project_credit_scoring.ipynb

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      "source": [
        "# Analysis on Loan Default Risk\n",
        "\n",
        "In this project, a credit division in a bank is asking for a report that analyzes the relation of marriage status, number of children owned, income, and borrowing purpose with probability of default on loan. A data on borrowers' debt eligibility is provided. The analyzes on this data will be used to evaluate borrowers' credit scoring that predicts the borrowers' repayment ability.\n",
        "\n",
        "Hypothesis to be tested are:\n",
        "1. Having children increases the probability of default\n",
        "2. Marriage status affects the probability of default\n",
        "3. Income affects the probability of default\n",
        "4. Borrowing purpose affects the probability of default\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPsZR8xckuyg"
      },
      "source": [
        "## Import libraries and load data"
      ]
    },
    {
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        "outputId": "623f58b9-571c-46bc-ae46-cfde7a818b49"
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      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# Import all libraries\n",
        "import pandas as pd, numpy as np, seaborn as sns, plotly.express as px\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data\n",
        "path ='/content/gdrive/MyDrive/data_projects/credit_scoring_eng.csv'\n",
        "credit_scoring = pd.read_csv(path)"
      ],
      "metadata": {
        "id": "LWx6lRMIlON1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bataRzEakuym"
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      "source": [
        "## Data Exploration\n",
        "\n",
        "**Data Description**\n",
        "- `children` - number of children in the household\n",
        "- `days_employed` - borrowers' number of days working experience\n",
        "- `dob_years` - borrowers' age in year\n",
        "- `education` - borrowers' level of education\n",
        "- `education_id` - ID assigned on borrowers' level of education\n",
        "- `family_status` - borrowers' marriage status\n",
        "- `family_status_id` - ID assigned on borrowers' marriage status\n",
        "- `gender` - borrowers' gender\n",
        "- `income_type` - borrowers' occupation status\n",
        "- `debt` - whether a borrower has past due loan\n",
        "- `total_income` - monthly income\n",
        "- `purpose` - borrowers' purpose of borrowing"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explore the data and observe on:**\n",
        "1. Column name and number\n",
        "2. Number of rows, and their non-null values\n",
        "3. Number of null values in every column\n",
        "4. Sample of rows"
      ],
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        "id": "o0SCN2TsJ91r"
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        "outputId": "73533a3c-0b4e-4afd-f065-12a97bf77406"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21525 entries, 0 to 21524\n",
            "Data columns (total 12 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   children          21525 non-null  int64  \n",
            " 1   days_employed     19351 non-null  float64\n",
            " 2   dob_years         21525 non-null  int64  \n",
            " 3   education         21525 non-null  object \n",
            " 4   education_id      21525 non-null  int64  \n",
            " 5   family_status     21525 non-null  object \n",
            " 6   family_status_id  21525 non-null  int64  \n",
            " 7   gender            21525 non-null  object \n",
            " 8   income_type       21525 non-null  object \n",
            " 9   debt              21525 non-null  int64  \n",
            " 10  total_income      19351 non-null  float64\n",
            " 11  purpose           21525 non-null  object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 2.0+ MB\n"
          ]
        }
      ],
      "source": [
        "# Load general info of the dataset\n",
        "credit_scoring.info()\n"
      ]
    },
    {
      "cell_type": "code",
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            "text/plain": [
              "           children  days_employed     dob_years  education_id  \\\n",
              "count  21525.000000   19351.000000  21525.000000  21525.000000   \n",
              "mean       0.538908   63046.497661     43.293380      0.817236   \n",
              "std        1.381587  140827.311974     12.574584      0.548138   \n",
              "min       -1.000000  -18388.949901      0.000000      0.000000   \n",
              "25%        0.000000   -2747.423625     33.000000      1.000000   \n",
              "50%        0.000000   -1203.369529     42.000000      1.000000   \n",
              "75%        1.000000    -291.095954     53.000000      1.000000   \n",
              "max       20.000000  401755.400475     75.000000      4.000000   \n",
              "\n",
              "       family_status_id          debt   total_income  \n",
              "count      21525.000000  21525.000000   19351.000000  \n",
              "mean           0.972544      0.080883   26787.568355  \n",
              "std            1.420324      0.272661   16475.450632  \n",
              "min            0.000000      0.000000    3306.762000  \n",
              "25%            0.000000      0.000000   16488.504500  \n",
              "50%            0.000000      0.000000   23202.870000  \n",
              "75%            1.000000      0.000000   32549.611000  \n",
              "max            4.000000      1.000000  362496.645000  "
            ],
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              "      <th></th>\n",
              "      <th>children</th>\n",
              "      <th>days_employed</th>\n",
              "      <th>dob_years</th>\n",
              "      <th>education_id</th>\n",
              "      <th>family_status_id</th>\n",
              "      <th>debt</th>\n",
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              "      <th>count</th>\n",
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              "      <th>mean</th>\n",
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              "      <td>63046.497661</td>\n",
              "      <td>43.293380</td>\n",
              "      <td>0.817236</td>\n",
              "      <td>0.972544</td>\n",
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              "      <td>26787.568355</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3306.762000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2747.423625</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16488.504500</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1203.369529</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23202.870000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-291.095954</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>32549.611000</td>\n",
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              "      <th>max</th>\n",
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              "      <td>401755.400475</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "# Load statistic description of the dataset\n",
        "credit_scoring.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
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          "text": [
            "   children  days_employed  dob_years            education  education_id  \\\n",
            "0         1   -8437.673028         42    bachelor's degree             0   \n",
            "1         1   -4024.803754         36  secondary education             1   \n",
            "2         0   -5623.422610         33  Secondary Education             1   \n",
            "3         3   -4124.747207         32  secondary education             1   \n",
            "4         0  340266.072047         53  secondary education             1   \n",
            "5         0    -926.185831         27    bachelor's degree             0   \n",
            "6         0   -2879.202052         43    bachelor's degree             0   \n",
            "7         0    -152.779569         50  SECONDARY EDUCATION             1   \n",
            "8         2   -6929.865299         35    BACHELOR'S DEGREE             0   \n",
            "9         0   -2188.756445         41  secondary education             1   \n",
            "\n",
            "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
            "0            married                 0      F    employee     0     40620.102   \n",
            "1            married                 0      F    employee     0     17932.802   \n",
            "2            married                 0      M    employee     0     23341.752   \n",
            "3            married                 0      M    employee     0     42820.568   \n",
            "4  civil partnership                 1      F     retiree     0     25378.572   \n",
            "5  civil partnership                 1      M    business     0     40922.170   \n",
            "6            married                 0      F    business     0     38484.156   \n",
            "7            married                 0      M    employee     0     21731.829   \n",
            "8  civil partnership                 1      F    employee     0     15337.093   \n",
            "9            married                 0      M    employee     0     23108.150   \n",
            "\n",
            "                               purpose  \n",
            "0                purchase of the house  \n",
            "1                         car purchase  \n",
            "2                purchase of the house  \n",
            "3              supplementary education  \n",
            "4                    to have a wedding  \n",
            "5                purchase of the house  \n",
            "6                 housing transactions  \n",
            "7                            education  \n",
            "8                     having a wedding  \n",
            "9  purchase of the house for my family  \n"
          ]
        }
      ],
      "source": [
        "# Load data sample\n",
        "print(credit_scoring.head(10))\n"
      ]
    },
    {
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          "text": [
            "Number of unique values in every column\n",
            "\n",
            "children\n",
            " 0     14149\n",
            " 1      4818\n",
            " 2      2055\n",
            " 3       330\n",
            " 20       76\n",
            "-1        47\n",
            " 4        41\n",
            " 5         9\n",
            "Name: children, dtype: int64\n",
            "\n",
            "days_employed\n",
            "-8437.673028      1\n",
            "-3507.818775      1\n",
            " 354500.415854    1\n",
            "-769.717438       1\n",
            "-3963.590317      1\n",
            "                 ..\n",
            "-1099.957609      1\n",
            "-209.984794       1\n",
            " 398099.392433    1\n",
            "-1271.038880      1\n",
            "-1984.507589      1\n",
            "Name: days_employed, Length: 19351, dtype: int64\n",
            "\n",
            "dob_years\n",
            "35    617\n",
            "40    609\n",
            "41    607\n",
            "34    603\n",
            "38    598\n",
            "42    597\n",
            "33    581\n",
            "39    573\n",
            "31    560\n",
            "36    555\n",
            "44    547\n",
            "29    545\n",
            "30    540\n",
            "48    538\n",
            "37    537\n",
            "50    514\n",
            "43    513\n",
            "32    510\n",
            "49    508\n",
            "28    503\n",
            "45    497\n",
            "27    493\n",
            "56    487\n",
            "52    484\n",
            "47    480\n",
            "54    479\n",
            "46    475\n",
            "58    461\n",
            "57    460\n",
            "53    459\n",
            "51    448\n",
            "59    444\n",
            "55    443\n",
            "26    408\n",
            "60    377\n",
            "25    357\n",
            "61    355\n",
            "62    352\n",
            "63    269\n",
            "64    265\n",
            "24    264\n",
            "23    254\n",
            "65    194\n",
            "22    183\n",
            "66    183\n",
            "67    167\n",
            "21    111\n",
            "0     101\n",
            "68     99\n",
            "69     85\n",
            "70     65\n",
            "71     58\n",
            "20     51\n",
            "72     33\n",
            "19     14\n",
            "73      8\n",
            "74      6\n",
            "75      1\n",
            "Name: dob_years, dtype: int64\n",
            "\n",
            "education\n",
            "secondary education    13750\n",
            "bachelor's degree       4718\n",
            "SECONDARY EDUCATION      772\n",
            "Secondary Education      711\n",
            "some college             668\n",
            "BACHELOR'S DEGREE        274\n",
            "Bachelor's Degree        268\n",
            "primary education        250\n",
            "Some College              47\n",
            "SOME COLLEGE              29\n",
            "PRIMARY EDUCATION         17\n",
            "Primary Education         15\n",
            "graduate degree            4\n",
            "Graduate Degree            1\n",
            "GRADUATE DEGREE            1\n",
            "Name: education, dtype: int64\n",
            "\n",
            "education_id\n",
            "1    15233\n",
            "0     5260\n",
            "2      744\n",
            "3      282\n",
            "4        6\n",
            "Name: education_id, dtype: int64\n",
            "\n",
            "family_status\n",
            "married              12380\n",
            "civil partnership     4177\n",
            "unmarried             2813\n",
            "divorced              1195\n",
            "widow / widower        960\n",
            "Name: family_status, dtype: int64\n",
            "\n",
            "family_status_id\n",
            "0    12380\n",
            "1     4177\n",
            "4     2813\n",
            "3     1195\n",
            "2      960\n",
            "Name: family_status_id, dtype: int64\n",
            "\n",
            "gender\n",
            "F      14236\n",
            "M       7288\n",
            "XNA        1\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "income_type\n",
            "employee                       11119\n",
            "business                        5085\n",
            "retiree                         3856\n",
            "civil servant                   1459\n",
            "unemployed                         2\n",
            "entrepreneur                       2\n",
            "student                            1\n",
            "paternity / maternity leave        1\n",
            "Name: income_type, dtype: int64\n",
            "\n",
            "debt\n",
            "0    19784\n",
            "1     1741\n",
            "Name: debt, dtype: int64\n",
            "\n",
            "total_income\n",
            "17312.717    2\n",
            "42413.096    2\n",
            "31791.384    2\n",
            "21005.772    1\n",
            "18591.443    1\n",
            "            ..\n",
            "27020.895    1\n",
            "23686.835    1\n",
            "9606.294     1\n",
            "28156.762    1\n",
            "13127.587    1\n",
            "Name: total_income, Length: 19348, dtype: int64\n",
            "\n",
            "purpose\n",
            "wedding ceremony                            797\n",
            "having a wedding                            777\n",
            "to have a wedding                           774\n",
            "real estate transactions                    676\n",
            "buy commercial real estate                  664\n",
            "buying property for renting out             653\n",
            "housing transactions                        653\n",
            "transactions with commercial real estate    651\n",
            "purchase of the house                       647\n",
            "housing                                     647\n",
            "purchase of the house for my family         641\n",
            "construction of own property                635\n",
            "property                                    634\n",
            "transactions with my real estate            630\n",
            "building a real estate                      626\n",
            "buy real estate                             624\n",
            "building a property                         620\n",
            "purchase of my own house                    620\n",
            "housing renovation                          612\n",
            "buy residential real estate                 607\n",
            "buying my own car                           505\n",
            "going to university                         496\n",
            "car                                         495\n",
            "second-hand car purchase                    489\n",
            "to own a car                                480\n",
            "buying a second-hand car                    479\n",
            "cars                                        478\n",
            "to buy a car                                472\n",
            "supplementary education                     462\n",
            "car purchase                                462\n",
            "purchase of a car                           455\n",
            "university education                        453\n",
            "education                                   447\n",
            "to get a supplementary education            447\n",
            "getting an education                        443\n",
            "profile education                           436\n",
            "getting higher education                    426\n",
            "to become educated                          412\n",
            "Name: purpose, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Load unique values in every column\n",
        "print('Number of unique values in every column')\n",
        "print()\n",
        "print('children')\n",
        "print(credit_scoring['children'].value_counts())\n",
        "print()\n",
        "print('days_employed')\n",
        "print(credit_scoring['days_employed'].value_counts())\n",
        "print()\n",
        "print('dob_years')\n",
        "print(credit_scoring['dob_years'].value_counts())\n",
        "print()\n",
        "print('education')\n",
        "print(credit_scoring['education'].value_counts())\n",
        "print()\n",
        "print('education_id')\n",
        "print(credit_scoring['education_id'].value_counts())\n",
        "print()\n",
        "print('family_status')\n",
        "print(credit_scoring['family_status'].value_counts())\n",
        "print()\n",
        "print('family_status_id')\n",
        "print(credit_scoring['family_status_id'].value_counts())\n",
        "print()\n",
        "print('gender')\n",
        "print(credit_scoring['gender'].value_counts())\n",
        "print()\n",
        "print('income_type')\n",
        "print(credit_scoring['income_type'].value_counts())\n",
        "print()\n",
        "print('debt')\n",
        "print(credit_scoring['debt'].value_counts())\n",
        "print()\n",
        "print('total_income')\n",
        "print(credit_scoring['total_income'].value_counts())\n",
        "print()\n",
        "print('purpose')\n",
        "print(credit_scoring['purpose'].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Several issues observed on the dataset:\n",
        "1. Nulls in `days_employed` and `total_income`\n",
        "2. Negative values in `days_employed`\n",
        "3. Type of `days_employed` is float\n",
        "4. Several values in `education` have the same meaning\n",
        "5. Negative values in `children`"
      ],
      "metadata": {
        "id": "3Lt0ZCt6K8WZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0vbcvESkuyu",
        "outputId": "6d0ad5d5-4548-4948-841a-9386afd949b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "children               0\n",
            "days_employed       2174\n",
            "dob_years              0\n",
            "education              0\n",
            "education_id           0\n",
            "family_status          0\n",
            "family_status_id       0\n",
            "gender                 0\n",
            "income_type            0\n",
            "debt                   0\n",
            "total_income        2174\n",
            "purpose                0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Load number of nulls in every column\n",
        "print(credit_scoring.isna().sum())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRrEHaKvkuyv"
      },
      "source": [
        "There are nulls in column `days_employed` and `total_income`, and their amount is the same. There might be a relation. Test is needed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "id": "tx_MpFUzkuyv",
        "outputId": "6b0296a6-2005-4e0c-8e17-bade48916c2c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      children  days_employed  dob_years            education  education_id  \\\n",
              "0            0            NaN         65  secondary education             1   \n",
              "1            0            NaN         41  secondary education             1   \n",
              "2            0            NaN         63  secondary education             1   \n",
              "3            0            NaN         50  secondary education             1   \n",
              "4            0            NaN         54  secondary education             1   \n",
              "...        ...            ...        ...                  ...           ...   \n",
              "2169         2            NaN         47  Secondary Education             1   \n",
              "2170         1            NaN         50  secondary education             1   \n",
              "2171         0            NaN         48    BACHELOR'S DEGREE             0   \n",
              "2172         1            NaN         42  secondary education             1   \n",
              "2173         2            NaN         28  secondary education             1   \n",
              "\n",
              "          family_status  family_status_id gender    income_type  debt  \\\n",
              "0     civil partnership                 1      M        retiree     0   \n",
              "1               married                 0      M  civil servant     0   \n",
              "2             unmarried                 4      F        retiree     0   \n",
              "3               married                 0      F  civil servant     0   \n",
              "4     civil partnership                 1      F        retiree     1   \n",
              "...                 ...               ...    ...            ...   ...   \n",
              "2169            married                 0      M       business     0   \n",
              "2170  civil partnership                 1      F       employee     0   \n",
              "2171            married                 0      F       business     0   \n",
              "2172            married                 0      F       employee     0   \n",
              "2173            married                 0      F       employee     0   \n",
              "\n",
              "      total_income                   purpose  \n",
              "0              NaN         to have a wedding  \n",
              "1              NaN                 education  \n",
              "2              NaN    building a real estate  \n",
              "3              NaN  second-hand car purchase  \n",
              "4              NaN         to have a wedding  \n",
              "...            ...                       ...  \n",
              "2169           NaN         purchase of a car  \n",
              "2170           NaN          wedding ceremony  \n",
              "2171           NaN       building a property  \n",
              "2172           NaN    building a real estate  \n",
              "2173           NaN              car purchase  \n",
              "\n",
              "[2174 rows x 12 columns]"
            ],
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              "      <div>\n",
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              "      <td>NaN</td>\n",
              "      <td>wedding ceremony</td>\n",
              "    </tr>\n",
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              "      <th>2171</th>\n",
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              "      <td>48</td>\n",
              "      <td>BACHELOR'S DEGREE</td>\n",
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              "      <td>car purchase</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2174 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8969fb1-2b03-494c-af2c-b0130b4433ac')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8969fb1-2b03-494c-af2c-b0130b4433ac');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Load dataset filtered on `days_employed`\n",
        "credit_scoring.loc[credit_scoring['days_employed'].isna()].reset_index(drop=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1rSGX_bjkuyw"
      },
      "source": [
        "At a glance the filtered table shows every null row on `days_employed`and `total_income` as symmetrical. It is worth checking for a symmetrical relationship. Both column are by filtered for null to see if the number of rows are the same with the number of rows of the previous filtered table."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "id": "ZxO7IIt_kuyw",
        "outputId": "6d94f2de-6830-430b-fba9-e8c9b826adf0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      children  days_employed  dob_years            education  education_id  \\\n",
              "0            0            NaN         65  secondary education             1   \n",
              "1            0            NaN         41  secondary education             1   \n",
              "2            0            NaN         63  secondary education             1   \n",
              "3            0            NaN         50  secondary education             1   \n",
              "4            0            NaN         54  secondary education             1   \n",
              "...        ...            ...        ...                  ...           ...   \n",
              "2169         2            NaN         47  Secondary Education             1   \n",
              "2170         1            NaN         50  secondary education             1   \n",
              "2171         0            NaN         48    BACHELOR'S DEGREE             0   \n",
              "2172         1            NaN         42  secondary education             1   \n",
              "2173         2            NaN         28  secondary education             1   \n",
              "\n",
              "          family_status  family_status_id gender    income_type  debt  \\\n",
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              "1               married                 0      M  civil servant     0   \n",
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              "3               married                 0      F  civil servant     0   \n",
              "4     civil partnership                 1      F        retiree     1   \n",
              "...                 ...               ...    ...            ...   ...   \n",
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              "\n",
              "      total_income                   purpose  \n",
              "0              NaN         to have a wedding  \n",
              "1              NaN                 education  \n",
              "2              NaN    building a real estate  \n",
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              "4              NaN         to have a wedding  \n",
              "...            ...                       ...  \n",
              "2169           NaN         purchase of a car  \n",
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              "2171           NaN       building a property  \n",
              "2172           NaN    building a real estate  \n",
              "2173           NaN              car purchase  \n",
              "\n",
              "[2174 rows x 12 columns]"
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              "<p>2174 rows × 12 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Load dataset null-filtered both on `days_employed`and `total_income`\n",
        "(credit_scoring.loc[(credit_scoring['days_employed'].isna()) & (credit_scoring['total_income'].isna())]).reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It appears that the number of rows is 2,174, exactly the same number with filtered only on one of either two columns."
      ],
      "metadata": {
        "id": "k6yXV9SLg7JG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B7-oYyX6kuyx",
        "outputId": "8c43e918-8b56-4015-e599-ee660c2e365d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "children             0.0\n",
              "days_employed       10.1\n",
              "dob_years            0.0\n",
              "education            0.0\n",
              "education_id         0.0\n",
              "family_status        0.0\n",
              "family_status_id     0.0\n",
              "gender               0.0\n",
              "income_type          0.0\n",
              "debt                 0.0\n",
              "total_income        10.1\n",
              "purpose              0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# percentage of missing values\n",
        "round(credit_scoring.isna().sum()/len(credit_scoring)*100,2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9J4_4oyCkuyy"
      },
      "source": [
        "**Interim Conclusion**\n",
        "\n",
        "From filtering dataset looking at nulls in both `days_employed` and `total_income`, it is shown that their rows are symmetrical. \n",
        "\n",
        "In the next step,  a relation in both columns will be observed more.\n",
        "\n",
        "Percentage of the missing values was computed and it showed around 10% portion. Before dealing with them, more checking is needed.\n",
        "\n",
        "Income is generally related to occupation. Hence distribution on `income_type` will be checked next. \n",
        "\n",
        "And a relation to `education` and other categorical columns: `family_status` and `gender`, will be checked as well."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zk5KDX2Nkuyy"
      },
      "outputs": [],
      "source": [
        "# Save filtered dataset on missing values `days_employed`\n",
        "data_nan = credit_scoring.loc[(credit_scoring['days_employed'].isna())].reset_index(drop=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_y5azLFVkuyz",
        "outputId": "0647812a-39b3-4cee-cd5f-7669b2b19069"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "employee         0.508280\n",
            "business         0.233671\n",
            "retiree          0.189972\n",
            "civil servant    0.067617\n",
            "entrepreneur     0.000460\n",
            "Name: income_type, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Load distribution of unique values of `income_type` for rows with missing values\n",
        "print(data_nan['income_type'].value_counts(normalize=True))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_385UtNkuy0",
        "outputId": "f39ab972-8831-44c4-9414-c961c6b17ea3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "employee                       0.516562\n",
            "business                       0.236237\n",
            "retiree                        0.179141\n",
            "civil servant                  0.067782\n",
            "unemployed                     0.000093\n",
            "entrepreneur                   0.000093\n",
            "student                        0.000046\n",
            "paternity / maternity leave    0.000046\n",
            "Name: income_type, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Load distribution of unique values of `income_type` for rows in original data\n",
        "print(credit_scoring['income_type'].value_counts(normalize=True))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When comparing distribution of `income_type` in data with missing values only against the original one, the distribution seems similar, with employee's portion around 50%, followed by business 23% and retiree 18%. That means no certain pattern is shown yet. That then means no conclusion to what has caused the missing values can be drawn so far.\n",
        "\n",
        "Hence the investigation is expanded to another columns. The same checking on distribution in column `education`, `family_status`, and `gender` will be the next step."
      ],
      "metadata": {
        "id": "CGxFNbVUSyoe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLaLYEUKkuy1",
        "outputId": "cd2b3da1-8acf-47ad-e8e3-df3ec3124e90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "secondary education    0.647654\n",
              "bachelor's degree      0.228151\n",
              "SECONDARY EDUCATION    0.030819\n",
              "Secondary Education    0.029899\n",
              "some college           0.025299\n",
              "Bachelor's Degree      0.011500\n",
              "BACHELOR'S DEGREE      0.010580\n",
              "primary education      0.008740\n",
              "Some College           0.003220\n",
              "SOME COLLEGE           0.003220\n",
              "Primary Education      0.000460\n",
              "PRIMARY EDUCATION      0.000460\n",
              "Name: education, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Load distribution of unique values of `education` for rows with missing values\n",
        "data_nan['education'].value_counts(normalize=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ozOATkVnkuy1",
        "outputId": "6e9acd9f-a818-430c-d899-83400b7af808"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "secondary education    0.638792\n",
              "bachelor's degree      0.219187\n",
              "SECONDARY EDUCATION    0.035865\n",
              "Secondary Education    0.033031\n",
              "some college           0.031034\n",
              "BACHELOR'S DEGREE      0.012729\n",
              "Bachelor's Degree      0.012451\n",
              "primary education      0.011614\n",
              "Some College           0.002184\n",
              "SOME COLLEGE           0.001347\n",
              "PRIMARY EDUCATION      0.000790\n",
              "Primary Education      0.000697\n",
              "graduate degree        0.000186\n",
              "Graduate Degree        0.000046\n",
              "GRADUATE DEGREE        0.000046\n",
              "Name: education, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Load distribution of unique values of `education` for rows in original data\n",
        "credit_scoring['education'].value_counts(normalize=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fd9Ni-iDkuy1",
        "outputId": "7b2189f4-f029-441b-ac72-a64ee114bcc3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "married              0.568997\n",
              "civil partnership    0.203312\n",
              "unmarried            0.132475\n",
              "divorced             0.051518\n",
              "widow / widower      0.043698\n",
              "Name: family_status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Load distribution of unique values of `family_status` for rows with missing values\n",
        "data_nan['family_status'].value_counts(normalize=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "IDZPyFTpkuy1",
        "outputId": "5b31ac82-8831-4703-f084-e9d9eb24bf1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "married              0.575145\n",
              "civil partnership    0.194053\n",
              "unmarried            0.130685\n",
              "divorced             0.055517\n",
              "widow / widower      0.044599\n",
              "Name: family_status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Load distribution of unique values of `family_status` for rows in original data\n",
        "credit_scoring['family_status'].value_counts(normalize=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tmq3mAIokuy2",
        "outputId": "12d82c49-21ec-4da0-dbb7-a2b3a82d2f69"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F    0.682613\n",
              "M    0.317387\n",
              "Name: gender, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Load distribution of unique values of `gender` for rows with missing values\n",
        "data_nan['gender'].value_counts(normalize=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0ynT5ZJkuy2",
        "outputId": "73437125-9225-40d6-9316-a6dbe74c2a45"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F      0.661370\n",
              "M      0.338583\n",
              "XNA    0.000046\n",
              "Name: gender, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Load distribution of unique values of `gender` for rows in original data\n",
        "credit_scoring['gender'].value_counts(normalize=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCbVG9REkuy3"
      },
      "source": [
        "**Interim Conclusion**\n",
        "\n",
        "When comparing distribution of `education`, `family_status`, and`gender` in data with missing values only against the original one, the distribution seems similar. That means once again there's no certain pattern is shown. \n",
        "\n",
        "Side note came from checking the distribution in `education`, there's an implicit duplicates in the data that are caused by use of capital letter."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqsBzfbhkuy3"
      },
      "source": [
        "**Conclusion**\n",
        "\n",
        "After making comparison over distribution of `education`, `family_status`, and`gender` in data containing only rows with all missing values to ditribution of the original one, there is no particular pattern appears that would show the possible cause of the missing values. \n",
        "\n",
        "In the next step:\n",
        "1. Categorisations will be made over data in column `dob_years`, `income_type` and `purpose` to show a clearer depiction of the dataset, as well as the possible cause of the missing values.\n",
        "2. Transformation of data by handling the missing values, replacing the values that do not make sense, handling the implicit duplicates, and also the explicit ones."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZrASueEkuy4"
      },
      "source": [
        "## Data Transformation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYExHtxFjxHk"
      },
      "source": [
        "In the next step, column `education` is checked\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vr9oRgAJkuy4",
        "outputId": "686f29b1-263a-4b87-9a7e-6687b168dc77"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "secondary education    13750\n",
              "bachelor's degree       4718\n",
              "SECONDARY EDUCATION      772\n",
              "Secondary Education      711\n",
              "some college             668\n",
              "BACHELOR'S DEGREE        274\n",
              "Bachelor's Degree        268\n",
              "primary education        250\n",
              "Some College              47\n",
              "SOME COLLEGE              29\n",
              "PRIMARY EDUCATION         17\n",
              "Primary Education         15\n",
              "graduate degree            4\n",
              "Graduate Degree            1\n",
              "GRADUATE DEGREE            1\n",
              "Name: education, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Load all unique values in education\n",
        "credit_scoring['education'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "Tg1Akevnkuy4"
      },
      "outputs": [],
      "source": [
        "# Fix writing in education\n",
        "credit_scoring['education'] = credit_scoring['education'].str.lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bk4rTdGkuy5",
        "outputId": "044f8baf-13a4-4b10-99f4-2c4dbaae4396"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "secondary education    15233\n",
              "bachelor's degree       5260\n",
              "some college             744\n",
              "primary education        282\n",
              "graduate degree            6\n",
              "Name: education, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Re-check all unique values\n",
        "credit_scoring['education'].value_counts()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7TmtXCcZkuy6"
      },
      "source": [
        "In the next step, column `children` is checked"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAynIAhSkuy6",
        "outputId": "c5694003-3c9f-4bb4-85c8-d371345cb9eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 0     14149\n",
              " 1      4818\n",
              " 2      2055\n",
              " 3       330\n",
              " 20       76\n",
              "-1        47\n",
              " 4        41\n",
              " 5         9\n",
              "Name: children, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# Load unique values in `children`\n",
        "credit_scoring['children'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9OhOGhUWkuy6"
      },
      "source": [
        "There is an odd value in `children`.\n",
        "The number of children should not be negative. It is possible that some respondents misunderstood by answering -1 for answer 1. Every -1 will be changed to 1. Aside, there is value of 20, which is unlikely for number of children. Probably a 0 was misplaced, so 20 will be changed to 2."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Erj2USKqkuy7"
      },
      "outputs": [],
      "source": [
        "# Change -1 to 1\n",
        "credit_scoring.loc[credit_scoring['children'] == -1, 'children'] = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n4CCDe4Ykuy7"
      },
      "outputs": [],
      "source": [
        "# Change 20 to 2\n",
        "credit_scoring.loc[credit_scoring['children'] == 20, 'children'] = 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-EuY63JKkuy7",
        "outputId": "2c1fda1e-6cea-4dbb-a352-7aa21e0faa5b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    14149\n",
              "1     4865\n",
              "2     2131\n",
              "3      330\n",
              "4       41\n",
              "5        9\n",
              "Name: children, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Re-check unique values in `children`\n",
        "credit_scoring['children'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mUv25hFkuy7"
      },
      "source": [
        "The `days_employed` column, in addition to having missing values, is also expressed in decimal, and also has many negative numbers.\n",
        "For now we will deal with negative numbers. This is probably caused by a technical error. \n",
        "\n",
        "But before replacing them with their absolute number, their total income will be checked to conclude that indeed the issue was caused by technical error."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SMMQ-1wgkuy8",
        "outputId": "b89223aa-5f98-4f6a-90f2-451a478ff0ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15906\n",
            "0.7389547038327526\n"
          ]
        }
      ],
      "source": [
        "# Load number of negative in days_employed\n",
        "print((credit_scoring['days_employed'] < 0).sum())\n",
        "\n",
        "# Load the portion\n",
        "print(len(credit_scoring['days_employed'][credit_scoring['days_employed'] < 0]) / \n",
        "      len(credit_scoring['days_employed']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EBNdM9A7kuy8"
      },
      "source": [
        "It appears that the negative values ​​in `days_employed` comprises around 74% of total data. It also appears that each negative `days_employed` has income. So it can be concluded that these negative values are probably caused by a technical error, so they can be replaced with their absolute values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "33YYdp8bkuy9"
      },
      "outputs": [],
      "source": [
        "# Replace all values with their absolute\n",
        "\n",
        "credit_scoring['days_employed'] = credit_scoring['days_employed'].abs()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIbN5XQBkuy9",
        "outputId": "d7d37d4a-d770-4ec2-e90d-e6220c109023"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    21525\n",
              "Name: days_employed, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Re-check negative values\n",
        "(credit_scoring['days_employed'] < 0).value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the next step, unreasonable values will be checked. Maximum age of the respondents is 75 years old. A value of more than 75 years or 27,375 days in `days_employed` will then be unreasonable."
      ],
      "metadata": {
        "id": "5qrEwsfpnGuX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
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        },
        "id": "gMYUYIL3kuy-",
        "outputId": "a5a49be6-ffe1-4502-900e-9211bdca44a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       days_employed\n",
              "count    3445.000000\n",
              "mean   365004.309916\n",
              "std     21075.016396\n",
              "min    328728.720605\n",
              "25%    346639.413916\n",
              "50%    365213.306266\n",
              "75%    383246.444219\n",
              "max    401755.400475"
            ],
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          },
          "metadata": {},
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      ],
      "source": [
        "# Load unreasonable values in days_employed\n",
        "\n",
        "credit_scoring.loc[credit_scoring['days_employed'] > 27375, ['days_employed']].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6O3MlFYkuy-",
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        {
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              "16.004645760743323"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# Check the percentage\n",
        "\n",
        "len(credit_scoring.loc[credit_scoring['days_employed'] > 27375, ['days_employed']])/len(credit_scoring)*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "EKL-b3Flkuy_"
      },
      "outputs": [],
      "source": [
        "# Drop unreasonable values\n",
        "credit_scoring = credit_scoring.drop(credit_scoring.loc[credit_scoring['days_employed'] > 27375].index)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "L28sStFKkuy_",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           children  days_employed     dob_years  education_id  \\\n",
              "count  18080.000000   15906.000000  18080.000000  18080.000000   \n",
              "mean       0.552876    2353.015932     40.276825      0.798673   \n",
              "std        0.790152    2304.243851     10.974709      0.551921   \n",
              "min        0.000000      24.141633      0.000000      0.000000   \n",
              "25%        0.000000     756.371964     32.000000      0.000000   \n",
              "50%        0.000000    1630.019381     40.000000      1.000000   \n",
              "75%        1.000000    3157.480084     48.000000      1.000000   \n",
              "max        5.000000   18388.949901     75.000000      4.000000   \n",
              "\n",
              "       family_status_id          debt   total_income  \n",
              "count      18080.000000  18080.000000   15906.000000  \n",
              "mean           0.970299      0.086228   27837.509634  \n",
              "std            1.439355      0.280708   16980.846677  \n",
              "min            0.000000      0.000000    3418.824000  \n",
              "25%            0.000000      0.000000   17323.415000  \n",
              "50%            0.000000      0.000000   24181.535000  \n",
              "75%            1.000000      0.000000   33839.106500  \n",
              "max            4.000000      1.000000  362496.645000  "
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              "      <td>0.798673</td>\n",
              "      <td>0.970299</td>\n",
              "      <td>0.086228</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>756.371964</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>48.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
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          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# Re-check statisical descprition\n",
        "credit_scoring.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGXM5QDBkuzA"
      },
      "source": [
        "In the next step `dob_years` will be observed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AISLCsVakuzB",
        "outputId": "da2eb9df-aac5-454b-f527-b46188d9fd26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dob_years\n",
              "0            84\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "# Check `dob_years` less than 15\n",
        "credit_scoring.loc[credit_scoring['dob_years'] < 15, ['dob_years']].value_counts()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfq9uBu-kuzB"
      },
      "source": [
        "There is 0 value in the `dob_years`. This value is unreasonable, so rows with 0 will be dropped."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop 0 value\n",
        "credit_scoring = credit_scoring.drop(credit_scoring.loc[credit_scoring['dob_years'] == 0].index)"
      ],
      "metadata": {
        "id": "1HyPslrJrHGR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Re-check for 0 value\n",
        "credit_scoring.loc[credit_scoring['dob_years'] == 0, ['dob_years']].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vrpMNCmsrQqi",
        "outputId": "94d30ccf-363d-496d-8a38-a63de300e934"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfkJ9fHzkuzC"
      },
      "source": [
        "In the next step, column `family_status` is observed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1hvRI59kuzD",
        "outputId": "6d8c4140-9225-4d58-bf55-b083d45572ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "married              10466\n",
              "civil partnership     3579\n",
              "unmarried             2488\n",
              "divorced               988\n",
              "widow / widower        475\n",
              "Name: family_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "# Load unique values in family_status\n",
        "credit_scoring['family_status'].value_counts(dropna=False)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EETEdu37kuzE"
      },
      "source": [
        "In the next step, column `gender` is observed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eX1sMslXkuzE",
        "outputId": "a69e6fb0-93de-410d-baae-b6f0a44a5d31"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F      11373\n",
              "M       6622\n",
              "XNA        1\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "# Load unique values in gender\n",
        "credit_scoring['gender'].value_counts(dropna=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "In the `gender` column there is 1 outlier, namely XNA. Since it maked only 1, it will be changed to F which makes the larget portion of all values."
      ],
      "metadata": {
        "id": "Z3BqYSzpsLxM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xq_XLAiskuzE"
      },
      "outputs": [],
      "source": [
        "# Change XNA to F\n",
        "\n",
        "credit_scoring.loc[credit_scoring['gender'] == 'XNA', 'gender'] = 'F'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXuqW6DokuzF",
        "outputId": "b2e273d4-eb19-48a4-8eb7-664f928b0e58"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F    11374\n",
              "M     6622\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "# Re-check unique values\n",
        "credit_scoring['gender'].value_counts(dropna=False)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "22hIyZoUkuzF"
      },
      "source": [
        "In the next step, `income_type` will be observed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RlXoyx4DkuzF",
        "outputId": "f7848f3f-ad5c-466a-e3dd-2f154ca76d4c"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "employee                       11064\n",
              "business                        5065\n",
              "civil servant                   1453\n",
              "retiree                          410\n",
              "entrepreneur                       2\n",
              "student                            1\n",
              "paternity / maternity leave        1\n",
              "Name: income_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# Mari kita lihat nilai dalam kolom ini\n",
        "credit_scoring['income_type'].value_counts(dropna=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is an `entrepreneur` value which is essentially the same as `business`, so `entrepreneur` will be replaced with `business`. Also then `paternity / maternity leave` will be changed to `employee`. Also `student` will be changed with `unemployed`. These four values ​​only have 1 or 2 rows. So replacing them will not change the data significantly, while it helps to have less categories."
      ],
      "metadata": {
        "id": "O8Tfc2Cyv8XK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EFL6KPTykuzG"
      },
      "outputs": [],
      "source": [
        "# Replace the values to combine them to other values\n",
        "credit_scoring.loc[credit_scoring['income_type'] == 'paternity / maternity leave', 'income_type'] = 'employee'\n",
        "credit_scoring.loc[credit_scoring['income_type'] == 'student', 'income_type'] = 'unemployed'\n",
        "credit_scoring.loc[credit_scoring['income_type'] == 'entrepreneur', 'income_type'] = 'business'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UohvxC5DkuzH",
        "outputId": "3d6a4109-dd02-492a-9526-4e075a7e4908"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "employee         11065\n",
              "business          5067\n",
              "civil servant     1453\n",
              "retiree            410\n",
              "unemployed           1\n",
              "Name: income_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "# Re-check unique values\n",
        "credit_scoring['income_type'].value_counts()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zRfnaeV4kuzH"
      },
      "source": [
        "In the next step, duplicate is checked."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SqtM1RSbkuzH",
        "outputId": "788df3c7-34dd-4981-ac1e-bb98033958ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "71"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "# Check duplicates\n",
        "credit_scoring.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "ed2f_ISekuzI",
        "outputId": "3d0e52e7-365d-4ca8-ab99-def7be7a3235"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    children  days_employed  dob_years            education  education_id  \\\n",
              "0          0            NaN         41  secondary education             1   \n",
              "1          0            NaN         58  secondary education             1   \n",
              "2          1            NaN         34    bachelor's degree             0   \n",
              "3          0            NaN         60  secondary education             1   \n",
              "4          0            NaN         58  secondary education             1   \n",
              "..       ...            ...        ...                  ...           ...   \n",
              "66         0            NaN         64  secondary education             1   \n",
              "67         0            NaN         60  secondary education             1   \n",
              "68         0            NaN         47  secondary education             1   \n",
              "69         1            NaN         30    bachelor's degree             0   \n",
              "70         0            NaN         54  secondary education             1   \n",
              "\n",
              "        family_status  family_status_id gender income_type  debt  \\\n",
              "0             married                 0      F    employee     0   \n",
              "1   civil partnership                 1      F     retiree     0   \n",
              "2   civil partnership                 1      F    employee     0   \n",
              "3   civil partnership                 1      F     retiree     0   \n",
              "4   civil partnership                 1      F     retiree     0   \n",
              "..                ...               ...    ...         ...   ...   \n",
              "66            married                 0      F     retiree     0   \n",
              "67            married                 0      F     retiree     0   \n",
              "68            married                 0      F    employee     0   \n",
              "69            married                 0      F    employee     0   \n",
              "70            married                 0      F     retiree     0   \n",
              "\n",
              "    total_income                              purpose  \n",
              "0            NaN  purchase of the house for my family  \n",
              "1            NaN                    to have a wedding  \n",
              "2            NaN                     wedding ceremony  \n",
              "3            NaN                     wedding ceremony  \n",
              "4            NaN                    to have a wedding  \n",
              "..           ...                                  ...  \n",
              "66           NaN              supplementary education  \n",
              "67           NaN                   to become educated  \n",
              "68           NaN                   housing renovation  \n",
              "69           NaN           buy commercial real estate  \n",
              "70           NaN                 housing transactions  \n",
              "\n",
              "[71 rows x 12 columns]"
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              "      <th>family_status_id</th>\n",
              "      <th>gender</th>\n",
              "      <th>income_type</th>\n",
              "      <th>debt</th>\n",
              "      <th>total_income</th>\n",
              "      <th>purpose</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41</td>\n",
              "      <td>secondary education</td>\n",
              "      <td>1</td>\n",
              "      <td>married</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>employee</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>purchase of the house for my family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>58</td>\n",
              "      <td>secondary education</td>\n",
              "      <td>1</td>\n",
              "      <td>civil partnership</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>retiree</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>to have a wedding</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34</td>\n",
              "      <td>bachelor's degree</td>\n",
              "      <td>0</td>\n",
              "      <td>civil partnership</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>employee</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>wedding ceremony</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60</td>\n",
              "      <td>secondary education</td>\n",
              "      <td>1</td>\n",
              "      <td>civil partnership</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>retiree</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>wedding ceremony</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>58</td>\n",
              "      <td>secondary education</td>\n",
              "      <td>1</td>\n",
              "      <td>civil partnership</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>retiree</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>to have a wedding</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64</td>\n",
              "      <td>secondary education</td>\n",
              "      <td>1</td>\n",
              "      <td>married</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>retiree</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>supplementary education</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60</td>\n",
              "      <td>secondary education</td>\n",
              "      <td>1</td>\n",
              "      <td>married</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>retiree</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>to become educated</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47</td>\n",
              "      <td>secondary education</td>\n",
              "      <td>1</td>\n",
              "      <td>married</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>employee</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>housing renovation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30</td>\n",
              "      <td>bachelor's degree</td>\n",
              "      <td>0</td>\n",
              "      <td>married</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>employee</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>buy commercial real estate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>54</td>\n",
              "      <td>secondary education</td>\n",
              "      <td>1</td>\n",
              "      <td>married</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>retiree</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>housing transactions</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>71 rows × 12 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# Load duplicate rows\n",
        "credit_scoring.loc[credit_scoring.duplicated()].reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ClDVOvwZkuzI"
      },
      "outputs": [],
      "source": [
        "# Drop duplicates\n",
        "credit_scoring.drop_duplicates(inplace=True, ignore_index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "674e3012-71b6-406b-cd48-0f67c1d1eed7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "# Re-check duplicates\n",
        "credit_scoring.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGSQUtfakuzJ",
        "outputId": "c84c6ab5-c4af-459e-d70f-0c901133a6af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17925 entries, 0 to 17924\n",
            "Data columns (total 12 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   children          17925 non-null  int64  \n",
            " 1   days_employed     15832 non-null  float64\n",
            " 2   dob_years         17925 non-null  int64  \n",
            " 3   education         17925 non-null  object \n",
            " 4   education_id      17925 non-null  int64  \n",
            " 5   family_status     17925 non-null  object \n",
            " 6   family_status_id  17925 non-null  int64  \n",
            " 7   gender            17925 non-null  object \n",
            " 8   income_type       17925 non-null  object \n",
            " 9   debt              17925 non-null  int64  \n",
            " 10  total_income      15832 non-null  float64\n",
            " 11  purpose           17925 non-null  object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ],
      "source": [
        "# Load general info on dataset\n",
        "credit_scoring.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QO8DS8BWkuzJ"
      },
      "source": [
        "Now the new dataset has fewer rows, that is 21,454. The number of columns remains 12. And with fewer missing values, that is 2,103 missing values ​​in each of the days_employed and total_income columns.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2I7HhsWkkuzK"
      },
      "source": [
        "# Work with missing values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rGiypQYukuzO"
      },
      "source": [
        "### Handle missing values in `total_income`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "51wrwwTfkuzO"
      },
      "source": [
        "In the next steps, the missing values ​​in total_income will be worked first. A new column that categorise ages will be created. From the description of statistics, it shows facts about age. The first, and third quartiles are 33 and 53, respectively. The median is 42. The highest is 75. And the mean is 43.\n",
        "Therefore categorisation of age will be made using the function below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J18oaClukuzP"
      },
      "outputs": [],
      "source": [
        "# Define function to categorize age\n",
        "def assign_age_group(age):\n",
        "    try:\n",
        "        if age < 18 or np.isnan(age):\n",
        "            return 'below18'\n",
        "        elif age < 30:\n",
        "            return 'young_adult'\n",
        "        elif age < 64:\n",
        "            return 'adult'\n",
        "        else:\n",
        "            return 'elderly'\n",
        "    except:\n",
        "        return 'NA'\n",
        "   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-OEk773ukuzP",
        "outputId": "b5d5ad1a-74ad-4dbc-c077-46615590b451"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "below18\n",
            "below18\n",
            "young_adult\n",
            "adult\n",
            "elderly\n"
          ]
        }
      ],
      "source": [
        "# Test function to see if it works\n",
        "test_Nan = assign_age_group(float('Nan'))\n",
        "print(test_Nan)\n",
        "test_17 = assign_age_group(17)\n",
        "print(test_17)\n",
        "test_27 = assign_age_group(27)\n",
        "print(test_27)\n",
        "test_40 = assign_age_group(40)\n",
        "print(test_40)\n",
        "test_82 = assign_age_group(82)\n",
        "print(test_82)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ydmr226DkuzP"
      },
      "outputs": [],
      "source": [
        "# Create new column for age using deob_years\n",
        "credit_scoring['dob_group'] = credit_scoring['dob_years'].apply(assign_age_group)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cZyl7sI6kuzQ",
        "outputId": "2fdb912f-985a-4abc-991e-6eab40c6d376"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "adult          14477\n",
              "young_adult     3173\n",
              "elderly          275\n",
              "Name: dob_group, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "# Check unique values in the new column\n",
        "credit_scoring['dob_group'].value_counts()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhT2SrpakuzQ"
      },
      "source": [
        "From the available observed data, factors that usually relate to a person's income are:\n",
        "1. Age\n",
        "2. Income source/ type\n",
        "3. Education level\n",
        "\n",
        "In the next steps, a new dataset that is clean from missing values will be created. Then typical data (mean or median) of total_income that are based on those 3 factors will be inferred."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtDvkmgZkuzR"
      },
      "source": [
        "Before filling in missing values, a new dataset excluding missing values will be created."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "muAbvgapkuzR",
        "outputId": "8f2147db-a004-4d0d-f5ca-9e3b9a0fe550"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   children  days_employed  dob_years            education  education_id  \\\n",
              "0         1    8437.673028         42    bachelor's degree             0   \n",
              "1         1    4024.803754         36  secondary education             1   \n",
              "2         0    5623.422610         33  secondary education             1   \n",
              "3         3    4124.747207         32  secondary education             1   \n",
              "4         0     926.185831         27    bachelor's degree             0   \n",
              "\n",
              "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
              "0            married                 0      F    employee     0     40620.102   \n",
              "1            married                 0      F    employee     0     17932.802   \n",
              "2            married                 0      M    employee     0     23341.752   \n",
              "3            married                 0      M    employee     0     42820.568   \n",
              "4  civil partnership                 1      M    business     0     40922.170   \n",
              "\n",
              "                   purpose    dob_group  \n",
              "0    purchase of the house        adult  \n",
              "1             car purchase        adult  \n",
              "2    purchase of the house        adult  \n",
              "3  supplementary education        adult  \n",
              "4    purchase of the house  young_adult  "
            ],
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              "      <td>17932.802</td>\n",
              "      <td>car purchase</td>\n",
              "      <td>adult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>5623.422610</td>\n",
              "      <td>33</td>\n",
              "      <td>secondary education</td>\n",
              "      <td>1</td>\n",
              "      <td>married</td>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "      <td>employee</td>\n",
              "      <td>0</td>\n",
              "      <td>23341.752</td>\n",
              "      <td>purchase of the house</td>\n",
              "      <td>adult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>4124.747207</td>\n",
              "      <td>32</td>\n",
              "      <td>secondary education</td>\n",
              "      <td>1</td>\n",
              "      <td>married</td>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "      <td>employee</td>\n",
              "      <td>0</td>\n",
              "      <td>42820.568</td>\n",
              "      <td>supplementary education</td>\n",
              "      <td>adult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>926.185831</td>\n",
              "      <td>27</td>\n",
              "      <td>bachelor's degree</td>\n",
              "      <td>0</td>\n",
              "      <td>civil partnership</td>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>business</td>\n",
              "      <td>0</td>\n",
              "      <td>40922.170</td>\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "# Create new dataset excluding missing values, and then load data sample\n",
        "data_clean = credit_scoring.dropna().reset_index(drop=True)\n",
        "data_clean.head()"
      ]
    },
    {
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      "metadata": {
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        },
        "id": "1kFQkRoakuzR",
        "outputId": "aaa21033-67f0-4497-8529-282a1dd7dd75"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15832 entries, 0 to 15831\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   children          15832 non-null  int64  \n",
            " 1   days_employed     15832 non-null  float64\n",
            " 2   dob_years         15832 non-null  int64  \n",
            " 3   education         15832 non-null  object \n",
            " 4   education_id      15832 non-null  int64  \n",
            " 5   family_status     15832 non-null  object \n",
            " 6   family_status_id  15832 non-null  int64  \n",
            " 7   gender            15832 non-null  object \n",
            " 8   income_type       15832 non-null  object \n",
            " 9   debt              15832 non-null  int64  \n",
            " 10  total_income      15832 non-null  float64\n",
            " 11  purpose           15832 non-null  object \n",
            " 12  dob_group         15832 non-null  object \n",
            "dtypes: float64(2), int64(5), object(6)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ],
      "source": [
        "# Load general info on the new clean dataset\n",
        "data_clean.info()"
      ]
    },
    {
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        {
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            "text/plain": [
              "                     mean       median\n",
              "             total_income total_income\n",
              "dob_group                             \n",
              "adult        28330.324243   24565.6280\n",
              "elderly      30135.480741   25648.0425\n",
              "young_adult  25559.861636   22794.7330"
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        "# Load mean and median of total_income based on dob_group\n",
        "data_clean.pivot_table(index = 'dob_group', values = 'total_income', aggfunc=['mean', 'median'])"
      ]
    },
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              "                             mean       median\n",
              "                     total_income total_income\n",
              "education                                     \n",
              "bachelor's degree    33895.773014   28744.4340\n",
              "graduate degree      27772.929500   25161.5835\n",
              "primary education    22990.762982   20823.9590\n",
              "secondary education  25506.021789   22678.9820\n",
              "some college         29419.492013   26119.0075"
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-af4bb6da-6486-487b-87e8-20394b9b46f7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "# Load mean and median of total_income based on education\n",
        "data_clean.pivot_table(index = 'education', values = 'total_income', aggfunc=['mean', 'median'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load mean and median of total_income based on income_type\n",
        "data_clean.pivot_table(index = 'income_type', values = 'total_income', aggfunc=['mean', 'median'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "fkjesmZPxaw4",
        "outputId": "7ff9e9f2-cfb9-4a95-9a47-4a11eeabea6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       mean       median\n",
              "               total_income total_income\n",
              "income_type                             \n",
              "business       32407.766937   27571.0825\n",
              "civil servant  27361.316126   24083.5065\n",
              "employee       25822.952345   22814.0140\n",
              "unemployed     15712.260000   15712.2600"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2270d190-e1c9-49b7-adad-2c090397fee7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>total_income</th>\n",
              "      <th>total_income</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>income_type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>business</th>\n",
              "      <td>32407.766937</td>\n",
              "      <td>27571.0825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>civil servant</th>\n",
              "      <td>27361.316126</td>\n",
              "      <td>24083.5065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>employee</th>\n",
              "      <td>25822.952345</td>\n",
              "      <td>22814.0140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unemployed</th>\n",
              "      <td>15712.260000</td>\n",
              "      <td>15712.2600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2270d190-e1c9-49b7-adad-2c090397fee7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2270d190-e1c9-49b7-adad-2c090397fee7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2270d190-e1c9-49b7-adad-2c090397fee7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "Wr2M8tTtkuzU",
        "outputId": "3255df5b-9a30-4de8-d998-bb7524bd2661"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f64812c7160>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Check for outliers\n",
        "\n",
        "sns.boxplot(data_clean['total_income'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MGEnHeaZkuzU"
      },
      "source": [
        "The original data has a median total_income of 23,202 and a mean of 26,787. The highest income is 36,249 and the lowest is 3,306.\n",
        "\n",
        "Just like the median and mean of the clean dataset, it appears that they differ significantly. Since the data contains many outliers, its median is a better representative of typical value. So the median will be used to fill in the missing values.\n",
        "\n",
        "The most related factors to income are age group and type of income. Therefore the median that is based on thse two columns will be used."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Hg38Ny0lkuzU",
        "outputId": "dc8e2e98-2f43-4b5b-db6d-b6ef78fac4ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     dob_group    income_type  total_income\n",
              "0        adult       business    28372.6700\n",
              "1        adult  civil servant    24137.1830\n",
              "2        adult       employee    23054.3295\n",
              "3      elderly       business    29127.8650\n",
              "4      elderly  civil servant    26089.6870\n",
              "5      elderly       employee    24643.1985\n",
              "6  young_adult       business    25599.1250\n",
              "7  young_adult  civil servant    23504.5105\n",
              "8  young_adult       employee    21537.4790\n",
              "9  young_adult     unemployed    15712.2600"
            ],
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              "\n",
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              "      <th>total_income</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>young_adult</td>\n",
              "      <td>civil servant</td>\n",
              "      <td>23504.5105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>young_adult</td>\n",
              "      <td>employee</td>\n",
              "      <td>21537.4790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>young_adult</td>\n",
              "      <td>unemployed</td>\n",
              "      <td>15712.2600</td>\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "# Load median of total_income based on dob_group and income_type\n",
        "data_clean.groupby(['dob_group', 'income_type'])['total_income'].median().reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v9CdjfYzkuzV"
      },
      "outputs": [],
      "source": [
        "#  Define function to fill in missing values\n",
        "def fill_na(df, agg_column_1, agg_column_2, value_column):\n",
        "    grouped_values = df.groupby([agg_column_1, agg_column_2])[value_column].median().reset_index()\n",
        "    size = len(grouped_values)\n",
        "    for n in range(size):\n",
        "        group1 = grouped_values[agg_column_1][n]\n",
        "        group2 = grouped_values[agg_column_2][n]\n",
        "        value = grouped_values[value_column][n]\n",
        "        df.loc[(df[agg_column_1]==group1) & (df[agg_column_2]==group2) & (df[value_column].isna()), value_column] = value\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dq4eN5B0kuzV"
      },
      "outputs": [],
      "source": [
        "# Apply function to create new dataset with filled-in missing values\n",
        "credit_scoring = fill_na(df=credit_scoring, \n",
        "                         agg_column_1='dob_group', \n",
        "                         agg_column_2='income_type', \n",
        "                         value_column='total_income')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7lR6zZfwkuzX",
        "outputId": "d7568172-ed0c-47e6-d0e1-775ee593d261"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17925 entries, 0 to 17924\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   children          17925 non-null  int64  \n",
            " 1   days_employed     15832 non-null  float64\n",
            " 2   dob_years         17925 non-null  int64  \n",
            " 3   education         17925 non-null  object \n",
            " 4   education_id      17925 non-null  int64  \n",
            " 5   family_status     17925 non-null  object \n",
            " 6   family_status_id  17925 non-null  int64  \n",
            " 7   gender            17925 non-null  object \n",
            " 8   income_type       17925 non-null  object \n",
            " 9   debt              17925 non-null  int64  \n",
            " 10  total_income      17542 non-null  float64\n",
            " 11  purpose           17925 non-null  object \n",
            " 12  dob_group         17925 non-null  object \n",
            "dtypes: float64(2), int64(5), object(6)\n",
            "memory usage: 1.8+ MB\n"
          ]
        }
      ],
      "source": [
        "# Load general info of the dataset\n",
        "credit_scoring.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vRjFi1GGkuzY"
      },
      "source": [
        "###  Handle missing values in `days_employed`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14mXSqabkuzY"
      },
      "source": [
        "From the available observed data, factors that usually relate to a person's days working are:\n",
        "1. Age\n",
        "2. Education level\n",
        "\n",
        "In the next steps, the typical data (mean or median) of days_employed that are based on those 2 factors will be inferred."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
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          "height": 175
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        "id": "GhZuFaB7kuzZ",
        "outputId": "ac6fa7d9-49ef-4724-e8b9-4fd4f86f58f0"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  median         mean\n",
              "dob_group                            \n",
              "adult        1874.546554  2589.399590\n",
              "elderly      2579.458493  4020.774858\n",
              "young_adult   996.761544  1209.928267"
            ],
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              "      <th>median</th>\n",
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              "  <tbody>\n",
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              "      <th>adult</th>\n",
              "      <td>1874.546554</td>\n",
              "      <td>2589.399590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>elderly</th>\n",
              "      <td>2579.458493</td>\n",
              "      <td>4020.774858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>young_adult</th>\n",
              "      <td>996.761544</td>\n",
              "      <td>1209.928267</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "# Load median and mean in days_employed based on dob_group\n",
        "data_clean.groupby('dob_group')['days_employed'].agg(['median', 'mean'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "8IYiXIG1kuza",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          median         mean\n",
              "education                                    \n",
              "bachelor's degree    1611.056758  2277.650856\n",
              "graduate degree      3851.735057  3520.186537\n",
              "primary education    1189.581396  1940.672957\n",
              "secondary education  1686.419677  2435.122216\n",
              "some college         1151.756703  1567.252403"
            ],
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              "    <tr>\n",
              "      <th>graduate degree</th>\n",
              "      <td>3851.735057</td>\n",
              "      <td>3520.186537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>primary education</th>\n",
              "      <td>1189.581396</td>\n",
              "      <td>1940.672957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>secondary education</th>\n",
              "      <td>1686.419677</td>\n",
              "      <td>2435.122216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>some college</th>\n",
              "      <td>1151.756703</td>\n",
              "      <td>1567.252403</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "# Load median and mean in days_employed based on education\n",
        "data_clean.groupby('education')['days_employed'].agg(['median', 'mean'])\n"
      ]
    },
    {
      "cell_type": "code",
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6481207190>"
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Check for outliers\n",
        "sns.boxplot(data_clean['days_employed'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O07Xsd8Nkuzb"
      },
      "source": [
        "There are many outliers in `days_employed`. It is better then taking the median as a representative of typical values, and using it to fill in the missing values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z7TfGntZkuzd"
      },
      "outputs": [],
      "source": [
        "# Fill in missing values in days_employed by applying the already defined function\n",
        "credit_scoring = fill_na(df=credit_scoring,\n",
        "                         agg_column_1='dob_group', \n",
        "                         agg_column_2='education', \n",
        "                         value_column='days_employed')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zL75HiR7kuzd",
        "outputId": "2a7446be-6314-48e6-8bb0-14ec11efea49"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17925 entries, 0 to 17924\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   children          17925 non-null  int64  \n",
            " 1   days_employed     17925 non-null  float64\n",
            " 2   dob_years         17925 non-null  int64  \n",
            " 3   education         17925 non-null  object \n",
            " 4   education_id      17925 non-null  int64  \n",
            " 5   family_status     17925 non-null  object \n",
            " 6   family_status_id  17925 non-null  int64  \n",
            " 7   gender            17925 non-null  object \n",
            " 8   income_type       17925 non-null  object \n",
            " 9   debt              17925 non-null  int64  \n",
            " 10  total_income      17542 non-null  float64\n",
            " 11  purpose           17925 non-null  object \n",
            " 12  dob_group         17925 non-null  object \n",
            "dtypes: float64(2), int64(5), object(6)\n",
            "memory usage: 1.8+ MB\n"
          ]
        }
      ],
      "source": [
        "# Check general info on the new dataset\n",
        "credit_scoring.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3VSyxhdkuzd"
      },
      "source": [
        "The missing values in days_employed have been filled in.\n",
        "\n",
        "In the next step, data type of days_employed is changed to type integer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vmG_16Vjkuze"
      },
      "outputs": [],
      "source": [
        "# Change data type\n",
        "credit_scoring['days_employed'] = credit_scoring['days_employed'].astype('int')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WwnX7zOUkuze"
      },
      "source": [
        "## Data Categorization\n",
        "\n",
        "In order to address the mentioned hypothesis, categorisation of values in total_income and purpose will be made."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzmVzm9vkuzf"
      },
      "source": [
        "In the next step, unique values in 'purpose' is checked"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPHAm6wNkuzf",
        "outputId": "d14a2c6d-20e4-403d-b6bc-b3f3cf4de424"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['purchase of the house', 'car purchase', 'supplementary education',\n",
              "       'housing transactions', 'education', 'having a wedding',\n",
              "       'purchase of the house for my family', 'buy real estate',\n",
              "       'buy commercial real estate', 'to have a wedding',\n",
              "       'buy residential real estate', 'construction of own property',\n",
              "       'property', 'building a property', 'buying my own car',\n",
              "       'buying a second-hand car', 'building a real estate', 'housing',\n",
              "       'transactions with my real estate', 'cars', 'to become educated',\n",
              "       'second-hand car purchase', 'getting an education', 'car',\n",
              "       'wedding ceremony', 'to get a supplementary education',\n",
              "       'purchase of my own house', 'real estate transactions',\n",
              "       'getting higher education',\n",
              "       'transactions with commercial real estate', 'to own a car',\n",
              "       'purchase of a car', 'profile education', 'university education',\n",
              "       'to buy a car', 'buying property for renting out',\n",
              "       'housing renovation', 'going to university'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "# Load unique values in purpose\n",
        "credit_scoring['purpose'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OyHRKvQSkuzf"
      },
      "source": [
        "It appears that unique values in 'purpose' can be grouped into general topics:\n",
        "1. Wedding\n",
        "2. Property\n",
        "3. Education\n",
        "4. Car\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HpxHiX4Ekuzg"
      },
      "outputs": [],
      "source": [
        "# Define function to categorize the values into the general group\n",
        "def purpose_topic(row):\n",
        "    if row in values_wedding:\n",
        "        return 'wedding'\n",
        "    elif row in values_property:\n",
        "        return 'property'\n",
        "    elif row in values_education:\n",
        "        return 'education'\n",
        "    elif row in values_car:\n",
        "         return 'car'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WYFpxz7-kuzg"
      },
      "outputs": [],
      "source": [
        "# Save values for wedding group\n",
        "values_wedding = ['to have a wedding', 'having a wedding', 'wedding ceremony']\n",
        "\n",
        "# Save values for property group\n",
        "values_property = ['purchase of the house', 'housing transactions', 'purchase of the house for my family', 'buy real estate', 'buy commercial real estate', 'buy residential real estate', 'construction of own property', 'property', 'building a property',\n",
        "      'transactions with commercial real estate', 'building a real estate', 'housing', 'transactions with my real estate', 'purchase of my own house', 'real estate transactions', 'buying property for renting out', 'housing renovation']\n",
        "\n",
        "# Save values for education group\n",
        "values_education = ['supplementary education', 'education', 'to become educated', 'getting an education', 'to get a supplementary education', \n",
        "                    'getting higher education', 'profile education', 'university education', 'going to university']\n",
        "\n",
        "# Save values for car group\n",
        "values_car = ['car purchase', 'buying a second-hand car',\n",
        "       'buying my own car', 'cars', 'second-hand car purchase', 'car', 'to own a car', 'purchase of a car', 'to buy a car']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3U6XQWhkuzg",
        "outputId": "4ff59ea8-dbb9-4867-ce95-b97ea1b7e06c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "property     9070\n",
              "car          3572\n",
              "education    3347\n",
              "wedding      1936\n",
              "Name: purpose_topic, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ],
      "source": [
        "# Create new column for general group by applying the function and check the values\n",
        "credit_scoring['purpose_topic'] = credit_scoring['purpose'].apply(purpose_topic)\n",
        "credit_scoring['purpose_topic'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qI6EgZVqkuzh"
      },
      "source": [
        "In the next step, total_income will be categorised and a new column will be created."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FmFRBnclkuzh",
        "outputId": "98d42a2d-14e3-44a2-f028-88d08f4cc154"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3418.824      1\n",
              "4036.463      1\n",
              "4212.770      1\n",
              "4245.348      1\n",
              "4494.861      1\n",
              "             ..\n",
              "273809.483    1\n",
              "274402.943    1\n",
              "276204.162    1\n",
              "352136.354    1\n",
              "362496.645    1\n",
              "Name: total_income, Length: 15835, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ],
      "source": [
        "# Load all unique values\n",
        "credit_scoring['total_income'].value_counts().sort_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQOwcoGPkuzi",
        "outputId": "618978eb-7880-4def-ca9a-6a1cccb0eafa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     17542.0\n",
              "mean      27511.0\n",
              "std       16202.0\n",
              "min        3419.0\n",
              "25%       17992.0\n",
              "50%       23724.0\n",
              "75%       32327.0\n",
              "max      362497.0\n",
              "Name: total_income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ],
      "source": [
        "# Load statistical description\n",
        "round(credit_scoring['total_income'].describe(),0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "22uylON-kuzi",
        "outputId": "eed3db8a-6270-49d9-95d9-7b895fda2479"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Check for outliers\n",
        "px.box(credit_scoring, y='total_income')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzpbxfVmkuzj"
      },
      "source": [
        "There are many outliers in `total_income`. The minimum value is 3.306. The maximum is 362.496, which falls beyond three times of the standard deviation from the mean. \n",
        "\n",
        "In categorising, two middle range around the median with the same size and touching the upper fence is defined. While top and bottom range are to contain the top and bottom outliers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QPczxO4qkuzj"
      },
      "outputs": [],
      "source": [
        "# Define function for categorisation\n",
        "def income_class(income):\n",
        "    try:\n",
        "        if income < 8000:\n",
        "            return 'lower'\n",
        "        elif 8000 <= income < 23000:\n",
        "            return 'lower middle'\n",
        "        elif 23000 <= income < 38000:\n",
        "            return 'middle'\n",
        "        else:\n",
        "            return 'upper middle'\n",
        "    except:\n",
        "        return 'NA'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AsMZv3B7kuzk"
      },
      "outputs": [],
      "source": [
        "# Create a column containing the categories by applying the function\n",
        "credit_scoring['income_class'] = credit_scoring['total_income'].apply(income_class)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XNyLtTIokuzl",
        "outputId": "dbc1bdfb-c097-48f6-a32a-68556cb06350"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "lower middle    40.59\n",
              "middle          40.15\n",
              "upper middle    18.20\n",
              "lower            1.07\n",
              "Name: income_class, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ],
      "source": [
        "# Load distribution of unique values\n",
        "round(credit_scoring['income_class'].value_counts(normalize=True)*100,2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2NmuBj1jkuzu"
      },
      "source": [
        "## Checking the hypothesis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the next step, the hypothesis are checked"
      ],
      "metadata": {
        "id": "5t0TBX6dG-VW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96DzvcWbkuzu",
        "outputId": "f85a07d8-4fce-4c03-bc0b-3f90872ee086"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17925 entries, 0 to 17924\n",
            "Data columns (total 15 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   children          17925 non-null  int64  \n",
            " 1   days_employed     17925 non-null  int64  \n",
            " 2   dob_years         17925 non-null  int64  \n",
            " 3   education         17925 non-null  object \n",
            " 4   education_id      17925 non-null  int64  \n",
            " 5   family_status     17925 non-null  object \n",
            " 6   family_status_id  17925 non-null  int64  \n",
            " 7   gender            17925 non-null  object \n",
            " 8   income_type       17925 non-null  object \n",
            " 9   debt              17925 non-null  int64  \n",
            " 10  total_income      17542 non-null  float64\n",
            " 11  purpose           17925 non-null  object \n",
            " 12  dob_group         17925 non-null  object \n",
            " 13  purpose_topic     17925 non-null  object \n",
            " 14  income_class      17925 non-null  object \n",
            "dtypes: float64(1), int64(6), object(8)\n",
            "memory usage: 2.1+ MB\n"
          ]
        }
      ],
      "source": [
        "# Load general info of the dataset\n",
        "credit_scoring.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHyfccgBkuzu",
        "outputId": "7e095b4f-31f3-44cc-bb2f-b26ea325afe8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ],
      "source": [
        "# Re-check for duplicate\n",
        "credit_scoring.duplicated().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KVpQ8SIckuzu"
      },
      "source": [
        "### **Is there a correlation between having children and paying off on time?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "colab": {
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          "height": 300
        },
        "id": "N64kPxVJkuzv",
        "outputId": "da689341-294b-4fde-d8f8-b67eabf1b0d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "debt            0       1  Total\n",
              "children                        \n",
              "0          9993.0   892.0  10885\n",
              "1          4149.0   430.0   4579\n",
              "2          1890.0   200.0   2090\n",
              "3           296.0    26.0    322\n",
              "4            36.0     4.0     40\n",
              "5             9.0     NaN      9\n",
              "Total     16373.0  1552.0  17925"
            ],
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              "\n",
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              "          document.querySelector('#df-6c707610-457e-442d-ade0-95100b4f4287 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6c707610-457e-442d-ade0-95100b4f4287');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ],
      "source": [
        "# Load pivot table that gorups event of timely payment and default by number of children\n",
        "credit_scoring.pivot_table(index='children', columns='debt', values='dob_years', aggfunc='count', margins=True,margins_name='Total')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCy20sSPkuzv",
        "outputId": "9819a6f3-4fd4-4f2b-bdb8-3f5bebc37cfd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "children\n",
              "0    892\n",
              "1    430\n",
              "2    200\n",
              "3     26\n",
              "4      4\n",
              "Name: debt, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ],
      "source": [
        "# Check number of of event of default based on number of children\n",
        "debt_1 = credit_scoring[credit_scoring['debt'] == 1]\n",
        "debt_1.groupby('children')['debt'].count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eUNYH8e3kuzv"
      },
      "source": [
        "**Conclusion**\n",
        "\n",
        "A relationship between having children and timeliness of repayment is observed. From the pivot table that compares the distribution of defaults and non-defaults, as well as from the grouping of defaults based on the number of children, it appears that there is no trend exists indicating such a relationship. In the event of default, the borrowers mostly do not have children. The event of defaults decreases as the number of children increases. However, in the event of timely repayment, a similar trend exists. \n",
        "\n",
        "In general, the distribution ratio in each value of the number of children between on timely repayment : default is around 91% : 9%. Hence it can be concluded that there is no relationship between the number of children and event of default."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TV-QWLdukuzw"
      },
      "source": [
        "### **Is there a correlation between family status and timely repayment?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "pzQlOsb2kuzw",
        "outputId": "bf06a4cb-fa2a-40d2-acaf-e70d9ece6585"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "debt                   0     1  Total    % timely      % late\n",
              "family_status                                                \n",
              "civil partnership   3196   357   3553   19.519941   23.002577\n",
              "divorced             914    74    988    5.582361    4.768041\n",
              "married             9596   829  10425   58.608685   53.414948\n",
              "unmarried           2224   261   2485   13.583338   16.817010\n",
              "widow / widower      443    31    474    2.705674    1.997423\n",
              "Total              16373  1552  17925  100.000000  100.000000"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th>debt</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
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              "      <th>% timely</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>civil partnership</th>\n",
              "      <td>3196</td>\n",
              "      <td>357</td>\n",
              "      <td>3553</td>\n",
              "      <td>19.519941</td>\n",
              "      <td>23.002577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>divorced</th>\n",
              "      <td>914</td>\n",
              "      <td>74</td>\n",
              "      <td>988</td>\n",
              "      <td>5.582361</td>\n",
              "      <td>4.768041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>married</th>\n",
              "      <td>9596</td>\n",
              "      <td>829</td>\n",
              "      <td>10425</td>\n",
              "      <td>58.608685</td>\n",
              "      <td>53.414948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unmarried</th>\n",
              "      <td>2224</td>\n",
              "      <td>261</td>\n",
              "      <td>2485</td>\n",
              "      <td>13.583338</td>\n",
              "      <td>16.817010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>widow / widower</th>\n",
              "      <td>443</td>\n",
              "      <td>31</td>\n",
              "      <td>474</td>\n",
              "      <td>2.705674</td>\n",
              "      <td>1.997423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total</th>\n",
              "      <td>16373</td>\n",
              "      <td>1552</td>\n",
              "      <td>17925</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ],
      "source": [
        "# Load pivot table gorups event of timely payment and default for each family_status \n",
        "family_pivot = credit_scoring.pivot_table(index='family_status', columns='debt', values='dob_years', aggfunc='count', margins=True, margins_name='Total')\n",
        "family_pivot['% timely'] = family_pivot[0]/(family_pivot.loc['Total',0])*100\n",
        "family_pivot['% late'] = family_pivot[1]/(family_pivot.loc['Total',1])*100\n",
        "\n",
        "family_pivot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Kvl29Nrkuzw",
        "outputId": "6154cd2a-7a24-4619-c723-19fb010c4af2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "family_status\n",
              "civil partnership    357\n",
              "divorced              74\n",
              "married              829\n",
              "unmarried            261\n",
              "widow / widower       31\n",
              "Name: debt, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ],
      "source": [
        "# Load event of defaults based on family_status\n",
        "\n",
        "debt_1.groupby('family_status')['debt'].count()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJB4GM5Mkuzx"
      },
      "source": [
        "**Conclusion**\n",
        "\n",
        "A relationship between marital status to timeliness of repayment is observed\n",
        "\n",
        "From the pivot table and groupby, it appears that the percentage of event of default is greater than of timely repayment for civil_partnership and unmarried. This is  contrast to married, widowed, and divorced. \n",
        "\n",
        "However, the difference between the percentage of default and timely repayment is not too significant so it can be concluded that there is no effect of marital status on timeliness in paying off loans."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jw7N6FIqkuzx"
      },
      "source": [
        "### **Is there a correlation between income levels and timely repayment?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "oTAKvLk9kuzx",
        "outputId": "ce6874d1-e92b-4cd9-b73c-36cceab2982c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "debt             0    1   % timely     % late\n",
              "income_class                                 \n",
              "lower          177   14   1.081048   0.902062\n",
              "lower middle  6586  689  40.224760  44.394330\n",
              "middle        6590  607  40.249191  39.110825\n",
              "upper middle  3020  242  18.445001  15.592784"
            ],
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          },
          "metadata": {},
          "execution_count": 126
        }
      ],
      "source": [
        "# Load pivot tabel that groups event of timely payment and default for each income level\n",
        "income_pivot = credit_scoring.pivot_table(index='income_class', columns='debt', values='dob_years', aggfunc='count')\n",
        "income_pivot['% timely'] = income_pivot[0]/(income_pivot[0].sum())*100\n",
        "income_pivot['% late'] = income_pivot[1]/(income_pivot[1].sum())*100\n",
        "\n",
        "income_pivot\n",
        "\n"
      ]
    },
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "income_class\n",
              "lower            14\n",
              "lower middle    689\n",
              "middle          607\n",
              "upper middle    242\n",
              "Name: debt, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ],
      "source": [
        "# Load event of default based on income_class\n",
        "debt_1.groupby('income_class')['debt'].count()\n"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "**Conclusion**\n",
        "\n",
        "A relationship between income_class to timeliness of repayment is observed.\n",
        "\n",
        "From the pivot and grouping results, it appears that in general the greater the income, the lower the default rate. This is what is happening in the lower middle, middle, and upper middle income classes. Only the lower classes are outliers. Consider that it has small amount of data, it can be ignored. Hence it can be concluded that the higher the income, the lower the default rate.\n"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "### **How ​​does borrowing purpose affect default rates?**"
      ]
    },
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            "text/plain": [
              "debt              0    1   % timely     % late\n",
              "purpose_topic                                 \n",
              "car            3214  358  19.629878  23.067010\n",
              "education      3019  328  18.438893  21.134021\n",
              "property       8369  701  51.114640  45.167526\n",
              "wedding        1771  165  10.816588  10.631443"
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          "metadata": {},
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      ],
      "source": [
        "# Load pivot tabel that groups event of timely payment and default for each purpose\n",
        "\n",
        "purpose_pivot = credit_scoring.pivot_table(index='purpose_topic', columns='debt', values='dob_years', aggfunc='count')\n",
        "purpose_pivot['% timely'] = purpose_pivot[0]/(purpose_pivot[0].sum())*100\n",
        "purpose_pivot['% late'] = purpose_pivot[1]/(purpose_pivot[1].sum())*100\n",
        "\n",
        "purpose_pivot\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "**Conclusion**\n",
        "\n",
        "A relationship between borrowing purpose and timeliness of repayment is observed.\n",
        "\n",
        "From the results of the pivot, it appears that the purpose of borrowing for cars and education is more likely to go on default. While for property and wedding purposes there is no significant difference between rate of defaults and timely repayment.\n"
      ]
    },
    {
      "cell_type": "markdown",
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        "# General conclusion\n",
        "\n",
        "First, the missing values ​​in days_employed and total_income are not clearly identified. The proportion is 10% of the total data. Because the portion is quite significant, keeping the obervations might be beneficial for the testing the hypothesis. Median then is used to fill in the missing values. The median is preferred over the mean because of the massive number of outliers in the data.\n",
        "On another note, the median used is the median that is grouped by other relating, obersed factors.\n",
        "\n",
        "Second, explicit duplicates are found, which account for 0.3% of the total data. The cause for the duplicates is not known. Since the portion is small, it is pragmatic to drop them.\n",
        "\n",
        "Third, it is found that values are written in redundant ways, causing many unique values have one exactly the same meaning. A standardisation the is done to the writing style using all lowercase letters.\n",
        "\n",
        "Fourth, unreasonable values exist, such as negative values ​​for the number of children, and implausible number of days working. A technical error is assumed to has happned that causes the unreasonable values, and they then so are replaced with their absolute values, or reasonable value.\n",
        "\n",
        "Fifth, the data type are changed to the suitable one.\n",
        "\n",
        "In the final steps, the hypothetical questions are addressed. The conclusions drawns are:\n",
        "1. There is no effect of the number of children had to the timeliness of repayment\n",
        "2. Civil partnership and unmarried marital status have a greater possibilities to default\n",
        "3. The greater the income level, the smaller the probability of default\n",
        "4. Loans with the purpose of buying cars and education have a greater probability to default"
      ]
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