",
-    "# Indice: X_train_bin, X_test_bin, y_train_bin, y_test_bin = train_test_split(X_bin, y_bin, test_size=0.3, random_state=42)\n",
+    "# Indice: X_bin = df_ml[features]\n",
+    "# Indice: y_bin = df_ml['alcool_eleve']\n",
+    "# Indice: X_train_bin, X_test_bin, y_train_bin, y_test_bin = train_test_split(X_bin, y_bin, test_size=0.3, random_state=42, stratify=y_bin)\n",
     "\n",
     "# Etape 3: Entrainez le modele et obtenez les probabilites\n",
     "# Indice: model_bin = LogisticRegression()\n",
@@ -756,35 +965,16 @@
     "\n",
     "# Etape 4: Tracez la courbe ROC\n",
     "# Indice: from sklearn.metrics import roc_curve, roc_auc_score\n",
-    "# Indice: fpr, tpr, thresholds = roc_curve(y_test_bin, y_proba)\n",
-    "# Indice: auc = roc_auc_score(y_test_bin, y_proba)\n",
-    "# plt.figure(figsize=(8, 6))\n",
-    "# plt.plot(fpr, tpr, linewidth=2, label=f'ROC (AUC = {auc:.2f})')\n",
-    "# plt.plot([0, 1], [0, 1], '--', color='gray', label='Hasard')\n",
-    "# plt.xlabel('Taux de faux positifs')\n",
-    "# plt.ylabel('Taux de vrais positifs')\n",
-    "# plt.title('Courbe ROC - Classification binaire (CA > 50)')\n",
+    "# Indice: fpr, tpr, _ = roc_curve(y_test_bin, y_proba)\n",
+    "# Indice: plt.plot(fpr, tpr, label=f\"AUC = {roc_auc_score(y_test_bin, y_proba):.2f}\")\n",
+    "# plt.plot([0, 1], [0, 1], 'k--', label=\"Hasard\")\n",
+    "# plt.xlabel(\"Taux de faux positifs\")\n",
+    "# plt.ylabel(\"Taux de vrais positifs\")\n",
+    "# plt.title(\"Courbe ROC\")\n",
     "# plt.legend()\n",
-    "# plt.grid(True, alpha=0.3)\n",
     "# plt.show()\n",
     "\n",
-    "print(\"Exercice 4 a completer : courbe ROC et AUC\")"
-   ],
-   "metadata": {
-    "execution": {
-     "iopub.status.busy": "2026-08-17T21:21:37.464694Z",
-     "iopub.execute_input": "2026-08-17T21:21:37.464834Z",
-     "shell.execute_reply": "2026-08-17T21:21:37.467881Z",
-     "iopub.status.idle": "2026-08-17T21:21:37.468522Z"
-    }
-   },
-   "execution_count": 10,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": "Exercice 4 a completer : courbe ROC et AUC\n"
-    }
+    "print(\"Exercice 4 a completer : courbe ROC pour la classification binaire\")\n"
    ]
   },
   {
@@ -792,10 +982,10 @@
    "id": "c111b9db",
    "metadata": {
     "papermill": {
-     "duration": 0.004496,
-     "end_time": "2026-05-15T02:45:56.593251+00:00",
+     "duration": 0.002985,
+     "end_time": "2026-10-07T07:12:59.138678",
      "exception": false,
-     "start_time": "2026-05-15T02:45:56.588755+00:00",
+     "start_time": "2026-10-07T07:12:59.135693",
      "status": "completed"
     },
     "tags": []
@@ -809,10 +999,10 @@
    "id": "e1d050cf",
    "metadata": {
     "papermill": {
-     "duration": 0.011949,
-     "end_time": "2026-05-15T02:45:56.605200+00:00",
+     "duration": 0.002999,
+     "end_time": "2026-10-07T07:12:59.144630",
      "exception": false,
-     "start_time": "2026-05-15T02:45:56.593251+00:00",
+     "start_time": "2026-10-07T07:12:59.141631",
      "status": "completed"
     },
     "tags": []
@@ -830,7 +1020,17 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "cell-d1c9f249",
+   "metadata": {
+    "papermill": {
+     "duration": 0.002897,
+     "end_time": "2026-10-07T07:12:59.150491",
+     "exception": false,
+     "start_time": "2026-10-07T07:12:59.147594",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "## References\n",
     "\n",
@@ -838,13 +1038,19 @@
     "2. T. Fawcett, *An Introduction to ROC Analysis*, Pattern Recognition Letters 27(8), 2006, pp. 861-874. Référence canonique de la courbe ROC et de l'AUC (Exercice 4).\n",
     "3. F. Pedregosa et al., *Scikit-learn: Machine Learning in Python*, JMLR 12, 2011, pp. 2825-2830. Implémentation `LogisticRegression` / `roc_curve` / `train_test_split` — référence détaillée au Lab 1.\n",
     "4. J. W. Tukey, *Exploratory Data Analysis*, Addison-Wesley, 1977. Cadre de l'EDA (Étape 2) — référence détaillée au Lab 4.\n"
-   ],
-   "id": "cell-d1c9f249"
+   ]
   },
   {
    "cell_type": "markdown",
    "id": "cell-lab5-retour",
    "metadata": {
+    "papermill": {
+     "duration": 0.002998,
+     "end_time": "2026-10-07T07:12:59.156550",
+     "exception": false,
+     "start_time": "2026-10-07T07:12:59.153552",
+     "status": "completed"
+    },
     "tags": []
    },
    "source": [
@@ -855,6 +1061,23 @@
   }
  ],
  "metadata": {
+  "cost": {
+   "api_provider": "none",
+   "api_usd_est": 0.0,
+   "cpu_min": 2,
+   "external_account": "none",
+   "free_alternative": "self",
+   "gpu_min": 0,
+   "gpu_required": false,
+   "metadata_written": "2026-07-23T13:00Z",
+   "network": false,
+   "notes": "Visualisation matplotlib (scatter, hist, line) + machine learning scikit-learn\n(regression lineaire, train/test split, score R^2). Aucune API externe,\naucun GPU requis.",
+   "reduced_pedagogical": "self",
+   "reproducibility": "HIGH",
+   "validator": "papermill",
+   "vram_gb": 0,
+   "vram_tier": "LITE"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -870,36 +1093,19 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.12"
+   "version": "3.13.13"
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 33.660207,
-   "end_time": "2026-05-15T02:45:57.173579+00:00",
+   "duration": 4.330673,
+   "end_time": "2026-10-07T07:12:59.610790",
    "environment_variables": {},
    "exception": null,
    "input_path": "Lab5-Viz-ML.ipynb",
    "output_path": "Lab5-Viz-ML.ipynb",
    "parameters": {},
-   "start_time": "2026-05-15T02:45:23.513372+00:00",
+   "start_time": "2026-10-07T07:12:55.280117",
    "version": "2.6.0"
-  },
-  "cost": {
-   "api_usd_est": 0.0,
-   "api_provider": "none",
-   "cpu_min": 2,
-   "gpu_min": 0,
-   "gpu_required": false,
-   "vram_gb": 0,
-   "vram_tier": "LITE",
-   "network": false,
-   "external_account": "none",
-   "free_alternative": "self",
-   "reduced_pedagogical": "self",
-   "reproducibility": "HIGH",
-   "metadata_written": "2026-07-23T13:00Z",
-   "validator": "papermill",
-   "notes": "Visualisation matplotlib (scatter, hist, line) + machine learning scikit-learn\n(regression lineaire, train/test split, score R^2). Aucune API externe,\naucun GPU requis."
   }
  },
  "nbformat": 4,