035-supervised-learning-logistic-regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c2414ff5",
   "metadata": {},
   "source": [
    "# Логістична регресія. Класифікація. Титанік"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "828e84b7-ad9d-4939-8d59-9b9248667ae7",
   "metadata": {},
   "source": [
    "Курс: \"Математичне моделювання в R\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ced297",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9910b214-644b-461d-95e6-28e8f95639ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# install.packages(\"mice\")\n",
    "# install.packages(\"Amelia\")\n",
    "# install.packages(\"InformationValue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607361bd-ec64-40da-9d45-497af39439f0",
   "metadata": {},
   "source": [
    "## Dataset overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8895342-1404-480f-a39a-8b8fd31ff78f",
   "metadata": {},
   "source": [
    "У даному навчальному матеріалі використано класичний приклад даних з інформацією про пасажирів корабля Титанік."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3191a739-7cc1-4a25-a17c-08af969d46f5",
   "metadata": {},
   "source": [
    "Source: https://github.com/Geoyi/Cleaning-Titanic-Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9603dea4-8656-4c05-9307-10c045f2ab29",
   "metadata": {},
   "source": [
    "**Dataset description:**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39bfbe51-8519-41e2-8087-c9f919e5a67b",
   "metadata": {},
   "source": [
    "You are provided hourly rental data spanning two years. For this competition, the training set is comprised of the first 19 days of each month, while the test set is the 20th to the end of the month. You must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a90aac-c2e2-4035-9498-491bc455d042",
   "metadata": {},
   "source": [
    "**Data Fields:**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84c03a47-25ce-4af1-b6fd-7291bcaa210c",
   "metadata": {},
   "source": [
    "|Variable|Definition|Key|\n",
    "|---|---|---|\n",
    "|survival|Survival (**TARGET**)|0 = No, 1 = Yes\n",
    "|pclass|Ticket class|1 = 1st, 2 = 2nd, 3 = 3rd||\n",
    "|sex|Sex||\n",
    "|Age|Age in years||\n",
    "|sibsp|# of siblings / spouses aboard the Titanic||\n",
    "|parch|# of parents / children aboard the Titanic||\n",
    "|ticket|Ticket number||\n",
    "|fare|Passenger fare||\n",
    "|cabin|Cabin number||\n",
    "|embarked|Port of Embarkation|C = Cherbourg, Q = Queenstown, S = Southampton|\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4518116b-4a81-49a5-807c-672c6a377a41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 14</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>pclass</th><th scope=col>survived</th><th scope=col>name</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>sibsp</th><th scope=col>parch</th><th scope=col>ticket</th><th scope=col>fare</th><th scope=col>cabin</th><th scope=col>embarked</th><th scope=col>boat</th><th scope=col>body</th><th scope=col>home.dest</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>1</td><td>Allen, Miss. Elisabeth Walton                  </td><td>female</td><td>29.0000</td><td>0</td><td>0</td><td>24160 </td><td>211.3375</td><td>B5     </td><td>S</td><td>2 </td><td> NA</td><td>St Louis, MO                   </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>1</td><td>Allison, Master. Hudson Trevor                 </td><td>male  </td><td> 0.9167</td><td>1</td><td>2</td><td>113781</td><td>151.5500</td><td>C22 C26</td><td>S</td><td>11</td><td> NA</td><td>Montreal, PQ / Chesterville, ON</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1</td><td>0</td><td>Allison, Miss. Helen Loraine                   </td><td>female</td><td> 2.0000</td><td>1</td><td>2</td><td>113781</td><td>151.5500</td><td>C22 C26</td><td>S</td><td>NA</td><td> NA</td><td>Montreal, PQ / Chesterville, ON</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1</td><td>0</td><td>Allison, Mr. Hudson Joshua Creighton           </td><td>male  </td><td>30.0000</td><td>1</td><td>2</td><td>113781</td><td>151.5500</td><td>C22 C26</td><td>S</td><td>NA</td><td>135</td><td>Montreal, PQ / Chesterville, ON</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1</td><td>0</td><td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td><td>female</td><td>25.0000</td><td>1</td><td>2</td><td>113781</td><td>151.5500</td><td>C22 C26</td><td>S</td><td>NA</td><td> NA</td><td>Montreal, PQ / Chesterville, ON</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1</td><td>1</td><td>Anderson, Mr. Harry                            </td><td>male  </td><td>48.0000</td><td>0</td><td>0</td><td>19952 </td><td> 26.5500</td><td>E12    </td><td>S</td><td>3 </td><td> NA</td><td>New York, NY                   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 14\n",
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & pclass & survived & name & sex & age & sibsp & parch & ticket & fare & cabin & embarked & boat & body & home.dest\\\\\n",
       "  & <int> & <int> & <chr> & <chr> & <dbl> & <int> & <int> & <chr> & <dbl> & <chr> & <chr> & <chr> & <int> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 1 & Allen, Miss. Elisabeth Walton                   & female & 29.0000 & 0 & 0 & 24160  & 211.3375 & B5      & S & 2  &  NA & St Louis, MO                   \\\\\n",
       "\t2 & 1 & 1 & Allison, Master. Hudson Trevor                  & male   &  0.9167 & 1 & 2 & 113781 & 151.5500 & C22 C26 & S & 11 &  NA & Montreal, PQ / Chesterville, ON\\\\\n",
       "\t3 & 1 & 0 & Allison, Miss. Helen Loraine                    & female &  2.0000 & 1 & 2 & 113781 & 151.5500 & C22 C26 & S & NA &  NA & Montreal, PQ / Chesterville, ON\\\\\n",
       "\t4 & 1 & 0 & Allison, Mr. Hudson Joshua Creighton            & male   & 30.0000 & 1 & 2 & 113781 & 151.5500 & C22 C26 & S & NA & 135 & Montreal, PQ / Chesterville, ON\\\\\n",
       "\t5 & 1 & 0 & Allison, Mrs. Hudson J C (Bessie Waldo Daniels) & female & 25.0000 & 1 & 2 & 113781 & 151.5500 & C22 C26 & S & NA &  NA & Montreal, PQ / Chesterville, ON\\\\\n",
       "\t6 & 1 & 1 & Anderson, Mr. Harry                             & male   & 48.0000 & 0 & 0 & 19952  &  26.5500 & E12     & S & 3  &  NA & New York, NY                   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 14\n",
       "\n",
       "| <!--/--> | pclass &lt;int&gt; | survived &lt;int&gt; | name &lt;chr&gt; | sex &lt;chr&gt; | age &lt;dbl&gt; | sibsp &lt;int&gt; | parch &lt;int&gt; | ticket &lt;chr&gt; | fare &lt;dbl&gt; | cabin &lt;chr&gt; | embarked &lt;chr&gt; | boat &lt;chr&gt; | body &lt;int&gt; | home.dest &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1 | 1 | Allen, Miss. Elisabeth Walton                   | female | 29.0000 | 0 | 0 | 24160  | 211.3375 | B5      | S | 2  |  NA | St Louis, MO                    |\n",
       "| 2 | 1 | 1 | Allison, Master. Hudson Trevor                  | male   |  0.9167 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | 11 |  NA | Montreal, PQ / Chesterville, ON |\n",
       "| 3 | 1 | 0 | Allison, Miss. Helen Loraine                    | female |  2.0000 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | NA |  NA | Montreal, PQ / Chesterville, ON |\n",
       "| 4 | 1 | 0 | Allison, Mr. Hudson Joshua Creighton            | male   | 30.0000 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | NA | 135 | Montreal, PQ / Chesterville, ON |\n",
       "| 5 | 1 | 0 | Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | female | 25.0000 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | NA |  NA | Montreal, PQ / Chesterville, ON |\n",
       "| 6 | 1 | 1 | Anderson, Mr. Harry                             | male   | 48.0000 | 0 | 0 | 19952  |  26.5500 | E12     | S | 3  |  NA | New York, NY                    |\n",
       "\n"
      ],
      "text/plain": [
       "  pclass survived name                                            sex   \n",
       "1 1      1        Allen, Miss. Elisabeth Walton                   female\n",
       "2 1      1        Allison, Master. Hudson Trevor                  male  \n",
       "3 1      0        Allison, Miss. Helen Loraine                    female\n",
       "4 1      0        Allison, Mr. Hudson Joshua Creighton            male  \n",
       "5 1      0        Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female\n",
       "6 1      1        Anderson, Mr. Harry                             male  \n",
       "  age     sibsp parch ticket fare     cabin   embarked boat body\n",
       "1 29.0000 0     0     24160  211.3375 B5      S        2     NA \n",
       "2  0.9167 1     2     113781 151.5500 C22 C26 S        11    NA \n",
       "3  2.0000 1     2     113781 151.5500 C22 C26 S        NA    NA \n",
       "4 30.0000 1     2     113781 151.5500 C22 C26 S        NA   135 \n",
       "5 25.0000 1     2     113781 151.5500 C22 C26 S        NA    NA \n",
       "6 48.0000 0     0     19952   26.5500 E12     S        3     NA \n",
       "  home.dest                      \n",
       "1 St Louis, MO                   \n",
       "2 Montreal, PQ / Chesterville, ON\n",
       "3 Montreal, PQ / Chesterville, ON\n",
       "4 Montreal, PQ / Chesterville, ON\n",
       "5 Montreal, PQ / Chesterville, ON\n",
       "6 New York, NY                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read local\n",
    "titanic_data <- read.csv(\"https://raw.githubusercontent.com/Geoyi/Cleaning-Titanic-Data/master/titanic_original.csv\", na.strings = c(\"\")) # empty strings is missing data and wiil be replaced with NA\n",
    "head(titanic_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35856746-ba96-4e89-877c-257f95ca0e53",
   "metadata": {},
   "source": [
    "Переглянемо структуру даних:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "463bf2eb-5c06-4b12-aadd-24db41266b37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t1310 obs. of  14 variables:\n",
      " $ pclass   : int  1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ survived : int  1 1 0 0 0 1 1 0 1 0 ...\n",
      " $ name     : chr  \"Allen, Miss. Elisabeth Walton\" \"Allison, Master. Hudson Trevor\" \"Allison, Miss. Helen Loraine\" \"Allison, Mr. Hudson Joshua Creighton\" ...\n",
      " $ sex      : chr  \"female\" \"male\" \"female\" \"male\" ...\n",
      " $ age      : num  29 0.917 2 30 25 ...\n",
      " $ sibsp    : int  0 1 1 1 1 0 1 0 2 0 ...\n",
      " $ parch    : int  0 2 2 2 2 0 0 0 0 0 ...\n",
      " $ ticket   : chr  \"24160\" \"113781\" \"113781\" \"113781\" ...\n",
      " $ fare     : num  211 152 152 152 152 ...\n",
      " $ cabin    : chr  \"B5\" \"C22 C26\" \"C22 C26\" \"C22 C26\" ...\n",
      " $ embarked : chr  \"S\" \"S\" \"S\" \"S\" ...\n",
      " $ boat     : chr  \"2\" \"11\" NA NA ...\n",
      " $ body     : int  NA NA NA 135 NA NA NA NA NA 22 ...\n",
      " $ home.dest: chr  \"St Louis, MO\" \"Montreal, PQ / Chesterville, ON\" \"Montreal, PQ / Chesterville, ON\" \"Montreal, PQ / Chesterville, ON\" ...\n"
     ]
    }
   ],
   "source": [
    "str(titanic_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e690612-56cd-4b27-8c33-26def40af6d0",
   "metadata": {},
   "source": [
    "Значення показників вибірки:\n",
    "\n",
    "* _survival_ – \"клас виживання\" (0 = No; 1 = Yes)\n",
    "* _pclass_ – клас пасажирів (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
    "* ~~_name_~~ – ім’я\n",
    "* _sex_ - стать\n",
    "* _age_ - вік\n",
    "* _sibsp_ – кількість членів сім'ї на борту (братів, сестер, подружжя)\n",
    "* _parch_ – кількість дітей або батьків на борту\n",
    "* ~~_ticket_~~ – номер квитка\n",
    "* _fare_ – вартість \n",
    "* _cabin_ – номер(и)\n",
    "* _embarked_ – порт посадки на судно (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
    "* ~~_boat_~~ – борт на якому відпливав під час порятунку (якщо врятований)\n",
    "* ~~_body_~~ – ідентифікаційни номер тіла\n",
    "* ~~_home.dest_~~ – місце доставки пасажира\n",
    "\n",
    "Детальніше: http://campus.lakeforest.edu/frank/FILES/MLFfiles/Bio150/Titanic/TitanicMETA.pdf.\n",
    "\n",
    "Закреслені поля надалі будуть видалені з датасету для моделювання."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ff8772-8d4d-4288-a9b3-11704133c500",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52b95a50-1ed2-4f3b-8757-172b413a0465",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "510be956-9c9b-40a1-99be-6029555f59f3",
   "metadata": {},
   "source": [
    "### Remove unused columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b41017d-f896-4e56-8f45-a0231c8ec0ec",
   "metadata": {},
   "source": [
    "Lets remove columns: \n",
    "- [x] `name` - just string\n",
    "- [x] `ticket` - it can be useful to analyze numbers and check if \n",
    "- [x] `boat` - if boat is not missing - survived, we cannot use it\n",
    "- [x] `body` - if body is not missing - not survived, cant use it\n",
    "- [x] `home.dest` - just text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b3cd09b-53ae-4cb1-a4c5-8a38e95e5c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>pclass</th><th scope=col>survived</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>sibsp</th><th scope=col>parch</th><th scope=col>fare</th><th scope=col>cabin</th><th scope=col>embarked</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>1</td><td>female</td><td>29.0000</td><td>0</td><td>0</td><td>211.3375</td><td>B5     </td><td>S</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>1</td><td>male  </td><td> 0.9167</td><td>1</td><td>2</td><td>151.5500</td><td>C22 C26</td><td>S</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1</td><td>0</td><td>female</td><td> 2.0000</td><td>1</td><td>2</td><td>151.5500</td><td>C22 C26</td><td>S</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1</td><td>0</td><td>male  </td><td>30.0000</td><td>1</td><td>2</td><td>151.5500</td><td>C22 C26</td><td>S</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1</td><td>0</td><td>female</td><td>25.0000</td><td>1</td><td>2</td><td>151.5500</td><td>C22 C26</td><td>S</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1</td><td>1</td><td>male  </td><td>48.0000</td><td>0</td><td>0</td><td> 26.5500</td><td>E12    </td><td>S</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & pclass & survived & sex & age & sibsp & parch & fare & cabin & embarked\\\\\n",
       "  & <int> & <int> & <chr> & <dbl> & <int> & <int> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 1 & female & 29.0000 & 0 & 0 & 211.3375 & B5      & S\\\\\n",
       "\t2 & 1 & 1 & male   &  0.9167 & 1 & 2 & 151.5500 & C22 C26 & S\\\\\n",
       "\t3 & 1 & 0 & female &  2.0000 & 1 & 2 & 151.5500 & C22 C26 & S\\\\\n",
       "\t4 & 1 & 0 & male   & 30.0000 & 1 & 2 & 151.5500 & C22 C26 & S\\\\\n",
       "\t5 & 1 & 0 & female & 25.0000 & 1 & 2 & 151.5500 & C22 C26 & S\\\\\n",
       "\t6 & 1 & 1 & male   & 48.0000 & 0 & 0 &  26.5500 & E12     & S\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | pclass &lt;int&gt; | survived &lt;int&gt; | sex &lt;chr&gt; | age &lt;dbl&gt; | sibsp &lt;int&gt; | parch &lt;int&gt; | fare &lt;dbl&gt; | cabin &lt;chr&gt; | embarked &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1 | 1 | female | 29.0000 | 0 | 0 | 211.3375 | B5      | S |\n",
       "| 2 | 1 | 1 | male   |  0.9167 | 1 | 2 | 151.5500 | C22 C26 | S |\n",
       "| 3 | 1 | 0 | female |  2.0000 | 1 | 2 | 151.5500 | C22 C26 | S |\n",
       "| 4 | 1 | 0 | male   | 30.0000 | 1 | 2 | 151.5500 | C22 C26 | S |\n",
       "| 5 | 1 | 0 | female | 25.0000 | 1 | 2 | 151.5500 | C22 C26 | S |\n",
       "| 6 | 1 | 1 | male   | 48.0000 | 0 | 0 |  26.5500 | E12     | S |\n",
       "\n"
      ],
      "text/plain": [
       "  pclass survived sex    age     sibsp parch fare     cabin   embarked\n",
       "1 1      1        female 29.0000 0     0     211.3375 B5      S       \n",
       "2 1      1        male    0.9167 1     2     151.5500 C22 C26 S       \n",
       "3 1      0        female  2.0000 1     2     151.5500 C22 C26 S       \n",
       "4 1      0        male   30.0000 1     2     151.5500 C22 C26 S       \n",
       "5 1      0        female 25.0000 1     2     151.5500 C22 C26 S       \n",
       "6 1      1        male   48.0000 0     0      26.5500 E12     S       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "suppressMessages(library(dplyr)) # filter()\n",
    "titanic_data <- titanic_data |>\n",
    "    select(-c(\"name\", \"ticket\", \"boat\", \"body\", \"home.dest\"))\n",
    "head(titanic_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a96727e0-bea4-41a7-8568-9d45f2dc437b",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a10cd480-50d9-4460-843a-fb860c703108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     pclass         survived         sex                 age         \n",
       " Min.   :1.000   Min.   :0.000   Length:1310        Min.   : 0.1667  \n",
       " 1st Qu.:2.000   1st Qu.:0.000   Class :character   1st Qu.:21.0000  \n",
       " Median :3.000   Median :0.000   Mode  :character   Median :28.0000  \n",
       " Mean   :2.295   Mean   :0.382                      Mean   :29.8811  \n",
       " 3rd Qu.:3.000   3rd Qu.:1.000                      3rd Qu.:39.0000  \n",
       " Max.   :3.000   Max.   :1.000                      Max.   :80.0000  \n",
       " NA's   :1       NA's   :1                          NA's   :264      \n",
       "     sibsp            parch            fare            cabin          \n",
       " Min.   :0.0000   Min.   :0.000   Min.   :  0.000   Length:1310       \n",
       " 1st Qu.:0.0000   1st Qu.:0.000   1st Qu.:  7.896   Class :character  \n",
       " Median :0.0000   Median :0.000   Median : 14.454   Mode  :character  \n",
       " Mean   :0.4989   Mean   :0.385   Mean   : 33.295                     \n",
       " 3rd Qu.:1.0000   3rd Qu.:0.000   3rd Qu.: 31.275                     \n",
       " Max.   :8.0000   Max.   :9.000   Max.   :512.329                     \n",
       " NA's   :1        NA's   :1       NA's   :2                           \n",
       "   embarked        \n",
       " Length:1310       \n",
       " Class :character  \n",
       " Mode  :character  \n",
       "                   \n",
       "                   \n",
       "                   \n",
       "                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(titanic_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74f22a3-b046-443a-9ca8-1d755ea618df",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "034e10e0-c6bf-49c7-9d1e-928e5420a1b6",
   "metadata": {},
   "source": [
    "### Check missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6668f5f5-8299-4b8a-a3a2-994b17153a45",
   "metadata": {},
   "source": [
    "Для оцінки кількості пропусків побудуємо матрицю та мапу:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f4a0e89-a5cf-4763-a898-8dba757a9dd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: 'mice'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:stats':\n",
      "\n",
      "    filter\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    cbind, rbind\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 8 × 10 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>pclass</th><th scope=col>survived</th><th scope=col>sex</th><th scope=col>sibsp</th><th scope=col>parch</th><th scope=col>fare</th><th scope=col>embarked</th><th scope=col>age</th><th scope=col>cabin</th><th scope=col></th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>270</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>  1</td><td>   1</td><td>   0</td></tr>\n",
       "\t<tr><th scope=row>773</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>  1</td><td>   0</td><td>   1</td></tr>\n",
       "\t<tr><th scope=row>23</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>  0</td><td>   1</td><td>   1</td></tr>\n",
       "\t<tr><th scope=row>240</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>  0</td><td>   0</td><td>   2</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>  1</td><td>   1</td><td>   1</td></tr>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>  1</td><td>   0</td><td>   2</td></tr>\n",
       "\t<tr><th scope=row>1</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>  0</td><td>   0</td><td>   9</td></tr>\n",
       "\t<tr><th scope=row></th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td><td>3</td><td>264</td><td>1015</td><td>1289</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 8 × 10 of type dbl\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & pclass & survived & sex & sibsp & parch & fare & embarked & age & cabin & \\\\\n",
       "\\hline\n",
       "\t270 & 1 & 1 & 1 & 1 & 1 & 1 & 1 &   1 &    1 &    0\\\\\n",
       "\t773 & 1 & 1 & 1 & 1 & 1 & 1 & 1 &   1 &    0 &    1\\\\\n",
       "\t23 & 1 & 1 & 1 & 1 & 1 & 1 & 1 &   0 &    1 &    1\\\\\n",
       "\t240 & 1 & 1 & 1 & 1 & 1 & 1 & 1 &   0 &    0 &    2\\\\\n",
       "\t2 & 1 & 1 & 1 & 1 & 1 & 1 & 0 &   1 &    1 &    1\\\\\n",
       "\t1 & 1 & 1 & 1 & 1 & 1 & 0 & 1 &   1 &    0 &    2\\\\\n",
       "\t1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 &   0 &    0 &    9\\\\\n",
       "\t & 1 & 1 & 1 & 1 & 1 & 2 & 3 & 264 & 1015 & 1289\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 8 × 10 of type dbl\n",
       "\n",
       "| <!--/--> | pclass | survived | sex | sibsp | parch | fare | embarked | age | cabin | <!----> |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 270 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |   1 |    1 |    0 |\n",
       "| 773 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |   1 |    0 |    1 |\n",
       "| 23 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |   0 |    1 |    1 |\n",
       "| 240 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |   0 |    0 |    2 |\n",
       "| 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |   1 |    1 |    1 |\n",
       "| 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 |   1 |    0 |    2 |\n",
       "| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |   0 |    0 |    9 |\n",
       "| <!----> | 1 | 1 | 1 | 1 | 1 | 2 | 3 | 264 | 1015 | 1289 |\n",
       "\n"
      ],
      "text/plain": [
       "    pclass survived sex sibsp parch fare embarked age cabin     \n",
       "270 1      1        1   1     1     1    1          1    1     0\n",
       "773 1      1        1   1     1     1    1          1    0     1\n",
       "23  1      1        1   1     1     1    1          0    1     1\n",
       "240 1      1        1   1     1     1    1          0    0     2\n",
       "2   1      1        1   1     1     1    0          1    1     1\n",
       "1   1      1        1   1     1     0    1          1    0     2\n",
       "1   0      0        0   0     0     0    0          0    0     9\n",
       "    1      1        1   1     1     2    3        264 1015  1289"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9KgwrA0FqnYlNBmSRpUGJbGIhWbEtos\nSYMKw9JYpGJTQpslaVBhWBqL9J1PuP6nyfv/lfL9qmlW29FPCm2WpEGFYWn+SpE2wY49XKTZ\nzcPDrH001iTUbYadlifU/JEibZN/UcWDqtfm7ebhulmd/rEcmxLaLEmDCsPS/I0ibWe/X6T9\nbHn7cNbsT7IxG+o2w07LE2r+RJE2zeL3i7Rse/Px4aF9kjc4JbRZkgYVhqX5E0Vq1uO/8T/A\nQ6rtMcTnh8endpuxKaHNkjSoMCzNnyjSduIZ1AM8pBr8gvRf0+vUxymhzZI0qDAszZ8o0uH3\ni7Q9vbLw6eFmOWtexqaENkvSoMKwNBbpe6yb14GHR1Zjz+1Qtxl2Wp5QY5G+x6wZenhkP/Zq\nA+o2w07LE2os0rfYXv+4aPvhT47GkqFuM+y0PKHGIn2LzfUJ3OVh9+dIu2Y+MiW0WZIGFYal\nsUjfYnn9SaDLw/YnG/ZLv0dSE7M8fZHm1xe/rw+7n7VbjE0JbZakQYVhaSzS9z6lGXq4njXz\nsT+PtUj/lsYilQJ1m2Gn5Qk1f6VI0SNeZ0posyQNKgxLY5GKTQltlqRBhWFpLFKxKaHNkjSo\nMCyNRSo2JbRZkgYVhqWxSMWmhDZL0qDCsDQWqdiU0GZJGlQYlsYiFZsS2ixJgwrD0likYlNC\nmyVpUGFYGotUbEposyQNKgxLY5GKTQltlqRBhWFpLFKxKaHNkjSoMCyNRSo2JbRZkgYVhqWx\nSMWmhDZL0qDCsDQWqdiU0GZJGlQYlsYiFZsS2ixJgwrD0likYlNCmyVpUGFYGotUbEposyQN\nKgxLY5GKTQltlqRBhWFpLFKxKaHNkjSoMCyNRSo2JbRZkgYVhqWxSMWmhDZL0qDCsDQWqdiU\n0GZJGlQYlsYiFZsS2ixJgwrD0likYlNCmyVpUGFYGotUbEposyQNKgxLY5GKTRH5Neoc8TpT\nQr9FkTSoMCxN6ktJJMz/LNLnlZA0qDAsjUUqNiW0EpIGFYalsUjFpoRWQtKgwrA0FqnYlNBK\nSBpUGJbGIhWbEloJSYMKw9JYpGJTQishaVBhWBqLVGxKaCUkDSoMS2ORik0JrYSkQYVhaSxS\nsSmhlZA0qDAsjUUqNiW0EpIGFYalsUjFpoRWQtKgwrA0FqnYlNBKSBpUGJbGIhWbEloJSYMK\nw9JYpGJTQishaVBhWBqLVGxKaCUkDSoMS2ORik0JrYSkQYVhaSxSsSmhlZA0qDAsjUUqNiW0\nEpIGFYalsUjFpoRWQtKgwrA0FqnYlNBKSBpUGJbGIhWbEloJSYMKw9JYpGJTQishaVBhWBqL\nVGxKaCUkDSoMS2ORik0JrYSkQYVhaSxSsSmhlZA0qDAsjUUqNiW0EpIGFYalsUjFpoRWQtKg\nwrA0FqnYlNBKSBpUGJbGIt3LZt7M1vvTo/2qaVbb0SmhlZA0qDAszV8p0ibYsR+p1u3fmjE7\nNWnWPhxrEuo2W6TSmj9SpG3yb3z5iWrbrPanWq9OlWr/sRybEloJSYMKw9L8jSJtZ5QiLbtP\nPsWZNfvzo+EpoZWQNKgwLM2fKNKmWVCK9K64OJrZ2C8JrYSkQYVhaf5EkZr1+G/8j7Tgx4Z9\nszg/WjebsSmhlZA0qDAszZ8o0nbiGdQD/Fy1aV7bf//XHDs+NiW0EpIGFYal+RNFOsCKtJud\nX2HYLGfNy9iU0EpIGlQYlsYi3c9+tri+sRp7boe6zRaptMYi3c9i3ntjP/ZqA+o2W6TSGot0\nL7v5YndjG3uFJLQSkgYVhqWxSHfyennBrvtzpF0zH/6FqNtskUprLNJ97C496n6yYb/0eyQ1\n/7NI97Jqzhzef9ZuMfIrUbfZIpXWWKQ7P7dXpMN61szH/jzWIv1bGotUCtRttkilNX+lSNEj\nXmdKaCUkDSoMS2ORik0JrYSkQYVhaSxSsSmhlZA0qDAsjUUqNiW0EpIGFYalsUjFpoRWQtKg\nwrA0FqnYlNBKSBpUGJbGIhWbEloJSYMKw9JYpGJTQishaVBhWBqLVGxKaCUkDSoMS2ORik0J\nrYSkQYVhaSxSsSmhlZA0qDAsjUUqNiW0EpIGFYalsUjFpoRWQtKgwrA0FqnYlNBKSBpUGJbG\nIhWbEloJSYMKw9JYpGJTQishaVBhWBqLVGxKaCUkDSoMS2ORik0JrYSkQYVhaSxSsSmhlZA0\nqDAsjUUqNiW0EpIGFYalsUjFpoRWQtKgwrA0FqnYlNBKSBpUGJbGIhWbEloJSYMKw9JYpGJT\nQishaVBhWBqLVGxKaCUkDSoMS2ORik0R+TXqHPE6U0K/t5A0qDAsTeorUkhT54jXmRK5P7DT\nErE8pcYiFZsSuT+w0xKxPKXGIhWbErk/sNMSsTylxiIVmxK5P7DTErE8pcYiFZsSuT+w0xKx\nPKXGIhWbErk/sNMSsTylxiIVmxK5P7DTErE8pcYiFZsSuT+w0xKxPKXGIhWbErk/sNMSsTyl\nxiIVmxK5P7DTErE8pcYiFZsSuT+w0xKxPKXGIhWbErk/sNMSsTylxiIVmxK5P7DTErE8pcYi\nFZsSuT+w0xKxPKXGIhWbErk/sNMSsTylxiIVmxK5P7DTErE8pcYiFZsSuT+w0xKxPKXGIhWb\nErk/sNMSsTylxiIVmxK5P7DTErE8pcYiFZsSuT+w0xKxPKXGIhWbErk/sNMSsTylxiIVmxK5\nP7DTErE8pcYiFZsSuT+w0xKxPKXGIhWbErk/sNMSsTylxiIVmxK5P7DTErE8pcYiFZsSuT+w\n0xKxPKXGIhWbErk/sNMSsTylxiJ9i828ma3372+9nQ3rWf+9H6dE7g/stEQsT6n5G0W6Pcc/\n5f4irdv/wv/snGA/6wyL9r3zsSmR+wM7LRHLU2r+RJFuz/GPubtI22Z1nL1pVt2by+5vzXhr\nZtvDdta8jUyJ3B/YaYlYnlLzF4r04Rz/mLuLtOw+4/y3zvx3/utn1s1r+9bLyJTI/YGdlojl\nKTV/oUi35/jnPOrpAuyaRfdg2ewOp5YvR3515P7ATkvE8pSav1Ckm3Mc4EHPvlmc/rVodl2S\nZrrfqNtskUpr/k6Rzuc4wINF2rRP5V6a/w4WSc0nS6gBIc2X5zjBY0XazU7P4dpnchZJzSdL\nqAEhzVfnOMJDRdrP2i+I89NrhxZJzSdLqAEhzVfnOMJDRVq0f160ar8sdt2ZWSQ1V0uoASHN\nF+c4wwNF2s0Xu/ZTe3//eveq3c5X7dT8768U6f0cZ7i/SK/vL3T0i/TSfnV6bdYjUyL3B3Za\nIpan1PyJIr3GXrDrjvi9n7D7MN+fbFDzyRJqQEjzrXP8U+4u0ur6ZagTdA/m7fvGsqFus0Uq\nrfkLRfp4jn/K3Z5muEj79qe/Rz8pcn9gpyVieUrNXyjSx3P8U/zfI/2u5Sk1f6FI8SNeZ0rk\n/sBOS8TylBqLVGxK5P7ATkvE8pQai1RsSuT+wE5LxPKUGotUbErk/sBOS8TylBqLVGxK5P7A\nTkvE8pQai1RsSuT+wE5LxPKUGotUbErk/sBOS8TylBqLVGxK5P7ATkvE8pQai1RsSuT+wE5L\nxPKUGotUbErk/sBOS8TylBqLVGxK5P7ATkvE8pQai1RsSuT+wE5LxPKUGotUbErk/sBOS8Ty\nlBqLVGxK5P7ATkvE8pQai1RsSuT+wE5LxPKUGotUbErk/sBOS8TylBqLVGxK5P7ATkvE8pQa\ni1RsSuT+wE5LxPKUGotUbErk/sBOS8TylBqLVGxK5P7ATkvE8pQai1RsSuT+wE5LxPKUGotU\nbErk/sBOS8TylBqLVGxK5P7ATkvE8pQai1RsSuT+wE5LxPKUGotUbErk/sBOS8TylBqLVGxK\n5P7ATkvE8pQai1RsisivUeeI15kS+Y0O9ttuxELTZL4GhMKENHWOeJ0poZWQNKgwFmlCU+eI\n15kSWglJgwpjkSY0dY54nSmhlZA0qDAWaUJT54jXmRJaCUmDCmORJjR1jnidKaGVkDSoMBZp\nQlPniNeZEloJSYMKY5EmNHWOeJ0poZWQNKgwFmlCU+eI15kSWglJgwpjkSY0dY54nSmhlZA0\nqDAWaUJT54jXmRJaCUmDCmORJjR1jnidKaGVkDSoMBZpQlPniNeZEloJSYMKY5EmNHWOeJ0p\noZWQNKgwFmlCU+eI15kSWglJgwpjkSY0dY54nSmhlZA0qDAWaUJT54jXmRJaCUmDCmORJjR1\njnidKaGVkDSoMBZpQlPniNeZEloJSYMKY5EmNHWOeJ0poZWQNKgwFmlCU+eI15kSWglJgwpj\nkSY0dY54nSmhlZA0qDAWaUJT54jXmRJaCUmDCmORJjR1jnidKaGVkDSoMBZpQlPniNeZEloJ\nSYMKY5EmNHWOeJ0poZWQNKgwFmlCU+eI15kSWglJgwpjkSY0dY54nSmhlZA0qDAWaUJT54in\nRJt5M1vvx6aEVkLSoMJYpAnN+JkNdiylWrf/3f/ZSJNgmwVZaJp/qUjb5F9UEVJtm9X+1PDV\nyJTQSkgaVBiLNKEZO7MzYJGWnWcsGWyzIAtN8+8UadMsgEV6t1mkP675d4rUrEeP60NHP6c6\nHPbNYmRKaCUkDSqMRZrQDJ/J7fjv+48QLdKmeR2ZEloJSYMKY5EmNOOHH1qk3Ww5NiW0EpIG\nFcYiTWjGDz+zSPvZyBM7i/R3NBbpQYKqxXx8SmglJA0qjEWa0IwfS2KRdvPFbnxKaCUkDSqM\nRZrQjB9LYJFex16w66aEVkLSoMJYpAnN+LHkFWk32SOL9Gc0FulBQqpVc2ZkSmglJA0qjEWa\n0Iwffl6RGov0HBqL9GgDcqqpKaGVkDSoMBZpQlPniNeZEloJSYMKY5EmNHWOeJ0poZWQNKgw\nFmlCU+eI15kSWglJgwpjkSY0dY54nSmhlZA0qDAWaUJT54jXmRJaCUmDCmORJjR1jnidKaGV\nkDSoMBZpQlPniNeZEloJSYMKY5EmNHWOeJ0poZWQNKgwFmlCU+eI15kSWglJgwpjkSY0dY54\nnSmhlZA0qDAWaUJT54jXmRJaCUmDCmORJjR1jnidKaGVkDSoMBZpQlPniNeZEloJSYMKY5Em\nNHWOeJ0poZWQNKgwFmlCU+eI15kSWglJgwpjkSY0dY54nSmhlZA0qDAWaUJT54jXmRJaCUmD\nCmORJjR1jnidKaGVkDSoMBZpQlPniNeZEloJSYMKY5EmNHWOeJ0poZWQNKgwFmlCU+eI15kS\nWglJgwpjkSY0dY54nSmhlZA0qDAWaUJT54jXmRJaCUmDCmORJjR1jnidKaGVkDSoMBZpQlPn\niNeZEloJSYMKY5EmNHWOeJ0poZWQNKgwFmlCU+eI15ki8mvUOeJ1poR+byFpUGFCX0pCX0tg\n11TniNeZEtosSYMKY5EmrqnOEa8zJbRZkgYVxiJNXFOdI15nSmizJA0qjEWauKY6R7zOlNBm\nSRpUGIs0cU11jnidKaHNkjSoMBZp4prqHPE6U0KbJWlQYSzSxDXVOeJ1poQ2S9KgwlikiWuq\nc8TrTAltlqRBhbFIE9dU54jXmRLaLEmDCmORJq6pzhGvMyW0WZIGFcYiTVxTnSNeZ0posyQN\nKoxFmrimOke8zpTQZkkaVBiLNHFNdY54nSmhzZI0qDAWaeKa6hzxOlNCmyVpUGEs0sQ11Tni\ndaaENkvSoMJYpIlrqnPE60wJbZakQYWxSBPXVOeI15kS2ixJgwpjkSauqc4RrzMltFmSBhXG\nIk1cU50jXmdKaLMkDSqMRZq4pjpHvM6U0GZJGlQYizRxTXWOeJ0poc2SNKgwFmnimuoc8TpT\nQpslaVBhLNLENdU54nWmhDZL0qDCWKSJa6pzxOtMCW2WpEGFsUgT11TniNeZEtosSYMKY5Em\nrqnOEa8zJbRZkgYVxiJNXFOdI15nSmizJA0qjEWauKY6Rzyn2oy7YIcOZKEdukyYiKV0kTbz\nZrbep05/rkjbif/sP+zQgSy0Q5cJE7EULtK6/ZsqZqkmxYq0nVmk39SEDl0mTMRStkjbZrU/\nPYtahc5/qkibZmGRflMTOnSZMBFL2SItu3fH/vakmGc9lQl26EAW2qHLhIlYqrzYgCvSdjIT\n7NCBLLRDlwkTsdQo0r5ZhAoQfNXOIv2mJnToMmEilhpF2jSvqdMf8hws0u9qQocuEyZiqVCk\n3WwZO/0pkUX6XU3o0GXCRCzli7SfpZ7YWaTfttAOXSZMxFK+SIt58PQHVRbpFzWhQ5cJE7GU\nLtJuviKgCwkAAAtnSURBVNgFT39QZZF+URM6dJkwEUvhIr3GXrDrjnhQZZF+URM6dJkwEUvZ\nIu2yPbJIv2yhHbpMmIilbJFWzZnU6Q95DhbpdzWhQ5cJE7GULVLDLdLUlNBmSRpUGIs0cU11\njnidKaHNkjSoMBZp4prqHPE6U0KbJWlQYSzSxDXVOeJ1poQ2S9KgwlikiWuqc8TrTAltlqRB\nhbFIE9dU54jXmRLaLEmDCmORJq6pzhGvMyW0WZIGFcYiTVxTnSNeZ0posyQNKoxFmrimOke8\nzpTQZkkaVBiLNHFNdY54nSmhzZI0qDAWaeKa6hzxOlNCmyVpUGEs0sQ11TnidaaENkvSoMJY\npIlrqnPE60wJbZakQYWxSBPXVOeI15kS2ixJgwpjkSauqc4RrzMltFmSBhXGIk1cU50jXmdK\naLMkDSqMRZq4pjpHvM6U0GZJGlQYizRxTXWOeJ0poc2SNKgwFmnimuoc8TpTQpslaVBhLNLE\nNdU54nWmhDZL0qDCWKSJa6pzxOtMCW2WpEGFsUgT11TniNeZEtosSYMKY5EmrqnOEa8zJbRZ\nkgYVxiJNXFOdI15nSmizJA0qjEWauKY6R7zOlNBmSRpUGIs0cU11jnidKaHNkjSoMBZp4prq\nHPE6U0R+jTpHvM6U0O8tJA0qDEuDCvNcX5FCKyFpUGFYGlQYizSwEpIGFYalQYWxSAMrIWlQ\nYVgaVBiLNLASkgYVhqVBhbFIAyshaVBhWBpUGIs0sBKSBhWGpUGFsUgDKyFpUGFYGlQYizSw\nEpIGFYalQYWxSAMrIWlQYVgaVBiLNLASkgYVhqVBhbFIAyshaVBhWBpUGIs0sBKSBhWGpUGF\nsUgDKyFpUGFYGlQYizSwEpIGFYalQYWxSAMrIWlQYVgaVBiLNLASkgYVhqVBhbFIAyshaVBh\nWBpUGIs0sBKSBhWGpUGFsUgDKyFpUGFYGlQYizSwEpIGFYalQYWxSAMrIWlQYVgaVBiLNLAS\nkgYVhqVBhbFIAyshaVBhWBpUGIs0sBKSBhWGpUGFsUgDKyFpUGFYGlQYizSwEpIGFYalQYWx\nSAMrIWlQYVgaVBiLNLASkgYVhqVBhfl7RdqMu2CbBVmeUoMKM1qk9axZvMZOf65I24m/PwO2\nWZDlKTWoMGNFWrR/5ctL6vjHirSdWSQ1SUvZIm2axf6wXzXb0PlPFemYyyKpSVrKFmnRvB3/\nuWvWoQKkinQMZJHUJC1li3Q+rc0iVYCQZ3uwSGqiljpFin0lCXkOFklN1lK2SPNmd/znm0X6\ndQ0qDEuDCjNSpJdmuT9sp76xv/P0hzwHi6Qmayn88vfs9Or30iL9ugYVhqVBhRkr0n7VzF78\nHun3NagwLA0qzOSPCG2beer0hzwHi6QmaylbpFmzP5z++HOZOv0hz8EiqclayhZp3awOh7d5\n81/q9Ic8B4ukJmspW6R9+2JD7AuSRfply1NqUGHGvkfarY41Iv709+SU0EpIGlQYlgYV5u/9\n75GmpoRWQtKgwrA0qDAWaWAlJA0qDEuDCmORBlZC0qDCsDSoMBZpYCUkDSoMS4MKY5EGVkLS\noMKwNKgwFmlgJSQNKgxLgwpjkQZWQtKgwrA0qDAWaWAlJA0qDEuDCmORBlZC0qDCsDSoMBZp\nYCUkDSoMS4MKY5EGVkLSoMKwNKgwFmlgJSQNKgxLgwpjkQZWQtKgwrA0qDAWaWAlJA0qDEuD\nCmORBlZC0qDCsDSoMBZpYCUkDSoMS4MKY5EGVkLSoMKwNKgwFmlgJSQNKgxLgwpjkQZWQtKg\nwrA0qDAWaWAlJA0qDEuDCmORBlZC0qDCsDSoMBZpYCUkDSoMS4MKY5EGVkLSoMKwNKgwFmlg\nJSQNKgxLgwpjkQZWQtKgwrA0qDDPVSSRX6POEa8yReTJsUgiASySSACLJBLAIokEsEgiASyS\nSACLJBLAIokEsEgiASySSACLJBLAIokEsEgiATBF2kSSZCwsTcSymTez9f6nlv2qaVbbn0fY\nnjy79w+8ffsCL6tYz3qX8/7eif/VxOUTryH2Pcfx4eL12xcyCKVI28j/bCRjYWkilnV7xGY/\nbdKs1TzWpF6E15s0+9l3L/CyikUrmN++dztepMsnXkPsumuZ7S66l4cu6x1IkbazxHHJWFia\niGXbrPan35ZXP9OsT4J1s/xphNlse9gvm3X3keV3L/CyirfmKDi+9Xbz3u1osN4vuYRYtePb\nK9o0i/3pi+2jX2pbGEU6XkrguGQsLE3GsuwUPzXNmv3Dll6E/9ozvG9m7Xv+++7/hvW6inXz\n2n7iy817N2NfU66/pBeiuT5ctI3cvRf7MRhFOl5D4LhkLCxNKMxZFjGdC/B4hJvf+3ff/p3i\nuoplc3pC1n0Fur5302y++sReiPPzydPFvHdqccd1fB7yk0+OsY3c5IyFpQmFadn/7KicWY+d\n129HmDeHl1n7JOtw+nKw++YFbm++jLz/6/reZfO6amYDX1Y+7rAN8XJ+avfy0fogjCIdYr9b\nZq4HpYkVadP88JWpQ/tE7CfPgNoITbNsv88/veOl+e+OCxwo0qFXpJbB3yxuRnR72JxebZid\nflOYt1/g3ixS3MLSpIq0mz30KsEtm+XsBy9vdRGa02sFx2/uX85Pz0JFao6dPOyHv2D2R5z3\n8HJ5qe6lWe4P2x9+L2qR8JpQmP0s8cTucPoW59HnducI3Qvou9PL1/PTK9GhIp1nvL8qPvSJ\nvRCb01fWfXct7Svh337xcCTaTz45CerQoTShMIuhA/YI+4dfbThHuPZg1T7JurtIs4kiDdt6\n7zyHmLevQHa1O/Zp9uL3SHkLSxOx7OaL3de/6ns8GOgS4foi9L3/EcebV+12739udE+RLiE+\nvcKwHfxS9m0sEl6TsLxGXrDr/hxp99iJu0Z4ab8O7Y5vP1ikTvD6/rLH5etUl27oO8H3AdcQ\n3Ve19qtr94mbx/6g+TLhJ5+chHPoaJqAZRfpUfdzAPvlQ98j9SIcm9j+JMF/57fvfmp385MN\nl/eu2+961oMvTZ5/SS/E8Vfvz5/TXtbb/JLnISwSXhOwrEL/GezZ+AvM90R4udXcXaTjNzh9\nwfm9+y7d4Kvz51/SD7G4OM6f+LPXNC0SXpP4+YjUf09+PWvmj71mdxPhddH/k9P7i9T95PbQ\ne0fSXb4j6oW4OnbHgi2f5Ke/Rf40FkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkk\ngEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJY\nJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUS\nCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEA\nFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCR\nRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkk\ngEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJY\nJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUS\nCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEA\nFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCRRAJYJJEAFkkkgEUSCWCR\nRAJYJJEAFkkkgEUSCfD/wiZxQPDF4HIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "suppressMessages(library(mice))\n",
    "md.pattern(titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e14bcda8-f966-4bc4-861c-c6b707d43105",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: Rcpp\n",
      "\n",
      "## \n",
      "## Amelia II: Multiple Imputation\n",
      "## (Version 1.8.0, built: 2021-05-26)\n",
      "## Copyright (C) 2005-2022 James Honaker, Gary King and Matthew Blackwell\n",
      "## Refer to http://gking.harvard.edu/amelia/ for more information\n",
      "## \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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dV6k2GPhMEYaaZIgJeRZooEeBlppkiAl5FmigR4GWmOUiSl1psLvTLSHKVIrVcx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\nAhQJEPg/GtTzSegniAQAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Missing data map\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "suppressMessages(library(Amelia))\n",
    "missmap(titanic_data, main = 'Missing data map', col = c('yellow', 'black'), legend = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d8a4b6-9222-4a26-ba7f-3dd4e0a0fb6e",
   "metadata": {},
   "source": [
    "One more way to check where missing data is present with `saplly()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e5aa079-ec49-46f0-b21f-aa97157abc3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>pclass</dt><dd>1</dd><dt>survived</dt><dd>1</dd><dt>sex</dt><dd>1</dd><dt>age</dt><dd>264</dd><dt>sibsp</dt><dd>1</dd><dt>parch</dt><dd>1</dd><dt>fare</dt><dd>2</dd><dt>cabin</dt><dd>1015</dd><dt>embarked</dt><dd>3</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[pclass] 1\n",
       "\\item[survived] 1\n",
       "\\item[sex] 1\n",
       "\\item[age] 264\n",
       "\\item[sibsp] 1\n",
       "\\item[parch] 1\n",
       "\\item[fare] 2\n",
       "\\item[cabin] 1015\n",
       "\\item[embarked] 3\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "pclass\n",
       ":   1survived\n",
       ":   1sex\n",
       ":   1age\n",
       ":   264sibsp\n",
       ":   1parch\n",
       ":   1fare\n",
       ":   2cabin\n",
       ":   1015embarked\n",
       ":   3\n",
       "\n"
      ],
      "text/plain": [
       "  pclass survived      sex      age    sibsp    parch     fare    cabin \n",
       "       1        1        1      264        1        1        2     1015 \n",
       "embarked \n",
       "       3 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sapply(titanic_data, function(x) sum(is.na(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "005cf012-ce7b-4615-ad64-83d729a51f42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>pclass</th><th scope=col>survived</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>sibsp</th><th scope=col>parch</th><th scope=col>fare</th><th scope=col>cabin</th><th scope=col>embarked</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 9\n",
       "\\begin{tabular}{lllllllll}\n",
       " pclass & survived & sex & age & sibsp & parch & fare & cabin & embarked\\\\\n",
       " <int> & <int> & <chr> & <dbl> & <int> & <int> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 9\n",
       "\n",
       "| pclass &lt;int&gt; | survived &lt;int&gt; | sex &lt;chr&gt; | age &lt;dbl&gt; | sibsp &lt;int&gt; | parch &lt;int&gt; | fare &lt;dbl&gt; | cabin &lt;chr&gt; | embarked &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  pclass survived sex age sibsp parch fare cabin embarked\n",
       "1 NA     NA       NA  NA  NA    NA    NA   NA    NA      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets check where survived is NA, target should be finite\n",
    "titanic_data %>% filter(is.na(survived))\n",
    "# looks like its wrong line in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f6cd3d0-e05b-4248-9764-70fa26229234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>pclass</dt><dd>0</dd><dt>survived</dt><dd>0</dd><dt>sex</dt><dd>0</dd><dt>age</dt><dd>263</dd><dt>sibsp</dt><dd>0</dd><dt>parch</dt><dd>0</dd><dt>fare</dt><dd>1</dd><dt>cabin</dt><dd>1014</dd><dt>embarked</dt><dd>2</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[pclass] 0\n",
       "\\item[survived] 0\n",
       "\\item[sex] 0\n",
       "\\item[age] 263\n",
       "\\item[sibsp] 0\n",
       "\\item[parch] 0\n",
       "\\item[fare] 1\n",
       "\\item[cabin] 1014\n",
       "\\item[embarked] 2\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "pclass\n",
       ":   0survived\n",
       ":   0sex\n",
       ":   0age\n",
       ":   263sibsp\n",
       ":   0parch\n",
       ":   0fare\n",
       ":   1cabin\n",
       ":   1014embarked\n",
       ":   2\n",
       "\n"
      ],
      "text/plain": [
       "  pclass survived      sex      age    sibsp    parch     fare    cabin \n",
       "       0        0        0      263        0        0        1     1014 \n",
       "embarked \n",
       "       2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets remove this row\n",
    "titanic_data <- titanic_data %>% filter(!is.na(survived))\n",
    "sapply(titanic_data, function(x) sum(is.na(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc5a0ba1-c3bc-47a4-b998-8c16847d9043",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">We will replace all missing data on the next stages for: `fare`, `cabin`, `age`, `embarked`.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf48c90b-1e36-4eb3-9c63-9681dfe1da12",
   "metadata": {},
   "source": [
    "### Visual analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cbbf8cfd-f120-47ea-a6af-6b07af4b9019",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ggplot2) # for plots\n",
    "library(gmodels) # for CrossTable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5d3e49-14d1-4db3-ad38-788cecb40b7c",
   "metadata": {},
   "source": [
    "**Survived**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e900f20b-4c38-466a-9a67-b4140f81edd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      "   Cell Contents\n",
      "|-------------------------|\n",
      "|                       N |\n",
      "|         N / Table Total |\n",
      "|-------------------------|\n",
      "\n",
      " \n",
      "Total Observations in Table:  1309 \n",
      "\n",
      " \n",
      "          |         0 |         1 | \n",
      "          |-----------|-----------|\n",
      "          |       809 |       500 | \n",
      "          |     0.618 |     0.382 | \n",
      "          |-----------|-----------|\n",
      "\n",
      "\n",
      "\n",
      " \n"
     ]
    },
    {
     "data": {
      "image/png": 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HvGflQVIRUaPYSUcM/Yj6oipEKjh5AS7hn7UVWEVGj0EFLCPWM/qoqQCo0eQkq4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\nwAUgJIALQEgAF+D/ASa7N1cIQyWTAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CrossTable(titanic_data$survived)\n",
    "ggplot(titanic_data, aes(survived)) + geom_bar(aes(fill = factor(survived))) + theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f8b0966-ce3d-47a1-8be2-393d256308ce",
   "metadata": {},
   "source": [
    "**Крос-таблиця survived vs pclass**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a76f5d9e-f323-4e68-90e8-77ff46c8d52b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      "   Cell Contents\n",
      "|-------------------------|\n",
      "|                       N |\n",
      "| Chi-square contribution |\n",
      "|           N / Row Total |\n",
      "|           N / Col Total |\n",
      "|         N / Table Total |\n",
      "|-------------------------|\n",
      "\n",
      " \n",
      "Total Observations in Table:  1309 \n",
      "\n",
      " \n",
      "                      | titanic_data$pclass \n",
      "titanic_data$survived |         1 |         2 |         3 | Row Total | \n",
      "----------------------|-----------|-----------|-----------|-----------|\n",
      "                    0 |       123 |       158 |       528 |       809 | \n",
      "                      |    29.411 |     1.017 |    18.411 |           | \n",
      "                      |     0.152 |     0.195 |     0.653 |     0.618 | \n",
      "                      |     0.381 |     0.570 |     0.745 |           | \n",
      "                      |     0.094 |     0.121 |     0.403 |           | \n",
      "----------------------|-----------|-----------|-----------|-----------|\n",
      "                    1 |       200 |       119 |       181 |       500 | \n",
      "                      |    47.587 |     1.645 |    29.788 |           | \n",
      "                      |     0.400 |     0.238 |     0.362 |     0.382 | \n",
      "                      |     0.619 |     0.430 |     0.255 |           | \n",
      "                      |     0.153 |     0.091 |     0.138 |           | \n",
      "----------------------|-----------|-----------|-----------|-----------|\n",
      "         Column Total |       323 |       277 |       709 |      1309 | \n",
      "                      |     0.247 |     0.212 |     0.542 |           | \n",
      "----------------------|-----------|-----------|-----------|-----------|\n",
      "\n",
      " \n"
     ]
    },
    {
     "data": {
      "image/png": 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E7DQwgUd7YZhLQQAuk0PIRAOg0PIVDc2WYQ0kIIpNPwEALpNDyEQHFnm0FICyGQ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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CrossTable(titanic_data$survived, titanic_data$pclass)\n",
    "ggplot(titanic_data, aes(pclass)) + geom_bar(aes(fill = factor(pclass)))+ theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df639a3e-d64d-4bf1-9af0-99d6c30f212a",
   "metadata": {},
   "source": [
    "**sex**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc610ec8-88e9-4bf1-b05f-812413682534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sbBOPllSCTuhBkaB+PklyGRuBNmaByM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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(titanic_data, aes(x = sex)) + geom_bar(aes(fill = factor(sex))) + theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "470faf71-7c5c-484c-b406-37a54eeb80e8",
   "metadata": {},
   "source": [
    "**Age***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "579db943-8375-4432-8628-e96801d19aa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 263 rows containing non-finite values (stat_bin).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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wIznLdF+vrp3e327lP4byUhEiJdmLnx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\nJCGIREhCEImQhCASIQlBJEIS8v/p7eBb7b4kOwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(titanic_data, aes(age)) + geom_histogram(bins = 25, alpha = 0.5, fill = 'blue', color='black')  + theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cee51d5-7be9-4aa4-8883-8b79d0ba83b2",
   "metadata": {},
   "source": [
    "**fare**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c2d9b504-e53f-46cf-9865-49bf0fb3cbe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 1 rows containing non-finite values (stat_bin).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(titanic_data, aes(fare)) + geom_histogram(bins = 15, alpha = 0.5, fill = 'blue', color='black')+ theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a651943d-3a0f-431f-8250-bf5cb88b3fb2",
   "metadata": {},
   "source": [
    "**sibsp**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f0a69a4f-5a6b-471d-bf90-bcf1248b8c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gPJAaYaUgwHkgNMNaQYDiQHmGpIMRxI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\n74AzJM874AzJ8w64/wfFUML29bnjwwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(titanic_data, aes(sibsp)) + geom_bar()+ theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d70890-dc93-445a-a40b-b0dcac6943b6",
   "metadata": {},
   "source": [
    "**parch**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7bc726de-cf96-40e1-b559-82e3c39c87d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(titanic_data, aes(parch)) + geom_bar()+ theme_bw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0786b873-6cf9-4b1f-8a43-f8b323b7e4c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UIanG9I5/NPzW8uPr92eSHtQ0jFEUJ6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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(titanic_data, aes(embarked)) + geom_bar(aes(fill = factor(embarked)))+ theme_bw()\n",
    "\n",
    "# it looks like S the most popular value in embarked, so it could be good idea to replace 1 missing with S"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85a887c-a6e6-4087-b14b-aac982f835a6",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29089ab-40e6-4f84-bba6-7c38507ab155",
   "metadata": {},
   "source": [
    "## Splitting data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9230e82-9ba4-49b1-ac52-b6d431feea86",
   "metadata": {},
   "source": [
    "Lest split out dataset for test and train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "76d21f63-80f8-4c8a-a87c-a63239819afe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      "   Cell Contents\n",
      "|-------------------------|\n",
      "|                       N |\n",
      "|         N / Table Total |\n",
      "|-------------------------|\n",
      "\n",
      " \n",
      "Total Observations in Table:  916 \n",
      "\n",
      " \n",
      "          |         0 |         1 | \n",
      "          |-----------|-----------|\n",
      "          |       566 |       350 | \n",
      "          |     0.618 |     0.382 | \n",
      "          |-----------|-----------|\n",
      "\n",
      "\n",
      "\n",
      " \n",
      "\n",
      " \n",
      "   Cell Contents\n",
      "|-------------------------|\n",
      "|                       N |\n",
      "|         N / Table Total |\n",
      "|-------------------------|\n",
      "\n",
      " \n",
      "Total Observations in Table:  393 \n",
      "\n",
      " \n",
      "          |         0 |         1 | \n",
      "          |-----------|-----------|\n",
      "          |       243 |       150 | \n",
      "          |     0.618 |     0.382 | \n",
      "          |-----------|-----------|\n",
      "\n",
      "\n",
      "\n",
      " \n"
     ]
    }
   ],
   "source": [
    "set.seed(1) # seed\n",
    "library(caTools)\n",
    "split <- sample.split(titanic_data$survived, SplitRatio = 0.7)\n",
    "titanic_train <- subset(titanic_data, split == TRUE)\n",
    "titanic_test <- subset(titanic_data, split == FALSE)\n",
    "\n",
    "library(gmodels)\n",
    "CrossTable(titanic_train$survived)\n",
    "CrossTable(titanic_test$survived) # its splitted by the same proportions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c85aca9-cfed-42c2-9ae7-becb3fae637a",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f17bff3e-b13b-49b8-839d-4e329940bfff",
   "metadata": {},
   "source": [
    "## Missing replacement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f25de19a-9a4b-4721-a0bd-30db78219765",
   "metadata": {},
   "source": [
    "Missing data replacement should have base points for each parameter. \n",
    "\n",
    "So, we will fix data for replacement from train and implement it to both train and test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "772f64ef-2067-4169-9b51-6804617e9140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>pclass</th><th scope=col>survived</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>sibsp</th><th scope=col>parch</th><th scope=col>fare</th><th scope=col>cabin</th><th scope=col>embarked</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>1</td><td>female</td><td>29.0000</td><td>0</td><td>0</td><td>211.3375</td><td>B5     </td><td>S</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>1</td><td>male  </td><td> 0.9167</td><td>1</td><td>2</td><td>151.5500</td><td>C22 C26</td><td>S</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1</td><td>0</td><td>female</td><td> 2.0000</td><td>1</td><td>2</td><td>151.5500</td><td>C22 C26</td><td>S</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1</td><td>0</td><td>male  </td><td>30.0000</td><td>1</td><td>2</td><td>151.5500</td><td>C22 C26</td><td>S</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1</td><td>0</td><td>female</td><td>25.0000</td><td>1</td><td>2</td><td>151.5500</td><td>C22 C26</td><td>S</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1</td><td>1</td><td>male  </td><td>48.0000</td><td>0</td><td>0</td><td> 26.5500</td><td>E12    </td><td>S</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & pclass & survived & sex & age & sibsp & parch & fare & cabin & embarked\\\\\n",
       "  & <int> & <int> & <chr> & <dbl> & <int> & <int> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 1 & female & 29.0000 & 0 & 0 & 211.3375 & B5      & S\\\\\n",
       "\t2 & 1 & 1 & male   &  0.9167 & 1 & 2 & 151.5500 & C22 C26 & S\\\\\n",
       "\t3 & 1 & 0 & female &  2.0000 & 1 & 2 & 151.5500 & C22 C26 & S\\\\\n",
       "\t4 & 1 & 0 & male   & 30.0000 & 1 & 2 & 151.5500 & C22 C26 & S\\\\\n",
       "\t5 & 1 & 0 & female & 25.0000 & 1 & 2 & 151.5500 & C22 C26 & S\\\\\n",
       "\t6 & 1 & 1 & male   & 48.0000 & 0 & 0 &  26.5500 & E12     & S\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | pclass &lt;int&gt; | survived &lt;int&gt; | sex &lt;chr&gt; | age &lt;dbl&gt; | sibsp &lt;int&gt; | parch &lt;int&gt; | fare &lt;dbl&gt; | cabin &lt;chr&gt; | embarked &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1 | 1 | female | 29.0000 | 0 | 0 | 211.3375 | B5      | S |\n",
       "| 2 | 1 | 1 | male   |  0.9167 | 1 | 2 | 151.5500 | C22 C26 | S |\n",
       "| 3 | 1 | 0 | female |  2.0000 | 1 | 2 | 151.5500 | C22 C26 | S |\n",
       "| 4 | 1 | 0 | male   | 30.0000 | 1 | 2 | 151.5500 | C22 C26 | S |\n",
       "| 5 | 1 | 0 | female | 25.0000 | 1 | 2 | 151.5500 | C22 C26 | S |\n",
       "| 6 | 1 | 1 | male   | 48.0000 | 0 | 0 |  26.5500 | E12     | S |\n",
       "\n"
      ],
      "text/plain": [
       "  pclass survived sex    age     sibsp parch fare     cabin   embarked\n",
       "1 1      1        female 29.0000 0     0     211.3375 B5      S       \n",
       "2 1      1        male    0.9167 1     2     151.5500 C22 C26 S       \n",
       "3 1      0        female  2.0000 1     2     151.5500 C22 C26 S       \n",
       "4 1      0        male   30.0000 1     2     151.5500 C22 C26 S       \n",
       "5 1      0        female 25.0000 1     2     151.5500 C22 C26 S       \n",
       "6 1      1        male   48.0000 0     0      26.5500 E12     S       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(titanic_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d1e3a889-61cd-49a9-a0cb-c4e9ca95b908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>pclass</dt><dd>0</dd><dt>survived</dt><dd>0</dd><dt>sex</dt><dd>0</dd><dt>age</dt><dd>182</dd><dt>sibsp</dt><dd>0</dd><dt>parch</dt><dd>0</dd><dt>fare</dt><dd>1</dd><dt>cabin</dt><dd>703</dd><dt>embarked</dt><dd>1</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[pclass] 0\n",
       "\\item[survived] 0\n",
       "\\item[sex] 0\n",
       "\\item[age] 182\n",
       "\\item[sibsp] 0\n",
       "\\item[parch] 0\n",
       "\\item[fare] 1\n",
       "\\item[cabin] 703\n",
       "\\item[embarked] 1\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "pclass\n",
       ":   0survived\n",
       ":   0sex\n",
       ":   0age\n",
       ":   182sibsp\n",
       ":   0parch\n",
       ":   0fare\n",
       ":   1cabin\n",
       ":   703embarked\n",
       ":   1\n",
       "\n"
      ],
      "text/plain": [
       "  pclass survived      sex      age    sibsp    parch     fare    cabin \n",
       "       0        0        0      182        0        0        1      703 \n",
       "embarked \n",
       "       1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>pclass</dt><dd>0</dd><dt>survived</dt><dd>0</dd><dt>sex</dt><dd>0</dd><dt>age</dt><dd>81</dd><dt>sibsp</dt><dd>0</dd><dt>parch</dt><dd>0</dd><dt>fare</dt><dd>0</dd><dt>cabin</dt><dd>311</dd><dt>embarked</dt><dd>1</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[pclass] 0\n",
       "\\item[survived] 0\n",
       "\\item[sex] 0\n",
       "\\item[age] 81\n",
       "\\item[sibsp] 0\n",
       "\\item[parch] 0\n",
       "\\item[fare] 0\n",
       "\\item[cabin] 311\n",
       "\\item[embarked] 1\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "pclass\n",
       ":   0survived\n",
       ":   0sex\n",
       ":   0age\n",
       ":   81sibsp\n",
       ":   0parch\n",
       ":   0fare\n",
       ":   0cabin\n",
       ":   311embarked\n",
       ":   1\n",
       "\n"
      ],
      "text/plain": [
       "  pclass survived      sex      age    sibsp    parch     fare    cabin \n",
       "       0        0        0       81        0        0        0      311 \n",
       "embarked \n",
       "       1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check missing\n",
    "sapply(titanic_train, function(x) sum(is.na(x)))\n",
    "sapply(titanic_test, function(x) sum(is.na(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6687bc84-c7fc-4bbe-924d-1e137d12d140",
   "metadata": {},
   "source": [
    "Створюємо dummy-змінну, що буде вказути на наявність запису про каюту у пасажира `1/0`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "209b6e87-783f-4d48-98af-6d4dd26cd63a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train\n",
    "titanic_train <- titanic_train %>%\n",
    "    mutate(hascabin = ifelse(!is.na(cabin), 1, 0)) %>%\n",
    "    select(-cabin) # remove cabin column\n",
    "\n",
    "titanic_test <- titanic_test %>%\n",
    "    mutate(hascabin = ifelse(!is.na(cabin), 1, 0)) %>%\n",
    "    select(-cabin) # remove cabin column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0979d1-838e-4039-9df2-20ad245eb402",
   "metadata": {},
   "source": [
    "Заповнюємо дані пропусків вартості квитка `fare`. Замінюємо `NA` на середнє значення вартості квитка:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e3849ab3-c616-4a30-991d-252664813ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "33.2955"
      ],
      "text/latex": [
       "33.2955"
      ],
      "text/markdown": [
       "33.2955"
      ],
      "text/plain": [
       "[1] 33.2955"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_fare <- round(mean(titanic_data$fare, na.rm = TRUE),4)\n",
    "avg_fare\n",
    "\n",
    "# lets study new function from tidyr replace NA\n",
    "library(tidyr)\n",
    "titanic_train <- titanic_train %>% \n",
    "    mutate(fare = replace_na(fare, avg_fare))\n",
    "\n",
    "# there are no missing in test$fare\n",
    "\n",
    "any(is.na(titanic_train$fare)) # check for missing\n",
    "any(is.na(titanic_test$fare))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e992529e-d2d7-48e8-9af3-740b36ecc9be",
   "metadata": {},
   "source": [
    "Заповнюємо пропуски для порту посадки (`embarked`). Варіантом для заміни оберемо найпопулярнішйи варінт значення, той який зустрічається частіше за інші. Скористаємося показником `МОДА`. З попереднього аналізу відомо, що таким показником є `S`. Проте варто написати універсальну функцію, що буде сама відбирати значення:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cb6b2765-f370-46a3-b707-4d3442e9bd6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "getmode <- function(v) {\n",
    "  uniqv <- unique(v) # select all unique values\n",
    "  uniqv[which.max(tabulate(match(v, uniqv)))] # select value that included most times\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b92853ca-ae01-4ef7-9acf-b7d7f3c0a1b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'S'"
      ],
      "text/latex": [
       "'S'"
      ],
      "text/markdown": [
       "'S'"
      ],
      "text/plain": [
       "[1] \"S\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the moda\n",
    "embarked_moda <- getmode(titanic_data$embarked)\n",
    "embarked_moda # its `S`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f93ef48f-c321-4f9e-a3dd-3231f4a26c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_train <- titanic_train %>% \n",
    "    mutate(embarked = replace_na(embarked, embarked_moda))\n",
    "\n",
    "titanic_test <- titanic_test %>% \n",
    "    mutate(embarked = replace_na(embarked, embarked_moda))\n",
    "\n",
    "any(is.na(titanic_train$embarked)) # check for missing\n",
    "any(is.na(titanic_test$embarked))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe6872f6-22b4-4f9a-bf2b-f73bc297e939",
   "metadata": {},
   "source": [
    "Для заміни пропусків по віку (age) використаємо показник “Клас пасажира” (pclass). Обчислимо середній вік пасажирів кожного класу і замінимо пропуски з урахуванням цієї інформації. Тобто для усіх пасажирів 1-го класу замінимо пропуски середнім по пасажирах середнього класу і т.д."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "22c4c9d1-c00c-4949-98ea-2767ef013a4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 182 rows containing non-finite values (stat_boxplot).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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8dITMhWMPsTcGUxC7IDlipS/liX07ZE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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets build boxplot for average age in each class\n",
    "\n",
    "age_plot <- ggplot(titanic_train, aes(pclass, age)) \n",
    "age_plot <- age_plot + geom_boxplot(aes(group = pclass, fill = factor(pclass), alpha = 0.4)) # alpha == opacity of chart elements\n",
    "age_plot <- age_plot + scale_y_continuous(breaks = seq(min(0), max(80), by = 2)) + theme_bw()\n",
    "age_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79fcfdcf-d731-44d5-b8f4-22d631d6e8b7",
   "metadata": {},
   "source": [
    "Середні значення віку для кожного класу пасажирів становлять:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e48a0287-3c09-44b6-bebf-e7b6c15cb896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>pclass</th><th scope=col>mean_age</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>38</td></tr>\n",
       "\t<tr><td>2</td><td>29</td></tr>\n",
       "\t<tr><td>3</td><td>24</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 2\n",
       "\\begin{tabular}{ll}\n",
       " pclass & mean\\_age\\\\\n",
       " <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 38\\\\\n",
       "\t 2 & 29\\\\\n",
       "\t 3 & 24\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 2\n",
       "\n",
       "| pclass &lt;int&gt; | mean_age &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 1 | 38 |\n",
       "| 2 | 29 |\n",
       "| 3 | 24 |\n",
       "\n"
      ],
      "text/plain": [
       "  pclass mean_age\n",
       "1 1      38      \n",
       "2 2      29      \n",
       "3 3      24      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pclass_ages <- titanic_train %>%\n",
    "    group_by(pclass) %>%\n",
    "    summarise(mean_age = floor(mean(age, na.rm =T)))\n",
    "pclass_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ecd6c508-20cf-4a0c-ad4f-ed98a64e266a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>pclass</th><th scope=col>survived</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>sibsp</th><th scope=col>parch</th><th scope=col>fare</th><th scope=col>embarked</th><th scope=col>hascabin</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>1</td><td>female</td><td>29.0000</td><td>0</td><td>0</td><td>211.3375</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>1</td><td>male  </td><td> 0.9167</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1</td><td>0</td><td>female</td><td> 2.0000</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1</td><td>0</td><td>male  </td><td>30.0000</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1</td><td>0</td><td>female</td><td>25.0000</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1</td><td>1</td><td>male  </td><td>48.0000</td><td>0</td><td>0</td><td> 26.5500</td><td>S</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & pclass & survived & sex & age & sibsp & parch & fare & embarked & hascabin\\\\\n",
       "  & <int> & <int> & <chr> & <dbl> & <int> & <int> & <dbl> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 1 & female & 29.0000 & 0 & 0 & 211.3375 & S & 1\\\\\n",
       "\t2 & 1 & 1 & male   &  0.9167 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t3 & 1 & 0 & female &  2.0000 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t4 & 1 & 0 & male   & 30.0000 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t5 & 1 & 0 & female & 25.0000 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t6 & 1 & 1 & male   & 48.0000 & 0 & 0 &  26.5500 & S & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | pclass &lt;int&gt; | survived &lt;int&gt; | sex &lt;chr&gt; | age &lt;dbl&gt; | sibsp &lt;int&gt; | parch &lt;int&gt; | fare &lt;dbl&gt; | embarked &lt;chr&gt; | hascabin &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1 | 1 | female | 29.0000 | 0 | 0 | 211.3375 | S | 1 |\n",
       "| 2 | 1 | 1 | male   |  0.9167 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 3 | 1 | 0 | female |  2.0000 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 4 | 1 | 0 | male   | 30.0000 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 5 | 1 | 0 | female | 25.0000 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 6 | 1 | 1 | male   | 48.0000 | 0 | 0 |  26.5500 | S | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  pclass survived sex    age     sibsp parch fare     embarked hascabin\n",
       "1 1      1        female 29.0000 0     0     211.3375 S        1       \n",
       "2 1      1        male    0.9167 1     2     151.5500 S        1       \n",
       "3 1      0        female  2.0000 1     2     151.5500 S        1       \n",
       "4 1      0        male   30.0000 1     2     151.5500 S        1       \n",
       "5 1      0        female 25.0000 1     2     151.5500 S        1       \n",
       "6 1      1        male   48.0000 0     0      26.5500 S        1       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_train <- titanic_train %>% \n",
    "    mutate(age = ifelse(is.na(age), pclass_ages$mean_age[pclass], age))\n",
    "           \n",
    "any(is.na(titanic_train$age)) # check for missing\n",
    "head(titanic_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7314530b-f41d-45c8-a9c6-b605d1cdcbe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_test <- titanic_test %>% \n",
    "       mutate(age = ifelse(is.na(age), pclass_ages$mean_age[pclass], age))\n",
    "any(is.na(titanic_test$age)) # check for missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a2aa8de3-cb12-4e36-8a85-0b7fe75320fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " /\\     /\\\n",
      "{  `---'  }\n",
      "{  O   O  }\n",
      "==>  V <==  No need for mice. This data set is completely observed.\n",
      " \\  \\|/  /\n",
      "  `-----'\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 10 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>pclass</th><th scope=col>survived</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>sibsp</th><th scope=col>parch</th><th scope=col>fare</th><th scope=col>embarked</th><th scope=col>hascabin</th><th scope=col></th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>916</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><th scope=row></th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 10 of type dbl\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & pclass & survived & sex & age & sibsp & parch & fare & embarked & hascabin & \\\\\n",
       "\\hline\n",
       "\t916 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0\\\\\n",
       "\t & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 10 of type dbl\n",
       "\n",
       "| <!--/--> | pclass | survived | sex | age | sibsp | parch | fare | embarked | hascabin | <!----> |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 916 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |\n",
       "| <!----> | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "    pclass survived sex age sibsp parch fare embarked hascabin  \n",
       "916 1      1        1   1   1     1     1    1        1        0\n",
       "    0      0        0   0   0     0     0    0        0        0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5971m7R7Z+aezoucEhn0z8r/9Y/Kd66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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "md.pattern(titanic_train) # OK, no missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "38945fbe-ddfb-4738-8a79-d633e93808fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " /\\     /\\\n",
      "{  `---'  }\n",
      "{  O   O  }\n",
      "==>  V <==  No need for mice. This data set is completely observed.\n",
      " \\  \\|/  /\n",
      "  `-----'\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 10 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>pclass</th><th scope=col>survived</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>sibsp</th><th scope=col>parch</th><th scope=col>fare</th><th scope=col>embarked</th><th scope=col>hascabin</th><th scope=col></th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>393</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><th scope=row></th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 10 of type dbl\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & pclass & survived & sex & age & sibsp & parch & fare & embarked & hascabin & \\\\\n",
       "\\hline\n",
       "\t393 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0\\\\\n",
       "\t & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 10 of type dbl\n",
       "\n",
       "| <!--/--> | pclass | survived | sex | age | sibsp | parch | fare | embarked | hascabin | <!----> |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 393 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |\n",
       "| <!----> | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "    pclass survived sex age sibsp parch fare embarked hascabin  \n",
       "393 1      1        1   1   1     1     1    1        1        0\n",
       "    0      0        0   0   0     0     0    0        0        0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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44ptcy7r9q9/XOv2o1/YvTW1zO++dTv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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "md.pattern(titanic_test) # OK, no missing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ce8739-a437-4a88-87d1-26f444643094",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a94756e-c613-4e19-beb1-b953ff084138",
   "metadata": {},
   "source": [
    "# Model building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6806b3bd-9126-4d7d-9cae-69faae12ca6d",
   "metadata": {},
   "source": [
    "Перед побудовою моделі визначимо категоріальні змінні як фактори (factor), щоб під час моделювання для них було сформовано dummy-змінні."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e06b9a4-cf5e-4186-9026-3d1d0d846433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>pclass</th><th scope=col>survived</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>sibsp</th><th scope=col>parch</th><th scope=col>fare</th><th scope=col>embarked</th><th scope=col>hascabin</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>1</td><td>female</td><td>29.0000</td><td>0</td><td>0</td><td>211.3375</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>1</td><td>male  </td><td> 0.9167</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1</td><td>0</td><td>female</td><td> 2.0000</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1</td><td>0</td><td>male  </td><td>30.0000</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1</td><td>0</td><td>female</td><td>25.0000</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1</td><td>1</td><td>male  </td><td>48.0000</td><td>0</td><td>0</td><td> 26.5500</td><td>S</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & pclass & survived & sex & age & sibsp & parch & fare & embarked & hascabin\\\\\n",
       "  & <int> & <int> & <chr> & <dbl> & <int> & <int> & <dbl> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 1 & female & 29.0000 & 0 & 0 & 211.3375 & S & 1\\\\\n",
       "\t2 & 1 & 1 & male   &  0.9167 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t3 & 1 & 0 & female &  2.0000 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t4 & 1 & 0 & male   & 30.0000 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t5 & 1 & 0 & female & 25.0000 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t6 & 1 & 1 & male   & 48.0000 & 0 & 0 &  26.5500 & S & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | pclass &lt;int&gt; | survived &lt;int&gt; | sex &lt;chr&gt; | age &lt;dbl&gt; | sibsp &lt;int&gt; | parch &lt;int&gt; | fare &lt;dbl&gt; | embarked &lt;chr&gt; | hascabin &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1 | 1 | female | 29.0000 | 0 | 0 | 211.3375 | S | 1 |\n",
       "| 2 | 1 | 1 | male   |  0.9167 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 3 | 1 | 0 | female |  2.0000 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 4 | 1 | 0 | male   | 30.0000 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 5 | 1 | 0 | female | 25.0000 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 6 | 1 | 1 | male   | 48.0000 | 0 | 0 |  26.5500 | S | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  pclass survived sex    age     sibsp parch fare     embarked hascabin\n",
       "1 1      1        female 29.0000 0     0     211.3375 S        1       \n",
       "2 1      1        male    0.9167 1     2     151.5500 S        1       \n",
       "3 1      0        female  2.0000 1     2     151.5500 S        1       \n",
       "4 1      0        male   30.0000 1     2     151.5500 S        1       \n",
       "5 1      0        female 25.0000 1     2     151.5500 S        1       \n",
       "6 1      1        male   48.0000 0     0      26.5500 S        1       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(titanic_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "592d3847-6d74-457c-8880-d748b82cddd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>pclass</th><th scope=col>survived</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>sibsp</th><th scope=col>parch</th><th scope=col>fare</th><th scope=col>embarked</th><th scope=col>hascabin</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>1</td><td>female</td><td>29.0000</td><td>0</td><td>0</td><td>211.3375</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>1</td><td>male  </td><td> 0.9167</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1</td><td>0</td><td>female</td><td> 2.0000</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1</td><td>0</td><td>male  </td><td>30.0000</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1</td><td>0</td><td>female</td><td>25.0000</td><td>1</td><td>2</td><td>151.5500</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1</td><td>1</td><td>male  </td><td>48.0000</td><td>0</td><td>0</td><td> 26.5500</td><td>S</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & pclass & survived & sex & age & sibsp & parch & fare & embarked & hascabin\\\\\n",
       "  & <fct> & <fct> & <fct> & <dbl> & <int> & <int> & <dbl> & <fct> & <fct>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 1 & female & 29.0000 & 0 & 0 & 211.3375 & S & 1\\\\\n",
       "\t2 & 1 & 1 & male   &  0.9167 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t3 & 1 & 0 & female &  2.0000 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t4 & 1 & 0 & male   & 30.0000 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t5 & 1 & 0 & female & 25.0000 & 1 & 2 & 151.5500 & S & 1\\\\\n",
       "\t6 & 1 & 1 & male   & 48.0000 & 0 & 0 &  26.5500 & S & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | pclass &lt;fct&gt; | survived &lt;fct&gt; | sex &lt;fct&gt; | age &lt;dbl&gt; | sibsp &lt;int&gt; | parch &lt;int&gt; | fare &lt;dbl&gt; | embarked &lt;fct&gt; | hascabin &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1 | 1 | female | 29.0000 | 0 | 0 | 211.3375 | S | 1 |\n",
       "| 2 | 1 | 1 | male   |  0.9167 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 3 | 1 | 0 | female |  2.0000 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 4 | 1 | 0 | male   | 30.0000 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 5 | 1 | 0 | female | 25.0000 | 1 | 2 | 151.5500 | S | 1 |\n",
       "| 6 | 1 | 1 | male   | 48.0000 | 0 | 0 |  26.5500 | S | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  pclass survived sex    age     sibsp parch fare     embarked hascabin\n",
       "1 1      1        female 29.0000 0     0     211.3375 S        1       \n",
       "2 1      1        male    0.9167 1     2     151.5500 S        1       \n",
       "3 1      0        female  2.0000 1     2     151.5500 S        1       \n",
       "4 1      0        male   30.0000 1     2     151.5500 S        1       \n",
       "5 1      0        female 25.0000 1     2     151.5500 S        1       \n",
       "6 1      1        male   48.0000 0     0      26.5500 S        1       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for train\n",
    "titanic_train$pclass <- factor(titanic_train$pclass, levels = c(1,2,3))\n",
    "titanic_train$survived <- factor(titanic_train$survived, levels = c(0, 1))\n",
    "titanic_train$hascabin<- factor(titanic_train$hascabin, levels = c(0, 1))\n",
    "\n",
    "titanic_train$embarked <- factor(titanic_train$embarked, levels = c('S', 'C', 'Q'))\n",
    "titanic_train$sex <- factor(titanic_train$sex, levels = c('female', 'male'))\n",
    "\n",
    "head(titanic_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a8bd625a-cb31-4c8e-8ba0-7f91586f001a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>pclass</th><th scope=col>survived</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>sibsp</th><th scope=col>parch</th><th scope=col>fare</th><th scope=col>embarked</th><th scope=col>hascabin</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>7</th><td>1</td><td>1</td><td>female</td><td>63</td><td>1</td><td>0</td><td> 77.9583</td><td>S</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>1</td><td>0</td><td>male  </td><td>47</td><td>1</td><td>0</td><td>227.5250</td><td>C</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>1</td><td>1</td><td>female</td><td>18</td><td>1</td><td>0</td><td>227.5250</td><td>C</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>1</td><td>1</td><td>female</td><td>24</td><td>0</td><td>0</td><td> 69.3000</td><td>C</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>17</th><td>1</td><td>0</td><td>male  </td><td>24</td><td>0</td><td>1</td><td>247.5208</td><td>C</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>20</th><td>1</td><td>0</td><td>male  </td><td>36</td><td>0</td><td>0</td><td> 75.2417</td><td>C</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & pclass & survived & sex & age & sibsp & parch & fare & embarked & hascabin\\\\\n",
       "  & <fct> & <fct> & <fct> & <dbl> & <int> & <int> & <dbl> & <fct> & <fct>\\\\\n",
       "\\hline\n",
       "\t7 & 1 & 1 & female & 63 & 1 & 0 &  77.9583 & S & 1\\\\\n",
       "\t11 & 1 & 0 & male   & 47 & 1 & 0 & 227.5250 & C & 1\\\\\n",
       "\t12 & 1 & 1 & female & 18 & 1 & 0 & 227.5250 & C & 1\\\\\n",
       "\t13 & 1 & 1 & female & 24 & 0 & 0 &  69.3000 & C & 1\\\\\n",
       "\t17 & 1 & 0 & male   & 24 & 0 & 1 & 247.5208 & C & 1\\\\\n",
       "\t20 & 1 & 0 & male   & 36 & 0 & 0 &  75.2417 & C & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | pclass &lt;fct&gt; | survived &lt;fct&gt; | sex &lt;fct&gt; | age &lt;dbl&gt; | sibsp &lt;int&gt; | parch &lt;int&gt; | fare &lt;dbl&gt; | embarked &lt;fct&gt; | hascabin &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 7 | 1 | 1 | female | 63 | 1 | 0 |  77.9583 | S | 1 |\n",
       "| 11 | 1 | 0 | male   | 47 | 1 | 0 | 227.5250 | C | 1 |\n",
       "| 12 | 1 | 1 | female | 18 | 1 | 0 | 227.5250 | C | 1 |\n",
       "| 13 | 1 | 1 | female | 24 | 0 | 0 |  69.3000 | C | 1 |\n",
       "| 17 | 1 | 0 | male   | 24 | 0 | 1 | 247.5208 | C | 1 |\n",
       "| 20 | 1 | 0 | male   | 36 | 0 | 0 |  75.2417 | C | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "   pclass survived sex    age sibsp parch fare     embarked hascabin\n",
       "7  1      1        female 63  1     0      77.9583 S        1       \n",
       "11 1      0        male   47  1     0     227.5250 C        1       \n",
       "12 1      1        female 18  1     0     227.5250 C        1       \n",
       "13 1      1        female 24  0     0      69.3000 C        1       \n",
       "17 1      0        male   24  0     1     247.5208 C        1       \n",
       "20 1      0        male   36  0     0      75.2417 C        1       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use the same levels for factors in test_set\n",
    "# different ordering data in \n",
    "\n",
    "titanic_test$pclass <- factor(titanic_test$pclass, levels = c(1,2,3))\n",
    "titanic_test$survived <- factor(titanic_test$survived, levels = c(0, 1))\n",
    "titanic_test$hascabin<- factor(titanic_test$hascabin, levels = c(0, 1))\n",
    "\n",
    "titanic_test$embarked <- factor(titanic_test$embarked, levels = c('S', 'C', 'Q'))\n",
    "titanic_test$sex <- factor(titanic_test$sex, levels = c('female', 'male'))\n",
    "\n",
    "head(titanic_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "755aa469-3437-47e2-8a81-bcdbecfc9a97",
   "metadata": {},
   "source": [
    "Для побудови логістичної регресії використовується функція `glm()`. Формула `survived ~ .` означає, що у модель будуть відібрані усі параметри із вибірки, а survived буде використано як вихідну змінну. `family = binomial(link = \"logit\")` вказує на використання логістиної регресії."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f296089f-65f0-4d27-a227-e5c6473b26d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = survived ~ ., family = binomial(link = \"logit\"), \n",
       "    data = titanic_train)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.5558  -0.6169  -0.4128   0.5801   2.6250  \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)  2.748e+00  5.167e-01   5.318 1.05e-07 ***\n",
       "pclass2     -2.653e-01  3.772e-01  -0.703 0.481831    \n",
       "pclass3     -1.397e+00  3.907e-01  -3.574 0.000351 ***\n",
       "sexmale     -2.753e+00  1.999e-01 -13.771  < 2e-16 ***\n",
       "age         -3.423e-02  7.987e-03  -4.285 1.83e-05 ***\n",
       "sibsp       -3.144e-01  1.111e-01  -2.829 0.004672 ** \n",
       "parch       -7.658e-02  1.196e-01  -0.640 0.521911    \n",
       "fare         4.771e-05  2.313e-03   0.021 0.983541    \n",
       "embarkedC    6.002e-01  2.336e-01   2.569 0.010193 *  \n",
       "embarkedQ   -1.329e-01  3.185e-01  -0.417 0.676547    \n",
       "hascabin1    1.008e+00  3.247e-01   3.104 0.001912 ** \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 1218.4  on 915  degrees of freedom\n",
       "Residual deviance:  805.4  on 905  degrees of freedom\n",
       "AIC: 827.4\n",
       "\n",
       "Number of Fisher Scoring iterations: 5\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def_glm <- glm(formula = survived ~ ., family = binomial(link = \"logit\"), data = titanic_train)\n",
    "summary(def_glm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf120fb-0e4f-4f16-901c-26047369e87f",
   "metadata": {},
   "source": [
    "Параметри з _***_ вказують на значимість показника у моделі. Здійснимо прогноз по тренувальній вибірці:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d678cec-b560-4229-af7a-6ba6dfdb4dbc",
   "metadata": {},
   "source": [
    "And test build model with an other package `caret` explained before in train/test/validation splitting paragraph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ae19f8bd-8898-4c75-a444-e4053e54449f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# install.packages(\"e1071\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "444fc203-1b7e-4bca-9ea5-23ae3c063254",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: lattice\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "NULL\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.5558  -0.6169  -0.4128   0.5801   2.6250  \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)  2.748e+00  5.167e-01   5.318 1.05e-07 ***\n",
       "pclass2     -2.653e-01  3.772e-01  -0.703 0.481831    \n",
       "pclass3     -1.397e+00  3.907e-01  -3.574 0.000351 ***\n",
       "sexmale     -2.753e+00  1.999e-01 -13.771  < 2e-16 ***\n",
       "age         -3.423e-02  7.987e-03  -4.285 1.83e-05 ***\n",
       "sibsp       -3.144e-01  1.111e-01  -2.829 0.004672 ** \n",
       "parch       -7.658e-02  1.196e-01  -0.640 0.521911    \n",
       "fare         4.771e-05  2.313e-03   0.021 0.983541    \n",
       "embarkedC    6.002e-01  2.336e-01   2.569 0.010193 *  \n",
       "embarkedQ   -1.329e-01  3.185e-01  -0.417 0.676547    \n",
       "hascabin1    1.008e+00  3.247e-01   3.104 0.001912 ** \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 1218.4  on 915  degrees of freedom\n",
       "Residual deviance:  805.4  on 905  degrees of freedom\n",
       "AIC: 827.4\n",
       "\n",
       "Number of Fisher Scoring iterations: 5\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#library(e1071) # required by caret\n",
    "suppressMessages(library(caret))\n",
    "\n",
    "# this is wrapper function for training different models\n",
    "caret_glm = train(survived ~ ., # formula\n",
    "                    data = titanic_train, # data for model training     \n",
    "                    method = 'glm', # model building algorithm, glm\n",
    "                    family = \"binomial\",\n",
    "                    ) # data preprocessing techniques, normalization and scaling\n",
    "summary(caret_glm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4529cd31-81ce-4f94-9bca-5a1702d19678",
   "metadata": {},
   "source": [
    "> You can see that we got the same variables significansy in both models, but coefficients are different. Lets check what model building algorithm is better."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16854469-ff99-45af-91bb-7c8a619e6316",
   "metadata": {},
   "source": [
    "To compare our modeling resutls for train lets create special data frames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2a4ba81c-60aa-4e54-ac69-c05833b324a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>survived</th><th scope=col>predicted_def</th><th scope=col>predicted_caret</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>0.9412193</td><td>0.9412193</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>0.6250488</td><td>0.6250488</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>0</td><td>0.9618407</td><td>0.9618407</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>0</td><td>0.3812235</td><td>0.3812235</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>0</td><td>0.9198190</td><td>0.9198190</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1</td><td>0.3455040</td><td>0.3455040</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & survived & predicted\\_def & predicted\\_caret\\\\\n",
       "  & <fct> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 0.9412193 & 0.9412193\\\\\n",
       "\t2 & 1 & 0.6250488 & 0.6250488\\\\\n",
       "\t3 & 0 & 0.9618407 & 0.9618407\\\\\n",
       "\t4 & 0 & 0.3812235 & 0.3812235\\\\\n",
       "\t5 & 0 & 0.9198190 & 0.9198190\\\\\n",
       "\t6 & 1 & 0.3455040 & 0.3455040\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | survived &lt;fct&gt; | predicted_def &lt;dbl&gt; | predicted_caret &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | 1 | 0.9412193 | 0.9412193 |\n",
       "| 2 | 1 | 0.6250488 | 0.6250488 |\n",
       "| 3 | 0 | 0.9618407 | 0.9618407 |\n",
       "| 4 | 0 | 0.3812235 | 0.3812235 |\n",
       "| 5 | 0 | 0.9198190 | 0.9198190 |\n",
       "| 6 | 1 | 0.3455040 | 0.3455040 |\n",
       "\n"
      ],
      "text/plain": [
       "  survived predicted_def predicted_caret\n",
       "1 1        0.9412193     0.9412193      \n",
       "2 1        0.6250488     0.6250488      \n",
       "3 0        0.9618407     0.9618407      \n",
       "4 0        0.3812235     0.3812235      \n",
       "5 0        0.9198190     0.9198190      \n",
       "6 1        0.3455040     0.3455040      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_check <- titanic_train %>% \n",
    "    select(survived) %>%\n",
    "    mutate(predicted_def = predict(def_glm, type = 'response', newdata = titanic_train), # predicted by default glm function\n",
    "           predicted_caret = predict(caret_glm, type = 'prob', newdata = titanic_train)[, 2]) # predicted by caret train algorithm\n",
    "\n",
    "head(train_check)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7896c373-c65d-4e12-bb02-82b393428ec2",
   "metadata": {},
   "source": [
    "As you see for logistic regression results are the same, so we can use default algorithm. But `train()` method is very powerfull with desicion trees, boosting, random forest, neural network and other complex algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94428f1c-dcf1-409a-8e95-df07c3861ebe",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e00677-97e6-434c-afc7-804503b9c28c",
   "metadata": {},
   "source": [
    "## Model accuracy check"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f6ef9c-4c75-4a69-b6fc-5da094c8f934",
   "metadata": {},
   "source": [
    "Lets create new datasets for train and test results storing. We will use them "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "818a8235-31f4-42d2-836d-6e23e859ae32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>survived</th><th scope=col>predicted</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>7</th><td>1</td><td>0.7839711</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>0</td><td>0.4233024</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>1</td><td>0.9688239</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>1</td><td>0.9717471</td></tr>\n",
       "\t<tr><th scope=row>17</th><td>0</td><td>0.6718810</td></tr>\n",
       "\t<tr><th scope=row>20</th><td>0</td><td>0.5925107</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & survived & predicted\\\\\n",
       "  & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t7 & 1 & 0.7839711\\\\\n",
       "\t11 & 0 & 0.4233024\\\\\n",
       "\t12 & 1 & 0.9688239\\\\\n",
       "\t13 & 1 & 0.9717471\\\\\n",
       "\t17 & 0 & 0.6718810\\\\\n",
       "\t20 & 0 & 0.5925107\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | survived &lt;fct&gt; | predicted &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 7 | 1 | 0.7839711 |\n",
       "| 11 | 0 | 0.4233024 |\n",
       "| 12 | 1 | 0.9688239 |\n",
       "| 13 | 1 | 0.9717471 |\n",
       "| 17 | 0 | 0.6718810 |\n",
       "| 20 | 0 | 0.5925107 |\n",
       "\n"
      ],
      "text/plain": [
       "   survived predicted\n",
       "7  1        0.7839711\n",
       "11 0        0.4233024\n",
       "12 1        0.9688239\n",
       "13 1        0.9717471\n",
       "17 0        0.6718810\n",
       "20 0        0.5925107"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_results <- titanic_train %>% \n",
    "    select(survived) %>%\n",
    "    mutate(predicted = predict(def_glm, type = 'response', newdata = titanic_train),\n",
    "          residuals = residuals(def_glm, type = \"response\"))\n",
    "\n",
    "test_results <- titanic_test %>%\n",
    "    select(survived) %>%\n",
    "    mutate(predicted = predict(def_glm, type = 'response', newdata = titanic_test))\n",
    "\n",
    "head(test_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4929538-df40-48d1-8767-0f8cb0530cc5",
   "metadata": {},
   "source": [
    "The next stage is converting out predicted probabilities into event classes 1/0. By default you can do this using next condition `predicted == 1 if prob >= 0.5 and 0 if prob < 0.5`.\n",
    "\n",
    "In this case `0.5` is cut-off line or classification splitter. So, lets do this for both train and test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "52c29b16-05d6-4d0e-9298-4f8f00704eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>survived</th><th scope=col>predicted</th><th scope=col>survived_05</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>7</th><td>1</td><td>0.7839711</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>0</td><td>0.4233024</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>1</td><td>0.9688239</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>1</td><td>0.9717471</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>17</th><td>0</td><td>0.6718810</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>20</th><td>0</td><td>0.5925107</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & survived & predicted & survived\\_05\\\\\n",
       "  & <fct> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t7 & 1 & 0.7839711 & 1\\\\\n",
       "\t11 & 0 & 0.4233024 & 0\\\\\n",
       "\t12 & 1 & 0.9688239 & 1\\\\\n",
       "\t13 & 1 & 0.9717471 & 1\\\\\n",
       "\t17 & 0 & 0.6718810 & 1\\\\\n",
       "\t20 & 0 & 0.5925107 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | survived &lt;fct&gt; | predicted &lt;dbl&gt; | survived_05 &lt;fct&gt; |\n",
       "|---|---|---|---|\n",
       "| 7 | 1 | 0.7839711 | 1 |\n",
       "| 11 | 0 | 0.4233024 | 0 |\n",
       "| 12 | 1 | 0.9688239 | 1 |\n",
       "| 13 | 1 | 0.9717471 | 1 |\n",
       "| 17 | 0 | 0.6718810 | 1 |\n",
       "| 20 | 0 | 0.5925107 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "   survived predicted survived_05\n",
       "7  1        0.7839711 1          \n",
       "11 0        0.4233024 0          \n",
       "12 1        0.9688239 1          \n",
       "13 1        0.9717471 1          \n",
       "17 0        0.6718810 1          \n",
       "20 0        0.5925107 1          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_results <- train_results %>%\n",
    "    mutate(survived_05 = as.factor(ifelse(predicted >= 0.5, 1 , 0)))\n",
    "\n",
    "test_results <- test_results %>%\n",
    "    mutate(survived_05 = as.factor(ifelse(predicted >= 0.5, 1 , 0)))\n",
    "\n",
    "head(test_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec2119e-9806-4543-afaf-5db0c295363c",
   "metadata": {},
   "source": [
    "Для демонстрації переглянемо графік реальних та прогнозованих/модельованих значень survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "52fb7458-f094-4291-9b41-d77058691d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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waRBm8uOSI5/RFCJG8VIpXm8kVKX9ks\nwLZJT+nEqwtWIZLHRSSF5ueSRHL5IiWndOLVBasQyeMikkLzcykipa9sFmD76BohUrAKkUqz\nBpFSVzYLsFlBJG8VIpVmBSIVTX/bgkjeKkQqzSpESi3xsVlBJG8VIpUGkZRViORxEUmh+UEk\njYtIkTs5QSRpFSKZg0ge10BBJImLSFox5xNJ2HWASFaaH0TSuFsVSbwYAiJZaX4QSeNuVqT6\nJyLlBZGUVdcnknwxBESy0vwgksbdsEgOkTKDSMqq6xNJPnIekaw0P4ikcTcrknTkPCJZaX4Q\nSeNuVSSmvwuCSMqqqxQpxUUkheYHkTRu8N83IuUHkaRVVyiS7dIsYRDJ4xooiCRxtyJS/ROR\nnO2U5ARXLwGRRO42RDJemiXM1kRKf8lKPIgkrbpOkRwiaV+yEg8iSauuTyTjpVnCbEwk5UtW\n4kEkadU1imS6NEuYDYrkEEkKImnctgnT33VsV+RMcPUSEEnkbkYkEy7I5kQyXZEzwdVLQCSR\ni0hz4hJcA24GkZj+jgWRNC4iaVxDEElahUjmIJLHNeAQSeIi0py4BNeAQ6TJQSRlFSJ5XERS\naH4QSeMiksY1BJGkVYhkDiJ5XAMOkSQuIs2JS3ANOESaHERSViGSx0UkheYHkTQuImlcQxBJ\nWoVI5iCSxzXgEEniItKcuATXgEOkyUEkZRUieVxEUmh+EEnjIpLGNQSRpFWIZA4ieVwD7opF\nSlyyE5HmxCW4BhwiTc5JRUp+YxUizYlLcA04RJqc04pUL0EkMYjkcQ24axUp/Y1ViDQnLsE1\n4BBpck4tkkMkOYjkcQ24axUp/Y1ViDQnLsE14BBpck492RD/xipEmhOX4BpwiDQ5TH8rqxDJ\n4yKSQvMzzw7ZOBeR5sQluAYcIk0OIimrEMnjIpJC84NIGheRNK4hiCStQiRzEMnjGnCIJHER\naU5cgmvAIdLkIJKyCpE8LiIpND+IpHERSeMagkjSKkQyB5E8rgGHSBIXkebEJbgGHCJNDiIp\nqzYjUurrixBpchBJWbURkcRzLMOREKk0iKSs2opINQ6RhkGkJDYrVyOSfI5lOBIilQaRlFXb\nEckh0iiIlMRm5WpEks+xDEdCpNIgkrJqKyJJ51iGIyFSaRBJWbURkZj+DoJISWxWrkmkVBBp\nchBJTv//NyJ53KsUabfP4G7zy26wEJHkDHZfIpLHvUaRdv3NYMFu1ASRpAx2XyKSx12HSKlP\nhG0miBSIhUhihrsvEcnjrkGkvUavVJUmijT2CJGkDHdfIpLHXYVI9abd6UQ6/Np/RHqvyZM5\nr4I7wapwjQGX0yRngAk1VP1NVu9p1c2MKxo640nJ6VNUS9aqUapDW+UJnCrSeBnvSFIGuy95\nR/K4K3hHqg5cxY+JInn3EEkO098x7gpEqs14Ot2mnT/r4NH8XLdIFi4iKX0sOdVkw9PpJht2\nwjJE0oJIHncNIp12+vso0uC9CZGUIJLHXYdIhseh5MiGoVGjAxsQSQsiedyrFEkPIilBJI+L\nSApNrQSRtN6IlB9EklYhkjmI5HENOESSuIg0Jy7BNQSRJgeRlCCSx0UkhaZWgkhab0TKDyJJ\nqxDJHETyuAYcIklcRJoTl+AagkiTg0hKEMnjIpJCUytBJK03IuUHkaRViGQOInlcAw6RJC4i\nzYlLcA1BpMlBJCWI5HERSaGplSCS1huR8oNI0ipEMgeRPK4Bh0gSF5HmxCW4hiDS5CCSEkTy\nuIik0NRKEEnrjUj5QSRpFSKZg0ge14BDJImLSHPiElxDEGlyEEkJInlcRFJoaiWIpPVGpPwg\nkrQKkcxBJI9rwCGSxEWkOXEJriGINDmIpASRPC4iKTS1EkTSek96osOrVSOSfZXODYJISWxW\nLkekwdc/F+FiXFsQaXIQSclSItW9ESmNQySJi0iDDL/+uQgX4RqDSJODSEqWE8khkoJDJImL\nSMNU/U0ZLsa1BZEmB5GULDfZEMzbIZJ9lc4NgkhJbFYuRySmvw04RJK4iKQFkeyrdG4QREpi\ns4JI3kiIVBpEUoJIHheRFJpaCSJpvREpP4gkrUIkcxDJ4xpwiCRxEUkLItlX6dwgiJTEZgWR\nvJEQqTSIpASRPC4iKTS1EkTSeiNSfhBJWoVI5iCSxzXgEEniIpIWRLKv0rlBECmJzQoieSMV\niBQeI5gVRJJWnVqkxJOGSGZuYqRskaRT5LOCSNKq04qUfNIQycxNjJQvUn0HkVYmUupJQyQz\nNzFSrkjiKfJZQSRp1UlFSj9piGTmJkYqEckhkludSA6RpnMTI5Vs2k17ISKStOrkm3bxPxqR\nzNzESCWTDdPm7RBJWnXyyYb4k4ZIZm5iJKa/S7MmkZj+noebGKlApIlBJGnVqUUyYLOCSN5I\niFQaRCrnItIMQSRpFSKZg0geVy8BkUQuImlBJPsqnRsEkZLYrCCSNxIilQaRyrmINEMQSVqF\nSOYgksfVS0AkkYtIWhDJvkrnBkGkJDYriOSNhEilQaRyLiLNEESSViGSOYjkcfUSEEnkIpIW\nRLKv0rlBECmJzQoieSMhUmkQqZyLSDMEkaRViGQOInlcvYR+leUUKESSRszAZgWRvJHWIJLt\npFxEkkbMwGYFkbyRViFSf5PFDYJISWxWEMkbaQUiGS9chEjSiBnYrCCSN9JKRHpAJETSuSW4\nHG5ipBWIZLy4KyJJI2Zgs4JI3kirEMl0uXFEkkbMwGYFkbyR1iAS099jLiJpQaTYqmnPWxtE\nSmKzgkjeSIhUDaMPI9DUShBJ641I+bk0ke5PLtIRi0iR3oiUn0sTaW/SW52eoqUrGU6VIFKk\nNyLl5+JEci92Oj1FS1cynLxHpEhvRMrP5Ynkdvc6PkFLVjLanYxIkd6IlJ8LFKko7z2ZUg1/\nvApWv/J+WmJom4MryrQBgt4z13vyPz8cKeNJOVV1CW5OmXrOM/09POSWd6RIb96R8rOZdyRj\nJcOTQBAp0huR8nNtIjH9beiNSPm5PpGOQaRIb0TKDyJJSxDJHETyuHoJiCRyEUkLIsVWIZJD\nJJ1bgsvhJkZCpG5RrlyIVM5FpBmCSNISRDIHkTyuXgIiiVxE0oJIsVWI5BBJ55bgcriJkRCp\nW4RIOUEkbyREKg0ilXMRaYYgkrQEkcxBJI+rl4BIIheRtCBSbBUiOUTSuSW4HG5iJEQqDSKV\ncxFphiCStASRzEEkj6uXsJhIzy/vqqq6e/msDyPQ7JUgUqT3+UQ67vZApAg3SFSk46XtMq6C\ngkjl3IsRaXj6MiJFuEEiIr3d7e7fPO7vPL65r3bma9whUjn3ckTqbxApyg0ii/Rm93rw2+vd\nG30oj2avBJEivc8k0ug77BApwg0ii/TCa+X/rtPslSBSpPcZRXpAJIUbhFm7JDYr2xBpdBlc\nRIpwg5zt2yj6IFKk9xknGx6YbFC4QRApic3KRkRi+tvADRLftLu7fXTu8fZOH0Wi2StBpEjv\n84l0bItIEW6QqEh31WFPbJVjEiKVc/3eWZsCiORx9RKWEql9Hp/ZtDNnTpFsX7Zt4cp8Y1tE\ninCDREW6rQ6bdrwjmTOrSIOpM0MQyePqJSwl0uPuMNWwe9SHEWj2ShBJ6D3amWMIInlcvYTF\nJhue72+q6ibrmFVEmsANRHKIlJ1LFKkkiFTODTbtcp4SRPK4egmIJHI3J1KVNW+HSB5XL2E5\nkV7f7Z/J23f6KBLNXgkiyb2Z/i7IBYr0fNMc1VBV5nMoHCJN4U56KSGSx9VLWEqkF9V9/Z/i\n+9WtPoxAs1eCSObeRdwS/spEShzSdH6Ruo10dsiag0he72VESp7Ri0jimjgXkbRsV6T+5iJF\najft7ivrSX1jmr0SRDL3LuKW8NckUvqM3vOL9MyRDblBJK/3YiI9XLBIzr2sj2y458gGcxDJ\n673Ypl38jN7zi5S1/yik2StBJHPvIm4Jf10ipc7oPb9I1Y310kEizV4JIpl7F3FL+KsS6cKn\nv/fbdbu8I1YdIk3hIlJ54vWeXyT3eL+rqruc4xoQaQoXkcpz0SLt8/a+qm7e14cRaPZKEMnc\nu4hbwkckL5OP/n7kKkL2IJLXG5HavH2xf0d6HS430OyVIJK5dxG3hI9IXiZ/RnrBZyR7EMnr\njUiumbW7ec2sXU4QyeuNSPWvd+xHygwieb0RaZ/cNyOPZq8Ekcy9i7glfETyUipSc2os1/7O\nDCJ5vREJkTLa5gyASB534yI5DlotCCJ5vRHJcdBqQRDJ641IjoNWC4JIXm9EqsNBq7lBJK83\nIrXhoNWsIJLXG5H6cNBqRhDJ641IbThoNSuI5PVGpDoctJobRPJ6I5LjoNWCIJLXG5EcB60W\nBJG83oi0z03OFVZDmr0SRDL3LuKW8BHJywSRdiVfPIZI5VxEKs8li/Tu9j7nYsU+zV4JIpl7\nF3FL+IjkZcpnJI7+zg0ieb0RySFSQRDJ641IpUGkci4ilQeRHCJl9C7ilvARyQubdsmBdGxW\nEMnrjUgOkQqCSF5vROrzePtSH0Wi2StBJHPvIm4JH5G8TP+M9FzlmIRI5VxEKs8KROJbze1B\nJK83Ih3zfrXThxFo9koQydy7iFvCRyQvc0w23OvDCDR7JYhk7l3ELeEjkpfpIu1yPEKkCVxE\nKs8li1QURCrnIlJ5EMkhUkbvIm4JH5G8TBHp9c65t9WO/UjmIJLXG5H2eV1V7nG3/5R0NGm3\nz/D+zluGSFO4iFSeSxbppnq7//f63XH6e9ffHH+OliHSFC4ileeSRdq/Ib2pboY7ZBFp+gCI\n5HG3L9KuenxRvas/JfVL+hvPJ0QyD4BIHnf7Ir2sdyLVb0j9jqSRSN1HpH7Ze02++tWvPu3z\n1Yyfr4Llr7yfebzoz1czcaI/p9UbPg7z/jw1X3gcXi0z3tTXwyyvs/is3X21e7OX5LhDNnhH\n2vGOlDsA70ged/vvSGE8aRCpYABE8rgbF8m/OGTzOyJNH2B7IglnByBSnze74XdQvN41Vy9m\n0276AFsTqdYoUAmRjnm7292/qS8Q+fhm/1np8JUUvkg7h0i5A2xOpP5G6o1Irp5rCM6j6I5i\nGB7RwJENWQNsTKRq9CPojUh1nl/e7S26y/pCZkQq565VpAdEqsPR30lsVq5PpPpF88CmXR1E\nSmKzco0iVe6ByYY6cZHuDh8kb3K+kwKRyrmrFInp7y6pyYZmaZXzhWOIVM5dqUiJ5w+R6uyq\nZtL7HZfjMgeRvCWI5I5v2YhkDiJ5SxDJ1R+RXjw793xf3erDCDR7JYhk7l3ELeEjkpcJIjWn\nmdenUrzThxFo9koQydy7iFvCRyQvk3bI3t9U1U3eF8kiUjkXkcpz0SKVBJHKuYhUHkRyiGTv\nnZj3QSSPq5ewiEj7p4wvGsvOaUUSz1gwcK18sS0iRbhBECmJzcqJRepv8rhWvtgWkSLcIGza\nJbFZOalI8hkLBq6RL7dFpAg3SFSk25xDg0KavRJEMvWWz1gwcI18uS0iRbhBEocI6fgEzV4J\nItl6i2csGLhWvtgWkSLcIFGR3t3m7ULyaPZKEMnWWzxjwcC18sW2iBThBtG/sY/JBmuY/vaW\nIJJDpIKcWiTLC3MSP2yLSBFuEGbtktisIJK3BJFKg0jlXEQqz2WL9Ppuv1l3m3PwNyJN4CJS\neS5ZpOeb5vNRdThR1hhEKuciUnkuWaQX1X09TfQ+J/aZg0jeEkRyx2MkmbUzB5G8JYjkEKkg\niOQtQSTXb9rdczkucxDJW4JI+zx312zgApHWIJK3BJGavGyu2ZBzDX1EmsBFpPKUiHT8zHJq\nkQqCSOXcE4uU9Vl3+yINDwFGJIdIOrdJ8tDxxEDbFalehUghF5GSSZ7MlBhoqyKNTpM88WTD\nC47+zszlipQ+vTYx0JZFcsuIdHeNp1H0f+wGRXKINMjwUjKnFamq3tf5cZq9kssRabD/eWMi\npS9BlBhouyINjjU4rUg313fNhsGrbXMi5R2jsn2Rlpv+fszcheTR7JVcjEjD7Z+ticT0t6V3\nPFNm7d6/ts9Iw0/k2xMpK4jkhcmG5EBeBnPEiGTmrkOk1MuYyQY3s0jHvZaIZOauQaT0R8RT\nvyPp+ATNXsnliLTd6e/cbE6k/ibZO54pn5HuXlzhBSK7Nohk5q5AJGU32qk37a7uM5JDJH+g\n7Yj0gEjJIJLGLcnGRFIONTzxpl1JEEkJInncxSYbEge/I5JDJJ1bkq2JdN7p74vZtEuVgEga\ntyTbEyk1wJWIlN4HgEgatySI5GXypt3j7Ut9FIlmr0QVqb9JcRFpziCSl+mfkZ6rHJNOIJKy\nDwCRNG5JEMnLDJMN5960U/YBIJLGLQkieZku0vvVTh9GoNkrMWzaJfYBIJLGLQkieZljsuFe\nH0ag2SsxTDYk9gEgksYtyYlEOj6NT/6SzYu0y/GI6W8t1yzScPr1qVvS/R+5XZGKcqIdsom/\nFJE0bklOI1J/04t03GpHpAjNXgkimXuvWqTR9OtTd7ebR9qwSPe7y9ghKzYJuIg0Z04l0kMg\nktu8SNkpA+EAABc3SURBVPeXcmSD2CTgXpJIiYfsikUaTb/2m3YZF2m01HCBIu2q1zo/TrNX\nsjWRkoc0XbVIg+nXfrLBbX6yIeudKKTZK9mcSP2NkGsW6Uqnv++q/MvaIZJ2SNN1i2R5wQex\n/I9+ySI97m4v5poNaxPpAZHkVfki2S4Re8kiXcxpFGKTgHsxIqUPaUKkCDcqUn9jqgGRpCUX\nKVLi8egnG+KHNBVxvQHyuEW5GJGUY/8zuGcTqSibFym5pfEwaBRJgmv4Rr3rFCm+oZzDRSSx\nScBdSqT+JjVA/gs+fYh7ObcwFyNS7gNzwSKxaTdIekujXKTc/3it3NJckEh5b9WIJC25TJHi\nL/hpIjlEErP26e+ibF2k9JbGtE27nMkpM7cwlyRS1lOLSNKSSxQptaUxRaS83SVmbmEQyQsi\nJQeKt5kw/Z0cienvCHfDIr3eOfe22p39clxik4C7mEgJzCSRLEEkrckFivR6/1/kY31K0lKX\n4wq/HBeRzEMjksY9m0g31dv9v9fvFrqKkPR17YhkHhqRNO7ZRNq/qt9UN4tNfw+nrRApe2hE\n0rhnE2lXPb6o3tWfkvRhBJq9kofB4OOTvBDJPDQiadyzifSyvhZX/Ya0yHXtRns6ESl7aETS\nuGcTyd1Xuzf7N6aFrms33NOJSNlDI5LGPZ9IJZk02fDAZIMSRNKaIBLT34YgktbkEkW6Lzmx\n7yk/r6J3wjUGyqQm0zAZ9VaVoZiMoWdOzkBh2/jjYHjsioqZ9pzM8rBe0HXtgjubfUeyHVmX\nMzTvSBr3bO9Iu+rdbfX4fFu91YcRaPZKrlGk/iYviKQ1uUCR9v9jvqzeuOfqVh9GoNkruT6R\njGcf5QyNSBr3nCK9qS+2yqadGZMj0gMiKRTLqgzu2US6q95/rG7cW0QyY3I27QwXIsgZGpE0\n7tlEqg26recaXujDCDR7JdcokulCBDlDI5LGPZtI7s2Ncy/yjhBCJK1Jxy26sjoiqU0uUaSS\nIJLS5ElvkojfKbwo/UxBJC+IJA1kaLMGkcLvZJ0tiOQFkaSBDG1WIVJ/g0g6F5EcIomdRnuj\nEEnjIpJDJLHTaG8UImlcRHKIJHca7o1CJI2LSA6R5E7hd7LOFkTygkjSQIY2axCJ6e8cLiI5\nRIp2ynnBFw2ESBFukAsWKTxlNsFFpDmDSF5WLJJ0EYcEF5HmDCJ5WbNI9c85RTId3oZI2VxE\nanKpIokXuktwtQfDeMA1ImVzEanJJYvk5hRp8AaXCCJlcxGpyaWKJF4MPMFVHgzrSamIlM1F\npCaXK5Lw9RQJrkEkh0jZAyFShBvkYkWaefrbeOEeRMrmIlKTCxZJaBLnGiYbLPN2iJTNRaQm\n1yIS099FAyFShBvkekQyBZGyuYjUBJHEkRDJzEWkJogkjoRIZi4iNUEkcSREMnMRqQkiiSMh\nkpm7PZGEaSlEygwiZXO3JpK4owSRMoNI2dygqMSO9FWI1N8MgkiZQaRsrldU8jyyFYgkH0yG\nSJlBpGyuL1K9ZOUiPSDS1CBSNjfcMOpfh2sUST7hBpEyg0jZXOETxrpFkk4BRaTMIFI2N9y0\ni59HtgaRmP6eI4iUzQ0nG+Lnka1DJKEtImUGkbK5QVErn/4W2yJSZhApm1v0CkUkK81eCSKZ\ng0haE0QS70QXhFxEmjOI5AWRMoNI2VxEaoJI4kiIZOYiUhNEEkdCJDMXkZogkjgSIpm5iNQE\nkcSREMnMRaQmiCSOhEhmLiI1QSRxJEQycxGpCSKJIyGSmYtITRBJHAmRzFxEaoJI4kiIZOYi\nUhNEEkfajkimS54XcMO2hlWIZAwiKU2WFsn4JRzZ3FRRiVWIZAwiKU0WF6m/KRsIkSLcIIgk\njrQRkaxfVJjLTRaVWIVIxiCS0uQMIj0gUu4AiDQ5GxPJ+mXu2dxUUYlViGQMIilNzjDZEF5b\nKmMgRIpwgyCSONJWRDrr9Hc4NCIZg0hKkzPskC0ZaQ6RpDdDRDIGkZQm1ySS8PEMkYxBJKXJ\n9YgkThgikjGIpDSZR6R+i+nCRXKIVBhEUprMIdLgsJ8LFkk8qAKRjEEkpcksIvU3ly2ScJgf\nIhmDSEqTGUQabjNdskhMf08IIilNZhLpYRUi5XARKUKzV4JI5vSbdg8r2LTL4yJShGavBJHM\n6ScbHlYw2ZDHRaQIzV4JIpnTdVrD9HcmF5EiNHsliGROMAAimbmIJC0IuYikdioZCJEi3CCI\nJI6ESCq3hI9IxiCS0gSRIlxEitDslSCSOYhUzkUkaUHIRSS1U8lAiBThBkEkcSREUrklfEQy\nBpGUJogU4SJShGavBJHMQaRyLiJJC0IuIqmdSgZCpAg3CCKJIyGSyi3hI1KT3T7+/d1wISJp\nTRApwr0mkXb9zeD+btQEkZQmiBThIlKEZq8EkcxBpHLuhYrULRh7hEhaE0SKcK9epP4j0ntN\nnvLzKn4nuiBBmZZJI8X/gKKBDANYKCUj5fSZp22CcvLnpOT1FmSCSLtwGe9IShPekSLca35H\n2gV3EElrgkgR7hWLJN1DJKUJIkW41yvSTliGSFoTRIpwr1akwTT4YGsPkZQmiBThXpNIx6MZ\n2tm63WCZT7NXgkjmIFI595JE0oNIShNEinARKUKzV4JI5iBSOReRpAUhF5HUTiUDLSVS6ptu\nEclWCSKZs1WR0t+9jki2ShDJnM2KVK9BJJFmrwSRzNmoSOLXzWYNgEgOkTKyYZEcIsk0eyWI\nZM5GRRK/bjZrAERyiJSRzYokfN1s1gCI5BApI1sVienvOM1eCSKZs12RJg6ASA6RMoJI2VxE\nEpvEuYikdioZCJEi3CCIJI6ESCq3hI9IxiCS0gSRzNycARDJzS1SavrHkCkiHYfOECmr3hOL\nJPwBiBThBtmWSOkdEoaUizQ88NIsUma9JxVpWAsiebk6kfqbwkwQqV6TLVJ/k1XdaUQa1IJI\nXq5MJOWgLUOKRRodeGkVKbfeU4o0qgWRvFyhSA/nE8mViJRT76lFCv8nQKQIN8imRFJObDFk\n0qZduGUUJNy0y6n31Jt24bYpIkW4QTYmUvJUS0MmTTaEn9WDhJMNOfWeerIhnC1BpAg3yLZE\nYvo70UkN09/RttcnUtbzGi9m2gszQ6Sy1+FpRBKKQqQINwgiiSMhksot4COSNYikNEEkMzdn\nAERyiKTjQi4iWbmIJDaJcxEpvxidj0gxbhBEEkdCJJVbwEckaxBJaYJIZm7OAIjkEEnHhVxE\nsnIRSWwS5yJSfjE6H5Fi3CCIJI6ESCq3gI9I1iCS0gSRzNycARDJIZKOC7mIZOUiktgkzkWk\n/GJ0PiLFuEEQSRwJkVRuAR+RrEEkpQkimbk5AyCSQyQdF3IRycpFJLFJnItI+cXofESKcYMg\nkjgSIqncAj4iWYNIShNEMnNzBkAkh0g6LuQikpWLSGKTOBeR8ovR+YgU4wZBJHEkRFK5BXxE\nsgaRlCaIZObmDIBIDpF0XMhFJCsXkcQmcS4i5Rej8xEpxg2CSOJIiKRyC/iIZA0iKU0QyczN\nGQCRHCLpuJCLSFYuIolN4lxEyi9G5yNSjBsEkcSREEnlFvARyRpEUpogkpmbMwAiOUTScSEX\nkaxcRBKbxLmIlF+MzkekGDcIIokjIZLKLeAjkjWIpDRBJDM3ZwBEcoik40IuIlm5iCQ2iXMR\nKb8YnY9IMW6QjYhk+UZxSxDJzC3gI5I1ZxJpr9FDpxIimYvR+YgU4wbZhkh1E0TKLUbnI1KM\nG2QLIlWHJpXW1hBEMnML+IhkzflEcoiUXYzOR6QYN8gWRDqUzqZdbjE6/8pFOk5hXYtI1fGP\nRiRzMTr/qkUavqquRCSmvxEpwp0iUn+zXZES77mIZC5G51+zSKNP3tsUKfmei0jmYnT+tYv0\nsHGR+htE8rk5xej8axZptHdykyKl33MRyVyMzr9ukQbHy2xWpPh7LiKZi9H5Vy3S9qe/k++5\niGQuRudfuUg59a5SpNR7LiKZi9H5iBTjBlmjSEx/I5KVi0hik3AJIo25OcXofESKcYMgktgb\nkVRuAR+RrEEkpQkimbk5AyCSQyQdF3IRycpFJLFJuASRxtycYnQ+IsW4QWYW6Sk/r+J3oguE\nJfqarGIWa5JTb9DJ0jljgGlFzdw2p5Ph5ZCgxF9vGfXyjiT25h1J5RbweUeyBpGUJohk5uYM\ngEgOkXRcyEUkKxeRxCbhEkQac3OK0fmIFOMGQSSxNyKp3AI+IlmDSEoTRDJzcwZAJIdIOi7k\nIpKVi0hik3AJIo25OcXofESKcYMgktgbkVRuAR+RrEEkpQkimbk5AyCSQyQdF3IRycpFJLFJ\nuASRxtycYnQ+IsW4QRBJ7I1IKreAj0jWIJLSBJHM3JwBEMkhko4LuYhk5SKS2CRcgkhjbk4x\nOh+RYtwgiCT2RiSVW8BHJGsQSWmCSGZuzgCI5BBJx4VcRLJyEUlsEi5BpDE3pxidj0gxbhBE\nEnsjksot4COSNYikNEEkMzdnAERyiKTjQi4iWbmIJDYJlyDSmJtTjM5HpBg3CCKJvRFJ5Rbw\nEckaRFKaIJKZmzMAIjlE0nEhF5GsXEQSm4RLEGnMzSlG5yNSjBsEkcTeiKRyC/iIZA0iKU0Q\nyczNGQCRHCLpuJCLSFYuIolNwiWINObmFKPzESnGDYJIYm9EUrkFfESyBpGUJohk5uYMgEgO\nkXRcyEUkKxeRxCbhEkQac3OK0fmIFOMGQSSxNyKp3AI+IlmDSEoTRDJzcwZAJIdIOi7kIpKV\ni0hik3AJIo25OcXofESKcYMgktgbkVRuAR+RrEEkpQkimbk5AyCSQyQdF3IRycpFJLFJuASR\nxtycYnQ+IsW4QRBJ7I1IKreAj0jWIJLSBJHM3JwBEMkhko4LuYhk5SKS2CRcgkhjbk4xOh+R\nYtwgiCT2RiSVW8BHJGsQSWmCSGZuzgCI5BBJx4VcRLJyEUlsEi5BpDE3pxidj0gxbhBEEnsj\nksot4COSNYikNEEkMzdnAERyiKTjQi4iWbmIJDYJlyDSmJtTjM5HpBg3CCKJvRFJ5RbwEcka\nRFKaIJKZmzMAIjlE0nEhF5GsXEQSm4RLEGnMzSlG5yNSjBsEkcTeiKRyC/iIZA0iKU0QyczN\nGQCRHCLpuJCLSFYuIolNwiWINObmFKPzESnGDYJIYm9EUrkFfESyBpGUJohk5uYMgEgOkXRc\nyEUkKxeRxCbhEkQac3OK0fmIFOMGQSSxNyKp3AI+IlmDSEoTRDJzcwZAJIdIOi7kIpKVi0hi\nk3AJIo25OcXofESKcYMgktgbkVRuAR+RrEEkpQkimbk5AyCSQyQdF3IRycpFJLFJuASRxtyc\nYnQ+IsW4QRBJ7I1IKreAj0jWIJLSBJHM3JwBEMkhko4LuYhk5SKS2CRcgkhjbk4xOh+RYtwg\niCT2RiSVW8BHJGsQSWmCSGZuzgCI5BBJx4VcRLJyEUlsEi5BpDE3pxidj0gxbpAckXb7+PeH\ny4pE+sAH2jv//M/tnQ99yGvyyU/6nb7ylRj3y1+OlmDIJz4RwR7z2c+qlE9/Orqq437uc+2d\nnHo//vH2zpe+dPj5+c/73DBf/KKZ39fykY/o3C4f+5iZ7z71KTu3z2c+ozb56Edj3A9+sL3z\nT/8U7f3hD3sLvvCF9s6//EuMGyZDpF1/c7w/XOYKRPpAE1drVMfVGtUZNPlkk2GnrzSRuF9u\nov0hsXyiSarcvUZ1kpRPN4msPHA/18Tl1fvxJq7WqE6tUZ0hN8wXm5j4fS0faZLmdvlYExN/\nr1EdG7fPZ5okm3y0icT9YBNXa1RH7P3hJoMFX2jiao3qWOtFpGMQCZFWKtIH2rQe7U360Ic8\nkz75Sd+kr3xlYNKI++UvTzHpE584mhR54D77WdWkT386ZVLD/VybrHo/3qb16Etf+vznByZF\n6v3iF80m9bV85CMDk5QX0Mc+ZjfpU58amGQV6TOfUU366EcHJo24H2zTeiSa9OEPeyZ9oU3r\nUWvSgiK91+QpL51IT51IT51IfZNOpGOnTqQQ170YMqto04mUaNKJlGjSiZRo0omUVW8n0pMv\nUqpTJ5KB39fSiWQpqhPJ0rYTydK2TydSokknUrimE+mpE0no3YnUL+hEeupEMlbKpt0xbNqx\nabfSTTtEQqR0EMkL098jLtPf5jD9PUrOHricnIi7snLhnp2LSEti4W6WW3Jkw254f+qRDROD\nSHAvgnv+Y+2mBZHgXgQXkZbEwt0sF5GWxMLdLBeRlsTC3SwXkZbEwt0sF5GWxMLdLBeRlsTC\n3SwXkZbEwt0sF5GWxMLdLBeRlsTC3SwXkZbEwt0sF5GWxMLdLBeRlsTC3SwXkZbEwt0sF5GW\nxMLdLBeRlsTC3SwXkZbEwt0sF5GWxMLdLBeRlsTC3SwXkZbEwt0sF5GWxMLdLBeRlsTC3SwX\nkZbEwt0sF5GWxMLdLBeRlsTC3SwXkZbEwt0sF5GWxMLdLBeRlsTC3Sx3ZpEIudLMKlJCsZPS\nZ8/KyqXeEyenXkQaZGXlUu+Jg0iFWVm51HviXI5IhFxJEImQGYJIhMwQRCJkhiASITMEkQiZ\nIacUabfPCfFzpit1TSU3Nyupd22Pb369JxRp199cfLpS11RyXeZa6l3b41tQLyLVWdsTXReJ\nSKcLIk3Jip7orta11Lsb/lxLvYhUGEQ6WXZut/VNUUTqsrYnelX1tsWupd5ukgGRCrKiF2Zf\n5krq5TPSLNWsI7vxzUVnd8hq6kWkWapZRXbH25WUzDvS6YJIpdkNfqyo5LXUi0jTylnJbuxu\nW8mtqeT2SV5JvRzZQAgxBJEImSGIRMgMQSRCZggiETJDEImQGYJIhMwQRCJkhiASITMEkZbJ\nmxfNj/uSx7vte0g1Irwe73d/Le2Gr5Jjpngv3hxvSTqItEjeHl6g9+kXdbJvmzHB44n4HJG8\ntru3x1uSDCItkt3r+vZFVSLSoW+XfJHSSfIOb1Di+xwZB5FOmMe7andf37lvj4B8275Q6x9V\n1a2uqne7233rvWcvHt2bqt6Se1vVG1R3+9um777DXXX72Hbtmu5/Gb3yD7+2uLd3VYcfDjYs\n77a6O/C6ti2v7+qGtyQVRDpdnnf1C/Nuf6c66NT/j394bTerm4W3e3kOrXfPrqrNua9aB57b\nOy8OK5uux6aySDXuTVX1+OFgXnl39dq+7QFw7Lov47m/Jakg0ulyv389N+9BL6vu8/rgdb9/\nvT+717U13Wt2/zZyu7/7onq3fxOoV7zbAw59m8a3nRZ9U3HT7oC7qd6vAVUnUjfYsLxb93xb\nrx23Hfy6d+plf0tSQaTT5ab7j/yuemwXjUTqttQO927q28fqpnnZvq3uq7f7V/6btm9Vy1Wv\ndIcX/uPgt0FakQ6jPb55eXsU6dH5rVtK5bcd/LpffdffklQQ6XTpX7fHF/BIpO72eK/9/bbe\nmNq/r9xJK6uw65jZLrs9bKCFQ4QFjNsOfpU7EiE8QqdLqUgvqufdnbvbufqNoFSkF9XN6zeP\nNpHGbQe/IpI5PEKnS79pZxLpuL2237bbf0p5f7919/6xWbPy1rZp1999Tol03LQbtx38ikjm\n8AidLvXMW/OZPfIZqbs93DvOINSTZ4/1K7kWsfuM1EwMvLRNNhzuvm2nEmIivTxMYFR+28Gv\nfEYyB5FOl8dmyvlGmLWTts/6Oe1m28rV7xh7XY6zdvXKtsOxaVV5hz3sjl5qn5GO09/Dtrvh\nr8zamYNIJ8y728N+0+d+B05CpH4va/3CrdvX23eu34/0eHtYeXij6Jp6M9qvjyLVh1Hcvk2J\nVO8vvus+FbVtD7z+V/YjmYNIS6ToWNVh33N9SDn8D1BxZIMaRFok4+Pl8vueSSSOtTMHkRbJ\n2wmvxbe7tEhVHwMsqzFHf9uDSMtkdE5Rft/ziMT5SPYgEiEzBJEImSGIRMgMQSRCZggiETJD\nEImQGYJIhMwQRCJkhvw/OqNCRztODeYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_data <- train_results[0:80,]\n",
    "\n",
    "ggplot(plot_train_data, aes(x = c(1:nrow(plot_train_data)), y = as.numeric(survived)-1)) +\n",
    "  geom_segment(aes(xend = c(1:nrow(plot_train_data)), yend = predicted), alpha = .2) +\n",
    "  geom_point(aes(color = residuals), size = 2) +\n",
    "  scale_color_gradient2(low = \"blue\", mid = \"white\", high = \"red\") +\n",
    "  guides(color = FALSE) +\n",
    "  geom_point(aes(y = predicted), shape = 1, size = 2) +\n",
    "  geom_hline(yintercept=0.5, linetype=\"dashed\", color = \"red\") +\n",
    "  theme_bw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0dc82f4e-ae74-414e-bf46-91a943a5dc7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your code contains a unicode char which cannot be displayed in your\n",
      "current locale and R will silently convert it to an escaped form when the\n",
      "R kernel executes this code. This can lead to subtle errors if you use\n",
      "such chars to do comparisons. For more information, please see\n",
      "https://github.com/IRkernel/repr/wiki/Problems-with-unicode-on-windowsWarning message:\n",
      "\"`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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GxFJKa/FbkeiWs22C9rh0i5osSu3ZjpnoVEGpeLSIJ7gcj6cdFrNiBSH/Ov9Tr5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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_test_data <- test_results[0:80,]\n",
    "\n",
    "ggplot(plot_test_data, aes(x = c(1:nrow(plot_test_data)), y = as.numeric(survived)-1)) +\n",
    "  geom_segment(aes(xend = c(1:nrow(plot_test_data)), yend = predicted), alpha = .2) +\n",
    "  # Увага, для тестової вибірки є зміни, за точку відліку враховується вихідна змінна\n",
    "  geom_point(aes(color =  as.numeric(survived)), size = 2) +\n",
    "  scale_color_gradient2(low = \"blue\", mid = \"white\", high = \"red\") +\n",
    "  guides(color = FALSE) +\n",
    "  geom_point(aes(y = predicted), shape = 1, size = 2) +\n",
    "  geom_hline(yintercept=0.5, linetype=\"dashed\", color = \"red\") +\n",
    "  theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249472a7-e037-4833-a529-828200308068",
   "metadata": {},
   "source": [
    "Побудуємо confusion matrix для тренувальної вибірки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "84edfa85-20fa-4396-b98c-5354367269ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Train CM:\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   \n",
       "      0   1\n",
       "  0 488  78\n",
       "  1 104 246"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Train CM:\")\n",
    "table(train_results$survived, train_results$survived_05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "804b95b1-d3f9-4967-b76d-dfc928092d72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Test CM:\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   \n",
       "      0   1\n",
       "  0 206  37\n",
       "  1  46 104"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Test CM:\")\n",
    "table(test_results$survived, test_results$survived_05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "436d23f9-6fe9-4d73-bca7-883e35330ebb",
   "metadata": {},
   "source": [
    "Більш детальну статистику по `confusion matrix` можна отримати скориставшись методом `confusionMatrix()` з пакету `caret`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "048f3fc7-de67-4683-a884-62e8a730cc67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction   0   1\n",
       "         0 488  78\n",
       "         1 104 246\n",
       "                                         \n",
       "               Accuracy : 0.8013         \n",
       "                 95% CI : (0.774, 0.8267)\n",
       "    No Information Rate : 0.6463         \n",
       "    P-Value [Acc > NIR] : < 2e-16        \n",
       "                                         \n",
       "                  Kappa : 0.5732         \n",
       "                                         \n",
       " Mcnemar's Test P-Value : 0.06386        \n",
       "                                         \n",
       "            Sensitivity : 0.7593         \n",
       "            Specificity : 0.8243         \n",
       "         Pos Pred Value : 0.7029         \n",
       "         Neg Pred Value : 0.8622         \n",
       "             Prevalence : 0.3537         \n",
       "         Detection Rate : 0.2686         \n",
       "   Detection Prevalence : 0.3821         \n",
       "      Balanced Accuracy : 0.7918         \n",
       "                                         \n",
       "       'Positive' Class : 1              \n",
       "                                         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train\n",
    "caret::confusionMatrix(train_results$survived, train_results$survived_05, positive = \"1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cd0aa51e-0a2c-4868-a6ae-1d8bcdf7ae24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction   0   1\n",
       "         0 206  37\n",
       "         1  46 104\n",
       "                                          \n",
       "               Accuracy : 0.7888          \n",
       "                 95% CI : (0.7451, 0.8281)\n",
       "    No Information Rate : 0.6412          \n",
       "    P-Value [Acc > NIR] : 1.522e-10       \n",
       "                                          \n",
       "                  Kappa : 0.5474          \n",
       "                                          \n",
       " Mcnemar's Test P-Value : 0.3799          \n",
       "                                          \n",
       "            Sensitivity : 0.7376          \n",
       "            Specificity : 0.8175          \n",
       "         Pos Pred Value : 0.6933          \n",
       "         Neg Pred Value : 0.8477          \n",
       "             Prevalence : 0.3588          \n",
       "         Detection Rate : 0.2646          \n",
       "   Detection Prevalence : 0.3817          \n",
       "      Balanced Accuracy : 0.7775          \n",
       "                                          \n",
       "       'Positive' Class : 1               \n",
       "                                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test\n",
    "caret::confusionMatrix(test_results$survived, test_results$survived_05, positive = \"1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15661a7c-6b55-4957-9953-5de06eb17e8e",
   "metadata": {},
   "source": [
    "One of most often qustions is \"What cutoff line should be selected?\". You can have 2 answers:\n",
    "\n",
    "- [x] You can select optimal cutoff using best classification criteria: while you best classify both 1 and 0 events\n",
    "\n",
    "- [x] You can select cutoff than help solve your business tasks, for, example, you need to select only 20% most risky clients, so, set your cutoff to 0.8. Or you need concrete count of clients, for example, 1000 and it can be at 0.94 line sometimes if you have big database. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "954b254b-426f-4b84-9931-b966ba1b2845",
   "metadata": {},
   "source": [
    "Lets try to find optimal cutoff with `InformationValue` package and special method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "562420d9-08e7-4218-bb4c-21beae4e9ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: 'InformationValue'\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:caret':\n",
      "\n",
      "    confusionMatrix, precision, sensitivity, specificity\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "0.556674085642972"
      ],
      "text/latex": [
       "0.556674085642972"
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       "0.556674085642972"
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      "text/plain": [
       "[1] 0.5566741"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "suppressMessages(library(InformationValue))\n",
    "\n",
    "opt_cutoff <- optimalCutoff(train_results$survived, train_results$predicted)\n",
    "opt_cutoff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ed8bef-e584-4e7d-b11a-8edb894ce310",
   "metadata": {},
   "source": [
    "Lest compare confusion matrix for previous 0.5 cutoff and current `opt_cutoff`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "94f5161f-bfdf-4a71-9a26-456dbdc234b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction   0   1\n",
       "         0 206  37\n",
       "         1  46 104\n",
       "                                          \n",
       "               Accuracy : 0.7888          \n",
       "                 95% CI : (0.7451, 0.8281)\n",
       "    No Information Rate : 0.6412          \n",
       "    P-Value [Acc > NIR] : 1.522e-10       \n",
       "                                          \n",
       "                  Kappa : 0.5474          \n",
       "                                          \n",
       " Mcnemar's Test P-Value : 0.3799          \n",
       "                                          \n",
       "            Sensitivity : 0.7376          \n",
       "            Specificity : 0.8175          \n",
       "         Pos Pred Value : 0.6933          \n",
       "         Neg Pred Value : 0.8477          \n",
       "             Prevalence : 0.3588          \n",
       "         Detection Rate : 0.2646          \n",
       "   Detection Prevalence : 0.3817          \n",
       "      Balanced Accuracy : 0.7775          \n",
       "                                          \n",
       "       'Positive' Class : 1               \n",
       "                                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cutoff = 0.5\n",
    "caret::confusionMatrix(test_results$survived, test_results$survived_05, positive = \"1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cf5282dd-9da1-4172-9810-4962184bea32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction   0   1\n",
       "         0 215  28\n",
       "         1  53  97\n",
       "                                          \n",
       "               Accuracy : 0.7939          \n",
       "                 95% CI : (0.7505, 0.8328)\n",
       "    No Information Rate : 0.6819          \n",
       "    P-Value [Acc > NIR] : 5.041e-07       \n",
       "                                          \n",
       "                  Kappa : 0.5489          \n",
       "                                          \n",
       " Mcnemar's Test P-Value : 0.007661        \n",
       "                                          \n",
       "            Sensitivity : 0.7760          \n",
       "            Specificity : 0.8022          \n",
       "         Pos Pred Value : 0.6467          \n",
       "         Neg Pred Value : 0.8848          \n",
       "             Prevalence : 0.3181          \n",
       "         Detection Rate : 0.2468          \n",
       "   Detection Prevalence : 0.3817          \n",
       "      Balanced Accuracy : 0.7891          \n",
       "                                          \n",
       "       'Positive' Class : 1               \n",
       "                                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# opt_cutoff\n",
    "\n",
    "test_results <- test_results %>%\n",
    "    mutate(survived_opt = ifelse(predicted >= opt_cutoff, 1, 0)) # calculate optima cutoff\n",
    "\n",
    "caret::confusionMatrix(test_results$survived, factor(test_results$survived_opt), positive = \"1\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0311a682-73bd-4d34-a45c-eafb5e3e1bd0",
   "metadata": {},
   "source": [
    "`Accuracy : 0.7888` -> `Accuracy : 0.7939`   increased"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1825a0f-d66e-454c-8741-27fcfd5fa894",
   "metadata": {},
   "source": [
    " `Balanced Accuracy : 0.7775` -> `Balanced Accuracy : 0.7891` inreased"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b176f7b7-15f0-47da-a538-9ded9f71417c",
   "metadata": {},
   "source": [
    "In this case if you need classificate more clients as `survived` decrease cutoff, for less `survived` increase it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39bb0df5-1620-479c-973e-b3ba8b2907e6",
   "metadata": {},
   "source": [
    "And the last one characteristics we will use is ROC-curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "29f19851-b404-4ceb-9a67-19c923f60839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.8573"
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      "text/latex": [
       "0.8573"
      ],
      "text/markdown": [
       "0.8573"
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      "text/plain": [
       "[1] 0.8573"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.7146"
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      "text/latex": [
       "0.7146"
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      "text/markdown": [
       "0.7146"
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      "text/plain": [
       "[1] 0.7146"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for train\n",
    "plotROC(train_results$survived, train_results$predicted) # from InformationValue package\n",
    "auroc <- round(AUROC(train_results$survived, train_results$predicted), 4)\n",
    "auroc\n",
    "gini <- 2*auroc - 1\n",
    "gini\n",
    "\n",
    "#if ROC is fully filled and AUROC close to 1 on train, its very big possibility that our model is overfitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0f11026c-6028-402e-81b4-e935e1282051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.8142"
      ],
      "text/latex": [
       "0.8142"
      ],
      "text/markdown": [
       "0.8142"
      ],
      "text/plain": [
       "[1] 0.8142"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.6284"
      ],
      "text/latex": [
       "0.6284"
      ],
      "text/markdown": [
       "0.6284"
      ],
      "text/plain": [
       "[1] 0.6284"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RMzeEhPZXEREiZm6JCsw+DW3h4hYWKGPLMhPlr3ZF1GSJgkzrUDBBASIICQAAGE\nBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGE\nBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGE\nBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIMDhkLQX\nDdAcIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQEC3A1Je8kALRASIICQAAGEBAggJEAA\nIQECCAkQQEiAAEICBBASIMDZkLQXDNAGIQECCAkQQEiAAEICBBASIEAnpAa0FwzQRpt1my0S\nUIFdO0AAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGE\nBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGE\nBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGE\nBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGE\nBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBAwR0lMg/7V9\nGSFhcgYI6Sn5T/q1fRkhYXoICRBASICAx4f0X6DBFO0FA7TSLA7BkIwGW6Qmt2mPqUzVmkpI\nTGWqwFRCYipTBaYSElOZKjCVkJjKVIGprUNKz2awv257ZsO0FiJTmdo+pPtE7lgXTGUqIQlg\nKlMJSQBTmUpIApjKVEISwFSmEpIApjKVkAQwlamEJICpTCUkAUxlKiEJYCpTCUkAU5lKSAKY\nylRCEsBUphKSAKYylZAEMJWphCSAqUwlJAFMZSohCWAqUwlJAFOZSkgCmMpUQhLAVKYSkgCm\nMpWQBDCVqYQkgKlMJSQBTGUqIQlgKlOnFFIDD/1hPXFfhzHV+0pIFbivw5jqfSWkCtzXYUz1\nvhJSBe7rMKZ6XwmpAvd1GFO9r2P6ewHOIiRAACEBAggJEEBIgIDBQ7J/Y3P5b3F2R9V9dfHO\n5u/r03UUy/XJvq9O3tlreq/arK9Dh5T8KnTra/syl5TdVxfvp5FZhvn77JjC/XJ4uZpqkq+i\n/zRaroSUIqRh5O+Xq/cz9HQlpL7K/uV08G6GCsv1Op7l6vC/TwYh9VUakqO78pnlGj/tsC5z\nSe5+Of3U80pI/VX+y+n4fR3Zcn0qucwlhNRX6b587jJXVO6Gun9fy75yCSH1NaYHfLwhlS0d\nM2YAAASjSURBVP1L5RRC6qvsAR/DfWW5iiKkvjL3y7rPDt7VwnIdy/M5OyQH76rhZkjpq8P2\n144esbHua+YVeOW7VWqkyzWt39H7mindoTMbgFkgJEAAIQECCAkQQEiAAEICBBASIICQAAGE\nBAggpMFtvMJFl/3K87zF+tBylHcbtfE97xh/U3L9uWbE0Vub26Wu1reLzSmaE/FX4V1cBz8O\ntQhpaIfiCn/wkxW1bp0vuo3amD95qg5pV7wi5fvX6pACu2vugk3w/SX8U6hBSAPbeIUV/hSs\nneG//MdV25JC/m1tr1RSWGLr7Yu3SL697Eyh1gXn4AKzTdp72/b3c1YIaVCnpVcMaRmuzKGN\n2dFqqy6UO9dfvEXJLaxvd+Edsi7Ye0vzv4V3aX0/Z4WQhhQ8E/KPhfXauuByL4oyPULahtuX\nmpAu0b5e/soDm6R6hDQkz9+VrNe+d8rdzLvuF56/jnfzdsF2bBGvuJftIvnOjEqe2kRzq65P\ntiH+wv7Rye2yPz/7dTGk4K/S9C89T4Q0pHAXrhDSxvP3mR2l4Cn+be0PNxfnhX0g4hwdmFjG\no3IhVV6/jXYgD9YzqmO0J1m3RfKvuX29cDIH7u4gpMEV97SW4bHv3Sm9hbc8Xy/r27/6C89s\nms6b23e+twq+O94OMNxGefZmpfL6c1TA0np2E8dVHdImPEqXXHDaeFFAO/btahHS4Eqesmzi\nzcghusVtnV+ZGvbhqnw1q+7O/GcVfnMKjxIUQ6q53g8LOkc3iH5A7nWizB8IpgQtX7LXx/2c\n7DkoIKTBlT73P+1WfvwqzTX+V/9oVtZVcnOT1yqzR1UMqeb6Tbj52XjWq75+tHUqhJS+AnvI\nXOBv4s3mhSdJtQhpcJUH0S6HtWftkV0zz3GyRxQyo+yQaq4/htu5zPof37qwaxdZbS/W9SfP\n3p3rcnxxRghpcOnaW3xNaW/tkV2FQwq3P8mOYu6ulN7D/AUHz9qcEVItQhpcPqTMGpmtoSqd\n7HdNQ1oH+3YL71y4vnFI5rSMc+WNYCOkweXXwFV6YkO05+VFxwDC50jLzLOeZXSVfVjaDqnm\n+mCDsjpFhzEiC8/edau8h+kFi2TPMDonAhUIaXD51fTg+cmB7/ho8+3lnTCx+IUb04E5YH27\namcdlrZDqrnefLGxo7WP2tXeQ+swXnzXOGp3ByENrrCaroIV3Gx1Luak1eho8/piXkcK/9H3\nveUxOX/04oevKgXfXK5lIZVff0x+UPZn76IXZxuHFIQapbi7c6bs3BHS4IpPLrbJ0YTF+XaL\nfcm5DLcn+iff+qbkBdmS61fxQY29lzsnNt6sNA8pyPp2r1b585qQQUiDK3mWft6ald2P39gX\n3OK8SM+uC8+189fRinvZLIIe7F2yTEgl15/X3u2pzcXLn9cTvZDUIqTj7dViXka6g5AcMNQB\nseLa33kHjT27OwjJAUOFlH0Ryei8YfF5P1I9QnLAQCFdsi8ihbbdtiycsnoPITlgmJDMKT/F\nS7t9+gKf2XAPITlgmJAWpW9jP3Z5cztvRrqLkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCA\nkAAB/wemqeJC5N/SAQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for test\n",
    "plotROC(test_results$survived, test_results$predicted) # from InformationValue package\n",
    "auroc <- round(AUROC(test_results$survived, test_results$predicted), 4)\n",
    "auroc\n",
    "gini <- 2*auroc - 1\n",
    "gini\n",
    "\n",
    "#if ROC is fully filled and AUROC close to 1 on test, its very big possibility that you test sample contains"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0232b76e-2c91-4453-b1a8-1cb00d0e6f7c",
   "metadata": {},
   "source": [
    "You should made your conclusions about test set, because test results shows how model predicting events on data it not know before. Test data is very close to prediction data but did not have target variable, so you can check accuracy on prediction set after some time cheking you experiment on real-world situation."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.1.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}