diff --git a/lab-hyper-tuning.ipynb b/lab-hyper-tuning.ipynb
index 847d487..692acbf 100644
--- a/lab-hyper-tuning.ipynb
+++ b/lab-hyper-tuning.ipynb
@@ -47,11 +47,182 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "PassengerId",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "HomePlanet",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "CryoSleep",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "Cabin",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Destination",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Age",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "VIP",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "RoomService",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "FoodCourt",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "ShoppingMall",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Spa",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "VRDeck",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Transported",
+         "rawType": "bool",
+         "type": "boolean"
+        }
+       ],
+       "ref": "c9312073-e8af-4b47-8f2b-7d7e1a8998fc",
+       "rows": [
+        [
+         "0",
+         "0001_01",
+         "Europa",
+         "False",
+         "B/0/P",
+         "TRAPPIST-1e",
+         "39.0",
+         "False",
+         "0.0",
+         "0.0",
+         "0.0",
+         "0.0",
+         "0.0",
+         "Maham Ofracculy",
+         "False"
+        ],
+        [
+         "1",
+         "0002_01",
+         "Earth",
+         "False",
+         "F/0/S",
+         "TRAPPIST-1e",
+         "24.0",
+         "False",
+         "109.0",
+         "9.0",
+         "25.0",
+         "549.0",
+         "44.0",
+         "Juanna Vines",
+         "True"
+        ],
+        [
+         "2",
+         "0003_01",
+         "Europa",
+         "False",
+         "A/0/S",
+         "TRAPPIST-1e",
+         "58.0",
+         "True",
+         "43.0",
+         "3576.0",
+         "0.0",
+         "6715.0",
+         "49.0",
+         "Altark Susent",
+         "False"
+        ],
+        [
+         "3",
+         "0003_02",
+         "Europa",
+         "False",
+         "A/0/S",
+         "TRAPPIST-1e",
+         "33.0",
+         "False",
+         "0.0",
+         "1283.0",
+         "371.0",
+         "3329.0",
+         "193.0",
+         "Solam Susent",
+         "False"
+        ],
+        [
+         "4",
+         "0004_01",
+         "Earth",
+         "False",
+         "F/1/S",
+         "TRAPPIST-1e",
+         "16.0",
+         "False",
+         "303.0",
+         "70.0",
+         "151.0",
+         "565.0",
+         "2.0",
+         "Willy Santantines",
+         "True"
+        ]
+       ],
+       "shape": {
+        "columns": 14,
+        "rows": 5
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -200,7 +371,7 @@
        "4         True  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -221,11 +392,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code here"
+    "#Drop missing values\n",
+    "spaceship = spaceship.dropna()\n",
+    "\n",
+    "# Extract the first letter of the 'Cabin' column\n",
+    "spaceship['Cabin'] = spaceship['Cabin'].str[0]\n",
+    "\n",
+    "# Drop unnecessary columns\n",
+    "spaceship = spaceship.drop(columns=['Name', 'PassengerId'])\n",
+    "\n",
+    "# Convert boolean columns to integers\n",
+    "spaceship['CryoSleep'] = spaceship['CryoSleep'].astype(int)\n",
+    "spaceship['VIP'] = spaceship['VIP'].astype(int)\n",
+    "spaceship['Transported'] = spaceship['Transported'].astype(int)\n",
+    "\n",
+    "# One-hot encoding for categorical variables\n",
+    "spaceship = pd.get_dummies(spaceship, columns=['HomePlanet', 'Destination', 'Cabin'], drop_first=True)\n",
+    "\n",
+    "# Convert dummied columns to integers\n",
+    "columns_dummed =[\n",
+    "    'HomePlanet_Europa',\n",
+    "    'HomePlanet_Mars',\n",
+    "    'Destination_PSO J318.5-22',\n",
+    "    'Destination_TRAPPIST-1e',\n",
+    "    'Cabin_B',\n",
+    "    'Cabin_C',\n",
+    "    'Cabin_D',\n",
+    "    'Cabin_E',\n",
+    "    'Cabin_F',\n",
+    "    'Cabin_G',\n",
+    "    'Cabin_T'\n",
+    "    ]\n",
+    "spaceship[columns_dummed] = spaceship[columns_dummed].astype(int)\n",
+    "\n",
+    "# Numerical Scaling\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "# Escalator Min-Max\n",
+    "scaler = MinMaxScaler()\n",
+    "spaceship = pd.DataFrame(\n",
+    "    scaler.fit_transform(spaceship), \n",
+    "    columns=spaceship.select_dtypes(include=['float64', 'int64']).columns\n",
+    ")"
    ]
   },
   {
@@ -237,11 +449,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data: (5284, 19)\n",
+      "Testing data: (1322, 19)\n",
+      "Predicciones: [1. 1. 1. 1. 0. 0. 1. 0. 0. 1.]\n",
+      "Probabilidades: [0.95588915 0.76593463 0.56842413 0.63780278 0.40456481 0.13942804\n",
+      " 0.92009723 0.04622036 0.12148288 0.92339599]\n",
+      "Accuracy Score: 0.8071\n"
+     ]
+    }
+   ],
    "source": [
-    "#your code here"
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "X = spaceship.drop('Transported', axis=1)\n",
+    "y = spaceship['Transported']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(f\"Training data: {X_train.shape}\")\n",
+    "print(f\"Testing data: {X_test.shape}\")\n",
+    "\n",
+    "model_gb = GradientBoostingClassifier(random_state=42)\n",
+    "\n",
+    "model_gb.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = model_gb.predict(X_test)\n",
+    "y_probs = model_gb.predict_proba(X_test)[:, 1]\n",
+    "\n",
+    "print(f'Predicciones: {y_pred[:10]}')\n",
+    "print(f'Probabilidades: {y_probs[:10]}')\n",
+    "print(f'Accuracy Score: {accuracy_score(y_test, y_pred):.4f}')"
    ]
   },
   {
@@ -253,11 +497,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precisión final del modelo: 0.8071\n",
+      "\n",
+      "Reporte de Clasificación:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.84      0.76      0.79       653\n",
+      "         1.0       0.78      0.86      0.82       669\n",
+      "\n",
+      "    accuracy                           0.81      1322\n",
+      "   macro avg       0.81      0.81      0.81      1322\n",
+      "weighted avg       0.81      0.81      0.81      1322\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "#your code here"
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(f\"Precisión final del modelo: {accuracy:.4f}\")\n",
+    "\n",
+    "print(\"\\nReporte de Clasificación:\")\n",
+    "print(classification_report(y_test, y_pred))"
    ]
   },
   {
@@ -283,11 +552,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code here"
+    "param_grid_gb = {\n",
+    "    'n_estimators': [100, 200],\n",
+    "    'learning_rate': [0.01, 0.1, 0.2],\n",
+    "    'max_depth': [3, 4, 5],\n",
+    "    'subsample': [0.8, 1.0]\n",
+    "}"
    ]
   },
   {
@@ -299,10 +573,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
+      "Mejores parámetros encontrados: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100, 'subsample': 0.8}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "\n",
+    "grid_gb = GridSearchCV(\n",
+    "    estimator=GradientBoostingClassifier(random_state=42),\n",
+    "    param_grid=param_grid_gb,\n",
+    "    cv=5,\n",
+    "    n_jobs=-1,\n",
+    "    verbose=1\n",
+    ")\n",
+    "\n",
+    "grid_gb.fit(X_train, y_train)\n",
+    "\n",
+    "best_gb = grid_gb.best_estimator_\n",
+    "\n",
+    "print(f\"Mejores parámetros encontrados: {grid_gb.best_params_}\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -313,10 +613,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Evaluación del Modelo ---\n",
+      "Accuracy: 0.8139\n",
+      "\n",
+      "Matriz de Confusión:\n",
+      "[[496 157]\n",
+      " [ 89 580]]\n",
+      "\n",
+      "Reporte de Clasificación:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.85      0.76      0.80       653\n",
+      "         1.0       0.79      0.87      0.83       669\n",
+      "\n",
+      "    accuracy                           0.81      1322\n",
+      "   macro avg       0.82      0.81      0.81      1322\n",
+      "weighted avg       0.82      0.81      0.81      1322\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "\n",
+    "y_pred = best_gb.predict(X_test)\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "\n",
+    "print(f\"--- Evaluación del Modelo ---\")\n",
+    "print(f\"Accuracy: {accuracy:.4f}\")\n",
+    "print(\"\\nMatriz de Confusión:\")\n",
+    "print(confusion_matrix(y_test, y_pred))\n",
+    "print(\"\\nReporte de Clasificación:\")\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
   }
  ],
  "metadata": {
@@ -335,7 +672,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,