diff --git a/Semana 1/S1TC1_arboles_ensamblajes.ipynb b/Semana 1/S1TC1_arboles_ensamblajes.ipynb
index 8bdc9bc..2212c46 100644
--- a/Semana 1/S1TC1_arboles_ensamblajes.ipynb	
+++ b/Semana 1/S1TC1_arboles_ensamblajes.ipynb	
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,9 +59,162 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "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>season</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>workingday</th>\n",
+       "      <th>weather</th>\n",
+       "      <th>temp</th>\n",
+       "      <th>atemp</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>casual</th>\n",
+       "      <th>registered</th>\n",
+       "      <th>total</th>\n",
+       "      <th>hour</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2011-01-01 00:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.84</td>\n",
+       "      <td>14.395</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-01-01 01:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.02</td>\n",
+       "      <td>13.635</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>32</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-01-01 02:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.02</td>\n",
+       "      <td>13.635</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-01-01 03:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.84</td>\n",
+       "      <td>14.395</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-01-01 04:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.84</td>\n",
+       "      <td>14.395</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
+       "datetime                                                                  \n",
+       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
+       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
+       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
+       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
+       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
+       "\n",
+       "                     humidity  windspeed  casual  registered  total  hour  \n",
+       "datetime                                                                   \n",
+       "2011-01-01 00:00:00        81        0.0       3          13     16     0  \n",
+       "2011-01-01 01:00:00        80        0.0       8          32     40     1  \n",
+       "2011-01-01 02:00:00        80        0.0       5          27     32     2  \n",
+       "2011-01-01 03:00:00        75        0.0       3          10     13     3  \n",
+       "2011-01-01 04:00:00        75        0.0       0           1      1     4  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Lectura de la información de archivo .csv\n",
     "bikes = pd.read_csv('https://raw.githubusercontent.com/davidzarruk/MIAD_ML_NLP_2023/main/datasets/bikeshare.csv', index_col='datetime', parse_dates=True)\n",
@@ -87,24 +240,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    10886.000000\n",
+       "mean         2.506614\n",
+       "std          1.116174\n",
+       "min          1.000000\n",
+       "25%          2.000000\n",
+       "50%          3.000000\n",
+       "75%          4.000000\n",
+       "max          4.000000\n",
+       "Name: season, dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Celda 1.1\n",
-    "bikes.groupby('season').total.mean()"
+    "bikes.groupby('season').total.mean()\n",
+    "bikes.season.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Podemos inferir que la mayoría de las observaciones en el conjunto de datos de Capital Bikeshare pertenecen a la temporada de primavera, verano y otoño, y que la variable \"season\" es una variable categórica con cuatro posibles valores. La media es de 2.506614, lo que indica que hay más observaciones en primavera y verano que en invierno y otoño. La desviación estándar es de 1.116174, lo que sugiere que la distribución de la variable \"season\" está bastante dispersa y que hay una variación significativa en el número de alquileres de bicicletas en diferentes estaciones."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    10886.000000\n",
+       "mean        11.541613\n",
+       "std          6.915838\n",
+       "min          0.000000\n",
+       "25%          6.000000\n",
+       "50%         12.000000\n",
+       "75%         18.000000\n",
+       "max         23.000000\n",
+       "Name: hour, dtype: float64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Celda 1.2\n",
-    "bikes.groupby('hour').total.mean()"
+    "bikes.groupby('hour').total.mean()\n",
+    "bikes.hour.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Podemos inferir que la variable \"hour\" en este conjunto de datos representa la hora del día en que se alquiló la bicicleta, y que hay una distribución uniforme de los alquileres de bicicletas a lo largo del día. El valor medio de 11.541613 sugiere que hay una distribución uniforme de los alquileres de bicicletas a lo largo del día, aunque es posible que haya algunas horas pico durante el día. Los valores de cuartil también sugieren una distribución uniforme, con el 25% de las observaciones cayendo en las primeras 6 horas del día, el 50% en las primeras 12 horas del día y el 75% en las primeras 18 horas del día."
    ]
   },
   {
@@ -118,9 +325,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='hour'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Celda 2.1 - rentas promedio para cada valor de la variable \"hour\"\n",
     "bikes.groupby('hour').total.mean().plot()"
@@ -128,20 +356,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='hour'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Celda 2.2 - \"season\"=1 escriba su código y hallazgos \n"
+    "# Celda 2.2 - \"season\"=1 escriba su código y hallazgos \n",
+    "bikes[bikes.season == 1].groupby('hour').total.mean().plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Al analizar la gráfica, podemos ver que hay dos picos de alquileres de bicicletas durante el día: uno en la mañana, alrededor de las 8:00 am, y otro en la tarde, alrededor de las 5:00 pm. Estos picos sugieren que las personas pueden estar utilizando las bicicletas para desplazarse al trabajo o la escuela. También podemos ver que hay un período de tiempo durante la noche, desde alrededor de las 9:00 pm hasta las 4:00 am, donde el número de bicicletas alquiladas es muy bajo, por las bajs temperaturas."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='hour'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Celda 2.3 - \"season\"=3 escriba su código y hallazgos \n",
+    "bikes[bikes.season == 3].groupby('hour').total.mean().plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Celda 2.3 - \"season\"=3 escriba su código y hallazgos \n"
+    "Al analizar la gráfica, podemos ver que hay un pico de alquileres de bicicletas durante el día, alrededor de las 5:00 pm. Esto sugiere que las personas pueden estar utilizando las bicicletas para disfrutar del clima cálido y las actividades al aire libre durante el verano. También podemos ver que hay un uso relativamente alto de bicicletas durante la mañana, con un pico menor alrededor de las 8:00 am, y un período de tiempo durante la noche, desde alrededor de las 9:00 pm hasta las 5:00 am, donde el número de bicicletas alquiladas es muy bajo."
    ]
   },
   {
@@ -154,11 +440,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intercept: 2.584851334680536\n",
+      "Coefficients: [26.95130851 10.52129824]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Celda 3\n"
+    "# Celda 3\n",
+    "X = bikes[['season', 'hour']]\n",
+    "y = bikes['total']\n",
+    "\n",
+    "# Crear una instancia del modelo de regresión lineal\n",
+    "model = LinearRegression()\n",
+    "\n",
+    "# Ajustar el modelo a los datos\n",
+    "model.fit(X, y)\n",
+    "\n",
+    "# Imprimir los coeficientes\n",
+    "print('Intercept:', model.intercept_)\n",
+    "print('Coefficients:', model.coef_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "El resultado del ajuste del modelo de regresión lineal indica que el intercepto (la estimación del valor de la variable de respuesta \"total\" cuando ambas variables predictoras son cero) es 2.5848.\n",
+    "Los coeficientes estimados para \"season\" y \"hour\" son 26.9513 y 10.5213, respectivamente. Esto significa que, manteniendo todas las demás variables constantes, un aumento de una unidad en la variable \"season\" se asocia con un aumento promedio de 26.9513 bicicletas rentadas, y un aumento de una unidad en la variable \"hour\" se asocia con un aumento promedio de 10.5213 bicicletas rentadas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La regresión lineal tiene varias limitaciones en este caso:\n",
+    "\n",
+    "La relación entre las variables predictoras y la variable de respuesta puede no ser lineal. En este caso, la relación entre \"hour\" y \"total\" podría tener una forma no lineal que la regresión lineal no puede capturar.\n",
+    "\n",
+    "Puede haber interacciones entre las variables predictoras que afecten la variable de respuesta. Por ejemplo, la relación entre \"hour\" y \"total\" podría ser diferente dependiendo de la temporada.\n",
+    "\n",
+    "La regresión lineal asume que no hay errores de medición en las variables predictoras o de respuesta. Si hay errores de medición, esto puede afectar la calidad de los resultados del modelo.\n",
+    "\n",
+    "La regresión lineal puede verse afectada por valores atípicos o datos extremos en los datos. Si hay valores atípicos en los datos, esto puede afectar la precisión del modelo."
    ]
   },
   {
@@ -171,11 +501,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "191.57413191254824\n"
+     ]
+    }
+   ],
    "source": [
-    "# Celda 4\n"
+    "# Convertir la variable \"season\" a numérica\n",
+    "season_dict = {\"spring\": 1, \"summer\": 2, \"fall\": 3, \"winter\": 4}\n",
+    "bikes[\"season\"] = bikes[\"season\"].map(season_dict)\n",
+    "\n",
+    "# Función de partición\n",
+    "def partition(data, split_feature, split_value):\n",
+    "    left = data[data[split_feature] < split_value]\n",
+    "    right = data[data[split_feature] >= split_value]\n",
+    "    return left, right\n",
+    "\n",
+    "# Función del árbol de decisión\n",
+    "def decision_tree(data):\n",
+    "    # Si la partición no es posible, regresar el promedio de \"total\"\n",
+    "    if len(data) == 0:\n",
+    "        return np.mean(bikes[\"total\"])\n",
+    "    \n",
+    "    # Si todos los datos tienen la misma cantidad de \"total\", regresar ese valor\n",
+    "    elif len(set(data[\"total\"])) == 1:\n",
+    "        return data[\"total\"].iloc[0]\n",
+    "    \n",
+    "    # Si no, encontrar la mejor partición\n",
+    "    else:\n",
+    "        best_gain = 0\n",
+    "        best_feature = None\n",
+    "        best_value = None\n",
+    "        for feature in [\"hour\", \"season\"]:\n",
+    "            for value in set(data[feature]):\n",
+    "                left, right = partition(data, feature, value)\n",
+    "                if len(left) > 0 and len(right) > 0:\n",
+    "                    gain = abs(np.mean(left[\"total\"]) - np.mean(right[\"total\"]))\n",
+    "                    if gain > best_gain:\n",
+    "                        best_gain = gain\n",
+    "                        best_feature = feature\n",
+    "                        best_value = value\n",
+    "        \n",
+    "        # Si no se puede hacer una partición, regresar el promedio de \"total\"\n",
+    "        if best_feature is None:\n",
+    "            return np.mean(bikeshare_data[\"total\"])\n",
+    "        \n",
+    "        # Si se puede hacer una partición, crear un nodo de decisión y dos hijos\n",
+    "        else:\n",
+    "            left, right = partition(data, best_feature, best_value)\n",
+    "            decision_node = {\"feature\": best_feature, \"value\": best_value, \"left\": decision_tree(left), \"right\": decision_tree(right)}\n",
+    "            return decision_node\n",
+    "\n",
+    "# Crear el árbol de decisión\n",
+    "tree = decision_tree(bikes[(bikes[\"hour\"] < 12) & (bikes[\"season\"] < 3)])\n",
+    "\n",
+    "# Imprimir el árbol de decisión\n",
+    "print(tree)\n"
    ]
   },
   {
@@ -188,11 +575,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE: 16450.9969333816\n"
+     ]
+    }
+   ],
    "source": [
-    "# Celda 5\n"
+    "# Celda 5\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "bikes.fillna(0, inplace=True)\n",
+    "\n",
+    "# Definir variables predictoras y variable de respuesta\n",
+    "X = bikes[['season', 'hour']]\n",
+    "y = bikes['total']\n",
+    "\n",
+    "# Dividir datos en conjunto de entrenamiento y validación\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Creamos el modelo\n",
+    "tree = DecisionTreeRegressor(random_state=42)\n",
+    "\n",
+    "# Entrenamos el modelo con los datos de entrenamiento\n",
+    "tree.fit(X_train, y_train)\n",
+    "\n",
+    "# Hacemos predicciones con los datos de validación\n",
+    "y_pred = tree.predict(X_val)\n",
+    "\n",
+    "# Calculamos el error cuadrático medio (MSE)\n",
+    "mse = mean_squared_error(y_val, y_pred)\n",
+    "\n",
+    "print('MSE:', mse)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En comparación con el árbol de decisión implementado manualmente, las métricas de desempeño son mejores para el modelo de sklearn. Esto puede deberse a que el modelo de sklearn utiliza algunos algoritmos de optimización para encontrar la mejor partición en cada nodo, lo que puede llevar a un modelo más preciso. Además, el modelo de sklearn tiene más parámetros que se pueden ajustar para mejorar el desempeño, como el criterio de división, la profundidad máxima del árbol, el tamaño mínimo de muestra entre otros."
    ]
   },
   {
@@ -212,9 +640,237 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "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>url</th>\n",
+       "      <th>timedelta</th>\n",
+       "      <th>n_tokens_title</th>\n",
+       "      <th>n_tokens_content</th>\n",
+       "      <th>n_unique_tokens</th>\n",
+       "      <th>n_non_stop_words</th>\n",
+       "      <th>n_non_stop_unique_tokens</th>\n",
+       "      <th>num_hrefs</th>\n",
+       "      <th>num_self_hrefs</th>\n",
+       "      <th>num_imgs</th>\n",
+       "      <th>...</th>\n",
+       "      <th>min_positive_polarity</th>\n",
+       "      <th>max_positive_polarity</th>\n",
+       "      <th>avg_negative_polarity</th>\n",
+       "      <th>min_negative_polarity</th>\n",
+       "      <th>max_negative_polarity</th>\n",
+       "      <th>title_subjectivity</th>\n",
+       "      <th>title_sentiment_polarity</th>\n",
+       "      <th>abs_title_subjectivity</th>\n",
+       "      <th>abs_title_sentiment_polarity</th>\n",
+       "      <th>Popular</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>http://mashable.com/2014/12/10/cia-torture-rep...</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>188.0</td>\n",
+       "      <td>0.732620</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.844262</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>-0.487500</td>\n",
+       "      <td>-0.60</td>\n",
+       "      <td>-0.250000</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>http://mashable.com/2013/10/18/bitlock-kicksta...</td>\n",
+       "      <td>447.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>297.0</td>\n",
+       "      <td>0.653199</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.815789</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.160000</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>-0.135340</td>\n",
+       "      <td>-0.40</td>\n",
+       "      <td>-0.050000</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>http://mashable.com/2013/07/24/google-glass-po...</td>\n",
+       "      <td>533.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.660377</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.775701</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.136364</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>http://mashable.com/2013/11/21/these-are-the-m...</td>\n",
+       "      <td>413.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>781.0</td>\n",
+       "      <td>0.497409</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.677350</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>-0.195701</td>\n",
+       "      <td>-0.40</td>\n",
+       "      <td>-0.071429</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>http://mashable.com/2014/02/11/parking-ticket-...</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>0.685714</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.830357</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>-0.175000</td>\n",
+       "      <td>-0.25</td>\n",
+       "      <td>-0.100000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 61 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 url  timedelta  \\\n",
+       "0  http://mashable.com/2014/12/10/cia-torture-rep...       28.0   \n",
+       "1  http://mashable.com/2013/10/18/bitlock-kicksta...      447.0   \n",
+       "2  http://mashable.com/2013/07/24/google-glass-po...      533.0   \n",
+       "3  http://mashable.com/2013/11/21/these-are-the-m...      413.0   \n",
+       "4  http://mashable.com/2014/02/11/parking-ticket-...      331.0   \n",
+       "\n",
+       "   n_tokens_title  n_tokens_content  n_unique_tokens  n_non_stop_words  \\\n",
+       "0             9.0             188.0         0.732620               1.0   \n",
+       "1             7.0             297.0         0.653199               1.0   \n",
+       "2            11.0             181.0         0.660377               1.0   \n",
+       "3            12.0             781.0         0.497409               1.0   \n",
+       "4             8.0             177.0         0.685714               1.0   \n",
+       "\n",
+       "   n_non_stop_unique_tokens  num_hrefs  num_self_hrefs  num_imgs  ...  \\\n",
+       "0                  0.844262        5.0             1.0       1.0  ...   \n",
+       "1                  0.815789        9.0             4.0       1.0  ...   \n",
+       "2                  0.775701        4.0             3.0       1.0  ...   \n",
+       "3                  0.677350       10.0             3.0       1.0  ...   \n",
+       "4                  0.830357        3.0             2.0       1.0  ...   \n",
+       "\n",
+       "   min_positive_polarity  max_positive_polarity  avg_negative_polarity  \\\n",
+       "0               0.200000                   0.80              -0.487500   \n",
+       "1               0.160000                   0.50              -0.135340   \n",
+       "2               0.136364                   1.00               0.000000   \n",
+       "3               0.100000                   1.00              -0.195701   \n",
+       "4               0.100000                   0.55              -0.175000   \n",
+       "\n",
+       "   min_negative_polarity  max_negative_polarity  title_subjectivity  \\\n",
+       "0                  -0.60              -0.250000                 0.9   \n",
+       "1                  -0.40              -0.050000                 0.1   \n",
+       "2                   0.00               0.000000                 0.3   \n",
+       "3                  -0.40              -0.071429                 0.0   \n",
+       "4                  -0.25              -0.100000                 0.0   \n",
+       "\n",
+       "   title_sentiment_polarity  abs_title_subjectivity  \\\n",
+       "0                       0.8                     0.4   \n",
+       "1                      -0.1                     0.4   \n",
+       "2                       1.0                     0.2   \n",
+       "3                       0.0                     0.5   \n",
+       "4                       0.0                     0.5   \n",
+       "\n",
+       "   abs_title_sentiment_polarity  Popular  \n",
+       "0                           0.8        1  \n",
+       "1                           0.1        0  \n",
+       "2                           1.0        0  \n",
+       "3                           0.0        0  \n",
+       "4                           0.0        0  \n",
+       "\n",
+       "[5 rows x 61 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Lectura de la información de archivo .csv\n",
     "df = pd.read_csv('https://raw.githubusercontent.com/davidzarruk/MIAD_ML_NLP_2023/main/datasets/mashable.csv', index_col=0)\n",
@@ -223,9 +879,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Definición variable de interes y variables predictoras\n",
     "X = df.drop(['url', 'Popular'], axis=1)\n",
@@ -235,7 +902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,11 +921,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Árbol de decisión:\n",
+      "Accuracy: 0.64\n",
+      "F1-Score: 0.6633416458852869\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import BaggingClassifier, VotingClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, f1_score\n",
+    "\n",
+    "tree_model = DecisionTreeClassifier(max_depth=5)\n",
+    "tree_model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = tree_model.predict(X_test)\n",
+    "acc = accuracy_score(y_test, y_pred)\n",
+    "f1 = f1_score(y_test, y_pred)\n",
+    "\n",
+    "print(\"Árbol de decisión:\")\n",
+    "print(\"Accuracy:\", acc)\n",
+    "print(\"F1-Score:\", f1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Regresión logística:\n",
+      "Accuracy: 0.614\n",
+      "F1-Score: 0.6106254203093476\n"
+     ]
+    }
+   ],
    "source": [
-    "# Celda 6\n"
+    "logit_model = LogisticRegression()\n",
+    "logit_model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = logit_model.predict(X_test)\n",
+    "acc = accuracy_score(y_test, y_pred)\n",
+    "f1 = f1_score(y_test, y_pred)\n",
+    "\n",
+    "print(\"Regresión logística:\")\n",
+    "print(\"Accuracy:\", acc)\n",
+    "print(\"F1-Score:\", f1)\n"
    ]
   },
   {
@@ -277,11 +996,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Árboles de decisión con max_depth:\n",
+      "Accuracy: 0.6473333333333333\n",
+      "F1-Score: 0.6499007279947056\n",
+      "Árboles de decisión con min_samples_leaf:\n",
+      "Accuracy: 0.6446666666666667\n",
+      "F1-Score: 0.6458471760797342\n",
+      "Regresión logística:\n",
+      "Accuracy: 0.6213333333333333\n",
+      "F1-Score: 0.6172506738544474\n"
+     ]
+    }
+   ],
    "source": [
-    "# Celda 7\n"
+    "from sklearn.ensemble import BaggingClassifier, VotingClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, f1_score\n",
+    "# Árboles de decisión con max_depth\n",
+    "tree_models_1 = BaggingClassifier(\n",
+    "    base_estimator=DecisionTreeClassifier(max_depth=10),\n",
+    "    n_estimators=100,\n",
+    "    random_state=1\n",
+    ")\n",
+    "tree_models_1.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_1 = tree_models_1.predict(X_test)\n",
+    "acc_1 = accuracy_score(y_test, y_pred_1)\n",
+    "f1_1 = f1_score(y_test, y_pred_1)\n",
+    "\n",
+    "print(\"Árboles de decisión con max_depth:\")\n",
+    "print(\"Accuracy:\", acc_1)\n",
+    "print(\"F1-Score:\", f1_1)\n",
+    "\n",
+    "\n",
+    "# Árboles de decisión con min_samples_leaf\n",
+    "tree_models_2 = BaggingClassifier(\n",
+    "    base_estimator=DecisionTreeClassifier(min_samples_leaf=5),\n",
+    "    n_estimators=100,\n",
+    "    random_state=1\n",
+    ")\n",
+    "tree_models_2.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_2 = tree_models_2.predict(X_test)\n",
+    "acc_2 = accuracy_score(y_test, y_pred_2)\n",
+    "f1_2 = f1_score(y_test, y_pred_2)\n",
+    "\n",
+    "print(\"Árboles de decisión con min_samples_leaf:\")\n",
+    "print(\"Accuracy:\", acc_2)\n",
+    "print(\"F1-Score:\", f1_2)\n",
+    "\n",
+    "\n",
+    "# Regresión logística\n",
+    "logit_models = BaggingClassifier(\n",
+    "    base_estimator=LogisticRegression(),\n",
+    "    n_estimators=100,\n",
+    "    random_state=1\n",
+    ")\n",
+    "logit_models.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_3 = logit_models.predict(X_test)\n",
+    "acc_3 = accuracy_score(y_test, y_pred_3)\n",
+    "f1_3 = f1_score(y_test, y_pred_3)\n",
+    "\n",
+    "print(\"Regresión logística:\")\n",
+    "print(\"Accuracy:\", acc_3)\n",
+    "print(\"F1-Score:\", f1_3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ensamble de modelos:\n",
+      "Accuracy: 0.648\n",
+      "F1-Score: 0.648936170212766\n"
+     ]
+    }
+   ],
+   "source": [
+    "ensemble = VotingClassifier(\n",
+    "    estimators=[\n",
+    "        ('tree1', tree_models_1),\n",
+    "        ('tree2', tree_models_2),\n",
+    "        ('logit', logit_models)\n",
+    "    ],\n",
+    "    voting='hard'\n",
+    ")\n",
+    "\n",
+    "ensemble.fit(X_train, y_train)\n",
+    "y_pred_ensemble = ensemble.predict(X_test)\n",
+    "acc_ensemble = accuracy_score(y_test, y_pred_ensemble)\n",
+    "f1_ensemble = f1_score(y_test, y_pred_ensemble)\n",
+    "\n",
+    "print(\"Ensamble de modelos:\")\n",
+    "print(\"Accuracy:\", acc_ensemble)\n",
+    "print(\"F1-Score:\", f1_ensemble)\n"
    ]
   },
   {
@@ -294,11 +1116,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Árboles de decisión con max_depth:\n",
+      "Accuracy: 0.6473333333333333\n",
+      "F1-Score: 0.6499007279947056\n",
+      "Árboles de decisión con min_samples_leaf:\n",
+      "Accuracy: 0.6446666666666667\n",
+      "F1-Score: 0.6458471760797342\n",
+      "Regresión logística:\n",
+      "Accuracy: 0.6213333333333333\n",
+      "F1-Score: 0.6172506738544474\n"
+     ]
+    }
+   ],
    "source": [
-    "# Celda 8\n"
+    "# Celda 8\n",
+    "# Árboles de decisión con max_depth\n",
+    "tree_models_1 = BaggingClassifier(\n",
+    "    base_estimator=DecisionTreeClassifier(max_depth=10),\n",
+    "    n_estimators=100,\n",
+    "    random_state=1\n",
+    ")\n",
+    "tree_models_1.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_1 = tree_models_1.predict(X_test)\n",
+    "acc_1 = accuracy_score(y_test, y_pred_1)\n",
+    "f1_1 = f1_score(y_test, y_pred_1)\n",
+    "\n",
+    "print(\"Árboles de decisión con max_depth:\")\n",
+    "print(\"Accuracy:\", acc_1)\n",
+    "print(\"F1-Score:\", f1_1)\n",
+    "\n",
+    "\n",
+    "# Árboles de decisión con min_samples_leaf\n",
+    "tree_models_2 = BaggingClassifier(\n",
+    "    base_estimator=DecisionTreeClassifier(min_samples_leaf=5),\n",
+    "    n_estimators=100,\n",
+    "    random_state=1\n",
+    ")\n",
+    "tree_models_2.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_2 = tree_models_2.predict(X_test)\n",
+    "acc_2 = accuracy_score(y_test, y_pred_2)\n",
+    "f1_2 = f1_score(y_test, y_pred_2)\n",
+    "\n",
+    "print(\"Árboles de decisión con min_samples_leaf:\")\n",
+    "print(\"Accuracy:\", acc_2)\n",
+    "print(\"F1-Score:\", f1_2)\n",
+    "\n",
+    "\n",
+    "# Regresión logística\n",
+    "logit_models = BaggingClassifier(\n",
+    "    base_estimator=LogisticRegression(),\n",
+    "    n_estimators=100,\n",
+    "    random_state=1\n",
+    ")\n",
+    "logit_models.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_3 = logit_models.predict(X_test)\n",
+    "acc_3 = accuracy_score(y_test, y_pred_3)\n",
+    "f1_3 = f1_score(y_test, y_pred_3)\n",
+    "\n",
+    "print(\"Regresión logística:\")\n",
+    "print(\"Accuracy:\", acc_3)\n",
+    "print(\"F1-Score:\", f1_3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ensamble de modelos:\n",
+      "Accuracy: 0.6493333333333333\n",
+      "F1-core: 0.6511936339522546\n"
+     ]
+    }
+   ],
+   "source": [
+    "ensemble = VotingClassifier(\n",
+    "    estimators=[\n",
+    "        ('tree1', tree_models_1),\n",
+    "        ('tree2', tree_models_2),\n",
+    "        ('logit', logit_models)\n",
+    "    ],\n",
+    "    voting='soft',\n",
+    "    weights=[0.3, 0.3, 0.4]\n",
+    ")\n",
+    "\n",
+    "ensemble.fit(X_train, y_train)\n",
+    "y_pred_ensemble = ensemble.predict(X_test)\n",
+    "acc_ensemble = accuracy_score(y_test, y_pred_ensemble)\n",
+    "f1_ensemble = f1_score(y_test, y_pred_ensemble)\n",
+    "\n",
+    "print(\"Ensamble de modelos:\")\n",
+    "print(\"Accuracy:\", acc_ensemble)\n",
+    "print(\"F1-core:\", f1_ensemble)\n"
    ]
   },
   {
@@ -309,14 +1232,25 @@
     "En la celda 9 comente sobre los resultados obtenidos con las metodologías usadas en los puntos 7 y 8, compare los resultados y enuncie posibles ventajas o desventajas de cada una de ellas."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Comparando los resultados obtenidos con ambas metodologías, se puede observar que los modelos construidos con votación ponderada obtuvieron mejores resultados que los modelos construidos con votación mayoritaria.\n",
+    "\n",
+    "En el caso de los modelos individuales, los árboles de decisión con min_samples_leaf obtuvieron el mejor desempeño, seguido de la regresión logística y los árboles de decisión con max_depth. Esto indica que los árboles de decisión con min_samples_leaf fueron capaces de capturar patrones relevantes en los datos y generar predicciones más precisas.\n",
+    "\n",
+    "En cuanto a las metodologías de ensamble, la votación ponderada obtuvo mejores resultados que la votación mayoritaria. Esto puede ser debido a que la ponderación permite asignar una mayor importancia a aquellos modelos que tienen mejor desempeño en los datos de prueba, mientras que en la votación mayoritaria todos los modelos tienen el mismo peso en la predicción final.\n",
+    "\n",
+    "Una posible ventaja de la votación mayoritaria es que es más fácil de implementar y menos propensa al sobreajuste, ya que todos los modelos contribuyen por igual en la predicción final. Por otro lado, una posible ventaja de la votación ponderada es que permite asignar diferentes pesos a cada modelo, lo que puede mejorar la precisión de la predicción final."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Celda 9"
-   ]
+   "source": []
   }
  ],
  "metadata": {
@@ -335,7 +1269,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.10.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3c06e3e46abf38078fe4dac36a0085ec2b134ebbd73dd076183d243eeca6918f"
+   }
   }
  },
  "nbformat": 4,
diff --git a/Semana 2/S2TC1_RandomForests_Boosting.ipynb b/Semana 2/S2TC1_RandomForests_Boosting.ipynb
index d7bbd38..6549540 100644
--- a/Semana 2/S2TC1_RandomForests_Boosting.ipynb	
+++ b/Semana 2/S2TC1_RandomForests_Boosting.ipynb	
@@ -1,247 +1,1416 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image info](https://raw.githubusercontent.com/albahnsen/MIAD_ML_and_NLP/main/images/banner_1.png)"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gaZq0pYsPt93"
+      },
+      "source": [
+        "![image info](https://raw.githubusercontent.com/albahnsen/MIAD_ML_and_NLP/main/images/banner_1.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U3XgibcbPt95"
+      },
+      "source": [
+        "# Taller: Construcción e implementación de modelos Bagging, Random Forest y XGBoost\n",
+        "\n",
+        "En este taller podrán poner en práctica sus conocimientos sobre la construcción e implementación de modelos de Bagging, Random Forest y XGBoost. El taller está constituido por 8 puntos, en los cuales deberan seguir las intrucciones de cada numeral para su desarrollo."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KkOe_CJFPt96"
+      },
+      "source": [
+        "## Datos predicción precio de automóviles\n",
+        "\n",
+        "En este taller se usará el conjunto de datos de Car Listings de Kaggle donde cada observación representa el precio de un automóvil teniendo en cuenta distintas variables como año, marca, modelo, entre otras. El objetivo es predecir el precio del automóvil. Para más detalles puede visitar el siguiente enlace: [datos](https://www.kaggle.com/jpayne/852k-used-car-listings)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "iy9aR7WyPt97"
+      },
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "I8Q4jMJDPt97",
+        "outputId": "a286b437-29b1-4ca5-f3e9-21ca5f1ec02e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Price  Year  Mileage  M_Camry  M_Camry4dr  M_CamryBase  M_CamryL  \\\n",
+              "7    21995  2014     6480        0           0            0         1   \n",
+              "11   13995  2014    39972        0           0            0         0   \n",
+              "167  17941  2016    18989        0           0            0         0   \n",
+              "225  12493  2014    51330        0           0            0         1   \n",
+              "270   7994  2007   116065        0           1            0         0   \n",
+              "\n",
+              "     M_CamryLE  M_CamrySE  M_CamryXLE  \n",
+              "7            0          0           0  \n",
+              "11           1          0           0  \n",
+              "167          0          1           0  \n",
+              "225          0          0           0  \n",
+              "270          0          0           0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3095e7c4-2ad4-4437-ba6f-41b660ac7a8d\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>Price</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>M_Camry</th>\n",
+              "      <th>M_Camry4dr</th>\n",
+              "      <th>M_CamryBase</th>\n",
+              "      <th>M_CamryL</th>\n",
+              "      <th>M_CamryLE</th>\n",
+              "      <th>M_CamrySE</th>\n",
+              "      <th>M_CamryXLE</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>21995</td>\n",
+              "      <td>2014</td>\n",
+              "      <td>6480</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>13995</td>\n",
+              "      <td>2014</td>\n",
+              "      <td>39972</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>167</th>\n",
+              "      <td>17941</td>\n",
+              "      <td>2016</td>\n",
+              "      <td>18989</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>225</th>\n",
+              "      <td>12493</td>\n",
+              "      <td>2014</td>\n",
+              "      <td>51330</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>270</th>\n",
+              "      <td>7994</td>\n",
+              "      <td>2007</td>\n",
+              "      <td>116065</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3095e7c4-2ad4-4437-ba6f-41b660ac7a8d')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-3095e7c4-2ad4-4437-ba6f-41b660ac7a8d button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-3095e7c4-2ad4-4437-ba6f-41b660ac7a8d');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ],
+      "source": [
+        "# Importación de librerías\n",
+        "%matplotlib inline\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.tree import DecisionTreeRegressor, export_graphviz\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score\n",
+        "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor, export_graphviz\n",
+        "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier\n",
+        "from sklearn import metrics\n",
+        "from sklearn.tree import plot_tree\n",
+        "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
+        "\n",
+        "# Lectura de la información de archivo .csv\n",
+        "data = pd.read_csv('https://raw.githubusercontent.com/albahnsen/MIAD_ML_and_NLP/main/datasets/dataTrain_carListings.zip')\n",
+        "\n",
+        "# Preprocesamiento de datos para el taller\n",
+        "data = data.loc[data['Model'].str.contains('Camry')].drop(['Make', 'State'], axis=1)\n",
+        "data = data.join(pd.get_dummies(data['Model'], prefix='M'))\n",
+        "data = data.drop(['Model'], axis=1)\n",
+        "\n",
+        "# Vatasetisualización d\n",
+        "data.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DYTkDxzUUiC7"
+      },
+      "source": [
+        "### **ANÁLISIS DESCRIPTIVO**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "xt-hajQuUhMr",
+        "outputId": "30503fdf-8c90-4bab-dbf9-e272fb887650"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Estadisticas descriptivas de variable Price\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "              Price\n",
+              "count  10495.000000\n",
+              "mean   14538.403716\n",
+              "std     3922.420961\n",
+              "min     5002.000000\n",
+              "25%    11999.000000\n",
+              "50%    15000.000000\n",
+              "75%    16999.000000\n",
+              "max    32444.000000"
+            ],
+            "text/html": [
+              "\n",
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+              "    <div class=\"colab-df-container\">\n",
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\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>Price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>10495.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>14538.403716</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>3922.420961</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>5002.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>11999.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>15000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>16999.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>32444.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c276e96-4a9d-4b4f-8d28-7c2c74ee7c70')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-8c276e96-4a9d-4b4f-8d28-7c2c74ee7c70 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-8c276e96-4a9d-4b4f-8d28-7c2c74ee7c70');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Variable a predecir\n",
+        "\n",
+        "p=sns.histplot(data['Price'], bins=\"auto\" )\n",
+        "plt.title(\"\\n Histograma de la variable precio\")\n",
+        "plt.xlabel(\"Precio\")\n",
+        "plt.ylabel(\"Número de vehículos\")\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Estadisticas descriptivas de variable Price\")\n",
+        "display(pd.DataFrame(data.Price.describe()))\n",
+        "\n",
+        "plt.boxplot(data['Price'])\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Respecto a la variable a predecir Price, se puede decir que posee una distribución normal pues los datos se agrupan hacia la media, con una media de 14.538, en los datos esta variable presenta un mínimo de 5002 y un máximo de 32.444. Posee una desviación estandar de 3922 y el 75% de los datos poseen un valor igual o inferior a 16.999 en la variable Price."
+      ],
+      "metadata": {
+        "id": "gG27YSXZgY-_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 843
+        },
+        "id": "8cs2L4-og7Bc",
+        "outputId": "3a05e86a-56f0-4add-fc8c-fe148440abfb"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Variables predictoras\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.scatter( data['Price'],data['Year'])\n",
+        "plt.show()\n",
+        "\n",
+        "#Variables predictoras\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.scatter(data['Price'],data['Mileage'] )\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Respecto al análisis de la variable Price con sus predictoras, se puede observar que hay dos relaciones que llaman la atención, con las variables Year y con la variable Mileage. En el scatter realizado en la parte superior se puede observar la correlación positiva que presenta esta variable con la variable Year en los datos, y además se puede observar la correlación negativa que presenta con la variable Mileage."
+      ],
+      "metadata": {
+        "id": "OWOV26yahMBf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "KpTkJYlkUp_E",
+        "outputId": "0c661595-7592-4f07-9ede-93702e6594ca"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2500x2000 with 2 Axes>"
+            ],
+            "image/png": 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XW3k0Tvf88bMKL3yCDACucOVOIrZu3Vo7duxQeHi4unXrpldffVVubm765JNP1Lhx48qIsUbKy5OKW4Xj7BKl+bmOx85ycbXNYuzYW+rQ2yqrRVr1uzTjVemh6VLjlpUTMwCUxs3sqrw8x2Vezpa5mS9+6SFPL7O69mmmzWtilHnG+Aebi4uz7np0mOZ/v1lHDib+taBRI5hdXZRfzJ7MefmF9uPlcWf/LurZtKFe+GmpzuSU8oaMGsvs5qL8YpaJtPcpc+l96tPZxj1zlq2O1rHjybpzQl/169Vcy1bb9qVzcXHSfXcM0C8Ldujw0YpJFqH6cHdxUV4xY9fZJUrLO3aV5ubP5xh+n7dtt3646xY9dFUv/bh9T7mXRcWVyd3FRXnFvJb2PuVScp9yLzpWfJ8ssLfPKyyUZ9FMi13x8Xr4t4WSpEX7Dygnv0CP9u+tng0baN3hI/IsWs7N39NDN8z+RjtO2mZvLD1wUCvuvl3/6NmNJGI15+7sojxLMX2msMB+vCLcueIHw+9zY3dq1sAbNaVVF83c96fis86U0BI1gZuTi/KLWU40r9C2BKTZqey/M/8bNUdeLmaFetTRsNAuMju5yslkMiRzzuRn60DGCa1I2KGotCOq6xmgWxoO1HNtbtMj2z5hSdMawt3Ftfj3urPjVgnvlaW+T9rHvHN9MTknS3uSTmlW1FbFpCSpVZ0gTW3bRa/1G6Z7l/7yl+8DqCgWsQ8aylbu5UyffvppWYr2annhhRcUFxenPn366Pfff9e77757UefIzc1Venq64Sc39+/1JZ2bm1RQzJLXZ2fTX7i34fm+eU/asUG68ympa3+p20Dpof/aZjF++2GlhAsAdi4uzvKr4234cXIyKS83X25ujh+4z5bl5V78Ov+9BrWS2d1Vy+bvcDg2+rae8q3tqS8/WHbpN4FqydXZSQHenoYfJ5NJufkFxe4d5la0D2du/sX/wT+0TTM9cHUvfb95l+ZsZHZOTefi4iT/2l6GHycnk3LzCord99Dep3LL/yXSdz//qcJCizq1b2gvG3ttZ/nW8tDnX6+99JvAFa+ksSunoKDYPerMLuUfu8orv9CirzZtl6+HuyLDKm65QVwerk5OCvDyNPzY+1QxY5e9TxWU3Kdyio4V3yddDO3P1v01yrgS0S9RtgckOtYNNdQ7kppmTyBKUlZ+vpYeOKi2oSFyLmEvRVQPOYUFcnMqps8UJQ9zCitvHJuxd7NcnZzVPbhBpV0DV4Y8S4FcnYr5O7MoYZNrKfvvzKj0w9qcvF+/HF+vR7d/qqtDOuquiOH2417O7vq/Tv/QnrTD+jR2gdae3qPvjqzUv3fNUtvajTU0tEvF3RCqVE5BfvHvdWfHrRLeK0t9n7SPeba+WN/HV9+MGKfv9u3WB9s3asnhA3pn6zo9s/YPjWjcXP3rhVfIvQDA5VLux8KGDBli/3eTJk0UHR2t5ORk+fn5yXSRfwBMnz5dzz//vKHs2Wef1YBJ5Y2m+vL1L37J0rRk2//WrlN8u4J8ac0i296HTuelgF1cpNZdpOW/2Oqw1ziAytKqfX29OmOKoWzisDeUnJghvwDHTcj9A21lSQkX/4TwwOHtlJGerU2rjF9OeXqbddOd/fTbd5vk5W2Wl7ftiQt3T7NMJpOCw2orJydfacmOS4yg+mvfIEwz7zJu/nv1KzOUeCZTgbW8HOoHFu1rmJDuuMxWcXo0aaDpNw7Rqn1xeuGnpX89YFzxWreoq3emjzeUjZvysZJTMlXH33Epo7NlZ5c1LY+8vAKln8lWLW93SZKXp5sm3NhDP/2+TV6ebvLytM3Y8XB3lckkhQTVUk5ugVLTssp9LVxZOtQP0+xJxrHrqreLxi6fYsYu76Kx60z5+1l5nEy3vS/7erhX6nVQ8TrWDdNXNxv7VL8PZygxI1OBXo59KqioT50qpU8lZtiOFdvey0sp2dn22Rdnl0dNyjKOT2d/93V3L6pnO2dSMUukJmdly83ZWR6ursrIy3M4juohITtDIR6On/+DipYxPVXMUqcV5USWbXnL2mbGsJouKTddAe6+DuV13Gx973QxS52WJqMgW1tTDmhQSEd9eOA3SVLfoDbyN9fS2kTjVho7Ug8qoyBbbXwb6Zfj6y/xDnAlScjKVIiX4+f8IM+i98qs4set1Nxs5RYU2OuV1nZMs9YyO7to2ZFYQ70lh237cHYKqasVx9gLHUD1cclrSxw4cECxsbHq27ev/P39ZS3Hes5PPvmkpk2bZigzm83acPLrSw2n2qkfIe3bIWVnSh7nvf/ERRcdL2Fl2Ix0yVJoktXi+N+7sECyWkyyFHMMACrKwX3xevKuLwxlyaczdHDfSUV2bCiTyWR4T2jeup5ysvN0vJT9w87nH+Cttl3C9ccv25Sfb1wqxKeWhzy9zLpxch/dOLmPQ9tZCx7WumV79cJDf5/3k7+TfScTNeUz43JWpzMyFX0yUZ0a1ZXJJJ3/caRt/VBl5eXr0OnUMs/dpn6I3r31Gu05lqBpX/9m2LMONdeBuARNe/o7Q1lySqYOHExQm8h6Dn2qVbNQZefk6ejxlHJfy8PDVb61PJWani1J8vF2l6enm24e0003j+nmUH/OjKlavSFGT7/0U7mvhStLdHyiJs82jl2JGZmKjk9Up4aOY1e7eraxKy4ptVLjqu9n+0I2JTO7Uq+Dirc3IVETvr2gT2Vmam9CojrXqyuTpPPfxdqFFr0fpqSWeM5TGZlKysxSm1DHmaltQ4O199S5JeR3x5+S1EbB3sYvYc/+nlyUTEzIyFRCRqaCfYr5stbbSzn5BcokgVitRSUnqEfLhvJ2dVNG/rnXsn1AWNHxi98PvbwaeNeWZFsyEDXbgYwT6uAXIU9ns7IKz61i1rKWbRbqgTOOe8GWxezkKi+Xcwlov6KEZHGzo53lJGenci/khitUVFKCeoQ1cBy3gkLtx4tjlRSdkqg2ASEOx9oHhelweqoy820zEQM9vGQymeR0QX9yLZq57WKiPwGoXsqdRExKStKNN96o5cuXy2QyKSYmRo0bN9aUKVPk5+enN954o8xzmM1mmc2lrNf5N9Cxj7T4e5NW/27V4KKHSPPzpHWLpfAWVvnb9ntWUoKUlyOFFq3QUau25Olt1bZ10qgJ52Yc5mRLOzdIIfWtcvt7/6cFUMkyzuRo28aDDuWr/9ijPoNbq9dVrbTmD9sTnLVqe6rP4NbasHKfISEYWs9PknTymOMX8f2GtpGzs5OW/e64lGlqcqaef9AxQXjtzd3Vsm19/feJuUo+zZ4oNVV6Tq42xB5xKF+8K0ZD2jTT1ZFNtXh3jCSptqe7BrdpqhV7Dxr2S6zvb/vS/Ghymr2scaC/Ppx4nY6npOsfs35ib7C/kYzMXP25w3E/rhVr96l/7+bq26OZVq7bL0nyreWh/r2ba92mWMN+iWEhtSVJJ+JTJdmWPHV2cVJ2tnFprYnjesrJyaRNf9qeOk5Jy9K/XvrR4do3jOyoyBZheuH135TErOoaIT0nV+sPOo5di6JiNDSymQa3bKpFUefGriGtmmr5/gvGrqKE39GUNIfzlMXP00MpWcZEoZebqyZ076jkzCztOVl5X/KjcqTn5mrdYcc+tWBfjIa1aKYhzZtq4T5bn/LzcNewFk21LPagYR+nBrVtfepI6rk+tXB/jK5v3UqhPt46WTRrsUfD+mpcx19fbNlmr/fHgVg9U9BfN7SJ1A+79tgTlje2ay1JWnPoXGzzo/dpcueO6tWogdYWlft5uGtQ0witP3JEPLJTvS04Eq2pkd10U9P2+jRqkyTJzclZYyPaaFvicZ0s2qswzLOWPFxcFJueXO5r+Lq560x+riznPW3hYnLSPa27K7ewQOvjHf+/gJplZcJOjW/YX9fU7a45R1ZKklxNzhoW1kVRaYeVmGsbx4LMteXu7KojWeceeqjt6q3UfOPMshB3P3X0b6p96cfsZceK2gwM7qCZcYvt5b0CIuXhYlbMmROVdn+4vBbE7dfUdl11U4t2+nTXZklF41azNtp26oROZhaNW14+8nBxVWxasqHtE137qU1AsHadtn1+auzrp55hDfTpzs32egfTkuVkMmlk4+b6Pubc7NZRES0kSXuS+OwFoHopdxLxoYcekqurq44cOaKWLVvay8eNG6dp06ZdVBIRUuMWUqc+Vs37QkpPk4LCpPVLpNOnpAnnTdL84jVp/06TPllk+8Ds5CxdfYP08yyTpj9oVY+rJItFWrtISjlt0pTHjX+G7dggHSv6rr+wUDoWJ80v+v69XXepXgkzHvH39tU8KT1DSiiaOLZ8nRRf9Dn81uulYh4mBrRmyR7t3XFE014YrQYRgUpPydLIcV3l5GTS/y7Yv/C/n0yWJE0c/qbDeQYMb6fTCenaufmQw7HcnHytX77XobzngBZq3rpuscdQ8y3eHaPtR07oP2MGKyLIXylZ2RrfrZ2cTSa9/4dx2aEZd9wgSRr86ueSJE83V31y+2jV8jDri1Vb1LeFcX+Ko8lp2nHk5OW5EVwxVq7brz3RJ/TEP4epUYM6SkvP1rXD28vJyaQvLti/8M3/3ChJGn/HJ5Ikfz8vffbORC1dtVdHjtm+dOjSoZF6dInQxj8Pas1G2xf7ubkFWrPhgMO1e3dvqhYWa7HHULMsiorR9qMn9PK1gxURaBu7burSTs5OJr233Dh2zZxoG7uuevtze1mYr49GtbP9PXZ2X8O7+3aVJJ1IPaNfdtreE2/p2k5XtYjQ8n0HdTLtjAJ9vHRDh0iF+tbSY/MWKr/QUun3istj4b4YbTt+Qv8dNlhN6vgrJTtbt3SwvR++s8bYp2aPt/Wp/h+d61Mfrd+kYc2b6X83jdXMLdvk5eaqO7p2VnRCon7Yde5L0NOZWfpg/SY91Kenvrjxei2JOaAWQYEa166NfomK1q74U+edc7OGN2+m968bqc83b9WZ3Fzd1L6tXJyc9MZK9oOt7rafPqnfDu3VYx36qY67pw6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+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Gráfico de correlaciones\n",
+        "corr = data.corr(method='spearman')\n",
+        "plt.figure(figsize=(25,20))\n",
+        "plt.title(\"Correlación entre variables\")\n",
+        "sns.heatmap(corr[(corr >= 0.0) | (corr <= -0)],\n",
+        "           cmap='viridis', vmax=1.0, vmin=-1.0, linewidths=0.1,\n",
+        "           annot=True, annot_kws={\"size\": 12}, square=True);"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WznWe4UgVKVQ"
+      },
+      "source": [
+        "En el gráfico de correlaciones entre las variables de los datos se puede observar que hay una correlación positiva entre la variable Precio y la variable Year, de aquí se puede inferir que la inflación anual puede ser la variable omitida que está causando esta correlación puesto que mientras pasan los años se van aumentando los niveles de precios en general en la economía. Además, se puede observar que hay una correlación negativa entre el precio con el Mileage que es la cantidad de millas que el carro ha recorrido, lo cual hace sentido desde que entre más usado esté un carro puede considerarse que está más depreciado respecto a su valor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "id": "6h4TxTlrPt98"
+      },
+      "outputs": [],
+      "source": [
+        "# Separación de variables predictoras (X) y variable de interés (y)\n",
+        "y = data['Price']\n",
+        "X = data.drop(['Price'], axis=1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "1wfq8UQwPt98"
+      },
+      "outputs": [],
+      "source": [
+        "# Separación de datos en set de entrenamiento y test\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yPJJI4mrPt98"
+      },
+      "source": [
+        "### Punto 1 - Árbol de decisión manual\n",
+        "\n",
+        "En la celda 1 creen un árbol de decisión **manualmente**  que considere los set de entrenamiento y test definidos anteriormente y presenten el RMSE y MAE del modelo en el set de test."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "id": "uwF_ha9nPt99"
+      },
+      "outputs": [],
+      "source": [
+        "# Celda 1\n",
+        "class Node():\n",
+        "    def __init__(self, feature_index=None, threshold=None, left=None, right=None, var_red=None, value=None):\n",
+        "        \n",
+        "        # Nodo de decisión\n",
+        "        self.feature_index = feature_index\n",
+        "        self.threshold = threshold\n",
+        "        self.left = left\n",
+        "        self.right = right\n",
+        "        self.var_red = var_red\n",
+        "        \n",
+        "        # Nodo hoja\n",
+        "        self.value = value\n",
+        "\n",
+        "class DecisionTreeRegressor():\n",
+        "    def __init__(self, min_samples_split=2, max_depth=2):\n",
+        "        self.root = None    \n",
+        "        # Para condiciones\n",
+        "        self.min_samples_split = min_samples_split\n",
+        "        self.max_depth = max_depth\n",
+        "        \n",
+        "    def build_tree(self, dataset, curr_depth=0):   \n",
+        "        X, Y = dataset[:,:-1], dataset[:,-1]\n",
+        "        num_samples, num_features = np.shape(X)\n",
+        "        best_split = {}\n",
+        "\n",
+        "        if num_samples>=self.min_samples_split and curr_depth<=self.max_depth:\n",
+        "            # Encuentra el mejor split\n",
+        "            best_split = self.get_best_split(dataset, num_samples, num_features)\n",
+        "            # Revisar si gain es positivo\n",
+        "            if best_split[\"var_red\"]>0:\n",
+        "                # Izquierda\n",
+        "                left_subtree = self.build_tree(best_split[\"dataset_left\"], curr_depth+1)\n",
+        "                # Derecha\n",
+        "                right_subtree = self.build_tree(best_split[\"dataset_right\"], curr_depth+1)\n",
+        "                # Retorno a nodo de decisión\n",
+        "                return Node(best_split[\"feature_index\"], best_split[\"threshold\"], \n",
+        "                            left_subtree, right_subtree, best_split[\"var_red\"])\n",
+        "        # Hoja nodo\n",
+        "        leaf_value = self.calculate_leaf_value(Y)\n",
+        "        # Retorna a hoja nodo\n",
+        "        return Node(value=leaf_value)\n",
+        "    \n",
+        "    def get_best_split(self, dataset, num_samples, num_features):\n",
+        "   \n",
+        "        # Diccionario para almacenar el mejor split\n",
+        "        best_split = {}\n",
+        "        max_var_red = -float(\"inf\")\n",
+        "        for feature_index in range(num_features):\n",
+        "            feature_values = dataset[:, feature_index]\n",
+        "            possible_thresholds = np.unique(feature_values)\n",
+        "            for threshold in possible_thresholds:\n",
+        "                dataset_left, dataset_right = self.split(dataset, feature_index, threshold)\n",
+        "                if len(dataset_left)>0 and len(dataset_right)>0:\n",
+        "                    y, left_y, right_y = dataset[:, -1], dataset_left[:, -1], dataset_right[:, -1]\n",
+        "                    # Información gain\n",
+        "                    curr_var_red = self.variance_reduction(y, left_y, right_y)\n",
+        "                    # Actualiza el mejor split\n",
+        "                    if curr_var_red>max_var_red:\n",
+        "                        best_split[\"feature_index\"] = feature_index\n",
+        "                        best_split[\"threshold\"] = threshold\n",
+        "                        best_split[\"dataset_left\"] = dataset_left\n",
+        "                        best_split[\"dataset_right\"] = dataset_right\n",
+        "                        best_split[\"var_red\"] = curr_var_red\n",
+        "                        max_var_red = curr_var_red\n",
+        "                        \n",
+        "        # Retorna el mejor split\n",
+        "        return best_split\n",
+        "    \n",
+        "    def split(self, dataset, feature_index, threshold):\n",
+        "           \n",
+        "        dataset_left = np.array([row for row in dataset if row[feature_index]<=threshold])\n",
+        "        dataset_right = np.array([row for row in dataset if row[feature_index]>threshold])\n",
+        "        return dataset_left, dataset_right\n",
+        "    \n",
+        "    def variance_reduction(self, parent, l_child, r_child):       \n",
+        "        weight_l = len(l_child) / len(parent)\n",
+        "        weight_r = len(r_child) / len(parent)\n",
+        "        reduction = np.var(parent) - (weight_l * np.var(l_child) + weight_r * np.var(r_child))\n",
+        "        return reduction\n",
+        "    \n",
+        "    def calculate_leaf_value(self, Y):      \n",
+        "        val = np.mean(Y)\n",
+        "        return val\n",
+        "                \n",
+        "    def print_tree(self, tree=None, indent=\" \"):     \n",
+        "        if not tree:\n",
+        "            tree = self.root\n",
+        "        if tree.value is not None:\n",
+        "            print(tree.value)\n",
+        "        else:\n",
+        "            print(\"X_\"+str(tree.feature_index), \"<=\", tree.threshold, \"?\", tree.var_red)\n",
+        "            print(\"%sleft:\" % (indent), end=\"\")\n",
+        "            self.print_tree(tree.left, indent + indent)\n",
+        "            print(\"%sright:\" % (indent), end=\"\")\n",
+        "            self.print_tree(tree.right, indent + indent)\n",
+        "    \n",
+        "    def fit(self, X, Y):       \n",
+        "        dataset = np.concatenate((X, Y), axis=1)\n",
+        "        self.root = self.build_tree(dataset)\n",
+        "        \n",
+        "    def make_prediction(self, x, tree):       \n",
+        "        if tree.value!=None: return tree.value\n",
+        "        feature_val = x[tree.feature_index]\n",
+        "        if feature_val<=tree.threshold:\n",
+        "            return self.make_prediction(x, tree.left)\n",
+        "        else:\n",
+        "            return self.make_prediction(x, tree.right)\n",
+        "    \n",
+        "    def predict(self, X):       \n",
+        "        preditions = [self.make_prediction(x, self.root) for x in X]\n",
+        "        return preditions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Y0XW-p2Z0IgL",
+        "outputId": "ee946e2f-d1bf-48ec-befe-67a705211e04"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X_0 <= 2012 ? 8790046.173046965\n",
+            " left:X_0 <= 2011 ? 2959465.8517095316\n",
+            "  left:X_1 <= 99121 ? 1170116.721087044\n",
+            "    left:X_0 <= 2007 ? 893698.6400833833\n",
+            "        left:8403.308823529413\n",
+            "        right:10479.29531568228\n",
+            "    right:X_0 <= 2009 ? 552919.3704753756\n",
+            "        left:7233.494485294118\n",
+            "        right:8878.844036697248\n",
+            "  right:X_1 <= 82133 ? 1363726.4906349215\n",
+            "    left:X_1 <= 47312 ? 504080.0386649659\n",
+            "        left:14652.202127659575\n",
+            "        right:13077.713080168776\n",
+            "    right:X_1 <= 112714 ? 363719.12316599186\n",
+            "        left:11426.666666666666\n",
+            "        right:10010.15909090909\n",
+            " right:X_0 <= 2016 ? 1620597.6461158656\n",
+            "  left:X_1 <= 49121 ? 910016.9812996429\n",
+            "    left:X_1 <= 25773 ? 381099.80697076535\n",
+            "        left:17245.66734279919\n",
+            "        right:15898.380538662033\n",
+            "    right:X_1 <= 70061 ? 776981.4839276252\n",
+            "        left:14624.099041533545\n",
+            "        right:12750.181229773463\n",
+            "  right:X_8 <= 0 ? 1093632.8568244968\n",
+            "    left:X_0 <= 2017 ? 800646.9773042481\n",
+            "        left:18672.143669985777\n",
+            "        right:25139.071428571428\n",
+            "    right:X_1 <= 405 ? 995412.853711037\n",
+            "        left:27977.666666666668\n",
+            "        right:21639.347826086956\n"
+          ]
+        }
+      ],
+      "source": [
+        "regressor = DecisionTreeRegressor(min_samples_split=3, max_depth=3)\n",
+        "regressor.fit(X_train.values,y_train.values.reshape(-1,1))\n",
+        "regressor.print_tree()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jm5ErOpl05S7",
+        "outputId": "779440be-3935-47be-cd86-ce7baf5cdf07"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 1786.7701175453772\n",
+            "MAE: 1338.7678256052181\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = regressor.predict(X_test.values) \n",
+        "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+        "mae = mean_absolute_error(y_test, y_pred)\n",
+        "print(\"RMSE:\", rmse)\n",
+        "print(\"MAE:\", mae)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jpU0ir1mPt99"
+      },
+      "source": [
+        "### Punto 2 - Bagging manual\n",
+        "\n",
+        "En la celda 2 creen un modelo bagging **manualmente** con 10 árboles de regresión y comenten sobre el desempeño del modelo."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "fepahK6kPt99"
+      },
+      "outputs": [],
+      "source": [
+        "# Celda 2\n",
+        "from random import randrange\n",
+        "def subsample(x,y, ratio):\n",
+        "\tsample = list()\n",
+        "\tysample=list()\n",
+        "\tn_sample = round(len(x) * ratio)\n",
+        "\twhile len(sample) < n_sample:\n",
+        "\t\tindex = randrange(len(x))\n",
+        "\t\tsample.append(x[index])\n",
+        "\t\tysample.append(y[index])\n",
+        "\treturn sample,ysample\n",
+        "\n",
+        "# Bootstrap\n",
+        "def bagging(X_train,y_train,X_test,sample_size=0.5):\n",
+        "\ttrees = list()\n",
+        "\tfor i in range(0,10):\n",
+        "\t\tsample,ysample = subsample(X_train.values,y_train.values.reshape(-1,1), sample_size)\n",
+        "\t\treg = DecisionTreeRegressor(min_samples_split=3, max_depth=3)\n",
+        "\t\treg.fit(sample,ysample)\n",
+        "\t\ttrees.append(reg)\n",
+        "\tpredictions = [tree.predict(X_test.values) for tree in trees]\n",
+        "\tpredictions =pd.DataFrame(predictions).mean().values\n",
+        "\treturn(predictions)\n",
+        "\n",
+        "preds=bagging(X_train,y_train,X_test, sample_size=0.5)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "v-g8Fs_o7_Kx",
+        "outputId": "f0076ceb-4f6b-47c6-c7ec-414d9ed64de9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 1681.409274104567\n",
+            "MAE: 1244.4111811524126\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
+        "mae = mean_absolute_error(y_test, preds)\n",
+        "print(\"RMSE:\", rmse)\n",
+        "print(\"MAE:\", mae)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "41QXP-q38g0i"
+      },
+      "source": [
+        "El modelo bagging manual obtiene un error mucho mayor tanto en términos de RMSE como de MAE. Esto sugiere que la técnica de bagging no es efectiva para mejorar el rendimiento del modelo. Por lo tanto, el modelo bagging manual no es una buena técnica para mejorar el rendimiento del modelo y reducir la varianza en los datos de prueba."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p64YpQwoPt9-"
+      },
+      "source": [
+        "### Punto 3 - Bagging con librería\n",
+        "\n",
+        "En la celda 3, con la librería sklearn, entrenen un modelo bagging con 10 árboles de regresión y el parámetro `max_features` igual a `log(n_features)` y comenten sobre el desempeño del modelo."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "id": "ul4DaD7_lrNU"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import BaggingRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "from math import log"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UsWSvYFDPt9-",
+        "outputId": "bede570a-bd87-44ce-b15f-ad04df0a5a8c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 1622.3450691556634\n",
+            "MAE: 1209.2665406544402\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Celda 3\n",
+        "import sklearn\n",
+        "from sklearn.metrics import mean_absolute_error\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "from sklearn.ensemble import BaggingRegressor\n",
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "\n",
+        "n_features=2\n",
+        "base_model = DecisionTreeRegressor(random_state=42,max_depth=5)\n",
+        "bagging_model = BaggingRegressor(base_estimator=base_model,n_estimators=10,max_features=np.log(n_features), random_state=42)\n",
+        "bagging_model.fit(X_train, y_train)\n",
+        "y_pred=bagging_model.predict(X_test)\n",
+        "rmserbagging =  np.sqrt(mean_squared_error(y_pred, y_test))\n",
+        "print('RMSE:', np.sqrt(mean_squared_error(y_pred, y_test)))\n",
+        "maerbagging = mean_absolute_error(y_pred, y_test)\n",
+        "print('MAE:', mean_absolute_error(y_pred, y_test))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ENleco1IqUQc"
+      },
+      "source": [
+        "El modelo Bagging con 10 árboles de clasificación y max_features igual a log(n_features) muestra un desempeño significativamente mejor que el modelo Bagging manual creado anteriormente. Tanto el RMSE como el MAE son  más bajos, lo que indica que el modelo está prediciendo mejor los precios de los automóviles.\n",
+        "\n",
+        "En comparación con el árbol de decisión simple, el modelo Bagging muestra una mejora significativa en la precisión de la predicción. Esto sugiere que el modelo Bagging es una mejor opción para este problema y que el ajuste de hiperparámetros es esencial para mejorar el desempeño del modelo."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qwUDWbdqPt9-"
+      },
+      "source": [
+        "### Punto 4 - Random forest con librería\n",
+        "\n",
+        "En la celda 4, usando la librería sklearn entrenen un modelo de Randon Forest para regresión  y comenten sobre el desempeño del modelo."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AwyxlbZmPt9-",
+        "outputId": "439d088b-b08e-4150-a288-3d4cd045cd4a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 1762.2342949305003\n",
+            "MAE: 1311.228249038875\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Celda 4\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "\n",
+        "# Definición de modelo Random Forest para un problema de regresión\n",
+        "rf = RandomForestRegressor()\n",
+        "rf.fit(X_train,y_train)\n",
+        "y_pred=rf.predict(X_test)\n",
+        "\n",
+        "#Impresión de desempeño del modelo\n",
+        "rmser=  np.sqrt(mean_squared_error(y_pred, y_test))\n",
+        "print('RMSE:', rmser)\n",
+        "maer = mean_absolute_error(y_pred, y_test)\n",
+        "print('MAE:', maer)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CGijKjGoxLmE"
+      },
+      "source": [
+        "Los resultados del modelo de Random Forest no son mejores que los del modelo Bagging con los mismos parámetros. Esto indica que el modelo de Random Forest no es una buena opción para este problema y que no puede proporcionar una mayor precisión de predicción en comparación con los modelos probados anteriormente.\n",
+        "\n",
+        "El modelo Random Forest no pudo proporcionar una mayor precisión de predicción y no es una buena opción cuando se requiere una mayor precisión y generalización del modelo."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LpsEvh3lPt9_"
+      },
+      "source": [
+        "### Punto 5 - Calibración de parámetros Random forest\n",
+        "\n",
+        "En la celda 5, calibren los parámetros max_depth, max_features y n_estimators del modelo de Randon Forest para regresión, comenten sobre el desempeño del modelo y describan cómo cada parámetro afecta el desempeño del modelo."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "id": "LAEodLZ9Pt9_"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import GridSearchCV\n",
+        "\n",
+        "# Definición de los valores de los parámetros a explorar\n",
+        "param_grid = {\n",
+        "    'max_depth': [5, 10, 15, 20],\n",
+        "    'max_features': [int(np.log(X_train.shape[1])), 'sqrt', 'log2'],\n",
+        "    'n_estimators': [10, 50, 100, 200]\n",
+        "}\n",
+        "\n",
+        "# Creación del modelo Random Forest\n",
+        "rf = RandomForestClassifier(random_state=42)\n",
+        "# Búsqueda de los mejores parámetros\n",
+        "rf_cv = GridSearchCV(rf, param_grid, cv=5)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7b-t61GnDQ6X",
+        "outputId": "1c9095ef-eca7-48ac-eaf3-ef5c76743446"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 1760.2374696019947\n",
+            "MAE: 1310.8053317328438\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "rmsefc = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+        "maefc = mean_absolute_error(y_test, y_pred)\n",
+        "\n",
+        "print('RMSE:', rmsefc)\n",
+        "print('MAE:', maefc)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bbPT3ACFDW2i"
+      },
+      "source": [
+        "Los mejores parámetros encontrados son max_depth=15, max_features=3 y n_estimators=200. Estos valores indican que un modelo con una profundidad máxima de 15, utilizando 3 características al azar para cada árbol y 200 árboles en el modelo, es el mejor para este problema.\n",
+        "\n",
+        "El resultado del modelo es ligeramente mejor que los modelos anteriores, al ver el RMSE y el MAE. La calibración de los parámetros permitió mejorar la precisión del modelo.\n",
+        "\n",
+        "Cada parámetro afecta el desempeño del modelo de la siguiente manera:\n",
+        "\n",
+        "1. max_depth: controla la profundidad máxima del árbol de decisión. Un valor más alto permite que el modelo capture relaciones más complejas en los datos de entrenamiento, pero también puede conducir a sobreajuste. En este caso, un valor de 15 para max_depth es óptimo para el modelo.\n",
+        "\n",
+        "2. max_features: controla la cantidad de características a considerar en cada división del árbol. Un valor más alto aumenta la complejidad del modelo, pero también puede aumentar la varianza. Los valores posibles son int(np.log(X_train.shape[1])), 'sqrt' y 'log2', que indican cuántas características se seleccionan al azar para cada árbol. En este caso, un valor de 3 para max_features es óptimo.\n",
+        "\n",
+        "3. n_estimators: controla el número de árboles en el modelo. Un valor más alto aumenta la precisión del modelo, pero también aumenta el tiempo de entrenamiento. En este caso, un valor de 200 para n_estimators"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HWJFK_mvPt9_"
+      },
+      "source": [
+        "### Punto 6 - XGBoost con librería\n",
+        "\n",
+        "En la celda 6 implementen un modelo XGBoost de regresión con la librería sklearn y comenten sobre el desempeño del modelo."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pUIw3pVzPt9_",
+        "outputId": "59318c6a-ab3b-4362-c458-75e888f562e0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 1621.4197004256812\n",
+            "MAE: 1186.634392366123\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Celda 6\n",
+        "from xgboost import XGBRegressor\n",
+        "\n",
+        "# Definición de modelo XGBoost para un problema de regresión\n",
+        "clf = XGBRegressor()\n",
+        "clf\n",
+        "\n",
+        "clf.fit(X_train, y_train)\n",
+        "y_pred = clf.predict(X_test)\n",
+        "\n",
+        "rmseXB =  np.sqrt(mean_squared_error(y_pred, y_test))\n",
+        "print('RMSE:', np.sqrt(mean_squared_error(y_pred, y_test)))\n",
+        "maeXB = mean_absolute_error(y_pred, y_test)\n",
+        "print('MAE:', mean_absolute_error(y_pred, y_test))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r1oVN3itPVYp"
+      },
+      "source": [
+        "El modelo XGBoost de regresión con la librería sklearn tiene un RMSE y un MAE que indican que el modelo tiene un desempeño mejor en la predicción del precio de los automóviles con respecto al random forest. Estos resultados se pueden mejorarse mediante la optimización de los hiperparámetros y la selección de características adecuadas."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "twmPaKMFPt9_"
+      },
+      "source": [
+        "### Punto 7 - Calibración de parámetros XGBoost\n",
+        "\n",
+        "En la celda 7 calibren los parámetros learning rate, gamma y colsample_bytree del modelo XGBoost para regresión, comenten sobre el desempeño del modelo y describan cómo cada parámetro afecta el desempeño del modelo."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "id": "rR1LAQcIjLB0"
+      },
+      "outputs": [],
+      "source": [
+        "# Celda 7\n",
+        "from xgboost import XGBRegressor\n",
+        "from sklearn.model_selection import GridSearchCV\n",
+        "\n",
+        "# Definir el modelo base\n",
+        "model = XGBRegressor()\n",
+        "\n",
+        "# Definir los parámetros a calibrar\n",
+        "params = {\n",
+        "    'learning_rate': [0.01, 0.1, 0.5],\n",
+        "    'gamma': [0, 0.1, 1],\n",
+        "    'colsample_bytree': [0.5, 0.8, 1]\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pFHQR4xduRIp",
+        "outputId": "f6f5803c-68e1-462f-9689-b9caca4a133c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Best Parameters:  {'colsample_bytree': 0.5, 'gamma': 0, 'learning_rate': 0.1}\n",
+            "Best Score:  2284814.1558396956\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Realizar la búsqueda de parámetros óptimos\n",
+        "grid = GridSearchCV(model, params, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n",
+        "grid.fit(X_train, y_train)\n",
+        "\n",
+        "# Imprimir los mejores parámetros y el mejor score obtenido\n",
+        "print(\"Best Parameters: \", grid.best_params_)\n",
+        "print(\"Best Score: \", -grid.best_score_)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ggcpe-HxjaYW",
+        "outputId": "b86a0b9c-070b-475e-d6f8-d1ac7523aab7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "xgboost RMSE: 1614.9339355118796\n",
+            "xgboost MAE: 1182.2714202387504\n"
+          ]
+        }
+      ],
+      "source": [
+        "import xgboost as xgb\n",
+        "\n",
+        "# Definir modelo con los parámetros óptimos\n",
+        "xgb_model = xgb.XGBRegressor(n_estimators=1000, learning_rate=0.1, gamma=0, colsample_bytree=0.5)\n",
+        "\n",
+        "# Entrenamiento del modelo\n",
+        "xgb_model.fit(X_train, y_train)\n",
+        "\n",
+        "# Predicción del modelo en datos de test\n",
+        "y_pred = xgb_model.predict(X_test)\n",
+        "\n",
+        "# Cálculo del RMSE y MAE\n",
+        "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+        "mae = mean_absolute_error(y_test, y_pred)\n",
+        "\n",
+        "print(\"xgboost RMSE:\", rmse)\n",
+        "print(\"xgboost MAE:\", mae)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EwPMyvs-XfcU"
+      },
+      "source": [
+        "El parámetro learning_rate controla la tasa de aprendizaje del modelo. Un learning_rate alto hace que el modelo aprenda más rápido, pero puede llevar a sobreajuste. Por otro lado, un learning_rate bajo hace que el modelo aprenda más lentamente, pero puede llevar a una mejor generalización. \n",
+        "\n",
+        "El parámetro gamma controla la cantidad mínima de reducción de pérdida necesaria para que se produzca una división adicional en un nodo hoja del árbol. Un valor alto de gamma hace que el modelo sea más conservador en la creación de nuevas divisiones, lo que puede evitar el sobreajuste. \n",
+        "\n",
+        "El parámetro colsample_bytree controla la fracción de columnas que se muestrean al construir cada árbol. Un valor alto de colsample_bytree hace que el modelo tenga más variedad en los árboles, lo que puede reducir el sobreajuste. \n",
+        "\n",
+        "En general, se espera que una calibración adecuada de los parámetros pueda mejorar el desempeño del modelo."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fjHl_VOVPt-A"
+      },
+      "source": [
+        "### Punto 8 - Comparación y análisis de resultados\n",
+        "En la celda 8 comparen los resultados obtenidos de los diferentes modelos (random forest y XGBoost) y comenten las ventajas del mejor modelo y las desventajas del modelo con el menor desempeño."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1jUPBjcjPt-A",
+        "outputId": "9717a6be-287f-4cd6-b119-5a58e1feae00"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Random forest con librería RMSE: 1762.2342949305003\n",
+            "Random forest Calibrado RMSE: 1760.2374696019947\n",
+            "Xgboost RMSE: 1621.4197004256812\n",
+            "Xgboost Calibrado RMSE: 1614.9339355118796\n",
+            " \n",
+            "Random forest con librería MAE : 1311.228249038875\n",
+            "Random forest Calibrado MAE: 1310.8053317328438\n",
+            "Xgboost MAE: 1186.634392366123\n",
+            "Xgboost Calibrado MAE: 1182.2714202387504\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Celda 8\n",
+        "print('Random forest con librería RMSE:', rmser)\n",
+        "print('Random forest Calibrado RMSE:', rmsefc)\n",
+        "print('Xgboost RMSE:', rmseXB)\n",
+        "print(\"Xgboost Calibrado RMSE:\", rmse)\n",
+        "print(\" \")\n",
+        "print('Random forest con librería MAE :', maer)\n",
+        "print('Random forest Calibrado MAE:', maefc)\n",
+        "print('Xgboost MAE:', maeXB)\n",
+        "print(\"Xgboost Calibrado MAE:\", mae)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SpzwL_e-iUih"
+      },
+      "source": [
+        "El mejor modelo en términos de desempeño es el modelo XGBoost de regresión calibrado, ya que tiene un RMSE de 1614.93 y un MAE de 1182.271, los cuales son más bajos que el modelo Random Forest de regresión con librería y calibrado, y más bajos que el XG Boost con librería. Además, XGBoost es conocido por su capacidad para manejar grandes cantidades de datos y por ser más rápido y escalable que Random Forest. Otra de las ventajas de XGBoost es que es menos propenso a sobreajuste y además puede manejar variables categóricas y variables de texto.\n",
+        "\n",
+        "Por otro lado, una desventaja del modelo Random Forest es que puede ser propenso al sobreajuste (overfitting) si no se ajustan adecuadamente los parámetros, ya que cada árbol de decisión en el bosque se entrena en una muestra aleatoria del conjunto de datos. Además, en general, los modelos basados en árboles de decisión como Random Forest pueden ser menos interpretables que otros modelos, lo que puede dificultar la comprensión de los resultados por parte de los usuarios."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.12"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Taller: Construcción e implementación de modelos Bagging, Random Forest y XGBoost\n",
-    "\n",
-    "En este taller podrán poner en práctica sus conocimientos sobre la construcción e implementación de modelos de Bagging, Random Forest y XGBoost. El taller está constituido por 8 puntos, en los cuales deberan seguir las intrucciones de cada numeral para su desarrollo."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Datos predicción precio de automóviles\n",
-    "\n",
-    "En este taller se usará el conjunto de datos de Car Listings de Kaggle donde cada observación representa el precio de un automóvil teniendo en cuenta distintas variables como año, marca, modelo, entre otras. El objetivo es predecir el precio del automóvil. Para más detalles puede visitar el siguiente enlace: [datos](https://www.kaggle.com/jpayne/852k-used-car-listings)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Importación de librerías\n",
-    "%matplotlib inline\n",
-    "import pandas as pd\n",
-    "\n",
-    "# Lectura de la información de archivo .csv\n",
-    "data = pd.read_csv('https://raw.githubusercontent.com/albahnsen/MIAD_ML_and_NLP/main/datasets/dataTrain_carListings.zip')\n",
-    "\n",
-    "# Preprocesamiento de datos para el taller\n",
-    "data = data.loc[data['Model'].str.contains('Camry')].drop(['Make', 'State'], axis=1)\n",
-    "data = data.join(pd.get_dummies(data['Model'], prefix='M'))\n",
-    "data = data.drop(['Model'], axis=1)\n",
-    "\n",
-    "# Visualización dataset\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Separación de variables predictoras (X) y variable de interés (y)\n",
-    "y = data['Price']\n",
-    "X = data.drop(['Price'], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Separación de datos en set de entrenamiento y test\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Punto 1 - Árbol de decisión manual\n",
-    "\n",
-    "En la celda 1 creen un árbol de decisión **manualmente**  que considere los set de entrenamiento y test definidos anteriormente y presenten el RMSE y MAE del modelo en el set de test."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Celda 1\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Punto 2 - Bagging manual\n",
-    "\n",
-    "En la celda 2 creen un modelo bagging **manualmente** con 10 árboles de clasificación y comenten sobre el desempeño del modelo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Celda 2\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Punto 3 - Bagging con librería\n",
-    "\n",
-    "En la celda 3, con la librería sklearn, entrenen un modelo bagging con 10 árboles de clasificación y el parámetro `max_features` igual a `log(n_features)` y comenten sobre el desempeño del modelo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Celda 3\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Punto 4 - Random forest con librería\n",
-    "\n",
-    "En la celda 4, usando la librería sklearn entrenen un modelo de Randon Forest para clasificación  y comenten sobre el desempeño del modelo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Celda 4\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Punto 5 - Calibración de parámetros Random forest\n",
-    "\n",
-    "En la celda 5, calibren los parámetros max_depth, max_features y n_estimators del modelo de Randon Forest para clasificación, comenten sobre el desempeño del modelo y describan cómo cada parámetro afecta el desempeño del modelo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Celda 5\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Punto 6 - XGBoost con librería\n",
-    "\n",
-    "En la celda 6 implementen un modelo XGBoost de clasificación con la librería sklearn y comenten sobre el desempeño del modelo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Celda 6\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Punto 7 - Calibración de parámetros XGBoost\n",
-    "\n",
-    "En la celda 7 calibren los parámetros learning rate, gamma y colsample_bytree del modelo XGBoost para clasificación, comenten sobre el desempeño del modelo y describan cómo cada parámetro afecta el desempeño del modelo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Celda 7\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Punto 8 - Comparación y análisis de resultados\n",
-    "En la celda 8 comparen los resultados obtenidos de los diferentes modelos (random forest y XGBoost) y comenten las ventajas del mejor modelo y las desventajas del modelo con el menor desempeño."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Celda 8\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Semana 3/model_deploy/api.py b/Semana 3/model_deploy/api.py
new file mode 100644
index 0000000..bffb834
--- /dev/null
+++ b/Semana 3/model_deploy/api.py	
@@ -0,0 +1,40 @@
+from flask import Flask
+from flask_restx import Api, Resource, fields
+from model_deployment import predict, CategoricalEncoder, DataFrameSelector
+
+app = Flask(__name__)
+
+api = Api(
+    app,
+    version='1.0',
+    title='Prediction API',
+    description='Prediction API')
+
+ns = api.namespace('predict',
+                   description='Regressor')
+
+parser = api.parser()
+
+parser.add_argument('Year', type=int, required=True, help='Year', location='args')
+parser.add_argument('Mileage', type=int, required=True, help='Mileage', location='args')
+parser.add_argument('State', type=str, required=True, help='State', location='args')
+parser.add_argument('Make', type=str, required=True, help='Make', location='args')
+parser.add_argument('Model', type=str, required=True, help='Model', location='args')
+
+resource_fields = api.model('Resource', {
+    'result': fields.String,
+})
+
+
+@ns.route('/')
+class ModelApi(Resource):
+
+    @api.doc(parser=parser)
+    @api.marshal_with(resource_fields)
+    def get(self):
+        args = parser.parse_args()
+        return {"result": predict(args)}, 200
+
+
+if __name__ == '__main__':
+    app.run(debug=True, use_reloader=False, host='0.0.0.0', port=5000)
\ No newline at end of file
diff --git a/Semana 3/model_deploy/model_deployment.py b/Semana 3/model_deploy/model_deployment.py
new file mode 100644
index 0000000..b8751f9
--- /dev/null
+++ b/Semana 3/model_deploy/model_deployment.py	
@@ -0,0 +1,134 @@
+#!/usr/bin/python
+
+import pandas as pd
+import numpy as np
+import joblib
+
+
+
+# funciones util
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.utils import check_array
+from sklearn.preprocessing import LabelEncoder
+from scipy import sparse
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import FeatureUnion
+
+class CategoricalEncoder(BaseEstimator, TransformerMixin):
+    def __init__(self, encoding='onehot', categories='auto', dtype=np.float64,
+                 handle_unknown='error'):
+        self.encoding = encoding
+        self.categories = categories
+        self.dtype = dtype
+        self.handle_unknown = handle_unknown
+
+    def fit(self, X, y=None):
+        if self.encoding not in ['onehot', 'onehot-dense', 'ordinal']:
+            template = ("encoding should be either 'onehot', 'onehot-dense' "
+                        "or 'ordinal', got %s")
+            raise ValueError(template % self.handle_unknown)
+
+        if self.handle_unknown not in ['error', 'ignore']:
+            template = ("handle_unknown should be either 'error' or "
+                        "'ignore', got %s")
+            raise ValueError(template % self.handle_unknown)
+
+        if self.encoding == 'ordinal' and self.handle_unknown == 'ignore':
+            raise ValueError("handle_unknown='ignore' is not supported for"
+                             " encoding='ordinal'")
+
+        X = check_array(X, dtype=object, accept_sparse='csc', copy=True)
+        n_samples, n_features = X.shape
+
+        self._label_encoders_ = [LabelEncoder() for _ in range(n_features)]
+
+        for i in range(n_features):
+            le = self._label_encoders_[i]
+            Xi = X[:, i]
+            if self.categories == 'auto':
+                le.fit(Xi)
+            else:
+                valid_mask = np.in1d(Xi, self.categories[i])
+                if not np.all(valid_mask):
+                    if self.handle_unknown == 'error':
+                        diff = np.unique(Xi[~valid_mask])
+                        msg = ("Found unknown categories {0} in column {1}"
+                               " during fit".format(diff, i))
+                        raise ValueError(msg)
+                le.classes_ = np.array(np.sort(self.categories[i]))
+
+        self.categories_ = [le.classes_ for le in self._label_encoders_]
+
+        return self
+
+    def transform(self, X):
+        X = check_array(X, accept_sparse='csc', dtype=object, copy=True)
+        n_samples, n_features = X.shape
+        X_int = np.zeros_like(X, dtype=int)
+        X_mask = np.ones_like(X, dtype=bool)
+
+        for i in range(n_features):
+            valid_mask = np.in1d(X[:, i], self.categories_[i])
+
+            if not np.all(valid_mask):
+                if self.handle_unknown == 'error':
+                    diff = np.unique(X[~valid_mask, i])
+                    msg = ("Found unknown categories {0} in column {1}"
+                           " during transform".format(diff, i))
+                    raise ValueError(msg)
+                else:
+                    # Set the problematic rows to an acceptable value and
+                    # continue `The rows are marked `X_mask` and will be
+                    # removed later.
+                    X_mask[:, i] = valid_mask
+                    X[:, i][~valid_mask] = self.categories_[i][0]
+            X_int[:, i] = self._label_encoders_[i].transform(X[:, i])
+
+        if self.encoding == 'ordinal':
+            return X_int.astype(self.dtype, copy=False)
+
+        mask = X_mask.ravel()
+        n_values = [cats.shape[0] for cats in self.categories_]
+        n_values = np.array([0] + n_values)
+        indices = np.cumsum(n_values)
+
+        column_indices = (X_int + indices[:-1]).ravel()[mask]
+        row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),
+                                n_features)[mask]
+        data = np.ones(n_samples * n_features)[mask]
+
+        out = sparse.csc_matrix((data, (row_indices, column_indices)),
+                                shape=(n_samples, indices[-1]),
+                                dtype=self.dtype).tocsr()
+        if self.encoding == 'onehot-dense':
+            return out.toarray()
+        else:
+            return out
+
+class DataFrameSelector(BaseEstimator, TransformerMixin):
+    def __init__(self, attribute_names):
+        self.attribute_names = attribute_names
+    def fit(self, X, y=None):
+        return self
+    def transform(self, X):
+        return X[self.attribute_names]
+
+
+def predict(args):
+    model = joblib.load('model_xgb.pkl')
+    print(model)
+    year = args['Year']
+    mileage = args['Mileage']
+    state = args['State']
+    make = args['Make']
+    car_model = args['Model']
+
+    input_df = pd.DataFrame(np.array([[year, mileage, state, make, car_model]]),
+                            columns=['Year', 'Mileage', 'State', 'Make', 'Model'])
+
+    # Make prediction
+    prediction = model.predict(input_df)
+    print(f'Predicted price: {prediction}')
+    return prediction
diff --git a/model_deployment/Price_Car_Grupo4.pkl b/model_deployment/Price_Car_Grupo4.pkl
new file mode 100644
index 0000000..4b6a3aa
Binary files /dev/null and b/model_deployment/Price_Car_Grupo4.pkl differ
diff --git a/model_deployment/api.py b/model_deployment/api.py
index ec65080..b24199a 100644
--- a/model_deployment/api.py
+++ b/model_deployment/api.py
@@ -1,45 +1,69 @@
 #!/usr/bin/python
 from flask import Flask
-from flask_restplus import Api, Resource, fields
+from flask_restx import Api, Resource, fields
 import joblib
-from m09_model_deployment import predict_proba
+from flask_cors import CORS
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import cross_val_score
+from sklearn.preprocessing import OrdinalEncoder
 
 app = Flask(__name__)
+CORS(app)  # Enable CORS for all routes and origins
 
 api = Api(
-    app, 
-    version='1.0', 
-    title='Phishing Prediction API',
-    description='Phishing Prediction API')
+    app,
+    version='1.0',
+    title='Predicción del precio de carro usado',
+    description='Predicción del precio de carro usado')
 
-ns = api.namespace('predict', 
-     description='Phishing Classifier')
+ns = api.namespace('predict',
+     description='Valor del precio del carro a predecir')
    
 parser = api.parser()
 
 parser.add_argument(
-    'URL', 
-    type=str, 
-    required=True, 
-    help='URL to be analyzed', 
-    location='args')
+    'Year', type=int, required=True, help='Year', location='args')
+parser.add_argument(
+    'Mileage', type=int, required=True, help='Mileage', location='args')
+parser.add_argument(
+    'State', type=str, required=True, help='State', location='args')
+parser.add_argument(
+    'Make', type=str, required=True, help='Make', location='args')
+parser.add_argument(
+    'Model', type=str, required=True, help='Model', location='args')
 
 resource_fields = api.model('Resource', {
-    'result': fields.String,
+    'result': fields.Float,
 })
 
+def predict_price(url):
+
+    #clf = joblib.load('Price_Car_Grupo4.pkl') 
+    clf = joblib.load('Price_Car_Grupo4.pkl')
+    #a = url.split('-')
+    url_ = pd.DataFrame(url).transpose()
+    url_.columns=['Year', 'Mileage', 'State', 'Make', 'Model']
+    url_[['Year', 'Mileage']]=url_[['Year', 'Mileage']].astype(float)
+    enc = OrdinalEncoder()
+    url_[['State','Make','Model']] = enc.fit_transform(url_[['State','Make','Model']])
+    p1= clf.predict(url_)
+
+    return p1
+
 @ns.route('/')
-class PhishingApi(Resource):
+class CarPrice(Resource):
 
     @api.doc(parser=parser)
     @api.marshal_with(resource_fields)
     def get(self):
         args = parser.parse_args()
-        
+        features = [args['Year'], args['Mileage'], args['State'], args['Make'], args['Model']]
+       
         return {
-         "result": predict_proba(args['URL'])
+            "result": predict_price(features)
         }, 200
-    
+ 
     
 if __name__ == '__main__':
-    app.run(debug=True, use_reloader=False, host='0.0.0.0', port=8888)
+    app.run(debug=True, use_reloader=False, host='0.0.0.0', port=5000)
diff --git a/model_deployment/m09_model_deployment.py b/model_deployment/m09_model_deployment.py
index de992fd..63510ec 100644
--- a/model_deployment/m09_model_deployment.py
+++ b/model_deployment/m09_model_deployment.py
@@ -4,28 +4,34 @@
 import joblib
 import sys
 import os
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import cross_val_score
+import joblib
+import os
+os.chdir('..')
+from xgboost import XGBRegressor
+from sklearn.preprocessing import OrdinalEncoder
+
+
+# Carga de datos de archivo .csv
+dataTraining = pd.read_csv('https://raw.githubusercontent.com/davidzarruk/MIAD_ML_NLP_2023/main/datasets/dataTrain_carListings.zip')
+dataTesting = pd.read_csv('https://raw.githubusercontent.com/davidzarruk/MIAD_ML_NLP_2023/main/datasets/dataTest_carListings.zip', index_col=0)
 
-def predict_proba(url):
+dataTotal= pd.concat([dataTraining,dataTesting], axis=0)
+enc = OrdinalEncoder()
+dataTotal[['State','Make','Model']] = enc.fit_transform(dataTotal[['State','Make','Model']])
 
-    clf = joblib.load(os.path.dirname(__file__) + '/phishing_clf.pkl') 
+X=dataTotal.iloc[:400000,:].drop(['Price'], axis=1)
+y=dataTraining['Price']
 
-    url_ = pd.DataFrame([url], columns=['url'])
-  
-    # Create features
-    keywords = ['https', 'login', '.php', '.html', '@', 'sign']
-    for keyword in keywords:
-        url_['keyword_' + keyword] = url_.url.str.contains(keyword).astype(int)
+XTest=dataTotal.iloc[400000:,:].drop(['Price'], axis=1)
 
-    url_['lenght'] = url_.url.str.len() - 2
-    domain = url_.url.str.split('/', expand=True).iloc[:, 2]
-    url_['lenght_domain'] = domain.str.len()
-    url_['isIP'] = (url_.url.str.replace('.', '') * 1).str.isnumeric().astype(int)
-    url_['count_com'] = url_.url.str.count('com')
+clf = XGBRegressor(max_depth=10, n_estimators=100, gamma=0, learning_rate=0.2,random_state=1)
+clf.fit(X, y)
 
-    # Make prediction
-    p1 = clf.predict_proba(url_.drop('url', axis=1))[0,1]
 
-    return p1
+joblib.dump(clf, 'Price_Car_Grupo4.pkl', compress=3)
 
 
 if __name__ == "__main__":
diff --git a/model_deployment/phishing_clf.pkl b/model_deployment/phishing_clf.pkl
index 75ab711..9f27f5c 100644
Binary files a/model_deployment/phishing_clf.pkl and b/model_deployment/phishing_clf.pkl differ
diff --git a/modelo2/Price_Car_Grupo4.pkl b/modelo2/Price_Car_Grupo4.pkl
new file mode 100644
index 0000000..03eb939
Binary files /dev/null and b/modelo2/Price_Car_Grupo4.pkl differ
diff --git a/modelo2/api.py b/modelo2/api.py
new file mode 100644
index 0000000..b24199a
--- /dev/null
+++ b/modelo2/api.py
@@ -0,0 +1,69 @@
+#!/usr/bin/python
+from flask import Flask
+from flask_restx import Api, Resource, fields
+import joblib
+from flask_cors import CORS
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import cross_val_score
+from sklearn.preprocessing import OrdinalEncoder
+
+app = Flask(__name__)
+CORS(app)  # Enable CORS for all routes and origins
+
+api = Api(
+    app,
+    version='1.0',
+    title='Predicción del precio de carro usado',
+    description='Predicción del precio de carro usado')
+
+ns = api.namespace('predict',
+     description='Valor del precio del carro a predecir')
+   
+parser = api.parser()
+
+parser.add_argument(
+    'Year', type=int, required=True, help='Year', location='args')
+parser.add_argument(
+    'Mileage', type=int, required=True, help='Mileage', location='args')
+parser.add_argument(
+    'State', type=str, required=True, help='State', location='args')
+parser.add_argument(
+    'Make', type=str, required=True, help='Make', location='args')
+parser.add_argument(
+    'Model', type=str, required=True, help='Model', location='args')
+
+resource_fields = api.model('Resource', {
+    'result': fields.Float,
+})
+
+def predict_price(url):
+
+    #clf = joblib.load('Price_Car_Grupo4.pkl') 
+    clf = joblib.load('Price_Car_Grupo4.pkl')
+    #a = url.split('-')
+    url_ = pd.DataFrame(url).transpose()
+    url_.columns=['Year', 'Mileage', 'State', 'Make', 'Model']
+    url_[['Year', 'Mileage']]=url_[['Year', 'Mileage']].astype(float)
+    enc = OrdinalEncoder()
+    url_[['State','Make','Model']] = enc.fit_transform(url_[['State','Make','Model']])
+    p1= clf.predict(url_)
+
+    return p1
+
+@ns.route('/')
+class CarPrice(Resource):
+
+    @api.doc(parser=parser)
+    @api.marshal_with(resource_fields)
+    def get(self):
+        args = parser.parse_args()
+        features = [args['Year'], args['Mileage'], args['State'], args['Make'], args['Model']]
+       
+        return {
+            "result": predict_price(features)
+        }, 200
+ 
+    
+if __name__ == '__main__':
+    app.run(debug=True, use_reloader=False, host='0.0.0.0', port=5000)
diff --git a/modelo2/m09_model_deployment.py b/modelo2/m09_model_deployment.py
new file mode 100644
index 0000000..0bfa0c6
--- /dev/null
+++ b/modelo2/m09_model_deployment.py
@@ -0,0 +1,52 @@
+#!/usr/bin/python
+
+import pandas as pd
+import joblib
+import sys
+import os
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import cross_val_score
+import joblib
+import os
+os.chdir('..')
+from xgboost import XGBRegressor
+from sklearn.preprocessing import OrdinalEncoder
+
+
+# Carga de datos de archivo .csv
+dataTraining = pd.read_csv('https://raw.githubusercontent.com/davidzarruk/MIAD_ML_NLP_2023/main/datasets/dataTrain_carListings.zip')
+dataTesting = pd.read_csv('https://raw.githubusercontent.com/davidzarruk/MIAD_ML_NLP_2023/main/datasets/dataTest_carListings.zip', index_col=0)
+
+dataTotal= pd.concat([dataTraining,dataTesting], axis=0)
+enc = OrdinalEncoder()
+dataTotal[['State','Make','Model']] = enc.fit_transform(dataTotal[['State','Make','Model']])
+
+X=dataTotal.iloc[:400000,:].drop(['Price'], axis=1)
+y=dataTraining['Price']
+
+XTest=dataTotal.iloc[400000:,:].drop(['Price'], axis=1)
+
+clf = XGBRegressor(colsample_bytree=0.6000000000000001,gamma=25.69025797151704,
+      learning_rate=0.04660442516384383,max_depth=12,min_child_weight=6,n_estimators=597, subsample=0.9911830515002495,n_jobs=1)
+
+clf.fit(X, y)
+
+
+joblib.dump(clf, 'Price_Car_Grupo4.pkl', compress=3)
+
+
+if __name__ == "__main__":
+    
+    if len(sys.argv) == 1:
+        print('Please add an URL')
+        
+    else:
+
+        url = sys.argv[1]
+
+        p1 = predict_proba(url)
+        
+        print(url)
+        print('Probability of Phishing: ', p1)
+        
\ No newline at end of file