w6_d2 - Intro ML, ML Workflow, Feature extraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML WorkFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CensusId</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Native</th>\n",
       "      <th>...</th>\n",
       "      <th>Walk</th>\n",
       "      <th>OtherTransp</th>\n",
       "      <th>WorkAtHome</th>\n",
       "      <th>MeanCommute</th>\n",
       "      <th>Employed</th>\n",
       "      <th>PrivateWork</th>\n",
       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1001</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>55221</td>\n",
       "      <td>26745</td>\n",
       "      <td>28476</td>\n",
       "      <td>2.6</td>\n",
       "      <td>75.8</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>26.5</td>\n",
       "      <td>23986</td>\n",
       "      <td>73.6</td>\n",
       "      <td>20.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1003</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>195121</td>\n",
       "      <td>95314</td>\n",
       "      <td>99807</td>\n",
       "      <td>4.5</td>\n",
       "      <td>83.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>26.4</td>\n",
       "      <td>85953</td>\n",
       "      <td>81.5</td>\n",
       "      <td>12.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1005</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour</td>\n",
       "      <td>26932</td>\n",
       "      <td>14497</td>\n",
       "      <td>12435</td>\n",
       "      <td>4.6</td>\n",
       "      <td>46.2</td>\n",
       "      <td>46.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>8597</td>\n",
       "      <td>71.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1007</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>22604</td>\n",
       "      <td>12073</td>\n",
       "      <td>10531</td>\n",
       "      <td>2.2</td>\n",
       "      <td>74.5</td>\n",
       "      <td>21.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>28.8</td>\n",
       "      <td>8294</td>\n",
       "      <td>76.8</td>\n",
       "      <td>16.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1009</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount</td>\n",
       "      <td>57710</td>\n",
       "      <td>28512</td>\n",
       "      <td>29198</td>\n",
       "      <td>8.6</td>\n",
       "      <td>87.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>34.9</td>\n",
       "      <td>22189</td>\n",
       "      <td>82.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CensusId    State   County  TotalPop    Men  Women  Hispanic  White  Black  \\\n",
       "0      1001  Alabama  Autauga     55221  26745  28476       2.6   75.8   18.5   \n",
       "1      1003  Alabama  Baldwin    195121  95314  99807       4.5   83.1    9.5   \n",
       "2      1005  Alabama  Barbour     26932  14497  12435       4.6   46.2   46.7   \n",
       "3      1007  Alabama     Bibb     22604  12073  10531       2.2   74.5   21.4   \n",
       "4      1009  Alabama   Blount     57710  28512  29198       8.6   87.9    1.5   \n",
       "\n",
       "   Native  ...  Walk  OtherTransp  WorkAtHome  MeanCommute  Employed  \\\n",
       "0     0.4  ...   0.5          1.3         1.8         26.5     23986   \n",
       "1     0.6  ...   1.0          1.4         3.9         26.4     85953   \n",
       "2     0.2  ...   1.8          1.5         1.6         24.1      8597   \n",
       "3     0.4  ...   0.6          1.5         0.7         28.8      8294   \n",
       "4     0.3  ...   0.9          0.4         2.3         34.9     22189   \n",
       "\n",
       "   PrivateWork  PublicWork  SelfEmployed  FamilyWork  Unemployment  \n",
       "0         73.6        20.9           5.5         0.0           7.6  \n",
       "1         81.5        12.3           5.8         0.4           7.5  \n",
       "2         71.8        20.8           7.3         0.1          17.6  \n",
       "3         76.8        16.1           6.7         0.4           8.3  \n",
       "4         82.0        13.5           4.2         0.4           7.7  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obtener datos\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "census=pd.read_csv('data/census.csv')\n",
    "census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CensusId</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Pacific</th>\n",
       "      <th>...</th>\n",
       "      <th>Walk</th>\n",
       "      <th>OtherTransp</th>\n",
       "      <th>WorkAtHome</th>\n",
       "      <th>MeanCommute</th>\n",
       "      <th>Employed</th>\n",
       "      <th>PrivateWork</th>\n",
       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3.220000e+03</td>\n",
       "      <td>3.220000e+03</td>\n",
       "      <td>3.220000e+03</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3.220000e+03</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "      <td>3220.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>31393.605280</td>\n",
       "      <td>9.940935e+04</td>\n",
       "      <td>4.889694e+04</td>\n",
       "      <td>5.051241e+04</td>\n",
       "      <td>11.011522</td>\n",
       "      <td>75.428789</td>\n",
       "      <td>8.665497</td>\n",
       "      <td>1.723509</td>\n",
       "      <td>1.229068</td>\n",
       "      <td>0.082733</td>\n",
       "      <td>...</td>\n",
       "      <td>3.323509</td>\n",
       "      <td>1.612733</td>\n",
       "      <td>4.631770</td>\n",
       "      <td>23.278758</td>\n",
       "      <td>4.559352e+04</td>\n",
       "      <td>74.219348</td>\n",
       "      <td>17.560870</td>\n",
       "      <td>7.931801</td>\n",
       "      <td>0.288106</td>\n",
       "      <td>8.094441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>16292.078954</td>\n",
       "      <td>3.193055e+05</td>\n",
       "      <td>1.566813e+05</td>\n",
       "      <td>1.626620e+05</td>\n",
       "      <td>19.241380</td>\n",
       "      <td>22.932890</td>\n",
       "      <td>14.279122</td>\n",
       "      <td>7.253115</td>\n",
       "      <td>2.633079</td>\n",
       "      <td>0.734931</td>\n",
       "      <td>...</td>\n",
       "      <td>3.756096</td>\n",
       "      <td>1.670988</td>\n",
       "      <td>3.178772</td>\n",
       "      <td>5.600466</td>\n",
       "      <td>1.496995e+05</td>\n",
       "      <td>7.863188</td>\n",
       "      <td>6.510354</td>\n",
       "      <td>3.914974</td>\n",
       "      <td>0.455137</td>\n",
       "      <td>4.096114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>4.200000e+01</td>\n",
       "      <td>4.300000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>19032.500000</td>\n",
       "      <td>1.121800e+04</td>\n",
       "      <td>5.637250e+03</td>\n",
       "      <td>5.572000e+03</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>4.550750e+03</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>30024.000000</td>\n",
       "      <td>2.603500e+04</td>\n",
       "      <td>1.293200e+04</td>\n",
       "      <td>1.305700e+04</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>84.100000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.050800e+04</td>\n",
       "      <td>75.700000</td>\n",
       "      <td>16.200000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>7.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>46105.500000</td>\n",
       "      <td>6.643050e+04</td>\n",
       "      <td>3.299275e+04</td>\n",
       "      <td>3.348750e+04</td>\n",
       "      <td>9.825000</td>\n",
       "      <td>93.200000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>26.800000</td>\n",
       "      <td>2.863275e+04</td>\n",
       "      <td>79.700000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>9.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>72153.000000</td>\n",
       "      <td>1.003839e+07</td>\n",
       "      <td>4.945351e+06</td>\n",
       "      <td>5.093037e+06</td>\n",
       "      <td>99.900000</td>\n",
       "      <td>99.800000</td>\n",
       "      <td>85.900000</td>\n",
       "      <td>92.100000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>35.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>71.200000</td>\n",
       "      <td>39.100000</td>\n",
       "      <td>37.200000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>4.635465e+06</td>\n",
       "      <td>88.300000</td>\n",
       "      <td>66.200000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>36.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           CensusId      TotalPop           Men         Women     Hispanic  \\\n",
       "count   3220.000000  3.220000e+03  3.220000e+03  3.220000e+03  3220.000000   \n",
       "mean   31393.605280  9.940935e+04  4.889694e+04  5.051241e+04    11.011522   \n",
       "std    16292.078954  3.193055e+05  1.566813e+05  1.626620e+05    19.241380   \n",
       "min     1001.000000  8.500000e+01  4.200000e+01  4.300000e+01     0.000000   \n",
       "25%    19032.500000  1.121800e+04  5.637250e+03  5.572000e+03     1.900000   \n",
       "50%    30024.000000  2.603500e+04  1.293200e+04  1.305700e+04     3.900000   \n",
       "75%    46105.500000  6.643050e+04  3.299275e+04  3.348750e+04     9.825000   \n",
       "max    72153.000000  1.003839e+07  4.945351e+06  5.093037e+06    99.900000   \n",
       "\n",
       "             White        Black       Native        Asian      Pacific  ...  \\\n",
       "count  3220.000000  3220.000000  3220.000000  3220.000000  3220.000000  ...   \n",
       "mean     75.428789     8.665497     1.723509     1.229068     0.082733  ...   \n",
       "std      22.932890    14.279122     7.253115     2.633079     0.734931  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%      64.100000     0.500000     0.100000     0.200000     0.000000  ...   \n",
       "50%      84.100000     1.900000     0.300000     0.500000     0.000000  ...   \n",
       "75%      93.200000     9.600000     0.600000     1.200000     0.000000  ...   \n",
       "max      99.800000    85.900000    92.100000    41.600000    35.300000  ...   \n",
       "\n",
       "              Walk  OtherTransp   WorkAtHome  MeanCommute      Employed  \\\n",
       "count  3220.000000  3220.000000  3220.000000  3220.000000  3.220000e+03   \n",
       "mean      3.323509     1.612733     4.631770    23.278758  4.559352e+04   \n",
       "std       3.756096     1.670988     3.178772     5.600466  1.496995e+05   \n",
       "min       0.000000     0.000000     0.000000     4.900000  6.200000e+01   \n",
       "25%       1.400000     0.900000     2.700000    19.500000  4.550750e+03   \n",
       "50%       2.400000     1.300000     3.900000    23.000000  1.050800e+04   \n",
       "75%       4.000000     1.900000     5.600000    26.800000  2.863275e+04   \n",
       "max      71.200000    39.100000    37.200000    44.000000  4.635465e+06   \n",
       "\n",
       "       PrivateWork   PublicWork  SelfEmployed   FamilyWork  Unemployment  \n",
       "count  3220.000000  3220.000000   3220.000000  3220.000000   3220.000000  \n",
       "mean     74.219348    17.560870      7.931801     0.288106      8.094441  \n",
       "std       7.863188     6.510354      3.914974     0.455137      4.096114  \n",
       "min      25.000000     5.800000      0.000000     0.000000      0.000000  \n",
       "25%      70.500000    13.100000      5.400000     0.100000      5.500000  \n",
       "50%      75.700000    16.200000      6.900000     0.200000      7.600000  \n",
       "75%      79.700000    20.500000      9.400000     0.300000      9.900000  \n",
       "max      88.300000    66.200000     36.600000     9.800000     36.500000  \n",
       "\n",
       "[8 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descripcion de los datos\n",
    "census.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CensusId', 'State', 'County', 'TotalPop', 'Men', 'Women', 'Hispanic',\n",
       "       'White', 'Black', 'Native', 'Asian', 'Pacific', 'Citizen', 'Income',\n",
       "       'IncomeErr', 'IncomePerCap', 'IncomePerCapErr', 'Poverty',\n",
       "       'ChildPoverty', 'Professional', 'Service', 'Office', 'Construction',\n",
       "       'Production', 'Drive', 'Carpool', 'Transit', 'Walk', 'OtherTransp',\n",
       "       'WorkAtHome', 'MeanCommute', 'Employed', 'PrivateWork', 'PublicWork',\n",
       "       'SelfEmployed', 'FamilyWork', 'Unemployment'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3220 entries, 0 to 3219\n",
      "Data columns (total 37 columns):\n",
      "CensusId           3220 non-null int64\n",
      "State              3220 non-null object\n",
      "County             3220 non-null object\n",
      "TotalPop           3220 non-null int64\n",
      "Men                3220 non-null int64\n",
      "Women              3220 non-null int64\n",
      "Hispanic           3220 non-null float64\n",
      "White              3220 non-null float64\n",
      "Black              3220 non-null float64\n",
      "Native             3220 non-null float64\n",
      "Asian              3220 non-null float64\n",
      "Pacific            3220 non-null float64\n",
      "Citizen            3220 non-null int64\n",
      "Income             3219 non-null float64\n",
      "IncomeErr          3219 non-null float64\n",
      "IncomePerCap       3220 non-null int64\n",
      "IncomePerCapErr    3220 non-null int64\n",
      "Poverty            3220 non-null float64\n",
      "ChildPoverty       3219 non-null float64\n",
      "Professional       3220 non-null float64\n",
      "Service            3220 non-null float64\n",
      "Office             3220 non-null float64\n",
      "Construction       3220 non-null float64\n",
      "Production         3220 non-null float64\n",
      "Drive              3220 non-null float64\n",
      "Carpool            3220 non-null float64\n",
      "Transit            3220 non-null float64\n",
      "Walk               3220 non-null float64\n",
      "OtherTransp        3220 non-null float64\n",
      "WorkAtHome         3220 non-null float64\n",
      "MeanCommute        3220 non-null float64\n",
      "Employed           3220 non-null int64\n",
      "PrivateWork        3220 non-null float64\n",
      "PublicWork         3220 non-null float64\n",
      "SelfEmployed       3220 non-null float64\n",
      "FamilyWork         3220 non-null float64\n",
      "Unemployment       3220 non-null float64\n",
      "dtypes: float64(27), int64(8), object(2)\n",
      "memory usage: 930.9+ KB\n"
     ]
    }
   ],
   "source": [
    "census.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3218 entries, 0 to 3219\n",
      "Data columns (total 37 columns):\n",
      "CensusId           3218 non-null int64\n",
      "State              3218 non-null object\n",
      "County             3218 non-null object\n",
      "TotalPop           3218 non-null int64\n",
      "Men                3218 non-null int64\n",
      "Women              3218 non-null int64\n",
      "Hispanic           3218 non-null float64\n",
      "White              3218 non-null float64\n",
      "Black              3218 non-null float64\n",
      "Native             3218 non-null float64\n",
      "Asian              3218 non-null float64\n",
      "Pacific            3218 non-null float64\n",
      "Citizen            3218 non-null int64\n",
      "Income             3218 non-null float64\n",
      "IncomeErr          3218 non-null float64\n",
      "IncomePerCap       3218 non-null int64\n",
      "IncomePerCapErr    3218 non-null int64\n",
      "Poverty            3218 non-null float64\n",
      "ChildPoverty       3218 non-null float64\n",
      "Professional       3218 non-null float64\n",
      "Service            3218 non-null float64\n",
      "Office             3218 non-null float64\n",
      "Construction       3218 non-null float64\n",
      "Production         3218 non-null float64\n",
      "Drive              3218 non-null float64\n",
      "Carpool            3218 non-null float64\n",
      "Transit            3218 non-null float64\n",
      "Walk               3218 non-null float64\n",
      "OtherTransp        3218 non-null float64\n",
      "WorkAtHome         3218 non-null float64\n",
      "MeanCommute        3218 non-null float64\n",
      "Employed           3218 non-null int64\n",
      "PrivateWork        3218 non-null float64\n",
      "PublicWork         3218 non-null float64\n",
      "SelfEmployed       3218 non-null float64\n",
      "FamilyWork         3218 non-null float64\n",
      "Unemployment       3218 non-null float64\n",
      "dtypes: float64(27), int64(8), object(2)\n",
      "memory usage: 955.3+ KB\n"
     ]
    }
   ],
   "source": [
    "census=census.dropna()\n",
    "census.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outliers (3 veces IQR)\n",
    "plt.figure(figsize=(7,7))\n",
    "census.boxplot(column=['FamilyWork'])\n",
    "plt.xticks(rotation=45)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "q1=np.percentile(census.FamilyWork, 25)\n",
    "q3=np.percentile(census.FamilyWork, 75)\n",
    "iqr=q3-q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [CensusId, State, County, TotalPop, Men, Women, Hispanic, White, Black, Native, Asian, Pacific, Citizen, Income, IncomeErr, IncomePerCap, IncomePerCapErr, Poverty, ChildPoverty, Professional, Service, Office, Construction, Production, Drive, Carpool, Transit, Walk, OtherTransp, WorkAtHome, MeanCommute, Employed, PrivateWork, PublicWork, SelfEmployed, FamilyWork, Unemployment]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "upper=q3+3*iqr\n",
    "lower=q1-3*iqr\n",
    "print (census[census.FamilyWork<lower])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3020 entries, 0 to 3219\n",
      "Data columns (total 37 columns):\n",
      "CensusId           3020 non-null int64\n",
      "State              3020 non-null object\n",
      "County             3020 non-null object\n",
      "TotalPop           3020 non-null int64\n",
      "Men                3020 non-null int64\n",
      "Women              3020 non-null int64\n",
      "Hispanic           3020 non-null float64\n",
      "White              3020 non-null float64\n",
      "Black              3020 non-null float64\n",
      "Native             3020 non-null float64\n",
      "Asian              3020 non-null float64\n",
      "Pacific            3020 non-null float64\n",
      "Citizen            3020 non-null int64\n",
      "Income             3020 non-null float64\n",
      "IncomeErr          3020 non-null float64\n",
      "IncomePerCap       3020 non-null int64\n",
      "IncomePerCapErr    3020 non-null int64\n",
      "Poverty            3020 non-null float64\n",
      "ChildPoverty       3020 non-null float64\n",
      "Professional       3020 non-null float64\n",
      "Service            3020 non-null float64\n",
      "Office             3020 non-null float64\n",
      "Construction       3020 non-null float64\n",
      "Production         3020 non-null float64\n",
      "Drive              3020 non-null float64\n",
      "Carpool            3020 non-null float64\n",
      "Transit            3020 non-null float64\n",
      "Walk               3020 non-null float64\n",
      "OtherTransp        3020 non-null float64\n",
      "WorkAtHome         3020 non-null float64\n",
      "MeanCommute        3020 non-null float64\n",
      "Employed           3020 non-null int64\n",
      "PrivateWork        3020 non-null float64\n",
      "PublicWork         3020 non-null float64\n",
      "SelfEmployed       3020 non-null float64\n",
      "FamilyWork         3020 non-null float64\n",
      "Unemployment       3020 non-null float64\n",
      "dtypes: float64(27), int64(8), object(2)\n",
      "memory usage: 896.6+ KB\n"
     ]
    }
   ],
   "source": [
    "census_clean=census[census.FamilyWork<upper]\n",
    "census_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3218, 37) (3020, 37)\n"
     ]
    }
   ],
   "source": [
    "print (census.shape, census_clean.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1225 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(census_clean);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reescalado y redondeo ([0,1])\n",
    "\n",
    "def escala(x):\n",
    "    return (x/100).round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/data/Library/Python/3.7/lib/python/site-packages/pandas/core/frame.py:3494: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    0.004\n",
       "1    0.006\n",
       "2    0.002\n",
       "3    0.004\n",
       "4    0.003\n",
       "Name: NativeRate, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversion=['Hispanic', 'White', 'Black', 'Native', 'Asian',\n",
    "            'Pacific', 'Poverty', 'ChildPoverty', 'Service', 'Office']\n",
    "\n",
    "n_columnas=[x+'Rate' for x in conversion]\n",
    "\n",
    "census_clean[n_columnas]=census_clean[conversion].apply(escala)\n",
    "census_clean.NativeRate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Cellar/ipython/7.8.0/libexec/vendor/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    3020.000000\n",
       "mean        0.010478\n",
       "std         0.032757\n",
       "min         0.000000\n",
       "25%         0.001265\n",
       "50%         0.002822\n",
       "75%         0.007227\n",
       "max         1.000000\n",
       "Name: TotalPopScaled, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalizar (standard, minmax, robusta....)\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "census_clean['TotalPopScaled']=MinMaxScaler().fit_transform(census_clean.TotalPop.values.reshape(-1, 1))\n",
    "\n",
    "census_clean.TotalPopScaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CensusId</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Native</th>\n",
       "      <th>...</th>\n",
       "      <th>South Dakota</th>\n",
       "      <th>Tennessee</th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>Vermont</th>\n",
       "      <th>Virginia</th>\n",
       "      <th>Washington</th>\n",
       "      <th>West Virginia</th>\n",
       "      <th>Wisconsin</th>\n",
       "      <th>Wyoming</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1001</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>55221</td>\n",
       "      <td>26745</td>\n",
       "      <td>28476</td>\n",
       "      <td>2.6</td>\n",
       "      <td>75.8</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1003</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>195121</td>\n",
       "      <td>95314</td>\n",
       "      <td>99807</td>\n",
       "      <td>4.5</td>\n",
       "      <td>83.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1005</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour</td>\n",
       "      <td>26932</td>\n",
       "      <td>14497</td>\n",
       "      <td>12435</td>\n",
       "      <td>4.6</td>\n",
       "      <td>46.2</td>\n",
       "      <td>46.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1007</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>22604</td>\n",
       "      <td>12073</td>\n",
       "      <td>10531</td>\n",
       "      <td>2.2</td>\n",
       "      <td>74.5</td>\n",
       "      <td>21.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1009</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount</td>\n",
       "      <td>57710</td>\n",
       "      <td>28512</td>\n",
       "      <td>29198</td>\n",
       "      <td>8.6</td>\n",
       "      <td>87.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CensusId    State   County  TotalPop    Men  Women  Hispanic  White  Black  \\\n",
       "0      1001  Alabama  Autauga     55221  26745  28476       2.6   75.8   18.5   \n",
       "1      1003  Alabama  Baldwin    195121  95314  99807       4.5   83.1    9.5   \n",
       "2      1005  Alabama  Barbour     26932  14497  12435       4.6   46.2   46.7   \n",
       "3      1007  Alabama     Bibb     22604  12073  10531       2.2   74.5   21.4   \n",
       "4      1009  Alabama   Blount     57710  28512  29198       8.6   87.9    1.5   \n",
       "\n",
       "   Native  ...  South Dakota  Tennessee  Texas  Utah  Vermont  Virginia  \\\n",
       "0     0.4  ...             0          0      0     0        0         0   \n",
       "1     0.6  ...             0          0      0     0        0         0   \n",
       "2     0.2  ...             0          0      0     0        0         0   \n",
       "3     0.4  ...             0          0      0     0        0         0   \n",
       "4     0.3  ...             0          0      0     0        0         0   \n",
       "\n",
       "   Washington  West Virginia  Wisconsin  Wyoming  \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 99 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding, get_dummies\n",
    "\n",
    "states=pd.get_dummies(census_clean.State, drop_first=True)\n",
    "census_dummy=pd.concat([census_clean, states], axis=1)\n",
    "census_dummy.head()\n",
    "\n",
    "# LabelEncoder, OrdinalEncoder...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CensusId</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Native</th>\n",
       "      <th>...</th>\n",
       "      <th>South Dakota</th>\n",
       "      <th>Tennessee</th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>Vermont</th>\n",
       "      <th>Virginia</th>\n",
       "      <th>Washington</th>\n",
       "      <th>West Virginia</th>\n",
       "      <th>Wisconsin</th>\n",
       "      <th>Wyoming</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3166</td>\n",
       "      <td>72049</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>Culebra</td>\n",
       "      <td>1557</td>\n",
       "      <td>880</td>\n",
       "      <td>677</td>\n",
       "      <td>95.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2617</td>\n",
       "      <td>48189</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Hale</td>\n",
       "      <td>35504</td>\n",
       "      <td>18337</td>\n",
       "      <td>17167</td>\n",
       "      <td>57.9</td>\n",
       "      <td>35.4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2444</td>\n",
       "      <td>47033</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Crockett</td>\n",
       "      <td>14600</td>\n",
       "      <td>6906</td>\n",
       "      <td>7694</td>\n",
       "      <td>9.5</td>\n",
       "      <td>75.7</td>\n",
       "      <td>13.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1288</td>\n",
       "      <td>26115</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Monroe</td>\n",
       "      <td>150436</td>\n",
       "      <td>74065</td>\n",
       "      <td>76371</td>\n",
       "      <td>3.3</td>\n",
       "      <td>91.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>285</td>\n",
       "      <td>8081</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Moffat</td>\n",
       "      <td>13117</td>\n",
       "      <td>6830</td>\n",
       "      <td>6287</td>\n",
       "      <td>14.3</td>\n",
       "      <td>82.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CensusId        State    County  TotalPop    Men  Women  Hispanic  \\\n",
       "3166     72049  Puerto Rico   Culebra      1557    880    677      95.7   \n",
       "2617     48189        Texas      Hale     35504  18337  17167      57.9   \n",
       "2444     47033    Tennessee  Crockett     14600   6906   7694       9.5   \n",
       "1288     26115     Michigan    Monroe    150436  74065  76371       3.3   \n",
       "285       8081     Colorado    Moffat     13117   6830   6287      14.3   \n",
       "\n",
       "      White  Black  Native  ...  South Dakota  Tennessee  Texas  Utah  \\\n",
       "3166    3.4    0.0     0.3  ...             0          0      0     0   \n",
       "2617   35.4    4.6     0.1  ...             0          0      1     0   \n",
       "2444   75.7   13.1     0.2  ...             0          1      0     0   \n",
       "1288   91.9    2.3     0.3  ...             0          0      0     0   \n",
       "285    82.2    0.5     1.0  ...             0          0      0     0   \n",
       "\n",
       "      Vermont  Virginia  Washington  West Virginia  Wisconsin  Wyoming  \n",
       "3166        0         0           0              0          0        0  \n",
       "2617        0         0           0              0          0        0  \n",
       "2444        0         0           0              0          0        0  \n",
       "1288        0         0           0              0          0        0  \n",
       "285         0         0           0              0          0        0  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shuffling\n",
    "\n",
    "s_census=census_dummy.sample(frac=1)\n",
    "s_census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 99)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# muestreo\n",
    "\n",
    "census_sample=census_clean.sample(frac=0.5)\n",
    "census_sample=census_dummy.sample(n=1000)\n",
    "census_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2416, 99)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(census_dummy, test_size=0.2, random_state=42)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(604, 99)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1932, 99)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train, val=train_test_split(train, test_size=0.2)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(484, 99)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CensusId</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Hispanic</th>\n",
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       "      <th>Black</th>\n",
       "      <th>Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Pacific</th>\n",
       "      <th>...</th>\n",
       "      <th>South Dakota</th>\n",
       "      <th>Tennessee</th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>Vermont</th>\n",
       "      <th>Virginia</th>\n",
       "      <th>Washington</th>\n",
       "      <th>West Virginia</th>\n",
       "      <th>Wisconsin</th>\n",
       "      <th>Wyoming</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1001</td>\n",
       "      <td>55221</td>\n",
       "      <td>26745</td>\n",
       "      <td>28476</td>\n",
       "      <td>2.6</td>\n",
       "      <td>75.8</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1003</td>\n",
       "      <td>195121</td>\n",
       "      <td>95314</td>\n",
       "      <td>99807</td>\n",
       "      <td>4.5</td>\n",
       "      <td>83.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1005</td>\n",
       "      <td>26932</td>\n",
       "      <td>14497</td>\n",
       "      <td>12435</td>\n",
       "      <td>4.6</td>\n",
       "      <td>46.2</td>\n",
       "      <td>46.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1007</td>\n",
       "      <td>22604</td>\n",
       "      <td>12073</td>\n",
       "      <td>10531</td>\n",
       "      <td>2.2</td>\n",
       "      <td>74.5</td>\n",
       "      <td>21.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1009</td>\n",
       "      <td>57710</td>\n",
       "      <td>28512</td>\n",
       "      <td>29198</td>\n",
       "      <td>8.6</td>\n",
       "      <td>87.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CensusId  TotalPop    Men  Women  Hispanic  White  Black  Native  Asian  \\\n",
       "0      1001     55221  26745  28476       2.6   75.8   18.5     0.4    1.0   \n",
       "1      1003    195121  95314  99807       4.5   83.1    9.5     0.6    0.7   \n",
       "2      1005     26932  14497  12435       4.6   46.2   46.7     0.2    0.4   \n",
       "3      1007     22604  12073  10531       2.2   74.5   21.4     0.4    0.1   \n",
       "4      1009     57710  28512  29198       8.6   87.9    1.5     0.3    0.1   \n",
       "\n",
       "   Pacific  ...  South Dakota  Tennessee  Texas  Utah  Vermont  Virginia  \\\n",
       "0      0.0  ...             0          0      0     0        0         0   \n",
       "1      0.0  ...             0          0      0     0        0         0   \n",
       "2      0.0  ...             0          0      0     0        0         0   \n",
       "3      0.0  ...             0          0      0     0        0         0   \n",
       "4      0.0  ...             0          0      0     0        0         0   \n",
       "\n",
       "   Washington  West Virginia  Wisconsin  Wyoming  \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 97 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleccion del target\n",
    "\n",
    "df=census_dummy._get_numeric_data()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     55221\n",
       "1    195121\n",
       "2     26932\n",
       "3     22604\n",
       "4     57710\n",
       "Name: TotalPop, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df.drop(columns=['TotalPop', 'CensusId'])\n",
    "y=df.TotalPop\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modelo\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "linreg=LinearRegression(fit_intercept=True, n_jobs=-1)\n",
    "linreg.fit(X_train, y_train)\n",
    "y_pred=linreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(linreg.coef_)==X.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-6.252230377867818e-07"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.00000000e+00,  1.00000000e+00,  1.23290267e-12, -3.10307335e-14,\n",
       "        1.10539355e-12,  2.33285613e-12, -6.51932067e-12, -1.77665938e-11,\n",
       "        3.32980171e-15,  8.90038504e-16, -2.54972638e-14,  4.59646833e-15,\n",
       "        1.67455059e-14, -1.95754191e-12,  1.46929623e-12, -4.60282768e-12,\n",
       "       -7.86735588e-12, -6.66801183e-12, -6.22132744e-12, -4.76931548e-12,\n",
       "       -8.85506702e-12, -1.28032278e-11, -5.10512106e-12, -1.03101272e-11,\n",
       "       -9.63521887e-12, -1.53801389e-11, -8.69678149e-13, -3.72653839e-15,\n",
       "        1.42148675e-10,  1.43855996e-10,  1.48161312e-10,  1.72190707e-10,\n",
       "        2.81464956e-13,  2.18713238e-12,  1.21149246e-13, -2.18931716e-15,\n",
       "       -8.00034801e-14, -7.35841537e-14, -2.03993637e-13, -1.94320535e-14,\n",
       "        1.53201968e-14, -7.84012760e-14, -6.66779788e-14, -2.30228610e-02,\n",
       "       -2.58672070e-11, -4.81215341e-11,  6.12653117e-12, -7.96519586e-11,\n",
       "       -2.44214910e-11, -5.02290187e-11, -1.14326351e-11,  8.67361738e-19,\n",
       "       -1.06481241e-11,  4.10104206e-12, -8.16208498e-11,  9.93899086e-12,\n",
       "       -2.07100152e-12, -3.93305688e-12,  4.50676328e-12, -5.37043738e-12,\n",
       "        4.63639388e-12, -2.57755100e-11, -1.44279041e-11, -1.51673648e-11,\n",
       "       -2.50273023e-11, -1.74357088e-11, -3.81906377e-12,  5.37075825e-12,\n",
       "        7.55340229e-12,  9.24878380e-12,  1.69295313e-11, -7.92862423e-12,\n",
       "       -1.45177587e-11, -3.60550596e-11,  2.51755554e-11,  2.16515729e-12,\n",
       "        1.48666734e-12, -1.22348298e-13, -9.57737947e-12,  7.97247844e-12,\n",
       "       -1.33811992e-11,  3.57905688e-11, -8.18624628e-13,  4.67618102e-11,\n",
       "        2.87069359e-13,  7.79177300e-12, -8.91651268e-12, -1.96945509e-11,\n",
       "       -3.20324750e-12,  4.30380959e-12, -1.51144627e-12, -3.68926295e-12,\n",
       "        2.13371841e-12,  1.13215861e-11,  4.78928995e-12])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Extraction and Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import zipfile\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>State</th>\n",
       "      <th>County</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>...</th>\n",
       "      <th>Walk</th>\n",
       "      <th>OtherTransp</th>\n",
       "      <th>WorkAtHome</th>\n",
       "      <th>MeanCommute</th>\n",
       "      <th>Employed</th>\n",
       "      <th>PrivateWork</th>\n",
       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CensusId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1001</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>55221</td>\n",
       "      <td>26745</td>\n",
       "      <td>28476</td>\n",
       "      <td>2.6</td>\n",
       "      <td>75.8</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>26.5</td>\n",
       "      <td>23986</td>\n",
       "      <td>73.6</td>\n",
       "      <td>20.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1003</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>195121</td>\n",
       "      <td>95314</td>\n",
       "      <td>99807</td>\n",
       "      <td>4.5</td>\n",
       "      <td>83.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>26.4</td>\n",
       "      <td>85953</td>\n",
       "      <td>81.5</td>\n",
       "      <td>12.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1005</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour</td>\n",
       "      <td>26932</td>\n",
       "      <td>14497</td>\n",
       "      <td>12435</td>\n",
       "      <td>4.6</td>\n",
       "      <td>46.2</td>\n",
       "      <td>46.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>8597</td>\n",
       "      <td>71.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1007</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>22604</td>\n",
       "      <td>12073</td>\n",
       "      <td>10531</td>\n",
       "      <td>2.2</td>\n",
       "      <td>74.5</td>\n",
       "      <td>21.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>28.8</td>\n",
       "      <td>8294</td>\n",
       "      <td>76.8</td>\n",
       "      <td>16.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1009</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount</td>\n",
       "      <td>57710</td>\n",
       "      <td>28512</td>\n",
       "      <td>29198</td>\n",
       "      <td>8.6</td>\n",
       "      <td>87.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>34.9</td>\n",
       "      <td>22189</td>\n",
       "      <td>82.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            State   County  TotalPop    Men  Women  Hispanic  White  Black  \\\n",
       "CensusId                                                                     \n",
       "1001      Alabama  Autauga     55221  26745  28476       2.6   75.8   18.5   \n",
       "1003      Alabama  Baldwin    195121  95314  99807       4.5   83.1    9.5   \n",
       "1005      Alabama  Barbour     26932  14497  12435       4.6   46.2   46.7   \n",
       "1007      Alabama     Bibb     22604  12073  10531       2.2   74.5   21.4   \n",
       "1009      Alabama   Blount     57710  28512  29198       8.6   87.9    1.5   \n",
       "\n",
       "          Native  Asian  ...  Walk  OtherTransp  WorkAtHome  MeanCommute  \\\n",
       "CensusId                 ...                                               \n",
       "1001         0.4    1.0  ...   0.5          1.3         1.8         26.5   \n",
       "1003         0.6    0.7  ...   1.0          1.4         3.9         26.4   \n",
       "1005         0.2    0.4  ...   1.8          1.5         1.6         24.1   \n",
       "1007         0.4    0.1  ...   0.6          1.5         0.7         28.8   \n",
       "1009         0.3    0.1  ...   0.9          0.4         2.3         34.9   \n",
       "\n",
       "          Employed  PrivateWork  PublicWork  SelfEmployed  FamilyWork  \\\n",
       "CensusId                                                                \n",
       "1001         23986         73.6        20.9           5.5         0.0   \n",
       "1003         85953         81.5        12.3           5.8         0.4   \n",
       "1005          8597         71.8        20.8           7.3         0.1   \n",
       "1007          8294         76.8        16.1           6.7         0.4   \n",
       "1009         22189         82.0        13.5           4.2         0.4   \n",
       "\n",
       "          Unemployment  \n",
       "CensusId                \n",
       "1001               7.6  \n",
       "1003               7.5  \n",
       "1005              17.6  \n",
       "1007               8.3  \n",
       "1009               7.7  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cargar datos\n",
    "\n",
    "zf=zipfile.ZipFile('data/acs2015_county_data.csv.zip')\n",
    "census=pd.read_csv(zf.open('acs2015_county_data.csv'), index_col='CensusId')\n",
    "census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3220 entries, 1001 to 72153\n",
      "Data columns (total 36 columns):\n",
      "State              3220 non-null object\n",
      "County             3220 non-null object\n",
      "TotalPop           3220 non-null int64\n",
      "Men                3220 non-null int64\n",
      "Women              3220 non-null int64\n",
      "Hispanic           3220 non-null float64\n",
      "White              3220 non-null float64\n",
      "Black              3220 non-null float64\n",
      "Native             3220 non-null float64\n",
      "Asian              3220 non-null float64\n",
      "Pacific            3220 non-null float64\n",
      "Citizen            3220 non-null int64\n",
      "Income             3219 non-null float64\n",
      "IncomeErr          3219 non-null float64\n",
      "IncomePerCap       3220 non-null int64\n",
      "IncomePerCapErr    3220 non-null int64\n",
      "Poverty            3220 non-null float64\n",
      "ChildPoverty       3219 non-null float64\n",
      "Professional       3220 non-null float64\n",
      "Service            3220 non-null float64\n",
      "Office             3220 non-null float64\n",
      "Construction       3220 non-null float64\n",
      "Production         3220 non-null float64\n",
      "Drive              3220 non-null float64\n",
      "Carpool            3220 non-null float64\n",
      "Transit            3220 non-null float64\n",
      "Walk               3220 non-null float64\n",
      "OtherTransp        3220 non-null float64\n",
      "WorkAtHome         3220 non-null float64\n",
      "MeanCommute        3220 non-null float64\n",
      "Employed           3220 non-null int64\n",
      "PrivateWork        3220 non-null float64\n",
      "PublicWork         3220 non-null float64\n",
      "SelfEmployed       3220 non-null float64\n",
      "FamilyWork         3220 non-null float64\n",
      "Unemployment       3220 non-null float64\n",
      "dtypes: float64(27), int64(7), object(2)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "census.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>reservation_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>checkin_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>channel_code</th>\n",
       "      <th>main_product_code</th>\n",
       "      <th>numberofadults</th>\n",
       "      <th>numberofchildren</th>\n",
       "      <th>persontravellingid</th>\n",
       "      <th>resort_region_code</th>\n",
       "      <th>...</th>\n",
       "      <th>season_holidayed_code</th>\n",
       "      <th>state_code_residence</th>\n",
       "      <th>state_code_resort</th>\n",
       "      <th>total_pax</th>\n",
       "      <th>member_age_buckets</th>\n",
       "      <th>booking_type_code</th>\n",
       "      <th>memberid</th>\n",
       "      <th>cluster_code</th>\n",
       "      <th>reservationstatusid_code</th>\n",
       "      <th>resort_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>7dae1ce6bc8f69481328f2be5c4943077dad5598b5f66d...</td>\n",
       "      <td>05/04/18</td>\n",
       "      <td>05/04/18</td>\n",
       "      <td>06/04/18</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>9f14025af0065b30e47e23ebb3b491d39ae8ed17d33739...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>fe0d4e444e1818436c88f72f1cf800536c2f785e59baeb...</td>\n",
       "      <td>24/05/15</td>\n",
       "      <td>23/06/15</td>\n",
       "      <td>27/06/15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
       "      <td>e7f6c011776e8db7cd330b54174fd76f7d0216b612387a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>540bd4285ad8168e8388f84ee74a82cd4f97dc0a404d3e...</td>\n",
       "      <td>21/07/15</td>\n",
       "      <td>07/08/15</td>\n",
       "      <td>10/08/15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>09593c907ae262e46f655b4db9e14f54a19eadcfdd2679...</td>\n",
       "      <td>31/07/15</td>\n",
       "      <td>22/10/15</td>\n",
       "      <td>25/10/15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
       "      <td>49d180ecf56132819571bf39d9b7b342522a2ac6d23c14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>f4c50caac68051faf37551d70bb17eebef2a20e2244cb1...</td>\n",
       "      <td>01/07/16</td>\n",
       "      <td>01/10/16</td>\n",
       "      <td>05/10/16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
       "      <td>e29c9c180c6279b0b02abd6a1801c7c04082cf486ec027...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      reservation_id booking_date  \\\n",
       "0  7dae1ce6bc8f69481328f2be5c4943077dad5598b5f66d...     05/04/18   \n",
       "1  fe0d4e444e1818436c88f72f1cf800536c2f785e59baeb...     24/05/15   \n",
       "2  540bd4285ad8168e8388f84ee74a82cd4f97dc0a404d3e...     21/07/15   \n",
       "3  09593c907ae262e46f655b4db9e14f54a19eadcfdd2679...     31/07/15   \n",
       "4  f4c50caac68051faf37551d70bb17eebef2a20e2244cb1...     01/07/16   \n",
       "\n",
       "  checkin_date checkout_date  channel_code  main_product_code  numberofadults  \\\n",
       "0     05/04/18      06/04/18             3                  2               2   \n",
       "1     23/06/15      27/06/15             1                  2               4   \n",
       "2     07/08/15      10/08/15             3                  2               3   \n",
       "3     22/10/15      25/10/15             1                  2               3   \n",
       "4     01/10/16      05/10/16             1                  2               4   \n",
       "\n",
       "   numberofchildren  persontravellingid  resort_region_code  ...  \\\n",
       "0                 0                  45                   3  ...   \n",
       "1                 0                  45                   1  ...   \n",
       "2                 0                  45                   2  ...   \n",
       "3                 0                  45                   1  ...   \n",
       "4                 0                  45                   1  ...   \n",
       "\n",
       "   season_holidayed_code  state_code_residence  state_code_resort  total_pax  \\\n",
       "0                    2.0                   2.0                  3          3   \n",
       "1                    2.0                   2.0                  4          2   \n",
       "2                    4.0                   2.0                  2          3   \n",
       "3                    2.0                   2.0                  6          3   \n",
       "4                    2.0                   2.0                 11          2   \n",
       "\n",
       "   member_age_buckets  booking_type_code  \\\n",
       "0                   H                  1   \n",
       "1                   H                  1   \n",
       "2                   H                  1   \n",
       "3                   H                  1   \n",
       "4                   H                  1   \n",
       "\n",
       "                                            memberid cluster_code  \\\n",
       "0  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...            A   \n",
       "1  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...            F   \n",
       "2  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...            D   \n",
       "3  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...            F   \n",
       "4  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...            F   \n",
       "\n",
       "   reservationstatusid_code                                          resort_id  \n",
       "0                         C  9f14025af0065b30e47e23ebb3b491d39ae8ed17d33739...  \n",
       "1                         A  e7f6c011776e8db7cd330b54174fd76f7d0216b612387a...  \n",
       "2                         A  d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...  \n",
       "3                         A  49d180ecf56132819571bf39d9b7b342522a2ac6d23c14...  \n",
       "4                         A  e29c9c180c6279b0b02abd6a1801c7c04082cf486ec027...  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zf=zipfile.ZipFile('data/mahindra.zip')\n",
    "ctr=pd.read_csv(zf.open('test.csv'))\n",
    "ctr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 146765 entries, 0 to 146764\n",
      "Data columns (total 23 columns):\n",
      "reservation_id              146765 non-null object\n",
      "booking_date                146765 non-null object\n",
      "checkin_date                146765 non-null object\n",
      "checkout_date               146765 non-null object\n",
      "channel_code                146765 non-null int64\n",
      "main_product_code           146765 non-null int64\n",
      "numberofadults              146765 non-null int64\n",
      "numberofchildren            146765 non-null int64\n",
      "persontravellingid          146765 non-null int64\n",
      "resort_region_code          146765 non-null int64\n",
      "resort_type_code            146765 non-null int64\n",
      "room_type_booked_code       146765 non-null int64\n",
      "roomnights                  146765 non-null int64\n",
      "season_holidayed_code       146730 non-null float64\n",
      "state_code_residence        144505 non-null float64\n",
      "state_code_resort           146765 non-null int64\n",
      "total_pax                   146765 non-null int64\n",
      "member_age_buckets          146765 non-null object\n",
      "booking_type_code           146765 non-null int64\n",
      "memberid                    146765 non-null object\n",
      "cluster_code                146765 non-null object\n",
      "reservationstatusid_code    146765 non-null object\n",
      "resort_id                   146765 non-null object\n",
      "dtypes: float64(2), int64(12), object(9)\n",
      "memory usage: 121.5 MB\n"
     ]
    }
   ],
   "source": [
    "# downcasting\n",
    "\n",
    "ctr.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 146765 entries, 0 to 146764\n",
      "Data columns (total 23 columns):\n",
      "reservation_id              146765 non-null category\n",
      "booking_date                146765 non-null category\n",
      "checkin_date                146765 non-null category\n",
      "checkout_date               146765 non-null category\n",
      "channel_code                146765 non-null int64\n",
      "main_product_code           146765 non-null int64\n",
      "numberofadults              146765 non-null int64\n",
      "numberofchildren            146765 non-null int64\n",
      "persontravellingid          146765 non-null int64\n",
      "resort_region_code          146765 non-null int64\n",
      "resort_type_code            146765 non-null int64\n",
      "room_type_booked_code       146765 non-null int64\n",
      "roomnights                  146765 non-null int64\n",
      "season_holidayed_code       146730 non-null float64\n",
      "state_code_residence        144505 non-null float64\n",
      "state_code_resort           146765 non-null int64\n",
      "total_pax                   146765 non-null int64\n",
      "member_age_buckets          146765 non-null category\n",
      "booking_type_code           146765 non-null int64\n",
      "memberid                    146765 non-null category\n",
      "cluster_code                146765 non-null category\n",
      "reservationstatusid_code    146765 non-null category\n",
      "resort_id                   146765 non-null category\n",
      "dtypes: category(9), float64(2), int64(12)\n",
      "memory usage: 46.9 MB\n"
     ]
    }
   ],
   "source": [
    "for e in ctr.select_dtypes('object').columns:\n",
    "    ctr[e]=ctr[e].astype('category')\n",
    "ctr.info(memory_usage='deep')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 146765 entries, 0 to 146764\n",
      "Data columns (total 23 columns):\n",
      "reservation_id              146765 non-null category\n",
      "booking_date                146765 non-null category\n",
      "checkin_date                146765 non-null category\n",
      "checkout_date               146765 non-null category\n",
      "channel_code                146765 non-null int8\n",
      "main_product_code           146765 non-null int8\n",
      "numberofadults              146765 non-null int8\n",
      "numberofchildren            146765 non-null int8\n",
      "persontravellingid          146765 non-null int16\n",
      "resort_region_code          146765 non-null int8\n",
      "resort_type_code            146765 non-null int8\n",
      "room_type_booked_code       146765 non-null int8\n",
      "roomnights                  146765 non-null int8\n",
      "season_holidayed_code       146730 non-null float64\n",
      "state_code_residence        144505 non-null float64\n",
      "state_code_resort           146765 non-null int8\n",
      "total_pax                   146765 non-null int8\n",
      "member_age_buckets          146765 non-null category\n",
      "booking_type_code           146765 non-null int8\n",
      "memberid                    146765 non-null category\n",
      "cluster_code                146765 non-null category\n",
      "reservationstatusid_code    146765 non-null category\n",
      "resort_id                   146765 non-null category\n",
      "dtypes: category(9), float64(2), int16(1), int8(11)\n",
      "memory usage: 35.2 MB\n"
     ]
    }
   ],
   "source": [
    "for e in ctr.select_dtypes('integer').columns:\n",
    "    ctr[e]=pd.to_numeric(ctr[e], downcast='integer')\n",
    "ctr.info(memory_usage='deep') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 146765 entries, 0 to 146764\n",
      "Data columns (total 23 columns):\n",
      "reservation_id              146765 non-null category\n",
      "booking_date                146765 non-null category\n",
      "checkin_date                146765 non-null category\n",
      "checkout_date               146765 non-null category\n",
      "channel_code                146765 non-null int8\n",
      "main_product_code           146765 non-null int8\n",
      "numberofadults              146765 non-null int8\n",
      "numberofchildren            146765 non-null int8\n",
      "persontravellingid          146765 non-null int16\n",
      "resort_region_code          146765 non-null int8\n",
      "resort_type_code            146765 non-null int8\n",
      "room_type_booked_code       146765 non-null int8\n",
      "roomnights                  146765 non-null int8\n",
      "season_holidayed_code       146730 non-null float32\n",
      "state_code_residence        144505 non-null float32\n",
      "state_code_resort           146765 non-null int8\n",
      "total_pax                   146765 non-null int8\n",
      "member_age_buckets          146765 non-null category\n",
      "booking_type_code           146765 non-null int8\n",
      "memberid                    146765 non-null category\n",
      "cluster_code                146765 non-null category\n",
      "reservationstatusid_code    146765 non-null category\n",
      "resort_id                   146765 non-null category\n",
      "dtypes: category(9), float32(2), int16(1), int8(11)\n",
      "memory usage: 34.1 MB\n"
     ]
    }
   ],
   "source": [
    "for e in ctr.select_dtypes('float').columns:\n",
    "    ctr[e]=pd.to_numeric(ctr[e], downcast='float')\n",
    "ctr.info(memory_usage='deep') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# binning\n",
    "\n",
    "census['Income'].hist(figsize=(8,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10386.046, 48150.333]    1984\n",
       "(48150.333, 85801.667]    1194\n",
       "(85801.667, 123453.0]       41\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(census.Income, 3).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(52074.0, 123453.0]     805\n",
       "(38191.5, 44749.0]      805\n",
       "(10498.999, 38191.5]    805\n",
       "(44749.0, 52074.0]      804\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(census.Income, [0, 0.25, 0.5, 0.75, 1]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>State</th>\n",
       "      <th>County</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>...</th>\n",
       "      <th>OtherTransp</th>\n",
       "      <th>WorkAtHome</th>\n",
       "      <th>MeanCommute</th>\n",
       "      <th>Employed</th>\n",
       "      <th>PrivateWork</th>\n",
       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>HighUnemploymentLowIncome</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CensusId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1001</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>55221</td>\n",
       "      <td>26745</td>\n",
       "      <td>28476</td>\n",
       "      <td>2.6</td>\n",
       "      <td>75.8</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>26.5</td>\n",
       "      <td>23986</td>\n",
       "      <td>73.6</td>\n",
       "      <td>20.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1003</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>195121</td>\n",
       "      <td>95314</td>\n",
       "      <td>99807</td>\n",
       "      <td>4.5</td>\n",
       "      <td>83.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>26.4</td>\n",
       "      <td>85953</td>\n",
       "      <td>81.5</td>\n",
       "      <td>12.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1005</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour</td>\n",
       "      <td>26932</td>\n",
       "      <td>14497</td>\n",
       "      <td>12435</td>\n",
       "      <td>4.6</td>\n",
       "      <td>46.2</td>\n",
       "      <td>46.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>8597</td>\n",
       "      <td>71.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>17.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1007</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>22604</td>\n",
       "      <td>12073</td>\n",
       "      <td>10531</td>\n",
       "      <td>2.2</td>\n",
       "      <td>74.5</td>\n",
       "      <td>21.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>28.8</td>\n",
       "      <td>8294</td>\n",
       "      <td>76.8</td>\n",
       "      <td>16.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1009</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount</td>\n",
       "      <td>57710</td>\n",
       "      <td>28512</td>\n",
       "      <td>29198</td>\n",
       "      <td>8.6</td>\n",
       "      <td>87.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>34.9</td>\n",
       "      <td>22189</td>\n",
       "      <td>82.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            State   County  TotalPop    Men  Women  Hispanic  White  Black  \\\n",
       "CensusId                                                                     \n",
       "1001      Alabama  Autauga     55221  26745  28476       2.6   75.8   18.5   \n",
       "1003      Alabama  Baldwin    195121  95314  99807       4.5   83.1    9.5   \n",
       "1005      Alabama  Barbour     26932  14497  12435       4.6   46.2   46.7   \n",
       "1007      Alabama     Bibb     22604  12073  10531       2.2   74.5   21.4   \n",
       "1009      Alabama   Blount     57710  28512  29198       8.6   87.9    1.5   \n",
       "\n",
       "          Native  Asian  ...  OtherTransp  WorkAtHome  MeanCommute  Employed  \\\n",
       "CensusId                 ...                                                   \n",
       "1001         0.4    1.0  ...          1.3         1.8         26.5     23986   \n",
       "1003         0.6    0.7  ...          1.4         3.9         26.4     85953   \n",
       "1005         0.2    0.4  ...          1.5         1.6         24.1      8597   \n",
       "1007         0.4    0.1  ...          1.5         0.7         28.8      8294   \n",
       "1009         0.3    0.1  ...          0.4         2.3         34.9     22189   \n",
       "\n",
       "          PrivateWork  PublicWork  SelfEmployed  FamilyWork  Unemployment  \\\n",
       "CensusId                                                                    \n",
       "1001             73.6        20.9           5.5         0.0           7.6   \n",
       "1003             81.5        12.3           5.8         0.4           7.5   \n",
       "1005             71.8        20.8           7.3         0.1          17.6   \n",
       "1007             76.8        16.1           6.7         0.4           8.3   \n",
       "1009             82.0        13.5           4.2         0.4           7.7   \n",
       "\n",
       "          HighUnemploymentLowIncome  \n",
       "CensusId                             \n",
       "1001                              0  \n",
       "1003                              0  \n",
       "1005                              1  \n",
       "1007                              1  \n",
       "1009                              0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nuevas caracteristicas\n",
    "\n",
    "census['HighUnemploymentLowIncome']=np.where((census.Unemployment>0.07)\\\n",
    "                                             & (pd.qcut(census.Income, \n",
    "                                                       [0, 1/3, 2/3, 1]).cat.codes==0), 1, 0)\n",
    "census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Pacific</th>\n",
       "      <th>Citizen</th>\n",
       "      <th>...</th>\n",
       "      <th>OtherTransp</th>\n",
       "      <th>WorkAtHome</th>\n",
       "      <th>MeanCommute</th>\n",
       "      <th>Employed</th>\n",
       "      <th>PrivateWork</th>\n",
       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>HighUnemploymentLowIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>TotalPop</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999877</td>\n",
       "      <td>0.999886</td>\n",
       "      <td>0.113882</td>\n",
       "      <td>-0.187370</td>\n",
       "      <td>0.075296</td>\n",
       "      <td>-0.046038</td>\n",
       "      <td>0.447874</td>\n",
       "      <td>0.027104</td>\n",
       "      <td>0.996378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036125</td>\n",
       "      <td>-0.027852</td>\n",
       "      <td>0.148545</td>\n",
       "      <td>0.997621</td>\n",
       "      <td>0.196913</td>\n",
       "      <td>-0.143553</td>\n",
       "      <td>-0.146419</td>\n",
       "      <td>-0.085016</td>\n",
       "      <td>0.030306</td>\n",
       "      <td>-0.149519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Men</td>\n",
       "      <td>0.999877</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999527</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>-0.186478</td>\n",
       "      <td>0.072807</td>\n",
       "      <td>-0.045818</td>\n",
       "      <td>0.449085</td>\n",
       "      <td>0.028143</td>\n",
       "      <td>0.995819</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036560</td>\n",
       "      <td>-0.027384</td>\n",
       "      <td>0.147537</td>\n",
       "      <td>0.997481</td>\n",
       "      <td>0.196025</td>\n",
       "      <td>-0.142667</td>\n",
       "      <td>-0.146133</td>\n",
       "      <td>-0.084826</td>\n",
       "      <td>0.029516</td>\n",
       "      <td>-0.150084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Women</td>\n",
       "      <td>0.999886</td>\n",
       "      <td>0.999527</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.113466</td>\n",
       "      <td>-0.188187</td>\n",
       "      <td>0.077676</td>\n",
       "      <td>-0.046240</td>\n",
       "      <td>0.446604</td>\n",
       "      <td>0.026096</td>\n",
       "      <td>0.996685</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035697</td>\n",
       "      <td>-0.028297</td>\n",
       "      <td>0.149481</td>\n",
       "      <td>0.997524</td>\n",
       "      <td>0.197722</td>\n",
       "      <td>-0.144373</td>\n",
       "      <td>-0.146661</td>\n",
       "      <td>-0.085180</td>\n",
       "      <td>0.031060</td>\n",
       "      <td>-0.148940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0.113882</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>0.113466</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.724959</td>\n",
       "      <td>-0.144540</td>\n",
       "      <td>-0.056119</td>\n",
       "      <td>0.044244</td>\n",
       "      <td>-0.002645</td>\n",
       "      <td>0.104092</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041738</td>\n",
       "      <td>-0.120975</td>\n",
       "      <td>0.035312</td>\n",
       "      <td>0.102515</td>\n",
       "      <td>-0.170047</td>\n",
       "      <td>0.212196</td>\n",
       "      <td>-0.005562</td>\n",
       "      <td>-0.047897</td>\n",
       "      <td>0.321975</td>\n",
       "      <td>0.149073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>White</td>\n",
       "      <td>-0.187370</td>\n",
       "      <td>-0.186478</td>\n",
       "      <td>-0.188187</td>\n",
       "      <td>-0.724959</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.466185</td>\n",
       "      <td>-0.231565</td>\n",
       "      <td>-0.199347</td>\n",
       "      <td>-0.075080</td>\n",
       "      <td>-0.183133</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.173506</td>\n",
       "      <td>0.217985</td>\n",
       "      <td>-0.077046</td>\n",
       "      <td>-0.176872</td>\n",
       "      <td>0.228257</td>\n",
       "      <td>-0.405057</td>\n",
       "      <td>0.202066</td>\n",
       "      <td>0.112044</td>\n",
       "      <td>-0.538941</td>\n",
       "      <td>-0.286174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Black</td>\n",
       "      <td>0.075296</td>\n",
       "      <td>0.072807</td>\n",
       "      <td>0.077676</td>\n",
       "      <td>-0.144540</td>\n",
       "      <td>-0.466185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.097140</td>\n",
       "      <td>0.019916</td>\n",
       "      <td>-0.035385</td>\n",
       "      <td>0.081138</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004333</td>\n",
       "      <td>-0.281791</td>\n",
       "      <td>0.186750</td>\n",
       "      <td>0.071231</td>\n",
       "      <td>0.064072</td>\n",
       "      <td>0.115743</td>\n",
       "      <td>-0.305339</td>\n",
       "      <td>-0.137477</td>\n",
       "      <td>0.352578</td>\n",
       "      <td>0.280962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Native</td>\n",
       "      <td>-0.046038</td>\n",
       "      <td>-0.045818</td>\n",
       "      <td>-0.046240</td>\n",
       "      <td>-0.056119</td>\n",
       "      <td>-0.231565</td>\n",
       "      <td>-0.097140</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002663</td>\n",
       "      <td>0.033927</td>\n",
       "      <td>-0.050108</td>\n",
       "      <td>...</td>\n",
       "      <td>0.347975</td>\n",
       "      <td>0.167324</td>\n",
       "      <td>-0.207657</td>\n",
       "      <td>-0.047252</td>\n",
       "      <td>-0.402015</td>\n",
       "      <td>0.452651</td>\n",
       "      <td>0.045619</td>\n",
       "      <td>0.077294</td>\n",
       "      <td>0.188074</td>\n",
       "      <td>0.056425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asian</td>\n",
       "      <td>0.447874</td>\n",
       "      <td>0.449085</td>\n",
       "      <td>0.446604</td>\n",
       "      <td>0.044244</td>\n",
       "      <td>-0.199347</td>\n",
       "      <td>0.019916</td>\n",
       "      <td>-0.002663</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.353935</td>\n",
       "      <td>0.456733</td>\n",
       "      <td>...</td>\n",
       "      <td>0.133118</td>\n",
       "      <td>0.013706</td>\n",
       "      <td>0.043008</td>\n",
       "      <td>0.463088</td>\n",
       "      <td>0.108109</td>\n",
       "      <td>-0.030053</td>\n",
       "      <td>-0.156825</td>\n",
       "      <td>-0.087749</td>\n",
       "      <td>-0.059663</td>\n",
       "      <td>-0.197351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pacific</td>\n",
       "      <td>0.027104</td>\n",
       "      <td>0.028143</td>\n",
       "      <td>0.026096</td>\n",
       "      <td>-0.002645</td>\n",
       "      <td>-0.075080</td>\n",
       "      <td>-0.035385</td>\n",
       "      <td>0.033927</td>\n",
       "      <td>0.353935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027806</td>\n",
       "      <td>...</td>\n",
       "      <td>0.160300</td>\n",
       "      <td>0.020612</td>\n",
       "      <td>-0.066947</td>\n",
       "      <td>0.026891</td>\n",
       "      <td>-0.118206</td>\n",
       "      <td>0.140242</td>\n",
       "      <td>0.004529</td>\n",
       "      <td>-0.004515</td>\n",
       "      <td>-0.037973</td>\n",
       "      <td>-0.041219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Citizen</td>\n",
       "      <td>0.996378</td>\n",
       "      <td>0.995819</td>\n",
       "      <td>0.996685</td>\n",
       "      <td>0.104092</td>\n",
       "      <td>-0.183133</td>\n",
       "      <td>0.081138</td>\n",
       "      <td>-0.050108</td>\n",
       "      <td>0.456733</td>\n",
       "      <td>0.027806</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035693</td>\n",
       "      <td>-0.031685</td>\n",
       "      <td>0.152106</td>\n",
       "      <td>0.995176</td>\n",
       "      <td>0.212811</td>\n",
       "      <td>-0.152932</td>\n",
       "      <td>-0.161904</td>\n",
       "      <td>-0.092302</td>\n",
       "      <td>0.031339</td>\n",
       "      <td>-0.160601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Income</td>\n",
       "      <td>0.234886</td>\n",
       "      <td>0.235859</td>\n",
       "      <td>0.233896</td>\n",
       "      <td>-0.219561</td>\n",
       "      <td>0.267041</td>\n",
       "      <td>-0.220597</td>\n",
       "      <td>-0.038644</td>\n",
       "      <td>0.439447</td>\n",
       "      <td>0.075974</td>\n",
       "      <td>0.248217</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026077</td>\n",
       "      <td>0.202900</td>\n",
       "      <td>0.059770</td>\n",
       "      <td>0.257709</td>\n",
       "      <td>0.237906</td>\n",
       "      <td>-0.245566</td>\n",
       "      <td>-0.063982</td>\n",
       "      <td>-0.047615</td>\n",
       "      <td>-0.509510</td>\n",
       "      <td>-0.673279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>IncomeErr</td>\n",
       "      <td>-0.263337</td>\n",
       "      <td>-0.262743</td>\n",
       "      <td>-0.263848</td>\n",
       "      <td>-0.027521</td>\n",
       "      <td>0.082250</td>\n",
       "      <td>-0.083293</td>\n",
       "      <td>0.046390</td>\n",
       "      <td>-0.163356</td>\n",
       "      <td>0.057022</td>\n",
       "      <td>-0.282625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044090</td>\n",
       "      <td>0.286125</td>\n",
       "      <td>-0.173274</td>\n",
       "      <td>-0.260500</td>\n",
       "      <td>-0.402052</td>\n",
       "      <td>0.236892</td>\n",
       "      <td>0.388015</td>\n",
       "      <td>0.220618</td>\n",
       "      <td>-0.227995</td>\n",
       "      <td>0.018102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>IncomePerCap</td>\n",
       "      <td>0.238516</td>\n",
       "      <td>0.237878</td>\n",
       "      <td>0.239075</td>\n",
       "      <td>-0.294180</td>\n",
       "      <td>0.345323</td>\n",
       "      <td>-0.207272</td>\n",
       "      <td>-0.095884</td>\n",
       "      <td>0.403223</td>\n",
       "      <td>0.090034</td>\n",
       "      <td>0.257302</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029183</td>\n",
       "      <td>0.299546</td>\n",
       "      <td>-0.034978</td>\n",
       "      <td>0.264614</td>\n",
       "      <td>0.191792</td>\n",
       "      <td>-0.284536</td>\n",
       "      <td>0.087274</td>\n",
       "      <td>0.005932</td>\n",
       "      <td>-0.547596</td>\n",
       "      <td>-0.605686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>IncomePerCapErr</td>\n",
       "      <td>-0.235936</td>\n",
       "      <td>-0.235582</td>\n",
       "      <td>-0.236222</td>\n",
       "      <td>-0.029884</td>\n",
       "      <td>0.093692</td>\n",
       "      <td>-0.081191</td>\n",
       "      <td>0.008278</td>\n",
       "      <td>-0.155552</td>\n",
       "      <td>0.087864</td>\n",
       "      <td>-0.252711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026601</td>\n",
       "      <td>0.350686</td>\n",
       "      <td>-0.207202</td>\n",
       "      <td>-0.233130</td>\n",
       "      <td>-0.405600</td>\n",
       "      <td>0.198416</td>\n",
       "      <td>0.460763</td>\n",
       "      <td>0.206154</td>\n",
       "      <td>-0.250070</td>\n",
       "      <td>0.019268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Poverty</td>\n",
       "      <td>-0.062075</td>\n",
       "      <td>-0.063009</td>\n",
       "      <td>-0.061161</td>\n",
       "      <td>0.482186</td>\n",
       "      <td>-0.618490</td>\n",
       "      <td>0.308611</td>\n",
       "      <td>0.153093</td>\n",
       "      <td>-0.153620</td>\n",
       "      <td>-0.028188</td>\n",
       "      <td>-0.069567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.053027</td>\n",
       "      <td>-0.269402</td>\n",
       "      <td>0.102818</td>\n",
       "      <td>-0.078917</td>\n",
       "      <td>-0.248676</td>\n",
       "      <td>0.379531</td>\n",
       "      <td>-0.125693</td>\n",
       "      <td>-0.053550</td>\n",
       "      <td>0.712344</td>\n",
       "      <td>0.641849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ChildPoverty</td>\n",
       "      <td>-0.057034</td>\n",
       "      <td>-0.058183</td>\n",
       "      <td>-0.055914</td>\n",
       "      <td>0.395252</td>\n",
       "      <td>-0.573871</td>\n",
       "      <td>0.371930</td>\n",
       "      <td>0.128454</td>\n",
       "      <td>-0.184514</td>\n",
       "      <td>-0.053754</td>\n",
       "      <td>-0.064229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024297</td>\n",
       "      <td>-0.299034</td>\n",
       "      <td>0.130842</td>\n",
       "      <td>-0.074957</td>\n",
       "      <td>-0.179588</td>\n",
       "      <td>0.302751</td>\n",
       "      <td>-0.132962</td>\n",
       "      <td>-0.070628</td>\n",
       "      <td>0.676583</td>\n",
       "      <td>0.637212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Professional</td>\n",
       "      <td>0.248403</td>\n",
       "      <td>0.247094</td>\n",
       "      <td>0.249607</td>\n",
       "      <td>-0.097219</td>\n",
       "      <td>0.085386</td>\n",
       "      <td>-0.125293</td>\n",
       "      <td>0.066968</td>\n",
       "      <td>0.363298</td>\n",
       "      <td>0.007936</td>\n",
       "      <td>0.269306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056443</td>\n",
       "      <td>0.391443</td>\n",
       "      <td>-0.095850</td>\n",
       "      <td>0.269650</td>\n",
       "      <td>-0.174397</td>\n",
       "      <td>0.095597</td>\n",
       "      <td>0.183207</td>\n",
       "      <td>0.070669</td>\n",
       "      <td>-0.300648</td>\n",
       "      <td>-0.350484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Service</td>\n",
       "      <td>-0.001064</td>\n",
       "      <td>-0.000958</td>\n",
       "      <td>-0.001165</td>\n",
       "      <td>0.222669</td>\n",
       "      <td>-0.317807</td>\n",
       "      <td>0.148077</td>\n",
       "      <td>0.112310</td>\n",
       "      <td>-0.029184</td>\n",
       "      <td>0.062767</td>\n",
       "      <td>-0.002952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093518</td>\n",
       "      <td>-0.125955</td>\n",
       "      <td>-0.038306</td>\n",
       "      <td>-0.015277</td>\n",
       "      <td>-0.224442</td>\n",
       "      <td>0.358497</td>\n",
       "      <td>-0.138458</td>\n",
       "      <td>-0.062446</td>\n",
       "      <td>0.364564</td>\n",
       "      <td>0.274709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Office</td>\n",
       "      <td>0.185946</td>\n",
       "      <td>0.185006</td>\n",
       "      <td>0.186808</td>\n",
       "      <td>0.104799</td>\n",
       "      <td>-0.122228</td>\n",
       "      <td>0.095943</td>\n",
       "      <td>-0.117021</td>\n",
       "      <td>0.079781</td>\n",
       "      <td>-0.000106</td>\n",
       "      <td>0.202534</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012360</td>\n",
       "      <td>-0.252340</td>\n",
       "      <td>0.190631</td>\n",
       "      <td>0.180124</td>\n",
       "      <td>0.317369</td>\n",
       "      <td>-0.168495</td>\n",
       "      <td>-0.334275</td>\n",
       "      <td>-0.195269</td>\n",
       "      <td>0.162646</td>\n",
       "      <td>-0.082698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Construction</td>\n",
       "      <td>-0.264243</td>\n",
       "      <td>-0.262402</td>\n",
       "      <td>-0.265956</td>\n",
       "      <td>0.131005</td>\n",
       "      <td>0.017874</td>\n",
       "      <td>-0.157809</td>\n",
       "      <td>0.043855</td>\n",
       "      <td>-0.306973</td>\n",
       "      <td>-0.027832</td>\n",
       "      <td>-0.289256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001230</td>\n",
       "      <td>0.158397</td>\n",
       "      <td>-0.048633</td>\n",
       "      <td>-0.271589</td>\n",
       "      <td>-0.294023</td>\n",
       "      <td>0.123838</td>\n",
       "      <td>0.357350</td>\n",
       "      <td>0.234626</td>\n",
       "      <td>-0.091410</td>\n",
       "      <td>0.131495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Production</td>\n",
       "      <td>-0.184503</td>\n",
       "      <td>-0.183940</td>\n",
       "      <td>-0.185003</td>\n",
       "      <td>-0.187876</td>\n",
       "      <td>0.161394</td>\n",
       "      <td>0.107941</td>\n",
       "      <td>-0.112290</td>\n",
       "      <td>-0.203708</td>\n",
       "      <td>-0.027796</td>\n",
       "      <td>-0.197393</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.115908</td>\n",
       "      <td>-0.330884</td>\n",
       "      <td>0.060354</td>\n",
       "      <td>-0.190441</td>\n",
       "      <td>0.375012</td>\n",
       "      <td>-0.330177</td>\n",
       "      <td>-0.192321</td>\n",
       "      <td>-0.102363</td>\n",
       "      <td>0.079671</td>\n",
       "      <td>0.164864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Drive</td>\n",
       "      <td>-0.103191</td>\n",
       "      <td>-0.101488</td>\n",
       "      <td>-0.104808</td>\n",
       "      <td>-0.021655</td>\n",
       "      <td>0.078510</td>\n",
       "      <td>0.154946</td>\n",
       "      <td>-0.336971</td>\n",
       "      <td>-0.277215</td>\n",
       "      <td>-0.161804</td>\n",
       "      <td>-0.098314</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.515444</td>\n",
       "      <td>-0.564711</td>\n",
       "      <td>0.231651</td>\n",
       "      <td>-0.109859</td>\n",
       "      <td>0.404189</td>\n",
       "      <td>-0.249669</td>\n",
       "      <td>-0.369549</td>\n",
       "      <td>-0.234478</td>\n",
       "      <td>0.098434</td>\n",
       "      <td>0.089661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Carpool</td>\n",
       "      <td>-0.080850</td>\n",
       "      <td>-0.079179</td>\n",
       "      <td>-0.082440</td>\n",
       "      <td>0.074306</td>\n",
       "      <td>-0.095702</td>\n",
       "      <td>0.032938</td>\n",
       "      <td>0.050257</td>\n",
       "      <td>-0.066114</td>\n",
       "      <td>0.024309</td>\n",
       "      <td>-0.093579</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021746</td>\n",
       "      <td>-0.082157</td>\n",
       "      <td>0.059327</td>\n",
       "      <td>-0.087432</td>\n",
       "      <td>-0.072880</td>\n",
       "      <td>0.079081</td>\n",
       "      <td>0.015877</td>\n",
       "      <td>-0.006345</td>\n",
       "      <td>0.088901</td>\n",
       "      <td>0.141333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Transit</td>\n",
       "      <td>0.402729</td>\n",
       "      <td>0.396624</td>\n",
       "      <td>0.408516</td>\n",
       "      <td>0.086346</td>\n",
       "      <td>-0.178568</td>\n",
       "      <td>0.083811</td>\n",
       "      <td>-0.013507</td>\n",
       "      <td>0.440298</td>\n",
       "      <td>0.016191</td>\n",
       "      <td>0.411518</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101531</td>\n",
       "      <td>0.004869</td>\n",
       "      <td>0.191493</td>\n",
       "      <td>0.414272</td>\n",
       "      <td>0.074310</td>\n",
       "      <td>-0.024188</td>\n",
       "      <td>-0.102205</td>\n",
       "      <td>-0.057884</td>\n",
       "      <td>0.030777</td>\n",
       "      <td>-0.088615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Walk</td>\n",
       "      <td>-0.047901</td>\n",
       "      <td>-0.048278</td>\n",
       "      <td>-0.047526</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>-0.048004</td>\n",
       "      <td>-0.169504</td>\n",
       "      <td>0.362889</td>\n",
       "      <td>0.187220</td>\n",
       "      <td>0.209134</td>\n",
       "      <td>-0.051720</td>\n",
       "      <td>...</td>\n",
       "      <td>0.465537</td>\n",
       "      <td>0.342670</td>\n",
       "      <td>-0.415897</td>\n",
       "      <td>-0.043496</td>\n",
       "      <td>-0.399775</td>\n",
       "      <td>0.302706</td>\n",
       "      <td>0.275276</td>\n",
       "      <td>0.208346</td>\n",
       "      <td>-0.084317</td>\n",
       "      <td>-0.060600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>OtherTransp</td>\n",
       "      <td>0.036125</td>\n",
       "      <td>0.036560</td>\n",
       "      <td>0.035697</td>\n",
       "      <td>0.041738</td>\n",
       "      <td>-0.173506</td>\n",
       "      <td>-0.004333</td>\n",
       "      <td>0.347975</td>\n",
       "      <td>0.133118</td>\n",
       "      <td>0.160300</td>\n",
       "      <td>0.035693</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.049403</td>\n",
       "      <td>-0.175421</td>\n",
       "      <td>0.036179</td>\n",
       "      <td>-0.182209</td>\n",
       "      <td>0.230593</td>\n",
       "      <td>-0.017683</td>\n",
       "      <td>0.004182</td>\n",
       "      <td>0.110418</td>\n",
       "      <td>0.002972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WorkAtHome</td>\n",
       "      <td>-0.027852</td>\n",
       "      <td>-0.027384</td>\n",
       "      <td>-0.028297</td>\n",
       "      <td>-0.120975</td>\n",
       "      <td>0.217985</td>\n",
       "      <td>-0.281791</td>\n",
       "      <td>0.167324</td>\n",
       "      <td>0.013706</td>\n",
       "      <td>0.020612</td>\n",
       "      <td>-0.031685</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.213501</td>\n",
       "      <td>-0.022087</td>\n",
       "      <td>-0.410801</td>\n",
       "      <td>0.074007</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>0.377613</td>\n",
       "      <td>-0.306548</td>\n",
       "      <td>-0.189550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MeanCommute</td>\n",
       "      <td>0.148545</td>\n",
       "      <td>0.147537</td>\n",
       "      <td>0.149481</td>\n",
       "      <td>0.035312</td>\n",
       "      <td>-0.077046</td>\n",
       "      <td>0.186750</td>\n",
       "      <td>-0.207657</td>\n",
       "      <td>0.043008</td>\n",
       "      <td>-0.066947</td>\n",
       "      <td>0.152106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.175421</td>\n",
       "      <td>-0.213501</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.147238</td>\n",
       "      <td>0.235821</td>\n",
       "      <td>-0.113674</td>\n",
       "      <td>-0.264526</td>\n",
       "      <td>-0.171779</td>\n",
       "      <td>0.293725</td>\n",
       "      <td>0.091209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Employed</td>\n",
       "      <td>0.997621</td>\n",
       "      <td>0.997481</td>\n",
       "      <td>0.997524</td>\n",
       "      <td>0.102515</td>\n",
       "      <td>-0.176872</td>\n",
       "      <td>0.071231</td>\n",
       "      <td>-0.047252</td>\n",
       "      <td>0.463088</td>\n",
       "      <td>0.026891</td>\n",
       "      <td>0.995176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036179</td>\n",
       "      <td>-0.022087</td>\n",
       "      <td>0.147238</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.201799</td>\n",
       "      <td>-0.149712</td>\n",
       "      <td>-0.146030</td>\n",
       "      <td>-0.084870</td>\n",
       "      <td>0.014012</td>\n",
       "      <td>-0.158740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PrivateWork</td>\n",
       "      <td>0.196913</td>\n",
       "      <td>0.196025</td>\n",
       "      <td>0.197722</td>\n",
       "      <td>-0.170047</td>\n",
       "      <td>0.228257</td>\n",
       "      <td>0.064072</td>\n",
       "      <td>-0.402015</td>\n",
       "      <td>0.108109</td>\n",
       "      <td>-0.118206</td>\n",
       "      <td>0.212811</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.182209</td>\n",
       "      <td>-0.410801</td>\n",
       "      <td>0.235821</td>\n",
       "      <td>0.201799</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.852914</td>\n",
       "      <td>-0.554611</td>\n",
       "      <td>-0.305991</td>\n",
       "      <td>-0.099744</td>\n",
       "      <td>-0.226546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PublicWork</td>\n",
       "      <td>-0.143553</td>\n",
       "      <td>-0.142667</td>\n",
       "      <td>-0.144373</td>\n",
       "      <td>0.212196</td>\n",
       "      <td>-0.405057</td>\n",
       "      <td>0.115743</td>\n",
       "      <td>0.452651</td>\n",
       "      <td>-0.030053</td>\n",
       "      <td>0.140242</td>\n",
       "      <td>-0.152932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.230593</td>\n",
       "      <td>0.074007</td>\n",
       "      <td>-0.113674</td>\n",
       "      <td>-0.149712</td>\n",
       "      <td>-0.852914</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.041945</td>\n",
       "      <td>0.069269</td>\n",
       "      <td>0.306015</td>\n",
       "      <td>0.276924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SelfEmployed</td>\n",
       "      <td>-0.146419</td>\n",
       "      <td>-0.146133</td>\n",
       "      <td>-0.146661</td>\n",
       "      <td>-0.005562</td>\n",
       "      <td>0.202066</td>\n",
       "      <td>-0.305339</td>\n",
       "      <td>0.045619</td>\n",
       "      <td>-0.156825</td>\n",
       "      <td>0.004529</td>\n",
       "      <td>-0.161904</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017683</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>-0.264526</td>\n",
       "      <td>-0.146030</td>\n",
       "      <td>-0.554611</td>\n",
       "      <td>0.041945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.383754</td>\n",
       "      <td>-0.294460</td>\n",
       "      <td>-0.007915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>FamilyWork</td>\n",
       "      <td>-0.085016</td>\n",
       "      <td>-0.084826</td>\n",
       "      <td>-0.085180</td>\n",
       "      <td>-0.047897</td>\n",
       "      <td>0.112044</td>\n",
       "      <td>-0.137477</td>\n",
       "      <td>0.077294</td>\n",
       "      <td>-0.087749</td>\n",
       "      <td>-0.004515</td>\n",
       "      <td>-0.092302</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004182</td>\n",
       "      <td>0.377613</td>\n",
       "      <td>-0.171779</td>\n",
       "      <td>-0.084870</td>\n",
       "      <td>-0.305991</td>\n",
       "      <td>0.069269</td>\n",
       "      <td>0.383754</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.122988</td>\n",
       "      <td>0.016451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unemployment</td>\n",
       "      <td>0.030306</td>\n",
       "      <td>0.029516</td>\n",
       "      <td>0.031060</td>\n",
       "      <td>0.321975</td>\n",
       "      <td>-0.538941</td>\n",
       "      <td>0.352578</td>\n",
       "      <td>0.188074</td>\n",
       "      <td>-0.059663</td>\n",
       "      <td>-0.037973</td>\n",
       "      <td>0.031339</td>\n",
       "      <td>...</td>\n",
       "      <td>0.110418</td>\n",
       "      <td>-0.306548</td>\n",
       "      <td>0.293725</td>\n",
       "      <td>0.014012</td>\n",
       "      <td>-0.099744</td>\n",
       "      <td>0.306015</td>\n",
       "      <td>-0.294460</td>\n",
       "      <td>-0.122988</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.463626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HighUnemploymentLowIncome</td>\n",
       "      <td>-0.149519</td>\n",
       "      <td>-0.150084</td>\n",
       "      <td>-0.148940</td>\n",
       "      <td>0.149073</td>\n",
       "      <td>-0.286174</td>\n",
       "      <td>0.280962</td>\n",
       "      <td>0.056425</td>\n",
       "      <td>-0.197351</td>\n",
       "      <td>-0.041219</td>\n",
       "      <td>-0.160601</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002972</td>\n",
       "      <td>-0.189550</td>\n",
       "      <td>0.091209</td>\n",
       "      <td>-0.158740</td>\n",
       "      <td>-0.226546</td>\n",
       "      <td>0.276924</td>\n",
       "      <td>-0.007915</td>\n",
       "      <td>0.016451</td>\n",
       "      <td>0.463626</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           TotalPop       Men     Women  Hispanic     White  \\\n",
       "TotalPop                   1.000000  0.999877  0.999886  0.113882 -0.187370   \n",
       "Men                        0.999877  1.000000  0.999527  0.114286 -0.186478   \n",
       "Women                      0.999886  0.999527  1.000000  0.113466 -0.188187   \n",
       "Hispanic                   0.113882  0.114286  0.113466  1.000000 -0.724959   \n",
       "White                     -0.187370 -0.186478 -0.188187 -0.724959  1.000000   \n",
       "Black                      0.075296  0.072807  0.077676 -0.144540 -0.466185   \n",
       "Native                    -0.046038 -0.045818 -0.046240 -0.056119 -0.231565   \n",
       "Asian                      0.447874  0.449085  0.446604  0.044244 -0.199347   \n",
       "Pacific                    0.027104  0.028143  0.026096 -0.002645 -0.075080   \n",
       "Citizen                    0.996378  0.995819  0.996685  0.104092 -0.183133   \n",
       "Income                     0.234886  0.235859  0.233896 -0.219561  0.267041   \n",
       "IncomeErr                 -0.263337 -0.262743 -0.263848 -0.027521  0.082250   \n",
       "IncomePerCap               0.238516  0.237878  0.239075 -0.294180  0.345323   \n",
       "IncomePerCapErr           -0.235936 -0.235582 -0.236222 -0.029884  0.093692   \n",
       "Poverty                   -0.062075 -0.063009 -0.061161  0.482186 -0.618490   \n",
       "ChildPoverty              -0.057034 -0.058183 -0.055914  0.395252 -0.573871   \n",
       "Professional               0.248403  0.247094  0.249607 -0.097219  0.085386   \n",
       "Service                   -0.001064 -0.000958 -0.001165  0.222669 -0.317807   \n",
       "Office                     0.185946  0.185006  0.186808  0.104799 -0.122228   \n",
       "Construction              -0.264243 -0.262402 -0.265956  0.131005  0.017874   \n",
       "Production                -0.184503 -0.183940 -0.185003 -0.187876  0.161394   \n",
       "Drive                     -0.103191 -0.101488 -0.104808 -0.021655  0.078510   \n",
       "Carpool                   -0.080850 -0.079179 -0.082440  0.074306 -0.095702   \n",
       "Transit                    0.402729  0.396624  0.408516  0.086346 -0.178568   \n",
       "Walk                      -0.047901 -0.048278 -0.047526  0.000392 -0.048004   \n",
       "OtherTransp                0.036125  0.036560  0.035697  0.041738 -0.173506   \n",
       "WorkAtHome                -0.027852 -0.027384 -0.028297 -0.120975  0.217985   \n",
       "MeanCommute                0.148545  0.147537  0.149481  0.035312 -0.077046   \n",
       "Employed                   0.997621  0.997481  0.997524  0.102515 -0.176872   \n",
       "PrivateWork                0.196913  0.196025  0.197722 -0.170047  0.228257   \n",
       "PublicWork                -0.143553 -0.142667 -0.144373  0.212196 -0.405057   \n",
       "SelfEmployed              -0.146419 -0.146133 -0.146661 -0.005562  0.202066   \n",
       "FamilyWork                -0.085016 -0.084826 -0.085180 -0.047897  0.112044   \n",
       "Unemployment               0.030306  0.029516  0.031060  0.321975 -0.538941   \n",
       "HighUnemploymentLowIncome -0.149519 -0.150084 -0.148940  0.149073 -0.286174   \n",
       "\n",
       "                              Black    Native     Asian   Pacific   Citizen  \\\n",
       "TotalPop                   0.075296 -0.046038  0.447874  0.027104  0.996378   \n",
       "Men                        0.072807 -0.045818  0.449085  0.028143  0.995819   \n",
       "Women                      0.077676 -0.046240  0.446604  0.026096  0.996685   \n",
       "Hispanic                  -0.144540 -0.056119  0.044244 -0.002645  0.104092   \n",
       "White                     -0.466185 -0.231565 -0.199347 -0.075080 -0.183133   \n",
       "Black                      1.000000 -0.097140  0.019916 -0.035385  0.081138   \n",
       "Native                    -0.097140  1.000000 -0.002663  0.033927 -0.050108   \n",
       "Asian                      0.019916 -0.002663  1.000000  0.353935  0.456733   \n",
       "Pacific                   -0.035385  0.033927  0.353935  1.000000  0.027806   \n",
       "Citizen                    0.081138 -0.050108  0.456733  0.027806  1.000000   \n",
       "Income                    -0.220597 -0.038644  0.439447  0.075974  0.248217   \n",
       "IncomeErr                 -0.083293  0.046390 -0.163356  0.057022 -0.282625   \n",
       "IncomePerCap              -0.207272 -0.095884  0.403223  0.090034  0.257302   \n",
       "IncomePerCapErr           -0.081191  0.008278 -0.155552  0.087864 -0.252711   \n",
       "Poverty                    0.308611  0.153093 -0.153620 -0.028188 -0.069567   \n",
       "ChildPoverty               0.371930  0.128454 -0.184514 -0.053754 -0.064229   \n",
       "Professional              -0.125293  0.066968  0.363298  0.007936  0.269306   \n",
       "Service                    0.148077  0.112310 -0.029184  0.062767 -0.002952   \n",
       "Office                     0.095943 -0.117021  0.079781 -0.000106  0.202534   \n",
       "Construction              -0.157809  0.043855 -0.306973 -0.027832 -0.289256   \n",
       "Production                 0.107941 -0.112290 -0.203708 -0.027796 -0.197393   \n",
       "Drive                      0.154946 -0.336971 -0.277215 -0.161804 -0.098314   \n",
       "Carpool                    0.032938  0.050257 -0.066114  0.024309 -0.093579   \n",
       "Transit                    0.083811 -0.013507  0.440298  0.016191  0.411518   \n",
       "Walk                      -0.169504  0.362889  0.187220  0.209134 -0.051720   \n",
       "OtherTransp               -0.004333  0.347975  0.133118  0.160300  0.035693   \n",
       "WorkAtHome                -0.281791  0.167324  0.013706  0.020612 -0.031685   \n",
       "MeanCommute                0.186750 -0.207657  0.043008 -0.066947  0.152106   \n",
       "Employed                   0.071231 -0.047252  0.463088  0.026891  0.995176   \n",
       "PrivateWork                0.064072 -0.402015  0.108109 -0.118206  0.212811   \n",
       "PublicWork                 0.115743  0.452651 -0.030053  0.140242 -0.152932   \n",
       "SelfEmployed              -0.305339  0.045619 -0.156825  0.004529 -0.161904   \n",
       "FamilyWork                -0.137477  0.077294 -0.087749 -0.004515 -0.092302   \n",
       "Unemployment               0.352578  0.188074 -0.059663 -0.037973  0.031339   \n",
       "HighUnemploymentLowIncome  0.280962  0.056425 -0.197351 -0.041219 -0.160601   \n",
       "\n",
       "                           ...  OtherTransp  WorkAtHome  MeanCommute  \\\n",
       "TotalPop                   ...     0.036125   -0.027852     0.148545   \n",
       "Men                        ...     0.036560   -0.027384     0.147537   \n",
       "Women                      ...     0.035697   -0.028297     0.149481   \n",
       "Hispanic                   ...     0.041738   -0.120975     0.035312   \n",
       "White                      ...    -0.173506    0.217985    -0.077046   \n",
       "Black                      ...    -0.004333   -0.281791     0.186750   \n",
       "Native                     ...     0.347975    0.167324    -0.207657   \n",
       "Asian                      ...     0.133118    0.013706     0.043008   \n",
       "Pacific                    ...     0.160300    0.020612    -0.066947   \n",
       "Citizen                    ...     0.035693   -0.031685     0.152106   \n",
       "Income                     ...     0.026077    0.202900     0.059770   \n",
       "IncomeErr                  ...     0.044090    0.286125    -0.173274   \n",
       "IncomePerCap               ...     0.029183    0.299546    -0.034978   \n",
       "IncomePerCapErr            ...     0.026601    0.350686    -0.207202   \n",
       "Poverty                    ...     0.053027   -0.269402     0.102818   \n",
       "ChildPoverty               ...     0.024297   -0.299034     0.130842   \n",
       "Professional               ...     0.056443    0.391443    -0.095850   \n",
       "Service                    ...     0.093518   -0.125955    -0.038306   \n",
       "Office                     ...    -0.012360   -0.252340     0.190631   \n",
       "Construction               ...     0.001230    0.158397    -0.048633   \n",
       "Production                 ...    -0.115908   -0.330884     0.060354   \n",
       "Drive                      ...    -0.515444   -0.564711     0.231651   \n",
       "Carpool                    ...     0.021746   -0.082157     0.059327   \n",
       "Transit                    ...     0.101531    0.004869     0.191493   \n",
       "Walk                       ...     0.465537    0.342670    -0.415897   \n",
       "OtherTransp                ...     1.000000    0.049403    -0.175421   \n",
       "WorkAtHome                 ...     0.049403    1.000000    -0.213501   \n",
       "MeanCommute                ...    -0.175421   -0.213501     1.000000   \n",
       "Employed                   ...     0.036179   -0.022087     0.147238   \n",
       "PrivateWork                ...    -0.182209   -0.410801     0.235821   \n",
       "PublicWork                 ...     0.230593    0.074007    -0.113674   \n",
       "SelfEmployed               ...    -0.017683    0.658149    -0.264526   \n",
       "FamilyWork                 ...     0.004182    0.377613    -0.171779   \n",
       "Unemployment               ...     0.110418   -0.306548     0.293725   \n",
       "HighUnemploymentLowIncome  ...     0.002972   -0.189550     0.091209   \n",
       "\n",
       "                           Employed  PrivateWork  PublicWork  SelfEmployed  \\\n",
       "TotalPop                   0.997621     0.196913   -0.143553     -0.146419   \n",
       "Men                        0.997481     0.196025   -0.142667     -0.146133   \n",
       "Women                      0.997524     0.197722   -0.144373     -0.146661   \n",
       "Hispanic                   0.102515    -0.170047    0.212196     -0.005562   \n",
       "White                     -0.176872     0.228257   -0.405057      0.202066   \n",
       "Black                      0.071231     0.064072    0.115743     -0.305339   \n",
       "Native                    -0.047252    -0.402015    0.452651      0.045619   \n",
       "Asian                      0.463088     0.108109   -0.030053     -0.156825   \n",
       "Pacific                    0.026891    -0.118206    0.140242      0.004529   \n",
       "Citizen                    0.995176     0.212811   -0.152932     -0.161904   \n",
       "Income                     0.257709     0.237906   -0.245566     -0.063982   \n",
       "IncomeErr                 -0.260500    -0.402052    0.236892      0.388015   \n",
       "IncomePerCap               0.264614     0.191792   -0.284536      0.087274   \n",
       "IncomePerCapErr           -0.233130    -0.405600    0.198416      0.460763   \n",
       "Poverty                   -0.078917    -0.248676    0.379531     -0.125693   \n",
       "ChildPoverty              -0.074957    -0.179588    0.302751     -0.132962   \n",
       "Professional               0.269650    -0.174397    0.095597      0.183207   \n",
       "Service                   -0.015277    -0.224442    0.358497     -0.138458   \n",
       "Office                     0.180124     0.317369   -0.168495     -0.334275   \n",
       "Construction              -0.271589    -0.294023    0.123838      0.357350   \n",
       "Production                -0.190441     0.375012   -0.330177     -0.192321   \n",
       "Drive                     -0.109859     0.404189   -0.249669     -0.369549   \n",
       "Carpool                   -0.087432    -0.072880    0.079081      0.015877   \n",
       "Transit                    0.414272     0.074310   -0.024188     -0.102205   \n",
       "Walk                      -0.043496    -0.399775    0.302706      0.275276   \n",
       "OtherTransp                0.036179    -0.182209    0.230593     -0.017683   \n",
       "WorkAtHome                -0.022087    -0.410801    0.074007      0.658149   \n",
       "MeanCommute                0.147238     0.235821   -0.113674     -0.264526   \n",
       "Employed                   1.000000     0.201799   -0.149712     -0.146030   \n",
       "PrivateWork                0.201799     1.000000   -0.852914     -0.554611   \n",
       "PublicWork                -0.149712    -0.852914    1.000000      0.041945   \n",
       "SelfEmployed              -0.146030    -0.554611    0.041945      1.000000   \n",
       "FamilyWork                -0.084870    -0.305991    0.069269      0.383754   \n",
       "Unemployment               0.014012    -0.099744    0.306015     -0.294460   \n",
       "HighUnemploymentLowIncome -0.158740    -0.226546    0.276924     -0.007915   \n",
       "\n",
       "                           FamilyWork  Unemployment  HighUnemploymentLowIncome  \n",
       "TotalPop                    -0.085016      0.030306                  -0.149519  \n",
       "Men                         -0.084826      0.029516                  -0.150084  \n",
       "Women                       -0.085180      0.031060                  -0.148940  \n",
       "Hispanic                    -0.047897      0.321975                   0.149073  \n",
       "White                        0.112044     -0.538941                  -0.286174  \n",
       "Black                       -0.137477      0.352578                   0.280962  \n",
       "Native                       0.077294      0.188074                   0.056425  \n",
       "Asian                       -0.087749     -0.059663                  -0.197351  \n",
       "Pacific                     -0.004515     -0.037973                  -0.041219  \n",
       "Citizen                     -0.092302      0.031339                  -0.160601  \n",
       "Income                      -0.047615     -0.509510                  -0.673279  \n",
       "IncomeErr                    0.220618     -0.227995                   0.018102  \n",
       "IncomePerCap                 0.005932     -0.547596                  -0.605686  \n",
       "IncomePerCapErr              0.206154     -0.250070                   0.019268  \n",
       "Poverty                     -0.053550      0.712344                   0.641849  \n",
       "ChildPoverty                -0.070628      0.676583                   0.637212  \n",
       "Professional                 0.070669     -0.300648                  -0.350484  \n",
       "Service                     -0.062446      0.364564                   0.274709  \n",
       "Office                      -0.195269      0.162646                  -0.082698  \n",
       "Construction                 0.234626     -0.091410                   0.131495  \n",
       "Production                  -0.102363      0.079671                   0.164864  \n",
       "Drive                       -0.234478      0.098434                   0.089661  \n",
       "Carpool                     -0.006345      0.088901                   0.141333  \n",
       "Transit                     -0.057884      0.030777                  -0.088615  \n",
       "Walk                         0.208346     -0.084317                  -0.060600  \n",
       "OtherTransp                  0.004182      0.110418                   0.002972  \n",
       "WorkAtHome                   0.377613     -0.306548                  -0.189550  \n",
       "MeanCommute                 -0.171779      0.293725                   0.091209  \n",
       "Employed                    -0.084870      0.014012                  -0.158740  \n",
       "PrivateWork                 -0.305991     -0.099744                  -0.226546  \n",
       "PublicWork                   0.069269      0.306015                   0.276924  \n",
       "SelfEmployed                 0.383754     -0.294460                  -0.007915  \n",
       "FamilyWork                   1.000000     -0.122988                   0.016451  \n",
       "Unemployment                -0.122988      1.000000                   0.463626  \n",
       "HighUnemploymentLowIncome    0.016451      0.463626                   1.000000  \n",
       "\n",
       "[35 rows x 35 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reduccion de dimensiones\n",
    "\n",
    "census.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "sns.heatmap(census.corr(), cmap='viridis');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE  # recursive feature elimination\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=['Poverty', 'WorkAtHome', 'SelfEmployed', 'Black', 'White', \n",
    "          'Native','Asian', 'Pacific', 'Hispanic', \n",
    "          'IncomePerCap']\n",
    "\n",
    "target='Unemployment'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(census[features], \n",
    "                                                  census[target],\n",
    "                                                  test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 7 1 5 3 6 2 1 4 8]\n"
     ]
    }
   ],
   "source": [
    "modelo=LinearRegression()\n",
    "selector=RFE(modelo, n_features_to_select=3, step=1)\n",
    "selector.fit(X_train, y_train)\n",
    "print (selector.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Poverty', 'SelfEmployed', 'Pacific']"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[features[i] for i,e in enumerate(selector.support_) if e]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(644, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(644, 2)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "print(X_test.shape)\n",
    "modelo=DecisionTreeRegressor()\n",
    "selector=SelectFromModel(modelo, threshold=0.15)\n",
    "selector.fit(X_train, y_train)\n",
    "selector.transform(X_test).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.45893759, 0.0499203 , 0.0950674 , 0.04829198, 0.06755061,\n",
       "       0.02924947, 0.02809525, 0.01079368, 0.04811881, 0.16397491])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo.fit(X_train, y_train)\n",
    "modelo.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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