{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "891be44f-6fcc-41f1-8081-332ee38a29a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "# Algorithmes\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Outils\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, mean_absolute_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7a030508-1d96-4c5e-ba5c-dd32d51288f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Config Graphique\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams['figure.figsize'] = (10, 6)\n",
    "plt.rcParams['font.size'] = 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b3560f2-58ed-4c63-86c5-944c2f025bdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environnement prêt.\n"
     ]
    }
   ],
   "source": [
    "# Fonction utilitaire pour afficher une jolie Matrice de Confusion\n",
    "def plot_confusion_matrix(y_true, y_pred, model_name):\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    labels = ['À Risque (0)', 'Moyen (1)', 'Bon (2)']\n",
    "    \n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
    "                xticklabels=labels, yticklabels=labels)\n",
    "    plt.title(f\"Matrice de Confusion - {model_name}\", fontsize=14, fontweight='bold')\n",
    "    plt.ylabel('Vraie Catégorie')\n",
    "    plt.xlabel('Catégorie Prédite')\n",
    "    plt.show()\n",
    "\n",
    "print(\"Environnement prêt.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6bcae0d1-6d82-4edd-b496-1c7b397b48ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>grade_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
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       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
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       "      <th>4</th>\n",
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       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
       "0       0    0   18        1        0        0     4     4     0     4  ...   \n",
       "1       0    0   17        1        0        1     1     1     0     2  ...   \n",
       "2       0    0   15        1        1        1     1     1     0     2  ...   \n",
       "3       0    0   15        1        0        1     4     2     1     3  ...   \n",
       "4       0    0   16        1        0        1     3     3     2     2  ...   \n",
       "\n",
       "   freetime  goout  Dalc  Walc  health  absences  G1  G2  G3  grade_category  \n",
       "0         3      4     1     1       3         4   0  11  11               1  \n",
       "1         3      3     1     1       3         2   9  11  11               1  \n",
       "2         3      2     2     3       3         6  12  13  12               1  \n",
       "3         2      2     1     1       5         0  14  14  14               2  \n",
       "4         3      2     1     2       5         0  11  13  13               1  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/processed/student_data_cleaned.csv\")\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc1e8f5f-5246-473c-8e8e-bef4436eae6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Séparation Features / Target\n",
    "X = df.drop(['G3', 'grade_category'], axis=1)\n",
    "y_class = df['grade_category']  # 0, 1, 2\n",
    "y_reg = df['G3']                # Note /20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0fcc74f0-1d56-47dc-a4ce-12d05cca50d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split (80% Train, 20% Test) - Random State fixe pour reproductibilité\n",
    "X_train, X_test, y_class_train, y_class_test, y_reg_train, y_reg_test = train_test_split(\n",
    "    X, y_class, y_reg, test_size=0.2, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd02e5e2-d726-4ffd-8933-48316639a42c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Données prêtes : 519 exemples d'entraînement, 130 de test.\n"
     ]
    }
   ],
   "source": [
    "# Liste pour stocker les résultats finaux\n",
    "final_results = []\n",
    "\n",
    "print(f\"Données prêtes : {X_train.shape[0]} exemples d'entraînement, {X_test.shape[0]} de test.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2512c2db-0f46-48cc-93df-bda1baa6c1a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔄 Entraînement de Random Forest...\n"
     ]
    }
   ],
   "source": [
    "MODEL_NAME = \"Random Forest\"\n",
    "print(f\"🔄 Entraînement de {MODEL_NAME}...\")\n",
    "\n",
    "# 1. Entraînement\n",
    "clf_rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "start_time = time.time()\n",
    "clf_rf.fit(X_train, y_class_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# 2. Prédiction\n",
    "y_pred_rf = clf_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a0535b7d-ed91-45b4-b0bb-fe0de28d8a8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Accuracy : 86.15%\n",
      "\n",
      "Rapport de Classification :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Risque       0.75      0.60      0.67        15\n",
      "       Moyen       0.83      0.94      0.88        71\n",
      "         Bon       0.97      0.82      0.89        44\n",
      "\n",
      "    accuracy                           0.86       130\n",
      "   macro avg       0.85      0.79      0.81       130\n",
      "weighted avg       0.87      0.86      0.86       130\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 3. Métriques\n",
    "acc_rf = accuracy_score(y_class_test, y_pred_rf)\n",
    "print(f\"✅ Accuracy : {acc_rf:.2%}\")\n",
    "print(\"\\nRapport de Classification :\")\n",
    "print(classification_report(y_class_test, y_pred_rf, target_names=['Risque', 'Moyen', 'Bon']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fff74c1c-ae42-44bf-aca0-6955cf728a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. Stockage pour comparaison\n",
    "final_results.append({'Modèle': MODEL_NAME, 'Accuracy': acc_rf, 'Temps': train_time})\n",
    "\n",
    "# 5. Visualisation\n",
    "plot_confusion_matrix(y_class_test, y_pred_rf, MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "43c499fe-ad91-4de2-bb72-20e871940ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Importance \n",
    "plt.figure(figsize=(10, 5))\n",
    "feat_importances = pd.Series(clf_rf.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(10).plot(kind='barh', color='teal')\n",
    "plt.title(\"Top 10 des Facteurs d'Influence (Random Forest)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8995a3b9-5f48-405e-9f65-cd430daae0d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔄 Entraînement de SVM (Linear)...\n"
     ]
    }
   ],
   "source": [
    "MODEL_NAME = \"SVM (Linear)\"\n",
    "print(f\"🔄 Entraînement de {MODEL_NAME}...\")\n",
    "\n",
    "# 1. Entraînement\n",
    "clf_svm = SVC(kernel='linear', random_state=42)\n",
    "start_time = time.time()\n",
    "clf_svm.fit(X_train, y_class_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# 2. Prédiction\n",
    "y_pred_svm = clf_svm.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "40b7ca1e-108d-4ad2-9939-eae6643c69d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Accuracy : 84.62%\n",
      "\n",
      "Rapport de Classification :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Risque       0.62      0.67      0.65        15\n",
      "       Moyen       0.86      0.86      0.86        71\n",
      "         Bon       0.91      0.89      0.90        44\n",
      "\n",
      "    accuracy                           0.85       130\n",
      "   macro avg       0.80      0.80      0.80       130\n",
      "weighted avg       0.85      0.85      0.85       130\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 3. Métriques\n",
    "acc_svm = accuracy_score(y_class_test, y_pred_svm)\n",
    "print(f\"✅ Accuracy : {acc_svm:.2%}\")\n",
    "print(\"\\nRapport de Classification :\")\n",
    "print(classification_report(y_class_test, y_pred_svm, target_names=['Risque', 'Moyen', 'Bon']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f8ee135b-46b6-42cb-9cf6-43f986c21b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. Stockage\n",
    "final_results.append({'Modèle': MODEL_NAME, 'Accuracy': acc_svm, 'Temps': train_time})\n",
    "\n",
    "# 5. Visualisation\n",
    "plot_confusion_matrix(y_class_test, y_pred_svm, MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2a79f7c0-4de8-4451-90a3-f1bd7ce2393f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔄 Entraînement de KNN (k=5)...\n"
     ]
    }
   ],
   "source": [
    "MODEL_NAME = \"KNN (k=5)\"\n",
    "print(f\"🔄 Entraînement de {MODEL_NAME}...\")\n",
    "\n",
    "# 1. Entraînement\n",
    "clf_knn = KNeighborsClassifier(n_neighbors=5)\n",
    "start_time = time.time()\n",
    "clf_knn.fit(X_train, y_class_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# 2. Prédiction\n",
    "y_pred_knn = clf_knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bff869e6-c1c3-4760-a6b8-14a15541eee7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Accuracy : 83.08%\n",
      "\n",
      "Rapport de Classification :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Risque       0.67      0.53      0.59        15\n",
      "       Moyen       0.82      0.90      0.86        71\n",
      "         Bon       0.90      0.82      0.86        44\n",
      "\n",
      "    accuracy                           0.83       130\n",
      "   macro avg       0.80      0.75      0.77       130\n",
      "weighted avg       0.83      0.83      0.83       130\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 3. Métriques\n",
    "acc_knn = accuracy_score(y_class_test, y_pred_knn)\n",
    "print(f\"✅ Accuracy : {acc_knn:.2%}\")\n",
    "print(\"\\nRapport de Classification :\")\n",
    "print(classification_report(y_class_test, y_pred_knn, target_names=['Risque', 'Moyen', 'Bon']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b1981cd3-e506-4b36-a988-30b94fee30c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. Stockage\n",
    "final_results.append({'Modèle': MODEL_NAME, 'Accuracy': acc_knn, 'Temps': train_time})\n",
    "\n",
    "# 5. Visualisation\n",
    "plot_confusion_matrix(y_class_test, y_pred_knn, MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5e769f10-5f0e-413f-98f3-570082bf0572",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔄 Entraînement de Gradient Boosting...\n",
      "✅ Accuracy : 85.38%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MODEL_NAME = \"Gradient Boosting\"\n",
    "print(f\"🔄 Entraînement de {MODEL_NAME}...\")\n",
    "\n",
    "clf_gb = GradientBoostingClassifier(random_state=42)\n",
    "start_time = time.time()\n",
    "clf_gb.fit(X_train, y_class_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "y_pred_gb = clf_gb.predict(X_test)\n",
    "acc_gb = accuracy_score(y_class_test, y_pred_gb)\n",
    "print(f\"✅ Accuracy : {acc_gb:.2%}\")\n",
    "\n",
    "final_results.append({'Modèle': MODEL_NAME, 'Accuracy': acc_gb, 'Temps': train_time})\n",
    "plot_confusion_matrix(y_class_test, y_pred_gb, MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2458a53c-4b08-4b4e-86ac-c09f4bf0a2a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏆 CLASSEMENT FINAL DES MODÈLES :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_824e2_row0_col1, #T_824e2_row1_col1 {\n",
       "  background-color: #00441b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_824e2_row2_col1 {\n",
       "  background-color: #238b45;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_824e2_row3_col1, #T_824e2_row4_col1 {\n",
       "  background-color: #75c477;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_824e2_row5_col1 {\n",
       "  background-color: #f7fcf5;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_824e2\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_824e2_level0_col0\" class=\"col_heading level0 col0\" >Modèle</th>\n",
       "      <th id=\"T_824e2_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_824e2_level0_col2\" class=\"col_heading level0 col2\" >Temps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_824e2_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_824e2_row0_col0\" class=\"data row0 col0\" >Random Forest</td>\n",
       "      <td id=\"T_824e2_row0_col1\" class=\"data row0 col1\" >0.861538</td>\n",
       "      <td id=\"T_824e2_row0_col2\" class=\"data row0 col2\" >0.335267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_824e2_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_824e2_row1_col0\" class=\"data row1 col0\" >Random Forest</td>\n",
       "      <td id=\"T_824e2_row1_col1\" class=\"data row1 col1\" >0.861538</td>\n",
       "      <td id=\"T_824e2_row1_col2\" class=\"data row1 col2\" >0.335267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_824e2_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
       "      <td id=\"T_824e2_row2_col0\" class=\"data row2 col0\" >Gradient Boosting</td>\n",
       "      <td id=\"T_824e2_row2_col1\" class=\"data row2 col1\" >0.853846</td>\n",
       "      <td id=\"T_824e2_row2_col2\" class=\"data row2 col2\" >0.922882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_824e2_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
       "      <td id=\"T_824e2_row3_col0\" class=\"data row3 col0\" >SVM (Linear)</td>\n",
       "      <td id=\"T_824e2_row3_col1\" class=\"data row3 col1\" >0.846154</td>\n",
       "      <td id=\"T_824e2_row3_col2\" class=\"data row3 col2\" >0.029717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_824e2_level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
       "      <td id=\"T_824e2_row4_col0\" class=\"data row4 col0\" >SVM (Linear)</td>\n",
       "      <td id=\"T_824e2_row4_col1\" class=\"data row4 col1\" >0.846154</td>\n",
       "      <td id=\"T_824e2_row4_col2\" class=\"data row4 col2\" >0.028387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_824e2_level0_row5\" class=\"row_heading level0 row5\" >4</th>\n",
       "      <td id=\"T_824e2_row5_col0\" class=\"data row5 col0\" >KNN (k=5)</td>\n",
       "      <td id=\"T_824e2_row5_col1\" class=\"data row5 col1\" >0.830769</td>\n",
       "      <td id=\"T_824e2_row5_col2\" class=\"data row5 col2\" >0.003604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x28e2a50c3b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Création du DataFrame comparatif\n",
    "df_res = pd.DataFrame(final_results).sort_values(by='Accuracy', ascending=False)\n",
    "\n",
    "print(\"🏆 CLASSEMENT FINAL DES MODÈLES :\")\n",
    "display(df_res.style.background_gradient(cmap='Greens', subset=['Accuracy']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ddeff938-2e1d-4294-96aa-17550a6a1801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨ Conclusion : Le modèle recommandé pour l'application est : Random Forest\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Graphique comparatif\n",
    "plt.figure(figsize=(10, 6))\n",
    "barplot = sns.barplot(x='Accuracy', y='Modèle', data=df_res)\n",
    "\n",
    "# Ajouter les % sur les barres\n",
    "for i, v in enumerate(df_res['Accuracy']):\n",
    "    barplot.text(v + 0.005, i, f\"{v:.1%}\", va='center', fontweight='bold')\n",
    "\n",
    "\n",
    "plt.title(\"Comparaison de la Précision des Modèles\", fontsize=16)\n",
    "plt.xlabel(\"Précision (Accuracy)\")\n",
    "plt.show()\n",
    "\n",
    "best_model = df_res.iloc[0]['Modèle']\n",
    "print(f\"✨ Conclusion : Le modèle recommandé pour l'application est : {best_model}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "31b23c7e-3070-46b2-9f7e-f9cb0c5aa868",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚔️ BATAILLE RÉGRESSION : Linear vs Random Forest\n"
     ]
    }
   ],
   "source": [
    "print(\"⚔️ BATAILLE RÉGRESSION : Linear vs Random Forest\")\n",
    "\n",
    "# 1. Linear Regression\n",
    "reg_lin = LinearRegression()\n",
    "reg_lin.fit(X_train, y_reg_train)\n",
    "pred_lin = reg_lin.predict(X_test)\n",
    "mae_lin = mean_absolute_error(y_reg_test, pred_lin)\n",
    "\n",
    "# 2. Random Forest Regressor\n",
    "reg_rf = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "reg_rf.fit(X_train, y_reg_train)\n",
    "pred_rf = reg_rf.predict(X_test)\n",
    "mae_rf = mean_absolute_error(y_reg_test, pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "36458010-23ed-47b3-ac1b-b0fade5009c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏆 Le modèle le plus précis est : Random Forest (Erreur moyenne plus faible)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Comparaison Visuelle\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot Linear\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(y_reg_test, pred_lin, alpha=0.6, color='orange')\n",
    "plt.plot([0, 20], [0, 20], 'r--')\n",
    "plt.title(f\"Régression Linéaire\\nMAE: {mae_lin:.2f} pts\")\n",
    "plt.xlabel(\"Note Réelle\"); plt.ylabel(\"Prédite\")\n",
    "\n",
    "# Plot Random Forest\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(y_reg_test, pred_rf, alpha=0.6, color='green')\n",
    "plt.plot([0, 20], [0, 20], 'r--')\n",
    "plt.title(f\"Random Forest\\nMAE: {mae_rf:.2f} pts\")\n",
    "plt.xlabel(\"Note Réelle\"); plt.ylabel(\"Prédite\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "winner = \"Random Forest\" if mae_rf < mae_lin else \"Linear Regression\"\n",
    "print(f\"🏆 Le modèle le plus précis est : {winner} (Erreur moyenne plus faible)\")"
   ]
  }
 ],
 "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}