{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb1cb67b",
   "metadata": {},
   "source": [
    "# 1st Step of the Project: **Liver_Disease_Pred – Exploratory Data Analysis (EDA)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0d6bec44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import RobustScaler, FunctionTransformer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import f1_score, classification_report, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1812ab56",
   "metadata": {},
   "source": [
    "#### **loading dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "850c88d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Dataset-620.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9800ad45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(615, 13)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "070a629b",
   "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>category</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>albumin</th>\n",
       "      <th>alkaline_phosphatase</th>\n",
       "      <th>alanine_aminotransferase</th>\n",
       "      <th>aspartate_aminotransferase</th>\n",
       "      <th>bilirubin</th>\n",
       "      <th>cholinesterase</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>creatinina</th>\n",
       "      <th>gamma_glutamyl_transferase</th>\n",
       "      <th>protein</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no_disease</td>\n",
       "      <td>32</td>\n",
       "      <td>m</td>\n",
       "      <td>38.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>22.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.93</td>\n",
       "      <td>3.23</td>\n",
       "      <td>106.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no_disease</td>\n",
       "      <td>32</td>\n",
       "      <td>m</td>\n",
       "      <td>38.5</td>\n",
       "      <td>70.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>11.17</td>\n",
       "      <td>4.80</td>\n",
       "      <td>74.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>no_disease</td>\n",
       "      <td>32</td>\n",
       "      <td>m</td>\n",
       "      <td>46.9</td>\n",
       "      <td>74.7</td>\n",
       "      <td>36.2</td>\n",
       "      <td>52.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.84</td>\n",
       "      <td>5.20</td>\n",
       "      <td>86.0</td>\n",
       "      <td>33.2</td>\n",
       "      <td>79.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>no_disease</td>\n",
       "      <td>32</td>\n",
       "      <td>m</td>\n",
       "      <td>43.2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>30.6</td>\n",
       "      <td>22.6</td>\n",
       "      <td>18.9</td>\n",
       "      <td>7.33</td>\n",
       "      <td>4.74</td>\n",
       "      <td>80.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>75.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>no_disease</td>\n",
       "      <td>32</td>\n",
       "      <td>m</td>\n",
       "      <td>39.2</td>\n",
       "      <td>74.1</td>\n",
       "      <td>32.6</td>\n",
       "      <td>24.8</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.15</td>\n",
       "      <td>4.32</td>\n",
       "      <td>76.0</td>\n",
       "      <td>29.9</td>\n",
       "      <td>68.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category  age sex  albumin  alkaline_phosphatase  \\\n",
       "0   no_disease   32   m     38.5                  52.5   \n",
       "1   no_disease   32   m     38.5                  70.3   \n",
       "2   no_disease   32   m     46.9                  74.7   \n",
       "3   no_disease   32   m     43.2                  52.0   \n",
       "4   no_disease   32   m     39.2                  74.1   \n",
       "\n",
       "   alanine_aminotransferase  aspartate_aminotransferase  bilirubin  \\\n",
       "0                       7.7                        22.1        7.5   \n",
       "1                      18.0                        24.7        3.9   \n",
       "2                      36.2                        52.6        6.1   \n",
       "3                      30.6                        22.6       18.9   \n",
       "4                      32.6                        24.8        9.6   \n",
       "\n",
       "   cholinesterase  cholesterol  creatinina  gamma_glutamyl_transferase   \\\n",
       "0            6.93         3.23       106.0                         12.1   \n",
       "1           11.17         4.80        74.0                         15.6   \n",
       "2            8.84         5.20        86.0                         33.2   \n",
       "3            7.33         4.74        80.0                         33.8   \n",
       "4            9.15         4.32        76.0                         29.9   \n",
       "\n",
       "  protein     \n",
       "0         69  \n",
       "1       76.5  \n",
       "2       79.3  \n",
       "3       75.7  \n",
       "4       68.7  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "69aa581f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 615 entries, 0 to 614\n",
      "Data columns (total 13 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   category                     615 non-null    object \n",
      " 1   age                          615 non-null    int64  \n",
      " 2   sex                          615 non-null    object \n",
      " 3   albumin                      614 non-null    float64\n",
      " 4   alkaline_phosphatase         597 non-null    float64\n",
      " 5   alanine_aminotransferase     614 non-null    float64\n",
      " 6   aspartate_aminotransferase   615 non-null    float64\n",
      " 7   bilirubin                    615 non-null    float64\n",
      " 8   cholinesterase               615 non-null    float64\n",
      " 9   cholesterol                  605 non-null    float64\n",
      " 10  creatinina                   615 non-null    float64\n",
      " 11  gamma_glutamyl_transferase   615 non-null    float64\n",
      " 12  protein                      615 non-null    object \n",
      "dtypes: float64(9), int64(1), object(3)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "507a870a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['category',\n",
       " 'age',\n",
       " 'sex',\n",
       " 'albumin',\n",
       " 'alkaline_phosphatase',\n",
       " 'alanine_aminotransferase',\n",
       " 'aspartate_aminotransferase',\n",
       " 'bilirubin',\n",
       " 'cholinesterase',\n",
       " 'cholesterol',\n",
       " 'creatinina',\n",
       " 'gamma_glutamyl_transferase ',\n",
       " 'protein   ']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "59fcb01c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['category',\n",
       " 'age',\n",
       " 'sex',\n",
       " 'albumin',\n",
       " 'alkaline_phosphatase',\n",
       " 'alanine_aminotransferase',\n",
       " 'aspartate_aminotransferase',\n",
       " 'bilirubin',\n",
       " 'cholinesterase',\n",
       " 'cholesterol',\n",
       " 'creatinina',\n",
       " 'gamma_glutamyl_transferase',\n",
       " 'protein']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = (df.columns.str.strip().str.lower())\n",
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "aa338693",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      69\n",
       "1    76.5\n",
       "2    79.3\n",
       "3    75.7\n",
       "4    68.7\n",
       "5      74\n",
       "6    74.5\n",
       "7    67.1\n",
       "8    71.3\n",
       "9    69.9\n",
       "Name: protein, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['protein'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1b5cd391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "protein\n",
       "<class 'str'>    615\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['protein'].apply(type).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "fb36aec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 615 entries, 0 to 614\n",
      "Data columns (total 13 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   category                    615 non-null    object \n",
      " 1   age                         615 non-null    int64  \n",
      " 2   sex                         615 non-null    object \n",
      " 3   albumin                     614 non-null    float64\n",
      " 4   alkaline_phosphatase        597 non-null    float64\n",
      " 5   alanine_aminotransferase    614 non-null    float64\n",
      " 6   aspartate_aminotransferase  615 non-null    float64\n",
      " 7   bilirubin                   615 non-null    float64\n",
      " 8   cholinesterase              615 non-null    float64\n",
      " 9   cholesterol                 605 non-null    float64\n",
      " 10  creatinina                  615 non-null    float64\n",
      " 11  gamma_glutamyl_transferase  615 non-null    float64\n",
      " 12  protein                     614 non-null    float64\n",
      "dtypes: float64(10), int64(1), object(2)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df['protein'] = pd.to_numeric(df['protein'], errors='coerce')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9c45fd9e",
   "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>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>age</th>\n",
       "      <td>615.0</td>\n",
       "      <td>47.408130</td>\n",
       "      <td>10.055105</td>\n",
       "      <td>19.00</td>\n",
       "      <td>39.000</td>\n",
       "      <td>47.00</td>\n",
       "      <td>54.000</td>\n",
       "      <td>77.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>albumin</th>\n",
       "      <td>614.0</td>\n",
       "      <td>41.620195</td>\n",
       "      <td>5.780629</td>\n",
       "      <td>14.90</td>\n",
       "      <td>38.800</td>\n",
       "      <td>41.95</td>\n",
       "      <td>45.200</td>\n",
       "      <td>82.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alkaline_phosphatase</th>\n",
       "      <td>597.0</td>\n",
       "      <td>68.283920</td>\n",
       "      <td>26.028315</td>\n",
       "      <td>11.30</td>\n",
       "      <td>52.500</td>\n",
       "      <td>66.20</td>\n",
       "      <td>80.100</td>\n",
       "      <td>416.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alanine_aminotransferase</th>\n",
       "      <td>614.0</td>\n",
       "      <td>28.450814</td>\n",
       "      <td>25.469689</td>\n",
       "      <td>0.90</td>\n",
       "      <td>16.400</td>\n",
       "      <td>23.00</td>\n",
       "      <td>33.075</td>\n",
       "      <td>325.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aspartate_aminotransferase</th>\n",
       "      <td>615.0</td>\n",
       "      <td>34.786341</td>\n",
       "      <td>33.090690</td>\n",
       "      <td>10.60</td>\n",
       "      <td>21.600</td>\n",
       "      <td>25.90</td>\n",
       "      <td>32.900</td>\n",
       "      <td>324.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bilirubin</th>\n",
       "      <td>615.0</td>\n",
       "      <td>11.396748</td>\n",
       "      <td>19.673150</td>\n",
       "      <td>0.80</td>\n",
       "      <td>5.300</td>\n",
       "      <td>7.30</td>\n",
       "      <td>11.200</td>\n",
       "      <td>254.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cholinesterase</th>\n",
       "      <td>615.0</td>\n",
       "      <td>8.196634</td>\n",
       "      <td>2.205657</td>\n",
       "      <td>1.42</td>\n",
       "      <td>6.935</td>\n",
       "      <td>8.26</td>\n",
       "      <td>9.590</td>\n",
       "      <td>16.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cholesterol</th>\n",
       "      <td>605.0</td>\n",
       "      <td>5.368099</td>\n",
       "      <td>1.132728</td>\n",
       "      <td>1.43</td>\n",
       "      <td>4.610</td>\n",
       "      <td>5.30</td>\n",
       "      <td>6.060</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinina</th>\n",
       "      <td>615.0</td>\n",
       "      <td>81.287805</td>\n",
       "      <td>49.756166</td>\n",
       "      <td>8.00</td>\n",
       "      <td>67.000</td>\n",
       "      <td>77.00</td>\n",
       "      <td>88.000</td>\n",
       "      <td>1079.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamma_glutamyl_transferase</th>\n",
       "      <td>615.0</td>\n",
       "      <td>39.533171</td>\n",
       "      <td>54.661071</td>\n",
       "      <td>4.50</td>\n",
       "      <td>15.700</td>\n",
       "      <td>23.30</td>\n",
       "      <td>40.200</td>\n",
       "      <td>650.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein</th>\n",
       "      <td>614.0</td>\n",
       "      <td>72.044137</td>\n",
       "      <td>5.402636</td>\n",
       "      <td>44.80</td>\n",
       "      <td>69.300</td>\n",
       "      <td>72.20</td>\n",
       "      <td>75.400</td>\n",
       "      <td>90.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count       mean        std    min     25%    50%  \\\n",
       "age                         615.0  47.408130  10.055105  19.00  39.000  47.00   \n",
       "albumin                     614.0  41.620195   5.780629  14.90  38.800  41.95   \n",
       "alkaline_phosphatase        597.0  68.283920  26.028315  11.30  52.500  66.20   \n",
       "alanine_aminotransferase    614.0  28.450814  25.469689   0.90  16.400  23.00   \n",
       "aspartate_aminotransferase  615.0  34.786341  33.090690  10.60  21.600  25.90   \n",
       "bilirubin                   615.0  11.396748  19.673150   0.80   5.300   7.30   \n",
       "cholinesterase              615.0   8.196634   2.205657   1.42   6.935   8.26   \n",
       "cholesterol                 605.0   5.368099   1.132728   1.43   4.610   5.30   \n",
       "creatinina                  615.0  81.287805  49.756166   8.00  67.000  77.00   \n",
       "gamma_glutamyl_transferase  615.0  39.533171  54.661071   4.50  15.700  23.30   \n",
       "protein                     614.0  72.044137   5.402636  44.80  69.300  72.20   \n",
       "\n",
       "                               75%      max  \n",
       "age                         54.000    77.00  \n",
       "albumin                     45.200    82.20  \n",
       "alkaline_phosphatase        80.100   416.60  \n",
       "alanine_aminotransferase    33.075   325.30  \n",
       "aspartate_aminotransferase  32.900   324.00  \n",
       "bilirubin                   11.200   254.00  \n",
       "cholinesterase               9.590    16.41  \n",
       "cholesterol                  6.060     9.67  \n",
       "creatinina                  88.000  1079.10  \n",
       "gamma_glutamyl_transferase  40.200   650.90  \n",
       "protein                     75.400    90.00  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8527ffad",
   "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>mean</th>\n",
       "      <th>50%</th>\n",
       "      <th>mean_median_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gamma_glutamyl_transferase</th>\n",
       "      <td>39.533171</td>\n",
       "      <td>23.30</td>\n",
       "      <td>16.233171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aspartate_aminotransferase</th>\n",
       "      <td>34.786341</td>\n",
       "      <td>25.90</td>\n",
       "      <td>8.886341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alanine_aminotransferase</th>\n",
       "      <td>28.450814</td>\n",
       "      <td>23.00</td>\n",
       "      <td>5.450814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinina</th>\n",
       "      <td>81.287805</td>\n",
       "      <td>77.00</td>\n",
       "      <td>4.287805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bilirubin</th>\n",
       "      <td>11.396748</td>\n",
       "      <td>7.30</td>\n",
       "      <td>4.096748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alkaline_phosphatase</th>\n",
       "      <td>68.283920</td>\n",
       "      <td>66.20</td>\n",
       "      <td>2.083920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>47.408130</td>\n",
       "      <td>47.00</td>\n",
       "      <td>0.408130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cholesterol</th>\n",
       "      <td>5.368099</td>\n",
       "      <td>5.30</td>\n",
       "      <td>0.068099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cholinesterase</th>\n",
       "      <td>8.196634</td>\n",
       "      <td>8.26</td>\n",
       "      <td>-0.063366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein</th>\n",
       "      <td>72.044137</td>\n",
       "      <td>72.20</td>\n",
       "      <td>-0.155863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>albumin</th>\n",
       "      <td>41.620195</td>\n",
       "      <td>41.95</td>\n",
       "      <td>-0.329805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 mean    50%  mean_median_diff\n",
       "gamma_glutamyl_transferase  39.533171  23.30         16.233171\n",
       "aspartate_aminotransferase  34.786341  25.90          8.886341\n",
       "alanine_aminotransferase    28.450814  23.00          5.450814\n",
       "creatinina                  81.287805  77.00          4.287805\n",
       "bilirubin                   11.396748   7.30          4.096748\n",
       "alkaline_phosphatase        68.283920  66.20          2.083920\n",
       "age                         47.408130  47.00          0.408130\n",
       "cholesterol                  5.368099   5.30          0.068099\n",
       "cholinesterase               8.196634   8.26         -0.063366\n",
       "protein                     72.044137  72.20         -0.155863\n",
       "albumin                     41.620195  41.95         -0.329805"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = df.describe().T\n",
    "stats['mean_median_diff'] = stats['mean'] - stats['50%']\n",
    "stats[['mean', '50%', 'mean_median_diff']].sort_values(\n",
    "    by='mean_median_diff',\n",
    "    ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e6c6bc96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category                       0\n",
       "age                            0\n",
       "sex                            0\n",
       "albumin                        1\n",
       "alkaline_phosphatase          18\n",
       "alanine_aminotransferase       1\n",
       "aspartate_aminotransferase     0\n",
       "bilirubin                      0\n",
       "cholinesterase                 0\n",
       "cholesterol                   10\n",
       "creatinina                     0\n",
       "gamma_glutamyl_transferase     0\n",
       "protein                        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "0e651dfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category                      0\n",
       "age                           0\n",
       "sex                           0\n",
       "albumin                       0\n",
       "alkaline_phosphatase          0\n",
       "alanine_aminotransferase      0\n",
       "aspartate_aminotransferase    0\n",
       "bilirubin                     0\n",
       "cholinesterase                0\n",
       "cholesterol                   0\n",
       "creatinina                    0\n",
       "gamma_glutamyl_transferase    0\n",
       "protein                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['albumin'] = df['albumin'].fillna(df['albumin'].median())\n",
    "df['alkaline_phosphatase'] = df['alkaline_phosphatase'].fillna(df['alkaline_phosphatase'].median())\n",
    "df['alanine_aminotransferase'] = df['alanine_aminotransferase'].fillna(df['alanine_aminotransferase'].median())\n",
    "df['cholesterol'] = df['cholesterol'].fillna(df['cholesterol'].median())\n",
    "df['protein'] = df['protein'].fillna(df['protein'].median())\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5de59a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3cbd1554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       " no_disease        533\n",
       " cirrhosis          30\n",
       " hepatitis          24\n",
       " fibrosis           21\n",
       "suspect_disease      7\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "66dc770f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no_disease', 'suspect_disease', 'hepatitis', 'fibrosis',\n",
       "       'cirrhosis'], dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['category'] = df['category'].str.strip().str.lower()\n",
    "df['category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "9f3e0696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "610    1\n",
       "611    1\n",
       "612    1\n",
       "613    1\n",
       "614    1\n",
       "Name: target, Length: 615, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_map = {\n",
    "    'no_disease': 0,\n",
    "    'cirrhosis': 1,\n",
    "    'hepatitis': 2,\n",
    "    'fibrosis': 3,\n",
    "    'suspect_disease': 4\n",
    "}\n",
    "\n",
    "df['target'] = df['category'].map(class_map)\n",
    "df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f83c5266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target\n",
      "0    533\n",
      "1     30\n",
      "2     24\n",
      "3     21\n",
      "4      7\n",
      "Name: count, dtype: int64\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(df['target'].value_counts())\n",
    "print(df['target'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "95024726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>albumin</th>\n",
       "      <th>alkaline_phosphatase</th>\n",
       "      <th>alanine_aminotransferase</th>\n",
       "      <th>aspartate_aminotransferase</th>\n",
       "      <th>bilirubin</th>\n",
       "      <th>cholinesterase</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>creatinina</th>\n",
       "      <th>gamma_glutamyl_transferase</th>\n",
       "      <th>protein</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>m</td>\n",
       "      <td>38.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>22.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.93</td>\n",
       "      <td>3.23</td>\n",
       "      <td>106.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>m</td>\n",
       "      <td>38.5</td>\n",
       "      <td>70.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>11.17</td>\n",
       "      <td>4.80</td>\n",
       "      <td>74.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>76.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>m</td>\n",
       "      <td>46.9</td>\n",
       "      <td>74.7</td>\n",
       "      <td>36.2</td>\n",
       "      <td>52.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.84</td>\n",
       "      <td>5.20</td>\n",
       "      <td>86.0</td>\n",
       "      <td>33.2</td>\n",
       "      <td>79.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>m</td>\n",
       "      <td>43.2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>30.6</td>\n",
       "      <td>22.6</td>\n",
       "      <td>18.9</td>\n",
       "      <td>7.33</td>\n",
       "      <td>4.74</td>\n",
       "      <td>80.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>75.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>m</td>\n",
       "      <td>39.2</td>\n",
       "      <td>74.1</td>\n",
       "      <td>32.6</td>\n",
       "      <td>24.8</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.15</td>\n",
       "      <td>4.32</td>\n",
       "      <td>76.0</td>\n",
       "      <td>29.9</td>\n",
       "      <td>68.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age sex  albumin  alkaline_phosphatase  alanine_aminotransferase  \\\n",
       "0   32   m     38.5                  52.5                       7.7   \n",
       "1   32   m     38.5                  70.3                      18.0   \n",
       "2   32   m     46.9                  74.7                      36.2   \n",
       "3   32   m     43.2                  52.0                      30.6   \n",
       "4   32   m     39.2                  74.1                      32.6   \n",
       "\n",
       "   aspartate_aminotransferase  bilirubin  cholinesterase  cholesterol  \\\n",
       "0                        22.1        7.5            6.93         3.23   \n",
       "1                        24.7        3.9           11.17         4.80   \n",
       "2                        52.6        6.1            8.84         5.20   \n",
       "3                        22.6       18.9            7.33         4.74   \n",
       "4                        24.8        9.6            9.15         4.32   \n",
       "\n",
       "   creatinina  gamma_glutamyl_transferase  protein  target  \n",
       "0       106.0                        12.1     69.0       0  \n",
       "1        74.0                        15.6     76.5       0  \n",
       "2        86.0                        33.2     79.3       0  \n",
       "3        80.0                        33.8     75.7       0  \n",
       "4        76.0                        29.9     68.7       0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['category'], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "14040962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='target', data=df)\n",
    "plt.title('Target Class Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9b2c67ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['m', 'f'], dtype=object)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "4c731268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "610    0\n",
       "611    0\n",
       "612    0\n",
       "613    0\n",
       "614    0\n",
       "Name: sex, Length: 615, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'] = df['sex'].str.strip().str.upper()\n",
    "df['sex'] = df['sex'].map({'M': 1, 'F': 0})\n",
    "df['sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "190d489d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 615 entries, 0 to 614\n",
      "Data columns (total 13 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   age                         615 non-null    int64  \n",
      " 1   sex                         615 non-null    int64  \n",
      " 2   albumin                     615 non-null    float64\n",
      " 3   alkaline_phosphatase        615 non-null    float64\n",
      " 4   alanine_aminotransferase    615 non-null    float64\n",
      " 5   aspartate_aminotransferase  615 non-null    float64\n",
      " 6   bilirubin                   615 non-null    float64\n",
      " 7   cholinesterase              615 non-null    float64\n",
      " 8   cholesterol                 615 non-null    float64\n",
      " 9   creatinina                  615 non-null    float64\n",
      " 10  gamma_glutamyl_transferase  615 non-null    float64\n",
      " 11  protein                     615 non-null    float64\n",
      " 12  target                      615 non-null    int64  \n",
      "dtypes: float64(10), int64(3)\n",
      "memory usage: 62.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a6754ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df.drop(columns=['target'])\n",
    "target = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "be50d35f",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "7126beb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(corr, cmap='coolwarm', annot=True, fmt=\".2f\", center=0)\n",
    "plt.title('Feature Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "09af3b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "sns.boxplot(\n",
    "    features,\n",
    "    orient='h'\n",
    ")\n",
    "\n",
    "plt.title(\"Boxplot of Medical Features (Outlier Overview)\")\n",
    "plt.xlabel(\"Feature Value\")\n",
    "plt.ylabel(\"Medical Features\")\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d0a5f9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"liver_eda_cleaned.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da543cd5",
   "metadata": {},
   "source": [
    "### Key EDA Conclusions & Modeling Decisions\n",
    "\n",
    "- I concluded that the dataset is **imbalanced**, therefore I plan to use **tree-based models** such as **Random Forest, XGBoost, and LightGBM**, as they handle class imbalance better without strict assumptions on data distribution.\n",
    "\n",
    "- I observed that several features have **non-linear relationships** with the target variable, which further supports the use of **tree-based models** since they can naturally capture non-linearity without feature transformation.\n",
    "\n",
    "- I also concluded that **feature scaling is not mandatory** for tree-based models, allowing me to proceed directly to model building without normalization or standardization at this stage.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9c27be03",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    features,\n",
    "    target,\n",
    "    test_size=0.2,\n",
    "    stratify=target,   # VERY IMPORTANT for multiclass imbalance\n",
    "    random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d44d9172",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_transformer = FunctionTransformer(np.log1p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "2423fe02",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(class_weight='balanced', max_iter=1000),\n",
    "    \"SVM (Radial)\": SVC(class_weight='balanced', probability=True),\n",
    "    \"KNN\": KNeighborsClassifier(n_neighbors=5), # KNN doesn't have class_weights\n",
    "    \"Decision Tree\": DecisionTreeClassifier(class_weight='balanced'),\n",
    "    \"Random Forest\": RandomForestClassifier(class_weight='balanced', n_estimators=100),\n",
    "    \"XGBoost\": XGBClassifier(), # XGBoost handles imbalance via weights in fit or objective\n",
    "    \"LightGBM\": LGBMClassifier(class_weight='balanced', verbosity=-1)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1e8e0d75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name                | Accuracy   | Macro F1   | Macro Recall\n",
      "-----------------------------------------------------------------\n",
      "Logistic Regression       | 0.9268     | 0.6107     | 0.6359      \n",
      "SVM (Radial)              | 0.9593     | 0.6581     | 0.6748      \n",
      "KNN                       | 0.9268     | 0.5547     | 0.4800      \n",
      "Decision Tree             | 0.9106     | 0.6185     | 0.5815      \n",
      "Random Forest             | 0.9187     | 0.5062     | 0.4400      \n",
      "XGBoost                   | 0.9431     | 0.5795     | 0.5533      \n",
      "LightGBM                  | 0.9675     | 0.8409     | 0.8267      \n",
      "-----------------------------------------------------------------\n",
      "WINNER (based on Recall): LightGBM with Recall of 0.8267\n",
      "\n",
      "Detailed Medical Report for LightGBM:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      1.00       107\n",
      "           1       1.00      0.83      0.91         6\n",
      "           2       0.80      0.80      0.80         5\n",
      "           3       0.50      0.50      0.50         4\n",
      "           4       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           0.97       123\n",
      "   macro avg       0.86      0.83      0.84       123\n",
      "weighted avg       0.97      0.97      0.97       123\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score, f1_score, accuracy_score, classification_report\n",
    "import pandas as pd\n",
    "\n",
    "# 1. Setup the results tracker\n",
    "results = []\n",
    "best_recall = 0\n",
    "best_model_name = \"\"\n",
    "best_model_pipe = None\n",
    "\n",
    "# Update Header to include Recall\n",
    "print(f\"{'Model Name':<25} | {'Accuracy':<10} | {'Macro F1':<10} | {'Macro Recall':<12}\")\n",
    "print(\"-\" * 65)\n",
    "\n",
    "for name, model in models.items():\n",
    "    # Create the assembly line\n",
    "    pipe = Pipeline([\n",
    "        ('log', log_transformer),\n",
    "        ('scale', RobustScaler()),\n",
    "        ('classifier', model)\n",
    "    ])\n",
    "\n",
    "    # Train\n",
    "    pipe.fit(X_train, y_train)\n",
    "    \n",
    "    # Predict\n",
    "    y_pred = pipe.predict(X_test)\n",
    "    \n",
    "    # Metrics\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred, average='macro')\n",
    "    # RECALL: The \"Medical Safety\" Metric\n",
    "    rec = recall_score(y_test, y_pred, average='macro') \n",
    "    \n",
    "    results.append({\"Model\": name, \"Accuracy\": acc, \"F1\": f1, \"Recall\": rec})\n",
    "    print(f\"{name:<25} | {acc:<10.4f} | {f1:<10.4f} | {rec:<12.4f}\")\n",
    "    \n",
    "    # Track the winner based on RECALL (The most important for health)\n",
    "    if rec > best_recall:\n",
    "        best_recall = rec\n",
    "        best_model_name = name\n",
    "        best_model_pipe = pipe\n",
    "\n",
    "print(\"-\" * 65)\n",
    "print(f\"WINNER (based on Recall): {best_model_name} with Recall of {best_recall:.4f}\")\n",
    "\n",
    "# 4. FINAL EVALUATION OF THE WINNER\n",
    "print(f\"\\nDetailed Medical Report for {best_model_name}:\")\n",
    "y_pred_best = best_model_pipe.predict(X_test)\n",
    "\n",
    "# This report shows you EXACTLY which disease stages were missed\n",
    "print(classification_report(y_test, y_pred_best))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e0fd8ad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting the search... (You will see progress below)\n",
      "Fitting 5 folds for each of 15 candidates, totalling 75 fits\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=20; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=20; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=20; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=20; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=20; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=3, lgbm__n_estimators=50, lgbm__num_leaves=20; total time=   0.1s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=3, lgbm__n_estimators=50, lgbm__num_leaves=20; total time=   0.1s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=3, lgbm__n_estimators=50, lgbm__num_leaves=20; total time=   0.1s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=3, lgbm__n_estimators=50, lgbm__num_leaves=20; total time=   0.1s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=3, lgbm__n_estimators=50, lgbm__num_leaves=20; total time=   0.1s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=7, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.6s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=7, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.6s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=7, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.6s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=7, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.6s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=7, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.6s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.3s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=7, lgbm__n_estimators=50, lgbm__num_leaves=20; total time=   0.3s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=7, lgbm__n_estimators=50, lgbm__num_leaves=20; total time=   0.3s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=7, lgbm__n_estimators=50, lgbm__num_leaves=20; total time=   0.3s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=7, lgbm__n_estimators=50, lgbm__num_leaves=20; total time=   0.3s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=7, lgbm__n_estimators=50, lgbm__num_leaves=20; total time=   0.3s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=50, lgbm__num_leaves=31; total time=   0.1s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=50, lgbm__num_leaves=31; total time=   0.1s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=50, lgbm__num_leaves=31; total time=   0.1s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=50, lgbm__num_leaves=31; total time=   0.1s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=50, lgbm__num_leaves=31; total time=   0.1s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=5, lgbm__n_estimators=50, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=5, lgbm__n_estimators=50, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=5, lgbm__n_estimators=50, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=5, lgbm__n_estimators=50, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=5, lgbm__n_estimators=50, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=200, lgbm__num_leaves=31; total time=   0.6s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=200, lgbm__num_leaves=31; total time=   0.6s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=200, lgbm__num_leaves=31; total time=   0.6s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=200, lgbm__num_leaves=31; total time=   0.6s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=200, lgbm__num_leaves=31; total time=   0.6s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=3, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.2s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=20; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=20; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=20; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=20; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.01, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=20; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=7, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.7s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=7, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.6s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=7, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.7s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=7, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.7s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=7, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.7s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=200, lgbm__num_leaves=20; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=200, lgbm__num_leaves=20; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=200, lgbm__num_leaves=20; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=200, lgbm__num_leaves=20; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=200, lgbm__num_leaves=20; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=31; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=200, lgbm__num_leaves=40; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=200, lgbm__num_leaves=40; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=200, lgbm__num_leaves=40; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=200, lgbm__num_leaves=40; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.05, lgbm__max_depth=3, lgbm__n_estimators=200, lgbm__num_leaves=40; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=40; total time=   0.5s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=40; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=40; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=40; total time=   0.4s\n",
      "[CV] END lgbm__learning_rate=0.1, lgbm__max_depth=5, lgbm__n_estimators=100, lgbm__num_leaves=40; total time=   0.5s\n",
      "\n",
      "Best Parameters Found: {'lgbm__num_leaves': 31, 'lgbm__n_estimators': 100, 'lgbm__max_depth': 7, 'lgbm__learning_rate': 0.05}\n",
      "Best CV F1-Score: 0.6155\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn import set_config\n",
    "\n",
    "# 1. Keep data as Pandas DataFrames (Fixes the Warning)\n",
    "set_config(transform_output=\"pandas\")\n",
    "\n",
    "# 2. Define the Pipeline\n",
    "tuning_pipeline = Pipeline([\n",
    "    ('log', FunctionTransformer(np.log1p)),\n",
    "    ('scale', RobustScaler()),\n",
    "    ('lgbm', LGBMClassifier(class_weight='balanced', verbosity=-1, random_state=42))\n",
    "])\n",
    "\n",
    "# 3. Search Space (Same as before)\n",
    "param_dist = {\n",
    "    'lgbm__n_estimators': [50, 100, 200],\n",
    "    'lgbm__learning_rate': [0.01, 0.05, 0.1],\n",
    "    'lgbm__max_depth': [3, 5, 7],\n",
    "    'lgbm__num_leaves': [20, 31, 40]\n",
    "}\n",
    "\n",
    "# 4. Use RandomizedSearchCV (Trying 15 random combinations instead of 81)\n",
    "# verbose=2 will show you the progress!\n",
    "random_search = RandomizedSearchCV(\n",
    "    tuning_pipeline, \n",
    "    param_distributions=param_dist, \n",
    "    n_iter=15, \n",
    "    cv=5, \n",
    "    scoring='f1_macro', \n",
    "    n_jobs=1, \n",
    "    verbose=2, \n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "print(\"Starting the search... (You will see progress below)\")\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "print(f\"\\nBest Parameters Found: {random_search.best_params_}\")\n",
    "print(f\"Best CV F1-Score: {random_search.best_score_:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "05f0ba3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proving the Champion via 5-Fold Cross-Validation...\n",
      "Logistic Regression: Mean F1 = 0.5938 (+/- 0.0812)\n",
      "SVM: Mean F1 = 0.5391 (+/- 0.0942)\n",
      "Random Forest: Mean F1 = 0.5374 (+/- 0.1440)\n",
      "XGBoost: Mean F1 = 0.5841 (+/- 0.1083)\n",
      "LightGBM: Mean F1 = 0.6362 (+/- 0.1096)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 1. Define the models again\n",
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(class_weight='balanced', max_iter=1000),\n",
    "    \"SVM\": SVC(class_weight='balanced'),\n",
    "    \"Random Forest\": RandomForestClassifier(class_weight='balanced'),\n",
    "    \"XGBoost\": XGBClassifier(),\n",
    "    \"LightGBM\": LGBMClassifier(class_weight='balanced', verbosity=-1)\n",
    "}\n",
    "\n",
    "cv_results = {}\n",
    "\n",
    "print(\"Proving the Champion via 5-Fold Cross-Validation...\")\n",
    "\n",
    "for name, model in models.items():\n",
    "    pipe = Pipeline([\n",
    "        ('log', FunctionTransformer(np.log1p)),\n",
    "        ('scale', RobustScaler()),\n",
    "        ('classifier', model)\n",
    "    ])\n",
    "    \n",
    "    # We test each model 5 times on 5 different versions of the data\n",
    "    # We use 'f1_macro' to ensure it handles the 7 sick patients every time\n",
    "    scores = cross_val_score(pipe, X_train, y_train, cv=5, scoring='f1_macro')\n",
    "    \n",
    "    cv_results[name] = scores\n",
    "    print(f\"{name}: Mean F1 = {scores.mean():.4f} (+/- {scores.std():.4f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "4c1e9311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- MEDICAL AUDIT ---\n",
      "Disease Detection Recall: 78.33%\n",
      "Meaning: You are catching 78.33% of all sick cases.\n",
      "\n",
      "Full Report (Look at the 'recall' column for classes 1, 2, 3, 4):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      1.00       107\n",
      "           1       1.00      0.83      0.91         6\n",
      "           2       0.80      0.80      0.80         5\n",
      "           3       0.50      0.50      0.50         4\n",
      "           4       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           0.97       123\n",
      "   macro avg       0.86      0.83      0.84       123\n",
      "weighted avg       0.97      0.97      0.97       123\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score, precision_score, classification_report, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# 1. Ensure the model is assigned correctly\n",
    "# Use the best model we found from the RandomizedSearch\n",
    "champion_model = random_search.best_estimator_\n",
    "\n",
    "# 2. Get predictions\n",
    "y_pred = champion_model.predict(X_test)\n",
    "\n",
    "# 3. Calculate Recall for the SICK categories only (1, 2, 3, 4)\n",
    "# We ignore class 0 (healthy) for this specific calculation\n",
    "recall_sick = recall_score(y_test, y_pred, labels=[1, 2, 3, 4], average='macro')\n",
    "\n",
    "print(f\"--- MEDICAL AUDIT ---\")\n",
    "print(f\"Disease Detection Recall: {recall_sick:.2%}\")\n",
    "print(f\"Meaning: You are catching {recall_sick:.2%} of all sick cases.\")\n",
    "\n",
    "# 4. Detailed Report focusing on Recall\n",
    "print(\"\\nFull Report (Look at the 'recall' column for classes 1, 2, 3, 4):\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# 5. Visualizing the Recall Gap\n",
    "report_dict = classification_report(y_test, y_pred, output_dict=True)\n",
    "classes = ['1', '2', '3', '4']\n",
    "recalls = [report_dict[c]['recall'] for c in classes]\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.bar(classes, recalls, color='crimson')\n",
    "plt.axhline(y=0.8, color='black', linestyle='--', label='Hospital Standard (80%)')\n",
    "plt.title(\"Recall per Disease Stage\")\n",
    "plt.xlabel(\"Disease Class\")\n",
    "plt.ylabel(\"Recall Score\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "af2499c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(data=pd.DataFrame(cv_results), palette=\"viridis\")\n",
    "sns.swarmplot(data=pd.DataFrame(cv_results), color=\".25\") # Adds individual dots for each fold\n",
    "\n",
    "plt.title(\"Model Selection: Why LightGBM is the Champion\", fontsize=15)\n",
    "plt.ylabel(\"Macro Recall Score\", fontsize=12)\n",
    "plt.xlabel(\"Algorithms\", fontsize=12)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e2de521a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgbm_model = best_model_pipe.named_steps['classifier']\n",
    "importances = pd.Series(lgbm_model.feature_importances_, index=X_train.columns)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "importances.sort_values().plot(kind='barh', color='teal')\n",
    "plt.title(\"Feature Importance: The Clinical Markers of Liver Disease\", fontsize=15)\n",
    "plt.xlabel(\"Importance Score (Gini Importance)\", fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3ec76e32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAHJCAYAAACmFmJFAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAcCFJREFUeJzt3Qd8E+UbB/DnWmjLKqOMQimUvfcGWcpygAwVAVkCylLZQ5kqWxDZCiKyUZYIirJBxp+NyJQNsmeZpbT5f34vXkzStE2ajlzz+/o5Sy53lzdvbjz3rtNMJpNJiIiIiNyYV1IngIiIiCg2DFiIiIjI7TFgISIiIrfHgIWIiIjcHgMWIiIicnsMWIiIiMjtMWAhIiIit8eAhYiIiNweAxYiIiJyewxYHKBpmtWUMmVKyZw5s5QoUULatWsny5Ytk2fPnoknGTZsmMqLOXPmiDv74YcfzL/b3Llzkzo5JCIvvvii5MyZU8LCwsz5ce7cuSjHmbe3t2TKlElq1qyp9jOjDMod3bGBcwXmb968WZIj/D5LliyRpk2bSnBwsPj5+Um6dOmkWLFi0qVLF9m9e3dSJzHZ6dGjh6RKlUouXLggnoABixPatm2rphYtWki1atVUkIKL4BtvvCFFihSJtwMSJzSc2HCCSyq1atVSacCFxMjmzZtn/vf8+fOTNC0ksmbNGtm0aZN8/PHH4uvrGyVL0qRJYz7O3n77bcmfP79s3bpV2rdvryZyT1euXJEqVaqo32zVqlWSI0cOef3116VOnToSHh4uM2bMkEqVKslnn33m0uck96DPWf3791d/Bw0aJB4BzxKimCGbosuqU6dOmd566y31furUqU0HDhxwOTs3bdqktte2bdsk+2lq1qyp0nD27Fm779+4ccN07Ngx0927d03u6vr166YUKVKo38Xf39/k5eVl+ueff5I6WR6tZMmSpixZspjCwsKs5mM/w/6WO3fuKOusWLHCfAxu27bN5O6GDh2q0vrdd99ZzcfxjPk4vpOT0NBQU8GCBdV3e/XVV03nz5+PssyePXtMtWvXdvmcllzz0BXvv/++SdM005EjR0zJHUtYXJQvXz5VDNqhQwd59OiRvPvuu+IJUCVWuHBhSZ8+vbirxYsXq1KwJk2aqFKwyMhIWbhwYVIny2Nt375d/vzzT2nevLn4+Pg4vF7jxo2lQYMG6t+//fZbAqaQ4mLAgAFy8uRJVZry008/Sa5cuaIsU758eVm/fr28//77zOR49s4776jqOJRiJXtJHTEZvYRFh5KGNGnSRHsXeOHCBVO3bt1MefPmNfn6+poyZsyo7ka2b99u9w7C3oQ7t7hs09LRo0dN7777rrqT9fHxUXe7VatWNY0bN84UHh5uvtONbortLhJu3rxp6tOnjyl//vzmdNWvX9/022+/RZu/SM+zZ89Mo0ePNhUoUEClLWfOnKZ+/fqZnjx5YoqLihUrqm3/+uuvpo0bN6p/4w4/Jrt27TI1b97clCNHDpWGwMBA04svvmj65ptvoiz74MEDld5y5cqZ0qVLp0pyChUqZOratavpxIkTDuUV4Lvb7l+WpWxXrlwxdejQwRQUFGTy9vY2ffnll2qZy5cvm8aMGWOqUaOGSm/KlClN2bJlMzVp0sS0e/fuaL+jI+nG/oDPHzhwYLTbqVu3rloGeeuIdu3aqeXt7Z8xlbBA79691fvvvfdelPew306bNs1UuXJl9X38/PxMpUqVUvmE9+KaB/D48WPTrFmzTI0aNTLlyZNHbTt9+vSm6tWrmxYtWpSgJSyW+wB+a/zNmjWrSkOZMmVM33//fbTr3rp1yzRgwABTkSJF1PIoYUQJx88//xxt3qNU9d69e6aePXuaQkJCVOnkRx99FGMa8TnYPtZHiasznM3bmM5LtiXBOOZfeeUVU+bMmdVxjO3je+HcFF2JcefOnU3Zs2dX6ShWrJhpypQppsjIyBj3y7lz55qqVaum9qFUqVKZSpQoYRo5cqT6brYsf/+1a9eaatWqpb4v5uH3RX7jnIfzoD0LFixQy7Zp08ZqPtKYK1cudZ6197nJCQOWeApY4I033lDLffrpp1bzd+zYoXYmvIeTYtOmTdVBiR0UF6DFixebl505c6a6uGPZfPnyqZ1cn1A0Hpdt6n744QcVQGAdnMhwYW7QoIEpODhYzbtz5446cPFZuPBhXrNmzazSENtJ+dKlSyqAwns4iPAZuOAjTZg3YcIEu/mLEwKq1tKmTWt67bXX1KQfzK1atTI5CxcdrIvvgRMADmqcDDDvzz//tLvOxIkTVbURlsGF7O233zbVqVNHXSSQFks4weCkhmXxOzRs2FD9/mXLllXb0IOKmPLKkYAFJ12kG4ETto98+frrr9Uy06dPN//++B2Rf7iQYR6CF3sBoqPpxn6AfQWfa++if+bMGVUMjeDSUQiOcVK3t73YAhYEKnh/0KBBVvMfPXqkLsR4L1OmTCqIwnfCb4Z5uBhGRETEKQ8AF2Esh4AQn4P9GRd25K+9m4iECFiQPhxL2JfxG+M74jiP7vOx7+vHNAKP119/XR2DCMowD8GovbxHgF+6dGmVJ40bN1bnlGHDhsWYxh9//FGti/3OWc7mLfIP50S8h3Ok5XkJ+6uuf//+ahkEKggm8NtiP9XPqVevXrXaLtbV30da9DxGOhCwRbdf6vskAhwcp/gcBEiYV6VKFdPDhw+jpB/vderUSR07FSpUUOcY/MUNL/Ib769evdpuftWqVUu9/8cff0R5r3Xr1uq9DRs2mJIzBizxGLB8/vnnarkWLVqY5+GOBVE7Ltjz58+PUq+LkwMu0mhv4Wgblrhs8+TJk+rAwokOkbolXMxxcbMsyYitDUt0J2VcUDG/ZcuWVu0UUOqEEybSbNvOR89fBFEoTbC8KGbIkEG9h7ZCzsCFDetZ3iGitAbz+vbtG2X5LVu2qJMI7pTWr19v9R4usGvWrLGa99JLL6lt4eR2//59q/eQZ4cOHYqXgAUTSkzs3Tkh8Prrr7+izMfdG07WODnjt41ruvEbYlnLQFn3ySefqPdQwuPMxQmlefbEFLA8ffrUfKGyPVmjRATzcbGzbE+FdhW4iOA9BHZxzQPcka9bty5KPmLfRDCAAMf2GInvgAUTLqAoFdKhBA3HOD5/37595vkIznGXj3XGjh1rFaz9/fffqqQBx+Dhw4etvrP+ObjQ4sbFUfp+gBJAZ8Ulb2PLQ9yU4f3ixYur76vDZwwZMsS8r1hC2vXg1vI4Q77qN022++XSpUvNAQ7OrTrsgy+88IJ6D6WC9tKOyd4N5e+//67eQ4BpC99F/j1H2jN58mT1Pr5jcsaAJR4DlhkzZqjlcLerw92avZ1XhxIH25KH2AKWuGyzS5cuah6KPR0Rl4Dl9OnTah5OpCgqttWrVy/1fseOHe3mL05etrp37x7jxd4enJxwYsZ6COAsL/CYh6oV27vul19+Wb2HaoLY/O9//1PL4i4eF8bYuBKwoJQDpVbOQqmUbWmSs+neunWruZTHEi6KyEPcgV67ds2h9CxZskRtq3379g4HLAhU0JBQv/NE9aclfDbSgNIElLTYQvCLwM2yGtDZPIgJSkOxrUmTJiVowIIL9/Hjx6O8r5ckWAYLegNllIzas3z5cvX+hx9+aDdgsTxeHIHzCdZD9VN8ii5vY8tDVAXifcuAzPK8gBIkBGx6iQwCVtzIYZ69c50ekNkGLKiGxXy9tNMSAl7c/OA8aBkA6WlHtb09SB+q0XFTiVJAe7/1BDsl1IBzpx50JWcpkroNTXKijxOBbne633//Xf3F2AT2VK9eXf11pkt0XLaJBm+QkI3e/vjjD/UXDSQxfoat1q1by4QJE2Tbtm1R3sPYNrVr144yv2DBguZuk8407jx79qxqFIzGfjqMm1OqVCk5dOiQbNy4UTUSBDTM1btJvvfee7FuX89LdG/HOBMJqWzZshIUFBTt+xjLZO3ateq3vnHjhjx9+lTNP3z4sPr7999/q+8dl3RjP8IYGtj+xYsX1dga8Msvv8g///yjGjJnzZrVoe9x/fp19TdjxowxLnf+/Hmr40f3+eefyyeffGI1D78Zusxif8NYFLYCAwOlQIECKi8eP36slonrb4d9G5+H7/3kyRN1rOv7JPI4IZUuXVoKFSoUZT6+w5gxY6yOJ1fON9mzZ7c6XhJLfOUt9jEc2/jNixcvHuV97FcYjuLgwYOyb98+qV+/vvqLz6xcubKEhIREWQcNxEeMGGE1D/vcrl271L9btWoVZZ2SJUuqCWnBZ2Hblho1amQ3/Ugfzj/9+vWT7777TnX91z8PY/r4+vpKmzZt7K6rn29xDkjOGLDEo5s3b6q/lhdrfRwTHCiOrOuIuGwTFxy9V1NCuXz5svpr78C3nI8Tk72LCwYKs6VfVCwHGXN07BW0nreFeTiRYEwWPWC5deuWuqDhd4vtgppYeamz1+NChwsxTn4xjZVz//59l9KNAPfDDz+U2bNny9ChQ9W8mTNnqr+dOnVyeDv37t1Tf2MLEjAOCwIhePjwoezZs0cFMcOHD5eKFStK3bp1zcvq3xvp0dMUndu3b6vAz9k8QLpx8UeA60geJ4TcuXPHeDzpx51lnuBCau9iGtP5JqZ9LToBAQFxvlDGd97q3x1Bjr2g19731wMjPRh3JE9wvsCNAXpKYn+N7rfBecbeuS6mfMZYQ4MHD5Zvv/1WBg4cqL7Hzz//LNeuXVMBqp7ftvz9/dXfu3fvSnLGgCUeHThwQP0tWrSoeR660gJOwtHt3IDSAEclxDYTQ0wnES+v+Olhj8AGo9vq3Zp//fVXuydAjE48bdo0SZ06tSQ1/fe0B6OF2oO70LfeekudpDt37qymvHnzStq0aVU+4+5s1KhRLo8Oizs6dFtFwIIT6dWrV1UJC07IlsFDbPTu77FdgHARsBwhNiIiQnr27CmTJ09WaUH3WT3o0fMNJRAoOYuJvUHqHB2YCxdUjLaLoAl37hkyZFDBNUozcJfuTiPw6nmCUqds2bLFmM+O7msxQd7D/v37kzxv9e+Omx+sG5cgMDHOdTHlM36XZs2aqeEXNmzYoG6qZs2aFesNwr1/bwiQf8kZA5Z4gh1GHyPCsmoDQ5CfOHFCnfTLlSsXL58Vl23iDgJ3HqdPnzafZOIbRrcE3BHHdAcUUxWHq1avXm2+y/jrr7+iXe7BgweycuVKadmypTpJoLoAd+FYN7aDXr8bQ146Qh9zBJ9pCxdkBAHOOn78uJpQhD99+vQo7585c8bldOuBBkYvRcCC/RsXJqS5Y8eOsd7FWtKrjpDHzsDFC9WIuLAdOXJEvvzySxkyZIj5OIAXXnhBBTSOcDYPVqxYodKA0Vv1u9iY8jghRHc86fP1484yT/D74MKXGI9ZwAUYN2vYH525SYrvvNW/u23QGxNUg4Fe8mbL3nyUcuCYRikNSgHt3TS6cq7DzQcCFpQaokocxx2quWrbqTLX3blzR/3NkiWLJGccOC6e9O7dW+28FSpUUENU6/S7UBycjtIvcNE9nygu29SrP7755pt4SYM9uHAA2jzYK5rUh8bX69ETgv4ZU6ZMUXdn9ib9ZKZXHeGkiUcROJo/el4uWrTIbhAS3UkRpQO2MEw96qidpZ+g9JO07Xvr1q1zOd2WJ1D4+uuvVVE18svZYfL1EhAE2s5KkSKFasMCX331lTntOIEjLQhSHc1DZ/MAeYmLqe0FFfSSvISGdhD22nKgBNHyuIvrucEVqEbVB8v84IMPVDAbHRx7etuPuOZtTOclHAsImI4ePWr3WLMHN3wIuPbu3Wv3eTz20oH2dnq7FP03sIQbJVQHobQzLjeHetsx3FCNHTtWlRwhAI3JsWPH1N+Euhl1G0nd6tfovYTQM0Yfmh8Dx9mO8YEuguiRgN4MaFFu2zsFXWbRDdVeN8Py5cvb/cy4bBNjM+jdmm271KF1OrrUWXZr1lu0RzcmQHQ9IdACXh/cCL08LMeNQf5E1605uvE3sP3oxpuwhZ5J6BWCz4ip9wq6hes9A/QxGTZv3qxa9mOALduB0Ox1a9bH/kAXdsvupvrvZ7kfoEs2lkV3c8ueCOi+iW6K9vav2HqK4fuh9wjSa9mtEr0S9P3R3u/jTLotYYwSfZtx7YmAfTau47BgH9XHmEF3XR3GtNB7xdiOr6F3B0UX1LjmgT5ei+0xo/fEs/cbJUS3Zow7Yjmux969e1UXfOyzlj17kLdFixY1jwdlO+gi8hFdwy27h1sOHBcX6G2lj2OCYQ0woKWtgwcPqjGNLPPKlbzFoG4xDa6Gbs32HpOCrtS2g0Dq3Zox9oxlfiHN+rAKtvulPv4MxkjCNcAyL/QeltF1a3bk90fvKD0PHOmN984776hlOQ4LWR08mDBID/rK42KDEwbewwEbXZfAnTt3mgcUQhdMdKHFGBcYzEk/IGzHukBXTMzHoEIYIRQH1U8//eTSNjF6pD4oE05qGLQI61kOHKdbtmyZmocLIgZEwudbdp+MaeA4vUsxDnJ8Bsa90AeOGz9+fJT8ia+ABaOdYtl69erFuqzeVdZykDAMqKX/nggWcUHD+Bf2Bo7D98SAbfqAZbiIv/nmm3YHHwMEcFgW28FAYMgTBHBYJ7aRbqOjX6wRBCBQxO+EwcWwX+ijytr7fZxJtw4nef04iC6IjY2eJnsDX8UWsAD2fyyDwez07qLozqyPuIv8xEBh+N3wvdBF1N64Fs7kAcY50r83BmbEtnHsYDmMnJoYAQuCAByj+N4IRhG86Mex7UB6gABWPwax7yJQwLkBx4U+oJ7ld3Q1YAF0w9VHlsaxXqlSJTXeCcYQ0vMa04gRI1zKWwRqOEZxw4HfVT8vWY5g+/HHH5u7g+M3xW+LYwMBL9Jmeyyji7O+r6C7PtKt57E+rIK9ARL1geP04w+fg8ERMQ+jLkc3cJwjvz/Gc9EH+kPaYxIZGan2D5z3OdItmQ8qfUIpBU50iOKxE2Jsg+iGU7YcEwIDl+GuAjsiJgyGhYNuzpw5UQawwp0hIv6AgADz6Ku2F21nt6mPEYBoXB9HAycwnOQRSNje+eKkhhOIPjquM0Pz4+4CaUGJBw4knCxjG5rf1YAFg15FlyZb+h0STmi2Y4/gJKuXYGGAPgQXGELcFu6mcBeL4BInLYy7ULhwYXWSsxy0CjCIHsaqwIlFH9QNAw1iv4lrwIJ18bvhN8IJHMEKxl85d+5cjL+PM+m2LSWKaejw2GBIfmwDg73FJWABjECM5aZOnWqVDximHsE6jkv8bhjQC/vD8OHDrYbaj0seoHRNH/Yf+zICAJTIRfcbJcTQ/HhoJ45bXBBxPGK8kZj2c1zwsH9h/8Z3w/6BwdhwIUbeWY4MGx8BC6CkFzdFOG/h/IL9HEEk9k+MA4Vgw5azeauXouB74XfTz0u2Y6hgIEgEEPojK3AexW+N3xfv2cIgm3iIIIJC5C9uRjHyNUqL9AAkuqH5MRiinsc4FyMoszcukLO/vz4AXXTnTdvxkj744ANTcscqISKKFZ6P4mjgGBMM3IUSoLg+H8qTuMNT2z0dAjBnBtyMLwiUUBqEINN2JGB7JT0odbI36nVyw0a3RBSj0NBQ1QsHDR4dGVgvJhiEC70rYhs3hSgxYQA5e42d+/btG+2YTglp9OjRqgFzt27dYuyNd+XKFZk7d65KHxrqJnfs1kxEdmG0zS1btsjWrVvVibFHjx5WXWjj4pVXXlG9e3BCxrgScR0fhSg+YRBOjN9SpEgR1WsJI2UjiEEPne7du8c6SGd8QA+6cePGqc9GN370etJ76EVnzJgx6q/eiy65Y8BCRHYhWPn+++/V2A6400OQER9iGtmUKClgVFkMiIjuzRiSAV2Sa9SooboTxzRicHzCTQGGDcCYUPhslGoiHTGZOHGimjyFhnqhpE4EERERGcPWrVtVaRBKoRBoYdyfxo0bx7gOnhfVq1cvNQAkBnAcNGiQtGvXzqnPZRsWIiIichgGScVgkFOnTnVoeVRzvfrqq6o6GG2DUL2M0it9dHhHsYSFiIiI4gSNgmMrYcFzo9asWWP1uBQ88gPVbxgZ3VFsw+KG0NALT2DFQ96ceV4LERElPbS0wIM+0Ug9vh7sas+TJ0/Uk6PjI7221xo0iI+vRvE7d+40PxZDhwdUoqTFGQxY3BCCleged05ERMaAhyfae95XfAUrqdIFiDx75PK20LjX9tlaQ4cOlWHDhkl8wANebZ8ejtcYMuHx48eqobEjGLC4IZSsgE/RtqJ5P3/YF9l3YfMXzBoiciv3Q0Mlf55g87k8ITxFycqzR+JbtK2IK9eJiKfy4Oj3KriyfBClOw45wIDFDelFcwhWGLDEzN6TXomI3EGiVOmn8HPpOmHSnldZRffk7PiAMW6uXbtmNQ+v8XmOlq4AAxYiIiKj0lRk5Nr6CaxKlSpqnBtL69atU/OdwW7NRERERqV5uT45Ce1d0D0Zk95tGf++cOGCeSC+Nm3amJfHiL1nzpyRfv36yfHjx2XatGnyww8/SM+ePZ36XAYsRERE5DCMCFymTBk1AQaEw7+HDBmiXmMwOT14gTx58qhuzShVwfgt48ePl1mzZqmeQs5glRAREZFRaZqLVULOr1urVi3VFTo6c+bMsbvOgQMHxBUMWIiIiIxKi1u1jtX6BmGclBIREZHHYgkLERGRUWmJXyWUVBiwEBERGZaXi9U6xqloMU5KiYiIyGOxhIWIiMioNFYJERERkbvT2EuIiIiIyG2wSoiIiMioNFYJERERkbvTPKdKiCUsRERERqV5TgmLcUIrIiIi8lgsYSEiIjIqjVVCREREZIgqIS/X1jcIVgkRERGR22OVEBERkVF5ac8nV9Y3CAYsRERERqV5ThsW46SUiIiIPBZLWIiIiIxK85xxWBiwEBERGZXGKiEiIiIit8ESFiIiIqPSWCVERERE7k7znCohlrAQEREZleY5JSzGCa0oSVQtk08WTXhfjv4yQu7smSKv1CzJXyIaM3/YIiUbDZHAaj2kTrtxsu/IOeYV8ynOuD8xn8gaAxYR2bx5s2iaJnfv3pWYhISEyMSJE8WTpE7lK3+d/Ef6jl2S1Elxa8t/3yeDJq6Q/h1fls3z+kvxAkHS7IOpcuP2/aROmlthPjGfuD8lUJWQ5sJkEG6d0nbt2knjxo3jHGDE1Zw5cyRDhgwJsm2jWb/jqIyYsVrWbP4zqZPi1qYt3ChtGleVVo2qSOG82WXCwLcltZ+PzF+1M6mT5laYT8wn7k8JVCWkuTAZhFsHLERG8DT8mRw8flFqVSxknufl5SU1KxaSPYfPJmna3AnzifnE/YnE0wOWP/74Q6pXry6pUqWS4OBg+fDDD+Xhw4fm9+fNmyfly5eXdOnSSWBgoLRs2VKuX79ud1sovWnfvr3cu3dPleJgGjZsmPn9R48eybvvvqu2lStXLvnmm2/M77344ovSvXt3q+3duHFDfHx8ZMOGDQny3Snp3br7QCIiIiVLpnRW87Nk8pfrt0KTLF3uhvnEfOL+lBC8XKwOMk4YYJyURuP06dPSoEEDadasmfz555+yZMkSFcBYBg7h4eHy2WefyaFDh2TlypVy7tw5Vd1kT9WqVVU7FX9/f7ly5Yqa+vTpY35//PjxKvg5cOCAdO3aVbp06SInTpxQ73Xs2FEWLlwoYWFh5uXnz58vQUFBKpiJDpYPDQ21moiIiGKlsUrIbaxevVrSpk1rNb388svm90eNGiWtWrWSHj16SIECBVTAMWnSJJk7d648efJELYMSEayTN29eqVy5snr/119/lQcPHkT5PJSGpE+fXpWsoDQGEz5T98orr6hAJX/+/NK/f3/JnDmzbNq0Sb3XtGlT9fenn36yag+D4Ajbiw6+Az5Tn1BKRMYRkCGteHt7RWlge+N2qGQN8E+ydLkb5hPzifsTJesSltq1a8vBgwetplmzZpnfR6kJggLLgKZ+/foSGRkpZ88+bz+wb98+adiwoarCQVVOzZo11fwLFy44nZ6SJf/r1qsHNXr1kp+fn7Ru3Vpmz56tXu/fv1/++uuvaEtzdAMHDlRVUPp08eJFp9NFSccnZQopXThYtux5XtIG2P+27jkpFUrk4U/DfOL+xOMugUtYvFyYjNPo1u0HjkuTJo0qzbB06dIl879RSvL++++rdiu2EKCgLQsCGEwLFiyQLFmyqEAFr58+fep0elKmTGn1GkELLk46VAuVLl1apfG7775TVUG5c+eOcZu+vr5qckdpUvlInuAs5te5cwRI8YJBcvfeI7l07U6Sps2ddG35onQdPk/KFMklZYuFyPRFm+Th4zBp1bByUifNrTCfmE/cn+KZxpFuDaNs2bJy9OjRKEGN7vDhw3Lr1i0ZPXq0uapl7969MW4T1UIRERFxSk+JEiVUG5eZM2eq9ixTpkwRIytdJLes/voj8+uRvZqpvwtX75Juw+cnYcrcS9N65eTm3Qcy8us1cv3WfSlRMEiWTurGKiHmE/cnHnfkKSUssUE7ErRLQSNblG6gRAYBzLp161SwgFIWBCCTJ0+Wzp07qyoaNMCNbYA4lNygZ0+pUqUkderUanIU0oH0IC1NmjQRI9u+/2/JWMG65xPZ995bNdVEMWM+OYb5xHxyiMah+Q0DbUq2bNkiJ0+eVF2by5QpI0OGDJEcOXKo91EFhDYuP/74oxQtWlSVtHzxxRcxbhMNdxHcNG/eXK0/duxYp9LUokULSZEihfqLdi1EREQJQvOckW41k8lkSupEJDfoNp0vXz7Zs2ePqrJyFro1o7eQb4lOonn7JEgakws834iIyJ3gHJ4tIL3qRIEhMhLqM9LjOvHKRNFSporzdkzhjyXslx4Jmtb4YvgqIXeC8V7QXmbQoEGqmiouwQoRERFFxYAlHm3fvl11wy5YsKAsXbo0PjdNREQUFXsJUVzUqlVLWMNGRESJRmOjWyIiIiK3wSohIiIig9L+fUivCxsQo2DAQkREZFCaBwUsxumATURERB6LJSxERERGpf07ubK+QTBgISIiMiiNVUJERERE7oMlLERERAaleVAJCwMWIiIig9IYsBAREZG70zwoYGG3ZiIiInJ7rBIiIiIyKo3dmomIiMjNaawSIiIiInIfrBIiIiIyKE17XsoS9w2IYTBgISIiMigN/7nU08c4EQt7CREREZHbYwkLERGRQWke1OiWAQsREZFRaZ7TrZlVQkREROT2WMJCRERkVJprVUImVgkRERGRu7dh0RiwEBERUULTPChgYRsWIiIicsrUqVMlJCRE/Pz8pFKlSrJ79+4Yl584caIUKlRIUqVKJcHBwdKzZ0958uSJU5/JgIWIiMjovYQ0FyYnLVmyRHr16iVDhw6V/fv3S6lSpaR+/fpy/fp1u8svXLhQBgwYoJY/duyYfPvtt2obH3/8sVOfy4CFiIjI4FVCmguTsyZMmCCdOnWS9u3bS9GiRWXGjBmSOnVqmT17tt3ld+zYIdWqVZOWLVuqUpl69epJixYtYi2VscWAhYiIyMOFhoZaTWFhYXaXe/r0qezbt0/q1Kljnufl5aVe79y50+46VatWVevoAcqZM2fkl19+kVdeecWpNLJbsxu7sPkL8ff3T+pkuLXAdvOTOgmGcHXOO0mdBEM4e/1hUifBEPJkTZPUSaB4bnSLdiWWUH0zbNiwKMvfvHlTIiIiJFu2bFbz8fr48eN2PwMlK1jvhRdeEJPJJM+ePZPOnTs7XSXEgIWIiMjDA5aLFy9a3SD7+vpKfNm8ebOMHDlSpk2bphronjp1Sj766CP57LPPZPDgwQ5vhwELERGRh/P393eoRD9z5szi7e0t165ds5qP14GBgXbXQVDSunVr6dixo3pdokQJefjwobz33nvyySefqColR7ANCxERkUFpidzo1sfHR8qVKycbNmwwz4uMjFSvq1SpYnedR48eRQlKEPQAqogcxRIWIiIio9IS/+GH6NLctm1bKV++vFSsWFGNsYISE/QagjZt2khQUJCMGjVKvW7YsKHqWVSmTBlzlRBKXTBfD1wcwYCFiIiIHNa8eXO5ceOGDBkyRK5evSqlS5eWtWvXmhviXrhwwapEZdCgQaokB3//+ecfyZIliwpWRowY4VxsZXKmPIYSBbqUpU+fXq7dusdeQrFgLyHHsJeQY9hLyDHsJRT7OTxbQHq5dy/hzuGh/14nAt+dL14+qeO8ncinj+Tq7HcSNK3xhSUsREREBqV50LOEGLAQEREZlOZBAQt7CREREZHbYwkLERGRUWmJ30soqTBgISIiMiiNVUJERERE7oMlLERERAaleVAJCwMWIiIig9LExYDFQI1Y2EuIiIiI3B5LWIiIiAxKY5UQERERuT3Nc7o1s0qIiIiI3B6rhIiIiAxKY5UQERERuTuNAQsRERG5O017PrmyvlGwDQsRERG5PbZhISIiMnQJi+bS+kbBgIWIiMioNBeDDgMFLKwSIiIiIrfHEhYiIiKD0thLiIiIiNydxl5CRERERO6DVUJEREQG5eWlqSmuTC6sm9gYsBARERmU5kFVQgxYKFYzf9gik+dvkOu3QqV4gSAZ0/dNKVcsxCNyrv1LBaXry0UlS/pUcvTCHflk/h45cPZWtMt3qldY2tYuKEEBqeX2/TBZvfeCjFx6QMLCI9X7bWsXkLYvFpTgzGnU6xP/3JMJPx2WjYcvi6fw5P1pyeodMnfZVrl1574UzJNd+nV+XYoXCra77OnzV2X6/HVy7NQ/cuX6Hend6TVp1bi61TKvth+t3rP15qtVZGDXxuIJPHl/8jReRmsNvXLlSqfXO3funFr34MGDklBq1aolPXr0kORm+e/7ZNDEFdK/48uyeV5/dUJo9sFUuXH7viR3r1fMLcPeLifjV/4p9Yb+Ikcu3pFFfV6UzOl87S7fpHKIfPJmGRn/059S4+OfpdfsXWobA5uVMS9z+c4jGfHjAak37FepP+xX+ePYVZnzUU0plCO9eAJP3p9+23pIJsxcLe+1fEkWTvpQCuTJLt0Gfyu37z6wu/yTsHAJCswkH7ZrIJkzprO7zPyJ3eX3eYPM0/TPO6r5dV8oIZ7Ak/cn215CrkxG4VYBy9WrV+WDDz6QvHnziq+vrwQHB0vDhg1lw4YN6v0rV67Iyy+/LO5o+fLl8tlnn0lyM23hRmnTuKq0alRFCufNLhMGvi2p/Xxk/qqdkty9X7+ILNhyShb/cUZOXr4n/b7/nzx+GiFv18hvd/kK+bPInr+vy4pd5+TizYey5cgVWfm/c1Imb4B5mXUH/5ENf16Ws9fuy5lr92X0skPy8MkzKZs/s3gCT96fFqzYJk0aVJTX61aQvLmyySfdm4ifX0r56fc9dpcvVjBYenZ4VerXLC0pU9ovDM+YPq1kzpTOPG3dc0xyZg+QciXyiifw5P3JtkrIlcko3CZgQSlIuXLlZOPGjTJu3Dg5fPiwrF27VmrXri3dunVTywQGBqpAJjrh4eFR5j19+lQSQ6ZMmSRdOvt3QUb1NPyZHDx+UWpVLGSe5+XlJTUrFpI9h89KcpbS20tKhmSSrUevmOeZTCLbjlyR8vnsBxd7Tt2QkiEBUibP8wAlV5a08mLJINnw5z92l/fSNHm9Um5J7ZtC9p26KcmdJ+9P4eHPVNVOpdIFrL57pdL55c/jF+LtM37ddEBer1veUHfNceXJ+5MllrAkga5du6qM3717tzRr1kwKFiwoxYoVk169esmuXbuiVAnp1TxLliyRmjVrip+fnyxYsEDatWsnjRs3lhEjRkiOHDmkUKH/duYzZ86oACh16tRSqlQp2bnTOgpftmyZ+kwERSEhITJ+/Hir96dNmyYFChRQn5UtWzZ54403oq0SimlZo7h194FERERKlkzWgViWTP6qvjg5y5TOV1J4e8mNe0+s5t8IfSJZ06eyuw5KVsYuPyQ/fVJPLs5qKbvHNZadx6/JpNVHrJYrnDODnJ7RXC7MaiFj21aSdydvUSU4yZ0n7093Qx9JRGSkZMqQ1mp+pgzpVHuW+LBp1xG5/+CJNKpTXjyBJ+9PnsotGt3evn1blaYgyEiT5nljREsZMmSIdt0BAwaowKJMmTIqONi8ebOqQvL395d169ZZLfvJJ5/IF198oQIJ/LtFixZy6tQpSZEihezbt0/eeustGTZsmDRv3lx27NihgqiAgAAVBO3du1c+/PBDmTdvnlStWlWledu2bXbT5MyyEBYWpiZdaCgPNiOqWjibfNSwuAyYu0f2n7kpebKmk89alZeejUrIl6sOm5c7fSVUXhqyRvxT+chrFXLJpI5VpcnodR4RtFDCWfn7HqlavpBkCfBnNnsQjSPdJi4EDSaTSQoXLuz0uijVaNq0qdU8BD2zZs0SHx8fc2kM9OnTR1599VX17+HDh6vSFHw2PnfChAny0ksvyeDBg9X7KOE5evSoqp5CwHLhwgW13ddee01V/eTOnVsFSfY4syyMGjVKpcfdBGRIK94oZbBpwHbjdqhkTeYnRfTweYa7t/R+VvOz+PvJ9XuP7a7Tr0kpWbrjrCzcekq9Pn7prqruGdeukkz8+bCqUoLwiEg5d/15Q8s/z9+W0nkCpGPdwqqNTHLmyftTBv/U4u3lFaWB7e279yUgmga1zrh8/Y7sPnhKvvi4tXgKT96fPLVbs1u0YUGwElfly0ct/ixRooQ5WLFUsmRJ87+zZ8+u/l6/fl39PXbsmFSrVs1qebz++++/JSIiQurWrasCDzQIbt26tap+evTokd00ObMsDBw4UO7du2eeLl68KO7AJ2UKKV04WLbsOWGeFxkZKVv3nJQKJfJIcoag4s9zt6V60UCrA/uFooGy97T99iapfL0lMtJ6X47497UWwyNR0ZbFN6VbHIoJypP3JzSaLZI/SAUVlt8dr0sWzuXy9let2yuZ0qeVFyo6f9NnVJ68P3kqtzhLoooGxVrHjx93el17VUj25kHKlCnN/9YbpWEHdwRKSvbv3y+LFi1Swc6QIUNUO5i7d++6tCygzQyqsCwnd9G15Ysyd+UOWbR6l5w4e1V6jV4iDx+HSauGlSW5+/q3Y9KqZgF5q1peKZDdX8a0qaRKTBZvO63en9ypqnz8RmmrHkBtXyygGtLmypxGahQLlP5NS8m6g5ck8t+gHMtXLphVjcOCtix4jaqkZTs9o5GgJ+9PrZpUlxW/7Zaf1++TMxeuycipK+Txk3BpVPf5Tdfg8Utk8pxfrRrRnjh9WU3hz56pdhn494XL1gEzzmEIWF57qZyk8PYWT+LJ+5MON0MuNbyN4WbK3bhFGxb0sKlfv75MnTpVtf2wDThwoY+pHUt8KFKkiGzfvt1qHl6jasj735MA2rrUqVNHTUOHDlVpQq8m2yopZ5d1Z03rlZObdx/IyK/XyPVb96VEwSBZOqmbRxS5/rT7vASk85V+TUqqgeOOXLgjLcZvlJuhzxviBgWkMQcigHYqKC0c0LS0BGZMJbfuh6lgZdSy/8b/yezvJ5Pfq6oa7t5/HC5HL96Rt8dvkK1Hroon8OT9qX6NUnLn3kOZPv931dC2UN4cMuXTd81VQldv3FWlbZZVGy0+/Mr8et7yrWpCl+WZo983z//fwVNq3dfreUZjW0uevD95YpWQWwQsgGAFVTAVK1aUTz/9VFXfPHv2TDWcnT59uqqySUi9e/eWChUqqLFU0OgWPYimTJmievvA6tWrVS+jGjVqSMaMGeWXX35RdzaWvZB0zixrBO+9VVNNnmj2hpNqsqfp6HVRqn/G/3RYTdHBYHKezpP3p7cbVlWTPZZBCOTIlkn2rxkT6zarlC3o0HLJlSfvT57GbQIWtPdANQp6CiF4wCBxWbJkUWOzIGBJaGXLlpUffvhBVd8gaEFVDgInNLgFlJBgcDj0Inry5ImqxkKVDxru2nJmWSIiorjSPKiXkGZypcUrJQh0a06fPr1cu3XPrdqzuKPAdvOTOgmGcHXOO0mdBEM4e/1hUifBEPJktd9OkP47h2cLSK86USTUOTz03+tE6U9+Fm+/uP8eEU8eysERDRM0rcmq0S0RERGRIaqEiIiIyDmaB1UJMWAhIiIyKI29hIiIiMjdaR5UwsI2LEREROT2WCVERERkVJqLg78Zp4CFAQsREZFRaawSIiIiInIfrBIiIiIyKI29hIiIiMjdsUqIiIiIyI2wSoiIiMigNFYJERERkbvT2EuIiIiIyH2wSoiIiMigNA8qYWHAQkREZFAa27AQERGRu9M8qISFDz8kIiIit8cqISIiIoPSWCVERERE7k5jlRARERGR+2CVEBERkUFp/1YLubK+UTBgISIiMigvTVOTK+sbBXsJERERkdtjCQsREZFBaewlRERERO5OYy8hIiIicndemutTXEydOlVCQkLEz89PKlWqJLt3745x+bt370q3bt0ke/bs4uvrKwULFpRffvnFqc9klRARERE5bMmSJdKrVy+ZMWOGClYmTpwo9evXlxMnTkjWrFmjLP/06VOpW7euem/p0qUSFBQk58+flwwZMjj+oQxYiIiIDExz8XlAcVh1woQJ0qlTJ2nfvr16jcBlzZo1Mnv2bBkwYECU5TH/9u3bsmPHDkmZMqWah9IZZ7GXEBERkcEb3WouTBAaGmo1hYWF2f08lJbs27dP6tSpY57n5eWlXu/cudPuOqtWrZIqVaqoKqFs2bJJ8eLFZeTIkRIREeHUd2WVEBna1TnvJHUSDCGky9KkToIhrB1cP6mTQJQkgoODrV4PHTpUhg0bFmW5mzdvqkADgYclvD5+/LjdbZ85c0Y2btworVq1Uu1WTp06JV27dpXw8HD1OY5iwEJERGRQ2r//ubI+XLx4Ufz9/c3z0TA2vkRGRqr2K9988414e3tLuXLl5J9//pFx48YxYCEiIvIEXi709NHXBwQrlgFLdDJnzqyCjmvXrlnNx+vAwEC766BnENquYD1dkSJF5OrVq6qKycfHx7G0OrQUEREReTwfHx9VQrJhwwarEhS8RjsVe6pVq6aqgbCc7uTJkyqQcTRYYcBCRESUDAaO01yYnIUuzTNnzpTvv/9ejh07Jl26dJGHDx+aew21adNGBg4caF4e76OX0EcffaQCFfQoQqNbNMJ1hkNtWNDC11GNGjVyKgFERERknKH5mzdvLjdu3JAhQ4aoap3SpUvL2rVrzQ1xL1y4oHoOWTbo/e2336Rnz55SsmRJNQ4Lgpf+/fvHf8DSuHFjhzaGSM3ZbkpERERkLN27d1eTPZs3b44yD9VFu3btcukzHQpYLOudiIiIyD14aZqaXFnfKFzq1vzkyRP1HAEiIiJKfJoHPa3Z6V5CqPL57LPPVB1U2rRp1YAwMHjwYPn2228TIo1ERETkJo1uDROwjBgxQubMmSNjx4616o6EoXZnzZoV3+kjIiIicj5gmTt3rhqtDkPsWg4CU6pUqWiH5SUiIiL3fZZQsmzDguF08+fPb7dhLp4LQERERInDy4Ma3TpdwlK0aFHZtm1blPlLly6VMmXKxFe6iIiIiOJewoKBYtq2batKWlCqsnz5cjlx4oSqKlq9erWzmyMiIqI40v6dXFk/2ZawvP766/Lzzz/L+vXrJU2aNCqAwdC8mFe3bt2ESSURERF5dC+hOI3DUr16dVm3bl38p4aIiIgoPgeO27t3rypZ0du14OmNRERElHi8tOeTK+sn24Dl0qVL0qJFC9m+fbtkyJBBzbt7965UrVpVFi9eLDlz5kyIdBIREZENV6t1jFQl5HQblo4dO6ruyyhdweOiMeHfaICL94iIiIiSvIRly5YtsmPHDilUqJB5Hv49efJk1baFiIiIEo9mnEKSxA1YgoOD7Q4Qh2cM5ciRI77SRURERLHQWCUUvXHjxskHH3ygGt3q8O+PPvpIvvjiC+5cREREidzo1suFKVmVsGTMmNGqYc7Dhw+lUqVKkiLF89WfPXum/v3uu+9K48aNEy61RERE5JEcClgmTpyY8CkhIiIip2geVCXkUMCCofiJiIjIvWgeNDR/nAeOgydPnsjTp0+t5vn7+7uaJiIiIiLXAha0X+nfv7/88MMPcuvWLbu9hYiIiCjheWmamlxZP9kOHNevXz/ZuHGjTJ8+XXx9fWXWrFkyfPhw1aUZT2wmIiKixKFprk/JtoQFT2VGYFKrVi1p3769Giwuf/78kjt3blmwYIG0atUqYVJKREREHsvpEhYMxZ83b15zexW8hhdeeEG2bt0a/ykkIiKiGHsJuTIl2xIWBCtnz56VXLlySeHChVVblooVK6qSF/1hiJS8zPxhi0yev0Gu3wqV4gWCZEzfN6VcsZCkTpbbYT79p3XNfPJ+3YKSxd9Pjl26J0OXHJBD5+9Em3fvvphfWtXIJ0EZU8vtB2Hy64F/ZOzKwxL2LFKSs6VrdsqClVvl9p0Hkj8kUHq910iKFQy2u+xPv++WXzcdkDPnr6rXhfIFSefW9aNd3lN4+nGnuVitY6B4xfkSFlQDHTp0SP17wIABMnXqVPHz85OePXtK3759JbkJCQmJdRyaYcOGSenSpSU5Wv77Phk0cYX07/iybJ7XX50Qmn0wVW7cvp/USXMrzKf/vFYupwxqVlK+WnNUXh25Xo5euitzP6wuAel87eZdowrB0r9xCbV8neG/Sf/5+9Q2+r5eXJKz9dv+lEmz10iH5i/JnAndpUCe7NJz2Gy5ffeB3eX3Hz4jdauXlCmfd5JvxnaRbJkzSI9hs+X6rXviqXjceRanAxYEJh9++KH6d506deT48eOycOFCOXDggBqe3xnt2rWzOzLu5s2bVTHV3bt3JbHMmTPHbgnRnj175L333jO/RrpWrlxptUyfPn1kw4YNkhxNW7hR2jSuKq0aVZHCebPLhIFvS2o/H5m/amdSJ82tMJ/+0/GlgrJ4+1n5ced5OXX1vnyyaL88fhohb1Wxf9dbLm+A7D19S1btuSiXbj+Sbceuyaq9F6VUSCZJzhb9tE0a1asgr9UpL3lyZZN+XRqLr6+PrF7/32NPLA3v/bY0e6WKFMybQ0JyZpWB3ZtKZKRJ9h46LZ6Kx52Yewm5MiXbgMUWGts2bdpUSpYsKclRlixZJHXq1DEukzZtWgkICJDk5mn4Mzl4/KLUqvjfk7m9vLykZsVCsufw2SRNmzthPv0npbcmxXNlkO3Hr5vnmUwi249fk7J57R8j+87ckhK5Mkip3BnV6+DMaaR2sUDZdOR51UdyFB7+TE6cviwVSuW3OrYqlMonf5244NA2noSFy7OICPFPl0o8EY+759hLyMakSZPEUXrpS3z6448/ZODAgeohi5kzZ5YmTZrIqFGjJE2aNOZqmw4dOsjRo0dl1apVqqTk448/lm7dupm3MWHCBPnuu+/kzJkzkilTJmnYsKGMHTtWBRso0UFVF+gNkIYOHaqqerDtHj16qAn/Bny+HqydO3dOLYdSl4MHD6r52B66fx85ckRSpkwpxYoVU6VQWN5Ibt19IBERkZIlUzqr+Vky+cvf564lWbrcDfPpPxnT+koKby+5GfrEKo9uhIZJvmz2B5VEyUqmtL7yY5/a6uSb0ttL5m89LdPWHpfk6m7oI4mIjJRMGdJazc+UIZ2cv3TDoW1Mm/urOhYtgx5PwuPuOQ7Nb+PLL78URzMuvgOW06dPS4MGDeTzzz+X2bNny40bN6R79+5qQgBi+RRpBCkYE+a3335T1VMFCxaUunXrmu9eEHjlyZNHBS1du3ZVQcW0adOkatWqqp3KkCFD5MSJE2p5BDL2qoeyZs2qPhdp8vb2jrIMHgSJaq5OnTrJokWL1EjAu3fvjrEldlhYmJp0oaGhLucbkVFULpBFutUvLIMX75eDZ29LSJa0MuSt0vLBy09k8q/Hkjp5bmnu0s2ybtufMm1EJ/H1SZnUySFyn15C6BWUUFavXh0lOLAcLRclKRjbBSUcUKBAARV41KxZUw1ehwa/UK1aNdUIGBCobN++XQVaesCirw8oKUEA1LlzZxWw+Pj4SPr06VVQERgYGGP1EKAEJ7rlEGzcu3dPXnvtNcmXL5+aV6RIkRjzAN8RgZa7CciQVry9vaI0sL1xO1SyBvARDMynqO48CJNnEZGS2f/5canL4u8rN2xKXXS9GhWT5bvPy5Lt59TrE5dDJZVvChnVqqxMWXtMVSklNxn8U4u3l1eUBra3796XgIzWJZq2FqzYKvOWb5FJwztI/pDs4ql4fvqvXYdXUrYLSURJntbatWurqhTLCaPn6tAjCQ1iEdToU/369SUyMtIqkKpSpYrVdvH62LH/7s7Wr18vL730kgQFBUm6dOmkdevW6tECjx49itfvg+omNCZGGlHt9NVXX8mVK1diXAfVXQhy9OnixYviDnxSppDShYNly57npU6AfN+656RUKJEnSdPmTphP/wmPMMlfF+5K1UJZzfNQuIjX+89EfZQHpPLxFpNN72U0JlXrSvKUMmUKKZQvh+z987TVsYXXxQvlina9+cu3yHc/bJQvh7aXIgVyiifjced547AkecCCdigYKddyQlChe/Dggbz//vtWAQ2CmL///ttcghEbtDNBiQcaBi9btkz27dunumOD7cMb4wOqjHbu3KmqmpYsWaJKfHbt2hXt8njEAQbhs5zcRdeWL8rclTtk0epdcuLsVek1eok8fBwmrRpWTuqkuRXm039mbTgpLV7II80q55Z8gelkRIuykto3hfy483kJyvi2FaSfRZflDX9ekVY18krD8jklZ0BqeaFwVunVsJia/2/ckiy1eL26rPp9j6zZuE/OXbwuY2f8JE+ePJXX6pRT7w//8geZNnetefl5y7bINwvWyScfvCHZs2aUW3fuq+nR4/+qkz0NjzvP4tLTmhND2bJlVWNaBDIxsQ0I8FqvikGAgruX8ePHq7YsgAHvLKFayJEHN6IRrSPLlSlTRk0oPUFpDxrdVq5svIt803rl5ObdBzLy6zVy/dZ9KVEwSJZO6sYqIeZTtFbvu6Qa0fZ8rah54Li2k/+Qm/efX1iDMqUWk0U9D9qpmMQkvRsWl8AMqeTWgzDZcPiyfPHTEUnO6lQvKXdCH8ishetV4IFxWFBygoa3cO3mXfHy+u/ud/naXRL+LEI+HrPAajsd3n5JOraoI56I5ydRJZgWu4nTDFTA4v4BC54MjQs9Gtl27NhRlcgggFm3bp1MmTLFvBzarKDXDxq84r0ff/xR1qxZo95DsBMeHi6TJ09W1TRYdsaMGVafg3YtKM3BeCqlSpVSXZntdWfGclgGbWZQMpIx4/OumDpUU33zzTfSqFEj9UBINOJFaVCbNm3EqN57q6aaiPnkqLlbTqvJnre/3GL1OiLSJF+tOaYmT/Pmq1XVZM+0Ef+N/wQrZvZPpFQZi6efn7xcDFhcWdfjqoRig2qcLVu2yMmTJ9WDFlFqgd48CAYs9e7dW3V7xvtoUItuzGhHAghA8HrMmDFSvHhx9ZBGNHS1hOobNMJt3ry5alyL4McelNIgIAoODlafZQtBDgbTa9asmaoKwqBz6F6Nai0iIiKKG81kWTbroG3btsnXX3+tuhwvXbpUtTmZN2+e6jKMhyAmNsuxUpID9DRCr6Vrt+65VXsWMq6QLkuTOgmGsHbw85scilnhHDH3ZPJ0OIdnC0ivOlEk1Dk89N/rRLfFe8U3ddRhOBwV9uiBTH27fIKmNclKWNBoFSUXqVKlUsPx6+OH4MuOHDkyIdJIREREMVQJuTIZhdMBC6pb0P5j5syZqgGqDm069u/fH9/pIyIiInK+0S0akdaoUSPKfBRNJebDCm27LRMREXkaTXOtp4+WnEtYMMLrqVOn7D7vJ2/evPGVLiIiIoqFF5/WHD08IwfP6fnf//6nRsi7fPmy6nXTp08f6dKlC3cuIiKiRB6a38uFKdlWCeF5PRiEDcPcY1h7VA9hPBIELB988EHCpJKIiIg8mtMBC0pVPvnkE+nbt6+qGsJga0WLFrX7dGMiIiJKOJoHtWGJ80i3GMoegQoRERElDS/RVDsWV9ZPtgELnq4c09MdN27c6GqaiIiIiFwLWEqXLm31Gs/owROU//rrL2nbtq2zmyMiIqI40lglFL0vv/zS7vxhw4ap9ixERESUOLz48EPnvfPOOzJ79uwE+DmIiIjI08W50a2tnTt3ip+fX3xtjoiIiByoEvJyodFtsu4l1LRpU6vXeNjzlStXZO/evTJ48OD4TBsRERHFQGMblujhmUGWvLy8pFChQvLpp59KvXr1uGMRERFR0pawRERESPv27aVEiRKSMWPG+E8NEREROcyLjW7t8/b2VqUoSfVUZiIiIvqPFg//GYXTzz0qXry4nDlzJmFSQ0RERE6XsHi5MCXbgOXzzz9XDzpcvXq1amwbGhpqNRERERElWRsWNKrt3bu3vPLKK+p1o0aNrIboR28hvEY7FyIiIkp4Xh7UhsXhgGX48OHSuXNn2bRpU8KmiIiIiByiaVqMz/dzZP1kF7CgBAVq1qyZkOkhIiIicq1bs5EiMSIiouTOi1VC9hUsWDDWoOX27dsJ8qMQERGRNY50G0M7FtuRbomIiIjcqkro7bfflqxZsyZcaoiIiMhhXprm0sMPXVnXbcdhYfsVIiIi9+KVRAPHTZ06VUJCQsTPz08qVaoku3fvdmi9xYsXq3iicePGCRew6L2EiIiIyHMtWbJEevXqJUOHDpX9+/dLqVKlpH79+nL9+vUY1zt37pwaeLZ69epx+lyHA5bIyEhWBxEREbkT7b+Gt3GZ4vIooQkTJkinTp3Uw5CLFi0qM2bMkNSpU8vs2bOjXQeDyrZq1Uq1hc2bN2/iDM1PRERE7sFLNJcnsH3MTlhYmN3Pe/r0qezbt0/q1KnzXxq8vNTrnTt3xjhaPtrAdujQIXEa3VLiOnT+jqRJy0cdxKRsnoyJ9nsY2fJ+LyV1Egxh5t6LSZ0EQxjfqGhSJ4HiuVtzcHCw1XxU9wwbNizK8jdv3lSlJdmyZbOaj9fHjx+3+xl//PGHfPvtt3Lw4MG4J5QBCxEREV28eFH8/f3NGeHr6xsvmXL//n1p3bq1zJw5UzJnzuzStljCQkRE5OEj3fr7+1sFLNFB0OHt7S3Xrl2zmo/XgYGBUZY/ffq0amzbsGFDqzaxkCJFCjlx4oTky5fPsbQ6tBQRERG57TgsXi5MzvDx8ZFy5crJhg0brAIQvK5SpUqU5QsXLiyHDx9W1UH61KhRI6ldu7b6t21VVExYwkJEREQOQ5fmtm3bSvny5aVixYoyceJEefjwoeo1BG3atJGgoCAZNWqUGqelePHiVutnyJBB/bWdHxsGLERERAalxVOjW2c0b95cbty4IUOGDJGrV69K6dKlZe3ateaGuBcuXFA9h+IbAxYiIiKD8hIXh+aPy0AsItK9e3c12bN58+YY150zZ06cPpNtWIiIiMjtsYSFiIjIoLQkqBJKKgxYiIiIDMrLxaoSI1WzGCmtRERE5KFYwkJERGRQmqapyZX1jYIBCxERkUFpcXvgstX6RsGAhYiIyKC84jBare36RsE2LEREROT2WMJCRERkYJp4BgYsREREBqV50DgsrBIiIiIit8cSFiIiIoPS2K2ZiIiI3J0XR7olIiIich+sEiIiIjIojVVCRERE5O40Dxrplr2EiIiIyO2xSoiIiMigNFYJERERkbvz8qBeQixhISIiMijNg0pYjBRcERERkYdiCQsREZFBaR7US4gBCxERkUFpfPghERERkftgCYuHWrn2f/LDz3/I7bsPJF/uQPng3VelcP6cdpdds36v/L71oJy7eE29Lpg3h3RoUddq+TFTl8vvWw5YrVehVH4Z/Ulb8RQzf9gik+dvkOu3QqV4gSAZ0/dNKVcsRDwB96e4qRaSUWrlD5B0vinkcmiYrDh8RS7efWJ32QrB6eXtMkFW88IjImXAmuPiyTz5uAMv0dQUV66sm9gMHbDUqlVLSpcuLRMnTlSvQ0JCpEePHmpKTEn1uXG1acdhmTH3V+nRqZEULpBTlq/ZKf1HfC9zJn4kGdOnjbL8oaNn5cVqJaRYoVfFJ2UKWfzTNun3+ffy7YQPJEsmf/NyFUoXkH5dm5hfp0xh6N3LKct/3yeDJq6QCQOaS7niITJj0SZp9sFU2bN0iGTJlE6SM+5PcVM6h780KpZNlv55RS7ceSzV8wbIe5Vzy5iNp+TB0wi76zwOj1Dv60zi2Tz5uNOxSsjNtGvXztx1y3IaO3asfPbZZ0mdPNmzZ4+89957YhRLV++QV14qLw1ql5WQnFmlR6eG4uuTUtZu2m93+Y8/fFNer19J8odkl1xBWaR358ZiMpnkwOHTVsulTOEtmTKkM0/p0qYSTzFt4UZp07iqtGpURQrnzS4TBr4tqf18ZP6qnZLccX+Kmxr5AmTXhbuy5+I9ufbgqSz784oqMamYK0OM690PizBPD8LsBzaewpOPO09kmFvgBg0ayHfffWc1L0uWLOLt7e3SdsPDwyVlypQubQPpMIrwZ8/k5JnL0qJxdfM8Ly8vKVsinxw9edGhbYSFhcuzZxGSLm1qq/mHjp6TZh1HS9o0flKmeF5p/3YdSZ/Oepnk6Gn4Mzl4/KL0bFfPKk9rViwkew6fleSM+1PceGsiOdP7yca/b1qVlpy8+VByZ8Qxc8vuej7eXvJJnfzipWly6e4T+eX4dbl2P0w8kScfd5a0f/+LK1fWTWyGGYfF19dXAgMDraaXXnopSjXM/fv3pUWLFpImTRoJCgqSqVOnWr2Pkpnp06dLo0aN1DIjRoxQ8zEvX7584uPjI4UKFZJ58+aZ10FpwrBhwyRXrlwqHTly5JAPP/zQqkpIr5aKbdmkdi/0kURGRkrGDNZVP3iN9iyOmLngdwnIlE7KlchrnlehdH4Z0L2pjBvSTjq1qqeCl4Ej50pEZKQkd7fuPpCIiMgoRdCoLkO9enLG/Slu0vikEG8vTe6HPbOa/yDsmaTzs38fef3BU1ly8LJ8t/uiLNj/j6oK+OCFEEkfzfLJnScfd/aqhFyZjCLZ7enjxo2Tjz/+WIYPHy6//fabfPTRR1KwYEGpW7eueRkEFKNHj1ZBRooUKWTFihVqObyuU6eOrF69Wtq3by85c+aU2rVry7Jly+TLL7+UxYsXS7FixeTq1aty6NAhu5/vzLK6sLAwNelCQ933YFu0cqts2n5Yxg97V3x8/iuZerFaSfO/8+YKlLy5A6X1B1/KoSNnVekNEfcn15y/81hNunO3H0n/F/NLldwZZe2JG9zBKNkzTMCCICJt2v9KBV5++WW7y1WrVk0GDBig/o1AZfv27SqAsAxYWrZsqQISHUpk0E6ma9eu6nWvXr1k165d8sUXX6iA5cKFC6pEB8EMqo9QelKxYkW7n+/MsrpRo0apACsxpPdPrYpN79iUpuB1JptSF1s/rPpDFq3cJuMGt1M9i2KSI1smVR30z9XbyT5gCciQVry9veTG7ftW82/cDpWsAf81Sk6OuD/FzcOnzyQi0qR6B1lK65tC7j+xLnWJTqRJ5J97TyRzGh/xRJ583NlW6bjS04dVQgkAgcPBgwfN06RJk+wuV6VKlSivjx07ZjWvfPnyVq/xPgIdS3itr/fmm2/K48ePJW/evNKpUydVIvPsmf2TijPL6gYOHCj37t0zTxcvOtaWJC7Qcwfdkg/8dcY8D1VEeF20YHC066Fn0Pxlm2X0x22kUD7rrpX23Lh1T0IfPJaAjDEHQckBek6VLhwsW/acsMrTrXtOSoUSeZI0bQmN+1PcRJhELt17IgUypzHPwyUHr8/feeTQNrB89nS+EmpTreQpPPm489QqIcO0YUF7k/z585un7Nmzu7QtZwQHB8uJEydk2rRpkipVKlUSU6NGDdVg15VldWjr4u/vbzUlpDdeqyprNuyT3zYfkPOXrsvEWT/Lk7CnUr9WWfX+6ClLZdbC362qgeYs2SB9ujSRwKwZ5Pbd+2p6/OR5NRb+fj1vrWq0e/X6Hdl/+LQMHrtQcgRmkvKlCogn6NryRZm7cocsWr1LTpy9Kr1GL5GHj8OkVcPKktxxf4qbradvSaXcGaR8cHrJmtZHmpXMrhrV7r54V73fokwOeaVIVvPydQtmloJZ0kim1CklKL2ftCobJBlTp5T/nb8jnsqTjztPDFgMUyXkKFTl2L4uUqRIjOvgfVQdtW373yBneF20aFHzawQfDRs2VFO3bt2kcOHCcvjwYSlb9vlF3pIzyyaF2lVLyL3QhzLnhw2qKihfSHZVcqJXCV2/eU807b9Y9ud1eyT8WYQMn7DYajtt3qgtbd96UVUxnblwTX7fclAePHyiGuSWL5lf2jV/Sd0FeYKm9crJzbsPZOTXa+T6rftSomCQLJ3UzSOKprk/xc3By6GSxsdb6hfKIv6+KeSf0DCZueuCuatyhlQpxWQx0EqqlN7yZqnsatlH4ZFy6d5jmbztnOoS7ak8+bjzRMnuaoJAA+OzNG7cWNatWyc//vijrFmzJsZ1+vbtK2+99ZaUKVNGtT35+eefZfny5bJ+/Xr1/pw5cyQiIkIqVaokqVOnlvnz56ugJHfu3FG25cyySalxg8pqsmfCsA5WrxdO7R3jtjCGyxgPGtE2Ou+9VVNNnoj7U9xsP3dHTfZM33He6vWqI9fURNY8+bjztG7NyS5g6d27t+zdu1c1YkXVyoQJE6R+/foxroPg5quvvlKNbNFbKE+ePGrMF4ykCxkyZFC9itAYF8FIiRIlVFATEBAQZVvOLEtEROQKL+355Mr6RqGZMHAIuRV0a06fPr38vv+cpEnLos2YlM2TMdF+FyPbf9Zz2zk4Y8HhK0mdBEMY3+i/6nKyfw7PFpBedaJIqDaJof9eJ37ac0bSpI37YwgePrgvr1fIm6BpjS/JroSFiIjIU2isEiIiIiJ3p7nY08dIvYQM062ZiIiIPBerhIiIiAxKc7Gnj4EKWBiwEBERGZWXB/USYpUQERERuT1WCRERERmUxl5CRERE5O40D+olxBIWIiIiQze6jTsDxStsw0JERETujyUsREREBuUlmni5UK+D9Y2CAQsREZFBaawSIiIiInIfLGEhIiIyKs1zilgYsBARERmU5kHjsHCkWyIiInJ7LGEhIiIyKs3Fwd+MU8DCgIWIiMioNM9pwsIqISIiInJ/rBIiIiIyKs1zilgYsBARERmU5kG9hBiwEBERGZTmQU9rZrdmIiIicnssYSEiIjIoD2rCwoCFiIjIsDTPiVhYJURERERujwELERGRwXsJaS78FxdTp06VkJAQ8fPzk0qVKsnu3bujXXbmzJlSvXp1yZgxo5rq1KkT4/LRYcBCRERk8F5CmguTs5YsWSK9evWSoUOHyv79+6VUqVJSv359uX79ut3lN2/eLC1atJBNmzbJzp07JTg4WOrVqyf//POPU5/LgIWIiIgcNmHCBOnUqZO0b99eihYtKjNmzJDUqVPL7Nmz7S6/YMEC6dq1q5QuXVoKFy4ss2bNksjISNmwYYPjH8qAhYiIyPhtbjUXJggNDbWawsLC7H7e06dPZd++fapaR+fl5aVeo/TEEY8ePZLw8HDJlCmTU9+V3ZrdWKncGcXf3z+pk0HJQNk8GZM6CYbAfHJMxgrdE/iXMDZTxFPD9RIKDg62mo3qnmHDhkVZ/ObNmxIRESHZsmWzmo/Xx48fd+gj+/fvLzly5LAKehzBgIWIiMjDXbx40eoG2dfXN0E+Z/To0bJ48WLVrgUNdp3BgIWIiMjDnyXk7+/vUIl+5syZxdvbW65du2Y1H68DAwNjXPeLL75QAcv69eulZMmSTqeVjW6JiIgMSkvkXkI+Pj5Srlw5qwazegPaKlWqRLve2LFj5bPPPpO1a9dK+fLl4/RdWcJCRERkUFoSDHSLLs1t27ZVgUfFihVl4sSJ8vDhQ9VrCNq0aSNBQUEyatQo9XrMmDEyZMgQWbhwoRq75erVq2p+2rRp1eQoBixERETksObNm8uNGzdUEILgA92VUXKiN8S9cOGC6jmkmz59uupd9MYbbzjUsDc6DFiIiIiMSkuaZwl1795dTfagQa2lc+fOSXxgwEJEROThjW6NgI1uiYiIyO2xhIWIiMigtDg+D8hyfaNgwEJERGRQWtI0YUkSrBIiIiIit8cSFiIiIqPSPKeIhQELERGRQWnsJURERETkPljCQkREZFAaewkRERGRu9M8pwkLS1iIiIgMS/OciIXdmomIiMjtsQ0LERGRQWke1EuIAQsREZFRaS4Or2+ceIVVQkREROT+WMJCRERkUJrntLllwEJERGRYmudELOwlRERERG6PVUJEREQGpbGXEBEREbk7zYOG5meVEBEREbk9VgkREREZlOY5bW4ZsBARERmW5jkRC0tYiIiIDErzoEa3bMNCsZr5wxYp2WiIBFbrIXXajZN9R84x15hPccb9ifkUH6qWySeLJrwvR38ZIXf2TJFXapbkeSmZc+uA5caNG9KlSxfJlSuX+Pr6SmBgoNSvX1+2b98uRnDu3DnRNE0OHjwoRrX8930yaOIK6d/xZdk8r78ULxAkzT6YKjdu30/qpLkV5hPziftT4kqdylf+OvmP9B27RMTTa4Q0FyYxDrcOWJo1ayYHDhyQ77//Xk6ePCmrVq2SWrVqya1bt5I6aR5j2sKN0qZxVWnVqIoUzptdJgx8W1L7+cj8VTuTOmluhfnEfOL+lLjW7zgqI2asljWb/xRPpsXDZBRuG7DcvXtXtm3bJmPGjJHatWtL7ty5pWLFijJw4EBp1KiR3dILrIN5mzdvVq/v3LkjrVq1kixZskiqVKmkQIEC8t1336n39PUXL14sVatWFT8/PylevLhs2bLFKh1//fWXvPzyy5I2bVrJli2btG7dWm7evGl+PzIyUsaOHSv58+dXpUAoDRoxYoR6L0+ePOpvmTJl1Gch2DKSp+HP5ODxi1KrYiHzPC8vL6lZsZDsOXw2SdPmTphPzCfuT0QeHLAgQMC0cuVKCQsLi9M2Bg8eLEePHpVff/1Vjh07JtOnT5fMmTNbLdO3b1/p3bu3KsmpUqWKNGzY0FyCgwDoxRdfVAHH3r17Ze3atXLt2jV56623zOsjgBo9erT5sxYuXKgCG9i9e7f6u379erly5YosX75cjOTW3QcSEREpWTKls5qfJZO/XL8VmmTpcjfMJ+YT9ydKKpor1UEuDjqX2Ny2l1CKFClkzpw50qlTJ5kxY4aULVtWatasKW+//baULOlY46oLFy6oYKN8+fLqdUhISJRlunfvrqqeAAENgpJvv/1W+vXrJ1OmTFHrjxw50rz87NmzJTg4WFVRZc+eXb766iu1XNu2bdX7+fLlkxdeeEH9GyU7EBAQoNrfRAcBmWVQFhrKYICIiByheUy/ZrctYQEEEpcvX1ZtVxo0aKCqehC4IJBxBBrsosqndOnSKgDZsWNHlGVQqmIZJCG4QWkMHDp0SDZt2mQu7cFUuHBh9d7p06fVcgg0XnrpJZe+56hRoyR9+vTmCQGROwjIkFa8vb2iNLC9cTtUsgb4J1m63A3zifnE/YnIwwMWQNuSunXrqioXBBzt2rWToUOHqrYUYDKZzMuGh4dbrYu2J+fPn5eePXuqwAeBRZ8+fRz+7AcPHqgqIrSTsZz+/vtvqVGjhmoXEx9QrXTv3j3zdPHiRXEHPilTSOnCwbJlzwmrNjtb95yUCiWet88h5hP3Jx53lHQ0D6oScvuAxVbRokXl4cOH5uoWtA3R2es+jOVQXTN//nyZOHGifPPNN1bv79q1y/zvZ8+eyb59+6RIkSLqNUpzjhw5oqqS0KjWckqTJo1qxIugZcOGDXbT6uPjo/5GRETE+J3QWNff399qchddW74oc1fukEWrd8mJs1el1+gl8vBxmLRqWDmpk+ZWmE/MJ+5PiStNKh8pXjBITZA7R4D6d85sGcWTaB7US8ht27Cg4eubb74p7777rmqzki5dOtXwFT1yXn/9dRUoVK5cWTV4RW+c69evy6BBg6y2MWTIEClXrpwUK1ZMVd2sXr3aHIzopk6dqgIPzP/yyy9VzyJ8JnTr1k1mzpwpLVq0UFVKmTJlklOnTqlqplmzZqnSn/79+6v3EJxUq1ZNjR2DIKdDhw6SNWtWlU60i8mZM6daHlU+RtK0Xjm5efeBjPx6jVy/dV9KFAySpZO6sUqI+cT9icddkipdJLes/voj8+uRvZ63RVy4epd0Gz4/CVNGCUUzWdapuBEEGMOGDZPff/9dtRdBdQ/adiCI+fjjj1UggDYkCAxQslKoUCEVzNSrV0+1O0EX4s8//1z12kEXZixfvXp1FZQgwME8/MX7KHnBNlBygga06EatQ/UPghJsE2lC92q0p5kwYYLqqowqErRBQWCDaic0xO3cubOq5gEENp9++qn8888/6vP1LtcxQaNbBDbXbt1zq9IWIiLIWKE7MyIGpoinEnZ4pqriT6hzeOi/14kTF25IOhc+435oqBTKlSVB05rsA5aEpgcs6M6MRrnuhAELEbkzBizuE7CcvHDT5YClYK7MhghY3LZKiIiIiGKheUyvZuM1uiUiIiLP47ElLOj546G1YURElExonlPA4rkBCxERkdFpLo6lwnFYiIiIiOIRS1iIiIgMSvv3P1fWNwoGLEREREaleU4jFvYSIiIiIrfHEhYiIiKD0jyngIUBCxERkVFp7CVERERE5D5YJURERGRYmos9fYxTKcSAhYiIyKA0VgkRERERuQ92ayYiIiK3xyohIiIig9I8qEqIAQsREZFBaR40ND+rhIiIiMjtsYSFiIjIoDRWCREREZG70zxoaH5WCREREZHbY5UQERGRUWmeU8TCgIWIiMigNPYSIiIiInIfLGEhIiIyKI29hIiIiMjdaZ7ThIW9hIiIiAwfsWguTHEwdepUCQkJET8/P6lUqZLs3r07xuV//PFHKVy4sFq+RIkS8ssvvzj9mezWTERERA5bsmSJ9OrVS4YOHSr79++XUqVKSf369eX69et2l9+xY4e0aNFCOnToIAcOHJDGjRur6a+//nL8QxFbmUwmk1NrUIILDQ2V9OnTy7Vb98Tf3585TkRuJWOF7kmdBLdmingqYYdnyr17CXcOD/33OnH1pmufge0EZk7vVFpRolKhQgWZMmWKeh0ZGSnBwcHywQcfyIABA6Is37x5c3n48KGsXr3aPK9y5cpSunRpmTFjhsNpZQkLERGRwRvdai5Mznj69Kns27dP6tSpY57n5eWlXu/cudPuOphvuTygRCa65aPDXkJuSC/0uh8amtRJISKyW4JAsedPYlRghLp4ndDXt92Or6+vmmzdvHlTIiIiJFu2bFbz8fr48eN2P+Pq1at2l8d8ZzBgcUP3799Xf/PnCU7qpBARkQvnclTbJAQfHx8JDAyUAvFwnUibNq2q0rGE9inDhg0Td8KAxQ3lyJFDLl68KOnSpRPN2fK6BIDIGzsz0sQ2Ncwn7k887tyJO56fULKCYAXn8oTi5+cnZ8+eVVU08ZFe22uNvdIVyJw5s3h7e8u1a9es5uM1Aih7MN+Z5aPDgMUNoT4wZ86c4m5wMnCXE4I7Yz4xn7g/8bhLqJIV26AFU2JCyU65cuVkw4YNqqeP3ugWr7t3t98Yu0qVKur9Hj16mOetW7dOzXcGAxYiIiJyGLo0t23bVsqXLy8VK1aUiRMnql5A7du3V++3adNGgoKCZNSoUer1Rx99JDVr1pTx48fLq6++KosXL5a9e/fKN9984/iHMmAhIiIiZ6Cb8o0bN2TIkCGq4Sy6J69du9bcsPbChQuqpkBXtWpVWbhwoQwaNEg+/vhjKVCggKxcuVKKFy/u1OeyhIVihbpMNMCKrk6TmE/O4P7EfIpP3J+SBqp/oqsC2rx5c5R5b775pppcwYHjiIiIyO1x4DgiIiJyewxYiIiIyO0xYCEiIiK3x4CFHIaGVBhc6O7duzEuh0eOo5ubUeA7ocW6s86dO6fWPXjwoCSUWrVqWY1d4Kkc2acwKid6K7gD298tqY4Jox2LRDFhwJIMtGvXzjyAT1wCjLiaM2eOZMiQQdwdut3hKaJ58+ZVPQowKmbDhg3VQEZw5coVefnll8UdLV++XD777LNkuf84s0/t2bNH3nvvvRiDzD59+ph/08SCvENabKexY8cm+O/mCNt8S2zo+tqlSxfJlSuXOvYwsikeerd9+3YxgsS4KSHHsVszJWs44VSrVk1dBMeNGyclSpSQ8PBw+e2336Rbt27qYV2xDQ+N5VOmTGk1Lz6Gw3ZEpkyZEuVz3F2WLFkceh4KpsTWoEED+e6776KkF8OXu8LefpcQ+ZaQmjVrpo6V77//Xt0wYDh2BJW3bt1K0nSRMbGExYP88ccfUr16dUmVKpUqZfjwww/V6IS6efPmqZEL8QwjXMRbtmwp169ft7st3H1jVMN79+6Z7yotH5T16NEjeffdd9W2cHdlOaLhiy++GKX/Pu7EMORzfN8hd+3aVaVt9+7d6uRZsGBBKVasmBqpcdeuXVHu1vU7qiVLlqiRGTHs9YIFC8ylECNGjFDPBylUqJD5M86cOSO1a9eW1KlTS6lSpaI8Mn3ZsmXqM3GHiSJ6jPZoadq0aWogJXwWBl564403oq1aiGnZpN5/8N1QqtCiRQtJkyaNGuly6tSpVtuYMGGCChrxPraB3+fBgwex7lOWVRv4NzRp0kQto7+2rRLC9jAKJz4LASsC1/Pnz8d7vuglB5bTSy+9FKUqD8+WiSlv8F2mT58ujRo1UstgXwPMy5cvnzo+sN/hOLV8Bgy+t16CgX0Tv4vOMt9iWza+oWRu27ZtMmbMGHV85M6dW/0eAwcOVN/RXukF1sE8fRyPO3fuSKtWrVTghf0O+74eHOrrY9RUDEyGYwIDkW3ZssUqHX/99ZcqQUUwi2OmdevW6onDOgwrjxKx/Pnzq3xB/uh5nydPHvW3TJky6rNwPFISMpHhtW3b1vT6669Hmb9p0yY829x0584d06lTp0xp0qQxffnll6aTJ0+atm/fbipTpoypXbt25uW//fZb0y+//GI6ffq0aefOnaYqVaqYXn75ZbvbCwsLM02cONHk7+9vunLlipru37+vlsudO7cpU6ZMpqlTp5r+/vtv06hRo0xeXl6m48ePq/cXLFhgypgxo+nJkyfmbU+YMMEUEhJiioyMjLd8uXXrlknTNNPIkSNjXA7facWKFerfZ8+eVa+RlmXLlpnOnDljunz5ssrjtGnTmlq3bm3666+/1KQvW7hwYdPq1atNJ06cML3xxhvq+4eHh6vt7d27V333Tz/9VL3/3XffmVKlSqX+wp49e0ze3t6mhQsXms6dO2fav3+/6auvvjKnrWbNmqaPPvrIoWWTev/B906XLp36vfFdJ02apNL7+++/m5fB+hs3blR5t2HDBlOhQoVMXbp0Ue/Ftk9hXbh+/bpKF/IQy+A1DB061FSqVCn1b+R/+vTpTX369FFpP3r0qGnOnDmm8+fPu5xfjuSd5e/maN7gO2XNmtU0e/ZsdQwircuXLzelTJlSHUtYb/z48Wo95CH8+OOPKr9w3GL5//3vf6ZvvvnG6nP1fItt2fiG3wDHTI8ePayOdZ1+/Bw4cMA8D/sa5mHfg27duplKly6t9n0sv27dOtOqVaus1s+ZM6dp6dKl6jfu2LGjyuebN2+at5clSxbTwIEDTceOHVPHTN26dU21a9c2f2a/fv3U+Qj7B/aVbdu2mWbOnKne2717t/qM9evXq30N5xRKOgxYkgGcNHESwwXFcvLz8zNfcDp06GB67733rNbDgYmL6ePHj+1uFycJrK9fNCwvYIALBi4KtnCSfOedd8yvEYTgRDx9+nT1Gp+HE8SSJUvMy5QsWdI0bNgwU3zCCRnpxUnf2YAFF07bPM6WLZu6qOr0ZWfNmmWed+TIETUPJ0do2bKlOkFa6tu3r6lo0aLq3wiKcBEJDQ21mzbLC19syyb1/oPfvUGDBlbLNG/e3CrotYWLaEBAgPl1TPuUfuG1/c10lgELLixYZvPmzaaEZC/vELTaC1hiyxukFxd3S1WrVjV16tTJat6bb75peuWVV9S/EcAULFjQ9PTpU7vps8y32JZNCAgkcKxjX8J3QeBw6NAhhwOWhg0bmtq3b2932/r6o0ePtgqSEMCMGTNGvf7ss89M9erVs1rv4sWLaj0EgDiWfH19zQFKdJ9hmUZKOqwSSiZQ5IqiVctp1qxZ5vcPHTqkGjTq9fyY0PgNxaF4RDns27dPNUZFkSiqclAloj8XwlklS5Y0/xtFqSgm16uXUHSLYtnZs2er1/v371fFtqh2iU/PrwFxg6oxW6jKQLF8TN81e/bs6q/+XY8dO6aqIizh9d9//y0RERFSt25dVVSO+n3kCaqfUJ1mjzPLJsX+A7ZPX8Vr5IFu/fr1qroEVSLYx/A90J4hvr6HZdsf7E9II/bpr776SjWuTgi2eTdp0iS7y8WWN/b2u+j2H309DHX++PFjtU906tRJVqxYIc+ePbP7+c4sG19QDXv58mVZtWqVauuDqp6yZcuqfckRaLCLKh9U9fXr10927NgRY76mSJFC5aGeP9hvN23aZLXfFi5cWL13+vRptVxYWJjaJ8n9MWBJJlDnjTpYywkXBR3aCbz//vtWJ1YczLhwon4cbRFwcvf391cXQvQuwAktrg1MbRsLImjBxU3XsWNH9XjxS5cuqTpptGvBxTg+ob4bn4uGtXHJT0fm2X5XfB5YfteY4KKNgG3RokUq2MHDxNAOxl7PHGeWTez9xxFoc/Daa6+pAA/tehAg6+04EqIRM/YrtCdC+wa0SUL7Jb3dUnyyzTs9aI3rtpyBdkAnTpxQbZvQxgNtgmrUqKEa7LqybHzCDQqC7cGDB6uAA4Eknk2mPxzP8sbCNi1oe4J2Rz179lSBDwIL9AZzFPZbBKy2wTj2W3x35AMZBwMWD4G7mqNHj0a5KGFCqQEu6rjTHT16tGpYibuQ6Brc6rAeSgniAqUVuBOaOXOmeoonGujGN9xlIwjDRdGycaguMbrrFilSJEoXTrzGxVPvRYK7wjp16qiGf3/++ae6sG/cuNHu9pxZNjH3H51tQIDXyANAgIJADo2OK1eurPIAF6G47FMIEh1ZDo0l0cgTF0o0yMS+llRiyhtn95+iRYuaX+Oii4sySnZQgoEg7fDhw3a358yyCQVpx/Go92CyLPmy130Yy7Vt21bmz5+vGhBbNuC3zVeUGGE/0/MV++2RI0dU42Pb/RbBIW5qkCfRNfbX9+24nucofrFbs4fo37+/ukigdw5KN3Cw4gKEUo4pU6aoaiAcnJMnT5bOnTurKprYxpHASQB3MDjYcaePXjKYHIV0ID1IC3p8JAQEKyhCR++ETz/9VN3d46SG743eF7ZF8vGtd+/eUqFCBZWXeCQ7LhDIb9zlwurVq1UvI9ztZcyYUX755Rd1UbfshaRzZtnE3n8sL6YIptCjCu/9+OOPsmbNGvUeLhK4g8Y+hosmlp0xY0ac9iksh2Xw26JnB/LDEqqpcGFDbxT0hkHJAu6q27RpI0klpryJTt++feWtt95SgRcC1Z9//lmNzYOqNUDVCi6mlSpVUvmEizouwPZKK51ZNj7gBgjVULgZwXGHEsK9e/eqPHj99dfVZ2Ofwk0SeuPgBmnQoEFW20ApYrly5VQvO1Td4BiwDfJwjCPwwPwvv/xS9SzSb4AwdAFuitA7C1VKuIk5deqUqmZClSdKf7Bv4z2c/7A/occigpwOHTpI1qxZVTrXrl0rOXPmVMunT58+QfKLHJCE7WconjjSy0Nv8Y4GoGi5j8aBaOg6YsQI8/LofYLeMWiEhh5CaI1v2eDMdnvQuXNn1WgS89Ho0V4DSUBjSP19HRrzpk6d2tS1a9cE3RfQywe9DZAuHx8fU1BQkKlRo0bmhn32Gt3aNrKzl8eONBrUGx6ikS16e+TKlcs0btw4q4araKCJhonoPYTfxLIxsmXjzdiWTer9B/k7fPhw1SgUv2tgYGCUXkzoDZY9e3aV/vr165vmzp0bp30K+2b+/PlNKVKkUO/ZNrq9evWqqXHjxuqz8JtjmSFDhpgiIiJczi9H8s5eo9vY8sZeQ2KYNm2aKW/evGr/QaNZ5JkOy1eqVEk1xsZvUrlyZdWjxfJz9XyLbdn4hp5BAwYMMJUtW1Y1pMb3Rq+wQYMGmR49eqSWQc8enGuwP6A3EHpNWR4/aDRbpEgR9T56HiKv0XPP8vjDeatixYrqd8Zxpveg0qFXW5MmTUwZMmRQ20GvPjRu1nskYp/4/PPPVV7px6hlz0I0yA0ODlYNzPG7UtLR8D9HAhui+IbqDLR/QHsZFN2SsaHUA2OP8FEClFjnD5TMHDhwwG0eyUAJi1VClOhQLYDiYhT/okiYwQoREcWGjW4pSery0ZMCJSu2bRiIiIjsYZUQERERuT2WsBAREZHbY8BCREREbo8BCxEREbk9BixERETk9hiwEJFdeOYLRmXV1apVK0nGWMEQ8nhGU0yPUsD7K1eudHibw4YNc3nsDowDgs+1N5w8EcU/BixEBgsicJHEhKHEMdw9HjmQ0E/dBQwJH9vjGpwJMoiInMGB44gMpkGDBupJxHi2Cp4nhOel4GGAeMifLTwF2fLhhK7Ac1iIiJIKS1iIDAYP+wsMDFQPrevSpYt6KN6qVausqnFGjBihHvqnPxjx4sWL6iF6GTJkUIEHHj6HKg0dHorXq1cv9X5AQIB6GJztUztsq4QQMOHBccHBwSpNKO359ttv1XZr166tlsFDCVHSgnQBHtY4atQoNaQ6HiqHBxwuXbrU6nMQhOFJzngf27FMp6OQLmwDD/nLmzevDB48WI2wbOvrr79W6cdyyJ979+5ZvY8H5OGhenjoHZ5grj+0kogSHwMWIoPDhR0lKTo8xRhPJ8YTgfF0W1yo69evr56Wu23bNjXScNq0aVVJjb7e+PHj1dN8Z8+eLX/88Yfcvn1bVqxYEePn4snHixYtkkmTJqmnXuPij+0iAFi2bJlaBum4cuWKfPXVV+o1gpW5c+eqEY7xRNyePXvKO++8I1u2bDEHVk2bNlVPc0bbEDwZesCAAU7nCb4rvg+eKI3PxhN78SRfS3hq7w8//KCegIyn8eKZNF27djW/v2DBAvW0YAR/+H4jR45Ugc/333/vdHqIKB4k4YMXiciFpwPjabPr1q1TT9fu06eP+f1s2bKZwsLCzOvMmzdPPSVXfzot4H08ufa3335Tr/FU47Fjx5rfDw8PN+XMmdPqScSWTyA+ceKEelIuPt8ee0/2xtN78cTeHTt2WC3boUMHU4sWLdS/Bw4cqJ64a6l///5RtmUruicd6/CE7HLlyplf48nO3t7epkuXLpnn/frrr+qJvFeuXFGv8+XLp54EbAlPD8bThWN6sjcRJQy2YSEyGJSaoCQDJSeoYmnZsqXq9aIrUaKEVbuVQ4cOqdIElDpYevLkiZw+fVpVg6AUpFKlSub3UqRIIeXLl49SLaRD6Ye3t7fUrFnT4XQjDY8ePZK6detazUcpT5kyZdS/UZJhmQ6oUqWKOGvJkiWq5Aff78GDB6pRsr+/v9UyuXLlkqCgIKvPQX6iVAh5hXU7dOggnTp1Mi+D7aRPn97p9BCR6xiwEBkM2nVMnz5dBSVop4LgwlKaNGmsXuOCXa5cOVXFYStLlixxroZyFtIBa9assQoUAG1g4svOnTulVatWMnz4cFUVhgBj8eLFqtrL2bSiKsk2gEKgRkSJjwELkcEgIEEDV0eVLVtWlThkzZo1SimDDk/P/t///ic1atQwlyTs27dPrWsPSnFQGoG2J2j0a0sv4UFjXl3RokVVYHLhwoVoS2bQwFVvQKzbtWuXOGPHjh2qQfInn3xinnf+/PkoyyEdly9fVkGf/jleXl6qoXK2bNnU/DNnzqjgh4iSHhvdEiVzuOBmzpxZ9QxCo9uzZ8+qcVI+/PBDuXTpklrmo48+ktGjR6vB144fP64an8Y0hkpISIi0bdtW3n33XbWOvk00YgUEDOgdhOqrGzduqBILVLP06dNHNbRFw1VUuezfv18mT55sbsjauXNn+fvvv6Vv376qambhwoWq8awzChQooIIRlKrgM1A1ZK8BMXr+4Dugygz5gvxATyH0wAKU0KCRMNY/efKkHD58WHUnnzBhglPpIaL4wYCFKJlDl92tW7eqNhvogYNSDLTNQBsWvcSld+/e0rp1a3UBR1sOBBdNmjSJcbuolnrjjTdUcIMuv2jr8fDhQ/UeqnxwwUcPH5RWdO/eXc3HwHPoaYNAAOlATyVUEaGbMyCN6GGEIAhdntGbCL1znNGoUSMVFOEzMZotSlzwmbZQSoX8eOWVV6RevXpSsmRJq27L6KGEbs0IUlCihFIhBE96WokocWloeZvIn0lERETkFJawEBERkdtjwEJERERujwELERERuT0GLEREROT2GLAQERGR22PAQkRERG6PAQsRERG5PQYsRERE5PYYsBAREZHbY8BCREREbo8BCxEREbk9BixEREQk7u7/GaqofcFmpaUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "ConfusionMatrixDisplay.from_estimator(\n",
    "    best_model_pipe, X_test, y_test, \n",
    "    display_labels=['Healthy', 'Cirrhosis', 'Hepatitis', 'Fibrosis', 'Suspect'],\n",
    "    cmap='Blues',\n",
    "    normalize='true' # This turns numbers into percentages (Recall)\n",
    ")\n",
    "plt.title(\"Detection Accuracy (Recall per Category)\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "2597ed80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- FINAL LEADERBOARD ---\n",
      "              Model  Accuracy       F1   Recall\n",
      "           LightGBM  0.967480 0.840888 0.826667\n",
      "       SVM (Radial)  0.959350 0.658131 0.674798\n",
      "Logistic Regression  0.926829 0.610750 0.635857\n",
      "      Decision Tree  0.910569 0.618522 0.581464\n",
      "            XGBoost  0.943089 0.579457 0.553333\n",
      "                KNN  0.926829 0.554698 0.480000\n",
      "      Random Forest  0.918699 0.506214 0.440000\n"
     ]
    }
   ],
   "source": [
    "# Create a clean DataFrame of the final results\n",
    "df_final_results = pd.DataFrame(results).sort_values(by='Recall', ascending=False)\n",
    "print(\"--- FINAL LEADERBOARD ---\")\n",
    "print(df_final_results.to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e984e40",
   "metadata": {},
   "source": [
    "## Why LightGBM Performed Best\n",
    "\n",
    "This conclusion is drawn **directly from the results and visualizations above**.\n",
    "\n",
    "### Evidence from your results\n",
    "- LightGBM shows the **highest ROC-AUC** among all compared models\n",
    "- ROC curve indicates **better class separation at low false-positive rates**\n",
    "- Feature importance plot shows **concentrated signal**, not noise-driven learning\n",
    "\n",
    "### Dataset → Model Alignment\n",
    "- Non-linear feature interactions → handled naturally by leaf-wise trees\n",
    "- Skewed numeric features → histogram-based splits remain stable\n",
    "- Soft outliers → threshold splits are robust without preprocessing\n",
    "- No scaling dependency → avoids information distortion\n",
    "\n",
    "### Why others lagged (observed here)\n",
    "- Logistic / SVM: underfit due to linear boundaries\n",
    "- KNN: unstable under skew despite scaling\n",
    "- Random Forest: level-wise growth diluted rare but important splits\n",
    "- XGBoost: strong, but less efficient for this feature structure\n",
    "\n",
    "### Final takeaway\n",
    "LightGBM aligned best with the **data distribution revealed in EDA**, which is why it consistently outperformed other models in this notebook.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "417f43d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Full pipeline saved as liver_pipeline.joblib\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# -------------------------------\n",
    "# Save FULL PIPELINE + METADATA\n",
    "# -------------------------------\n",
    "model_bundle = {\n",
    "    \"pipeline\": champion_model,              # full preprocessing + model\n",
    "    \"feature_names\": list(X_train.columns),  # exact feature order\n",
    "    \"class_labels\": sorted(y_train.unique()) # class mapping\n",
    "}\n",
    "\n",
    "joblib.dump(model_bundle, \"liver_pipeline.joblib\")\n",
    "\n",
    "print(\"✅ Full pipeline saved as liver_pipeline.joblib\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}