{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1489800c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns \n",
    "import plotly.express as px \n",
    "import matplotlib.pyplot as plt \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e0e5d9",
   "metadata": {},
   "source": [
    "## 1.Business Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5825ab8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2b89f87",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9bbcbc9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4394daa9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "be09463e",
   "metadata": {},
   "source": [
    "## Hypothesis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55c9f906",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6115c64f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3a53ac2a",
   "metadata": {},
   "source": [
    "## 2.Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2b2706e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Loading dataset\n",
    "\n",
    "data= pd.read_csv(\"Desktop/Pandas/Sepsis/Paitients_Files_Train.csv\")\n",
    "\n",
    "data_test= pd.read_csv(\"Desktop/Pandas/Sepsis/Paitients_Files_Test.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67ca2509",
   "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>ID</th>\n",
       "      <th>PRG</th>\n",
       "      <th>PL</th>\n",
       "      <th>PR</th>\n",
       "      <th>SK</th>\n",
       "      <th>TS</th>\n",
       "      <th>M11</th>\n",
       "      <th>BD2</th>\n",
       "      <th>Age</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>Sepssis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ICU200010</td>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICU200011</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ICU200012</td>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICU200013</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ICU200014</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  PRG   PL  PR  SK   TS   M11    BD2  Age  Insurance   Sepssis\n",
       "0  ICU200010    6  148  72  35    0  33.6  0.627   50          0  Positive\n",
       "1  ICU200011    1   85  66  29    0  26.6  0.351   31          0  Negative\n",
       "2  ICU200012    8  183  64   0    0  23.3  0.672   32          1  Positive\n",
       "3  ICU200013    1   89  66  23   94  28.1  0.167   21          1  Negative\n",
       "4  ICU200014    0  137  40  35  168  43.1  2.288   33          1  Positive"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b124309f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>ID</th>\n",
       "      <th>PRG</th>\n",
       "      <th>PL</th>\n",
       "      <th>PR</th>\n",
       "      <th>SK</th>\n",
       "      <th>TS</th>\n",
       "      <th>M11</th>\n",
       "      <th>BD2</th>\n",
       "      <th>Age</th>\n",
       "      <th>Insurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ICU200609</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>120</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.407</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICU200610</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>88</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.400</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ICU200611</td>\n",
       "      <td>6</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.190</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICU200612</td>\n",
       "      <td>1</td>\n",
       "      <td>124</td>\n",
       "      <td>74</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.100</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ICU200613</td>\n",
       "      <td>7</td>\n",
       "      <td>150</td>\n",
       "      <td>78</td>\n",
       "      <td>29</td>\n",
       "      <td>126</td>\n",
       "      <td>35.2</td>\n",
       "      <td>0.692</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  PRG   PL  PR  SK   TS   M11    BD2  Age  Insurance\n",
       "0  ICU200609    1  109  38  18  120  23.1  0.407   26          1\n",
       "1  ICU200610    1  108  88  19    0  27.1  0.400   24          1\n",
       "2  ICU200611    6   96   0   0    0  23.7  0.190   28          1\n",
       "3  ICU200612    1  124  74  36    0  27.8  0.100   30          1\n",
       "4  ICU200613    7  150  78  29  126  35.2  0.692   54          0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "475b41ee",
   "metadata": {},
   "source": [
    "### 2.1 Checking Data information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "932555fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 599 entries, 0 to 598\n",
      "Data columns (total 11 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   ID         599 non-null    object \n",
      " 1   PRG        599 non-null    int64  \n",
      " 2   PL         599 non-null    int64  \n",
      " 3   PR         599 non-null    int64  \n",
      " 4   SK         599 non-null    int64  \n",
      " 5   TS         599 non-null    int64  \n",
      " 6   M11        599 non-null    float64\n",
      " 7   BD2        599 non-null    float64\n",
      " 8   Age        599 non-null    int64  \n",
      " 9   Insurance  599 non-null    int64  \n",
      " 10  Sepssis    599 non-null    object \n",
      "dtypes: float64(2), int64(7), object(2)\n",
      "memory usage: 51.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dbd79244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID           0\n",
       "PRG          0\n",
       "PL           0\n",
       "PR           0\n",
       "SK           0\n",
       "TS           0\n",
       "M11          0\n",
       "BD2          0\n",
       "Age          0\n",
       "Insurance    0\n",
       "Sepssis      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a8757c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "## there are no missing values; however, Sepsis feature is mispelt as 'Sepssis'; therefore, we will go ahead and rename it \n",
    "\n",
    "data.rename(columns= {\"Sepssis\":\"Sepsis\"}, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "937bb785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance',\n",
       "       'Sepsis'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3076e0",
   "metadata": {},
   "source": [
    "### 2.2 Getting a Describtion of My Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "617477f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRG</th>\n",
       "      <th>PL</th>\n",
       "      <th>PR</th>\n",
       "      <th>SK</th>\n",
       "      <th>TS</th>\n",
       "      <th>M11</th>\n",
       "      <th>BD2</th>\n",
       "      <th>Age</th>\n",
       "      <th>Insurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>599.000000</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.824708</td>\n",
       "      <td>120.153589</td>\n",
       "      <td>68.732888</td>\n",
       "      <td>20.562604</td>\n",
       "      <td>79.460768</td>\n",
       "      <td>31.920033</td>\n",
       "      <td>0.481187</td>\n",
       "      <td>33.290484</td>\n",
       "      <td>0.686144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.362839</td>\n",
       "      <td>32.682364</td>\n",
       "      <td>19.335675</td>\n",
       "      <td>16.017622</td>\n",
       "      <td>116.576176</td>\n",
       "      <td>8.008227</td>\n",
       "      <td>0.337552</td>\n",
       "      <td>11.828446</td>\n",
       "      <td>0.464447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.100000</td>\n",
       "      <td>0.248000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.383000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>123.500000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>0.647000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              PRG          PL          PR          SK          TS         M11  \\\n",
       "count  599.000000  599.000000  599.000000  599.000000  599.000000  599.000000   \n",
       "mean     3.824708  120.153589   68.732888   20.562604   79.460768   31.920033   \n",
       "std      3.362839   32.682364   19.335675   16.017622  116.576176    8.008227   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000   99.000000   64.000000    0.000000    0.000000   27.100000   \n",
       "50%      3.000000  116.000000   70.000000   23.000000   36.000000   32.000000   \n",
       "75%      6.000000  140.000000   80.000000   32.000000  123.500000   36.550000   \n",
       "max     17.000000  198.000000  122.000000   99.000000  846.000000   67.100000   \n",
       "\n",
       "              BD2         Age   Insurance  \n",
       "count  599.000000  599.000000  599.000000  \n",
       "mean     0.481187   33.290484    0.686144  \n",
       "std      0.337552   11.828446    0.464447  \n",
       "min      0.078000   21.000000    0.000000  \n",
       "25%      0.248000   24.000000    0.000000  \n",
       "50%      0.383000   29.000000    1.000000  \n",
       "75%      0.647000   40.000000    1.000000  \n",
       "max      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6dee478",
   "metadata": {},
   "source": [
    "### 2.3 Univariate Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80cc414a",
   "metadata": {},
   "source": [
    "#### 2.3.1 Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "58a12832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'PRG'}>, <Axes: title={'center': 'PL'}>,\n",
       "        <Axes: title={'center': 'PR'}>],\n",
       "       [<Axes: title={'center': 'SK'}>, <Axes: title={'center': 'TS'}>,\n",
       "        <Axes: title={'center': 'M11'}>],\n",
       "       [<Axes: title={'center': 'BD2'}>, <Axes: title={'center': 'Age'}>,\n",
       "        <Axes: title={'center': 'Insurance'}>]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(figsize= (10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55cdb523",
   "metadata": {},
   "source": [
    "#### 2.3.2 Visualizing Outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd1a4242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb16c408",
   "metadata": {},
   "source": [
    "#### Notes: \n",
    "\n",
    "there are some outliers; therefore we will:\n",
    "\n",
    "- Use an algorithm robust to outliers \n",
    "- For algorithms that are not robust to outliers, we will use L1 regularization to minimize the effect of the outliers. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e59615",
   "metadata": {},
   "source": [
    "#### 2.3.3 Visualizing Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2b983552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sepsis', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data= data, x= data['Sepsis'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80defc2e",
   "metadata": {},
   "source": [
    "#### Notes:\n",
    "\n",
    "- There is relatively a low class imbalance "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "387d0982",
   "metadata": {},
   "source": [
    "### 2.4 Bivariate Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1960915c",
   "metadata": {},
   "source": [
    "#### I am just going to paste the metadata to help me understand \n",
    "\n",
    "\n",
    "- PRG Plasma glucose\n",
    "- PL Blood Work Result-1 (mu U/ml)\n",
    "- PR Blood Pressure (mm Hg)\n",
    "- SK Blood Work Result-2 (mm)\n",
    "- TS Blood Work Result-3 (mu U/ml)\n",
    "- M11 Body mass index (weight in kg/(height in m)^2\n",
    "- BD2 Blood Work Result-4 (mu U/ml)\n",
    "- Age patients age (years)\n",
    "- Insurance If a patient holds a valid insurance card\n",
    "- Sepssis Positive: if a patient in ICU will develop a sepsis , and Negative: otherwise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb5e493",
   "metadata": {},
   "source": [
    "#### 2.4.1 Outlier Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "092eda71",
   "metadata": {},
   "outputs": [],
   "source": [
    "##there are a lot of features outliers; therefore, it might be a good idea to see how these feature vary with the label (Sepsis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "653f9aaf",
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   "source": [
    "##Let's see how blood work varies with Sepsis\n",
    "\n",
    "for  col in data.drop(['ID',\"Insurance\", \"SK\", 'Sepsis'], axis= 1):\n",
    "    fig= px.box(data_frame=data, color= \"Sepsis\", y= col, title= f\"Relationship of {col}, with respect to Sepsis\")\n",
    "    fig.show()"
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   "source": [
    "#### Notes:\n",
    "\n",
    "- There were some people with very high Plasma Glucose even though they didn't have Sepsis \n",
    "- "
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