datos/cargar_datos.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3bf1fef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar las librerías necesarias\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "38b3ca10",
   "metadata": {
    "scrolled": false
   },
   "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>AF365</th>\n",
       "      <th>AF450</th>\n",
       "      <th>35 deg</th>\n",
       "      <th>42 deg</th>\n",
       "      <th>Grupo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.320158</td>\n",
       "      <td>1.229614</td>\n",
       "      <td>13.01</td>\n",
       "      <td>19.39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.529274</td>\n",
       "      <td>0.616514</td>\n",
       "      <td>8.18</td>\n",
       "      <td>24.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.908257</td>\n",
       "      <td>0.955847</td>\n",
       "      <td>7.75</td>\n",
       "      <td>16.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.590625</td>\n",
       "      <td>1.018495</td>\n",
       "      <td>4.10</td>\n",
       "      <td>16.30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.617071</td>\n",
       "      <td>1.242961</td>\n",
       "      <td>5.76</td>\n",
       "      <td>21.05</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>4.093525</td>\n",
       "      <td>3.037288</td>\n",
       "      <td>3.97</td>\n",
       "      <td>5.35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>4.730000</td>\n",
       "      <td>3.040000</td>\n",
       "      <td>7.04</td>\n",
       "      <td>14.44</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>4.010000</td>\n",
       "      <td>4.110000</td>\n",
       "      <td>6.65</td>\n",
       "      <td>17.37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>6.70</td>\n",
       "      <td>10.57</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>3.885714</td>\n",
       "      <td>2.403073</td>\n",
       "      <td>6.61</td>\n",
       "      <td>14.30</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        AF365     AF450  35 deg  42 deg  Grupo\n",
       "0    2.320158  1.229614   13.01   19.39      0\n",
       "1    1.529274  0.616514    8.18   24.78      0\n",
       "2    1.908257  0.955847    7.75   16.90      0\n",
       "3    1.590625  1.018495    4.10   16.30      0\n",
       "4    1.617071  1.242961    5.76   21.05      0\n",
       "..        ...       ...     ...     ...    ...\n",
       "119  4.093525  3.037288    3.97    5.35      2\n",
       "120  4.730000  3.040000    7.04   14.44      2\n",
       "121  4.010000  4.110000    6.65   17.37      2\n",
       "122  3.200000  2.360000    6.70   10.57      2\n",
       "123  3.885714  2.403073    6.61   14.30      2\n",
       "\n",
       "[124 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cargar el dataset\n",
    "datos = pd.read_csv('datos.csv')\n",
    "\n",
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16427c8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    62\n",
       "0    48\n",
       "2    14\n",
       "Name: Grupo, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contar el número de instancias de cada grupo\n",
    "\n",
    "datos['Grupo'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5d59170",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Separar datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d434bbec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se separa la variable a predecir (Grupo)\n",
    "X = datos.drop('Grupo', axis=1)\n",
    "y = datos['Grupo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf9eef7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar librería de scikit-learn para separar los datos\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Separar datos en conjunto de entrenamiento y test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.2,\n",
    "                                                    stratify=y,\n",
    "                                                    random_state=1234)\n",
    "\n",
    "# Al tratarse de un dataset desbalanceado, se hace una separación estratificada con stratify\n",
    "# Así se asegura que la distribución de cada grupo será igual en los dos conjuntos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a3c7614",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features Shape: (99, 4)\n",
      "Training Labels Shape: (99,)\n",
      "Testing Features Shape: (25, 4)\n",
      "Testing Labels Shape: (25,)\n"
     ]
    }
   ],
   "source": [
    "# Visualizar datos separados\n",
    "print('Training Features Shape:', X_train.shape)\n",
    "print('Training Labels Shape:', y_train.shape)\n",
    "print('Testing Features Shape:', X_test.shape)\n",
    "print('Testing Labels Shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "134b3ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AF365</th>\n",
       "      <th>AF450</th>\n",
       "      <th>35 deg</th>\n",
       "      <th>42 deg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1.964789</td>\n",
       "      <td>1.321641</td>\n",
       "      <td>12.81</td>\n",
       "      <td>20.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>3.120000</td>\n",
       "      <td>1.630000</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.617071</td>\n",
       "      <td>1.242961</td>\n",
       "      <td>5.76</td>\n",
       "      <td>21.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2.180147</td>\n",
       "      <td>1.489262</td>\n",
       "      <td>5.69</td>\n",
       "      <td>8.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2.350000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>6.77</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.360190</td>\n",
       "      <td>2.417640</td>\n",
       "      <td>9.17</td>\n",
       "      <td>15.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2.360000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>6.27</td>\n",
       "      <td>10.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.200820</td>\n",
       "      <td>0.671578</td>\n",
       "      <td>8.17</td>\n",
       "      <td>17.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>3.034884</td>\n",
       "      <td>2.051768</td>\n",
       "      <td>4.55</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.320158</td>\n",
       "      <td>1.229614</td>\n",
       "      <td>13.01</td>\n",
       "      <td>19.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        AF365     AF450  35 deg  42 deg\n",
       "43   1.964789  1.321641   12.81   20.09\n",
       "60   3.120000  1.630000    7.00   11.09\n",
       "4    1.617071  1.242961    5.76   21.05\n",
       "74   2.180147  1.489262    5.69    8.41\n",
       "57   2.350000  1.750000    6.77   10.65\n",
       "..        ...       ...     ...     ...\n",
       "14   2.360190  2.417640    9.17   15.97\n",
       "58   2.360000  1.400000    6.27   10.41\n",
       "20   1.200820  0.671578    8.17   17.66\n",
       "118  3.034884  2.051768    4.55    8.50\n",
       "0    2.320158  1.229614   13.01   19.39\n",
       "\n",
       "[99 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f348660d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43     0\n",
       "60     1\n",
       "4      0\n",
       "74     1\n",
       "57     1\n",
       "      ..\n",
       "14     0\n",
       "58     1\n",
       "20     0\n",
       "118    2\n",
       "0      0\n",
       "Name: Grupo, Length: 99, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "96659e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AF365</th>\n",
       "      <th>AF450</th>\n",
       "      <th>35 deg</th>\n",
       "      <th>42 deg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2.710000</td>\n",
       "      <td>2.610000</td>\n",
       "      <td>12.63</td>\n",
       "      <td>15.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2.740964</td>\n",
       "      <td>1.831435</td>\n",
       "      <td>6.10</td>\n",
       "      <td>10.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2.262295</td>\n",
       "      <td>1.052067</td>\n",
       "      <td>6.58</td>\n",
       "      <td>20.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.971609</td>\n",
       "      <td>0.876543</td>\n",
       "      <td>5.84</td>\n",
       "      <td>15.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1.973585</td>\n",
       "      <td>1.543846</td>\n",
       "      <td>7.64</td>\n",
       "      <td>14.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2.745763</td>\n",
       "      <td>2.050569</td>\n",
       "      <td>8.46</td>\n",
       "      <td>14.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2.225962</td>\n",
       "      <td>1.726468</td>\n",
       "      <td>9.00</td>\n",
       "      <td>18.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.410494</td>\n",
       "      <td>0.270591</td>\n",
       "      <td>3.91</td>\n",
       "      <td>16.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2.148500</td>\n",
       "      <td>1.125300</td>\n",
       "      <td>10.21</td>\n",
       "      <td>20.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1.948529</td>\n",
       "      <td>1.697131</td>\n",
       "      <td>5.06</td>\n",
       "      <td>14.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.689723</td>\n",
       "      <td>1.216485</td>\n",
       "      <td>5.27</td>\n",
       "      <td>15.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>3.510000</td>\n",
       "      <td>2.060000</td>\n",
       "      <td>2.33</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.063241</td>\n",
       "      <td>0.833135</td>\n",
       "      <td>11.64</td>\n",
       "      <td>32.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>6.70</td>\n",
       "      <td>10.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2.507812</td>\n",
       "      <td>2.328594</td>\n",
       "      <td>7.48</td>\n",
       "      <td>12.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>4.076923</td>\n",
       "      <td>1.730280</td>\n",
       "      <td>5.39</td>\n",
       "      <td>11.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2.654762</td>\n",
       "      <td>1.793103</td>\n",
       "      <td>3.89</td>\n",
       "      <td>13.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.747126</td>\n",
       "      <td>2.115298</td>\n",
       "      <td>7.97</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.406667</td>\n",
       "      <td>1.475761</td>\n",
       "      <td>14.00</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>4.093525</td>\n",
       "      <td>3.037288</td>\n",
       "      <td>3.97</td>\n",
       "      <td>5.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2.607306</td>\n",
       "      <td>1.342122</td>\n",
       "      <td>11.96</td>\n",
       "      <td>16.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>3.410000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>6.34</td>\n",
       "      <td>12.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2.079208</td>\n",
       "      <td>1.937500</td>\n",
       "      <td>6.78</td>\n",
       "      <td>20.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2.733100</td>\n",
       "      <td>2.121000</td>\n",
       "      <td>7.64</td>\n",
       "      <td>10.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2.011696</td>\n",
       "      <td>1.637843</td>\n",
       "      <td>6.11</td>\n",
       "      <td>10.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        AF365     AF450  35 deg  42 deg\n",
       "56   2.710000  2.610000   12.63   15.48\n",
       "85   2.740964  1.831435    6.10   10.71\n",
       "39   2.262295  1.052067    6.58   20.41\n",
       "26   1.971609  0.876543    5.84   15.02\n",
       "93   1.973585  1.543846    7.64   14.59\n",
       "92   2.745763  2.050569    8.46   14.77\n",
       "30   2.225962  1.726468    9.00   18.72\n",
       "12   0.410494  0.270591    3.91   16.32\n",
       "47   2.148500  1.125300   10.21   20.22\n",
       "79   1.948529  1.697131    5.06   14.76\n",
       "19   1.689723  1.216485    5.27   15.32\n",
       "115  3.510000  2.060000    2.33    3.70\n",
       "24   2.063241  0.833135   11.64   32.04\n",
       "122  3.200000  2.360000    6.70   10.57\n",
       "89   2.507812  2.328594    7.48   12.20\n",
       "68   4.076923  1.730280    5.39   11.88\n",
       "64   2.654762  1.793103    3.89   13.34\n",
       "23   3.747126  2.115298    7.97   15.00\n",
       "15   2.406667  1.475761   14.00   22.00\n",
       "119  4.093525  3.037288    3.97    5.35\n",
       "38   2.607306  1.342122   11.96   16.74\n",
       "54   3.410000  1.400000    6.34   12.66\n",
       "73   2.079208  1.937500    6.78   20.78\n",
       "109  2.733100  2.121000    7.64   10.06\n",
       "105  2.011696  1.637843    6.11   10.06"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f3bd0ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56     1\n",
       "85     1\n",
       "39     0\n",
       "26     0\n",
       "93     1\n",
       "92     1\n",
       "30     0\n",
       "12     0\n",
       "47     0\n",
       "79     1\n",
       "19     0\n",
       "115    2\n",
       "24     0\n",
       "122    2\n",
       "89     1\n",
       "68     1\n",
       "64     1\n",
       "23     0\n",
       "15     0\n",
       "119    2\n",
       "38     0\n",
       "54     1\n",
       "73     1\n",
       "109    1\n",
       "105    1\n",
       "Name: Grupo, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77898621",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Visualizar distribución de cada conjunto (entrenamiento y test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8afad015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(15,5))\n",
    "\n",
    "#fig.suptitle(\"Distribución de los grupos en los conjuntos de entrenamiento y test\")\n",
    "\n",
    "# Representación del conjunto de entrenamiento\n",
    "plt.subplot(121)\n",
    "y_train.value_counts().plot(kind='bar')\n",
    "plt.title('Distribución del conjunto de entrenamiento')\n",
    "plt.xlabel('Grupo')\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Representación del conjunto de test\n",
    "plt.subplot(122)\n",
    "y_test.value_counts().plot(kind='bar', color='seagreen')\n",
    "plt.title('Distribución del conjunto de test')\n",
    "plt.xlabel('Grupo')\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "25323e55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    50\n",
       "0    38\n",
       "2    11\n",
       "Name: Grupo, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribución de los grupos en el conjunto de entrenamiento\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7b04cb6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.505051\n",
       "0    0.383838\n",
       "2    0.111111\n",
       "Name: Grupo, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Valores normalizados\n",
    "y_train.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eeb10115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    12\n",
       "0    10\n",
       "2     3\n",
       "Name: Grupo, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribución de los grupos en el conjunto de test\n",
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c0a19535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.48\n",
       "0    0.40\n",
       "2    0.12\n",
       "Name: Grupo, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Valores normalizados\n",
    "y_test.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4d2091",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**Las clases (grupos) en los dos conjuntos tienen una proporción similar.**\n",
    "\n",
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}