project_new_draft.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cbcd66da-c2b1-4f40-b703-28e0356722d7",
   "metadata": {
    "tags": []
   },
   "source": [
    "**DSCI100 Project Proposal**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32e6ce41-8fc1-47b9-aa14-9c745eb59233",
   "metadata": {},
   "source": [
    "Introduction:\n",
    "Every day, children and adults around the world suffer from a medical complication known as a stroke. A stroke is when there’s disruption in the blood supply to the brain. Strokes can have serious and potentially life-threatening consequences, as they can lead to brain damage, disability, or even death. There are several factors that contribute to a person’s chances of getting a stroke which include: blood pressure levels, BMI, smoking, physical activity, age, and more. Our project aims to predict the likelihood of someone getting a stroke based on a person’s age and stress levels. We want to attempt to answer the following questions: How does age and stress levels affect the risks of getting a stroke?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b609d5ef-1ec3-4e52-94c4-bd5fe25ba6c7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.2 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.4.2     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 1.0.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.2.1     \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.1.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.3.0     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.5.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 2.1.3     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.2\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",
      "── \u001b[1mAttaching packages\u001b[22m ────────────────────────────────────── tidymodels 1.0.0 ──\n",
      "\n",
      "\u001b[32m✔\u001b[39m \u001b[34mbroom       \u001b[39m 1.0.2     \u001b[32m✔\u001b[39m \u001b[34mrsample     \u001b[39m 1.1.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mdials       \u001b[39m 1.1.0     \u001b[32m✔\u001b[39m \u001b[34mtune        \u001b[39m 1.0.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34minfer       \u001b[39m 1.0.4     \u001b[32m✔\u001b[39m \u001b[34mworkflows   \u001b[39m 1.1.2\n",
      "\u001b[32m✔\u001b[39m \u001b[34mmodeldata   \u001b[39m 1.0.1     \u001b[32m✔\u001b[39m \u001b[34mworkflowsets\u001b[39m 1.0.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mparsnip     \u001b[39m 1.0.3     \u001b[32m✔\u001b[39m \u001b[34myardstick   \u001b[39m 1.1.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mrecipes     \u001b[39m 1.0.4     \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[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m      masks \u001b[34mstats\u001b[39m::lag()\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 Use \u001b[32mtidymodels_prefer()\u001b[39m to resolve common conflicts.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)\n",
    "library(tidymodels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6ec2842-599b-4c46-8e48-70cf5da8be3c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m15000\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m22\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (14): Patient Name, Gender, Marital Status, Work Type, Residence Type, S...\n",
      "\u001b[32mdbl\u001b[39m  (8): Patient ID, Age, Hypertension, Heart Disease, Average Glucose Leve...\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"
     ]
    }
   ],
   "source": [
    "stroke <- read_csv(\"https://raw.githubusercontent.com/Megunight/dsci100-project/main/stroke_prediction_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4d6956e6-dfbf-4f69-ad2c-469053c6f6af",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 22</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>patient_id</th><th scope=col>patient_name</th><th scope=col>age</th><th scope=col>gender</th><th scope=col>hypertension</th><th scope=col>heart_disease</th><th scope=col>marital_status</th><th scope=col>work_type</th><th scope=col>residence_type</th><th scope=col>average_glucose_level</th><th scope=col>⋯</th><th scope=col>alcohol_intake</th><th scope=col>physical_activity</th><th scope=col>stroke_history</th><th scope=col>family_history_of_stroke</th><th scope=col>dietary_habits</th><th scope=col>stress_levels</th><th scope=col>blood_pressure_levels</th><th scope=col>cholesterol_levels</th><th scope=col>symptoms</th><th scope=col>diagnosis</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</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;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>18153</td><td>Mamooty Khurana  </td><td>56</td><td>Male  </td><td>0</td><td>1</td><td>Married </td><td>Self-employed</td><td>Rural</td><td>130.91</td><td>⋯</td><td>Social Drinker  </td><td>Moderate</td><td>0</td><td>Yes</td><td>Vegan      </td><td>3.48</td><td>140/108</td><td>HDL: 68, LDL: 133</td><td>Difficulty Speaking, Headache                                </td><td>Stroke   </td></tr>\n",
       "\t<tr><td>62749</td><td>Kaira Subramaniam</td><td>80</td><td>Male  </td><td>0</td><td>0</td><td>Single  </td><td>Self-employed</td><td>Urban</td><td>183.73</td><td>⋯</td><td>Never           </td><td>Low     </td><td>0</td><td>No </td><td>Paleo      </td><td>1.73</td><td>146/91 </td><td>HDL: 63, LDL: 70 </td><td>Loss of Balance, Headache, Dizziness, Confusion              </td><td>Stroke   </td></tr>\n",
       "\t<tr><td>32145</td><td>Dhanush Balan    </td><td>26</td><td>Male  </td><td>1</td><td>1</td><td>Married </td><td>Never Worked </td><td>Rural</td><td>189.00</td><td>⋯</td><td>Rarely          </td><td>High    </td><td>0</td><td>Yes</td><td>Paleo      </td><td>7.31</td><td>154/97 </td><td>HDL: 59, LDL: 95 </td><td>Seizures, Dizziness                                          </td><td>Stroke   </td></tr>\n",
       "\t<tr><td> 6154</td><td>Ivana Baral      </td><td>73</td><td>Male  </td><td>0</td><td>0</td><td>Married </td><td>Never Worked </td><td>Urban</td><td>185.29</td><td>⋯</td><td>Frequent Drinker</td><td>Moderate</td><td>0</td><td>No </td><td>Paleo      </td><td>5.35</td><td>174/81 </td><td>HDL: 70, LDL: 137</td><td>Seizures, Blurred Vision, Severe Fatigue, Headache, Confusion</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>48973</td><td>Darshit Jayaraman</td><td>51</td><td>Male  </td><td>1</td><td>1</td><td>Divorced</td><td>Self-employed</td><td>Urban</td><td>177.34</td><td>⋯</td><td>Rarely          </td><td>Low     </td><td>0</td><td>Yes</td><td>Pescatarian</td><td>6.84</td><td>121/95 </td><td>HDL: 65, LDL: 68 </td><td>Difficulty Speaking                                          </td><td>Stroke   </td></tr>\n",
       "\t<tr><td>29307</td><td>Advika Kota      </td><td>62</td><td>Female</td><td>0</td><td>0</td><td>Single  </td><td>Private      </td><td>Urban</td><td> 91.60</td><td>⋯</td><td>Social Drinker  </td><td>High    </td><td>0</td><td>No </td><td>Gluten-Free</td><td>4.85</td><td>132/64 </td><td>HDL: 80, LDL: 69 </td><td>Severe Fatigue                                               </td><td>Stroke   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 22\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " patient\\_id & patient\\_name & age & gender & hypertension & heart\\_disease & marital\\_status & work\\_type & residence\\_type & average\\_glucose\\_level & ⋯ & alcohol\\_intake & physical\\_activity & stroke\\_history & family\\_history\\_of\\_stroke & dietary\\_habits & stress\\_levels & blood\\_pressure\\_levels & cholesterol\\_levels & symptoms & diagnosis\\\\\n",
       " <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <dbl> & ⋯ & <chr> & <chr> & <dbl> & <chr> & <chr> & <dbl> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 18153 & Mamooty Khurana   & 56 & Male   & 0 & 1 & Married  & Self-employed & Rural & 130.91 & ⋯ & Social Drinker   & Moderate & 0 & Yes & Vegan       & 3.48 & 140/108 & HDL: 68, LDL: 133 & Difficulty Speaking, Headache                                 & Stroke   \\\\\n",
       "\t 62749 & Kaira Subramaniam & 80 & Male   & 0 & 0 & Single   & Self-employed & Urban & 183.73 & ⋯ & Never            & Low      & 0 & No  & Paleo       & 1.73 & 146/91  & HDL: 63, LDL: 70  & Loss of Balance, Headache, Dizziness, Confusion               & Stroke   \\\\\n",
       "\t 32145 & Dhanush Balan     & 26 & Male   & 1 & 1 & Married  & Never Worked  & Rural & 189.00 & ⋯ & Rarely           & High     & 0 & Yes & Paleo       & 7.31 & 154/97  & HDL: 59, LDL: 95  & Seizures, Dizziness                                           & Stroke   \\\\\n",
       "\t  6154 & Ivana Baral       & 73 & Male   & 0 & 0 & Married  & Never Worked  & Urban & 185.29 & ⋯ & Frequent Drinker & Moderate & 0 & No  & Paleo       & 5.35 & 174/81  & HDL: 70, LDL: 137 & Seizures, Blurred Vision, Severe Fatigue, Headache, Confusion & No Stroke\\\\\n",
       "\t 48973 & Darshit Jayaraman & 51 & Male   & 1 & 1 & Divorced & Self-employed & Urban & 177.34 & ⋯ & Rarely           & Low      & 0 & Yes & Pescatarian & 6.84 & 121/95  & HDL: 65, LDL: 68  & Difficulty Speaking                                           & Stroke   \\\\\n",
       "\t 29307 & Advika Kota       & 62 & Female & 0 & 0 & Single   & Private       & Urban &  91.60 & ⋯ & Social Drinker   & High     & 0 & No  & Gluten-Free & 4.85 & 132/64  & HDL: 80, LDL: 69  & Severe Fatigue                                                & Stroke   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 22\n",
       "\n",
       "| patient_id &lt;dbl&gt; | patient_name &lt;chr&gt; | age &lt;dbl&gt; | gender &lt;chr&gt; | hypertension &lt;dbl&gt; | heart_disease &lt;dbl&gt; | marital_status &lt;chr&gt; | work_type &lt;chr&gt; | residence_type &lt;chr&gt; | average_glucose_level &lt;dbl&gt; | ⋯ ⋯ | alcohol_intake &lt;chr&gt; | physical_activity &lt;chr&gt; | stroke_history &lt;dbl&gt; | family_history_of_stroke &lt;chr&gt; | dietary_habits &lt;chr&gt; | stress_levels &lt;dbl&gt; | blood_pressure_levels &lt;chr&gt; | cholesterol_levels &lt;chr&gt; | symptoms &lt;chr&gt; | diagnosis &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 18153 | Mamooty Khurana   | 56 | Male   | 0 | 1 | Married  | Self-employed | Rural | 130.91 | ⋯ | Social Drinker   | Moderate | 0 | Yes | Vegan       | 3.48 | 140/108 | HDL: 68, LDL: 133 | Difficulty Speaking, Headache                                 | Stroke    |\n",
       "| 62749 | Kaira Subramaniam | 80 | Male   | 0 | 0 | Single   | Self-employed | Urban | 183.73 | ⋯ | Never            | Low      | 0 | No  | Paleo       | 1.73 | 146/91  | HDL: 63, LDL: 70  | Loss of Balance, Headache, Dizziness, Confusion               | Stroke    |\n",
       "| 32145 | Dhanush Balan     | 26 | Male   | 1 | 1 | Married  | Never Worked  | Rural | 189.00 | ⋯ | Rarely           | High     | 0 | Yes | Paleo       | 7.31 | 154/97  | HDL: 59, LDL: 95  | Seizures, Dizziness                                           | Stroke    |\n",
       "|  6154 | Ivana Baral       | 73 | Male   | 0 | 0 | Married  | Never Worked  | Urban | 185.29 | ⋯ | Frequent Drinker | Moderate | 0 | No  | Paleo       | 5.35 | 174/81  | HDL: 70, LDL: 137 | Seizures, Blurred Vision, Severe Fatigue, Headache, Confusion | No Stroke |\n",
       "| 48973 | Darshit Jayaraman | 51 | Male   | 1 | 1 | Divorced | Self-employed | Urban | 177.34 | ⋯ | Rarely           | Low      | 0 | Yes | Pescatarian | 6.84 | 121/95  | HDL: 65, LDL: 68  | Difficulty Speaking                                           | Stroke    |\n",
       "| 29307 | Advika Kota       | 62 | Female | 0 | 0 | Single   | Private       | Urban |  91.60 | ⋯ | Social Drinker   | High     | 0 | No  | Gluten-Free | 4.85 | 132/64  | HDL: 80, LDL: 69  | Severe Fatigue                                                | Stroke    |\n",
       "\n"
      ],
      "text/plain": [
       "  patient_id patient_name      age gender hypertension heart_disease\n",
       "1 18153      Mamooty Khurana   56  Male   0            1            \n",
       "2 62749      Kaira Subramaniam 80  Male   0            0            \n",
       "3 32145      Dhanush Balan     26  Male   1            1            \n",
       "4  6154      Ivana Baral       73  Male   0            0            \n",
       "5 48973      Darshit Jayaraman 51  Male   1            1            \n",
       "6 29307      Advika Kota       62  Female 0            0            \n",
       "  marital_status work_type     residence_type average_glucose_level ⋯\n",
       "1 Married        Self-employed Rural          130.91                ⋯\n",
       "2 Single         Self-employed Urban          183.73                ⋯\n",
       "3 Married        Never Worked  Rural          189.00                ⋯\n",
       "4 Married        Never Worked  Urban          185.29                ⋯\n",
       "5 Divorced       Self-employed Urban          177.34                ⋯\n",
       "6 Single         Private       Urban           91.60                ⋯\n",
       "  alcohol_intake   physical_activity stroke_history family_history_of_stroke\n",
       "1 Social Drinker   Moderate          0              Yes                     \n",
       "2 Never            Low               0              No                      \n",
       "3 Rarely           High              0              Yes                     \n",
       "4 Frequent Drinker Moderate          0              No                      \n",
       "5 Rarely           Low               0              Yes                     \n",
       "6 Social Drinker   High              0              No                      \n",
       "  dietary_habits stress_levels blood_pressure_levels cholesterol_levels\n",
       "1 Vegan          3.48          140/108               HDL: 68, LDL: 133 \n",
       "2 Paleo          1.73          146/91                HDL: 63, LDL: 70  \n",
       "3 Paleo          7.31          154/97                HDL: 59, LDL: 95  \n",
       "4 Paleo          5.35          174/81                HDL: 70, LDL: 137 \n",
       "5 Pescatarian    6.84          121/95                HDL: 65, LDL: 68  \n",
       "6 Gluten-Free    4.85          132/64                HDL: 80, LDL: 69  \n",
       "  symptoms                                                      diagnosis\n",
       "1 Difficulty Speaking, Headache                                 Stroke   \n",
       "2 Loss of Balance, Headache, Dizziness, Confusion               Stroke   \n",
       "3 Seizures, Dizziness                                           Stroke   \n",
       "4 Seizures, Blurred Vision, Severe Fatigue, Headache, Confusion No Stroke\n",
       "5 Difficulty Speaking                                           Stroke   \n",
       "6 Severe Fatigue                                                Stroke   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names(stroke) <- c(\"patient_id\", \"patient_name\", \"age\", \"gender\", \"hypertension\", \"heart_disease\", \"marital_status\",\n",
    "                  \"work_type\", \"residence_type\", \"average_glucose_level\", \"bmi\", \"smoking_status\", \"alcohol_intake\", \"physical_activity\",\n",
    "                  \"stroke_history\", \"family_history_of_stroke\", \"dietary_habits\", \"stress_levels\", \"blood_pressure_levels\",\n",
    "                  \"cholesterol_levels\", \"symptoms\", \"diagnosis\")\n",
    "head(stroke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b3236118-3a38-43f1-b5ec-51e56ce36900",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Seven different variables have been selected for use\n",
    "\n",
    "stroke_selected <- stroke |>\n",
    "                    select(age,\n",
    "                           hypertension,\n",
    "                           stress_levels,\n",
    "                           diagnosis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8790c8ee-f8db-4a57-b996-56235b72c73c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "stroke_sample <- stroke_selected |>\n",
    "                sample_n(5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bdd54da-8aa1-499d-801e-00de8b1522d4",
   "metadata": {},
   "source": [
    "Due to the large number of rows in the stroke data set, we decided to sample 5000 random rows from the data set to ensure that our visualizations wouldn't be overwhelmed with data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aea651e4-8b1b-4a9b-a48c-bb4a574bfe09",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3749 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>age</th><th scope=col>hypertension</th><th scope=col>stress_levels</th><th scope=col>diagnosis</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;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>75</td><td>1</td><td>9.65</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>58</td><td>0</td><td>8.72</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>77</td><td>0</td><td>0.07</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>26</td><td>0</td><td>8.36</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>37</td><td>1</td><td>0.88</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>83</td><td>0</td><td>1.52</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>40</td><td>0</td><td>7.96</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>21</td><td>0</td><td>5.07</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>85</td><td>1</td><td>7.32</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>81</td><td>1</td><td>6.02</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>60</td><td>0</td><td>3.07</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>65</td><td>0</td><td>9.17</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>65</td><td>0</td><td>9.86</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>52</td><td>0</td><td>9.93</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>28</td><td>0</td><td>2.18</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>64</td><td>1</td><td>3.95</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>89</td><td>0</td><td>4.96</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>49</td><td>0</td><td>2.48</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>74</td><td>0</td><td>8.64</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>39</td><td>0</td><td>4.98</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>56</td><td>0</td><td>8.75</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>85</td><td>0</td><td>6.52</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>71</td><td>0</td><td>4.25</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>35</td><td>0</td><td>9.05</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>84</td><td>0</td><td>8.26</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>75</td><td>0</td><td>6.24</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>34</td><td>0</td><td>6.17</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>80</td><td>0</td><td>5.79</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>31</td><td>0</td><td>5.40</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>42</td><td>1</td><td>9.28</td><td>No Stroke</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>70</td><td>0</td><td>4.76</td><td>Stroke</td></tr>\n",
       "\t<tr><td>21</td><td>0</td><td>6.95</td><td>Stroke</td></tr>\n",
       "\t<tr><td>84</td><td>1</td><td>4.29</td><td>Stroke</td></tr>\n",
       "\t<tr><td>47</td><td>0</td><td>3.18</td><td>Stroke</td></tr>\n",
       "\t<tr><td>78</td><td>0</td><td>1.01</td><td>Stroke</td></tr>\n",
       "\t<tr><td>22</td><td>0</td><td>0.15</td><td>Stroke</td></tr>\n",
       "\t<tr><td>89</td><td>0</td><td>4.76</td><td>Stroke</td></tr>\n",
       "\t<tr><td>68</td><td>1</td><td>8.35</td><td>Stroke</td></tr>\n",
       "\t<tr><td>90</td><td>0</td><td>3.07</td><td>Stroke</td></tr>\n",
       "\t<tr><td>20</td><td>1</td><td>8.48</td><td>Stroke</td></tr>\n",
       "\t<tr><td>26</td><td>0</td><td>4.67</td><td>Stroke</td></tr>\n",
       "\t<tr><td>57</td><td>0</td><td>6.32</td><td>Stroke</td></tr>\n",
       "\t<tr><td>23</td><td>0</td><td>1.99</td><td>Stroke</td></tr>\n",
       "\t<tr><td>27</td><td>0</td><td>9.73</td><td>Stroke</td></tr>\n",
       "\t<tr><td>82</td><td>0</td><td>5.92</td><td>Stroke</td></tr>\n",
       "\t<tr><td>90</td><td>0</td><td>1.27</td><td>Stroke</td></tr>\n",
       "\t<tr><td>79</td><td>0</td><td>2.40</td><td>Stroke</td></tr>\n",
       "\t<tr><td>71</td><td>0</td><td>8.80</td><td>Stroke</td></tr>\n",
       "\t<tr><td>53</td><td>0</td><td>6.03</td><td>Stroke</td></tr>\n",
       "\t<tr><td>20</td><td>0</td><td>2.48</td><td>Stroke</td></tr>\n",
       "\t<tr><td>72</td><td>0</td><td>4.75</td><td>Stroke</td></tr>\n",
       "\t<tr><td>61</td><td>0</td><td>2.54</td><td>Stroke</td></tr>\n",
       "\t<tr><td>39</td><td>0</td><td>5.68</td><td>Stroke</td></tr>\n",
       "\t<tr><td>69</td><td>1</td><td>5.29</td><td>Stroke</td></tr>\n",
       "\t<tr><td>26</td><td>0</td><td>9.87</td><td>Stroke</td></tr>\n",
       "\t<tr><td>43</td><td>0</td><td>4.62</td><td>Stroke</td></tr>\n",
       "\t<tr><td>71</td><td>0</td><td>1.54</td><td>Stroke</td></tr>\n",
       "\t<tr><td>75</td><td>0</td><td>0.46</td><td>Stroke</td></tr>\n",
       "\t<tr><td>53</td><td>0</td><td>9.21</td><td>Stroke</td></tr>\n",
       "\t<tr><td>64</td><td>0</td><td>1.10</td><td>Stroke</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3749 × 4\n",
       "\\begin{tabular}{llll}\n",
       " age & hypertension & stress\\_levels & diagnosis\\\\\n",
       " <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t 75 & 1 & 9.65 & No Stroke\\\\\n",
       "\t 58 & 0 & 8.72 & No Stroke\\\\\n",
       "\t 77 & 0 & 0.07 & No Stroke\\\\\n",
       "\t 26 & 0 & 8.36 & No Stroke\\\\\n",
       "\t 37 & 1 & 0.88 & No Stroke\\\\\n",
       "\t 83 & 0 & 1.52 & No Stroke\\\\\n",
       "\t 40 & 0 & 7.96 & No Stroke\\\\\n",
       "\t 21 & 0 & 5.07 & No Stroke\\\\\n",
       "\t 85 & 1 & 7.32 & No Stroke\\\\\n",
       "\t 81 & 1 & 6.02 & No Stroke\\\\\n",
       "\t 60 & 0 & 3.07 & No Stroke\\\\\n",
       "\t 65 & 0 & 9.17 & No Stroke\\\\\n",
       "\t 65 & 0 & 9.86 & No Stroke\\\\\n",
       "\t 52 & 0 & 9.93 & No Stroke\\\\\n",
       "\t 28 & 0 & 2.18 & No Stroke\\\\\n",
       "\t 64 & 1 & 3.95 & No Stroke\\\\\n",
       "\t 89 & 0 & 4.96 & No Stroke\\\\\n",
       "\t 49 & 0 & 2.48 & No Stroke\\\\\n",
       "\t 74 & 0 & 8.64 & No Stroke\\\\\n",
       "\t 39 & 0 & 4.98 & No Stroke\\\\\n",
       "\t 56 & 0 & 8.75 & No Stroke\\\\\n",
       "\t 85 & 0 & 6.52 & No Stroke\\\\\n",
       "\t 71 & 0 & 4.25 & No Stroke\\\\\n",
       "\t 35 & 0 & 9.05 & No Stroke\\\\\n",
       "\t 84 & 0 & 8.26 & No Stroke\\\\\n",
       "\t 75 & 0 & 6.24 & No Stroke\\\\\n",
       "\t 34 & 0 & 6.17 & No Stroke\\\\\n",
       "\t 80 & 0 & 5.79 & No Stroke\\\\\n",
       "\t 31 & 0 & 5.40 & No Stroke\\\\\n",
       "\t 42 & 1 & 9.28 & No Stroke\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 70 & 0 & 4.76 & Stroke\\\\\n",
       "\t 21 & 0 & 6.95 & Stroke\\\\\n",
       "\t 84 & 1 & 4.29 & Stroke\\\\\n",
       "\t 47 & 0 & 3.18 & Stroke\\\\\n",
       "\t 78 & 0 & 1.01 & Stroke\\\\\n",
       "\t 22 & 0 & 0.15 & Stroke\\\\\n",
       "\t 89 & 0 & 4.76 & Stroke\\\\\n",
       "\t 68 & 1 & 8.35 & Stroke\\\\\n",
       "\t 90 & 0 & 3.07 & Stroke\\\\\n",
       "\t 20 & 1 & 8.48 & Stroke\\\\\n",
       "\t 26 & 0 & 4.67 & Stroke\\\\\n",
       "\t 57 & 0 & 6.32 & Stroke\\\\\n",
       "\t 23 & 0 & 1.99 & Stroke\\\\\n",
       "\t 27 & 0 & 9.73 & Stroke\\\\\n",
       "\t 82 & 0 & 5.92 & Stroke\\\\\n",
       "\t 90 & 0 & 1.27 & Stroke\\\\\n",
       "\t 79 & 0 & 2.40 & Stroke\\\\\n",
       "\t 71 & 0 & 8.80 & Stroke\\\\\n",
       "\t 53 & 0 & 6.03 & Stroke\\\\\n",
       "\t 20 & 0 & 2.48 & Stroke\\\\\n",
       "\t 72 & 0 & 4.75 & Stroke\\\\\n",
       "\t 61 & 0 & 2.54 & Stroke\\\\\n",
       "\t 39 & 0 & 5.68 & Stroke\\\\\n",
       "\t 69 & 1 & 5.29 & Stroke\\\\\n",
       "\t 26 & 0 & 9.87 & Stroke\\\\\n",
       "\t 43 & 0 & 4.62 & Stroke\\\\\n",
       "\t 71 & 0 & 1.54 & Stroke\\\\\n",
       "\t 75 & 0 & 0.46 & Stroke\\\\\n",
       "\t 53 & 0 & 9.21 & Stroke\\\\\n",
       "\t 64 & 0 & 1.10 & Stroke\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3749 × 4\n",
       "\n",
       "| age &lt;dbl&gt; | hypertension &lt;dbl&gt; | stress_levels &lt;dbl&gt; | diagnosis &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| 75 | 1 | 9.65 | No Stroke |\n",
       "| 58 | 0 | 8.72 | No Stroke |\n",
       "| 77 | 0 | 0.07 | No Stroke |\n",
       "| 26 | 0 | 8.36 | No Stroke |\n",
       "| 37 | 1 | 0.88 | No Stroke |\n",
       "| 83 | 0 | 1.52 | No Stroke |\n",
       "| 40 | 0 | 7.96 | No Stroke |\n",
       "| 21 | 0 | 5.07 | No Stroke |\n",
       "| 85 | 1 | 7.32 | No Stroke |\n",
       "| 81 | 1 | 6.02 | No Stroke |\n",
       "| 60 | 0 | 3.07 | No Stroke |\n",
       "| 65 | 0 | 9.17 | No Stroke |\n",
       "| 65 | 0 | 9.86 | No Stroke |\n",
       "| 52 | 0 | 9.93 | No Stroke |\n",
       "| 28 | 0 | 2.18 | No Stroke |\n",
       "| 64 | 1 | 3.95 | No Stroke |\n",
       "| 89 | 0 | 4.96 | No Stroke |\n",
       "| 49 | 0 | 2.48 | No Stroke |\n",
       "| 74 | 0 | 8.64 | No Stroke |\n",
       "| 39 | 0 | 4.98 | No Stroke |\n",
       "| 56 | 0 | 8.75 | No Stroke |\n",
       "| 85 | 0 | 6.52 | No Stroke |\n",
       "| 71 | 0 | 4.25 | No Stroke |\n",
       "| 35 | 0 | 9.05 | No Stroke |\n",
       "| 84 | 0 | 8.26 | No Stroke |\n",
       "| 75 | 0 | 6.24 | No Stroke |\n",
       "| 34 | 0 | 6.17 | No Stroke |\n",
       "| 80 | 0 | 5.79 | No Stroke |\n",
       "| 31 | 0 | 5.40 | No Stroke |\n",
       "| 42 | 1 | 9.28 | No Stroke |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 70 | 0 | 4.76 | Stroke |\n",
       "| 21 | 0 | 6.95 | Stroke |\n",
       "| 84 | 1 | 4.29 | Stroke |\n",
       "| 47 | 0 | 3.18 | Stroke |\n",
       "| 78 | 0 | 1.01 | Stroke |\n",
       "| 22 | 0 | 0.15 | Stroke |\n",
       "| 89 | 0 | 4.76 | Stroke |\n",
       "| 68 | 1 | 8.35 | Stroke |\n",
       "| 90 | 0 | 3.07 | Stroke |\n",
       "| 20 | 1 | 8.48 | Stroke |\n",
       "| 26 | 0 | 4.67 | Stroke |\n",
       "| 57 | 0 | 6.32 | Stroke |\n",
       "| 23 | 0 | 1.99 | Stroke |\n",
       "| 27 | 0 | 9.73 | Stroke |\n",
       "| 82 | 0 | 5.92 | Stroke |\n",
       "| 90 | 0 | 1.27 | Stroke |\n",
       "| 79 | 0 | 2.40 | Stroke |\n",
       "| 71 | 0 | 8.80 | Stroke |\n",
       "| 53 | 0 | 6.03 | Stroke |\n",
       "| 20 | 0 | 2.48 | Stroke |\n",
       "| 72 | 0 | 4.75 | Stroke |\n",
       "| 61 | 0 | 2.54 | Stroke |\n",
       "| 39 | 0 | 5.68 | Stroke |\n",
       "| 69 | 1 | 5.29 | Stroke |\n",
       "| 26 | 0 | 9.87 | Stroke |\n",
       "| 43 | 0 | 4.62 | Stroke |\n",
       "| 71 | 0 | 1.54 | Stroke |\n",
       "| 75 | 0 | 0.46 | Stroke |\n",
       "| 53 | 0 | 9.21 | Stroke |\n",
       "| 64 | 0 | 1.10 | Stroke |\n",
       "\n"
      ],
      "text/plain": [
       "     age hypertension stress_levels diagnosis\n",
       "1    75  1            9.65          No Stroke\n",
       "2    58  0            8.72          No Stroke\n",
       "3    77  0            0.07          No Stroke\n",
       "4    26  0            8.36          No Stroke\n",
       "5    37  1            0.88          No Stroke\n",
       "6    83  0            1.52          No Stroke\n",
       "7    40  0            7.96          No Stroke\n",
       "8    21  0            5.07          No Stroke\n",
       "9    85  1            7.32          No Stroke\n",
       "10   81  1            6.02          No Stroke\n",
       "11   60  0            3.07          No Stroke\n",
       "12   65  0            9.17          No Stroke\n",
       "13   65  0            9.86          No Stroke\n",
       "14   52  0            9.93          No Stroke\n",
       "15   28  0            2.18          No Stroke\n",
       "16   64  1            3.95          No Stroke\n",
       "17   89  0            4.96          No Stroke\n",
       "18   49  0            2.48          No Stroke\n",
       "19   74  0            8.64          No Stroke\n",
       "20   39  0            4.98          No Stroke\n",
       "21   56  0            8.75          No Stroke\n",
       "22   85  0            6.52          No Stroke\n",
       "23   71  0            4.25          No Stroke\n",
       "24   35  0            9.05          No Stroke\n",
       "25   84  0            8.26          No Stroke\n",
       "26   75  0            6.24          No Stroke\n",
       "27   34  0            6.17          No Stroke\n",
       "28   80  0            5.79          No Stroke\n",
       "29   31  0            5.40          No Stroke\n",
       "30   42  1            9.28          No Stroke\n",
       "⋮    ⋮   ⋮            ⋮             ⋮        \n",
       "3720 70  0            4.76          Stroke   \n",
       "3721 21  0            6.95          Stroke   \n",
       "3722 84  1            4.29          Stroke   \n",
       "3723 47  0            3.18          Stroke   \n",
       "3724 78  0            1.01          Stroke   \n",
       "3725 22  0            0.15          Stroke   \n",
       "3726 89  0            4.76          Stroke   \n",
       "3727 68  1            8.35          Stroke   \n",
       "3728 90  0            3.07          Stroke   \n",
       "3729 20  1            8.48          Stroke   \n",
       "3730 26  0            4.67          Stroke   \n",
       "3731 57  0            6.32          Stroke   \n",
       "3732 23  0            1.99          Stroke   \n",
       "3733 27  0            9.73          Stroke   \n",
       "3734 82  0            5.92          Stroke   \n",
       "3735 90  0            1.27          Stroke   \n",
       "3736 79  0            2.40          Stroke   \n",
       "3737 71  0            8.80          Stroke   \n",
       "3738 53  0            6.03          Stroke   \n",
       "3739 20  0            2.48          Stroke   \n",
       "3740 72  0            4.75          Stroke   \n",
       "3741 61  0            2.54          Stroke   \n",
       "3742 39  0            5.68          Stroke   \n",
       "3743 69  1            5.29          Stroke   \n",
       "3744 26  0            9.87          Stroke   \n",
       "3745 43  0            4.62          Stroke   \n",
       "3746 71  0            1.54          Stroke   \n",
       "3747 75  0            0.46          Stroke   \n",
       "3748 53  0            9.21          Stroke   \n",
       "3749 64  0            1.10          Stroke   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stroke_split <- initial_split(stroke_sample, prop = 0.75, strata = diagnosis)\n",
    "stroke_training <- training(stroke_split)\n",
    "stroke_testing <- testing(stroke_split)\n",
    "\n",
    "stroke_training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4d84f577-7acd-4be1-a268-136ff10582bb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mThere was 1 warning in `mutate()`.\n",
      "\u001b[1m\u001b[22m\u001b[36mℹ\u001b[39m In argument: `hypertension = fct_recode(hypertension, False = \"0\", True =\n",
      "  \"1\")`.\n",
      "Caused by warning:\n",
      "\u001b[33m!\u001b[39m Unknown levels in `f`: 0, 1”\n"
     ]
    }
   ],
   "source": [
    "stroke_training <- stroke_training |>\n",
    "  mutate(stress_level_category = case_when(\n",
    "    stress_levels <= 2.5 ~ \"Low\",\n",
    "    stress_levels > 2.5 & stress_levels <= 5.0 ~ \"Moderate\",\n",
    "    stress_levels > 5.0 & stress_levels <= 7.5 ~ \"High\",\n",
    "    stress_levels > 7.5 ~ \"Very High\") )\n",
    "\n",
    "\n",
    "stroke_training <- stroke_training |>\n",
    "    mutate(age_group = cut(age, breaks = seq(0, 100, by = 10), labels = seq(0, 90, by = 10), right = FALSE)) |>\n",
    "    mutate(hypertension = as_factor(hypertension)) |>\n",
    "    mutate(hypertension = fct_recode(hypertension, \"False\" = \"0\", \"True\" = \"1\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b65935d-4eb1-4b6b-8b12-e411005d07d1",
   "metadata": {},
   "source": [
    "We split the data set into a training set and testing set. After that, we mutated stress levels and age into categories because there are too many values too plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "74961d86-de0c-48c3-a421-7913f10caeaa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>diagnosis</th><th scope=col>count</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>No Stroke</td><td>1898</td></tr>\n",
       "\t<tr><td>Stroke   </td><td>1851</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 2\n",
       "\\begin{tabular}{ll}\n",
       " diagnosis & count\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t No Stroke & 1898\\\\\n",
       "\t Stroke    & 1851\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 2\n",
       "\n",
       "| diagnosis &lt;chr&gt; | count &lt;int&gt; |\n",
       "|---|---|\n",
       "| No Stroke | 1898 |\n",
       "| Stroke    | 1851 |\n",
       "\n"
      ],
      "text/plain": [
       "  diagnosis count\n",
       "1 No Stroke 1898 \n",
       "2 Stroke    1851 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stroke_obs <- stroke_training |>\n",
    "                group_by(diagnosis) |>\n",
    "                summarise(count = n())\n",
    "stroke_obs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d9cc5d8-29ef-45bb-94d0-fdc87ab5a60c",
   "metadata": {},
   "source": [
    "This table summarizes the count of the diagnostics of people who have had a stroke or no stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9253d192-4324-47de-968c-f5484361a62f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in geom_histogram(stat = \"count\", position = \"dodge\"):\n",
      "“\u001b[1m\u001b[22mIgnoring unknown parameters: `binwidth`, `bins`, and `pad`”\n",
      "Warning message in geom_histogram(stat = \"count\", position = \"dodge\"):\n",
      "“\u001b[1m\u001b[22mIgnoring unknown parameters: `binwidth`, `bins`, and `pad`”\n"
     ]
    },
    {
     "data": {
      "application/pdf": 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XTuv17u4PY2np374sKxH9239PPpwAM/p/s/TAS/lMic/qYUFlSM1I9ikmFkrOgP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\nV2x6MgOZeD2ZgNxGY1OHw2R8mLwfTlxPVP7/AT+I7VkKZW5kc3R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f4y/8lJ1j/tj/AOiUrY/ZI/5mv/t0/wDa1Y/xl/5KTrH/AGx/9EpX4/xZ/Gl6r/0k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\n8u/Gf/ku8w/7h/8ApqAV7L+yt/y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0/sPnAtyrPtwDaexi5SbAAAAJANqTLjxgGGgCvbVq/dfvziHf/ACLWtq0exsrVb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eESnybw/109/MGR8b+M/0v263Elqx7panyoq7h805Ptpfesbw9m1w9f2rTrh4Pz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\nCiUlRU9GCg==",
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H96a4LyKVTAc5UMcRtxwM818WP20/A3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\nCHxL8XeNv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Gcg7FwrEnB6Y716/XmH7RfwA0P9pH4bTe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\n9GfcIvrZr1rMXERu1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Tu8ThXI3HKl+3lpuRvo/4PCgLsTgNRl\nr2AXFfj0ytHtK2bP3fskjZbWbm55RUTk7du3aTRNVdwFXLdu3tTc4NatWyIiUtzNLekHqlHT\n+SeXdCxk5TF2w+x6diJRt2d3Hbwz0e9NiLu7u4iIPLt3L/RDxqmb6PsLJq16KSKSs1OPxind\neTjGudNnokREqg0YWin578RfvZp8zogqT57cIiLy5Imml0l9/vwFnUeswYe8NAmYOZWq22Ps\nilPnlrdwFpGAXSu2vNBtIOnsoUSPHpVVIuqTW7Y+E5ELGzbeEhGr5r06OKW16oePOVbmv6H0\ntMPrVAG9lSsV6X5eWu4GGflBkRn1AaC97BXs/lvQpmydNv1GfPPl7JORqbZ8c+HCQxERKVCg\nwIdssWStWjlFRN7u8/TW9GM7V7z2+4qIOH76aamkj6ns7GLuEm9U5Iu1i9vnFJGna/r0XvEk\nQUeFa9fOLyISvnfLjgBNA3iy/38r913xS35cLP0CToxtP+5UuIiY1RgzsZmGH3lIINzfP0RE\nRHLmdE72YOiRpas1zMGL+xK64e3tn7xDrw1bXyVbmg66vzTq0Ff3zh3Yefa5htZmhXt3q60S\nEXnw4EGK2/zwHhIo2K3Hp0Yi6uP/7HojFzduvCUi9m17t7ZN2EivW0wm899Quu/wOlUgY8uV\nivS/kbXaDfT1QWGw+gDQgaHPBWcu39XNY86qGRfpvupG8tuqxQi6tqSVq0pExMhj8rX4xaF/\nthQREdPO2xLNY0j16q7oS2PcRETErsmKJ0mvPXmxqVPMqoVGnEzQpcZLW97s6ldQJSJiVXPG\n9QRXKN+YVDbmFm3Fhx1KdinYsy2d8oiIWLda9VqdtjQvDIp8ee6PXqVi71ri+Nmi20kmZGi4\nqswrtjpFx5xPPPsj+vn2fsWsHBxiTogmmqBw77eYea+m1affS1yzoDPjysReif+B19jp/tIE\nboj5Fac8vXckLbRarY64NCYmjtaYFXdXsWTV0LmHNPj+0cBERMzbbDgzqZSIiNPnexNfh5mO\nLQY8vnzhwoULF6481lTIpCPI9DeUrju8ThXQ3wuk+y9PpP+NnPZukM7+k30Q6XsHBpARslmw\nU0c/3tglX+yJNVOXqp2+nbZsi+dR73MXLp4/dXjvjk3LZnzXrbpr7MlFy/LjTyb84YODX+YS\nERGHhr8evvPk2X/XbjwNVaf1PaR+u29QTCIzL9FpjueN1+FqtVod9urmvvm9SsdEpHy9diX6\ntE7hmuWgkyNKmYiImHqMPf3+OzTo+LclYi5MtynTb97e6y9CoqLDA3zvHFs1vmlhUxERE/fx\nZ5N/0GsQ921k0/b3UwmcOOK1c/PyGd/3/qxo3OE5lVONiUeSf8VoCHZBO3rFTO21qfL19tsB\nUWq1Oirg/tEV39bPa2JZbca+2Fup2jdf+uR9XH2xvFnMJUFm7r2WnHwSFK1Wq0N9zqweXi2H\nOLXt2dQ6eX10D3Y6vzQhh4YWEhERi5IdJq06eOXRC//QiLCAV0+vHlw9rnFeIxER2+Yr4n8H\nIHk1dO0hLa9XNjETEdvYm5DlGXo86dRp3bfoNSh2VoxWkcQAbygdd3idKqC3FyjurWTdYppX\nai49j/+TIv1v5LR3g/T1n/yDSN87MIAMkN2CnVqtjni0a1yDfMkv90pE5VC2x7zTSf4qDTk0\nJPE90j6Z/Vid5veQWh1688/uxeI3aGRubWUWfwrcvGinFTcS/2xWypPRwi5NqRzz09+FB3i9\nH1zkf1sHl0lwRtQo4fXP1u4DNt7X8R4NqbNxazF+10ONSVHTPNDoh8tb5IobkMrc1sHaVCUi\nYl3u693PoqOv/OAe+6CRpb1d57UxxwCjHm/qUiB+brCRua2dlYmIiF3V7w/57emtp2Cn80sT\nfGlus7xxRwyTs6sywjONX87VrYc0BaxpYxW/ctGEP5iW7i3qFuzUhnhD6bjD61QBPb1A2r2V\nRFqvTnDQO91vZC12g/T0r+mDSM87MAD9y4bBTq1Wq9Vhz7w3zv6+T8vaFYrnyWFraWZsZGJh\n65i7oHv1pl2G/LzC6/ZbjT+N8+7C0oG13VxtzMysHV3dPhm6/ZVai+8htVqtDvnP6/cRXeqX\nLZjTzsLU1NIhV6EKDbt/t2D/o9DkbVO5y0DU3QUNYi6eyd1+XcK7WkT4eC//oV+zKsVdc1ib\nGZta2ecuWqlRzzFLjj4O074qKX0bGZnZOOUp7FGzzcDx87dffp3yrwalcIOP8Id7fxvQsFx+\nR0tTM9tchUtXb/P173vvxf462rt/F/X4pLCTpZlljiLVJh6J7zz69bll33eu6ebqYGlm6VTA\nvVrLr+bvfxiiVqt39bRMvpX0BTu1WreXRq2Ofnt164yvOtYrV8TF3tLU2MjEwsapQOkarT6f\ntPqMr1b3sdOhh7QF/dU17g6BJX+8mkIjnbaoc7BTq9UGeEPptsPrVAF9vEDpCna6P684Wu0G\nOvefwgeRXndgAHqnUqs1XTgOZFlha1ta9Ngp4jL08PN5dQw9GgAAspDsNSsWHw912Jsnt58k\nnxUr0WfPxtwJrVSpZBOJAQDI3gh2yHqij40oZm2VI79bzTFHQ5I85rtm+qrnImJUuXmTXIYY\nHAAAWRfBDlmPUfWunQqLiDxc1K7h1ysO33oZGBER6HP7zPbpnesM2vFGRPL2mjigiIGHCQBA\nVsM1dsiSIm4t7dz4y20PNd321si14W//bBtZKfVbIwMAkP0Q7JBlBd7Y/sf8lX8fPnfj4Qv/\nUJWlvXO+UpXrNO8++Iv25Rw51gwAQDIEOwAAAIXguAcAAIBCEOwAAAAUgmAHAACgEAQ7AAAA\nhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCmBh6AJnHz8/P0EPIQGZmZnZ2dsHB\nwcHBwYYeS/ZibGzs6OgYGhoaGBho6LFkLyqVysnJKSIiwt/f39BjyXZy5MghIq9fvzb0QDKQ\nubm5ra2toUcB6IwjdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAaYFRt64KdOc88l\nX164+8K5nfPF/iPovte6dbtP33j8KtTYzqVE5QaderQu45gghqbZAAAAIJsxQLALDAoSMSta\nu2X5nImW53CPm1geeXf9+DHr75kUqlG/fWO78OcXjxxcMeHykx9mD6too10DAACA7McAwS4o\nKEjEoXyb3r2LaW7gu2vJpnvRJftM+6VdfmMRkc6tK/42dJrXok2NlvRzU2nRAAAAIBsywLnL\noMAgEWtr65Qe9zly6GaUZY0OLWNCm4iIQ81OjVzF99DBa2ptGgAAAGRHhgh2QXHBLjrsnd/L\nV+9CoxM+HH779n8iJdxLmyZcWsjd3VL8b9321aIBAABAtpT5p2Kjg4JDRUJv7/ht927vh4FR\nIkbWecs36NK/Z538ZiLi9+JFtFg450x8RE+VM1dOER8fX5HotBq4ZOKzAQAAyDIyP9gFBQWJ\nyN1jR+0athvU3sno7ePLB3cf3T7z1sOQ2T81cZGQ4BARCwvzJKtZWliIhIYES9oN4kyZMuXA\ngQMx/+/g4PDXX39l4NPKGiwtLS0tLQ09iuzIwsLC3DzpPolMYGpq6uTkZOhRZDsqlUpElF35\nyMhIQw8BSI/MD3bm5TuPGtXcplDFcnktYpY0almv8Pff/Hlx9cZ/G3xdUUREVJJ0CoRaLSKi\nilucZgMRsbS0jP8JZxsbm+joaFEulUqlUqnUarVazWWGmc3Y2FitVit7B8uaqLyhGBsbiwiV\nB7KgzA92ZnnLfpo38SLj/K1aVV4z89Tlyw+koo2VlYh/SEiS1YJDgkUsLS1FrNJqEGfEiBEj\nRoyI/6efn59en0jWYmZmZmdnFxoaGhwcnHZr6I+xsbGjo2NYWFhgYKChx5K9qFQqJyenyMhI\nf39/Q48l28mRI4eIvHnzxtADyUDm5uampqZptwOymCxyR19TB3srkdDQUJGcLi7GEvryRUCi\nBtE+z1+I5HHNo0UDAACAbCnTg13oI+892zYdvR+VaOnbR48CRHLmyiliUrJkUZXcvnolLMHj\n6puXr4aLs7u7Ng0AAACypUwPduZBlzatWDN/6e4n8dFO/fb0ym3XRVXk02ouIuJcp2F5szDv\nLVvvR8Q2iPbx3HjwtapgowYlRJsGAAAA2VGmX2OnKtVlcP0zUw4uHTH0fK0qxXMY+T+6eML7\nfoCVW69hrWPOo+ZoOKDn4e+WrR87/H696kVtQx6fO3T8blTBdkPaFhTtGgAAAGRDKkNMoox6\ne3P/po27vG8+exOqNrd3KVq+VuvO7aq6mr1vEvro8PrV209ce/wqzMQhb6lqTbt1a1rcVnRo\nkEx2mDwRHBzM5IlMFjN5IjQ0lMkTmSxm8kRERASTJzJfzOSJ169fG3ogGcjc3Dz+vgrAR8Qg\nwc4wCHbICAQ7QyHYGRDBDsiyssisWAAAAHwogh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDY\nAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCmBh6ANAn6zMXDD0Erbz0cDP0EAAAUCCO\n2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEA\nACgEwQ4AAEAh+EkxAEnZTv/J0EPQSpiI0eSZhh4FAGQhHLEDAABQCIIdAACAQnAqFgCyio/l\nJHiEiOkvcww9CgAaEOzS9rF81IaJSNPOhh4FAAAwGE7FAgAAKARH7JB1fUzHSidNM/QoAADg\niB0AAIBSEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAArBfewAPbD0Pm/o\nIWjlpYeboYcAAMhAHLEDAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4A\nAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAmhh4AAKSf2Ykzhh6Ctl56uBl6\nCACUjyN2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQ2WhWrKmpqaGHgFi8FoZC5Q1I\necVX3jNKyMiIAx/4KGWjYGdubm7oISAWr4WhUHkDUljxVSqVwp4RoAzZKNgFBgamb0Vb/Y4D\nWr8WVF7vqLwBaVP8j6jyarU63R+qHwVzc3MLCwtDjwLQGYeaAQAAFIJgBwAAoBAEOwAAAIUg\n2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQiGx0HzsAgL4YHz1l6CFo66WHm6GHAGQejtgBAAAo\nBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEO\nAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABA\nIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2\nAAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAA\nCkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGw\nAwAAUAiCHQAAgEIQ7AAAABTCxNADCL+x7OvR259aNvxxw7CK7xcH3fdat2736RuPX4Ua27mU\nqNygU4/WZRyNdGgAAACQzRg4CUXe3Tj/n6fqZEvXjx81759rUcXqtu/WqVklu6cHV0z4fsG/\ngVo3AAAAyH4MesQu6uHm+Vt9ChbO9+DB6wSLfXct2XQvumSfab+0y28sItK5dcXfhk7zWrSp\n0ZJ+biotGgAAAGRDBjxip37897zND3K17N3IKdFynyOHbkZZ1ujQMia0iYg41OzUyFV8Dx28\nptamAQAAQHZksGCn9tk5f/3tHE2+7OpmmuiB8Nu3/xMp4V460eJC7u6W4n/rtq8WDQAAALIl\nQwU7P88Fq29Y1x/cu5x50kdevIgWC+ec1omWqnLmyini4+OrRQMAAIBsyTDX2L0+sPDPS2a1\nRverZCUSlPixkOAQEQuLpHnP0sJCJDQkWIsGcfbt23fr1q3YRy0tu3fvru/ngc+k55wAACAA\nSURBVHSytrZOuxEyAJU3IIpvKOmrvErFBdv4KBki2Pkf+2P5OVWVbz6vYZdSE5UkfUep1SIi\n8W+0NBuIyNGjRz09PWP+39HRccCAAekbb1j6VkPKLC0ttWlG5fWOyhuQNsWn8hlBy90+icjI\nSL2PBMgEmR/sAk8uWeodUXbIF/UdND5uZWUl4h8SkmRxcEhw7NszzQZx+vbt26pVq5j/NzEx\n8ff3T9+ILdK3GlKm5WtB5fWOyhuQNsWn8hkhfR/+JiYmJiYGv9UroLPM3muDzy9ffCyoWOeu\nlVR+fn4iIhISGCEiYe/8/PyMrRwdc7q4GMuDly8CRGzfrxft8/yFSEHXPCKOaTWIU7Ro0aJF\ni8b/M257OuOjVu8iIiK0aUbl9Y7KG5A2xafyGUHL3T4JIyPueI+PUmYHu8eXLr+RiDcbx/Tb\nmPiBo7P6HRWX9rOW9C5Zsqjq1O2rV8Ka14i/jk598/LVcHF2d88pImk2AAAAyI4yO9jla/jV\nD2USX0YSemHN9J3PK3T7rkVRC9c8IlZ1GpZfu8B7y9b7VboWMRURifbx3HjwtapgtwYlRESc\n02oAAACQHWV2sLPOX7ZK/sSLgt7sEPF1LlGlSuxvxeZoOKDn4e+WrR87/H696kVtQx6fO3T8\nblTBdkPaFtSyAQAAQDaUJa8MNcrfeuI0+/Wrt584su1cmIlD3lLNBnfr1rS4udYNAAAAsp8s\nEOysG/28o1HShRYF6vYdV7dvymul2QAAACCbYdYPAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg\n2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEA\nACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgE\nwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4A\nAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAh\nCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYA\nAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAK\nYWLoAWQeR0fH9K0Yqd9xQOvXgsrrHZU3IG2KT+UzQvo+/KOjo/U+EiATZKNg9/bt2/StaKPf\ncUDr14LK6x2VNyBtik/lM0L6PvzNzc1NTU31Phggo2WjYKdWqw09BMTitTAUKm9AFN9Q0ld5\nXi98pLjGDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAA\nKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATB\nDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAA\nQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEI\ndgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAA\nAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApB\nsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgECYG2Wq4z7+7//r70KX/nvsFipVzgVJVGnbs0qiE\nrSq+RdB9r3Xrdp++8fhVqLGdS4nKDTr1aF3GMUEMTbMBAABANmOAYBf11HPy9wsvhjiVrlm7\nlat5yPNrx4/9s+DcBZ9f5vQuaSYiEnl3/fgx6++ZFKpRv31ju/DnF48cXDHh8pMfZg+raCNa\nNQAAAMh+Mj/Y+e9fvPRiYJ6WU2d9XtoqZlHn6jOH/HJkx46zPUp+aiziu2vJpnvRJftM+6Vd\nfmMRkc6tK/42dJrXok2NlvRzU2nRAAAAIBvK/HOXwbZuTZq079kmLtWJiN0nNcoYS8TTJy9E\nRHyOHLoZZVmjQ8uY0CYi4lCzUyNX8T108JpamwYAAADZUeYHO9ca3T//stenORMue+PnFyWm\nuXM7ikj47dv/iZRwL22asEUhd3dL8b9121eLBgAAANmSYSZPxFFHhb3zuX1669JVNy2L9exU\n00JE/F68iBYL55zWiVqqcubKKeLj4ysSnVYDl9hFISEhERERsQ+rVCoVJ2mzCl4LQ6HyBkTx\nDSV9lef1wkfKgMHu0rwuP3gFi4hlwbpdfxzboqKLiYhISHCIiIWFeZLWlhYWIqEhwVo0iDNl\nyhRPT8+Y/3d0dPTy8krfQMPStxpS5uTkpE0zKq93VN6AtCk+lc8IWu72SURGRup9JEAmMGCw\ncy7frKVZQODLh9cuH1kz98WrL77pW90ldjwqSfqnklotIhL/F1SaDUSkaNGiVatWjfl/Gxub\n+KN3MDheC0Oh8gZE8Q0lfZWPjo42MTHsSS0gPQy41+at1evzWiIiav8rqyb9/Ne0qTZz5nYt\naGVlJeIfEpKkdXBIsIilpaVImg3i9O3bt2/fvvH/9PPzS99AbdO3GlLm7++vTTMqr3dU3oC0\nKT6Vzwha7vZJmJubm5snPTUEZH1Z4Y6+KvsyPTt+Yh7133HvJyI5XVyMJfTli4BEbaJ9nr8Q\nyeOaR4sGAAAA2VKmB7tXh2Z+NXjg7GOhiZaqI8IjRcJCw0RMSpYsqpLbV68kvNhEffPy1XBx\ndnfPqUUDAACAbCnTg51THrvApz7HNm+5/T7aRT3d43khSizcShYQEec6DcubhXlv2Xo/7rKI\naB/PjQdfqwo2alBCtGkAAACQHWX+NXZuHQfW9/714KZRg6/UqFrK1Tri1f1zJ/59HmpddlC3\nqmYiIjkaDuh5+Ltl68cOv1+velHbkMfnDh2/G1Ww3ZC2BWO6SLMBAABANmSAyRMO1b6eNa3U\npi1eZy8f2nEiwsjS0dWtfs+WXdpUdYmd0mqUv/XEafbrV28/cWTbuTATh7ylmg3u1q1p8fjL\nWNNsAAAAkP0YZFasyr5E48/HNv48lSYWBer2HVe37wc0AAAAyGaywqxYAAAA6AHBDgAAQCEI\ndgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAA\nAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApB\nsAMAIPv5u4eJSqUqNPKcoQeiwZYuqiw7Nr3IyCdIsAMAIJOFPT78v/H9WlQvXcDZ1tLUxNTS\nNke+klWb9Phuvue9EEMPTq88+9ioEjOxtHPOU7hsnXYDxszbceNttKFHqDQEOwAAMlHkvTU9\nyhav9/mUFbu8b/ipHPMWLZrf0TTU59bZvWtnDGvqXqbr6vtRida480sllaranCcGGrA+GJtb\nxzGNDHz1/L8rR7ct+/Wr1u55S3aafeq1OnHrRjOuXLlyZd9ID8MMNuNl5BMk2AEAkGnUl6e2\n77v2dph5ia5zPG+/CXj56O7NW/efvQ72v39o8cBK1hJ+b0P/HnPvJVglxNv7ssHGqydN/vAJ\njBUSERHy8u6ZXf/7oUtFZ6PgO5tH1Kraf8eLhK3t8nl4eHiUcLEw1HAzWkY+QYIdAACZxnvZ\n4kuRIhV+2LHu68bF7Y3jH7AuXHfg4gNre7iIRJz6ff7p9wftzp8+HWmIoWYYYwvnolWa9f9p\n/dlr+8d9aidR91Z07TTvXtorQgsEOwAAMkvY/fvPRMShWnU3TQ/bt/557Yr1u0/uGVXGSERk\nZx8blarWAl8ROT08f8wF994iIlu7GatUqrrz/cLvrB9Sr1gua4tio87G9RL6aP+cYW2ru7k6\nWpmZWefI41aj3bA5+x+Fpj28yLuLGjurVCr7mr9cStA8yvf0sjHd6nrkc7QyM7dxyudeq8t3\nS069jEq5I+0Y5ao3ecfqnnlFgo9M+mFHQNxyzXMLonyOL/m2Ux2P/E7W5qamVg55S9Xs9N2y\nc0nP44pI0NW1o9tXK5bL1sLSMW/pBgNmHXwaLZfGFFepVI6DD8S12t7TNKaGEvlw3/R+9T0K\nOFmbW9jnLf3ZgNlHXybrVcuqRr04teS7LnU8CjhZm5ta2LsUdq/Rdvj8ffcTtdP0BLVaUQsE\nOwAAMot57twOIvLu2rUUrpgrVL9Pl6bVS7lYqUREXKu0bd+ivJOISI5yzdq3b9++WVknEREz\nM1MRCQo8Pb1zr4WH770MDgsMChMRkTdHx9fyaDh8/t/nXliXqPFZnfJ5jH3Obps/vGGZOj+e\nfJfq4F7vHdZ82L5XZiW/3LpjTLm404ShF39vXK7GgF/XH3tiVuyTerXL5jV6emrjjEGfujee\ne0XX1JFcjlZTx9Q0EXn118p/AlNuFn1rcYuytQfN2nz0blhO9yrVKxdzDL53YvOMAdUr9935\nMmHL0LOT6lXt8dvW0w/CcpevVa209d0NIxt90mPzjXfvRMTKyiquoalpTA2vru5UvemozXdU\nuYuVLGwf/uz6wWUjGjSa9G/Cw6TaVvXpps4Vag6asfHog0jX0p/Uqu7hEv3k9N9zhjWu0nz2\npdSKle4VkyHYAQCQaao2a+okEn10dNMBy075hKXRutKQ1VuW9i8pIlK8z+ItW7ZsWdiruEh8\nKNk3c5Fvk6n/XLj/6OHJ8RVE5N2eb7pMORdgVLjbymtP7p7ev8frxNXHz68t75hf3p2Z1HXU\nkRTn3IZfmdmu0x+3o13brvCc91mOuMVhJ0a1H37A17TMoHXXHt07e2iv18nLDx+dX9q1mJHf\ngRGdfjwf8cElyde2bRURCT+w73CKU2SD/ho7wvOl2rz8yANPnt88e/zoqcsPnl5f2zm/RD74\nc/DkY+9XvD130M9nQ8T60x9PPLzrvW/PvjP3HxweW3jX4O92BIiISqWKaxlTwycrBn17s/ma\nGz6Pr5w9ff7m4+sbehZVScTFOXP2xT81bauqPjbt27+eRdvU/PncsydXzxw9eOjExYcvbm3u\nX8Lk9cExYza9SenppXtFDQh2AABkGrt205b0LGYmwVeXDaiRz7VUo57Dpy7ZeuzGi7DkZxRT\nYWxsLCI3Dt1tuWrzmBblC+cvUMTFWuTZ6mlrn4vk7Llwae8S1nGNrUr0XTqjra3Io5Vzt/lr\n7M/n789bfH/knd2nv+1Z163g+2zwct3UxfejVWVGb1zYtaRdbCZS2ZcbsGLpgDwSfXPh3D0f\nftAuT4UKuUTk3X//vU6pyePQXC1aNazzxaQJ9Z3jrku0KtZt+vAaIvLE0/NaXMNLq1deiBJx\n+3rRhGoOMYuMctb+aeO0Cq+fJAm1MRnP547VsM2Lu7rFVsuscOepQ6uKiL+3943YhlpX9dWl\nS09EpHKXzyvZx9fQoliHucvmjZv8a4ciKabqdK+oAcEOAIBMlK/dqtOnln3TpKiNKurNTa81\nc8YNal/bPbd9bo9GvccuOfCfLl/iru26108wszLYy/NYlIhjy86NrBI3tG/Vrr6JSOjBAyeT\n9xJ8/udW3Vc9Min51dYd35VLOFMz7MCuA2EiHh06lUqSF8zrtGnqKBKwf/8ZHcabAicnJxER\nPz+/lFqU7Llo4/Z9h+e0sk20OH/x4hYi4uPjE7vg9YkTN0Ukf7PmZVQJG+bpNbi1tWhUtnPX\n0omeXD43N6uEo9G+qjaOjiYicnHXtv8SHci0rvnF5HHf9KuZJ6Wnl+4VNTDRoS0AAPhwOSr2\nm72n3y/Pznnt3nvw8NGjx7wvPXr38prXqmteq6ZPrDd6/YZJdXNpc+SlXIUKCfPLrevXo0Sk\nlIdHspUtSpYsKNvv+d+8+VyauiZ4QP14Q4+WE8+GuHZY4zn7/SnYGHeuXg0TkcdrPq970DRJ\nj0H/hYnI89u3A6W2jVZPO0VhYWEiImZmZqk2i3p755jXkYv3nvj4vPQPjVKLyNOz0SISFRU3\nj+PBgwciIiVKlEiyrmWNGuVl/YnknRoXLVooaVtLS5Hg8PDwmH9qX1WLll8NKb5r7p09X5Yr\nva1Lz44tmzSsW6mgTdqvZLpX1EDLYHdl1cg/L5ftMaNX+ZTbXJrVashW1yHbFnfNmZ6RAACQ\nnVjkqdxyQOWWA8aJRAU+uXjUc9vqBQs3XvQ5NLlxC+sr3qNLpPm9buHsnOgw1Nu3b0VEHBwc\nkre1t7ePb/I+2IWeGddivtdztRhZ58vnnGyDb968ERF5e+fkkTspjMHf31/kw4Kd2tf3pYiI\ns7Nzim0CLywY2HX0+lupzK+IH43GCuR2cVGJJD/fHXuwLBU6VNWu3qz9f1kOHD5v7519Sybs\nWzJBTByL12zeqc+wr3tUzWmcfP046V4xOS3T4K0dM2fO3HEz1TYmb+6cOLH50BVdNg8AAIxt\n8lVqNmDy+vOXN3RxFQk/O2XyzrQmVoiIubl5on8nmBmQjFqtFhExMkr0xe97zOuyqkixvKro\nu3N7fe2V9AK82A7rzvdRp+TUt3m1eoqpuHLyZICIFCxb1i6FFq+2fdFk6PpbgfaV+8/8y/ve\ni7fBkTFb9xpkr+lZaipEasVJlU5VNSrQ8hfPW4+ueq6YMrRDjaIO6jd3Dq+Z0ueTErUnHH2b\n2lbSvWKyjlJ9NOT1kxivQxL+K7n/bp/duvifeyIhIcr6kTsAADKNUb5Oo3sXE5HACxfu6r66\no6OjSPxxtsRiF8Y2iaNy/mzKwX8vbxle0lj9cFmfIdteJVop9uK358+f6z4YbUWdXbPhjog4\nNGpUJYUmLzfOW/9CJEenpfv/N6LdJ0Vy2lvGHMRSBwcnnrthY2MjIhIQEJCsD1/fdP4sre5V\nNc5RunGfsfM2n7j70vfqztm9y1rJ25M/9/jxeHgam0r3igmkHux2DcwfY9BuEdk9JH9KCrtV\nbT/vUoQYly+v2F92AwDgg7w7OntAu7rlPhlzPOV7+5qYmIikfb2ZRm4eHqYicv3KlWT9B1y9\n+khEcnp45Eq4uEDvX8dWt7esNnXN2DKm8mzt5wPXJ8xwxcuUsRCRB2fPJg58+hP94I/RfzwQ\nkRKDvmyQ0inHu7duRYtIjWbNkhyfO3fiZOIDmwUKFBARuX//fpIuQk+evJDOIepe1fdMnEo3\n/2blkYUtLEQe79uX+plPvayYRrCr1O+XkX1b1SyV2zLt45cqc5eqvZcuHVpQ+40DAJCN2Fn5\nHtl25PKZBWPnXNF8fivkzJ+bb4qIU5UqRWIXxZ4JjIxM+3fFLBo0q2cq4r9zg2dQ4kdebtl0\nOFrEvkmTahrXNK80Yc3EKubyauvgPssfxV+JZvJZi8YWIuH7l/75X5I1fJd39Kjd9btNtz7g\n9yeinv4ztPXIgwEiubv/OrJ8ipEk9jhcaGjiw3M+63/+3z2RhJMnXCtXzisid/bue5C46epF\nf6dxeV6KtK7qU8/JX3Ru2GPxg6Q92OXIYSqpnNRM94qapB7sCjcbPX359mPXfVa1E5EWfzxP\nic8L/9CQ56dX9vUwT7VDAACyr8ojZ3R1VUnAsZF16345f/fl58FxpwcjAx+d3jitf63G02+J\nWNYY8239uMNXjjlyqETk3oULyU4vJpO72+h+BVTit27YoHUP4o5lqd9dmttnzL5QMS3z1bfN\nrVJY1aTs6DWTa1iJ/76ve/1+L25Yjh1HDy5uJNFnJ3b+/sDzuGgZ+mj36DbfbLl2bNedaN2u\n7I8dUfibeyc2/da7ark2i66EimX5UZv+aJvyxAkpVraslYgcW7noWly0C7yxfkDDL58171xK\nREJv334cu7x6l84FROTfGUPnXwuOWRTtd3Rix+9uuRZNOq9XW9pW1TH69pZN+9eO6jneM8E9\n86Jee0+bsSNAxKFu3XKaN5DuFTXRclZsoXq9e9tUKe/i4qJ91wAAIBHn1sv2LY1uN2zjnTOL\nhjVfNExlbpvDwUoV/OZVQHjMcTIT17pj1m35tnj8gRezajWrGO8483Zd54KniuWOKvbVmR2D\nc6fUv3W96Zt+vtJk/Mm13Uvu+cGjTFGH0Kc3r918FhhtnLflwk3jy6USw4xLjFg9Y1f5Lw8f\nGd192mfHR3uYiIh5tSlb5l5t9JXXmekNCq8qXcXD1ejtg2uX772KELOSQ7f8r0uOlDtMYO+X\n+Ry+if3/6PCggJC4jGhTovPUdUuHVUp1Xq1lhx++99j241XvH6qW2F6rYl7xvXLyzAObJgsP\nLi4+c8/GG35nfqhb50id7lOWDyxdffTcnlvarX60e1j5/PMqVyxo+frmmQt+hb/dNfF50573\ntBpsMlpW1arZb38OPt9h0YkpTQv+nrdkiYLO1urA53eu3PGLEONCXRdPbm6huf90r6iJlrNi\nKw9ZuXLlkE+07xcAACRn6dF/w9V7x1f+9EXbOuWK5LKICvDz84+0cMrvXqN579EL91y7dein\nuk4J1yg8ZNmSXp8UdDAK8vEJtHXNmfrFd7afjDt4+cCcoa0r5Ai6e/rQ8as+xoXr9hj359nL\nfw8omcZ1e0ZFBv85p5mDhJ6e0P3n87HHpizLDt1zxft/o7rWKWby7N+jh71v+tt6NOk/eevF\ns/MapXKYLZHIEP94AWHGdjnzl67Zbsjk1WfvX98wrJJtWqsbl/3Bc8/U7jWLWfldOnzI+064\nW6/Z+89s/6Kk5Wc/LBlcPb995NNLFx8Hq0REcrZZftLzl951SjpHPLxw5uoT0ypDVp08Nr2e\nXXS0xP2OmM60rKpr84XHvTdO/aJ5RRejl3f+PXny3zv+9mUb9/lx3bkLazulMn043Ssmp4qd\nqauliLf/3bj5yO9dSHh0Cqu5VGhSPsW/JAwqlTtap852+k/6HUnGsWja2dBD0MpLDzdtmlF5\nvaPyBqRN8al8RtByt0/C3Nzc1jbNvIGPh/+yhg4D9kul6Q/OjSxk6MFkJK1/eUL9fN/EL4b9\nvvO2f+rThdtvVm/p8MHDAgAA0JX62enNnqfv+eRtP6Z9iQQTPwO8vE6LiFOlSkqf46ltsLs9\nq32rn0+FiYioTK3sbC1NUpgna8fsCQAAYBAqOT9/0NfHIu12hzv9Na5uLlMRUb86s6D/iE0B\nIkX69KmdztsUfzS0DHY31i4/FSbGbt1+/2Nyr9qF0/fzZQAAABkpT/8FM7fX+3rf8Un18s8v\n6l7MWe13//aDlyHRYlNh9J8/Vk/HJN6Pi5YJ7e7duyK5+i5Y8WVdUh0AAMiizMt8tePC4cUj\n2lUrYPzi+rnzt16o8lRsOvDXHReO/1Lzw37U9qOg5RE7a2trkcJubum4DzYAAEDmMc9fZ+DM\nOgNnGnocBqHl0bfS5coZy7NnzzJ2MAAAAEg/LYNd7p7f983zZOVPyx/qcnMUAAAAZB5tr5ez\na7rgwJ9tHo6u22z0qpP3fAPT/sU6AAAAZCotr7E7+mOdsZ7BwZHGvp6/9fb8TcTYws7GXNOU\n4dar3v7ZSq9DBAAAgDa0DHYvrh49cTrhgqjQd/6hmloGhX/4oAAAAKA7LYNd9e//3tHX1NTE\n1MQ4jTv75S774YMCAACA7rQMdnmrttbpJ2gBAACQ2bjZMAAAgEJoecTu3eOrj/zTbBUdFR5q\nVbBqcacPHBQAAIoTEBCQEd3a2tpmRLf4SGkZ7PYNL9PxL61att+s3tLhAwYEAIBSmU0ep98O\nw8dP0W+H+NhpGezSZmqdw8HW0sraNcv+4WBhYWHoISAWr4WhUHkDoviGkr7KGxsr/sfioUxa\nBrt260NCojQsjw599eT+3avHNsz6bad5/2Wrf2qUJ41Zs4ajUmXZoWU7vBaGQuUNiOIbCpVH\ntqJlsDMytbAw1fSARd4SFfOWqFindctyzSu0bBB56N9famTRv0pDQkLSt2KWPQb58dLytaDy\nekflDUib4lP5jJC+D39zc3O9jwTIBPqaFWtcZND4njluzJmy/rWeegQAAIBO9He7E1WhQgUl\n9Ny5a3rrEQAAADrQX7CLfvjwicjbt2/11iMAAEBSW7qYqFTV5jwx9DiyJP0EO3XII8/vv1n6\nVKRgwYJ66REAAOhP6Jo2KpVKZVFt1u3oZA9eHF1MpSo/+e6HbCD69aUtM75s8Umpwq4Olqam\nlna5i1Zs2HP8mksJD/jc3/Hr1B0ftBmkQcvJE14jPIbv0/yQOjLE3+fJU/9wETHy6Nq5jN7G\nBgAA9Crs9IQvF3fcPzi/nvt9uq5rtV6bnpgVrNOhwyB3F+tof9+75/dsWz9l/7bd1/ccmlrL\nXkTk+a7fxkwtVX5sq2J63jziaRns/B9du5bGxXNGDuV7zd04vhzTygEAyJJyflrL6dSBMcPW\ntPu7R2499ht1aOrwTU8s68z+d/83xd8ni+lT/ulXvdXKX7+a3+/CuGIiUWfPXhAppccNIxkt\ng12VoStWtND8kMrI1NzWKZ9bxcruubLojU4AAIBIuMe3izwe1Vv87fDtzde1dky53cO9M36c\nvm7fuXsvQ4zt8pSq0erz8RMGVk3xF0P9rl9/IVK3Y9fiiWKFyrXltDWra/q6VHOMDl3ZyrLv\nLhGRZU1VyyT318d85tSULV1MOm5sucH3q6vdB80//rjVxpA/W+k+gOjHaztW67FN3X7VyU09\nChmJRPueWPDT1OU7T918HmRin7fUp60/Hzf+8yrZ4hdPtQx2Bev26ZOh4wAAABksPNy07qx5\nPbe1Wv3V6L4NFje01tjq8ZoOVXv9E1yy/eCxw0rninx6fuvCBYNqe93ff+7XmlYa13DOn99C\n5KrXnseD+uRPlCxy1uzRX0REouuP37Yox8jBq582mLB+SAWLou4iImZmXQY/qQAAIABJREFU\nZiJvdwzv623aZvgEp1JF0jGAd4dHNu+3NajujKNrexQyEpGX//T9pN2qtyU7fTHhKw/HkMdn\n/1q4cGAtz1t7z86oY5P+4n0k9PaTYgAAIGuLjo4Wh5YzZ7be2XPplxP7XJlRPfmptrCdP3zz\nzwunbluPr22bQ0RE+nzRNk/VMmOnf7foy1PfFtDUr3HT4aMrbPlxe78KVfb079exxWe1q5TK\nZZHo2iyjAtXaNDg8WcSnYPU2bZrELjU1NRU5sctv5qU9wwoapWMAkTcXdGg3+477d57bvy1r\nLiISfnDi4FVP3cedOjm5ckxw7T24R/lGpT+fPXLBkLOjCqe3dh8LHWfFhjw+vm7at/3bN/y0\ncvnSJUuVqfhJ7aZdvxy3YMeV15p+cQwAAGQxOXvM+7W+1d25g6Zejkz2oProtr9fSe5OX7TJ\nEb/MuFS/XtUl2nvnnlcpdGlaYeLhk/8bUs/61qZpX3WsXTq3fc4Sn7b94uflBx4EpzYUlUol\noqrfu2/B+DyiywB8/xnU7OuDDr3W7fmtjl3s6ic2bn4qZdu0yxfgE8fX9LPWNUyizx06Ephm\ncT56OgS7tyenNSlZrFb3UbOWb91/8vyl67duXr1w5pjnhkVTh7YuW6hC3zU3wzJuoAAAQD/y\nf77oxxrGV34bNPumOslDPjdv+ouU9vBIdLwtt5ubvcjt27dT7tOufP/5B/57+ej0juXTR/Vv\n4qa6sWvxhP4NiheqNXa/T6qjyePmluAMqdYDCD7/c8uuy59V+3XfsrYu8Y1fXL/uJ/LvT5Vc\nEyoybG+kyKNHj1IdiCJofSr24dIOTUYdCBAj20LV6tepVLJgTnsrM3VY4Buf+1dPHzz8r8+V\nlb0aRjte+rN5jrQ7AwAAhqMq8c2i71dX+vnHwYs7Hfoi4R1og4KCRMTaOsnld5aWlnGPpdqv\ndf6qLftWbdl3pEjkm1sH1kz++rs1v3QYVObu9q7OKa1kb2+fjgFc+7Xn6YAgkat7Dz35tlih\nuGQXEBAgIuWHbfmlWbIrCM0L5Ulj/AqgZbBTH54+8UCAcaFOC7YvGVjWPuktTaJenJrRq+3o\nvatGzh7e/Ofyeh8lAADQJ5OyY//4ev2nM8cMW9V2R3mj+BN4NjY2EpugzBI0DwoKFrGztdVl\nC45ujYetWvnwRPWZe3Ycieja3lSr1bQdQJB1tV+3DXk6qN28b7rPqHX0u5LGIiJiG9Mkd/km\nTYrqMFjl0PJU7FUvr+fi0GXeykHJU52IGOeqPmrj7FZWcnPHP3f0O0AAAJABLKpPWjgg/9t/\nRo7YFmAWn6Fc3N1ziNy4fj3ROdon16+/E1XJkm6aOoq6tGxwp4Ytf72Q/CctROXgYCcSERio\n9cVa2g6g6qjloz5rPW3t9x7hJ8d1nXQ+PGZx7tKlnUVunjqV+BdO3730C9d2BB83LYPd48eP\nRTyqVLFMuYl9jRruIvfv39fPwAAAQIaybvjb751zvlz31c/e71NUzfbtcsnzjYu2v4lfFH5x\n8cozYla/fQsHTd0Yu6gf7N6/c/LAMQdeJp5JGXFv7bjFl8SkWoO6NiJibGwsEhYSoiEAJqDT\nACw++WndhMrGF6d2HX0sZmZEzU4dXSXUc9b0s++nbbzcNrCsi0vL5alf66cM2p6KVatj/5My\nKysrkcjI5DNsAABAVuTYZs6MVp69d+wVkXIxi8waT/q9/c5ua3vWivxqQEM3+6D/Tm1Y8L9r\nNtWmzeqfwq9V5O63dPmhuj3XTWtQcH2NFs2ru+W2UQe+enLrpOe+f30jnD+b9cfggiIirkWK\nWMiZXb8O/OVBiQK1hnSvovE+ejoOwLTM2LW/7av49ZzuwxpfXtHYwaT+j4t67Gq/ZmqjOs9H\nDqiTN/rZ+a1/LN3/qsjQr9q46KVoWZuWR+zy5csncuX06ZCUm4R6e18SyZ9f3z8/BwAAMopL\nr3lT6iUOWHk6bziz95eOua+smDik/xfjFhw2qj920+n935VN8SI5o/yd1l68+Nevgxvlf3Nq\n06LfJk+evnD1vtsmlXpMXH3mxr5vysWc6bVoNX52ew+TK6t+mjz/wMOUDwTpOADjEsPWzG1q\n93hl7883vhCRXK3/PH14zuDqkftnDh/w+Yhp21+V//J/x0/MbZgtZneq0jgMFyv60JC89Re+\nLNJtuefSXsWT3/Y56Nafg5r3X3sv99DDT+bVyZo/F+vn55e+FW2n/6TfkWQci6adDT0Erbz0\n0HiZRlJUXu+ovAFpU3wqnxG03O2TMDc3t9VppoAWAgICzCaP02+f4eOn6H2c+KhpeSrWqN73\nk+qvHnRwXe/SXrMat2xY1aOIq4O1qTos8M3ze1e89+30uvYqUhwaTxmVRVMdAACA4ml9H7uC\nAzfv9G3fedJhn0s7l1/amexxswLNftm0vk8+vY4O+H979x1QVf3/cfx9ucBl740ICgIi4sCJ\ninvPXKlppVlp65u/6mu5SrMcDdt9Lcuy0rJy5cCNDBEEAcWtaE4E3MqG+/vDhbiucPHK4fn4\nC875nHPf5wN+eHk+ZwAAAJ09xLtiHcInb9o3ePUv8xevjUne8++ZC5cLVObW9q61gpuE9xj6\n3LCOPnd/mTAAAAAehYcIdiKisg3o+crMnq9UUjEAAAAov4d4V+y9FBcXP7gRAAAAKpkOZ+zy\nDi/98K1fbab+/Wb9u6zNXftC6ESTN76fNarR3d5JAQAAriuY9IGhS4DCPeiM3bnIt1o37P/+\n0iXz/ki72/ozCz5esDdp7uhmzcasyqqE+gAAAKCj+5+xO/nj8P4fJ10RI4cmrf2NC0XueDSg\n68gflp0cNXr6xgPfDerutjlmanOzSqsVAIAqzSZpp353eCk0RL87RFV3vzN2+Zs+mLjmvKjc\n+87bFjfv6cC7PvDZtGbPaas3fd3dWXKTPhz7xYFKqhMAAAAPcJ9gV7Thl98zRGwHzPl5ZJ37\nntkzrTv251k9LKUo+X/zEvRdIQAAAHRyn2C3Ly7uvIjz8JcH2j54P84j3nrWVeTIhg3p+isO\nAAAAurtPsDt+/LiIBNStq9ZlR8ZNmzYSkcOHD+unMAAAADyc+wS7goICEXF2dtJtTxqNRkRy\nc3P1URYAAKi6/hpirFK1+OyEoeuofu4T7BwdHUUkM1PHp5hcO8Hn5KRjDgQAAI9UybnUvz5+\nqVfzurXc7cxNTMxtXH0bdx4x6dfUC6Uapa+Y+eGKQwarERV0n2Dn6+cnIklRUVd12dGpVatS\nRMTXt7Z+CgMAAHp0cuHQBqGD3vopzbzpgBfffv/DqW++0DMgP3HRByNadZgQffF6q9OrZr1D\nsKvC7nO3q3uXLvUldtfyj788MOht//u/VeLqxndnbSkRadS9u5ueKwQAABVVvPnDcYtPmLed\ns2PD66UedfHRB/+Matnnp5mvfTUqeaKfSPH27ckidQ1YKCrmfs+xCxr9agdzKdj+3oDXIrK0\n9253dddn/YfOOyFi1e0/o/z1XiIAAKig7D17MkWaDBp6+wPMVO69Z//6y7w/P+pnX5L3Uy+V\ncd9fcuXqD91VKpXb6zEi16+We+KPzM2TO/vbm5s/s+L6lgX/rv1wZKdgTztzU42VU62mff7z\nXcLZe358yfHfBniqjDwG/nq05NqCM7FfvtyzkbeDuanG2rl2s37jvt9+782hs/u+Usxj1Jx3\nW1hIftpXPYPbvPK/DYcul4l3+ae3/TalT4Nm49Zlidi0+3DO05yvAwDg8ePk5WUmkrZ+zfGi\nMmucWw9/bkCneo5Gph0mLf12hK+IWacpS5cu/em5IBERU1NTkQsrxo1caNJj3JQJva5dcXX8\n14HNuk/861TgUxM++37urP/rZbPj6xfD27wdk3O3D78U+WbPUUuutvso4rfhPkYikvXPyObt\nXvvtmP/QKd/8+N2s/+thnfTNC21av7nlSqV2QnVw/1eKqUPGL//jeKchX+/KjP16bOevX7Or\nFdIwsIaTjab4yrmM9NTkfVl517KeXfOJy5a9Gnj/CVsAAGAQ6u7j3m7013vLRzVquua5UYN6\ndQxvWtfF7LY/20Y1W/TrFDldJMO7Zb9+3a4vNTExEYldlf1J6ppXva+fDspfOfn1fzIdhy2J\n+e0JBxEReXbMEx7N6k/46K1vX4p7o+Ztn1y07+uB/eccDHorYvkbIRoRkYJN745dcDJoYtzW\n6U0sRUTkmbHDG3ap9/ycN79+efv4WpXZD4p33zN2IiIuvb5KSPpjfA9/a5VI4YUjSZFrlv/1\nx+KlqzbE7c3K04qoHUMGTFu9M2Z6Wx2eYwwAAAzBpNG7kVvnvdzecv/i2a8NCq/nauvs3+qJ\nMe//uPHIXc+y3aBSqURUHZ4Z6X0zMmijli47K66Dx/RzuNlMXXfU0y2lZNvKNbdPqJ7558Ue\n/9lk9/TCNbPa2lzfPPaPP09KSL/+NS5n3HDGpGPfMOOSxM2cs6ug+5+xu8YsYPDMVYMmHIpe\ntz46Lnnfiazzl/KMLOwcXGvVa9IivHPn5l7mlV4nAACoGJuGz321cdSs49s3bYiKjY2Ojo5e\nNXfrsrlT327934V/ftjpPldTeQQEWN36LmPfvosiocHBt53wcw0IsJWYAwcOiLS8vign6f3e\nT/14qsVHaT884XazceaePdki2dNC3afd8UHHjh0TCarYYVZvugQ7ERFR2fiFD/QLH1iZxQAA\ngMqlsvRq1ntks94j3xQpOr9/46/T//PWrzMGvlj/0PKh93wUra1t6Vm5q1evioilpeXtjczN\nzW+su2b3zBHxl6+KpK3dfOINP58bye7y5csi0vDVv2b0KLMHEY2PRwWODQ8R7AAAgLIY2wd0\nfXXBT//GtvxkzYothUMHmOi0mZWVlVyPcKalFl+9miNiY219a4Fli5lLXz75Yv8vX3/q4zZR\nbwVee0ep9bUmrg27dfPV26HgugdeYwcAAKq44tQfxg7u3Htmcsmd61R2djYihVeu5Ou6N7eg\nIAeRvXv23PasjBN79lwSVWBgwM0lzcb/OL5j39m//Te4YOvEoVOTCq4tdq1Xz0lkX1zchdv2\neikru+Bhjgl3RbADAEDp1G7aI6s3rJz+wjsbs4pvW1N4+LeJc1PFuEWndlYiolarRfJzc+8S\nAEtpPaC/i5z+49vl528uKkiZ+1OCmHYY0MuuTGOz5tMWTmmiTvlw6NvR1+6MaD14kLvkRXz6\n0fZbt21kLX0hxM2t948ZFTpOMBULAIDyuY76/sfN7UYsnN3Je1FYr54tA1yttFfOnti/NWLd\njjOFTh0//d9YbxER99q1zSRh1cwXZhzxr9nm5aea3nEVnIiIadepXwxYOey3EW2KXhvdOcD2\n6tG437+et9uqxexPn3O9s7lJ/Qm/zVrX+D+fPfVq153zu9oZd3jv2+GrBvz6YZe2p98c3daz\n5FTSkv99v+Fs7Vde68fzcCuIYAcAgPIZeQ3+LaX+gG++XLAiMm7xt8su5qtMrRy9AkKHvzt0\n7CvDmjhdm8Iz6zNpzoC0SasXTDvoNvS7F59qeo/deTz5e4Ljx+/O+GX+u8uy8kwcvBt0mLB4\nyjuD6t79Kj21/6u/fh7RYPRPzzzfbeefT7q49P05PrLJtA9/WvnJuF8ua63c64a/NO/Pd0Y2\nc7jr5tCdSqu9z8vCFCU7O7t8G1p/dOf92I8ps+5PGroEnWQFBzy4ET1fCeh5A9Kl8+n5yqDj\nr30ZGo3GutRdAHpx+fJlm6Sd+t3npdAQvdeJKo1r7AAAABSCYAcAAKAQBDsAAACFINgBAAAo\nBMEOAABAIQh2AAAACsFz7AAAeEQuhYYYugQoHMEOAIBHgQfO4REwTLDTXknfvHjhitjdJ84X\nmTm4etSq32Hg0K4BNqqbLa6mr1+4cHX83uNn89Q2bv5NOg0e3re+famJ4wc2AAAAqGYMEewu\nJ305btqGTDOvJuG9OtgWndkVHb3qm+T9Vz75aKC3WkSk6NCiSe8sOmzsE9ZhQFebgtMpWzbN\nn7LzxOQ5rza2Ep0aAAAAVD+PPthpdy38YkOmWYMXP53a0/PaGbZBLT95ZcaW337e3GNKJwuR\nM6u+W3y4JPDZ2TP6e6lFRJ7s23jWK7PXf7u4y3ejAlQ6NAAAAKiGHv3c5flLar8GDfoM7uZ5\n87NtW4Q3MpXiY8dOiYhkbNm8r9g8bGDva6FNRMSu9eAu7nJm86bdWl0aAAAAVEeP/oydQ6vR\nk1uVWVZUkF8s4ujkKCIFBw4cFakfVM+kdAufoCBz2bj/wBkJdnhQA7fKPQAAAIDH0+Nwt4H2\ndMTqpGKT4PAW9iKSnZlZImZOzpa3tVE5uziLZGSc0aEBAABAtWT4x51cTpv/4fxdxkGjxnZz\nERHJzckVMTPTlGlmbmYmkpebo0ODG37//feUlJRrX1taWv73v/+tvKPAQ+Gef0Oh5w2IzjcU\neh7VimGDXdHpqK+nfb7xrHe/yRP73bpiTkQlZW+B0GpFRFQqXRuISFpa2oYNG659bW9vP3ny\n5PJVmV++zXBvGk3ZXH5X9Lze0fMGpEvn0/OVQcdf+zKKior0XgnwCBgu2Gkv7Vo0Y+bve0wa\nj/pwfD8/8xvLLSwsRC7m5pZpnpObI2Jubq5Dgxv+7//+b+zYsde+NjIyOn/+fPkqtSjfZrg3\nHX8W9Lze0fMGpEvn0/OVoXyDv4mJiZUVD9BC1WOgYKc9v+3Ld2ZtOOfda+Lk0c0cS1/p5+zm\nppYjWZmXRUqdPS/JOJ0p4u3uIWL/oAY3ODg4lP7M7OzsSjoaPKzi4mJDl1BN0fMGROcbSvl6\n3tjY8JcqAeVgkJsnrqbOm/LRhot1n/5g5gu3pzoRMQ4M9FXJgbRdpecktPt2phWIU1CQsw4N\nAAAAqiUDBLtLcd9+/M8x196TpgysY3aX9U5tOzc0zd/215L0wutLSjIi/th0TuXdpZO/Tg0A\nAACqo0d/qjl96fyoi+JUW5v4x8+Jt69yajKgZz0rceg8ekTkWz8smjAuvX1LX+vc44mbYw4V\ne/d/+Qnva+0e2AAAAKAaevTB7vTpDBHJTl75d3LZVX423XvWsxIx8ur77mzbRb8sj92yNDHf\n2M6zbo+xw4Z1r3PzxqYHNgAAAKh+Hn2wa/X2ihUPbmVWs93Iie1GVqABAABANfM4vHkCAAAA\nekCwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGw\nAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAA\nUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiC\nHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAA\ngEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ\n7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAA\nABSCYAcAAKAQBDsAAACFINgBAAAohLGhC3h0HB0dy7dhgX7rgM4/C3pe7+h5A9Kl8+n5ylC+\nwb+oqEjvlQCPQDUKdmfPni3fhtb6rQM6/yzoeb2j5w1Il86n5ytD+QZ/jUZjYmKi92KAysZU\nLAAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAA\ngEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ\n7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAA\nABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSC\nYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcA\nAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQ\nBDsAAACFINgBAAAoBMEOAABAIQh2AAAACmFssE8uztr+25wv/k676Pv0vE8HupRZezV9/cKF\nq+P3Hj+bp7Zx82/SafDwvvXtjR6iAQAAQDVjmGCXf3zLD59+G3FKLO66uujQoknvLDps7BPW\nYUBXm4LTKVs2zZ+y88TkOa82ttKtAQAAQPVjiGCXE/XRuE921+wx/rP68a/Mirxj/ZlV3y0+\nXBL47OwZ/b3UIiJP9m0865XZ679d3OW7UQEqHRoAAABUQ4aYuyxWe/eY+PlHY1q5m95tdcaW\nzfuKzcMG9r4W2kRE7FoP7uIuZzZv2q3VpQEAAEB1ZIhgZ91qxKjmLup7rC04cOCoiH9QPZPS\nS32Cgszl4v4DZ3RoAAAAUC0Z7uaJe8nOzCwRMydny9uWqpxdnEUyMs6IlDyogdv1RadOnbp4\n8eK1r9VqtYtL2Ts0YCjGxo/fL171QM8bEJ1vKOXreSMj7sZDlfT4DTS5ObkiZmaaMovNzcxE\n8nJzdGhwwzfffBMREXHta3t7+/Xr15evovzybYZ7s7Oz06UZPa939LwB6dL59Hxl0PHXvoyi\noiK9VwI8Ao9fsBMREZWUvQVCqxURUal0bSAiTZs2tbC4ft+thYVFXl5eeYuBnun4s6Dn9Y6e\nNyBdOp+erwzlHvw5yYqq6PH7rbWwsBC5mJtbZnFObo6Iubm5Dg1u6Nu3b9++fW9+m52dXb6K\nrMu3Ge7typUrujSj5/WOnjcgXTqfnq8MOv7al6HRaMzMzPReDFDZHr9rCJzd3NSSl5V5+bal\nJRmnM0U83D10aAAAAFAtPX7Bzjgw0FclB9J2lb7YRLtvZ1qBOAUFOevQAAAAoFp6/IKdOLXt\n3NA0f9tfS9ILry8pyYj4Y9M5lXeXTv46NQAAAKiODHCN3eW0VUuSrl3ulpFeLHJ2x98/X7UQ\nEXEI7d872FocOo8eEfnWD4smjEtv39LXOvd44uaYQ8Xe/V9+wvvaHh7YAAAAoBoyQLC7sm/j\n338fuvX9+bQ1f6eJiEgtyy69g61FjLz6vjvbdtEvy2O3LE3MN7bzrNtj7LBh3evcfMLJAxsA\nAABUPwYIdu4DP10x8EGNzGq2Gzmx3cgKNAAAAKhmHsNr7AAAAFAeBDsAAACFINgBAAAoBMEO\nAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABA\nIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2\nAAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAA\nCkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGw\nAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAA\nUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAhj\nQxfw6FhaWhq6BFzHz8JQ6HkDovMNpXw9r1Kp9F4J8AhUo2BXVFRUvg1N9VsHdP5Z0PN6R88b\nkC6dT89XhvIN/mq1Wu+VAI9ANQp2+fn55duQoVbvdPxZ0PN6R88bkC6dT89XhvIN/hqNRu+V\nAI8A19gBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACF\nINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgB\nAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAo\nBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEO\nAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABA\nIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2\nAAAACkGwAwAAUAiCHQAAgEIQ7AAAABTC2NAFlNfV9PULF66O33v8bJ7axs2/SafBw/vWtyen\nAgCA6qtqJqGiQ4smjf/yn93Ffu0GDBvcI9Tm5Kb5U/779Y4rhi4MAADAcKrkGbszq75bfLgk\n8NnZM/p7qUVEnuzbeNYrs9d/u7jLd6MCVIYuDwAAwCCq4hm7jC2b9xWbhw3sfS3ViYjYtR7c\nxV3ObN60W2vIygAAAAyoCga7ggMHjor4B9UzKb3UJyjIXC7uP3DGQFUBAAAYWhUMdtmZmSVi\n5uRsedtSlbOLs0hGBsEOAABUV1XwGrvcnFwRMzNNmcXmZmYiebk5t5Z88803cXFx1762trb+\n4osvyveBxeXbDPdmZ2enSzN6Xu/oeQPSpfPp+cqg4699GSUlJXqvBHgEqmCwExERlZS9R0Kr\nFRFRlVp86tSpvXv3Xvva3t7e2LicB2s8q5yJ8NFT2BWG9Lyh0POGQs8/PoqKigxdAlAeVTDY\nWVhYiFzMzS2zOCc3R8Tc3PzWkunTp0+fPv3mt9nZ2Y+mQIMwNTW1sbHJycnJycl5cGvoj1qt\ntre3z8vLu3KFx+08UiqVytHRsbCw8OLFi4aupdpxcHAQkXPnzhkObETEAAAgAElEQVS6kEqk\n0Wisra0NXQXw0KrgNXbObm5qycvKvHzb0pKM05kiHu4eBqoKAADA0KpgsDMODPRVyYG0Xfml\nFmr37UwrEKegIGeD1QUAAGBYVTDYiVPbzg1N87f9tSS98PqSkoyIPzadU3l36eRv0MoAAAAM\nqApeYyfi0Hn0iMi3flg0YVx6+5a+1rnHEzfHHCr27v/yE96GLg0AAMBgqmSwEyOvvu/Otl30\ny/LYLUsT843tPOv2GDtsWPc6ZR+BAgAAUI1UzWAnImY1242c2G6kocsAAAB4bFTFa+wAAABw\nFwQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7\nAAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAA\nhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIlVarNXQNj8j58+cNXUIlKi4uzs3NNTU1NTU1\nNXQt1UtJSUlOTo6JiYlGozF0LdWLVqu9evWqWq02Nzc3dC3VzpUrV1QqlaWlpaELqUQmJiZW\nVlaGrgJ4aNUo2ClbTEzM66+/PmbMmNGjRxu6lurlyJEjgwYN6tOnz5QpUwxdS/VSUFAQFhYW\nGho6d+5cQ9dS7XTv3l2tVq9cudLQhQAoi6lYAAAAhSDYAQAAKATBDgAAQCHU7733nqFrgB6Y\nmpr6+PiEhoY6OTkZupbqxdjY2NPTs1mzZu7u7oaupXpRqVTOzs4tWrTw9vY2dC3VjoODQ/Pm\nzf38/AxdCICyuHkCAABAIZiKBQAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDtAPwpPrJ0x659M\nQ5cBAKjOCHaAXmiPblwaF7twfsxlQ1cCAKi+CHYKkncycc1ffyxZt/NMgaFLqYZUdQY938H+\nauxPC/fQ/Y+OtvBS5slT2TnFhi6kumG0AR5XPMeuaio6uW7+RoeBI5rYq64tKD69+ePJX8Rm\nFouIaLy6vDZ5TBs3Y0OWWB1djPzwxU+3ezz9xScDvVSGLkbxSs7uWPT13GWJp/NFjCw8m/R8\n+rknW7qbGros5WG0AaoU3jxRJV2M/GLqgvVRSed9w5p6mKukcN9Pb//vYu8pM955pq1XwaEd\nidujog/bNWlVx5ZTspWq8ET8lqPmNV0trvWzmU+AeVrE+pgTbp3a1TI3cG3Kps2MnDX+u9MN\nhowe3jvM3y7nSEpiwpaNiVd8mzd2NydU6xOjDVC1EOyqJLNaoXULUqO2bY9OOu/bqqnd9u++\nKx4y/ekQK2ONvU9ouyZWB2K3Jm/dymhbubK3zBg/++/Va7edNvWp6+9sphKx9PNXJaxcG385\noFtzd85hVJrMldPfS2k+bebTTTxdXb3qNGzXNczxePzWXYev1GzdtraVoctTFEYboGoh2FVR\npi4NWwcVpEZt2x6z46q95khe3REdfNTX1hnbBbQOtd4fE8toW7lM8vav3XDMMcQ7a/2vf23N\nMq9Vt46TmW1ArdyYf1anGDXvFmJPx1eOo8vmLDjd4rlnGzlcX1ByNnXpX4kOQ9+f0L2G2qCl\nKRGjDVCVEOyqruuj7Za4bYmHLpd4tuzVwP7mOrWdP6NtpVM71XXPjFhxMGjctCds965a+NuK\nhLNWvvWatfLN3rQs4pBdh47+FoauUZn2bfgh+t8aYU80c1eJSElm9CcTvr/cZ+qkPrVMi8/t\nWvVT5OX6wR6cMNUjRhugyiDYVWnXR9vo3WfPHz1t37q9n9Wtq4sYbR8BE4+6toeX/hpl0v/t\n/xsS5n5pxz+LFq5MvlqnV1vN9n/Wn/HpFubFxfwVduVQ2mlLFzvjwpN7j4izg0ak+MiWNTsO\n5Hp0CfcxKZ3qRKRo54KpPyZYN+vTyMnQdSsMow1QNRDsqqTCE5uWpZgHelupboy2MTv3Jibf\nuLr5hhujbXJuzVbh9Z1MDFixUhSeWDt76oKjNgF1vazVIiJmPgFmKX8uSjBu1TOsQZNOXZs4\nnotfsWjJzgI79dk9e4ub9GjkyJ+4iklfPPGtufEmLjlLP5l/zKdtixpmYueQmxCRuGvXv6JK\nnLfwVqoTEXXBofUR2QH9ugfbGrZsxWC0AaoWgl1VlLl86qSfV8eddG/R0ufWaBsVvz066W6j\nbcfObZvU4B5NfSg5k7ph05at0Rsjthws8agb6GGpVln5+RVvW/RXql27jnWsTB18m3bp1Nj6\nzI7tB/J9ez/dN4S/cBVl72N7ZOWyVZHJZk/MnNLNVSUiYuPvp03cnLQnNeVC6LhZI+ub3Whc\ncnzdz0vzO740KIA7KPSD0QaoYgh2VU3J5X9T916WK/v3pB+I31Z2tN12t9HWzIxsoScqa++m\nnbs2dys8tiNq49o10UeNvOoGuHsGel/ctHD5YbfO4bXMRFRmTv4tunQMD+8c5sNfuIorzNq3\nJTI5q6Dk7JHjFo1aB9obi4jaIbiZ97nt2w5nH0ndnecWGOBla6LKOxH1zYzfCwdNfDGUqUB9\nYLQBqiCCXVVSdDr6ywnv/XWoSG1ibJx7NuvyxX91G21RYcWXThzYdzQrX2NrZ+NUq3HH7m28\n5WTy5oiVq7eeMKndtk/IpYhFqzN8u7b0vDYlaGRuY8VfOL0oPn/0gt8zLzS7EB+VGBu71/x6\ntlOZ12gWHmJ2Ii05NTFq9bIVa9cu+335bufBU8b38uHCxopjtAGqKILdY6no5PoFEXl+9VxK\n/4HK3TV3/JzMHrNnv9QtrHnrjr26N3U8vydl966tdx1tc4I7NnbhvkB9KMlO/PmDyTPnr9i4\nacOa5Wu2Z5r71vNzcarZoF33tv6aMzvWr1i6eodxoyZme9fFXAzp2pRu1y+1Xe2gmtY2Pi2b\nu5+Iuy3bica5bnj3zqE+rs5uNXxD2g8ZO6Z/Qye6/6HdOeAw2gBVFsHucXR23UfvLVgfdci+\nQwe/m8/LOL/+i4/j/F6Y2PX6y6pUpg5+LdqFlOyMSkiOLTPaHsis1btPI67u0gNtZuSs8XNP\nhz43/rWnuzZ2Kz6+KzEpbuPWDI9mzbytjK086rXu1inY+mxyxKrEMyXaKyeL/Xu1rMGfOL3Q\n5l/MOpentjAzVomIqKzulu3EyNyxpn9wgwb1A31cLHmGXXncOeAw2gBVF8HucWTh27h2wVWf\nfv0b2d+KCCdi/1i72yi4Vwc/za2Wpk4hLWqciozes/+2WZKOHbgrTS+0GcumTUtrOW3miEZO\nNtYOngHNO4fXPJ+8LXVn7PazfuHNPcxEjMxdA1p069LEPidDmr36n25u9HzFFWcl/vLRuzO+\nWbR06bINabkeDep7WhiVyXZmDVvVdTDWZiZtPWlf05FeL787BxxGG6DqItg9llRmHg2bBdob\nF55Y+8lnMXahDV1MxeJi2vKtO44XN+zS2Ln0eQkzr3pW+1clnLp8LH7bSY+WYd6WXO2iN/sX\nz15k9MTEgf43p6iMLGs2a+N3Pj46JTUpw6NTG59r92OqNI5+Tdu3DeYvnB6UZGya+c53Zxo8\nNXpwW+/i9MTEhMjYY27Nw2pZ3Zbttm7ZnpGfFblgcXrNduG1eBR0Bdwx4HjnMtoAVRXB7rGi\nzYr9+qN/Sho0rWGuEpGig8u++HnDtuiU/KDWDT1qO1/asiFxR3KmT5sWXqWvVTZzvJq6VVXX\n6/yeHTE78kM6NnJmKlA/Cnat+nlbtn/HHsE2pRdrPEIbmaVuiE8+at6qJ49L07Pio4smf3Si\n+8z3hzSs4VEzqFW408HV2w4djY+7LduF+eYdSNqemHrGbeCEt7p58RtfLvcccBo82VkTx2gD\nVEkEu8dJ0f7fP/wuMjV2e7ZPq2Y1zFVGTiFhvhcTtyTER6fkB4V3ah9clLA5MXVr0sXaLZt4\n3hptL+2J2O74wvuDLXZExm3bb9qm++05BOWlzj8YsT7hYK5v1xaet52KU9n418qNXLstr1bv\n9n7cgqlP+du+n7HaecSknj5GIlJ8euNnc6Lde3e0Sk9JiSmV7Sw9G3Xu1/+JgU90DHIkWZTT\nfQacndaDRwUfiWW0Aaoegt3jxMipYXPPE9u27t51c6g182jS6ma2C+n7dK862QkxKcnRMUfM\nAhoGOGlUoj2/9dsvdzV8elBI7fr1tAmrozPcOncJZGJKLxyd8uIitqekHHMJa13bqvSsk8qh\n6NCSaG3LJ9v4kCv0KWPr4uU7s1yadm/kaHQx4au3v7s4cPqEAe3DvDOiovYciI87Yt+wma92\n99Z0y5quFsZqZgIr4L4DztYTASOH1TiSwGgDVDEEu8eLytK7ZYv7ZLuCJkNeeCKkaN/2pNT4\nDWsidx5Mi/7jp7UyaPLYJrZGorLV/rty0+WGAzrVMXvwZ0FEe/Z0prG11T3vpVTZBtQpSdic\ntDs+/pRb0+Y+1jcfe1t4cN0vmyy6v9jRR3OvjfEQSvLzi4yN1SIW2mMbU1Sh7cL8C9a//25U\n0DsfDK2jEdF41jPfszwh4+rJ7etWrVwdd65WePs6NsS6Crr/gJOY3eCF54LPpjLaAFUJwe6x\n88BsFzrwmSHdGtgVXzqXlXmu2KXF02+83MnLRESk+OiGBWuMOo7pWYf/QutAm7318wkT5++w\nDG0VYHevbKd2DG7uc2771p1pW7ekXnX0D/S2M1Hln9zy1cy/jYeMfzbYmmyhBxfiPn5t8maL\nsLa+VkaudTt07FTfTZX0w7Q/jfpNHF7/+ts7NE5XU6MsO/Tx1mgC+/7n1W41uU9FL+4/4GzP\nCHx58jP+akYboMog2D2OVJbeLRtIXMTO4+lb7zonG9ypc6uW4V169urZuXUDx5yTF01tzfOP\nbv5m5l/aoe88H8KJDJ2otJcOxUYn70mIOWB1n2ynMq/RLLy+5sSu5NQdsWtWRERFrVm8JNlu\nwOTxvbzJFnpxauWn3x4NeXFke3eNSsRUo1GJnNi8YNlu00b9OgfeeC1b4aHNEdoek1/uHx7q\na8/8tx7df8CJ3W/T77XXhj/BaANUDQQ7A7r3PGDeocUfzztav1vw1f177n7erqRhhxBHtYic\nj/70zUlfL1z6999rDjg9OfntnoQNnZm6hLQOKk6NfWC2u/aGgy6NvJ0cHJ096rTo/+LLg5vw\npH09KDyx9usf9zo5ZKXXGDKqiV2pNcaZicvjdx294NP22l2Z2sz1P/6tbj+ipQf9Xk4VH3AY\nbYAqgGBnKPeeB8w79Md706K8X/vwlZ4dWt51imR3pm+vXg2dTEREW1RiZGHv4duww9CXxw7k\n0QMPS/dsJ0Zmjt6BIY0aN6zn52ZNP+tF4cEVX81fuWnrWTNrjW/HFp6lOt+0hnt+3OaknbEx\nB/JMJXvHH1/9ox76xhB/HpxWPvoYcBhtgKqAYGco95gHvDnIjmliq7rH5S9Nu3S8+ah3lcbe\nK6B+SEjdWi6WDLPlolO2u5gyd8IXx4M71mXiSZ/UTiGtA3JTt8QfyT520rRlp3p2RqXXtfAt\nOJiSsjM5IS75jOegCW929+IEUXnpY8BhtAGqAoKdwdwlT9w+yF5z+1Bbq1XzGub33S3K4QHZ\n7mLK3EmfHG7x6vNhzgQLfdAWXsrKOJtrbGFhYqRxa9QmqCA1ZvfhlKTM2m1a1Lj1xDSVhUfD\nTr16hIe17Td8ePf6TmSJimDAAaoJgp0h3TbUmthnLPw8+vZB9pobQ21KcXDXzryWsXLcM9td\nS3VhE6cODeQvXMWVnN2x8OOpH3y1cPnK5Uv+iTqU61inbm3f0NZBBakxiTtiks77hjX1KP2m\nA7XGxt7eytTo3ruErhhwgOpApdVqDV1DdXd196+Tpy4+lCfGdZ/738y+LveY6yu5erXA0pIn\nRlWqGz8L64bPfzCpt48pqU6vtJmRM9/+OaftU/0auxQc37Fu2cqkjALz2r3Hvzu6sX1O2s+T\np/59qMSr6zvTX2piz5R3ZWHAAZSNM3aGd+O/0buzMw4ds77n9fsqU1Omoirbbac0jMz2//R9\nOqlObzJXTn8vpfm0mU838XR19arTsF3XMMfj8Vt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       "plot without title"
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   "source": [
    "set.seed(560)\n",
    "options(repr.plot.width = 7, repr.plot.height = 7)\n",
    "\n",
    "stroke_age <- stroke_training |>\n",
    "            ggplot(aes(x = age_group, fill = diagnosis)) +\n",
    "            geom_histogram(stat = \"count\", position = \"dodge\") +\n",
    "            labs(x = \"Age Group\", y = \"Count\", fill = \"Stroke Diagnosis\") +\n",
    "            ggtitle(\"Stroke Diagnosis vs. Age\") +\n",
    "            theme(text = element_text(size = 15))  +\n",
    "            theme(axis.text.x = element_text(angle = 45, hjust = 1))\n",
    "stroke_age\n",
    "\n",
    "stroke_stress <- stroke_training |>\n",
    "            ggplot(aes(x = stress_level_category, fill = diagnosis)) +\n",
    "            geom_histogram(stat = \"count\", position = \"dodge\") +\n",
    "            labs(x = \"Stress Levels\", y = \"Count\", fill = \"Stroke Diagnosis\") +\n",
    "            ggtitle(\"Stroke Diagnosis vs. Stress Levels\") +\n",
    "            theme(text = element_text(size = 15))  +\n",
    "            theme(axis.text.x = element_text(angle = 45, hjust = 1))\n",
    "stroke_stress"
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   "id": "cf3062f5-ccd9-42d0-8051-2488c5c2b430",
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    "We made a histogram for hypertension to compare the number of people with and without hypertension who have suffered from a stroke. Although we had hypothesized that abnormal blood pressure levels could affect a persons risk of getting a stroke, we weren’t expecting this kind of outcome. If a person doesn’t have hypertension, they are more likely to get a stroke than a person who does. In fact, more people with hypertension don’t get strokes than those who do."
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    "**Methods:**\n",
    "\n",
    "Our dataset contains 19 predictors for stroke probability. We will use age and stress levels for prediction. From outside research, it seems like age can affect a person’s arteries, which directly increases one’s chance of getting a stroke. Stress levels cause the heart to work harder, which increases the risks of clots forming and causing a stroke. Since we already know blood pressure levels directly cause strokes, we want to investigate seemingly related, but not confirmed factors. Since we are using multiple predictors, we have no way to visualize it on a 2D plane, so we decided to use the k-nearest neighbours algorithm to perform classification prediction.  "
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   "id": "b86761c5-1f85-4c43-8495-04c8636ec1f3",
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    "**Expected Outcomes and Significance:**\n",
    "\n",
    "\n",
    "We expect to find correlation between some of theses variables, and that all the variables play a big part in predicting strokes. Some of these variables lead to outside factors such as economic status and mental wellbeing, etc. If we can accurately predict strokes based on these variables, it can prove the connection between personal wellbeing and strokes, which can influence policies aimed for health. This can also lead to future questions such as to what extent is there correlation between these variables and others outside the scope of our project, and how could that influence the prediction of strokes. "
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