Pipeline-Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "postulaciones = pd.read_csv('Sources/postulaciones_for_training.csv').drop('Unnamed: 0', axis=1)\n",
    "postulantes = pd.read_csv('Sources/postulantes_proc.csv').drop('Unnamed: 0', axis=1)\n",
    "avisos = pd.read_csv('Sources/fiuba_6_avisos_detalle_limpio.csv').drop('Unnamed: 0', axis=1)\n",
    "test = pd.read_csv('Sources/test_final_100k.csv')\n",
    "visitas = pd.read_csv('Sources/visitas_proc.csv').drop('Unnamed: 0', axis=1)\n",
    "preds = pd.DataFrame()"
   ]
  },
  {
   "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>fechanacimiento</th>\n",
       "      <th>sexo</th>\n",
       "      <th>nivel</th>\n",
       "      <th>tipo_de_trabajo</th>\n",
       "      <th>nivel_laboral</th>\n",
       "      <th>nombre_area</th>\n",
       "      <th>denominacion_empresa</th>\n",
       "      <th>cluster</th>\n",
       "      <th>FEM</th>\n",
       "      <th>MASC</th>\n",
       "      <th>...</th>\n",
       "      <th>TendenciaSexo</th>\n",
       "      <th>prom_edad</th>\n",
       "      <th>sabados</th>\n",
       "      <th>domingos</th>\n",
       "      <th>ingles</th>\n",
       "      <th>3 anos</th>\n",
       "      <th>2 anos</th>\n",
       "      <th>4 anos</th>\n",
       "      <th>5 anos</th>\n",
       "      <th>visito</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.0</td>\n",
       "      <td>FEM</td>\n",
       "      <td>-1</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Senior / Semi-Senior</td>\n",
       "      <td>Calidad</td>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.0</td>\n",
       "      <td>MASC</td>\n",
       "      <td>11</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Senior / Semi-Senior</td>\n",
       "      <td>Calidad</td>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.0</td>\n",
       "      <td>MASC</td>\n",
       "      <td>3</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Senior / Semi-Senior</td>\n",
       "      <td>Calidad</td>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.0</td>\n",
       "      <td>FEM</td>\n",
       "      <td>10</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Senior / Semi-Senior</td>\n",
       "      <td>Calidad</td>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.0</td>\n",
       "      <td>FEM</td>\n",
       "      <td>2</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Senior / Semi-Senior</td>\n",
       "      <td>Calidad</td>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   fechanacimiento  sexo  nivel tipo_de_trabajo         nivel_laboral  \\\n",
       "0             55.0   FEM     -1       Full-time  Senior / Semi-Senior   \n",
       "1             31.0  MASC     11       Full-time  Senior / Semi-Senior   \n",
       "2             34.0  MASC      3       Full-time  Senior / Semi-Senior   \n",
       "3             27.0   FEM     10       Full-time  Senior / Semi-Senior   \n",
       "4             26.0   FEM      2       Full-time  Senior / Semi-Senior   \n",
       "\n",
       "  nombre_area denominacion_empresa  cluster  FEM  MASC   ...    TendenciaSexo  \\\n",
       "0     Calidad  Abbott Laboratories       82  NaN   NaN   ...              NaN   \n",
       "1     Calidad  Abbott Laboratories       82  NaN   NaN   ...              NaN   \n",
       "2     Calidad  Abbott Laboratories       82  NaN   NaN   ...              NaN   \n",
       "3     Calidad  Abbott Laboratories       82  NaN   NaN   ...              NaN   \n",
       "4     Calidad  Abbott Laboratories       82  NaN   NaN   ...              NaN   \n",
       "\n",
       "  prom_edad  sabados  domingos  ingles  3 anos  2 anos  4 anos  5 anos  visito  \n",
       "0       NaN        0         0       0       0       0       0       0     NaN  \n",
       "1       NaN        0         0       0       0       0       0       0     NaN  \n",
       "2       NaN        0         0       0       0       0       0       0     NaN  \n",
       "3       NaN        0         0       0       0       0       0       0     NaN  \n",
       "4       NaN        0         0       0       0       0       0       0     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postulaciones = postulaciones.merge(postulantes, on='idpostulante')\n",
    "postulaciones = postulaciones.merge(avisos, on='idaviso')\n",
    "postulaciones = postulaciones.merge(visitas, on=['idaviso','idpostulante'], how='left')\n",
    "media_visitas = np.nanmean(postulaciones.loc[postulaciones['sepostulo']==1,'visito'])\n",
    "postulaciones.loc[postulaciones['sepostulo']==1,'visito'].fillna(media_visitas,inplace=True)\n",
    "resultados = postulaciones['sepostulo']\n",
    "postulaciones.drop(['titulo','descripcion','idaviso','idpostulante','sepostulo'],axis=1, inplace=True)\n",
    "postulaciones.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>fechanacimiento</th>\n",
       "      <th>sexo</th>\n",
       "      <th>nivel</th>\n",
       "      <th>tipo_de_trabajo</th>\n",
       "      <th>nivel_laboral</th>\n",
       "      <th>nombre_area</th>\n",
       "      <th>denominacion_empresa</th>\n",
       "      <th>cluster</th>\n",
       "      <th>FEM</th>\n",
       "      <th>MASC</th>\n",
       "      <th>...</th>\n",
       "      <th>TendenciaSexo</th>\n",
       "      <th>prom_edad</th>\n",
       "      <th>sabados</th>\n",
       "      <th>domingos</th>\n",
       "      <th>ingles</th>\n",
       "      <th>3 anos</th>\n",
       "      <th>2 anos</th>\n",
       "      <th>4 anos</th>\n",
       "      <th>5 anos</th>\n",
       "      <th>visito</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>FEM</td>\n",
       "      <td>21</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Jefe / Supervisor / Responsable</td>\n",
       "      <td>Comercial</td>\n",
       "      <td>BUMERAN.COM ARGENTINA</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.0</td>\n",
       "      <td>MASC</td>\n",
       "      <td>9</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Jefe / Supervisor / Responsable</td>\n",
       "      <td>Comercial</td>\n",
       "      <td>BUMERAN.COM ARGENTINA</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>FEM</td>\n",
       "      <td>12</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Jefe / Supervisor / Responsable</td>\n",
       "      <td>Comercial</td>\n",
       "      <td>BUMERAN.COM ARGENTINA</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.0</td>\n",
       "      <td>MASC</td>\n",
       "      <td>15</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Tecnologia / Sistemas</td>\n",
       "      <td>TeleTech</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>FEM</td>\n",
       "      <td>11</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Tecnologia / Sistemas</td>\n",
       "      <td>TeleTech</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   fechanacimiento  sexo  nivel tipo_de_trabajo  \\\n",
       "0             42.0   FEM     21       Full-time   \n",
       "1             31.0  MASC      9       Full-time   \n",
       "2             36.0   FEM     12       Full-time   \n",
       "3             69.0  MASC     15       Full-time   \n",
       "4             32.0   FEM     11       Full-time   \n",
       "\n",
       "                     nivel_laboral            nombre_area  \\\n",
       "0  Jefe / Supervisor / Responsable              Comercial   \n",
       "1  Jefe / Supervisor / Responsable              Comercial   \n",
       "2  Jefe / Supervisor / Responsable              Comercial   \n",
       "3                             Otro  Tecnologia / Sistemas   \n",
       "4                             Otro  Tecnologia / Sistemas   \n",
       "\n",
       "    denominacion_empresa  cluster  FEM  MASC   ...    TendenciaSexo prom_edad  \\\n",
       "0  BUMERAN.COM ARGENTINA      102  NaN   NaN   ...              NaN       NaN   \n",
       "1  BUMERAN.COM ARGENTINA      102  NaN   NaN   ...              NaN       NaN   \n",
       "2  BUMERAN.COM ARGENTINA      102  NaN   NaN   ...              NaN       NaN   \n",
       "3               TeleTech       27  NaN   NaN   ...              NaN       NaN   \n",
       "4               TeleTech       27  NaN   NaN   ...              NaN       NaN   \n",
       "\n",
       "   sabados  domingos  ingles  3 anos  2 anos  4 anos  5 anos  visito  \n",
       "0        0         0       1       0       0       0       0     NaN  \n",
       "1        0         0       1       0       0       0       0     NaN  \n",
       "2        0         0       1       0       0       0       0     NaN  \n",
       "3        0         0       0       0       0       0       0     NaN  \n",
       "4        0         0       0       0       0       0       0     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = test.merge(postulantes, on='idpostulante')\n",
    "test = test.merge(avisos, on='idaviso')\n",
    "test = test.merge(visitas, on=['idaviso','idpostulante'], how='left')\n",
    "test.drop(['titulo','descripcion','idaviso','idpostulante','id'],axis=1, inplace=True)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.concat([test,postulaciones])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['fechanacimiento'].fillna(np.nanmean(test['fechanacimiento']),inplace=True)\n",
    "test['FEM'].fillna(np.nanmean(test['FEM']),inplace=True)\n",
    "test['MASC'].fillna(np.nanmean(test['MASC']),inplace=True)\n",
    "test['NO_DECLARA'].fillna(np.nanmean(test['NO_DECLARA']),inplace=True)\n",
    "test['prom_edad'].fillna(np.nanmean(test['prom_edad']),inplace=True)\n",
    "test['FEM'].fillna(np.nanmean(test['FEM']),inplace=True)\n",
    "lista = ['sabados', 'domingos','ingles', '3 anos', '2 anos', '4 anos', '5 anos', 'visito']\n",
    "for i in lista:\n",
    "    test[i].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>fechanacimiento</th>\n",
       "      <th>sexo</th>\n",
       "      <th>nivel</th>\n",
       "      <th>tipo_de_trabajo</th>\n",
       "      <th>nivel_laboral</th>\n",
       "      <th>nombre_area</th>\n",
       "      <th>denominacion_empresa</th>\n",
       "      <th>cluster</th>\n",
       "      <th>FEM</th>\n",
       "      <th>MASC</th>\n",
       "      <th>...</th>\n",
       "      <th>TendenciaSexo</th>\n",
       "      <th>prom_edad</th>\n",
       "      <th>sabados</th>\n",
       "      <th>domingos</th>\n",
       "      <th>ingles</th>\n",
       "      <th>3 anos</th>\n",
       "      <th>2 anos</th>\n",
       "      <th>4 anos</th>\n",
       "      <th>5 anos</th>\n",
       "      <th>visito</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>FEM</td>\n",
       "      <td>21</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Jefe / Supervisor / Responsable</td>\n",
       "      <td>Comercial</td>\n",
       "      <td>BUMERAN.COM ARGENTINA</td>\n",
       "      <td>102</td>\n",
       "      <td>681.674548</td>\n",
       "      <td>439.710842</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.836947</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.0</td>\n",
       "      <td>MASC</td>\n",
       "      <td>9</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Jefe / Supervisor / Responsable</td>\n",
       "      <td>Comercial</td>\n",
       "      <td>BUMERAN.COM ARGENTINA</td>\n",
       "      <td>102</td>\n",
       "      <td>681.674548</td>\n",
       "      <td>439.710842</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.836947</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>FEM</td>\n",
       "      <td>12</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Jefe / Supervisor / Responsable</td>\n",
       "      <td>Comercial</td>\n",
       "      <td>BUMERAN.COM ARGENTINA</td>\n",
       "      <td>102</td>\n",
       "      <td>681.674548</td>\n",
       "      <td>439.710842</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.836947</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.0</td>\n",
       "      <td>MASC</td>\n",
       "      <td>15</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Tecnologia / Sistemas</td>\n",
       "      <td>TeleTech</td>\n",
       "      <td>27</td>\n",
       "      <td>681.674548</td>\n",
       "      <td>439.710842</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.836947</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>FEM</td>\n",
       "      <td>11</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Tecnologia / Sistemas</td>\n",
       "      <td>TeleTech</td>\n",
       "      <td>27</td>\n",
       "      <td>681.674548</td>\n",
       "      <td>439.710842</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.836947</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   fechanacimiento  sexo  nivel tipo_de_trabajo  \\\n",
       "0             42.0   FEM     21       Full-time   \n",
       "1             31.0  MASC      9       Full-time   \n",
       "2             36.0   FEM     12       Full-time   \n",
       "3             69.0  MASC     15       Full-time   \n",
       "4             32.0   FEM     11       Full-time   \n",
       "\n",
       "                     nivel_laboral            nombre_area  \\\n",
       "0  Jefe / Supervisor / Responsable              Comercial   \n",
       "1  Jefe / Supervisor / Responsable              Comercial   \n",
       "2  Jefe / Supervisor / Responsable              Comercial   \n",
       "3                             Otro  Tecnologia / Sistemas   \n",
       "4                             Otro  Tecnologia / Sistemas   \n",
       "\n",
       "    denominacion_empresa  cluster         FEM        MASC   ...    \\\n",
       "0  BUMERAN.COM ARGENTINA      102  681.674548  439.710842   ...     \n",
       "1  BUMERAN.COM ARGENTINA      102  681.674548  439.710842   ...     \n",
       "2  BUMERAN.COM ARGENTINA      102  681.674548  439.710842   ...     \n",
       "3               TeleTech       27  681.674548  439.710842   ...     \n",
       "4               TeleTech       27  681.674548  439.710842   ...     \n",
       "\n",
       "   TendenciaSexo  prom_edad  sabados  domingos  ingles  3 anos  2 anos  \\\n",
       "0            NaN  30.836947        0         0       1       0       0   \n",
       "1            NaN  30.836947        0         0       1       0       0   \n",
       "2            NaN  30.836947        0         0       1       0       0   \n",
       "3            NaN  30.836947        0         0       0       0       0   \n",
       "4            NaN  30.836947        0         0       0       0       0   \n",
       "\n",
       "   4 anos  5 anos  visito  \n",
       "0       0       0     0.0  \n",
       "1       0       0     0.0  \n",
       "2       0       0     0.0  \n",
       "3       0       0     0.0  \n",
       "4       0       0     0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "for col in test.columns:\n",
    "    if not test[col].dtype == 'O': continue\n",
    "    test[col] = test[col].fillna('na')\n",
    "    le.fit(test[col])\n",
    "    test[col] = le.transform(test[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "postulaciones = test[100000:].fillna(-1)\n",
    "test = test[:100000].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "postulaciones.rename(columns={x:int(y) for x,y in zip(postulaciones.columns,range(1,len(postulaciones.columns)+1))},inplace=True)\n",
    "test.rename(columns={x:int(y) for x,y in zip(test.columns,range(1,len(test.columns)+1))},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "partitions = []\n",
    "partitions_res = []\n",
    "n = 5\n",
    "index = int(postulaciones.shape[0]/n)\n",
    "for i in range(n):\n",
    "    partitions.append(postulaciones[index*i:index*(i+1)])\n",
    "    partitions_res.append(resultados[index*i:index*(i+1)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "aviso = None\n",
    "postulantes = None\n",
    "visitas = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def proba(result):\n",
    "    prob_1 = []\n",
    "    for x in result:\n",
    "        prob_1.append(x[1])\n",
    "    return prob_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arranca XG\n",
      "Termino XG\n"
     ]
    }
   ],
   "source": [
    "print('Arranca XG')\n",
    "\n",
    "import xgboost as xgb\n",
    "dtrain = xgb.DMatrix(data=partitions[0],label=partitions_res[0])\n",
    "dtest = xgb.DMatrix(data=partitions[4])\n",
    "param = {'max_depth':20, 'eta':1, 'silent':1, 'objective':'binary:logistic', 'subsample':0.8}\n",
    "num_round = 50\n",
    "bst = xgb.train(param, dtrain, num_round)\n",
    "preds['XGB'] = bst.predict(dtest)\n",
    "\n",
    "print('Termino XG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arranca RF\n",
      "Termino RF\n"
     ]
    }
   ],
   "source": [
    "print('Arranca RF')\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "clf=RandomForestClassifier(n_estimators=50, warm_start=True, criterion='entropy', n_jobs=-1)\n",
    "clf.fit(partitions[1],partitions_res[1])\n",
    "preds['RF'] = proba(clf.predict_proba(partitions[4]))\n",
    "\n",
    "print('Termino RF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arranca NB\n",
      "Termino NB\n"
     ]
    }
   ],
   "source": [
    "print('Arranca NB')\n",
    "\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "nb = BernoulliNB()\n",
    "nb.fit(partitions[2] ,partitions_res[2])\n",
    "BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)\n",
    "preds['NB'] = proba(nb.predict_proba(partitions[4]))\n",
    "\n",
    "print('Termino NB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arranca PERCEPTRON\n",
      "Termino PERCEPTRON\n"
     ]
    }
   ],
   "source": [
    "print('Arranca PERCEPTRON')\n",
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)\n",
    "clf.fit(partitions[3], partitions_res[3])\n",
    "preds['PERCEPTRON'] = proba(clf.predict_proba(partitions[4]))\n",
    "\n",
    "print('Termino PERCEPTRON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entreno KNN\n",
      "KNN ya esta entrenado\n"
     ]
    }
   ],
   "source": [
    "print('Entreno KNN')\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neigh = KNeighborsClassifier(n_neighbors=25 ,n_jobs=-1, algorithm='auto')\n",
    "neigh.fit(preds, partitions_res[4])\n",
    "\n",
    "print('KNN ya esta entrenado')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>XGB</th>\n",
       "      <th>RF</th>\n",
       "      <th>NB</th>\n",
       "      <th>PERCEPTRON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.939662</td>\n",
       "      <td>0.603714</td>\n",
       "      <td>0.477862</td>\n",
       "      <td>0.200281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.954823</td>\n",
       "      <td>0.735927</td>\n",
       "      <td>0.477862</td>\n",
       "      <td>0.200281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.998544</td>\n",
       "      <td>0.692747</td>\n",
       "      <td>0.477862</td>\n",
       "      <td>0.200281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.985069</td>\n",
       "      <td>0.811429</td>\n",
       "      <td>0.477862</td>\n",
       "      <td>0.200281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.994120</td>\n",
       "      <td>0.700602</td>\n",
       "      <td>0.477862</td>\n",
       "      <td>0.200281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        XGB        RF        NB  PERCEPTRON\n",
       "0  0.939662  0.603714  0.477862    0.200281\n",
       "1  0.954823  0.735927  0.477862    0.200281\n",
       "2  0.998544  0.692747  0.477862    0.200281\n",
       "3  0.985069  0.811429  0.477862    0.200281\n",
       "4  0.994120  0.700602  0.477862    0.200281"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arranca XGB final\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "invalid index to scalar variable.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-dd643111e9a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mnum_round\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mbst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_round\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mfinal\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'XGB'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Termino XGB final'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-13-5a2a0ad7afdb>\u001b[0m in \u001b[0;36mproba\u001b[0;34m(result)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mprob_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m         \u001b[0mprob_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mprob_1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: invalid index to scalar variable."
     ]
    }
   ],
   "source": [
    "print('Arranca XGB final')\n",
    "\n",
    "aviso = None\n",
    "postulantes = None\n",
    "import xgboost as xgb\n",
    "dtrain = xgb.DMatrix(data=postulaciones, label=resultados)\n",
    "dtest = xgb.DMatrix(data=test)\n",
    "param = {'max_depth':20, 'eta':1, 'silent':1, 'objective':'binary:logistic', 'subsample':0.8}\n",
    "num_round = 50\n",
    "bst = xgb.train(param, dtrain, num_round)\n",
    "final['XGB'] = bst.predict(dtest)\n",
    "\n",
    "print('Termino XGB final')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arranca RF final\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'RandomForestClassifier' object has no attribute 'precit_proba'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-3845da56c649>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mclfr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarm_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'entropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mclfr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpostulaciones\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mresultados\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mfinal\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RF'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclfr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprecit_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Termino RF final'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'RandomForestClassifier' object has no attribute 'precit_proba'"
     ]
    }
   ],
   "source": [
    "print('Arranca RF final')\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "clfr=RandomForestClassifier(n_estimators=50, warm_start=True, criterion='entropy', n_jobs=-1)\n",
    "clfr.fit(postulaciones,resultados)\n",
    "final['RF'] = proba(clfr.predict_proba(test))\n",
    "\n",
    "print('Termino RF final')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arranca NB final\n",
      "Termino NB final\n"
     ]
    }
   ],
   "source": [
    "print('Arranca NB final')\n",
    "\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "nb = BernoulliNB()\n",
    "nb.fit(postulaciones, resultados)\n",
    "BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)\n",
    "final['NB'] = proba(nb.predict_proba(test))\n",
    "\n",
    "print('Termino NB final')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arranca PPERCEPTRON final\n",
      "Termino PERCEPTRON final\n"
     ]
    }
   ],
   "source": [
    "print('Arranca PPERCEPTRON final')\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)\n",
    "clf.fit(postulaciones, resultados)\n",
    "final['PERCEPTRON'] = proba(clf.predict_proba(test))\n",
    "\n",
    "print('Termino PERCEPTRON final')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>XGB</th>\n",
       "      <th>RF</th>\n",
       "      <th>NB</th>\n",
       "      <th>PERCEPTRON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.999293e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.199370</td>\n",
       "      <td>0.318002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.588827e-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.199370</td>\n",
       "      <td>0.318002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.629230e-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.199370</td>\n",
       "      <td>0.318002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.431283e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.244178</td>\n",
       "      <td>0.318002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.114742e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.244178</td>\n",
       "      <td>0.318002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            XGB   RF        NB  PERCEPTRON\n",
       "0  4.999293e-08  0.0  0.199370    0.318002\n",
       "1  3.588827e-06  0.0  0.199370    0.318002\n",
       "2  4.629230e-06  0.0  0.199370    0.318002\n",
       "3  4.431283e-08  0.0  0.244178    0.318002\n",
       "4  2.114742e-07  0.0  0.244178    0.318002"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predigo con KNN\n",
      "Termino KNN\n"
     ]
    }
   ],
   "source": [
    "print('Predigo con KNN')\n",
    "\n",
    "resultado = neigh.predict(final)\n",
    "\n",
    "print('Termino KNN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('Sources/test_final_100k.csv')\n",
    "test['sepostulo'] = resultado\n",
    "test.drop(['idaviso','idpostulante'],axis=1,inplace=True)\n",
    "test.to_csv('Sources/resultado_final_pipe.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}