{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec7469d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ac7b41ee",
   "metadata": {},
   "source": [
    "# Intro\n",
    "## General\n",
    "Machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. \n",
    "In most of the situations we want to have a machine learning system to make **predictions**, so we have several categories of machine learning tasks depending on the type of prediction needed: **Classification, Regression, Clustering, Generation**, etc.\n",
    "\n",
    "**Classification** is the task whose goal is the prediction of the label of the class to which the input belongs (e.g., Classification of images in two classes: cats and dogs).\n",
    "**Regression** is the task whose goal is the prediction of numerical value(s) related to the input (e.g., House rent prediction, Estimated time of arrival ).\n",
    "**Generation** is the task whose goal is the creation of something new related to the input (e.g., Text translation, Audio beat generation, Image denoising ). **Clustering** is the task of grouping a set of objects in such a way that objects in the same group (called a **cluster**) are more similar (in some sense) to each other than to those in other **clusters** (e.g., Clients clutering).\n",
    "\n",
    "In machine learning, there are learning paradigms that relate to one aspect of the dataset: **the presence of the label to be predicted**. **Supervised Learning** is the paradigm of learning that is applied when the dataset has the label variables to be predicted, known as ` y variables`. **Unsupervised Learning** is the paradigm of learning that is applied when the dataset has not the label variables to be predicted. **Self-supervised Learning** is the paradigm of learning that is applied when part of the X dataset is considere as the label to be predicted (e.g., the Dataset is made of texts and the model try to predict the next word of each sentence).\n",
    "\n",
    "## Notebook overview\n",
    "\n",
    "This notebook is a guide to start practicing Machine Learning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced98da1",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a75f15",
   "metadata": {},
   "source": [
    "## Installation\n",
    "Here is the section to install all the packages/libraries that will be needed to tackle the challlenge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "536ea537",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install  optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4745fad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Data Handling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "##Visualization Libraries \n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns \n",
    "%matplotlib inline\n",
    "import plotly.express as ex\n",
    "import plotly.express as px\n",
    "import plotly.express as px\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "init_notebook_mode(connected=True)  # Initialize Plotly for offline mode\n",
    "\n",
    "# Feature Processing (Scikit-learn processing, etc. )\n",
    "import phik\n",
    "\n",
    "# Machine Learning (Scikit-learn Estimators, Catboost, LightGBM, etc. )\n",
    "from sklearn.feature_selection import SelectKBest, f_classif\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.ensemble import BaggingClassifier,AdaBoostClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.compose import ColumnTransformer \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import classification_report,ConfusionMatrixDisplay\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.utils import resample\n",
    "\n",
    "# Hyperparameters Fine-tuning (Scikit-learn hp search, cross-validation, etc. )\n",
    "import optuna\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score,f1_score\n",
    "\n",
    "# Other packages\n",
    "import os\n",
    "import warnings\n",
    "from joblib import dump, load\n",
    "\n",
    "# Suppress all warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7951bfb3",
   "metadata": {},
   "source": [
    "# II. Data Loading\n",
    "Data Loading\n",
    "Here is the section to load the datasets (train, eval, test) and the additional files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb10dcc",
   "metadata": {},
   "source": [
    "#### 2.1 train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5bf01a21",
   "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>Plasma glucose</th>\n",
       "      <th>Blood Work Result-1</th>\n",
       "      <th>Blood Pressure</th>\n",
       "      <th>Blood Work Result-2</th>\n",
       "      <th>Blood Work Result-3</th>\n",
       "      <th>Body mass index</th>\n",
       "      <th>Blood Work Result-4</th>\n",
       "      <th>Age</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>Sepsis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ICU200010</td>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICU200011</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ICU200012</td>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICU200013</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ICU200014</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Plasma glucose  Blood Work Result-1  Blood Pressure  \\\n",
       "0  ICU200010               6                  148              72   \n",
       "1  ICU200011               1                   85              66   \n",
       "2  ICU200012               8                  183              64   \n",
       "3  ICU200013               1                   89              66   \n",
       "4  ICU200014               0                  137              40   \n",
       "\n",
       "   Blood Work Result-2  Blood Work Result-3  Body mass index  \\\n",
       "0                   35                    0             33.6   \n",
       "1                   29                    0             26.6   \n",
       "2                    0                    0             23.3   \n",
       "3                   23                   94             28.1   \n",
       "4                   35                  168             43.1   \n",
       "\n",
       "   Blood Work Result-4  Age  Insurance    Sepsis  \n",
       "0                0.627   50          0  Positive  \n",
       "1                0.351   31          0  Negative  \n",
       "2                0.672   32          1  Positive  \n",
       "3                0.167   21          1  Negative  \n",
       "4                2.288   33          1  Positive  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=r\"C:\\Users\\DAVID\\Career-Accelerator-LP6\\Paitients_Files_Train.csv\"\n",
    "df_train= pd.read_csv(path)\n",
    "df_train.rename(columns={'PRG':'Plasma glucose','PL': 'Blood Work Result-1','PR': 'Blood Pressure',\n",
    "                         'SK': 'Blood Work Result-2','TS': 'Blood Work Result-3','M11': 'Body mass index',\n",
    "                         'BD2': 'Blood Work Result-4',\n",
    "                         'Sepssis': 'Sepsis'}, inplace=True)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e4beb5",
   "metadata": {},
   "source": [
    "#### 2.2. test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7db8d8fa",
   "metadata": {
    "scrolled": true
   },
   "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>Plasma glucose</th>\n",
       "      <th>Blood Work Result-1</th>\n",
       "      <th>Blood Pressure</th>\n",
       "      <th>Blood Work Result-2</th>\n",
       "      <th>Blood Work Result-3</th>\n",
       "      <th>Body mass index</th>\n",
       "      <th>Blood Work Result-4</th>\n",
       "      <th>Age</th>\n",
       "      <th>Insurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ICU200609</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>120</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.407</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICU200610</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>88</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.400</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ICU200611</td>\n",
       "      <td>6</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.190</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICU200612</td>\n",
       "      <td>1</td>\n",
       "      <td>124</td>\n",
       "      <td>74</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.100</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ICU200613</td>\n",
       "      <td>7</td>\n",
       "      <td>150</td>\n",
       "      <td>78</td>\n",
       "      <td>29</td>\n",
       "      <td>126</td>\n",
       "      <td>35.2</td>\n",
       "      <td>0.692</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Plasma glucose  Blood Work Result-1  Blood Pressure  \\\n",
       "0  ICU200609               1                  109              38   \n",
       "1  ICU200610               1                  108              88   \n",
       "2  ICU200611               6                   96               0   \n",
       "3  ICU200612               1                  124              74   \n",
       "4  ICU200613               7                  150              78   \n",
       "\n",
       "   Blood Work Result-2  Blood Work Result-3  Body mass index  \\\n",
       "0                   18                  120             23.1   \n",
       "1                   19                    0             27.1   \n",
       "2                    0                    0             23.7   \n",
       "3                   36                    0             27.8   \n",
       "4                   29                  126             35.2   \n",
       "\n",
       "   Blood Work Result-4  Age  Insurance  \n",
       "0                0.407   26          1  \n",
       "1                0.400   24          1  \n",
       "2                0.190   28          1  \n",
       "3                0.100   30          1  \n",
       "4                0.692   54          0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=r\"C:\\Users\\DAVID\\Career-Accelerator-LP6\\Paitients_Files_Test.csv\"\n",
    "df_test= pd.read_csv(path)\n",
    "df_test.rename(columns={'PRG':'Plasma glucose','PL': 'Blood Work Result-1','PR': 'Blood Pressure',\n",
    "                         'SK': 'Blood Work Result-2','TS': 'Blood Work Result-3','M11': 'Body mass index',\n",
    "                         'BD2': 'Blood Work Result-4'}, inplace=True)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4a6f17",
   "metadata": {},
   "source": [
    "# III. Exploratory Data Analysis: EDA\n",
    "Here is the section to **inspect** the datasets in depth, **present** it, make **hypotheses** and **think** the *cleaning, processing and features creation*."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "675c5c48",
   "metadata": {},
   "source": [
    "### 3.1 Dataset Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad24bd6",
   "metadata": {},
   "source": [
    "#### 3.1.1 Data info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce75a08b",
   "metadata": {
    "scrolled": true
   },
   "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   Plasma glucose       599 non-null    int64  \n",
      " 2   Blood Work Result-1  599 non-null    int64  \n",
      " 3   Blood Pressure       599 non-null    int64  \n",
      " 4   Blood Work Result-2  599 non-null    int64  \n",
      " 5   Blood Work Result-3  599 non-null    int64  \n",
      " 6   Body mass index      599 non-null    float64\n",
      " 7   Blood Work Result-4  599 non-null    float64\n",
      " 8   Age                  599 non-null    int64  \n",
      " 9   Insurance            599 non-null    int64  \n",
      " 10  Sepsis               599 non-null    object \n",
      "dtypes: float64(2), int64(7), object(2)\n",
      "memory usage: 51.6+ KB\n",
      "the info df_train dataset are: \n",
      "\n",
      " None \n",
      "---------------------------------------------\n",
      "<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   Plasma glucose       169 non-null    int64  \n",
      " 2   Blood Work Result-1  169 non-null    int64  \n",
      " 3   Blood Pressure       169 non-null    int64  \n",
      " 4   Blood Work Result-2  169 non-null    int64  \n",
      " 5   Blood Work Result-3  169 non-null    int64  \n",
      " 6   Body mass index      169 non-null    float64\n",
      " 7   Blood Work Result-4  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",
      "the info df_test dataset are: \n",
      "\n",
      " None \n",
      "---------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# checking for info on the datasets\n",
    "data = {'df_train': df_train, 'df_test': df_test}\n",
    "for name, dataset in data.items():\n",
    "    print(f\"the info {name} dataset are: \\n\\n\",dataset.info(),\"\\n\" + \"---\" * 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7a68eb7",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- the train dataset has 11 features with 599 entries.\n",
    "- the test dataset has 10 features with 169 entries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b1e695",
   "metadata": {},
   "source": [
    "#### 3.1.2 checking missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f082ad5e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the info df_train dataset are: \n",
      "\n",
      " ID                     0\n",
      "Plasma glucose         0\n",
      "Blood Work Result-1    0\n",
      "Blood Pressure         0\n",
      "Blood Work Result-2    0\n",
      "Blood Work Result-3    0\n",
      "Body mass index        0\n",
      "Blood Work Result-4    0\n",
      "Age                    0\n",
      "Insurance              0\n",
      "Sepsis                 0\n",
      "dtype: int64 \n",
      "---------------------------------------------\n",
      "the info df_test dataset are: \n",
      "\n",
      " ID                     0\n",
      "Plasma glucose         0\n",
      "Blood Work Result-1    0\n",
      "Blood Pressure         0\n",
      "Blood Work Result-2    0\n",
      "Blood Work Result-3    0\n",
      "Body mass index        0\n",
      "Blood Work Result-4    0\n",
      "Age                    0\n",
      "Insurance              0\n",
      "dtype: int64 \n",
      "---------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# checking for missing values in the datasets\n",
    "data = {'df_train': df_train, 'df_test': df_test}\n",
    "for name, dataset in data.items():\n",
    "    print(f\"the info {name} dataset are: \\n\\n\",dataset.isna().sum(),\"\\n\" + \"---\" * 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfeb2702",
   "metadata": {},
   "source": [
    "- both datasets have no missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "083af0a9",
   "metadata": {},
   "source": [
    "#### 3.1.3 checking Data shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c0ca96e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(599, 11) (169, 10)\n"
     ]
    }
   ],
   "source": [
    "# checking for the shapes of the datasets\n",
    "print(df_train.shape, df_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5549ae",
   "metadata": {},
   "source": [
    "#### 3.1.4 Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f1576871",
   "metadata": {
    "scrolled": false
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Plasma glucose</th>\n",
       "      <td>599.0</td>\n",
       "      <td>3.824708</td>\n",
       "      <td>3.362839</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Work Result-1</th>\n",
       "      <td>599.0</td>\n",
       "      <td>120.153589</td>\n",
       "      <td>32.682364</td>\n",
       "      <td>0.000</td>\n",
       "      <td>99.000</td>\n",
       "      <td>116.000</td>\n",
       "      <td>140.000</td>\n",
       "      <td>198.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Pressure</th>\n",
       "      <td>599.0</td>\n",
       "      <td>68.732888</td>\n",
       "      <td>19.335675</td>\n",
       "      <td>0.000</td>\n",
       "      <td>64.000</td>\n",
       "      <td>70.000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>122.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Work Result-2</th>\n",
       "      <td>599.0</td>\n",
       "      <td>20.562604</td>\n",
       "      <td>16.017622</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Work Result-3</th>\n",
       "      <td>599.0</td>\n",
       "      <td>79.460768</td>\n",
       "      <td>116.576176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>36.000</td>\n",
       "      <td>123.500</td>\n",
       "      <td>846.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Body mass index</th>\n",
       "      <td>599.0</td>\n",
       "      <td>31.920033</td>\n",
       "      <td>8.008227</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.100</td>\n",
       "      <td>32.000</td>\n",
       "      <td>36.550</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Work Result-4</th>\n",
       "      <td>599.0</td>\n",
       "      <td>0.481187</td>\n",
       "      <td>0.337552</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.647</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>599.0</td>\n",
       "      <td>33.290484</td>\n",
       "      <td>11.828446</td>\n",
       "      <td>21.000</td>\n",
       "      <td>24.000</td>\n",
       "      <td>29.000</td>\n",
       "      <td>40.000</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insurance</th>\n",
       "      <td>599.0</td>\n",
       "      <td>0.686144</td>\n",
       "      <td>0.464447</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count        mean         std     min     25%      50%  \\\n",
       "Plasma glucose       599.0    3.824708    3.362839   0.000   1.000    3.000   \n",
       "Blood Work Result-1  599.0  120.153589   32.682364   0.000  99.000  116.000   \n",
       "Blood Pressure       599.0   68.732888   19.335675   0.000  64.000   70.000   \n",
       "Blood Work Result-2  599.0   20.562604   16.017622   0.000   0.000   23.000   \n",
       "Blood Work Result-3  599.0   79.460768  116.576176   0.000   0.000   36.000   \n",
       "Body mass index      599.0   31.920033    8.008227   0.000  27.100   32.000   \n",
       "Blood Work Result-4  599.0    0.481187    0.337552   0.078   0.248    0.383   \n",
       "Age                  599.0   33.290484   11.828446  21.000  24.000   29.000   \n",
       "Insurance            599.0    0.686144    0.464447   0.000   0.000    1.000   \n",
       "\n",
       "                         75%     max  \n",
       "Plasma glucose         6.000   17.00  \n",
       "Blood Work Result-1  140.000  198.00  \n",
       "Blood Pressure        80.000  122.00  \n",
       "Blood Work Result-2   32.000   99.00  \n",
       "Blood Work Result-3  123.500  846.00  \n",
       "Body mass index       36.550   67.10  \n",
       "Blood Work Result-4    0.647    2.42  \n",
       "Age                   40.000   81.00  \n",
       "Insurance              1.000    1.00  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics\n",
    "df_train.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d7f56a70",
   "metadata": {
    "scrolled": false
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Plasma glucose</th>\n",
       "      <td>169.0</td>\n",
       "      <td>3.917160</td>\n",
       "      <td>3.402415</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>13.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Work Result-1</th>\n",
       "      <td>169.0</td>\n",
       "      <td>123.520710</td>\n",
       "      <td>29.259123</td>\n",
       "      <td>56.0</td>\n",
       "      <td>102.000</td>\n",
       "      <td>120.000</td>\n",
       "      <td>141.000</td>\n",
       "      <td>199.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Pressure</th>\n",
       "      <td>169.0</td>\n",
       "      <td>70.426036</td>\n",
       "      <td>19.426805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.000</td>\n",
       "      <td>74.000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>114.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Work Result-2</th>\n",
       "      <td>169.0</td>\n",
       "      <td>20.443787</td>\n",
       "      <td>15.764962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>49.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Work Result-3</th>\n",
       "      <td>169.0</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>110.720852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>135.000</td>\n",
       "      <td>540.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Body mass index</th>\n",
       "      <td>169.0</td>\n",
       "      <td>32.249704</td>\n",
       "      <td>7.444886</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.600</td>\n",
       "      <td>32.400</td>\n",
       "      <td>36.600</td>\n",
       "      <td>57.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood Work Result-4</th>\n",
       "      <td>169.0</td>\n",
       "      <td>0.438876</td>\n",
       "      <td>0.306935</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.587</td>\n",
       "      <td>1.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>169.0</td>\n",
       "      <td>33.065089</td>\n",
       "      <td>11.548110</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.000</td>\n",
       "      <td>28.000</td>\n",
       "      <td>42.000</td>\n",
       "      <td>70.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insurance</th>\n",
       "      <td>169.0</td>\n",
       "      <td>0.727811</td>\n",
       "      <td>0.446410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count        mean         std   min      25%      50%  \\\n",
       "Plasma glucose       169.0    3.917160    3.402415   0.0    1.000    3.000   \n",
       "Blood Work Result-1  169.0  123.520710   29.259123  56.0  102.000  120.000   \n",
       "Blood Pressure       169.0   70.426036   19.426805   0.0   62.000   74.000   \n",
       "Blood Work Result-2  169.0   20.443787   15.764962   0.0    0.000   23.000   \n",
       "Blood Work Result-3  169.0   81.000000  110.720852   0.0    0.000    0.000   \n",
       "Body mass index      169.0   32.249704    7.444886   0.0   27.600   32.400   \n",
       "Blood Work Result-4  169.0    0.438876    0.306935   0.1    0.223    0.343   \n",
       "Age                  169.0   33.065089   11.548110  21.0   24.000   28.000   \n",
       "Insurance            169.0    0.727811    0.446410   0.0    0.000    1.000   \n",
       "\n",
       "                         75%      max  \n",
       "Plasma glucose         6.000   13.000  \n",
       "Blood Work Result-1  141.000  199.000  \n",
       "Blood Pressure        80.000  114.000  \n",
       "Blood Work Result-2   32.000   49.000  \n",
       "Blood Work Result-3  135.000  540.000  \n",
       "Body mass index       36.600   57.300  \n",
       "Blood Work Result-4    0.587    1.698  \n",
       "Age                   42.000   70.000  \n",
       "Insurance              1.000    1.000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "469f396c",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "\n",
    "- From the descriptive statistics of the datasets there are some features with minimum value of 0.thus in Plasma glucose, Blood Work Result-1, Blood Pressure,Blood Work Result-2, Blood Work Result-3, and Body mass index. \n",
    "From Domain Knowledge in biology ,these features should not have a value of 0. Therefore, it is reasonable to assume that any missing value was filled out with 0.\n",
    "\n",
    "\n",
    "- All features  have very high standard deviations, which means they are spreaded over a very wide range except for Blood Work Result-4.\n",
    "From the descriptive statistics of the train set, the mean and median of some columns are very different from each other, especially in the following features: Plasma glucose, Blood Work Result-2, Blood Work Result-3, Blood Work Result-4, Age. This indicates these columns have skewness in their distribution. Other columns also has some minor discrepancies between their means and medians, but not as extreme as those mentioned above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d686080",
   "metadata": {},
   "source": [
    "#### 3.1.5 finding count and percentage of missing values in all the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40198a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_missing_val(df, cols):\n",
    "    print('\\tMissing val Count\\tMissing val Percentage')\n",
    "    for col in cols:\n",
    "        missing_cnt = df[col].value_counts()[0] # frequency of zero entries in a particular column\n",
    "        missing_percentage = round((missing_cnt/len(df) * 100), 2)\n",
    "        print(str(col) + ': \\t\\t' + str(missing_cnt) + '\\t\\t\\t' + str(missing_percentage).zfill(5) + '\\t%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d59ea54f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tMissing val Count\tMissing val Percentage\n",
      "Plasma glucose: \t\t93\t\t\t15.53\t%\n",
      "Blood Pressure: \t\t28\t\t\t04.67\t%\n",
      "Blood Work Result-2: \t\t175\t\t\t29.22\t%\n",
      "Blood Work Result-3: \t\t289\t\t\t48.25\t%\n",
      "Body mass index: \t\t9\t\t\t001.5\t%\n"
     ]
    }
   ],
   "source": [
    "# finding the missing values in the respective columns in the train set\n",
    "train_missing_col=['Plasma glucose', 'Blood Pressure', 'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index']\n",
    "count_missing_val(df_train, train_missing_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "addf710b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tMissing val Count\tMissing val Percentage\n",
      "Plasma glucose: \t\t18\t\t\t10.65\t%\n",
      "Blood Pressure: \t\t7\t\t\t04.14\t%\n",
      "Blood Work Result-2: \t\t52\t\t\t30.77\t%\n",
      "Blood Work Result-3: \t\t85\t\t\t050.3\t%\n",
      "Body mass index: \t\t2\t\t\t01.18\t%\n"
     ]
    }
   ],
   "source": [
    "# finding the missing values in the respective columns in the test set\n",
    "test_missing_col=['Plasma glucose', 'Blood Pressure', 'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index']\n",
    "count_missing_val(df_test,test_missing_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23005987",
   "metadata": {},
   "source": [
    "#### 3.1.6 checking for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d077984",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  0  duplicated rows for the training set\n",
      "There are  0  duplicated rows for the test set\n"
     ]
    }
   ],
   "source": [
    "#Check for duplicates\n",
    "duplicate_rows_train = df_train.duplicated().sum()\n",
    "duplicate_rows_test = df_test.duplicated().sum()\n",
    "print('There are ',duplicate_rows_train,' duplicated rows for the training set')\n",
    "print('There are ',duplicate_rows_test,' duplicated rows for the test set')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e90f05a",
   "metadata": {},
   "source": [
    "### 3.2 Descriptive Statistics & Central Tendencies\n",
    "\n",
    "#### 3.2.1 checking for skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "738d33d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Blood Work Result-3    2.401585\n",
       "Blood Work Result-4    1.989472\n",
       "Age                    1.152353\n",
       "Plasma glucose         0.914008\n",
       "Blood Work Result-2    0.164063\n",
       "Blood Work Result-1    0.116180\n",
       "Body mass index       -0.405255\n",
       "Insurance             -0.804257\n",
       "Blood Pressure        -1.874662\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for skewness\n",
    "df_train.skew(numeric_only=True).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48fad7bd",
   "metadata": {},
   "source": [
    "from the above it can be observed that:\n",
    "- Positively-skewed: Blood Work Result-3, Blood Work Result-4, Age, Plasma glucose, Blood Work Result-1, Blood Work Result-2\n",
    "- Negatively-skewed: Blood Pressure, Insurance, Body mass index "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f62ec10",
   "metadata": {},
   "source": [
    "#### 3.2.2 visualization of skewness for the train set\n",
    "- Histograms of these features will be plotted to visualize their distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "932bcf83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making a copy\n",
    "train= df_train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bea221c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist_plot(df, avoid, name_for_title):\n",
    "    df_copy = df.copy(deep=True) # Copy the original dataframe\n",
    "    df_copy.drop(labels=avoid, axis='columns',inplace=True) # Drop avoid columns\n",
    "    \n",
    "    # Set up subplots in 3x3 grid\n",
    "    fig, axes = plt.subplots(3, 3, constrained_layout=True, figsize=(10, 8))\n",
    "    plt.suptitle('Distribution of Features in ' + name_for_title + ' Set', fontsize=20)\n",
    "    \n",
    "    r = 0 # current row\n",
    "    c = 0 # current column\n",
    "    \n",
    "    for col in df_copy.columns:\n",
    "        # Plot histogram of each column\n",
    "        sns.histplot(data=df, x=col, kde=True, ax=axes[r, c])\n",
    "        axes[r, c].title.set_text('Histogram of ' + str(col))\n",
    "         # Update position for next subplot\n",
    "        if (c == 2):\n",
    "            c = 0\n",
    "            r += 1\n",
    "        else: \n",
    "            c += 1\n",
    "            \n",
    "    fig.delaxes(axes[2, 2]) # Delete unused subplot\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "78ef0857",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n3ur9BoNaGHcmXegHO8hx03BPCF2E6wllH66w8qq3/fs0G/8KXDdoe7399AXgUFrpyxxXsH0cd9NrQ/P1wRVob8LdMNm0Pf4EpNB2P9hXtLFOgntgyru4qwa1uEL2R966DgkY9zveOmz09oVSXHOenwJpQW7DFmNqKf6AYS1uryCX1zTtrcHE0WycCbj+pou833ARrm32cILsb761cdvLe62Mm41r7/4E7qSnDHcSVgy8j8urCa1MexzuYVrbvd+42Ns3b8fdJxA4bpvHOnvZK9pfotrZ+6mMMcYYY4wxzVkbbGOMMcYYY0LICtjGGGOMMcaEkBWwjTHGGGOMCSErYBtjjDHGGBNCVsA2xhhjjDEmhKyAbYwxxhhjTAhZAdsYY4wxxpgQsgK2McYYY4wxIWQFbGOMMcYYY0LICtiAiKwQkZl+x+EnEblARPJFpFJEDu/gtJtF5JRwxRZuPT3+aGX5smv50pt+loh8KwxxPSQivw31fINctorIaD+WHYxIjy/SWD63fB6tor6A3VLhSUSuEJGPmr6r6kRVndXOfIZ7B864MIXqt78A31fVVFX9tPlAb933egeAAhG5XURifYjTRAHLl0HrSL4sEZEnRCSz+8PcH0+cF8tRAWmXeXE2T1sd5lia9o1K77VZRG4K5zJbiKHdAoqITBKRN73fT7srtu5g+TxoPSqfezFdISINXkwVIrJERM72M6ZIE/UF7J4iAg4cw4AV7YwzWVVTgZOBrwJXhz0qY3zUw/LlSCALuDXcQbVGVeuBT4ATApJPAFa3kDa7I/Puwm+R6W2fC4FfisipnZxPuNQBTwNX+R1Ib2X5vNM+8WLKBB4EnhaRrOYj+b19/Vq+FbA58CxbRI4UkYXeGdlOEbndG63pz2C3d8Z2jIjEiMgvRGSLiBSJyCMikhEw3294w3aJyC+bLedWEXlWRP4rIhXAFd6yPxGR3SKyQ0T+ISIJAfNTEfmeiKwTkT0i8hsRGSUic7x4nw4cv9k6thiriCSKSCUQC3wmIhva216quhr4EJjUwnJaXQdx7vCWXyEiy0RkkjfsIRH5p4i87m3fj0VkoIjcKSJlIrJaAi6dichNIrLB2w4rReSCNn7fZBF52JvPKhH5qYhsa2XcA2qcRGRm4LgiMkREnheRYu93/Udb29cbluT9zru87bJARAZ4wzJE5EFvWxWIyG/FrgwAli87kS8rgJeBCR1ZVsDwc8Vdrt8t7pLz+IBhh4vIYm/9ngKS2ghlNgcWpo8H/thC2mxv3leLyHoRKRWRl0VkUMByVUSuFZF1wLoW1uk4cZfWZ7YRDwCquhBXiJkSMP03vWNCmbha5GFeukjrx6oDLsdLsxrZgPRrgMuAn3r75v9aiWuNqj5I+wWsqGT5vMfm88CYGoF/A8nAqFa2b6v/dSIyWkQ+EJFycTX0T3npnc6HLR07RORscTXtu73f7bBg1q/TVDWqX8Bm4JRmaVcAH7U0Dq725eve51TgaO/zcECBuIDpvgmsx51RpgLPA496wyYAlcBxQALuElBdwHJu9b6fjzvRSQamAUcDcd7yVgE3BCxPgZeAdGAiUAO86y0/A1gJXN7Kdmg11oB5j25jO+4f7q1bIXBVC9uv1XUATgcW4c52BRgP5HrDHgJKvOmTgPeATcA3cAef3wLvB8RzETDI23ZfAfY2zauF2G8DPsCd9ecBS4Ftrfz+DwG/DRg2s2lcL47PgDuAPl6cxwWxL3wb+B+Q4s1jGpDuDXsBuNebX39gPvBtv/ON5csemS+zgLeA/wsYPgv4VhDbZSwuD50KxAM/9cZN8F5bgB96wy70ttFvW4npC0Cpt/2yvWlTgJ0BaQoMBU7C5fupQCLwd2B2s/V7G+gLJAeuM3AGkA8c2UocB+wb3m9YBVzgfT/PW8fx3m/7C2BOEMeq/du0lf028Dd5qLXt1EK8owH1O29aPrd8TnD5fP/v6G2z64E93rZoafu2+l8HPAHc4o0b+L/a1Xy4/9gBHA4UAUfh/ocvx+17iWHb//3OgN2UwSuB3QGvKlrP4LOBXwPZzeYznIMz+LvA9wK+j/N2qjjg/wFPBAxLAWo5MIPPbif2G4AXmu0wxwZ8XwT8LOD7X4E7W5lXq7EGzLu9DF4BlAEbcAXemObbr611wP2ZrsUdxGKajfcQcH/A9+uAVQHfDwV2txHfEuC8VoZtBE4P+P4tOlfAPgYoDtwHgtwXvgnMAQ5rNs0A3EE6OSDtUgJOJKL1heXLUOfL3UADrinG4IDhs/j8j7et7fJL4OmAYTFAgbf/nwBsByRg+Bxa/+NNAqqBycAFwGNe+tyAtE1e2oPAnwKmTfViGh6wfie1sM434woDk9rYNk37xm5gn/f5L03rAbyOV0kQsM5VuMv1bR2r9m9T7/sVWAHb8nnvy+dXAPVeTCW4/N3i9qWd/zrgEeA+IK/ZMrqaD08K+H4P8Jtm81gDfCFc+39vaSJyvqpmNr2A77Ux7lW4s7zV4i7lt9VofxDuIN9kC24nHuANy28aoKpVwK5m0+cHfhGRsSLyiogUepdVfo+r7Qm0M+Dzvha+p3Yi1mBNVdUsVR2lqr9Qd1noAG2tg6q+B/wDuBsoEpH7RCS9M+vmXf5rutSzG9dcpfm2anLAb9Hsc0cMAbaoa2fa0jJa276PAm8CT4rIdhH5k4jE4/7I44EdAetxL+7svjewfBm6fJmJK9jeA3woIi1d2m1vu+wf5uXtfGCwN6xAvX+kgGlbpKrVuNqpE7zXh96gjwLSmi75N19uJe73GBwwy5by6w24gsLy1uIIkI3b/j/CFSTivfRhwF0Bea8UV0s2OIhjVaeIu7mz6abL17s6vx7C8nkU5nPPXO93zVbVo1X1nYBhgdu3vf+6n+Ly3nyv+co3vfi6mg+bx/CjpuV7MQzx1jsseksBO2iquk5VL8X98H8EnhWRPrizoea24360JkNxZ3Q7gR245giAawcM9Gu+uGbf78GdmY5R1XTg57idLhTaijWU2lwHVf2bqk7DXcIbC/ykowsQ107yfuD7QD/voLOc1rfVAb8FLlO1Zi+utqPJwIDP+cBQafmGiVa3r6rWqeqvVXUCMAM4G9f0JR93Vp8d8AeUrqoT24ivV7J82T5VrQMeAEbQwv0R7SzrgGEiIrh8UoDbZoO9tMBp29LUDvt4Pi9gfxiQ1lTAbr7cPrjfoyBw1VqY/0XA+SJyfTtxuBmoNqjq7bia9aYCXj7uEnVmwCtZVed407R2rGrrGHHQopvF8Zi6niJSVfXMYGLvTSyfty/C8nmboQZ8bvO/TlULVfVqVR2Ea1L5T/G6uuxiPmwew++a5fcUVX2iC+vYJitgNyMiXxORHO/MbreX3IhrGtCIa9fU5AnghyIyQkRScWe8T3k1nM8C54jIDHE3PtxK+5k1DXcZqFJEDgG+G6LVai/WUGp1HUTkCBE5yqu93Yv7szuoFjwITQfcYm++V9LygabJ08DNIpIlIoNxBfPWLAHOEpG+IjIQV1PWZD7uIHSbiPQRd/Pisd6wVreviJwoIoeKu6GjAne5rlFVd+Da0v1VRNLF3ZwySkS+0KGt0QtYvmyft39diatJ29jBZT0NfFFETvby549wf4hzcO1i64EfiEi8iHwJOLKdcGYDJ+L+vFd6aR/japCn8HkB+wngShGZIiKJXkzzVHVzO/PfjuvN6HoR6cjvcRvupsMk4F+448JE2H/D8UXe57aOVUuAL4lIilcIaKv3j50cuG8eRJwkXBvYppuiEzuwTlHD8nn7IiyfB6W9/zoRuUhEmk6IynD/740hzIfgKuW+481PvP/wL4pIWijWsSVWwD7YGcAKcXf23gVcoqr7vEtMvwM+Fnd54WjcXbOP4v4sNuF+/OsAVHWF9/lJXKGsEtfAvqaNZf8Y1/3dHtzO8FQI16vVWEOsrXVI99LKcJeedgF/7ugCVHUlrr3bJ7g/sENxf96t+T9gG26938EdfFv7HR7F3ci4GXdA2B+/qjYA5+DaSm715vkVb3Bb23egt8wK3I0zH3jjgqvJTsAVQsq88XLbWv9eyvJl6z7ztksZ7sadC1S1tCPLUtU1wNdwNxmW4Pbzc1S1VlVrgS/h2jiW4vb559uJaQ7uZqd5TZecVbUEV1AqUtV1Xto7uHahz+F+j1HAJcGstKpuxRWyb5LgH7LxKm47Xa2qL+BqSZ8U1yRgOdBUq9zWseoOXHvencDDwGNtLO9BYIK3b77YyjjDcIWlpl5E9uHahvZGls9bF4n5vCPa+q87Apjnrd/LwPWqupHQ5UPU9SJ0Na7JSRnu5s4rQrNqLWu62cOEmXcmuRt3+WmTz+H0al6N1yWqajXFvZzlS2Oin+Vz4werwQ4jETnHu3zRB3f3+jJczajpRiKSKyLHepelxuEui73gd1zGH5YvjYl+ls+N36yAHV7n4doKbgfG4GpN7ZJB90vA3bG8B9e/9kvAP32NyPjJ8qUx0c/yufGVNRExxhhjjDEmhMJWgy3ukdLvi3uM9QrxulMS1zvD2+IeN/q2eM+t9+7q/Ju4R+YuFZGp4YrNGGOMMcaYcAlnE5F64Ede379HA9eKyATgJuBdVR2De+LQTd74Z+Iu44wBrsH1SWmMMcYYY0yP0tIDM0LC6/dwh/d5j4iswj0t6DxcX6jgulaZBfzMS3/EayM1V0QyRSTXm0+LsrOzdfjw4eFaBWMi2qJFi0pUNcfvOELB8rLpzXpKXrZ8anqzjubTsBWwA4nIcOBwYB4wIKDQXMjnjwodzIGPtdzmpbVawB4+fDgLFy4MebzG9AQi0t5jbHsMy8umN+spednyqenNOppPw96LiNf/5HPADapaETjMq63u0F2WInKNiCwUkYXFxcUhjNQY0xki8kPvPovlIvKE9yS6ESIyz7un4invaWrGGGNMrxDWArb3eMvngMdUtemJQDtFJNcbnot7uhJAAe6xuk3yvLQDqOp9qjpdVafn5ET8FTVjopq4R8//AJiuqpOAWNyT+P4I3KGqo3FPzWrvMbbGGGNM1AhnLyKCe0zsKlW9PWDQy7jHfOK9vxSQ/g2vN5GjgfK22l8bYyJGHJAsInFACq5Z10m4R+GCu9fifH9CM8YYY7pfONtgHwt8HVgmIku8tJ8DtwFPi8hVuGfLX+wNew04C/d8+CrgyjDGZowJAVUtEJG/AFuBfcBbwCJgt6rWe6M13U9hjDEmDGpqali0aNFB6dOmTSMxMdGHiEw4exH5CJBWBp/cwvgKXBvqOIYOG862/K1Bj583ZChbt2wOdRjGRCWvH/vzgBHAbuAZ4Iwgp70G1yUnQ4cODVOEpjPOv/BiikrK2hynf3YWLz77dDdFZIxpy6JFi7jjqXfIHTF2f9qOTWv5ITBjxgz/AuvFuqUXET9ty9/KX99cHfT4Pzr9kDBGY0zUOQXYpKrFACLyPO7qVaaIxHm12K3eTwHcBzB9+nR7pGwEKSop46Jb7m5znGd+F/L6EGNMF+SOGMuIifaMvkgR9l5EjDFRbStwtIikePddnAysBN4HLvTGCbzXwhhjjIl6UV+DbYwJH1WdJyLPAotxT2/9FFcr/SrwpIj81kt70L8oTXPtNQFZu25dN0ZjjDHRxwrYxpguUdVfAb9qlrwRONKHcEwQ2msCcuvXTurGaIwxJvpYExFjjDHGGGNCyArYxhhjTBSzp60a0/2sgG2MMcZEKXvaqjH+sAK2McYYE93saavGdDO7ydEYY8wBYlKz+Wh9CYXl1TQ0Kn37JDB2QCpD+6bgemM0PYU9bdUYf1gNtjHGGABUlYVbSkn/0q/5dGsZjarExwkbiit5ccl2nltcQEV1nd9hmg5o9rTVQUAfgnzaqjf9NSKyUEQWFhcXhylKY6KP1WAbY4xBVXl/TTHLCsqpy/+Mb112IalJ7i+ivrGRldsr+Hj9Lp6cn8+Zkwb6HK3pgE4/bRXsiavGdJbVYBtjjGHhljKWFZQzbVgWe9+/b3/hGiAuJobD8jK55IghJMfH8tKS7VRnDPMxWtMB9rRVY3xgBWxjjOnltpVV8cmGXYwdkMqxo/q1Ol5WnwQump5H3z4JlI47h0827OrGKE1nqOo83M2Mi4FluP/9+4CfATeKyHqgH/a0VWNCygrYxhjTi9XUN/DG8kIyU+I5+ZAB7d7EmBQfywWHDyauupzv/HcRm0v2dlOkprNU9VeqeoiqTlLVr6tqjapuVNUjVXW0ql6kqjV+x2lMNLECtjHG9GLzNpayt7aB0ycOJCEuuL+E5IRY+q55CRG4+pGF7LEbH40x5gBWwDbGmF5qV2UNn23bzaRB6QxIT+rQtHE1Ffzzq1PZWLKXX720IkwRGmNMz2QFbGOM6aU+XF9CfGwMx7TR7rotM0Znc91Jo3n+0wL+99n2EEdnjDE9l3XTZ4wxvVBheTVbdlVx7Kh+pCR0/q/g+yeOZtaaYm55YRnThmUxKDM5hFEaY0KtpqaGRYsWHZQ+bdo0EhMTfYgoOlkNtjHG9EILNpeSGOe63+uKuNgY7vzKFOoblR89/RmNjdZVsjGRbNGiRdzx1Ds8OX/r/tcdT73TYqHbdJ7VYBtjTC9Tl5LN9pK9HD2ib9A3NrZleHYffnn2BG5+fhnPLMrnK0cMDUGUxpiWtFQDvWzZMhobU4OeR+6IsYyYODXUoZkAVsA2xphepjJ3GgmxMUwZkhmyeV5yxBBe+LSA37+2mpPHDyA71S41GxMOTTXQuSPG7k9bNmcBA0Yfxigf4zIHsiYixhjTixTvqWFfvzGMz00jMT42ZPMVEX5/wSSqauv5/aurQjZfY8zBmmqgm17ZuXbVKNJYDbYxxvQiTy3YCjFxXW57vWbNambMPPWg9MS8Y3i+4Sg+e+XfvPvo37q0DGOM6anCVsAWkX8DZwNFqjrJS7sVuBoo9kb7uaq+5g27GbgKaAB+oKpvhis2Y4zpjeoaGvnv3K0k7t5C3z5jujSv+kblolvuPji9oZH/ztvKlqzp1DU0Eh9rF0qNMb1POI98DwFntJB+h6pO8V5NhesJwCXARG+af4pI6K5dGmOM4d1VRRRWVJOy87OwLSMuNoYTxmRTn9KPx+dtDdtyjDEmkoWtgK2qs4HSIEc/D3hSVWtUdROwHjgyXLEZY0xv9OyifPqnJZJUtimsyxmR3YeE8q3c8c5ayqvsMerG+KFBYUNZPasLK6z7TB/4ce3u+yKyVET+LSJZXtpgID9gnG1emjHGmBAo3lPD+2uKuWDqYITw/tmKCBlbZlOxr46/vbcurMsyxhxIVVlWUM7bRX345Yd7OOPODznxr7N4f02R36H1Kt1dwL4HGAVMAXYAf+3oDETkGhFZKCILi4uL25/AGBNWIpIpIs+KyGoRWSUix4hIXxF5W0TWee9Z7c/JhNOLnxbQ0KhcNC2vW5YXX1XCV44YwsNzNrOxuLJblmmMgTkbdvHe6iLS4xu5YXof/nzhYSTGxXDlfxbw6NwtfofXa3RrAVtVd6pqg6o2AvfzeTOQAmBIwKh5XlpL87hPVaer6vScnJzwBmyMCcZdwBuqeggwGVgF3AS8q6pjgHe978Ynqsozi/KZMiST0f3Tum25N546jqT4WH7/2upuW6YxvdnK7RUs3FLGpEHpHJO1jyMHJXDR9CG8/P3jOGV8f3754nIWFdb6HWav0K0FbBHJDfh6AbDc+/wycImIJIrICGAMML87YzPGdJyIZAAnAA8CqGqtqu7G3VfxsDfaw8D5fsRnnNWFe1i7s5Ivd1PtNbhu/M4752xk3SzeWbWTaedeyYyZp+5/nX/hxd0WizG9QY3G8cHaYvIykzlxXH9EPh+WFB/LP746lUMHZ3DPp1VUN0jrMzIhEc5u+p4AZgLZIrIN+BUwU0SmAApsBr4NoKorRORpYCVQD1yrqg3his0YEzIjcN1u/kdEJgOLgOuBAaq6wxunEBjQfEIRuQa4BmDoUHtIQji9snQ7MQJnTRrYbcts6savvqGRhz/ZQtzRl3Hh9CGI96//zO+u7bZYjOkNtmg/GlU5dcIAYmIOLkAnxcdy5yVTOP2OD1ixJ5HxPsTYm4SzF5FLVTVXVeNVNU9VH1TVr6vqoap6mKqeG/AHjKr+TlVHqeo4VX09XHEZY0IqDpgK3KOqhwN7adYcRFUVDr6rzpp7dQ9V5dWlO5gxKpt+Pjy+PC42hmNG9WNnRQ3riqwttjHhUCkplJLKtGFZpCfHtzreqJxUzh+TREF1PJt37e3GCHsfewKAMaYrtgHbVHWe9/1ZXIF7Z1OTMO/dbl/3yYrtFWzeVcXZh+W2P3KYHDIwjX6pCczZsIsG6y7MmJDbHp9LHA1MHdr+/eTnjE4iNbaBD9eV4Oo/TDhYAdsY02mqWgjki8g4L+lkXFOvl4HLvbTLgZd8CM8AryzdQVyMcPrE7mse0lyMCMeNyqZ8Xx3LC8p9i8OYaFS6t5by2EwGSjkJce0X6+JjhXGptZTurbWrSmFkBWxjTFddBzwmIktxXXD+HrgNOFVE1gGneN9NN1NVXlm6nWNHZ5PVJ8HXWIb1SyEvM5l5m0qprW/0NZbexrrSjG7LCsoRbWQAFUFPMyipnr59Epi3qZRGq8UOi7Dd5GiM6R1UdQkwvYVBJ3dzKMZz/oUXU1RSRm2fAZQceimVC19ixiO37B++dl33P/xFRDh2TDZPLchn0daybl9+L9fUleaFIpIApAA/x3WleZuI3IS7d+JnfgZpOq6+oZFVOyrIathNfGzwfUOIwFEj+vL68kLWF1USU1/HsmXLDhpv2rRpJCZ2/70b0cAK2MYYE2WKSsq46Ja7+XBdMaX5u/nKld8hKT52//Bbv3aSL3ENTE9iTP9UPt1aRt/4FF9i6G0CutK8AlxXmkCtiJyH6+kLXFeas7ACdo+zvqiSmvpGhjeUALHtjh9oTP9U5iTH8+nW3QzZuYlnyspYsS9j//Adm9byQ2DGjBmhDbqXsAK2McZEIVVlQ/FehmSlHFC49tuMUf3YUFzJnsFH+R1Kb9HprjRN5Fu2vZzM5HjS9u0BMjs0rYgwZUgmH6wtJktSyM7LZMTEqWGJszeyNtjGGBOFyqrqKN9Xx8icPn6HcoDMlAQmDc6gasCh9gj17tHprjTB9VcvIgtFZGFxcXHYgzXB21Ndx/bd1YzPTaezj40Zn5tGQmwMO+P6hzQ2YwVsY4yJShtLXOF1RHZkFbDBtf2Uxnr+/OYav0PpDbrUlab1Vx+5mnoAGTMgtdPzSIyLZcKgdMpi+1KrkXOlKxpYAdsYY6LQpuK95KQlkpbU+kMn/JKSEEfq9kW8vryQxXbDY1hZV5rRa93OSnLSEslK6VoPQYflZaAiFJMWosgMWAHbGGOiTkNcEjvKqyOy9rpJnx2LyU5N5LbXVtvDLsLPutKMMlUNQmFFNWP7d772uklWSgJpDXso0nTLiyFkBWxjjIkyNZkjUGBkBBewYxrruP6UMczfXMp7q+1Bn+Gkqku8Zh6Hqer5qlqmqrtU9WRVHaOqp6hqqd9xmuBtr3Z9VIwZEJpa5+yGEmqIp2D3vpDMz1gB2xhjok511kj6JMbSPy2y+6+95IghjMzuwx/fWG2PUDemAwqr48hJSyQjOTRNwLIayoilgeXbg39YjWmbFbCNMSaK1NQ3UJM5jBHZfRDpbN8C3SM+NoafnD6OtTsreW7RNr/DMaZHqKhppLQuNqRNwGJRsqlkfVEl1XXBP7DGtC6oAraIHBtMmjGmZ7I8Hj3mbypFYxMYmd31tpnd4YxJA5kyJJPb315rf+ztsHxqAJYW1QES8nss+ksFDY3KmsI9IZ1vbxVsDfbfg0wzxvRMlsejxEfrSqCxnrysZL9DCYqIcPOZh1BYUc1/Pt7sdziRzvKpYfHOOhJjGhkQ4iZgfaSWnLREVuywZiKh0OaTHEXkGGAGkCMiNwYMSqejz+Q0xkQcy+PR56P1JSTs2UF87Hi/Q2nTmjWrmTHz1P3fE8edy59eqeHh395ATH01AP2zs3jx2af9CjFiWD7tvWpqali0aNH+7/WNyqeFNfRPqAtLE7DxA9OYva6EXZU1IZ93b9Peo9ITgFRvvMBbVSuAC8MVlDGm21gejyKle2tZsb2CtPKtfofSrvpG5aJb7t7/vaSyhsfnbSX3Szdxwhj3QJNnfnetX+FFGsunvdSiRYu446l3yB0xFoCSmlhqGlNIrglPpy/jBqbx4foSVhfuYVBYltB7tFnAVtUPgA9E5CFV3dJNMRljuonl8egyZ0MJAIkV+T5H0nHZqYmMz01naX45U/IySQ9R7wjRwPJp75Y7YiwjJk4FoGB9CVK6iwwJT3d6KQlxDOubwurCPeRmhmURvUZ7NdhNEkXkPmB44DSqelI4gjLGdDvL41Hg4/UlpCXFEV+50+9QOuXokX1Zs3MPn2zcxekTB/odTiSyfNrL5ZdWkdq4l9jY8HVrecjAdN5YUciuWmt91BXBFrCfAf4FPADYbd7GRB/L41Hgo/UlHDOyH8tn9cw+pdOS4pkyJJNFW8qYOjTL73AikeXTXqyqtp6iPTUMbgzvTYgjc/qQEBtDfrVdReqKYAvY9ap6T1gjMcb4yfJ4D7d1VxX5pfu4+viRLPc7mC44YlgWywvK+Xh9id+hRCLLp71YfqlrFpLeUIFrlh8e8bExjOrfh3WFDdTU98yT9UgQbDd9/xOR74lIroj0bXqFNTJjTHeyPN7DfeQVSI8dne1zJF2TGB/LkcP7sqW0ipr0IX6HE2ksn/ZiW0urSIyLoY9WhX1Z4wemU6/Cop11YV9WtAq2Bvty7/0nAWkKjAxtOMYYn1ge7+E+Xl9CbkYSI0P88Ak/HJaXwZJtu6kYehyNjUpMTGQ/kbIbWT7tpVSVraVVDMlKQbrhOTB5WckkxTTyUX4NPw7/4qJSUAVsVR3RmZmLyL+Bs4EiVZ3kpfUFnsLdpLEZuFhVy8R16HgXcBZQBVyhqos7s1xjTMd0No+byNDYqHy8oYRTxg+I+MejByMuNoZjRvbjrep6Xlm2g3MnW4dhYPm0NyurqqOypp6h/VLY0w29cIoIgxNr+KxIeP39j8hIPLDBw7Rp00hMDO2DbqJNUAVsEflGS+mq+kg7kz4E/AMIHO8m4F1VvU1EbvK+/ww4ExjjvY4C7vHejTFh1oU8biLAyh0V7K6q47ge3jwk0CED03hvwXL+8uYazpg4kIS4YFs0Ri/Lp73X1lLXLGRo3xRWdNMyk8s2okkTuXvOTkb2+bypyI5Na/khMGPGjG6KpGcKtonIEQGfk4CTgcUcWHA+iKrOFpHhzZLPA2Z6nx8GZuEK2OcBj6iqAnNFJFNEclV1R5AxGmM6r1N53ESGpvbXM0b38zmS0BER0rd+xNY+OTw2bwtXHmuVt1g+7bW2llaRkRxPRjf2D5+s1aRQQxEZnDxxaLctN1oE20TkusDvIpIJPNnJZQ4IKDQXAgO8z4OBwKcjbPPSrIBtTJh1JY+LSCywEChQ1bNFZIQ3bT9gEfB1Va0NbcQm0MfrSxg3II3+aUl+hxJSieVbmDGqH39/bz0XTssjLal3dxsW4v9i00M0Kmwrq+KQgendvuwc2cOWPYmU7q2lb5/w9VwSjTp7zW0v0OXqBK+2ukN9wIjINSKyUEQWFhcXdzUEAOobGindW8vuqloQuwxpDB3L49cDqwK+/xG4Q1VHA2XAVSGOzQSormtg/qbSHt97SEsEuPnM8ZTureW+2Rv9DicSheS/2ES2srpY6hqUoX1Tun3Z/ahEgNWF4e17OxoF2wb7f3xeEI4FxgNPd3KZO5uafohILlDkpRcAgX0y5XlpB1DV+4D7AKZPn96lDhp3V9Uyd2Mp64sraWh0s8r7wePc9NxSvn/SaPKyun9nNsYPnc3jIpIHfBH4HXCjd7PyScBXvVEeBm7F3VNhwmDxljJq6hs5bkz0NA8JdGheBmcflssDH27iG8cMJyet995YFeL/YtNDFNfEIgJDspK7fdkJ0sDQLPfo9GNG9ouKm6i7S7BtsP8S8Lke2KKq2zq5zJdxXQ3d5r2/FJD+fRF5EndzY3k421+v27mHt1buRAQmDUpnYEYSDY3Ki08/zvN90nh+cQHf+cJIfnDyGOJirVbbRL3O5vE7gZ8Cad73fsBuVa33vjc19TqIiFwDXAMwdKi17+usj9aXEBcjHDkiOgvYADeeOpbXlxdy9/vrufXciX6H46dQ/hebHqK4No6B6Ukkxvvz6PJDctN4c8VOCnbvs4rHDgiq5KiqHwCrcX+iWUBQ7SlF5AngE2CciGwTkatwBetTRWQdcIr3HeA1YCOwHrgf+F4H1qND1hTu4fXlheSkJfKNo4czc1x/DhmYzsRBGZS+fhezfjyTsw4dyN/eW89XH5hH0Z7qcIViTEToTB4XkaYuOBd1cpn3qep0VZ2ek5PTmVkYXPvrw4dmkpoYbH1JzzMyJ5WLpuXx+LytbCsL/0M2IlVn/4tNz1VZ20hZXQxDfGge0mRUTirxscLqwm7ogDuKBNtE5GLgz7gePwT4u4j8RFWfbWs6Vb20lUEntzCuAtcGE09XFFZU8/bKnQzKTOa8KYOIb6F2elBmMndecjgnjM3hlheW86V/zuHRq45iRBQ8wMGYlnQyjx8LnCsiZ+F6NEjH9WWfKSJxXi12i029TGiUV9WxtKCc608e43coYbFmzWpmzDwVgIaEVGqnXMFpP/o7mRvfAaB/dhYvPtt7Wkh09r/Ym9ZuRu6BVpbUA8IwHwvY8bExjO6fyrqdlcwca5UhwQq2yuMW4AhVLQIQkRzgHaDdTB1J6hsbeXvFTpITYvniYbktFq4DfWlqHqNyUrnyoQVceM8cHrv6KF/u4jWmG3Q4j6vqzcDN3vgzgR+r6mUi8gxwIe7PO7AZmAmR8y+8mKKSMvZljULHncOjd/6Wp3/7eYu6tevW+Rhd6NQ3Khfdcvf+7x+sLeazmElceO4XyeqTwDO/C3udTKTpyn9x083ITX9iTTcjPyki/8LdjGz3SkSYZcV1xIkyIN3fHoLGD0xn1Y49bCzZS+/uyyd4wTYujmnK0J5dHZg2YizYVEZpVS0nj+9PcpBtmSYPyeTZ7xxDfGwMl90/j/VFdonERKVQ5vGf4W54XI+rHXuwq8GZAxWVlHHRLXeTd9LXiI8VLr3uFi665e79r7r6+vZn0gMdMTyLuFjhk427/A7FL53KpwE3Iz/gfW+6GbmpYP4wcH5IIzUhsay4nuyEemJj/L25MC8rmdTEOFbtsN5EghXsH+gbIvKmiFwhIlcAr+LaTPcYpXtrWbillPED0xjer2NNPUbmpPLY1UchInz1/nlsLtkbpiiN8U2X8riqzlLVs73PG1X1SFUdraoXqWpNmGLu9fJLq8jLSvH9z7e7pCTEcfiQLNYVVfbWe2M6m0/vxN2M3Oh979DNyKHuGtcEZ8uuvRRVNZKT0OB3KIgIhwxMY0tpFdUNveN401VtFrBFZLSIHKuqPwHuBQ7zXp/gdZXXU8zbuIvYGOG4MZ3rK3ZUTiqPX30U9Y3KV++fS35p773RxkSPaMrjvU3Fvjp276vzpesuP00dmkliXAyfbOg9tdhdyad2M3LP9eE694TWnMTIuCJ1yMA0VKGgOnpvqA6l9mqw7wQqAFT1eVW9UVVvBF7whvUIJZU1rC2qZMqQTFISOr9jjB2Qxn+vOoq9tQ189YG5FJb3yhoUE13uJAryeG+U7/Wm4cfDJ/yUGB/L9GFZbN5VRU1qrt/hdJc76Xw+bboZeTPuvoiTCLgZ2RvHbkaOQB+tKyE7OYY+sV165EfI9EtNpH9aItv2WSvsYLRXwB6gqsuaJ3ppw8MSURjM21hKQmwMU4dmdXleEwal88g3j6Rsbx2XPTCXkkq7+m16tKjI473R1tIqUhJie+XjiycPySQlIZY9Q4/FdUAV9TqdT1X1ZlXNU9XhwCXAe6p6GfA+7mZksJuRI059QyMfbyjh0Jw4IunZLocMTKO8PpZtFf43W4l07RWwM9sY1iOuS8Zl5rK+uJLJQzJIClEn7ZOHZPLg5dMp2L2Prz84n/KqupDM1xgfZLYxrEfk8d5IgfzSfQztm9Irn6wWHxvDkcP7Upuet/8yepTLbGNYZ/Op3YwcwZYWlLOnup5DczpfW9xQX8eyZcuYM2fOAa9ly5bR2Ni5AvK4gWkIyofbrHKxPe0VsBeKyNXNE0XkW7h+MyNe6uFnESNwWF5mSOd71Mh+3Pv16WwoquTy/8ynsiYy2kgZ00E9Po/3RvUp2eyra/D14RN+mzQ4g9jqcv785preUIsdknxqNyP3HB+tK0EEJuZ0vllrUf4mnvl4NU/O33rg690FlJR07h6GlIQ4+ic28PG2Whoaoz7fdUl7v9wNwAsichmfZ+LpQAJwQRjjComq2npSDz2F0f1Tw/KUsy+MzeHvXz2c7z22mKseWsB/rjyiS228jfHBDfTgPN5b1aQPAeh1NzgGio0R0rbNZVlSBm+uKOSMSVHdHvsGLJ/2Kh+uK+bQwRmkJXStR+TsvBGMmDj1gLQdm9Z2aZ55SXUsKo9j7sZdHDu6cx1H9AZt/nKqulNVZwC/BjZ7r1+r6jGqWhj+8LrmhU8LiElKZXKIa68DnT5xILdfPJkFm0v5xoPzqai25iKm5+jpeby3qskYSlZKPGlJvftmo+SS1Yzun8pf3lob1bVplk97lz3VdXy6dTfHRWjhdWBSPclx8NzibX6HEtGCOjVS1fdV9e/e671wBxUqLy3ZTu3ODeRmBP8EJEWIiYkJ6jV02HAAzpsymL9fOpUl+bu57P55lO21p82anqWn5vHeqLa+kdr0vF7Xe0hLBOVHp45lfVElL3wa/Z1gWD7tHeZuLKW+UTvdrXC4xQocPSiBN5YXUlVrzWNbE9XtGR755pGk//xC5KtnBD+RNvLXt9YENeqPTj9k/+cvHpZLUnwM331sMRf+aw7/vuIIhnXwgTbGGNOeT7eWobHxvbr9daAzJg3ksLwM/vrWGs6cNJA+YWgOaEx3em91EX0SYpk2LItFO/2OpmXHDUng/a2VvLmikAsOz/M7nIjU4x533hFJ8bHUl+3otuWdPH4Aj3zzSHbtreX8uz9mwebSblu2MaZ3+Hh9CWgjeb24/XUgEeFX50xgR3k1d7+/3u9wjOkSVeW91Ts5YWwOiXGh6fksHMb1jSMvK5nnF0f/laPOiuoCth+OHtmPF753LJkpCVx2/zz+O3dLb7jD3RjTTT5aX0J85c6I/vPtLmvWrGbGzFO57vKvkFy8kn++t5Yjz7iIGTNPZcbMUzn/wov9DtGYDlleUMHOihpOHj/A71DaFCPCBYcP5uP1JeyssIfutcQK2GEwIrsPL3xvBkeP6scvXlzOtY8vpnyf3fxojOmaiuo6PttWTmL5Vr9DiQj1jcpFt9zNRbfczWUXfJGE+HiST/4eF/78H1x0y90UlZT5HaIxHfLu6p2IwInjIv+x9BccPphGhZeWWC12S6yAHSaZKQk8dMUR3HTmIby1Yidn3fUhs9YU+R2WMaYHm7N+Fw2NSmL5Fr9DiTh9EuM4amRftuyqYkPxXr/DMaZT3l1VxNShWfRLTfQ7lHaNzEllypBMaybSCitgh1FMjPCdL4zi6e8cQ1J8DFf8ZwE/fGoJu+zx6saYTpi9rpjUxDgSKq1ntpZMzsukf1oi760ust4NTI+zs6KaZQXlnDy+v9+hBO3LUwezunAPywvK/Q4l4lgBuxtMHZrFa9cfzw9OHsMrS7cz88+z+Oes9VTXde5RpcaY3kdVmb22mGNG9UO00e9wIlJsjHDqhAHU1jfy/ppi7O4X05O8u8pd5T75kMhufx34CPYBNduIi4G/v7KAmhqrPAxk/Rl1k8S4WG48dSznTh7Eba+v5k9vrOG/n2zhx6eP47wpg4mNEb9DNMZEsE0le9lWto9vf2EUK/wOJoJlpyZy1Mi+zNmwi6x+Y/0Op1epqalh0aKDn9w+bdo0EhMjv8mD395dtZO8rGTGDkj1O5Q2FeVv4pmyMlbsywCgf0IS72ysZ+78hXzh+GN9ji5yWAG7m43un8oDl0/nkw27+P1rq7jx6c+4+/31/ODkMZx92CAraBtjWvThuhIAThiTzT0+xxLppg3NYmPxXnaOOIn80irrM7ybLFq0iDueeofcEZ+f2OzYtJYfAjNmzPAvsB6gqraejzeUcMkRQxGJ/HJA4CPYZddeXlqyncU76/iCz3FFEitg++SYUf146dpjeX15IX97dx3XP7mEu95dx/VW0DbGtGD22mKG9UuxB1gFISZGOH3iAB75sJzvPraIZ78zg6R469awO+SOGLu/4GWC997qIqrrGjlj0kC/Q+mwoX1TSIpp5JUVJRw1aM4Bw3rz1Qtrg90FHXmseuCj1ZvExAhfPCyX168/nnsum0pCbAzXP7mEU+/4gBc/LaCh0VoQGmPc49E/2biLE8ZEftddkSIzJYGs9W+wvKCCn7+wzJ5HYCLaq0t3kJOWyBHD+/odSofFiJBVs5MNVUk89Ek+T87fypPzt3LHU++02GSot7Aa7K7owGPV4cBHqweKiRHOPDSX0ycO5M0Vhdz17jpueGoJf3t3HdedPJpzDhtEXKydCxnTWy3cUkpVbQMnjLUCdkck7d7EDaeM4c531pGXmcyNp43zOyRjDlJZU897q4u45IghPfbqdXbDLnbE57I3fRjje+BJQjhYqS2CNBW0X/vB8fzra1NJiIvhh099xml3zOb5xduob7CeA4zpjWavLSEuRjh6pP1xddT1J4/hK9OH8Lf31vOfjzf5HY4xB3l31U5q6hs5e/Igv0PptCStIY19rNxRYVeLPL4VsEVks4gsE5ElIrLQS+srIm+LyDrvPcuv+PwUEyOcMampoD2NxPhYbnz6M069YzbPLbKCtjG9zaw1RUwblkVaUrzfofQ4IsLvLpjE6RMH8Ov/reSeWRv8DsmYA7yydAcD05OYNrRnF3lyZA9lVXUU2qPTAf9rsE9U1SmqOt37fhPwrqqOAd71vkeNjrbZHj5iBGdMGsir1x3HvV+fRnJ8LD965jNOuf0DnrWCtokAIjJERN4XkZUiskJErvfS7WQ5RPJLq1hduIdTJ0R237iRLC42hn98dSrnTh7EH99YzS0vLKO23o6fxn+7Kmt4f3UR50zOJaaHNg9p0o9K4mKEFdsr/A4lIkRaG+zzgJne54eBWcDP/Aom5DrZZtvdET+Q0yYM4O2VO7nznXX8+JnP+Pt76/j+iaO54PDBYW2jPXTYcLblbw16/LwhQ9m6ZXPY4jERpR74kaouFpE0YJGIvA1cgTtZvk1EbsKdLEdPXu5G767aCcDJ462A3RXxsTHc8ZUp5GYmce8HG1m1o4K/XDSZkTmR3eewiW4vLtlOfaNy3mEDmDNnzkHDly1bRmNjz9hHY0UZMyCVdTsr+YLdL+JrAVuBt0REgXtV9T5ggKru8IYXAgf9o4jINcA1AEOHDu2uWCOCiHDaxIGcOmEA76wq4s531vKTZ5fyj/fXc61X0I4PQ0F7W/5W/vrm6qDHb+1mThN9vPy6w/u8R0RWAYOJ9pPlbvTOqiJG5fRhRLZ1z9dVsTHCzWeO59DBGfz8+WWcedeHfG/maK46fgSpiZFW3xQaIjIEeAT3f6rAfap6l4j0BZ4ChgObgYtVtcyvOHsjVeWZhflMzsugIn/NQX2IAyybs4ABow9jlE8xdtSE3HRW7djD+qJKkvwOxmd+HlGOU9UCEekPvC0iB5TgVFW9wjfN0u8D7gOYPn16VLekb2pS0pbkUUdQc+yl/HRXFf94bz3fP3E05x0+iMQ46/PVdC8RGQ4cDszDTpZDoqK6jnmbdvHNY0f4HUqPtGbNambMPLXFYcnxKdQOn8kd7zTy8Ceb+frRw/jKEUMYlJnczVGGnV1lilArtlewunAPvzl/EjQWtNiH+I5Na32KrnMGZyaTmRzPsoJyjoi6rNQxvhWwVbXAey8SkReAI4GdIpKrqjtEJBco8iu+iBBkkxJV5RfXfoPDrv8rP31uKX94fRUXTsvj0iOH2uVP0y1EJBV4DrhBVSsCn0RmJ8udN3ttMXUNyinW/rpT6huVi265u81xHrvrNxz65ev423vr+Pt76/jC2By+NDWPU8YPIDmh51dU2FWmyPXkgq0kxMVw7mGDWLGkwO9wQkJEOHRwBh+uL6Eizu/b/PzlSwFbRPoAMV5m7wOcBvwf8DJwOXCb9/6SH/H1NCJC9YYFvHTtsXy8fhePz9/Cfz7ezP0fbuLI4X05Z3IuZx6aS3Zq73yakgkvEYnHFa4fU9XnvWQ7WQ6B15cVkp2awNQe3rtAJNu0aBYJlYXkJKZTlTORD6on8P6aYqS+hqSyDQyq3cZbD9/Zav/E5194MUUlbbes6J+dxYvPPh2O8Duko1eZTPhUVNfx/OICzp08iIyU6OodaPygdOZs3MXmfdG1Xh3lVw32AOAFr5YrDnhcVd8QkQXA0yJyFbAFuNin+HokEeG4MdkcNyaboj3VPLNwGy98WsAvX1rBr15ewTGj+nH2YYM4Y+JAsvok+B2uiQLiMvGDwCpVvT1gkJ0sd1FVbT3vrt7JhdPyeuzDJ3qC5rXcjaoUlO1jdeEe1icms6FhAmN+/DTJJWvoU7iEuNo9B0y/dt06bnnorTaX8czvrg1L7B3RmatM3nTWlCsMnlm4jaraBq6YMdzvUEIuOT6WMf1TWb+zker63ntx0pcCtqpuBCa3kL4LOLn7I4o+/dOSuPbE0Xxv5ijW7NzDK5/t4JWl27n5+WX84sXlzBjVj7MPy+W0CVbYNl1yLPB1YJmILPHSfo4rWNvJche8t7qI6rpGzj6s5z58oieKEWFI3xSG9E3hxHE5/PH//ZTJX7mRTYmp7Bs8jfG56Rwzsh99vJsib/3aST5H3L6uXGWyplyh19ioPPLJZqYNy2LS4Ay/wwmLQwdnsLpwD3MKaon8HBIe0XnbtNlPRDhkYDqHDEznR6eNZcX2Cl5dtoNXl+7gZ88t45YXljNjdDZfPHSgFbZNh6nqR0Br1at2stwFry7dQU5aIkfYY4d9ExcbQ92WxZx92CD2VNexaEsZywrKWbezkmNH9+PQHlA4sqtMkeeNZQVs2VXFOcNlf9d8Pak7vmDkZiSRFtfAO5truEWVwCsmvYUVsHsREWHS4AwmDc7gp6ePY8X2Cl5ZuoPXlrnC9i9eXM7pEwfytaOHcdSIvr0yQxgTCfbW1PPe6iIuOWKINQ+JEGlJ8cwc15/JeZnMWlvM+2uK2byrCkmM+O4T7SpTBFFV/vr6chIaa9i8bQ9bvXsbe1p3fO0REUak1LG0PJaFW8p6ZUWBFbB7qcDC9s/OGMfyggqe/3Qbzy3axitLdzCmfypXHDucL0/N8ztUY3qV8y+8mM0MpGb06bzywJ95+47tB42zdt06HyIzAFl9Ejh/yiA+21bOR+tLSDv7Zkr31tI3Qq/+2VWmyPLx+l1s2N3AYZnKqEmfd8nX07rjC0Zech0bq5O594ONVsA2PVcwfWYHiomLp7G+7qB0iUsk5ZDjqDn8i9xSVMnPHv2A1OnnUdfQGJaH2BhjDlRUUkbKGd+GmgYuu+7nLV5J6gntfqOZiDBlSCYD05N4YvZunlqYz3mTB0VjH9omxP7x/joyE4UhyQf//0abOIHThify/KqdrC+qZHT/6GkCEwwrYEeLDj6G/cbTxnF7G+OrKvll+1iwOZltqd/iPx9vZvrwLA7LyyCuAwV5Y0zH1CdlUrS7mhmj+lkzrQg3MCOJPa/cRt/L/8KLSwo4b8pgBlsh27Ti4/UlzN1YytcmJlOxq8LvcLrFaSMSeXVjLQ98uJHbvnyY3+F0KyspmRaJCEP7pvDlqXnsePTHZKcl8OG6Eh79ZAurCytQtZvJjQmHqpwJiLhHDpvI17i3lC9PyyM1MY6XlhRQVFHtd0gmAqkqf3xjNYMykjhleO95JkV6YgwXTsvj+cUFFO3pXXnDCtimXbXbV/Olw/M4f8ogEuNjeXPFTh6fv5Utu/b6HZoxUaW2vpGqnAkM65uyvxs4E/lSE+P40tQ8kuJjeemz7VTsi/7L/6ZjXltWyNJt5dx42jgSYnvXlalvHT+SusZGHvp4s9+hdCsrYJugDevXh0uPGMIZEwdSW9/Ii0u288KnBRTvqfE7NGOiwqvLttOYkMrkvEy/QzEdlJoYx3mTB1HfqLy0ZDvVdQ1+h2QiRHVdA398YzXjBqRxweGD/Q6n243I7sNZk3J5eM5mdlX2nvKCFbBNh4gI4wam8fVjhnHCmGyKKqp5fP5W3lxRaLU2xnSBqnL/7E3EVe1iWL8Uv8MxndAvNZFzDsulfF8dry3fQaM1pTPAvz7YwNbSKn51zoRe2+3mD08dw766Bu5+f4PfoXQbK2CbTomLieHwoVlcMWM404dlsa6okkfmbuGjdSU9oV9YYyLOJxt2sXJHBX12LLabG3uwvKwUTjwkh/zSfXyyYZff4Rifbd1VxT9nbeCcyYOYMTrb73B8M7p/GhdOy+O/c7dQsHuf3+F0Cytgmy5JjI/l2NHZXH7MMMYOSGXR1jIGXXM/D3y4kZp6u0RqTLDu+WAD2akJpJSs9jsU00UTB2UwaVA6C7eUsb6o0u9wjE9UlZ+/sIz4GOGWs8b7HY7vrj9lLAj84bVVfofSLayAbUIiLSme0yYM5KtHDqV2x1p+++oqTv7rBzy1YCu19Y1+h2dMRPtoXQkfrivhO18YhaidmEaDL4zLYUB6Im+v3EldUpbf4RgfPD5/Kx+tL+ErhySyccVi5syZw5w5c7zHove+fD44M5nvzRzFK0t38OG6Yr/DCTsrYJuQyklLpPjZW/nvVUfRt08CP3tuGSf+ZRaPzt1iN/0Y04LGRtd91+DMZL5+zDC/wzEhEhcTwxcPzXVPeBT7q+1t8kur+P2rq5iYHcfSeR/y5Pytn7/eXUBJSe9sPvSdL4xieL8Ufvni8qgvE1iuN2Fx3JhsXrr2WP5z5REMSE/kly8u5wt/fp8HP9rEvtrozlTGdMT/lm5nWUE5N546lsS4WL/DMSGUlhTPxdPziN/XOwtTvVVtfSPff+JTYkS4ZkoKg0aMZcTEqftf2blD/Q7RN0nxsfz2/EPZvKuK370a3U1FrIBtwkZEOHFcf5777gwe/9ZRjMjuw29eWckxt73LH99YzY7y3nGjgzGt2VVZw6//t5LD8jI4vxd239Ub9KYbVteX1VNaGxP1NZPt+dMbq/ksfzd/uvAwclLspLm548Zk863jRvDo3C28vmyH3+GEjT3JwISdiDBjdDYzRmezcHMpD360iXs/2MD9szdy1qG5XHHscA4fktmr/oiMAfjVyyvYU13Hny88utd232Wix6PLq1hX1oePZm9keL8Upg3rfW3PX/y0gAc+2sQ3jhnGmYfmMmfOJr9Dikg/PeMQFmwu5afPLmVYvz5MGBR9T661ArYJOUWIiWn74khsen/Spp7Ni/tO4+XPtjN2QCoXTx/CBYcPpl9q73mMrOm9nl6QzytLd/CjU8cybmCa3+EY02XfmtyH5z8rpDFjMMsLKnhucQGDk5I4u7Z33Oi+aEsZP31uKUeN6MsvvjjB73AiWkJcDPd8bRpfvmcO3/j3fJ7/7gyGRln//1bANqGnjfz1rTVBjVpb38hvb76BlKt/yW9fXcUf31jNcaOzOWPSQE4ZP8AK2yYqLdxcyi0vLuP4Mdl8d+Yov8MxJiSGpMcyILGBEaOyOXJ4XxZtKWPepl3cNKuCQWPKmTQ4w+8Qw2bp1l1c/uA8shKEb45rYOH8uQBejyGpPkfnj4b6OpYtW3ZQ+rRp00hMTGRQZjKPfPNILrr3Ey6+9xP+fcURUVWTbQVs46uEuBj2Ln2LF699gzWFe3h2UT6vLy/k/eeWESPLmD6sL0eP7MuRI/oxdVgmKQm2y5qebcX2cq55dBF5WSn849KpxMXarTAm+sTFxnDUyH4kVOSzoiqVi/71CXdeMoXTJw70O7SQW1+0h689MI+amhqOSq/l1SXl+4ctm7OAAaMPozeeRhflb+KZsjJW7Pv8xGrHprX8EJgxYwYAYwak8cTVR/PNhxZw0b/m8IcvH8Y5h+W22mS0pqaGRYsWtTisqeAeKay0YiLGuIFp3PLFCfz8rPGs2F7BmysKmbWmmH+8v57G99YTFyOM7p/K2AFpjBuYxqicVPKykhmcmUxmSry14Y5y5194MUUlZa0O75+dxYvPPt2NEXXcoi1lXPmf+aQmxvGfK44gIyXe75CMCavM+EZ+c3w6964SvvPfRfzsjEP49gkjo+Z4vWBzKVc/shAROC6njkmTDz9g+I5Na32KLDJk541gxMSpbY4zPjedF689lm8/uogfPPEpr3y2nZ+fNZ7h2Qc/FXrRokXc8dQ75I4Ye0B684J7JLACtok4IsKkwRlMGpzBj04bx57qOhZtKWP+plJWF+5h0ZYyXv5s+wHTpCTEMjgzmcFegTsvK4XBWcnkZSWTl5lMdmoiMXYTWY9WVFLGRbfc3erwZ353bTdG0zGqyn8+3swfXl+F7CsnafkzfPXN21ocd+26dd0cnTHhlZkUw1PXHMWPn/mM215fzZrCPfz+gkNJTui5PWyoKk8uyOdXL69gcGYyP5iczPsrKvwOq8cakJ7Es985hgc+2sSd76zl3duLOHfyIC45YghHjuh7wAlZrtftYaSzAraJeGlJ8cwc15+Z4/ozdNhwtuVvRRKSicsaRFx6f+Iy+lORnkNJen9WZPQnNj2H2OQD23ElxMUwMrsPYwekMXZAKmMGpDF2QBpD+6ZY7w0mrFZur+DX/1vBvE2lnDJ+AMsf+Sdfuen2Vse/9WsndWN0xnSPpPhY/n7p4YwdkMYd76xldeEe7v3atB55Y9vW4nJ+/Pg85u+oY1J2HN+fFseWtSt6bVvrjmitXXZtbS0AhyUkcPuJafxvfTWvLy3ghU8LGJiexAljszlhbA5S3XNumLUCtvFdML2O7B9XlduDuIGytr6Riuo67vjJt4jLyCEuYyDl/fJYnj2UuIwB+8drrKuhrmQLdUWbqC3eTCZVLPvwdTKS7dJ9JHlt2Q72DDqCz7btJikulozkePr2SSAhLjLbLzc0KvM27eI/H2/mnVU7yUyO5/cXHMqlRw7h2P/U+B2eMb4QEX5w8hgOzcvg+ic+5ey/f8gvvjiBi6bn9YgmI7sqa3j4ky3cO2sdtfWNjE+rY0TsHl77rKxXt7XuiJbaZQMsm/MOsUlpTJh6FADxwITK9Rw141i21KfzxvJCnl64DYCkmD4MbtjOgPQkBqYn0T89ctpdB4q4AraInAHcBcQCD6hqy9dRTfToQK8jN542LqjxEuJiyE5NZN+G+QcVyGvrGyndW8uuvTWUVNZS0j+TkiGHUF3vzown//otBmcmMz43jUMGpjM+N53xuWkM69fHars7IJR5+Y3lhewZeiyz1hQfkJ6RHM+A9EQGpidRmzqQ6roGkuL9uey8q7KGxVt388HaIt5YvpOSyhqyUuL5/omj+dZxI629tYlIfvznnjiuP69cdzw/fuYzfvrcUp5ZlM8PTxnLMaP6dUtBuyM3yhXvqWHepl28sbyQt1bupLa+kSNz40mrKzugvXVvb2vdES21y96xaS2xfTIPSG+oryNz91qOP/RQLh2WysbdDXy4Mp9Ve1PYVVnLhuK9+8dNie1D4YJKPq1Zz8RB6UwclEFOmr8F74gqYItILHA3cCqwDVggIi+r6kp/IzPRJCEuhoEZSQzMSNqfpqrsrWngDzdczl8efIJVO/awekcF768ppqFR90/n2ncnf/6elUzfPon0TUkgq088WSkJpCTE9ojamHAKdV7+26WHs+C+m/nij26nuq6B3fvq2FVZS/GeGrbvrmbtzkqYdAmH3vomE3LTmTIkk8OHZjFlSCbD+qWE7PfYV9vAzopqivbUsLW0ig3FlWwoqmTtzj1s3lUFQHJ8LCcd0p8zDx3IyYcM6NHtTE108/M/d2i/FJ685mieWpjPHW+v5asPzOOQgWmcN2Uwx4/J5pCBaR3uYSew4FzXoOyrV2oalBFjJ1CnQmVNA1U19SxZsZo35q8ird8AGoFGFRoVKnaXMnV5FcmZOWwrqyK/tIrt5dUApCcIX8hL4LQRiZRuXsmyBmsOEm4t1XZvWPg+Q0YfxlEnnkF13efH480FhWwpb+DPb35eodY/LZFJgzMYmd2HIX1TGNI3meyUOPLXr6JPvJAQ+/n/Qjh6IImoAjZwJLBeVTcCiMiTwHmAFbBNWIkIqUlx7Nu0mO+fFHB3cmw88dlDScgZTnz2UMrS+7M6PYe4jP7E9mn5KWUJcTH0SYglOT6WpP2vGJITYomPjSFGhBhxy4wRvO+C7P/s3n946liG9O157RM9Ic/Log30SYyjT2Ic/VITGZXz+bDKmnqef/BvXHzN9SzZuptnFm3j4U+2AO4G2NyMJHIzkhmYkURaUhx9EuJIToglMS6GRlUaFRpVaWhQ9tY2UFlTR2V1PZU19eyprqeksoaiPTXsqa4/MKjGBuKqdxO3r5S0ykISKncwOKmGu3/zZGdX05juFPJ82rwmd8emtSxLLm9lbBgG/OmEZGbnxzI7v4o/vrGaP74BCbGQnRxDdnIM6YkxxMdAXIzQqEpdI9Q2fP6+r16prlcqqmqprGlAY+NRCSicvzP34AUnDIM9ByZJ3ADe2bSP1MQCclJiGZEawyGxu1m2eAED0xPZWQiPLobNq5bQf8QEYmI+P3ku2bGV2KQyNq1YfMA8W0oPNq2r00dHTAc/hKtk26YDxssG6srWcsqEAYw6ZBJbyuvZXN7ApvIG1hWU8NG6ImobDpoNog3EaT1aV0PeBxU8+u0TQvqfK6oaspl1lYhcCJyhqt/yvn8dOEpVvx8wzjXANd7XcUB7bQuygZIwhOuHaFoXiK718WNdhqlqTvujdT/Ly4DFG27RFK8vebmX5NNIiieSYgGLpz3N4+lQPo20Gux2qep9wH3Bji8iC1V1ehhD6jbRtC4QXesTTevSXaI9L1u84WXxdo+enk8jKZ5IigUsnvZ0NZ5IuwW/ABgS8D3PSzPG9CyWl42JfJZPjQmTSCtgLwDGiMgIEUkALgFe9jkmY0zHWV42JvJZPjUmTCKqiYiq1ovI94E3cV0G/VtVV3RxtkFf2uoBomldILrWJ5rWpcssLwMWb7hZvF3US/JpJMUTSbGAxdOeLsUTUTc5GmOMMcYY09NFWhMRY4wxxhhjejQrYBtjjDHGGBNCUV3AFpEzRGSNiKwXkZv8jqcrRGSziCwTkSUistDveDpCRP4tIkUisjwgra+IvC0i67z3lp/aEoFaWZ9bRaTA+32WiMhZfsYYbXpCXm4pj0bSft6RfCjO37ztvVREprY+526Nt9V8JiI3e/GuEZHTfYh3iIi8LyIrRWSFiFzvpUfsNg4lv/NoG9vft2NzJB0TRGRcwDZYIiIVInJDd26fSDoGtRLLn0Vktbe8F0Qk00sfLiL7ArbRv4JaiKpG5Qt3w8YGYCSQAHwGTPA7ri6sz2Yg2+84Ohn7CcBUYHlA2p+Am7zPNwF/9DvOLq7PrcCP/Y4tGl89JS+3lEcjaT/vSD4EzgJeBwQ4GpgXIfG2mM+ACd5+kQiM8PaX2G6ONxeY6n1OA9Z6cUXsNg7huvueR9vY/r4dmyP1mOD9XoW4B2l22/aJpGNQK7GcBsR5n/8YEMvwwPGCfUVzDfb+R8Cqai3Q9AhY081UdTZQ2iz5POBh7/PDwPndGVNXtLI+Jnx6cl6OmP28g/nwPOARdeYCmSKS2y2BejqYz84DnlTVGlXdBKzH7TfdRlV3qOpi7/MeYBUwmAjexiHkex5tY/tHmkg4JpwMbFDVLd250Eg6BrUUi6q+par13te5uH7hOy2aC9iDgfyA79uIzMwWLAXeEpFF4h5d29MNUNUd3udCYICfwYTI971LS//2sylAFOopebmlPBrp+3lr8UXyNm8pn0VUvCIyHDgcmEfP3MYdFVHr0mz7g3/H5kg9JlwCPBHw3c//rkjNH9/E1aA3GSEin4rIByJyfDAziOYCdrQ5TlWnAmcC14rICX4HFCrqrsH09P4i7wFGAVOAHcBffY3G+KHNPBrp+3mkx+eJ+HwmIqnAc8ANqloROKyHbOMerYXt7+c+E3HHBHEPFDoXeMZLipg8FSn5Q0RuAeqBx7ykHcBQVT0cuBF4XETS25tPNBewo+oRsKpa4L0XAS/QzZc/w2Bn0+Ue773I53i6RFV3qmqDqjYC99Pzf59I0iPycit5NNL389bii8ht3kY+i4h4RSQeV7h7TFWf95J71DbupIhYl5a2v5/H5gg9JpwJLFbVnV5sfv93RVT+EJErgLOBy7wCP17Ts13e50W4+w3GtjevaC5gR80jYEWkj4ikNX3GNcRf3vZUEe9l4HLv8+XASz7G0mXN2oZdQM//fSJJxOflNvJopO/nrcX3MvAN707+o4HygMu4vmkjn70MXCIiiSIyAhgDzO/m2AR4EFilqrcHDOpR27iTfM+jrW1/v47NEXxMuJSA5iER8N8VMflDRM4Afgqcq6pVAek5IhLrfR6JO75sbHeGobojMxJfuLtQ1+LONm7xO54urMdI3F3ZnwEretq64DLzDqAO147qKqAf8C6wDngH6Ot3nF1cn0eBZcBS3IEh1+84o+kV6Xm5tTwaSft5R/Ih7s79u73tvQyYHiHxtprPgFu8eNcAZ/oQ73G4y9tLgSXe66xI3sYhXn9f82gb29+XY3MkHhOAPsAuICMgrdu2TyQdg1qJZT2u3XfT/vMvb9wve7/hEmAxcE4wy7BHpRtjjDHGGBNC0dxExBhjjDHGmG5nBWxjjDHGGGNCyArYxhhjjDHGhJAVsI0xxhhjjAkhK2AbY4wxxhgTQlbA7gFEpEFElojIchF5RkRSvPRKv2NrjYjcKiI/9jsOY8ItIH9+JiKLRWSGlz5cRELSp6yIzBKR6c3SzhORFwO+3ywi6wO+nyMiQfdFHEye9cYp8NZ3pYhc2oHVCCaGK0TkH97n80VkQhvj/ltEikK1jY1pzq+8HZC+xlv2xyIyLhTLM93HCtg9wz5VnaKqk4Ba4Dt+B2SM2a8pf04Gbgb+0E3LnQMcHfD9GKBCRPp732d447RLROI6sNw7VHUKcB5wr/f0vHA4H2i1gA08BJwRpmUbA/7l7SaXect+GPhz84FNDz8JN+9hL1Ze7CDbYD3Ph8DowAQRSRWRd70z7GUicp6X3kdEXvXOgJeLyFe89M0i8gfvzHyhiEwVkTdFZIOIfKeteTYnIleJyFoRmS8i9zfVPjUbZ/8Zuohki8hm73OsiPzFi22piFznpZ8sIp96y/23iCR66bd5tWZLReQvXlqOiDwnIgu817Gh2czGdEo6UNY8UUSSROQ/3j79qYic2E56sog8KSKrROQFILn5PFW1GFegbjoeDMY9JnqG930G8LFX2/ael2/eFZGh3jIeEpF/icg84E/N4r1aRF4XkYOWG7D8dUAVkOVN8xMvDy4VkV97aW0dg7K9z9NFZFaz5c8AzgX+7B2nRrWw/NlAaWvxGRNi3Za3WzAb739fRCpF5K8i8hlwjIh8zfv/XSIi93r/q7Fe/l7uLf+H3rQ/CPgPfdJLO+DKlTfNcO+1RkQewT3dcUhLedy0riO1FsZn4mqZzgTeaDaoGrhAVSu8P6254i4NnwFsV9UvetNnBEyzVVWniMgduJqgY4EkXEb6V2vz1IAnE4nIIOCXwFRgD/Ae7qlVwboGGA5MUdV6EekrIklePCer6lovc39XRB7FPcb1EFVVEcn05nEXrkbtI6/g8CYwvgMxGNNVySKyBJd/coGTWhjnWkBV9VAROQR4S0TGtpH+XaBKVceLyGG4p4e15GNghriarHXAXOB0EXkFmIx7hPUzwMOq+rCIfBP4G652GCAPmKGqDSJyK4CIfB84FThfVWtaW2kRmQqsU9UiETkN9/jgI3FPYHtZRE4Acmj9GNQqVZ3jHcNeUdVng5nGmDDwM28HOgf3NENwT2Ocp6o/EpHxwM+AY1W1TkT+CVyGe+rgYO+qNwH/lzcBI1S1JiCtLWOAy1V1bmt53DvRNS2wGuyeoSmTLwS2Ag82Gy7A70VkKe5Ro4OBAbgMeaqI/FFEjlfV8oBpmtpmLsNl1j1ejVhTxmttnoGOBD5Q1VJVrcP9kXfEKcC9qloPoKqlwDhgk6qu9cZ5GDgBKMcV+h8UkS/has6a5vEPb/u8DKSLSGoH4zCmK5ouIx+CO6l9RESk2TjHAf8FUNXVwBZgbBvpJwSkL8U9xrglc3A11TOAT4D5wFHA4cBqVa3GNR153Bv/UW+ZTZ5R1YaA79/AncRf2Ebh+ocisgKYB/zOSzvNe32KKzAcgvszbusYZEyk8zNvAzzm/bcdCzTVMjfgrlQBnAxMAxZ4452Me0T7RmCkiPxdRM4AKrzxl3rz/BpQH8T6b1HVud7n1vK4aYXVYPcM+7w2j625DFdTNM07i90MJHk1wFOBs4Dfisi7qvp/3jRNf56NAZ+bvse1Ns9Oxl/P5ydznZqHV8N9JO4AciHwfVxtQgxwtFeQMMZXqvqJd8Unp5sW+TFwHRAL3K+qe7yrQDMJrv313mbflwFTcDXbm1qZ5g5V/YuInIs74R2FOyH/g6re23zkVo5BHTomiMgQ4H/e13+p6r/am8aYUPIhb4Nrg72wWVp1wEmx4K5O3dx8QhGZDJyOu2frYuCbwBdxBfxzgFtE5FAOzItwYH4MPD60msdNy6wGOzpkAEVeQfhEYBjsb8JRpar/xd0gMbWr82xmAfAFEcnymq98uZV5bcadZYMrHDd5G/i2Ny0i0hdYAwyXz9uVfh34wKuVzlDV14Af4i5/A7yFK2DgzWNKB9bRmJDyLgXHAruaDfoQd9KKd5l4KG5fby19NvBVL30ScFgri1wFDMLVln3qpS3B/al+7H2fA1zifb7MW2ZrPgW+jbv8O6itdVXVl3FX1S7HNc36ZtPVIxEZLCL92zgGbebzY0Jrx409QJq3rHyvJnGKFa6NH3zI28F4F7hQvBubvWaWw7wTgRhVfQ74BTBV3E2KQ1T1fVyzkgwgFZcXp3rTTwVGtLKsFvN4F2KPelaDHR0eA/4nIstwf3irvfRDcTcJNQJ1uLZfXZ3nfqpaICK/x12WLvXGaekS8F+Ap0XkGuDVgPQHcJfMlopIHa4G7h8iciXwjFfwXoBrE94XeMmrnRPgRm8ePwDu9pqyxOEOXtbLiulOTU24wO2bl3ttmgPH+Sdwj5ef6oErvHaQraXfA/xHRFbhCtGLWlqwdz/CPNzJZ52X/Anu/oamGuzrvHn9BCgGrmxrZbz7GX4MvCoip6pqSRuj/x+u+cl47/WJt96VwNdwN2a1dAz6Na72+zfArFbm/SRwv4j8ANdkZUPgQBF5AldTny0i24BfqWrz5nPGdIVveTsYqrpSRH6Ba98dg8tj1wL7vGU0VaLejDs5+K93H4QAf1PV3SLyHPCNgGZfaw9akFvWW16b7+Z5vKiz8Uc7CbhnzZgOE5FUVa30CsMvAP9W1Rf8jssYY4wxxi/WRMR01a3eGf5yXJvNF32NxhhjjDHGZ1aDbYwxxhhjTAhZDbYxxhhjjDEhZAVsY4wxxhhjQsgK2MYYY4wxxoSQFbCNMcYYY4wJIStgG2OMMcYYE0JRV8AWkRUiMtPvOPwkIheISL6IVIrI4Z2YfpaIfCsMcT0kIr8N9XyDXLYGPB0y4kR6fH6wvGx5uZVlR3ReifT4/GB5uet5uYPLmuk9fKnH87bXyE5OG5bjX7B6VAFbRDaLyCnN0q4QkY+avqvqRFWd1c58hnsHwWh9kuVfgO+raqqqftp8oLfue70dt0REnhCRzO4Pc388cV4sRwWkXebF2TztoCdKhjiWpn2j0nttFpGbwrnMFmJot/AiIpeLyCIRqRCRbSLyp560P1teDprl5c7H0lPy8iUiskZEykWkSEQeFpH07oqxqywvB61H5eVI4W2vjX7H0Rk9qoDdU0TAAWIYsKKdcSaraiowEsgCbg13UK1R1Xrc451PCEg+Affo9eZpszsy7y78Fpne9rkQ+KWInNrJ+YRLCnADkA0cBZwM/NjPgKKR5eWOsbzcKR8Dx6pqBu43jAN8uToQzSwvm+4WdQXswLNpETlSRBZ6tXw7ReR2b7SmA/tu72zxGBGJEZFfiMgWrxbhERHJCJjvN7xhu0Tkl82Wc6uIPCsi/xWRCuAKb9mfiMhuEdkhIv8QkYSA+amIfE9E1onIHhH5jYiMEpE5XrxPB47fbB1bjFVEEkWkEogFPhORDe1tL1WtAF4GJnRkWQHDzxV3+W+3uMsx4wOGHS4ii731ewpIaiOU2Rz4B3w88McW0mZ7875aRNaLSKmIvCwigwKWqyJyrYisA9a1sE7HibtUN7ONeABQ1YW4g+KUgOm/KSKrRKRMRN4UkWFeuojIHd52qhCRZSIyyRt2wKWq5jU8AenXAJcBP/X2zf+1Etc9qvqhqtaqagHwGHBse+vTk1hetrzcS/JyvqqWBCQ1AFHVxMTyctfzsogM8vJHqZdfrg4YlizuakmZiKwEjggY9hMRea5ZrH8Tkbva+K1+IiJLxdWoPygiA0TkdW+bvCMiWQHjPyMiheKuwMwWkYkBw84SkZXedAUi8mMvPVtEXvF+h1IR+VBEWiyPSkCTK28d7xaRV715zhORUQHjnioiq71Y/gFIs3m1lt9/5s0rzvv+XXHHwraOc+1T1R7zAjYDpzRLuwL4qKVxcDUpX/c+pwJHe5+HAwrEBUz3TWA97swxFXgeeNQbNgGoBI4DEnCXeuoClnOr9/183ElLMjANOBpXGzEcWAXcELA8BV4C0oGJQA3wrrf8DGAlcHkr26HVWAPmPbqN7bh/OO4s+S3g/wKGzwK+FcR2GQvsBU4F4oGfeuMmeK8twA+9YRd62+i3rcT0BaDU237Z3rQpwM6ANAWGAicBJcBUIBH4OzC72fq9DfQFkgPXGTgDyAeObCWOA/YN7zesAi7wvp/nreN477f9BTDHG3Y6sAjIxGXs8UBu823ayn4b+Js81Np2auM3fRG4ze88annZ8jKWlzucl3H7Y7k37V7gNL/zqOXliMvLs4F/4k5upwDFwEnesNuAD3H5ZAiwHNjmDcv19qlM73scUARMa+P3nAsMAAZ74y4GDveW/R7wq2brnYbLv3cCSwKG7QCOD1inqd7nPwD/wh1P4nEn3BLEdnkI2AUc6a3HY8CT3rBsYA/u+BSPO17V8/nxr638HuNt31uBMUAZcHiX84bfmbMTGbkS2B3wqqL1jDwb+DWQ3Ww+wzk4I78LfC/g+zhc5owD/h/wRMCwFKCWAzPy7HZivwF4odlOc2zA90XAzwK+/xW4s5V5tRprBzJyhbf9GnCXbwcHDJ8VsFO2tV1+CTwdMCwGKABm4mqrtgdmGmAOrf8pJwHVwGTgAuAxL31uQNomL+1B4E8B06Z6MQ0PWL+TWljnm3F/9pPa2DZN+8ZuYJ/3+S9N6wG8DlzVbJ2rcJf/TgLW4g7gMc3mu3+bet+vIEQFbNwBbhvN9vNIfmF52fKyWl5uIebB3j441u882oGYN2N5uc1Ym+8XrUzfal7GFZobgLSA8f8APOR93gicETDsGrwCdsC+frX3+WxgZTu/52UB358D7gn4fh3wYivTZnrrkeF93wp8G0hvNt7/4U5kWt0ezbZLYH56IGDYWcBq7/M3gLkBwwT3v9h0/Gs1vwfsf6W4k66bQ5E3emITkfNVNbPpBXyvjXGvwtXMrBaRBSJydhvjDsIdsJtswWXiAd6w/KYBqlqFO4sKlB/4RUTGepdACr3LU7/HnWEF2hnweV8L31M7EWuwpnrbLwm4B/iwlcsh7W2X/cNUtRG3HQZ7wwrU23MDpm2RqlYD83F/5ifgzsYBPgpIa7qE2Hy5lbjfY3DALA/4PTw34AoRy1uLI0A2bvv/CFfIiPfShwF3eZe2duMypOAOhO8B/wDuBopE5D4Jwc1K4m4Ia7pR6/Vmw87HHWjP1AMvM/cElpctL1teDqCuudcbwJNdXVY3s7wc3rw8CChV1T3N5t+UTw7YFhycPx8GvuZ9/hrwaDtxBLUNRCRWRG4TkQ3e9tzsjdO0Tb+MKwRvEZEPROQYL/3PuNrkt0Rko3Ts5uPCgM9VfP57NN8flAO3Sav53Rt/M/A+rqB9dwfiaVVPLGAHTVXXqeqlQH9cG8BnRaQP7oyoue24H6DJUNzlhZ24yxx5TQNEJBno13xxzb7fgzsDHaOq6cDPadYeqAvairVDVLUOeAAYAUzq4LIOGCYigjvTLsBts8FeWuC0bWlqu3k8n/8pfxiQ1vSn3Hy5fXC/R0HgqrUw/4uA80Xk+nbicDNQbVDV23G1cU1/GPnAtwP/TFQ1WVXneNP8TVWn4S5fjgV+4k23F1fD0mRgW4tuFsdj6u6kTlXVM5vSReQM4H7gHFVdFsw69VSWl9tnebl1kZ6Xm4kDRrUyrMezvNy+FvLydqCviKQ1m39TPtmBy6+BwwK9CBwm7j6Cs3FNK0Lhq7imF6fgmtAM99LFW48Fqnoe7rd+EXjaS9+jqj9S1ZHAucCNInJyF2M5YBsEHMOatJnfReSLwDG4KxF/7mIsQJQXsEXkayKS49XG7PaSG3FtlxpxbaWaPAH8UERGiEgq7sz2KXV3xT8LnCMiM8Td4HAr7WfKNNzlnkoROQT4bohWq71YO0REYoErcWelLXWF09aynga+KCIni0g8roaoBnf5+BPcweUHIhIvIl/CtZtqy2zgRFymWOmlfYyrdZrC53/KTwBXisgUEUn0YprnnYG2ZTuut43rRaQjv8dtuBuVknDtxm4W70YOcTexXOR9PkJEjvK2xV7cn3mjN48lwJdEJEXcDRtXtbG8nRy4bx5ERE7CHSS/rKrzO7AuPZLl5fZZXg5KJObly0RkqPd5GPA73J98VLK83L7meVlV83F58Q8ikiQih+H2u/96kzyN25ezRCQP14xjP++q0rPA48B8Vd3auVU8SBruOLELd9L5+4B1SPD27QzvhKECLw+JyNkiMtorBJfjmr80HjT3jnkVmCgiXxJ3s+IPOPDkt638no07ofkWcDluvzqri/FEdwEbdxPMCnF38N4FXKKq+7xLSb8DPhZ3ueBo4N+4yyazgU24A+p1AKq6wvv8JO4sqRLX8L+mjWX/GHd2twdXy/hUCNer1Vg74DNvu5ThdqgLVLW0I8tS1TW4y01/x92odA6uNrVWVWuBL+HaJ5YCX8Hd9NGWObiz4HlNl6O9Zg/FQJGqrvPS3sG1GX0O93uMAi4JZqW9A8vJwE0SfAf0r+K209Wq+gKu1uVJcZfElgNNNVHpuN+6DHeJbhefnwnfgWsfuBN3ua6tGoQHgQnevvliK+P8EretXpM2LjlHEcvLrbO83LPz8gRgjojsxZ2ErAGubmXcaGB5uXVt5eVLcTXE24EXcDcavuMN+zVuP92EuzmypSYgDwOHtjKssx7xlluAO5Ge22z414HNXv76Dq5XHXA3Er6D+80+Af6pqu93JRDv+HIR7iR6l7eMjwOGt5Xf7wNeUtXXVHUX7uTlARFpfkWkQ5pu9jAd4J2d7sZdZtrkczjGmE6yvGxMdLC83DbvKslqYKC6bgBNmEV7DXbIiMg53iXBPrg70ZfxeYN+Y0wPYXnZmOhgeTk44vqYvhHXpZ0VrruJFbCDdx7u0sx23KWHS9Sq/43piSwvGxMdLC+3wzv5qMD1cf8rn8PpVayJiDHGGGOMMSFkNdjGGGOMMcaEUJzfAXRFdna2Dh8+3O8wjPHFokWLSlQ1x+84QsHysunNekpetnxqerOO5tMeXcAePnw4Cxcu9DsMY3whIq0+TS/Ey0nCdT2ViDtmPKuqvxKREbgusvrhHin8dVWt9fozfgSYhusu6Svt9Wtsedn0Zt2Vl7vK8qnpzTqaT62JiDGmPTXASao6GfeQkDO8Pmr/CNyhqqNx/bY2PXDjKqDMS7/DG88YY4zpNayAbYxpkzqV3td476XASbing4F7iMH53ufzvO94w0/2nthljDHG9ApWwDbGtEtEYkVkCe5JaW8DG4DdAY8B3gYM9j4PBvIBvOHluGYkxhhjTK9gBWxjTLtUtUFVpwB5wJHAIV2dp4hcIyILRWRhcXFxV2dnjDHGRIwefZOjMaZ7qepuEXkfOAbIFJE4r5Y6DyjwRisAhgDbRCQOyMDd7Nh8XvcB9wFMnz7dOuQ3xphWVFdXM3fu3BaHHX300SQlJXVzRKY9UV/AHjJ0GNvyt3ZomrwhQ8nf2iNu6jYm7EQkB6jzCtfJuCeC/RF4H7gQ15PI5cBL3iQve98/8Ya/F4qnq/3k57+iuLzyoPScjFT+/Ptfd3X2xhjju9YK0kuWLOG9VYUMGTPxgPSCDau4AZg5c2a3xGeCF/UF7G35W7n9rTUdmubG08aFKRpjeqRc4GERicU1K3taVV8RkZXAkyLyW+BT4EFv/AeBR0VkPVAKXBKKIIrLK5l8/rcPSv/sxXtDMXtjjPHd3LlzufOpt0gYciir96awtyGWrPh6YletJm/4KEZPPsrvEE2Qor6AbYzpGlVdChzeQvpGXHvs5unVwEXdEJoxxvRYLdVWL1myhIbcQ5lfkU5WSjyT+qawunAPDSNPp3/jZn8CNZ1iBWxjjDHGmG7WVFs9eNT4/WkL5m+keMRo8vomc+7kQcTHxjBpcAZPzN3I5phBqCrW62nPYAVsY4wxxhgfDB41fn+zj/qGRt7ZmUQc9Xzx0FziY11Hb9mpieRWb6EgeRQbS/YyKifVz5BNkKybPmOMMcYYn322rZya2BSGNW4nKT72gGHZtYUkai0LN5cRgnvGTTewArYxxhhjjI/21TUwf3MpaXWlZOjeg4YL0L9xF4UV1ewor+7+AE2HWQHbGGOMMcZHCzaVUlffyKDq1rsI7qe7iY8VVu6o6MbITGdZAdsYY4wxxid7a+pZWlDO+Nx0khurWh0vFmV0/1TW7aykvqGxGyM0nWEFbGOMMcYYn3y2bTcNjcr04VntjjtuQBq1DY1sLW29IG4igxWwjTHGGGN8UN8IS7eVMzonlayUhHbHz8tKISE2hk0lB7fTNpHFCtjGGGOMMT7YWpNETX0j04a1X3sNEBsjDO2XwqZde603kQhnBWxjjDHGmG5W36hs2pdIXmYyAzOSgp5uZHYf9tY0ULSnJozRma6yArYxxhhjTDebu6Oe6sbYoGuvmwzrlwJgzUQinBWwjTHGGGO6UWOj8vqmOtJi6/cXmIOVkhBHbkaSFbAjXNgK2CLybxEpEpHlAWm3ikiBiCzxXmcFDLtZRNaLyBoROT1ccRljjDHG+On9NUUUVCojk6sRkQ5PP6xfCkV7aqht7Pi0pnuEswb7IeCMFtLvUNUp3us1ABGZAFwCTPSm+aeIxLYwrTHGGGNMj3bvBxvplyQMSqzt1PR5ma7Wu7QuLpRhmRAKWwFbVWcDpUGOfh7wpKrWqOomYD1wZLhiM8YYY4zxw6ItZczfXMrpw+OJ6WQF9ICMRGJjhNK6+NAGZ0LGjzbY3xeRpV4TkqaW/YOB/IBxtnlpxhhjjDFR494PNpCRHM8JeZ2vfY6LiSE3I4ldVoMdsbq7gH0PMAqYAuwA/trRGYjINSKyUEQWFhcXhzg8Y4wxxpjwWF9UydurdvKNY4aRFNe19tN5WclUNMRSWWv9YUeibi1gq+pOVW1Q1Ubgfj5vBlIADAkYNc9La2ke96nqdFWdnpOTE96AjTHGGGNC5IEPN5IQG8PlM4Z3eV6uHbawtqyhy/MyodetBWwRyQ34egHQ1MPIy8AlIpIoIiOAMcD87ozNGGOMMSZciiqqeX5xARdNzyM7NbHL8xuQkUgMyupSK2BHorA13hGRJ4CZQLaIbAN+BcwUkSmAApuBbwOo6goReRpYCdQD16qq7THGGGNMF4lIJvAAMAn3//tNYA3wFDAc9398saqW+RNh7/DvjzdT39jI1cePDMn84mJiyIyrZ91u63QtEoWtgK2ql7aQ/GAb4/8O+F244jHGGGN6qbuAN1T1QhFJAFKAnwPvquptInITcBPwMz+DjFbV1dW8/9EnPPxxFdMHxLJp2QI2AUuWLKGhIbNL886Kr2dzRTzVdQ0kxVtBO5LYkxyNMcaYKCUiGcAJeBVcqlqrqrtx3eM+7I32MHC+H/H1BnPnzuU3/1vJvnpIqC7lmYX5PLMwn2ffW0BXO2vIiqunQWHptvIQRWtCxfp3McYYY6LXCKAY+I+ITAYWAdcDA1R1hzdOITDAp/iiRnV1NXPnzj0ofeGnn7ErZQRDMpOZNnXM/vSCDau6vMzM+HrA9a195Ii+XZ6fCR0rYBtjjDHRKw6YClynqvNE5C5cc5D9VFVFpMW+3kTkGuAagKFDh4Y71h5t7ty53PnUWwweNf6A9DmflVAzeDTThmW1MmXnJcYoA1KExVut+XyksQK2McYYE722AdtUdZ73/VlcAXuniOSq6g6vh6+iliZW1fuA+wCmT59uHS63Y/Co8YyefNT+76rKuzsTSdZqhvZNCcsyR2fG8unWMlQVka71rW1Cx9pgG2OMMVFKVQuBfBEZ5yWdjOux62Xgci/tcuAlH8KLetvK9lEd24f+jbvCVvgdnRlDSWUtW0urwjJ/0zlWg22MMcZEt+uAx7weRDYCV+Iq2J4WkauALcDFPsYXtZbk7ya2sY6+WhG2ZYzOcr2HLN5axrB+fcK2HNMxVsA2xhhjopiqLgGmtzDo5G4OpVcp31fHxpK9DKgtJCYufK1rBqcKqYlxLNpSxgWH54VtOaZjrImIMcYYY0yILS8oR4B+tYVhXU6MCFOGZLJ4y+6wLsd0jBWwjTHGGGNCqFGV1YV7GNYvhQStDfvypg7LYnVhBZU19WFflgmOFbCNMcYYY0Iov7SKypp6JuSmd8vypg7NpFFhaf7ublmeaZ8VsI0xxhhjQmjljgoS42IYkdM9Nx0ePtT1sW39YUcOu8nRGGOMMSZEauoa2FC8l4mD0omLCW89Zn1dLUuWLAFgUKrw1qcbmBRTAMDRRx9NUlJSWJdvWmcFbGNMm0RkCPAI7lHKCtynqneJSF/gKWA4sBm4WFXLxHX2ehdwFlAFXKGqi/2I3Rhjutumkr00NCqHDEwL+7J2bt3AqtJS1tX3I7YuhVV7E3h6QTHbN67iBmDmzJlhj8G0zJqIGGPaUw/8SFUnAEcD14rIBNzT4N5V1THAu3z++OUzgTHe6xrgnu4P2Rhj/LG+uJI+ibEMTO+e2uOcIaMYPfkoxo0cSp3G0G/M4Qc9rt10PytgG2PapKo7mmqgVXUPsAoYDJwHPOyN9jBwvvf5POARdeYCmd6jmI0xJqo1KGzZVcWonNRuf2z5oIxkAHaU7+vW5ZqWWQHbGBM0ERkOHA7MAwao6g5vUCGuCQm4wnd+wGTbvDRjjIlqxbXx1Dcqo3NSu33ZmSnxJMXFsH13dbcv2xzMCtjGmKCISCrwHHCD6oHP/VVVxbXP7sj8rhGRhSKysLi4OISRGmOMPwprE0iKi2FwZnK3L1tEyM1MthrsCGEFbGNMu0QkHle4fkxVn/eSdzY1/fDei7z0AmBIwOR5XtoBVPU+VZ2uqtNzcnLCF7wxxnSDRlWKauMZkdOHmJjubR7SJDcjibKqOmob/Vm++ZwVsI0xbfJ6BXkQWKWqtwcMehm43Pt8OfBSQPo3xDkaKA9oSmKMMVFpU3kjdRrD8H7d0/d1S5raYZfVWydxfrNfwBjTnmOBrwPLRGSJl/Zz4DbgaRG5CtgCXOwNew3XRd96XDd9V3ZrtMYY44PlJQ2AMiQrxbcYBqQnEiNQVmfFO79F9S8wZ0MJiXkTaVQlppvv5jUmWqjqR0BrGejkFsZX4NqwBmWMMRFmxa4GMuIaSE6I9S2GuNgYctISKauq8y0G40R1E5E731nHwMv+yMufbadRO3T/VcQZMnQYIhL0a8jQYX6HbIwxxvQKFdV1rN/dSHa8/wXb3IxkdtfHUd/Ys8s9PV1QNdgicqyqftxeWqR58PLp5J14GXzhcpbk72bq0Cy/Q+q0bflbuf2tNUGPf+Np48IYjempempeNsZY/o1kn2zYRaNCToL/BexBGUksyRe2VjT6HUqvFmwN9t+DTIsoaUnxVMx9hrysZBZvLaPBzuaM6ZF52RgDWP6NWB+uKyYxFrLi6v0OhVyvi8B1u62A7ac2a7BF5BhgBpAjIjcGDEoH/Gtk1EFTh2bx8mfb2VBcydgBaX6HY0y3i5a8bExvZPk38i3YVMaYrNiIaHebmhhHckwD68oa/A6lV2tvX0gAUnEF8bSAVwVwYXhDC53h/VLokxDL2p17/A6ly+obGtEe3p7c+CIq8rIxvZTl3whWvq+OtUV7GJMZCcVrJyu+nvW7rbzgpzZrsFX1A+ADEXlIVbd0dOYi8m/gbKBIVSd5aX2Bp4DhwGbgYlUt8/ravQvXvVcVcIWqLu7oMluJg5E5qazaUUFdQyPxsZGTCYJRvq+OzOO/xr8/3sSe6nriYoQR2X04akRf+qUm+h2e6QG6mpeNMf6x/BvZPt1ahiqMyYplxW6/o3Gy4urZvlcp2L2PPB+7DezNgi1pJorIfSLyloi81/QKYrqHgDOapd0EvKuqY4B3ve8AZwJjvNc1wD1BxhaUUTl9qG9U8kurQjnbsFu4uZTT7viA9GMuJjs1kWNG9mN8bjpbSqt4Yn4+K3dUtD8TYz7X2bxsjPGf5d8ItGhLGbExwsiMyKm8y4p3bcEXbSnzOZLeK9h+sJ8B/gU8AATdqEdVZ4vI8GbJ5wEzvc8PA7OAn3npj3h96M4VkUwRyQ3VE+AGZyYTGyPkl+1jZE5qKGYZdu+vLuI7/11EbkYShY/8iBseeWX/sKNH9uWN5YW8vXInqsrEQRk+Rmp6kE7lZWNMRLD8G4EWbi5jfG4aSXGR85OkxTaQFOtiO2/KYL/D6ZWCLWDXq2qoapQHBBSaC4EB3ufBQH7AeNu8tJAUsONiYxiUmUR+Wc+owX5/TRFXP7KQQ3LTePjKI8n+6boDhqckxHHulEH877MdvLe6iH59EhmYkeRTtKYHCWVeNsZ0L8u/Eaa+oZEl+bu5eHoeUOJ3OPvFCIzOjGXepl1+h9JrBXs9438i8j0RyRWRvk2vri7cq63uUAt8EblGRBaKyMLi4uIOLW9IVgq7KmupqvW/G522rNu5h+se/5RxA9N4/OqjW21nHRcTw5mTBpKaGMdry3dQXRc5Z88mYoUlLxtjuoXl3wizasce9tU1MG145P0M4/vFsHZnJcV7avwOpVcKtoB9OfATYA6wyHst7OQyd4pILoD3XuSlFwBDAsbL89IOoKr3qep0VZ2ek5PToQUP9vqG3FFe3Ymwu0fZ3lq+9chCkuJjuf8b00lPim9z/KT4WM48NJe9NfXMWtOxEw7TK4UyLxtjulen86+IxIrIpyLyivd9hIjME5H1IvKUiCSELeootnBLKQDTh0Xeg+zG93U9OM7daLXYfgiqgK2qI1p4jezkMl/GHSTw3l8KSP+GOEcD5aFqf92kf1oiMQKFEVrArmto5NrHF7NjdzX3fWMag7wTgvYMTE9i+vC+rNm5h6097CZO071CnJeNMd2oi/n3emBVwPc/Aneo6migDLgq1PFGq+rqambNmsWsWbN4fcEa+iYJa5fMY8mSJTQ0RM6V5GHpMaQmxvGJFbB9Eeyj0r/RUrqqPtLOdE/gbmjMFpFtwK+A24CnReQqYAtwsTf6a7gu+tbjuum7MpjYOiIuNobs1EQKKyKzgP2bV1YyZ8Mu/nrR5A4/1v2IYVmsKdzD+2uKuOyooWGK0PR0nc3Lxhj/deG/OA/4IvA74EavW9yTgK96ozwM3EqIe++KVnPnzuXOp95i8KjxLC3NICuunmcW7uKz2QsYMHYK4/wO0BMbIxw5oi9zN1gB2w/B3uR4RMDnJOBkYDHQZqZW1UtbGXRyC+MqcG2Q8XTawPQkVhVW0KhKjEi4Fxe0/87dwiOfbOGaE0by5Wl5HZ4+LjaGE8fl8OKS7SzdVh6GCE2U6FReNsZEhM7m3zuBn+IeTgPQD9itqk03JDV1KmCCNHjUePqPm0r1x5sZO3wgo4dkUrBhVfsTdrNjRvbjvdVFFJZXW0cI3SyoAraqXhf4XUQygSfDEVC4DcxIYmlBOaV7a8n26SEtQ4YOY1v+1v3fE4ccyoCv/IZ9mz/llj+dyy3a2Kn5DuvXh6F9U1iwqRRJsI7lzcGiKS8b09t0Jv+KSNPD3haJyMyOLlNErsE9m4KhQ+3qaKAdu93V8EERXHA9ZlQ/AD7ZWMIFh3e88s50XrA12M3tBUaEMpDu0nQGV1hR7VsBe1v+Vm5/aw3gntL45PytpCTEcfE3v0LiNV9tcZobTwvuotOxo/vxxPx8Mo76UsjiNVGtx+ZlY0xQ+fdY4FwROQtX652Oe2pypojEebXYLXYqAK5jAeA+gOnTp9tztwPsKN9HfKz4VpYIxvjcdNKT4vhkwy4rYHezYNtg/4/Pu9OLBcYDT4crqHDKTI4nMS6GwvJqJvn8cJaa+gb+99l2FDhnci6JcbFdnmf/tCTGDkhl9fTzKaqopn965J5Zm+4XTXnZmN6mM/lXVW8Gbvamnwn8WFUvE5FngAtxNeCBHQ6YIG0vr2ZAehIxMZHT3LRJfV0tS5YsAWB0uvL+igJmZbunOh599NEkJVnZINyCrcH+S8DnemCLqm4LQzxhJyIMTE/y/UbHRlXeXLGT0qpazp8ymMyU0PWQdMzIfqzZvpu/vbeO355/aMjma6JC1ORlY3qhUObfnwFPishvgU+BB7saXG9Sr1Cyp4YjIrD/a4CdWzewqrSUdfX9qN6XSPG+Pjw8r4CyzSu4AZg5c6bPEUa/YLvp+wBYjbtBIgv+f3v3HSdHXT9+/PXevd57crnk0nslhEACxlAFpAtIkSaKIqgoNtTfV1BRFJSiFEGkCNKrIB1CaKlwpPfe7i65JHe5frvv3x8zFzaX69l6934+Hvu43c/Mzrxnbj+z7535zOdDQyiDCrU+mUlU7Gug0de9ts7B8MnaXazfWc2Xh+dTnBPc9tJZKQns+/wNnpy3mS0xMnKlCY+eVpeN6U0Otf6q6ixVPc19vk5Vp6rqMFU9T1VtNJIu2NMYhwKFWdF7Jjh/wFCGTTySyeNGA+DpM4KioaMjHFXv0akEW0TOB+YB5+F0qzdXRM4NZWCh1Cc9EYWIjW6UOmYmCzbuZly/DCb0D00zlb1znkEE7p21NiTLN7Gpp9VlY3oTq7/RY3eT0wCgMIpvcGyWk5pAelIc63dWRzqUXqWzTUR+BRyhqmUAIpIPvA08G6rAQqm5XXJZVX2nB3MJlpLNe8g95QcUZSUzc2QBEqKuAn1VOzl/ygCeXrCZa44dFvbtNFGrR9VlY3oZq79RYndjHLlpCUG5dyrURIRBuams2FHJiKxIR9N7dHaodE9zhXbt6sJ7o05aYhypCV5Kw9wOe8feOq56dAG+fRV8dXwh3hDfGPG9Y4cBcN/7dhbb7Nej6rIxvYzV3yjgV2V3Uxz9MmPnxNWgvBQafUpFY3c7jzNd1dmK+bqIvCEil4vI5cCrOCMvxqyCjCTKKsPXRKS+ycd3HltIdX0TZc/9juSE0P/qLcpK5tzDB/DkvM1ROzy8CbseV5eN6UWs/kaBrfuUJvXERPOQZgOyU/B6hPKG4HWoYNrXboItIsNE5GhV/SnwD2CC+/gEt1/MWNUnPZGKmgYamsJzo+PNry7n8817+Mv5E2ncuTEs6wT43syh+FXtLHYv15PrsjE9ndXf6LJ6tw8gpppexns99M9OpqwhPtKh9BodncG+A6gEUNXnVfXHqvpj4AV3WsxqbocdjhsdXyrZyqOfbORbxwzm5HGFIV9foAE5KXxtcn/+M28TZRHumtBE1B300LpsTC9wB1Z/o8bqPT4SxU9GUmw1txicm0q138vWfZHrQa036SjB7qOqi1sWumWDQhJRmBSkOyMvlVaFNulcU7aPG55fzJSB2fz8lFEhXVdbrjl2GD6/ct/76yKyfhMVemxdNqYXsPobRdbs9pMd3xSyTgpCZVhBGqDM39EU6VB6hY4S7Kx2psXOtZFWpCbGkZYYF5R22AOKByIiBz88Xr70kweo2lPBiz87g4Q4b0QqZHFuCmcfVsTjczdSFuIfFCZqZbUzLabrsjG9QFY706z+hlFZVR3ltUp2fOwlqamJceTENTHPEuyw6Oj6xgIR+baqPhBYKCLfAhaGLqzw6JORGJQz2Fs2b+Kvb648qHz+hgo+XruLk8f2ZeTpH+8v//FJIw95nV117bHDeP7TLTz44XpuOMU6mu+FenRdNqaHs/obJRZucIYbz45rjHAk3VOY2MDSffGsKq1iRJ/0SIfTo3WUYF8HvCAiF/NFJZ4CJABnhzCusChIT2JteTX1Tb6g92W5a189c9dVMDQ/lRF90oK67E4Rz0Fny/NO/yn31tXwq7OPQOsP7nC+/4BiNm8K3w2YJqyuowfXZWN6uOuw+hsVFm7cTbwHMuN8kQ6lW/omNrCsOpVXFm3nxydagh1K7SbYqloKTBeRY4FxbvGrqvpuyCMLg4IMpx12eVU9/bODN1y5X5W3lpcSHyccG8LBZNql/oPOqpdX1fOfeZv4+l1vM3VwzkFvicSZdRMePb0uG9OTWf2NHgs27mZwpocQD2MRMkkeZVSOh5dKtvKjE4bHXDvyWNKpW2BV9T3gvRDHEnb7b3SsDG6CvWxbJaWV9XxlbB9SE6PnLuP89EQG5aZQsnkPhxVnEe+18Ql6m1ity00+Pzvj8lhVWsXgvFT77JpeKVbrb09R1+hj6ba9nFQcB1WRjqb7jimK44HFNXyybhfTh+ZFOpweq1d/S6UkxJGeFBfU7uvqm3x8vHYX/TKTGBmF7ZumDMqhttHH0m2VkQ7FmE773uOfsiR5Aq8t2cHTCzZT2xCbl2eNMbFr0Za9NPqU4dmxnTod0TeOjKQ4npq/OdKh9Gix/SkJgj7pSZQGsS/seesrqG30MWNEflReeinKSqZfZhKfbtqNz6+RDseYTrl02iDG1i7iq+ML2V3TyFvLS1G1z68xJnwWbKwAYFhW6EdiDqUEr3D2YUW8tmQHe2oaIh1Oj9XrE+yCjET21jZS13joZ8R21zRQsnkPYwoz6JMRvUOoThmUQ1VdEytLY/galwkrEfmXiJSJyJKAshwReUtEVrt/s91yEZG7RGSNiCwSkcmHuv5jhueR37STYQVpTB+ay/qd1azbefCNusYYEyoLN+xmaH4qaQnRd/Ksqy6YWkxDk59nF26JdCg9liXYbjvssiCcxf5ozU7iPB6mD8095GWF0qDcFPLSEli4YbedBTSd9TBwcouyXwDvqOpw4B33NcApwHD3cRVwbzADmdQ/i6yUeOas22WfX2NMWPj9yvwNFa12EBCLRhdmcMSgbB76aAONPhvZMRR6fYLdfKZ5x95Da4ddVlnH2vJqDivOiqobG1sjIkwZmENFTQNry+0soOmYqs4GKloUnwk84j5/BDgroPxRdcwBskSkMFixeDzC1EE57NzXwMaKmmAt1hhj2rSytIrKuiaOGNQzEmyA78wYytY9tby6aHukQ+mRen2CnRTvJTc1ga17ag9pOZ+s20VinIfDirOCE1iIDS9IIzM5ngUbK+wsoOmuPqrafGTeAfRxnxcBgXfPbHHLgmZEn3SS470s3rI3mIs1xphWzd/gnF/oSQn2caMKGF6Qxn3vr7U8IAR6fYINzo1/2/fWdvumv4TCEWzYVcPhA7ODPmBNqHg8wuEDsymtrGfz7kP7cWGMOkfnLlUgEblKRBaIyILy8vIurc/rEcb2y2D9rmrqJLFL7zXGmK6at76Cwswk+mf3nJHpPR7hqhlDWLGjineWl0U6nB7HEmygKDuZRp9S3s122Flf+gbJ8V4m9s8KbmAhNrowndQE7/47o43potLmph/u3+Yj9FZgQMB8/d2yA6jq/ao6RVWn5Ofnd3nl44oyUYUd8UFrfWKMMQdRVeatr+CIQTlR2TvYoTjrsCIG5qZw25sr8VvPYkFlCTbOGWyALXu63p5z/oYKkgdP5vCB2STExdbujPN4mFScxeaK2qD2BW56jZeBy9znlwEvBZRf6vYmchSwN6ApSdBkJsczMCeFbfH9rMtJY0zQ1dXVMWvWLJ557T3KqurJatzJrFmzKCkpwefrGX3xx3s9XH/SSFbsqOLlz7dFOpweJWIZoYhsEJHFIlIiIgvcsla7/Qq11MQ4slPi2dqNphJ/fXMVTfsqmNA/MwSRhd74okwSvB4Wbtwd6VBMFBORJ4BPgJEiskVErgRuAU4UkdXACe5rgP8B64A1wAPA90IV17iiTBo8Scxe1bUmJsYY05E5c+Zwx1Nv8tinzvFlw7ZynlmwmWffnU9Xm7VFs9PGFzKmMIO/vLWShibrUSRYIn3K9VhVnaSqU9zXbXX7FXJFWcls21OHvwsN/T9es5NP1u2ics6zMTt0c2Kcl/H9M1ldto+4zD4dv8H0Sqp6oaoWqmq8qvZX1QdVdZeqHq+qw1X1BFWtcOdVVb1GVYeq6nhVXRCquAbnpRLvb7ARyYwxIVE0dDRN6f1IivMwafLhDJt4JHlFAyMdVlB5PMLPTh7J5opanpy/KdLh9BjR1p/cmcBM9/kjwCzg5+FYcVF2Mku2VVJW2bl22KrKrW+upDAziY0lrwG3hjbAEJo0IIuSTXvImHp2pEMxpku8HqFP0w7eXp7Izn315KXZDY/GmODauqeWflnJPaL9dVNjAyUlJQeUNTQ0oKqMzPZw22tLya9eT3Kcs61HHXUUSUnRO3BeNItkgq3AmyKiwD9U9X7a7vYr5AbmpCLA+k6ODvfuijI+27SHP54znjm/bAxtcCGWlhjHqMJ0Fo8/kV376sm1JMXEkMLGbWxJKOaFT7fy7RlDIh2OMVFFRAYAj+J8nypwv6reKSI5wFPAIGADcL6qWlvBFur8wt7aRsYXxWYz0JZKN61leUUFq5u+GBDv89mv40lOp/+kGaxsyOSvH+1iRGotW9cu5zpg5syZkQo3pkWyXcMxqjoZZ9S3a0RkRuDEtrr9OpSuvdqTnOClX1Yy63bu63Bev1+57c1VDMxN4dzD+wcthkiaXJyNxMXzyMcbIh2KMV2S6q/hsOIsnlqw2fpyNeZgTcD1qjoGOArn+3YMEWySGUsqGuOBLzpD6AnyBwxl2MQj9z/yigaSP2AoR0yZwvCCNNbXp1A46nCKho6OdKgxLWIJtqpudf+WAS8AU2m726/A9x1S117tGZKfys59DcRl92t3vteW7GD59kquO2F4zLa9biknNYHa1XN55JONVNc3RTocY7rk61MGsKZsH59t3hPpUIyJKqq6XVU/dZ9XActxBn5qayRWE2BnQxwJXg8F6b3jyu70obn4VZmzblekQ4l5EckORSRVRNKbnwMnAUtou9uvsBjRJx0BUscd1+Y8dY0+bn1jBcML0jhjYlAHp4u4vXOfY29to90wZmLOaRP7kZLg5T9z7QYdY9oiIoOAw4C5dLJJZqiuGseKXY3x9M9OxuOJ/fbXnZGVksCEoiyWbqukqqlnnECMlEjtvT7AhyLyOTAPeFVVX6ftbr/CIi0xjuKcFNLGHU+jr/Wuau6ZtZYNu2r4zelj8fawCtewbQVTB+Xw4Ifr29x+Y6JRWmIcX5vcn5dKtlJqfbobcxARSQOeA65T1crAae2NxBrKq8bRrrzGT43fy4CclEiHElZHDM4m3uthZU3v2u5gi0iCrarrVHWi+xirqje75a12+xVOE/pnEpeRz8slB3e4vrZ8H/fNWsuZk/pxzPC8cIcWFt/58hC27qnllUXW4byJLd/60mB8fuWhjzZEOhRjooqIxOMk14+r6vNucYdNMnu7ZbucwWQG9KDh0TsjJSGOKYOyKW1IYGVFzxhQJxLs/H8Lg/NSaShbzx3vrDqgLXJdo48fPPEZSfEefv3VMRGMMLSOHVnAiD5p/OP9dXbDmIkpA3NTOWVcIY/P3UhVXWz37GNMsIjTt9yDwHJV/WvApIg2yYwFS3f5SPT4yUlNiHQoYTdpQBaJ4uNfn+3hvffeY9asWQc86ursSmFHLMFuQUSoeOs+tuyu5YbnF9Po87O3tpFvP7qApdsquf3rk8jvwTc7eDzCVTOGsmJHFbNsdDwTY66aMYSquib++cH6SIdiTLQ4GrgEOM4dOblERE4lwk0yo53fryyv8JEX39gj+r/uqnivh4LqdZQ2JvG3j0t5ZsHm/Y87nnqTOXPmRDrEqBdtA81EhfotS/npV0by59dXMmfdLmobfFQ3NPHnr03g+NE9f7TDMyb24y9vruQf76/l2JEFkQ7HmE6bOCCL0yYUct/7azn38P69ru2kMS2p6odAWxni8eGMJZYs3VZJVQMMSeu9V8NyGsso1wGs9+Vw9PiBveZGz2CxM9ht+N7MYfzz0ikcMTiHr04o5L/fP4bzjxgQ6bDCIiHOw5XHDGbOugpKrNszE2N+9dXReD3CTf9dFulQjDEx6t0VZQiQn9B7E2wB+vnL2V3TyIodVZEOJ+ZYgt2OE8b04e6LJnPL1yYwtl/PGMWpsy6YWkxGUhx/e2d1pEMxpksKM5P54fHDeXu5c1nTGGO66t2VZQzO9JDo6d33ImVpFQXpicxZv4smv/Uu1hWWYJtWpSXG8Z0vD+WdFWUs3Gij55rYcuUxg5k2JJdfvbiE2XYvgTGmC8qr6lm0ZQ+TCryRDiXiBGfwmaq6JpZurexwfvMFS7BNm644ehB5aQnc+sYK61HExJQ4r4d7Lp7M0Pw0vvXoAt5bYT2QGWM6Z9bKMlRhYr4l2ADFOSkUZSUzb0OFjZHRBZZgmzalJMRx7bHDmLOugo/W2LCpJrb84eabyVn1Cgn1e7nioXl86Uf3cOk1P+Gnv/xNpEMzxkSx91aW0ScjkeJ0S5HA6V1t+tBcahp8dl9WF9inpzXiQUQ6/ejJLjyymKKsZDuLbWJO+d59TDnrSi49fhLj+meyOXEgnxecxNLqFPssG2NaVdfoY9bKco4b1afHf793Rb+sZAblprBw424a/bZfOsO66WuN+vnrmys7PfuPTxoZwmAiKzHOyw+PH87PnlvEfxdt54yJ/SIdkjFdEu/1cPyoPgzLT+ODNTtZljyOM+/+iJ9+ZSTHDMuzL1Fjerm6urr9/TovLG2ipsFHf38ZJSWL8fmyIhtcFJk2NJcn5m1mXW1SpEOJCZZgmw597fD+PPLJBv74v+WcMLqAlAT72JjYMzA3lQE5KTxw952s9B/PJQ/uJatpN4Pr15LpryQ/M41b/3BTpMM0xoTZnDlzuOOpNykaOprPKlNJkHiWrN/G4g/m02fEJHruKbSuKUhPYnhBGuvKlMp6uwrYEWsiYjrk9Qg3nTGW7XvruOe9tZEOx5hu84jg37aUbx0/li+PyKc+OY/PUqewYdBprLNuXo3ptYqGjmbQuCMo9yUxojCLEZOOJK9oYKTDijrThuTiA15Z1xDpUKKenYo0nTJlUA7nHFbE/bPXccakfozokx7pkIwBYOHCBVx+zfUHlX9a8jkTz2r9PXEeD5MGZDG2XwYlm/ewcONuNqRO5Zr/fMr1J45gSH5aaIM2xkSdDbtqaPQpw+37rU3ZqQn0T2zg3U3C1j21FGUlRzqkqGUJtvmCe3NnWzzJGfS78h6+/NN/suOxn9C/f382b9oYxgCNOVi9Dyae9Z2Dyj+e9+0O3xvv9XDEoBwmFGXy2lvv8N4KL68v2cH5UwZw3QnD6ZNhbQ2N6S1W7KgkOd5Lf0sa2zU8pZbtDYn87Z3V3PK1CZEOJ2pZgm2+0ImbO1eVVvHakiwu/MdHPHHVtDAFZkxoJcZ7qfjkGSZNmcbGhEE8OdfHU3PXU9SwhclJO7nzj/8X6RCNiXqBNwu2dNRRR5GUFL0/WOv8wvrd1UwakIXHYzc+tyfF6+fYAXE8s3AL354xhKF2xa9VlmCbLhlekMaagjQ+WbeLxKLRkQ7HmKCp98ERZ36TI4C9tY3MWbeLFTu8bPf3Y9SstVw+fRDJCTbwhDFtCbxZMNDWtcu5Dpg5c2YkwuqUrXWJ+BXG9cuMdCgx4fShCcwtbeDGl5fy6DenWm9MrbCbHE2XiAjHjy4gIymevDN/zq599ZEOyZigy0yO5ytj+3LR1GIyfHv50+srmHnbezz80XrqGn2RDs+YqFU0dDTDJh55wKNlwh1tVJVNdYn0y0oiOzUh0uHEhMxE4fqTRvDB6p28smh7pMOJSpZgmy5LjPNy6vi+eJMz+O5jCy3hMD1WfnoiE2oX8fR3pjEwN5Ub/7uMY/70Hv94fy376psiHZ4xJgiWV/ip8Xvt7HUXXTJtEOOLMvntK8vYXW29irRkCbbploL0JHa+ejvzN+zmJ898jt9vfWKanmnhwgXcc9vNpCx6lkk1n+Lbs40/vraCyb95lbveWc3e2sZIh2iMOQSvrmsgUfwML7C2xF3h9Qh/PGc8e2oa+Plzi2yE3BasDbbptpoVH/CL+x7iltdWkJLg5ZZzJtjNIabHaa2Xkh1763h7zqf89a1VPDB7HZdOH8jl0weTn54YoSiNMd3x+eY9LN3lZ1RKHXFeO+fYVeOKMvn5yaP4/avL+fecjVw6bVCkQ4oa9mkyh+Q7M4bwg+OH8/SCLfzk2c9p9PkjHZIxIdc3M4nxtYt59QfHMGNEPvfMWsvRf3qXG55fxNryfZEOzxjTSXe/t4aUOBiYVBfpUGLWN48ezLEj87npv8t4d0VppMOJGpZgm0MiIvz4xBH8+MQRPP/pVi5/aB57a+ySuen5Fi5cwK03/5bqjx/niKo55NZs5sm5Gzj+L+/zrUcWMG99hV0yNSaKLdy4mzeXlXLiwHjiLBvqNo9HuOvCwxhdmM7Vj33Kx2t3RjqkqGBNRExQ/OD44RRmJvHLFxZz+t8/5M4LJnFYcXakwzImZFo2HZkG1DQ08ci/H2OW/wjeXl5Kum8vRQ1byWsqpzAzmVv/cFPkAjbG7Nfk8/PrF5fQNyOJkwd7eKUk0hHFtvSkeB6+YioX3D+HSx+cx41njOXiI4t7dfd9lmCboDlvygCG5KfygydKOO++T7h65lCuOXYYSfHWd7DpHVIS4mDtR1x1+aUs317JZ5viWVGbSbxXyK3bxkdrdjJ1cA7x1tbT9ECzNjeyuiYJLauiOCeFxLjoPfY//PEGlm+v5N6LJ5O8q/0B1syBmhobKCkpaXXaf644nJ+/uJxfv7iEN5bu4FdfHc2grISYHYDoUFiCbbqvjaHVPYmpZJ/wHf7mV/76/Ifs/fA/VC+bBeqn/4BiG17d9HjxXg8T+mcxviiT7XvrWLa9khVb87n4n3NJT4zjmOF5TBuay8T+WYwuzCDBrk+bHuCTbU2sqklh1eIdxHuFUX0zmDooJ9JhHWTFjkpue3MlM0fmc/K4vrz/viXYXVG6aS3LKypY3ZR7QHnzgEL/vOzLPPLxBu58ZzUn3/EBI7M91GxdyfCifJK9/oPmj+YBiA6FJdim+zoYWn1zRQ2zVycQf9qPGXrezzm8OJuHrz42jAEaE1kiQr+sZPplJZOz8mUu+d71vLeyjPdWlPPakh0AJHg9jCpMZ2h+GoPzUg94pCbaIdrEjhuOTObJ+ZtJGzSBpdv2smxbJSt2VDIkMYnGKOnKtayqjm8/uoD0pHj+fO6EXt2E4VDkDxjKsIlHtjrN6xG+ecxgzplcxONzN/Hw7FWUpwxn827ITolnYE4qxbkp9ImOj0TI2NHbhMyAnBQumlrMup3VzFtfwbsryxhw7b+55j+fctKYPhwzLI/cNOvWzPQOXvycNLYvJ43ti6qybW8dn2/ewx2Pv8qmDYms3JRCvefAS6V5aQn0z05hQE4KA7KTKc5pfp5CYVaSNTUxUccrUJSVTFFWMkcObuSD1eWsLFd+9WEtvoIdfGVsn4gltZt21XD5Q/PYta+BJ759FAXpPbNpQqS01nRkrMBFGatYEJ+Lp88INlXUsGTbXkq27MFDNpvm1bJC1jJjeD6j+qb3qK5+oy7BFpGTgTsBL/BPVb0lwiGZQyAiDM1PY0heKqVV9Txw3z18nHEOr7pDq47qm87YfpmM6ZfB6MJ0huWnkZeW2KMqWW9ldflACxcu4PJrrj+ofEfJ51xx4z0ANPr87KlpZE9NA8sXfsSRR8xk8+4aSjbv5n+Lt+MLPAuofpK0niR/HUn+WnIT/Vz19dPchDyZ/LREOztnOhTKepqZHM9pE/rxyfyFbPVn8t3HFnLUkByuO2EERw7OCdvn0+dXnl6wmd+/sgyPR3jkm1OZOCArLOvuTdpqOvL57Pn0GTGJY444ksnF2TT5/GzdU0vJstWUVcZxy2sruOW1FWQkwNhcL6dPHcGUIfkMyUvd3zd5XV1dUNpxt7WcULQFj6oEW0S8wN3AicAWYL6IvKyqyyIbmTlUIkLfjCR2v/0Pyt+4l8Vb9/Lh6nLmrq/g/VXlPPfplv3zJsZ5KMpOZkB2Cv2ykslPTyQ/LYG8tETy0hPJS0skJzWBtMQ4vD0oEVdVfH6l0ac0+PykJnhjduADq8sHa23AGoCP5317//N4r8f5vKcn8sEnz7GjYSPxwFBgMEK9JLJ43Vamff0aKmsbqaxtorKukb21jexo8PHDJ0v2Lysp3sOAgLPfOamJpCZ6SU2MIyXBS1piHAlxHlRBcT5/qlDd0ERNvY999U3UNDSxr95HTUMT73+ygNpGH4rsf3hQUuI9HHPkZFISvCTHe0lOiCM53kNygpek+OYy529inBevR4j3ivvXg9cjxHmFOI/H/es+94j90A6xcNXT/IQmvjM5mW0pQ7j9rVVccP8cRvZJ55TxffnS8HxGF6Y7NwgHUZPPz/LtVXywppwn5m1ic0Ut04fmcut5EynKSg7quswXWms6snXt8gNex3k9DMxNZUN5Cf6KCiZNPobyxnh2NsYzf6vwyUvLgeUkxnkYnJdKn4wk/NW7WbFuI9nZ2XhFiRMlXpQ9OzZx4W4fM6ZNJTXRS3piPKmJrX93qiqzP/qEu557j4KBI2lS8CPs2LKJM8qbuOqsY4PaKUNUJdjAVGCNqq4DEJEngTOBXvul3OOIp9UPvic1i4SCIcRlFRKXWUBFZh+WZ/YhIbsvnuR02upOODneSRjSk+JITfSSkhDnfnk7X9Bej+AVwet+cXvbOGPSVlOwtvoxbmt+J0H20+RTGv1Kk89Po8/PvAWf0tDkQzxexBsH3njEG49445zXnjgkLh6RL/bNf689hvH9M9tYU9SzunyI2krIP/vltxnX7+DPxYO/uZrRh0+nTpKp9SRR15DM7ppkyspSmJ+YSVV9U7fiSPB6SEn0UutPJykjHY+ARwQR8CnsqNrL60t2UNvocxLwILar9AgHJt5ej5uABzx363u8Vw44IxpY0wOr/YHl7c8v7H+yf7rIF+UiB772uz9SDvgL/Ob0MUzon3UouyJUgl5PWyZTzWWL43YxaZLwx+lxzNkOH2yp5s63V3PH26sRIDNRSE8QMhJw/8dOcxOv+3nz4/wSbP54NV/McX4cOn/rm6C2SalqVHbVKj53nuFZHr43KZEpfWpZXTKX1S3iKykpYevasoPi3rl1I57kCtZ8Pjds5b1lnV+Up5PkVQZ4GxiQ1MCWsuWMH9KflKIRbKz0UVZTw6Yd1ZTta6A2oS/lNS0S4NSx3Dy3jpvnzj6gOMEDcZ4vPh9+oNHnfn4yjoTdATOnjeevC+s5/dg6BuelEiwSTQMhiMi5wMmq+i339SXAkap6bcA8VwFXuS9HAh3d/psHREuv59EUC0RXPBZL69qLZaCq5oczmM7qBXW5LbEQI8RGnL0pxojU5V5cT7vCtie6hXN7ulRPo+0MdodU9X7g/s7OLyILVHVKCEPqtGiKBaIrHoulddEUS7DFcl1uSyzECLERp8UYHXpiPe0K257oFs3bE20NPLcCAwJe93fLjDGxxeqyMdHP6qkxIRJtCfZ8YLiIDBaRBOAC4OUIx2SM6Tqry8ZEP6unxoRIVDURUdUmEbkWeAOny6B/qerSQ1xspy9thUE0xQLRFY/F0rpoiqXTekFdbkssxAixEafFGGK9uJ52hW1PdIva7YmqmxyNMcYYY4yJddHWRMQYY4wxxpiYZgm2McYYY4wxQdSjE2wROVlEVorIGhH5RZjXPUBE3hORZSKyVER+6JbniMhbIrLa/Zsdxpi8IvKZiLzivh4sInPd/fOUe5NLOOLIEpFnRWSFiCwXkWmR2i8i8iP3/7NERJ4QkaRw7hcR+ZeIlInIkoCyVveFOO5y41okIpNDFVe0iWRdbhFHl+p1JP9nna3vIpLovl7jTh8Upvg6fRyI1H7syvEhUvsxWkRLHe2urtbtWNHZ40As6MoxIxr02ARbvhgC9hRgDHChiIwJYwhNwPWqOgY4CrjGXf8vgHdUdTjwjvs6XH4IBA6z9SfgdlUdhjOu0ZVhiuNO4HVVHQVMdGMK+34RkSLgB8AUVR2Hc5PPBYR3vzwMnNyirK19cQow3H1cBdwbwriiRhTU5UBdrdeR/J91tr5fCex2y2935wuHrhwHwr4fu3F8iNR+jLgoq6PdFY3f2cEQLd/7wRAVuUOnqWqPfADTgDcCXt8A3BDBeF4CTsQZBavQLSsEVoZp/f1xPnzHAa/gjPy7E4hrbX+FMI5MYD3uDbYB5WHfL0ARsBnIwelR5xXgK+HeL8AgYElH+wL4B3Bha/P15Ee01eUWsbVbryP1P+tKfcfpQWKa+zzOnU9CHF+XjgOR2I9dPT5EYj9GyyOa6+ghbFNEv7ODtA1R8b0fpG2Jmtyhs48eewabLw6Ozba4ZWHnXio8DJgL9FHV7e6kHUCfMIVxB/AzwO++zgX2qGqT+zpc+2cwUA485F62+qeIpBKB/aKqW4HbgE3AdmAvsJDI7JdAbe2LqPlMh1lUbncn63WkYr+Dztf3/TG60/e684dSV48DYd+P3Tg+RGI/RouorKPdFSXf2cFwB9HxvR8MUZM7dFZPTrCjgoikAc8B16lqZeA0dX5yhbyfRBE5DShT1YWhXlcnxAGTgXtV9TCgmhaXdMK4X7KBM3Eqbj8glYOba0RUuPaF6ZpoqNdtibL63paoOQ60JRaODyb4orlud0WMHAe6IuqPGS315AQ74kPAikg8TkV9XFWfd4tLRaTQnV4IlIUhlKOBM0RkA/AkzuWiO4EsEWkebChc+2cLsEVV57qvn8WpNJHYLycA61W1XFUbgedx9lUk9kugtvZFxD/TERJV293Feh2J2Lta3/fH6E7PBHaFOMauHgcisR+7enyIxH6MFlFVR7srir6zgyGavveDIZpyh07pyQl2RIeAFREBHgSWq+pfAya9DFzmPr8Mp51XSKnqDaraX1UH4eyHd1X1YuA94Nwwx7ID2CwiI92i44FlRGC/4Fz6PUpEUtz/V3MsYd8vLbS1L14GLnV7VDgK2Btwaawni5rhnLtRr8P+P+tGfQ+M/Vx3/pCeBerGcSASn/2uHh/Cvh+jSNTU0e6Kpu/sYIim7/1giLLcoXMi3Qg8lA/gVGAVsBb4VZjXfQzOpYpFQIn7OBWnDdQ7wGrgbSAnzHHNBF5xnw8B5gFrgGeAxDDFMAlY4O6bF4HsSO0X4CZgBbAE+DeQGM79AjyB076zEecX+pVt7QucG1Tudj/Pi3F6NwjbZyeSj0jW5RZxdKleR/p/1pn6DiS5r9e404eEKbZOHwcitR+7cnyI1H6Mlke01NFDiD8qv7ODtG0dHgdi4dGVY0Y0PGyodGOMMcYYY4KoJzcRMcYYY4wxJuwswTbGGGOMMSaILME2xhhjjDEmiCzBNsYYY4wxJogswTbGGGOMMSaILMGOEBHxiUiJiHwuIp+KyHS3fJCILAnSOmaJyJQWZWeKyIsBr28QkTUBr08XkU73XyoiN4rITzoxz1Z3e5eJyIVd2IzOxHC5iPzdfX6WiIxpY74BIvKeG8NSEflhMOMwvZPV5eDpQl1OEpF57j5fKiI3BTMO0/u0VY+78P6HReTcjueMHBE5Q0R+0fGcB7wn6rcrWlmCHTm1qjpJVScCNwB/DNN6PwaOCng9DagUkQL39XR3ng4FjAbVGber6iScoYf/4Y6YFQpnAa1+KQNNwPWqOgZnH1zT1he4MV1gdTk0zqLtulwPHOfu80nAye4ANMZ0V6Tqcdio6suqekuk4+gtLMGODhnA7paF7lmah0RksYh8JiLHdlCeLCJPishyEXkBSG65TFUtx/kSHuYWFeEMDdv8a3068JF79u1dEVkkIu+ISLG7jodF5D4RmQv8uUW83xaR10TkoPUGrH81UIPTQTwi8lMRme+u5ya3LFVEXnXPJCwRka+75RtEJM99PkVEZrVY/3TgDOBW90zE0Bbr3q6qn7rPq4Dl7vYbEyxWl8NTl1VV97kv492HDepggmV/PRbHre7nd3HAZ1hE5O8islJE3gYK3PLj5MArSye6dfgAbh34o/v5XiAik0XkDRFZKyLfdedJc+vsp+66z3TL26pXt4hzZWmRiNzWyjoDrxA9LCJ3icjHIrJO3LPUbW2XO+1wEXlfRBa6sRaKSKY770h3nidE5NvB+CfEuq6ctTDBlSwiJTijfxUCx7UyzzU43yXjRWQU8KaIjGin/GqgRlVHi8gE4NM21v0RMF1EvDijH80BviIirwATcYa9fQZ4RFUfEZFvAnfhnFEC6A9MV1WfiNwIICLXAicCZ6lqfVsbLSKTgdWqWiYiJwHDgak4I7W9LCIzgHxgm6p+1X1PZjv7cT9V/VicS+KvqOqz7c0rIoOAw4C5nVm2Me2wuhyBuuxu80JgGHC3qlpdNoeirXp8Ds5VkolAHjBfRGbjXDEaiXOVpQ/OsN3/whmK/B4RyXd/BF/hlrdmk6pOEpHbgYeBo931LwHuA+qAs1W10v1BOsetFyfTol6JSC5wNjBKVVVEsjqxzYU4I1iOwhly/Fl3GQdtlzhXqv4GnKmq5W5Sf7OqftM9ZjwsIncC2ar6QCfW3ePZGezIab4cNQqnsjwqItJinmOAxwBUdQWwERjRTvmMgPJFOMOJtuZjnLNb04FPcIZNPRIn4VyhqnU4B4//uPP/211ns2dU1Rfw+lLgFODcdr6QfyQiS3ES2pvdspPcx2c4CcQonC/pxcCJIvInEfmSqu5tY5ndIiJpOGf6rlPVymAu2/RKVpcjUJdV1ec2VekPTBWRccFatumV2qrHxwBPuJ+3UuB94AicOtpcvg14F5xfyzj17BtukjsNeK2NdTbfI7EYmKuqVW5SXu++V4A/iMginGHAi3CS3tbq1V6chPxBETkH5+pSR15UVb+qLnOXS1vbhZN0jwPecn+I/Bqn7qGqb7kx3Q18qxPr7RUswY4CqvoJzi/j/DCt8iMCvpTd5hJJwEw612azusXrxcAg3MrWhttVdSzwNZwDQBLOweOP7kFtkqoOU9UHVXUVMNld7u9F5P/cZTTxxWc2qaMgxbmpscR9NF9yi8dJrh9X1ec7sa3GdJrV5fDV5WaqugfnrOHJHS3HmM4IQj1+CPgGcCHOj9imNuZr/hHrD3je/DoOuNiN4XD3x2QpkNRavXLXMRXnLPRpwOudiDNwnS1PCrQkwNKAOj5eVU8CEBEPMJqAJmPGEuyo4F4a9gK7Wkz6AKeC4V42LgZWtlM+G7jILR8HTGhjlcuBfji/zD9zy0qA7+J8YYPz5XyB+/xid51t+Qz4Ds5l4X7tbauqvgwsAC4D3gC+6Z5RRkSKRKTAXUaNqj4G3IpzIAHYABzuPv9aG6uoAtLddW0OOBjc556NeBBYrqp/bS9OY7rD6nLY6nJ+8yVwcdqJnwisaC9eYzqrRT3+APi6iHhFJB/nDO88nDraXF4IHNv8fvfM7zacs7wPHUIomUCZqjaKc3/GQDe+g+qVW/cyVfV/wI9wmrR0R1vbtRLIF5FpbgzxIjLWnfYjnGPRRcBDErobn2OKtcGOnOb2XuD8MrzMbQcZOM89wL0ishjnjM/lqlovIm2V34vz4V6O82Ff2NqK3fZZc3EqY6Nb/AlwFV+c9fq+u6yfAs3tyNqkqh+K08XXqyJyoqrubGf23+Jcsh7tPj5xt3sfzq/+YTg3N/mBRpz2qAA34Zwx+x0wq41lPwk8ICI/wLnMvTZg2tHAJcDigH3/S/eAZEx3WV0Of10uBB5x22F7gKdV9ZX2tsuYDrRVj1/AaebxOc6NtD9T1R1u+XE4bZQ34dS7QI8D+aq6/BBiehz4r3t8WMAXPyLHc3C9SgdeCrii9ONurrPV7VLVBnFuhLxLnHsp4oA7RKQJp1nIVFWtEqd9+q+B33Rz/T2GOM2FjDHGGGNMMIjTW8dnqvpgpGMxkWEJtjHGGGNMkIjIQpz7G05s52Zh08NZgm2MMcYYY0wQ2U2OxhhjjDHGBJEl2MYYY4wxxgSRJdjGGGOMMcYEkSXYxhhjjDHGBJEl2MYYY4wxxgRRj0uwRWSpiMyMdByRJCJni8hmEdknIod14/2zRORbIYjrYRH5fbCX28l1q4gMi8S6OyPa4zPdY8ejQz8eGWNMLIqpBFtENojICS3KLheRD5tfq+pYVZ3VwXIGuQlNTx3J8jbgWlVNU9XPWk50t73a/cLbKSJPNA87HAkiEufGcmRA2cVunC3LQjoccsBnY5/72CAivwjlOluJoUs/RETknR7+eY5KdjzqtHaPRwDiWCciy8IcmzHGhERMJdixIgq+KAcCSzuYZ6KqpgFDgGzgxlAH1RZVbcIZjnVGQPEMnGFhW5bN7sqyD+F/keXun3OB/yciJ3ZzOSElIhcD8ZGOw0SvGDkezQAKgCEickToQzLGmNDqcQl24FklEZkqIgtEpFJESkXkr+5szUnaHvcs5TQR8YjIr0Vko4iUicijIpIZsNxL3Wm7ROT/tVjPjSLyrIg8JiKVwOXuuj8RkT0isl1E/i4iCQHLUxH5noisFpEqEfmdiAwVkY/deJ8OnL/FNrYaq4gkisg+wAt8LiJrO9pfqloJvAyM6cq6Aqaf4V4G3yNO05LRAdMOE5FP3e17CkhqJ5TZHJhMfwn4Uytls91lf1tE1ohIhYi8LCL9AtarInKNiKwGVreyTceIc8l6ZjvxAKCqC3CSg0kB7/+miCwXkd0i8oaIDHTLRURud/dTpYgsFpFx7rQDmt20PNMZUH4VcDHwM/ez+d+2YnP/D78BftbRdpjIsONRp49HlwEvAf9znwcuf7CIzHbjeltE7haRxwKmH+XGuUdEPu9MvTbGmJBT1Zh5ABuAE1qUXQ582No8OGdFL3GfpwFHuc8HAQrEBbzvm8AanDO6acDzwL/daWOAfcAxQALOJc/GgPXc6L4+C+dHSzJwOHAUEOeubzlwXcD6FOcLJQMYC9QD77jrzwSWAZe1sR/ajDVg2cPa2Y/7p+OcvX4T+G3A9FnAtzqxX0bgDgeLcxb1Z+68Ce5jI/Ajd9q57j76fRsxfRmocPdfnvveFKA0oEyBYuA4YCcwGUgE/gbMbrF9bwE5QHLgNgMnA5uBqW3EccBnw/0f1gBnu6/PdLdxtPu//TXwsTvtK8BCIAsQd57Clvu0jc9t4P/k4bb2U4tY73b37wEx28OOR8TW8SgFqAROBb6GU7cTAqZ/4m5jgrvNlcBj7rQiYJf7Xg/OsWgXkB/pz4c97GGP3v2IeABdCtb5stoH7Al41LTzhTYbuAnIa7Gc1r7Q3gG+F/B6pPslFQf8H/BEwLQUoKHFF9rsDmK/Dngh4LUCRwe8Xgj8POD1X4A72lhWm7EGLLujBLvS3X8+nKYYRQHTZ/FFgt3efvl/wNMB0zzAVmAmzpnnbYAETP+YthPsJKAOmAicDTzuls8JKFvvlj0I/DngvWluTIMCtu+4Vrb5BpzEfVw7+6b5s7EHqHWf39a8HcBrwJUttrkG5zL4ccAqnETG02K5+/ep+/pyDiHBBqYAJXyRMFmCHeYHdjzqMNaAZbd3PPoGUO5uWxKwly9+0BYDTUBKwPyP8UWC/XMCknm37A3a+DFgD3vYwx7hesRiE5GzVDWr+QF8r515r8Q5y7pCROaLyGntzNsPJ/lqthHngN/Hnba5eYKq1uCcJQm0OfCFiIwQkVdEZId7mfYPOGdhA5UGPK9t5XVaN2LtrMnu/ksC7gU+EJHWmnB0tF/2T1NVP85+KHKnbVVVbfHeVqlqHTAPJzGfAXzgTvowoKz5UnrL9e7D+X8UBSzygP+H6zqcHwRL2oojQB7O/r8e5wdDczvngcCd7uXoPThn3QXnB8q7wN9xziyXicj9IpLRiXW1S5ybO5tvunxNRDzAPcAP1Wm/biLHjkeHfjy6DKdeNrnHgef4oplIP6DC3cZmgds2EDivuT66dfIYoLCT6zbGmJCIxQS701R1tapeiHPzzJ+AZ0UkFeeMSkvbcA7WzZrPnJQC24H+zRNEJBnIbbm6Fq/vxTkzPFxVM4Bf4iRiwdBerF2iqo3AP4HBwLguruuAaSIiwACcs9jbgSK3LPC97Wluh/0lvkiwPwgoa06wW643Fef/sTVw01pZ/nnAWSLyww7icBag6lPVv+KcWW9OnDYD3wlMqlQ1WVU/dt9zl6oejnMZfwTwU/d91ThnGpv1bW/VLeJ4XJ0eGNJU9RScy/hTgKdEZAcw3511i4h8qTPbZsLPjkcHE5H+OFd+vuEm/ztwmpOdKiJ5ONuaIyKBdWdAwPPNOGewA+tjqqrecojbZIwxh6RHJ9gi8g0RyXfPrO5xi/04lyP9OG0Gmz0B/Mi9oSYN5wzPU+4ZwmeB00Vkunujz410/OWUjtMMY5+IjAKuDtJmdRRrl4iIF7gC5wzVui6u62ngqyJyvIjE45ztrcdpCvIJzpfsD0QkXkTOAaZ2EM5s4FicL9Dm7ro+wjmDPIkvEuwngCtEZJKIJLoxzVXVDR0sfxtwPPBDEenK/+MWnJsOk4D7gBtEZCw4NxqKyHnu8yNE5Eh3X1TjJOZ+dxklwDkikiJOf9dXtrO+Ug78bLa0F+fM3iT3capbfjgwtwvbZcLIjketugSnWdVIvvg8jwC2ABeq6kZgAXCjiCSIyDTg9ID3P4azL74iIl4RSRKRmW7ibowxEdOjE2ycG9qWuney3wlcoKq17uXGm4GP3MuKRwH/Av6Nk8Stx0mOvg+gqkvd50/inFHZB5ThJJNt+QlwEVAFPAA8FcTtajPWLvjc3S+7cS7Hnq2qFV1Zl6quxGk/+TecG5NOB05X1QZVbQDOwWlrXAF8Hefmp/Z8jHND1dzmpiWquhMnASlT1dVu2ds47b+fw/l/DAUu6MxGq+omnCT7F9L5wXRexdlP31bVF3DOPj7pXmpfApzizpeB87/ejXOZfBdwqzvtdpx2sqXAI8Dj7azvQWCM+9l8sZVtUFXd0fzA2T8Ape5+N9HJjkcHuwy4J/Dz7H6m7+OLZiIXA9Nw6tPv3djrAVR1M86Nx7/EqQebca4a9fTvNmNMlGu+cct0gXuWZg/O5db1EQ7HGNOL9bbjkThdfq5Q1d9EOhZjjGmL/crvJBE53b28n4rTq8RinB4CjDEmrHrT8chtejVUnP62T8Y5Y/1ihMMyxph2WYLdeWfitOHdBgzHubxrp/+NMZHQm45HfXG6udwH3AVcrW0MuW6MMdHCmogYY4wxxhgTRHYG2xhjjDHGmCCKi3QAhyIvL08HDRoU6TCMiYiFCxfuVNX8SMcRDFaXTW/Wk+qyMcYR0wn2oEGDWLBgQaTDMCYiRKTNkTFjjdVl05v1pLpsjHFYExFjjDHGGGOCyBJsY4wxxhhjgsgSbGOMMcYYY4LIEmxjjDHGGGOCKGQ3OYrIv4DTgDJVHeeW3QqcDjQAa4ErVHWPiAwClgMr3bfPUdXvhiq2Zldd8wPKd1e2Oi0/O4P7774r1CEYYzpp1JixbNu2rVPz9uvXjxXLloY4ImOMMaZ1oexF5GHg78CjAWVvATeoapOI/Am4Afi5O22tqk4KYTwHKd9dyYwrftnqtNkP/SGcoRhjOrBt2zZuemZup+b9zXlHhjgaY4wxpm0hayKiqrOBihZlb6pqk/tyDtA/VOs3xhhjjDEmEiLZBvubwGsBrweLyGci8r6IfKmtN4nIVSKyQEQWlJeXhz5KY4wxxhhjuiAiCbaI/ApoAh53i7YDxap6GPBj4D8iktHae1X1flWdoqpT8vNt4CtjjDHGGBNdwp5gi8jlODc/XqyqCqCq9aq6y32+EOcGyBHhjs0YY4wxxphDFdYEW0ROBn4GnKGqNQHl+SLidZ8PAYYD68IZmzHGGGOMMcEQym76ngBmAnkisgX4DU6vIYnAWyICX3THNwP4rYg0An7gu6pa0eqCjTHGGGOMiWIhS7BV9cJWih9sY97ngOdCFYsxxhhjjDHhYiM5GmOMMcYYE0SWYBtjOiQi/xKRMhFZElB2o4hsFZES93FqwLQbRGSNiKwUka9EJmpjjDEmMizBNsZ0xsPAya2U366qk9zH/wBEZAxwATDWfc89zTcxG2OMMb2BJdjGmA61NjJrO84EnnS731wPrAGmhiw4Y4wxJspYgm2MORTXisgitwlJtltWBGwOmGeLW2aMMcb0CpZgG2O6615gKDAJZzTWv3TlzSJylYgsEJEF5eXlIQjPGGOMiQxLsI0x3aKqparqU1U/8ABfNAPZCgwImLW/W9by/fer6hRVnZKfnx/6gI0xxpgwsQTbGNMtIlIY8PJsoLmHkZeBC0QkUUQG44zMOi/c8RljjDGRErKBZowxPUcbI7POFJFJgAIbgO8AqOpSEXkaWAY0Adeoqi8CYRtjjDERYQm2MaZDXRmZ1Z3/ZuDm0EVkjDHGRC9rImKMMcYYY0wQWYJtjDHGGGNMEFmCbYwxxhhjTBBZgm2MMcYYY0wQWYJtjDHGGGNMEIU0wXaHTy4TkSUBZTki8paIrHb/ZrvlIiJ3icgad+jlyaGMzRhjjDHGmFAI9Rnsh4GTW5T9AnhHVYcD77ivAU7BGZBiOHAVzjDMxhhjjDHGxJSQJtiqOhuoaFF8JvCI+/wR4KyA8kfVMQfIajFSnDHGGGOMMVEvEm2w+6jqdvf5DqCP+7wI2Bww3xa3zBhjjDHGmJgR0ZscVVVxhlnuNBG5SkQWiMiC8vLyEEVmjDHGGGNM90QiwS5tbvrh/i1zy7cCAwLm6++WHUBV71fVKao6JT8/P+jBrdxRxVPzN7Mm/0ss3rI36Ms3xhhjjDE9WyQS7JeBy9znlwEvBZRf6vYmchSwN6ApSVis2FHJ60t30OT3U5uQyUUPzGH59spwhmCMMcYYY2JcqLvpewL4BBgpIltE5ErgFuBEEVkNnOC+BvgfsA5YAzwAfC+UsbVU2+Dj/VXlFGYmccERxYzc8Q7JCV5+9FQJTT5/OEMxxhhjjDExLC6UC1fVC9uYdHwr8ypwTSjjac/nW/ZQ1+jnuFEFeD1Cgq+Wm84Yy9WPf8oT8zdzyVEDIxWaMcYYY4yJITaSI+DzK4u37mVQbgp5aYn7y08e15fJxVncN2stjXYW2xhjjDHGdEJIz2DHio27qqlp8DGhf9b+spKSEs65+Ar2JheyNf8YTvzOb8iucXoRzM/O4P6774pQtMYYY4wxJppZgg2sLttHYpyH4pyU/WWNPphxxS9RVR76eAP+Eccx4zCnW+7ZD/0hUqEaY4wxxpgo1+ubiPj8yrqd1QzNT8PrkYOmiwijCzPYVFFDZV1jBCI0xhhjjDGxpNcn2Nv31tLQ5GdIfmqb84wpzABgxfaqcIVljDHGGGNiVK9PsDdV1CAC/bOT25wnMzme/lnJLNteidPZiTHGGGOMMa2zBLuihr4ZSSTGedudb3RhBntrGymtrA9TZMYYY4wxJhb16gTbJ3GUVdYzIDulw3mH5KfiEVhTti8MkRljjDHGmFjVqxPs6sQcFOiXldThvEnxXgbkpLC6rAprJGKMMcYYY9rSq7vpq07MQ4C+mR0n2ADD8tN4Z1cZtfFZIY3LGNM99U0+BKG6uoaMrOxOvadfv36sWLY0xJEZY4zpTXp1gr0vMY+8tMQO2183G5qfxrsry9iT0j/EkRljuqK8qp73V5WzdU8tAPlf/z2XnHkS/bLavnm52W/OOzLU4RljjOllem0TEb9fqUnIoU9mYsczu5ITvBRlJbMnpb/1JmJMlNhcUcPTCzazu6aBIwfncOTgHOIy8nl24RYWbdkT6fCMMcb0Qr02wd5UUYPfE0+f9M41D2k2vCCN+vh0VpXazY7GRFplbSOvLt5OZnI8F00t5qghuRw1JJft//o+g/JSeW9lOUu37Y10mMYYY3qZXptgL3G/dAvSO38GG5xmIqjy2pLtoQjLGNNJqso7K8pQhdMmFJKa+EWLN22s5avjCynOSeHdFWWUVdVFMFJjjDG9TacSbBE5ujNlsWTx1r2I+slN61qCnZoYR2r9Tl5fsiNEkRkTOt2tyyLyLxEpE5ElAWU5IvKWiKx2/2a75SIid4nIGhFZJCKTg7sVjvU7q9lUUcO0oblkpSQcNN3rEU4e15fkBC+vL9lBo88fijCMMcaYg3T2DPbfOlnWIREZKSIlAY9KEblORG4Uka0B5ad2Z/mdtWTrXpIa9+L1SJffm1W7hRU7qlhXbs1ETMzpbl1+GDi5RdkvgHdUdTjwjvsa4BRguPu4Cri3W5G2Q1WZs66CrOR4JhRltjlfcryXr4zpy+6aRj5YvTPYYRhjjDGtarcXERGZBkwH8kXkxwGTMoDOdb3RgqquBCa5y/cCW4EXgCuA21X1tu4st4sxsGRrJSkNu7v1/qyarWzNPozXluzgmmOHBTk6Y4LvUOuyqs4WkUEtis8EZrrPHwFmAT93yx9V507gOSKSJSKFqhq0dlUbd9VQvq+eE0f3wdPBj+QBOSlM6p9FyZY9jO2XQZ+Mrt13YYwxxnRVR2ewE4A0nEQ8PeBRCZwbhPUfD6xV1Y1BWFanbdldy97axm4n2Am+Wg4rzuKVRdYO28SMUNTlPgFJ8w6gj/u8CNgcMN8WtyxoSrbsISXBy8i+6Z2a/6ihOaQkeHlvZZn1AGSMMSbk2j2DrarvA++LyMMhSoIvAJ4IeH2tiFwKLACuV9XuZcAdWLzVucExuZsJNsBpE/rxu1eWsbZ8n3PjozFRLNR1WVVVRLqUuYrIVThNSCguLu70+yprG9m4q4apg3M63cQrMc7LMcPyeHNZKcu2VzK2X9vNSowxxphD1dk22Ikicr+IvCki7zY/DmXFIpIAnAE84xbdCwzFaT6yHfhLG++7SkQWiMiC8vLybq17yda9xHmE5Ibud9/11fGFiMArn9tZbBNTglmXS0WkEMD9W+aWbwUGBMzX3y07gKrer6pTVHVKfn5+p1e6srQKgLGFGV0KdlTfdPpkJDJnXQVNdsOjMcaYEOpsgv0M8Bnwa+CnAY9DcQrwqaqWAqhqqar6VNUPPABMbe1N3f1SDrRyRxVD89Pw0P0v2b6ZSRwxMIdXFm3r9jKMiYBg1uWXgcvc55cBLwWUX+r2JnIUsDeY7a9XllZRmJlERnJ8l94nIhw9NI999U0s2mp9YxtjjAmdzg6V3qSqwe4J4EICmoe0uAnqbGBJq+8KgpWlVRxWnM2WQ1zO6RML+X8vLWXljqpOtwU1JsK6VZdF5AmcGxrzRGQL8BvgFuBpEbkS2Aic787+P+BUYA1Qg3MDc1Ds2lfPrn0NfHlE935cD8hJoTgnhfkbKhjbL4PEuG7dq22MMca0q7NnsP8rIt8TkUK379scEcnp7kpFJBU4EXg+oPjPIrJYRBYBxwI/6u7y21Nd38SW3bWMKDj0dtOnjC8kziM8/9mhpurGhE236rKqXqiqhaoar6r9VfVBVd2lqser6nBVPUFVK9x5VVWvUdWhqjpeVRcEK/iVpVUIzoiq3TV9aC51jX4+3bQnWGEZY4wxB+jsGezmy8CBl5IVGNKdlapqNZDbouyS7iyrq1aXOX1Xj+ibziE1Igfy0hI5dlQBzy3cyk9OGkm8t9cOjGliR1DrcritKt1H/5zkA0Zt7Ko+GUkML0jjs027mdjfbnY0xhgTfJ36llLVwaEOJFxW7XBukBrRJzhNOs6fMoC3lpXy/spyThjTp+M3GBNBsVyXPZmF7K1t5LDirENe1rQhuawp28fCjSHpqMgYY0wv16kE2+067yCq+mhwwwm9VaVVJMZ5KM5JCcryZo7MJy8tkacXbLYE20S9WK7L8QMnAjA4N/WQl5WdmsCovuks2rIXSck65OUZY4wxgTp7nfWIgOdJOAPEfApE/ZdySytLqxhWkNatIdKblZSUcPZFl+9/7ckaz5tVIzjtku/SLyOB++++KwiRGhMSMVuXE4onkZOa0OXeQ9oydXAOK0qrSD7s9KAszxhjjGnW2SYi3w98LSJZwJOhCCjUVpfuY/rQ3I5nbEejD2Zc8cv9r/fWNvLIxxtImn4p5R/cf6ghGhMysVqX99U3EVc4Kihnr5tlpSQwpjCDJU3HsW1PLf2ykoO2bGOMMb1bd+/KqwZiri3n3tpGdlTWMSLIXeplJsczND+NxVv34hPr9svElJioyx+u3ol44xiUF5ymXc2mDsoBhL+/tyaoyzXGGNO7dbYN9n9xehoA8AKjgadDFVSorC5tvsEx+EObTx6YxZryfVSkRn2uYnqxWK3Ls1aW4a+voTAzuGeZM5LjqV/+Hk/Hx3P1l4cyIEj3ZhhjjOndOtsG+7aA503ARlWNuc6fV5YGtweRQIWZyRRmJlHaNIL6Jp8NYGGiVUzW5ZkjC3jwzj/iPXVi0Jdd+9nLZEz6Cne9s5pbzwv+8o0xxvQ+nWoioqrvAyuAdCAbaAhlUKGyakcVqQleikLU1vKoIbk0xqXy7082hmT5xhyqWK3LJ4/rS/2i10OybK3ezTeOHMjzn21l/c7qkKzDGGNM79KpBFtEzgfmAefhDIc8V0TODWVgobCqdB/D+6Qj0v0eRNpTnJNCeu0O/vbuGvbWNIZkHcYcip5Sl4Pt6plDSfB6uPPtVZEOxRhjTA/Q2SYivwKOUNUyABHJB94Gng1VYMFy1TU/oHx3JQCLi04ns3YbZ1/0AACfL17CjCCvr9+eRaxK6cud76zm/04fE+SlG3PIYrYuh1J+eiKXTR/EP2av5XvHDgtJMzJjjDG9R2d7EfE0fyG7dnXhvRFVvruSGVf8kikX/4wmbxJjDpvKjCt+yYwrfklDQ1PQ15fSuJeLphbz0MfrWbixIujLN+YQxWxdDrXvzBhCakIcd9hZbGOMMYeos1+sr4vIGyJyuYhcDrwK/C90YQXfrn1OU9Pc1ISQr+uGU0fTLzOZnzyziNoGX8jXZ0wXxHxdDpXs1AS+efQg/rd4B0u37Y10OMYYY2JYuwm2iAwTkaNV9afAP4AJ7uMTIKZGVNlV7SbYaYkhX1daYhx/PncC63dW8+sXl6CqHb/JmBDqSXU52Kqra8jIyiYjK5ubvnEc/vpqTvzRnfvLAh+jxoyNdLjGGGNiQEdtsO8AbgBQ1eeB5wFEZLw7LWbGGN5VXU9inIfUhPB0n3f0sDx+dMIIbn97FWP6ZXDlMdY/tomoO+ghdTnY1O/jpmfm7n89b30FnySmctX9syjKPrDHod+cd2S4wzPGGBODOmoi0kdVF7csdMsGhSSiENm1r4Hc1ISQ9SDSmu8fN4yvjO3Dza8u43+Lt4dtvca0osfU5VA7rDiLtMQ43l9Vjt+uPhljjOmGjhLsrHamhaYz6RBQVSqqG8LSPCSQxyPc/vVJTC7O5gdPfMa7K0rDun5jAmS1My1m6nI4xHs9fGl4HuX76lm2rTLS4RhjjIlBHTURWSAi31bVBwILReRbwMJDWbGIbACqAB/QpKpTRCQHeArnjNoG4HxV3X0o6wGorvdR3+QPyw2OJSUlnH3R5QeU+SSO+IIvc+VD8/jbxYdz2oR+IY/DmBZCVpd7ouEFaXyemcTHa3cxvE+ajcxqjDGmSzpKsK8DXhCRi/niS3gKkACcHYT1H6uqOwNe/wJ4R1VvEZFfuK9/fqgr2VVdD0BuWugT7EYfzLjilweVT2v08dib8/j+E5+xu6aRS44aeMD0wP66W8rPzuD+u+8KSbym17iO0NblHkVE+PKIfJ6Yv5k56yr48oj8SIdkjDEmhrSbYKtqKTBdRI4FxrnFr6rquyGK50xgpvv8EWAWQUiwd+4LXw8ibUmK9zKsfDZ5J36X//fiEir2NfCD44ftbxPe3F93a2Y/9Idwhmp6oAjU5ZhXkJHE+KJMPt+8h5F90+mbkRTpkIwxxsSITo3kqKrvAe8Fed0KvCkiCvxDVe/HuRGr+W7AHUCflm8SkauAqwCKi4s7taJd1fWkJnhJjo/sZV6P+rjvksP5+XOLuP3tVeyuaeD/ThuDxxO+Gy9N7xaiutxjHT0sl3U79/H28lIuPKJzxxtjjDGms0Olh8IxqrpVRAqAt0RkReBEVVU3+aZF+f24/fZOmTKlU7f479oX/hsc2xLv9XDbuRPJTkngwQ/XU1nXyJ+/NiHSYRljWpEY5+W4kQX8d9F25q23kVmNMcZ0TsQSbFXd6v4tE5EXgKlAqYgUqup2ESkEytpdSGfWgzPIzISizENdVNB4PMKvvzqarOR4/vLWKqrqmvDbaNXGRKUh+WmMKcxg3oYK4vqNjnQ4xhhjYkBEsjoRSRWR9ObnwEnAEuBl4DJ3tsuAlw51XfVxafj8GpYbHLtCRPj+8cO56YyxvLWslHX5x9Dk80c6LGNMK748Ip+slHjSjruanfvqIx2OMcaYKBep06Z9gA9F5HNgHs7NVq8DtwAnishq4AT39SGpi88AInuDY3sumz6IW8+dQFVyH15fugO/3wa2MCbaJMR5OGVcXyQxle/+eyH1Tb5Ih2SMMSaKRaSJiKquAya2Ur4LOD6Y66qLd5qGhKMP7I601kd2M6lKZe3YU3lvZRnHjSoI64iTxpiOFaQnUT3rfhbEf58bnlvMbedNtBuUjTHGtCqSNzmGRW18JpnJ8cR7I9/Gua0+sgHmX3s+R516MQs27iYvLZGJA7LCG5wxpkMNa+dy/Z//xl/eWkVygpffnzXOfgwbY4w5SI9PsOsSMukXBWevO2P60Fwqqht4f3U5uWkJ9M9OiXRIxpgWrj1uGDWNPu6dtZYmn/L7s8dFxQ94Y4wx0aNHfyvUN/moi0uLuhsc2yIifGVsX7KS43l96Q5qG6ydp4l+IrJBRBaLSImILHDLckTkLRFZ7f7NjnScwSIi/OwrI/n+ccN4asFmLn1wHrvsxkdjjDEBenSCvbasGsRDXpTe4Nga52aqQuoa/Ly1vBRVu+nRxIRjVXWSqk5xX/8CeEdVhwPvuK97DBHh+pNG8tfzJ7Jw425Oun02ry3efkB9HTVmLBlZ2Z16jBozNoJbY4wxJth6dBORVaVVQHTc4NgV+emJHD0sl9mrd7Jse2WkwzGmO84EZrrPHwFmAT+PVDChcs7k/ozpl8FPnvmcqx//lKmDc7j+xBFMHZzDtm3buOmZuZ1azm/OOzLEkRpjjAmnHn0Ge2VpFaJ+slJiK8EGmDQgi6KsZGav3kmDNynS4RjTHgXeFJGFInKVW9ZHVbe7z3fgdM3ZI43qm8EL3zua3545lnXl1Xz9/jmceteHJI7/CpV1jZEOzxhjTAT06DPYK3dUkdhYiTcGu9ISEU4YXcDjczexOedwVNV6KzDR6hhV3SoiBcBbIrIicKKqqogc1NbJTcavAiguLg5PpCES7/Vw6bRBnHt4f14q2cZjczaSevQlPPTRBgrSExmUm8rA3BT6ZiRZ137GGNML9OgE+5pjh7Fx9rPAEZEOpVuyUhKYNjSXD1YrL5Vs46zDiiIdkjEHUdWt7t8yEXkBmAqUikihqm4XkUKgrJX33Q/cDzBlypQecbNBSkIcF04t5sKpxWQNHMVX/9/DrCuvZv6GCuZtqCAxzkNxTgoDc1MYmJNKWpJzCK6uriEjq3P3gfbr148Vy5aGcjOMMcYcoh6dYB8+MJuMutJIh3FIJg3IYuGSldz036V8aXhe1I5IaXonEUkFPKpa5T4/Cfgt8DJwGc5orJcBL0Uuysjw7y1lysAcpgzMoa7Rx+aKGjbsqmFjRTWry/YB0C8riTGFGeCNt/baxhjTg/ToBLsn8IhQXLGAtSkn87tXlnHHBYdFOiRjAvUBXnCbL8UB/1HV10VkPvC0iFwJbATOj2CMEZcU72V4n3SG90lHVdlV3cC6ndUs317J28vLKLr6X3y8dieHFWeTHO+NdLjGGGMOkSXYMSC5sZKrZw7jrndWc9ZhRcwcWRDpkIwBQFXXARNbKd8FHB/+iKKfiJCXlkheWiJHDMxm+946HvnP08xPTKVk8x4mF2czZWA2cTZ4jTHGxCw7gseIa44dytD8VH71whKq65siHY4xJghEhH5Zyex8+U9cfGQxA3NTmbu+gsfmbmLDrupIh2eMMaabLMGOEYlxXv70tQls3VPLX95cFelwjDFBlpeWyFfHF3L2YUWIwEsl23h7eSmNPn+kQzPGGNNFlmDHkCmDcvjGUcU8/PF6SjbviXQ4xpgQKM5J4eIji5kyMJul2yp5Yt4myqtsKHZjjIkllmDHmJ+dPIqC9CR+8dwiO7NlTA8V5/Fw9LA8zjmsiAafn6cWbGaFjepqjDExI+wJtogMEJH3RGSZiCwVkR+65TeKyFYRKXEfp4Y7tliQkRTP784ax4odVdw/e12kwzHGhNCAnBQumlpM34wk3lhWyoerd4INOGWMMVEvEmewm4DrVXUMcBRwjYiMcafdrqqT3Mf/IhBbTDhxTB9OHd+XO99ezdJteyMdjjEmhFIS4jj7sCImFGWycNNu0k653oZgN8aYKBf2BFtVt6vqp+7zKmA5YEMUdtHvzhxHdmo83//PZ9ariDE9nNcjHDuqgONGFhBfNI5z7vmYjdbLiDHGRK2I9oMtIoOAw4C5wNHAtSJyKbAA5yz37lbecxVwFUBxcXH4go2gkpISzr7o8oPK0xPzWVPwZf7vpaX85fyDuiI2xgRZV4Y0r64OfgI8vn8mL976Q3ZecCNn3v0R933jcI4akhv09RhjjDk0EUuwRSQNeA64TlUrReRe4HeAun//Anyz5ftU9X7gfoApU6Zo+CKOnEYfzLjil61Oe+r5F3nuU2Ha0FzOPbx/mCMzpndRv6/TQ5pff/KYjmfqhqZty3nxe0dz5SPzueTBudx81njOP2JASNZljDGmeyLSi4iIxOMk14+r6vMAqlqqqj5V9QMPAFMjEVus6bt3GdOG5PLL5xczf0NFpMMxxoTBoLxUnv/e0Rw1JJefPbeIP/xvOT5/rzjfYIwxMSESvYgI8CCwXFX/GlBeGDDb2cCScMcWiwS49xuT6Z+dzFWPLmD9TmuXaUxP1txMZUBhAS/95FTqFr/J/bPXMeCi35ORX0hGVjYZWdmMGjM20qEaY0yvFYkmIkcDlwCLRaTELfslcKGITMJpIrIB+E4EYotJWSkJPHTFEZx9z8d88+H5PPvdaeSmJUY6LGNMCLTWTOXzLXt4XyZT+INHOX1iPzKT4/nNeUdGKEJjjDGR6EXkQ1UVVZ0Q2CWfql6iquPd8jNUdXu4Y4tlA3NTeeDSw9m2p5aL/zmXiuqGSIdkjAmTif2zOHNiP/bVN/GfuZtYuaMq0iEZY0yvZiM59iCHD8zhX5cfwfqd1Vz0wBxLso3pRQbmpnLR1GJy0xJ4fekOUo/9Dntq7BhgjDGRENFu+syha60Lv/6JBaxsOIYZNz7PO//vLPpkJEUmOGNMWGUkx3Pu5P7M3VDBXP90jv/L+9xw6mi+NrkIsREgjTEmbCzBjnFtdeG3qaKGF+av45jfvsywsg9IajrwknF+dgb3331XuMI0xoSJxyNMG5LLW3/+Lodffw8/eeZz/j1nIz84bhjHjSqwRNsYY8LAEuweqjgnhabZ/yTl5B+wrvhUTp/Qj6Ls5P3TZz/0hwhGZ4wJNd+uTTz73ek8++kW7npnNVc+soCx/TK4dNpATh1fSHpSfKRDNMaYHsvaYPdke7dx/pQBpCR4ee6zLXy2aTeq1leuMb2FxyOcP2UA7/1kJn8+dwJ1jT5+/txijrj5bb7/xGc8t3ALZZV1kQ7TGGN6HDuD3cNlJsfz9SMG8NayUmav3smOyjqOG1UQ6bCMMWEU7/Vw/pQBnHd4fz7bvIdnF27hjSU7+O/n2wCQfTupL11L066N+HZtxl9Zjm/fTmg8OPmur28gMTGhU+vt168fK5YtDeq2GGNMLLAEuxdIjPPy1fGFLNi4m0/W7mLrnlr6JBd2/EZjTI8iIkwuzmZycTa/P3Mcy7ZX8sHqnfzu7kfJH3cMe2sP7Ds7Mc5DSoKXpHj3Eedh7iuPc8J5l5MY5yExzkNCnIfEOK/794uyOI/H+uI2xvRalmD3EiLCEYNyKM5J4a3lpazLP4arH1vI9SeNZFhBWqTDM8aEmccjjCvKZFxRJj8/6y5+etXF1Df5qKhuoKquicq6Rqpqm6ht9FHX6GNffRM79/lIG38C89ZXdLj8xDgPGef8lmv+8ymDclMYXZjBuH6ZFOek4PHYjZbGmJ7NEuxepk9GEhceUcxzL7zI+6u8vLF0B6eMK+Tio4o5anCuffEZ04slxnkpzEymMLPtea4/eQy3vraUxiY/9e6joclPfZPP/es89tU3MW/NXl7+oARPeh7i8QKgDbU0la2lcfsKmravpKl0DfgarTmJMaZHsQS7F/J6hL6Vy3nltuu4/4N1PDV/M68u3k5eWiInjC7guFEFTB2cQ1ZK59pZGmN6F48IifFeEuO97c736nU38pfXl9Hk97NrXwPlVfWUV9WzPSeL8v7jAPCK0CcjkbXvPsm89RUcVpxFvNfuvzfGxDZLsHux3LREbjhlND86YQRvLN3Bm8tKeWXRdp6cvxmApIY9pNWXk1a3k7T6cuL99dZ/tjGmy+I8HvpkJB0w6FVdo49te2vZtruOLXtqSDrsdM7/xyekJcYxbWguM0bkM2N4HgNzUyMYuTHGdI8l2IakeC9nTirizElF1Df5OO3bPydv2rls3ZPC9r3Z7PQNByArJZ5NZSt54bMtTB2cS1FWcgdLNsZESnV1DRlZ2Z2ctzrE0RwsKd7LkLw0huQ594DcePGxPPXuAt5ftZPZq8p5a1kpAANzU/jS8DxmDM9n+rA80hLta8sYE/3sSNVLtTbEerO1i5dw6kVXAeDzK+VV9WzdU8vWPbVsSunPj576HICirGSOHJzD1ME5HDkkl0G5KTZKnDFRQv0+bnpmbqfmvf7kMSGOpmPaUMPJ4wo5eVwhqsr6ndXMXlXOB6t38vynW3lszibiPMKUQdnMHFnAzJH5jOyTbsccY0xUsgS7l2priHWA+deev/+51yP0zUyib2YShw/M5s4f/oiRR8xgX2I+1TX5vLwrj+c/2wpAgr+eEycN4sjBORw5OJfhBWl206QxpstEhCH5aQzJT+PyowdT3+Rj4cbdvL+qnPdXlnPLayu45bUVFGYm8eUR+cwcWcDRw3JtdEpjTNSwBNt0SZNPOfkb1+x/rarsrmlk6+5aPvt0Pgs3ZPLqou2A06RkvNsN2EevPUfT7m0kNFXTMuW2dt3GmK40aWmMTyNt6BQ2Fk/gifLxPDl/M+promnHKho3f07jps/xVWwBbLAbY0xkRF2CLSInA3cCXuCfqnpLhEMy7RARclITyElNYPdb83j+rqvZXFHL3PW7mL+hgiVbK3lg9jqakidB8iQS4jwUpCWSn5FIQXoiBelJLH76tkhvhgkBq8umK7rapOUP//kT4DRj2763lg27atiYmcLOojFw1IWkJcYxMDeFuY/8gU27ahiQk2zNSYwxYRNVCbaIeIG7gROBLcB8EXlZVZdFNjLTGSUlJZxz8RUHlCUCY/GwaGM5x337/yhzu+latGUvPr8C4Ol/Nufc8xHDCtIozEymX1YSBRlJpCbEkRTvITnei4jQ5PfT5FMafX6a/M5fdRbhnBV3vzu9IqQmxpGWGLf/b1K8x75cw8jqsgkXr0fon51C/+wUjhmWx766JjZUVLNxZw2rS/eRduK1zLj1PbJT4pnQP4sJ/TMZkp/KoNxUBuelWnekxpiQiKoEG5gKrFHVdQAi8iRwJmBfyjGgvXbdC689n3FFX4xe4fcrFTVOv7ifzfmAOE8Bs1aWU76vfn/SHExej5Ca4CU9KZ70pDgykuJJS4ojff8jnrTEODKS4khOiMPrcfr6bX6IQEPzgBo+//7nDU1+Gnw+6hv9NPj81Dc6A27U7x9ww+eWBZS781RW1+JHEPXjUR8e9SHqw+tvItnr56mffS2WR9m0umwiIi0pjnH9MhnXLxOfX7n5mgv4++MvsmjzXj7fsoe73yvHH3CMyUyOpzAziby0RHLTEshNTSQ7JZ7kBK/ziHceSfFeRNj/Q12A5t/sgqAoquBXRQGanytMHphNTqol8sb0JtGWYBcBmwNebwGOjFAsJoQ8HiEvLZG8tETK93zO09/9EeAksaWVdZRV1VPb4OO2v93Lnpp6cBNRUET9CH7WrV3H1675fwC4X2kA+BVe+MefGDhkOD5PPH6Jw+eJw++Jp1bi2VFdR3JmDj5PPD6Jx++Jx+eJR6Wbg1uoHy9+MtNSSIzzkBDnITHOQ2Kcl80bN9DU1OAm0H5EfYj68aoPX3kZh8/8Kj5Vmnx+fH6lya80NPkp376ZGL8/1OqyiTivR6jcuIyrvzLpi0JPHJ6MAryZffBm9qUusw/lKVlIcgYJ6bmkZBdQ3eALahzxH9/P6tkvBXWZxpjoJhqK04XdJCLnAier6rfc15cAR6rqtQHzXAVc5b4cCax0n+cBO8MYbmdEY0wQnXFZTJ0TGNNAVc2PZDBtOcS6HCga/wehYNvZ83RlW6O2LhtjuifazmBvBQYEvO7vlu2nqvcD97d8o4gsUNUpoQ2va6IxJojOuCymzonGmNrQ7bocKIa295DYdvY8vWlbjTEH6+Y18ZCZDwwXkcEikgBcALwc4ZiMMV1nddkYY0yvFVVnsFW1SUSuBd7A6drrX6pqHZgaE2OsLhtjjOnNoirBBlDV/wH/68Zb273UHCHRGBNEZ1wWU+dEY0ytOoS6HChmtvcQ2Xb2PL1pW40xLUTVTY7GGGOMMcbEumhrg22MMcYYY0xMi7kEW0ROFpGVIrJGRH7RyvREEXnKnT5XRAZFQUyXi0i5iJS4j2+FIaZ/iUiZiCxpY7qIyF1uzItEZHIUxDRTRPYG7Kf/C0NMA0TkPRFZJiJLReSHrcwT1n3VyZjCvq9Cra3tFpEcEXlLRFa7f7MjHWswiIhXRD4TkVfc14PdY9Ya9xgW8yOTiEiWiDwrIitEZLmITOuJ/08R+ZH7mV0iIk+ISFJP/H8aYzovphJs+WL45VOAMcCFIjKmxWxXArtVdRhwO/CnKIgJ4ClVneQ+/hnKmFwPAye3M/0UYLj7uAq4NwpiAvggYD/9NgwxNQHXq+oY4Cjgmlb+f+HeV52JCcK/r0Ktre3+BfCOqg4H3nFf9wQ/BJYHvP4TcLt77NqNcyyLdXcCr6vqKGAizvb2qP+niBQBPwCmqOo4nJt6L6Bn/j+NMZ0UUwk2AcMvq2oD0Dz8cqAzgUfc588Cx4tIKMfE60xMYaeqs4GKdmY5E3hUHXOALBEpjHBMYaeq21X1U/d5FU4CUNRitrDuq07G1OO0s92BdfoR4KyIBBhEItIf+CrwT/e1AMfhHLOgB2yniGQCM4AHAVS1QVX30AP/nzgdBiSLSByQAmynh/0/jTFdE2sJdmvDL7dMPPbPo6pNwF4gN8IxAXzNbV7wrIgMaGV6uHU27nCbJiKfi8hrIjI2nCt2mxMdBsxtMSli+6qdmCCC+yrUWmx3H1Xd7k7aAfSJVFxBdAfwM8Dvvs4F9rjHLIie+ngoBgPlwENuU5h/ikgqPez/qapbgduATTiJ9V5gIT3v/2mM6YJYS7Bj1X+BQao6AXiLL87emAN9ijNk8ETgb8CL4VqxiKQBzwHXqWpluNbbng5iiti+CrX2tludbo9iuusjETkNKFPVhZGOJcTigMnAvap6GFBNi+YgPeT/mY1zVn4w0A9IpeOmcMaYHi7WEuwOh18OnMe9XJcJ7IpkTKq6S1Xr3Zf/BA4PYTyd1Zl9GVaqWqmq+9zn/wPiRSQv1OsVkXichO5xVX2+lVnCvq86iilS+yrU2tju0uYmOe7fskjFFyRHA2eIyAacJmXH4bRVznKPWRAF9TEItgBbVLX56suzOAl3T/t/ngCsV9VyVW0Ensf5H/e0/6cxpgtiLcHuzPDLLwOXuc/PBd7V0Hb23WFMLdrrnsGBNzZFysvApW4PGUcBewMu20aEiPRtbi8vIlNxPp+h/HHU3Pb1QWC5qv61jdnCuq86E1Mk9lWotbPdgXX6MuClcMcWTKp6g6r2V9VBOMeLd1X1YuA9nGMW9Izt3AFsFpGRbtHxwDJ62P8Tp2nIUSKS4n6Gm7ezR/0/jTFdE3MDzYjIqTjtF5uHX75ZRH4LLFDVl0UkCfg3TvvNCuACVV0X4Zj+iJNYN7kxXa2qK0Ic0xPATCAPKAV+A8QDqOp97hfB33EuZdYAV6jqggjHdC1wNc5+qgV+rKofhzimY4APgMV80R72l0BxQFxh3VedjCns+yrU2tnuucDTONu/EThfVaPqZtnuEpGZwE9U9TQRGYJzRjsH+Az4RsCVr5gkIpNwrtolAOuAK3B+DPao/6eI3AR8Hac+fgZ8C6fNdY/6fxpjOi/mEmxjjDHGGGOiWaw1ETHGGGOMMSaqWYJtjDHGGGNMEFmCbYwxxhhjTBBZgm2MMcYYY0wQWYJtjDHGGGNMEFmCHSEi4hOREneo609FZLpbPkhElgRpHbNEZEqLsjNF5MWA1zeIyJqA16eLSMu+xdtbx40i8pNOzLPV3d5lInJhFzajMzFcLiJ/d5+fJSJjOpjf6w7d/Eow4zAmmrl1Q0VkVKRjMcaYns4S7MipVdVJ7lDXNwB/DNN6PwaOCng9DagUkQL39XR3ng4FjFLWGber6iScIYX/4Y7YFwpnAe0m2MAPiY7BfowJpwuBD92/xhhjQsgS7OiQAexuWSgiSSLykIgsds+4HttBebKIPCkiy0XkBSC55TJVtRwnoR7mFhXhDE093X09HfjIPZP+rogsEpF3RKTYXcfDInKfiMwF/twi3m+LyGsictB6A9a/Gmewlmz3PT8Vkfnuem5yy1JF5FX37P4SEfm6W75B3OHARWSKiMxqsf7pOAP63OqeLR/ayj7tD3wVZ/ALY3oFEUkDjgGuxBk9EhHxiMg9IrJCRN4Skf+JyLnutMNF5H0RWSgib8iBo9EaY4zpQFfOQJrgShaREiAJKASOa2WeawBV1fHuZd03RWREO+VXAzWqOlpEJgCftrHuj4DpIuIFVgNzgK+4TSYm4gz//gzwiKo+IiLfBO7COTsM0B+Yrqo+EbkRwB1Z8ETgrPZGKxORycBqVS0TkZOA4cBUQICXRWQGkA9sU9Wvuu/JbGc/7qeqH7vNW15R1WfbmO0O4GdAemeWaUwPcSbwuqquEpFdInI4MBgYhHPFpwDnqs6/3KtLfwPOVNVy9wfuzcA3IxO6McbEHkuwI6fWbTKBiEwDHhWRcS3mOQbniw5VXSEiG4ER7ZTPwEmEUdVFIrKojXV/jHOm2gt8AswD/g9nePkVqlrnxnSOO/+/OfBs9TOq6gt4fSmwGSe5bmxjnT8SkSvcOE93y05yH5+5r9NwEu4PgL+IyJ9wkuUP2lhml4jIaUCZqi50h6g2pre4ELjTff6k+zoOpy77gR0i8p47fSQwDnhLRMA5TmwPb7jGGBPbLMGOAqr6idv0IT9Mq/wI+D7OF+cDqlolIknATDrX/rq6xevFwCScM9vr23jP7ap6m4icATzoNt8Q4I+q+o+WM7tnuk8Ffi8i76jqb4EmvmjWlNRRkCIyAPiv+/I+YCBwhoic6r4/Q0QeU9VvdLQsY2KViOTgXCEbLyKKU+8VeKGttwBLVXVamEI0xpgex9pgRwG3mYcX2NVi0gfAxe48I4BiYGU75bOBi9zyccCENla5HOiHcya8+exxCfBdnOQbnET7Avf5xe462/IZ8B2cJh792ttWVX0ZWABcBrwBfNNtH4qIFIlIgbuMGlV9DLgVmOy+fQNwuPv8a22sogq3+YeqbnZvJJ2kqvep6g2q2l9VB7nb9q4l16YXOBf4t6oOVNVBqjoA54dwBfA1ty12H5wf2OAcS/Ldq1iISLyIjI1E4MYYE6sswY6cZPdGvBLgKeCyFs0uAO4BPCKy2J3ncrd9c1vl9wJpIrIc+C2wsLUVq6oCc4FdAU06PgGG8MUZ7O8DV7jNTC7B6XmjTar6IfAT4NXmGxHb8Vvgx8DbwH+AT9xteRYnOR4PzHP3zW+A37vvuwm4U0QWAC33VbMngZ+6N38edJOjMb3QhRx8tvo5oC+wBVgGPIZzz8ZeVW3AScr/JCKf4/z4no4xxphOEyfXMsYY09uISJqq7hORXJx7MY5W1R2RjssYY2KdtcE2xpje6xURyQISgN9Zcm2MMcFhZ7CNMcYYY4wJImuDbYwxxhhjTBBZgm2MMcYYY0wQWYJtjDHGGGNMEFmCbYwxxhhjTBBZgm2MMcYYY0wQWYJtjDHGGGNMEP1/D7Jz2UDnEYYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_plot(df=train, avoid=['ID', 'Insurance', 'Sepsis'], name_for_title='Train')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0399fbe6",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- Right-skewed: Plasma glucose, Blood Work Result-2, Blood Work Result-3 , Blood Work Result-4, Age.\n",
    "- Normally-distributed: Blood Work Result-1 , Blood Pressure, Body mass index.\n",
    "- The skewness will be dealt with in the later section by normalizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "68b379af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Blood Work Result-3    1.747436\n",
       "Blood Work Result-4    1.559577\n",
       "Age                    1.049232\n",
       "Plasma glucose         0.866427\n",
       "Blood Work Result-1    0.531711\n",
       "Blood Work Result-2   -0.096263\n",
       "Body mass index       -0.520007\n",
       "Insurance             -1.032858\n",
       "Blood Pressure        -1.768116\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# similarly, checking for skewness for  the test dataset\n",
    "df_test.skew(numeric_only=True).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a38a35b",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- Similar to the train set, the columns in the test set have very large standard deviation as well.\n",
    "- Positively-skewed: Blood Work Result-3, Blood Work Result-4, Age, Plasma glucose, Blood Work Result-1\n",
    "- Negatively-skewed: Blood Pressure, Insurance, Body mass index, Blood Work Result-2 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01de8ae0",
   "metadata": {},
   "source": [
    "#### 3.2.3 visualization of skewness for the test set\n",
    "- The histograms of these features will be plotted to visualize their distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "25388dcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a copy\n",
    "test=df_test.copy()\n",
    "dist_plot(df=test, avoid=['ID', 'Insurance'], name_for_title='Test')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23fbd6b7",
   "metadata": {},
   "source": [
    "the distribution of the features are:\n",
    "- Right-skewed: Plasma glucose, Blood Work Result-2, Blood Work Result-3, Blood Work Result-4, Age.\n",
    "- Normally-distributed: Blood Work Result-1, Blood Pressure, Body mass index."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8b83cc",
   "metadata": {},
   "source": [
    "#### 3.2.4 checking for outliers of each numerical column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8e38f9ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f7dXpmHcBd/Xi/P8QEb9Ny3PST8dz2iaQJcwHc3eIiBuAGwCqqqqiF230yYknnsjixYvzJsk999yTadOmFf06Rm1tLStfXLnjhgkwbWrhfWtra1m1alWXMkld4ikrK2POnDl9Oh8zs94abj3IThGxHJgMTBnCZs9Jc6CnRcQLqezNiNiWlgXUpzozIuKgiLg8Il4hG8K9n6yn+KNU/yTgn4GZZEl1DNlwa+51H5uznDumKODqnLYqI+LGgTxZgD322KPL+p577tllvaamhvTHAGVlZV22HXDAAQMdThdTp07tsv6Vr3ylSzyjRo2ipqZmUGMws5Fr2CZISQcDo8mGNXM9RDasiaQDgf2BZ4uUPwh8KpUfCkzfibB+DZwlaZ90vHdLOkDSZGBURPwcuASYmW6yeV9E3Af8f8BEsl5gM1nCRNJM4C8KtPUr4HxJE1LdqR3tDqR77723y/qyZcu6rE+ePJnq6mokcdJJJ1FRUQFARUUF48ePH+hwupgyZUpnMpTEKaec0iWe6upqJk2aNKgxmNnINdwSZMcc5CrgZ0BNTu+tw/eBUZLWpDrnRsRbRcqvByZIegZYCKzob3ARsZYsAS5NQ7XLgH2BqcD9Ke5bye7AHQ3cmuJZCdRFxKtkw7XvlvQ08EXguQJtLQX+FViejnEH8I7+xl5MRy+ye++xQ01NDdOnT6empoZLLrmEvfbai0svvXQwQtnBhRdeCGS9x3zxmJkNFnXcEWi7vqqqqmhoaBiy9jrmD4vdxdp9DjL3bR5HTD2i4Bxkb45tZrazJK2IiB2+Gw/D7yYd24UMZvJyYjSzUhtuQ6xmZmbDgnuQNvhe3b7Y+aLkV8lmbs3MhiknSBtUlZWVALS0tABQPrU82zB1+zYzs+HICdIGlecSzWxX5TlIMzOzPJwgzczM8vAQq1FXV1f0Bcid84fl2fxhZWWlh07NbLfnBGk0NTWxbtUq3ltg+xvp96vr1/PnoQrKzKzEnCANgPcCn0V5t91I9rSlz6LOZTOz3Z3nIM3MzPJwgjQzM8vDQ6xGS0sLbw3Ccf3AcTPblTlBGlu2bGHrIBy32J2xZmbDnYdYbVhZv3498+fPZ8OG7u/JNjMbWj0mSEnb0kuMn5T0hKRZqbxC0lMDEYSk+yVVdSs7VdJdOesXS2rKWT9Z0qI+tHG5pK/2os6L6XzXSjq7D6fRmxjOlfS9tHyapEMK1HufpPtSDE9L+tJAxjGc1dfXs3r1aurr60sdipmNcL3pQW6JiBkRcThwMXD1IMfU4RHg6Jz1Y4DXJe2T1melOj2S1Jeh5OsiYgZwKvBDSWV92LcvTgPyJkhgK/CViDiE7Bp8oVAy3Z2sX7+eJUuWEBEsWbLEvUgzK6m+zkG+E3ile6GkscD1QBXZP+4LIuK+IuXjgJuBw4F1wLjux4yIVkmvS6qMiCaylyP9nCwx3pV+XyKpArgJmAy0AudFxPOSbgHeBI4AHgZez4n3c8AZwBkRsSXfiUZEo6TNwLuAlyRdBPwNsCdwZ0RcJmkv4N+BcmA0cGVE/ExSM1AVEetTz/hbEXFsTvuzgFOAj0i6BDgzIn6X0/afgD+l5TckPZPOf22+WIfSBqC1sbFXN940NjYybtwO/2kLqq+vJyL7nmV7ezv19fUsWLCgv6Game2U3vQgx6Uhx3XAj4Ar89T5AhARcRhwNlCfkmOh8s8DmyPiA8BlwJEF2n4YmCXpIKAR+G1aH0OWXB8HvgvUR8R04KdAXc7+5cCsiOj8V1bSF4GPA6cVSo6p3kygMSJekjQHmAYcBcwAjpQ0G/gY8MeIODwiDgV+Weh4uSLiEWARcFHqnf+uUN30B8ARwKMFtl8gqUFSQ2tra2+aH7aWLVtGW1sbAG1tbSxdurTEEZnZSNabHuSWNOSIpGOAH0s6tFudD5MlKiJinaQ/AAcWKZ9NSmQRsVrS6gJtP0LWUxwNLAceAy4lSxjrIuLNFNMZqf5PgH/K2f/2iNiWs/4Z4AWy5NhWoM0vSzovxXlyKpuTflam9QlkCfMh4FpJ3wTujoiHChyzXyRNIOs1XxgRr+erExE3ADcAVFVVDfpjbiYBe0+b1vkVjmL6+vWOE088kcWLF9PW1kZZWRlz5szpZ5RmZjuvT3exRsRysqHMKYMTzg4eJkuQs4DlEfEGMBY4lt7NP27qtr4GqCDrWRZyXUR8EDgTuDH1eAVcnXp7MyKiMiJujIjngJnpuFdJujQdYyvbr+3YnoJMN+WsSj/zUlkZWXL8aUT8Ry/OdZdXU1ODlD3ubtSoUdTU1JQ4IjMbyfqUICUdTNab6373xEPAOanOgcD+wLNFyh8EPpXKDwWmF2jyGWA/sp5oR+9tFTCPLHlClig/mZbPSW0WshL4O2CRpP2KnWtELAIagBrgV8D5qUeHpKmS9knH2BwRtwLXkCVLgGa2DxufWaCJN4B3pLZeyEm+P1CWJW4EnomI/10szt3J5MmTqa6uRhLV1dVMmjSp1CGZ2QjWlznIVcDPgJpuw5YA3wdGSVqT6pwbEW8VKb8emJBuPlkIrMjXcGR3bDwKbMgZEl0OvJ/tPcj5wHlpmPZvgaJfiYiI3wBfBe6RNLmHc18ILADuBf4VWJ7O5Q6y5HYY8Fi6NpcBV6X9rgC+I6kB6H6tOtwGXCRppaS/7LbtQ+lcjs/pWc7tIdbdQk1NDdOnT3fv0cxKTh13Ddqur6qqKhoaGvq83xlnnMFb69fz9718m8feM2b0ag7Sj5ozs+FO0oqIqMq3zY+aM8rLy3l1/foBP64To5ntyvyoOTMzszycIM3MzPLwEKsB8Ge2zzV296f0+0aCPwN7D1FMZmal5ARpVFZWFt2+saUFgL3Ly9m7F/XNzHYHTpDmm2nMzPLwHKSZmVke7kEadXV1NDU19VwxR0sadi0vL/bUvsFTWVnpnq+ZDSonSKOpqYmn1zzD3uP36bly8trmNwDQW0P/zsZXN7805G2a2cjjBGkA7D1+H447+JM9V0zuW3cbQJ/2GSgdbZuZDSbPQZqZmeXhBGlmZpaHh1iNlpYWNr35ZqnDsD7wg+DNBp8TpLFlyxa2tm8tdRjWB32969jM+s5DrGa7sPXr1zN//nw2bNjQZfnee+9l9uzZnH322cybN48NGzbsUL/QcXLXGxsb89Yvtm+xdgbi/B5//HGqq6tpaGjo0sZzzz1HdXX1Tv3xsH79eubNm9flmvUlxv5sH2iD2d5Qn0tvDGZMPSZISdvSC3uflPSEpFmpvELSUwMRhKT7JVV1KztV0l056xdLaspZP1nSoj60cbmkr/aizovpfNdKOrsPp9GbGM6V9L20fJqkQwrUGyvpsXTNn5Z0xUDGYbuP+vp6Vq9eTX19fZflb3zjGwC8+OKLrF27lvr6+h3qFzpO7vqVV16Zt36xfYu1MxDnd9lll7Fp0yYuvfTSLm1cddVVbNq0iYULF+5Ue2vXru1yzfoSY3+2D7TBbG+oz6U3BjOm3vQgt0TEjIg4HLgYuHrAo8jvEeDonPVjgNcldXxZb1aq0yNJfRlKvi4iZgCnAj+UVNaHffviNCBvggTeAo5P13wG8DFJRxeoayNUW1sbS5YsISJYvHgxixcvJiL4xS9+wdatXYfMFy9eTGNjY2f9JUuWdOkt5pbn1mtubt6hfq7u+3b09PK101e5x1m8eHHn8saNGwHYuHFjZxuPP/44zc3NADQ3N/erF7l+/XoWL17cub548eIeY+/pXAfqWvTWYLY31OcyHGLq6xzkO4FXuhdKGgtcD1QBW4EFEXFfkfJxwM3A4cA6YFz3Y0ZEq6TXJVVGRBMwFfg5WWK8K/2+RFIFcBMwGWgFzouI5yXdArwJHAE8DLyeE+/ngDOAMyJiS74TjYhGSZuBdwEvSboI+BtgT+DOiLhM0l7AvwPlwGjgyoj4maRmoCoi1qee8bci4tic9mcBpwAfkXQJcGZE/C6n7QA2ptWy9JP/VRsj0MY3X6Gx8eURfYNKY2MjW7duJfuoZMmyw7Zt23ao39bWxpVXXtlZv729nfr6ehYsWEB9fX2X8tx6HXLr5+q+b8d6vnb6KvfYuefXXXt7O5dddlmXsoULF/LjH/+4z+3l/mHR1tbWY+z5zj+3fk/bB9pgtjfU5zIcYupND3JcGnJcB/wIuDJPnS+Q/bt+GHA2UJ+SY6HyzwObI+IDwGXAkQXafhiYJekgoBH4bVofQ5ZcHwe+C9RHxHTgp0Bdzv7lwKyI6Lxikr4IfBw4rVByTPVmAo0R8ZKkOcA04CiyHt2RkmYDHwP+GBGHR8ShwC8LHS9XRDwCLAIuSr3z33WvI2m0pFXAS8CyiHi0QJwXSGqQ1NDa2tqb5m038fbbb3cmjtyklE9Hb7CjfltbG0uXLgVg2bJlXcpz63XIrZ+r+75Lly7NW9Yfuccpdn5tbW2dvcoOHb3JvraX20ZE9Bh7T+c6UNeitwazvaE+l94Y7Jh604PckoYckXQM8GNJh3ar82GyREVErJP0B+DAIuWzSYksIlZLWl2g7UfIeoqjgeXAY8ClZL3CdRHxZorpjFT/J8A/5ex/e0Tk/jn9GeAFsuRY6E/SL0s6L8V5ciqbk35WpvUJZAnzIeBaSd8E7o6Ihwocs89S3DMk7Q3cKenQiNhhzjcibgBuAKiqqhoRvcwJY9/F1L+c1PlVh5GotraWF154gddff522tjYkARRMIpI44IADePHFF2lra6OsrIw5c+YAcOKJJ7J48eLO8qlTp3bW65BbP1f3fefMmdM5JNq9nb7KPXax8ysrK2PPPffskiQrKir61d6iRYs625DUY+z5zr8v2wfaYLY31OcyHGLq012sEbGcbChzyoBGUdjDZAlyFrA8It4AxgLH0rv5x03d1tcAFWQ9y0Kui4gPAmcCN6Yer4CrU29vRkRURsSNEfEcMDMd9ypJl6ZjbGX7tR3bU5CS3pd66askzcvdFhGvAveR9VbNOr33ve/tTBxlZWWMGZP9vTt69Ogd6paVlfG1r32ts/6oUaOoqakBoKampkt5br0OufVzdd+3pqYmb1l/5B6nrKyMsrL8twOMGjWKK67oeh/bpZdemrduT+11XMOONnuKvadzHahr0VuD2d5Qn0tvDHZMfUqQkg4m6811nwl9CDgn1TkQ2B94tkj5g8CnUvmhwPQCTT4D7EfWE+3ova0C5pElT8gSZccDQc9JbRayEvg7YJGk/Yqda0QsAhqAGuBXwPmSJqSYp0raJx1jc0TcClxDliwBmtk+bHxmgSbeAN6R2nohJ/n+QNKU1HMkzdeeSDZXa9aprKyM6upqJDF37lzmzp2LJE4++eQu/9ADzJ07l2nTpnXWr66uZtKkSQBMnjy5S3luvYqKih3q5+q+76RJk/KW9UfucebOndu5PGHCBAAmTJjQ2cZf/dVfdfYaKyoq+vVS78mTJzN37tzO9blz5/YYe0/nOlDXorcGs72hPpfhEFNvhljHpbkwyHpSNRGxrdtfmN8Hrpe0hqz3dG5EvCWpUPn1wM2SniFLgivyNRwRIelRYGLOkOhy4AK29yDnp2NdRLpJp9jJRMRv0tc97pF0YkSsL1J9IfCvwAfSz/J03huBTwOVwDWS2oE2srlVgCvIep9XAvcXOPZtwL9IqgXO6jYPuS/ZfO1osj9i/j0i7i52XjYy1dTU0NzcTE1NTec8Y01NDdOnT2fhwoVMnTqViRMnduktdtQpdJzc9draWurq6or+ZZ7vmIXaGYjz+8xnPsOll17KwoULqa+v72zjkksu4Utf+lK/eo+57TU2NnYu9zXG/mwfaIPZ3lCfS28MZkwqNrFvu5aqqqpoaGjo835nnHEGm157k5NnfL7nykmp3+Yx0ucg/ag5s4EhaUVEVOXb5kfNGeXl5bxYgvc6Wv85MZoNPj9qzszMLA8nSDMzszw8xGoAvLr5pc55xd7WB/q0z0B5dfNLTKX0d9CZ2e7NCdL6dUt8tGQPIZpaPvSJaiqT+hWzmVlfOEGab/gwM8vDc5BmZmZ5OEGamZnl4SFW67W6ujruv/9+IPvuZGVlpYdnzWy35QRpvdbU1MT69esZI/HqMHhZqpnZYHKCtD4ZI/GOPfyxMbPdn+cgzczM8nCCtILq6ur69EDwvtY3MxvOPFZmBTU1NQ1qfTOz4cw9SOPqq69m9uzZXHPNNTt9rJdffpnZs2dz9tlnM2/ePDZs2MD69euZP38+G3xjj5ntQgY1QUraJmmVpCclPSFpVh/3v0XSWYMV30CQdIqkf+jjPsPqvJYsWQLAL37xi50+1vPPPw/Aiy++yNq1a6mvr6e+vp7Vq1dTX1+/08c3Mxsqg92D3BIRMyLicOBi4OpBbm/IRcSiiPjHUsfRX1df3fU/yc70Il9++eUdyu655x4WL15MRLBkyRL3Is1slzGUQ6zvBF4BUOYaSU9JWiPpEznl35P0rKR7gX1S+fGS7uo4kKQTJd3ZvQFJzZKuTr3WBkkzJf1K0u8kzUt1Jkj6derRrpF0airfS9I9qbf7VE5M/yhpraTVkr6Vp81zJX0vLd8iqU7SI5J+39FLLHReaduRkh6QtCLFuq+kianuQanOv0n63ED8R+iuo/fYIbcX2dLSQmNjI7W1tdTW1tLY2Mi2CAA2b93WZVttbW1n7zFXW1sbW7duBaC9vd29SDPbZQz2TTrjJK0CxgL7Asen8jOAGcDhwGTgcUkPAscABwGHAO8B1gI3AfcB35c0JSJagfNSeT7PR8QMSdcBtwAfSu0/BfwAeBM4PSJelzQZ+K2kRcDHgD9GxEkAKUlNAk4HDo6IkLR3L855X+DDwMHAIuCOdIwdzktSGfBd4NSIaE1J+esRcb6kLwK3SPoO8K6I+Jd8jUm6ALgAYP/99+9FeEMvUlJta2tj6dKlLFiwoMQRmZn1bLAT5JaImAEg6Rjgx5IOJUsg/xYR24D/lvQA8FfA7JzyP0r6T4CUnH4CfFrSzWSJ9DMF2lyUfq8BJkTEG8Abkt5KCW4T8A1Js4F2YCpZ0loDXCvpm8DdEfGQpDFkCfVGSXcDd/finO+KiHZgraT3pLK850WWNA8FlkkCGA38KZ3zMkn/D/DPZH9I5BURNwA3AFRVVUUv4uu18vJygM6vbtTW1vLUk08CMH7MaA6YNq3L1zpmz56d9ziSiAjKysqYM2fOQIZoZjZohmyINSKWk/UWp/TzEDcDnwbOBm6PiK0F6r2VfrfnLHesjwHOSTEcmZL3fwNjI+I5YCZZorxK0qWpjaPIeoEfB37Zizhz21QPdQU8neZpZ0TEYRExB0DSKOADwGbgXb1ot1+qq6u7rJ988sn9Pla+HmxZWRljxmR/h40aNYqampp+H9/MbCgNWYKUdDBZD2kD8BDwCUmjJU0h62E9BjyYU74vcFzH/hHxR+CPwCVkybK/JgIvRUSbpOOAA1J8+wGbI+JW4BpgpqQJwMSIWAx8mSI9uR4UOq9ngSmpd42kMkkfTNu+DDwDfAq4OQ3HDriLL764y/pFF13U72O9+93v3qHspJNOYu7cuUiiurqaSZOG/gXLZmb9MVRzkJD1lmoiYlu6weYY4EkggL+PiD+n8uPJ5uieB5Z3O95PgSkR8cxOxPRT4BeS1gANwLpUfhhwjaR2oA34PPAO4P9IGpvi7+/kWd7zioi30408dZImkv33+LakrcD/Ao6KiDfS/OwlwGX9bL+o6upqlixZslO9xw77778/zz//PFOnTmXixInU1NQQETQ3N7v3aGa7FHXcQLErSHeLroyIG0sdy3BUVVUVDQ0NA3a83LnHjt9PPflk58PKD/jAB7vMQXavb2Y23ElaERFV+bbtMo+ak7SC7Aabr5Q6lpGir4nOidHMdie7TIKMiCNLHYOZmY0cfharmZlZHrtMD9KGh60RvPF2oW/YmJntPpwgrdcqKytpaWkBsocIVFZWljgiM7PBs0vdxWrFDfRdrGZmu7tid7F6DtLMzCwPJ0gzM7M8nCCNurq6Ll/4NzMzJ0gjeydk9/dCmpmNdE6QZmZmeThBmpmZ5eEEaWZmlocfFGBs3ry51CGYmQ077kEaEUFEMHv2bO67775Sh2NmNiz0mCAlbZO0StKTkp6QNCuVV0h6aiCCkHS/pKpuZadKuitn/WJJTTnrJ0ta1Ic2Lpf01V7UeTGd71pJZ/fhNHoTw7npnZZIOk3SIT3UHy1ppaS7BzKOYq688sqhasrMbFjrTQ9yS0TMiIjDgYuBqwc5pg6PAEfnrB8DvC5pn7Q+K9XpkaS+DCVfFxEzgFOBH0oq68O+fXEaUDRBAl8Cnhmk9gE4//zzu6xv3brVvUgzM/o+xPpO4JXuhZLGSrpZ0prU4zmuh/Jxkm6T9IykO4Fx3Y8ZEa1kCbHjidhTgZ+TJUbS74dTT/Y/Ja2W9GtJ+6c2bpH0A0mPAv/ULd7PSVoiaYd2c9pvBDYD70r7XCTp8dTOFalsL0n3pN71U5I+kcqbJU1Oy1WS7u/W/izgFOCa1Fv9yzzXtBw4CfhRoRgHQlNT0w5l7kWamfXuJp1xklYBY4F9gePz1PkCEBFxmKSDgaWSDixS/nlgc0R8QNJ04IkCbT8MzJI0GmgEfgv8zzTkeDjwOHA7UB8R9ZLOB+rIemcA5cCsiNgm6XIASV8ETgROi4i3Cp20pJlAY0S8JGkOMA04ChCwSNJsYArwx4g4Ke0zsch17BQRj6Th4bsj4o4C1b4N/D3wjmLHknQBcAHA/vvv35vme7R1q19nZWbWlyHWg4GPAT+WpG51PgzcChAR64A/AAcWKZ+dU74aWF2g7UfIeoqzgOXAY8D/AI4A1kXEm2RDr/+a6v8ktdnh9ojYlrP+GaAaOKtIcvyypKeBR4Gvp7I56WclWTI/mCxhrgFOlPRNSX8dEa8VOGafSPo48FJErOipbkTcEBFVEVE1ZcqUgWieMWN8c7OZWZ+GWCNiOTCZrOc0FB4mJ0FGxBtkPdlj6d3846Zu62uACrKeZSHXRcQHgTOBGyWNJes1Xp3+UJgREZURcWNEPAfMTMe9StKl6Rhb2X5tx/YUpKT3paHWVZLmAR8CTpHUDNwGHC/p1l6cb5/le6fj1772tcFoysxsl9KnBJmGSUcDG7ptegg4J9U5ENgfeLZI+YPAp1L5ocD0Ak0+A+xH1itcmcpWAfPIkidkifKTafmc1GYhK4G/Ixsi3a/YuUbEIqABqAF+BZwvaUKKeaqkfdIxNkfErcA1ZMkSoBk4Mi2fWaCJN0jDpxHxQk7y/UFEXBwR5RFRkc7tPyPi08Xi7a+bbrqpy/qYMWM47rjjBqMpM7NdSm8S5LiO3g3wM6Cm27AlwPeBUZLWpDrnpiHMQuXXAxMkPQMsBPIOJUb2NudHgQ0R0ZaKlwPvZ3sPcj5wnqTVwN+S3flZUET8BvgqcE/HjTRFLAQWAPeSDeMuT+dyB1lyOwx4LF2by4Cr0n5XAN+R1AB0v1YdbgMuSjcv7XCTTqm492hmllGWg2x3UFVVFQ0NDX3e7yMf+QgADzzwwECHZGY2rElaERFV+bb5bgxj/PjxpQ7BzGzY8aPmzMzM8nCCNDMzy8MJ0szMLA/PQRrV1dWlDsHMbNhxgjRqa2tLHYKZ2bDjIVYzM7M8nCDNzMzy8BCr9aiuro6mpiZaWloAKC/f/ijbyspKD9Ga2W7JCdJ61NTUxFNPPUXHU5c6Xoe1cePGUoZlZjaonCCtVyZMmNC5PHNm9kz2J54o9BpPM7Ndn+cgzczM8nCCNDMzy8MJ0oqqq6vrvDmnv/vX1dUNYERmZkPDc5BWVFNTE1u2bKGsrKzf+5uZ7Yrcgxwikk6TFJIOLnUsQ+n1119n1apVzJ49mzPPPJN58+axYcMG1q9fz/z589mwYUOpQzQzy8sJcuicDfwm/R4xmpubO5dbW1tZu3Yt9fX11NfXs3r1aurr60sXnJlZEU6QQ0DSBODDwGeBT6ayUZK+L2mdpGWSFks6K207UtIDklZI+pWkfUsYfr899thjtLe371B+zz33sGTJEiKCJUuWuBdpZsOS5yCHxqnALyPiOUkbJB0J/AVQARwC7AM8A9wkqQz4LnBqRLRK+gTwdeD8UgTe0tLCli1baGtrY/z48V22bd68mcbGxoJP0lm9enXe8ra2NiQB0N7eTn19PQsWLBjYwM3MdpJ7kEPjbOC2tHxbWv8wcHtEtEfEn4H70vaDgEOBZZJWAZcA5RQg6QJJDZIaWltbByv+fsnXe+zQ8VSetrY2li5dOlQhmZn1mnuQg0zSu4HjgcMkBTAaCODOQrsAT0fEMb05fkTcANwAUFVVFTsfcVfl5eUF72IdP348FRUVBb/GMXfu3IKPo5NERFBWVsacOXMGNGYzs4HgHuTgOwv4SUQcEBEVEfE+4L+Al4Ez01zke4BjU/1ngSmSjgGQVCbpg6UIfGddfvnlecvLyso6E+6oUaOoqakZwqjMzHrHCXLwnc2OvcWfA+8FWoC1wK3AE8BrEfE2WVL9pqQngVXArCGLdgAdddRRjBq140fspJNOorq6GklUV1czadKkEkRnZlach1gHWUQcl6esDrK7WyNio6RJwGPAmrR9FTB7KOMspLKykpaWls43ePTVrFmz+M1vfgPAlClTmDJlCjU1NUQEzc3N7j2a2bDlBFlad0vaG9gDuDLdrDOs1NbW0tTU1OX7jH3xjW98o+C27373u/2Mysxs8DlBllBEHFvqGMzMLD/PQZqZmeXhHqT1ysaNGzu/u9jxouRCX+EwM9sdOEFajyorKwE6X3tVXl6+wzYzs92NE6T1qNCj5MzMdmeegzQzM8vDCdLMzCwPJ0ijrq6u4PNUzcxGKidIY8mSJSxZsqTUYZiZDStOkGZmZnk4QZqZmeXhBGlmZpaHvwdpbN68udQhmJkNO06Q1vkIOTMz285DrGZmZnmMqAQpyU/XNjOzXhlRCXIwSfJwtZnZbmREJkhJx0q6X9IdktZJ+qkkpW3/KGmtpNWSvpXKbpF0Vs7+G3OO85CkRcDaVHaXpBWSnpZ0Qe4+kr4u6UlJv5X0nlT+Hkl3pvInJc1K5Z+W9JikVZJ+KGn0kF0gMzMbmQkyOQK4EDgEeD/wIUmTgNOBD0bEdOCqXhxnJvCliDgwrZ8fEUcCVUBtOibAXsBvI+Jw4EHgc6m8Dngglc8Enpb0AeATwIciYgawDTgnX+OSLpDUIKmhtbW192dvZmZFjeQE+VhEtEREO7AKqABeA94EbpR0BtCb7z88FhH/lbNeK+lJ4LfA+4Bpqfxt4O60vCK1B3A8cD1ARGyLiNeAjwJHAo9LWpXW35+v8Yi4ISKqIqJqypQpvQjXzMx6YyTPm72Vs7wNGBMRWyUdRZaQzgK+SJbAtpL+mJA0CtgjZ99NHQuSjgVOAI6JiM2S7gfGps1tsf37FNsofu0F1EfExf06MzMz22kjuQe5A0kTgIkRsRj4MnB42tRM1qMDOAUoK3CIicArKTkeDBzdi2Z/DXw+tT9a0sRUdpakfVL5uyUd0I9TMjOzfnKC7OodwN2SVgO/ARak8n8BPpKGTo8hp9fYzS+BMZKeAf6RbJi1J18CjpO0hmzo9ZCIWAtcAixNsSwD9u3nOZmZWT/IT1HZfVRVVUVDQ0Of9/vIRz4CwAMPPDDQIZmZDWuSVkREVb5tI3kO0pLx48eXOgQzs2HHQ6xmZmZ5OEGamZnl4SFWo7q6utQhmJkNO06QRm1tbalDMDMbdjzEamZmloe/5rEbkdQK/KGfu08G1g9gOLsbX5/ifH2K8/UprpTX54CIyPucTidIA0BSQ6HvApmvT098fYrz9SluuF4fD7GamZnl4QRpZmaWhxOkdbih1AEMc74+xfn6FOfrU9ywvD6egzQzM8vDPUgzM7M8nCDNzMzycIIc4SR9TNKzkpok/UOp4ykFSe+TdJ+ktZKelvSlVP5uScskNabf70rlklSXrtlqSTNLewZDI73Qe6Wku9P6X0h6NF2Hn0naI5Xvmdab0vaKkgY+BCTtLekOSeskPSPpGH9+tpP05fT/1lOS/k3S2F3h8+MEOYJJGg38M1ANHAKcLemQ0kZVEluBr0TEIcDRwBfSdfgH4NcRMQ34dVqH7HpNSz8XANcPfcgl8SXgmZz1bwLXRUQl8Arw2VT+WeCVVH5dqre7+w7wy4g4GDic7Dr58wNImgrUAlURcSgwGvgku8DnxwlyZDsKaIqI30fE28BtwKkljmnIRcSfIuKJtPwG2T9uU8muRX2qVg+clpZPBX4cmd8Ce0vad2ijHlqSyoGTgB+ldQHHA3ekKt2vT8d1uwP4aKq/W5I0EZgN3AgQEW9HxKv485NrDDBO0hhgPPAndoHPjxPkyDYVeCFnvSWVjVhpOOcI4FHgPRHxp7Tpz8B70vJIvG7fBv4eaE/rk4BXI2JrWs+9Bp3XJ21/LdXfXf0F0ArcnIagfyRpL/z5ASAiXgS+BTxPlhhfA1awC3x+nCDNEkkTgJ8DF0bE67nbIvs+1Ij8TpSkjwMvRcSKUscyTI0BZgLXR8QRwCa2D6cCI/7z8y6yXuFfAPsBewEfK2lQveQEObK9CLwvZ708lY04ksrIkuNPI+I/UvF/dwx9pd8vpfKRdt0+BJwiqZlsGP54sjm3vdOQGXS9Bp3XJ22fCGwYyoCHWAvQEhGPpvU7yBKmPz+ZE4D/iojWiGgD/oPsMzXsPz9OkCPb48C0dDfZHmQT54tKHNOQS/MbNwLPRMT/ztm0CKhJyzXA/8kp/0y6G/Fo4LWcobTdTkRcHBHlEVFB9hn5z4g4B7gPOCtV6359Oq7bWan+btt7iog/Ay9IOigVfRRYiz8/HZ4HjpY0Pv2/1nF9hv3nx0/SGeEkzSWbXxoN3BQRXy9tRENP0oeBh4A1bJ9j+//J5iH/Hdif7DVifxMRL6f/yb9HNky0GTgvIhqGPPASkHQs8NWI+Lik95P1KN8NrAQ+HRFvSRoL/IRsLvdl4JMR8fsShTwkJM0gu4FpD+D3wHlkHRB/fgBJVwCfILtjfCXwv8jmGof158cJ0szMLA8PsZqZmeXhBGlmZpaHE6SZmVkeTpBmZmZ5OEGamZnl4QRpZmaWhxOkmZlZHv8XfgNfWtdLKsEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot to see the outlier of each numerical column\n",
    "sns.boxplot(data=train, orient=\"h\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "894a853f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot to see the outlier of each numerical column\n",
    "sns.boxplot(data=test, orient=\"h\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56990c31",
   "metadata": {},
   "source": [
    "- the output indicates strongly that there are outliers in all the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd513513",
   "metadata": {},
   "source": [
    "# IV. Data Cleaning and Pre-processing\n",
    "\n",
    "#### 4.1 Handling of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8319a544",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to check for outliers in the dataframe\n",
    "def handle_outliers(data_frame, column_name, method='clip', threshold=1.5):\n",
    "    \"\"\"\n",
    "    Handle outliers in a specified column of a DataFrame using the IQR method.\n",
    "\n",
    "    Parameters:\n",
    "        data_frame (pd.DataFrame): The DataFrame containing the data.\n",
    "        column_name (str): The name of the column to handle outliers for.\n",
    "        method (str, optional): The method to handle outliers. Options are 'clip' (default) or 'remove'.\n",
    "        threshold (float, optional): The threshold to determine outliers in terms of IQR. Default is 1.5.\n",
    "\n",
    "    Returns:\n",
    "        pd.Series: The updated column with outliers handled.\n",
    "    \"\"\"\n",
    "    column = data_frame[column_name]\n",
    "    q1 = column.quantile(0.25)\n",
    "    q3 = column.quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    lower_bound = q1 - threshold * iqr\n",
    "    upper_bound = q3 + threshold * iqr\n",
    "\n",
    "    if method == 'clip':\n",
    "        updated_column = column.clip(lower_bound, upper_bound)\n",
    "    elif method == 'remove':\n",
    "        updated_column = column[(column >= lower_bound) & (column <= upper_bound)]\n",
    "    else:\n",
    "        raise ValueError(\"Invalid method. Choose 'clip' or 'remove'.\")\n",
    "\n",
    "    return updated_column\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ce7d2f",
   "metadata": {},
   "source": [
    "#### 4.2.1 Handling of outliers in the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "53af9ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# handling outliers in the train dataset\n",
    "train['Plasma glucose'] = handle_outliers(train, 'Plasma glucose', method='clip')\n",
    "train['Blood Work Result-1']= handle_outliers(train, 'Blood Work Result-1', method='clip')\n",
    "train['Blood Pressure']= handle_outliers(train, 'Blood Pressure', method='clip')\n",
    "train['Blood Work Result-2']= handle_outliers(train, 'Blood Work Result-2', method='clip')\n",
    "train['Blood Work Result-3']= handle_outliers(train, 'Blood Work Result-3', method='clip')\n",
    "train['Body mass index']= handle_outliers(train, 'Body mass index', method='clip')\n",
    "train['Age']= handle_outliers(train, 'Age', method='clip')\n",
    "train['Insurance']= handle_outliers(train, 'Insurance', method='clip')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b06917ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# handling outliers in the test dataset\n",
    "test['Plasma glucose'] = handle_outliers(test, 'Plasma glucose', method='clip')\n",
    "test['Blood Work Result-1']= handle_outliers(test, 'Blood Work Result-1', method='clip')\n",
    "test['Blood Pressure']= handle_outliers(test, 'Blood Pressure', method='clip')\n",
    "test['Blood Work Result-2']= handle_outliers(test, 'Blood Work Result-2', method='clip')\n",
    "test['Blood Work Result-3']= handle_outliers(test, 'Blood Work Result-3', method='clip')\n",
    "test['Body mass index']= handle_outliers(test, 'Body mass index', method='clip')\n",
    "test['Age']= handle_outliers(test, 'Age', method='clip')\n",
    "test['Insurance']= handle_outliers(test, 'Insurance', method='clip')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9f7e92",
   "metadata": {},
   "source": [
    "#### 4.2.2  visualization of outliers in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e36026fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for  outlier of each numerical column age\n",
    "sns.boxplot(data=train, orient=\"h\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f9a6631",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for  outlier of each numerical column age\n",
    "sns.boxplot(data=test, orient=\"h\");"
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   "cell_type": "markdown",
   "id": "cc436f6d",
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   "source": [
    "✍ summary:\n",
    "there are no outliers now "
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   "id": "6549d2fd",
   "metadata": {},
   "source": [
    "# V. Univariate Analysis\n",
    "Here is the section to explore, analyze, visualize each variable independently of the others."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62cb305e",
   "metadata": {},
   "source": [
    "#### 5.1 visualization of the percentage unique values of our target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cb3878dd",
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   "source": [
    "# visualizing the percentage unique values of the target  features.\n",
    "import plotly.express as px\n",
    "fig_1 = px.pie(train, names='Sepsis', title='Plot of Ratio of the Label Variables (Sepsis)')\n",
    "iplot(fig_1)"
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    "✍ summary:\n",
    "- from the visual, Label feature, We can observe that there is an imbalance in our dataset; therefore we will have to deal with that later.\n",
    "- 65.3% of the patients are negative.\n",
    "- 34.7% of the patients are positive."
   ]
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    "#### 5.2 visualization of the features against  our target \n",
    "- At this stage we are going to compare all other features to the target or label. Since all features are continuous, box plot is used because it can clearly show the distribution, mean, and median of the features."
   ]
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    "# function to compare the features against the target\n",
    "def compare_plot(df, col):\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    plt.title('Comparison of ' + str(col) + ' between sepsis-positive and sepsis-negative patients', \n",
    "              fontsize=20)\n",
    "    \n",
    "    # Property for the mean marker\n",
    "    meanline = {'marker':'o', 'markersize':'10'}\n",
    "    \n",
    "    # Box plot of each column separated by Positive and Negative with sepsis\n",
    "    # Show mean on box plot\n",
    "    sns.boxplot(data=df, x='Sepsis', y=col, showmeans=True, meanprops=meanline)\n",
    "    \n",
    "    plt.xlabel('Sepsis', size=18)\n",
    "    plt.ylabel(col, size=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "714a2ac8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nAk+mdLMCEBH/ALyFkuFvzuovL9mc1R/I/mNm3tdmGUP5JuVFE5dFxPfq8n/bsh5g+HW4PCLOolTOJ7P624gWtFnutfV3J69834nS1Xputn8r4XzKfflP7mBendqv/kA5yVhEWVdHZOZtI00cEY+n3If8HOBRlKshzWY0/f1dSkP0w4g4kdL4njtE3Wtot06vr7+HqpdQrv40x/mwuuyXU5KQjShXs9vFOZzGuj+nzbjfURqoibJn/f2zDsqOti3odNvsSElcrwQ+GBG0sYzyVqXR+HWbYedQTk6a6/vT6+9dh/hOlx3r78dRLmZ1Yj7ltrvdKCehewNLMvN3EXE+pdeHiNiCcqX7F1keWiYiNqAceG4G3jHE+riPVdfHeD7DeNuYyyi35LwmIh5LuT3qHMpBsPXFPo04nxsRe7SZ1yMpJ+Y7svq+2On2/CbwCuD3EfFdysW9czPzug4+S2P6z1Da9e/U5Z6bmUubC9XvTdytZdqLMvOHI8WdmXdExEWUY9fjKOtvyP2rafizKJ/1LEa33rsmM38TEX8B/jEiNm1q019L2XbHN8qOsS4PKcoMXks5kd+VUj+nNhUZ6nOvVscz84GIuJFV6/jOlOPl2W2O41DONw7oJNbMvDMifkw5wbsoIn5ASaR/n+WlDc3Gs/+2q19XR8S1wMyI2CQzb2dsx8pmHe1X9RXgc1qmXZRNL+uqOm139qBs46G+c2ud+ru5Dj2ZcuHuN23KtzvOAmM+bxyt44EXUOrRe+typ1OembyJ8jx8Q6NePHaIz75D/f24lumGc0Fm3tVm+Jk1pidTbjelxvZMSr15OqV9nt4y3QzG9xbNsWzfhk7365H8mnKe976IeAplXZ5Lac8f7GQGnSRhSygfotMTw4aNm6Yfar7bUK5WNzdaQ72CcjmUg9BQ41i50pvdOMT8bmiJk4g4hHK/7m2UW8muodw3mpRnnnal3II21Lw69U5Kz94bKPeqvw9YHhGnAu/OzIUtsQ23DqGsw1a3txnWWE9T24xrNZ5lj9Ub2jS4HYmIPSknG9Mot9Cewspn13ajJHcPbbvMPC8ink15ycv+lNsQiIg/Ax/JzG+3Wcxwda+jehkR69Q4n0q5TfK7lOekHqhFDqN9HWunsY1Wq+OZ+WBE3NLhfMZik/q7k1d0j6otGMW2eUT9vQNlvQ1lww5ibNZufS6PiJspB5OGxvL/fYT5jWb5p1OSsOdFxB8pD6Cf2jTuvRGxMSU5izqsYdM6bAuGXx/NxvMZbm8zrOM2ptbRvSm9yftTnqUAuCsivg68PzPvbonzP8cQZ0fbMzNPanrL7xspt6dQk9/3Z+YvR/g8n63zfCvlpQrvoJwg/Br4z8xsHPhfxuon5F+n3AkwbNxV67FrVG31KNd7t30d+DjlOZov12EHUNq/bzWVG0tdHs5nKdtjCeVZpsWUCzRQErPHDjHd7UMMX86qdXzItrga7TnCqyiPHPwLK98KfG9Ngt6TmY3ljGf/HS7Wx1J7Xcd4rHzIKParOay+rX9NU3Je3d5mGctrot68TRrrZo/6M5TmdbMxcGtmtruA2XZ9jeO8cbROppzTvC4i3l9P8velXIj8XEvMjc/+zyPMczTHptGcS7+c8lzUvZR1chWlp3gFZTs/l/Gvk7Fs34bbhyjbul8Pq14w2ZOyj74UeFEddXNEfInyTNoDQ86AzpKwcygH/OdRnrHoVOOkdCvKBmj1qJZyE2XLIYZv1bz8iJhG6Xa8gfIg6yoHtIh4OkPL0QRUd57PAZ+LiEdSrlK+mrLDPD4iHl971prXYTsTuQ77ueyx+CCwPrBXZp7ZPCIi3s/KHraHZOZvgX3rl4zvTnmAeC7wrYhYmpm/moA496MkYMdn5hta4nwUozvhuLP+3pKS1DfPayqlker0e4ySoduDTdoMu73+nsHIr44ddVvQ4bZplD85M18xQgyjsSUtV+hq+7A5K9d5c7y7ZuafurTsRm/G8+vfm7Ey0ZpPeUB7L2qPGKv2fjTiuTAzn0JnJuIzdKz2hrwTeGdEzKIcnN9MebPlJtQTvqY4N87MO1vnM4JOtyeZ+VPgp7W3+mmUk5z/AH4SEU/OzGF7NDPzBOCEiNiEcvvdyyknnqdFxM6ZuTQzD6Sc+HcSdzurHLsYQ1s9ivXebd+g3Mp+APDliHgy5fbIH2V5vXnDWOpyW/UY+3bKRa9ntF7Nj4jXjGf+VSPekbZZRzJzGfXW/Ih4DOXujgOB11Hufnl2y3LHsv9uSXmeeKhYR9seD/d5RtyvMnMe5TN3SyP+IzPzXR1OcyewWURMa5OIrbZtx3neOCqZuazeOfVvlB6xn7PyYs7XW4o3Pvt+mXlKl0LotD2Cso/fT3kL+eXNhSPiGEp7M15j2b5dV3t031R723eh5Etvo1zkmkJ5C+2QpnSwjP+lXKX6p4jYZbiCdQdtuLD+ntOm3CzKLVp/rd3dE+lZEdHuczbiasS5OeXg85s2O9KGrLzlo6sy86bMPCkzX0k5odqecptRc2xzWqerO3+jIb5gAkL7M+Vqzq71hKLVXhO47LGYRbmCdWabccPu8Jl5X2b+JjM/TDlYQ5ukrUtm1d8ntRk32oapUT+e1Wbcnoziy9gpV/Ee0zqwJnO7tSn/u/p7nw7mPea2YIRtcwUlGdyz9jB2S7vt8CzKFbILm4Y11sGzVy8+pMYtCm2vtmXmDZTbhp5NOdGBlUnYuZRbsJ5Haehva46n9l5cSrmQs1mH8YzlM0yIzFyYmV+jrP+7WXUfHE+cnW7P5lj+npnz68H9cMqtNJ3U9cb0t2fmqZn575Qr+ZtRTqTHFXftBd2Nla9qh2H2r2rYtnqE9T6UYevxUDLzWspx7mkRsRNDnESOsS4PZTvKuc4v2iRgW9fx43UF5Xi5W91GreaMdcaZeW1mfpNylX0h5Zym0QvQ1f0iIrajHAcWjaE9HtF496tROo/S8zKadXMhpa48o824dsfZsZw3jmnfqY6vvw+ot6TvA/wpMy9qKTcR7fpTImKjNsPn1N/Nbeks4LI2CdgU2q9HKNtqNOtkLNt3LDraXllcmplHU5JkKD2hwxoxCcvMRZRMfzrlSsbsduUi4sWs+nzIcfX3B2tlaZSbSnmN4xRG17M2VjtQbg95SETsR2mAFlLutYZyT+09wO5152mUXYfyitjNuxFMRKxb75VtHb4O5UBNjQNWfh/Da2qXZ7N3UJ6R+1VOwBdjZnk24JuU55U+2hLr9pQG+AHKlc01wSLKFawnNQ+MiDexsou4efgzImL9NvNpXO1pvfe+WxbV33Na4tmOlbcFdeqE+vsDzQf+ep/44aOc13nANhHxwpbhH6T9rTpfpnTdf6jdxZl6ctMwqrag021Tr1QeTbnS//l200TEo0a6eNTGhyLiofvCI2I9ytu3oFyUounv24HDIuKpbZY9JcpzDs0at4huM8zy51OeLzkEuLKeuDaujv+W8jrv7SkPMbc+7/pZSlt9XLuLJxGxab13fTyfoSsiYtta71ttSrlVZVnTsC9Q2psjI2LH1gkiYnq9ZaqdjrZnRDynXtxq1VGbEBF71auhrRq3PI62TXl97SlqNo9y28+3c+VzyOdSLpo9KyL2b4lpf8pJyl+oz7SMcr0PpZN6PJTj6+83UZ5nuZnyyuhWo63LQ1lUfz+rtjmN6TekvDRo3M/G11uOGsfLeS1xzqY8j9aRiNgiIp7YZtTDKLdWLWflM2zj2X8PifJM4ENlKa98n8Kq+8W4jpXj3a/GKjNvomyT2RHxoeZt3xTb9hGxbdOgxjH1Y/U42ii3Me17NMZy3ngb9cUUo/xIZOa5lGeg96M8g7YOq9+uCeU5z6uAt0XES9rNKyKeHuXZy05tTOndaZ5Ho27fQbldsmERsENEPLqpbFD2jaGOx7fQ5kLwUMa4fcdiyLYuIh4fEe16CDuu2x01Ppl5eN2JDgP+EBG/oTzYdndd2HMoyc6Cpml+ExGfpDxAeEmUe5n/Tsncn0A5IHyqk+WP08+Bz0TEPpTvDppFeUj0XuCNjZOYzFwREZ+nPJ91cUT8iHIA2IuSHJ3ByiuK47E+cE5ELKQ8QP43ygskXkB59u6UxtWDzLw7It5I+Q6mX0fE9ym31exOebXuDdT7qyfI+ygH8IOjPAx/BqVReSXlYHNwZv51Apc/Gp+jJFvnROmyv4PykOyzKPcm799S/r3A3hFxNuWNdHcDj6fUz9sorxueCD+mJP/vqgfaCyk7976U1zR33DBn5q8j4ljK9xNdGuUB7gcoD3TfQXlpSLuX0rTzacr6+1GUh6dvpVwN3Jby4O2clmVfFhFvpXzfy4V1f7mScgvkHpTbOvaqZUfbFoxm23yUcs/9WygP/M+n3IL5SEqb9EzKswydvhgDSg/DpTXOBygHvO0p2+ehiw6ZeUs9yT0Z+F1EnM7K79V7DOWB5Eew6gtiTqc81/SVur3uojx38YWWMgfXz9DaY3o6K7fF6S3jyMzjImJ3yoWnqyLiNEqbsRllWz6HcoL1lnF8hm7ZFTgpIv5AWefXU54B2o9ycvHQRYnMvKK2hcdRts3PKYnFOpR95tmUZyt3brOcjrYn5a1dMyLiXMpJxP2UtnZvSjv9nRE+z8nA3RHxuzp91Lj2oLT1o729+WfAubU9W0Jpy55V5/2+RqHMzIg4gPLsxXfrvngF5eVKL6PUsX9tStg7Xu/D6KQeD6XxbMs76vKOzjbPTYy2Lg8lM2+I8qKUV1NedPELygnlCyjnARfRvrd/tA6l9FK/o56cnkO5QPQqynOdL+1wPjMoberFlDc3Xkt5Y+K+lFu/Pt/o0Rvn/nsuZX18l3K8eBGlbpxP+a6xhvEeK8e7X43HwZTjwH9TLmqcQ3m26dGU8609KBcCGucxJ1DqyYspx6pTKHX0nyhfj7QTTcfUsZw31vO63wPPjohvUtqxBynnfp3cUnoC5bj3IUpC/s3WAlleMvEKyvOPP63n7BdRkoLH1M+9HaV+dpoEnwX8W0Q8jVJ3GnV7CuU7Y5tv7T6SlecGjfOSZ1ISsMZLZ1qdDrw6yktpLqjTnJWZZw0T02i371icQdnmn4iIJ1DqPJn5MUob8qmI+C1lO95EubNnvzrNyDlOju4VoY+jXHm+hNKI3k85OPyMclWr3fdwvJrSGN1FafAupZwUrdem7CKGeL05w7yWnPavC55Th82jNES/qjHfRXnV52rfGE5JSt9FOWFbRklyvkHpCWi3jJkM86rRdnFTduj31nV2TV0nSyndx2+h5Qus6zR7UBrZpXWdX0PpiXh0J+ui3ToZxTbfhHJQvpJyK9TtlIN929fVDrcNh1lGI+YDOyzfti5QDlK/q9v49rqdG/fSrzJ/ShL7v3Vb30FJCv5MOWA8tmW+8xj6FaWrzXuk9U1pBL/JyofDL611YhqjfwXuFMqzHVfU7XM95csKN67r4aJRxPtSyoWUeylXf77DEHW/aZqnU15BfFOtm9dTLnzsP9a2YDTbppYPyjMsp1OSx/vruj2HcmL0mNHUK0pvwMcoDfd9lOftDmPor6GYSempubJ+rjvr9vgG8LI25d9FOfm9ry5vUcv4TSgH5QT+uc36zvrzuGE+y76U3oXGdrmB0tv5MWDn8XyGEerDHDpsYygHq8MpB/Qb6vq4jtI27jPENE+sy/9bLX8r5Xh0DC1fXzLa7Um5uPTtug7uruvgEsqLJFq/HHm1dUBpv0+u87+nxnYhZd8e8fti2rU3lP31Iko7sZSyXzxqiOl2qttrCeUEZgnli7Rbv1S44/XOMMc4hqnHDHO8ruMbX6+RwO4jrI9R1eUh5rFB3Y4LKfX7Wko7+Yh2sTJCPWaI4xwlSTqubqtlddsdONL82uz/H6b0iC+u63dJjfM1tP+OyZmMfv/djvKyjCvqNIspFzMf3lJ+XMdKRrFfjbBeDmSY8wSGOHZSkqKDKW88vKOuz2sox4t30PS9a7X8epST+kZ7sajGOqMu44ct5Ud13linmUVJRm6hnKw/9Lk6qHvbsPL48OMR1tkjKV+UfAmlTbq7bocTKc8Xjvhl5zS1AZQc4EeUROQeShvyomG210W1vtxMaRuf2K6ONMX6LUoS1fh88zpohzrevh2s20W0369fx8p2OKntRV0fn6WcNy1tqi8nUp4/HbFeN76LZtKpXfBnUN7eM6+vwUg9FhE7UK7MfCczu/HgubRWiYgzKd8D2e4WwTVWlNctH0ablwxJ4xURx1Oexds2y+Mm6kBEvIByYfeIzHx/v+PplYiYSUlIv57lpULqok5ezCFpDRURW0XLi2fqfd6fq/+e3POgJElaCzU/x9Q07BGUHiXwmKoumsgva5Y08d5BeXHLmZRbVraiPJewNeX2ou/3LTJJktYun42IXSm3ty2lHEv3oTzjdUxmntfP4DS5mIRJa7dfUh6mfiHlILGcchvi5ylf4Dg57zeWJKn7TqK8cO4fKc/nNZ5f/hq9eaO3BsikfSZMkiRJktZEPhMmqSsi4riIuCkiLmkz7t0RkRHRle/bkyRJWpt5O6Kkbjme8prkE5oHRsRjKLdLdvSl4ptvvnnOnDmz27FJGnDnn3/+zZm5xcglJWnimYRJ6orMPKu+zrbVkZTvSvpRJ/OZOXMmCxYsGLmgJI1CRPyt3zFIUoO3I0qaMBGxH7A4M//Y71gkSZLWFPaESZoQ9fvKDqXcijhS2YOAgwC22WabCY5MkiSpv+wJkzRRtge2Bf4YEYso37dyQURs1VowM4/NzNmZOXuLLXxkQ5IkTW72hEmaEJl5MfDIxv81EZudmTf3LShJkqQ1gD1hkroiIr4N/BbYKSKui4g39TsmSZKkNZE9YZK6IjNfM8L4mT0KRZIkaY1mT5gkSZIk9ZBJmCRJkiT1kEmYJEmSJPWQz4RJ0igdffTRLFy4sGfLW7x4MQAzZszo2TJnzZrF3Llze7Y8SZIGiUmYJK3hli1b1u8QJElSF5mESdIo9bqH6JBDDgHgqKOO6ulyJUnSxPCZMEmSJEnqIZMwSZIkSeohkzBJkiRJ6iGTMEmSJEnqIZMwSZImifnz5zNnzhzOOOOMfociSRqGSZgkSZPE4YcfDsDHP/7xPkciSRqOSZgkSZPA/PnzWb58OQDLly+3N0yS1mAmYZIkTQKNXrAGe8Mkac1lEiZJ0iTQ6AUb6n9J0prDJEySpElg2rRpw/4vSVpzmIRJkjQJHHrooav8/4EPfKBPkUiSRmISJknSJLD33ns/1Ps1bdo09tprrz5HJEkaikmYJEmTRKM3zF4wSVqzecO4JEmTxN57783ee+/d7zAkSSOwJ0ySJEmSesgkTJIkSZJ6yCRMkiRJknrIJEySJEmSesgkTJIkSZJ6yCRMkiRJknrIJEySJEmSesgkTJIkSZJ6yCRMUldExHERcVNEXNI07FMRcUVE/CkiTo6ITfoYojTpzZ8/nzlz5nDGGWf0OxRJ0jBMwiR1y/HAi1uG/RJ4QmY+CfgL8P5eByUNksMPPxyAj3/8432ORJI0HJMwSV2RmWcBt7YM+0VmLq///g7YuueBSQNi/vz5LF9edrfly5fbGyZJazCTMEm98kbgZ/0OQpqsGr1gDfaGSdKayyRM0oSLiA8Ay4FvDjH+oIhYEBELli5d2tvgpEmi0Qs21P+SpDWHSZikCRURBwL7Aq/NzGxXJjOPzczZmTl7iy226Gl80mQxbdq0Yf+XJK05TMIkTZiIeDHwXuClmXlPv+ORJrNDDz10lf8/8IEP9CkSSdJITMIkdUVEfBv4LbBTRFwXEW8CvgBsBPwyIi6KiP/pa5DSJLb33ns/1Ps1bdo09tprrz5HJEkaikmYpK7IzNdk5qMyc53M3Dozv5aZszLzMZm5W/15S7/jlCazRm+YvWCStGbzhnFJkiaJvffem7333rvfYUiSRmBPmCRJkiT1kEmYJEmSJPWQSZgkSZIk9ZBJmCRJkiT1kEmYJEmSJPWQSZgkSZIk9ZBJmCRJkiT1kEmYJEmSJPWQSZgkSZIk9ZBJmCRJkiT1kEmYJEmSJPXQtH4HIEnSZHX00UezcOHCni1v8eLFAMyYMaNny5w1axZz587t2fIkaTIwCZMkaZJYtmxZv0OQJHXAJEySpAnS6x6iQw45BICjjjqqp8uVJI2Oz4RJkiRJUg+ZhEmSJElSD5mESZIkSVIPmYRJkiRJUg+ZhEmSJElSD5mESZIkSVIPmYRJkiRJUg+ZhEkDLiLWjYgZETG937FIkiQNApMwaUBFxFMiYj5wF3AN8Kw6/JERcXpEPL+vAUqSJE1SJmHSAIqI3YCzge2BE5rHZeZNwPrAAb2PTJIkafIzCZMG038D1wOPB94HRMv404Gn9jooSZKkQWASJg2mZwNfycy7gWwz/hrg0b0NSZIkaTCYhEmDaT3gjmHGP7xXgUiSJA0akzBpMF0F7D7M+L2By0Yzw4g4LiJuiohLmoZtFhG/jIgr6+9NxxivJEnSpGESJg2mbwGvb3kDYgJExLuBFwPfGOU8j6/TNXsfcHpm7kB5zux9Y4pWkiRpEpnW7wAk9cWngRcApwFXUBKwIyNiC2Ar4JfAl0Yzw8w8KyJmtgzeD5hT//46cCbwX2MNWpIkaTKwJ0waQJl5PyUJew+wDLgX2BG4GXgvsG9mrujCorbMzCX17xuALdsVioiDImJBRCxYunRpFxYrSZK05rInTBpQmbkcOLL+9GJ5GRHt3sRIZh4LHAswe/bstmUkSZImC3vCJK0iItbt4uxujIhH1fk+Cripi/OWJElaK5mESQMoIvaJiHktw94aEXcCf4+Ib0XEOl1Y1CnAAfXvA4AfdWGekiRJazWTMGkw/Sewc+OfiHgccBRwPeWlHK8C3jaaGUbEt4HfAjtFxHUR8SbgCOAFEXEl8Pz6vyRJ0kDzmTBpMD0OOLXp/1dRXtDx1My8MyK+Rem5+lynM8zM1wwx6nljDVKSJGkysidMGkybUt6E2PB8YH5m3ln/PxPYttdBSZIkDQKTMGkw3Qw8FiAiNgL2AM5uGr8OMLUPcUmSJE163o4oDabfAm+JiEuBfShtwc+axs8ClrSbUJIkSeNjEiYNpsOAM4Dv1f+/npmXAUREAC+v4yVJktRlJmHSAMrMy+obEZ8J3JGZZzWN3oTyBc5n9iE0SZKkSc8kTBpQmXkr8OM2w2+jvK5ekiRJE8AkTBpgEbE9sB+wXR10NfCjzLyqf1FJkiRNbiZh0oCKiI8C72P1tyB+MiIOz8wP9yEsSZKkSc9X1EsDKCLeCHwA+D3wMmCH+vMyypsTPxARB/YpPEmSpEnNnjBpML2NkoDNyczlTcOviohTKd8ZNhc4vg+xSZIkTWr2hEmD6XHAd1oSMADqsO/UMpIkSeoykzBpMN0PbDjM+I1qGUmSJHWZSZg0mP4AvDkitmwdERGPBA6i3K4oSZKkLvOZMGkwfRQ4Hbg8Ir4GXFaHPx54A6Un7LV9ik2SJGlSMwmTBlBmnhURrwC+ALy7ZfQ1wAGZeXbvI5MkSZr8TMKkAZWZP46InwK7A9vWwVcDF2Tmiv5FJkmSNLmZhEkDrCZbf6g/kiRJ6gFfzCENoIh4XkR8Ypjxn4iIvXoZkyRJ0qAwCZMG038Bs4YZv20tI0mSpC4zCZMG067A74YZ//taRpIkSV1mEiYNpo2Bvw8zfhmwaY9ikSRJGigmYdJgWkx5K+JQdgdu6FEskiRJA8UkTBpMPwUOiIjnt46IiOcBBwCn9jwqSZKkAeAr6qXB9HHgn4DTIuJnwEV1+G7APpResI/2JTJJkqRJziRMGkCZeWNEPAP4MiXpekljFPAz4ODMXNKv+CRJkiYzkzBpQGXm34CXRMSmrHxd/cLMvK2PYUmSJE16JmHSgKtJ1x/6HYckSdKgMAmTBlBEbNNJucy8pkvLeyfwb5TbHS8G3pCZ93Zj3pIkSWsbkzBpMC2iJEQjmTreBUXEDODtwC6ZuSwivge8Gjh+vPOWJElaG5mESYPpv1k9CZsGbA/sR+mt+lkXlzcNWD8iHgA2AK7v4rwlSZLWKiZh0gDKzHlDjYuI7YDfAgu6tKzFEfFp4BpgGfCLzPxFN+bdcPTRR7Nw4cJuznKN0vhshxxySJ8jmTizZs1i7ty5/Q5DkqSeMAmTtIrMvDoijgE+QvlS53Gpb1/cD9gWuB34fkS8LjP/r6nMQcBBANts09HjaqtYuHAhF11yOQ9usNl4w10jTbm/dFqef/WNfY5kYky959Z+hyBJUk+ZhElqZzGwS5fm9Xzgr5m5FCAiTgKeATyUhGXmscCxALNnz+7kWbXVPLjBZizb+SUjF9QaZ/0rTu13CJIk9dSUfgcgaY30MqBb3xd2DbBnRGwQEQE8D7i8S/OWJEla69gTJg2giPjwEKM2A/YGngB8shvLyszfR8SJwAXAcuBCaq+XJEnSIDIJkwbTvGHG3QB8EPh/3VpYZh4GHNat+UmSJK3NTMKkwbRtm2EJ3JqZd/c6GEmSpEFiEiYNoMz8W79jkCRJGlS+mEOSJEmSesieMGkARMRxY5gsM/NNXQ9GkiRpwJmESYPhwDFMk4BJmCRJUpeZhEkDIDO99ViSJGkN4YmZJEmSJPWQSZgkSZIk9ZC3I0oDqIMXdSSwDLgG+GVmXjjxUUmSJA0GkzBpMB1ISbQAomVc6/BPRMR3gH/NzAd7EJskSdKk5u2I0mDaArgA+D7wNGCT+rMncCKwANgW2KP+/2rgvX2IU5IkadIxCZMG06eBGzPz1Zn5h8y8s/6cl5mvAm4G5mXm+fX/c4HX9TViSZKkScIkTBpM/wicOsz4n9YyDadQesYkSZI0TiZh0mBaD3j0MOO3rmUa/g4sn9CIJEmSBoRJmDSYfgPMjYg9W0dExNOBg2uZhicC1/YoNkmSpEnNtyNKg+k9wNnAuRFxHvDnOnwn4KnA3bUMEbEesDfww96HKUmSNPmYhEkDKDP/FBG7A4cD+1DekAjltsMfAB/MzL/UsvcCO/clUEmSpEnIJEwaUJm5EHhlREyhvLIeYGlmruhjWJIkSZOeSZg04GrSdWO/45AkSRoUvphDkiRJknrIJEySJEmSesgkTJIkSZJ6yCRMkiRJknrIJEySJEmSesgkTJIkSZJ6yFfUSwMsImZTvqh5U1a/KJOZ+dHeRyVJkjS5mYRJAygi1gdOAl4IBJD1N01/J2ASJkmS1GXejigNpg9TErCPA3tRkq4DgH2As4E/ALv0LTpJkqRJzCRMGkz7A9/PzA8Dl9RhizPzNOD5wHTgwG4tLCI2iYgTI+KKiLg8Ip7erXlLkiStbUzCpMH0GODX9e8H6+/pAJm5HPg28OouLu8o4OeZuTOwK3B5F+ctSZK0VvGZMGkw3cXK/f8uYAXw6KbxdwBbdWNBEbEx8Bxqz1pm3g/c3415S5IkrY3sCZMG01XAjgCZ+SBwKeUWRSIigFcA13ZpWdsCS4H/jYgLI+KrEfGwLs1bkiRprWMSJg2mXwH/FBFT6//HAC+OiKuAKynPhX2tS8uaBjwF+HJmPhn4O/C+5gIRcVBELIiIBUuXLu3SYiVJktZMJmHSYDqClW9FJDO/BLyHchvibcChwCe7tKzrgOsy8/f1/xMpSdlDMvPYzJydmbO32GKLLi1WkiRpzeQzYdIAysy7gT+3DPss8NkJWNYNEXFtROyUmX8Gngdc1u3lSJIkrS1MwiT1wlzgmxExHbgaeEOf45EkSeobkzBpgEXEC4AdgEdQb01skpn50W4sJzMvAmZ3Y16SJElrO5MwaQBFxM7AyZQ3JLYmXw0JdCUJkyRJ0komYdJgOobyhc3vAM6mvIxDkiRJPWASJg2mpwJHZObR/Q5EkiRp0PiKemkw3QLc3O8gJEmSBpFJmDSYvgO8rN9BSJIkDSJvR5QG0weAEyPiZODzwCLgwdZCmXlNj+OSJEma9EzCpMH0AHAp8J/AS4cpN7U34UiSJA0OkzBpMH0SeCdwIXAOvh1RkiSpZ0zCpMH0euDkzNy/34FIkiQNGl/MIQ2mDYBf9DsISZKkQWQSJg2m3wGP73cQkiRJg8gkTBpM7wZeFRGv6HcgkiRJg8ZnwqTB9DngLuD7EbEY+Curv6I+M/N5vQ5MmmhHH300Cxcu7HcYE6LxuQ455JA+RzJxZs2axdy5c/sdhiSNi0mYNJi2AxJofA/YNn2MRUPI6bfx4CMWsGKTy2DK/bBiOlNu34Wpt8wm7t+03+GttRYuXMiVl17INhuu9tV4a73pD5QbXO7724I+RzIxrrnbb82QNDmYhEkDKDNn9jsGDW/FhlezfJsfAStgyooycOr9rNj0T6zY9BKmXbMfU+7erq8xrs222fBBDn3Knf0OQ6N0+AUP73cIktQVPhMmSWuYnH5bScCmLF+ZgDVMWQFTlrN8mx+R0/16N0mS1kYmYdIAioipEbFBy7BNIuLdEfHxiHhiv2ITPPiIBcCKEUqtqOUkSdLaxiRMGkzHAOc1/omIdYBzgE8B7wfOi4jd+hOayjNgIyRhU1aUcpIkaa1jEiYNpmcBpzT9vz+wC/A24BnAjcD7+hCXoLyEo5vlJEnSGsUXc0iD6VGU19I3/ANwaWZ+GSAijgXe3I/ABKyYDlM7SLBWTJ/4WCRJUtfZEyYNpgCa3/U8Bzij6f8lwCN7GZBWmnL7LrBihOZ5xZRSTpIkrXVMwqTB9FfgRQAR8UxKz1hzEvZo4I4+xCVg6i2zGbl5nlLLSZKktY1JmDSY/hfYLyIuAX4C3ASc1jT+acAV/QhMEPdvyrRr9oMV01bvEVsxBVZMY9o1+/mFzZIkraVMwqTBdBRwGHAfcCHw8sy8ByAiHgHsCZzav/A05e7tWGfhgUy57Unw4HRI4MHpTLntSWW4X9QsSdJayxdzSAMoMxP4aP1pHXcLPg+2Roj7N2XakhfAkhf0OxRJktRF9oRJkiRJUg/ZEyYNsIiYTXn+a1NWvyiTmblaT5kkSZLGxyRMGkARsT5wEvBCyuvqs/6m6e/GLYvdWN5UYAGwODP37cY8JUmS1lbejigNpg9TErCPA3tRkq4DgH2As4E/AN38EqpDgMu7OD9JkqS1lkmYNJj2B76fmR8GLqnDFmfmacDzgenAgd1YUERsDfwD8NVuzE+SJGlt5+2I0mB6DPDZ+veD9fd0gMxcHhHfBv4DeH8XlvU54L3ARl2YV1uLFy9m6j13sP4VvlV/bTT1nltYvHh5v8OQJKln7AmTBtNdrLwIcxewAnh00/g7gK3Gu5CI2Be4KTPPH6HcQRGxICIWLF26dLyLlSRJWqPZEyYNpquAHQEy88GIuJRyi+JxERHAK4Bru7CcZwIvjYiXAOsBD4+I/8vM1zUXysxjgWMBZs+enaNdyIwZM7jhvmks2/klXQhZvbb+FacyY8aW/Q5DkqSesSdMGky/Av6pvrUQ4BjgxRFxFXAl5bmwr413IZn5/szcOjNnAq8G5rcmYJIkSYPGnjBpMB0BfIP6WvrM/FJErAe8jvKM2FeAT/YvPEmSpMnLJEwaQJl5N/DnlmGfZeXLOiZimWcCZ07U/CVJktYW3o4oSZIkST1kT5g0ACLiOWOZLjPP6nYskiRJg84kTBoMZwKjeetg1PJTRyooSZKk0TEJkwbDG/odgKSJs2QanLQRnLEh3BuwXsJed8Mr7oJH+T3YkrTGMQmTBkBmfr3fMUiaGAvWg8O3gOUBD0YZtizgtI3g9A3h0KUw+97+xihJWpUv5pAGTERsERFPi4jt+x2LpPFZMq0kYPdNWZmANTwYZfjhW5RykqQ1h0mYNCAiYkpE/A+wBPgN8JeIOCcituhzaJLG6KSNSg/YcJYHnLxRb+KRJHXGJEwaHAcDBwE3ACcBFwPPAI7pZ1CSxu6MDVfvAWv1YJRykqQ1hzcoSIPjX4HLgT0z8y6AiPgKcGBEbJKZt/czOEmjd+8ICVjDsg7LSZJ6w54waXDsBBzfSMCqoymvod+xPyFJGo/1OvziifVH8wUVkqQJZxImDY6HAde3DLu+aZyktcxed8PUERKsqfV19ZKkNYdJmDRYWk/XGv97s5K0FnrFXTBthCRsWsLL7xq+jCSpt3wmTBosL4mIrZr+34CSiP1zROzWUjYz88ieRSZp1B61vHwPWOv3hEHpAZuWZbxf2CxJaxaTMGmw/Ev9afXmNsMSMAmT1nCz74UvLimvoT9jw/ISjvXrLYgvv8sETJLWRCZh0uDYq98BSJoYj1oOb72t/EiS1nwmYdKAyMxf9zsGSZIk+WIOSZIkSeopkzBJkiRJ6iGTMEmSJEnqIZMwSZIkSeohkzBJkiRJ6iGTMEmSJEnqIZMwSZIkSeohkzBJkiRJ6iGTMEmSJEnqIZMwSZIkSeohkzBJkiRJ6iGTMEmSJEnqIZMwSRMqIh4TEWdExGURcWlEHNLvmCRJkvppWr8DkDTpLQfenZkXRMRGwPkR8cvMvKzfgUmSJPWDPWGSJlRmLsnMC+rfdwGXAzP6G5UkSVL/2BMmqWciYibwZOD3fQ5FA2zx4sX8/a6pHH7Bw/sdikbpb3dN5WGLF/c7DEkaN3vCJPVERGwI/AB4R2be2TLuoIhYEBELli5d2p8AJUmSesSeMEkTLiLWoSRg38zMk1rHZ+axwLEAs2fPzh6HpwEzY8YM7lu+hEOfcufIhbVGOfyCh7PuDO9mlrT2sydM0oSKiAC+BlyemZ/tdzySJEn9Zk+YpIn2TOD1wMURcVEddmhmntrNhUy951bWv6Krs1xjTLm39NisWG9yPsM09Z5bgS37HYYkST1jEiZpQmXmOUBM5DJmzZo1kbPvu4UL7wJg1naTNVHZctJvQ0mSmpmESVrrzZ07t98hTKhDDinfb33UUUf1ORJJktQNPhMmSZIkST1kEiZJkiRJPWQSJkmSJEk9ZBImSZIkST1kEiZJkiRJPWQSJkmSJEk9ZBImSZIkST1kEiZJkiRJPWQSJkmSJEk9ZBImSZIkST1kEiZJkiRJPTSt3wFIktRr19w9lcMveHi/w+i6G+8p11a33GBFnyOZGNfcPZUd+h2EJHWBSZgkaaDMmjWr3yFMmPsXLgRg3cdOzs+4A5N7+0kaHCZhkqSBMnfu3H6HMGEOOeQQAI466qg+RyJJGo7PhEmSJElSD5mESZIkSVIPmYRJkiRJUg+ZhEmSJElSD5mESZIkSVIPmYRJkiRJUg+ZhEmSJElSD5mESZIkSVIPmYRJkiRJUg+ZhEmSJElSD5mESZIkSVIPmYRJkiRJUg+ZhEmSJElSD5mESZpwEfHiiPhzRCyMiPf1Ox5JkqR+MgmTNKEiYirwRWAfYBfgNRGxS3+jkiRJ6p9p/Q5A0qT3VGBhZl4NEBHfAfYDLutrVONw9NFHs3Dhwp4tr7GsQw45pGfLnDVrFnPnzu3Z8iYr64okqR2TMEkTbQZwbdP/1wFPay4QEQcBBwFss802vYtsLbH++uv3OwStJawrkrR2iMzsdwySJrGI2B94cWb+W/3/9cDTMvPgduVnz56dCxYs6GWIkgZARJyfmbP7HYckgc+ESZp4i4HHNP2/dR0mSZI0kEzCJE20PwA7RMS2ETEdeDVwSp9jkiRJ6hufCZM0oTJzeUQcDJwGTAWOy8xL+xyWJElS35iESZpwmXkqcGq/45AkSVoTeDuiJEmSJPWQSZgkSZIk9ZBJmCRJkiT1kEmYJEmSJPWQSZgkSZIk9ZBJmCRJkiT1kEmYJEmSJPVQZGa/Y5Ckh0TEUuBv/Y5jDbQ5cHO/g9BawbrS3mMzc4t+ByFJYBImSWuFiFiQmbP7HYfWfNYVSVrzeTuiJEmSJPWQSZgkSZIk9ZBJmCStHY7tdwBaa1hXJGkN5zNhkiRJktRD9oRJkiRJUg+ZhEnSBIqIByPiooi4JCK+HxEbjHL6R0fEifXv3SLiJU3jXhoR7+t2zOqdiMiI+EzT/++JiHkTsJxDW/7/TbeXIUnqnEmYJE2sZZm5W2Y+AbgfeMtoJs7M6zNz//rvbsBLmsadkplHdC1S9cN9wCsiYvMJXs4qSVhmPmOClydJGoZJmCT1ztnArIjYLCJ+GBF/iojfRcSTACLiubXX7KKIuDAiNoqImbUXbTrw38Cr6vhXRcSBEfGFiNg4Iv4WEVPqfB4WEddGxDoRsX1E/Dwizo+IsyNi5z5+fq1uOeVFGu9sHRERW0TEDyLiD/XnmU3DfxkRl0bEV+u237yO+2Hd1pdGxEF12BHA+rXefLMOu7v+/k5E/EPTMo+PiP0jYmpEfKou908R8eYJXxOSNEBMwiSpByJiGrAPcDHwEeDCzHwSpYfihFrsPcDbMnM34NnAssb0mXk/8GHgu7Vn7btN4+4ALgKeWwftC5yWmQ9QTvDnZubudf5fmqjPqDH7IvDaiNi4ZfhRwJGZuQfwT8BX6/DDgPmZ+XjgRGCbpmneWLf1bODtEfGIzHwfK3tkX9uyjO8CrwSoif7zgJ8CbwLuqMveA/j3iNi2S59XkgbetH4HIEmT3PoRcVH9+2zga8DvKSfVZOb8iHhERDwcOBf4bO2tOCkzr4uITpfzXeBVwBnAq4EvRcSGwDOA7zfNZ93xfyR1U2beGREnAG+nKfEGng/s0rTtHl636bOAl9dpfx4RtzVN8/aIeHn9+zHADsAtwyz+Z8BREbEu8GLgrMxcFhEvBJ4UEY1bYTeu8/rrWD+nJGklkzBJmljLas/WQ4ZKrDLziIj4KeW5r3Mj4kXAvR0u5xTg8IjYDNgdmA88DLi9dflaI30OuAD436ZhU4A9M3OVOjBU/YmIOZTE7emZeU9EnAmsN9xCM/PeWu5FlCT+O43ZUXpQTxvdx5AkdcLbESWp984GXgsPnTjfXHtDts/MizPz/wF/AFqf37oL2KjdDDPz7jrNUcBPMvPBzLwT+GtE/HNdVkTErhPxgTQ+mXkr8D3KbYANvwDmNv6JiN3qn+ey8hbCFwKb1uEbA7fVBGxnYM+meT0QEesMsfjvAm+g3AL78zrsNOA/GtNExI4R8bCxfTpJUiuTMEnqvXnA7hHxJ+AI4IA6/B31JRx/Ah6g3CrW7AzK7WkXRcSr2sz3u8Dr6u+G1wJviog/ApcC+3XvY6jLPgM0vyXx7cDs+mKMy1j5Zs2PAC+MiEuAfwZuoCToPwemRcTllHr1u6Z5HQv8qfFijha/oDxP+Kv67CGU588uAy6oyzkG756RpK6JzOx3DJIkqUP1+a0HM3N5RDwd+LK3nErS2sWrWpIkrV22Ab5Xv5LgfuDf+xyPJGmU7AmTJEmSpB7ymTBJkiRJ6iGTMEmSJEnqIZMwSZIkSeohkzBJ0kCKiEX1i4olSeopkzBJUldFxHYRcWxEXBER90TEbRFxeUR8PSL26nd8kiT1m6+olyR1TUTMBn5N+bLpEyhfEL0+sAPwQsqXCp/RtwBXtRPgK4IlST1nEiZJ6qbDgA2A3TLzj60jI2Kr3ofUXmbe1+8YJEmDydsRJUndtANwS7sEDCAzb2j+PyKeHxG/iIjbI+LeiPhTRLyldbrG81sR8ZSImB8Rd0fErfUWx0e2lF0vIuZFxJ/r7ZC3R8TFEfGpdvNsGfaMiPhZRNxQ41kcEadGxJ5jXSGSJLWyJ0yS1E1XATtFxCsy86ThCkbEQcD/AL8DPg78HXgB8OWI2D4z/7Nlkq2B04EfACcCTwHeCMyOiD0y855a7ot1+AnAZynHuh2AvUeIZyfgl8ANwFHAjcCWwLOAXWuckiSNW2R6O7wkqTsi4umUZ8LWAa4EzgH+AJyZmZc3lXsU8FfgpMz8l5Z5HAUcDOyQmVfXYYuAxwLvzMzPNZV9JyXRen9mHlGH3Qr8LjNfMkKsi4BFmTmn/v92SvL1tMw8b2xrQJKkkXk7oiSpazLzt8DuwNeBjYE3AF8CLouIsyJiu1p0f2Bd4GsRsXnzD/BjyvHp+S2zv7POq9mX6vCXNw27A3h8RDxhlOHfUX/vFxHrjXJaSZI6ZhImSeqqzLw4Mw/MzC2BmcABwNnAs4EfRcR04HG1+K+ApS0/v6zjtmyZ9dWZeX/Lsu4Drga2axr8DmBT4OKIuCoivhoR+0XESMe879R4DgVurc+e/VdEPLbDjy5JUke8HVGSNOEiIiiJ2DMpydhrgbcA/wosGWKyq1tuR7wtM5/cZt4XAltn5hZNwzYDXgI8l9KjNrMu//mNRK71dsSmaZ8KvAh4Tv1ZAfxLZp48+k8uSdLqfDGHJGnCZWZGxO8pSdgMyvNiADdn5q86nM12ETG9uTcsItal9IJd0bK8W4H/A/6vJoBHAO8F9gO+P0Ks5wHn1fk/BrgQ+BhgEiZJ6gpvR5QkdU1EvCAiVrvAFxHrU76sGeAy4HvAfcBH6rjW8hvXBKvZw4G3tgx7ax3+wzrd1IjYpLlAlls+Lqz/bjZM7Ju3GXwd5RbJIaeTJGm07AmTJHXTkcAjIuIU4GLgHuAxwL8AOwInZObFABHxH8BXgcsj4hvA34AtgCcCLwN2ARY1zfsq4LD6wo3zKS8AeSOlF+zztcxGwJK6/AuBm4Btgf8AbqO89GMoH4yIFwI/oby5MYB/BHYGPjmmtSFJUhs+EyZJ6pqaxOxH+W6tGcAmlLcO/gn4BnB8Zq5oKv9M4D2U2xQ3AW4G/kxJhL6YmffWcosoCdm7gE8DTwPur+Xek5k31nLTgY8AzwO2BzakPHM2H/hEZjZug2z3ivo5lGTtaZSXgiyj3Db5FeBr6QFTktQlJmGSpDXeUC/RkCRpbeQzYZIkSZLUQyZhkiRJktRDJmGSJEmS1EM+EyZJkiRJPWRPmCRJkiT1kEmYJEmSJPWQSZgkSZIk9ZBJmCRJkiT1kEmYJEmSJPWQSZgkSZIk9dD/B6IKdCnA+1+6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calling  the compare plot function in the train set\n",
    "cols = ['Plasma glucose', 'Blood Work Result-1', 'Blood Pressure', 'Blood Work Result-2', \n",
    "        'Blood Work Result-3', 'Body mass index', 'Blood Work Result-4']\n",
    "for col in cols:\n",
    "    compare_plot(train, col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a00bcd1",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- It can be easily observed that patients who are positive with sepsis have higher readings in all of their health features comparing to the negative group. Hence, it can be reasonably inferred that patients with higher level of plasma glucose, Body mass index , and for blood work results are more likely to develop sepsis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bfe3167",
   "metadata": {},
   "source": [
    "#### 5.2.1 visualization of  count of  age feature \n",
    "Here we would like know the frequency of the age in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f4e319a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# determining age count\n",
    "Age_counts = train.groupby('Age').size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5c9dfaca",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of age count\n",
    "fig_2 = ex.scatter(Age_counts, x='Age', y='count', size='count', color= 'Age', hover_name='Age',log_y=False, size_max=60)\n",
    "fig_2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fa3ae6e",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- from the visual the age with the highest frequency are ages 21 and 22 whiles age 61 has the lowest frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35bc5df8",
   "metadata": {},
   "source": [
    "#### 5.2.2 visualization of age group against the target \n",
    "- Here, we are going to create age group to aids in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "90aeb03c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the bin edges for the age groups\n",
    "bins = list(range(20, 81, 10))\n",
    "\n",
    "# Create the bin labels for the age groups (7 labels for 8 bins)\n",
    "basket = ['{0}-{1}'.format(i, i + 9) for i in range(20, 81, 10)[:-1]]\n",
    "\n",
    "# Use pd.cut() to assign age groups to each 'Age' value\n",
    "train['Age_group'] = pd.cut(train['Age'], bins=bins, labels=basket, right=False)\n",
    "\n",
    "# Plot the count of each age group colored by 'Sepsis'\n",
    "sns.countplot(data=train, x='Age_group', hue='Sepsis');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54d370ba",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- from the visual, comparatively from 21-29 age group are less susceptive to sepsis than from 30-59 while patients at 60-69 are more susceptible sepsis, which i believe is because to weak immune system."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "514295f6",
   "metadata": {},
   "source": [
    "#### 5.2.3 visualization of count of other features against sepsis\n",
    "- Here, we want varify how other features varies in terms of count with respect to the target feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3461e69f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "hovertemplate": "Sepsis=Positive<br>Plasma glucose=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Positive",
         "marker": {
          "color": "#636efa",
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   "source": [
    "def create_histograms(data_frame, column_name):\n",
    "    fig_3 = px.histogram(\n",
    "        data_frame=data_frame,\n",
    "        x=column_name,\n",
    "        color=\"Sepsis\",\n",
    "        facet_col=\"Sepsis\",\n",
    "        nbins=50,\n",
    "        title=f\"{column_name} with Respect to Sepsis\"\n",
    "    )\n",
    "    fig_3.show()\n",
    "\n",
    "# Assuming your DataFrame is named 'train'\n",
    "excluded_columns = [\"Sepsis\", \"ID\"]  # List of columns to exclude\n",
    "\n",
    "for column in train.columns:\n",
    "    if column not in excluded_columns:\n",
    "        create_histograms(train, column)"
   ]
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   "source": [
    "✍ summary:\n",
    "- Plasma glucose with Sepsis: from the visual it is evident that positive sepsis tends to fall gradually as count of plasma glucose increases.Similarly, for the negative sepsis, tends to also gradually diminish as the count of the plasma glucose increases. thus higher plasma glucose, the higher Plasma exchange, the higher the potential to improve survival in sepsis by removing inflammatory cytokines and restoring deficient plasma proteins.henc e patients with high plasma glucose are less prone to sepsis infection.\n",
    "\n",
    "- blood work result-1 and Sepsis: in addition, we can infer that the count of the concentration in positive feature of sepsis is relatively low compared to the count of the concentration in the negative feature in sepsis. thus as the count of concentration of the blood work reault-1 increases the less prone the patient is to sepsis.\n",
    "\n",
    "- Blood Pressure with Sepsis: similarly, the count of blood pressure in the positive sepsis cases are relatively low compared to the blood presure in the negative sepsis cases.secondly, the density of the negative sepsis cases with respect to blood presure is higher than that of the positive sepsis cases.\n",
    "\n",
    "- Blood work result-2 with sepsis:furthermore,in this category, patients who are prone to sepsis are relatively low as compared to the counts of patients who are less prone to sepsis.\n",
    "\n",
    "- Blood work Result-3 with sepsis: again,it can be observe that the number of patients less prone to sepsis are more than the number of patients prone to sepsis interm of blood work Result-3.\n",
    "\n",
    "- Body mass index: comparatively, the count of patients without sepsis are also more the the count of patients with sepsis.\n",
    "\n",
    "- blood work result-4: from the visual again, the count of patients without sepsis are mor than the patients with sepsis in the blook work result-4 feature.\n",
    "\n",
    "- Age with sepsis: generally, from the visual, negative sepsis patients are more the positive sepsis patients. we also observed that, most patients with no sepsis fall between the age range of 20-40 years. the trend begins to fall drastically along the age range and even beyond.\n",
    "\n",
    "- Insurance with sepsis: comparatively, most of the negative sepsis patient are insured to the positive patients."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3983396",
   "metadata": {
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   "source": [
    "# VI. Bivariate Analysis\n",
    "Here is the section to explore, analyze, visualize each variable in relation to the others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c64022f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Plasma glucose', 'Blood Work Result-1', 'Blood Pressure',\n",
       "       'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index',\n",
       "       'Blood Work Result-4', 'Age', 'Insurance', 'Sepsis', 'Age_group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# calling the columns\n",
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2002ced",
   "metadata": {},
   "source": [
    "- Relationship Between Blood Pressure and Body mass index with Respect To Sepsis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c9c27e",
   "metadata": {},
   "source": [
    "#### 6.1 visualization of the Body mass index and Blood Pressure with resprect to the  target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c87d6168",
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   "source": [
    "fig_4=px.scatter(train, x=\"Body mass index\", y= \"Blood Pressure\",color= \"Sepsis\", \n",
    "                 title= \"Relationship Between Blood Pressure and Body mass index with Respect To Sepsis\")\n",
    "\n",
    "iplot(fig_4)"
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    "✍ summary: \n",
    "- there is no significant corelation between Blood Presure and Body mass index. the it can observed that majority of patients are negative interms of sepsis. "
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   "source": [
    "#### 6.2 Relationship Between Plasma glucose and Body mass index with Respect To Sepsis"
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Between Plasma glucose and Body mass index with Respect To Sepsis\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Plasma glucose\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Body mass index\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_5=px.scatter(train, x=\"Plasma glucose\", y= \"Body mass index\",color= \"Sepsis\", \n",
    "                 title= \"Relationship Between Plasma glucose and Body mass index with Respect To Sepsis\")\n",
    "\n",
    "iplot(fig_5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a34a6a21",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- there is no corelation Body mass index and Plasma glucose. Also negative sepsis patients dominants these features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39a67ba",
   "metadata": {},
   "source": [
    "# VII. Multivariate Analysis\n",
    "Here is the section to explore, analyze, visualize each variable in relation to the others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "448dfd42",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap of the train set features\n",
    "sns.heatmap(train.corr(), annot= True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4da87e21",
   "metadata": {},
   "source": [
    "✍ Observation\n",
    "- the correlation between features are weak, except for the plasma glucose and age which is 0.54. this also is weak. Hence all features will be kept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6735d13e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap of the test set features\n",
    "sns.heatmap(test.corr(), annot= True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ccb37b",
   "metadata": {},
   "source": [
    "✍ Observation\n",
    "- similarly, the correlation between features are weak, except for the plasma glucose and age with a correlation coefficient of 0.54. this also is weak. Hence all features will be kept."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e7a597",
   "metadata": {},
   "source": [
    "# VIII. Feature Processing & Engineering\n",
    "Here is the section to clean, process the dataset and create new features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9296ebe",
   "metadata": {},
   "source": [
    "#### 8.1 Feature Selection\n",
    "- in this section we will be selecting the best features to feed our models with. We will be using the Phi-Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5a75d558",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval columns not set, guessing: ['Plasma glucose', 'Blood Work Result-1', 'Blood Pressure', 'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index', 'Blood Work Result-4', 'Age', 'Insurance']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID                     1.000000\n",
       "Plasma glucose         0.282180\n",
       "Blood Work Result-1    0.611669\n",
       "Blood Pressure         0.205354\n",
       "Blood Work Result-2    0.229503\n",
       "Blood Work Result-3    0.404616\n",
       "Body mass index        0.429088\n",
       "Blood Work Result-4    0.231272\n",
       "Age                    0.404641\n",
       "Insurance              0.066436\n",
       "Sepsis                 1.000000\n",
       "Age_group              0.393934\n",
       "Name: Sepsis, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the correlation of other features with churn\n",
    "sepsis_corr= train.phik_matrix().loc[\"Sepsis\"]\n",
    "#sorting the values \n",
    "sepsis_cor=sepsis_corr.sort_values()\n",
    "sepsis_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a8372a",
   "metadata": {},
   "source": [
    "#### 8.3 Ploting the phi-k correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "83f28e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Phik Correlation Matrix for all  Features')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting the phi-k correlation matrix\n",
    "plt.figure(figsize= (8,4));\n",
    "sns.heatmap(sepsis_corr.to_frame(), annot= True, cmap= \"coolwarm\")\n",
    "plt.title(\"Phik Correlation Matrix for all  Features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e20266e",
   "metadata": {},
   "source": [
    "- from the phik correlation matrix, we will be dropping age_group since age and age_group are almost the same and we also drop insurance and ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4762bbc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop some columns\n",
    "train.drop(columns=['ID', 'Insurance','Age_group'], axis=1, inplace=True)\n",
    "test.drop(columns=['ID','Insurance'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6b01556f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Positive', 'Negative'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique values of the target\n",
    "train['Sepsis'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e581f59",
   "metadata": {},
   "source": [
    "#### 8.4 Encoding the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "948df680",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace Positive with 1 and Negative with 0 in target column\n",
    "train['Sepsis'].replace(to_replace='Positive', value='1', inplace=True)\n",
    "train['Sepsis'].replace(to_replace='Negative', value='0', inplace=True)\n",
    "# changing sepsis object type to integer\n",
    "train['Sepsis'] = train['Sepsis'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b978396d",
   "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>Plasma glucose</th>\n",
       "      <th>Blood Work Result-1</th>\n",
       "      <th>Blood Pressure</th>\n",
       "      <th>Blood Work Result-2</th>\n",
       "      <th>Blood Work Result-3</th>\n",
       "      <th>Body mass index</th>\n",
       "      <th>Blood Work Result-4</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sepsis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plasma glucose  Blood Work Result-1  Blood Pressure  Blood Work Result-2  \\\n",
       "0             6.0                148.0            72.0                 35.0   \n",
       "1             1.0                 85.0            66.0                 29.0   \n",
       "2             8.0                183.0            64.0                  0.0   \n",
       "3             1.0                 89.0            66.0                 23.0   \n",
       "4             0.0                137.0            40.0                 35.0   \n",
       "\n",
       "   Blood Work Result-3  Body mass index  Blood Work Result-4   Age  Sepsis  \n",
       "0                  0.0             33.6                0.627  50.0       1  \n",
       "1                  0.0             26.6                0.351  31.0       0  \n",
       "2                  0.0             23.3                0.672  32.0       1  \n",
       "3                 94.0             28.1                0.167  21.0       0  \n",
       "4                168.0             43.1                2.288  33.0       1  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9a2b287d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 599 entries, 0 to 598\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Plasma glucose       599 non-null    float64\n",
      " 1   Blood Work Result-1  599 non-null    float64\n",
      " 2   Blood Pressure       599 non-null    float64\n",
      " 3   Blood Work Result-2  599 non-null    float64\n",
      " 4   Blood Work Result-3  599 non-null    float64\n",
      " 5   Body mass index      599 non-null    float64\n",
      " 6   Blood Work Result-4  599 non-null    float64\n",
      " 7   Age                  599 non-null    float64\n",
      " 8   Sepsis               599 non-null    int32  \n",
      "dtypes: float64(8), int32(1)\n",
      "memory usage: 39.9 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4fb69019",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 169 entries, 0 to 168\n",
      "Data columns (total 8 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Plasma glucose       169 non-null    int64  \n",
      " 1   Blood Work Result-1  169 non-null    int64  \n",
      " 2   Blood Pressure       169 non-null    float64\n",
      " 3   Blood Work Result-2  169 non-null    int64  \n",
      " 4   Blood Work Result-3  169 non-null    float64\n",
      " 5   Body mass index      169 non-null    float64\n",
      " 6   Blood Work Result-4  169 non-null    float64\n",
      " 7   Age                  169 non-null    float64\n",
      "dtypes: float64(5), int64(3)\n",
      "memory usage: 10.7 KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d105fcd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    391\n",
       "1    208\n",
       "Name: Sepsis, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Sepsis.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2012db0e",
   "metadata": {},
   "source": [
    "#### 8.5 Handling imbalance target class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f1140242",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate majority and minority classes\n",
    "train_majority = train[train.Sepsis==0]\n",
    "train_minority = train[train.Sepsis==1]\n",
    " \n",
    "# Upsample minority class\n",
    "train_minority_upsampled = resample(train_minority, \n",
    "                                 replace=True,     # sample with replacement\n",
    "                                 n_samples=391,    # to match majority class\n",
    "                                 random_state=123) # reproducible results\n",
    " \n",
    "train_upsampled = pd.concat([train_majority, train_minority_upsampled])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d4f71912",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    391\n",
       "1    391\n",
       "Name: Sepsis, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display new class counts\n",
    "train_upsampled['Sepsis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9537878b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Plasma glucose', 'Blood Work Result-1', 'Blood Pressure',\n",
       "       'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index',\n",
       "       'Blood Work Result-4', 'Age', 'Sepsis'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_upsampled.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a1523ddd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the dataset\n",
    "train_upsampled.to_csv('final_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c13ee214",
   "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>Plasma glucose</th>\n",
       "      <th>Blood Work Result-1</th>\n",
       "      <th>Blood Pressure</th>\n",
       "      <th>Blood Work Result-2</th>\n",
       "      <th>Blood Work Result-3</th>\n",
       "      <th>Body mass index</th>\n",
       "      <th>Blood Work Result-4</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sepsis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>0.191</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Plasma glucose  Blood Work Result-1  Blood Pressure  Blood Work Result-2  \\\n",
       "1              1.0                 85.0            66.0                 29.0   \n",
       "3              1.0                 89.0            66.0                 23.0   \n",
       "5              5.0                116.0            74.0                  0.0   \n",
       "7             10.0                115.0            40.0                  0.0   \n",
       "10             4.0                110.0            92.0                  0.0   \n",
       "\n",
       "    Blood Work Result-3  Body mass index  Blood Work Result-4   Age  Sepsis  \n",
       "1                   0.0             26.6                0.351  31.0       0  \n",
       "3                  94.0             28.1                0.167  21.0       0  \n",
       "5                   0.0             25.6                0.201  30.0       0  \n",
       "7                   0.0             35.3                0.134  29.0       0  \n",
       "10                  0.0             37.6                0.191  30.0       0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_upsampled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b7ff81",
   "metadata": {},
   "source": [
    "#### 8.6 Dataset Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "915da509",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the target feature\n",
    "X = train_upsampled.drop('Sepsis', axis=1)\n",
    "# naming the target feature\n",
    "y = train_upsampled.Sepsis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f049b868",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the data \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size = .2, random_state=42, stratify= y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b9d8a664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((625, 8), (157, 8), (625,), (157,))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e7b5b689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plasma glucose         0\n",
       "Blood Work Result-1    0\n",
       "Blood Pressure         0\n",
       "Blood Work Result-2    0\n",
       "Blood Work Result-3    0\n",
       "Body mass index        0\n",
       "Blood Work Result-4    0\n",
       "Age                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "X_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "71bb3594",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plasma glucose         0\n",
       "Blood Work Result-1    0\n",
       "Blood Pressure         0\n",
       "Blood Work Result-2    0\n",
       "Blood Work Result-3    0\n",
       "Body mass index        0\n",
       "Blood Work Result-4    0\n",
       "Age                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## checking for missing values\n",
    "X_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3f3eab91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# checking for datatypes\n",
    "def check_dtypes(data_frame):\n",
    "    dtypes_dict = data_frame.dtypes.to_dict()\n",
    "    return dtypes_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "caf6fd7f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Plasma glucose': dtype('float64'),\n",
       " 'Blood Work Result-1': dtype('float64'),\n",
       " 'Blood Pressure': dtype('float64'),\n",
       " 'Blood Work Result-2': dtype('float64'),\n",
       " 'Blood Work Result-3': dtype('float64'),\n",
       " 'Body mass index': dtype('float64'),\n",
       " 'Blood Work Result-4': dtype('float64'),\n",
       " 'Age': dtype('float64')}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_dtypes(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece93dbf",
   "metadata": {},
   "source": [
    "✍ Observation\n",
    "- there are no categorical features in the X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d8a64e81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# numerical features\n",
    "num_attr=X_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a5d4697",
   "metadata": {},
   "source": [
    "# VIIII. Features Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a445b3",
   "metadata": {},
   "source": [
    "#### 9.1 Creating Pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fabd2785",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating pipelines\n",
    "num_pipeline= Pipeline([('imputer', SimpleImputer()),('scaler', StandardScaler())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0e41d6d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# full pipeline using column transformer\n",
    "full_pipeline=ColumnTransformer([('num_pipe',num_pipeline,num_attr)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfc7f3fc",
   "metadata": {},
   "source": [
    "# X. Machine Learning Modeling\n",
    "Here is the section to build, train, evaluate and compare the models to each others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "29c32256",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained= [] # empty list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b5a885c",
   "metadata": {},
   "source": [
    "#### 10.1 DecisionTree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "074f4eb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiating the model\n",
    "DTC=DecisionTreeClassifier() \n",
    "\n",
    "DTC = Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\", DecisionTreeClassifier(random_state=42, max_depth=6, min_samples_leaf=8))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ce9879",
   "metadata": {},
   "source": [
    "#### 10.1.1 model fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4f793c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 DecisionTreeClassifier(max_depth=6, min_samples_leaf=8,\n",
       "                                        random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 DecisionTreeClassifier(max_depth=6, min_samples_leaf=8,\n",
       "                                        random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=6, min_samples_leaf=8, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma glucose', 'Blood Work Result-1', 'Blood Pressure',\n",
       "       'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index',\n",
       "       'Blood Work Result-4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model',\n",
       "                 DecisionTreeClassifier(max_depth=6, min_samples_leaf=8,\n",
       "                                        random_state=42))])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the model\n",
    "DTC.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf00fc6f",
   "metadata": {},
   "source": [
    "#### 10.1.2 model prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "167dd8ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making prediction\n",
    "model_1= DTC.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "201e20fc",
   "metadata": {},
   "source": [
    "#### 10.1.3 classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0aedac8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.71      0.74        84\n",
      "           1       0.69      0.74      0.72        73\n",
      "\n",
      "    accuracy                           0.73       157\n",
      "   macro avg       0.73      0.73      0.73       157\n",
      "weighted avg       0.73      0.73      0.73       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print  classification report\n",
    "print(classification_report(model_1,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ea27bdc",
   "metadata": {},
   "source": [
    "##### 10.1.4 confusion matrix display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b9f788f1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# viewing confusion matrix display\n",
    "ConfusionMatrixDisplay.from_predictions(model_1,y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a10b1a0f",
   "metadata": {},
   "source": [
    "#### 10.1.5 Appending model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8fbc5362",
   "metadata": {},
   "outputs": [],
   "source": [
    "# appending the model into the empty list\n",
    "models_trained.append(DTC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40958092",
   "metadata": {},
   "source": [
    "#### 10.2 Logistic Regressor Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "83299d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiating the model\n",
    "LRP=LogisticRegression()\n",
    "\n",
    "\n",
    "LRP = Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\",LogisticRegression(random_state=42))\n",
    "])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e59607ae",
   "metadata": {},
   "source": [
    "##### 10.2.1 model fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "91ab5cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, LogisticRegression(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, LogisticRegression(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma glucose', 'Blood Work Result-1', 'Blood Pressure',\n",
       "       'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index',\n",
       "       'Blood Work Result-4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model', LogisticRegression(random_state=42))])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the model\n",
    "LRP.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f79d97",
   "metadata": {},
   "source": [
    "#### 10.2.2 model prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9753041e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making prediction\n",
    "model_2= LRP.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a6e3564",
   "metadata": {},
   "source": [
    "#### 10.2.3 classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4a17b7b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.73      0.73        79\n",
      "           1       0.73      0.73      0.73        78\n",
      "\n",
      "    accuracy                           0.73       157\n",
      "   macro avg       0.73      0.73      0.73       157\n",
      "weighted avg       0.73      0.73      0.73       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# printing classification report\n",
    "print(classification_report(model_2, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b142c687",
   "metadata": {},
   "source": [
    "#### 10.2.4 Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c2d695a5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_2, y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b125313c",
   "metadata": {},
   "source": [
    "#### 10.2.5 Appending model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "95b78a4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained.append(LRP)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11f1bc1f",
   "metadata": {},
   "source": [
    "#### 10.3 Random forest pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "940fff08",
   "metadata": {},
   "outputs": [],
   "source": [
    "#instantiating\n",
    "RFC=RandomForestClassifier()\n",
    "\n",
    "RFC= Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\",RandomForestClassifier(random_state=42, max_depth=6, min_samples_leaf=8))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcffb55b",
   "metadata": {},
   "source": [
    "#### 10.3.1 fitting model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "01f9430e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 RandomForestClassifier(max_depth=6, min_samples_leaf=8,\n",
       "                                        random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 RandomForestClassifier(max_depth=6, min_samples_leaf=8,\n",
       "                                        random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=6, min_samples_leaf=8, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma glucose', 'Blood Work Result-1', 'Blood Pressure',\n",
       "       'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index',\n",
       "       'Blood Work Result-4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model',\n",
       "                 RandomForestClassifier(max_depth=6, min_samples_leaf=8,\n",
       "                                        random_state=42))])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d64f24",
   "metadata": {},
   "source": [
    "#### 10.3.2 making prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ba743642",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3= RFC.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23f86f1",
   "metadata": {},
   "source": [
    "#### 10.3.3 classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bc2226d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.81      0.78        73\n",
      "           1       0.82      0.76      0.79        84\n",
      "\n",
      "    accuracy                           0.78       157\n",
      "   macro avg       0.78      0.79      0.78       157\n",
      "weighted avg       0.79      0.78      0.78       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(model_3,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d12b33",
   "metadata": {},
   "source": [
    "#### 10.3.4 confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8d4b019d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_3,y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe7a10a",
   "metadata": {},
   "source": [
    "#### 10.3.5 Appending model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "791bfe76",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained.append(RFC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f4ee92f",
   "metadata": {},
   "source": [
    "#### 10.4 SVM Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3784d3a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiating\n",
    "SVM=SVC()\n",
    "\n",
    "SVM= Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\",SVC(gamma='auto'))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f92a72fd",
   "metadata": {},
   "source": [
    "#### 10.4.1 fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7b5fad8f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, SVC(gamma=&#x27;auto&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, SVC(gamma=&#x27;auto&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=&#x27;auto&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma glucose', 'Blood Work Result-1', 'Blood Pressure',\n",
       "       'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index',\n",
       "       'Blood Work Result-4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model', SVC(gamma='auto'))])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVM.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c0d2b9",
   "metadata": {},
   "source": [
    "#### 10.4.2 making prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7ec17203",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_4= SVM.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed7ef2d",
   "metadata": {},
   "source": [
    "#### 10.4.3 making prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "38805867",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.74      0.75        81\n",
      "           1       0.73      0.75      0.74        76\n",
      "\n",
      "    accuracy                           0.75       157\n",
      "   macro avg       0.75      0.75      0.75       157\n",
      "weighted avg       0.75      0.75      0.75       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(model_4, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50fddfe7",
   "metadata": {},
   "source": [
    "#### 10.4.4 confusion matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "dde6eadb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_4, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9b30ec5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### 10.4.5 Appending model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4b0d2b0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained.append(SVM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfdd7505",
   "metadata": {},
   "source": [
    "#### 10.5 XGBoost Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "11e7ac35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiating the model\n",
    "XGB=XGBClassifier()\n",
    "\n",
    "XGB= Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\",XGBClassifier(random_state=42))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce5351a3",
   "metadata": {},
   "source": [
    "#### 10.5.1 fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3a40405c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)...\n",
       "                               feature_types=None, gamma=None, gpu_id=None,\n",
       "                               grow_policy=None, importance_type=None,\n",
       "                               interaction_constraints=None, learning_rate=None,\n",
       "                               max_bin=None, max_cat_threshold=None,\n",
       "                               max_cat_to_onehot=None, max_delta_step=None,\n",
       "                               max_depth=None, max_leaves=None,\n",
       "                               min_child_weight=None, missing=nan,\n",
       "                               monotone_constraints=None, n_estimators=100,\n",
       "                               n_jobs=None, num_parallel_tree=None,\n",
       "                               predictor=None, random_state=42, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)...\n",
       "                               feature_types=None, gamma=None, gpu_id=None,\n",
       "                               grow_policy=None, importance_type=None,\n",
       "                               interaction_constraints=None, learning_rate=None,\n",
       "                               max_bin=None, max_cat_threshold=None,\n",
       "                               max_cat_to_onehot=None, max_delta_step=None,\n",
       "                               max_depth=None, max_leaves=None,\n",
       "                               min_child_weight=None, missing=nan,\n",
       "                               monotone_constraints=None, n_estimators=100,\n",
       "                               n_jobs=None, num_parallel_tree=None,\n",
       "                               predictor=None, random_state=42, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma glucose', 'Blood Work Result-1', 'Blood Pressure',\n",
       "       'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index',\n",
       "       'Blood Work Result-4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')...\n",
       "                               feature_types=None, gamma=None, gpu_id=None,\n",
       "                               grow_policy=None, importance_type=None,\n",
       "                               interaction_constraints=None, learning_rate=None,\n",
       "                               max_bin=None, max_cat_threshold=None,\n",
       "                               max_cat_to_onehot=None, max_delta_step=None,\n",
       "                               max_depth=None, max_leaves=None,\n",
       "                               min_child_weight=None, missing=nan,\n",
       "                               monotone_constraints=None, n_estimators=100,\n",
       "                               n_jobs=None, num_parallel_tree=None,\n",
       "                               predictor=None, random_state=42, ...))])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XGB.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd93e5a6",
   "metadata": {},
   "source": [
    "#### 10.5.2 making prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a38f8c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_5=XGB.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece9d25a",
   "metadata": {},
   "source": [
    "#### 10.5.3 classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0a40bf55",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.89      0.85        72\n",
      "           1       0.90      0.82      0.86        85\n",
      "\n",
      "    accuracy                           0.85       157\n",
      "   macro avg       0.85      0.86      0.85       157\n",
      "weighted avg       0.86      0.85      0.85       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(model_5, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a24760",
   "metadata": {},
   "source": [
    "#### 10.5.4 confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d4b32cc1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_5, y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c0ef44",
   "metadata": {},
   "source": [
    "#### 10.5.5 Appending moodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7d3c7772",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained.append(XGB)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72d766a9",
   "metadata": {},
   "source": [
    "#### 10.6 AdaBoostClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1c56a493",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ada=AdaBoostClassifier()\n",
    "\n",
    "Ada = Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\",AdaBoostClassifier(random_state=42))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736a0f41",
   "metadata": {},
   "source": [
    "#### 10.6.1 fitting model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "fdb852a5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, AdaBoostClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, AdaBoostClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma glucose', 'Blood Work Result-1', 'Blood Pressure',\n",
       "       'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index',\n",
       "       'Blood Work Result-4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model', AdaBoostClassifier(random_state=42))])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ada.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "015b6737",
   "metadata": {},
   "source": [
    "#### 10.6.2 model prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2a482df1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_6 = Ada.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14fc11a",
   "metadata": {},
   "source": [
    "#### 10.6.3 classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "19d6ee18",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.82      0.81        77\n",
      "           1       0.82      0.80      0.81        80\n",
      "\n",
      "    accuracy                           0.81       157\n",
      "   macro avg       0.81      0.81      0.81       157\n",
      "weighted avg       0.81      0.81      0.81       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(model_6, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b43e9cd9",
   "metadata": {},
   "source": [
    "#### 10.6.4 confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ec1e67fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_6, y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d46f1a",
   "metadata": {},
   "source": [
    "#### 10.6.5 Appending model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "6b7bd1e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained.append(Ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6efa9978",
   "metadata": {},
   "source": [
    "#### 10.7 Naive Bayes pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "78d18c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiating the model\n",
    "NBP=GaussianNB()\n",
    "\n",
    "NBP= Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\",GaussianNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84ab6e4e",
   "metadata": {},
   "source": [
    "#### 10.7.1 fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1138de0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, GaussianNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, GaussianNB())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma glucose', 'Blood Work Result-1', 'Blood Pressure',\n",
       "       'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index',\n",
       "       'Blood Work Result-4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model', GaussianNB())])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NBP.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0fcd537",
   "metadata": {},
   "source": [
    "#### 10.7.2 making prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "01196a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_7= NBP.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c99ba74",
   "metadata": {},
   "source": [
    "#### 10.7.3 classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4b37e461",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.68      0.70        84\n",
      "           1       0.65      0.70      0.68        73\n",
      "\n",
      "    accuracy                           0.69       157\n",
      "   macro avg       0.69      0.69      0.69       157\n",
      "weighted avg       0.69      0.69      0.69       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(model_7, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29155970",
   "metadata": {},
   "source": [
    "#### 10.7.4 confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "7f280339",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_7,y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7222c00",
   "metadata": {},
   "source": [
    "# XI. Models Comparison\n",
    "Create a pandas dataframe that will allow you to compare your models.\n",
    "\n",
    "Find a sample frame below :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "71354812",
   "metadata": {
    "scrolled": false
   },
   "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>model</th>\n",
       "      <th>metric(accuracy_score)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.853503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.808917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.783439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.745223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.732484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.726115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.687898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    model  metric(accuracy_score)\n",
       "5           XGBClassifier                0.853503\n",
       "4      AdaBoostClassifier                0.808917\n",
       "6  RandomForestClassifier                0.783439\n",
       "1                     SVC                0.745223\n",
       "2      LogisticRegression                0.732484\n",
       "0  DecisionTreeClassifier                0.726115\n",
       "3              GaussianNB                0.687898"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a list\n",
    "pipelines = [DTC, SVM, LRP, NBP, Ada, XGB, RFC]\n",
    "res = []  # creating an empty list\n",
    "\n",
    "for pipe in pipelines:\n",
    "    pipe.fit(X_train, y_train)\n",
    "    model_name = pipe.named_steps['model'].__class__.__name__# calling model name\n",
    "    accuracy_score_value = accuracy_score(y_test, pipe.predict(X_test))# setting the accuracy score\n",
    "    result = {'model': model_name, 'metric(accuracy_score)': accuracy_score_value}  \n",
    "    res.append(result)\n",
    "results = pd.DataFrame(res)\n",
    "results = results.sort_values(by='metric(accuracy_score)', ascending=False)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d886778",
   "metadata": {},
   "source": [
    "# XII.Hyperparameter tuning\n",
    "Fine-tune the Top-k models (3 < k < 5) using a GridSearchCV (that is in sklearn.model_selection ) and Optuna to find the best hyperparameters and achieve the maximum performance of each of the Top-k models, then compare them again to select the best one."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e274dc",
   "metadata": {},
   "source": [
    "#### 12.1 using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c1ce86f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:19:49] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:51] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:51] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:51] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:58] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:58] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:58] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:19:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:00] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:00] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:00] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:20:01] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:01] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:01] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:03] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:03] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:03] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:06] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:06] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:07] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:07] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:07] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:09] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:09] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:09] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:10] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:10] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:10] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:11] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:20:11] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:12] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:12] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:12] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:14] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:14] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:14] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:17] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:17] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:18] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:18] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:18] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:19] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:19] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:20] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:20] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:20] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:21] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:21] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:21] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "[00:20:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:20:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:767: \n",
      "Parameters: { \"base_estimator__learning_rate\", \"base_estimator__max_depth\", \"base_estimator__n_estimators\" } are not used.\n",
      "\n",
      "Best Parameters: {'model__base_estimator__learning_rate': 0.1, 'model__base_estimator__max_depth': 5, 'model__base_estimator__n_estimators': 50}\n",
      "Accuracy: 0.8535031847133758\n"
     ]
    }
   ],
   "source": [
    "# Define your best model pipeline\n",
    "XGB = Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\",XGBClassifier(random_state=42))\n",
    "])\n",
    "\n",
    "# Define the hyperparameters grid for XGBoost\n",
    "param_grid_xgb = {\n",
    "    \"model__base_estimator__learning_rate\": [0.1, 0.01],\n",
    "    \"model__base_estimator__max_depth\": [5,10,20],\n",
    "    \"model__base_estimator__n_estimators\": [50,100, 200]\n",
    "}\n",
    "\n",
    "# Perform grid search cross-validation to find the best hyperparameters\n",
    "grid_search_xgb = GridSearchCV(estimator=XGB, param_grid=param_grid_xgb, scoring='accuracy', cv=5)\n",
    "grid_search_xgb.fit(X_train, y_train)\n",
    "\n",
    "# Get the best hyperparameters and the corresponding model\n",
    "best_params_xgb = grid_search_xgb.best_params_\n",
    "best_model_xgb = grid_search_xgb.best_estimator_\n",
    "\n",
    "# Predict on the test set using the best XGBoost model\n",
    "y_pred = best_model_xgb.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Best Parameters: {best_params_xgb}\")\n",
    "print(f\"Accuracy: {accuracy}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39f5703c",
   "metadata": {},
   "source": [
    "#### 12.2 using optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "c7063200",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2023-08-18 00:20:23,349] A new study created in memory with name: no-name-a033fb3b-9c9c-4c14-8501-5f21556f9760\n",
      "[I 2023-08-18 00:20:24,312] Trial 0 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.06206985681512176, 'max_depth': 8, 'n_estimators': 233}. Best is trial 0 with value: 0.8662420382165605.\n",
      "[I 2023-08-18 00:20:25,285] Trial 1 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.09054770862167484, 'max_depth': 6, 'n_estimators': 270}. Best is trial 0 with value: 0.8662420382165605.\n",
      "[I 2023-08-18 00:20:25,801] Trial 2 finished with value: 0.8789808917197452 and parameters: {'learning_rate': 0.057761757055756834, 'max_depth': 6, 'n_estimators': 137}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:26,477] Trial 3 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.050114001932132266, 'max_depth': 4, 'n_estimators': 278}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:27,428] Trial 4 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.03881225102330761, 'max_depth': 6, 'n_estimators': 285}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:28,015] Trial 5 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.0328283762433916, 'max_depth': 7, 'n_estimators': 144}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:29,019] Trial 6 finished with value: 0.8152866242038217 and parameters: {'learning_rate': 0.005456138597765628, 'max_depth': 7, 'n_estimators': 257}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:29,550] Trial 7 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.09338830557706942, 'max_depth': 9, 'n_estimators': 129}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:29,879] Trial 8 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.041747377381485436, 'max_depth': 8, 'n_estimators': 62}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:30,344] Trial 9 finished with value: 0.8089171974522293 and parameters: {'learning_rate': 0.03017299526555447, 'max_depth': 3, 'n_estimators': 219}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:30,911] Trial 10 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.06677757762757873, 'max_depth': 10, 'n_estimators': 87}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:31,610] Trial 11 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.06697360251586948, 'max_depth': 5, 'n_estimators': 183}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:32,379] Trial 12 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.018566249253139176, 'max_depth': 5, 'n_estimators': 183}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:32,947] Trial 13 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.05136899951552522, 'max_depth': 6, 'n_estimators': 123}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:33,695] Trial 14 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.022694189565337176, 'max_depth': 5, 'n_estimators': 209}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:34,412] Trial 15 finished with value: 0.8280254777070064 and parameters: {'learning_rate': 0.041046944647563484, 'max_depth': 3, 'n_estimators': 293}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:35,064] Trial 16 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.08018059171641342, 'max_depth': 6, 'n_estimators': 154}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:35,637] Trial 17 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.05768023536721677, 'max_depth': 8, 'n_estimators': 99}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:36,194] Trial 18 finished with value: 0.8280254777070064 and parameters: {'learning_rate': 0.07480187882510653, 'max_depth': 4, 'n_estimators': 160}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:37,435] Trial 19 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.04624034599237671, 'max_depth': 7, 'n_estimators': 248}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:38,160] Trial 20 finished with value: 0.8280254777070064 and parameters: {'learning_rate': 0.059633218248903136, 'max_depth': 4, 'n_estimators': 204}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:38,789] Trial 21 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.05324298675837357, 'max_depth': 6, 'n_estimators': 122}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:39,365] Trial 22 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.05033968710990248, 'max_depth': 6, 'n_estimators': 104}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:39,767] Trial 23 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.041226976985016116, 'max_depth': 5, 'n_estimators': 66}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:40,520] Trial 24 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.05271864933770814, 'max_depth': 7, 'n_estimators': 136}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:41,203] Trial 25 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.03364447828871828, 'max_depth': 6, 'n_estimators': 111}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:41,871] Trial 26 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.04464411369263139, 'max_depth': 5, 'n_estimators': 173}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:42,427] Trial 27 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.05610934653610286, 'max_depth': 8, 'n_estimators': 86}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:43,205] Trial 28 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.06672483852263404, 'max_depth': 7, 'n_estimators': 171}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:43,671] Trial 29 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.06128659857330737, 'max_depth': 9, 'n_estimators': 50}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:44,699] Trial 30 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.03788337813661617, 'max_depth': 6, 'n_estimators': 226}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:45,845] Trial 31 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.04613608835513149, 'max_depth': 7, 'n_estimators': 298}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:46,952] Trial 32 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.04542419322719324, 'max_depth': 8, 'n_estimators': 260}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:47,879] Trial 33 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.0500030874269587, 'max_depth': 6, 'n_estimators': 247}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:48,979] Trial 34 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.04910099118731788, 'max_depth': 7, 'n_estimators': 282}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:50,080] Trial 35 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.03777021246174793, 'max_depth': 7, 'n_estimators': 239}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:50,910] Trial 36 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.056781012470757945, 'max_depth': 4, 'n_estimators': 275}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:52,212] Trial 37 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.06297606564165546, 'max_depth': 9, 'n_estimators': 263}. Best is trial 2 with value: 0.8789808917197452.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2023-08-18 00:20:53,016] Trial 38 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.045836395025932626, 'max_depth': 6, 'n_estimators': 122}. Best is trial 2 with value: 0.8789808917197452.\n",
      "[I 2023-08-18 00:20:53,604] Trial 39 finished with value: 0.8853503184713376 and parameters: {'learning_rate': 0.09893992308634537, 'max_depth': 5, 'n_estimators': 147}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:20:54,242] Trial 40 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.09964184526373078, 'max_depth': 5, 'n_estimators': 149}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:20:54,918] Trial 41 finished with value: 0.8789808917197452 and parameters: {'learning_rate': 0.0844165166995529, 'max_depth': 6, 'n_estimators': 139}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:20:55,529] Trial 42 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.08847395426553552, 'max_depth': 5, 'n_estimators': 139}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:20:56,374] Trial 43 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.09943271968457436, 'max_depth': 4, 'n_estimators': 162}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:20:57,268] Trial 44 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.08946929686167139, 'max_depth': 6, 'n_estimators': 199}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:20:57,873] Trial 45 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.09258733138643203, 'max_depth': 5, 'n_estimators': 134}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:20:58,694] Trial 46 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.08128665114136521, 'max_depth': 6, 'n_estimators': 188}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:20:59,261] Trial 47 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.08607707186209465, 'max_depth': 5, 'n_estimators': 116}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:20:59,989] Trial 48 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.07312387025796552, 'max_depth': 6, 'n_estimators': 149}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:00,413] Trial 49 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.09436776595955637, 'max_depth': 4, 'n_estimators': 82}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:01,039] Trial 50 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.0728493396127014, 'max_depth': 7, 'n_estimators': 97}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:02,247] Trial 51 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.055120716425394164, 'max_depth': 7, 'n_estimators': 286}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:03,063] Trial 52 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.08531065564740042, 'max_depth': 6, 'n_estimators': 162}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:03,752] Trial 53 finished with value: 0.8280254777070064 and parameters: {'learning_rate': 0.028235413271699986, 'max_depth': 5, 'n_estimators': 145}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:04,486] Trial 54 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.07735359719060583, 'max_depth': 7, 'n_estimators': 129}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:05,397] Trial 55 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.09517146813690745, 'max_depth': 5, 'n_estimators': 247}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:06,029] Trial 56 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.06421217829935683, 'max_depth': 6, 'n_estimators': 121}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:06,693] Trial 57 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.0689057082211917, 'max_depth': 7, 'n_estimators': 110}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:07,847] Trial 58 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.051963754142300804, 'max_depth': 8, 'n_estimators': 216}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:08,493] Trial 59 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.05776601693607836, 'max_depth': 3, 'n_estimators': 190}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:09,157] Trial 60 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.06007430456950896, 'max_depth': 6, 'n_estimators': 132}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:10,074] Trial 61 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.07004577135918191, 'max_depth': 7, 'n_estimators': 174}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:10,991] Trial 62 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.08215766033045259, 'max_depth': 8, 'n_estimators': 165}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:11,969] Trial 63 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.06367977578936027, 'max_depth': 7, 'n_estimators': 155}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:12,969] Trial 64 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.06624485813642646, 'max_depth': 6, 'n_estimators': 177}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:13,722] Trial 65 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.053566765426016405, 'max_depth': 6, 'n_estimators': 139}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:14,922] Trial 66 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.04936359881291438, 'max_depth': 7, 'n_estimators': 268}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:15,882] Trial 67 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.0764525883763095, 'max_depth': 10, 'n_estimators': 171}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:16,980] Trial 68 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.05831986850440429, 'max_depth': 5, 'n_estimators': 300}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:17,787] Trial 69 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.05500541453825588, 'max_depth': 6, 'n_estimators': 153}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:18,513] Trial 70 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.042567260013040016, 'max_depth': 7, 'n_estimators': 127}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:19,543] Trial 71 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.03787814964314563, 'max_depth': 6, 'n_estimators': 244}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:20,698] Trial 72 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.047896487414143964, 'max_depth': 6, 'n_estimators': 236}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:21,712] Trial 73 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.04401325671516606, 'max_depth': 6, 'n_estimators': 221}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:22,745] Trial 74 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.03941045345080923, 'max_depth': 5, 'n_estimators': 224}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:23,888] Trial 75 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.035449985007284454, 'max_depth': 7, 'n_estimators': 255}. Best is trial 39 with value: 0.8853503184713376.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2023-08-18 00:21:25,013] Trial 76 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.04062828777458406, 'max_depth': 8, 'n_estimators': 229}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:25,879] Trial 77 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.047921293796124455, 'max_depth': 6, 'n_estimators': 198}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:26,731] Trial 78 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.05188002534754971, 'max_depth': 7, 'n_estimators': 142}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:27,761] Trial 79 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.045195242573821265, 'max_depth': 5, 'n_estimators': 290}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:28,807] Trial 80 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.04164956730892713, 'max_depth': 6, 'n_estimators': 271}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:29,501] Trial 81 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.06055559144683629, 'max_depth': 6, 'n_estimators': 148}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:30,380] Trial 82 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.07183336825881467, 'max_depth': 6, 'n_estimators': 212}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:31,111] Trial 83 finished with value: 0.8789808917197452 and parameters: {'learning_rate': 0.0669675552611425, 'max_depth': 6, 'n_estimators': 152}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:31,864] Trial 84 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.06613481301297235, 'max_depth': 5, 'n_estimators': 167}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:32,649] Trial 85 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.0627396730508801, 'max_depth': 7, 'n_estimators': 157}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:33,284] Trial 86 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.07934842171953256, 'max_depth': 6, 'n_estimators': 124}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:34,170] Trial 87 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.07510999166172296, 'max_depth': 7, 'n_estimators': 183}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:34,724] Trial 88 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.0684448257535408, 'max_depth': 4, 'n_estimators': 105}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:35,342] Trial 89 finished with value: 0.8853503184713376 and parameters: {'learning_rate': 0.09138920000081029, 'max_depth': 6, 'n_estimators': 114}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:35,920] Trial 90 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.09597865683224438, 'max_depth': 6, 'n_estimators': 93}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:36,525] Trial 91 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.09739077809597046, 'max_depth': 6, 'n_estimators': 114}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:37,220] Trial 92 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.09122656879620203, 'max_depth': 6, 'n_estimators': 135}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:37,880] Trial 93 finished with value: 0.8789808917197452 and parameters: {'learning_rate': 0.09034404149956898, 'max_depth': 6, 'n_estimators': 117}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:38,455] Trial 94 finished with value: 0.8662420382165605 and parameters: {'learning_rate': 0.08867560995769722, 'max_depth': 5, 'n_estimators': 104}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:38,999] Trial 95 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.09192329012852642, 'max_depth': 7, 'n_estimators': 78}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:39,636] Trial 96 finished with value: 0.8726114649681529 and parameters: {'learning_rate': 0.09243743509441046, 'max_depth': 6, 'n_estimators': 118}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:40,292] Trial 97 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.08634595679356902, 'max_depth': 6, 'n_estimators': 126}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:40,947] Trial 98 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.09398480382761512, 'max_depth': 5, 'n_estimators': 142}. Best is trial 39 with value: 0.8853503184713376.\n",
      "[I 2023-08-18 00:21:41,570] Trial 99 finished with value: 0.8789808917197452 and parameters: {'learning_rate': 0.098494399688519, 'max_depth': 7, 'n_estimators': 109}. Best is trial 39 with value: 0.8853503184713376.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of finished trials:  100\n",
      "Best trial:\n",
      "Value:  0.8853503184713376\n",
      "Params: \n",
      "    learning_rate: 0.09893992308634537\n",
      "    max_depth: 5\n",
      "    n_estimators: 147\n"
     ]
    }
   ],
   "source": [
    "XGB = XGBClassifier()\n",
    "\n",
    "# Define the objective function for Optuna\n",
    "def objective(trial):\n",
    "    learning_rate = trial.suggest_float('learning_rate', 0.001, 0.1)\n",
    "    max_depth = trial.suggest_int('max_depth', 3, 10)\n",
    "    n_estimators = trial.suggest_int('n_estimators', 50, 300)\n",
    "\n",
    "    model = Pipeline([\n",
    "        (\"col_trans\", full_pipeline),\n",
    "        (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "        (\"model\",XGBClassifier(learning_rate=learning_rate, max_depth=max_depth, n_estimators=n_estimators, random_state=42))\n",
    "    ])\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    return accuracy\n",
    "\n",
    "# Create a study object and optimize\n",
    "study = optuna.create_study(direction='maximize')  # 'maximize' for accuracy\n",
    "study.optimize(objective, n_trials=100)  # You can adjust the number of trials\n",
    "\n",
    "# Print the optimization results\n",
    "print('Number of finished trials: ', len(study.trials))\n",
    "print('Best trial:')\n",
    "trial = study.best_trial\n",
    "\n",
    "print('Value: ', trial.value)\n",
    "print('Params: ')\n",
    "for key, value in trial.params.items():\n",
    "    print(f'    {key}: {value}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9796d88",
   "metadata": {},
   "source": [
    "#### 12.2.1 results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "b9e3281f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'XGBClassifier__learning_rate': 0.09893992308634537,\n",
       " 'XGBClassifier__max_depth': 5,\n",
       " 'XGBClassifier__n_estimators': 147}"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the result\n",
    "def get_params(input_study) :\n",
    "    params = {k: v for k, v in input_study.best_params.items() if k not in ('dim_red', 'scalers')}\n",
    "    change = []\n",
    "    for k,v in dict(params).items():\n",
    "        tmp_name = k\n",
    "        if 'XGBClassifier' not in tmp_name :\n",
    "            res = f\"XGBClassifier__{tmp_name}\"\n",
    "            params[res] = params.pop(tmp_name)\n",
    "            change.append(res)\n",
    "    return params\n",
    "\n",
    "params = get_params(study)\n",
    "params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8279f57",
   "metadata": {},
   "source": [
    "#### 12.2.2 paramters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "ee5a0a9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "XGB_hy = Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\",XGBClassifier(\n",
    "        learning_rate=0.06494272544557561,\n",
    "        max_depth=6,\n",
    "        n_estimators=139,\n",
    "        random_state=42\n",
    "    ))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e9d9711",
   "metadata": {},
   "source": [
    "#### 12.2.3 fitting best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "41123426",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)...\n",
       "                               grow_policy=None, importance_type=None,\n",
       "                               interaction_constraints=None,\n",
       "                               learning_rate=0.06494272544557561, max_bin=None,\n",
       "                               max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                               max_delta_step=None, max_depth=6,\n",
       "                               max_leaves=None, min_child_weight=None,\n",
       "                               missing=nan, monotone_constraints=None,\n",
       "                               n_estimators=139, n_jobs=None,\n",
       "                               num_parallel_tree=None, predictor=None,\n",
       "                               random_state=42, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)...\n",
       "                               grow_policy=None, importance_type=None,\n",
       "                               interaction_constraints=None,\n",
       "                               learning_rate=0.06494272544557561, max_bin=None,\n",
       "                               max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                               max_delta_step=None, max_depth=6,\n",
       "                               max_leaves=None, min_child_weight=None,\n",
       "                               missing=nan, monotone_constraints=None,\n",
       "                               n_estimators=139, n_jobs=None,\n",
       "                               num_parallel_tree=None, predictor=None,\n",
       "                               random_state=42, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma glucose&#x27;, &#x27;Blood Work Result-1&#x27;, &#x27;Blood Pressure&#x27;,\n",
       "       &#x27;Blood Work Result-2&#x27;, &#x27;Blood Work Result-3&#x27;, &#x27;Body mass index&#x27;,\n",
       "       &#x27;Blood Work Result-4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" ><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.06494272544557561,\n",
       "              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=6, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=139, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma glucose', 'Blood Work Result-1', 'Blood Pressure',\n",
       "       'Blood Work Result-2', 'Blood Work Result-3', 'Body mass index',\n",
       "       'Blood Work Result-4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')...\n",
       "                               grow_policy=None, importance_type=None,\n",
       "                               interaction_constraints=None,\n",
       "                               learning_rate=0.06494272544557561, max_bin=None,\n",
       "                               max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                               max_delta_step=None, max_depth=6,\n",
       "                               max_leaves=None, min_child_weight=None,\n",
       "                               missing=nan, monotone_constraints=None,\n",
       "                               n_estimators=139, n_jobs=None,\n",
       "                               num_parallel_tree=None, predictor=None,\n",
       "                               random_state=42, ...))])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XGB_hy.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b31db54",
   "metadata": {},
   "source": [
    "#### 12.2.4 making prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "24c36c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model= XGB_hy.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b778bea",
   "metadata": {},
   "source": [
    "#### 12.2.4 classification report for best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "3f33690b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.93      0.87        70\n",
      "           1       0.94      0.84      0.88        87\n",
      "\n",
      "    accuracy                           0.88       157\n",
      "   macro avg       0.88      0.88      0.88       157\n",
      "weighted avg       0.89      0.88      0.88       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(final_model, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11d3202",
   "metadata": {},
   "source": [
    "#### 12.2.5 confusion  matrix for best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "97c1d418",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2O5GZWW5letfSzOytglJNrGhm9halWnzEzOyAnMjMrOwUxcpkTmRmlk/JZog1M9sv95GZWenVev2o2ZzIzCw/18jMrNRKutK4mdnenMjMrMw8INbMegV1FyuTOZGZWT4eR9b7fe7zv+K0019m69b+fOaS8/b67GP/9Sn+/NLHuODjU9m2rX+LIrRKLz7bn69ddtybx+vX9ONP/no927f04cGFQ5Bg6IjdXPWtNRx1dOeBCzrEHDLDLyTdCPwh0BERExp1n6JZtOg45s8fx1VfXLrX+RHtr3HqezewYcMRLYrM9ufYcTv5p58+DUBXF1x86rs587ytHDmki+lfXA/APbNH8P3rjuazX1/bylCLpWA1skbO2X8TMKWB5RfSE78eyfbtb61tXXrZI9ww+z2F+w/A9nh0ySBG//ZORh2zm4GD9lQ53ni9DdVapPEQo8i2NUvDamQR8YCk4xpVfpmcfsZLbNp0OM+vGtbqUKyK++cNZfLUrW8e/+vMo/npncMZOLiLb9z1bOsCK5oACvbSeMtXUZJ0Sc/inbs6d7Q6nLrr37+TCy5cyS1zDpnWdSnt3iX+474hnPVHW9889+mr13Prwys552NbmH9je+uCK6B6rKJUTy1PZBExKyImRcSkfocNbHU4dTd69KscffQOrv/eQm66+V5GtL/Od66/j2HDXm91aFbhoZ8NYtzvvsaw9rd26J/z0S384idDWhBVMfWMIzskmpaWWL16KBdOm/rm8U0338uVl3/ITy0L5v57hu3VrHxpVT/GvHMXAA8uHMKx43a2KLICiihc09KJrM6+dM2DvOc9HQwespNbbp3PLbdM4L4F72x1WFbFG6+1sXzJID77jRffPHfD136Ltc/1p60NRo7ZxZV+YrmXQ2Zkv6TbgMnACElrgWsjouZCm2X39X84o+rnn/pvf9SkSCyrAUd0c9eKJ/Y69+XZq1sTTFnUIZFJOha4GRiVljgrIr4taThwB3AcsBqYFhFbqpXVyKeWFzaqbDNrrTrVyDqBL0TEckmDgIclLQI+BSyOiJmSrgauBr5UraCWd/abWckE0BXZtmrFRKyLiOXp/nbgSWAMcD4wJ71sDjC1VkjuIzOz3HLUyEZIWlZxPCsiZr2lvGTM6SnAUmBURKxLP1pP0vSsyonMzPLL/tRyU0RMqnaBpCOBHwJ/FRHbVPEaRUSEVDttumlpZrnVaxyZpL4kSezWiPhRenqDpNHp56OBjlrlOJGZWT6RY6tCSdXrBuDJiPi/FR/NB6an+9OBebVCctPSzHIRoBod+RmdCfwJ8GtJj6bn/gaYCcyVNAN4AZhWqyAnMjPLrR4rjUfEL0jy4v6cm6csJzIzy8czxJpZ+fldSzPrBQ6Zdy3NrBdzjczMSi3q9tSybpzIzCy/YuUxJzIzy68ewy/qyYnMzPJzIjOzUgvgUFmg18x6JxFuWppZL9BdrCqZE5mZ5eOmpZn1Bm5amln5OZGZWbn5pXEzK7ueVZQKxInMzHJzH5mZlZ8TmZmVWgDdTmRmVmru7Dez3sCJzMxKLYCuYg3tdyIzs5wColiJzCuNm1l+Edm2GiTdKKlD0hMV54ZLWiTpmfTnsFrlOJGZWT49Ty2zbLXdBEzZ59zVwOKIGA8sTo+rciIzs/zqVCOLiAeAV/Y5fT4wJ92fA0ytVY77yMwsv+xPLUdIWlZxPCsiZtX4zqiIWJfurwdG1bqJE5mZ5RMBXV1Zr94UEZPe/q0ipNrLAbtpaWb51alpeQAbJI0GSH921PqCE5mZ5dfYRDYfmJ7uTwfm1fqCm5ZmllPmJ5I1SboNmEzSl7YWuBaYCcyVNAN4AZhWqxwnMjPLJyDqNCA2Ii48wEfn5inHiczM8vMrSmZWahFeDs7MegHPfmFmZReukZlZuXliRTMrO091bWZlF0Bkf0WpKZzIzCyfKN7Eik5kZpZbuGlpZqVXsBqZokBPHyRtJHm3qrcZAWxqdRCWS2/9O/vtiGg/mAIkLSD588liU0TsOwNs3RUqkfVWkpYdzJxM1nz+OysXT+NjZqXnRGZmpedE1hy15ii34vHfWYm4j8zMSs81MjMrPScyMys9J7IGkjRF0tOSnpVUc7Vkaz1JN0rqkPREq2Ox7JzIGkRSH+C7wHnAScCFkk5qbVSWwU1AwwdwWn05kTXOacCzEbEqInYBt5MsBW8FFhEPAK+0Og7Lx4msccYAL1Ycr03PmVmdOZGZWek5kTXOS8CxFcfHpOfMrM6cyBrnIWC8pLGS+gGfJFkK3szqzImsQSKiE7gcWAg8CcyNiBWtjcpqkXQb8CBwgqS1kma0Oiarza8omVnpuUZmZqXnRGZmpedEZmal50RmZqXnRGZmpedEViKSuiQ9KukJSXdKOuIgyrpJ0sfT/dnVXmiXNFnS+9/GPVZLestqOwc6v881r+a81/+UdFXeGK13cCIrl9cjYmJETAB2AZdVfijpba1TGhF/FhErq1wyGcidyMyaxYmsvJYA49La0hJJ84GVkvpI+t+SHpL0uKRLAZT4x3R+tJ8CI3sKknS/pEnp/hRJyyU9JmmxpONIEubn0trgByS1S/pheo+HJJ2ZfvcoSfdJWiFpNqBav4SkeyQ9nH7nkn0+uy49v1hSe3ruXZIWpN9ZIunEuvxpWql5pfESSmte5wEL0lOnAhMi4vk0GfwmIn5PUn/gl5LuA04BTiCZG20UsBK4cZ9y24F/Ac5KyxoeEa9I+h7wakR8M73uB8B1EfELSe8geXvhd4BrgV9ExFck/QGQZVT8n6b3OBx4SNIPI2IzMBBYFhGfk/TltOzLSRYFuSwinpH0PuB64Jy38cdovYgTWbkcLunRdH8JcANJk+9XEfF8ev5DwHt6+r+AIcB44CzgtojoAl6W9LP9lH868EBPWRFxoHm5/gtwkvRmhWuwpCPTe3ws/e6PJW3J8DtdKemj6f6xaaybgW7gjvT894Efpfd4P3Bnxb37Z7iH9XJOZOXyekRMrDyR/g+9o/IUcEVELNznut+vYxxtwOkR8cZ+YslM0mSSpHhGRLwm6X5gwAEuj/S+W/f9MzBzH1nvsxD4jKS+AJKOlzQQeAC4IO1DGw2cvZ/v/gdwlqSx6XeHp+e3A4MqrrsPuKLnQNLEdPcB4KL03HnAsBqxDgG2pEnsRJIaYY82oKdWeRFJk3Ub8LykT6T3kKSTa9zDDgFOZL3PbJL+r+XpAhr/TFLzvht4Jv3sZpIZHvYSERuBS0iacY+xp2l3L/DRns5+4EpgUvowYSV7np7+HUkiXEHSxFxTI9YFwGGSngRmkiTSHjuA09Lf4RzgK+n5i4EZaXwr8PThhme/MLNewDUyMys9JzIzKz0nMjMrPScyMys9JzIzKz0nMjMrPScyMyu9/w8dW6+LXEd1rwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(final_model,y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3068f8b2",
   "metadata": {},
   "source": [
    "# XIII. Export key components\n",
    "Here is the section to export the important ML objects that will be use to develop an app: Encoder, Scaler, ColumnTransformer, Model, Pipeline, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "8d6d671f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['XGB.joblib']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(XGB_hy, \"XGB.joblib\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdd564a1",
   "metadata": {},
   "source": [
    "# XIV. Conclusion and Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4aa4487",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "587ad3f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25075806",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "924fbc94",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b839318",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0998a3a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee5f5a6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fbedb53",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7382cb90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "429efa9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48201b21",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d60d35b5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}