proposal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "49d40b5e-88cf-4ce5-8f08-20e590eb79ae",
   "metadata": {},
   "source": [
    "## Predicting Heart Failure \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f7493d-7a1e-468f-9385-25638d90225e",
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   "source": [
    "Heart disease is a leading cause of death among adults.  According to the World Health Organization, nearly 18 million people die each year from heart disease (WHO, 2022).  Heart failure is a common result of heart disease and is identified by a variety of factors influencing the heart’s ability to properly function.  \n",
    "\n",
    "- age: age of the patient (years)\n",
    "- creatinine phosphokinase (CPK): level of the CPK enzyme in the blood (mcg/L)\n",
    "- ejection fraction: percentage of blood leaving the heart at each contraction (percentage)\n",
    "- platelets: platelets in the blood (kiloplatelets/mL)\n",
    "- serum creatinine: level of serum creatinine in the blood (mg/dL)\n",
    "- serum sodium: level of serum sodium in the blood (mEq/L) \n",
    "\n",
    "These are some of the clinical features to consider when predicting if a patient will die from heart failure.  A data set compiled by the University of California, Irvine showcases these factors in patients experiencing heart failure and the outcome of survival.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "480ed631-3986-4079-8f9d-560850403ac0",
   "metadata": {},
   "source": [
    "**Question :**\n",
    "Predicting if a patient will die of heart failure based on the clinical features mentioned above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7eeca007-10aa-40e4-a94f-8dd62c939f2c",
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    {
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      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.1 ──\n",
      "\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.6     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
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      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.2.0     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
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      "\n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\n",
      "\n",
      "Attaching package: ‘testthat’\n",
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      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    matches\n",
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      "The following object is masked from ‘package:purrr’:\n",
      "\n",
      "    is_null\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:readr’:\n",
      "\n",
      "    edition_get, local_edition\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:tidyr’:\n",
      "\n",
      "    matches\n",
      "\n",
      "\n",
      "── \u001b[1mAttaching packages\u001b[22m ────────────────────────────────────── tidymodels 1.0.0 ──\n",
      "\n",
      "\u001b[32m✔\u001b[39m \u001b[34mbroom       \u001b[39m 1.0.0     \u001b[32m✔\u001b[39m \u001b[34mrsample     \u001b[39m 1.0.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mdials       \u001b[39m 1.0.0     \u001b[32m✔\u001b[39m \u001b[34mtune        \u001b[39m 1.0.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34minfer       \u001b[39m 1.0.2     \u001b[32m✔\u001b[39m \u001b[34mworkflows   \u001b[39m 1.0.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mmodeldata   \u001b[39m 1.0.0     \u001b[32m✔\u001b[39m \u001b[34mworkflowsets\u001b[39m 1.0.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mparsnip     \u001b[39m 1.0.0     \u001b[32m✔\u001b[39m \u001b[34myardstick   \u001b[39m 1.0.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mrecipes     \u001b[39m 1.0.1     \n",
      "\n",
      "── \u001b[1mConflicts\u001b[22m ───────────────────────────────────────── tidymodels_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mscales\u001b[39m::\u001b[32mdiscard()\u001b[39m   masks \u001b[34mpurrr\u001b[39m::discard()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m     masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mrecipes\u001b[39m::\u001b[32mfixed()\u001b[39m    masks \u001b[34mstringr\u001b[39m::fixed()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mtestthat\u001b[39m::\u001b[32mis_null()\u001b[39m masks \u001b[34mpurrr\u001b[39m::is_null()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m        masks \u001b[34mstats\u001b[39m::lag()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mrsample\u001b[39m::\u001b[32mmatches()\u001b[39m  masks \u001b[34mtestthat\u001b[39m::matches(), \u001b[34mdplyr\u001b[39m::matches(), \u001b[34mtidyr\u001b[39m::matches()\n",
      "\u001b[31m✖\u001b[39m \u001b[34myardstick\u001b[39m::\u001b[32mspec()\u001b[39m   masks \u001b[34mreadr\u001b[39m::spec()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mrecipes\u001b[39m::\u001b[32mstep()\u001b[39m     masks \u001b[34mstats\u001b[39m::step()\n",
      "\u001b[34m•\u001b[39m Dig deeper into tidy modeling with R at \u001b[32mhttps://www.tmwr.org\u001b[39m\n",
      "\n"
     ]
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   ],
   "source": [
    "library(tidyverse)\n",
    "library(testthat)\n",
    "library(digest)\n",
    "library(repr)\n",
    "library(tidymodels)\n",
    "options(repr.matrix.max.rows = 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b37e4dc1-5afe-4278-8bac-614d951eda26",
   "metadata": {},
   "source": [
    "## Reading the dataset"
   ]
  },
  {
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   "execution_count": 2,
   "id": "cb2e7a36-b178-4bfa-b3f7-c49fd54a1f81",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m299\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m13\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[32mdbl\u001b[39m (13): age, anaemia, creatinine_phosphokinase, diabetes, ejection_fractio...\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A spec_tbl_df: 299 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>age</th><th scope=col>anaemia</th><th scope=col>creatinine_phosphokinase</th><th scope=col>diabetes</th><th scope=col>ejection_fraction</th><th scope=col>high_blood_pressure</th><th scope=col>platelets</th><th scope=col>serum_creatinine</th><th scope=col>serum_sodium</th><th scope=col>sex</th><th scope=col>smoking</th><th scope=col>time</th><th scope=col>DEATH_EVENT</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>75</td><td>0</td><td> 582</td><td>0</td><td>20</td><td>1</td><td>265000</td><td>1.9</td><td>130</td><td>1</td><td>0</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><td>55</td><td>0</td><td>7861</td><td>0</td><td>38</td><td>0</td><td>263358</td><td>1.1</td><td>136</td><td>1</td><td>0</td><td>6</td><td>1</td></tr>\n",
       "\t<tr><td>65</td><td>0</td><td> 146</td><td>0</td><td>20</td><td>0</td><td>162000</td><td>1.3</td><td>129</td><td>1</td><td>1</td><td>7</td><td>1</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>45</td><td>0</td><td>2060</td><td>1</td><td>60</td><td>0</td><td>742000</td><td>0.8</td><td>138</td><td>0</td><td>0</td><td>278</td><td>0</td></tr>\n",
       "\t<tr><td>45</td><td>0</td><td>2413</td><td>0</td><td>38</td><td>0</td><td>140000</td><td>1.4</td><td>140</td><td>1</td><td>1</td><td>280</td><td>0</td></tr>\n",
       "\t<tr><td>50</td><td>0</td><td> 196</td><td>0</td><td>45</td><td>0</td><td>395000</td><td>1.6</td><td>136</td><td>1</td><td>1</td><td>285</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A spec\\_tbl\\_df: 299 × 13\n",
       "\\begin{tabular}{lllllllllllll}\n",
       " age & anaemia & creatinine\\_phosphokinase & diabetes & ejection\\_fraction & high\\_blood\\_pressure & platelets & serum\\_creatinine & serum\\_sodium & sex & smoking & time & DEATH\\_EVENT\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 75 & 0 &  582 & 0 & 20 & 1 & 265000 & 1.9 & 130 & 1 & 0 & 4 & 1\\\\\n",
       "\t 55 & 0 & 7861 & 0 & 38 & 0 & 263358 & 1.1 & 136 & 1 & 0 & 6 & 1\\\\\n",
       "\t 65 & 0 &  146 & 0 & 20 & 0 & 162000 & 1.3 & 129 & 1 & 1 & 7 & 1\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 45 & 0 & 2060 & 1 & 60 & 0 & 742000 & 0.8 & 138 & 0 & 0 & 278 & 0\\\\\n",
       "\t 45 & 0 & 2413 & 0 & 38 & 0 & 140000 & 1.4 & 140 & 1 & 1 & 280 & 0\\\\\n",
       "\t 50 & 0 &  196 & 0 & 45 & 0 & 395000 & 1.6 & 136 & 1 & 1 & 285 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A spec_tbl_df: 299 × 13\n",
       "\n",
       "| age &lt;dbl&gt; | anaemia &lt;dbl&gt; | creatinine_phosphokinase &lt;dbl&gt; | diabetes &lt;dbl&gt; | ejection_fraction &lt;dbl&gt; | high_blood_pressure &lt;dbl&gt; | platelets &lt;dbl&gt; | serum_creatinine &lt;dbl&gt; | serum_sodium &lt;dbl&gt; | sex &lt;dbl&gt; | smoking &lt;dbl&gt; | time &lt;dbl&gt; | DEATH_EVENT &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 75 | 0 |  582 | 0 | 20 | 1 | 265000 | 1.9 | 130 | 1 | 0 | 4 | 1 |\n",
       "| 55 | 0 | 7861 | 0 | 38 | 0 | 263358 | 1.1 | 136 | 1 | 0 | 6 | 1 |\n",
       "| 65 | 0 |  146 | 0 | 20 | 0 | 162000 | 1.3 | 129 | 1 | 1 | 7 | 1 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 45 | 0 | 2060 | 1 | 60 | 0 | 742000 | 0.8 | 138 | 0 | 0 | 278 | 0 |\n",
       "| 45 | 0 | 2413 | 0 | 38 | 0 | 140000 | 1.4 | 140 | 1 | 1 | 280 | 0 |\n",
       "| 50 | 0 |  196 | 0 | 45 | 0 | 395000 | 1.6 | 136 | 1 | 1 | 285 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "    age anaemia creatinine_phosphokinase diabetes ejection_fraction\n",
       "1   75  0        582                     0        20               \n",
       "2   55  0       7861                     0        38               \n",
       "3   65  0        146                     0        20               \n",
       "⋮   ⋮   ⋮       ⋮                        ⋮        ⋮                \n",
       "297 45  0       2060                     1        60               \n",
       "298 45  0       2413                     0        38               \n",
       "299 50  0        196                     0        45               \n",
       "    high_blood_pressure platelets serum_creatinine serum_sodium sex smoking\n",
       "1   1                   265000    1.9              130          1   0      \n",
       "2   0                   263358    1.1              136          1   0      \n",
       "3   0                   162000    1.3              129          1   1      \n",
       "⋮   ⋮                   ⋮         ⋮                ⋮            ⋮   ⋮      \n",
       "297 0                   742000    0.8              138          0   0      \n",
       "298 0                   140000    1.4              140          1   1      \n",
       "299 0                   395000    1.6              136          1   1      \n",
       "    time DEATH_EVENT\n",
       "1   4    1          \n",
       "2   6    1          \n",
       "3   7    1          \n",
       "⋮   ⋮    ⋮          \n",
       "297 278  0          \n",
       "298 280  0          \n",
       "299 285  0          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart_failure <- read_csv(\"https://raw.githubusercontent.com/Parcode19/Group_project/main/heart_failure_clinical_records_dataset.csv\")\n",
    "heart_failure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c773b51-e807-4899-aeb6-ad410bc0eb39",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Cleaning and wrangling the data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f900b8c0-dd67-4b5e-9fdc-9cb7e860b9ec",
   "metadata": {},
   "source": [
    "### Selecting the columns to be used for the data analysis\n",
    "\n",
    "We clean the data and only select the columns which are needed for our analysis and answering our question. We also modify the `DEATH_EVENT` column and change the `1` to `Fatal` and `0` to `Not Fatal`. We then use mutate to change the items in the `DEATH_EVENT` column so that it contains factors since our proposal made us realize that we are working on a classifiction problem which requires the target variable to be in the form of factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6545f4cd-34b3-4ba6-8fbc-517405cee2b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 299 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>age</th><th scope=col>creatinine_phosphokinase</th><th scope=col>ejection_fraction</th><th scope=col>platelets</th><th scope=col>serum_creatinine</th><th scope=col>serum_sodium</th><th scope=col>DEATH_EVENT</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>75</td><td> 582</td><td>20</td><td>265000</td><td>1.9</td><td>130</td><td>Fatal</td></tr>\n",
       "\t<tr><td>55</td><td>7861</td><td>38</td><td>263358</td><td>1.1</td><td>136</td><td>Fatal</td></tr>\n",
       "\t<tr><td>65</td><td> 146</td><td>20</td><td>162000</td><td>1.3</td><td>129</td><td>Fatal</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>45</td><td>2060</td><td>60</td><td>742000</td><td>0.8</td><td>138</td><td>Not Fatal</td></tr>\n",
       "\t<tr><td>45</td><td>2413</td><td>38</td><td>140000</td><td>1.4</td><td>140</td><td>Not Fatal</td></tr>\n",
       "\t<tr><td>50</td><td> 196</td><td>45</td><td>395000</td><td>1.6</td><td>136</td><td>Not Fatal</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 299 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " age & creatinine\\_phosphokinase & ejection\\_fraction & platelets & serum\\_creatinine & serum\\_sodium & DEATH\\_EVENT\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t 75 &  582 & 20 & 265000 & 1.9 & 130 & Fatal\\\\\n",
       "\t 55 & 7861 & 38 & 263358 & 1.1 & 136 & Fatal\\\\\n",
       "\t 65 &  146 & 20 & 162000 & 1.3 & 129 & Fatal\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 45 & 2060 & 60 & 742000 & 0.8 & 138 & Not Fatal\\\\\n",
       "\t 45 & 2413 & 38 & 140000 & 1.4 & 140 & Not Fatal\\\\\n",
       "\t 50 &  196 & 45 & 395000 & 1.6 & 136 & Not Fatal\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 299 × 7\n",
       "\n",
       "| age &lt;dbl&gt; | creatinine_phosphokinase &lt;dbl&gt; | ejection_fraction &lt;dbl&gt; | platelets &lt;dbl&gt; | serum_creatinine &lt;dbl&gt; | serum_sodium &lt;dbl&gt; | DEATH_EVENT &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 75 |  582 | 20 | 265000 | 1.9 | 130 | Fatal |\n",
       "| 55 | 7861 | 38 | 263358 | 1.1 | 136 | Fatal |\n",
       "| 65 |  146 | 20 | 162000 | 1.3 | 129 | Fatal |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 45 | 2060 | 60 | 742000 | 0.8 | 138 | Not Fatal |\n",
       "| 45 | 2413 | 38 | 140000 | 1.4 | 140 | Not Fatal |\n",
       "| 50 |  196 | 45 | 395000 | 1.6 | 136 | Not Fatal |\n",
       "\n"
      ],
      "text/plain": [
       "    age creatinine_phosphokinase ejection_fraction platelets serum_creatinine\n",
       "1   75   582                     20                265000    1.9             \n",
       "2   55  7861                     38                263358    1.1             \n",
       "3   65   146                     20                162000    1.3             \n",
       "⋮   ⋮   ⋮                        ⋮                 ⋮         ⋮               \n",
       "297 45  2060                     60                742000    0.8             \n",
       "298 45  2413                     38                140000    1.4             \n",
       "299 50   196                     45                395000    1.6             \n",
       "    serum_sodium DEATH_EVENT\n",
       "1   130          Fatal      \n",
       "2   136          Fatal      \n",
       "3   129          Fatal      \n",
       "⋮   ⋮            ⋮          \n",
       "297 138          Not Fatal  \n",
       "298 140          Not Fatal  \n",
       "299 136          Not Fatal  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 299 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>age</th><th scope=col>creatinine_phosphokinase</th><th scope=col>ejection_fraction</th><th scope=col>platelets</th><th scope=col>serum_creatinine</th><th scope=col>serum_sodium</th><th scope=col>DEATH_EVENT</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>75</td><td> 582</td><td>20</td><td>265000</td><td>1.9</td><td>130</td><td>Fatal</td></tr>\n",
       "\t<tr><td>55</td><td>7861</td><td>38</td><td>263358</td><td>1.1</td><td>136</td><td>Fatal</td></tr>\n",
       "\t<tr><td>65</td><td> 146</td><td>20</td><td>162000</td><td>1.3</td><td>129</td><td>Fatal</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>45</td><td>2060</td><td>60</td><td>742000</td><td>0.8</td><td>138</td><td>Not Fatal</td></tr>\n",
       "\t<tr><td>45</td><td>2413</td><td>38</td><td>140000</td><td>1.4</td><td>140</td><td>Not Fatal</td></tr>\n",
       "\t<tr><td>50</td><td> 196</td><td>45</td><td>395000</td><td>1.6</td><td>136</td><td>Not Fatal</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 299 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " age & creatinine\\_phosphokinase & ejection\\_fraction & platelets & serum\\_creatinine & serum\\_sodium & DEATH\\_EVENT\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t 75 &  582 & 20 & 265000 & 1.9 & 130 & Fatal\\\\\n",
       "\t 55 & 7861 & 38 & 263358 & 1.1 & 136 & Fatal\\\\\n",
       "\t 65 &  146 & 20 & 162000 & 1.3 & 129 & Fatal\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 45 & 2060 & 60 & 742000 & 0.8 & 138 & Not Fatal\\\\\n",
       "\t 45 & 2413 & 38 & 140000 & 1.4 & 140 & Not Fatal\\\\\n",
       "\t 50 &  196 & 45 & 395000 & 1.6 & 136 & Not Fatal\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 299 × 7\n",
       "\n",
       "| age &lt;dbl&gt; | creatinine_phosphokinase &lt;dbl&gt; | ejection_fraction &lt;dbl&gt; | platelets &lt;dbl&gt; | serum_creatinine &lt;dbl&gt; | serum_sodium &lt;dbl&gt; | DEATH_EVENT &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 75 |  582 | 20 | 265000 | 1.9 | 130 | Fatal |\n",
       "| 55 | 7861 | 38 | 263358 | 1.1 | 136 | Fatal |\n",
       "| 65 |  146 | 20 | 162000 | 1.3 | 129 | Fatal |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 45 | 2060 | 60 | 742000 | 0.8 | 138 | Not Fatal |\n",
       "| 45 | 2413 | 38 | 140000 | 1.4 | 140 | Not Fatal |\n",
       "| 50 |  196 | 45 | 395000 | 1.6 | 136 | Not Fatal |\n",
       "\n"
      ],
      "text/plain": [
       "    age creatinine_phosphokinase ejection_fraction platelets serum_creatinine\n",
       "1   75   582                     20                265000    1.9             \n",
       "2   55  7861                     38                263358    1.1             \n",
       "3   65   146                     20                162000    1.3             \n",
       "⋮   ⋮   ⋮                        ⋮                 ⋮         ⋮               \n",
       "297 45  2060                     60                742000    0.8             \n",
       "298 45  2413                     38                140000    1.4             \n",
       "299 50   196                     45                395000    1.6             \n",
       "    serum_sodium DEATH_EVENT\n",
       "1   130          Fatal      \n",
       "2   136          Fatal      \n",
       "3   129          Fatal      \n",
       "⋮   ⋮            ⋮          \n",
       "297 138          Not Fatal  \n",
       "298 140          Not Fatal  \n",
       "299 136          Not Fatal  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart_data <- heart_failure |>\n",
    "    select(age, \n",
    "           creatinine_phosphokinase, \n",
    "           ejection_fraction, \n",
    "           platelets, \n",
    "           serum_creatinine, \n",
    "           serum_sodium, \n",
    "           DEATH_EVENT) \n",
    "\n",
    "heart_data$DEATH_EVENT <- ifelse(heart_data$DEATH_EVENT == 0, \"Not Fatal\", \"Fatal\") \n",
    "heart_data \n",
    "\n",
    "heart_data <- heart_data  |>\n",
    "      mutate(DEATH_EVENT = as_factor(DEATH_EVENT))\n",
    "heart_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc705478-557e-4882-bf9f-16554f7c39b3",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Optimising the number of predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aaf5be98-ce97-4706-a15b-1eb8b8d57b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "names <- colnames(heart_data |> select(-DEATH_EVENT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2f0e1b90-a87c-4687-a3cc-a3ecf2e483c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>size</th><th scope=col>model_string</th><th scope=col>accuracy</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>DEATH_EVENT ~ ejection_fraction                                                                     </td><td>0.6820571</td></tr>\n",
       "\t<tr><td>2</td><td>DEATH_EVENT ~ ejection_fraction+serum_creatinine                                                    </td><td>0.7662415</td></tr>\n",
       "\t<tr><td>3</td><td>DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium                                       </td><td>0.7763527</td></tr>\n",
       "\t<tr><td>4</td><td>DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium+creatinine_phosphokinase              </td><td>0.7699046</td></tr>\n",
       "\t<tr><td>5</td><td>DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium+creatinine_phosphokinase+platelets    </td><td>0.7562925</td></tr>\n",
       "\t<tr><td>6</td><td>DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium+creatinine_phosphokinase+platelets+age</td><td>0.7464583</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " size & model\\_string & accuracy\\\\\n",
       " <int> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & DEATH\\_EVENT \\textasciitilde{} ejection\\_fraction                                                                      & 0.6820571\\\\\n",
       "\t 2 & DEATH\\_EVENT \\textasciitilde{} ejection\\_fraction+serum\\_creatinine                                                     & 0.7662415\\\\\n",
       "\t 3 & DEATH\\_EVENT \\textasciitilde{} ejection\\_fraction+serum\\_creatinine+serum\\_sodium                                        & 0.7763527\\\\\n",
       "\t 4 & DEATH\\_EVENT \\textasciitilde{} ejection\\_fraction+serum\\_creatinine+serum\\_sodium+creatinine\\_phosphokinase               & 0.7699046\\\\\n",
       "\t 5 & DEATH\\_EVENT \\textasciitilde{} ejection\\_fraction+serum\\_creatinine+serum\\_sodium+creatinine\\_phosphokinase+platelets     & 0.7562925\\\\\n",
       "\t 6 & DEATH\\_EVENT \\textasciitilde{} ejection\\_fraction+serum\\_creatinine+serum\\_sodium+creatinine\\_phosphokinase+platelets+age & 0.7464583\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| size &lt;int&gt; | model_string &lt;chr&gt; | accuracy &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 1 | DEATH_EVENT ~ ejection_fraction                                                                      | 0.6820571 |\n",
       "| 2 | DEATH_EVENT ~ ejection_fraction+serum_creatinine                                                     | 0.7662415 |\n",
       "| 3 | DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium                                        | 0.7763527 |\n",
       "| 4 | DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium+creatinine_phosphokinase               | 0.7699046 |\n",
       "| 5 | DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium+creatinine_phosphokinase+platelets     | 0.7562925 |\n",
       "| 6 | DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium+creatinine_phosphokinase+platelets+age | 0.7464583 |\n",
       "\n"
      ],
      "text/plain": [
       "  size\n",
       "1 1   \n",
       "2 2   \n",
       "3 3   \n",
       "4 4   \n",
       "5 5   \n",
       "6 6   \n",
       "  model_string                                                                                        \n",
       "1 DEATH_EVENT ~ ejection_fraction                                                                     \n",
       "2 DEATH_EVENT ~ ejection_fraction+serum_creatinine                                                    \n",
       "3 DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium                                       \n",
       "4 DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium+creatinine_phosphokinase              \n",
       "5 DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium+creatinine_phosphokinase+platelets    \n",
       "6 DEATH_EVENT ~ ejection_fraction+serum_creatinine+serum_sodium+creatinine_phosphokinase+platelets+age\n",
       "  accuracy \n",
       "1 0.6820571\n",
       "2 0.7662415\n",
       "3 0.7763527\n",
       "4 0.7699046\n",
       "5 0.7562925\n",
       "6 0.7464583"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create an empty tibble to store the results\n",
    "accuracies <- tibble(size = integer(), \n",
    "                     model_string = character(), \n",
    "                     accuracy = numeric())\n",
    "\n",
    "# create a model specification\n",
    "knn_spec <- nearest_neighbor(weight_func = \"rectangular\", \n",
    "                             neighbors = tune()) |>\n",
    "     set_engine(\"kknn\") |>\n",
    "     set_mode(\"classification\")\n",
    "\n",
    "# create a 5-fold cross-validation object\n",
    "heart_vfold <- vfold_cv(heart_data, v = 5, strata = DEATH_EVENT)\n",
    "\n",
    "# store the total number of predictors\n",
    "n_total <- length(names)\n",
    "\n",
    "# stores selected predictors\n",
    "selected <- c()\n",
    "\n",
    "# for every size from 1 to the total number of predictors\n",
    "for (i in 1:n_total) {\n",
    "    # for every predictor still not added yet\n",
    "    accs <- list()\n",
    "    models <- list()\n",
    "    for (j in 1:length(names)) {\n",
    "        # create a model string for this combination of predictors\n",
    "        preds_new <- c(selected, names[[j]])\n",
    "        model_string <- paste(\"DEATH_EVENT\", \"~\", paste(preds_new, collapse=\"+\"))\n",
    "\n",
    "        # create a recipe from the model string\n",
    "        heart_recipe <- recipe(as.formula(model_string), \n",
    "                                data = heart_data) |>\n",
    "                          step_scale(all_predictors()) |>\n",
    "                          step_center(all_predictors())\n",
    "\n",
    "        # tune the KNN classifier with these predictors, \n",
    "        # and collect the accuracy for the best K\n",
    "        acc <- workflow() |>\n",
    "          add_recipe(heart_recipe) |>\n",
    "          add_model(knn_spec) |>\n",
    "          tune_grid(resamples = heart_vfold, grid = 10) |>\n",
    "          collect_metrics() |>\n",
    "          filter(.metric == \"accuracy\") |>\n",
    "          summarize(mx = max(mean))\n",
    "        acc <- acc$mx |> unlist()\n",
    "\n",
    "        # add this result to the dataframe\n",
    "        accs[[j]] <- acc\n",
    "        models[[j]] <- model_string\n",
    "    }\n",
    "    jstar <- which.max(unlist(accs))\n",
    "    accuracies <- accuracies |> \n",
    "      add_row(size = i, \n",
    "              model_string = models[[jstar]], \n",
    "              accuracy = accs[[jstar]])\n",
    "    selected <- c(selected, names[[jstar]])\n",
    "    names <- names[-jstar]\n",
    "}\n",
    "accuracies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875bd8fd-8950-4241-ab2e-bbe775c42474",
   "metadata": {},
   "source": [
    "!!!!!!!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8ff98ae-595b-4c3e-a6c2-0b1eadf83937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 299 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>ejection_fraction</th><th scope=col>serum_creatinine</th><th scope=col>DEATH_EVENT</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>20</td><td>1.9</td><td>Fatal</td></tr>\n",
       "\t<tr><td>38</td><td>1.1</td><td>Fatal</td></tr>\n",
       "\t<tr><td>20</td><td>1.3</td><td>Fatal</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>60</td><td>0.8</td><td>Not Fatal</td></tr>\n",
       "\t<tr><td>38</td><td>1.4</td><td>Not Fatal</td></tr>\n",
       "\t<tr><td>45</td><td>1.6</td><td>Not Fatal</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 299 × 3\n",
       "\\begin{tabular}{lll}\n",
       " ejection\\_fraction & serum\\_creatinine & DEATH\\_EVENT\\\\\n",
       " <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t 20 & 1.9 & Fatal\\\\\n",
       "\t 38 & 1.1 & Fatal\\\\\n",
       "\t 20 & 1.3 & Fatal\\\\\n",
       "\t ⋮ & ⋮ & ⋮\\\\\n",
       "\t 60 & 0.8 & Not Fatal\\\\\n",
       "\t 38 & 1.4 & Not Fatal\\\\\n",
       "\t 45 & 1.6 & Not Fatal\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 299 × 3\n",
       "\n",
       "| ejection_fraction &lt;dbl&gt; | serum_creatinine &lt;dbl&gt; | DEATH_EVENT &lt;fct&gt; |\n",
       "|---|---|---|\n",
       "| 20 | 1.9 | Fatal |\n",
       "| 38 | 1.1 | Fatal |\n",
       "| 20 | 1.3 | Fatal |\n",
       "| ⋮ | ⋮ | ⋮ |\n",
       "| 60 | 0.8 | Not Fatal |\n",
       "| 38 | 1.4 | Not Fatal |\n",
       "| 45 | 1.6 | Not Fatal |\n",
       "\n"
      ],
      "text/plain": [
       "    ejection_fraction serum_creatinine DEATH_EVENT\n",
       "1   20                1.9              Fatal      \n",
       "2   38                1.1              Fatal      \n",
       "3   20                1.3              Fatal      \n",
       "⋮   ⋮                 ⋮                ⋮          \n",
       "297 60                0.8              Not Fatal  \n",
       "298 38                1.4              Not Fatal  \n",
       "299 45                1.6              Not Fatal  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart_new_data <- select (heart_data, ejection_fraction, serum_creatinine, DEATH_EVENT)\n",
    "heart_new_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acedb2e3-57cf-4611-b27d-0b954695d6b8",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Scaling the dataset for equal contributions of each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20841b12-766e-42b8-8499-3ada4be88ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 299 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>ejection_fraction</th><th scope=col>serum_creatinine</th><th scope=col>DEATH_EVENT</th><th scope=col>scaled_ejection_fraction</th><th scope=col>scaled_serum_creatinine</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>20</td><td>1.9</td><td>Fatal</td><td>-1.527997920</td><td> 0.48923681</td></tr>\n",
       "\t<tr><td>38</td><td>1.1</td><td>Fatal</td><td>-0.007064906</td><td>-0.28407611</td></tr>\n",
       "\t<tr><td>20</td><td>1.3</td><td>Fatal</td><td>-1.527997920</td><td>-0.09074788</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>60</td><td>0.8</td><td>Not Fatal</td><td> 1.851853222</td><td>-0.574068459</td></tr>\n",
       "\t<tr><td>38</td><td>1.4</td><td>Not Fatal</td><td>-0.007064906</td><td> 0.005916232</td></tr>\n",
       "\t<tr><td>45</td><td>1.6</td><td>Not Fatal</td><td> 0.584409044</td><td> 0.199244462</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 299 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " ejection\\_fraction & serum\\_creatinine & DEATH\\_EVENT & scaled\\_ejection\\_fraction & scaled\\_serum\\_creatinine\\\\\n",
       " <dbl> & <dbl> & <fct> & <dbl{[},1{]}> & <dbl{[},1{]}>\\\\\n",
       "\\hline\n",
       "\t 20 & 1.9 & Fatal & -1.527997920 &  0.48923681\\\\\n",
       "\t 38 & 1.1 & Fatal & -0.007064906 & -0.28407611\\\\\n",
       "\t 20 & 1.3 & Fatal & -1.527997920 & -0.09074788\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 60 & 0.8 & Not Fatal &  1.851853222 & -0.574068459\\\\\n",
       "\t 38 & 1.4 & Not Fatal & -0.007064906 &  0.005916232\\\\\n",
       "\t 45 & 1.6 & Not Fatal &  0.584409044 &  0.199244462\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 299 × 5\n",
       "\n",
       "| ejection_fraction &lt;dbl&gt; | serum_creatinine &lt;dbl&gt; | DEATH_EVENT &lt;fct&gt; | scaled_ejection_fraction &lt;dbl[,1]&gt; | scaled_serum_creatinine &lt;dbl[,1]&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 20 | 1.9 | Fatal | -1.527997920 |  0.48923681 |\n",
       "| 38 | 1.1 | Fatal | -0.007064906 | -0.28407611 |\n",
       "| 20 | 1.3 | Fatal | -1.527997920 | -0.09074788 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 60 | 0.8 | Not Fatal |  1.851853222 | -0.574068459 |\n",
       "| 38 | 1.4 | Not Fatal | -0.007064906 |  0.005916232 |\n",
       "| 45 | 1.6 | Not Fatal |  0.584409044 |  0.199244462 |\n",
       "\n"
      ],
      "text/plain": [
       "    ejection_fraction serum_creatinine DEATH_EVENT scaled_ejection_fraction\n",
       "1   20                1.9              Fatal       -1.527997920            \n",
       "2   38                1.1              Fatal       -0.007064906            \n",
       "3   20                1.3              Fatal       -1.527997920            \n",
       "⋮   ⋮                 ⋮                ⋮           ⋮                       \n",
       "297 60                0.8              Not Fatal    1.851853222            \n",
       "298 38                1.4              Not Fatal   -0.007064906            \n",
       "299 45                1.6              Not Fatal    0.584409044            \n",
       "    scaled_serum_creatinine\n",
       "1    0.48923681            \n",
       "2   -0.28407611            \n",
       "3   -0.09074788            \n",
       "⋮   ⋮                      \n",
       "297 -0.574068459           \n",
       "298  0.005916232           \n",
       "299  0.199244462           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart_subset <- heart_new_data |>\n",
    "    mutate(scaled_ejection_fraction = scale(ejection_fraction, center = TRUE),\n",
    "           scaled_serum_creatinine = scale(serum_creatinine, center = TRUE))\n",
    "heart_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e07c50f-1e64-421a-947e-7447bcf14cf0",
   "metadata": {},
   "source": [
    "## Splitting the table into training and testing dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "addb4023-a441-4db9-b0a7-ba321cfe66aa",
   "metadata": {},
   "source": [
    "We split the scaled data into a training set and a testing set. \n",
    "\n",
    "We will only use the training set when building the classifier. Then, we will use the testing data set to evaluate the accuracy of our classifier. We split the data so that 75% of the data ends up in the training set and 25% in the testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "73cdf0b9-5f4c-4df6-aa04-8f79383712c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 224 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>ejection_fraction</th><th scope=col>serum_creatinine</th><th scope=col>DEATH_EVENT</th><th scope=col>scaled_ejection_fraction</th><th scope=col>scaled_serum_creatinine</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>20</td><td>1.9</td><td>Fatal</td><td>-1.527997920</td><td> 0.48923681</td></tr>\n",
       "\t<tr><td>38</td><td>1.1</td><td>Fatal</td><td>-0.007064906</td><td>-0.28407611</td></tr>\n",
       "\t<tr><td>20</td><td>1.3</td><td>Fatal</td><td>-1.527997920</td><td>-0.09074788</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>38</td><td>1.2</td><td>Not Fatal</td><td>-0.007064906</td><td>-0.1874120</td></tr>\n",
       "\t<tr><td>60</td><td>0.8</td><td>Not Fatal</td><td> 1.851853222</td><td>-0.5740685</td></tr>\n",
       "\t<tr><td>45</td><td>1.6</td><td>Not Fatal</td><td> 0.584409044</td><td> 0.1992445</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 224 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " ejection\\_fraction & serum\\_creatinine & DEATH\\_EVENT & scaled\\_ejection\\_fraction & scaled\\_serum\\_creatinine\\\\\n",
       " <dbl> & <dbl> & <fct> & <dbl{[},1{]}> & <dbl{[},1{]}>\\\\\n",
       "\\hline\n",
       "\t 20 & 1.9 & Fatal & -1.527997920 &  0.48923681\\\\\n",
       "\t 38 & 1.1 & Fatal & -0.007064906 & -0.28407611\\\\\n",
       "\t 20 & 1.3 & Fatal & -1.527997920 & -0.09074788\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 38 & 1.2 & Not Fatal & -0.007064906 & -0.1874120\\\\\n",
       "\t 60 & 0.8 & Not Fatal &  1.851853222 & -0.5740685\\\\\n",
       "\t 45 & 1.6 & Not Fatal &  0.584409044 &  0.1992445\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 224 × 5\n",
       "\n",
       "| ejection_fraction &lt;dbl&gt; | serum_creatinine &lt;dbl&gt; | DEATH_EVENT &lt;fct&gt; | scaled_ejection_fraction &lt;dbl[,1]&gt; | scaled_serum_creatinine &lt;dbl[,1]&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 20 | 1.9 | Fatal | -1.527997920 |  0.48923681 |\n",
       "| 38 | 1.1 | Fatal | -0.007064906 | -0.28407611 |\n",
       "| 20 | 1.3 | Fatal | -1.527997920 | -0.09074788 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 38 | 1.2 | Not Fatal | -0.007064906 | -0.1874120 |\n",
       "| 60 | 0.8 | Not Fatal |  1.851853222 | -0.5740685 |\n",
       "| 45 | 1.6 | Not Fatal |  0.584409044 |  0.1992445 |\n",
       "\n"
      ],
      "text/plain": [
       "    ejection_fraction serum_creatinine DEATH_EVENT scaled_ejection_fraction\n",
       "1   20                1.9              Fatal       -1.527997920            \n",
       "2   38                1.1              Fatal       -0.007064906            \n",
       "3   20                1.3              Fatal       -1.527997920            \n",
       "⋮   ⋮                 ⋮                ⋮           ⋮                       \n",
       "222 38                1.2              Not Fatal   -0.007064906            \n",
       "223 60                0.8              Not Fatal    1.851853222            \n",
       "224 45                1.6              Not Fatal    0.584409044            \n",
       "    scaled_serum_creatinine\n",
       "1    0.48923681            \n",
       "2   -0.28407611            \n",
       "3   -0.09074788            \n",
       "⋮   ⋮                      \n",
       "222 -0.1874120             \n",
       "223 -0.5740685             \n",
       "224  0.1992445             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 75 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>ejection_fraction</th><th scope=col>serum_creatinine</th><th scope=col>DEATH_EVENT</th><th scope=col>scaled_ejection_fraction</th><th scope=col>scaled_serum_creatinine</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>65</td><td>1.5</td><td>Fatal</td><td> 2.274334615</td><td> 0.1025803</td></tr>\n",
       "\t<tr><td>38</td><td>4.0</td><td>Fatal</td><td>-0.007064906</td><td> 2.5191832</td></tr>\n",
       "\t<tr><td>25</td><td>0.9</td><td>Fatal</td><td>-1.105516528</td><td>-0.4774043</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>30</td><td>3.8</td><td>Not Fatal</td><td>-0.683035135</td><td>2.325854996</td></tr>\n",
       "\t<tr><td>35</td><td>1.7</td><td>Not Fatal</td><td>-0.260553742</td><td>0.295908577</td></tr>\n",
       "\t<tr><td>38</td><td>1.4</td><td>Not Fatal</td><td>-0.007064906</td><td>0.005916232</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 75 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " ejection\\_fraction & serum\\_creatinine & DEATH\\_EVENT & scaled\\_ejection\\_fraction & scaled\\_serum\\_creatinine\\\\\n",
       " <dbl> & <dbl> & <fct> & <dbl{[},1{]}> & <dbl{[},1{]}>\\\\\n",
       "\\hline\n",
       "\t 65 & 1.5 & Fatal &  2.274334615 &  0.1025803\\\\\n",
       "\t 38 & 4.0 & Fatal & -0.007064906 &  2.5191832\\\\\n",
       "\t 25 & 0.9 & Fatal & -1.105516528 & -0.4774043\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 30 & 3.8 & Not Fatal & -0.683035135 & 2.325854996\\\\\n",
       "\t 35 & 1.7 & Not Fatal & -0.260553742 & 0.295908577\\\\\n",
       "\t 38 & 1.4 & Not Fatal & -0.007064906 & 0.005916232\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 75 × 5\n",
       "\n",
       "| ejection_fraction &lt;dbl&gt; | serum_creatinine &lt;dbl&gt; | DEATH_EVENT &lt;fct&gt; | scaled_ejection_fraction &lt;dbl[,1]&gt; | scaled_serum_creatinine &lt;dbl[,1]&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 65 | 1.5 | Fatal |  2.274334615 |  0.1025803 |\n",
       "| 38 | 4.0 | Fatal | -0.007064906 |  2.5191832 |\n",
       "| 25 | 0.9 | Fatal | -1.105516528 | -0.4774043 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 30 | 3.8 | Not Fatal | -0.683035135 | 2.325854996 |\n",
       "| 35 | 1.7 | Not Fatal | -0.260553742 | 0.295908577 |\n",
       "| 38 | 1.4 | Not Fatal | -0.007064906 | 0.005916232 |\n",
       "\n"
      ],
      "text/plain": [
       "   ejection_fraction serum_creatinine DEATH_EVENT scaled_ejection_fraction\n",
       "1  65                1.5              Fatal        2.274334615            \n",
       "2  38                4.0              Fatal       -0.007064906            \n",
       "3  25                0.9              Fatal       -1.105516528            \n",
       "⋮  ⋮                 ⋮                ⋮           ⋮                       \n",
       "73 30                3.8              Not Fatal   -0.683035135            \n",
       "74 35                1.7              Not Fatal   -0.260553742            \n",
       "75 38                1.4              Not Fatal   -0.007064906            \n",
       "   scaled_serum_creatinine\n",
       "1   0.1025803             \n",
       "2   2.5191832             \n",
       "3  -0.4774043             \n",
       "⋮  ⋮                      \n",
       "73 2.325854996            \n",
       "74 0.295908577            \n",
       "75 0.005916232            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the seed. Don't remove this!\n",
    "set.seed(2020)\n",
    "\n",
    "heart_data_split <- initial_split(heart_subset, prop = 0.75, strata = DEATH_EVENT)  \n",
    "heart_data_train <- training(heart_data_split)   \n",
    "heart_data_test <- testing(heart_data_split)\n",
    "\n",
    "heart_data_train\n",
    "heart_data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b70263e-011c-4e22-b215-0c40121f663e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>DEATH_EVENT</th><th scope=col>n</th><th scope=col>percent</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Fatal    </td><td> 72</td><td>32.14286</td></tr>\n",
       "\t<tr><td>Not Fatal</td><td>152</td><td>67.85714</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 3\n",
       "\\begin{tabular}{lll}\n",
       " DEATH\\_EVENT & n & percent\\\\\n",
       " <fct> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Fatal     &  72 & 32.14286\\\\\n",
       "\t Not Fatal & 152 & 67.85714\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 3\n",
       "\n",
       "| DEATH_EVENT &lt;fct&gt; | n &lt;int&gt; | percent &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| Fatal     |  72 | 32.14286 |\n",
       "| Not Fatal | 152 | 67.85714 |\n",
       "\n"
      ],
      "text/plain": [
       "  DEATH_EVENT n   percent \n",
       "1 Fatal        72 32.14286\n",
       "2 Not Fatal   152 67.85714"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart_proportions <- heart_data_train |>\n",
    "                      group_by(DEATH_EVENT) |>\n",
    "                      summarize(n = n()) |>\n",
    "                      mutate(percent = 100*n/nrow(heart_data_train))\n",
    "\n",
    "heart_proportions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b69e1ed5-06c2-4663-8141-7a5b999cebb5",
   "metadata": {},
   "source": [
    "## Evaluating accuracy of the classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d272525a-ea1a-46ba-b1d5-1fc3c4c0776e",
   "metadata": {},
   "source": [
    "### Preprocessing the data\n",
    "We preprocess the data to standardize it and create `heart_recipe` using only the training data set. We set our target variable and also the variables which we want to use as predictors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c33670f7-8aef-4a4f-a16d-feb8eabe370f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Recipe\n",
       "\n",
       "Inputs:\n",
       "\n",
       "      role #variables\n",
       "   outcome          1\n",
       " predictor          2\n",
       "\n",
       "Operations:\n",
       "\n",
       "Scaling for all_predictors()\n",
       "Centering for all_predictors()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(2020) # DO NOT REMOVE\n",
    "\n",
    "heart_recipe <- recipe(DEATH_EVENT ~ scaled_ejection_fraction + scaled_serum_creatinine , data = heart_data_train) |>\n",
    "            step_scale(all_predictors()) |>\n",
    "            step_center(all_predictors())\n",
    "heart_recipe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aee3eea6-f04a-4e9d-a328-f78fab73ffea",
   "metadata": {},
   "source": [
    "### Training the classifier\n",
    "\n",
    "We first create a model specification for the knn classification. We then build the classifier by combining the model specification and the recipe into a workflow and fit the model to our training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "14b8358d-466b-41ba-9990-3f4c0a45eb3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
       "\u001b[3mModel:\u001b[23m nearest_neighbor()\n",
       "\n",
       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
       "2 Recipe Steps\n",
       "\n",
       "• step_scale()\n",
       "• step_center()\n",
       "\n",
       "── Model ───────────────────────────────────────────────────────────────────────\n",
       "\n",
       "Call:\n",
       "kknn::train.kknn(formula = ..y ~ ., data = data, ks = min_rows(3,     data, 5), kernel = ~\"rectangular\")\n",
       "\n",
       "Type of response variable: nominal\n",
       "Minimal misclassification: 0.2678571\n",
       "Best kernel: rectangular\n",
       "Best k: 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(2020) # DO NOT REMOVE\n",
    "\n",
    "knn_spec <- nearest_neighbor(weight_func = \"rectangular\", neighbors = 3) |>\n",
    "     set_engine(\"kknn\") |>\n",
    "     set_mode(\"classification\")\n",
    "\n",
    "knn_fit <- workflow() |>\n",
    "  add_recipe(heart_recipe) |>\n",
    "  add_model(knn_spec) |>\n",
    "  fit(data = heart_data_train)\n",
    "\n",
    "knn_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e8f019c-3599-437c-9e1a-ef4d0d90372f",
   "metadata": {},
   "source": [
    "### Predicting labels in the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7054b032-4bc8-45fb-9e24-29b0ebd36d17",
   "metadata": {},
   "source": [
    "We use the classifier which we just created to predict the labels for our test data. The `DEATH_EVENT` column contains the true labels while the `.pred_class` columns contains the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c59f580b-86b8-410f-ac65-f7ebf48ce5c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 75 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>.pred_class</th><th scope=col>ejection_fraction</th><th scope=col>serum_creatinine</th><th scope=col>DEATH_EVENT</th><th scope=col>scaled_ejection_fraction</th><th scope=col>scaled_serum_creatinine</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th><th scope=col>&lt;dbl[,1]&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Not Fatal</td><td>65</td><td>1.5</td><td>Fatal</td><td> 2.274334615</td><td> 0.1025803</td></tr>\n",
       "\t<tr><td>Fatal    </td><td>38</td><td>4.0</td><td>Fatal</td><td>-0.007064906</td><td> 2.5191832</td></tr>\n",
       "\t<tr><td>Fatal    </td><td>25</td><td>0.9</td><td>Fatal</td><td>-1.105516528</td><td>-0.4774043</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>Not Fatal</td><td>30</td><td>3.8</td><td>Not Fatal</td><td>-0.683035135</td><td>2.325854996</td></tr>\n",
       "\t<tr><td>Not Fatal</td><td>35</td><td>1.7</td><td>Not Fatal</td><td>-0.260553742</td><td>0.295908577</td></tr>\n",
       "\t<tr><td>Not Fatal</td><td>38</td><td>1.4</td><td>Not Fatal</td><td>-0.007064906</td><td>0.005916232</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 75 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " .pred\\_class & ejection\\_fraction & serum\\_creatinine & DEATH\\_EVENT & scaled\\_ejection\\_fraction & scaled\\_serum\\_creatinine\\\\\n",
       " <fct> & <dbl> & <dbl> & <fct> & <dbl{[},1{]}> & <dbl{[},1{]}>\\\\\n",
       "\\hline\n",
       "\t Not Fatal & 65 & 1.5 & Fatal &  2.274334615 &  0.1025803\\\\\n",
       "\t Fatal     & 38 & 4.0 & Fatal & -0.007064906 &  2.5191832\\\\\n",
       "\t Fatal     & 25 & 0.9 & Fatal & -1.105516528 & -0.4774043\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t Not Fatal & 30 & 3.8 & Not Fatal & -0.683035135 & 2.325854996\\\\\n",
       "\t Not Fatal & 35 & 1.7 & Not Fatal & -0.260553742 & 0.295908577\\\\\n",
       "\t Not Fatal & 38 & 1.4 & Not Fatal & -0.007064906 & 0.005916232\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 75 × 6\n",
       "\n",
       "| .pred_class &lt;fct&gt; | ejection_fraction &lt;dbl&gt; | serum_creatinine &lt;dbl&gt; | DEATH_EVENT &lt;fct&gt; | scaled_ejection_fraction &lt;dbl[,1]&gt; | scaled_serum_creatinine &lt;dbl[,1]&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Not Fatal | 65 | 1.5 | Fatal |  2.274334615 |  0.1025803 |\n",
       "| Fatal     | 38 | 4.0 | Fatal | -0.007064906 |  2.5191832 |\n",
       "| Fatal     | 25 | 0.9 | Fatal | -1.105516528 | -0.4774043 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| Not Fatal | 30 | 3.8 | Not Fatal | -0.683035135 | 2.325854996 |\n",
       "| Not Fatal | 35 | 1.7 | Not Fatal | -0.260553742 | 0.295908577 |\n",
       "| Not Fatal | 38 | 1.4 | Not Fatal | -0.007064906 | 0.005916232 |\n",
       "\n"
      ],
      "text/plain": [
       "   .pred_class ejection_fraction serum_creatinine DEATH_EVENT\n",
       "1  Not Fatal   65                1.5              Fatal      \n",
       "2  Fatal       38                4.0              Fatal      \n",
       "3  Fatal       25                0.9              Fatal      \n",
       "⋮  ⋮           ⋮                 ⋮                ⋮          \n",
       "73 Not Fatal   30                3.8              Not Fatal  \n",
       "74 Not Fatal   35                1.7              Not Fatal  \n",
       "75 Not Fatal   38                1.4              Not Fatal  \n",
       "   scaled_ejection_fraction scaled_serum_creatinine\n",
       "1   2.274334615              0.1025803             \n",
       "2  -0.007064906              2.5191832             \n",
       "3  -1.105516528             -0.4774043             \n",
       "⋮  ⋮                        ⋮                      \n",
       "73 -0.683035135             2.325854996            \n",
       "74 -0.260553742             0.295908577            \n",
       "75 -0.007064906             0.005916232            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(2020) # DO NOT REMOVE\n",
    "\n",
    "heart_test_predictions <- predict(knn_fit, heart_data_test) |>\n",
    "  bind_cols(heart_data_test)\n",
    "\n",
    "heart_test_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce018883-25e6-41a7-967a-a82ab977ee10",
   "metadata": {},
   "source": [
    "## Compute the accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "191446cf-9020-4cac-82ca-3c41efc7a368",
   "metadata": {},
   "source": [
    "Finally, we can access the classifiers accuracy using the metrics function.\n",
    "\n",
    "We can also look at the confusion matrix for the classifier which shows the table of predicted lables and true labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ab47d1b6-25aa-454e-b5dd-6b3549445353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>accuracy</td><td>binary</td><td>0.6933333</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 3\n",
       "\\begin{tabular}{lll}\n",
       " .metric & .estimator & .estimate\\\\\n",
       " <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t accuracy & binary & 0.6933333\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 3\n",
       "\n",
       "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| accuracy | binary | 0.6933333 |\n",
       "\n"
      ],
      "text/plain": [
       "  .metric  .estimator .estimate\n",
       "1 accuracy binary     0.6933333"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "           Truth\n",
       "Prediction  Fatal Not Fatal\n",
       "  Fatal        17        16\n",
       "  Not Fatal     7        35"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(2020) # DO NOT REMOVE\n",
    "\n",
    "heart_test_predictions |>\n",
    "  metrics(truth = DEATH_EVENT, estimate = .pred_class) |>\n",
    "  filter(.metric == \"accuracy\")\n",
    "\n",
    "confusion <- heart_test_predictions |>\n",
    "             conf_mat(truth = DEATH_EVENT, estimate = .pred_class)\n",
    "confusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ecaaf9-93ff-4b27-9e77-af40b35776b0",
   "metadata": {},
   "source": [
    "We can see that our classifier has the accuracy of around 69.33% and that it predicted 17 labels corectly as Fatal and 35 labels correctly as Not Fatal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c605ff-c017-47e8-8073-5e2d7e49178b",
   "metadata": {},
   "source": [
    "## Tuning the classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f972c427-db75-4044-a2b0-4b3772fdbe18",
   "metadata": {},
   "source": [
    "### Cross validation\n",
    "We will now perform a 5 fold cross validation where we split our training dataset into c evenly sized chunks and then iteratively use 1 chunk as the validation set for evaluation and the remaining c-1 chunks as the training sets.\n",
    "\n",
    "Here, we will set c to be 5 and perform a 5 fold cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "69f7b7c9-bfa2-47f2-b279-402a0f4af2c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "set.seed(2020) # DO NOT REMOVE\n",
    "\n",
    "heart_vfold <- vfold_cv(heart_data_train, v = 5, strata = DEATH_EVENT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d0cd73-3032-4fd5-83bd-d3e333298477",
   "metadata": {},
   "source": [
    "Then we will create our data analysis workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e47bdb25-6511-4e9e-9c4a-9763f938afa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>mean</th><th scope=col>n</th><th scope=col>std_err</th><th scope=col>.config</th></tr>\n",
       "\t<tr><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;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>accuracy</td><td>binary</td><td>0.6697628</td><td>5</td><td>0.01861383</td><td>Preprocessor1_Model1</td></tr>\n",
       "\t<tr><td>roc_auc </td><td>binary</td><td>0.7025883</td><td>5</td><td>0.02036722</td><td>Preprocessor1_Model1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " .metric & .estimator & mean & n & std\\_err & .config\\\\\n",
       " <chr> & <chr> & <dbl> & <int> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t accuracy & binary & 0.6697628 & 5 & 0.01861383 & Preprocessor1\\_Model1\\\\\n",
       "\t roc\\_auc  & binary & 0.7025883 & 5 & 0.02036722 & Preprocessor1\\_Model1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 6\n",
       "\n",
       "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | mean &lt;dbl&gt; | n &lt;int&gt; | std_err &lt;dbl&gt; | .config &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| accuracy | binary | 0.6697628 | 5 | 0.01861383 | Preprocessor1_Model1 |\n",
       "| roc_auc  | binary | 0.7025883 | 5 | 0.02036722 | Preprocessor1_Model1 |\n",
       "\n"
      ],
      "text/plain": [
       "  .metric  .estimator mean      n std_err    .config             \n",
       "1 accuracy binary     0.6697628 5 0.01861383 Preprocessor1_Model1\n",
       "2 roc_auc  binary     0.7025883 5 0.02036722 Preprocessor1_Model1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(2020) # DO NOT REMOVE\n",
    "\n",
    "heart_recipe <- recipe(DEATH_EVENT ~ scaled_ejection_fraction + scaled_serum_creatinine , data = heart_data_train) |>\n",
    "            step_scale(all_predictors()) |>\n",
    "            step_center(all_predictors())\n",
    "\n",
    "\n",
    "# fit the knn model (we can reuse the old knn_spec model from before)\n",
    "knn_fit <- workflow() |>\n",
    "  add_recipe(heart_recipe) |>\n",
    "  add_model(knn_spec) |>\n",
    "  fit_resamples(resamples = heart_vfold)\n",
    "\n",
    "knn_fit |> \n",
    "  collect_metrics() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da290f52-bb36-44de-a578-b6afa98ffd4f",
   "metadata": {},
   "source": [
    "The collect_metrics function is used to aggregate the mean and standard error of the classifier’s validation accuracy across the folds. So if your estimated mean is 0.67 and standard error is 0.018, you can expect the true average accuracy of the classifier to be somewhere roughly between 65.2 % and 68.8 % (although it may fall outside this range)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fbf8581-25fc-4715-9eeb-fc1a52488b6b",
   "metadata": {},
   "source": [
    "### Parameter value selection\n",
    "With cross-validation, we have established a prediction accuracy for our classifier. \n",
    "\n",
    "If we had to improve our classifier, we have to change the parameter: number of neighbours, $K$. \n",
    "We can use cross-validation to calculate an accuracy for each value of $K$ in a reasonable range, and then pick the value of $K$ that gives us the best accuracy. \n",
    "\n",
    "Using `tune()`, each parameter in the model can be adjusted rather than given a specific value. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa46a2a0-f8fa-4726-bdfd-ea37b4701d5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "K-Nearest Neighbor Model Specification (classification)\n",
       "\n",
       "Main Arguments:\n",
       "  neighbors = tune()\n",
       "  weight_func = rectangular\n",
       "\n",
       "Computational engine: kknn \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(2020) # DO NOT REMOVE\n",
    "\n",
    "knn_tune <- nearest_neighbor(weight_func = \"rectangular\", \n",
    "                             neighbors = tune()) |>\n",
    "      set_engine(\"kknn\") |>\n",
    "      set_mode(\"classification\")\n",
    "knn_tune"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1edd29b-8be2-492f-be53-2f6827ff50ea",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23cb5263-0ca3-4b35-9ec6-0cca3fad3a2c",
   "metadata": {},
   "source": [
    "We then create a workflow analysis which combines our `heart_recipe` and our new `knn_tune` model specification. \n",
    "\n",
    "We use the `tune_grid` function to fit the model for each value in a range of parameter values.\n",
    "We then aggregate the mean and standard error by using the `collect_metrics` function.\n",
    "\n",
    "Finally, we filter for `accuracy` from the `.metric` column of `knn_results`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1535ffbc-7e39-407c-bfb7-63061e167d8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 20 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>neighbors</th><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>mean</th><th scope=col>n</th><th scope=col>std_err</th><th scope=col>.config</th></tr>\n",
       "\t<tr><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;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> 1</td><td>accuracy</td><td>binary</td><td>0.6513834</td><td>5</td><td>0.02578540</td><td>Preprocessor1_Model01</td></tr>\n",
       "\t<tr><td> 6</td><td>accuracy</td><td>binary</td><td>0.6788538</td><td>5</td><td>0.01319087</td><td>Preprocessor1_Model02</td></tr>\n",
       "\t<tr><td>11</td><td>accuracy</td><td>binary</td><td>0.7063241</td><td>5</td><td>0.03448942</td><td>Preprocessor1_Model03</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>86</td><td>accuracy</td><td>binary</td><td>0.6786561</td><td>5</td><td>0.001936356</td><td>Preprocessor1_Model18</td></tr>\n",
       "\t<tr><td>91</td><td>accuracy</td><td>binary</td><td>0.6786561</td><td>5</td><td>0.001936356</td><td>Preprocessor1_Model19</td></tr>\n",
       "\t<tr><td>96</td><td>accuracy</td><td>binary</td><td>0.6786561</td><td>5</td><td>0.001936356</td><td>Preprocessor1_Model20</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 20 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " neighbors & .metric & .estimator & mean & n & std\\_err & .config\\\\\n",
       " <dbl> & <chr> & <chr> & <dbl> & <int> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t  1 & accuracy & binary & 0.6513834 & 5 & 0.02578540 & Preprocessor1\\_Model01\\\\\n",
       "\t  6 & accuracy & binary & 0.6788538 & 5 & 0.01319087 & Preprocessor1\\_Model02\\\\\n",
       "\t 11 & accuracy & binary & 0.7063241 & 5 & 0.03448942 & Preprocessor1\\_Model03\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 86 & accuracy & binary & 0.6786561 & 5 & 0.001936356 & Preprocessor1\\_Model18\\\\\n",
       "\t 91 & accuracy & binary & 0.6786561 & 5 & 0.001936356 & Preprocessor1\\_Model19\\\\\n",
       "\t 96 & accuracy & binary & 0.6786561 & 5 & 0.001936356 & Preprocessor1\\_Model20\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 20 × 7\n",
       "\n",
       "| neighbors &lt;dbl&gt; | .metric &lt;chr&gt; | .estimator &lt;chr&gt; | mean &lt;dbl&gt; | n &lt;int&gt; | std_err &lt;dbl&gt; | .config &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "|  1 | accuracy | binary | 0.6513834 | 5 | 0.02578540 | Preprocessor1_Model01 |\n",
       "|  6 | accuracy | binary | 0.6788538 | 5 | 0.01319087 | Preprocessor1_Model02 |\n",
       "| 11 | accuracy | binary | 0.7063241 | 5 | 0.03448942 | Preprocessor1_Model03 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 86 | accuracy | binary | 0.6786561 | 5 | 0.001936356 | Preprocessor1_Model18 |\n",
       "| 91 | accuracy | binary | 0.6786561 | 5 | 0.001936356 | Preprocessor1_Model19 |\n",
       "| 96 | accuracy | binary | 0.6786561 | 5 | 0.001936356 | Preprocessor1_Model20 |\n",
       "\n"
      ],
      "text/plain": [
       "   neighbors .metric  .estimator mean      n std_err     .config              \n",
       "1   1        accuracy binary     0.6513834 5 0.02578540  Preprocessor1_Model01\n",
       "2   6        accuracy binary     0.6788538 5 0.01319087  Preprocessor1_Model02\n",
       "3  11        accuracy binary     0.7063241 5 0.03448942  Preprocessor1_Model03\n",
       "⋮  ⋮         ⋮        ⋮          ⋮         ⋮ ⋮           ⋮                    \n",
       "18 86        accuracy binary     0.6786561 5 0.001936356 Preprocessor1_Model18\n",
       "19 91        accuracy binary     0.6786561 5 0.001936356 Preprocessor1_Model19\n",
       "20 96        accuracy binary     0.6786561 5 0.001936356 Preprocessor1_Model20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>neighbors</th><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>mean</th><th scope=col>n</th><th scope=col>std_err</th><th scope=col>.config</th></tr>\n",
       "\t<tr><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;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>21</td><td>accuracy</td><td>binary</td><td>0.7328063</td><td>5</td><td>0.02593492</td><td>Preprocessor1_Model05</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " neighbors & .metric & .estimator & mean & n & std\\_err & .config\\\\\n",
       " <dbl> & <chr> & <chr> & <dbl> & <int> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t 21 & accuracy & binary & 0.7328063 & 5 & 0.02593492 & Preprocessor1\\_Model05\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 7\n",
       "\n",
       "| neighbors &lt;dbl&gt; | .metric &lt;chr&gt; | .estimator &lt;chr&gt; | mean &lt;dbl&gt; | n &lt;int&gt; | std_err &lt;dbl&gt; | .config &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 21 | accuracy | binary | 0.7328063 | 5 | 0.02593492 | Preprocessor1_Model05 |\n",
       "\n"
      ],
      "text/plain": [
       "  neighbors .metric  .estimator mean      n std_err    .config              \n",
       "1 21        accuracy binary     0.7328063 5 0.02593492 Preprocessor1_Model05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(2020) # DO NOT REMOVE\n",
    "\n",
    "k_vals <- tibble(neighbors = seq(from = 1, to = 100, by = 5))\n",
    "\n",
    "knn_results <- workflow() |>\n",
    "  add_recipe(heart_recipe) |>\n",
    "  add_model(knn_tune) |>\n",
    "  tune_grid(resamples = heart_vfold, grid = k_vals) |>\n",
    "  collect_metrics() \n",
    "\n",
    "accuracies <- knn_results |>\n",
    "  filter(.metric == \"accuracy\")\n",
    "accuracies\n",
    "\n",
    "k_best <- accuracies |>\n",
    "    filter(mean==max(mean))\n",
    "k_best"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2334c7c2-0a74-48c0-82cb-6945fe1df487",
   "metadata": {},
   "source": [
    "To find the best value of the number of neighboors, \n",
    "\n",
    "we create a line plot using the `accuracies` dataset with `neighbors` on the x-axis and the `mean` on the y-axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "29ffece2-bc17-4b0c-856d-e78892de7632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PHS+60CEs8vkCbRvY5mdg2kT6l/0jFtit/FtgxpvfR+q4DE8wuk+2mCo5ldA2lL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\nKej/AYSG4oOZSANeAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_vs_k <- ggplot(accuracies, aes(x = neighbors, y = mean)) +\n",
    "  geom_point() +\n",
    "  geom_line() +\n",
    "  labs(x = \"Neighbors\", y = \"Accuracy Estimate\") + \n",
    "  theme(text = element_text(size = 12)) \n",
    "\n",
    "accuracy_vs_k"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c93c25-c60c-4e59-8afc-8ea7a564fb12",
   "metadata": {},
   "source": [
    "From the plot above, we can see that $K = 21$ provides the highest accuracy. $K$ values that are smaller or larger than this result in a reduced accuracy estimate.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef7772d7-f651-4df1-b43d-20eb60fb64d4",
   "metadata": {},
   "source": [
    "## Building the classifier using the best K "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c8440192-7088-413d-9090-9fbb80301854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
       "\u001b[3mModel:\u001b[23m nearest_neighbor()\n",
       "\n",
       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
       "2 Recipe Steps\n",
       "\n",
       "• step_scale()\n",
       "• step_center()\n",
       "\n",
       "── Model ───────────────────────────────────────────────────────────────────────\n",
       "\n",
       "Call:\n",
       "kknn::train.kknn(formula = ..y ~ ., data = data, ks = min_rows(21,     data, 5), kernel = ~\"rectangular\")\n",
       "\n",
       "Type of response variable: nominal\n",
       "Minimal misclassification: 0.2633929\n",
       "Best kernel: rectangular\n",
       "Best k: 21"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(2020) # DO NOT REMOVE\n",
    "\n",
    "knn_spec <- nearest_neighbor(weight_func = \"rectangular\", neighbors = 21) |>\n",
    "     set_engine(\"kknn\") |>\n",
    "     set_mode(\"classification\")\n",
    "\n",
    "knn_fit <- workflow() |>\n",
    "  add_recipe(heart_recipe) |>\n",
    "  add_model(knn_spec) |>\n",
    "  fit(data = heart_data_train)\n",
    "\n",
    "knn_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad74e0d4-6dcd-4d64-8352-7dc9241f2be0",
   "metadata": {},
   "source": [
    "## Predicting new observations using the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6d4bc5e8-405e-4729-9572-46ec5a6ac13a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pwyxm0wBDAUAKhZjfcQVpWTkyMi1V4pWlV0dLSIZGdn1y8WAAAISZqm8eXTnnF/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\n5sPbINfSmhCq2RWpt8ApCJLc7nk4wCvY2mt2+Ti/4TVzNC/ITXLXaWeR8yG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pZmNBpbWloqKyszMjLGPVZcM4lmz4Ae30pI\nx0PlvFYdCUnYpUuXPvjgA0JIYGDgyZMn+bptom9Zzoqc7ncyNjY2OjrK9lwBAJAgdAgB3ARb\nuRQUFMSS9U/oQLraZzLI5XJaMBgMfHVMJhPrC7GQpp2WlpaKigpCyIwZMxobG20XR5lMJs4D\nJdgKrB/4+PFjB5OXpKSkZGVl7d+/nxBSV1c3bodQdDOJxgasBMajONtl8kIVHZKw7u7udevW\njY6Oenp6arVagfnJom9ZzoqcXuoeHh7oDQKAxCHLKICbiIqKooV79+6JO7C3t9fJMf1fREQE\nLbS3t/PV6ejooAWFQiEwp3GKO3PmDC1kZGRwpspgP9OKBFuBPdYLbCDR1dXFMogIY0kj//nn\nn3Eri24m0ZRKJS3o9Xq+Om1tbbYvTl6ookMSYDQa09PTe3p6CCGfffbZm2++KVBZ9C3LKZGb\nzWY6NVpg3ikAgESgQwjgJtg6t4aGBqPRaP+BKpWKFq5cucI5F6u1tTUnJycnJ2ffvn3iYlu8\neDEt6HS6sbExzjo6nc6q8nTEEqWwzQCssM3frUiwFdi2eJxZPWpqakJDQyMjI1NSUviitcQ+\nhG9tpyXRzSQa+yK+TQ7+/PNPlljI0uSFKjokAZs3b6bb+q1fv55uPyhA9C3LKZH39PTQwVXb\nrUQBAKQGHUIANxEdHU0HSfr7+2k6B1sXLlyYP3/+tm3bLPPvLVy4MDY2lhDS29t76tQp26OO\nHj1aWlpaWlrKOZvRnrU6ixYtio6OJoR0d3ezEQ8rLOa0tLRxP3DKYtMg+/r6bN/t7u7+6quv\naNnqvEmwFdhuE5zP7iqVis76+/3337/99lvhj7K85jknH1qdH9HNJNqSJUvovMTm5mbOAbGy\nsjLOPwRMXqiiQ+Kzf//+gwcPEkLi4uLKysrGrS/6luWUyNnIqvCuJwAAUoAOIYD70Gg0tLBj\nx44bN25YvdvR0ZGdnd3W1rZv377R0VHLt7Zv304LmzdvttqVq6mpiT5xenl5ZWVlsdfZgjR7\npnvJZDL2Ffn5+bZdmtLS0vPnzxNClEqlPWk/pyw2qa+6utrqAV2v1ycnJ0dERNAdRIaGhqyy\nLEqtFdgmcpzjPEqlkl3P+fn5O3fu5EtKeePGjcTExD/++IMQMm/evDVr1rC3+M6PI80kjlwu\nT0pKIoSYzeYtW7ZYfem1a9eKi4tZ0pSnE6rokDhdvXo1Pz+fEKJQKAQSyVgRd8tySuTsquPb\nzBAAQDqQVAbAlf766y/2SDSu8PDwrVu3ClTYuHFjVVVVZWWlwWBYunSpWq1OSkpSKBQ9PT0N\nDQ1lZWV0b4P33nuPTVCkcnJytFptfX29Xq9/8cUXs7Ky4uPjh4eHdTrdDz/8QB/Fdu3aNW/e\nPHYIHWsihGi12vDw8JiYGL1e/+GHH7LMgVZyc3OPHz9eV1fX1tamUqm2b9+ekJDg6+vb2dlZ\nWVn5448/EkI8PT3Ly8un7wJCQsjKlSsDAwP7+vru3r2blJSk0WgiIiJ6e3tra2tLSkqMRuO1\na9fy8vIuXbpECCksLMzLy1MoFM899xyRXiuwjd3pJENbH3/88a+//lpVVWU2m/fs2bN3797X\nX389Li4uJCTEy8vr77//fvjwYVNTU0tLC60fFBR07Ngxy5SbfOfHkWYS7ZNPPqmpqTGZTGfO\nnElISMjJyYmMjDQYDPX19eXl5Uqlcvny5bYDZZMaqriQbPX19aWnp9Npn2q1+v79+/fv3+er\nHBMTw5azir5lOR45mx29ZMmScX8gAICbMwPAU3fu3DkR/1tfeukl9glLly6lL7a2tlp+stFo\nVKvVfBsGeHh4bN269cmTJ7YhDQwMrFq1ivMomUxWWFhoVf/Jkye2i5pGR0cFYhscHExPT+f7\ndYGBgT/99JNtYKtXr6YVGhoaOE9mXl4erXDo0CG7zr7F+Y+NjbXzEPvjqa6u5syZOWvWrAsX\nLpjNZquNLnbu3MmOlVQrGI1GhUJBCPHz8xsaGuKsYzKZiouL2UCfgNTU1Pb2dvvPj+hmcuRU\nfP/995wJLYODgxsbGwsKCug/T5w4YXnU5IUqOiSrq+v69eu2n8Dniy++sPwo0bcscZFTY2Nj\ndOmgt7f3o0eP+E4OAIBEYMoogFvx9vY+cODAzZs3t2zZsmjRIrlc7uXlJZfLVSrVtm3bfvnl\nl71793JOpvL396+urq6pqdm4cWNUVNTMmTP9/Pyio6PVavWtW7eKioqs6nt6etbW1q5duzYk\nJMTHxycsLOytt97iG5iiZs6cWVlZefHixXfffTcmJsbf3/+ZZ56ZPXv2ihUrvvzyy46OjpSU\nFGeeCxdZtWrV1atXN2zYMGfOHC8vrxkzZsTHx+/evfvevXtvvPEGISQ3N7ewsDA8PNzHx2f+\n/PksPSaRWCt4e3vTHsvw8HBNTQ1nHQ8Pj4KCgs7OziNHjmRmZqpUquDgYB8fH3pVR0VFpaam\nfvrpp7/99tupU6dY1kpG4Pw40kyiZWZm3r59Ozs7e+7cub6+vrNmzXrhhRcKCgpu3rz52muv\nBQUF0Wo0+yUzqaGKC8mJRN+yHIn88uXLNFvPihUr7PlzAwCAe5OZJ7JkHACmiGXLljU2NhJC\nWltbFyxY4OpwAMTQ6XR0wl5SUlJtba2rwwGp2LRp0+HDhwkhVVVVbBwVAECyMEIIAACukZCQ\nsGzZMkLI2bNn7dxvEMBBDx480Gq1hJDo6OjU1FRXhwMA4HroEAJMSzTXAiHE19fXtZEAOGLP\nnj2EELPZvGvXLlfHApLw0Ucf0fw3n3/+ufD8agAAicCUUYDpZ3h4OCQkZGhoiBDy33//WaZV\nBJh2MjMzjxw5Qgipq6tLTEx0dTjgzm7fvv3yyy+bTKbExMS6ujpXhwMAMCWgQwgwnZw/f76/\nv//w4cOnT58mhERFRbW3t7s6KACH9Pf3x8fHd3V1RUZG3rlz59lnn3V1ROCeHj9+vHjx4ubm\nZrlcfufOnfDwcFdHBAAwJWCyBMB0otFo1q1bR3uDhBCWWh1g+pLL5Vqt1sfHp7OzU61Wuzoc\ncFsajaa5uVkmkx06dAi9QQAABh1CgOkkICCAEOLt7b1w4cKSkpLc3FxXRwTgBK+++mpZWRkh\npKKiYvfu3a4OB9zQwYMH6YaNxcXFa9ascXU4AABTCKaMAgAAAAAASBRGCAEAAAAAACQKHUIA\nAAAAAACJQocQAAAAAABAotAhBAAAAAAAkCh0CAEAAAAAACQKHUIAAAAAAACJQocQAAAAAABA\notAhBAAAAAAAkKj/AS0EHiUuHnofAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height = 8, repr.plot.width = 10)\n",
    "\n",
    "plot_1 <- heart_subset |>\n",
    "    ggplot(aes(x = scaled_ejection_fraction, y = scaled_serum_creatinine, col = as_factor(DEATH_EVENT))) +\n",
    "        geom_point() +\n",
    "        labs(x = \"Ejection Fraction (Standardized)\", y = \"Serum Creatinine (Standardized)\", color = \"Fatality\")+\n",
    "        theme(text = element_text(size = 20)) \n",
    "\n",
    "        \n",
    "plot_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e1f29ae6-0004-42de-813d-6828c0f4640b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xd7vd6qR7pcruXLl4tIs2bNahMOAAAAABA8fhTCYcOGiUhycvK999576tSpyoad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37l23jsPhWLx48eOPP+5wOCRJUtYaBVow\npefH7XZXVFQoxy6XSzlWupi8vyqdP95fldtc769KFufb+dNYym2odyeJ+GXfiH/ePTP+z2I0\nGjUajSzLfnrVJEnyziX88L619cPn3+WH535aqnXoN30mhG+Xo6P/xe701t630X6o1ep6N92p\ny/ve3Y8Ab9N9OrK8SQ67Oft18XMqFa/TalQqIUTNdTfV9uwTSKiNoP7xuGnZe3XLq+55yJ2Y\nVLc8VJRnHMqx8jREOfZ+uuH9R6T86XmOPZPbffq9vY+981LvvuKqqipP/613n6ryIMYThnJc\nXl4eeE945HnnqN6/0t7XB5+/WZ9j775Tz4XCO0f1SZW981KDweA59u7O9e7CrTdVBgCEXBAJ\n4YABA6ZPn56dnb1r166srKxLL720b9++yluLFy9et27d4cOHN2/e7Pk/9Z577unfv3/oQwYa\nxZOYeVI1n0TO7XYrPRVWq1W5U5RlWRlup/xWl5WVeco9dUISm3LXpfQJJCUlJSYmKjdASgKg\nHCt3aUqJcqz0LSi3Tcqxcjul9D/4dMJEgMlkcrvdgYyfjCJ1fp6pJK9uuX30ZY6Lh0Y+nlBR\nFRaYddqd+QXPb/76qzNnhRBdLYnT+/edNGJMGE96nt9/qbxUhDMhNBqNzaKbS6PRmEwmz1Bh\n7+RQufJ4jr2HE3sP8VWuM8qxd17qPa7Yu/PfO0f1SY89x95hKE+m6oZUXFwcqotbyPnkqAkJ\nCZIkabXa5OTk5ORki8WSkpKSmprqObZYLBkZGRG+GAJA8xLcFIjXXnuttLR05cqVsixv3bp1\n69atSvmSJUt8ak6cOPH1118PTYy4kPgMhvT+6hkD6fmq9KepVCqr1Wqz2ZSH90o/m6cXztMv\nF5LwPKlXYmJifHx8x44dPemZJ4XzTuSUNM+T7HmOlUlKym2N94QlhJvbXH8PmxwXUJdpzJLj\n4refPXfF0pXtE+L/MHxInFb74aHc+z79/EzbzFnDRobrpOdZQqa5fzPDRKvVev+x+595EVO8\nx/QqT9CUY+9Lq/eYXu8c1bs71ztH9Z4E652jenfheueo4pepsk9ITqczNzfXzz9BpVIpyaEn\nS0xLS7NYLMleUlJSAhzCAAAtjBTIag0+3n///RdeeOHgwYP1vjtgwIDHHnvstttua3JswSkq\nKorwGZOSkhqxakKL4XQ6y8vLKysrvUc8KvlY3ZzNJ6NTpr35zHzzvm9oImUMkjIqyXOg0Ov1\nyigmpVyZ8KOUeL4qfWveX5UkkDUk/GsWPYRCCONHyzS5h7xL3AmJtqkz5Z/HtjVTN464dO+J\nk3vv/m262SSEcMvy0PeW51orc3NzAxl/2wiSy2lavEBV8otrr6tDJ9utvw3H6Zodnx5ChJXT\n6SwpKSkpKSktLS0uLi4uLi75WXl5eVlZ2blz54qKijw9ovXSaDTePY1KlujJGC0WS2pqakpK\nSoAjxlGXxWJRVqFHoxkMBn4DEXKNSQgVhw4d2rp1a15eXmlpqdIT0rlz5yFDhnTt2jW0IQaI\nhDDCbrzxxi1btjSlBaVjzXvEo+erTz6mTBXzydyUjM5sNqelpWm1WofDYTQalawvVP9GBKW5\nJIRStc24ZoX61EnlpTvJUnPdTa7WGdGNqumWLn4nYf/u29J/6ndyJyQ+sOfQP5avOHToUPg6\no9RFBYY1Kz05obtte9v1N9NDqCAhjBFGo9FsNlutVofD4XA4SktLPbliUVGRdxpZUlKifPU/\n81On03n3LiojVOumkYxTrYuEsOlICBEOjU8IYw0JYYQNGDCgqKho7NixyiCopKQkn1GRykS4\nesdSBr6oSYNUKlVycrLD4eCuK+qaS0KoUOWdUZUUyXHx7vaZcgAL5zQX6vw8s61KY7GUxifd\nPGnS/v37Dxw4EN5TulzqMz9KVqucnOJq07YlberYRCSEMcI7IQzwI3a7vW7SqOSN3iX+l2jW\n6/Xnm9bonUY2iwmxoUJC2HQkhAgHhsCh8ZKSkhYtWhTtKHABqLapC84Jrdad1koO0ejH46np\nuQlJrTWaXip1w8uANh+u1hmqpCSVRrPmH//Iycn54x//GPZTqtWuDp3CfhYggvR6fZs2bdq0\naeO/Wk1NTVlZWVlZmTIqVRmYmp+f73lZXl5++vRp/w9l9Hp9UlKSspyYctC6detWrVp5XiYm\nJmZkZLDOKoDwCTQhdLvdTqdTV98cm9ra2pUrV37++efFxcVpaWmjR4++8cYbIz9sL/LP2FQq\nlcFgaDFdrMGSJEmSpKg/2lTWPVer1VGPBMocSynUHUSqrTmqLz8XyhoV5jj3+Ovdvfs2pcEK\nl3vm8ZOrS39aRHGg2bSwc2Z3Q8O7TTQXKpVqw4YNDz744LXXXvvII48Esu8FwkGlUnFpigXK\nHFqdThfIJjpBMRqNFksDu24KIaqqqoqLi4uKirxHpSovlc7G4uLi48eP++/ANJvNP/74YyD7\n4sQylUrFX0QTcUlHODScEDocjnnz5s2dO/ett966/vrrfd49efLkNddcs3//fk/JggULunTp\nsnz58oEDB4Y4WL8in5jJP4vweWNKjPzz+UHEiJD/IFT796j+++n/XldVqtascFuS5SZM+Xvk\n5ClPNiiE2Flluy33+Je9uhlbyv+yb7zxxiOPPDJhwoT58+dLUsuZF9DsKN95vv+xIyo/C6fT\nWVFRUVlZWVVVZbPZKisrKysrKyoqlJeeEv/ZoFarVUYJNvdfJ/6zBmJTAwlhRUXFuHHjtm3b\nJoTYs2ePT0JYU1MzYcIE72xQcfTo0bFjx27fvr1z586hDdePyM9cMhgMdrv9gp1DqFzWoz5h\nTKVSmc3mZjR1rQVTqVQh/0GYtn3pW1RbK3+9pWac78OpABU6XcuKfWewHKyu2VBUcm1CS5iV\n8fTTT7/99ttPP/30Qw89xN9FdGk0Go1Gw48g6iRJ0ul0yooyoW25pqZGGSPqZ+BoUVGRZwPJ\neun1+tatW/fu3dvPwNG0tDS1Wt0C/qKNRmNz/ydEHSvnIRwaSAhnzpypZINCiLqD4OfNm7dz\n504hhF6vnzp1ar9+/fLy8hYvXnzq1Kni4uJHHnlkzZo14QgaMaKkpOTOO+/0LArqWSbUs3Co\n9x59yrHPZuuMfIB/Kms9u2PXWxigM7W19T2dlk//vG1as/bCCy8sWLBg/vz59957b+TX2QJa\nBlmWvVciPd/SMiUlJX4akSRJWTwmMzPTs8+hzyYWykHE/l0AcD7+EsJ9+/b985//FEJotdrn\nn3/+gQce8H7X6XS+9tprQghJklavXj1u3Dil/He/+93o0aN37969du3a3NzcaO1CgXDr1q3b\n6dOnP/744ya20+Cm7fVu7O5ZwlSj0bRr187lcqnV6pAvYYqoc8cnqG2+67+74xu/uEIbbb0X\nPalteHbqi6ScnJy5c+e+8MILd911V7RjAWJUWVmZMmfPz+YTpaWl/sf+JCUlpaSkdOnSxc/m\nExaLhSeeAJoLfwnh8uXLlaHeCxYsmDJlis+7X3755ZkzZ4QQEydO9GSDQoikpKQFCxZccskl\nQohly5b94Q9/CHnQiAXLly8XQtT8rKysTNlfvu6O84GXlJeXKzvXhyRC7+3pPbvSN7GE7ekj\nzDFwiPGTXww0kNWa2gGDG91gK43m14nxq8srvAs763RXxjXvHcOcTuf//d//WSwWo9H4j3/8\nQ6VSeTbgHjVqVPv27aMbHhBu3gt++gzjrKioKCgoOHPmTElJif//X5QFP7t165aYmKiM2PQe\nxqmUpKSkaJv/8yMA8Obv1lYZLNqtW7c777yz7ruffPKJcjBt2jSft4YMGdKrV68ffvhh+/bt\nIYoTMcqzEXzr1q1D1abT6aysrHQ6nVVVVbW1tZ6vSonD4VCSRpvNVltbW11dLUmSMilfKa+u\nrvZJOMvLy/Pz8+12e0jCU/LD86WOJpNJp9Mp5UajUafTKSWer1qtVhlh6/mqjLMlz6yXM6uf\nvbxM980WyeUSQsgGY80VVzVxE/lXMlpVud2fVfzU8dhLr3u7Q1ujqnnvnme1Wo8ePSqE+N3v\nfufz1pIlS0gI0QjK9VM5Vq6oyrFyaVWOlUuxcuxyuTyPIVwuV0XFT49dZFkuL//fMG/vY++9\nfCsqKjxz7ZQLvnLs/ZRQubZ7wvAcN/gkMS4uzmKx9OnTx9N9p3z1dOspPXv1LqUOAC2ev3vQ\nI0eOCCHGjRtX7zrymzZtEkIYDIZRo0bVfXfQoEE//PBD3fVmgAZpNBpl1GhqamqDlYPamN67\nc9KTPSo3N955pifbrJuRKpmq56vy7Nn/ggGB856E6Rn76pmEqUy59J6E6Rk96xlPq3zfPGNu\nlXdDElsUOYaPqu0/SH0uX1ap3G0yZH1T59MnqdX/zGx3qMae63C00WgvMuo1zX8v9eTk5MLC\nQuU4KSlJo9EwhzB2lJeXe1ZWrKio8KRAypVEOfZOaZQrknLsnQJ5Z1/KAzLPsScTc7vdnouh\nLMveF0bvMLwzscrKSu9MrDZW59Mqj9WUY+Whm7IHg/I8zme4pjKGUznOyMhISUkJamN6ALig\n+EsIlQnT3bt3r/tWVVXV7t27hRCDBg2qd72jVq1aeVoAYoSnPzPklLRQudVTjpX7LaVEOVbu\nxqxWq/K8XJblsrIy8fPz8rKyMk+5sjad93P0pvBOGhusrNxmNVit3omayoQZn7k3Su7aYIMB\n/mgCT3GVrtcGq53S6faZTIE0qKTZfjhl+ajd4dDrO5pM7XQNjChTMv9Azhvg2ktKB7VyLMuy\nRqNRfrt8qFSqJGetuqjAbTS627STY7Vfutzl+r66psrt7m80eKZ3+qQ9fjqgGsy+vNMe7+zL\nu0/Mux+strbWk4kpj4eUY+9+MO+QhBD1fv9jhPcvc3x8vL4o8icAACAASURBVOdXUXkCpRwr\nQxiUY2Vog3LsfYlQxkEox1qt1vTzn5Iy9kE59r5WKA+z6g1DuUZ5wvD8zodk9ASrMgKAf/6u\ns8r/f/Wuz/HNN98o/7Neeuml9X42PT1dCOH9vyPQgil/Jg3mDMHyJJOexNIn7ayoqFDukpXB\nWsqx9whb5di7Y8HnttWH9411CDs/0ewEeCOuDJBusFqDmbx39lVeYy+3Vf2U0jmd4ufyWOOT\n9sTFxSn/RqUnPzMzU5IkpdzzEaUn33Ncb9rj/SzDu0/M+6GJ97ddGYuuHHs/aPDOvlrGYAEA\nQJj4+/9ep9PV1NTUO8Ri69atysH5EkIlXWRaFNAUKpVKSTJjeWlyT7ZpMpn8bJPlM3rNjwAH\nrXlnuf4pvbINVgtwQSPvfipFtVt+u6S0wvWLrtEBRsMoSQ5kn1LvMYF+eA8F9M9ut7tcrrrf\nQ1VJseS1Y4fd5bK53O7UNNFQD2SAnV3KQOsGG/HuYVOpVCqVSqvVejqdNHp9uVotx8ULIYRO\nJ/R6IURbrTbDZPSkPd5ZlncHlE/a433snX359IM1mH0p84Q9x4EkwD/9WzQak8kU4E8NAIBo\n8ZewJSUl5efnFxQU1H1ry5YtQghJkkaOHFnvZ5XZLKz+D7R4Op1OuY32nxAKISwWSwTjipz3\nSssrzuT7FO6VpFU9u8aro7DufP1zCF2uuL//RXLWCvGLCZP2MWMdFw+NaHx+/d/ZgkXFpT6F\nep3u4+6dohIPAAAtnr+blc6dOwshduzY4VNeUVHx5ZdfCiEuuuiilJSUej+7Z88eIURmZmZo\nwgSAWHW2vm4xpywXupyRD+Z8JHuN5HT6ZINCCKkitvqv8mvr+aaddcboMicAALQA/hLCiy++\nWAjx6aefehavUyxatEjpBLjhhhvq/WBJSYnShdi7d++QRQoAMal9fUvV61RS61gaMy/rDbK2\nnjjlxBBPfG2ittp6vmkd2AwAAICw8ZcQXn/99UIIm8129913e4aBff/9988//7wQQqVSTZ48\nud4Pvvjii8pUnLFjx4Y2XACINdfGx3Wos1H1FEuSKYDVQSNHrXZcfIlPmRwX7+iVFZVwzmda\ncj3ft/tTY3cOLQAAzZ2/+5XLL7+8f//+Qoi1a9d26dLltttuu/baay+55BJlHcKJEyd269bN\n5yMul+vvf//7X//6VyGExWJRUkoAaMHi1ap3M9v2Mfy8XYcsbrckPtc6LapB1cNx6ajaAYM9\nL92padU33iqMAe26ETFd9LpF7TMyfu4nNEjSk61SJyU1vGMKAABoHMn/4nu7du0aPnx43aX8\nOnTo8N1336Wl/eKOZ//+/ddcc83JkyeVly+//PLjjz8e2nD9iPwuzElJSd57+yIqgtqYHmHV\n4KIyLZtLlg87agtqa3vo9a3rG/cYMf43ppeqKlVFhcJkcqU0vL5otNhl+Ycae6XbnWXQJ/28\nImizwyqjMcJoNJrNZjamjwUWi6W01HfVKATFYDAEuI0tELgG7gb69+//xRdf+EwFvOKKK/77\n3//6ZINCiLi4OE82+Otf//rRRx8NYaAAEMvUktRLrxsVZ45uNqg6c0reud19+AfpPAuxyOY4\nV2YnV1qrmM0GhRB6SepvNIwwm5pvNggAQHPR8I3LxRdfvH///m+//fbw4cMulysrK2vgwIH1\n1szMzExNTa2oqHjsscf++Mc/qmL4bgMAWhiputq4ZoX61Am3EG4hzAmJ1ddOcLVtH+24AABA\nTAv0SfbgwYMHDx7cYLV333134MCBrVq1alpUAIDg6P+9Xn3qhOelZC03rl1ZNXWGbAh0I3UA\nAHABCnEn3vjx48kGASDCpGqb9shB38LKCk3u4ajEAwAAmgtGdQJAsydVVor6VgiTKisiHwwA\nAGhGSAgBoNmTExJEfeuvyBZ28AMAAP6QEAJAsyfrDY7+F/sUutPSnV27RyUeAADQXERzeXQA\nQKg4Rl0puVzaPTuF2y2EcLXrUDP+elnNRR4AAPjDvQIAtASyWl3zq6vtI8Yk1No1luRilzva\nEQEAgGaAhBBAREnV1ZqTxyRblSst3dUuU0hS5GNwuVw/HDpYU1SgTUjq1rOXyWCIfAyByLU7\nvrZVy0IMMRp6GPSBfEQ2GqU2bSSNRhQV1VtBfeqkuvCcbDI7MzvJRlNI4wUAAM0PCSGAyNHk\nHjJ8slaqqRZCFkJytetQPeE3sj6i+VhhaVntiveHlZcoLwu+/M/Bq2/oGXtz7V44V/hmcanD\n/dPaoTNSLH9uk96UBiWHw/jhB+pTJ5Rvvmww1Fx1nbN7rxDECgAAmi0WlQEQIZK13PDxaqmm\nWnklhFCf/tHwn40RDqN83coeP2eDQoh0e3Xmx2ustqoIh+Hfemvl3MISTzYohJhfXPpBaXlT\n2tT/d+PPO9dLQgippsbw8RpVeVlT2gQAAM0dCSGACNEe/kGy230KNQf3SbW1EYvhXHHxgHN5\nPoXp9urcA/sjFkMg3i+pJ09b2oSEUHI5tT/s9S2sdWgO7mt0mwAAoAUgIQQQIZLNVk+p2y2q\n6ysPj8rzbNTurqqMWAyBKHa56hYW1VcYKLtd1Pfx+n8oAADggkFCCCBC3BZL3UJZr5fNcRGL\nIS0lzVXfMjb61LSIxRCIzjpd3cIu9RUGSDYYZYOxbrk7OaXRbQIAgBaAhBBAhDh7ZrmTU30K\nHUNHCLU6YjEkxJk39+jrU7g/Jb1Xz94RiyEQD6UlG36ZuOol6ZG05Ma3qFI5ho30KXNbkv8/\ne+cdJ0dx5v2n0+S0szmvMooghAQISYCJIoMIxjZwYAwGzueAD/tsv8Y44OzDBzYG7owxNmBs\nE0UwOQgJkEQSKK82x9kwOXZ3vX/Ubqtnpmd31L2hFz3fP+bTU9NTUz3T012/eqqen7gw99tA\nEARBEOSwAgUhgiBTBBGExEWflRqaRp7yfPqEE9MrV09xM5atP+e1hcsy7MjV793aBvtFl/FT\nKEqLYbHN+qfG2gZBoE9rBeHe+uoVDo0QX/GkVxybXnMy4UfqlOobExddTgxEHREEQRAE+RTA\nEELG32smMFDAdGvy8Pl84XBYltH9eTphWdbv96fT6XA4PN1tOdxxOByyLCeTyXH3ZBIJJhqR\n/aVTGRvMIZXJ9Pb3l5T4PA7ndLVhXAhARyYjE2gQBLZov0afz8fzfMFLoiSxw4PE6UITwsmG\n53mHw4GXpmnHbrc7nc5wOJxOp6e7LYc7JSUlw8PD092KmY3NZnO5pm6dBXKYgD6ECIJMNcRu\nJ3ZDwS7jWAWhsbZ2AitkYlG+eR8bj0mlZeKc+cBOwPwLBkAJEk4YHCeXGfIzRBAEQRDk0wQK\nQgRBEKPwe3fZnnuSGY0/SOWViUu/QEwce0QQBEEQBKHgGkIEQRBDMOGQWg0CABfosz3/9DQ2\nCUEQBEEQpEhQECIIghiC37+HyVubxB/YN5X+igiCIAiCIPpAQYggCGIINpnQKCWELSK/DoIg\nCIIgyPSCghBBEMQQ+eaKAEAEC/F4p74xCIIgCIIghwQKQgRBEENk5h0hVVbnFKZXryUm8zZE\nEARBEATJBwUhgiCIMTguceFl4vwjgGEAgFhtqXWnpFeunu5mIQiCIAiCjA/aTiAIghiFuD2J\n8y9lxAzEYsTjpcoQQRAEQRDE/KAgRBDE7Fi3bGLb9jOcIC5aml68bLqbUxDCC+D1TWMD2HRS\nePUlKThEPF72yBVyTd00NsZcEMI372V7u0GwSLPnSeUV090gBEEQBDELKAgRBDEx6bTr3juZ\nRIw+41qb+e3vxK/80vQ2ypxwXR2ORx4AWQYAGcD58YeZlccnTzptuts1/TBixv73v3Kd7SPP\n33g5veak1PHrprVRCIIgCGIWcA0hgiDmxfH4w4oapHB9PdbXXp6u9pgZxz8fompQQdi6he3p\nma72mAfLG68cVIO0ZNNrXHvrNDUHQRAEQcwFCkIEQcwL19mZX8jv3jH1LTE5bE8PpFL55Zbt\nm6e+MWZD2PWxRuFujUIEQRAEOQxBQYggiIkhcn4ZI4pT3xCTw8ZC2uXJ5BS3xIyktL4E/GYQ\nBEEQBABQECIIYmaI1ZpfKKPhex5yXYNmuVRVM8UtMSFymUYKGYJ5ZRAEQRAEAFAQIghiZpIn\nng6QY+HAJNefZ7xmJhzi2lrYwQEgxHhtAACiyO/dLWzbwvZNw7I92eYQZ8/LKSQ2W2r1iVPf\nGLORWvcZ9VMCQDze9PKV09UeBEEQBDEVmGUUQRDzIi47imx6hYlFlRK5qlYurzRSJ5NJW/+1\nUVlXJtfUJc46Xy4pNVKnsGe39dnHlLmscklp4sovyRaLkToPldT6c/l774RMZuQ5wyTXngIs\njvqB1DQnccGl1jdeZocGgeOkhlmpU84gNvt0twtBEARBTAEKQgRBzIvjnw+r1SAAsL2d1q3v\npFYeq7tO20vP8aosI0x3p/3Jv8evuJZwOq+HbDhs2/gPdYZPdnjQ9vCf4lddp7uROrA/8uBB\nNQgAhNhfeT42f6HscExlM8yJOO8Icd4RTCpJeAE4brqbgyAIgiAmAgUhgiDmhW3Zn1tEwLL5\ndd2CkInH+E8+yioBYAL9XPN+cf4R+uq0bH4tx+8BALj+XjYelR0ufXVSXorENsfjhMCxDvuZ\nnrGqYkMhdjAQ4fm/1jTtdnurk4kNve1zY1HL5teTp6430oZxYcIhYdcONhKRS/yZRcuIfQIi\nb+/EEy9FokmZLHfYzve4OYYZ/z1FQKy2CakHQRAEQT5NoCBEEMS8MPkL/BgAMa27QjYc0lw0\nyIaDuutkgtrvZfsDcpNOQUgArm3vfiocoU/vAjjd7fpzQ00hacT29exzuE8/7uQe64ge++nc\nRb//eOtlw0P6GlAkfPNe21P/ZMSRyKR1y5vxiz9nMJPN93r67xkcHnkyCPc4hh9vqnfg3FcE\nQRAEmRzwFosgiHkhmvqH1z+SJbvc2h/k9uiuk3i03yv79a9L/NNQUFGDlBci0bsVmZSHWF5+\nzVHH9ljtMKp2kyz3lcUrW/3lutswLkwiYXvuSUUNAgAk4raNj+XHS4vnhUj0nuzDfC+e/FFv\nQHeFCIIgCIKMDQpCBEHMi1xbl1+YXH6c7gqJy52ZvxAgK0go+0ryU3QWT/r4EyFPuMolpXIB\noVgMT2erQcpTIY1CSpfLs9VbCpCVkzXGcc8sXKq7DePCd7QyiUROITs8xAX6dNf5dDhaZCGC\nIAiCIBMCCkIEQcxL/PKr5ezYnVTXmFl3kpE6U6efI82aqzyVS8uSF1xKBEF3hXJJSeK0s9T5\nPInblfjc1UYaGdMKskULR96ikvZLEWEyM52mU4dWXgSaxzjGgSMIgiAIYhBcQ4ggiKmJfflr\nXHOz5aP3wGpNH71KqqoyWCGx2+MXf54L9LGDA7LbI1XVGE87KR65IrZwKbfjAy4cFOubxLnz\nDVa4yGp9L57MKVxisxbav8lqcbJsvoxcOhEpXgqh7f/BsppG8EWy2GrdCLmB0MWFDxxBEARB\nEIOgIEQQxOxIc+Yk5syZ4DrLKyVjfoY5yBaLvGJVZvwdi+KbFaUbw9GgJCklLpb9r8qCCwKt\nDPO9yrL/6ulXF57icp7kmkTPCamyWly8LCdra+q4tcSu/0OvKy15aDjYkRGVEivD/KBqEldC\nIgiCIMhhDk4ZRRAEMR21gvDU7IaT3U4rw1hYZq3L8eSs+lmWsea1frG05I7aqlkWgQEoFfjr\nS0vuq6+ZGLuGwiRPPyd1/FricAIA8fpSnzkjffxaIxV6OPaJ2Q3neFxOlhUY5miH7ZGmupUO\nNJFHEARBkMmCIVoZ2GciAwMDU/yJPp8vHA7LuLhlWmFZ1u/3p9PpcDg83W053HE4HLIsJ5O5\nEx0RI0iEEAD+UIz47B6P02KZ4ksiI0lkQg3fCUBGJhZ2siXtJMLzvMPhwEvTtGO3251OZzgc\nTqf1O9YgE0JJScnwcMFsyUgx2Gw2l8uQwy2C5INTRhEEOewYGuh/ZMfHe4GtJtKGutq5ei3p\n1Qi7dgjbt7KJmFRanjplvez1GqyQADwdjmyKxgnAcQ77hV7PuOKoNZX5dk9viyRXCMJ1Xve5\n3lyPjagsPzgU2plMlvH82R7XMRMXeZtYNQgADMCMVoMIgiAIMlPACKF+MEJoBjBCaB5mSoRw\n5ycfn58mwdH0mxYi/08wsGHtOiN12p94lN+3++Bzhklc8jmxUf+6R4mQL7R3vRSJKSVrnI5H\nm+qEwqHCZ8PRf+voOnhFZ2CDx/OH+mplh4505uyW9h7V8rzvVpZ/rdyvu5HI2GCE0CRghNA8\nYITQOBghRCYDXEOIIMhhBJHl6yOJoMqMIc2wN3vKujs6dNfJN+/j9+3J/hhif+LvuisEgP8b\nCqrVIABsisXvGhirI3V9R3fW+B6Bf4bCm2JxpeAb3X09GVFtwfiTvsCHCbMLeARBEARBJhUU\nhAiCHEa0thzY7cr1i49x3JstB3TXKXy4PcfpHgAgnWYN+LM/q2XF/oyWWz3l3VgiqTXd4/6h\nIN2IyfIb0RhAlnM9ADwfQc93BEEQBDmsQUGIIMhhRDKTAtCQbwkDc78ZUXseGpPS78+eIBrt\nGaORwQIvxaWRQ00RrRoBEvKnZNUAgiAIgiD6QEGIIMhhxOyGJqckQd5CvKUlPt11SnWNuUUE\ngAGpqkZ3nctstvzCI+0ahZTVTrvm4sI1zpG3lHBcnZZrxRh1IgiCIAhyOICCEEGQwwirw/nD\nYO5MzksDPSuOXK67ztRxa4ndmVXEQGb5McDrT+P8zYrS0uy8nV6O+6+KskL7u1j2Ml9uXtMq\nnr+pvHS0RXB7VUXODqudjvM8mJwAQRAEQQ5rUBAiCHJ4ccXaE+8L9i2NhixEbkrEvtff9d/H\nHWuoRpaNX3O9VFMLLAsAhBfSq1YnTznLSJWVPP/snIZzvG4fx3k57gy365lZ9fVjGtPfWVd1\nXWmJjWGAAZ6BYx2O1+fOUu+w3uN6qLFuhcNuYZgagb++tOTBhlruUBwOEQRBEAT59IG2E/pB\n2wkzgLYT5mGm2E586vH5fDzPT/0lEckBbSdMAtpOmAe0nTAO2k4gkwEa0yMIYmoYSRQ+fI/t\n6gCeFxtniwuXgOGgVndG/ONQsDmdruL5S32e5ROxjm7bB+89NhgM8NwRknT1kiX+itz5mZ9W\nPkmm/joc7M5Is63C1f6SegFvKwiCIAgyk8A7N4Ig5oVJpx1//T92IECfCh9/KO7+JHHhZUY0\n4dZ44uLWzvhobP9/B4d/Xl1xTWmJkXb+7pVXflBRC5UjKwnv7up/bnho3oIjjNQ5I3hkOHRz\nT196NFXpfQPDDzXWrXU5prdVCIIgCIIUD64hRBDEvFjffEVRgxS+ea+w433dFcoEbuzsiWfP\n9L61N9Ceyeiuc9/e3T+oqFWXhAThhsGQ7gpnCv2i+K2e/rTKuCJJyI1dPWm0skAQBEGQmQMK\nQgRBzAvXvDe/kG/ep7vC/el0aypX+yUJeS0S013nK13d+c6GH3p8/d3duuucEbwVS8TzFlH3\nZsQduI4UQRAEQWYOKAgRBDEvjCRplIqi7grThOSbEAKAkVwTaUJAq9KUgajjjCBVIKVWGgOE\nCIIgCDJzQEGIIIh5kapr82JvINXUau1bFHOtFjencd07WssIvkiWu935hZWpZE2NfmP6GYFm\nMh4rwyy2Wae+MQiCIAiC6AMFIYIg5iV10qnEalGXyD5/ZuVq3RXaGObHef7snyvxHu3QLwjX\nrFx1/mBvTuFv0jFOGMs28FPAApv1urxkPLdWVXi0JDeCIAiCIOYEs4wiCGJeZJ8/fsW1ljdf\n5bs7CcdJTXNSa04iFsv47yzM50q8JRx318DQ/nS6iucvL/F+0e8z2M67j1t11Oa3/2539Vls\ni+KRm238CcfrV60ziB9Wlc+xCH8ZDnWJ4myL5culJed7NeKlCIIgCIKYFjSm1w8a05sBNKY3\nD2hMbxLQmN4koDG9SUBjevOAxvTGQWN6ZDLACCGCIMgEwLfsF3Z9DPGYXFqeXnEs8XgNVpiW\nyZ+Gg1viCZnAsQ7bF0tLrAbcF5EJ59lw9Pa+QECS/Bz3H6Wll/s9xusU9u7i9u5mkgm5ojJ9\nzHHE4TReJ4IgCIKMDQpCBEEQo1jefMX69qaRJy3NwofbE5f/m1RZrbvCNCHntra/Fx8Jtz4b\njvwtGH5+dqOdRU1oCn7SN3DHwCDNeDQkSv/R3bM5Hr+zrspInbYXnxU+2DbypGW/8OH2+Oe/\nKPtLDTcWQRAEQcYCl/4jCIIYguvrOagGAQCAyWRszz1ppM67BoYUNUjZmUz9KjBopE5kogjK\n8m9H1aDCI6HQTgPzpbn2loNqEAAAmGTS9vxTuitEEARBkCLBCCGCIGYklUolEolxdyOEFLlG\nKx6PF7OCSBTFaDRaTIWRSIQuIeb37eb37M99ec/+pN0HdkeRBwIAhBBmdFLoPxIpyIx6MNrt\nwPMA8JRFOHPuLMtoTh2bzWYbdcuwWq3KtiAIXq+hCavBYDCnRJblSCSSv6fmtyrL8hg/SiKR\nGOOHiMVimSL8Gw9p/bYoirFYrMidi+HDRJJEYwAAySSoWvtlm3Wlw648ZVmW5/ki161xPV2+\n0DCXMyuYgfTeNuA4HY10Op08fwi3eJfLNfb+6vNNE7fbzbIao8yCIDid2nNfnU6nkJeM1263\nW/ISR43bPARBEEQ3mFRGP5hUxgyMkVSG9ixjsRjt4qsfpTy78yLVgiRJmt3ifDQ/JZ90Oh2P\nx4upMBQKFfNvTSaTqVRq3N3G7rKrKbKDXnyfG/81UwnDMIxKY7Asq3z5SnkxJyqCmBCPx6OW\noBzH5STbcDgcFouFZVmWZSVJIoR4vV71P8LlcnGjepthGPVISo6OtdlsVuuIwSbP88oHsSzr\n8RxcPqquQd085XMtFovdPjJq4Ha7OV1qf+aCSWWMg0llkMnARONtsiy//vrrr7zySktLSywW\nc7vdCxYsOOuss5YvXz7dTUMmEaqI0ul0IpFIJpNUzyQSiVQqlUwmlQ3lJWVnuls6nU6n06lU\nKhKJ0KqoCCxGwyCFsFqtSpdlDBiGUXpCPM+XlJQQQjRVK+2WjVuhups1Nm63m2EYURQBQJIk\nKnLojy5JElU4oijyPC8IApX6hBC6gyzLyhuVPWm/TZIk+hIhRBkgSKfTtH66P8MwsiwXo7qn\nF5ZllY4v/VFyojeEEDMrc/UZaLfbmVFsNptyIDabTelPWywWGkFiGEZ9vgmCoGzzPK/EuFiW\nVT6CZVmHw6F8XEVFhfLVqU9Il8tFywdE8etDYVC+T5uNbv+munKZytCS4zibzVbkQAnf3mJ5\n7aWcQuLzJ87bAEWPBxVCkqQi495QxOjY2MNY9Pqs+VKhwSB6ec8ppGN5OYWRSCRn/IJe8HPa\nr1z/Q6EQaEW8zYb60qeEdhVFqrzKsqzbPWLr4vONmOUospNGaBWBarFY6FntcDhoGJaKUqU2\nWl78VRdBkE8xZokQZjKZn/70p9u2bQMAq9XqdrtDoRC9pl9wwQXXXHPNuDVghHBSoQElOm2M\nRsmUx5zgG40UKaqM3tRjsZgi/OjdOh6PFz+VblzonY+OvFIxQ0voPDoKLVeP8ir3y3HJGVQu\nhLryMcgZUR4DzflU+fA8X2hGVg45A+oTS47tBO3kUdGeL+OVkyGdTtOThO6vzNikPTl6FtF3\nKZ1aeppN0lHkoExUU8b11T+f8io3ECgRR/rQAsc6BYs0e67s9qhn2aljEeofQi2/FY0dk+Vv\ndvcFeB6EETFTarE8smh+iSqekDMvVIk26LOdUF/NlNmwkB0iVksFdSxa3aFXqwV13FgdXc8J\nUNPfWtlW7krq3r865F5kPH9aUGtResGh36T6TFBfedR/XmtPlyc98pWyDHit1syipcTtUY+8\nQPblSH0i0ROM53m/3+/3+0tKSooZhTkcyLGdyBG0dMxReZoznqgWk2pdnXMOq3dT/33UFytl\nH7VmVv53amWriGFNqTxJ0AuIcqGjgpPeUqkQVS599CVaQt9Fb1X0zkuvivTM9Hq9HMcpIhYw\nQjgRYIQQmQzMEiF86KGHtm3bZrFYbrrppnXr1nEcl06nN27c+MADDzzxxBPz5s1bu3btdLdx\nZqBINUXCKY+EENrZUh5h9BYVDAbpWiz1q1T+FT+3cFzohBx6CykpKaE3DPpIbyrqR3pTUT/S\nW4760eFw2Gy2uro69CGcQKLRaCKRiMfjkUgkmUzGYrFoNKpspFKpaDRK+0zBYJD2kKjSo1I/\nGAzmj9kbQZnH5Xa7vV6v1WptbGyE0a6w0p+mJ4nS+ejv73/nnXd6e3szmUxlZeXxxx9/5pln\n0hAT7dMoeyrD8EpPKGfmWFHIsuX9rcLuj5lYTCotTx+/VqqpM3jgq0TpV4HBTbG4TMjxTsd/\nlpdWCZN4xVZLDiX4YH7ouUe31T179XiT+oQs1KdXLokUejFUtjX79zuGgx2JhESABSgV00pK\nWWVoY2BgIJVK0TMzFAoVPz/8IO9sP7T9s3G73aWlpaWlpSUlJfTR7/fTEqoYaeHhtjbPYrHM\nOKmsnDzKIIsywqKcuspCX2VnZeiEns/KcJuiM+kb6W5KhXTwJRAIHPLpWhh6mbVarfTRYrG4\n3W6qHukYitfrpQMldAePx2Oz2ex2u8fjcTqdVNJP6oAmghzmmCJCGIlErrrqKlEUv/zlL591\n1lnql+65555nnnmmqqrqnnvuGTtEM7MihIVCJUr0LCd4oo650Yu1OiJHZVv+XBrdKMJMkWqK\nbFMkHB2l9ng8tPfMMAztRNKeNN32+XzqfSakbTmgMf0YBIPBSCQSVkGfRiKRUCgUi8VisVg8\nHg8Gg3QjFoup+8SHhHIvp2PDgiC43W61jKc7UBmvRHSVuK5a9dGJdlardYxcFGPzwgsvXHHF\nFUuWLLn88ss5jnvsscfefvvtb3/72zfffLO+o5tZEgVXJQAAIABJREFUoDH9tPOnP/3p+9//\nvt/v7+rqeuKJJ0444YScHdQaVZ2wZ4y4k1qj5sROFY1K7xpDowwPDw8ODg4PD499n/J6vYpu\n9I9SVlamKEb6OHP74mhMbwQ6Rkw7J7TrQnsvtK9Cz2QqJtXDyrSE7kP1p7qTo7vvZLVaHQ6H\nx+NxOBwul8vlcrndbo/Ho2y7XC6fz+dyuTwej9fr9fl8VGpO+NcyjWCEEJkMTCEIn3vuubvv\nvtvhcPzlL3/JGars6em5/vrrAeBnP/vZokWLxqhkGgXhli1b3nrrLUXjqZfA5aC8NCENyJkM\nqX7M6XCrO+JKB13prCtROCUuNyHNmxoOQ0Eoy/Lg4ODAwMDg4GB/f//g4ODg4GAoFBoeHh4e\nHg4Gg8pjkRVSke92u6mic7vdbrfbZrM5HA63203HdGmJ0+l0uVx0g55sVPXR4FLOlNGJgkkk\nLFu3cN2dwHNi4+zM0avIeGkYVq5cSQjZtGkTnaSXyWTWrVsXCoV27txJd+AC/cK2LdzwkOR0\niUuOFOfMn9g2TwhDovylzq4diRQBsshmvbe+pnK8/+YfB4d+3D8UlyQLy57stj9QbzRKOS1k\nCPnTUOilSDRJyHK77Svl/lJTJt54L568b2i4LZ2pFfgrS3xrXSOzQLdu3XrhhRfedtttbrf7\npptu0hSEU8yQCqoSBwYG1IqRvjR2JX4VVCXmhx/9fv/UHNEhgYLQPChTRumItjKvhGrFaDRK\n5SJdbhAOh+PxOC2PRCKJRCKRSIRCIWUOS5EfarfbqTj0eDzK2VtWVqYe/qiqqipm5bwZQEGI\nTAam6Prv3r0bABYvXpwvRaqrq8vKygYGBnbv3j22IJxGbrnlFnoImqi1ltvtztFj+ZotR9HR\nVShq1afowKk8RmTqGRoaam5ubmlp6e7u7u3t7erq6unp6enpGRgYGGNslWVZn8/n9/vnzJlD\nZZ7X6/V6vXTbMwodQHW5XE6n08znEpOIOx+4l4mMqH2upZnfuyv+2avGSMQviuK1117b2Nio\ntmFYuXLlww8/nEwmbTYb39Jsf/wRkCQAYAGEvbvSJ5yYWn3iFBxO8QRledme/anRAbvNscTy\nvQfenz97DE14S0/f/YMjAaWELD8bii2NN+9YMGcqmjtxyAQ+29b5RnRkrtrmWPxvwdArc5qq\nJ3O6rA6eCEW+1NFNt7cCPBGK/LSm8lq/DwDKyspeeOGFRYsWPfroo9PaxoMUI9VkWc6JKwYC\nAUUrUgKBwP79eQ4rKugInXo+qrrbrcjISZowgswgeJ73+Xx0MhFdBaCDUCgUjUapXIxGo3T+\nC90IqQgGg+FwuLOzc+zUSi6Xi/Y5q6qq6urq6urqGhoa6uvrm5qaiskOgCAzGlPcYtva2gCg\ntrZW89WampqBgYHW1tYpbdOhkMlkXC7Xgw8+mDPTEpN3IYdET0/P5s2bt23b9sEHHzQ3N+eH\n+CwWS0VFxdFHH00XAlVWVtLOFp3cVVJSQqfHTEvjJwnray8qapDCdXda3t+aPua4Qm/heZ5O\nK1AghDQ3N9fV1dlsNpBl23NPQvbkastbr2cWLJZLyya28Ua4orUzlT19IyOTz7V2vjy3SXP/\ntASKGlTozYh3Dgx9pcyMQZtC/DUYUtQgZUCUvtvT98cG7RvEtBCT5Zu7e3MKb+3pP9vtqhb4\nWbNmTUurDMKybFlZWVnZOP8CURTVunFoaIg+KoUDAwPd3d179+4doxKalFiJLtILWr6MVCcj\nQZB86HBn8fuLoqiMd9DJNfQEHhgYoIOtvb29+/bty3kXx3GNjY2LFi1atGjRiSeeuGLFisPN\nLAQ5HDCFIKRx/0JpDEpKSgAgf0LgK6+8osRJ6urqCunJyYNhGIvFQr2kBUE45ZRTprgBCIx6\nqbEsO6MH8ILB4Ne+9rUnn3ySrgviOK6+vv7oo4+eO3funDlz6uvra2pqqqurKysri0l2Ol1w\nHDfhPwTf3ppfKHS0MSeMH9BLpVKBQKCnp+cPf/jD+++/f//991utVqa/l4lpDBJbezrlGhNJ\njk9SGnPb9qYzhb7efxSY7/ePcOSbtdWaL5mTzYn8KcfkzVjCVH/wrZFoWMqN0qcJ2ZbOXOw6\nuOqV9hoFQTBV4w1C55DX19ePvRtdyqjucCu6kXbEBwYGOjo6xphcAwAWi0XRinSOH932+/0X\nXHBB8d+q4uJg5uvnYQLDMNP4d6Bnb13dWHPpE4lEb29vR0dHe3t7R0dHa2trc3Pzrl27Nm7c\nuHHjxl/84hfl5eV33XXX+vXrp6zZOczc1byImTGFIKS54ApdI+hq4PxsV9/5zneUlfSXXHLJ\nt771rclsozaKNRDDMDiWOY3wPD+jv/9//etfjz32GAAcd9xxX//6188555wi/TBMyMROQE0z\nTP4qZ57j7EX83O+8885pp50GAE1NTU8++SS9f5NoRHMVkc1q5WbCKVToPLdECuUDnGGXJlZj\nTiwDJrvA2vPUIMVmt6nbSW9qdC3uFLXMTJSWls6bN2/sfZLJ5ODgYGdn5+7du3fv3r1r1649\ne/a0tLTQlfbpdLq3t7e3NzcYCwCXX375oWacmikrxD71mPzv4Ha7Kyoqli1bpi6k00weeOCB\nX//614FAIJFITONRoM0yMhmYQhCODU17kz+wd+ONNyoZcRYsWDCBae6LxG63J5NJau5MCJn6\nBiAwakItSdKUmTVNBsuWLVu2bNlHH3309ttvX3bZZY2NjQsWLJgzZ868efNmzZpVX19fW1tr\n8psoANCA+cTeq9iGJnbHBzmFYn1Duoi/2/z58x999NGhoaEXX3zx7LPP/uY3v3nrrbeC08U7\nnBDPfXuqqoaY6S+80G57N6pqDwFgYK7VUug6c55Lu7N7oc87sy5Nx9tt+Qvv1ridpjqKI1jW\nybKx7KW8FpZZzvPqdtIUJtSyZaqbaA5SqZQSJwwEAkNDQzSZDS2hYcPBwcGx7Q2sVqsSGCwv\nL6erEA/Jw1YQBIvFkkwmJyoRN6Ibu90+Uf7DkwchpLe3t62trb29va2trbm5+ZNPPtm1axft\nZvA8H4lEpvFPzbJsMQbFCHJImEIQOhwO6m+m+Sotzw+YXHnlleqnU59l1Gq1JpNJqgZlWW5r\na1MywUxxSw5nWJalgtD895gx8Pv9L7/88ubNm998883t27e///77L7zwQs4+Lpertra2urq6\noqKCdowqKipo36i8vNzn8027YmQYZsKzjDJrP+Ns3seo9JtUWZ1YtoIU8XM7HI6TTz4ZADZs\n2NDY2PjLX/7ytNNOW758OX/62fYnskRHetXqlNsLZjqF7q+rOmrvgYw8Gh9lgGfgTw01Y5zn\nl5Z4Hh3Omlrv57iv+b0z66/xWZfjIYdta/zgWeTjuB9WlJnqKHiAn9ZU/kdnj7rwlvLSMjnr\nQkTnsKgdET81ZDIZZdGgIvPUKwmp/Bu70ywIgt/vb2xspBlKy8vL1b4XdHW03+8vFAk8pG/V\nYrGk02nMMjrt2Gy2af875Jy9gUAgEAj09/f39fXRVQYDAwM5p4ogCHPnzl28ePG6detOPfXU\n8vLyaTwKM+eBQ2YuphCEHo+nv7+/UJZ8mgvbzEbJLMuGQqHFixcrJerEoTl26jlm6zQBaY4t\n+xjuf9N4mMhks3r16tWrV9NtmmK0ubm5tbW1q6urt7e3u7u7s7Nzz549hd5Ok7bR1DI0xwxN\nLqrkF1U/dbvdM2ICFXG6Yv92vfXtTWxXB3Cc2DQns2r12LYTgUDgqaeeOuqoo1asWKEUHnPM\nMQCwc+fO5cuXi/OOiH3+i5atm7mhQeJ2Z5YclVlgugzGFTz/ztym6zp7d6WShDDzrdb76qob\nxxwV/l1t9WzB8tuBwSQhPDDHOm3/aGqYsgZPFDzDPDar4e6B4Zcj0bgsH+2wf6O8tMZkKUYB\n4HKfp17g7x0cbkln6gT+qhLfmZ5PQwoxSZLGzhlDl/+NnfGf4zi/319XV5eTM0aBjmRN+xgW\n8umAeh4qKUaj0WgoG5plNBgMBoPBoaGhMc5emlpp/vz5NMWokmV07ty5ONaPfLoxxV22qalp\n//79HR0d+S8RQjo7OwFgzhzzJk+/+eabn3/+eWouTG2FZVmmXq5K7uOJ8nsc1w5eKVf2UcSk\nIixzZCfelU0I7TatXLkypzwSifT19dE+mWJCODg4SI0Hh4aG+vr68pOkaUJPD+o8odgP0m1q\n8mu1Wumr1G/Q4XDYbDaPx0NtUaZsjIY4XclTzix+f47jvvOd7xx//PGPPfaYMoby2muvAYCS\nS0CuqU2ef8lEt3SCqbdYnpt9aIru5orSmytKZ7oxvY1hvl7u/3q52ZOjrnE61jhnzFpfQkiO\nq4Qye1PtLTG2fynDMH6/v6qqauHChUo6UCVBqNpeYsqOC/kUQI2aqVFkLBaLxWLUWzjfhDAY\nDNKNSCRC5R99qZhPEQTB4/FUVVUtWrRIPTBBN6qqqioqKsrKyjCDKHJ4YgpBuGTJkpdeemnn\nzp3pdDpnDKa5uTkUCgHA0qVLp6l147Nhw4YNGzaMu1swGAQAejjBYJAQoohGuq2ISbWwVEro\nGJjyGI1GBwcHxzbVKZ4cY3qXy5VjXq9YJioG9+pHapCofkSzxEmCyra5c+eOsY8kSVQfhkcJ\nhUJ0AJWOoYbD4VgsFgqF6K23t7eXnpaHCj0xqEkmtbD3uN2WeNSZyTg4VnC7XXPmMw6HOsSt\nPCojF/SRnmya5wwb6LNvfJwJDTMMI1bWpM67WB4z447f7//KV77y29/+9pxzzjnvvPNsNtuW\nLVsef/zxY445Zu3atXSfn/QF/jA4nCKEZ5g1TvvDjfUGuwAygYeDocdDkX5RPMJq+Wp56WKb\n0Ux6O5PJy9q6+8UMECjluUca6pc5xqnzo86Ouzp79nJ8NZE3OGwbFi+egrSKz+3e/WAo0sly\nc2Xp+sqyY5uMmi70pVLn7m1uB5YAeID8vrL8tAoTOYIovHXgwH2BgQMsXy9J/1biOW3BAlr+\n7rvvUtOFbdu2AcCLL77Y0tICAGvXri3Sb009hkhvB3Q7Go0qqdTi8bgyqy2RSCjb8XhcWaGX\n4zs/9rgkVXRz584t5DtPHzFR5+EM7asAAO230N5ILBbLZDL0JKQTpKnAo6bzdJ9QKER7LwCg\n2NBHIhG6g46W0I6K1+stLy93u910EJNOhKHb1JGCWjFRDjULEYIcVjATFbkyQjKZvPLKK5PJ\n5DXXXHPBBReoX/r5z3/+1ltvzZ079ze/+c3YlUz9cLjP56NqbYo/NwdRFGkvgV6U1Y+0x6B+\nTCQS9HpNS1KpVHIU5TpONwot6TxUqESkmkGtFWmUSa0n89VmjiIVBEGtWmn91AeZjiZOSIMP\nW2KjhMNhuqY3Go3GYrFkMhmJROjZEg6HldFZep7Q8yocDmcymYkanlCi1h6Ph5VldyopcKyV\n4+w8zzKMx26T5sy3u1x08IgGKul5NfIWlhUEYdu2ba+//npvby/DMBUVFccee+wVV1zhcDi8\nXu8tPb2vRhPgcgMAsAw4XZU8//ERhuYgfL2r9y/DB0W1hWH+1lRnJHx0IJ0+bm8rgazr8yvz\nmpYWztj+2v59lySzLkfXx0M/XrVKdxuK4b+3v3e7NaubdQ8jXqSaP3+oRDOZuXuaJchSHX8s\nKzm3quJQq6LXRrpNh9XottKppdChOopafdGOLN2mV1S6Tbu5uwOBF4CFVBrSI5fKI5PxRocD\nAD766CNN49zGxka32600Q92knGZMLG63m8bx1EE8al6qVnqf4sCI3W53Op00ADXdbZlIlOWp\n9HYPo6E2UJ2x9FRX/gvKDtFoVJIk2luA0TEI2gFQlBvtWtCeg1JoHDoOSG/3LpfLYrF4PB7a\nSfB4PBaLxeVyORwO+tTpdNKfj05OcTgcHo/nME/WYLPZ0OMamXBMIQgB4B//+Mef//xnQRBu\nuOGGk08+meO4eDz+t7/97fHHHweA22+/fcmSJWPXcNgKwsmD3jkU9ZivJ9WPOVo0FospSlV5\nVMKbE9I8KgvdbrcgCFQ/qBdhjh2PoiVj1093LqYlVKyOuxsVt8VUSBtZzJ6mwtm8N/3oXxOi\nGE2nM7IcTKZkQoJub/yk03JC3LQrrD43lM4x/U/R8yqTycSHh4gsh7RM+SYWluM8o3Onld9d\n+WXpIAUA0JMn59WALD8njV5IeR7sDgDwcux/1tXYRwOe6h4MPUXptjooqj7l1h9oD/A88KPn\nFcuB0+HnuD0LtYPDkiQt//DjHktufPVln3PZmKZbxaB0IgGAjiLR7da+vos7Ry0BRJFm5XHI\n4j211enRfeg0B6UqteyhQQa6rVwZnhkaHkwkQLlKJBIgiizAWpdTHRNTN4lempTtsVNWTiN0\nYItu04Etuk2vUXQ757KjvlKpl5GrB8XowBndpqeZMm+TKj1MSHioglB9dgEAvZ3R7TFGE9Tn\ns7pvoJzb6iEA5WRWV6j+cylRYiUsTO+zkB0cnjyUix49Ienljo7e0gE4ugON1NEVKABA56vT\nSUZ0lYHNZqOnJR3PbWxs1DchBVFAQYhMBmYRhLIs33HHHXSdD51+Njw8LEkSwzDXXnvtueee\nO24NKAhnEGphkDMzVll+qZ5PS2+6ypxb5VVl7gp91zQf1ZRD783j7qbueo4NVdTj7kaDvRof\nNDQI4YO/AsswXqsFGCYz7wgYU9+qe7c5CB+9pw6S2XlekuUMy2WWLEun04QQOsgNAPSpLMuZ\nTMZut6fTafrfpB0sOvgNAH2ZTFcsDgBAZBAlYBmQZMikBYYtY0Z8AmRZViLkNJPwuN/JFMOy\nrPLT02u4YBEiMgECQGQAAMECQECW7YRYeZ7nefVAjCiKDMNQdZHJZBRrH0mSlIMVRdGEB06x\n2WyKyLFYLFRp0+9E+VoEQVCUNs/zSp5qlmWVvwPDMLSTSk+/HCWmbNN96DZVX3v6+25PimC1\ngXLeWm0gCLey4omz5yjN4Hne6/UqWmJiobNC9L23yGjkuOo6J8hZ5KcUqlY98VVBHZtVyL/z\n0gFH5WlOOItlWTr/Renw5LTctOMIykWeBtBAdflVxhcUMabMrVD2UQYL6CAjlWqgmlJBy6ls\nU+oZ44JsnJKSkrHXqSLjgoIQmQzMIggpb7311gsvvLB///54PO7z+RYtWnTBBReM62xLQUF4\neKKeMlpoyWV+PEp5uzLmOjbKvJpxKTKBkGbXJ5/iZ+kUGXpVj0CPQU5UB0EQZIZCZRLDHOzt\n0OUJyg458w9zxsXUwwTqHGw5cV31CII64ZYy3UORWzmfqOysiDTIjgZ/ykBBaBwUhMhkYC5B\naAQUhIcnuIZwelGP3zvbW5kn/56zg1zfGL/gUmWJy7jkRBXsTzzKhHNjv1J94/Cxa4pU1Dkr\nG5OyfFNXL+Rd9i4WuOPt44dbc+aSUboy4h+HsoMhsuRLJq/0j5+IVZkPxjAMx3FWq5V2Ve8f\nHI5YrKB0W60WsFjdLPODqgp1hJbOywIAQuDfW9v6PKOfyLLgdALAw17ngupq2hOlUXR1Bzcn\nrgIAmuuHNS90mUymo6/vu0OhDJPVc3VEQrdWVfBaoeaxhy3odIC/BwZ78l7iCdxYUZozmlOI\nIncblzHCX5Isvx2Li9lxb5bAKpvFqlIaNAxrcJK8EhoyQpHT2pVPHDsfmHLWaVJoBgEUnoOg\n1kIKmuvE8oNX+VPx8w/207qGcCaCgtA4KAiRycAUWUYRBJmhqLtijprVTPsBdtfHSgmx2uLn\nX2w5FIOKnDSMbH2t849/GJkGSbFYIjd8FQxkFNjU2fdwKKjWhA2CcPeC2borBAChp/+ewYO9\nHCvDPDSrYaVDf5bdL0nSsl371bqVYeClebNmFz7w+1oOXBJJpVSBhW8koqeuPGjGmO8UYtw7\nJPP+B98Vsnr/f+DJ+iOO0F3ht0TSuGu3mC0y/1pe+plKcyUafXrnzmvkLG3zMyl59ZFHqkvo\nVFUcq0IQBEFMDkYI9YMRQjOAEULz4HA4ZEmStm7h9+9lkgmpsjp97AnEZdTlkg0N2555nB0Y\nAJaVautTZ18oG84vd89Q8Oe9/XGZCCyz3uO6t67GYIUA8FQ48ngo0pcRF9qsN5WVjKHciqQr\nLV3Y2t6ZyRAgNbzw91l149a5v6/v7ta2vQxXzZANHtcZC/QLs+LZ1Lz/wYGhLmBnA7mutnqJ\n4Rw24XT6gr3NewgjM1Aqyw801KyYKtPLQ+Kjzs77urpbgK0D+cqKstWzcscUUBCaBIwQmgeM\nEBoHI4TIZICCUD8oCM0ACkLz4HA4ZFkuZo0iMqnMdGP6Tw0oCE0CCkLzgILQOCgIkckAp4wi\nCDJxZDLWzW9w+/cwqaRUWZ0+fp1cfsj2cTmw4ZD1rdfYrk7C81LT7PRxa8mYC5yK4bFXXn6E\nEw44XFWp5Jmx4Jc/cwYvGDJhS6bTt29++02rLcgLDanEVU77RcuXG2zknmT6S509B9IpQkiT\n1XpPXdWS8Q78u92B+4eHRUJYgKUO+1ONtfbsJVtXt3c/E44QBoCAk2M3z5lTYzGWu4IQ/pOP\nLB+9x0Yjcok/veI4cba2Mcanj5f27b1nKHiAtzSImStdjgsWLZqJbjEIgiAIghFC/WCE0Axg\nhNA8OOx29s/3Ma0HlBLC8YnPXy1VVuuukwmHnA/cyyQTSolcVhG/4ouE12+tdv8L/7qlpkld\ncmV3269PP113hQBw+RtvvuTPkr6/iA9fveo43RV2pNOr9rWIhMCoRTsLzKb5TfMKzxq9qqPz\n2VBWLlwPxzYvPJil+fzmzs2JrB0YgK4lC4y41FnfeMXyziYR2L0u96JoCACSZ5yTWXa0gSpH\nSBGSkGWfWd3SH/jwg29yWYsnv5WKfHPFMeoSjBAeEiIhQUku4yf+F8cIoXnACKFxMEKITAaf\nzrzGCIJMPcxH76vVIAAwkmh76VkjdVpfe1GtBgGAHei3bH1bd4WJWOz28twVg3+uaXx1yybd\ndT60fVuOGgSA33A2UdQ/WnR1R7dIQFGDACAD+bf27kL7RyXp2XCuM0pYkn/Ye3CkLEcNAgAB\nOHFvi+5GssODgQ/eO2btma71lxy99kz7+ktPP/bkzGsvM8a63buTqQtbOhp37pu3a/+xe1ue\nDRflvDKVhOPx/we5yvznVnf74OC0tGemMyRK/9HZ07hz38Ld+xfs3n9nYEj+lIxUIwiCzAxQ\nECIIMjEwHW35hWxPNxhIu893d2jU2dWuu8Ktn+wIChpBtg+C+sM426Majhq9VvuHna2669yb\n0rDHbEkVFFpPhKL5XhoA8K9R44SNQW0jzVZRv3hjuzrXrD7tY9eIjwUB5g1/xZkr1rL9+c4R\nxTIoSZe0dW6KxSVCAOBAOn1Ve/ebWt/wNPJxX29CK3T5Xl/v1DdmpiMTuLaz++FgOE0IAAyJ\n0g/7Ar8dGJrudiEIghxGoCBEEGSC0HRSZhgwsLCKMFp1svonlfEsqymcOAOLvzoLTONkWP2L\ntFNac9HFwjP82QJfcnK0HmeBL00yEIp5kOV78pY1bveUfGJgHv09A0O9GTGn8Ed9Af01TgIx\neoB5X11QwhUEh8yrsVie4Ce/6h+I4nIMBEGQqQIFIYIgEwOZOz+/UGycrS0Ui0OaNUejTq3C\nIjlmybL6VG64ySrLa2r0O0+UFLDWKKkysHjyEAXqkXZt7/KFo4KtUPIYh4FfZ4vdo1HKwGt2\n/etb9qU1QqN7CodGpwWmpKQkk4bs38gui5yvZJpaNIPZm8z7cQmTJqRV60xAEARBJgMUhAiC\nTAxk/kKyLCu1JrHbU6efbaTO1LpTZG+WB53UOCtz5IpC+4+LxWa7NTZsk7NmsX6lu+XoZUfp\nrnNFTW1+IQPg5fRfYD1aAT2HZrwUAABKCyRfOc3tpBuVFu1w5Qq7XbO8GMoc2iq0yoAHo0dL\noPoMfJOTQZndPjcetZCsENbRweEKU/olmhyNvwkDAOA1MFSBIAiCHBJoO4EgyIQhnnex1Dib\n27eb2k5kjjmeGNAbAEBs9vjVX7Zsf5ft6iA8LzXNySw9ykjIEQDOP/Ez9Ts+fKi7o83qKM+k\nzhDY8888y0iFZ7qdP+ljY9kz3E52Of0GMmSe7XX/ZSiYU3iKx1lo/yqBP8HpeCuWFfx0c+wZ\n7pFgnY9la3ihW8yNuny5VH9Q64sl/t8FgnL21Ekrw5zj1g6ZFsNFXvdDw6Gcwot9Xt0VTgZL\nbdZQRVXD0FBVKhHjeYckDgvW5srqNU7HdDdt5nGq2+nluFD2SuNjHfZ6i5H0twiCIMghgLYT\n+kHbCTOAthPm4XA2pn88FPlqV09iNDfiPKvlsab6KsHQiNvqvS37VOk6GyzC1vmzx5DCHenM\nhtaOltGJdk6W/V1d1dmeg9qsOZ0+aX9bUnXJutzn+Z86/fNaAeB3gcHb+gaUuwjHwP31tes9\nhlKi/6J/4Jf9B9N1nuhyPtRYazGZxd8nydRn2zqV5Y5+nvtTfe3xzqzhD7SdKJLnwtEbO3uU\nRYNNFuGfs+obhAkThGg7YR7QdsI4aDuBTAYYIUQQxNSwQwPWN1/lejoJy0lNs1NrTiaOgoGy\nIrl7y5YTP3l/bizcbXO80DDv8lNOcRuY5QgAG+zCut3vPytDgLcsTCXOamok82YZbOQxLuu+\nocxo6hLmKMc4FvL1FmFWcLjN7pIZYAjxJOPHclkHNcdiebC++mvd/f2iaGXgbLf75zVVBhv5\npdKSN+OJ1yIxCYBnmM97PQbVIACc5nLfMxgMSxIACAx8xukwmxoEgIUWYX1o6CGbM81yvCyv\nC4dXCA3T3aiZynqPa8u8Wc9Foj2ZzHyr9Tyv24S/OIIgyKcYjBDqByOEZgAjhOZhMiKEbHDI\n8cC9al872eePX3UdMaDffv/yy9967y11yUMNs8+97Av6WwngvPu/2WgEDtrIQ3rV6tSJp+qu\n8I6BgZ/0Debksfyiz/ezuspCb1n/7rZtjqzRAIdkAAAgAElEQVS5mjZZ6li2SHn6aiR2aVsn\nAMBoQ092OR5prGcN9L1Pbm75OJEVdTnH47m/QX/UsVeSlu3an3XcDPykqvK6UnMtz7vinXef\nd2ZNZD06HvnXKjSmNyMYITQPGCE0DkYIkckAF20jCGJerK+9xGRnmGSDQ8K7m3VX2J9K3bDj\n3ZzCz7UfeHDHJ7rrtLy9iY1GANQ28mDZugXEXPuE4vlJ7yDkjdb9XzB3VaHCUDKZowYBIMly\nN7y7VXl6S0/f6OZIQ1+Nxp+JRHU38l/hSI4aBICN4XCHgW73RQfacw+bwA96+3VXOBkcCARy\n1CAAvOdwv9G8b1ragyAIgiBGQEGIIIh54Xq6IC9+xfV06a7wtbY2TyY/nT2J9XTqrpNva9Eo\nJYTr7dZdJwBA/pEXDuX9Za+2FHmHG1mINSRJmnn834sn9DUOAJ6PaJvdP2dAZHZo/DqQMdlM\nlhc7tc/A5wYLKnYEQRAEMS0oCBEEMS9EM1Enr3/xs5XXnGvKEAOZS0mB9hAjWTG0tV9BRejl\ntV8SRqe0WxjtVVlWA4u17AW+NGdhe4xx4cZQvabBXmCWrR1XviEIgiAzEBSECIKYF3HOPK3C\n+borXN/Q2Jw3tTLBcfOOOEJ3naKWhyERBLlS/1I6lmhKi4KBss8vWKj5hiuEEUXtYtnjtUwR\nTnXrz9DzhRJPvkblGLjAq2VYXxyrnLa8MuI2ssxxErhowTw276dgAD7XWDcdzUEQBEEQQ6Ag\nRBDEvKTXniKXVahLxLkLMkv1m8hbeOb5E0+LZQce71xyzKl19brrzMxbKDXOVpcQBpJnXai7\nQgDYPrchX2u9PKex0P48x92QpBM1DyqVplTi349aqjy9o7Yqxxrxa+X+Yxz6jSIX2WxfKMnV\nft+vqHAa8JF/tLHekS3/GGCeaCp44NOCy+b4XjqeU/ileGhuhdGsrQiCIAgy9WCWUf1gllEz\ngFlGzcMk+RAykiR8+B7b1Q48LzXNzhyxBAxPzNvU23fgnbcaw8F+u0NcfOTlCxcab6fwwXbL\ne+9CKimXlKROO1suLTdYYW8yueJAR1omwABPyNa5jXW2/OhZFs807/vPcDLE8zZJvjCT+NXK\nY3J2GBalPw4HP04kS3n+fI97rWsCjNSfCkX+OzAUEKU6C39bVcWxjnEaOS4SwJXtnW/HEhlC\nmiyWhxvqay1aM4enm7dbWm8LDHTylkox858e5xl5QWbMMmoSMMuoecAso8bBLKPIZICCUD8o\nCM0ACkLzcDgb05sKn8/H8/zUXxKRHFAQmgQUhOYBBaFxUBAikwEa0yMIMnVEJPmXgYGnw9H+\njLjEbr25vPR09zg3to8SyR/3D2yLJwRgPuN2fq+yrNZIshYAOZ2+44Xn/1le02Z3VqSTJw/0\n/+iYpY6yGiN17opELjzQMchxAMACHA/iE0sWG6kQAH738Y4fgSABAwAsgVsgc/PSJWO/5bOt\nna9GYzIAA1Aj8M/MasqJrc37eF8QpJHJqIT8tqLqc5WG/P2Corz+QGtzOkMAGAZW2WxPzmk0\nGM7b/fEnPx4IbPf6kwx3RCx8Qzpx3smfMVYlbI0nf9wX+DCRdLLsqW7ndyvLKgykJgKAaBqW\n7NsbGxlRZawMeXferBoD9pgIgiAIMl1ghFA/GCE0AxghNA/jRghlApe0dbwRzVp8dX99zTne\n3CwvCnuSqdMPtMdH/mUEgKm3CK/OafRqZh8tjh8+8/SdjVlpaU4Z6HvkpHW6KxQJafh4dyY7\ntebRUuZfR46j38bg6QP7ronnXlt+w8pXLCo4u/Wc5rZ3Ellfvo1lOhYdPNLZH++J5L1rx+z6\nKof+iaNzd+8PiVJWiUXYMn92of3HZSjQe0Zrd6v9YKobmyT9Mdh32okn667zvXjyvJb2lOpm\nt8BmfXF2Y6FkocVQ/ckeMfvmyTBM/+Ks8wojhCYBI4TmASOExsEIITIZYFIZBEGmiGcj0Rw1\nCAD/1dsvFx6Vuq0vED845sIAgY505q4B/f2JaE/n/9XNySl8uazy8Wee0l3nF3buyeQZLbzH\nCUEDvc8bohqm9rdI+WUjDElSjhoEgKRMvt7dM7JNSL4aBICVLR162wi/DwzlqEEA2J/JfBRP\n6a7zZ+99qFaDAJDkuF8J+jPfAMD3evtS2UOfe5Kp/xvSfxY9EAyKeSctIeTaNv1ulgiCIAgy\nXaAgRBBkivgwoeGB3psR+0QN8UP5KJEtLRgAgA8NLFN8+6MP4xyXb9/wgYH5858UaP/fe/t1\n15nSCl6JhbPpPBnS9oJ/JzryBf6hp0dzh6SBKQ7PR7WM6Qk8HNKvtQ7YNcKVe5wFY8jFsCOh\nIVA/zNPPxXNv/5Bm+etxXECLIAiCzDxQECIIMkU4WI15nkxhm28o8JLDgO+5mxrT59VqI4Wj\nb+NRaN1YrU3/WkdG25+94BdVXmAOrW20eJ69QJCN0b9qwFlAoPo4/Qdu1ZqE75AKDhkUg13L\nBsPBGjiLBO3hA6vuGhEEQRBk+kBBiCDIFHF6jgc6AQBY7XT4Ci8IPMujERpab8BLfeXaE2fH\nc+dO2mXxvBKv7jo/79V4L0vIWRWVuuusSmsEtfyFQ6nneF2aV/MbS/104+ySkpG4aLYAPMKA\nZ+A3Kks1JepNpfoT1axJ5E4qBoBjg9oRuSJZr5W46CyP/kU499RoG9DfVqn/F0cQBEGQ6QIF\nIYIgU8Rim/X7lSp3PgYqef63tWN5eX+7ouxIe5av3UVez6UGxBtrsfw40OPNZJQSC5Fv6mhe\nfIL+pDLfaKqfJWZyCr9vzOJv8xFz2ZycJQBbjshd/ajmR9W5amS53Xax76Bx/MXUg14l4VgC\nby6cD3pZabef6swV5zeVlbgMpPy5Yf369YGs2a3zYpFfztPfSAD4YVX5fGtWHPdqv++M8dLb\njkGjlV1kzQ0MV3LcBr+hqa0IgiAIMi1gllH9YJZRM4BZRs1DkT6E2+OJjeFoQJIWWS1XlPjc\n40WoREIeDYa3xhNWlj3J6TjTQGBH4cBH793T09dpcfgz6csgs+b0s4zX+d19zf9IJBOELQPp\n3obaY0pKDFYoStJZH3y4U7ATBualks8vXWazjiO0Poqnru/s6pVkD89f5/PcVO7P2eHJYPC6\nzj4ZAAg08ezWhfMMNhIAHhoK/SIQCIpQJrC/rq08MU8i6uBPLz63mbMkGW5BMvH1dSc5nIaS\nygBAmpCHhkPvJ5Iulj3V5TzZQJBZ4dd9Q78KDIgAHMCVJd5f1OYKcswyahIwy6h5wCyjxsEs\no8hkgIJQPygIzQAKQvMw44zp2XRanmjjuCFJ8huIj+XTG41KALWHcvvPWCzVHs8Yl8T2TKbB\nmJdjPhN+4AQgIxOLAWeIqaFXFKsKWBqiIDQJKAjNAwpC46AgRCYDNKZHEOQwQ5Ztzz4u7NkF\nsgwMSFW1yYsukx2G7q87UqkNLZ3DkggEGAZW2+1PzG4w2MwNO3a+CRxhAAAYAqvE1Mbly8bY\nXyLkvI93b2VGppqWy9JTTY1zPVmhsKP3HOjIZIAAMMAC3NdQfZ7Ho1lbkfRK0pn7W7pEida5\nyGp5flaD3ZgybM9kvt8TeDUayxCyxGb9flX5Gqeh+beTQVAUV+9vDYy4bjAejnl93qw6Y2b3\nCIIgCDIt4BpCBEEOL+xPPCrs+gRobJ8A19PleOA+IxWmJTh9f+uwKNJ8LYTAW/HE2QfajdR5\ny969bzAjahAACAPvCNZrd+0e4y2nf7zrXebgwsMAy53Y1iGp5oCMqEEYWUYoA3yxvac/k7v6\n8ZA4bs+BroykpKvZmUyf0NxqpMKwJF9woP2ZcCQuyxlC3k8kP9vauTWuYVgyvSzf2xI46MFI\nwpK8au+B6WwQgiAIgugFBSGCIIcRbDzKN+/LKWSiEWH727rrvK6rM9+m/N14Iizpt7J4MKUx\nF/3pwuYLA+nUR0xuXC7NMF/bM3KwGYCOvMw3AHDKgTbdjfx1/2Asb858R1rcqmU4WST3DA53\nZLKOM0XIrQYcHSeDjeFwNO/AMzK5tdtc7UQQBEGQYkBBiCDIYQTX2pJrvAAAAHyH/oDejoT2\nwqQXIlq+7cUhai2dkwsb0z/doy1FtqdHRODT4bDWccOgqH8V9GsxDYsIAPhnUP+quZ0pDb+N\nT5LmWvr1l+GQZvlL8egUtwRBEARBjIOCEEGQwwhSYK0gsdk0y4vBWSBRah1vxJhek4I5wGrs\n2tlxXKP263N4XvPdXGGz+3HxstprBcs5/UvpXFp+8W4DJvKTQQWrfYBmayeCIAiCFAPevRAE\nOYwQGxpJduYPqpLSy1fqrvPGMg0fdo5hjnPpN0twa003dRaeg3pGReXI1Txb9X111Jj+SIeD\n0Qownm/Aju+Wcg1jegaYGw0Y05+rZStyntdcKfV+UF2uWf7DqoopbgmCIAiCGAcFIYIghxMs\nmzz3IlBJIwaYzNGr5Mpq3VV+1uc7Js8o7w/1VborBIBPli7MNaYn8PacxjHecofPBZAVW1zH\nSGer3Op/VpUtYxjwsMxdDfoPfJnDeoHXnaMJv1VRaiTL6Olu1/WlWRaORzts36vUFmDThZ/n\nL/N5cg58rcuxymG6bKgIgiAIMi7oQ6gf9CE0A+hDaB5mkA8hGwlZX3mRHRogLnfq+LVSnVGL\nCAC4fyj0+4HBsCQ3WoT76+tqLRNgyrfig496OR4IUy6LHxy1dNz922LRa5vb2hm2BJhv+N2X\n1tbm7HAgkzmruT0oigLDXlTi+W1Nrpe6Dp6PRG7rCQxJcqXA31VTvcxhNV7nu/HEi5FoUibL\nHbbzPW6u8OLJaeT1SOKm7u6QKLlY7odVFZeUuHN2QB9Ck4A+hOYBfQiNgz6EyGSAglA/KAjN\nAApCM8AkEsRqdbhckycIo7JsYRjLhAqDA6lMgyDwEzdPIi3LPZLUOKGe7zvS6Xg6feyh3P5l\np6vUZh0eHCxYZyo1i+ddE+cjTwCGRcnPT6Qx/WQQlmQbw0ys2f1T4fB6j0fzJ6eCsG1o2Mtx\nE/qZyKGBgtA8oCA0DgpCZDJAF10EQfRCiOX9rZYtbzLxGOF4WHqUfMqZMNF5NV6NxG7t7d+V\nSnMMc5zD/tOayoVW7QQqRZKW5avae16OxggQYGC+xfpIY029xVCde5LpL3R0tabSAMACc67H\ndW9DjcEv4pzmtncSSWr4DgBH2m0vjTllVCZw79DwbwODA6JkZ9mLvO7vV5bniLSzD7S/Sz39\nGLAzzAP1tSe7ndrVFUdUlm/vC/x1OByXZR/H3VBW8pUyv2C+gN4z4ciP+gaaU2meYdY5HbdX\nV8wxdhZ1p+VV+/enZDqi2sMzsHFW/QrVlFGZwJ39A3cGhgKZjI1hLvF5vl9V7ps4EY4gCIIg\nEwVGCPWDEUIzgBHCacSy7W3rqy+oCghpmBW95AsTqAm3xxPnt3SkVJepUo57fV5TJa9/MOui\n1o43o1l+CaUct3PhXN2NTsrywj37o1LWtfRcr/uP9TV6q4RbuvruHw5mFTFwrtv1x4bcWaAK\ndw0M3dYbUJescTr+2VSvxKbOa+nYkm0UwQLsWjjXb0ClXN3etTGc5bXw72X+W6vMteTvtWjs\nktZOdUmdRXh1TqMReVb98V7xYAIfAsAwAP1LFig73BEY+klf1s9xksv5t8Y6DBVOPRghNA8Y\nITQORgiRyQCTyiAIogdGEi2bXsspY9pb+ea9E/gpt/cPpLIHrQYl6c7AkO4Ke9Lim3nueYOS\n9LuBghMsx+Wn/UM5ahAANoYjMUn/aNH9wWBuEYGnwwVt7hIy+UV/7qDYplj8pejIW9ISbMk7\ncBngqvZu3Y3cHk9szGvS7weG+kVRc//p4kd92d8Mgc505n8H877horlvIChmnZYMABCAa9u6\n6POoLP+yfyAn5etr0djrBZwbEQRBEGQaQUGIIIgemFCIyWgMt3MDgfxC3ezWciTfk9I/zL8p\nHte049sW17/08aNEIr+QENie1PBYL5ZDnLrRlk4nZI337Bptw3tajQSAZi0j+CLZO+p6r0YG\n2GvgB5oMduf8EAwAwG4DB/73cFDTvvHt+MiX3JrOpAnJt5PcNRNSLiEIgiCHGygIEQTRQyEn\nd9mAw3s+Xi3Pd4+BKanVgvZcUyPTJjUbCQC1wtQtGPMWaL8yK7LQIkmngS/TXWCtoNlWymn+\nQIW+sWKoErS/TN/oLz7uz4EgCIIg5gEFIYIgeiAOp9QwK6sEgAgWcc78CfyUi7ye/MINvtz8\n/sWzxulwcrkyhgH48qiBuw5uKCvNjwX5eW6OgUQ1Hk5DuNoKKE8AqBb4o+y5UtzGMqeP+s7X\nWjib1vK1G8tKdTdynctZrk5aQwAAjrBZF1knwHliAtE8iy706j+L7lQWSWYHZX8zauNRL/Ar\nHbnWlF6OO9VYCh8EQRAEmQxQECIIopPE+vNk30EdxQgW+dyLiMc7gR/x1XL/Ka6sPvQNZSVn\nefR35QHggYZaXqWMGICvlZUusOkXb8c6bFf7s45aYJlHG+t1VwgAzQvnMHnxt11z5o3xlnRe\nhjCZQEYlWR6bVZ9T4zFOe07LDwkPx/6+rvpgNIyBaoG/t77GbHlTvldVfly2PPt2Zdkap34T\nea8gXOjzAGSNA6xy2tXG9L+vq26wHHSjcLLs/9RWVhhIhoQgCIIgkwRmGdUPZhk1A5hldHph\nJInb8wk3MECcTn7ZctntmQwfwlej8XfjcSvDnOhyLs+Lg+kgKMu39QZ2J1M1Av/VMv+yiajz\n3Vji94ND/aK0xGa9tbLcWTiaVzynNrd9kkwCYebZLG/MbRpjz850ZvneA/nlP6uu+GJpifI0\nKknXdvTsSqU8LP/V8pKLfRqhs0NlQJSeDkc6M+Jsi3CB121kDurkQQBeiETfiyedLHOq27XI\nNgExzHfj8Svbe8Ky6GT5X9eUn+fJ/TJFjnsmltgRClcJ/NluV6Hpyshkg1lGzQNmGTUOZhlF\nJgMUhPpBQWgGUBCaB4fDMXnG9Ew8BixLbLnT8ExFmyhuikY3uHy2iev5D4kSYaB0vLVne5Kp\nNftb88v/X2XZf5RnTQpNA/y2f3idQzgks/uxScukXxSrBJ6fOAfCrrQYksVFE7okdcKJStIb\n0fhqp9PHa8hgakyPl6ZpBwWheUBBaBwUhMhkgAOWCIKYGn7fbusr/2LDIQCQqmpSp66Xqgt6\n8U0Xfx+OfKWrRyIEGPga9JVy7Afz5hmUhZtj8W/39NM0ofOslp9UVYxhIt9ktbhYNpo3PrU0\nW1DVf7InCQAEfkEAGLihrOyHVfrXEAJAUJJu7Q08GgyLhNgY5roy/7cqSi3GZOETocjXu3rp\nsXDAXFPqu726wkiFk0GvJJ2yr1Ux2PDy3DNNDUYmHiMIgiDIdGHGuT0IgiAUrrPN/sSjVA0C\nANfbbf/HQ8zoU5MQyMg3dnVLQJQVZYOSvHz/fiN17k2lL2/rUkwj9qXSV7Z37SjsY2FlmNV5\ni+J8HLfGdbCwYdf+JBnNg8IAANw9MLA9qj9mQgBu7Ox5aDhETfmShPxPYPDWnn7dFQLAzmTy\n+s5uRdlKQO4bHP5NQL9L5CSxZm+L2m4xJEqnNLdNY3sQBEEQRDcoCBEEMS/WTa/llDDJhGXr\n5uloS0EubWvPLxwQpU4D/ux3BAbj2eG+JCG/6i+oi+Ky/Ho0NvJkdB1AUJJejIwYx0fTkJCk\n/Ddu6NBofJFsiydfjMRyCv84FOzN6D/wb3b35/sp3hEY0l3hZPBsKBISc7/MFJF/1ms64Yog\nCIIg44KCEEEQ88IOafSwOa3CaaQjra1/Hg8Gdde5X8vbfW9hL/X2dCalLAhXTdjcN1rPoyHt\nxiRkDZVYJPu02iMD7DewUqtDy+w+Qcy1VPuFWKyAMX2uPEYQBEEQ84OCEEEQ80LsGllkZJOl\nlnFoJRQBgKOt+k3nfFpJSv2FU8uU8Nov+Ud9DlZ6tV0WOKJ/vd9II/MCemO0c1xcWgfOacqv\n6aOeF3KLCABAlZBXjiAIgiCmBwUhgiDmJbNoaX6huPjIqW/JGHyrwp8vWFiAE9z6heulPg17\nwMsKu0RU8vxJrlz96eO4M0bz0Cy1WCA/1wsDl/n1Z6tb53JWC3zOsS+1WRcaMHXQ9EU8Rmtc\nYBr5Smlp1kETAAaAge9WGsrQgyAIgiDTAgpCBEHMS3rl6syCReqS1Op14uy509UeTT7v8x1t\nteXoovsb6ozUebHP86VSHwAoAbgvlHiv8PvGeMv/1FYdoVJiXo77XV2V2gn93obqnLeUMNx/\n19TobqSLZe+trylTBScbLcK99TVGwnnXlfo/43Kov8xynn+0yVx5ZS0cZCU+ZQCA+Uppab0F\ns4wiCIIgMw/0IdQP+hCaAfQhNA+T50PIdXVwXe3AW8SGRrnMdA4ElI3h8G29gyFJnmsRHm2s\ncfETYOqzI5naHItLhBzvdCy3j+/IJxLyr0i07f+3d6fhUVTpw4dPL0lnIWQnMRhACAGBGCbC\ngMoiiCIgiywqAxMhLOqgoBBBdBwZ4ALBQcBXHVABAVHUyM7gAiYIASJhR1ETlkDYl5A96fTy\n/1Az9fbV6YSmE1Kdrt/9gauoOlX95JxKdT2pU+dotHf7+DykdTB7YZFRdD1z8rLR7K0RE8LD\nX20U7PA4tyXfbP62oCjXZIrx9n68YQNDbUxF+GNh8ed5+UUWc4+ABs+F1kKQd8Ils3lS7sWT\n5cZob/38iMiWvvbZIPMQugnmIXQfzENYc8xDiDuBhNB1JITugITQfdzRienhJKOp4mppWXRo\nmMXoeAQao8V6ymgM99LfcrJ7ZWkKCzSmCktgsNDW154sJIRugoTQfZAQ1hwJIe4EJqYHAE9Q\nYTLNP3z4Q4O/UaMVORd6F+fPaxnTOCRELmAVYuHV64uuXi+1WIUQD/j7vhsVGWNwu16O2vPn\nfH/Yqr16RQghfP3Kuj9SEfcnpYMCAMBj1de/vAIAbP3r0JFFPgFGzX+v6t/5B47OPm20mS5v\nybW8uZevlf5vmr+9xaV/ycktcrM+DpqCfL/1a/+bDQohSkt8vt2s/+M3RYMCAMCTkRACQL1X\nXFb2vo/9xBKH/BrsyM6Sls1W67tX7edvPG2sSLnpXh0avQ9lakpL7Vfu2alIMAAAqAEJIQDU\ne+dv5snPBm2dLCmRFm6YLTfNDuagP1nuXm9VaW/aZ61CCG3ejbqPBAAAlSAhBIB6L8Tf8RgD\n4f+bKr2hTuutdTD+Z3gVM9orxepr/5xTCGH1c7ASAADUChJCAKj3wgIC+hTbd/4MMxofvece\nadmg0QxpGGBXoIFW+2SQg4ngFWRsG195pem+hLqPBAAAlSAhBABPsLBN6/tLCuX/Nqoo/zjQ\nL8RmdPI5URFd/P//o7ZAne7/NY6M9nKvsaYtjaPLHnncajOLY8W9ceWdHlIwJAAAPJt73QoA\nAFwTGhDwnw737zl98lRZ+d3+fh0jmgT4+toWaKDVrr8nek9xyS9lxjC9rlsDP/ecirAi4c+m\nmFa6s2c0pgrLXY3NEXcpHREAAJ6MhBAAaoFViNwK05WKihiDd6BCiZZWq+nSIuaJoCC9Xn/t\n2jWHZR7093vQ391fybM2DDS1c9B3FAAA1DoSQgCoqaxy46Tzl/aXlAoh9BpNUkjQjMhwL42D\nQVwAAADcCgkhANRIicWSePZ89v/mbzBZrR9dz/PVav8eEaZsYAAAALfEoDIAUCOb8wuzK83m\nt+TajVKLVZF4AAAAnEdCCAA1crbCVHlludV60VRR98EAAADcFhJCAKiRRo7mdtdpNGE6+uQD\nAAB3R0IIADXyRGBAmJQT2nQRHRwY0FDHBRYAALg77lcAoEZCdbqPo6MivfTif6OKPtzA/+27\nIhQNCgAAwCn0aAKAmuri77ev5T17i0uvms2tvL0T/HyUjggAAMApJIQAUAv8tdpeAf5KRwEA\nAHB76DIKAAAAACpFQggAAAAAKkVCCAAAAAAqRUIIAAAAACpFQggAAAAAKkVCCAAAAAAq5TnT\nTnh5edXxJ2o0Gi8vL4vFUsefC1sajUYIodVq6/4EgB2tViv9UigdiNpJvxQ0hOJ0Oh2XJneg\n0+mEEHq93mq1Kh2L2vEdUXNaLc9yUPs8JyE0GAx1/Ikajcbb25svGGXJCWHdnwCwI91vSS0C\nBUlNwG+E4jQaDZcmd6DX66V/uZNWnEaj4TcCcEOekxAWFRXV8ScGBQUVFxfzhFBZ0v2WyWSq\n+xMAdvz8/CwWS1lZmdKBqF1QUJBWq+U3QnF6vV6j0dAQivP19dXr9WVlZUajUelY1M7Ly4vf\niBry8fFROgR4IP5aBgAAAAAqRUIIAAAAACpFQggAAAAAKkVCCAAAAAAqRUIIAAAAACpFQggA\nAAAAKkVCCAAAAAAqRUIIAAAAACpFQggAAAAAKkVCCAAAAAAqRUIIAAAAACpFQggAAAAAKkVC\nCAAAAAAqRUIIAAAAACpFQggAAAAAKkVCCAAAAAAqRUIIAAAAACpFQggAAAAAKkVCCAAAAAAq\nRUIIAAAAACpFQggAAAAAKkVCCAAAAAAqRUIIAAAAACpFQggAAAAAKkVCCAAAAAAqpbFarUrH\nALiupKRk0aJFMTExTz31lNKxAG5h5cqV58+fnz59ukajUToWQHl79uxJS0sbOnRobGys0rEA\ngDviCSHqN6PRuG7dur179yodCOAudu7cuW7dOqWjANzF77//vm7duosXLyodCAC4KRJCAAAA\nAFApEkIAAAAAUCkSQgAAAABQKQaVAQAAAACV4gkhAAAAAKgUCSEAAAAAqBQJIQAAAAColF7p\nAIDal56ePm/ePCFEYmLi0KFDlQ4HqAsWi2Xnzp0//vjj6dOni4uLAwICWrVq1bdv3z/96U9K\nhwYoj+8FAKgKCSE8TV5e3ocffqh0FECdqqiomDt3bmZmphDCYDAEBwfn5+dnZGRkZGQMGjQo\nKSlJ6QABJfG9AADVICGEp3n//fcLC5rsPwMAABqoSURBVAsNBkN5ebnSsQB15PPPP8/MzPT2\n9p4wYUK3bt10Op3RaNyyZcvKlSs3bNjQsmXLrl27Kh0joBi+FwCgGrxDCI/y3Xff7d+/v3Xr\n1q1bt1Y6FqCOFBYWbty4UQiRlJTUo0cPnU4nhPD29h48eHDfvn2FEKtXr2aGIagW3wsAUD0S\nQniOy5cvL1u2TK/X/+1vf1M6FqDu7N6922Qy+fn5PfbYY3abBgwYIIS4dOnSiRMnlAgNUBjf\nCwBwSySE8BBWq3XRokVlZWV/+ctfmjVrpnQ4QN357bffhBBt27bV6+3fArjrrrvCwsLkMoCq\n8L0AAM4gIYSHWL9+/S+//NKqVavBgwcrHQtQp3JycoQQjRs3drg1KipKCHHmzJm6DAlwB3wv\nAIAzSAjhCXJyctasWWMwGF555RWtlrMa6lJYWCiECAoKcrg1ODhYCFFQUFCnMQFK43sBAJzE\nJRL1ntlsXrhwYUVFxahRo6SHIYCqlJaWCiEMBoPDrd7e3kKIkpKSOo0JUBTfCwDgPKadQD1g\nMpksFovtGp1OJw2lKIRYs2bNqVOn4uPjpQEVAdiSxhfVaDRKBwLUHb4XAMB5JISoB6ZMmXL6\n9GnbNR06dPjHP/4hhPj999/XrVvn5+c3ceJEbnmhTn5+fkVFRVVNsCat9/Pzq9ugAMXwvQAA\nt4Uuo6jHysvLFy5caLFYxo8fHx4ernQ4gDIaNmwohMjLy3O49caNG6LqNwwBD8P3AgDcLp4Q\noh5YvHixw/Xp6ekXLlzQ6XQbN26UJuaWXbx4UQixefPmXbt2CSHeeecd6U0qwPM0a9YsOzv7\n3LlzlTdZrdbc3FwhRIsWLeo8LkABfC8AwO0iIUQ9ZjKZhBBms9muQ6ksLy9Pemxi9woi4Ena\ntWu3ffv2X3/91Wg02t3gnjx5Mj8/XwgRFxenUHRAneJ7AQBul0YabwDwMG+++eaRI0cSExOH\nDh2qdCzAnVVWVpaYmFhWVpaUlDRo0CDbTfPmzUtPT4+JiXn33XeVCg9wE3wvAIBDvEMIAPWb\nj4/PU089JYRYvXr19u3bzWazEKKkpGTFihXp6elCiKSkJIVDBAAA7oouowBQ7w0ePPjs2bNp\naWnvvffe0qVLAwIC8vLyzGazRqMZO3Zsu3btlA4QAAC4KRJCAKj3tFrt5MmTO3Xq9P3332dn\nZ+fl5QUFBbVp02bQoEEtW7ZUOjoAAOC+eIcQAAAAAFSKdwgBAAAAQKVICAEAAABApUgIAQAA\nAEClSAgBAAAAQKVICAEAAABApUgIAQAAAEClSAgBAAAAQKVICIF6qUuXLhqNRqPR/Pbbb0rH\nYs+dY1OPetcKX3zxhRTwrFmzlI5FGSNHjpRqYMuWLUrH8l9VheT+Z1flyD/66CNpzb/+9S9l\nYwMAd0NCCChg+/btmtt35swZpQP3EC7Uf1lZmdJRe7K9e/eOHj1aCDFs2LA333yzcoG0tLQp\nU6Z07949KirKz89Pr9c3bNiwadOmvXr1euutt3755Zc6Dxn1zPjx4ydMmCCEmDZt2saNG5UO\nBwDcCAkhUC+1bNkyPj4+Pj7ex8dHqRief/55jUbz9ttv2613h9jUwwNaIT8/f/jw4eXl5U2a\nNPn444/tth47dqxDhw49evR49913f/rpp4sXL5aWlprN5sLCwrNnz+7YsWPmzJlxcXEjR44s\nLCysfPCq6gc1UY/OLlsLFixo27atxWIZPXp0bm6u0uEAgLvQKx0AoGohISHSgxFnBAYGyssr\nVqy4MxHdhoyMDIfr3SE2Jzlf/3q9m14tPaAVJk2alJOTI4RYtmyZ7UkuhDh48GDXrl1LSkqE\nEH5+fr17905ISGjUqJHBYCgoKMjKytq2bVt2drbVal2zZs3p06dTU1O9vb1tj1BV/aAm6tHZ\nZctgMKxatapjx455eXmjR4/+4YcflI4IANyCm97iACoRHh5eT19oKSkpOX78uNJR1FT9rX+J\nB7TCvn37Vq1aJYTo379/r1697LYmJiZK2eCAAQM++eST8PBwuwJWq3X58uUvvPBCRUXFnj17\nFi9e/Oqrr8pbPaB+ULsSEhJGjRq1fPny7du3r1u3bvDgwUpHBADKo8soAFccOHDAZDIpHYXa\neUArTJ061Wq1CiHmzJljt2n//v3Sy4FRUVFffvll5WxQCKHRaMaMGTNjxgzpv4sXL7ZYLPJW\nD6gf1LpZs2ZJj5Ffe+0127MFAFSLhBCol6of5e/o0aMTJ06Mj48PCgoyGAyNGzfu1q3b/Pnz\nr1+/Xv1hd+7cOW7cuNjY2ICAAH9//9jY2PHjxx8+fNi2zIwZMzQaTbdu3aT/Tp8+XYrk8ccf\ndya21NTUcePG3XvvvUFBQd7e3pGRkQ888MDf//73c+fOOQzp4Ycflo5mNpuFEEeOHElKSoqO\njvb29g4ICIiLi5s2bdqVK1duVWG1pmvXrhqNRqvVWq3WoqKiSZMmST0YZ8+ebVuspKRkyZIl\nTzzxRJMmTfz9/b28vMLDw7t27Tp79uyrV69W/xHqaYX9+/fv2rVLCPHYY4+1a9fObusff/wh\nLXTv3r36d9VefPHF4cOHz5w584MPPpAywFvWj8SFZqphVZw5c+all16KjY318/MLDg6Oi4t7\n/fXXz58/X31FuRaqk+eqCyFVPruSk5OdGZwpJibG4QFdu2S5VplRUVFPPfWUECIrK8t9BnQF\nACVZAdQ5+d2VVq1auXaEhx56SDrCiRMnbNeXl5c///zzVf2+h4SEfP311w4PmJ+fP3DgQId7\nabXa6dOnyyXfeusth8V69+5dfWwFBQX9+/evKjaDwbBw4cLKgfXu3VsqUFhY+O9//1un01Xe\nt3HjxmfOnHG+9mpS/3K3xuLi4h49esgxTJkyRS6zf//+6Ojoqn7S0NDQH3/80eHBVdUKVqtV\nfoHT4Wm5cuVKaWu/fv1u67BWJ+rH6moz1aQqtm7d6u/vX7l8WFjYTz/9NGLECOm/mzdvttvR\ntVCdOVddC6ny2TVlypSqwrPVokULuyBdvmS5XJlWq1X6M4QQom/fvg4PDgCqQkIIKODOJYRP\nP/20tD4yMnL27Nnbtm07cODAxo0bk5KSpDtXnU63adMmu6OZTKaHH35Y2rFZs2YzZsz4/PPP\nly5dmpSUJA+mMmPGDKnw9evXs7KykpOTpfXJyclZWVlZWVkXLlyoJjaTydSlSxdpfVRU1Ntv\nv71z586DBw9u3br1+eef9/LykjZ98MEHdrH169dP2iSlBy1atJgzZ8769eu//vrr1157LSAg\nQNo6aNAg52uvJvXfp08faV9pXA2DwdClS5devXotWLBAKnDlypWwsDCpzP333//ee+9t3br1\nxx9/XL58eUJCgrQ+ICAgNzfX7shqa4WKioqQkBAhhI+PT1FRUeUCmZmZ0mG9vLwOHz7s/JGd\nqR+Xm8nlqjh16pSfn5+0tUePHl9//fWBAwfS0tJmzpwZEhISGRkpH9kuh3E51Fueqy6HVPns\n+uOPP36oWvv27aXyo0aNsgvStUuWy5FLzGZzZGSkEMLb2zsvL69yAQBQFRJCQAF3KCFcvXq1\ntDI+Pv7atWt2u2zZskW6wYqKiiosLLTd9OGHH0o7du7c2e7WPDU1VcpG9Hq97UOPuXPnSrvM\nnTvXmdjeffddaWXr1q2vXLlit8uGDRukrf7+/hcvXrTdNGDAAGlTYGDggAEDysrKbLempaVJ\nW3U6nfM3djWpf/nxWqdOnTp06CAnGDL5fbZu3brZRWuxWIYOHSqnKHY7qq0Vdu/eLe316KOP\nVlVGzpADAwMXLlxYUFDg5MEl1dSPy83kclWMHDlS2jRw4ECLxWK7KTs72/YNSbscxuVQb3mu\nuhxSVX+QcmjhwoVS4fbt25eUlNhucvmS5XLkssTERKlASkrKLX8EAPBsvEMIeA5pWA6NRvP5\n55+Hhobabe3Xr9+zzz4rhLhw4UJKSortJnmkzY8++siuF9bDDz8s3XuZTCZpNEgXWK3W9957\nT1p+//33K48OMnDgwCeffFIIUVxcbPcpGo1GWvDy8lq9erXBYLDd2r1797i4OCGE2Ww+cuSI\na+HdFq32v5fNQ4cOpaSk3HXXXXYFfH19H3/88fbt2ycnJ9tFq9FoJk+eLC3v2LHDbke1tcLe\nvXulhc6dO1dVZunSpXfffbcQIj8//5VXXomIiOjXr9/cuXPT0tKKi4ud/CCHXG4m16qitLR0\n3bp10u4LFiyQDyJp0aKF3Xt9tRJq9edqTUJy3s6dO6VxX0NDQ9evX+/r62u71bVLVq1E3qlT\nJ2lh3759LvxcAOBJSAgBJf3+++/OjMRQ1dAgdoc6ceKEEOLBBx9s06aNwzJ//etfpYXNmzfL\nK48ePXrq1CkhRLt27aR7WTvJyckrVqzYvHmzNBKDC44cOXLmzBkhRJMmTXr27OmwzPDhw6WF\nrVu3OiwwcuTIhg0bVl4vD0ZSl0PLCCH69+/ftGnTyuunTp26bdu2Q4cOOXxV795775UWLly4\nYLteha1w9OhRaUHuT1hZbGxsZmbmkCFDpJv+0tLS//znP6+//nqPHj2CgoI6duw4derU1NRU\nF4YSda2ZbN1WVezbt0+aPyM+Pr5FixaV9xo+fLjdDIq1GKrDc7UmITnp3Llzw4YNM5lMOp3u\nyy+/bNasme1Wly9ZtRK5fNbVzR+SAMCdMQ8h4CHS09OlBYfphOT++++XFuR7cSGE/KaWvNVO\n27Zt27ZtW5PY5I/o1KmT3Z/zZR06dJAWpLfFKhd74IEHHO4oT2Uu3SPeFikhv2WxESNGfPbZ\nZ3Yru3bt6uSnVFRUSD3lhBDl5eXSyrKyMtsyKmwFKTsVQtjlCXYiIiJSUlJOnDixevXqTZs2\nSRNRCCFMJlNmZmZmZuY777xz9913T5o0aeLEiTVJYJxpJlu3VRW//vqrtHDfffc53CsgIKBV\nq1bHjh27E6E6PFdrN6TKysrKBg8eLI2AOm/evEceecSugMuXrFqJ/J577pEWcnJyqikGAGpA\nQggoKTQ0tJoR9mzJo0pURX4+sGTJkiVLllRf+OzZs/KyfF8eFRXlTCQukD9OvgmrTH6CUVBQ\nUFhYWPnZS6NGjRzuKA/zKN0f1xlpUIqqpKamfvbZZxkZGZcuXbpx48YtY1NhK1y8eFFacOZH\nvvfee+fMmTNnzpwrV66kp6fv2bNn7969mZmZUjqUm5v76quvfvXVV9988001o3FWdrvNZOu2\nqkKeC6Fx48ZVHbBp06bV5DA1CdXhuVrzkKr3wgsvSH+DeOaZZxyOQeryJatWIo+MjNTpdGaz\nuZonqwCgEiSEgJLCwsJq5UUdIUReXp7zhY1Go9FolB6nFBYWSisdjuFeK/Lz86WFBg0aVFVG\nq9X6+vqWlpYKIQoKCiqnIvJIm7UoKChIHp6+Gn/+858rr3Q4T7oQoqioKDExcf369bcViQpb\noaioSFq4rR+5UaNGTz75pPSuY1lZWWpq6ieffCK9UbZ///6+ffseOnTImSBdayZbt1UV8g8r\nj41ZWVX1UPNQHZ6rNQnplj744INPP/1UCBEfH79s2TKHZVy+ZNVK5BqNxtfXt6ioyIWeBQDg\nYUgIAQ8hDyDx7LPPjho16pbl5ecY8o7VdDmrG/JDD2e6cdaKiIiI999/37V9q+qdOGbMGOne\nPSAgIDk5+YknnmjcuHFISIg0qUNZWZnduBoSFbaC3NfRbqAU5/n4+PTp06dPnz5bt24dPHiw\n0Wg8fvx4SkrKM888c8t9XWsmlznzQK+qNyFrHqrDc7UmIVVv9+7dr7zyihAiJCRk/fr1VaVt\nLl+yaityab4Ti8VSUVEhz7kCACpEQgh4CPnNpdDQUHmw/tvaUXrb504ICgqSFgoKCqoqYzab\n5VxIDqneOX78+FdffSWE8PPzS09Pr/xylNlsdrijCltBzgPLy8trOHhJv379Ro8evXTpUiHE\njh07bpkQutxMLpMfWFXzPMphu9y5UF0OqXoXLlwYNmxYRUWFTqdbu3ZtNf2TXb5k1Vbk0qmu\n1WrJBgGoHKOMAh6iefPm0kJWVpZrO16+fLmWY/qfJk2aSAsnT56sqszp06elheDg4Gr6NLq5\n7777Tlp45plnHA6VIf+YdlTYCvJtfTUTSJw9e1YeQaR68qCR169fv2Vhl5vJZREREdJCbm5u\nVWWys7Mrr7xzobocUjWMRuOQIUMuXbokhJg7d+6jjz5aTWGXL1m1ErnVapW6RlfT7xQAVIKE\nEPAQ8ntuu3btMhqNzu+YkJAgLezdu9dhX6wTJ06MHTt27Nixixcvdi22jh07SgsZGRkWi8Vh\nmYyMDLvC9ZE8UIo8GYAdefJ3OypsBXlaPIejemzbtq1Ro0ZNmzbt169fVdHakg9S1budtlxu\nJpfJH1TVJAfnz5+XBxaydedCdTmkarz44ovStH5PP/20NP1gNVy+ZNVK5JcuXZIerlaeShQA\n1IaEEPAQMTEx0kOSmzdvSsM5VJaWltayZcuXX37Zdvy9Nm3atGrVSghx+fLlTZs2Vd7rs88+\nW7Zs2bJlyxz2ZnTmXZ24uLiYmBghxIULF+QnHnbkmAcPHnzLA7otuRvkjRs3Km+9cOHCwoUL\npWW7elNhK8izTTi8d09ISJB6/Z05c+bDDz+s/lC257zDzod29eNyM7msc+fOUr/EY8eOOXwg\ntnz5cod/CLhzobocUlWWLl368ccfCyHuu+++5cuX37K8y5esWolcfrJa/awnAKAGJISA50hO\nTpYWXn311QMHDthtPX369JgxY7KzsxcvXlxRUWG7afLkydLCiy++aDcrV2ZmpnTHqdfrR48e\nLa+XX0hzpruXRqORP2LixImVU5ply5Zt375dCBEREeHMsJ9uS+7Ut3HjRrsb9Nzc3D59+jRp\n0kSaQaS4uNhulEW1tYI8iZzD5zwRERHy+Txx4sRp06ZVNSjlgQMHevbsee7cOSFEixYtBg0a\nJG+qqn5q0kyuCQoK6t27txDCarW+9NJLdh/6888/z5s3Tx40pW5CdTkkh/bt2zdx4kQhRHBw\ncDUDydhx7ZJVK5HLZ11VkxkCgHowqAygpKtXr8q3RLcUHR09adKkagqMGDFiw4YNKSkpBQUF\nDz300Lhx43r37h0cHHzp0qVdu3YtX75cmttg/PjxcgdFydixY9euXZuampqbm9u+ffvRo0fH\nx8eXlpZmZGSsWbNGuhV74403WrRoIe8iPWsSQqxduzY6Ojo2NjY3N/e1116TRw6089xzz33z\nzTc7duzIzs5OSEiYPHlyp06dfHx8cnJyUlJSvvjiCyGETqf79NNP6+8LhEKIJ554IiQk5MaN\nG7/++mvv3r2Tk5ObNGly+fLlb7/9dsmSJUaj8eeff54wYcLu3buFENOnT58wYUJwcPDdd98t\n1NcK8sTuUifDyv75z3/+8ssvGzZssFqt8+fPX7RoUdeuXe+7777w8HC9Xn/t2rUrV65kZmYe\nP35cKh8aGvrll1/aDrlZVf3UpJlcNnPmzG3btpnN5u+++65Tp05jx45t2rRpQUFBamrqp59+\nGhER8cgjj1R+UHZHQ3UtpMpu3LgxZMgQqdvnuHHjTp06derUqaoKx8bGyq+zunzJqnnkcu/o\nzp073/IHBAAPZwVQ53744QcXflvvv/9++QgPPfSQtPLEiRO2RzYajePGjatqwgCtVjtp0iST\nyVQ5pMLCwgEDBjjcS6PRTJ8+3a68yWSq/FJTRUVFNbEVFRUNGTKkqp8uJCRky5YtlQMbOHCg\nVGDXrl0OK3PChAlSgRUrVjhV+zb136pVKyd3cT6ejRs3OhwzMzAwMC0tzWq12k10MW3aNHlf\nVbWC0WgMDg4WQvj6+hYXFzssYzab582bJz/oq0b//v1PnjzpfP243Ew1qYqVK1c6HNAyLCws\nPT196tSp0n/XrVtnu9edC9XlkOzOrv3791c+QlXeeecd20O5fMlyLXKJxWKRXh308vLKy8ur\nqnIAQCXoMgp4FC8vr48++ujgwYMvvfRSXFxcUFCQXq8PCgpKSEh4+eWXjxw5smjRIoedqRo0\naLBx48Zt27aNGDGiefPm/v7+vr6+MTEx48aNO3To0Jw5c+zK63S6b7/99sknnwwPDzcYDI0b\nN3788cerejAl8ff3T0lJ2blzZ1JSUmxsbIMGDby9vSMjI3v16rVgwYLTp0/369evNutCIQMG\nDNi3b9/w4cOjoqL0er2fn198fPysWbOysrK6d+8uhHjuueemT58eHR1tMBhatmwpD48pVNYK\nXl5eUsZSWlq6bds2h2W0Wu3UqVNzcnJWr16dmJiYkJAQFhZmMBiks7p58+b9+/efPXv2H3/8\nsWnTJnnUSlk19VOTZnJZYmLi4cOHx4wZc8899/j4+AQGBrZt23bq1KkHDx588MEHQ0NDpWLS\n6JeyOxqqayHVIpcvWTWJfM+ePdJoPb169XLmzw0A4Nk01tt5ZRyAm+jSpUt6eroQ4sSJE61b\nt1Y6HMAVGRkZUoe93r17f/vtt0qHA7V49tlnV61aJYTYsGGD/BwVAFSLJ4QAAGV06tSpS5cu\nQojvv//eyfkGgRq6ePHi2rVrhRAxMTH9+/dXOhwAUB4JIVAvSWMtCCF8fHyUjQSoifnz5wsh\nrFbrG2+8oXQsUIW33npLGv/m7bffrr5/NQCoBF1GgfqntLQ0PDy8uLhYCFFSUmI7rCJQ7yQm\nJq5evVoIsWPHjp49eyodDjzZ4cOHO3ToYDabe/bsuWPHDqXDAQC3QEII1Cfbt2+/efPmqlWr\nNm/eLIRo3rz5yZMnlQ4KqJGbN2/Gx8efPXu2adOmR48ebdiwodIRwTOVl5d37Njx2LFjQUFB\nR48ejY6OVjoiAHALdJYA6pPk5ORhw4ZJ2aAQQh5aHai/goKC1q5dazAYcnJyxo0bp3Q48FjJ\nycnHjh3TaDQrVqwgGwQAGQkhUJ8EBAQIIby8vNq0abNkyZLnnntO6YiAWvDAAw8sX75cCPHV\nV1/NmjVL6XDggT7++GNpwsZ58+YNGjRI6XAAwI3QZRQAAAAAVIonhAAAAACgUiSEAAAAAKBS\nJIQAAAAAoFIkhAAAAACgUiSEAAAAAKBSJIQAAAAAoFIkhAAAAACgUiSEAAAAAKBS/wczJq1L\n2jM/uQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "point1 <- c(2, 0)\n",
    "point2 <- c(0, 1)\n",
    "point3 <- c(-1.25, 0)\n",
    "\n",
    "options(repr.plot.height = 8, repr.plot.width = 10)\n",
    "plot_1 <- heart_subset |>\n",
    "    ggplot(aes(x = scaled_ejection_fraction, y = scaled_serum_creatinine, col = as_factor(DEATH_EVENT))) +\n",
    "        geom_point() +\n",
    "        labs(x = \"Ejection Fraction (Standardized)\", y = \"Serum Creatinine (Standardized)\", color = \"Fatality\")+\n",
    "        annotate(\"path\", \n",
    "                 x=point1[1] + 5*cos(seq(0,2*pi,length.out=100)),\n",
    "                 y=point1[2] + 0.1*sin(seq(0,2*pi,length.out=100))) +\n",
    "        annotate(\"text\", x = 2, y =  0, label = \"1\") +\n",
    "        annotate(\"path\",\n",
    "                 x=point2[1] + 5*cos(seq(0,2*pi,length.out=10)),\n",
    "                 y=point2[2] + 0.1*sin(seq(0,2*pi,length.out=10))) +\n",
    "        annotate(\"text\", x = 0, y =  1, label = \"2\") +\n",
    "        annotate(\"path\",\n",
    "                 x=point3[1] + 5*cos(seq(0,2*pi,length.out=10)),\n",
    "                 y=point3[2]+0.1*sin(seq(0,2*pi,length.out=10))) +\n",
    "        annotate(\"text\", x = -1.25, y =  0, label = \"3\") +\n",
    "        theme(text = element_text(size = 20)) \n",
    "\n",
    "        \n",
    "plot_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322a0ccb-bc87-4b04-8991-ba110c9383fb",
   "metadata": {},
   "source": [
    "Let's select an observation for predicting the fatality for it. \n",
    "\n",
    "Let the:\n",
    "- ejection fraction be 2 \n",
    "- serum creatinine be 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "03fa6df6-bddc-4c5c-b33a-b08bb528233a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "observation_1 <- tibble(scaled_ejection_fraction = 2, scaled_serum_creatinine = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d946ab7-8ec2-41f4-a3a7-40e665a061d6",
   "metadata": {},
   "source": [
    "Just by looking at the plot, we can say that fatality for (2,0) will be \"Not fatal\" because the points closer to it are mostly not fatal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7cb22538-4ac5-4495-983b-e7ee9812c06a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>.pred_class</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Not Fatal</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 1\n",
       "\\begin{tabular}{l}\n",
       " .pred\\_class\\\\\n",
       " <fct>\\\\\n",
       "\\hline\n",
       "\t Not Fatal\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 1\n",
       "\n",
       "| .pred_class &lt;fct&gt; |\n",
       "|---|\n",
       "| Not Fatal |\n",
       "\n"
      ],
      "text/plain": [
       "  .pred_class\n",
       "1 Not Fatal  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction <- predict(knn_fit, observation_1)\n",
    "\n",
    "prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d64e763-027e-46af-815f-db11e3e315cf",
   "metadata": {},
   "source": [
    "YAYY! It matches our prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc3c3c9-efd8-4fbe-b991-b4c6414c8181",
   "metadata": {},
   "source": [
    "Let's try a new observation for predicting the fatality for it. \n",
    "\n",
    "Let the:\n",
    "- ejection fraction be 0 \n",
    "- serum creatinine be 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c1454af3-e9cb-4a34-880d-c1c4f28fe05a",
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_2 <- tibble(scaled_ejection_fraction = 0, scaled_serum_creatinine = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e2f0b0-6c0e-4efb-a5e1-abe945fe1c2a",
   "metadata": {},
   "source": [
    "Looking at the plot, we can say that fatality for (0,1) will be \"Fatal\" because the points closer to it are mostly fatal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e89319dd-31ee-4f94-a8f7-e6b484eea8da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>.pred_class</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Fatal</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 1\n",
       "\\begin{tabular}{l}\n",
       " .pred\\_class\\\\\n",
       " <fct>\\\\\n",
       "\\hline\n",
       "\t Fatal\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 1\n",
       "\n",
       "| .pred_class &lt;fct&gt; |\n",
       "|---|\n",
       "| Fatal |\n",
       "\n"
      ],
      "text/plain": [
       "  .pred_class\n",
       "1 Fatal      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction <- predict(knn_fit, observation_2)\n",
    "\n",
    "prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fe03fa1-3783-409f-9760-5d0d84279383",
   "metadata": {},
   "source": [
    "YAYY! It matches our prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d802d1b3-91c9-4ae5-b8aa-0c10569965de",
   "metadata": {},
   "source": [
    "Let's try a new observation for predicting the fatality for it. \n",
    "\n",
    "Let the:\n",
    "- ejection fraction be -1.25\n",
    "- serum creatinine be 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "13cc06ff-8b2e-4f8d-8270-281f86c05c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_3 <- tibble(scaled_ejection_fraction = -1.25, scaled_serum_creatinine = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70f8ff79-fd14-44ff-a441-c8e2c5a72bc6",
   "metadata": {},
   "source": [
    "Looking at the plot, we can say that fatality for (0,1) will be \"Fatal\" because the points closer to it are mostly fatal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dbc6bfc6-676a-4e67-b52a-bf9519d29f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>.pred_class</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Fatal</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 1\n",
       "\\begin{tabular}{l}\n",
       " .pred\\_class\\\\\n",
       " <fct>\\\\\n",
       "\\hline\n",
       "\t Fatal\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 1\n",
       "\n",
       "| .pred_class &lt;fct&gt; |\n",
       "|---|\n",
       "| Fatal |\n",
       "\n"
      ],
      "text/plain": [
       "  .pred_class\n",
       "1 Fatal      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "prediction <- predict(knn_fit, observation_3)\n",
    "\n",
    "prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f61b74d-c1a4-4dda-972d-48c1ab5379f8",
   "metadata": {},
   "source": [
    "YAYY! It matches our prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebd94d5c-005e-41f2-8e72-bdf6a2720331",
   "metadata": {},
   "source": [
    "## Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b88d178-901e-4554-992d-0fb1e36757f3",
   "metadata": {},
   "source": [
    "- We chose to remove all of the columns which had boolean observations and were not relevant except the death event which was the column to be predicted. We then scaled the data so that each column had equal contribution in the scatter plot.\n",
    "\n",
    "- We used the scatter plot because it was the only visualization that would give us a logical relationship . The bar was very congested and the histogram didn’t show an accurate relationship. However, going forward, we might choose to further analyze the data and try using other forms of visualizations for the final project. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd17d208-e593-4100-b707-6b4ae663913a",
   "metadata": {},
   "source": [
    "## Expected outcomes and significance:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba14672-8d21-4f10-a3ef-96fc1ec58d80",
   "metadata": {},
   "source": [
    "- **What do you expect to find?**\n",
    "    \n",
    "    We are expected to find if our model works well enough and is successful in predicting the relationship between different factors and how they\n",
    "    affect death in the case of heart failure. For example, if a person with a high amount of ejection fraction is more or less likely to encounter \n",
    "    death upon undergoing heart failure. \n",
    "   \n",
    "- **What impact could such findings have?**\n",
    "    \n",
    "    Finding the answer to such questions will allow us to control the variables which result in death in the case of heart failure, and minimize deaths\n",
    "    which occur as a result of heart failure. This will have a huge impact as it can save numerous lives because heart failure is a leading cause of \n",
    "    death amongst adults.\n",
    "    \n",
    "- **What future questions could this lead to?**\n",
    "    \n",
    "    Some future questions which might potentially arise from this analysis are:\n",
    "     \n",
    "    1. How accurate is the analysis performed and how can the model be improved and refined in the future for results which are more \n",
    "          precise?\n",
    "    2. Can such analysis and models be applied to different diseases across the medical field and help reduce the percentage of deaths \n",
    "          occuring overall? "
   ]
  }
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