Teorema de Bayes Ejercicio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import seaborn as sb\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (16, 9)\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.feature_selection import SelectKBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "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>ingresos</th>\n",
       "      <th>gastos_comunes</th>\n",
       "      <th>pago_coche</th>\n",
       "      <th>gastos_otros</th>\n",
       "      <th>ahorros</th>\n",
       "      <th>vivienda</th>\n",
       "      <th>estado_civil</th>\n",
       "      <th>hijos</th>\n",
       "      <th>trabajo</th>\n",
       "      <th>comprar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>50000</td>\n",
       "      <td>400000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6745</td>\n",
       "      <td>944</td>\n",
       "      <td>123</td>\n",
       "      <td>429</td>\n",
       "      <td>43240</td>\n",
       "      <td>636897</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6455</td>\n",
       "      <td>1033</td>\n",
       "      <td>98</td>\n",
       "      <td>795</td>\n",
       "      <td>57463</td>\n",
       "      <td>321779</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7098</td>\n",
       "      <td>1278</td>\n",
       "      <td>15</td>\n",
       "      <td>254</td>\n",
       "      <td>54506</td>\n",
       "      <td>660933</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6167</td>\n",
       "      <td>863</td>\n",
       "      <td>223</td>\n",
       "      <td>520</td>\n",
       "      <td>41512</td>\n",
       "      <td>348932</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5692</td>\n",
       "      <td>911</td>\n",
       "      <td>11</td>\n",
       "      <td>325</td>\n",
       "      <td>50875</td>\n",
       "      <td>360863</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6830</td>\n",
       "      <td>1298</td>\n",
       "      <td>345</td>\n",
       "      <td>309</td>\n",
       "      <td>46761</td>\n",
       "      <td>429812</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6470</td>\n",
       "      <td>1035</td>\n",
       "      <td>39</td>\n",
       "      <td>782</td>\n",
       "      <td>57439</td>\n",
       "      <td>606291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6251</td>\n",
       "      <td>1250</td>\n",
       "      <td>209</td>\n",
       "      <td>571</td>\n",
       "      <td>50503</td>\n",
       "      <td>291010</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6987</td>\n",
       "      <td>1258</td>\n",
       "      <td>252</td>\n",
       "      <td>245</td>\n",
       "      <td>40611</td>\n",
       "      <td>324098</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ingresos  gastos_comunes  pago_coche  gastos_otros  ahorros  vivienda  \\\n",
       "0      6000            1000           0           600    50000    400000   \n",
       "1      6745             944         123           429    43240    636897   \n",
       "2      6455            1033          98           795    57463    321779   \n",
       "3      7098            1278          15           254    54506    660933   \n",
       "4      6167             863         223           520    41512    348932   \n",
       "5      5692             911          11           325    50875    360863   \n",
       "6      6830            1298         345           309    46761    429812   \n",
       "7      6470            1035          39           782    57439    606291   \n",
       "8      6251            1250         209           571    50503    291010   \n",
       "9      6987            1258         252           245    40611    324098   \n",
       "\n",
       "   estado_civil  hijos  trabajo  comprar  \n",
       "0             0      2        2        1  \n",
       "1             1      3        6        0  \n",
       "2             2      1        8        1  \n",
       "3             0      0        3        0  \n",
       "4             0      0        3        1  \n",
       "5             1      4        5        1  \n",
       "6             1      1        5        1  \n",
       "7             0      0        1        0  \n",
       "8             0      0        3        1  \n",
       "9             2      1        7        1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# carguemos la info del archivo csv:\n",
    "dataframe = pd.read_csv(r\"comprar_alquilar.csv\")\n",
    "dataframe.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "comprar\n",
      "0    135\n",
      "1     67\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Visualización de Datos\n",
    "print(dataframe.groupby('comprar').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histograma\n",
    "dataframe.drop(['comprar'], axis=1).hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preparar los datos de entrada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>ingresos</th>\n",
       "      <th>ahorros</th>\n",
       "      <th>vivienda</th>\n",
       "      <th>estado_civil</th>\n",
       "      <th>hijos</th>\n",
       "      <th>trabajo</th>\n",
       "      <th>comprar</th>\n",
       "      <th>financiar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6000</td>\n",
       "      <td>50000</td>\n",
       "      <td>400000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6745</td>\n",
       "      <td>43240</td>\n",
       "      <td>636897</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>593657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6455</td>\n",
       "      <td>57463</td>\n",
       "      <td>321779</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>264316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7098</td>\n",
       "      <td>54506</td>\n",
       "      <td>660933</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>606427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6167</td>\n",
       "      <td>41512</td>\n",
       "      <td>348932</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>307420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5692</td>\n",
       "      <td>50875</td>\n",
       "      <td>360863</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>309988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6830</td>\n",
       "      <td>46761</td>\n",
       "      <td>429812</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>383051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6470</td>\n",
       "      <td>57439</td>\n",
       "      <td>606291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>548852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6251</td>\n",
       "      <td>50503</td>\n",
       "      <td>291010</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>240507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6987</td>\n",
       "      <td>40611</td>\n",
       "      <td>324098</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>283487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ingresos  ahorros  vivienda  estado_civil  hijos  trabajo  comprar  \\\n",
       "0      6000    50000    400000             0      2        2        1   \n",
       "1      6745    43240    636897             1      3        6        0   \n",
       "2      6455    57463    321779             2      1        8        1   \n",
       "3      7098    54506    660933             0      0        3        0   \n",
       "4      6167    41512    348932             0      0        3        1   \n",
       "5      5692    50875    360863             1      4        5        1   \n",
       "6      6830    46761    429812             1      1        5        1   \n",
       "7      6470    57439    606291             0      0        1        0   \n",
       "8      6251    50503    291010             0      0        3        1   \n",
       "9      6987    40611    324098             2      1        7        1   \n",
       "\n",
       "   financiar  \n",
       "0     350000  \n",
       "1     593657  \n",
       "2     264316  \n",
       "3     606427  \n",
       "4     307420  \n",
       "5     309988  \n",
       "6     383051  \n",
       "7     548852  \n",
       "8     240507  \n",
       "9     283487  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe['financiar']=dataframe['vivienda']-dataframe['ahorros']\n",
    "dataframe.drop(['gastos_comunes','gastos_otros','pago_coche'], axis=1).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#resumen estadístico que nos brinda la librería Pandas "
   ]
  },
  {
   "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>ingresos</th>\n",
       "      <th>ahorros</th>\n",
       "      <th>vivienda</th>\n",
       "      <th>estado_civil</th>\n",
       "      <th>hijos</th>\n",
       "      <th>trabajo</th>\n",
       "      <th>comprar</th>\n",
       "      <th>financiar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>202.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4958.995050</td>\n",
       "      <td>38749.668317</td>\n",
       "      <td>373349.638614</td>\n",
       "      <td>1.024752</td>\n",
       "      <td>1.232673</td>\n",
       "      <td>4.490099</td>\n",
       "      <td>0.331683</td>\n",
       "      <td>334599.970297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1682.862556</td>\n",
       "      <td>17365.231870</td>\n",
       "      <td>136371.525622</td>\n",
       "      <td>0.837184</td>\n",
       "      <td>1.367833</td>\n",
       "      <td>2.535794</td>\n",
       "      <td>0.471988</td>\n",
       "      <td>126607.099497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>10319.000000</td>\n",
       "      <td>176553.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>154716.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3513.750000</td>\n",
       "      <td>24964.250000</td>\n",
       "      <td>274810.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>240410.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4947.500000</td>\n",
       "      <td>38523.000000</td>\n",
       "      <td>340783.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>301177.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6374.500000</td>\n",
       "      <td>52150.750000</td>\n",
       "      <td>444482.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>393413.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7984.000000</td>\n",
       "      <td>69934.000000</td>\n",
       "      <td>669540.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>618621.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ingresos       ahorros       vivienda  estado_civil       hijos  \\\n",
       "count   202.000000    202.000000     202.000000    202.000000  202.000000   \n",
       "mean   4958.995050  38749.668317  373349.638614      1.024752    1.232673   \n",
       "std    1682.862556  17365.231870  136371.525622      0.837184    1.367833   \n",
       "min    2008.000000  10319.000000  176553.000000      0.000000    0.000000   \n",
       "25%    3513.750000  24964.250000  274810.000000      0.000000    0.000000   \n",
       "50%    4947.500000  38523.000000  340783.500000      1.000000    1.000000   \n",
       "75%    6374.500000  52150.750000  444482.000000      2.000000    2.000000   \n",
       "max    7984.000000  69934.000000  669540.000000      2.000000    4.000000   \n",
       "\n",
       "          trabajo     comprar      financiar  \n",
       "count  202.000000  202.000000     202.000000  \n",
       "mean     4.490099    0.331683  334599.970297  \n",
       "std      2.535794    0.471988  126607.099497  \n",
       "min      0.000000    0.000000  154716.000000  \n",
       "25%      2.000000    0.000000  240410.250000  \n",
       "50%      5.000000    0.000000  301177.000000  \n",
       "75%      7.000000    1.000000  393413.000000  \n",
       "max      8.000000    1.000000  618621.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced = dataframe.drop(['gastos_comunes','gastos_otros','pago_coche'], axis=1)\n",
    "reduced.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Selection ó Selección de Características"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ingresos', 'ahorros', 'hijos', 'trabajo', 'financiar'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "X=dataframe.drop(['comprar'], axis=1)\n",
    "y=dataframe['comprar']\n",
    " \n",
    "best=SelectKBest(k=5)\n",
    "X_new = best.fit_transform(X, y)\n",
    "X_new.shape\n",
    "selected = best.get_support(indices=True)\n",
    "print(X.columns[selected])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grado de correlación tienen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8fb5ac7290>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_features =X.columns[selected]\n",
    " \n",
    "colormap = plt.cm.viridis\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sb.heatmap(dataframe[used_features].astype(float).corr(),linewidths=0.1,vmax=1.0,\n",
    "           square=True, cmap=colormap, linecolor='white', annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Crear el modelo Gaussian Naive Bayes con SKLearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Primero vamos a dividir nuestros datos de entrada en entrenamiento y test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset in training and test datasets\n",
    "X_train, X_test = train_test_split(dataframe, test_size=0.2, random_state=6) \n",
    "y_train =X_train[\"comprar\"]\n",
    "y_test = X_test[\"comprar\"]\n",
    "\n",
    "# Split dataset in training and test datasets\n",
    "X_train, X_test = train_test_split(dataframe, test_size=0.2, random_state=6) \n",
    "y_train =X_train[\"comprar\"]\n",
    "y_test = X_test[\"comprar\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lo ponemos a aprender con fit() y obtenemos predicciones sobre nuestro conjunto de test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión en el set de Entrenamiento: 0.87\n",
      "Precisión en el set de Test: 0.90\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the classifier\n",
    "gnb = GaussianNB()\n",
    "# Train classifier\n",
    "gnb.fit(\n",
    "    X_train[used_features].values,\n",
    "    y_train\n",
    ")\n",
    "y_pred = gnb.predict(X_test[used_features])\n",
    " \n",
    "print('Precisión en el set de Entrenamiento: {:.2f}'\n",
    "     .format(gnb.score(X_train[used_features], y_train)))\n",
    "print('Precisión en el set de Test: {:.2f}'\n",
    "     .format(gnb.score(X_test[used_features], y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Probemos el modelo: ¿Comprar o Alquilar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "#                 ['ingresos', 'ahorros', 'hijos', 'trabajo', 'financiar']\n",
    "print(gnb.predict([[2000,        5000,     0,       5,         200000],\n",
    "                   [6000,        34000,    2,       5,         320000] ]))\n",
    "#Resultado esperado 0-Alquilar, 1-Comprar casa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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