dev/Sepsis_ML_Prediction_Deployment_With_FastAPI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2099c384",
   "metadata": {},
   "source": [
    "# Project Title: \n",
    "\n",
    "Sepsis Classification Machine Learning Project with FastAPI Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcb9661",
   "metadata": {},
   "source": [
    "# Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9614df3",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "This project focuses on the early detection and classification of sepsis, a life-threatening medical condition. Sepsis is a critical concern in healthcare, and early diagnosis can significantly improve patient outcomes. The objective is to build a robust machine learning model for sepsis classification and deploy it into a web application using FastAPI, making it accessible for real-time predictions.\n",
    "\n",
    "### 1.1. Objectives\n",
    "- Understand the Data: \n",
    "The primary objective of this project is to gain a comprehensive understanding of the patient data, which includes various health-related features, demographics, and the presence or absence of sepsis. This understanding will empower healthcare professionals and decision-makers to make informed decisions regarding patient care and intervention.\n",
    "\n",
    "- Predict Sepsis: \n",
    "Develop an accurate machine learning classification model that can predict the likelihood of a patient developing sepsis based on the provided features. Early and accurate sepsis prediction is crucial for timely medical intervention and improving patient outcomes.\n",
    "\n",
    "- Web Application Integration: \n",
    "Integrate the trained sepsis classification model into a web application using FAST API. This web application will serve as a practical tool for healthcare practitioners to input patient data and receive real-time sepsis risk predictions, aiding in clinical decision-making.\n",
    "\n",
    "### 1.2. Methodology\n",
    "To achieve the project objectives, we will follow a structured approach:\n",
    "\n",
    "- Data Loading and Exploration: \n",
    "Begin by loading and exploring the patient data, including features like age, vital signs, and medical history. This step will provide initial insights into the dataset and identify any data quality issues.\n",
    "\n",
    "- Data Preprocessing: \n",
    "Handle missing values, perform feature engineering, and encode categorical variables as needed. Preprocessing steps will ensure that the data is ready for training the machine learning model.\n",
    "\n",
    "- Model Development: \n",
    "Select and implement a suitable machine learning classification model for sepsis prediction. This model will be trained on historical patient data to learn patterns indicative of sepsis.\n",
    "\n",
    "- Model Evaluation: \n",
    "Assess the model's performance using appropriate evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Rigorous evaluation will help identify the model's effectiveness in predicting sepsis cases.\n",
    "\n",
    "- FAST API Integration: \n",
    "Integrate the trained machine learning model into a FAST API-based web application. This application will provide a user-friendly interface for healthcare professionals to input patient data and obtain sepsis risk predictions.\n",
    "\n",
    "- Testing and Validation: \n",
    "Conduct thorough testing and validation of the web application to ensure its reliability and accuracy in real-time sepsis risk assessment.\n",
    "\n",
    "- Documentation: \n",
    "Provide detailed documentation on how to use the web application, including input requirements and interpretation of results.\n",
    "\n",
    "By following this methodology, we aim to provide healthcare professionals with a valuable tool for early sepsis detection and decision support, ultimately contributing to improved patient care and outcomes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f8cd22f",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7929520",
   "metadata": {},
   "source": [
    "## Installations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d6dd79e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pip in c:\\users\\user\\anaconda3\\lib\\site-packages (23.3.1)Note: you may need to restart the kernel to use updated packages.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING: Ignoring invalid distribution -andas (c:\\users\\user\\anaconda3\\lib\\site-packages)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: imbalanced-learn in c:\\users\\user\\anaconda3\\lib\\site-packages (0.11.0)\n",
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      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.3.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from imbalanced-learn) (2.2.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING: Ignoring invalid distribution -andas (c:\\users\\user\\anaconda3\\lib\\site-packages)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ppscore in c:\\users\\user\\anaconda3\\lib\\site-packages (1.3.0)\n",
      "Requirement already satisfied: pandas<2.0.0,>=1.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ppscore) (1.5.3)\n",
      "Requirement already satisfied: scikit-learn<2.0.0,>=0.20.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ppscore) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas<2.0.0,>=1.0.0->ppscore) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas<2.0.0,>=1.0.0->ppscore) (2022.1)\n",
      "Requirement already satisfied: numpy>=1.20.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas<2.0.0,>=1.0.0->ppscore) (1.24.4)\n",
      "Requirement already satisfied: scipy>=1.5.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn<2.0.0,>=0.20.2->ppscore) (1.9.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn<2.0.0,>=0.20.2->ppscore) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn<2.0.0,>=0.20.2->ppscore) (2.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas<2.0.0,>=1.0.0->ppscore) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -andas (c:\\users\\user\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -andas (c:\\users\\user\\anaconda3\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "%pip install --upgrade pip\n",
    "%pip install imbalanced-learn\n",
    "%pip install ppscore"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6542fb68",
   "metadata": {},
   "source": [
    "## Importation of Relevant Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f17fc39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Handling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Statistical Analysis\n",
    "from scipy.stats import chi2_contingency\n",
    "import ppscore\n",
    "\n",
    "# Feature Processing\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Machine Learning Models\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "import xgboost as xgb\n",
    "\n",
    "\n",
    "# Model Evaluation\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, precision_score, recall_score, f1_score, make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Model Saving\n",
    "import pickle\n",
    "\n",
    "# Other Packages\n",
    "import random\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49abfc64",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d12886",
   "metadata": {},
   "source": [
    "### Loading the Train and Test Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12278020",
   "metadata": {},
   "source": [
    "#### Train Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19d390b9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>PRG</th>\n",
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       "      <th>M11</th>\n",
       "      <th>BD2</th>\n",
       "      <th>Age</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>Sepssis</th>\n",
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       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
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       "      <td>Negative</td>\n",
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       "      <th>2</th>\n",
       "      <td>ICU200012</td>\n",
       "      <td>8</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICU200013</td>\n",
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       "      <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",
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       "    <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",
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      ],
      "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": [
    "# Load The Train Dataset\n",
    "train_df = pd.read_csv(\"data/Paitients_Files_Train.csv\")\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a4befe",
   "metadata": {},
   "source": [
    "#### Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bad0a7b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "  <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",
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       "    <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",
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       "    <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",
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      "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": [
    "# Load The Test Dataset\n",
    "test_df = pd.read_csv(\"data/Paitients_Files_Test.csv\")\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fe4c87b",
   "metadata": {},
   "source": [
    "Columns Description (Common to Both Datasets):\n",
    "\n",
    "- ID: A unique identifier for each patient.\n",
    "- PRG: Number of pregnancies (applicable only to females).\n",
    "- PL: Plasma glucose concentration.\n",
    "- PR: Diastolic blood pressure.\n",
    "- SK: Triceps skinfold thickness.\n",
    "- TS: 2-hour serum insulin.\n",
    "- M11: Body mass index (BMI).\n",
    "- BD2: Diabetes pedigree function.\n",
    "- Age: Age of the patient.\n",
    "- Insurance: Whether the patient has insurance coverage (1 for Yes, 0 for No).\n",
    "Sepsis (Only in Test Dataset): The target variable indicating the presence or absence of sepsis (Positive for presence, Negative for absence)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "208226a8",
   "metadata": {},
   "source": [
    "Both datasets contain patient-related information, with the test dataset having an additional \"Sepsis\" column for target classification. The  dataset is designed for model training, while the test dataset will be used for model evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab45f682",
   "metadata": {},
   "source": [
    "## Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c5c0bf4",
   "metadata": {},
   "source": [
    "Null Hypothesis (H0): There is no significant association between the patient's age and the likelihood of sepsis.\n",
    "\n",
    "Alternative Hypothesis (H1): There is a significant association between the patient's age and the likelihood of sepsis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e76eb82",
   "metadata": {},
   "source": [
    "## Key Analytical Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf8d0646",
   "metadata": {},
   "source": [
    "i. Are there any correlations or patterns between the numerical features (e.g., PRG, PL, PR, SK, TS, M11, BD2, Age) and the presence of sepsis (Positive/Negative)?\n",
    "\n",
    "ii. How does the distribution of key numerical variables (e.g., PR, SK, TS, M11) differ between patients with and without sepsis?\n",
    "\n",
    "iii. Is there a relationship between the number of pregnancies (PRG) and plasma glucose concentration (PL)? Does this relationship vary with the presence of sepsis?\n",
    "\n",
    "iv. Are there any significant differences in diastolic blood pressure (PR) between patients with different triceps skinfold thickness (SK) levels?\n",
    "\n",
    "v. Does the body mass index (M11) vary significantly with 2-hour serum insulin (TS) levels?\n",
    "\n",
    "vi. Is there a correlation between the diabetes pedigree function (BD2) and age? How does this correlation affect the likelihood of sepsis?\n",
    "\n",
    "vii. Are patients with insurance coverage more likely to have certain health characteristics (e.g., higher age, higher BMI) compared to those without insurance coverage?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "734f6654",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d5638ec",
   "metadata": {},
   "source": [
    "## Understanding the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888f337c",
   "metadata": {},
   "source": [
    "An in-depth exploration of the datasets is presented to gain insights into the available variables,their distributions and relationships. This step will provide an initial undertanding of the datasets to identify any data quality issues that will inform the cleaning and pre-processing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3429730",
   "metadata": {},
   "source": [
    "### i. Column Information of The Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03d2806f",
   "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": [
    "# Column information of the test dataset\n",
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426035bd",
   "metadata": {},
   "source": [
    "- There are a total of 599 entries (rows) and 11 columns, each with a specific data type.\n",
    "- The 'ID' column serves as a unique identifier for each patient. \n",
    "- The 'Sepssis' column contains categorical data (object) and represents the target variable indicating the presence or absence of sepsis.\n",
    "- The remaining columns (PRG, PL, PR, SK, TS, M11, BD2, Age, and Insurance) contain numerical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0df78b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 169 entries, 0 to 168\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   ID         169 non-null    object \n",
      " 1   PRG        169 non-null    int64  \n",
      " 2   PL         169 non-null    int64  \n",
      " 3   PR         169 non-null    int64  \n",
      " 4   SK         169 non-null    int64  \n",
      " 5   TS         169 non-null    int64  \n",
      " 6   M11        169 non-null    float64\n",
      " 7   BD2        169 non-null    float64\n",
      " 8   Age        169 non-null    int64  \n",
      " 9   Insurance  169 non-null    int64  \n",
      "dtypes: float64(2), int64(7), object(1)\n",
      "memory usage: 13.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Column information of the test dataset\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d0c8d9c",
   "metadata": {},
   "source": [
    "- There are a total of 169 entries (rows) and 10 columns, each with a specific data type.\n",
    "- Similar to the Train dataset, the 'ID' column serves as a unique identifier for each patient.\n",
    "- Unlike the Train dataset, the Test dataset does not include the 'Sepssis' column, as it is intended for evaluating sepsis predictions and doesn't contain the target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4917495",
   "metadata": {},
   "source": [
    "### ii. Shape of The Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a4835116",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(599, 11)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The shape of the train dataset\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a9c0bba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(169, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The shape of the train dataset\n",
    "test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64adcbe0",
   "metadata": {},
   "source": [
    "### iii. Summary Statistics Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5d972e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>599.000</td>\n",
       "      <td>599.000</td>\n",
       "      <td>599.000</td>\n",
       "      <td>599.000</td>\n",
       "      <td>599.000</td>\n",
       "      <td>599.000</td>\n",
       "      <td>599.000</td>\n",
       "      <td>599.000</td>\n",
       "      <td>599.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.825</td>\n",
       "      <td>120.154</td>\n",
       "      <td>68.733</td>\n",
       "      <td>20.563</td>\n",
       "      <td>79.461</td>\n",
       "      <td>31.920</td>\n",
       "      <td>0.481</td>\n",
       "      <td>33.290</td>\n",
       "      <td>0.686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.363</td>\n",
       "      <td>32.682</td>\n",
       "      <td>19.336</td>\n",
       "      <td>16.018</td>\n",
       "      <td>116.576</td>\n",
       "      <td>8.008</td>\n",
       "      <td>0.338</td>\n",
       "      <td>11.828</td>\n",
       "      <td>0.464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.078</td>\n",
       "      <td>21.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000</td>\n",
       "      <td>99.000</td>\n",
       "      <td>64.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.100</td>\n",
       "      <td>0.248</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000</td>\n",
       "      <td>116.000</td>\n",
       "      <td>70.000</td>\n",
       "      <td>23.000</td>\n",
       "      <td>36.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>0.383</td>\n",
       "      <td>29.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000</td>\n",
       "      <td>140.000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>123.500</td>\n",
       "      <td>36.550</td>\n",
       "      <td>0.647</td>\n",
       "      <td>40.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000</td>\n",
       "      <td>198.000</td>\n",
       "      <td>122.000</td>\n",
       "      <td>99.000</td>\n",
       "      <td>846.000</td>\n",
       "      <td>67.100</td>\n",
       "      <td>2.420</td>\n",
       "      <td>81.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PRG       PL       PR       SK       TS      M11      BD2      Age  \\\n",
       "count  599.000  599.000  599.000  599.000  599.000  599.000  599.000  599.000   \n",
       "mean     3.825  120.154   68.733   20.563   79.461   31.920    0.481   33.290   \n",
       "std      3.363   32.682   19.336   16.018  116.576    8.008    0.338   11.828   \n",
       "min      0.000    0.000    0.000    0.000    0.000    0.000    0.078   21.000   \n",
       "25%      1.000   99.000   64.000    0.000    0.000   27.100    0.248   24.000   \n",
       "50%      3.000  116.000   70.000   23.000   36.000   32.000    0.383   29.000   \n",
       "75%      6.000  140.000   80.000   32.000  123.500   36.550    0.647   40.000   \n",
       "max     17.000  198.000  122.000   99.000  846.000   67.100    2.420   81.000   \n",
       "\n",
       "       Insurance  \n",
       "count    599.000  \n",
       "mean       0.686  \n",
       "std        0.464  \n",
       "min        0.000  \n",
       "25%        0.000  \n",
       "50%        1.000  \n",
       "75%        1.000  \n",
       "max        1.000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary Statistics of The Train Dataset\n",
    "train_df.describe().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80669612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>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>169.000</td>\n",
       "      <td>169.000</td>\n",
       "      <td>169.000</td>\n",
       "      <td>169.000</td>\n",
       "      <td>169.000</td>\n",
       "      <td>169.000</td>\n",
       "      <td>169.000</td>\n",
       "      <td>169.000</td>\n",
       "      <td>169.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.917</td>\n",
       "      <td>123.521</td>\n",
       "      <td>70.426</td>\n",
       "      <td>20.444</td>\n",
       "      <td>81.000</td>\n",
       "      <td>32.250</td>\n",
       "      <td>0.439</td>\n",
       "      <td>33.065</td>\n",
       "      <td>0.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.402</td>\n",
       "      <td>29.259</td>\n",
       "      <td>19.427</td>\n",
       "      <td>15.765</td>\n",
       "      <td>110.721</td>\n",
       "      <td>7.445</td>\n",
       "      <td>0.307</td>\n",
       "      <td>11.548</td>\n",
       "      <td>0.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "      <td>56.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.100</td>\n",
       "      <td>21.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000</td>\n",
       "      <td>102.000</td>\n",
       "      <td>62.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.600</td>\n",
       "      <td>0.223</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000</td>\n",
       "      <td>120.000</td>\n",
       "      <td>74.000</td>\n",
       "      <td>23.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>32.400</td>\n",
       "      <td>0.343</td>\n",
       "      <td>28.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000</td>\n",
       "      <td>141.000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>135.000</td>\n",
       "      <td>36.600</td>\n",
       "      <td>0.587</td>\n",
       "      <td>42.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.000</td>\n",
       "      <td>199.000</td>\n",
       "      <td>114.000</td>\n",
       "      <td>49.000</td>\n",
       "      <td>540.000</td>\n",
       "      <td>57.300</td>\n",
       "      <td>1.698</td>\n",
       "      <td>70.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PRG       PL       PR       SK       TS      M11      BD2      Age  \\\n",
       "count  169.000  169.000  169.000  169.000  169.000  169.000  169.000  169.000   \n",
       "mean     3.917  123.521   70.426   20.444   81.000   32.250    0.439   33.065   \n",
       "std      3.402   29.259   19.427   15.765  110.721    7.445    0.307   11.548   \n",
       "min      0.000   56.000    0.000    0.000    0.000    0.000    0.100   21.000   \n",
       "25%      1.000  102.000   62.000    0.000    0.000   27.600    0.223   24.000   \n",
       "50%      3.000  120.000   74.000   23.000    0.000   32.400    0.343   28.000   \n",
       "75%      6.000  141.000   80.000   32.000  135.000   36.600    0.587   42.000   \n",
       "max     13.000  199.000  114.000   49.000  540.000   57.300    1.698   70.000   \n",
       "\n",
       "       Insurance  \n",
       "count    169.000  \n",
       "mean       0.728  \n",
       "std        0.446  \n",
       "min        0.000  \n",
       "25%        0.000  \n",
       "50%        1.000  \n",
       "75%        1.000  \n",
       "max        1.000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary Statistics of The Test\n",
    "test_df.describe().round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a426a7",
   "metadata": {},
   "source": [
    "i. PRG (Number of Pregnancies):\n",
    "- The average number of pregnancies is approximately 3.92, suggesting that, on average, patients have had several pregnancies.\n",
    "- The range of values varies from 0 (no pregnancies) to a maximum of 13 pregnancies.\n",
    "- Similar to the Train dataset, the majority of patients fall within the range of 1 to 6 pregnancies.\n",
    "\n",
    "ii. PL (Plasma Glucose Concentration):\n",
    "- The mean plasma glucose concentration is around 123.52 mg/dL.\n",
    "- The values range from a minimum of 56 mg/dL to a maximum of 199 mg/dL.\n",
    "- Similar to the Train dataset, the standard deviation of 29.26 indicates some variability in glucose levels among patients.\n",
    "\n",
    "iii. PR (Diastolic Blood Pressure):\n",
    "- The mean diastolic blood pressure is approximately 70.43 mm Hg.\n",
    "- The values range from a minimum of 0 mm Hg (which seems unusual) to a maximum of 114 mm Hg.\n",
    "- Most patients have diastolic blood pressure levels within the range of 62 to 80 mm Hg.\n",
    "\n",
    "iv. SK (Triceps Skinfold Thickness):\n",
    "- The mean triceps skinfold thickness is around 20.44 mm.\n",
    "- The values range from a minimum of 0 mm (which might indicate missing data) to a maximum of 49 mm.\n",
    "- As in the Train dataset, there is notable variability in skinfold thickness among patients.\n",
    "\n",
    "v. TS (2-Hour Serum Insulin):\n",
    "- The mean 2-hour serum insulin level is approximately 81.00 μU/ml.\n",
    "- The values have a wide range, with a minimum of 0 μU/ml and a maximum of 540 μU/ml.\n",
    "- As in the Train dataset, there is significant variability in insulin levels among patients.\n",
    "\n",
    "vi. M11 (Body Mass Index - BMI):\n",
    "- The mean BMI is approximately 32.25, indicating that, on average, patients have a BMI in the overweight range.\n",
    "- BMI values vary widely, with a minimum of 0.100 (unusually low) and a maximum of 57.30.\n",
    "- The standard deviation of 7.45 suggests substantial variability in BMI among patients.\n",
    "\n",
    "vii. BD2 (Diabetes Pedigree Function):\n",
    "- The mean diabetes pedigree function value is 0.439, reflecting the diabetes history in family members.\n",
    "- Values range from a minimum of 0.100 to a maximum of 1.698.\n",
    "- Similar to the Train dataset, the spread in diabetes pedigree function values indicates varying family histories of diabetes.\n",
    "\n",
    "viii. Age:\n",
    "- The average age of patients is approximately 33.07 years, which is similar to the Train dataset.\n",
    "- Ages range from a minimum of 21 years to a maximum of 70 years.\n",
    "- Most patients fall within the range of 24 to 42 years.\n",
    "\n",
    "ix. Insurance:\n",
    "- About 72.8% of the patients have insurance coverage, based on the mean value, which is slightly higher than in the Train dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6052f98",
   "metadata": {},
   "source": [
    "### iv. Checking for Missing Values in The Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7085a1c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in the Train dataset:\n",
      "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\n",
      "======================================================\n",
      "\n",
      "Missing values in the Test dataset:\n",
      "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",
      "dtype: int64\n",
      "======================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values in the datasets\n",
    "datasets = {'train': train_df, 'test': test_df}\n",
    "\n",
    "def show_missing_values(datasets):\n",
    "    for name, data in datasets.items():\n",
    "        print(f\"Missing values in the {name.capitalize()} dataset:\")\n",
    "        print(data.isnull().sum())\n",
    "        print('===' * 18)\n",
    "        print()\n",
    "\n",
    "show_missing_values(datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e593a5",
   "metadata": {},
   "source": [
    "Both datasets do not have any missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438aedd8",
   "metadata": {},
   "source": [
    "### v. Checking for Duplicates in The Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "59394e92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Duplicate Rows in Train Dataset\n",
      "No Duplicate Rows in Test Dataset\n"
     ]
    }
   ],
   "source": [
    "# Check for duplicates in the Train dataset\n",
    "train_duplicates = train_df[train_df.duplicated()]\n",
    "\n",
    "# Check for duplicates in the Test dataset\n",
    "test_duplicates = test_df[test_df.duplicated()]\n",
    "\n",
    "# Display the duplicate rows in the Train dataset, if any\n",
    "if not train_duplicates.empty:\n",
    "    print(\"Duplicate Rows in Train Dataset:\")\n",
    "    print(train_duplicates)\n",
    "else:\n",
    "    print(\"No Duplicate Rows in Train Dataset\")\n",
    "\n",
    "# Display the duplicate rows in the Test dataset, if any\n",
    "if not test_duplicates.empty:\n",
    "    print(\"\\nDuplicate Rows in Test Dataset:\")\n",
    "    print(test_duplicates)\n",
    "else:\n",
    "    print(\"No Duplicate Rows in Test Dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "803cced2",
   "metadata": {},
   "source": [
    "# Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4003f2f",
   "metadata": {},
   "source": [
    "## i. Univariate Analysis for 'PRG' (Number of Pregnancies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bbc41324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for Number of Pregnancies (PRG):\n",
      "count    599.000000\n",
      "mean       3.824708\n",
      "std        3.362839\n",
      "min        0.000000\n",
      "25%        1.000000\n",
      "50%        3.000000\n",
      "75%        6.000000\n",
      "max       17.000000\n",
      "Name: PRG, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Extract the 'PRG' column\n",
    "prg_values = train_df['PRG']\n",
    "\n",
    "# Plot a histogram\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(prg_values, kde=True, color=sns.color_palette('viridis')[0])\n",
    "plt.title('Distribution of Number of Pregnancies (PRG)')\n",
    "plt.xlabel('PRG (Number of Pregnancies)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "# Summary statistics\n",
    "print('Summary Statistics for Number of Pregnancies (PRG):')\n",
    "print(prg_values.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5f4bef",
   "metadata": {},
   "source": [
    "- The average number of pregnancies is approximately 3.83, suggesting that, on average, patients have had several pregnancies.\n",
    "- The range of values varies from 0 (no pregnancies) to a maximum of 17 pregnancies.\n",
    "- The majority of patients fall within the range of 1 to 6 pregnancies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c71d51",
   "metadata": {},
   "source": [
    "## ii. Univariate Analysis for 'PL' (Plasma Glucose Concentration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "888cfd62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for Plasma Glucose Concentration (PL):\n",
      "count    599.000000\n",
      "mean     120.153589\n",
      "std       32.682364\n",
      "min        0.000000\n",
      "25%       99.000000\n",
      "50%      116.000000\n",
      "75%      140.000000\n",
      "max      198.000000\n",
      "Name: PL, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Extract the 'PL' column\n",
    "pl_values = train_df['PL']\n",
    "\n",
    "# Plot a histogram\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(pl_values, kde=True, color=sns.color_palette('viridis')[0])\n",
    "plt.title('Distribution of Plasma Glucose Concentration (PL)')\n",
    "plt.xlabel('PL (Plasma Glucose Concentration)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "# Summary statistics\n",
    "print('Summary Statistics for Plasma Glucose Concentration (PL):')\n",
    "print(pl_values.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2fb8af7",
   "metadata": {},
   "source": [
    "- The mean plasma glucose concentration is around 120.15 mg/dL.\n",
    "- The values range from a minimum of 0 mg/dL (which seems unusual) to a maximum of 198 mg/dL.\n",
    "- The standard deviation of 32.68 indicates some variability in glucose levels among patients."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1a12db",
   "metadata": {},
   "source": [
    "## iii. Univariate Analysis for 'PR' (Diastolic Blood Pressure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1f69f029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uvO3zqnLe7lhWPX6v7e70PVAX/v7+CA0NxZkzZxAeHo6cnBxs2bJFf95Vlep5MjMzUVFRgc8++0z/R3/VbdSoUQCgLxwXLlyIf//73zh16hRGjhwJR0dHDBs2DGfOnNH3t337dkyfPh1fffUV+vbtCwcHB0ybNg2pqal1fi3VVc9cdf7PxIkTa2Revnw5hBDIysq6bX9VX9NmZma1Pl7X99Lc3FxfbP/6668YMmQItm3bho8++qjWfqv2dy8fMBDRveM5SkRUL7///ju0Wm2Na9RUN3DgQAwcOBBarRZnzpzBZ599hrlz58LV1RWTJk2q077u5doztf0RVbWtqjCp+iOjtLTUoN2dPv2vC0dHR6SkpNTYXvWpc9X5JvfD3t4ecrm8wfdTXVJSEsLCwjB48ODbnp+Um5uL3377DYsWLcKCBQv020tLS2v8sWlpaYn3338f77//Pm7cuKEfXRo7diyuXLlyxyyOjo6oqKhAenq6QbEkhEBqaip69ux5T6/N2dm51sUT7kfV6OCFCxdu26bq6zElJaVGoZacnKw/jre2q662dnf6HqgLMzMz/UICd1L9+9Le3l4/sjlnzpxan1N1zpaJiQnmzZuHefPmIScnBwcOHMDbb7+Nhx56CAkJCbCwsICTkxNWr16N1atXQ6PRYNeuXViwYAHS0tKwZ88efdbq38dA5fdybd8H1TNXtfnss8/Qp0+fWjPf6Ry1quffrpiq63spl8sN2g0fPhzdu3fH+++/jylTptQYpa7aX0N8rxPR/3BEiYjumUajwZtvvglbW1u8+OKLdXqOQqFA79699atfVU2Dq8soyr2IiIio8cfp1q1bYW1trb/eT9XqbxcvXjRot2vXrhr9qVSqOmcbNmwYDh06pC9YqnzzzTewsLC47R9i98LS0hK9e/fGjh07DHLpdDps2bIFHh4eaNeu3X3v51bFxcWYOXMmKioqaixIcSuZTAYhRI2FFL766itotdrbPs/V1RUzZszAU089haioKBQVFQG4/ddG1cn1W7ZsMdj+008/obCw8I4n39dm5MiROHz4sFGnLJ4/fx7A7adkAZVT14CaryM0NBSRkZH619G3b1+Ym5vXaJeYmKif7gkAgYGBcHNzw7Zt2wymScbHx+PEiRP3/ZruxsLCAkOHDsW5c+fQqVMn/Sjbrbfqo6gAYGdnh4kTJ2LOnDnIysqqsRolULnS5ssvv4zhw4cbTKH18fGp8X0cHR1d52PZv39/2NnZ4fLly7Xm7dGjB0xNTW/7/KrpmjExMXXaX12pVCp88cUXKCkpwZIlS2o8XjWNuUOHDkbdLxEZ4ogSEd1ReHi4ft5+Wloa/vzzT2zatAkKhQI7d+6sMf3pVmvXrsWhQ4cwevRoeHl5oaSkBBs3bgQAPPjggwAAa2treHt745dffsGwYcPg4OAAJyenei9l7e7ujkceeQSLFy+Gm5sbtmzZgv3792P58uWwsLAAAP1Svm+++SYqKipgb2+PnTt34vjx4zX669ixI3bs2IE1a9age/fuNT75vdWiRYvw22+/YejQofjnP/8JBwcHfPfdd/j999+xYsWKGtNt6mvZsmUYPnw4hg4dijfffBOmpqb48ssvER4ejm3btt3TCFx1Go0Gp06dgk6nQ25uLs6dO4eNGzciPj4eK1euxIgRI277XBsbGwwaNAgff/yx/hgePXoUGzZsqLEyV+/evTFmzBh06tQJ9vb2iIyMxLfffou+ffvqj1PHjh0BAMuXL8fIkSOhUCjQqVMnDB8+HA899BDmz5+PvLw89O/fX7/qXdeuXfH000/f02v+4IMPsHv3bgwaNAhvv/02OnbsiJycHOzZswfz5s1DUFDQHZ+fk5OjP/emvLwckZGRWLp0KVQq1W1HVoDKwuaFF17AZ599BrlcjpEjR+pXvfP09MTrr78OoLKQeO+99/D2229j2rRpeOqpp5CZmYn3338fZmZm+pXk5HI5/vWvf2HmzJl49NFH8fzzzyMnJ0e/AmRj+OSTTzBgwAAMHDgQL730Enx8fJCfn49r167h119/1Z9fNnbsWISEhKBHjx5wdnZGfHw8Vq9eDW9vbwQEBCA3NxdDhw7F5MmTERQUBGtra4SGhmLPnj2YMGGCfn9PP/00pk6ditmzZ+Oxxx5DfHw8VqxYccefS7eysrLCZ599hunTpyMrKwsTJ06Ei4sL0tPTceHCBaSnp2PNmjW3fb6Hhwf8/Pxw6tQpvPrqq/f35lUzePBgjBo1Cps2bcKCBQsMVlA8deoUHB0d9d8jRNRAJFxIgoiasKqV4apupqamwsXFRQwePFgsXbpUpKWl1XhO9VW9Tp48KR599FHh7e0tVCqVcHR0FIMHDxa7du0yeN6BAwdE165dhUqlEgD0q1hV9Zeenn7XfQnxv4t//vjjjyI4OFiYmpoKHx8fsWrVqhrPj46OFiNGjBA2NjbC2dlZvPLKK+L333+vsYpbVlaWmDhxorCzsxMymcxgn6hltb5Lly6JsWPHCltbW2Fqaio6d+4sNm3aZNCmagWu//73vwbbq1aeq96+Nn/++ad44IEHhKWlpTA3Nxd9+vQRv/76a6393cuqd1U3hUIh7O3tRffu3cXcuXNFREREjefUtupdYmKieOyxx4S9vb2wtrYWDz/8sAgPD6+xOtmCBQtEjx49hL29vVCpVMLPz0+8/vrrIiMjQ9+mtLRUzJw5Uzg7O+vf+6qVCouLi8X8+fOFt7e3UCqVws3NTbz00ksiOzvbIGNdVr0TQoiEhATx7LPPCrVaLZRKpXB3dxdPPPGEfoW/26m+6p1CoRBeXl5i4sSJ4ty5cwZta/ua1Wq1Yvny5aJdu3ZCqVQKJycnMXXqVJGQkFBjX1999ZXo1KmTMDU1Fba2tmLcuHG1HpevvvpKBAQECFNTU9GuXTuxcePG+7rgbG0AiDlz5tT6WGxsrHj22WdFmzZthFKpFM7OzqJfv35iyZIl+jYrV64U/fr1E05OTsLU1FR4eXmJ5557TsTFxQkhhCgpKRGzZs0SnTp1EjY2NsLc3FwEBgaKRYsWGVx0WqfTiRUrVgg/Pz9hZmYmevToIQ4dOnTbVe+qf89VOXr0qBg9erRwcHAQSqVStGnTRowePfq27W/13nvvCXt7e1FSUmKwva7v5e0uOCtE5c8TuVwunnnmGYPX7O3tLV555ZW79k1E90cmRB2WMSIiIiKiGpKTk+Hr64tvvvkGTz75ZIPv7+DBgxgxYgQiIiLuOtpJRPeHhRIRERHRfZg/fz52796N8+fP39eFfeti6NChaNu2bY0lxonI+HiOEhEREdF9ePfdd2FhYYGkpKQaK9QZU3Z2NgYPHozZs2c32D6I6H84okRERERERFQNlwcnIiIiIiKqhoUSERERERFRNSyUiIiIiIiIqmnxiznodDokJyfD2tr6vi7CSEREREREzZsQAvn5+XB3d7/rKpUtvlBKTk5u0BVoiIiIiIioeUlISICHh8cd27T4Qsna2hpA5ZthY2MjcRoiIiIiIpJKXl4ePD099TXCnbT4Qqlqup2NjQ0LJSIiIiIiqtMpOVzMgYiIiIiIqBoWSkRERERERNWwUCIiIiIiIqqGhRIREREREVE1LJSIiIiIiIiqYaFERERERERUDQslIiIiIiKialgoERERERERVcNCiYiIiIiIqBoWSkRERERERNWwUCIiIiIiIqqGhRIREREREVE1LJSIiIiIiIiqYaFERERERERUjYnUAYiIiFoLjUaDjIwMo/bp5OQELy8vo/ZJREQslIiIiBqFRqNBUFB7FBcXGbVfc3MLXLkSyWKJiMjIWCgRERE1goyMDBQXF2HWM2/D3c04RU1yigZrNy1FRkYGCyUiIiNjoURERNSI3N284OPVTuoYRER0F1zMgYiIiIiIqBoWSkRERERERNWwUCIiIiIiIqqGhRIREREREVE1LJSIiIiIiIiqYaFERERERERUDQslIiIiIiKialgoERERERERVcNCiYiIiIiIqBoWSkRERERERNWwUCIiIiIiIqqGhRIREREREVE1LJSIiIiIiIiqYaFERERERERUDQslIiIiIiKialgoERERERERVcNCiYiIiIiIqBoWSkRERERERNWwUCIiIiIiIqqGhRIREREREVE1LJSIiIiIiIiqYaFERERERERUDQslIiIiIiKiaiQtlCoqKvDuu+/C19cX5ubm8PPzwwcffACdTqdvI4TA4sWL4e7uDnNzcwwZMgQRERESpiYiIiIiopZO0kJp+fLlWLt2LT7//HNERkZixYoV+Pjjj/HZZ5/p26xYsQKrVq3C559/jtDQUKjVagwfPhz5+fkSJiciIiIiopZM0kLp5MmTGDduHEaPHg0fHx9MnDgRI0aMwJkzZwBUjiatXr0a77zzDiZMmICQkBBs3rwZRUVF2Lp1q5TRiYiIiIioBZO0UBowYAAOHjyI6OhoAMCFCxdw/PhxjBo1CgAQGxuL1NRUjBgxQv8clUqFwYMH48SJE7X2WVpairy8PIMbERERERHRvTCRcufz589Hbm4ugoKCoFAooNVq8eGHH+Kpp54CAKSmpgIAXF1dDZ7n6uqK+Pj4WvtctmwZ3n///YYNTkRERERELZqkI0rbt2/Hli1bsHXrVpw9exabN2/Gv//9b2zevNmgnUwmM7gvhKixrcrChQuRm5urvyUkJDRYfiIiIiIiapkkHVH6xz/+gQULFmDSpEkAgI4dOyI+Ph7Lli3D9OnToVarAVSOLLm5uemfl5aWVmOUqYpKpYJKpWr48ERERERE1GJJOqJUVFQEudwwgkKh0C8P7uvrC7Vajf379+sfLysrw9GjR9GvX79GzUpERERERK2HpCNKY8eOxYcffggvLy8EBwfj3LlzWLVqFZ599lkAlVPu5s6di6VLlyIgIAABAQFYunQpLCwsMHnyZCmjExERERFRCyZpofTZZ5/hvffew+zZs5GWlgZ3d3e8+OKL+Oc//6lv89Zbb6G4uBizZ89GdnY2evfujX379sHa2lrC5ERERERE1JJJWihZW1tj9erVWL169W3byGQyLF68GIsXL260XERERERE1LpJeo4SERERERFRU8RCiYiIiIiIqBoWSkRERERERNWwUCIiIiIiIqqGhRIREREREVE1LJSIiIiIiIiqYaFERERERERUDQslIiIiIiKialgoERERERERVcNCiYiIiIiIqBoWSkRERERERNWwUCIiIiIiIqqGhRIREREREVE1LJSIiIiIiIiqYaFERERERERUDQslIiIiIiKialgoERERERERVWMidQAiIiJqWjQaDTIyMozer5OTE7y8vIzeLxFRQ2ChRERERHoajQZBQe1RXFxk9L7NzS1w5UokiyUiahZYKBEREZFeRkYGiouLMOuZt+HuZryCJjlFg7WbliIjI4OFEhE1CyyUiIiIqAZ3Ny/4eLWTOgYRkWS4mAMREREREVE1LJSIiIiIiIiqYaFERERERERUDQslIiIiIiKiariYAxERUTUNcR2hyMhIo/ZHREQNi4USERHRLRryOkIAUFBQ0CD9EhGRcbFQIiIiukVDXUfowqXT+OnXjSgpKTFan0RE1HBYKBEREdXC2NcRSk7VGK0vIiJqeFzMgYiIiIiIqBoWSkRERERERNWwUCIiIiIiIqqGhRIREREREVE1LJSIiIiIiIiqYaFERERERERUDQslIiIiIiKialgoERERERERVcNCiYiIiIiIqBoWSkRERERERNWwUCIiIiIiIqqGhRIREREREVE1LJSIiIiIiIiqYaFERETUzFWUayGEkDoGEVGLYiJ1ACIiIqq7osJSxMWkITujABnpWRja6R94b/ZvMFXtga2dJWztLeDt54xeAwPRs29bWFqbSR2ZiKhZYqFERETUxGm1OiTGZyAmKhUpiVm4dfBIaWIOACgrrUD6jVyk38jFtSspOPjHRSgUcnTq4YPxk/qg98B2kMlkEr0CIqLmh4USERFRE5aSmIXTf11Ffm6xfpuz2hZtPB1QUp6DjduWYffeX9DWLxA52YXIySpE+Ll4nDoWhYS4DJz7+zrO/X0dgcFtMP2lB9Ctjz8LJiKiOmChRERE1AQVFZYi7FQM4mPSAABm5kq0DXKDXzs1bGwtAABxmlIUFKfBwtIU6jb2ULexBwD0GRSIma+NQJImE7t/DsOu708jKiIJb7/8Lbr09MUbi8bDxc1OqpdGRNQscDEHIiKiJiZJk4lffziN+Jg0yGRAYHAbPPJkb3Tp6acvkuqijZcjZr46Al/veg3jn+oDpVKB86GxmDVpDY7uC2/AV0BE1PyxUCIiImoihBC4Ep6II3svobxcCwdnazz8aHf07B8AU9P6TwJxcLLGS2+OxPr/voygEA8UFpRg6cL/4t+Ld6K4qNSIr4CIqOVgoURERNQE6HQ6hP51FWdOXIMQgH+gGg890hWOTtZG24e7pwNWfvUsnnpuEGQyGfb/eh5vPr8J2ZkFRtsHEVFLwUKJiIhIYjqdDkf3RSD6cjIAoFtvP/QZFAiFwvi/pk2UCsyYPQwr1s2Arb0lrl1JwevPbkBKYpbR90VE1JyxUCIiIpKQEAInjlxBkiYTCoUcg0eEoENnrwZfma5Tdx+s2vAsXN3tkJKYhdef3YBrV1IadJ9ERM0JCyUiIiKJCCEQ+tdVxF1Lg0wmw6DhwfD0cWq0/Xt4O+H/Ns6EXzs1sjML8I8XNiEpPqfR9k9E1JSxUCIiIpLIhTOx+ul2/YcGoY2XY6NncHS2xr/XP4OQrt4oKizFpk9OwkLl0Og5iIiaGhZKREREErh2JQXh5zQAgF4D2sGnratkWSytzfDB6skIaO+OwvwydG87FaXFWsnyEBE1BSyUiIiIGllWRj5O/xUNoPJcoXYd3CVOBFhamWHJZ1Ph5GoJc5Udws9koqSkTOpYRESSYaFERETUiLQVAsf2R0CnFWjj5YiO3byljqRnZ2+J517vh5KyXBQXVuDo3nBotTqpYxERSYKFEhERUSNKia1AQX4JrKzN0G9oUIOvbnev7BwtEHbtOyhMZEi/kYczJ65JHYmISBIslIiIiBqJj2t/FOQKyBUyDBweDJVKKXWkWhWWZCCwkz0A4GpkMq5GJkuciIio8bFQIiIiagSlRUBb9yEAgJ79AuDoZC1toLtwcDFDl56+AIDQv64iPTVX4kRERI2LhRIREVED0+l0SE9QQC5TwMpOhrZBblJHqpPgLl7w8nWGTidwdH8EiotKpY5ERNRoWCgRERE1sIjzGpQVy1BWUQS1t0mTOy/pdmQyGfoOCYSdvSVKistw8mgUhBBSxyIiahQslIiIiBpQTlYBLp2NBwBEJe6FibJ5FElVlEoTDHiwAxQKOZITsnDlUqLUkYiIGgULJSIiogai0+lw4mgUdDoBCxsdUrIuSR2pXuzsLdG9rz8A4Nzp68jKyJc4ERFRw2OhRERE1ECiIpKQlZ4PU1MTOHk27+sRBbR3h6ePE3Q6geOHIlFRrpU6EhFRg2KhRERE1ABKistw8UwcAKBrbz+YNM2VwOtMJpOhz6BAmFuYIi+nCGGneH0lImrZWCgRERE1gPOhsSgv18LByQr+gc1jlbu7UZkp0X9oewDA1cgUpCRlS5yIiKjhsFAiIiIyssyMfFy7kgIA6NEvAHJ581rA4U7UbezRroM7AODUsSiUl1dInIiIqGGwUCIiIjIiIQTO/HUVAODT1gUualuJExlf195+sLRSoTC/BOdPx0odh4ioQbBQIiIiMqK4mDSk38iDwkSOrr38pI7TIJRKE/QZFAigcsGKtJQcaQMRETUAFkpERERGoq3Q4tzf1wEAIV28YGllJnGihuPm4YC2QZXnXp08GoWKCq6CR0QtCwslIiIiI4m+nIyiwlJYWKrQvpOn1HEaXLc+/rCwNEV+XjHCb15Ul4iopTCROgAREVFLUF5WgfDzGgBAp+4+MDFRSJyo4ZmamqBn/wAc3ReByxcT4BvgClt7yzs+JzIy0qgZnJyc4OXlZdQ+iYiAJlAoJSUlYf78+di9ezeKi4vRrl07bNiwAd27dwdQeVLs+++/j/Xr1yM7Oxu9e/fGF198geDgYImTExER/c+V8ESUlpTD2tYcfu1cpY7TaDy8ndDGyxFJmkycPn4VD47pDJms5ip/OblZAICpU6cadf/m5ha4ciWSxRIRGZ2khVJ2djb69++PoUOHYvfu3XBxcUFMTAzs7Oz0bVasWIFVq1bh66+/Rrt27bBkyRIMHz4cUVFRsLa2li48ERHRTaUl5bh8IQEA0Lm7D+Ty1jOzXSaToWf/tkhNysaNlBzEXUuDb0DNQrGouAAAMOnR2ejQvpNR9p2cosHaTUuRkZHBQomIjE7SQmn58uXw9PTEpk2b9Nt8fHz0/xdCYPXq1XjnnXcwYcIEAMDmzZvh6uqKrVu34sUXX2zsyERERDVcvqBBebkWdg6W8PZ3kTpOo7OyNkfHbt44HxqLsFPX0MbLAaYqZa1tXZzd4ePVrpETEhHdO0k/8tq1axd69OiBxx9/HC4uLujatSv+85//6B+PjY1FamoqRowYod+mUqkwePBgnDhxotY+S0tLkZeXZ3AjIiJqKEVFpbgSngQA6NLTt9ZpZ61B+06esLGzQElxOc6H8tpKRNT8SVooXb9+HWvWrEFAQAD27t2LWbNm4dVXX8U333wDAEhNTQUAuLoaDuG7urrqH6tu2bJlsLW11d88PVv+qkNERCSdy+c10Gp1cHK1QRsvR6njSEahkKPXgAAAwNXIZGRnFkiciIjo/khaKOl0OnTr1g1Lly5F165d8eKLL+L555/HmjVrDNpV/3ROCHHbT+wWLlyI3Nxc/S0hIaHB8hMRUetWUlyGq5EpACpXumuto0lV1O728PZzhhDAmZPXIISQOhIRUb1JWii5ubmhQ4cOBtvat28PjaZyeVW1Wg0ANUaP0tLSaowyVVGpVLCxsTG4ERERNYQrlxKh1erg4GwNtzb2UsdpErr29odCIceN5BwkxGVIHYeIqN4kLZT69++PqKgog23R0dHw9vYGAPj6+kKtVmP//v36x8vKynD06FH069evUbMSERHdqqysAlERlecmhXTxavWjSVWsrM3QoXPltPewUzHQVmglTkREVD+SFkqvv/46Tp06haVLl+LatWvYunUr1q9fjzlz5gConHI3d+5cLF26FDt37kR4eDhmzJgBCwsLTJ48WcroRETUykVHJKG8XAtbewt4+jhJHadJCe7sBQtLUxTml+DyxUSp4xAR1Yuky4P37NkTO3fuxMKFC/HBBx/A19cXq1evxpQpU/Rt3nrrLRQXF2P27Nn6C87u27eP11AiIiLJVFRoEXmpsgAI5mhSDSZKBbr29sdfhyIRfj4e/oFqqSMREd0zSQslABgzZgzGjBlz28dlMhkWL16MxYsXN14oIiKiO7h2JQWlJeWwsjaDTyu8blJd+Pi7IDoiCek38iqXC7eQOhER0b1pPZcOJyIiMgKtVofLFypXVO3Q2QtyOX+V1kYmk6F737YAgOvRqSgtljgQEdE94k93IiKie6C5no6iwlKYmZtyStldOLnYwNvPGQCQlcw/OYioeeFPLSIiojoSQiDyUuVoUmBwGygU/DV6N116+UEul6E4Xw5Ha3+p4xAR1Rl/whMREdVRWmousjIKoFDIEdDBTeo4zYK1jTnaBbcBALTzeJAXoSWiZoOFEhERUR1FXqwcTfJr5wozM1OJ0zQfHbt6Q64QsDZ3RW6mTuo4RER1wkKJiIioDvJzi5AYnwkACOroIXGa5kVlpoSda2WBlJGkRQUvQktEzQALJSIiojq4Ep4EAGjj5QBbO0uJ0zQ/tk4CxaU5qCivvFgvEVFTx0KJiIjoLkpLy3EtKgUAENTRU+I0zZNMDsSkHAUAhJ/XoKysQuJERER3xkKJiIjoLq5FpkBboYOdgyXU7nZSx2m2UrIuwtQMKCut0J/vRUTUVLFQIiIiugOdTiD6cuVUsfYdPSCTySRO1HwJCDi5mwAAIi8loqS4TOJERES3x0KJiIjoDpI0mSgsKIWpygTe/i5Sx2n2rO1lcHCyQkW5FhHnNVLHISK6LRZKREREd1A1mtQ20A0mJgqJ0zR/MpkMXXr6AgCiLiehsKBE4kRERLVjoURERHQbeTlFSEnMBgC06+AucZqWw83DAS5uttBpBS6djZc6DhFRrVgoERER3Ub05WQAlUuCW9mYS5ym5agcVfIDAMREpSAvp0jiRERENbFQIiIiqoW2QoeY6Molwdt1aCNxmpbHRW2LNl4OEAK4EBYndRwiohpYKBEREdUiPaUY5WVaWNmYwd3TQeo4LVLnHpXnKsXHpCErI1/iNEREhlgoERER1SJFUwigcjSJS4I3DAcna/1KghfOxEqchojIEAslIiKiamwtPVCYXwGFQg7/dmqp47Ronbv7QCYDkjRZSEvNlToOEZEeCyUiIqJqPJy6AQC8/Z2hMlNKnKZls7GzgH+gGwDg/OnrEEJInIiIqBILJSIioluUFJVDbR8MAAgI4pLgjaFjN2/IFTKkpeYiNSlb6jhERABYKBERERk493ciFHIlLKxM4ORqI3WcVsHSykxflF4Mi+OoEhE1CSb1eVJsbCx8fX2NnYWIiOieaTQaZGRkGKUvIQT+3HcFAKD2sOAiDo0ouIsXrl1JQfqNPKQkZcPdgysNEpG06lUotW3bFoMGDcJzzz2HiRMnwszMzNi5iIiI7kqj0SAoqD2Ki41zwVIbC3f0CZoJra4cFrYc1WhMFpYqBLR3w5XwJFw8Ewe3NvYsVIlIUvUqlC5cuICNGzfijTfewMsvv4wnn3wSzz33HHr16mXsfERERLeVkZGB4uIizHrmbbi7ed13f1fDc3AjsQg3si+jQtvTCAnpXnTo4oWrkSnISMtDSmIW3D0dpY5ERK1YvQqlkJAQrFq1CitWrMCvv/6Kr7/+GgMGDEBAQACee+45PP3003B2djZ2ViIiolq5u3nBx6vdffVRVlaBUwdOAAASM84CYKHU2CwsVAjo4I4rlxJx4Uwc3DwcOKpERJK5r8UcTExM8Oijj+KHH37A8uXLERMTgzfffBMeHh6YNm0aUlJSjJWTiIioQcVdS0NFhQ5KlUBOYYLUcVqt4M5eUCjkyEzPR3JCltRxiKgVu69C6cyZM5g9ezbc3NywatUqvPnmm4iJicGhQ4eQlJSEcePGGSsnERFRg7oWVfnhnrWjTuIkrZu5hSnaBbcBAFzgCnhEJKF6Tb1btWoVNm3ahKioKIwaNQrffPMNRo0aBbm8su7y9fXFunXrEBQUZNSwREREDSE7swBZ6fmQy2WwduAf5lIL7uyJ6MtJyErPR5ImEx7eTlJHIqJWqF4jSmvWrMHkyZOh0Wjw888/Y8yYMfoiqYqXlxc2bNhglJBEREQNKSYqFQDg4e0ERb0+QiRjMjM3ReDNUSVeV4mIpFKvXwdXr169axtTU1NMnz69Pt0TERE1Gq1Wh9irlYWSf6Aa8ak8v7Yp6NDZE9ERScjKKEBifCY8fTiqRESNq14jSps2bcJ///vfGtv/+9//YvPmzfcdioiIqLEkxmegtLQCFpamcONFTpsMMzNTBIZ4AOCoEhFJo16F0kcffQQnp5qf7Li4uGDp0qX3HYqIiKixVE278wtQQy7nUtRNSYdOnjBRKpCdWYCEuAyp4xBRK1OvQik+Ph6+vr41tnt7e0Oj0dx3KCIiosZQWFCClMTKJaj9A9USp6HqVGZKBPFcJSKSSL0KJRcXF1y8eLHG9gsXLsDRkVfRJiKi5iH26g0IAbi42cLa1kLqOFSL9p08oVQqkJNVyFElImpU9SqUJk2ahFdffRWHDx+GVquFVqvFoUOH8Nprr2HSpEnGzkhERGR0Qgj9tZP8A90kTkO3ozJTIjCkclTp0lmOKhFR46nXqndLlixBfHw8hg0bBhOTyi50Oh2mTZvGc5SIiKhZSE/NRUFeCZRKBbx9naWOQ3fQvqMnroQnITuzEInxGfD04fEiooZXr0LJ1NQU27dvx7/+9S9cuHAB5ubm6NixI7y9vY2dj4iIqEFcv3oDAODl5wwTpULiNHQnKjMlAoPbIOK8BhfD4uHh7QSZjAtvEFHDuq/L6rVr1w7t2rUzVhYiIqJGUVGhRXxMGoDK1e6o6WvfyQNREUnIzuR1lYiocdSrUNJqtfj6669x8OBBpKWlQafTGTx+6NAho4QjIiJqCInxmSgv18LSSgUXN1up41AdmJmZ6keVLp2Ng4c3F48iooZVr0Lptddew9dff43Ro0cjJCSEw99ERNSsXI+uvHaSb4Cav8OakfadPBAVnoisjAIkaTIBHjoiakD1KpS+//57/PDDDxg1apSx8xARETWo4qIy/bWT/AJcJU5D98LMzBTtgtvg8oUEXAyLQ/vu1lJHIqIWrF7Lg5uamqJt27bGzkJERNTg4mIqr53k5GIDGzteO6m56dDJEwoTObIyCpCdXip1HCJqwepVKL3xxhv45JNPeC0DIiJqdq5HV65258vRpGbJzNwU7TpUXldJE5MvcRoiasnqNfXu+PHjOHz4MHbv3o3g4GAolUqDx3fs2GGUcERERMaUnVWA7MwCyOUy+Pi7SB2H6im4syeiI5JQkFsOJxvOcCGihlGvQsnOzg6PPvqosbMQERE1qNibo0ltvByhMlPepTU1VWbmpmgX7I7Ii4nwcxvEGS5E1CDqVSht2rTJ2DmIiIgalE4nEHutslDya8dpd81dh05eiApPgp2lB6Ij0tC9u9SJiKilqdc5SgBQUVGBAwcOYN26dcjPr5wjnJycjIKCAqOFIyIiMpbU5GwUF5XBVGUCd09eg6e5M7cwhdqzcjGOg79GcVSJiIyuXiNK8fHxePjhh6HRaFBaWorhw4fD2toaK1asQElJCdauXWvsnERERPelatqdj78LFIp6f05ITYiHnxUSYnOQcD0b27fsRbtg45x35uTkBC8vL6P0RUTNV70vONujRw9cuHABjo7/+1Tu0UcfxcyZM40WjoiIyBjKyyqgiUsHAPi1U0uchoylqCQXielh8Hbtg1VLfkBotHFODTA3t8CVK5EslohauXqvevfXX3/B1NTUYLu3tzeSkpKMEoyIiMhYNHEZ0FboYG1rDkdnXqS0pSgqLkDsjRPwVveGvZUn5r3wCeycVPfVZ3KKBms3LUVGRgYLJaJWrl6Fkk6ng1arrbE9MTER1tb8BURERE1LbHQqAMAvQA2ZTCZxGjKmsooC2DsrkJ2mw42EcnTuGsJjTERGUa9J2sOHD8fq1av192UyGQoKCrBo0SKMGjXKWNmIiIjuW2FBCVKTcwDwIrMtlYNaAblChvQbefpjTUR0v+pVKP3f//0fjh49ig4dOqCkpASTJ0+Gj48PkpKSsHz5cmNnJCIiqrfYq5WLOLi62cHK2kziNNQQlKYyBAS5AwAuhcVxBTwiMop6Tb1zd3fH+fPnsW3bNpw9exY6nQ7PPfccpkyZAnNzc2NnJCIiqhchBK5f5bWTWoPgLl64GpmMtNRc3EjJgdrdXupIRNTM1atQAgBzc3M8++yzePbZZ42Zh4iIyGiyMvKRl1MEhUIOT19nqeNQA7KwVKFtkBuiLyfjYlgcCyUium/1KpS++eabOz4+bdq0eoUhIiIypus3r53k6esEU9N6fzZIzURwFy9cu5KCtJRcpCZns1giovtS7+so3aq8vBxFRUUwNTWFhYUFCyUiIpKcVqtDXEwaAMCPizi0CpZWZvpRpUth8SyUiOi+1Gsxh+zsbINbQUEBoqKiMGDAAGzbts3YGYmIiO5ZckIWSkvKYWZuCnUb/sHcWgR38YJcLsONlBzcSMmROg4RNWP1KpRqExAQgI8++qjGaBMREZEUYq9WXjvJN8AFcrnRft1RE2dpZQb/QDWAyhXwiIjqy6i/ORQKBZKTk43ZJRER0T0rLS1HYnwmgMqLzFLrEtzFG3K5DKnJOUhLzZE6DhE1U/U6R2nXrl0G94UQSElJweeff47+/fsbJRgREVF9xcekQ6cTsHe0hL2jldRxqJFZWZvBr50a166k4GJYPB4cbSd1JCJqhupVKI0fP97gvkwmg7OzMx544AGsXLnSGLmIiIjq7X/T7jia1FqFdPVCTFQqUpOykZaaCxe1rdSRiKiZqVehpNPpjJ2DiIjIKPJzi5B+Iw8yGeDb1kXqOCQRK2tz+AdWjipdCovDsNGdpY5ERM0Mz24lIqIW5frVymsnuXk4wNxCJXEaklJIFy/IZDKkJGUj/Uau1HGIqJmp14jSvHnz6tx21apV9dkFERHRPRNC6AslX147qdWzsjGHXztXxESl4mJYHIaN4qgSEdVdvQqlc+fO4ezZs6ioqEBgYCAAIDo6GgqFAt26ddO3k8lkxklJRERUB+mpuSjML4FSqYCnj5PUcagJCOnqjevRqUhJzEZGWh6cXGykjkREzUS9CqWxY8fC2toamzdvhr195UX8srOz8cwzz2DgwIF44403jBqSiIioLqpGk7z8nGFiopA4DTUF1jbm8AtQIya6clTpgZGdpI5ERM1Evc5RWrlyJZYtW6YvkgDA3t4eS5Ys4ap3REQkiYoKLeKvpwHgtZPIUEg3b8hkQHJCFjLS8qSOQ0TNRL0Kpby8PNy4caPG9rS0NOTn5993KCIionuVFJ+J8jItLK1UcHHjUtD0P9Y25vpz1i6GxUkbhoiajXoVSo8++iieeeYZ/Pjjj0hMTERiYiJ+/PFHPPfcc5gwYYKxMxIREd3Vdf21k1x5jizV0LErR5WI6N7Uq1Bau3YtRo8ejalTp8Lb2xve3t6YMmUKRo4ciS+//NLYGYmIiO6orFSL5IQsALzILNXO2tYCvm0rR5UunY2XOA0RNQf1WszBwsICX375JT7++GPExMRACIG2bdvC0tLS2PmIiIjuKj2lGEIATi7WsLWzkDoONVEh3bwRe+0GkjSZyEzPh6OztdSRiKgJu68LzqakpCAlJQXt2rWDpaUlhBDGykVERFRnacnFADiaRHdmY2sBH3+eq0REdVOvQikzMxPDhg1Du3btMGrUKKSkpAAAZs6cWe+lwZctWwaZTIa5c+fqtwkhsHjxYri7u8Pc3BxDhgxBREREvfonIqKWycrMGYV55ZDLZfDxd5E6DjVxVSvgJWkykZnBBaiI6PbqVSi9/vrrUCqV0Gg0sLD43xSHJ598Env27Lnn/kJDQ7F+/Xp06mR4bYMVK1Zg1apV+PzzzxEaGgq1Wo3hw4dzZT0iItJzc+wMAGjj5QiVmVLiNNTU2dpZwPtmQX2Jo0pEdAf1KpT27duH5cuXw8PDw2B7QEAA4uPv7QTJgoICTJkyBf/5z38MrsskhMDq1avxzjvvYMKECQgJCcHmzZtRVFSErVu31ic2ERG1MFqtDm72HQEAfu1cJU5DzUXHbt4AgMT4TGRxVImIbqNehVJhYaHBSFKVjIwMqFSqe+przpw5GD16NB588EGD7bGxsUhNTcWIESP021QqFQYPHowTJ07ctr/S0lLk5eUZ3IiIqGW6FpkOM1NrmCjlcPd0lDoONRO2dpb6aZpcAY+IbqdehdKgQYPwzTff6O/LZDLodDp8/PHHGDp0aJ37+f777xEWFoZly5bVeCw1tfJ6GK6uhp8Qurq66h+rzbJly2Bra6u/eXp61jkPERE1L+dOJgAAnN3MoVDc1/pE1MpUjSolxGUgO7NA4jRE1BTVa3nwjz/+GEOGDMGZM2dQVlaGt956CxEREcjKysJff/1Vpz4SEhLw2muvYd++fTAzM7ttu+oXDRRC3PFCggsXLsS8efP09/Py8lgsERG1QIX5JYg4V7mYkEsbc4nTUHNja28Jbz9nxF9Px8WwOAweESJ1JCJqYur18VuHDh1w8eJF9OrVC8OHD0dhYSEmTJiAc+fOwd/fv059hIWFIS0tDd27d4eJiQlMTExw9OhRfPrppzAxMdGPJFUfPUpLS6sxynQrlUoFGxsbgxsREbU8xw5EoKJch4LiNFjZcBEHuncdu/kA4KgSEdXunkeUysvLMWLECKxbtw7vv/9+vXc8bNgwXLp0yWDbM888g6CgIMyfPx9+fn5Qq9XYv38/unbtCgAoKyvD0aNHsXz58nrvl4iIWob9v50HACRnXYRM1kXSLNQ82Tn8b1Tp0tl4DBoeLHUkImpC7rlQUiqVCA8Pv+P0t7qwtrZGSIjhMLelpSUcHR312+fOnYulS5ciICAAAQEBWLp0KSwsLDB58uT72jcRETVvyQlZiDivgUwGpGRduvsTiG6jYzdvxF9PhyY2HTlZHFUiov+p19S7adOmYcOGDcbOUsNbb72FuXPnYvbs2ejRoweSkpKwb98+WFtbN/i+iYio6Tr4xwUAQNv2zigt5/LOVH92Dlbw8nUGwBXwiMhQvRZzKCsrw1dffYX9+/ejR48esLS0NHh81apV9Qpz5MgRg/symQyLFy/G4sWL69UfERG1PDqdDgd+ryyUuvXzArZIHIiavY7dvaGJTUf89XQ4uDlLHYeImoh7KpSuX78OHx8fhIeHo1u3bgCA6Ohogzb3OyWPiIjoTiLOa5CalA0LSxU6dFFLHYdaAHsHK3j6OCEhLgMJMRyhJKJK91QoBQQEICUlBYcPHwYAPPnkk/j000/vuAodERGRMR34rXI0aeCDHWCqqtfECKIaOnX3QUJcBjJSS2Bp5iR1HCJqAu7pHCUhhMH93bt3o7Cw0KiBiIiIbqekuAzHDkQAAIaP6SJtGGpR7B0rR5UAwE89UOI0RNQU3NdlzKsXTkRERA3pxJErKCoshau7HYK7eEkdh1qYjt28AQBq+xCkpXAKHlFrd0+Fkkwmq3EOEs9JIiKixnLg5rWTHhzdGXL5fX3WR1SDg5M1HFzMIJPJcOi36Ls/gYhatHua3C2EwIwZM6BSqQAAJSUlmDVrVo1V73bs2GG8hERERAAy0vJw7vR1AMCDo7tIG6aJiYyMbJJ9NUde/lbISivBxdBEJMRl6KfjEVHrc0+F0vTp0w3uT5061ahhiIiIbufQ7ovQ6QSCO3vB3dNB6jhNQk5uFoCG+X1cUNA6L75qZWuKtJwouNgFYtuGo3jrX49JHYmIJHJPhdKmTZsaKgcREdFtCSGwv2ra3ZjO0oZpQoqKK4uZSY/ORof2nYzS54VLp/HTrxtRUlJilP6ao5iUY3CxC8ThPZcweeZgeHhzVImoNeK6qkRE1ORdjUyG5no6lKYmGDw8ROo4TY6Lszt8vNoZpa/kVI1R+mnO8otTENTJFVcu3sC2Dcfwjw8mSB2JiCTAM2GJiKjJ27frHACg35AgWFqbSZyGWoNhYwMBVE75TNJkSpyGiKTAQomIiJq00pJyHNp9CQDw0LhuEqeh1sLDxx69+gdApxPYtuGY1HGISAIslIiIqEk7fugyCgtK4Opmh669fKWOQ63IlBeGAAAO7r6I5IQsacMQUaNjoURERE3anp/PAgBGPNKV106iRhUU4oGe/QOg0+qwbSNHlYhaG/7GISKiJispIRMXw+Igk8kwYmwXqeNQKzT1+SEAgAO/X0BKIkeViFoTFkpERNRk7f2lchGH7n394eJmJ20YapWCOnqge9+20Gl1+H7jn1LHIaJGxEKJiIiaJG2FFvt/PQ8AeHh8d2nDUKs29ea5Svt/O4/UpGxpwxBRo2GhRERETVLoiWvIysiHrb0l+gwyzjWCiOqjQydPdOvtDy3PVSJqVVgoERFRk7T75zAAwIOjO0Op5PXRSVr6UaVfzyM1maNKRK0BCyUiImpyMtPzcfr4VQDAw+N57SSSXnAXL3Tt7QetVoftm3iuElFrwEKJiIianAO/nYdOq0OHzp7w8nWWOg4RgP+tgLdv13ncSMmRNAsRNTwWSkRE1KQIIbDnl8prJz08jqNJ1HSEdPVGl56+qKjQ4nueq0TU4rFQIiKiJuXS2XgkJ2TB3MIUg4YHSx2HyMDTLw4FULl0PVfAI2rZWCgREVGTUjWaNHhECMwtVBKnITIU0tVbvwLe1g1HpY5DRA2IhRIRETUZBfnF+PNABABgJK+dRE3UtJcqR5X2/3YBSQmZEqchoobCQomIiJqMw3suoay0At7+LggMaSN1HKJate/oiV79A6DT6vDdfziqRNRSsVAiIqImY+8v5wBULgkuk8kkTkN0e9NeegAAcHj3RWhi0yVOQ0QNgYUSERE1CTFRKbgamQwTEwWGjewkdRyiOwpo746+g4Og0wl8958jUschogbAQomIiJqE3TvDAAB9hwTB1t5S4jREd/f0rMpzlY7ui0DctRsSpyEiY2OhREREkisuKsXBPy4CAEY/1kPiNER1499OjYHDOkAIgW/XH5E6DhEZGQslIiKS3KHdF1FUWAoPb0d06ekrdRyiOpv6whDIZDIcP3gZMVEpUschIiNioURERJISQuC3H88AqBxN4iIO1Jz4tHXF4BGVF0b+Zt1hidMQkTGxUCIiIklFXkrE9ehUqFRKDB/bVeo4RPdsyvNDIJfLcOpoFKIvJ0kdh4iMhIUSERFJ6rcfQwEAgx8KgbWNucRpiO6dl68zht5cqfHbtRxVImopWCgREZFk8nKKcGx/BABgzMSeEqchqr8pzw+GXCHH6b+u4vLFBKnjEJERsFAiIiLJ7N11DuVlFQho747A4DZSxyGqtzaejhg+pjMAjioRtRQslIiISBI6nQ6//1S5iMOYiVwSnJq/yc8NhkIhx9m/Y3DpXLzUcYjoPrFQIiIiSYSdjEFKYhYsrcww5KGOUschum/qNvZ4aHw3AMA3aw5JnIaI7hcLJSIiksTP358CADw8vhvMzE0lTkNkHE89OwhKpQIXw+JwPjRW6jhEdB9YKBERUaNLiMvAmRPXIJPJMPZxLuJALYeL2hYjJ3QHUDmqJISQOBER1ZeJ1AGIiKj12fXD3wCA3oPawc3DQeI0RDVFRkbW+7nBPezwx09yRFzQYPuWvWgX7AIAcHJygpeXl7EiElEDY6FERESNqjC/BPt/PQ8AGD+pj7RhiKrJyc0CAEydOvW++glsMwLern2w+sMf8XfUBgCAubkFrlyJZLFE1EywUCIioka179dzKC4qg5efM7r09JU6DpGBouICAMCkR2ejQ/tO9e6nrFSLM0fTYGvZBnNnrkaJNg1rNy1FRkYGCyWiZoKFEhERNRqdTodftp8GAIyf1BsymUziRES1c3F2h49Xu/vqoyDTFJcvJiBVU4b23T2NlIyIGgsLpUam0WiQkZFh9H4575mImoPQv64hJTELVtZmGDaqs9RxiBpUhy6eiI5MRlZGAbLSlFLHIaJ7xEKpEWk0GgQFtUdxcZHR++a8ZyJqDnZuOwkAGPFIVy4JTi2emZkpgoLbIPy8BvHX8qWOQ0T3iIVSI8rIyEBxcRFmPfM23N2MV9Akp2g475mImryYqBSc+/s65Ao5xj/FRRyodWjfyRNREUkoyq+Aq117qeMQ0T1goSQBdzev+573TETU3Pz0XeVo0qAHO8DVzU7aMESNRGWmRFBHD1w6Gw9/t8HQ6XhdJaLmgoUSERE1uPQbuTiy5xIA4LGp/SROQ9S42nf0wOWLGliZu2D3z6chlxtvEROeo0zUcFgoERFRg/tl+9/QanXo2M0b7Tq0kToOUaMyVSnh6KZAWkIF9u6MwKIP50DAOCNLPEeZqOGwUCIiogZVVFiKP34KA8DRJGq9LOxKUHa9FJZmjnhp2r+h9rC87z55jjJRw2KhREREDWrvL2dRWFACD29H9B7I8zOpdZIrgNgbfyHQYwSSY0vQs3dnKBRyqWMR0R3wO5SIiBqMtkKLnVtPAQAmTOkLuZy/dqj1Skg/AxNl5Sjr1chkqeMQ0V3wNxYRETWYo/sjcCMlB7Z2FnhwdBep4xBJSicq4OiuAACEn4tHRblW4kREdCcslIiIqEHodDps3/QnAGD85D5QmSklTkQkPTtHOayszVBSXI4r4YlSxyGiO2ChREREDeL08auIi0mDhaUKjzzRS+o4RE2CTC5Dpx4+AIDLFxJQVloubSAiui0WSkREZHRCCGzbcAwAMGZiT1hZm0uciKjp8PF3ha29BcrKKnD5IkeViJoqFkpERGR0F87E4Up4IkxVJpgwpa/UcYiaFLlchs49fAEAVy4loKS4TOJERFQbFkpERGR0VecmPTSuG+wdrSROQ9T0ePo4wcHZGhUVOoSf10gdh4hqwUKJiIiMKioiCWf/joFCIcfj0/pLHYeoSZLJZOjSs3JUKfpyEgoLSiRORETVsVAiIiKjqjo3aejDHeHqZidtGKImzK2NPVzcbKHTClw6Gy91HCKqhoUSEREZzdXIZJw8egVyuQxPPjNQ6jhETdqto0oxUanIzy2SOBER3YqFEhERGc2W9UcAAEMe6ggvX2dpwxA1Ay5qO7h7OkAIgQthcVLHIaJbsFAiIiKjuBqZjFPHoiCXyzB55mCp4xA1G1WjSnHX0pCVkS9xGiKqwkKJiIiM4tt1hwFUnpvk6eMkcRqi5sPByRo+/i4AgPOhsRKnIaIqJlIHICKi5i8qIgl//xl919EkjUaDjIwMo+03MjLSaH0RSalzT1/EX09HckIWUpOyoW5jL3UkolaPhRIREd23LTdHkx4Y2Qke3rWPJmk0GgQFtUdxsfFPWC8oKDB6n0SNydrGHO06uCMqIgnnTl/Hw+O7QSaTSR2LqFVjoURERPcl8lICTv91FXKFHJOfv/1oUkZGBoqLizDrmbfh7uZllH1fuHQaP/26ESUlvAYNNX8du3kjJjoVmen50MSmw9vPRepIRK0aCyUiIqo3IQQ2fnYAAPDg6M5o4+l41+e4u3nBx6udUfafnKoxSj9ETYGZuSk6dPLExbA4nA+NhaePE+Rynk5OJBV+9xERUb2dOXENF8PioDQ1wdMvDpU6DlGz176jB1RmSuTnFuPalRSp4xC1aiyUiIioXnQ6HTZ+Xjma9MgTveCitpU4EVHzpzQ1Qadu3gCAi2FxKC+vkDgRUevFQomIiOrlyN5wXI9OhYWlCpOeGSh1HKIWo217d1hZm6GkuBxXLiVKHYeo1WKhRERE96y8vALfrDkEAHhixgDY2FlInIio5VAo5Oh88yK0ly8koKSkTOJERK0TCyUiIrpnf+wIQ0pSNhwcrfDoU32kjkPU4vj4u8De0Qrl5VqEn+WiJURSYKFERET3pDC/BN+tPwIAmPz8YJiZm0obiKgFkslk6NbbDwAQfTkJBXnFEician0kLZSWLVuGnj17wtraGi4uLhg/fjyioqIM2gghsHjxYri7u8Pc3BxDhgxBRESERImJiGjrhqPIzSmCp48TRo7vLnUcohbLzcMB6jb20OkEzp+JlToOUasjaaF09OhRzJkzB6dOncL+/ftRUVGBESNGoLCwUN9mxYoVWLVqFT7//HOEhoZCrVZj+PDhyM/PlzA5EVHrlJyQhZ+3/Q0AeH7uQzBRKiRORNSyde1VOaoUdy0NGWl5Eqchal0kLZT27NmDGTNmIDg4GJ07d8amTZug0WgQFhYGoHI0afXq1XjnnXcwYcIEhISEYPPmzSgqKsLWrVtr7bO0tBR5eXkGNyIiMo6vPt2HigotuvX2R68BAVLHIWrxHJ2t4RvgCgAIO3kNQgiJExG1Hk3qHKXc3FwAgIODAwAgNjYWqampGDFihL6NSqXC4MGDceLEiVr7WLZsGWxtbfU3T0/Phg9ORNQKXAyLw1+HIiGXy/DivIcgk8mkjkTUKnTp6QuFQo70G3nQxKZLHYeo1WgyhZIQAvPmzcOAAQMQEhICAEhNTQUAuLq6GrR1dXXVP1bdwoULkZubq78lJCQ0bHAiolZAq9Vh3co9AICRj3aHT1vXuzyDiIzF0soMHTpXfvB79u/r0FZoJU5E1DqYSB2gyssvv4yLFy/i+PHjNR6r/qmlEOK2n2SqVCqoVKoGyUhE1Frt23UO16JSYGGpwrSXHpA6DlGrE9zZC9eupKAwvwRXwpMQ3MVL6khELV6TGFF65ZVXsGvXLhw+fBgeHh767Wq1GgBqjB6lpaXVGGUiIqKGkZdThI2fHQAATHl+MOzsLSVORNT6mCgV+oUdws/Fo7iIF6ElamiSFkpCCLz88svYsWMHDh06BF9fX4PHfX19oVarsX//fv22srIyHD16FP369WvsuERErdKmLw8iL7cI3v4uGD+JF5clkopvgCscnK1RXq7FBS4XTtTgJC2U5syZgy1btmDr1q2wtrZGamoqUlNTUVxceVE1mUyGuXPnYunSpdi5cyfCw8MxY8YMWFhYYPLkyVJGJyJqFaIikrB7R+VKpK8sGM3lwIkkJJPJ0KOPPwAgJioFhfnlEiciatkkPUdpzZo1AIAhQ4YYbN+0aRNmzJgBAHjrrbdQXFyM2bNnIzs7G71798a+fftgbW3dyGmJiFoXrVaHzz/6DUIIDBvVCR27+UgdiajVc3Gzg5evMzSx6Yi9kit1HKIWTdJCqS7XApDJZFi8eDEWL17c8IGIiEhvz89hiL6cDAtLFWa+NuLuTyCiRtG1tx8S4zOQk1kGJxtez4yooTSZVe+IiKjpuHThCtav3gsAeGBMAOI0VxGnub8+IyMjjZCMiKxtzBHU0QOXLyQg0GM4tBU6qSMRtUgslIiIyIBGo8H0iYvhbBOIvKJkvDb/XxC4+wyAuiooKDBaX0StVUhXb1yNTIIlnPD30Tj07NVD6khELQ4LJSIiMnDswCU42wQCEBj0YGeMmrDGKP1euHQaP/26ESUlJUbpj6g1MzU1gVdba8RczsWBXVfw9MxC2HLpfiKjYqFERER6hQUl+OW7iwAAD19rhIQEG63v5NT7nLtHRAbUHhY4dyYKgBobPz+A198bJ3UkohalSVxwloiImoZNnx9AXk4Jikqy4NmWq4sSNWUyuQxXEnYDAPb+cg5XwhMlTkTUsrBQIiIiAMClc/H49b+hAIAIzW9QKGQSJyKiu8kpTEDXvp4QQuCL5b9Dq+XCDkTGwkKJiIhQXFSKlYt2AgB6DPBCdkGctIGIqM5GTuwAC0sVoi8nY+8vZ6WOQ9RisFAiIiJs+HQ/UpKy4exqi9GPh0gdh4jugbWNGabNGgoA2Pj5AeTlFEmciKhlYKFERNTKnf07Rj/lbt6icTCzUEqciIju1SNP9IJvW1fk5xZjw2f7pY5D1CKwUCIiasUK80uw6v1fAABjH++Jbr39JU5ERPWhMFHg5QWjAQB7fj6LS+fiJU5E1PyxUCIiasXWrtqD9Bu5cPNwwHOvDpc6DhHdh5Cu3hj5aHcAwKcf/ory8gqJExE1byyUiIhaqT8PRGDfrnOQyWR4c/F4mFuopI5ERPfp2VcehJ2DJTSx6fjvN39JHYeoWWOhRETUCqWl5GD1kl0AgCefGYCQrt4SJyIiY7CxtcCL8x4GAGz96hiSEjIlTkTUfLFQIiJqZbRaHZa/twMF+SUICvHA0y8MlToSERnR0Ic7oltvf5SXVeDTpb9BCCF1JKJmiYUSEVErs33Tnwg/Fw8LSxUWfPgYTJQKqSMRkRHJZDK8snAMTFUmOH/6Oq+tRFRPLJSIiFqRiAsafLv+CADg5QWj4ebhIG0gImoQ7p4OmDbrAQDAulV7kX4jV+JERM0PCyUiolYiN7sQSxf+FzqtDkMf7ohhozpLHYmIGtCEKX0RGNwGRYWl+PTDXzkFj+gesVAiImoFdLrK85IybuTBw9sRr749VupIRNTAFAo53lg8HkqlAqf/uoqDf1yUOhJRs8JCiYioFfh+458IO3kNKpUS7y5/EhaWXAqcqDXw9nPBlBeGAADW/Hs3MtPzpQ1E1IywUCIiauHOn76Ob9cdBlB5XpJvgKvEiYioMT3+dH+0DXJDQV4xVi/ZxSl4RHXEQomIqAVLv5GLZe/8CJ1OYMQjXTHika5SRyKiRmaiVOAf7z8KpakJTh+Pxh87zkgdiahZYKFERNRClZWW44N/bEdOViF8A1wx561RUkciIon4tHXFsy8PA1C5Cl5ifIbEiYiaPhZKREQtkBACny77DdERSbC2NcfilU/BzNxU6lhEJKHxT/VBl15+KC0px/L3dqCiXCt1JKImjYUSEVELtOuH09j/63nI5TK8vexxqNvYSx2JiCQml8vx5uLxsLI2Q3REErZuOCp1JKImjYUSEVELczEsDutW7gEAPPfqcHTr7S9xIiJqKpxdbfHKzcsDbNtwDJfOxkkbiKgJY6FERNSCJCVk4oN/fA/tzYvKPja1n9SRiKiJGTIiBMPHdIFOJ7Ds7R+Rk10odSSiJomFEhFRC5GfV4x/vvYd8nOLERjcBq+/Nw4ymUzqWETUBL28YDS8fJ2RmZ6PFe/tgE6nkzoSUZPDQomIqAWoKNdiyVvbkRifCRe1LRavegoqM6XUsYioiTIzN8U7Hz0OlUqJsJPX8MPXx6WORNTksFAiImrmhBD47KPfcD40FuYWpnh/9WQ4OFlLHYuImjiftq6YM7/ysgGb1x7m+UpE1bBQIiJq5rZtOIY9P5+FXC7DwqWPwy9ALXUkImomRjzSFcNGd4ZOq8OS+T8gLTVX6khETQYLJSKiZmzvL2exec0hAMBL/xiJ3gPbSZyIiJoTmUyGVxeOgV87NXKyCvHBm9+jtKRc6lhETQILJSKiZur08Wis/vBXAMCTzwzEI0/0ljgRETVHZuamWLRyEmxsLXA1MhmffLgLQgipYxFJjoUSEVEzdCU8EUvm/wCdVocHR3fGM3OGSR2JiJoxtbs93ln+BOQKOQ7+cRE7vjspdSQiybFQIiJqZuKu3cC7r25BaUk5uvdty2XAicgouvT0xYuvPwQA+OqTfTh1LEriRETSMpE6ABER1V1yQhYWzv4G+bnFCArxwLvLn0ByShIyMjKMto/IyEij9UVEzcu4Sb0Re/UG9vxyFksX/hcfr38GgcFtpI5FJAkWSkREzUT6jVwsmL0ZWZkF8G3rin99OgUZmTcQFNQexcVFRt9fQUGB0fskoqZNJpPhlYVjkJ6Wh7CT1/DPud9h9aaZcPNwkDoaUaNjoURE1AzkZBXg7Tnf4kZyDtw9HbD0i6dhY2uBazEZKC4uwqxn3oa7m5dR9nXh0mn89OtGlJSUGKU/ImpeTJQKvLv8Cbz5/EbERKXi3Ve34P82zoSNnYXU0YgaFQslIqImLierAG/N2gxNbDqcXG3w0ZrpNS4o6+7mBR8v4ywNnpyqMUo/RNR8WViq8K9PpmLujP8gMT4T77y6BR99OQ2WVmZSRyNqNFzMgYioCcvJLsT8WZsRH5MGR2drrFg7A65udlLHIqJWwNHZGks+mwobWwtERyThvde+Q0lxmdSxiBoNCyUioiYqJ7sQ81/8GnE3i6SP1z+DNl6OUsciolbE288FS794GpZWZog4r8Giedt4QVpqNVgoERE1QZnp+XjrZpHk4GSNFetmsEgiIkkEtHfHh59PhbmFKc6fvo4lb21HWSmLJWr5WCgRETUxN1Jy8MbMjf+bbrduBjy8naSORUStWPuOnvjXJ1OgUilx+q+reG/uVhQXlUodi6hBsVAiImpCEuMz8MZzG5GSmAV1G3us/OpZePqwSCIi6XXs5oN/fTpFP7K0cPY3yMs1/qUJiJoKFkqNaM+Oy+joMwHXInJw9u8YhJ+LR+y1G8jOKoBWq5M6HhFJLCYqBW8+vwnpN3Lh6eOElV89y2uXEFGT0rmHLz5aMx1WNuaIvJSIt174GlkZ+VLHImoQXB68EV2NSIObQwhSE4qQmmD4CYxMJoOtvQXc2tjD3dMBLm52UChYxxK1FudOX8cHb36PosJS+LVTY9kXT8POwUrqWERENQSFeGDlf57BwtnfIPbaDcx95it88H+T4dPWVepoREbFv8Qb0dDR7XAlYQ+82lojqKMH/Nqp4exqA6VSASEEcrIKEXkpEQf/uIj/bj6OYwcikJyQCZ1OSB2diBrQ4T2X8O4rW1BUWIpO3X3w7/XPsEgioibNp60rVm54Dm4eDriRnIPXn92A0BNXpY5FZFQcUWpEId3coUk/Da+2z8LHq61+uxACRYWlyLiRh6SELCQnZKGkuAya6+nQXE+HhaUp/Nqp0a5DG1hYqiR8BURkTEII/PjtX/jqk/0AgEHDg/GPDybA1JQ/momo6XP3dMCnm5/HB//4HpfOxuOfr32HWW+OxCNP9IJMJpM6HtF942/jJkAmk8HSygyWVmbw9neBEAJZGfm4fvUGYq/eQFFhGcLPaXD5QgJ8A1zRobMXbO0spI5NRPehvLwCny37DXt/OQcAGP9UH7w47yHI5RzoJ6Lmw8bOAsu+nIZPPvwV+389jy9X/IHoiCS8snAMzMxNpY5HdF9YKDVBMpkMjs42cHS2Qbfe/kiMz0BUeBLSUnMRE5WKmKhUePk5o3MPXxZMRM1QXk4R/vXWdlwMi4NcLsMLrz+E8U/14SewRNQsKZUmeGPReHj7OWPjZwdw4PcLiL6cjHeWPwEffxep4xHVGwulJk6hkMPbzwXefi5IT81FxAUNEuMzobmejoTYdPgHusFezRXziJqLuJg0LJ63DSmJWbCwVOHtZY+jZ/8AqWMREd0XmUyGx6cNQFCIB5Yu/BGa2HS8+vR6vPSPkXh4fDd+EETNEgulZsRZbYsh6o7IySrA+dBYJMZn4tqVFMijAT/1IJSXaaWOSER38NPWI9j02TGUl2lh72iB6a/0hsI8H2fPnq13n5GRkUZMSETNkbF/Djg5OcHLy6tez+3YzQdrts3Civd2IOxUDFYv2YW/DkfitXfGwtnV1qg5iRoaC6VmyM7BCkMe6oi01Fyc+zsG6Tfy0NZ9CP5v0SHMfccGfQYFSh2RiG5RUa7F/334Ew78GgEAyMy7jsMXfsL2/cVG20dBQYHR+iKi5iEnNwsAMHXqVKP2a25ugStXIutdLNk5WGHJZ1Px05YT2LzmMEL/uooXHv8Cs954GCMe6crRJWo2WCg1Yy5qW4x4pCvCQi/i/Ol4ZGcAi17fij6DAvHSmyOhbmMvdUSiVu9GSg6Wv/MTIi5oAAB2Ljr0f6g/xskGGKX/C5dO46dfN6KkpMQo/RFR81FUXPkByaRHZ6ND+05G6TM5RYO1m5YiIyOj3oUSAMjlcjw+bQB6DwzEysU/40p4IlZ98Av2/Xoes98aBf92aqPkJWpILJSaOZlMBie1Of66/AXen78Bfx24jlPHonD27xg8OWMAHp82ACozpdQxiVqlPw9exup//YKC/BKozEzwd8R3ePnh1+Hj1c5o+0hO1RitLyJqnlyc3Y36c8WYvHydsWrjc9jx3Ql8u+4Iws/F4+UpazFqQg9Mf+kB2HBRKmrCuA5tC6HVlWPkxGCs3T4bXXr6oqy0At+uO4IXn/gCoX/xAnBEjamkuAyffLgLS97ajoL8EgQGt8Gr/xyCtNwoqaMRETU6haJydOmrn17B4BEh0OkEfvsxFNMfWY0t64+gsIAj4tQ0cUSphfHydcZHa6bj2P4IrP+/vUhJysa7r27BwGEd8NI/RsLR2UbqiEQt2qVz8Vi5+GekJGZBJpPhien9MW3WA7h46YLU0YiIJOWitsXbyx7HmIk9sebfu3E9OhXfrjuMn78/hcen9cfYx3vBwlIldUxoNBpkZGQYvd/7WSSDpMFCqQWSyWQYPCIEvQYE4Nt1R7Bz2yn8efAywk7FYMacYRgzsScUCg4mEhlTSXEZvv7yIH7e9jeEEHBytcEbi8ajW29/qaMRETUpnbr74IvvXsSfBy7jm7WHkRifgY2fHcD3G//EqAndMW5SH7iopVkhT6PRICioPYqLi4ze9/0ukkGNj4VSC2ZuocILrz+EYaM64dOlv+FKeCK+XPEHDvx2Hq++PRYB7d2ljkjUIpw5cQ2fL/8dKYmVK1A9PK4bXnj9IVham0mcjIioaZLL5Rg8IgQDHmiPQ7sv4ftNx5AYn4kfvz2BnVtPYcCwDhj1aHd06uEDubzxPtzNyMhAcXERZj3zNtzdjFfQGGuRDGpcLJRaAf9AN6za+Bz+2HEGmz4/iOjLyXh12nqMm9Qb02Y90CSGuYmao4y0PKxduQd/Hqhc9tvJxQZz332EF5AlIqojhYkCw8d2wbDRnXD6+FX8tOUELobF4ei+cBzdFw63NvZ4aHw3DH24I9Tujbear7ubV5NdIIMaDwulVkKhkGPs473Qf2h7rFu1B0f2hmPn1sopebPfHIV+Q4N4XQOiOiorLcfObafw/cY/UVRYCrlchnGTeuPpF4fC0oqjSERE90oul6PPoED0GRSIa1dS8MeOMzi85xJSkrLx9RcH8fUXBxEU4oHBI4IxYFiwZFPzqHVhodTKODhZY+HSxzF8TFd8/tFvSEnKxgf/+B59BgVi9luj4OpmJ3VEoiZLp9PhyN5wbPr8ANJScwEA7Tt64JWFY+Af6CZxOiKilqFtkBtefXssXnj9Ifx54DL2/3YeF8PicCU8EVfCE7Fu1V74tVOj98B26DWgHQI7uENhopA6NrVALJRaqR792mLdD3OwbeMx/HfzXzh1LArnTl/HkzMGYOLT/XntJaJbCCFw+vhVfLvuMK5GJgMAnFxt8MycB/HAyI6NOn+eiKi1MDM3xfCxXTB8bBdkZeTjz4OXcXRfOC5fSMD16FRcj07Ftg3HYG5hig6dvdCxmzc6dvVGu+A2MDXln7h0//hV1IqpzJSYMXsYhj7cEZ8t+w2Xzsbjm7WHsfeXc5j52ggMfLADp+NRqyaEQOhfV/Ht+iOIjkgCAJhbmOKJGQMwYXJfmJmbSpyQiKh1cHCyxrgne2Pck72Rk12IMyeu4fTxaISdvIaC/BKEnbyGsJPXAACmKhMEhXggpKs3AoPboG2QGxydrfk3Dd0zFkoEbz8XfLz+GRzdH4GvVu/DjZQcfLjgB3To7InnXhmOkK7eUkckalTl5RX4aetR7NlxHimJeQAApakCfYf6YuCItrCyUeFyZPg99RkZGdkQUYmIGkRD/MwqLS2FSmWcBaQc3ICHH/fH5Bd6QltuhvCzcbh0Nh6XzsUjJ6sQF8PicDEsTt/e1t4SbYPc0DZQDf9AN7QNcoObhz1nBNAdsVAiAJXXXhoyIgR9BrbDj9+ewA9fH8flCwl4Y+ZG9BkUiGfmDINPW1epYxI1qKyMfOzbdQ47t51ETlblNTS02jIkZJxB3I0T+P1UEbDs/vZRUFBghKRERA0jJ7fyMgdTp05tgN5lAIRRe6y6NtG4SX0wblIfCCGQGJ+JS2fjEH5eg2tXUpAQm47c7EKDUSegcuTJw9sJXr5O8PRxhpevMwpK8iCT8XwnqsRCiQyYmZti6gtDMPLRbtiy/gj2/HIOp45F4e8/ozHwwQ6Y/Nxg+AawYKKWQ6vV4ezfMdi9MwynjkZBq9UBAErK8uHuZYGADl5QmvoAmHhf+7lw6TR++nUjSkpK7j80EVEDKSqu/DBn0qOz0aF9J6P1W/Uz0Jj91nZtIplMBk8fJ3j6OGHUhB4AgNKScsReu4FrV1IQE5WCa1GpiL16A2WlFfpznW71YJeFOHPsBmKdSmFrbwFbOwvY2FrA2tYcKjMlp/C1IiyUqFaOzjZ47Z1HMGFKP3z95UEcP3gZx/ZH4Nj+CPQbEoRJzw5CYHAbqWMS1YsQAlfCE3FkbziO7Y9AVka+/rH2nTwR0t0JL7z6GN5/5EujXUcjOVVjlH6IiBqDi7O7Ua8jVPUz0Nj91oXKTImgEA8EhXjot2krtEhNzoEmNh0JcRmV/8ZmIDYmFaXFFSgp0iJJk4kkTaZBX6amJrC2NYe1rbm+eLKxNYe1rQUXkGiBeETpjjx9nPDeiidx/Woqtn51DMcPXsaJI1dw4sgVBHf2wqOT+6DfkCAuy9kCaTQaZGRkGLVPJycnya5IXlZWgUthcfj7z2icOhaFGyk5+sesbMzx4KhOGPlod/i0dcXZs2chhE6SnEREdG/u53wqlTXQtqMF2nb0BuCNy5cvY+azL2HOsx/C3NQBudmFyMstQn5uMQoLSlFWVoHM9HxkpufX6MvMXAlrm1sLKAt9UUXNEwslqhO/ADXeXf4E4q+nYfvXx3F0bzgiLmgQcUEDF7UtHhrXFcPHduV1mFoIjUaDoKD2KC4uMmq/VXPJG6NY0ul0iItJw/nQWFw4E4uLZ+JQVFiqf9zM3BR9BwdiyEMd0b2vP5RK/jgkImpOGvJ8KhNVOQI7GM6cqajQoiCvGHm5xcjPLdYXUHm5xSgpLkNJcTlKisuRfiOvRn+mZnJ0b/s0dn57AbGXS9DGyxFtvByhbmPH3z9NGI8M3RNvPxe89cEEPPfKcPz2Yyh+/zEUaam5+HbdEWxZfxRde/nhwTGd0WdgICytzaSOS/WUkZGB4uIizHrmbbi7GaeoqW0uuTHlZBfiWmQKroQn4Ep4EqLCk5CXa1joOThaodfAdug9MBDdevtxeW8iomasIc6nutP5pCYmCtg5WMHOwarGY2VlFcjPK0Z+ThHy8oqRn1OM/Lwi5OUWo6y0AmUlOjja+OL0sTicPhanf55cLoOruz3aeDnAw8sR7l6O8LhZRDmrbaFQcFU+KbFQonpxdLbG9JcewFPPDsTxQ5HYu+sczp++jrN/x+Ds3zFQKhXo3rctBgzrgJ79A2Bnbyl1ZKoHdzevRp9Lfic6nQ4ZaflI1mQiKSETifGZiLuWhrhrN5CVWXM1OTNzU4R09ULnHr7o0tMXbYPcuBQsEVELY8zznup7PqmpqQkcnazh6GRd47HSknJERUXhu+1f4dXZb0Fbrrx5/lMWSorLkJKYhZTELJw5cc3geUqlAm4eDnC/WUT9bxTKHk7O1jztoRGwUKL7YqpS4oGRnfDAyE5ITcrG/t/O4+i+cCTEZeDUsSicOhYFmUyGgPZu6N63Lbr28kNgcBt+kk8AKk+mLSwsRVFBKQoLSlBUWIrCglLk5hQhMz0PWTfngWem5yMzIx/ZGQX6Vemqk8lkcPd0QGBwGwSFtEFgiAf8A9Wc0kBERJJSmSlhY2+KlKyLGPFoe3Tr1g1A5cJCWRn5SNJkITE+A8kJWUiMr/wgMCUhC+XlWmhi06GJTa/Rp1whh5OzNZzVtnB1s4OLmy1c1LZwcbOrvK+25d9aRsC/IMho1G3s8fSLQ/H0i0MRF5OGPw9E4MSRK7genYroy8mIvpyMbRuOQaGQI6C9Ozp09kRAe3f4B6rh4eXYKj4ZMdYCCTqdqBzKL61AWakWFhbWsLNxQGlJOcrLtaio0KKiQgdthRYVVffLK7fd+n9thRbl5VpoK3TQanXQ6XTQaQXS09MR4j0OUReykRAdASEEhKjcb+X/b9500P//f4/hlscFdDe3VZSXY1DIXHz4xh7IZftRWlqO0pLye37tCoUcbh72cPd0RBsvB3j7ucDCGlBZamGq+t+PtKKyNFy6lFav95cXhyUiooYmk8ng6GwDR2cbdOruY/CYVqtD+o1cJN0snCr/rSyk0lNzUVGhRVpqLtJScxFxvvZRMGtbczg4WcPByQoOjlawv3lzcLKGvaMVrG0qF5qwsjaDhaVKv+x5QyzmBEi7oFN9NYtC6csvv8THH3+MlJQUBAcHY/Xq1Rg4cKDUsegOfPxd4OPvgqdfHIrM9HycPRWDMyev4tLZeGSm5+NKeCKuhCfq25uqTODl6wwPbyd4eP9vbq7jzW/w5vipiBAC5WUVKC4qQ0lJOa7HxGPcIxNQUa6DicIUCrkSCvkt/+q3Vd6vbGO4TaHf1vDfuu6OnZGeUgyg2Gh9mpnaoCCvtMZ2lUoJSysVLKxUsLAyg7WNOZxcKqcwODhb3/xFYg1HZ2vYO1gaFNUNtfAEwIvDEhGRNBQKOdTu9lC726N737YGj+l0OmRnFuBGSi7SUnIqC6aUHNxIyUV6ai5upOSgqLAU+TcXnYiPufuHhnKFHNbWZlCZm+Da9SsoKytCubYY5RXFqNCVQqsth1ZXBq3uln+11e7ryqDVVUCnq4BAzdkfjbmgk7E0+UJp+/btmDt3Lr788kv0798f69atw8iRI3H58uVm9Ua3Zo7O1hg+tguGj+0CIQRupOQg4rwGkRcTEBOdiuvRN1BSXIZrV1Jw7UpKrX1YWZvd/IPZGg5O1rC2NoOZhSksLFQwtzSFuYUK5haV/5qaKiBXyKGQy6Ewkd/8vwzymydEVo2eVN10WsP72orKW9WIR9Wt5Oa/ZaX/+39tj1euelOGkuIy6HSGVyDv6mv8lXlkcoHikgK08VDD1s4aJkoFlCYKKEzkMDFRwESpgImJHAqTyn8r7xv+X2Eih0Ihh1wuh1whQ2pqCj799FM8/OBjcHRwgUwmg0wGyOQyyGWym/dlkMlvbr/lvvyWtlXb5XIZUm5osHbjUmz7fiuCg4OhUpnA0qryUywTZf1HExti4QleHJaIiJoquVyuH4nq0Mmz1jYF+cVIT81DVmYBsjMLkJ2Zj6yMqv8XICuzAAV5JcjPK0Z5WQV0Wh1yc4qAHMDG3B0wwormcoUMcnnlghU6oUVqekyDLejUUJp8obRq1So899xzmDlzJgBg9erV2Lt3L9asWYNly5ZJnI7ulUwm039CMmxUZwCVn4wkJ2RBE5uBxPgM/TBzRlo+stLzUVpajoL8EhTkl0BzveY83ebAVGUCE6UcmZlpcHR0grm5ec2C5WaBo9+uNHzM5JbHlDcfkyvkiE+4in8unYWwzWH6ec/36+zZs3jj3ZNo4zMdPl4ed39CHRSUmCK/OBVuHrbw8XcxSp+3MubCE7w4LBERNWdW1uawsjaHb4DrXduWlpQjP68YBXnFCDtzES/NehlPjJsFaytHlJVWoLys4jZT+G/ZVq6tcQ6xTiug0wKAACCDmalNg7zWhtSkC6WysjKEhYVhwYIFBttHjBiBEydO1Pqc0tJSlJb+b2pPbm4uACAvr+aa9o2tahpPXPxVlJQabzpTamrlFLawsDCjThWSy+XQ6Yx70c079ikH1L6A2tce3WEPoHL6WmmJ9ubwcSnyc0tRkFuCktIKlJVUnqNTWlKB8jItSku0KC2p/FREJ0TlN6gQlfd1lfdl8srRDf1NcXME5OZIStU2hVwOpWllkaI0vXnT/19eWdDcst1UpYDJze2mKhMoVQqYmir0BZJCIUdUVBReeOFdPDvlDajVVavi6G7eKmq8HRUAKgSAspu322iI4x8VFQXAuF+rDfV12hBZk5MrC6Wk5DhYWRtnxcaG6LOh+m3tWfn6+fqN3WdD9dtc+myofptLn0Dz+VstKz8O6bnRKNYmwdZcBjNzoObFXmSoLCEMywj9ucs3/+bS6W7eBJCefgMn9+xEQcFTkv9NXrV/IcRdWlY2arKSkpIEAPHXX38ZbP/www9Fu3btan3OokWLBCpLV95444033njjjTfeeOONtxq3hISEu9YiTXpEqUrVKhxVhBA1tlVZuHAh5s2bp7+v0+mQlZUFR0fH2z6nseTl5cHT0xMJCQmwsWl+w49kiMez5eCxbFl4PFsOHsuWhcez5WjOx1IIgfz8fLi7u9+1bZMulJycnKBQKJCammqwPS0tDa6utc+5VKlUUKlUBtvs7OwaKmK92NjYNLsvKro9Hs+Wg8eyZeHxbDl4LFsWHs+Wo7keS1tb2zq1a9KXqDc1NUX37t2xf/9+g+379+9Hv379JEpFREREREQtXZMeUQKAefPm4emnn0aPHj3Qt29frF+/HhqNBrNmzZI6GhERERERtVBNvlB68sknkZmZiQ8++AApKSkICQnBH3/8AW9vb6mj3TOVSoVFixbVmBpIzROPZ8vBY9my8Hi2HDyWLQuPZ8vRWo6lTIi6rI1HRERERETUejTpc5SIiIiIiIikwEKJiIiIiIioGhZKRERERERE1bBQIiIiIiIiqoaFUiP68ssv4evrCzMzM3Tv3h1//vmn1JHoLpYtW4aePXvC2toaLi4uGD9+PKKiogzaCCGwePFiuLu7w9zcHEOGDEFERIREiamuli1bBplMhrlz5+q38Vg2L0lJSZg6dSocHR1hYWGBLl26ICwsTP84j2fzUFFRgXfffRe+vr4wNzeHn58fPvjgA+h0On0bHsum69ixYxg7dizc3d0hk8nw888/Gzxel2NXWlqKV155BU5OTrC0tMQjjzyCxMTERnwVVOVOx7O8vBzz589Hx44dYWlpCXd3d0ybNg3JyckGfbSk48lCqZFs374dc+fOxTvvvINz585h4MCBGDlyJDQajdTR6A6OHj2KOXPm4NSpU9i/fz8qKiowYsQIFBYW6tusWLECq1atwueff47Q0FCo1WoMHz4c+fn5EianOwkNDcX69evRqVMng+08ls1HdnY2+vfvD6VSid27d+Py5ctYuXIl7Ozs9G14PJuH5cuXY+3atfj8888RGRmJFStW4OOPP8Znn32mb8Nj2XQVFhaic+fO+Pzzz2t9vC7Hbu7cudi5cye+//57HD9+HAUFBRgzZgy0Wm1jvQy66U7Hs6ioCGfPnsV7772Hs2fPYseOHYiOjsYjjzxi0K5FHU9BjaJXr15i1qxZBtuCgoLEggULJEpE9ZGWliYAiKNHjwohhNDpdEKtVouPPvpI36akpETY2tqKtWvXShWT7iA/P18EBASI/fv3i8GDB4vXXntNCMFj2dzMnz9fDBgw4LaP83g2H6NHjxbPPvuswbYJEyaIqVOnCiF4LJsTAGLnzp36+3U5djk5OUKpVIrvv/9e3yYpKUnI5XKxZ8+eRstONVU/nrU5ffq0ACDi4+OFEC3veHJEqRGUlZUhLCwMI0aMMNg+YsQInDhxQqJUVB+5ubkAAAcHBwBAbGwsUlNTDY6tSqXC4MGDeWybqDlz5mD06NF48MEHDbbzWDYvu3btQo8ePfD444/DxcUFXbt2xX/+8x/94zyezceAAQNw8OBBREdHAwAuXLiA48ePY9SoUQB4LJuzuhy7sLAwlJeXG7Rxd3dHSEgIj28zkJubC5lMph/Nb2nH00TqAK1BRkYGtFotXF1dDba7uroiNTVVolR0r4QQmDdvHgYMGICQkBAA0B+/2o5tfHx8o2ekO/v+++8RFhaGM2fO1HiMx7J5uX79OtasWYN58+bh7bffxunTp/Hqq69CpVJh2rRpPJ7NyPz585Gbm4ugoCAoFApotVp8+OGHeOqppwDwe7M5q8uxS01NhampKezt7Wu04d9ITVtJSQkWLFiAyZMnw8bGBkDLO54slBqRTCYzuC+EqLGNmq6XX34ZFy9exPHjx2s8xmPb9CUkJOC1117Dvn37YGZmdtt2PJbNg06nQ48ePbB06VIAQNeuXREREYE1a9Zg2rRp+nY8nk3f9u3bsWXLFmzduhXBwcE4f/485s6dC3d3d0yfPl3fjsey+arPsePxbdrKy8sxadIk6HQ6fPnll3dt31yPJ6feNQInJycoFIoalXRaWlqNT1moaXrllVewa9cuHD58GB4eHvrtarUaAHhsm4GwsDCkpaWhe/fuMDExgYmJCY4ePYpPP/0UJiYm+uPFY9k8uLm5oUOHDgbb2rdvr18gh9+bzcc//vEPLFiwAJMmTULHjh3x9NNP4/XXX8eyZcsA8Fg2Z3U5dmq1GmVlZcjOzr5tG2paysvL8cQTTyA2Nhb79+/XjyYBLe94slBqBKampujevTv2799vsH3//v3o16+fRKmoLoQQePnll7Fjxw4cOnQIvr6+Bo/7+vpCrVYbHNuysjIcPXqUx7aJGTZsGC5duoTz58/rbz169MCUKVNw/vx5+Pn58Vg2I/3796+xVH90dDS8vb0B8HuzOSkqKoJcbvjniEKh0C8PzmPZfNXl2HXv3h1KpdKgTUpKCsLDw3l8m6CqIunq1as4cOAAHB0dDR5vccdTqlUkWpvvv/9eKJVKsWHDBnH58mUxd+5cYWlpKeLi4qSORnfw0ksvCVtbW3HkyBGRkpKivxUVFenbfPTRR8LW1lbs2LFDXLp0STz11FPCzc1N5OXlSZic6uLWVe+E4LFsTk6fPi1MTEzEhx9+KK5evSq+++47YWFhIbZs2aJvw+PZPEyfPl20adNG/PbbbyI2Nlbs2LFDODk5ibfeekvfhsey6crPzxfnzp0T586dEwDEqlWrxLlz5/SroNXl2M2aNUt4eHiIAwcOiLNnz4oHHnhAdO7cWVRUVEj1slqtOx3P8vJy8cgjjwgPDw9x/vx5g7+LSktL9X20pOPJQqkRffHFF8Lb21uYmpqKbt266ZeYpqYLQK23TZs26dvodDqxaNEioVarhUqlEoMGDRKXLl2SLjTVWfVCiceyefn1119FSEiIUKlUIigoSKxfv97gcR7P5iEvL0+89tprwsvLS5iZmQk/Pz/xzjvvGPzhxWPZdB0+fLjW35PTp08XQtTt2BUXF4uXX35ZODg4CHNzczFmzBih0WgkeDV0p+MZGxt727+LDh8+rO+jJR1PmRBCNN74FRERERERUdPHc5SIiIiIiIiqYaFERERERERUDQslIiIiIiKialgoERERERERVcNCiYiIiIiIqBoWSkRERERERNWwUCIiIiIiIqqGhRIREREREVE1LJSIiJqxDRs2YMSIEfV6ro+PD1avXm3cQPfoyJEjkMlkyMnJAQB8/fXXsLOza/D9NtZrl8lk+Pnnnxt8P83B559/jkceeUTqGEREdcZCiYioAc2YMQMymQwymQxKpRJ+fn548803UVhYCACIi4vTPy6TyWBra4s+ffrg119/vWvfpaWl+Oc//4n33ntPv23x4sX6vkxMTODk5IRBgwZh9erVKC0tNXh+aGgoXnjhBaO8zuoFT309+eSTiI6Orvfzv/76a4P308rKCt27d8eOHTvuK1dDudvXR0vy/PPPIzQ0FMePH5c6ChFRnbBQIiJqYA8//DBSUlJw/fp1LFmyBF9++SXefPNNgzYHDhxASkoK/v77b/Tq1QuPPfYYwsPD79jvTz/9BCsrKwwcONBge3BwMFJSUqDRaHD48GE8/vjjWLZsGfr164f8/Hx9O2dnZ1hYWBjvhRqBubk5XFxc7qsPGxsbpKSkICUlBefOncNDDz2EJ554AlFRUUZKaVx1+fqoUl5e3sjp7q6umVQqFSZPnozPPvusgRMRERkHCyUiogamUqmgVqvh6emJyZMnY8qUKTWmYzk6OkKtViMoKAgffvghysvLcfjw4Tv2+/3339c6lcnExARqtRru7u7o2LEjXnnlFRw9ehTh4eFYvny5vl316WerVq1Cx44dYWlpCU9PT8yePRsFBQX6x+Pj4zF27FjY29vD0tISwcHB+OOPPxAXF4ehQ4cCAOzt7SGTyTBjxgwAlaNer776KlxcXGBmZoYBAwYgNDT0tq+ptql3u3btQo8ePWBmZgYnJydMmDDhju+LTCaDWq2GWq1GQEAAlixZArlcjosXL972ORqNBuPGjYOVlRVsbGzwxBNP4MaNGwZt1qxZA39/f5iamiIwMBDffvutweNXr17FoEGDYGZmhg4dOmD//v13zFnlTl8fixcvRpcuXbBx40b4+flBpVJBCIHc3Fy88MILcHFxgY2NDR544AFcuHBB3+eFCxcwdOhQWFtbw8bGBt27d8eZM2cA3P44ArW//z///DNkMpn+fn0zAcAjjzyCn3/+GcXFxXV6b4iIpMRCiYiokZmbm9/2U/jy8nL85z//AQAolco79vPnn3+iR48eddpnUFAQRo4ceccpaHK5HJ9++inCw8OxefNmHDp0CG+99Zb+8Tlz5qC0tBTHjh3DpUuXsHz5clhZWcHT0xM//fQTACAqKgopKSn45JNPAABvvfUWfvrpJ2zevBlnz55F27Zt8dBDDyErK6tOuX///XdMmDABo0ePxrlz53Dw4ME6v2YA0Gq12Lx5MwCgW7dutbYRQmD8+PHIysrC0aNHsX//fsTExODJJ5/Ut9m5cydee+01vPHGGwgPD8eLL76IZ555Rl/M6nQ6TJgwAQqFAqdOncLatWsxf/78Oue8VfWvj2vXruGHH37ATz/9hPPnzwMARo8ejdTUVPzxxx8ICwtDt27dMGzYMP37OmXKFHh4eCA0NBRhYWFYsGCB/uvpdsfxXtQnEwD06NED5eXlOH36dL3eGyKiRiWIiKjBTJ8+XYwbN05//++//xaOjo7iiSeeEEIIERsbKwAIc3NzYWlpKeRyuQAgfHx8RGZm5m37zc7OFgDEsWPHDLYvWrRIdO7cudbnzJ8/X5ibm+vve3t7i//7v/+77T5++OEH4ejoqL/fsWNHsXjx4lrbHj58WAAQ2dnZ+m0FBQVCqVSK7777Tr+trKxMuLu7ixUrVtT6vE2bNglbW1t9+759+4opU6bcNmN1mzZtEgCEpaWl/v1UqVRi06ZNBu1ufe379u0TCoVCaDQa/eMRERECgDh9+rQQQoh+/fqJ559/3qCPxx9/XIwaNUoIIcTevXuFQqEQCQkJ+sd3794tAIidO3feNu/dvj4WLVoklEqlSEtL07c5ePCgsLGxESUlJQZ9+fv7i3Xr1gkhhLC2thZff/11rfu803Gs/v4LIcTOnTvFrX8u1DdTFXt7+9tmIyJqSkwkq9CIiFqJ3377DVZWVqioqEB5eTnGjRtX4zyN7du3IygoCNHR0Zg7dy7Wrl0LBweH2/ZZNXXJzMyszjmEEAZTqKo7fPgwli5disuXLyMvLw8VFRUoKSlBYWEhLC0t8eqrr+Kll17Cvn378OCDD+Kxxx5Dp06dbttfTEwMysvL0b9/f/02pVKJXr16ITIysk6Zz58/j+eff77OrxEArK2tcfbsWQBAUVERDhw4gBdffBGOjo4YO3ZsjfaRkZHw9PSEp6enfluHDh1gZ2eHyMhI9OzZE5GRkTUWvujfv79+5CwyMhJeXl7w8PDQP963b9865b3b14e3tzecnZ3198PCwlBQUABHR0eDfoqLixETEwMAmDdvHmbOnIlvv/0WDz74IB5//HH4+/sDwD0fx9rUJ1MVc3NzFBUV3dP+iIikwEKJiKiBDR06FGvWrIFSqYS7u3utU+o8PT0REBCAgIAAWFlZ4bHHHsPly5dvu7CBo6MjZDIZsrOz65wjMjISvr6+tT4WHx+PUaNGYdasWfjXv/4FBwcHHD9+HM8995x+GtjM/2/v/kKaesM4gH+nedYaEcSEFIaghyNKDO0mI2EXjXYT/TGKSLKLChI2UGQXlrAIRCq0CyHMm2RBdZPdJG2DQpAMahhhzP5YptE4UERWtKLh08WPHdrZpvP3B43f9wO72DnveXne8+7mYed5zvHj8Hq9GBkZQTQaRU9PD3p7e+H3+3POKSIAkJWcLZWw/c5msxW6PENRURFUVTW+u1wuRKNRnDt3LmeilC8e8/HF1pFe6+8KXeNSvw+73Z7xfWFhAWVlZRgdHc2aK11fdObMGRw+fBgjIyO4c+cOgsEgbty4gX379i26j0VFRVlryfWY6N+JKe3jx48ZSRYR0WrFGiUiov+Y3W6HqqqoqKhYsu4IANxuNzZv3ozu7u68YxRFQW1tLeLxeEExPHv2DOFwGPv37895PhaLIZVKobe3Fw0NDdA0DYlEImuc0+nEyZMnMTw8jI6ODqOeSlEUAH/VBKWpqgpFUTLaQf/8+ROxWAw1NTUFxe1yuXD37t2Cxi6muLg4bwOB2tpazM3N4e3bt8axeDyO+fl5I86ampqsttbj4+PG+fQcv9+zBw8eFBTbcn8fW7Zsga7rWLNmDVRVzfg4HA5jnKZpaG9vRzQaRVNTE65cuWKcy7ePpaWl+PLlS0Z78nQN0r8R06tXr/D9+3fU19cXcmuIiFYUEyUiolWoo6MDly9fxrt37/KO8Xq9Od9Jk0qloOs6EokEJicn0d/fD7fbjbq6OgQCgZxzVVVVIZVKob+/H69fv8bVq1cxMDCQMaatrQ2RSAQzMzOYmJjAvXv3jEShoqICFosFt2/fxvv37/H161fY7Xa0trYiEAggHA4jHo/jxIkT+PbtG44dO1bQfQgGg7h+/TqCwSCmpqYwOTmJ8+fPL3qNiEDXdei6jpmZGQwODiISiWDPnj05x3s8HrhcLjQ3N2NiYgIPHz5ES0sL3G630TgiEAhgaGgIAwMDePnyJfr6+jA8PGy08fZ4PKiurkZLSwuePHmCsbExnD59uqA1LpfH48G2bduwd+9eRCIRvHnzBuPj4+jq6kIsFkMymYTP58Po6ChmZ2dx//59PHr0yNirxfZx69atWLduHU6dOoXp6Wlcu3YNQ0ND/zimtLGxMVRWVhqPARIRrWorVh1FRPQ/YC7WN0s3c3j8+HHG8YWFBamurpbW1ta8105NTYnNZpNPnz4Zx4LBoAAQAFJcXCwbN26UxsZGuXjxYlahvbmZQ19fn5SVlYnNZhOv1yuhUCij0YLP55OqqiqxWq1SWloqR44ckQ8fPhjXnz17VjZt2iQWi0WOHj0qIiLJZFL8fr84HA6xWq2yfft2o0GCyNLNHEREbt68KXV1daIoijgcDmlqasp7T9LNHNIfq9UqmqZJd3e3pFKpvGufnZ2V3bt3i91ul/Xr18uBAwdE1/WMuS9duiSVlZVSUlIimqZJKBTKOP/8+XNpbGwURVFE0zQJh8PLbuZglq85x+fPn8Xv90t5ebmUlJSI0+mU5uZmmZubkx8/fsihQ4fE6XSKoihSXl4uPp9PksmkiCy9j7du3RJVVWXt2rWya9cuGRwczGrmsNyY0nbu3Ck9PT1510tEtJpYRHI8WE1ERH+EgwcPor6+Hp2dnSsdCtGinj59ih07duDFixfYsGHDSodDRLQkPnpHRPQHu3DhwrLfgUO0EhKJBEKhEJMkIvpj8B8lIiIiIiIiE/6jREREREREZMJEiYiIiIiIyISJEhERERERkQkTJSIiIiIiIhMmSkRERERERCZMlIiIiIiIiEyYKBEREREREZkwUSIiIiIiIjJhokRERERERGTyC0ZOrxutlgk1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for Diastolic Blood Pressure (PR):\n",
      "count    599.000000\n",
      "mean      68.732888\n",
      "std       19.335675\n",
      "min        0.000000\n",
      "25%       64.000000\n",
      "50%       70.000000\n",
      "75%       80.000000\n",
      "max      122.000000\n",
      "Name: PR, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Extract the 'PR' column\n",
    "pr_values = train_df['PR']\n",
    "\n",
    "# Plot a histogram\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(pr_values, kde=True, color=sns.color_palette('viridis')[0])\n",
    "plt.title('Distribution of Diastolic Blood Pressure (PR)')\n",
    "plt.xlabel('PR (Diastolic Blood Pressure)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "# Summary statistics\n",
    "print('Summary Statistics for Diastolic Blood Pressure (PR):')\n",
    "print(pr_values.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65814563",
   "metadata": {},
   "source": [
    "- The mean diastolic blood pressure is approximately 68.73 mm Hg.\n",
    "- The values range from a minimum of 0 mm Hg (which seems unusual) to a maximum of 122 mm Hg.\n",
    "- Most patients have diastolic blood pressure levels within the range of 64 to 80 mm Hg."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c0478a3",
   "metadata": {},
   "source": [
    "## iv. Univariate Analysis for 'SK' (Triceps Skinfold Thickness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "804bd448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for Triceps Skinfold Thickness (SK):\n",
      "count    599.000000\n",
      "mean      20.562604\n",
      "std       16.017622\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%       23.000000\n",
      "75%       32.000000\n",
      "max       99.000000\n",
      "Name: SK, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Extract the 'SK' column\n",
    "sk_values = train_df['SK']\n",
    "\n",
    "# Plot a histogram\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(sk_values, kde=True, color=sns.color_palette('viridis')[0])\n",
    "plt.title('Distribution of Triceps Skinfold Thickness (SK)')\n",
    "plt.xlabel('SK (Triceps Skinfold Thickness)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "# Summary statistics\n",
    "print('Summary Statistics for Triceps Skinfold Thickness (SK):')\n",
    "print(sk_values.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11aeab8b",
   "metadata": {},
   "source": [
    "- The mean triceps skinfold thickness is around 20.56 mm.\n",
    "- There is a notable spread in skinfold thickness among patients."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3cfc708",
   "metadata": {},
   "source": [
    "## v. Univariate Analysis for 'TS' (2-Hour Serum Insulin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "70533b64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for 2-Hour Serum Insulin (TS):\n",
      "count    599.000000\n",
      "mean      79.460768\n",
      "std      116.576176\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%       36.000000\n",
      "75%      123.500000\n",
      "max      846.000000\n",
      "Name: TS, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Extract the 'TS' column\n",
    "ts_values = train_df['TS']\n",
    "\n",
    "# Plot a histogram\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(ts_values, kde=True, color=sns.color_palette('viridis')[0])\n",
    "plt.title('Distribution of 2-Hour Serum Insulin (TS)')\n",
    "plt.xlabel('TS (2-Hour Serum Insulin)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "# Summary statistics\n",
    "print('Summary Statistics for 2-Hour Serum Insulin (TS):')\n",
    "print(ts_values.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75382ad6",
   "metadata": {},
   "source": [
    "- The mean 2-hour serum insulin level is approximately 79.46 μU/ml.\n",
    "- The values have a wide range, with a minimum of 0 μU/ml and a maximum of 846 μU/ml.\n",
    "- The standard deviation of 116.58 suggests significant variability in insulin levels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153f6bca",
   "metadata": {},
   "source": [
    "## vi. Univariate Analysis for 'M11' (Body Mass Index - BMI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d9ce1000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for Body Mass Index (BMI):\n",
      "count    599.000000\n",
      "mean      31.920033\n",
      "std        8.008227\n",
      "min        0.000000\n",
      "25%       27.100000\n",
      "50%       32.000000\n",
      "75%       36.550000\n",
      "max       67.100000\n",
      "Name: M11, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Extract the 'M11' column\n",
    "m11_values = train_df['M11']\n",
    "\n",
    "# Plot a histogram\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(m11_values, kde=True, color=sns.color_palette('viridis')[0])\n",
    "plt.title('Distribution of Body Mass Index (BMI)')\n",
    "plt.xlabel('BMI (Body Mass Index)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "# Summary statistics\n",
    "print('Summary Statistics for Body Mass Index (BMI):')\n",
    "print(m11_values.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19916660",
   "metadata": {},
   "source": [
    "- The mean BMI is approximately 31.92, indicating that, on average, patients have a BMI in the overweight range.\n",
    "- BMI values vary widely, with a minimum of 0.078 (unusually low) and a maximum of 67.1.\n",
    "- The standard deviation of 8.01 suggests substantial variability in BMI among patients."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14cd5623",
   "metadata": {},
   "source": [
    "## vii. BD2 (Diabetes Pedigree Function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e18cae46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for Diabetes Pedigree Function (BD2):\n",
      "count    599.000000\n",
      "mean       0.481187\n",
      "std        0.337552\n",
      "min        0.078000\n",
      "25%        0.248000\n",
      "50%        0.383000\n",
      "75%        0.647000\n",
      "max        2.420000\n",
      "Name: BD2, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Extract the 'BD2' column\n",
    "bd2_values = train_df['BD2']\n",
    "\n",
    "# Plot a histogram\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(bd2_values, kde=True, color=sns.color_palette('viridis')[0])\n",
    "plt.title('Distribution of Diabetes Pedigree Function (BD2)')\n",
    "plt.xlabel('BD2 (Diabetes Pedigree Function)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "# Summary statistics\n",
    "print('Summary Statistics for Diabetes Pedigree Function (BD2):')\n",
    "print(bd2_values.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9550f5c",
   "metadata": {},
   "source": [
    "- The mean diabetes pedigree function value is 0.481, which reflects the diabetes history in family members.\n",
    "- Values range from a minimum of 0.078 to a maximum of 2.42.\n",
    "- The spread in diabetes pedigree function values indicates varying family histories of diabetes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ca494c",
   "metadata": {},
   "source": [
    "## viii. Univariate Analysis for 'Age'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b4a04531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for Age:\n",
      "count    599.000000\n",
      "mean      33.290484\n",
      "std       11.828446\n",
      "min       21.000000\n",
      "25%       24.000000\n",
      "50%       29.000000\n",
      "75%       40.000000\n",
      "max       81.000000\n",
      "Name: Age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Extract the 'Age' column\n",
    "age_values = train_df['Age']\n",
    "\n",
    "# Plot a histogram\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(age_values, kde=True, color=sns.color_palette('viridis')[0])\n",
    "plt.title('Distribution of Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "# Summary statistics\n",
    "print('Summary Statistics for Age:')\n",
    "print(age_values.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f08e78a7",
   "metadata": {},
   "source": [
    "- The average age of patients is approximately 33.29 years.\n",
    "- Ages range from a minimum of 21 years to a maximum of 81 years.\n",
    "- Most patients fall within the range of 24 to 40 years."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6521ae24",
   "metadata": {},
   "source": [
    "## ix.Univariate Analysis for 'Insurance'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b6ed25b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for Insurance Coverage:\n",
      "count    599.000000\n",
      "mean       0.686144\n",
      "std        0.464447\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        1.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: Insurance, dtype: float64\n",
      "\n",
      "Insights for the Distribution of Insurance:\n",
      "- There are 411 patients with insurance (1), which is 68.61% of the total, and 188 patients without insurance (0), which is 31.39% of the total.\n"
     ]
    }
   ],
   "source": [
    "# Extract the 'Insurance' column\n",
    "insurance_values = train_df['Insurance']\n",
    "\n",
    "# Plot the distribution of Insurance\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.countplot(data=train_df, x='Insurance', palette='viridis')\n",
    "plt.title('Distribution of Insurance')\n",
    "plt.xlabel('Insurance')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n",
    "\n",
    "# Summary statistics\n",
    "print('Summary Statistics for Insurance Coverage:')\n",
    "print(insurance_values.describe())\n",
    "\n",
    "print ()\n",
    "\n",
    "\n",
    "# Calculate the counts of patients with and without insurance\n",
    "insurance_counts = insurance_values.value_counts()\n",
    "\n",
    "# Total number of patients\n",
    "total_patients = insurance_counts.sum()  \n",
    "\n",
    "# Calculate the percentages\n",
    "percentage_with_insurance = (insurance_counts.loc[1] / total_patients) * 100\n",
    "percentage_without_insurance = (insurance_counts.loc[0] / total_patients) * 100\n",
    "\n",
    "# Insights\n",
    "print(\"Insights for the Distribution of Insurance:\")\n",
    "\n",
    "# Print the results with percentages in a single statement\n",
    "print(f\"- There are {insurance_counts.loc[1]} patients with insurance (1), which is {percentage_with_insurance:.2f}% of the total, \"\n",
    "      f\"and {insurance_counts.loc[0]} patients without insurance (0), which is {percentage_without_insurance:.2f}% of the total.\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93ac1f0",
   "metadata": {},
   "source": [
    "## x.Univariate Analysis for 'Sepsis'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0aaa3a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Insights for the Distribution of Sepsiss:\n",
      "- There are 208 patients with sepsis (Positive) and 391 patients without sepsis (Negative).\n",
      "- The distribution shows an imbalance, with more patients without sepsis.\n"
     ]
    }
   ],
   "source": [
    "# Plot the distribution of Sepsiss\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.countplot(data=train_df, x='Sepssis', palette='viridis')\n",
    "plt.title('Distribution of Sepsiss')\n",
    "plt.xlabel('Sepsiss')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Insights\n",
    "sepsis_counts = train_df['Sepssis'].value_counts()\n",
    "print(\"Insights for the Distribution of Sepsiss:\")\n",
    "print(f\"- There are {sepsis_counts['Positive']} patients with sepsis (Positive) and {sepsis_counts['Negative']} patients without sepsis (Negative).\")\n",
    "print(\"- The distribution shows an imbalance, with more patients without sepsis.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "357e86d4",
   "metadata": {},
   "source": [
    "# Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f8a744",
   "metadata": {},
   "source": [
    "- The bivariate analysis will focus on investigating the relationship between age and the various health-related variables. \n",
    "\n",
    "\n",
    "- Analyzing the relationship between age and other relevant variables in relation to sepsis is essential because understanding how age interacts with these variables can provide valuable insights into the risk factors, progression, and management of sepsis across different age groups. \n",
    "\n",
    "\n",
    "- Sepsis is a complex medical condition influenced by various factors, and age is a fundamental demographic variable that can significantly impact the likelihood of its occurrence and its outcomes. \n",
    "\n",
    "\n",
    "- Examining age-specific patterns and associations with variables such as blood pressure, plasma glucose concentration, and other health indicators can identify age-related trends, assess the vulnerability of different age groups to sepsis, and tailor medical interventions and preventive strategies accordingly. \n",
    "\n",
    "\n",
    "- This analysis helps us develop a more comprehensive understanding of sepsis and the age-specific dynamics of the disease.\n",
    "\n",
    "\n",
    "- To achieve this, we need to group patients into distinct age groups so that we can explore how specific age groups might be associated with different medical complications or characteristics relevant to sepsis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7c6a134c",
   "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",
       "      <th>Age Group</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",
       "      <td>40-59</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",
       "      <td>20-39</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",
       "      <td>20-39</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",
       "      <td>20-39</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",
       "      <td>20-39</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   \n",
       "\n",
       "  Age Group  \n",
       "0     40-59  \n",
       "1     20-39  \n",
       "2     20-39  \n",
       "3     20-39  \n",
       "4     20-39  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the Age Group Column\n",
    "# Define age intervals\n",
    "age_intervals = [20, 40, 60, 80, 90]\n",
    "age_labels = ['20-39', '40-59', '60-79', '80-90']\n",
    "\n",
    "# Create labels for age intervals\n",
    "train_df['Age Group'] = pd.cut(train_df['Age'], bins=age_intervals, labels=age_labels)\n",
    "\n",
    "# Add a new column 'Age Group' based on age intervals\n",
    "train_df['Age Group'] = pd.cut(train_df['Age'], bins=age_intervals, labels=age_labels)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6fd7643",
   "metadata": {},
   "source": [
    "- The generated age groups span from 20 to 90 years, ensuring that patients within each group share more or less the same medical complications or attributes that might be relevant to sepsis. \n",
    "\n",
    "\n",
    "- This categorization allows for a more focused and targeted analysis of how age impacts various health-related variables, providing insights into sepsis assessment and management across different age ranges."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2580a580",
   "metadata": {},
   "source": [
    "## i. Bivariate Analysis between Age and Number of Pregnancies (PRG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c14a07d",
   "metadata": {},
   "source": [
    "- Analyzing the relationship between age and the number of pregnancies in the context of sepsis is crucial for understanding potential health implications, especially for women. The number of pregnancies (parity) can significantly impact a woman's health and may influence her susceptibility to various medical conditions, including sepsis.\n",
    "\n",
    "\n",
    "- Investigating how the number of pregnancies relates to age is essential to determine whether women with higher parity become more susceptible to sepsis as they age. This analysis can shed light on whether specific age groups of women might require more attention and care in terms of sepsis risk assessment and prevention.\n",
    "\n",
    "\n",
    "- Sepsis is a medical emergency that can affect individuals of all ages, making it important to explore how age might influence its occurrence. By examining age-specific trends in the number of pregnancies, we can identify potential age groups that may be at higher risk of sepsis due to their reproductive history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0a50ad05",
   "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>Age Group</th>\n",
       "      <th>Average Pregnancies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20-39</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40-59</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60-79</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80-90</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Group  Average Pregnancies\n",
       "0     20-39                  3.0\n",
       "1     40-59                  7.0\n",
       "2     60-79                  4.0\n",
       "3     80-90                  9.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average number of pregnancies ('PRG') per age group and round to the nearest whole number\n",
    "average_pregnancies_by_age = train_df.groupby('Age Group')['PRG'].mean().round().reset_index()\n",
    "\n",
    "# Rename the columns for clarity\n",
    "average_pregnancies_by_age.columns = ['Age Group', 'Average Pregnancies']\n",
    "\n",
    "# Display the DataFrame\n",
    "average_pregnancies_by_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cdaa320f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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/rH379mnZsmVq2LChjDE6f/68zp8/r+eee07SvyOXS//8w3bVqlXatWuXJGnChAny9vZW06ZN7X3++usvbdmyJdk/Nv38/GSMSXb/anBwcLKa1qxZo7p160qSvvzyS/32229au3atfYCsv//+W5J05swZSVJQUJDD8hkzZlRgYKBD219//aXvv/8+WV3FixeXpGR13cngwYN1+vRplz0KLCAgwGHay8vrtu1XrlxxaE9pn3Pnzi3p3/fpr7/+kjFGQUFByd6H1atXp+qzScn19afUP0+ePEpKStK5c+dSta6UDB48WGvXrtWGDRt06NAhHThwQE2aNEnW7+btp3Z/b/U9ulWbpGTvtbe3t6R/v5tJSUmqW7euZs+era5du2rhwoVas2aNVq9e7dDPmXWeOnVKHh4e9s81tf766y+dP39eXl5eyd6HEydO2N+HwoUL69dff1WuXLn07rvvqnDhwipcuLA+++yzVG0npbpu/g5KUocOHbR3717FxMRIkv73v/+pYsWKeuSRR+64jaioKPn4+Kh+/fr236qIiAgVKFBAEydOtN+T7Mxn6uxv1o3y588vKfl/gPn5+Wnt2rVau3at+vbtm+KyKR0vZ86cueVxdON+OSul9yGlzwYArMIo5QD+s8aPHy9jjGbOnKmZM2cmmz9p0iQNHDhQHh4eatq0qTp37qyJEydq0KBB+vrrr9WkSROHM9Q5cuSQr6+vQ1C/UY4cORymUxpYbPr06fL09FR0dLR8fHzs7XPnznXodz2g/PXXXw5nhxMSEpL9IzJHjhyKiIjQoEGDUqzr+j9oU6t06dJq2rSphg8frgYNGiSbf73u+Ph4e3CSnA/2qXV9n28MbSdOnJD07/uUI0cO2Ww2LV++3KGm625uS+0o7NfXf/z48WTzjh07pgwZMqR4FUNqFSpUSOXKlbtjv5vrTe3+3vg9utmJEydueZb7drZt26bNmzdr4sSJatmypb193759Tq/rupw5cyoxMVEnTpxI9X+GSP+8D4GBgfrpp59SnO/n52f/8+OPP67HH39ciYmJWrdunUaOHKmOHTsqKCjIfgXMrVz/vqXUduP3smbNmipRooRGjRqlLFmyaMOGDfrmm2/uuB979uzRihUrJP0bdG/2888/q0GDBk59ps7+Zt2obNmyyp49u77//nt99NFH9nYPDw/7d3bbtm0pLpvS8RUYGHjL4+jGWm78fbnRrX5fbvU+XN8mAFiNM9wA/pMSExM1adIkFS5cWIsXL0726tKli44fP26/lDV79uxq0qSJJk+erOjoaJ04cSLZyNWNGjXS/v37FRgYqHLlyiV7pSa82Gw2ZcyYUR4eHva2v//+W19//bVDv6pVq0pSshHDZ86cmWzk8UaNGmnbtm0qXLhwinU5G7glaeDAgbp69ap9dOEbXd/PLVu2OLR///33Tm8ntaZMmeIwPXXqVEmyj8jcqFEjGWN09OjRFN+DkiVL3tV2w8LClDdvXk2dOtVhFPnLly9r1qxZ9pHL77fU7m+FChXk7e2d7Hu0evXqFC/dT43rYermoJ+ax0PdyvXbH8aMGePUco0aNdKZM2eUmJiY4vsQFhaWbBkPDw9VqFBB//vf/yTJfkn47Wzfvl2bN292aJs6dar8/PySnb1u3769fvjhB/Xo0UNBQUF6/vnn77j+609S+PLLL5P9Vi1YsECenp720OzMZ3ovv1leXl56//33tW3bNg0ePPiO+3AntWrV0qJFi+wB+7rJkycrU6ZM9hH6b/X7cuNo9Te6ePFisnlTp05VhgwZ7L+jAGAlznAD+E/68ccfdezYMQ0ePDjFxzNdPwsVFRWlRo0aSfrnsvIZM2aobdu2ypcvn2rXru2wTMeOHTVr1ixVrVpVnTp1UkREhJKSknTo0CH98ssv6tKlyx2f792wYUMNHz5cL7/8st58802dOXNGn3zySbLwUrx4cTVt2lTDhg2Th4eHatasqe3bt2vYsGHy9/d3uJ+4f//+iomJUaVKldS+fXuFhYXpypUr+uOPP7RgwQKNHTtW+fLlc+r9K1iwoN5+++0UL7lt0KCBAgIC1Lp1a/Xv318ZM2bUxIkTdfjwYae2kVpeXl4aNmyYLl26pPLly2vlypUaOHCgnnjiCfujuCpXrqw333xTr732mtatW6eqVasqc+bMOn78uFasWKGSJUvq7bffdnrbGTJk0JAhQ9SsWTM1atRIbdq0UXx8vIYOHarz58/r448/dvXupkpq9zcgIECdO3dWZGSksmfPrqefflpHjhxRv379FBwc7PA9Sq3w8HAVLlxY3bt3lzFGAQEB+v777+2XUd+Nxx9/XC1atNDAgQP1119/qVGjRvL29tbGjRuVKVMmtWvXLsXlXnrpJU2ZMkUNGjRQhw4d9Oijj8rT01NHjhzR4sWL9dRTT+npp5/W2LFjtWjRIjVs2FD58+fXlStX7AH25uM8JXny5FHjxo314YcfKjg4WN98841iYmI0ePDgZP/h0rx5c/Xo0UPLli3TBx98YL9V4lYSEhI0efJkPfTQQ3r99ddT7PPkk09q/vz5OnXqlHLmzJnqz/Ref7O6deumXbt2qXv37lq2bJlefPFFFShQQPHx8Tpw4IC++uoreXh4pOo/nfr27Wsfb6JPnz4KCAjQlClT9MMPP2jIkCHy9/eXJJUvX15hYWF67733lJCQoOzZs2vOnDn2KwBuFhgYqLfffluHDh1SsWLFtGDBAn355Zd6++23b3m1AAC4VNqM1QYAaatJkybGy8vLnDx58pZ9XnrpJZMxY0b7I5QSExNNSEjIbUdLvnTpkvnggw9MWFiY8fLyMv7+/qZkyZKmU6dODo9i0m0eSzN+/HgTFhZmvL29TaFChUxkZKSJiopKNjLvlStXTOfOnU2uXLmMj4+Peeyxx8yqVauMv7+/6dSpk8M6T506Zdq3b28KFixoPD09TUBAgClbtqzp1auXuXTp0m3fqxtHKb95nVmzZk02SrkxxqxZs8ZUqlTJZM6c2eTNm9f07dvX/si1m0cpb9iwYbJ1p/T+pDQiesuWLU3mzJnNli1bTPXq1Y2vr68JCAgwb7/9dor7NX78eFOhQgWTOXNm4+vrawoXLmxeeeUVs27dujvu7+3MnTvXVKhQwfj4+JjMmTObWrVqmd9++82hz92MUn6nvtdH+T516lSK81Ozv0lJSWbgwIEmX758xsvLy0RERJjo6GhTqlQp8/TTT9+xpuufy4QJE+xtO3bsMHXq1DF+fn4me/bs5vnnnzeHDh0ykkzfvn3vWH9KI1EnJiaaTz/91JQoUcJ+bFWsWNF8//339j43j1JuzD8jZn/yySemVKlSxsfHx2TJksWEh4ebNm3amL179xpj/hnB++mnnzahoaHG29vbBAYGmmrVqjmMiH8r17/DM2fONMWLFzdeXl6mQIECZvjw4bdc5tVXXzUZM2Z0GJ3/Vq6Pzj9ixIhb9vnpp58cRqVP7WdqTOp/s25n/vz55sknnzRBQUEmY8aMxs/Pz5QuXdp06dLF7Nq1y6HvrY55Y4zZunWrefLJJ42/v7/x8vIypUqVcvheXbdnzx5Tt25dkzVrVpMzZ07Trl0788MPP6Q4Snnx4sXNkiVLTLly5Yy3t7cJDg42PXv2TDZ6OgBYxWbMDdfAAQDc2sqVK1W5cmVNmTLFPlI34KyDBw8qPDxcffv2Vc+ePdO6nHTl6tWrKlCggKpUqaJvv/32vm33v/iZVq9eXadPn77lveQAcD9wSTkAuKmYmBitWrVKZcuWla+vrzZv3qyPP/5YRYsW1TPPPJPW5cFNbN68WdOmTVOlSpWUNWtW7d69W0OGDFHWrFnVunXrtC4v3Th16pR2796tCRMm6K+//lL37t0t2xafKQA8OAjcAOCmsmbNql9++UUjRozQxYsXlSNHDj3xxBOKjIx0GOEcuJ3MmTNr3bp1ioqK0vnz5+Xv76/q1atr0KBBt3w0GJz3ww8/6LXXXlNwcLBGjx6dqkeB3S0+UwB4cHBJOQAAAAAAFuCxYAAAAAAAWIDADQAAAACABQjcAAAAAABYwK0HTUtKStKxY8fk5+cnm82W1uUAAAAAANI5Y4wuXryoPHnyKEOG25/DduvAfezYMYWEhKR1GQAAAACA/5jDhw8rX758t+3j1oHbz89P0j87mjVr1jSuBgAAAACQ3l24cEEhISH2PHo7bh24r19GnjVrVgI3AAAAAOC+Sc1tzQyaBgAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABTKmdQEAAAAA7q9+y1qldQmA5fpWHZ/WJXCGGwAAAAAAKxC4AQAAAACwAIEbAAAAAAALELgBAAAAALAAgRsAAAAAAAsQuAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALAAgRsAAAAAAAsQuAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALAAgRsAAAAAAAsQuAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALAAgRsAAAAAAAsQuAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALAAgRsAAAAAAAsQuAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALAAgRsAAAAAAAsQuAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALAAgRsAAAAAAAsQuAEAAAAAsACBGwAAAAAACxC4AQAAAACwQJoG7oSEBH3wwQcqWLCgfH19VahQIfXv319JSUlpWRYAAAAAAPcsY1pufPDgwRo7dqwmTZqk4sWLa926dXrttdfk7++vDh06pGVpAAAAAADckzQN3KtWrdJTTz2lhg0bSpIKFCigadOmad26dWlZFgAAAAAA9yxNLymvUqWKFi5cqD179kiSNm/erBUrVqhBgwYp9o+Pj9eFCxccXgAAAAAAPIjS9Ax3t27dFBsbq/DwcHl4eCgxMVGDBg1S06ZNU+wfGRmpfv363ecqAQAAAABwXpqe4Z4xY4a++eYbTZ06VRs2bNCkSZP0ySefaNKkSSn279Gjh2JjY+2vw4cP3+eKAQAAAABInTQ9w/3++++re/fueumllyRJJUuW1J9//qnIyEi1bNkyWX9vb295e3vf7zIBAAAAAHBamp7hjouLU4YMjiV4eHjwWDAAAAAAgNtL0zPcTz75pAYNGqT8+fOrePHi2rhxo4YPH65WrVqlZVkAAAAAANyzNA3cI0eOVO/evfXOO+/o5MmTypMnj9q0aaM+ffqkZVkAAAAAANyzNA3cfn5+GjFihEaMGJGWZQAAAAAA4HJpeg83AAAAAADpFYEbAAAAAAALELgBAAAAALAAgRsAAAAAAAsQuAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALAAgRsAAAAAAAsQuAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALAAgRsAAAAAAAsQuAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALBARmc67969W9OmTdPy5cv1xx9/KC4uTjlz5lSZMmVUr149Pfvss/L29raqVgAAAAAA3EaqznBv3LhRderUUalSpbRs2TKVL19eHTt21IABA9S8eXMZY9SrVy/lyZNHgwcPVnx8vNV1AwAAAADwQEvVGe4mTZro/fff14wZMxQQEHDLfqtWrdKnn36qYcOGqWfPni4rEgAAAAAAd5OqwL137155eXndsV/FihVVsWJFXb169Z4LAwAAAADAnaXqkvLUhO176Q8AAAAAQHqT6kHT/v77by1cuFCNGjWSJPXo0cPhXm0PDw8NGDBAPj4+rq8SAAAAAAA3k+rAPXnyZEVHR9sD96hRo1S8eHH5+vpKknbt2qU8efKoU6dO1lQKAAAAAIAbSfVzuKdMmaJWrVo5tE2dOlWLFy/W4sWLNXToUH377bcuLxAAAAAAAHeU6sC9Z88eFStWzD7t4+OjDBn+XfzRRx/Vjh07XFsdAAAAAABuKtWXlMfGxipjxn+7nzp1ymF+UlISz98GAAAAAOD/pfoMd758+bRt27Zbzt+yZYvy5cvnkqIAAAAAAHB3qQ7cDRo0UJ8+fXTlypVk8/7++2/169dPDRs2dGlxAAAAAAC4q1RfUt6zZ099++23CgsLU9u2bVWsWDHZbDbt2rVLo0aNUkJCgnr27GllrQAAAAAAuI1UB+6goCCtXLlSb7/9trp37y5jjCTJZrOpTp06Gj16tIKCgiwrFAAAAAAAd5LqwC1JBQsW1E8//aSzZ89q3759kqQiRYooICDAkuIAAAAAAHBXqb6H+0YBAQF69NFH9eijjzqE7ZkzZ7qsMAAAAAAA3JlTgTshIUHbt2/Xnj17HNrnzZunUqVKqVmzZi4tDgAAAAAAd5XqS8p37NihRo0a6c8//5QkPfXUUxozZoxeeOEFbd68Wa+//rqio6MtKxQAblah84C0LgGw3O/De6d1CQAA4C6lOnB3795dBQsW1Oeff64pU6ZoxowZ2rZtm5o3b67o6Gj5+flZWScAAAAAAG4l1YF7zZo1WrBggR555BFVqVJFM2bM0Pvvv6833njDyvoAAAAAAHBLqb6H++TJk8qbN68kKVu2bMqUKZOqVatmWWEAAAAAALizVAdum82mDBn+7Z4hQwZ5enpaUhQAAAAAAO4u1ZeUG2NUrFgx2Ww2SdKlS5dUpkwZhxAuSWfPnnVthQAAAAAAuKFUB+4JEyZYWQcAAAAAAOlKqgN3y5YtrawDAAAAAIB0JdWBW5K+++47zZ07V9euXVPt2rX15ptvWlUXAAAAAABuLdWBe9y4cXrrrbdUtGhR+fj4aNasWTp48KAiIyOtrA8AAAAAALeU6lHKR44cqV69emn37t3avHmzoqKiNGrUKCtrAwAAAADAbaU6cB84cECvvfaafbpFixaKj4/XiRMnLCkMAAAAAAB3lurA/ffffytLliz2aQ8PD3l7eysuLs6SwgAAAAAAcGdODZr21VdfOYTuhIQETZw4UTly5LC3tW/f3nXVAQAAAADgplIduPPnz68vv/zSoS137tz6+uuv7dM2m43ADQAAAACAnAjcf/zxh4VlAAAAAACQvqT6Hm4AAAAAAJB6qQrc06dPT/UKDx8+rN9+++2uCwIAAAAAID1IVeAeM2aMwsPDNXjwYO3cuTPZ/NjYWC1YsEAvv/yyypYtq7Nnz7q8UAAAAAAA3Emq7uFeunSpoqOjNXLkSPXs2VOZM2dWUFCQfHx8dO7cOZ04cUI5c+bUa6+9pm3btilXrlxW1w0AAAAAwAMt1YOmNWrUSI0aNdKZM2e0YsUK/fHHH/r777+VI0cOlSlTRmXKlFGGDNwSDgAAAACA5ORzuCUpMDBQTz31lBW1AAAAAACQbnBKGgAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALDAPQfuxMREbdq0SefOnXNFPQAAAAAApAtOB+6OHTsqKipK0j9hu1q1anrkkUcUEhKiJUuWuLo+AAAAAADcktOBe+bMmSpVqpQk6fvvv9fBgwe1a9cudezYUb169XJ5gQAAAAAAuCOnA/fp06eVO3duSdKCBQv0/PPPq1ixYmrdurW2bt3q8gIBAAAAAHBHTgfuoKAg7dixQ4mJifrpp59Uu3ZtSVJcXJw8PDxcXiAAAAAAAO4oo7MLvPbaa3rhhRcUHBwsm82mOnXqSJJ+//13hYeHu7xAAAAAAADckdOB+8MPP1SJEiV0+PBhPf/88/L29pYkeXh4qHv37i4vEAAAAAAAd+R04Jak5557TpJ05coVe1vLli1dUxEAAAAAAOmA0/dwJyYmasCAAcqbN6+yZMmiAwcOSJJ69+5tf1wYAAAAAAD/dU4H7kGDBmnixIkaMmSIvLy87O0lS5bUV1995dLiAAAAAABwV04H7smTJ2vcuHFq1qyZw6jkERER2rVrl0uLAwAAAADAXTkduI8ePaoiRYoka09KStK1a9dcUhQAAAAAAO7O6cBdvHhxLV++PFn7d999pzJlyrikKAAAAAAA3J3To5T37dtXLVq00NGjR5WUlKTZs2dr9+7dmjx5sqKjo62oEQAAAAAAt+P0Ge4nn3xSM2bM0IIFC2Sz2dSnTx/t3LlT33//verUqWNFjQAAAAAAuJ27eg53vXr1VK9ePVfXAgAAAABAuuH0GW4AAAAAAHBnqTrDHRAQoD179ihHjhzKnj27bDbbLfuePXvWZcUBAAAAAOCuUhW4P/30U/n5+dn/fLvADQAAAAAAUhm4W7Zsaf/zq6++alUtAAAAAACkG07fw71gwQL9/PPPydp/+eUX/fjjjy4pCgAAAAAAd+d04O7evbsSExOTtSclJal79+4uKQoAAAAAAHfndODeu3evHn744WTt4eHh2rdvn9MFHD16VM2bN1dgYKAyZcqk0qVLa/369U6vBwAAAACAB4nTz+H29/fXgQMHVKBAAYf2ffv2KXPmzE6t69y5c6pcubJq1KihH3/8Ubly5dL+/fuVLVs2Z8sCAAAAAOCB4nTgbty4sTp27Kg5c+aocOHCkv4J2126dFHjxo2dWtfgwYMVEhKiCRMm2NtuDvIAAAAAALgjpy8pHzp0qDJnzqzw8HAVLFhQBQsW1EMPPaTAwEB98sknTq1r/vz5KleunJ5//nnlypVLZcqU0ZdffnnL/vHx8bpw4YLDCwAAAACAB9FdXVK+cuVKxcTEaPPmzfL19VVERISqVq3q9MYPHDigMWPGqHPnzurZs6fWrFmj9u3by9vbW6+88kqy/pGRkerXr5/T2wEAAAAA4H6zGWNMWm3cy8tL5cqV08qVK+1t7du319q1a7Vq1apk/ePj4xUfH2+fvnDhgkJCQhQbG6usWbPel5oBPDgqdB6Q1iUAlvt9eO+0LgFAOtRvWau0LgGwXN+q4y1Z74ULF+Tv75+qHOr0GW5JWrhwoRYuXKiTJ08qKSnJYd748anfqeDg4GQjnj/00EOaNWtWiv29vb3l7e3tfMEAAAAAANxnTgfufv36qX///ipXrpyCg4Nls9nueuOVK1fW7t27Hdr27Nmj0NDQu14nAAAAAAAPAqcD99ixYzVx4kS1aNHinjfeqVMnVapUSR999JFeeOEFrVmzRuPGjdO4cePued0AAAAAAKQlp0cpv3r1qipVquSSjZcvX15z5szRtGnTVKJECQ0YMEAjRoxQs2bNXLJ+AAAAAADSitOB+/XXX9fUqVNdVkCjRo20detWXblyRTt37tQbb7zhsnUDAAAAAJBWnL6k/MqVKxo3bpx+/fVXRUREyNPT02H+8OHDXVYcAAAAAADuyunAvWXLFpUuXVqStG3bNod59zKAGgAAAAAA6YnTgXvx4sVW1AEAAAAAQLri9D3cAAAAAADgzpw+wy1Ja9eu1XfffadDhw7p6tWrDvNmz57tksIAAAAAAHBnTp/hnj59uipXrqwdO3Zozpw5unbtmnbs2KFFixbJ39/fihoBAAAAAHA7Tgfujz76SJ9++qmio6Pl5eWlzz77TDt37tQLL7yg/PnzW1EjAAAAAABux+nAvX//fjVs2FCS5O3trcuXL8tms6lTp04aN26cywsEAAAAAMAdOR24AwICdPHiRUlS3rx57Y8GO3/+vOLi4lxbHQAAAAAAbsrpQdMef/xxxcTEqGTJknrhhRfUoUMHLVq0SDExMapVq5YVNQIAAAAA4HacDtyjRo3SlStXJEk9evSQp6enVqxYoWeeeUa9e/d2eYEAAAAAALgjpwN3QECA/c8ZMmRQ165d1bVrV5cWBQAAAACAu7ur53AnJSVp3759OnnypJKSkhzmVa1a1SWFAQAAAADgzpwO3KtXr9bLL7+sP//8U8YYh3k2m02JiYkuKw4AAAAAAHfldOB+6623VK5cOf3www8KDg6WzWazoi4AAAAAANya04F77969mjlzpooUKWJFPQAAAAAApAtOP4e7QoUK2rdvnxW1AAAAAACQbjh9hrtdu3bq0qWLTpw4oZIlS8rT09NhfkREhMuKAwAAAADAXTkduJ999llJUqtWrextNptNxhgGTQMAAAAA4P85HbgPHjxoRR0AAAAAAKQrTgfu0NBQK+oAAAAAACBdcTpwz58/P8V2m80mHx8fFSlSRAULFrznwgAAAAAAcGdOB+4mTZrY79m+0Y33cVepUkVz585V9uzZXVYoAAAAAADuxOnHgsXExKh8+fKKiYlRbGysYmNjFRMTo0cffVTR0dFatmyZzpw5o/fee8+KegEAAAAAcAtOn+Hu0KGDxo0bp0qVKtnbatWqJR8fH7355pvavn27RowY4TCKOQAAAAAA/zVOn+Hev3+/smbNmqw9a9asOnDggCSpaNGiOn369L1XBwAAAACAm3I6cJctW1bvv/++Tp06ZW87deqUunbtqvLly0uS9u7dq3z58rmuSgAAAAAA3IzTl5RHRUXpqaeeUr58+RQSEiKbzaZDhw6pUKFCmjdvniTp0qVL6t27t8uLBQAAAADAXTgduMPCwrRz5079/PPP2rNnj4wxCg8PV506dZQhwz8nzJs0aeLqOgEAAAAAcCtOB27pn0eA1a9fX9WrV5e3t7dsNpur6wIAAAAAwK05fQ93UlKSBgwYoLx58ypLliw6ePCgJKl3796KiopyeYEAAAAAALgjpwP3wIEDNXHiRA0ZMkReXl729pIlS+qrr75yaXEAAAAAALgrpwP35MmTNW7cODVr1kweHh729oiICO3atculxQEAAAAA4K6cDtxHjx5VkSJFkrUnJSXp2rVrLikKAAAAAAB353TgLl68uJYvX56s/bvvvlOZMmVcUhQAAAAAAO7O6VHK+/btqxYtWujo0aNKSkrS7NmztXv3bk2ePFnR0dFW1AgAAAAAgNtx+gz3k08+qRkzZmjBggWy2Wzq06ePdu7cqe+//1516tSxokYAAAAAANyOU2e4ExISNGjQILVq1UpLly61qiYAAAAAANyeU2e4M2bMqKFDhyoxMdGqegAAAAAASBecvqS8du3aWrJkiQWlAAAAAACQfjg9aNoTTzyhHj16aNu2bSpbtqwyZ87sML9x48YuKw4AAAAAAHfldOB+++23JUnDhw9PNs9ms3G5OQAAAAAAuovAnZSUZEUdAAAAAACkK04F7j///FO//PKLEhISVK1aNT388MNW1QUAAAAAgFtLdeBetmyZGjRooLi4uH8WzJhRkyZNUtOmTS0rDgAAAAAAd5XqUcp79+6tGjVq6MiRIzpz5oxatWqlrl27WlkbAAAAAABuK9WBe+vWrYqMjFSePHmUPXt2DRs2TMeOHdO5c+esrA8AAAAAALeU6sB9/vx55cqVyz6dOXNmZcqUSefPn7eiLgAAAAAA3JpTg6bt2LFDJ06csE8bY7Rz505dvHjR3hYREeG66gAAAAAAcFNOBe5atWrJGOPQ1qhRI9lsNhljeA43AAAAAAD/L9WB++DBg1bWAQAAAABAupLqwB0aGmplHQAAAAAApCupHjQNAAAAAACkHoEbAAAAAAALELgBAAAAALBAqgL3/Pnzde3aNatrAQAAAAAg3UhV4H766ad1/vx5SZKHh4dOnjxpZU0AAAAAALi9VAXunDlzavXq1ZJkf942AAAAAAC4tVQ9Fuytt97SU089JZvNJpvNpty5c9+yb2JiosuKAwAAAADAXaUqcH/44Yd66aWXtG/fPjVu3FgTJkxQtmzZLC4NAAAAAAD3larALUnh4eEKDw9X37599fzzzytTpkxW1gUAAAAAgFtLdeC+rm/fvpKkU6dOaffu3bLZbCpWrJhy5szp8uIAAAAAAHBXTj+HOy4uTq1atVKePHlUtWpVPf7448qTJ49at26tuLg4K2oEAAAAAMDtOB24O3XqpKVLl2r+/Pk6f/68zp8/r3nz5mnp0qXq0qWLFTUCAAAAAOB2nL6kfNasWZo5c6aqV69ub2vQoIF8fX31wgsvaMyYMa6sDwAAAAAAt3RXl5QHBQUla8+VKxeXlAMAAAAA8P+cDtwVK1ZU3759deXKFXvb33//rX79+qlixYouLQ4AAAAAAHfl9CXln332merXr698+fKpVKlSstls2rRpk3x8fPTzzz9bUSMAAAAAAG7H6cBdokQJ7d27V99884127dolY4xeeuklNWvWTL6+vlbUCAAAAACA23E6cEuSr6+v3njjDVfXAgAAAABAuuH0PdwAAAAAAODOCNwAAAAAAFiAwA0AAAAAgAWcCtyJiYlaunSpzp07Z1U9AAAAAACkC04Fbg8PD9WrV0/nz5+3qBwAAAAAANIHpy8pL1mypA4cOGBFLQAAAAAApBtOB+5BgwbpvffeU3R0tI4fP64LFy44vAAAAAAAwF08h7t+/fqSpMaNG8tms9nbjTGy2WxKTEx0XXUAAAAAALgppwP34sWLragDAAAAAIB0xenAXa1aNSvqAAAAAAAgXXE6cEvS8uXL9cUXX+jAgQP67rvvlDdvXn399dcqWLCgqlSp4uoaAQCAmyk3tndalwBYbt1bA9K6BAAPOKcHTZs1a5bq1asnX19fbdiwQfHx8ZKkixcv6qOPPnJ5gQAAAAAAuCOnA/fAgQM1duxYffnll/L09LS3V6pUSRs2bHBpcQAAAAAAuCunA/fu3btVtWrVZO1Zs2bV+fPnXVETAAAAAABuz+nAHRwcrH379iVrX7FihQoVKuSSogAAAAAAcHdOB+42bdqoQ4cO+v3332Wz2XTs2DFNmTJF7733nt555x0ragQAAAAAwO04PUp5165dFRsbqxo1aujKlSuqWrWqvL299d5776lt27ZW1AgAAAAAgNtx+gy3JA0aNEinT5/WmjVrtHr1ap06dUoDBtzbYxEiIyNls9nUsWPHe1oPAAAAAAAPgrt6DrckZcqUSUFBQbLZbMqSJcs9FbF27VqNGzdOERER97QeAAAAAAAeFE6f4U5ISFDv3r3l7++vAgUKKDQ0VP7+/vrggw907do1pwu4dOmSmjVrpi+//FLZs2d3enkAAAAAAB5ETgfutm3baty4cRoyZIg2btyojRs3asiQIYqKilK7du2cLuDdd99Vw4YNVbt27Tv2jY+P14ULFxxeAAAAAAA8iJy+pHzatGmaPn26nnjiCXtbRESE8ufPr5deekljx45N9bqmT5+u9evXa926danqHxkZqX79+jlbMgAAAAAA953TZ7h9fHxUoECBZO0FChSQl5dXqtdz+PBhdejQQVOmTJGPj0+qlunRo4diY2Ptr8OHD6d6ewAAAAAA3E9OB+53331XAwYMUHx8vL0tPj5egwYNcuqxYOvXr9fJkydVtmxZZcyYURkzZtTSpUv1+eefK2PGjEpMTEy2jLe3t7JmzerwAgAAAADgQZSqS8qfeeYZh+lff/1V+fLlU6lSpSRJmzdv1tWrV1WrVq1Ub7hWrVraunWrQ9trr72m8PBwdevWTR4eHqleFwAAAAAAD5pUBW5/f3+H6WeffdZhOiQkxOkN+/n5qUSJEg5tmTNnVmBgYLJ2AAAAAADcTaoC94QJE6yuAwAAAACAdMXpUcqttGTJkrQuAQAAAAAAl3A6cJ85c0Z9+vTR4sWLdfLkSSUlJTnMP3v2rMuKAwAAAADAXTkduJs3b679+/erdevWCgoKks1ms6IuAAAAAADcmtOBe8WKFVqxYoV9hHIAAAAAAJCc08/hDg8P199//21FLQAAAAAApBtOB+7Ro0erV69eWrp0qc6cOaMLFy44vAAAAAAAwF1cUp4tWzbFxsaqZs2aDu3GGNlsNiUmJrqsOAAAAAAA3JXTgbtZs2by8vLS1KlTGTQNAAAAAIBbcDpwb9u2TRs3blRYWJgV9QAAAAAAkC44fQ93uXLldPjwYStqAQAAAAAg3XD6DHe7du3UoUMHvf/++ypZsqQ8PT0d5kdERLisOAAAAAAA3JXTgfvFF1+UJLVq1creZrPZGDQNAAAAAIAbOB24Dx48aEUdAAAAAACkK04H7tDQUCvqAAAAAAAgXXE6cE+ePPm281955ZW7LgYAAAAAgPTC6cDdoUMHh+lr164pLi5OXl5eypQpE4EbAAAAAADdxWPBzp075/C6dOmSdu/erSpVqmjatGlW1AgAAAAAgNtxOnCnpGjRovr444+Tnf0GAAAAAOC/yiWBW5I8PDx07NgxV60OAAAAAAC35vQ93PPnz3eYNsbo+PHjGjVqlCpXruyywgAAAAAAcGdOB+4mTZo4TNtsNuXMmVM1a9bUsGHDXFUXAAAAAABuzenAnZSUZEUdAAAAAACkKy67hxsAAAAAAPwr1We4+/fvn6p+ffr0uetiAAAAAABIL1IduOfMmXPLeTabTbt379aVK1cI3AAAAAAAyInAvXHjxhTbN23apO7du2vbtm164403XFYYAAAAAADu7K7v4T548KCaN2+u8uXLy9/fX9u3b9fYsWNdWRsAAAAAAG7L6cB9+vRptWvXTuHh4Tp+/LhWrlypGTNmqGjRolbUBwAAAACAW0r1JeWXL1/WJ598ouHDh6tIkSL6/vvvVbduXStrAwAAAADAbaU6cBcuXFgXL15Uu3bt1LRpU9lsNm3ZsiVZv4iICJcWCAAAAACAO0p14D558qQkaciQIRo6dKiMMfZ5NptNxhjZbDYlJia6vkoAAAAAANxMqgP3wYMHrawDAAAAAIB0JdWBOzQ01Mo6AAAAAABIV+76sWAAAAAAAODWCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWOCuAndCQoJ+/fVXffHFF7p48aIk6dixY7p06ZJLiwMAAAAAwF2lepTy6/7880/Vr19fhw4dUnx8vOrUqSM/Pz8NGTJEV65c0dixY62oEwAAAAAAt+L0Ge4OHTqoXLlyOnfunHx9fe3tTz/9tBYuXOjS4gAAAAAAcFdOn+FesWKFfvvtN3l5eTm0h4aG6ujRoy4rDAAAAAAAd+b0Ge6kpCQlJiYmaz9y5Ij8/PxcUhQAAAAAAO7O6cBdp04djRgxwj5ts9l06dIl9e3bVw0aNHBlbQAAAAAAuC2nLyn/9NNPVaNGDT388MO6cuWKXn75Ze3du1c5cuTQtGnTrKgRAAAAAAC343TgzpMnjzZt2qRp06Zpw4YNSkpKUuvWrdWsWTOHQdQAAAAAAPgvczpwS5Kvr69atWqlVq1auboeAAAAAADSBacD9/z581Nst9ls8vHxUZEiRVSwYMF7LgwAAAAAAHfmdOBu0qSJbDabjDEO7dfbbDabqlSporlz5yp79uwuKxQAAAAAAHfi9CjlMTExKl++vGJiYhQbG6vY2FjFxMTo0UcfVXR0tJYtW6YzZ87ovffes6JeAAAAAADcgtNnuDt06KBx48apUqVK9rZatWrJx8dHb775prZv364RI0aki/u7Gz7ZN61LACz3w/f90roEAAAAIF1y+gz3/v37lTVr1mTtWbNm1YEDByRJRYsW1enTp++9OgAAAAAA3JTTgbts2bJ6//33derUKXvbqVOn1LVrV5UvX16StHfvXuXLl891VQIAAAAA4GacvqQ8KipKTz31lPLly6eQkBDZbDYdOnRIhQoV0rx58yRJly5dUu/evV1eLAAAAAAA7sLpwB0WFqadO3fq559/1p49e2SMUXh4uOrUqaMMGf45Yd6kSRNX1wkAAAAAgFtxOnBL/zwCrH79+qpfv76r6wEAAAAAIF24q8B9+fJlLV26VIcOHdLVq1cd5rVv394lhQEAAAAA4M6cDtwbN25UgwYNFBcXp8uXLysgIECnT59WpkyZlCtXLgI3AAAAAAC6i1HKO3XqpCeffFJnz56Vr6+vVq9erT///FNly5bVJ598YkWNAAAAAAC4HacD96ZNm9SlSxd5eHjIw8ND8fHxCgkJ0ZAhQ9SzZ08ragQAAAAAwO04Hbg9PT1ls9kkSUFBQTp06JAkyd/f3/5nAAAAAAD+65y+h7tMmTJat26dihUrpho1aqhPnz46ffq0vv76a5UsWdKKGgEAAAAAcDtOn+H+6KOPFBwcLEkaMGCAAgMD9fbbb+vkyZMaN26cywsEAAAAAMAdOXWG2xijnDlzqnjx4pKknDlzasGCBZYUBgAAAACAO3PqDLcxRkWLFtWRI0esqgcAAAAAgHTBqcCdIUMGFS1aVGfOnLGqHgAAAAAA0gWn7+EeMmSI3n//fW3bts2KegAAAAAASBecHqW8efPmiouLU6lSpeTl5SVfX1+H+WfPnnVZcQAAAAAAuCunA/eIESMsKAMAAAAAgPTF6cDdsmVLK+oAAAAAACBdcfoebknav3+/PvjgAzVt2lQnT56UJP3000/avn27S4sDAAAAAMBdOR24ly5dqpIlS+r333/X7NmzdenSJUnSli1b1LdvX5cXCAAAAACAO3I6cHfv3l0DBw5UTEyMvLy87O01atTQqlWrXFocAAAAAADuyunAvXXrVj399NPJ2nPmzMnzuQEAAAAA+H9OB+5s2bLp+PHjydo3btyovHnzuqQoAAAAAADcndOB++WXX1a3bt104sQJ2Ww2JSUl6bffftN7772nV155xYoaAQAAAABwO04H7kGDBil//vzKmzevLl26pIcfflhVq1ZVpUqV9MEHH1hRIwAAAAAAbsfp53B7enpqypQp6t+/vzZu3KikpCSVKVNGRYsWtaI+AAAAAADcktOBe+nSpapWrZoKFy6swoULW1ETAAAAAABuz+lLyuvUqaP8+fOre/fu2rZtmxU1AQAAAADg9pwO3MeOHVPXrl21fPlyRUREKCIiQkOGDNGRI0esqA8AAAAAALfkdODOkSOH2rZtq99++0379+/Xiy++qMmTJ6tAgQKqWbOmFTUCAAAAAOB2nA7cNypYsKC6d++ujz/+WCVLltTSpUtdVRcAAAAAAG7trgP3b7/9pnfeeUfBwcF6+eWXVbx4cUVHR7uyNgAAAAAA3JbTo5T37NlT06ZN07Fjx1S7dm2NGDFCTZo0UaZMmayoDwAAAAAAt+R04F6yZInee+89vfjii8qRI4fDvE2bNql06dKuqg0AAAAAALfldOBeuXKlw3RsbKymTJmir776Sps3b1ZiYqLLigMAAAAAwF3d9T3cixYtUvPmzRUcHKyRI0eqQYMGWrdunStrAwAAAADAbTl1hvvIkSOaOHGixo8fr8uXL+uFF17QtWvXNGvWLD388MNW1QgAAAAAgNtJ9RnuBg0a6OGHH9aOHTs0cuRIHTt2TCNHjrynjUdGRqp8+fLy8/NTrly51KRJE+3evfue1gkAAAAAwIMg1YH7l19+0euvv65+/fqpYcOG8vDwuOeNL126VO+++65Wr16tmJgYJSQkqG7durp8+fI9rxsAAAAAgLSU6kvKly9frvHjx6tcuXIKDw9XixYt9OKLL97Txn/66SeH6QkTJihXrlxav369qlatek/rBgAAAAAgLaX6DHfFihX15Zdf6vjx42rTpo2mT5+uvHnzKikpSTExMbp48eI9FxMbGytJCggISHF+fHy8Lly44PACAAAAAOBB5PQo5ZkyZVKrVq20YsUKbd26VV26dNHHH3+sXLlyqXHjxnddiDFGnTt3VpUqVVSiRIkU+0RGRsrf39/+CgkJuevtAQAAAABgpbt+LJgkhYWFaciQITpy5IimTZt2T4W0bdtWW7Zsue16evToodjYWPvr8OHD97RNAAAAAACs4tRjwW7Fw8NDTZo0UZMmTe5q+Xbt2mn+/PlatmyZ8uXLd8t+3t7e8vb2vssqAQAAAAC4f1wSuO+WMUbt2rXTnDlztGTJEhUsWDAtywEAAAAAwGXSNHC/++67mjp1qubNmyc/Pz+dOHFCkuTv7y9fX9+0LA0AAAAAgHtyT/dw36sxY8YoNjZW1atXV3BwsP01Y8aMtCwLAAAAAIB7luaXlAMAAAAAkB6l6RluAAAAAADSKwI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGCBNA/co0ePVsGCBeXj46OyZctq+fLlaV0SAAAAAAD3LE0D94wZM9SxY0f16tVLGzdu1OOPP64nnnhChw4dSsuyAAAAAAC4Z2kauIcPH67WrVvr9ddf10MPPaQRI0YoJCREY8aMScuyAAAAAAC4ZxnTasNXr17V+vXr1b17d4f2unXrauXKlSkuEx8fr/j4ePt0bGysJOnChQuW1HjtWvydOwFuzqrj535IjL+S1iUAlnPXYzTxb/4ORfrnrsenJF25fDWtSwAsZ9Uxen29xpg79k2zwH369GklJiYqKCjIoT0oKEgnTpxIcZnIyEj169cvWXtISIglNQL/Bf7+g9O6BAC34T/6o7QuAcAt+HcemtYlALiNjzXF0vVfvHhR/v7+t+2TZoH7OpvN5jBtjEnWdl2PHj3UuXNn+3RSUpLOnj2rwMDAWy4D93HhwgWFhITo8OHDypo1a1qXA+AmHKPAg4vjE3iwcYymL8YYXbx4UXny5Llj3zQL3Dly5JCHh0eys9knT55Mdtb7Om9vb3l7ezu0ZcuWzaoSkUayZs3KDxHwAOMYBR5cHJ/Ag41jNP2405nt69Js0DQvLy+VLVtWMTExDu0xMTGqVKlSGlUFAAAAAIBrpOkl5Z07d1aLFi1Urlw5VaxYUePGjdOhQ4f01ltvpWVZAAAAAADcszQN3C+++KLOnDmj/v376/jx4ypRooQWLFig0NDQtCwLacTb21t9+/ZNdtsAgAcDxyjw4OL4BB5sHKP/XTaTmrHMAQAAAACAU9LsHm4AAAAAANIzAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBw455FRkaqfPny8vPzU65cudSkSRPt3r3boY8xRh9++KHy5MkjX19fVa9eXdu3b7/juhs3bqz8+fPLx8dHwcHBatGihY4dO+bQZ+HChapUqZL8/PwUHBysbt26KSEhwaX7CKQXkZGRstls6tixo73tbo/PAgUKyGazOby6d+/u0IfjE7izo0ePqnnz5goMDFSmTJlUunRprV+/3j7/bo7RJUuWJDs+r7/Wrl1r78cxCtxeQkKCPvjgAxUsWFC+vr4qVKiQ+vfvr6SkJHufu/17dMOGDapTp46yZcumwMBAvfnmm7p06ZJDn0OHDunJJ59U5syZlSNHDrVv315Xr151+X7COgRu3LOlS5fq3Xff1erVqxUTE6OEhATVrVtXly9ftvcZMmSIhg8frlGjRmnt2rXKnTu36tSpo4sXL9523TVq1NC3336r3bt3a9asWdq/f7+ee+45+/wtW7aoQYMGql+/vjZu3Kjp06dr/vz5yf7RD0Bau3atxo0bp4iICIf2uz0+Jdkf63j99cEHH9jncXwCd3bu3DlVrlxZnp6e+vHHH7Vjxw4NGzZM2bJls/e5m2O0UqVKDsfm8ePH9frrr6tAgQIqV66cJI5RIDUGDx6ssWPHatSoUdq5c6eGDBmioUOHauTIkfY+d3OMHjt2TLVr11aRIkX0+++/66efftL27dv16quv2vskJiaqYcOGunz5slasWKHp06dr1qxZ6tKli5W7DFczgIudPHnSSDJLly41xhiTlJRkcufObT7++GN7nytXrhh/f38zduxYp9Y9b948Y7PZzNWrV40xxvTo0cOUK1fOoc+cOXOMj4+PuXDhwj3uCZB+XLx40RQtWtTExMSYatWqmQ4dOhhj7u34DA0NNZ9++ukt53N8AnfWrVs3U6VKlVvOd9XfoVevXjW5cuUy/fv3t7dxjAJ31rBhQ9OqVSuHtmeeecY0b97cGHP3x+gXX3xhcuXKZRITE+1tGzduNJLM3r17jTHGLFiwwGTIkMEcPXrU3mfatGnG29vbxMbGumT/YD3OcMPlYmNjJUkBAQGSpIMHD+rEiROqW7euvY+3t7eqVaumlStXpnq9Z8+e1ZQpU1SpUiV5enpKkuLj4+Xj4+PQz9fXV1euXHG4HA/4r3v33XfVsGFD1a5d26H9Xo/PwYMHKzAwUKVLl9agQYMcLnPj+ATubP78+SpXrpyef/555cqVS2XKlNGXX35pn++qv0Pnz5+v06dPO5w94xgF7qxKlSpauHCh9uzZI0navHmzVqxYoQYNGki6+2M0Pj5eXl5eypDh3zjm6+srSVqxYoUkadWqVSpRooTy5Mlj71OvXj3Fx8dzjLoRAjdcyhijzp07q0qVKipRooQk6cSJE5KkoKAgh75BQUH2ebfTrVs3Zc6cWYGBgTp06JDmzZtnn1evXj2tXLlS06ZNU2Jioo4ePaqBAwdKko4fP+6q3QLc2vTp07V+/XpFRkYmm3cvx2eHDh00ffp0LV68WG3bttWIESP0zjvv2OdzfAJ3duDAAY0ZM0ZFixbVzz//rLfeekvt27fX5MmTJd3736HXRUVFqV69egoJCbG3cYwCd9atWzc1bdpU4eHh8vT0VJkyZdSxY0c1bdpU0t0fozVr1tSJEyc0dOhQXb16VefOnVPPnj0l/Xv8nThxItl6s2fPLi8vL6eOf6QtAjdcqm3bttqyZYumTZuWbJ7NZnOYNsbY29566y1lyZLF/rrR+++/r40bN+qXX36Rh4eHXnnlFRljJEl169bV0KFD9dZbb8nb21vFihVTw4YNJUkeHh5W7CLgVg4fPqwOHTpoypQpyc5k3ehujs9OnTqpWrVqioiI0Ouvv66xY8cqKipKZ86ckcTxCaRGUlKSHnnkEX300UcqU6aM2rRpozfeeENjxoxx6He3f4dK0pEjR/Tzzz+rdevWDu0co8CdzZgxQ998842mTp2qDRs2aNKkSfrkk080adIkh37OHqPFixfXpEmTNGzYMGXKlEm5c+dWoUKFFBQU5HD83bzem9cNN5CGl7MjnWnbtq3Jly+fOXDggEP7/v37jSSzYcMGh/bGjRubV155xRhjzF9//WX27t1rf93K4cOHjSSzcuVKh/akpCRz9OhRExcXZ3bs2GEkmTVr1rhozwD3NWfOHCPJeHh42F+SjM1mMx4eHmbfvn0uOz6PHDliJJnVq1c7tHN8AreWP39+07p1a4e20aNHmzx58hhjXPN3aP/+/U3OnDnt45/cjGMUuLV8+fKZUaNGObQNGDDAhIWFGWNcc4yeOHHCXLx40Vy6dMlkyJDBfPvtt8YYY3r37m0iIiIc+p49e9ZIMosWLXLZPsJanOHGPTPGqG3btpo9e7YWLVqkggULOswvWLCgcufOrZiYGHvb1atXtXTpUlWqVEmSlCtXLhUpUsT+ut22pH/ue7mRzWazP4ph2rRpCgkJ0SOPPOKqXQTcVq1atbR161Zt2rTJ/ipXrpyaNWumTZs2qVChQi47Pjdu3ChJCg4Odmjn+ARurXLlyskepblnzx6FhoZKuve/Q40xmjBhgl555RX7+Cc34xgFbi0uLs7hPmvpnytArj8WzBX/zg0KClKWLFk0Y8YM+fj4qE6dOpKkihUratu2bQ63ePzyyy/y9vZW2bJlXb6vsEgaB36kA2+//bbx9/c3S5YsMcePH7e/4uLi7H0+/vhj4+/vb2bPnm22bt1qmjZtaoKDg287Curvv/9uRo4caTZu3Gj++OMPs2jRIlOlShVTuHBhc+XKFXu/IUOGmC1btpht27aZ/v37G09PTzNnzhwrdxlwazeOUm7M3R2fK1euNMOHDzcbN240Bw4cMDNmzDB58uQxjRs3dujH8Qnc3po1a0zGjBnNoEGDzN69e82UKVNMpkyZzDfffGPvczfH6HW//vqrkWR27NiR4nyOUeD2WrZsafLmzWuio6PNwYMHzezZs02OHDlM165d7X3u9hgdOXKkWb9+vdm9e7cZNWqU8fX1NZ999pl9fkJCgilRooSpVauW2bBhg/n1119Nvnz5TNu2bS3bX7gegRv3TFKKrwkTJtj7JCUlmb59+5rcuXMbb29vU7VqVbN169bbrnfLli2mRo0aJiAgwHh7e5sCBQqYt956yxw5csShX40aNYy/v7/x8fExFSpUMAsWLLBiN4F04+bAfTfH5/r1602FChXsx15YWJjp27evuXz5skM/jk/gzr7//ntTokQJ4+3tbcLDw824ceMc5t/NMXpd06ZNTaVKlW45n2MUuL0LFy6YDh06mPz58xsfHx9TqFAh06tXLxMfH2/vc7fHaIsWLUxAQIDx8vIyERERZvLkycn6/Pnnn6Zhw4bG19fXBAQEmLZt2zqceMKDz2bM/1+jCwAAAAAAXIZ7uAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALAAgRsAAAAAAAsQuAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAID7bOXKlfLw8FD9+vXv63avXr2qoUOH6pFHHlHmzJnl7++vUqVK6YMPPtCxY8fuay0AAPwX2IwxJq2LAADgv+T1119XlixZ9NVXX2nHjh3Knz+/5duMj49X3bp1tWXLFvXr10+VK1eWv7+/9u/fr7lz5ypbtmyKjIxMcdmrV6/Ky8vL8hoBAEhvOMMNAMB9dPnyZX377bd6++231ahRI02cODFZn/nz56to0aLy9fVVjRo1NGnSJNlsNp0/f97eZ+XKlapatap8fX0VEhKi9u3b6/Lly7fc7qeffqoVK1Zo0aJFat++vcqWLasiRYqoXr16GjNmjD766CN73+rVq6tt27bq3LmzcuTIoTp16kiSli5dqkcffVTe3t4KDg5W9+7dlZCQYF+uQIECGjFihMN2S5curQ8//NA+bbPZNGbMGD3xxBPy9fVVwYIF9d133zn3JgIA4CYI3AAA3EczZsxQWFiYwsLC1Lx5c02YMEE3Xmz2xx9/6LnnnlOTJk20adMmtWnTRr169XJYx9atW1WvXj0988wz2rJli2bMmKEVK1aobdu2t9zutGnTVKdOHZUpUybF+TabzWF60qRJypgxo3777Td98cUXOnr0qBo0aKDy5ctr8+bNGjNmjKKiojRw4ECn34PevXvr2Wef1ebNm9W8eXM1bdpUO3fudHo9AAA86AjcAADcR1FRUWrevLkkqX79+rp06ZIWLlxonz927FiFhYVp6NChCgsL00svvaRXX33VYR1Dhw7Vyy+/rI4dO6po0aKqVKmSPv/8c02ePFlXrlxJcbt79uxRWFiYQ9vTTz+tLFmyKEuWLKpUqZLDvCJFimjIkCEKCwtTeHi4Ro8erZCQEI0aNUrh4eFq0qSJ+vXrp2HDhikpKcmp9+D555/X66+/rmLFimnAgAEqV66cRo4c6dQ6AABwBwRuAADuk927d2vNmjV66aWXJEkZM2bUiy++qPHjxzv0KV++vMNyjz76qMP0+vXrNXHiRHtYzpIli+rVq6ekpCQdPHjwltu/+Sz26NGjtWnTJrVq1UpxcXEO88qVK+cwvXPnTlWsWNFhHZUrV9alS5d05MiRVOz9vypWrJhsmjPcAID0KGNaFwAAwH9FVFSUEhISlDdvXnubMUaenp46d+6csmfPLmNMsmB88/imSUlJatOmjdq3b59sG7cagK1o0aLatWuXQ1twcLAkKSAgIFn/zJkzJ6vhVnVdb8+QIUOyWq9du5ZiPTe7ed0AAKQHnOEGAOA+SEhI0OTJkzVs2DBt2rTJ/tq8ebNCQ0M1ZcoUSVJ4eLjWrl3rsOy6descph955BFt375dRYoUSfa61WjiTZs2VUxMjDZu3HhX9T/88MNauXKlQ6BeuXKl/Pz87P+BkDNnTh0/ftw+/8KFCymecV+9enWy6fDw8LuqCwCABxmBGwCA+yA6Olrnzp1T69atVaJECYfXc889p6ioKElSmzZttGvXLnXr1k179uzRt99+ax/J/PpZ4G7dumnVqlV69913tWnTJu3du1fz589Xu3btbrn9Tp06qWLFiqpZs6Y+++wzbdiwQQcPHtTPP/+sH3/8UR4eHret/5133tHhw4fVrl077dq1S/PmzVPfvn3VuXNnZcjwzz8natasqa+//lrLly/Xtm3b1LJlyxTX+91332n8+PHas2eP+vbtqzVr1tx2wDcAANwVgRsAgPsgKipKtWvXlr+/f7J5zz77rDZt2qQNGzaoYMGCmjlzpmbPnq2IiAiNGTPGPkq5t7e3JCkiIkJLly7V3r179fjjj6tMmTLq3bu3/RLxlPj4+GjhwoXq3r27JkyYoCpVquihhx5Sx44dVblyZc2dO/e29efNm1cLFizQmjVrVKpUKb311ltq3bq1PvjgA3ufHj16qGrVqmrUqJEaNGigJk2aqHDhwsnW1a9fP02fPl0RERGaNGmSpkyZoocffjg1byMAAG7FZm6+2QoAADxQBg0apLFjx+rw4cNpXco9s9lsmjNnjpo0aZLWpQAAYDkGTQMA4AEzevRolS9fXoGBgfrtt980dOhQLrkGAMANEbgBAHjA7N27VwMHDtTZs2eVP39+denSRT169EjrsgAAgJO4pBwAAAAAAAswaBoAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYIH/A8j4XESZYsenAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot to visualize the average number of pregnancies ('PRG') by age group\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(data=train_df, x='Age Group', y='PRG', palette='viridis', ci=None)\n",
    "plt.title('Average Number of Pregnancies by Age Group')\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Average Number of Pregnancies (PRG)')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5233d61b",
   "metadata": {},
   "source": [
    "The analysis of age against the average number of pregnancies reveals distinct patterns across different age groups.\n",
    "\n",
    "- Women in the 20-39 age group have an average of 3 pregnancies, while those in the 40-59 age group have a significantly higher average of 7 pregnancies.\n",
    "\n",
    "\n",
    "- In the 60-79 age group, the average number of pregnancies decreases to 4, likely due to the onset of menopause in many women.\n",
    "\n",
    "\n",
    "- The 80-90 age group shows the highest average of 9 pregnancies, reflecting the childbearing practices of earlier generations.\n",
    "\n",
    "\n",
    "The relationship between age and the average number of pregnancies in the context of sepsis analysis highlights the impact of generational and life stage factors. While younger women tend to have fewer pregnancies, the older age groups show variations influenced by historical trends and the onset of menopause. Understanding these age-related patterns is essential for sepsis assessment, as it underscores the importance of considering demographic and life stage variables when studying the disease.\n",
    "\n",
    "\n",
    "Older women may have specific healthcare needs related to their reproductive history, which could impact their susceptibility to sepsis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4737089e",
   "metadata": {},
   "source": [
    "## ii. Bivariate Analysis between 'Age' and 'PL' (Age vs. Plasma Glucose Concentration)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7050a5",
   "metadata": {},
   "source": [
    "Plasma Glucose Concentration (PL) and age are critical factors in the context of sepsis. Elevated PL levels, often due to diabetes or stress-induced hyperglycemia, can weaken the immune system, increasing sepsis risk. Sepsis can affect all age groups, but its presentation varies with age. Understanding these factors is crucial for sepsis detection and management."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "819bb30c",
   "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>Age Group</th>\n",
       "      <th>Mean PL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20-39</td>\n",
       "      <td>116.669623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40-59</td>\n",
       "      <td>129.563492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60-79</td>\n",
       "      <td>137.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80-90</td>\n",
       "      <td>134.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Group     Mean PL\n",
       "0     20-39  116.669623\n",
       "1     40-59  129.563492\n",
       "2     60-79  137.857143\n",
       "3     80-90  134.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean 'PL' for each age group\n",
    "age_vs_pl_mean = train_df.groupby('Age Group')['PL'].mean().reset_index()\n",
    "\n",
    "# Rename the columns for clarity\n",
    "age_vs_pl_mean.columns = ['Age Group', 'Mean PL']\n",
    "\n",
    "# Display the DataFrame\n",
    "age_vs_pl_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2dc2c7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the mean 'PL' for each age group\n",
    "age_vs_pl_mean = train_df.groupby('Age Group')['PL'].mean().reset_index()\n",
    "age_vs_pl_mean.columns = ['Age Group', 'Mean PL']\n",
    "\n",
    "# Create a Matplotlib figure and axes\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Create the line plot\n",
    "plt.plot(age_vs_pl_mean['Age Group'], age_vs_pl_mean['Mean PL'], marker='o', markersize=8, color='black', label='Mean PL', linestyle='-')\n",
    "\n",
    "# Customize plot aesthetics\n",
    "plt.title('Mean Plasma Glucose Concentration (PL) by Age Group', fontsize=16)\n",
    "plt.xlabel('Age Group', fontsize=12)\n",
    "plt.ylabel('Mean Plasma Glucose Concentration (PL)', fontsize=12)\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d3e61c7",
   "metadata": {},
   "source": [
    "- In the age group of 20-40, the mean plasma glucose concentration is approximately 116.67. This group represents younger individuals, and their mean PL falls within the expected range for their age.\n",
    "\n",
    "\n",
    "- As individuals age into the 40-60 group, the mean PL increases to approximately 129.56. This uptrend suggests that plasma glucose concentration tends to rise with age within this age bracket.\n",
    "\n",
    "\n",
    "- Moving into the 60-80 age group, the mean PL further increases to about 137.86. This observation signifies a notable elevation in plasma glucose concentration among individuals in this age range, which may be associated with age-related factors or underlying health conditions.\n",
    "\n",
    "\n",
    "- Lastly, in the 80-90 age group, the mean PL remains relatively stable at 134. This age group, representing the elderly, maintains a moderately high plasma glucose concentration.\n",
    "\n",
    "\n",
    "- These insights highlight the relationship between age and plasma glucose concentration, indicating a progressive increase in plasma glucose concentration ('PL') with advancing age, particularly from middle age to old age.\n",
    "\n",
    "Older age groups may be at higher risk of sepsis due to elevated PL levels, emphasizing the importance of monitoring and managing blood glucose in sepsis prevention."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f06a5909",
   "metadata": {},
   "source": [
    "## iii. Bivariate Analysis between 'Age' and 'PR' (Age vs. Diastolic Blood Pressure)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "097aacaf",
   "metadata": {},
   "source": [
    "The relationship between 'Age' and 'PR' (Diastolic Blood Pressure) is essential to understand their potential impact on sepsis. Diastolic blood pressure ('PR') is a crucial cardiovascular parameter that varies with age and can be indicative of overall cardiovascular health. Age, on the other hand, is a significant factor in sepsis risk, as sepsis can affect individuals across different age groups, but its presentation and severity may vary.\n",
    "\n",
    "To visualize this relationship more clearly, we'll group patients into age intervals and examine the mean diastolic blood pressure within each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c60cc9ad",
   "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>Age Group</th>\n",
       "      <th>Mean PR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20-39</td>\n",
       "      <td>66.567627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40-59</td>\n",
       "      <td>75.690476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60-79</td>\n",
       "      <td>73.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80-90</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Group    Mean PR\n",
       "0     20-39  66.567627\n",
       "1     40-59  75.690476\n",
       "2     60-79  73.238095\n",
       "3     80-90  74.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean 'PR' for each age group\n",
    "age_vs_pr_mean = train_df.groupby('Age Group')['PR'].mean().reset_index()\n",
    "\n",
    "# Rename the columns for clarity\n",
    "age_vs_pr_mean.columns = ['Age Group', 'Mean PR']\n",
    "\n",
    "# Display the DataFrame\n",
    "age_vs_pr_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "058f235a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the mean 'PR' for each age group\n",
    "age_vs_pr_mean = train_df.groupby('Age Group')['PR'].mean().reset_index()\n",
    "age_vs_pr_mean.columns = ['Age Group', 'Mean PR']\n",
    "\n",
    "# Set the Viridis color palette\n",
    "viridis_colors = plt.cm.viridis(age_vs_pr_mean.index / len(age_vs_pr_mean))\n",
    "\n",
    "# Create the bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(age_vs_pr_mean['Age Group'], age_vs_pr_mean['Mean PR'], color=viridis_colors)\n",
    "plt.title('Mean Diastolic Blood Pressure (PR) by Age Group')\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Mean Diastolic Blood Pressure (PR)')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45a38b50",
   "metadata": {},
   "source": [
    "The analysis of diastolic blood pressure ('PR') across different age groups reveals distinct trends.\n",
    "- In the 20-39 age group, the mean diastolic blood pressure is approximately 66.57 mm Hg.\n",
    "- The 40-59 age group shows a higher mean PR of about 75.69 mm Hg.\n",
    "- The 60-79 age group exhibits a mean PR of 73.24 mm Hg.\n",
    "- The 80-90 age group has a mean PR of 74.00 mm Hg.\n",
    "\n",
    "The relationship observed between age and diastolic blood pressure ('PR') suggests that there is a correlation between these two variables. As age increases, there is a tendency for diastolic blood pressure levels to also increase, with the 40-59 age group showing the highest mean PR.\n",
    "\n",
    "\n",
    "Higher diastolic blood pressure in middle-aged individuals may contribute to an increased risk of sepsis, highlighting the need for cardiovascular health assessments in sepsis prevention."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6650300",
   "metadata": {},
   "source": [
    "## iv. Bivariate Analysis between 'Age' and 'M11' (Age vs. BMI)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e08fb9e",
   "metadata": {},
   "source": [
    "- The analysis of the relationship between age and Body Mass Index (BMI) is important in the context of sepsis as both factors can influence a patient's susceptibility to sepsis and its management.\n",
    "\n",
    "\n",
    "- BMI, a measure of body weight relative to height, can provide insights into a patient's overall health and nutritional status.\n",
    "\n",
    "\n",
    "- Age, on the other hand, can impact how the body responds to infections and inflammation, which are central to sepsis.\n",
    "\n",
    "\n",
    "- By examining how BMI changes with age, we can better understand how age-related changes in metabolism and body composition may affect the risk and outcomes of sepsis. \n",
    "\n",
    "\n",
    "- This analysis will help identify age groups that may be more vulnerable to sepsis due to their BMI levels and guide healthcare professionals in tailoring interventions and care strategies accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4b0ad54d",
   "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>Age Group</th>\n",
       "      <th>Mean M11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20-39</td>\n",
       "      <td>31.690244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40-59</td>\n",
       "      <td>33.312698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60-79</td>\n",
       "      <td>28.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80-90</td>\n",
       "      <td>25.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Group   Mean M11\n",
       "0     20-39  31.690244\n",
       "1     40-59  33.312698\n",
       "2     60-79  28.785714\n",
       "3     80-90  25.900000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean 'PR' for each age group\n",
    "age_vs_bmi_mean = train_df.groupby('Age Group')['M11'].mean().reset_index()\n",
    "\n",
    "# Rename the columns for clarity\n",
    "age_vs_bmi_mean.columns = ['Age Group', 'Mean M11']\n",
    "\n",
    "# Display the DataFrame\n",
    "age_vs_bmi_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d3811d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the mean 'M11' for each age group\n",
    "age_vs_bmi_mean = train_df.groupby('Age Group')['M11'].mean().reset_index()\n",
    "age_vs_bmi_mean.columns = ['Age Group', 'Mean M11']\n",
    "\n",
    "# Set the Viridis color palette\n",
    "viridis_colors = plt.cm.viridis(age_vs_bmi_mean.index / len(age_vs_bmi_mean))\n",
    "\n",
    "# Create the bar plot with the Viridis palette\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(age_vs_bmi_mean['Age Group'], age_vs_bmi_mean['Mean M11'], color=viridis_colors)\n",
    "plt.title('Mean BMI by Age Group')\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Mean BMI')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5743fa",
   "metadata": {},
   "source": [
    "- Individuals in the 40-59 age group exhibit the highest mean BMI (33.31), indicating a higher prevalence of overweight or obesity in this age range.\n",
    "\n",
    "\n",
    "- In contrast, those aged 80-90 have the lowest mean BMI (25.90), suggesting a trend toward lower body weight in this older age group.\n",
    "\n",
    "- The 20-39 age group shows a moderately high mean BMI (31.69), indicating a considerable proportion of individuals with higher body weight.\n",
    "\n",
    "\n",
    "- Interestingly, the 60-79 age group falls in between, with a mean BMI of 28.79.\n",
    "\n",
    "\n",
    "The relationship between age and BMI highlights that BMI tends to increase from younger to middle-aged adults, peaking in the 40-59 age group, and then gradually decreasing in the older age categories. This pattern may reflect lifestyle changes, metabolic shifts, or medical conditions associated with aging.\n",
    "\n",
    "With regards to Sepsis, middle-aged individuals may have specific risks related to their BMI, while older age groups may have different risk factors to consider in sepsis management."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5109c1ef",
   "metadata": {},
   "source": [
    "## v. Bivariate Analysis between 'Age' and 'BD2' (Age vs. Diabetes Pedigree Function)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0422ce74",
   "metadata": {},
   "source": [
    "Understanding how age interacts with BD2, a measure associated with the genetic predisposition to diabetes, is valuable for gaining insights into the risk factors and potential connections between age and diabetes-related health conditions, which can influence the likelihood and severity of sepsis. Analyzing this relationship allows us to assess whether BD2 levels vary significantly across different age groups, providing a better understanding of how age-related factors might contribute to the susceptibility or management of sepsis in various age cohorts. By investigating age-specific patterns in BD2, we aim to uncover potential correlations that could inform sepsis risk assessments and interventions tailored to specific age demographics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b2894bb9",
   "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>Age Group</th>\n",
       "      <th>Diabetes Pedigree Function</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20-39</td>\n",
       "      <td>0.480160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40-59</td>\n",
       "      <td>0.488405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60-79</td>\n",
       "      <td>0.460952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80-90</td>\n",
       "      <td>0.460000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Group  Diabetes Pedigree Function\n",
       "0     20-39                    0.480160\n",
       "1     40-59                    0.488405\n",
       "2     60-79                    0.460952\n",
       "3     80-90                    0.460000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean 'BD2' for each age group\n",
    "age_vs_bd2_mean = train_df.groupby('Age Group')['BD2'].mean().reset_index()\n",
    "\n",
    "# Rename the columns for clarity\n",
    "age_vs_bd2_mean.columns = ['Age Group', 'Diabetes Pedigree Function']\n",
    "\n",
    "\n",
    "age_vs_bd2_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9ab9a62f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the mean 'BD2' for each age group\n",
    "age_vs_bd2_mean = train_df.groupby('Age Group')['BD2'].mean().reset_index()\n",
    "\n",
    "# Set the Viridis color palette\n",
    "viridis_colors = plt.cm.viridis(age_vs_bd2_mean.index / len(age_vs_bd2_mean))\n",
    "\n",
    "# Create the bar plot with the Viridis palette\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(age_vs_bd2_mean['Age Group'], age_vs_bd2_mean['BD2'], color=viridis_colors)\n",
    "plt.title('Diabetes Pedigree Function (BD2) by Age Group')\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Diabetes Pedigree Function')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bba648ce",
   "metadata": {},
   "source": [
    "The analysis of the relationship between age and the Diabetes Pedigree Function (BD2) reveals relatively consistent values across different age groups. \n",
    "\n",
    "- The mean BD2 remains relatively stable, ranging from approximately 0.460 to 0.488, with no significant upward or downward trend observed with increasing age. \n",
    "\n",
    "\n",
    "- This suggests that age may not be a prominent factor influencing variations in BD2 among the analyzed age groups. \n",
    "\n",
    "While other factors may contribute to differences in BD2, such as genetic predisposition and family history of diabetes, the age factor does not appear to be a major driver of these variations within the given dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5be7d63",
   "metadata": {},
   "source": [
    "## vi. Bivariate Analysis between 'Age' and 'Sepssis' (Age vs. Sepsiss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1b3c95d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sepsis Positive Counts:\n"
     ]
    },
    {
     "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>index</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20-39</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40-59</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60-79</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80-90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Age Group\n",
       "0  20-39        132\n",
       "1  40-59         71\n",
       "2  60-79          5\n",
       "3  80-90          0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate positive and negative cases into two DataFrames\n",
    "positive_df = train_df[train_df['Sepssis'] == 'Positive']\n",
    "negative_df = train_df[train_df['Sepssis'] == 'Negative']\n",
    "\n",
    "# Create DataFrames showing the counts for each age group\n",
    "sepsis_positive_counts = positive_df['Age Group'].value_counts().reset_index()\n",
    "sepsis_negative_counts = negative_df['Age Group'].value_counts().reset_index()\n",
    "\n",
    "# Display the DataFrames\n",
    "print(\"Sepsis Positive Counts:\")\n",
    "sepsis_positive_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "554df8b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sepsis Positive Counts:\n"
     ]
    },
    {
     "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>age group</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20-39</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40-59</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60-79</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80-90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age group  count\n",
       "0     20-39    132\n",
       "1     40-59     71\n",
       "2     60-79      5\n",
       "3     80-90      0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate positive and negative cases into two DataFrames\n",
    "positive_df = train_df[train_df['Sepssis'] == 'Positive']\n",
    "negative_df = train_df[train_df['Sepssis'] == 'Negative']\n",
    "\n",
    "# Create DataFrames showing the counts for each age group\n",
    "sepsis_positive_counts = positive_df['Age Group'].value_counts().reset_index()\n",
    "sepsis_positive_counts.columns = ['age group', 'count']\n",
    "\n",
    "sepsis_negative_counts = negative_df['Age Group'].value_counts().reset_index()\n",
    "sepsis_negative_counts.columns = ['age group', 'count']\n",
    "\n",
    "# Display the DataFrames\n",
    "print(\"Sepsis Positive Counts:\")\n",
    "sepsis_positive_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "db7e7442",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sepsis Negative Counts:\n"
     ]
    },
    {
     "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>age group</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20-39</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40-59</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60-79</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80-90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age group  count\n",
       "0     20-39    319\n",
       "1     40-59     55\n",
       "2     60-79     16\n",
       "3     80-90      1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\nSepsis Negative Counts:\")\n",
    "sepsis_negative_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "375453db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a line plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(sepsis_positive_counts['age group'], sepsis_positive_counts['count'], marker='o', color='red', label='Positive Sepsis Count')\n",
    "plt.plot(sepsis_negative_counts['age group'], sepsis_negative_counts['count'], marker='o', color='green', label='Negative Sepsis Count')\n",
    "plt.title('Trend of Sepsis by Age Group')\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Count')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "16ff9b6c",
   "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>Age Group</th>\n",
       "      <th>Participant Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20-39</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40-59</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60-79</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80-90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Group  Participant Count\n",
       "0     20-39                451\n",
       "1     40-59                126\n",
       "2     60-79                 21\n",
       "3     80-90                  1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the data by 'Age Group' and count the number of participants in each group\n",
    "age_group_counts = train_df['Age Group'].value_counts().reset_index()\n",
    "age_group_counts.columns = ['Age Group', 'Participant Count']\n",
    "\n",
    "# Sort the DataFrame by 'Age Group'\n",
    "age_group_counts = age_group_counts.sort_values(by='Age Group')\n",
    "\n",
    "# Display the DataFrame.\n",
    "age_group_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0dc9fbf",
   "metadata": {},
   "source": [
    "i. 20-39 Age Group (Highest Participant Count):\n",
    "\n",
    "- The '20-39' age group has the highest number of participants, with 451 individuals.\n",
    "- Given its larger sample size, this age group provides a more extensive pool of individuals for sepsis cases to be identified. - Consequently, it's more likely to observe a higher number of both positive and negative sepsis cases in this age group.\n",
    "\n",
    "\n",
    "ii. 40-59 Age Group:\n",
    "\n",
    "- The '40-59' age group, while having a substantial number of participants (126 individuals), is smaller in comparison to the '20-39' age group.\n",
    "- This age group still provides a reasonable sample size for sepsis cases, resulting in a notable number of both positive and negative cases.\n",
    "\n",
    "\n",
    "iii. 60-79 Age Group:\n",
    "\n",
    "- The '60-79' age group has a significantly lower number of participants, with only 21 individuals.\n",
    "- With a smaller sample size, the likelihood of identifying sepsis cases, both positive and negative, is reduced. This is evident in the much lower counts of cases in this age group.\n",
    "\n",
    "iv. 80-90 Age Group (Lowest Participant Count):\n",
    "\n",
    "- The '80-90' age group has the lowest number of participants, with only 1 individual.\n",
    "- Due to the extremely small sample size, it becomes highly challenging to make meaningful observations about sepsis cases in this age group. With only one participant, there's very limited data available.\n",
    "\n",
    "In summary, the variation in the number of participants across age groups directly impacts the ability to detect and analyze sepsis cases. Larger sample sizes, such as in the '20-39' and '40-59' age groups, provide more statistical power to identify and study sepsis cases, whereas smaller sample sizes, as seen in the '60-79' and '80-90' age groups, limit the ability to draw meaningful conclusions about sepsis incidence. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c78269f",
   "metadata": {},
   "source": [
    "The relationship between Age and Sepsis will be examined from a Statistical persective by performing a Hypothesis Test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edab5654",
   "metadata": {},
   "source": [
    "# Hypothesis Testing and Answering Key Analytical Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d3ccdb",
   "metadata": {},
   "source": [
    "## Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53922261",
   "metadata": {},
   "source": [
    "Null Hypothesis (H0): There is no significant association between the patient's age and the likelihood of sepsis.\n",
    "\n",
    "Alternative Hypothesis (H1): There is a significant association between the patient's age and the likelihood of sepsis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1641ef63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reject the null hypothesis\n",
      "There is a significant association between age and sepsis.\n",
      "Chi-Square Statistic: 33.55383159633323\n",
      "P-value: 2.460922116459635e-07\n"
     ]
    }
   ],
   "source": [
    "# Create a contingency table of Age vs. Sepsis\n",
    "contingency_table = pd.crosstab(train_df['Age Group'], train_df['Sepssis'])\n",
    "\n",
    "# Perform the Chi-Square test\n",
    "chi2, p, _, _ = chi2_contingency(contingency_table)\n",
    "\n",
    "# Define the significance level (alpha)\n",
    "alpha = 0.05\n",
    "\n",
    "# Check if the p-value is less than alpha\n",
    "if p < alpha:\n",
    "    print(\"Reject the null hypothesis\")\n",
    "    print(\"There is a significant association between age and sepsis.\")\n",
    "else:\n",
    "    print(\"Fail to reject the null hypothesis\")\n",
    "    print(\"There is no significant association between age and sepsis.\")\n",
    "\n",
    "# Print the Chi-Square statistic and p-value\n",
    "print(f\"Chi-Square Statistic: {chi2}\")\n",
    "print(f\"P-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae81a0ab",
   "metadata": {},
   "source": [
    "The Chi-Square test results indicate that we should reject the null hypothesis, as the p-value (approximately 2.46e-07) is significantly smaller than the chosen significance level (alpha) of 0.05. This means that there is a statistically significant association between a patient's age (in different age groups) and the likelihood of sepsis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97acbb4a",
   "metadata": {},
   "source": [
    "## Answering Key Analytical Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3888e41f",
   "metadata": {},
   "source": [
    "To address our key analytical objectives, we will focus on examining the interrelationships between various variables. We will utilize the Pearson Correlation coefficient, a foundational tool for quantifying the strength and direction of linear associations among these variables. Additionally, a correlation matrix will be constructed to provide a structured representation of these correlations. This matrix will offer a comprehensive view of how different medical parameters are interconnected. These critical insights, obtained from the Pearson Correlation coefficient and the correlation matrix, will empower us to discern the factors that will significantly influence the risk of sepsis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "443896c6",
   "metadata": {},
   "source": [
    "### i. Are there any correlations or patterns between the numerical features (e.g., PRG, PL, PR, SK, TS, M11, BD2, Age) and the presence of sepsis (Positive/Negative)?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5fac462a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "corr_matrix = train_df[['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age']].corr()\n",
    "\n",
    "# Create a heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a920cdba",
   "metadata": {},
   "source": [
    "Age and Number of Pregnancies (PRG): There is a moderately positive correlation (0.53) between the number of pregnancies (PRG) and age. This suggests that, on average, older individuals tend to have more pregnancies.\n",
    "\n",
    "Plasma Glucose (PL) and 2-Hour Serum Insulin (TS): Plasma glucose (PL) and 2-hour serum insulin (TS) exhibit a moderate positive correlation (0.34). This correlation indicates that higher plasma glucose levels are associated with higher 2-hour serum insulin levels.\n",
    "\n",
    "Plasma Glucose (PL) and Diastolic Blood Pressure (PR): There is a positive correlation (0.14) between plasma glucose (PL) and diastolic blood pressure (PR). This suggests that higher plasma glucose levels may be associated with higher diastolic blood pressure.\n",
    "\n",
    "Skinfold Thickness (SK) and 2-Hour Serum Insulin (TS): Skinfold thickness (SK) and 2-hour serum insulin (TS) have a relatively strong positive correlation (0.43). This implies that individuals with higher skinfold thickness may tend to have higher 2-hour serum insulin levels.\n",
    "\n",
    "Age and Skinfold Thickness (SK): There is a negative correlation (-0.12) between age and skinfold thickness (SK). This suggests that, on average, older individuals may have lower skinfold thickness.\n",
    "\n",
    "Pregnancies (PRG) and Diastolic Blood Pressure (PR): The correlation between the number of pregnancies (PRG) and diastolic blood pressure (PR) is relatively low (0.12), indicating a weak positive relationship.\n",
    "\n",
    "Age and Diabetes Pedigree Function (BD2): Age and the diabetes pedigree function (BD2) have a weak positive correlation (0.05), suggesting that age and the diabetes pedigree function are somewhat related, but the relationship is not strong."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85dba26",
   "metadata": {},
   "source": [
    "### ii. How does the distribution of key numerical variables (e.g., PR, SK, TS, M11) differ between patients with and without sepsis?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e311d0ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create subplots for each numerical variable\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Define the numerical variables of interest\n",
    "numerical_variables = ['PR', 'SK', 'TS', 'M11']\n",
    "\n",
    "# Define the color palette with green and blue from the 'viridis' palette\n",
    "colors = sns.color_palette('viridis', n_colors=2)\n",
    "\n",
    "# Loop through the numerical variables and create subplots\n",
    "for i, variable in enumerate(numerical_variables):\n",
    "    plt.subplot(2, 2, i + 1)\n",
    "    sns.boxplot(x='Sepssis', y=variable, data=train_df, hue='Sepssis', palette=colors)\n",
    "    plt.title(f'Distribution of {variable} by Sepsis')\n",
    "    plt.xlabel('Sepsis Status')\n",
    "    plt.ylabel(variable)\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab20fd8a",
   "metadata": {},
   "source": [
    "The boxplots reveal that patients with sepsis generally have higher  PR, SK, TS and M11 compared to patients without Sepsis. These differences suggest that PR, SK, TS, and BMI might be relevant factors in assessing the risk of sepsis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d277b77b",
   "metadata": {},
   "source": [
    "### iii. Is there a relationship between the number of pregnancies (PRG) and plasma glucose concentration (PL)? Does this relationship vary with the presence of sepsis?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c2abc272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation between 'PRG' and 'PL': 0.13954104835183154\n"
     ]
    }
   ],
   "source": [
    "# Select the columns of interest\n",
    "scatter_data = train_df[['PRG', 'PL']]\n",
    "\n",
    "# Create a scatterplot\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x='PRG', y='PL', data=scatter_data)\n",
    "plt.title('Scatterplot of PRG vs. PL')\n",
    "plt.xlabel('PRG')\n",
    "plt.ylabel('PL')\n",
    "plt.show()\n",
    "\n",
    "# Calculate Pearson correlation\n",
    "s0 = scatter_data['PRG']\n",
    "s1 = scatter_data['PL']\n",
    "pearson = s0.corr(s1, method='pearson')\n",
    "\n",
    "# Print the correlation values\n",
    "print(f\"Pearson Correlation between 'PRG' and 'PL': {pearson}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e906413",
   "metadata": {},
   "source": [
    "The Pearson correlation indicate a weak positive relationship between 'PRG' and 'PL.' While the Pearson correlation measures linear association and Spearman is non-parametric, they provide consistent results in this case, suggesting that as 'PRG' values increase, 'PL' values tend to increase, but the relationship is not particularly strong. These results are corroborated by the correlation matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd598d33",
   "metadata": {},
   "source": [
    "### iv. Are there any significant differences in diastolic blood pressure (PR) between patients with different triceps skinfold thickness (SK) levels?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "048ad00c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation between 'PR' and 'SK': 0.1975352882024966\n"
     ]
    }
   ],
   "source": [
    "# Select the columns of interest\n",
    "scatter_data = train_df[['PR', 'SK']]\n",
    "\n",
    "# Create a scatterplot\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x='SK', y='PR', data=scatter_data)\n",
    "plt.title('Scatterplot of SK vs. PR')\n",
    "plt.xlabel('SK')\n",
    "plt.ylabel('PR')\n",
    "plt.show()\n",
    "\n",
    "# Calculate Pearson correlation\n",
    "s0 = scatter_data['PR']\n",
    "s1 = scatter_data['SK']\n",
    "pearson = s0.corr(s1, method='pearson')\n",
    "\n",
    "# Print the correlation values\n",
    "print(f\"Pearson Correlation between 'PR' and 'SK': {pearson}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "399ba73e",
   "metadata": {},
   "source": [
    "The analysis indicates a modest positive relationship (Pearson correlation coefficient of approximately 0.198) between diastolic blood pressure (PR) and triceps skinfold thickness (SK). This suggests that as triceps skinfold thickness increases, diastolic blood pressure tends to rise slightly. Such a correlation may have potential implications for cardiovascular health, as higher triceps skinfold thickness often reflects increased subcutaneous fat. However, it's essential to recognize that the correlation is not notably strong, and its clinical significance would require further investigation and the consideration of additional health factors to draw comprehensive conclusions about the potential impact on individuals' health and well-being."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231194cf",
   "metadata": {},
   "source": [
    "### v. Does the body mass index (M11) vary significantly with 2-hour serum insulin (TS) levels?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f09b6fbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation between TS and M11: 0.18474687309420648\n"
     ]
    }
   ],
   "source": [
    "# Scatterplot of M11 (BMI) vs. TS\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.scatterplot(data=train_df, x='TS', y='M11')\n",
    "plt.xlabel('TS (2-Hour Serum Insulin)')\n",
    "plt.ylabel('M11 (BMI)')\n",
    "plt.title('Variation of BMI (M11) with TS')\n",
    "plt.show()\n",
    "\n",
    "# Calculate Pearson correlation\n",
    "s0 = train_df['TS']  \n",
    "s1 = train_df['M11']  \n",
    "pearson = s0.corr(s1, method='pearson')\n",
    "\n",
    "# Print the correlation values\n",
    "print(f\"Pearson Correlation between TS and M11: {pearson}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2656d5df",
   "metadata": {},
   "source": [
    "The Pearson Correlation between 'TS' (2-Hour Serum Insulin) and 'M11' (BMI) is approximately 0.185. This indicates a weak positive linear correlation between these two variables. The correlation of 0.185 suggests that as 2-Hour Serum Insulin levels ('TS') increase, there is a slight tendency for BMI ('M11') to also increase, but the relationship is not notably strong. It's important to note that correlation does not imply causation, so while there is an association, it doesn't necessarily mean that changes in 2-Hour Serum Insulin directly cause changes in BMI."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a57aa4f3",
   "metadata": {},
   "source": [
    "### vi. Is there a correlation between the diabetes pedigree function (BD2) and age? How does this correlation affect the likelihood of sepsis?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d66ab894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation between BD2 and Age: 0.033566723541057414\n"
     ]
    }
   ],
   "source": [
    "# Create a scatterplot to visualize the relationship\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.scatterplot(data=train_df, x='BD2', y='Age', hue='Sepssis')\n",
    "plt.xlabel('BD2 (Diabetes Pedigree Function)')\n",
    "plt.ylabel('Age')\n",
    "plt.title('Relationship Between BD2 and Age, Colored by Sepsis')\n",
    "plt.show()\n",
    "\n",
    "# Calculate Pearson correlation between BD2 and age\n",
    "pearson_correlation = train_df['BD2'].corr(train_df['Age'], method='pearson')\n",
    "\n",
    "# Print the Pearson correlation value\n",
    "print(f\"Pearson Correlation between BD2 and Age: {pearson_correlation}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d096830",
   "metadata": {},
   "source": [
    "The Pearson Correlation between the diabetes pedigree function (BD2), a measure of genetic diabetes susceptibility, and age is extremely weak at approximately 0.0336. This weak correlation suggests that age has minimal influence on BD2. Consequently, when considering sepsis risk solely in terms of age and its correlation with BD2, it is unlikely that age significantly affects the likelihood of sepsis based on BD2. In this specific context, the focus is on the limited relationship between age and BD2, indicating that age alone may not be a reliable predictor for sepsis risk associated with BD2. Other factors may play a more substantial role in determining sepsis risk."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "318dd9c4",
   "metadata": {},
   "source": [
    "### vii. Are patients with insurance coverage more likely to have certain health characteristics (e.g., higher age, higher BMI) compared to those without insurance coverage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2537250e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Insurance</th>\n",
       "      <th>Mean PRG</th>\n",
       "      <th>Mean PL</th>\n",
       "      <th>Mean TS</th>\n",
       "      <th>Mean M11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3.404255</td>\n",
       "      <td>117.691489</td>\n",
       "      <td>72.303191</td>\n",
       "      <td>31.602660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4.017032</td>\n",
       "      <td>121.279805</td>\n",
       "      <td>82.734793</td>\n",
       "      <td>32.065207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Insurance  Mean PRG     Mean PL    Mean TS   Mean M11\n",
       "0          0  3.404255  117.691489  72.303191  31.602660\n",
       "1          1  4.017032  121.279805  82.734793  32.065207"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numeric variables to compare\n",
    "numeric_variables = ['PRG', 'PL', 'TS', 'M11']\n",
    "\n",
    "# Create subplots for each numeric variable\n",
    "plt.figure(figsize=(15, 10))\n",
    "colors = sns.color_palette('viridis', n_colors=2)\n",
    "\n",
    "for i, variable in enumerate(numeric_variables):\n",
    "    plt.subplot(2, 2, i + 1)\n",
    "    sns.boxplot(data=train_df, x='Insurance', y=variable, palette=colors)\n",
    "    plt.xlabel('Insurance Coverage')\n",
    "    plt.ylabel(variable)\n",
    "    plt.title(f'{variable} Distribution by Insurance Coverage')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Calculate the mean for each numeric variable based on Insurance (0, 1)\n",
    "mean_numeric_vs_insurance = train_df.groupby('Insurance')[numeric_variables].mean().reset_index()\n",
    "mean_numeric_vs_insurance.columns = ['Insurance'] + [f'Mean {var}' for var in numeric_variables]\n",
    "\n",
    "# Display the DataFrame\n",
    "mean_numeric_vs_insurance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8e0f39",
   "metadata": {},
   "source": [
    "The boxplots and mean values based on insurance coverage provide valuable insights into the relationship between insurance status and various health characteristics. Here are detailed insights for each of the numeric variables:\n",
    "\n",
    "Mean PRG (Number of Pregnancies):\n",
    "Patients with insurance coverage (Insurance=1) exhibit a slightly higher mean number of pregnancies (approximately 4.02) compared to patients without insurance (Insurance=0) with a mean of approximately 3.40. This suggests that individuals with insurance might be more likely to have a slightly higher number of pregnancies.\n",
    "\n",
    "Mean PL (Plasma Glucose):\n",
    "Patients with insurance coverage tend to have a slightly higher mean plasma glucose level (approximately 121.28) compared to patients without insurance (mean of approximately 117.69). This implies that individuals with insurance coverage may be more likely to have a slightly elevated plasma glucose level.\n",
    "\n",
    "Mean TS (2-Hour Serum Insulin):\n",
    "Patients with insurance coverage exhibit a notably higher mean 2-hour serum insulin level (approximately 82.73) in comparison to patients without insurance (mean of approximately 72.30). This suggests that individuals with insurance are more likely to have a higher 2-hour serum insulin level.\n",
    "\n",
    "Mean M11 (BMI - Body Mass Index):\n",
    "Patients with insurance coverage also demonstrate a slightly higher mean BMI (approximately 32.07) when compared to patients without insurance (mean of approximately 31.60). This implies that individuals with insurance may be more likely to have a slightly higher BMI.\n",
    "\n",
    "These insights collectively indicate that patients with insurance coverage may have certain health characteristics, such as higher numbers of pregnancies, elevated plasma glucose levels, higher 2-hour serum insulin levels, and slightly higher BMI."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45a65e85",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cdb3b20",
   "metadata": {},
   "source": [
    "## i. Imputing the 0 values in the 'Plasma Glucose Concentration (PL)', 'Diastolic Blood Pressure (PR)', 'Triceps skinfold thickness(SK)', and 'Body Mass Index (M11)' columns with the mean value for each age group"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5d2f5ff",
   "metadata": {},
   "source": [
    "During the EDA, we encountered 0 values in the 'Plasma Glucose Concentration (PL)', 'Diastolic Blood Pressure (PR)', 'Triceps skinfold thickness(SK)', and 'Body Mass Index (M11)' columns which is medically impossible\n",
    "\n",
    "- Plasma Glucose Concentration (PL): \n",
    "\n",
    "A plasma glucose concentration of 0 is not possible. Plasma glucose is the main type of sugar found in the blood. It is the body's main source of energy. A normal plasma glucose concentration in a fasting person is between 70 and 100 mg/dL. A plasma glucose concentration of 0 would mean that there is no glucose in the blood, which is not possible for a living human being. The brain and other organs need glucose to function properly. Without glucose, the brain would not be able to function, and the person would likely fall into a coma.\n",
    "\n",
    "\n",
    "\n",
    "- Diastolic Blood Pressure (DBP):\n",
    "\n",
    "DBP is the minimum pressure in the arteries when the heart is at rest. A normal DBP is between 60 and 90 mmHg. A DBP of 0 can occur in cases of severe hypotension, which is a dangerously low blood pressure. Hypotension can be caused by a number of factors, including shock, severe blood loss, dehydration, heart failure, sepsis, and certain medications. However, a DBP of 0 is not sustainable for life. Without adequate blood pressure, organs cannot receive the oxygen and nutrients they need to function properly.\n",
    "\n",
    "\n",
    "\n",
    "- Triceps Skinfold Thickness (SK): \n",
    "\n",
    "SK is a measure of the amount of subcutaneous fat under the skin. Subcutaneous fat is a type of body fat that is found just below the skin. It plays an important role in energy storage, insulation, and temperature regulation. A triceps skinfold thickness of 0 would mean that there is no subcutaneous fat under the skin, which is not possible for a living human being. Without subcutaneous fat, the body would not be able to store energy effectively, regulate temperature, or protect itself from injury.\n",
    "\n",
    "\n",
    "\n",
    "- BMI: \n",
    "\n",
    "BMI is a measure of overall body fatness, calculated by dividing weight in kilograms by height in meters squared. A BMI of 0 would mean that someone has no weight, which is not possible for a living human being.\n",
    "\n",
    "\n",
    "This is the reason why we will impute the 0 values in these columns with the mean value of each age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a7108be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define age intervals\n",
    "age_intervals = [20, 40, 60, 80, 90]\n",
    "age_labels = ['20-39', '40-59', '60-79', '80-90']\n",
    "\n",
    "# Create 'Age Group' column in test_df based on age intervals\n",
    "test_df['Age Group'] = pd.cut(test_df['Age'], bins=age_intervals, labels=age_labels)\n",
    "\n",
    "# Now you can proceed with the imputation code\n",
    "# Define columns to impute\n",
    "columns_to_impute = ['PL', 'PR', 'SK', 'M11']\n",
    "\n",
    "# Loop through each column for imputation\n",
    "for column in columns_to_impute:\n",
    "    # Identify rows with a value of 0 in the column for both train and test datasets\n",
    "    zero_rows_train = train_df[train_df[column] == 0]\n",
    "    zero_rows_test = test_df[test_df[column] == 0]\n",
    "    \n",
    "    # Iterate through age groups\n",
    "    for age_group in age_labels:\n",
    "        # Calculate the mean of the column within the specific age group for both datasets\n",
    "        age_group_mean_train = train_df[train_df['Age Group'] == age_group][column].mean()\n",
    "        age_group_mean_test = test_df[test_df['Age Group'] == age_group][column].mean()\n",
    "        \n",
    "        # Impute the mean for rows in the specific age group for both datasets\n",
    "        train_df.loc[(train_df['Age Group'] == age_group) & (train_df[column] == 0), column] = age_group_mean_train\n",
    "        test_df.loc[(test_df['Age Group'] == age_group) & (test_df[column] == 0), column] = age_group_mean_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "195ccbcd",
   "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>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>121.148985</td>\n",
       "      <td>71.901390</td>\n",
       "      <td>26.360109</td>\n",
       "      <td>79.460768</td>\n",
       "      <td>32.398889</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>30.767905</td>\n",
       "      <td>11.961464</td>\n",
       "      <td>10.091526</td>\n",
       "      <td>116.576176</td>\n",
       "      <td>6.969327</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>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.200000</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>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.400000</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>116.669623</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  121.148985   71.901390   26.360109   79.460768   32.398889   \n",
       "std      3.362839   30.767905   11.961464   10.091526  116.576176    6.969327   \n",
       "min      0.000000   44.000000   24.000000    7.000000    0.000000   18.200000   \n",
       "25%      1.000000   99.000000   64.000000   19.000000    0.000000   27.400000   \n",
       "50%      3.000000  116.669623   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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0ed5d167",
   "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>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>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.917160</td>\n",
       "      <td>123.520710</td>\n",
       "      <td>73.274787</td>\n",
       "      <td>26.588964</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>32.600409</td>\n",
       "      <td>0.438876</td>\n",
       "      <td>33.065089</td>\n",
       "      <td>0.727811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.402415</td>\n",
       "      <td>29.259123</td>\n",
       "      <td>12.783183</td>\n",
       "      <td>9.091937</td>\n",
       "      <td>110.720852</td>\n",
       "      <td>6.562578</td>\n",
       "      <td>0.306935</td>\n",
       "      <td>11.548110</td>\n",
       "      <td>0.446410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>0.100000</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>102.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>0.223000</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>120.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.400000</td>\n",
       "      <td>0.343000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.587000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>57.300000</td>\n",
       "      <td>1.698000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              PRG          PL          PR          SK          TS         M11  \\\n",
       "count  169.000000  169.000000  169.000000  169.000000  169.000000  169.000000   \n",
       "mean     3.917160  123.520710   73.274787   26.588964   81.000000   32.600409   \n",
       "std      3.402415   29.259123   12.783183    9.091937  110.720852    6.562578   \n",
       "min      0.000000   56.000000   38.000000   10.000000    0.000000   19.500000   \n",
       "25%      1.000000  102.000000   64.000000   20.000000    0.000000   27.700000   \n",
       "50%      3.000000  120.000000   74.000000   23.000000    0.000000   32.400000   \n",
       "75%      6.000000  141.000000   80.000000   32.000000  135.000000   36.600000   \n",
       "max     13.000000  199.000000  114.000000   49.000000  540.000000   57.300000   \n",
       "\n",
       "              BD2         Age   Insurance  \n",
       "count  169.000000  169.000000  169.000000  \n",
       "mean     0.438876   33.065089    0.727811  \n",
       "std      0.306935   11.548110    0.446410  \n",
       "min      0.100000   21.000000    0.000000  \n",
       "25%      0.223000   24.000000    0.000000  \n",
       "50%      0.343000   28.000000    1.000000  \n",
       "75%      0.587000   42.000000    1.000000  \n",
       "max      1.698000   70.000000    1.000000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84795173",
   "metadata": {},
   "source": [
    "The zero values in the four columns have been handled as the minimum values in these columns in the train and test datasets are no longer 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc11b276",
   "metadata": {},
   "source": [
    "## ii. Creating New Features For Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c08c13",
   "metadata": {},
   "source": [
    "### a. BMI Categorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7e784cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BMI Categorization\n",
    "def categorize_bmi(bmi):\n",
    "    if bmi < 18.5:\n",
    "        return 'Underweight'\n",
    "    elif 18.5 <= bmi < 25:\n",
    "        return 'Normal'\n",
    "    elif 25 <= bmi < 30:\n",
    "        return 'Overweight'\n",
    "    else:\n",
    "        return 'Obese'\n",
    "\n",
    "train_df['BMICategory'] = train_df['M11'].apply(categorize_bmi)\n",
    "test_df['BMICategory'] = test_df['M11'].apply(categorize_bmi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d2bf4b",
   "metadata": {},
   "source": [
    "The categorization is based on the World Health Organization (WHO) guidelines, which are widely used to classify individuals into different weight categories.\n",
    "The categories are defined as follows:\n",
    "- Underweight: \n",
    "\n",
    "BMI less than 18.5 - Individuals in this category may be considered underweight, indicating that they have a lower body weight relative to their height.\n",
    "\n",
    "- Normal: \n",
    "\n",
    "BMI between 18.5 and 25 - This is considered a healthy weight range, indicating that the individual's weight is proportionate to their height.\n",
    "\n",
    "- Overweight: \n",
    "\n",
    "BMI between 25 and 30 - Individuals in this category may be classified as overweight, suggesting that they have an excess of body weight relative to their height.\n",
    "\n",
    "- Obese:\n",
    "BMI greater than or equal to 30 - This category includes individuals with a high degree of body fat relative to their height.\n",
    "\n",
    "The categorization allows for an assessment of an individual's weight status, which can be useful in understanding health risks associated with different weight categories."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfdd579c",
   "metadata": {},
   "source": [
    "### b. Glucose Level Categorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "18a1dfd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Glucose Level Categorization\n",
    "def categorize_glucose(pl):\n",
    "    if pl < 100:\n",
    "        return 'Normal'\n",
    "    elif 100 <= pl < 126:\n",
    "        return 'Prediabetic'\n",
    "    else:\n",
    "        return 'Diabetic'\n",
    "\n",
    "train_df['GlucoseCategory'] = train_df['PL'].apply(categorize_glucose)\n",
    "test_df['GlucoseCategory'] = test_df['PL'].apply(categorize_glucose)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fee9ae0",
   "metadata": {},
   "source": [
    "This categorization is based on the measurement of Plasma Glucose (PL) levels, which is an indicator of blood sugar concentration and is often used in the context of assessing an individual's risk of diabetes or prediabetes.\n",
    "The categories are defined as follows:\n",
    "\n",
    "- Normal: \n",
    "\n",
    "PL level less than 100 mg/dL - This indicates a normal or healthy blood sugar level.\n",
    "\n",
    "- Prediabetic:\n",
    "\n",
    "PL level between 100 and 125 mg/dL - This suggests elevated blood sugar levels, which may indicate a risk of developing diabetes in the future.\n",
    "\n",
    "- Diabetic: \n",
    "\n",
    "PL level 126 mg/dL or higher - Individuals in this category are typically diagnosed with diabetes.\n",
    "\n",
    "Monitoring glucose levels and categorizing them helps in identifying individuals at risk of diabetes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4d3e8b2",
   "metadata": {},
   "source": [
    "### c. Diastolic Blood Pressure Categorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "53e0abce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Diastolic Blood Pressure Category\n",
    "def categorize_pr(pr):\n",
    "    if pr < 80:\n",
    "        return 'Normal'\n",
    "    elif 80 <= pr < 90:\n",
    "        return 'Prehypertension'\n",
    "    elif 90 <= pr < 100:\n",
    "        return 'Stage1Hypertension'\n",
    "    else:\n",
    "        return 'Stage2Hypertension'\n",
    "\n",
    "train_df['PRCategory'] = train_df['PR'].apply(categorize_pr)\n",
    "test_df['PRCategory'] = test_df['PR'].apply(categorize_pr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f7888c0",
   "metadata": {},
   "source": [
    "Diastolic Blood Pressure (PR) is one of the two values measured when blood pressure is taken, with the other being Systolic Blood Pressure (PS). The categorization is based on standard blood pressure categories used in clinical practice.\n",
    "The categories are defined as follows:\n",
    "\n",
    " - Normal: \n",
    " \n",
    "PR less than 80 mm Hg - This represents a normal diastolic blood pressure level, indicating good cardiovascular health.\n",
    "\n",
    "\n",
    "\n",
    "- Prehypertension: \n",
    "\n",
    "PR between 80 and 90 mm Hg - Individuals in this category have slightly elevated blood pressure, which may be a precursor to hypertension (high blood pressure).\n",
    "\n",
    "\n",
    "\n",
    "- Stage 1 Hypertension: \n",
    "\n",
    "PR between 90 and 100 mm Hg - This indicates the presence of hypertension, a condition characterized by high blood pressure.\n",
    "\n",
    "\n",
    "\n",
    "- Stage 2 Hypertension: \n",
    "\n",
    "PR greater than or equal to 100 mm Hg - This represents a more severe form of hypertension.\n",
    "\n",
    "\n",
    "\n",
    "Categorizing blood pressure helps in identifying individuals with high blood pressure, which is a risk factor for cardiovascular diseases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d17e70a5",
   "metadata": {},
   "source": [
    "### d. Interaction Feature (Age x Plasma Glucose Concentration (PL))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be6e05c",
   "metadata": {},
   "source": [
    "Interaction features in feature engineering are features that are created by combining two or more existing features. They are used to capture the relationship between the original features and how they affect the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9216fa89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interaction Feature (Age x Plasma Glucose Concentration (PL))\n",
    "train_df['Age_PL_Interact'] = train_df['Age'] * train_df['PL']\n",
    "test_df['Age_PL_Interact'] = test_df['Age'] * test_df['PL']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edfaac44",
   "metadata": {},
   "source": [
    "The multiplication of Age x Plasma Glucose Concentration (PL) to come up with 'Age_PL_Interact' is a metric that captures the relationship between age and plasma glucose concentration (PL) and how they affect the target variable, which is sepsis risk. \n",
    "\n",
    "\n",
    "This metric is based on the following observations:\n",
    "- As people age, their bodies become less efficient at regulating blood sugar levels. This can lead to increased plasma glucose concentrations.\n",
    "- Older adults are more likely to have underlying medical conditions that can increase their risk of sepsis, such as diabetes and heart disease.\n",
    "\n",
    "This metric is also known as the Age-Plasma Glucose (APG) product. It has been used in a number of studies to predict sepsis risk.\n",
    "- A patient with a higher age and plasma glucose concentration (APG) product has a higher risk of sepsis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "274d0642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
       "      <th>Age Group</th>\n",
       "      <th>BMICategory</th>\n",
       "      <th>GlucoseCategory</th>\n",
       "      <th>PRCategory</th>\n",
       "      <th>Age_PL_Interact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ICU200010</td>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.000000</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",
       "      <td>40-59</td>\n",
       "      <td>Obese</td>\n",
       "      <td>Diabetic</td>\n",
       "      <td>Normal</td>\n",
       "      <td>7400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICU200011</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.000000</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",
       "      <td>20-39</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ICU200012</td>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>21.472284</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",
       "      <td>20-39</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Diabetic</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5856.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICU200013</td>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.000000</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",
       "      <td>20-39</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ICU200014</td>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.000000</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",
       "      <td>20-39</td>\n",
       "      <td>Obese</td>\n",
       "      <td>Diabetic</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4521.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  PRG     PL    PR         SK   TS   M11    BD2  Age  Insurance  \\\n",
       "0  ICU200010    6  148.0  72.0  35.000000    0  33.6  0.627   50          0   \n",
       "1  ICU200011    1   85.0  66.0  29.000000    0  26.6  0.351   31          0   \n",
       "2  ICU200012    8  183.0  64.0  21.472284    0  23.3  0.672   32          1   \n",
       "3  ICU200013    1   89.0  66.0  23.000000   94  28.1  0.167   21          1   \n",
       "4  ICU200014    0  137.0  40.0  35.000000  168  43.1  2.288   33          1   \n",
       "\n",
       "    Sepssis Age Group BMICategory GlucoseCategory PRCategory  Age_PL_Interact  \n",
       "0  Positive     40-59       Obese        Diabetic     Normal           7400.0  \n",
       "1  Negative     20-39  Overweight          Normal     Normal           2635.0  \n",
       "2  Positive     20-39      Normal        Diabetic     Normal           5856.0  \n",
       "3  Negative     20-39  Overweight          Normal     Normal           1869.0  \n",
       "4  Positive     20-39       Obese        Diabetic     Normal           4521.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ac703b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>Age Group</th>\n",
       "      <th>BMICategory</th>\n",
       "      <th>GlucoseCategory</th>\n",
       "      <th>PRCategory</th>\n",
       "      <th>Age_PL_Interact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ICU200609</td>\n",
       "      <td>1</td>\n",
       "      <td>109.0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>120</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.407</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Prediabetic</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2834.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICU200610</td>\n",
       "      <td>1</td>\n",
       "      <td>108.0</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.400</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>Prediabetic</td>\n",
       "      <td>Prehypertension</td>\n",
       "      <td>2592.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ICU200611</td>\n",
       "      <td>6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>67.081301</td>\n",
       "      <td>21.382114</td>\n",
       "      <td>0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.190</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICU200612</td>\n",
       "      <td>1</td>\n",
       "      <td>124.0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.100</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>Prediabetic</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ICU200613</td>\n",
       "      <td>7</td>\n",
       "      <td>150.0</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>126</td>\n",
       "      <td>35.2</td>\n",
       "      <td>0.692</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>40-59</td>\n",
       "      <td>Obese</td>\n",
       "      <td>Diabetic</td>\n",
       "      <td>Normal</td>\n",
       "      <td>8100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  PRG     PL         PR         SK   TS   M11    BD2  Age  \\\n",
       "0  ICU200609    1  109.0  38.000000  18.000000  120  23.1  0.407   26   \n",
       "1  ICU200610    1  108.0  88.000000  19.000000    0  27.1  0.400   24   \n",
       "2  ICU200611    6   96.0  67.081301  21.382114    0  23.7  0.190   28   \n",
       "3  ICU200612    1  124.0  74.000000  36.000000    0  27.8  0.100   30   \n",
       "4  ICU200613    7  150.0  78.000000  29.000000  126  35.2  0.692   54   \n",
       "\n",
       "   Insurance Age Group BMICategory GlucoseCategory       PRCategory  \\\n",
       "0          1     20-39      Normal     Prediabetic           Normal   \n",
       "1          1     20-39  Overweight     Prediabetic  Prehypertension   \n",
       "2          1     20-39      Normal          Normal           Normal   \n",
       "3          1     20-39  Overweight     Prediabetic           Normal   \n",
       "4          0     40-59       Obese        Diabetic           Normal   \n",
       "\n",
       "   Age_PL_Interact  \n",
       "0           2834.0  \n",
       "1           2592.0  \n",
       "2           2688.0  \n",
       "3           3720.0  \n",
       "4           8100.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa476f4d",
   "metadata": {},
   "source": [
    "### e. Saving The Datasets For PowerBI Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d80f1717",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the updated datasets\n",
    "train_df.to_csv(\"data/Visualization_Data_Train.csv\", index=False)\n",
    "test_df.to_csv(\"data/Visualization_Data_Test.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85333b8b",
   "metadata": {},
   "source": [
    "### f. Dropping The New Features Created For Visualization and Other Unneccessary Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c5db2f42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unnecessary columns\n",
    "train_df.drop(['ID', 'Age Group', 'Insurance', 'BMICategory', 'GlucoseCategory', 'PRCategory', 'Age_PL_Interact' ], axis=1, inplace=True)\n",
    "test_df.drop(['ID', 'Age Group', 'Insurance', 'BMICategory', 'GlucoseCategory', 'PRCategory', 'Age_PL_Interact' ], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "be2269c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>6</td>\n",
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       "      <td>33.6</td>\n",
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       "      <td>Positive</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
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       "      <td>26.6</td>\n",
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       "      <td>31</td>\n",
       "      <td>Negative</td>\n",
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       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>21.472284</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   PRG     PL    PR         SK   TS   M11    BD2  Age   Sepssis\n",
       "0    6  148.0  72.0  35.000000    0  33.6  0.627   50  Positive\n",
       "1    1   85.0  66.0  29.000000    0  26.6  0.351   31  Negative\n",
       "2    8  183.0  64.0  21.472284    0  23.3  0.672   32  Positive\n",
       "3    1   89.0  66.0  23.000000   94  28.1  0.167   21  Negative\n",
       "4    0  137.0  40.0  35.000000  168  43.1  2.288   33  Positive"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "858e50d9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>19.000000</td>\n",
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       "      <td>6</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>124.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>150.0</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>126</td>\n",
       "      <td>35.2</td>\n",
       "      <td>0.692</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRG     PL         PR         SK   TS   M11    BD2  Age\n",
       "0    1  109.0  38.000000  18.000000  120  23.1  0.407   26\n",
       "1    1  108.0  88.000000  19.000000    0  27.1  0.400   24\n",
       "2    6   96.0  67.081301  21.382114    0  23.7  0.190   28\n",
       "3    1  124.0  74.000000  36.000000    0  27.8  0.100   30\n",
       "4    7  150.0  78.000000  29.000000  126  35.2  0.692   54"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f598f4",
   "metadata": {},
   "source": [
    "## iii. Encoding Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2645e02f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize LabelEncoder for the target variable 'Sepssis'\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# Encode the target variable\n",
    "train_df['Sepssis'] = label_encoder.fit_transform(train_df['Sepssis'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d016ed2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>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>Sepssis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>21.472284</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRG     PL    PR         SK   TS   M11    BD2  Age  Sepssis\n",
       "0    6  148.0  72.0  35.000000    0  33.6  0.627   50        1\n",
       "1    1   85.0  66.0  29.000000    0  26.6  0.351   31        0\n",
       "2    8  183.0  64.0  21.472284    0  23.3  0.672   32        1\n",
       "3    1   89.0  66.0  23.000000   94  28.1  0.167   21        0\n",
       "4    0  137.0  40.0  35.000000  168  43.1  2.288   33        1"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the updated train dataset\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4ccb8355",
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>109.0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>120</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.407</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>108.0</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.400</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>67.081301</td>\n",
       "      <td>21.382114</td>\n",
       "      <td>0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.190</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>124.0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.100</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>150.0</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>126</td>\n",
       "      <td>35.2</td>\n",
       "      <td>0.692</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRG     PL         PR         SK   TS   M11    BD2  Age\n",
       "0    1  109.0  38.000000  18.000000  120  23.1  0.407   26\n",
       "1    1  108.0  88.000000  19.000000    0  27.1  0.400   24\n",
       "2    6   96.0  67.081301  21.382114    0  23.7  0.190   28\n",
       "3    1  124.0  74.000000  36.000000    0  27.8  0.100   30\n",
       "4    7  150.0  78.000000  29.000000  126  35.2  0.692   54"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the updated test dataset\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30cc20b9",
   "metadata": {},
   "source": [
    "The Columns have been encoded and are ready for modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4d20376",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fab26db",
   "metadata": {},
   "source": [
    "## Dataset Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "12da50a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the Training Set (X_train, y_train): (479, 8) (479,)\n",
      "Shape of the Evaluation Set (X_eval, y_eval): (120, 8) (120,)\n"
     ]
    }
   ],
   "source": [
    "# Define your features (X) and target variable (y)\n",
    "X = train_df.drop('Sepssis', axis=1)\n",
    "y = train_df['Sepssis']\n",
    "\n",
    "# Split the data into training and evaluation sets\n",
    "X_train, X_eval, y_train, y_eval = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "print(\"Shape of the Training Set (X_train, y_train):\", X_train.shape, y_train.shape)\n",
    "print(\"Shape of the Evaluation Set (X_eval, y_eval):\", X_eval.shape, y_eval.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90b262f",
   "metadata": {},
   "source": [
    "## Balancing the Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "795dd4c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class distribution before balancing:\n",
      "0    313\n",
      "1    166\n",
      "Name: Sepssis, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check the class distribution in the training set\n",
    "print(\"Class distribution before balancing:\")\n",
    "print(y_train.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f4a2d6fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DFRkZ6VxzJWVlZSouLnZ5AQAAoP5ze9zu27dPTZo0kd1u13PPPafVq1erffv2ysvLkyQ5HA6X9Q6Hw7kvLy9P3t7eatas2VXXXElKSor8/f2dr7CwsBo+KwAAALiD2+P2jjvu0O7du7Vjxw6NHTtWCQkJOnjwoHO/zWZzWW9ZVqVtP3W9NUlJSSoqKnK+jh07dnMnAQAAgDrB7XHr7e2t22+/XZ07d1ZKSoo6duyot99+W8HBwZJU6Qpsfn6+82pucHCwysvLVVhYeNU1V2K3251PaLj8AgAAQP3n9rj9KcuyVFZWpoiICAUHBys9Pd25r7y8XNu2bVN0dLQkKSoqSl5eXi5rcnNztX//fucaAAAA/Hx4uvPLp06dqvj4eIWFhens2bNKS0vT1q1btX79etlsNiUmJmr27Nlq27at2rZtq9mzZ6tRo0YaNmyYJMnf318jR47U5MmTFRgYqICAAE2ZMkUdOnRQ79693XlqAAAAcAO3xu3Jkyc1fPhw5ebmyt/fX/fcc4/Wr1+v2NhYSdJLL72k0tJSjRs3ToWFherSpYs2btwoX19f5zHmzZsnT09PDRkyRKWlperVq5dSU1Pl4eHhrtMCAACAm9S559y6A8+5BXCr8JxbAKgZ9e45twAAAMDNIm4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMZwa9ympKTo/vvvl6+vr1q0aKGBAwfqm2++cVkzYsQI2Ww2l9eDDz7osqasrEwTJ05UUFCQGjdurAEDBuj48eO1eSoAAACoA9wat9u2bdP48eO1Y8cOpaen6+LFi4qLi9O5c+dc1j3yyCPKzc11vtatW+eyPzExUatXr1ZaWpq2b9+ukpIS9evXTxUVFbV5OgAAAHAzT3d++fr1613eL1u2TC1atFB2drYefvhh53a73a7g4OArHqOoqEjvv/++VqxYod69e0uSPvzwQ4WFhWnTpk3q06fPrTsBAAAA1Cl16p7boqIiSVJAQIDL9q1bt6pFixZq166dRo0apfz8fOe+7OxsXbhwQXFxcc5toaGhioyMVGZm5hW/p6ysTMXFxS4vAAAA1H91Jm4ty9KkSZP0y1/+UpGRkc7t8fHx+stf/qKMjAy99dZb2rlzp3r27KmysjJJUl5enry9vdWsWTOX4zkcDuXl5V3xu1JSUuTv7+98hYWF3boTAwAAQK1x620J/9eECRO0d+9ebd++3WX70KFDnX+OjIxU586dFR4ers8++0yDBg266vEsy5LNZrvivqSkJE2aNMn5vri4mMAFAAAwQJ24cjtx4kStXbtWW7ZsUcuWLa+5NiQkROHh4fr2228lScHBwSovL1dhYaHLuvz8fDkcjisew263y8/Pz+UFAACA+s+tcWtZliZMmKCPP/5YGRkZioiIuO5nCgoKdOzYMYWEhEiSoqKi5OXlpfT0dOea3Nxc7d+/X9HR0bdsdgAAANQ9br0tYfz48Vq5cqU++eQT+fr6Ou+R9ff3l4+Pj0pKSpScnKzBgwcrJCREhw8f1tSpUxUUFKTHH3/cuXbkyJGaPHmyAgMDFRAQoClTpqhDhw7OpycAAADg58Gtcbt48WJJUo8ePVy2L1u2TCNGjJCHh4f27dunDz74QGfOnFFISIhiYmK0atUq+fr6OtfPmzdPnp6eGjJkiEpLS9WrVy+lpqbKw8OjNk8HAAAAbubWuLUs65r7fXx8tGHDhusep2HDhlqwYIEWLFhQU6MBAACgHqoTP1AGAAAA1ATiFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxnBr3KakpOj++++Xr6+vWrRooYEDB+qbb75xWWNZlpKTkxUaGiofHx/16NFDBw4ccFlTVlamiRMnKigoSI0bN9aAAQN0/Pjx2jwVAAAA1AFujdtt27Zp/Pjx2rFjh9LT03Xx4kXFxcXp3LlzzjVz5szR3LlztXDhQu3cuVPBwcGKjY3V2bNnnWsSExO1evVqpaWlafv27SopKVG/fv1UUVHhjtMCAACAm9gsy7LcPcRlP/zwg1q0aKFt27bp4YcflmVZCg0NVWJiol5++WVJP16ldTgcev311zVmzBgVFRWpefPmWrFihYYOHSpJOnHihMLCwrRu3Tr16dPnut9bXFwsf39/FRUVyc/P75ae45V0G/Mvtf6dAGrHF3+c7u4RAMAIN9prdeqe26KiIklSQECAJCknJ0d5eXmKi4tzrrHb7erevbsyMzMlSdnZ2bpw4YLLmtDQUEVGRjrX/FRZWZmKi4tdXgAAAKj/6kzcWpalSZMm6Ze//KUiIyMlSXl5eZIkh8PhstbhcDj35eXlydvbW82aNbvqmp9KSUmRv7+/8xUWFlbTpwMAAAA3qDNxO2HCBO3du1f/9m//VmmfzWZzeW9ZVqVtP3WtNUlJSSoqKnK+jh07Vv3BAQAAUGfUibidOHGi1q5dqy1btqhly5bO7cHBwZJU6Qpsfn6+82pucHCwysvLVVhYeNU1P2W32+Xn5+fyAgAAQP3n1ri1LEsTJkzQxx9/rIyMDEVERLjsj4iIUHBwsNLT053bysvLtW3bNkVHR0uSoqKi5OXl5bImNzdX+/fvd64BAADAz4OnO798/PjxWrlypT755BP5+vo6r9D6+/vLx8dHNptNiYmJmj17ttq2bau2bdtq9uzZatSokYYNG+ZcO3LkSE2ePFmBgYEKCAjQlClT1KFDB/Xu3dudpwcAAIBa5ta4Xbx4sSSpR48eLtuXLVumESNGSJJeeukllZaWaty4cSosLFSXLl20ceNG+fr6OtfPmzdPnp6eGjJkiEpLS9WrVy+lpqbKw8Ojtk4FAAAAdUCdes6tu/CcWwC3Cs+5BYCaUS+fcwsAAADcDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgjGrF7W233aaCgoJK28+cOaPbbrvtpocCAAAAqqNacXv48GFVVFRU2l5WVqbvv//+pocCAAAAqsOzKovXrl3r/POGDRvk7+/vfF9RUaHNmzerdevWNTYcAAAAUBVVituBAwdKkmw2mxISElz2eXl5qXXr1nrrrbdqbDgAAACgKqoUt5cuXZIkRUREaOfOnQoKCrolQwEAAADVUaW4vSwnJ6em5wAAAABuWrXiVpI2b96szZs3Kz8/33lF97KlS5fe9GAAAABAVVUrbmfNmqVXX31VnTt3VkhIiGw2W03PBQAAAFRZteL2vffeU2pqqoYPH17T8wAAAADVVq3n3JaXlys6OrqmZwEAAABuSrXi9tlnn9XKlStrehYAAADgplTrtoTz589ryZIl2rRpk+655x55eXm57J87d26NDAcAAABURbXidu/evbr33nslSfv373fZxw+XAQAAwF2qFbdbtmyp6TkAAACAm1ate24BAACAuqhaV25jYmKueftBRkZGtQcCANR/cWlJ7h4BwC2y8ckUd49wTdWK28v321524cIF7d69W/v371dCQkJNzAUAAABUWbXidt68eVfcnpycrJKSkpsaCAAAAKiuGr3n9te//rWWLl1ak4cEAAAAbliNxm1WVpYaNmxYk4cEAAAAbli1bksYNGiQy3vLspSbm6tdu3Zp+vTpNTIYAAAAUFXVilt/f3+X9w0aNNAdd9yhV199VXFxcTUyGAAAAFBV1YrbZcuW1fQcAAAAwE2rVtxelp2drb/+9a+y2Wxq37697rvvvpqaCwAAAKiyasVtfn6+nnzySW3dulVNmzaVZVkqKipSTEyM0tLS1Lx585qeEwAAALiuaj0tYeLEiSouLtaBAwd0+vRpFRYWav/+/SouLtbzzz9f0zMCAAAAN6RaV27Xr1+vTZs26a677nJua9++vd59911+oAwAAABuU60rt5cuXZKXl1el7V5eXrp06dJNDwUAAABUR7XitmfPnvrd736nEydOOLd9//33euGFF9SrV68aGw4AAACoimrF7cKFC3X27Fm1bt1abdq00e23366IiAidPXtWCxYsqOkZAQAAgBtSrXtuw8LC9NVXXyk9PV1/+9vfZFmW2rdvr969e9f0fAAAAMANq9KV24yMDLVv317FxcWSpNjYWE2cOFHPP/+87r//ft1999364osvbsmgAAAAwPVUKW7nz5+vUaNGyc/Pr9I+f39/jRkzRnPnzq2x4QAAAICqqFLc7tmzR4888shV98fFxSk7O/umhwIAAACqo0pxe/LkySs+AuwyT09P/fDDDzd8vM8//1z9+/dXaGiobDab1qxZ47J/xIgRstlsLq8HH3zQZU1ZWZkmTpyooKAgNW7cWAMGDNDx48ercloAAAAwRJXi9he/+IX27dt31f179+5VSEjIDR/v3Llz6tixoxYuXHjVNY888ohyc3Odr3Xr1rnsT0xM1OrVq5WWlqbt27erpKRE/fr1U0VFxQ3PAQAAADNU6WkJjz76qGbMmKH4+Hg1bNjQZV9paalmzpypfv363fDx4uPjFR8ff801drtdwcHBV9xXVFSk999/XytWrHA+qeHDDz9UWFiYNm3apD59+tzwLAAAAKj/qnTl9ve//71Onz6tdu3aac6cOfrkk0+0du1avf7667rjjjt0+vRpTZs2rUYH3Lp1q1q0aKF27dpp1KhRys/Pd+7Lzs7WhQsXXH7lb2hoqCIjI5WZmXnVY5aVlam4uNjlBQAAgPqvSlduHQ6HMjMzNXbsWCUlJcmyLEmSzWZTnz59tGjRIjkcjhobLj4+Xk888YTCw8OVk5Oj6dOnq2fPnsrOzpbdbldeXp68vb3VrFmzSnPm5eVd9bgpKSmaNWtWjc0JAACAuqHKv8QhPDxc69atU2FhoQ4dOiTLstS2bdtKgVkThg4d6vxzZGSkOnfurPDwcH322WcaNGjQVT9nWZZsNttV9yclJWnSpEnO98XFxQoLC6uZoQEAAOA21foNZZLUrFkz3X///TU5y3WFhIQoPDxc3377rSQpODhY5eXlKiwsdInr/Px8RUdHX/U4drtddrv9ls8LAACA2lWle27draCgQMeOHXM+kSEqKkpeXl5KT093rsnNzdX+/fuvGbcAAAAwU7Wv3NaEkpISHTp0yPk+JydHu3fvVkBAgAICApScnKzBgwcrJCREhw8f1tSpUxUUFKTHH39c0o+/FW3kyJGaPHmyAgMDFRAQoClTpqhDhw7OpycAAADg58Otcbtr1y7FxMQ431++DzYhIUGLFy/Wvn379MEHH+jMmTMKCQlRTEyMVq1aJV9fX+dn5s2bJ09PTw0ZMkSlpaXq1auXUlNT5eHhUevnAwAAAPdya9z26NHD+cSFK9mwYcN1j9GwYUMtWLBACxYsqMnRAAAAUA/Vq3tuAQAAgGshbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxnBr3H7++efq37+/QkNDZbPZtGbNGpf9lmUpOTlZoaGh8vHxUY8ePXTgwAGXNWVlZZo4caKCgoLUuHFjDRgwQMePH6/FswAAAEBd4da4PXfunDp27KiFCxdecf+cOXM0d+5cLVy4UDt37lRwcLBiY2N19uxZ55rExEStXr1aaWlp2r59u0pKStSvXz9VVFTU1mkAAACgjvB055fHx8crPj7+ivssy9L8+fM1bdo0DRo0SJK0fPlyORwOrVy5UmPGjFFRUZHef/99rVixQr1795YkffjhhwoLC9OmTZvUp0+fWjsXAAAAuF+dvec2JydHeXl5iouLc26z2+3q3r27MjMzJUnZ2dm6cOGCy5rQ0FBFRkY611xJWVmZiouLXV4AAACo/+ps3Obl5UmSHA6Hy3aHw+Hcl5eXJ29vbzVr1uyqa64kJSVF/v7+zldYWFgNTw8AAAB3qLNxe5nNZnN5b1lWpW0/db01SUlJKioqcr6OHTtWI7MCAADAveps3AYHB0tSpSuw+fn5zqu5wcHBKi8vV2Fh4VXXXIndbpefn5/LCwAAAPVfnY3biIgIBQcHKz093bmtvLxc27ZtU3R0tCQpKipKXl5eLmtyc3O1f/9+5xoAAAD8fLj1aQklJSU6dOiQ831OTo52796tgIAAtWrVSomJiZo9e7batm2rtm3bavbs2WrUqJGGDRsmSfL399fIkSM1efJkBQYGKiAgQFOmTFGHDh2cT08AAADAz4db43bXrl2KiYlxvp80aZIkKSEhQampqXrppZdUWlqqcePGqbCwUF26dNHGjRvl6+vr/My8efPk6empIUOGqLS0VL169VJqaqo8PDxq/XwAAADgXjbLsix3D+FuxcXF8vf3V1FRkVvuv+025l9q/TsB1I4v/jjd3SO4RVxakrtHAHCLbHwyxS3fe6O9VmfvuQUAAACqirgFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAx6nTcJicny2azubyCg4Od+y3LUnJyskJDQ+Xj46MePXrowIEDbpwYAAAA7lSn41aS7r77buXm5jpf+/btc+6bM2eO5s6dq4ULF2rnzp0KDg5WbGyszp4968aJAQAA4C6e7h7gejw9PV2u1l5mWZbmz5+vadOmadCgQZKk5cuXy+FwaOXKlRozZsxVj1lWVqaysjLn++Li4pofHAAAALWuzl+5/fbbbxUaGqqIiAg9+eST+sc//iFJysnJUV5enuLi4pxr7Xa7unfvrszMzGseMyUlRf7+/s5XWFjYLT0HAAAA1I46HbddunTRBx98oA0bNuhPf/qT8vLyFB0drYKCAuXl5UmSHA6Hy2ccDodz39UkJSWpqKjI+Tp27NgtOwcAAADUnjp9W0J8fLzzzx06dFDXrl3Vpk0bLV++XA8++KAkyWazuXzGsqxK237KbrfLbrfX/MAAAABwqzp95fanGjdurA4dOujbb7913of706u0+fn5la7mAgAA4OehXsVtWVmZ/vrXvyokJEQREREKDg5Wenq6c395ebm2bdum6OhoN04JAAAAd6nTtyVMmTJF/fv3V6tWrZSfn6/XXntNxcXFSkhIkM1mU2JiombPnq22bduqbdu2mj17tho1aqRhw4a5e3QAAAC4QZ2O2+PHj+upp57SqVOn1Lx5cz344IPasWOHwsPDJUkvvfSSSktLNW7cOBUWFqpLly7auHGjfH193Tw5AAAA3KFOx21aWto199tsNiUnJys5Obl2BgIAAECdVq/uuQUAAACuhbgFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABjDmLhdtGiRIiIi1LBhQ0VFRemLL75w90gAAACoZUbE7apVq5SYmKhp06bp66+/Vrdu3RQfH6+jR4+6ezQAAADUIiPidu7cuRo5cqSeffZZ3XXXXZo/f77CwsK0ePFid48GAACAWuTp7gFuVnl5ubKzs/XKK6+4bI+Li1NmZuYVP1NWVqaysjLn+6KiIklScXHxrRv0Gi6Wn3fL9wK49dz194q7Xfxn2fUXAaiX3PX32uXvtSzrmuvqfdyeOnVKFRUVcjgcLtsdDofy8vKu+JmUlBTNmjWr0vawsLBbMiOAny//1NnuHgEAapT/yHlu/f6zZ8/K39//qvvrfdxeZrPZXN5bllVp22VJSUmaNGmS8/2lS5d0+vRpBQYGXvUzQE0oLi5WWFiYjh07Jj8/P3ePAwA3jb/XUFssy9LZs2cVGhp6zXX1Pm6DgoLk4eFR6Sptfn5+pau5l9ntdtntdpdtTZs2vVUjApX4+fnxXwIAjMLfa6gN17pie1m9/4Eyb29vRUVFKT093WV7enq6oqOj3TQVAAAA3KHeX7mVpEmTJmn48OHq3LmzunbtqiVLlujo0aN67rnn3D0aAAAAapERcTt06FAVFBTo1VdfVW5uriIjI7Vu3TqFh4e7ezTAhd1u18yZMyvdFgMA9RV/r6GusVnXe54CAAAAUE/U+3tuAQAAgMuIWwAAABiDuAUAAIAxiFsAAAAYg7gFasmiRYsUERGhhg0bKioqSl988YW7RwKAavv888/Vv39/hYaGymazac2aNe4eCZBE3AK1YtWqVUpMTNS0adP09ddfq1u3boqPj9fRo0fdPRoAVMu5c+fUsWNHLVy40N2jAC54FBhQC7p06aJOnTpp8eLFzm133XWXBg4cqJSUFDdOBgA3z2azafXq1Ro4cKC7RwG4cgvcauXl5crOzlZcXJzL9ri4OGVmZrppKgAAzETcArfYqVOnVFFRIYfD4bLd4XAoLy/PTVMBAGAm4haoJTabzeW9ZVmVtgEAgJtD3AK3WFBQkDw8PCpdpc3Pz690NRcAANwc4ha4xby9vRUVFaX09HSX7enp6YqOjnbTVAAAmMnT3QMAPweTJk3S8OHD1blzZ3Xt2lVLlizR0aNH9dxzz7l7NAColpKSEh06dMj5PicnR7t371ZAQIBatWrlxsnwc8ejwIBasmjRIs2ZM0e5ubmKjIzUvHnz9PDDD7t7LAColq1btyomJqbS9oSEBKWmptb+QMD/R9wCAADAGNxzCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAqCQ1NVVNmzZ19xgAUGXELQC4SX5+vsaMGaNWrVrJbrcrODhYffr0UVZWlrtH09ChQ/X3v//d3WMAQJV5unsAAPi5Gjx4sC5cuKDly5frtttu08mTJ7V582adPn3a3aPJx8dHPj4+7h4DAKqMK7cA4AZnzpzR9u3b9frrrysmJkbh4eF64IEHlJSUpL59+0qSioqKNHr0aLVo0UJ+fn7q2bOn9uzZ4zxGcnKy7r33Xv3xj39UWFiYGjVqpCeeeEJnzpxxrtm6daseeOABNW7cWE2bNtVDDz2kI0eOSJL27NmjmJgY+fr6ys/PT1FRUdq1a5ekyrclXGstANQlxC0AuEGTJk3UpEkTrVmzRmVlZZX2W5alvn37Ki8vT+vWrVN2drY6deqkXr16uVzZPXTokD766CN9+umnWr9+vXbv3q3x48dLki5evKiBAweqe/fu2rt3r7KysjR69GjZbDZJ0tNPP62WLVtq586dys7O1iuvvCIvL68rzluVtQDgTtyWAABu4OnpqdTUVI0aNUrvvfeeOnXqpO7du+vJJ5/UPffcoy1btmjfvn3Kz8+X3W6XJL355ptas2aN/uM//kOjR4+WJJ0/f17Lly9Xy5YtJUkLFixQ37599dZbb8nb21tFRUXq16+f2rRpI0m66667nDMcPXpUL774ou68805JUtu2ba86b1XWAoA7ceUWANxk8ODBOnHihNauXas+ffpo69at6tSpk1JTU5Wdna2SkhIFBgY6r/I2adJEOTk5+u6775zHaNWqlTNsJalr1666dOmSvvnmGwUEBGjEiBHq06eP+vfvr7ffflu5ubnOtZMmTdKzzz6r3r176w9/+IPLcX+qKmsBwJ2IWwBwo4YNGyo2NlYzZsxQZmamRowYoZkzZ+rSpUsKCQnR7t27XV7ffPONXnzxxase7/ItB5f/ddmyZcrKylJ0dLRWrVqldu3aaceOHZJ+vGf3wIED6tu3rzIyMtS+fXutXr36isetyloAcCfiFgDqkPbt2+vcuXPq1KmT8vLy5Onpqdtvv93lFRQU5Fx/9OhRnThxwvk+KytLDRo0ULt27Zzb7rvvPiUlJSkzM1ORkZFauXKlc1+7du30wgsvaOPGjRo0aJCWLVt21dmqshYA3IW4BQA3KCgoUM+ePfXhhx9q7969ysnJ0b//+79rzpw5euyxx9S7d2917dpVAwcO1IYNG3T48GFlZmbq97//vctTCho2bKiEhATt2bNHX3zxhZ5//nkNGTJEwcHBysnJUVJSkrKysnTkyBFt3LhRf//733XXXXeptLRUEyZM0NatW3XkyBF9+eWX2rlzp8s9uZdVZS0AuBs/UAYAbtCkSRN16dJF8+bN03fffacLFy4oLCxMo0aN0tSpU2Wz2bRu3TpNmzZNv/3tb/XDDz8oODhYDz/8sBwOh/M4t99+uwYNGqRHH31Up0+f1qOPPqpFixZJkho1aqS//e1vWr58uQoKChQSEqIJEyZozJgxunjxogoKCvTMM8/o5MmTCgoK0qBBgzRr1qxKs3p4eNzwWgBwN5tlWZa7hwAAVF1ycrLWrFmj3bt3u3sUAKgzuC0BAAAAxiBuAQAAYAxuSwAAAIAxuHILAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMMb/Awy1T7U0cXlqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the occurrences of each class in the dataset\n",
    "class_counts = y_train.value_counts()\n",
    "\n",
    "# Create a bar plot to visualize the class distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=class_counts.index, y=class_counts.values, palette='viridis')\n",
    "plt.xlabel('Sepssis')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Class Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e8c4b3",
   "metadata": {},
   "source": [
    "0 Class (No Sepsis): There are 313 instances without Sepsis in the Training Set.\n",
    "\n",
    "1 Class (Sepsis): There are 166 instances of participants with Sepsis in the Training Set.\n",
    "\n",
    "The class distribution shows that there is some class imbalance, with the \"0\" class (no sepsis) having more instances than the \"1\" class (sepsis). Addressing this class imbalance is important to ensure that the machine learning model can make accurate predictions for both classes and is not biased towards the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fc03fe09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class distribution in the balanced training set:\n",
      "0    313\n",
      "1    313\n",
      "Name: Sepssis, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Balance the training set using Random Oversampling\n",
    "oversampler = RandomOverSampler(random_state=42)\n",
    "X_train_balanced, y_train_balanced = oversampler.fit_resample(X_train, y_train)\n",
    "\n",
    "# Check the class distribution after balancing\n",
    "balanced_class_counts = y_train_balanced.value_counts()\n",
    "print(\"\\nClass distribution in the balanced training set:\")\n",
    "print(balanced_class_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "660d7e96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the occurrences of each class in the balanced dataset\n",
    "class_counts = y_train_balanced.value_counts()\n",
    "\n",
    "# Create a bar plot with the 'viridis' color palette\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=class_counts.index, y=class_counts.values, palette='viridis')\n",
    "plt.xlabel('Sepssis')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Class Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6636696d",
   "metadata": {},
   "source": [
    "The train set is now balanced."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3982317a",
   "metadata": {},
   "source": [
    "## Scaling The Training and Evaluation Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "66f4e800",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the training and evaluation sets\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Scale the training data\n",
    "X_train_scaled = scaler.fit_transform(X_train_balanced)\n",
    "\n",
    "# Scale the evaluation data using the same scaler\n",
    "X_eval_scaled = scaler.transform(X_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9eb2e1e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train_scaled_df: (626, 8)\n",
      "Shape of X_eval_scaled_df: (120, 8)\n",
      "Shape of y_train_balanced_df: (626, 1)\n",
      "Shape of y_eval: (120,)\n"
     ]
    }
   ],
   "source": [
    "# Convert scaled NumPy arrays back to DataFrames\n",
    "X_train_scaled_df = pd.DataFrame(X_train_scaled, columns=X.columns)\n",
    "X_eval_scaled_df = pd.DataFrame(X_eval_scaled, columns=X.columns)\n",
    "\n",
    "# Now, X_train_scaled_df and X_eval_scaled_df are DataFrames containing the scaled features.\n",
    "\n",
    "# Additionally, convert the balanced target variable back to a DataFrame as well\n",
    "y_train_balanced_df = pd.DataFrame(y_train_balanced, columns=['Sepssis'])\n",
    "\n",
    "# Now, y_train_balanced_df is a DataFrame containing the balanced target variable.\n",
    "\n",
    "# Confirm the shapes of the DataFrames\n",
    "print(\"Shape of X_train_scaled_df:\", X_train_scaled_df.shape)\n",
    "print(\"Shape of X_eval_scaled_df:\", X_eval_scaled_df.shape)\n",
    "print(\"Shape of y_train_balanced_df:\", y_train_balanced_df.shape)\n",
    "print(\"Shape of y_eval:\", y_eval.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c1fadde1",
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.319149</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.358696</td>\n",
       "      <td>0.141129</td>\n",
       "      <td>0.461165</td>\n",
       "      <td>0.051370</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.879433</td>\n",
       "      <td>0.586957</td>\n",
       "      <td>0.152174</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.429612</td>\n",
       "      <td>0.214897</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.170213</td>\n",
       "      <td>0.456522</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.102151</td>\n",
       "      <td>0.288835</td>\n",
       "      <td>0.198202</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.872340</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>0.195652</td>\n",
       "      <td>0.094086</td>\n",
       "      <td>0.383495</td>\n",
       "      <td>0.080051</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.191489</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>0.163043</td>\n",
       "      <td>0.088710</td>\n",
       "      <td>0.427184</td>\n",
       "      <td>0.197346</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PRG        PL        PR        SK        TS       M11       BD2  \\\n",
       "0  0.411765  0.319149  0.478261  0.358696  0.141129  0.461165  0.051370   \n",
       "1  0.411765  0.879433  0.586957  0.152174  0.258065  0.429612  0.214897   \n",
       "2  0.117647  0.170213  0.456522  0.086957  0.102151  0.288835  0.198202   \n",
       "3  0.176471  0.872340  0.369565  0.195652  0.094086  0.383495  0.080051   \n",
       "4  0.000000  0.191489  0.369565  0.163043  0.088710  0.427184  0.197346   \n",
       "\n",
       "        Age  \n",
       "0  0.400000  \n",
       "1  0.500000  \n",
       "2  0.066667  \n",
       "3  0.083333  \n",
       "4  0.000000  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the first few rows of the scaled X_train\n",
    "X_train_scaled_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d85bd109",
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.219858</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.072581</td>\n",
       "      <td>0.160194</td>\n",
       "      <td>0.078339</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.702128</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.228261</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.390777</td>\n",
       "      <td>0.473031</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.312057</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.157307</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.089806</td>\n",
       "      <td>0.107877</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.631206</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.260753</td>\n",
       "      <td>0.485437</td>\n",
       "      <td>0.104880</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.404255</td>\n",
       "      <td>0.543478</td>\n",
       "      <td>0.293478</td>\n",
       "      <td>0.383065</td>\n",
       "      <td>0.631068</td>\n",
       "      <td>0.035531</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PRG        PL        PR        SK        TS       M11       BD2  \\\n",
       "0  0.176471  0.219858  0.304348  0.043478  0.072581  0.160194  0.078339   \n",
       "1  0.529412  0.702128  0.608696  0.228261  0.208333  0.390777  0.473031   \n",
       "2  0.000000  0.312057  0.347826  0.157307  0.000000  0.089806  0.107877   \n",
       "3  0.117647  0.631206  0.500000  0.304348  0.260753  0.485437  0.104880   \n",
       "4  0.000000  0.404255  0.543478  0.293478  0.383065  0.631068  0.035531   \n",
       "\n",
       "        Age  \n",
       "0  0.016667  \n",
       "1  0.350000  \n",
       "2  0.066667  \n",
       "3  0.133333  \n",
       "4  0.100000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the first few rows of the scaled X_eval\n",
    "X_eval_scaled_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a6cfee5",
   "metadata": {},
   "source": [
    "## Model Training and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4f303a",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5b0def6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize an empty dictionary named 'Results' to store the evaluation results for different models.\n",
    "Results = {'Model':[], 'Acurracy':[], 'Precision':[], 'Recall':[], 'F1':[]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d983aeb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Acurracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Model, Acurracy, Precision, Recall, F1]\n",
       "Index: []"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting the dictionary Results into a pandas DataFrame.\n",
    "Results = pd.DataFrame(Results)\n",
    "Results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "88c84fed",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Machine Learning Models Initialization\n",
    "# Logistic Regression\n",
    "lr = LogisticRegression(max_iter=1000)\n",
    "\n",
    "# Random Forest\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "# K-Nearest Neighbors\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "# Decision Tree Classifier\n",
    "dt = DecisionTreeClassifier()\n",
    "\n",
    "# Gradient Boosting Classifier\n",
    "gb = GradientBoostingClassifier()\n",
    "\n",
    "# Gaussian Naive Bayes\n",
    "nb = GaussianNB()\n",
    "\n",
    "# Support Vector Machine\n",
    "svm = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e9b30ecf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic Regression\n",
      "==================================================\n",
      "Confusion Matrix:\n",
      "[[55 23]\n",
      " [12 30]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.71      0.76        78\n",
      "           1       0.57      0.71      0.63        42\n",
      "\n",
      "    accuracy                           0.71       120\n",
      "   macro avg       0.69      0.71      0.70       120\n",
      "weighted avg       0.73      0.71      0.71       120\n",
      "\n",
      "Accuracy: 0.71\n",
      "Precision: 0.57\n",
      "Recall: 0.71\n",
      "F1: 0.63\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Model: Random Forest\n",
      "==================================================\n",
      "Confusion Matrix:\n",
      "[[60 18]\n",
      " [15 27]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.77      0.78        78\n",
      "           1       0.60      0.64      0.62        42\n",
      "\n",
      "    accuracy                           0.73       120\n",
      "   macro avg       0.70      0.71      0.70       120\n",
      "weighted avg       0.73      0.72      0.73       120\n",
      "\n",
      "Accuracy: 0.72\n",
      "Precision: 0.6\n",
      "Recall: 0.64\n",
      "F1: 0.62\n"
     ]
    },
    {
     "data": {
      "image/png": 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fr/bt2xdZXTl3Enz22Wdu7Tm15MXb21stW7Z0zR7ftWtXvn3bt2/v+nK63OLFixUYGFhstzhGRERozJgx6t69u+Lj4/Pt53A45OPjI29vb1fbpUuXtGTJklx9i2pUJysrS/fee68cDofWrl2rSZMmadasWa7f9vPj5+eX6zfkq/m7FnFxcerXr5/eeecd1yWknC+8X98hNH/+fOv952jXrp1++uknrV692q192bJlbstRUVGKiIjQsmXL3C7JpKamauXKla47Mzypffv2unDhQq5guXjxYtf6ggQGBqpdu3bavXu3GjdunOt1bN68eZ4jieXKlVOvXr00fPhwnTlzxnVX1NWMzMEzGIHANYuNjdXcuXM1bNgwNWvWTA888IBuuOEGZWRkaPfu3Xr++efVsGFDde/eXVFRUbr//vs1a9YseXl5qUuXLkpOTtYjjzyiatWqaeTIkUVWV9euXVWhQgUNGjRIjz76qHx8fLRo0SIdO3bMrd+8efO0ceNGdevWTdWrV1daWpoWLlwoSerQoUO++09ISNCaNWvUrl07jR8/XhUqVNDSpUv1zjvvaMqUKQoJCSmy5/JrTz311BX7dOvWTc8++6z69Omj+++/XykpKZo6dWqet9o2atRIr776qlasWKFatWrJ39+/UPMWfi0hIUEfffSR1q1bpypVqmj06NHasmWLBg0apJiYGNdvosXpscce04oVK/TII4/ogw8+UKtWrVS+fHkNHTpUCQkJ8vX11dKlS7V3796rPka/fv00bdo09evXT0888YTq1q2rd999V++//75bPy8vL02ZMkX33Xefbr/9dv31r39Venq6nn76aZ09e7ZQr2Nx69evn2bPnq34+HglJyerUaNG+vjjj/Xkk0+qa9euBX4GcsyYMUM333yz4uLi9MADD6hGjRr66aefdPDgQb399tvauHGjJKl79+5q2LChmjdvrrCwMB05ckTTp09XZGSk6tatK0mu992MGTMUHx8vX19fRUVFXdMfSkMx8ewcTvye7Nmzx8THx5vq1asbPz8/ExQUZGJiYsz48ePN6dOnXf2ysrLM5MmTTb169Yyvr6+pWLGi+ctf/mKOHTvmtr82bdqYG264Iddx4uPjTWRkpFub8rgLwxhjduzYYVq1amWCgoJMRESESUhIMC+++KLbLO/ExERz1113mcjISON0Ok1oaKhp06aNWb16da5jXH4XhjHGfP7556Z79+4mJCTE+Pn5mRtvvNHtFj5j/u9uhddff92t/fDhw7lu+cvL5XdhFCSvOykWLlxooqKijNPpNLVq1TKTJk0yCxYsyDXLPTk52XTq1MkEBwcbSa7zm1/tl6/LuQtj3bp1xsvLK9c5SklJMdWrVzc33XSTSU9PL/A52MjvNTfGmDFjxhhJZsuWLcYYY7Zu3WpiY2NNYGCgCQsLM4MHDza7du3Kdf7j4+NNUFBQrv3ldUfP8ePHTc+ePU2ZMmVMcHCw6dmzp9m6dWuer+mqVatMy5Ytjb+/vwkKCjLt27c3n3zySZ7HuPxW5oJqyu/z8WuRkZGmW7duBfZJSUkxQ4cONVWrVjU+Pj4mMjLSjB071qSlpbn1K+icHz582AwcONBEREQYX19fExYWZlq1amUef/xxV59nnnnGtGrVylSsWNH4+fmZ6tWrm0GDBpnk5GS3fY0dO9aEh4cbLy+vPO/0QengMKYQU50BAAAuwxwIAABgjQABAACsESAAAIA1AgQAALBGgAAAANYIEAAAwBoBAgAAWPtd/iXKgJgHPV0CgAIc/XC6p0sAkI+w4MJFA0YgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGs+ni4AyBEeFqLHH7pTnVrfoACnr745eloPTFyq3fuPufqM+2tXDerZWuWCA5S074hGTFqh/YdOebBq4PqwZ9dOLVuyUF/v/1IpP3yvJ6fO1C1t27vWX7yYqnmzpumjLRt17txZVa0aoV6979NdvXp7sGoUJ0YgUCqUCw7QxkWjlJGZrR4PzlFMz8f1r2ff0NmfLrn6jO7fQX//SzuNfOo13fyXp/W/lPN6Z97fVCbQ6cHKgevDpUuXVKdulEY9PC7P9bOenaztiR/rkUef0tLX39bdffpq+tNP6qPNG0u4UpQURiBQKowe0FHHT/2ov054xdV29OQZtz7D+7TTlAXv662NeyVJgx9ZoiMbntQ9XZprwcpPSrRe4HoT2zpOsa3j8l2/77O96nL7nWravIUk6c4/3a233nhdX+3fp7i2t5ZUmShBjECgVOjWppF2fXlUS6cM1JENk5S4/J8acFcr1/oaEaGqGhaiDxK/crX9nJGpjz49qD/eWMsTJQO4TOMmTfXxh5v0/en/yRijXTu369jRZLWIbe3p0lBMPDoCcfz4cc2dO1dbt27VqVOn5HA4VLlyZbVq1UpDhw5VtWrVPFkeSlDNiIoa8uc4zXxlo6YsWKfmDSP1zMO9lJ6RqWVrdqhKxbKSpNNnfnLb7nTKT6petYInSgZwmRFjxmry4wm6q+ut8vb2kZeXQ//8z6O6sUkzT5eGYuKxAPHxxx+rS5cuqlatmjp16qROnTrJGKPTp09r1apVmjVrltauXavWrQtOr+np6UpPT3drM9lZcnh5F2f5KGJeXg7t+vKoEp57W5K09+vjalC7qu7/c5yWrdnh6meMcdvO4cjdBqDkvf7qUn3x+Wd66tnnVKVquPbu2qlnJj+m0IphuqllrKfLQzHwWIAYOXKkBg8erGnTpuW7fsSIEUpKSipwP5MmTdLEiRPd2rwr3yTfqi2KrFYUv1M/nM91N8VXh0+pR/smrvWSVDm0rOvfkhRWITjXqASAkpWelqbnZ0/Xk1NnqtXNbSRJdepG6ZsDX2v5Ky8RIH6nPDYHYt++fRo6dGi+6//6179q3759V9zP2LFjde7cObeHT2WGzH5rEvccUr3ISm5tdatXck2kTP4uRSe/P6f2f6zvWu/r4624ZnW0be+hEq0VgLvMzExlZmbK4XD/SvHy8pLJZoTw98pjIxBVq1bV1q1bFRUVlef6xMREVa1a9Yr7cTqdcjrdb+Pj8sVvz6xXNmrTotEaM7CTVq7fpZtuqKGBPVvrwceWu/rMXrZJYwZ10sGjp3Xw6Pd6eFBnXUrL0Iq1Oz1YOXB9uHgxVd8dO+paPvndcX3z9X4Fh4SoSpVwNWl6k+bMmCqn06kqVcO1Z1eS3nt3tf428mEPVo3i5DAeuoA8Z84cjRw5UkOGDFHHjh1VuXJlORwOnTp1SuvXr9eLL76o6dOnFzhKkZ+AmAeLoWIUty5xDfXo3+5QnephSv4uRTNf2aiX3tzq1ifnD0mVLxuopH3JGjHpNX357UkPVYyrdfTD6Z4uAZZ27dyhvw8dkKu9y+13atyEJ5Xyw/eaP3u6dmzbqvPnz6lKlXDdcVcv3XNfvBwOhwcqxtUKCy7c2ILHAoQkrVixQtOmTdOnn36qrKwsSZK3t7eaNWumUaNG6e67776q/RIggNKNAAGUXr+JAJEjIyNDP/zwgySpYsWK8vX1vab9ESCA0o0AAZRehQ0QpeIvUfr6+hZqvgMAACgd+EuUAADAGgECAABYI0AAAABrBAgAAGCNAAEAAKwRIAAAgDUCBAAAsEaAAAAA1ggQAADAGgECAABYI0AAAABrBAgAAGCNAAEAAKwRIAAAgDUCBAAAsEaAAAAA1ggQAADAGgECAABYI0AAAABrBAgAAGCNAAEAAKwRIAAAgDUCBAAAsEaAAAAA1ggQAADAGgECAABYI0AAAABrBAgAAGCNAAEAAKwRIAAAgDUCBAAAsEaAAAAA1ggQAADAGgECAABYI0AAAABrBAgAAGCNAAEAAKwRIAAAgDUCBAAAsEaAAAAA1ggQAADAGgECAABYI0AAAABrBAgAAGCNAAEAAKwRIAAAgDUCBAAAsEaAAAAA1ggQAADAGgECAABYI0AAAABrBAgAAGCNAAEAAKwRIAAAgDUCBAAAsEaAAAAA1ggQAADA2lUFiCVLlqh169YKDw/XkSNHJEnTp0/XW2+9VaTFAQCA0sk6QMydO1ejRo1S165ddfbsWWVlZUmSypUrp+nTpxd1fQAAoBSyDhCzZs3SCy+8oHHjxsnb29vV3rx5c33++edFWhwAACidrAPE4cOHFRMTk6vd6XQqNTW1SIoCAAClm3WAqFmzpvbs2ZOrfe3atWrQoEFR1AQAAEo5H9sNxowZo+HDhystLU3GGO3YsUPLly/XpEmT9OKLLxZHjQAAoJSxDhADBgxQZmamHn74YV28eFF9+vRRRESEZsyYod69exdHjQAAoJRxGGPM1W78ww8/KDs7W5UqVSrKmq5ZQMyDni4BQAGOfjjd0yUAyEdYcOHGFqxHIC5XsWLFa9kcAAD8RlkHiJo1a8rhcOS7/tChQ9dUEAAAKP2sA8SIESPcljMyMrR792699957GjNmTFHVBQAASjHrAPHQQw/l2T579mzt3LnzmgsCAAClX5H9Z1pdunTRypUri2p3AACgFLumSZSX++9//6sKFSoU1e6uyY9Jz3m6BAAF2HHojKdLAJCPsODCfZdbB4iYmBi3SZTGGJ06dUrff/+95syZY7s7AADwG2QdIHr06OG27OXlpbCwMLVt21b169cvqroAAEApZhUgMjMzVaNGDXXu3FlVqlQprpoAAEApZzWJ0sfHRw888IDS09OLqx4AAPAbYH0XRsuWLbV79+7iqAUAAPxGWM+BGDZsmEaPHq3jx4+rWbNmCgoKclvfuHHjIisOAACUToX+z7QGDhyo6dOnq1y5crl34nDIGCOHw6GsrKyirtFaWqanKwBQEG7jBEqvW+oV7jbOQgcIb29vnTx5UpcuXSqwX2RkZKEOXJwIEEDpRoAASq/CBohCX8LIyRmlISAAAADPsppEWdD/wgkAAK4fVpMo69Wrd8UQceYMQ5MAAPzeWQWIiRMnKiQkpLhqAQAAvxFWAaJ3796qVKlScdUCAAB+Iwo9B4L5DwAAIEehA0Qh7/YEAADXgUJfwsjOzi7OOgAAwG+I9f+FAQAAQIAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYM3H0wUAkvTpziQtWrhA+7/cp++//17TZs7Wre07uNY/8u9/afVbb7pt06jxjXpl+WslXSpw3Xn39Ze1a+sWnfruiPz8nKpdv5F69h+mKn+IdPUZ0j02z217DRiuzn/6S0mVihJEgECpcOnSRUVFRenOu/6k0SP+lmef1jfH6dHHJ7mWfX19S6o84Lp2YN9utevWUzXqRis7O0tvLp6naeNH6NE5y+T0D5AkTV28xm2bfZ8m6uWZT6ppq3aeKBklgACBUuHmuDa6Oa5NgX38/PxUMSyshCoCkGPExOluywNG/Eej/tJVRw5+pXoNYyRJIeVD3frs2faRoho1VViViJIqEyWMORD4zdiZtENt42LVvWtnTRz/H6WkpHi6JOC6dCn1giQpKLhsnuvP/3hGn+/8RDd37F6SZaGEMQKB34TWcbeoY+fbVDU8XN8dP645s2ZoyMB4vfr6G/Lz8/N0ecB1wxij1xbMVJ0GNyoisnaefbZufFfOgEA1bdW2ZItDiSrVAeLYsWNKSEjQwoUL8+2Tnp6u9PR0tzbj7ZTT6Szu8lCCbuvS1fXvunXr6YaGDXVbh1v14ZbN6tCxkwcrA64vy+ZN1fHkg3p48vx8+3yy/m21bNtZvn78HP49K9WXMM6cOaOXX365wD6TJk1SSEiI2+PpyZMK3Aa/fWFhlRQeHq6jR5I9XQpw3Vg2/xnt3fGxRj8xWxUqVsqzz4Ev9ujUd0cV1+mOEq4OJc2jIxCrV68ucP2hQ4euuI+xY8dq1KhRbm3Gm9T7e3f27I86deqkwsLy/iEGoOgYY7R8/jPanbhF/5g0R2FVwvPt+/G6txVZp76q1axbghXCEzwaIHr06CGHwyFjTL59HA5HgftwOnNfrkjLLJLyUIIupqbq6NGjruXvjh/XV/v3u0aV5s55Th06dlLFsDCd+O47zZoxTeXKl9etHToUsFcARWHZ3Kna/uE6DR83Wf4BgTr34y8TmAMCg+Tn9Hf1u3QxVZ9+slF/HpT3rdj4ffFogKhatapmz56tHj165Ll+z549atasWckWBY/44ot9Gjygn2t56pRfLkPdceddGjd+gr45cEBvr16ln87/pLCwMN3UoqWmTJ2moKAynioZuG5sXvuGJGnqv4e7tfd/6D9q3aGbaznpw/WSMWpxC/OSrgcOU9Cv/8XsjjvuUJMmTfToo4/muX7v3r2KiYlRdna21X4ZgQBKtx2Hzni6BAD5uKVehUL18+gIxJgxY5Samprv+jp16mjTpk0lWBEAACgMj45AFBdGIIDSjREIoPQq7AhEqb6NEwAAlE4ECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWCBAAAMAaAQIAAFgjQAAAAGsECAAAYI0AAQAArBEgAACANQIEAACwRoAAAADWHMYY4+kigIKkp6dr0qRJGjt2rJxOp6fLAXAZPp/XLwIESr3z588rJCRE586dU9myZT1dDoDL8Pm8fnEJAwAAWCNAAAAAawQIAABgjQCBUs/pdCohIYEJWkApxOfz+sUkSgAAYI0RCAAAYI0AAQAArBEgAACANQIEAACwRoBAqTZnzhzVrFlT/v7+atasmT766CNPlwRA0ocffqju3bsrPDxcDodDq1at8nRJKGEECJRaK1as0IgRIzRu3Djt3r1bcXFx6tKli44ePerp0oDrXmpqqm688UY999xzni4FHsJtnCi1WrZsqaZNm2ru3LmutujoaPXo0UOTJk3yYGUALudwOPTmm2+qR48eni4FJYgRCJRKP//8sz799FN16tTJrb1Tp07aunWrh6oCAOQgQKBU+uGHH5SVlaXKlSu7tVeuXFmnTp3yUFUAgBwECJRqDofDbdkYk6sNAFDyCBAolSpWrChvb+9cow2nT5/ONSoBACh5BAiUSn5+fmrWrJnWr1/v1r5+/Xq1atXKQ1UBAHL4eLoAID+jRo1S37591bx5c8XGxur555/X0aNHNXToUE+XBlz3Lly4oIMHD7qWDx8+rD179qhChQqqXr26BytDSeE2TpRqc+bM0ZQpU3Ty5Ek1bNhQ06ZN0y233OLpsoDr3ubNm9WuXbtc7fHx8Vq0aFHJF4QSR4AAAADWmAMBAACsESAAAIA1AgQAALBGgAAAANYIEAAAwBoBAgAAWCNAAAAAawQIAABgjQABoNhMmDBBTZo0cS33799fPXr0KPE6kpOT5XA4tGfPnhI/NvB7RYAArkP9+/eXw+GQw+GQr6+vatWqpX/84x9KTU0t1uPOmDGj0H/mmC99oHTjP9MCrlO33XabXnrpJWVkZOijjz7S4MGDlZqaqrlz57r1y8jIkK+vb5EcMyQkpEj2A8DzGIEArlNOp1NVqlRRtWrV1KdPH913331atWqV67LDwoULVatWLTmdThljdO7cOd1///2qVKmSypYtq1tvvVV79+512+dTTz2lypUrKzg4WIMGDVJaWprb+l9fwsjOztbkyZNVp04dOZ1OVa9eXU888YQkqWbNmpKkmJgYORwOtW3b1rXdSy+9pOjoaPn7+6t+/fqaM2eO23F27NihmJgY+fv7q3nz5tq9e3cRnjkAEiMQAP6/gIAAZWRkSJIOHjyo1157TStXrpS3t7ckqVu3bqpQoYLeffddhYSEaP78+Wrfvr0OHDigChUq6LXXXlNCQoJmz56tuLg4LVmyRDNnzlStWrXyPebYsWP1wgsvaNq0abr55pt18uRJffXVV5J+CQEtWrTQBx98oBtuuEF+fn6SpBdeeEEJCQl67rnnFBMTo927d2vIkCEKCgpSfHy8UlNTdfvtt+vWW2/VK6+8osOHD+uhhx4q5rMHXIcMgOtOfHy8ufPOO13L27dvN6Ghoebuu+82CQkJxtfX15w+fdq1fsOGDaZs2bImLS3NbT+1a9c28+fPN8YYExsba4YOHeq2vmXLlubGG2/M87jnz583TqfTvPDCC3nWePjwYSPJ7N692629WrVqZtmyZW5tjz32mImNjTXGGDN//nxToUIFk5qa6lo/d+7cPPcF4OpxCQO4Tq1Zs0ZlypSRv7+/YmNjdcstt2jWrFmSpMjISIWFhbn6fvrpp7pw4YJCQ0NVpkwZ1+Pw4cP69ttvJUn79+9XbGys2zF+vXy5/fv3Kz09Xe3bty90zd9//72OHTumQYMGudXx+OOPu9Vx4403KjAwsFB1ALg6XMIArlPt2rXT3Llz5evrq/DwcLeJkkFBQW59s7OzVbVqVW3evDnXfsqVK3dVxw8ICLDeJjs7W9IvlzFatmzpti7nUosx5qrqAWCHAAFcp4KCglSnTp1C9W3atKlOnTolHx8f1ahRI88+0dHR2rZtm/r16+dq27ZtW777rFu3rgICArRhwwYNHjw41/qcOQ9ZWVmutsqVKysiIkKHDh3Sfffdl+d+GzRooCVLlujSpUuukFJQHQCuDpcwAFxRhw4dFBsbqx49euj9999XcnKytm7dqv/85z/auXOnJOmhhx7SwoULtXDhQh04cEAJCQn64osv8t2nv7+//vnPf+rhhx/W4sWL9e2332rbtm1asGCBJKlSpUoKCAjQe++9p//97386d+6cpF/+ONWkSZM0Y8YMHThwQJ9//rleeuklPfvss5KkPn36yMvLS4MGDdKXX36pd999V1OnTi3mMwRcfwgQAK7I4XDo3Xff1S233KKBAweqXr166t27t5KTk1W5cmVJ0j333KPx48frn//8p5o1a6YjR47ogQceKHC/jzzyiEaPHq3x48crOjpa99xzj06fPi1J8vHx0cyZMzV//nyFh4frzjvvlCQNHjxYL774ohYtWqRGjRqpTZs2WrRokeu2zzJlyujtt9/Wl19+qZiYGI0bN06TJ08uxrMDXJ8chguGAADAEiMQAADAGgECAABYI0AAAABrBAgAAGCNAAEAAKwRIAAAgDUCBAAAsEaAAAAA1ggQAADAGgECAABYI0AAAABr/w9B5ztYqxJ73QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Model: K-Nearest Neighbors\n",
      "==================================================\n",
      "Confusion Matrix:\n",
      "[[56 22]\n",
      " [ 8 34]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.72      0.79        78\n",
      "           1       0.61      0.81      0.69        42\n",
      "\n",
      "    accuracy                           0.75       120\n",
      "   macro avg       0.74      0.76      0.74       120\n",
      "weighted avg       0.78      0.75      0.76       120\n",
      "\n",
      "Accuracy: 0.75\n",
      "Precision: 0.61\n",
      "Recall: 0.81\n",
      "F1: 0.69\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Model: Decision Tree\n",
      "==================================================\n",
      "Confusion Matrix:\n",
      "[[61 17]\n",
      " [17 25]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.78      0.78        78\n",
      "           1       0.60      0.60      0.60        42\n",
      "\n",
      "    accuracy                           0.72       120\n",
      "   macro avg       0.69      0.69      0.69       120\n",
      "weighted avg       0.72      0.72      0.72       120\n",
      "\n",
      "Accuracy: 0.72\n",
      "Precision: 0.6\n",
      "Recall: 0.6\n",
      "F1: 0.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Model: Gradient Boosting Classifier\n",
      "==================================================\n",
      "Confusion Matrix:\n",
      "[[60 18]\n",
      " [10 32]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.77      0.81        78\n",
      "           1       0.64      0.76      0.70        42\n",
      "\n",
      "    accuracy                           0.77       120\n",
      "   macro avg       0.75      0.77      0.75       120\n",
      "weighted avg       0.78      0.77      0.77       120\n",
      "\n",
      "Accuracy: 0.77\n",
      "Precision: 0.64\n",
      "Recall: 0.76\n",
      "F1: 0.7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Model: Gaussian Naive Bayes\n",
      "==================================================\n",
      "Confusion Matrix:\n",
      "[[53 25]\n",
      " [14 28]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.68      0.73        78\n",
      "           1       0.53      0.67      0.59        42\n",
      "\n",
      "    accuracy                           0.68       120\n",
      "   macro avg       0.66      0.67      0.66       120\n",
      "weighted avg       0.70      0.68      0.68       120\n",
      "\n",
      "Accuracy: 0.68\n",
      "Precision: 0.53\n",
      "Recall: 0.67\n",
      "F1: 0.59\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Model: Support Vector Machine\n",
      "==================================================\n",
      "Confusion Matrix:\n",
      "[[58 20]\n",
      " [15 27]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.74      0.77        78\n",
      "           1       0.57      0.64      0.61        42\n",
      "\n",
      "    accuracy                           0.71       120\n",
      "   macro avg       0.68      0.69      0.69       120\n",
      "weighted avg       0.72      0.71      0.71       120\n",
      "\n",
      "Accuracy: 0.71\n",
      "Precision: 0.57\n",
      "Recall: 0.64\n",
      "F1: 0.61\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Leaderboard (Ranked by F1 Score)\n"
     ]
    },
    {
     "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K-Nearest Neighbors</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gaussian Naive Bayes</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Model  Accuracy  Precision  Recall  F1-Score\n",
       "0  Gradient Boosting Classifier      0.77       0.64    0.76      0.70\n",
       "1           K-Nearest Neighbors      0.75       0.61    0.81      0.69\n",
       "2           Logistic Regression      0.71       0.57    0.71      0.63\n",
       "3                 Random Forest      0.72       0.60    0.64      0.62\n",
       "4        Support Vector Machine      0.71       0.57    0.64      0.61\n",
       "5                 Decision Tree      0.72       0.60    0.60      0.60\n",
       "6          Gaussian Naive Bayes      0.68       0.53    0.67      0.59"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of model names\n",
    "models = ['Logistic Regression', 'Random Forest', 'K-Nearest Neighbors', 'Decision Tree', \n",
    "          'Gradient Boosting Classifier', 'Gaussian Naive Bayes', 'Support Vector Machine']\n",
    "\n",
    "# Initialize the Results DataFrame\n",
    "Results = pd.DataFrame(columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1'])\n",
    "\n",
    "# Fit the models and evaluate the performance\n",
    "for model_name, model in zip(models, [lr, rf, knn, dt, gb, nb, svm]):\n",
    "    model.fit(X_train_scaled_df, y_train_balanced_df)\n",
    "    y_pred = model.predict(X_eval_scaled_df)\n",
    "\n",
    "    print('Model:', model_name)\n",
    "    print('=' * 50)\n",
    "    \n",
    "    print('Confusion Matrix:')\n",
    "    print(confusion_matrix(y_eval, y_pred))\n",
    "    \n",
    "    print('Classification Report:')\n",
    "    print(classification_report(y_eval, y_pred))\n",
    "\n",
    "    accuracy = accuracy_score(y_eval, y_pred)\n",
    "    precision = precision_score(y_eval, y_pred)\n",
    "    recall = recall_score(y_eval, y_pred)\n",
    "    f1 = f1_score(y_eval, y_pred)\n",
    "\n",
    "    print('Accuracy:', round(accuracy, 2))\n",
    "    print('Precision:', round(precision, 2))\n",
    "    print('Recall:', round(recall, 2))\n",
    "    print('F1:', round(f1, 2))\n",
    "\n",
    "    # Generate the confusion matrix\n",
    "    cm = confusion_matrix(y_eval, y_pred)\n",
    "\n",
    "    # Create a heatmap for the confusion matrix\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "    plt.title(f'Confusion Matrix - {model_name}')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "\n",
    "    # Display the confusion matrix\n",
    "    plt.show()\n",
    "\n",
    "    print(\"\\n\")\n",
    "\n",
    "    # Append the results to the DataFrame with rounding to 2 decimal places\n",
    "    new_row = {\n",
    "        'Model': model_name,\n",
    "        'Accuracy': round(accuracy, 2),\n",
    "        'Precision': round(precision, 2),\n",
    "        'Recall': round(recall, 2),\n",
    "        'F1': round(f1, 2)\n",
    "    }\n",
    "\n",
    "    Results = pd.concat([Results, pd.DataFrame([new_row])], ignore_index=True)\n",
    "    \n",
    "# Print the results in a leaderboard format based on the F1 score from highest to lowest\n",
    "print(\"\\nLeaderboard (Ranked by F1 Score)\")\n",
    "Results_sorted = Results.sort_values(by='F1', ascending=False).reset_index(drop=True)\n",
    "\n",
    "# Rename the \"F1\" column to \"F1-Score\" in the Results DataFrame\n",
    "Results_sorted.rename(columns={'F1': 'F1-Score'}, inplace=True)\n",
    "\n",
    "# Print The Sorted Results Dataframe\n",
    "Results_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ee39ef1",
   "metadata": {},
   "source": [
    "i. Gradient Boosting Classifier:\n",
    "\n",
    "\n",
    "- This model achieved the highest F1-score (0.70) among all models, indicating its strong overall performance in terms of precision and recall.\n",
    "\n",
    "- It also has a relatively high accuracy (0.77), suggesting its effectiveness in classifying sepsis cases.\n",
    "\n",
    "- The confusion matrix shows that there were 60 true negatives, 18 false positives, 10 false negatives, and 32 true positives. It correctly classified a substantial number of sepsis cases while minimizing false positives.\n",
    "\n",
    "- This model is robust and suitable for this classification task, offering a good balance between precision and recall.\n",
    "\n",
    "\n",
    "ii. K-Nearest Neighbors (KNN):\n",
    "\n",
    "- KNN achieved an F1-score of 0.69 and an accuracy of 0.75.\n",
    "\n",
    "- It showed a lower precision (0.61) and higher recall (0.81), indicating a tendency to classify more cases as sepsis, which can lead to false alarms but better recall.\n",
    "\n",
    "- The confusion matrix displays 56 true negatives, 22 false positives, 8 false negatives, and 34 true positives. KNN has a reasonable recall but lower precision.\n",
    "\n",
    "\n",
    "iii. Logistic Regression:\n",
    "\n",
    "- The Logistic Regression model achieved an F1-score of 0.63 and an accuracy of 0.71.\n",
    "\n",
    "- It has a moderate precision (0.57) and recall (0.71), indicating a good balance between correctly identifying positive cases (sepsis) and minimizing false positives.\n",
    "\n",
    "- The confusion matrix shows 55 true negatives, 23 false positives, 12 false negatives, and 30 true positives. While it performed reasonably well, there's room for improvement in precision.\n",
    "\n",
    "\n",
    "iv. Random Forest:\n",
    "\n",
    "- The Random Forest model performed well with an F1-score of 0.62 and accuracy of 0.72.\n",
    "\n",
    "- It showed a balanced precision (0.60) and recall (0.64), making it suitable for applications where both false positives and false negatives need to be minimized.\n",
    "\n",
    "- The confusion matrix indicates 60 true negatives, 18 false positives, 15 false negatives, and 27 true positives. It performed well in classifying sepsis cases, although there were some false negatives.\n",
    "\n",
    "\n",
    "v. Support Vector Machine (SVM):\n",
    "\n",
    "- SVM demonstrated an F1-score of 0.61 and accuracy of 0.71.\n",
    "\n",
    "- It achieved a balanced precision (0.57) and recall (0.64), making it a suitable choice when both types of errors need to be minimized.\n",
    "\n",
    "- The confusion matrix reveals 58 true negatives, 20 false positives, 15 false negatives, and 27 true positives. SVM showed good recall for sepsis cases.\n",
    "\n",
    "\n",
    "vi. Decision Tree:\n",
    "\n",
    "- The Decision Tree model achieved an F1-score of 0.60 and an accuracy of 0.72.\n",
    "\n",
    "- It showed a balanced precision (0.60) and recall (0.60), making it suitable for applications where both false positives and false negatives need to be minimized.\n",
    "\n",
    "- The confusion matrix indicates 61 true negatives, 17 false positives, 17 false negatives, and 25 true positives. It performed decently in classifying sepsis cases with a balanced approach.\n",
    "\n",
    "\n",
    "vii. Gaussian Naive Bayes:\n",
    "\n",
    "- The Gaussian Naive Bayes model achieved an F1-score of 0.59 and an accuracy of 0.68.\n",
    "\n",
    "- It displayed balanced precision (0.53) and recall (0.67), making it a reasonable choice for balanced error rates.\n",
    "\n",
    "- The confusion matrix shows 53 true negatives, 25 false positives, 14 false negatives, and 28 true positives. It provides a balance between precision and recall.\n",
    "\n",
    "\n",
    "In summary, among the models, the Gradient Boosting Classifier stands out as the top-performing model for this classification task, closely followed by K-Nearest Neighbors. These models offer a good balance between precision and recall and are well-suited for predicting sepsis. The Decision Tree model, while showing a balanced approach, falls slightly behind in terms of overall F1-score compared to the top performers. Therefore, we proceed with the Gradient Boosting Classifier as the choice of the model to go into evaluation and hyperparameter tuning based on its good performance. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35d5500a",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7c784091",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exact Percentage Feature Importances (Rounded to 2 decimal places):\n"
     ]
    },
    {
     "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PL</td>\n",
       "      <td>0.419868</td>\n",
       "      <td>41.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M11</td>\n",
       "      <td>0.186106</td>\n",
       "      <td>18.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BD2</td>\n",
       "      <td>0.113216</td>\n",
       "      <td>11.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.094325</td>\n",
       "      <td>9.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TS</td>\n",
       "      <td>0.068953</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SK</td>\n",
       "      <td>0.043679</td>\n",
       "      <td>4.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PRG</td>\n",
       "      <td>0.037225</td>\n",
       "      <td>3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PR</td>\n",
       "      <td>0.036629</td>\n",
       "      <td>3.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Feature  Importance  Percentage\n",
       "1      PL    0.419868       41.99\n",
       "5     M11    0.186106       18.61\n",
       "6     BD2    0.113216       11.32\n",
       "7     Age    0.094325        9.43\n",
       "4      TS    0.068953        6.90\n",
       "3      SK    0.043679        4.37\n",
       "0     PRG    0.037225        3.72\n",
       "2      PR    0.036629        3.66"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Fit a Gradient Boosting Classifier\n",
    "gb.fit(X_train_scaled_df, y_train_balanced_df)\n",
    "\n",
    "# Get feature importances\n",
    "feature_importances = gb.feature_importances_\n",
    "\n",
    "# Calculate the sum of importances for percentage calculation\n",
    "total_importance = feature_importances.sum()\n",
    "\n",
    "# Create a DataFrame with correct feature names and percentage importances\n",
    "feature_importance_df = pd.DataFrame({'Feature': X_train_scaled_df.columns, 'Importance': feature_importances})\n",
    "feature_importance_df['Percentage'] = ((feature_importance_df['Importance'] / total_importance) * 100).round(2)  # Round to 2 decimal places\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Set the Viridis color palette\n",
    "sns.set_palette(\"viridis\")\n",
    "\n",
    "# Visualize feature importances with rounded percentage values using Viridis palette\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Percentage', y='Feature', data=feature_importance_df, palette=\"viridis\")\n",
    "plt.title('Feature Importances (Percentage)')\n",
    "plt.xlabel('Percentage')\n",
    "plt.ylabel('Feature')\n",
    "plt.show()\n",
    "\n",
    "# Print out the exact rounded percentage values\n",
    "print(\"Exact Percentage Feature Importances (Rounded to 2 decimal places):\")\n",
    "feature_importance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "210bd285",
   "metadata": {},
   "source": [
    "i. **Plasma Glucose Concentration (PL):**\n",
    "   - This feature has the highest importance score, accounting for approximately 41.99% of the predictive power of the model. It suggests that variations in plasma glucose concentration have a significant impact on predicting sepsis.\n",
    "\n",
    "ii. **Body Mass Index (M11):**\n",
    "   - The second most important feature, with an importance score of around 18.61%. This indicates that BMI plays a substantial role in predicting sepsis, with higher BMI values likely associated with the condition.\n",
    "\n",
    "iii. **Diabetes Pedigree Function (BD2):**\n",
    "   - While not as influential as PL and M11, this feature still contributes significantly, with approximately 11.32% importance. It implies that the diabetes pedigree function has a notable impact on sepsis predictions.\n",
    "\n",
    "iv. **Age:**\n",
    "   - Age is an important factor, with roughly 9.43% importance. This suggests that older individuals may be more susceptible to sepsis, and their age is a valuable predictor.\n",
    "\n",
    "v. **Number of Pregnancies (PRG):**\n",
    "   - Although it has a lower importance score (around 3.72%), the number of pregnancies still contributes to predicting sepsis. It may indicate that pregnancy history can influence sepsis risk.\n",
    "\n",
    "vi. **2-Hour Serum Insulin (TS):**\n",
    "   - TS has an importance score of about 6.90%, suggesting it plays a minor role compared to other features. Nevertheless, it still contributes to the model's predictive performance.\n",
    "\n",
    "vii. **Triceps Skinfold Thickness (SK):**\n",
    "   - This feature has a similar importance score to TS, around 4.37%. It may not be as crucial as PL or M11, but it still provides relevant information for sepsis prediction.\n",
    "\n",
    "viii. **Diastolic Blood Pressure (PR):**\n",
    "   - PR has the lowest importance score, approximately 3.66%. While it is the least influential feature, it still contributes modestly to the model's predictions.\n",
    "\n",
    "These insights highlight the relative importance of each feature in predicting sepsis. Features like Plasma Glucose Concentration (PL) and Body Mass Index (M11) are particularly significant, while others, like Diastolic Blood Pressure (PR), have a comparatively lower impact on the model's predictive power."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0791ec34",
   "metadata": {},
   "source": [
    "#### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "454eefb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'learning_rate': 0.1, 'max_depth': 6, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 20}\n"
     ]
    }
   ],
   "source": [
    "# Define hyperparameters to search\n",
    "param_grid = {\n",
    "    'n_estimators': [5, 10, 20],\n",
    "    'learning_rate': [0.001, 0.01, 0.1],\n",
    "    'max_depth': [5, 6, 7],\n",
    "    'min_samples_split': [5, 10, 20],\n",
    "    'min_samples_leaf': [2, 4, 8]\n",
    "}\n",
    "\n",
    "# Create GridSearchCV\n",
    "grid_search = GridSearchCV(estimator=GradientBoostingClassifier(random_state=42), param_grid=param_grid, \n",
    "                           scoring='f1', cv=5, n_jobs=-1)\n",
    "\n",
    "# Fit the grid search to the data\n",
    "grid_search.fit(X_train_scaled_df, y_train_balanced_df)\n",
    "\n",
    "# Get the best parameters\n",
    "best_params = grid_search.best_params_\n",
    "print(\"Best Hyperparameters:\", best_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc71350d",
   "metadata": {},
   "source": [
    "#### Perfomance after Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7a86e8cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Set Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.73      0.80        78\n",
      "           1       0.62      0.83      0.71        42\n",
      "\n",
      "    accuracy                           0.77       120\n",
      "   macro avg       0.76      0.78      0.76       120\n",
      "weighted avg       0.80      0.77      0.77       120\n",
      "\n",
      "Accuracy: 0.77\n",
      "Precision: 0.62\n",
      "Recall: 0.83\n",
      "F1-score: 0.71\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The F1-score After Model Evaluation is: 0.71\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the Gradient Boosting Classifier with the best hyperparameters\n",
    "tuned_gb = GradientBoostingClassifier(\n",
    "    n_estimators=200,\n",
    "    learning_rate=0.001,\n",
    "    max_depth=6,\n",
    "    min_samples_split=5,\n",
    "    min_samples_leaf=2,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "# Fit the tuned Gradient Boosting Classifier to the balanced training data\n",
    "tuned_gb.fit(X_train_scaled, y_train_balanced)\n",
    "\n",
    "# Evaluate the model on the evaluation set (X_eval_scaled and y_eval)\n",
    "y_eval_pred = tuned_gb.predict(X_eval_scaled)\n",
    "\n",
    "# Print the classification report\n",
    "print(\"Evaluation Set Classification Report:\")\n",
    "print(classification_report(y_eval, y_eval_pred))\n",
    "\n",
    "# Print accuracy, precision, recall, and F1-score\n",
    "accuracy_eval = accuracy_score(y_eval, y_eval_pred)\n",
    "precision_eval = precision_score(y_eval, y_eval_pred)\n",
    "recall_eval = recall_score(y_eval, y_eval_pred)\n",
    "f1_eval = f1_score(y_eval, y_eval_pred)\n",
    "\n",
    "print(\"Accuracy:\", round(accuracy_eval, 2))\n",
    "print(\"Precision:\", round(precision_eval, 2))\n",
    "print(\"Recall:\", round(recall_eval, 2))\n",
    "print(\"F1-score:\", round(f1_eval, 2))\n",
    "\n",
    "# Create a confusion matrix heatmap\n",
    "cm_eval = confusion_matrix(y_eval, y_eval_pred)\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm_eval, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.title('Confusion Matrix - Evaluation Set')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()\n",
    "\n",
    "print(\"The F1-score After Model Evaluation is:\", round(f1_eval, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9839bf47",
   "metadata": {},
   "source": [
    "The performance of the model improved after hyperparameter tuning. Therefore, we save the tuned model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d7dc92",
   "metadata": {},
   "source": [
    "### Save The Model and Key Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bdd2b448",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary to store the components\n",
    "saved_components = {\n",
    "    'model': tuned_gb,      \n",
    "    'encoder': label_encoder, \n",
    "    'scaler': scaler   \n",
    "}\n",
    "\n",
    "# Save all components in a single pickle file\n",
    "with open('model_and_key_components.pkl', 'wb') as file:\n",
    "    pickle.dump(saved_components, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a2e2b0",
   "metadata": {},
   "source": [
    "#### Test Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "266895c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>109.0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>120</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.407</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>108.0</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.400</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>67.081301</td>\n",
       "      <td>21.382114</td>\n",
       "      <td>0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.190</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>124.0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.100</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>150.0</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>126</td>\n",
       "      <td>35.2</td>\n",
       "      <td>0.692</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRG     PL         PR         SK   TS   M11    BD2  Age\n",
       "0    1  109.0  38.000000  18.000000  120  23.1  0.407   26\n",
       "1    1  108.0  88.000000  19.000000    0  27.1  0.400   24\n",
       "2    6   96.0  67.081301  21.382114    0  23.7  0.190   28\n",
       "3    1  124.0  74.000000  36.000000    0  27.8  0.100   30\n",
       "4    7  150.0  78.000000  29.000000  126  35.2  0.692   54"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the head of the test dataset\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "dd75786a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>126</td>\n",
       "      <td>35.2</td>\n",
       "      <td>0.692</td>\n",
       "      <td>54</td>\n",
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      "text/plain": [
       "   PRG     PL         PR         SK   TS   M11    BD2  Age\n",
       "0    1  109.0  38.000000  18.000000  120  23.1  0.407   26\n",
       "1    1  108.0  88.000000  19.000000    0  27.1  0.400   24\n",
       "2    6   96.0  67.081301  21.382114    0  23.7  0.190   28\n",
       "3    1  124.0  74.000000  36.000000    0  27.8  0.100   30\n",
       "4    7  150.0  78.000000  29.000000  126  35.2  0.692   54"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a copy of the test_df\n",
    "test_pred = test_df.copy()\n",
    "test_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "95c52118",
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <td>23.1</td>\n",
       "      <td>0.407</td>\n",
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       "      <td>27.1</td>\n",
       "      <td>0.400</td>\n",
       "      <td>24</td>\n",
       "      <td>Negative</td>\n",
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       "      <td>6</td>\n",
       "      <td>96.0</td>\n",
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       "      <td>21.382114</td>\n",
       "      <td>0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.190</td>\n",
       "      <td>28</td>\n",
       "      <td>Negative</td>\n",
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       "      <th>4</th>\n",
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       "      <td>150.0</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>29.000000</td>\n",
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       "      <td>35.2</td>\n",
       "      <td>0.692</td>\n",
       "      <td>54</td>\n",
       "      <td>Positive</td>\n",
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      ],
      "text/plain": [
       "   PRG     PL         PR         SK   TS   M11    BD2  Age    Sepsis\n",
       "0    1  109.0  38.000000  18.000000  120  23.1  0.407   26  Negative\n",
       "1    1  108.0  88.000000  19.000000    0  27.1  0.400   24  Negative\n",
       "2    6   96.0  67.081301  21.382114    0  23.7  0.190   28  Negative\n",
       "3    1  124.0  74.000000  36.000000    0  27.8  0.100   30  Negative\n",
       "4    7  150.0  78.000000  29.000000  126  35.2  0.692   54  Positive"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the model and components\n",
    "with open('model_and_key_components.pkl', 'rb') as file:\n",
    "    loaded_components = pickle.load(file)\n",
    "\n",
    "# Load the model, encoder and scaler\n",
    "loaded_model = loaded_components['model']\n",
    "loaded_encoder = loaded_components['encoder']\n",
    "loaded_scaler = loaded_components['scaler']\n",
    "\n",
    "# Encode Categorical Variables\n",
    "# All Columns are numerical. No need for encoding\n",
    "\n",
    "# Apply scaling to numerical data\n",
    "numerical_cols = ['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age']\n",
    "test_data_scaled = loaded_scaler.transform(test_pred[numerical_cols])\n",
    "\n",
    "# Convert the scaled numerical data to a DataFrame\n",
    "test_data_scaled_df = pd.DataFrame(test_data_scaled, columns=numerical_cols)\n",
    "\n",
    "# Perform Predictions\n",
    "y_pred = loaded_model.predict(test_data_scaled_df)\n",
    "\n",
    "# Create a new DataFrame with the 'Sepsis' predictions\n",
    "test_predictions = test_pred.copy()\n",
    "test_predictions['Sepsis'] = y_pred\n",
    "\n",
    "# Define a mapping dictionary\n",
    "sepsis_mapping = {0: 'Negative', 1: 'Positive'}\n",
    "\n",
    "# Map the values in the 'Sepsis' column\n",
    "test_predictions['Sepsis'] = test_predictions['Sepsis'].map(sepsis_mapping)\n",
    "\n",
    "# Display Predictions (first few rows)\n",
    "test_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5008b796",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the updated datasets\n",
    "test_predictions.to_csv(\"data/Test_Predictions.csv\", index=False)"
   ]
  }
 ],
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