hw3_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O6_OCTFzu8lE"
   },
   "source": [
    "## Домашнее задание 3 (10 баллов)\n",
    "\n",
    "## НИУ ВШЭ, 2020-21 учебный год"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rIc6VNYLu8lK"
   },
   "source": [
    "### О задании"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xRsXxtgNu8lM"
   },
   "source": [
    "В этом домашнем задании вы получите опыт подготовки данных и EDA (Exploratory Data Analysis), приблизившись к реальной работе с данными с помощью уже знакомых вам инструментов: numpy, pandas, matplotlib и seaborn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "01c_ZXgNu8ld"
   },
   "source": [
    "Вы будет работать с задачей кредитного скоринга (оценка надёжности клиента). Предоставляются данные о 1000 клиентов, про которых известно около 20 признаков и положительная или отрицательная оценка в качестве кредитуемого. Эти данные нужно будет подготовить, исследовать (в том числе с помощью средств визуализации) и даже попробовать предложить какую-нибудь формулу для условной оценки надёжности клиентов исходя из предоставленных данных. Описание данных:\n",
    "https://www.rdocumentation.org/packages/evtree/versions/1.0-8/topics/GermanCredit\n",
    "\n",
    "Каждый построенный график необходимо красиво оформить - для этого требуется как минимум указать его название, а также отметить, что изображено по каждой из осей.\n",
    "Также по каждому графику ожидается небольшой комментарий с выводами.\n",
    "\n",
    "*Функции, которые могут пригодиться при оформлении графиков: `.show()`, `.title()`, `.xlabel()`, `.ylabel()`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "kbKMD992u8ld"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PoKE3PySu8ld"
   },
   "source": [
    "### Задание 1 (0.0 балла)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sMh8vvlNu8ld"
   },
   "source": [
    "Прочитайте средствами pandas файл с данными по ссылке https://raw.githubusercontent.com/PersDep/data-mining-intro-2021/main/hw03-EDA-data/german_credit.csv. Выведите базовое представление таблицы (первые 5 и последние 5 строк, размер)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "cRtVjoMlu8le"
   },
   "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>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>purpose</th>\n",
       "      <th>amount</th>\n",
       "      <th>savings</th>\n",
       "      <th>employment_duration</th>\n",
       "      <th>installment_rate</th>\n",
       "      <th>personal_status_sex</th>\n",
       "      <th>other_debtors</th>\n",
       "      <th>...</th>\n",
       "      <th>property</th>\n",
       "      <th>age</th>\n",
       "      <th>other_installment_plans</th>\n",
       "      <th>housing</th>\n",
       "      <th>number_credits</th>\n",
       "      <th>job</th>\n",
       "      <th>people_liable</th>\n",
       "      <th>telephone</th>\n",
       "      <th>foreign_worker</th>\n",
       "      <th>credit_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no checking account</td>\n",
       "      <td>18</td>\n",
       "      <td>all credits at this bank paid back duly</td>\n",
       "      <td>car (used)</td>\n",
       "      <td>1049</td>\n",
       "      <td>unknown/no savings account</td>\n",
       "      <td>&lt; 1 yr</td>\n",
       "      <td>&lt; 20</td>\n",
       "      <td>female : non-single or male : single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>car or other</td>\n",
       "      <td>21</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee/official</td>\n",
       "      <td>0 to 2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no checking account</td>\n",
       "      <td>9</td>\n",
       "      <td>all credits at this bank paid back duly</td>\n",
       "      <td>others</td>\n",
       "      <td>2799</td>\n",
       "      <td>unknown/no savings account</td>\n",
       "      <td>1 &lt;= ... &lt; 4 yrs</td>\n",
       "      <td>25 &lt;= ... &lt; 35</td>\n",
       "      <td>male : married/widowed</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>unknown / no property</td>\n",
       "      <td>36</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>2-3</td>\n",
       "      <td>skilled employee/official</td>\n",
       "      <td>3 or more</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>... &lt; 0 DM</td>\n",
       "      <td>12</td>\n",
       "      <td>no credits taken/all credits paid back duly</td>\n",
       "      <td>retraining</td>\n",
       "      <td>841</td>\n",
       "      <td>... &lt;  100 DM</td>\n",
       "      <td>4 &lt;= ... &lt; 7 yrs</td>\n",
       "      <td>25 &lt;= ... &lt; 35</td>\n",
       "      <td>female : non-single or male : single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>unknown / no property</td>\n",
       "      <td>23</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>1</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>0 to 2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>no checking account</td>\n",
       "      <td>12</td>\n",
       "      <td>all credits at this bank paid back duly</td>\n",
       "      <td>others</td>\n",
       "      <td>2122</td>\n",
       "      <td>unknown/no savings account</td>\n",
       "      <td>1 &lt;= ... &lt; 4 yrs</td>\n",
       "      <td>20 &lt;= ... &lt; 25</td>\n",
       "      <td>male : married/widowed</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>unknown / no property</td>\n",
       "      <td>39</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>2-3</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>3 or more</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>no checking account</td>\n",
       "      <td>12</td>\n",
       "      <td>all credits at this bank paid back duly</td>\n",
       "      <td>others</td>\n",
       "      <td>2171</td>\n",
       "      <td>unknown/no savings account</td>\n",
       "      <td>1 &lt;= ... &lt; 4 yrs</td>\n",
       "      <td>&lt; 20</td>\n",
       "      <td>male : married/widowed</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>car or other</td>\n",
       "      <td>38</td>\n",
       "      <td>bank</td>\n",
       "      <td>rent</td>\n",
       "      <td>2-3</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>0 to 2</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>no checking account</td>\n",
       "      <td>24</td>\n",
       "      <td>no credits taken/all credits paid back duly</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>1987</td>\n",
       "      <td>unknown/no savings account</td>\n",
       "      <td>1 &lt;= ... &lt; 4 yrs</td>\n",
       "      <td>25 &lt;= ... &lt; 35</td>\n",
       "      <td>male : married/widowed</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>unknown / no property</td>\n",
       "      <td>21</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>1</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>3 or more</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>no checking account</td>\n",
       "      <td>24</td>\n",
       "      <td>no credits taken/all credits paid back duly</td>\n",
       "      <td>others</td>\n",
       "      <td>2303</td>\n",
       "      <td>unknown/no savings account</td>\n",
       "      <td>&gt;= 7 yrs</td>\n",
       "      <td>&lt; 20</td>\n",
       "      <td>male : married/widowed</td>\n",
       "      <td>co-applicant</td>\n",
       "      <td>...</td>\n",
       "      <td>unknown / no property</td>\n",
       "      <td>45</td>\n",
       "      <td>none</td>\n",
       "      <td>rent</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee/official</td>\n",
       "      <td>0 to 2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>... &gt;= 200 DM / salary for at least 1 year</td>\n",
       "      <td>21</td>\n",
       "      <td>all credits at this bank paid back duly</td>\n",
       "      <td>others</td>\n",
       "      <td>12680</td>\n",
       "      <td>... &gt;= 1000 DM</td>\n",
       "      <td>&gt;= 7 yrs</td>\n",
       "      <td>&lt; 20</td>\n",
       "      <td>male : married/widowed</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real estate</td>\n",
       "      <td>30</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>manager/self-empl./highly qualif. employee</td>\n",
       "      <td>0 to 2</td>\n",
       "      <td>yes (under customer name)</td>\n",
       "      <td>no</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>... &lt; 0 DM</td>\n",
       "      <td>12</td>\n",
       "      <td>no credits taken/all credits paid back duly</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>6468</td>\n",
       "      <td>... &gt;= 1000 DM</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>25 &lt;= ... &lt; 35</td>\n",
       "      <td>male : married/widowed</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real estate</td>\n",
       "      <td>52</td>\n",
       "      <td>none</td>\n",
       "      <td>rent</td>\n",
       "      <td>1</td>\n",
       "      <td>manager/self-empl./highly qualif. employee</td>\n",
       "      <td>0 to 2</td>\n",
       "      <td>yes (under customer name)</td>\n",
       "      <td>no</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>no checking account</td>\n",
       "      <td>30</td>\n",
       "      <td>no credits taken/all credits paid back duly</td>\n",
       "      <td>car (used)</td>\n",
       "      <td>6350</td>\n",
       "      <td>... &gt;= 1000 DM</td>\n",
       "      <td>&gt;= 7 yrs</td>\n",
       "      <td>&lt; 20</td>\n",
       "      <td>male : married/widowed</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>car or other</td>\n",
       "      <td>31</td>\n",
       "      <td>none</td>\n",
       "      <td>rent</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee/official</td>\n",
       "      <td>0 to 2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         status  duration  \\\n",
       "0                           no checking account        18   \n",
       "1                           no checking account         9   \n",
       "2                                    ... < 0 DM        12   \n",
       "3                           no checking account        12   \n",
       "4                           no checking account        12   \n",
       "..                                          ...       ...   \n",
       "995                         no checking account        24   \n",
       "996                         no checking account        24   \n",
       "997  ... >= 200 DM / salary for at least 1 year        21   \n",
       "998                                  ... < 0 DM        12   \n",
       "999                         no checking account        30   \n",
       "\n",
       "                                  credit_history              purpose  amount  \\\n",
       "0        all credits at this bank paid back duly           car (used)    1049   \n",
       "1        all credits at this bank paid back duly               others    2799   \n",
       "2    no credits taken/all credits paid back duly           retraining     841   \n",
       "3        all credits at this bank paid back duly               others    2122   \n",
       "4        all credits at this bank paid back duly               others    2171   \n",
       "..                                           ...                  ...     ...   \n",
       "995  no credits taken/all credits paid back duly  furniture/equipment    1987   \n",
       "996  no credits taken/all credits paid back duly               others    2303   \n",
       "997      all credits at this bank paid back duly               others   12680   \n",
       "998  no credits taken/all credits paid back duly  furniture/equipment    6468   \n",
       "999  no credits taken/all credits paid back duly           car (used)    6350   \n",
       "\n",
       "                        savings employment_duration installment_rate  \\\n",
       "0    unknown/no savings account              < 1 yr             < 20   \n",
       "1    unknown/no savings account    1 <= ... < 4 yrs   25 <= ... < 35   \n",
       "2                 ... <  100 DM    4 <= ... < 7 yrs   25 <= ... < 35   \n",
       "3    unknown/no savings account    1 <= ... < 4 yrs   20 <= ... < 25   \n",
       "4    unknown/no savings account    1 <= ... < 4 yrs             < 20   \n",
       "..                          ...                 ...              ...   \n",
       "995  unknown/no savings account    1 <= ... < 4 yrs   25 <= ... < 35   \n",
       "996  unknown/no savings account            >= 7 yrs             < 20   \n",
       "997              ... >= 1000 DM            >= 7 yrs             < 20   \n",
       "998              ... >= 1000 DM          unemployed   25 <= ... < 35   \n",
       "999              ... >= 1000 DM            >= 7 yrs             < 20   \n",
       "\n",
       "                      personal_status_sex other_debtors  ...  \\\n",
       "0    female : non-single or male : single          none  ...   \n",
       "1                  male : married/widowed          none  ...   \n",
       "2    female : non-single or male : single          none  ...   \n",
       "3                  male : married/widowed          none  ...   \n",
       "4                  male : married/widowed          none  ...   \n",
       "..                                    ...           ...  ...   \n",
       "995                male : married/widowed          none  ...   \n",
       "996                male : married/widowed  co-applicant  ...   \n",
       "997                male : married/widowed          none  ...   \n",
       "998                male : married/widowed          none  ...   \n",
       "999                male : married/widowed          none  ...   \n",
       "\n",
       "                  property age  other_installment_plans   housing  \\\n",
       "0             car or other  21                     none  for free   \n",
       "1    unknown / no property  36                     none  for free   \n",
       "2    unknown / no property  23                     none  for free   \n",
       "3    unknown / no property  39                     none  for free   \n",
       "4             car or other  38                     bank      rent   \n",
       "..                     ...  ..                      ...       ...   \n",
       "995  unknown / no property  21                     none  for free   \n",
       "996  unknown / no property  45                     none      rent   \n",
       "997            real estate  30                     none       own   \n",
       "998            real estate  52                     none      rent   \n",
       "999           car or other  31                     none      rent   \n",
       "\n",
       "    number_credits                                         job people_liable  \\\n",
       "0                1                   skilled employee/official        0 to 2   \n",
       "1              2-3                   skilled employee/official     3 or more   \n",
       "2                1                        unskilled - resident        0 to 2   \n",
       "3              2-3                        unskilled - resident     3 or more   \n",
       "4              2-3                        unskilled - resident        0 to 2   \n",
       "..             ...                                         ...           ...   \n",
       "995              1                        unskilled - resident     3 or more   \n",
       "996              1                   skilled employee/official        0 to 2   \n",
       "997              1  manager/self-empl./highly qualif. employee        0 to 2   \n",
       "998              1  manager/self-empl./highly qualif. employee        0 to 2   \n",
       "999              1                   skilled employee/official        0 to 2   \n",
       "\n",
       "                     telephone foreign_worker credit_risk  \n",
       "0                           no             no        good  \n",
       "1                           no             no        good  \n",
       "2                           no             no        good  \n",
       "3                           no            yes        good  \n",
       "4                           no            yes        good  \n",
       "..                         ...            ...         ...  \n",
       "995                         no             no         bad  \n",
       "996                         no             no         bad  \n",
       "997  yes (under customer name)             no         bad  \n",
       "998  yes (under customer name)             no         bad  \n",
       "999                         no             no         bad  \n",
       "\n",
       "[1000 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "data = pd.read_csv('https://raw.githubusercontent.com/PersDep/data-mining-intro-2021/main/hw03-EDA-data/german_credit.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JBbxRdOQu8le"
   },
   "source": [
    "### Задание 2 (0.5 балла)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hIPt1r6ju8le"
   },
   "source": [
    "Определите, есть ли пропуски в данных. Разделите признаки на количественные, бинарные, порядковые и категориальные.\n",
    "Пойдем с конца \n",
    "#credit_risk - очевидно бинарный, так как имеет всего два значения в домене good, bad\n",
    "#foreign_worker - так же бинарный, два значения yes и no \n",
    "#telephone - бинарный, сначала хотел определить как матегориальный или порядковый принзнак, но оказалось, что значения всего два, поэтому бинарный\n",
    "#people_liable - тоже бинарный (но вообще-то странно) значения всего два, может эта категория окажется важна, поэтому оставим бинарный\n",
    "#job - однозначно категориальный признак, в нашем случае конечно домен ограничен набором, который при желании можно рассматривать как порядковый, но это категориальный\n",
    "#number_credits - однозначно порядковый признак потому что он не достаточно свободен для арифметических операций, но мы можем сравнить друг с другом его значения\n",
    "#housing - порядковый, всего три сравнимых значения\n",
    "#other_installment_plans - можно рассматривать его как категориальный, так как так много всего, что можно взять в кредит, а на кредитной истории скажется однозначно\n",
    "#age - однозначно вещественный признак, который даже не требует пояснения на мой взгляд\n",
    "#property - категориальный по принципу признака работы (владеть можно много чем, сравнить это невозможно, сложить или умножить тоже)\n",
    "#present_residence - порядковый, потому что значения - ограниченное количество сравнимых промежутков\n",
    "#other_debtors - порядковый, потому что опций всего три, их можно явно сравнить друг с другом по уровню надежности, которую мы и рассматриваем\n",
    "#personal_status_sex - я склонен определить как категориальный, потому что даже если сократить до множества категорий мужина-женщина, опций много и сравнить их нельзя\n",
    "#installment_rate - порядковый, понятные промежутки, ограничены и сравнимы, но не подвергаются арифметике\n",
    "#employment_duration - порядковый по той же причине\n",
    "#savings - порядковый, то же самое\n",
    "#amount - однозначно вещественный, даже однозначнее чем age, числа, сравнимые и арифметически доступные\n",
    "#purpose - я склонен отправить это в категориальные признаки, потому что причин взять кредит может быть чудовищное множество и они не сравнимы сами по себе\n",
    "#credit_history - однозначно порядковый, ограниченный домен с понятным сравнением и невозможностью арифметических операций не оставляяет нам шансов\n",
    "#duration - вещественный (числа, можно сравнить, можно применять арифметику)\n",
    "#status - порядковый, потому что у нас интервалы "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "jD04ZLhdM6DT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 21 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   status                   1000 non-null   object\n",
      " 1   duration                 1000 non-null   int64 \n",
      " 2   credit_history           1000 non-null   object\n",
      " 3   purpose                  1000 non-null   object\n",
      " 4   amount                   1000 non-null   int64 \n",
      " 5   savings                  1000 non-null   object\n",
      " 6   employment_duration      1000 non-null   object\n",
      " 7   installment_rate         1000 non-null   object\n",
      " 8   personal_status_sex      1000 non-null   object\n",
      " 9   other_debtors            1000 non-null   object\n",
      " 10  present_residence        1000 non-null   object\n",
      " 11  property                 1000 non-null   object\n",
      " 12  age                      1000 non-null   int64 \n",
      " 13  other_installment_plans  1000 non-null   object\n",
      " 14  housing                  1000 non-null   object\n",
      " 15  number_credits           1000 non-null   object\n",
      " 16  job                      1000 non-null   object\n",
      " 17  people_liable            1000 non-null   object\n",
      " 18  telephone                1000 non-null   object\n",
      " 19  foreign_worker           1000 non-null   object\n",
      " 20  credit_risk              1000 non-null   object\n",
      "dtypes: int64(3), object(18)\n",
      "memory usage: 164.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.isnull().sum()\n",
    "#второй вариант, но мне больше нравится первый\n",
    "data.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SqPrAHdKRTIE"
   },
   "source": [
    "### Задание 3 (0.5 балла)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KlMiEcHBRXhD"
   },
   "source": [
    "Выведите среднюю сумму кредита (колонка amount) в каждой категории (колонка purpose), воспользовавшись методом groupby."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "oXE_DvksRbpn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "purpose\n",
       "business               8209.333333\n",
       "car (new)              5370.223301\n",
       "car (used)             3066.983425\n",
       "domestic appliances    2728.090909\n",
       "furniture/equipment    2487.685714\n",
       "others                 3062.948718\n",
       "radio/television       1498.000000\n",
       "repairs                3180.400000\n",
       "retraining             4158.041237\n",
       "vacation               1205.888889\n",
       "Name: amount, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('purpose')['amount'].apply(lambda x: x.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mG3ALQRdu8le"
   },
   "source": [
    "### Задание 4 (0.5 балла)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dlb3bBzTu8lf"
   },
   "source": [
    "Какой процент клиентов моложе 30 лет (колонка age) снимает жилье (колонка housing)? А какой процент клиентов старше 40 лет снимает жилье?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "MbGsJVWQu8lf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "моложе 30 и снимают:  66.66666666666666\n",
      "старше 40 и снимают:  68.01470588235294\n"
     ]
    }
   ],
   "source": [
    "print('моложе 30 и снимают: ', data[(data['age']<30) & (data['housing']=='rent')].age.count()/data[data['age']<30].age.count()*100)\n",
    "print('старше 40 и снимают: ', data[(data['age']>40)&(data['housing']=='rent')].age.count()/data[data['age']>40].age.count()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-JG_63_nu8lf"
   },
   "source": [
    "### Задание 5 (0.5 балла)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aEC7Jj5eu8lf"
   },
   "source": [
    " У скольких иностранцев (foreign_worker) число кредитов (number_credits) больше 3?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ScD09Oa4u8lf"
   },
   "outputs": [],
   "source": [
    "data[(data['foreign_worker']=='yes')&((data['number_credits']=='4-5') | (data['number_credits']=='>= 6'))].age.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KAe_K_upu8lg"
   },
   "source": [
    "### Задание 6 (1 балл)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Q1pVde3gjPNB"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aW45ioAJjWMo"
   },
   "source": [
    "Используя numpy и matplotlib, начнём с тренировки: постройте график функций $sin(x)$ и $cos(x)$ от $-\\pi$ до $\\pi$ из 100 точек c легендой и сеткой. Используйте для линий разные цвета, одна из них пусть будет пунктирной. Результат в этом и следующих заданиях на построение графиков должен получиться примерно как на картинке. Содержание подписей не должно обязательно совпадать, однако осмысленные подписи должны присутствовать на графиках."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "8rMlt0nbu8lg",
    "outputId": "02bfcae5-2a4f-4b8b-eb41-51ac7ad41379"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe65a587a30>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-np.pi, np.pi, 100)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "plt.title(\"sin(x), cos(x)\", fontsize=18)\n",
    "plt.grid()\n",
    "plt.plot(x, y1, \"r--\", label='sin(x)')\n",
    "plt.plot(x, y2, \"b\", label='cos(x)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "toFoOig2u8lh"
   },
   "source": [
    "### Задание 7 (1 балл)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9I3yl5dEu8lh"
   },
   "source": [
    "Рассмотрим сравнительное количество положительных и отрицательных кейсов в зависимости от размера текущего счёта. Постройте зеркальные горизонтальные гистограммы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 373
    },
    "id": "sqXmg0V3u8li",
    "outputId": "1bb93982-356f-4db5-e6c1-e1d5611d6fd5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Status of checking account')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15,6))\n",
    "good_ones = data[data['credit_risk']=='good'].groupby('status', as_index=False).count()\n",
    "bad_ones = data[data['credit_risk']=='bad'].groupby('status', as_index=False).count()\n",
    "axs[0].barh(good_ones['status'], good_ones['amount'], color='g')\n",
    "axs[0].set_xlim(370, 0)\n",
    "axs[0].set_title('Good Risk')\n",
    "axs[1].barh(bad_ones['status'], bad_ones['amount'], color='r')\n",
    "axs[1].set_xlim(0, 370)\n",
    "axs[1].yaxis.tick_right()\n",
    "axs[1].set_title('Bad Risk')\n",
    "fig.suptitle('Status of checking account')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bfJALnuMu8li"
   },
   "source": [
    "### Задание 8  (0.5 балла)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Yi_RIvTx00EE"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mFbzcUiwu8li"
   },
   "source": [
    "Теперь постройте гистограмму для сравнения количества положительных и отрицательных кейсов в зависимости от размера сберегательного счёта с помощью seaborn. Какой способ вам больше нравится с точки зрения трудоёмкости написания кода? А с точки зрения восприятия результата?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "id": "ZFe21FDJu8li",
    "outputId": "b6b62e36-4dc7-481d-f972-bc5296f4a505"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe65772ddf0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BkiRp1CY7K2mHN+xpY/sn+VSSe5LcneQTSfbfzGTPBX4TOKolteXtB27eBfxqktuBX23946nxYuAWulu1neSdxiRJkiSNG/ZHKj9Md1Tk1a3/N1rbr041QVV9kakT4/OnmOY0usNekiRJkuaQJG+kO9Vs0Jeq6hG3WR6VYcPL/Kr68ED/R5KcPIJ6JEmSJM1BLQ98eLMjjtCwF+zfm+Q3kuzUHr8BfHuUhUmSJEnSoGHDy28BrwH+f7p7Mr8KeOOoipIkSZKkiYY9bex/AcdX1Xr4yf2jT6cLNZIkSZI0csOGl6ePBxeAqvpOkmn9GqYkSZKkbWfeW+dt0988WX/G+hm/PXOShcClVfXUYcYf9rSxRyX5yY+jtCMvwwYfSZIkSdpqwwaQ9wL/J8nHgaK7/sVbGkuSJEk7kCR/CLwOuBO4F7ge+Bzwl8BjgW8Av1VV65MsnqL9mcC5wA+AL05n+UMdeamq84FXAncD64BXVNUF01mQJEmSpP5KMkaXCQ4DXgGMtUHnA79XVU8HbgL+aDPtHwbeUlXPmW4NQ5/6VVW3ALdMdwGSJEmStgtHAJ+pqh8CJPl74HHAHlX1hTbOecDfJXnikO0XAC8atoBhr3mRJEmStGPbFhf0h+4ylC1ieJEkSZI0jC8CL0uya5LdgZcA3wfWJ/mlNs5vAl+oqu9O0X4f8N0kR7T2102nAO8YJkmSJPXQTN/auKquS3IJcAPwb8Ay4LvA8cBfJnkscAcbf8x+qvY3Aucm+QFw+XRqMLxIkiRJGtbpVXVqCyRXA++tquXAsyeOuIn264FDB5pOHXbhhhdJkiRJwzo7ycHArsB5VfXVmVy44UWSJEnSUKrqtbO5fC/YlyRJktQLhhdJkiRJvWB4kSRJktQLhhdJkiRJveAF+5IkSVIPrV60aIt/qX4yC1et2uzvxiRZCFxaVU+d7vy3ZtpxHnmRJEmS1AuGF0mSJEnTsXOS85LcmOTjSR6b5J1JrkuyIsnZSQKQ5JlJbkjyZeCkrV2w4UWSJEnSdPwccHZVPR34HvDfgDOr6lntlLDdgJe2cT8MvKWqnrMtFmx4kSRJkjQdd1bVl1r33wBHAM9Lck2Sm4CjgEOSPBHYo6q+0Ma9YGsX7AX7kiRJkqZj4o0CCjgLGKuqO5OcCuwKZJJxt4pHXiRJkiRNx4FJxk8DOw74Yuu+N8nuwKsAquo+4LtJjmjDX7e1Cx7ZkZck59Kd63bP+O3QWgr7bWBdG+33q+qzbdgpwAnABrrz4i4fVW2SJElS3w1za+MRuRU4PslfAbcDHwTmATcBq4HrBsZ9I3Bukh8AW/39fpSnjX0EOBM4f0L7GVV1+mBDkoOBpcAhwL7A55I8uao2jLA+SZIkSdNQVauBgycZ9D/bY+L41wOHDjSdujXLH9lpY1V1NfCdIUdfAlxYVQ9W1SpgJXD4qGqTJEmS1D+zccH+m5O8HlgGvK2q1gP7AV8ZGGdNa3uEJCcCJwIceOCBIy5VkqTRmPfWebNdwsisP2P9bJcgaTs10xfsfxB4ErAYuAt4b2uf7Hy9Se9MUFVnV9VYVY3Nnz9/JEVKkiRJmntmNLxU1d1VtaGqfgycw8ZTw9YABwyMuj+wdiZrkyRJkjS3zWh4SbJgoPcYYEXrvgRYmuQxSRYBBwHXzmRtkiRJkua2Ud4q+WPAkcBeSdYAfwQcmWQx3Slhq4E3AVTVzUkuBm4BHgJO8k5jkiRJkgaNLLxU1XGTNP/1JsY/DThtVPVIkiRJ6reZvmBfkiRJkraI4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSL4wsvCQ5N8k9SVYMtO2Z5Iokt7fneQPDTkmyMsltSV44qrokSZIk9dMoj7x8BDh6Qts7gCur6iDgytZPkoOBpcAhbZqzkuw0wtokSZIk9czIwktVXQ18Z0LzEuC81n0e8PKB9gur6sGqWgWsBA4fVW2SJEmS+memr3nZp6ruAmjPe7f2/YA7B8Zb09oeIcmJSZYlWbZu3bqRFitJkiRp7pgrF+xnkraabMSqOruqxqpqbP78+SMuS5IkSdJcMdPh5e4kCwDa8z2tfQ1wwMB4+wNrZ7g2SZIkSXPYTIeXS4DjW/fxwGcG2pcmeUySRcBBwLUzXJskSZKkOWznUc04yceAI4G9kqwB/gh4F3BxkhOAbwKvBqiqm5NcDNwCPAScVFUbRlWbJEmSpP4ZWXipquOmGPT8KcY/DThtVPVIkiRJ6re5csG+JEmSJG2S4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSL+w82wVIc928t86b7RJGZv0Z62e7BEmSpKF55EWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSLxheJEmSJPWC4UWSJElSL+w8GwtNshq4H9gAPFRVY0n2BC4CFgKrgddUlT//LUmSJAmY3SMvz6uqxVU11vrfAVxZVQcBV7Z+SZIkSQJm6cjLFJYAR7bu84CrgN+brWIkabrmvXXebJcwUuvP8GC4JGl2zdaRlwL+Kcn1SU5sbftU1V0A7XnvWapNkiRJ0hw0W0denltVa5PsDVyR5OvDTtjCzokABx544KjqkyRJkjTHzMqRl6pa257vAT4FHA7cnWQBQHu+Z4ppz66qsaoamz9//kyVLEmSJGmWzXh4SfK4JI8f7wZ+DVgBXAIc30Y7HvjMTNcmSZIkae6ajdPG9gE+lWR8+X9bVZcluQ64OMkJwDeBV89CbZIkSZLmqBkPL1V1B3DoJO3fBp4/0/VIkiRJ6oe5dKtkSZIkTeBt2KWNZvNHKiVJkiRpaIYXSZIkSb1geJEkSZLUC4YXSZIkSb1geJEkSZLUC95tTNqBrV60aLZLGKmFq1bNdgmSJGkbMrxIkqRtyh0jkkbF08YkSZIk9YLhRZIkSVIvGF4kSZIk9YLhRZIkSVIvGF4kSZIk9YLhRZIkSVIvGF4kSZIk9YLhRZIkSVIvGF4kSZIk9YLhRZIkSVIvGF4kSZIk9YLhRZIkSVIvGF4kSZIk9YLhRZIkSVIv7DzbBUiS+mH1okWzXcJILVy1arZLkCRthkdeJEmSJPWC4UWSJElSLxheJEmSJPWC17xIkiRp1ng9naZjzh15SXJ0ktuSrEzyjtmuR5IkSdLcMKfCS5KdgA8ALwIOBo5LcvDsViVJkiRpLphT4QU4HFhZVXdU1X8AFwJLZrkmSZIkSXPAXLvmZT/gzoH+NcAvDI6Q5ETgxNb7QJLbZqi23ljEfXsB9852HSOTzHYF2w23FU2H24uG5bai6XB7mdJlVXX0tixlezDXwstk7249rKfqbODsmSmnn5Isq6qx2a5Dc5/biqbD7UXDclvRdLi9aDrm2mlja4ADBvr3B9bOUi2SJEmS5pC5Fl6uAw5KsijJo4GlwCWzXJMkSZKkOWBOnTZWVQ8leTNwObATcG5V3TzLZfWRp9VpWG4rmg63Fw3LbUXT4faioaWqNj+WJEmSJM2yuXbamCRJkiRNyvAiSZIkqRd2iPCS5A1JzpwDdTwnyTmzXQdAkv+S5PWzXcdMadvAvrNdx2xK8uokNyf5cZKxCcNOSbIyyW1JXjjQ/swkN7Vhf5E88mb1bd2uS/K1JLcnuTzJLw4M/0iSHyR5/EDb+5JUkr1G9XpnW5Jzk9yTZMWE9j2TXNHW1RVJ5g0Mm/R92F4kOTXJt5Isb48XDwxzG+ypJD+f5MtJHkzy9gnDjm7v6cok7xho36q/gyRXteE3Jvl6kjOT7DEwvJJcMNC/c9tGLt2mL34GJVnd/haWJ1k20L5DfaYkeXN7TQ/7+03nL9qwG5M8Y2DYtLfDgXEWJvlh+3y5Ncm1SY4fGP6GVsvzB9qOaW2vGs1a2LHtEOFlDjkauGy2iwCoqr+sqvNnu44Z9AZguw0vSR6X7g59m7ICeAVw9YRpD6a7s98hdNvoWUl2aoM/SPejsAe1x1Q/lnVRVR1WVQcB7wI+meQpA8NXAkva8h4FPA/41pAvr68+wuTr6x3AlW1dXdn6N/c+bNZk/3Rn0pDbIMAZVbW4PT7bpnUbnEFJHp3kcdtwlt8B3gKcPmE5OwEfAF4EHAwc195r2DZ/B6+rqqcDTwceBD4zMOz7wFOT7Nb6f5Xt4/1+XvvbGdwBtaN9pnwJeAHwbxPaX8TGz4kT6T47tmg7nMQ32ufLU+jW6VuTvHFg+E3AcQP9S4EbhnqhmrbehZeWgFcM9L89yamt+6ok726p+F+T/NIk07+k7SHaq+2N+4sk/yfJHeMJuaX39yRZ0fZyHNvaz0ry6637U0nObd0nJPnfrbZbk5yTbg/3Pw18cAI8H/hcS+mfTHJZS/t/NlDfcW2ZK5K8e4p18K4kt7Q9C6e3tpcluabtGfhckn2SPCrdnpo9BqZd2YadmraHbKr1luSxSS5uy7mozX8syU5t3Y2vn7dOUuMj6mntuyf5cJvuxiSvbO1HJ/lqkhuSXNna9kzy6TbeV5I8vbX/pPbWv6Kt+0nXf3tfx4CPpttjtdvEevsqybOS/BVwM7DJfzRVdWtV3TbJoCXAhVX1YFWtovuSd3iSBcATqurL1d3Z43zg5Zurqao+T3fnmBMHmj8GHNu6j6T75/PQ5ubVZ1V1Nd2XuomWAOe17vPYuE4nfR82tYwke7fPwBVsXL8zajrb4Ca4Dc6secDNSf4qybO2dmZVdU9VXQf8aMKgw4GVVXVHVf0HcCEtQLIN/w7avH8XODDJoQOD/hF4Ses+jm4b2B7tUJ8pVfW1qlo9yaRLgPOr8xVgj/YZsiXb4ZSq6g7gf9AF9nH/QveZtUuS3YH/BCzf3Ly0ZXoXXoawc1UdDpwM/NHggCTH0KXqF1fVva15AXAE8FK6vXXQ7Z1eDBxKl+7f0/4ArgbGA9F+dAmeNv2/tO6DgA9U1SHAfcD4l/O9gB9V1XfbeIvpPhieBhyb5IB0pzW9GziqDX9WkpdPeA17AscAh7Q9Tv+7Dfoi8OyqOozuD/N3q+rHdHuijmnT/gKwuqruHnK9/TdgfVvO/wKeOVD7flX11Kp6GvDhSeb3iHpa+x8C362qp7X5/nOS+cA5wCur6lDg1W3cPwa+1sb7fbovLpvziPVfVR8HltHtpVtcVT8cYj5zVgt1b0nyNbr3/5+Bn5vifR3GfsCdA/1rWtt+rXti+zC+Cvz8QP/twPx0e/KOo9smdlT7VNVdAO1579Y+1fvwMG2nxNFJPg5cBewKHF1Vf9mGvy4bT8safHx8W72ArdwG39x2SJybjXt23QZnUHuffg74PHBa28n0lvb/BYC2w2qy7Wg6pxtvapveqr+DSV7TBro93YPv+YXA0iS70h2duWYatc9FBfxTkuuTDAbzHf0zZdymPkemux1uzsTPlwI+B7yQLhD5G4UjNKd+52Ub+WR7vh5YOND+PLq9779WVd8baP90+5J/y/jRAbow8rH2YXh3ki8Az6ILKCenO9x4CzCvhZrn0CXwnwJWVdXySWr4NeCfBpZ75XiQSXIL8DNt+quqal1r/yjwy8CnB6b7HvDvwIeS/AMwfv7u/sBFrZ5HA6ta+0XAO+kCxtLWP+x6OwJ4H0BVrUhyY2u/A/jZJO8H/mHC6xo3VT0vaHXQ5rs+ycuAq9ueIapqfG/1EbTwV1X/nOSnkjxxivrHTbX+twst4N5Bd/rhr1fVnZuZZKjZTtJWm2jf0nl+ku69/wXgTUPOZ0cy7Pr+NPAM4D8Dl9eE+91X1UeBj27z6pqt3AY/SLcjpNrze4Hfwm1wxlXVg3Rf7i9MciBwJvBnSX62qtZW1bbY674l7982e8+r6sYkC+nC6meHnMdc9tyqWptkb+CKJF9vR3insiN8pjxsVpO0be3nyHSWdSHdd8EnAm+j2+mqEejjkZeHeHjdu04Y/mB73sDDw9kdwOOBJ08xPmzcGCfbKKmqb9Edwjya7ijMvwCvAR6oqvsnmd9gDS/i4de7TDbepMudUMNDdIdAP0F3eHN8nu8HzmxHQt7ExvXyZeA/taMbL2djSJlosvU21XpYT3dU6irgJOBDk4w2VT3hkR8ak7VNtfxi09vAVOt/e3E38FpgF+Dvk/yP9o9sa6wBDhjo3x9Y29r3n6R9GIcBt05ou5DuC+sVbYfBjuruFuppz/e09qneh4lOAT5O9zf2gYmn/czAXtIt3gar6u6q2tDe/3PYeAqL2+AsaKcIvQ34e7ofhn4t3fu7rY68bGqb3tq/g4mvZSe6MxkmvueX0F2L0/tTxqpqbXu+B/gUG/9+dtjPlAk29Tky3e1wcx7x+VJV1wJPBfaqqn+ddvUaWh/Dy93A3m0v/GPoTvcaxr/RnQ52fpJDNjPu1XSncu3UvvT/MnBtG/ZlulOrxsPL29l4ytikkoTukPXyzSz3GuBX0l2PsxPd3qIvTJjX7sATq7vQ9WS6U7igS/rjFyP+5C4YbQ/Kp4A/B26tqm9vpoZBX6QLZ+MX/j2tde8FPKqqPkF3GtgzJpl20nrojtK8eeD1zKNbp7+SZFFrGz914Wrgda3tSODedtRs9fgy091NZNEQr+V+uvDaa+2L3yer6iV053I/Frg63bVBmzsqNZVL6E6teEx7Dw4Crm2H0O9P8uy2Db+eh18QO6kkv0J3rcHD7qxXVd8E/gA4awvr3F5cwsa/iePZuE4nfR8mTlxVN1fVyXQX4X6B7rSfG5P8Whv+0dp4QfzgY5vc9WZrtsHxLwnNMXQ3kQC3wRmV5IlJPk33Gbsb3anUL2nv6waAqjp2iu1oOjd6uQ44KMmidBdeL2Xj6TRb9Xcw4fXsAvwpcGdV3Thh8LnAn1TVTdOoe85Jd/H648e76c7mGPz72SE/Uya4BHh9Os+mO0X9LrZsO5xSO5p3Ol3Ym+gUPOIyelXVuwfdYbmVwBV0d/Q5tbVfBYy17r3oru+A7k5TZ7buw+hO+XpSm/ZVA/N9oD0HeA/dB8NNwLED45wArG3du9Dd0eQVrX8hsGJg3LcDp9KdrvaRgfaf1NP6LwWObN2vbctcAfzZJK99Ad2Hz41tvONb+xK6o0v/0mq/amCaMbojFscPtJ0KvH0z6+1xdHtjbqS7kO16ug+/Q+nO91zeHi+apM5J6wF2b/NaQXd+8vi6exHwtdZ2RWvbk+6D5EbgK8DTW/tudCFoOd2Xk1vbup90/bfuVwK3tWl2m+1teKDGzwL7tu4/oTtkDvDrdP9wh5lH6K6TeuJmxjuGbg/Ug3Q7AS4fGPYHwDfaOnrRQPtYe6++QXdaSSaZ7xuAdW3d/itwOd3pDePDP8LA39lA+2q6PVSz/j6M6L39GHAX3UXMa4ATWvtP0d3V5vb2vOfm3ochlvUzwDN7sA1eQPe5dSPdl4YFboPbZFub1ntIt3PpqMnW5RYu/6fbNv49umsN19DdaAHgxe09+QbwBwPTbNXfAd3/rdvatnQb3d2k9hgY/sAk0xwJXDrb79cWruOfpfv/eAPdhezbbF1Osbw5+5lC9x1wDd1ZGGuBDw1M84H2em+ifa/Z0u1wYJyFwA/pvqPcSvcd7I0Dw9/AwHe6gfZJP3d8bP0jbQVrhJL8T7o7XfTqAtF29GeXqvr3JE+i+8N+cnV365AkSZJmlOFFU2qHqD9Pd4QpwO9V1T/OblWSJEnaURleJEmSJPVCHy/YlyRJkrQDMrxIkiRJ6gXDiyRJkqReMLxIkjYpyYfabz1JkjSrvGBfkiRJUi945EWStmPtl7n/IckNSVYkOTbJO5Nc1/rPbr9I/ZQk1w5MtzDJja37qiRjrfuBJKe1+X0lyT6t/Umt/7okf5Lkgda+IMnVSZa35f3SbKwHSdL2wfAiSdu3o4G1VXVoVT0VuIzu16Cf1fp3A15aVbcCj07ys226Y4GLJ5nf44CvVNWhwNXAb7f29wHvq6pn0f3q9bjXApdX1WLgUGD5Nn11kqQdiuFFkrZvNwEvSPLuJL9UVd8FnpfkmiQ3AUcBh7RxLwZe07qPBS6aZH7/AVzauq8HFrbu5wB/17r/dmD864A3JjkVeFpV3b/1L0mStKMyvEjSdqyq/hV4Jl2I+dMk7wTOAl5VVU8DzgF2baNfBLwmyZO7Sev2SWb5o9p4seQGYOfNLP9q4JeBbwEXJHn91r4mSdKOy/AiSduxJPsCP6iqvwFOB57RBt2bZHfgVePjVtU36ALJHzL5UZdN+Qrwyta9dGD5PwPcU1XnAH89sHxJkqZtk3vMJEm99zTgPUl+DPwI+K/Ay+mOxKymO61r0EXAe4BF01zOycDfJHkb8A/Ad1v7kcD/k+RHwAOAR14kSVvMWyVLkrZakscCP6yqSrIUOK6qlsx2XZKk7YtHXiRJ28IzgTOTBLgP+K3ZLUeStD3yyIskSZKkXvCCfUmSJEm9YHiRJEmS1AuGF0mSJEm9YHiRJEmS1AuGF0mSJEm98H8BR46t+uOhPW4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 805.95x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='savings', palette=['green', 'red'], hue='credit_risk', kind=\"count\", data=data, height=4.5, aspect=2.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "86JCla6mu8li"
   },
   "source": [
    "### Задание 9  (1 балл)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KDWT_Zx4u8lj"
   },
   "source": [
    "seaborn легко позволяет добавить ещё больше информации на один график. Для каждого размера сберегательного счёта мы можем оценить не только количество положительных и отрицательных кейсов, но и их распределение по возрасту, например. Постройте подходящую для этого визуализацию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "id": "_jyzuliwu8lj",
    "outputId": "686f5314-d24e-46e3-ec51-b3e96ed3e1ec"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe658b33520>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 805.95x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='savings', y='age', hue='credit_risk', palette=['green', 'red'], kind='violin', split=True, data=data, height=4.5, aspect=2.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q6ZBM9nqu8lj"
   },
   "source": [
    "### Задание 10  (0.5 балла)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9w1ch-1cu8lj"
   },
   "source": [
    "Заканчивая с гистограммами, посмотрим на распределение положительных и отрицательных кейсов по возрастам, разместив их друг над другом. Добавим распределение всех взятых кредитов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 663
    },
    "id": "_hWi9-MVEQf0",
    "outputId": "90b55c97-a3c7-4285-9f81-deaf5471be8b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe65bc4e6a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 1, tight_layout=True, figsize=(7, 9))\n",
    "axs[0].hist(data.loc[data['credit_risk']=='good']['age'].values.tolist(), bins=11, color='green', label='Good credits age distribution')\n",
    "axs[1].hist(data.loc[data['credit_risk']=='bad']['age'].values.tolist(), bins=11, color='red', label='Bad credits age distribution')\n",
    "axs[2].hist(data['age'].values.tolist(), bins=11, color='blue', label='Overall credits age distribution')\n",
    "axs[0].set_ylabel('Count')\n",
    "axs[1].set_ylabel('Count')\n",
    "axs[2].set_ylabel('Count')\n",
    "axs[2].set_xlabel('Age')\n",
    "axs[0].set_ylim(0, 200)\n",
    "axs[1].set_ylim(0, 200)\n",
    "fig.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PVrJUff5u8lj"
   },
   "source": [
    "### Задание 11  (0.5 балла)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vh20VqxOu8lk"
   },
   "source": [
    "Не менее полезной иногда может быть диаграмма рассеяния. Постройте такую диаграмму с продолжительностью и размерами кредитов в качестве осей. Обозначьте положительные и отрицательные кейсы разными цветами."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "id": "PEAmiINUu8lk",
    "outputId": "4886ccea-b046-4e51-fd17-2a8a47ea24ea"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize = (12, 8))\n",
    "sns.scatterplot(x='amount', y='duration', hue='credit_risk', palette=['green', 'red'], data=data, ax=axs)\n",
    "plt.xlabel('Credit amount')\n",
    "plt.ylabel('Duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mePIgqPqu8lk"
   },
   "source": [
    "### Задание 12  (1 балл)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HWD2qUbLu8lk"
   },
   "source": [
    "Продвинемся глубже. Постройте 3D версию диаграммы рассеяния, добавив ось с возрастом."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "SU2bmkPIu8lk",
    "outputId": "bccaab35-5fc2-4249-8ae2-309e53dcbd26"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'соглашусь, можно было сделать и через data=data, но как по мне, то в данном случае понятнее, что откуда выходит'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "ax.scatter(data[data['credit_risk']=='good']['amount'], data[data['credit_risk']=='good']['duration'].values.tolist(), data[data['credit_risk']=='good']['age'].values.tolist(), c='g', marker='o')\n",
    "ax.set_xlabel('Amount')\n",
    "ax.set_ylabel('Duration')\n",
    "ax.set_zlabel('Age')\n",
    "ax.scatter(data[data['credit_risk']=='bad']['amount'], data[data['credit_risk']=='bad']['duration'].values.tolist(), data[data['credit_risk']=='bad']['age'].values.tolist(), c='r', marker='o')\n",
    "\n",
    "plt.show()\n",
    "'''соглашусь, можно было сделать и через data=data, но как по мне, то в данном случае понятнее, что откуда выходит'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8Zxx_kxSYa_l"
   },
   "source": [
    "### Задание 13  (0.5 балла)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5YBhNGg6Yjuw"
   },
   "source": [
    "Представьте все порядковые и бинарные признаки в виде числовых значений {0, ..., n - 1}, где n это количество значений признака. Избавьтесь от категориальных признаков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "81_7p_bEYjuw"
   },
   "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>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>amount</th>\n",
       "      <th>savings</th>\n",
       "      <th>employment_duration</th>\n",
       "      <th>installment_rate</th>\n",
       "      <th>other_debtors</th>\n",
       "      <th>present_residence</th>\n",
       "      <th>age</th>\n",
       "      <th>...</th>\n",
       "      <th>property_car or other</th>\n",
       "      <th>property_real estate</th>\n",
       "      <th>property_unknown / no property</th>\n",
       "      <th>other_installment_plans_bank</th>\n",
       "      <th>other_installment_plans_none</th>\n",
       "      <th>other_installment_plans_stores</th>\n",
       "      <th>job_manager/self-empl./highly qualif. employee</th>\n",
       "      <th>job_skilled employee/official</th>\n",
       "      <th>job_unemployed/unskilled - non-resident</th>\n",
       "      <th>job_unskilled - resident</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>1049</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2799</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>841</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2122</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2171</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2303</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>12680</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>6468</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>6350</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     status  duration  credit_history  amount  savings  employment_duration  \\\n",
       "0         0        18               4    1049        0                    1   \n",
       "1         0         9               4    2799        0                    2   \n",
       "2         1        12               3     841        1                    3   \n",
       "3         0        12               4    2122        0                    2   \n",
       "4         0        12               4    2171        0                    2   \n",
       "..      ...       ...             ...     ...      ...                  ...   \n",
       "995       0        24               3    1987        0                    2   \n",
       "996       0        24               3    2303        0                    4   \n",
       "997       3        21               4   12680        4                    4   \n",
       "998       1        12               3    6468        4                    0   \n",
       "999       0        30               3    6350        4                    4   \n",
       "\n",
       "     installment_rate  other_debtors  present_residence  age  ...  \\\n",
       "0                   0              0                  3   21  ...   \n",
       "1                   2              0                  1   36  ...   \n",
       "2                   2              0                  3   23  ...   \n",
       "3                   1              0                  1   39  ...   \n",
       "4                   0              0                  3   38  ...   \n",
       "..                ...            ...                ...  ...  ...   \n",
       "995                 2              0                  3   21  ...   \n",
       "996                 0              2                  0   45  ...   \n",
       "997                 0              0                  3   30  ...   \n",
       "998                 2              0                  0   52  ...   \n",
       "999                 0              0                  3   31  ...   \n",
       "\n",
       "     property_car or other  property_real estate  \\\n",
       "0                        1                     0   \n",
       "1                        0                     0   \n",
       "2                        0                     0   \n",
       "3                        0                     0   \n",
       "4                        1                     0   \n",
       "..                     ...                   ...   \n",
       "995                      0                     0   \n",
       "996                      0                     0   \n",
       "997                      0                     1   \n",
       "998                      0                     1   \n",
       "999                      1                     0   \n",
       "\n",
       "     property_unknown / no property  other_installment_plans_bank  \\\n",
       "0                                 0                             0   \n",
       "1                                 1                             0   \n",
       "2                                 1                             0   \n",
       "3                                 1                             0   \n",
       "4                                 0                             1   \n",
       "..                              ...                           ...   \n",
       "995                               1                             0   \n",
       "996                               1                             0   \n",
       "997                               0                             0   \n",
       "998                               0                             0   \n",
       "999                               0                             0   \n",
       "\n",
       "     other_installment_plans_none  other_installment_plans_stores  \\\n",
       "0                               1                               0   \n",
       "1                               1                               0   \n",
       "2                               1                               0   \n",
       "3                               1                               0   \n",
       "4                               0                               0   \n",
       "..                            ...                             ...   \n",
       "995                             1                               0   \n",
       "996                             1                               0   \n",
       "997                             1                               0   \n",
       "998                             1                               0   \n",
       "999                             1                               0   \n",
       "\n",
       "     job_manager/self-empl./highly qualif. employee  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "..                                              ...   \n",
       "995                                               0   \n",
       "996                                               0   \n",
       "997                                               1   \n",
       "998                                               1   \n",
       "999                                               0   \n",
       "\n",
       "     job_skilled employee/official  job_unemployed/unskilled - non-resident  \\\n",
       "0                                1                                        0   \n",
       "1                                1                                        0   \n",
       "2                                0                                        0   \n",
       "3                                0                                        0   \n",
       "4                                0                                        0   \n",
       "..                             ...                                      ...   \n",
       "995                              0                                        0   \n",
       "996                              1                                        0   \n",
       "997                              0                                        0   \n",
       "998                              0                                        0   \n",
       "999                              1                                        0   \n",
       "\n",
       "     job_unskilled - resident  \n",
       "0                           0  \n",
       "1                           0  \n",
       "2                           1  \n",
       "3                           1  \n",
       "4                           1  \n",
       "..                        ...  \n",
       "995                         1  \n",
       "996                         0  \n",
       "997                         0  \n",
       "998                         0  \n",
       "999                         0  \n",
       "\n",
       "[1000 rows x 41 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "# Кодируем бинарные признаки\n",
    "y = ['credit_risk', 'foreign_worker', 'telephone', 'people_liable']\n",
    "le = LabelEncoder()\n",
    "for i in y:\n",
    "    le.fit(data[i])\n",
    "    data[i] = le.transform(data[i])\n",
    "\n",
    "# Кодируем порядковые признаки словарями \n",
    "status = {'no checking account': 0, '... < 0 DM': 1, '0<= ... < 200 DM': 2, '... >= 200 DM / salary for at least 1 year': 3}\n",
    "credit_history = {'critical account/other credits elsewhere': 0, 'delay in paying off in the past': 1, 'existing credits paid back duly till now': 2, 'no credits taken/all credits paid back duly': 3, 'all credits at this bank paid back duly': 4}\n",
    "savings = {'unknown/no savings account': 0, '... <  100 DM': 1, '100 <= ... <  500 DM': 2, '500 <= ... < 1000 DM': 3, '... >= 1000 DM': 4}\n",
    "employment_duration = {'unemployed': 0, '< 1 yr': 1, '1 <= ... < 4 yrs': 2, '4 <= ... < 7 yrs': 3, '>= 7 yrs': 4}\n",
    "installment_rate = {'< 20': 0, '20 <= ... < 25': 1, '25 <= ... < 35': 2, '>= 35': 3}\n",
    "other_debtors = {'none': 0, 'guarantor': 1, 'co-applicant': 2}\n",
    "present_residence = {'< 1 yr': 0, '1 <= ... < 4 yrs': 1, '4 <= ... < 7 yrs': 2, '>= 7 yrs': 3}\n",
    "housing = {'for free': 0, 'rent': 1, 'own': 2}\n",
    "number_credits = {'>= 6': 0, '4-5': 1, '2-3': 2, '1': 3}\n",
    "# Теперь у нас будет два списка с именами колонок и названиями списков\n",
    "# Я пытался сделать понятнее и проще, хотя бы без счетчика, но раз уж до дедлайна всего ничего, то я забил и сделал так, вроде все понятно\n",
    "z = ['status', 'credit_history', 'savings', 'employment_duration', 'installment_rate', 'other_debtors', 'present_residence', 'housing', 'number_credits']\n",
    "s = [status, credit_history, savings, employment_duration, installment_rate, other_debtors, present_residence, housing, number_credits]\n",
    "# А теперь собственно перебьем значения\n",
    "counter = 0\n",
    "for i in z:\n",
    "    data[i] = data[i].replace(s[counter])\n",
    "    counter += 1\n",
    "\n",
    "# У нас еще остались категориальные признаки, которые тоже надо закодировать (оказывается)\n",
    "x = ['purpose', 'personal_status_sex', 'property', 'other_installment_plans', 'job']    #Список с именами признаков\n",
    "data = pd.get_dummies(data, prefix=x)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Iz7VTiFpu8lo"
   },
   "source": [
    "### Задание 14  (2 балла)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mv52wusiu8lo"
   },
   "source": [
    "По результатам предыдущего задания у вас должна получиться таблица, в которой все признаки так или иначе представлены числовыми значениями. Попробуйте придумать на основе проведённых исследований данных какую-нибудь формулу, связывающую целевую переменную с признаками, не используя готовых инструментов машинного обучения. По желанию вы можете исключить ещё какие-то признаки, или, наоборот, сформировать новые на основе имеющихся. Возможно, вы захотите отмасштабировать большие наборы численных значений некоторых признаков с помощью интервалов. Цель: реализовать функцию, которая будет принимать на вход набор признаков для объекта выборки и возвращать прогноз целевой переменной (0 или 1). Например, вы можете просто сложить все признаки и возращать ноль, если сумма отрицательная, а в противном случае единицу. Но наверняка можно придумать лучше!\n",
    "Данные уже разделены на набор для обучения, который вы можете использовать как угодно, и тестовый, на котором нужно будет проверить вашу точность. Постарайтесь достичь точности хотя бы 0.6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "V8vjz88qu8lo"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict(Xtest: np.array) -> np.array:\n",
    "    Xtest = np.where(2*Xtest[:,2] + Xtest[:,0]+ Xtest[:,4]+Xtest[:,11]>=8, 1, 0)\n",
    "    return Xtest\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "X = data.drop('credit_risk', 1).values\n",
    "y = data['credit_risk'].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=10)\n",
    "# Используйте X_train и y_train на ваше усмотрение\n",
    "# your code here\n",
    "accuracy_score(y_test, predict(X_test))\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "hw1.ipynb",
   "provenance": []
  },
  "kernelspec": {
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   "name": "python3"
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  "language_info": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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