diff --git a/TP05/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/TP05/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/TP05/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TP05/Untitled.ipynb b/TP05/Untitled.ipynb
new file mode 100644
index 0000000..7c2efc5
--- /dev/null
+++ b/TP05/Untitled.ipynb
@@ -0,0 +1,1325 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ae8de2f8-124f-4b64-ab28-b862a5c15242",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading data...\n",
+      "Téléchargement des données...\n",
+      "Data shape: (297, 14)\n",
+      "\n",
+      "Preprocessing data...\n",
+      "\n",
+      "Training Model 1...\n",
+      "Epoch 1/50\n",
+      "6/6 [==============================] - 2s 118ms/step - loss: 0.6855 - accuracy: 0.5450 - val_loss: 0.6612 - val_accuracy: 0.6875\n",
+      "Epoch 2/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.6296 - accuracy: 0.6825 - val_loss: 0.6479 - val_accuracy: 0.7083\n",
+      "Epoch 3/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.5430 - accuracy: 0.7619 - val_loss: 0.6314 - val_accuracy: 0.7292\n",
+      "Epoch 4/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.5014 - accuracy: 0.7831 - val_loss: 0.6138 - val_accuracy: 0.7708\n",
+      "Epoch 5/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.4822 - accuracy: 0.7884 - val_loss: 0.5954 - val_accuracy: 0.7708\n",
+      "Epoch 6/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.4436 - accuracy: 0.8042 - val_loss: 0.5778 - val_accuracy: 0.7708\n",
+      "Epoch 7/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.4270 - accuracy: 0.8148 - val_loss: 0.5608 - val_accuracy: 0.7500\n",
+      "Epoch 8/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.4405 - accuracy: 0.8201 - val_loss: 0.5414 - val_accuracy: 0.7500\n",
+      "Epoch 9/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.3840 - accuracy: 0.8413 - val_loss: 0.5271 - val_accuracy: 0.7500\n",
+      "Epoch 10/50\n",
+      "6/6 [==============================] - 0s 18ms/step - loss: 0.3704 - accuracy: 0.8413 - val_loss: 0.5168 - val_accuracy: 0.7500\n",
+      "Epoch 11/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3865 - accuracy: 0.8307 - val_loss: 0.5075 - val_accuracy: 0.7708\n",
+      "Epoch 12/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.3560 - accuracy: 0.8466 - val_loss: 0.5013 - val_accuracy: 0.7708\n",
+      "Epoch 13/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.3422 - accuracy: 0.8466 - val_loss: 0.4952 - val_accuracy: 0.7708\n",
+      "Epoch 14/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3238 - accuracy: 0.8624 - val_loss: 0.4893 - val_accuracy: 0.7708\n",
+      "Epoch 15/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.3038 - accuracy: 0.8836 - val_loss: 0.4846 - val_accuracy: 0.7708\n",
+      "Epoch 16/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3116 - accuracy: 0.8677 - val_loss: 0.4787 - val_accuracy: 0.7917\n",
+      "Epoch 17/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.3145 - accuracy: 0.8624 - val_loss: 0.4771 - val_accuracy: 0.7917\n",
+      "Epoch 18/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3088 - accuracy: 0.8836 - val_loss: 0.4765 - val_accuracy: 0.7917\n",
+      "Epoch 19/50\n",
+      "6/6 [==============================] - 0s 19ms/step - loss: 0.3098 - accuracy: 0.8730 - val_loss: 0.4775 - val_accuracy: 0.7917\n",
+      "Epoch 20/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.2802 - accuracy: 0.8995 - val_loss: 0.4742 - val_accuracy: 0.7917\n",
+      "Epoch 21/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.2991 - accuracy: 0.8783 - val_loss: 0.4721 - val_accuracy: 0.7917\n",
+      "Epoch 22/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2697 - accuracy: 0.8836 - val_loss: 0.4724 - val_accuracy: 0.7917\n",
+      "Epoch 23/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2678 - accuracy: 0.9153 - val_loss: 0.4709 - val_accuracy: 0.7708\n",
+      "Epoch 24/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.3058 - accuracy: 0.8677 - val_loss: 0.4699 - val_accuracy: 0.7708\n",
+      "Epoch 25/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2338 - accuracy: 0.9101 - val_loss: 0.4730 - val_accuracy: 0.7500\n",
+      "Epoch 26/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2420 - accuracy: 0.9048 - val_loss: 0.4731 - val_accuracy: 0.7500\n",
+      "Epoch 27/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2506 - accuracy: 0.8836 - val_loss: 0.4737 - val_accuracy: 0.7500\n",
+      "Epoch 28/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2331 - accuracy: 0.9259 - val_loss: 0.4732 - val_accuracy: 0.7500\n",
+      "Epoch 29/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2395 - accuracy: 0.9048 - val_loss: 0.4753 - val_accuracy: 0.7500\n",
+      "Epoch 30/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2838 - accuracy: 0.8730 - val_loss: 0.4808 - val_accuracy: 0.7292\n",
+      "Epoch 31/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2263 - accuracy: 0.9153 - val_loss: 0.4850 - val_accuracy: 0.7292\n",
+      "Epoch 32/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.2270 - accuracy: 0.9153 - val_loss: 0.4917 - val_accuracy: 0.7292\n",
+      "Epoch 33/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.2288 - accuracy: 0.9101 - val_loss: 0.4948 - val_accuracy: 0.7292\n",
+      "Epoch 34/50\n",
+      "1/6 [====>.........................] - ETA: 0s - loss: 0.1590 - accuracy: 0.9688Restoring model weights from the end of the best epoch: 24.\n",
+      "6/6 [==============================] - 0s 19ms/step - loss: 0.2019 - accuracy: 0.9206 - val_loss: 0.4963 - val_accuracy: 0.7292\n",
+      "Epoch 34: early stopping\n",
+      "\n",
+      "Model 1 - Test Accuracy: 0.9333\n",
+      "\n",
+      "Training Model 2...\n",
+      "Epoch 1/50\n",
+      "6/6 [==============================] - 3s 105ms/step - loss: 0.6759 - accuracy: 0.5767 - val_loss: 0.6780 - val_accuracy: 0.6042\n",
+      "Epoch 2/50\n",
+      "6/6 [==============================] - 0s 18ms/step - loss: 0.6347 - accuracy: 0.6349 - val_loss: 0.6638 - val_accuracy: 0.6667\n",
+      "Epoch 3/50\n",
+      "6/6 [==============================] - 0s 18ms/step - loss: 0.5800 - accuracy: 0.6772 - val_loss: 0.6402 - val_accuracy: 0.7292\n",
+      "Epoch 4/50\n",
+      "6/6 [==============================] - 0s 18ms/step - loss: 0.5013 - accuracy: 0.7725 - val_loss: 0.6169 - val_accuracy: 0.7500\n",
+      "Epoch 5/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.4566 - accuracy: 0.7937 - val_loss: 0.5964 - val_accuracy: 0.7917\n",
+      "Epoch 6/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.4297 - accuracy: 0.8201 - val_loss: 0.5788 - val_accuracy: 0.8125\n",
+      "Epoch 7/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3851 - accuracy: 0.8254 - val_loss: 0.5624 - val_accuracy: 0.7917\n",
+      "Epoch 8/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.4309 - accuracy: 0.8148 - val_loss: 0.5463 - val_accuracy: 0.7917\n",
+      "Epoch 9/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3926 - accuracy: 0.8360 - val_loss: 0.5362 - val_accuracy: 0.8125\n",
+      "Epoch 10/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3602 - accuracy: 0.8519 - val_loss: 0.5278 - val_accuracy: 0.8125\n",
+      "Epoch 11/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3761 - accuracy: 0.8042 - val_loss: 0.5178 - val_accuracy: 0.8125\n",
+      "Epoch 12/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3413 - accuracy: 0.8624 - val_loss: 0.5081 - val_accuracy: 0.8125\n",
+      "Epoch 13/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3255 - accuracy: 0.8624 - val_loss: 0.4994 - val_accuracy: 0.8125\n",
+      "Epoch 14/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.3181 - accuracy: 0.8942 - val_loss: 0.4877 - val_accuracy: 0.8125\n",
+      "Epoch 15/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.3330 - accuracy: 0.8519 - val_loss: 0.4778 - val_accuracy: 0.8125\n",
+      "Epoch 16/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.3159 - accuracy: 0.8730 - val_loss: 0.4703 - val_accuracy: 0.8125\n",
+      "Epoch 17/50\n",
+      "6/6 [==============================] - 0s 18ms/step - loss: 0.3236 - accuracy: 0.8677 - val_loss: 0.4637 - val_accuracy: 0.8333\n",
+      "Epoch 18/50\n",
+      "6/6 [==============================] - 0s 23ms/step - loss: 0.3000 - accuracy: 0.8624 - val_loss: 0.4583 - val_accuracy: 0.8333\n",
+      "Epoch 19/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2777 - accuracy: 0.8624 - val_loss: 0.4540 - val_accuracy: 0.8333\n",
+      "Epoch 20/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2938 - accuracy: 0.8783 - val_loss: 0.4512 - val_accuracy: 0.8333\n",
+      "Epoch 21/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2958 - accuracy: 0.8624 - val_loss: 0.4482 - val_accuracy: 0.8125\n",
+      "Epoch 22/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2643 - accuracy: 0.9259 - val_loss: 0.4420 - val_accuracy: 0.8125\n",
+      "Epoch 23/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2544 - accuracy: 0.9153 - val_loss: 0.4378 - val_accuracy: 0.8125\n",
+      "Epoch 24/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.2371 - accuracy: 0.8942 - val_loss: 0.4343 - val_accuracy: 0.8333\n",
+      "Epoch 25/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2255 - accuracy: 0.8942 - val_loss: 0.4308 - val_accuracy: 0.8125\n",
+      "Epoch 26/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.2518 - accuracy: 0.8571 - val_loss: 0.4260 - val_accuracy: 0.8125\n",
+      "Epoch 27/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2424 - accuracy: 0.8942 - val_loss: 0.4233 - val_accuracy: 0.8125\n",
+      "Epoch 28/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2345 - accuracy: 0.8995 - val_loss: 0.4240 - val_accuracy: 0.7917\n",
+      "Epoch 29/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2326 - accuracy: 0.8836 - val_loss: 0.4254 - val_accuracy: 0.7708\n",
+      "Epoch 30/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2498 - accuracy: 0.8836 - val_loss: 0.4285 - val_accuracy: 0.7708\n",
+      "Epoch 31/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2155 - accuracy: 0.8995 - val_loss: 0.4297 - val_accuracy: 0.7500\n",
+      "Epoch 32/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2161 - accuracy: 0.9153 - val_loss: 0.4323 - val_accuracy: 0.7500\n",
+      "Epoch 33/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2137 - accuracy: 0.9101 - val_loss: 0.4345 - val_accuracy: 0.7500\n",
+      "Epoch 34/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.1759 - accuracy: 0.9365 - val_loss: 0.4328 - val_accuracy: 0.7708\n",
+      "Epoch 35/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.2121 - accuracy: 0.9101 - val_loss: 0.4295 - val_accuracy: 0.7917\n",
+      "Epoch 36/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.1779 - accuracy: 0.9153 - val_loss: 0.4326 - val_accuracy: 0.7917\n",
+      "Epoch 37/50\n",
+      "1/6 [====>.........................] - ETA: 0s - loss: 0.2512 - accuracy: 0.8438Restoring model weights from the end of the best epoch: 27.\n",
+      "6/6 [==============================] - 0s 18ms/step - loss: 0.2044 - accuracy: 0.9048 - val_loss: 0.4354 - val_accuracy: 0.7708\n",
+      "Epoch 37: early stopping\n",
+      "\n",
+      "Model 2 - Test Accuracy: 0.8667\n",
+      "\n",
+      "Plotting results...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n## Exercice 1 : \\nadapter le programme sur les données suivantes : \\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data\\n\\n\\n## Exercice 2 : \\nOn vous demande d'implémenter 2 autres modèles en suivant le schéma du programme donné. Sur les 2 data-set. \\n\\nL'objectif est de rendre un rapport explicatif complet sur au moins un des modèles ; le code doit être commenté et des tests (changement de paramètres : itération, taux, couches réseaux) doivent être fait.\\n\\n### Premier Modèle : Random Forest Classifier\\n\\nCe modèle est particulièrement intéressant car il offre :\\n- Une excellente performance sur les données médicales\\n- Une interprétabilité des résultats\\n- Une facilité relative d'implémentation\\n\\nVoici un exemple de structure pour l'implémentation :\\n\\n```python\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom sklearn.model_selection import GridSearchCV\\n\\ndef create_model_rf(X_train, y_train):\\n    # Création du modèle avec des hyperparamètres de base\\n    rf_model = RandomForestClassifier(\\n        n_estimators=100,\\n        max_depth=10,\\n        random_state=42\\n    )\\n    \\n    # Définition des paramètres à optimiser\\n    param_grid = {\\n        'n_estimators': [50, 100, 200],\\n        'max_depth': [5, 10, 15],\\n        'min_samples_split': [2, 5, 10]\\n    }\\n    \\n    # Recherche des meilleurs paramètres\\n    grid_search = GridSearchCV(\\n        rf_model,\\n        param_grid,\\n        cv=5,\\n        scoring='accuracy',\\n        n_jobs=-1\\n    )\\n    \\n    # Entraînement avec recherche des meilleurs paramètres\\n    grid_search.fit(X_train, y_train)\\n    \\n    return grid_search.best_estimator_\\n```\\n\\n### Deuxième Modèle : XGBoost\\n\\nXGBoost est un algorithme de boosting très performant qui permet souvent d'obtenir d'excellents résultats. Voici une structure d'implémentation :\\n\\n```python\\nimport xgboost as xgb\\nfrom sklearn.model_selection import cross_val_score\\n\\ndef create_model_xgb(X_train, y_train):\\n    # Création du modèle avec des paramètres de base\\n    xgb_model = xgb.XGBClassifier(\\n        learning_rate=0.1,\\n        n_estimators=100,\\n        max_depth=5,\\n        random_state=42\\n    )\\n    \\n    # Paramètres à optimiser\\n    param_grid = {\\n        'learning_rate': [0.01, 0.1, 0.3],\\n        'n_estimators': [50, 100, 200],\\n        'max_depth': [3, 5, 7]\\n    }\\n    \\n    # Optimisation des hyperparamètres\\n    grid_search = GridSearchCV(\\n        xgb_model,\\n        param_grid,\\n        cv=5,\\n        scoring='accuracy',\\n        n_jobs=-1\\n    )\\n    \\n    grid_search.fit(X_train, y_train)\\n    \\n    return grid_search.best_estimator_\\n```\\n\\nPour faciliter l'implémentation, voici les points essentiels à comprendre :\\n\\nPour le Random Forest :\\n- C'est un ensemble d'arbres de décision\\n- Chaque arbre est entraîné sur un sous-ensemble aléatoire des données\\n- La prédiction finale est obtenue par vote majoritaire des arbres\\n- Les paramètres clés sont le nombre d'arbres (n_estimators) et la profondeur maximale (max_depth)\\n\\nPour XGBoost :\\n- C'est un algorithme de boosting qui construit les arbres séquentiellement\\n- Chaque nouvel arbre corrige les erreurs des arbres précédents\\n- Le learning_rate contrôle la contribution de chaque arbre\\n- La profondeur des arbres (max_depth) limite la complexité du modèle\\n\\nPour l'évaluation des modèles, on peut réutiliser les fonctions de visualisation existantes en les adaptant légèrement. Par exemple :\\n\\n```python\\ndef plot_model_comparison(models_results):\\n    plt.figure(figsize=(10, 6))\\n    \\n    for model_name, scores in models_results.items():\\n        plt.plot(scores['val_accuracy'], label=f'{model_name} validation accuracy')\\n    \\n    plt.title('Model Comparison')\\n    plt.xlabel('Iteration')\\n    plt.ylabel('Accuracy')\\n    plt.legend()\\n    plt.show()\\n```\\n\\n\""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Projet de Machine Learning : Prédiction de Maladies Cardiaques (Version corrigée)\n",
+    "Dataset : UCI Heart Disease Dataset\n",
+    "Objectif : Comparer deux architectures de réseaux de neurones pour la prédiction de maladies cardiaques\n",
+    "\"\"\"\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import urllib.request\n",
+    "import ssl\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# 1. Chargement des données avec gestion du SSL\n",
+    "def load_data():\n",
+    "    try:\n",
+    "        # Créer un contexte SSL non-vérifié (à utiliser avec précaution)\n",
+    "        ssl._create_default_https_context = ssl._create_unverified_context\n",
+    "        \n",
+    "        # URL du dataset\n",
+    "        url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\"\n",
+    "        \n",
+    "        # Définir les noms des colonnes\n",
+    "        columns = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
+    "                  'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target']\n",
+    "        \n",
+    "        print(\"Téléchargement des données...\")\n",
+    "        \n",
+    "        # Télécharger directement dans un DataFrame\n",
+    "        data = pd.read_csv(url, names=columns)\n",
+    "        \n",
+    "        # En cas d'erreur, utiliser un dataset de démonstration\n",
+    "        if data.empty:\n",
+    "            raise Exception(\"Le dataset est vide\")\n",
+    "            \n",
+    "    except Exception as e:\n",
+    "        print(f\"Erreur lors du téléchargement des données: {e}\")\n",
+    "        print(\"Utilisation d'un dataset de démonstration...\")\n",
+    "        \n",
+    "        # Créer un petit dataset de démonstration\n",
+    "        np.random.seed(42)\n",
+    "        n_samples = 300\n",
+    "        \n",
+    "        data = pd.DataFrame({\n",
+    "            'age': np.random.normal(55, 10, n_samples),\n",
+    "            'sex': np.random.binomial(1, 0.5, n_samples),\n",
+    "            'cp': np.random.randint(0, 4, n_samples),\n",
+    "            'trestbps': np.random.normal(130, 20, n_samples),\n",
+    "            'chol': np.random.normal(240, 40, n_samples),\n",
+    "            'fbs': np.random.binomial(1, 0.2, n_samples),\n",
+    "            'restecg': np.random.randint(0, 3, n_samples),\n",
+    "            'thalach': np.random.normal(150, 20, n_samples),\n",
+    "            'exang': np.random.binomial(1, 0.3, n_samples),\n",
+    "            'oldpeak': np.random.normal(1, 1, n_samples),\n",
+    "            'slope': np.random.randint(0, 3, n_samples),\n",
+    "            'ca': np.random.randint(0, 4, n_samples),\n",
+    "            'thal': np.random.randint(0, 3, n_samples),\n",
+    "            'target': np.random.binomial(1, 0.4, n_samples)\n",
+    "        })\n",
+    "    \n",
+    "    # Nettoyer les données\n",
+    "    data = data.replace('?', np.nan)\n",
+    "    data = data.dropna()\n",
+    "    \n",
+    "    # Convertir les colonnes en nombres\n",
+    "    for column in data.columns:\n",
+    "        data[column] = pd.to_numeric(data[column])\n",
+    "    \n",
+    "    # Binariser la target (0 pour pas de maladie, 1 pour maladie)\n",
+    "    data['target'] = (data['target'] > 0).astype(int)\n",
+    "    \n",
+    "    return data\n",
+    "\n",
+    "# 2. Prétraitement des données\n",
+    "def preprocess_data(data):\n",
+    "    # Séparer features et target\n",
+    "    X = data.drop('target', axis=1)\n",
+    "    y = data['target']\n",
+    "    \n",
+    "    # Split train/test\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "    \n",
+    "    # Standardisation\n",
+    "    scaler = StandardScaler()\n",
+    "    X_train_scaled = scaler.fit_transform(X_train)\n",
+    "    X_test_scaled = scaler.transform(X_test)\n",
+    "    \n",
+    "    return X_train_scaled, X_test_scaled, y_train, y_test\n",
+    "\n",
+    "# 3. Premier modèle : Réseau dense classique\n",
+    "def create_model_1(input_shape):\n",
+    "    model = Sequential([\n",
+    "        Dense(64, activation='relu', input_shape=input_shape),\n",
+    "        BatchNormalization(),\n",
+    "        Dense(32, activation='relu'),\n",
+    "        Dropout(0.3),\n",
+    "        Dense(16, activation='relu'),\n",
+    "        Dense(1, activation='sigmoid')\n",
+    "    ])\n",
+    "    \n",
+    "    model.compile(optimizer=Adam(learning_rate=0.001),\n",
+    "                 loss='binary_crossentropy',\n",
+    "                 metrics=['accuracy'])\n",
+    "    \n",
+    "    return model\n",
+    "\n",
+    "# 4. Second modèle : Réseau plus profond avec régularisation plus forte\n",
+    "def create_model_2(input_shape):\n",
+    "    model = Sequential([\n",
+    "        Dense(128, activation='relu', input_shape=input_shape),\n",
+    "        BatchNormalization(),\n",
+    "        Dropout(0.3),\n",
+    "        Dense(64, activation='relu'),\n",
+    "        BatchNormalization(),\n",
+    "        Dropout(0.3),\n",
+    "        Dense(32, activation='relu'),\n",
+    "        BatchNormalization(),\n",
+    "        Dense(16, activation='relu'),\n",
+    "        Dense(1, activation='sigmoid')\n",
+    "    ])\n",
+    "    \n",
+    "    model.compile(optimizer=Adam(learning_rate=0.001),\n",
+    "                 loss='binary_crossentropy',\n",
+    "                 metrics=['accuracy'])\n",
+    "    \n",
+    "    return model\n",
+    "\n",
+    "# 5. Fonction d'entraînement et d'évaluation\n",
+    "def train_and_evaluate(model, X_train, X_test, y_train, y_test, model_name):\n",
+    "    # Early stopping pour éviter le surapprentissage\n",
+    "    early_stopping = EarlyStopping(\n",
+    "        monitor='val_loss',\n",
+    "        patience=10,\n",
+    "        restore_best_weights=True,\n",
+    "        verbose=1\n",
+    "    )\n",
+    "    \n",
+    "    # Entraînement\n",
+    "    history = model.fit(\n",
+    "        X_train, y_train,\n",
+    "        validation_split=0.2,\n",
+    "        epochs=50,  # Réduit pour la démonstration\n",
+    "        batch_size=32,\n",
+    "        callbacks=[early_stopping],\n",
+    "        verbose=1\n",
+    "    )\n",
+    "    \n",
+    "    # Évaluation\n",
+    "    test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
+    "    print(f\"\\n{model_name} - Test Accuracy: {test_accuracy:.4f}\")\n",
+    "    \n",
+    "    return history\n",
+    "\n",
+    "# 6. Visualisation des résultats\n",
+    "def plot_training_history(history1, history2):\n",
+    "    plt.figure(figsize=(12, 4))\n",
+    "    \n",
+    "    # Plot accuracy\n",
+    "    plt.subplot(1, 2, 1)\n",
+    "    plt.plot(history1.history['accuracy'], label='Model 1 accuracy')\n",
+    "    plt.plot(history1.history['val_accuracy'], label='Model 1 val accuracy')\n",
+    "    plt.plot(history2.history['accuracy'], label='Model 2 accuracy')\n",
+    "    plt.plot(history2.history['val_accuracy'], label='Model 2 val accuracy')\n",
+    "    plt.title('Model Accuracy')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.legend()\n",
+    "    \n",
+    "    # Plot loss\n",
+    "    plt.subplot(1, 2, 2)\n",
+    "    plt.plot(history1.history['loss'], label='Model 1 loss')\n",
+    "    plt.plot(history1.history['val_loss'], label='Model 1 val loss')\n",
+    "    plt.plot(history2.history['loss'], label='Model 2 loss')\n",
+    "    plt.plot(history2.history['val_loss'], label='Model 2 val loss')\n",
+    "    plt.title('Model Loss')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Loss')\n",
+    "    plt.legend()\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "# 7. Programme principal\n",
+    "def main():\n",
+    "    print(\"Loading data...\")\n",
+    "    data = load_data()\n",
+    "    print(\"Data shape:\", data.shape)\n",
+    "    \n",
+    "    print(\"\\nPreprocessing data...\")\n",
+    "    X_train, X_test, y_train, y_test = preprocess_data(data)\n",
+    "    input_shape = (X_train.shape[1],)\n",
+    "    \n",
+    "    print(\"\\nTraining Model 1...\")\n",
+    "    model1 = create_model_1(input_shape)\n",
+    "    history1 = train_and_evaluate(model1, X_train, X_test, y_train, y_test, \"Model 1\")\n",
+    "    \n",
+    "    print(\"\\nTraining Model 2...\")\n",
+    "    model2 = create_model_2(input_shape)\n",
+    "    history2 = train_and_evaluate(model2, X_train, X_test, y_train, y_test, \"Model 2\")\n",
+    "    \n",
+    "    print(\"\\nPlotting results...\")\n",
+    "    plot_training_history(history1, history2)\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()\n",
+    "    \n",
+    "\n",
+    "    \n",
+    "'''\n",
+    "Modèle 1 : Réseau Dense Classique\n",
+    "- C'est une architecture relativement simple et légère avec 4 couches :\n",
+    "1. Première couche : 64 neurones avec activation ReLU\n",
+    "    - Cette couche initiale capture les patterns de base dans les données\n",
+    "    - Suivie d'une normalisation par lots (BatchNormalization) pour stabiliser l'apprentissage\n",
+    "2. Deuxième couche : 32 neurones avec activation ReLU\n",
+    "    - Suivie d'un Dropout de 30% pour éviter le surapprentissage\n",
+    "3. Troisième couche : 16 neurones avec activation ReLU\n",
+    "    - Réduit progressivement la dimensionnalité\n",
+    "4. Couche de sortie : 1 neurone avec activation sigmoid\n",
+    "    - Pour la prédiction binaire (malade/non malade)\n",
+    "    \n",
+    "Modèle 2 : Réseau Plus Profond\n",
+    "- C'est une architecture plus complexe avec 5 couches et plus de régularisation :\n",
+    "1. Première couche : 128 neurones avec activation ReLU\n",
+    "    - Commence avec plus de neurones pour capturer des patterns plus complexes\n",
+    "    - Suivie de BatchNormalization et Dropout 30%\n",
+    "2. Deuxième couche : 64 neurones avec activation ReLU\n",
+    "    - Également suivie de BatchNormalization et Dropout\n",
+    "3. Troisième couche : 32 neurones avec activation ReLU\n",
+    "    - Avec BatchNormalization\n",
+    "4. Quatrième couche : 16 neurones avec activation ReLU\n",
+    "5. Couche de sortie : 1 neurone avec activation sigmoid\n",
+    "\n",
+    "Les principales différences sont :\n",
+    "1. Complexité : Le modèle 2 a plus de paramètres et de couches\n",
+    "2. Régularisation : Le modèle 2 utilise plus de BatchNormalization et de Dropout\n",
+    "3. Capacité d'apprentissage : Le modèle 2 peut capturer des relations plus complexes dans les données\n",
+    "\n",
+    "L'idée est de comparer :\n",
+    "- Une approche simple qui pourrait suffire pour ce problème médical relativement simple\n",
+    "- Une approche plus complexe qui pourrait potentiellement capturer des patterns plus subtils\n",
+    "\n",
+    "Les deux modèles utilisent le même optimiseur (Adam) avec le même learning rate (0.001) pour une comparaison équitable.\n",
+    "\n",
+    "Cette configuration permet d'observer si la complexité supplémentaire du deuxième modèle apporte réellement un avantage en termes de performances, ou si le modèle plus simple est suffisant.\n",
+    "\n",
+    "- ReLU (Rectified Linear Unit) est une fonction d'activation très populaire en deep learning : ReLu (x) = max (0,x)\n",
+    "\n",
+    "- Le Dropout est une technique de régularisation cruciale en deep learning. Voici une explication détaillée :\n",
+    "Principe de base :\n",
+    "Pendant l'entraînement, à chaque itération\n",
+    "Désactive aléatoirement un certain pourcentage de neurones\n",
+    "Ces neurones sont temporairement \"éteints\" avec toutes leurs connexions\n",
+    "Le pourcentage est défini par le paramètre de dropout (ex: 0.3 = 30% des neurones)\n",
+    "\n",
+    "- La BatchNormalization (ou normalisation par lots) est une technique très importante en deep learning. Voici une explication détaillée :\n",
+    "Principe fondamental :\n",
+    "Normalise les activations d'une couche pour chaque batch\n",
+    "Maintient la moyenne proche de 0 et l'écart-type proche de 1\n",
+    "S'applique avant la fonction d'activation\n",
+    "'''\n",
+    "    \n",
+    "'''\n",
+    "## Exercice 1 : \n",
+    "adapter le programme sur les données suivantes : \n",
+    "https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data\n",
+    "\n",
+    "\n",
+    "## Exercice 2 : \n",
+    "On vous demande d'implémenter 2 autres modèles en suivant le schéma du programme donné. Sur les 2 data-set. \n",
+    "\n",
+    "L'objectif est de rendre un rapport explicatif complet sur au moins un des modèles ; le code doit être commenté et des tests (changement de paramètres : itération, taux, couches réseaux) doivent être fait.\n",
+    "\n",
+    "### Premier Modèle : Random Forest Classifier\n",
+    "\n",
+    "Ce modèle est particulièrement intéressant car il offre :\n",
+    "- Une excellente performance sur les données médicales\n",
+    "- Une interprétabilité des résultats\n",
+    "- Une facilité relative d'implémentation\n",
+    "\n",
+    "Voici un exemple de structure pour l'implémentation :\n",
+    "\n",
+    "```python\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "def create_model_rf(X_train, y_train):\n",
+    "    # Création du modèle avec des hyperparamètres de base\n",
+    "    rf_model = RandomForestClassifier(\n",
+    "        n_estimators=100,\n",
+    "        max_depth=10,\n",
+    "        random_state=42\n",
+    "    )\n",
+    "    \n",
+    "    # Définition des paramètres à optimiser\n",
+    "    param_grid = {\n",
+    "        'n_estimators': [50, 100, 200],\n",
+    "        'max_depth': [5, 10, 15],\n",
+    "        'min_samples_split': [2, 5, 10]\n",
+    "    }\n",
+    "    \n",
+    "    # Recherche des meilleurs paramètres\n",
+    "    grid_search = GridSearchCV(\n",
+    "        rf_model,\n",
+    "        param_grid,\n",
+    "        cv=5,\n",
+    "        scoring='accuracy',\n",
+    "        n_jobs=-1\n",
+    "    )\n",
+    "    \n",
+    "    # Entraînement avec recherche des meilleurs paramètres\n",
+    "    grid_search.fit(X_train, y_train)\n",
+    "    \n",
+    "    return grid_search.best_estimator_\n",
+    "```\n",
+    "\n",
+    "### Deuxième Modèle : XGBoost\n",
+    "\n",
+    "XGBoost est un algorithme de boosting très performant qui permet souvent d'obtenir d'excellents résultats. Voici une structure d'implémentation :\n",
+    "\n",
+    "```python\n",
+    "import xgboost as xgb\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "def create_model_xgb(X_train, y_train):\n",
+    "    # Création du modèle avec des paramètres de base\n",
+    "    xgb_model = xgb.XGBClassifier(\n",
+    "        learning_rate=0.1,\n",
+    "        n_estimators=100,\n",
+    "        max_depth=5,\n",
+    "        random_state=42\n",
+    "    )\n",
+    "    \n",
+    "    # Paramètres à optimiser\n",
+    "    param_grid = {\n",
+    "        'learning_rate': [0.01, 0.1, 0.3],\n",
+    "        'n_estimators': [50, 100, 200],\n",
+    "        'max_depth': [3, 5, 7]\n",
+    "    }\n",
+    "    \n",
+    "    # Optimisation des hyperparamètres\n",
+    "    grid_search = GridSearchCV(\n",
+    "        xgb_model,\n",
+    "        param_grid,\n",
+    "        cv=5,\n",
+    "        scoring='accuracy',\n",
+    "        n_jobs=-1\n",
+    "    )\n",
+    "    \n",
+    "    grid_search.fit(X_train, y_train)\n",
+    "    \n",
+    "    return grid_search.best_estimator_\n",
+    "```\n",
+    "\n",
+    "Pour faciliter l'implémentation, voici les points essentiels à comprendre :\n",
+    "\n",
+    "Pour le Random Forest :\n",
+    "- C'est un ensemble d'arbres de décision\n",
+    "- Chaque arbre est entraîné sur un sous-ensemble aléatoire des données\n",
+    "- La prédiction finale est obtenue par vote majoritaire des arbres\n",
+    "- Les paramètres clés sont le nombre d'arbres (n_estimators) et la profondeur maximale (max_depth)\n",
+    "\n",
+    "Pour XGBoost :\n",
+    "- C'est un algorithme de boosting qui construit les arbres séquentiellement\n",
+    "- Chaque nouvel arbre corrige les erreurs des arbres précédents\n",
+    "- Le learning_rate contrôle la contribution de chaque arbre\n",
+    "- La profondeur des arbres (max_depth) limite la complexité du modèle\n",
+    "\n",
+    "Pour l'évaluation des modèles, on peut réutiliser les fonctions de visualisation existantes en les adaptant légèrement. Par exemple :\n",
+    "\n",
+    "```python\n",
+    "def plot_model_comparison(models_results):\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    \n",
+    "    for model_name, scores in models_results.items():\n",
+    "        plt.plot(scores['val_accuracy'], label=f'{model_name} validation accuracy')\n",
+    "    \n",
+    "    plt.title('Model Comparison')\n",
+    "    plt.xlabel('Iteration')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.legend()\n",
+    "    plt.show()\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2a0e5d3c-a532-4d08-84b9-40e9a8fc8db3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chargement des données...\n",
+      "Téléchargement des données...\n",
+      "Dimension des données: (569, 31)\n",
+      "\n",
+      "Prétraitement des données...\n",
+      "\n",
+      "Entraînement du Modèle 1...\n",
+      "Epoch 1/50\n",
+      "12/12 [==============================] - 2s 38ms/step - loss: 0.5129 - accuracy: 0.7995 - val_loss: 0.5310 - val_accuracy: 0.8901\n",
+      "Epoch 2/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.3201 - accuracy: 0.9203 - val_loss: 0.4203 - val_accuracy: 0.9341\n",
+      "Epoch 3/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.2287 - accuracy: 0.9505 - val_loss: 0.3310 - val_accuracy: 0.9231\n",
+      "Epoch 4/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.1699 - accuracy: 0.9615 - val_loss: 0.2650 - val_accuracy: 0.9451\n",
+      "Epoch 5/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.1497 - accuracy: 0.9533 - val_loss: 0.2195 - val_accuracy: 0.9451\n",
+      "Epoch 6/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.1279 - accuracy: 0.9698 - val_loss: 0.1896 - val_accuracy: 0.9560\n",
+      "Epoch 7/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0874 - accuracy: 0.9890 - val_loss: 0.1657 - val_accuracy: 0.9560\n",
+      "Epoch 8/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0948 - accuracy: 0.9698 - val_loss: 0.1493 - val_accuracy: 0.9670\n",
+      "Epoch 9/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0878 - accuracy: 0.9725 - val_loss: 0.1399 - val_accuracy: 0.9560\n",
+      "Epoch 10/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0675 - accuracy: 0.9780 - val_loss: 0.1285 - val_accuracy: 0.9560\n",
+      "Epoch 11/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0668 - accuracy: 0.9890 - val_loss: 0.1198 - val_accuracy: 0.9560\n",
+      "Epoch 12/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0584 - accuracy: 0.9918 - val_loss: 0.1134 - val_accuracy: 0.9670\n",
+      "Epoch 13/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0701 - accuracy: 0.9753 - val_loss: 0.1099 - val_accuracy: 0.9670\n",
+      "Epoch 14/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0639 - accuracy: 0.9808 - val_loss: 0.1070 - val_accuracy: 0.9670\n",
+      "Epoch 15/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0494 - accuracy: 0.9808 - val_loss: 0.1042 - val_accuracy: 0.9670\n",
+      "Epoch 16/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0662 - accuracy: 0.9780 - val_loss: 0.0980 - val_accuracy: 0.9670\n",
+      "Epoch 17/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0422 - accuracy: 0.9890 - val_loss: 0.0939 - val_accuracy: 0.9670\n",
+      "Epoch 18/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0375 - accuracy: 0.9890 - val_loss: 0.0903 - val_accuracy: 0.9560\n",
+      "Epoch 19/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0431 - accuracy: 0.9863 - val_loss: 0.0889 - val_accuracy: 0.9780\n",
+      "Epoch 20/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0518 - accuracy: 0.9863 - val_loss: 0.0891 - val_accuracy: 0.9670\n",
+      "Epoch 21/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0540 - accuracy: 0.9863 - val_loss: 0.0949 - val_accuracy: 0.9670\n",
+      "Epoch 22/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0441 - accuracy: 0.9835 - val_loss: 0.0992 - val_accuracy: 0.9670\n",
+      "Epoch 23/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0323 - accuracy: 0.9890 - val_loss: 0.0922 - val_accuracy: 0.9670\n",
+      "Epoch 24/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0661 - accuracy: 0.9835 - val_loss: 0.0836 - val_accuracy: 0.9780\n",
+      "Epoch 25/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0613 - accuracy: 0.9863 - val_loss: 0.0873 - val_accuracy: 0.9670\n",
+      "Epoch 26/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0342 - accuracy: 0.9863 - val_loss: 0.0932 - val_accuracy: 0.9560\n",
+      "Epoch 27/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0552 - accuracy: 0.9808 - val_loss: 0.1022 - val_accuracy: 0.9560\n",
+      "Epoch 28/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.0242 - accuracy: 0.9918 - val_loss: 0.0961 - val_accuracy: 0.9670\n",
+      "Epoch 29/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0522 - accuracy: 0.9780 - val_loss: 0.0930 - val_accuracy: 0.9670\n",
+      "Epoch 30/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0543 - accuracy: 0.9808 - val_loss: 0.0868 - val_accuracy: 0.9670\n",
+      "Epoch 31/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0216 - accuracy: 0.9945 - val_loss: 0.0919 - val_accuracy: 0.9670\n",
+      "Epoch 32/50\n",
+      "12/12 [==============================] - 0s 16ms/step - loss: 0.0465 - accuracy: 0.9863 - val_loss: 0.0962 - val_accuracy: 0.9670\n",
+      "Epoch 33/50\n",
+      "12/12 [==============================] - 0s 8ms/step - loss: 0.0227 - accuracy: 0.9918 - val_loss: 0.0994 - val_accuracy: 0.9670\n",
+      "Epoch 34/50\n",
+      " 1/12 [=>............................] - ETA: 0s - loss: 0.0190 - accuracy: 1.0000Restoring model weights from the end of the best epoch: 24.\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0455 - accuracy: 0.9918 - val_loss: 0.0943 - val_accuracy: 0.9780\n",
+      "Epoch 34: early stopping\n",
+      "\n",
+      "Model 1 - Test Accuracy: 0.9561\n",
+      "\n",
+      "Entraînement du Modèle 2...\n",
+      "Epoch 1/50\n",
+      "12/12 [==============================] - 3s 47ms/step - loss: 0.5959 - accuracy: 0.7005 - val_loss: 0.5395 - val_accuracy: 0.9451\n",
+      "Epoch 2/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.3752 - accuracy: 0.8599 - val_loss: 0.4585 - val_accuracy: 0.9341\n",
+      "Epoch 3/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.2824 - accuracy: 0.9203 - val_loss: 0.3877 - val_accuracy: 0.9341\n",
+      "Epoch 4/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.2312 - accuracy: 0.9368 - val_loss: 0.3325 - val_accuracy: 0.9341\n",
+      "Epoch 5/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.1754 - accuracy: 0.9505 - val_loss: 0.2880 - val_accuracy: 0.9341\n",
+      "Epoch 6/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.1667 - accuracy: 0.9560 - val_loss: 0.2487 - val_accuracy: 0.9451\n",
+      "Epoch 7/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.1353 - accuracy: 0.9560 - val_loss: 0.2176 - val_accuracy: 0.9451\n",
+      "Epoch 8/50\n",
+      "12/12 [==============================] - 0s 18ms/step - loss: 0.1445 - accuracy: 0.9505 - val_loss: 0.1950 - val_accuracy: 0.9560\n",
+      "Epoch 9/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.1151 - accuracy: 0.9670 - val_loss: 0.1781 - val_accuracy: 0.9560\n",
+      "Epoch 10/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.0926 - accuracy: 0.9753 - val_loss: 0.1631 - val_accuracy: 0.9560\n",
+      "Epoch 11/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0940 - accuracy: 0.9698 - val_loss: 0.1501 - val_accuracy: 0.9560\n",
+      "Epoch 12/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.0928 - accuracy: 0.9725 - val_loss: 0.1382 - val_accuracy: 0.9560\n",
+      "Epoch 13/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0958 - accuracy: 0.9670 - val_loss: 0.1255 - val_accuracy: 0.9560\n",
+      "Epoch 14/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0690 - accuracy: 0.9808 - val_loss: 0.1299 - val_accuracy: 0.9560\n",
+      "Epoch 15/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0840 - accuracy: 0.9698 - val_loss: 0.1176 - val_accuracy: 0.9560\n",
+      "Epoch 16/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0628 - accuracy: 0.9753 - val_loss: 0.1157 - val_accuracy: 0.9560\n",
+      "Epoch 17/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0584 - accuracy: 0.9808 - val_loss: 0.1213 - val_accuracy: 0.9560\n",
+      "Epoch 18/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0611 - accuracy: 0.9863 - val_loss: 0.1150 - val_accuracy: 0.9560\n",
+      "Epoch 19/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0505 - accuracy: 0.9918 - val_loss: 0.1111 - val_accuracy: 0.9560\n",
+      "Epoch 20/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0411 - accuracy: 0.9863 - val_loss: 0.1211 - val_accuracy: 0.9451\n",
+      "Epoch 21/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0469 - accuracy: 0.9863 - val_loss: 0.1150 - val_accuracy: 0.9451\n",
+      "Epoch 22/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9863 - val_loss: 0.1176 - val_accuracy: 0.9451\n",
+      "Epoch 23/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0691 - accuracy: 0.9780 - val_loss: 0.1092 - val_accuracy: 0.9341\n",
+      "Epoch 24/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0645 - accuracy: 0.9780 - val_loss: 0.1223 - val_accuracy: 0.9451\n",
+      "Epoch 25/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0582 - accuracy: 0.9808 - val_loss: 0.1338 - val_accuracy: 0.9451\n",
+      "Epoch 26/50\n",
+      "12/12 [==============================] - 0s 9ms/step - loss: 0.0433 - accuracy: 0.9835 - val_loss: 0.1201 - val_accuracy: 0.9560\n",
+      "Epoch 27/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0469 - accuracy: 0.9808 - val_loss: 0.1089 - val_accuracy: 0.9451\n",
+      "Epoch 28/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0349 - accuracy: 0.9890 - val_loss: 0.1112 - val_accuracy: 0.9451\n",
+      "Epoch 29/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0393 - accuracy: 0.9890 - val_loss: 0.1391 - val_accuracy: 0.9451\n",
+      "Epoch 30/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.0459 - accuracy: 0.9808 - val_loss: 0.1096 - val_accuracy: 0.9451\n",
+      "Epoch 31/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.0322 - accuracy: 0.9890 - val_loss: 0.1250 - val_accuracy: 0.9451\n",
+      "Epoch 32/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9890 - val_loss: 0.1146 - val_accuracy: 0.9451\n",
+      "Epoch 33/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0637 - accuracy: 0.9753 - val_loss: 0.1191 - val_accuracy: 0.9560\n",
+      "Epoch 34/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0440 - accuracy: 0.9863 - val_loss: 0.1113 - val_accuracy: 0.9560\n",
+      "Epoch 35/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0233 - accuracy: 0.9973 - val_loss: 0.1071 - val_accuracy: 0.9560\n",
+      "Epoch 36/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.0873 - accuracy: 0.9753 - val_loss: 0.0946 - val_accuracy: 0.9670\n",
+      "Epoch 37/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0640 - accuracy: 0.9725 - val_loss: 0.0857 - val_accuracy: 0.9670\n",
+      "Epoch 38/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0478 - accuracy: 0.9835 - val_loss: 0.0810 - val_accuracy: 0.9670\n",
+      "Epoch 39/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0414 - accuracy: 0.9808 - val_loss: 0.0805 - val_accuracy: 0.9780\n",
+      "Epoch 40/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.0238 - accuracy: 0.9890 - val_loss: 0.0967 - val_accuracy: 0.9670\n",
+      "Epoch 41/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.0419 - accuracy: 0.9918 - val_loss: 0.0902 - val_accuracy: 0.9670\n",
+      "Epoch 42/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0290 - accuracy: 0.9890 - val_loss: 0.0686 - val_accuracy: 0.9780\n",
+      "Epoch 43/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0216 - accuracy: 0.9973 - val_loss: 0.0676 - val_accuracy: 0.9890\n",
+      "Epoch 44/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0170 - accuracy: 0.9973 - val_loss: 0.0766 - val_accuracy: 0.9780\n",
+      "Epoch 45/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0211 - accuracy: 0.9918 - val_loss: 0.0742 - val_accuracy: 0.9780\n",
+      "Epoch 46/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0349 - accuracy: 0.9863 - val_loss: 0.0739 - val_accuracy: 0.9780\n",
+      "Epoch 47/50\n",
+      "12/12 [==============================] - 0s 18ms/step - loss: 0.0548 - accuracy: 0.9808 - val_loss: 0.0623 - val_accuracy: 0.9780\n",
+      "Epoch 48/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0352 - accuracy: 0.9863 - val_loss: 0.0686 - val_accuracy: 0.9670\n",
+      "Epoch 49/50\n",
+      "12/12 [==============================] - 0s 10ms/step - loss: 0.0255 - accuracy: 0.9945 - val_loss: 0.0751 - val_accuracy: 0.9780\n",
+      "Epoch 50/50\n",
+      "12/12 [==============================] - 0s 11ms/step - loss: 0.0308 - accuracy: 0.9945 - val_loss: 0.0704 - val_accuracy: 0.9780\n",
+      "\n",
+      "Model 2 - Test Accuracy: 0.9649\n",
+      "\n",
+      "Visualisation des résultats...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import urllib.request\n",
+    "import ssl\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# 1. Chargement des données\n",
+    "# Cette fonction télécharge le dataset depuis l'URL et le nettoie\n",
+    "# Elle gère également les valeurs manquantes et convertit les types de données\n",
+    "def load_data():\n",
+    "    try:\n",
+    "        ssl._create_default_https_context = ssl._create_unverified_context\n",
+    "        url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data\"\n",
+    "        columns = ['id', 'diagnosis'] + [f'feature_{i}' for i in range(30)]\n",
+    "        print(\"Téléchargement des données...\")\n",
+    "        data = pd.read_csv(url, names=columns)\n",
+    "        if data.empty:\n",
+    "            raise Exception(\"Le dataset est vide\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Erreur lors du téléchargement des données: {e}\")\n",
+    "        raise\n",
+    "    \n",
+    "    # Convertir 'diagnosis' en variable binaire (M = 1, B = 0)\n",
+    "    data['diagnosis'] = data['diagnosis'].map({'M': 1, 'B': 0})\n",
+    "    \n",
+    "    # Supprimer la colonne ID car elle n'est pas utile pour l'apprentissage\n",
+    "    data = data.drop(columns=['id'])\n",
+    "    \n",
+    "    return data\n",
+    "\n",
+    "# 2. Prétraitement des données\n",
+    "# Sépare les features (X) et la target (y), puis effectue une standardisation des données\n",
+    "def preprocess_data(data):\n",
+    "    X = data.drop('diagnosis', axis=1)\n",
+    "    y = data['diagnosis']\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "    scaler = StandardScaler()\n",
+    "    X_train_scaled = scaler.fit_transform(X_train)\n",
+    "    X_test_scaled = scaler.transform(X_test)\n",
+    "    return X_train_scaled, X_test_scaled, y_train, y_test\n",
+    "\n",
+    "# 3. Modèle de réseau de neurones classique\n",
+    "# Un modèle simple avec quelques couches cachées\n",
+    "def create_model_1(input_shape):\n",
+    "    model = Sequential([\n",
+    "        Dense(64, activation='relu', input_shape=input_shape),\n",
+    "        BatchNormalization(),\n",
+    "        Dense(32, activation='relu'),\n",
+    "        Dropout(0.3),\n",
+    "        Dense(16, activation='relu'),\n",
+    "        Dense(1, activation='sigmoid')\n",
+    "    ])\n",
+    "    model.compile(optimizer=Adam(learning_rate=0.001),\n",
+    "                 loss='binary_crossentropy',\n",
+    "                 metrics=['accuracy'])\n",
+    "    return model\n",
+    "\n",
+    "# 4. Modèle plus profond avec régularisation plus forte\n",
+    "# Ajoute plus de couches et de Dropout pour éviter l'overfitting\n",
+    "def create_model_2(input_shape):\n",
+    "    model = Sequential([\n",
+    "        Dense(128, activation='relu', input_shape=input_shape),\n",
+    "        BatchNormalization(),\n",
+    "        Dropout(0.3),\n",
+    "        Dense(64, activation='relu'),\n",
+    "        BatchNormalization(),\n",
+    "        Dropout(0.3),\n",
+    "        Dense(32, activation='relu'),\n",
+    "        BatchNormalization(),\n",
+    "        Dense(16, activation='relu'),\n",
+    "        Dense(1, activation='sigmoid')\n",
+    "    ])\n",
+    "    model.compile(optimizer=Adam(learning_rate=0.001),\n",
+    "                 loss='binary_crossentropy',\n",
+    "                 metrics=['accuracy'])\n",
+    "    return model\n",
+    "\n",
+    "def train_and_evaluate(model, X_train, X_test, y_train, y_test, model_name):\n",
+    "    early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True, verbose=1)\n",
+    "    history = model.fit(X_train, y_train, validation_split=0.2, epochs=50, batch_size=32, callbacks=[early_stopping], verbose=1)\n",
+    "    test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
+    "    print(f\"\\n{model_name} - Test Accuracy: {test_accuracy:.4f}\")\n",
+    "    return history\n",
+    "\n",
+    "def plot_training_history(history1, history2):\n",
+    "    plt.figure(figsize=(12, 4))\n",
+    "    plt.subplot(1, 2, 1)\n",
+    "    plt.plot(history1.history['accuracy'], label='Model 1 accuracy')\n",
+    "    plt.plot(history1.history['val_accuracy'], label='Model 1 val accuracy')\n",
+    "    plt.plot(history2.history['accuracy'], label='Model 2 accuracy')\n",
+    "    plt.plot(history2.history['val_accuracy'], label='Model 2 val accuracy')\n",
+    "    plt.title('Model Accuracy')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.legend()\n",
+    "    plt.subplot(1, 2, 2)\n",
+    "    plt.plot(history1.history['loss'], label='Model 1 loss')\n",
+    "    plt.plot(history1.history['val_loss'], label='Model 1 val loss')\n",
+    "    plt.plot(history2.history['loss'], label='Model 2 loss')\n",
+    "    plt.plot(history2.history['val_loss'], label='Model 2 val loss')\n",
+    "    plt.title('Model Loss')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Loss')\n",
+    "    plt.legend()\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "def main():\n",
+    "    print(\"Chargement des données...\")\n",
+    "    data = load_data()\n",
+    "    print(\"Dimension des données:\", data.shape)\n",
+    "    print(\"\\nPrétraitement des données...\")\n",
+    "    X_train, X_test, y_train, y_test = preprocess_data(data)\n",
+    "    input_shape = (X_train.shape[1],)\n",
+    "    print(\"\\nEntraînement du Modèle 1...\")\n",
+    "    model1 = create_model_1(input_shape)\n",
+    "    history1 = train_and_evaluate(model1, X_train, X_test, y_train, y_test, \"Model 1\")\n",
+    "    print(\"\\nEntraînement du Modèle 2...\")\n",
+    "    model2 = create_model_2(input_shape)\n",
+    "    history2 = train_and_evaluate(model2, X_train, X_test, y_train, y_test, \"Model 2\")\n",
+    "    print(\"\\nVisualisation des résultats...\")\n",
+    "    plot_training_history(history1, history2)\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1cf760cd-464b-485a-808b-5d0213629b6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chargement des données...\n",
+      "Téléchargement des données...\n",
+      "Dimension des données: (297, 14)\n",
+      "\n",
+      "Prétraitement des données...\n",
+      "\n",
+      "Entraînement du Modèle 1...\n",
+      "Epoch 1/50\n",
+      "6/6 [==============================] - 2s 128ms/step - loss: 0.6726 - accuracy: 0.5714 - val_loss: 0.6936 - val_accuracy: 0.4375\n",
+      "Epoch 2/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.6053 - accuracy: 0.6508 - val_loss: 0.6665 - val_accuracy: 0.5833\n",
+      "Epoch 3/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.5555 - accuracy: 0.7037 - val_loss: 0.6432 - val_accuracy: 0.7292\n",
+      "Epoch 4/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.5297 - accuracy: 0.7407 - val_loss: 0.6233 - val_accuracy: 0.7292\n",
+      "Epoch 5/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.5108 - accuracy: 0.7725 - val_loss: 0.6030 - val_accuracy: 0.7500\n",
+      "Epoch 6/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.4443 - accuracy: 0.8095 - val_loss: 0.5857 - val_accuracy: 0.7500\n",
+      "Epoch 7/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.4542 - accuracy: 0.8360 - val_loss: 0.5711 - val_accuracy: 0.7500\n",
+      "Epoch 8/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.4128 - accuracy: 0.8307 - val_loss: 0.5559 - val_accuracy: 0.7708\n",
+      "Epoch 9/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.4183 - accuracy: 0.8095 - val_loss: 0.5407 - val_accuracy: 0.7708\n",
+      "Epoch 10/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.3841 - accuracy: 0.8360 - val_loss: 0.5273 - val_accuracy: 0.7708\n",
+      "Epoch 11/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.3808 - accuracy: 0.8413 - val_loss: 0.5168 - val_accuracy: 0.7708\n",
+      "Epoch 12/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.3372 - accuracy: 0.8730 - val_loss: 0.5076 - val_accuracy: 0.7917\n",
+      "Epoch 13/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.3501 - accuracy: 0.8413 - val_loss: 0.4959 - val_accuracy: 0.7917\n",
+      "Epoch 14/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.3666 - accuracy: 0.8519 - val_loss: 0.4896 - val_accuracy: 0.7708\n",
+      "Epoch 15/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.3219 - accuracy: 0.8624 - val_loss: 0.4819 - val_accuracy: 0.7708\n",
+      "Epoch 16/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.3346 - accuracy: 0.8783 - val_loss: 0.4751 - val_accuracy: 0.7708\n",
+      "Epoch 17/50\n",
+      "6/6 [==============================] - 0s 13ms/step - loss: 0.2917 - accuracy: 0.9048 - val_loss: 0.4702 - val_accuracy: 0.7708\n",
+      "Epoch 18/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.3021 - accuracy: 0.8783 - val_loss: 0.4684 - val_accuracy: 0.7708\n",
+      "Epoch 19/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.2949 - accuracy: 0.8730 - val_loss: 0.4693 - val_accuracy: 0.7708\n",
+      "Epoch 20/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.3028 - accuracy: 0.8624 - val_loss: 0.4744 - val_accuracy: 0.7917\n",
+      "Epoch 21/50\n",
+      "6/6 [==============================] - 0s 13ms/step - loss: 0.2837 - accuracy: 0.8995 - val_loss: 0.4743 - val_accuracy: 0.7917\n",
+      "Epoch 22/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2644 - accuracy: 0.8942 - val_loss: 0.4703 - val_accuracy: 0.7917\n",
+      "Epoch 23/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.2787 - accuracy: 0.8783 - val_loss: 0.4687 - val_accuracy: 0.7917\n",
+      "Epoch 24/50\n",
+      "6/6 [==============================] - 0s 13ms/step - loss: 0.2607 - accuracy: 0.8942 - val_loss: 0.4725 - val_accuracy: 0.7708\n",
+      "Epoch 25/50\n",
+      "6/6 [==============================] - 0s 22ms/step - loss: 0.2635 - accuracy: 0.9101 - val_loss: 0.4762 - val_accuracy: 0.7708\n",
+      "Epoch 26/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.2532 - accuracy: 0.8942 - val_loss: 0.4795 - val_accuracy: 0.7708\n",
+      "Epoch 27/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2781 - accuracy: 0.8836 - val_loss: 0.4803 - val_accuracy: 0.7917\n",
+      "Epoch 28/50\n",
+      "1/6 [====>.........................] - ETA: 0s - loss: 0.2628 - accuracy: 0.8438Restoring model weights from the end of the best epoch: 18.\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2181 - accuracy: 0.9153 - val_loss: 0.4796 - val_accuracy: 0.7917\n",
+      "Epoch 28: early stopping\n",
+      "\n",
+      "Model 1 - Test Accuracy: 0.9000\n",
+      "\n",
+      "Entraînement du Modèle 2...\n",
+      "Epoch 1/50\n",
+      "6/6 [==============================] - 3s 99ms/step - loss: 0.7850 - accuracy: 0.5556 - val_loss: 0.6804 - val_accuracy: 0.6458\n",
+      "Epoch 2/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.6244 - accuracy: 0.6984 - val_loss: 0.6675 - val_accuracy: 0.6250\n",
+      "Epoch 3/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.5951 - accuracy: 0.7037 - val_loss: 0.6540 - val_accuracy: 0.6667\n",
+      "Epoch 4/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.5336 - accuracy: 0.7407 - val_loss: 0.6397 - val_accuracy: 0.7083\n",
+      "Epoch 5/50\n",
+      "6/6 [==============================] - 0s 18ms/step - loss: 0.4938 - accuracy: 0.7672 - val_loss: 0.6284 - val_accuracy: 0.6875\n",
+      "Epoch 6/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.4418 - accuracy: 0.7884 - val_loss: 0.6183 - val_accuracy: 0.7292\n",
+      "Epoch 7/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.4246 - accuracy: 0.8360 - val_loss: 0.6073 - val_accuracy: 0.7292\n",
+      "Epoch 8/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.4053 - accuracy: 0.8360 - val_loss: 0.5955 - val_accuracy: 0.7500\n",
+      "Epoch 9/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.3941 - accuracy: 0.8254 - val_loss: 0.5831 - val_accuracy: 0.7500\n",
+      "Epoch 10/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3762 - accuracy: 0.8571 - val_loss: 0.5713 - val_accuracy: 0.7500\n",
+      "Epoch 11/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.3831 - accuracy: 0.8095 - val_loss: 0.5591 - val_accuracy: 0.7708\n",
+      "Epoch 12/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3737 - accuracy: 0.8254 - val_loss: 0.5458 - val_accuracy: 0.7500\n",
+      "Epoch 13/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3206 - accuracy: 0.8519 - val_loss: 0.5351 - val_accuracy: 0.7292\n",
+      "Epoch 14/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3356 - accuracy: 0.8413 - val_loss: 0.5237 - val_accuracy: 0.7500\n",
+      "Epoch 15/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.2974 - accuracy: 0.8624 - val_loss: 0.5149 - val_accuracy: 0.7500\n",
+      "Epoch 16/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.3358 - accuracy: 0.8466 - val_loss: 0.5082 - val_accuracy: 0.7500\n",
+      "Epoch 17/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.3013 - accuracy: 0.8677 - val_loss: 0.5025 - val_accuracy: 0.7708\n",
+      "Epoch 18/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.3208 - accuracy: 0.8783 - val_loss: 0.4991 - val_accuracy: 0.7708\n",
+      "Epoch 19/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2584 - accuracy: 0.8942 - val_loss: 0.5003 - val_accuracy: 0.7708\n",
+      "Epoch 20/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.3307 - accuracy: 0.8624 - val_loss: 0.4953 - val_accuracy: 0.7708\n",
+      "Epoch 21/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.3249 - accuracy: 0.8624 - val_loss: 0.4936 - val_accuracy: 0.7708\n",
+      "Epoch 22/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.2679 - accuracy: 0.8836 - val_loss: 0.4936 - val_accuracy: 0.7708\n",
+      "Epoch 23/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.2829 - accuracy: 0.8995 - val_loss: 0.4963 - val_accuracy: 0.7708\n",
+      "Epoch 24/50\n",
+      "6/6 [==============================] - 0s 14ms/step - loss: 0.3027 - accuracy: 0.8836 - val_loss: 0.4958 - val_accuracy: 0.7708\n",
+      "Epoch 25/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2284 - accuracy: 0.8889 - val_loss: 0.4924 - val_accuracy: 0.7708\n",
+      "Epoch 26/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2505 - accuracy: 0.8942 - val_loss: 0.4890 - val_accuracy: 0.7708\n",
+      "Epoch 27/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.2924 - accuracy: 0.8889 - val_loss: 0.4864 - val_accuracy: 0.7708\n",
+      "Epoch 28/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2524 - accuracy: 0.9153 - val_loss: 0.4880 - val_accuracy: 0.7708\n",
+      "Epoch 29/50\n",
+      "6/6 [==============================] - 0s 15ms/step - loss: 0.2920 - accuracy: 0.8889 - val_loss: 0.4899 - val_accuracy: 0.7708\n",
+      "Epoch 30/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2480 - accuracy: 0.8677 - val_loss: 0.4903 - val_accuracy: 0.7708\n",
+      "Epoch 31/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2126 - accuracy: 0.9153 - val_loss: 0.4957 - val_accuracy: 0.7500\n",
+      "Epoch 32/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2365 - accuracy: 0.9048 - val_loss: 0.5023 - val_accuracy: 0.7500\n",
+      "Epoch 33/50\n",
+      "6/6 [==============================] - 0s 17ms/step - loss: 0.2467 - accuracy: 0.8942 - val_loss: 0.5030 - val_accuracy: 0.7500\n",
+      "Epoch 34/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2289 - accuracy: 0.9101 - val_loss: 0.5044 - val_accuracy: 0.7500\n",
+      "Epoch 35/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2143 - accuracy: 0.8889 - val_loss: 0.5049 - val_accuracy: 0.7500\n",
+      "Epoch 36/50\n",
+      "6/6 [==============================] - 0s 16ms/step - loss: 0.2786 - accuracy: 0.8783 - val_loss: 0.5102 - val_accuracy: 0.7500\n",
+      "Epoch 37/50\n",
+      "1/6 [====>.........................] - ETA: 0s - loss: 0.2511 - accuracy: 0.8750Restoring model weights from the end of the best epoch: 27.\n",
+      "6/6 [==============================] - 0s 18ms/step - loss: 0.1923 - accuracy: 0.9365 - val_loss: 0.5136 - val_accuracy: 0.7500\n",
+      "Epoch 37: early stopping\n",
+      "\n",
+      "Model 2 - Test Accuracy: 0.8833\n",
+      "\n",
+      "Entraînement du Random Forest...\n",
+      "\n",
+      "Random Forest - Test Accuracy: 0.8833\n",
+      "\n",
+      "Entraînement de XGBoost...\n",
+      "\n",
+      "XGBoost - Test Accuracy: 0.8167\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import urllib.request\n",
+    "import ssl\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "import xgboost as xgb\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# 1. Chargement des données\n",
+    "# Cette fonction télécharge le dataset depuis l'URL et le nettoie\n",
+    "# Elle gère également les valeurs manquantes et convertit les types de données\n",
+    "def load_data():\n",
+    "    try:\n",
+    "        ssl._create_default_https_context = ssl._create_unverified_context\n",
+    "        url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\"\n",
+    "        columns = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
+    "                   'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target']\n",
+    "        print(\"Téléchargement des données...\")\n",
+    "        data = pd.read_csv(url, names=columns)\n",
+    "        if data.empty:\n",
+    "            raise Exception(\"Le dataset est vide\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Erreur lors du téléchargement des données: {e}\")\n",
+    "        raise\n",
+    "    \n",
+    "    data = data.replace('?', np.nan)\n",
+    "    data = data.dropna()\n",
+    "    for column in data.columns:\n",
+    "        data[column] = pd.to_numeric(data[column])\n",
+    "    data['target'] = (data['target'] > 0).astype(int)\n",
+    "    return data\n",
+    "\n",
+    "# 2. Prétraitement des données\n",
+    "# Sépare les features (X) et la target (y), puis effectue une standardisation des données\n",
+    "def preprocess_data(data):\n",
+    "    X = data.drop('target', axis=1)\n",
+    "    y = data['target']\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "    scaler = StandardScaler()\n",
+    "    X_train_scaled = scaler.fit_transform(X_train)\n",
+    "    X_test_scaled = scaler.transform(X_test)\n",
+    "    return X_train_scaled, X_test_scaled, y_train, y_test\n",
+    "\n",
+    "\n",
+    "# 3. Modèle de réseau de neurones classique\n",
+    "# Un modèle simple avec quelques couches cachées\n",
+    "def create_model_1(input_shape):\n",
+    "    model = Sequential([\n",
+    "        Dense(64, activation='relu', input_shape=input_shape),\n",
+    "        BatchNormalization(),\n",
+    "        Dense(32, activation='relu'),\n",
+    "        Dropout(0.3),\n",
+    "        Dense(16, activation='relu'),\n",
+    "        Dense(1, activation='sigmoid')\n",
+    "    ])\n",
+    "    model.compile(optimizer=Adam(learning_rate=0.001),\n",
+    "                  loss='binary_crossentropy',\n",
+    "                  metrics=['accuracy'])\n",
+    "    return model\n",
+    "\n",
+    "# 4. Modèle plus profond avec régularisation plus forte\n",
+    "# Ajoute plus de couches et de Dropout pour éviter l'overfitting\n",
+    "def create_model_2(input_shape):\n",
+    "    model = Sequential([\n",
+    "        Dense(128, activation='relu', input_shape=input_shape),\n",
+    "        BatchNormalization(),\n",
+    "        Dropout(0.3),\n",
+    "        Dense(64, activation='relu'),\n",
+    "        BatchNormalization(),\n",
+    "        Dropout(0.3),\n",
+    "        Dense(32, activation='relu'),\n",
+    "        BatchNormalization(),\n",
+    "        Dense(16, activation='relu'),\n",
+    "        Dense(1, activation='sigmoid')\n",
+    "    ])\n",
+    "    model.compile(optimizer=Adam(learning_rate=0.001),\n",
+    "                  loss='binary_crossentropy',\n",
+    "                  metrics=['accuracy'])\n",
+    "    return model\n",
+    "\n",
+    "# 5. Modèle Random Forest avec recherche d'hyperparamètres\n",
+    "def create_model_rf(X_train, y_train):\n",
+    "    rf_model = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)\n",
+    "    param_grid = {'n_estimators': [50, 100, 200], 'max_depth': [5, 10, 15], 'min_samples_split': [2, 5, 10]}\n",
+    "    grid_search = GridSearchCV(rf_model, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
+    "    grid_search.fit(X_train, y_train)\n",
+    "    return grid_search.best_estimator_\n",
+    "\n",
+    "# 6. Modèle XGBoost avec GridSearchCV pour optimiser les hyperparamètres\n",
+    "def create_model_xgb(X_train, y_train):\n",
+    "    xgb_model = xgb.XGBClassifier(learning_rate=0.1, n_estimators=100, max_depth=5, random_state=42)\n",
+    "    \n",
+    "    param_grid = {\n",
+    "        'learning_rate': [0.01, 0.1, 0.3],\n",
+    "        'n_estimators': [50, 100, 200],\n",
+    "        'max_depth': [3, 5, 7]\n",
+    "    }\n",
+    "    \n",
+    "    grid_search = GridSearchCV(estimator=xgb_model, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=1)\n",
+    "    grid_search.fit(X_train, y_train)\n",
+    "    \n",
+    "    return grid_search.best_estimator_\n",
+    "\n",
+    "# 7. Fonction d'entraînement et d'évaluation pour les réseaux de neurones\n",
+    "def train_and_evaluate(model, X_train, X_test, y_train, y_test, model_name):\n",
+    "    early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True, verbose=1)\n",
+    "    history = model.fit(X_train, y_train, validation_split=0.2, epochs=50, batch_size=32, callbacks=[early_stopping], verbose=1)\n",
+    "    test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
+    "    print(f\"\\n{model_name} - Test Accuracy: {test_accuracy:.4f}\")\n",
+    "    return history\n",
+    "\n",
+    "# 8. Fonction pour Random Forest et XGBoost\n",
+    "def train_and_evaluate_rf_xgb(model, X_train, X_test, y_train, y_test, model_name):\n",
+    "    model.fit(X_train, y_train)\n",
+    "    test_accuracy = model.score(X_test, y_test)\n",
+    "    print(f\"\\n{model_name} - Test Accuracy: {test_accuracy:.4f}\")\n",
+    "    return test_accuracy\n",
+    "\n",
+    "# 9. Visualisation des performances de tous les modèles\n",
+    "def plot_training_history(history1, history2, rf_accuracy, xgb_accuracy):\n",
+    "    plt.figure(figsize=(12, 4))\n",
+    "    \n",
+    "    # Graphique de l'accuracy\n",
+    "    plt.subplot(1, 2, 1)\n",
+    "    plt.plot(history1.history['accuracy'], label='Model 1 accuracy')\n",
+    "    plt.plot(history1.history['val_accuracy'], label='Model 1 val accuracy')\n",
+    "    plt.plot(history2.history['accuracy'], label='Model 2 accuracy')\n",
+    "    plt.plot(history2.history['val_accuracy'], label='Model 2 val accuracy')\n",
+    "    plt.axhline(y=rf_accuracy, color='g', linestyle='--', label='Random Forest accuracy')\n",
+    "    plt.axhline(y=xgb_accuracy, color='r', linestyle='--', label='XGBoost accuracy')\n",
+    "    plt.title('Model Accuracy')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.legend()\n",
+    "\n",
+    "    # Graphique de la loss\n",
+    "    plt.subplot(1, 2, 2)\n",
+    "    plt.plot(history1.history['loss'], label='Model 1 loss')\n",
+    "    plt.plot(history1.history['val_loss'], label='Model 1 val loss')\n",
+    "    plt.plot(history2.history['loss'], label='Model 2 loss')\n",
+    "    plt.plot(history2.history['val_loss'], label='Model 2 val loss')\n",
+    "    plt.title('Model Loss')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Loss')\n",
+    "    plt.legend()\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "# 10. Programme principal\n",
+    "def main():\n",
+    "    print(\"Chargement des données...\")\n",
+    "    data = load_data()\n",
+    "    print(\"Dimension des données:\", data.shape)\n",
+    "    print(\"\\nPrétraitement des données...\")\n",
+    "    X_train, X_test, y_train, y_test = preprocess_data(data)\n",
+    "    \n",
+    "    # Réseaux de neurones\n",
+    "    print(\"\\nEntraînement du Modèle 1...\")\n",
+    "    model1 = create_model_1((X_train.shape[1],))\n",
+    "    history1 = train_and_evaluate(model1, X_train, X_test, y_train, y_test, \"Model 1\")\n",
+    "\n",
+    "    print(\"\\nEntraînement du Modèle 2...\")\n",
+    "    model2 = create_model_2((X_train.shape[1],))\n",
+    "    history2 = train_and_evaluate(model2, X_train, X_test, y_train, y_test, \"Model 2\")\n",
+    "\n",
+    "    print(\"\\nEntraînement du Random Forest...\")\n",
+    "    rf_model = create_model_rf(X_train, y_train)\n",
+    "    rf_accuracy = train_and_evaluate_rf_xgb(rf_model, X_train, X_test, y_train, y_test, \"Random Forest\")\n",
+    "\n",
+    "    print(\"\\nEntraînement de XGBoost...\")\n",
+    "    xgb_model = create_model_xgb(X_train, y_train)\n",
+    "    xgb_accuracy = train_and_evaluate_rf_xgb(xgb_model, X_train, X_test, y_train, y_test, \"XGBoost\")\n",
+    "\n",
+    "    plot_training_history(history1, history2, rf_accuracy, xgb_accuracy)\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8371c4d-21a1-48a4-bbba-2c1857feac9a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (tensorflow_env)",
+   "language": "python",
+   "name": "tensorflow_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TP05/tp05.py b/TP05/tp05.py
new file mode 100644
index 0000000..b3beed4
--- /dev/null
+++ b/TP05/tp05.py
@@ -0,0 +1,390 @@
+"""
+Projet de Machine Learning : Prédiction de Maladies Cardiaques (Version corrigée)
+Dataset : UCI Heart Disease Dataset
+Objectif : Comparer deux architectures de réseaux de neurones pour la prédiction de maladies cardiaques
+"""
+
+import pandas as pd
+import numpy as np
+import urllib.request
+import ssl
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import EarlyStopping
+import matplotlib.pyplot as plt
+
+# 1. Chargement des données avec gestion du SSL
+def load_data():
+    try:
+        # Créer un contexte SSL non-vérifié (à utiliser avec précaution)
+        ssl._create_default_https_context = ssl._create_unverified_context
+        
+        # URL du dataset
+        url = "https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data"
+        
+        # Définir les noms des colonnes
+        columns = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',
+                  'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target']
+        
+        print("Téléchargement des données...")
+        
+        # Télécharger directement dans un DataFrame
+        data = pd.read_csv(url, names=columns)
+        
+        # En cas d'erreur, utiliser un dataset de démonstration
+        if data.empty:
+            raise Exception("Le dataset est vide")
+            
+    except Exception as e:
+        print(f"Erreur lors du téléchargement des données: {e}")
+        print("Utilisation d'un dataset de démonstration...")
+        
+        # Créer un petit dataset de démonstration
+        np.random.seed(42)
+        n_samples = 300
+        
+        data = pd.DataFrame({
+            'age': np.random.normal(55, 10, n_samples),
+            'sex': np.random.binomial(1, 0.5, n_samples),
+            'cp': np.random.randint(0, 4, n_samples),
+            'trestbps': np.random.normal(130, 20, n_samples),
+            'chol': np.random.normal(240, 40, n_samples),
+            'fbs': np.random.binomial(1, 0.2, n_samples),
+            'restecg': np.random.randint(0, 3, n_samples),
+            'thalach': np.random.normal(150, 20, n_samples),
+            'exang': np.random.binomial(1, 0.3, n_samples),
+            'oldpeak': np.random.normal(1, 1, n_samples),
+            'slope': np.random.randint(0, 3, n_samples),
+            'ca': np.random.randint(0, 4, n_samples),
+            'thal': np.random.randint(0, 3, n_samples),
+            'target': np.random.binomial(1, 0.4, n_samples)
+        })
+    
+    # Nettoyer les données
+    data = data.replace('?', np.nan)
+    data = data.dropna()
+    
+    # Convertir les colonnes en nombres
+    for column in data.columns:
+        data[column] = pd.to_numeric(data[column])
+    
+    # Binariser la target (0 pour pas de maladie, 1 pour maladie)
+    data['target'] = (data['target'] > 0).astype(int)
+    
+    return data
+
+# 2. Prétraitement des données
+def preprocess_data(data):
+    # Séparer features et target
+    X = data.drop('target', axis=1)
+    y = data['target']
+    
+    # Split train/test
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    # Standardisation
+    scaler = StandardScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+    
+    return X_train_scaled, X_test_scaled, y_train, y_test
+
+# 3. Premier modèle : Réseau dense classique
+def create_model_1(input_shape):
+    model = Sequential([
+        Dense(64, activation='relu', input_shape=input_shape),
+        BatchNormalization(),
+        Dense(32, activation='relu'),
+        Dropout(0.3),
+        Dense(16, activation='relu'),
+        Dense(1, activation='sigmoid')
+    ])
+    
+    model.compile(optimizer=Adam(learning_rate=0.001),
+                 loss='binary_crossentropy',
+                 metrics=['accuracy'])
+    
+    return model
+
+# 4. Second modèle : Réseau plus profond avec régularisation plus forte
+def create_model_2(input_shape):
+    model = Sequential([
+        Dense(128, activation='relu', input_shape=input_shape),
+        BatchNormalization(),
+        Dropout(0.3),
+        Dense(64, activation='relu'),
+        BatchNormalization(),
+        Dropout(0.3),
+        Dense(32, activation='relu'),
+        BatchNormalization(),
+        Dense(16, activation='relu'),
+        Dense(1, activation='sigmoid')
+    ])
+    
+    model.compile(optimizer=Adam(learning_rate=0.001),
+                 loss='binary_crossentropy',
+                 metrics=['accuracy'])
+    
+    return model
+
+# 5. Fonction d'entraînement et d'évaluation
+def train_and_evaluate(model, X_train, X_test, y_train, y_test, model_name):
+    # Early stopping pour éviter le surapprentissage
+    early_stopping = EarlyStopping(
+        monitor='val_loss',
+        patience=10,
+        restore_best_weights=True,
+        verbose=1
+    )
+    
+    # Entraînement
+    history = model.fit(
+        X_train, y_train,
+        validation_split=0.2,
+        epochs=50,  # Réduit pour la démonstration
+        batch_size=32,
+        callbacks=[early_stopping],
+        verbose=1
+    )
+    
+    # Évaluation
+    test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)
+    print(f"\n{model_name} - Test Accuracy: {test_accuracy:.4f}")
+    
+    return history
+
+# 6. Visualisation des résultats
+def plot_training_history(history1, history2):
+    plt.figure(figsize=(12, 4))
+    
+    # Plot accuracy
+    plt.subplot(1, 2, 1)
+    plt.plot(history1.history['accuracy'], label='Model 1 accuracy')
+    plt.plot(history1.history['val_accuracy'], label='Model 1 val accuracy')
+    plt.plot(history2.history['accuracy'], label='Model 2 accuracy')
+    plt.plot(history2.history['val_accuracy'], label='Model 2 val accuracy')
+    plt.title('Model Accuracy')
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy')
+    plt.legend()
+    
+    # Plot loss
+    plt.subplot(1, 2, 2)
+    plt.plot(history1.history['loss'], label='Model 1 loss')
+    plt.plot(history1.history['val_loss'], label='Model 1 val loss')
+    plt.plot(history2.history['loss'], label='Model 2 loss')
+    plt.plot(history2.history['val_loss'], label='Model 2 val loss')
+    plt.title('Model Loss')
+    plt.xlabel('Epoch')
+    plt.ylabel('Loss')
+    plt.legend()
+    
+    plt.tight_layout()
+    plt.show()
+
+# 7. Programme principal
+def main():
+    print("Loading data...")
+    data = load_data()
+    print("Data shape:", data.shape)
+    
+    print("\nPreprocessing data...")
+    X_train, X_test, y_train, y_test = preprocess_data(data)
+    input_shape = (X_train.shape[1],)
+    
+    print("\nTraining Model 1...")
+    model1 = create_model_1(input_shape)
+    history1 = train_and_evaluate(model1, X_train, X_test, y_train, y_test, "Model 1")
+    
+    print("\nTraining Model 2...")
+    model2 = create_model_2(input_shape)
+    history2 = train_and_evaluate(model2, X_train, X_test, y_train, y_test, "Model 2")
+    
+    print("\nPlotting results...")
+    plot_training_history(history1, history2)
+
+if __name__ == "__main__":
+    main()
+    
+
+    
+'''
+Modèle 1 : Réseau Dense Classique
+- C'est une architecture relativement simple et légère avec 4 couches :
+1. Première couche : 64 neurones avec activation ReLU
+    - Cette couche initiale capture les patterns de base dans les données
+    - Suivie d'une normalisation par lots (BatchNormalization) pour stabiliser l'apprentissage
+2. Deuxième couche : 32 neurones avec activation ReLU
+    - Suivie d'un Dropout de 30% pour éviter le surapprentissage
+3. Troisième couche : 16 neurones avec activation ReLU
+    - Réduit progressivement la dimensionnalité
+4. Couche de sortie : 1 neurone avec activation sigmoid
+    - Pour la prédiction binaire (malade/non malade)
+    
+Modèle 2 : Réseau Plus Profond
+- C'est une architecture plus complexe avec 5 couches et plus de régularisation :
+1. Première couche : 128 neurones avec activation ReLU
+    - Commence avec plus de neurones pour capturer des patterns plus complexes
+    - Suivie de BatchNormalization et Dropout 30%
+2. Deuxième couche : 64 neurones avec activation ReLU
+    - Également suivie de BatchNormalization et Dropout
+3. Troisième couche : 32 neurones avec activation ReLU
+    - Avec BatchNormalization
+4. Quatrième couche : 16 neurones avec activation ReLU
+5. Couche de sortie : 1 neurone avec activation sigmoid
+
+Les principales différences sont :
+1. Complexité : Le modèle 2 a plus de paramètres et de couches
+2. Régularisation : Le modèle 2 utilise plus de BatchNormalization et de Dropout
+3. Capacité d'apprentissage : Le modèle 2 peut capturer des relations plus complexes dans les données
+
+L'idée est de comparer :
+- Une approche simple qui pourrait suffire pour ce problème médical relativement simple
+- Une approche plus complexe qui pourrait potentiellement capturer des patterns plus subtils
+
+Les deux modèles utilisent le même optimiseur (Adam) avec le même learning rate (0.001) pour une comparaison équitable.
+
+Cette configuration permet d'observer si la complexité supplémentaire du deuxième modèle apporte réellement un avantage en termes de performances, ou si le modèle plus simple est suffisant.
+
+- ReLU (Rectified Linear Unit) est une fonction d'activation très populaire en deep learning : ReLu (x) = max (0,x)
+
+- Le Dropout est une technique de régularisation cruciale en deep learning. Voici une explication détaillée :
+Principe de base :
+Pendant l'entraînement, à chaque itération
+Désactive aléatoirement un certain pourcentage de neurones
+Ces neurones sont temporairement "éteints" avec toutes leurs connexions
+Le pourcentage est défini par le paramètre de dropout (ex: 0.3 = 30% des neurones)
+
+- La BatchNormalization (ou normalisation par lots) est une technique très importante en deep learning. Voici une explication détaillée :
+Principe fondamental :
+Normalise les activations d'une couche pour chaque batch
+Maintient la moyenne proche de 0 et l'écart-type proche de 1
+S'applique avant la fonction d'activation
+'''
+    
+'''
+## Exercice 1 : 
+adapter le programme sur les données suivantes : 
+https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data
+
+
+## Exercice 2 : 
+On vous demande d'implémenter 2 autres modèles en suivant le schéma du programme donné. Sur les 2 data-set. 
+
+L'objectif est de rendre un rapport explicatif complet sur au moins un des modèles ; le code doit être commenté et des tests (changement de paramètres : itération, taux, couches réseaux) doivent être fait.
+
+### Premier Modèle : Random Forest Classifier
+
+Ce modèle est particulièrement intéressant car il offre :
+- Une excellente performance sur les données médicales
+- Une interprétabilité des résultats
+- Une facilité relative d'implémentation
+
+Voici un exemple de structure pour l'implémentation :
+
+```python
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import GridSearchCV
+
+def create_model_rf(X_train, y_train):
+    # Création du modèle avec des hyperparamètres de base
+    rf_model = RandomForestClassifier(
+        n_estimators=100,
+        max_depth=10,
+        random_state=42
+    )
+    
+    # Définition des paramètres à optimiser
+    param_grid = {
+        'n_estimators': [50, 100, 200],
+        'max_depth': [5, 10, 15],
+        'min_samples_split': [2, 5, 10]
+    }
+    
+    # Recherche des meilleurs paramètres
+    grid_search = GridSearchCV(
+        rf_model,
+        param_grid,
+        cv=5,
+        scoring='accuracy',
+        n_jobs=-1
+    )
+    
+    # Entraînement avec recherche des meilleurs paramètres
+    grid_search.fit(X_train, y_train)
+    
+    return grid_search.best_estimator_
+```
+
+### Deuxième Modèle : XGBoost
+
+XGBoost est un algorithme de boosting très performant qui permet souvent d'obtenir d'excellents résultats. Voici une structure d'implémentation :
+
+```python
+import xgboost as xgb
+from sklearn.model_selection import cross_val_score
+
+def create_model_xgb(X_train, y_train):
+    # Création du modèle avec des paramètres de base
+    xgb_model = xgb.XGBClassifier(
+        learning_rate=0.1,
+        n_estimators=100,
+        max_depth=5,
+        random_state=42
+    )
+    
+    # Paramètres à optimiser
+    param_grid = {
+        'learning_rate': [0.01, 0.1, 0.3],
+        'n_estimators': [50, 100, 200],
+        'max_depth': [3, 5, 7]
+    }
+    
+    # Optimisation des hyperparamètres
+    grid_search = GridSearchCV(
+        xgb_model,
+        param_grid,
+        cv=5,
+        scoring='accuracy',
+        n_jobs=-1
+    )
+    
+    grid_search.fit(X_train, y_train)
+    
+    return grid_search.best_estimator_
+```
+
+Pour faciliter l'implémentation, voici les points essentiels à comprendre :
+
+Pour le Random Forest :
+- C'est un ensemble d'arbres de décision
+- Chaque arbre est entraîné sur un sous-ensemble aléatoire des données
+- La prédiction finale est obtenue par vote majoritaire des arbres
+- Les paramètres clés sont le nombre d'arbres (n_estimators) et la profondeur maximale (max_depth)
+
+Pour XGBoost :
+- C'est un algorithme de boosting qui construit les arbres séquentiellement
+- Chaque nouvel arbre corrige les erreurs des arbres précédents
+- Le learning_rate contrôle la contribution de chaque arbre
+- La profondeur des arbres (max_depth) limite la complexité du modèle
+
+Pour l'évaluation des modèles, on peut réutiliser les fonctions de visualisation existantes en les adaptant légèrement. Par exemple :
+
+```python
+def plot_model_comparison(models_results):
+    plt.figure(figsize=(10, 6))
+    
+    for model_name, scores in models_results.items():
+        plt.plot(scores['val_accuracy'], label=f'{model_name} validation accuracy')
+    
+    plt.title('Model Comparison')
+    plt.xlabel('Iteration')
+    plt.ylabel('Accuracy')
+    plt.legend()
+    plt.show()
+```
+
+'''
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