n04_2_clusterizacion_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Librerías"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importación datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edad</th>\n",
       "      <th>educacion_madre</th>\n",
       "      <th>educacion_padre</th>\n",
       "      <th>tiempo_viaje</th>\n",
       "      <th>tiempo_estudio</th>\n",
       "      <th>relacion_familiar</th>\n",
       "      <th>tiempo_libre</th>\n",
       "      <th>salir_amigos</th>\n",
       "      <th>consumo_alcohol_entre_semana</th>\n",
       "      <th>consumo_alcohol_fin_de_semana</th>\n",
       "      <th>...</th>\n",
       "      <th>independizado_padres_True</th>\n",
       "      <th>ocupacion_madre_True</th>\n",
       "      <th>ocupacion_padre_True</th>\n",
       "      <th>razon_matricula_True</th>\n",
       "      <th>interes_educacion_superior_True</th>\n",
       "      <th>internet_casa_True</th>\n",
       "      <th>apoyo_escolar_True</th>\n",
       "      <th>apoyo_familiar_True</th>\n",
       "      <th>actividades_True</th>\n",
       "      <th>relacion_romantica_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   edad  educacion_madre  educacion_padre  tiempo_viaje  tiempo_estudio  \\\n",
       "0  15.0              4.0              3.0           1.0             3.0   \n",
       "1  18.0              2.0              2.0           2.0             3.0   \n",
       "2  17.0              3.0              2.0           1.0             1.0   \n",
       "3  18.0              4.0              4.0           1.0             2.0   \n",
       "4  19.0              4.0              2.0           2.0             2.0   \n",
       "\n",
       "   relacion_familiar  tiempo_libre  salir_amigos  \\\n",
       "0                5.0           4.0           3.0   \n",
       "1                4.0           2.0           1.0   \n",
       "2                5.0           5.0           5.0   \n",
       "3                2.0           4.0           4.0   \n",
       "4                5.0           4.0           4.0   \n",
       "\n",
       "   consumo_alcohol_entre_semana  consumo_alcohol_fin_de_semana  ...  \\\n",
       "0                           1.0                            1.0  ...   \n",
       "1                           1.0                            1.0  ...   \n",
       "2                           2.0                            4.0  ...   \n",
       "3                           1.0                            1.0  ...   \n",
       "4                           1.0                            1.0  ...   \n",
       "\n",
       "   independizado_padres_True  ocupacion_madre_True  ocupacion_padre_True  \\\n",
       "0                          1                     1                     1   \n",
       "1                          1                     1                     1   \n",
       "2                          1                     1                     1   \n",
       "3                          1                     1                     1   \n",
       "4                          1                     1                     1   \n",
       "\n",
       "   razon_matricula_True  interes_educacion_superior_True  internet_casa_True  \\\n",
       "0                     1                                1                   1   \n",
       "1                     1                                1                   1   \n",
       "2                     1                                1                   1   \n",
       "3                     1                                1                   1   \n",
       "4                     1                                1                   1   \n",
       "\n",
       "   apoyo_escolar_True  apoyo_familiar_True  actividades_True  \\\n",
       "0                   0                    1                 1   \n",
       "1                   0                    0                 0   \n",
       "2                   0                    1                 1   \n",
       "3                   1                    1                 1   \n",
       "4                   0                    1                 1   \n",
       "\n",
       "   relacion_romantica_True  \n",
       "0                        0  \n",
       "1                        1  \n",
       "2                        0  \n",
       "3                        1  \n",
       "4                        1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CSV_FILE = 'EDA/datos_clasificacion.csv'\n",
    "\n",
    "df = pd.read_csv(CSV_FILE)\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separación de varibles por grupos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividir las columnas en académicas y sociales/demográficas\n",
    "academicas = [\n",
    "    'tiempo_estudio', 'relacion_familiar', \n",
    "    'salir_amigos', 'consumo_alcohol_entre_semana', \n",
    "    'consumo_alcohol_fin_de_semana'\n",
    "]\n",
    "\n",
    "sociales = [\n",
    "    'edad', 'educacion_madre', 'educacion_padre', \n",
    "    'ocupacion_madre_True', 'ocupacion_padre_True', \n",
    "    'independizado_padres_True'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Escalar las variables para ambas categorías\n",
    "scaler = StandardScaler()\n",
    "academicas_scaled = scaler.fit_transform(df[academicas].dropna())\n",
    "sociales_scaled = scaler.fit_transform(df[sociales].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Realizar clusterización para académicas\n",
    "kmeans_academicas = KMeans(\n",
    "    n_clusters=3, \n",
    "    random_state=42\n",
    ")\n",
    "df['cluster_academicas'] = kmeans_academicas.fit_predict(academicas_scaled)\n",
    "\n",
    "# Realizar clusterización para sociales\n",
    "kmeans_sociales = KMeans(\n",
    "    n_clusters=3, \n",
    "    random_state=42\n",
    ")\n",
    "df['cluster_sociales'] = kmeans_sociales.fit_predict(sociales_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cruce de Clústeres:\n",
      "cluster_sociales      0   1   2\n",
      "cluster_academicas             \n",
      "0                    64  36  26\n",
      "1                   142  93  74\n",
      "2                    80  32  40\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizar cómo se cruzan los clústeres\n",
    "cruce = pd.crosstab(\n",
    "    df['cluster_academicas'], \n",
    "    df['cluster_sociales']\n",
    ")\n",
    "\n",
    "# Mostrar la tabla de cruce\n",
    "print(\"Cruce de Clústeres:\")\n",
    "print(cruce)\n",
    "\n",
    "# Visualización de los clústeres\n",
    "sns.heatmap(cruce, annot=True, fmt=\"d\", cmap=\"YlGnBu\")\n",
    "plt.title(\"Cruce de Clústeres Académicos y Sociales\")\n",
    "plt.xlabel(\"Clúster Sociales\")\n",
    "plt.ylabel(\"Clúster Académicos\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpretación del Cruce de Clústeres Académicos y Sociales\n",
    "\n",
    "## Análisis General\n",
    "El gráfico de calor muestra cómo se distribuyen los estudiantes entre los clústeres académicos y sociales. Las cifras en cada celda representan el número de estudiantes que pertenecen a ambos clústeres al mismo tiempo.\n",
    "\n",
    "### Puntos Clave\n",
    "1. **Clúster Académico 0:**\n",
    "   - Este clúster está moderadamente distribuido entre los clústeres sociales, con un mayor número de estudiantes en el clúster social `0` (64 estudiantes).\n",
    "   - Esto sugiere que los estudiantes de este clúster académico tienen características demográficas relativamente homogéneas con una ligera inclinación hacia el clúster social `0`.\n",
    "\n",
    "2. **Clúster Académico 1:**\n",
    "   - Es el grupo más grande y está dominado por estudiantes en el clúster social `0` (142 estudiantes).\n",
    "   - Esto indica una fuerte correlación entre el clúster social `0` y las características académicas de este grupo, sugiriendo que el rendimiento académico está fuertemente influenciado por factores demográficos del clúster social `0`.\n",
    "\n",
    "3. **Clúster Académico 2:**\n",
    "   - Este grupo tiene una distribución más equilibrada entre los clústeres sociales, con un número ligeramente mayor de estudiantes en el clúster social `0` (80 estudiantes).\n",
    "   - Esto podría indicar que los estudiantes de este clúster académico tienen una diversidad más amplia de características demográficas.\n",
    "\n",
    "## Observaciones\n",
    "- **Clúster Social `0`:** Parece ser el clúster demográfico más influyente, ya que tiene la mayor cantidad de estudiantes en todos los clústeres académicos.\n",
    "- **Clúster Social `1` y `2`:** Estos clústeres tienen una representación más baja en comparación con el clúster social `0`, lo que podría indicar menor relevancia demográfica o una menor población en estos grupos.\n",
    "\n",
    "## Conclusiones\n",
    "- Existe una clara correlación entre el clúster académico `1` y el clúster social `0`, lo que sugiere que los factores demográficos asociados al clúster social `0` están relacionados con el rendimiento académico.\n",
    "- Los clústeres académicos `0` y `2` tienen una distribución más equilibrada entre los clústeres sociales, lo que podría representar una mayor diversidad en las características sociales."
   ]
  }
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