Update notebooks/TP1_Titanic_Survival.ipynb

notebooks/TP1_Titanic_Survival.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 🚢 TP-1 : Survie sur le Titanic — Classification\n",
+    "# TP-1 : Survie sur le Titanic — Classification\n",
     "\n",
     "**Objectif** : Prédire la survie des passagers du Titanic à partir de leurs caractéristiques.\n",
     "\n",
@@ -21,7 +21,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 📋 Table des matières\n",
+    "##  Table des matières\n",
     "\n",
     "1. [Import des bibliothèques](#section-1)\n",
     "2. [Chargement et exploration des données](#section-2)\n",
@@ -86,7 +86,7 @@
     "train_df = pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')\n",
     "\n",
     "print(f\"📊 Dimensions du dataset : {train_df.shape}\")\n",
-    "print(f\"\\n📋 Colonnes : {list(train_df.columns)}\")"
+    "print(f\"\\n Colonnes : {list(train_df.columns)}\")"
    ]
   },
   {
@@ -134,7 +134,7 @@
     "    'Pourcentage': missing_percent\n",
     "}).sort_values('Pourcentage', ascending=False)\n",
     "\n",
-    "print(\"🔍 Valeurs manquantes :\")\n",
+    "print(\"Valeurs manquantes :\")\n",
     "print(missing_df[missing_df['Valeurs manquantes'] > 0])"
    ]
   },
@@ -332,7 +332,7 @@
     "lr_pred = lr_model.predict(X_test)\n",
     "lr_accuracy = accuracy_score(y_test, lr_pred)\n",
     "\n",
-    "print(f\"🎯 Régression Logistique - Accuracy : {lr_accuracy:.4f}\")"
+    "print(f\" Régression Logistique - Accuracy : {lr_accuracy:.4f}\")"
    ]
   },
   {
@@ -377,7 +377,7 @@
     "    n_jobs=-1\n",
     ")\n",
     "\n",
-    "print(\"⏳ Optimisation en cours...\")\n",
+    "print(\" Optimisation en cours...\")\n",
     "grid_search.fit(X_train, y_train)\n",
     "\n",
     "print(f\"\\n✅ Meilleurs paramètres : {grid_search.best_params_}\")\n",
@@ -402,7 +402,7 @@
     "best_model = grid_search.best_estimator_\n",
     "best_pred = best_model.predict(X_test)\n",
     "\n",
-    "print(\"📊 Rapport de classification :\")\n",
+    "print(\" Rapport de classification :\")\n",
     "print(classification_report(y_test, best_pred, target_names=['Non survécu', 'Survécu']))"
    ]
   },
@@ -454,7 +454,7 @@
     "# Cross-validation finale\n",
     "cv_scores = cross_val_score(best_model, X, y, cv=5, scoring='accuracy')\n",
     "\n",
-    "print(f\"📊 Cross-validation (5-fold) :\")\n",
+    "print(f\" Cross-validation (5-fold) :\")\n",
     "print(f\"  Mean accuracy : {cv_scores.mean():.4f}\")\n",
     "print(f\"  Std accuracy : {cv_scores.std():.4f}\")\n",
     "print(f\"  Scores : {cv_scores}\")"
@@ -464,7 +464,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 🎓 Conclusion\n",
+    "##  Conclusion\n",
     "\n",
     "Dans ce TP, nous avons :\n",
     "\n",