INCOME QUALIFICATION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a80684d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "420806f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_income_train = pd.read_csv(r\"C:\\Users\\prave\\Downloads\\Dataset for the project\\Dataset for the project\\train.csv\")\n",
    "df_income_test =  pd.read_csv(r\"C:\\Users\\prave\\Downloads\\Dataset for the project\\Dataset for the project\\test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a4ccec5c",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>v2a1</th>\n",
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       "      <th>SQBmeaned</th>\n",
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       "             Id      v2a1  hacdor  rooms  hacapo  v14a  refrig  v18q  v18q1  \\\n",
       "0  ID_279628684  190000.0       0      3       0     1       1     0    NaN   \n",
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       "\n",
       "   r4h1  ...  SQBescolari  SQBage  SQBhogar_total  SQBedjefe  SQBhogar_nin  \\\n",
       "0     0  ...          100    1849               1        100             0   \n",
       "1     0  ...          144    4489               1        144             0   \n",
       "2     0  ...          121    8464               1          0             0   \n",
       "3     0  ...           81     289              16        121             4   \n",
       "4     0  ...          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",
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       "3         1.777778            1.0      121.0    289       4  \n",
       "4         1.777778            1.0      121.0   1369       4  \n",
       "\n",
       "[5 rows x 143 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "53dda3cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "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": [
    "df_income_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5fa8c5ff",
   "metadata": {},
   "outputs": [
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       "             Id      v2a1  hacdor  rooms  hacapo  v14a  refrig  v18q  v18q1  \\\n",
       "0  ID_2f6873615       NaN       0      5       0     1       1     0    NaN   \n",
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       "\n",
       "   r4h1  ...  age  SQBescolari  SQBage  SQBhogar_total  SQBedjefe  \\\n",
       "0     1  ...    4            0      16               9          0   \n",
       "1     1  ...   41          256    1681               9          0   \n",
       "2     1  ...   41          289    1681               9          0   \n",
       "3     0  ...   59          256    3481               1        256   \n",
       "4     0  ...   18          121     324               1          0   \n",
       "\n",
       "   SQBhogar_nin  SQBovercrowding  SQBdependency  SQBmeaned  agesq  \n",
       "0             1             2.25           0.25     272.25     16  \n",
       "1             1             2.25           0.25     272.25   1681  \n",
       "2             1             2.25           0.25     272.25   1681  \n",
       "3             0             1.00           0.00     256.00   3481  \n",
       "4             1             0.25          64.00        NaN    324  \n",
       "\n",
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b35bda06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23856 entries, 0 to 23855\n",
      "Columns: 142 entries, Id to agesq\n",
      "dtypes: float64(8), int64(129), object(5)\n",
      "memory usage: 25.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_income_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "418252b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integer Type: \n",
      "Index(['hacdor', 'rooms', 'hacapo', 'v14a', 'refrig', 'v18q', 'r4h1', 'r4h2',\n",
      "       'r4h3', 'r4m1',\n",
      "       ...\n",
      "       'area1', 'area2', 'age', 'SQBescolari', 'SQBage', 'SQBhogar_total',\n",
      "       'SQBedjefe', 'SQBhogar_nin', 'agesq', 'Target'],\n",
      "      dtype='object', length=130)\n",
      "\n",
      "\n",
      "Float Type: \n",
      "Index(['v2a1', 'v18q1', 'rez_esc', 'meaneduc', 'overcrowding',\n",
      "       'SQBovercrowding', 'SQBdependency', 'SQBmeaned'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "Object Type: \n",
      "Index(['Id', 'idhogar', 'dependency', 'edjefe', 'edjefa'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#List the columns for different datatypes:\n",
    "print('Integer Type: ')\n",
    "print(df_income_train.select_dtypes(np.int64).columns)\n",
    "print('\\n')\n",
    "print('Float Type: ')\n",
    "print(df_income_train.select_dtypes(np.float64).columns)\n",
    "print('\\n')\n",
    "print('Object Type: ')\n",
    "print(df_income_train.select_dtypes(np.object).columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "33ba00b2",
   "metadata": {},
   "outputs": [
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       "      <th>age</th>\n",
       "      <th>SQBescolari</th>\n",
       "      <th>SQBage</th>\n",
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       "      <th>SQBedjefe</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>...</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>...</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>...</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>...</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 130 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hacdor  rooms  hacapo  v14a  refrig  v18q  r4h1  r4h2  r4h3  r4m1  ...  \\\n",
       "0       0      3       0     1       1     0     0     1     1     0  ...   \n",
       "1       0      4       0     1       1     1     0     1     1     0  ...   \n",
       "2       0      8       0     1       1     0     0     0     0     0  ...   \n",
       "3       0      5       0     1       1     1     0     2     2     1  ...   \n",
       "4       0      5       0     1       1     1     0     2     2     1  ...   \n",
       "\n",
       "   area1  area2  age  SQBescolari  SQBage  SQBhogar_total  SQBedjefe  \\\n",
       "0      1      0   43          100    1849               1        100   \n",
       "1      1      0   67          144    4489               1        144   \n",
       "2      1      0   92          121    8464               1          0   \n",
       "3      1      0   17           81     289              16        121   \n",
       "4      1      0   37          121    1369              16        121   \n",
       "\n",
       "   SQBhogar_nin  agesq  Target  \n",
       "0             0   1849       4  \n",
       "1             0   4489       4  \n",
       "2             0   8464       4  \n",
       "3             4    289       4  \n",
       "4             4   1369       4  \n",
       "\n",
       "[5 rows x 130 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_train.select_dtypes('int64').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dbf47abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find columns with null values\n",
    "null_counts=df_income_train.select_dtypes('int64').isnull().sum()\n",
    "null_counts[null_counts > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5411a5f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>190000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.0</td>\n",
       "      <td>144.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>64.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.777778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.777778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       v2a1  v18q1  rez_esc  meaneduc  overcrowding  SQBovercrowding  \\\n",
       "0  190000.0    NaN      NaN      10.0      1.000000         1.000000   \n",
       "1  135000.0    1.0      NaN      12.0      1.000000         1.000000   \n",
       "2       NaN    NaN      NaN      11.0      0.500000         0.250000   \n",
       "3  180000.0    1.0      1.0      11.0      1.333333         1.777778   \n",
       "4  180000.0    1.0      NaN      11.0      1.333333         1.777778   \n",
       "\n",
       "   SQBdependency  SQBmeaned  \n",
       "0            0.0      100.0  \n",
       "1           64.0      144.0  \n",
       "2           64.0      121.0  \n",
       "3            1.0      121.0  \n",
       "4            1.0      121.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_train.select_dtypes('float64').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b3ab69b3",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find columns with null values\n",
    "null_counts=df_income_train.select_dtypes('float64').isnull().sum()\n",
    "null_counts[null_counts > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cb7515fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_train.select_dtypes('object').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5008615f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find columns with null values\n",
    "null_counts=df_income_train.select_dtypes('object').isnull().sum()\n",
    "null_counts[null_counts > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9bf7f6dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dependency</th>\n",
       "      <th>edjefe</th>\n",
       "      <th>edjefa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9557.000000</td>\n",
       "      <td>9557.000000</td>\n",
       "      <td>9557.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.149550</td>\n",
       "      <td>5.096788</td>\n",
       "      <td>2.896830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.605993</td>\n",
       "      <td>5.246513</td>\n",
       "      <td>4.612056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dependency       edjefe       edjefa\n",
       "count  9557.000000  9557.000000  9557.000000\n",
       "mean      1.149550     5.096788     2.896830\n",
       "std       1.605993     5.246513     4.612056\n",
       "min       0.000000     0.000000     0.000000\n",
       "25%       0.333333     0.000000     0.000000\n",
       "50%       0.666667     6.000000     0.000000\n",
       "75%       1.333333     9.000000     6.000000\n",
       "max       8.000000    21.000000    21.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping={'yes':1,'no':0}\n",
    "\n",
    "for df in [df_income_train, df_income_test]:\n",
    "    df['dependency'] =df['dependency'].replace(mapping).astype(np.float64)\n",
    "    df['edjefe'] =df['edjefe'].replace(mapping).astype(np.float64)\n",
    "    df['edjefa'] =df['edjefa'].replace(mapping).astype(np.float64)\n",
    "    \n",
    "df_income_train[['dependency','edjefe','edjefa']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aa2f5708",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df_income_train[df_income_train['v2a1'].isnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "50a61141",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tipovivi1  tipovivi2  tipovivi3  tipovivi4  tipovivi5\n",
       "2           1          0          0          0          0\n",
       "13          1          0          0          0          0\n",
       "14          1          0          0          0          0\n",
       "26          1          0          0          0          0\n",
       "32          1          0          0          0          0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['tipovivi1','tipovivi2','tipovivi3','tipovivi4','tipovivi5']\n",
    "data[columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "22897e7c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>SQBescolari</th>\n",
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       "      <th>SQBedjefe</th>\n",
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       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>90.2500</td>\n",
       "      <td>1521</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ID_e5cdba865</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>784</td>\n",
       "      <td>4</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "              Id  v2a1  hacdor  rooms  hacapo  v14a  refrig  v18q  v18q1  \\\n",
       "2   ID_68de51c94   NaN       0      8       0     1       1     0    NaN   \n",
       "13  ID_064b57869   NaN       0      4       0     1       1     1    1.0   \n",
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       "32  ID_e24d9c3c9   NaN       0      5       0     1       1     0    NaN   \n",
       "\n",
       "    r4h1  ...  SQBescolari  SQBage  SQBhogar_total  SQBedjefe  SQBhogar_nin  \\\n",
       "2      0  ...          121    8464               1          0             0   \n",
       "13     0  ...           16    6241               4          0             0   \n",
       "14     0  ...          225    1521               4          0             0   \n",
       "26     0  ...          225    1936               1        225             0   \n",
       "32     0  ...            1     784              25        121             1   \n",
       "\n",
       "    SQBovercrowding  SQBdependency  SQBmeaned  agesq  Target  \n",
       "2          0.250000        64.0000   121.0000   8464       4  \n",
       "13         1.000000         1.0000    90.2500   6241       4  \n",
       "14         1.000000         1.0000    90.2500   1521       4  \n",
       "26         0.250000         0.0000   225.0000   1936       4  \n",
       "32         2.777778         0.0625    27.5625    784       4  \n",
       "\n",
       "[5 rows x 143 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e49299a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tipovivi1</th>\n",
       "      <th>tipovivi2</th>\n",
       "      <th>tipovivi3</th>\n",
       "      <th>tipovivi4</th>\n",
       "      <th>tipovivi5</th>\n",
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       "    <tr>\n",
       "      <th>9552</th>\n",
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       "      <th>9553</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>9557 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      tipovivi1  tipovivi2  tipovivi3  tipovivi4  tipovivi5\n",
       "0             0          0          1          0          0\n",
       "1             0          0          1          0          0\n",
       "2             1          0          0          0          0\n",
       "3             0          0          1          0          0\n",
       "4             0          0          1          0          0\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",
       "[9557 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "own_variables = [x for x in df_income_train if x.startswith('tipo')]\n",
    "df_income_train[own_variables]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ec53a775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Status Of Home Ownership for Missing Rent Payments')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df_income_train.loc[df_income_train['v2a1'].isnull(), own_variables].sum().plot.bar(figsize = (10, 8), color = 'green', edgecolor = 'k', linewidth = 2)\n",
    "plt.xticks([0,1,2,3,4],['Owns and Paid Off', 'Owns and Paying', 'Rented', 'Precarious', 'Other'], rotation = 20)\n",
    "plt.title('Status Of Home Ownership for Missing Rent Payments', size = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9cd317fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "v2a1    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for df in [df_income_train, df_income_test]:\n",
    "    df['v2a1'].fillna(value=0, inplace=True)\n",
    "\n",
    "df_income_train[['v2a1']].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b8f5e4ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "v18q\n",
       "0    2318\n",
       "1       0\n",
       "Name: v18q1, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Heads of household\n",
    "heads = df_income_train.loc[df_income_train['parentesco1'] == 1].copy()\n",
    "heads.groupby('v18q')['v18q1'].apply(lambda x: x.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c483bc0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "col='v18q1'\n",
    "df_income_train[col].value_counts().sort_index().plot.bar(color = 'blue',\n",
    "                                             edgecolor = 'k',\n",
    "                                             linewidth = 2)\n",
    "plt.xlabel(f'{col}'); plt.title(f'{col} Value Counts'); plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a60bf9c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "v18q1    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for df in [df_income_train, df_income_test]:\n",
    "    df['v18q1'].fillna(value=0, inplace=True)\n",
    "\n",
    "df_income_train[['v18q1']].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "248bcb1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1629.000000\n",
       "mean       12.258441\n",
       "std         3.218325\n",
       "min         7.000000\n",
       "25%         9.000000\n",
       "50%        12.000000\n",
       "75%        15.000000\n",
       "max        17.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#look at the data with not null values first.\n",
    "df_income_train[df_income_train['rez_esc'].notnull()]['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c8c75525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7928.000000\n",
       "mean       38.833249\n",
       "std        20.989486\n",
       "min         0.000000\n",
       "25%        24.000000\n",
       "50%        38.000000\n",
       "75%        54.000000\n",
       "max        97.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_train.loc[df_income_train['rez_esc'].isnull()]['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "579f84ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     1.0\n",
       "mean     10.0\n",
       "std       NaN\n",
       "min      10.0\n",
       "25%      10.0\n",
       "50%      10.0\n",
       "75%      10.0\n",
       "max      10.0\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_train.loc[(df_income_train['rez_esc'].isnull() & ((df_income_train['age'] > 7) & (df_income_train['age'] < 17)))]['age'].describe()\n",
    "#There is one value that has Null for the 'behind in school' column with age between 7 and 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c56250c3",
   "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>Id</th>\n",
       "      <th>v2a1</th>\n",
       "      <th>hacdor</th>\n",
       "      <th>rooms</th>\n",
       "      <th>hacapo</th>\n",
       "      <th>v14a</th>\n",
       "      <th>refrig</th>\n",
       "      <th>v18q</th>\n",
       "      <th>v18q1</th>\n",
       "      <th>r4h1</th>\n",
       "      <th>...</th>\n",
       "      <th>SQBescolari</th>\n",
       "      <th>SQBage</th>\n",
       "      <th>SQBhogar_total</th>\n",
       "      <th>SQBedjefe</th>\n",
       "      <th>SQBhogar_nin</th>\n",
       "      <th>SQBovercrowding</th>\n",
       "      <th>SQBdependency</th>\n",
       "      <th>SQBmeaned</th>\n",
       "      <th>agesq</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2514</th>\n",
       "      <td>ID_f012e4242</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>9</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>182.25</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 143 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id      v2a1  hacdor  rooms  hacapo  v14a  refrig  v18q  \\\n",
       "2514  ID_f012e4242  160000.0       0      6       0     1       1     1   \n",
       "\n",
       "      v18q1  r4h1  ...  SQBescolari  SQBage  SQBhogar_total  SQBedjefe  \\\n",
       "2514    1.0     0  ...            0     100               9        121   \n",
       "\n",
       "      SQBhogar_nin  SQBovercrowding  SQBdependency  SQBmeaned  agesq  Target  \n",
       "2514             1             2.25           0.25     182.25    100       4  \n",
       "\n",
       "[1 rows x 143 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_train[(df_income_train['age'] ==10) & df_income_train['rez_esc'].isnull()].head()\n",
    "df_income_train[(df_income_train['Id'] =='ID_f012e4242')].head()\n",
    "#there is only one member in household for the member with age 10 and who is 'behind in school'. This explains why the member is \n",
    "#behind in school."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2615ae55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rez_esc    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from above we see that  the 'behind in school' column has null values \n",
    "# Lets use the above to fix the data\n",
    "for df in [df_income_train, df_income_test]:\n",
    "    df['rez_esc'].fillna(value=0, inplace=True)\n",
    "df_income_train[['rez_esc']].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "099e23e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edjefe</th>\n",
       "      <th>edjefa</th>\n",
       "      <th>instlevel1</th>\n",
       "      <th>instlevel2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       edjefe  edjefa  instlevel1  instlevel2\n",
       "count     0.0     0.0         0.0         0.0\n",
       "mean      NaN     NaN         NaN         NaN\n",
       "std       NaN     NaN         NaN         NaN\n",
       "min       NaN     NaN         NaN         NaN\n",
       "25%       NaN     NaN         NaN         NaN\n",
       "50%       NaN     NaN         NaN         NaN\n",
       "75%       NaN     NaN         NaN         NaN\n",
       "max       NaN     NaN         NaN         NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df_income_train[df_income_train['meaneduc'].isnull()].head()\n",
    "\n",
    "columns=['edjefe','edjefa','instlevel1','instlevel2']\n",
    "data[columns][data[columns]['instlevel1']>0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e91a1881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "meaneduc    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets fix the data\n",
    "for df in [df_income_train, df_income_test]:\n",
    "    df['meaneduc'].fillna(value=0, inplace=True)\n",
    "df_income_train[['meaneduc']].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "03f363e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edjefe</th>\n",
       "      <th>edjefa</th>\n",
       "      <th>instlevel1</th>\n",
       "      <th>instlevel2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       edjefe  edjefa  instlevel1  instlevel2\n",
       "count     0.0     0.0         0.0         0.0\n",
       "mean      NaN     NaN         NaN         NaN\n",
       "std       NaN     NaN         NaN         NaN\n",
       "min       NaN     NaN         NaN         NaN\n",
       "25%       NaN     NaN         NaN         NaN\n",
       "50%       NaN     NaN         NaN         NaN\n",
       "75%       NaN     NaN         NaN         NaN\n",
       "max       NaN     NaN         NaN         NaN"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df_income_train[df_income_train['SQBmeaned'].isnull()].head()\n",
    "\n",
    "columns=['edjefe','edjefa','instlevel1','instlevel2']\n",
    "data[columns][data[columns]['instlevel1']>0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6a737132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SQBmeaned    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets fix the data\n",
    "for df in [df_income_train, df_income_test]:\n",
    "    df['SQBmeaned'].fillna(value=0, inplace=True)\n",
    "df_income_train[['SQBmeaned']].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1cc41831",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets look at the overall data\n",
    "null_counts = df_income_train.isnull().sum()\n",
    "null_counts[null_counts > 0].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8d19d3c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 85 households where the family members do not all have the same target.\n"
     ]
    }
   ],
   "source": [
    "# Groupby the household and figure out the number of unique values\n",
    "all_equal = df_income_train.groupby('idhogar')['Target'].apply(lambda x: x.nunique() == 1)\n",
    "\n",
    "# Households where targets are not all equal\n",
    "not_equal = all_equal[all_equal != True]\n",
    "print('There are {} households where the family members do not all have the same target.'.format(len(not_equal)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "43ccdff4",
   "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>idhogar</th>\n",
       "      <th>parentesco1</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7651</th>\n",
       "      <td>0172ab1d9</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7652</th>\n",
       "      <td>0172ab1d9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7653</th>\n",
       "      <td>0172ab1d9</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7654</th>\n",
       "      <td>0172ab1d9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7655</th>\n",
       "      <td>0172ab1d9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        idhogar  parentesco1  Target\n",
       "7651  0172ab1d9            0       3\n",
       "7652  0172ab1d9            0       2\n",
       "7653  0172ab1d9            0       3\n",
       "7654  0172ab1d9            1       3\n",
       "7655  0172ab1d9            0       2"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets check one household\n",
    "df_income_train[df_income_train['idhogar'] == not_equal.index[0]][['idhogar', 'parentesco1', 'Target']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ef234ad8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 15 households without a head.\n"
     ]
    }
   ],
   "source": [
    "#Lets use Target value of the parent record (head of the household) and update rest. But before that lets check\n",
    "# if all families has a head. \n",
    "\n",
    "households_head = df_income_train.groupby('idhogar')['parentesco1'].sum()\n",
    "\n",
    "# Find households without a head\n",
    "households_no_head = df_income_train.loc[df_income_train['idhogar'].isin(households_head[households_head == 0].index), :]\n",
    "\n",
    "print('There are {} households without a head.'.format(households_no_head['idhogar'].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5b464893",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Households with no head have different Target value.\n"
     ]
    }
   ],
   "source": [
    "# Find households without a head and where Target value are different\n",
    "households_no_head_equal = households_no_head.groupby('idhogar')['Target'].apply(lambda x: x.nunique() == 1)\n",
    "print('{} Households with no head have different Target value.'.format(sum(households_no_head_equal == False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "361f8b49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 0 households where the family members do not all have the same target.\n"
     ]
    }
   ],
   "source": [
    "#Lets fix the data\n",
    "#Set poverty level of the members and the head of the house within a family.\n",
    "# Iterate through each household\n",
    "for household in not_equal.index:\n",
    "    # Find the correct label (for the head of household)\n",
    "    true_target = int(df_income_train[(df_income_train['idhogar'] == household) & (df_income_train['parentesco1'] == 1.0)]['Target'])\n",
    "    \n",
    "    # Set the correct label for all members in the household\n",
    "    df_income_train.loc[df_income_train['idhogar'] == household, 'Target'] = true_target\n",
    "    \n",
    "    \n",
    "# Groupby the household and figure out the number of unique values\n",
    "all_equal = df_income_train.groupby('idhogar')['Target'].apply(lambda x: x.nunique() == 1)\n",
    "\n",
    "# Households where targets are not all equal\n",
    "not_equal = all_equal[all_equal != True]\n",
    "print('There are {} households where the family members do not all have the same target.'.format(len(not_equal)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e93487bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     222\n",
       "2     442\n",
       "3     355\n",
       "4    1954\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets look at the dataset and plot head of household and Target\n",
    "# 1 = extreme poverty 2 = moderate poverty 3 = vulnerable households 4 = non vulnerable households \n",
    "target_counts = heads['Target'].value_counts().sort_index()\n",
    "target_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8321c18e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Target vs Total_Count'}>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cAIDByGZZlhHXn7lUjv6K+TMXc9d7XK44SdKkjLIgj+TmOLT1CUnS4cPH+2R/vX6pHAAABAfhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDdCrcPp9PM2bM0NmzZyVJeXl5Sk5O1syZMzVz5kz98Y9/lCQdO3ZM6enpSklJ0bJly3T16lVJUn19vbKyspSamqqFCxeqqanpJh0OAAADW4fhPnLkiGbPnq26urrAutraWm3evFlVVVWqqqpSUlKSJCk3N1fLly/X7t27ZVmWKioqJElFRUXKzMyU2+3W+PHjVVZWdnOOBgCAAa7DcFdUVKiwsFBOp1OSdOXKFdXX1ys/P19paWlav369/H6/zp07p+bmZrlcLklSenq63G63WltbdfDgQaWkpLRbDwAAus7e0QbPPPNMu+WGhgZNnjxZhYWFioiI0IIFC7R9+3aNHTtWDocjsJ3D4ZDH49GlS5cUHh4uu93ebj0AAOi6DsP9WTExMdqwYUNgec6cOaqsrFRsbKxsNltgvWVZstlsgT8/7bPLnTFyZHiX72MShyMi2ENADzB/5mLu0BX94fnS5XCfOHFCdXV1gUvflmXJbrcrKipKXq83sF1DQ4OcTqciIyPV2NiotrY2DRkyRF6vN3DZvSsuXvTJ77e6fD8TOBwR8nobgz0MdBPzZy7mDl3VV8+XkBDbDU9Yu/xxMMuytGrVKl2+fFmtra0qLy9XUlKSoqOjFRYWppqaGklSVVWVEhISNHToUMXHx6u6ulqSVFlZqYSEhB4cDgAAg1eXz7jj4uL02GOPafbs2bp69aqSk5M1Y8YMSVJxcbEKCgrk8/k0btw4zZ07V5JUWFiopUuX6oUXXtCoUaNUUlLSu0cBAMAgYbMsy4jrz1wqR3/F/JmLues9LlecJGlSxsD8uO+hrU9Ikg4fPt4n++vVS+UAACB4CDcAAAYh3AAAGIRwAwBgEMINAIBBCDcAAAYh3AAAGIRwAwBgEMINAIBBCDcAAAYh3AAAGIRwAwBgEMINAIBBCDcAAAYh3AAAGIRwAwBgEMINAIBBCDcAAAYh3AAAGIRwAwBgEMINAIBBCDcAAAYh3AAAGIRwAwBgEMINAIBBCDcAAAYh3AAAGIRwAwBgEMINAIBBCDcAAAYh3AAAGKRT4fb5fJoxY4bOnj0rSSovL9eMGTOUlpamvLw8ffzxx5Kk0tJSTZ06VTNnztTMmTO1ZcsWSVJ9fb2ysrKUmpqqhQsXqqmp6SYdDgAAA1uH4T5y5Ihmz56turo6SdKpU6f08ssva+vWrXr99dfl9/v16quvSpJqa2tVUlKiqqoqVVVVKSsrS5JUVFSkzMxMud1ujR8/XmVlZTfviAAAGMA6DHdFRYUKCwvldDolSaGhoSosLFR4eLhsNpu+/vWvq76+XtK1cL/00ktKS0vTihUr1NLSotbWVh08eFApKSmSpPT0dLnd7pt4SAAADFz2jjZ45pln2i1HR0crOjpakvThhx9qy5YtWr16tZqamvTNb35Tubm5Gj16tJYuXaqysjJlZWUpPDxcdvu1XTkcDnk8ni4PdOTI8C7fxyQOR0Swh4AeYP7MxdyhK/rD86XDcN+Ix+PRj370Iz388MO65557JEkbN24M3D5//nzl5+crMzNTNput3X0/u9wZFy/65Pdb3R1uv+ZwRMjrbQz2MNBNzJ+5mDt0VV89X0JCbDc8Ye3Wu8pPnjypjIwMPfTQQ8rOzpZ07Q1o27dvD2xjWZbsdrsiIyPV2NiotrY2SZLX6w1cdgcAAF3T5XD7fD49+uij+tnPfqb58+cH1g8bNky/+tWvdObMGVmWpS1btigpKUlDhw5VfHy8qqurJUmVlZVKSEjovSMAAGAQ6XK4t2/froaGBv33f/934GNfzz33nCIjI7VixQotXLhQqampsixL8+bNkyQVFhaqoqJC999/v9599109+eSTvX0cAAAMCjbLsox44ZjXuNFfMX/mYu56j8sVJ0malDEwP+57aOsTkqTDh4/3yf56/TVuAAAQHIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDdCrcPp9PM2bM0NmzZyVJe/fuVVpampKTk/Xss88Gtjt27JjS09OVkpKiZcuW6erVq5Kk+vp6ZWVlKTU1VQsXLlRTU9NNOBQAAAa+DsN95MgRzZ49W3V1dZKk5uZm5efnq6ysTNXV1aqtrdXbb78tScrNzdXy5cu1e/duWZaliooKSVJRUZEyMzPldrs1fvx4lZWV3bwjAgBgAOsw3BUVFSosLJTT6ZQkHT16VKNHj1ZMTIzsdrvS0tLkdrt17tw5NTc3y+VySZLS09PldrvV2tqqgwcPKiUlpd16AADQdfaONnjmmWfaLV+4cEEOhyOw7HQ65fF4rlvvcDjk8Xh06dIlhYeHy263t1sPAAC6rsNwf5bf75fNZgssW5Ylm812w/Wf/Plpn13ujJEjw7t8H5M4HBHBHgJ6gPkzF3OHrugPz5cuhzsqKkperzew7PV65XQ6r1vf0NAgp9OpyMhINTY2qq2tTUOGDAls31UXL/rk91tdvp8JHI4Ieb2NwR4Guon5Mxdzh67qq+dLSIjthiesXf442IQJE3Tq1CmdPn1abW1t2rVrlxISEhQdHa2wsDDV1NRIkqqqqpSQkKChQ4cqPj5e1dXVkqTKykolJCT04HAAABi8unzGHRYWpjVr1ugnP/mJWlpalJiYqNTUVElScXGxCgoK5PP5NG7cOM2dO1eSVFhYqKVLl+qFF17QqFGjVFJS0rtHAQDAIGGzLMuI689cKkd/xfyZi7nrPS5XnCRpUsbA/Ljvoa1PSJIOHz7eJ/vr1UvlAAAgeAg3AAAGIdwAABiEcAMAYBDCDQCAQQg3AAAGIdwAABiEcAMAYBDCDQCAQQg3AAAGIdwAABiEcAMAYBDCDQCAQQg3AAAGIdwAABiEcAMAYBDCDQCAQQg3AAAGIdwAABiEcAMAYBDCDQCAQQg3AAAGIdwAABiEcAMAYBDCDQCAQQg3AAAGIdwAABiEcAMAYBDCDQCAQQg3AAAGIdwAABjE3t07btu2TZs3bw4snz17VjNnztSVK1dUU1OjW265RZKUk5OjpKQkHTt2TMuWLVNTU5Pi4+NVVFQku73buwcAYFDqdjlnzZqlWbNmSZLee+89ZWdnKycnRz/4wQ+0efNmOZ3Odtvn5uZq5cqVcrlcys/PV0VFhTIzM3s2egAABpleuVT+1FNPadGiRbrllltUX1+v/Px8paWlaf369fL7/Tp37pyam5vlcrkkSenp6XK73b2xawAABpUeh3vv3r1qbm7WtGnT1NDQoMmTJ2vVqlWqqKjQu+++q+3bt+vChQtyOByB+zgcDnk8np7uGgCAQafHLzJv3bpV8+bNkyTFxMRow4YNgdvmzJmjyspKxcbGymazBdZbltVuuTNGjgzv6VD7NYcjIthDQA8wf+Zi7tAV/eH50qNwf/zxxzp48KDWrFkjSTpx4oTq6uqUkpIi6Vqg7Xa7oqKi5PV6A/draGi47jXwjly86JPfb/VkuP2WwxEhr7cx2MNANzF/5mLu0FV99XwJCbHd8IS1R5fKT5w4oTFjxujWW2+VdC3Uq1at0uXLl9Xa2qry8nIlJSUpOjpaYWFhqqmpkSRVVVUpISGhJ7sGAGBQ6tEZ95kzZxQVFRVYjouL02OPPabZs2fr6tWrSk5O1owZMyRJxcXFKigokM/n07hx4zR37tyejRwAgEHIZlmWEdefuVSO/or5Mxdz13tcrjhJ0qSMsiCP5OY4tPUJSdLhw8f7ZH837VI5AADoW4QbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG4AAAxi78md58yZow8//FB2+7WHWbFihZqamrR69Wq1tLRo2rRpWrRokSTp2LFjWrZsmZqamhQfH6+ioqLA/QAAQOd0u5yWZamurk5/+ctfAgFubm5WamqqNm3apFGjRmnBggV6++23lZiYqNzcXK1cuVIul0v5+fmqqKhQZmZmrx0IAACDQbcvlb///vuSpPnz5+uBBx7Q5s2bdfToUY0ePVoxMTGy2+1KS0uT2+3WuXPn1NzcLJfLJUlKT0+X2+3ulQMAAGAw6Xa4P/roI02ZMkUbNmzQb37zG23dulX19fVyOByBbZxOpzwejy5cuNBuvcPhkMfj6dnIAQAYhLp9qXzixImaOHFiYPmRRx7R+vXr9a1vfSuwzrIs2Ww2+f1+2Wy269Z3xciR4d0dqhEcjohgDwE9wPyZi7lDV/SH50u3w/3uu++qtbVVU6ZMkXQtxtHR0fJ6vYFtvF6vnE6noqKi2q1vaGiQ0+ns0v4uXvTJ77e6O9x+zeGIkNfbGOxhoJuYP3Mxd+iqvnq+hITYbnjC2u1L5Y2NjVq7dq1aWlrk8/m0c+dOLV68WKdOndLp06fV1tamXbt2KSEhQdHR0QoLC1NNTY0kqaqqSgkJCd3dNQAAg1a3z7inTp2qI0eO6MEHH5Tf71dmZqYmTpyoNWvW6Cc/+YlaWlqUmJio1NRUSVJxcbEKCgrk8/k0btw4zZ07t9cOAgCAwcJmWZYR15+5VI7+ivkzF3PXe1yuOEnSpIyyII/k5ji09QlJ0uHDx/tkfzflUjkAAOh7hBsAAIMQbgAADMKXhWNA+eR1toGur15nA9D/cMYNAIBBOOPGgDTQ39kKYPDijBsAAIMQbgAADEK4AQAwCOEGAMAghBsAAIMQbgAADEK4AQAwCOEGAMAghBsAAIMQbgAADEK4AQAwCN9VDqBf4JfdgM7hjBsAAINwxg2gX+GX3YAvxhk3AAAGIdwAABiEcAMAYBDCDQCAQQg3AAAGIdwAABiEcAMAYBDCDQCAQQg3AAAGIdwAABiEcAMAYJAefVd5aWmp3nzzTUlSYmKilixZory8PNXU1OiWW26RJOXk5CgpKUnHjh3TsmXL1NTUpPj4eBUVFclu56vSAQDoim6Xc+/evdqzZ4927twpm82mH/3oR/rjH/+o2tpabd68WU6ns932ubm5WrlypVwul/Lz81VRUaHMzMweHwAAAINJty+VOxwOLV26VKGhoRo6dKhiY2NVX1+v+vp65efnKy0tTevXr5ff79e5c+fU3Nwsl8slSUpPT5fb7e6tYwAAYNDo9hn32LFjA3+vq6vTm2++qS1btujAgQMqLCxURESEFixYoO3bt2vs2LFyOByB7R0OhzweT5f2N3JkeHeHagSHIyLYQ4BBeL6Yi7kzW3+Yvx6/yPzee+9pwYIFWrJkib72ta9pw4YNgdvmzJmjyspKxcbGymazBdZbltVuuTMuXvTJ77d6Otx+yeGIkNfbGOxhwCA8X8zF3Jmtr+YvJMR2wxPWHr2rvKamRj/84Q/185//XA899JBOnDih3bt3B263LEt2u11RUVHyer2B9Q0NDde9Bg4AADrW7XCfP39e2dnZKi4u1vTp0yVdC/WqVat0+fJltba2qry8XElJSYqOjlZYWJhqamokSVVVVUpISOidIwAAYBDp9qXyl19+WS0tLVqzZk1gXUZGhh577DHNnj1bV69eVXJysmbMmCFJKi4uVkFBgXw+n8aNG6e5c+f2fPQ3gcsVF+wh9InDh48HewgAgG7odrgLCgpUUFDwubdlZWVdty4uLk7bt2/v7u4AAIB64c1pA9WkjLJgD+GmOLT1iWAPAQDQA3zlKQAABiHcAAAYhHADAGAQwg0AgEEINwAABiHcAAAYhHADAGAQwg0AgEEINwAABiHcAAAYhHADAGAQwg0AgEEINwAABiHcAAAYhHADAGAQwg0AgEEINwAABiHcAAAYhHADAGAQwg0AgEEINwAABiHcAAAYhHADAGAQwg0AgEEINwAABiHcAAAYhHADAGAQwg0AgEEINwAABunTcL/xxhu6//77lZycrC1btvTlrgEAGBDsfbUjj8ejZ599Vq+99ppCQ0OVkZGhe+65R3fddVdfDQEAAOP12Rn33r17NXnyZN1222269dZblZKSIrfb3Ve7BwBgQOizM+4LFy7I4XAElp1Op44ePdrp+4eE2G7GsK5z5513SpIu7FnVJ/vra58cX1/9e/Y15s9czJ3ZmL/e9UX76bNw+/1+2Wz/fyCWZbVb7sjttw+/GcO6zt///vc+2Q9uDubPXMyd2Zi/vtNnl8qjoqLk9XoDy16vV06ns692DwDAgNBn4b733nu1b98+ffjhh7py5Yr+8Ic/KCEhoa92DwDAgNBnl8rvuOMOLVq0SHPnzlVra6seeeQR3X333X21ewAABgSbZVlWsAcBAAA6h29OAwDAIIQbAACDEG4AAAxCuAEAMAjhBgDAIIQbAACDEG6gC/70pz9p06ZN+uCDD9qtLy8vD9KI0BV1dXXyeDySpG3btmnlypWqrq4O8qjQHWvWrAn2EIKmz76ABTBdcXGxamtrFRsbqxdffFFLlizRzJkzJUlbt27V97///SCPEF/kN7/5jTZt2iS/36/Jkyfr/PnzSkpK0o4dO3Tq1CllZ2cHe4i4gby8vOvWvfXWW7p8+bIkafXq1X09pKAi3H2svr7+C2//8pe/3EcjQVe9/fbb2rlzp+x2u+bMmaP58+crNDRU06ZNE99j1P/t2LFD1dXVamho0IwZM7R//36FhYVp1qxZeuSRRwh3P3bbbbepsrJSjz/+uEaMGCFJ2r9/v77zne8EeWTBQbj72IIFC1RXVyen03ndf+xtNpv+/Oc/B2lk6Minf9FuzJgxeumllzRv3jxFRkZ26ZfuEBx+v1+hoaGKjo7W/PnzFRYWFritra0tiCNDR/7rv/5LCQkJWrdunRYvXqx77rlHv/3tb/XQQw8Fe2hBwVee9jGfz6fMzEwVFhbqW9/6VrCHgy4oLS3V3r17tXTp0sD37NfU1CgnJ0cff/yxampqgjxCfJHnnntOBw4c0O9+9zsNGTJEknT8+HEVFBToe9/7nnJycoI8QnTkn//8pwoLC/XlL39Ze/bs0RtvvBHsIQUF4Q6Co0ePatu2bXr66aeDPRR00b59++R0OhUbGxtYd/78eb3yyitatmxZEEeGzjh48KC+/e1vB5bff/99nTlzRomJiUEcFbpq27ZtevPNN/XKK68EeyhBQbgBADAIHwcDAMAghBsAAIMQbgAADEK4AQAwCOEGAMAg/w9u02/Sxa2qdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_counts.plot.bar(figsize = (8, 6),linewidth = 2,edgecolor = 'k',title=\"Target vs Total_Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1dacf6d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extreme poverty is the smallest count in the train dataset. The dataset is biased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6b40a701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9557, 143)\n",
      "(9557, 134)\n"
     ]
    }
   ],
   "source": [
    "#Lets remove them\n",
    "print(df_income_train.shape)\n",
    "cols=['SQBescolari', 'SQBage', 'SQBhogar_total', 'SQBedjefe', \n",
    "        'SQBhogar_nin', 'SQBovercrowding', 'SQBdependency', 'SQBmeaned', 'agesq']\n",
    "\n",
    "\n",
    "for df in [df_income_train, df_income_test]:\n",
    "    df.drop(columns = cols,inplace=True)\n",
    "\n",
    "print(df_income_train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c8a3f984",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_ = ['Id', 'idhogar', 'Target']\n",
    "\n",
    "ind_bool = ['v18q', 'dis', 'male', 'female', 'estadocivil1', 'estadocivil2', 'estadocivil3', \n",
    "            'estadocivil4', 'estadocivil5', 'estadocivil6', 'estadocivil7', \n",
    "            'parentesco1', 'parentesco2',  'parentesco3', 'parentesco4', 'parentesco5', \n",
    "            'parentesco6', 'parentesco7', 'parentesco8',  'parentesco9', 'parentesco10', \n",
    "            'parentesco11', 'parentesco12', 'instlevel1', 'instlevel2', 'instlevel3', \n",
    "            'instlevel4', 'instlevel5', 'instlevel6', 'instlevel7', 'instlevel8', \n",
    "            'instlevel9', 'mobilephone']\n",
    "\n",
    "ind_ordered = ['rez_esc', 'escolari', 'age']\n",
    "\n",
    "hh_bool = ['hacdor', 'hacapo', 'v14a', 'refrig', 'paredblolad', 'paredzocalo', \n",
    "           'paredpreb','pisocemento', 'pareddes', 'paredmad',\n",
    "           'paredzinc', 'paredfibras', 'paredother', 'pisomoscer', 'pisoother', \n",
    "           'pisonatur', 'pisonotiene', 'pisomadera',\n",
    "           'techozinc', 'techoentrepiso', 'techocane', 'techootro', 'cielorazo', \n",
    "           'abastaguadentro', 'abastaguafuera', 'abastaguano',\n",
    "            'public', 'planpri', 'noelec', 'coopele', 'sanitario1', \n",
    "           'sanitario2', 'sanitario3', 'sanitario5',   'sanitario6',\n",
    "           'energcocinar1', 'energcocinar2', 'energcocinar3', 'energcocinar4', \n",
    "           'elimbasu1', 'elimbasu2', 'elimbasu3', 'elimbasu4', \n",
    "           'elimbasu5', 'elimbasu6', 'epared1', 'epared2', 'epared3',\n",
    "           'etecho1', 'etecho2', 'etecho3', 'eviv1', 'eviv2', 'eviv3', \n",
    "           'tipovivi1', 'tipovivi2', 'tipovivi3', 'tipovivi4', 'tipovivi5', \n",
    "           'computer', 'television', 'lugar1', 'lugar2', 'lugar3',\n",
    "           'lugar4', 'lugar5', 'lugar6', 'area1', 'area2']\n",
    "\n",
    "hh_ordered = [ 'rooms', 'r4h1', 'r4h2', 'r4h3', 'r4m1','r4m2','r4m3', 'r4t1',  'r4t2', \n",
    "              'r4t3', 'v18q1', 'tamhog','tamviv','hhsize','hogar_nin',\n",
    "              'hogar_adul','hogar_mayor','hogar_total',  'bedrooms', 'qmobilephone']\n",
    "\n",
    "hh_cont = ['v2a1', 'dependency', 'edjefe', 'edjefa', 'meaneduc', 'overcrowding']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e44015f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2973, 98)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for redundant household variables\n",
    "heads = df_income_train.loc[df_income_train['parentesco1'] == 1, :]\n",
    "heads = heads[id_ + hh_bool + hh_cont + hh_ordered]\n",
    "heads.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "83a6d59c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['coopele', 'area2', 'tamhog', 'hhsize', 'hogar_total']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create correlation matrix\n",
    "corr_matrix = heads.corr()\n",
    "\n",
    "# Select upper triangle of correlation matrix\n",
    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "\n",
    "# Find index of feature columns with correlation greater than 0.95\n",
    "to_drop = [column for column in upper.columns if any(abs(upper[column]) > 0.95)]\n",
    "\n",
    "to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b485e07f",
   "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>r4t3</th>\n",
       "      <th>tamhog</th>\n",
       "      <th>tamviv</th>\n",
       "      <th>hhsize</th>\n",
       "      <th>hogar_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>r4t3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996884</td>\n",
       "      <td>0.929237</td>\n",
       "      <td>0.996884</td>\n",
       "      <td>0.996884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tamhog</th>\n",
       "      <td>0.996884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.926667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tamviv</th>\n",
       "      <td>0.929237</td>\n",
       "      <td>0.926667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.926667</td>\n",
       "      <td>0.926667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hhsize</th>\n",
       "      <td>0.996884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.926667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hogar_total</th>\n",
       "      <td>0.996884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.926667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 r4t3    tamhog    tamviv    hhsize  hogar_total\n",
       "r4t3         1.000000  0.996884  0.929237  0.996884     0.996884\n",
       "tamhog       0.996884  1.000000  0.926667  1.000000     1.000000\n",
       "tamviv       0.929237  0.926667  1.000000  0.926667     0.926667\n",
       "hhsize       0.996884  1.000000  0.926667  1.000000     1.000000\n",
       "hogar_total  0.996884  1.000000  0.926667  1.000000     1.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix.loc[corr_matrix['tamhog'].abs() > 0.9, corr_matrix['tamhog'].abs() > 0.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "02d4b1d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_matrix.loc[corr_matrix['tamhog'].abs() > 0.9, corr_matrix['tamhog'].abs() > 0.9],\n",
    "            annot=True, cmap = plt.cm.Accent_r, fmt='.3f');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "28f4c67d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9557, 131)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=['tamhog', 'hogar_total', 'r4t3']\n",
    "for df in [df_income_train, df_income_test]:\n",
    "    df.drop(columns = cols,inplace=True)\n",
    "\n",
    "df_income_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b93af1cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9557, 39)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for redundant Individual variables\n",
    "ind = df_income_train[id_ + ind_bool + ind_ordered]\n",
    "ind.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "77db0309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['female']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create correlation matrix\n",
    "corr_matrix = ind.corr()\n",
    "\n",
    "# Select upper triangle of correlation matrix\n",
    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "\n",
    "# Find index of feature columns with correlation greater than 0.95\n",
    "to_drop = [column for column in upper.columns if any(abs(upper[column]) > 0.95)]\n",
    "\n",
    "to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bfa8cbaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9557, 130)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is simply the opposite of male! We can remove the male flag.\n",
    "for df in [df_income_train, df_income_test]:\n",
    "    df.drop(columns = 'male',inplace=True)\n",
    "\n",
    "df_income_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "252bdf67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9557, 129)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets check area1 and area2 also\n",
    "# area1, =1 zona urbana \n",
    "# area2, =2 zona rural \n",
    "#area2 redundant because we have a column indicating if the house is in a urban zone\n",
    "\n",
    "for df in [df_income_train, df_income_test]:\n",
    "    df.drop(columns = 'area2',inplace=True)\n",
    "\n",
    "df_income_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "dbf094e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9557, 127)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finally lets delete 'Id', 'idhogar'\n",
    "cols=['Id','idhogar']\n",
    "for df in [df_income_train, df_income_test]:\n",
    "    df.drop(columns = cols,inplace=True)\n",
    "\n",
    "df_income_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "45e9dd6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9557, 126)\n",
      "(9557,)\n"
     ]
    }
   ],
   "source": [
    "x_features=df_income_train.iloc[:,0:-1]\n",
    "y_features=df_income_train.iloc[:,-1]\n",
    "print(x_features.shape)\n",
    "print(y_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "eb2da890",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,f1_score,classification_report\n",
    "\n",
    "x_train,x_test,y_train,y_test=train_test_split(x_features,y_features,test_size=0.2,random_state=1)\n",
    "rmclassifier = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b659d8b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmclassifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "876d3bf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=10)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
    "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                       min_samples_leaf=1, min_samples_split=2,\n",
    "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
    "                       n_jobs=None, oob_score=False, random_state=None,\n",
    "                       verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ed523cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = rmclassifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "823f4984",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9461297071129707\n",
      "[[ 134    1    0   22]\n",
      " [   1  285    0   31]\n",
      " [   0    2  188   43]\n",
      " [   0    2    1 1202]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.99      0.85      0.92       157\n",
      "           2       0.98      0.90      0.94       317\n",
      "           3       0.99      0.81      0.89       233\n",
      "           4       0.93      1.00      0.96      1205\n",
      "\n",
      "    accuracy                           0.95      1912\n",
      "   macro avg       0.97      0.89      0.93      1912\n",
      "weighted avg       0.95      0.95      0.94      1912\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,y_predict))\n",
    "print(confusion_matrix(y_test,y_predict))\n",
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "68fd5157",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_testdata = rmclassifier.predict(df_income_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "790c47af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, ..., 4, 4, 4], dtype=int64)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_testdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54204cc1",
   "metadata": {},
   "source": [
    "# check for the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "80ad7953",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold,cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99dc248f",
   "metadata": {},
   "source": [
    "# checking the score using default 10 trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e7e65041",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.94246862 0.94979079 0.94557823 0.94243851 0.94976452]\n",
      "94.60081361157272\n"
     ]
    }
   ],
   "source": [
    "seed=7\n",
    "kfold=KFold(n_splits=5,random_state=seed,shuffle=True)\n",
    "\n",
    "rmclassifier=RandomForestClassifier(random_state=10,n_jobs = -1)\n",
    "print(cross_val_score(rmclassifier,x_features,y_features,cv=kfold,scoring='accuracy'))\n",
    "results=cross_val_score(rmclassifier,x_features,y_features,cv=kfold,scoring='accuracy')\n",
    "print(results.mean()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "368f8180",
   "metadata": {},
   "source": [
    "## checking the score using 100 trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "26753599",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.94246862 0.94979079 0.94557823 0.94243851 0.94976452]\n",
      "94.60081361157272\n"
     ]
    }
   ],
   "source": [
    "num_trees= 100\n",
    "\n",
    "rmclassifier=RandomForestClassifier(n_estimators=100, random_state=10,n_jobs = -1)\n",
    "print(cross_val_score(rmclassifier,x_features,y_features,cv=kfold,scoring='accuracy'))\n",
    "results=cross_val_score(rmclassifier,x_features,y_features,cv=kfold,scoring='accuracy')\n",
    "print(results.mean()*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "db3b5704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, ..., 4, 4, 4], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmclassifier.fit(x_features,y_features)\n",
    "y_predict_testdata = rmclassifier.predict(df_income_test)\n",
    "y_predict_testdata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731e93fa",
   "metadata": {},
   "source": [
    "Looking at the accuracy score, RandomForestClassifier with cross validation has the highest accuracy score of 94.60%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7176e64",
   "metadata": {},
   "source": [
    "lets visualize how our model uses the different features and which features have greater effect.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f540b8cb",
   "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>v2a1</td>\n",
       "      <td>0.018653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rooms</td>\n",
       "      <td>0.025719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>r4h2</td>\n",
       "      <td>0.020706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>r4h3</td>\n",
       "      <td>0.019808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>r4m1</td>\n",
       "      <td>0.015271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature  importance\n",
       "0     v2a1    0.018653\n",
       "2    rooms    0.025719\n",
       "9     r4h2    0.020706\n",
       "10    r4h3    0.019808\n",
       "11    r4m1    0.015271"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = list(x_features)\n",
    "feature_importances = pd.DataFrame({'feature': labels, 'importance': rmclassifier.feature_importances_})\n",
    "feature_importances=feature_importances[feature_importances.importance>0.015]\n",
    "feature_importances.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bb27003e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Importance')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances.sort_values(by=['importance'], ascending=True, inplace=True)\n",
    "feature_importances['positive'] = feature_importances['importance'] > 0\n",
    "feature_importances.set_index('feature',inplace=True)\n",
    "feature_importances.head()\n",
    "\n",
    "feature_importances.importance.plot(kind='barh', figsize=(11, 6),color = feature_importances.positive.map({True: 'blue', False: 'red'}))\n",
    "plt.xlabel('Importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "009db85b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}