final_calcs (4).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80b766d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports all of the libraries we will be needing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60e1e63f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtains the data from the csv file\n",
    "data = pd.read_csv('survey_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7896b761",
   "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",
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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>CustomerID</th>\n",
       "      <th>Region</th>\n",
       "      <th>TownSize</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EducationYears</th>\n",
       "      <th>JobCategory</th>\n",
       "      <th>UnionMember</th>\n",
       "      <th>EmploymentLength</th>\n",
       "      <th>Retired</th>\n",
       "      <th>...</th>\n",
       "      <th>CallWait</th>\n",
       "      <th>CallForward</th>\n",
       "      <th>ThreeWayCalling</th>\n",
       "      <th>EBilling</th>\n",
       "      <th>TVWatchingHours</th>\n",
       "      <th>OwnsPC</th>\n",
       "      <th>OwnsMobileDevice</th>\n",
       "      <th>OwnsGameSystem</th>\n",
       "      <th>OwnsFax</th>\n",
       "      <th>NewsSubscriber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3964-QJWTRG-NPN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0648-AIPJSP-UVM</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5195-TLUDJE-HVO</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4459-VLPQUH-3OL</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8158-SMTQFB-CNO</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CustomerID  Region TownSize  Gender  Age  EducationYears  JobCategory  \\\n",
       "0  3964-QJWTRG-NPN       1        2       1   20              15            1   \n",
       "1  0648-AIPJSP-UVM       5        5       0   22              17            2   \n",
       "2  5195-TLUDJE-HVO       3        4       1   67              14            2   \n",
       "3  4459-VLPQUH-3OL       4        3       0   23              16            2   \n",
       "4  8158-SMTQFB-CNO       2        2       0   26              16            2   \n",
       "\n",
       "   UnionMember  EmploymentLength  Retired  ...  CallWait  CallForward  \\\n",
       "0            1                 0        0  ...         1            1   \n",
       "1            0                 0        0  ...         0            1   \n",
       "2            0                16        0  ...         0            0   \n",
       "3            0                 0        0  ...         0            0   \n",
       "4            0                 1        0  ...         1            1   \n",
       "\n",
       "   ThreeWayCalling  EBilling  TVWatchingHours  OwnsPC  OwnsMobileDevice  \\\n",
       "0                1         0               13       0                 1   \n",
       "1                0         1               18       1                 1   \n",
       "2                0         0               21       0                 0   \n",
       "3                0         1               26       1                 1   \n",
       "4                1         0               27       1                 0   \n",
       "\n",
       "   OwnsGameSystem  OwnsFax  NewsSubscriber  \n",
       "0               1        0               0  \n",
       "1               1        1               1  \n",
       "2               0        0               1  \n",
       "3               1        0               1  \n",
       "4               1        0               0  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks at the first 5 entries of the data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "40bc8db4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 60 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   CustomerID           5000 non-null   object \n",
      " 1   Region               5000 non-null   int64  \n",
      " 2   TownSize             5000 non-null   object \n",
      " 3   Gender               5000 non-null   int64  \n",
      " 4   Age                  5000 non-null   int64  \n",
      " 5   EducationYears       5000 non-null   int64  \n",
      " 6   JobCategory          5000 non-null   int64  \n",
      " 7   UnionMember          5000 non-null   int64  \n",
      " 8   EmploymentLength     5000 non-null   int64  \n",
      " 9   Retired              5000 non-null   int64  \n",
      " 10  HouseholdIncome      5000 non-null   int64  \n",
      " 11  DebtToIncomeRatio    5000 non-null   float64\n",
      " 12  CreditDebt           5000 non-null   float64\n",
      " 13  OtherDebt            5000 non-null   float64\n",
      " 14  LoanDefault          5000 non-null   int64  \n",
      " 15  MaritalStatus        5000 non-null   int64  \n",
      " 16  HouseholdSize        5000 non-null   int64  \n",
      " 17  NumberPets           5000 non-null   int64  \n",
      " 18  NumberCats           5000 non-null   int64  \n",
      " 19  NumberDogs           5000 non-null   int64  \n",
      " 20  NumberBirds          5000 non-null   int64  \n",
      " 21  HomeOwner            5000 non-null   int64  \n",
      " 22  CarsOwned            5000 non-null   int64  \n",
      " 23  CarOwnership         5000 non-null   int64  \n",
      " 24  CarBrand             5000 non-null   int64  \n",
      " 25  CarValue             5000 non-null   float64\n",
      " 26  CommuteTime          5000 non-null   int64  \n",
      " 27  Telecommute          0 non-null      float64\n",
      " 28  PoliticalPartyMem    5000 non-null   int64  \n",
      " 29  Votes                5000 non-null   int64  \n",
      " 30  CreditCard           5000 non-null   int64  \n",
      " 31  CardTenure           5000 non-null   int64  \n",
      " 32  CardItemsMonthly     5000 non-null   int64  \n",
      " 33  CardSpendMonthly     5000 non-null   float64\n",
      " 34  ActiveLifestyle      5000 non-null   int64  \n",
      " 35  PhoneCoTenure        5000 non-null   int64  \n",
      " 36  VoiceLastMonth       5000 non-null   float64\n",
      " 37  VoiceOverTenure      5000 non-null   object \n",
      " 38  EquipmentRental      5000 non-null   int64  \n",
      " 39  EquipmentLastMonth   5000 non-null   float64\n",
      " 40  EquipmentOverTenure  5000 non-null   float64\n",
      " 41  CallingCard          5000 non-null   int64  \n",
      " 42  WirelessData         5000 non-null   int64  \n",
      " 43  DataLastMonth        5000 non-null   float64\n",
      " 44  DataOverTenure       5000 non-null   float64\n",
      " 45  Multiline            5000 non-null   int64  \n",
      " 46  VM                   5000 non-null   int64  \n",
      " 47  Pager                5000 non-null   int64  \n",
      " 48  Internet             5000 non-null   int64  \n",
      " 49  CallerID             5000 non-null   int64  \n",
      " 50  CallWait             5000 non-null   int64  \n",
      " 51  CallForward          5000 non-null   int64  \n",
      " 52  ThreeWayCalling      5000 non-null   int64  \n",
      " 53  EBilling             5000 non-null   int64  \n",
      " 54  TVWatchingHours      5000 non-null   int64  \n",
      " 55  OwnsPC               5000 non-null   int64  \n",
      " 56  OwnsMobileDevice     5000 non-null   int64  \n",
      " 57  OwnsGameSystem       5000 non-null   int64  \n",
      " 58  OwnsFax              5000 non-null   int64  \n",
      " 59  NewsSubscriber       5000 non-null   int64  \n",
      "dtypes: float64(11), int64(46), object(3)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n",
    "# there are 5000 entries, no null values, 60 columns, and the following data types float64(11), int64(46), object(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8feb6671",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EducationYears</th>\n",
       "      <th>HouseholdIncome</th>\n",
       "      <th>DebtToIncomeRatio</th>\n",
       "      <th>EmploymentLength</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "      <td>9.9</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>10</td>\n",
       "      <td>196</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>17</td>\n",
       "      <td>108</td>\n",
       "      <td>10.8</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>19</td>\n",
       "      <td>189</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "      <td>77</td>\n",
       "      <td>6.2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender  Age  EducationYears  HouseholdIncome  DebtToIncomeRatio  \\\n",
       "0          1   20              15               31               11.1   \n",
       "1          0   22              17               15               18.6   \n",
       "2          1   67              14               35                9.9   \n",
       "3          0   23              16               20                5.7   \n",
       "4          0   26              16               23                1.7   \n",
       "...      ...  ...             ...              ...                ...   \n",
       "4995       0   68              10              196                8.0   \n",
       "4996       0   51              14               83               15.0   \n",
       "4997       0   75              17              108               10.8   \n",
       "4998       0   47              19              189               10.8   \n",
       "4999       1   41              10               77                6.2   \n",
       "\n",
       "      EmploymentLength  \n",
       "0                    0  \n",
       "1                    0  \n",
       "2                   16  \n",
       "3                    0  \n",
       "4                    1  \n",
       "...                ...  \n",
       "4995                24  \n",
       "4996                 6  \n",
       "4997                24  \n",
       "4998                 3  \n",
       "4999                20  \n",
       "\n",
       "[5000 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creates a dataframe for the data we are selecting\n",
    "# Quasi-identifiers: 'Gender', 'Age', 'EducationYears', 'HouseholdIncome', 'DebtToIncomeRatio', 'EmploymentLength'\n",
    "df = pd.DataFrame(data[['Gender', 'Age', 'EducationYears', 'HouseholdIncome', 'DebtToIncomeRatio', 'EmploymentLength']])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b652fcc",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EducationYears</th>\n",
       "      <th>HouseholdIncome</th>\n",
       "      <th>DebtToIncomeRatio</th>\n",
       "      <th>EmploymentLength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.503600</td>\n",
       "      <td>47.025600</td>\n",
       "      <td>14.543000</td>\n",
       "      <td>54.759600</td>\n",
       "      <td>9.954160</td>\n",
       "      <td>9.730400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500037</td>\n",
       "      <td>17.770338</td>\n",
       "      <td>3.281083</td>\n",
       "      <td>55.377511</td>\n",
       "      <td>6.399783</td>\n",
       "      <td>9.690929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1073.000000</td>\n",
       "      <td>43.100000</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Gender          Age  EducationYears  HouseholdIncome  \\\n",
       "count  5000.000000  5000.000000     5000.000000      5000.000000   \n",
       "mean      0.503600    47.025600       14.543000        54.759600   \n",
       "std       0.500037    17.770338        3.281083        55.377511   \n",
       "min       0.000000    18.000000        6.000000         9.000000   \n",
       "25%       0.000000    31.000000       12.000000        24.000000   \n",
       "50%       1.000000    47.000000       14.000000        38.000000   \n",
       "75%       1.000000    62.000000       17.000000        67.000000   \n",
       "max       1.000000    79.000000       23.000000      1073.000000   \n",
       "\n",
       "       DebtToIncomeRatio  EmploymentLength  \n",
       "count        5000.000000       5000.000000  \n",
       "mean            9.954160          9.730400  \n",
       "std             6.399783          9.690929  \n",
       "min             0.000000          0.000000  \n",
       "25%             5.100000          2.000000  \n",
       "50%             8.800000          7.000000  \n",
       "75%            13.600000         15.000000  \n",
       "max            43.100000         52.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describes our data\n",
    "# we have 5000 unique customers\n",
    "# the min value of Age is 18, the average age is 47, while the max value is 79\n",
    "# the min value of Education years is 6, the average is 14, while the max value is 23\n",
    "# the min value of Household income is 9, the average is 54, while the max value is 1073\n",
    "# the min value of Debt to income ratio is 0, the average is 10, while the max value is 43\n",
    "# the min value of Employment length is 0, the average age is 10, while the max value is 52\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7dd18204",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminating some outliers\n",
    "\n",
    "# the min value of Household income is 9, the average is 54, while the max value is 1073. \n",
    "# I decided to eliminate values above 1000 as they would be easily identified compared to the average of 47..That's a total of 1 value which is 1073\n",
    "df = df[df['HouseholdIncome'] <= 1000]\n",
    "\n",
    "# the min value of Debt to income ratio is 0, the average is 10, while the max value is 43\n",
    "# I decided to eliminate values below 0.1 as they would be easily identified compared to the average of 10 and greater than 40 as they would be easily identifiable\n",
    "# there were 3 people with debt to income ratio greater than 40 and one with less than 0.1\n",
    "df = df.loc[(df['DebtToIncomeRatio'] >= 0.01) & (df['DebtToIncomeRatio'] < 40)]\n",
    "# the min value of Employment length is 0, the average age is 10, while the max value is 52\n",
    "# I decided to eliminate the values above 45 as they are very few and easily identifiable\n",
    "df = df.loc[(df['EmploymentLength'] < 46)& (df['EmploymentLength'] > 0)]\n",
    "\n",
    "clean_data = df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03eb5a37",
   "metadata": {},
   "source": [
    "## Mask the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b657c485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               0\n",
       "Age                  0\n",
       "EducationYears       0\n",
       "HouseholdIncome      0\n",
       "DebtToIncomeRatio    0\n",
       "EmploymentLength     0\n",
       "Age_group            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mask the data\n",
    "# step 1: Age masking \n",
    "# the min value of Age is 18, the average age is 47, while the max value is 79\n",
    "# group the ages into bins of range 3\n",
    "bins = range(16, 82, 3)\n",
    "labels = ['{}-{}'.format(i, i+2) for i in bins[:-1]]\n",
    "df['Age_group'] = pd.cut(df['Age'], bins=bins, labels=labels)\n",
    "\n",
    "# make sure all of our values are grouped correctly\n",
    "nan_count = df.isna().sum()\n",
    "nan_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "427f92c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets take a look at our df after age has been dropped and age group has been created\n",
    "# df = df.drop('Age', axis=1)\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f5ce4b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                  0\n",
       "Age                     0\n",
       "EducationYears          0\n",
       "HouseholdIncome         0\n",
       "DebtToIncomeRatio       0\n",
       "EmploymentLength        0\n",
       "Age_group               0\n",
       "EducationYears_group    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mask the data\n",
    "# step 2: Education Years masking \n",
    "# group the education years into bins of range 3\n",
    "# the min value of Education years is 6, the average is 14, while the max value is 23\n",
    "bins = range(1, 26, 3)\n",
    "labels = ['{}-{}'.format(i, i+2) for i in bins[:-1]]\n",
    "df['EducationYears_group'] = pd.cut(df['EducationYears'], bins=bins, labels=labels)\n",
    "\n",
    "# make sure all of our values are grouped correctly\n",
    "nan_count = df.isna().sum()\n",
    "nan_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f9300bbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                   0\n",
       "Age                      0\n",
       "EducationYears           0\n",
       "HouseholdIncome          0\n",
       "DebtToIncomeRatio        0\n",
       "EmploymentLength         0\n",
       "Age_group                0\n",
       "EducationYears_group     0\n",
       "HouseholdIncome_group    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mask the data\n",
    "# step 3: Household income masking \n",
    "# group the household income into bins of range 3\n",
    "# the min value of Household income is 9, the average is 54, while the max value is 1073\n",
    "bins = range(1, 1000, 3)\n",
    "labels = ['{}-{}'.format(i, i+2) for i in bins[:-1]]\n",
    "df['HouseholdIncome_group'] = pd.cut(df['HouseholdIncome'], bins=bins, labels=labels)\n",
    "\n",
    "# make sure all of our values are grouped correctly\n",
    "nan_count = df.isna().sum()\n",
    "nan_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "73e94aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                     0\n",
       "Age                        0\n",
       "EducationYears             0\n",
       "HouseholdIncome            0\n",
       "DebtToIncomeRatio          0\n",
       "EmploymentLength           0\n",
       "Age_group                  0\n",
       "EducationYears_group       0\n",
       "HouseholdIncome_group      0\n",
       "DebtToIncomeRatio_group    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mask the data\n",
    "# step 4: Debt to income ratio masking \n",
    "# group the Debt to income ratio into bins of range 3\n",
    "# the min value of Debt to income ratio is 0, the average is 10, while the max value is 43.1\n",
    "bins = range(0, 46, 3)\n",
    "labels = ['{}-{}'.format(i, i+2) for i in bins[:-1]]\n",
    "df['DebtToIncomeRatio_group'] = pd.cut(df['DebtToIncomeRatio'], bins=bins, labels=labels)\n",
    "\n",
    "\n",
    "# make sure all of our values are grouped correctly\n",
    "nan_count = df.isna().sum()\n",
    "nan_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2917107",
   "metadata": {},
   "outputs": [],
   "source": [
    "# There was a value that was ungrouped above. '0.0'\n",
    "# The range was chanted to start a -1 to include 0.0\n",
    "# ungrouped = df['DebtToIncomeRatio_group'].isna()\n",
    "# df[ungrouped]\n",
    "#df[df['DebtToIncomeRatio'] == 0.0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "efa09d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                     0\n",
       "Age                        0\n",
       "EducationYears             0\n",
       "HouseholdIncome            0\n",
       "DebtToIncomeRatio          0\n",
       "EmploymentLength           0\n",
       "Age_group                  0\n",
       "EducationYears_group       0\n",
       "HouseholdIncome_group      0\n",
       "DebtToIncomeRatio_group    0\n",
       "EmploymentLength_group     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mask the data\n",
    "# step 5: Employment length masking \n",
    "# group the Employment Length into bins of range 3\n",
    "# the min value of Employment length is 0, the average age is 10, while the max value is 52\n",
    "bins = range(0, 56, 3)\n",
    "labels = ['{}-{}'.format(i, i+2) for i in bins[:-1]]\n",
    "df['EmploymentLength_group'] = pd.cut(df['EmploymentLength'], bins=bins, labels=labels)\n",
    "\n",
    "\n",
    "# make sure all of our values are grouped correctly\n",
    "nan_count = df.isna().sum()\n",
    "nan_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e75f8478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                     0\n",
       "Age                        0\n",
       "EducationYears             0\n",
       "HouseholdIncome            0\n",
       "DebtToIncomeRatio          0\n",
       "EmploymentLength           0\n",
       "Age_group                  0\n",
       "EducationYears_group       0\n",
       "HouseholdIncome_group      0\n",
       "DebtToIncomeRatio_group    0\n",
       "EmploymentLength_group     0\n",
       "Sex                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mask the data\n",
    "# step 6: Gender masking \n",
    "# Encode the gender into F or M\n",
    "\n",
    "# Define a mapping of values to F or M\n",
    "gender_map = {0: 'F', 1: 'M'}\n",
    "\n",
    "# Apply the mapping to the column\n",
    "df['Sex'] = df['Gender'].map(gender_map)\n",
    "\n",
    "\n",
    "# make sure all of our values are grouped correctly\n",
    "nan_count = df.isna().sum()\n",
    "nan_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4cbe115",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EducationYears</th>\n",
       "      <th>HouseholdIncome</th>\n",
       "      <th>DebtToIncomeRatio</th>\n",
       "      <th>EmploymentLength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4329.000000</td>\n",
       "      <td>4329.000000</td>\n",
       "      <td>4329.000000</td>\n",
       "      <td>4329.000000</td>\n",
       "      <td>4329.000000</td>\n",
       "      <td>4329.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.500578</td>\n",
       "      <td>50.638023</td>\n",
       "      <td>14.389928</td>\n",
       "      <td>58.435897</td>\n",
       "      <td>9.953384</td>\n",
       "      <td>11.146916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500057</td>\n",
       "      <td>16.061097</td>\n",
       "      <td>3.332325</td>\n",
       "      <td>55.924689</td>\n",
       "      <td>6.331875</td>\n",
       "      <td>9.476386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>995.000000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Gender          Age  EducationYears  HouseholdIncome  \\\n",
       "count  4329.000000  4329.000000     4329.000000      4329.000000   \n",
       "mean      0.500578    50.638023       14.389928        58.435897   \n",
       "std       0.500057    16.061097        3.332325        55.924689   \n",
       "min       0.000000    18.000000        6.000000         9.000000   \n",
       "25%       0.000000    37.000000       12.000000        26.000000   \n",
       "50%       1.000000    51.000000       14.000000        42.000000   \n",
       "75%       1.000000    64.000000       17.000000        72.000000   \n",
       "max       1.000000    79.000000       23.000000       995.000000   \n",
       "\n",
       "       DebtToIncomeRatio  EmploymentLength  \n",
       "count        4329.000000       4329.000000  \n",
       "mean            9.953384         11.146916  \n",
       "std             6.331875          9.476386  \n",
       "min             0.100000          1.000000  \n",
       "25%             5.200000          4.000000  \n",
       "50%             8.800000          8.000000  \n",
       "75%            13.600000         16.000000  \n",
       "max            37.500000         45.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets find out more information of our new data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d88e276c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram for our 'EducationYears_group'\n",
    "\n",
    "sns.histplot(data=df, x='EducationYears_group', color='red', bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "760310dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram for our 'HouseholdIncome_group' \n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.histplot(data=df, x='HouseholdIncome_group', color='green', bins=10)\n",
    "plt.xlim(2, 100)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b3c6bdc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram for our 'DebtToIncomeRatio_group' \n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.histplot(data=df, x='DebtToIncomeRatio_group', color='blue', bins=1)\n",
    "plt.xlim(0, 12)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a58bf7d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram for our 'Sex' \n",
    "# we can see that our sex category is almost evenly distributed between males and females\n",
    "\n",
    "sns.histplot(data=df, x='Sex', hue='Sex', bins=1, palette={'M':'green', 'F':'pink'})\n",
    "plt.xlim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dc61845f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram for our 'EmploymentLength_group' \n",
    "plt.figure(figsize=(10,5))\n",
    "sns.histplot(data=df, x='EmploymentLength_group', color='purple', bins=10)\n",
    "plt.xlim(0, 14)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c6f9463d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram for our 'Age group'\n",
    "sns.histplot(data=df, x='Age_group', color='yellow', bins=1)\n",
    "plt.xlim(5, 20)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9e5190e4",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EducationYears</th>\n",
       "      <th>HouseholdIncome</th>\n",
       "      <th>DebtToIncomeRatio</th>\n",
       "      <th>EmploymentLength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4329.000000</td>\n",
       "      <td>4329.000000</td>\n",
       "      <td>4329.000000</td>\n",
       "      <td>4329.000000</td>\n",
       "      <td>4329.000000</td>\n",
       "      <td>4329.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.500578</td>\n",
       "      <td>50.638023</td>\n",
       "      <td>14.389928</td>\n",
       "      <td>58.435897</td>\n",
       "      <td>9.953384</td>\n",
       "      <td>11.146916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500057</td>\n",
       "      <td>16.061097</td>\n",
       "      <td>3.332325</td>\n",
       "      <td>55.924689</td>\n",
       "      <td>6.331875</td>\n",
       "      <td>9.476386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>995.000000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Gender          Age  EducationYears  HouseholdIncome  \\\n",
       "count  4329.000000  4329.000000     4329.000000      4329.000000   \n",
       "mean      0.500578    50.638023       14.389928        58.435897   \n",
       "std       0.500057    16.061097        3.332325        55.924689   \n",
       "min       0.000000    18.000000        6.000000         9.000000   \n",
       "25%       0.000000    37.000000       12.000000        26.000000   \n",
       "50%       1.000000    51.000000       14.000000        42.000000   \n",
       "75%       1.000000    64.000000       17.000000        72.000000   \n",
       "max       1.000000    79.000000       23.000000       995.000000   \n",
       "\n",
       "       DebtToIncomeRatio  EmploymentLength  \n",
       "count        4329.000000       4329.000000  \n",
       "mean            9.953384         11.146916  \n",
       "std             6.331875          9.476386  \n",
       "min             0.100000          1.000000  \n",
       "25%             5.200000          4.000000  \n",
       "50%             8.800000          8.000000  \n",
       "75%            13.600000         16.000000  \n",
       "max            37.500000         45.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab056b33",
   "metadata": {},
   "source": [
    "# DATASET WITH QUASI-IDENTIFIERS GROUPED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b1a6d2be",
   "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>Age_group</th>\n",
       "      <th>EducationYears_group</th>\n",
       "      <th>HouseholdIncome_group</th>\n",
       "      <th>DebtToIncomeRatio_group</th>\n",
       "      <th>EmploymentLength_group</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64-66</td>\n",
       "      <td>13-15</td>\n",
       "      <td>34-36</td>\n",
       "      <td>9-11</td>\n",
       "      <td>15-17</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25-27</td>\n",
       "      <td>13-15</td>\n",
       "      <td>22-24</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0-2</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>61-63</td>\n",
       "      <td>16-18</td>\n",
       "      <td>106-108</td>\n",
       "      <td>3-5</td>\n",
       "      <td>21-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49-51</td>\n",
       "      <td>13-15</td>\n",
       "      <td>76-78</td>\n",
       "      <td>0-2</td>\n",
       "      <td>9-11</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43-45</td>\n",
       "      <td>13-15</td>\n",
       "      <td>94-96</td>\n",
       "      <td>12-14</td>\n",
       "      <td>9-11</td>\n",
       "      <td>M</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>67-69</td>\n",
       "      <td>7-9</td>\n",
       "      <td>193-195</td>\n",
       "      <td>6-8</td>\n",
       "      <td>21-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>49-51</td>\n",
       "      <td>13-15</td>\n",
       "      <td>82-84</td>\n",
       "      <td>12-14</td>\n",
       "      <td>3-5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>73-75</td>\n",
       "      <td>16-18</td>\n",
       "      <td>106-108</td>\n",
       "      <td>9-11</td>\n",
       "      <td>21-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>46-48</td>\n",
       "      <td>16-18</td>\n",
       "      <td>187-189</td>\n",
       "      <td>9-11</td>\n",
       "      <td>0-2</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>40-42</td>\n",
       "      <td>7-9</td>\n",
       "      <td>76-78</td>\n",
       "      <td>6-8</td>\n",
       "      <td>18-20</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4329 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age_group EducationYears_group HouseholdIncome_group  \\\n",
       "2        64-66                13-15                 34-36   \n",
       "4        25-27                13-15                 22-24   \n",
       "5        61-63                16-18               106-108   \n",
       "6        49-51                13-15                 76-78   \n",
       "7        43-45                13-15                 94-96   \n",
       "...        ...                  ...                   ...   \n",
       "4995     67-69                  7-9               193-195   \n",
       "4996     49-51                13-15                 82-84   \n",
       "4997     73-75                16-18               106-108   \n",
       "4998     46-48                16-18               187-189   \n",
       "4999     40-42                  7-9                 76-78   \n",
       "\n",
       "     DebtToIncomeRatio_group EmploymentLength_group Sex  \n",
       "2                       9-11                  15-17   M  \n",
       "4                        0-2                    0-2   F  \n",
       "5                        3-5                  21-23   F  \n",
       "6                        0-2                   9-11   M  \n",
       "7                      12-14                   9-11   M  \n",
       "...                      ...                    ...  ..  \n",
       "4995                     6-8                  21-23   F  \n",
       "4996                   12-14                    3-5   F  \n",
       "4997                    9-11                  21-23   F  \n",
       "4998                    9-11                    0-2   F  \n",
       "4999                     6-8                  18-20   M  \n",
       "\n",
       "[4329 rows x 6 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "groupeQuasi_df = df[[\"Age_group\", \"EducationYears_group\", \"HouseholdIncome_group\", \"DebtToIncomeRatio_group\", \"EmploymentLength_group\", \"Sex\"]]\n",
    "groupeQuasi_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "130284e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age_group</th>\n",
       "      <th>EquivClassSize</th>\n",
       "      <th>re-identificationRisk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16-18</td>\n",
       "      <td>10</td>\n",
       "      <td>0.002310</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19-21</td>\n",
       "      <td>58</td>\n",
       "      <td>0.013398</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22-24</td>\n",
       "      <td>136</td>\n",
       "      <td>0.031416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25-27</td>\n",
       "      <td>187</td>\n",
       "      <td>0.043197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28-30</td>\n",
       "      <td>236</td>\n",
       "      <td>0.054516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31-33</td>\n",
       "      <td>247</td>\n",
       "      <td>0.057057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34-36</td>\n",
       "      <td>283</td>\n",
       "      <td>0.065373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37-39</td>\n",
       "      <td>216</td>\n",
       "      <td>0.049896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40-42</td>\n",
       "      <td>230</td>\n",
       "      <td>0.053130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43-45</td>\n",
       "      <td>229</td>\n",
       "      <td>0.052899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>46-48</td>\n",
       "      <td>246</td>\n",
       "      <td>0.056826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>49-51</td>\n",
       "      <td>242</td>\n",
       "      <td>0.055902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>52-54</td>\n",
       "      <td>241</td>\n",
       "      <td>0.055671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>55-57</td>\n",
       "      <td>244</td>\n",
       "      <td>0.056364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>58-60</td>\n",
       "      <td>227</td>\n",
       "      <td>0.052437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>61-63</td>\n",
       "      <td>237</td>\n",
       "      <td>0.054747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>64-66</td>\n",
       "      <td>233</td>\n",
       "      <td>0.053823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67-69</td>\n",
       "      <td>239</td>\n",
       "      <td>0.055209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>70-72</td>\n",
       "      <td>187</td>\n",
       "      <td>0.043197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>73-75</td>\n",
       "      <td>192</td>\n",
       "      <td>0.044352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76-78</td>\n",
       "      <td>209</td>\n",
       "      <td>0.048279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age_group  EquivClassSize  re-identificationRisk\n",
       "0      16-18              10               0.002310\n",
       "1      19-21              58               0.013398\n",
       "2      22-24             136               0.031416\n",
       "3      25-27             187               0.043197\n",
       "4      28-30             236               0.054516\n",
       "5      31-33             247               0.057057\n",
       "6      34-36             283               0.065373\n",
       "7      37-39             216               0.049896\n",
       "8      40-42             230               0.053130\n",
       "9      43-45             229               0.052899\n",
       "10     46-48             246               0.056826\n",
       "11     49-51             242               0.055902\n",
       "12     52-54             241               0.055671\n",
       "13     55-57             244               0.056364\n",
       "14     58-60             227               0.052437\n",
       "15     61-63             237               0.054747\n",
       "16     64-66             233               0.053823\n",
       "17     67-69             239               0.055209\n",
       "18     70-72             187               0.043197\n",
       "19     73-75             192               0.044352\n",
       "20     76-78             209               0.048279"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# *********************** EXAMPLE ONLY ******************** EXAMPLE ONLY \n",
    "\n",
    "# re-identification example of Risk of Age group \n",
    "\n",
    "# first group the data by age group and count the number of people in each age group. \n",
    "# Then, calculate the re-identification risk for each age group by dividing the size of each age group by the total size of all age groups combined.\n",
    "\n",
    "age_groupcounts = df.groupby('Age_group')['Age'].agg([('EquivClassSize', 'count')]).reset_index()\n",
    "\n",
    "# Calculate the re-identification risk for each age group\n",
    "total_count = age_groupcounts['EquivClassSize'].sum()\n",
    "age_groupcounts['re-identificationRisk'] = age_groupcounts['EquivClassSize'] / total_count\n",
    "\n",
    "age_groupcounts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c25c3704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# smallest equivalence class = 10\n",
    "# formula 1/ total of equivalence class \n",
    "risk_id = 1/10\n",
    "risk_id\n",
    "# risk identification is 10% "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dbba520",
   "metadata": {},
   "source": [
    "# TRIAL 1: EQUIVALENCE CLASSES\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c316e697",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>29223835</th>\n",
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       "      <td>10-12</td>\n",
       "      <td>7-9</td>\n",
       "      <td>9-11</td>\n",
       "      <td>9-11</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29224894</th>\n",
       "      <td>76-78</td>\n",
       "      <td>10-12</td>\n",
       "      <td>13-15</td>\n",
       "      <td>6-8</td>\n",
       "      <td>33-35</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29583492</th>\n",
       "      <td>76-78</td>\n",
       "      <td>16-18</td>\n",
       "      <td>13-15</td>\n",
       "      <td>9-11</td>\n",
       "      <td>36-38</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age_group EducationYears_group HouseholdIncome_group  \\\n",
       "1795537      19-21                  7-9                 16-18   \n",
       "1796076      19-21                  7-9                 19-21   \n",
       "1974276      19-21                10-12                 13-15   \n",
       "1974348      19-21                10-12                 13-15   \n",
       "1974852      19-21                10-12                 16-18   \n",
       "...            ...                  ...                   ...   \n",
       "29044626     76-78                  7-9                   7-9   \n",
       "29044998     76-78                  7-9                 10-12   \n",
       "29223835     76-78                10-12                   7-9   \n",
       "29224894     76-78                10-12                 13-15   \n",
       "29583492     76-78                16-18                 13-15   \n",
       "\n",
       "         DebtToIncomeRatio_group EmploymentLength_group Sex  EquivClass  \n",
       "1795537                      3-5                    0-2   M           2  \n",
       "1796076                      3-5                    0-2   F           2  \n",
       "1974276                      3-5                    0-2   F           3  \n",
       "1974348                     9-11                    0-2   F           4  \n",
       "1974852                      6-8                    0-2   F           2  \n",
       "...                          ...                    ...  ..         ...  \n",
       "29044626                   15-17                   9-11   F           2  \n",
       "29044998                     0-2                  27-29   F           2  \n",
       "29223835                    9-11                   9-11   M           2  \n",
       "29224894                     6-8                  33-35   F           2  \n",
       "29583492                    9-11                  36-38   F           2  \n",
       "\n",
       "[105 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = groupeQuasi_df.groupby(['Age_group', 'EducationYears_group', 'HouseholdIncome_group', 'DebtToIncomeRatio_group', 'EmploymentLength_group', 'Sex']).size().reset_index(name='EquivClass')\n",
    "grouped[grouped['EquivClass'] > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd57cc18",
   "metadata": {},
   "source": [
    "# Trial 1: Risk identification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bbac868b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group                  105\n",
       "EducationYears_group       105\n",
       "HouseholdIncome_group      105\n",
       "DebtToIncomeRatio_group    105\n",
       "EmploymentLength_group     105\n",
       "Sex                        105\n",
       "EquivClass                 105\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of equivalence classes we have\n",
    "grouped[grouped['EquivClass'] > 1].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "97756dcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group                  19-21\n",
       "EducationYears_group         7-9\n",
       "HouseholdIncome_group        7-9\n",
       "DebtToIncomeRatio_group      0-2\n",
       "EmploymentLength_group       0-2\n",
       "Sex                            F\n",
       "EquivClass                     2\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finds the min value amongst the equivalence classes\n",
    "grouped[grouped['EquivClass'] > 1].min()\n",
    "\n",
    "# it seems as 2 is minimum value amongst the equivalence classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "51bde3bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# smallest equivalence class = 2\n",
    "# formula 1/ total of equivalence class \n",
    "risk_id = 1/2\n",
    "risk_id\n",
    "# risk identification is 50% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4d5a86ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4 risk scenarios: Scenario 1: Deliberate Data Attack\n",
    "\n",
    "numberof_employees = 300.0\n",
    "rogue_employees = 60.0\n",
    "#formula = rogue_employees/numberofemployees\n",
    "risk_identification = (rogue_employees/numberof_employees) * 100\n",
    "risk_identification\n",
    "# risk identification is 20% \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "348c4cdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 risk scenarios: Scenario 2: Inadvertent Data Attack\n",
    "# resource https://www.fundera.com/resources/small-business-lending-statistics#:~:text=Alternative%20lenders%20have%20a%2056.8,have%20a%2027.7%25%20approval%20rate.\n",
    "\n",
    "bank_approval_rate = 27.70/100\n",
    "average_friends = 150.00\n",
    "# formula = 1-(1-0.02770)^150\n",
    "#probabilityof_acquaintance = 1.00 - (1.00 - bank_approval_rate)^average_friends\n",
    "# probabilityof_acquaintance is 99%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "33359b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 risk scenarios: Scenario 3: Data Breach\n",
    "\n",
    "# average percent of people who get their credentials stolen = 19% \n",
    "# probability of breach is 19% \n",
    "# resource https://www.ibm.com/reports/data-breach?utm_content=SRCWW&p1=Search&p4=43700072379268724&p5=p&gclid=CjwKCAiAjPyfBhBMEiwAB2CCIsX8n31iNCT5XQbRg6IFte3ohXSURwF86uA5mFSlkIGRweg5WIisrRoCdSYQAvD_BwE&gclsrc=aw.ds \n",
    "probability_of_breach = 19.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "71bcba98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 risk scenarios: Scenario 4: Demonstration attack\n",
    "# 6 out of 6 quasi identifiers\n",
    "risk = 100.0\n",
    "# 100% risk of getting identified"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "614f972f",
   "metadata": {},
   "source": [
    "## Trial # 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ed3f88f8",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EducationYears</th>\n",
       "      <th>HouseholdIncome</th>\n",
       "      <th>DebtToIncomeRatio</th>\n",
       "      <th>EmploymentLength</th>\n",
       "      <th>Age_group</th>\n",
       "      <th>EducationYears_group</th>\n",
       "      <th>HouseholdIncome_group</th>\n",
       "      <th>DebtToIncomeRatio_group</th>\n",
       "      <th>EmploymentLength_group</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_group2</th>\n",
       "      <th>EducationYears_group2</th>\n",
       "      <th>HouseholdIncome_group2</th>\n",
       "      <th>DebtToIncomeRatio_group2</th>\n",
       "      <th>EmploymentLength_group2</th>\n",
       "      <th>Sex2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "      <td>9.9</td>\n",
       "      <td>16</td>\n",
       "      <td>64-66</td>\n",
       "      <td>13-15</td>\n",
       "      <td>34-36</td>\n",
       "      <td>9-11</td>\n",
       "      <td>15-17</td>\n",
       "      <td>M</td>\n",
       "      <td>65-70</td>\n",
       "      <td>11-16</td>\n",
       "      <td>31-36</td>\n",
       "      <td>6-11</td>\n",
       "      <td>12-17</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "      <td>25-27</td>\n",
       "      <td>13-15</td>\n",
       "      <td>22-24</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0-2</td>\n",
       "      <td>F</td>\n",
       "      <td>23-28</td>\n",
       "      <td>11-16</td>\n",
       "      <td>19-24</td>\n",
       "      <td>0-5</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>17</td>\n",
       "      <td>107</td>\n",
       "      <td>5.6</td>\n",
       "      <td>22</td>\n",
       "      <td>61-63</td>\n",
       "      <td>16-18</td>\n",
       "      <td>106-108</td>\n",
       "      <td>3-5</td>\n",
       "      <td>21-23</td>\n",
       "      <td>F</td>\n",
       "      <td>59-64</td>\n",
       "      <td>11-16</td>\n",
       "      <td>103-108</td>\n",
       "      <td>0-5</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>14</td>\n",
       "      <td>77</td>\n",
       "      <td>1.9</td>\n",
       "      <td>10</td>\n",
       "      <td>49-51</td>\n",
       "      <td>13-15</td>\n",
       "      <td>76-78</td>\n",
       "      <td>0-2</td>\n",
       "      <td>9-11</td>\n",
       "      <td>M</td>\n",
       "      <td>47-52</td>\n",
       "      <td>11-16</td>\n",
       "      <td>73-78</td>\n",
       "      <td>0-5</td>\n",
       "      <td>6-11</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>16</td>\n",
       "      <td>97</td>\n",
       "      <td>14.4</td>\n",
       "      <td>11</td>\n",
       "      <td>43-45</td>\n",
       "      <td>13-15</td>\n",
       "      <td>94-96</td>\n",
       "      <td>12-14</td>\n",
       "      <td>9-11</td>\n",
       "      <td>M</td>\n",
       "      <td>41-46</td>\n",
       "      <td>11-16</td>\n",
       "      <td>91-96</td>\n",
       "      <td>12-17</td>\n",
       "      <td>6-11</td>\n",
       "      <td>M</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>10</td>\n",
       "      <td>196</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24</td>\n",
       "      <td>67-69</td>\n",
       "      <td>7-9</td>\n",
       "      <td>193-195</td>\n",
       "      <td>6-8</td>\n",
       "      <td>21-23</td>\n",
       "      <td>F</td>\n",
       "      <td>65-70</td>\n",
       "      <td>5-10</td>\n",
       "      <td>193-198</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6</td>\n",
       "      <td>49-51</td>\n",
       "      <td>13-15</td>\n",
       "      <td>82-84</td>\n",
       "      <td>12-14</td>\n",
       "      <td>3-5</td>\n",
       "      <td>F</td>\n",
       "      <td>47-52</td>\n",
       "      <td>11-16</td>\n",
       "      <td>79-84</td>\n",
       "      <td>12-17</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>17</td>\n",
       "      <td>108</td>\n",
       "      <td>10.8</td>\n",
       "      <td>24</td>\n",
       "      <td>73-75</td>\n",
       "      <td>16-18</td>\n",
       "      <td>106-108</td>\n",
       "      <td>9-11</td>\n",
       "      <td>21-23</td>\n",
       "      <td>F</td>\n",
       "      <td>71-76</td>\n",
       "      <td>11-16</td>\n",
       "      <td>103-108</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>19</td>\n",
       "      <td>189</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3</td>\n",
       "      <td>46-48</td>\n",
       "      <td>16-18</td>\n",
       "      <td>187-189</td>\n",
       "      <td>9-11</td>\n",
       "      <td>0-2</td>\n",
       "      <td>F</td>\n",
       "      <td>41-46</td>\n",
       "      <td>17-22</td>\n",
       "      <td>187-192</td>\n",
       "      <td>6-11</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "      <td>77</td>\n",
       "      <td>6.2</td>\n",
       "      <td>20</td>\n",
       "      <td>40-42</td>\n",
       "      <td>7-9</td>\n",
       "      <td>76-78</td>\n",
       "      <td>6-8</td>\n",
       "      <td>18-20</td>\n",
       "      <td>M</td>\n",
       "      <td>35-40</td>\n",
       "      <td>5-10</td>\n",
       "      <td>73-78</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4329 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender  Age  EducationYears  HouseholdIncome  DebtToIncomeRatio  \\\n",
       "2          1   67              14               35                9.9   \n",
       "4          0   26              16               23                1.7   \n",
       "5          0   64              17              107                5.6   \n",
       "6          1   52              14               77                1.9   \n",
       "7          1   44              16               97               14.4   \n",
       "...      ...  ...             ...              ...                ...   \n",
       "4995       0   68              10              196                8.0   \n",
       "4996       0   51              14               83               15.0   \n",
       "4997       0   75              17              108               10.8   \n",
       "4998       0   47              19              189               10.8   \n",
       "4999       1   41              10               77                6.2   \n",
       "\n",
       "      EmploymentLength Age_group EducationYears_group HouseholdIncome_group  \\\n",
       "2                   16     64-66                13-15                 34-36   \n",
       "4                    1     25-27                13-15                 22-24   \n",
       "5                   22     61-63                16-18               106-108   \n",
       "6                   10     49-51                13-15                 76-78   \n",
       "7                   11     43-45                13-15                 94-96   \n",
       "...                ...       ...                  ...                   ...   \n",
       "4995                24     67-69                  7-9               193-195   \n",
       "4996                 6     49-51                13-15                 82-84   \n",
       "4997                24     73-75                16-18               106-108   \n",
       "4998                 3     46-48                16-18               187-189   \n",
       "4999                20     40-42                  7-9                 76-78   \n",
       "\n",
       "     DebtToIncomeRatio_group EmploymentLength_group Sex Age_group2  \\\n",
       "2                       9-11                  15-17   M      65-70   \n",
       "4                        0-2                    0-2   F      23-28   \n",
       "5                        3-5                  21-23   F      59-64   \n",
       "6                        0-2                   9-11   M      47-52   \n",
       "7                      12-14                   9-11   M      41-46   \n",
       "...                      ...                    ...  ..        ...   \n",
       "4995                     6-8                  21-23   F      65-70   \n",
       "4996                   12-14                    3-5   F      47-52   \n",
       "4997                    9-11                  21-23   F      71-76   \n",
       "4998                    9-11                    0-2   F      41-46   \n",
       "4999                     6-8                  18-20   M      35-40   \n",
       "\n",
       "     EducationYears_group2 HouseholdIncome_group2 DebtToIncomeRatio_group2  \\\n",
       "2                    11-16                  31-36                     6-11   \n",
       "4                    11-16                  19-24                      0-5   \n",
       "5                    11-16                103-108                      0-5   \n",
       "6                    11-16                  73-78                      0-5   \n",
       "7                    11-16                  91-96                    12-17   \n",
       "...                    ...                    ...                      ...   \n",
       "4995                  5-10                193-198                     6-11   \n",
       "4996                 11-16                  79-84                    12-17   \n",
       "4997                 11-16                103-108                     6-11   \n",
       "4998                 17-22                187-192                     6-11   \n",
       "4999                  5-10                  73-78                     6-11   \n",
       "\n",
       "     EmploymentLength_group2 Sex2  \n",
       "2                      12-17    M  \n",
       "4                        0-5    F  \n",
       "5                      18-23    F  \n",
       "6                       6-11    M  \n",
       "7                       6-11    M  \n",
       "...                      ...  ...  \n",
       "4995                   18-23    F  \n",
       "4996                     0-5    F  \n",
       "4997                   18-23    F  \n",
       "4998                     0-5    F  \n",
       "4999                   18-23    M  \n",
       "\n",
       "[4329 rows x 18 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets make a dataframe from our copy of our clean original df\n",
    "df2 = pd.DataFrame(clean_data)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26205818",
   "metadata": {},
   "source": [
    "## Mask the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "57e04c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group                   0\n",
       "EducationYears_group        0\n",
       "HouseholdIncome_group       0\n",
       "DebtToIncomeRatio_group     0\n",
       "EmploymentLength_group      0\n",
       "Sex                         0\n",
       "Age_group2                  0\n",
       "EducationYears_group2       0\n",
       "HouseholdIncome_group2      0\n",
       "DebtToIncomeRatio_group2    0\n",
       "EmploymentLength_group2     0\n",
       "Sex2                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Masking\n",
    "\n",
    "# new range: 6\n",
    "\n",
    "# step 1: Age masking \n",
    "# the min value of Age is 18, the average age is 47, while the max value is 79\n",
    "# group the ages into bins of range 6\n",
    "bins = range(17, 84, 6)\n",
    "labels = ['{}-{}'.format(i, i+5) for i in bins[:-1]]\n",
    "df2['Age_group2'] = pd.cut(df2['Age'], bins=bins, labels=labels)\n",
    "\n",
    "# step 2: Education Years masking \n",
    "# group the education years into bins of range 6\n",
    "# the min value of Education years is 6, the average is 14, while the max value is 23\n",
    "bins = range(5, 25, 6)\n",
    "labels = ['{}-{}'.format(i, i+5) for i in bins[:-1]]\n",
    "df2['EducationYears_group2'] = pd.cut(df2['EducationYears'], bins=bins, labels=labels)\n",
    "\n",
    "\n",
    "# step 3: Household income masking \n",
    "# group the household income into bins of range 6\n",
    "# the min value of Household income is 9, the average is 54, while the max value is 1073\n",
    "bins = range(1, 1000, 6)\n",
    "labels = ['{}-{}'.format(i, i+5) for i in bins[:-1]]\n",
    "df2['HouseholdIncome_group2'] = pd.cut(df2['HouseholdIncome'], bins=bins, labels=labels)\n",
    "\n",
    "# step 4: Debt to income ratio masking \n",
    "# group the Debt to income ratio into bins of range 6\n",
    "# the min value of Debt to income ratio is 0, the average is 10, while the max value is 43.1\n",
    "bins = range(0, 46, 6)\n",
    "labels = ['{}-{}'.format(i, i+5) for i in bins[:-1]]\n",
    "df2['DebtToIncomeRatio_group2'] = pd.cut(df2['DebtToIncomeRatio'], bins=bins, labels=labels)\n",
    "\n",
    "# step 5: Employment length masking \n",
    "# group the Employment Length into bins of range 6\n",
    "# the min value of Employment length is 0, the average age is 10, while the max value is 52\n",
    "bins = range(0, 56, 6)\n",
    "labels = ['{}-{}'.format(i, i+5) for i in bins[:-1]]\n",
    "df2['EmploymentLength_group2'] = pd.cut(df2['EmploymentLength'], bins=bins, labels=labels)\n",
    "\n",
    "\n",
    "# step 6: Gender masking \n",
    "# Encode the gender into F or M\n",
    "\n",
    "# Define a mapping of values to F or M\n",
    "gender_map = {0: 'F', 1: 'M'}\n",
    "\n",
    "# Apply the mapping to the column\n",
    "df2['Sex2'] = df2['Gender'].map(gender_map)\n",
    "\n",
    "# lets take a look at our df after dropping DebtToIncomeRatio, EmploymentLength and creating DebtToIncomeRatio_group, and EmploymentLength_group\n",
    "df2 = df2.drop(['Age','EducationYears', 'HouseholdIncome','EmploymentLength','DebtToIncomeRatio', 'Gender'], axis=1)\n",
    "\n",
    "# make sure all of our values are grouped correctly\n",
    "nan_count = df2.isna().sum()\n",
    "nan_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c5b305f",
   "metadata": {},
   "source": [
    "##  DATASET WITH QUASI-IDENTIFIERS GROUPED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "91e36a42",
   "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>Age_group2</th>\n",
       "      <th>EducationYears_group2</th>\n",
       "      <th>HouseholdIncome_group2</th>\n",
       "      <th>DebtToIncomeRatio_group2</th>\n",
       "      <th>EmploymentLength_group2</th>\n",
       "      <th>Sex2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65-70</td>\n",
       "      <td>11-16</td>\n",
       "      <td>31-36</td>\n",
       "      <td>6-11</td>\n",
       "      <td>12-17</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23-28</td>\n",
       "      <td>11-16</td>\n",
       "      <td>19-24</td>\n",
       "      <td>0-5</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>59-64</td>\n",
       "      <td>11-16</td>\n",
       "      <td>103-108</td>\n",
       "      <td>0-5</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47-52</td>\n",
       "      <td>11-16</td>\n",
       "      <td>73-78</td>\n",
       "      <td>0-5</td>\n",
       "      <td>6-11</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>41-46</td>\n",
       "      <td>11-16</td>\n",
       "      <td>91-96</td>\n",
       "      <td>12-17</td>\n",
       "      <td>6-11</td>\n",
       "      <td>M</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>65-70</td>\n",
       "      <td>5-10</td>\n",
       "      <td>193-198</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>47-52</td>\n",
       "      <td>11-16</td>\n",
       "      <td>79-84</td>\n",
       "      <td>12-17</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>71-76</td>\n",
       "      <td>11-16</td>\n",
       "      <td>103-108</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>41-46</td>\n",
       "      <td>17-22</td>\n",
       "      <td>187-192</td>\n",
       "      <td>6-11</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>35-40</td>\n",
       "      <td>5-10</td>\n",
       "      <td>73-78</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4329 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age_group2 EducationYears_group2 HouseholdIncome_group2  \\\n",
       "2         65-70                 11-16                  31-36   \n",
       "4         23-28                 11-16                  19-24   \n",
       "5         59-64                 11-16                103-108   \n",
       "6         47-52                 11-16                  73-78   \n",
       "7         41-46                 11-16                  91-96   \n",
       "...         ...                   ...                    ...   \n",
       "4995      65-70                  5-10                193-198   \n",
       "4996      47-52                 11-16                  79-84   \n",
       "4997      71-76                 11-16                103-108   \n",
       "4998      41-46                 17-22                187-192   \n",
       "4999      35-40                  5-10                  73-78   \n",
       "\n",
       "     DebtToIncomeRatio_group2 EmploymentLength_group2 Sex2  \n",
       "2                        6-11                   12-17    M  \n",
       "4                         0-5                     0-5    F  \n",
       "5                         0-5                   18-23    F  \n",
       "6                         0-5                    6-11    M  \n",
       "7                       12-17                    6-11    M  \n",
       "...                       ...                     ...  ...  \n",
       "4995                     6-11                   18-23    F  \n",
       "4996                    12-17                     0-5    F  \n",
       "4997                     6-11                   18-23    F  \n",
       "4998                     6-11                     0-5    F  \n",
       "4999                     6-11                   18-23    M  \n",
       "\n",
       "[4329 rows x 6 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupeQuasi_df2 = df2[[\"Age_group2\", \"EducationYears_group2\", \"HouseholdIncome_group2\", \"DebtToIncomeRatio_group2\", \"EmploymentLength_group2\", \"Sex2\"]]\n",
    "groupeQuasi_df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52f8a7a1",
   "metadata": {},
   "source": [
    "# TRIAL 2 : EQUIVALENCE CLASSES\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "aa7ab911",
   "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>Age_group2</th>\n",
       "      <th>EducationYears_group2</th>\n",
       "      <th>HouseholdIncome_group2</th>\n",
       "      <th>DebtToIncomeRatio_group2</th>\n",
       "      <th>EmploymentLength_group2</th>\n",
       "      <th>Sex2</th>\n",
       "      <th>EquivClass2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>17-22</td>\n",
       "      <td>5-10</td>\n",
       "      <td>13-18</td>\n",
       "      <td>0-5</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>17-22</td>\n",
       "      <td>5-10</td>\n",
       "      <td>13-18</td>\n",
       "      <td>0-5</td>\n",
       "      <td>0-5</td>\n",
       "      <td>M</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>17-22</td>\n",
       "      <td>5-10</td>\n",
       "      <td>13-18</td>\n",
       "      <td>6-11</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>17-22</td>\n",
       "      <td>5-10</td>\n",
       "      <td>13-18</td>\n",
       "      <td>12-17</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>17-22</td>\n",
       "      <td>5-10</td>\n",
       "      <td>19-24</td>\n",
       "      <td>0-5</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648558</th>\n",
       "      <td>77-82</td>\n",
       "      <td>11-16</td>\n",
       "      <td>7-12</td>\n",
       "      <td>12-17</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648650</th>\n",
       "      <td>77-82</td>\n",
       "      <td>11-16</td>\n",
       "      <td>13-18</td>\n",
       "      <td>0-5</td>\n",
       "      <td>6-11</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648669</th>\n",
       "      <td>77-82</td>\n",
       "      <td>11-16</td>\n",
       "      <td>13-18</td>\n",
       "      <td>6-11</td>\n",
       "      <td>6-11</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648676</th>\n",
       "      <td>77-82</td>\n",
       "      <td>11-16</td>\n",
       "      <td>13-18</td>\n",
       "      <td>6-11</td>\n",
       "      <td>30-35</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648818</th>\n",
       "      <td>77-82</td>\n",
       "      <td>11-16</td>\n",
       "      <td>19-24</td>\n",
       "      <td>12-17</td>\n",
       "      <td>24-29</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>676 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age_group2 EducationYears_group2 HouseholdIncome_group2  \\\n",
       "252         17-22                  5-10                  13-18   \n",
       "253         17-22                  5-10                  13-18   \n",
       "270         17-22                  5-10                  13-18   \n",
       "288         17-22                  5-10                  13-18   \n",
       "378         17-22                  5-10                  19-24   \n",
       "...           ...                   ...                    ...   \n",
       "648558      77-82                 11-16                   7-12   \n",
       "648650      77-82                 11-16                  13-18   \n",
       "648669      77-82                 11-16                  13-18   \n",
       "648676      77-82                 11-16                  13-18   \n",
       "648818      77-82                 11-16                  19-24   \n",
       "\n",
       "       DebtToIncomeRatio_group2 EmploymentLength_group2 Sex2  EquivClass2  \n",
       "252                         0-5                     0-5    F            6  \n",
       "253                         0-5                     0-5    M            6  \n",
       "270                        6-11                     0-5    F           11  \n",
       "288                       12-17                     0-5    F            2  \n",
       "378                         0-5                     0-5    F            3  \n",
       "...                         ...                     ...  ...          ...  \n",
       "648558                    12-17                     0-5    F            2  \n",
       "648650                      0-5                    6-11    F            2  \n",
       "648669                     6-11                    6-11    M            2  \n",
       "648676                     6-11                   30-35    F            2  \n",
       "648818                    12-17                   24-29    F            2  \n",
       "\n",
       "[676 rows x 7 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped2 = groupeQuasi_df2.groupby(['Age_group2', 'EducationYears_group2', 'HouseholdIncome_group2', 'DebtToIncomeRatio_group2', 'EmploymentLength_group2', 'Sex2']).size().reset_index(name='EquivClass2')\n",
    "grouped2[grouped2['EquivClass2'] > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25d57215",
   "metadata": {},
   "source": [
    "# Trial 2: Risk identification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a0ced30a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group2                  676\n",
       "EducationYears_group2       676\n",
       "HouseholdIncome_group2      676\n",
       "DebtToIncomeRatio_group2    676\n",
       "EmploymentLength_group2     676\n",
       "Sex2                        676\n",
       "EquivClass2                 676\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of equivalence classes we have\n",
    "grouped2[grouped2['EquivClass2'] > 1].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "db1bbba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group2                  17-22\n",
       "EducationYears_group2        5-10\n",
       "HouseholdIncome_group2       7-12\n",
       "DebtToIncomeRatio_group2      0-5\n",
       "EmploymentLength_group2       0-5\n",
       "Sex2                            F\n",
       "EquivClass2                     2\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finds the min value amongst the equivalence classes\n",
    "grouped2[grouped2['EquivClass2'] > 1].min()\n",
    "\n",
    "# it seems as 2 is minimum value amongst the equivalence classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "74af15b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# smallest equivalence class = 2\n",
    "# formula 1/ total of equivalence class \n",
    "risk_id = 1/2\n",
    "risk_id\n",
    "# risk identification is 50% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8e8a3698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group2                    77-82\n",
       "EducationYears_group2         17-22\n",
       "HouseholdIncome_group2      139-144\n",
       "DebtToIncomeRatio_group2      30-35\n",
       "EmploymentLength_group2       30-35\n",
       "Sex2                              M\n",
       "EquivClass2                      14\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# our max value in an equivalence class is 14 so we can go higher than our group range of 6\n",
    "grouped2[grouped2['EquivClass2'] > 1].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778ee2cb",
   "metadata": {},
   "source": [
    "# Trial #3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3ff173b5",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EducationYears</th>\n",
       "      <th>HouseholdIncome</th>\n",
       "      <th>DebtToIncomeRatio</th>\n",
       "      <th>EmploymentLength</th>\n",
       "      <th>Age_group</th>\n",
       "      <th>EducationYears_group</th>\n",
       "      <th>HouseholdIncome_group</th>\n",
       "      <th>DebtToIncomeRatio_group</th>\n",
       "      <th>EmploymentLength_group</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_group2</th>\n",
       "      <th>EducationYears_group2</th>\n",
       "      <th>HouseholdIncome_group2</th>\n",
       "      <th>DebtToIncomeRatio_group2</th>\n",
       "      <th>EmploymentLength_group2</th>\n",
       "      <th>Sex2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "      <td>9.9</td>\n",
       "      <td>16</td>\n",
       "      <td>64-66</td>\n",
       "      <td>13-15</td>\n",
       "      <td>34-36</td>\n",
       "      <td>9-11</td>\n",
       "      <td>15-17</td>\n",
       "      <td>M</td>\n",
       "      <td>65-70</td>\n",
       "      <td>11-16</td>\n",
       "      <td>31-36</td>\n",
       "      <td>6-11</td>\n",
       "      <td>12-17</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "      <td>25-27</td>\n",
       "      <td>13-15</td>\n",
       "      <td>22-24</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0-2</td>\n",
       "      <td>F</td>\n",
       "      <td>23-28</td>\n",
       "      <td>11-16</td>\n",
       "      <td>19-24</td>\n",
       "      <td>0-5</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>17</td>\n",
       "      <td>107</td>\n",
       "      <td>5.6</td>\n",
       "      <td>22</td>\n",
       "      <td>61-63</td>\n",
       "      <td>16-18</td>\n",
       "      <td>106-108</td>\n",
       "      <td>3-5</td>\n",
       "      <td>21-23</td>\n",
       "      <td>F</td>\n",
       "      <td>59-64</td>\n",
       "      <td>11-16</td>\n",
       "      <td>103-108</td>\n",
       "      <td>0-5</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>14</td>\n",
       "      <td>77</td>\n",
       "      <td>1.9</td>\n",
       "      <td>10</td>\n",
       "      <td>49-51</td>\n",
       "      <td>13-15</td>\n",
       "      <td>76-78</td>\n",
       "      <td>0-2</td>\n",
       "      <td>9-11</td>\n",
       "      <td>M</td>\n",
       "      <td>47-52</td>\n",
       "      <td>11-16</td>\n",
       "      <td>73-78</td>\n",
       "      <td>0-5</td>\n",
       "      <td>6-11</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>16</td>\n",
       "      <td>97</td>\n",
       "      <td>14.4</td>\n",
       "      <td>11</td>\n",
       "      <td>43-45</td>\n",
       "      <td>13-15</td>\n",
       "      <td>94-96</td>\n",
       "      <td>12-14</td>\n",
       "      <td>9-11</td>\n",
       "      <td>M</td>\n",
       "      <td>41-46</td>\n",
       "      <td>11-16</td>\n",
       "      <td>91-96</td>\n",
       "      <td>12-17</td>\n",
       "      <td>6-11</td>\n",
       "      <td>M</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>10</td>\n",
       "      <td>196</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24</td>\n",
       "      <td>67-69</td>\n",
       "      <td>7-9</td>\n",
       "      <td>193-195</td>\n",
       "      <td>6-8</td>\n",
       "      <td>21-23</td>\n",
       "      <td>F</td>\n",
       "      <td>65-70</td>\n",
       "      <td>5-10</td>\n",
       "      <td>193-198</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6</td>\n",
       "      <td>49-51</td>\n",
       "      <td>13-15</td>\n",
       "      <td>82-84</td>\n",
       "      <td>12-14</td>\n",
       "      <td>3-5</td>\n",
       "      <td>F</td>\n",
       "      <td>47-52</td>\n",
       "      <td>11-16</td>\n",
       "      <td>79-84</td>\n",
       "      <td>12-17</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>17</td>\n",
       "      <td>108</td>\n",
       "      <td>10.8</td>\n",
       "      <td>24</td>\n",
       "      <td>73-75</td>\n",
       "      <td>16-18</td>\n",
       "      <td>106-108</td>\n",
       "      <td>9-11</td>\n",
       "      <td>21-23</td>\n",
       "      <td>F</td>\n",
       "      <td>71-76</td>\n",
       "      <td>11-16</td>\n",
       "      <td>103-108</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>19</td>\n",
       "      <td>189</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3</td>\n",
       "      <td>46-48</td>\n",
       "      <td>16-18</td>\n",
       "      <td>187-189</td>\n",
       "      <td>9-11</td>\n",
       "      <td>0-2</td>\n",
       "      <td>F</td>\n",
       "      <td>41-46</td>\n",
       "      <td>17-22</td>\n",
       "      <td>187-192</td>\n",
       "      <td>6-11</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "      <td>77</td>\n",
       "      <td>6.2</td>\n",
       "      <td>20</td>\n",
       "      <td>40-42</td>\n",
       "      <td>7-9</td>\n",
       "      <td>76-78</td>\n",
       "      <td>6-8</td>\n",
       "      <td>18-20</td>\n",
       "      <td>M</td>\n",
       "      <td>35-40</td>\n",
       "      <td>5-10</td>\n",
       "      <td>73-78</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4329 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender  Age  EducationYears  HouseholdIncome  DebtToIncomeRatio  \\\n",
       "2          1   67              14               35                9.9   \n",
       "4          0   26              16               23                1.7   \n",
       "5          0   64              17              107                5.6   \n",
       "6          1   52              14               77                1.9   \n",
       "7          1   44              16               97               14.4   \n",
       "...      ...  ...             ...              ...                ...   \n",
       "4995       0   68              10              196                8.0   \n",
       "4996       0   51              14               83               15.0   \n",
       "4997       0   75              17              108               10.8   \n",
       "4998       0   47              19              189               10.8   \n",
       "4999       1   41              10               77                6.2   \n",
       "\n",
       "      EmploymentLength Age_group EducationYears_group HouseholdIncome_group  \\\n",
       "2                   16     64-66                13-15                 34-36   \n",
       "4                    1     25-27                13-15                 22-24   \n",
       "5                   22     61-63                16-18               106-108   \n",
       "6                   10     49-51                13-15                 76-78   \n",
       "7                   11     43-45                13-15                 94-96   \n",
       "...                ...       ...                  ...                   ...   \n",
       "4995                24     67-69                  7-9               193-195   \n",
       "4996                 6     49-51                13-15                 82-84   \n",
       "4997                24     73-75                16-18               106-108   \n",
       "4998                 3     46-48                16-18               187-189   \n",
       "4999                20     40-42                  7-9                 76-78   \n",
       "\n",
       "     DebtToIncomeRatio_group EmploymentLength_group Sex Age_group2  \\\n",
       "2                       9-11                  15-17   M      65-70   \n",
       "4                        0-2                    0-2   F      23-28   \n",
       "5                        3-5                  21-23   F      59-64   \n",
       "6                        0-2                   9-11   M      47-52   \n",
       "7                      12-14                   9-11   M      41-46   \n",
       "...                      ...                    ...  ..        ...   \n",
       "4995                     6-8                  21-23   F      65-70   \n",
       "4996                   12-14                    3-5   F      47-52   \n",
       "4997                    9-11                  21-23   F      71-76   \n",
       "4998                    9-11                    0-2   F      41-46   \n",
       "4999                     6-8                  18-20   M      35-40   \n",
       "\n",
       "     EducationYears_group2 HouseholdIncome_group2 DebtToIncomeRatio_group2  \\\n",
       "2                    11-16                  31-36                     6-11   \n",
       "4                    11-16                  19-24                      0-5   \n",
       "5                    11-16                103-108                      0-5   \n",
       "6                    11-16                  73-78                      0-5   \n",
       "7                    11-16                  91-96                    12-17   \n",
       "...                    ...                    ...                      ...   \n",
       "4995                  5-10                193-198                     6-11   \n",
       "4996                 11-16                  79-84                    12-17   \n",
       "4997                 11-16                103-108                     6-11   \n",
       "4998                 17-22                187-192                     6-11   \n",
       "4999                  5-10                  73-78                     6-11   \n",
       "\n",
       "     EmploymentLength_group2 Sex2  \n",
       "2                      12-17    M  \n",
       "4                        0-5    F  \n",
       "5                      18-23    F  \n",
       "6                       6-11    M  \n",
       "7                       6-11    M  \n",
       "...                      ...  ...  \n",
       "4995                   18-23    F  \n",
       "4996                     0-5    F  \n",
       "4997                   18-23    F  \n",
       "4998                     0-5    F  \n",
       "4999                   18-23    M  \n",
       "\n",
       "[4329 rows x 18 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new equivalence classes based on a copy of our df\n",
    "df3 = pd.DataFrame(clean_data)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ccfb74fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EducationYears_group        0\n",
       "HouseholdIncome_group       0\n",
       "DebtToIncomeRatio_group     0\n",
       "EmploymentLength_group      0\n",
       "Sex                         0\n",
       "Age_group3                  0\n",
       "EducationYears_group3       0\n",
       "HouseholdIncome_group3      0\n",
       "DebtToIncomeRatio_group3    0\n",
       "EmploymentLength_group3     0\n",
       "Sex3                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Masking\n",
    "\n",
    "# step 1: Age masking \n",
    "# the min value of Age is 18, the average age is 47, while the max value is 79\n",
    "# group the ages into bins of range 15\n",
    "bins = range(17, 105, 15)\n",
    "labels = ['{}-{}'.format(i, i+14) for i in bins[:-1]]\n",
    "df3['Age_group3'] = pd.cut(df3['Age'], bins=bins, labels=labels)\n",
    "\n",
    "# step 2: Education Years masking \n",
    "# group the education years into bins of range 15\n",
    "# the min value of Education years is 6, the average is 14, while the max value is 23\n",
    "bins = range(5, 51, 15)\n",
    "labels = ['{}-{}'.format(i, i+14) for i in bins[:-1]]\n",
    "df3['EducationYears_group3'] = pd.cut(df3['EducationYears'], bins=bins, labels=labels)\n",
    "\n",
    "\n",
    "# step 3: Household income masking \n",
    "# group the household income into bins of range 15\n",
    "# the min value of Household income is 9, the average is 54, while the max value is 100\n",
    "bins = range(1, 1100, 15)\n",
    "labels = ['{}-{}'.format(i, i+14) for i in bins[:-1]]\n",
    "df3['HouseholdIncome_group3'] = pd.cut(df3['HouseholdIncome'], bins=bins, labels=labels)\n",
    "\n",
    "# step 4: Debt to income ratio masking \n",
    "# group the Debt to income ratio into bins of range 15\n",
    "# the min value of Debt to income ratio is 0, the average is 10, while the max value is 43.1\n",
    "bins = range(0, 60, 15)\n",
    "labels = ['{}-{}'.format(i, i+14) for i in bins[:-1]]\n",
    "df3['DebtToIncomeRatio_group3'] = pd.cut(df3['DebtToIncomeRatio'], bins=bins, labels=labels)\n",
    "\n",
    "# step 5: Employment length masking \n",
    "# group the Employment Length into bins of range 15\n",
    "# the min value of Employment length is 0, the average age is 10, while the max value is 52\n",
    "bins = range(0, 60, 15)\n",
    "labels = ['{}-{}'.format(i, i+14) for i in bins[:-1]]\n",
    "df3['EmploymentLength_group3'] = pd.cut(df3['EmploymentLength'], bins=bins, labels=labels)\n",
    "\n",
    "\n",
    "# step 6: Gender masking \n",
    "# Encode the gender into F or M\n",
    "\n",
    "# Define a mapping of values to F or M\n",
    "gender_map = {0: 'F', 1: 'M'}\n",
    "\n",
    "# Apply the mapping to the column\n",
    "df3['Sex3'] = df3['Gender'].map(gender_map)\n",
    "\n",
    "# lets take a look at our df after dropping DebtToIncomeRatio, EmploymentLength and creating DebtToIncomeRatio_group, and EmploymentLength_group\n",
    "df3 = df3.drop(['Age_group2', 'EducationYears_group2','HouseholdIncome_group2', 'DebtToIncomeRatio_group2', 'Age_group', 'EmploymentLength_group2', 'Sex2' ,'Age','EducationYears', 'HouseholdIncome','EmploymentLength','DebtToIncomeRatio', 'Gender'], axis=1)\n",
    "\n",
    "# make sure all of our values are grouped correctly\n",
    "nan_count = df3.isna().sum()\n",
    "nan_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa1590ca",
   "metadata": {},
   "source": [
    "##  DATASET WITH QUASI-IDENTIFIERS GROUPED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c8d06287",
   "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>Age_group3</th>\n",
       "      <th>EducationYears_group3</th>\n",
       "      <th>HouseholdIncome_group3</th>\n",
       "      <th>DebtToIncomeRatio_group3</th>\n",
       "      <th>EmploymentLength_group3</th>\n",
       "      <th>Sex3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62-76</td>\n",
       "      <td>5-19</td>\n",
       "      <td>31-45</td>\n",
       "      <td>0-14</td>\n",
       "      <td>15-29</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17-31</td>\n",
       "      <td>5-19</td>\n",
       "      <td>16-30</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>62-76</td>\n",
       "      <td>5-19</td>\n",
       "      <td>106-120</td>\n",
       "      <td>0-14</td>\n",
       "      <td>15-29</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47-61</td>\n",
       "      <td>5-19</td>\n",
       "      <td>76-90</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32-46</td>\n",
       "      <td>5-19</td>\n",
       "      <td>91-105</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>M</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>62-76</td>\n",
       "      <td>5-19</td>\n",
       "      <td>181-195</td>\n",
       "      <td>0-14</td>\n",
       "      <td>15-29</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>47-61</td>\n",
       "      <td>5-19</td>\n",
       "      <td>76-90</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>62-76</td>\n",
       "      <td>5-19</td>\n",
       "      <td>106-120</td>\n",
       "      <td>0-14</td>\n",
       "      <td>15-29</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>32-46</td>\n",
       "      <td>5-19</td>\n",
       "      <td>181-195</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>32-46</td>\n",
       "      <td>5-19</td>\n",
       "      <td>76-90</td>\n",
       "      <td>0-14</td>\n",
       "      <td>15-29</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4329 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age_group3 EducationYears_group3 HouseholdIncome_group3  \\\n",
       "2         62-76                  5-19                  31-45   \n",
       "4         17-31                  5-19                  16-30   \n",
       "5         62-76                  5-19                106-120   \n",
       "6         47-61                  5-19                  76-90   \n",
       "7         32-46                  5-19                 91-105   \n",
       "...         ...                   ...                    ...   \n",
       "4995      62-76                  5-19                181-195   \n",
       "4996      47-61                  5-19                  76-90   \n",
       "4997      62-76                  5-19                106-120   \n",
       "4998      32-46                  5-19                181-195   \n",
       "4999      32-46                  5-19                  76-90   \n",
       "\n",
       "     DebtToIncomeRatio_group3 EmploymentLength_group3 Sex3  \n",
       "2                        0-14                   15-29    M  \n",
       "4                        0-14                    0-14    F  \n",
       "5                        0-14                   15-29    F  \n",
       "6                        0-14                    0-14    M  \n",
       "7                        0-14                    0-14    M  \n",
       "...                       ...                     ...  ...  \n",
       "4995                     0-14                   15-29    F  \n",
       "4996                     0-14                    0-14    F  \n",
       "4997                     0-14                   15-29    F  \n",
       "4998                     0-14                    0-14    F  \n",
       "4999                     0-14                   15-29    M  \n",
       "\n",
       "[4329 rows x 6 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupeQuasi_df3 = df3[[\"Age_group3\", \"EducationYears_group3\", \"HouseholdIncome_group3\", \"DebtToIncomeRatio_group3\", \"EmploymentLength_group3\", \"Sex3\"]]\n",
    "groupeQuasi_df3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d89857",
   "metadata": {},
   "source": [
    "# TRIAL 3 : EQUIVALENCE CLASSES\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4addb619",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age_group3</th>\n",
       "      <th>EducationYears_group3</th>\n",
       "      <th>HouseholdIncome_group3</th>\n",
       "      <th>DebtToIncomeRatio_group3</th>\n",
       "      <th>EmploymentLength_group3</th>\n",
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       "      <th>EquivClass3</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1-15</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>F</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17-31</td>\n",
       "      <td>5-19</td>\n",
       "      <td>1-15</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>M</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>1-15</td>\n",
       "      <td>15-29</td>\n",
       "      <td>0-14</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17-31</td>\n",
       "      <td>5-19</td>\n",
       "      <td>1-15</td>\n",
       "      <td>15-29</td>\n",
       "      <td>0-14</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>17-31</td>\n",
       "      <td>5-19</td>\n",
       "      <td>16-30</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>F</td>\n",
       "      <td>160</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",
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       "      <th>15796</th>\n",
       "      <td>77-91</td>\n",
       "      <td>5-19</td>\n",
       "      <td>16-30</td>\n",
       "      <td>15-29</td>\n",
       "      <td>30-44</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15805</th>\n",
       "      <td>77-91</td>\n",
       "      <td>5-19</td>\n",
       "      <td>31-45</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15809</th>\n",
       "      <td>77-91</td>\n",
       "      <td>5-19</td>\n",
       "      <td>31-45</td>\n",
       "      <td>0-14</td>\n",
       "      <td>30-44</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15824</th>\n",
       "      <td>77-91</td>\n",
       "      <td>5-19</td>\n",
       "      <td>46-60</td>\n",
       "      <td>0-14</td>\n",
       "      <td>15-29</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15827</th>\n",
       "      <td>77-91</td>\n",
       "      <td>5-19</td>\n",
       "      <td>46-60</td>\n",
       "      <td>0-14</td>\n",
       "      <td>30-44</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>322 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age_group3 EducationYears_group3 HouseholdIncome_group3  \\\n",
       "0          17-31                  5-19                   1-15   \n",
       "1          17-31                  5-19                   1-15   \n",
       "6          17-31                  5-19                   1-15   \n",
       "7          17-31                  5-19                   1-15   \n",
       "18         17-31                  5-19                  16-30   \n",
       "...          ...                   ...                    ...   \n",
       "15796      77-91                  5-19                  16-30   \n",
       "15805      77-91                  5-19                  31-45   \n",
       "15809      77-91                  5-19                  31-45   \n",
       "15824      77-91                  5-19                  46-60   \n",
       "15827      77-91                  5-19                  46-60   \n",
       "\n",
       "      DebtToIncomeRatio_group3 EmploymentLength_group3 Sex3  EquivClass3  \n",
       "0                         0-14                    0-14    F           32  \n",
       "1                         0-14                    0-14    M           21  \n",
       "6                        15-29                    0-14    F            2  \n",
       "7                        15-29                    0-14    M            2  \n",
       "18                        0-14                    0-14    F          160  \n",
       "...                        ...                     ...  ...          ...  \n",
       "15796                    15-29                   30-44    F            2  \n",
       "15805                     0-14                    0-14    M            2  \n",
       "15809                     0-14                   30-44    M            3  \n",
       "15824                     0-14                   15-29    F            2  \n",
       "15827                     0-14                   30-44    M            2  \n",
       "\n",
       "[322 rows x 7 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped3 = groupeQuasi_df3.groupby(['Age_group3', 'EducationYears_group3', 'HouseholdIncome_group3', 'DebtToIncomeRatio_group3', 'EmploymentLength_group3', 'Sex3']).size().reset_index(name='EquivClass3')\n",
    "grouped3[grouped3['EquivClass3'] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ca08c10f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group3                  322\n",
       "EducationYears_group3       322\n",
       "HouseholdIncome_group3      322\n",
       "DebtToIncomeRatio_group3    322\n",
       "EmploymentLength_group3     322\n",
       "Sex3                        322\n",
       "EquivClass3                 322\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of equivalence classes we have\n",
    "grouped3[grouped3['EquivClass3'] > 1].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bde52df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group3                  17-31\n",
       "EducationYears_group3        5-19\n",
       "HouseholdIncome_group3       1-15\n",
       "DebtToIncomeRatio_group3     0-14\n",
       "EmploymentLength_group3      0-14\n",
       "Sex3                            F\n",
       "EquivClass3                     2\n",
       "dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finds the min value amongst the equivalence classes\n",
    "grouped3[grouped3['EquivClass3'] > 1].min()\n",
    "\n",
    "# it seems as 2 is minimum value amongst the equivalence classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e89f0ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# smallest equivalence class = 2\n",
    "# formula 1/ total of equivalence class \n",
    "risk_id = 1/2\n",
    "risk_id\n",
    "# risk identification is 50% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a60ae0fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group3                    77-91\n",
       "EducationYears_group3         20-34\n",
       "HouseholdIncome_group3      406-420\n",
       "DebtToIncomeRatio_group3      30-44\n",
       "EmploymentLength_group3       30-44\n",
       "Sex3                              M\n",
       "EquivClass3                     163\n",
       "dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# our max value in an equivalence class is 163, I'll go higher by 5 points in our range. \n",
    "# However, this is now the point where our data is being questioned for utility\n",
    "grouped3[grouped3['EquivClass3'] > 1].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34bdf739",
   "metadata": {},
   "source": [
    "## Trial # 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "35d47049",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "      <td>25-27</td>\n",
       "      <td>13-15</td>\n",
       "      <td>22-24</td>\n",
       "      <td>0-2</td>\n",
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       "      <td>0-5</td>\n",
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       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>F</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>17</td>\n",
       "      <td>107</td>\n",
       "      <td>5.6</td>\n",
       "      <td>22</td>\n",
       "      <td>61-63</td>\n",
       "      <td>16-18</td>\n",
       "      <td>106-108</td>\n",
       "      <td>3-5</td>\n",
       "      <td>...</td>\n",
       "      <td>103-108</td>\n",
       "      <td>0-5</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "      <td>62-76</td>\n",
       "      <td>5-19</td>\n",
       "      <td>106-120</td>\n",
       "      <td>0-14</td>\n",
       "      <td>15-29</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>14</td>\n",
       "      <td>77</td>\n",
       "      <td>1.9</td>\n",
       "      <td>10</td>\n",
       "      <td>49-51</td>\n",
       "      <td>13-15</td>\n",
       "      <td>76-78</td>\n",
       "      <td>0-2</td>\n",
       "      <td>...</td>\n",
       "      <td>73-78</td>\n",
       "      <td>0-5</td>\n",
       "      <td>6-11</td>\n",
       "      <td>M</td>\n",
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       "      <td>5-19</td>\n",
       "      <td>76-90</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>16</td>\n",
       "      <td>97</td>\n",
       "      <td>14.4</td>\n",
       "      <td>11</td>\n",
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       "      <td>94-96</td>\n",
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       "      <td>6-11</td>\n",
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       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>M</td>\n",
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       "      <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>4995</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>10</td>\n",
       "      <td>196</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24</td>\n",
       "      <td>67-69</td>\n",
       "      <td>7-9</td>\n",
       "      <td>193-195</td>\n",
       "      <td>6-8</td>\n",
       "      <td>...</td>\n",
       "      <td>193-198</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "      <td>62-76</td>\n",
       "      <td>5-19</td>\n",
       "      <td>181-195</td>\n",
       "      <td>0-14</td>\n",
       "      <td>15-29</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6</td>\n",
       "      <td>49-51</td>\n",
       "      <td>13-15</td>\n",
       "      <td>82-84</td>\n",
       "      <td>12-14</td>\n",
       "      <td>...</td>\n",
       "      <td>79-84</td>\n",
       "      <td>12-17</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "      <td>47-61</td>\n",
       "      <td>5-19</td>\n",
       "      <td>76-90</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>17</td>\n",
       "      <td>108</td>\n",
       "      <td>10.8</td>\n",
       "      <td>24</td>\n",
       "      <td>73-75</td>\n",
       "      <td>16-18</td>\n",
       "      <td>106-108</td>\n",
       "      <td>9-11</td>\n",
       "      <td>...</td>\n",
       "      <td>103-108</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>F</td>\n",
       "      <td>62-76</td>\n",
       "      <td>5-19</td>\n",
       "      <td>106-120</td>\n",
       "      <td>0-14</td>\n",
       "      <td>15-29</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>19</td>\n",
       "      <td>189</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3</td>\n",
       "      <td>46-48</td>\n",
       "      <td>16-18</td>\n",
       "      <td>187-189</td>\n",
       "      <td>9-11</td>\n",
       "      <td>...</td>\n",
       "      <td>187-192</td>\n",
       "      <td>6-11</td>\n",
       "      <td>0-5</td>\n",
       "      <td>F</td>\n",
       "      <td>32-46</td>\n",
       "      <td>5-19</td>\n",
       "      <td>181-195</td>\n",
       "      <td>0-14</td>\n",
       "      <td>0-14</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "      <td>77</td>\n",
       "      <td>6.2</td>\n",
       "      <td>20</td>\n",
       "      <td>40-42</td>\n",
       "      <td>7-9</td>\n",
       "      <td>76-78</td>\n",
       "      <td>6-8</td>\n",
       "      <td>...</td>\n",
       "      <td>73-78</td>\n",
       "      <td>6-11</td>\n",
       "      <td>18-23</td>\n",
       "      <td>M</td>\n",
       "      <td>32-46</td>\n",
       "      <td>5-19</td>\n",
       "      <td>76-90</td>\n",
       "      <td>0-14</td>\n",
       "      <td>15-29</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4329 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender  Age  EducationYears  HouseholdIncome  DebtToIncomeRatio  \\\n",
       "2          1   67              14               35                9.9   \n",
       "4          0   26              16               23                1.7   \n",
       "5          0   64              17              107                5.6   \n",
       "6          1   52              14               77                1.9   \n",
       "7          1   44              16               97               14.4   \n",
       "...      ...  ...             ...              ...                ...   \n",
       "4995       0   68              10              196                8.0   \n",
       "4996       0   51              14               83               15.0   \n",
       "4997       0   75              17              108               10.8   \n",
       "4998       0   47              19              189               10.8   \n",
       "4999       1   41              10               77                6.2   \n",
       "\n",
       "      EmploymentLength Age_group EducationYears_group HouseholdIncome_group  \\\n",
       "2                   16     64-66                13-15                 34-36   \n",
       "4                    1     25-27                13-15                 22-24   \n",
       "5                   22     61-63                16-18               106-108   \n",
       "6                   10     49-51                13-15                 76-78   \n",
       "7                   11     43-45                13-15                 94-96   \n",
       "...                ...       ...                  ...                   ...   \n",
       "4995                24     67-69                  7-9               193-195   \n",
       "4996                 6     49-51                13-15                 82-84   \n",
       "4997                24     73-75                16-18               106-108   \n",
       "4998                 3     46-48                16-18               187-189   \n",
       "4999                20     40-42                  7-9                 76-78   \n",
       "\n",
       "     DebtToIncomeRatio_group  ... HouseholdIncome_group2  \\\n",
       "2                       9-11  ...                  31-36   \n",
       "4                        0-2  ...                  19-24   \n",
       "5                        3-5  ...                103-108   \n",
       "6                        0-2  ...                  73-78   \n",
       "7                      12-14  ...                  91-96   \n",
       "...                      ...  ...                    ...   \n",
       "4995                     6-8  ...                193-198   \n",
       "4996                   12-14  ...                  79-84   \n",
       "4997                    9-11  ...                103-108   \n",
       "4998                    9-11  ...                187-192   \n",
       "4999                     6-8  ...                  73-78   \n",
       "\n",
       "     DebtToIncomeRatio_group2 EmploymentLength_group2 Sex2 Age_group3  \\\n",
       "2                        6-11                   12-17    M      62-76   \n",
       "4                         0-5                     0-5    F      17-31   \n",
       "5                         0-5                   18-23    F      62-76   \n",
       "6                         0-5                    6-11    M      47-61   \n",
       "7                       12-17                    6-11    M      32-46   \n",
       "...                       ...                     ...  ...        ...   \n",
       "4995                     6-11                   18-23    F      62-76   \n",
       "4996                    12-17                     0-5    F      47-61   \n",
       "4997                     6-11                   18-23    F      62-76   \n",
       "4998                     6-11                     0-5    F      32-46   \n",
       "4999                     6-11                   18-23    M      32-46   \n",
       "\n",
       "     EducationYears_group3 HouseholdIncome_group3 DebtToIncomeRatio_group3  \\\n",
       "2                     5-19                  31-45                     0-14   \n",
       "4                     5-19                  16-30                     0-14   \n",
       "5                     5-19                106-120                     0-14   \n",
       "6                     5-19                  76-90                     0-14   \n",
       "7                     5-19                 91-105                     0-14   \n",
       "...                    ...                    ...                      ...   \n",
       "4995                  5-19                181-195                     0-14   \n",
       "4996                  5-19                  76-90                     0-14   \n",
       "4997                  5-19                106-120                     0-14   \n",
       "4998                  5-19                181-195                     0-14   \n",
       "4999                  5-19                  76-90                     0-14   \n",
       "\n",
       "     EmploymentLength_group3 Sex3  \n",
       "2                      15-29    M  \n",
       "4                       0-14    F  \n",
       "5                      15-29    F  \n",
       "6                       0-14    M  \n",
       "7                       0-14    M  \n",
       "...                      ...  ...  \n",
       "4995                   15-29    F  \n",
       "4996                    0-14    F  \n",
       "4997                   15-29    F  \n",
       "4998                    0-14    F  \n",
       "4999                   15-29    M  \n",
       "\n",
       "[4329 rows x 24 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets make a dataframe from our copy of our clean original df\n",
    "df4 = pd.DataFrame(clean_data)\n",
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f966ad",
   "metadata": {},
   "source": [
    "## Mask the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9085da29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group                   0\n",
       "EducationYears_group        0\n",
       "HouseholdIncome_group       0\n",
       "DebtToIncomeRatio_group     0\n",
       "EmploymentLength_group      0\n",
       "Sex                         0\n",
       "Age_group2                  0\n",
       "EducationYears_group2       0\n",
       "HouseholdIncome_group2      0\n",
       "DebtToIncomeRatio_group2    0\n",
       "EmploymentLength_group2     0\n",
       "Sex2                        0\n",
       "Age_group3                  0\n",
       "EducationYears_group3       0\n",
       "HouseholdIncome_group3      0\n",
       "DebtToIncomeRatio_group3    0\n",
       "EmploymentLength_group3     0\n",
       "Sex3                        0\n",
       "Age_group4                  0\n",
       "EducationYears_group4       0\n",
       "HouseholdIncome_group4      0\n",
       "DebtToIncomeRatio_group4    0\n",
       "EmploymentLength_group4     0\n",
       "Sex4                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Masking\n",
    "\n",
    "# new range: 20\n",
    "\n",
    "# step 1: Age masking \n",
    "# the min value of Age is 18, the average age is 47, while the max value is 79\n",
    "# group the ages into bins of range 20\n",
    "bins = range(17, 117, 20)\n",
    "labels = ['{}-{}'.format(i, i+19) for i in bins[:-1]]\n",
    "df4['Age_group4'] = pd.cut(df4['Age'], bins=bins, labels=labels)\n",
    "\n",
    "# step 2: Education Years masking \n",
    "# group the education years into bins of range 20\n",
    "# the min value of Education years is 6, the average is 14, while the max value is 23\n",
    "bins = range(0, 80, 20)\n",
    "labels = ['{}-{}'.format(i, i+19) for i in bins[:-1]]\n",
    "df4['EducationYears_group4'] = pd.cut(df4['EducationYears'], bins=bins, labels=labels)\n",
    "\n",
    "\n",
    "# step 3: Household income masking \n",
    "# group the household income into bins of range 20\n",
    "# the min value of Household income is 9, the average is 54, while the max value is 100\n",
    "bins = range(1, 1200, 20)\n",
    "labels = ['{}-{}'.format(i, i+19) for i in bins[:-1]]\n",
    "df4['HouseholdIncome_group4'] = pd.cut(df4['HouseholdIncome'], bins=bins, labels=labels)\n",
    "\n",
    "# step 4: Debt to income ratio masking \n",
    "# group the Debt to income ratio into bins of range 20\n",
    "# the min value of Debt to income ratio is 0, the average is 10, while the max value is 43.1\n",
    "bins = range(0, 60, 20)\n",
    "labels = ['{}-{}'.format(i, i+19) for i in bins[:-1]]\n",
    "df4['DebtToIncomeRatio_group4'] = pd.cut(df4['DebtToIncomeRatio'], bins=bins, labels=labels)\n",
    "\n",
    "# step 5: Employment length masking \n",
    "# group the Employment Length into bins of range 20\n",
    "# the min value of Employment length is 0, the average age is 10, while the max value is 52\n",
    "bins = range(0, 80, 20)\n",
    "labels = ['{}-{}'.format(i, i+19) for i in bins[:-1]]\n",
    "df4['EmploymentLength_group4'] = pd.cut(df4['EmploymentLength'], bins=bins, labels=labels)\n",
    "\n",
    "\n",
    "# step 6: Gender masking \n",
    "# Encode the gender into F or M\n",
    "\n",
    "# Define a mapping of values to F or M\n",
    "gender_map = {0: 'F', 1: 'M'}\n",
    "\n",
    "# Apply the mapping to the column\n",
    "df4['Sex4'] = df4['Gender'].map(gender_map)\n",
    "\n",
    "# lets take a look at our df after dropping DebtToIncomeRatio, EmploymentLength and creating DebtToIncomeRatio_group, and EmploymentLength_group\n",
    "df4 = df4.drop(['Age','EducationYears', 'HouseholdIncome','EmploymentLength','DebtToIncomeRatio', 'Gender'], axis=1)\n",
    "\n",
    "# make sure all of our values are grouped correctly\n",
    "nan_count = df4.isna().sum()\n",
    "nan_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20bbbab1",
   "metadata": {},
   "source": [
    "##  DATASET WITH QUASI-IDENTIFIERS GROUPED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fbba08ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Age_group4</th>\n",
       "      <th>EducationYears_group4</th>\n",
       "      <th>HouseholdIncome_group4</th>\n",
       "      <th>DebtToIncomeRatio_group4</th>\n",
       "      <th>EmploymentLength_group4</th>\n",
       "      <th>Sex4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57-76</td>\n",
       "      <td>0-19</td>\n",
       "      <td>21-40</td>\n",
       "      <td>0-19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17-36</td>\n",
       "      <td>0-19</td>\n",
       "      <td>21-40</td>\n",
       "      <td>0-19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57-76</td>\n",
       "      <td>0-19</td>\n",
       "      <td>101-120</td>\n",
       "      <td>0-19</td>\n",
       "      <td>20-39</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>37-56</td>\n",
       "      <td>0-19</td>\n",
       "      <td>61-80</td>\n",
       "      <td>0-19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37-56</td>\n",
       "      <td>0-19</td>\n",
       "      <td>81-100</td>\n",
       "      <td>0-19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>M</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>57-76</td>\n",
       "      <td>0-19</td>\n",
       "      <td>181-200</td>\n",
       "      <td>0-19</td>\n",
       "      <td>20-39</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>37-56</td>\n",
       "      <td>0-19</td>\n",
       "      <td>81-100</td>\n",
       "      <td>0-19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>57-76</td>\n",
       "      <td>0-19</td>\n",
       "      <td>101-120</td>\n",
       "      <td>0-19</td>\n",
       "      <td>20-39</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>37-56</td>\n",
       "      <td>0-19</td>\n",
       "      <td>181-200</td>\n",
       "      <td>0-19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>37-56</td>\n",
       "      <td>0-19</td>\n",
       "      <td>61-80</td>\n",
       "      <td>0-19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4329 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age_group4 EducationYears_group4 HouseholdIncome_group4  \\\n",
       "2         57-76                  0-19                  21-40   \n",
       "4         17-36                  0-19                  21-40   \n",
       "5         57-76                  0-19                101-120   \n",
       "6         37-56                  0-19                  61-80   \n",
       "7         37-56                  0-19                 81-100   \n",
       "...         ...                   ...                    ...   \n",
       "4995      57-76                  0-19                181-200   \n",
       "4996      37-56                  0-19                 81-100   \n",
       "4997      57-76                  0-19                101-120   \n",
       "4998      37-56                  0-19                181-200   \n",
       "4999      37-56                  0-19                  61-80   \n",
       "\n",
       "     DebtToIncomeRatio_group4 EmploymentLength_group4 Sex4  \n",
       "2                        0-19                    0-19    M  \n",
       "4                        0-19                    0-19    F  \n",
       "5                        0-19                   20-39    F  \n",
       "6                        0-19                    0-19    M  \n",
       "7                        0-19                    0-19    M  \n",
       "...                       ...                     ...  ...  \n",
       "4995                     0-19                   20-39    F  \n",
       "4996                     0-19                    0-19    F  \n",
       "4997                     0-19                   20-39    F  \n",
       "4998                     0-19                    0-19    F  \n",
       "4999                     0-19                    0-19    M  \n",
       "\n",
       "[4329 rows x 6 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupeQuasi_df4 = df4[[\"Age_group4\", \"EducationYears_group4\", \"HouseholdIncome_group4\", \"DebtToIncomeRatio_group4\", \"EmploymentLength_group4\", \"Sex4\"]]\n",
    "groupeQuasi_df4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec735f3",
   "metadata": {},
   "source": [
    "# TRIAL 4 : EQUIVALENCE CLASSES\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "20829145",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Age_group4</th>\n",
       "      <th>EducationYears_group4</th>\n",
       "      <th>HouseholdIncome_group4</th>\n",
       "      <th>DebtToIncomeRatio_group4</th>\n",
       "      <th>EmploymentLength_group4</th>\n",
       "      <th>Sex4</th>\n",
       "      <th>EquivClass4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17-36</td>\n",
       "      <td>0-19</td>\n",
       "      <td>1-20</td>\n",
       "      <td>0-19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>F</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17-36</td>\n",
       "      <td>0-19</td>\n",
       "      <td>1-20</td>\n",
       "      <td>0-19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>M</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17-36</td>\n",
       "      <td>0-19</td>\n",
       "      <td>1-20</td>\n",
       "      <td>20-39</td>\n",
       "      <td>0-19</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17-36</td>\n",
       "      <td>0-19</td>\n",
       "      <td>1-20</td>\n",
       "      <td>20-39</td>\n",
       "      <td>0-19</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17-36</td>\n",
       "      <td>0-19</td>\n",
       "      <td>21-40</td>\n",
       "      <td>0-19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>F</td>\n",
       "      <td>251</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6386</th>\n",
       "      <td>77-96</td>\n",
       "      <td>0-19</td>\n",
       "      <td>21-40</td>\n",
       "      <td>0-19</td>\n",
       "      <td>20-39</td>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6387</th>\n",
       "      <td>77-96</td>\n",
       "      <td>0-19</td>\n",
       "      <td>21-40</td>\n",
       "      <td>0-19</td>\n",
       "      <td>20-39</td>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6396</th>\n",
       "      <td>77-96</td>\n",
       "      <td>0-19</td>\n",
       "      <td>41-60</td>\n",
       "      <td>0-19</td>\n",
       "      <td>0-19</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6399</th>\n",
       "      <td>77-96</td>\n",
       "      <td>0-19</td>\n",
       "      <td>41-60</td>\n",
       "      <td>0-19</td>\n",
       "      <td>20-39</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6411</th>\n",
       "      <td>77-96</td>\n",
       "      <td>0-19</td>\n",
       "      <td>61-80</td>\n",
       "      <td>0-19</td>\n",
       "      <td>20-39</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age_group4 EducationYears_group4 HouseholdIncome_group4  \\\n",
       "0         17-36                  0-19                   1-20   \n",
       "1         17-36                  0-19                   1-20   \n",
       "6         17-36                  0-19                   1-20   \n",
       "7         17-36                  0-19                   1-20   \n",
       "12        17-36                  0-19                  21-40   \n",
       "...         ...                   ...                    ...   \n",
       "6386      77-96                  0-19                  21-40   \n",
       "6387      77-96                  0-19                  21-40   \n",
       "6396      77-96                  0-19                  41-60   \n",
       "6399      77-96                  0-19                  41-60   \n",
       "6411      77-96                  0-19                  61-80   \n",
       "\n",
       "     DebtToIncomeRatio_group4 EmploymentLength_group4 Sex4  EquivClass4  \n",
       "0                        0-19                    0-19    F          111  \n",
       "1                        0-19                    0-19    M           92  \n",
       "6                       20-39                    0-19    F            8  \n",
       "7                       20-39                    0-19    M            7  \n",
       "12                       0-19                    0-19    F          251  \n",
       "...                       ...                     ...  ...          ...  \n",
       "6386                     0-19                   20-39    F            6  \n",
       "6387                     0-19                   20-39    M            9  \n",
       "6396                     0-19                    0-19    F            2  \n",
       "6399                     0-19                   20-39    M            3  \n",
       "6411                     0-19                   20-39    M            2  \n",
       "\n",
       "[193 rows x 7 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped4 = groupeQuasi_df4.groupby(['Age_group4', 'EducationYears_group4', 'HouseholdIncome_group4', 'DebtToIncomeRatio_group4', 'EmploymentLength_group4', 'Sex4']).size().reset_index(name='EquivClass4')\n",
    "grouped4[grouped4['EquivClass4'] > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f3e7eeb",
   "metadata": {},
   "source": [
    "## Trial 4: Risk identification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "06d4a651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group4                  193\n",
       "EducationYears_group4       193\n",
       "HouseholdIncome_group4      193\n",
       "DebtToIncomeRatio_group4    193\n",
       "EmploymentLength_group4     193\n",
       "Sex4                        193\n",
       "EquivClass4                 193\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of equivalence classes we have\n",
    "grouped4[grouped4['EquivClass4'] > 1].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c9cfb2d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group4                  17-36\n",
       "EducationYears_group4        0-19\n",
       "HouseholdIncome_group4       1-20\n",
       "DebtToIncomeRatio_group4     0-19\n",
       "EmploymentLength_group4      0-19\n",
       "Sex4                            F\n",
       "EquivClass4                     2\n",
       "dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finds the min value amongst the equivalence classes\n",
    "grouped4[grouped4['EquivClass4'] > 1].min()\n",
    "\n",
    "# it seems as 2 is minimum value amongst the equivalence classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "89e6121f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# smallest equivalence class = 2\n",
    "# formula 1/ total of equivalence class \n",
    "risk_id = 1/2\n",
    "risk_id\n",
    "# risk identification is 50% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2cc46970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group4                    77-96\n",
       "EducationYears_group4         20-39\n",
       "HouseholdIncome_group4      401-420\n",
       "DebtToIncomeRatio_group4      20-39\n",
       "EmploymentLength_group4       40-59\n",
       "Sex4                              M\n",
       "EquivClass4                     286\n",
       "dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# our max value in an equivalence class is 286, this is already a pretty big value so I do not believe we should increase it any higher.\n",
    "grouped4[grouped4['EquivClass4'] > 1].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5725efa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group4                  17-36\n",
       "EducationYears_group4        0-19\n",
       "HouseholdIncome_group4       1-20\n",
       "DebtToIncomeRatio_group4     0-19\n",
       "EmploymentLength_group4      0-19\n",
       "Sex4                            F\n",
       "EquivClass4                     3\n",
       "dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finds the next min value amongst the equivalence classes\n",
    "grouped4[grouped4['EquivClass4'] > 2].min()\n",
    "\n",
    "# it seems as 3 is minimum value amongst the equivalence classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c9fea55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# second smallest equivalence class = 3\n",
    "# formula 1/ total of equivalence class \n",
    "risk_id = 1/3\n",
    "risk_id\n",
    "# risk identification is 33% \n",
    "# we'll use this to find different levels of risk"
   ]
  }
 ],
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