C3_2_Clustering_Kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importando los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\caleb.terrel\\\\Desktop\\\\3-Python_para_machine_learning\\\\datasets'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()\n",
    "os.chdir('C:\\\\Users\\\\caleb.terrel\\\\Desktop\\\\3-Python_para_machine_learning\\\\datasets')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>AGENCIAS</th>\n",
       "      <th>Mora.Media</th>\n",
       "      <th>CAR.Media</th>\n",
       "      <th>Cartera.Pesada.Promedio</th>\n",
       "      <th>Ingreso.Promedio</th>\n",
       "      <th>Intereses.de.Credito.Jun.17</th>\n",
       "      <th>Utilidad.Mes.M</th>\n",
       "      <th>Clientes</th>\n",
       "      <th>Desembolsos</th>\n",
       "      <th>Meses.en.Funcion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agencia_1</td>\n",
       "      <td>0.061715</td>\n",
       "      <td>0.073098</td>\n",
       "      <td>0.081526</td>\n",
       "      <td>374027.081188</td>\n",
       "      <td>2240211.79</td>\n",
       "      <td>121183.2091</td>\n",
       "      <td>1819</td>\n",
       "      <td>1912700.0</td>\n",
       "      <td>7.326489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Agencia_2</td>\n",
       "      <td>0.078396</td>\n",
       "      <td>0.079581</td>\n",
       "      <td>0.106191</td>\n",
       "      <td>279416.337518</td>\n",
       "      <td>1775647.47</td>\n",
       "      <td>31513.4712</td>\n",
       "      <td>1267</td>\n",
       "      <td>1473600.0</td>\n",
       "      <td>19.219713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Agencia_3</td>\n",
       "      <td>0.071103</td>\n",
       "      <td>0.076640</td>\n",
       "      <td>0.076054</td>\n",
       "      <td>408598.215412</td>\n",
       "      <td>2700761.90</td>\n",
       "      <td>210296.2826</td>\n",
       "      <td>3036</td>\n",
       "      <td>2272720.0</td>\n",
       "      <td>26.513347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agencia_4</td>\n",
       "      <td>0.061930</td>\n",
       "      <td>0.068141</td>\n",
       "      <td>0.112533</td>\n",
       "      <td>719976.185012</td>\n",
       "      <td>4511839.64</td>\n",
       "      <td>427145.0846</td>\n",
       "      <td>5616</td>\n",
       "      <td>2128870.0</td>\n",
       "      <td>79.572895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agencia_5</td>\n",
       "      <td>0.060584</td>\n",
       "      <td>0.069302</td>\n",
       "      <td>0.091240</td>\n",
       "      <td>450459.667241</td>\n",
       "      <td>2802044.98</td>\n",
       "      <td>309514.5234</td>\n",
       "      <td>3431</td>\n",
       "      <td>2362080.0</td>\n",
       "      <td>70.176591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGENCIAS  Mora.Media  CAR.Media  Cartera.Pesada.Promedio  \\\n",
       "0  Agencia_1    0.061715   0.073098                 0.081526   \n",
       "1  Agencia_2    0.078396   0.079581                 0.106191   \n",
       "2  Agencia_3    0.071103   0.076640                 0.076054   \n",
       "3  Agencia_4    0.061930   0.068141                 0.112533   \n",
       "4  Agencia_5    0.060584   0.069302                 0.091240   \n",
       "\n",
       "   Ingreso.Promedio  Intereses.de.Credito.Jun.17  Utilidad.Mes.M  Clientes  \\\n",
       "0     374027.081188                   2240211.79     121183.2091      1819   \n",
       "1     279416.337518                   1775647.47      31513.4712      1267   \n",
       "2     408598.215412                   2700761.90     210296.2826      3036   \n",
       "3     719976.185012                   4511839.64     427145.0846      5616   \n",
       "4     450459.667241                   2802044.98     309514.5234      3431   \n",
       "\n",
       "   Desembolsos  Meses.en.Funcion  \n",
       "0    1912700.0          7.326489  \n",
       "1    1473600.0         19.219713  \n",
       "2    2272720.0         26.513347  \n",
       "3    2128870.0         79.572895  \n",
       "4    2362080.0         70.176591  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AM = pd.read_excel('AgenciasMicrof_Indic2.xlsx')\n",
    "AM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 119 entries, 0 to 118\n",
      "Data columns (total 10 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   AGENCIAS                     119 non-null    object \n",
      " 1   Mora.Media                   119 non-null    float64\n",
      " 2   CAR.Media                    119 non-null    float64\n",
      " 3   Cartera.Pesada.Promedio      119 non-null    float64\n",
      " 4   Ingreso.Promedio             119 non-null    float64\n",
      " 5   Intereses.de.Credito.Jun.17  119 non-null    float64\n",
      " 6   Utilidad.Mes.M               119 non-null    float64\n",
      " 7   Clientes                     119 non-null    int64  \n",
      " 8   Desembolsos                  119 non-null    float64\n",
      " 9   Meses.en.Funcion             119 non-null    float64\n",
      "dtypes: float64(8), int64(1), object(1)\n",
      "memory usage: 9.4+ KB\n"
     ]
    }
   ],
   "source": [
    "AM.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " [Text(1, 0, 'Mora.Media'),\n",
       "  Text(2, 0, 'CAR.Media'),\n",
       "  Text(3, 0, 'Cartera.Pesada.Promedio'),\n",
       "  Text(4, 0, 'Ingreso.Promedio'),\n",
       "  Text(5, 0, 'Intereses.de.Credito.Jun.17'),\n",
       "  Text(6, 0, 'Utilidad.Mes.M'),\n",
       "  Text(7, 0, 'Clientes'),\n",
       "  Text(8, 0, 'Desembolsos'),\n",
       "  Text(9, 0, 'Meses.en.Funcion')])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.rcParams['figure.figsize'] = (6.4,4.8)\n",
    "plt.rcParams['figure.figsize'] = (13,6)\n",
    "\n",
    "AM.boxplot(return_type='axes')\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estandarizacion de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 119 entries, 0 to 118\n",
      "Data columns (total 9 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   Mora.Media                   119 non-null    float64\n",
      " 1   CAR.Media                    119 non-null    float64\n",
      " 2   Cartera.Pesada.Promedio      119 non-null    float64\n",
      " 3   Ingreso.Promedio             119 non-null    float64\n",
      " 4   Intereses.de.Credito.Jun.17  119 non-null    float64\n",
      " 5   Utilidad.Mes.M               119 non-null    float64\n",
      " 6   Clientes                     119 non-null    int64  \n",
      " 7   Desembolsos                  119 non-null    float64\n",
      " 8   Meses.en.Funcion             119 non-null    float64\n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 8.5 KB\n"
     ]
    }
   ],
   "source": [
    "X = AM.drop('AGENCIAS', axis=1)\n",
    "X.head()\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "\n",
    "X_array = sc.fit_transform(X.values)\n",
    "\n",
    "X_sc = pd.DataFrame(X_array, index=X.index, columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Mora.Media</th>\n",
       "      <th>CAR.Media</th>\n",
       "      <th>Cartera.Pesada.Promedio</th>\n",
       "      <th>Ingreso.Promedio</th>\n",
       "      <th>Intereses.de.Credito.Jun.17</th>\n",
       "      <th>Utilidad.Mes.M</th>\n",
       "      <th>Clientes</th>\n",
       "      <th>Desembolsos</th>\n",
       "      <th>Meses.en.Funcion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.437825</td>\n",
       "      <td>1.270919</td>\n",
       "      <td>0.996998</td>\n",
       "      <td>0.182798</td>\n",
       "      <td>0.027921</td>\n",
       "      <td>-0.234485</td>\n",
       "      <td>-0.366595</td>\n",
       "      <td>-0.063125</td>\n",
       "      <td>-0.935135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.190664</td>\n",
       "      <td>1.500867</td>\n",
       "      <td>1.776375</td>\n",
       "      <td>-0.221181</td>\n",
       "      <td>-0.312048</td>\n",
       "      <td>-0.492853</td>\n",
       "      <td>-0.755900</td>\n",
       "      <td>-0.467095</td>\n",
       "      <td>-0.733942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.861494</td>\n",
       "      <td>1.396545</td>\n",
       "      <td>0.824062</td>\n",
       "      <td>0.330414</td>\n",
       "      <td>0.364952</td>\n",
       "      <td>0.022279</td>\n",
       "      <td>0.491711</td>\n",
       "      <td>0.268092</td>\n",
       "      <td>-0.610559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.447515</td>\n",
       "      <td>1.095123</td>\n",
       "      <td>1.976801</td>\n",
       "      <td>1.659968</td>\n",
       "      <td>1.690301</td>\n",
       "      <td>0.647090</td>\n",
       "      <td>2.311290</td>\n",
       "      <td>0.135750</td>\n",
       "      <td>0.287026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.386781</td>\n",
       "      <td>1.136301</td>\n",
       "      <td>1.303947</td>\n",
       "      <td>0.509158</td>\n",
       "      <td>0.439071</td>\n",
       "      <td>0.308158</td>\n",
       "      <td>0.770290</td>\n",
       "      <td>0.350303</td>\n",
       "      <td>0.128073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mora.Media  CAR.Media  Cartera.Pesada.Promedio  Ingreso.Promedio  \\\n",
       "0    1.437825   1.270919                 0.996998          0.182798   \n",
       "1    2.190664   1.500867                 1.776375         -0.221181   \n",
       "2    1.861494   1.396545                 0.824062          0.330414   \n",
       "3    1.447515   1.095123                 1.976801          1.659968   \n",
       "4    1.386781   1.136301                 1.303947          0.509158   \n",
       "\n",
       "   Intereses.de.Credito.Jun.17  Utilidad.Mes.M  Clientes  Desembolsos  \\\n",
       "0                     0.027921       -0.234485 -0.366595    -0.063125   \n",
       "1                    -0.312048       -0.492853 -0.755900    -0.467095   \n",
       "2                     0.364952        0.022279  0.491711     0.268092   \n",
       "3                     1.690301        0.647090  2.311290     0.135750   \n",
       "4                     0.439071        0.308158  0.770290     0.350303   \n",
       "\n",
       "   Meses.en.Funcion  \n",
       "0         -0.935135  \n",
       "1         -0.733942  \n",
       "2         -0.610559  \n",
       "3          0.287026  \n",
       "4          0.128073  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Mora.Media</th>\n",
       "      <th>CAR.Media</th>\n",
       "      <th>Cartera.Pesada.Promedio</th>\n",
       "      <th>Ingreso.Promedio</th>\n",
       "      <th>Intereses.de.Credito.Jun.17</th>\n",
       "      <th>Utilidad.Mes.M</th>\n",
       "      <th>Clientes</th>\n",
       "      <th>Desembolsos</th>\n",
       "      <th>Meses.en.Funcion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.190000e+02</td>\n",
       "      <td>1.190000e+02</td>\n",
       "      <td>1.190000e+02</td>\n",
       "      <td>1.190000e+02</td>\n",
       "      <td>1.190000e+02</td>\n",
       "      <td>1.190000e+02</td>\n",
       "      <td>1.190000e+02</td>\n",
       "      <td>1.190000e+02</td>\n",
       "      <td>1.190000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.478211e-17</td>\n",
       "      <td>-5.411171e-16</td>\n",
       "      <td>3.134747e-16</td>\n",
       "      <td>4.646143e-16</td>\n",
       "      <td>-4.888713e-16</td>\n",
       "      <td>-3.172066e-17</td>\n",
       "      <td>5.597763e-17</td>\n",
       "      <td>-1.100893e-16</td>\n",
       "      <td>-1.334134e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.004228e+00</td>\n",
       "      <td>1.004228e+00</td>\n",
       "      <td>1.004228e+00</td>\n",
       "      <td>1.004228e+00</td>\n",
       "      <td>1.004228e+00</td>\n",
       "      <td>1.004228e+00</td>\n",
       "      <td>1.004228e+00</td>\n",
       "      <td>1.004228e+00</td>\n",
       "      <td>1.004228e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.347476e+00</td>\n",
       "      <td>-1.321723e+00</td>\n",
       "      <td>-1.579200e+00</td>\n",
       "      <td>-1.414158e+00</td>\n",
       "      <td>-1.607406e+00</td>\n",
       "      <td>-2.217546e+00</td>\n",
       "      <td>-1.603626e+00</td>\n",
       "      <td>-1.561061e+00</td>\n",
       "      <td>-1.059074e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.414015e-01</td>\n",
       "      <td>-6.326425e-01</td>\n",
       "      <td>-7.350260e-01</td>\n",
       "      <td>-8.843183e-01</td>\n",
       "      <td>-7.946660e-01</td>\n",
       "      <td>-5.855424e-01</td>\n",
       "      <td>-7.541368e-01</td>\n",
       "      <td>-7.850912e-01</td>\n",
       "      <td>-7.545064e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.492963e-01</td>\n",
       "      <td>-1.686827e-01</td>\n",
       "      <td>-1.065112e-01</td>\n",
       "      <td>-1.270064e-01</td>\n",
       "      <td>-9.680074e-02</td>\n",
       "      <td>-1.154826e-01</td>\n",
       "      <td>-1.655946e-01</td>\n",
       "      <td>-1.882443e-01</td>\n",
       "      <td>-3.121052e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.309011e-01</td>\n",
       "      <td>4.459211e-01</td>\n",
       "      <td>5.587967e-01</td>\n",
       "      <td>7.098186e-01</td>\n",
       "      <td>7.097200e-01</td>\n",
       "      <td>5.378665e-01</td>\n",
       "      <td>7.166897e-01</td>\n",
       "      <td>4.997125e-01</td>\n",
       "      <td>4.487583e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.566617e+00</td>\n",
       "      <td>3.753802e+00</td>\n",
       "      <td>3.916989e+00</td>\n",
       "      <td>2.018906e+00</td>\n",
       "      <td>2.096374e+00</td>\n",
       "      <td>3.849604e+00</td>\n",
       "      <td>2.632890e+00</td>\n",
       "      <td>3.189923e+00</td>\n",
       "      <td>4.806077e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mora.Media     CAR.Media  Cartera.Pesada.Promedio  Ingreso.Promedio  \\\n",
       "count  1.190000e+02  1.190000e+02             1.190000e+02      1.190000e+02   \n",
       "mean   4.478211e-17 -5.411171e-16             3.134747e-16      4.646143e-16   \n",
       "std    1.004228e+00  1.004228e+00             1.004228e+00      1.004228e+00   \n",
       "min   -1.347476e+00 -1.321723e+00            -1.579200e+00     -1.414158e+00   \n",
       "25%   -6.414015e-01 -6.326425e-01            -7.350260e-01     -8.843183e-01   \n",
       "50%   -1.492963e-01 -1.686827e-01            -1.065112e-01     -1.270064e-01   \n",
       "75%    5.309011e-01  4.459211e-01             5.587967e-01      7.098186e-01   \n",
       "max    3.566617e+00  3.753802e+00             3.916989e+00      2.018906e+00   \n",
       "\n",
       "       Intereses.de.Credito.Jun.17  Utilidad.Mes.M      Clientes  \\\n",
       "count                 1.190000e+02    1.190000e+02  1.190000e+02   \n",
       "mean                 -4.888713e-16   -3.172066e-17  5.597763e-17   \n",
       "std                   1.004228e+00    1.004228e+00  1.004228e+00   \n",
       "min                  -1.607406e+00   -2.217546e+00 -1.603626e+00   \n",
       "25%                  -7.946660e-01   -5.855424e-01 -7.541368e-01   \n",
       "50%                  -9.680074e-02   -1.154826e-01 -1.655946e-01   \n",
       "75%                   7.097200e-01    5.378665e-01  7.166897e-01   \n",
       "max                   2.096374e+00    3.849604e+00  2.632890e+00   \n",
       "\n",
       "        Desembolsos  Meses.en.Funcion  \n",
       "count  1.190000e+02      1.190000e+02  \n",
       "mean  -1.100893e-16     -1.334134e-16  \n",
       "std    1.004228e+00      1.004228e+00  \n",
       "min   -1.561061e+00     -1.059074e+00  \n",
       "25%   -7.850912e-01     -7.545064e-01  \n",
       "50%   -1.882443e-01     -3.121052e-01  \n",
       "75%    4.997125e-01      4.487583e-01  \n",
       "max    3.189923e+00      4.806077e+00  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sc.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " [Text(1, 0, 'Mora.Media'),\n",
       "  Text(2, 0, 'CAR.Media'),\n",
       "  Text(3, 0, 'Cartera.Pesada.Promedio'),\n",
       "  Text(4, 0, 'Ingreso.Promedio'),\n",
       "  Text(5, 0, 'Intereses.de.Credito.Jun.17'),\n",
       "  Text(6, 0, 'Utilidad.Mes.M'),\n",
       "  Text(7, 0, 'Clientes'),\n",
       "  Text(8, 0, 'Desembolsos'),\n",
       "  Text(9, 0, 'Meses.en.Funcion')])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplots = X_sc.boxplot(return_type='axes')\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizacion de datos con PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA con 2 componentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Especificar la cantidad de componentes que queremos mantener\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components = 2)\n",
    "pca.fit(X_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.6218934 , 0.20049677])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % varianza explicada por cada componente\n",
    "\n",
    "pca.explained_variance_ratio_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8223901717016836"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# % varianza explicada por cada componente acumulada\n",
    "\n",
    "np.sum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tamaño data Original:  (119, 9)\n",
      "Tamaño data reducida:  (119, 2)\n"
     ]
    }
   ],
   "source": [
    "# transformar datos en los dos primeros componentes principales\n",
    "\n",
    "X_sc_PCA = pca.transform(X_sc)\n",
    "\n",
    "print(\"Tamaño data Original: \", (X_sc.shape))\n",
    "print(\"Tamaño data reducida: \", (X_sc_PCA.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.877092  , 2.05158292],\n",
       "       [0.73347984, 3.27968623],\n",
       "       [1.7420941 , 1.71276109],\n",
       "       [3.86164099, 0.68188368],\n",
       "       [2.05963988, 1.28299194]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# muestra 5 registros\n",
    "\n",
    "X_sc_PCA[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'PCA 2')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficar los dos primeros componentes principales\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "plt.scatter(X_sc_PCA[:, 0], X_sc_PCA[:, 1], s=60, edgecolors=\"black\", alpha=0.7)\n",
    "plt.gca().set_aspect(\"equal\")\n",
    "plt.xlabel(\"PCA 1\")\n",
    "plt.ylabel(\"PCA 2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Añadir los datos de PCA en el dataset original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.877092</td>\n",
       "      <td>2.051583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.733480</td>\n",
       "      <td>3.279686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.742094</td>\n",
       "      <td>1.712761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.861641</td>\n",
       "      <td>0.681884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.059640</td>\n",
       "      <td>1.282992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1\n",
       "0  0.877092  2.051583\n",
       "1  0.733480  3.279686\n",
       "2  1.742094  1.712761\n",
       "3  3.861641  0.681884\n",
       "4  2.059640  1.282992"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformar a Data Frame\n",
    "\n",
    "pca_df = pd.DataFrame(X_sc_PCA, index = X_sc.index)\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.877092</td>\n",
       "      <td>2.051583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.733480</td>\n",
       "      <td>3.279686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.742094</td>\n",
       "      <td>1.712761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.861641</td>\n",
       "      <td>0.681884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.059640</td>\n",
       "      <td>1.282992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PCA1      PCA2\n",
       "0  0.877092  2.051583\n",
       "1  0.733480  3.279686\n",
       "2  1.742094  1.712761\n",
       "3  3.861641  0.681884\n",
       "4  2.059640  1.282992"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df.columns = ['PCA1', 'PCA2']\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sc2 = pd.concat([X_sc, pca_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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>Mora.Media</th>\n",
       "      <th>CAR.Media</th>\n",
       "      <th>Cartera.Pesada.Promedio</th>\n",
       "      <th>Ingreso.Promedio</th>\n",
       "      <th>Intereses.de.Credito.Jun.17</th>\n",
       "      <th>Utilidad.Mes.M</th>\n",
       "      <th>Clientes</th>\n",
       "      <th>Desembolsos</th>\n",
       "      <th>Meses.en.Funcion</th>\n",
       "      <th>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.437825</td>\n",
       "      <td>1.270919</td>\n",
       "      <td>0.996998</td>\n",
       "      <td>0.182798</td>\n",
       "      <td>0.027921</td>\n",
       "      <td>-0.234485</td>\n",
       "      <td>-0.366595</td>\n",
       "      <td>-0.063125</td>\n",
       "      <td>-0.935135</td>\n",
       "      <td>0.877092</td>\n",
       "      <td>2.051583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.190664</td>\n",
       "      <td>1.500867</td>\n",
       "      <td>1.776375</td>\n",
       "      <td>-0.221181</td>\n",
       "      <td>-0.312048</td>\n",
       "      <td>-0.492853</td>\n",
       "      <td>-0.755900</td>\n",
       "      <td>-0.467095</td>\n",
       "      <td>-0.733942</td>\n",
       "      <td>0.733480</td>\n",
       "      <td>3.279686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.861494</td>\n",
       "      <td>1.396545</td>\n",
       "      <td>0.824062</td>\n",
       "      <td>0.330414</td>\n",
       "      <td>0.364952</td>\n",
       "      <td>0.022279</td>\n",
       "      <td>0.491711</td>\n",
       "      <td>0.268092</td>\n",
       "      <td>-0.610559</td>\n",
       "      <td>1.742094</td>\n",
       "      <td>1.712761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.447515</td>\n",
       "      <td>1.095123</td>\n",
       "      <td>1.976801</td>\n",
       "      <td>1.659968</td>\n",
       "      <td>1.690301</td>\n",
       "      <td>0.647090</td>\n",
       "      <td>2.311290</td>\n",
       "      <td>0.135750</td>\n",
       "      <td>0.287026</td>\n",
       "      <td>3.861641</td>\n",
       "      <td>0.681884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.386781</td>\n",
       "      <td>1.136301</td>\n",
       "      <td>1.303947</td>\n",
       "      <td>0.509158</td>\n",
       "      <td>0.439071</td>\n",
       "      <td>0.308158</td>\n",
       "      <td>0.770290</td>\n",
       "      <td>0.350303</td>\n",
       "      <td>0.128073</td>\n",
       "      <td>2.059640</td>\n",
       "      <td>1.282992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mora.Media  CAR.Media  Cartera.Pesada.Promedio  Ingreso.Promedio  \\\n",
       "0    1.437825   1.270919                 0.996998          0.182798   \n",
       "1    2.190664   1.500867                 1.776375         -0.221181   \n",
       "2    1.861494   1.396545                 0.824062          0.330414   \n",
       "3    1.447515   1.095123                 1.976801          1.659968   \n",
       "4    1.386781   1.136301                 1.303947          0.509158   \n",
       "\n",
       "   Intereses.de.Credito.Jun.17  Utilidad.Mes.M  Clientes  Desembolsos  \\\n",
       "0                     0.027921       -0.234485 -0.366595    -0.063125   \n",
       "1                    -0.312048       -0.492853 -0.755900    -0.467095   \n",
       "2                     0.364952        0.022279  0.491711     0.268092   \n",
       "3                     1.690301        0.647090  2.311290     0.135750   \n",
       "4                     0.439071        0.308158  0.770290     0.350303   \n",
       "\n",
       "   Meses.en.Funcion      PCA1      PCA2  \n",
       "0         -0.935135  0.877092  2.051583  \n",
       "1         -0.733942  0.733480  3.279686  \n",
       "2         -0.610559  1.742094  1.712761  \n",
       "3          0.287026  3.861641  0.681884  \n",
       "4          0.128073  2.059640  1.282992  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sc2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Segmentacion K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.877092</td>\n",
       "      <td>2.051583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.733480</td>\n",
       "      <td>3.279686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.742094</td>\n",
       "      <td>1.712761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.861641</td>\n",
       "      <td>0.681884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.059640</td>\n",
       "      <td>1.282992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PCA1      PCA2\n",
       "0  0.877092  2.051583\n",
       "1  0.733480  3.279686\n",
       "2  1.742094  1.712761\n",
       "3  3.861641  0.681884\n",
       "4  2.059640  1.282992"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionando las variables PCA1 y PCA2\n",
    "\n",
    "X_sc2.iloc[:, 9:11].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=5, random_state=2020)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 5, random_state=2020)\n",
    "kmeans.fit(X_sc2.iloc[:, 9:11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 0 2 0 0 2 0 1 2 0 1 2 0 0 0 2 0 0 2 1 1\n",
      " 0 2 2 0 2 1 0 0 2 0 0 0 1 1 0 0 1 2 1 0 1 1 0 1 2 0 0 1 0 0 2 0 0 1 2 2 2\n",
      " 1 2 1 1 1 1 0 1 4 1 1 1 1 1 4 4 1 1 1 2 1 1 1 0 1 4 4 1 1 4 4 1 1 4 4 1 4\n",
      " 4 4 4 4 4 4 4 4]\n"
     ]
    }
   ],
   "source": [
    "# Etiquetas del cluster del modelo\n",
    "\n",
    "print(kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 0 2 0 0 2 0 1 2 0 1 2 0 0 0 2 0 0 2 1 1\n",
      " 0 2 2 0 2 1 0 0 2 0 0 0 1 1 0 0 1 2 1 0 1 1 0 1 2 0 0 1 0 0 2 0 0 1 2 2 2\n",
      " 1 2 1 1 1 1 0 1 4 1 1 1 1 1 4 4 1 1 1 2 1 1 1 0 1 4 4 1 1 4 4 1 1 4 4 1 4\n",
      " 4 4 4 4 4 4 4 4]\n"
     ]
    }
   ],
   "source": [
    "# Asignación de clusters en cualquier data\n",
    "\n",
    "print(kmeans.predict(X_sc2.iloc[:, 9:11]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.84658835 -0.22719215]\n",
      " [-1.46424425  0.12070443]\n",
      " [ 3.28903681 -1.4642178 ]\n",
      " [ 1.9788245   2.41082159]\n",
      " [-3.58482869 -0.47947215]]\n"
     ]
    }
   ],
   "source": [
    "print(kmeans.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'PCA 2')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x defecto el tamaño del ancho y alto es de 6.4 y 4.8\n",
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "\n",
    "# https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html\n",
    "\n",
    "plt.scatter(X_sc2.iloc[:, 9], X_sc2.iloc[:, 10], c=kmeans.labels_, cmap='viridis', s=60, edgecolors=\"black\", alpha=0.6)\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], \n",
    "            marker='^', s=140, linewidth=2, c=[0, 1, 2, 3, 4], cmap='viridis', edgecolors=\"black\")\n",
    "plt.xlabel(\"PCA 1\")\n",
    "plt.ylabel(\"PCA 2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Buscando el numero optimo de clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inercia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_clusters = [2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "inertias = []\n",
    "for n in nb_clusters:\n",
    "    km = KMeans(n_clusters = n)\n",
    "    km.fit(X_sc2.iloc[:, 9:11])\n",
    "    inertias.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[397.37563286359733,\n",
       " 266.61157624692294,\n",
       " 188.86041880690792,\n",
       " 141.46528807725034,\n",
       " 114.72453883601547,\n",
       " 97.84491888757536,\n",
       " 82.02141458048624,\n",
       " 67.58157005152735,\n",
       " 60.004132650672446]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inertias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'inercias de cada modelo k-means')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nb_clusters, inertias, marker='o')\n",
    "plt.xlabel(\"modelos clusters k-means\")\n",
    "plt.ylabel(\"inercias de cada modelo k-means\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Silueta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "nb_clusters = [2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "avg_silhouettes = []\n",
    "\n",
    "for n in nb_clusters:\n",
    "    km = KMeans(n_clusters = n)\n",
    "    Y = km.fit_predict(X_sc2.iloc[:, 9:11])\n",
    "    avg_silhouettes.append(silhouette_score(X_sc2.iloc[:, 9:11], Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.4758344226430164,\n",
       " 0.4574740104349773,\n",
       " 0.4195371893744552,\n",
       " 0.4170698878326849,\n",
       " 0.4328179393575662,\n",
       " 0.4350990333024475,\n",
       " 0.4385937636536939,\n",
       " 0.41400935572241077,\n",
       " 0.4266680236439462]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_silhouettes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'siluetas de cada modelo k-means')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "\n",
    "plt.plot(nb_clusters, avg_silhouettes, marker='o')\n",
    "plt.xlabel(\"modelos clusters k-means\")\n",
    "plt.ylabel(\"siluetas de cada modelo k-means\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion: modelo k-means con 3 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 0 0 0 2 0 0 1 0 2 1 0 0 2 0 0 1 0 0 1 1\n",
      " 0 0 0 0 0 2 0 2 0 2 0 1 1 1 0 0 1 0 1 2 1 1 2 1 0 0 0 1 2 2 0 2 2 1 0 0 0\n",
      " 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 3)\n",
    "kmeans.fit(X_sc2.iloc[:, 9:11])\n",
    "\n",
    "# Etiquetas del cluster del modelo\n",
    "\n",
    "print(kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.38738156 -1.14622092]\n",
      " [-2.05282132 -0.15851774]\n",
      " [ 1.43857303  1.6792614 ]]\n"
     ]
    }
   ],
   "source": [
    "print(kmeans.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'PCA 2')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_sc2.iloc[:, 9], X_sc2.iloc[:, 10], c=kmeans.labels_, cmap='viridis', s=60, edgecolors=\"black\", alpha=0.6)\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], \n",
    "            marker='^', s=140, linewidth=2, c=[0, 1, 2], cmap='viridis', edgecolors=\"black\")\n",
    "plt.xlabel(\"PCA 1\")\n",
    "plt.ylabel(\"PCA 2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agregar el vector Clusters en la data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Mora.Media</th>\n",
       "      <th>CAR.Media</th>\n",
       "      <th>Cartera.Pesada.Promedio</th>\n",
       "      <th>Ingreso.Promedio</th>\n",
       "      <th>Intereses.de.Credito.Jun.17</th>\n",
       "      <th>Utilidad.Mes.M</th>\n",
       "      <th>Clientes</th>\n",
       "      <th>Desembolsos</th>\n",
       "      <th>Meses.en.Funcion</th>\n",
       "      <th>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.437825</td>\n",
       "      <td>1.270919</td>\n",
       "      <td>0.996998</td>\n",
       "      <td>0.182798</td>\n",
       "      <td>0.027921</td>\n",
       "      <td>-0.234485</td>\n",
       "      <td>-0.366595</td>\n",
       "      <td>-0.063125</td>\n",
       "      <td>-0.935135</td>\n",
       "      <td>0.877092</td>\n",
       "      <td>2.051583</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.190664</td>\n",
       "      <td>1.500867</td>\n",
       "      <td>1.776375</td>\n",
       "      <td>-0.221181</td>\n",
       "      <td>-0.312048</td>\n",
       "      <td>-0.492853</td>\n",
       "      <td>-0.755900</td>\n",
       "      <td>-0.467095</td>\n",
       "      <td>-0.733942</td>\n",
       "      <td>0.733480</td>\n",
       "      <td>3.279686</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.861494</td>\n",
       "      <td>1.396545</td>\n",
       "      <td>0.824062</td>\n",
       "      <td>0.330414</td>\n",
       "      <td>0.364952</td>\n",
       "      <td>0.022279</td>\n",
       "      <td>0.491711</td>\n",
       "      <td>0.268092</td>\n",
       "      <td>-0.610559</td>\n",
       "      <td>1.742094</td>\n",
       "      <td>1.712761</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.447515</td>\n",
       "      <td>1.095123</td>\n",
       "      <td>1.976801</td>\n",
       "      <td>1.659968</td>\n",
       "      <td>1.690301</td>\n",
       "      <td>0.647090</td>\n",
       "      <td>2.311290</td>\n",
       "      <td>0.135750</td>\n",
       "      <td>0.287026</td>\n",
       "      <td>3.861641</td>\n",
       "      <td>0.681884</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.386781</td>\n",
       "      <td>1.136301</td>\n",
       "      <td>1.303947</td>\n",
       "      <td>0.509158</td>\n",
       "      <td>0.439071</td>\n",
       "      <td>0.308158</td>\n",
       "      <td>0.770290</td>\n",
       "      <td>0.350303</td>\n",
       "      <td>0.128073</td>\n",
       "      <td>2.059640</td>\n",
       "      <td>1.282992</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mora.Media  CAR.Media  Cartera.Pesada.Promedio  Ingreso.Promedio  \\\n",
       "0    1.437825   1.270919                 0.996998          0.182798   \n",
       "1    2.190664   1.500867                 1.776375         -0.221181   \n",
       "2    1.861494   1.396545                 0.824062          0.330414   \n",
       "3    1.447515   1.095123                 1.976801          1.659968   \n",
       "4    1.386781   1.136301                 1.303947          0.509158   \n",
       "\n",
       "   Intereses.de.Credito.Jun.17  Utilidad.Mes.M  Clientes  Desembolsos  \\\n",
       "0                     0.027921       -0.234485 -0.366595    -0.063125   \n",
       "1                    -0.312048       -0.492853 -0.755900    -0.467095   \n",
       "2                     0.364952        0.022279  0.491711     0.268092   \n",
       "3                     1.690301        0.647090  2.311290     0.135750   \n",
       "4                     0.439071        0.308158  0.770290     0.350303   \n",
       "\n",
       "   Meses.en.Funcion      PCA1      PCA2  Clusters  \n",
       "0         -0.935135  0.877092  2.051583         2  \n",
       "1         -0.733942  0.733480  3.279686         2  \n",
       "2         -0.610559  1.742094  1.712761         2  \n",
       "3          0.287026  3.861641  0.681884         0  \n",
       "4          0.128073  2.059640  1.282992         2  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sc2['Clusters'] = kmeans.labels_\n",
    "\n",
    "X_sc2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    58\n",
       "0    33\n",
       "2    28\n",
       "Name: Clusters, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sc2['Clusters'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sc3 = X_sc2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mora.Media</th>\n",
       "      <th>CAR.Media</th>\n",
       "      <th>Cartera.Pesada.Promedio</th>\n",
       "      <th>Ingreso.Promedio</th>\n",
       "      <th>Intereses.de.Credito.Jun.17</th>\n",
       "      <th>Utilidad.Mes.M</th>\n",
       "      <th>Clientes</th>\n",
       "      <th>Desembolsos</th>\n",
       "      <th>Meses.en.Funcion</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.437825</td>\n",
       "      <td>1.270919</td>\n",
       "      <td>0.996998</td>\n",
       "      <td>0.182798</td>\n",
       "      <td>0.027921</td>\n",
       "      <td>-0.234485</td>\n",
       "      <td>-0.366595</td>\n",
       "      <td>-0.063125</td>\n",
       "      <td>-0.935135</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.190664</td>\n",
       "      <td>1.500867</td>\n",
       "      <td>1.776375</td>\n",
       "      <td>-0.221181</td>\n",
       "      <td>-0.312048</td>\n",
       "      <td>-0.492853</td>\n",
       "      <td>-0.755900</td>\n",
       "      <td>-0.467095</td>\n",
       "      <td>-0.733942</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.861494</td>\n",
       "      <td>1.396545</td>\n",
       "      <td>0.824062</td>\n",
       "      <td>0.330414</td>\n",
       "      <td>0.364952</td>\n",
       "      <td>0.022279</td>\n",
       "      <td>0.491711</td>\n",
       "      <td>0.268092</td>\n",
       "      <td>-0.610559</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.447515</td>\n",
       "      <td>1.095123</td>\n",
       "      <td>1.976801</td>\n",
       "      <td>1.659968</td>\n",
       "      <td>1.690301</td>\n",
       "      <td>0.647090</td>\n",
       "      <td>2.311290</td>\n",
       "      <td>0.135750</td>\n",
       "      <td>0.287026</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.386781</td>\n",
       "      <td>1.136301</td>\n",
       "      <td>1.303947</td>\n",
       "      <td>0.509158</td>\n",
       "      <td>0.439071</td>\n",
       "      <td>0.308158</td>\n",
       "      <td>0.770290</td>\n",
       "      <td>0.350303</td>\n",
       "      <td>0.128073</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mora.Media  CAR.Media  Cartera.Pesada.Promedio  Ingreso.Promedio  \\\n",
       "0    1.437825   1.270919                 0.996998          0.182798   \n",
       "1    2.190664   1.500867                 1.776375         -0.221181   \n",
       "2    1.861494   1.396545                 0.824062          0.330414   \n",
       "3    1.447515   1.095123                 1.976801          1.659968   \n",
       "4    1.386781   1.136301                 1.303947          0.509158   \n",
       "\n",
       "   Intereses.de.Credito.Jun.17  Utilidad.Mes.M  Clientes  Desembolsos  \\\n",
       "0                     0.027921       -0.234485 -0.366595    -0.063125   \n",
       "1                    -0.312048       -0.492853 -0.755900    -0.467095   \n",
       "2                     0.364952        0.022279  0.491711     0.268092   \n",
       "3                     1.690301        0.647090  2.311290     0.135750   \n",
       "4                     0.439071        0.308158  0.770290     0.350303   \n",
       "\n",
       "   Meses.en.Funcion  Clusters  \n",
       "0         -0.935135         2  \n",
       "1         -0.733942         2  \n",
       "2         -0.610559         2  \n",
       "3          0.287026         0  \n",
       "4          0.128073         2  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sc3 = X_sc3.drop(['PCA1','PCA2'], axis=1)\n",
    "X_sc3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Caracterización de Clusters (variables originales estandarizadas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clusters</th>\n",
       "      <th>Mora.Media</th>\n",
       "      <th>CAR.Media</th>\n",
       "      <th>Cartera.Pesada.Promedio</th>\n",
       "      <th>Ingreso.Promedio</th>\n",
       "      <th>Intereses.de.Credito.Jun.17</th>\n",
       "      <th>Utilidad.Mes.M</th>\n",
       "      <th>Clientes</th>\n",
       "      <th>Desembolsos</th>\n",
       "      <th>Meses.en.Funcion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.179680</td>\n",
       "      <td>0.106602</td>\n",
       "      <td>0.173705</td>\n",
       "      <td>1.211110</td>\n",
       "      <td>1.224889</td>\n",
       "      <td>1.037849</td>\n",
       "      <td>1.184408</td>\n",
       "      <td>1.172118</td>\n",
       "      <td>0.349358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.722115</td>\n",
       "      <td>-0.687510</td>\n",
       "      <td>-0.679589</td>\n",
       "      <td>-0.805575</td>\n",
       "      <td>-0.784524</td>\n",
       "      <td>-0.697507</td>\n",
       "      <td>-0.728528</td>\n",
       "      <td>-0.684697</td>\n",
       "      <td>-0.208960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.284044</td>\n",
       "      <td>1.298489</td>\n",
       "      <td>1.202997</td>\n",
       "      <td>0.241313</td>\n",
       "      <td>0.181466</td>\n",
       "      <td>0.221657</td>\n",
       "      <td>0.113186</td>\n",
       "      <td>0.036877</td>\n",
       "      <td>0.021103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Clusters  Mora.Media  CAR.Media  Cartera.Pesada.Promedio  Ingreso.Promedio  \\\n",
       "0         0    0.179680   0.106602                 0.173705          1.211110   \n",
       "1         1   -0.722115  -0.687510                -0.679589         -0.805575   \n",
       "2         2    1.284044   1.298489                 1.202997          0.241313   \n",
       "\n",
       "   Intereses.de.Credito.Jun.17  Utilidad.Mes.M  Clientes  Desembolsos  \\\n",
       "0                     1.224889        1.037849  1.184408     1.172118   \n",
       "1                    -0.784524       -0.697507 -0.728528    -0.684697   \n",
       "2                     0.181466        0.221657  0.113186     0.036877   \n",
       "\n",
       "   Meses.en.Funcion  \n",
       "0          0.349358  \n",
       "1         -0.208960  \n",
       "2          0.021103  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PromVarCust = X_sc3.groupby(by = 'Clusters', as_index=False).mean()\n",
    "PromVarCust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mora.Media</th>\n",
       "      <th>CAR.Media</th>\n",
       "      <th>Cartera.Pesada.Promedio</th>\n",
       "      <th>Ingreso.Promedio</th>\n",
       "      <th>Intereses.de.Credito.Jun.17</th>\n",
       "      <th>Utilidad.Mes.M</th>\n",
       "      <th>Clientes</th>\n",
       "      <th>Desembolsos</th>\n",
       "      <th>Meses.en.Funcion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.179680</td>\n",
       "      <td>0.106602</td>\n",
       "      <td>0.173705</td>\n",
       "      <td>1.211110</td>\n",
       "      <td>1.224889</td>\n",
       "      <td>1.037849</td>\n",
       "      <td>1.184408</td>\n",
       "      <td>1.172118</td>\n",
       "      <td>0.349358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.722115</td>\n",
       "      <td>-0.687510</td>\n",
       "      <td>-0.679589</td>\n",
       "      <td>-0.805575</td>\n",
       "      <td>-0.784524</td>\n",
       "      <td>-0.697507</td>\n",
       "      <td>-0.728528</td>\n",
       "      <td>-0.684697</td>\n",
       "      <td>-0.208960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.284044</td>\n",
       "      <td>1.298489</td>\n",
       "      <td>1.202997</td>\n",
       "      <td>0.241313</td>\n",
       "      <td>0.181466</td>\n",
       "      <td>0.221657</td>\n",
       "      <td>0.113186</td>\n",
       "      <td>0.036877</td>\n",
       "      <td>0.021103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mora.Media  CAR.Media  Cartera.Pesada.Promedio  Ingreso.Promedio  \\\n",
       "0    0.179680   0.106602                 0.173705          1.211110   \n",
       "1   -0.722115  -0.687510                -0.679589         -0.805575   \n",
       "2    1.284044   1.298489                 1.202997          0.241313   \n",
       "\n",
       "   Intereses.de.Credito.Jun.17  Utilidad.Mes.M  Clientes  Desembolsos  \\\n",
       "0                     1.224889        1.037849  1.184408     1.172118   \n",
       "1                    -0.784524       -0.697507 -0.728528    -0.684697   \n",
       "2                     0.181466        0.221657  0.113186     0.036877   \n",
       "\n",
       "   Meses.en.Funcion  \n",
       "0          0.349358  \n",
       "1         -0.208960  \n",
       "2          0.021103  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PromVarCust = PromVarCust.drop('Clusters', axis=1)\n",
    "PromVarCust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]), [Text(0, 0, '0'), Text(1, 0, '1'), Text(2, 0, '2')])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.rcParams['figure.figsize'] = (6.4, 4.8)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 8)\n",
    "\n",
    "PromVarCust.plot(kind=\"bar\")\n",
    "plt.xticks(rotation=360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 15)\n",
    "\n",
    "boxplots = X_sc3.boxplot(by='Clusters')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}