Practica1_MineríaDeDatos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6206a25d",
   "metadata": {},
   "source": [
    "### <span style=\"color:#20B2AA\"> Máster en Tecnologías de Análisis de Datos Masivos: Big Data\n",
    "***\n",
    "Minería de datos                                                                 <br>\n",
    "Profesor: Juan Carlos Vidal Aguiar                                                                                  <br>\n",
    "Práctica 1                                                                                                              <br>\n",
    "<br>\n",
    "Alumna: Irlui Ramírez Hernández                                                                                           <br>\n",
    "Febrero 2023                                                                                                                <br> \n",
    "******* "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de6cff8",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#20B2AA\">   Librerías a implementar           <br>\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "8ec2f456",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import missingno as mi\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#Imputar valores ausentes\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.impute import KNNImputer\n",
    "#Estandarizar con MIN-MAX y Z-Score\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "#Balanceo submuestreo y sobremuestreo\n",
    "from sklearn.utils import resample\n",
    "# Balanceo SMOTE\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.combine import SMOTEENN\n",
    "from imblearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95f634d",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#20B2AA\">   Extracción de los datos del fichero echocardiogram.data           <br>\n",
    "1. Importar la tabla correctamente. Esto implica que las columnas deben tener definidas \n",
    "correctamente su nombre y tipo de dato. Se debe mostrar la estructura de la tabla. \n",
    "• Los nombres de las columnas mencionadas en el fichero echocardiogram.names \n",
    "deben tener los siguientes nombres: Survival, StillAlive, AgeAttack, PericardEffu, \n",
    "FracShort, EPSS, LVDD, WMS, WMI, Mult, Name, Group, AliveAt1. \n",
    "2. Generar un fichero en formato CSV con la tabla importada y transformada \n",
    "correctamente. La tabla tendrá el mismo nombre y la extensión será CSV. El fichero \n",
    "creado se deberá entregar junto con la memoria. \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2f79aca",
   "metadata": {},
   "source": [
    "##### <span style=\"color:#20B2AA\">Pasos:           <br>\n",
    "Paso 1: \"Nombramos las columnas\"    <br>\n",
    "Paso 2: \"Cargar los datos del archivo .date con la librería Pandas\"    <br>\n",
    "Paso 3: \"Asignamos las columnas al dataframe datos\"    <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "2796491b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ruta_archivo = \"https://raw.githubusercontent.com/IrluiDev/BigData/main/echocardiogram.data\"\n",
    "usercols = [\"Survival\", \"StillAlive\", \"AgeAttack\", \"PericardEffu\", \n",
    "\"FracShort\", \"EPSS\", \"LVDD\", \"WMS\", \"WMI\", \"Mult\", \"Name\", \"Group\", \"AliveAt1\"]\n",
    "datos = pd.read_csv(ruta_archivo, header = None)\n",
    "datos.columns = usercols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e055fcda",
   "metadata": {},
   "source": [
    "##### <span style=\"color:#20B2AA\">Pasos:           <br>\n",
    "Paso 4: \"Generamos un archivo .csv con el nombre echocardiogram y mostramos el dataframe\"    <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "22b6dc87",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
       "      <th>AgeAttack</th>\n",
       "      <th>PericardEffu</th>\n",
       "      <th>FracShort</th>\n",
       "      <th>EPSS</th>\n",
       "      <th>LVDD</th>\n",
       "      <th>WMS</th>\n",
       "      <th>WMI</th>\n",
       "      <th>Mult</th>\n",
       "      <th>Name</th>\n",
       "      <th>Group</th>\n",
       "      <th>AliveAt1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0.260</td>\n",
       "      <td>9</td>\n",
       "      <td>4.600</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>name</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0.380</td>\n",
       "      <td>6</td>\n",
       "      <td>4.100</td>\n",
       "      <td>14</td>\n",
       "      <td>1.700</td>\n",
       "      <td>0.588</td>\n",
       "      <td>name</td>\n",
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       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>3.420</td>\n",
       "      <td>14</td>\n",
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       "      <td>1</td>\n",
       "      <td>name</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.253</td>\n",
       "      <td>12.062</td>\n",
       "      <td>4.603</td>\n",
       "      <td>16</td>\n",
       "      <td>1.450</td>\n",
       "      <td>0.788</td>\n",
       "      <td>name</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.160</td>\n",
       "      <td>22</td>\n",
       "      <td>5.750</td>\n",
       "      <td>18</td>\n",
       "      <td>2.250</td>\n",
       "      <td>0.571</td>\n",
       "      <td>name</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Survival StillAlive AgeAttack PericardEffu FracShort    EPSS   LVDD WMS  \\\n",
       "0       11          0        71            0     0.260       9  4.600  14   \n",
       "1       19          0        72            0     0.380       6  4.100  14   \n",
       "2       16          0        55            0     0.260       4  3.420  14   \n",
       "3       57          0        60            0     0.253  12.062  4.603  16   \n",
       "4       19          1        57            0     0.160      22  5.750  18   \n",
       "\n",
       "     WMI   Mult  Name Group AliveAt1  \n",
       "0      1      1  name     1        0  \n",
       "1  1.700  0.588  name     1        0  \n",
       "2      1      1  name     1        0  \n",
       "3  1.450  0.788  name     1        0  \n",
       "4  2.250  0.571  name     1        0  "
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datos.to_csv(\"C:/Users/Irlui ma/OneDrive - Universidade de Santiago de Compostela/Documentos/Máster BD USC/Minería de Datos/practica1/echocardiogram.csv\")\n",
    "datos.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a2d6cc8e",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#20B2AA\">   Análisis de los datos           <br>\n",
    "3. Mostrar la distribución de los valores ausentes tanto por columnas como por filas, en el \n",
    "caso de que existan, tanto a través de una salida de texto como a través de\n",
    "representaciones gráficas. \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caea68ac",
   "metadata": {},
   "source": [
    "##### <span style=\"color:#20B2AA\">Mostrar la distribución de los  datos ausentes:           <br>\n",
    "Para los datos ausentes sustituimos el ? por datos ausentes representados por nan para que calcular la distribución    <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "bcb6c13a",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
       "      <th>AgeAttack</th>\n",
       "      <th>PericardEffu</th>\n",
       "      <th>FracShort</th>\n",
       "      <th>EPSS</th>\n",
       "      <th>LVDD</th>\n",
       "      <th>WMS</th>\n",
       "      <th>WMI</th>\n",
       "      <th>Mult</th>\n",
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       "      <td>name</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <th>127</th>\n",
       "      <td>7.5</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>12.9</td>\n",
       "      <td>4.72</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0.857</td>\n",
       "      <td>name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.28</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.47</td>\n",
       "      <td>11</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.714</td>\n",
       "      <td>name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>7.00</td>\n",
       "      <td>5.05</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.857</td>\n",
       "      <td>name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>16.1</td>\n",
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       "      <td>15</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.786</td>\n",
       "      <td>name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>20</td>\n",
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       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0</td>\n",
       "      <td>4.51</td>\n",
       "      <td>15.5</td>\n",
       "      <td>1.409</td>\n",
       "      <td>0.786</td>\n",
       "      <td>name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>132 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survival StillAlive AgeAttack PericardEffu FracShort    EPSS   LVDD   WMS  \\\n",
       "0         11          0        71            0     0.260       9  4.600    14   \n",
       "1         19          0        72            0     0.380       6  4.100    14   \n",
       "2         16          0        55            0     0.260       4  3.420    14   \n",
       "3         57          0        60            0     0.253  12.062  4.603    16   \n",
       "4         19          1        57            0     0.160      22  5.750    18   \n",
       "..       ...        ...       ...          ...       ...     ...    ...   ...   \n",
       "127      7.5          1        64            0      0.24    12.9   4.72    12   \n",
       "128       41          0        64            0      0.28    5.40   5.47    11   \n",
       "129       36          0        69            0      0.20    7.00   5.05  14.5   \n",
       "130       22          0        57            0      0.14    16.1   4.36    15   \n",
       "131       20          0        62            0      0.15       0   4.51  15.5   \n",
       "\n",
       "       WMI   Mult  Name Group AliveAt1  \n",
       "0        1      1  name     1        0  \n",
       "1    1.700  0.588  name     1        0  \n",
       "2        1      1  name     1        0  \n",
       "3    1.450  0.788  name     1        0  \n",
       "4    2.250  0.571  name     1        0  \n",
       "..     ...    ...   ...   ...      ...  \n",
       "127      1  0.857  name   NaN      NaN  \n",
       "128   1.10  0.714  name   NaN      NaN  \n",
       "129   1.21  0.857  name   NaN      NaN  \n",
       "130   1.36  0.786  name   NaN      NaN  \n",
       "131  1.409  0.786  name   NaN      NaN  \n",
       "\n",
       "[132 rows x 13 columns]"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos = datos.replace('?', math.nan)\n",
    "datos"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5675907a",
   "metadata": {},
   "source": [
    "##### <span style=\"color:#20B2AA\">Contar los valores nulos por columna           <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "bf5b3a42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tabla: Total de datos faltantes por columna:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Survival         2\n",
       "StillAlive       1\n",
       "AgeAttack        5\n",
       "PericardEffu     1\n",
       "FracShort        8\n",
       "EPSS            15\n",
       "LVDD            11\n",
       "WMS              4\n",
       "WMI              1\n",
       "Mult             4\n",
       "Name             0\n",
       "Group           22\n",
       "AliveAt1        58\n",
       "dtype: int64"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Tabla: Total de datos faltantes por columna:\")\n",
    "null_counts = datos.isnull().sum()\n",
    "null_counts"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4c2788e9",
   "metadata": {},
   "source": [
    "<span style=\"color:#20B2AA\">Podemos visualizar la distribución de los nulos por columnas con la siguiente gráfica: <br>\n",
    "\n",
    "La columna con más datos nulos es AliveAt1, la columna de clasificación, seguida de \"Group\" con 22 datos nulos. <br>\n",
    "La columna \"name\" no tiene datos nulos y las columnas con menos datos nulos son: <br>\n",
    "StillAlive, PericardEffu y  WMI<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "109edf49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi.bar(datos)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "673efaed",
   "metadata": {},
   "source": [
    "##### <span style=\"color:#20B2AA\">Contar los valores nulos por filas           <br>\n",
    "Para conseguirlo se construye la matriz de nulos con datos.isnull() y se recorre con un for por fila para contar sus valores nulos, <br>\n",
    "se carga el DataFrame de las filas con nulos únicamente y totalizamos las filas sin nulos, las filas con nulos y el total general de datos nulos. <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     nulosFila\n",
      "24           1\n",
      "25           1\n",
      "26           1\n",
      "27           3\n",
      "28           6\n",
      "..         ...\n",
      "127          2\n",
      "128          2\n",
      "129          2\n",
      "130          2\n",
      "131          2\n",
      "\n",
      "[71 rows x 1 columns]\n",
      "Total valores nulos: 132\n",
      "   - Fila con más valores nulos: #49 con: 9 datos nulos.\n",
      "Total general de filas:\n",
      "   - No Nulos:61 - Con Nulos: 71\n"
     ]
    }
   ],
   "source": [
    "matrizNulos = datos.isnull()\n",
    "acum= 0\n",
    "mayor = 0\n",
    "sinNulos = 0\n",
    "dic = {}\n",
    "for fila in range(len(matrizNulos)):\n",
    "    if (matrizNulos.iloc[fila]==True).sum() > 0:\n",
    "        nulosFila = (matrizNulos.iloc[fila]==True).sum()\n",
    "        if (nulosFila >= mayor): \n",
    "            mayor = nulosFila\n",
    "            filaMayor = fila\n",
    "        dic[fila] = nulosFila\n",
    "        acum = acum + nulosFila\n",
    "    else: sinNulos = sinNulos + 1\n",
    "totalFilasNulos = (len(matrizNulos) - sinNulos)\n",
    "dfNulos = pd.DataFrame.from_dict(dic, orient='index', columns=['nulosFila'])\n",
    "print(dfNulos)\n",
    "print('Total valores nulos: {}\\n   - Fila con más valores nulos: #{} con: {} datos nulos.\\nTotal general de filas:\\n   - No Nulos:{} - Con Nulos: {}'.format(acum, filaMayor, mayor,sinNulos,totalFilasNulos))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5935d798",
   "metadata": {},
   "source": [
    "<span style=\"color:#20B2AA\">Podemos visualizar la distribución de los nulos por filas con la siguiente gráfica:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "2fcb7326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "dfNulos.plot(kind='bar', rot=0, ax=ax)\n",
    "ax.legend(title='Filas')\n",
    "ax.set_xlabel('Fila')\n",
    "ax.set_ylabel('Número de nulos')\n",
    "ax.set_title('Número de nulos por fila')\n",
    "ax.set_xticklabels(dfNulos.index, rotation=45, fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92a48889",
   "metadata": {},
   "source": [
    "<span style=\"color:#20B2AA\">Podemos visualizar la distribución de los nulos por filas y columnas con la siguiente gráfica:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "2b410ab6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GRAFICA #3: VALORES AUSENTES POR COLUMNA Y FILA\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "keyword grid_b is not recognized; valid keywords are ['size', 'width', 'color', 'tickdir', 'pad', 'labelsize', 'labelcolor', 'zorder', 'gridOn', 'tick1On', 'tick2On', 'label1On', 'label2On', 'length', 'direction', 'left', 'bottom', 'right', 'top', 'labelleft', 'labelbottom', 'labelright', 'labeltop', 'labelrotation', 'grid_agg_filter', 'grid_alpha', 'grid_animated', 'grid_antialiased', 'grid_clip_box', 'grid_clip_on', 'grid_clip_path', 'grid_color', 'grid_dash_capstyle', 'grid_dash_joinstyle', 'grid_dashes', 'grid_data', 'grid_drawstyle', 'grid_figure', 'grid_fillstyle', 'grid_gapcolor', 'grid_gid', 'grid_in_layout', 'grid_label', 'grid_linestyle', 'grid_linewidth', 'grid_marker', 'grid_markeredgecolor', 'grid_markeredgewidth', 'grid_markerfacecolor', 'grid_markerfacecoloralt', 'grid_markersize', 'grid_markevery', 'grid_mouseover', 'grid_path_effects', 'grid_picker', 'grid_pickradius', 'grid_rasterized', 'grid_sketch_params', 'grid_snap', 'grid_solid_capstyle', 'grid_solid_joinstyle', 'grid_transform', 'grid_url', 'grid_visible', 'grid_xdata', 'grid_ydata', 'grid_zorder', 'grid_aa', 'grid_c', 'grid_ds', 'grid_ls', 'grid_lw', 'grid_mec', 'grid_mew', 'grid_mfc', 'grid_mfcalt', 'grid_ms']",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[291], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m get_ipython()\u001b[39m.\u001b[39mrun_line_magic(\u001b[39m'\u001b[39m\u001b[39mmatplotlib\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39minline\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      2\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mGRAFICA #3: VALORES AUSENTES POR COLUMNA Y FILA\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m----> 3\u001b[0m mi\u001b[39m.\u001b[39;49mmatrix(datos)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\missingno\\missingno.py:73\u001b[0m, in \u001b[0;36mmatrix\u001b[1;34m(df, filter, n, p, sort, figsize, width_ratios, color, fontsize, labels, label_rotation, sparkline, freq, ax)\u001b[0m\n\u001b[0;32m     71\u001b[0m \u001b[39m# Remove extraneous default visual elements.\u001b[39;00m\n\u001b[0;32m     72\u001b[0m ax0\u001b[39m.\u001b[39mset_aspect(\u001b[39m'\u001b[39m\u001b[39mauto\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m---> 73\u001b[0m ax0\u001b[39m.\u001b[39;49mgrid(b\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n\u001b[0;32m     74\u001b[0m ax0\u001b[39m.\u001b[39mxaxis\u001b[39m.\u001b[39mtick_top()\n\u001b[0;32m     75\u001b[0m ax0\u001b[39m.\u001b[39mxaxis\u001b[39m.\u001b[39mset_ticks_position(\u001b[39m'\u001b[39m\u001b[39mnone\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3196\u001b[0m, in \u001b[0;36m_AxesBase.grid\u001b[1;34m(self, visible, which, axis, **kwargs)\u001b[0m\n\u001b[0;32m   3194\u001b[0m _api\u001b[39m.\u001b[39mcheck_in_list([\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39my\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mboth\u001b[39m\u001b[39m'\u001b[39m], axis\u001b[39m=\u001b[39maxis)\n\u001b[0;32m   3195\u001b[0m \u001b[39mif\u001b[39;00m axis \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mboth\u001b[39m\u001b[39m'\u001b[39m]:\n\u001b[1;32m-> 3196\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mxaxis\u001b[39m.\u001b[39;49mgrid(visible, which\u001b[39m=\u001b[39;49mwhich, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m   3197\u001b[0m \u001b[39mif\u001b[39;00m axis \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39my\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mboth\u001b[39m\u001b[39m'\u001b[39m]:\n\u001b[0;32m   3198\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39myaxis\u001b[39m.\u001b[39mgrid(visible, which\u001b[39m=\u001b[39mwhich, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\matplotlib\\axis.py:1655\u001b[0m, in \u001b[0;36mAxis.grid\u001b[1;34m(self, visible, which, **kwargs)\u001b[0m\n\u001b[0;32m   1652\u001b[0m \u001b[39mif\u001b[39;00m which \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mmajor\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mboth\u001b[39m\u001b[39m'\u001b[39m]:\n\u001b[0;32m   1653\u001b[0m     gridkw[\u001b[39m'\u001b[39m\u001b[39mgridOn\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m (\u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_major_tick_kw[\u001b[39m'\u001b[39m\u001b[39mgridOn\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m   1654\u001b[0m                         \u001b[39mif\u001b[39;00m visible \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m visible)\n\u001b[1;32m-> 1655\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_tick_params(which\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mmajor\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mgridkw)\n\u001b[0;32m   1656\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstale \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\matplotlib\\axis.py:927\u001b[0m, in \u001b[0;36mAxis.set_tick_params\u001b[1;34m(self, which, reset, **kwargs)\u001b[0m\n\u001b[0;32m    914\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    915\u001b[0m \u001b[39mSet appearance parameters for ticks, ticklabels, and gridlines.\u001b[39;00m\n\u001b[0;32m    916\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    924\u001b[0m \u001b[39m    gridlines.\u001b[39;00m\n\u001b[0;32m    925\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    926\u001b[0m _api\u001b[39m.\u001b[39mcheck_in_list([\u001b[39m'\u001b[39m\u001b[39mmajor\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mminor\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mboth\u001b[39m\u001b[39m'\u001b[39m], which\u001b[39m=\u001b[39mwhich)\n\u001b[1;32m--> 927\u001b[0m kwtrans \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_translate_tick_params(kwargs)\n\u001b[0;32m    929\u001b[0m \u001b[39m# the kwargs are stored in self._major/minor_tick_kw so that any\u001b[39;00m\n\u001b[0;32m    930\u001b[0m \u001b[39m# future new ticks will automatically get them\u001b[39;00m\n\u001b[0;32m    931\u001b[0m \u001b[39mif\u001b[39;00m reset:\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\matplotlib\\axis.py:1071\u001b[0m, in \u001b[0;36mAxis._translate_tick_params\u001b[1;34m(kw, reverse)\u001b[0m\n\u001b[0;32m   1069\u001b[0m \u001b[39mfor\u001b[39;00m key \u001b[39min\u001b[39;00m kw_:\n\u001b[0;32m   1070\u001b[0m     \u001b[39mif\u001b[39;00m key \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m allowed_keys:\n\u001b[1;32m-> 1071\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m   1072\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mkeyword \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m is not recognized; valid keywords are \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m   1073\u001b[0m             \u001b[39m%\u001b[39m (key, allowed_keys))\n\u001b[0;32m   1074\u001b[0m kwtrans\u001b[39m.\u001b[39mupdate(kw_)\n\u001b[0;32m   1075\u001b[0m \u001b[39mreturn\u001b[39;00m kwtrans\n",
      "\u001b[1;31mValueError\u001b[0m: keyword grid_b is not recognized; valid keywords are ['size', 'width', 'color', 'tickdir', 'pad', 'labelsize', 'labelcolor', 'zorder', 'gridOn', 'tick1On', 'tick2On', 'label1On', 'label2On', 'length', 'direction', 'left', 'bottom', 'right', 'top', 'labelleft', 'labelbottom', 'labelright', 'labeltop', 'labelrotation', 'grid_agg_filter', 'grid_alpha', 'grid_animated', 'grid_antialiased', 'grid_clip_box', 'grid_clip_on', 'grid_clip_path', 'grid_color', 'grid_dash_capstyle', 'grid_dash_joinstyle', 'grid_dashes', 'grid_data', 'grid_drawstyle', 'grid_figure', 'grid_fillstyle', 'grid_gapcolor', 'grid_gid', 'grid_in_layout', 'grid_label', 'grid_linestyle', 'grid_linewidth', 'grid_marker', 'grid_markeredgecolor', 'grid_markeredgewidth', 'grid_markerfacecolor', 'grid_markerfacecoloralt', 'grid_markersize', 'grid_markevery', 'grid_mouseover', 'grid_path_effects', 'grid_picker', 'grid_pickradius', 'grid_rasterized', 'grid_sketch_params', 'grid_snap', 'grid_solid_capstyle', 'grid_solid_joinstyle', 'grid_transform', 'grid_url', 'grid_visible', 'grid_xdata', 'grid_ydata', 'grid_zorder', 'grid_aa', 'grid_c', 'grid_ds', 'grid_ls', 'grid_lw', 'grid_mec', 'grid_mew', 'grid_mfc', 'grid_mfcalt', 'grid_ms']"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "print(\"GRAFICA #3: VALORES AUSENTES POR COLUMNA Y FILA\")\n",
    "mi.matrix(datos)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6366ee86",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#20B2AA\">   Análisis de los datos - Valores ausentes en la columna de clasificación.           <br>\n",
    "4. De acuerdo con la información suministrada, calcula los valores ausentes de la columna\n",
    "de clasificación (la última).  \n",
    "\n",
    "***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "7caccd3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datos ausentes para la variable categorica:58 valores nulos.\n"
     ]
    }
   ],
   "source": [
    "print('Datos ausentes para la variable categorica:{} valores nulos.'.format(datos[\"AliveAt1\"].isnull().sum()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b8c3c7d5",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#20B2AA\">   Análisis de los datos - Descartando atributos.           <br>\n",
    "5. Antes del proceso de imputación, y en función de la distribución de NA’s, indicar si\n",
    "sería conveniente eliminar alguna instancia o atributo. Razona tu respuesta.   <br>\n",
    "\n",
    "Para ello consideramos la medida de correlación entre las variables o atributos.\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
       "      <th>AgeAttack</th>\n",
       "      <th>PericardEffu</th>\n",
       "      <th>FracShort</th>\n",
       "      <th>EPSS</th>\n",
       "      <th>LVDD</th>\n",
       "      <th>WMS</th>\n",
       "      <th>WMI</th>\n",
       "      <th>Mult</th>\n",
       "      <th>Name</th>\n",
       "      <th>Group</th>\n",
       "      <th>AliveAt1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survival</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.704403</td>\n",
       "      <td>-0.024611</td>\n",
       "      <td>0.704403</td>\n",
       "      <td>0.228410</td>\n",
       "      <td>0.150999</td>\n",
       "      <td>0.186989</td>\n",
       "      <td>0.339860</td>\n",
       "      <td>-0.010837</td>\n",
       "      <td>0.339860</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.055470</td>\n",
       "      <td>0.015146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StillAlive</th>\n",
       "      <td>0.704403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.017336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.343978</td>\n",
       "      <td>0.244012</td>\n",
       "      <td>0.289775</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>-0.007634</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.039073</td>\n",
       "      <td>0.098688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeAttack</th>\n",
       "      <td>-0.024611</td>\n",
       "      <td>-0.017336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.017336</td>\n",
       "      <td>0.115916</td>\n",
       "      <td>-0.071045</td>\n",
       "      <td>0.083756</td>\n",
       "      <td>-0.035076</td>\n",
       "      <td>-0.017336</td>\n",
       "      <td>-0.035076</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.230713</td>\n",
       "      <td>0.144165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PericardEffu</th>\n",
       "      <td>0.704403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.017336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.343978</td>\n",
       "      <td>0.244012</td>\n",
       "      <td>0.289775</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>-0.007634</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.039073</td>\n",
       "      <td>0.098688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FracShort</th>\n",
       "      <td>0.228410</td>\n",
       "      <td>0.343978</td>\n",
       "      <td>0.115916</td>\n",
       "      <td>0.343978</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.409260</td>\n",
       "      <td>0.727548</td>\n",
       "      <td>0.325535</td>\n",
       "      <td>-0.022192</td>\n",
       "      <td>0.325535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.113592</td>\n",
       "      <td>0.031017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPSS</th>\n",
       "      <td>0.150999</td>\n",
       "      <td>0.244012</td>\n",
       "      <td>-0.071045</td>\n",
       "      <td>0.244012</td>\n",
       "      <td>0.409260</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.496609</td>\n",
       "      <td>0.215207</td>\n",
       "      <td>-0.031284</td>\n",
       "      <td>0.215207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.160128</td>\n",
       "      <td>0.019675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LVDD</th>\n",
       "      <td>0.186989</td>\n",
       "      <td>0.289775</td>\n",
       "      <td>0.083756</td>\n",
       "      <td>0.289775</td>\n",
       "      <td>0.727548</td>\n",
       "      <td>0.496609</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.266501</td>\n",
       "      <td>-0.026343</td>\n",
       "      <td>0.266501</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.134840</td>\n",
       "      <td>0.009205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMS</th>\n",
       "      <td>0.339860</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>-0.035076</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>0.325535</td>\n",
       "      <td>0.215207</td>\n",
       "      <td>0.266501</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.079057</td>\n",
       "      <td>0.110632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMI</th>\n",
       "      <td>-0.010837</td>\n",
       "      <td>-0.007634</td>\n",
       "      <td>-0.017336</td>\n",
       "      <td>-0.007634</td>\n",
       "      <td>-0.022192</td>\n",
       "      <td>-0.031284</td>\n",
       "      <td>-0.026343</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.039073</td>\n",
       "      <td>-0.077350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mult</th>\n",
       "      <td>0.339860</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>-0.035076</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>0.325535</td>\n",
       "      <td>0.215207</td>\n",
       "      <td>0.266501</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.494242</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.079057</td>\n",
       "      <td>0.110632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <td>-0.055470</td>\n",
       "      <td>-0.039073</td>\n",
       "      <td>0.230713</td>\n",
       "      <td>-0.039073</td>\n",
       "      <td>-0.113592</td>\n",
       "      <td>-0.160128</td>\n",
       "      <td>-0.134840</td>\n",
       "      <td>-0.079057</td>\n",
       "      <td>-0.039073</td>\n",
       "      <td>-0.079057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.505146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AliveAt1</th>\n",
       "      <td>0.015146</td>\n",
       "      <td>0.098688</td>\n",
       "      <td>0.144165</td>\n",
       "      <td>0.098688</td>\n",
       "      <td>0.031017</td>\n",
       "      <td>0.019675</td>\n",
       "      <td>0.009205</td>\n",
       "      <td>0.110632</td>\n",
       "      <td>-0.077350</td>\n",
       "      <td>0.110632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.505146</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Survival  StillAlive  AgeAttack  PericardEffu  FracShort  \\\n",
       "Survival      1.000000    0.704403  -0.024611      0.704403   0.228410   \n",
       "StillAlive    0.704403    1.000000  -0.017336      1.000000   0.343978   \n",
       "AgeAttack    -0.024611   -0.017336   1.000000     -0.017336   0.115916   \n",
       "PericardEffu  0.704403    1.000000  -0.017336      1.000000   0.343978   \n",
       "FracShort     0.228410    0.343978   0.115916      0.343978   1.000000   \n",
       "EPSS          0.150999    0.244012  -0.071045      0.244012   0.409260   \n",
       "LVDD          0.186989    0.289775   0.083756      0.289775   0.727548   \n",
       "WMS           0.339860    0.494242  -0.035076      0.494242   0.325535   \n",
       "WMI          -0.010837   -0.007634  -0.017336     -0.007634  -0.022192   \n",
       "Mult          0.339860    0.494242  -0.035076      0.494242   0.325535   \n",
       "Name               NaN         NaN        NaN           NaN        NaN   \n",
       "Group        -0.055470   -0.039073   0.230713     -0.039073  -0.113592   \n",
       "AliveAt1      0.015146    0.098688   0.144165      0.098688   0.031017   \n",
       "\n",
       "                  EPSS      LVDD       WMS       WMI      Mult  Name  \\\n",
       "Survival      0.150999  0.186989  0.339860 -0.010837  0.339860   NaN   \n",
       "StillAlive    0.244012  0.289775  0.494242 -0.007634  0.494242   NaN   \n",
       "AgeAttack    -0.071045  0.083756 -0.035076 -0.017336 -0.035076   NaN   \n",
       "PericardEffu  0.244012  0.289775  0.494242 -0.007634  0.494242   NaN   \n",
       "FracShort     0.409260  0.727548  0.325535 -0.022192  0.325535   NaN   \n",
       "EPSS          1.000000  0.496609  0.215207 -0.031284  0.215207   NaN   \n",
       "LVDD          0.496609  1.000000  0.266501 -0.026343  0.266501   NaN   \n",
       "WMS           0.215207  0.266501  1.000000  0.494242  1.000000   NaN   \n",
       "WMI          -0.031284 -0.026343  0.494242  1.000000  0.494242   NaN   \n",
       "Mult          0.215207  0.266501  1.000000  0.494242  1.000000   NaN   \n",
       "Name               NaN       NaN       NaN       NaN       NaN   NaN   \n",
       "Group        -0.160128 -0.134840 -0.079057 -0.039073 -0.079057   NaN   \n",
       "AliveAt1      0.019675  0.009205  0.110632 -0.077350  0.110632   NaN   \n",
       "\n",
       "                 Group  AliveAt1  \n",
       "Survival     -0.055470  0.015146  \n",
       "StillAlive   -0.039073  0.098688  \n",
       "AgeAttack     0.230713  0.144165  \n",
       "PericardEffu -0.039073  0.098688  \n",
       "FracShort    -0.113592  0.031017  \n",
       "EPSS         -0.160128  0.019675  \n",
       "LVDD         -0.134840  0.009205  \n",
       "WMS          -0.079057  0.110632  \n",
       "WMI          -0.039073 -0.077350  \n",
       "Mult         -0.079057  0.110632  \n",
       "Name               NaN       NaN  \n",
       "Group         1.000000  0.505146  \n",
       "AliveAt1      0.505146  1.000000  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mat = datos.isnull().corr()\n",
    "corr_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "f5f87821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi.heatmap(datos)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e715e4c9",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#20B2AA\"> Consideraciones para eliminar las siguientes variables en función de los valores Nulos:<br>\n",
    "1.- La variable \"Name\" se elimina, no tiene datos nulos, no significativa ya que a partir de ella no se puede explicar la correlación de datos faltantes. <br>\n",
    "2.- La variable \"Multi\" se elimina porque tiene una correlación alta con la variable \"VMS\", \"StillAlive\" y \"PericardEffu\" por lo que \"Mult\" no estará <br>\n",
    "aportando información relevante.<br>\n",
    "3.- La variable PericardEffu es una variable binaria y tiene una correlación alta con la variable Survival que se refiere al número de meses que estuvo vivo <br>(variable cuantitativa continua) de la que depende la variable \"AliveAt1\" que es la variable de clasificación. <br>\n",
    "Cualquiera de las dos podría eliminarse debido al grado de correlación, pera cada una aporta información de forma distinta a la variable respuesta y una <br>\n",
    "no podría explicar con certeza la distribución de los datos faltantes de la otra.<br>\n",
    "4.- La variable \"LVDD\" tiene un grado de correlación alta con las variables \"FracShort\" y \"EPSS\" por lo que puede estar representando datos redundantes y  <br>\n",
    "las variables anteriores pueden portar suficiente información al modelo al eliminar \"LVDD\".<br>\n",
    "5.- La variable \"WMI\" tiene una correlación fuerte con la variable \"WMS\" y al mismo tiempo una correlación muy baja con los valores ausentes de la <br>\n",
    "variable objetivo. Además conocemos por la descripción de los datos que esta variable es la media de los valores de  \"WMI\", por tal razón la eliminamos. <br>\n",
    "6.- Por los datos arrojados en la \"Tabla de valores faltantes por columna\" podemos determinar que sumados los datos faltantes de las variables \"FracShort\" y <br>\n",
    "\"EPSS\" son iguales a los datos faltantes en la variable \"WMS\" en la suma total y de correlación entre otras variables. A su vez esta variable \"WMS\" tiene <br>\n",
    "una correlación fuerte con las variables \"StillAlive\" y \"PericardEffu\", con lo que la variabilidad de los datos faltantes en esta variable \"WMS\" estarían <br>\n",
    "explicados en las variables \"StillAlive\" y \"PericardEffu\".\n",
    "6.- Group no aporta información sobre los datos faltantes ya que sus datos están asociados a la variable de objetivo. \n",
    "\n",
    "Bajo este análisis nos quedaríamos con las variables: Survival,\tStillAlive,\tAgeAttack,\tPericardEffu, FracShort, EPSS y AliveAt1 como variable objetivo <br>\n",
    "y eliminamos: Name, Multi, LVDD, WMI, WMS y Group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "26a34a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "10d5c65f",
   "metadata": {},
   "source": [
    "Otra forma de eliminar varibles es considerando el resultado de aplicar el Análisis de los Componentes Principales, ignorando la variable name y <br>\n",
    "la variable objetivo (clase):<br>\n",
    "Con esta técnica se puede determinar cuáles atributos o variables tienen menor importancia en la explicación de la variabilidad de los datos, y <br>\n",
    "por lo tanto pueden ser eliminados sin afectar significativamente el rendimiento del modelo o el análisis posterior. <br>\n",
    "\n",
    "<span style=\"color:#20B2AA\">NOTA: Esta técnica no toma en cuenta los valores nulos pero nos permite identificar si el análisis anterior tiene coherencia. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "1df995a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
       "      <th>AgeAttack</th>\n",
       "      <th>PericardEffu</th>\n",
       "      <th>FracShort</th>\n",
       "      <th>EPSS</th>\n",
       "      <th>LVDD</th>\n",
       "      <th>WMS</th>\n",
       "      <th>WMI</th>\n",
       "      <th>Mult</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0.260</td>\n",
       "      <td>9</td>\n",
       "      <td>4.600</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0.380</td>\n",
       "      <td>6</td>\n",
       "      <td>4.100</td>\n",
       "      <td>14</td>\n",
       "      <td>1.700</td>\n",
       "      <td>0.588</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0.260</td>\n",
       "      <td>4</td>\n",
       "      <td>3.420</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.253</td>\n",
       "      <td>12.062</td>\n",
       "      <td>4.603</td>\n",
       "      <td>16</td>\n",
       "      <td>1.450</td>\n",
       "      <td>0.788</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.160</td>\n",
       "      <td>22</td>\n",
       "      <td>5.750</td>\n",
       "      <td>18</td>\n",
       "      <td>2.250</td>\n",
       "      <td>0.571</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>14.3</td>\n",
       "      <td>5.49</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.643</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.228</td>\n",
       "      <td>9.7</td>\n",
       "      <td>4.29</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0.786</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036</td>\n",
       "      <td>7</td>\n",
       "      <td>4.12</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.786</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>.75</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>40</td>\n",
       "      <td>6.23</td>\n",
       "      <td>14</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.714</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>0.26</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.42</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survival StillAlive AgeAttack PericardEffu FracShort    EPSS   LVDD   WMS  \\\n",
       "0         11          0        71            0     0.260       9  4.600    14   \n",
       "1         19          0        72            0     0.380       6  4.100    14   \n",
       "2         16          0        55            0     0.260       4  3.420    14   \n",
       "3         57          0        60            0     0.253  12.062  4.603    16   \n",
       "4         19          1        57            0     0.160      22  5.750    18   \n",
       "..       ...        ...       ...          ...       ...     ...    ...   ...   \n",
       "105       24          0        59            0      0.17    14.3   5.49  13.5   \n",
       "106       25          0        57            0     0.228     9.7   4.29    11   \n",
       "107       24          0        57            0     0.036       7   4.12  13.5   \n",
       "108      .75          1        78            0      0.23      40   6.23    14   \n",
       "109        3          1        62            0      0.26     7.6   4.42    14   \n",
       "\n",
       "       WMI   Mult Group  \n",
       "0        1      1     1  \n",
       "1    1.700  0.588     1  \n",
       "2        1      1     1  \n",
       "3    1.450  0.788     1  \n",
       "4    2.250  0.571     1  \n",
       "..     ...    ...   ...  \n",
       "105   1.50  0.643     2  \n",
       "106      1  0.786     2  \n",
       "107   1.23  0.786     2  \n",
       "108    1.4  0.714     2  \n",
       "109      1      1     2  \n",
       "\n",
       "[87 rows x 11 columns]"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datosCp = datos.iloc[:, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]]\n",
    "# Eliminar filas con NaN\n",
    "datosCp = datosCp.dropna()\n",
    "datosCp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "95c6e1f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seleccionamos los 5 primeros componentes que explican la varianza de los datos un 99%\n",
      "Se eliminan las variables: EPSS, LVDD, WMI, Mult y Group\n",
      "Tabla por componente:              Porcentaje de varianza explicada\n",
      "Survival                              0.668870\n",
      "StillAlive                            0.173928\n",
      "AgeAttack                             0.111152\n",
      "PericardEffu                          0.043957\n",
      "FracShort                             0.000851\n",
      "EPSS                                  0.000456\n",
      "LVDD                                  0.000306\n",
      "WMS                                   0.000265\n",
      "WMI                                   0.000192\n",
      "Mult                                  0.000020\n",
      "Group                                 0.000005\n"
     ]
    }
   ],
   "source": [
    "# Crear el objeto PCA y especificar el número de componentes a generar\n",
    "pca = PCA(n_components=11)\n",
    "# Aplicar PCA a los datos\n",
    "pca.fit(datosCp)\n",
    "# Transformar los datos para obtener los componentes principales\n",
    "transformed_data = pca.transform(datosCp)\n",
    "\n",
    "# Obtener los porcentajes de varianza explicada por cada componente principal\n",
    "variance_ratios = pca.explained_variance_ratio_\n",
    "\n",
    "# Crear un dataframe con los porcentajes de varianza explicada por cada variable\n",
    "variance_df = pd.DataFrame(variance_ratios, columns=[\"Porcentaje de varianza explicada\"], index=datosCp.columns)\n",
    "suma = int((variance_df.iloc[0:4]['Porcentaje de varianza explicada'].sum())*100)\n",
    "# Imprimir el dataframe\n",
    "print('Seleccionamos los 5 primeros componentes que explican la varianza de los datos un {}%'.format(suma))\n",
    "print(\"Se eliminan las variables: EPSS, LVDD, WMI, Mult y Group\")\n",
    "print('Tabla por componente:{}'.format(variance_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "2abeea0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15,5))  \n",
    "sns.heatmap(datos.isnull().transpose(), cmap = 'Blues')\n",
    "plt.title(\"Mapa de calor de los datos ausentes por # de fila y atributo (columna).\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9de7643f",
   "metadata": {},
   "source": [
    "### <span style=\"color:#20B2AA\">   Imputando los valores ausentes.           <br>\n",
    "6. Imputar los valores ausentes aplicando una imputación por mediana y una imputación <br>\n",
    "por KNN. Genera un dataset para cada uno de los tipos de imputación con los siguientes<br>\n",
    "nombres: echo_mediana y echo_knn. Muestra los resultados de la imputación por<br>\n",
    "pantalla. \n",
    "\n",
    "***"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9a620e5a",
   "metadata": {},
   "source": [
    "##### <span style=\"color:#20B2AA\">   Tipos de datos           <br>\n",
    "\n",
    "Pasos: <br>\n",
    "1.- Eliminamos la columna Name ya que no contiene datos ausentes.<br>\n",
    "2.- Convertimos los datos de tipo object al correspondiente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "c5903264",
   "metadata": {},
   "outputs": [],
   "source": [
    "datos = datos.drop('Name', axis=1)\n",
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "26a5a308",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
       "      <th>AgeAttack</th>\n",
       "      <th>PericardEffu</th>\n",
       "      <th>FracShort</th>\n",
       "      <th>EPSS</th>\n",
       "      <th>LVDD</th>\n",
       "      <th>WMS</th>\n",
       "      <th>WMI</th>\n",
       "      <th>Mult</th>\n",
       "      <th>Name</th>\n",
       "      <th>Group</th>\n",
       "      <th>AliveAt1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>False</td>\n",
       "      <td>71</td>\n",
       "      <td>False</td>\n",
       "      <td>0.26</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
       "      <td>72</td>\n",
       "      <td>False</td>\n",
       "      <td>0.38</td>\n",
       "      <td>6.00</td>\n",
       "      <td>4.1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>55</td>\n",
       "      <td>False</td>\n",
       "      <td>0.26</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57.0</td>\n",
       "      <td>False</td>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12.06</td>\n",
       "      <td>4.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.0</td>\n",
       "      <td>True</td>\n",
       "      <td>57</td>\n",
       "      <td>False</td>\n",
       "      <td>0.16</td>\n",
       "      <td>22.00</td>\n",
       "      <td>5.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>7.5</td>\n",
       "      <td>True</td>\n",
       "      <td>64</td>\n",
       "      <td>False</td>\n",
       "      <td>0.24</td>\n",
       "      <td>12.90</td>\n",
       "      <td>4.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>name</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>41.0</td>\n",
       "      <td>False</td>\n",
       "      <td>64</td>\n",
       "      <td>False</td>\n",
       "      <td>0.28</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>name</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>36.0</td>\n",
       "      <td>False</td>\n",
       "      <td>69</td>\n",
       "      <td>False</td>\n",
       "      <td>0.20</td>\n",
       "      <td>7.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>name</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>22.0</td>\n",
       "      <td>False</td>\n",
       "      <td>57</td>\n",
       "      <td>False</td>\n",
       "      <td>0.14</td>\n",
       "      <td>16.10</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>name</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>20.0</td>\n",
       "      <td>False</td>\n",
       "      <td>62</td>\n",
       "      <td>False</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>name</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survival  StillAlive  AgeAttack  PericardEffu  FracShort   EPSS  LVDD  \\\n",
       "0        11.0       False         71         False       0.26   9.00   4.6   \n",
       "1        19.0       False         72         False       0.38   6.00   4.1   \n",
       "2        16.0       False         55         False       0.26   4.00   3.4   \n",
       "3        57.0       False         60         False       0.25  12.06   4.6   \n",
       "4        19.0        True         57         False       0.16  22.00   5.8   \n",
       "..        ...         ...        ...           ...        ...    ...   ...   \n",
       "127       7.5        True         64         False       0.24  12.90   4.7   \n",
       "128      41.0       False         64         False       0.28   5.40   5.5   \n",
       "129      36.0       False         69         False       0.20   7.00   5.0   \n",
       "130      22.0       False         57         False       0.14  16.10   4.4   \n",
       "131      20.0       False         62         False       0.15   0.00   4.5   \n",
       "\n",
       "      WMS  WMI  Mult  Name  Group AliveAt1  \n",
       "0    14.0  1.0   1.0  name   True  FALLECE  \n",
       "1    14.0  1.7   0.6  name   True  FALLECE  \n",
       "2    14.0  1.0   1.0  name   True  FALLECE  \n",
       "3    16.0  1.4   0.8  name   True  FALLECE  \n",
       "4    18.0  2.2   0.6  name   True  FALLECE  \n",
       "..    ...  ...   ...   ...    ...      ...  \n",
       "127  12.0  1.0   0.9  name   <NA>      NaN  \n",
       "128  11.0  1.1   0.7  name   <NA>      NaN  \n",
       "129  14.5  1.2   0.9  name   <NA>      NaN  \n",
       "130  15.0  1.4   0.8  name   <NA>      NaN  \n",
       "131  15.5  1.4   0.8  name   <NA>      NaN  \n",
       "\n",
       "[132 rows x 13 columns]"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos['Survival'] = ((pd.to_numeric(datos['Survival'], errors='coerce')).round(1)).astype('float')\n",
    "datos['StillAlive'] = (datos['StillAlive'].astype('Int64')).astype('boolean')\n",
    "datos['AgeAttack'] = ((pd.to_numeric(datos['AgeAttack'], errors='coerce')).round(0)).astype('Int64')\n",
    "datos['PericardEffu'] = (datos['PericardEffu'].astype('Int64')).astype('boolean')\n",
    "datos['FracShort'] = ((pd.to_numeric(datos['FracShort'], errors='coerce')).round(2)).astype('float')\n",
    "datos['EPSS'] = ((pd.to_numeric(datos['EPSS'], errors='coerce')).round(2)).astype('float')\n",
    "datos['LVDD'] = ((pd.to_numeric(datos['LVDD'], errors='coerce')).round(1)).astype('float')\n",
    "datos['WMS'] = ((pd.to_numeric(datos['WMS'], errors='coerce')).round(2)).astype('float')\n",
    "datos['WMI'] = ((pd.to_numeric(datos['WMI'], errors='coerce')).round(1)).astype('float')\n",
    "datos['Mult'] = ((pd.to_numeric(datos['Mult'], errors='coerce')).round(1)).astype('float')\n",
    "datos['Group'] = (datos['Group'].astype('Int64')).astype('boolean')\n",
    "\n",
    "# Crear un diccionario de mapeo\n",
    "mapping_dict = {\"1\": \"VIVE\", \"0\": \"FALLECE\"}\n",
    "# Aplicar el mapeo a la columna \"AliveAt1\"\n",
    "datos[\"AliveAt1\"] = datos[\"AliveAt1\"].map(mapping_dict).astype(\"category\")\n",
    "\n",
    "\n",
    "\n",
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "77e9237d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 132 entries, 0 to 131\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype   \n",
      "---  ------        --------------  -----   \n",
      " 0   Survival      130 non-null    float64 \n",
      " 1   StillAlive    131 non-null    boolean \n",
      " 2   AgeAttack     127 non-null    Int64   \n",
      " 3   PericardEffu  131 non-null    boolean \n",
      " 4   FracShort     124 non-null    float64 \n",
      " 5   EPSS          117 non-null    float64 \n",
      " 6   LVDD          121 non-null    float64 \n",
      " 7   WMS           128 non-null    float64 \n",
      " 8   WMI           131 non-null    float64 \n",
      " 9   Mult          128 non-null    float64 \n",
      " 10  Group         110 non-null    boolean \n",
      " 11  AliveAt1      0 non-null      category\n",
      "dtypes: Int64(1), boolean(3), category(1), float64(7)\n",
      "memory usage: 9.5 KB\n"
     ]
    }
   ],
   "source": [
    "datos.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ea66bb44",
   "metadata": {},
   "source": [
    "##### <span style=\"color:#20B2AA\">   Imputando con sklearn           <br>\n",
    "Vamos a imputar (rellenar) los valores faltantes o nulos en un conjunto de datos por mediana y una por KNN. <br>\n",
    "Esta función de skelearn se utiliza principalmente para preprocesar los datos antes de aplicar un modelo de aprendizaje automático.\n",
    "Pasos:\n",
    "1.- Imputamos los datos tipo Float (continuos)\n",
    "2.- Imputamos los datos tipo Int (enteros) y booleanos.\n",
    "3.- Imputamos la variable categorica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "b9f45da1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
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       "      <td>0.6</td>\n",
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       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
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       "      <td>7.5</td>\n",
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       "      <td>0.24</td>\n",
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       "      <td>name</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>41.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survival  StillAlive  AgeAttack  PericardEffu  FracShort   EPSS  LVDD  \\\n",
       "0        11.0       False         71         False       0.26   9.00   4.6   \n",
       "1        19.0       False         72         False       0.38   6.00   4.1   \n",
       "2        16.0       False         55         False       0.26   4.00   3.4   \n",
       "3        57.0       False         60         False       0.25  12.06   4.6   \n",
       "4        19.0        True         57         False       0.16  22.00   5.8   \n",
       "..        ...         ...        ...           ...        ...    ...   ...   \n",
       "127       7.5        True         64         False       0.24  12.90   4.7   \n",
       "128      41.0       False         64         False       0.28   5.40   5.5   \n",
       "129      36.0       False         69         False       0.20   7.00   5.0   \n",
       "130      22.0       False         57         False       0.14  16.10   4.4   \n",
       "131      20.0       False         62         False       0.15   0.00   4.5   \n",
       "\n",
       "      WMS  WMI  Mult  Name  Group AliveAt1  \n",
       "0    14.0  1.0   1.0  name   True  FALLECE  \n",
       "1    14.0  1.7   0.6  name   True  FALLECE  \n",
       "2    14.0  1.0   1.0  name   True  FALLECE  \n",
       "3    16.0  1.4   0.8  name   True  FALLECE  \n",
       "4    18.0  2.2   0.6  name   True  FALLECE  \n",
       "..    ...  ...   ...   ...    ...      ...  \n",
       "127  12.0  1.0   0.9  name   <NA>      NaN  \n",
       "128  11.0  1.1   0.7  name   <NA>      NaN  \n",
       "129  14.5  1.2   0.9  name   <NA>      NaN  \n",
       "130  15.0  1.4   0.8  name   <NA>      NaN  \n",
       "131  15.5  1.4   0.8  name   <NA>      NaN  \n",
       "\n",
       "[132 rows x 13 columns]"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean Imputation \n",
    "echo_mediana = datos.copy()\n",
    "si = SimpleImputer(strategy='median') #reemplazará los datos faltantes sobre la mediana de los valores de la columna\n",
    "echo_mediana['Survival'] = si.fit_transform(echo_mediana['Survival'].values.reshape(-1,1))\n",
    "echo_mediana['FracShort'] = si.fit_transform(echo_mediana['FracShort'].values.reshape(-1,1))\n",
    "echo_mediana['EPSS'] = si.fit_transform(echo_mediana['EPSS'].values.reshape(-1,1))\n",
    "echo_mediana['LVDD'] = si.fit_transform(echo_mediana['LVDD'].values.reshape(-1,1))\n",
    "echo_mediana['WMS'] = si.fit_transform(echo_mediana['WMS'].values.reshape(-1,1))\n",
    "echo_mediana['WMI'] = si.fit_transform(echo_mediana['WMI'].values.reshape(-1,1))\n",
    "echo_mediana['Mult'] = si.fit_transform(echo_mediana['Mult'].values.reshape(-1,1))\n",
    "\n",
    "echo_mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "65984903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "null_values = datos['Survival'].isnull()\n",
    "\n",
    "fig = plt.Figure()\n",
    "fig = echo_mediana.plot(x=\"AgeAttack\", y='Survival', kind='scatter', c=null_values, cmap='winter', title='Mean Imputation', colorbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "90cdd973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
       "      <th>AgeAttack</th>\n",
       "      <th>PericardEffu</th>\n",
       "      <th>FracShort</th>\n",
       "      <th>EPSS</th>\n",
       "      <th>LVDD</th>\n",
       "      <th>WMS</th>\n",
       "      <th>WMI</th>\n",
       "      <th>Mult</th>\n",
       "      <th>Name</th>\n",
       "      <th>Group</th>\n",
       "      <th>AliveAt1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>False</td>\n",
       "      <td>71</td>\n",
       "      <td>False</td>\n",
       "      <td>0.26</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
       "      <td>72</td>\n",
       "      <td>False</td>\n",
       "      <td>0.38</td>\n",
       "      <td>6.00</td>\n",
       "      <td>4.1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>55</td>\n",
       "      <td>False</td>\n",
       "      <td>0.26</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57.0</td>\n",
       "      <td>False</td>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12.06</td>\n",
       "      <td>4.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.0</td>\n",
       "      <td>True</td>\n",
       "      <td>57</td>\n",
       "      <td>False</td>\n",
       "      <td>0.16</td>\n",
       "      <td>22.00</td>\n",
       "      <td>5.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survival StillAlive AgeAttack PericardEffu  FracShort   EPSS  LVDD   WMS  \\\n",
       "0      11.0      False        71        False       0.26   9.00   4.6  14.0   \n",
       "1      19.0      False        72        False       0.38   6.00   4.1  14.0   \n",
       "2      16.0      False        55        False       0.26   4.00   3.4  14.0   \n",
       "3      57.0      False        60        False       0.25  12.06   4.6  16.0   \n",
       "4      19.0       True        57        False       0.16  22.00   5.8  18.0   \n",
       "\n",
       "   WMI  Mult  Name Group AliveAt1  \n",
       "0  1.0   1.0  name  True  FALLECE  \n",
       "1  1.7   0.6  name  True  FALLECE  \n",
       "2  1.0   1.0  name  True  FALLECE  \n",
       "3  1.4   0.8  name  True  FALLECE  \n",
       "4  2.2   0.6  name  True  FALLECE  "
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "si = SimpleImputer(strategy='most_frequent', missing_values=pd.NA) #Imputamos Enteros y Booleanos\n",
    "echo_mediana['AgeAttack'] = si.fit_transform(echo_mediana['AgeAttack'].values.reshape(-1,1))\n",
    "\n",
    "echo_mediana['StillAlive'] = si.fit_transform(echo_mediana['StillAlive'].values.reshape(-1,1))\n",
    "echo_mediana['PericardEffu'] = si.fit_transform(echo_mediana['PericardEffu'].values.reshape(-1,1))\n",
    "echo_mediana['Group'] = si.fit_transform(echo_mediana['Group'].values.reshape(-1,1))\n",
    "\n",
    "echo_mediana.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "569f9408",
   "metadata": {},
   "outputs": [],
   "source": [
    "echo_mediana['AliveAt1'] = si.fit_transform(echo_mediana['AliveAt1'].values.reshape(-1,1)) #Imputamos la variable categorica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "2ed8e949",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
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       "      <td>16.0</td>\n",
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       "      <td>57.0</td>\n",
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       "      <th>128</th>\n",
       "      <td>41.0</td>\n",
       "      <td>False</td>\n",
       "      <td>64</td>\n",
       "      <td>False</td>\n",
       "      <td>0.28</td>\n",
       "      <td>5.40</td>\n",
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       "      <td>11.0</td>\n",
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       "      <td>0.7</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
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       "      <th>129</th>\n",
       "      <td>36.0</td>\n",
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       "      <td>69</td>\n",
       "      <td>False</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>0.14</td>\n",
       "      <td>16.10</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15.0</td>\n",
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       "      <td>0.8</td>\n",
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       "      <td>False</td>\n",
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       "<p>132 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survival StillAlive AgeAttack PericardEffu  FracShort   EPSS  LVDD   WMS  \\\n",
       "0        11.0      False        71        False       0.26   9.00   4.6  14.0   \n",
       "1        19.0      False        72        False       0.38   6.00   4.1  14.0   \n",
       "2        16.0      False        55        False       0.26   4.00   3.4  14.0   \n",
       "3        57.0      False        60        False       0.25  12.06   4.6  16.0   \n",
       "4        19.0       True        57        False       0.16  22.00   5.8  18.0   \n",
       "..        ...        ...       ...          ...        ...    ...   ...   ...   \n",
       "127       7.5       True        64        False       0.24  12.90   4.7  12.0   \n",
       "128      41.0      False        64        False       0.28   5.40   5.5  11.0   \n",
       "129      36.0      False        69        False       0.20   7.00   5.0  14.5   \n",
       "130      22.0      False        57        False       0.14  16.10   4.4  15.0   \n",
       "131      20.0      False        62        False       0.15   0.00   4.5  15.5   \n",
       "\n",
       "     WMI  Mult  Name Group AliveAt1  \n",
       "0    1.0   1.0  name  True  FALLECE  \n",
       "1    1.7   0.6  name  True  FALLECE  \n",
       "2    1.0   1.0  name  True  FALLECE  \n",
       "3    1.4   0.8  name  True  FALLECE  \n",
       "4    2.2   0.6  name  True  FALLECE  \n",
       "..   ...   ...   ...   ...      ...  \n",
       "127  1.0   0.9  name  True  FALLECE  \n",
       "128  1.1   0.7  name  True  FALLECE  \n",
       "129  1.2   0.9  name  True  FALLECE  \n",
       "130  1.4   0.8  name  True  FALLECE  \n",
       "131  1.4   0.8  name  True  FALLECE  \n",
       "\n",
       "[132 rows x 13 columns]"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "echo_mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "28bb51ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 132 entries, 0 to 131\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Survival      132 non-null    float64\n",
      " 1   StillAlive    132 non-null    object \n",
      " 2   AgeAttack     132 non-null    object \n",
      " 3   PericardEffu  132 non-null    object \n",
      " 4   FracShort     132 non-null    float64\n",
      " 5   EPSS          132 non-null    float64\n",
      " 6   LVDD          132 non-null    float64\n",
      " 7   WMS           132 non-null    float64\n",
      " 8   WMI           132 non-null    float64\n",
      " 9   Mult          132 non-null    float64\n",
      " 10  Name          132 non-null    object \n",
      " 11  Group         132 non-null    object \n",
      " 12  AliveAt1      132 non-null    object \n",
      "dtypes: float64(7), object(6)\n",
      "memory usage: 13.5+ KB\n"
     ]
    }
   ],
   "source": [
    "echo_mediana.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "a9834d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>16.0</td>\n",
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       "      <td>0.26</td>\n",
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       "      <td>3.4</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>name</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12.06</td>\n",
       "      <td>4.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>name</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>22.00</td>\n",
       "      <td>5.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>name</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survival  StillAlive  AgeAttack  PericardEffu  FracShort   EPSS  LVDD  \\\n",
       "0      11.0         0.0       71.0           0.0       0.26   9.00   4.6   \n",
       "1      19.0         0.0       72.0           0.0       0.38   6.00   4.1   \n",
       "2      16.0         0.0       55.0           0.0       0.26   4.00   3.4   \n",
       "3      57.0         0.0       60.0           0.0       0.25  12.06   4.6   \n",
       "4      19.0         1.0       57.0           0.0       0.16  22.00   5.8   \n",
       "\n",
       "    WMS  WMI  Mult  Name  Group AliveAt1  \n",
       "0  14.0  1.0   1.0  name    1.0  FALLECE  \n",
       "1  14.0  1.7   0.6  name    1.0  FALLECE  \n",
       "2  14.0  1.0   1.0  name    1.0  FALLECE  \n",
       "3  16.0  1.4   0.8  name    1.0  FALLECE  \n",
       "4  18.0  2.2   0.6  name    1.0  FALLECE  "
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imputación KNN\n",
    "echo_knn = datos.copy()\n",
    "# seleccionar las columnas a imputar\n",
    "cols = ['Survival', 'StillAlive', 'AgeAttack', 'PericardEffu', 'FracShort', \n",
    "        'EPSS', 'LVDD', 'WMS', 'WMI', 'Mult', 'Group']\n",
    "# crear un objeto KNNImputer con k=5\n",
    "# knni = KNNImputer(n_neighbors=5)\n",
    "knni = KNNImputer(n_neighbors=2, weights=\"uniform\")\n",
    "# echo_knn['Survival'] = knni.fit_transform(echo_knn['Survival'].values.reshape(-1,1))\n",
    "# imputar los valores faltantes en el dataframe\n",
    "echo_knn[cols] = knni.fit_transform(echo_knn[cols])\n",
    "echo_knn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "ebf8ae92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 132 entries, 0 to 131\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype   \n",
      "---  ------        --------------  -----   \n",
      " 0   Survival      132 non-null    float64 \n",
      " 1   StillAlive    132 non-null    float64 \n",
      " 2   AgeAttack     132 non-null    float64 \n",
      " 3   PericardEffu  132 non-null    float64 \n",
      " 4   FracShort     132 non-null    float64 \n",
      " 5   EPSS          132 non-null    float64 \n",
      " 6   LVDD          132 non-null    float64 \n",
      " 7   WMS           132 non-null    float64 \n",
      " 8   WMI           132 non-null    float64 \n",
      " 9   Mult          132 non-null    float64 \n",
      " 10  Name          132 non-null    object  \n",
      " 11  Group         132 non-null    float64 \n",
      " 12  AliveAt1      74 non-null     category\n",
      "dtypes: category(1), float64(11), object(1)\n",
      "memory usage: 12.8+ KB\n"
     ]
    }
   ],
   "source": [
    "echo_knn.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "044f5f28",
   "metadata": {},
   "source": [
    "##### <span style=\"color:#20B2AA\">   Comparamos la imputación en la fila con más valores nulos           <br>\n",
    "En este caso comparamos el resultado de ambas técnicas. <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "76b7e260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Observaciones</th>\n",
       "      <th>Mediana_Imputer</th>\n",
       "      <th>KNN_Imputer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survival</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StillAlive</th>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeAttack</th>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PericardEffu</th>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FracShort</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPSS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LVDD</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMI</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mult</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>name</td>\n",
       "      <td>name</td>\n",
       "      <td>name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AliveAt1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>FALLECE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Observaciones Mediana_Imputer KNN_Imputer\n",
       "Survival               NaN            23.5         5.0\n",
       "StillAlive            <NA>           False         1.0\n",
       "AgeAttack               77              77        77.0\n",
       "PericardEffu          <NA>           False         0.0\n",
       "FracShort              NaN           0.205       0.195\n",
       "EPSS                   NaN            11.0        17.7\n",
       "LVDD                   NaN             4.6         4.5\n",
       "WMS                    NaN            14.0        19.5\n",
       "WMI                    2.0             2.0         2.0\n",
       "Mult                   NaN             0.8         0.7\n",
       "Name                  name            name        name\n",
       "Group                 True            True         1.0\n",
       "AliveAt1               NaN         FALLECE         NaN"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printImputer = pd.DataFrame({'Observaciones':datos.iloc[49],'Mediana_Imputer': echo_mediana.iloc[49], 'KNN_Imputer':echo_knn.iloc[49]})\n",
    "printImputer"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "80679517",
   "metadata": {},
   "source": [
    "### <span style=\"color:#20B2AA\">   Estandarizando.           <br>\n",
    "7. Estandarizar las columnas numéricas utilizando los métodos min-max y z-score para\n",
    "cada uno de los datasets imputados. Como resultado, se generarán 4 datasets:\n",
    "echo_mediana_minmax, echo_mediana_z, echo_knn_minmax y echo_knn_z.\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "6cb42a6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 132 entries, 0 to 131\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype   \n",
      "---  ------        --------------  -----   \n",
      " 0   Survival      132 non-null    float64 \n",
      " 1   StillAlive    132 non-null    float64 \n",
      " 2   AgeAttack     132 non-null    float64 \n",
      " 3   PericardEffu  132 non-null    float64 \n",
      " 4   FracShort     132 non-null    float64 \n",
      " 5   EPSS          132 non-null    float64 \n",
      " 6   LVDD          132 non-null    float64 \n",
      " 7   WMS           132 non-null    float64 \n",
      " 8   WMI           132 non-null    float64 \n",
      " 9   Mult          132 non-null    float64 \n",
      " 10  Name          132 non-null    object  \n",
      " 11  Group         132 non-null    float64 \n",
      " 12  AliveAt1      74 non-null     category\n",
      "dtypes: category(1), float64(11), object(1)\n",
      "memory usage: 12.8+ KB\n"
     ]
    }
   ],
   "source": [
    "echo_knn.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "4b367b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "colsNumericasMediana = ['Survival',   'FracShort', 'EPSS', 'LVDD', 'WMS', 'WMI', 'Mult']\n",
    "colsNumericasKNN = ['Survival', 'StillAlive', 'AgeAttack', 'PericardEffu', 'FracShort', \n",
    "        'EPSS', 'LVDD', 'WMS', 'WMI', 'Mult', 'Group']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "aee92499",
   "metadata": {},
   "source": [
    "#### MIN-MAX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "1f77ab71",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
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       "      <th>PericardEffu</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.192982</td>\n",
       "      <td>False</td>\n",
       "      <td>71</td>\n",
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       "      <td>0.416667</td>\n",
       "      <td>0.2250</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.280702</td>\n",
       "      <td>False</td>\n",
       "      <td>55</td>\n",
       "      <td>False</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.244444</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.3015</td>\n",
       "      <td>0.511111</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>57</td>\n",
       "      <td>False</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.432432</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survival StillAlive AgeAttack PericardEffu  FracShort    EPSS      LVDD  \\\n",
       "0  0.192982      False        71        False   0.416667  0.2250  0.511111   \n",
       "1  0.333333      False        72        False   0.616667  0.1500  0.400000   \n",
       "2  0.280702      False        55        False   0.416667  0.1000  0.244444   \n",
       "3  1.000000      False        60        False   0.400000  0.3015  0.511111   \n",
       "4  0.333333       True        57        False   0.250000  0.5500  0.777778   \n",
       "\n",
       "        WMS   WMI      Mult  Name Group AliveAt1  \n",
       "0  0.324324  0.00  0.473684  name  True  FALLECE  \n",
       "1  0.324324  0.35  0.263158  name  True  FALLECE  \n",
       "2  0.324324  0.00  0.473684  name  True  FALLECE  \n",
       "3  0.378378  0.20  0.368421  name  True  FALLECE  \n",
       "4  0.432432  0.60  0.263158  name  True  FALLECE  "
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aplicar min-max\n",
    "echo_mediana_minmax = echo_mediana.copy()\n",
    "scaler_minmax = MinMaxScaler()\n",
    "echo_mediana_minmax[colsNumericasMediana] = scaler_minmax.fit_transform(echo_mediana_minmax[colsNumericasMediana])\n",
    "echo_mediana_minmax.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "9a6e2de5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
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       "      <th>PericardEffu</th>\n",
       "      <th>FracShort</th>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0.473684</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.20</td>\n",
       "      <td>0.368421</td>\n",
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       "      <td>FALLECE</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.431373</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.432432</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survival  StillAlive  AgeAttack  PericardEffu  FracShort    EPSS      LVDD  \\\n",
       "0  0.192982         0.0   0.705882           0.0   0.416667  0.2250  0.511111   \n",
       "1  0.333333         0.0   0.725490           0.0   0.616667  0.1500  0.400000   \n",
       "2  0.280702         0.0   0.392157           0.0   0.416667  0.1000  0.244444   \n",
       "3  1.000000         0.0   0.490196           0.0   0.400000  0.3015  0.511111   \n",
       "4  0.333333         1.0   0.431373           0.0   0.250000  0.5500  0.777778   \n",
       "\n",
       "        WMS   WMI      Mult  Name  Group AliveAt1  \n",
       "0  0.324324  0.00  0.473684  name    0.0  FALLECE  \n",
       "1  0.324324  0.35  0.263158  name    0.0  FALLECE  \n",
       "2  0.324324  0.00  0.473684  name    0.0  FALLECE  \n",
       "3  0.378378  0.20  0.368421  name    0.0  FALLECE  \n",
       "4  0.432432  0.60  0.263158  name    0.0  FALLECE  "
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aplicar min-max\n",
    "echo_knn_minmax = echo_knn.copy()\n",
    "scaler_minmax = MinMaxScaler()\n",
    "echo_knn_minmax[colsNumericasKNN] = scaler_minmax.fit_transform(echo_knn_minmax[colsNumericasKNN])\n",
    "echo_knn_minmax.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9a74565a",
   "metadata": {},
   "source": [
    "### Z-SCORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "3d2e5e52",
   "metadata": {},
   "outputs": [],
   "source": [
    "echo_mediana_z = echo_mediana.copy()\n",
    "scaler_zscore = StandardScaler()\n",
    "echo_mediana_z[colsNumericasMediana] = scaler_zscore.fit_transform(echo_mediana_z[colsNumericasMediana])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "bd68dedd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
       "      <th>AgeAttack</th>\n",
       "      <th>PericardEffu</th>\n",
       "      <th>FracShort</th>\n",
       "      <th>EPSS</th>\n",
       "      <th>LVDD</th>\n",
       "      <th>WMS</th>\n",
       "      <th>WMI</th>\n",
       "      <th>Mult</th>\n",
       "      <th>Name</th>\n",
       "      <th>Group</th>\n",
       "      <th>AliveAt1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.714584</td>\n",
       "      <td>False</td>\n",
       "      <td>71</td>\n",
       "      <td>False</td>\n",
       "      <td>0.423737</td>\n",
       "      <td>-0.438268</td>\n",
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       "      <td>-0.086296</td>\n",
       "      <td>-0.850488</td>\n",
       "      <td>1.075824</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.204305</td>\n",
       "      <td>False</td>\n",
       "      <td>72</td>\n",
       "      <td>False</td>\n",
       "      <td>1.580976</td>\n",
       "      <td>-0.871860</td>\n",
       "      <td>-0.844124</td>\n",
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       "      <td>0.705652</td>\n",
       "      <td>-0.917282</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.395660</td>\n",
       "      <td>False</td>\n",
       "      <td>55</td>\n",
       "      <td>False</td>\n",
       "      <td>0.423737</td>\n",
       "      <td>-1.160922</td>\n",
       "      <td>-1.746867</td>\n",
       "      <td>-0.086296</td>\n",
       "      <td>-0.850488</td>\n",
       "      <td>1.075824</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.219521</td>\n",
       "      <td>False</td>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "      <td>0.327300</td>\n",
       "      <td>0.003996</td>\n",
       "      <td>-0.199307</td>\n",
       "      <td>0.319946</td>\n",
       "      <td>0.038735</td>\n",
       "      <td>0.079271</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.204305</td>\n",
       "      <td>True</td>\n",
       "      <td>57</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.540630</td>\n",
       "      <td>1.440633</td>\n",
       "      <td>1.348253</td>\n",
       "      <td>0.726188</td>\n",
       "      <td>1.817181</td>\n",
       "      <td>-0.917282</td>\n",
       "      <td>name</td>\n",
       "      <td>True</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survival StillAlive AgeAttack PericardEffu  FracShort      EPSS      LVDD  \\\n",
       "0 -0.714584      False        71        False   0.423737 -0.438268 -0.199307   \n",
       "1 -0.204305      False        72        False   1.580976 -0.871860 -0.844124   \n",
       "2 -0.395660      False        55        False   0.423737 -1.160922 -1.746867   \n",
       "3  2.219521      False        60        False   0.327300  0.003996 -0.199307   \n",
       "4 -0.204305       True        57        False  -0.540630  1.440633  1.348253   \n",
       "\n",
       "        WMS       WMI      Mult  Name Group AliveAt1  \n",
       "0 -0.086296 -0.850488  1.075824  name  True  FALLECE  \n",
       "1 -0.086296  0.705652 -0.917282  name  True  FALLECE  \n",
       "2 -0.086296 -0.850488  1.075824  name  True  FALLECE  \n",
       "3  0.319946  0.038735  0.079271  name  True  FALLECE  \n",
       "4  0.726188  1.817181 -0.917282  name  True  FALLECE  "
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "echo_mediana_z.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "842b7d2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
       "      <th>AgeAttack</th>\n",
       "      <th>PericardEffu</th>\n",
       "      <th>FracShort</th>\n",
       "      <th>EPSS</th>\n",
       "      <th>LVDD</th>\n",
       "      <th>WMS</th>\n",
       "      <th>WMI</th>\n",
       "      <th>Mult</th>\n",
       "      <th>Name</th>\n",
       "      <th>Group</th>\n",
       "      <th>AliveAt1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.707056</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>0.940248</td>\n",
       "      <td>-0.471405</td>\n",
       "      <td>0.434731</td>\n",
       "      <td>-0.460292</td>\n",
       "      <td>-0.224674</td>\n",
       "      <td>-0.084694</td>\n",
       "      <td>-0.857321</td>\n",
       "      <td>1.090275</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.201640</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.060280</td>\n",
       "      <td>-0.471405</td>\n",
       "      <td>1.574821</td>\n",
       "      <td>-0.881509</td>\n",
       "      <td>-0.855673</td>\n",
       "      <td>-0.084694</td>\n",
       "      <td>0.680862</td>\n",
       "      <td>-0.874452</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.391171</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-0.980258</td>\n",
       "      <td>-0.471405</td>\n",
       "      <td>0.434731</td>\n",
       "      <td>-1.162321</td>\n",
       "      <td>-1.739072</td>\n",
       "      <td>-0.084694</td>\n",
       "      <td>-0.857321</td>\n",
       "      <td>1.090275</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.199087</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-0.380100</td>\n",
       "      <td>-0.471405</td>\n",
       "      <td>0.339724</td>\n",
       "      <td>-0.030650</td>\n",
       "      <td>-0.224674</td>\n",
       "      <td>0.318211</td>\n",
       "      <td>0.021641</td>\n",
       "      <td>0.107911</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.201640</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>-0.740195</td>\n",
       "      <td>-0.471405</td>\n",
       "      <td>-0.515343</td>\n",
       "      <td>1.364984</td>\n",
       "      <td>1.289724</td>\n",
       "      <td>0.721116</td>\n",
       "      <td>1.779565</td>\n",
       "      <td>-0.874452</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survival  StillAlive  AgeAttack  PericardEffu  FracShort      EPSS  \\\n",
       "0 -0.707056   -0.707107   0.940248     -0.471405   0.434731 -0.460292   \n",
       "1 -0.201640   -0.707107   1.060280     -0.471405   1.574821 -0.881509   \n",
       "2 -0.391171   -0.707107  -0.980258     -0.471405   0.434731 -1.162321   \n",
       "3  2.199087   -0.707107  -0.380100     -0.471405   0.339724 -0.030650   \n",
       "4 -0.201640    1.414214  -0.740195     -0.471405  -0.515343  1.364984   \n",
       "\n",
       "       LVDD       WMS       WMI      Mult  Name  Group AliveAt1  \n",
       "0 -0.224674 -0.084694 -0.857321  1.090275  name    0.0  FALLECE  \n",
       "1 -0.855673 -0.084694  0.680862 -0.874452  name    0.0  FALLECE  \n",
       "2 -1.739072 -0.084694 -0.857321  1.090275  name    0.0  FALLECE  \n",
       "3 -0.224674  0.318211  0.021641  0.107911  name    0.0  FALLECE  \n",
       "4  1.289724  0.721116  1.779565 -0.874452  name    0.0  FALLECE  "
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "echo_knn_z = echo_knn.copy()\n",
    "scaler_minmax = StandardScaler()\n",
    "echo_knn_z[colsNumericasKNN] = scaler_minmax.fit_transform(echo_knn_z[colsNumericasKNN])\n",
    "echo_knn_z.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9d3913b6",
   "metadata": {},
   "source": [
    "### <span style=\"color:#20B2AA\">   Ficheros Imputación y Estandarización           <br>\n",
    "8. Generar un fichero CSV resultado para cada una de las diferentes técnicas de\n",
    "imputación y estandarización utilizadas. Los nombres de los ficheros tendrán el nombre\n",
    "del dataset con la extensión .csv. \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b709d69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# guardar el dataframe como archivo CSV\n",
    "\n",
    "#Imputaciones\n",
    "echo_mediana.to_csv('echo_mediana.csv', index=False)\n",
    "echo_knn.to_csv('echo_knn.csv', index=False)\n",
    "\n",
    "#Estandarizaciones\n",
    "\n",
    "#MIN-MAX\n",
    "echo_mediana_minmax.to_csv('echo_mediana_minmax.csv', index=False)\n",
    "echo_knn_minmax.to_csv('echo_knn_minmax.csv', index=False)\n",
    "\n",
    "#Z-SCORE\n",
    "echo_mediana_z.to_csv('echo_mediana_z.csv', index=False)\n",
    "echo_knn_z.to_csv('echo_knn_z.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "86f204ca",
   "metadata": {},
   "source": [
    "### <span style=\"color:#20B2AA\">   Balanceando echo_knn_z           <br>\n",
    "9. Para el dataset echo_knn_z, balancea los datos en base a la variable AliveAt1,\n",
    "empleando los métodos de submuestreo, sobremuestreo y SMOTE. \n",
    "\n",
    "***"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "abfee2f6",
   "metadata": {},
   "source": [
    "Podemos ver que la clase `AliveAt1` está claramente desbalanceada hacia las personas que FALLECEN:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "916d35fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FALLECE    50\n",
      "VIVE       24\n",
      "Name: AliveAt1, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(datos['AliveAt1'].value_counts())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f44833da",
   "metadata": {},
   "source": [
    "Primero, creemos dos conjuntos con los datos de la clase `AliveAt1` por separado, uno con las personas que viven y otra con las que fallecen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "467cabb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 13)\n",
      "(50, 13)\n"
     ]
    }
   ],
   "source": [
    "vive = echo_knn_z[echo_knn_z['AliveAt1'] == \"VIVE\"]\n",
    "fallece  = echo_knn_z[echo_knn_z['AliveAt1'] == \"FALLECE\"]\n",
    "print(vive.shape)\n",
    "print(fallece.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "44d92fdf",
   "metadata": {},
   "source": [
    "Podemos balancear el conjunto de datos haciendo un submuestreo a través de la función `resample` de `sklearn.utils`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "b6aa53ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 13)\n"
     ]
    }
   ],
   "source": [
    "echo_knn_z_submuestreo = resample(fallece, replace=True, n_samples=len(vive), random_state=42)\n",
    "\n",
    "print(echo_knn_z_submuestreo.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1cc594ab",
   "metadata": {},
   "source": [
    "Para generar el conjunto de datos balanceado simplemente concatenamos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "1af84065",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>72</th>\n",
       "      <td>0.430130</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-0.380100</td>\n",
       "      <td>-0.471405</td>\n",
       "      <td>-0.895373</td>\n",
       "      <td>-0.291805</td>\n",
       "      <td>-0.603273</td>\n",
       "      <td>0.116759</td>\n",
       "      <td>0.680862</td>\n",
       "      <td>-0.874452</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1.440962</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-0.020005</td>\n",
       "      <td>-0.471405</td>\n",
       "      <td>-0.610351</td>\n",
       "      <td>0.101332</td>\n",
       "      <td>-0.224674</td>\n",
       "      <td>-0.286146</td>\n",
       "      <td>-0.637580</td>\n",
       "      <td>0.599093</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.012109</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>0.340090</td>\n",
       "      <td>-0.471405</td>\n",
       "      <td>-1.180396</td>\n",
       "      <td>0.662955</td>\n",
       "      <td>1.289724</td>\n",
       "      <td>-1.293409</td>\n",
       "      <td>-0.198099</td>\n",
       "      <td>-1.856816</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.303776</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-1.100290</td>\n",
       "      <td>2.121320</td>\n",
       "      <td>-1.370411</td>\n",
       "      <td>0.634874</td>\n",
       "      <td>-0.729473</td>\n",
       "      <td>0.721116</td>\n",
       "      <td>0.241382</td>\n",
       "      <td>0.599093</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.756847</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-0.380100</td>\n",
       "      <td>-0.471405</td>\n",
       "      <td>1.099783</td>\n",
       "      <td>-0.600698</td>\n",
       "      <td>0.532525</td>\n",
       "      <td>-0.084694</td>\n",
       "      <td>-0.857321</td>\n",
       "      <td>1.090275</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FALLECE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survival  StillAlive  AgeAttack  PericardEffu  FracShort      EPSS  \\\n",
       "72  0.430130   -0.707107  -0.380100     -0.471405  -0.895373 -0.291805   \n",
       "56  1.440962   -0.707107  -0.020005     -0.471405  -0.610351  0.101332   \n",
       "18 -0.012109    1.414214   0.340090     -0.471405  -1.180396  0.662955   \n",
       "81  0.303776   -0.707107  -1.100290      2.121320  -1.370411  0.634874   \n",
       "7   1.756847   -0.707107  -0.380100     -0.471405   1.099783 -0.600698   \n",
       "\n",
       "        LVDD       WMS       WMI      Mult  Name  Group AliveAt1  \n",
       "72 -0.603273  0.116759  0.680862 -0.874452  name    0.0  FALLECE  \n",
       "56 -0.224674 -0.286146 -0.637580  0.599093  name    0.0  FALLECE  \n",
       "18  1.289724 -1.293409 -0.198099 -1.856816  name    0.0  FALLECE  \n",
       "81 -0.729473  0.721116  0.241382  0.599093  name    0.0  FALLECE  \n",
       "7   0.532525 -0.084694 -0.857321  1.090275  name    0.0  FALLECE  "
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "echo_knn_z_submuestreo = pd.concat([echo_knn_z_submuestreo, vive])\n",
    "echo_knn_z_submuestreo.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bd7a2b0d",
   "metadata": {},
   "source": [
    "Se puede hacer un sobremuestreo de una forma similar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "8d1050e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 13)\n"
     ]
    }
   ],
   "source": [
    "echo_knn_z_sobremuestreo = resample(vive, replace=True, n_samples=len(fallece), random_state=42)\n",
    "\n",
    "print(echo_knn_z_sobremuestreo.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cb29e9e0",
   "metadata": {},
   "source": [
    "Para generar el conjunto de datos balanceado simplemente concatenamos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "ce3579ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survival</th>\n",
       "      <th>StillAlive</th>\n",
       "      <th>AgeAttack</th>\n",
       "      <th>PericardEffu</th>\n",
       "      <th>FracShort</th>\n",
       "      <th>EPSS</th>\n",
       "      <th>LVDD</th>\n",
       "      <th>WMS</th>\n",
       "      <th>WMI</th>\n",
       "      <th>Mult</th>\n",
       "      <th>Name</th>\n",
       "      <th>Group</th>\n",
       "      <th>AliveAt1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-1.351462</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>2.620691</td>\n",
       "      <td>2.121320</td>\n",
       "      <td>-0.325329</td>\n",
       "      <td>0.943766</td>\n",
       "      <td>0.911124</td>\n",
       "      <td>-0.118941</td>\n",
       "      <td>0.021641</td>\n",
       "      <td>-0.383271</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>VIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>-1.086118</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>0.220058</td>\n",
       "      <td>2.121320</td>\n",
       "      <td>-0.515343</td>\n",
       "      <td>-0.530495</td>\n",
       "      <td>0.911124</td>\n",
       "      <td>0.318211</td>\n",
       "      <td>0.021641</td>\n",
       "      <td>0.107911</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>VIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>-1.389368</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>2.740723</td>\n",
       "      <td>-0.471405</td>\n",
       "      <td>0.054701</td>\n",
       "      <td>-0.010993</td>\n",
       "      <td>0.532525</td>\n",
       "      <td>1.929831</td>\n",
       "      <td>1.779565</td>\n",
       "      <td>0.107911</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>VIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>-1.389368</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>-0.020005</td>\n",
       "      <td>2.121320</td>\n",
       "      <td>-0.467840</td>\n",
       "      <td>1.042050</td>\n",
       "      <td>0.280125</td>\n",
       "      <td>1.728379</td>\n",
       "      <td>1.999305</td>\n",
       "      <td>-0.383271</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>VIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-1.370415</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>1.180311</td>\n",
       "      <td>-0.471405</td>\n",
       "      <td>0.149709</td>\n",
       "      <td>0.063422</td>\n",
       "      <td>1.668323</td>\n",
       "      <td>-1.394135</td>\n",
       "      <td>0.241382</td>\n",
       "      <td>-1.856816</td>\n",
       "      <td>name</td>\n",
       "      <td>0.0</td>\n",
       "      <td>VIVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survival  StillAlive  AgeAttack  PericardEffu  FracShort      EPSS  \\\n",
       "21 -1.351462    1.414214   2.620691      2.121320  -0.325329  0.943766   \n",
       "98 -1.086118    1.414214   0.220058      2.121320  -0.515343 -0.530495   \n",
       "70 -1.389368    1.414214   2.740723     -0.471405   0.054701 -0.010993   \n",
       "48 -1.389368    1.414214  -0.020005      2.121320  -0.467840  1.042050   \n",
       "22 -1.370415    1.414214   1.180311     -0.471405   0.149709  0.063422   \n",
       "\n",
       "        LVDD       WMS       WMI      Mult  Name  Group AliveAt1  \n",
       "21  0.911124 -0.118941  0.021641 -0.383271  name    0.0     VIVE  \n",
       "98  0.911124  0.318211  0.021641  0.107911  name    0.0     VIVE  \n",
       "70  0.532525  1.929831  1.779565  0.107911  name    0.0     VIVE  \n",
       "48  0.280125  1.728379  1.999305 -0.383271  name    0.0     VIVE  \n",
       "22  1.668323 -1.394135  0.241382 -1.856816  name    0.0     VIVE  "
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "echo_knn_z_sobremuestreo = pd.concat([echo_knn_z_sobremuestreo, fallece])\n",
    "echo_knn_z_sobremuestreo.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f2b5e7a6",
   "metadata": {},
   "source": [
    "Para utilizar `SMOTE`, primero instalamos la librería `imbalanced-learn`:\n",
    "Paso 1: Seleccionamos las variables no categoricas ni booleanas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "c7904bf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 132 entries, 0 to 131\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Survival      132 non-null    float64\n",
      " 1   StillAlive    132 non-null    float64\n",
      " 2   AgeAttack     132 non-null    float64\n",
      " 3   PericardEffu  132 non-null    float64\n",
      " 4   FracShort     132 non-null    float64\n",
      " 5   EPSS          132 non-null    float64\n",
      " 6   LVDD          132 non-null    float64\n",
      " 7   WMS           132 non-null    float64\n",
      " 8   WMI           132 non-null    float64\n",
      " 9   Mult          132 non-null    float64\n",
      " 10  Group         132 non-null    float64\n",
      "dtypes: float64(11)\n",
      "memory usage: 11.5 KB\n"
     ]
    }
   ],
   "source": [
    "X = echo_knn_z.filter([\"Survival\", \"StillAlive\", \"AgeAttack\", \"PericardEffu\", \"FracShort\", \"EPSS\", \"LVDD\", \"WMS\", \"WMI\", \"Mult\", \"Group\"], axis=1).copy()\n",
    "y = echo_knn_z.filter(['AliveAt1'], axis=1).copy()\n",
    "X.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a7cc1b84",
   "metadata": {},
   "source": [
    "Para utilizar SMOTE, creamos una instancia y la aplicamos al dataset. Sin embargo, para manejar valores NaN, creamos un pipeline que primero aplica SimpleImputer de sklearn y luego SMOTE.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "057db1b9",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'float' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[419], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[39m# Es necesario este pipeline para dar soporte a los NaN. Por defecto smote no los soporta.\u001b[39;00m\n\u001b[0;32m      4\u001b[0m smote_enn \u001b[39m=\u001b[39m make_pipeline(SimpleImputer(), SMOTEENN(smote\u001b[39m=\u001b[39msmote))\n\u001b[1;32m----> 5\u001b[0m A, b \u001b[39m=\u001b[39m smote_enn\u001b[39m.\u001b[39;49mfit_resample(X, y)\n\u001b[0;32m      6\u001b[0m \u001b[39mprint\u001b[39m(A\u001b[39m.\u001b[39mshape)\n\u001b[0;32m      7\u001b[0m \u001b[39mprint\u001b[39m(b\u001b[39m.\u001b[39mshape)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\imblearn\\pipeline.py:380\u001b[0m, in \u001b[0;36mPipeline.fit_resample\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m    378\u001b[0m fit_params_last_step \u001b[39m=\u001b[39m fit_params_steps[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msteps[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m][\u001b[39m0\u001b[39m]]\n\u001b[0;32m    379\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(last_step, \u001b[39m\"\u001b[39m\u001b[39mfit_resample\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m--> 380\u001b[0m     \u001b[39mreturn\u001b[39;00m last_step\u001b[39m.\u001b[39;49mfit_resample(Xt, yt, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_params_last_step)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\imblearn\\base.py:203\u001b[0m, in \u001b[0;36mBaseSampler.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    182\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Resample the dataset.\u001b[39;00m\n\u001b[0;32m    183\u001b[0m \n\u001b[0;32m    184\u001b[0m \u001b[39mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[39m    The corresponding label of `X_resampled`.\u001b[39;00m\n\u001b[0;32m    201\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    202\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_params()\n\u001b[1;32m--> 203\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mfit_resample(X, y)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\imblearn\\base.py:80\u001b[0m, in \u001b[0;36mSamplerMixin.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m     59\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit_resample\u001b[39m(\u001b[39mself\u001b[39m, X, y):\n\u001b[0;32m     60\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Resample the dataset.\u001b[39;00m\n\u001b[0;32m     61\u001b[0m \n\u001b[0;32m     62\u001b[0m \u001b[39m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     78\u001b[0m \u001b[39m        The corresponding label of `X_resampled`.\u001b[39;00m\n\u001b[0;32m     79\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 80\u001b[0m     check_classification_targets(y)\n\u001b[0;32m     81\u001b[0m     arrays_transformer \u001b[39m=\u001b[39m ArraysTransformer(X, y)\n\u001b[0;32m     82\u001b[0m     X, y, binarize_y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_X_y(X, y)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\multiclass.py:199\u001b[0m, in \u001b[0;36mcheck_classification_targets\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m    187\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcheck_classification_targets\u001b[39m(y):\n\u001b[0;32m    188\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Ensure that target y is of a non-regression type.\u001b[39;00m\n\u001b[0;32m    189\u001b[0m \n\u001b[0;32m    190\u001b[0m \u001b[39m    Only the following target types (as defined in type_of_target) are allowed:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    197\u001b[0m \u001b[39m        Target values.\u001b[39;00m\n\u001b[0;32m    198\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 199\u001b[0m     y_type \u001b[39m=\u001b[39m type_of_target(y, input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39my\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m    200\u001b[0m     \u001b[39mif\u001b[39;00m y_type \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m [\n\u001b[0;32m    201\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mbinary\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m    202\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mmulticlass\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    205\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mmultilabel-sequences\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m    206\u001b[0m     ]:\n\u001b[0;32m    207\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mUnknown label type: \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m y_type)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\multiclass.py:363\u001b[0m, in \u001b[0;36mtype_of_target\u001b[1;34m(y, input_name)\u001b[0m\n\u001b[0;32m    361\u001b[0m \u001b[39m# Check multiclass\u001b[39;00m\n\u001b[0;32m    362\u001b[0m first_row \u001b[39m=\u001b[39m y[\u001b[39m0\u001b[39m] \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m issparse(y) \u001b[39melse\u001b[39;00m y\u001b[39m.\u001b[39mgetrow(\u001b[39m0\u001b[39m)\u001b[39m.\u001b[39mdata\n\u001b[1;32m--> 363\u001b[0m \u001b[39mif\u001b[39;00m xp\u001b[39m.\u001b[39;49munique_values(y)\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m] \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m \u001b[39mor\u001b[39;00m (y\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m2\u001b[39m \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(first_row) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m):\n\u001b[0;32m    364\u001b[0m     \u001b[39m# [1, 2, 3] or [[1., 2., 3]] or [[1, 2]]\u001b[39;00m\n\u001b[0;32m    365\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mmulticlass\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m suffix\n\u001b[0;32m    366\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\_array_api.py:84\u001b[0m, in \u001b[0;36m_NumPyApiWrapper.unique_values\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39munique_values\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[1;32m---> 84\u001b[0m     \u001b[39mreturn\u001b[39;00m numpy\u001b[39m.\u001b[39;49munique(x)\n",
      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36munique\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\lib\\arraysetops.py:274\u001b[0m, in \u001b[0;36munique\u001b[1;34m(ar, return_index, return_inverse, return_counts, axis, equal_nan)\u001b[0m\n\u001b[0;32m    272\u001b[0m ar \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masanyarray(ar)\n\u001b[0;32m    273\u001b[0m \u001b[39mif\u001b[39;00m axis \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 274\u001b[0m     ret \u001b[39m=\u001b[39m _unique1d(ar, return_index, return_inverse, return_counts, \n\u001b[0;32m    275\u001b[0m                     equal_nan\u001b[39m=\u001b[39;49mequal_nan)\n\u001b[0;32m    276\u001b[0m     \u001b[39mreturn\u001b[39;00m _unpack_tuple(ret)\n\u001b[0;32m    278\u001b[0m \u001b[39m# axis was specified and not None\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\lib\\arraysetops.py:336\u001b[0m, in \u001b[0;36m_unique1d\u001b[1;34m(ar, return_index, return_inverse, return_counts, equal_nan)\u001b[0m\n\u001b[0;32m    334\u001b[0m     aux \u001b[39m=\u001b[39m ar[perm]\n\u001b[0;32m    335\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 336\u001b[0m     ar\u001b[39m.\u001b[39msort()\n\u001b[0;32m    337\u001b[0m     aux \u001b[39m=\u001b[39m ar\n\u001b[0;32m    338\u001b[0m mask \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mempty(aux\u001b[39m.\u001b[39mshape, dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mbool_)\n",
      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'float' and 'str'"
     ]
    }
   ],
   "source": [
    "# transform the dataset\n",
    "smote = SMOTE()\n",
    "# Es necesario este pipeline para dar soporte a los NaN. Por defecto smote no los soporta.\n",
    "smote_enn = make_pipeline(SimpleImputer(), SMOTEENN(smote=smote))\n",
    "A, b = smote_enn.fit_resample(X, y)\n",
    "print(A.shape)\n",
    "print(b.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "c9c1286e",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'float' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[421], line 9\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[39m# se crea el pipeline para dar soporte a los NaN y SMOTE\u001b[39;00m\n\u001b[0;32m      8\u001b[0m smote_enn \u001b[39m=\u001b[39m make_pipeline(SimpleImputer(), SMOTEENN(smote\u001b[39m=\u001b[39msmote))\n\u001b[1;32m----> 9\u001b[0m A, b \u001b[39m=\u001b[39m smote_enn\u001b[39m.\u001b[39;49mfit_resample(X, y)\n\u001b[0;32m     10\u001b[0m \u001b[39mprint\u001b[39m(A\u001b[39m.\u001b[39mshape)\n\u001b[0;32m     11\u001b[0m \u001b[39mprint\u001b[39m(b\u001b[39m.\u001b[39mshape)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\imblearn\\pipeline.py:380\u001b[0m, in \u001b[0;36mPipeline.fit_resample\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m    378\u001b[0m fit_params_last_step \u001b[39m=\u001b[39m fit_params_steps[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msteps[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m][\u001b[39m0\u001b[39m]]\n\u001b[0;32m    379\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(last_step, \u001b[39m\"\u001b[39m\u001b[39mfit_resample\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m--> 380\u001b[0m     \u001b[39mreturn\u001b[39;00m last_step\u001b[39m.\u001b[39;49mfit_resample(Xt, yt, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_params_last_step)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\imblearn\\base.py:203\u001b[0m, in \u001b[0;36mBaseSampler.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    182\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Resample the dataset.\u001b[39;00m\n\u001b[0;32m    183\u001b[0m \n\u001b[0;32m    184\u001b[0m \u001b[39mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[39m    The corresponding label of `X_resampled`.\u001b[39;00m\n\u001b[0;32m    201\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    202\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_params()\n\u001b[1;32m--> 203\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mfit_resample(X, y)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\imblearn\\base.py:80\u001b[0m, in \u001b[0;36mSamplerMixin.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m     59\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit_resample\u001b[39m(\u001b[39mself\u001b[39m, X, y):\n\u001b[0;32m     60\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Resample the dataset.\u001b[39;00m\n\u001b[0;32m     61\u001b[0m \n\u001b[0;32m     62\u001b[0m \u001b[39m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     78\u001b[0m \u001b[39m        The corresponding label of `X_resampled`.\u001b[39;00m\n\u001b[0;32m     79\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 80\u001b[0m     check_classification_targets(y)\n\u001b[0;32m     81\u001b[0m     arrays_transformer \u001b[39m=\u001b[39m ArraysTransformer(X, y)\n\u001b[0;32m     82\u001b[0m     X, y, binarize_y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_X_y(X, y)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\multiclass.py:199\u001b[0m, in \u001b[0;36mcheck_classification_targets\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m    187\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcheck_classification_targets\u001b[39m(y):\n\u001b[0;32m    188\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Ensure that target y is of a non-regression type.\u001b[39;00m\n\u001b[0;32m    189\u001b[0m \n\u001b[0;32m    190\u001b[0m \u001b[39m    Only the following target types (as defined in type_of_target) are allowed:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    197\u001b[0m \u001b[39m        Target values.\u001b[39;00m\n\u001b[0;32m    198\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 199\u001b[0m     y_type \u001b[39m=\u001b[39m type_of_target(y, input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39my\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m    200\u001b[0m     \u001b[39mif\u001b[39;00m y_type \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m [\n\u001b[0;32m    201\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mbinary\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m    202\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mmulticlass\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    205\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mmultilabel-sequences\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m    206\u001b[0m     ]:\n\u001b[0;32m    207\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mUnknown label type: \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m y_type)\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\multiclass.py:363\u001b[0m, in \u001b[0;36mtype_of_target\u001b[1;34m(y, input_name)\u001b[0m\n\u001b[0;32m    361\u001b[0m \u001b[39m# Check multiclass\u001b[39;00m\n\u001b[0;32m    362\u001b[0m first_row \u001b[39m=\u001b[39m y[\u001b[39m0\u001b[39m] \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m issparse(y) \u001b[39melse\u001b[39;00m y\u001b[39m.\u001b[39mgetrow(\u001b[39m0\u001b[39m)\u001b[39m.\u001b[39mdata\n\u001b[1;32m--> 363\u001b[0m \u001b[39mif\u001b[39;00m xp\u001b[39m.\u001b[39;49munique_values(y)\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m] \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m \u001b[39mor\u001b[39;00m (y\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m2\u001b[39m \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(first_row) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m):\n\u001b[0;32m    364\u001b[0m     \u001b[39m# [1, 2, 3] or [[1., 2., 3]] or [[1, 2]]\u001b[39;00m\n\u001b[0;32m    365\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mmulticlass\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m suffix\n\u001b[0;32m    366\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\_array_api.py:84\u001b[0m, in \u001b[0;36m_NumPyApiWrapper.unique_values\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39munique_values\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[1;32m---> 84\u001b[0m     \u001b[39mreturn\u001b[39;00m numpy\u001b[39m.\u001b[39;49munique(x)\n",
      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36munique\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\lib\\arraysetops.py:274\u001b[0m, in \u001b[0;36munique\u001b[1;34m(ar, return_index, return_inverse, return_counts, axis, equal_nan)\u001b[0m\n\u001b[0;32m    272\u001b[0m ar \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masanyarray(ar)\n\u001b[0;32m    273\u001b[0m \u001b[39mif\u001b[39;00m axis \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 274\u001b[0m     ret \u001b[39m=\u001b[39m _unique1d(ar, return_index, return_inverse, return_counts, \n\u001b[0;32m    275\u001b[0m                     equal_nan\u001b[39m=\u001b[39;49mequal_nan)\n\u001b[0;32m    276\u001b[0m     \u001b[39mreturn\u001b[39;00m _unpack_tuple(ret)\n\u001b[0;32m    278\u001b[0m \u001b[39m# axis was specified and not None\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\itramirez.INDRA\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\lib\\arraysetops.py:336\u001b[0m, in \u001b[0;36m_unique1d\u001b[1;34m(ar, return_index, return_inverse, return_counts, equal_nan)\u001b[0m\n\u001b[0;32m    334\u001b[0m     aux \u001b[39m=\u001b[39m ar[perm]\n\u001b[0;32m    335\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 336\u001b[0m     ar\u001b[39m.\u001b[39msort()\n\u001b[0;32m    337\u001b[0m     aux \u001b[39m=\u001b[39m ar\n\u001b[0;32m    338\u001b[0m mask \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mempty(aux\u001b[39m.\u001b[39mshape, dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mbool_)\n",
      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'float' and 'str'"
     ]
    }
   ],
   "source": [
    "# seleccionar las columnas adecuadas\n",
    "X = echo_knn_z[['Survival', 'StillAlive', 'AgeAttack', 'PericardEffu', 'FracShort', 'EPSS', 'LVDD', 'WMS', 'WMI', 'Mult', 'Group']]\n",
    "y = echo_knn_z['AliveAt1']\n",
    "\n",
    "# transformar el dataset\n",
    "smote = SMOTE()\n",
    "# se crea el pipeline para dar soporte a los NaN y SMOTE\n",
    "smote_enn = make_pipeline(SimpleImputer(), SMOTEENN(smote=smote))\n",
    "A, b = smote_enn.fit_resample(X, y)\n",
    "print(A.shape)\n",
    "print(b.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "987cc36d",
   "metadata": {},
   "source": [
    "### <span style=\"color:#20B2AA\">  Ficheros sub_muestreo          <br>\n",
    "10. Genera un fichero echo_knn_z_submuestreo, echo_knn_z_sobremuestreo, y\n",
    "echo_knn_z_smote\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "936c216d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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