2_ML_Process.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54bc018e-82cd-4526-8925-ac7bf46e164f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc0ce36b-952c-423c-ae18-4adefd1d71fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e6a6d3-f6d8-444b-99e5-881a1a065a0f",
   "metadata": {},
   "source": [
    "# Process Model for Machine Learning in Digital Health\n",
    "\n",
    "<img src=\"assets/process_model.png\" width=\"40%\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2920e6e-40db-4aa8-b6e5-8a76ec928700",
   "metadata": {},
   "source": [
    "## 1. Requirements Analysis\n",
    "\n",
    "- [x] No identifiable data on GitHub\n",
    "- [x] Interpretability"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "546ded4d-6763-4044-bd5f-28b486cbfb75",
   "metadata": {},
   "source": [
    "## 2. Data Acquisition\n",
    "\n",
    "We will use the **South African Heart Disease Dataset**, which was introduced in the [previous notebook](./1_ML_Intro.ipynb).\n",
    "\n",
    "The dataset is already de-identified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b6c7433-2419-41de-bda0-a4aa3c3c9dd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9843005-4a9f-4530-8952-7748c96aea25",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row.names</th>\n",
       "      <th>sbp</th>\n",
       "      <th>tobacco</th>\n",
       "      <th>ldl</th>\n",
       "      <th>adiposity</th>\n",
       "      <th>famhist</th>\n",
       "      <th>typea</th>\n",
       "      <th>obesity</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>age</th>\n",
       "      <th>chd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>12.00</td>\n",
       "      <td>5.73</td>\n",
       "      <td>23.11</td>\n",
       "      <td>Present</td>\n",
       "      <td>49</td>\n",
       "      <td>25.30</td>\n",
       "      <td>97.20</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4.41</td>\n",
       "      <td>28.61</td>\n",
       "      <td>Absent</td>\n",
       "      <td>55</td>\n",
       "      <td>28.87</td>\n",
       "      <td>2.06</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>118</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3.48</td>\n",
       "      <td>32.28</td>\n",
       "      <td>Present</td>\n",
       "      <td>52</td>\n",
       "      <td>29.14</td>\n",
       "      <td>3.81</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>170</td>\n",
       "      <td>7.50</td>\n",
       "      <td>6.41</td>\n",
       "      <td>38.03</td>\n",
       "      <td>Present</td>\n",
       "      <td>51</td>\n",
       "      <td>31.99</td>\n",
       "      <td>24.26</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>134</td>\n",
       "      <td>13.60</td>\n",
       "      <td>3.50</td>\n",
       "      <td>27.78</td>\n",
       "      <td>Present</td>\n",
       "      <td>60</td>\n",
       "      <td>25.99</td>\n",
       "      <td>57.34</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row.names  sbp  tobacco  ldl  adiposity  famhist  typea  obesity  alcohol  \\\n",
       "0          1  160    12.00 5.73      23.11  Present     49    25.30    97.20   \n",
       "1          2  144     0.01 4.41      28.61   Absent     55    28.87     2.06   \n",
       "2          3  118     0.08 3.48      32.28  Present     52    29.14     3.81   \n",
       "3          4  170     7.50 6.41      38.03  Present     51    31.99    24.26   \n",
       "4          5  134    13.60 3.50      27.78  Present     60    25.99    57.34   \n",
       "\n",
       "   age  chd  \n",
       "0   52    1  \n",
       "1   63    1  \n",
       "2   46    0  \n",
       "3   58    1  \n",
       "4   49    1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import load_messy_heart_disease_dataset\n",
    "heart_disease_data = load_messy_heart_disease_dataset()\n",
    "heart_disease_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d6a1b3c-5172-4d5e-bd1d-cfb1fbd60957",
   "metadata": {},
   "source": [
    "## 3. Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8caae94f-823e-4f9d-a528-3acdb77cd4da",
   "metadata": {},
   "source": [
    "### Labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63d339bd-5db6-42f4-be29-9b176224eff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patient ID</th>\n",
       "      <th>Systolic Blood Pressure</th>\n",
       "      <th>Tobacco (kg)</th>\n",
       "      <th>LDL</th>\n",
       "      <th>Adiposity</th>\n",
       "      <th>Fam. Hist</th>\n",
       "      <th>Type A</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Age</th>\n",
       "      <th>Coronary Heart Disease</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>12.00</td>\n",
       "      <td>5.73</td>\n",
       "      <td>23.11</td>\n",
       "      <td>Present</td>\n",
       "      <td>49</td>\n",
       "      <td>25.30</td>\n",
       "      <td>97.20</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4.41</td>\n",
       "      <td>28.61</td>\n",
       "      <td>Absent</td>\n",
       "      <td>55</td>\n",
       "      <td>28.87</td>\n",
       "      <td>2.06</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>118</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3.48</td>\n",
       "      <td>32.28</td>\n",
       "      <td>Present</td>\n",
       "      <td>52</td>\n",
       "      <td>29.14</td>\n",
       "      <td>3.81</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>170</td>\n",
       "      <td>7.50</td>\n",
       "      <td>6.41</td>\n",
       "      <td>38.03</td>\n",
       "      <td>Present</td>\n",
       "      <td>51</td>\n",
       "      <td>31.99</td>\n",
       "      <td>24.26</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>134</td>\n",
       "      <td>13.60</td>\n",
       "      <td>3.50</td>\n",
       "      <td>27.78</td>\n",
       "      <td>Present</td>\n",
       "      <td>60</td>\n",
       "      <td>25.99</td>\n",
       "      <td>57.34</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Patient ID  Systolic Blood Pressure  Tobacco (kg)  LDL  Adiposity  \\\n",
       "0           1                      160         12.00 5.73      23.11   \n",
       "1           2                      144          0.01 4.41      28.61   \n",
       "2           3                      118          0.08 3.48      32.28   \n",
       "3           4                      170          7.50 6.41      38.03   \n",
       "4           5                      134         13.60 3.50      27.78   \n",
       "\n",
       "  Fam. Hist  Type A  Obesity  Alcohol  Age  Coronary Heart Disease  \n",
       "0   Present      49    25.30    97.20   52                       1  \n",
       "1    Absent      55    28.87     2.06   63                       1  \n",
       "2   Present      52    29.14     3.81   46                       0  \n",
       "3   Present      51    31.99    24.26   58                       1  \n",
       "4   Present      60    25.99    57.34   49                       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use more user-friendly column names\n",
    "columns = [\n",
    "    'Patient ID',\n",
    "    'Systolic Blood Pressure', \n",
    "    'Tobacco (kg)', \n",
    "    'LDL', \n",
    "    'Adiposity', \n",
    "    'Fam. Hist', \n",
    "    'Type A', \n",
    "    'Obesity', \n",
    "    'Alcohol', \n",
    "    'Age',\n",
    "    'Coronary Heart Disease'\n",
    "]\n",
    "\n",
    "heart_disease_data.columns = columns\n",
    "heart_disease_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9a6eb5b-8778-404f-92e9-93cd61ef0dbe",
   "metadata": {},
   "source": [
    "Define one variable as the target y, all other variables (except for patient ID) as features X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e45ed29a-39fc-4211-8ebd-8577b8e6de85",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Coronary Heart Disease'\n",
    "features = [c for c in columns if c != target and c != 'Patient ID']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f83011-3ff5-4a05-9468-d8a454b0e082",
   "metadata": {},
   "source": [
    "### Exploration & Quality Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f08eecd4-9301-465a-9d57-916adfede511",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patient ID</th>\n",
       "      <th>Systolic Blood Pressure</th>\n",
       "      <th>Tobacco (kg)</th>\n",
       "      <th>LDL</th>\n",
       "      <th>Adiposity</th>\n",
       "      <th>Type A</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Age</th>\n",
       "      <th>Coronary Heart Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>462.00</td>\n",
       "      <td>462.00</td>\n",
       "      <td>462.00</td>\n",
       "      <td>462.00</td>\n",
       "      <td>462.00</td>\n",
       "      <td>462.00</td>\n",
       "      <td>462.00</td>\n",
       "      <td>462.00</td>\n",
       "      <td>462.00</td>\n",
       "      <td>462.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>231.94</td>\n",
       "      <td>138.33</td>\n",
       "      <td>3.64</td>\n",
       "      <td>4.74</td>\n",
       "      <td>25.41</td>\n",
       "      <td>53.10</td>\n",
       "      <td>26.04</td>\n",
       "      <td>17.04</td>\n",
       "      <td>67.24</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>133.94</td>\n",
       "      <td>20.50</td>\n",
       "      <td>4.59</td>\n",
       "      <td>2.07</td>\n",
       "      <td>7.78</td>\n",
       "      <td>9.82</td>\n",
       "      <td>4.21</td>\n",
       "      <td>24.48</td>\n",
       "      <td>524.63</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>101.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>6.74</td>\n",
       "      <td>13.00</td>\n",
       "      <td>14.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>116.25</td>\n",
       "      <td>124.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3.28</td>\n",
       "      <td>19.77</td>\n",
       "      <td>47.00</td>\n",
       "      <td>22.98</td>\n",
       "      <td>0.51</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>231.50</td>\n",
       "      <td>134.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.34</td>\n",
       "      <td>26.12</td>\n",
       "      <td>53.00</td>\n",
       "      <td>25.80</td>\n",
       "      <td>7.51</td>\n",
       "      <td>45.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>347.75</td>\n",
       "      <td>148.00</td>\n",
       "      <td>5.50</td>\n",
       "      <td>5.79</td>\n",
       "      <td>31.23</td>\n",
       "      <td>60.00</td>\n",
       "      <td>28.50</td>\n",
       "      <td>23.89</td>\n",
       "      <td>55.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>463.00</td>\n",
       "      <td>218.00</td>\n",
       "      <td>31.20</td>\n",
       "      <td>15.33</td>\n",
       "      <td>42.49</td>\n",
       "      <td>78.00</td>\n",
       "      <td>46.58</td>\n",
       "      <td>147.19</td>\n",
       "      <td>11,315.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Patient ID  Systolic Blood Pressure  Tobacco (kg)    LDL  Adiposity  \\\n",
       "count      462.00                   462.00        462.00 462.00     462.00   \n",
       "mean       231.94                   138.33          3.64   4.74      25.41   \n",
       "std        133.94                    20.50          4.59   2.07       7.78   \n",
       "min          1.00                   101.00          0.00   0.98       6.74   \n",
       "25%        116.25                   124.00          0.05   3.28      19.77   \n",
       "50%        231.50                   134.00          2.00   4.34      26.12   \n",
       "75%        347.75                   148.00          5.50   5.79      31.23   \n",
       "max        463.00                   218.00         31.20  15.33      42.49   \n",
       "\n",
       "       Type A  Obesity  Alcohol       Age  Coronary Heart Disease  \n",
       "count  462.00   462.00   462.00    462.00                  462.00  \n",
       "mean    53.10    26.04    17.04     67.24                    0.35  \n",
       "std      9.82     4.21    24.48    524.63                    0.48  \n",
       "min     13.00    14.70     0.00     15.00                    0.00  \n",
       "25%     47.00    22.98     0.51     31.25                    0.00  \n",
       "50%     53.00    25.80     7.51     45.00                    0.00  \n",
       "75%     60.00    28.50    23.89     55.00                    1.00  \n",
       "max     78.00    46.58   147.19 11,315.00                    1.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da123759-f4b4-43d5-aaf4-e5cf07916b3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart_disease_data.Age.plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2485e6c-aea3-4ed6-a93c-4b3b29ff3087",
   "metadata": {},
   "source": [
    "Remove outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e33ecf52-c32b-4518-9eda-d3883869ef09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patient ID</th>\n",
       "      <th>Systolic Blood Pressure</th>\n",
       "      <th>Tobacco (kg)</th>\n",
       "      <th>LDL</th>\n",
       "      <th>Adiposity</th>\n",
       "      <th>Fam. Hist</th>\n",
       "      <th>Type A</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Age</th>\n",
       "      <th>Coronary Heart Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>104</td>\n",
       "      <td>128</td>\n",
       "      <td>0.42</td>\n",
       "      <td>4.60</td>\n",
       "      <td>26.68</td>\n",
       "      <td>Absent</td>\n",
       "      <td>41</td>\n",
       "      <td>30.97</td>\n",
       "      <td>10.33</td>\n",
       "      <td>11315</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Patient ID  Systolic Blood Pressure  Tobacco (kg)  LDL  Adiposity  \\\n",
       "103         104                      128          0.42 4.60      26.68   \n",
       "\n",
       "    Fam. Hist  Type A  Obesity  Alcohol    Age  Coronary Heart Disease  \n",
       "103    Absent      41    30.97    10.33  11315                       0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease_data[heart_disease_data.Age > 10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4677a263-b873-4197-9f23-757ca5656128",
   "metadata": {},
   "outputs": [],
   "source": [
    "heart_disease_data_clean = heart_disease_data[~(heart_disease_data.Age > 10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cfd3a444-cfc3-4350-8cb1-bd6e57b06256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart_disease_data_clean.Age.plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "107e93c4-e762-45ca-a8a9-659a6d40d07b",
   "metadata": {},
   "source": [
    "### Training / Test Set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c3cfee-e166-4962-a8c9-357234b57045",
   "metadata": {},
   "source": [
    "We create a training and test set first without and then with stratified sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c8c8fb63-7614-4233-8630-79393b42dc54",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c59f8b9a-7229-4450-8998-7ed675d6cf05",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = heart_disease_data_clean[features]\n",
    "y = heart_disease_data_clean[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52dce414-f5d1-408a-8c03-a947324fb485",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa6f99c-4484-4231-b1ba-d9523d370023",
   "metadata": {},
   "source": [
    "Mean of the labels in training and test set without stratitication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1ef47672-23b3-4d3c-adad-b579c4d56e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.358695652173913, 0.3010752688172043)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same distribution?\n",
    "y_train.mean(), y_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "289a56ac-94a7-452c-8101-8dd798160bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff3816c-fd79-4e47-8bcb-9cef1a3007a5",
   "metadata": {},
   "source": [
    "Mean of the labels in training and test set with stratitication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6c4b183c-7fbf-4dcb-ab1f-31a72849cfc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.34782608695652173, 0.34408602150537637)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same distribution?\n",
    "y_train.mean(), y_test.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf98591-18aa-4592-9543-3bccd2c2303a",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ade084e2-a8af-4c9f-a85b-6ede572d9923",
   "metadata": {},
   "source": [
    "Encode Family history as integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "059cc94f-d7b8-4904-85f4-61f410926ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "for ds in [X_train, X_test]:\n",
    "    ds['Fam. Hist'] = ds['Fam. Hist'].map({'Absent': 0, 'Present': 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d407094-fc7f-4879-aeff-e4f80d9c6c5c",
   "metadata": {},
   "source": [
    "#### Constructing Polynomial Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "31ab8aaf-d263-4483-b25e-6b362d7eb20b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "57fcad98-3f40-4d08-93be-89122c97267a",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c696a4db-bdeb-43ff-b56a-921654218ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_poly = poly.fit_transform(X_train)\n",
    "X_poly_names = poly.get_feature_names_out(X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f972ee19-ddef-4b32-a5a8-54ada3a0ee67",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>Systolic Blood Pressure</th>\n",
       "      <th>Tobacco (kg)</th>\n",
       "      <th>LDL</th>\n",
       "      <th>Adiposity</th>\n",
       "      <th>Fam. Hist</th>\n",
       "      <th>Type A</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Age</th>\n",
       "      <th>...</th>\n",
       "      <th>Type A^2</th>\n",
       "      <th>Type A Obesity</th>\n",
       "      <th>Type A Alcohol</th>\n",
       "      <th>Type A Age</th>\n",
       "      <th>Obesity^2</th>\n",
       "      <th>Obesity Alcohol</th>\n",
       "      <th>Obesity Age</th>\n",
       "      <th>Alcohol^2</th>\n",
       "      <th>Alcohol Age</th>\n",
       "      <th>Age^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.00</td>\n",
       "      <td>154.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.68</td>\n",
       "      <td>39.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>61.00</td>\n",
       "      <td>33.17</td>\n",
       "      <td>1.54</td>\n",
       "      <td>64.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3,721.00</td>\n",
       "      <td>2,023.37</td>\n",
       "      <td>93.94</td>\n",
       "      <td>3,904.00</td>\n",
       "      <td>1,100.25</td>\n",
       "      <td>51.08</td>\n",
       "      <td>2,122.88</td>\n",
       "      <td>2.37</td>\n",
       "      <td>98.56</td>\n",
       "      <td>4,096.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>134.00</td>\n",
       "      <td>6.40</td>\n",
       "      <td>8.49</td>\n",
       "      <td>37.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>28.94</td>\n",
       "      <td>10.49</td>\n",
       "      <td>51.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3,136.00</td>\n",
       "      <td>1,620.64</td>\n",
       "      <td>587.44</td>\n",
       "      <td>2,856.00</td>\n",
       "      <td>837.52</td>\n",
       "      <td>303.58</td>\n",
       "      <td>1,475.94</td>\n",
       "      <td>110.04</td>\n",
       "      <td>534.99</td>\n",
       "      <td>2,601.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "      <td>120.00</td>\n",
       "      <td>1.40</td>\n",
       "      <td>6.25</td>\n",
       "      <td>20.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>25.85</td>\n",
       "      <td>8.51</td>\n",
       "      <td>28.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3,600.00</td>\n",
       "      <td>1,551.00</td>\n",
       "      <td>510.60</td>\n",
       "      <td>1,680.00</td>\n",
       "      <td>668.22</td>\n",
       "      <td>219.98</td>\n",
       "      <td>723.80</td>\n",
       "      <td>72.42</td>\n",
       "      <td>238.28</td>\n",
       "      <td>784.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.00</td>\n",
       "      <td>152.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>9.12</td>\n",
       "      <td>30.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>28.64</td>\n",
       "      <td>0.37</td>\n",
       "      <td>42.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3,136.00</td>\n",
       "      <td>1,603.84</td>\n",
       "      <td>20.72</td>\n",
       "      <td>2,352.00</td>\n",
       "      <td>820.25</td>\n",
       "      <td>10.60</td>\n",
       "      <td>1,202.88</td>\n",
       "      <td>0.14</td>\n",
       "      <td>15.54</td>\n",
       "      <td>1,764.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.00</td>\n",
       "      <td>134.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.66</td>\n",
       "      <td>14.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>21.03</td>\n",
       "      <td>2.06</td>\n",
       "      <td>37.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2,704.00</td>\n",
       "      <td>1,093.56</td>\n",
       "      <td>107.12</td>\n",
       "      <td>1,924.00</td>\n",
       "      <td>442.26</td>\n",
       "      <td>43.32</td>\n",
       "      <td>778.11</td>\n",
       "      <td>4.24</td>\n",
       "      <td>76.22</td>\n",
       "      <td>1,369.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>1.00</td>\n",
       "      <td>134.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.66</td>\n",
       "      <td>30.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>27.19</td>\n",
       "      <td>23.66</td>\n",
       "      <td>49.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2,704.00</td>\n",
       "      <td>1,413.88</td>\n",
       "      <td>1,230.32</td>\n",
       "      <td>2,548.00</td>\n",
       "      <td>739.30</td>\n",
       "      <td>643.32</td>\n",
       "      <td>1,332.31</td>\n",
       "      <td>559.80</td>\n",
       "      <td>1,159.34</td>\n",
       "      <td>2,401.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>1.00</td>\n",
       "      <td>174.00</td>\n",
       "      <td>2.02</td>\n",
       "      <td>6.57</td>\n",
       "      <td>31.90</td>\n",
       "      <td>1.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>28.75</td>\n",
       "      <td>11.83</td>\n",
       "      <td>64.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2,500.00</td>\n",
       "      <td>1,437.50</td>\n",
       "      <td>591.50</td>\n",
       "      <td>3,200.00</td>\n",
       "      <td>826.56</td>\n",
       "      <td>340.11</td>\n",
       "      <td>1,840.00</td>\n",
       "      <td>139.95</td>\n",
       "      <td>757.12</td>\n",
       "      <td>4,096.00</td>\n",
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       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>1.00</td>\n",
       "      <td>136.00</td>\n",
       "      <td>7.36</td>\n",
       "      <td>2.19</td>\n",
       "      <td>28.11</td>\n",
       "      <td>1.00</td>\n",
       "      <td>61.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>61.71</td>\n",
       "      <td>54.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3,721.00</td>\n",
       "      <td>1,525.00</td>\n",
       "      <td>3,764.31</td>\n",
       "      <td>3,294.00</td>\n",
       "      <td>625.00</td>\n",
       "      <td>1,542.75</td>\n",
       "      <td>1,350.00</td>\n",
       "      <td>3,808.12</td>\n",
       "      <td>3,332.34</td>\n",
       "      <td>2,916.00</td>\n",
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       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>1.00</td>\n",
       "      <td>116.00</td>\n",
       "      <td>4.28</td>\n",
       "      <td>7.02</td>\n",
       "      <td>19.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>23.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>...</td>\n",
       "      <td>4,624.00</td>\n",
       "      <td>1,585.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3,536.00</td>\n",
       "      <td>543.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,212.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,704.00</td>\n",
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       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>1.00</td>\n",
       "      <td>142.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.19</td>\n",
       "      <td>18.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>23.65</td>\n",
       "      <td>20.78</td>\n",
       "      <td>42.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3,136.00</td>\n",
       "      <td>1,324.40</td>\n",
       "      <td>1,163.68</td>\n",
       "      <td>2,352.00</td>\n",
       "      <td>559.32</td>\n",
       "      <td>491.45</td>\n",
       "      <td>993.30</td>\n",
       "      <td>431.81</td>\n",
       "      <td>872.76</td>\n",
       "      <td>1,764.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>368 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1  Systolic Blood Pressure  Tobacco (kg)  LDL  Adiposity  Fam. Hist  \\\n",
       "0   1.00                   154.00          4.50 4.68      39.97       0.00   \n",
       "1   1.00                   134.00          6.40 8.49      37.25       1.00   \n",
       "2   1.00                   120.00          1.40 6.25      20.47       0.00   \n",
       "3   1.00                   152.00          0.90 9.12      30.23       0.00   \n",
       "4   1.00                   134.00          2.00 3.66      14.69       0.00   \n",
       "..   ...                      ...           ...  ...        ...        ...   \n",
       "363 1.00                   134.00          2.50 3.66      30.90       0.00   \n",
       "364 1.00                   174.00          2.02 6.57      31.90       1.00   \n",
       "365 1.00                   136.00          7.36 2.19      28.11       1.00   \n",
       "366 1.00                   116.00          4.28 7.02      19.99       1.00   \n",
       "367 1.00                   142.00          0.00 4.19      18.04       0.00   \n",
       "\n",
       "     Type A  Obesity  Alcohol   Age  ...  Type A^2  Type A Obesity  \\\n",
       "0     61.00    33.17     1.54 64.00  ...  3,721.00        2,023.37   \n",
       "1     56.00    28.94    10.49 51.00  ...  3,136.00        1,620.64   \n",
       "2     60.00    25.85     8.51 28.00  ...  3,600.00        1,551.00   \n",
       "3     56.00    28.64     0.37 42.00  ...  3,136.00        1,603.84   \n",
       "4     52.00    21.03     2.06 37.00  ...  2,704.00        1,093.56   \n",
       "..      ...      ...      ...   ...  ...       ...             ...   \n",
       "363   52.00    27.19    23.66 49.00  ...  2,704.00        1,413.88   \n",
       "364   50.00    28.75    11.83 64.00  ...  2,500.00        1,437.50   \n",
       "365   61.00    25.00    61.71 54.00  ...  3,721.00        1,525.00   \n",
       "366   68.00    23.31     0.00 52.00  ...  4,624.00        1,585.08   \n",
       "367   56.00    23.65    20.78 42.00  ...  3,136.00        1,324.40   \n",
       "\n",
       "     Type A Alcohol  Type A Age  Obesity^2  Obesity Alcohol  Obesity Age  \\\n",
       "0             93.94    3,904.00   1,100.25            51.08     2,122.88   \n",
       "1            587.44    2,856.00     837.52           303.58     1,475.94   \n",
       "2            510.60    1,680.00     668.22           219.98       723.80   \n",
       "3             20.72    2,352.00     820.25            10.60     1,202.88   \n",
       "4            107.12    1,924.00     442.26            43.32       778.11   \n",
       "..              ...         ...        ...              ...          ...   \n",
       "363        1,230.32    2,548.00     739.30           643.32     1,332.31   \n",
       "364          591.50    3,200.00     826.56           340.11     1,840.00   \n",
       "365        3,764.31    3,294.00     625.00         1,542.75     1,350.00   \n",
       "366            0.00    3,536.00     543.36             0.00     1,212.12   \n",
       "367        1,163.68    2,352.00     559.32           491.45       993.30   \n",
       "\n",
       "     Alcohol^2  Alcohol Age    Age^2  \n",
       "0         2.37        98.56 4,096.00  \n",
       "1       110.04       534.99 2,601.00  \n",
       "2        72.42       238.28   784.00  \n",
       "3         0.14        15.54 1,764.00  \n",
       "4         4.24        76.22 1,369.00  \n",
       "..         ...          ...      ...  \n",
       "363     559.80     1,159.34 2,401.00  \n",
       "364     139.95       757.12 4,096.00  \n",
       "365   3,808.12     3,332.34 2,916.00  \n",
       "366       0.00         0.00 2,704.00  \n",
       "367     431.81       872.76 1,764.00  \n",
       "\n",
       "[368 rows x 55 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_poly, columns=X_poly_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "838dcf17-f122-4b28-b9e4-8178c89964a3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>...</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.00</td>\n",
       "      <td>138.16</td>\n",
       "      <td>3.31</td>\n",
       "      <td>4.67</td>\n",
       "      <td>25.27</td>\n",
       "      <td>0.40</td>\n",
       "      <td>53.34</td>\n",
       "      <td>26.01</td>\n",
       "      <td>17.46</td>\n",
       "      <td>42.23</td>\n",
       "      <td>...</td>\n",
       "      <td>2,934.53</td>\n",
       "      <td>1,390.82</td>\n",
       "      <td>937.75</td>\n",
       "      <td>2,239.32</td>\n",
       "      <td>693.06</td>\n",
       "      <td>464.13</td>\n",
       "      <td>1,116.36</td>\n",
       "      <td>934.74</td>\n",
       "      <td>774.98</td>\n",
       "      <td>1,996.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.00</td>\n",
       "      <td>20.36</td>\n",
       "      <td>4.42</td>\n",
       "      <td>2.07</td>\n",
       "      <td>7.62</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.45</td>\n",
       "      <td>4.08</td>\n",
       "      <td>25.13</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>1,000.38</td>\n",
       "      <td>349.26</td>\n",
       "      <td>1,423.04</td>\n",
       "      <td>840.05</td>\n",
       "      <td>228.37</td>\n",
       "      <td>678.84</td>\n",
       "      <td>449.67</td>\n",
       "      <td>2,465.67</td>\n",
       "      <td>1,183.88</td>\n",
       "      <td>1,181.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>101.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>6.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>17.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>...</td>\n",
       "      <td>400.00</td>\n",
       "      <td>536.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>561.00</td>\n",
       "      <td>315.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>287.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>225.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>124.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>3.24</td>\n",
       "      <td>19.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>47.75</td>\n",
       "      <td>23.05</td>\n",
       "      <td>0.51</td>\n",
       "      <td>30.75</td>\n",
       "      <td>...</td>\n",
       "      <td>2,280.25</td>\n",
       "      <td>1,157.82</td>\n",
       "      <td>27.16</td>\n",
       "      <td>1,655.00</td>\n",
       "      <td>531.54</td>\n",
       "      <td>11.65</td>\n",
       "      <td>780.02</td>\n",
       "      <td>0.26</td>\n",
       "      <td>17.02</td>\n",
       "      <td>945.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>134.00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4.24</td>\n",
       "      <td>25.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>25.80</td>\n",
       "      <td>7.51</td>\n",
       "      <td>44.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2,809.00</td>\n",
       "      <td>1,356.65</td>\n",
       "      <td>394.11</td>\n",
       "      <td>2,273.00</td>\n",
       "      <td>665.90</td>\n",
       "      <td>185.95</td>\n",
       "      <td>1,161.62</td>\n",
       "      <td>56.41</td>\n",
       "      <td>271.40</td>\n",
       "      <td>1,936.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>148.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5.64</td>\n",
       "      <td>30.80</td>\n",
       "      <td>1.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>28.42</td>\n",
       "      <td>23.66</td>\n",
       "      <td>55.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3,600.00</td>\n",
       "      <td>1,586.85</td>\n",
       "      <td>1,243.45</td>\n",
       "      <td>2,817.75</td>\n",
       "      <td>807.70</td>\n",
       "      <td>599.79</td>\n",
       "      <td>1,432.96</td>\n",
       "      <td>559.80</td>\n",
       "      <td>1,057.62</td>\n",
       "      <td>3,025.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00</td>\n",
       "      <td>218.00</td>\n",
       "      <td>31.20</td>\n",
       "      <td>15.33</td>\n",
       "      <td>42.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>78.00</td>\n",
       "      <td>45.72</td>\n",
       "      <td>145.29</td>\n",
       "      <td>64.00</td>\n",
       "      <td>...</td>\n",
       "      <td>6,084.00</td>\n",
       "      <td>2,587.68</td>\n",
       "      <td>10,080.00</td>\n",
       "      <td>4,320.00</td>\n",
       "      <td>2,090.32</td>\n",
       "      <td>4,426.56</td>\n",
       "      <td>2,423.16</td>\n",
       "      <td>21,109.18</td>\n",
       "      <td>6,912.00</td>\n",
       "      <td>4,096.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           1  Systolic Blood Pressure  Tobacco (kg)    LDL  Adiposity  \\\n",
       "count 368.00                   368.00        368.00 368.00     368.00   \n",
       "mean    1.00                   138.16          3.31   4.67      25.27   \n",
       "std     0.00                    20.36          4.42   2.07       7.62   \n",
       "min     1.00                   101.00          0.00   0.98       6.74   \n",
       "25%     1.00                   124.00          0.03   3.24      19.98   \n",
       "50%     1.00                   134.00          1.65   4.24      25.91   \n",
       "75%     1.00                   148.00          4.50   5.64      30.80   \n",
       "max     1.00                   218.00         31.20  15.33      42.49   \n",
       "\n",
       "       Fam. Hist  Type A  Obesity  Alcohol    Age  ...  Type A^2  \\\n",
       "count     368.00  368.00   368.00   368.00 368.00  ...    368.00   \n",
       "mean        0.40   53.34    26.01    17.46  42.23  ...  2,934.53   \n",
       "std         0.49    9.45     4.08    25.13  14.62  ...  1,000.38   \n",
       "min         0.00   20.00    17.75     0.00  15.00  ...    400.00   \n",
       "25%         0.00   47.75    23.05     0.51  30.75  ...  2,280.25   \n",
       "50%         0.00   53.00    25.80     7.51  44.00  ...  2,809.00   \n",
       "75%         1.00   60.00    28.42    23.66  55.00  ...  3,600.00   \n",
       "max         1.00   78.00    45.72   145.29  64.00  ...  6,084.00   \n",
       "\n",
       "       Type A Obesity  Type A Alcohol  Type A Age  Obesity^2  Obesity Alcohol  \\\n",
       "count          368.00          368.00      368.00     368.00           368.00   \n",
       "mean         1,390.82          937.75    2,239.32     693.06           464.13   \n",
       "std            349.26        1,423.04      840.05     228.37           678.84   \n",
       "min            536.00            0.00      561.00     315.06             0.00   \n",
       "25%          1,157.82           27.16    1,655.00     531.54            11.65   \n",
       "50%          1,356.65          394.11    2,273.00     665.90           185.95   \n",
       "75%          1,586.85        1,243.45    2,817.75     807.70           599.79   \n",
       "max          2,587.68       10,080.00    4,320.00   2,090.32         4,426.56   \n",
       "\n",
       "       Obesity Age  Alcohol^2  Alcohol Age    Age^2  \n",
       "count       368.00     368.00       368.00   368.00  \n",
       "mean      1,116.36     934.74       774.98 1,996.29  \n",
       "std         449.67   2,465.67     1,183.88 1,181.05  \n",
       "min         287.25       0.00         0.00   225.00  \n",
       "25%         780.02       0.26        17.02   945.75  \n",
       "50%       1,161.62      56.41       271.40 1,936.00  \n",
       "75%       1,432.96     559.80     1,057.62 3,025.00  \n",
       "max       2,423.16  21,109.18     6,912.00 4,096.00  \n",
       "\n",
       "[8 rows x 55 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_poly, columns=X_poly_names).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8279cbab-3eb8-40ab-8d1c-da1d31ae724c",
   "metadata": {},
   "source": [
    "Number of features before / after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "07fd62da-191e-4468-b013-4bc8f7ab9e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 55)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[1], X_poly.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f61c7ca-0352-4d16-87ef-75b9e241b44a",
   "metadata": {},
   "source": [
    "#### Standardization (Scaling)\n",
    "\n",
    "Scale features to have mean = 0 and variance / std = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4f332d25-676a-423e-8a84-4c93e7ee873a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "528ab3cc-9b41-4fd7-b0f0-ec2a2cf3c582",
   "metadata": {},
   "source": [
    "Before scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "896d9678-83f9-48c5-9b3d-4c89a4742ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Systolic Blood Pressure</th>\n",
       "      <th>Tobacco (kg)</th>\n",
       "      <th>LDL</th>\n",
       "      <th>Adiposity</th>\n",
       "      <th>Fam. Hist</th>\n",
       "      <th>Type A</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>138.16</td>\n",
       "      <td>3.31</td>\n",
       "      <td>4.67</td>\n",
       "      <td>25.27</td>\n",
       "      <td>0.40</td>\n",
       "      <td>53.34</td>\n",
       "      <td>26.01</td>\n",
       "      <td>17.46</td>\n",
       "      <td>42.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20.36</td>\n",
       "      <td>4.42</td>\n",
       "      <td>2.07</td>\n",
       "      <td>7.62</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.45</td>\n",
       "      <td>4.08</td>\n",
       "      <td>25.13</td>\n",
       "      <td>14.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>101.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>6.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>17.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>124.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>3.24</td>\n",
       "      <td>19.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>47.75</td>\n",
       "      <td>23.05</td>\n",
       "      <td>0.51</td>\n",
       "      <td>30.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>134.00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4.24</td>\n",
       "      <td>25.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>25.80</td>\n",
       "      <td>7.51</td>\n",
       "      <td>44.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>148.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5.64</td>\n",
       "      <td>30.80</td>\n",
       "      <td>1.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>28.42</td>\n",
       "      <td>23.66</td>\n",
       "      <td>55.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>218.00</td>\n",
       "      <td>31.20</td>\n",
       "      <td>15.33</td>\n",
       "      <td>42.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>78.00</td>\n",
       "      <td>45.72</td>\n",
       "      <td>145.29</td>\n",
       "      <td>64.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Systolic Blood Pressure  Tobacco (kg)    LDL  Adiposity  Fam. Hist  \\\n",
       "count                   368.00        368.00 368.00     368.00     368.00   \n",
       "mean                    138.16          3.31   4.67      25.27       0.40   \n",
       "std                      20.36          4.42   2.07       7.62       0.49   \n",
       "min                     101.00          0.00   0.98       6.74       0.00   \n",
       "25%                     124.00          0.03   3.24      19.98       0.00   \n",
       "50%                     134.00          1.65   4.24      25.91       0.00   \n",
       "75%                     148.00          4.50   5.64      30.80       1.00   \n",
       "max                     218.00         31.20  15.33      42.49       1.00   \n",
       "\n",
       "       Type A  Obesity  Alcohol    Age  \n",
       "count  368.00   368.00   368.00 368.00  \n",
       "mean    53.34    26.01    17.46  42.23  \n",
       "std      9.45     4.08    25.13  14.62  \n",
       "min     20.00    17.75     0.00  15.00  \n",
       "25%     47.75    23.05     0.51  30.75  \n",
       "50%     53.00    25.80     7.51  44.00  \n",
       "75%     60.00    28.42    23.66  55.00  \n",
       "max     78.00    45.72   145.29  64.00  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6edaaad-2f37-4d2c-9f87-bbd7d0ac8ecf",
   "metadata": {},
   "source": [
    "After scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "313f2e44-4723-4446-a6b9-92ee75e3374d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Systolic Blood Pressure</th>\n",
       "      <th>Tobacco (kg)</th>\n",
       "      <th>LDL</th>\n",
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       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>368.00</td>\n",
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       "      <td>368.00</td>\n",
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       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.83</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-3.53</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.93</td>\n",
       "      <td>6.32</td>\n",
       "      <td>5.15</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.61</td>\n",
       "      <td>4.83</td>\n",
       "      <td>5.09</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Systolic Blood Pressure  Tobacco (kg)    LDL  Adiposity  Fam. Hist  \\\n",
       "count                   368.00        368.00 368.00     368.00     368.00   \n",
       "mean                     -0.00         -0.00  -0.00      -0.00       0.00   \n",
       "std                       1.00          1.00   1.00       1.00       1.00   \n",
       "min                      -1.83         -0.75  -1.78      -2.43      -0.82   \n",
       "25%                      -0.70         -0.74  -0.69      -0.69      -0.82   \n",
       "50%                      -0.20         -0.38  -0.21       0.08      -0.82   \n",
       "75%                       0.48          0.27   0.47       0.73       1.23   \n",
       "max                       3.93          6.32   5.15       2.26       1.23   \n",
       "\n",
       "       Type A  Obesity  Alcohol    Age  \n",
       "count  368.00   368.00   368.00 368.00  \n",
       "mean    -0.00    -0.00    -0.00   0.00  \n",
       "std      1.00     1.00     1.00   1.00  \n",
       "min     -3.53    -2.03    -0.70  -1.86  \n",
       "25%     -0.59    -0.72    -0.68  -0.79  \n",
       "50%     -0.04    -0.05    -0.40   0.12  \n",
       "75%      0.71     0.59     0.25   0.87  \n",
       "max      2.61     4.83     5.09   1.49  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_train_scaled, columns=scaler.feature_names_in_).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62123efe-4c4b-444d-b556-389e609072f6",
   "metadata": {},
   "source": [
    "## 4. Predictive Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f358d6d9-0d35-4a44-9ebf-5c9b685864c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "log_reg = LogisticRegression(max_iter=1000)\n",
    "log_reg.fit(X_train.values, y_train)\n",
    "y_test_pred = log_reg.predict(X_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb0196a-ce3b-4a80-aebf-afe81f4a1ed7",
   "metadata": {},
   "source": [
    "### Polynomial Features\n",
    "\n",
    "Note: training might not converge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "48e65125-f99b-46d3-ab9e-4aa65636564d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/phlobo/miniconda3/envs/dm4dh/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Fit logistic regression with polynomial features\n",
    "log_reg_poly = Pipeline([('poly', PolynomialFeatures()), ('classifier', LogisticRegression(max_iter=1000))])\n",
    "log_reg_poly.fit(X_train.values, y_train)\n",
    "# Get predictions on test set\n",
    "y_test_poly_pred = log_reg_poly.predict(X_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff8582f4-12bb-499f-a9e6-52a9338313c4",
   "metadata": {},
   "source": [
    "### Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e087e974-1e54-475d-9337-4fb79b23375c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit logistic regression with standardized features\n",
    "log_reg_scaled = Pipeline([('scaler', StandardScaler()), ('classifier', LogisticRegression(max_iter=1000))])\n",
    "log_reg_scaled.fit(X_train.values, y_train)\n",
    "\n",
    "# Get predictions on test set\n",
    "y_test_scaled_pred = log_reg_scaled.predict(X_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df8c44b-2f81-4211-81e4-3c93aa58b867",
   "metadata": {},
   "source": [
    "### Standardization + Polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9d5f0fc4-b488-4f56-8c6e-32d6331a2fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit logistic regression with standardized, polynomial features\n",
    "log_reg_poly_scaled = Pipeline([('poly', PolynomialFeatures()), ('scaler', StandardScaler()), ('classifier', LogisticRegression(max_iter=1000))])\n",
    "log_reg_poly_scaled.fit(X_train.values, y_train)\n",
    "\n",
    "# Get predictions on test set\n",
    "y_test_poly_scaled_pred = log_reg_poly_scaled.predict(X_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69767822-4aba-44ac-9dc4-9d34ee69acd7",
   "metadata": {},
   "source": [
    "## 5. Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5d08b823-30a7-44bc-bfc9-f510f385b05d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay, precision_score, recall_score, accuracy_score, roc_auc_score\n",
    "\n",
    "def do_eval(y_true, y_pred):\n",
    "    ConfusionMatrixDisplay.from_predictions(y_true, y_pred)\n",
    "    print('Accuracy: %.2f' % accuracy_score(y_true, y_pred))\n",
    "    print('Precision: %.2f' % precision_score(y_true, y_pred))\n",
    "    print('Recall: %.2f' % recall_score(y_true, y_pred))\n",
    "    print('AUC: %.2f' % roc_auc_score(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a544b5aa-57c3-47b2-8cfe-56ca981f6ae1",
   "metadata": {},
   "source": [
    "Vanilla Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "295ccb66-7b6a-4560-9238-c579923f2599",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.72\n",
      "Precision: 0.59\n",
      "Recall: 0.62\n",
      "AUC: 0.70\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "do_eval(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c89eab1-0dee-4f5a-b348-86eb13c99fb9",
   "metadata": {},
   "source": [
    "Logistic Regression Model + Polynomial Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "677762be-9e9f-459b-97fc-d65eaf48f09b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.73\n",
      "Precision: 0.59\n",
      "Recall: 0.75\n",
      "AUC: 0.74\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "do_eval(y_test, y_test_poly_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a94faef-9fa9-41a4-a08e-671da75929e6",
   "metadata": {},
   "source": [
    "Logistic Regression Model + Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f355e024-5c78-4772-ac39-3b957d7fb445",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.73\n",
      "Precision: 0.60\n",
      "Recall: 0.66\n",
      "AUC: 0.71\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "do_eval(y_test, y_test_scaled_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "220a5986-501e-41ca-ab1d-3f64045301c3",
   "metadata": {},
   "source": [
    "Logistic Regression Model + Polynomial Features + Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9e5df17e-b49e-4e82-a56e-2ff4a2e7c47f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.77\n",
      "Precision: 0.65\n",
      "Recall: 0.75\n",
      "AUC: 0.77\n"
     ]
    },
    {
     "data": {
      "image/png": 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DzPhdrUR6cXYpQvgbF/BySHK5IHJgXwt8srh7lYNcieqaOh9GylZ9AwB/f/9qy7Zq1QqAegpwYmKiQetFDymVwNLJLVCYbw47RyVmfHGr1kHh76OO2LOpMeRZlnBuVIoBY+8hfHYaRr51B606qAfC/v2nE6a+3Bo/RzaV8KsgevyaNKl8cHe/F29i3U9/IKh76mOuEelCBZner4aiznfTpKY+/I/R29u72rIeHg8X2EpNTRVbSsiwor5qiktn1INUJ32cUmF9EG39/r0bVs/3hiDI0GuwHO9F3oK9o+YaJek4st0Fn0/3QXGhOTZEesK1SSleek37pbiJTMmdO3aY8PoAZGTYw9pKiXYBWRg9Tr28vJtbIeb+Xww+WdQdMcebGbuq9Ahjr8Ba19T5lpGyVd8AwNW1+lHlLi4u4rHmDoNkOFf+scXG5eoQ+OxAOV4Y9aBW9zlz1AGr5jWDIMgQEJiHD1bdeCSIqD0/VI7pyx/OXli3xBOF+XX+nzk1UDnZNki57YRShTny861w+pQXZk7rgz+PqMfGmZsLeGfKGVjblBq5pkT6qfM/pYuKHg6CfHQWzaOsrB6OcNe8jgyjqECGZZNbQFkqg6u7AlOWaTfFsTK/rHzY5fKvWRkwr6ZNr/cQOdp0Vs+iycu2wF/7uHcH1R8qlRm+XP4McnLUP8+cXYoR1D3NyLWiytSHRc8elzrfTVNa+vAvAs2wURnN85rXkWFs+qopUv63l4x/pwLs+6XylfnSbz4MkTev2GDLavWCaPZOSgwKvQ9FiQzxp9TdPJbWKrTvllfpfTQ983wurpxVX5N8wQ59hsn1+VKITEphgSXiTnmib7+bAIBWre/j6OHmRq4VlSfVcu4NY9xInQ8jNjY24rFCoag2kJSUPFxCvKbgQvq7l/Hwe3zygDNOHnCu8ZrEc3ZIPKdeN6FpsxIMCr2PnPvmUJSo/0Kwc1DCXIsJBK5NHo5Lyc9tOH9dUN1nbVNaYU+ayty9aysel607QqZDACQZgNpQxozU+TBiZ/dwwZ/i4mKtw0hNXTpkOjSXfc99YIGSYhmsrKv/T1RzSXhHFy4JT6bNzEyFyC8OwdM7D7Y2pRg5bChKSqr/8ezo+PDnWVmXDVFdVefDiJPTw/EAcrkcjo6OVZbVHLRa2818SHszvriFGV/cqrHcuRgHzByhnnb9wqj7Fa5xcH64FLxKJUPcYUf0GFD9Mu9njz38d/BEu8Ja1J7o8VGpzGDvoICTkzpgdA1Kx/E/K1/AEVCHl85P3xHfJyXy55kpMoVdd+uKOt9+7evrKx6np6dXWzYj4+GumF5eXoaqEhlAjwEPF6n7OdIDpdXMDo474ojzser9PWztlXimT27VhYlMxPFjD6fnjh57CWZmFWeLlXk5JBmeXupB2tlya5yJ86iyLBkPB7Bqr85/pX5+fuKx5gJolSk7L5PJ0Lp1a4PWi6Q15t1MWNmofzhfjbfDwtdbIud+xcEjZ4444pO3WojvR7yVyW4aqhN2bGuN3FxLAIBfKzlmzDoFK6tHx4IIGDwkCW9MOCd+8uP69lqNMSEyZXX+X3Dnzp1haWkJhUKBkydPIjQ0tNJySqUSp0+fBgC0bdu2XPcOmT4PnxJMX34Ln05qAUGQ4dRBZ7wa2A49BmSjmV8xShUyXPjLARdPPtzxtMvzORg75U41dyUyHXK5DZZHdMXc/4uBubmA5/veQodOmYg53gz3smzh5FyMZ7qmo3nzhy19u6L9sOcPv2ruSsbEbhrt1fkw4uTkhKCgIBw7dgyHDh1CWlpapV0wu3btEhdIGzhw4OOuJkng+RA5bO1ViJzaHNn3LVBcaI7D2yr2lctkAgaG3sOkRalazbwhMhV/xXpj4fyemP7+KTi7FKNx4yK8HJJcoVxJiRl+/rE9fv2lrRFqSdrgbBrd1PkwAgDh4eE4duwYFAoFpk2bhrVr18LB4eFfyMnJyVi8eDEAwN7eHiNHjjRWVUlP3frl4MeTCdizqRFOHXDCjcu2yHlgDisbAe5eJejYPQ8DQ+/BL4CL2lHdFHfKE2/8ayAGDLqGbt3T0bxFNuztFcjPt0R6mgP+PuOBP3b64f4925pvRlRH1Isw0rNnT/Tv3x979+7F2bNnERISgrFjx8LT0xMJCQnYtGkT8vPVg71mzpzJmTQmplOPPOxN+0fr8rb2Kgx7MwvD3swyXKWIJLJxQ3ts3NBep2sKCqyw9de22PorWz7qLEGiRc8aSFdPvQgjALB06VLk5+fj+PHjSElJQURERLnzMpkMkydPxpgxY4xUQyIiakg4ZkR79SaM2NraYu3atYiOjsb27dtx6dIl5ObmwsXFBV26dEFYWBi6dOli7GoSERHRI+pNGAHUrR9DhgzBkCFDjF0VIiJq4Ngyor16FUaIiIhMgQBpwghn0xAREVGtSTG1t6Go8yuwEhERUd3GlhEiIiID4JgR7TGMEBERSYxjRnTDbhoiIiIyKraMEBERGQC7abTHMEJERCQ1LgevE3bTEBERkVFp1TLy9ddfG7QSkydPNuj9iYiIHjehgbRqSEHrMCKTGe6byjBCRET1DRc9057WY0YEwTATjAwZcoiIiMj0aRVGPvnkE0PXg4iIqN7gOiO60SqMDBs2zND1ICIiqlc4ZkR7nNpLRERkAFxnRHsGndpbVFQEhUJhyEcQERFRHSdpy8hff/2FHTt24MyZM0hPT4dCocDMmTMxfvx4AMDEiRPRunVrjB8/Ho0aNZLy0URERCaF3TTakySMZGZmYvr06YiLixM/EwShwkyZM2fO4OjRo9i0aROWLFmCF198UYrHExERmRQOYNWN3t006enpGDFiBOLi4iAIgvh6VE5ODnJzcwEAeXl5eO+997B//359H09ERER1nN5h5N1330VmZiYEQUCbNm3w+eef4+DBgxXKOTg44IsvvoCfnx8AQKlU4sMPP4RcLte3CkRERCZHEPR/NRR6hZE9e/bgwoULkMlkGDhwILZt24ZBgwbB29u74oPMzDBgwABs3boVwcHBANStJb/++qs+VSAiIjJBMqgkeKGBrOKqVxjZvXs3AMDZ2Rkff/wxzMxqvp2VlRWWLFkCR0dHAMDRo0f1qQIRERHVcXqFkfPnz0Mmk6FPnz5wcHDQ+jpnZ2f069cPgiAgOTlZnyoQERGZHkE9m0bfV0MZwarXbJp79+4BAFq0aKHztT4+PgAgDmolIiKqT7jomfb0ahmxsbEBAJSUlOh8bX5+PgDA3t5enyoQERFRHadXGPHw8AAAnDt3TudrY2JiAACenp76VIGIiMgkcTaN9vQKIz169IAgCIiNjcXly5e1vm7nzp24dOkSZDIZunXrpk8ViIiITI4AacaMNJQ8olcYGTlyJMzNzSEIAt555x3cunWrxmt27dqFDz/8EAAgk8nwyiuv6FMFIiIikyTJANYGQq8BrH5+fggLC8P69euRkpKCl19+Gf3790e7du3EMjdu3MCBAweQnJyM/fv3IyEhQVwq/pVXXoG/v7/eXwQRERHVXXrvTTNz5kxkZGRg9+7dKC4uRnR0NKKjo8V9aX755Rf88ssvYvmypeK7deuG+fPn6/t4IiIik8TZNNrTezl4mUyG5cuXY86cOXB1da2wP43me0EQYGtri//85z/4/vvvYWlpqfcXQEREZIo4gFV7kuzaCwBhYWEYPXo09u/fj1OnTuHq1avIzs5GaWkpnJ2d0axZMwQGBmLAgAFwcXGR6rFERERUx0kWRgDA2toagwcPxuDBg6W8LRERUZ3TkAag6kvSMEJEREQApJoN00ACjeRhpKCgAGfPnsWNGzeQk5MDKysrODk5wd/fHwEBAbCwYP4hIiKihyRLBhcuXMB3332HQ4cOQalUVlrGxsYGQ4cOxZtvvglvb2+pHk1ERGRyGtD4U73pPZsGAJYvX44xY8Zg//79KC0trTCDpuxVWFiIqKgohISEYPfu3VI8moiIyORwBVbd6N0y8sUXX+Dbb7+FTCaDIAiwsLBAp06d0KpVKzg7O0MQBGRnZ+PKlSu4cOECVCoV8vLyMGPGDFhaWqJfv35SfB1ERERUR+kVRpKSkvDdd98BUK8nEhYWhokTJ6JRo0aVls/IyEBkZCSio6OhVCoxZ84cPP3001WWJyIiqrMaSrOGBPTqpomKioJSqYRMJsPs2bMxZ86caoOFh4cHIiIi8MYbbwAAcnNzsWHDBn2qQEREZJK4N4329AojJ06cgEwmw5NPPonw8HCtr5s2bRpatGgBQRBw6NAhfapARERkkrgCq/b0CiN37twBAPTq1Uun68zNzREcHAwASElJ0acKREREVMfpNWbEysoKRUVFsLKy0vlaBwcHABA31CMiIqpPGlI3i770ahnx9/cHAPzzzz86X5uUlAQAaNmypT5VICIiMk2CTP9XA6FXGHnllVcgCAJOnDiBkydPan3dtWvXcODAAchkMgwZMkSfKhAREVEdp1cYGTp0KHr27AmVSoXJkyfj+PHjNV5z9epVvPXWW1AoFOjYsSPGjRunTxWIiIhMjwSDVwUBDWZ6sFZjRrZv317lueDgYFy4cAHZ2dn497//jaCgILz44oto27YtnJ2dAQB5eXm4efMmYmJi8Mcff6C4uBi+vr6YNWsWrl+/jlatWknyxRAREZmMBhIkpKBVGJk9e3aNA03LVmD966+/8Ndff1VZThAEyGQy3Lx5E6GhoZDJZEhISNCt1kRERFRvaD2bRtBywrM25bS9FxERUV3F2TTa0yqMTJ482dD1ICIiql/4d7fWGEaIiIjquaysLGzYsAFHjx7FzZs3Aai3aOnZsydGjx5d49hNQRCwc+dO/Pbbb7h06RIKCgrg7u6OwMBAhIaGomPHjnrVT+9de4mIiKgiU+mmOXHiBKZNmwa5XF7u82vXruHatWvYtGkTJk+ejIkTJ1Z6fVFREaZMmYIjR46U+zw1NRWpqamIjo7G1KlTMWHChFrXkWGEiIjIEEygm+by5ct46623UFxcDAB4/vnn0bNnTzg4OODSpUvYvHkzioqKsHz5cjg4OODVV1+tcI+5c+eKQcTPzw+jRo2Cm5sb4uPjERUVhYKCAkRGRqJp06YICQmpVT0NEkYEQagwSFUQBKhUKpSWlqKwsBByuRzx8fHYvn07vv/+e0NUg4iIyIiM3zLy8ccfi0FkwYIFGDt2rHhu2LBhGDduHEaOHImcnBwsX74cQ4cOFbdrAdStKjt37gQABAUFYc2aNbC2tgYADB48GCNGjMC4ceMgl8uxZMkSBAcHl7teW5KEkX379uG///0v4uPjkZ+fz9kyRERERpacnIy4uDgAQM+ePcsFkTK+vr5488038fnnnyMvLw9//vknBg0aJJ5ft24dAMDCwgKLFi0Sg0gZPz8/zJs3D9OnT4dcLseWLVswfvx4neuq1wqsALB8+XJMmTIFJ0+eRG5uLlQqldgyos2LiIioXhIkeOlBLpcjKCgIbm5uGDBgQJXl2rZtKx6npqaWuz4mJgYA0KtXL/j4+FR6/aBBg9C4cWMAwJ49e2pVV71aRhITE7FmzRoAFdcOKVskrarPLSws8PLLL6Nnz576VIGIiMg0Gfnv7WeeeQY//vhjjeUyMjLEY3d3d/E4Li4OKpUKgLqLpipmZmYIDAzEnj17cO7cOWRnZ4srsGtLr5aRLVu2iGGjS5cu2LJlC86fP4/ffvsNgiDA3NwcMTEx+Oeff3Do0CEsWbIEnp6eEAQBpaWlaN++fbnmICIiInp85HK52BVja2uL5557TjyXlJQkHvv7+1d7n7KpwYIgIDExUed66BVGyvqiHBwcsGrVKnTo0AFWVlYICAiAk5MTlEolTpw4ARsbG3h5eWH48OH4/fff4e/vD0EQEBERgaysLH2qQEREZHoEmXQviRUXF+PatWv47rvv8PLLL+PGjRsA1Fu/NGrUSCyn2WXj7e1d7T09PDwqvU5beoWRjIwMyGQyPP/88xWaZAICAgAAZ86cKfe5o6MjIiMjIZPJUFRUhF9++UWfKhAREZkcAdLs2it1T8/FixfRsWNHDBw4EJ999hkyMzPh7u6O5cuXY8yYMeXK3r9/Xzx2dXWt9r4uLi7i8aPrmWhDrzEjubm5AICWLVtWONeqVSvExsbi8uXLFc61bt0azzzzDE6fPo3Tp0/rUwUiIqJ6LT09HcHBwVWeP3jwoNb30hwfUkYul2P37t1o164dfH19xc+LiorE40dn0TzKysqq0uu0pVfLiK2tLQDA0tKywrmyL+jq1auVXtu+fftqzxMREdVpRp5NUxlXV1fMmzcPy5cvx6xZs9C2bVsoFArs27cPI0aMQHx8vFi2tLRUPNYMG5XRPK95nbb0ahlxcXFBXl5epeM+yqYA5eXlITMzE02aNCl3vqxbJzs7W58qEBERmSaJxnt4enrq1PpRnS5duqBLly7i+/DwcCxYsACbN29Gbm4uZsyYgZ07d8Lc3Bw2NjZiOYVCUW0gKSkpEY9rCi6V0atlpF27dhAEQZyHrKl58+bi8fnz5yucL2sq4lojRERExmFmZob/+7//Q5s2bQCo96s5fvw4AMDOzk4sV7aKa1U0w0hNXTqV1kPnKzT06NEDgHqVt1WrVpU716JFC7EbZ8eOHeXOFRYW4sCBAwAANzc3fapARERkcmQAZIIEr8dQV3Nzc4wYMUJ8//fffwMAnJycxM9qGpSqeV5zRo629AojISEh4kNXrFiBESNGiE1JMpkMffv2hSAI2L9/PxYtWoTExEScPHkSb775JrKysiCTyfDUU0/pUwUiIiLTZIJjRqqiORGlbBaN5mDW9PT0aq/XHBjr5eWl8/P1CiM2Njb46KOPYGZmBkEQEB8fX252zIQJE2Bmpn7Exo0bERISgvDwcDF1Aah0rXwiIqI6z8hrjGzbtg3vvvsuhg0bhpycnGrLanbD2NvbA1DvO1NGcwG0ypSdl8lkaN26tc511Xtvmn79+mHVqlXw9vaGIAho1qyZeK5NmzaYO3cuAKCyPWn+/e9/IzAwUN8qEBER0SMSExOxd+9eJCQkIDY2ttqymmM7n3jiCQBA586dxdmyJ0+erPJapVIpNkS0bdu2XPeOtvQOIwDQu3dv7N+/Hz/++GOFvWZCQ0Px008/oVevXnBycoKNjQ2eeuopfP7555g2bZoUjyciIjItUnTR6NlV06tXL/H4v//9b5XlynbbBdRLdfTu3RuAesxI2Z40hw4dQlpaWqXX79q1S+zaGThwYK3qqtfUXk1mZmbo1q1bpecCAwPZAkJERA2LkSeLBgUFoW3btrh8+TL++usvfP/993jjjTfKlcnLy8M777wjholRo0ahadOm4vnw8HAcO3YMCoUC06ZNw9q1a+Hg4CCeT05OxuLFiwGou3dGjhxZq7pKFkaIiIjIdJiZmWHJkiUIDQ1FYWEhli1bhpiYGLz44otwdHREUlIStmzZgrt37wJQL9fx/vvvl7tHz5490b9/f+zduxdnz55FSEgIxo4dC09PTyQkJGDTpk3Iz88HAMycObNWM2kAhhEiIiLDMIFltAICArBmzRpMnToV9+7dw/Hjx8V1RDT16tULn3/+ubgkh6alS5ciPz8fx48fR0pKCiIiIsqdl8lkmDx5coW9bXShVRjZvn17rR+gjaFDhxr0/kRERI+dAXbcrY2uXbti9+7d2LhxIw4dOoTr16+juLgYjRs3RufOnTFs2DBxnEhlbG1tsXbtWkRHR2P79u24dOkScnNz4eLigi5duiAsLKzcqq61oVUYmT17NmQyw3xTZTIZwwgREZEBOTs7Y9KkSZg0aVKtrpfJZBgyZAiGDBkicc3UtO6m4bLtRERE2pPx16bWtAojkydPNnQ9iIiI6heGEa0xjBAREZFRSbLoGREREVFtcWovERGRAXDMiPYYRmoh/ZY1+nu1NXY1iAwmdVZjY1eByCCUex5jh4CJTO2tC9hNQ0REREbFlhEiIiKp6bnJXbn7NAAMI0RERIbQQIKEFNhNQ0REREbFlhEiIiID4Gwa7TGMEBERGQLDiNbYTUNERERGxZYRIiIiQ2DLiNYkDSOCIODMmTM4c+YM0tLSkJ2djSFDhqBv374AgK1bt6JTp07w8/OT8rFEREQmh2NGtCdZGNm2bRtWrlyJ1NTUcp936tRJPP7qq6+QmZmJAQMGYP78+XBxcZHq8URERFRH6R1GBEHABx98gN9//118X0Yme7gUrkKhQGZmJgRBwO7du3HhwgX8/PPPaNq0qb5VICIiMj1cDl5reg9gjYyMxPbt28UQ0rNnT0yePLlCuZKSEvTq1Ussd/v2bbz33nv6Pp6IiMj0CBK+GgC9wsi1a9ewfv16yGQyeHh4YPPmzVi7dm2lYcTe3h7ffvstNmzYgMaN1ZtwnT17FgcOHNCnCkRERCZJJuj/aij0CiNRUVFQKpWQyWRYsWIFOnbsWOM1gYGBWLFihfg+OjpanyoQERFRHadXGImNjYVMJkP37t3RoUMHra/r3Lkznn32WQiCgAsXLuhTBSIiItPELhqt6RVGMjIyAECrFpFHBQQEAADu3bunTxWIiIhMErtptKdXGCkpKQEA2NjY6HytlZWVugJmXASWiIioIdMrCZQNRL1x44bO1165cgUA0KhRI32qQEREZJrYTaM1vcJIx44dIQgCDh8+jIKCAq2vu337No4cOQKZTKbTWBMiIqI6g2FEa3qFkYEDBwIAsrOzsXDhQq2ukcvlePfdd6FQKAAAL774oj5VICIiojpOrzDSv39/tG/fHoIgYMeOHXj99dcRGxuL7OzsCmUzMzOxceNGhISE4PLly5DJZPDz8xMDDRERUX0hgzQDWBvKGq56Lwf/5ZdfYvTo0cjKykJsbCxiY2PLnV+9ejVWrVqFvLw88TNBEODg4IAvvvii3JLxRERE1PDoPZXF29sbmzZtEltIyl5lISM3Nxe5ubnlzjVv3hwbNmxAq1at9P4CiIiIqG6TZNdeHx8f/PLLL9izZw9+++03/P333ygsLCz/IAsLBAQEYOjQoRg+fDisra2leDQREZFpakADUPUlSRgB1OuFDBo0CIMGDYJSqURaWhrkcjmUSiWcnJzg5eVVq/VIiIiI6hyJFi0TGkigkSyMaDI3N4ePjw98fHwMcXsiIiLT10CChBS4/CkREREZlV4tI2lpaZJUwsvLS5L7EBERmQy2jGhNrzDSt29fvafmymQyJCQk6HUPIiIiUyPJmBH9b1En6D1mRGgoo2uIiIjIIPQKI4GBgVqVKy4uxt27d5Geng5A3RrSr18/PPPMM/o8noiIyHTxb3Wt6RVGNmzYoFP5rKwsfPvtt9i4cSOOHj2KYcOGoW/fvvpUgYiIyCRJ0U3TUDzW2TRubm6YO3cu3nvvPZSUlOCDDz5AZmbm46wCERERmRijTO1988030axZM+Tk5GDjxo3GqAIREZFhCRK8GgijhBGZTIbnn38egiDg0KFDxqgCERGR4UgRRBpQIDHaomeNGzcGIN1aJURERFQ3GWQ5eG3cunULAPRep4SIiMgUcQCr9owSRlJSUrB7927IZDI0b97cGFUgIiIyLIYRrT22MFJSUoIHDx7gxIkT+PLLL1FUVASZTMapvUREVD8xjGhNrzDy5JNP6vVwJycnvPbaa3rdg4iIiOo2vcKIPkvBOzo64uuvv4azs7M+VSAiIjJJHDOiPb3CiC677Zqbm8PGxgaenp4ICgrC8OHD4eLios/jiYiITBfDiNb0CiNcI4SIiIj0pVcYuXLlCszNzdGqVSup6kNERFQvsJtGe3oterZy5Uq8/PLLePnll3HgwAGp6kRERFT3cfVVrekVRs6fPw9BEJCcnAxXV1ep6kREREQNiF7dNA8ePBCPAwIC9K4MERFRvSBVy0YDaR3Rq2XE19dXPE5PT9e3LkRERPWGTIJXQ6FXGJk0aZK4t8yyZcugVColqRQRERE1HHqFkf79++PLL7+Es7Mzjhw5gmHDhuHnn3/GxYsXIZfLoVKppKonERFR3cIBrFrTa8zIv//9bwCAj48P5HI5kpKSsHjxYp3uIZPJkJCQoE81iIiITIoM0kztbShdNXqFkWPHjondNGX/X58l4omIiOoN/jrU2mNbDp6IiIioMlwOnoiIyBDYMqI1rcLIBx98AAB46aWX0LNnT4NWiIiIqD7gcvDa0yqMbNu2DTKZDP7+/gwjREREJCm9ummIiIioCmwZ0RrDCBERkQGwm0Z7ei16RkRERKQvtowQERFJjRvl6YRhhIiIyADYTaM9dtMQERGRUenUMrJp0yYcPnxY0grIZDL8+OOPkt6TiIjI6EysZSQ7OxtRUVE4fPgwrl+/jvz8fDg6OqJNmzYYMGAAhg8fDisrqyqvFwQBO3fuxG+//YZLly6hoKAA7u7uCAwMRGhoKDp27FjruukURm7fvo3bt2/X+mGPEgRB3NOGiIioXjGhMBIbG4tp06bh/v375T6/f/8+YmNjERsbi59//hmrV6+Gj49PheuLioowZcoUHDlypNznqampSE1NRXR0NKZOnYoJEybUqn46hRFugkdERKQdUxkzcvnyZbz11lsoLCwEAPTs2RPBwcFwcXFBWloatm/fjqSkJCQlJeGNN97Ar7/+Cicnp3L3mDt3rhhE/Pz8MGrUKLi5uSE+Ph5RUVEoKChAZGQkmjZtipCQEJ3rqFMY4XLwREREdcuiRYvEILJgwQKMHTu23Pnw8HDMnj0b0dHRuHnzJlatWoXZs2eL50+cOIGdO3cCAIKCgrBmzRpYW1sDAAYPHowRI0Zg3LhxkMvlWLJkCYKDg+Hg4KBTHXUKI+3bt8ewYcN0egAREVGDZAItI1evXsXp06cBAP369asQRADAwsICixcvxsmTJ5GZmYmtW7fi/fffh7m5OQBg3bp1YrlFixaJQaSMn58f5s2bh+nTp0Mul2PLli0YP368TvXkbBoiIiIDkAmC3i99xcbGisfVdZ9YW1ujT58+ANQDXW/cuAEAkMvliImJAQD06tWr0vEkADBo0CA0btwYALBnzx6d68kwQkREVE+ZmZmhdevWcHBwgK+vb7VlnZ2dxeOcnBwAQFxcHFQqFQB1F011zwkMDAQAnDt3DtnZ2TrVk4ueERERSc1EVmAdN24cxo0bp1XZ5ORk8djFxQUAkJSUJH7m7+9f7fWtWrUCoJ7skpiYKIYTbbBlhIiIyABkgv6vx+XOnTs4duwYAMDV1RUtWrQAoJ66W8bb27vae3h4eIjHmtdpg2GEiIiogVu6dCkUCgUA9cxZMzN1PNBcl8TV1bXae5S1pgDqsSa60LqbhmuMEBER6UCiX5vp6ekIDg6u8vzBgwf1un9UVBR27doFALCzsyu3cFlRUZF4/Ogsmkdprt6qeZ02tAojP/30EwCgefPmOt2ciIiooTKVRc+qc+DAAXz00Ufi+wULFqBp06bi+9LSUvG4uqXiHz2veZ02tAojXbt21emmREREJA1PT0+9Wz8qs2/fPkybNg1KpRIAEBYWVmH6r42NjXisUCiqDSQlJSXicU3B5VEcM0JERGQIggQvA9myZQvee+89cZzIsGHDMGfOnArl7OzsxOPi4uJq76kZRmrq0nkUp/YSERFJTAZpumkMsZXs119/jRUrVojvX3nlFSxatKjSjWs196iRy+VwdHSs8r6ag1YbNWqkU50YRsjkePkWY8j4LDzVMw9NvEtgaS0g94E5ki/Y4s+dLji8zRXKUu72TKapfdM7GB5wGZ090+HhmA8bi1LkFFsj+Z4r/rzeAr/Gt0N+iW5N2Jo+6H0coU9dAAC8uC4UablONVxBRmNiY0ZUKhUWLFiAzZs3i5+FhYVhzpw5lQYRAOUWSktPT69yBVYAyMjIEI+9vLx0qhvDCJmUYW/exZvz0mBhWf7zxh6laOyRi24v5OKVCXfx8QRfpF3XrRmQyJBsLRSY1/dPDHkyscK5xnaFaGxXiG4+aXjjmbOYvbcfYm5V/UO9Kj2a38bYThekqC41MCqVCjNnzkR0dLT42ZQpUzBp0qRqr/Pz8xOPk5KSqh1DWrZAmkwmQ+vWrXWqH8eMkMkY/K8sTPzoYRC5Fm+DbWvdsCGyKf7c4YySYnVyfyKgCMu2XIWLm8KItSV6yEymwvKX9pYLIqdue2HNqafxZUxX/HaxLR4UqgcCNrIrwsohf6CbT4pOz3C2KcKiFw7BjI2CdYYpLXq2YMECMYiYmZlhwYIFNQYRAOjcuTMsLdU/lE+ePFllOaVSKW7I17Zt23LdO9pgywiZBBc3Bd78MB0AoFIBq+d5Y8f6xtDsMfVqWYwF666jRZtiuHspMP6DDCyfrvtfl0RSG9k+AT19bwMA5IXWmLprAOJSyzdTL/2zBEtePIR+ra7D0lyFxS8cwks/jkOxUrsfw//X9yiaOBRIXncyEAGAFOtzSXCLrVu3il0zZmZm+OSTTzB06FCtrnVyckJQUBCOHTuGQ4cOIS0trdIumF27dokLpA0cOFDnOtb7lpGFCxeiTZs25QbrkOnpNTgbtvbqzZgOb3PBjvVueHToVtp1a3z6dgvx/XMvy2FmbmKdstQg/evpc+Lx3P19KwQRAChQWGHG7heQmKUe2OfhmI8B/skVylUm5MnLeLH1NQBASWm9/7FNEsrKysLixYvF97NmzdI6iJQJDw8HoJ7aO23aNOTl5ZU7n5ycLD7D3t4eI0eO1Lme9bplJDY2FlFRUcauBmnBt83D1fou/OVQZblrCbZ4cNcCru6lsHNQwcWtFPfvWFZZnsjQ/BrdR3MX9Q6n1x844+h13yrLlqrMsfl8AOb1Ve8BEuSTit8vta32/t5OOfig93EAwLEbPrCxUCKwWZo0lSeDMoVFz3788UcxPHh5eaFZs2Y4cOBAjde1a9dObAHp2bMn+vfvj7179+Ls2bMICQnB2LFj4enpiYSEBGzatAn5+fkAgJkzZ+o8kwaox2Hk4sWLmDx5srj1MZk2zf+Z3DyrHgtiYamCrYN6gR6lEsiVmxu6akTVat344d4d8Xea1Fj+dvbDbdrd7POrLWsmU+GT/gfhYK2AvNAa8/b3QcTAmn+RkIkwgTCybds28TgtLQ1vv/22Vtd98sknGD58uPh+6dKlyM/Px/Hjx5GSkoKIiIhy5WUyGSZPnowxY8bUqp71MowcPXoUM2bMqNCURKbrZuLDVf4GjL2H39e5Ied+xX+eQ9/Mgo2t+r/w8zEOUBSzyZqMa3/yE+j3/Wtwt89HUWnNP1LdNQJIgaL6Vr03nzmLp73U0yU/OtQbWQX2+lWWGpT79+/j7t27ktzL1tYWa9euRXR0NLZv345Lly4hNzcXLi4u6NKlC8LCwtClS5da379ehZGSkhJ88803WL16NVtE6phDW10RPisDji5KuHmW4us9ifhxqQfOxzog54E5vJ8oQcjrWXhxtPqv0NwH5lizULd57ESGoBTMkJHngIy8qrsXNb3Q6pp4fPVe1c3Z7ZrcxVvd4gAAOy+3xr5kvyrLkmmSGfnXUKNGjXDlyhXJ7ieTyTBkyBAMGTJEsnuWqTdhJCYmBvPmzUNKinq6nJ2dHUaPHo3169cbuWakjYJcc3z87xZY+MMN2Nqr0LSZAjNX3K607NljDlg9z7tcawpRXdDZMx3Ptbwpvt+f/ESl5azNS/Fp/wOwNFchPdcBiw/3elxVJCmZQDdNXVFv2rh37NghBpH27dtjy5Yt6NOnj5FrRbo4d8IRb/Xzx9EdzlWWyZWb4+9jDki/VfsVLImMwcWmEEv6HxTXCTlyrQUu3XWvtOz7z8XgiUZyqATgw/19kFvCBf6ofqs3LSOAukmqbACNubk57t27Z+wqkQ7sHJUY/K97eLqXeqzPhb/sceEve5QUm8GnVRGCXsyBo4sSb8zJQP/R9zHvtSeQdoM/pMn02VmW4Oshu+HjnAsAyC6ywqdHe1ZatpfvTYzpGA8A2PhPB5y83eyx1ZOkZQqzaeqKehNGQkNDsWDBgnLbHVPd0aiJAos3XsMTAUXIyzbD3HEtEXek/Ap+zo1KMfPrm3jm+Tw08yvBop+v4a0X/FFcyBk1ZLocrYqxKuQPPOV5BwCgVMkwZ18wUnIqrlDpaluIj/sdBgBcveeK5SeCHmtdSUImtOhZXVBvumk6dOjAIFKHzV1zA08EqNca+WRSiwpBBACy71tgwfiWSLpgCwDwfqIEwydkPdZ6EunC3T4fP47cjs7/mxGjVMnwfwefr3ItkgXBR+BmXwiF0gwf7AtGiZars5JpMqXl4E1dvQkjVHe175qH9l3Vy1yfi7FH3OGq9zRQFJvhh089xPfBIx4YvH5EtdHW/S42jf4N/m7qGWAKpRk+3N8H2xMqX+TslYAEBPvdAAB8c6oLEjIrH09CVB8xdpPRPdXr4XowZ/90rLH8P8cdoCiRwdJKgI9fMWzslCgqYFcNmY7nW17HsgEHYGdVCgAoVFjg/d39cOR6y0rLO1kXY+ZzMQCAAoUFFEpzhD/9T6VlPRwe/vcyov0l5BSrx00du9EcV+/rvvIlGVADatnQF8MIGV3jpg9XXM3NrjlUlCrMkJdtDld39Q96O0cVwwiZjLEdL2B27xMwN1P/JrpfYIPJ0QNxPsOjymscrIphb6X+78DOshTTev6l1bMmdP1bPH5QaMMwYmIaUjeLvhhGyOiKCx/2Frp5VL0UfBkzMwH2jkrxPZeEJ1Px78AzmNLjlPj+xgNnvPX7S+WWgCeiihhGyOhSrj2cnhsYnIMflnpWW759t3xY2aj/5Lh91ZpLwpNJCO10vlwQOZfeBG/vGAR5kW2N16blOqH9l29p9Zz1r/wubpT34rpQpOVWPcaKjEyK2TQNBH+Kk9Gd3O8EpbrHBa3aF6HXYHmVZc3MBPxrZob4/lg0/+Ik4+vslY73/zfmAwD+uu2NN7cO0SqIUP0kgzSzaWTG/kIeE4YRMrq7aVbYvbGx+H7a57crDSS29krMWnkL7bupNxrLvm+OrWs444CMy8JMiU9ePAiL/40RuXy3Md6JHojC0uo3wSOih9hNQyZhzUdeaN2xAG06F8LOQYUP19zE1fg7+OeYI3IemMOjRQm698+GS2P1WJGSYhkWT/BFrpz/hMm4hrW7jGb/W1kVAC7eaYLRHeK1uvZOnj12J7Y2VNXI2NhLozX+JCeTUFxohlmj/PBe5G30HpINAPALKILf/xZC03Q3zRJL3mqBhNPcTp2ML6Rd+V1RR7S/pPW1f6d5MIzUY5xNoz2GETIZhfnmWDLRF799m4/+ox+gfVAe3DwVsLQSkPvAHNcSbPHXPifs3dyIg1bJZPg35h5YRPpiGCGTc+WsPa6cZasH1Q1dV//7sT5v/G8hj/V5VEsCABX3ptEWwwgREZEhNJAgIQWGESIiIgPgmBHt1esw0q1bN1y5cqXmgkRERGQ09TqMEBERGYcg0QqsDaN5hWGEiIjIANhNoz3OjyQiIiKjYssIERGRIbBlRGsMI0RERFITAJkUY0YaSKBhNw0REREZFVtGiIiIDEFl7ArUHQwjREREBiBJN00DwW4aIiIiMiq2jBARERkCG0a0xjBCRERkCOym0RrDCBERkQFwBVbtccwIERERGRVbRoiIiAyB3TRaYxghIiKSmgDIpFhnpIHkGXbTEBERkVGxZYSIiMgQ2E2jNYYRIiIiQ2AW0Rq7aYiIiMio2DJCREQkMRmk2ZtGpn9V6gSGESIiIkPgmBGtsZuGiIiIjIotI0RERFITAHCdEa0xjBAREUlOkGTMSENJIwwjREREhsAxI1rjmBEiIiIyKraMEBERGQJbRrTGMEJERGQIUgxgbSDYTUNERERGxZYRIiIiA5BmNk3DwDBCRERkCAwjWmM3DRERERkVW0aIiIikJkCalpEG0rjCMEJERGQI7KbRGsMIERGRIXBqr9Y4ZoSIiIiMii0jREREkuNGebpgGCEiIjIEjhnRGrtpiIiIyKjYMkJERGQIKraMaIthhIiISGqC+H9IC+ymISIiIqNiywgREZEhSDGAVab/LeoChhEiIiJDYBjRGrtpiIiIyKjYMkJERGQIUsymaSBNBgwjREREkhMAQYrNaRrGjByGESIiIkPgCqxaayANQERERGSq2DJCREQkNQHSjBlpII0rbBkhIiIyBEHQ/2VACxcuRJs2bbBixQqtyh89ehQTJ05Ejx490L59e/Tu3RvvvPMOYmJi9K4LW0aIiIgamNjYWERFRWlVVqVSYf78+diyZUu5zzMyMpCRkYF9+/bh1VdfxYcffgiZrHYLozCMEBERGYKJDmC9ePEiJk+eDJVKu9k+X375pRhEPD09MW7cOHh7e+PatWv473//i/v37+Pnn39G48aNMWnSpFrViWGEiIjIEEwwjBw9ehQzZsxAXl6eVuWvXr2K7777DgDQunVrbNy4Ec7OzuL50aNH49VXX8XNmzexatUqhISEwNvbW+d6ccwIERFRPVdSUoKvvvoKEydORE5OjtbX/fjjj1AqlQCABQsWlAsiANCkSRMsXboUAKBQKLB+/fpa1Y9hhIiIyBBUKv1fEoiJicHAgQOxcuVKqFQq2NnZYfz48VpUX4W9e/cCAPz9/fHMM89UWq5z584ICAgAAOzduxdCLVqEGEaIiIgkJ8FMGkGAFHN7d+zYgZSUFABA+/btsWXLFvTp06fG6xITEyGXywEAQUFB1ZYtO5+ZmYkrV67oXEeGESIionquUaNGmD9/Pn755Re0atVKq2sSExPFY39//2rLat7z8uXLOtePA1iJiIikJkCaAawS3CI0NBQLFiyAjY2NTtelpqaKxzUNSvX09Kz0Om0xjBARERmCFCuwSqBDhw61uu7+/fvisaura7VlNQe2lnXt6IJhhIiIyAAESXbtBdLT0xEcHFzl+YMHD0rynEcVFRWJx1ZWVtWWtba2rvQ6bXHMCBEREVVQWloqHtcURjTPa16nLbaMEBERGYJE3TSenp4Ga/2ojuYYE4VCUW3ZkpIS8djS0lLnZzGMEBERGYIJrsCqCzs7O/FYM2xUpri4WDzWdaAswG4aIiIiqoSTk5N4XNOg1OzsbPG4UaNGOj+LLSNERERSEwRpVlA1YuuKr6+veJyenl5tWc3zXl5eOj+LYYSIiMgQ6ng3jeZCZklJSdWW1Txf0wJplWE3DREREVXQsmVLuLm5AQBOnjxZbdmy8y4uLgwjREREpkJQqfR+GZOZmRn69esHALh48SL++eefSsudOXMG8fHxAID+/fvDzEz3aMEwQkREZAiSbJRnXK+99hosLNQjOmbNmoW7d++WO3/nzh3Mnj0bAGBubo5//etftXoOx4wQERFRpVq1aoWwsDCsW7cON27cQEhICMaNG4eWLVvi+vXr+O9//4t79+4BAN544w34+fnV6jkMI0RERIZgInvT6GvGjBl48OABtm3bhnv37mHFihUVyowcORLvvfderZ/BMEJERCQ1QQCk2JvGBLpqzM3N8emnn2LgwIGIiorChQsXIJfL4eDggE6dOmHs2LF4/vnn9XoGwwgREZEBCCbcMtKtWzdcuXJFp2t69+6N3r17G6Q+HMBKRERERsWWESIiIkOQopumgWAYISIikpgAabppTLejR1rspiEiIiKjYsuIjjIzM6G0KEGq72VjV4XIYJR7rhu7CkSGUZgHyAz/d7jSogSpLRIkuU9DwDCiI2tra8hkMri7uxu7KkREpKO7d0tgZWVl0Gd4enqa9P1MkUwQTGASMxERETVYHDNCRERERsUwQkREREbFMEJERERGxTBCRERERsUwQkREREbFMEJERERGxTBCRERERsUwQkREREbFMEJERERGxTBCRERERsUwQkREREbFMEJERERGxTBCJkcQBERHRyM8PBzdunVDhw4d0LdvX8yaNQvnz583dvWIDGLhwoVo06YNVqxYYeyqED12FsauAJGmoqIiTJkyBUeOHCn3eWpqKlJTUxEdHY2pU6diwoQJxqkgkQHExsYiKirK2NUgMhqGETIpc+fOFYOIn58fRo0aBTc3N8THxyMqKgoFBQWIjIxE06ZNERISYtzKEkng4sWLmDx5MlQqlbGrQmQ0DCNkMk6cOIGdO3cCAIKCgrBmzRpYW1sDAAYPHowRI0Zg3LhxkMvlWLJkCYKDg+Hg4GDMKhPp5ejRo5gxYwby8vKMXRUio+KYETIZ69atAwBYWFhg0aJFYhAp4+fnh3nz5gEA5HI5tmzZ8tjrSCSFkpISfPXVV5g4cSJycnKMXR0io2MYIZMgl8sRExMDAOjVqxd8fHwqLTdo0CA0btwYALBnz57HVj8iqcTExGDgwIFYuXIlVCoV7OzsMH78eGNXi8ioGEbIJMTFxYl95kFBQVWWMzMzQ2BgIADg3LlzyM7Ofiz1I5LKjh07kJKSAgBo3749tmzZgj59+hi5VkTGxTBCJiEpKUk89vf3r7Zsq1atAKinACcmJhq0XkSG0KhRI8yfPx+//PKL+O+ZqCFjGCGTkJqaKh57e3tXW9bDw6PS64jqgtDQUBw+fBihoaEwNzc3dnWITAJn05BJuH//vnjs6upabVkXFxfxWC6XG6hGRIbRoUMHY1eByOSwZYRMQlFRkXj86CyaR1lZWVV6HRER1U0MI2QSSktLxWPNsFEZzfOa1xERUd3EMEImwcbGRjxWKBTVli0pKRGPawouRERk+hhGyCTY2dmJx8XFxdWW1QwjNXXpEBGR6WMYIZPg5OQkHtc0KFXzfKNGjQxUIyIielwYRsgk+Pr6isfp6enVls3IyBCPvby8DFUlIiJ6TBhGyCT4+fmJx5oLoFWm7LxMJkPr1q0NWi8iIjI8hhEyCZ07d4alpSUA4OTJk1WWUyqVOH36NACgbdu25bp3iIiobmIYIZPg5OQk7klz6NAhpKWlVVpu165d4gJpAwcOfGz1IyIiw2EYIZMRHh4OQD21d9q0acjLyyt3Pjk5GYsXLwYA2NvbY+TIkY+7ikREZABcDp5MRs+ePdG/f3/s3bsXZ8+eRUhICMaOHQtPT08kJCRg06ZNyM/PBwDMnDmTM2mIiOoJhhEyKUuXLkV+fj6OHz+OlJQURERElDsvk8kwefJkjBkzxkg1JCIiqTGMkEmxtbXF2rVrER0dje3bt+PSpUvIzc2Fi4sLunTpgrCwMHTp0sXY1SQiIgnJBEEQjF0JIiIiarg4gJWIiIiMimGEiIiIjIphhIiIiIyKYYSIiIiMimGEiIiIjIphhIiIiIyKYYSIiIiMimGEiIiIjIphhIiIiIyKYYSoARAEAUql0tjVICKqFMMIGdzWrVvRpk2bGl/t2rVD586dERwcjP/85z9Yt24d7t27Z+zq6+W1114Tv76UlJRy506ePCmemz17tsHqkJycjFdffRXp6ekGe4auNP9NrFixolb3qO57K6XH9RxdzJ49W6zTyZMnjV0dIr0xjJDJUCqVKCgoQEpKCo4cOYKlS5eiX79+2Lhxo7GrVmetX78eQ4cORVxcnLGrQkRUJe7aS4+Vj48Pxo4dW+k5lUqFwsJCpKenY9++fcjLy0NBQQE++ugjmJmZVXkdVe3QoUNQKBTGrgYRUbUYRuix8vT0xBtvvFFjudmzZ+Ptt9/G6dOnAQDLli1Dv3794O7ubugqPjbdunXDlStXjF0NIiKjYzcNmSRnZ2esXLkSDg4OAICCggL8/vvvRq4VEREZAsMImSxnZ2cMHjxYfB8bG2vE2hARkaGwm4ZMmp+fn3ickZFR7tyKFSvw9ddfAwCOHz+O4uJifPrpp4iJiYGlpSV8fX0xfPhwjB49utx1CoUCv//+Ow4cOICEhAQ8ePAAdnZ28Pb2Rs+ePTFu3Dh4eHjUWDeVSoU//vgD27ZtQ3x8PPLy8uDu7o7u3bsjPDwc/v7+1V5/8uRJhIWFAQCGDRuGTz/9tMqyp0+fxtatW/H333+Ls2Lc3d3x9NNPY9SoUQgMDCxXvm/fvkhNTS33WXBwsHh88OBBNGvWrMJzzp07h61bt+LkyZPIzMyEUqmEm5sbunTpgiFDhqBnz57Vf1P+58qVK9i4cSNiY2ORnp4Oe3t7+Pv7Y9SoUXj55Ze1uodUMjIysG3bNpw6dQrXr1+HXC6HUqmEs7MzWrRoge7du2Ps2LFo3LixVvcrKCjAjz/+iN27d+P27duwsLCAj48P+vbti3HjxqFRo0Za3ef48ePYuXMn4uLikJWVBZlMhiZNmqBbt2545ZVX0KlTJ32+bKI6hWGETJq5uXmlx4/Kzs7GhAkTyv0C/ueff/D000+XK3fp0iVMnToVN27cKPd5SUkJ5HI54uPj8cMPP2DatGkIDw+v9nmaY1rKpKWl4bfffsOOHTswb948Lb7C6j148AAffPABDh8+XOFcSkoKUlJSsGPHDgwfPhwfffQRLC0ta/Wc4uJizJs3r9KusLLn/P777+jVqxciIyPh7Oxc5b2+//57fPbZZ1CpVOJncrkcp06dwqlTp7Bjxw706dOnVvXU1YoVK7BmzRqUlJRUOHf37l3cvXsXcXFx+P777xEREYF+/fpVe7/bt28jPDwct2/fLvd5fHw84uPj8dNPP2HRokV48cUXq7yHXC7H9OnTcfz48Qrnbty4gRs3bmDz5s0YNmwYFi5cCGtray2/WqK6i2GETNrly5fF4+bNm1dZ7tNPP63QEgAAAwYMEI/Pnz+P8PBw5OfnAwCaNGmCPn36wMvLC3l5eYiLi8PZs2dRXFyMTz75BNnZ2ZgyZUqFexYUFCA0NBRJSUkAACsrKwQHB8Pf3x+5ubk4fPgwrl+/jgULFsDR0bHWX3t+fj7CwsKQmJgIAJDJZOjevTs6deoEQRBw4cIFnDhxAoB63Q4A+OSTTwAAEydORG5uLjZt2iT+4pw4cSKcnJwAAC4uLuJzSkpKMH78eJw5cwYAYGlpiV69eqFdu3aQyWS4evUqjhw5goKCAhw7dgyhoaGIiooSx/No+vrrr8utG9K+fXs8++yzsLKywoULF/Dnn3/izz//xPnz52v9fdFWZGQk1qxZA0D9vevWrRs6duwIR0dH5OfnIyEhASdOnBCnlM+YMQN//PEHvLy8qrznlClTkJ2dDVtbW/Tr1w9PPPEE7t+/j3379uHOnTvIzs7Ge++9h1WrVqF3794Vrs/OzsaYMWNw/fp1AICtrS369u0LPz8/KJVKXLp0CceOHYNCocC2bduQlpaGdevWwcKCP6qpfuO/cDJZWVlZ+OOPP8T3zz33XJVljx07Bnd3dyxcuBBBQUHiL4iypu68vDxMnTpVDCJvvPEGpk6dCisrq3L3OXr0KKZPn47c3FysXr0aXbt2Rffu3cuV+eabb8Qg4u3tje+++65cd9KMGTPw9ddfY9WqVcjOzq711x8ZGSkGEXd3d6xYsQKdO3cuV+bQoUN45513UFpaiq1bt2LYsGHo2rUrRo0aBQA4cuSIGEZGjhxZaddMZGSkGETatWuHL7/8skLwy8zMxPTp03Hq1CkkJSXh448/xtKlS8uVSU5OxurVqwEAZmZmmDdvHsaNG1euzNmzZ/HWW2/hwYMHtf22aOX69etYt24dAHW4+vbbb/Hss89WKHflyhWMHz8e9+7dQ2FhIbZu3YrJkydXed/s7Gz4+/tj9erV5b6X06dPx5w5c/DHH3+gtLQUc+fOxZ49eyoEtrlz54pB5Nlnn0VERESF7qEbN27gnXfeQWJiIk6ePIlVq1bh3XffrfX3gqgu4ABWMkk3b97EhAkTkJeXB0DdihESElLtNStWrEBwcDDs7e3h4+NTbgrxpk2bxJaT4cOHY+bMmRWCCAD07t0bixYtAqBeQv3R1UHlcjnWr18PQP1LbvXq1eWCCKDuTpoyZQqGDx+u41f9UHZ2Nn755RfxfitXrqwQRAD12JBJkyaJ7zdv3qzTc+7cuSMuKteoUSN8//33lbZANWnSBKtXrxanVu/YsaNCV9dXX32F0tJSAOqw92gQAYDOnTvjq6++gkwm06meuoqOjhbrEhoaWmkQAYA2bdrg9ddfF9/Hx8dXe19nZ2esXbu2QqiztbXFZ599hoCAAADqLqBt27aVK3P+/Hns378fgHos1KpVqyodp+Lr64tvv/0WNjY2AIAffvgBOTk51daLqK5jywg9Vunp6fj+++8rPadQKPDgwQMkJCQgLi5OHHNgbm6OZcuWVdt3XraUfFV+/fVX8fg///lPtXUcMGAAWrZsievXr+PMmTNITU2Ft7c3AHULTNn4g759+6JNmzZV3mfKlCnYvn17ubET2jp69Ki4WNlzzz1X7WDG0aNH448//oCPjw9at26t03O2bdsmPmfUqFHVDr50cHBAWFgYIiMjoVKpsHPnTrEVoaSkBMeOHQOgDmlvvvlmlffp2rUrevToIXYxGUJQUBBsbW2RkpJSYQDzozT/NyxrOavKv/71LzRt2rTSc+bm5pg0aRLefvttAOrA9tprr4nnt2zZIh6Hh4eLYaMyXl5eCAkJwebNm5Gfn4+DBw9i2LBh1daNqC5jGKHH6vbt21i2bJnW5d3d3REREVGhq+RRTz31VJXnMjMzxb/i7ezs4OvrW+NzO3fuLDannzlzRgwjMTExYplevXpVew8PDw+0a9cOFy9erPF5j9J8Tk2DPd3c3LBr1y6dnwGg3ADcdu3a1Vhe8/tc1rUDqAcLFxQUAAA6duxYbkxKZYKDgw0aRrp27YquXbvWWK6kpAQ3b94U35e1plSlf//+1Z5/7rnnYGFhgdLSUiQkJKCgoAB2dnYAgFOnTonltPled+7cWWzpOnPmDMMI1WsMI2QyrKys4ODgAHd3dwQEBKBHjx7o379/pd0pj6psLESZq1eviscFBQXVtmZURnODOc1Bso92z1TG39+/VmFE85mtWrXS+XptJScni8e6jktIS0ur9Fjb78vjVFxcjOvXr+PWrVu4desWbty4gcTERFy6dKncTBtBEKq8h6WlJZ544olqn2NlZQUfHx9cv34dpaWlSElJgb+/P0pLS8uFnldeeUWn+pvSJodEhsAwQo9V165dsWHDBsnvWzZLpDJyuVyve2ter7mLcHXPLOPq6lqrZ96/f188rm4arb70GWCreW1WVpZ4rM0Motp+X3R18OBB/PTTT4iLi6uy1cPc3BxKpbLGezk5OcHMrOZhdpr/e5WN9cjJyak26NRE33/DRKaOYYTqherWINH8RePt7Y3Q0FCd7l02KBGAzgMva7vux+Pa3E7zF/SECRNq7F7RpDmG53F9X7RVWlqK2bNnIzo6usI5d3d3+Pn5ISAgAIGBgTAzM8OECRMke7Zm6Chr1Xs0CM2YMUOrYFNG2wXZiOoqhhGq9zRbMBwcHLTaqK8qbm5u4rRebf5aLZsNpCvNOhtyJoWzs7PYqjFw4ECtxjJUxs3NTTzWprUlNze3Vs/R1rfffisGEUtLS4wdOxZ9+/ZFQEBAhRato0ePanVPbf+31Pz6y571aOvWqFGjDNriRVTXcGov1Xs+Pj7i8c2bN7VqdcjNza20Wb9sICsArXbc1RyvogvNOl+7dq3G8pGRkVi0aBF++OEHnQKQ5nPKQlZ1SkpKKr3/4/q+aKO4uBg//PCD+D4iIgJz585F9+7dK+1a0+wSq64rpbi4uMKWBI8qKCjArVu3AKgHS5eNZbK2ti6347Q23+vCwkIUFRXVWI6oPmAYoXqvZcuW4l/uRUVF4hTU6owbNw4dO3ZE37598eeff4qfP//88+LxgQMHqr1Hbm4u/vnnn1rVWXMZ+5rqW1JSgo0bN2LDhg347LPPdOoCeeaZZ8Tjmr4eANiwYQO6dOmCbt26Yf78+eLnTz31lNjFEx8fX+OAS83vqdSuX78utiY5Oztj4MCB1ZY/efKkeFzTuA7NWU6VOXjwoDiVu1OnTuVWTtXcP0ib73VERAQ6deqEZ599FitXrqyxPFFdxjBCDcLQoUPF4y+++ALFxcVVlt2+fTsSExOhVCqRlZWFDh06iOd69eolDtCMjY2t9pfT2rVrUVhYWKv6as4iOnjwYLV/SW/fvl1cH6Nnz57lxnJojqWpbL2ToUOHiuM9Dhw4gLi4uCqf8+DBA3GNGLlcXm5WkoWFhTjtVaVS4fPPP6/yPomJidi7d2+V5/WlOTumoKCg0n1pyly4cAE7d+4U39fUarZmzRpxCvOjioqKxI0bAfWKt5o0/w1u3ry5wqJxmm7cuCGuS5KVlYX27dtXWy+iuo5hhBqE8PBw8S/3K1euYOLEieVmgJQ5dOgQFi5cKL5/9dVXy838sLGxwXvvvQdA/Vf01KlTy60fUWbjxo3ivii14ebmJi7WpVAoMHHixHLTcMucOnVK3I9GJpNVWGxMcznyyvbuadWqFV566SUA6hDx9ttvV7r+x507d/DWW2+Js4m8vLwwYsSIcmXefvttMajt2LEDERERFbq6kpOTMXHiRIMO0H3iiSfE1iGFQoHPP/+80haPAwcO4M033yxXl5q6Ra5fv44pU6ZU6KqSy+WYNGmSGDCefPLJCmuS9O7dW1yYr6CgAK+//nqlK75evXoVEydOFENUx44dK93nhqg+4QBWahDc3d2xbNkyvP3221AoFIiJicELL7wgblKWm5uLs2fP4uzZs+I17du3r3SjvLFjx+Lw4cM4duwYsrOzERYWhl69eqFTp05QKBQ4duyY+EvG19e32r+Aq/P+++/j77//Rnx8PFJSUjB06FD07t0bTz75JAD1Pi8nTpwQf9GOHz++XLcLUH79lTlz5uCVV16BIAgYPXo0mjRpAgBYuHAhLl++jOTkZMjlcrz++uvo0qULAgMDYWlpiWvXruHAgQNia5K1tTU+++yzCiviNm3aFPPnz8esWbOgUqmwdu1aHDhwAH379oWTkxOuXLmCAwcOQKFQ6PV9qYmDgwOGDx8uLhi2fv16nDhxAt27d4erqysyMzMRGxsrLmpnYWEBpVIJQRCqHZRsa2sLR0dH/PnnnwgODkb//v3h6emJ9PR07N27V7zWxcUFkZGRlW5ut3z5cowcORJ3795FamoqRowYgR49eqBjx44QBAFJSUk4fPiwOAPM2dkZERER0n6DiEwQwwg1GL1798a6devw/vvvIyMjAwUFBeWa6DX16dMHERERlS5Bb2ZmhtWrV+PDDz/E9u3bIQiCuBttGZlMhnfeeQdyubzWv3Stra3x008/YcaMGTh8+DAUCgUOHDhQYbxB2dTUqVOnVrjH8OHD8fPPP0OhUCAtLU3ca6dt27Z44YUXAKh/eW/atAkzZ87E4cOHAahX/NRcYbWMh4cHIiIi0KVLl0rrPGTIENja2uL9999HYWEhbty4IW5YV6Z9+/aYOXMmwsLCdP6eaOuDDz7AzZs38ddffwFQdw2VbTqoycvLC0uXLsWcOXNw+/Zt3Lt3DxkZGfDw8KhQ1traGt988w3+85//4O7du5XuA+Tr64tVq1ZVufCbp6cnfv31V0ydOhVnz56FSqXC8ePHcfz48Qpl/fz88MUXX2i1YjBRXccwQg1K165dsX//fmzbtg2HDx9GQkICHjx4ADMzMzRp0gRPPfUUhg0bhh49elR7H0tLSyxduhRDhgzB5s2b8ffff0Mul8PFxQUdO3ZEWFgYgoKCsHjxYr3q6+DggG+++QaxsbH4/fffERcXh6ysLAiCAA8PD3Tr1g2hoaFVrirr7++Pn376CStXrsTFixeRl5cHJyenCi0ATk5O+OabbxAXF4cdO3bg9OnTyMzMRHFxMZycnNCmTRsEBwdj2LBhsLe3r7bOL7zwAvbs2YOffvoJR48exe3bt2FhYQFfX1+8/PLLCA0NNehsGkDdirF+/Xps374d0dHRuHTpEnJycmBtbY1GjRrB398fvXv3xtChQ2FjY4Nnn30WUVFRANRjcCZOnFjpfQMCArBjxw6sXbsWBw8eRHp6OmxtbdGqVSsMGjQII0eOrHHFYA8PD0RFReHw4cPYvXs3zp49i6ysLCgUCri6uqJdu3bo378/Bg8erNXqw0T1gUzQZ1lAIiIiIj1xACsREREZFcMIERERGRXDCBERERkVwwgREREZFcMIERERGRXDCBERERkVwwgREREZFcMIERERGRXDCBERERkVwwgREREZFcMIERERGRXDCBERERkVwwgREREZFcMIERERGRXDCBERERkVwwgREREZ1f8Du/OrcTdqJLgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "do_eval(y_test, y_test_poly_scaled_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "972211bb-f624-45b0-9ab5-8798df5b2244",
   "metadata": {},
   "source": [
    "## 6. Deployment (Optional)\n",
    "\n",
    "**Note**: this will only work in an interactive Jupyter environment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de023a5f-0bfe-4029-b50c-319f0725fa4a",
   "metadata": {},
   "source": [
    "### Serialize model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3c58c0c8-925d-4784-9e34-df008987b550",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "376692f4-0b07-4430-bb8c-d58f309541b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['log_reg.joblib']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(log_reg_scaled, 'log_reg.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6880efdc-e2f5-4795-9147-3de653dea073",
   "metadata": {},
   "source": [
    "### Simple app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "819bd64f-5711-40dc-90c0-1c2fbf554b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact, FloatSlider, Checkbox, IntSlider, Output, widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7a2c85b5-1782-40cc-adf8-cc0fc0b3d44c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load('log_reg.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "80cab93d-8afc-49eb-a36c-6a73afbb39c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "systolic_bp_slider = FloatSlider(min=80, max=200, value=120)\n",
    "tobacco_slider = FloatSlider(min=0, max=50, value=0)\n",
    "ldl_slider = FloatSlider(min=0, max=20, value=0)\n",
    "adiposity_slider = FloatSlider(min=0, max=50, value=0)\n",
    "fam_hist_cb = Checkbox()\n",
    "type_a_slider = FloatSlider(min=0, max=100, value=50)\n",
    "obesity_slider = FloatSlider(min=0, max=100, value=25)\n",
    "alcohol_slider = FloatSlider(min=0, max=200, value=50)\n",
    "age_slider = IntSlider(min=0, max=100, value=30)\n",
    "\n",
    "output = Output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "031bbf40-f3be-4c73-9004-da274c696ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(systolic_bp, tobacco, ldl, adiposity, fam_hist, type_a, obesity, alcohol, age):\n",
    "    df = pd.DataFrame({\n",
    "        'Systolic Blood Pressure': [systolic_bp],\n",
    "        'Tobacco (kg)': [tobacco],\n",
    "        'LDL': [ldl],\n",
    "        'Adiposity': [adiposity],\n",
    "        'Fam. Hist': [fam_hist],\n",
    "        'Type A': [type_a],\n",
    "        'Obesity': [obesity],\n",
    "        'Alcohol': [alcohol],\n",
    "        'Age': [age]\n",
    "    })\n",
    "    prediction = model.predict_proba(df.values)[:, 1][0]\n",
    "    \n",
    "    with output:\n",
    "        output.clear_output()\n",
    "        print(f'Prediction: {prediction:.4f} (from 0.0 to 1.0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b970e513-e089-4a8d-a327-f6f0fde29079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c986eb6193414c96815a1715e1d3677e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=120.0, description='systolic_bp', max=200.0, min=80.0), FloatSlider(va…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7de1ced15ba465db1e4a7b570f45c45",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(\n",
    "    predict,\n",
    "    systolic_bp=systolic_bp_slider,\n",
    "    tobacco=tobacco_slider,\n",
    "    ldl=ldl_slider,\n",
    "    adiposity=adiposity_slider,\n",
    "    fam_hist=fam_hist_cb,\n",
    "    type_a=type_a_slider,\n",
    "    obesity=obesity_slider,\n",
    "    alcohol=alcohol_slider,\n",
    "    age=age_slider\n",
    ")\n",
    "\n",
    "display(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "628bd8d8-3d8e-41c8-b7ed-dd2c8ef20fa0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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