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 "cells": [
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   "source": [
    "# Catching Up & Happy Birthday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, let me say I'm sorry for missing last week's tutorial. There is no excuse:\n",
    "\n",
    "<figure>\n",
    "    <center><img src=\"https://github.com/GalKepler/ml_for_neuro/blob/04e93eb771df3cdfb2aa4941c9ea0932ff49b24e/ml_for_neuro/chapters/catching_up/Luna.jpeg?raw=true\" alt=\"Luna Kepler\" width=\"400\"/></center>\n",
    "    <figcaption>\n",
    "        <i>\n",
    "            Luna Kepler was born on January 18th, 2024, and was the cause of ML for Neuroscientists' tutor to be absent from class.\n",
    "            <a href=\"https://www.fatherly.com/parenting/excuses-for-missing-work-baby\">\n",
    "                Fatherly\n",
    "            </a>\n",
    "        </i>\n",
    "    </figcaption>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)\n",
    "\n",
    "Our main goal while doing EDA is to [summarize main characteristics of our dataset](https://en.wikipedia.org/wiki/Exploratory_data_analysis).\n",
    "\n",
    "It's crucial that we understand what our data is composed of.\n",
    "\n",
    "### First, we take a look."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````{toggle} configuration.py\n",
    "    :show:\n",
    "```{literalinclude} configuration.py   \n",
    "```\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "from read_data import read_data # read data from file at a destinaiton defined in configuration.py\n",
    "from get_scattered_chunks import get_scattered_chunks # get scattered chunks of data\n",
    "from print_table import print_table # Styling and printing the table\n",
    "import pandas as pd\n",
    "pd.set_option(\"future.no_silent_downcasting\", True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove-input"
    ]
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   "outputs": [
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       "#T_12fad_row0_col5, #T_12fad_row0_col20, #T_12fad_row0_col21, #T_12fad_row0_col29, #T_12fad_row0_col31, #T_12fad_row1_col5, #T_12fad_row1_col20, #T_12fad_row1_col21, #T_12fad_row1_col26, #T_12fad_row1_col31, #T_12fad_row2_col4, #T_12fad_row2_col5, #T_12fad_row2_col20, #T_12fad_row2_col21, #T_12fad_row2_col22, #T_12fad_row2_col23, #T_12fad_row2_col24, #T_12fad_row2_col25, #T_12fad_row2_col26, #T_12fad_row2_col28, #T_12fad_row2_col29, #T_12fad_row2_col30, #T_12fad_row2_col31, #T_12fad_row2_col32, #T_12fad_row2_col33, #T_12fad_row2_col34, #T_12fad_row2_col35, #T_12fad_row2_col36, #T_12fad_row2_col37, #T_12fad_row2_col38, #T_12fad_row2_col39, #T_12fad_row3_col20, #T_12fad_row3_col21, #T_12fad_row3_col23, #T_12fad_row3_col24, #T_12fad_row3_col25, #T_12fad_row3_col26, #T_12fad_row3_col28, #T_12fad_row3_col31, #T_12fad_row3_col40, #T_12fad_row4_col20, #T_12fad_row4_col21, #T_12fad_row4_col22, #T_12fad_row4_col26, #T_12fad_row4_col28, #T_12fad_row4_col31, #T_12fad_row4_col40, #T_12fad_row5_col20, #T_12fad_row5_col21, #T_12fad_row5_col22, #T_12fad_row5_col28, #T_12fad_row5_col31, #T_12fad_row5_col40, #T_12fad_row6_col20, #T_12fad_row6_col21, #T_12fad_row6_col26, #T_12fad_row6_col28, #T_12fad_row6_col31, #T_12fad_row6_col40, #T_12fad_row7_col14, #T_12fad_row7_col15, #T_12fad_row7_col16, #T_12fad_row7_col17, #T_12fad_row7_col18, #T_12fad_row7_col19, #T_12fad_row7_col20, #T_12fad_row7_col21, #T_12fad_row7_col22, #T_12fad_row7_col23, #T_12fad_row7_col24, #T_12fad_row7_col25, #T_12fad_row7_col26, #T_12fad_row7_col28, #T_12fad_row7_col31, #T_12fad_row7_col40, #T_12fad_row8_col20, #T_12fad_row8_col21, #T_12fad_row8_col22, #T_12fad_row8_col24, #T_12fad_row8_col25, #T_12fad_row8_col40, #T_12fad_row9_col5, #T_12fad_row9_col14, #T_12fad_row9_col15, #T_12fad_row9_col16, #T_12fad_row9_col17, #T_12fad_row9_col18, #T_12fad_row9_col19, #T_12fad_row9_col20, #T_12fad_row9_col21, #T_12fad_row9_col22, #T_12fad_row9_col23, #T_12fad_row9_col24, #T_12fad_row9_col25, #T_12fad_row9_col26, #T_12fad_row9_col28, #T_12fad_row9_col29, #T_12fad_row9_col31, #T_12fad_row9_col40, #T_12fad_row10_col18, #T_12fad_row10_col20, #T_12fad_row10_col21, #T_12fad_row10_col31, #T_12fad_row10_col40, #T_12fad_row11_col20, #T_12fad_row11_col21, #T_12fad_row11_col22, #T_12fad_row11_col24, #T_12fad_row11_col25, #T_12fad_row11_col28, #T_12fad_row11_col31, #T_12fad_row11_col40, #T_12fad_row12_col5, #T_12fad_row12_col14, #T_12fad_row12_col15, #T_12fad_row12_col16, #T_12fad_row12_col17, #T_12fad_row12_col18, #T_12fad_row12_col19, #T_12fad_row12_col20, #T_12fad_row12_col21, #T_12fad_row12_col22, #T_12fad_row12_col23, #T_12fad_row12_col24, #T_12fad_row12_col25, #T_12fad_row12_col26, #T_12fad_row12_col28, #T_12fad_row12_col29, #T_12fad_row12_col31, #T_12fad_row12_col40, #T_12fad_row13_col14, #T_12fad_row13_col15, #T_12fad_row13_col16, #T_12fad_row13_col17, #T_12fad_row13_col18, #T_12fad_row13_col19, #T_12fad_row13_col20, #T_12fad_row13_col21, #T_12fad_row13_col22, #T_12fad_row13_col23, #T_12fad_row13_col24, #T_12fad_row13_col25, #T_12fad_row13_col26, #T_12fad_row13_col28, #T_12fad_row13_col31, #T_12fad_row13_col40, #T_12fad_row14_col5, #T_12fad_row14_col14, #T_12fad_row14_col15, #T_12fad_row14_col16, #T_12fad_row14_col17, #T_12fad_row14_col18, #T_12fad_row14_col19, #T_12fad_row14_col20, #T_12fad_row14_col21, #T_12fad_row14_col22, #T_12fad_row14_col23, #T_12fad_row14_col24, #T_12fad_row14_col25, #T_12fad_row14_col26, #T_12fad_row14_col28, #T_12fad_row14_col29, #T_12fad_row14_col31, #T_12fad_row14_col40 {\n",
       "  background-color: grey;\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_12fad\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_12fad_level0_col0\" class=\"col_heading level0 col0\" >test_name</th>\n",
       "      <th id=\"T_12fad_level0_col1\" class=\"col_heading level0 col1\" >swab_type</th>\n",
       "      <th id=\"T_12fad_level0_col2\" class=\"col_heading level0 col2\" >covid19_test_results</th>\n",
       "      <th id=\"T_12fad_level0_col3\" class=\"col_heading level0 col3\" >age</th>\n",
       "      <th id=\"T_12fad_level0_col4\" class=\"col_heading level0 col4\" >high_risk_exposure_occupation</th>\n",
       "      <th id=\"T_12fad_level0_col5\" class=\"col_heading level0 col5\" >high_risk_interactions</th>\n",
       "      <th id=\"T_12fad_level0_col6\" class=\"col_heading level0 col6\" >diabetes</th>\n",
       "      <th id=\"T_12fad_level0_col7\" class=\"col_heading level0 col7\" >chd</th>\n",
       "      <th id=\"T_12fad_level0_col8\" class=\"col_heading level0 col8\" >htn</th>\n",
       "      <th id=\"T_12fad_level0_col9\" class=\"col_heading level0 col9\" >cancer</th>\n",
       "      <th id=\"T_12fad_level0_col10\" class=\"col_heading level0 col10\" >asthma</th>\n",
       "      <th id=\"T_12fad_level0_col11\" class=\"col_heading level0 col11\" >copd</th>\n",
       "      <th id=\"T_12fad_level0_col12\" class=\"col_heading level0 col12\" >autoimmune_dis</th>\n",
       "      <th id=\"T_12fad_level0_col13\" class=\"col_heading level0 col13\" >smoker</th>\n",
       "      <th id=\"T_12fad_level0_col14\" class=\"col_heading level0 col14\" >temperature</th>\n",
       "      <th id=\"T_12fad_level0_col15\" class=\"col_heading level0 col15\" >pulse</th>\n",
       "      <th id=\"T_12fad_level0_col16\" class=\"col_heading level0 col16\" >sys</th>\n",
       "      <th id=\"T_12fad_level0_col17\" class=\"col_heading level0 col17\" >dia</th>\n",
       "      <th id=\"T_12fad_level0_col18\" class=\"col_heading level0 col18\" >rr</th>\n",
       "      <th id=\"T_12fad_level0_col19\" class=\"col_heading level0 col19\" >sats</th>\n",
       "      <th id=\"T_12fad_level0_col20\" class=\"col_heading level0 col20\" >rapid_flu_results</th>\n",
       "      <th id=\"T_12fad_level0_col21\" class=\"col_heading level0 col21\" >rapid_strep_results</th>\n",
       "      <th id=\"T_12fad_level0_col22\" class=\"col_heading level0 col22\" >ctab</th>\n",
       "      <th id=\"T_12fad_level0_col23\" class=\"col_heading level0 col23\" >labored_respiration</th>\n",
       "      <th id=\"T_12fad_level0_col24\" class=\"col_heading level0 col24\" >rhonchi</th>\n",
       "      <th id=\"T_12fad_level0_col25\" class=\"col_heading level0 col25\" >wheezes</th>\n",
       "      <th id=\"T_12fad_level0_col26\" class=\"col_heading level0 col26\" >days_since_symptom_onset</th>\n",
       "      <th id=\"T_12fad_level0_col27\" class=\"col_heading level0 col27\" >cough</th>\n",
       "      <th id=\"T_12fad_level0_col28\" class=\"col_heading level0 col28\" >cough_severity</th>\n",
       "      <th id=\"T_12fad_level0_col29\" class=\"col_heading level0 col29\" >fever</th>\n",
       "      <th id=\"T_12fad_level0_col30\" class=\"col_heading level0 col30\" >sob</th>\n",
       "      <th id=\"T_12fad_level0_col31\" class=\"col_heading level0 col31\" >sob_severity</th>\n",
       "      <th id=\"T_12fad_level0_col32\" class=\"col_heading level0 col32\" >diarrhea</th>\n",
       "      <th id=\"T_12fad_level0_col33\" class=\"col_heading level0 col33\" >fatigue</th>\n",
       "      <th id=\"T_12fad_level0_col34\" class=\"col_heading level0 col34\" >headache</th>\n",
       "      <th id=\"T_12fad_level0_col35\" class=\"col_heading level0 col35\" >loss_of_smell</th>\n",
       "      <th id=\"T_12fad_level0_col36\" class=\"col_heading level0 col36\" >loss_of_taste</th>\n",
       "      <th id=\"T_12fad_level0_col37\" class=\"col_heading level0 col37\" >runny_nose</th>\n",
       "      <th id=\"T_12fad_level0_col38\" class=\"col_heading level0 col38\" >muscle_sore</th>\n",
       "      <th id=\"T_12fad_level0_col39\" class=\"col_heading level0 col39\" >sore_throat</th>\n",
       "      <th id=\"T_12fad_level0_col40\" class=\"col_heading level0 col40\" >er_referral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_12fad_row0_col0\" class=\"data row0 col0\" >SARS COV 2 RNA RTPCR</td>\n",
       "      <td id=\"T_12fad_row0_col1\" class=\"data row0 col1\" >Nasopharyngeal</td>\n",
       "      <td id=\"T_12fad_row0_col2\" class=\"data row0 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col3\" class=\"data row0 col3\" >58</td>\n",
       "      <td id=\"T_12fad_row0_col4\" class=\"data row0 col4\" >True</td>\n",
       "      <td id=\"T_12fad_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
       "      <td id=\"T_12fad_row0_col6\" class=\"data row0 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col7\" class=\"data row0 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col8\" class=\"data row0 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col9\" class=\"data row0 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col10\" class=\"data row0 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col11\" class=\"data row0 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col12\" class=\"data row0 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col13\" class=\"data row0 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col14\" class=\"data row0 col14\" >36.950000</td>\n",
       "      <td id=\"T_12fad_row0_col15\" class=\"data row0 col15\" >81.000000</td>\n",
       "      <td id=\"T_12fad_row0_col16\" class=\"data row0 col16\" >126.000000</td>\n",
       "      <td id=\"T_12fad_row0_col17\" class=\"data row0 col17\" >82.000000</td>\n",
       "      <td id=\"T_12fad_row0_col18\" class=\"data row0 col18\" >18.000000</td>\n",
       "      <td id=\"T_12fad_row0_col19\" class=\"data row0 col19\" >97.000000</td>\n",
       "      <td id=\"T_12fad_row0_col20\" class=\"data row0 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row0_col21\" class=\"data row0 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row0_col22\" class=\"data row0 col22\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col23\" class=\"data row0 col23\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col24\" class=\"data row0 col24\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col25\" class=\"data row0 col25\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col26\" class=\"data row0 col26\" >28.000000</td>\n",
       "      <td id=\"T_12fad_row0_col27\" class=\"data row0 col27\" >True</td>\n",
       "      <td id=\"T_12fad_row0_col28\" class=\"data row0 col28\" >Severe</td>\n",
       "      <td id=\"T_12fad_row0_col29\" class=\"data row0 col29\" >nan</td>\n",
       "      <td id=\"T_12fad_row0_col30\" class=\"data row0 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col31\" class=\"data row0 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row0_col32\" class=\"data row0 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col33\" class=\"data row0 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col34\" class=\"data row0 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col35\" class=\"data row0 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col36\" class=\"data row0 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col37\" class=\"data row0 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col38\" class=\"data row0 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col39\" class=\"data row0 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row0_col40\" class=\"data row0 col40\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_12fad_row1_col0\" class=\"data row1 col0\" >SARS-CoV-2, NAA</td>\n",
       "      <td id=\"T_12fad_row1_col1\" class=\"data row1 col1\" >Oropharyngeal</td>\n",
       "      <td id=\"T_12fad_row1_col2\" class=\"data row1 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col3\" class=\"data row1 col3\" >35</td>\n",
       "      <td id=\"T_12fad_row1_col4\" class=\"data row1 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
       "      <td id=\"T_12fad_row1_col6\" class=\"data row1 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col7\" class=\"data row1 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col8\" class=\"data row1 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col9\" class=\"data row1 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col10\" class=\"data row1 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col11\" class=\"data row1 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col12\" class=\"data row1 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col13\" class=\"data row1 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col14\" class=\"data row1 col14\" >36.750000</td>\n",
       "      <td id=\"T_12fad_row1_col15\" class=\"data row1 col15\" >77.000000</td>\n",
       "      <td id=\"T_12fad_row1_col16\" class=\"data row1 col16\" >131.000000</td>\n",
       "      <td id=\"T_12fad_row1_col17\" class=\"data row1 col17\" >86.000000</td>\n",
       "      <td id=\"T_12fad_row1_col18\" class=\"data row1 col18\" >16.000000</td>\n",
       "      <td id=\"T_12fad_row1_col19\" class=\"data row1 col19\" >98.000000</td>\n",
       "      <td id=\"T_12fad_row1_col20\" class=\"data row1 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row1_col21\" class=\"data row1 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row1_col22\" class=\"data row1 col22\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col23\" class=\"data row1 col23\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col24\" class=\"data row1 col24\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col25\" class=\"data row1 col25\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col26\" class=\"data row1 col26\" >nan</td>\n",
       "      <td id=\"T_12fad_row1_col27\" class=\"data row1 col27\" >True</td>\n",
       "      <td id=\"T_12fad_row1_col28\" class=\"data row1 col28\" >Mild</td>\n",
       "      <td id=\"T_12fad_row1_col29\" class=\"data row1 col29\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col30\" class=\"data row1 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col31\" class=\"data row1 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row1_col32\" class=\"data row1 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col33\" class=\"data row1 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col34\" class=\"data row1 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col35\" class=\"data row1 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col36\" class=\"data row1 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col37\" class=\"data row1 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col38\" class=\"data row1 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col39\" class=\"data row1 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row1_col40\" class=\"data row1 col40\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_12fad_row2_col0\" class=\"data row2 col0\" >SARS CoV w/CoV 2 RNA</td>\n",
       "      <td id=\"T_12fad_row2_col1\" class=\"data row2 col1\" >Oropharyngeal</td>\n",
       "      <td id=\"T_12fad_row2_col2\" class=\"data row2 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row2_col3\" class=\"data row2 col3\" >12</td>\n",
       "      <td id=\"T_12fad_row2_col4\" class=\"data row2 col4\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col6\" class=\"data row2 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row2_col7\" class=\"data row2 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row2_col8\" class=\"data row2 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row2_col9\" class=\"data row2 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row2_col10\" class=\"data row2 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row2_col11\" class=\"data row2 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row2_col12\" class=\"data row2 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row2_col13\" class=\"data row2 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row2_col14\" class=\"data row2 col14\" >36.950000</td>\n",
       "      <td id=\"T_12fad_row2_col15\" class=\"data row2 col15\" >74.000000</td>\n",
       "      <td id=\"T_12fad_row2_col16\" class=\"data row2 col16\" >122.000000</td>\n",
       "      <td id=\"T_12fad_row2_col17\" class=\"data row2 col17\" >73.000000</td>\n",
       "      <td id=\"T_12fad_row2_col18\" class=\"data row2 col18\" >17.000000</td>\n",
       "      <td id=\"T_12fad_row2_col19\" class=\"data row2 col19\" >98.000000</td>\n",
       "      <td id=\"T_12fad_row2_col20\" class=\"data row2 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col21\" class=\"data row2 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col22\" class=\"data row2 col22\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col23\" class=\"data row2 col23\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col24\" class=\"data row2 col24\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col25\" class=\"data row2 col25\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col26\" class=\"data row2 col26\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col27\" class=\"data row2 col27\" >False</td>\n",
       "      <td id=\"T_12fad_row2_col28\" class=\"data row2 col28\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col29\" class=\"data row2 col29\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col30\" class=\"data row2 col30\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col31\" class=\"data row2 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col32\" class=\"data row2 col32\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col33\" class=\"data row2 col33\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col34\" class=\"data row2 col34\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col35\" class=\"data row2 col35\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col36\" class=\"data row2 col36\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col37\" class=\"data row2 col37\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col38\" class=\"data row2 col38\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col39\" class=\"data row2 col39\" >nan</td>\n",
       "      <td id=\"T_12fad_row2_col40\" class=\"data row2 col40\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row3\" class=\"row_heading level0 row3\" >23498</th>\n",
       "      <td id=\"T_12fad_row3_col0\" class=\"data row3 col0\" >SARS-CoV-2, NAA</td>\n",
       "      <td id=\"T_12fad_row3_col1\" class=\"data row3 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row3_col2\" class=\"data row3 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col3\" class=\"data row3 col3\" >35</td>\n",
       "      <td id=\"T_12fad_row3_col4\" class=\"data row3 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col5\" class=\"data row3 col5\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col6\" class=\"data row3 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col7\" class=\"data row3 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col8\" class=\"data row3 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col9\" class=\"data row3 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col10\" class=\"data row3 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col11\" class=\"data row3 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col12\" class=\"data row3 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col13\" class=\"data row3 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col14\" class=\"data row3 col14\" >37.000000</td>\n",
       "      <td id=\"T_12fad_row3_col15\" class=\"data row3 col15\" >69.000000</td>\n",
       "      <td id=\"T_12fad_row3_col16\" class=\"data row3 col16\" >136.000000</td>\n",
       "      <td id=\"T_12fad_row3_col17\" class=\"data row3 col17\" >84.000000</td>\n",
       "      <td id=\"T_12fad_row3_col18\" class=\"data row3 col18\" >12.000000</td>\n",
       "      <td id=\"T_12fad_row3_col19\" class=\"data row3 col19\" >100.000000</td>\n",
       "      <td id=\"T_12fad_row3_col20\" class=\"data row3 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row3_col21\" class=\"data row3 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row3_col22\" class=\"data row3 col22\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col23\" class=\"data row3 col23\" >nan</td>\n",
       "      <td id=\"T_12fad_row3_col24\" class=\"data row3 col24\" >nan</td>\n",
       "      <td id=\"T_12fad_row3_col25\" class=\"data row3 col25\" >nan</td>\n",
       "      <td id=\"T_12fad_row3_col26\" class=\"data row3 col26\" >nan</td>\n",
       "      <td id=\"T_12fad_row3_col27\" class=\"data row3 col27\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col28\" class=\"data row3 col28\" >nan</td>\n",
       "      <td id=\"T_12fad_row3_col29\" class=\"data row3 col29\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col30\" class=\"data row3 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col31\" class=\"data row3 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row3_col32\" class=\"data row3 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col33\" class=\"data row3 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col34\" class=\"data row3 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col35\" class=\"data row3 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col36\" class=\"data row3 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col37\" class=\"data row3 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col38\" class=\"data row3 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col39\" class=\"data row3 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row3_col40\" class=\"data row3 col40\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row4\" class=\"row_heading level0 row4\" >23499</th>\n",
       "      <td id=\"T_12fad_row4_col0\" class=\"data row4 col0\" >SARS-CoV-2, NAA</td>\n",
       "      <td id=\"T_12fad_row4_col1\" class=\"data row4 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row4_col2\" class=\"data row4 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col3\" class=\"data row4 col3\" >24</td>\n",
       "      <td id=\"T_12fad_row4_col4\" class=\"data row4 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col5\" class=\"data row4 col5\" >True</td>\n",
       "      <td id=\"T_12fad_row4_col6\" class=\"data row4 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col7\" class=\"data row4 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col8\" class=\"data row4 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col9\" class=\"data row4 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col10\" class=\"data row4 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col11\" class=\"data row4 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col12\" class=\"data row4 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col13\" class=\"data row4 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col14\" class=\"data row4 col14\" >36.750000</td>\n",
       "      <td id=\"T_12fad_row4_col15\" class=\"data row4 col15\" >70.000000</td>\n",
       "      <td id=\"T_12fad_row4_col16\" class=\"data row4 col16\" >128.000000</td>\n",
       "      <td id=\"T_12fad_row4_col17\" class=\"data row4 col17\" >78.000000</td>\n",
       "      <td id=\"T_12fad_row4_col18\" class=\"data row4 col18\" >12.000000</td>\n",
       "      <td id=\"T_12fad_row4_col19\" class=\"data row4 col19\" >99.000000</td>\n",
       "      <td id=\"T_12fad_row4_col20\" class=\"data row4 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row4_col21\" class=\"data row4 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row4_col22\" class=\"data row4 col22\" >nan</td>\n",
       "      <td id=\"T_12fad_row4_col23\" class=\"data row4 col23\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col24\" class=\"data row4 col24\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col25\" class=\"data row4 col25\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col26\" class=\"data row4 col26\" >nan</td>\n",
       "      <td id=\"T_12fad_row4_col27\" class=\"data row4 col27\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col28\" class=\"data row4 col28\" >nan</td>\n",
       "      <td id=\"T_12fad_row4_col29\" class=\"data row4 col29\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col30\" class=\"data row4 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col31\" class=\"data row4 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row4_col32\" class=\"data row4 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col33\" class=\"data row4 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col34\" class=\"data row4 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col35\" class=\"data row4 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col36\" class=\"data row4 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col37\" class=\"data row4 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col38\" class=\"data row4 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col39\" class=\"data row4 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row4_col40\" class=\"data row4 col40\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row5\" class=\"row_heading level0 row5\" >23500</th>\n",
       "      <td id=\"T_12fad_row5_col0\" class=\"data row5 col0\" >SARS-CoV-2, NAA</td>\n",
       "      <td id=\"T_12fad_row5_col1\" class=\"data row5 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row5_col2\" class=\"data row5 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col3\" class=\"data row5 col3\" >52</td>\n",
       "      <td id=\"T_12fad_row5_col4\" class=\"data row5 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col5\" class=\"data row5 col5\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col6\" class=\"data row5 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col7\" class=\"data row5 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col8\" class=\"data row5 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col9\" class=\"data row5 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col10\" class=\"data row5 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col11\" class=\"data row5 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col12\" class=\"data row5 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col13\" class=\"data row5 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col14\" class=\"data row5 col14\" >37.000000</td>\n",
       "      <td id=\"T_12fad_row5_col15\" class=\"data row5 col15\" >94.000000</td>\n",
       "      <td id=\"T_12fad_row5_col16\" class=\"data row5 col16\" >165.000000</td>\n",
       "      <td id=\"T_12fad_row5_col17\" class=\"data row5 col17\" >82.000000</td>\n",
       "      <td id=\"T_12fad_row5_col18\" class=\"data row5 col18\" >12.000000</td>\n",
       "      <td id=\"T_12fad_row5_col19\" class=\"data row5 col19\" >98.000000</td>\n",
       "      <td id=\"T_12fad_row5_col20\" class=\"data row5 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row5_col21\" class=\"data row5 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row5_col22\" class=\"data row5 col22\" >nan</td>\n",
       "      <td id=\"T_12fad_row5_col23\" class=\"data row5 col23\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col24\" class=\"data row5 col24\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col25\" class=\"data row5 col25\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col26\" class=\"data row5 col26\" >7.000000</td>\n",
       "      <td id=\"T_12fad_row5_col27\" class=\"data row5 col27\" >True</td>\n",
       "      <td id=\"T_12fad_row5_col28\" class=\"data row5 col28\" >nan</td>\n",
       "      <td id=\"T_12fad_row5_col29\" class=\"data row5 col29\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col30\" class=\"data row5 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col31\" class=\"data row5 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row5_col32\" class=\"data row5 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col33\" class=\"data row5 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col34\" class=\"data row5 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col35\" class=\"data row5 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col36\" class=\"data row5 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col37\" class=\"data row5 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col38\" class=\"data row5 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col39\" class=\"data row5 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row5_col40\" class=\"data row5 col40\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row6\" class=\"row_heading level0 row6\" >46996</th>\n",
       "      <td id=\"T_12fad_row6_col0\" class=\"data row6 col0\" >SARS-CoV-2, NAA</td>\n",
       "      <td id=\"T_12fad_row6_col1\" class=\"data row6 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row6_col2\" class=\"data row6 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col3\" class=\"data row6 col3\" >11</td>\n",
       "      <td id=\"T_12fad_row6_col4\" class=\"data row6 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col5\" class=\"data row6 col5\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col6\" class=\"data row6 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col7\" class=\"data row6 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col8\" class=\"data row6 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col9\" class=\"data row6 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col10\" class=\"data row6 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col11\" class=\"data row6 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col12\" class=\"data row6 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col13\" class=\"data row6 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col14\" class=\"data row6 col14\" >36.900000</td>\n",
       "      <td id=\"T_12fad_row6_col15\" class=\"data row6 col15\" >78.000000</td>\n",
       "      <td id=\"T_12fad_row6_col16\" class=\"data row6 col16\" >116.000000</td>\n",
       "      <td id=\"T_12fad_row6_col17\" class=\"data row6 col17\" >79.000000</td>\n",
       "      <td id=\"T_12fad_row6_col18\" class=\"data row6 col18\" >16.000000</td>\n",
       "      <td id=\"T_12fad_row6_col19\" class=\"data row6 col19\" >100.000000</td>\n",
       "      <td id=\"T_12fad_row6_col20\" class=\"data row6 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row6_col21\" class=\"data row6 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row6_col22\" class=\"data row6 col22\" >True</td>\n",
       "      <td id=\"T_12fad_row6_col23\" class=\"data row6 col23\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col24\" class=\"data row6 col24\" >True</td>\n",
       "      <td id=\"T_12fad_row6_col25\" class=\"data row6 col25\" >True</td>\n",
       "      <td id=\"T_12fad_row6_col26\" class=\"data row6 col26\" >nan</td>\n",
       "      <td id=\"T_12fad_row6_col27\" class=\"data row6 col27\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col28\" class=\"data row6 col28\" >nan</td>\n",
       "      <td id=\"T_12fad_row6_col29\" class=\"data row6 col29\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col30\" class=\"data row6 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col31\" class=\"data row6 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row6_col32\" class=\"data row6 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col33\" class=\"data row6 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col34\" class=\"data row6 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col35\" class=\"data row6 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col36\" class=\"data row6 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col37\" class=\"data row6 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col38\" class=\"data row6 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col39\" class=\"data row6 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row6_col40\" class=\"data row6 col40\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row7\" class=\"row_heading level0 row7\" >46997</th>\n",
       "      <td id=\"T_12fad_row7_col0\" class=\"data row7 col0\" >SARS-CoV-2, NAA</td>\n",
       "      <td id=\"T_12fad_row7_col1\" class=\"data row7 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row7_col2\" class=\"data row7 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col3\" class=\"data row7 col3\" >30</td>\n",
       "      <td id=\"T_12fad_row7_col4\" class=\"data row7 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col5\" class=\"data row7 col5\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col6\" class=\"data row7 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col7\" class=\"data row7 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col8\" class=\"data row7 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col9\" class=\"data row7 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col10\" class=\"data row7 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col11\" class=\"data row7 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col12\" class=\"data row7 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col13\" class=\"data row7 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col14\" class=\"data row7 col14\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col15\" class=\"data row7 col15\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col16\" class=\"data row7 col16\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col17\" class=\"data row7 col17\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col18\" class=\"data row7 col18\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col19\" class=\"data row7 col19\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col20\" class=\"data row7 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col21\" class=\"data row7 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col22\" class=\"data row7 col22\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col23\" class=\"data row7 col23\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col24\" class=\"data row7 col24\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col25\" class=\"data row7 col25\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col26\" class=\"data row7 col26\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col27\" class=\"data row7 col27\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col28\" class=\"data row7 col28\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col29\" class=\"data row7 col29\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col30\" class=\"data row7 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col31\" class=\"data row7 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row7_col32\" class=\"data row7 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col33\" class=\"data row7 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col34\" class=\"data row7 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col35\" class=\"data row7 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col36\" class=\"data row7 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col37\" class=\"data row7 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col38\" class=\"data row7 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col39\" class=\"data row7 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row7_col40\" class=\"data row7 col40\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row8\" class=\"row_heading level0 row8\" >46998</th>\n",
       "      <td id=\"T_12fad_row8_col0\" class=\"data row8 col0\" >SARS-CoV-2, NAA</td>\n",
       "      <td id=\"T_12fad_row8_col1\" class=\"data row8 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row8_col2\" class=\"data row8 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col3\" class=\"data row8 col3\" >36</td>\n",
       "      <td id=\"T_12fad_row8_col4\" class=\"data row8 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col5\" class=\"data row8 col5\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col6\" class=\"data row8 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col7\" class=\"data row8 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col8\" class=\"data row8 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col9\" class=\"data row8 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col10\" class=\"data row8 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col11\" class=\"data row8 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col12\" class=\"data row8 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col13\" class=\"data row8 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col14\" class=\"data row8 col14\" >36.850000</td>\n",
       "      <td id=\"T_12fad_row8_col15\" class=\"data row8 col15\" >81.000000</td>\n",
       "      <td id=\"T_12fad_row8_col16\" class=\"data row8 col16\" >122.000000</td>\n",
       "      <td id=\"T_12fad_row8_col17\" class=\"data row8 col17\" >81.000000</td>\n",
       "      <td id=\"T_12fad_row8_col18\" class=\"data row8 col18\" >14.000000</td>\n",
       "      <td id=\"T_12fad_row8_col19\" class=\"data row8 col19\" >100.000000</td>\n",
       "      <td id=\"T_12fad_row8_col20\" class=\"data row8 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row8_col21\" class=\"data row8 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row8_col22\" class=\"data row8 col22\" >nan</td>\n",
       "      <td id=\"T_12fad_row8_col23\" class=\"data row8 col23\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col24\" class=\"data row8 col24\" >nan</td>\n",
       "      <td id=\"T_12fad_row8_col25\" class=\"data row8 col25\" >nan</td>\n",
       "      <td id=\"T_12fad_row8_col26\" class=\"data row8 col26\" >7.000000</td>\n",
       "      <td id=\"T_12fad_row8_col27\" class=\"data row8 col27\" >True</td>\n",
       "      <td id=\"T_12fad_row8_col28\" class=\"data row8 col28\" >Mild</td>\n",
       "      <td id=\"T_12fad_row8_col29\" class=\"data row8 col29\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col30\" class=\"data row8 col30\" >True</td>\n",
       "      <td id=\"T_12fad_row8_col31\" class=\"data row8 col31\" >Mild</td>\n",
       "      <td id=\"T_12fad_row8_col32\" class=\"data row8 col32\" >True</td>\n",
       "      <td id=\"T_12fad_row8_col33\" class=\"data row8 col33\" >True</td>\n",
       "      <td id=\"T_12fad_row8_col34\" class=\"data row8 col34\" >True</td>\n",
       "      <td id=\"T_12fad_row8_col35\" class=\"data row8 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col36\" class=\"data row8 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col37\" class=\"data row8 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col38\" class=\"data row8 col38\" >True</td>\n",
       "      <td id=\"T_12fad_row8_col39\" class=\"data row8 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row8_col40\" class=\"data row8 col40\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row9\" class=\"row_heading level0 row9\" >70494</th>\n",
       "      <td id=\"T_12fad_row9_col0\" class=\"data row9 col0\" >Rapid COVID-19 PCR Test</td>\n",
       "      <td id=\"T_12fad_row9_col1\" class=\"data row9 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row9_col2\" class=\"data row9 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col3\" class=\"data row9 col3\" >32</td>\n",
       "      <td id=\"T_12fad_row9_col4\" class=\"data row9 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col5\" class=\"data row9 col5\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col6\" class=\"data row9 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col7\" class=\"data row9 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col8\" class=\"data row9 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col9\" class=\"data row9 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col10\" class=\"data row9 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col11\" class=\"data row9 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col12\" class=\"data row9 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col13\" class=\"data row9 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col14\" class=\"data row9 col14\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col15\" class=\"data row9 col15\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col16\" class=\"data row9 col16\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col17\" class=\"data row9 col17\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col18\" class=\"data row9 col18\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col19\" class=\"data row9 col19\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col20\" class=\"data row9 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col21\" class=\"data row9 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col22\" class=\"data row9 col22\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col23\" class=\"data row9 col23\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col24\" class=\"data row9 col24\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col25\" class=\"data row9 col25\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col26\" class=\"data row9 col26\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col27\" class=\"data row9 col27\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col28\" class=\"data row9 col28\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col29\" class=\"data row9 col29\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col30\" class=\"data row9 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col31\" class=\"data row9 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row9_col32\" class=\"data row9 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col33\" class=\"data row9 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col34\" class=\"data row9 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col35\" class=\"data row9 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col36\" class=\"data row9 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col37\" class=\"data row9 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col38\" class=\"data row9 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col39\" class=\"data row9 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row9_col40\" class=\"data row9 col40\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row10\" class=\"row_heading level0 row10\" >70495</th>\n",
       "      <td id=\"T_12fad_row10_col0\" class=\"data row10 col0\" >SARS-CoV-2, NAA</td>\n",
       "      <td id=\"T_12fad_row10_col1\" class=\"data row10 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row10_col2\" class=\"data row10 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col3\" class=\"data row10 col3\" >59</td>\n",
       "      <td id=\"T_12fad_row10_col4\" class=\"data row10 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col5\" class=\"data row10 col5\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col6\" class=\"data row10 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col7\" class=\"data row10 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col8\" class=\"data row10 col8\" >True</td>\n",
       "      <td id=\"T_12fad_row10_col9\" class=\"data row10 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col10\" class=\"data row10 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col11\" class=\"data row10 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col12\" class=\"data row10 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col13\" class=\"data row10 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col14\" class=\"data row10 col14\" >36.600000</td>\n",
       "      <td id=\"T_12fad_row10_col15\" class=\"data row10 col15\" >75.000000</td>\n",
       "      <td id=\"T_12fad_row10_col16\" class=\"data row10 col16\" >137.000000</td>\n",
       "      <td id=\"T_12fad_row10_col17\" class=\"data row10 col17\" >99.000000</td>\n",
       "      <td id=\"T_12fad_row10_col18\" class=\"data row10 col18\" >nan</td>\n",
       "      <td id=\"T_12fad_row10_col19\" class=\"data row10 col19\" >97.000000</td>\n",
       "      <td id=\"T_12fad_row10_col20\" class=\"data row10 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row10_col21\" class=\"data row10 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row10_col22\" class=\"data row10 col22\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col23\" class=\"data row10 col23\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col24\" class=\"data row10 col24\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col25\" class=\"data row10 col25\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col26\" class=\"data row10 col26\" >7.000000</td>\n",
       "      <td id=\"T_12fad_row10_col27\" class=\"data row10 col27\" >True</td>\n",
       "      <td id=\"T_12fad_row10_col28\" class=\"data row10 col28\" >Moderate</td>\n",
       "      <td id=\"T_12fad_row10_col29\" class=\"data row10 col29\" >True</td>\n",
       "      <td id=\"T_12fad_row10_col30\" class=\"data row10 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col31\" class=\"data row10 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row10_col32\" class=\"data row10 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col33\" class=\"data row10 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col34\" class=\"data row10 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col35\" class=\"data row10 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col36\" class=\"data row10 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col37\" class=\"data row10 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col38\" class=\"data row10 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row10_col39\" class=\"data row10 col39\" >True</td>\n",
       "      <td id=\"T_12fad_row10_col40\" class=\"data row10 col40\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row11\" class=\"row_heading level0 row11\" >70496</th>\n",
       "      <td id=\"T_12fad_row11_col0\" class=\"data row11 col0\" >SARS-CoV-2, NAA</td>\n",
       "      <td id=\"T_12fad_row11_col1\" class=\"data row11 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row11_col2\" class=\"data row11 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col3\" class=\"data row11 col3\" >27</td>\n",
       "      <td id=\"T_12fad_row11_col4\" class=\"data row11 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col5\" class=\"data row11 col5\" >True</td>\n",
       "      <td id=\"T_12fad_row11_col6\" class=\"data row11 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col7\" class=\"data row11 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col8\" class=\"data row11 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col9\" class=\"data row11 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col10\" class=\"data row11 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col11\" class=\"data row11 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col12\" class=\"data row11 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col13\" class=\"data row11 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col14\" class=\"data row11 col14\" >37.000000</td>\n",
       "      <td id=\"T_12fad_row11_col15\" class=\"data row11 col15\" >63.000000</td>\n",
       "      <td id=\"T_12fad_row11_col16\" class=\"data row11 col16\" >113.000000</td>\n",
       "      <td id=\"T_12fad_row11_col17\" class=\"data row11 col17\" >71.000000</td>\n",
       "      <td id=\"T_12fad_row11_col18\" class=\"data row11 col18\" >15.000000</td>\n",
       "      <td id=\"T_12fad_row11_col19\" class=\"data row11 col19\" >98.000000</td>\n",
       "      <td id=\"T_12fad_row11_col20\" class=\"data row11 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row11_col21\" class=\"data row11 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row11_col22\" class=\"data row11 col22\" >nan</td>\n",
       "      <td id=\"T_12fad_row11_col23\" class=\"data row11 col23\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col24\" class=\"data row11 col24\" >nan</td>\n",
       "      <td id=\"T_12fad_row11_col25\" class=\"data row11 col25\" >nan</td>\n",
       "      <td id=\"T_12fad_row11_col26\" class=\"data row11 col26\" >2.000000</td>\n",
       "      <td id=\"T_12fad_row11_col27\" class=\"data row11 col27\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col28\" class=\"data row11 col28\" >nan</td>\n",
       "      <td id=\"T_12fad_row11_col29\" class=\"data row11 col29\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col30\" class=\"data row11 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col31\" class=\"data row11 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row11_col32\" class=\"data row11 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col33\" class=\"data row11 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col34\" class=\"data row11 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col35\" class=\"data row11 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col36\" class=\"data row11 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col37\" class=\"data row11 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col38\" class=\"data row11 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col39\" class=\"data row11 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row11_col40\" class=\"data row11 col40\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row12\" class=\"row_heading level0 row12\" >93992</th>\n",
       "      <td id=\"T_12fad_row12_col0\" class=\"data row12 col0\" >SARS-CoV-2, NAA</td>\n",
       "      <td id=\"T_12fad_row12_col1\" class=\"data row12 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row12_col2\" class=\"data row12 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col3\" class=\"data row12 col3\" >33</td>\n",
       "      <td id=\"T_12fad_row12_col4\" class=\"data row12 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col5\" class=\"data row12 col5\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col6\" class=\"data row12 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col7\" class=\"data row12 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col8\" class=\"data row12 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col9\" class=\"data row12 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col10\" class=\"data row12 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col11\" class=\"data row12 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col12\" class=\"data row12 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col13\" class=\"data row12 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col14\" class=\"data row12 col14\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col15\" class=\"data row12 col15\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col16\" class=\"data row12 col16\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col17\" class=\"data row12 col17\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col18\" class=\"data row12 col18\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col19\" class=\"data row12 col19\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col20\" class=\"data row12 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col21\" class=\"data row12 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col22\" class=\"data row12 col22\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col23\" class=\"data row12 col23\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col24\" class=\"data row12 col24\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col25\" class=\"data row12 col25\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col26\" class=\"data row12 col26\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col27\" class=\"data row12 col27\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col28\" class=\"data row12 col28\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col29\" class=\"data row12 col29\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col30\" class=\"data row12 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col31\" class=\"data row12 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row12_col32\" class=\"data row12 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col33\" class=\"data row12 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col34\" class=\"data row12 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col35\" class=\"data row12 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col36\" class=\"data row12 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col37\" class=\"data row12 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col38\" class=\"data row12 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col39\" class=\"data row12 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row12_col40\" class=\"data row12 col40\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row13\" class=\"row_heading level0 row13\" >93993</th>\n",
       "      <td id=\"T_12fad_row13_col0\" class=\"data row13 col0\" >Rapid COVID-19 PCR Test</td>\n",
       "      <td id=\"T_12fad_row13_col1\" class=\"data row13 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row13_col2\" class=\"data row13 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col3\" class=\"data row13 col3\" >46</td>\n",
       "      <td id=\"T_12fad_row13_col4\" class=\"data row13 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col5\" class=\"data row13 col5\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col6\" class=\"data row13 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col7\" class=\"data row13 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col8\" class=\"data row13 col8\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col9\" class=\"data row13 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col10\" class=\"data row13 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col11\" class=\"data row13 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col12\" class=\"data row13 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col13\" class=\"data row13 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col14\" class=\"data row13 col14\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col15\" class=\"data row13 col15\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col16\" class=\"data row13 col16\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col17\" class=\"data row13 col17\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col18\" class=\"data row13 col18\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col19\" class=\"data row13 col19\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col20\" class=\"data row13 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col21\" class=\"data row13 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col22\" class=\"data row13 col22\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col23\" class=\"data row13 col23\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col24\" class=\"data row13 col24\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col25\" class=\"data row13 col25\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col26\" class=\"data row13 col26\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col27\" class=\"data row13 col27\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col28\" class=\"data row13 col28\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col29\" class=\"data row13 col29\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col30\" class=\"data row13 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col31\" class=\"data row13 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row13_col32\" class=\"data row13 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col33\" class=\"data row13 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col34\" class=\"data row13 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col35\" class=\"data row13 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col36\" class=\"data row13 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col37\" class=\"data row13 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col38\" class=\"data row13 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col39\" class=\"data row13 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row13_col40\" class=\"data row13 col40\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_12fad_level0_row14\" class=\"row_heading level0 row14\" >93994</th>\n",
       "      <td id=\"T_12fad_row14_col0\" class=\"data row14 col0\" >Rapid COVID-19 PCR Test</td>\n",
       "      <td id=\"T_12fad_row14_col1\" class=\"data row14 col1\" >Nasal</td>\n",
       "      <td id=\"T_12fad_row14_col2\" class=\"data row14 col2\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col3\" class=\"data row14 col3\" >53</td>\n",
       "      <td id=\"T_12fad_row14_col4\" class=\"data row14 col4\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col5\" class=\"data row14 col5\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col6\" class=\"data row14 col6\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col7\" class=\"data row14 col7\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col8\" class=\"data row14 col8\" >True</td>\n",
       "      <td id=\"T_12fad_row14_col9\" class=\"data row14 col9\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col10\" class=\"data row14 col10\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col11\" class=\"data row14 col11\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col12\" class=\"data row14 col12\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col13\" class=\"data row14 col13\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col14\" class=\"data row14 col14\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col15\" class=\"data row14 col15\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col16\" class=\"data row14 col16\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col17\" class=\"data row14 col17\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col18\" class=\"data row14 col18\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col19\" class=\"data row14 col19\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col20\" class=\"data row14 col20\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col21\" class=\"data row14 col21\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col22\" class=\"data row14 col22\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col23\" class=\"data row14 col23\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col24\" class=\"data row14 col24\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col25\" class=\"data row14 col25\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col26\" class=\"data row14 col26\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col27\" class=\"data row14 col27\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col28\" class=\"data row14 col28\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col29\" class=\"data row14 col29\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col30\" class=\"data row14 col30\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col31\" class=\"data row14 col31\" >nan</td>\n",
       "      <td id=\"T_12fad_row14_col32\" class=\"data row14 col32\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col33\" class=\"data row14 col33\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col34\" class=\"data row14 col34\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col35\" class=\"data row14 col35\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col36\" class=\"data row14 col36\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col37\" class=\"data row14 col37\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col38\" class=\"data row14 col38\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col39\" class=\"data row14 col39\" >False</td>\n",
       "      <td id=\"T_12fad_row14_col40\" class=\"data row14 col40\" >nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcef462b940>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = read_data()\n",
    "data_chunks = get_scattered_chunks(data, n_chunks=5, chunk_size=3)\n",
    "print_table(data_chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "93995"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "n_observations"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/plain": "41"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "n_columns"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/plain": "'covid19_test_results'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "target_column_name"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/plain": "1313"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "n_positives"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from configuration import TARGET_COLUMN_NAME\n",
    "from myst_nb import glue\n",
    "\n",
    "target = data[TARGET_COLUMN_NAME]\n",
    "n_positives = target.sum()\n",
    "\n",
    "glue(\"n_observations\", len(data), display=False)\n",
    "glue(\"n_columns\", len(data.columns), display=False)\n",
    "glue(\"target_column_name\", TARGET_COLUMN_NAME, display=False)\n",
    "glue(\"n_positives\", n_positives, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright! Some things that we can already learn about our dataset from this table are:\n",
    "* It contains a total of {glue:}`n_observations` observations.\n",
    "* There are {glue:}`n_columns` columns with mixed data types (numeric and categorical).\n",
    "* Missing values certainly exist (we can easily spot `nan` entries).\n",
    "* The subsample raises a strong suspicion that dataset is imbalanced, i.e. when examining our target variable ({glue:}`target_column_name`) it seems there are far more negative observations than positive ones. A quick `sum()` call reveals that indeed only {glue:}`n_positives` of the {glue:}`n_observations` observations are positive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Missing Values\n",
    "    :class: note\n",
    "> Handling missing values requires careful judgement. Possible solutions include:\n",
    "* Removing the entire feature (column) containing the missing values.\n",
    "* Removing all observations with missing values.\n",
    "* *Imputation*: \"Filling in\" missing values with some constant or statistic, such as the mean or the mode. \n",
    "\n",
    "The approach we'll take when dealing with missing values depends heavily on the structure of our data, for example:\n",
    "* If a column contains a small number of observations (relative to the size of the dataset) and the dataset is rich enough and offers more features that could be expected to be informative, it might be best to remove it.\n",
    "* If the dataset is large and the feature in question is crucial for the purposes of our analysis, remove all observations with missing values. \n",
    "* Imputation might sound like a good trade-off if there is a good reason to believe some statistic may adequately approximate the missing values, but it is also the subject of many misconceptions and often used poorly.\n",
    "* There are also ML methods that can safely include missing values (such as decision trees). We will learn when and how these are used later in this course.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "from missing_values import clean_missing_values\n",
    "# Extract columns with null values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = clean_missing_values(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the dataset is clean of any missing values, we can go on to inspect the kind of features that are available for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorial features:\n",
      "test_name, swab_type, high_risk_exposure_occupation, high_risk_interactions, diabetes, chd, htn, cancer, asthma, copd, autoimmune_dis, smoker, labored_respiration, cough, fever, sob, diarrhea, fatigue, headache, loss_of_smell, loss_of_taste, runny_nose, muscle_sore, sore_throat\n",
      "\n",
      "Numerical features:\n",
      "age, temperature, pulse, sats\n"
     ]
    }
   ],
   "source": [
    "X = data.drop(TARGET_COLUMN_NAME, axis=1)\n",
    "\n",
    "categorial_features = X.select_dtypes([\"object\", \"bool\"])\n",
    "numerical_featuers = X.select_dtypes(exclude=[\"object\", \"bool\"])\n",
    "\n",
    "print(\"Categorial features:\\n\" + \", \".join(categorial_features.columns))\n",
    "print(\"\\nNumerical features:\\n\" + \", \".join(numerical_featuers.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "28"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "n_features_after"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/plain": "37754"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "n_observations_after"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/plain": "718"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "n_positives_after"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_features_after = len(data.columns) - 1\n",
    "n_observations_after = len(data)\n",
    "target = data[TARGET_COLUMN_NAME]\n",
    "n_positives_after = target.sum()\n",
    "\n",
    "glue(\"n_features_after\", n_features_after, display=False)\n",
    "glue(\"n_observations_after\", n_observations_after, display=False)\n",
    "glue(\"n_positives_after\", n_positives_after, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are left with {glue:}`n_features_after` features and {glue:}`n_observations_after` observations ({glue:}`n_positives_after` of which are positive)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "Simply looking at the values in our data isn't enough. It's helpful to visualize how different variables interact with each out, how the distribute, etc.\n",
    "\n",
    "### Feature Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "correlation_matrix = X.corr(numeric_only=True)\n",
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "_ = sns.heatmap(correlation_matrix, annot=True,vmin=-.3, vmax=.3, center=0, cmap=\"RdBu_r\", ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorial Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "X = X.convert_dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "plt.rcParams['axes.grid'] = True\n",
    "\n",
    "boolean_features = X.select_dtypes([\"bool\"])\n",
    "positives = boolean_features[target]\n",
    "value_counts = {}\n",
    "for column in positives:\n",
    "    value_counts[column] = positives[column].value_counts()\n",
    "value_counts = pd.DataFrame(value_counts)\n",
    "\n",
    "fig, axes = plt.subplots(2,1,figsize=(15, 12))\n",
    "_ = value_counts.T.plot(kind=\"bar\", ax=axes[0])\n",
    "_ = plt.title(\"Symptoms in Positive Patients\")\n",
    "_ = ax.set_xlabel(\"Feature\")\n",
    "_ = ax.set_ylabel(\"Count\")\n",
    "plt.grid()\n",
    "\n",
    "# Group all boolean features by the target variable (COVID-19 test result)\n",
    "grouped = boolean_features.groupby(target)\n",
    "\n",
    "# Calculate the fraction of positives in each group.\n",
    "fractions = grouped.sum() / grouped.count()\n",
    "\n",
    "_ = fractions.T.plot(kind=\"bar\", ax=axes[1])\n",
    "_ = plt.title(\"Fraction of Positive Responses by Test Result\")\n",
    "_ = ax.set_xlabel(\"Feature\")\n",
    "_ = ax.set_ylabel(\"Fraction\")\n",
    "\n",
    "plt.subplots_adjust(hspace=0.8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x3000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select all numerical features.\n",
    "numerical_features = X.select_dtypes([\"float64\", \"int64\"])\n",
    "\n",
    "# Create distribution plots.\n",
    "nrows = len(numerical_features.columns)\n",
    "fig, ax = plt.subplots(nrows=nrows, ncols=2, figsize=(15, 30))\n",
    "for i, feature in enumerate(numerical_features):\n",
    "    sns.violinplot(x=TARGET_COLUMN_NAME, y=feature, hue=TARGET_COLUMN_NAME, data=data, ax=ax[i, 0], legend=False)\n",
    "    if i == 0:\n",
    "        ax[i, 0].set_title(\"Violin Plots\")\n",
    "        ax[i, 1].set_title(\"Box Plots\")        \n",
    "    sns.boxplot(x=TARGET_COLUMN_NAME, y=feature, hue=TARGET_COLUMN_NAME, data=data, ax=ax[i, 1], legend=False)\n",
    "    ax[i, 0].set_xlabel(\"\")\n",
    "    ax[i, 1].set_xlabel(\"\")\n",
    "    ax[i, 1].set_ylabel(\"\")\n",
    "_ = fig.text(0.5, 0, \"COVID-19 Test Results\", ha='center')\n",
    "_ = fig.suptitle(\"Numerical Feature Distributions\", y=1)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "[Violin plots](https://en.wikipedia.org/wiki/Violin_plot) are essentially [box plots](https://en.wikipedia.org/wiki/Box_plot) combined with a [kernel density estimation](https://en.wikipedia.org/wiki/Kernel_density_estimation). While box plots give us a good understanding of the data's quartiles and outliers, violin plots provide us with an informative representation of it's entire distribution.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbors (k-NN)\n",
    "[k-NN](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) is a simple and useful non-parametric method that is commonly used for both classification and regression. It relies on having some method of calculating distance between data points, and using the the \"nearest\" observations to predict the target value for new ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/KnnClassification.svg/1024px-KnnClassification.svg.png\" alt=\"KNN Illustration\" width=\"400\"/></center>\n",
    "    <figcaption>\n",
    "        <i>\n",
    "            Example of k-NN classification. The test sample (green dot) should be classified either to blue squares or to red triangles. If k = 3 (solid line circle) it is assigned to the red triangles because there are 2 triangles and only 1 square inside the inner circle. If k = 5 (dashed line circle) it is assigned to the blue squares (3 squares vs. 2 triangles inside the outer circle).\n",
    "            <a href=\"https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm\">\n",
    "                Wikipedia\n",
    "            </a>\n",
    "        </i>\n",
    "    </figcaption>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "TARGET_NAME = \"class\"\n",
    "\n",
    "# Read a type of dictionary with the dataset as well as some metadata.\n",
    "iris_dataset = load_iris()\n",
    "\n",
    "# Read the features and targets.\n",
    "X = pd.DataFrame(iris_dataset.data, columns=iris_dataset.feature_names)\n",
    "y = pd.Series(iris_dataset.target, name=TARGET_NAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "#T_feaae_row0_col3, #T_feaae_row1_col3, #T_feaae_row2_col2, #T_feaae_row2_col3, #T_feaae_row3_col0, #T_feaae_row3_col3, #T_feaae_row4_col3, #T_feaae_row5_col1, #T_feaae_row11_col1 {\n",
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       "#T_feaae_row0_col4, #T_feaae_row1_col4, #T_feaae_row2_col4, #T_feaae_row3_col4, #T_feaae_row4_col4 {\n",
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       "  background-color: #0c74b2;\n",
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       "}\n",
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       "}\n",
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       "  background-color: #056fae;\n",
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       "  text-align: center;\n",
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       "#T_feaae_row8_col3, #T_feaae_row9_col3 {\n",
       "  background-color: #509ac6;\n",
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       "}\n",
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       "  background-color: #045b8f;\n",
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       "  text-align: center;\n",
       "}\n",
       "#T_feaae_row12_col3 {\n",
       "  background-color: #045d92;\n",
       "  color: #f1f1f1;\n",
       "  text-align: center;\n",
       "}\n",
       "#T_feaae_row13_col0 {\n",
       "  background-color: #0d75b3;\n",
       "  color: #f1f1f1;\n",
       "  text-align: center;\n",
       "}\n",
       "#T_feaae_row14_col0 {\n",
       "  background-color: #4697c4;\n",
       "  color: #f1f1f1;\n",
       "  text-align: center;\n",
       "}\n",
       "#T_feaae_row14_col2 {\n",
       "  background-color: #034b76;\n",
       "  color: #f1f1f1;\n",
       "  text-align: center;\n",
       "}\n",
       "#T_feaae_row14_col3 {\n",
       "  background-color: #056dac;\n",
       "  color: #f1f1f1;\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_feaae\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_feaae_level0_col0\" class=\"col_heading level0 col0\" >sepal length (cm)</th>\n",
       "      <th id=\"T_feaae_level0_col1\" class=\"col_heading level0 col1\" >sepal width (cm)</th>\n",
       "      <th id=\"T_feaae_level0_col2\" class=\"col_heading level0 col2\" >petal length (cm)</th>\n",
       "      <th id=\"T_feaae_level0_col3\" class=\"col_heading level0 col3\" >petal width (cm)</th>\n",
       "      <th id=\"T_feaae_level0_col4\" class=\"col_heading level0 col4\" >class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_feaae_row0_col0\" class=\"data row0 col0\" >5.10</td>\n",
       "      <td id=\"T_feaae_row0_col1\" class=\"data row0 col1\" >3.50</td>\n",
       "      <td id=\"T_feaae_row0_col2\" class=\"data row0 col2\" >1.40</td>\n",
       "      <td id=\"T_feaae_row0_col3\" class=\"data row0 col3\" >0.20</td>\n",
       "      <td id=\"T_feaae_row0_col4\" class=\"data row0 col4\" >setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_feaae_row1_col0\" class=\"data row1 col0\" >4.90</td>\n",
       "      <td id=\"T_feaae_row1_col1\" class=\"data row1 col1\" >3.00</td>\n",
       "      <td id=\"T_feaae_row1_col2\" class=\"data row1 col2\" >1.40</td>\n",
       "      <td id=\"T_feaae_row1_col3\" class=\"data row1 col3\" >0.20</td>\n",
       "      <td id=\"T_feaae_row1_col4\" class=\"data row1 col4\" >setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_feaae_row2_col0\" class=\"data row2 col0\" >4.70</td>\n",
       "      <td id=\"T_feaae_row2_col1\" class=\"data row2 col1\" >3.20</td>\n",
       "      <td id=\"T_feaae_row2_col2\" class=\"data row2 col2\" >1.30</td>\n",
       "      <td id=\"T_feaae_row2_col3\" class=\"data row2 col3\" >0.20</td>\n",
       "      <td id=\"T_feaae_row2_col4\" class=\"data row2 col4\" >setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_feaae_row3_col0\" class=\"data row3 col0\" >4.60</td>\n",
       "      <td id=\"T_feaae_row3_col1\" class=\"data row3 col1\" >3.10</td>\n",
       "      <td id=\"T_feaae_row3_col2\" class=\"data row3 col2\" >1.50</td>\n",
       "      <td id=\"T_feaae_row3_col3\" class=\"data row3 col3\" >0.20</td>\n",
       "      <td id=\"T_feaae_row3_col4\" class=\"data row3 col4\" >setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_feaae_row4_col0\" class=\"data row4 col0\" >5.00</td>\n",
       "      <td id=\"T_feaae_row4_col1\" class=\"data row4 col1\" >3.60</td>\n",
       "      <td id=\"T_feaae_row4_col2\" class=\"data row4 col2\" >1.40</td>\n",
       "      <td id=\"T_feaae_row4_col3\" class=\"data row4 col3\" >0.20</td>\n",
       "      <td id=\"T_feaae_row4_col4\" class=\"data row4 col4\" >setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row5\" class=\"row_heading level0 row5\" >72</th>\n",
       "      <td id=\"T_feaae_row5_col0\" class=\"data row5 col0\" >6.30</td>\n",
       "      <td id=\"T_feaae_row5_col1\" class=\"data row5 col1\" >2.50</td>\n",
       "      <td id=\"T_feaae_row5_col2\" class=\"data row5 col2\" >4.90</td>\n",
       "      <td id=\"T_feaae_row5_col3\" class=\"data row5 col3\" >1.50</td>\n",
       "      <td id=\"T_feaae_row5_col4\" class=\"data row5 col4\" >versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row6\" class=\"row_heading level0 row6\" >73</th>\n",
       "      <td id=\"T_feaae_row6_col0\" class=\"data row6 col0\" >6.10</td>\n",
       "      <td id=\"T_feaae_row6_col1\" class=\"data row6 col1\" >2.80</td>\n",
       "      <td id=\"T_feaae_row6_col2\" class=\"data row6 col2\" >4.70</td>\n",
       "      <td id=\"T_feaae_row6_col3\" class=\"data row6 col3\" >1.20</td>\n",
       "      <td id=\"T_feaae_row6_col4\" class=\"data row6 col4\" >versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row7\" class=\"row_heading level0 row7\" >74</th>\n",
       "      <td id=\"T_feaae_row7_col0\" class=\"data row7 col0\" >6.40</td>\n",
       "      <td id=\"T_feaae_row7_col1\" class=\"data row7 col1\" >2.90</td>\n",
       "      <td id=\"T_feaae_row7_col2\" class=\"data row7 col2\" >4.30</td>\n",
       "      <td id=\"T_feaae_row7_col3\" class=\"data row7 col3\" >1.30</td>\n",
       "      <td id=\"T_feaae_row7_col4\" class=\"data row7 col4\" >versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row8\" class=\"row_heading level0 row8\" >75</th>\n",
       "      <td id=\"T_feaae_row8_col0\" class=\"data row8 col0\" >6.60</td>\n",
       "      <td id=\"T_feaae_row8_col1\" class=\"data row8 col1\" >3.00</td>\n",
       "      <td id=\"T_feaae_row8_col2\" class=\"data row8 col2\" >4.40</td>\n",
       "      <td id=\"T_feaae_row8_col3\" class=\"data row8 col3\" >1.40</td>\n",
       "      <td id=\"T_feaae_row8_col4\" class=\"data row8 col4\" >versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row9\" class=\"row_heading level0 row9\" >76</th>\n",
       "      <td id=\"T_feaae_row9_col0\" class=\"data row9 col0\" >6.80</td>\n",
       "      <td id=\"T_feaae_row9_col1\" class=\"data row9 col1\" >2.80</td>\n",
       "      <td id=\"T_feaae_row9_col2\" class=\"data row9 col2\" >4.80</td>\n",
       "      <td id=\"T_feaae_row9_col3\" class=\"data row9 col3\" >1.40</td>\n",
       "      <td id=\"T_feaae_row9_col4\" class=\"data row9 col4\" >versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row10\" class=\"row_heading level0 row10\" >145</th>\n",
       "      <td id=\"T_feaae_row10_col0\" class=\"data row10 col0\" >6.70</td>\n",
       "      <td id=\"T_feaae_row10_col1\" class=\"data row10 col1\" >3.00</td>\n",
       "      <td id=\"T_feaae_row10_col2\" class=\"data row10 col2\" >5.20</td>\n",
       "      <td id=\"T_feaae_row10_col3\" class=\"data row10 col3\" >2.30</td>\n",
       "      <td id=\"T_feaae_row10_col4\" class=\"data row10 col4\" >virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row11\" class=\"row_heading level0 row11\" >146</th>\n",
       "      <td id=\"T_feaae_row11_col0\" class=\"data row11 col0\" >6.30</td>\n",
       "      <td id=\"T_feaae_row11_col1\" class=\"data row11 col1\" >2.50</td>\n",
       "      <td id=\"T_feaae_row11_col2\" class=\"data row11 col2\" >5.00</td>\n",
       "      <td id=\"T_feaae_row11_col3\" class=\"data row11 col3\" >1.90</td>\n",
       "      <td id=\"T_feaae_row11_col4\" class=\"data row11 col4\" >virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row12\" class=\"row_heading level0 row12\" >147</th>\n",
       "      <td id=\"T_feaae_row12_col0\" class=\"data row12 col0\" >6.50</td>\n",
       "      <td id=\"T_feaae_row12_col1\" class=\"data row12 col1\" >3.00</td>\n",
       "      <td id=\"T_feaae_row12_col2\" class=\"data row12 col2\" >5.20</td>\n",
       "      <td id=\"T_feaae_row12_col3\" class=\"data row12 col3\" >2.00</td>\n",
       "      <td id=\"T_feaae_row12_col4\" class=\"data row12 col4\" >virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row13\" class=\"row_heading level0 row13\" >148</th>\n",
       "      <td id=\"T_feaae_row13_col0\" class=\"data row13 col0\" >6.20</td>\n",
       "      <td id=\"T_feaae_row13_col1\" class=\"data row13 col1\" >3.40</td>\n",
       "      <td id=\"T_feaae_row13_col2\" class=\"data row13 col2\" >5.40</td>\n",
       "      <td id=\"T_feaae_row13_col3\" class=\"data row13 col3\" >2.30</td>\n",
       "      <td id=\"T_feaae_row13_col4\" class=\"data row13 col4\" >virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_feaae_level0_row14\" class=\"row_heading level0 row14\" >149</th>\n",
       "      <td id=\"T_feaae_row14_col0\" class=\"data row14 col0\" >5.90</td>\n",
       "      <td id=\"T_feaae_row14_col1\" class=\"data row14 col1\" >3.00</td>\n",
       "      <td id=\"T_feaae_row14_col2\" class=\"data row14 col2\" >5.10</td>\n",
       "      <td id=\"T_feaae_row14_col3\" class=\"data row14 col3\" >1.80</td>\n",
       "      <td id=\"T_feaae_row14_col4\" class=\"data row14 col4\" >virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fce8ebaabf0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Class colors\n",
    "COLORS = \"rgba(255, 0, 0, 0.3)\", \"rgba(0, 255, 0, 0.3)\", \"rgba(0, 0, 255, 0.3)\"\n",
    "\n",
    "# Create a unified dataframe.\n",
    "data = pd.concat([X, y], axis=\"columns\")\n",
    "\n",
    "\n",
    "# Set class background color\n",
    "def set_class_color(class_index: str) -> str:\n",
    "    return f\"background-color: {COLORS[class_index]};\"\n",
    "\n",
    "\n",
    "def set_class_name(class_index: str) -> str:\n",
    "    return iris_dataset.target_names[class_index]\n",
    "\n",
    "\n",
    "# Select some sample indices\n",
    "sample_indices = np.linspace(0, len(data) - 5, 3, dtype=int)\n",
    "sample_indices = [index for i in sample_indices for index in range(i, i + 5)]\n",
    "\n",
    "# Display table\n",
    "data.iloc[sample_indices, :].style.background_gradient().map(lambda x: set_class_color(x), subset=[TARGET_NAME]).format(\n",
    "    {TARGET_NAME: set_class_name}).set_properties(**{\n",
    "        \"border\": \"1px solid black\"\n",
    "    }, subset=[TARGET_NAME]).set_properties(**{\n",
    "        \"text-align\": \"center\"\n",
    "    }).set_table_styles([\n",
    "        dict(selector=\"th\", props=[(\"font-size\", \"14px\")]),\n",
    "        dict(selector=\"td\", props=[(\"font-size\", \"12px\")]),\n",
    "    ]).format(\"{:.2f}\", subset=[col for col in data.columns if col != TARGET_NAME])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train/Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} [Train-Test Split](https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/)\n",
    "    :class: note\n",
    "A lot can be said regarding the different approaches for model validation and evaluation (which we'll discuss later in the course), but the general guideline is that, since our model should detect underlying trends in the data, we would evaluate its performance over unseen data. Therefore, we'll split our data into a training (for model fitting) and testing (for evaluation).\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    random_state=0,\n",
    "                                                    test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "112"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "n_train"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/plain": "38"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "n_test"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from myst_nb import glue\n",
    "\n",
    "glue(\"n_train\", len(X_train), display=False)\n",
    "glue(\"n_test\", len(X_test), display=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a training dataset consisting of {glue:}`n_train` observations and a test dataset with {glue:}`n_test` observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} [Models and Estimators](https://scikit-learn.org/stable/getting_started.html)\n",
    "    :class: note\n",
    "Machine Learning algorithms and models are easily accesible through numberous packages, namely [scikit-learn](https://scikit-learn.org/stable/index.html). While different estimators provide different advantages and pitfalls, they share some basic properties, making it easy for users to use them.\n",
    "\n",
    "*Since these estimators are basically just algorithms and formulas that need to be tailored to the dataset being used, all estimators have a 'fit' method, (unsurprisingly) fitting the estimators to generalize to any specific data.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "k = 1\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=k)\n",
    "_ = knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Misclassification Rate / Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center><img src=\"https://wikimedia.org/api/rest_v1/media/math/render/svg/f0453f2614cd29f5dd49c2c9a0ef807985128e9e\" alt=\"Accuracy\" width=\"400\"/></center>\n",
    "    <figcaption>\n",
    "        <i>\n",
    "            Accuracy is defined as 1-error rate. It is used as a statistical measure of how well a classification test performes.\n",
    "            <a href=\"https://en.wikipedia.org/wiki/Accuracy_and_precision\">\n",
    "                Wikipedia\n",
    "            </a>\n",
    "        </i>\n",
    "    </figcaption>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "y_predicted = knn.predict(X_test)\n",
    "\n",
    "misclassification_rate = np.mean(y_predicted != y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'2.632'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "misclassification_rate"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/plain": "37"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "n_correct"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glue(\"misclassification_rate\", f\"{misclassification_rate:.3f}\", display=False)\n",
    "glue(\"n_correct\", (y_predicted == y_test).sum(), display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model achieved a misclassification_rate of {glue:}`misclassification_rate`%, meaning it correctly predicted {glue:}`n_correct` of {glue:}`n_test` target values in our test set.\n",
    "\n",
    "Another way to look at it is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.36842105263158"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_predicted) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center><img src=\"https://miro.medium.com/v2/resize:fit:640/format:webp/1*qcK0IMq6gxIsoKzaTmSYiw.jpeg\" alt=\"Confusion Matrix\" width=\"400\"/></center>\n",
    "    <figcaption>\n",
    "        <i>\n",
    "            A confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.\n",
    "            <a href=\"https://shivang-ahd.medium.com/all-about-confusion-matrix-preparing-for-interview-questions-fddea115a7ee\">\n",
    "                Medium\n",
    "            </a>\n",
    "        </i>\n",
    "    </figcaption>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13,  0,  0],\n",
       "       [ 0, 15,  1],\n",
       "       [ 0,  0,  9]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "\n",
    "disp = ConfusionMatrixDisplay.from_estimator(knn,\n",
    "                             X_test,\n",
    "                             y_test,\n",
    "                             display_labels=iris_dataset.target_names,\n",
    "                             cmap=plt.cm.Blues,\n",
    "                             normalize=\"true\")\n",
    "_ = disp.ax_.set_title(f\"Confusion Matrix (k={k})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). [*Wikipedia*](https://en.wikipedia.org/wiki/Linear_regression)\n",
    "\n",
    "Using linear regression with Python is as easy as running:\n",
    "\n",
    "```python\n",
    ">>> from sklearn.linear_model import LinearRegression\n",
    ">>> model = LinearRegression()\n",
    ">>> model.fit(X_train, y_train)\n",
    ">>> predictions = model.predict(X_test)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's reproduce `scikit-learn`'s [Linear Regerssion Example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#linear-regression-example) using the prepackaged `diabetes` dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "# Read the dataset as a pandas DataFrame\n",
    "dataset = datasets.load_diabetes(as_frame=True)\n",
    "\n",
    "# Create observations matrix and target vector\n",
    "X, y = dataset.data, dataset.target\n",
    "\n",
    "# Create a unified DataFrame containing both\n",
    "data = pd.concat([X, y], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Raw Inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  text-align: center;\n",
       "  background-color: #bcc7e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row1_col5, #T_a7948_row4_col0, #T_a7948_row7_col3, #T_a7948_row11_col6 {\n",
       "  text-align: center;\n",
       "  background-color: #b4c4df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row1_col6 {\n",
       "  text-align: center;\n",
       "  background-color: #4697c4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row1_col7, #T_a7948_row11_col7, #T_a7948_row15_col7 {\n",
       "  text-align: center;\n",
       "  background-color: #d7d6e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row1_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #ede7f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row2_col2 {\n",
       "  text-align: center;\n",
       "  background-color: #157ab5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row2_col3, #T_a7948_row10_col3 {\n",
       "  text-align: center;\n",
       "  background-color: #0c74b2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row2_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #fbf3f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row2_col5 {\n",
       "  text-align: center;\n",
       "  background-color: #d4d4e8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row2_col6, #T_a7948_row10_col6 {\n",
       "  text-align: center;\n",
       "  background-color: #e7e3f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row2_col8, #T_a7948_row14_col5 {\n",
       "  text-align: center;\n",
       "  background-color: #529bc7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row2_col9, #T_a7948_row5_col4, #T_a7948_row14_col9 {\n",
       "  text-align: center;\n",
       "  background-color: #79abd0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row2_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #69a5cc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row3_col2, #T_a7948_row13_col5 {\n",
       "  text-align: center;\n",
       "  background-color: #a8bedc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row3_col3 {\n",
       "  text-align: center;\n",
       "  background-color: #8fb4d6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row3_col4, #T_a7948_row4_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #83afd3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row3_col5 {\n",
       "  text-align: center;\n",
       "  background-color: #328dbf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row3_col6, #T_a7948_row12_col3 {\n",
       "  text-align: center;\n",
       "  background-color: #eae6f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row3_col7, #T_a7948_row4_col7, #T_a7948_row5_col8, #T_a7948_row12_col7 {\n",
       "  text-align: center;\n",
       "  background-color: #0872b1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row3_col8 {\n",
       "  text-align: center;\n",
       "  background-color: #1278b4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row3_col9, #T_a7948_row8_col6 {\n",
       "  text-align: center;\n",
       "  background-color: #3f93c2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row3_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #034e7b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row4_col2, #T_a7948_row4_col3, #T_a7948_row14_col3 {\n",
       "  text-align: center;\n",
       "  background-color: #0567a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row4_col5, #T_a7948_row5_col7 {\n",
       "  text-align: center;\n",
       "  background-color: #2182b9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row4_col8 {\n",
       "  text-align: center;\n",
       "  background-color: #2987bc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row4_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #0771b1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row5_col0 {\n",
       "  text-align: center;\n",
       "  background-color: #dedcec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row5_col3, #T_a7948_row6_col3 {\n",
       "  text-align: center;\n",
       "  background-color: #4295c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row5_col5 {\n",
       "  text-align: center;\n",
       "  background-color: #3790c0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row5_col6, #T_a7948_row6_col0, #T_a7948_row12_col6, #T_a7948_row14_col6 {\n",
       "  text-align: center;\n",
       "  background-color: #e3e0ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row5_col9 {\n",
       "  text-align: center;\n",
       "  background-color: #023f64;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row5_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #8bb2d4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row6_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #2c89bd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row6_col5 {\n",
       "  text-align: center;\n",
       "  background-color: #0569a4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row6_col6 {\n",
       "  text-align: center;\n",
       "  background-color: #a9bfdc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row6_col8 {\n",
       "  text-align: center;\n",
       "  background-color: #b9c6e0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row6_col9, #T_a7948_row9_col9 {\n",
       "  text-align: center;\n",
       "  background-color: #2685bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row6_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #d1d2e6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row7_col2 {\n",
       "  text-align: center;\n",
       "  background-color: #99b8d8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row7_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #d8d7e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row7_col5 {\n",
       "  text-align: center;\n",
       "  background-color: #b7c5df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row7_col9, #T_a7948_row8_col9 {\n",
       "  text-align: center;\n",
       "  background-color: #9fbad9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row7_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #8fb4d6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row8_col0 {\n",
       "  text-align: center;\n",
       "  background-color: #549cc7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row8_col2 {\n",
       "  text-align: center;\n",
       "  background-color: #bdc8e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row8_col3, #T_a7948_row12_col0 {\n",
       "  text-align: center;\n",
       "  background-color: #7dacd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row8_col8 {\n",
       "  text-align: center;\n",
       "  background-color: #f2ecf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row8_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #e1dfed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row9_col0 {\n",
       "  text-align: center;\n",
       "  background-color: #f0eaf4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row9_col3 {\n",
       "  text-align: center;\n",
       "  background-color: #034f7d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row9_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #67a4cc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row9_col5, #T_a7948_row13_col0 {\n",
       "  text-align: center;\n",
       "  background-color: #056faf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row9_col6 {\n",
       "  text-align: center;\n",
       "  background-color: #c4cbe3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row9_col8 {\n",
       "  text-align: center;\n",
       "  background-color: #e8e4f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row9_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #04598c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row10_col0 {\n",
       "  text-align: center;\n",
       "  background-color: #2d8abd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row10_col2, #T_a7948_row14_col0, #T_a7948_row15_col0 {\n",
       "  text-align: center;\n",
       "  background-color: #d0d1e6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row10_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #d1d2e6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row10_col5 {\n",
       "  text-align: center;\n",
       "  background-color: #4a98c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row10_col9 {\n",
       "  text-align: center;\n",
       "  background-color: #93b5d6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row10_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row11_col0 {\n",
       "  text-align: center;\n",
       "  background-color: #d9d8ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row11_col2, #T_a7948_row14_col2 {\n",
       "  text-align: center;\n",
       "  background-color: #2383ba;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row11_col3 {\n",
       "  text-align: center;\n",
       "  background-color: #9cb9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row11_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #b5c4df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row11_col5 {\n",
       "  text-align: center;\n",
       "  background-color: #a5bddb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row11_col8 {\n",
       "  text-align: center;\n",
       "  background-color: #2484ba;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row11_col9, #T_a7948_row15_col9 {\n",
       "  text-align: center;\n",
       "  background-color: #197db7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row11_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #e0dded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row12_col2, #T_a7948_row13_col2 {\n",
       "  text-align: center;\n",
       "  background-color: #b0c2de;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row12_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #0771b1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row12_col8 {\n",
       "  text-align: center;\n",
       "  background-color: #97b7d7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row12_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #c0c9e2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row13_col3 {\n",
       "  text-align: center;\n",
       "  background-color: #03446a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row13_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #f1ebf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row13_col6 {\n",
       "  text-align: center;\n",
       "  background-color: #e0dded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row13_col7 {\n",
       "  text-align: center;\n",
       "  background-color: #94b6d7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row13_col8 {\n",
       "  text-align: center;\n",
       "  background-color: #dbdaeb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a7948_row13_col9 {\n",
       "  text-align: center;\n",
       "  background-color: #0569a5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row13_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #81aed2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row14_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #76aad0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row14_col7 {\n",
       "  text-align: center;\n",
       "  background-color: #1e80b8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row14_col8 {\n",
       "  text-align: center;\n",
       "  background-color: #04598c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row14_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row15_col5 {\n",
       "  text-align: center;\n",
       "  background-color: #2a88bc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row15_col8 {\n",
       "  text-align: center;\n",
       "  background-color: #6ba5cd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a7948_row15_col10 {\n",
       "  text-align: center;\n",
       "  border-left: 4px solid black;\n",
       "  background-color: #fdf5fa;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a7948\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a7948_level0_col0\" class=\"col_heading level0 col0\" >age</th>\n",
       "      <th id=\"T_a7948_level0_col1\" class=\"col_heading level0 col1\" >sex</th>\n",
       "      <th id=\"T_a7948_level0_col2\" class=\"col_heading level0 col2\" >bmi</th>\n",
       "      <th id=\"T_a7948_level0_col3\" class=\"col_heading level0 col3\" >bp</th>\n",
       "      <th id=\"T_a7948_level0_col4\" class=\"col_heading level0 col4\" >s1</th>\n",
       "      <th id=\"T_a7948_level0_col5\" class=\"col_heading level0 col5\" >s2</th>\n",
       "      <th id=\"T_a7948_level0_col6\" class=\"col_heading level0 col6\" >s3</th>\n",
       "      <th id=\"T_a7948_level0_col7\" class=\"col_heading level0 col7\" >s4</th>\n",
       "      <th id=\"T_a7948_level0_col8\" class=\"col_heading level0 col8\" >s5</th>\n",
       "      <th id=\"T_a7948_level0_col9\" class=\"col_heading level0 col9\" >s6</th>\n",
       "      <th id=\"T_a7948_level0_col10\" class=\"col_heading level0 col10\" >target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_a7948_row0_col0\" class=\"data row0 col0\" >0.038</td>\n",
       "      <td id=\"T_a7948_row0_col1\" class=\"data row0 col1\" >0.051</td>\n",
       "      <td id=\"T_a7948_row0_col2\" class=\"data row0 col2\" >0.062</td>\n",
       "      <td id=\"T_a7948_row0_col3\" class=\"data row0 col3\" >0.022</td>\n",
       "      <td id=\"T_a7948_row0_col4\" class=\"data row0 col4\" >-0.044</td>\n",
       "      <td id=\"T_a7948_row0_col5\" class=\"data row0 col5\" >-0.035</td>\n",
       "      <td id=\"T_a7948_row0_col6\" class=\"data row0 col6\" >-0.043</td>\n",
       "      <td id=\"T_a7948_row0_col7\" class=\"data row0 col7\" >-0.003</td>\n",
       "      <td id=\"T_a7948_row0_col8\" class=\"data row0 col8\" >0.020</td>\n",
       "      <td id=\"T_a7948_row0_col9\" class=\"data row0 col9\" >-0.018</td>\n",
       "      <td id=\"T_a7948_row0_col10\" class=\"data row0 col10\" >151.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_a7948_row1_col0\" class=\"data row1 col0\" >-0.002</td>\n",
       "      <td id=\"T_a7948_row1_col1\" class=\"data row1 col1\" >-0.045</td>\n",
       "      <td id=\"T_a7948_row1_col2\" class=\"data row1 col2\" >-0.051</td>\n",
       "      <td id=\"T_a7948_row1_col3\" class=\"data row1 col3\" >-0.026</td>\n",
       "      <td id=\"T_a7948_row1_col4\" class=\"data row1 col4\" >-0.008</td>\n",
       "      <td id=\"T_a7948_row1_col5\" class=\"data row1 col5\" >-0.019</td>\n",
       "      <td id=\"T_a7948_row1_col6\" class=\"data row1 col6\" >0.074</td>\n",
       "      <td id=\"T_a7948_row1_col7\" class=\"data row1 col7\" >-0.039</td>\n",
       "      <td id=\"T_a7948_row1_col8\" class=\"data row1 col8\" >-0.068</td>\n",
       "      <td id=\"T_a7948_row1_col9\" class=\"data row1 col9\" >-0.092</td>\n",
       "      <td id=\"T_a7948_row1_col10\" class=\"data row1 col10\" >75.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_a7948_row2_col0\" class=\"data row2 col0\" >0.085</td>\n",
       "      <td id=\"T_a7948_row2_col1\" class=\"data row2 col1\" >0.051</td>\n",
       "      <td id=\"T_a7948_row2_col2\" class=\"data row2 col2\" >0.044</td>\n",
       "      <td id=\"T_a7948_row2_col3\" class=\"data row2 col3\" >-0.006</td>\n",
       "      <td id=\"T_a7948_row2_col4\" class=\"data row2 col4\" >-0.046</td>\n",
       "      <td id=\"T_a7948_row2_col5\" class=\"data row2 col5\" >-0.034</td>\n",
       "      <td id=\"T_a7948_row2_col6\" class=\"data row2 col6\" >-0.032</td>\n",
       "      <td id=\"T_a7948_row2_col7\" class=\"data row2 col7\" >-0.003</td>\n",
       "      <td id=\"T_a7948_row2_col8\" class=\"data row2 col8\" >0.003</td>\n",
       "      <td id=\"T_a7948_row2_col9\" class=\"data row2 col9\" >-0.026</td>\n",
       "      <td id=\"T_a7948_row2_col10\" class=\"data row2 col10\" >141.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_a7948_row3_col0\" class=\"data row3 col0\" >-0.089</td>\n",
       "      <td id=\"T_a7948_row3_col1\" class=\"data row3 col1\" >-0.045</td>\n",
       "      <td id=\"T_a7948_row3_col2\" class=\"data row3 col2\" >-0.012</td>\n",
       "      <td id=\"T_a7948_row3_col3\" class=\"data row3 col3\" >-0.037</td>\n",
       "      <td id=\"T_a7948_row3_col4\" class=\"data row3 col4\" >0.012</td>\n",
       "      <td id=\"T_a7948_row3_col5\" class=\"data row3 col5\" >0.025</td>\n",
       "      <td id=\"T_a7948_row3_col6\" class=\"data row3 col6\" >-0.036</td>\n",
       "      <td id=\"T_a7948_row3_col7\" class=\"data row3 col7\" >0.034</td>\n",
       "      <td id=\"T_a7948_row3_col8\" class=\"data row3 col8\" >0.023</td>\n",
       "      <td id=\"T_a7948_row3_col9\" class=\"data row3 col9\" >-0.009</td>\n",
       "      <td id=\"T_a7948_row3_col10\" class=\"data row3 col10\" >206.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row4\" class=\"row_heading level0 row4\" >146</th>\n",
       "      <td id=\"T_a7948_row4_col0\" class=\"data row4 col0\" >-0.031</td>\n",
       "      <td id=\"T_a7948_row4_col1\" class=\"data row4 col1\" >0.051</td>\n",
       "      <td id=\"T_a7948_row4_col2\" class=\"data row4 col2\" >0.060</td>\n",
       "      <td id=\"T_a7948_row4_col3\" class=\"data row4 col3\" >0.001</td>\n",
       "      <td id=\"T_a7948_row4_col4\" class=\"data row4 col4\" >0.012</td>\n",
       "      <td id=\"T_a7948_row4_col5\" class=\"data row4 col5\" >0.032</td>\n",
       "      <td id=\"T_a7948_row4_col6\" class=\"data row4 col6\" >-0.043</td>\n",
       "      <td id=\"T_a7948_row4_col7\" class=\"data row4 col7\" >0.034</td>\n",
       "      <td id=\"T_a7948_row4_col8\" class=\"data row4 col8\" >0.015</td>\n",
       "      <td id=\"T_a7948_row4_col9\" class=\"data row4 col9\" >0.007</td>\n",
       "      <td id=\"T_a7948_row4_col10\" class=\"data row4 col10\" >178.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row5\" class=\"row_heading level0 row5\" >147</th>\n",
       "      <td id=\"T_a7948_row5_col0\" class=\"data row5 col0\" >-0.056</td>\n",
       "      <td id=\"T_a7948_row5_col1\" class=\"data row5 col1\" >-0.045</td>\n",
       "      <td id=\"T_a7948_row5_col2\" class=\"data row5 col2\" >0.093</td>\n",
       "      <td id=\"T_a7948_row5_col3\" class=\"data row5 col3\" >-0.019</td>\n",
       "      <td id=\"T_a7948_row5_col4\" class=\"data row5 col4\" >0.015</td>\n",
       "      <td id=\"T_a7948_row5_col5\" class=\"data row5 col5\" >0.023</td>\n",
       "      <td id=\"T_a7948_row5_col6\" class=\"data row5 col6\" >-0.029</td>\n",
       "      <td id=\"T_a7948_row5_col7\" class=\"data row5 col7\" >0.025</td>\n",
       "      <td id=\"T_a7948_row5_col8\" class=\"data row5 col8\" >0.026</td>\n",
       "      <td id=\"T_a7948_row5_col9\" class=\"data row5 col9\" >0.040</td>\n",
       "      <td id=\"T_a7948_row5_col10\" class=\"data row5 col10\" >128.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row6\" class=\"row_heading level0 row6\" >148</th>\n",
       "      <td id=\"T_a7948_row6_col0\" class=\"data row6 col0\" >-0.060</td>\n",
       "      <td id=\"T_a7948_row6_col1\" class=\"data row6 col1\" >0.051</td>\n",
       "      <td id=\"T_a7948_row6_col2\" class=\"data row6 col2\" >0.015</td>\n",
       "      <td id=\"T_a7948_row6_col3\" class=\"data row6 col3\" >-0.019</td>\n",
       "      <td id=\"T_a7948_row6_col4\" class=\"data row6 col4\" >0.037</td>\n",
       "      <td id=\"T_a7948_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
       "      <td id=\"T_a7948_row6_col6\" class=\"data row6 col6\" >0.019</td>\n",
       "      <td id=\"T_a7948_row6_col7\" class=\"data row6 col7\" >-0.003</td>\n",
       "      <td id=\"T_a7948_row6_col8\" class=\"data row6 col8\" >-0.031</td>\n",
       "      <td id=\"T_a7948_row6_col9\" class=\"data row6 col9\" >-0.001</td>\n",
       "      <td id=\"T_a7948_row6_col10\" class=\"data row6 col10\" >96.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row7\" class=\"row_heading level0 row7\" >149</th>\n",
       "      <td id=\"T_a7948_row7_col0\" class=\"data row7 col0\" >-0.049</td>\n",
       "      <td id=\"T_a7948_row7_col1\" class=\"data row7 col1\" >0.051</td>\n",
       "      <td id=\"T_a7948_row7_col2\" class=\"data row7 col2\" >-0.005</td>\n",
       "      <td id=\"T_a7948_row7_col3\" class=\"data row7 col3\" >-0.047</td>\n",
       "      <td id=\"T_a7948_row7_col4\" class=\"data row7 col4\" >-0.021</td>\n",
       "      <td id=\"T_a7948_row7_col5\" class=\"data row7 col5\" >-0.020</td>\n",
       "      <td id=\"T_a7948_row7_col6\" class=\"data row7 col6\" >-0.069</td>\n",
       "      <td id=\"T_a7948_row7_col7\" class=\"data row7 col7\" >0.071</td>\n",
       "      <td id=\"T_a7948_row7_col8\" class=\"data row7 col8\" >0.061</td>\n",
       "      <td id=\"T_a7948_row7_col9\" class=\"data row7 col9\" >-0.038</td>\n",
       "      <td id=\"T_a7948_row7_col10\" class=\"data row7 col10\" >126.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row8\" class=\"row_heading level0 row8\" >292</th>\n",
       "      <td id=\"T_a7948_row8_col0\" class=\"data row8 col0\" >0.009</td>\n",
       "      <td id=\"T_a7948_row8_col1\" class=\"data row8 col1\" >-0.045</td>\n",
       "      <td id=\"T_a7948_row8_col2\" class=\"data row8 col2\" >-0.022</td>\n",
       "      <td id=\"T_a7948_row8_col3\" class=\"data row8 col3\" >-0.032</td>\n",
       "      <td id=\"T_a7948_row8_col4\" class=\"data row8 col4\" >-0.050</td>\n",
       "      <td id=\"T_a7948_row8_col5\" class=\"data row8 col5\" >-0.069</td>\n",
       "      <td id=\"T_a7948_row8_col6\" class=\"data row8 col6\" >0.078</td>\n",
       "      <td id=\"T_a7948_row8_col7\" class=\"data row8 col7\" >-0.071</td>\n",
       "      <td id=\"T_a7948_row8_col8\" class=\"data row8 col8\" >-0.063</td>\n",
       "      <td id=\"T_a7948_row8_col9\" class=\"data row8 col9\" >-0.038</td>\n",
       "      <td id=\"T_a7948_row8_col10\" class=\"data row8 col10\" >84.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row9\" class=\"row_heading level0 row9\" >293</th>\n",
       "      <td id=\"T_a7948_row9_col0\" class=\"data row9 col0\" >-0.071</td>\n",
       "      <td id=\"T_a7948_row9_col1\" class=\"data row9 col1\" >-0.045</td>\n",
       "      <td id=\"T_a7948_row9_col2\" class=\"data row9 col2\" >0.093</td>\n",
       "      <td id=\"T_a7948_row9_col3\" class=\"data row9 col3\" >0.013</td>\n",
       "      <td id=\"T_a7948_row9_col4\" class=\"data row9 col4\" >0.020</td>\n",
       "      <td id=\"T_a7948_row9_col5\" class=\"data row9 col5\" >0.043</td>\n",
       "      <td id=\"T_a7948_row9_col6\" class=\"data row9 col6\" >0.001</td>\n",
       "      <td id=\"T_a7948_row9_col7\" class=\"data row9 col7\" >0.000</td>\n",
       "      <td id=\"T_a7948_row9_col8\" class=\"data row9 col8\" >-0.055</td>\n",
       "      <td id=\"T_a7948_row9_col9\" class=\"data row9 col9\" >-0.001</td>\n",
       "      <td id=\"T_a7948_row9_col10\" class=\"data row9 col10\" >200.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row10\" class=\"row_heading level0 row10\" >294</th>\n",
       "      <td id=\"T_a7948_row10_col0\" class=\"data row10 col0\" >0.024</td>\n",
       "      <td id=\"T_a7948_row10_col1\" class=\"data row10 col1\" >0.051</td>\n",
       "      <td id=\"T_a7948_row10_col2\" class=\"data row10 col2\" >-0.031</td>\n",
       "      <td id=\"T_a7948_row10_col3\" class=\"data row10 col3\" >-0.006</td>\n",
       "      <td id=\"T_a7948_row10_col4\" class=\"data row10 col4\" >-0.017</td>\n",
       "      <td id=\"T_a7948_row10_col5\" class=\"data row10 col5\" >0.018</td>\n",
       "      <td id=\"T_a7948_row10_col6\" class=\"data row10 col6\" >-0.032</td>\n",
       "      <td id=\"T_a7948_row10_col7\" class=\"data row10 col7\" >-0.003</td>\n",
       "      <td id=\"T_a7948_row10_col8\" class=\"data row10 col8\" >-0.074</td>\n",
       "      <td id=\"T_a7948_row10_col9\" class=\"data row10 col9\" >-0.034</td>\n",
       "      <td id=\"T_a7948_row10_col10\" class=\"data row10 col10\" >55.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row11\" class=\"row_heading level0 row11\" >295</th>\n",
       "      <td id=\"T_a7948_row11_col0\" class=\"data row11 col0\" >-0.053</td>\n",
       "      <td id=\"T_a7948_row11_col1\" class=\"data row11 col1\" >0.051</td>\n",
       "      <td id=\"T_a7948_row11_col2\" class=\"data row11 col2\" >0.039</td>\n",
       "      <td id=\"T_a7948_row11_col3\" class=\"data row11 col3\" >-0.040</td>\n",
       "      <td id=\"T_a7948_row11_col4\" class=\"data row11 col4\" >-0.006</td>\n",
       "      <td id=\"T_a7948_row11_col5\" class=\"data row11 col5\" >-0.013</td>\n",
       "      <td id=\"T_a7948_row11_col6\" class=\"data row11 col6\" >0.012</td>\n",
       "      <td id=\"T_a7948_row11_col7\" class=\"data row11 col7\" >-0.039</td>\n",
       "      <td id=\"T_a7948_row11_col8\" class=\"data row11 col8\" >0.016</td>\n",
       "      <td id=\"T_a7948_row11_col9\" class=\"data row11 col9\" >0.003</td>\n",
       "      <td id=\"T_a7948_row11_col10\" class=\"data row11 col10\" >85.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row12\" class=\"row_heading level0 row12\" >438</th>\n",
       "      <td id=\"T_a7948_row12_col0\" class=\"data row12 col0\" >-0.006</td>\n",
       "      <td id=\"T_a7948_row12_col1\" class=\"data row12 col1\" >0.051</td>\n",
       "      <td id=\"T_a7948_row12_col2\" class=\"data row12 col2\" >-0.016</td>\n",
       "      <td id=\"T_a7948_row12_col3\" class=\"data row12 col3\" >-0.068</td>\n",
       "      <td id=\"T_a7948_row12_col4\" class=\"data row12 col4\" >0.049</td>\n",
       "      <td id=\"T_a7948_row12_col5\" class=\"data row12 col5\" >0.079</td>\n",
       "      <td id=\"T_a7948_row12_col6\" class=\"data row12 col6\" >-0.029</td>\n",
       "      <td id=\"T_a7948_row12_col7\" class=\"data row12 col7\" >0.034</td>\n",
       "      <td id=\"T_a7948_row12_col8\" class=\"data row12 col8\" >-0.018</td>\n",
       "      <td id=\"T_a7948_row12_col9\" class=\"data row12 col9\" >0.044</td>\n",
       "      <td id=\"T_a7948_row12_col10\" class=\"data row12 col10\" >104.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row13\" class=\"row_heading level0 row13\" >439</th>\n",
       "      <td id=\"T_a7948_row13_col0\" class=\"data row13 col0\" >0.042</td>\n",
       "      <td id=\"T_a7948_row13_col1\" class=\"data row13 col1\" >0.051</td>\n",
       "      <td id=\"T_a7948_row13_col2\" class=\"data row13 col2\" >-0.016</td>\n",
       "      <td id=\"T_a7948_row13_col3\" class=\"data row13 col3\" >0.017</td>\n",
       "      <td id=\"T_a7948_row13_col4\" class=\"data row13 col4\" >-0.037</td>\n",
       "      <td id=\"T_a7948_row13_col5\" class=\"data row13 col5\" >-0.014</td>\n",
       "      <td id=\"T_a7948_row13_col6\" class=\"data row13 col6\" >-0.025</td>\n",
       "      <td id=\"T_a7948_row13_col7\" class=\"data row13 col7\" >-0.011</td>\n",
       "      <td id=\"T_a7948_row13_col8\" class=\"data row13 col8\" >-0.047</td>\n",
       "      <td id=\"T_a7948_row13_col9\" class=\"data row13 col9\" >0.015</td>\n",
       "      <td id=\"T_a7948_row13_col10\" class=\"data row13 col10\" >132.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row14\" class=\"row_heading level0 row14\" >440</th>\n",
       "      <td id=\"T_a7948_row14_col0\" class=\"data row14 col0\" >-0.045</td>\n",
       "      <td id=\"T_a7948_row14_col1\" class=\"data row14 col1\" >-0.045</td>\n",
       "      <td id=\"T_a7948_row14_col2\" class=\"data row14 col2\" >0.039</td>\n",
       "      <td id=\"T_a7948_row14_col3\" class=\"data row14 col3\" >0.001</td>\n",
       "      <td id=\"T_a7948_row14_col4\" class=\"data row14 col4\" >0.016</td>\n",
       "      <td id=\"T_a7948_row14_col5\" class=\"data row14 col5\" >0.015</td>\n",
       "      <td id=\"T_a7948_row14_col6\" class=\"data row14 col6\" >-0.029</td>\n",
       "      <td id=\"T_a7948_row14_col7\" class=\"data row14 col7\" >0.027</td>\n",
       "      <td id=\"T_a7948_row14_col8\" class=\"data row14 col8\" >0.045</td>\n",
       "      <td id=\"T_a7948_row14_col9\" class=\"data row14 col9\" >-0.026</td>\n",
       "      <td id=\"T_a7948_row14_col10\" class=\"data row14 col10\" >220.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7948_level0_row15\" class=\"row_heading level0 row15\" >441</th>\n",
       "      <td id=\"T_a7948_row15_col0\" class=\"data row15 col0\" >-0.045</td>\n",
       "      <td id=\"T_a7948_row15_col1\" class=\"data row15 col1\" >-0.045</td>\n",
       "      <td id=\"T_a7948_row15_col2\" class=\"data row15 col2\" >-0.073</td>\n",
       "      <td id=\"T_a7948_row15_col3\" class=\"data row15 col3\" >-0.081</td>\n",
       "      <td id=\"T_a7948_row15_col4\" class=\"data row15 col4\" >0.084</td>\n",
       "      <td id=\"T_a7948_row15_col5\" class=\"data row15 col5\" >0.028</td>\n",
       "      <td id=\"T_a7948_row15_col6\" class=\"data row15 col6\" >0.174</td>\n",
       "      <td id=\"T_a7948_row15_col7\" class=\"data row15 col7\" >-0.039</td>\n",
       "      <td id=\"T_a7948_row15_col8\" class=\"data row15 col8\" >-0.004</td>\n",
       "      <td id=\"T_a7948_row15_col9\" class=\"data row15 col9\" >0.003</td>\n",
       "      <td id=\"T_a7948_row15_col10\" class=\"data row15 col10\" >57.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fce8cc52830>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Select some sample indices\n",
    "sample_indices = np.linspace(0, len(X) - 4, 4, dtype=int)\n",
    "sample_indices = [index for i in sample_indices for index in range(i, i + 4)]\n",
    "\n",
    "# Print data table (features and target)\n",
    "data.iloc[sample_indices, :].style.set_properties(**{\n",
    "    \"text-align\": \"center\",\n",
    "}).set_properties(**{\n",
    "    \"border-left\": \"4px solid black\"\n",
    "}, subset=['target']).set_table_styles([\n",
    "    dict(selector=\"th\", props=[(\"font-size\", \"13px\")]),\n",
    "    dict(selector=\"td\", props=[(\"font-size\", \"11px\")]),\n",
    "]).background_gradient().format(\"{:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `seaborn`'s [`heatmap`](http://seaborn.pydata.org/generated/seaborn.heatmap.html) function to inspect feature correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Calculate correlation matrix using NumPy\n",
    "correlation_matrix = np.corrcoef(data.values.T)\n",
    "\n",
    "# Plot correlation matrix using seaborn\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "tick_labels = list(X.columns) + ['diabetes']\n",
    "hm = sns.heatmap(\n",
    "    correlation_matrix,\n",
    "    ax=ax,\n",
    "    cbar=True,  # Show colorbar\n",
    "    cmap=\"vlag\",  # Specify colormap\n",
    "    vmin=-1,  # Min. value for colormapping\n",
    "    vmax=1,  # Max. value for colormapping\n",
    "    annot=True,  # Show the value of each cell\n",
    "    square=True,  # Square aspect ratio in cell sizing\n",
    "    fmt='.2f',  # Float formatting\n",
    "    annot_kws={'size':\n",
    "               12},  # Font size of the values displayed within the cells\n",
    "    xticklabels=tick_labels,  # x-axis labels\n",
    "    yticklabels=tick_labels)  # y-axis labels\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple linear regression is a linear regression model with a single explanatory variable. `bmi` seems to show a discernible linear relationship with the target variable, so let's go with that one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Create a vector of the single predictor values\n",
    "simple_X = X.bmi.to_numpy().reshape(len(X), 1)\n",
    "\n",
    "# Split for simple linear regression\n",
    "simple_X_train, simple_X_test, y_train, y_test = train_test_split(simple_X,\n",
    "                                                                  y,\n",
    "                                                                  random_state=0,\n",
    "                                                                  test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "_ = model.fit(simple_X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_y_pred = model.predict(simple_X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll plot our predicted values alongside the observed values, as well as the regression line estimated by our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure\n",
    "fig, ax = plt.subplots(figsize=(15, 7))\n",
    "\n",
    "# Plot real values scatter plot\n",
    "_ = plt.scatter(simple_X_test, y_test, color=\"black\", label=\"Real Values\")\n",
    "\n",
    "# Plot predicted values scatter plot\n",
    "_ = plt.scatter(simple_X_test,\n",
    "                simple_y_pred,\n",
    "                color=\"red\",\n",
    "                label=\"Predicted Values\")\n",
    "\n",
    "# Plot regression line\n",
    "_ = plt.plot(simple_X_test,\n",
    "             simple_y_pred,\n",
    "             color=\"blue\",\n",
    "             label=\"Regression Line\")\n",
    "\n",
    "# Show legend\n",
    "_ = plt.legend()\n",
    "\n",
    "# Set title\n",
    "title = \"Disease Progression by BMI\"\n",
    "plt.title(title)\n",
    "\n",
    "# Sex axis labels\n",
    "ax.set_xlabel(\"Standardized BMI score\")\n",
    "_ = ax.set_ylabel(\"Disease Progression\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the [`sklearn.metrics`](https://scikit-learn.org/stable/modules/model_evaluation.html) module to calculate the [MSE](https://en.wikipedia.org/wiki/Mean_squared_error) and [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination) ($R^2$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient: 981.66\n",
      "Intercept: 152.29\n",
      "Mean Squared Error: 4150.68\n",
      "Coefficient of Determination: 0.38\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "mse = mean_squared_error(y_test, simple_y_pred)\n",
    "r_squared = r2_score(y_train, model.predict(simple_X_train))\n",
    "print(f\"Coefficient: {model.coef_[0]:.2f}\")\n",
    "print(f\"Intercept: {model.intercept_:.2f}\")\n",
    "print(f'Mean Squared Error: {mse:.2f}')\n",
    "print(f'Coefficient of Determination: {r_squared:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "General notation:\n",
    "\n",
    "![OLS](https://wikimedia.org/api/rest_v1/media/math/render/svg/5d4d7a9152344a9b203e651e82834c16cb085365)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the coefficient vector of the least-squares hyperplane:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![OLS beta hat](https://wikimedia.org/api/rest_v1/media/math/render/svg/b6001f896b83cf90fdc5c544743ceec61b7c83fd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiple Linear Regression using `sklearn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    random_state=0,\n",
    "                                                    test_size=0.2)\n",
    "\n",
    "multiple_model = LinearRegression()\n",
    "_ = multiple_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_all = multiple_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse_all = mean_squared_error(y_train, multiple_model.predict(X_train))\n",
    "r2_score_all = r2_score(y_train, multiple_model.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient: [-35.55, -243.17, 562.76, 305.46, -662.7, 324.21, 24.75, 170.32, 731.64, 43.03]\n",
      "Mean Squared Error: 2734.75\n",
      "Coefficient of Determination: 0.55\n"
     ]
    }
   ],
   "source": [
    "betas_all = [beta.round(2) for beta in multiple_model.coef_.flatten()]\n",
    "\n",
    "print(f\"Coefficient: {betas_all}\")\n",
    "print(f'Mean Squared Error: {mse_all:.2f}')\n",
    "print(f'Coefficient of Determination: {r2_score_all:.2f}')"
   ]
  }
 ],
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