diff --git a/lab-ensemble.ipynb b/lab-ensemble.ipynb
index 5c5b3c7..83397f7 100644
--- a/lab-ensemble.ipynb
+++ b/lab-ensemble.ipynb
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 304,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 305,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spaceship = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/spaceship_titanic.csv\")\n",
+    "spaceship.shape\n",
+    "\n",
+    "spaceship.dropna(inplace=True)\n",
+    "spaceship.reset_index(inplace=True, drop=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now perform the same as before:\n",
+    "- Feature Scaling\n",
+    "- Feature Selection\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
    "metadata": {},
    "outputs": [
     {
@@ -67,200 +89,1734 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>PassengerId</th>\n",
-       "      <th>HomePlanet</th>\n",
-       "      <th>CryoSleep</th>\n",
-       "      <th>Cabin</th>\n",
-       "      <th>Destination</th>\n",
        "      <th>Age</th>\n",
-       "      <th>VIP</th>\n",
        "      <th>RoomService</th>\n",
        "      <th>FoodCourt</th>\n",
        "      <th>ShoppingMall</th>\n",
        "      <th>Spa</th>\n",
        "      <th>VRDeck</th>\n",
-       "      <th>Name</th>\n",
        "      <th>Transported</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.074783</td>\n",
+       "      <td>0.135844</td>\n",
+       "      <td>0.042314</td>\n",
+       "      <td>0.123820</td>\n",
+       "      <td>0.105031</td>\n",
+       "      <td>-0.082553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RoomService</th>\n",
+       "      <td>0.074783</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.013614</td>\n",
+       "      <td>0.060478</td>\n",
+       "      <td>0.012472</td>\n",
+       "      <td>-0.026002</td>\n",
+       "      <td>-0.247291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <td>0.135844</td>\n",
+       "      <td>-0.013614</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.012320</td>\n",
+       "      <td>0.215995</td>\n",
+       "      <td>0.216997</td>\n",
+       "      <td>0.055025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <td>0.042314</td>\n",
+       "      <td>0.060478</td>\n",
+       "      <td>-0.012320</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.022168</td>\n",
+       "      <td>0.000383</td>\n",
+       "      <td>0.011602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Spa</th>\n",
+       "      <td>0.123820</td>\n",
+       "      <td>0.012472</td>\n",
+       "      <td>0.215995</td>\n",
+       "      <td>0.022168</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.149447</td>\n",
+       "      <td>-0.219854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VRDeck</th>\n",
+       "      <td>0.105031</td>\n",
+       "      <td>-0.026002</td>\n",
+       "      <td>0.216997</td>\n",
+       "      <td>0.000383</td>\n",
+       "      <td>0.149447</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.207950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transported</th>\n",
+       "      <td>-0.082553</td>\n",
+       "      <td>-0.247291</td>\n",
+       "      <td>0.055025</td>\n",
+       "      <td>0.011602</td>\n",
+       "      <td>-0.219854</td>\n",
+       "      <td>-0.207950</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Age  RoomService  FoodCourt  ShoppingMall       Spa  \\\n",
+       "Age           1.000000     0.074783   0.135844      0.042314  0.123820   \n",
+       "RoomService   0.074783     1.000000  -0.013614      0.060478  0.012472   \n",
+       "FoodCourt     0.135844    -0.013614   1.000000     -0.012320  0.215995   \n",
+       "ShoppingMall  0.042314     0.060478  -0.012320      1.000000  0.022168   \n",
+       "Spa           0.123820     0.012472   0.215995      0.022168  1.000000   \n",
+       "VRDeck        0.105031    -0.026002   0.216997      0.000383  0.149447   \n",
+       "Transported  -0.082553    -0.247291   0.055025      0.011602 -0.219854   \n",
+       "\n",
+       "                VRDeck  Transported  \n",
+       "Age           0.105031    -0.082553  \n",
+       "RoomService  -0.026002    -0.247291  \n",
+       "FoodCourt     0.216997     0.055025  \n",
+       "ShoppingMall  0.000383     0.011602  \n",
+       "Spa           0.149447    -0.219854  \n",
+       "VRDeck        1.000000    -0.207950  \n",
+       "Transported  -0.207950     1.000000  "
+      ]
+     },
+     "execution_count": 306,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 1. Feature selection\n",
+    "\n",
+    "spaceship.select_dtypes(include=['number', 'bool']).corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code here\n",
+    "\n",
+    "features = spaceship.select_dtypes(include='number')    # Mejor manual\n",
+    "\n",
+    "target = spaceship['Transported']       # Estamos haciendo una clasificación\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0001_01</td>\n",
-       "      <td>Europa</td>\n",
-       "      <td>False</td>\n",
-       "      <td>B/0/P</td>\n",
-       "      <td>TRAPPIST-1e</td>\n",
        "      <td>39.0</td>\n",
-       "      <td>False</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>Maham Ofracculy</td>\n",
-       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0002_01</td>\n",
-       "      <td>Earth</td>\n",
-       "      <td>False</td>\n",
-       "      <td>F/0/S</td>\n",
-       "      <td>TRAPPIST-1e</td>\n",
        "      <td>24.0</td>\n",
-       "      <td>False</td>\n",
        "      <td>109.0</td>\n",
        "      <td>9.0</td>\n",
        "      <td>25.0</td>\n",
        "      <td>549.0</td>\n",
        "      <td>44.0</td>\n",
-       "      <td>Juanna Vines</td>\n",
-       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0003_01</td>\n",
-       "      <td>Europa</td>\n",
-       "      <td>False</td>\n",
-       "      <td>A/0/S</td>\n",
-       "      <td>TRAPPIST-1e</td>\n",
        "      <td>58.0</td>\n",
-       "      <td>True</td>\n",
        "      <td>43.0</td>\n",
        "      <td>3576.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>6715.0</td>\n",
        "      <td>49.0</td>\n",
-       "      <td>Altark Susent</td>\n",
-       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0003_02</td>\n",
-       "      <td>Europa</td>\n",
-       "      <td>False</td>\n",
-       "      <td>A/0/S</td>\n",
-       "      <td>TRAPPIST-1e</td>\n",
        "      <td>33.0</td>\n",
-       "      <td>False</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1283.0</td>\n",
        "      <td>371.0</td>\n",
        "      <td>3329.0</td>\n",
        "      <td>193.0</td>\n",
-       "      <td>Solam Susent</td>\n",
-       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0004_01</td>\n",
-       "      <td>Earth</td>\n",
-       "      <td>False</td>\n",
-       "      <td>F/1/S</td>\n",
-       "      <td>TRAPPIST-1e</td>\n",
        "      <td>16.0</td>\n",
-       "      <td>False</td>\n",
        "      <td>303.0</td>\n",
        "      <td>70.0</td>\n",
        "      <td>151.0</td>\n",
        "      <td>565.0</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>Willy Santantines</td>\n",
-       "      <td>True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
-       "0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
-       "1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n",
-       "2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n",
-       "3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n",
-       "4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n",
-       "\n",
-       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
-       "0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
-       "1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
-       "2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
-       "3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
-       "4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
-       "\n",
-       "   Transported  \n",
-       "0        False  \n",
-       "1         True  \n",
-       "2        False  \n",
-       "3        False  \n",
-       "4         True  "
+       "    Age  RoomService  FoodCourt  ShoppingMall     Spa  VRDeck\n",
+       "0  39.0          0.0        0.0           0.0     0.0     0.0\n",
+       "1  24.0        109.0        9.0          25.0   549.0    44.0\n",
+       "2  58.0         43.0     3576.0           0.0  6715.0    49.0\n",
+       "3  33.0          0.0     1283.0         371.0  3329.0   193.0\n",
+       "4  16.0        303.0       70.0         151.0   565.0     2.0"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 308,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "spaceship = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/spaceship_titanic.csv\")\n",
-    "spaceship.head()"
+    "features.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now perform the same as before:\n",
-    "- Feature Scaling\n",
-    "- Feature Selection\n"
+    "**Perform Train Test Split**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 309,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code here"
+    "#your code here\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.20, random_state=42)     "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Perform Train Test Split**"
+    "**Model Selection** - now you will try to apply different ensemble methods in order to get a better model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Bagging and Pasting"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 310,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code here"
+    "from sklearn.ensemble import BaggingClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "bagging_clf = BaggingClassifier(DecisionTreeClassifier(max_depth=20),\n",
+    "                                n_estimators=100,               # 100 arboles\n",
+    "                                max_samples=1000                # Del 80% del X_train va a coger paquetes de 1000\n",
+    "                                )\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 311,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-34 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-34 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-34 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-34 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-34 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-34 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-34 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-34 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-34 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-34 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-34 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-34 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-34 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-34 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-34 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-34 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-34 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-34\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(max_depth=20),\n",
+       "                  max_samples=1000, n_estimators=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-100\" type=\"checkbox\" ><label for=\"sk-estimator-id-100\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;BaggingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.BaggingClassifier.html\">?<span>Documentation for BaggingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(max_depth=20),\n",
+       "                  max_samples=1000, n_estimators=100)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-101\" type=\"checkbox\" ><label for=\"sk-estimator-id-101\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=20)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-102\" type=\"checkbox\" ><label for=\"sk-estimator-id-102\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=20)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "BaggingClassifier(estimator=DecisionTreeClassifier(max_depth=20),\n",
+       "                  max_samples=1000, n_estimators=100)"
+      ]
+     },
+     "execution_count": 311,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bagging_clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 312,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.83      0.74      0.78       653\n",
+      "        True       0.77      0.85      0.81       669\n",
+      "\n",
+      "    accuracy                           0.80      1322\n",
+      "   macro avg       0.80      0.79      0.79      1322\n",
+      "weighted avg       0.80      0.80      0.79      1322\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "**Model Selection** - now you will try to apply different ensemble methods in order to get a better model"
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "# Make predictions\n",
+    "y_pred = bagging_clf.predict(X_test)\n",
+    "\n",
+    "# Generate the classification report\n",
+    "report = classification_report(y_test, y_pred)\n",
+    "print(report)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 313,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "- Bagging and Pasting"
+    "# 2. Repetición con Normalization\n",
+    "\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "normalizer = MinMaxScaler()\n",
+    "\n",
+    "X_train_norm = normalizer.fit_transform(X_train)        \n",
+    "\n",
+    "X_test_norm = normalizer.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_norm = pd.DataFrame(X_train_norm, columns = X_train.columns)        # Vuelvo a dar formato de df\n",
+    "X_test_norm = pd.DataFrame(X_test_norm, columns = X_test.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bagging_clf_norm = BaggingClassifier(DecisionTreeClassifier(max_depth=20),\n",
+    "                                n_estimators=100, \n",
+    "                                bootstrap=True,         # Esto es para que sea bagging o pasting (no es obligatorio especificar)             \n",
+    "                                max_samples=1000        # Toma 1000 muestras para cada uno (si pusiera 0.X cogeria el X0% de las muestras)             \n",
+    "                                )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-35 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-35 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-35 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-35 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-35 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-35 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-35 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-35 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-35 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-35 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-35 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-35 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-35 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-35 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-35 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-35 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-35 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-35\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(max_depth=20),\n",
+       "                  max_samples=1000, n_estimators=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-103\" type=\"checkbox\" ><label for=\"sk-estimator-id-103\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;BaggingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.BaggingClassifier.html\">?<span>Documentation for BaggingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(max_depth=20),\n",
+       "                  max_samples=1000, n_estimators=100)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-104\" type=\"checkbox\" ><label for=\"sk-estimator-id-104\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=20)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-105\" type=\"checkbox\" ><label for=\"sk-estimator-id-105\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=20)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "BaggingClassifier(estimator=DecisionTreeClassifier(max_depth=20),\n",
+       "                  max_samples=1000, n_estimators=100)"
+      ]
+     },
+     "execution_count": 316,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bagging_clf_norm.fit(X_train_norm, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.83      0.74      0.78       653\n",
+      "        True       0.77      0.85      0.81       669\n",
+      "\n",
+      "    accuracy                           0.80      1322\n",
+      "   macro avg       0.80      0.80      0.80      1322\n",
+      "weighted avg       0.80      0.80      0.80      1322\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions\n",
+    "y_pred_norm = bagging_clf_norm.predict(X_test_norm)\n",
+    "\n",
+    "# Generate the classification report\n",
+    "report = classification_report(y_test, y_pred_norm)\n",
+    "print(report)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 318,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3. Repetición con Scaling\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()       \n",
+    "X_train_scaling = scaler.fit_transform(X_train)\n",
+    "X_train_scaling = pd.DataFrame(X_train_scaling, columns = X_train.columns)\n",
+    "\n",
+    "X_test_scaling = scaler.transform(X_test)\n",
+    "X_test_scaling = pd.DataFrame(X_test_scaling, columns = X_test.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 319,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-36 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-36 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-36 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-36 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-36 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-36 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-36 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-36 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-36 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-36 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-36 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-36 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-36 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-36 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-36 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-36 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-36 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-36\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(max_depth=20),\n",
+       "                  max_samples=1000, n_estimators=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-106\" type=\"checkbox\" ><label for=\"sk-estimator-id-106\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;BaggingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.BaggingClassifier.html\">?<span>Documentation for BaggingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(max_depth=20),\n",
+       "                  max_samples=1000, n_estimators=100)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-107\" type=\"checkbox\" ><label for=\"sk-estimator-id-107\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=20)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-108\" type=\"checkbox\" ><label for=\"sk-estimator-id-108\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=20)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "BaggingClassifier(estimator=DecisionTreeClassifier(max_depth=20),\n",
+       "                  max_samples=1000, n_estimators=100)"
+      ]
+     },
+     "execution_count": 319,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bagging_clf_scaling = BaggingClassifier(DecisionTreeClassifier(max_depth=20),\n",
+    "                                n_estimators=100,               \n",
+    "                                max_samples=1000                \n",
+    "                                )\n",
+    "\n",
+    "bagging_clf_scaling.fit(X_train_scaling, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.83      0.74      0.78       653\n",
+      "        True       0.77      0.85      0.81       669\n",
+      "\n",
+      "    accuracy                           0.80      1322\n",
+      "   macro avg       0.80      0.80      0.79      1322\n",
+      "weighted avg       0.80      0.80      0.80      1322\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions\n",
+    "y_pred_scaling = bagging_clf_scaling.predict(X_test_scaling)\n",
+    "\n",
+    "# Generate the classification report\n",
+    "report = classification_report(y_test, y_pred_scaling)\n",
+    "print(report)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 321,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your code here"
+    "### EN VISTA DE LAS 3 FORMAS DE REALIZAR ESTA PREDICCIÓN, LA MEJOR MANERA, ATENDIENDO AL f1-score (POR UNA LIGERA DIFERENCIA) ES LA NORMALIZACIÓN"
    ]
   },
   {
@@ -272,11 +1828,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 322,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.83      0.74      0.78       653\n",
+      "        True       0.77      0.85      0.81       669\n",
+      "\n",
+      "    accuracy                           0.79      1322\n",
+      "   macro avg       0.80      0.79      0.79      1322\n",
+      "weighted avg       0.80      0.79      0.79      1322\n",
+      "\n",
+      "Accuracy: 0.7934947049924357\n"
+     ]
+    }
+   ],
    "source": [
-    "#your code here"
+    "#your code here\n",
+    "# Import necessary libraries\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import root_mean_squared_error, mean_absolute_error, r2_score\n",
+    "from sklearn.metrics import classification_report, accuracy_score\n",
+    "# Create and train the Random Forest regressor\n",
+    "\n",
+    "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "rf_classifier.fit(X_train_norm, y_train)\n",
+    "\n",
+    "# Make predictions\n",
+    "y_pred_norm = rf_classifier.predict(X_test_norm)\n",
+    "\n",
+    "# Evaluate the model\n",
+    "report = classification_report(y_test, y_pred_norm)              \n",
+    "print(report)\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred_norm)\n",
+    "print(f\"Accuracy: {accuracy}\")\n"
    ]
   },
   {
@@ -288,49 +1879,201 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 323,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.83      0.74      0.78       653\n",
+      "        True       0.77      0.85      0.81       669\n",
+      "\n",
+      "    accuracy                           0.79      1322\n",
+      "   macro avg       0.80      0.79      0.79      1322\n",
+      "weighted avg       0.80      0.79      0.79      1322\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "#your code here"
+    "#your code here\n",
+    "from sklearn.ensemble import BaggingRegressor, RandomForestRegressor, GradientBoostingClassifier\n",
+    "\n",
+    "gb_clas = GradientBoostingClassifier(max_depth=20,\n",
+    "                                   n_estimators=100)\n",
+    "\n",
+    "gb_clas.fit(X_train_norm, y_train)\n",
+    "\n",
+    "pred_gb_norm = gb_clas.predict(X_test_norm)\n",
+    "\n",
+    "report = classification_report(y_test, y_pred_norm)              \n",
+    "print(report)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- Adaptive Boosting"
+    "- Adaptive Boosting (AdaBoost)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 324,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Anaconda\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.83      0.74      0.78       653\n",
+      "        True       0.77      0.85      0.81       669\n",
+      "\n",
+      "    accuracy                           0.79      1322\n",
+      "   macro avg       0.80      0.79      0.79      1322\n",
+      "weighted avg       0.80      0.79      0.79      1322\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "#your code here"
+    "#your code here\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import AdaBoostClassifier\n",
+    "\n",
+    "ada_clas = AdaBoostClassifier(DecisionTreeClassifier(max_depth=20),\n",
+    "                            n_estimators=200)\n",
+    "\n",
+    "ada_clas.fit(X_train_norm, y_train)\n",
+    "\n",
+    "report = classification_report(y_test, y_pred_norm)              \n",
+    "print(report)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Which model is the best and why?"
+    "## Which model is the best and why?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The best model is bagging with scaling (very close to the others) because its f1-score is 0.80, and for the rest 0.79"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Una vez que tenemos el mejor modelo, podriamos predecir los resultados de la Spaceship.\n",
+    "\n",
+    "predictions = bagging_clf_norm.predict(features)\n",
+    "\n",
+    "\n",
+    "predictions = pd.DataFrame(predictions, columns=['Resuls_predicted'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spaceship[\"predictions\"] = predictions  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 327,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spaceship[\"Resta\"] = spaceship[\"Transported\"].astype(int) - spaceship[\"predictions\"].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7275204359673024"
+      ]
+     },
+     "execution_count": 328,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spaceship[\"Resta\"].value_counts()[0] / spaceship[\"Resta\"].value_counts().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 329,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "cm = confusion_matrix(spaceship[\"Transported\"], spaceship[\"predictions\"])\n",
+    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
+    "plt.xlabel('Predicción')\n",
+    "plt.ylabel('Real')\n",
+    "plt.title('Matriz de Confusión')\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# ESTO ES MUY ÚTIL PERO SOLO EN CLASIFICACIÓN"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 330,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#comment here"
+    "# La razón de que que el f1-score sea 0.80 pero tenga un % de aciertos de apenas el 50% es porque "
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -344,7 +2087,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,