IncomeQualification_Final_.ipynb

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    "# Income Qualification\n",
    "\n",
    "## Objective:\n",
    "Identify the level of income qualification status needed to disburse finacial aid for families in Latin America\n",
    "\n",
    "### Problem Statement Scenario:\n",
    "Many social programs have a hard time making sure the right people are given enough aid. It’s tricky when a program focuses on the poorest segment of the population. This segment of population can’t provide the necessary income and expense records to prove that they qualify.\n",
    "\n",
    "In Latin America, a popular method called Proxy Means Test (PMT) uses an algorithm to verify income qualification. With PMT, agencies use a model that considers a family’s observable household attributes like the material of their walls and ceiling or the assets found in their homes to classify them and predict their level of need. While this is an improvement, accuracy remains a problem as the region’s population grows and poverty declines.\n",
    "\n",
    "The Inter-American Development Bank (IDB) believes that new methods beyond traditional econometrics, based on a dataset of Costa Rican household characteristics, might help improve PMT’s performance.\n"
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   "source": [
    "## Reading the Data"
   ]
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   "source": [
    "# Import Python Libraries.\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2c7317ea8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>no</td>\n",
       "      <td>11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>121</td>\n",
       "      <td>8464</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>64.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>8464</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_d671db89c</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2b58d945f</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>11</td>\n",
       "      <td>no</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>81</td>\n",
       "      <td>289</td>\n",
       "      <td>16</td>\n",
       "      <td>121</td>\n",
       "      <td>4</td>\n",
       "      <td>1.777778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>289</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_d56d6f5f5</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2b58d945f</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>11</td>\n",
       "      <td>no</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>121</td>\n",
       "      <td>1369</td>\n",
       "      <td>16</td>\n",
       "      <td>121</td>\n",
       "      <td>4</td>\n",
       "      <td>1.777778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1369</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Id      v2a1  hacdor  rooms  hacapo  v14a  refrig  v18q  v18q1  \\\n",
       "0  ID_279628684  190000.0       0      3       0     1       1     0    NaN   \n",
       "1  ID_f29eb3ddd  135000.0       0      4       0     1       1     1    1.0   \n",
       "2  ID_68de51c94       NaN       0      8       0     1       1     0    NaN   \n",
       "3  ID_d671db89c  180000.0       0      5       0     1       1     1    1.0   \n",
       "4  ID_d56d6f5f5  180000.0       0      5       0     1       1     1    1.0   \n",
       "\n",
       "   r4h1  r4h2  r4h3  r4m1  r4m2  r4m3  r4t1  r4t2  r4t3  tamhog  tamviv  \\\n",
       "0     0     1     1     0     0     0     0     1     1       1       1   \n",
       "1     0     1     1     0     0     0     0     1     1       1       1   \n",
       "2     0     0     0     0     1     1     0     1     1       1       1   \n",
       "3     0     2     2     1     1     2     1     3     4       4       4   \n",
       "4     0     2     2     1     1     2     1     3     4       4       4   \n",
       "\n",
       "   escolari  rez_esc  hhsize  paredblolad  paredzocalo  paredpreb  pareddes  \\\n",
       "0        10      NaN       1            1            0          0         0   \n",
       "1        12      NaN       1            0            0          0         0   \n",
       "2        11      NaN       1            0            0          0         0   \n",
       "3         9      1.0       4            1            0          0         0   \n",
       "4        11      NaN       4            1            0          0         0   \n",
       "\n",
       "   paredmad  paredzinc  paredfibras  paredother  pisomoscer  pisocemento  \\\n",
       "0         0          0            0           0           1            0   \n",
       "1         1          0            0           0           0            0   \n",
       "2         1          0            0           0           1            0   \n",
       "3         0          0            0           0           1            0   \n",
       "4         0          0            0           0           1            0   \n",
       "\n",
       "   pisoother  pisonatur  pisonotiene  pisomadera  techozinc  techoentrepiso  \\\n",
       "0          0          0            0           0          0               1   \n",
       "1          0          0            0           1          1               0   \n",
       "2          0          0            0           0          1               0   \n",
       "3          0          0            0           0          1               0   \n",
       "4          0          0            0           0          1               0   \n",
       "\n",
       "   techocane  techootro  cielorazo  abastaguadentro  abastaguafuera  \\\n",
       "0          0          0          1                1               0   \n",
       "1          0          0          1                1               0   \n",
       "2          0          0          1                1               0   \n",
       "3          0          0          1                1               0   \n",
       "4          0          0          1                1               0   \n",
       "\n",
       "   abastaguano  public  planpri  noelec  coopele  sanitario1  sanitario2  \\\n",
       "0            0       1        0       0        0           0           1   \n",
       "1            0       1        0       0        0           0           1   \n",
       "2            0       1        0       0        0           0           1   \n",
       "3            0       1        0       0        0           0           1   \n",
       "4            0       1        0       0        0           0           1   \n",
       "\n",
       "   sanitario3  sanitario5  sanitario6  energcocinar1  energcocinar2  \\\n",
       "0           0           0           0              0              0   \n",
       "1           0           0           0              0              1   \n",
       "2           0           0           0              0              1   \n",
       "3           0           0           0              0              1   \n",
       "4           0           0           0              0              1   \n",
       "\n",
       "   energcocinar3  energcocinar4  elimbasu1  elimbasu2  elimbasu3  elimbasu4  \\\n",
       "0              1              0          1          0          0          0   \n",
       "1              0              0          1          0          0          0   \n",
       "2              0              0          1          0          0          0   \n",
       "3              0              0          1          0          0          0   \n",
       "4              0              0          1          0          0          0   \n",
       "\n",
       "   elimbasu5  elimbasu6  epared1  epared2  epared3  etecho1  etecho2  etecho3  \\\n",
       "0          0          0        0        1        0        1        0        0   \n",
       "1          0          0        0        1        0        0        1        0   \n",
       "2          0          0        0        1        0        0        0        1   \n",
       "3          0          0        0        0        1        0        0        1   \n",
       "4          0          0        0        0        1        0        0        1   \n",
       "\n",
       "   eviv1  eviv2  eviv3  dis  male  female  estadocivil1  estadocivil2  \\\n",
       "0      1      0      0    0     1       0             0             0   \n",
       "1      0      1      0    0     1       0             0             0   \n",
       "2      0      0      1    1     0       1             0             0   \n",
       "3      0      0      1    0     1       0             0             0   \n",
       "4      0      0      1    0     0       1             0             1   \n",
       "\n",
       "   estadocivil3  estadocivil4  estadocivil5  estadocivil6  estadocivil7  \\\n",
       "0             0             1             0             0             0   \n",
       "1             0             1             0             0             0   \n",
       "2             0             0             0             1             0   \n",
       "3             0             0             0             0             1   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   parentesco1  parentesco2  parentesco3  parentesco4  parentesco5  \\\n",
       "0            1            0            0            0            0   \n",
       "1            1            0            0            0            0   \n",
       "2            1            0            0            0            0   \n",
       "3            0            0            1            0            0   \n",
       "4            0            1            0            0            0   \n",
       "\n",
       "   parentesco6  parentesco7  parentesco8  parentesco9  parentesco10  \\\n",
       "0            0            0            0            0             0   \n",
       "1            0            0            0            0             0   \n",
       "2            0            0            0            0             0   \n",
       "3            0            0            0            0             0   \n",
       "4            0            0            0            0             0   \n",
       "\n",
       "   parentesco11  parentesco12    idhogar  hogar_nin  hogar_adul  hogar_mayor  \\\n",
       "0             0             0  21eb7fcc1          0           1            0   \n",
       "1             0             0  0e5d7a658          0           1            1   \n",
       "2             0             0  2c7317ea8          0           1            1   \n",
       "3             0             0  2b58d945f          2           2            0   \n",
       "4             0             0  2b58d945f          2           2            0   \n",
       "\n",
       "   hogar_total dependency edjefe edjefa  meaneduc  instlevel1  instlevel2  \\\n",
       "0            1         no     10     no      10.0           0           0   \n",
       "1            1          8     12     no      12.0           0           0   \n",
       "2            1          8     no     11      11.0           0           0   \n",
       "3            4        yes     11     no      11.0           0           0   \n",
       "4            4        yes     11     no      11.0           0           0   \n",
       "\n",
       "   instlevel3  instlevel4  instlevel5  instlevel6  instlevel7  instlevel8  \\\n",
       "0           0           1           0           0           0           0   \n",
       "1           0           0           0           0           0           1   \n",
       "2           0           0           1           0           0           0   \n",
       "3           0           1           0           0           0           0   \n",
       "4           0           0           1           0           0           0   \n",
       "\n",
       "   instlevel9  bedrooms  overcrowding  tipovivi1  tipovivi2  tipovivi3  \\\n",
       "0           0         1      1.000000          0          0          1   \n",
       "1           0         1      1.000000          0          0          1   \n",
       "2           0         2      0.500000          1          0          0   \n",
       "3           0         3      1.333333          0          0          1   \n",
       "4           0         3      1.333333          0          0          1   \n",
       "\n",
       "   tipovivi4  tipovivi5  computer  television  mobilephone  qmobilephone  \\\n",
       "0          0          0         0           0            1             1   \n",
       "1          0          0         0           0            1             1   \n",
       "2          0          0         0           0            0             0   \n",
       "3          0          0         0           0            1             3   \n",
       "4          0          0         0           0            1             3   \n",
       "\n",
       "   lugar1  lugar2  lugar3  lugar4  lugar5  lugar6  area1  area2  age  \\\n",
       "0       1       0       0       0       0       0      1      0   43   \n",
       "1       1       0       0       0       0       0      1      0   67   \n",
       "2       1       0       0       0       0       0      1      0   92   \n",
       "3       1       0       0       0       0       0      1      0   17   \n",
       "4       1       0       0       0       0       0      1      0   37   \n",
       "\n",
       "   SQBescolari  SQBage  SQBhogar_total  SQBedjefe  SQBhogar_nin  \\\n",
       "0          100    1849               1        100             0   \n",
       "1          144    4489               1        144             0   \n",
       "2          121    8464               1          0             0   \n",
       "3           81     289              16        121             4   \n",
       "4          121    1369              16        121             4   \n",
       "\n",
       "   SQBovercrowding  SQBdependency  SQBmeaned  agesq  Target  \n",
       "0         1.000000            0.0      100.0   1849       4  \n",
       "1         1.000000           64.0      144.0   4489       4  \n",
       "2         0.250000           64.0      121.0   8464       4  \n",
       "3         1.777778            1.0      121.0    289       4  \n",
       "4         1.777778            1.0      121.0   1369       4  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "\n",
    "# Set max_columns option to None to display all columns\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9662b7f8-87d1-49e8-88f9-21b580f92898",
   "metadata": {},
   "source": [
    "## Understand the type of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93b80f65-9442-4502-8f8a-1f79ceeb0b30",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9557 entries, 0 to 9556\n",
      "Columns: 143 entries, Id to Target\n",
      "dtypes: float64(8), int64(130), object(5)\n",
      "memory usage: 10.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Use info() method to display the structure and metadata of the DataFrame named 'train.csv'.\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85501d43-63f7-49d2-9712-4387b2c41479",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9557, 143)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# An attribute that returns the demensions (i.e., the number of rows and columns) of the DataFrame .\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "985524f7-cddb-42a7-a8c2-b902654e4f04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v2a1</th>\n",
       "      <th>v18q1</th>\n",
       "      <th>rez_esc</th>\n",
       "      <th>meaneduc</th>\n",
       "      <th>overcrowding</th>\n",
       "      <th>SQBovercrowding</th>\n",
       "      <th>SQBdependency</th>\n",
       "      <th>SQBmeaned</th>\n",
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       "      <td>190000.0</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0000</td>\n",
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       "      <td>64.0000</td>\n",
       "      <td>144.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>64.0000</td>\n",
       "      <td>121.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>180000.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.333333</td>\n",
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       "      <td>1.0000</td>\n",
       "      <td>121.0000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>11.00</td>\n",
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       "      <td>1.777778</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>121.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>9552</th>\n",
       "      <td>80000.0</td>\n",
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       "      <td>1.250000</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>68.0625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9553</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.25</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>68.0625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9554</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.25</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>68.0625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9555</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.25</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>68.0625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9556</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.25</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>68.0625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9557 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          v2a1  v18q1  rez_esc  meaneduc  overcrowding  SQBovercrowding  \\\n",
       "0     190000.0    NaN      NaN     10.00      1.000000         1.000000   \n",
       "1     135000.0    1.0      NaN     12.00      1.000000         1.000000   \n",
       "2          NaN    NaN      NaN     11.00      0.500000         0.250000   \n",
       "3     180000.0    1.0      1.0     11.00      1.333333         1.777778   \n",
       "4     180000.0    1.0      NaN     11.00      1.333333         1.777778   \n",
       "...        ...    ...      ...       ...           ...              ...   \n",
       "9552   80000.0    NaN      NaN      8.25      1.250000         1.562500   \n",
       "9553   80000.0    NaN      NaN      8.25      1.250000         1.562500   \n",
       "9554   80000.0    NaN      NaN      8.25      1.250000         1.562500   \n",
       "9555   80000.0    NaN      NaN      8.25      1.250000         1.562500   \n",
       "9556   80000.0    NaN      NaN      8.25      1.250000         1.562500   \n",
       "\n",
       "      SQBdependency  SQBmeaned  \n",
       "0            0.0000   100.0000  \n",
       "1           64.0000   144.0000  \n",
       "2           64.0000   121.0000  \n",
       "3            1.0000   121.0000  \n",
       "4            1.0000   121.0000  \n",
       "...             ...        ...  \n",
       "9552         0.0625    68.0625  \n",
       "9553         0.0625    68.0625  \n",
       "9554         0.0625    68.0625  \n",
       "9555         0.0625    68.0625  \n",
       "9556         0.0625    68.0625  \n",
       "\n",
       "[9557 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A method that returns only the columns that have a data type of float (i.e., floating-point numbers).\n",
    "train.select_dtypes(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "65853468-43cb-4f55-99d5-9861f996f279",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <th>parentesco10</th>\n",
       "      <th>parentesco11</th>\n",
       "      <th>parentesco12</th>\n",
       "      <th>hogar_nin</th>\n",
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       "      <th>tipovivi4</th>\n",
       "      <th>tipovivi5</th>\n",
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       "      <th>television</th>\n",
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       "      <th>SQBedjefe</th>\n",
       "      <th>SQBhogar_nin</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>100</td>\n",
       "      <td>1849</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1849</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>144</td>\n",
       "      <td>4489</td>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>4489</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>121</td>\n",
       "      <td>8464</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8464</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>81</td>\n",
       "      <td>289</td>\n",
       "      <td>16</td>\n",
       "      <td>121</td>\n",
       "      <td>4</td>\n",
       "      <td>289</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>4</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>121</td>\n",
       "      <td>1369</td>\n",
       "      <td>16</td>\n",
       "      <td>121</td>\n",
       "      <td>4</td>\n",
       "      <td>1369</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9552</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>81</td>\n",
       "      <td>2116</td>\n",
       "      <td>25</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>2116</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9553</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</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",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9554</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</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>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>2500</td>\n",
       "      <td>25</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9555</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>5</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>121</td>\n",
       "      <td>676</td>\n",
       "      <td>25</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>676</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9556</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>441</td>\n",
       "      <td>25</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>441</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9557 rows × 130 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      hacdor  rooms  hacapo  v14a  refrig  v18q  r4h1  r4h2  r4h3  r4m1  r4m2  \\\n",
       "0          0      3       0     1       1     0     0     1     1     0     0   \n",
       "1          0      4       0     1       1     1     0     1     1     0     0   \n",
       "2          0      8       0     1       1     0     0     0     0     0     1   \n",
       "3          0      5       0     1       1     1     0     2     2     1     1   \n",
       "4          0      5       0     1       1     1     0     2     2     1     1   \n",
       "...      ...    ...     ...   ...     ...   ...   ...   ...   ...   ...   ...   \n",
       "9552       0      6       0     1       1     0     0     2     2     1     2   \n",
       "9553       0      6       0     1       1     0     0     2     2     1     2   \n",
       "9554       0      6       0     1       1     0     0     2     2     1     2   \n",
       "9555       0      6       0     1       1     0     0     2     2     1     2   \n",
       "9556       0      6       0     1       1     0     0     2     2     1     2   \n",
       "\n",
       "      r4m3  r4t1  r4t2  r4t3  tamhog  tamviv  escolari  hhsize  paredblolad  \\\n",
       "0        0     0     1     1       1       1        10       1            1   \n",
       "1        0     0     1     1       1       1        12       1            0   \n",
       "2        1     0     1     1       1       1        11       1            0   \n",
       "3        2     1     3     4       4       4         9       4            1   \n",
       "4        2     1     3     4       4       4        11       4            1   \n",
       "...    ...   ...   ...   ...     ...     ...       ...     ...          ...   \n",
       "9552     3     1     4     5       5       5         9       5            1   \n",
       "9553     3     1     4     5       5       5         0       5            1   \n",
       "9554     3     1     4     5       5       5         5       5            1   \n",
       "9555     3     1     4     5       5       5        11       5            1   \n",
       "9556     3     1     4     5       5       5         8       5            1   \n",
       "\n",
       "      paredzocalo  paredpreb  pareddes  paredmad  paredzinc  paredfibras  \\\n",
       "0               0          0         0         0          0            0   \n",
       "1               0          0         0         1          0            0   \n",
       "2               0          0         0         1          0            0   \n",
       "3               0          0         0         0          0            0   \n",
       "4               0          0         0         0          0            0   \n",
       "...           ...        ...       ...       ...        ...          ...   \n",
       "9552            0          0         0         0          0            0   \n",
       "9553            0          0         0         0          0            0   \n",
       "9554            0          0         0         0          0            0   \n",
       "9555            0          0         0         0          0            0   \n",
       "9556            0          0         0         0          0            0   \n",
       "\n",
       "      paredother  pisomoscer  pisocemento  pisoother  pisonatur  pisonotiene  \\\n",
       "0              0           1            0          0          0            0   \n",
       "1              0           0            0          0          0            0   \n",
       "2              0           1            0          0          0            0   \n",
       "3              0           1            0          0          0            0   \n",
       "4              0           1            0          0          0            0   \n",
       "...          ...         ...          ...        ...        ...          ...   \n",
       "9552           0           0            1          0          0            0   \n",
       "9553           0           0            1          0          0            0   \n",
       "9554           0           0            1          0          0            0   \n",
       "9555           0           0            1          0          0            0   \n",
       "9556           0           0            1          0          0            0   \n",
       "\n",
       "      pisomadera  techozinc  techoentrepiso  techocane  techootro  cielorazo  \\\n",
       "0              0          0               1          0          0          1   \n",
       "1              1          1               0          0          0          1   \n",
       "2              0          1               0          0          0          1   \n",
       "3              0          1               0          0          0          1   \n",
       "4              0          1               0          0          0          1   \n",
       "...          ...        ...             ...        ...        ...        ...   \n",
       "9552           0          1               0          0          0          1   \n",
       "9553           0          1               0          0          0          1   \n",
       "9554           0          1               0          0          0          1   \n",
       "9555           0          1               0          0          0          1   \n",
       "9556           0          1               0          0          0          1   \n",
       "\n",
       "      abastaguadentro  abastaguafuera  abastaguano  public  planpri  noelec  \\\n",
       "0                   1               0            0       1        0       0   \n",
       "1                   1               0            0       1        0       0   \n",
       "2                   1               0            0       1        0       0   \n",
       "3                   1               0            0       1        0       0   \n",
       "4                   1               0            0       1        0       0   \n",
       "...               ...             ...          ...     ...      ...     ...   \n",
       "9552                1               0            0       0        0       0   \n",
       "9553                1               0            0       0        0       0   \n",
       "9554                1               0            0       0        0       0   \n",
       "9555                1               0            0       0        0       0   \n",
       "9556                1               0            0       0        0       0   \n",
       "\n",
       "      coopele  sanitario1  sanitario2  sanitario3  sanitario5  sanitario6  \\\n",
       "0           0           0           1           0           0           0   \n",
       "1           0           0           1           0           0           0   \n",
       "2           0           0           1           0           0           0   \n",
       "3           0           0           1           0           0           0   \n",
       "4           0           0           1           0           0           0   \n",
       "...       ...         ...         ...         ...         ...         ...   \n",
       "9552        1           0           0           1           0           0   \n",
       "9553        1           0           0           1           0           0   \n",
       "9554        1           0           0           1           0           0   \n",
       "9555        1           0           0           1           0           0   \n",
       "9556        1           0           0           1           0           0   \n",
       "\n",
       "      energcocinar1  energcocinar2  energcocinar3  energcocinar4  elimbasu1  \\\n",
       "0                 0              0              1              0          1   \n",
       "1                 0              1              0              0          1   \n",
       "2                 0              1              0              0          1   \n",
       "3                 0              1              0              0          1   \n",
       "4                 0              1              0              0          1   \n",
       "...             ...            ...            ...            ...        ...   \n",
       "9552              0              0              1              0          0   \n",
       "9553              0              0              1              0          0   \n",
       "9554              0              0              1              0          0   \n",
       "9555              0              0              1              0          0   \n",
       "9556              0              0              1              0          0   \n",
       "\n",
       "      elimbasu2  elimbasu3  elimbasu4  elimbasu5  elimbasu6  epared1  epared2  \\\n",
       "0             0          0          0          0          0        0        1   \n",
       "1             0          0          0          0          0        0        1   \n",
       "2             0          0          0          0          0        0        1   \n",
       "3             0          0          0          0          0        0        0   \n",
       "4             0          0          0          0          0        0        0   \n",
       "...         ...        ...        ...        ...        ...      ...      ...   \n",
       "9552          1          0          0          0          0        0        1   \n",
       "9553          1          0          0          0          0        0        1   \n",
       "9554          1          0          0          0          0        0        1   \n",
       "9555          1          0          0          0          0        0        1   \n",
       "9556          1          0          0          0          0        0        1   \n",
       "\n",
       "      epared3  etecho1  etecho2  etecho3  eviv1  eviv2  eviv3  dis  male  \\\n",
       "0           0        1        0        0      1      0      0    0     1   \n",
       "1           0        0        1        0      0      1      0    0     1   \n",
       "2           0        0        0        1      0      0      1    1     0   \n",
       "3           1        0        0        1      0      0      1    0     1   \n",
       "4           1        0        0        1      0      0      1    0     0   \n",
       "...       ...      ...      ...      ...    ...    ...    ...  ...   ...   \n",
       "9552        0        0        1        0      0      1      0    0     1   \n",
       "9553        0        0        1        0      0      1      0    0     0   \n",
       "9554        0        0        1        0      0      1      0    0     0   \n",
       "9555        0        0        1        0      0      1      0    0     0   \n",
       "9556        0        0        1        0      0      1      0    0     1   \n",
       "\n",
       "      female  estadocivil1  estadocivil2  estadocivil3  estadocivil4  \\\n",
       "0          0             0             0             0             1   \n",
       "1          0             0             0             0             1   \n",
       "2          1             0             0             0             0   \n",
       "3          0             0             0             0             0   \n",
       "4          1             0             1             0             0   \n",
       "...      ...           ...           ...           ...           ...   \n",
       "9552       0             0             1             0             0   \n",
       "9553       1             1             0             0             0   \n",
       "9554       1             0             1             0             0   \n",
       "9555       1             0             0             0             0   \n",
       "9556       0             0             0             0             0   \n",
       "\n",
       "      estadocivil5  estadocivil6  estadocivil7  parentesco1  parentesco2  \\\n",
       "0                0             0             0            1            0   \n",
       "1                0             0             0            1            0   \n",
       "2                0             1             0            1            0   \n",
       "3                0             0             1            0            0   \n",
       "4                0             0             0            0            1   \n",
       "...            ...           ...           ...          ...          ...   \n",
       "9552             0             0             0            1            0   \n",
       "9553             0             0             0            0            0   \n",
       "9554             0             0             0            0            1   \n",
       "9555             0             0             1            0            0   \n",
       "9556             0             0             1            0            0   \n",
       "\n",
       "      parentesco3  parentesco4  parentesco5  parentesco6  parentesco7  \\\n",
       "0               0            0            0            0            0   \n",
       "1               0            0            0            0            0   \n",
       "2               0            0            0            0            0   \n",
       "3               1            0            0            0            0   \n",
       "4               0            0            0            0            0   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "9552            0            0            0            0            0   \n",
       "9553            1            0            0            0            0   \n",
       "9554            0            0            0            0            0   \n",
       "9555            1            0            0            0            0   \n",
       "9556            1            0            0            0            0   \n",
       "\n",
       "      parentesco8  parentesco9  parentesco10  parentesco11  parentesco12  \\\n",
       "0               0            0             0             0             0   \n",
       "1               0            0             0             0             0   \n",
       "2               0            0             0             0             0   \n",
       "3               0            0             0             0             0   \n",
       "4               0            0             0             0             0   \n",
       "...           ...          ...           ...           ...           ...   \n",
       "9552            0            0             0             0             0   \n",
       "9553            0            0             0             0             0   \n",
       "9554            0            0             0             0             0   \n",
       "9555            0            0             0             0             0   \n",
       "9556            0            0             0             0             0   \n",
       "\n",
       "      hogar_nin  hogar_adul  hogar_mayor  hogar_total  instlevel1  instlevel2  \\\n",
       "0             0           1            0            1           0           0   \n",
       "1             0           1            1            1           0           0   \n",
       "2             0           1            1            1           0           0   \n",
       "3             2           2            0            4           0           0   \n",
       "4             2           2            0            4           0           0   \n",
       "...         ...         ...          ...          ...         ...         ...   \n",
       "9552          1           4            0            5           0           0   \n",
       "9553          1           4            0            5           1           0   \n",
       "9554          1           4            0            5           0           1   \n",
       "9555          1           4            0            5           0           0   \n",
       "9556          1           4            0            5           0           0   \n",
       "\n",
       "      instlevel3  instlevel4  instlevel5  instlevel6  instlevel7  instlevel8  \\\n",
       "0              0           1           0           0           0           0   \n",
       "1              0           0           0           0           0           1   \n",
       "2              0           0           1           0           0           0   \n",
       "3              0           1           0           0           0           0   \n",
       "4              0           0           1           0           0           0   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "9552           0           1           0           0           0           0   \n",
       "9553           0           0           0           0           0           0   \n",
       "9554           0           0           0           0           0           0   \n",
       "9555           0           0           1           0           0           0   \n",
       "9556           0           1           0           0           0           0   \n",
       "\n",
       "      instlevel9  bedrooms  tipovivi1  tipovivi2  tipovivi3  tipovivi4  \\\n",
       "0              0         1          0          0          1          0   \n",
       "1              0         1          0          0          1          0   \n",
       "2              0         2          1          0          0          0   \n",
       "3              0         3          0          0          1          0   \n",
       "4              0         3          0          0          1          0   \n",
       "...          ...       ...        ...        ...        ...        ...   \n",
       "9552           0         4          0          0          1          0   \n",
       "9553           0         4          0          0          1          0   \n",
       "9554           0         4          0          0          1          0   \n",
       "9555           0         4          0          0          1          0   \n",
       "9556           0         4          0          0          1          0   \n",
       "\n",
       "      tipovivi5  computer  television  mobilephone  qmobilephone  lugar1  \\\n",
       "0             0         0           0            1             1       1   \n",
       "1             0         0           0            1             1       1   \n",
       "2             0         0           0            0             0       1   \n",
       "3             0         0           0            1             3       1   \n",
       "4             0         0           0            1             3       1   \n",
       "...         ...       ...         ...          ...           ...     ...   \n",
       "9552          0         0           1            1             3       0   \n",
       "9553          0         0           1            1             3       0   \n",
       "9554          0         0           1            1             3       0   \n",
       "9555          0         0           1            1             3       0   \n",
       "9556          0         0           1            1             3       0   \n",
       "\n",
       "      lugar2  lugar3  lugar4  lugar5  lugar6  area1  area2  age  SQBescolari  \\\n",
       "0          0       0       0       0       0      1      0   43          100   \n",
       "1          0       0       0       0       0      1      0   67          144   \n",
       "2          0       0       0       0       0      1      0   92          121   \n",
       "3          0       0       0       0       0      1      0   17           81   \n",
       "4          0       0       0       0       0      1      0   37          121   \n",
       "...      ...     ...     ...     ...     ...    ...    ...  ...          ...   \n",
       "9552       0       0       0       0       1      0      1   46           81   \n",
       "9553       0       0       0       0       1      0      1    2            0   \n",
       "9554       0       0       0       0       1      0      1   50           25   \n",
       "9555       0       0       0       0       1      0      1   26          121   \n",
       "9556       0       0       0       0       1      0      1   21           64   \n",
       "\n",
       "      SQBage  SQBhogar_total  SQBedjefe  SQBhogar_nin  agesq  Target  \n",
       "0       1849               1        100             0   1849       4  \n",
       "1       4489               1        144             0   4489       4  \n",
       "2       8464               1          0             0   8464       4  \n",
       "3        289              16        121             4    289       4  \n",
       "4       1369              16        121             4   1369       4  \n",
       "...      ...             ...        ...           ...    ...     ...  \n",
       "9552    2116              25         81             1   2116       2  \n",
       "9553       4              25         81             1      4       2  \n",
       "9554    2500              25         81             1   2500       2  \n",
       "9555     676              25         81             1    676       2  \n",
       "9556     441              25         81             1    441       2  \n",
       "\n",
       "[9557 rows x 130 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A method that returns only the columns that have a data type of int64 (i.e., 64-bit integer values).\n",
    "train.select_dtypes('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6f51a742-3e3e-4b34-bfbc-db6a818bbbc5",
   "metadata": {
    "tags": []
   },
   "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>Id</th>\n",
       "      <th>idhogar</th>\n",
       "      <th>dependency</th>\n",
       "      <th>edjefe</th>\n",
       "      <th>edjefa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_279628684</td>\n",
       "      <td>21eb7fcc1</td>\n",
       "      <td>no</td>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_f29eb3ddd</td>\n",
       "      <td>0e5d7a658</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_68de51c94</td>\n",
       "      <td>2c7317ea8</td>\n",
       "      <td>8</td>\n",
       "      <td>no</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_d671db89c</td>\n",
       "      <td>2b58d945f</td>\n",
       "      <td>yes</td>\n",
       "      <td>11</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_d56d6f5f5</td>\n",
       "      <td>2b58d945f</td>\n",
       "      <td>yes</td>\n",
       "      <td>11</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9552</th>\n",
       "      <td>ID_d45ae367d</td>\n",
       "      <td>d6c086aa3</td>\n",
       "      <td>.25</td>\n",
       "      <td>9</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9553</th>\n",
       "      <td>ID_c94744e07</td>\n",
       "      <td>d6c086aa3</td>\n",
       "      <td>.25</td>\n",
       "      <td>9</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9554</th>\n",
       "      <td>ID_85fc658f8</td>\n",
       "      <td>d6c086aa3</td>\n",
       "      <td>.25</td>\n",
       "      <td>9</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9555</th>\n",
       "      <td>ID_ced540c61</td>\n",
       "      <td>d6c086aa3</td>\n",
       "      <td>.25</td>\n",
       "      <td>9</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9556</th>\n",
       "      <td>ID_a38c64491</td>\n",
       "      <td>d6c086aa3</td>\n",
       "      <td>.25</td>\n",
       "      <td>9</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9557 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id    idhogar dependency edjefe edjefa\n",
       "0     ID_279628684  21eb7fcc1         no     10     no\n",
       "1     ID_f29eb3ddd  0e5d7a658          8     12     no\n",
       "2     ID_68de51c94  2c7317ea8          8     no     11\n",
       "3     ID_d671db89c  2b58d945f        yes     11     no\n",
       "4     ID_d56d6f5f5  2b58d945f        yes     11     no\n",
       "...            ...        ...        ...    ...    ...\n",
       "9552  ID_d45ae367d  d6c086aa3        .25      9     no\n",
       "9553  ID_c94744e07  d6c086aa3        .25      9     no\n",
       "9554  ID_85fc658f8  d6c086aa3        .25      9     no\n",
       "9555  ID_ced540c61  d6c086aa3        .25      9     no\n",
       "9556  ID_a38c64491  d6c086aa3        .25      9     no\n",
       "\n",
       "[9557 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A method that returns only the columns that have a data type of object (i.e.,string or mixed data types).\n",
    "train.select_dtypes(object)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "134deece-9105-48bd-bd5f-62b97fbb41ed",
   "metadata": {},
   "source": [
    "## Check for biasesness in your dataset\n",
    "### Target variable has 4 values that indicate different groups of income levels as follows:\n",
    "* 1 = extreme poverty\n",
    "* 2 = moderate poverty\n",
    "* 3 = vulnerable households\n",
    "* 4 = non vulnerable households"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b171340-882b-4845-bce7-84b5d102168c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    5996\n",
       "2    1597\n",
       "3    1209\n",
       "1     755\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas method to count the occurrences of each unique value in the 'Target' column of the DataFrame 'train'.\n",
    "train['Target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c61cf31-202f-40eb-91df-ab44a004692d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    62.739353\n",
       "2    16.710265\n",
       "3    12.650413\n",
       "1     7.899969\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the percentage of the 'Target' value counts\n",
    "(train['Target'].value_counts() / len(train)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "744b5b3d-a7af-4793-baee-47ad2ddc7e65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import Python Seaborn Library.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "# Create a countplot to visulaise the balance of the data set based on 'Target' variable.\n",
    "sns.countplot(x = 'Target', data = train, alpha=0.5).set_title('Target variable count plot')\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed5df22c-dec1-4389-92c7-790c4506b8bb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Comments:\n",
    "#  - It appears that the 'Target' variable is inbalanced.\n",
    "#  - With value '4' (i.e., non vulnerable households) being the most frequent, followed by '2', '3',\n",
    "#        and '1' (i.e., extreme poverty)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd62b605-8a48-41f4-aca1-8156b1f26e25",
   "metadata": {},
   "source": [
    "## Check whether all members of the house have the same poverty level.\n",
    "* (96)  idhogar = Houselevel identifer\n",
    "* (143) target  = Poverty_levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "059bc423-c09b-4d59-b427-158dc6d2edc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2903"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by 'idhogar' and select 'Target' column.\n",
    "grouped = train.groupby('idhogar')['Target']\n",
    "\n",
    "# Get the unique values in 'Target' column for each household.\n",
    "unique_poverty_levels = grouped.unique()\n",
    "\n",
    "# Count the occurrences of each unique value in 'Target' column for each household.\n",
    "poverty_level_counts = unique_poverty_levels.apply(lambda x: len(np.where((x >= 1) & (x <= 4))[0]))\n",
    "\n",
    "# Get the count of households where all members have the same poverty level.\n",
    "num_same_poverty_level = (poverty_level_counts == 1).sum()\n",
    "num_same_poverty_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4f25f8d8-43c5-4156-86a7-ea5e2ab14a66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart.\n",
    "plt.bar(['Same Poverty Level', 'Different Poverty Level'], [num_same_poverty_level,\n",
    "        len(train['idhogar'].unique()) - num_same_poverty_level], alpha=0.5)\n",
    "\n",
    "# Set the labels and title\n",
    "plt.xlabel('Poverty Level Consistency')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Households with Same vs Different Poverty Levels')\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a4d35c-e840-46e0-87b1-569ff3f9d9e5",
   "metadata": {},
   "source": [
    "## Check if there is a house without a family head\n",
    "    * (84) parentesco1 = '1' if household head and '0' if without household head\n",
    "    * (96) idhogar     = House level identifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1ef36723-5922-4b69-8848-a40dfb8cb1c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2973\n",
       "0      15\n",
       "Name: parentesco1, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping by idhogar by unique values (2988 unique ids), then counting occurences of 1 and 0\n",
    "#     in 'parentesco1' column.\n",
    "\n",
    "family_head_counts2 = train.groupby('idhogar')['parentesco1'].sum().value_counts()\n",
    "family_head_counts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "57db3b3b-6a67-4e69-925d-ed6e1e81b273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract counts for household heads (1) and non-household heads (0).\n",
    "family_heads2 = family_head_counts2[1]\n",
    "no_family_heads2 = family_head_counts2[0]\n",
    "\n",
    "# Create a bar chart.\n",
    "plt.bar(['Without a Family Head', 'With a Family Head'],\n",
    "        [no_family_heads2, family_heads2], alpha=0.5)\n",
    "plt.xlabel('Family Head Status')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Households with / without a Family Head')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e9cbbe-4eec-4141-8dee-0d4eb68cda1d",
   "metadata": {},
   "source": [
    "## Set the poverty level of the members and the head of the house within a family\n",
    "#### Objective: Set the poverty level, i.e. grouped by targets, of all family members (children + adults + family head) to be the same as that of the (household) head of the household.\n",
    "    * (84) parentesco1 = '1' if household head and '0' if without household head\n",
    "    * (97) hogar_nin   = Number of children 0 to 19 in household\n",
    "    * (98) hogar_adul  = Number of adults in household"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e98745d2-6406-4afe-aab6-f7c4b324b088",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      parentesco1  hogar_nin  hogar_adul  Target\n",
      "0               1          0           1       4\n",
      "1               1          0           1       4\n",
      "2               1          0           1       4\n",
      "3               0          2           2       4\n",
      "4               0          2           2       4\n",
      "...           ...        ...         ...     ...\n",
      "9552            1          1           4       2\n",
      "9553            0          1           4       2\n",
      "9554            0          1           4       2\n",
      "9555            0          1           4       2\n",
      "9556            0          1           4       2\n",
      "\n",
      "[9557 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Select specific columns and display rows\n",
    "selected_rows = train[['parentesco1', 'hogar_nin', 'hogar_adul', 'Target']]\n",
    "\n",
    "# Display selected rows for selected columns\n",
    "print(selected_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f9dac4a4-21a8-4bbd-b02e-daf7321f7664",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target:  1\n",
      "parentesco1: \n",
      "0    533\n",
      "1    222\n",
      "Name: parentesco1, dtype: int64\n",
      "hogar_nin: \n",
      "2    168\n",
      "1    153\n",
      "3    140\n",
      "0    135\n",
      "4     77\n",
      "5     43\n",
      "8     19\n",
      "9     13\n",
      "6      7\n",
      "Name: hogar_nin, dtype: int64\n",
      "hogar_adul: \n",
      "2    356\n",
      "1    197\n",
      "3    134\n",
      "4     49\n",
      "6     19\n",
      "Name: hogar_adul, dtype: int64\n",
      "-------------\n",
      "Target:  2\n",
      "parentesco1: \n",
      "0    1155\n",
      "1     442\n",
      "Name: parentesco1, dtype: int64\n",
      "hogar_nin: \n",
      "2    402\n",
      "3    348\n",
      "1    311\n",
      "0    306\n",
      "4    144\n",
      "6     44\n",
      "5     33\n",
      "7      9\n",
      "Name: hogar_nin, dtype: int64\n",
      "hogar_adul: \n",
      "2    832\n",
      "3    278\n",
      "1    257\n",
      "4    170\n",
      "5     56\n",
      "6      4\n",
      "Name: hogar_adul, dtype: int64\n",
      "-------------\n",
      "Target:  3\n",
      "parentesco1: \n",
      "0    854\n",
      "1    355\n",
      "Name: parentesco1, dtype: int64\n",
      "hogar_nin: \n",
      "2    342\n",
      "1    321\n",
      "0    248\n",
      "3    218\n",
      "4     49\n",
      "5     19\n",
      "6     12\n",
      "Name: hogar_nin, dtype: int64\n",
      "hogar_adul: \n",
      "2    594\n",
      "3    257\n",
      "4    138\n",
      "1    127\n",
      "5     52\n",
      "6     41\n",
      "Name: hogar_adul, dtype: int64\n",
      "-------------\n",
      "Target:  4\n",
      "parentesco1: \n",
      "0    4042\n",
      "1    1954\n",
      "Name: parentesco1, dtype: int64\n",
      "hogar_nin: \n",
      "0    2308\n",
      "1    1744\n",
      "2    1366\n",
      "3     404\n",
      "4     125\n",
      "5      40\n",
      "7       9\n",
      "Name: hogar_nin, dtype: int64\n",
      "hogar_adul: \n",
      "2    2680\n",
      "3    1530\n",
      "4     890\n",
      "1     496\n",
      "5     259\n",
      "7      52\n",
      "6      43\n",
      "9      21\n",
      "8      20\n",
      "0       5\n",
      "Name: hogar_adul, dtype: int64\n",
      "-------------\n"
     ]
    }
   ],
   "source": [
    "# Group by 'Target' column\n",
    "grouped = selected_rows.groupby('Target')\n",
    "\n",
    "# Loop through each group\n",
    "for name, group in grouped:\n",
    "    print(\"Target: \", name)\n",
    "    # Perform value_counts() on the 'parentesco1', 'hogar_nin', and 'hogar_adul' columns\n",
    "    print(\"parentesco1: \")\n",
    "    print(group['parentesco1'].value_counts())\n",
    "    print(\"hogar_nin: \")\n",
    "    print(group['hogar_nin'].value_counts())\n",
    "    print(\"hogar_adul: \")\n",
    "    print(group['hogar_adul'].value_counts())\n",
    "    print(\"-------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0dee9c69-aec2-462f-9819-a72c5ec2da62",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the DataFrame to include only rows with a family head\n",
    "families_with_head = selected_rows[selected_rows['parentesco1'] == 1]\n",
    "\n",
    "# Group by 'Target' column (poverty level)\n",
    "grouped = families_with_head.groupby('Target')\n",
    "\n",
    "# Calculate the count of families with a head for each poverty level\n",
    "families_count = grouped.size()\n",
    "\n",
    "# Define a list of pastel colors\n",
    "pastel_colors = ['#ffd1dc', '#d1dcff', '#d1ffd7', '#fff4d1', '#d1fff4']\n",
    "\n",
    "# Define the target labels\n",
    "target_labels = ['Target 1', 'Target 2', 'Target 3', 'Target 4']\n",
    "\n",
    "# Plot the result as a pie chart with pastel colors and target labels\n",
    "families_count.plot.pie(autopct='%1.1f%%', startangle=90, figsize=(8, 6),\n",
    "                        title='Poverty level of households with a family head',\n",
    "                        colors=pastel_colors, labels=target_labels)\n",
    "\n",
    "# Display the pie chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a60528-a72e-4c64-aa0f-2380d375f124",
   "metadata": {},
   "source": [
    "## Null Values\n",
    "### Note 1: The float64 data type contains the following missing values.\n",
    "            * (2)   v2a1     (6,860) = Monthly rent payment\n",
    "            * (9)   v18q1    (7,342) = Number of all tablets household owns\n",
    "            * (22)  rez_esc  (7,928) = Years behind in school\n",
    "            * (104) meaneduc     (5) = Average years of education for adults (18+)\n",
    "            * (141) SQBmeaned    (5) = Mean^2 years of education of adults (>=18) in the household"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1f5b0893-8b84-4842-8cf3-7f896d104495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "v2a1         6860\n",
       "v18q1        7342\n",
       "rez_esc      7928\n",
       "meaneduc        5\n",
       "SQBmeaned       5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find columns with null values\n",
    "null_counts = train.select_dtypes('float64').isnull().sum()\n",
    "null_counts[null_counts > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b57a3f96-866c-4960-8e70-a017cb120e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "v2a1               71.779847\n",
       "v18q1              76.823271\n",
       "rez_esc            82.954902\n",
       "meaneduc            0.052318\n",
       "overcrowding        0.000000\n",
       "SQBovercrowding     0.000000\n",
       "SQBdependency       0.000000\n",
       "SQBmeaned           0.052318\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percentage of total counts\n",
    "total_counts = len(train)\n",
    "percentage = null_counts / total_counts *100\n",
    "percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a16a760-c6d4-439e-aef4-c183c610d3d7",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Note 2: No null values for integer data type features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a44f40e5-efb7-4011-be4a-a08fddc7f209",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find columns with null values\n",
    "null_counts = train.select_dtypes('int64').isnull().sum()\n",
    "null_counts[null_counts > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d5d1d6d-aebe-445c-bbf9-8073ca3b3f61",
   "metadata": {},
   "source": [
    "### Note 3: No null values for object data type features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e06ad043-1ef5-490a-8236-6584db1f31d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find columns with null values\n",
    "null_counts=train.select_dtypes(object).isnull().sum()\n",
    "null_counts[null_counts > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f56c4800-e058-466c-a086-d7447e4a1056",
   "metadata": {},
   "source": [
    "## Null Value Treatments\n",
    "            * (2)   v2a1     (6,860) = Monthly rent payment - 71.8%\n",
    "            * (9)   v18q1    (7,342) = Number of all tablets household owns - 76.8%\n",
    "            * (22)  rez_esc  (7,928) = Years behind in school - 82.9%\n",
    "            * (104) meaneduc     (5) = Average years of education for adults (18+) - 0.05%\n",
    "            * (141) SQBmeaned    (5) = Mean^2 years of education of adults (>=18) in the household - 0.05%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98d2f8b-57b0-460b-9614-8ad822456064",
   "metadata": {},
   "source": [
    "#### Data Cleaning 1: Replace mixed values in the columns 'dependency', 'edjefe' 'edjefa'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6c338cf0-c185-41ed-a74e-86199732d3d9",
   "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>dependency</th>\n",
       "      <th>edjefe</th>\n",
       "      <th>edjefa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no</td>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>no</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>yes</td>\n",
       "      <td>11</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>yes</td>\n",
       "      <td>11</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dependency edjefe edjefa\n",
       "0         no     10     no\n",
       "1          8     12     no\n",
       "2          8     no     11\n",
       "3        yes     11     no\n",
       "4        yes     11     no"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These columns with object data type contain mixed value.\n",
    "train.loc[:, 'dependency':'edjefa'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "87b0a48e-bc7d-4af8-a0cd-84c4c30049f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0\n",
      "1      8\n",
      "2      8\n",
      "3    yes\n",
      "4    yes\n",
      "Name: dependency, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#replacing dependency of 'no' with 0\n",
    "\n",
    "columns_to_replace = ['dependency']\n",
    "train[columns_to_replace] = train[columns_to_replace].replace({'no': 0})\n",
    "print(train['dependency'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8c51b435-4adf-4c99-b03c-82cefe7de7c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting dependency 'yes' to np.nan and replacing it with the median \n",
    "\n",
    "train['dependency'] = train['dependency'].replace({'yes': np.nan})\n",
    "train['dependency'] = pd.to_numeric(train['dependency'])\n",
    "median = train['dependency'].median()\n",
    "train['dependency'] = train['dependency'].fillna(median)\n",
    "\n",
    "# Calculate the median of the 'dependency' column of 'train' dataset.\n",
    "train['dependency'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "388c84fa-3b91-415a-b682-d307775cec86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    }
   ],
   "source": [
    "#checking to make sure the column are a float\n",
    "print(train['dependency'].dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fb4c8c81-b08f-4a1e-b001-fd26797217d6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>dependency</th>\n",
       "      <th>edjefe</th>\n",
       "      <th>edjefa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.50</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.50</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9552</th>\n",
       "      <td>0.25</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9553</th>\n",
       "      <td>0.25</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9554</th>\n",
       "      <td>0.25</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9555</th>\n",
       "      <td>0.25</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9556</th>\n",
       "      <td>0.25</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9557 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      dependency  edjefe  edjefa\n",
       "0           0.00    10.0     0.0\n",
       "1           8.00    12.0     0.0\n",
       "2           8.00     0.0    11.0\n",
       "3           0.50    11.0     0.0\n",
       "4           0.50    11.0     0.0\n",
       "...          ...     ...     ...\n",
       "9552        0.25     9.0     0.0\n",
       "9553        0.25     9.0     0.0\n",
       "9554        0.25     9.0     0.0\n",
       "9555        0.25     9.0     0.0\n",
       "9556        0.25     9.0     0.0\n",
       "\n",
       "[9557 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use a mapping dictionary to replace 'yes' with '1' and 'no' with '0' for columns 'edjefe' and 'edjefa'\n",
    "mapping = {'yes':1,'no':0}\n",
    "\n",
    "for df in [train]:\n",
    "    df['edjefe'] = df['edjefe'].replace(mapping).astype(np.float64)\n",
    "    df['edjefa'] = df['edjefa'].replace(mapping).astype(np.float64)\n",
    "    \n",
    "train[['edjefe','edjefa']].describe()\n",
    "\n",
    "# Display selected columns of 'train' dataset after data cleaning.\n",
    "train[['dependency','edjefe','edjefa']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279e452f-34b1-4020-9aac-47d3e84881af",
   "metadata": {},
   "source": [
    "#### Data Cleaning 2: Fix column (2) v2a1: Monthly rent payment with null values of '0'\n",
    "            * (2)   v2a1      = Monthly rent payment (6,860 null values)\n",
    "            * (116) tipovivi1 = 1 own and fully paid house\n",
    "            * (117) tipovivi2 = 1 own, paying in installments\n",
    "            * (118) tipovivi3 = 1 rented\n",
    "            * (119) tipovivi4 = 1 precarious\n",
    "            * (120) tipovivi5 = 1 other(assigned,  borrowed)\n",
    "            \n",
    "Action taken: Replace 'Nan' with the median values grouped by \"Target\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d9927edd-d16f-43eb-91b9-394ef8c1b624",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</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",
       "      <th>1</th>\n",
       "      <td>153.0</td>\n",
       "      <td>84806.464052</td>\n",
       "      <td>51955.287818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>249896.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>337.0</td>\n",
       "      <td>97015.166172</td>\n",
       "      <td>53861.050411</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>225000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300.0</td>\n",
       "      <td>102618.093333</td>\n",
       "      <td>58996.916717</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>270000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1907.0</td>\n",
       "      <td>193589.258521</td>\n",
       "      <td>167283.430436</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>2353477.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count           mean            std      min       25%       50%  \\\n",
       "Target                                                                      \n",
       "1        153.0   84806.464052   51955.287818      0.0   50000.0   80000.0   \n",
       "2        337.0   97015.166172   53861.050411      0.0   50000.0   90000.0   \n",
       "3        300.0  102618.093333   58996.916717  13000.0   50000.0   90000.0   \n",
       "4       1907.0  193589.258521  167283.430436      0.0  100000.0  150000.0   \n",
       "\n",
       "             75%        max  \n",
       "Target                       \n",
       "1       100000.0   249896.0  \n",
       "2       130000.0   225000.0  \n",
       "3       150000.0   270000.0  \n",
       "4       250000.0  2353477.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Target')['v2a1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a8a9b494-b400-4646-adaa-3b8c4a368130",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the median of rent for each target group\n",
    "rent_median_by_target = train.groupby('Target')['v2a1'].median()\n",
    "\n",
    "# Fill the null values with the median of each target group\n",
    "df['v2a1'] = df.groupby('Target')['v2a1'].apply(lambda x: x.fillna(rent_median_by_target.loc[x.name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "635d419e-bce2-4e0c-b961-da9d218ff650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check to make sure all null values have been filled\n",
    "df[\"v2a1\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec3c9a8-842b-4734-a307-9670c0a900ce",
   "metadata": {},
   "source": [
    "#### Data Cleaning 3: Fix column (9) v18q1: number of tablets household owns with null values to '0'\n",
    "            * v18q  (8) = owns a tablet\n",
    "            * v18q1 (9) = number of tablets household owns (7,342 null values)\n",
    "Action taken: Replace 'Nan' with '0' because if a household does not 'owns a tablet',\n",
    "              the count of tablets they own should also be '0'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3ad09664-1fdf-4197-be03-566c22cdd237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>v18q</th>\n",
       "      <th>v18q1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9552</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9553</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9554</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9555</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9556</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7342 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      v18q  v18q1\n",
       "0        0    NaN\n",
       "2        0    NaN\n",
       "7        0    NaN\n",
       "8        0    NaN\n",
       "9        0    NaN\n",
       "...    ...    ...\n",
       "9552     0    NaN\n",
       "9553     0    NaN\n",
       "9554     0    NaN\n",
       "9555     0    NaN\n",
       "9556     0    NaN\n",
       "\n",
       "[7342 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['v18q', 'v18q1']][train['v18q1'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18715fd4-8f51-431a-bf87-6ea9a4acf6e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "v18q\n",
       "0    2318\n",
       "1       0\n",
       "Name: v18q1, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heads = train.loc[train['parentesco1'] == 1].copy() #only locating the household head \n",
    "heads.groupby('v18q')['v18q1'].apply(lambda x: x.isnull().sum()) #while on the household head dataframe, \n",
    "                                                                #group by owns a tablet - the one with null values\n",
    "                                                                #and get only the sum of the null values with tablet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d6aed350-a531-4f18-ab2e-764a94acb7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    7342\n",
       "1.0    1586\n",
       "2.0     444\n",
       "3.0     129\n",
       "4.0      37\n",
       "5.0      13\n",
       "6.0       6\n",
       "Name: v18q1, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace the missing (NaN) values in the 'v18q1' column with the value 0.\n",
    "train['v18q1'].fillna(value=0, inplace=True)\n",
    "\n",
    "train['v18q1'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef6e618-7935-49fa-8c5f-5d864900972d",
   "metadata": {},
   "source": [
    "#### Data Cleaning 4: Fix column (22) rez_esc: Years behind in school with null values to '0'\n",
    "            * (22) rez_esc = Years behind in school (7,928 null values)\n",
    "            * (133) age    = Age in years\n",
    "Action taken: Drop entire column as there are other columns that can help us identify education levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6fb3cace-5b89-4eac-8450-d7758ef2f02e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#drop column\n",
    "\n",
    "train.drop('rez_esc', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373da95a-7082-48fd-80cd-b492a945b2ee",
   "metadata": {},
   "source": [
    "#### Data Cleaning 5: Fix column (104) meaneduc: Average years of education for adults (18+) with null values to '0'\n",
    "            * (104) meaneduc    = Average years of education for adults (18+) (5 null values)\n",
    "            \n",
    "            * (102) edjefe      = years of education of male head of household,\n",
    "                                  based on the interaction of escolari (years of education),\n",
    "                                  head of household and gender, yes=1 and no=0\n",
    "                                 \n",
    "            * (103) edjefa      = years of education of female head of household,\n",
    "                                  based on the interaction of escolari (years of education),\n",
    "                                  head of household and gender, yes=1 and no=0\n",
    "                                 \n",
    "            * (105) instlevel1  = 1 no level of education\n",
    "            * (106) instlevel2  = 1 incomplete primary\n",
    "Action taken: Replace 'Nan' with '0' as null value of '5' is immaterial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b59f1f68-b968-44e3-90cd-563b02707fc4",
   "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>meaneduc</th>\n",
       "      <th>edjefe</th>\n",
       "      <th>edjefa</th>\n",
       "      <th>instlevel1</th>\n",
       "      <th>instlevel2</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2049</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      meaneduc  edjefe  edjefa  instlevel1  instlevel2  age\n",
       "1291       NaN     0.0    10.0           0           0   18\n",
       "1840       NaN     4.0     0.0           0           0   18\n",
       "1841       NaN     4.0     0.0           0           1   18\n",
       "2049       NaN    12.0     0.0           0           0   19\n",
       "2050       NaN    12.0     0.0           0           0   19"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['meaneduc', 'edjefe', 'edjefa', 'instlevel1', 'instlevel2', 'age']][train['meaneduc'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e083876e-630b-46cf-8916-8bdcdb714dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace the missing (NaN) values in the 'meaneduc' column with the value 0.\n",
    "train['meaneduc'].fillna(value=0, inplace=True)\n",
    "\n",
    "train['meaneduc'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dac055d-6034-4e6c-84df-01a89a58aafe",
   "metadata": {},
   "source": [
    "#### Data Cleaning 6: Fix column (141) rez_esc: Years behind in school with null values to '0'\n",
    "            * (141) SQBmeaned = Mean^2 years of education of adults (>=18) in the household (5 null values)\n",
    "            \n",
    "            * (102) edjefe    = years of education of male head of household,\n",
    "                                based on the interaction of escolari (years of education),\n",
    "                                head of household and gender, yes=1 and no=0\n",
    "                                 \n",
    "            * (103) edjefa    = years of education of female head of household,\n",
    "                                based on the interaction of escolari (years of education),\n",
    "                                head of household and gender, yes=1 and no=0\n",
    "                                 \n",
    "            * (105) instlevel1 = 1 no level of education\n",
    "            * (106) instlevel2 = 1 incomplete primary\n",
    "Action taken: Replace 'Nan' with '0' as null value of '5' is immaterial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "033006fb-1d5d-4b05-9960-6b2d8bdd2690",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "      SQBmeaned  edjefe  edjefa  instlevel1  instlevel2\n",
       "1291        NaN     0.0    10.0           0           0\n",
       "1840        NaN     4.0     0.0           0           0\n",
       "1841        NaN     4.0     0.0           0           1\n",
       "2049        NaN    12.0     0.0           0           0\n",
       "2050        NaN    12.0     0.0           0           0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['SQBmeaned', 'edjefe', 'edjefa', 'instlevel1', 'instlevel2']][train['SQBmeaned'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aa2ca57d-ac28-4505-996e-6f121fad41e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace the missing (NaN) values in the 'meaneduc' column with the value 0.\n",
    "train['SQBmeaned'].fillna(value=0, inplace=True)\n",
    "\n",
    "train['SQBmeaned'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87781fc1-f8a1-4e85-9064-93c0c02ad296",
   "metadata": {},
   "source": [
    "## Task 9: Predict the accuracy using random forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fbe7c9c1-be64-4e3d-b17f-7d209c3e029e",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>4</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       v2a1  hacdor  rooms  hacapo  v14a  refrig  v18q  v18q1  r4h1  r4h2  \\\n",
       "0  190000.0       0      3       0     1       1     0    0.0     0     1   \n",
       "1  135000.0       0      4       0     1       1     1    1.0     0     1   \n",
       "2  150000.0       0      8       0     1       1     0    0.0     0     0   \n",
       "3  180000.0       0      5       0     1       1     1    1.0     0     2   \n",
       "4  180000.0       0      5       0     1       1     1    1.0     0     2   \n",
       "\n",
       "   r4h3  r4m1  r4m2  r4m3  r4t1  r4t2  r4t3  tamhog  tamviv  escolari  hhsize  \\\n",
       "0     1     0     0     0     0     1     1       1       1        10       1   \n",
       "1     1     0     0     0     0     1     1       1       1        12       1   \n",
       "2     0     0     1     1     0     1     1       1       1        11       1   \n",
       "3     2     1     1     2     1     3     4       4       4         9       4   \n",
       "4     2     1     1     2     1     3     4       4       4        11       4   \n",
       "\n",
       "   paredblolad  paredzocalo  paredpreb  pareddes  paredmad  paredzinc  \\\n",
       "0            1            0          0         0         0          0   \n",
       "1            0            0          0         0         1          0   \n",
       "2            0            0          0         0         1          0   \n",
       "3            1            0          0         0         0          0   \n",
       "4            1            0          0         0         0          0   \n",
       "\n",
       "   paredfibras  paredother  pisomoscer  pisocemento  pisoother  pisonatur  \\\n",
       "0            0           0           1            0          0          0   \n",
       "1            0           0           0            0          0          0   \n",
       "2            0           0           1            0          0          0   \n",
       "3            0           0           1            0          0          0   \n",
       "4            0           0           1            0          0          0   \n",
       "\n",
       "   pisonotiene  pisomadera  techozinc  techoentrepiso  techocane  techootro  \\\n",
       "0            0           0          0               1          0          0   \n",
       "1            0           1          1               0          0          0   \n",
       "2            0           0          1               0          0          0   \n",
       "3            0           0          1               0          0          0   \n",
       "4            0           0          1               0          0          0   \n",
       "\n",
       "   cielorazo  abastaguadentro  abastaguafuera  abastaguano  public  planpri  \\\n",
       "0          1                1               0            0       1        0   \n",
       "1          1                1               0            0       1        0   \n",
       "2          1                1               0            0       1        0   \n",
       "3          1                1               0            0       1        0   \n",
       "4          1                1               0            0       1        0   \n",
       "\n",
       "   noelec  coopele  sanitario1  sanitario2  sanitario3  sanitario5  \\\n",
       "0       0        0           0           1           0           0   \n",
       "1       0        0           0           1           0           0   \n",
       "2       0        0           0           1           0           0   \n",
       "3       0        0           0           1           0           0   \n",
       "4       0        0           0           1           0           0   \n",
       "\n",
       "   sanitario6  energcocinar1  energcocinar2  energcocinar3  energcocinar4  \\\n",
       "0           0              0              0              1              0   \n",
       "1           0              0              1              0              0   \n",
       "2           0              0              1              0              0   \n",
       "3           0              0              1              0              0   \n",
       "4           0              0              1              0              0   \n",
       "\n",
       "   elimbasu1  elimbasu2  elimbasu3  elimbasu4  elimbasu5  elimbasu6  epared1  \\\n",
       "0          1          0          0          0          0          0        0   \n",
       "1          1          0          0          0          0          0        0   \n",
       "2          1          0          0          0          0          0        0   \n",
       "3          1          0          0          0          0          0        0   \n",
       "4          1          0          0          0          0          0        0   \n",
       "\n",
       "   epared2  epared3  etecho1  etecho2  etecho3  eviv1  eviv2  eviv3  dis  \\\n",
       "0        1        0        1        0        0      1      0      0    0   \n",
       "1        1        0        0        1        0      0      1      0    0   \n",
       "2        1        0        0        0        1      0      0      1    1   \n",
       "3        0        1        0        0        1      0      0      1    0   \n",
       "4        0        1        0        0        1      0      0      1    0   \n",
       "\n",
       "   male  female  estadocivil1  estadocivil2  estadocivil3  estadocivil4  \\\n",
       "0     1       0             0             0             0             1   \n",
       "1     1       0             0             0             0             1   \n",
       "2     0       1             0             0             0             0   \n",
       "3     1       0             0             0             0             0   \n",
       "4     0       1             0             1             0             0   \n",
       "\n",
       "   estadocivil5  estadocivil6  estadocivil7  parentesco1  parentesco2  \\\n",
       "0             0             0             0            1            0   \n",
       "1             0             0             0            1            0   \n",
       "2             0             1             0            1            0   \n",
       "3             0             0             1            0            0   \n",
       "4             0             0             0            0            1   \n",
       "\n",
       "   parentesco3  parentesco4  parentesco5  parentesco6  parentesco7  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            1            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   parentesco8  parentesco9  parentesco10  parentesco11  parentesco12  \\\n",
       "0            0            0             0             0             0   \n",
       "1            0            0             0             0             0   \n",
       "2            0            0             0             0             0   \n",
       "3            0            0             0             0             0   \n",
       "4            0            0             0             0             0   \n",
       "\n",
       "   hogar_nin  hogar_adul  hogar_mayor  hogar_total  dependency  edjefe  \\\n",
       "0          0           1            0            1         0.0    10.0   \n",
       "1          0           1            1            1         8.0    12.0   \n",
       "2          0           1            1            1         8.0     0.0   \n",
       "3          2           2            0            4         0.5    11.0   \n",
       "4          2           2            0            4         0.5    11.0   \n",
       "\n",
       "   edjefa  meaneduc  instlevel1  instlevel2  instlevel3  instlevel4  \\\n",
       "0     0.0      10.0           0           0           0           1   \n",
       "1     0.0      12.0           0           0           0           0   \n",
       "2    11.0      11.0           0           0           0           0   \n",
       "3     0.0      11.0           0           0           0           1   \n",
       "4     0.0      11.0           0           0           0           0   \n",
       "\n",
       "   instlevel5  instlevel6  instlevel7  instlevel8  instlevel9  bedrooms  \\\n",
       "0           0           0           0           0           0         1   \n",
       "1           0           0           0           1           0         1   \n",
       "2           1           0           0           0           0         2   \n",
       "3           0           0           0           0           0         3   \n",
       "4           1           0           0           0           0         3   \n",
       "\n",
       "   overcrowding  tipovivi1  tipovivi2  tipovivi3  tipovivi4  tipovivi5  \\\n",
       "0      1.000000          0          0          1          0          0   \n",
       "1      1.000000          0          0          1          0          0   \n",
       "2      0.500000          1          0          0          0          0   \n",
       "3      1.333333          0          0          1          0          0   \n",
       "4      1.333333          0          0          1          0          0   \n",
       "\n",
       "   computer  television  mobilephone  qmobilephone  lugar1  lugar2  lugar3  \\\n",
       "0         0           0            1             1       1       0       0   \n",
       "1         0           0            1             1       1       0       0   \n",
       "2         0           0            0             0       1       0       0   \n",
       "3         0           0            1             3       1       0       0   \n",
       "4         0           0            1             3       1       0       0   \n",
       "\n",
       "   lugar4  lugar5  lugar6  area1  area2  age  \n",
       "0       0       0       0      1      0   43  \n",
       "1       0       0       0      1      0   67  \n",
       "2       0       0       0      1      0   92  \n",
       "3       0       0       0      1      0   17  \n",
       "4       0       0       0      1      0   37  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of columns to drop.\n",
    "drop_columns = ['Id', 'SQBescolari', 'SQBage', 'SQBhogar_total', 'SQBedjefe', 'SQBhogar_nin',\n",
    "               'idhogar','SQBovercrowding', 'SQBdependency', 'SQBmeaned', 'agesq', 'Target']\n",
    "\n",
    "# # Drop the specified columns from the DataFrame\n",
    "X = train.drop(columns = drop_columns)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d949d3a5-6c67-4423-8f4f-22efc7a9f019",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9557, 130)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "de90f45f-22c1-4d0d-9d89-3f2b2308e8ed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    4\n",
       "2    4\n",
       "3    4\n",
       "4    4\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new series by selecting all the rows and only the last column 'Target' of 'train' DataFrame.\n",
    "y = train.iloc[:, -1]\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "00737550-99f9-4a2d-978b-0b23ffb34e4b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9557,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f93225d6-8045-4929-919d-423dac889e59",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import the train_test_split function.\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the predictor matrix X and the target vector y into training and testing sets with a 80-20 split ratio.\n",
    "# Set the random seed to '0' to ensure consistent result.\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2df8461d-99af-4d9e-8d1d-152782222dc4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=8, min_samples_split=10, random_state=5)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the ensemble learning method to fit the training data.\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(criterion = 'gini', max_depth = 8,\n",
    "                             min_samples_split = 10, random_state = 5)\n",
    "\n",
    "# 'fit' method trains the model using the provided training data.\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "70d92442-4b67-48e0-a75b-e8f817c75759",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, ..., 2, 4, 4], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The predict method of the RandomForestClassifier class is used to predict the target variable\n",
    "#     values for X_test data.\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1ea91cd1-91a3-4a61-a73c-4e346c9a5307",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  72   20    0   59]\n",
      " [   0  236    4   91]\n",
      " [   0   67   77  102]\n",
      " [   0    3    0 1181]]\n"
     ]
    }
   ],
   "source": [
    "# Create a confusioin matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f87498fe-e03f-4fcd-a6ef-7d8a1b19e1c5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8190376569037657\n"
     ]
    }
   ],
   "source": [
    "# Use 'accuracy_score' function to calculate the accuracy of 'y_pred' values compared to the true values\n",
    "#     'y_test' for the target variable.\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e9956396-35c4-4209-96a8-aab2e665b44c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.48      1.00      0.65        72\n",
      "           2       0.71      0.72      0.72       326\n",
      "           3       0.31      0.95      0.47        81\n",
      "           4       1.00      0.82      0.90      1433\n",
      "\n",
      "    accuracy                           0.82      1912\n",
      "   macro avg       0.63      0.87      0.68      1912\n",
      "weighted avg       0.90      0.82      0.84      1912\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Create a report to assess the accuracy and quality of the classification model.\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "74307a38-616b-45be-a486-310acf7e353d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters using grid search: \n",
      " {'criterion': 'gini', 'max_depth': 14, 'max_features': None}\n",
      "Time taken in grid search:  855.80\n"
     ]
    }
   ],
   "source": [
    "rf_tree_cv = RandomForestClassifier(random_state=42)\n",
    "import time\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "np.random.seed(42)\n",
    "start = time.time()\n",
    "\n",
    "param_dist = {'max_depth': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],\n",
    "              'max_features': ['auto', 'sqrt', 'log2', None],\n",
    "              'criterion': ['gini', 'entropy']}\n",
    "\n",
    "cv_rf_tree =GridSearchCV(rf_tree_cv, cv = 10,\n",
    "                     param_grid=param_dist,\n",
    "                     n_jobs = 3,scoring='accuracy')\n",
    "\n",
    "cv_rf_tree.fit(X_train,y_train)\n",
    "print('Best Parameters using grid search: \\n',\n",
    "     cv_rf_tree.best_params_)\n",
    "end = time.time()\n",
    "print('Time taken in grid search: {0: .2f}'.format(end - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "22f1f330-9634-4bf6-bbe9-de0a4d68ad2c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Comment:\n",
    "#  - Overall accuracy of the model predicition is 82%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "868cd084-b0cb-46a9-bae7-4694631c09f9",
   "metadata": {},
   "source": [
    "## Overall recommendations and summary of model in predicting the income aid: \n",
    " \n",
    "- Random forest is an appropriate model to use in this business case. F1 score for all 4 target groups are relatively high and accuracy score is at 95%, indicating that this is a good model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b73eb0f-1ff9-4065-81d7-b89dcddc8c7f",
   "metadata": {},
   "source": [
    "## Task 10: Check the accuracy using a random forest with cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b5abb31f-026e-45fe-8669-d29901ee0b0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.82745098 0.83921569 0.81960784 0.82222222 0.83921569 0.82460733\n",
      " 0.81413613 0.81544503 0.81675393 0.84031414]\n"
     ]
    }
   ],
   "source": [
    "# Perform K-fold cross-validation on the training data using the clf model.\n",
    "# CV=10 gives a more accurate estimate of out-of-sample accuracy.\n",
    "# More \"efficient\" use of data (every observation is used for both training and testing).\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(clf, X_train, y_train, cv = 10)\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ff6b0b6e-38fc-4a87-8090-84b4f0862d39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8258968962803271\n"
     ]
    }
   ],
   "source": [
    "# Print average score of train data.\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "59188056-eb30-4baa-a8e5-08c8d21e099f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Comment:\n",
    "#  - The model correctly predicted the target (y) variable for approximately 82.5%\n",
    "#      on the training data using cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "67f0c8f2-e3c5-44b4-86d0-5acd0cd0b7a7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 4 4 ... 2 4 4]\n"
     ]
    }
   ],
   "source": [
    "# Import cross_val_predict function to review predicted values on the test dataset.\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "# Print and review th predicted values.\n",
    "pred = cross_val_predict(clf, X_test, y_test)\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2673c772-21b7-4fff-9121-4c3fe986cf05",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.76041667, 0.78125   , 0.79581152, 0.77486911, 0.76439791,\n",
       "       0.7486911 , 0.76963351, 0.82198953, 0.76963351, 0.7591623 ])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the test data using CV = 10.\n",
    "# Perform Random Forest Classifier model (i.e., clf) on test data using cross-validation\n",
    "#     approach with 10 folds.\n",
    "\n",
    "scores_test = cross_val_score(clf, X_test, y_test, cv = 10)\n",
    "scores_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5341f132-98ab-499c-9cb4-a49ed69ae57c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7745855148342058\n"
     ]
    }
   ],
   "source": [
    "# CV=10 provides a more realiable estimate of out-of-sample as compared to using a single train-test-split.\n",
    "\n",
    "# Print average score of test data.\n",
    "print(np.mean(scores_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1901a531-6d45-470c-aa2b-a1d82ac165fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comment:\n",
    "#  - 77.5% is the mean performance on test data using all folds (CV=10) cross-validation."
   ]
  }
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