solvency_analysis/solvency_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb3bfb5b-b311-48f7-9ed0-9f60535d3b2e",
   "metadata": {},
   "source": [
    "# Getting data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12cf81df-ad22-4e91-a87f-9c54a1223349",
   "metadata": {},
   "source": [
    "We will have all imports in a separate cell for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d32c0174-0880-4402-997d-5a35111711fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "\n",
    "RANDOM_STATE = 12345"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3f2b58-9b6a-4736-b267-31bdf4da65fe",
   "metadata": {},
   "source": [
    "Now, let's read the data into a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "90048710-1809-4f60-9c44-094b6340a31e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <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>10494</th>\n",
       "      <td>0</td>\n",
       "      <td>-777.565227</td>\n",
       "      <td>41</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>166520.601913</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>3</td>\n",
       "      <td>-2789.878442</td>\n",
       "      <td>31</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>115323.047716</td>\n",
       "      <td>строительство недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9914</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>автомобили</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3165</th>\n",
       "      <td>0</td>\n",
       "      <td>329050.424240</td>\n",
       "      <td>59</td>\n",
       "      <td>начальное</td>\n",
       "      <td>3</td>\n",
       "      <td>вдовец / вдова</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>296160.300798</td>\n",
       "      <td>получение дополнительного образования</td>\n",
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       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>0</td>\n",
       "      <td>360739.755029</td>\n",
       "      <td>51</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>184394.932593</td>\n",
       "      <td>операции со своей недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14925</th>\n",
       "      <td>0</td>\n",
       "      <td>368945.757693</td>\n",
       "      <td>62</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>144149.728877</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6437</th>\n",
       "      <td>2</td>\n",
       "      <td>-3173.437878</td>\n",
       "      <td>29</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>79874.036949</td>\n",
       "      <td>покупка своего жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20182</th>\n",
       "      <td>0</td>\n",
       "      <td>-3288.307489</td>\n",
       "      <td>44</td>\n",
       "      <td>неоконченное высшее</td>\n",
       "      <td>2</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>216611.469978</td>\n",
       "      <td>покупка жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2661</th>\n",
       "      <td>0</td>\n",
       "      <td>336548.862836</td>\n",
       "      <td>64</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>82212.733538</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4599</th>\n",
       "      <td>2</td>\n",
       "      <td>-528.394952</td>\n",
       "      <td>26</td>\n",
       "      <td>ВЫСШЕЕ</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>171056.175618</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12975</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>среднее</td>\n",
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       "      <td>женат / замужем</td>\n",
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       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>покупка жилья для семьи</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7901</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ремонт жилью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20613</th>\n",
       "      <td>0</td>\n",
       "      <td>-4137.331615</td>\n",
       "      <td>69</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>госслужащий</td>\n",
       "      <td>0</td>\n",
       "      <td>146187.858312</td>\n",
       "      <td>недвижимость</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17092</th>\n",
       "      <td>2</td>\n",
       "      <td>-2826.964554</td>\n",
       "      <td>43</td>\n",
       "      <td>среднее</td>\n",
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       "      <td>0</td>\n",
       "      <td>269176.262406</td>\n",
       "      <td>ремонт жилью</td>\n",
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       "    <tr>\n",
       "      <th>5046</th>\n",
       "      <td>0</td>\n",
       "      <td>-423.938041</td>\n",
       "      <td>24</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>182491.031136</td>\n",
       "      <td>профильное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>0</td>\n",
       "      <td>-3690.815732</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>439770.605953</td>\n",
       "      <td>высшее образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3621</th>\n",
       "      <td>0</td>\n",
       "      <td>-1587.837522</td>\n",
       "      <td>55</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>109356.711511</td>\n",
       "      <td>строительство жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18517</th>\n",
       "      <td>0</td>\n",
       "      <td>-537.327354</td>\n",
       "      <td>24</td>\n",
       "      <td>ВЫСШЕЕ</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>119002.008399</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4844</th>\n",
       "      <td>2</td>\n",
       "      <td>-2716.177578</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>138185.845117</td>\n",
       "      <td>строительство собственной недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16233</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>строительство недвижимости</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years            education  education_id  \\\n",
       "10494         0    -777.565227         41              среднее             1   \n",
       "1460          3   -2789.878442         31              среднее             1   \n",
       "9914          0            NaN         58               высшее             0   \n",
       "3165          0  329050.424240         59            начальное             3   \n",
       "3080          0  360739.755029         51               высшее             0   \n",
       "14925         0  368945.757693         62              среднее             1   \n",
       "6437          2   -3173.437878         29              среднее             1   \n",
       "20182         0   -3288.307489         44  неоконченное высшее             2   \n",
       "2661          0  336548.862836         64              среднее             1   \n",
       "4599          2    -528.394952         26               ВЫСШЕЕ             0   \n",
       "12975         1            NaN         38              среднее             1   \n",
       "7901          0            NaN         59              среднее             1   \n",
       "20613         0   -4137.331615         69               высшее             0   \n",
       "17092         2   -2826.964554         43              среднее             1   \n",
       "5046          0    -423.938041         24              среднее             1   \n",
       "14986         0   -3690.815732         36              среднее             1   \n",
       "3621          0   -1587.837522         55              среднее             1   \n",
       "18517         0    -537.327354         24               ВЫСШЕЕ             0   \n",
       "4844          2   -2716.177578         36              среднее             1   \n",
       "16233         0            NaN         61              СРЕДНЕЕ             1   \n",
       "\n",
       "               family_status  family_status_id gender  income_type  debt  \\\n",
       "10494       гражданский брак                 1      M    сотрудник     0   \n",
       "1460         женат / замужем                 0      M    сотрудник     0   \n",
       "9914         женат / замужем                 0      M    сотрудник     0   \n",
       "3165          вдовец / вдова                 2      F    пенсионер     0   \n",
       "3080         женат / замужем                 0      M    пенсионер     0   \n",
       "14925       гражданский брак                 1      F    пенсионер     0   \n",
       "6437         женат / замужем                 0      M    сотрудник     0   \n",
       "20182  Не женат / не замужем                 4      M    компаньон     0   \n",
       "2661   Не женат / не замужем                 4      F    пенсионер     0   \n",
       "4599         женат / замужем                 0      M    компаньон     0   \n",
       "12975        женат / замужем                 0      F    сотрудник     0   \n",
       "7901         женат / замужем                 0      F    пенсионер     0   \n",
       "20613        женат / замужем                 0      M  госслужащий     0   \n",
       "17092        женат / замужем                 0      M    сотрудник     0   \n",
       "5046   Не женат / не замужем                 4      M    сотрудник     0   \n",
       "14986  Не женат / не замужем                 4      F    сотрудник     0   \n",
       "3621         женат / замужем                 0      F    сотрудник     0   \n",
       "18517       гражданский брак                 1      F    сотрудник     0   \n",
       "4844         женат / замужем                 0      F    сотрудник     0   \n",
       "16233        женат / замужем                 0      M    сотрудник     0   \n",
       "\n",
       "        total_income                                 purpose  \n",
       "10494  166520.601913              дополнительное образование  \n",
       "1460   115323.047716              строительство недвижимости  \n",
       "9914             NaN                              автомобили  \n",
       "3165   296160.300798   получение дополнительного образования  \n",
       "3080   184394.932593         операции со своей недвижимостью  \n",
       "14925  144149.728877                         сыграть свадьбу  \n",
       "6437    79874.036949                    покупка своего жилья  \n",
       "20182  216611.469978              покупка жилой недвижимости  \n",
       "2661    82212.733538                             образование  \n",
       "4599   171056.175618                    покупка недвижимости  \n",
       "12975            NaN                 покупка жилья для семьи  \n",
       "7901             NaN                            ремонт жилью  \n",
       "20613  146187.858312                            недвижимость  \n",
       "17092  269176.262406                            ремонт жилью  \n",
       "5046   182491.031136                  профильное образование  \n",
       "14986  439770.605953                      высшее образование  \n",
       "3621   109356.711511        строительство жилой недвижимости  \n",
       "18517  119002.008399                         сыграть свадьбу  \n",
       "4844   138185.845117  строительство собственной недвижимости  \n",
       "16233            NaN              строительство недвижимости  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank_data = pd.read_csv(\"datasets/bank_clients_data.csv\")\n",
    "display(bank_data.sample(n=20, random_state=RANDOM_STATE))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38ec4182-757b-4a9a-8f0c-a746325288a6",
   "metadata": {},
   "source": [
    "We notice that the data is characterized by a diverse set of features. Let's look at its concise summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3098edfb-7b03-4bf0-b95d-132dde83b6d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": [
    "bank_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec987167-4e66-4417-b70b-afa7f4b4f7ec",
   "metadata": {},
   "source": [
    "We can see that the DataFrame contains the following information in its columns:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e1a7e4e-af8c-4b72-86a2-b2d16e53aed2",
   "metadata": {},
   "source": [
    "<table>\n",
    "<thead>\n",
    "<tr><th>Features</th><th>Description</th></tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "<tr><td><code>children</code></td><td><i>Number of children in a family</i></td></tr>\n",
    "<tr><td><code>days_employed</code></td><td><i>Total number of days being employed</i></td></tr>\n",
    "<tr><td><code>dob_years</code></td><td><i>Client's age</i></td></tr>\n",
    "<tr><td><code>education</code></td><td><i>Client's education level</i></td></tr>\n",
    "<tr><td><code>education_id</code></td><td><i>Education level identifier</i></td></tr>\n",
    "<tr><td><code>family_status</code></td><td><i>Client's family status</i></td></tr>\n",
    "<tr><td><code>family_status_id</code></td><td><i>Family status identifier</i></td></tr>\n",
    "<tr><td><code>gender</code></td><td><i>Client's gender</i></td></tr>\n",
    "<tr><td><code>income_type</code></td><td><i>Type of employment</i></td></tr>\n",
    "<tr><td><code>debt</code></td><td><i>Indicator of owing debt to the bank</i></td></tr>\n",
    "<tr><td><code>total_income</code></td><td><i>Monthly income</i></td></tr>\n",
    "<tr><td><code>purpose</code></td><td><i>Purpose for taking out a loan</i></td></tr>\n",
    "</tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca5e11c-71cc-4b27-93b1-5c25fe861b24",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "\n",
    "The dataset contains information about borrowers of the bank with numerous attributes, where each row identifies a distinct debtor with specific data. The DataFrame can be seen to have missing values present as well as potential data format problems, all of which warrant data preprocessing being carried out."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afdc809-113f-409e-8d5c-9cc9fdb04891",
   "metadata": {},
   "source": [
    "# Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09dc58a-7620-4ca7-9253-5f203bdf6e0c",
   "metadata": {},
   "source": [
    "## Missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db9c95c-f74f-4cf6-9902-12484b951a3c",
   "metadata": {},
   "source": [
    "In order to compute missing values, we will make use of a function called `identify_missing_values()` which we define below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "23e54248-d484-499e-b6a9-f8c530321cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def identify_missing_values(data):\n",
    "    \"\"\"Performs missing values computation.\n",
    "\n",
    "    Function computes a number and share of missing values\n",
    "    in DataFrame columns which have NaN-values present\n",
    "    and displays data types of such columns.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : DataFrame\n",
    "        DataFrame which needs to be checked for missing values.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    DataFrame or None\n",
    "        DataFrame with column names/their data types, number of\n",
    "        missing values and shares of NaN-values in such columns,\n",
    "        or None if no missing values have been found.\n",
    "    \"\"\"\n",
    "    # Verifying the presence of missing values\n",
    "    miss_vals_num = data.isnull().sum()[data.isnull().sum() > 0]\n",
    "    if miss_vals_num.empty:\n",
    "        return None\n",
    "\n",
    "    # Creating a table with numbers of missing values\n",
    "    cols = {\"missing_count\": miss_vals_num.values}\n",
    "    nans_df = pd.DataFrame(data=cols, index=miss_vals_num.index).sort_values(\n",
    "        by=\"missing_count\", ascending=False\n",
    "    )\n",
    "\n",
    "    # Adding shares of missing values\n",
    "    nans_df[\"missing_fraction\"] = nans_df[\"missing_count\"] / data.shape[0]\n",
    "    nans_df[\"missing_fraction\"] = nans_df[\"missing_fraction\"].round(4)\n",
    "\n",
    "    # Adding data types\n",
    "    nans_df[\"dtype\"] = data[nans_df.index].dtypes\n",
    "    nans_df = nans_df[[\"dtype\", \"missing_count\", \"missing_fraction\"]]\n",
    "\n",
    "    return nans_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7d806e95-0413-49a2-94fa-3307b248e044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtype</th>\n",
       "      <th>missing_count</th>\n",
       "      <th>missing_fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>days_employed</th>\n",
       "      <td>float64</td>\n",
       "      <td>2174</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_income</th>\n",
       "      <td>float64</td>\n",
       "      <td>2174</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dtype  missing_count  missing_fraction\n",
       "days_employed  float64           2174             0.101\n",
       "total_income   float64           2174             0.101"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing the number and percentage of missing values in the DataFrame\n",
    "identify_missing_values(data=bank_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "047d75f4-82e2-436a-92a7-75b3a5512e70",
   "metadata": {},
   "source": [
    "We see that there are two columns that contain missing values: `days_employed` and `total_income`. \n",
    "\n",
    "Deal first with the latter column. In order to get rid of missing values, we need to understand what the variable represents. `total_income` stores information about clients' incomes, meaning that the column possesses numeric values. Income is dependent upon type of employment shown in `income_type` and therefore we can fill in missing values with median income relative to a particular employment type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6fa7b207-d49a-43d1-913c-47d23430e61c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling in missing values with median in 'total_income' column depending on 'income_type'\n",
    "for inc_type in bank_data[\"income_type\"].unique():\n",
    "    bank_data.loc[\n",
    "        (bank_data[\"income_type\"] == inc_type) & (bank_data[\"total_income\"].isna()),\n",
    "        \"total_income\",\n",
    "    ] = bank_data.loc[(bank_data[\"income_type\"] == inc_type), \"total_income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "06a62d1a-2911-47a7-a099-239d027de8ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtype</th>\n",
       "      <th>missing_count</th>\n",
       "      <th>missing_fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>days_employed</th>\n",
       "      <td>float64</td>\n",
       "      <td>2174</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dtype  missing_count  missing_fraction\n",
       "days_employed  float64           2174             0.101"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identify_missing_values(data=bank_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c441297-a782-49e7-9368-6e5fc200f373",
   "metadata": {},
   "source": [
    "## Abnormalities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46a03e6f-fd4b-4704-9154-863822047ac7",
   "metadata": {},
   "source": [
    "As can be seen from the data, we have abnormal values found in `days_employed` column: by no means can duration of employment be negative. Thus, we are dealing with abnormalities in the data. We can easily fix this by taking the absolute value of each data point in this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "381ed564-9563-4f14-9ffc-6a471799312d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the data to a correct format\n",
    "bank_data[\"days_employed\"] = np.absolute(bank_data[\"days_employed\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1810cf1d-1dd7-472c-b585-bcfcbfcf3023",
   "metadata": {},
   "source": [
    "Let's further explore this column and display median employment duration in relation to employment type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41f78e27-96eb-45f1-824e-8c506e486de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_type\n",
       "безработный        366413.652744\n",
       "в декрете            3296.759962\n",
       "госслужащий          2689.368353\n",
       "компаньон            1547.382223\n",
       "пенсионер          365213.306266\n",
       "предприниматель       520.848083\n",
       "сотрудник            1574.202821\n",
       "студент               578.751554\n",
       "Name: days_employed, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying median values\n",
    "bank_data.groupby(\"income_type\")[\"days_employed\"].agg(\"median\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "863d2a00-abb7-40f9-a7d1-2c468145564f",
   "metadata": {},
   "source": [
    "We see that some income types, specifically, unemployed (`безработный`) and retired (`пенсионер`) have unusually high values. It is difficult to deal with this kind of abnormalities and since this column will be very informative to use in the subsequent analysis, we just leave the data as it is here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be493414-0c8e-4fb1-866d-523bd91befeb",
   "metadata": {},
   "source": [
    "Now, display the possible unique numbers of children that borrowers have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8db5aa06-ce2b-4725-9974-9931b55fb3f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1  0  1  2  3  4  5 20]\n"
     ]
    }
   ],
   "source": [
    "print(bank_data[\"children\"].sort_values().unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc23c5d-4d67-42ea-84f9-c974d9e11db8",
   "metadata": {},
   "source": [
    "It is clearly seen that there are two impossible values: 20 and -1. Let's get rid of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef7fd26a-f998-4cf5-bba4-d8ceb4bb1aa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "# Getting rid of abnormal values\n",
    "bank_data = bank_data[bank_data[\"children\"].between(0, 5)]\n",
    "print(bank_data[\"children\"].sort_values().unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3674a298-e147-4fd7-936d-17547dccf890",
   "metadata": {},
   "source": [
    "## Missing values (cont.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d940b56a-2c21-4401-9b91-927e5d6d978a",
   "metadata": {},
   "source": [
    "Now we can fill in missing values in `days_employed` column with their median values depending on income type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6159e4a9-9751-4ec2-9d04-0ab736d6bb09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling in missing values with median in 'days_employed' column depending on 'income_type'\n",
    "for inc_type in bank_data[\"income_type\"].unique():\n",
    "    bank_data.loc[\n",
    "        (bank_data[\"income_type\"] == inc_type) & (bank_data[\"days_employed\"].isna()),\n",
    "        \"days_employed\",\n",
    "    ] = bank_data.loc[(bank_data[\"income_type\"] == inc_type), \"days_employed\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "be53603b-2bc0-45ea-82ab-9e02581f4f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "identify_missing_values(data=bank_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02fcc216-c1eb-4229-a090-70344a77e5d9",
   "metadata": {},
   "source": [
    "As can be seen, the function for missing values computation returned `None` meaning that we have managed to deal with all missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a9b81f7-3889-46bf-ab70-b3427b5f60c8",
   "metadata": {},
   "source": [
    "## Data type changes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82f112f0-5fdb-4421-b9b6-6df933c62db1",
   "metadata": {},
   "source": [
    "Let's change the data in `total_income` column from `float64` to `int64` for convenience. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e88d4d85-9e04-4b86-9c12-2d662ddc4c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_data[\"total_income\"] = bank_data[\"total_income\"].astype(\"int64\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed8b0b4-1d88-47e5-9d40-6f330603546e",
   "metadata": {},
   "source": [
    "## Duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d29658d6-4f5f-4c8e-a068-5a916a822987",
   "metadata": {},
   "source": [
    "We have some implicit duplicates in `education` column, specifically, using both capital and small letters to write down the same education level. Thus, let's convert all values in this column to strings with lowercase letters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c6f70eb0-347d-4e85-9827-f9bb9a7fe815",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_data[\"education\"] = bank_data[\"education\"].str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2035acad-9762-46ed-b64b-32fc8cee9e30",
   "metadata": {},
   "source": [
    "Now, we can take a look how many explicit duplicates (identical DataFrame rows) we have by using `preprocess_duplicates()` function defined below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "348ea796-95c0-4e29-a754-b6d699f77958",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_duplicates(data):\n",
    "    \"\"\"Deletes duplicates from a DataFrame.\"\"\"\n",
    "    # Computing the number of duplicates in DataFrame\n",
    "    num_duplicates = data.duplicated().sum()\n",
    "    # Checking the number of duplicates for nullity\n",
    "    if num_duplicates != 0:\n",
    "        # Deleting duplicates\n",
    "        data.drop_duplicates(inplace=True)\n",
    "        # Displaying a message attesting the deletion\n",
    "        print(f\"{num_duplicates:,} duplicates found and deleted.\")\n",
    "        return None\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d84e209a-e138-4c95-84a1-e633130540e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71 duplicates found and deleted.\n"
     ]
    }
   ],
   "source": [
    "preprocess_duplicates(data=bank_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7117cf4a-c6d1-4687-9b65-5d544ff511cf",
   "metadata": {},
   "source": [
    "## Data categorization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ab2130-677e-495b-b62c-1d1286a1de4c",
   "metadata": {},
   "source": [
    "### Income"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f7705d9-850d-4e8f-8ec3-1302a0ed6454",
   "metadata": {},
   "source": [
    "To make further data analysis more convenient, let's create a categorical variable that will classify clients' incomes (in roubles) according to the following ranges:\n",
    "\n",
    "* 0-30,000 => `E`\n",
    "* 30,001-50,000 => `D`\n",
    "* 50,001-200,000 => `C`\n",
    "* 200,001-1,000,000 => `B`\n",
    "* 1,000,001 and higher => `A`\n",
    "\n",
    "For instance, a borrower with monthly income of, say, 200,500 roubles will be put in `B` income category. We can easily program such a categorization by using `cut()` function of `pandas` library in order to be able to categorize the data in `total_income` column in accordance with ranges and income group identifiers defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "065499e8-095b-410e-b005-feca6e8081fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A' 'B' 'C' 'D' 'E']\n"
     ]
    }
   ],
   "source": [
    "# Classifying into income groups\n",
    "bank_data[\"total_income_category\"] = pd.cut(\n",
    "    bank_data[\"total_income\"],\n",
    "    bins=[0, 30000, 50000, 200000, 1000000, np.max(bank_data[\"total_income\"])],\n",
    "    labels=[\"E\", \"D\", \"C\", \"B\", \"A\"],\n",
    ")\n",
    "\n",
    "bank_data[\"total_income_category\"] = bank_data[\"total_income_category\"].astype(\"object\")\n",
    "print(bank_data[\"total_income_category\"].sort_values().unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef29682e-4a58-45b3-862d-74259434d5f8",
   "metadata": {},
   "source": [
    "### Loan purpose"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efdf2b40-575d-47b6-8730-1c2c0cf7d693",
   "metadata": {},
   "source": [
    "Next, consider possible purposes that clients taking out loans may have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "47820dfa-f41a-457c-a77f-8034f190f3b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['автомобили' 'автомобиль' 'высшее образование'\n",
      " 'дополнительное образование' 'жилье' 'заняться высшим образованием'\n",
      " 'заняться образованием' 'на покупку автомобиля'\n",
      " 'на покупку подержанного автомобиля' 'на покупку своего автомобиля'\n",
      " 'на проведение свадьбы' 'недвижимость' 'образование' 'операции с жильем'\n",
      " 'операции с коммерческой недвижимостью' 'операции с недвижимостью'\n",
      " 'операции со своей недвижимостью' 'покупка жилой недвижимости'\n",
      " 'покупка жилья' 'покупка жилья для сдачи' 'покупка жилья для семьи'\n",
      " 'покупка коммерческой недвижимости' 'покупка недвижимости'\n",
      " 'покупка своего жилья' 'получение высшего образования'\n",
      " 'получение дополнительного образования' 'получение образования'\n",
      " 'приобретение автомобиля' 'профильное образование' 'ремонт жилью'\n",
      " 'свадьба' 'свой автомобиль' 'сделка с автомобилем'\n",
      " 'сделка с подержанным автомобилем' 'строительство жилой недвижимости'\n",
      " 'строительство недвижимости' 'строительство собственной недвижимости'\n",
      " 'сыграть свадьбу']\n"
     ]
    }
   ],
   "source": [
    "# Printing unique purposes of taking a loan\n",
    "print(bank_data[\"purpose\"].sort_values().unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f24becf7-e1ac-4a8d-a946-bfe47050f784",
   "metadata": {},
   "source": [
    "We can see that it seems that there are lost of reasons for taking out loans in a bank, including real estate (`жилье`), buying a car (`автомобиль`), wedding (`свадьба`) and education (`образование`). But, in fact, all these purposes are simply written differently but actually refer to similar things. Hence, it is possible to also classify all these reasons into purpose categories. In order to accomplish this, we can use `categorize_purpose()` function we define below and use `apply()` method on it. Specifically, we will check if the word stem of a loan purpose (`жилье`, `автомобиль`, `свадьба` or `образование`) is included into the full purpose of taking out a loan and then classify these purposes accordingly into 4 categories:\n",
    "\n",
    "* `Car`\n",
    "* `Real estate`\n",
    "* `Wedding`\n",
    "* `Education`\n",
    "\n",
    "Purposes that are different from these will be allocated to `Other`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "511e869a-d79a-4e90-aaea-f61207ccafb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_purpose(loan_purpose):\n",
    "    \"\"\"Classifies people into loan purpose categories depending on loan purpose.\"\"\"\n",
    "    loan_purpose = loan_purpose.lower()\n",
    "    if \"автом\" in loan_purpose:\n",
    "        return \"Car\"\n",
    "    elif \"жил\" in loan_purpose or \"недвиж\" in loan_purpose:\n",
    "        return \"Real estate\"\n",
    "    elif \"свад\" in loan_purpose:\n",
    "        return \"Wedding\"\n",
    "    elif \"образов\" in loan_purpose:\n",
    "        return \"Education\"\n",
    "    else:\n",
    "        return \"Other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "00505082-73eb-4da1-a361-6040733a9d19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Car' 'Education' 'Real estate' 'Wedding']\n"
     ]
    }
   ],
   "source": [
    "# Applying the function to the DataFrame column\n",
    "bank_data[\"purpose_category\"] = bank_data[\"purpose\"].apply(categorize_purpose)\n",
    "print(bank_data[\"purpose_category\"].sort_values().unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a06ea14d-6dcd-4ffb-8c79-70d3fc9f44dc",
   "metadata": {},
   "source": [
    "### Family status"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a1164a-ab1a-4ab2-b066-bee3bb27233b",
   "metadata": {},
   "source": [
    "Let's use the same logic and categorize family statuses into 5 categories:\n",
    "\n",
    "* `Unmarried`\n",
    "* `Divorced`\n",
    "* `Widow(er)`\n",
    "* `Civil` => civil marriage\n",
    "* `Married`\n",
    "\n",
    "It can be achieved by using another defined function - `categorize_family_status()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "96e33229-4831-4d01-9142-0b5d922c5563",
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_family_status(family_status):\n",
    "    \"\"\"Classifies people into family status categories depending on family status.\"\"\"\n",
    "    family_status = family_status.lower()\n",
    "    if \"не жен\" in family_status or \"не замуж\" in family_status:\n",
    "        return \"Unmarried\"\n",
    "    elif \"разв\" in family_status:\n",
    "        return \"Divorced\"\n",
    "    elif \"вдов\" in family_status:\n",
    "        return \"Widow(er)\"\n",
    "    elif \"гражд\" in family_status:\n",
    "        return \"Civil\"\n",
    "    elif \"жен\" in family_status or \"замуж\" in family_status:\n",
    "        return \"Married\"\n",
    "    else:\n",
    "        return \"Other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5a15d40a-fac6-4554-b65c-3b2fa64b1c4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Не женат / не замужем' 'в разводе' 'вдовец / вдова' 'гражданский брак'\n",
      " 'женат / замужем']\n"
     ]
    }
   ],
   "source": [
    "print(bank_data[\"family_status\"].sort_values().unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "31cb872c-121a-4179-b33a-97c4b272fa29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Civil' 'Divorced' 'Married' 'Unmarried' 'Widow(er)']\n"
     ]
    }
   ],
   "source": [
    "bank_data[\"family_status_category\"] = bank_data[\"family_status\"].apply(\n",
    "    categorize_family_status\n",
    ")\n",
    "print(bank_data[\"family_status_category\"].sort_values().unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f705762-6d4b-468d-a9da-f601198c90b5",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "\n",
    "We have managed to properly preprocess data by dealing with missing and abnormal values, changing the format of data and deleting duplicates, both expicit and implicit. \n",
    "\n",
    "Omissions in the source data were noted in the columns `days_employed` and `total_income`. One possible reason for missing data may be the failure of an automated system for collecting information about borrowers. For example, when a borrower specifies information about themselves in a form on a website and does not specify some information, the website may not notice this and simply send the information further (if the programmer forgot to make some cells mandatory). Another reason may be a purely human factor: if a borrower comes to the bank and tells the bank employee information about themselves, an employee may simply make a mistake in something and forget to specify the data, which will result in the appearance of `NaN` values in the data.\n",
    "\n",
    "Besides, we have managed to fill in missing values by using a median. As a rule, there are \"outliers\" in quantitative variables - values that are very different from other values in a particular column. The use of average values can distort the data in this case and in order to avoid this, a median is used, which will take into account such sharply distinguished values and will not exaggerate the estimate of the missing value.\n",
    "\n",
    "Next step is to actually start conducting creditworthiness analysis itself."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67bdc8e-7448-4475-ab62-52e4c309e72f",
   "metadata": {},
   "source": [
    "# Solvency determinants analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56255c8f-8c6f-4a1d-b8f5-cd809bff9bfa",
   "metadata": {},
   "source": [
    "Now that the data has been preprocessed, we can get to analyzing possible factors that determine the ability of bank's clients to repay a loan on time. We will conduct the analysis by considering multiple features:\n",
    "\n",
    "    1. Number of children. \n",
    "    2. Family status.\n",
    "    3. Total income. \n",
    "    4. Purpose of a loan. \n",
    "    \n",
    "We will compute some descriptive statistics about the borrowers. In order not to repeat the code over and over again, let's use `show_solvency_by_cat()` that will calculate the number of borrowers, including those in debt and those who repaid the loan, as well as the share of people who do not have debt, depending on a specific feature of the bank clients. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8477e5c1-26fe-485e-8f20-f1f8110f1a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_solvency_by_cat(feature, data):\n",
    "    \"\"\"Displays solvency statistics for borrowers by category of a feature.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    feature : str\n",
    "        Characteristic of a borrower.\n",
    "    data : DataFrame\n",
    "        Bank clients' data.\n",
    "\n",
    "    Returns:\n",
    "    -------\n",
    "    solvency_info : DataFrame\n",
    "        Table with solvency statistics for each unique element of feature.\n",
    "    \"\"\"\n",
    "    # Aggregating information about borrowers\n",
    "    data_grouped = data.groupby(feature).agg({\"debt\": [\"count\", \"sum\"]})\n",
    "\n",
    "    # Computing number of borrowers who managed to repay on time\n",
    "    data_grouped[\"no_debt\"] = (\n",
    "        data_grouped[\"debt\"][\"count\"] - data_grouped[\"debt\"][\"sum\"]\n",
    "    )\n",
    "\n",
    "    # Computing debt repayment ratio\n",
    "    data_grouped[\"no_debt_share\"] = np.round(\n",
    "        data_grouped[\"no_debt\"] / data_grouped[\"debt\"][\"count\"], 4\n",
    "    )\n",
    "\n",
    "    # Getting rid of multiindex\n",
    "    data_grouped.columns = [\n",
    "        \"total_borrowers\",\n",
    "        \"has_debt\",\n",
    "        \"no_debt\",\n",
    "        \"repay_ratio\",\n",
    "    ]\n",
    "\n",
    "    solvency_info = data_grouped.copy()\n",
    "\n",
    "    return solvency_info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e87f0034-d571-4f99-8bc2-83fb2ec3bc0f",
   "metadata": {},
   "source": [
    "Since reading statistics in a DataFrame may sometimes be inconvenient, let's add visualization part here as well. Hence, we will make figures by `plot_solvency_by_cat()` function that will create a set of subplots for each category of a specific feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "36dd9f4c-2a4e-4566-ae23-fa4eed0697b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_solvency_by_cat(feature, data):\n",
    "    \"\"\"Plots solvency statistics for borrowers by category of feature.\n",
    "\n",
    "    Function creates two plots:\n",
    "        - Left one depicts the number of borrowers who repayed\n",
    "            and did not repay the loan for each category within feature.\n",
    "        - Right plot shows debt repayment ratios for each category of\n",
    "            clients within feature.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    feature : str\n",
    "        Characteristic of borrowers.\n",
    "    data : pd.DataFrame\n",
    "        Bank clients' data.\n",
    "    \"\"\"\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    fig.suptitle(f\"Solvency of clients by {feature}\")\n",
    "\n",
    "    # Plotting the number of borrowers\n",
    "    figure_left = sns.barplot(\n",
    "        x=feature,\n",
    "        y=\"debt\",\n",
    "        data=data,\n",
    "        estimator=lambda x: len(x),\n",
    "        ci=None,\n",
    "        hue=\"debt\",\n",
    "        ax=axes[0],\n",
    "    )\n",
    "    axes[0].legend(title=\"Status\", loc=\"upper right\", labels=[\"Repaid\", \"In debt\"])\n",
    "    axes[0].set_title(\"Number of borrowers by categories\")\n",
    "    axes[0].set_ylabel(\"People\")\n",
    "\n",
    "    # Plotting the debt repayment ratio\n",
    "    figure_right = sns.barplot(\n",
    "        x=feature,\n",
    "        y=\"debt\",\n",
    "        data=data,\n",
    "        estimator=lambda x: 100 - sum(x) / len(x) * 100,\n",
    "        ci=None,\n",
    "        ax=axes[1],\n",
    "        color=\"dodgerblue\",\n",
    "    )\n",
    "    axes[1].set_title(\"Repayment rate by categories\")\n",
    "    axes[1].set_ylabel(\"Percent\")\n",
    "\n",
    "    # Responsively setting y-limits for the right plot\n",
    "    repay_rate = (\n",
    "        data.groupby(feature)[\"debt\"].sum() / data.groupby(feature)[\"debt\"].count()\n",
    "    )\n",
    "    lower_bound = (100 - 100 * np.max(repay_rate)) - 0.5\n",
    "    upper_bound = (100 - 100 * np.min(repay_rate)) + 0.5\n",
    "    axes[1].set_ylim(lower_bound, upper_bound)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec90d48a-1765-4aeb-9a80-59a69ff826ea",
   "metadata": {},
   "source": [
    "## Number of children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "48f492ba-9b23-4be8-9c79-f11a08a6dc28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14091\n",
       "1     4808\n",
       "2     2052\n",
       "3      330\n",
       "4       41\n",
       "5        9\n",
       "Name: children, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing the number of borrowers in each children group\n",
    "bank_data[\"children\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37e708b4-0102-4e6b-b51d-bbae1bb9af1e",
   "metadata": {},
   "source": [
    "To begin with, let's calculate the total number of borrowers by the number of children using `value_counts()` method in the `children` column. It can be seen that the number of borrowers broken down by each group varies quite widely, which suggests that people with a large number of children do not take out loans so often."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "88216832-a616-4114-998f-160af0318a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_borrowers</th>\n",
       "      <th>has_debt</th>\n",
       "      <th>no_debt</th>\n",
       "      <th>repay_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14091</td>\n",
       "      <td>1063</td>\n",
       "      <td>13028</td>\n",
       "      <td>0.9246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4808</td>\n",
       "      <td>444</td>\n",
       "      <td>4364</td>\n",
       "      <td>0.9077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2052</td>\n",
       "      <td>194</td>\n",
       "      <td>1858</td>\n",
       "      <td>0.9055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>330</td>\n",
       "      <td>27</td>\n",
       "      <td>303</td>\n",
       "      <td>0.9182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>0.9024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          total_borrowers  has_debt  no_debt  repay_ratio\n",
       "children                                                 \n",
       "0                   14091      1063    13028       0.9246\n",
       "1                    4808       444     4364       0.9077\n",
       "2                    2052       194     1858       0.9055\n",
       "3                     330        27      303       0.9182\n",
       "4                      41         4       37       0.9024\n",
       "5                       9         0        9       1.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solvency_children = show_solvency_by_cat(feature=\"children\", data=bank_data)\n",
    "display(solvency_children)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "af6300fb-964f-4497-be82-01853387cb70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "plot_solvency_by_cat(feature=\"children\", data=bank_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdb50df9-2286-4788-abdf-f8a1f5ef4613",
   "metadata": {},
   "source": [
    "Let's look at the data from the bank's point of view and use as a metric of loan repayment on time - *\"the proportion of people who have paid off a loan on time in a certain group\"* (`repay_ratio`). \n",
    "\n",
    "It can be seen that samples of borrowers with three or more children do not have many observations, especially for a group of people with five children. In other words, such samples are not representative enough and therefore it makes sense to compare samples with 0, 1 and 2 children only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "759b5944-9d29-43e6-90f3-e57f1cb4504c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_borrowers</th>\n",
       "      <th>has_debt</th>\n",
       "      <th>no_debt</th>\n",
       "      <th>repay_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14091</td>\n",
       "      <td>1063</td>\n",
       "      <td>13028</td>\n",
       "      <td>0.9246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4808</td>\n",
       "      <td>444</td>\n",
       "      <td>4364</td>\n",
       "      <td>0.9077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2052</td>\n",
       "      <td>194</td>\n",
       "      <td>1858</td>\n",
       "      <td>0.9055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          total_borrowers  has_debt  no_debt  repay_ratio\n",
       "children                                                 \n",
       "0                   14091      1063    13028       0.9246\n",
       "1                    4808       444     4364       0.9077\n",
       "2                    2052       194     1858       0.9055"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing a table for subsamples of borrowers with 0, 1, 2 children\n",
    "solvency_children.loc[solvency_children[\"total_borrowers\"] > 2000].sort_values(\n",
    "    by=\"repay_ratio\", ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "365332e3-d16f-4d9d-a33d-4b1cac2e4ebb",
   "metadata": {},
   "source": [
    "Considering this grouping, it is obvious that with a larger number of children, the probability of repayment of the loan decreases: childless borrowers repay the loan on time in about 92.5% of cases, while for borrowers with a large number of children, this indicator is in the region of 90-91%. In other words, the repayment of the loan on time has an inverse relationship with the number of children of the borrower."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71290f3-0add-4cae-a0ef-ee1246d59dfb",
   "metadata": {},
   "source": [
    "## Family status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c0759f35-d06b-48db-b463-859b2a440ffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married      12261\n",
       "Civil         4134\n",
       "Unmarried     2796\n",
       "Divorced      1189\n",
       "Widow(er)      951\n",
       "Name: family_status_category, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing the number of borrowers in each group by family status\n",
    "bank_data[\"family_status_category\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2155fbc-cb51-4b5d-b394-7932c51acd5c",
   "metadata": {},
   "source": [
    "Let's apply the same strategy and look at the number of borrowers by marital status. Mostly loans are taken by people who are married, and least of all by borrowers without official relations - widower/widow and divorced. However, this does not mean that people who take out loans less often pay less on time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "49e1fb85-9afb-4f58-bac4-9b59401b2439",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_borrowers</th>\n",
       "      <th>has_debt</th>\n",
       "      <th>no_debt</th>\n",
       "      <th>repay_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_status_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Civil</th>\n",
       "      <td>4134</td>\n",
       "      <td>385</td>\n",
       "      <td>3749</td>\n",
       "      <td>0.9069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Divorced</th>\n",
       "      <td>1189</td>\n",
       "      <td>84</td>\n",
       "      <td>1105</td>\n",
       "      <td>0.9294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>12261</td>\n",
       "      <td>927</td>\n",
       "      <td>11334</td>\n",
       "      <td>0.9244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unmarried</th>\n",
       "      <td>2796</td>\n",
       "      <td>273</td>\n",
       "      <td>2523</td>\n",
       "      <td>0.9024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Widow(er)</th>\n",
       "      <td>951</td>\n",
       "      <td>63</td>\n",
       "      <td>888</td>\n",
       "      <td>0.9338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        total_borrowers  has_debt  no_debt  repay_ratio\n",
       "family_status_category                                                 \n",
       "Civil                              4134       385     3749       0.9069\n",
       "Divorced                           1189        84     1105       0.9294\n",
       "Married                           12261       927    11334       0.9244\n",
       "Unmarried                          2796       273     2523       0.9024\n",
       "Widow(er)                           951        63      888       0.9338"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solvency_family_status = show_solvency_by_cat(\n",
    "    feature=\"family_status_category\", data=bank_data\n",
    ")\n",
    "display(solvency_family_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dd5f4a92-6298-4cfc-a7b4-e27443397379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_solvency_by_cat(feature=\"family_status_category\", data=bank_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13f93198-8063-44a6-82a4-403b481042d2",
   "metadata": {},
   "source": [
    "As in the previous point, the number of observations in each sample has a fairly strong influence on the results of the study, since a smaller sample will absorb less information, thus distorting them. Therefore, in this case it makes sense to consider categories that include, say, more than 2000 people. Thus, we will exclude the categories of widower/widows and divorced from the analysis and consider others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1caa431f-b280-428a-9fe2-8680c21a3a9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_borrowers</th>\n",
       "      <th>has_debt</th>\n",
       "      <th>no_debt</th>\n",
       "      <th>repay_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_status_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>12261</td>\n",
       "      <td>927</td>\n",
       "      <td>11334</td>\n",
       "      <td>0.9244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civil</th>\n",
       "      <td>4134</td>\n",
       "      <td>385</td>\n",
       "      <td>3749</td>\n",
       "      <td>0.9069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unmarried</th>\n",
       "      <td>2796</td>\n",
       "      <td>273</td>\n",
       "      <td>2523</td>\n",
       "      <td>0.9024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        total_borrowers  has_debt  no_debt  repay_ratio\n",
       "family_status_category                                                 \n",
       "Married                           12261       927    11334       0.9244\n",
       "Civil                              4134       385     3749       0.9069\n",
       "Unmarried                          2796       273     2523       0.9024"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Considering a subsample\n",
    "solvency_family_status.loc[\n",
    "    solvency_family_status[\"total_borrowers\"] > 2000\n",
    "].sort_values(by=\"repay_ratio\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9d14a7b-91a8-449b-9efc-5d8c3626bbf1",
   "metadata": {},
   "source": [
    "Now the dependence is more clearly traced: more family people are more responsible for timely repayment of debt, which is manifested in a high value of the share of repayments for such categories of borrowers. Thus, we have a direct relationship between the \"formality\" of family relations and the repayment of the loan on time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6be0345-970c-49ed-9f8f-088c62470bc4",
   "metadata": {},
   "source": [
    "## Total income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9123a83b-58fc-411b-9f4f-213447a3cb3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C    15921\n",
       "B     5014\n",
       "D      349\n",
       "A       25\n",
       "E       22\n",
       "Name: total_income_category, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing the number of borrowers in each group by income\n",
    "bank_data[\"total_income_category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d513d0c2-9c76-4055-b1ee-f67bbc7316fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_borrowers</th>\n",
       "      <th>has_debt</th>\n",
       "      <th>no_debt</th>\n",
       "      <th>repay_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_income_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>0.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5014</td>\n",
       "      <td>354</td>\n",
       "      <td>4660</td>\n",
       "      <td>0.9294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>15921</td>\n",
       "      <td>1353</td>\n",
       "      <td>14568</td>\n",
       "      <td>0.9150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>349</td>\n",
       "      <td>21</td>\n",
       "      <td>328</td>\n",
       "      <td>0.9398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0.9091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       total_borrowers  has_debt  no_debt  repay_ratio\n",
       "total_income_category                                                 \n",
       "A                                   25         2       23       0.9200\n",
       "B                                 5014       354     4660       0.9294\n",
       "C                                15921      1353    14568       0.9150\n",
       "D                                  349        21      328       0.9398\n",
       "E                                   22         2       20       0.9091"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solvency_income = show_solvency_by_cat(feature=\"total_income_category\", data=bank_data)\n",
    "display(solvency_income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8ff4f6c0-2797-4ca9-82c1-99e32e4cad62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_solvency_by_cat(feature=\"total_income_category\", data=bank_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09abba56-5eed-4236-b709-32c04ba90a99",
   "metadata": {},
   "source": [
    "The results show a similar situation: the number of borrowers of categories A, E and D is extremely small, which may affect the results of the study. Borrowers in these groups are underrepresented and it would be better to exclude them from the analysis and consider the remaining categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "61c26a51-9143-4a7b-883b-811569261735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_borrowers</th>\n",
       "      <th>has_debt</th>\n",
       "      <th>no_debt</th>\n",
       "      <th>repay_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_income_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5014</td>\n",
       "      <td>354</td>\n",
       "      <td>4660</td>\n",
       "      <td>0.9294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>15921</td>\n",
       "      <td>1353</td>\n",
       "      <td>14568</td>\n",
       "      <td>0.9150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       total_borrowers  has_debt  no_debt  repay_ratio\n",
       "total_income_category                                                 \n",
       "B                                 5014       354     4660       0.9294\n",
       "C                                15921      1353    14568       0.9150"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Considering a subsample\n",
    "solvency_income.loc[solvency_income[\"total_borrowers\"] > 2000].sort_values(\n",
    "    by=\"repay_ratio\", ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f4089c4-0cc9-4bc1-a4f7-7ba2d871a23d",
   "metadata": {},
   "source": [
    "So, we can see that the higher the borrower's income category, the higher the probability of debt repayment, so we have a direct relationship between these two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765bd2fc-ba2a-4c77-89e1-fb195a58bef7",
   "metadata": {},
   "source": [
    "## Loan purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "517b7777-364c-43f3-8840-107f27006f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Real estate    10751\n",
       "Car             4279\n",
       "Education       3988\n",
       "Wedding         2313\n",
       "Name: purpose_category, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing the number of borrowers in each group by loan purpose\n",
    "bank_data[\"purpose_category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "836f74aa-7310-499e-8e75-f24a655c144f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_borrowers</th>\n",
       "      <th>has_debt</th>\n",
       "      <th>no_debt</th>\n",
       "      <th>repay_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Car</th>\n",
       "      <td>4279</td>\n",
       "      <td>400</td>\n",
       "      <td>3879</td>\n",
       "      <td>0.9065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>3988</td>\n",
       "      <td>369</td>\n",
       "      <td>3619</td>\n",
       "      <td>0.9075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real estate</th>\n",
       "      <td>10751</td>\n",
       "      <td>780</td>\n",
       "      <td>9971</td>\n",
       "      <td>0.9274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wedding</th>\n",
       "      <td>2313</td>\n",
       "      <td>183</td>\n",
       "      <td>2130</td>\n",
       "      <td>0.9209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  total_borrowers  has_debt  no_debt  repay_ratio\n",
       "purpose_category                                                 \n",
       "Car                          4279       400     3879       0.9065\n",
       "Education                    3988       369     3619       0.9075\n",
       "Real estate                 10751       780     9971       0.9274\n",
       "Wedding                      2313       183     2130       0.9209"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solvency_loan_purpose = show_solvency_by_cat(feature=\"purpose_category\", data=bank_data)\n",
    "display(solvency_loan_purpose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0aec6512-f7f7-4c61-9e9f-2533e38eee66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_solvency_by_cat(feature=\"purpose_category\", data=bank_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "98f41e50-139e-41d0-9759-9901d9481e2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_borrowers</th>\n",
       "      <th>has_debt</th>\n",
       "      <th>no_debt</th>\n",
       "      <th>repay_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Real estate</th>\n",
       "      <td>10751</td>\n",
       "      <td>780</td>\n",
       "      <td>9971</td>\n",
       "      <td>0.9274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wedding</th>\n",
       "      <td>2313</td>\n",
       "      <td>183</td>\n",
       "      <td>2130</td>\n",
       "      <td>0.9209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>3988</td>\n",
       "      <td>369</td>\n",
       "      <td>3619</td>\n",
       "      <td>0.9075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car</th>\n",
       "      <td>4279</td>\n",
       "      <td>400</td>\n",
       "      <td>3879</td>\n",
       "      <td>0.9065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  total_borrowers  has_debt  no_debt  repay_ratio\n",
       "purpose_category                                                 \n",
       "Real estate                 10751       780     9971       0.9274\n",
       "Wedding                      2313       183     2130       0.9209\n",
       "Education                    3988       369     3619       0.9075\n",
       "Car                          4279       400     3879       0.9065"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solvency_loan_purpose.loc[solvency_loan_purpose[\"total_borrowers\"] > 2000].sort_values(\n",
    "    by=\"repay_ratio\", ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78872743-9f12-4e6e-82e9-bc567d5cae31",
   "metadata": {},
   "source": [
    "Basically, loans are predominantly taken for real estate transactions and, moreover, borrowers of this group more often repay loans, so this direction of lending should be considered by bank analysts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d929666-bf62-4df0-b6ef-175cb7c1d16f",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "\n",
    "By considering separate solvency-affecting factors, we have managed to identify possible profit-enhacing channels that a bank can focus on."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ec64e5-4f62-4716-968f-0036c1139824",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8f92e0-1780-4360-8dae-2f44c77f86a7",
   "metadata": {},
   "source": [
    "So, in this study, the statistics of the solvency of customers of a credit institution have been analyzed. To obtain the results, the data obtained was carefully preprocessed: duplicates were removed, abnormal and missing values were processed, and data categorization was carried out to simplify further data analysis.\n",
    "\n",
    "Based on the results of the study, the following conclusion can be drawn. In order for the bank to increase its profits, the following groups of borrowers are the most profitable sources of funds for the bank:\n",
    "\n",
    "* Childless;\n",
    "\n",
    "* Married;\n",
    "\n",
    "* People with salaries from 200,000 to 1,000,000 roubles;\n",
    "\n",
    "* People taking out loans for real estate transactions.\n",
    "\n",
    "In the future, it is recommendable that in the process of collecting data, a more careful approach to their processing is applied. In particular, it is advisable that missing values in the data be processed appropriately at the stage of sending them for analysis, or explanations be provided regarding the nature of their absence. The same applies to checking the correctness of the data format. This will lead to more efficient data analysis and the provision of more accurate recommendations and results."
   ]
  }
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