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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fd78eddb",
+   "metadata": {},
+   "source": [
+    "# TP1 du module 5 : l'apprentissage supervisé\n",
+    "\n",
+    "Dans ce TP, nous allons mettre en pratique les principes de l'apprentissage supervisé. Objectifs :\n",
+    "* Préparer des jeux de données pour l'apprentissage supervisé\n",
+    "* Entraîner un modèle d'arbre de décision\n",
+    "* Evaluer les performances d'un modèle de classification\n",
+    "* Entraîner et évaluer un modèle de régression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f423b9af",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:49.883205Z",
+     "start_time": "2025-09-17T13:02:42.389996Z"
+    }
+   },
+   "source": [
+    "# Ajoutez ici les imports de librairies nécessaires\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from sklearn.datasets import load_diabetes\n",
+    "from sklearn.linear_model import LinearRegression, Lasso\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, ConfusionMatrixDisplay, mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.tree import DecisionTreeClassifier, plot_tree, DecisionTreeRegressor"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5baf53df",
+   "metadata": {},
+   "source": [
+    "## Création de modèles de classification pour le Titanic\n",
+    "\n",
+    "1. Commencez par recharger votre jeu de données sur le Titanic, à partir du csv que vous aviez enregistré à la fin du TP du module 4. Ainsi, vous obtenez un jeu de données déjà préparé pour l'apprentissage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "fb6a795c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:49.913092Z",
+     "start_time": "2025-09-17T13:02:49.894555Z"
+    }
+   },
+   "source": [
+    "titanic = pd.read_csv(\"Titanic.csv\")\n",
+    "titanic.head()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   Survived  Pclass   Age     Fare  Famille  Sex_male  Embarked_C  Embarked_Q  \\\n",
+       "0         0       3  22.0   7.2500        1       1.0         0.0         0.0   \n",
+       "1         1       1  38.0  71.2833        1       0.0         1.0         0.0   \n",
+       "2         1       3  26.0   7.9250        0       0.0         0.0         0.0   \n",
+       "3         1       1  35.0  53.1000        1       0.0         0.0         0.0   \n",
+       "4         0       3  35.0   8.0500        0       1.0         0.0         0.0   \n",
+       "\n",
+       "   Embarked_S  Embarked_U  \n",
+       "0         1.0         0.0  \n",
+       "1         0.0         0.0  \n",
+       "2         1.0         0.0  \n",
+       "3         1.0         0.0  \n",
+       "4         1.0         0.0  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Famille</th>\n",
+       "      <th>Sex_male</th>\n",
+       "      <th>Embarked_C</th>\n",
+       "      <th>Embarked_Q</th>\n",
+       "      <th>Embarked_S</th>\n",
+       "      <th>Embarked_U</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca2175c6",
+   "metadata": {},
+   "source": [
+    "2. Séparer vos données en mettant d'un côté les attributs, de l'autre la cible à prédire. Ensuite, séparer encore ces groupes entre entraînement et test (proportion de 0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "eaf3faeb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:49.933629Z",
+     "start_time": "2025-09-17T13:02:49.925558Z"
+    }
+   },
+   "source": [
+    "X = titanic.drop(['Survived'], axis=1)\n",
+    "y = titanic['Survived']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
+   ],
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fffa149",
+   "metadata": {},
+   "source": [
+    "3. Créez un arbre de décision, sans option particulière pour l'instant. Entraînez-le, puis évaluez-le à l'aide de l'accuracy, sur les jeux de données crées ci-dessus. Comparez ce score avec le score moyen obtenu en effectuant une validation croisée sur l'ensemble du jeu de données."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "75244332",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:50.021459Z",
+     "start_time": "2025-09-17T13:02:49.983049Z"
+    }
+   },
+   "source": [
+    "tree = DecisionTreeClassifier()\n",
+    "tree.fit(X_train, y_train)\n",
+    "print(\"Accuracy sur les données de test : \", np.round(tree.score(X_test, y_test),2))\n",
+    "print(\"Score moyen par validation croisée : \", np.round(np.mean(cross_val_score(tree, X, y, cv=5)), 2))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy sur les données de test :  0.79\n",
+      "Score moyen par validation croisée :  0.78\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "id": "949b2215",
+   "metadata": {},
+   "source": [
+    "4. Calculer également la précision et le rappel. Cherchez notamment dans la documentation comment indiquer laquelle des deux classes considérer comme la classe positive. Quelles observations pouvez-vous faire ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "21576d9a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:50.184497Z",
+     "start_time": "2025-09-17T13:02:50.171528Z"
+    }
+   },
+   "source": [
+    "y_pred = tree.predict(X_test)\n",
+    "\n",
+    "print(\"Précision : \", np.round(precision_score(y_test, y_pred, average='binary', pos_label=1),2))\n",
+    "print(\"Rappel : \", np.round(recall_score(y_test, y_pred, average='binary', pos_label=1),2))\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Précision :  0.7\n",
+      "Rappel :  0.68\n"
+     ]
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43b14d19",
+   "metadata": {},
+   "source": [
+    "**Observations :** il n'y a pas d'écart important entre accuracy, précision et rappel. Il semble donc qu'il n'y ait pas de déséquilibre notable au niveau des prédictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ebfa508",
+   "metadata": {},
+   "source": [
+    "5. Afficher la matrice de confusion pour cet arbre de décision, sur le jeu de test. Commencez par simplement l'afficher de manière textuelle, puis travailler votre affichage à l'aide de matplotlib afin d'ajouter des couleurs relatives au nombre d'éléments dans chaque case de la matrice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "0eadce28",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:50.272301Z",
+     "start_time": "2025-09-17T13:02:50.266077Z"
+    }
+   },
+   "source": [
+    "# Affichage textuel\n",
+    "confusion_matrix(y_test, y_pred)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[98, 18],\n",
+       "       [20, 43]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "code",
+   "id": "68993a30",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:50.509249Z",
+     "start_time": "2025-09-17T13:02:50.324774Z"
+    }
+   },
+   "source": [
+    "# Affichage plus visuel\n",
+    "ConfusionMatrixDisplay.from_estimator(tree, X_test, y_test, cmap=plt.cm.Blues, display_labels=['Died', 'Survived'])\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35152af4",
+   "metadata": {},
+   "source": [
+    "6. Quelle est la profondeur de l'arbre de décision que vous avez créé ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "8e11c4ce",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:50.553882Z",
+     "start_time": "2025-09-17T13:02:50.551346Z"
+    }
+   },
+   "source": [
+    "print(tree.tree_.max_depth)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "17\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81749549",
+   "metadata": {},
+   "source": [
+    "7. Créer un deuxième arbre de décision, en limitant sa profondeur à trois niveaux. Affichez son accuracy sur les données de test : que constatez-vous, et comment pouvez-vous l'expliquer ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "37d8da95",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:50.646047Z",
+     "start_time": "2025-09-17T13:02:50.638537Z"
+    }
+   },
+   "source": [
+    "tree_max_depth_3 = DecisionTreeClassifier(max_depth=3)\n",
+    "tree_max_depth_3.fit(X_test, y_test)\n",
+    "print(\"Accuracy : \", np.round(tree_max_depth_3.score(X_test, y_test),2))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy :  0.84\n"
+     ]
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6a60fc7",
+   "metadata": {},
+   "source": [
+    "**Observation :** en limitant la profondeur de l'arbre, le score augmente. Cela est du au fait que limiter la profondeur de l'arbre permet d'éviter un phénomène de surapprentissage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa541c9a",
+   "metadata": {},
+   "source": [
+    "8. Visualisez ce nouvel arbre de décision : prenez garde à bien faire apparaître les labels du jeu de données, et remplacez les valeurs 0 et 1 par des labels textuels de votre choix (ex : 'Died' et 'Survived'). Quelles observations pouvez-vous faire sur cet arbre ? Les décisions vous paraissent-elles cohérentes avec l'analyse des données faites dans le module 4 ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3db9ec07",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:51.004765Z",
+     "start_time": "2025-09-17T13:02:50.698668Z"
+    }
+   },
+   "source": [
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plot_tree(tree_max_depth_3, filled=True, feature_names=X.columns, class_names=['Died', 'Survived'])\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7810558",
+   "metadata": {},
+   "source": [
+    "9. Vous allez à présent comparer les performances d'arbre de profondeurs différentes. Créez différents arbre, en faisant varier la profondeur entre 1 et la profondeur trouvée à la question 6. Pour chaque arbre, calculer son score (accuracy) à l'aide d'une validation croisée à 5 feuilles. Sur un graphique, représentez l'évolution du score en fonction de la profondeur de l'arbre. \n",
+    "Affichez également la profondeur pour lequel le score est maximal.\n",
+    "Qu'observez-vous ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "12852820",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:51.538976Z",
+     "start_time": "2025-09-17T13:02:51.015353Z"
+    }
+   },
+   "source": [
+    "scores = []\n",
+    "\n",
+    "for i in range(1, 20):\n",
+    "    model = DecisionTreeClassifier(max_depth=i)\n",
+    "    score_val = np.mean(cross_val_score(model, X, y, cv=5))\n",
+    "    scores.append(score_val)\n",
+    "    \n",
+    "plt.plot(range(1, 20), scores)\n",
+    "plt.xticks(range(1, 20))\n",
+    "plt.xlabel('Profondeur de l\\'arbre')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.title('Evolution du score en fonction de la profondeur de l\\'arbre de décision')\n",
+    "plt.show()\n",
+    "\n",
+    "prof_max = np.argmax(scores) + 1\n",
+    "print(\"Score maximum pour une profondeur de\", prof_max)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score maximum pour une profondeur de 5\n"
+     ]
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14bb73c3",
+   "metadata": {},
+   "source": [
+    "**Observation :** au départ, le score augmente avec la profondeur. Avec une profondeur trop basse, on a du sous-apprentissage. Mais ensuite, le score diminue alors que la profondeur augmente : on bascule dans du sur-apprentissage. Une profondeur de 5 semble donner les meilleurs résultats."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "906f7111",
+   "metadata": {},
+   "source": [
+    "10. Comparez deux critères pouvant être utilisé pour constuire l'arbre de décision : coefficient de gini et mesure d'entropie. Pour la profondeur de l'arbre,\n",
+    "Avec un validation croisée, affichez sur un histogramme les trois valeurs moyennes obtenues. Voyez-vous un critère qui se détache des autres par ses performances ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "bdfe567e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:51.650439Z",
+     "start_time": "2025-09-17T13:02:51.548595Z"
+    }
+   },
+   "source": [
+    "scores = []\n",
+    "criteres = ['gini', 'entropy']\n",
+    "\n",
+    "for c in criteres : \n",
+    "    model = DecisionTreeClassifier(max_depth=prof_max, criterion=c)\n",
+    "    score_val = np.mean(cross_val_score(model, X, y, cv=5))\n",
+    "    scores.append(score_val)\n",
+    "    \n",
+    "plt.bar(criteres, scores)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e718e4d",
+   "metadata": {},
+   "source": [
+    "**Observations :** les performances des deux modèles ne présentent pas de différence pour le jeu de données du Titanic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3819b10a",
+   "metadata": {},
+   "source": [
+    "## Créer un modèle de regression\n",
+    "\n",
+    "Pour tester le concept de modèle de régression, nous ne pouvons pas utiliser le jeu de données sur le Titanic. A la place, nous allons nous intéresser à un jeu de données présentant l'évolution de la maladie chez des patients diabétiques. L'objectif sera de prédire l'évolution de la maladie en un an, en se basant sur des données mesurées un an avant. Ce jeu de données est directement disponible via la librairie scikit-learn. Documentation : https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html\n",
+    "\n",
+    "1. Commencez par charger les données dans deux dataframe : un pour les attributs, un pour la cible à prédire."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "39d7c581",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:51.672386Z",
+     "start_time": "2025-09-17T13:02:51.657081Z"
+    }
+   },
+   "source": [
+    "diabetes_X, diabetes_y = load_diabetes(return_X_y=True, as_frame=True)"
+   ],
+   "outputs": [],
+   "execution_count": 13
+  },
+  {
+   "cell_type": "markdown",
+   "id": "971982f5",
+   "metadata": {},
+   "source": [
+    "2. Affichez les premières lignes des attributs. Avez-vous bien repéré contenant la valeur à prédire ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3a508658",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:51.688736Z",
+     "start_time": "2025-09-17T13:02:51.680622Z"
+    }
+   },
+   "source": [
+    "diabetes_X.head()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "        age       sex       bmi        bp        s1        s2        s3  \\\n",
+       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
+       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
+       "2  0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
+       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
+       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
+       "\n",
+       "         s4        s5        s6  \n",
+       "0 -0.002592  0.019907 -0.017646  \n",
+       "1 -0.039493 -0.068332 -0.092204  \n",
+       "2 -0.002592  0.002861 -0.025930  \n",
+       "3  0.034309  0.022688 -0.009362  \n",
+       "4 -0.002592 -0.031988 -0.046641  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>bp</th>\n",
+       "      <th>s1</th>\n",
+       "      <th>s2</th>\n",
+       "      <th>s3</th>\n",
+       "      <th>s4</th>\n",
+       "      <th>s5</th>\n",
+       "      <th>s6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.038076</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.061696</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>-0.044223</td>\n",
+       "      <td>-0.034821</td>\n",
+       "      <td>-0.043401</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.019907</td>\n",
+       "      <td>-0.017646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.001882</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.051474</td>\n",
+       "      <td>-0.026328</td>\n",
+       "      <td>-0.008449</td>\n",
+       "      <td>-0.019163</td>\n",
+       "      <td>0.074412</td>\n",
+       "      <td>-0.039493</td>\n",
+       "      <td>-0.068332</td>\n",
+       "      <td>-0.092204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.085299</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.044451</td>\n",
+       "      <td>-0.005670</td>\n",
+       "      <td>-0.045599</td>\n",
+       "      <td>-0.034194</td>\n",
+       "      <td>-0.032356</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.002861</td>\n",
+       "      <td>-0.025930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.089063</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.011595</td>\n",
+       "      <td>-0.036656</td>\n",
+       "      <td>0.012191</td>\n",
+       "      <td>0.024991</td>\n",
+       "      <td>-0.036038</td>\n",
+       "      <td>0.034309</td>\n",
+       "      <td>0.022688</td>\n",
+       "      <td>-0.009362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.005383</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.036385</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>0.003935</td>\n",
+       "      <td>0.015596</td>\n",
+       "      <td>0.008142</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>-0.031988</td>\n",
+       "      <td>-0.046641</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9530d95f",
+   "metadata": {},
+   "source": [
+    "3. Commencez par effectuer une régression linéaire, en prenant bien soin d'avoir des données d'entraînement et de test. Quelle erreur quadratique moyenne obtenez-vous ? Comment analysez-vous ce résultat ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "8db48a4d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:51.748070Z",
+     "start_time": "2025-09-17T13:02:51.740531Z"
+    }
+   },
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(diabetes_X, diabetes_y, test_size=0.2)\n",
+    "\n",
+    "regressor = LinearRegression() \n",
+    "regressor.fit(X_train, y_train) \n",
+    "\n",
+    "y_pred = regressor.predict(X_test)\n",
+    "\n",
+    "print(\"Erreur quadratique moyenne : \", mean_squared_error(y_test, y_pred)) "
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Erreur quadratique moyenne :  3184.3145775880325\n"
+     ]
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e99bcc0",
+   "metadata": {},
+   "source": [
+    "4. Faites une représentation graphique des données réelles par rapport aux données prédites par le modèle : pour chaque attribut, représentez graphiquement les valeurs sur l'abscisse, et l'évolution de la maladie (réelle et prédite) sur les ordonnées. Colorez différement les données en fonction de si elles sont réelles ou prédites."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "dd1f38e6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:52.860068Z",
+     "start_time": "2025-09-17T13:02:51.874554Z"
+    }
+   },
+   "source": [
+    "fig, axs = plt.subplots(4, 3, figsize=(10,10))\n",
+    "print(len(X_test.columns))\n",
+    "number = 0\n",
+    "for column in X_test.columns:\n",
+    "    i = number%4\n",
+    "    j=int(number/4)\n",
+    "    axs[i,j].scatter(X_test[column], y_test, color=\"black\", label=\"Valeur réelle\")\n",
+    "    axs[i,j].scatter(X_test[column], y_pred, color=\"blue\", linewidth=3, label=\"Valeur prédite\")\n",
+    "    axs[i,j].set_title(column)\n",
+    "    axs[i,j].legend()\n",
+    "    number+=1\n",
+    "\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x1000 with 12 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cca8ad81",
+   "metadata": {},
+   "source": [
+    "5. Sur un seul graphe, affichez les valeurs prédites en fonction des valeurs réelles. Tracez également la droite d'équation y=x. Quelles observations faites-vous ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "36cc6ccc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:53.107441Z",
+     "start_time": "2025-09-17T13:02:53.005079Z"
+    }
+   },
+   "source": [
+    "#plt.figure(figsize=(10, 7))\n",
+    "plt.scatter(y_test, y_pred)\n",
+    "plt.plot(y_test, y_test, color='r')# droite d'équation y=x\n",
+    "plt.title('Valeurs réelles vs valeurs prédites')\n",
+    "plt.xlabel('Valeurs réelles')\n",
+    "plt.ylabel('Valeurs prédites')\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd4c4dc8",
+   "metadata": {},
+   "source": [
+    "6. Pour une régression linéaire, les valeurs sur les attributs sont cruciales. Par défaut lorsque vous avez chargé vos données avec Scikit-learn, celles-ci était normalisées. Rechargez-les en ajoutant l'option pour obtenir les données brutes. Affichez les premières lignes du dataset pour constater les différences de plage de valeurs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "07d7fce8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:53.133028Z",
+     "start_time": "2025-09-17T13:02:53.118658Z"
+    }
+   },
+   "source": [
+    "diabetes_X_normal, diabetes_y_normal = load_diabetes(return_X_y=True, as_frame=True, scaled=False)\n",
+    "diabetes_X_normal.head()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "    age  sex   bmi     bp     s1     s2    s3   s4      s5    s6\n",
+       "0  59.0  2.0  32.1  101.0  157.0   93.2  38.0  4.0  4.8598  87.0\n",
+       "1  48.0  1.0  21.6   87.0  183.0  103.2  70.0  3.0  3.8918  69.0\n",
+       "2  72.0  2.0  30.5   93.0  156.0   93.6  41.0  4.0  4.6728  85.0\n",
+       "3  24.0  1.0  25.3   84.0  198.0  131.4  40.0  5.0  4.8903  89.0\n",
+       "4  50.0  1.0  23.0  101.0  192.0  125.4  52.0  4.0  4.2905  80.0"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>bp</th>\n",
+       "      <th>s1</th>\n",
+       "      <th>s2</th>\n",
+       "      <th>s3</th>\n",
+       "      <th>s4</th>\n",
+       "      <th>s5</th>\n",
+       "      <th>s6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>59.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>32.1</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>93.2</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.8598</td>\n",
+       "      <td>87.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>103.2</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.8918</td>\n",
+       "      <td>69.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>72.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>30.5</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>93.6</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.6728</td>\n",
+       "      <td>85.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>25.3</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>198.0</td>\n",
+       "      <td>131.4</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.8903</td>\n",
+       "      <td>89.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>192.0</td>\n",
+       "      <td>125.4</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.2905</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "cell_type": "markdown",
+   "id": "655bd7ea",
+   "metadata": {},
+   "source": [
+    "7. Réentraînez un modèle de régression linéaire sur ces données non normalisées? Que constatez-vous ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3650381a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:02:53.307493Z",
+     "start_time": "2025-09-17T13:02:53.300699Z"
+    }
+   },
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(diabetes_X_normal, diabetes_y_normal, test_size=0.2)\n",
+    "\n",
+    "regressor = LinearRegression() \n",
+    "regressor.fit(X_train, y_train) \n",
+    "\n",
+    "y_pred = regressor.predict(X_test)\n",
+    "\n",
+    "print(\"Erreur quadratique moyenne : \", mean_squared_error(y_test, y_pred)) "
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Erreur quadratique moyenne :  2918.657668476358\n"
+     ]
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f9fcf55",
+   "metadata": {},
+   "source": [
+    "8. Proposez des modèles de régression polynomiale : tester plusieurs degrés de polynôme, entre 1 et 20. Pour chacun, calculez le score obtenu, et affichez-le. Représentez graphiquement l'évolution de l'erreur quadratique moyenne en fonction du degré du polynome.\n",
+    "**Attention :** pour cet partie, réfléchissez aux données que vous voulez utiliser : normalisées ou non ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d8877d23",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:03:28.914985Z",
+     "start_time": "2025-09-17T13:02:53.395390Z"
+    }
+   },
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(diabetes_X, diabetes_y, test_size=0.2)\n",
+    "\n",
+    "scores = []\n",
+    "\n",
+    "for i in range(1, 12):\n",
+    "    # On prépare les deux objets dont on a besoin : le préprocesseur (selon le degré considéré), et la régression linéaire\n",
+    "    polynomial_features = PolynomialFeatures(degree=i)\n",
+    "    linear_regression = LinearRegression()\n",
+    "    \n",
+    "    #On transforme les attributs d'entraînement et de test avec le préprocesseur\n",
+    "    train_X = polynomial_features.fit_transform(X_train) \n",
+    "    test_X = polynomial_features.transform(X_test) \n",
+    "    \n",
+    "    # On entraîne le modèle\n",
+    "    linear_regression.fit(train_X, y_train) \n",
+    "    \n",
+    "    # On évalue le modèle\n",
+    "    pred_y = linear_regression.predict(test_X) \n",
+    "    score = mean_squared_error(y_test, pred_y)\n",
+    "    scores.append(score)"
+   ],
+   "outputs": [],
+   "execution_count": 20
+  },
+  {
+   "cell_type": "code",
+   "id": "05d7bf91",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:03:29.116728Z",
+     "start_time": "2025-09-17T13:03:29.010284Z"
+    }
+   },
+   "source": [
+    "print(scores)\n",
+    "plt.plot(range(1, 12), scores)\n",
+    "plt.xticks(range(1, 12))\n",
+    "plt.xlabel('Degré du polynôme')\n",
+    "plt.ylabel('Erreur quadratique moyenne')\n",
+    "plt.title('Evolution du score en fonction du degré du polynome')\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2983.7819234553604, 4126.326872198257, 2681483.6754619093, 229711.79635034958, 194770.50037077378, 194388.03248086877, 194370.08713620013, 194369.49721707308, 194369.47302106168, 194369.4720771027, 194369.4720382594]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc404583",
+   "metadata": {},
+   "source": [
+    "Constatez-vous une différence avec la régression linéaire simple ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7341463",
+   "metadata": {},
+   "source": [
+    "## Régression avec un arbre de décision\n",
+    "\n",
+    "Il est aussi possible d'utiliser les arbres de décision pour construire un modèle de régression. En utilisant la classe adéquate de scikit-learn, proposez un arbre de régression sur le jeu de données du diabètes. Prenez soin d'analyser la profondeur de l'arbre, afin de choisir celle qui vous parait la plus pertinente. Affichez l'arbre obtenant le meilleur score.\n",
+    "\n",
+    "Comparez les résultats obtenus avec ceux de la régression linéaire et de la régression polynomiale."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "af19bd1f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:03:29.651147Z",
+     "start_time": "2025-09-17T13:03:29.125524Z"
+    }
+   },
+   "source": [
+    "scores = []\n",
+    "\n",
+    "for i in range(1, 20):\n",
+    "    model = DecisionTreeRegressor(max_depth=i)\n",
+    "    score_val = np.mean(cross_val_score(model, diabetes_X, diabetes_y, cv=5))\n",
+    "    scores.append(score_val)\n",
+    "    \n",
+    "plt.plot(range(1, 20), scores)\n",
+    "plt.xticks(range(1, 20))\n",
+    "plt.xlabel('Profondeur de l\\'arbre')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.title('Evolution du score en fonction de la profondeur de l\\'arbre de décision')\n",
+    "plt.show()\n",
+    "\n",
+    "prof_max = np.argmax(scores) + 1\n",
+    "print(\"Score maximum pour une profondeur de\", prof_max)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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0fXVeRsvUaNM5+99FB9oG5gbanCK2QPMUNCFAlSYCMQRemfUkxX6CIDyj8yv2PVzIoB2nZVOQrM4fNhew5QTq+lFHjKtGLFAcEJFdQacCLWQWhadvMGpNmfVeSd+AWM/5nDFjhmqbhAa2uMrA1T1Oemhsmf6EpJWMrraxU6ANF4LHjKARdmbM2TO0K0G7n4ykPwnkx7rJjJ7Tzml2ztplNK8ztGtKn+0xy+2wKcjEIFhDprNZs2bqogPzi2NNZo2f8woHf717Omq5jrB8sKxwPM7ody33n6yOORl9N69DNmRVVq23nfSQBcLJHwEE2u3i4hkdBpCpRXvT9NuTVvOY/nfM00G7L8vAzkyrCzp7mY6Dg4NqE4zzF9q6IcOMLB3Oc3gv/XlAz33PpgI2NGTF1RSqRdHYEFcxlr0/AD21kHXASrY8OJrT0fh7ZhDxZtS7KKOr3JyexMzTQ4Nyy9QpqsPOnTunqii0gOlgns1ZFjNM90HmMzMIlPDA1SQabaMx79y5c1UvHxyoUBY02s4sGNJiuWA6wcHBqgozt1V+5kwkDq5arYPcsMZ2gXlCpwBknTPLbuRmOZm3KVzhmjM2cO3aNbX9ZLUv5QZ+Bx0l0GTB8uCYfts1LyfMm1brDAdqZFlxcDZDx4HMehlmxJwBsIROL1k1uLacd1SPo/rMMkuWk+OVFt9Pz9yzLqN5Sr8+sL3hZIMMjzn7k5msjjmW278tbTvpoYcrqlhR84NzlRn259wyZwQt91dsU5DddmU+tqFDVF7mFcs0p+vfSNMJ+m8dp++hnVOonsUDnRNQk4eqWfRWRmeajPYTdBrK6PyKfQ+xR1YJg7wyTBu2nMBCQC80RMGIttG7xLI6FNBlHnX8lm3d8Dn0XMIOjSugzGDDQNrasprp6tWr9/WGA/RoyclBHRsY0t7ojWUZSX/77bdqWlr14jT3FMR0LKEnyoPMZ3pI82N5WkLghnVjHuoB2U+8xlVm+qtK8zLQYrmgPQqqsXD3gPRQrWPuMZUR9FzEckBvPRzs08ttd+vcssZ2gbQ+2nbMmjXrvr+Zp2Hu9ZiTbRf7UkbbkDmjqdW2i+lgm0KvL8vMC/ZZSzhgo7oCWXZsr1qsM1z1pr/CxXRzk1VHm03L9k+7d+9WgXN2vbrN845ppV9nqJrByTq733jQ72e0PJCBwjyh56gZmh4g82AJvYzxeVQlpV+GeI3gxQz7GrIVuCAxQ5uh9MN/2NK2k1kGxXJZYH4xdE9uXblyJc3xGMdd9EbGBXBGWSZLWH+4EMXg7hm1ycpuXrFMsa6wHVt+B01gjDwdf39/FSijd7rltptdFgtVkun/bk40ZDY8FNY1epailslyeBBczCLYQ0Ipt81kbCrDhrR6+kaZgGEhLK/AEKBhZ0TXWAQKllf+5oZ92ClRZYcxbnA1gqtoDJOAE0/6th6WUA2C1DUae6Kaz9x1GFeP6Rsg44SPK1ucvNCGBFeZGdXHYyOaMGGCOqhhWAa090JUjp0Y4xxZNiR/ENjA0CgTv4sTPpYbrjxxpfYg85kesproiowu5vg8DpYInrEBI2AwVy0g84mu+WjjhgM7qoMwhAKWFapPtVguGLEa1eLoDICGp8jy4WCN7Qjv4wSTWbtIBJQYlgMnNLQzQONftCnBiRe/hZ0NFwbWYo3tAo2ScVBHG0McBLHsEbRiO0XXfwx/gOwJ2hbiggbrD+NRoc1HRu0+0NQA2Sd0tTdX9+B3McwHgnJ0yddC9+7d1bpDl3oc/MxjTmXUhgydk3AwxL6PxtY4NuAguXPnTjXmIDKuuYFhULD9oioU08XvYHmZh7jICWzvKBMavuMAj+MMvo+u/jmZdyxH7C+YdyxzNDPAiQDVtFkNF6TF9zOCbRLDR2D7wXZjvuDFfmJ5kYffRkYd2zGmjW0Cx1dklBBs4FhsHh8TWQoch7Gt40ILNQEYgigv5TPKtpMejrnIJGKfwXEVATO2rbxUN2PfxDAtOGZiuA8EISgrqu+zg2MXjuc4PjZo0EAd73G8QRCDjkJYXhld1Jlhu0W5sa4wNJB5uA1z7ZWRp/PFF1+odYzPYfvDeRnbBT6HjmYZwfEMx12cD7E9IluNJACma75ozQi2fXSIwPSwn6BqFrEHjgE5HTs110wGHtYjfbdsQLfYMmXKZNiV3ezatWumYcOGmfz8/FS3W3TlTv87mXXTXrNmjalWrVrqe1WrVlXDg2Q03MWJEydU11106cffzF3W03cZthyuoVq1aiYXFxdTiRIlTCNGjDDdunUrzWcy6raf1TAc6d25c8c0evRoNdQEhh3p3r27KTQ09IHmM72zZ8+q4S0qVqyouv77+vqa2rVrZ1q3bt19n8UwE+iGjqEdihYtquZv7dq1mi0X85AtH3/8sfq7eToNGzY0vffee6orfHYwTEnv3r3VMsP3sZwff/xx0/r167McliOrdZ1eZt+3xnaBYQUmTpyohibBb2LoDwzxYNn1fMeOHWoZYd1bbhsZrf/ExES1LM2/h31vwoQJaYYoAJSjW7du2Q7bkJkbN26YnnrqKZO3t7fqqo/n5iFk0u+7mJfBgwereUOZSpUqZXr00UdNS5YsyXY66fcFLGvzsQLD/mCYF+zbmQ1DkdGwHtOnTzfNmDFDLRtsQxhSAsNlWMJvYZ/MCIYtQBf/wMBANT8YYgK/aTkEQFbbQU6/j7K+9NJL930/o3ndvHlz6jaCITYw/ERmxwcMP4GhJTB/eGB7xnRCQkLSfA7LCOsKy6hFixamvXv3ZjqsR1bDBhll28kIhhJq2rSpOi9gfbz++uupQ1Jh3nJyTDPvS/henTp11PLCMk2/TMzHn8yGo8D0sD1jmeBYjWP20KFD1XLPzqFDh1QZ8T0so8mTJ5u+/fbbDI93RpvOkSNH1JBLGAoEn8P57e23375vuZl/f//+/aaBAweaypYtq5Z18eLF1TaR/nczWv/4LsqEYweGbsG5EMfXnKwn87ZuuV1kx+G/ghARUS7gyh1X8Oi4YnmnFSIia7CpNmxEREREBREDNiIiIiKDY8BGREREZHBsw0ZERERkcMywERERERkcAzYiIiIigzPMwLlGhZH6Meo0BoTM7e2PiIiISB8mk0kNhIsB2/W+j68WGLBlA8GaNe4JRkRERNYXGhoqpUuXFlvHgC0b5ltZYYVb495gREREpL2oqCiVcMnqlpS2hAFbNszVoAjWGLARERHZFgc7ac5k+5W6RERERHaOARsRERGRwTFgIyIiIjI4BmxEREREBseAjYiIiMjgGLARERERGRwDNiIiIiKDY8BGREREZHAM2IiIiIgMjgEbERERkcExYCMiIiIyOAZsRERERAbHgM3OJSan6F0EIiIiekAM2OzYHwcvS+WJ/0jfOTtk3bFrkpJi0rtIRERElAcM2OzYkn2X1P97L9ySZ37YK51nblHvJSQx60ZERGRLGLDZcVXo3vO31PM+DUpLYTdnORUeI68uDpY20zfKN1vPSkx8kt7FJCIiohxgwGanDl2KlDuJyVLU00Wm960j2ye0lze6VBP/wm5yNfKufPDXcWk+db1MX31CrkfH611cIiIiygIDNjv179kb6v8mFYqJo6ODeLu7yIi2FWXbG+3ko961JcjPS6LuJsnsjWekxccbZOKyw3LhRqzexSYiIqIMMGCzU7vO3VT/Nw3yTfO+m7OTDHiorKwd20bmPtlQ6pUpotq0/bzrorT7ZJO89PN+OXwpUqdSExERUUacM3yX7KD92n8BW8ViGX7GydFButQKkM41S6jgbu7mM7Ip5Lr8dfiqerSoVExeaFNRWlbyEwcHh3yeAyIiIrLEgM0OHb4cKXEJ99qvVSleOMvPIhhrGlRMPY5fjZKvt5yVP4OvyPbTN9SjZqC3PN+monStFSDOTkzIEhER6YFn4ALQfi2nqpf0ls/615PNr7WVoc3Li4eLkxy9EiWjfz0g7WZskh92npc7CclWLDkRERFlhAGbHfr37L3q0Cbp2q/lVOminvJuj5qyY3x7eaVjFfH1cpXQm3fknT+Oqg4KX6w/JbfjEjQuNREREWWGAZs9t18Lyrj9Wk4V9XKVlztWlu1vtJf3H6sppYt6yM3YBPl07Ulp/tEGeW/FUbl8+45GJSciIqLMMGCz0/ZrRTxdpGqJrNuv5ZSHq5MMblZeNr3aVj4fUE9qlPRW05i//by0mbZRxi46KGevx2gyLSIiIrofAza7bb/mm6v2azmBTgeP1Sslf41uKT88/ZA0r1hMklJMsvTAZen02RaZ9McRuRHDQXiJiIi0xoDNTtuvPWh1aHY9S1tX8Zdfnm0qf45sIe2rFVeB24KdF6Tt9E3yv02n5W4iOycQERFphQGbHdGy/VpO1SldRL4b2lh+eaaJGgIkOj5Jpq0KkfafbJJlBy5JSoopX8pBRERkz2wuYJs9e7aUL19e3N3dpUmTJrJ79+5MP7t06VJp1KiRFClSRLy8vKRevXry448/ir2yRvu1nGpeyU9WjGwpnz5eVwJ93OVK5F15ZVGw9Ji9TXacicjXshAREdkbmwrYFi1aJGPHjpVJkybJ/v37pW7dutK5c2cJDw/P8PO+vr4yceJE2blzpxw6dEiGDRumHqtXrxZ7ZM32azmBafZuUFo2vNpWXu9SVQq7OcuRy1HyxLxdMvz7PXLqWnS+l4mIiMgeOJhMJpups0JGrXHjxjJr1iz1OiUlRcqUKSOjRo2S8ePH5+g3GjRoIN26dZPJkydn+Pf4+Hj1MIuKilLTiIyMFG9vbzGywd/tli0nr8uk7jVkWIsKehdHdUDAmG24TynauCGGxH1Mx3SsLMULu+tdPCIismNRUVHi4+NjE+dvu8qwJSQkyL59+6Rjx46p7zk6OqrXyKBlB3Hp+vXrJSQkRFq3bp3p56ZOnapWsPmBYM0W6NF+LTvFCrnJe4/VkjWvtFb3LEVztl92XVQdExDIxSUk6V1EIiIim2AzAVtERIQkJydLiRIl0ryP12FhYZl+D5F1oUKFxNXVVWXWvvzyS3n44Ycz/fyECRPUd8yP0NBQsQV6tl/LTpB/IfnqqUby2/PNpG6ZIqqcGHy33Seb5Le9oZLMjglEREQF++bvhQsXloMHD0pMTIzKsKENXFBQkLRt2zbDz7u5uamHrdG7/VpOPFTBV5a/2FxWHroqH686IZdu3ZHXlxyS77adkze7VldDhRAREZENB2x+fn7i5OQk165dS/M+XgcEBGT6PVSbVqpUST1HL9Hjx4+ras/MAjZbtSsfxl/Tagy37nUDpVPNEvLDjgvy5YZTciIsWrW/a1XZTwVuuAk9ERER2WCVKKo0GzZsqLJkZuh0gNfNmjXL8e/gO5adCuyBEduvZcfN2UmebR0kW15vJ8NbVhAXJwfZeipCun6xVV5fEixhkXf1LiIREZFh2EzABqjOnDdvnixYsEBlykaMGCGxsbFqqA4YPHiwaoNmhkza2rVr5ezZs+rzM2bMUOOwPfnkk2JPjlyOlFiDtl/LThFPV3n70Rqybmwb6VanpKDP8m97L0nbTzbKp2tCJCaeHROIiIhspkoU+vfvL9evX5d33nlHdTRAFeeqVatSOyJcvHhRVYGaIZh78cUX5dKlS+Lh4SHVqlWTn376Sf2OPd6O6qHyxm2/lp1yxbxk9hMNZHjLW/LhX8dl74Vb8sWG0/LL7lB55eHK0r9RGXUvUyIiooLIpsZh04MtjOMy5LvdsvnkdXnn0RrydEv9x197UNgkVx8Nk4/+OSHnb8Sp9yoVLySfD6gnNQN99C4eERHZgCgbOH/nBlMWNs4W26/lpGNCl1olZc0rbeTd7jWkqKeLnA6PkdG/HlDzS0REVNAwYLOT9ms+Hi5SLcC22q9lx9XZUYa2qCDrx7WVYl6ucuZ6rPy484LexSIiIsp3DNjspP2akcdfe1C+Xq4yrlNV9XzmupNyMzZB7yIRERHlKwZsNs48YK69VIdmpn/jMmp8tqi7SfLZ2pN6F4eIiChfMWCzYfbYfi0zTo4OqlMF/LzrgoSERetdJCIionzDgM2G2XP7tYw0q1hMutQMUDeRn7zymOpNSkREVBAwYLNhBaH9Wnq4dZWrk6NsOx0h646H610cIiKifMGAzYYVlPZrlsoW85Thre6NNTflr2MSn5Ssd5GIiIisjgGbjSpI7dfSe6ldJfEv7KYG1V2w47zexSEiIrI6Bmw2qqC1X7NUyM1ZXut8b5iPL9efloiYeL2LREREZFUM2GzUrnMFr/2apb4NSkvtUj4SHZ8kM9aE6F0cIiIiq2LAZqMKYvs1SwhS3+l+b5iPhXtC5eiVSL2LREREZDUM2GxQUnKK7DFn2IJ8paBqXN5XHq1TUjC6x/srOMwHERHZLwZsNujIlajU9mvVA7ylIJvQtbq4OTuqKuJVR8L0Lg4REZFVMGCz4erQhwpo+zVLpYp4yPOtg9TzKX8fl7uJHOaDiIjsDwM2G1TQ26+l90LbihLg7S6Xbt2Rb7ed07s4REREmmPAZsPt15oW4PZrljxdneWNR+4N8zF742kJj7qrd5GIiIg0xYDNxrD9WsYeq1tK6pUpInEJyTJtNYf5ICIi+8KAzcaw/VrGsCwm/TfMx5J9l+TQpdt6F4mIiEgzDNhsDNuvZa5+2aLSq34p9ZzDfBARkT1hwGZD2H4te290qSYeLk6y98ItWXHoqt7FISIi0gQDNhvC9mvZC/BxlxFtK6rnH/19XO4kcJgPIiKyfQzYbAjbr+XMc62D1PhsVyLvytdbzupdHCIiogfGgM2GsP1azri7OMn4R6qp53M3n5GrkXf0LhIREdEDYcBmI9h+LXdwj9HG5YvKncRk+fifE3oXh4iI6IEwYLMRbL+WOw4ODvLOozXFwUFk+cErsv/iLb2LRERElGcM2GzErv+qQxuXZ/u1nKpd2kf6Niitnr+34pikpHCYDyIisk0M2Gyu/RqrQ3PjtS5VxcvVSYJDb8vyg5f1Lg4REVGeMGCzlfZr5+9V6bHDQe4UL+wuL7WvpJ5/vOqExMYn6V0kIiKiXGPAZgOOXomSmPgk8XZ3luol2X4tt55uUUHK+HrItah41WuUiIjI1jBgs6nx14qJE9uv5WmYj4ldq6vnGJft0q04vYtERESUKwzYbADbrz24zjUD1PKLT0qRqRzmg4iIbAwDNoNj+zVth/lAgvKvQ1dl939j2hEREdkCBmwGx/Zr2qkR6C39G5dVz99feZTDfBARkc1gwGZwbL+mrVc7VZHC7s5y5HKULNl3Se/iEBER5QgDNoNj+zVtFSvkJi93qKyeT1t9QqLvJupdJCIiomwxYDMwtl+zjsHNykuQn5dExCTIrI2n9S4OERFRthiwGRjbr1mHq7OjTOx2b5iP+dvOy4UbsXoXiYiIKEsM2AyM7desp3214tKqsp8kJKfIlL+O610cIiKiLDFgMzC2X7P2MB81VCC85tg12XE6Qu8iERERZYoBm0Gx/Zr1VS5RWJ5sYh7m45ha5kREREbEgM2gjl29134NQ1Cw/Zr1jOlYRXw8XOREWLQs3BOqd3GIiIgyxIDN4NWhTSr4sv2aFRX1cpVXOt4b5uPTtScl8g6H+SAiIuNhwGZQ/569d+skVoda36Cm5aRS8UJyMzZBZm04pXdxiIiI7sOAzajt1/671yUDNutzcfr/YT5+2HlBrkXd1btIREREaTBgM2j7tWi2X8tXbav4S6NyRSU+KUX+x8F0iYjIYBiwGRDbr+kzzMfYh6uo57/uDpXLt+/oXSQiIqJUDNgMiO3X9NG8kp8a8w6D6c5mlo2IiAyEAZvBsP2avsY+XFX9/9ueUAm9Gad3cYiIiBQGbAbD9mv6eqiCr7plVVKKSb5Yzx6jRERkDAzYDIbt1/Rnbsu29MBlORfBG8MTEZH+GLAZDNuv6a9+2aLq5vDJzLIREZFBMGAzELZfM45XOt7Lsi0/eFlOh0frXRwiIirgGLAZCNuvGUft0j7SqUYJMZlEZq5jlo2IiPRlcwHb7NmzpXz58uLu7i5NmjSR3bt3Z/rZefPmSatWraRo0aLq0bFjxyw/rze2XzOWV/5ry7by0FU5ERald3GIiKgAs6mAbdGiRTJ27FiZNGmS7N+/X+rWrSudO3eW8PDwDD+/adMmGThwoGzcuFF27twpZcqUkU6dOsnly5fFyO3XmlRgdagRIMvZrXZJ9fyztSf1Lg4RERVgDiYTKn1sAzJqjRs3llmzZqnXKSkpKggbNWqUjB8/PtvvJycnq0wbvj948OAcTTMqKkp8fHwkMjJSvL2tV02JBu713lujqkRXjGypquRIf6euRUunmVtU1ejKUS2lVimuFyIiWxCVT+fv/GIzGbaEhATZt2+fqtY0c3R0VK+RPcuJuLg4SUxMFF9f30w/Ex8fr1ay5SM/HLvyX/s1N2epEWj7G5a9qFyisDxWN1A9Z5aNiIj0YjMBW0REhMqQlShRIs37eB0WFpaj33jjjTckMDAwTdCX3tSpU1VEbn4gg5ef7dcwcCvbrxnL6A6VBatk/YlwOXDxlt7FISKiAshmArYH9dFHH8nChQtl2bJlqsNCZiZMmKDSp+ZHaGhovgZsHM7DeIL8C0nvBqXV88/YY5SIiHRgMwGbn5+fODk5ybVr19K8j9cBAQFZfveTTz5RAduaNWukTp06WX7Wzc1N1XVbPqwN7dd2c/w1QxvdvrI4OzrIlpPXZe/5e+uKiIgov9hMwObq6ioNGzaU9evXp76HTgd43axZs0y/N23aNJk8ebKsWrVKGjVqJEbE9mvGV7aYp/RrdC/L9inbshERUT6zmYANMKQHxlZbsGCBHD9+XEaMGCGxsbEybNgw9Xf0/ESVptnHH38sb7/9tnz33Xdq7Da0dcMjJiZGjITt12zDyPaVxcXJQXacuSE7z9xbZ0RERPnBpgK2/v37q+rNd955R+rVqycHDx5UmTNzR4SLFy/K1atXUz8/Z84c1bu0b9++UrJkydQHfsNI2H7NNpQq4iEDGpdVzz9dGyI2NCIOERHZOJsah80ex3Hh+Gu2JSzyrrSevlESklLkx+EPSavK/noXiYiIMsBx2EhTbL9mWwJ83OXJJuXU8xlrTjLLRkRE+YIBm87Yfs32vNA2SNxdHOVg6G3ZGJLxbdGIiIi0xIBNZ2y/ZnuKF3aXIc3Kp/YYZZaNiIisjQGbjjj+mu16vk1F8XJ1kiOXo2TNsbRjAxIREWmNAZuO2H7Ndvl6ucrQFuVT7zGaksIsGxERWQ8DNgNUhzZm+zWb9GyrIBVsnwiLln+O5Ox+tkRERHnBgM0Q7dd89S4K5UERT1cZ3qqCev7ZupOqipuIiMgaGLDphO3X7MPTLSuIj4eLnA6PkRXBV/QuDhER2SkGbDo5ftWi/VpJtl+zVd7uLvJc6yD1/PP1pyQpOUXvIhERkR1iwGaA9mvOTlwNtmxI8/KqE8K5iFhZduCy3sUhIiI7xEhBJ2y/Zj8KuTnL8/9l2b7YcEoSmWUjIiKNMWDTyfS+dWXukw3lkVol9S4KaWBws/LiV8hNQm/ekSX7LuldHCIisjMM2HRS1MtVutQKkDK+nnoXhTTg4eokL7atqJ5/uf6UxCcl610kIiKyIwzYiDTyRJOyUsLbTa5E3pVFe0L1Lg4REdkRBmxEGnF3cZKR7Sqp57M3npa7icyyERGRNhiwEWno8cZlpFQRD7kWFS8/77qod3GIiMhOMGAj0pCbs5OMbH8vyzZn02mJS0jSu0hERGQHGLARaaxvw9JSxtdDImIS5MedF/QuDhER2QEGbEQac3FylNHtK6vnczefkZh4ZtmIiOjBMGAjsoJe9UtJkJ+X3IpLlAU7zutdHCIisnEM2IisALcbe7njvSzb11vOStTdRL2LRERENowBG5GVPFonUCoXLySRdxLl263n9C4OERHZMAZsRFbi5OggYzpWUc+/23ZObscl6F0kIiKyUQzYiKzokVoBUi2gsETHJ8m8rWf1Lg4REdkoBmxEVuTo6CCvPHwvyzZ/+3m5EROvd5GIiMgGMWAjsrJONUpI7VI+EpeQrDogEBER5RYDNiIrc3BwkLH/ZdkW7Dwv4dF39S4SERHZGAZsRPmgbVV/qVemiNxNTJFZG07rXRwiIrIxDNiI8inL9lrnqur5j/9ekH0XbuldJCIisiEM2IjySYtKftK7QSkxmUReXxIsdxOT9S4SERHZCAZsRPnonUdriH9hNzlzPVZmrjuld3GIiMhGMGAjykdFPF1lSs9a6vnXW85IcOhtvYtEREQ2gAEbUT7rVDNAetQNlBSTyKuLgyU+iVWjRESUNQZsRDp4t0dN8SvkKqfCY+TL9ew1SkREWWPARqQDXy9XmfzYvarROZvPyJHLkXoXiYiIDIwBG5FOHqldUrrVLinJKSZVNZqQlKJ3kYiIyKAYsBHp6L3Haqps24mwaPnfJlaNEhFRxhiwEenIr5Cbas8GuAPC8atReheJiIgMiAEbkc661ympbhCflGKS15YES2Iyq0aJiCgtBmxEBrht1Qe9aomPh4scuRwlX20+o3eRiIjIYBiwERlA8cLu8m6PGur5F+tPy8lr0XoXiYiIDIQBG5FB9KxXSjpUKy4JySny2uJgSWLVKBER/YcBG5GBqkan9Kothd2dJfhSpHyz7ZzeRSIiIoNgwEZkIAE+7vL2o/eqRj9de1JOh8foXSQiIjIABmxEBtOvYWlpXcVfDaT7+pJgNbAuEREVbAzYiAxYNfpR79pSyM1Z9l+8LfO3s2qUiKigY8BGZECBRTxkYrfq6vn01SFyLiJW7yIREZGOGLARGdSAxmWkZSU/iU9KkTeWHJIUVo0SERVYDNiIDFw1OrV3bfF0dZLd52/KDzvP610kIiKylYCtfPny8v7778vFixetUyIiSlXG11MmPFJNPf94VYhcvBGnd5GIiMgWArYxY8bI0qVLJSgoSB5++GFZuHChxMfHW6d0RCSDmpSTpkG+cicxWV7/PZhVo0REBVCeAraDBw/K7t27pXr16jJq1CgpWbKkjBw5Uvbv32+dUhIVYI6ODvJxnzri4eIk/569KT/vZnabiKigyXMbtgYNGsgXX3whV65ckUmTJsk333wjjRs3lnr16sl3330nJhOzAERaKVfMS17vUlU9/+jv43LpFqtGiYgKkjwHbImJifLbb79Jjx49ZNy4cdKoUSMVtPXp00fefPNNGTRokLYlJSrghjQrL43LF5XYhGSZsPQwL4qIiAqQXAdsqPa0rAatWbOmHDlyRLZt2ybDhg2Tt99+W9atWyfLli2zSoFnz56tOj64u7tLkyZNVNVsZo4ePaoCSHwePe5mzpxplTIR5VfV6LS+dcXN2VG2noqQRXtC9S4SEREZNWBDteepU6dkzpw5cvnyZfnkk0+kWrV7vdjMKlSoIAMGDBCtLVq0SMaOHauqYBE41q1bVzp37izh4eEZfj4uLk51jvjoo48kICBA8/IQ5bcKfl7yaqd7VaNT/jouVyPv6F0kIiLKBw6mXNarXLhwQcqVKyd6QEYNAeOsWbPU65SUFClTpozK+I0fPz7L7yLLhg4TeORGVFSU+Pj4SGRkpHh7ez9Q+Ym0gHuL9p27Qw5cvC1tq/rL/KGNVQaZiIjs9/yd6wwbslm7du267328t3fvXrGWhIQE2bdvn3Ts2DH1PUdHR/V6586dmk0HQ5RgJVs+iIzEydFBpvetI67OjrIp5Los2XdJ7yIREZHRAraXXnpJQkPvbzuD6lH8zVoiIiIkOTlZSpQokeZ9vA4LC9NsOlOnTlURufmBDB6R0VQqXljGdKysnk9eeUyuRd3Vu0hERGSkgO3YsWNqSI/06tevr/5m6yZMmKDSp+ZHRsEpkRE81ypI6pT2kai7STJxGXuNEhHZs1wHbG5ubnLt2rX73r969ao4OzuLtfj5+YmTk9N908ZrLTsUYP5Q1235IDIiZydHmd63rrg4Oci64+Hyx8EreheJiIiMErB16tQpNQtldvv2bTX2Gm5VZS2urq7SsGFDWb9+fep76HSA182aNbPadImMrGpAYRnd/l7V6Lsrjkp4NKtGiYjsUa4DNgzjgWpC9BRt166demAYD7QjmzFjhlgThvSYN2+eLFiwQI4fPy4jRoyQ2NhYNf4bDB48WAWTlh0VcBstPPAc7ezw/PTp01YtJ1F+eqFtRakZ6C234xLl7eVHWDVKRGSHcj2sByBI+vnnnyU4OFg8PDykTp06MnDgQHFxcRFrw5Ae06dPVwEiboOF22NhuA9o27atGr7j+++/V6/Pnz+vgsn02rRpI5s2bSqQ3YLJPh27EiU9Zm2TpBSTfDmwvnSvG6h3kYiIdBVlZ+fvPAVsBYm9rXCyX5+uPSlfrD8lvl6usvaV1lKskJveRSIi0k2UnZ2/89xLAD1CL168qKoaLeHeokSU/0a2qyRrjobJibBomfL3cfn08Xp6F4mIiPQK2M6ePSu9evWSw4cPq9HVzQk680jrGCuNiPIfBtKd2ru29PrfDlkRfEXe6lZDZduIiKgAdjp4+eWXVbsw3PHA09NT3WB9y5Yt0qhRoxy3CyMi66hftqjULuUjickmWXbgst7FISIivQI23Abq/fffV+Oi4dZQeLRs2VLdIWD06NFalYuI8ujxxvfuzvHbnlD2GCUiKqgBG6o8CxcurJ4jaLty5d5gnRjmIyQkRPsSElGu9KgbKG7OjhJyLVoOXfr/8RKJiKgABWy1atVSw3kAhtOYNm2abN++XWXdgoKCrFFGIsoFHw8XeaTWvbt/LNrLW6sRERXIgO2tt95SdxgABGnnzp2TVq1ayd9//63GRCMi41SLrjh4Re4ksCMQEVGB6yXauXPn1OeVKlWSEydOyM2bN6Vo0aKpPUWJSF9NKxSTsr6ecvFmnPxz5Kr0blBa7yIREVF+ZdgSExPVDd6PHDmS5n1fX18Ga0QG4ujoIP0a3gvSFu1htSgRUYEK2HDrqbJly3KsNSIb0LdRaXF0ENl17qacj4jVuzhERJSfbdgmTpwob775pqoGJSLjKunjIa2r+Kvni/cxy0ZEVKDasOHm66dPn5bAwEA1lIeXl1eav+/fv1/L8hHRA3i8URnZFHJdluy7JK90rCLOTrm+RiMiIlsM2Hr27GmdkhCR5jpWL6FuT3UtKl62nLou7auV0LtIRESUHwHbpEmT8jIdItLp/qK96peSb7edU50PGLAREdkm1o8QFYBqUVh/PFwiYuL1Lg4REeVHwIZ7hzo5OWX6ICJjqRpQWOqWKSJJKSZZtp83hCciKhBVosuWLbtvbLYDBw7IggUL5L333tOybESkkf6Nykhw6G11q6pnWlXguIlERDbGwWQymbT4oV9++UUWLVokf/zxh9iTqKgo8fHxkcjISPH29ta7OER5EnU3UR6ask7uJqbI0hebS4OyRfUuEhGRVUXZ2flbszZsTZs2lfXr12v1c0SkIW93F+lau6R6/hvvfEBEVDADtjt37qgbv5cqVUqLnyMiK1WLworgKxIbn6R3cYiIyJpt2NLf5B01qtHR0eLp6Sk//fRTbn+OiPLJQxV8pXwxTzl/I07+PnxV+v0XwBERkR0GbJ999lmagA29Rv39/aVJkyYqmCMiY8J+iyBt+uoQ+W1vKAM2IiJ7DtiGDh1qnZIQkdX1bVhaZqwJkT3nb8mZ6zFS0b+Q3kUiIiJrtGGbP3++LF68+L738R6G9iAi4yrh7S5tqxZXzxfvvaR3cYiIyFoB29SpU8XPz+++94sXLy4ffvhhbn+OiHS688Hv+y9JUnKK3sUhIiJrBGwXL16UChUq3Pd+uXLl1N+IyNg6VC8ufoVc5Xp0vGwMua53cYiIyBoBGzJphw4duu/94OBgKVasWG5/jojymYvTvRvCAzofEBGRHQZsAwcOlNGjR8vGjRslOTlZPTZs2CAvv/yyDBgwwDqlJCJN9W98r1p0w4lwCY++q3dxiIhI64Bt8uTJagiPDh06iIeHh3p06tRJ2rdvzzZsRDaiUvHC0qBsEUlOMclS3hCeiMh+7yV66tQpOXjwoArYateurdqw2SN7uxcZkdnC3Rdl/NLDEuTnJevHteEN4YnIrkTZ2fk71+OwmVWuXFk9iMg2PVo3UN5feUzORsTKvgu3pFF5X72LREREWlWJ9unTRz7++OP73p82bZr069cvtz9HRDop5OYs3f67Ifwi3hCeiMi+ArYtW7ZI165d73v/kUceUX8jItvrfPDX4asSwxvCExHZT8AWExMjrq6u973v4uKi6ouJyHY0LFdUgvy9JC4hWf46dEXv4hARkVYBGzoYLFq06L73Fy5cKDVq1MjtzxGRjtDRwHznA1aLEhHZUaeDt99+W3r37i1nzpxRQ3nA+vXr5ZdffpElS5ZYo4xEZEW9G5SS6atDZP/F23I6PFoN+UFERDaeYevevbssX75cTp8+LS+++KKMGzdOLl++rAbPrVSpknVKSURWU7ywu7T774bwv/GG8ERE9hGwQbdu3WT79u0SGxsrZ8+elccff1xeffVVqVu3rvYlJKJ863ywdP8lSeQN4YmI7CNgA/QIHTJkiAQGBsqMGTNU9ei///6rbemIKF+0q+ov/oXdJCImQdYfD9e7OERE9CABW1hYmHz00UdqwFyMuYaRg+Pj41UVKd5v3Lhxbn6OiAzC2clRtWWDxbwhPBGR7QZsaLtWtWpVOXTokMycOVOuXLkiX375pXVLR0T5xtxbdGNIuFyL4g3hiYhsMmD7559/ZPjw4fLee++pNmxOTk7WLRkR5auK/oWkcfmikmISWbKPnQ+IiGwyYNu2bZtER0dLw4YNpUmTJjJr1iyJiIiwbumIKF/1+y/LhmpRk8mkd3GIiCi3AVvTpk1l3rx5cvXqVXn++efVQLnocJCSkiJr165VwRwR2TbcW9TL1UnO34iT3edu6l0cIiLKay9RLy8vefrpp1XG7fDhw2ocNnQ4KF68uPTo0SO3P0dEBuLl5izd6waq54vY+YCIyPaH9QB0Qpg2bZpcunRJfv31V+1KRUS6V4v+ffiqRN1N1Ls4RET0oAGbGTog9OzZU/78808tfo6IdNSgbBGpVLyQ3E1MkZXBV/UuDhERaRWwEZF93RC+v/mG8KwWJSIyBAZsRHSfXg1KibOjgwSH3paQMHYoIiLSGwM2IrqPXyE36VDdfEN4ZtmIiPTGgI2Isrwh/LIDlyUhiTeEJyLSEwM2IspQ68r+Urywm9yMTZB1x6/pXRwiogKNARsRZXpD+L4NS6vnrBYlItIXAzYiyvaG8FtOXperkXf0Lg4RUYHFgI2IMlXez0sequB774bwe3lDeCIivdhcwDZ79mwpX768uLu7q5vQ7969O8vPL168WKpVq6Y+X7t2bfn777/zraxE9sA8JtvifZckBZEbERHlO5sK2BYtWiRjx46VSZMmyf79+6Vu3brSuXNnCQ8Pz/DzO3bskIEDB8rw4cPlwIED6m4MeBw5ciTfy05kq7rWLimF3Jzl4s04+ffcDb2LQ0RUIDmYTCabuWRGRq1x48Yya9Ys9TolJUXKlCkjo0aNkvHjx9/3+f79+0tsbKysXLky9b2mTZtKvXr1ZO7cuRlOIz4+Xj3MoqKi1DQiIyPF29vbKvNFZHQTlh6WX3dflJ71AmXmgPp6F4eIKFs4f/v4+NjN+dtmMmwJCQmyb98+6dixY+p7jo6O6vXOnTsz/A7et/w8ICOX2edh6tSpagWbHwjWiAo685hs/xwJk8g7vCE8EVF+s5mALSIiQpKTk6VEiRJp3sfrsLCwDL+D93PzeZgwYYKKxs2P0FAOZ0BUt7SPVC1RWOKTUuTP4Ct6F4eIqMCxmYAtv7i5uanUqeWDqKDDDeH7NfpvTLY9vIghIspvNhOw+fn5iZOTk1y7lnbEdbwOCAjI8Dt4PzefJ6LM9W5QWlycHOTw5Ug5diVK7+IQERUoNhOwubq6SsOGDWX9+vWp76HTAV43a9Ysw+/gfcvPw9q1azP9PBFlztfLVR6uca+JAe98QESUv2wmYAMM6TFv3jxZsGCBHD9+XEaMGKF6gQ4bNkz9ffDgwaoNmtnLL78sq1atkhkzZsiJEyfk3Xfflb1798rIkSN1nAsi29XvvzHZlh+8LPFJyXoXh4iowHAWG4JhOq5fvy7vvPOO6jiA4TkQkJk7Fly8eFH1HDVr3ry5/PLLL/LWW2/Jm2++KZUrV5bly5dLrVq1dJwLItu+IXxJH3e5GnlX1hy9Jt3rBupdJCKiAsGmxmHTg72N40L0oGasCZEvN5yWh8r7yqLnm6oOCURERhNlZ+dvm6oSJSJjjMnm5uwou8/flIXsMUpElC8YsBFRrpQu6imvda6qnn+w8piE3ozTu0hERHaPARsR5dqwFhWkcfmiEpuQLK8vOcSbwhMRWRkDNiLKNSdHB/mkX13xcHGSnWdvyI//XtC7SEREdo0BGxHlSbliXjKhazX1/KN/Tsj5iFi9i0REZLcYsBFRnj3ZpJw0r1hM7iQmy6uLgyWZVaNERFbBgI2I8szR0UGm9a0jhdycZe+FW/LdtnN6F4mIyC4xYCOiB+41+la36ur59DUhcjo8Wu8iERHZHQZsRKTJ2Gxtq/pLQlKKjPstWJKSU/QuEhGRXWHARkQPDHc7+Kh3HfF2d5bgS5Hy1ZazeheJiMiuMGAjIk0E+LjLuz1qqucz152U41ej9C4SEZHdYMBGRJrpVb+UPFyjhCQmm2Tsb8GqipSIiB4cAzYi0rRq9MNetaWop4vKsM3acErvIhER2QUGbESkKf/CbjK5Zy31fPamM3Lo0m29i0REZPMYsBGR5h6tEyjd6pRUA+mi1+jdxGS9i0REZNMYsBGRVUx+rJb4FXKVU+Ex8tm6k3oXh4jIpjFgIyKr8PVyVe3ZYN6Ws7Lvwi29i0REZLMYsBGR1XSqGSC965cS3GIU9xq9k8CqUSKivGDARkRWNal7TSnh7SbnImJl2uoTeheHiMgmMWAjIqvy8XSRj/vUUc/nbz8v/569oXeRiIhsDgM2IrK6tlWLy4DGZdTz15YES2x8kt5FIiKyKQzYiChfTOxWXUoV8ZDQm3fkw7+P610cIiKbwoCNiPJFYXcXmd73XtXoz7suytZT1/UuEhGRzWDARkT5pnklPxncrJx6/vqSQxJ1N1HvIhER2QQGbESUr8Y/Uk3KFfOUq5F3ZfKKY3oXh4jIJjBgI6J85enqLJ/0qysODiKL912S9cev6V0kIiLDY8BGRPmucXlfGd6igno+fulhuR2XoHeRiIgMjQEbEeni1c5VpaK/l1yPjpdJfx7VuzhERIbGgI2IdOHu4qSqRh0dRP44eEVWHbmqd5GIiAyLARsR6aZ+2aLyQpuK6vnEZUfkRky83kUiIjIkBmxEpKuXO1aWagGF5UZsgry1/IiYTCa9i0REZDgM2IhIV27O96pGnR0d5J8jYfJn8BW9i0REZDgM2IhId7VK+cjI9pXU83f+OCrhUXf1LhIRkaEwYCMiQ3ipXSWpVcpbIu8kyoSlh1k1SkRkgQEbERmCi5OjzOhXT1ydHGX9iXBZsu+S3kUiIjIMBmxEZBhVAwrLmIcrq+fvrzgmV27f0btIRESGwICNiAzluVZBUq9MEYmOT5LRvx6QyDjeIJ6IiAEbERmKM6pGH68rXq5OsvfCLek9Z7tcuBGrd7GIiHTFgI2IDKeifyH57YVmUtLHXc5cj5Wes7fLnvM39S4WEZFuGLARkSHVDPSRP15qIbVL+cituEQZNG+XLDvAjghEVDAxYCMiwyru7S6Lnm8qXWoGSEJyiryyKFhmrAmRlBQO+UFEBQsDNiIyNE9XZ/nfoAap9xz9csNpGbXwgNxNTNa7aERE+YYBGxEZnqOjg4x/pJpM61tH3cLqr0NXZcDX/8r1aN4snogKBgZsRGQzHm9URn4c3kR8PFzkYOht1RnhRFiU3sUiIrI6BmxEZFOaVSwmy15sLhX8vOTy7TvSd85O2RgSrnexiIisigEbEdmcIP9CKmhrUsFXYuKTZPj3e+T77ef0LhYRkdUwYCMim1TE01VVj/ZrWFrQafTdFcfknT+OSFJyit5FIyLSHAM2IrJZrs6OqiPC612qqtc/7Lwgwxfslei7vJ0VEdkXBmxEZNMcHBzkxbaVZM6gBuLu4iibT16XPnN2SOjNOL2LRkSkGQZsRGQXHqldUn57vpn4F3aTk9dipNf/tsv+i7f0LhYRkSYYsBGR3ahTuoi6nVX1kt4SEZOgxmpbEXxF72IRET0wBmxEZFcCi3jIkheaSYdqxSUhKUVG/XpAvlh/Skwm3s6KiGwXAzYisjtebs7y9eBGMrxlBfX607UnZexvwRKfxNtZEZFtYsBGRHbJydFB3n60hkzpVUs9X3bgsgyat0tuxPB2VkRke2wmYLt586YMGjRIvL29pUiRIjJ8+HCJiYnJ8jtff/21tG3bVn0HPclu376db+UlImMY1KScfD+ssRR2d5a9F25Jr//tkNPh0XoXi4jIPgM2BGtHjx6VtWvXysqVK2XLli3y3HPPZfmduLg46dKli7z55pv5Vk4iMp5Wlf1l6YjmUsbXQy7ejFNB27ZTEXoXi4goxxxMNtAS9/jx41KjRg3Zs2ePNGrUSL23atUq6dq1q1y6dEkCAwOz/P6mTZukXbt2cuvWLZWdy42oqCjx8fGRyMhIlakjItuF6tDnf9ynMm2oJn3/sZoqA0dE9ifKzs7fNpFh27lzpwq0zMEadOzYURwdHWXXrl2aTis+Pl6tZMsHEdmHYoXc5KdnmkjPeoGSnGKSicuOyPTVJ/QuFhGRfQRsYWFhUrx48TTvOTs7i6+vr/qblqZOnaoicvOjTJkymv4+EenL3cVJPutfT8Y+XEW9nr3xjKw9dk3vYhERGTdgGz9+vOoMkNXjxIn8vfqdMGGCSp+aH6Ghofk6fSKyPhxbRneoLM+1DlKvx/9+SK5Hs/coERmXs54THzdunAwdOjTLzwQFBUlAQICEh4eneT8pKUn1HMXftOTm5qYeRGT/xnWqIltOXpcTYdEyYekhmTe4kQrmiIiMRteAzd/fXz2y06xZMzUkx759+6Rhw4bqvQ0bNkhKSoo0adIkH0pKRPbIzfle9ehjs7bLuuPhsmhPqAx4qKzexSIiss02bNWrV1fDczz77LOye/du2b59u4wcOVIGDBiQ2kP08uXLUq1aNfV3M7RvO3jwoJw+fVq9Pnz4sHqNzBwREeC+o8i0wfsrj8mFG7F6F4mIyDYDNvj5559VQNahQwc1nEfLli3VwLhmiYmJEhISosZeM5s7d67Ur19fBXrQunVr9frPP//UZR6IyJieaRUkTSr4SlxCsryy6KAkJafoXSSyYRdvxHFwZiqY47Dpyd7GcSGijF26FSddZm6VmPgkea1zVXmpXSW9i0Q2IvJOouw8EyFbTkWoAZkxOLOjg8ii55tJ4/K+ehevwIqys/O3rm3YiIiMonRRT3mvR00ZtzhYPlt7UtpU8ZdapXz0LhYZUGJyihwMvS1bT16XracjJDj0tqSkS33gNbajX55tqlcxyc4wYCMi+k/vBqVk3fFr8s+RMBmz6KCsHNVSjdtGBRsqos5FxMq20xGy5WSE/Hv2hsrEWqro76Vugdaqsp+U9fWUrl9slR1nbsiuszekSVAx3cpO9oMBGxHRfzCkx5RetdWtq06Hx8hH/5yQd3vU1LtYpIPbcQmy/fQN2Xrqumw9FSGXb99J8/eini7SopKftK7sLy0r+0lgEY80f3+8URn5eddF+Xz9KfmFARtpgAEbEZEFXy9Xmda3jgybv0e+33FeOlQvrjInZH1xCUkqk+Xj4aIehdyc821cvISkFNl/8ZYK0NAO7dDlSLFs4e3i5CCNyvlKqyp+0qqSv9QM9BZHNFTLxIvtKslve0OZZSPNMGAjIkqnXdXi8mTTsvLTvxfl1cXBsnpMayni6ap3seza9tMRMurXA3IzNiH1PWdHh3vBm+e9AK4IHp6uqQFdEU/L/13TvOfi5JhtNeeZ6zGqihNVnajmRC9hS1VKFJKWlfxVkIZexJ6uOT9lliriwSwbaYoBGxFRBt7sWl12nL4hZyNi5e0/jsqXA+vrXSS7lJJikjmbz8iMNSGqoT6yagnJKSrjlZRikhuxCeqRW/idzAK7GzHxqpozLOpumu/4FXJV1ZzIqLas5CcBPu4PNG+WWbbd527KQxXYY5TyjsN6FLBuwUSUc+gJ2GfODklOMcnnA+rJY/VK6V0kuxsOY9xvB9VdJqBfw9IyuWct1dHjbmKy3I5LlNt3EiRS/Z+o/sd38N7t/56r1xafi7qbtjNAVlydHeWh8r6qowDaoVUPyLqaMy/eXHZYftl1UVpUKiY/P8Meo/kpys7O38ywERFlol6ZIjKqfSWZue6UvL38iBpTK33jcsqbY1eiZMTP++TCjTgVOL3fo2aa24IhaAvwccp1lgvBdZQ5kFPBXEKawA7/o7q0ecViKuNl7V7AL7atKIv3hqoODMyy0YNghq2ARehElPsxt/rO3anG2kKW5Menm2iehSloluy7JBOXHZb4pBTV1mvukw2ldmn7HfOOWTZ9RNnZ+dtmbk1FRKQHZGM+e7yuuLs4qizJ/B3n9S6SzYpPSlbBCzpyIFjD4MQY686egzVzlg29TM1ZNqK8YMBGRJSNIP9CMrFbDfX841Un5OQ13icytzCO2eNzd6pME0bqGNOxsswf2liKerkWiLto9GtURj3/fP1JvYtDNooBGxFRDjzZpKy0reqvei+OWXhQ/U85s+XkdXn0i60SfClS9dREoDamY5UCVbXMLBs9KAZsREQ5gAFcp/Wpo0a4P3Y1SmauY6YkJ0N2fLH+lAyZv1tuxSVK7VI+smJkS2lbtbgUNMiy9W3ILBvlHQM2IqIcKu7tLlN711bP524+I3vOM1OSGQyx8cwPe+XTtSfVHQMGPlRGFr/QTMr4ekpB9VK7imowYGTZuO1QbjFgIyLKhS61SkqfBqXVIK9jfzt4303ASeTI5Uh5dNZW2XAiXNycHdWtvqb2rmP1ITRsqi3bulN6F4dsDAM2IqJcmtSjhhqOIvTmHZm84pjexTEUjOzfe84OtWzK+HrI7yOaq1s0UdosG26HxSwb5QYDNiKiXPJ2d5EZj9dVvR0X7Q2VNUfDpKDDnQkmLD0kry85pDpktK9WXFaObCW1Stn3kB25xSwb5RUDNiKiPGgaVEyeaxWknk9YeliuR8dLQRV6M076zd0pv+4OVUHsuIeryDeDG6mbttP9mGWjvGDARkSUR2M7VZFqAYXVzcnH/35ICuKNYzaFhEv3Wdvk8OVI1YN2wbCHZFSHygVqyI68ZdlKq+fMslFOMWAjIsojN2cnmTmgnrg6Ocr6E+GycE+oFKQhOzC0ybDv96h7dNYt7SMrR7eS1lX89S6aTXixbaXULNteZtkoBxiwERE9gGoB3vJa56rq+eSVx+R8RKzYu1uxCSpQm7nulBqyY1CTsvLbC81URwzKGQxvkpplW88sG2WPARsR0QMa3rKCNA3ylbiEZDXUR1Ky/d4F4fClSHn0y22y+eR1NWTHjH51ZUqv2irbSHnLsm09xSwbZY8BGxHRA0J7rU/61ZXCbs6y/+JtNaiuPVq4+6L0mbtD3Re0XDFPWfZiC+nT8F6WiHKPWTbKDQZsREQaNSR/77Ga6jmqCpGJsgcYomP98Wvy/I97ZfzSw+p1x+ol5M+RLaVGoLfexbN5zLJRTjFgIyLSSK/6paRr7QBJSjHJmEUH5E5CstgiVOluPXVdXl8SLI2nrJPhC/bK6qPXBB0/0V7v66caio8Hh+zQKsvW978sJbNslBXnLP9KRES5ukH8lJ61Ze/5W3Lmeqx8vOqEvNvjXtbNFnp9YkywFYeuyD+Hw9RQJWbFC7tJ19olVWDBgXC191K7SrJk3yWVZdt34aY0LOerd5HIgBiwERFpqKiXq0zvV1eGfLdbvt9xXo34b9ShLjBu3IHQ27Iy+Kr8dfiKXIv6/8F/fb1c5ZFaAfJonUB5qIKvOHFcNatn2TAsDKrTfxzeRO8ikQExYCMi0libKv4yuFk5+WHnBXltSbCsHtNaini6ilGCtKNXolQm7a9DV+XSrTupfyvs7ixdagZI97qB0rxiMXF2YquZgpxli7qbKCfDoqVB2aIcCNkAGLAREVnBhEeqy7ZTEXI2IlYGf7dbWlTyk/LFPKVcMS8pX8xLVTPm50nw1LVoWRF8RVYcuirnLMaK83J1kodrlFCZtFZV/Dg8h06MlmU7ERYlw7/fq3oENy5fVD7oWVuqBhTWtUwFnYOpIN5LJReioqLEx8dHIiMjxdubPaKIKOeCQ29Lnzk7VCeE9NxdHFXghuEx7v2PQM5Tyvl5SUlvd02COQziu/LQFVkRfFVCrkWnvo/x0zpULy7d6wRKu2rFxd2FQZpR7sna7pNNanv5fUQz3bJsG0+Ey6hfD0hMfFLqe+jJivEGX+5YWTxdbSPXE2Vn528GbAVshRNR/kJma8upCLlwI1bO34hT/6MaMjmDIM7M1dlRyvreC+TMQRz+x+vAIh5Ztie7dCtOVXWuPHRV3d/TzMXJQVXVorqzQ/USUsjNNk66Bc0bSw7Jor2h0qqyny5Ztu+3n5P3Vx4TbJ4YDPqdR2vK5+tPql7CgLtZTOpeQzrVDBCji7Kz8zcDtgK2wolIf4nJKXL51h05jyAu4v8DuQs34uTizbgMM3KWgVeZop5S3u//s3Nli3mq30GVJwbuNUNgh6rYR+uUlM41AsTHk0NxGN3FG3HSfoY5y9ZcGpYrmm9DuSBQQ7tLeLxRaVUNiosHwFh8k/48mtrmsWP14qoHNMYfNKooOzt/M2ArYCuciIwNJ86rkXfvBXM34lQgZs7O4WSekM1trxwcRJpU8FWZNHQgKFbILd/KTtpm2dC7+IenH8qXzgUjfzkgW05eV9vP+C7V5LnWQWqYGksYV/DLDadk3tazkphsEg8XJxndobI806qCuBiwg0qUnZ2/GbAVsBVORLYL1ahhUXflgkVW7vx/mTn08MRYaXiU8HbXu6hkI1k2tJt7+vs9cio8RgVgMwfUk87ZVHeimn/i8iOy+9y9OzNUKVFIZeMw/IuRRNnZ+ZsBWwFb4UREZHz5kWXD8CHP/bBPDZJcwttNvh3SOMcDIyN0WLr/skz5+7jc/G+QZfRynfBINcNkdaPs7PxtvBwmERFRAYdx2dAGEdWU+y7c0vz3/zh4WQbO26WCtZqB3vLHSy1zdRcLVJf2aVhaNoxrIwMfKqvewzhyHT7dLAt3X1R3ziBtMWAjIiIyGHQk6dOglOb3GEVm7NO1J+XlhQclISlFOtUoIYtfaCYBPnmrRseA0FN711ZVt9UCCsvtuEQZv/Sw9Ptqpxy/GqVZuYkBGxERkSGNbFdZ0yzb3cRkGb3woHzxXwD4fJsgmftkQ03GVUM7u5WjWspb3aqrwZhR3ke/3CZT/jomsRbjuVHeMWAjIiKy8yzb9eh4GTjvXzX0CwbB/bhPbXU3Di3vtoFbmT3TKkjWjWuj7kOLTjLztp6Tjp9ullVHwlR2j/KOARsREZENZNn2X8xbli0kLFp6zt4uBy7eFh8PF/lh+EPSv/G9dmfWUNLHQ+Y82VDmD20sZXw91DA1L/y0T4Yv2Kt6pVLeMGAjIiKyhSzbutxn2TaGhKvbo+GeoLhbxrIXm0vzin6SH3DbszVj2shL7SqqAZ83nAiXhz/bLLM3nlbt5yh3GLARERHZQJZtcy6zbLjN1PDv96h7gmIw5WUvtpAg/0KSnzxcneS1ztXkn5dbqVtd3U1MkemrQ6TrF1vl37M38rUsto4BGxERkcGzbL3r5zzLhrtlvPPHEXl3xb17gvZrWFrdl7Sol6vopVLxwvLrs03l08frSjEvVzkdHiMDvv5Xxv52UCJi4nUrly1hwEZERGRwI9tXylGWDbeZQlsx8z1B3+hSTab1rZN6T1A9Yey23g0wdltbeaJJWXUbLAy+22HGZjUuHGVN/zVIREREWSpXzCvbLBsa9Peds0MFde4ujjL3yQYyom3F++4JqjcfTxf5sFdtWTqiudQo6S2RdxL1LpJNYMBGRERkY1m2A+mybBj3DD1BT16LkeKF3WTx882lS62SYmT1yxaVP0e2kFlP1JcedQP1Lo7hMWAjIiKytSybxbhs924z9e//32ZqZAupXTrnt5nSE8Zue7ROoOGygEb04MMbExERUb5l2ZYeuCybQu61Zdsccj01eHu4RgmZ2b+eeLnx1G6PuFaJiIhsKMvWq34pdaP1Id/uluj/bvv0fOsgeb1LNVVlSvaJVaJEREQ2ZGS7e23ZEKzhNlMf9a4tE7pWZ7Bm5xiwERER2ZDyfl4y9uEqUqVEIfnh6YdkwEPWu80UGYeDiXdjzVJUVJT4+PhIZGSkeHt7610cIiIiKoDnb2bYiIiIiAyOARsRERGRwdlMwHbz5k0ZNGiQSmsWKVJEhg8fLjExMVl+ftSoUVK1alXx8PCQsmXLyujRo1VqlIiIiMiW2EzAhmDt6NGjsnbtWlm5cqVs2bJFnnvuuUw/f+XKFfX45JNP5MiRI/L999/LqlWrVKBHREREZEtsotPB8ePHpUaNGrJnzx5p1KiReg/BV9euXeXSpUsSGJizW1osXrxYnnzySYmNjRVnZ+cC2WiRiIioIIiys/O3TWTYdu7cqapBzcEadOzYURwdHWXXrl05/h3zSssqWIuPj1cr2fJBREREpCebCNjCwsKkePHiad5D0OXr66v+lhMREREyefLkLKtRYerUqSoiNz/KlCnzQGUnIiIisumAbfz48eqGr1k9Tpw48cDTQZasW7duqlr13XffzfKzEyZMUJk48yM0NPSBp09ERERks/cSHTdunAwdOjTLzwQFBUlAQICEh4eneT8pKUn1BMXfshIdHS1dunSRwoULy7Jly8TFxSXLz7u5uakHERERkVHoGrD5+/urR3aaNWsmt2/fln379knDhg3Vexs2bJCUlBRp0qRJlpm1zp07qwDszz//FHd3d03LT0RERJQfbKINW/Xq1VWW7Nlnn5Xdu3fL9u3bZeTIkTJgwIDUHqKXL1+WatWqqb+bg7VOnTqpHqHffvuteo32bngkJyfrPEdERERENpJhy42ff/5ZBWkdOnRQvUP79OkjX3zxRerfExMTJSQkROLi4tTr/fv3p/YgrVSpUprfOnfunJQvXz6f54CIiIjIjsdh05O9jeNCRERUEETZ2fnbJqpEiYiIiAoym6kS1Ys5AckBdImIiGxH1H/nbXupSGTAlg0MCwIcQJeIiMg2z+M+Pj5i69iGLRsYOgQ3kcc4bhjIV8vIH0EgBua1Vt16fkwjv6bDaXAanAanwWlwGrmB8AbBGkaTQGdFW8cMWzawkkuXLm2138cGau3GkPkxjfyaDqfBaXAanAanwWnklD1k1sxsP+QkIiIisnMM2IiIiIgMjgGbTnC7rEmTJln1vqX5MY38mg6nwWlwGpwGp8FpFGTsdEBERERkcMywERERERkcAzYiIiIig2PARkRERGRwDNiIiIiIDI4Bmw62bNki3bt3V6Mv4+4Jy5cv1/T3p06dKo0bN1Z3ZyhevLj07NlTQkJCNJ3GnDlzpE6dOqmDHTZr1kz++ecfsaaPPvpILa8xY8Zo9pvvvvuu+k3LR7Vq1URrly9flieffFKKFSsmHh4eUrt2bdm7d6+m0yhfvvx984LHSy+9pMnvJycny9tvvy0VKlRQ81CxYkWZPHmy5vfpw8jkWMflypVT02nevLns2bPHqvsc5uGdd96RkiVLqml27NhRTp06pek0li5dKp06dVLbAP5+8OBBTecjMTFR3njjDbVteXl5qc8MHjxY3alFy/nAPoN9BNMoWrSoWla7du3SdBqWXnjhBfWZmTNnajqNoUOH3revdOnSRfP5OH78uPTo0UMN4IplhmPzxYsXNZtGRvs8HtOnT9dsGjExMTJy5Eg1iDz2jxo1asjcuXNz/Ps5mca1a9fUOsHfPT091brI7T5o7xiw6SA2Nlbq1q0rs2fPtsrvb968WZ2k//33X1m7dq06kONEgelqBTsuAqh9+/apwKN9+/by2GOPydGjR8UacML+6quvVJCotZo1a8rVq1dTH9u2bdP092/duiUtWrQQFxcXFdQeO3ZMZsyYoU52Wi8jy/nAuod+/fpp8vsff/yxCtRnzZqlTkJ4PW3aNPnyyy9FS88884wq+48//iiHDx9W2y6CAgS91trnMB9ffPGFOgkh+MCJtXPnznL37l3NpoG/t2zZUi03a8xHXFyc7N+/XwXV+B8BIi7UECxoNQ2oUqWK2gawbrCv4EIB6+j69euaTcNs2bJl6jiGk3hu5WQaCAos95lff/1V02mcOXNGrXMEuJs2bZJDhw6p9ePu7q7ZNCzLj8d3332nAqI+ffpoNo2xY8fKqlWr5KefflL7Pi6oEMD9+eefmkwDF0xILJw9e1b++OMPOXDggLpgw36v5XnL5mFYD9IPVsGyZcusOo3w8HA1nc2bN1t1OkWLFjV98803mv9udHS0qXLlyqa1a9ea2rRpY3r55Zc1++1JkyaZ6tata7KmN954w9SyZUtTfsNyqlixoiklJUWT3+vWrZvp6aefTvNe7969TYMGDTJpJS4uzuTk5GRauXJlmvcbNGhgmjhxolX2OSyfgIAA0/Tp01Pfu337tsnNzc3066+/ajINS+fOnVN/P3DgQJ5+OyfTMNu9e7f63IULF6w2jcjISPW5devWaTqNS5cumUqVKmU6cuSIqVy5cqbPPvssT7+f2TSGDBlieuyxx/L8mzmZRv/+/U1PPvmkVaeRHuapffv2mk6jZs2apvfff1+zfTL9NEJCQtR7WNdmycnJJn9/f9O8efPyNA17xAxbARAZGan+9/X1tcrvo6ps4cKF6koIVaNaQ7awW7du6mrLGpB2xxV8UFCQDBo0KFfVFTmBq9BGjRqpTBeqqOvXry/z5s0Ta0pISFBXw08//bS62tYCqibXr18vJ0+eVK+Dg4NVhuWRRx4RrSQlJantKX0GAtUwWmc+zc6dOydhYWFpti9UXzVp0kR27twptr7vY/0XKVLEatvZ119/rZYXsidaSUlJkaeeekpee+01lQG3FmS9sE9WrVpVRowYITdu3NB0Hv766y+VkUS2FtPBNqV1E5j01YqY5vDhwzX9Xez7OI4hy414a+PGjeo4gMyqFuLj49X/lvs97uONwXSttd/bIgZsdg4HDaSvUSVXq1YtTX8bVSKFChVSOxXamaD6Am0btIRAENU7aJdnDTiAfv/99yrdj+o+nLxbtWql2lFpBWl+/HblypVl9erV6sQwevRoWbBggVgLTgq3b99WbUK0Mn78eBkwYICq3kH1LgJPbFsIcrWCdpcI+tE2Dm2vELwh8ETghOoea0CwBiVKlEjzPl6b/2aLUJ2LNm0DBw7U/KbaK1euVPs+TrCfffaZqsL28/PT7PdRbezs7Kz2E2tBdegPP/ygLkIwPTQlwcUHtjkthIeHq7ZfaDqCaa1Zs0Z69eolvXv3VtOyBhxTsA9hGlpCswcc29EUxtXVVc0PqjZbt26tye/jmFK2bFmZMGGCakKCCwGsk0uXLlltv7dFznoXgKwL2akjR45Y5SoFV6VoOI2r+CVLlsiQIUPUgUiroC00NFRefvlldTLITZuP3LDMDqF9HAI4tJ347bffNLtKRdCMDNuHH36oXiPQwTpBeyksM2v49ttv1bzlpe1PZrBMfv75Z/nll19U1gPrHgEbpqHlfKDtGjKDpUqVEicnJ2nQoIEKOtBeknIG7VYff/xxlQ3BxYLW2rVrp9Z/RESEyhZjWmj7hyzSg8J6/vzzz9WFmlbZ4Yzg4sMMHTWw/6MjDbJuHTp00GS/B7TtfeWVV9TzevXqyY4dO9S+36ZNG9Ea2q/hAkrr4yUCNrQlRJYNx0d0IMC5Bfu+FjUfuABEm0scc1EThP0ev4tjGG/G9P+YYbNjaBSKK2Gkr3FlpDVcaVWqVEkaNmyoMmCoEsGBVis4cOMqFSdsXG3jgYAQjcPxXKsrYUuoOkIVxunTpzX7TfQ8TB/EVq9eXfOqV7MLFy7IunXrVON9LaF6ypxlwwkOVVY4EWmd/cRJE+sZ2QkE7bt371YBCKqsrSEgICC1OskSXpv/ZovBGrYDXOxonV0DdMrAvt+0aVN1cYD9Ef9rYevWrWq/R8bFvN9jXsaNG6c6OFgLti9kCbXa9/FbKHt+7ftYbuhkovV+f+fOHXnzzTfl008/Vb08Edji3NK/f3/55JNPNJsOziO4CEDNALJqqPVAFbW19ntbxIDNDuGKBDsUqig3bNighmHID7iiNLdF0AKuclHtip3Y/ECmCleQeI6rMK0hSEDPLgRZWkF1dPphVdD+A1eq1jB//nyV6UC7Py2hFyLalVjCOjBnEqwRFGA9oIoEVcnIVFgD9g8EZqgaM4uKilIZI2u0ycyPYA3tMhG0YwgRW9v3cSGA3pSW+z0yObhgwHZgLah+Q4Cg1b6PC1oM4ZFf+z4CZgQ9WrYlNG9TeOTXvo/2kP7+/mobxggE1trvbRGrRHWAoMDyKg7tpnBQQioYV5UPCqlqVFuhezTaM5jb4WBHQONtLaCtAdLVKC/ae2F6qErQ8oCKsqdvd4eTOE5CWrXHe/XVV9VVIw6gaDM1adIkdSBCFZxWkIVCo11UieJkiowRGmrjoTUcQBGwoYoSV/dawnKaMmWKWueoEkXXe1x1o/pSS9iGcNGBKnfsJzhRo43LsGHDrLbPoWr3gw8+UO0MEcBh6AUECRhqQKtp3Lx5U2VWzOOimU/kCBZzmsnLahoINPr27auqEpFZRwbavO/j7wggHnQa2PewDWCoEEwPVaJoy4TG6LkZPia7ZZU+0ESVGZYRtgktpoHHe++9p4a+wO/iIu31119XWUN0ENBqPrDtIhOFtl6oRkbWaMWKFepYqdU0zBcYixcvVsMF5UV200D1LeYF5w8cK5EBR/s/7P9aTQPlR6CG57hQR3MY7H9adWywC3p3Uy2INm7cqLowp3+gm7kWMvptPObPn2/SCoZ3QFd7V1dX1fW6Q4cOpjVr1pisTethPdDtvmTJkmo+MIQAXp8+fdqktRUrVphq1aqlhoqoVq2a6euvvzZZw+rVq9W6Rjd5rUVFRallX7ZsWZO7u7spKChIdeuPj4/XdDqLFi1Sv411guE2XnrpJTXMhjX3OQzt8fbbb5tKlCih1hG259wuw+ymgf0vo79jaBktpmEeLiSjB76nxTTu3Llj6tWrlykwMFCtH+w7PXr0UMOHaLms0svLsB5ZTQPDx3Tq1Ekdu1xcXNTvP/vss6awsDDN5+Pbb781VapUSe0zGEJo+fLlmk/jq6++Mnl4eOR5P8luGlevXjUNHTpUrXfMR9WqVU0zZszI1ZBB2U3j888/N5UuXVqtDxxj3nrrLc2PLbbOAf/oHTQSERERUebYho2IiIjI4BiwERERERkcAzYiIiIig2PARkRERGRwDNiIiIiIDI4BGxEREZHBMWAjIiIiMjgGbEREREQGx4CNiPLk3XfflRIlSoiDg4MsX77catP5/vvvpUiRImIU58+fV/OM2+oYbZ5wyyOUDTfQJiL7woCNyI4NHTpUncDxwL0kca/E999/X5KSkh7od48fP67uxfjVV1/J1atX1X1lSR4ogGvbtq3exSAiA+PN34nsXJcuXdQN4ePj4+Xvv/+Wl156Sd1Me8KECfd9NiEhIUc3CcfNsuGxxx5TwaCtwx36cLN0Z2fbPSQmJibm6XuYb6xDR0devxMZGfdQIjvn5uYmAQEBUq5cORkxYoR07NhR/vzzz9QMXM+ePWXKlCkSGBgoVatWVe8fPnxY2rdvLx4eHlKsWDF57rnnJCYmJrUqtHv37uo5TvLmgC0lJUVl70qXLq2mWa9ePVm1atV9VYlLly6Vdu3aiaenp9StW1d27tx5X7apbNmy6u+9evWSGzdu3DdPf/zxhzRo0EDc3d0lKChIZfvMWcOMqixRRYj3UGVoWXX4zz//SMOGDVV5t23bluHy2717t9SvX19Nq1GjRnLgwIH7PnPkyBGVZSxUqJCqJn7qqackIiJC8mrPnj3y8MMPi5+fn/j4+EibNm1k//79aT6D8s+ZM0d69OghXl5eah2abd++XerUqaPK3LRpU1W+9NWx2AZq1Kih5v3ixYsqoH/11VelVKlS6veaNGmSuryISH8M2IgKGARhyKSZrV+/XkJCQmTt2rWycuVKiY2Nlc6dO0vRokVV4LB48WJZt26djBw5Un0eJ3Vk7ADVoXjA559/LjNmzJBPPvlEDh06pH4DwcSpU6fSTH/ixInqNxBQValSRQYOHJgabO3atUuGDx+upoW/I7D74IMP0nx/69atMnjwYHn55Zfl2LFjqloWQYhlwJJT48ePl48++khV8SLASQ9B6qOPPqoCm3379qlgFWW3hGAQwS2Cur1796og9dq1a/L4449LXkVHR8uQIUNUEPnvv/9K5cqVpWvXrup9SygPgloE2E8//XTq+6+99ppaF1h//v7+KsC2zMDFxcXJxx9/LN98840cPXpUihcvrpY5gueFCxeq9devXz+VnU2//ohIJyYisltDhgwxPfbYY+p5SkqKae3atSY3NzfTq6++mvr3EiVKmOLj41O/8/XXX5uKFi1qiomJSX3vr7/+Mjk6OprCwsLU62XLlpnSHz4CAwNNU6ZMSfNe48aNTS+++KJ6fu7cOfWdb775JvXvR48eVe8dP35cvR44cKCpa9euaX6jf//+Jh8fn9TXHTp0MH344YdpPvPjjz+aSpYsmWY6Bw4cSP37rVu31HsbN25Ur/E/Xi9fvjzL5ffVV1+ZihUrZrpz507qe3PmzEnz+5MnTzZ16tQpzfdCQ0PVZ0JCQjL83fnz56eZp+wkJyebChcubFqxYkXqe/j9MWPGpPmceb4WLlyY+t6NGzdMHh4epkWLFqVOG585ePBg6mcuXLhgcnJyMl2+fDnN72FZT5gwIcflJCLrsd0GG0SUI8iaoaoOGRZUWz7xxBMqM2NWu3btNO3WkG1CVSWqxcxatGihvotMHKr80ouKipIrV66oz1nC6+Dg4DTvWWaySpYsqf4PDw+XatWqqWkjY2SpWbNmaapW8Xuo8rPMqKEd1t27d1XmKDdQxZkVc+YNVYuW5bGE8mzcuFEt44za+iGLmFvI0L311luqShLLBvOHeUPVZU7Kb1lGX19fVdWNeTHD+rZcD8jQYRrpy4pqUlSJE5H+GLAR2TlUK6KtE07SaKeWvmG9ZWCWH9Dhwcyy/VtOoZoSbdZ69+59398QWJkbz99LQmXdIF+LeUd5UOWIKsb0zAFpbqE6FG33UM2MtodoZ4YgzLIq+0HKj2pxy84imAcnJydV7Yv/LWUUiBJR/mPARmTncFLHcB45Vb16ddUmDG3ZzAEBMloIhMydEtLz9vZWwSA+hwbyZnj90EMP5WraaMdmCW24LKGzATJ9mc0T2mwB2tahXRnkdcw0lOfHH39U2Ttzli2j8vz+++9Svnx5zXqZYrn973//U+3WIDQ0NFedGFBGdNyAW7duycmTJ9W8ZAbLCRk2ZPNatWqlwRwQkdbY6YCI0hg0aJAKTpDlQe9CVPeNGjVK9XzMqDrUsqE7skyLFi1SARUa9CNQQueAnBo9erSq/kTHBTR2nzVrVprqUHjnnXfkhx9+UFk2NJhHVR8ayqMK0Zw9Qs9Ic2eCzZs3p/4tt1B9jEzUs88+qzo4YFgUlM0Shkm5efOm6jyBRv6oBl29erUMGzZMBUF5gU4GCBRRfgSwWCeYr5xCb110JsH6Q09g9DZFb+DMoCoU00BnDvTiPXfunOodO3XqVPnrr7/yNA9EpC0GbESUBobTQMCBIKRx48bSt29f6dChgwqesgu2xo4dK+PGjVPt4hBoYegIBB85hUBr3rx5qioQ7ejWrFlzX7CF3qdol4e/oXz4zmeffaaqDs2+++471fMUQ3aMGTPmvp6mOYXqwBUrVqg2XshCoYdr+qpPc2YRwVmnTp3UvGOaGDojr2ObffvttyozhuwdAmUsW/TkzCkEqwiUMf9hYWFqHrIbXw89fxGwYf0hk4oADwGoOVNHRPpyQM8DnctARERERFlgho2IiIjI4BiwERERERkcAzYiIiIig2PARkRERGRwDNiIiIiIDI4BGxEREZHBMWAjIiIiMjgGbEREREQGx4CNiIiIyOAYsBEREREZHAM2IiIiIjG2/wMlfFwXsNsS5wAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score maximum pour une profondeur de 2\n"
+     ]
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "cell_type": "code",
+   "id": "1fcfe5df",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:03:29.830296Z",
+     "start_time": "2025-09-17T13:03:29.658255Z"
+    }
+   },
+   "source": [
+    "model = DecisionTreeRegressor(max_depth=prof_max)\n",
+    "model.fit(X_train, y_train)\n",
+    "plot_tree(model, filled=True, feature_names=diabetes_X.columns)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 0.8333333333333334, 'bmi <= 0.009\\nsquared_error = 5794.059\\nsamples = 353\\nvalue = 152.32'),\n",
+       " Text(0.25, 0.5, 's5 <= 0.007\\nsquared_error = 3573.722\\nsamples = 219\\nvalue = 119.868'),\n",
+       " Text(0.375, 0.6666666666666667, 'True  '),\n",
+       " Text(0.125, 0.16666666666666666, 'squared_error = 2240.884\\nsamples = 156\\nvalue = 103.026'),\n",
+       " Text(0.375, 0.16666666666666666, 'squared_error = 4432.499\\nsamples = 63\\nvalue = 161.571'),\n",
+       " Text(0.75, 0.5, 's5 <= -0.001\\nsquared_error = 4888.558\\nsamples = 134\\nvalue = 205.358'),\n",
+       " Text(0.625, 0.6666666666666667, '  False'),\n",
+       " Text(0.625, 0.16666666666666666, 'squared_error = 3774.763\\nsamples = 39\\nvalue = 156.513'),\n",
+       " Text(0.875, 0.16666666666666666, 'squared_error = 3964.242\\nsamples = 95\\nvalue = 225.411')]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "cell_type": "code",
+   "id": "888a47b2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-17T13:03:29.837340Z",
+     "start_time": "2025-09-17T13:03:29.835732Z"
+    }
+   },
+   "source": [],
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/M06_TP01_Exercice.ipynb b/M06_TP01_Exercice.ipynb
new file mode 100644
index 0000000..a5b79ee
--- /dev/null
+++ b/M06_TP01_Exercice.ipynb
@@ -0,0 +1,614 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0c601b14",
+   "metadata": {},
+   "source": [
+    "# TP n°1 du module 6 : Les algorithmes de classification pour le _Machine Learning_\n",
+    "\n",
+    "Dans ce TP, nous allons mettre en pratique les principes de l'apprentissage supervisé.\n",
+    "\n",
+    "## Objectifs :\n",
+    "- Savoir mettre en place les principaux algorithmes de classification\n",
+    "- Etudier l'impact de leurs paramètres sur leurs performances\n",
+    "- Comparer les performances de différents algorithmes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8423b3aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ajoutez ici les imports de librairies nécessaires\n",
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82e63125",
+   "metadata": {},
+   "source": [
+    "## Question n°0\n",
+    "Commencez par charger à nouveau le jeu de données Titanic, à partir du csv généré dans le TP1 du module 4.\n",
+    "- Préparez les données d'entraînement et de test qui seront utilisées par la suite."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "79f23c2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lambda nommée pour afficher un score en pourcentage avec un libellé (avec détail) :\n",
+    "pscore = lambda lib, score, detail='': print(F\"{lib}{('',f\" ({detail})\")[len(str(detail))>0]} : {100*score:.2f}%\")\n",
+    "\n",
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc39d16d-6a9f-4fa6-ac5d-da5266c98583",
+   "metadata": {},
+   "source": [
+    "## Partie 1 : découvrir Naive Bayes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f7717c4",
+   "metadata": {},
+   "source": [
+    "### Question n°1\n",
+    "Commencez par créer un modèle basé sur Naive Bayes, sans changer les paramètres par défaut, en supposant que la répartition des données correspond à une Gaussienne (loi normale).\n",
+    "- Entraînez-le et testez-le.\n",
+    "- Quelle score (accuracy) obtenez-vous ?\n",
+    "- Que pouvez-vous dire de la précision et du rappel ?\n",
+    "- Comparez avec les scores obtenus sur les arbres de décision au module 5\n",
+    "- Avez-vous des hypothèses pour expliquer cette différence ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dd0f1d68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a68e940-c814-44fe-927b-e66c8904d99a",
+   "metadata": {},
+   "source": [
+    "#### Observations :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1af14ab5",
+   "metadata": {},
+   "source": [
+    "### Question n°2\n",
+    "Affichez une matrice de corrélation des données du jeu d'entraînement, en y incluant un affichage textuel de la valeur de la corrélation.\n",
+    "- Voyez-vous des informations permettant d'expliquer les performance de l'algorithme _Naive Bayes_ ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3b3d1c81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00130884-072e-4d7b-9d16-7f6dded39a8c",
+   "metadata": {},
+   "source": [
+    "#### Observations :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "351eae75",
+   "metadata": {},
+   "source": [
+    "### Question n°3\n",
+    "Proposez une représentation graphique des attributs continus, permettant de vérifier l'hypothèse que nous avons faite, selon laquelle ces données suivent une loi normale (Gaussienne)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b6b5b059",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "627d1608-9f5b-42cf-9666-10f86709f5b9",
+   "metadata": {},
+   "source": [
+    "#### Observations :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dab510d0",
+   "metadata": {},
+   "source": [
+    "## Partie 2 : découvrir KNN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90779015",
+   "metadata": {},
+   "source": [
+    "### Question n°1\n",
+    "Commencez par créer un modèle KNN, en gardant le nombre de voisins par défaut (à regarder dans la documentation).\n",
+    "- Que pouvez-vous dire de l'accuracy, de la précision et du rappel ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8f8e1696",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e4734d3-58a3-483f-8ffb-a898dadab63c",
+   "metadata": {},
+   "source": [
+    "#### Observations :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f991f919",
+   "metadata": {},
+   "source": [
+    "### Question n°2\n",
+    "Nous allons maintenant observer l'impact du nombre de voisins à prendre en considération.\n",
+    "- Faite varier k entre 1 et 20.\n",
+    "- Calculez à chaque fois accuracy, précision, et rappel.\n",
+    "- Tracez l'évolution de ces trois scores en fonction de k, sur un même graphique.\n",
+    "- Que constatez-vous ?\n",
+    "- Affichez la valeur de k pour laquelle l'accuracy est la plus élevée."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b65bb998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88905973-56e2-42a7-9fce-6619592b9b7a",
+   "metadata": {},
+   "source": [
+    "#### Observations :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18ec66e2",
+   "metadata": {},
+   "source": [
+    "### Question n°3\n",
+    "En prenant la valeur de _k_ qui vous semble la plus pertinente, faite varier la dimension (p) utilisée pour calculer la distance de Minkowski entre 2 données.\n",
+    "- Cette distance a-t'elle un fort impact sur les résultats d'accuracy obtenus ?\n",
+    "- Montrez-le en montrant l'évolution de ce score en fonction de _p_<br/> (faire varier entre 1 et 10).\n",
+    "- Ajoutez également la précision et le rappel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ce6b99d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d13a3756-fc4c-441d-846d-26e848db111b",
+   "metadata": {},
+   "source": [
+    "#### Observations :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fec73153-fbe1-4503-8bda-be6125a3691e",
+   "metadata": {},
+   "source": [
+    "## Partie 3 : découvrir les SVM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a7c6024",
+   "metadata": {},
+   "source": [
+    "### Question n°1\n",
+    "Créez un modèle de classification basée sur les machines à vecteur de support.\n",
+    "- Dans un premier temps, gardez les options par défaut.\n",
+    "- Que pouvez-vous dire des performances obtenues (accuracy, précision, rappel) ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "3b136dbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b07b1658-849c-4cdc-9f9b-c763291c1890",
+   "metadata": {},
+   "source": [
+    "#### Observations :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "905b31a7",
+   "metadata": {},
+   "source": [
+    "### Question n°2\n",
+    "Testez les différents noyaux disponibles pour l'algorithme SVM (linéaire, polynomial, rbf et sigmoïde).\n",
+    "- Représentez graphiquement l'accuracy, la précision et le rappel, pour chaque noyau.\n",
+    "- Il y en a t'il un qui semble plus pertinent que les autres ?\n",
+    "- Affichez-le, ainsi que les scores obtenus pour ce noyau."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e68429cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "014b5138",
+   "metadata": {},
+   "source": [
+    "### Question n°3\n",
+    "Nous allons essayer d'améliorer les performances obtenues avec le noyau polynomial.\n",
+    "- Utilisez ce noyau (`poly`), et faites varier le degré du polynôme utilisé de 1 à 10.\n",
+    "- Représentez graphiquement l'accuracy, la précision et le rappel, en fonction du degré du polynôme.\n",
+    "- Il y en a-t-il un qui semble plus pertinent que les autres ?\n",
+    "- Affichez-le, ainsi que les scores obtenus pour cette valeur.\n",
+    "- Comparez avec le meilleur score obtenu à la question précédente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "544318b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25f11b31",
+   "metadata": {},
+   "source": [
+    "## Partie 4 : découvrir les réseaux de neurones (ANN)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77448c8c",
+   "metadata": {},
+   "source": [
+    "### Question n°1\n",
+    "Commençons par étudier le réseau le plus simple : un _perceptron_.\n",
+    "- À l'aide de la classe `sklearn.linear_model.Perceptron`,<br/> créez un perceptron, en gardant les options par défaut.\n",
+    "- Affichez `accuracy`, `précision` et `rappel` : Que pensez-vous de ces performances ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "0d2620da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "646a3f3a-7382-4728-951d-d14637df4ca4",
+   "metadata": {},
+   "source": [
+    "#### Observation :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bda13ed8",
+   "metadata": {},
+   "source": [
+    "### Question n°2\n",
+    "Regardez la documentation pour créer un réseau de neurones (`sklearn.neural_network.MLPClassifier`) :\n",
+    "- Quelle est la structure d'un réseau de neurones par défaut avec scikit-learn ?\n",
+    "- Combien de couches cachées ?\n",
+    "- Combien de neurones par couche ?\n",
+    "\n",
+    "_N.B. : Un message d'alerte (⚠Warning: Stochastic Optimizer: Maximum iterations) est suceptible d'apparaître._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e627e744-c82a-4cd9-b215-58694948a2c3",
+   "metadata": {},
+   "source": [
+    "#### Réponse :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4ae8d40",
+   "metadata": {},
+   "source": [
+    "### Question n°3\n",
+    "- Créer un réseau de neurones, en gardant ces options par défaut.\n",
+    "- Affichez `accuracy`, `précision` et `rappel` :\n",
+    "    - Que pensez-vous de ces performances, notamment en comparant par rapport au perceptron ?\n",
+    "    - Avez-vous un message d'alerte ?<br/>(⚠Warning: Stochastic Optimizer: Maximum iterations)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "652f5cca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06a6ba7f-2e4b-4a51-9ed8-42a1a5c36551",
+   "metadata": {},
+   "source": [
+    "#### Observation :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f9b9f82",
+   "metadata": {},
+   "source": [
+    "### Question n°4\n",
+    "Si vous avez observé un message d'alerte sur la question précédent :\n",
+    "- Que signifie-t'il selon vous ?\n",
+    "- Que pouvez-vous faire pour y remédier ?\n",
+    "- Proposez un code permettant d'obtenir des résultats, sans message d'alerte.\n",
+    "- Qu'observez-vous sur l'évolution des scores ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "7268b9a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "720ecf9a-c376-41e1-bbf3-7e3261a53120",
+   "metadata": {},
+   "source": [
+    "#### Observation :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "132b2069",
+   "metadata": {},
+   "source": [
+    "### Question n°5\n",
+    "Nous allons à présent comparer différentes architectures du réseau de neurones :\n",
+    "- 3 couches de 50 neurones chacune\n",
+    "- 5 couches de 50 neurones chacune\n",
+    "- 3 couches :\n",
+    "    1. 50 neurones,\n",
+    "    2. 100 neurones,\n",
+    "    3. 50 neurones\n",
+    "- 5 couches :\n",
+    "    1. 50 neurones,\n",
+    "    2. 100 neurones,\n",
+    "    3. 50 neurones,\n",
+    "    4. 100 neurones,\n",
+    "    5. 50 neurones\n",
+    "\n",
+    "**Les attendus :**\n",
+    "- Représentez graphiquement l'_accuracy_, la _précision_ et le _rappel_, pour chaque architecture.\n",
+    "- Il y en a t'il une qui semble plus pertinente que les autres ?\n",
+    "- Affichez-la, ainsi que les scores obtenus pour cette architecture.\n",
+    "- Comparez avec le score obtenu par l'architecture par défaut.\n",
+    "- Votre code ne doit générer aucun message d'alerte."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "1027a554",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e437de59",
+   "metadata": {},
+   "source": [
+    "### Question n°6\n",
+    "En utilisant l'architecture qui vous donnait les meilleures performances, étudier l'impact de la fonction d'activation utilisée sur les performances.\n",
+    "- Représentez sur un graphiques les scores (accuracy, précision et rappel) obtenus pour les quatres fonctions d'activation proposées par _Scikit-Learn_.\n",
+    "- Affichez la fonction qui vous parait la plus pertinente, ainsi que les scores associés."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "9ad2a684",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60141a50",
+   "metadata": {},
+   "source": [
+    "## Partie 5 : comparer les performances des différents algorithmes\n",
+    "\n",
+    "Nous allons à présent résumer les différentes performances des algorithmes que vous avez testé dans ce TP :\n",
+    "- Récupérez les meilleurs scores (accuracy) obtenu pour chaque algorithme.\n",
+    "- Représentez-les sur un diagramme en barres, en regroupant par algorithme, et en représentant chaque score par une couleur.\n",
+    "- Un algorithme semble-t'il obtenir de meilleures performances que les autres ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "2318f1a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe7a5b28",
+   "metadata": {},
+   "source": [
+    "## Partie 6 : optimiser la recherche des paramètres optimaux"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62b98a2e-281c-49fb-93b4-0a71d690e572",
+   "metadata": {},
+   "source": [
+    "Dans ce TP, nous avons souvent cherché à identifier la meilleur combinaison de paramètres. Nous avons procédé par itération, en cherchant à fixer un paramètre avant de faire évoluer les autres. Cette méthode est coûteuse, et pour faire une recherche exhaustive, nécessite, de répéter très souvent le même code. Scikit-learn propose une classe, `sklearn.model_selection.GridSearchCV`, qui va permettre d'optimiser cette recherche de paramétrage optimal.\n",
+    "\n",
+    "_Lien vers la documentation :_ [sklearn.model_selection.GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)\n",
+    "\n",
+    "Le principe est de définir un dictionnaire, où la clé correspond à un paramètre, et la valeur à la liste de valeurs possibles à tester pour le paramètre considéré. \n",
+    "\n",
+    "### Consigne :\n",
+    "Appliquez ce principe pour déterminer la meilleure combinaison possible pour le réseau de neurones, en repartant des différentes configurations testées dans les parties précédentes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "7f6eeac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "40f04782-02e8-47f6-8f01-195c0b7f882f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82eca36d-3c7d-4440-ab03-066762fea747",
+   "metadata": {},
+   "source": [
+    "# Fin du TP !"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/M06_TP01_Solution.ipynb b/M06_TP01_Solution.ipynb
new file mode 100644
index 0000000..3c96cbc
--- /dev/null
+++ b/M06_TP01_Solution.ipynb
@@ -0,0 +1,1142 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0c601b14",
+   "metadata": {},
+   "source": [
+    "# TP1 du module 6 : les algorithmes de classification\n",
+    "\n",
+    "Dans ce TP, nous allons mettre en pratique les principes de l'apprentissage supervisé. Objectifs :\n",
+    "* Savoir mettre en place les principaux algorithmes de classification\n",
+    "* Etudier l'impact de leurs paramètres sur leurs performances\n",
+    "* Comparer les performances de différents algorithmes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "8423b3aa",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:35.673605Z",
+     "start_time": "2025-09-18T11:38:34.623332Z"
+    }
+   },
+   "source": [
+    "# Ajoutez ici les imports de librairies nécessaires\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "\n",
+    "from sklearn.linear_model import Perceptron\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.svm import SVC"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82e63125",
+   "metadata": {},
+   "source": [
+    "Commencez par charger à nouveau le jeu de données Titanic, à partir du csv généré dans le TP1 du module 4. Préparez les données d'entraînement et de test qui seront utilisées par la suite."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "79f23c2b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:35.699577Z",
+     "start_time": "2025-09-18T11:38:35.679773Z"
+    }
+   },
+   "source": [
+    "titanic = pd.read_csv(\"Titanic.csv\")\n",
+    "\n",
+    "X = titanic.drop(['Survived'], axis=1)\n",
+    "y = titanic['Survived']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
+   ],
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f7717c4",
+   "metadata": {},
+   "source": [
+    "## Partie 1 : découvrir Naive Bayes\n",
+    "\n",
+    "1. Commencez par créer un modèle basé sur Naive Bayes, sans changer les paramètres par défaut, en supposant que la répartition des données correspond à une Gaussienne (loi normale). Entraînez-le et testez-le. Quelle score (accuracy) obtenez-vous ? Que pouvez-vous dire de la précision et du rappel ? Comparez avec les scores obtenus sur les arbres de décision au module 5 : avez-vous des hypothèses pour expliquer cette différence ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "dd0f1d68",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:35.731457Z",
+     "start_time": "2025-09-18T11:38:35.710071Z"
+    }
+   },
+   "source": [
+    "gnb = GaussianNB()\n",
+    "\n",
+    "#Entraînement\n",
+    "gnb.fit(X_train, y_train)\n",
+    "y_pred=gnb.predict(X_test)\n",
+    "\n",
+    "#Test\n",
+    "print(\"Accuracy : \", gnb.score(X_test, y_test))\n",
+    "print(\"Precision : \", precision_score(y_test, y_pred))\n",
+    "print(\"Rappel : \", recall_score(y_test, y_pred))\n",
+    "\n",
+    "# sauvegarde des scores\n",
+    "nb_best_accuracy = gnb.score(X_test, y_test)\n",
+    "nb_best_pred = precision_score(y_test, y_pred)\n",
+    "nb_best_recall = recall_score(y_test, y_pred)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy :  0.6703910614525139\n",
+      "Precision :  0.8235294117647058\n",
+      "Rappel :  0.2\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "id": "140c9a8c",
+   "metadata": {},
+   "source": [
+    "**Observation :** le score d'accuracy est faible, d'autant plus en comparaison de performances de l'arbre de décision. Il est possible que l'hypothèse d'indépendance des variables ne soit pas adapté pour les données du Titanic.\n",
+    "\n",
+    "De plus, il est très intéressant de noter un bon rappel mais une mauvaise précision : cela signifie que si le modèle arrive à prédire correctement les dècés, il le fait en faisant énormément d'erreur sur la survie."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1af14ab5",
+   "metadata": {},
+   "source": [
+    "2. Affichez une matrice de corrélation des données du jeu d'entraînement, en y incluant un affichage textuel de la valeur de la corrélation. Voyez-vous des informations permettant d'expliquer les performance de l'algorithme Naive Bayes ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3b3d1c81",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:36.142110Z",
+     "start_time": "2025-09-18T11:38:35.757567Z"
+    }
+   },
+   "source": [
+    "sns.heatmap(titanic.corr(),annot=True)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6763d0ca",
+   "metadata": {},
+   "source": [
+    "**Observation :** Il existe en effet une certaine corrélation entre plusieurs variable (par exemple entre Fare et Pclass), qui mettent à mal l'hypothèse d'indépendance des variables supposée par l'algorithme Naive Bayes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "351eae75",
+   "metadata": {},
+   "source": [
+    "3. Proposez une représentation graphique des attributs continus, permettant de vérifier l'hypothèse que nous avons faite, selon laquelle ces données suivent une loi normale (Gaussienne)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b6b5b059",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:36.455574Z",
+     "start_time": "2025-09-18T11:38:36.150489Z"
+    }
+   },
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(20, 5))\n",
+    "i=0\n",
+    "for c in ['Age', 'Fare']:\n",
+    "    sns.histplot(X, x=c, kde=True, ax=axes[i])\n",
+    "    i+=1"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2000x500 with 2 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b701994c",
+   "metadata": {},
+   "source": [
+    "**Observation :** la répartition des données continues semble cohérente avec une loi Normale pour l'âge, mais moins adaptée pour le prix des billets. Cet élément peut également expliquer le faible score obtenu avec l'agorithme Naive Bayes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dab510d0",
+   "metadata": {},
+   "source": [
+    "## Partie 2 : découvrir KNN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90779015",
+   "metadata": {},
+   "source": [
+    "1. Commencez par créer un modèle knn, en gardant le nombre de voisins par défaut (à regarder dans la documentation). Que pouvez-vous dire de l'accuracy, de la précision et du rappel ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "8f8e1696",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:36.480242Z",
+     "start_time": "2025-09-18T11:38:36.463982Z"
+    }
+   },
+   "source": [
+    "knn = KNeighborsClassifier()\n",
+    "\n",
+    "#Entraînement\n",
+    "knn.fit(X_train, y_train)\n",
+    "y_pred=knn.predict(X_test)\n",
+    "\n",
+    "#Test\n",
+    "print(\"Accuracy : \", knn.score(X_test, y_test))\n",
+    "print(\"Precision : \", precision_score(y_test, y_pred))\n",
+    "print(\"Rappel : \", recall_score(y_test, y_pred))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy :  0.7094972067039106\n",
+      "Precision :  0.6551724137931034\n",
+      "Rappel :  0.5428571428571428\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ddd7d42",
+   "metadata": {},
+   "source": "**Observation :** on obtient cette fois un meilleur score pour le rappel. L'écart avec la précision est bien moins important, indiquant que le modèle semble faire de bonnes prédictions de manière équilibrée entre les deux classes."
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f991f919",
+   "metadata": {},
+   "source": [
+    "2. Nous allons maintenant observer l'impact du nombre de voisins à prendre en considération. Faite varier k entre 1 et 20. Calculez à chaque fois accuracy, précision, et rappel. Tracez l'évolution de ces trois scores en fonction de k, sur un même graphique. Que constatez-vous ? Affichez la valeur de k pour laquelle l'accuracy est la plus élevée."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b65bb998",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:36.755691Z",
+     "start_time": "2025-09-18T11:38:36.488005Z"
+    }
+   },
+   "source": [
+    "accuracies = []\n",
+    "precisions = []\n",
+    "recalls = []\n",
+    "k_range = range(1,20)\n",
+    "\n",
+    "for k in k_range:\n",
+    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
+    "    knn.fit(X_train, y_train)\n",
+    "    y_pred = knn.predict(X_test)\n",
+    "    accuracies.append(accuracy_score(y_test, y_pred))\n",
+    "    precisions.append(precision_score(y_test, y_pred))\n",
+    "    recalls.append(recall_score(y_test, y_pred))\n",
+    "    \n",
+    "plt.plot(k_range, accuracies, label='Accuracy')\n",
+    "plt.plot(k_range, precisions, label='Precision')\n",
+    "plt.plot(k_range, recalls, label='Rappel')\n",
+    "plt.xticks(range(1, 20))\n",
+    "plt.xlabel('Nombre de voisins')\n",
+    "plt.ylabel('Score')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "k_max = np.argmax(accuracies) + 1\n",
+    "print(\"Score maximum pour k =\", k_max)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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T/ytPvCfSX0RSo0kwuPlBaqjJXWz3tfDd2BfK6FD4ru8pRI22QHXzYzxdlehUpSA6Vy34dH2LwQDPA5/A9fpOGBSuiHvlJ+j8ykg+z/wGCxqGYRgLQREKOgvfceEeZkTMF7WS32k6QitzQeOS/qJfSbMMS3rp2KuQy0RqpISfO9pUeLwyiM7oqb7CKDQK4adbw/FW8gK8J1+Jg1FVsCWyZKax/dQqc52F6cyfoiBqFwX0egNux6ZkFk1RSbgVk5Jl+QOJjeL+7qgUoMKHMfNRMuEKtC4+kHdfjenPaOoml8tQ1FeNIj5qscJHXm059Ks7oI7mCvZW3Y1thcaIMU3CKT5Vi2vRyeKyHZHm/Xi5Ks1iyfR8SDSRENwWcg8zt1wUPVeIwt6uGNm4pCi4lT9DBHn8O10smU8vWAeplV6DpaBVXbHd1givJdWD0/DZ0FvUGGkL13/h/6pPfSccr6m+J77tN6LomHkxLGgYhmEkhCIGR28Y6yT2XYkSKaSu8n9QwuUB4mTeKNBkGLZULp7jpmt0Rl+jiI+4CAwTkLb1MlzDd2Cl/zIsKb8ModFaIRBuxaYgJiUdx27GiosJWhFT2McNMcnpmQpPM0KrcIwi6HE6iASWq1wP7+2j4JpwDAalOxI6/wp9DjrU6n1KiJVZPluHodjV5XitXBNoqncwPhWDQUSermZIa9E29YpJSNOKJdd0MUFSxc9dJQpsTam6oY1K4NXqhZ6btlLd3A+3q5tgkMmR0GxG7lypcwBFf+K6roT3368LmwLfTf0Q1/FHYSnwLFwvr4fnoZliO6nJp9CUecWic8xPsKBhGIbJI3RAvnA/EVsv3MfOS5HmAy1R3McFH8u2AKmAsv6b6FG3rDSDymRIaDkHyvun4Z10DW9pf0Fi5/+ZU0cU6TALBIqEPEgUqaPbsanmgt1SAR6Zoh8UyQnwcHmGd9B7cL22zZwC0RasneMpa0q/jOSaI4WxI3kRxQRUEjUnlBai1BJdGpfyf/x4rR7XHyY/juQ8iuZQ4TG9xhTZ6l+nCPrXLSoE33PRpQlPLCKl2hvQBVWBNaB+QWQc6bNtOFxu7oPP5tcR334RNKXbP/VY1e1/xetCJNcYgZQaw6wyx/wCCxqGYZhcQmkbKuylaMzNmMe+Nr5qFdpVCBJLcmsn/QOf7dSJ1hsp1QZLOr5BHSCiHr6bBkB97mdoircUy2hpGa+pTb/5sQYDopM0YmVN2aK+8IQBiuzU2FA9x8EpcLu0FgaZAvHtv3+m91R2SGr4EVT3T4qeOt7bRiH21b8ApVuWj6VoS/kCnuKSkZhkjXi9a5UNgiFVk63eKu6nFouVRzr3AkiubzT9tBoqNeI6/iD68bhe2wrvbSOQ0OYr0TDRhCI6VLhnk/t6aplOSGpsFF9M9mFBw1gPvQ7QOpYLL8M8SWxyOnZcisTWCw/MXWdNDdtalA0Q9RsNS/iJFS10pHVf8624P6X6GzC4Zl5tIwXpxVsgufoQ4YTstec9POyzK8uOtxQFCfR0RZCXa46arLkfmQP1ueWinoMOwppSbfM2YYUK8e0Wwm/Vy1BFhcDzn6lIbPFFjnZBy779PVwQ4OmKqNTHy8SfhTz+prCNIJIaT7HI+/BCFK4iMkPvEYlDr53jIEtPRlrV/kD8XXhvGgi5JgHpheqL19nS6bD8CAsaxmp4bR8N3DoARa9N0PpKFHZnGCvVxVA9zK6rodh3ORK6Rz45VJNSr7ivWKHUolwAPFwy/6S63NwLVeQ5GJRqpFQfarH5JTWaCJfb/4qlyF5730d8x5+yZanwItQnF8HjxDdiO7H5zEwRhbxAvXPi234LH4osnf9NHMTTKvSApfA8+KlwNtcUeUn0/7EZciUSWs+HQeUhin6pl5A8NRoI3yJWwGn9yopIzrMiVszzYUHDWAXVnf/gGva32FafWixy/wxj71CaZu/VaHx3MFykakxULOAp0kmUVqKoxzP+WTSTI1KqDIRB/bg2RHKUaiEQ/NZ0guv1XXA7/xtSqw7M0y7dQn6D56HPxXZio0lIrZp7a4CsSC/eHMl1x8Pj+FfiwK4Nqgqdf3lIjUv4TuPyZ7kSic0+l0To5QmZXMzDoHIXrtkeh2eLm/XuBUStjSWWkTsLLGgYy2MwwOPIXPNV10vrxA8kf3EZe+bErVgsOBhubm5HdTH9GhRHi5K+opj2RajuHoIq4rgook2pNcLi89UFVhaRGs9/p4kLOVzr/HIXCXW9/Bc8908U20l1xiKl9mhYguR674jXyOX2P/DeNhIxvf4GVI+6+UpBeorZnTql5gjo/MvBLpDJkNRoMgwuXvA4MgdQeSCu03LovYvZemYODSfpGIujunUAqntHxQ87AsqJ0K/b+RW2nhbDZMnlB4kYv+4cRq0+K8QM9TsZ1rA4/hpWDx++XBGlA18sZgj348bamdRKvaH3KAhrkFJjKDRFm0KmTYXXzrGiO3FuIhpeu8YLV2cqYk5u8CEshlwhIks692AoY67Aa99H4gRIKsgiQpFwCzrPwkiq+zbsCplMRKhie6wDRh2ErkA1W8/I4WFBw1ghOmMMqYoQeFPj6gJ1yHLhp8Iw9sLduFR8siUUA349if/CY0SDu541CmHd0PqiWdsLlwVnQBlxEi63D4o0R3KtN2E1qL9Km/miVT7V7ngcfRwZzQ60bJh6zcgMOqRWeBWJTT+zeIqGGtAltP9OrKByu7wObhdWSOYC7n7SaDWR2GSqtJEfCRGN9gK4C7AUsKBhLIrL9Z1QPTgDrUKNz+NfxtATxZCs8hcFcC7h2209PYYRq5bm7Q1Dz5+OieXXFB9oWyEIa16viwltyiEwq74sL4Cch4m08j2snkagaFBCqznmol6qX8uuCPPeMkREUNNKtUdCqy+tttImvXADs6cSFfAqI0PytkNhHfAxZHoNNMVbQFPa2MCPyd+woGEsxpUH8UjeM0NsL01rg98uarD7ShyWprQQt0Xs/QY7Qh+IJmAMY21S0nX44fANdPvhqDA1TNcZxIqlXwbUwoxOlVDMT52r/SqiLhiLUCFDcp0xsAV0AE+p1Eekjbx2vQ1Z6uNuwVmhiL4In80DhSWApmgTsayaVuRYk5Rao5BWso0QVFRPI0t7vCQ+p7iE/Q2XWwdEmjvBClEmxj7gomBGUiLiU7E9NFI0Gyv/cDe+c7mKBIMaKxTd0L1yQVQs4ov9x7tCE78BFTQX8N6WLfhcWQ4ty5M5XwHULeYrQv0MYym0Oj02hJCZ4U3RaI6oUMATY5uWQoOSeS9UN0dnynaGzrc0bEVik2lQ3T0MZdx1eO6fhAQSKVkc2OWx4fDZ2B/ytDjhbxTXwUbLhild1no+lKs7QBF/A1573kX8y0tzLEZkmkTR24ZIrj0aet9SFpowY2+woGHyTEKqFnuuRIpw/clbcSJkL4ce37uuFfffLDMQq9q2hatKLhp6vVqlAOI3vYLAGxsxWr0Lo5NL4+/z98WFwvvtKxYQ4qZ8AY8cuwUzzPOWYO++HIVF/143d/Ut4uOG0U1KChPIZ5kZ5gRFTBhcr24W27aKzphx8UBCm2/gu6473K5uhKZkK6RV6JnpIfLEu8IRWpH8ANqASsI8kf7PVtDKR+pE7Luuh7BZUJ9ZhpSaw3O0D/dj86FIioDOu4QQNIzzwIKGyRXksfJf+EMhYv65Fg2N7vHKhNpFfTA24DjKXroDvasPgluNh+EJwzhFvRHAjY3ogP/wS7dp2BCux65LkcKjZcWJ2+JSKsBdCBvqvFrI2z4aTQlvmZhkhESnwF8pQ0EvVxZdDsCxmzH49kA4Lt5PNDtRD2tUHN2rF4KKOvpKuKqG0jxpJduJZdS2hvyWkuu/K5YGe+7/WDSwM/gUF/fJUqLhs7EfFAm3ofUphdguv8Pg5mvrKUMbXBOJTT6B14GP4XHoc6QXrA1twTrZ+l+yDyARRCQ2nS768zDOAwsaJtvoDQacuROPrRfvizPd+FSt+b7SAe7oWDkY7SsGoaCHAv6/G8+MaIWHwfWRO/ATP1oU3lZFnECd6A2o1OZdvNeyjFhdsu3ifRwIi0Z4dDK+++e6uNQq4o2XKwejTflAeLuprHI2fz8h7SlTvBsPk5FBu8HDRSEM/YTB3yNzvzKBHjlaEcNYjkv3E0UvmcM3YsR1d5UCA+oWRb+6RZ7q6ptX5PG3RI8lIrnuWNgLybXHCFNE4Z20azziuq8BUmJFmkkZc1UsaSZHaFptZC+kVh0M1d0jwhmbVl3FvLb9xY0JRSHwZLFCi4qaydOKcS74V5d5Ideik0RNDF3uxT/2YgryfJweooO5KVLhduEPkQPXqwOEq+2zSKk+RAgadcivIjyvUrigedkAcUlM02LP5ShsDX2AEzdjcepOvLjM3XNVuPF2qBws/pJ/Tl5J1pAz8WPhQs7E9Dch7bFgy4iXqxLBPm64HpWEJI0OZ+7Gi0tGCvu4PRY6j/4W81VzfZAVTSO///e6qOcilHIZXq1RCEMaFoe/e85XLWUH91OLxMFUU6wZtMG1YDdQr5c2X8NvZTshatypyeWD42IlkV4dKMSM3qsI7AqZDInkJB51HsrYa/DeNVZ00X3eqivXy2vhcveIsJmg+iHG+WBBw2RJVGKaOBhQSunSA2OY3hSRaFUuUKSB6mRVwKtLg/uxr8Rmcu23npuPTyvdETqPgiLf7Xp1E9IqvGq+jyIcXaoVFBeKlNBqKJoLiY59V6PFxdNVgdblg4SgqlXU54U1EBRhuhOb+kiwJBoFTFQSbsemZvl4hQwo4e9ujryUC/IUwiTYywVBQd64dz8O16NTHkdxohKFEHqQqBE9TehCkSYTJL4okiX2F+RpFDqBHvB1t3zEyVl4mKTBD4dvYu2Ze8J/iaCo4ajGJVHU13LpB3lSBNwurBTbyXXsJzpjQu9dHInN/wfvXW/D/cQC420u3iLNZMvC5edhcPFE/MuLhZ2Dy839Yt7Jdcdl+VhZWhw8//2f2E6qOx5676JWni1jD7CgYcxQVGRzSIQQDsduxuLR8UCIlpdK+omoSNPS/nBTKZ65DxGdSbwjOn+mvMhLRqESoWWPI7OgPvuj6NmR1YqGYC9XDKxXTFyuRCaao0UkHDacixAXegyJLBI3VHgcl5KeKeIS9ijqkqrVZzmVAA8XcySFBAeljUr5u8MliwiQaYpUe0GPp8vLlR7fH5uSLsbLmKqiS5pWL2o4jHUc9zNFumi8jONT/RCTfZI0Wvy26zKW7L+G5EdtAMjxekzTUqgQ7Gnx8dWnloieJ1Sjkl64IeyRtPKvIvXGXrhd2SCazMV3/sUu6nyehy6gEhKaz4D3nvfgfnSuSFOnF2381OOoeac8JUqYO5LFAeOcsKBhBEv+u4Ffjt1CavrjA371wt5CILQpH5S9KII2xdzuXdQQZKMgL6VKP7gf/0o031PeP/nC4j+KktDlraalcOp2HLZeeIBdlyNFFGf50Vvi4ueuQkxy1l2IXRQylA7IXPNC21KmIcjzh6JXdDFB7sx34lIzRYboL90WmagRl8PXjXUeJhFJ0SEqMLUk/p6uGNagGGoXtX0xaG6h4vRp2y7h4aP3vFKwpxAy9UtYxytMlvIQ6vO/mn2P7LbnCaVxWsyCLqACPKp1gNa1HCz88ZKEtEq9kXL3KNShq+C9Ywxiem+D3iPYfL/iwTm4hRhff2E+qbBMSpGxf1jQMPjr7D0haIjifmrzyqKchuipFkaRfB86zyJIrdw3W/9jUAcgtXw3qC+ugvrMD0jI5moGSi+ZRMMHrcuKlVYUtfnn2kOzmCnk7fooVfQ4xVPUTy3qKawNCRR6benSqnxQpshCWFSyMYojIjqJIqKUmKbDtagki8+Lxj59KxbTO1QUS5cdjU0hEfh8x2VRqF0ywB0jG5VAq/KBkizBzi60qkamTUF6UHWkFzc2jbRXKI2TUnccPAK9gCij6aYjkNjsf1BFnoEyOhReO0YjrusqQKEE9HrRY0dm0CO1XNcsozeM88CCxskJuReP2Xuuiu132pRHvxp05pOLg4EmCe4nF4rN5HpvA2REmU1Sqg8VgsY17G8kJd6F3rNwjoam2hSqpaFLfGo6EiGHj9wg+SoWS+DhohSRMLpkXGH1IDEN8QYZ4uKSpfTqywQd8zecj8S28xGYtPmiaDLXu7adFYc+A3qNfj56S6yAIzpWLoCv+tVBfGySxV6vZ9VuqM/99Dgqaa/RGUdHpUb8y0vgu7qDKPz1ODIXyS99BJz6Bar7p6BXeSKp8RRbz5KxMfb/i89YDOr5MmHjBdHyvWW5AIxtVRYPHybm6oBAP+rylGjRzCr1ieZdL4Ly+JrCDeFy97AIHSc/8nTJDT5qFcoEeiEqKsGqBzYpodViBb3dUNXCz4OOve1qFsNHq09hzel7mLs3TNQljWla0q5761D67su9YVhz+q64PqheMYxtVjLLeidLoz63HHJNArT+FaAp1d7q4zsTVLyc2HIuvHe8Kfr96PzKAP9NF/clN3jfao7mjP1iUy+ntLQ0TJo0CXXr1kWTJk3w448/Zvm4gQMHokKFCk9dJk6caPU55xfSdXpM3HRBHMCo+HVqhwqQ5zIVQ54rtGSVSKr/rij2zSkpNYaKv+rzv4laHMZ6qbAPW5cV3XIJqqOauu2SsAewR6iwmqJJJGbo00q9i8Y2K2UbAZaeDPWZpY+7AlvJyNGZSSvXGSnVXhfbXrvfAVJiRIdj022Mc2PTCM3s2bMREhKC5cuX4+7du5gwYQIKFy6Ml19+OdPjvv32W6SnPy7yPHPmDN5++23069fPBrPOH3y17xpO34kXy7Bnd62cp/QM/aiTD4zWrxzSynXL1T401FnVq6joWup2+a9s1+AweYfEwBsNigvbCapH2XLhgSiwndW5Mtxdnr2izRYWG+9tOC+KwVUKGaZ1qChcsW0FiW95aoyISpJvE2MdEhtPgfL+KbGQQFxvMcPqRpqMfWKzU4rk5GSsWbMGkydPRpUqVdC2bVsMGzYMK1aseOqxvr6+CAoKEhd/f3/Mnz9fPLZatWo2mbujQ4WUqx+F66d3rIiS/rlfIixLjYH6tPEsNan+e6KJV66QK8xnWbSE22HzRQ5M56oF8WW3qnBTysWKq1Grz5jNG20NrWIbvuq0EDMkwr99tZpNxQy0qVCfWiw2k+u8xQdUa6JwFX5P6cE1gWYfQFuonq1nxDi7oAkNDYVWq0WtWo87atapU0dEX/T6Z4e7161bh7i4OAwfnjPDMsbIhYgEfLHritge0agEmpUJyNP+KNUkT0+ENqAyNGU65mlfqZX6iC6fyuiLUN09lKd9MbmjcWl/fP9adbH0nPrlDFt5GrceGTnaCurpM+T3U2JFFkWRlvapkWlJvC1wC139aEVfoRzXjDF5R+9dDHG9NgOtPrb1VBg7wmanFZGRkfDz84OLy+OeAYGBgaKuJjY2VkRislrZsGzZMgwaNAgeHrlzhJU61W7anyVT+FKNQV1UP9x4QRhJNi3jj2EvFX9q3zkZQ5YcCfXZRys8Gr4Pmfz5+viFY6h9kVqxF9Qhv0B99gdoi76U/clkdwwJyO9jVC3sjR/71cTYP8+JLspD/ziNr1+tisoFvSQbI7ucvh2Hd9afFzYUJf3VIjJTyMfNtq+XPh3uJ78T2ym13oRMKV3fk/z+2eIxeIzckN192kzQpKSkZBIzhOm6RpN1mPvIkSOIiIjAa6+9lutxAwJy/qP8POZsD8X16GR81rWq6DZrSfIydyoCfmttiAjdlw70wMKBdbM0eczRGNtmGAt4C9eGd92su/xmxXPHaD4GCPkFruE74aqIBvyMxaq2fp+dbQzqtrx+TGO88dMxnL8bj1Grz2LRgDponqGHTl7HeBHbQiIw/s9zohC4dnFf/DC4Hvxe8B2zyut1dxuQcBvwCIJnsxHwVElvqZCfP1s8Bo9hKWwmaFxdXZ8SLqbrbm5Pn4ER27dvR7NmzURNTW6JjpZ2Gey2c/dEKDzkViy+ebUqiljAL4Z0An1I8jJ3WuZ6JPyhcBue1bkSNImpiEpMzfUY8sS78Dv2g1hpElf3PaRHJ0rzPGSF4V2sOVxu7Ufy/u+Q3GSK1V8rHsMIxdu+61kVH2y4gKM3YjHk52P4pH15vFIlWLIxnsWfp+9i9u6rwn6D0qIzOlWELiUNUSlpko6TE8QYfu7Q7ZsDqhRLqjEcKXFkYJqQr953HoPHsLcxTPu2W0ETHByMmJgYUUejVCrNaSgSM97ej5uMZeTgwYMYM2ZMnsalF1rKF/uLzpXx9vrzuBGTgiF/nMHXPaqiQgHLeMfkdu5bLtzHHyfuiG1ank1FwM/aT3bHUB9fAJkuTXjXaIo2y1EL9ReNQS7cJGjIFyqp3rvPNbjM7RhS4AxjuKuU+Kp7VWEtQGaln269JGwaBtUrmqOl0tl9HpRW/v6/G/jx8E1xvXv1gviwdTnR3flZ/y+PvwmPo3MB3wJwdSsGrU8Z6PxKQ+9RSPr494UNUMReg97VBylVBlnsvbH1+85j8Bj2PIbdCZpKlSoJIXP69GnRh4Y4ceKEWLkkz6IW4+HDh7h165YoHLYnyERw3eiXMGDpYeHNM3LVGczuUtlqPjIv4tL9RMzYaSwCHtKwOFqWC8zzPuXxt4TYIJIafCD5QUNToiW0PqWgjAuH2+W1SK06CHaFXgtEhwEoAGeATDhpNVyQpyt+O34bCw6GIzIxDe+0KPO023oeIHfsmTsvY2PIfXPR+rBGVOf1/DE8Ds8yGi6SS3uG2w1Kd2h9S4sGbNSUTedLQqcMtD6lcyWSxa/0wS/N3a3JRoBhGPvBZoJGrVajW7dumDp1KmbMmIEHDx6IxnozZ840R2u8vLzM6acrV66INFXRovZnCx/s7SZWXrz313mcuBWH8etCMK1DBbSraNsDXmxyOj7YeF7UIDQu5S8OEFJAZpJUGKkp2hTpRRpBcmRypFR/A14HPxFLuFOrDLCfpmW6NPhs6AvcOwq3JlORUmMYnAHyRhrfvLRwBp+/7xpWnborlnRP7VBRWE/klZR0nWiYR15cpJE+alMO3asXeuH/yVJj4Xptm/FK7UHQPLwLeUwYFPE3IdMmQxUVIi5PovMoaBY4JHa0j7b1nkWe2XrA5fou4H4I9CoP8flkGMa+sGnzBOr0S4Jm8ODB8PT0xNixY9GuXTtxH3UOJnHTo0cPcT06Olqkouy1JbunqxLf9KgmQvLk/jz571ARmu9f1zYCjM52J/19Effi01DM1w2fdawoydk0hdvdQv8U20kN3oelSKv4GjwOz4Yy5ipUtw7Yh+mfwQDPA1OgundUXPX4d7poeZ9erCmchX51ioql08bPeRQeJp/D3K5V4OWmzJPwfuevEITcSxDi6PNXKqF52ey1E3C9skGkPrUBFaHs/A3iox9Zd+g0QtQoSNzEXoMiNgzKR3/JokORFCEuuPNvpv0ZFK7Q+ZQ0RnJI5IiLMcqjPv61eExqtcEwuNlHBJZhGDsRNBSlmTVrlrg8yaVLlzJd79ixo7jYM+Ql83mnigjYqxJnsF/tvyZEzbjmpazq/kssPBiOYzdjoVbJMTuPB5yMuB+dB5lBh7QSraHNpjN2bqBwfmql3nA/+4OI0tiDoHE7/xvUF36HQSaHrPhLkN34B97b30RMr7+h95Em+uUIUOTRz10lioVP3o7DiFXG2rECXtk3JDVxJy4F49aG4GZMCnzclPiyWxXUKOKTo34wph5Gnhm/YwoX6PzKiktWzSCNIucalELwmERPuBBHyoeXxCXLZ6N0Q0pN7oHFMPYIt7eUGBIu5C9TwNMV3x4Mx4oTtxGVlIZPX64gahGswY7QB6LWgaBxywbmrmfPkyiiL4kzYpMZnKWhzsEkZlxv7BEHHDpTthWqu0fgedC44iq50UfwaDke6UvbifbrPluHIubVjYAq9x2XHY16xf2wuHcNkV69So3v/jgtesRQTVlO6rvGrw8RqauCXq7i/0vm4P8VURfE62+Qq5BWoXum+pnnQdEVEuN0ybRmSq+DPOE2lCaBYxY7YVAkGet60GAUDO5BOSqCZxjGOthJYUL+gtJig+oXE3U0lOah1SFvrwtBYhot8bQslx8kYvr2y2J7cP1iaJ3LviFZ4XHsS8hgQFrpDtAGWd52Qu9bCpqSrR/bIdgIWqLuvW0kZHotUst2Ec3UoHJDQsel0KuDoIwOhdfud53OroFW8/3YtyZK+KlFfyPqKnzmTly2/vfojRiMfGStUC7IQzTyy4mYyRid0ZRsA4M6bx2vBXKFiLRpSrQStVGJLWYirttqPHz9BKKGh+LhgANA60/yPg7DMBaBBY0F6Vg5GF91ryLSPkdvxooVUFEW9MaJS6Ei4AuiCLhhCT+82Th3TemyQhkZAtewLTBAZvRsshK0moRwDV0jXL2tjjYV3luHQ54SJewdElrNNa/q0nsWRlyHJSJC4Ba2GeqTC+FsFPZxw7I+NVG1kBfiU7V4689z2H816rn/s/3iAxHZSdLoUKeYD5b0riFWUOUInQZul9aZ002WhlKgeooQ5tarjGEYi8OCxsI0LEneODXg767C5cgk0Ub+xsNkycfR6Q34+O9Q3I1LFQeZ/70iTRGwCXfq80HFuuW6QBdQEdYivWgTaP3KQ56eZD4jtxoGA7z2TxRpDb2bH+I6/vBUWomM8RKbfmZePuxyYw+cDV93Fb7rVR1NSvsLMU32GuvOGM1Pn2TF8dv4eEuoKFpvUz5IFNJTQX1OoRVH8tSH0LkHQ1O8uQTPgmEYR4cFjRUgD5wf+tZEUV83ITiGrTyDkHvSRhsW/Xsdh2/ECKfkuV0rw0f9tK1BblFGnIDr9V2iGDbZitEZgUwmGu0RwjdKr7Pa0JTmcgtdA4NMIdx9yRAvK1KrDkBKlQEiHee1Y4yov3A21CoF5nStgq5VC4ruvjN3XcXif6+LRnmE3mDA/H1holCe6FO7iCigp0L63OB2cZX4m1axJztdMwwjYEFjJYr6qoWoqRTsidiUdLy5+iz+vfZQkn3vuhSJ5Udvie0p7cujXJC0Db88jhijM6kVetmkMDe1wquiM6si/obVIiCq2/+KZdlEUuMpSC/a+LmPT2w6HemF6kGuiYf3lqGQaaRrh+8oUDffye3KYWjD4uL6ssM38fmOK0jR6DDl71D8/qhb9bhmpfBui9K5XvknT4qAy829YptWwjEMwxAsaKyIv7uLSD81LOmHVK0e7/0Vgo0hEXnaJ60wmb7duMR9QN2ikjfzU905BJfbB2GQK5Fcb7yk+87+JNRIrdxPbJILt6WhTsje20eJ5empFXqa63iei8IFce0XQ+cRDGXMFXjtehsw6OGMBfGjGpfExDZlRYO8v85F4KUvdovCeEqBUqH8wHrF8tRPyjX0T8gMeiEgbbnyjWEY+4IFjZVxd1FgfrcqeKVyAegMwGfbLwvfGlNoPifEp6bjgw3nkZKuR73ivniraSlpJ2swwN0UnanUF3pv45m3LUipOlikvFxu/wNFdKjlBkpPgc+WoZCnxiC9QA0ktJiZbWsHg0cBxHdYBoPcBa7h2+F+7CvYPVrLFKn3qFEYszpXFo3yYpLTRWE8FchToXyeMBge956pyNEZhmEew4LGBigVctEfhpZVm+pfyFmYCnuzCz12ypZQ3I5NRWFvV8zoVEmE/KVEdfsgXO4dEd1Tk+uOhS3RexeFpvTLj2tpLFUEvPd9KKMvQK8ORPzLSwFlztzTtcG1jCJILHOfB5dr22GPyDSJ8Nr2JjCzCFxM1gES06JcIL5/rTp61SkqrEGoQD6vKCOOi46/5NOUVraTJPNkGCZ/wILGRlDIfUzTUvigVRmQDPnzzD1M3HxRrBLJDksO3cB/4THiDJg6AftKWAQsMBiE9QBBBa+0RNnWmFI/ZFhJ3V6lRn3qe2FySOm1+JcXQ++Vu+ecVqk3kqsZvX68do2D4qGxL5C9QEXLvn92gevVTWL5s/uhLyyWHqtW2BtzetVAxWAvSfbndnGl+JtatjObQzIMkwkWNDbmtVpFMLNzJagUMuy9EoWxf54VqaTnsedKlEhTER+3Ky8anEkNLYtVPTgNg1KN5NpvwR5IL1Qf6YFVINOmwu3C75LuW3VzHzwOz3xc4Fu4QZ72l9T4E2iKNBLLzUWRcFr2Gs5ZGpfru+G7phOUMZfFkme4+gi/LEtFaSRFkwS3K5vEZlql12w9G4Zh7AwWNHYAdfOltu+ergqcuhOP4SvPICI+NcvHXotOwrStxiLgfnWK4OVKFnD0NujNfWfIfoBqQ+wCsYTbGKVRn1sO6KXpvCyPDYf3jrdEoWlK5b5IrTIw7ztVqMRSb51nESjjwsVybmsuOc/yPT32Fbz/fl2sxEovWBexvbcADUaIu92pKaCddzp2DdssHLS1PiWFuGUYhskICxo7oU4xXyztXRNBni64Fp0sGvCFRSVlegxZJ5AhYHK6scPq2GaWWeHhErYFqqjz0Ks8kVx7NOwJauynVwdAkXhXkqgC1ZL4bB0GeVoc0oNrI7HZ/7JdBPwiqB1/fMcfYFC6wfXmXngcmQNbQEvIqduxx9G5oldOStVBiO22GgaPYKM3kdJNNA9U3f4H9ozbxcdGlFK9RwzD5B9Y0NgRZcnTpm9NlPJ3x4NEjYjUnLptTFXoqQj471DhShzs5YqZFigCNg6kg8fRL8Um+dmQkZ9dQW7HVQaITfe8+jsZ9PDa/bZwVqb0S3yHJYAi547Rz0MbVBUJLY1Cxv3kArg+SplYC0XMVfj+2VmsuqLVVzSXxOYzxDJzgUegeUm8+4kFsFeo7kcUqMvkSKvwqq2nwzCMHcKCxs4o6O0mVoRUL+yNhDQtxvx5FnsuR+Hr3Vdw8NpDuChkmNO1MvzcHx2QJIbctKmPCjWyS6k5HPZIatWBonBXde8olJHncr0f9xPfwvXaNnGgJzGj9ygIS5BWvjuSa44U21573hUu0dbAJXzHo3qZq9B5FERsj7VIrdz3qcel1BopXk+XO/9Cef8U7Dk6oynWHHrPQraeDsMwdggLGjuEbAsW9qyG5mUCoNEZMGHjBSFoiElty6OSRCtGnkKnhfvReWIzpeYoGFx9YI+Q8Egr0ylPLtwu4TvNPXYoYqEtWAeWJKnRRGiKNYNMmyJSXJZYpZWpXubIXPhsGQJ5eiI0hRsg5rWtYkl5Vui9iiCtfA/7jdLodXC9tEZscmdghmGeBQsaO8VNpcAXXSqjR/VCMJVq9q5VGK9UyWNjsudx5g8o4q5D7+aP5Ef+SfaKyd/J9fIGyJIjc5yGoeXUop6k2mCkVra8WzP5DcW3Wwiddwko4m/Ce/toyYqaM0Krqby3DIHHcWNTP3of47qshME96Ln/R7VS5KROqSl7W2bucms/FEn3hUGoplRbW0+HYRg7hQWNHUM1Mh+1KYtJbcthbKuyeKeFBdu869KA/bPEplim7eIBe0ZbsDbSg2tBptdAfX5Ftv9Plmb0WpJrEkTkIrHxVFgLqkeK67hMNIUjOwmPQ8Zl4lJBQoRSTMJIVOGK+NZfIanpdLHi6kXo/MqaGxe6n/wO9oTJiDKVokgS1zgxDJN/YEHjAA34etQohPfaVRAdhi2F2/nfgbhbojiWVsE4AuZGeyG/iAZx2SoC3jUOytgw6DwLIb794mwd7KVEF1AJ8W3mi23304vhemmdZCvTqPiXlojTUvHYHuuNTtQ5wNRvyPXKX5DH34Y9IEt5KGqBCE43MQzzPFjQODmylGh47p0Aj4OfiuspZHGgylm7f1uRVqajEGCK5Adwvbr5hY93P/rl4+hFhx9gcA+ELdCUeQVJdcaJba+9H0D54Gzud6bXwf3wLPhsGyGa+GmKvISY17ZAW6B6jnelDa4JTdEmkOm1cD/9PewBt8vrINOnIz2oGnSBlW09HYZh7BgWNM6KTgP16aXw/60p1BdWiKZyqN4bqVWMS3gdAoULUqsNylZxsEvY3/A4/rXYTmg5K1cHfClJrv8e0kq0hkyXBm8qEk6OyvE+ZKmx8Pl7MDxOfGvcZ40RiOvyu+h/k+t51R4j/rpd+CNXc5IUMqI0pZs4OsMwzAtgQeOEuNzYA7+VbeH57zRj19jAqmJJL3pQHxbLLAe3FCmV+4tl12TToIw4keVjyJ3be9c7Yju5xnCkVchZKsYiyBVIaPsNtL6lRZNA7+0jAd3zLS+efE5+a16By819ojFefNtvkdTkE1F8nBfSizYWLuMktNRnf4AtUUaFQBl9UUTU0sp1telcGIaxf1jQOBGKmDB4bx4En82DRB0JOUpTo7XYXn9Dm0fvIltBaaO08t2eGaWh5dE+5KWkTRbplKSXJsNeoGXxlPqijswud48IgZkdqDmf35+doYi/AZ1XMcT02CB63UiCTIbkOmPM9hLUZdhWmIwo00q1t78GjwzD2B0saJwAWtnj8c90+K1sDdcbe2CQq0Sjt4f9DxgbrckVcGTMS7jD/oY88d7jO/Q6eO8YYz7wx7dflOcIhtTo/MuJSA2hPvcz3C4YD+LP7OL83+fw3vGm6GdDfW2oXkYXVEXSOWlKtYfWr5yI3omCa1ugTYXr5b/EJqebGIbJDixo8jN6HdzOr4D/iqZwP7NEFHumlWyDmL67kdR4Cgyu3sgPkL2AplAD8fzcQn413+5x+AvRw4Qcw+PIU8lOz/I1pdohqf57Yttz/yQoI05mHWnaPBDupxaZ+8bEdfrVMs9JJjd7eLmfXgZoU2BtXMN3CH8tnWdhpBdtYvXxGYZxPFjQ5FNUdw/Dd01HeO2bAHlKNLR+ZRHb6VfEv/IzdL4W7GdjI1JqGKM0biG/AempcLn8l/ngn9Bqnt2vkEmuOx5ppV8WfXW8tw2HLOm++T5F5Hn4re4Il1sHhDiLb7cISY0mWTSyllaum1j+LU+JhFuosUuvNTEXA1fs5fARRIZhrAMLmnwG9Q/x2v4mfNf3NDpmu3gjsclUxPTeifQSLZFfoTSJOACnPgR2TYXXnvfNvVXSynWG3SOTI6H1V9D6lRddcb23jgC0aXC9tB5+67pCkXBLdBmO6bnROs9HoUJyLaP/lPup7y3S1fhZyBPuQHXrgNhOrfia1cZlGMaxYUGTX0hPFv49/r83h9vVTcKVmFypHw44KFyzrd1AzurIlcLGQHBkEWTaVGiKt0BSgw/hKBhcPEVqjIxBVbRia0lLeO0cK55LWvGWiOn1t2jMZy1SK/WFXh0grBpcr1rPJZwiQmRLoSnSCHqfElYbl2EYx4YFjaNjMIjiSRIy5N9Dy201hRsi5rVtSGzxRZ56kjgaVOBMS5gJnU9JxLdd4HDpCr1vKTFv8lXCg/PiNmrCR6lCg5uvdSejUpu7MbufXCg+axbHoDenuLgYmGGYnMCCxoGhDrO+63vAe+cYKBLvQedVFHHtv0dctzV2XzNiCahANrn++0DBaoh/5UfrCwCJoNRgUouZQIEqiO+wBMkNP7SZMKOoFy0rV0aHwuXGbqvUftGqNBozrfQrFh+PYZj8g32tYWWyhSzpATyOzILbxdUiNE+FotQ7JLnmCEDpGLYFliKl9ih4tPsAuqgEmG3KHZDUqgPg2eJNaGz8PKhXTmpV4+oq9xMLoCnRWvSqsXQxsGik5yAWHAzD2AcsaBwJ6t56+ke4H/8a8vREcVNq+e5IajQRes/Ctp4dk0+h7srUtFAVcRyqe0eQXrihxfolUS8hIrUSFwMzDJMzWNA4AlS7cGkr/LZ8BEXcdXETtadPbDIN2kJ1bT07Jp9j8CggVhupz/8qojRxFhI0VHhMBdDU1E8bXNsiYzAMk39hQWPv6NLgtXMcEPY3qIpCrw5CYqOJSKvYUyz1ZRhrkFxrFNwurBDeUcrIENHMUGoyGVFaMK3FMEz+hI+I9ow2Fd5bhxvD8GRXUHs0Hg44gDQKx7OYYawILZ9OK2vsf6OmFU8So3h4Gar7J2GQKZBa4VXJ988wTP6Hj4r2LmbIe4mWIg9Yi+SXJsHg4mXrmTFOCjUpJEhgK2KvWSQ6Q0XHBvcgSffNMIxzwILGHtGmwmfrMLje3CvETHyn5UDp5raeFePkUCuAtBKtITPooX5kKyHNjtPhdmmd2OTeMwzD5BYWNHYpZoaKWgVhqtjpF6QXbWzrWTGMgNoDEG6hf2Z2Ns8DLjf3Cs8ovToQmhKtJNknwzDOBwsae0KbAp8tJGb2PxYzRV6y9awYxoy2UL1HzubpUJ9eKm0xMNXO5HeLDoZhLAYLGnsTM7cyiplGtp4VwzxFSh1jLY36/G+QpcbkaV+y5Ei4XN8ltjndxDBMXmBBYw+kp8Dn7yFwuXUABqU74jr/ymKGsVs0xVsiPbAKZNpkqM/+lKd9Ue2MzKBDenAt6PzLSzZHhmGcDxY09iBmtrwBl9sHH4sZCzUuYxhJkMmQ8mjFE3UQhiYpd/sxGDL3nmEYhskDLGhsHpl5HS63/4Fe5YHYzr8hvXADW8+KYV5IWplXoPUpCXlaLNQXfs/VPpT3T0EZc1ms5Esr20XyOTIM41ywoLEV6cnw+XswXO78K8RMXOffoC1c39azYpjsIVcgpdabYlN9erHoaJ1rI8rSHWFw9ZZ8igzDOBcsaGwqZv6DXuWJuM4rxOoRhnEkUiv2hM4jGIqkCHMfmWyTngLXKxuM++F0E8MwEsCCxhZiZvMguNw5ZBQzXUjMsMEk44AoXJFSY4TYVJ/8DtDrsv2vrte2CMd4nXdxLoBnGEYSWNBYE00SfDYPhMvdw9C7eBnFTME6tp4Vw+Sa1Cr9oXf1gTIuHC7Xtmb7/8zFwBXZl4xhGGngXxKriplBcLl7xChmKM3EYoZxcAwunkip9obYdj+xQKxcehHyuBsi3WqADKkVe1lhlgzDOAMsaKyATJMIX4rM3MsoZmrbeloMIwkpNYaKZpCqqBCobu1/4ePdQleLv+nFmkLvVcQKM2QYxhlgQWMFMUNpJtW9o9C7eCOuy+8sZph8hcHNDylV+ott95MLn/9gvQ5uoWvEJhcDMwwjJSxoLIhMkwCfTQOgunfssZgJrmXraTGM5KTUHAGDXCWK3ZURJ575ONWdf6FIvCvqbtJKtbfqHBmGyd+woLG0mIk4Ln6847r+AW1wTVtPi2Esgt6zMFLL9xDb7icWvrj3TPlugNLNavNjGCb/w4LGomLmhFHMdPkD2gI1bD0thrEoKbVHi0Jf1+s7oIgOfep+WWosXK9tE9upFTndxDCMtLCgkRhZWjx8NvZ/LGa6roS2QHVbT4thLI7Orww0ZTqIbXfqS/ME1EhPpkuDNqAStEHVbDBDhmHyMyxopBYzm/pDdf/kYzHDP9yME5Fce4xZvMjjb2a6L5MRpUxmk/kxDJN/YUEjEbK0OPhs7AfV/VPQu/oirusqFjOM00HRSE2xZpAZdHA/tdh8uyLqAlSRZ0XhsKnWhmEYRkpY0EhBSiy8N/SD6sFpIWZihZipautZMYxNSK79lvjrdnElZMmRmaIzmlJtYVD723R+DMPkT1jQ5BEqdMSv3aB6cAZ6Nz/EdlsNXVAVW0+LYWxGepGXkB5cS9TLqE8vA7QauD4yr+RiYIZhLAULmjziteMt4O4po5jpugq6wMq2nhLD2BaZzFxL4xbyC3B2FeSpMcKZW1O8ua1nxzBMPoUFTV4hh2HvIojrxmKGYUxQaknrVx5yTQKw5X1xW1qFXoBcaeupMQyTT2FBk0fiu/4BjD/LYoZhMiKTI7nOaOO2NlX8Sa30mm3nxDBMvoYFTV6h5acKPutkmCdJK9sVOq+iYju9UH3ofEvbekoMw+RjWNAwDGMZFCokNfkE8AxGcr3xtp4NwzD5HA4tMAxjMTRlOgINeiM9KgEw2Ho2DMPkZzhCwzAMwzCMw8OChmEYhmEYh4cFDcMwDMMwDg8LGoZhGIZhHB4WNAzDMAzDODw2FTRpaWmYNGkS6tatiyZNmuDHH3985mMvXbqEvn37onr16ujcuTMOHz5s1bkyDMMwDGO/2FTQzJ49GyEhIVi+fDk+/fRTLFiwANu2bXvqcQkJCRgyZAjKli2LTZs2oW3bthgzZgyio6NtMm+GYRiGYewLmwma5ORkrFmzBpMnT0aVKlWESBk2bBhWrFjx1GPXr18Pd3d3TJ06FSVKlMC4cePEXxJDDMMwDMMwNmusFxoaCq1Wi1q1aplvq1OnDr7//nvo9XrI5Y+11tGjR9G6dWsoFArzbWvXrs21U4GU/HLlRyRdiceb5cZDJvXOH2HarYV2z2PwGA49hrXG4TF4DB7DNmNkd58yg8Fgk/6d27dvx/Tp0/Hvv/+abwsLC0PHjh1x6NAh+Pv7m2/v2rUrXnnlFdy6dQt79uxBkSJFMGHCBCGAbE3TlU0RmxaLjd02opRPKVtPh2EYhmGcEptFaFJSUuDi4pLpNtN1jUbzVHpqyZIlGDRoEJYuXYq///4bQ4cOxdatW1GoUKEcjRsdnQApJZyfi78QNJfuhcErPRCWgNRpQICX5HPnMXiM/DCGtcbhMXgMHsM2Y5j2bbeCxtXV9SnhYrru5uaW6XZKNVWqVEnUzhCVK1cWkZ0NGzZg1KhRORqXXmgpX+xgdUGEJ1xDRHKERX+wLTF3HoPHyE9jWGscHoPH4DFs/123q6Lg4OBgxMTEiDoaE5GRkULMeHt7Z3psUFAQSpcunem2kiVL4t69e7A1JGiI+ykRtp4KwzAMwzgtNhM0FHFRKpU4ffq0+bYTJ06gWrVqmQqCiZo1a4o+NBm5du2aqKWxNSxoGIZhGMaJBY1arUa3bt3EUuyzZ89i165dorEe1cmYojWpqaliu0+fPkLQfPvtt7hx4wa+/vprUSBMxcK2hgUNwzAMwzh5Y72JEyeKHjSDBw/GtGnTMHbsWLRr107cR52Dt2zZIrYpErNs2TLs3bsXnTp1En+pSJjSVramIAsahmEYhrE5NisKNkVpZs2aJS5P8mSKiZZor1u3DvaGKUITmRoFnV4LhdymLynDMAzDOCVsTplH/Fz9oZKroDfoEJkWaevpMAzDMIxTwoImj8hlchTyMPbC4bQTwzAMw9gGFjQSUMiTBQ3DMAzD2BIWNBJQ2KOw+Hs/mQUNwzAMw9gCFjQSwBEahmEYhrEtLGikjNCwoGEYhmEYm8CCRgIKe7KgYRiGYRhbwoJGAsyrnFLvQ2/Q23o6DMMwDON0sKCRgGCPYMghR7peg1hNjK2nwzAMwzBOBwsaCaDGegFugWKb004MwzAMY31Y0EgEm1QyDMMwjO1gQSOxoIlgQcMwDMMwVocFjURwhIZhGIZhbAcLGolgQcMwDMMwtoMFjUQUfCRoHrCgYRiGYRirw4JGIjhCwzAMwzAOKGgSEhKwYsUK/O9//8PDhw+xd+9e3Lx5E85KAXWw+JukTUJieoKtp8MwDMMwTkWuBM3ly5fRrl07rF27FitXrkRSUhJ27NiBrl274ujRo3BG1Eo1fFx8xTZHaRiGYRjGAQQNRWX69u2LdevWQaVSidtmzpyJfv36Yfbs2XBWgt146TbDMAzDOIygOXfuHLp16/bU7X369MHVq1fhrHAdDcMwDMM4kKDx9/dHeHj4U7efPHkSAQEBcFaCH9XRsKBhGIZhGOuizM0/DR8+HB9//DFGjRoFg8GAw4cPY/369Vi+fDneeecdOCscoWEYhmEYBxI0lFoqUKAAfvjhB7i5uYm6mVKlSuGzzz5Dx44d4aywoGEYhmEYBxI0y5YtQ6dOncSybeZpQcPN9RiGYRjGAWpovv/+e6Snp0s/GwcnWF1I/I3RxCBNl2br6TAMwzCM05ArQUPRmUWLFuH69evQaDTSz8pB8VJ5Qa1wF9ucdmIYhmEYO085HThwAHfv3hWFwFlx8eJFOCMymUysdLqeGC4ETXHPEraeEsMwDMM4BbkSNF988YX0M8lHdTQmQcMwDMMwjB0Lmvr164u/lHIKCwuDXq8Xq5zKli0LZ4dXOjEMwzCMgwia+Ph4TJw4Ebt374aPjw90Op3wc6pXrx4WLlwILy8vOCssaBiGYRjGgbycIiIisGXLFhw5cgTHjx/Hpk2bkJycLDydnJmCj1Y6saBhGIZhGDsXNHv27MHUqVNRunRp822Ubvrkk09E1MaZ4QgNwzAMwziIoHF1dYVcLs9ylQ+ln5wZk6CJSouCTq+19XQYhmEYxinIlaBp1aoVpk2bhps3b5pvowJhSkU1b94czoyfqz9UchX0Bh0i0yJtPR2GYRiGcQpyJWg++OADEaVp164dGjRoIC4vv/yyKBCeMmUKnBm5TI4Cbuy6zTAMwzB2v8rJ29sbv/76Ky5duiSWbZO4oWXbGWtqnD3tdCf5NgsahmEYhrFnQUN2B1999RWKFCmC/v37i9t69OiBl156CePHj4dKpYIzw4XBDMMwDOMgy7b379+PihUrmm8bPXo09u3bh1mzZsHZYUFjOzbe+Auvb3sdD1Luw5HZc3cX+m7ui/CEa7aeCgMgLP4qRv0zBPtv7bf1VBgAkamRePvQW/jwwIe4FBdq6+kwjixoduzYgblz56JOnTrm29q0aSN60FBvGmeHBY1t0OjSsDR0EU7cP4EloYvgqMRr4jH/3ByERIfgm5B5MBgMtp6S07MibLk4cE49NBUp2mRbT8ep0Rl0mHF6Ks48PIWt4VuF0Hz3yBgcjTzM3xUnJ1eChj40aWlpWd6enp4OZ+exoHHsKIGjcfjBf0jUJojt3Xd34LKDnrn9HvaL+XmcfngSRyIP2XpKTk1ieiL+vX9QbEelRGF1+EpbT8mpoe8HiRk3hRodSnaAXKbA6eiT+OjYuxj+zyDsuLMVWm6Z4ZTkStC0b99erGaiDsHUHZguJ0+eFM322rZtC2cnY4RGb9DbejpOw86728RftVIt/i4OXehwZ2wRKfew/sYasV27QG3xd2nod+KslLENByL2Il2vgZvCTVxfGbYCD9Me2npaTknIw7NYfuVHsf121fcxu/ls/N5yDXqW7C0EzrWEMHxx5jP039cTq6/9jqT0JFtPmbF3QUM+TuXKlcPgwYNF2okugwYNQqVKlTB58mQ4O0FuBSCHXPwIxmpibD0dpyBOE4cjD4yRjLnN54peQKeiT+BY1BE4Ej9dXop0fTpqBdTBN62+gZfKC+GJ17Dj9lZbT81p2XnHKJT7lx2MqgFVkapLwa+PDqqM9UhMT8D/Tn8qeny1KdwO7Yq8bD6BHF15PFa1Wo9h5UfB3zUAkakP8H3oAvTZ2x1LQr9DVCr3BHMGcixooqKi4OLignnz5uHQoUNYtWoV3n//fXz00UdCzHh4eMDZUcqVCHALFNtcR2Md9t3bBa1Bi3Le5dGsaDN0K/GquH1J6EKHiW5cibuEXXe2i+2Rld6Cj6sPBpQdLK7/dGUpUnWpNp6h80ERM0pvEG2LtMe7dd8V25tubcDNxBs2np3zQJHWuee+wIPU+yjsXgTjq3wgOtNnxEvljX5lB+H3FmvxfrWJKOZRHEnaRKy89hv67X0Vs89+jusJ4TZ7DowdCRpy0x41ahSaNm0qugIT5NvUp08frFixQlw6d+4sTCsZLgy21Vl020dnbQPKvg5PpZcIQZtEgr2z5NJ3MMCAVoXaooKPcQUhCTP6LNEZ5rrw1baeotOx+84O8bemf23xPtQrWA+NCjQWUYIfLi229fSchr9vbRSpP4VMgY9rToOH6tknzi4KF3Qs1hk/Nfsdn9WZhWp+NcTJzrbbf2PIwf6YdPwDnH142uHS0YyEgubbb7/FnTt38Ntvv4kGelQ3Q8u3q1evju3bt2Pr1q1o0qSJWP3EPBY0ESxoLM7tpFu4EHtepPlaFzbWcHm7GM/WiB8vL0Ga7ukidnviWOQRnIg6BqVMiaEVRppvd1G4Ymh54/U/rv2KOE2sDWfpXNAB70mhTAyv+Kb4rB28vw/nY87ZcIbOAUVVFl74SmwPrTAKFX0rZ7tre+Pgpvi60SJ822gxmgQ3hwwyHH7wL94+PBpjDo3AgXt7HSaCy0goaGipNqWUqF6GQn3//POPiNoMHDjQ3EiPmuvR7QxHaKyJKQJTN6g+/N0CzLf3KNFT2FBQPn3ddfuNblDh+NJL34ntriVeRSH3wpnub1W4Lcp6l0eSNgm/Xv3ZRrN0PmiV3M2kG3CRu6BZwZbm20t5lcbLRV9x2MJzR2vF8L/TnyBNn4Y6gfXwWqm+udpPFb9qmF5nJn5u9gc6FesKldwFF2PPY+qpyXh9f19svLHe7k96GAkFTWRkJIoXL26+/t9//0GhUIiojInAwECkpKRkd5f5GhY0VjyLfrS6qW3hx2fRpujGkPIjxPbvYRTdiIM9QkvMr8ZfgYfSU6TKsjrTHFnxLbG98cY63E2+Y4NZOh+mzxWd5T+Z4hhcfhhc5a4IiTmL/x4Yl3Qz0vN96EKRNvZ18cXEGp+I70JeKOZZHO9Wm4A/Wq4T3zUquiebmq/Oz0Hfvd3x65Wf7PZ3gnkx2f50BAcH49atW+aDCHUKrlGjhjCkNHHq1CkUKlQou7t0CkHzgAWNRaGQ/73ku1Ar3NG4YLOn7m9dpB1Ke5UVxYEr7DC6QWegplqMvmUGwMfl8fcpI3R2Wi+wgagF4NoNy0N9TPbc3flUuslEkFsQepbqLbZpFY2O+55IDvX++evGn2J7QvUpYvWSVPi7+ouTnZUt12NM5bfF73WsJlYU35Ow+eb8PPG7wuRTL6euXbvi888/F15Nhw8fxr179/Dee++Z7w8NDRUrn7p06WKpuToUHKGxDqYah2YFW5j7hGSEighHVhyNCcfexYab69C9ZK+nUjq2ZP2NtWLlBi31f7Wk8QD5LEZUHI3j/xzF3nu70KtUn2zXEjA553jUEXGAo8hA3cAGWT6md+kB2HxrI24l3cSW25vRuXg32Dt0Mkq1WvU9awFQ2bW1wZyzn4tt+qw3KNDIIuOole7oUfI1dC3eA/sj9mLltRW4Gn9ZCCmKhjYv1BJNSryEpKQ0wFKZRRnQQtUEXjCujHVULsddwkOFN/xRyP4FzZtvvonExERMmjRJ1NCMGzcOnTp1EveRf9NPP/2EFi1aiMcxELUbBNU9UP8ET5WXraeU76Doxt57u595Fm2CDkh1AurhRPQxUSA8ueZU2IvFwYqry8X2G+WHw1Xh+tzHl/EuJ54ndUKlqMCXDb59aukqI61QpvolasOQFZ4qTwws+wYWXJiP5Vd+EL1R6ABpz/x8ZRl+vfoTCpwrgC/qfYmSnmVgb1CR7szT0xCfHo9y3hUwtPwoi4+pkCvFe92yUBvRv2rVtRWihxX9vph+YyzJwgtfY0qt6XipQFM4In9dX4tvL8yDn5sf/my1yajS7FnQKJVK0VCPLk/SrVs3sWS7cmU+YzRB3Wp9XHzFqhSK0rCgsZzVQaBbEGoE0Bln1tBBf3jF0Tjx7xuiXoXO+Mo/WhZtDxYHpb3KPFeQZYSED/3AmiwRGhZ4yeLzdGargyfrsp6EojJUcE51TWvCV2JQuSGwV8ge4LdHadcHKQ8w7r/RmFFvLqr6VYM9Qd8L+nxT5186yNMybGtBvxW1A+uKS1j8FWy8uR6JhjhoNJZLKdJ7cSX+Ej45MQnvV/vIXHDuCBgMBiHmf7lqbDTZpUwX8Rraqk4+24LmeVSoUEGK3eQ7gt0KCkFDS7fp7JqxTNEmnRlTaul5lPepIB636+4OsTJlbv1vbBrdyGhxMLzC6BfOP2Mqs0fJXuIMkiwR6gU1yPb/MtmD+p1o9BoU9yjxQuFLHamHVRiF6aemYNW139GpeDdRn2FvUKHrjDPTjH2OCrfFw/RInI48jQ+OjMOntT+3G2Gcydqgyvso6lHMZnOh3+x3q32IwEAvREUlWOwgrTdo8c3ludgYtlE0/6NUZ5/S/WHv6Aw6LDg/X6TyicHlhuDdOm8jOjrRZnPKW8k481y4jsY6VgfZjW4MKT/SbiwRTBYHNQNqo35Qwxz9b78yA9kSwQrppnZFOmRL9DYv2AoVfCohRZdsl5YIxi67M0RzRuqeS1GAJe2WoEFQI7EcesqJCXbRfPJJa4Psfq8dHUp3fdb4M/OSdOpubu/tANL16cLxnMQM9fYZW/ldvF5+mM1T4CxoLEiw2lhHw4LGclYH1J+llFf26gAKuhfKYIlgO8NHKjo0WxxUeCvHPwLU4r1/GbZEsLTVAa2Qyw70/o2qOMZsiXAr8SbsiY0314kUGol56rJL6XC6/K/uLLQp3F58Dyh6szZ8lc3mSAfvL8/NEgXyVLSflbVBfoaWo79ZeSxGVBgtrlMEds65GXa5ei5Fm4xJx98XqW9qBEo1id1L9oQ9wILGgnCExnJk1cE1O/QvY7JEuGqzs1ISUxT6pwLECr6VcrWPTJYIdtw00NGtDrIL1XA1NFkiXP4e9kJY/FV8d/FbsU0Hy3I+j8sDqNj5oxpTzKvrFl78WrQEsEVkYMvtTdgfseeRtcH051ob5Gf6lBmAD6pNEp2oyarh05OT7KrhX5wmFu8dGSdWytGq0s/rzhEpTHuBBY0FYUFjPauD7CIsEcoMtJklAlkcHI86+pTFQU7J2DTwD9E0kC0RLGV1kF2GVzBaIhyIsA9LBIrcUZfddL0GDYNeEsuTs4oMjK40zmyvsSJsOeaHzLZq9PJG4nVRi0HQPCo5eTuCDsU6YVqdGaKb8X8P/sGEY++IQnVbcz8lAuMPvYnQuAvwVnmLVZZUw2dPsKCxINxczwpWB7lotkW9aEyWCOuvGwtzbWFxQK7BeaF14XYo612OLREsbHWQXezNEuG7C18LsUDfkQ+rT35mCodu7192MN6t+qEQZJtvbcBnp6ZAo9NYpfXCZ6cyWBuU7mfxMR2BxsHNMLvefHgoPYSR5juH38LDtGibzedG4nWMOzRKfD+oZ9bXjb5HJd8qsDdY0FiQYLWxwVCMJsauwob51eogu7hmiG6sCPvFaq3OH1sceGBAWWMNTF4wWiIYazfYEsGyVgfZxV4sEfbf2yOECRVsTqrxKXxd/V74P7RC65Nan4laG4oyTTz+HpK1SVawNrgqmbVBfoLSmPMaLICfix/CEq5g7KGRuJN02+rzuBh7QURm6ASQisq/afQ9SniWhD3Cnx4LQitRqCU/wWkn61gdZJeMlgi/hxmb21nP4mCg6FEkBXRWWzewPlsiWNjqILtktERYGrrIJkWdVNhMBbZEn9IDRE+V7NKsUEvMrPul+H7RasB3D49FTNpDh7M2yC9QzdM3jRajkLqw+N2jKAn1x7EWxyOP4r0jYxGfHidW8pGYyUltmbVhQWNBKJTLK52sa3WQXUyWCMRfN9Za3LfFZHFATQBfZHGQU8gSgc7EyRIhNPaCpPt2FrJjdZBdyBKBBCuF57fe3gxrQgJqxulpomEjpQSoEWNOIQE0v+EC8Vpcjg8VZ+cRyfcc0togP1DEo6gQEnQCFqN5iLcPvyXSUJZm791dYjVTqi5FdFr/ssE3kp2IWQoWNBbGUQqDqZnVkO1D7NpplnL6+yJebHWQXejAVTugruipQAXCVrE4KPdii4OcQkvX2xRp/3gFlR33r3Bkq4PsYrREMLqm/3zlB7HM1Vr8cvUnke6itCYt0c7tc6GGgl83NJ6N306+JdId4QlhDmtt4OgEuAXiq4YLUc2vhogqf3j0bfx3/x+LjbfhxlrRE4givy0KtRarmdyV9r/yjAWNhXEEQUPFqivDVuBYxDH8euUn2CuHI/9DQvqLrQ5yEkEbUfEtc30LmatZ0uKglGdptCvawSJj0Jk4rYowWSIwlrE6yC6di3cX/VSokJMsEaxtbfB21Q/ybMJazJPqJRajpGcpRKdF4e3DoxEiweotWpVnsjYg0WVNawNHhuxzZtWfL9oDUCfrT05OFEu7pcRgMODny8vw9fkvRWuJLsW7iz4zjvIesaCxMI4gaCjdQmFFYsONdTYpPMsOO+9szbbVQXYxWSKYOnRKHd0wWhwY6wTIT8pSNgUF1YWEJQKx1IZNAx2RgxH7sm11kF2osJaWcRNkifDQQnUoWVkb0EorWgEnBVQT9FXDRajsW1WcTJBVgqlDd24gQURRK2J8lfeEaGKyD6XZp9eeKbpYU88jskogh3CpTmy/uTDP7Ms0qOwQjK/yvkNZq7CgsTCOIGjCEq6atynE+OPlxfnC6iC7mCwRTkYfF7UU0lscaITFAbWatyRsiZA7MvaekbI7rbUsETJaGxT1KI6xld+RdP/Uu2lug29Q/5FVwscnPsxVU0qyNvg8g7UBHZSZnENpRFqG30tCq4R0fbp4byjVZE9WBjmFBY2FcQRBcy3eKGiqBVZ7VFy62+6KS3NjdZBdMloiLJYwupFXi4OcwpYIuYugUfqDMNUhSQW93yMfpTQtaYmQ0dpgirA2MK6slDoy8L86ZJXQLldWCSZrA/oddEZrA4tYJVSSxiohxY6tDHIKCxorCZqotCi79OXIGKHpUKqDOfphb8WleengaitLBCksDnIKWyJYx+ogu1BkzpKWCNfiw8zWBsOfsDaQGqNVwifmjsM5sUpgawP7tEqIs3Mrg5zCgsbCUG8FOnOiH7TItEjYI3QQJyr4VcCQCvZXXJoXq4PcWCJQmiivjRClsjjIKWyJYD2rA1tbIlAE7rPTU0RKk9KZr2ZhbWCJyMBblcZnaEy5HF+FzHluVJOtDSxvlTC19uc5tkp4kHLf7q0McgoLGiv8AFCbfXtNOyWlJ5l7sJT3Ky/OUk3FpZSbtYfi0rxaHeTUEoH6xeTFEiGjxUGXEj3ybHGQU9gSwTpWB7mxRJAy8pnR2mBC9Y+tlsKhcQaUfd1slbDp1l/PtErIZG0QwNYGlqJJweY5skq4+YSVARV+26OVgUMJmrS0NEyaNAl169ZFkyZN8OOPzy6ce/PNN1GhQoVMl71798IRsOc6GlNvCVoK7evmm6m49HpiuM2LS6WwOsgu1B/G1IgsL5YIGS0OTP1IrC2iR1Qw1m6wJYJlrQ5yaolwLuaMJJYIubE2kJrsWCVktDYgZ2+2NrCeVcK4Q6Oy/O6TlcG4Q2+KEzeTlUFJr1LID9j00zV79myEhIRg+fLl+PTTT7FgwQJs22b8kXmSsLAwzJkzB//884/50rhxYzgC9ixoTPUzZbzK2mVxqVRWB9mFCkPzYomQyeKgtHQWBzmFollsiWB5qwNbWCLkxdpAap5nlbDv1j6sv/7Y2oCawzHWs0q4m3znKasER7MycBhBk5ycjDVr1mDy5MmoUqUK2rZti2HDhmHFiqfX1Gs0Gty+fRvVqlVDUFCQ+eLi4hjNfuxZ0JhWOJXxfixoniouDV/t8FYHubVEyGnL978yWBz0KGX5mobsWiJcir1o07nkZ6sDa1oiZLQ2qOhTOVfWBlJDgmpeg2/FcxNWCdSA7+E5TPl3irifrQ1sZZVQRqSdTFYJ265vw8RjjmVl4DCCJjQ0FFqtFrVqPe74WqdOHZw5cwZ6vT7TY69duybytsWKFYMj4ggRGvrwP7O49JptikultjrInSXC4hxZHPyWweLAGgIsu5YIee1TkVuo8PDva3/b3RJyKa0OrGmJQDVRZG3grnTHx7Vyb20gNbSK75uG34satNtJN4VVQmxaLFsb2IgAt0DMb7gQVf2qi2jz+0fexof7PxQRW+qP5ChWBg4jaCIjI+Hn55cpyhIYGCjqamJjY58SNJ6envjwww9FrU3Pnj2xf//+XI1LdXNSX16034LuBc0/7pYaIzcXA/S49qiGpqxPuafGaFPkcXEptVS3xmuV8XIkg9VBzcBaVnut5HIZRlYy1qDsursDV+IvZWuMP649sjjwKo32xTpY9bV61mVIBkuEo1GHrPK5ogvVTcw8Mx399vbERwc/wuenplJFlEXGyulzoR94k9VBO9FMz3rvSZcS3VHYZIlwfWWOxjjz8JTZ2oAKcot4FLHpZ+vJS3Gv4ljQeImwSiDUSjU+qT0NrkoXm7/nzjiGt4s35jT4Co0KNBYr4aiFRNcS3THFgu+JJV+r7GAzeZ+SkvJUysh0nVJMTwqa1NRUIWZGjBiBnTt3iiLhVatWiTRUTggI8JJg9jnbb0VXY/SDUhEBAZ65Xo0g9dxvxd8S4Uda5VGjeOUsx/iwwQcYsXMENtxch6G1X0cxr7xHybL7PPaf3SX+di7bCcFBvlZ9rQID66LjnY7YEr4FP4Z9j6Vtlz71vmUc427iXXO9wPv138vxfC32POCF/pX64efzP+OHK9+jQ8U2UMgVFvlcUQToaMRR/HT+J/x751/z7ZT2+uf+AeyO3oI+FfvAkmTnuRy8sktYHZTyKYWXytTL8fcxr6/XO3XfwQcHPsDqa79jcM3+CFQHvnCM2NRYzNw7DXro0bVMV/SuYWwEmRcs8VtIn7ffgn7FTyE/oUmRJqhZ0PIrZyz1m54/xvDCwvbfYvn55fBx9UHPcj0tvhrOGq+V3QkaV1fXp4SL6bqbW+ZQ/ejRozFw4ED4+PiI6xUrVsT58+exevXqHAua6OgESBl5p88GvYHP269S7yGWN1Jvkyt3b8Lf1V/yMXLDsXtGC/oSnqUQF5OS5RjlXKqK4lLqqTL38DxMqTU91+Pl5HnQCqMDtw+I7ab+rRAVlSD5GC9iQMkh2HljJ47cO4KtobtQP6jhM8eYe3q+OEhSg7ZKrjWzPV9rPI8eRfpg7eW1uBp7Fb+fWS36Vkg5BtV17I/YJwxOKZpF0Oe9aaEW6FumH8JSL2HO8TmYfWwOSrtURGlvaTs95/S5rAv9S/xtVbAdoqMTLTLG86jt2QgVfSohNO4ivjryLd6u+v5zxyChOOXEJDxIfiBWpYwsOzZPny9L/Z48Ro6BJYdaeAxrPI/8M0b3wr0d+nmY9m23Kafg4GDExMSIOpqMaSgSM97e3pkeK5fLzWLGROnSpXH//v0cj0svtNSXF+1XIVOaK/wjkiMsMkZuLmHxj+tnnjeGqbh0z91duBhzwaKvlemy9+7uR1YH5VDSs4xNXquC6sKPLREufgetXpflGFfiHlsc0GtFMQlrfK6ye/FUPl619uPlpUjRpkoyRnJ6CtaGr8GAfb1FrxESM7Q0uWuJV7G8+Up8Wut/qOBTGQMrDxSN3yjsPf3UJ5nGl/KSnedC3z+T1QH167HFeyKD3OzyvvnmBtxMuPncMcgw1mRtQO7Ubgp3u/ls8Rg8hsFKY2QHmwmaSpUqQalU4vRpY5SAOHHihIi4kIDJyEcffYSJEyc+VVRMosZRsMfCYPOSbW9j/Ux2ikutZYlgctZua2MDu35lBsND6SlqQkxt8p9tcdAaFe20CyoJMyrYlMISgZblUjflvnu7Y8GF+WIZMa2WGFxuKFa2Wi9clGmlhQkKcU+oMVk0gLuRGI5FF76BPVgdkEO5rRCWCEEvicaVz7NEsKa1AcM4OjYTNGq1Gt26dcPUqVNx9uxZ7Nq1SzTWGzRokDlaQ3UzRKtWrbBp0yb89ddfuHHjhuhXQ+JnwIABcDRBQz/+9mZ58OSS7ayg5aHWskSwhtVBdvFx8UH/MsbP5I+XlzxliUB9HUwWB+Taba/QqjWTBUNuLRHuJN3G/JA56Lu3B369+hPi0+MfGQ2+hz9arhOC5lnLQP1c/TGxxici0kedZQ/c22vbJo1WXDX3LIZXfL4lgtHa4BOrWhswjCNj08Z6FHWhHjSDBw/GtGnTMHbsWLRr107cRwXAW7ZsEdt0GzXeW7RoETp16oQ9e/Zg2bJlKFr08VmgvWNvEZqMlgfUSO5F0NmsyRJhqYSO1La0OsirJQJZHCy5tNBscZAxKmGPZLREMC0vzw4XY8/j0xOTMGh/b2y6uV7UClFTLkop/dJ8lUgxZWeJep3Aeuhdur/YnnvuC6t/FyglRlYBlrY6yC7kGv88SwSKZFFEi74DH1afzO7UDGPPgoaiNLNmzcKpU6dw8OBBvP764zbxly5dQo8ePczXe/Xqhe3bt+PcuXNYt24d6tWrB0fC3gRNRssDikJkB5MlQnjiNYtZIljT6iCvlgiUvrClxUFeLBE23Fj7XEsEEmuH7v8rzOve+m84Dt7fJ9JqlCaZ32AhvntpGZoXaiUaEeYE6m1EDeFoeTs1iLOmA/2OO9azOsirJcL+e3tFJIsiWhTZoggXwzDPh401rCxoHtiJoHncUO/F0RlrWiJY2+ogN5YIK64uF6mnHy7b3uJAaksEama49dZmDD04AJNPfCAOtJROa1+kI35o+htm1JsrPGNyGy2gRnDUEI4aw9G+rWWeabQ62GE36abnWSJQC4C5Z78Qt1FEiyJbDMO8GBY0ThqhMVse5EDQPFVcagFLBGtbHWQXikQYVzAZ7Q3mHZ+H+yn2YXGQUzKuWjsfdV7clpieIGpr+u/riTnnZojUDEWe+pTujxUt12JCjY9Fw0ApIPfxd6p+KLapUdyZ6FPIj1YHObFE8Fb5CEuETTc3iEaEJmsDU7duhmFeDAsaK0EigKD6BTp42E2EJoc9QTIVl0psiWArq4PsUi+DJcLvob/bjcVBTsm4am32sdlYdOFb9NnbHUsvLUJ0WpQQaSMrjsHKln+J5cUURbBEPQ9FfahR3Iwz03LtbG7PVgc5sUQYVO4Nsf3t+fk49eCU3VkbMIwjwILGSlAbcFNawtZRGqqPMFkelPF6/pLtFxWXSpkyOJzB6oDSGvYGpVlM/UMIavHerqhtl5XnFtOqtZMPTmJ1+B9I1iajlGdpTKj+MVa0+BO9S/ezeJ3JuCrvoqh7MUSmPsDcczMt1g4gMf2x1YG91GU9Sefi3cWKMRJ4JmsDimQxDJN9WNBYkWA309Jt2woaqlEhywM6oBXzKJan4tKNN9Y9t7g0N2fRbQq3y3GxqbUo71MBnYt3E3UlY6q8bbfzzM6qtQFljfVQtQJqY2bdL7Gs6a9oX7SjaOBmDdQiCjFdvJb/3j+AjTfXW2ScgxH7xMqs4h4lUN6nIuwRes1HVxovlnH3Kt8LrYsYV3syDJN9WNA4YR2NKd1EEQZFLkPaLyouzSmUcjjy4D+7TTdlhFrVH+xz0OGLNSnNcajvIcxruAANCjSyybJgEojDH9UmfXfxG9FITmpMQpk+V/a89JlWX21otw1TGk6x9VQYxiFhQWNFgtXBdiFormWwPJCiuHTvvV0Ijb2Qp33tu/fY6oD6c9gzFKHydPFEfsAengc1jKv/yBrhf6c/kXT1HH3XzFYHDhD1oHoaexZdDGPPsKBx4gjNiywPXoSUlgjms2g7rXFgLCsQJ1Q3WiNcl9gawWR1UMO/lk2tDhiGsTwsaJxQ0JgsD/IaoZHKEoFa6l+IDRH1A7QKhXE+LGGNYG9WBwzDWBYWNE7WXC+j5UFeIzQEnfV2L9EzT5YIpugM1eWYXMkZ50NqawR7szpgGMaysKCxIsGPQt4xmpinTA6tBdkW5NTy4EX0Lzso15YI9mh1wNgOKa0RMlodUG0KwzD5GxY0VoQO+tTS35Zpp7D4Kzm2PMiOJUK/XFoi2KvVAWMbqJHc5JpT82yNQEJo792dYpvTTQzjHLCgsSK0esHWK51ya3nwIrpntES4vtrhrQ4Y20Gu5e9UyWCN8DDn1gjHoo6KSKg9Wh0wDGMZWNA4WWFwbi0PcmSJEJY9SwR7tzpgbActsTZbI5yehnhNfL6xOmAYxjKwoHEiQZNXywOpLRHs3eqAsS0ZrRHIMDO7bQGo8J06DxNcl8UwzgMLGicSNHm1PJDaEsERrA4Y25Fba4QDEXvt3uqAYRjpYUHjRIJGCsuDF0FLr2n57YssERzJ6oCxHbmxRnAUqwOGYaSFBY0TCRqpLA9exIgKjy0RLsVedHirA8ZxrBEczeqAYRjpYEFjI0ETlRaVpx4buUEqy4MXUc6ngtkSYXHowixrH9jqgMmpNYKfi7/RGuHit8987C62OmAYp4UFjZUhvxqVXAW9QYfItEiHtTzIniWCKktLBLY6YHJrjUBsurkeByL2Zd2kMUO6iWEY54IFjQ3ONoPcClg97ZTZ8kDaHjTPtkTolaUlgumgQ7U2bHXA5KQ+q4/JGuHszKe+PxceXmCrA4ZxYljQOEkdjcnyIMA1ED4uvlYZs1+ZQfBUZrZEyHgW3a5IB6vMg8k/DCk/8pnWCJvDNou/bHXAMM4JCxonETQmywNL189kxNvFG/3LZrZEOB15GnfZ6oCR2BqBhM2W8C1im9NNDOOcsKBxEkFjKcuDHFkihK/GprBN4na2OmCktEYgq4OHqQ/Z6oBhnBgWNDbAtPrCqhEaC1keZMcSYUiFEWL797Bfse06F20y0lsj/HV9rbidrQ4YxnlhQWPTCM39fGF58CLaFG4vxiVLhAQNWx0w0jC2yjtmawTTSrp2LJQZxmlhQWNDQfMgJSLb/jT2bHmQnZVdIysaLREItjpgpMBd6YGPa00T1ghEKZ9SbHXAME4MCxobQMu2qQcL+c3EaGLyheVBdpbctijUWqw+6VS8q03mwOQ/SMC8VXm8+D4NrjyYrQ4YxonhZLMNoBw/9V+hUDnV0fi7+ucLy4MXMaXWNPgHeCD2YQqsEJhinISuJV5Fx2KdUTg4AFFRCbaeDsMwNoIjNE6w0slalgfZST1xwSZjCVwULraeAsMwNoYFjRMIGmtaHjAMwzCMLWBBk88FjbUtDxiGYRjGFrCgyeeCxhaWBwzDMAxjbVjQ5HNBYwvLA4ZhGIaxNixo8rmgedxQj9NNDMMwTP6FBY2NIH8jIkmbiMT0BItHaKxtecAwDMMw1oQFjY1QK9XmmhZLRWlsbXnAMAzDMNaCBY0NCXYzpp0iLCRobG15wDAMwzDWggVNPq6jsQfLA4ZhGIaxBixobEiwOtiigsZeLA8YhmEYxtKwoHGCCA0v2WYYhmHyOyxo8rGgYcsDhmEYxllgQWMHguaBBQQNWx4wDMMwzgQLGhsSrC4k/sZoYpCmS5N032x5wDAMwzgTLGhsiJfKC2qFu0XSTmx5wDAMwzgTLGhsiEwms9hKJ1NDPa6fYRiGYZwBFjT5tDD4cYSG62cYhmGY/A8LmnwoaMjyIDzBWEPDlgcMwzCMM8CCJh8KGlrdlKJLZssDhmEYxmlgQZMPBQ1bHjAMwzDOBguafCho2PKAYRiGcTZY0NiJoIlKi4JOr5Vkn2x5wDAMwzgbLGhsjL9rAFRyFfQGHSLTIiXZJ1seMAzDMM4GCxobI5fJEeRWQLK0E1seMAzDMM4IC5p8VkfDlgcMwzCMM8KCJp8JGrY8YBiGYZwRFjT5TNCw5QHDMAzjjLCgybcRGq6fYRiGYZwHFjR2JWjuS2Z5UNqLBQ3DMAzjPLCgsSNB8yAlAgaDQRLLg+IexSWcIcMwDMPYNyxo7ABati2DDBq9BjGamFzvhy0PGIZhGGeFj3p2ADXWC3QLQmTqA1FH4+/qn6v9sOUBwzDM0+j1euh0Oe/ELpMBqampSE/XIA/Bcx7jBSgUSsjleY+vsKCxo7STSdBU8q2cq32w5QHDMMxjKIUfH/8QKSmJud7Hw4dyIYgsCY8BqNWe8Pb2h4yUUS5hQWNHgiYk5myeVjqx5QHDMMxjTGLG09MPLi6uuTpYKhQy6HQWCmvwGCDRqdGkITHRWG7h4xOQ6/FZ0OSTpdtsecAwDPMYvV5nFjOent653o9SKYdWa9nIhrOP4eLiKv6SqPHy8st1+omLgvOJoGHLA4ZhmMfodLpMB0vGvjG9T7mpdTLBgiafCBq2PGAYhnmavNRkMI71PrGgySeChi0PGIZhGGfGpoImLS0NkyZNQt26ddGkSRP8+OOPL/yf27dvo1atWjhy5AjyEwXcgsXfJG0iEtNzXpHPlgcMwzD5jy1bNqFJk7rYvPkvW0/F7rGpoJk9ezZCQkKwfPlyfPrpp1iwYAG2bdv23P+ZOnUqkpOTkd9QK9Xm2pecRmnY8oBhGCZ/smvXdhQpUhTbtm2x9VTsHpsJGhIla9asweTJk1GlShW0bdsWw4YNw4oVK575Pxs3bkRSUhLyK8FuuUs7PbY8ULHlAcMwTD4hJuYhTpw4hjfeGI4zZ07h7t07tp6SXWMzQRMaGgqtVivSRybq1KmDM2fOZNmYJyYmBnPmzMH06dORX8ltHY2pfqYEWx4wDMO8sO9JSrou+xdNDh77jEtuPfr27NkFT09PtGvXAYGBQdi27W/zfSkpKZg9+3N07NhaXGbN+lyUcZiE0CefTES7ds3RpUt7LF68UMzh3r27In1Ff0388MNijBkzwpzeevPNIZg48X20b98cO3ZsRVJSImbMmIZOndqiRYuG6NfvVRw4sM/8/xnH6tixrXmsWbP+hwkT3sn0fObPn43PPpsCS2Gzo19kZCT8/KjZkYv5tsDAQPGGxMbGwt8/c/v/L774At27d0e5cnlbxSN1wbtpf1LsN9jdWEdzP+Vepv29aIxrCcb6mbLeZXM9DymfB4/BY1hzDGuNw2M41hhZ3U4H2mErz+Ds3XhYkxqFvbG0T40cr+TZvXsHGjVqIvqyNG7cTAgaitbQfr744jOEhV3FF198CVdXNyEUli5dhDFj3haCRKFQYMGCxSIb8umnE1GgQBAaNmzywjHPnTuLQYOGYOTIt+Dr64evv/4St27dwPz5C+Dmpsbvv/+CWbM+Q6NGjaFSqTKNlZaWgo8//kgcy9u0aY8PPhgvBJGHh6cIVOzbtwcTJnz83PHpJXryZcruy2YzQUPqMqOYIUzXNRpNptv/++8/nDhxAps3b87zuAEBXnneh6X2WyawJBAOxOijEBjole0xbp29Lv5WL1Q1y/+zh9eHx+AxrEF+eS48Rt7HIF8hasVP3Wup4ZtJ0MhtsIqbDsg0h+cJGtMcTdy/H4Fz586gX78B4r5WrVrjr7/+xPnzZ1C6dFns27cb33yzCLVr1xaP/+ijybh8+TLCw68iJOQs1q3bhMKFi4j7JkyYLISNQmEcg/6axpPLZWJedN20PWTIMLi5uYn7a9eug/79B6JMGWN95oABg7Bp01+Ij49BfHz8M8eqV68evL29cejQP3j55Y44efIU0tPT8dJLjZ56roReLxPCzc/Pwzx2TrGZoHF1dX1KuJiuZ3wy9KH85JNPRNFwbp9kRqKjEyQ156LPJ32hpNivp95YFHwr9jaiohKyPUZodKj4Gywvmun/bPU8eAwew5pjWGscHsOxxiCTRKMppSFT99olvWsgNQfdbJUKObS6vHXYdVPKH1kCGLLdYXf79m3iJL9u3YbivurVa8HLyxubN29C1649ROPAcuUqmP+vatWa4kJpKm9vHxQoUMh830svNRNj3Lp1W1zX6fTm+/R6gxB6dJ22/fz8oVS6mO9v164jDh7ch/Xr1+LGjeu4dMl4vNFotAgPv55pLBqDxjLuF2jZsi12796JNm1exs6dO9C8eUuSU1l2E6bXh96vmJgkqFTpWb7XditogoODRV0M1dEolUpzGopEC6k6E2fPnsWtW7cwbty4TP8/fPhwdOvWLcc1NfTBt8QXTIr9FshQFJzVvrIagywP7j6yPKAVTnmdg6VeHx6Dx7D0GNYah8dwjDGeNS5FINQqRbb3bxQbMpusbqISDKplMUEiZu/eXejUqesz/0/56HiaFVlFiEwdlU08mTn53/8+FWkoirJ069YTAQGBGDXqjReORVDaaezYkSLtdODAHkyZ8hks+ZmxmaCpVKmSeDFOnz4t+tAQlFaqVq1aJh+H6tWrY8eOHZn+t127dvjf//6Hxo0bIz8RrC4k/sZoYpCmS4Or4sUtu9nygGEYJn9x8+YNXL58CW+//T5q1zYeH4nw8Gv49NNJuHXrpqhbuXLlCmrUqCnuO3hwH376aSk+/ng64uPjRMoqONh4krxmzUqcOnUc7777kbiesfXJ81ZOkRDZuXMbliz5GZUqVRG3UQqJoKhO0aLFshzr5MljmDnzS1SpUhVBQUFYseIXIVJq1aqDfLnKSa1WiwgL9ZWhKMyuXbtEY71BgwaZozWUbqKITYkSJTJdTBGegIDcu3LaI14qL6gV7jla6cSWBwzDMPkLis5QKqdLlx6iXsZ0ad26HUqWLC1Exssvv4Kvv56DCxdCEBp6AYsXf4c6deqjdOkyqFOnnrlo+OTJ4/jtt59Rv34DsdimQIFgUdh7585tsarJJFCe5a9EhcBUzEsro44cOYR58+aI+6ge5smxTpwwjlW3bgPzPmjOK1euQMuWrYUIy7eN9SZOnCh60AwePBjTpk3D2LFjRfSFoM7BW7Y4VyMhCgcGq4NzJGjY8oBhGCZ/QaubaKn2k+kfonv3V3H8+FGx2qls2fJ455238P7740Tx7vDhb4rHUGqHhMjIka9j2rSP0aVLd7z66msi+zFx4hRcvHgeAwe+JtJXtKLpWdAqpk8+mS4KkAcM6IVvv52PwYOHiLTT5cuhT41F0SMaq0ePXpkEjUaTJv5aGpkhtwvkHRQqmpW6KJhWFkm134nH3sORyEN4t+oEdCre9YVjjPlvBC7EhmByzaloXbid3TwPHoPHsNYY1hqHx3CsMagoODr6HgICCkGleloYZJesCnalJj+PcezYYdEjZ82ajc9d5fW898v0Xr9w/DzMnbFxcz22PGAYhmHskaioKJw9exq//vqjKGK2hus5u207sKBhywOGYRjGHklMTMDMmdPh4+OLPn0GWGVMjtA4sKBhywOGYRjGHilZshR27jxg1TE5QuPAgsa8wonTTQzDMIyTw4LGTgVNVFoUdHrtcx8blnBV/OUl2wzDMIyzw4LGzvB3DRA1MXqDDpFpkc997LVHgoaXbDMMwzDODgsaO0MukyPIrcAL005keUBFwUQZb045MQzDMM4NCxoHraNhywOGYRiGeQwLGgcVNGx5wDAMwzCP4bW+Dipo2PKAYRgmf9KzZ2dERNwzXycPpCJFiqJbt1fx2mv9JB3rhx8W49SpE1iwYIkkj7MlLGgcPkLD9TMMwzD5jXHj3kPr1m3FtlarFSaTZALp5eWNDh06STZO374D0atXH8keZ0s45WTXguZ+lvez5QHDMEz+xtPTU5hA0iU4uKAQMeSmfeDAXknHcXd3F87eUj3OlnCExo4FzYOUCJB36JMeGGx5wDAMk0vIyVKbkoPHy4G8mjoq1UaHxTyiVCqgVKowZswIlClTFv/99y90Oi1++201EhISMG/eLOHE7efnj44dO2Pw4KEiXUUcOvQvFi1agBs3rqNo0eIYO/Yd1K1bP1MqiSJBX375hRBNGo0GtWvXxfvvT0RQUIGnUk4hIWexcOHXuHLlkhivf/9B6NnzNXHf559Phbe3NyIjI/HvvweE/cGIEaPx8suv5Pk1eO7rY9G9M7mClm3LIINGr0GMJgYBbv6Z7mfLA4ZhmFxgMMB3XXeoIo5bddj0QvUQ231drkUNCQ0SBkePHsakSZ9i48b12LJlE+bNWyCcqdVqd4wb9ybKli2Hn35aIYwh58yZAblcjtdfH4Zr18Lw/vvv4I03hqF163bYt28PJk58DytXrs80ztq1q3Dq1EnMm7cQbm5umDt3Jr75Zh4+++yLTI+7fj1cjNe7dz9MnDgF58+HCCEUGBiIJk1aPNrXagwf/iZGjnwLf/65SsynSZPmIvJkKfhoaIdQ5CXALRBRqZGijuZJQcOWBwzDMLnECq7PUkBiYv782WI7LS0Nrq5uoiC4XbsOQtC89FITVKtWQ9xPUZmIiHtYsuRnIWKKFy+Jt956GzNmTBOC5u+/N6B69Rpimxg48HWkpqYgMTEx05j37t2Dq6srChUqJNJLkydPRVxc3FNz27RpPcqXryDECkHjkcj57bflZkFTtmx59O8/WGwPGzYSa9b8gfDwMPOcLQELGjtOO5kETWW/ylmvcOKCYIZhmOwjkxkjJTlIOSmVcmhtkHIaOnQkmjdvJbZdXFxELY0pfUQULFjYvH3jRjji4+PQvn1z8216vV4Iobi4WNy8eQMVK1bKtH+KnjxJly7dsWvXdnTp0h61atVBs2Yt0bHj0wXI169fR+XKVTLdVq1adWzYsNZ8vWjRYuZtDw9Pc6TJkrCgsVOC3QriPM5ludIpLIEjNAzDMLmChIXKPfuPV8oBWR4FTS6gupSMouBJSOSY0Ol0IkryxRdfPvU4EhNKZfYO9aVLl8Gff27Cf//9g//+O4jFixdg585tWLhw6TPHfjwHvRBRJlQq1VOPoZpQS8KCxsGWbrPlAcMwDJORYsVK4P79CPj6+plrVI4dO4wtWzbj44+niSJgKt7NyKhRQ9CzZ+9Mt23dulmIFaqzadWqDUJCzmHUqDcQE/Mw0+OKFy+B06dPZrrt/Pmz4nZbwsu27ZSC7oWyFDRsecAwDMNkpH79hihYsCCmT5+CsLCrOHPmFGbPniEKeylNRQ356LaVK3/D7du38OuvP4l6lpo1a2faT1JSIr7++ktRk3P37h3s3LkVBQoEi1VKGenevReuXLmMxYsXinQWCaF169aYVznZCo7QOFiEhi0PGIZhmIyQaPnii3n46qs5GDFisFj11LJlG4wZM17cT12GZ86cg4ULv8GSJd+hZMnSmDVrPgIDgzLtp0eP1/DgwQN89tknSEiIR4UKlUQaK2PtDkHiafbs+fjuu6+FSKI+OWPGvINOnbrmvd4oD8gMlk5q2RlRUQmiDYGU6djAQC/J93sj8TreONAPHkpPbG6/wzzGvHNzsOnmevQpPQAjKo62++fBY/AYlh7DWuPwGI41Rnq6BtHR9xAQUEgsbc4tkhQF8xgv5Hnvl+m9fhGccrJTCrgFi79J2kQkpj9eWseWBwzDMAzzNCxo7BS1Um2ukTGlndjygGEYhmGyhgWNnS/dziho2PKAYRiGYbKGBY0DFQaz5QHDMAzDZA0LGjsmWB2cSdCw5QHDMAzDZA0LGgeI0ESYIjTxbHnAMAzDMFnBgsaBUk5secAwDMMwWcOCxkEETaImEXfZ8oBhGIZhsoQFjR0TrDbaH8SkPURIdIjYZssDhmEYhnkaXipjx3ipvKBWuIul2vtv7Re3seUBwzBM/qZnz86IiLhnvi6TyeDp6YUaNWrinXc+FFYDtuLkyeMYN24U/vnnOOwNFjR2DH2IaaXT9cRw7Lu1T9xW2quMrafFMAzDWJhx495D69ZtxbZer8f169cwZ85MfP75VHzzzfe2np5dwiknB6mjuZ14W/zl+hmGYZj8j6enJwICAsUlKKgA6tVriGHDRokISWLiYzsc5jEcoXEQQWOCLQ8YhmFyD/kxp+pSs/14JfJu6uimcBMR97yiUqnEX7lcjvDwa/j223k4d+4sdDotKlasjA8/nIySJUsJ0UOO2f36DcJPPy0VbtmvvdYHAwcOEf9PUR4PDw/cvXsXx48fRYkSJfDuuxNQrVoNcX9CQgK++mo2Dh48ALVajRYtWmH06HFwdXWDPcOCxoEEDVseMAzD5E3MjDs8Cudjzll13Kp+1fF1w0V5EjV37tzGr7/+jAYNXoKbmxsmTHgH9eo1wHvvfSQiNvPmzcKiRd9g1qz54vEPH0Zj27a/MX/+Qty/HyFEjI+PH7p06S7u/+uvtejduz/eemu82P7gg/FYufIv+Pr64osvpkOr1WLRoh+QlpaKr76ai3nzZmPixE9gz7CgcSBBU5ItDxiGYfKEDHmPlFiDuXNnYv782WJbp9NBqVShadNmorYmLS0N3bq9iu7de4kICtGhQyf8/vsv5v+n//nooykoV648KlSoiLCwvtiwYZ1Z0JQqVRpvvjlWbI8d+w7++ecAdu/ejoYNG+Pgwf3YsmWPSHsREyZ8jDfe6IexY9+FPcNHRwcSNFw/wzAMk3soQkKRkhylnJS2STkNHToSzZu3QnJyEn78cQnu3buHkSPHwMfH2LajW7eeIgITGnoBN29ex6VLl+Dv72/+f7XaXYgZE5SSWrHiV/N1U3rJlMIqX748rl+/joIFC4si5O7dO2SaD912+/Yt2DMsaBxI0HD9DMMwTN4gYaFWGqMa2RY0yJugyQ1+fv4oWrSY2P7ss1kYNmwQPvroPSxZ8jM0Gg2GDx8kxE2TJs3Qpk17IWr++OM38/9T3cyTgkQme7wOSKlUPnW/XC4TkR2KzCxb9lj8mAgKCsL588aeaPYIr3Kyc/xdA0TtDMERGoZhGOeDioE/+uhjXL16GatWrcCpUycQFRUplm9T4S/V0ty/HyFqhEwkJibg3j1jd3ni4sULKFv28THkypXL5m0SMXS9TJlyKF68hKjJIeFHgooulOJauPBraDTpsGdY0Ng5cpkcLxd9BVUDqqKyX1VbT4dhGIaxAZUqVcErr3TFzz//AC8vL6SkpODgwX1CtGza9BfWrl2N9PTMgmPWrP/h2rWr2LdvN1avXonu3V8z30eiiCI6FNn5+uu5SE1NRcuWbcQqKSo8njbtY1y8eB6XLoWKguKUlGQxrj3DKScH4N1qHyIw0AtRUQnIIMAZhmEYJ2LkyLeEOKFVSa+/PgxffjlLpJ/KlCkrll1/8cVniIx8YH58w4YvYfToYaKe5s0330K7di+b76NU1cmTx7B06SJUqFBBrIYyCZYpU6aLguTx40eL1FWDBo3wzjsfwN5hQcMwDMMwdsSff27K8nZaUr116x7z9SFDRmS6/5VXuoi/t27dFH/79BkgLlkVN3t5eWPy5KnPHGfatBlZ3le7dl27tD0gOOXEMAzDMIzDw4KGYRiGYRiHh1NODMMwDJOPqP2CtNCzUk2ODkdoGIZhGIZxeFjQMAzDMPmWjL1ZmPz9PrGgYRiGYfIdpk65Gk2arafCZAPT+6RQ5L4ShmtoGIZhmHyHXK6AWu2JxMQYcd3FxTVXbtd6PdkBWDbK48xjGAwGIWbofaL3i3ylcgsLGoZhGCZf4u1tNGs0iZrcQAdY8jmyJDwGhJgxvV+5hQUNwzAMky+hiIyPTwC8vPyg02lz8f9kEumBmJgki3Vp5zEg0kx5icyYYEHDMAzD5GvoYCmXu+TqIO3m5gaVKt2iQoDHkAYuCmYYhmEYxuFhQcMwDMMwjMPDgoZhGIZhGIfH6WpocrFqL1v7k3q/PAaPwWPY1zg8Bo/BY9hmjOzuU2bgNooMwzAMwzg4nHJiGIZhGMbhYUHDMAzDMIzDw4KGYRiGYRiHhwUNwzAMwzAODwsahmEYhmEcHhY0DMMwDMM4PCxoGIZhGIZxeFjQMAzDMAzj8LCgYRiGYRjG4WFBIwEajQadOnXCkSNHJN/3/fv3MW7cONSvXx9NmzbFzJkzkZaWJukYN27cwNChQ1GrVi20aNECy5YtgyUZMWIEPvroI8n3u3PnTlSoUCHThV47qd/radOmoV69enjppZcwb948SNlse926dU89B7pUrFgRUnLv3j2MHDkStWvXRqtWrfDzzz9DaqKjo8XrX7duXbRt21Y8N0t+527duoXXX38dNWvWRMeOHfHPP/9YZBzTd6Z69eoW2f/p06fRp08f8X1s37491qxZI/kYBw8eRJcuXcRzoL/79+/P0xjPGsdEQkKC+P3K62cgqzH+97//PfV9+e233yQd4+7duxg+fDhq1KghPstbtmyR9HnQ72FW3/tBgwZJ+jyOHz+OHj16iO9I165d8d9//0n6PIiQkBD07t1bfH5fe+018Xm2Fk7n5SQ1JC7ee+89XLlyRfJ904GSDgje3t5YsWIF4uLiMGnSJMjlckyYMEGSMfR6vRAY1apVw/r168UP9bvvvovg4GB07twZUvP333+LH8/u3btLvu+rV6+iZcuW+Oyzz8y3ubq6SjoG/XjSl/eHH35AUlIS3nnnHRQuXFgcgKSADsT0w29Cq9Vi8ODBQmhKydtvvy3mTQcYet3ef/99FClSRPxYS/XZfeutt8Tn65dffhHCnD6znp6eaNeuneTfOdN45cuXx9q1a7Fr1y6MGTNGHHjoeUo1TkYxmNcTi6z2HxkZKQ6cffv2xRdffIHz589j4sSJCAoKytVnIKsx6DtOrw19dlu3bi1eK3rttm3bhqJFi0r2XDIyZ84cPHjwIFf7ftEYYWFh4vaMvyn0OZNqDPoO0vtNrw39Rh49ehQffvghypYtKz5vUowxefJkcZuJO3fuYODAgbkWNGlZjEEnGKNGjRIXEsr0Wzx69GjxvhcsWFCyMeikokOHDpgxY4YQzm+88YYYK7ffw5zAEZo8QAcCUqA3b960yP6vXbsm1C1FZcqVKyfOdEngbN68WbIxoqKiUKlSJUydOhUlS5ZE8+bN0ahRI5w4cQJSExsbi9mzZwvxZAnoh41+YOjH33QhMSjl/OlgSYKJzmzpdRoyZAjOnDkj2Rhubm6Z5r9x40ZxsCbBIRUkjOlz9eabb4r3vE2bNkJEHTp0SLIx6Czt1KlT+PLLL1G5cmUhNIcNGyaEoCW+c4cPHxYRmunTp6NMmTLiAERnofR+STkOHfzpDNfFxcUiz4P2HxgYKE4q6L155ZVX0K1bN2zatEmyMSIiIsTtdOApVqyYOOC4u7vj7Nmzkj6XjFEBen/o85xbnjcGfe/pM5bxe6NWqyUbg07ASMSSKCtdurQ4eWnWrJn4fEs1hpeXV6b5f/vtt3j55ZfFd1OqMU6ePAmFQiG+h/S+k7ChE77cRFCeNcZff/0FX19fcTyh7yF9xurUqYM//vgD1oAFTR4gpd6gQQOsWrXKIvunDzalf+gHLiOJiYmSjVGgQAF89dVX4oyGDpwkZI4dOyZSXFIza9YsEeakMxtLQD9sdBCwFPTa0OuU8bWh6BYJTktAAmrp0qXiLCivB9AnRRP94FN0Jj09XQhn+rEjYSsVJC78/f3FD6cJCqGT0KExpf7OkaikgxodmE3QD2luw93PGmffvn0YP368OKPOC8/avymt/CS5+c4/awy6zTR/ei8opUWpg9ym0J73O0j7nTJlCj755JM8fYafNQa9LhT9k+J7/6wx6HY6eckY9fnuu+9EWkWqMTJCJxb0G0yiNjccfcYYJDToN2XHjh3it57EM0WZcxNletYY9L2vUqWKEE4Zv/fWSjtxyikP9OvXz6L7p+hCxvQDhe8pN9ywYUOLjEe1FJQrprNpCklKCX1J6UyNzjRJvUsNfUHDw8NF3cTixYuh0+nEGQ5FtKQSA/RlpbQMnYV8//334mBAZ+sU6aA0oNTQWQ0JTnoeUkJnZXSAoUgTpYPotaLn0atXL8nGIBFOdRMpKSnms2WKDFD4nm4nsSPld45SNfRaZSQgIECMKeU4lHIk8lov96z9U1ojY9qHQvgUrh87dqxkY2RMPVFqgN5/Es25TTc9bxz6npDQbNKkSa72/aIx6CRGJpOJcQ4cOCAO2hRxyk1K+1ljmL73c+fOxYYNG+Dn5yd+V3ITPcnOMWPJkiVi/oUKFcrx/p83BkX4+/fvL+ZOv1f0vpN4pqiTVGPQ9z40NDTTbfQdjImJgTXgCI0DQSHPCxcuiNy3Jfjmm2/ED8PFixcljTpQrvXTTz8VB1GKDlgCEmJ08CTxQhEnqtcg8UQpLqlITk4WB4GVK1eK14fG+PXXXy1SUEsCjc6cBwwYAEtABwISrnSGRc+F8uiU3pIKKp4kgUGiyfS6/fTTT+K+vERonoXpvc8IXacIgaOSmpoqhAwdJHITDXgRJCr//PNP8b2kFMf27dsl3T+lJei7QjVAloKiiyRo6KBMQoBEOUWEaIGAVNDnl2pn4uPjxe8jpQBJFJw7dw5SQ+KJ0nNUPyM1SUlJYv9UP0W/LZRyIoFOvwVSQfVxlLpcvXq1OHmhGprdu3db5DufFRyhcSAxs3z5csyfPz9XIcLsYKptIQFCNRtU+CZFdGPBggWoWrVqpmiT1NAZFJ01+/j4iB84Sp9QROuDDz4QP6gZQ6C5RalUihA31YXQeCYhRZEUqqWREvqxpFA61VBIDUXL6EBGtQEkMOl9p7EWLVokVrxIFQUiYUnFx5T6oWgJ5e5JPOW2YPNF41E4PSMkZiwloC0NHXyoYPP69ev4/fffc1UT8iKoboOiJ3ShgxpFf6WKzJIg//jjj8WB/8mUuZSQuCBhTpEZglYD0mtG30mpCtzpt8NUF0KRDUqpULSZDtpS1wOSqKTfLkuk5ZctWybeFxI0BD0PEh8UpaWVm1JAxyY6iSGhRCex9FyowN0SK4CzgiM0DgB9QOjslkSN1KkgKgqmXGpG6MtEilqqWh0KmdMYtIyPLhQ5oQttSwn96JCYMUFFaSTOqAhWqpomOnCaxAxRqlQpUTAoNXRmQyFiEmhSQ3UsJUqUyHSwp4MaiTMpoZqMPXv2iFQA1Z7Qa0Xheg8PD0gNrcqjz3JG6PqTaShHgL531EaBVo/QSYzUdWG0XzogZ4S+K1KmBeizREWzVDdn+t7TbXSQI2ErFfR9N4kZExStIYEuFfQZovcgY1rZkt97WnlmCc6fP/9U+wcSHFJ/71999VXx+aITJqrTo/cot+nMnMKCxs6h6AaFbanfiSXO1m/fvi0Ue8YfADrgUTg6t3UOT0JpGRIwVHtCF6rVoQttS/lDQEVqlHowQakz+rGT6nlQGoUEEtXqZAx5ZxQ4UkFnTtQjxhLQDzSlgDKmY+h5SPmjQ9ESOjOjgyQJQYpukaixRLG56b2hH2xK02Qs4qbbHQmKKtL3kb6X9L2h1Y1Ss3fvXhE9ydg/iV673NRSPE9gUvGp6TtPF/rcUcTm888/l2ycr7/+WqykyQjVcEj5XOgzRCKQak5MUERL6u89vR8UmbXk9/7q1auZbpP6e0/pMiqJoKgWjUfPyfTbbA1Y0Ngx9KWhanrqS0Fheyp8NF2kgkKmFHqk/jb0YSdVTZEgyq9KBX3xKSJgutAZOl1oWyroDJCiJ/RDTV9Seh5UPyPl2SD9SFIvEEph0Y8mfVEpb08HbqmhH1BLrQYjMalSqcRrReKMoihUGyBl3p6EJNUe0GeJ8vaUs6cl1FK+HxkhoURFlPTe0GtH7wuJwp49e8KRoFQghecpZE+LAkzf9yfTaXmB0oq0TypypfQM9bii+ila6i4VJGAzfufpQrdR6pHEjlRQuolWBFE7AFpCTOk5Ek9SpoCpcRwJTUrL0IkAvV703adly1JCvWco1Wip732vXr1EtJRq/ug7SX9pEYWUi1sockWCmd4HGoNeM4qQU2rQGnANjR1DxVR0VkC1DXTJyKVLlyQZg5Q0iSZKa1HhIeXq89LQyVZQXQb9qFEzJwp5kmCifhFSH0DpIECvFYkYeq1o1YAlCvgoXSJlD50nayfox4zOlOmATxEsWqkldeEp1XtRioEaNNJZIJ1NS9Fd93mfY1qOTCu26AC6cOFCqzTzkrqGgg6eT4oLEmwUsZECaqJm+q5Q3QydcNB7Qyc2jgZ9nmjutKCB/tJzoRo3KdPZ9NtCKX+qoSFxQ58p+mxL/XrRijbCEmlmgvoyUfG36bUi8UHCX8ooIIlVqp2jVCOdUFJ0i147S6SZs0JmkLJvO8MwDMMwjA3glBPDMAzDMA4PCxqGYRiGYRweFjQMwzAMwzg8LGgYhmEYhnF4WNAwDMMwDOPwsKBhGIZhGMbhYUHDMAzDMIzDw4KGYRiGYRiHhwUNwzgBFSpUwHvvvffU7WQeR1YIloD2S/u3FmQZQM/T2lCnaOrAKtXjGIbJHWx9wDBOwubNm4XVQaNGjWw9lXwFiRTyxpLqcQzD5A6O0DCMk0A+N9OnT8/kss1IY8SZHa+a7D6OYZjcwYKGYZyEt99+G/fv3xfGhM8iIiIC48ePF2aIDRo0EK7PJgFE6SNKm5BRar169dC4cWPhbLxt2zbhely3bl3hrp0Rcr4mp11ydR86dCju3r0rbr99+7ZID5GBJO2LhBaxc+dOdOzYUZjaUTTp6NGjz5xrYmIi3n33XWFE2L59e5w7dy7T/ffu3ROu8bQvSn8tWLBAmL0+SUJCgpjf4cOHM+2bbjt+/Li4Tg7C3bt3F2aINL8dO3ZkmUqi50dOzzQnioSRkWl6evpTj/voo48wc+ZM8Z7Q/Jo3by5eSxOHDh1C165dxRxat26NlStXPvN1YBjGCAsahnESyAl33Lhx+P7773Hr1q2n7ifhMnjwYKSkpAhnZ3LN3bdvn3DNNXHq1Cnxv3/++SdeeeUV4UD8yy+/CJFDB+lly5bhwoUL5sf/8ccfwvF87dq10Gq1mDBhQqYxT548Ke4jd/fQ0FBxPzl/b9y4EV26dMHw4cNx48aNLJ8POXlfu3ZNOEZ//PHHwtXXBHnujhkzBgEBAVi/fr0QD5s2bRLPPSv38aZNmwoxZYKeN7mQ16lTR4iLsWPHCoGxYcMG9OrVC++88w5CQkKe2hcJGHd3dyFOSKyRe/bq1auznP+KFSuEYzOlAtu1ayeeD4krEl0kdF5++WVs3bpVCMxp06bh6tWrWe6HYRgjLGgYxomgKEGJEiXw+eefP3XfwYMHRQSHoiwUPaEIwyeffCJESVJSklkokHigffTu3VuIHzrYV6xYUURUSECQyDDRt29fdOrUCeXLlxdjUsQlLCzMfD8JqOLFi6NkyZIicvTaa6+hc+fOYv8kcpo1aybGfxI68NPBnuZCooAEyejRo833U7SFoiUkMEqXLi2iTSSWSHxlBYkzEjT0/AgSIh06dIBMJhPCgyJAr7/+OkqVKoU33nhDCJAff/zxqf3cuXNHCKTChQujdu3aWLJkiYi+ZAW9xiTYihUrJkRLamqqiGjRc4uNjUVgYCCKFi0qhB2JtaCgoBe8uwzj3LCgYRgnQqFQiKgKRSB27dqV6T4SGiQsfHx8zLfRQZkiKzdv3hTXSbBQBIJwdXUVf+mga8LNzS1TjQ6laEzQ46iOJKPgobqejONTtIXSNaYLpXquX7/+1PMIDw8XkQwSUiYoPZNxXyQKKMJi2hdFVei2mJiYp/ZHKbP4+HicOXNGiDQSd5RaMu0r4/MgaH8ZhZkJikZRJIjEIKXDSFRlfH0yQq+1CU9PT/GXXmt6jUgIklijeVE6jkRSxveFYZin4VVODONkkEh59dVXRcSEDsAmTAIlI6aaE9NfpfLpnwyKYjxPQGVEr9dnWumTcUwagyIWVHOTERJJ2cHFxcW8TcKAIjPffffdU48jcfAkJNJIPFBkhqJUFB0xiZisXhd6HnR5EoqmkJghsUiikVJ89JxITD1JViueTBEiEp39+/cX+6HLqlWrxHN5VrSHYRiO0DCMU/L+++8jOTk5U4EwpVMoGkJRDBOnT58WIobSQrnh8uXL5m3aN0VBaJysoNupWJjSTaYLHcgPHDjw1GNJrJAgyFgInLF2h/ZF0RGqgzHti/b9zTffPFOAUdpp//79QkCYojOmfVHkJiNUS5TV85g/fz6io6NFhGXx4sWiFiZjAXF2iIyMFDUzNGeqJ6Iao4YNG2LPnj052g/DOBssaBjGCfHz8xOihmo+TNCqJarn+PDDD3Hp0iVRh0I1KFQD4+3tnatxqPaDDuhU8Dtx4kQRBaEDdVZQjcqWLVtEnQuluH7++WdxyZiayZiioSJdmh+JDWqqR6uYTDRp0kSksz744APxXGi10pQpU6BWq5+KGpmgep0HDx48JWhoXhS5Wb58uRBlNCeqtyHR8iSUTqMUET1fqochgVS5cuUcvWaUWqL9z5gxQ7wOx44dE/vL6X4YxtlgQcMwTgoV8VItiAk60JtSNFScSzUgtGTYtKQ6N1ABLa2Wov1R/Q0dpJ9FzZo1xYqq33//XQgKWh305ZdfimXdWUECheZPY9AKqwEDBmR6LrTyitJCNDYVLlO6hupSnpeyatOmDQoWLJipNoeWVdO8qDiZxB1FTOg5ZdWgkFJFlK6i4msat0CBApg8eXIOXjHjPOh9IBFDKSyK8tB7RaurGIZ5NjKDKWnLMAzDMAzjoHCEhmEYhmEYh4cFDcMwDMMwDg8LGoZhGIZhHB4WNAzDMAzDODwsaBiGYRiGcXhY0DAMwzAM4/CwoGEYhmEYxuFhQcMwDMMwjMPDgoZhGIZhGIeHBQ3DMAzDMA4PCxqGYRiGYeDo/B8VRDKU1NRlDwAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score maximum pour k = 16\n"
+     ]
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18ec66e2",
+   "metadata": {},
+   "source": [
+    "3. En prenant la valeur de k qui vous semble la plus pertinente, faite varier la dimension (p) utilisée pour calculer la distance de Minkowski entre deux données. Cette distance a-t'elle un fort impact sur les résultats d'accuracy obtenus ? Montrez-le en montrant l'évolution de ce score en fonction de p (faire varier entre 1 et 10). Ajoutez également la précision et le rappel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "ce6b99d8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:36.943793Z",
+     "start_time": "2025-09-18T11:38:36.764548Z"
+    }
+   },
+   "source": [
+    "accuracies = []\n",
+    "precisions = []\n",
+    "recalls = []\n",
+    "\n",
+    "p_range = range(1,10)\n",
+    "\n",
+    "for dim in p_range:\n",
+    "    knn = KNeighborsClassifier(n_neighbors=k_max, p=dim)\n",
+    "    knn.fit(X_train, y_train)\n",
+    "    y_pred = knn.predict(X_test)\n",
+    "    accuracies.append(accuracy_score(y_test, y_pred))\n",
+    "    precisions.append(precision_score(y_test, y_pred))\n",
+    "    recalls.append(recall_score(y_test, y_pred))\n",
+    "    \n",
+    "plt.plot(p_range, accuracies, label='Accuracy')\n",
+    "plt.plot(p_range, precisions, label='Precision')\n",
+    "plt.plot(p_range, recalls, label='Rappel')\n",
+    "plt.xticks(p_range)\n",
+    "plt.xlabel('Degré de la distance de Minkowski')\n",
+    "plt.ylabel('Scores')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "prof_max = np.argmax(accuracies) + 1\n",
+    "print(\"Score maximum pour p =\", prof_max)\n",
+    "\n",
+    "# sauvegarde des scores\n",
+    "knn_best_accuracy = accuracies[prof_max-1]\n",
+    "knn_best_pred = precisions[prof_max-1]\n",
+    "knn_best_recall = recalls[prof_max-1]"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score maximum pour p = 1\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb3a1544",
+   "metadata": {},
+   "source": "**Observation :** la différence n'est pas énorme mais elle existe. Ainsi, prendre cette prof_max semble légèrement améliorer les performances."
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a7c6024",
+   "metadata": {},
+   "source": [
+    "## Partie 3 : découvrir les SVM\n",
+    "\n",
+    "1. Créez un modèle de classification basée sur les machines à vecteur de support. Dans un premier temps, gardez les options par défaut. Que pouvez-vous dire des performances obtenues (accuracy, précision, rappel) ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3b136dbf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T12:01:52.300932Z",
+     "start_time": "2025-09-18T12:01:52.259774Z"
+    }
+   },
+   "source": [
+    "svm = SVC()\n",
+    "\n",
+    "#Entraînement\n",
+    "svm.fit(X_train, y_train)\n",
+    "y_pred=svm.predict(X_test)\n",
+    "\n",
+    "#Test\n",
+    "print(\"Accuracy : \", svm.score(X_test, y_test))\n",
+    "print(\"Precision : \", precision_score(y_test, y_pred))\n",
+    "print(\"Rappel : \", recall_score(y_test, y_pred))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy :  0.6759776536312849\n",
+      "Precision :  0.6875\n",
+      "Rappel :  0.3142857142857143\n"
+     ]
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d02a1e2e",
+   "metadata": {},
+   "source": [
+    "**Observation :** on obtient une accuracy similaire à KNN, mais on peut remarquer que le rappel est bien meilleur que la précision. Cela peut s'expliquer par le noyau utilisé, qui ne correspond peut-être pas à la distribution des données."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "905b31a7",
+   "metadata": {},
+   "source": [
+    "2. Testez les différents noyaux disponibles pour l'algorithme SVM (linéaire, polynomial, rbf et sigmoïde). Représentez graphiquement l'accuracy, la précision et le rappel, pour chaque noyau. Il y en a t'il un qui semble plus pertinent que les autres ? Affichez-le, ainsi que les scores obtenus pour ce noyau."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e68429cd",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:41.054882Z",
+     "start_time": "2025-09-18T11:38:37.003015Z"
+    }
+   },
+   "source": [
+    "noyaux = ['linear', 'poly', 'rbf', 'sigmoid']\n",
+    "\n",
+    "accuracies = []\n",
+    "precisions = []\n",
+    "recalls = []\n",
+    "\n",
+    "for n in noyaux:\n",
+    "    svm = SVC(kernel=n)\n",
+    "    svm.fit(X_train, y_train)\n",
+    "    y_pred = svm.predict(X_test)\n",
+    "    accuracies.append(accuracy_score(y_test, y_pred))\n",
+    "    precisions.append(precision_score(y_test, y_pred))\n",
+    "    recalls.append(recall_score(y_test, y_pred))\n",
+    "    \n",
+    "plt.plot(noyaux, accuracies, label='Accuracy')\n",
+    "plt.plot(noyaux, precisions, label='Precision')\n",
+    "plt.plot(noyaux, recalls, label='Rappel')\n",
+    "plt.xticks(noyaux)\n",
+    "plt.xlabel('Noyau utilisé')\n",
+    "plt.ylabel('Score')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "pos_meilleur_noyau = np.argmax(accuracies)\n",
+    "meilleur_noyau = noyaux[pos_meilleur_noyau]\n",
+    "print(\"Noyau optimal : \", meilleur_noyau)\n",
+    "print(\"Accuracy du noyau\", meilleur_noyau, \": \", accuracies[pos_meilleur_noyau])\n",
+    "print(\"Précision du noyau\", meilleur_noyau, \": \", precisions[pos_meilleur_noyau])\n",
+    "print(\"Rappel du noyau\", meilleur_noyau, \": \", recalls[pos_meilleur_noyau])"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Noyau optimal :  linear\n",
+      "Accuracy du noyau linear :  0.776536312849162\n",
+      "Précision du noyau linear :  0.7586206896551724\n",
+      "Rappel du noyau linear :  0.6285714285714286\n"
+     ]
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "cell_type": "markdown",
+   "id": "014b5138",
+   "metadata": {},
+   "source": [
+    "3. Nous allons essayer d'améliorer les performances obtenues avec le noyau polynomial. Utilisez ce noyau, et faites varier le degré du polynôme utilisé de 1 à 10. Représentez graphiquement l'accuracy, la précision et le rappel, en fonction du degré du polynôme. Il y en a t'il un qui semble plus pertinent que les autres ? Affichez-le, ainsi que les scores obtenus pour cette valeur. Comparez avec le meilleur score obtenu à la question précédente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "544318b0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:46.297848Z",
+     "start_time": "2025-09-18T11:38:41.513313Z"
+    }
+   },
+   "source": [
+    "degrees = range(1,11)\n",
+    "\n",
+    "accuracies = []\n",
+    "precisions = []\n",
+    "recalls = []\n",
+    "\n",
+    "for d in degrees:\n",
+    "    svm = SVC(kernel='poly', degree=d)\n",
+    "    svm.fit(X_train, y_train)\n",
+    "    y_pred = svm.predict(X_test)\n",
+    "    accuracies.append(accuracy_score(y_test, y_pred))\n",
+    "    precisions.append(precision_score(y_test, y_pred))\n",
+    "    recalls.append(recall_score(y_test, y_pred))\n",
+    "    \n",
+    "plt.plot(degrees, accuracies, label='Accuracy')\n",
+    "plt.plot(degrees, precisions, label='Precision')\n",
+    "plt.plot(degrees, recalls, label='Rappel')\n",
+    "plt.xticks(degrees)\n",
+    "plt.xlabel('Degré du polynôme')\n",
+    "plt.ylabel('Score')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "pos_meilleur_noyau = np.argmax(accuracies)\n",
+    "meilleur_degré = degrees[pos_meilleur_noyau]\n",
+    "print(\"Degré optimal : \", meilleur_degré)\n",
+    "print(\"Accuracy du degré\", meilleur_degré, \": \", accuracies[pos_meilleur_noyau])\n",
+    "print(\"Précision du degré\", meilleur_degré, \": \", precisions[pos_meilleur_noyau])\n",
+    "print(\"Rappel du degré\", meilleur_degré, \": \", recalls[pos_meilleur_noyau])\n",
+    "\n",
+    "# sauvegarde des scores\n",
+    "svm_best_accuracy = accuracies[pos_meilleur_noyau]\n",
+    "svm_best_pred = precisions[pos_meilleur_noyau]\n",
+    "svm_best_recall = recalls[pos_meilleur_noyau]"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Degré optimal :  10\n",
+      "Accuracy du degré 10 :  0.6759776536312849\n",
+      "Précision du degré 10 :  0.9285714285714286\n",
+      "Rappel du degré 10 :  0.18571428571428572\n"
+     ]
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25f11b31",
+   "metadata": {},
+   "source": [
+    "## Partie 4 : découvrir les réseaux de neurones"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77448c8c",
+   "metadata": {},
+   "source": [
+    "1. Commençons par étudier le réseau le plus simple : un perceptron. A l'aide de la classe `sklearn.linear_model.Perceptron`, créez un perceptron, en gardant les options par défaut. Affichez accuracy, précision et rappel : que pensez-vous de ces performances ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "0d2620da",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:46.418668Z",
+     "start_time": "2025-09-18T11:38:46.403116Z"
+    }
+   },
+   "source": [
+    "perceptron = Perceptron()\n",
+    "#Entraînement\n",
+    "perceptron.fit(X_train, y_train)\n",
+    "y_pred=svm.predict(X_test)\n",
+    "\n",
+    "#Test\n",
+    "print(\"Accuracy : \", perceptron.score(X_test, y_test))\n",
+    "print(\"Precision : \", precision_score(y_test, y_pred))\n",
+    "print(\"Rappel : \", recall_score(y_test, y_pred))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy :  0.7094972067039106\n",
+      "Precision :  0.9285714285714286\n",
+      "Rappel :  0.18571428571428572\n"
+     ]
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb4bf1a7",
+   "metadata": {},
+   "source": [
+    "**Observation :** on observe à nouveau une accuracy assez basse, avec un énorme déséquilibre entre précision et rappel : les bonnes prédictions des décès semblent se faire en se trompant énormément sur les survies."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bda13ed8",
+   "metadata": {},
+   "source": [
+    "2. Regardez la documentation pour créer un réseau de neurones (`sklearn.neural_network.MLPClassifier`) : quelle est la structure d'un réseau de neurones par défaut avec scikit-learn ? Combien de couches cachées ? Combien de neurones par couche ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25fe018c",
+   "metadata": {},
+   "source": [
+    "**Réponse :**\n",
+    "Lien vers la documentation : https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier\n",
+    "\n",
+    "Par défaut, il n'y qu'une seule couche cachée, composée de 100 neurones."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4ae8d40",
+   "metadata": {},
+   "source": [
+    "2. Créer un réseau de neurones, en gardant ces options par défaut. Affichez accuracy, précision et rappel : que pensez-vous de ces performances, notamment en comparant par rapport au perceptron ? Avez-vous un message d'alerte ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "7268b9a1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:46.779472Z",
+     "start_time": "2025-09-18T11:38:46.432196Z"
+    }
+   },
+   "source": [
+    "ann = MLPClassifier()\n",
+    "#Entraînement\n",
+    "ann.fit(X_train, y_train)\n",
+    "y_pred=ann.predict(X_test)\n",
+    "\n",
+    "#Test\n",
+    "print(\"Accuracy : \", ann.score(X_test, y_test))\n",
+    "print(\"Precision : \", precision_score(y_test, y_pred))\n",
+    "print(\"Rappel : \", recall_score(y_test, y_pred))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy :  0.770949720670391\n",
+      "Precision :  0.7735849056603774\n",
+      "Rappel :  0.5857142857142857\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ffotre\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:781: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff623621",
+   "metadata": {},
+   "source": [
+    "**Observation :** on obtient un bien meilleur score qu'avec un perceptron. On peut notamment voir que la précision s'est considérablement améliorée."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f9b9f82",
+   "metadata": {},
+   "source": [
+    "3. Si vous avez observé un message d'alerte sur la question précédent, que signifie-t'il selon vous ? Que pouvez-vous faire pour y remédier ? Proposez un code permettant d'obtenir des résultats, sans message d'alerte. Qu'observez-vous sur l'évolution des scores ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "478df749",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:47.181881Z",
+     "start_time": "2025-09-18T11:38:46.789079Z"
+    }
+   },
+   "source": [
+    "ann = MLPClassifier(max_iter=300)\n",
+    "#Entraînement\n",
+    "ann.fit(X_train, y_train)\n",
+    "y_pred=ann.predict(X_test)\n",
+    "\n",
+    "#Test\n",
+    "print(\"Accuracy : \", ann.score(X_test, y_test))\n",
+    "print(\"Precision : \", precision_score(y_test, y_pred))\n",
+    "print(\"Rappel : \", recall_score(y_test, y_pred))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy :  0.7932960893854749\n",
+      "Precision :  0.7894736842105263\n",
+      "Rappel :  0.6428571428571429\n"
+     ]
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "cell_type": "markdown",
+   "id": "132b2069",
+   "metadata": {},
+   "source": [
+    "4. Nous allons à présent comparer différentes architectures du réseau de neurones :\n",
+    "- Trois couches de 50 neurones chacune\n",
+    "- Cinq couches de 50 neurones chacune\n",
+    "- Trois couches : première avec 50, deuxième avec 100, troisième avec 50 neurones\n",
+    "- Cinq couches : première avec 50, deuxième avec 100, troisième avec 50 neurones, quatrième avec 100, cinquième avec 50 neurones\n",
+    "\n",
+    "Représentez graphiquement l'accuracy, la précision et le rappel, pour chaque architecture. Il y en a t'il une qui semble plus pertinente que les autres ? Affichez-la, ainsi que les scores obtenus pour cette architecture. Comparez avec le score obtenu par l'architecture par défaut. Votre code ne doit générer aucun message d'alerte."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "1027a554",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:49.204732Z",
+     "start_time": "2025-09-18T11:38:47.195634Z"
+    }
+   },
+   "source": [
+    "architectures = [(50,50,50),(50,50,50,50,50),(50,100,50),(50,100,50,100,50),]\n",
+    "labels = ['(50,50,50)','(50,50,50,50,50)','(50,100,50)','(50,100,50,100,50)']\n",
+    "\n",
+    "accuracies = []\n",
+    "precisions = []\n",
+    "recalls = []\n",
+    "\n",
+    "for archi in architectures:\n",
+    "    ann = MLPClassifier(max_iter=500, hidden_layer_sizes=archi)\n",
+    "    ann.fit(X_train, y_train)\n",
+    "    y_pred = ann.predict(X_test)\n",
+    "    accuracies.append(accuracy_score(y_test, y_pred))\n",
+    "    precisions.append(precision_score(y_test, y_pred))\n",
+    "    recalls.append(recall_score(y_test, y_pred))\n",
+    "    \n",
+    "plt.plot(labels, accuracies, label='Accuracy')\n",
+    "plt.plot(labels, precisions, label='Precision')\n",
+    "plt.plot(labels, recalls, label='Rappel')\n",
+    "plt.xticks(labels)\n",
+    "plt.xlabel('Architecture du réseau de neurones')\n",
+    "plt.ylabel('Score')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "pos_meilleure_archi = np.argmax(accuracies)\n",
+    "meilleure_archi = architectures[pos_meilleure_archi]\n",
+    "print(\"Architecture optimale : \", meilleure_archi)\n",
+    "print(\"Accuracy de l'architecture\", meilleure_archi, \": \", accuracies[pos_meilleure_archi])\n",
+    "print(\"Précision de l'architecture\", meilleure_archi, \": \", precisions[pos_meilleure_archi])\n",
+    "print(\"Rappel de l'architecture\", meilleure_archi, \": \", recalls[pos_meilleure_archi])"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Architecture optimale :  (50, 50, 50)\n",
+      "Accuracy de l'architecture (50, 50, 50) :  0.7932960893854749\n",
+      "Précision de l'architecture (50, 50, 50) :  0.8666666666666667\n",
+      "Rappel de l'architecture (50, 50, 50) :  0.5571428571428572\n"
+     ]
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e437de59",
+   "metadata": {},
+   "source": [
+    "5. En utilisant l'architecture qui vous donnait les meilleures performances, étudier l'impact de la fonction d'activation utilisée sur les performances. Représentez sur un graphiques les scores (accuracy, précision et rappel) obtenus pour les quatres fonctions d'activation proposées par scikit-learn. Affichez la fonction qui vous parait la plus pertinente, ainsi que les scores associés."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "9ad2a684",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:52.542120Z",
+     "start_time": "2025-09-18T11:38:49.324242Z"
+    }
+   },
+   "source": [
+    "activations = ['identity', 'logistic', 'tanh', 'relu']\n",
+    "\n",
+    "accuracies = []\n",
+    "precisions = []\n",
+    "recalls = []\n",
+    "\n",
+    "for f in activations:\n",
+    "    ann = MLPClassifier(hidden_layer_sizes=meilleure_archi,max_iter=500, activation=f)\n",
+    "    ann.fit(X_train, y_train)\n",
+    "    y_pred = ann.predict(X_test)\n",
+    "    accuracies.append(accuracy_score(y_test, y_pred))\n",
+    "    precisions.append(precision_score(y_test, y_pred))\n",
+    "    recalls.append(recall_score(y_test, y_pred))\n",
+    "    \n",
+    "plt.plot(activations, accuracies, label='Accuracy')\n",
+    "plt.plot(activations, precisions, label='Precision')\n",
+    "plt.plot(activations, recalls, label='Rappel')\n",
+    "plt.xticks(activations)\n",
+    "plt.xlabel('Fonction d\\'activation des neurones')\n",
+    "plt.ylabel('Score')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "pos_meilleure_fun = np.argmax(accuracies)\n",
+    "meilleure_fun = activations[pos_meilleure_fun]\n",
+    "print(\"Fonction d'activation optimale : \", meilleure_fun)\n",
+    "print(\"Accuracy de la fonction\", meilleure_fun, \": \", accuracies[pos_meilleure_fun])\n",
+    "print(\"Précision de la fonction\", meilleure_fun, \": \", precisions[pos_meilleure_fun])\n",
+    "print(\"Rappel de la fonction\", meilleure_fun, \": \", recalls[pos_meilleure_fun])\n",
+    "\n",
+    "# sauvegarde des scores\n",
+    "ann_best_accuracy = accuracies[pos_meilleure_fun]\n",
+    "ann_best_pred = precisions[pos_meilleure_fun]\n",
+    "ann_best_recall = recalls[pos_meilleure_fun]"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fonction d'activation optimale :  logistic\n",
+      "Accuracy de la fonction logistic :  0.7988826815642458\n",
+      "Précision de la fonction logistic :  0.8541666666666666\n",
+      "Rappel de la fonction logistic :  0.5857142857142857\n"
+     ]
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60141a50",
+   "metadata": {},
+   "source": [
+    "## Partie 5 : comparer les performances des différents algorithmes\n",
+    "\n",
+    "Nous allons à présent résumer les différentes performances des algorithmes que vous avez testé dans ce TP : récupérez les meilleurs scores (accuracy) obtenu pour chaque algorithme. Représentez-les sur un diagramme en barres, en regroupant par algorithme, et en représentant chaque score par une couleur. Un algorithme semble-t'il obtenir de meilleures performances que les autres ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "2318f1a5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:38:52.845128Z",
+     "start_time": "2025-09-18T11:38:52.676660Z"
+    }
+   },
+   "source": [
+    "algos = ['Naive Bayes', 'KNN', 'SVM', 'ANN']\n",
+    "\n",
+    "scores = {\n",
+    "    'Accuracy': (nb_best_accuracy, knn_best_accuracy, svm_best_accuracy, ann_best_accuracy),\n",
+    "    'Precision': (nb_best_pred, knn_best_pred, svm_best_pred, ann_best_pred),\n",
+    "    'Rappel': (nb_best_recall, knn_best_recall, svm_best_recall, ann_best_recall)\n",
+    "}\n",
+    "\n",
+    "x = np.arange(len(algos))  # the label locations\n",
+    "width = 0.25  # the width of the bars\n",
+    "multiplier = 0\n",
+    "\n",
+    "fig, ax = plt.subplots(layout='constrained')\n",
+    "\n",
+    "for attribute, value in scores.items():\n",
+    "    offset = width * multiplier\n",
+    "    rects = ax.bar(x + offset, value, width, label=attribute)\n",
+    "    ax.bar_label(rects, padding=3)\n",
+    "    multiplier += 1\n",
+    "\n",
+    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
+    "ax.set_ylabel('Scores')\n",
+    "ax.set_title('Performances comparée des différents algorithmes de classification')\n",
+    "ax.set_xticks(x + width, algos)\n",
+    "ax.legend(loc='upper left', ncols=3)\n",
+    "ax.set_ylim(0, 1.1)\n",
+    "\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe7a5b28",
+   "metadata": {},
+   "source": [
+    "## Partie 6 : optimiser la recherche des paramètres optimaux\n",
+    "\n",
+    "Dans ce TP, nous avons souvent cherché à identifier la meilleur combinaison de paramètres. Nous avons procédé par itération, en cherchant à fixer un paramètre avant de faire évoluer les autres. Cette méthode est couteuse, et pour faire une recherche exhaustive, nécessite, de répéter très souvent le même code. Scikit-learn propose une classe, `sklearn.model_selection.GridSearchCV`, qui va permettre d'optimiser cette recherche de paramétrage optimal.\n",
+    "\n",
+    "Lien vers la documentation : https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\n",
+    "\n",
+    "Le principe est de définir un dictionnaire, où la clé correspond à un paramètre, et la valeur à la liste de valeurs possibles à tester pour le paramètre considéré. \n",
+    "\n",
+    "**Consigne :** Appliquez ce principe pour déterminer la meilleure combinaison possible pour le réseau de neurones, en repartant des différentes configurations testées dans les parties précédentes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "7f6eeac1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T12:23:53.803671Z",
+     "start_time": "2025-09-18T12:22:40.984760Z"
+    }
+   },
+   "source": [
+    "parameters = {\n",
+    "    \"hidden_layer_sizes\": [(50,50,50),(50,50,50,50,50),(50,100,50),(50,100,50,100,50),],\n",
+    "    \"activation\": ['identity', 'logistic', 'tanh', 'relu']\n",
+    "}\n",
+    "\n",
+    "ann = MLPClassifier(max_iter=700)\n",
+    "\n",
+    "ann = GridSearchCV(\n",
+    "    ann, \n",
+    "    parameters, \n",
+    "    cv=5,\n",
+    "    scoring='accuracy',\n",
+    ")\n",
+    "\n",
+    "ann.fit(X_train, y_train)\n",
+    "\n",
+    "print('-----')\n",
+    "print(f'Meilleurs paramètres {ann.best_params_}')\n",
+    "print(\n",
+    "    f'Score moyen de validation croisée pour la meilleure combinaison de paramètres: ' + \n",
+    "    f'{ann.best_score_:.3f}'\n",
+    ")\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----\n",
+      "Meilleurs paramètres {'activation': 'logistic', 'hidden_layer_sizes': (50, 100, 50)}\n",
+      "Score moyen de validation croisée pour la meilleure combinaison de paramètres: 0.813\n"
+     ]
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "cell_type": "code",
+   "id": "d6221ddb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-18T11:40:11.923701Z",
+     "start_time": "2025-09-18T11:40:11.922279Z"
+    }
+   },
+   "source": [],
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/M07_TP01_Exercice.ipynb b/M07_TP01_Exercice.ipynb
new file mode 100644
index 0000000..ddc9712
--- /dev/null
+++ b/M07_TP01_Exercice.ipynb
@@ -0,0 +1,332 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c6c97ebc",
+   "metadata": {},
+   "source": [
+    "# TP n°1 du module 7 : L'apprentissage non supervisé pour le _Machine Learning_\n",
+    "\n",
+    "Dans ce TP, nous allons mettre en pratique les principes de l'apprentissage non supervisé.\n",
+    "\n",
+    "Objectifs :\n",
+    "- Passer en revue les principaux algorithmes de clustering\n",
+    "- Comparer les performances de ces différents algorithmes\n",
+    "- Comparer avec les performances de la classification supervisée.\n",
+    "\n",
+    "La recherche d'itemsets fréquents et de règles d'associaion ne sera pas abordée dans ce TP.\n",
+    "\n",
+    "_NB. : Des messages d'alertes sont suceptibles d'apparaître…_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3d02d7de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ajoutez ici les imports de librairies nécessaires\n",
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "925311c6",
+   "metadata": {},
+   "source": [
+    "Même si ce module concerne l'apprentissage non supervisé, nous allons continuer à explorer le jeu de données du Titanic :\n",
+    "- Cela nous permettra, à la fin, de comparer les clusters obtenus, avec les deux classes réelles obtenus via les labels du jeu de données.\n",
+    "\n",
+    "Nous allons donc commencer par charger les données. Pour cela, repartez du csv obtenu à la fin du TP n°1 du module 4 :\n",
+    "- Construisez, comme pour l'apprentissage supervisé, deux dataframe : un avec les attributs, l'autre avec les labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8660a70c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ab5c627",
+   "metadata": {},
+   "source": [
+    "## 1 - Découverte de KMeans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "596a7460-2256-40dc-9fcf-d1cbdd4164d2",
+   "metadata": {},
+   "source": [
+    "### Question n°1\n",
+    "Nous allons commencer par faire un premier clustering avec KMeans.\n",
+    "- Comme nous connaissons le nombre de clusters à rechercher, créer un modèle avec la classe de scikit-learn, en fixant le nombre de clusters.\n",
+    "- Appliquez ce clustering aux attributs de _Titanic.csv_\n",
+    "- Récupérer dans une liste le numéro du cluster prédit pour chaque donnée."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ad5f4f32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23f5200c",
+   "metadata": {},
+   "source": [
+    "### Question n°2\n",
+    "- Quelle est la classe majoritaire dans le premier cluster ?\n",
+    "- Dans le deuxième cluster ?\n",
+    "- Affichez une matrice, qui donne pour chaque classe le nombre de fois où elle apparaît dans chaque cluster.\n",
+    "- Qu'observez-vous ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "35da6202",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b356cb5",
+   "metadata": {},
+   "source": [
+    "### Question n°3\n",
+    "Faites un pairplot des données du Titanic, en colorant chaque donnée en fonction du cluster auquel elle a été affectée.\n",
+    "- Remarquez-vous des phénomènes intéressants ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "32bf66f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20edcaa2",
+   "metadata": {},
+   "source": [
+    "### Question n°4\n",
+    "Les algorithmes de clustering impliquant des mesures de distances sont très sensibles aux plages de valeurs des différents attributs.\n",
+    "- Normalisez vos données, et refaites les mêmes étapes que précédemment.\n",
+    "- Observez-vous une différence ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ee230dda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "69d9c149",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b188d1d-94b5-4c11-806f-374a797986ca",
+   "metadata": {},
+   "source": [
+    "#### Observation :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "462ecef1",
+   "metadata": {},
+   "source": [
+    "### Question n°5\n",
+    "Étudions l'évolution du coefficient de _silhouette_.\n",
+    "- Pour k variant de 2 à 10, tracez l'évolution de ce coefficient pour un clustering kmeans appliqué au données normalisées.\n",
+    "- Le nombre de clusters utilisé aux questions précédentes vous semble-t'il pertinent ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3b0c59a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08525d4b",
+   "metadata": {},
+   "source": [
+    "## 2 - Clustering hiérarchique\n",
+    "\n",
+    "⚠ Attention : Pour toute la suite du TP, nous travaillerons avec les données normalisées. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "975de584-bd9e-4837-b443-3a17c49983e8",
+   "metadata": {},
+   "source": [
+    "### Question n°1\n",
+    "Créez un modèle de clustering hiérarchique (`AgglomerativeClustering`). En utilisant le coefficient de silhouette, comparez le score obtenu pour deux clusters avec celui obtenu par KMeans sur la section précédente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6c8c8175",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6516e52f-c916-4d0a-a4db-87128de828ce",
+   "metadata": {},
+   "source": [
+    "#### Observation :\n",
+    "`à compléter`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7527fa93",
+   "metadata": {},
+   "source": [
+    "### Question n°2\n",
+    "Représentez graphiquement ce nouveau clustering, à l'aide d'un pairplot.\n",
+    "- Remarquez-vous des tendances ou des changements intéressants ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6cb2ad2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8305067c",
+   "metadata": {},
+   "source": [
+    "### Question n°3\n",
+    "Étudiez l'impact du paramètre `linkage` sur les résultats de votre clustering hiérarchique.\n",
+    "- Pour rappel, ce paramètre désigne la manière dont est calculée la distance entre deux clusters, pour décider lesquels réunir à une itération donnée.\n",
+    "- Construisez un graphique montrant la valeur du coefficient silhouette en fonction de la méthode utilisée."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "5e3fa74f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34eef8e2",
+   "metadata": {},
+   "source": [
+    "## 3 - Comparaison des différents clustering\n",
+    "\n",
+    "Nous allons à présent comparer les résultats obtenus avec les autres algorithmes de clustering proposés par _Scikit-Learn_.\n",
+    "- Testez ces différents algorithmes, et calculez à chaque fois le coefficient de slihouette obtenu.\n",
+    "- Pour les algorithmes qui ne demandent pas de préciser le nombre de clusters à construire :\n",
+    "    - Affichez le nombre de clusters déduit par l'algorithme."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b1d9bc9-b50f-4c35-91fd-310bf3948e71",
+   "metadata": {},
+   "source": [
+    "Présentez une synthèse de vos résultats sous forme d'un tableau et d'un graphique.\n",
+    "\n",
+    "Liste des algorithmes à prendre en compte : `KMeans`, `DBScan`, `Spectral`, `Affinity_Propagation`, `agglomerativeClustering`, `OPTICS`, `BIRCH`, …\n",
+    "\n",
+    "⚠Attention : Pour certains algorithmes, vous devrez jouer avec les paramètres à votre disposition pour parvenir à obtenir au moins deux clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "cd9df865",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fe16763-a151-4516-80cd-8f8ba0069fa3",
+   "metadata": {},
+   "source": [
+    "# Fin du TP !"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
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+   "codemirror_mode": {
+    "name": "ipython",
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+   "mimetype": "text/x-python",
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