ipynb_1_data_preparation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CodeStates AI Section2 Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AI_15_정경재"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Project : Hypertension Predictive Model\n",
    "- 프로젝트 : 한국형 고혈압 예측 모델 개발\n",
    "    - 고혈압 진단기준에 따른 차이가 있을까?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# from pandas_profiling import ProfileReport\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.family'] = 'Malgun Gothic'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. DATA 불러오기\n",
    "- .sas7bdat -> csv(한국어) -> csv(utf-8) -> pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\dankj\\anaconda3\\envs\\machine-inter\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3553: DtypeWarning: Columns (111,317,333,387,406,512,522,534,663,666) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(8110, 831)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_2019 = pd.read_csv('data/2019_utf8.csv')\n",
    "raw_2019.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\dankj\\anaconda3\\envs\\machine-inter\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3553: DtypeWarning: Columns (140,192,301,360,480,490,502,594,597) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(7359, 762)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_2020 = pd.read_csv('data/2020_utf8.csv')\n",
    "raw_2020.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Feature & Target 정의, 추출, 병합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ID',\n",
       " 'year',\n",
       " 'sex',\n",
       " 'age',\n",
       " 'DI1_2',\n",
       " 'DI2_2',\n",
       " 'DE1_31',\n",
       " 'DE1_32',\n",
       " 'DE1_dg',\n",
       " 'BD1_11',\n",
       " 'BD2_1',\n",
       " 'sm_presnt',\n",
       " 'HE_HPfh1',\n",
       " 'HE_HPfh2',\n",
       " 'HE_sbp',\n",
       " 'HE_dbp',\n",
       " 'HE_wt',\n",
       " 'HE_wc',\n",
       " 'HE_BMI',\n",
       " 'HE_glu',\n",
       " 'HE_HbA1c',\n",
       " 'HE_chol',\n",
       " 'HE_TG']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo_list = ['ID',\n",
    "             'year',\n",
    "             'sex',\n",
    "             'age']\n",
    "ques_list = ['DI1_2',\n",
    "             'DI2_2',\n",
    "             'DE1_31',\n",
    "             'DE1_32',\n",
    "             'DE1_dg',\n",
    "             'BD1_11',\n",
    "             'BD2_1',\n",
    "             'sm_presnt']\n",
    "exam_list = ['HE_HPfh1',\n",
    "             'HE_HPfh2',\n",
    "             'HE_sbp',\n",
    "             'HE_dbp',\n",
    "             'HE_wt',\n",
    "             'HE_wc',\n",
    "             'HE_BMI',\n",
    "             'HE_glu',\n",
    "             'HE_HbA1c',\n",
    "             'HE_chol',\n",
    "             'HE_TG']\n",
    "column_list = demo_list+ques_list+exam_list\n",
    "column_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8110, 23)\n",
      "(7359, 23)\n"
     ]
    }
   ],
   "source": [
    "raw2_2019 = raw_2019[column_list]\n",
    "raw2_2020 = raw_2020[column_list]\n",
    "print(raw2_2019.shape)\n",
    "print(raw2_2020.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15469, 23)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.concat([raw2_2019,raw2_2020],ignore_index=True)\n",
    "raw_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.ID.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 결측치 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15469 entries, 0 to 15468\n",
      "Data columns (total 23 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   ID         15469 non-null  object\n",
      " 1   year       15469 non-null  int64 \n",
      " 2   sex        15469 non-null  int64 \n",
      " 3   age        15469 non-null  int64 \n",
      " 4   DI1_2      15469 non-null  object\n",
      " 5   DI2_2      15469 non-null  object\n",
      " 6   DE1_31     15469 non-null  object\n",
      " 7   DE1_32     15469 non-null  object\n",
      " 8   DE1_dg     15469 non-null  object\n",
      " 9   BD1_11     15469 non-null  object\n",
      " 10  BD2_1      15469 non-null  object\n",
      " 11  sm_presnt  15469 non-null  object\n",
      " 12  HE_HPfh1   15469 non-null  object\n",
      " 13  HE_HPfh2   15469 non-null  object\n",
      " 14  HE_sbp     15469 non-null  object\n",
      " 15  HE_dbp     15469 non-null  object\n",
      " 16  HE_wt      15469 non-null  object\n",
      " 17  HE_wc      15469 non-null  object\n",
      " 18  HE_BMI     15469 non-null  object\n",
      " 19  HE_glu     15469 non-null  object\n",
      " 20  HE_HbA1c   15469 non-null  object\n",
      " 21  HE_chol    15469 non-null  object\n",
      " 22  HE_TG      15469 non-null  object\n",
      "dtypes: int64(3), object(20)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15469 entries, 0 to 15468\n",
      "Data columns (total 23 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   ID         15469 non-null  object\n",
      " 1   year       15469 non-null  int64 \n",
      " 2   sex        15469 non-null  int64 \n",
      " 3   age        15469 non-null  int64 \n",
      " 4   DI1_2      14802 non-null  object\n",
      " 5   DI2_2      14802 non-null  object\n",
      " 6   DE1_31     14802 non-null  object\n",
      " 7   DE1_32     14802 non-null  object\n",
      " 8   DE1_dg     14802 non-null  object\n",
      " 9   BD1_11     14802 non-null  object\n",
      " 10  BD2_1      14802 non-null  object\n",
      " 11  sm_presnt  12048 non-null  object\n",
      " 12  HE_HPfh1   13409 non-null  object\n",
      " 13  HE_HPfh2   13409 non-null  object\n",
      " 14  HE_sbp     13479 non-null  object\n",
      " 15  HE_dbp     13479 non-null  object\n",
      " 16  HE_wt      14760 non-null  object\n",
      " 17  HE_wc      14073 non-null  object\n",
      " 18  HE_BMI     14659 non-null  object\n",
      " 19  HE_glu     13101 non-null  object\n",
      " 20  HE_HbA1c   13097 non-null  object\n",
      " 21  HE_chol    13101 non-null  object\n",
      " 22  HE_TG      13101 non-null  object\n",
      "dtypes: int64(3), object(20)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "def fillnan(value):\n",
    "    if value == '.':\n",
    "        value = np.nan\n",
    "    return value\n",
    "nanfill = raw_data.copy()\n",
    "filled = nanfill.applymap(fillnan)\n",
    "filled.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15469 entries, 0 to 15468\n",
      "Data columns (total 23 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   ID         15469 non-null  object \n",
      " 1   year       15469 non-null  int64  \n",
      " 2   sex        15469 non-null  int64  \n",
      " 3   age        15469 non-null  int64  \n",
      " 4   DI1_2      14802 non-null  float64\n",
      " 5   DI2_2      14802 non-null  float64\n",
      " 6   DE1_31     14802 non-null  float64\n",
      " 7   DE1_32     14802 non-null  float64\n",
      " 8   DE1_dg     14802 non-null  float64\n",
      " 9   BD1_11     14802 non-null  float64\n",
      " 10  BD2_1      14802 non-null  float64\n",
      " 11  sm_presnt  12048 non-null  float64\n",
      " 12  HE_HPfh1   13409 non-null  float64\n",
      " 13  HE_HPfh2   13409 non-null  float64\n",
      " 14  HE_sbp     13479 non-null  float64\n",
      " 15  HE_dbp     13479 non-null  float64\n",
      " 16  HE_wt      14760 non-null  float64\n",
      " 17  HE_wc      14073 non-null  float64\n",
      " 18  HE_BMI     14659 non-null  float64\n",
      " 19  HE_glu     13101 non-null  float64\n",
      " 20  HE_HbA1c   13097 non-null  float64\n",
      " 21  HE_chol    13101 non-null  float64\n",
      " 22  HE_TG      13101 non-null  float64\n",
      "dtypes: float64(19), int64(3), object(1)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "fix1 = filled.copy()\n",
    "fix1.iloc[:,1:] = fix1.iloc[:,1:].apply(pd.to_numeric)\n",
    "fix1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11573 entries, 0 to 11572\n",
      "Data columns (total 23 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   ID         11573 non-null  object \n",
      " 1   year       11573 non-null  int64  \n",
      " 2   sex        11573 non-null  int64  \n",
      " 3   age        11573 non-null  int64  \n",
      " 4   DI1_2      11573 non-null  float64\n",
      " 5   DI2_2      11573 non-null  float64\n",
      " 6   DE1_31     11573 non-null  float64\n",
      " 7   DE1_32     11573 non-null  float64\n",
      " 8   DE1_dg     11573 non-null  float64\n",
      " 9   BD1_11     11573 non-null  float64\n",
      " 10  BD2_1      11573 non-null  float64\n",
      " 11  sm_presnt  11573 non-null  float64\n",
      " 12  HE_HPfh1   11573 non-null  float64\n",
      " 13  HE_HPfh2   11573 non-null  float64\n",
      " 14  HE_sbp     11573 non-null  float64\n",
      " 15  HE_dbp     11573 non-null  float64\n",
      " 16  HE_wt      11573 non-null  float64\n",
      " 17  HE_wc      11573 non-null  float64\n",
      " 18  HE_BMI     11573 non-null  float64\n",
      " 19  HE_glu     11573 non-null  float64\n",
      " 20  HE_HbA1c   11573 non-null  float64\n",
      " 21  HE_chol    11573 non-null  float64\n",
      " 22  HE_TG      11573 non-null  float64\n",
      "dtypes: float64(19), int64(3), object(1)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "fix2 = fix1.copy()\n",
    "fix2 = fix2.dropna().reset_index(drop=True)\n",
    "fix2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Target Data 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>DI1_2</th>\n",
       "      <th>HE_sbp</th>\n",
       "      <th>HE_dbp</th>\n",
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       "  </thead>\n",
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      "text/plain": [
       "   DI1_2  HE_sbp  HE_dbp\n",
       "0    1.0   122.0    84.0\n",
       "1    8.0   111.0    73.0\n",
       "2    8.0   125.0    85.0\n",
       "3    8.0   109.0    77.0\n",
       "4    1.0   131.0    77.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_list = ['DI1_2','HE_sbp','HE_dbp']\n",
    "target_df = fix2[target_list]\n",
    "target_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>DI1_2</th>\n",
       "      <th>HE_sbp</th>\n",
       "      <th>HE_dbp</th>\n",
       "      <th>bp_drug</th>\n",
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      "text/plain": [
       "   DI1_2  HE_sbp  HE_dbp  bp_drug\n",
       "0    1.0   122.0    84.0        1\n",
       "1    8.0   111.0    73.0        0\n",
       "2    8.0   125.0    85.0        0\n",
       "3    8.0   109.0    77.0        0\n",
       "4    1.0   131.0    77.0        1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bp_drug = []\n",
    "for i in fix2.DI1_2:\n",
    "    if i < 5:\n",
    "        bp_drug.append(1)\n",
    "    else:\n",
    "        bp_drug.append(0)\n",
    "bp_drug = np.array(bp_drug)\n",
    "\n",
    "target_df2 = target_df.copy()\n",
    "target_df2['bp_drug'] = bp_drug\n",
    "target_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DI1_2</th>\n",
       "      <th>HE_sbp</th>\n",
       "      <th>HE_dbp</th>\n",
       "      <th>bp_drug</th>\n",
       "      <th>HBP_US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DI1_2  HE_sbp  HE_dbp  bp_drug  HBP_US\n",
       "0    1.0   122.0    84.0        1       1\n",
       "1    8.0   111.0    73.0        0       0\n",
       "2    8.0   125.0    85.0        0       1\n",
       "3    8.0   109.0    77.0        0       0\n",
       "4    1.0   131.0    77.0        1       1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hbp_us = []\n",
    "for i in range(len(target_df2)):\n",
    "    if (target_df2.loc[i,'HE_sbp']<130) & (target_df2.loc[i,'HE_dbp']<80) & (target_df2.loc[i,'bp_drug']==0):\n",
    "        hbp_us.append(0)\n",
    "    else:\n",
    "        hbp_us.append(1)\n",
    "hbp_us = np.array(hbp_us)\n",
    "target_df3 = target_df2.copy()\n",
    "target_df3['HBP_US'] = hbp_us\n",
    "target_df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>DI1_2</th>\n",
       "      <th>HE_sbp</th>\n",
       "      <th>HE_dbp</th>\n",
       "      <th>bp_drug</th>\n",
       "      <th>HBP_US</th>\n",
       "      <th>HBP_EU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DI1_2  HE_sbp  HE_dbp  bp_drug  HBP_US  HBP_EU\n",
       "0    1.0   122.0    84.0        1       1       1\n",
       "1    8.0   111.0    73.0        0       0       0\n",
       "2    8.0   125.0    85.0        0       1       0\n",
       "3    8.0   109.0    77.0        0       0       0\n",
       "4    1.0   131.0    77.0        1       1       1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hbp_eu = []\n",
    "for i in range(len(target_df2)):\n",
    "    if (target_df2.loc[i,'HE_sbp']<140) & (target_df2.loc[i,'HE_dbp']<90) & (target_df2.loc[i,'bp_drug']==0):\n",
    "        hbp_eu.append(0)\n",
    "    else:\n",
    "        hbp_eu.append(1)\n",
    "hbp_eu = np.array(hbp_eu)\n",
    "target_df4 = target_df3.copy()\n",
    "target_df4['HBP_EU'] = hbp_eu\n",
    "target_df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11573, 6)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_df4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11573 entries, 0 to 11572\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   DI1_2    11573 non-null  float64\n",
      " 1   HE_sbp   11573 non-null  float64\n",
      " 2   HE_dbp   11573 non-null  float64\n",
      " 3   bp_drug  11573 non-null  int32  \n",
      " 4   HBP_US   11573 non-null  int32  \n",
      " 5   HBP_EU   11573 non-null  int32  \n",
      "dtypes: float64(3), int32(3)\n",
      "memory usage: 407.0 KB\n"
     ]
    }
   ],
   "source": [
    "target_df4.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cfm = confusion_matrix(target_df4.HBP_US,target_df4.HBP_EU)\n",
    "group_names = ['정상','-','당뇨병 전단계','당뇨병']\n",
    "group_counts = ['{0:0.0f}'.format(value) for value in cfm.flatten()]\n",
    "group_percentages = ['{0:.2%}'.format(value) for value in cfm.flatten()/np.sum(cfm)]\n",
    "labels = [f'{v1}\\n\\n{v2}\\n\\n{v3}' for v1,v2,v3 in zip(group_names,group_counts,group_percentages)]\n",
    "labels = np.asarray(labels).reshape(2,2)\n",
    "tick = ['정상','고혈압']\n",
    "sns.heatmap(cfm, annot=labels, fmt='',cmap='Purples',xticklabels=tick,yticklabels=tick)\n",
    "plt.xlabel('EU criteria')\n",
    "plt.ylabel('US criteria')\n",
    "plt.title('진단기준에 따른 분포\\n[US : 130mmHg/80mmHg]\\n[EU : 140mmHg/90mmHg]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11573, 23)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11573, 20)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_df = fix2.drop(columns=target_list,axis=1)\n",
    "feature_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5379</th>\n",
       "      <td>N760288401</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3912</th>\n",
       "      <td>H790227201</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7093</th>\n",
       "      <td>A835210202</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6998</th>\n",
       "      <td>A831291801</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5769</th>\n",
       "      <td>O763317302</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  year  sex  age\n",
       "5379  N760288401  2019    2   53\n",
       "3912  H790227201  2019    1   42\n",
       "7093  A835210202  2020    2   52\n",
       "6998  A831291801  2020    1   66\n",
       "5769  O763317302  2019    2   67"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_df0 = feature_df[demo_list]\n",
    "feature_df0.columns = ['ID','year','sex','age']\n",
    "feature_df0.sample(5,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>heavy_drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5379</th>\n",
       "      <td>N760288401</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3912</th>\n",
       "      <td>H790227201</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7093</th>\n",
       "      <td>A835210202</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6998</th>\n",
       "      <td>A831291801</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5769</th>\n",
       "      <td>O763317302</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  year  sex  age  heavy_drink\n",
       "5379  N760288401  2019    2   53            0\n",
       "3912  H790227201  2019    1   42            1\n",
       "7093  A835210202  2020    2   52            0\n",
       "6998  A831291801  2020    1   66            1\n",
       "5769  O763317302  2019    2   67            0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heavy_drink = []\n",
    "for i in range(len(feature_df)):\n",
    "    if (feature_df.loc[i,'sex']==1) & (feature_df.loc[i,'BD1_11'] in [3,4,5,6]) & (feature_df.loc[i,'BD2_1'] in [4,5]):\n",
    "        heavy_drink.append(1)\n",
    "    elif (feature_df.loc[i,'sex']==2) & (feature_df.loc[i,'BD1_11'] in [3,4,5,6]) & (feature_df.loc[i,'BD2_1'] in [3,4,5]):\n",
    "        heavy_drink.append(1)\n",
    "    else:\n",
    "        heavy_drink.append(0)\n",
    "heavy_drink = np.array(heavy_drink)\n",
    "\n",
    "feature_df1 = feature_df0.copy()\n",
    "feature_df1['heavy_drink'] = heavy_drink\n",
    "feature_df1.sample(5,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>heavy_drink</th>\n",
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       "      <th>5379</th>\n",
       "      <td>N760288401</td>\n",
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       "      <th>3912</th>\n",
       "      <td>H790227201</td>\n",
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       "      <td>66</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "              ID  year  sex  age  heavy_drink  smoke\n",
       "5379  N760288401  2019    2   53            0      0\n",
       "3912  H790227201  2019    1   42            1      0\n",
       "7093  A835210202  2020    2   52            0      0\n",
       "6998  A831291801  2020    1   66            1      0\n",
       "5769  O763317302  2019    2   67            0      0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_df2 = feature_df1.copy()\n",
    "feature_df2['smoke'] = feature_df['sm_presnt'].astype(int)\n",
    "feature_df2.sample(5,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
       "      <th>heavy_drink</th>\n",
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       "      <th>genetic_hbp</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5379</th>\n",
       "      <td>N760288401</td>\n",
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       "      <td>2</td>\n",
       "      <td>53</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
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       "    <tr>\n",
       "      <th>3912</th>\n",
       "      <td>H790227201</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7093</th>\n",
       "      <td>A835210202</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6998</th>\n",
       "      <td>A831291801</td>\n",
       "      <td>2020</td>\n",
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       "      <td>66</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5769</th>\n",
       "      <td>O763317302</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  year  sex  age  heavy_drink  smoke  genetic_hbp\n",
       "5379  N760288401  2019    2   53            0      0          1.5\n",
       "3912  H790227201  2019    1   42            1      0          1.5\n",
       "7093  A835210202  2020    2   52            0      0          2.0\n",
       "6998  A831291801  2020    1   66            1      0          1.0\n",
       "5769  O763317302  2019    2   67            0      0          1.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genetic = []\n",
    "for i in range(len(feature_df)):\n",
    "    if (feature_df.loc[i,'HE_HPfh1']==1) & (feature_df.loc[i,'HE_HPfh2']==1):\n",
    "        genetic.append(2)\n",
    "    elif (feature_df.loc[i,'HE_HPfh1']==1) & (feature_df.loc[i,'HE_HPfh2']!=1):\n",
    "        genetic.append(1.5)\n",
    "    elif (feature_df.loc[i,'HE_HPfh1']!=1) & (feature_df.loc[i,'HE_HPfh2']==1):\n",
    "        genetic.append(1.5)\n",
    "    else:\n",
    "        genetic.append(1)\n",
    "genetic = np.array(genetic)\n",
    "feature_df3 = feature_df2.copy()\n",
    "feature_df3['genetic_hbp'] = genetic\n",
    "feature_df3.sample(5,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
       "      <th>heavy_drink</th>\n",
       "      <th>smoke</th>\n",
       "      <th>genetic_hbp</th>\n",
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       "      <th>waist</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5379</th>\n",
       "      <td>N760288401</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>68.7</td>\n",
       "      <td>89.5</td>\n",
       "      <td>25.6</td>\n",
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       "    <tr>\n",
       "      <th>3912</th>\n",
       "      <td>H790227201</td>\n",
       "      <td>2019</td>\n",
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       "      <td>42</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>63.3</td>\n",
       "      <td>88.1</td>\n",
       "      <td>23.7</td>\n",
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       "    <tr>\n",
       "      <th>7093</th>\n",
       "      <td>A835210202</td>\n",
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       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.6</td>\n",
       "      <td>83.2</td>\n",
       "      <td>23.0</td>\n",
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       "    <tr>\n",
       "      <th>6998</th>\n",
       "      <td>A831291801</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.4</td>\n",
       "      <td>84.3</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>83.6</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  year  sex  age  heavy_drink  smoke  genetic_hbp  weight  \\\n",
       "5379  N760288401  2019    2   53            0      0          1.5    68.7   \n",
       "3912  H790227201  2019    1   42            1      0          1.5    63.3   \n",
       "7093  A835210202  2020    2   52            0      0          2.0    57.6   \n",
       "6998  A831291801  2020    1   66            1      0          1.0    67.4   \n",
       "5769  O763317302  2019    2   67            0      0          1.0    50.1   \n",
       "\n",
       "      waist   BMI  \n",
       "5379   89.5  25.6  \n",
       "3912   88.1  23.7  \n",
       "7093   83.2  23.0  \n",
       "6998   84.3  22.0  \n",
       "5769   83.6  20.6  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exam_list0 = ['HE_wt','HE_wc','HE_BMI']\n",
    "exam_list1 = ['weight','waist','BMI']\n",
    "feature_df4 = feature_df3.copy()\n",
    "feature_df4[exam_list1] = feature_df[exam_list0].astype(float)\n",
    "feature_df4.sample(5,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5379</th>\n",
       "      <td>N760288401</td>\n",
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       "      <td>53</td>\n",
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       "      <td>68.7</td>\n",
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       "      <th>3912</th>\n",
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       "      <td>63.3</td>\n",
       "      <td>88.1</td>\n",
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       "      <th>7093</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>57.6</td>\n",
       "      <td>83.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6998</th>\n",
       "      <td>A831291801</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.4</td>\n",
       "      <td>84.3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5769</th>\n",
       "      <td>O763317302</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.1</td>\n",
       "      <td>83.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  year  sex  age  heavy_drink  smoke  genetic_hbp  weight  \\\n",
       "5379  N760288401  2019    2   53            0      0          1.5    68.7   \n",
       "3912  H790227201  2019    1   42            1      0          1.5    63.3   \n",
       "7093  A835210202  2020    2   52            0      0          2.0    57.6   \n",
       "6998  A831291801  2020    1   66            1      0          1.0    67.4   \n",
       "5769  O763317302  2019    2   67            0      0          1.0    50.1   \n",
       "\n",
       "      waist   BMI  diabetes  \n",
       "5379   89.5  25.6         0  \n",
       "3912   88.1  23.7         0  \n",
       "7093   83.2  23.0         0  \n",
       "6998   84.3  22.0         0  \n",
       "5769   83.6  20.6         1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes = []\n",
    "for i in range(len(feature_df)):\n",
    "    if (feature_df.loc[i,'DE1_31']!=1) & (feature_df.loc[i,'DE1_32']!=1) & (feature_df.loc[i,'DE1_dg']!=1) & (feature_df.loc[i,'HE_glu']<126) & (feature_df.loc[i,'HE_HbA1c']<6.5):\n",
    "        diabetes.append(0)\n",
    "    else:\n",
    "        diabetes.append(1)\n",
    "diabetes = np.array(diabetes)\n",
    "\n",
    "feature_df5 = feature_df4.copy()\n",
    "feature_df5['diabetes'] = diabetes\n",
    "feature_df5.sample(5,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5379</th>\n",
       "      <td>N760288401</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>68.7</td>\n",
       "      <td>89.5</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3912</th>\n",
       "      <td>H790227201</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>63.3</td>\n",
       "      <td>88.1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7093</th>\n",
       "      <td>A835210202</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.6</td>\n",
       "      <td>83.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6998</th>\n",
       "      <td>A831291801</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.4</td>\n",
       "      <td>84.3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5769</th>\n",
       "      <td>O763317302</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.1</td>\n",
       "      <td>83.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  year  sex  age  heavy_drink  smoke  genetic_hbp  weight  \\\n",
       "5379  N760288401  2019    2   53            0      0          1.5    68.7   \n",
       "3912  H790227201  2019    1   42            1      0          1.5    63.3   \n",
       "7093  A835210202  2020    2   52            0      0          2.0    57.6   \n",
       "6998  A831291801  2020    1   66            1      0          1.0    67.4   \n",
       "5769  O763317302  2019    2   67            0      0          1.0    50.1   \n",
       "\n",
       "      waist   BMI  diabetes  hyper_chol  \n",
       "5379   89.5  25.6         0           0  \n",
       "3912   88.1  23.7         0           1  \n",
       "7093   83.2  23.0         0           0  \n",
       "6998   84.3  22.0         0           0  \n",
       "5769   83.6  20.6         1           0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyper_chol = []\n",
    "for i in range(len(feature_df)):\n",
    "    if (feature_df.loc[i,'DI2_2'] > 4) & (feature_df.loc[i,'HE_chol'] < 240):\n",
    "        hyper_chol.append(0)\n",
    "    else:\n",
    "        hyper_chol.append(1)\n",
    "hyper_chol = np.array(hyper_chol)\n",
    "feature_df6 = feature_df5.copy()\n",
    "feature_df6['hyper_chol'] = hyper_chol\n",
    "feature_df6.sample(5,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>heavy_drink</th>\n",
       "      <th>smoke</th>\n",
       "      <th>genetic_hbp</th>\n",
       "      <th>weight</th>\n",
       "      <th>waist</th>\n",
       "      <th>BMI</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>hyper_chol</th>\n",
       "      <th>triglycerides</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5379</th>\n",
       "      <td>N760288401</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>68.7</td>\n",
       "      <td>89.5</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3912</th>\n",
       "      <td>H790227201</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>63.3</td>\n",
       "      <td>88.1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7093</th>\n",
       "      <td>A835210202</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.6</td>\n",
       "      <td>83.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6998</th>\n",
       "      <td>A831291801</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.4</td>\n",
       "      <td>84.3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5769</th>\n",
       "      <td>O763317302</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.1</td>\n",
       "      <td>83.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  year  sex  age  heavy_drink  smoke  genetic_hbp  weight  \\\n",
       "5379  N760288401  2019    2   53            0      0          1.5    68.7   \n",
       "3912  H790227201  2019    1   42            1      0          1.5    63.3   \n",
       "7093  A835210202  2020    2   52            0      0          2.0    57.6   \n",
       "6998  A831291801  2020    1   66            1      0          1.0    67.4   \n",
       "5769  O763317302  2019    2   67            0      0          1.0    50.1   \n",
       "\n",
       "      waist   BMI  diabetes  hyper_chol  triglycerides  \n",
       "5379   89.5  25.6         0           0           55.0  \n",
       "3912   88.1  23.7         0           1           78.0  \n",
       "7093   83.2  23.0         0           0           83.0  \n",
       "6998   84.3  22.0         0           0          132.0  \n",
       "5769   83.6  20.6         1           0           62.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_df7 = feature_df6.copy()\n",
    "feature_df7['triglycerides'] = feature_df['HE_TG'].astype(float)\n",
    "feature_df7.sample(5,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature : (11573, 13)\n",
      "target : (11573, 6)\n"
     ]
    }
   ],
   "source": [
    "print('feature : {}'.format(feature_df7.shape))\n",
    "print('target : {}'.format(target_df4.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11573 entries, 0 to 11572\n",
      "Data columns (total 15 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   ID             11573 non-null  object \n",
      " 1   year           11573 non-null  int64  \n",
      " 2   sex            11573 non-null  int64  \n",
      " 3   age            11573 non-null  int64  \n",
      " 4   heavy_drink    11573 non-null  int32  \n",
      " 5   smoke          11573 non-null  int32  \n",
      " 6   genetic_hbp    11573 non-null  float64\n",
      " 7   weight         11573 non-null  float64\n",
      " 8   waist          11573 non-null  float64\n",
      " 9   BMI            11573 non-null  float64\n",
      " 10  diabetes       11573 non-null  int32  \n",
      " 11  hyper_chol     11573 non-null  int32  \n",
      " 12  triglycerides  11573 non-null  float64\n",
      " 13  HBP_US         11573 non-null  int32  \n",
      " 14  HBP_EU         11573 non-null  int32  \n",
      "dtypes: float64(5), int32(6), int64(3), object(1)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "fix_data0 = feature_df7.copy()\n",
    "fix_data0[['HBP_US','HBP_EU']] = target_df4[['HBP_US','HBP_EU']]\n",
    "fix_data0.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Github 출력 오류로 인한 비활성화\n",
    "# profile0 = ProfileReport(fix_data0,minimal=True)\n",
    "# profile0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 상관관계확인 & 다중공선성(노이즈 제거)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  background-color: #f5c4ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa974_row9_col1 {\n",
       "  background-color: #f4c5ad;\n",
       "  color: #000000;\n",
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       "  background-color: #efcebd;\n",
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       "#T_aa974_row10_col1 {\n",
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       "#T_aa974_row11_col0, #T_aa974_row12_col1 {\n",
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       "}\n",
       "#T_aa974_row11_col1, #T_aa974_row12_col0 {\n",
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       "</style>\n",
       "<table id=\"T_aa974_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >HBP_US</th>\n",
       "      <th class=\"col_heading level0 col1\" >HBP_EU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row0\" class=\"row_heading level0 row0\" >sex</th>\n",
       "      <td id=\"T_aa974_row0_col0\" class=\"data row0 col0\" >-0.137865</td>\n",
       "      <td id=\"T_aa974_row0_col1\" class=\"data row0 col1\" >-0.054865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row1\" class=\"row_heading level0 row1\" >age</th>\n",
       "      <td id=\"T_aa974_row1_col0\" class=\"data row1 col0\" >0.361483</td>\n",
       "      <td id=\"T_aa974_row1_col1\" class=\"data row1 col1\" >0.456246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row2\" class=\"row_heading level0 row2\" >heavy_drink</th>\n",
       "      <td id=\"T_aa974_row2_col0\" class=\"data row2 col0\" >0.027241</td>\n",
       "      <td id=\"T_aa974_row2_col1\" class=\"data row2 col1\" >-0.028808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row3\" class=\"row_heading level0 row3\" >smoke</th>\n",
       "      <td id=\"T_aa974_row3_col0\" class=\"data row3 col0\" >0.013057</td>\n",
       "      <td id=\"T_aa974_row3_col1\" class=\"data row3 col1\" >-0.024246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row4\" class=\"row_heading level0 row4\" >genetic_hbp</th>\n",
       "      <td id=\"T_aa974_row4_col0\" class=\"data row4 col0\" >0.058492</td>\n",
       "      <td id=\"T_aa974_row4_col1\" class=\"data row4 col1\" >0.044765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row5\" class=\"row_heading level0 row5\" >weight</th>\n",
       "      <td id=\"T_aa974_row5_col0\" class=\"data row5 col0\" >0.190571</td>\n",
       "      <td id=\"T_aa974_row5_col1\" class=\"data row5 col1\" >0.080518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row6\" class=\"row_heading level0 row6\" >waist</th>\n",
       "      <td id=\"T_aa974_row6_col0\" class=\"data row6 col0\" >0.363874</td>\n",
       "      <td id=\"T_aa974_row6_col1\" class=\"data row6 col1\" >0.315237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row7\" class=\"row_heading level0 row7\" >BMI</th>\n",
       "      <td id=\"T_aa974_row7_col0\" class=\"data row7 col0\" >0.284717</td>\n",
       "      <td id=\"T_aa974_row7_col1\" class=\"data row7 col1\" >0.226068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row8\" class=\"row_heading level0 row8\" >diabetes</th>\n",
       "      <td id=\"T_aa974_row8_col0\" class=\"data row8 col0\" >0.190552</td>\n",
       "      <td id=\"T_aa974_row8_col1\" class=\"data row8 col1\" >0.251410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row9\" class=\"row_heading level0 row9\" >hyper_chol</th>\n",
       "      <td id=\"T_aa974_row9_col0\" class=\"data row9 col0\" >0.194117</td>\n",
       "      <td id=\"T_aa974_row9_col1\" class=\"data row9 col1\" >0.246635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row10\" class=\"row_heading level0 row10\" >triglycerides</th>\n",
       "      <td id=\"T_aa974_row10_col0\" class=\"data row10 col0\" >0.167029</td>\n",
       "      <td id=\"T_aa974_row10_col1\" class=\"data row10 col1\" >0.115099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row11\" class=\"row_heading level0 row11\" >HBP_US</th>\n",
       "      <td id=\"T_aa974_row11_col0\" class=\"data row11 col0\" >1.000000</td>\n",
       "      <td id=\"T_aa974_row11_col1\" class=\"data row11 col1\" >0.664088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aa974_level0_row12\" class=\"row_heading level0 row12\" >HBP_EU</th>\n",
       "      <td id=\"T_aa974_row12_col0\" class=\"data row12 col0\" >0.664088</td>\n",
       "      <td id=\"T_aa974_row12_col1\" class=\"data row12 col1\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1eeb35b1188>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr0 = fix_data0.corr(method='pearson')\n",
    "corr0.iloc[1:,-2:].style.background_gradient(cmap='coolwarm',vmin=-1,vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  background-color: #b40426;\n",
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       "  background-color: #c6d6f1;\n",
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       "  background-color: #d6dce4;\n",
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       "  background-color: #cad8ef;\n",
       "  color: #000000;\n",
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       "#T_b34d4_row1_col5, #T_b34d4_row5_col1 {\n",
       "  background-color: #bed2f6;\n",
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       "  background-color: #f6bda2;\n",
       "  color: #000000;\n",
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       "  background-color: #f6bfa6;\n",
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       "  background-color: #e3d9d3;\n",
       "  color: #000000;\n",
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       "  background-color: #f3c8b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row2_col6, #T_b34d4_row5_col8, #T_b34d4_row6_col2, #T_b34d4_row8_col5 {\n",
       "  background-color: #e7d7ce;\n",
       "  color: #000000;\n",
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       "  background-color: #d8dce2;\n",
       "  color: #000000;\n",
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       "  background-color: #eed0c0;\n",
       "  color: #000000;\n",
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       "#T_b34d4_row3_col4, #T_b34d4_row4_col3 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row3_col5, #T_b34d4_row5_col3 {\n",
       "  background-color: #f1ccb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row3_col6, #T_b34d4_row6_col3 {\n",
       "  background-color: #e9d5cb;\n",
       "  color: #000000;\n",
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       "  background-color: #dfdbd9;\n",
       "  color: #000000;\n",
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       "#T_b34d4_row3_col10, #T_b34d4_row10_col3 {\n",
       "  background-color: #efcebd;\n",
       "  color: #000000;\n",
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       "#T_b34d4_row4_col5, #T_b34d4_row5_col4 {\n",
       "  background-color: #e5d8d1;\n",
       "  color: #000000;\n",
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       "#T_b34d4_row4_col6, #T_b34d4_row6_col4 {\n",
       "  background-color: #dcdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row4_col7, #T_b34d4_row7_col4 {\n",
       "  background-color: #e4d9d2;\n",
       "  color: #000000;\n",
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       "#T_b34d4_row4_col8, #T_b34d4_row8_col4 {\n",
       "  background-color: #d7dce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row5_col6, #T_b34d4_row6_col5 {\n",
       "  background-color: #d65244;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b34d4_row5_col7, #T_b34d4_row7_col5 {\n",
       "  background-color: #d55042;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b34d4_row5_col9, #T_b34d4_row9_col5 {\n",
       "  background-color: #dddcdc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row5_col10, #T_b34d4_row10_col5 {\n",
       "  background-color: #f5c4ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row6_col7, #T_b34d4_row7_col6 {\n",
       "  background-color: #cb3e38;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b34d4_row6_col8, #T_b34d4_row8_col6 {\n",
       "  background-color: #f5c2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row6_col9, #T_b34d4_row7_col8, #T_b34d4_row8_col7, #T_b34d4_row9_col6 {\n",
       "  background-color: #f0cdbb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row6_col10, #T_b34d4_row10_col6 {\n",
       "  background-color: #f6bea4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row7_col9, #T_b34d4_row9_col7 {\n",
       "  background-color: #edd2c3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row7_col10, #T_b34d4_row10_col7 {\n",
       "  background-color: #f4c5ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row8_col9, #T_b34d4_row9_col8 {\n",
       "  background-color: #f2c9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row8_col10, #T_b34d4_row10_col8 {\n",
       "  background-color: #ead4c8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b34d4_row9_col10, #T_b34d4_row10_col9 {\n",
       "  background-color: #ecd3c5;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b34d4_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >sex</th>\n",
       "      <th class=\"col_heading level0 col1\" >age</th>\n",
       "      <th class=\"col_heading level0 col2\" >heavy_drink</th>\n",
       "      <th class=\"col_heading level0 col3\" >smoke</th>\n",
       "      <th class=\"col_heading level0 col4\" >genetic_hbp</th>\n",
       "      <th class=\"col_heading level0 col5\" >weight</th>\n",
       "      <th class=\"col_heading level0 col6\" >waist</th>\n",
       "      <th class=\"col_heading level0 col7\" >BMI</th>\n",
       "      <th class=\"col_heading level0 col8\" >diabetes</th>\n",
       "      <th class=\"col_heading level0 col9\" >hyper_chol</th>\n",
       "      <th class=\"col_heading level0 col10\" >triglycerides</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b34d4_level0_row0\" class=\"row_heading level0 row0\" >sex</th>\n",
       "      <td id=\"T_b34d4_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_b34d4_row0_col1\" class=\"data row0 col1\" >0.038326</td>\n",
       "      <td id=\"T_b34d4_row0_col2\" class=\"data row0 col2\" >-0.248890</td>\n",
       "      <td id=\"T_b34d4_row0_col3\" class=\"data row0 col3\" >-0.350524</td>\n",
       "      <td id=\"T_b34d4_row0_col4\" class=\"data row0 col4\" >0.040384</td>\n",
       "      <td id=\"T_b34d4_row0_col5\" class=\"data row0 col5\" >-0.527505</td>\n",
       "      <td id=\"T_b34d4_row0_col6\" class=\"data row0 col6\" >-0.352720</td>\n",
       "      <td id=\"T_b34d4_row0_col7\" class=\"data row0 col7\" >-0.157245</td>\n",
       "      <td id=\"T_b34d4_row0_col8\" class=\"data row0 col8\" >-0.051748</td>\n",
       "      <td id=\"T_b34d4_row0_col9\" class=\"data row0 col9\" >0.074212</td>\n",
       "      <td id=\"T_b34d4_row0_col10\" class=\"data row0 col10\" >-0.194612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b34d4_level0_row1\" class=\"row_heading level0 row1\" >age</th>\n",
       "      <td id=\"T_b34d4_row1_col0\" class=\"data row1 col0\" >0.038326</td>\n",
       "      <td id=\"T_b34d4_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_b34d4_row1_col2\" class=\"data row1 col2\" >-0.259724</td>\n",
       "      <td id=\"T_b34d4_row1_col3\" class=\"data row1 col3\" >-0.124239</td>\n",
       "      <td id=\"T_b34d4_row1_col4\" class=\"data row1 col4\" >-0.140292</td>\n",
       "      <td id=\"T_b34d4_row1_col5\" class=\"data row1 col5\" >-0.212365</td>\n",
       "      <td id=\"T_b34d4_row1_col6\" class=\"data row1 col6\" >0.240594</td>\n",
       "      <td id=\"T_b34d4_row1_col7\" class=\"data row1 col7\" >0.043742</td>\n",
       "      <td id=\"T_b34d4_row1_col8\" class=\"data row1 col8\" >0.297689</td>\n",
       "      <td id=\"T_b34d4_row1_col9\" class=\"data row1 col9\" >0.286857</td>\n",
       "      <td id=\"T_b34d4_row1_col10\" class=\"data row1 col10\" >0.047392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b34d4_level0_row2\" class=\"row_heading level0 row2\" >heavy_drink</th>\n",
       "      <td id=\"T_b34d4_row2_col0\" class=\"data row2 col0\" >-0.248890</td>\n",
       "      <td id=\"T_b34d4_row2_col1\" class=\"data row2 col1\" >-0.259724</td>\n",
       "      <td id=\"T_b34d4_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_b34d4_row2_col3\" class=\"data row2 col3\" >0.287233</td>\n",
       "      <td id=\"T_b34d4_row2_col4\" class=\"data row2 col4\" >0.036807</td>\n",
       "      <td id=\"T_b34d4_row2_col5\" class=\"data row2 col5\" >0.225755</td>\n",
       "      <td id=\"T_b34d4_row2_col6\" class=\"data row2 col6\" >0.078772</td>\n",
       "      <td id=\"T_b34d4_row2_col7\" class=\"data row2 col7\" >0.071332</td>\n",
       "      <td id=\"T_b34d4_row2_col8\" class=\"data row2 col8\" >-0.034885</td>\n",
       "      <td id=\"T_b34d4_row2_col9\" class=\"data row2 col9\" >-0.053002</td>\n",
       "      <td id=\"T_b34d4_row2_col10\" class=\"data row2 col10\" >0.154579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b34d4_level0_row3\" class=\"row_heading level0 row3\" >smoke</th>\n",
       "      <td id=\"T_b34d4_row3_col0\" class=\"data row3 col0\" >-0.350524</td>\n",
       "      <td id=\"T_b34d4_row3_col1\" class=\"data row3 col1\" >-0.124239</td>\n",
       "      <td id=\"T_b34d4_row3_col2\" class=\"data row3 col2\" >0.287233</td>\n",
       "      <td id=\"T_b34d4_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_b34d4_row3_col4\" class=\"data row3 col4\" >-0.024376</td>\n",
       "      <td id=\"T_b34d4_row3_col5\" class=\"data row3 col5\" >0.195278</td>\n",
       "      <td id=\"T_b34d4_row3_col6\" class=\"data row3 col6\" >0.096584</td>\n",
       "      <td id=\"T_b34d4_row3_col7\" class=\"data row3 col7\" >0.035057</td>\n",
       "      <td id=\"T_b34d4_row3_col8\" class=\"data row3 col8\" >0.016959</td>\n",
       "      <td id=\"T_b34d4_row3_col9\" class=\"data row3 col9\" >-0.038498</td>\n",
       "      <td id=\"T_b34d4_row3_col10\" class=\"data row3 col10\" >0.167180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b34d4_level0_row4\" class=\"row_heading level0 row4\" >genetic_hbp</th>\n",
       "      <td id=\"T_b34d4_row4_col0\" class=\"data row4 col0\" >0.040384</td>\n",
       "      <td id=\"T_b34d4_row4_col1\" class=\"data row4 col1\" >-0.140292</td>\n",
       "      <td id=\"T_b34d4_row4_col2\" class=\"data row4 col2\" >0.036807</td>\n",
       "      <td id=\"T_b34d4_row4_col3\" class=\"data row4 col3\" >-0.024376</td>\n",
       "      <td id=\"T_b34d4_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_b34d4_row4_col5\" class=\"data row4 col5\" >0.068481</td>\n",
       "      <td id=\"T_b34d4_row4_col6\" class=\"data row4 col6\" >-0.000316</td>\n",
       "      <td id=\"T_b34d4_row4_col7\" class=\"data row4 col7\" >0.054945</td>\n",
       "      <td id=\"T_b34d4_row4_col8\" class=\"data row4 col8\" >-0.044133</td>\n",
       "      <td id=\"T_b34d4_row4_col9\" class=\"data row4 col9\" >0.017839</td>\n",
       "      <td id=\"T_b34d4_row4_col10\" class=\"data row4 col10\" >0.017991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b34d4_level0_row5\" class=\"row_heading level0 row5\" >weight</th>\n",
       "      <td id=\"T_b34d4_row5_col0\" class=\"data row5 col0\" >-0.527505</td>\n",
       "      <td id=\"T_b34d4_row5_col1\" class=\"data row5 col1\" >-0.212365</td>\n",
       "      <td id=\"T_b34d4_row5_col2\" class=\"data row5 col2\" >0.225755</td>\n",
       "      <td id=\"T_b34d4_row5_col3\" class=\"data row5 col3\" >0.195278</td>\n",
       "      <td id=\"T_b34d4_row5_col4\" class=\"data row5 col4\" >0.068481</td>\n",
       "      <td id=\"T_b34d4_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_b34d4_row5_col6\" class=\"data row5 col6\" >0.801364</td>\n",
       "      <td id=\"T_b34d4_row5_col7\" class=\"data row5 col7\" >0.811359</td>\n",
       "      <td id=\"T_b34d4_row5_col8\" class=\"data row5 col8\" >0.080922</td>\n",
       "      <td id=\"T_b34d4_row5_col9\" class=\"data row5 col9\" >0.003831</td>\n",
       "      <td id=\"T_b34d4_row5_col10\" class=\"data row5 col10\" >0.257491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b34d4_level0_row6\" class=\"row_heading level0 row6\" >waist</th>\n",
       "      <td id=\"T_b34d4_row6_col0\" class=\"data row6 col0\" >-0.352720</td>\n",
       "      <td id=\"T_b34d4_row6_col1\" class=\"data row6 col1\" >0.240594</td>\n",
       "      <td id=\"T_b34d4_row6_col2\" class=\"data row6 col2\" >0.078772</td>\n",
       "      <td id=\"T_b34d4_row6_col3\" class=\"data row6 col3\" >0.096584</td>\n",
       "      <td id=\"T_b34d4_row6_col4\" class=\"data row6 col4\" >-0.000316</td>\n",
       "      <td id=\"T_b34d4_row6_col5\" class=\"data row6 col5\" >0.801364</td>\n",
       "      <td id=\"T_b34d4_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_b34d4_row6_col7\" class=\"data row6 col7\" >0.874840</td>\n",
       "      <td id=\"T_b34d4_row6_col8\" class=\"data row6 col8\" >0.262076</td>\n",
       "      <td id=\"T_b34d4_row6_col9\" class=\"data row6 col9\" >0.177944</td>\n",
       "      <td id=\"T_b34d4_row6_col10\" class=\"data row6 col10\" >0.291078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b34d4_level0_row7\" class=\"row_heading level0 row7\" >BMI</th>\n",
       "      <td id=\"T_b34d4_row7_col0\" class=\"data row7 col0\" >-0.157245</td>\n",
       "      <td id=\"T_b34d4_row7_col1\" class=\"data row7 col1\" >0.043742</td>\n",
       "      <td id=\"T_b34d4_row7_col2\" class=\"data row7 col2\" >0.071332</td>\n",
       "      <td id=\"T_b34d4_row7_col3\" class=\"data row7 col3\" >0.035057</td>\n",
       "      <td id=\"T_b34d4_row7_col4\" class=\"data row7 col4\" >0.054945</td>\n",
       "      <td id=\"T_b34d4_row7_col5\" class=\"data row7 col5\" >0.811359</td>\n",
       "      <td id=\"T_b34d4_row7_col6\" class=\"data row7 col6\" >0.874840</td>\n",
       "      <td id=\"T_b34d4_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_b34d4_row7_col8\" class=\"data row7 col8\" >0.179504</td>\n",
       "      <td id=\"T_b34d4_row7_col9\" class=\"data row7 col9\" >0.132896</td>\n",
       "      <td id=\"T_b34d4_row7_col10\" class=\"data row7 col10\" >0.243004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b34d4_level0_row8\" class=\"row_heading level0 row8\" >diabetes</th>\n",
       "      <td id=\"T_b34d4_row8_col0\" class=\"data row8 col0\" >-0.051748</td>\n",
       "      <td id=\"T_b34d4_row8_col1\" class=\"data row8 col1\" >0.297689</td>\n",
       "      <td id=\"T_b34d4_row8_col2\" class=\"data row8 col2\" >-0.034885</td>\n",
       "      <td id=\"T_b34d4_row8_col3\" class=\"data row8 col3\" >0.016959</td>\n",
       "      <td id=\"T_b34d4_row8_col4\" class=\"data row8 col4\" >-0.044133</td>\n",
       "      <td id=\"T_b34d4_row8_col5\" class=\"data row8 col5\" >0.080922</td>\n",
       "      <td id=\"T_b34d4_row8_col6\" class=\"data row8 col6\" >0.262076</td>\n",
       "      <td id=\"T_b34d4_row8_col7\" class=\"data row8 col7\" >0.179504</td>\n",
       "      <td id=\"T_b34d4_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_b34d4_row8_col9\" class=\"data row8 col9\" >0.211524</td>\n",
       "      <td id=\"T_b34d4_row8_col10\" class=\"data row8 col10\" >0.116015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b34d4_level0_row9\" class=\"row_heading level0 row9\" >hyper_chol</th>\n",
       "      <td id=\"T_b34d4_row9_col0\" class=\"data row9 col0\" >0.074212</td>\n",
       "      <td id=\"T_b34d4_row9_col1\" class=\"data row9 col1\" >0.286857</td>\n",
       "      <td id=\"T_b34d4_row9_col2\" class=\"data row9 col2\" >-0.053002</td>\n",
       "      <td id=\"T_b34d4_row9_col3\" class=\"data row9 col3\" >-0.038498</td>\n",
       "      <td id=\"T_b34d4_row9_col4\" class=\"data row9 col4\" >0.017839</td>\n",
       "      <td id=\"T_b34d4_row9_col5\" class=\"data row9 col5\" >0.003831</td>\n",
       "      <td id=\"T_b34d4_row9_col6\" class=\"data row9 col6\" >0.177944</td>\n",
       "      <td id=\"T_b34d4_row9_col7\" class=\"data row9 col7\" >0.132896</td>\n",
       "      <td id=\"T_b34d4_row9_col8\" class=\"data row9 col8\" >0.211524</td>\n",
       "      <td id=\"T_b34d4_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_b34d4_row9_col10\" class=\"data row9 col10\" >0.125274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b34d4_level0_row10\" class=\"row_heading level0 row10\" >triglycerides</th>\n",
       "      <td id=\"T_b34d4_row10_col0\" class=\"data row10 col0\" >-0.194612</td>\n",
       "      <td id=\"T_b34d4_row10_col1\" class=\"data row10 col1\" >0.047392</td>\n",
       "      <td id=\"T_b34d4_row10_col2\" class=\"data row10 col2\" >0.154579</td>\n",
       "      <td id=\"T_b34d4_row10_col3\" class=\"data row10 col3\" >0.167180</td>\n",
       "      <td id=\"T_b34d4_row10_col4\" class=\"data row10 col4\" >0.017991</td>\n",
       "      <td id=\"T_b34d4_row10_col5\" class=\"data row10 col5\" >0.257491</td>\n",
       "      <td id=\"T_b34d4_row10_col6\" class=\"data row10 col6\" >0.291078</td>\n",
       "      <td id=\"T_b34d4_row10_col7\" class=\"data row10 col7\" >0.243004</td>\n",
       "      <td id=\"T_b34d4_row10_col8\" class=\"data row10 col8\" >0.116015</td>\n",
       "      <td id=\"T_b34d4_row10_col9\" class=\"data row10 col9\" >0.125274</td>\n",
       "      <td id=\"T_b34d4_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1eeb3d4e2c8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_features0 = fix_data0.iloc[:,2:-2]\n",
    "feat_corr0 = check_features0.corr(method='pearson')\n",
    "feat_corr0.style.background_gradient(cmap='coolwarm',vmin=-1,vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(feat_corr0.round(2),\n",
    "            cmap='coolwarm',\n",
    "            annot=True,\n",
    "            linewidths=.5,\n",
    "            vmin=-1,\n",
    "            vmax=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11573 entries, 0 to 11572\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   ID             11573 non-null  object \n",
      " 1   year           11573 non-null  int64  \n",
      " 2   sex            11573 non-null  int64  \n",
      " 3   age            11573 non-null  int64  \n",
      " 4   heavy_drink    11573 non-null  int32  \n",
      " 5   smoke          11573 non-null  int32  \n",
      " 6   genetic_hbp    11573 non-null  float64\n",
      " 7   BMI            11573 non-null  float64\n",
      " 8   diabetes       11573 non-null  int32  \n",
      " 9   hyper_chol     11573 non-null  int32  \n",
      " 10  triglycerides  11573 non-null  float64\n",
      " 11  HBP_US         11573 non-null  int32  \n",
      " 12  HBP_EU         11573 non-null  int32  \n",
      "dtypes: float64(3), int32(6), int64(3), object(1)\n",
      "memory usage: 904.3+ KB\n"
     ]
    }
   ],
   "source": [
    "drop_list = ['weight','waist']\n",
    "fix_data = fix_data0.copy()\n",
    "fix_data = fix_data.drop(columns=drop_list,axis=1)\n",
    "fix_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >sex</th>\n",
       "      <th class=\"col_heading level0 col1\" >age</th>\n",
       "      <th class=\"col_heading level0 col2\" >heavy_drink</th>\n",
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       "      <th class=\"col_heading level0 col5\" >BMI</th>\n",
       "      <th class=\"col_heading level0 col6\" >diabetes</th>\n",
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       "      <td id=\"T_a5ebc_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_a5ebc_row0_col1\" class=\"data row0 col1\" >0.038326</td>\n",
       "      <td id=\"T_a5ebc_row0_col2\" class=\"data row0 col2\" >-0.248890</td>\n",
       "      <td id=\"T_a5ebc_row0_col3\" class=\"data row0 col3\" >-0.350524</td>\n",
       "      <td id=\"T_a5ebc_row0_col4\" class=\"data row0 col4\" >0.040384</td>\n",
       "      <td id=\"T_a5ebc_row0_col5\" class=\"data row0 col5\" >-0.157245</td>\n",
       "      <td id=\"T_a5ebc_row0_col6\" class=\"data row0 col6\" >-0.051748</td>\n",
       "      <td id=\"T_a5ebc_row0_col7\" class=\"data row0 col7\" >0.074212</td>\n",
       "      <td id=\"T_a5ebc_row0_col8\" class=\"data row0 col8\" >-0.194612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5ebc_level0_row1\" class=\"row_heading level0 row1\" >age</th>\n",
       "      <td id=\"T_a5ebc_row1_col0\" class=\"data row1 col0\" >0.038326</td>\n",
       "      <td id=\"T_a5ebc_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_a5ebc_row1_col2\" class=\"data row1 col2\" >-0.259724</td>\n",
       "      <td id=\"T_a5ebc_row1_col3\" class=\"data row1 col3\" >-0.124239</td>\n",
       "      <td id=\"T_a5ebc_row1_col4\" class=\"data row1 col4\" >-0.140292</td>\n",
       "      <td id=\"T_a5ebc_row1_col5\" class=\"data row1 col5\" >0.043742</td>\n",
       "      <td id=\"T_a5ebc_row1_col6\" class=\"data row1 col6\" >0.297689</td>\n",
       "      <td id=\"T_a5ebc_row1_col7\" class=\"data row1 col7\" >0.286857</td>\n",
       "      <td id=\"T_a5ebc_row1_col8\" class=\"data row1 col8\" >0.047392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5ebc_level0_row2\" class=\"row_heading level0 row2\" >heavy_drink</th>\n",
       "      <td id=\"T_a5ebc_row2_col0\" class=\"data row2 col0\" >-0.248890</td>\n",
       "      <td id=\"T_a5ebc_row2_col1\" class=\"data row2 col1\" >-0.259724</td>\n",
       "      <td id=\"T_a5ebc_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_a5ebc_row2_col3\" class=\"data row2 col3\" >0.287233</td>\n",
       "      <td id=\"T_a5ebc_row2_col4\" class=\"data row2 col4\" >0.036807</td>\n",
       "      <td id=\"T_a5ebc_row2_col5\" class=\"data row2 col5\" >0.071332</td>\n",
       "      <td id=\"T_a5ebc_row2_col6\" class=\"data row2 col6\" >-0.034885</td>\n",
       "      <td id=\"T_a5ebc_row2_col7\" class=\"data row2 col7\" >-0.053002</td>\n",
       "      <td id=\"T_a5ebc_row2_col8\" class=\"data row2 col8\" >0.154579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5ebc_level0_row3\" class=\"row_heading level0 row3\" >smoke</th>\n",
       "      <td id=\"T_a5ebc_row3_col0\" class=\"data row3 col0\" >-0.350524</td>\n",
       "      <td id=\"T_a5ebc_row3_col1\" class=\"data row3 col1\" >-0.124239</td>\n",
       "      <td id=\"T_a5ebc_row3_col2\" class=\"data row3 col2\" >0.287233</td>\n",
       "      <td id=\"T_a5ebc_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_a5ebc_row3_col4\" class=\"data row3 col4\" >-0.024376</td>\n",
       "      <td id=\"T_a5ebc_row3_col5\" class=\"data row3 col5\" >0.035057</td>\n",
       "      <td id=\"T_a5ebc_row3_col6\" class=\"data row3 col6\" >0.016959</td>\n",
       "      <td id=\"T_a5ebc_row3_col7\" class=\"data row3 col7\" >-0.038498</td>\n",
       "      <td id=\"T_a5ebc_row3_col8\" class=\"data row3 col8\" >0.167180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5ebc_level0_row4\" class=\"row_heading level0 row4\" >genetic_hbp</th>\n",
       "      <td id=\"T_a5ebc_row4_col0\" class=\"data row4 col0\" >0.040384</td>\n",
       "      <td id=\"T_a5ebc_row4_col1\" class=\"data row4 col1\" >-0.140292</td>\n",
       "      <td id=\"T_a5ebc_row4_col2\" class=\"data row4 col2\" >0.036807</td>\n",
       "      <td id=\"T_a5ebc_row4_col3\" class=\"data row4 col3\" >-0.024376</td>\n",
       "      <td id=\"T_a5ebc_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_a5ebc_row4_col5\" class=\"data row4 col5\" >0.054945</td>\n",
       "      <td id=\"T_a5ebc_row4_col6\" class=\"data row4 col6\" >-0.044133</td>\n",
       "      <td id=\"T_a5ebc_row4_col7\" class=\"data row4 col7\" >0.017839</td>\n",
       "      <td id=\"T_a5ebc_row4_col8\" class=\"data row4 col8\" >0.017991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5ebc_level0_row5\" class=\"row_heading level0 row5\" >BMI</th>\n",
       "      <td id=\"T_a5ebc_row5_col0\" class=\"data row5 col0\" >-0.157245</td>\n",
       "      <td id=\"T_a5ebc_row5_col1\" class=\"data row5 col1\" >0.043742</td>\n",
       "      <td id=\"T_a5ebc_row5_col2\" class=\"data row5 col2\" >0.071332</td>\n",
       "      <td id=\"T_a5ebc_row5_col3\" class=\"data row5 col3\" >0.035057</td>\n",
       "      <td id=\"T_a5ebc_row5_col4\" class=\"data row5 col4\" >0.054945</td>\n",
       "      <td id=\"T_a5ebc_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_a5ebc_row5_col6\" class=\"data row5 col6\" >0.179504</td>\n",
       "      <td id=\"T_a5ebc_row5_col7\" class=\"data row5 col7\" >0.132896</td>\n",
       "      <td id=\"T_a5ebc_row5_col8\" class=\"data row5 col8\" >0.243004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5ebc_level0_row6\" class=\"row_heading level0 row6\" >diabetes</th>\n",
       "      <td id=\"T_a5ebc_row6_col0\" class=\"data row6 col0\" >-0.051748</td>\n",
       "      <td id=\"T_a5ebc_row6_col1\" class=\"data row6 col1\" >0.297689</td>\n",
       "      <td id=\"T_a5ebc_row6_col2\" class=\"data row6 col2\" >-0.034885</td>\n",
       "      <td id=\"T_a5ebc_row6_col3\" class=\"data row6 col3\" >0.016959</td>\n",
       "      <td id=\"T_a5ebc_row6_col4\" class=\"data row6 col4\" >-0.044133</td>\n",
       "      <td id=\"T_a5ebc_row6_col5\" class=\"data row6 col5\" >0.179504</td>\n",
       "      <td id=\"T_a5ebc_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_a5ebc_row6_col7\" class=\"data row6 col7\" >0.211524</td>\n",
       "      <td id=\"T_a5ebc_row6_col8\" class=\"data row6 col8\" >0.116015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5ebc_level0_row7\" class=\"row_heading level0 row7\" >hyper_chol</th>\n",
       "      <td id=\"T_a5ebc_row7_col0\" class=\"data row7 col0\" >0.074212</td>\n",
       "      <td id=\"T_a5ebc_row7_col1\" class=\"data row7 col1\" >0.286857</td>\n",
       "      <td id=\"T_a5ebc_row7_col2\" class=\"data row7 col2\" >-0.053002</td>\n",
       "      <td id=\"T_a5ebc_row7_col3\" class=\"data row7 col3\" >-0.038498</td>\n",
       "      <td id=\"T_a5ebc_row7_col4\" class=\"data row7 col4\" >0.017839</td>\n",
       "      <td id=\"T_a5ebc_row7_col5\" class=\"data row7 col5\" >0.132896</td>\n",
       "      <td id=\"T_a5ebc_row7_col6\" class=\"data row7 col6\" >0.211524</td>\n",
       "      <td id=\"T_a5ebc_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_a5ebc_row7_col8\" class=\"data row7 col8\" >0.125274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5ebc_level0_row8\" class=\"row_heading level0 row8\" >triglycerides</th>\n",
       "      <td id=\"T_a5ebc_row8_col0\" class=\"data row8 col0\" >-0.194612</td>\n",
       "      <td id=\"T_a5ebc_row8_col1\" class=\"data row8 col1\" >0.047392</td>\n",
       "      <td id=\"T_a5ebc_row8_col2\" class=\"data row8 col2\" >0.154579</td>\n",
       "      <td id=\"T_a5ebc_row8_col3\" class=\"data row8 col3\" >0.167180</td>\n",
       "      <td id=\"T_a5ebc_row8_col4\" class=\"data row8 col4\" >0.017991</td>\n",
       "      <td id=\"T_a5ebc_row8_col5\" class=\"data row8 col5\" >0.243004</td>\n",
       "      <td id=\"T_a5ebc_row8_col6\" class=\"data row8 col6\" >0.116015</td>\n",
       "      <td id=\"T_a5ebc_row8_col7\" class=\"data row8 col7\" >0.125274</td>\n",
       "      <td id=\"T_a5ebc_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1eeb8155a48>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_features = fix_data.iloc[:,2:-2]\n",
    "corr = check_features.corr(method='pearson')\n",
    "corr.style.background_gradient(cmap='coolwarm',vmin=-1,vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(corr.round(2),\n",
    "            cmap='coolwarm',\n",
    "            annot=True,\n",
    "            linewidths=.5,\n",
    "            vmin=-1,\n",
    "            vmax=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 이상치 제거(노이즈 제거)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11573, 13)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PyuVyqayszKvfbrcrPj5ex44d6/W6AQCA7zMl3LzzzjsKCQnRwYMHNW7cOI0ePVp33HGHGhsb5XA4NHToUK/9o6KiVFdXJ6fTKbfbLbvd3ml/Z1paWtTY2Oi1AQAA6zIl3DQ1NXnOwpSWlurNN99UbW2tsrOz5XK5ZBiG1/5ut1t+fn5yuVySdMn+zqxatUoRERGebeTIkb0zKAAA4BNMCTd2u11tbW0qKChQcHCwBg4cqEceeUS7d++WzWbr8OCs2tpaRUdHKzIyUoZh6OzZs532dyY3N1cNDQ2erbq6utfGBQAAzGdKuImLi9OAAQN04cKFvxfi76/g4GCNHz9eJSUlXvuXlJRo8uTJCgsLU1JSkle/w+HQmTNnlJqa2unPCgoKUnh4uNcGAACsy5RwExwcrO9973u699575XK51NLSory8PC1ZskSLFy9WUVGRiouLJUl79+5VeXm5MjIyJElZWVnKz8/XuXPn1NraqtzcXGVmZio0NNSMoQAAAB9j2q3gjz32mJqbmzV8+HB99atfVUJCglasWKERI0Zo+/btWrZsmaKiorRy5Urt2bNHYWFhkqTs7GxNnTpViYmJio+PV0hIiAoKCswaBgAA8DGmPaH4K1/5irZs2dJp3+zZs1VRUdFpn7+/vwoLC1VYWNib5QEAgH7KtDM3AAAAvYFwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALCXQ7AIAAPiiqqqq5HQ6u3UMu92u2NjYHqoIvoRwAwDoV6qqqpScPFbNzee7dZyQkFBVVJQTcCyIcAMA6FecTqeam88r7ft5Co+J79IxGh0ndXhDvpxOJ+HGggg3AIB+KTwmXrbYJLPLgA9iQTEAALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAU08LN3XffrYiICMXHx3u2yspKSdKRI0c0adIkxcXFKSUlRQcOHPD67urVq5WQkKDhw4drwYIFqqurM2MIAADAB5l65iYnJ0cnT570bHFxcWpqatLcuXO1cuVKVVZWat26dcrIyNDp06clSTt37tTmzZtVWlqqqqoqRUdHKysry8xhAAAAH2JquBk0aFCHtm3btmnChAmaOXOmJGnq1KmaMmWKduzYIemTszZ5eXmy2WwKCAjQihUrtHv3btXX1/dl6QAAwEf5XLg5dOiQ0tPTvdrS0tJ09OhRuVwulZWVefXb7XbFx8fr2LFjvV0uAADoBwLN/OG5ubnKy8vTmDFjlJubq29+85tyOByaMWOG135RUVE6fPiwnE6n3G637HZ7h/5LrbtpaWlRS0uL53NjY2PPDwQAviTKy8u79X273a7Y2NgeqgbonGnh5oknntDatWvldru1f/9+3XzzzSoqKpLL5ZJhGF77ut1u+fn5yeVySZIMw5Cfn1+H/s6sWrVK+fn5vTcQAPgSaG6ok+SnJUuWdOs4ISGhqqgoJ+CgV5kWbvz9P7kiFhAQoBtuuEHf+c539Nxzz8lms8npdHrtW1tbq+joaEVGRsowDJ09e1Y2m61Df2dyc3O1fPlyz+fGxkaNHDmyF0YEANbVdr5JkqGrFj2gIaOSu3SMRsdJHd6QL6fTSbhBrzL1stRnuVwuDRgwQOPHj1dJSYlXICkpKdEtt9yisLAwJSUlqaSkRHPmzJEkORwOnTlzRqmpqZ0eNygoSEFBQX0yBgCwuq9ExcoWm2R2GcA/ZNqC4v3796u9vV2S9Mc//lHPPPOMbrrpJi1evFhFRUUqLi6WJO3du1fl5eXKyMiQJGVlZSk/P1/nzp1Ta2urcnNzlZmZqdDQULOGAgAAfIhpZ25+/etf67vf/a5CQ0MVGxurXbt2KSUlRZK0fft2LVu2TPX19UpISNCePXsUFhYmScrOzlZNTY0SExMVGBioefPmqaCgwKxhAAAAH2NauNm3b98l+2bPnq2KiopO+/z9/VVYWKjCwsLeKg0AAPRjvFsKAABYCuEGAABYis/cLYWeV1VV1eG2+i+KB24BAPobwo1FVVVVKTl5rJqbz3frODxwCwDQ3xBuLMrpdKq5+bzSvp+n8Jj4Lh2DB24BAPojwo3FhcfE88AtAPBxLCPoWYQbAABMxDKCnke4AQDARCwj6HmEGwAAfADLCHoOz7kBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWwt1SAIA+VV5ebur3YX2EGwDoRd39RWylp842N9RJ8tOSJUt65HhtLa09chxYD+EGAHpBT/0it9JTZ9vON0kydNWiBzRkVHKXj+M4dkhv7f4PuVyunisOltKlcPOHP/xBGRkZXm1ut1t79uzR/Pnze6IuAOjXeuIX+adPnX3ttdc0duzYLtfia5dxvhIV262H1TU6TvZcMbCkLoWbBx54oEO4CQgI0PLlywk3APAZ3flFzmUcoGu+ULi5//779dFHH6murk7Lli3z6jtx4oQGDx7co8UBwJcZl3GArvlC4SYlJUWVlZXy9/fX0KFDvfrGjh2rW265pUeLAwBwGQf4or5QuLn11lslSe+9957y8vJ6ox4AAIBu6dJD/LZs2dLTdQAAAPSILoWb48ePa9q0aRo4cKACAgIUEBAgf39/BQQE9HR9AAAAX0iX7pa69dZbNWXKFK1fv16RkZE9XRMAAECXdSncnDp1Sr/85S97uhYAAIBu69JlqcTERNXX1/d0LQAAAN3WpTM3t99+u2666Sbl5OQoJibGq2/ixIk9UhgAAEBXdCnc/PSnP5Uk5eTkeLX7+fnpxIkT3S4KAACgq7oUbj744IOergMAAKBHdGnNDQAAgK/q0pmbsWPHys/Pr9O+v/3tb90qCAAAoDu6FG7Wr1/v9bmurk7/+Z//qWnTpvVETQC+xMrLy7v1fbvdrtjY2B6qBkB/1KVwM3Xq1A5tc+bM0be+9S098MAD3S4KwJdPc0OdJD8tWbKkW8cJCQlVRUU5AQf4EutSuOnMgAEDdP78+S59d+nSpTp48KAqKiokSUeOHNHSpUvlcDgUFhamNWvWaNasWZ79V69erbVr16q5uVkTJ07Uk08+qcGDB/fIOACYo+18kyRDVy16QENGJXfpGI2Okzq8IV9Op5NwA3yJdSnc/O///q/X548++kjPPfecWlpavvCxqqurtXnzZo0cOVKS1NTUpLlz5+qpp57SzJkz9corr2jevHmqqKhQdHS0du7cqc2bN6u0tFQRERG6++67lZWVpWeeeaYrQwHgY74SFStbbJLZZQDox7p0t1R0dLRiYmIUHR2t6OhoXXXVVXrxxRf15JNPfuFj/ehHP9Jtt93m+bxt2zZNmDBBM2fOlPTJJbApU6Zox44dkj45a5OXlyebzaaAgACtWLFCu3fv5onJAABAUhfDTXt7u9xut9rb29Xe3q7GxkYVFRVp7NixX+g4L774ourq6rRw4UJP26FDh5Senu61X1pamo4ePSqXy6WysjKvfrvdrvj4eB07dqwrQwEAABbTrTU3f/3rX1VdXa0xY8YoOfmLXSOvq6vTPffcoxdffFGnT5/2tDscDs2YMcNr36ioKB0+fFhOp1Nut1t2u71Df11dXac/p6WlxetyWWNj4xeqE0D/0907rrr7fQDm6lK4OX36tObPn6/KykrFxcWpqqpKqamp2rFjh8LDwz/3+4Zh6Pbbb1dOTo6Sk5O9wo3L5ZJhGF77u91u+fn5yeVyeb7/2efsfNrfmVWrVik/P78rw4SPqaqqktPp7NYxuE3Y2nrqjqtPtbW09shxAPStLoWbe++9V9ddd51WrFghf39/GYahhx9+WA8++KDWrl37ud8vKChQW1ub7r777g59Nputwy+w2tpaRUdHKzIyUoZh6OzZs7LZbB36O5Obm6vly5d7Pjc2NnoWL6P/qKqqUnLyWDU3d+2OvE9xm7C19cQdV5LkOHZIb+3+D8//UAHoX7oUbkpKSvT00097zpb4+fnpkUce0Ve/+tXL+v4TTzyhjz/+WJGRkZI+OVvT3NysQYMGKTc3VyUlJV6BpKSkRLfccovCwsKUlJSkkpISzZkzR9Inl7HOnDmj1NTUTn9WUFCQgoKCujJM+BCn06nm5vNK+36ewmPiu3QMbhP+8ujuHVeNjpM9VwyAPtelcBMQENDhMlBAQMBlP+fG4XB4fX755Zd15513qqKiQh9++KEKCgpUXFysGTNmaO/evSovL1dGRoYkKSsrS/n5+br22msVGhqq3NxcZWZmKjQ0tCtDQT8THhPPbcIAgH+oy++W+q//+i+vu5yeeeYZJSYmdrugESNGaPv27Vq2bJnq6+uVkJCgPXv2KCwsTJKUnZ2tmpoaJSYmKjAwUPPmzVNBQUG3fy4AALCGLoWbxx57TDNmzNAzzzyj5ORkvfPOO9q/f7/+9Kc/damIadOmeZ5OLEmzZ8/2+vxZ/v7+KiwsVGFhYZd+FgAAsLbLDjcXLlxQQECArrjiCqWkpOitt97Sli1b9P7772vcuHH6xS9+oSFDhvRmrQB6QU/chcat0wB8yWWHmxkzZugXv/iF5wF6drtdP/rRjzz9L7zwgrZt26bf//73PV8lgF7RU3ehfYpbpwH4gssONydOnOjw5ODPuv7663XPPff0SFEA+kZP3IUmces0+q/unnXk2Vm+6bLDzacLei8lICCg28UAMEd370Lj1mn0Nz31wEeeneWbLjvcBAcHq6mpSQMHDuy0v62tTW1tbT1WGAAAvaUnHvjIs7N812WHm/nz52vNmjX6yU9+0mn/1q1bNWnSpB4rDPBVLMAFrKO7D3yEb7rscPPAAw9o8uTJGjBggHJycjRgwABJn7znaf369XrkkUdUXFzca4UCvoAFuADg+y473ISHh+vll19WZmamfv7zn2vcuHFyu916++23NXjwYD377LOX/foFoL9iAS6Ai/EWet/zhR7iN2TIED333HOqqqrSG2+8oZaWFo0ZM0bXXHPNJd/KDVgRC3AB8BZ639WlJxTHxsayeAoA8KXGW+h9V5fCDQAA+ARvofc9/mYXAAAA0JM4cwP0c91ZjMhCRgBWRLgB+qmeXMzIQkYAVkK4AfqpnljMyEJGAFZEuAH6ue4sZmQhIwArYkExAACwFMINAACwFMINAACwFMINAACwFMINAACwFO6W6mFVVVVyOp3dOobdbufdXQAAdBHhpgdVVVUpOXmsmpvPd+s4ISGhqqgoJ+AAANAFhJse5HQ61dx8Xmnfz1N4THyXjtHoOKnDG/LldDoJNwAAdAHhpheEx8R36w2xAACg61hQDAAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALMXUcPP4448rMTFRsbGxuvLKK7V7925P35EjRzRp0iTFxcUpJSVFBw4c8Pru6tWrlZCQoOHDh2vBggWqq6vr6/IBAIAPMjXcpKWl6fjx46qqqtJvf/tb3XLLLaqrq1NTU5Pmzp2rlStXqrKyUuvWrVNGRoZOnz4tSdq5c6c2b96s0tJSVVVVKTo6WllZWWYOBQAA+AhT3y01depUz5+nTJmi0NBQ1dbW6tVXX9WECRM0c+ZMz35TpkzRjh07lJ2drdWrVysvL082m02StGLFCsXExKi+vt7TBlxKeXm5Kd8FAPQNn3hx5oULF7R+/XpNmDBBycnJeuyxx5Senu61T1pamo4ePSqXy6WysjKvfrvdrvj4eB07dswrMAGf1dxQJ8lPS5Ys6fax2lpau18QAKBXmBpu3n//fU2bNk01NTWaOHGitm7dKklyOByaMWOG175RUVE6fPiwnE6n3G637HZ7h/7O1t20tLSopaXF87mxsbEXRoL+oO18kyRDVy16QENGJXfpGI5jh/TW7v+Qy+Xq2eIAAD3G1HAzZswYVVdX68KFC3r22Wc1efJk/fd//7dcLpcMw/Da1+12y8/Pz/NLxTAM+fn5dei/2KpVq5Sfn9+7A0G/8pWoWNlik7r03UbHyZ4tBgDQ43zislRwcLAWLVqkoqIibdq0STabTU6n02uf2tpaRUdHKzIyUoZh6OzZs17raz7tv1hubq6WL1/u+dzY2KiRI0f23mAAADAJawo/4RPh5lNBQUEKCQnR+PHjVVJS4hVKSkpKdMsttygsLExJSUkqKSnRnDlzJH1yGevMmTNKTU3t9JhBQUF9NgYAAPoaawq9mRZuampq9OqrryojI0OBgYF69dVXtWvXLr3yyisKDQ1VQUGBiouLNWPGDO3du1fl5eXKyMiQJGVlZSk/P1/XXnutQkNDlZubq8zMTIWGhpo1HAAATMOaQm+mhZugoCD97ne/U3Z2tgYOHKj4+Hjt2rVLiYmJkqTt27dr2bJlqq+vV0JCgvbs2aOwsDBJUnZ2tmpqapSYmKjAwEDNmzdPBQUFZg2lV3T39KCVTi8CAC4Pawo/YVq4sdvt+tOf/nTJ/tmzZ6uioqLTPn9/fxUWFqqwsLC3yjNNT55alKxxehEAgC/Cp9bcoGdOLUrWOr0IAMAXQbjxUd05tShZ6/QiAABfBG8FBwAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlsLdUvhc3X0goN1uV2xsbA9VAwDAP0a4wSX11AMFQ0JCVVFRTsABAPQJwg0uqSceKNjoOKnDG/LldDoJNwCAPkG4wefq7gMFAQDoSywoBgAAlsKZG/QJ3nIOAOgrhBv0Kt5yDgDoa4Qb9Crecg4A6GuEG/QJ3nIOAOgrLCgGAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWYlq4KS4uVnp6uhISEjRmzBj95je/8fSdPHlSs2bNUlxcnBISEvT00097fXfbtm0aO3asRowYoenTp+uDDz7o6/IBAICPMi3cPP/889qwYYPee+89HThwQI899pj27dsnt9utuXPnavHixaqsrNTu3bt1zz336OjRo5KkQ4cO6cEHH9T+/fv14YcfatasWcrIyDBrGAAAwMeYFm7WrFmjpKQkSdLo0aN18803q7i4WEVFRQoMDNStt94qSUpJSdGSJUu0adMmSdJvfvMb5eTkKDY2VpJ0//3364MPPtCbb75pyjgAAIBv8Zk1N7W1tYqIiNChQ4eUnp7u1ZeWluZ15uaz/YGBgbr66qs9/QAA4MvNJ8JNaWmpXnjhBS1atEgOh0NDhw716o+KilJdXZ0kfW7/xVpaWtTY2Oi1AQAA6zI93Gzfvl033nijNm3apFGjRsnlcskwDK993G63/Pz8JOlz+y+2atUqRUREeLaRI0f2zkAAAIBPMC3cuN1uLVu2TPn5+dq/f79uvPFGSZLNZpPT6fTat7a2VtHR0ZfVf7Hc3Fw1NDR4turq6l4YDQAA8BWmhZucnBydOHFCZWVlSk1N9bSPHz9eJSUlXvuWlJRo8uTJnfa3trbqjTfe0KRJkzr9OUFBQQoPD/faAACAdZkSbi5cuKB169Zp48aNCgsL8+qbO3euTp065Xm2TVlZmZ5//nn94Ac/kCRlZWXpl7/8pT788EO53W6tWLFC06dP16hRo/p8HAAAwPcEmvFDT5w4ofb2ds/ZmE8lJSVp//792rNnjzIzM7V8+XJFR0dr69atGjFihCRpwYIFeu+99zRx4kS1t7dr2rRp2rBhgxnDAAAAPsiUcJOSkqL29vZL9o8fP15/+ctfLtl/33336b777uuN0gAAQD9n+t1SAAAAPYlwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALMXUcGMYhjZv3qzJkyd7tR85ckSTJk1SXFycUlJSdODAAa/+1atXKyEhQcOHD9eCBQtUV1fXl2UDAAAfZlq42bdvn8aNG6ef/exnOnv2rKe9qalJc+fO1cqVK1VZWal169YpIyNDp0+fliTt3LlTmzdvVmlpqaqqqhQdHa2srCyzhgEAAHyMaeHm448/1mOPPaYnn3zSq33btm2aMGGCZs6cKUmaOnWqpkyZoh07dkj65KxNXl6ebDabAgICtGLFCu3evVv19fV9PgYAAOB7TAs3N910k2644YYO7YcOHVJ6erpXW1pamo4ePSqXy6WysjKvfrvdrvj4eB07dqzXawYAAL7P5xYUOxwODR061KstKipKdXV1cjqdcrvdstvtnfZ3pqWlRY2NjV4bAACwLp8LNy6XS4ZheLW53W75+fnJ5XJJ0iX7O7Nq1SpFRER4tpEjR/ZO4QAAwCf4XLix2WxyOp1ebbW1tYqOjlZkZKQMw/BagPzZ/s7k5uaqoaHBs1VXV/da7QAAwHw+F27Gjx+vkpISr7aSkhJNnjxZYWFhSkpK8up3OBw6c+aMUlNTOz1eUFCQwsPDvTYAAGBdPhduFi9erKKiIhUXF0uS9u7dq/LycmVkZEiSsrKylJ+fr3Pnzqm1tVW5ubnKzMxUaGiomWUDAAAfEWh2ARcbMWKEtm/frmXLlqm+vl4JCQnas2ePwsLCJEnZ2dmqqalRYmKiAgMDNW/ePBUUFJhcNQAA8BWmh5tp06apoqLCq2327Nkd2j7l7++vwsJCFRYW9kV5AACgn/G5y1IAAADdQbgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACW0m/DTXNzs7KyshQXF6cRI0bo/vvvl2EYZpcFAABM1m/Dzb333qv29na9//77On78uA4ePKi1a9eaXRYAADBZvww3H330kTZt2qTHH39cgYGBioiIUG5urjZs2GB2aQAAwGT9Mty88cYbGjVqlGw2m6ctLS1Nb731ltxut4mVAQAAswWaXUBXOBwODR061KstKipKLpdLDQ0NXqGnpaVFLS0tns8NDQ2SpMbGxh6v66OPPpIk1Ve+LVdLc5eO0eiolCQ11LyrKwL9ulxLTxzHV47hS7UwHt+uhfH4di2Mx7dr6bHxnK6S9MnvxJ78XfvpsS5rfa3RD23ZssWYPn26V1tzc7Mhyaivr/dqz8vLMySxsbGxsbGxWWCrrq7+3JzQL8/c2Gw2OZ1Or7ba2loFBwcrIiLCqz03N1fLly/3fG5vb1d9fb0GDx4sP7+uJ9MvqrGxUSNHjlR1dbXCw8P77Of2N8zT5WGeLg/zdHmYp8vDPF2e3ponwzDU1NSkYcOGfe6+/TLcXH311Xr77bd19uxZRUZGSpJKSkqUlpYmf3/vZURBQUEKCgryahs0aFBfldpBeHg4/yguA/N0eZiny8M8XR7m6fIwT5enN+bp4hMYl9IvFxRHR0frn//5n/Xggw/K5XLJ6XTq0UcfVU5OjtmlAQAAk/XLcCNJv/vd73Tq1CnFxMTommuuUVZWlubPn292WQAAwGT98rKUJNntdj3//PNml3HZgoKClJeX1+ESGbwxT5eHebo8zNPlYZ4uD/N0eXxhnvwMg3cWAAAA6+i3l6UAAAA6Q7gBAACWQrgBAACWQrjpBcXFxUpPT1dCQoLGjBmj3/zmN56+kydPatasWYqLi1NCQoKefvppEys11+OPP67ExETFxsbqyiuv1O7duz19R44c0aRJkxQXF6eUlBQdOHDAxEp9w9KlS5WcnOz5zBz93d13362IiAjFx8d7tsrKTx4lzzx1VFpaqilTpiguLk7Dhg3Ts88+K4m5+tRLL73k9XcpPj5eQ4cO1cCBAyUxT59VU1OjuXPnavjw4Ro9erRWrFjh6TN1nnrupQj41D333GNUVFQYhmEY77//vjF8+HDjpZdeMlwul/G1r33N2Lhxo2EYhnH8+HEjMjLSOHLkiHnFmujll182WltbDcMwjFdeecUIDg42nE6n0djYaAwfPtw4cOCAZ7+IiAjD4XCYWa6pqqqqjNDQUCMpKckwDIM5ushdd91lPPzwwx3amaeOysvLjZiYGM+ctLS0GGfOnGGuPscdd9xhPPTQQ8zTRWbMmGHcf//9Rnt7u1FXV2ekpqYaGzduNH2eOHPTC9asWaOkpCRJ0ujRo3XzzTeruLhYRUVFCgwM1K233ipJSklJ0ZIlS7Rp0yYTqzXP1KlTdcUVV0iSpkyZotDQUNXW1mrbtm2aMGGCZs6c6dlvypQp2rFjh5nlmupHP/qRbrvtNs9n5qijzp48zjx19NBDD+mHP/yhZ04GDBigqKgo5uofOHHihHbt2qX77ruPebrIkSNH9N3vfld+fn6y2WyaM2eOysrKTJ8nwk0fqK2tVUREhA4dOqT09HSvvrS0NB09etScwnzEhQsXtHr1ak2YMEHJycnM00VefPFF1dXVaeHChZ425qijzsIN8+TtwoULeuGFF7yC8qeYq0srKCjQXXfdxX/HO7Fw4UKtXbtWra2tqqys1PPPP6+FCxeaPk+Em15WWlqqF154QYsWLZLD4dDQoUO9+qOiolRXV2dSdeZ6//33NXLkSIWGhmr79u36t3/7N0linj6jrq5O99xzj9atW+fVzhx1lJubq9jYWE2fPl1//OMfJTFPF3vnnXcUEhKigwcPaty4cRo9erTuuOMONTY2MleXUFtbqx07dujOO++UxN+piz366KPat2+fIiMjNWrUKE2fPl3Tpk0zfZ4IN71o+/btuvHGG7Vp0yaNGjVKLpdLxkXPTHS73X36dnJfMmbMGFVXV+v8+fO65557NHnyZL377rvM0/8zDEO33367cnJyvBYSS2KOLvLEE0/o9OnT+uCDD3Tffffp5ptv1htvvME8XaSpqUkul0tlZWUqLS3Vm2++qdraWmVnZzNXl7BlyxYtWLBAUVFRkvi391lut1s33HCDcnJy1NDQoJqaGr355ptas2aN6fPUb1+/4Mvcbrd++MMf6uDBg9q/f79SU1MlSTabTU6n02vf2tpaRUdHm1GmzwgODtaiRYtUVFSkTZs2MU//r6CgQG1tbbr77rs79DFH3vz9P/n/tICAAN1www36zne+o+eee455uojdbldbW5sKCgp0xRVXKDg4WI888oimT5+u6667jrnqxMaNG/WrX/3K85m/U39XXFys1tZWz0urY2Ji9Ktf/Uo33nij0tPTTZ0nztz0gpycHJ04cUJlZWWeYCNJ48ePV0lJide+JSUlmjx5cl+X6JOCgoIUEhLCPP2/J554Qq+99poiIyM1aNAgzZkzR++++64GDRrEHH0Ol8ulAQMGME8XiYuL04ABA3ThwgVPm7+/v4KDg5mrThw9elSnTp3S9OnTPW3M09+1trYqMND7HMkVV1yh1tZW8+epT+7J+hJpbm42AgICjFOnTnXo+/jjj42YmBhjy5YthmEYxuuvv27ExMQY1dXVfV2m6T788ENj69atRltbm2EYn9wKHh0dbbz99ttGdXW1MWjQIKOoqMgwDMN48cUXjbi4OOOjjz4ys2TTHTx40HMrOHPkbd++fYbb7TYMwzD2799vREZGGsePH2eeOrFs2TIjMzPTaGtrMy5cuGB861vfMu6//37mqhOrVq0y5s+f79XGPP3duXPnjGHDhhlbt241DMMwmpqajDlz5hh33nmn6fNEuOlhx48fN/z8/Iy4uDiv7Zvf/KZhGIZRVlZmfP3rXzeGDBliXHnllcbBgwfNLdgktbW1xnXXXWcMGTLEGD16tDFjxgzj0KFDnv59+/YZSUlJxpAhQ4zJkycbf/3rX02s1jd8NtwYBnP0WbNnzzaGDBlixMXFGd/4xjeMl19+2dPHPHlramoylixZYkRFRRljxowx7r//fqOlpcUwDObqYvPnzzd+9rOfdWhnnv7u2LFjxqxZs4y4uDhj1KhRRk5OjvHxxx8bhmHuPPFWcAAAYCmsuQEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAHQL2zbtk2pqamKjY3VmDFj9PTTT0uS2tra9K//+q8aPXq0YmNjlZWVpZkzZ+qpp57yfPfZZ59Vamqq4uPjdd111+m9994zaRQA+gLhBkC/sW/fPlVVVWnHjh3KyspSQ0ODHn74Yf3lL3/RkSNHVFVVpW984xs6ePCg5ztFRUW699579eyzz+rkyZP69re/rZtvvlm8Vg+wLl6cCaDfaGtr07vvvqv3339fixcv1ssvv6zp06frz3/+s8aOHevZLy0tTUuXLtWtt96qOXPm6KabbtJtt93m6bfb7Xr99dc1atQoM4YBoJcFml0AAFyO5cuXa9++fRo3bpzi4+MVGBiojz/+WE1NTUpKSvLaNzIy0vPnEydO6MEHH1R+fr6nzTAMnT59mnADWBThBoDPKy4u1t69e/XWW28pMDBQhmFo3bp1MgxD/v7+qqmp0ciRIz37f/DBB54/Dxs2TD//+c81f/58EyoHYAbW3ADweS0tLWppadH58+dlGIZ+/vOfq7m5WQMGDNCCBQv04x//WBcuXJBhGCooKFBNTY3nu//yL/+iRx991NN2/vx57d2716yhAOgDhBsAPm/27NmaNWuWEhMTlZSUpEGDBmnYsGGSpH//939XQECARo0apeTkZAUGBiotLU1hYWGSpCVLlmjRokWaNm2a4uPjNXHiRFVXV5s5HAC9jAXFACylvb1dsbGx2rNnj77+9a+bXQ4AE3DmBkC/9uqrr8rpdEqSWltblZubq9jYWF111VXmFgbANIQbAP3a3/72N6WlpSk2NlYpKSmqra3Vrl275OfnZ3ZpAEzCZSkAAGApnLkBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACW8n/GxEdy6lg+1QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_cols = ['age','BMI','triglycerides']\n",
    "for i in num_cols:\n",
    "    sns.histplot(x=i,data=fix_data)\n",
    "    plt.title(i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이상치 기준 : 심각 수준 이상인 경우\n",
    "- 체질량지수(BMI)\n",
    "    - $40kg/m^2$ 이상\n",
    "- 중성지방(Triglyceride)\n",
    "    - $500mg/dL$ 이상"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='BMI',data=fix_data)\n",
    "plt.axvline(25,label='1단계 비만(BMI 25 이상)',linestyle='--',color='red',alpha=0.4)\n",
    "plt.axvline(30,label='2단계 비만(BMI 30 이상)',linestyle='--',color='red',alpha=0.6)\n",
    "plt.axvline(35,label='3단계 비만(BMI 35 이상)',linestyle='--',color='red',alpha=0.8)\n",
    "plt.axvline(40,label='이상치 기준 (BMI 40 이상)',linestyle='-',color='red',alpha=1)\n",
    "plt.legend()\n",
    "plt.title('BMI')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='triglycerides',data=fix_data)\n",
    "plt.axvline(200,label='고중성지방혈증 기준 (200mg/dL 이상)',linestyle='--',color='red',alpha=0.5)\n",
    "plt.axvline(500,label='이상치 기준 (500mg/dL 이상)',linestyle='-',color='red',alpha=1)\n",
    "plt.legend()\n",
    "plt.title('triglycerides')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11573, 13)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_BMI = fix_data.query('BMI >= 40')\n",
    "len(outlier_BMI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11556, 13)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix_data1 = fix_data.drop(index=outlier_BMI.index).reset_index(drop=True)\n",
    "fix_data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_chol = fix_data1.query('triglycerides >= 500')\n",
    "len(outlier_chol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11422, 13)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fix_data2 = fix_data1.drop(index=outlier_chol.index).reset_index(drop=True)\n",
    "fix_data2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 최종 DATA EDA & 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11422 entries, 0 to 11421\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   ID             11422 non-null  object \n",
      " 1   year           11422 non-null  int64  \n",
      " 2   sex            11422 non-null  int64  \n",
      " 3   age            11422 non-null  int64  \n",
      " 4   heavy_drink    11422 non-null  int32  \n",
      " 5   smoke          11422 non-null  int32  \n",
      " 6   genetic_hbp    11422 non-null  float64\n",
      " 7   BMI            11422 non-null  float64\n",
      " 8   diabetes       11422 non-null  int32  \n",
      " 9   hyper_chol     11422 non-null  int32  \n",
      " 10  triglycerides  11422 non-null  float64\n",
      " 11  HBP_US         11422 non-null  int32  \n",
      " 12  HBP_EU         11422 non-null  int32  \n",
      "dtypes: float64(3), int32(6), int64(3), object(1)\n",
      "memory usage: 892.5+ KB\n"
     ]
    }
   ],
   "source": [
    "final_data = fix_data2.copy()\n",
    "final_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Github 출력 오류로 인한 비활성화\n",
    "# profile_final = ProfileReport(final_data,minimal=True)\n",
    "# profile_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_cols = ['sex','heavy_drink','smoke','genetic_hbp','diabetes','hyper_chol']\n",
    "for i in cat_cols:\n",
    "    ax = sns.countplot(x=i,data=final_data)\n",
    "    ax.bar_label(ax.containers[0])\n",
    "    plt.title(i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RMaMQ6RMpOiIpgJ3vaHbNu4x/v0ZzZWUlbrnlFjQ1NUGn02HMmDH4/PPPRUd0CQWVjVh9uASZh0qwt6AaZgtboCNaLC3YUrQFW4q24IUdLyApMAmjYkZhZOxI9AnpA5Wk6BHlZCecJdXGlP7zyCmtR+ahYmQeLsGhwlrRcdqtdfTRS6JjtFuQZxCGRw/HqNhRGBI1hKeZyGZ4+oi67HBRDTIPleD7QyXIUdi1AVEqDZVYcWIFVpxYYT3NNCJmBMbGj0WoPlR0PHJhLAXqlOrGFny5pxDLdxXg+Jk60XEU7fenmf61618YFj0MNybdiBExIziaiTqMpUDtJssytp2owLJdBcg8XIIWk0V0JPoDk2zChtMbsOH0BoR5heG6xOtwY9KNiPXlwAhqH5YCXVRpnQGf7T6Nz3YX4GRFo+g41E6lTaVYcHAB3j/4PmbEX4tHQgcDPScCav6zp/Pjbwed155TVfjvxlz8ePQMTBw55LJkyOhTdAjY8A7gGwVcMQMYMB3wCRMdjZwQS4HOsv54Kd7ZcAI78ypFRyEbCPMMxqjj21o/qCsC1s8DNv0L6DsFGP4oENRDbEByKiwFAgCYzBZ8e6AY7248gWMlvHDsTibroqCx7G37oLkF2PsRsO8T4NIbgeF/BcJ6iQlIToWloHAGoxnLdxXgfz/l4nRVk+g4ZGNqSY3JeXvPv4FsBg5+Bhz8HOg5obUcoi93XEByOnYthT6L+thz920cvOOgw57LHTSbzFi8NR/vbjyBioYW0XHITkYGpCA8N7MdW8rAsW9b/ySMBkY8BsQPsXs+cj6Kvk9+3bp1GDp0KBITE5GQkIA333zznNudPHkSY8eORXx8PBITE7FkyRIHJ7Uds0XG8l2ncNW/NmDed0dZCG5uSnV1x7/oxDpg4Z+Aj9OB0mM2z0TOTdGnj1asWIEPPvgAKSkpyM3NxYgRI5CUlITx48dbtzGbzUhLS8Nf//pXTJ8+HUeOHMGwYcNw6aWXon///uLCd8IPh0vwyurjvOtYIeL0kUg9vOPiG55P9g9Azlrg8tuAq57iaCWFUPSRwvz585GSkgIA6NGjBzIyMrBu3bo226xduxYajQbTp08HAPTu3RvTpk3DokWLHB230w4V1uDm/27DPR/9zEJQkJvUgZDQxaHEshn4+UPgjcuBjf8CjLzu5O4UXQp/VFZWdtZazNu2bcPQoUPbPDZo0CDs27fPgck6p7TOgEc/3Ye0tzZjey6HlyqJVqXF9Sd22W6HLXXA+rnAmwNaRyxx3nO3xVL4xc6dO/Htt9/i1ltvbfN4cXExwsPD2zwWFhaGiooKR8brEFmWsWR7Psa8uhFf7inkv18FusY/GYENdvgdrS0Evr4P+N9VQPF+2++fhGMpAFi2bBmuu+46LFq0CN27d2/zOZPJhD/OLm42m512feasM3W46d1tePrrQ6gzmETHIUGmlBXb9wmK9gL/Gw388AxPKbkZRV9oNpvNePDBB7F+/XqsXr0a/fr1O2uboKAglJeXt3msrKwMERERjorZLgajGW+uy8Z/N+XCaOahgZIl+8Sh/8HN9n8iiwnY+gZw9Btg4utAwlX2f06yO0UfKTz88MPIzc3F7t27z1kIADBgwABs3bq1zWNbt25FamqqIyK2y5accox/fRPeXn+ChUDIkB284E7VSeCj64Gv7gMaee3K1Sm2FAwGA9555x0sXLgQ3t7e590uLS0NRUVF1nsTdu/ejRUrVuCuu+5yVNTzqjUY8ein+zB1wQ7OXkoAAL1Gj4nZ28Q8+f5PgLcGAgc+FfP8ZBN2PX3kzHcZ5+bmwmKxnPWOPyUlBY8//jjee+89fPrpp9Dr9Vi5ciXuvvtuPProo4iIiMAnn3yCmJgYQclb7T5ZiVnL9qGwmudz6TcTfRPg3SzwhrPGcuDLu4Fjq4C0+YBXgLgs1CmKvabQu3dvWCznXyRmzJgx1v8eMGAA9uzZ44hYF2W2yHhjbTbeWp8DM6ezpj/IKMoVHaHVka+Bwj3A5P8BcYNFp6EOUOzpI1d0uqoRU97bhvlrs1kIdJZ+fglIKTkqOsZvak4BC68FNrwEWMyi01A7sRRcxMr9RfjT/J+wO79KdBRyUlNanHCYtGwGNrwIfDgRqDktOg21A0vByTW2mPC3z/bjwaV7ed8BnVeA1h/jsraIjnF+p7YC7wwFjqwQnYQugqXgxE5VNOKGt7fi85/5DosubJI+Dlpzs+gYF2aoBj69HVj9FE8nOTGblcKFLtoqyR/vfu6srTnlmPT2Zhw/w1XQ6MIkSMgoOCI6Rvtte6t1Wu6matFJ6By6PPpIq9VCpVKhqKgIoaGh0Gq1TjsFhL3JsoyysjJIkgQPD49O72fR1pOY8+0RmHgxmdphcEAy4vLWiI7RMSfWAgvGADcvBUKTRaeh3+lyKahUKnTv3h3FxcUoKiqyRSaXJkkSYmJioFarO/y1RrMFs1ccwtKdBXZIRu4qo94gOkLnVOQAC64GbnofSBorOg39QpJtdL5DlmWYTCaYzco+V+jh4dGpQiivb8Z9S37GrpMcXSRCUmw1SnxeEh2jw8I8g7H6+EFoLC48CEFSAWOeBYY9LDoJwYY3r/16yqQrp02U6khRLe5evJt3J1OHTdZFQWPZKzpG18gW4MdngdIjwHVvARqt6ESKxtFHgu3IrcCU97axEKjDNJIGk/NcvBB+78ByYOkUoKVBdBJFYykItPboGdyxcCfqml340J+EGRGQjPAaN7uOd2IdsOg6zrYqEEtBkK/3FuLej36GwcihvNQ5U6qrRUewj8LdwMI/AbVuVngugqUgwIdb8vDIp/s45JQ6LU4fidTcHaJj2E/ZMeD9cUDFCdFJFIel4GCv/5iF51Ye4brJ1CXp6iBIcPNfoppTwAfjuBa0g7EUHESWZTz3zWG8/mO26Cjk4rQqLa7PceOjhN9rKGudTC9f0MJBCsRScJDZKw7jw60nRccgN3CNfzIClHQhtrm2dVqM0z+LTqIILAUHePG7o/hoe77oGOQmppQVi47geC11wJIbgeIDopO4PZaCnb2xNhvvbXKS1bDI5SX7xKF/gRvdm9ARhmrgo+uBUoHLjSoAS8GOFvyUi9fWZImOQW4kQ9aLjiBWYwWw+DqOSrIjloKdLN15CnNXOdHSiOTy9Bo9Jmbzgivqz7Te4FZ9SnQSt8RSsIMV+wrx1FcHRccgNzPRNwHezVxfAwBQexpYlAbUKvD6ip2xFGxszZEz+Oun+8H70sjWMop4baqNqpPAksmAoVZ0ErfCUrChQ4U1mLVsL+9UJpvr55eAlBKejjxL6eHWJT7NnD/MVlgKNlJaa8Bdi3ajsUXZ60mQfUxpUeZqhu2Sux74dpboFG6DpWADBqMZdy3ejZJaF10Bi5xagNYf47K2iI7h3PYuATb9W3QKt8BS6CJZlvHXT/fjwOka0VHITV2vj4fW3Cw6hvNbNxc48o3oFC6PpdBF//kxG6sOcgQE2YcECekFh0XHcBEy8NW9nECvi1gKXbBiXyHeWMsJ7sh+BgckI648T3QM12FsBJbeAtSdEZ3EZdlsjWal2VdQjcc/5zws7qYxtxEly0pgrDBCNsuInBYJ/yv8z9rOVG/CmU/PQBuuReiEUOvjsklG8bJi1O6phSRJ0CfrETUtCmpvNWSTjMJFhWg42gCVVoWoGVHwTvIGABirjMh/PR89nuoBlfa392pT6nmdqsNqC4Ev7gRuXwGo1KLTuByWQifUNBrxwMd70GziqmnupLmoGafeOIWYu2Pgc4kPLCYLLI1n/z8uWV6Cqs1VUHmoEBQW1OZzZavK0FzYjKQXkqDyUKHww0IUf1KMmLtjULWpCiqtCin/TkHTqSYU/q8QiXMSAQBFHxUhIiOiTSGEeYZg1LGt9v2m3dXJn4ANLwGjnxKdxOXw9FEnPP7FfhRWN4mOQTZ25oszCL46GD6X+AAAVBoVNH5nv29SeamQ8EwCvHt5n/U5wykD/Ab4Qe2phqSWEDA4AE0nW39XDKcN8O3nCwDwivOC2dA6fLlmVw3UXmrr8/5qsjYCaplDnDvtp3+3rvlMHcJS6KDF205i9WGer3Q3ZmML6vbXIWBYwEW3DbsuDNow7Tk/53eFH6q3VsNUa4Kl2YLKdZUISG3dpy5Kh9o9tZAtMhqON0AbooW50Yyyb8oQcUtEm/1oJA0mn9zXxe9K4WQL8MXdnAqjg1gKHXC4qIaT3LmpxtISSB4SGo41IPvpbBx/7DgKPyyEualj79T9B/lD46fBsYeP4egDR2GsMiL4mmAAQODIQMhmGVlPZKF0RSmiZkSh5NMSBI8Phsan7RHJyIBkhNdw4fouaywHPp8JWHjE1V68ptBODc0mPPjJXrTwOoJbMhsMgAVoymtCwuwEyGYZhQsKUfxxMWLuimn3fkqWlUDlqUKvt3tBUkso+bQEBe8WIP6heKg8VIi587d9NWQ1wFhuhP+t/iheWozmwmaovdWIuDkCGaiyx7epTKe2AuvmAFc/JzqJS+CRQjs98/Uh5JY3iI5BduLh4wPZLCMivfVir9pLjbDrw1C3t/2zklqaLahcW4mo26Kg9lJDpVUh8tZINBxrQHNJ25vPLEYLij8uRtTtUSjPLIdHgAe6/a0bAlIDUPtZNVLzdtr6W1S2za8D2WtEp3AJLIV2+Pzn0/hyb6HoGGRHnkEhkDQSLMbfHQlKgOTR/jmHZIsM2SK3/VclAZIkQTa3nSSx7Nsy+A/yhzZMi8acRvj0a73I7N3bG9KpJkjgpIq2JQMr/gI08QjsYlgKF1FQ2YhnVxwSHYPsTO2hRcCQAJQsK4FslmExWlD6VSkChgS0fx9eavj08cGZz89ANsuQZRll35RBE6iBLlJn3c5QaED9wXqEjAsBAGhDtajfX9/6uQMGDArijJ92UV8CZD4pOoXTYylcgCzLeOKLA2jgzKeKEJ4RDkuLBcceOYacp3KgDdci7MYwVG+tRvHH7RvBEnNPDCwtFmQ9kYWsv2XBcMqA+FnxkFStRxyyLKNocRGibouCpG59LDQtFHUH6nD8r8dh3liP+aP5z9Ju9i8FslaLTuHUJFmWeZx6Hh/vyMdTX/EoQQmSYqtR4vOS6Bj4yBSE/gX7RMdwb76RwP3bAa8A0UmcEt+SnEdhdRNe/O6Y6BikIMk+cSwER6gr5mmkC2ApnMdTXx1EfTPP7ZLjTJH1oiMox/5PgKwfRKdwSiyFc/hmfxE2HC8THYMUxFujx8RsznPkUCtnAU3VolM4HZbCH9Q0GvH8yiOiY5DCTPBNgL65XnQMZakral2Yh9pgKfzBS5lHUV7PVa7IsTKKckVHUKbdHwBnuIjR77EUfufA6Wos21UgOgYpTH+/BKSUcE4tIWQz8P0TolM4FZbC78xddRQcoEuOltHS/rumyQ5O/gQcWSE6hdNgKfwi81AJduZVio5BChOg9ce4rC2iY9APTwNGrnIHsBQAAEazBS9n8p4Ecrzr9fHQmnkNS7jqU8CW+aJTOAWWAoDF2/KRxxlQycEkSEgv4EVOp7HldaDmtOgUwim+FGoajXhjbbboGKRAqQHJiCvPEx2DfmVsBNbMFp1COMWXwhvrslHTZBQdgxQoo57nsJ3OoS8VP0RV0aWQX9GAj7bli45BChTmGYJRObyD2fnIwIYXRYcQStGl8PqP2Wgxc3lNcrybdJFQy5yS3Skd/RYoOSg6hTCKLYVTFY34Zj8XRifH00gaTM7bIzoGnZcMrFfu0YJiS+GdjSdgtvBONXK8kQHJCKtp36I9JMjxVUDRPtEphFBkKZTUGPDFzxx6RmJkVHGdYJewQfyiSyIoshT+uymX1xJIiDh9JFLzdoqOQe2R9T1QqLzTfIorhYr6ZizdeUp0DFKodHUgJPC0pcvY+IroBA6nuFJ4f3Memowc9UGOp1PrcH0OjxJcSlYmUHFCdAqHUlQp1DQZeV8CCXONXzICGjnpomuRgR3viQ7hUIoqhU92nEId110mQTLKCkVHoM7Y9wlgqBWdwmEUUwqyLOOTnTxKIDFSfOPRv2Cf6BjUGS11wN4lolM4jGJKYWNWGQoqm0THIIXKsHiJjkBdsfO/gEUZIxYVUwof7+CIIxLDW6PHxGzOc+TSqvKA7NWiUziEIkqhpMaAdcdKRccghZromwB9c73oGNRV298RncAhFFEKS3ee4pQWJEx6Ua7oCGQLeRuBUvdfodHtS8FktmD5rgLRMUih+vslIKXkqOgYZCv7l4pOYHduXwo/Hi1FSS0XMyExMlok0RHIlg5+5vYXnN2+FD7hlBYkSKDWH+OytoiOQbZUWwic3CQ6hV25dSmU1TVjS0656BikUJP0cdCam0XHIFvbv1x0Arty61LIPFTMC8wkhAQJ6QVHRMcgezj6DdDSKDqF3bh1KXx7gAuZkBipAcmIK88THYPsoaUeOLZKdAq7cdtSKK0zYNdJTj5GYmTUc3CDWzuwTHQCu3HbUvj+YAl45ohECPMMwagc3sHs1k6sB+rOiE5hF25bCqt46ogEuUkXCbXMNTvcmmxuXZnNDbllKZypNWB3Pk8dkeNpJA0m5ylvCUdFOp4pOoFduGUpfHewmKeOSIhRASkIq+FRqiLkbQSM7nftyC1L4ftDJaIjkEKlV1WIjkCOYmxsLQY343alUN9swp78KtExSIHi9JFIzdslOgY5UpZtTyHJsozFixcjNTX1vNv4+PggOjoa3bp1Q7du3ZCenm7TDBqb7s0JbD9RARPPHZEAGepASODvnqJk/WCzXWVmZuKxxx5DU1MTNJoLvzRv3rwZ3bt3t9lz/57bHSls5rQWJIBOrcP1OTtExyBHqz0NFB+wya4aGhrw8ssvY8GCBRfdNiAgwCbPeS4sBSIbuMYvCf6NPG2pSFm2WZFt8uTJuPbaay+6nUqlgr+/v02e85z7t9ueBSipMSCnlCtckeNllBWJjkCi5Pzo0KeTJAkJCQlITk7GnXfeiaIi2/7uuVUp8CiBREjxjUf/gn2iY5AoRXsAY5PDnq6qqgp5eXnYtWsX9Ho90tLSIMu2u5blXqWQXSY6AilQhtlTdAQSydwCFOx02NOpVK0v2/7+/pg/fz6OHz+O3FzbLfnqXqWQwzHi5FjeGj0m5mwTHYNEyxcz15XFYoHFYoFWq7XZPt2mFLLP1KG8nguakGNN9E2AvpnXsRQv3zEr7J04cQJZWVkAgObmZsyaNQsDBw5EbGyszZ7DbUphX0G16AikQBmFJ0RHIGdQuAew2GcSxCVLlmDWrFkAgMrKSlx77bWIjo5Gr1690NLSgs8//9ymzyfJtrxCIdCzKw5h0bZ80THIRSXFVqPE56UOfc1l/olYvG+dnRKRy7l3ExDZT3SKLnObI4UDhTWiI5DCpBvc4v0U2YoDLzbbk1uUgslswdHiWtExSEECtf4Yl82FdOh3WArOI7u0HgajRXQMUpDr9XHQmjmwgX6nxDbTXYjmFqVw8DRPHZHjSJCQfuqw6BjkbCpOAKYW0Sm6zC1K4UBhtegIpCCpAcmIrTgpOgY5G4sRqMgRnaLL3GLqbB4pkCNl1Lvfalu/emVLMxbsMcJgkuHvKWHeaB2uS/EAAOwtNuO+VQYU11vg7SFh/nhPjE04+yXk+2wjnlzbjJrm1gvxd1+uxT+G66yf/+ywES9vaUZFkwxPjYR/jtIh45LW5yioseCOr5uQW2VB90AVlk72QoRP63vXlceNWHrIiE8m6+39Y+i80iNAeG/RKbrE5UvBYpFxrKROdAxSiHCvEIw66r4XmAdFq/HIYC081BI25ZswbkkjTj+ihlYtIW1pIz683gtX99Bg40kTJi1rxLG/+FhftH8V5q3Cuju8EeQloajOgsELGtAnTIW0X8olM8eEFTfrEe2nws9FZlz9UQN6h6pwaZgaj60x4P6BWtzU2wNv7WzBvE3NePNaL9Q1y3hmfTN+uM2JCwEASo+KTtBlLn/6qKimCc0mXmQmx5isjYRats9NSs5gZDcNPNQSAGBEvAZ6DwlljTKWHjJiYLQaV/fQWLcbEa/B8kPGs/YxIEqNIK/WfUT5qjAwWo3Cut+G774/yQvRfirrtld102BTfuvP9MAZCyYktT7HmO5q5Fa3ft0/1howa5AWYd5O/pLFUhAvv6JRdARSCI2kweS8PaJjOITBJOP17c0YGKVCzxA1tp02Y2isus02g6LV2Hfm/G/ILLKMNSdMOF5uwY29zn9SoqxRhr+utUR6h6rwxdHWovnyqAlXRqmx/bQJR8stmHGZ7eb3sZvSI6ITdJnLnz5iKZCjjApIQVju96Jj2NWJSgtGLWpAYa2MK6PV+GSyFwCguM6C0d3alkKYt4Qdhee+gW/g/+px8IwFIXoJCyd5nfcd/opjRmRVWJCW0vpS9Oo1nrhrZRNmr2/GqG4avDbOE2M/asAnN3rZ8Lu0o+p8oKUR0Dr5aa4LcP1SqGwQHYEUIqPK/WfhTQhSoeARXxhMMr48akLq+w3YPEMPkwVnrT5tlgFJOvd+dt3tA7NFxvbTZkxfYcCcq3S4+VKPNtu8vr0Zr2xpwYqbveD3y5FCfIAKa27ztm4zb1MzbujpAU+NhFu/aERlk4xLw9R4cYzOeprLqcgWoDwLiOovOkmnuf7po3IeKZD9xXtHYXDeLtExHMZTI+HWPh6YmKTBov1GBHlJKG9sWwtlDTIivM//wqxWSRgap8HsEVq8tfO38fuNRhk3LG/Ep4dN2HqnNwbHnPu9aVaFGSuOG/HYEC3uWtmEhwfrkDnNGxKAD/aefS3DadQUiE7QJa5fCpUsBbK/dFUApLPeK7s/nQbw0kgYEKnG1oK2F9i3njYh9Q/XGc69DwlevztImPJ5E/x1EjbN0KNbwLlfgmRZxn2rDHjjT57wUEvIrrDgyujW5xrdXY0DZ5z4Yn+tay/N6vKlcKqCp4/IvnRqHa7P2SE6ht0V1lqw9KARJktr+W3KN+GrYyakX6LB1L4eWJtnwro8EwDgu2wjjpZZkN7b46z9vLWzBXW/3KOQX23BvJ+aMb1f60Xi7AozNpw04b9pntCozn+U8f5eI3qHqKxHET5aCTsLW4tgVbYJl4ZdvIyEqS0UnaBLXPqaQnl9MxpanPgdA7mFcX7J8M/JFh3D7nQa4P29LZiVaYCvDugWoMJXU7yQHNz6Arxssh73rzKgsklGYpAKK2/Rw1vb+sKe/lkj7rtCi9HdNTh4xozkt5qhUwOBXhL+lqrF1L6t5ZFdaUGzCUh+s+3CRGO6a/D+pNaLyWfqLXhjRwu2zPzt2sI7EzwxY0UTGo0yruqmwV2Xn11GTqPGtUvBpddT2HOqCjf+n/veSESOc6H1FD4yBaJ/wX4HJyKXFTcEmOm6o9Rc+vRRaS1nqST7SvGNZyFQx7j46SOXLoWKBpYC2VeG2VN0BHI1dcWA656AcfFSqHf9aWrJeXlr9JiYs010DHI15hagoUx0ik5z8VLgkQLZz0SfBOib6y++IdEf1Z8RnaDTXLoUqhqd+AYWcnkZRSdERyBX1ey6Mze7dCnUNLEUyD4u809E8pljomOQq3LhI0yXLoVaA0uB7CPDfdfRIUdo4ZGCEDxSIHsI1PrjmuwtomOQK+ORghh1BpPoCOSGrtfHQWvmIAbqAl5TEKPZyCkuyLYkSEg/dVh0DHJ1LTxSEMLiuveHkJMaEpCC2IqTomOQq+ORghhmtgLZWHo9p2InG+CRghhmF76VnJxPuFcIRvEOZrIFs+sOgnHpUrDwSIFsaLI2EmqZ16nIBs63TqkLcOlS4JEC2YpaBibn7REdg9yF5LovrS6bXJZlV56IkJxMRZk39NoI0THIXbAUHI8XmcmWypo9MLTkEZRGjREdhdwBS8HxeOqIbK3GqEFq3gwci50iOgq5OpaC42lULhudnJhZVmF89iSsi30AMlz3YiEJJqlFJ+g0l31lVaskeHq4bHxycjOzh2JR5NOQ1VrRUcgV8UhBDB+dRnQEcmPP5fXCnIA5kHX+oqOQq2EpiKHXshTIvj4ojMU9HvNg8o0WHYVcic5HdIJOc+lS8OaRAjnAmvIgTDI8B0Nwb9FRyFV4BohO0GkuXQo+Ote9mEOu5XCdN0aUP4GqiKGio5Ar8AoUnaDTXLoUeKRAjlTa7IEhBX/GyZhJoqOQs/MKEJ2g01gKRB3QZFZjVM4U7Iy9U3QUcmY8fSSGDy80kyAZ2WPwZfRjkF14PDrZEU8fiRGg9xAdgRTs0ROX4fXQf0L28BYdhZwNTx+JEeHvKToCKdz8Uz3wiH4eLPpQ0VHImfD0kRiR/l6iIxDh6zNhuNkyBy0BPURHIWfgoQc8XPcNq0uXQlSA6/7gyb3srPbDNTVPoz5sgOgoJJp/jOgEXeLSpcAjBXImJ5s8kVo0CyXRY0VHIZEC4kUn6BKXLoUQHy20apf+FsjN1Jk0GJp7Bw7H3io6CokSyFIQRpIkXmwmp2OWVZiQPRE/xDzE6beVKCBOdIIucelSAIBIlgI5qXtyBuODyGcgq3Wio5Aj8fSRWFEBvK5AzmtOXk886z8PFhceokgdxNNHYsUEshTIuS0uisJd6nkw+br2qBRqJx4piJUc7is6AtFFrasIRFrTc2gKvlR0FLInrS+gDxKdoktcvhR6RrAUyDUcrddjRNljqIgcIToK2Uuw69/A6PKl0D3Em8NSyWWUtXhgSP49yI25QXQUsofwPqITdJnLv5pq1CokhLnu0nekPM0WFUbnpGNb7N2io5CtRfYVnaDLXL4UAKAXTyGRC7ol+yp8Gv0EZBWngHcbETxScAopLAVyUY+f6IdXg5+HrOX0265PAsJdfyABS4FIsLcKuuEhzxdg9g4THYW6IrAb4OknOkWXuUUp9Ixw/f8RpGwrS0ORbpqDloBE0VGos9zg1BHgJqUQ4e+JYG+t6BhEXbKnxhejq59CbdhA0VGoM9zgIjMAuM0VrgHxgfjhyBnRMdqtueg4qtZ/AFNtKWAxI+jqP0MX3xcV374KY8VpyKZmaCOSEHTN/dD4Bp/19Q3HNqP8239D7f3bjTKBo6bDu1frGHhzUy0qV7+N5qIsQAL8BqTB78obAQAtZSdR8f18mBtqoA3vgZCJf4NK2zqHVO3PK2Gur0TgyDsc8FOgPzpt0CG16CGsif8YUYWZouNQR0SwFJzKld2DXKYUjBUFKPtqHoInPAqvbv0hm42wGBoAAP5Db4UuMgmyxYzKNe+g6sf3EHrDP865H11UT0Tc+tI5P1f+7avQRaYgZNLfYa6vRMmSx6AJioY+cRAqM99qLaGoFFStex/1+76D35U3wlRbhoaDPyJi2r/s9r3TxTWY1Biaexu+SQxGn4KPRcehdpGA6CtEh7AJtzh9BACDup/9btpZVW/6CL6XT4RXt/4AAEntAbV3ANSePtBFJrU+plLDK2EgTHUV592PSnfuESvGykK0lOTAf8gUSJIEjW8w/Aakof7AGgCAqaEKuqgUAIBnfD8Yq1vLtHLNOwi4aiYkDU/FiSbLEtKyJyAzZhZkyW3+mbqvkGTA23Vegy7EbX7bekf5wVfn/Ac+sqkFjSd2wqfPhVfnMtWWom7PKvgOmHjebVSe5y6F5sJj0EUmQ1KprY9po1LQUpoHAFDr/WAoOARZtqAxezt0kcloOPoTVJ5+8Irv14nviuzlzzmD8L/w2ZA1nCLeqcWnik5gM87/KtpOapWEy+MDsTGrTHSUCzJWFkLS6GA4dQA12z+D3NIEz26XIfCqmVDp9KjZ/jlqd3wBS3MD/K680XqN4Fwas7bjdP4MqPV+8O5zNXwvnwhJkmBuqIRKH9BmW7XeH5amWgBA8PgHUbH6bViaaqFPHAyvxIE4s/QfCL/lRXt+69RJL5xMRn7kXMwxvABVU6XoOHQu8UNFJ7AZtykFoPW6grOXgqWlCbCY0VySjcjbX4NsNqH8u/+g8sf/ImTCw/AffBP8B98EU10FqjctRtkXcxCW/txZ+9GnDIV3z2EAWi8cl694BZBl+F1xHWSLGYDc9gtkC/DLKmDasB6IvO1V66cqvn8D/oNvgrm+EhWZb0BuMcAzrg/8UzPs9FOgjvq4OAqng+fgfb+Xoak9JToO/ZEblYLbnD4CWkvB2am9/CBbTAgcOR2SRguVTo+AobeiKWdHm+00vsEIHv8gDKcOwFhVdNZ+JOm3ZR61od3gP/RmNB7f3Pocnr7Wo4JfmRtroPYJPGs/hlMHYaqvgHfvUahY9RoCr7oTYRnPo6U0D025P9viWyYb2VgRiGsbnkVjiHuMh3cbwYmAf7ToFDbjVqXQLyYAOo1zf0sa/zBIag/IppbfHpQkSBqPszdWqQCVul0XfmWLGZK69cBPG5GI5qIsyLLF+vnmwmPQRfVs+zUmI6rWLUDwNffD3FgDqNTwCIiAJEnwjOuDlrKTnfoeyX6yGrwwvPRvqIgcKToK/aq7e/2/cO5X0A7SalS4otvZ74adiaTRwvuS0aha/z5kixmyyYiazZ/A+5Kr0Ji9Ay1l+QAA2WxE9YYPoYvqCY1vyFn7MRQcgqXFAAAwVhWhZutyePe+CgCgjUyG2icQtds/hyxbYKwuQd3eVfAdkNZmH9Vbl8G790ho/MOh8vSBub4SppozkC1mNOX+DG2Ia68g5a4qWjwwOP8e5MROFh2FAKCHe5WCJMuyfPHNXMfCLXn458ojomNckKWlCZU//B+a8vZCpfWCPmUIAoZPQ9OJ3ajauAhySyMkDx28ug+A//BpUHu2Tg1e9vWL8LnsWnjF90P15o9Rt/d7SGoPqHRe8B1wHXz7j7c+h7G6BBXfvQ5j+SmoPH0QMOJ26zUI4Ncb2N5ExLRXrKOUGrO2oWr9B4Bsgb7ncASOmu7Qnwt13JKkjRhW8J7oGMolqYHHclx+tbXfc7tSOF3ViGEvrxcdg8hhXuxxEDeX/BuSxSg6ivJ0Gw5M/1Z0Cptyq9NHABATqEevSE6QR8rxZG4fvBL8PGQtF5tyuJ7nv4/IVbldKQDA2N7hoiMQOdQ7BfF4QPcCzN4RoqMoS88JohPYnFuWwjUsBVKg78pCMNn4PJoDU0RHUYbI/kBArOgUNueWpXBptD+i/DktACnPvlofjK76O2rCB4uO4v56ud+pI8BNSwEArubRAilUoUGHIaf/gtMx7ndqw6n0TLv4Ni7IbUuB1xVIyRrMKgw/cSv2xd4uOop7Ck4EwnpefDsX5LalMLhHMIK4GhspmCxLuD57PL6NeZTTb9uaG446+pXb/qZ4qFWY1D9KdAwi4f6ScwXeCXsWssZLdBT30SdddAK7cdtSAID0Ae43MoCoM17JT8LffefB4uUeC8EIFdkfiLhUdAq7cetS6B3lh0uieCMbEQAsL47A7dJcGP27iY7i2i6bJjqBXbl1KQBA+oAY0RGInMbmSn+Mr3sGDaH9RUdxTRpPtz51BCigFCb1j4ZW7fbfJlG7nWj0wrCSR1EWNVp0FNfTcyLgFSA6hV25/atloLcWY3qFiY5B5FSqjBoMOXknsmLd+12vzbn5qSNAAaUAAOlX8BQS0R8ZLRKuyb4BG2Pvgwzp4l+gdP5xQI9RolPYnSJKYWRyGMJ8daJjEDmlO7KH4+Oof0BWnWP1P/pN/1sByf3LUxGloFZJuHVQnOgYRE7r6dxLMC9oLmQdR+udk0oDXK6Mu8MVUQoAMG1wPLROvn4zkUgLTsfiPu08mH140+dZLrkR8I8WncIhFPMqGeKjw6R+/GUnupDMsmBc3/wcDEHuOa9Ppw15UHQCh1FMKQDAzGHdRUcgcnoH63xwVeXfUR2RKjqKc+g+EojsKzqFwyiqFHpF+mF4UojoGEROr9igxdCCB3Aqxj2nh+4QBR0lAIJLQZZlLF68GKmp539HsnfvXgwePBjx8fHo3bs31qxZ06Xn/PPIhC59PZFSNJhVGHniZvwcO110FHFCewFJY0WncChhpZCZmYm+ffvi+eefR1VV1Tm3qaurQ1paGubOnYv8/Hy88847SE9PR0lJSaefd2hiCPpE+3f664mURJYlTM6+Biui/wZZUouO43hD/iI6gcMJK4WGhga8/PLLWLBgwXm3Wbp0KQYOHIirr74aADBy5EiMGDECy5cv79Jz3zuyR5e+nkhpZp24HG+FPQfZQy86iuP4RAB9MkSncDhhpTB58mRce+21F9xm27ZtGDp0aJvHBg0ahH379nXpua+9NBIJod5d2geR0ryan4DHvOfB4qWQ63JDHgQ0yluoy6kvNBcXFyM8vO2ymmFhYaioqOjSflUqCX+7JqVL+yBSos9LwjEVc2D0d/Ojbb9oYOBdolMI4dSlYDKZIMtym8fMZjMkG9xq/qc+kegbw2sLRB21rcof4+qeRn3oZaKj2M/IxwEPT9EphHDqUggKCkJ5eXmbx8rKyhAREWGT/T82jkcLRJ2R2+iJoSWPoDRqjOgotheUAPR3/9lQz8epS2HAgAHYunVrm8e2bt16wSGsHTE8KRRDErg8IVFn1Bg1SM2bgWOxU0RHsa3RTwFqjegUwjh1KUydOhVr167FunXrAADfffcdjh49ivR0280B//h43s5P1FlmWYXx2ZOwLvYB95h+O6Jv6zxHCuZ0pbBkyRLMmjULABATE4Nly5bh/vvvR1hYGObOnYuVK1fC29t2I4f6xwZg3CXhF9+QiM5rZvZQLIp8GrLaxUfrjH5GEdNjX4gk//FKrgJln6nD+Pk/wWxR/I+CqEtmRhfgmfoXIDXXiI7ScXGpwMxM0SmEc7ojBRGSwn2RPoCrsxF11QeFsbjHYx5Mvi42zbSkAsa/KDqFU2Ap/OKJ8T0RqOfKU0RdtaY8CJMMz8EQ3Ft0lPa7YiYQ5cZDbDuApfCLQG8tnuBFZyKbOFznjRHlT6AqYujFNxZNH9J6LYEAsBTamDIwFgPiA0XHIHILpc0eGFLwZ5yMmSQ6yoWNfR7wChCdwmmwFH5HkiTMvf5SaFTKHn1AZCtNZjVG5UzBztg7RUc5t9jBQP9bRadwKiyFP+gV6Yc7hnQTHYPIrWRkj8GX0Y851/TbkhqY8Krih6D+EUvhHB4Zm4wIP2XOe0JkL4+euAyvh/4TsoeTzFA86F4g4lLRKZwOS+EcfHQazE5zoZETRC5i/qkeeEQ/DxZ9qNggftHAqCfFZnBSLIXzuLZPJK7uxTudiWzt6zNhuNkyBy0BoqbfloBJbwOefoKe37mxFC7g5cl9EOKjEx2DyO3srPbDNTVPoz5sgOOf/Mq7gYSrHP+8LoKlcAHBPjq8clMf0TGI3NLJJk+kFs1CSfRYxz1pcFLrEFQ6L5bCRYzuGY5bB8WJjkHklupMGgzNvQOHYx0wLFSlAW58D/Dwsv9zuTCWQjs8PaEXuoc4yYgJIjdjllWYkD0RP8Q8ZN/pt4c9CkQLOF3lYlgK7aDXavCfKf15UxuRHd2TMxgfRD4DWW2H63iR/VuX2KSLYim0U//YADw4Okl0DCK3NievJ571nweLZ4DtdqrxAm54D1Bzwsv2YCl0wF9GJ+LyuADRMYjc2uKiKNylngeTr42ms097HQjjZJftxVLoALVKwttTL0eIj4uvLkXk5NZVBCKt6Tk0BXfxjuMr7gT63WybUArBUuigSH8vvH3r5by+QGRnR+v1GFH2GCoiR3RuBzEDgfEv2TaUArAUOmFQj2A8PaGX6BhEbq+sxQND8u9BbswNHftC71AgYzGg4VF9R7EUOmn60O6YfDmX8CSyt2aLCqNz0rEt9u72fYGkBm76APCLsm8wN8VS6IJ5N1yKPtH+omMQKcIt2Vfh0+gnIKs0F95wzGygeydPORFLoSs8PdR477YBCPbmISqRIzx+oh9eDX4esvY8N5P2ngQMe9ihmdwNS6GLogK88PZUXngmcpS3CrrhIc8XYPYOa/uJ6AGt9yNQl7AUbGBwj2C8cAMnziNylJWloUg3zUFLQGLrAwFxwC3LOa+RDUiyLMuiQ7iLt9fn4F+rj4uOQaQYMZ7NWBmzBIGTXgRCU0THcQs8UrChB65KxHSu70zkMKUmL2SN/h8LwYZYCjb2bFpvTOwbKToGkdtTScD8Kf0xqEew6ChuhaVgY5Ik4bWM/hiayF9UInv653WX4E99+AbM1lgKdqDVqPDebVfg0miuAUtkDw9fnYTbUruJjuGWWAp24qPT4MMZV6JbsF50FCK38tCYJDx8dbLoGG6LpWBHIT46LLsnFT24ahuRTTw0JgmPjmUh2BOHpDpAaZ0Bt/5vB3JK60VHIXJZLATH4JGCA4T5emLZPYPRM8JXdBQil8RCcBweKThQVUMLpr2/A4eLakVHIXIZLATH4pGCAwV6a/HJXYPRL4YzqxK1x0OjE1kIDsYjBQHqDEZMX7gLP+dXiY5C5JRUEvDUhN64c1h30VEUh6UgSEOzCQ98sgcbjpeJjkLkVHQaFf4zpT+u5Y1pQrAUBDJbZMxecQgf7zglOgqRU/D38sCCO67AwG5BoqMoFkvBCfxvUy5e/P4oLPw/QQoWHeCFRTMHIjGMo/REYik4icxDxXh4+T4YjBbRUYgc7pIoPyycPhBhfp6ioygeS8GJ7Cuoxl2LdqO8vll0FCKHGZEcinemXg5v3UXWXiaHYCk4mdNVjZixcBeyefczKcBdw7rj73/qCY2ao+OdBUvBCdUajHh0+T78eLRUdBQiu/DWqvHKTf0wgWuPOB2WgpOSZRn/t+EEXluTBTOvQJMbSQzzwbvTBiAxzEd0FDoHloKT25JTjoeW7kVFQ4voKERdNqFvJF6Z3JfXD5wYS8EFlNQYMGvZXuzIqxQdhahTNCoJT17bi3couwCWgoswW2TMX5uNt9Zl834GcikRfp5489bLeEOai2ApuJitJ8rxyPJ9OFPLYavk/G64LBrPXXcJ/L08REehdmIpuKCaRiP++e1hfLmnUHQUonMK9tZi3g2XYvylHF3kalgKLmzdsTN48suDPGogp3JN73C8cGMfhPjoREehTmApuLiaJiPmfHsEn/98WnQUUjhfTw2eS7sEkwfEiI5CXcBScBMbjpfiyS8PorjGIDoKKdCwxBC8clNfRAV4iY5CXcRScCN1BiPmrTqKZbsKREchhYjw88ST1/bEpP7RoqOQjbAU3NDuk5X458ojOFhYIzoKuSkPtYSZw7rjodFJvBHNzbAU3JQsy/hs92m8svo4Z10lmxqeFILnrrsECaGcpsIdsRTcXH2zCW+uy8bCzSfRYuZaDdR50QFeeGZiLw4zdXMsBYU4Wd6Aed8dxZojZ0RHIRfjrVXjzuE9cP+oBHh6qEXHITtjKSjM5uxyvJx5jNcb6KI8PVS4bXA87huViCBvreg45CAsBYVad+wM5v+Yjf2nWQ7Ullajwq1XxuH+UQlcHlOBWAoKt/54Keb/mI19BdWio5BgHmoJNw2IxYOjE3m/gYKxFAhA681v89dmY++patFRyME81BKu6xeNWWOSEBesFx2HBGMpUBubssrwfxtysD2Xaze4u0C9B265Mg63p3ZDhD9PE1ErlgKdU9aZOizaehJf7S1EY4tZdByyoeRwH8wY2h03XBbN0UR0FpYCXVCtwYjPd5/Gku35yC1vEB2HOkmSgFHJoZg5rDuGJ4WKjkNOjKVA7SLLMn7KLsfibSex7lgpV39zEUHeWkzqH4XbBsejB+9ApnZgKVCHna5qxIp9RVixrxBZZ+pFx6E/0KgkjEoJxU0DYjGmVxg81CrRkciFsBSoS46V1GLFviKs3F+E01VNouMo2mVxAZjULwoT+0VxgRvqNJYC2czP+ZX4Zl8RVh0sRnl9i+g4inBJlB/GXxKBSf2jOZyUbIKlQDZntsjYkVuB9cdLsTGrjKeYbEivVWNoYghG9wzD6J5hCOcdx2RjLAWyu+KaJmw8XoZN2WXYnF2OWoNJdCSXEhvkhdEpYRjdKxyDewRBp+EwUrIflgI5lNkiY++pKmzMKsOWnHIcKqpFi4lTev9ebJAXBsQFYkB8IAb3CEZSuK/oSKQgLAUSymi24GhxLfYVVGPvqWrsK6hGnoLuh/BQS7gkyh8D4gNxRXxrEXASOhKJpUBOp7qxBfsKWgviUGENskvrUVDZ6PL3RgR7a5EY5oOkcB8khfmiV6Qf+sb4865iciosBXIJBqMZuWUNyCmrR15ZA/IrG5Bf0Yj8iganGunk5aFGuJ8OMYF6JIb5tJZAmA+Swn25JgG5BJYCubzGFhPK61pQ3tCMivoWVNQ3o6KhBWV1rX9X1DejsqEFBqMZRrOMFrMFLSYLjOZf/7T9J6CSAE8PNTw91PDyUMPTQ2X9by+tGn5eHgj39US4nw5hfjqE+3oizK/1Y19PD0E/BSLbYCkQAdaSUKskns4hRWMpEBGRFSdFISIiK5YCERFZsRSIiMiKpUBERFYsBSIismIpEBGRFUuBiIisWApERGTFUiAiIiuWAhERWbEUiIjIiqVARERWLAUiIrJiKRARkRVLgYiIrFgKRERkxVIgIiIrlgIREVmxFIiIyIqlQEREViwFIiKyYikQEZEVS4GIiKxYCkREZMVSICIiK5YCERFZsRSIiMiKpUBERFYsBSIismIpEBGRFUuBiIisWApERGTFUiAiIiuWAhERWbEUiIjIiqVARERWLAUiIrJiKRARkRVLgYiIrFgKRERkxVIgIiIrlgIREVmxFIiIyIqlQEREViwFIiKy+n+Frx6Z45CGmgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in cat_cols:\n",
    "    count_cat = final_data[i].value_counts().sort_index()\n",
    "    count_label = count_cat.index\n",
    "    plt.pie(x = count_cat,labels=count_label,autopct='%.2f%%',startangle=90)\n",
    "    plt.title(i)\n",
    "    plt.legend(loc = 'upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ISJWVlTnHAADApcsr5tzMnTtXDz30kIKDg7Vp0yYlJSW51BMSErR9+3bZ7XZt3brVpR4WFqaYmBjt2LGjsdsGAABeyONnboqLi5WTk6PvvvtOkmSz2TRo0CCXdcLDw7V582aVlJTI4XAoLCzsnHpN826qqqpUVVXl/LmysrKe9wAAAHgTj5+5eeuttzRq1CiFh4dLkux2u4wxLus4HA75+PjIbrdLUo11d+bMmaPg4GDn0rlz5wbYCwAA4C08Hm4WLlyolJQU58+hoaEqKSlxWae4uFgREREKCQmRMUbl5eVu6+5kZGTo6NGjzqWwsLD+dwIAAHgNj4ab7du369ChQxo4cKBzrFevXsrLy3NZLy8vT4mJiQoKClJcXJxL3Waz6ciRI+rZs6fb7wgICFDr1q1dFgAAYF0eDTcff/yx+vfvL3///5v6k5KSonXr1mn9+vWSpI8++kg7d+7UmDFjJElpaWmaOXOmKioqVF1drYyMDE2cOFGBgYEe2QcAAOBdPDqhePPmzbr22mtdxjp16qQlS5ZoypQpKisrU2xsrFauXKmgoCBJUnp6uoqKitStWzf5+/trxIgRmjt3rifaBwAAXsjH/Hx2rsVVVlYqODhYR48evWQvUfXuP0Sx4591W9v79tPaunGt2xoAAJ5yIf9+e3xCMQAAQH0i3AAAAEsh3AAAAEsh3AAAAEvx+OsX4F3y9+5V7/5D3NYi24Vq5bKcRu4IAIALQ7iBC7vxqfVOKgAAvB2XpQAAgKUQbgAAgKUQbgAAgKUQbgAAgKUwodjCho0eK1tx2Tnj+fsPKNYD/QAA0BgINxZmKy5ze+fTnswUD3QDAEDj4LIUAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFF6/gHpR03usJCmyXahWLstp5I4AAJcqwg3OW/7everdf4j72v4DuumpbLe1vW8/3ZBtAQDggnCD82Y3Pm5fxCnxMk4AgPdgzg0AALAUztw0cbXNdcnff0CxjdwPAACeRrhp4mzFZVwqAgDg33BZCgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWIpHw82WLVvUv39/RUdHq0OHDnr//fclSdu2bVPfvn0VHR2t+Ph4rV271mW7rKwsxcbGqmPHjho1apRKS0s90T4AAPBCHgs3u3bt0siRI/X000+roKBABw4cUL9+/XTs2DENGzZMs2fPVkFBgV599VWNGTNGhw8fliQtXbpU2dnZ2rJliw4ePKiIiAilpaV5ajcAAICX8Vi4+f3vf6/f/va3Gjx4sCSpefPmCg8P1+LFi9WnTx/neHJysvr376+cnBxJP521yczMVGhoqPz8/DRr1iytWLFCZWXuX0EAAAAuLR4JN6dOndKqVat03333nVPbtGmTkpKSXMYSEhK0fft22e12bd261aUeFhammJgY7dixo8H7BgAA3s8j4WbPnj1q2bKlPv30U/Xo0UOXX365Jk2apMrKStlsNrVv395l/fDwcJWWlqqkpEQOh0NhYWFu6+5UVVWpsrLSZQEAANblkXBz7Ngx51mYLVu26KuvvlJxcbHS09Nlt9tljHFZ3+FwyMfHR3a7XZJqrLszZ84cBQcHO5fOnTs3zE4BAACv4JFwExYWptOnT2vu3Llq0aKFWrVqpWeeeUYrVqxQaGioSkpKXNYvLi5WRESEQkJCZIxReXm527o7GRkZOnr0qHMpLCxssP0CAACe55FwEx0drebNm+vUqVP/14ivr1q0aKFevXopLy/PZf28vDwlJiYqKChIcXFxLnWbzaYjR46oZ8+ebr8rICBArVu3dlkAAIB1eSTctGjRQvfee68eeeQR2e12VVVVKTMzU+PHj1dKSorWrVun9evXS5I++ugj7dy5U2PGjJEkpaWlaebMmaqoqFB1dbUyMjI0ceJEBQYGemJXAACAl/HYreDPP/+8Tp48qY4dO+pXv/qVYmNjNWvWLHXq1ElLlizRlClTFB4ertmzZ2vlypUKCgqSJKWnpys5OVndunVTTEyMWrZsqblz53pqNwAAgJfx99QXX3bZZXrrrbfc1oYOHapdu3a5rfn6+mrevHmaN29eQ7YHAACaKN4tBQAALIVwAwAALIVwAwAALMVjc25w6cjfu1e9+w9xW4tsF6qVy3IauSMAgJURbtDg7MZHseOfdVvb+/bTjdwNAMDquCwFAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshScUNwHDRo+VrbjMbS1//wHFNnI/AAB4M8JNE2ArLqvx9QV7MlMauRsAALwbl6UAAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAICleCzcTJ06VcHBwYqJiXEuBQUFkqRt27apb9++io6OVnx8vNauXeuybVZWlmJjY9WxY0eNGjVKpaWlntgFAADghTx65mb69Ok6cOCAc4mOjtaxY8c0bNgwzZ49WwUFBXr11Vc1ZswYHT58WJK0dOlSZWdna8uWLTp48KAiIiKUlpbmyd0AAABexKPhpk2bNueMLV68WH369NHgwYMlScnJyerfv79ycnIk/XTWJjMzU6GhofLz89OsWbO0YsUKlZWVNWbrAADAS3lduNm0aZOSkpJcxhISErR9+3bZ7XZt3brVpR4WFqaYmBjt2LGjodsFAABNgL8nvzwjI0OZmZnq2rWrMjIydNNNN8lms2nQoEEu64WHh2vz5s0qKSmRw+FQWFjYOfWa5t1UVVWpqqrK+XNlZWX97wjqLH/vXvXuP8RtLbJdqFYuy3FbGzZ6rGzF7s/W1bYdAMD6PBZuXn75ZS1YsEAOh0O5ubm68847tW7dOtntdhljXNZ1OBzy8fGR3W6XJBlj5OPjc07dnTlz5mjmzJkNtyO4KHbjo9jxz7qt7X376Rq3sxWX1Wk7AID1eeyylK/vT1/t5+enW2+9VXfffbc+/PBDhYaGqqSkxGXd4uJiRUREKCQkRMYYlZeXu627k5GRoaNHjzqXwsLChtkhAADgFbzmOTd2u13NmzdXr169lJeX51LLy8tTYmKigoKCFBcX51K32Ww6cuSIevbs6fZzAwIC1Lp1a5cFAABYl8fCTW5urs6cOSNJ+uSTT7Rs2TKNHj1aKSkpWrdundavXy9J+uijj7Rz506NGTNGkpSWlqaZM2eqoqJC1dXVysjI0MSJExUYGOipXQEAAF7EY3NuXnzxRd1zzz0KDAxUVFSUPvjgA8XHx0uSlixZoilTpqisrEyxsbFauXKlgoKCJEnp6ekqKipSt27d5O/vrxEjRmju3Lme2g0AAOBlPBZuPv744xprQ4cO1a5du9zWfH19NW/ePM2bN6+hWgMAAE2Y18y5AQAAqA+EGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCkefbcUUJva3juVv/+AYhu5HwBA00C4gdeq7b1TezJTGrkbAEBTwWUpAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKXUKN3/729/OGXM4HPrwww8vth8AAICLUqdw8/jjj58z5ufnpxkzZlx0QwAAABfD/0JWfuyxx3T8+HGVlpZqypQpLrX8/Hy1bdu2XpsDAAC4UBcUbuLj41VQUCBfX1+1b9/epXbllVdq7Nix9docAADAhbqgcJOamipJ2rt3rzIzMxuiHwAAgItSpzk3b731Vn33AQAAUC/qFG6+/fZbDRgwQK1atZKfn5/8/Pzk6+srPz+/+u4PAADgglzQZamzUlNT1b9/f/3lL39RSEhIffcEAABQZ3UKN4cOHdILL7xQ370AAABctDpdlurWrZvKysrquxcAAICLVqczNw888IBGjx6t6dOnKzIy0qV23XXX1UtjAAAAdVGncPPUU09JkqZPn+4y7uPjo/z8/ItuCgAAoK7qFG72799f330AAADUC94KDgAALKVOZ26uvPJK+fj4uK3961//uqiGAAAALkadws1f/vIXl59LS0v12muvacCAAXVq4sEHH9Snn36qXbt2SZK2bdumBx98UDabTUFBQXrppZc0ZMgQ5/pZWVlasGCBTp48qeuuu06vv/46L+0EAACS6nhZKjk52WX5zW9+o+XLl+vzzz+/4M8qLCxUdna28+djx45p2LBhmj17tgoKCvTqq69qzJgxOnz4sCRp6dKlys7O1pYtW3Tw4EFFREQoLS2tLrsBAAAsqN7m3DRv3lw//vjjBW/38MMP67777nP+vHjxYvXp00eDBw+W9FOQ6t+/v3JyciT9dNYmMzNToaGh8vPz06xZs7RixQqeuwMAACTVMdz88MMPLkt+fr7mz5+vqqqqC/qc1atXq7S0VHfccYdzbNOmTUpKSnJZLyEhQdu3b5fdbtfWrVtd6mFhYYqJidGOHTvqsisAAMBi6jTnJiIiQj4+PjLGSJIuu+wy9enTR6+//vp5f0ZpaammTZum1atXOy85SZLNZtOgQYNc1g0PD9fmzZtVUlIih8OhsLCwc+qlpaVuv6eqqsoldFVWVp53jwAAoOmpU7g5c+bMRX2pMUYPPPCApk+fru7du7uEG7vd7gxNZzkcDvn4+Mhutzu3//e7tc7W3ZkzZ45mzpx5Uf0CAICm46Lm3Hz99ddavXq18y6n8zV37lydPn1aU6dOPacWGhqqkpISl7Hi4mJFREQoJCRExhiVl5e7rbuTkZGho0ePOpfCwsIL6hUAADQtdTpzc/jwYY0cOVIFBQWKjo7WwYMH1bNnT+Xk5Kh169a/uP3LL7+sEydOKCQkRNJPZ2tOnjypNm3aKCMjQ3l5eZoxY4Zz/by8PI0dO1ZBQUGKi4tTXl6ebr/9dkk/XcY6cuSIevbs6fa7AgICFBAQUJfdBAAATVCdztw88sgjuvHGG1VUVKR//OMfKioqUu/evfXEE0+c1/Y2m02VlZWqqKhQRUWFVq1apSuuuEIVFRVKSUnRunXrtH79eknSRx99pJ07d2rMmDGSpLS0NM2cOVMVFRWqrq5WRkaGJk6cqMDAwLrsCgAAsJg6nbnJy8vT22+/7Zzn4uPjo2eeeUa/+tWvLrqhTp06acmSJZoyZYrKysoUGxurlStXKigoSJKUnp6uoqIidevWTf7+/hoxYoTmzp170d8LAACsoU7hxs/P75wJvH5+fnV6zo0kDRgwwGXeztChQ2ucx+Pr66t58+Zp3rx5dfouAABgbXW6LHXllVfqvffecxlbtmyZunXrVi9NAQAA1FWdztw8//zzGjRokJYtW6bu3btrz549ys3N1d///vf67g8AAOCCnHe4OXXqlPz8/NSsWTPFx8frm2++0VtvvaV9+/apR48e+tOf/qR27do1ZK/ARRs2eqxsxe5f1RHZLlQrl+U0ckcAgPp23uFm0KBB+tOf/uR89UFYWJgefvhhZ33VqlVavHix3nnnnfrvEqgntuIyxY5/1m1t79tPN3I3AICGcN5zbvLz889559O/u+WWW7Rp06Z6aQoAAKCuzvvMzdlbsWvi5+d30c0A9SF/71717j/EfW3/AcU2cj8AgMZ13uGmRYsWOnbsmFq1auW2fvr0aZ0+fbreGgPqym58arz0tCczpZG7AQA0tvO+LDVy5Ei99NJLNdbfffdd9e3bt16aAgAAqKvzPnPz+OOPKzExUc2bN9f06dPVvHlzST+9ofsvf/mLnnnmGecrEwAAADzlvMNN69attWHDBk2cOFF/+MMf1KNHDzkcDu3evVtt27bV+++/Xy+vXwAAALgYF/QQv3bt2unDDz/UwYMH9cUXX6iqqkpdu3ZV7969z3kdAwAAgCfU6QnFUVFRioqKqu9eAAAALlqd3i0FAADgrQg3AADAUgg3AADAUuo05wawotqebMxLNQGg6SDcAP+/2p5szEs1AaDp4LIUAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFI+Gmz/+8Y/q1q2boqKidPXVV2vFihXO2rZt29S3b19FR0crPj5ea9euddk2KytLsbGx6tixo0aNGqXS0tLGbh8AAHghf09+eUJCgh5++GE1a9ZMGzdu1NChQ/X999+refPmGjZsmN58800NHjxYn332mUaMGKFdu3YpIiJCS5cuVXZ2trZs2aLg4GBNnTpVaWlpWrZsmSd356IMGz1WtuIyt7X8/QcU28j9AADQVHk03CQnJzv/d//+/RUYGKji4mJt3LhRffr00eDBg53r9e/fXzk5OUpPT1dWVpYyMzMVGhoqSZo1a5YiIyNVVlbmHGtqbMVlih3/rNvansyURu4GAICmyyvm3Jw6dUpZWVnq06ePunfvrk2bNikpKcllnYSEBG3fvl12u11bt251qYeFhSkmJkY7duxo7NYBAICX8Wi42bdvnzp37qzAwEAtWbJEf/7znyVJNptN7du3d1k3PDxcpaWlKikpkcPhUFhYmNv6z1VVVamystJlAQAA1uXRcNO1a1cVFhbqxx9/1LRp05SYmKjvvvtOdrtdxhiXdR0Oh3x8fGS32yWpxvrPzZkzR8HBwc6lc+fODbdDAADA4zw65+asFi1aaNy4cVq3bp0WLVqk0NBQlZSUuKxTXFysiIgIhYSEyBij8vJyl/k1Z+s/l5GRoRkzZjh/rqysJODgguXv3ave/Ye4rUW2C9XKZTmN3BEAoCZeEW7OCggIUMuWLdWrVy/l5eW5hJK8vDyNHTtWQUFBiouLU15enm6//XZJP13GOnLkiHr27On2MwMCAhptH2BNduNT44TvvW8/3cjdAABq47HLUkVFRVq8eLHzMtPGjRv1wQcfaMyYMUpJSdG6deu0fv16SdJHH32knTt3asyYMZKktLQ0zZw5UxUVFaqurlZGRoYmTpyowMBAT+0OAADwEh47cxMQEKC//vWvSk9PV6tWrRQTE6MPPvhA3bp1kyQtWbJEU6ZMUVlZmWJjY7Vy5UoFBQVJktLT01VUVKRu3brJ399fI0aM0Ny5cz21KwAAwIt4LNyEhYXp73//e431oUOHateuXW5rvr6+mjdvnubNm9dQ7QHnjfk4AOBdvGrODdAU1TYf55NnxhF8AKCREW6ABsREZABofF7xhGIAAID6QrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWwrulAA/hbeIA0DAIN4CH8FJNAGgYXJYCAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACW4u/pBgCcv2Gjx8pWXOa2FtkuVCuX5TRyRwDgfQg3QBNiKy5T7Phn3db2vv10I3cDAN6Jy1IAAMBSPBZu1q9fr6SkJMXGxqpr16565ZVXnLUDBw5oyJAhio6OVmxsrN5++22XbRcvXqwrr7xSnTp10sCBA7V///7Gbh8AAHgpj4Wb5cuX64033tDevXu1du1aPf/88/r444/lcDg0bNgwpaSkqKCgQCtWrNC0adO0fft2SdKmTZv0xBNPKDc3V99//72GDBmiMWPGeGo3AACAl/FYuHnppZcUFxcnSbr88st15513av369Vq3bp38/f2VmpoqSYqPj9f48eO1aNEiSdIrr7yi6dOnKyoqSpL02GOPaf/+/frqq688sh8AAMC7eM2cm+LiYgUHB2vTpk1KSkpyqSUkJLicufn3ur+/v6699lpnHQAAXNq8Itxs2bJFq1at0rhx42Sz2dS+fXuXenh4uEpLSyXpF+s/V1VVpcrKSpcFAABYl8fDzZIlSzR8+HAtWrRIXbp0kd1ulzHGZR2HwyEfHx9J+sX6z82ZM0fBwcHOpXPnzg2zIwAAwCt4LNw4HA5NmTJFM2fOVG5uroYPHy5JCg0NVUlJicu6xcXFioiIOK/6z2VkZOjo0aPOpbCwsAH2BgAAeAuPhZvp06crPz9fW7duVc+ePZ3jvXr1Ul5ensu6eXl5SkxMdFuvrq7WF198ob59+7r9noCAALVu3dplAQAA1uWRcHPq1Cm9+uqrWrhwoYKCglxqw4YN06FDh5zPttm6dauWL1+u//iP/5AkpaWl6YUXXtD3338vh8OhWbNmaeDAgerSpUuj7wcAAPA+Hnn9Qn5+vs6cOeM8G3NWXFyccnNztXLlSk2cOFEzZsxQRESE3n33XXXq1EmSNGrUKO3du1fXXXedzpw5owEDBuiNN97wxG4AAAAv5JFwEx8frzNnztRY79Wrl7788ssa648++qgeffTRhmgNAAA0cbw4E/BC+Xv3qnf/IeeO7z+gWA/0AwBNCeEG8EJ24+P27d97MlM80A0ANC0ef84NAABAfSLcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAAS+E5N4BF1PTgP0mKbBeqlctyGrkjAPAMwg1gETU9+E+S9r79dCN3AwCew2UpAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKTzErxENGz1WtuIyt7X8/QcU28j9AABgRYSbRmQrLqvxCbJ7MlMauRsAAKyJcANcAnjvFIBLCeEGuATw3ikAlxImFAMAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEvhOTfAJa62B/wVHSxQx6hotzUe/gfAWxFugEtcbQ/425OZwsP/ADQ5XJYCAACWQrgBAACW4tFwY4xRdna2EhMTXca3bdumvn37Kjo6WvHx8Vq7dq1LPSsrS7GxserYsaNGjRql0tLSxmwbAAB4MY+Fm48//lg9evTQs88+q/Lycuf4sWPHNGzYMM2ePVsFBQV69dVXNWbMGB0+fFiStHTpUmVnZ2vLli06ePCgIiIilJaW5qndAAAAXsZjE4pPnDih559/XoGBgZo8ebJzfPHixerTp48GDx4sSUpOTlb//v2Vk5Oj9PR0ZWVlKTMzU6GhoZKkWbNmKTIyUmVlZc4xAJ41bPRY2YrL3Na4ywpAQ/NYuBk9erQkacOGDS7jmzZtUlJSkstYQkKCtm/fLrvdrq1bt7rUw8LCFBMTox07dig5ObnB+wbwy2zFZdxlBcBjvG5Csc1mU/v27V3GwsPDVVpaqpKSEjkcDoWFhbmtu1NVVaXKykqXBQAAWJfXhRu73S5jjMuYw+GQj4+P7Ha7JNVYd2fOnDkKDg52Lp07d26YxgEAgFfwuof4hYaGqqSkxGWsuLhYERERCgkJkTFG5eXlLvNrztbdycjI0IwZM5w/V1ZWEnCAelDbk43z9x9QbCP3AwBneV246dWrl/Ly8lwCSV5ensaOHaugoCDFxcUpLy9Pt99+u6SfLmMdOXJEPXv2dPt5AQEBCggIaJTegUvJLz3ZGAA8xesuS6WkpGjdunVav369JOmjjz7Szp07NWbMGElSWlqaZs6cqYqKClVXVysjI0MTJ05UYGCgJ9sGAABewuvO3HTq1ElLlizRlClTVFZWptjYWK1cuVJBQUGSpPT0dBUVFalbt27y9/fXiBEjNHfuXA93DQAAvIXHw82AAQO0a9cul7GhQ4eeM3aWr6+v5s2bp3nz5jVGewAAoInxustSAAAAF8PjZ24AXFpqu8uKpxcDqA+EGwCNqra7rHh6MYD6wGUpAABgKYQbAABgKYQbAABgKcy5AeA1mGwMoD4QbgB4DSYbA6gPXJYCAACWQrgBAACWQrgBAACWwpwbAE3esNFjZSsuc1tjIjJw6SHcAGjybMVlTEQG4MRlKQAAYCmEGwAAYClclqpntV37z99/QLGN3A9wqePBgMClh3BTz2q79r8nM6WRuwGso7aQUtt/ONT2YMBPnhlH8AEsiHADoEmoLaTU9T8ceCIyYE3MuQEAAJZCuAEAAJZCuAEAAJbCnBsAuEA13RVZ10nIPGEZqF+EGwC4QDXdFVnXScg8YRmoX4QbAHCjLree80wdwDsQbgDAjbrces6t5YB3INwAQCOo60MIAVw4wg0ANIKGeAghAPcINwBgQdyBhUsZ4QYAvFhdJylzBxYuZYQbAPBiTFIGLhxPKAYAAJbCmRsAuMTU9VJXbfN4ig4WqGNU9AV/JtAQCDcA0ETV9fbyul7qqm0ez57MlBprnzwzjocbolERbgCgiWoqt5c3hXlDdT0rxRkr70S4AQB4pca8nb2uZ6Vqq3lLcLsUNdlwc/LkSaWnpys3N1cOh0Pjxo3T888/Lx8fH0+3BgBNVmM/SfmXvu+mp7Ld1rjUhdo02XDzyCOP6MyZM9q3b59OnDihwYMHa8GCBfrtb3/r6dYAoMlq7Etddf2+2rarLfjUdBmpsV+B0RCTs3lw4/9pkuHm+PHjWrRokQoLC+Xv76/g4GBlZGRo1qxZhBsAuMT9UmByV2vsOUoNMTm7rme6rBimmmS4+eKLL9SlSxeFhoY6xxISEvTNN9/I4XDIz8/Pg90BANAwl/ga4kxXQ4QpT883apLhxmazqX379i5j4eHhstvtOnr0qEvoqaqqUlVVlfPno0ePSpIqKysbpDeH3a7TJ0+4rZkzZ7yi5i19UOPPzmo1b+mDmnf82Z12GEWPftxtbfcf/sMr9vuXanXdB4fdXu//zp79PGPML69smqC33nrLDBw40GXs5MmTRpIpKytzGc/MzDSSWFhYWFhYWCywFBYW/mJOaJJnbkJDQ1VSUuIyVlxcrBYtWig4ONhlPCMjQzNmzHD+fObMGZWVlalt27bcWdXIKisr1blzZxUWFqp169aebueSwrH3HI6953DsPachjr0xRseOHVOHDh1+cd0mGW6uvfZa7d69W+Xl5QoJCZEk5eXlKSEhQb6+rq/LCggIUEBAgMtYmzZtGqtVuNG6dWv+ovEQjr3ncOw9h2PvOfV97H9+AqMmTfLFmREREbr55pv1xBNPyG63q6SkRM8995ymT5/u6dYAAICHNclwI0l//etfdejQIUVGRqp3795KS0vTyJEjPd0WAADwsCZ5WUqSwsLCtHz5ck+3gQsQEBCgzMzMcy4TouFx7D2HY+85HHvP8fSx9zHmfO6pAgAAaBqa7GUpAAAAdwg3AADAUgg3AADAUgg3qDfGGGVnZysxMdFlfNu2berbt6+io6MVHx+vtWvXutSzsrIUGxurjh07atSoUSotLW3Mtpu89evXKykpSbGxseratateeeUVZ+3AgQMaMmSIoqOjFRsbq7fffttl28WLF+vKK69Up06dNHDgQO3fv7+x22/S/vjHP6pbt26KiorS1VdfrRUrVjhr/N43jgcffFDdu3d3/sxxb1hTp05VcHCwYmJinEtBQYEkLzv29fZOBFzS1qxZY6666irTtWtXExcX5xyvrKw0HTt2NGvXrjXGGLNhwwYTHBxsbDabMcaYnJwcc80115jS0lJjt9vN5MmTzW9+8xuP7ENTNW3aNLNr1y5jjDH79u0zHTt2NGvWrDF2u91cddVVZuHChcYYY7799lsTEhJitm3bZowxJi8vz8TExJiCggJjjDHPPfec6dWrlyd2ocnasGGDqa6uNsYY89lnn5kWLVqYkpISfu8bycGDB01gYKDz7xyOe8N76KGHzNNPP33OuLcde8IN6sV7771nVq9ebT799FOXcPNf//VfZuTIkS7rDhs2zGRlZRljjElMTDQffvihs1ZcXGz8/f1NaWlp4zRuQQ8//LB59NFHTW5urvn1r3/tUvvtb39rpk+fbowx5u6773b+ORhjzOnTp01oaKjZvn17o/ZrJaGhoWbnzp383jeS0aNHm4ceesj5dw7HveE99NBDZv78+eeMe9ux57IU6sXo0aN16623njO+adMmJSUluYwlJCRo+/btstvt2rp1q0s9LCxMMTEx2rFjR4P3bFXFxcUKDg6u9dhL5/7Z+Pv769prr3XWcf5OnTqlrKws9enTR927d+f3vhGsXr1apaWluuOOO5xjHPfG4e4VRt527Ak3aFA2m03t27d3GQsPD1dpaalKSkrkcDgUFhbmto4Lt2XLFq1atUrjxo2r9dhLtf/Z4Pzs27dPnTt3VmBgoJYsWaI///nPkvi9b2ilpaWaNm2aXn31VZdxjnvjyMjIUFRUlAYOHKhPPvlEkvcde8INGpTdbpf52XMiHQ6HfHx8ZLfbJanGOi7MkiVLNHz4cC1atEhdunSp9dhLtf/Z4Px07dpVhYWF+vHHHzVt2jQlJibqu+++4/e+ARlj9MADD2j69OkuE4kl/r5pDC+//LIOHz6s/fv369FHH9Wdd96pL774wuuOPeEGDSo0NFQlJSUuY8XFxYqIiFBISIiMMSovL3dbx/lxOByaMmWKZs6cqdzcXA0fPlxS7cf+fOo4fy1atNC4ceN0++23a9GiRfzeN6C5c+fq9OnTmjp16jk1jnvD8/X9KTb4+fnp1ltv1d13360PP/zQ64494QYNqlevXsrLy3MZy8vLU2JiooKCghQXF+dSt9lsOnLkiHr27NnYrTZZ06dPV35+vrZu3epy3Go79u7q1dXV+uKLL9S3b9/GadyCAgIC1LJlS37vG9DLL7+szz//XCEhIWrTpo1uv/12fffdd2rTpg3H3QPsdruaN2/ufce+QaYp45L187ulCgsLTZs2bcy6deuMMcasXr3aREdHm+PHjxtjjJk/f77p3bu3KS8vN1VVVWbChAnOu3nwy06ePGn8/PzMoUOHzqmdOHHCREZGmrfeessYY8w///lPExkZaQoLC40xxrz//vsmJibGFBYWGrvdbp588slz7nZAzb7//nvz7rvvmtOnTxtjfroVPCIiwuzevZvf+0b073/ncNwb3scff2wcDocxxpjc3FwTEhJivv32W6879oQb1Kufhxtjfvo/Q1xcnGnXrp1JTEw0X3/9tbPmcDjMI488Ytq1a2ciIyPN5MmTzalTpxq77Sbr22+/NT4+PiY6Otpluemmm4wxxmzdutVcc801pl27dubqq682n376qcv2f/zjH01kZKRp3769GTt2rCkrK/PAXjRNxcXF5sYbbzTt2rUzl19+uRk0aJDZtGmTs87vfeP4+d85HPeGNXToUNOuXTsTHR1tbrjhBrNhwwZnzZuOPW8FBwAAlsKcGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwANateuXbrtttvOeWleTVJTUzV37twG7srVjBkz9OGHH9ZYnzt3rlJTUxutHwAXx9/TDQBo2hYuXKhvv/1W8+bNc1vv3r27Vq9e3chdXZj58+d7ugUA9YgzNwAuSkFBgY4fP+62dubMmUbu5sJ4e38A6oZwA6DOxo8fr6ysLL3zzjuKiYnR888/rxYtWujdd99VbGysnnzySW3YsEHdu3d3bnPkyBGNGTNGnTt3VteuXfWf//mfatGihQ4cOODy2T/88INatmypwsJC55jdbldERIS+/vprSdL777+v3r17KyYmRlFRUdq0aZMk6eDBgxo+fLi6dOmi7t27Kzs72/kZqampeuKJJ3TzzTcrIiJCx48f14ABA7RkyRLnOosXL9ZVV12lzp07a8CAATp48KBLbzt27NDAgQMVExOjHj166OOPP3bWVq9erV69eikmJkZdunRRfn7+xR9oABemwd5aBeCSkJmZaSZNmmSMMWb//v3Gz8/PPP744+bMmTPG4XCc82LDxMREk5GRYex2u6murjYPPvigkWT2799vjDFmwoQJZs6cOcYYY+68807zhz/8wbntBx98YJKSkowxxrz33numa9eu5ptvvjHGGFNWVmaKiorMqVOnzBVXXGFef/11Y4wxBQUFpmPHjubLL790fn5MTIzZtWuXOXPmjDlz5oxJTk42ixcvNsYY88knn5ioqCiza9cuY4wx27dvN2FhYWbChAnGGGN++OEH0759e7NmzRpjjDFfffWVadu2rSkqKjInTpwwLVq0MLt37zbGGHP48GFTUVFRvwccwC/izA2AeuVwOJSeni4fHx/5+rr+FfPll1/qwIEDmjVrlvz8/NSsWTP96U9/Ome9syZPnuxy1uWvf/2rpkyZIkn6wx/+oHnz5ulXv/qVJCkkJEQdOnTQypUrFRkZqQceeECSFBUVpbvuustlwvBNN92kuLg4+fj4yMfHx+U7X3nlFf3ud79TXFycJKlnz566//77nfVFixZp6NChuvnmmyVJPXr00IABA5SbmysfHx81a9ZM//znP2WMUfv27RUcHFyXwwjgIhBuANSrZs2aKTIy0m1t3759uuKKK+Tn5+ccCwoKUrNmzdyuP3DgQBljtGXLFh06dEhffvml7rjjDknS7t271aNHj3O2yc/P15dffqmYmBjn8s4776isrMy5TnR0dI3979u3T1deeaXLWEhIiMvnr1ixwuXzP//8c5WUlKhly5Zat26dFi5cqCuuuEKvv/56jd8DoOFwtxSAelXTWRhJatu2rYqKilzGDh06pKqqqhq3mTRpkhYtWqROnTopNTVVzZs3lyRFRkZq3759uvzyy13W79Chg2688cZab+2urcewsLBz5tj8+7yZDh06aMKECcrKynK7fZ8+ffT3v/9dO3bs0KhRo9SqVSuNHTu2xu8DUP84cwPgooSGhjr/8bfb7bWum5iYqNOnT+vFF1+UJJ04cUKPPvporWEjNTVVK1as0Ntvv61JkyY5xydPnqzHHntMBQUFkn6agFxYWKjbbrtNmzdv1qpVq5zrbty4USUlJee1P3feeafmzJnjnMj86aefugSlu+66S4sXL9bmzZsl/XTH1YoVK2S321VeXq6tW7dKkuLj49WlS5ca7yQD0HAINwAuytixY1VWVqaYmBitWLGi1nVbtmypVatW6f3331dkZKT69eun8ePHy9fXV0FBQW63CQkJUXJysq644gpFRUU5xx9++GGNHz9eN910k6KjozV06FCVl5crJCREq1at0vPPP69OnTrpiiuu0GuvvVbjpa+fmzx5skaPHq3rr79eMTExWrRokR566CFnPTY2Vm+99ZYefPBBRUVFKS4uTrm5ufL19dXp06c1ceJERUREKD4+XldffbUmTJhwXt8LoP74GHOejw0FgAawZ88e9enTR0ePHq1xnV69emnOnDm66aabGrEzAE0VZ24ANKrVq1c759j88MMPmjp1qvMOKHeys7Pl7+9PsAFw3phQDKBRLV++XJMmTZKfn58CAwOVkpKixx577Jz1qqqqdPnllysqKko5OTke6BRAU8VlKQAAYClclgIAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJby/wG7UxTVs8KozwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in num_cols:\n",
    "    sns.histplot(x=i,data=final_data)\n",
    "    plt.title(i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f91a3_row0_col0, #T_f91a3_row1_col1, #T_f91a3_row2_col2, #T_f91a3_row3_col3, #T_f91a3_row4_col4, #T_f91a3_row5_col5, #T_f91a3_row6_col6, #T_f91a3_row7_col7, #T_f91a3_row8_col8 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_f91a3_row0_col1, #T_f91a3_row1_col0, #T_f91a3_row2_col4, #T_f91a3_row4_col2 {\n",
       "  background-color: #e1dad6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row0_col2, #T_f91a3_row2_col0 {\n",
       "  background-color: #b9d0f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row0_col3, #T_f91a3_row3_col0 {\n",
       "  background-color: #a7c5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row0_col4, #T_f91a3_row4_col0 {\n",
       "  background-color: #e2dad5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row0_col5, #T_f91a3_row5_col0 {\n",
       "  background-color: #c6d6f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row0_col6, #T_f91a3_row6_col0 {\n",
       "  background-color: #d6dce4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row0_col7, #T_f91a3_row7_col0 {\n",
       "  background-color: #e7d7ce;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row0_col8, #T_f91a3_row8_col0 {\n",
       "  background-color: #bfd3f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row1_col2, #T_f91a3_row2_col1 {\n",
       "  background-color: #b6cefa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row1_col3, #T_f91a3_row3_col1 {\n",
       "  background-color: #ccd9ed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row1_col4, #T_f91a3_row4_col1 {\n",
       "  background-color: #c9d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row1_col5, #T_f91a3_row4_col5, #T_f91a3_row5_col1, #T_f91a3_row5_col4 {\n",
       "  background-color: #e4d9d2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row1_col6, #T_f91a3_row6_col1 {\n",
       "  background-color: #f6bda2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row1_col7, #T_f91a3_row5_col8, #T_f91a3_row7_col1, #T_f91a3_row8_col5 {\n",
       "  background-color: #f6bea4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row1_col8, #T_f91a3_row8_col1 {\n",
       "  background-color: #e8d6cc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row2_col3, #T_f91a3_row3_col2 {\n",
       "  background-color: #f5c0a7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row2_col5, #T_f91a3_row5_col2 {\n",
       "  background-color: #e5d8d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row2_col6, #T_f91a3_row6_col2 {\n",
       "  background-color: #d8dce2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row2_col7, #T_f91a3_row7_col2 {\n",
       "  background-color: #d5dbe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row2_col8, #T_f91a3_row6_col8, #T_f91a3_row7_col8, #T_f91a3_row8_col2, #T_f91a3_row8_col6, #T_f91a3_row8_col7 {\n",
       "  background-color: #ecd3c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row3_col4, #T_f91a3_row4_col3 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row3_col5, #T_f91a3_row4_col8, #T_f91a3_row5_col3, #T_f91a3_row8_col4 {\n",
       "  background-color: #e0dbd8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row3_col6, #T_f91a3_row6_col3 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row3_col7, #T_f91a3_row4_col6, #T_f91a3_row6_col4, #T_f91a3_row7_col3 {\n",
       "  background-color: #d7dce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row3_col8, #T_f91a3_row8_col3 {\n",
       "  background-color: #eed0c0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row4_col7, #T_f91a3_row7_col4 {\n",
       "  background-color: #dfdbd9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row5_col6, #T_f91a3_row6_col5 {\n",
       "  background-color: #f0cdbb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row5_col7, #T_f91a3_row7_col5 {\n",
       "  background-color: #edd2c3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f91a3_row6_col7, #T_f91a3_row7_col6 {\n",
       "  background-color: #f2c9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f91a3_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >sex</th>\n",
       "      <th class=\"col_heading level0 col1\" >age</th>\n",
       "      <th class=\"col_heading level0 col2\" >heavy_drink</th>\n",
       "      <th class=\"col_heading level0 col3\" >smoke</th>\n",
       "      <th class=\"col_heading level0 col4\" >genetic_hbp</th>\n",
       "      <th class=\"col_heading level0 col5\" >BMI</th>\n",
       "      <th class=\"col_heading level0 col6\" >diabetes</th>\n",
       "      <th class=\"col_heading level0 col7\" >hyper_chol</th>\n",
       "      <th class=\"col_heading level0 col8\" >triglycerides</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f91a3_level0_row0\" class=\"row_heading level0 row0\" >sex</th>\n",
       "      <td id=\"T_f91a3_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_f91a3_row0_col1\" class=\"data row0 col1\" >0.035612</td>\n",
       "      <td id=\"T_f91a3_row0_col2\" class=\"data row0 col2\" >-0.243173</td>\n",
       "      <td id=\"T_f91a3_row0_col3\" class=\"data row0 col3\" >-0.347957</td>\n",
       "      <td id=\"T_f91a3_row0_col4\" class=\"data row0 col4\" >0.040761</td>\n",
       "      <td id=\"T_f91a3_row0_col5\" class=\"data row0 col5\" >-0.156856</td>\n",
       "      <td id=\"T_f91a3_row0_col6\" class=\"data row0 col6\" >-0.049934</td>\n",
       "      <td id=\"T_f91a3_row0_col7\" class=\"data row0 col7\" >0.079979</td>\n",
       "      <td id=\"T_f91a3_row0_col8\" class=\"data row0 col8\" >-0.204335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f91a3_level0_row1\" class=\"row_heading level0 row1\" >age</th>\n",
       "      <td id=\"T_f91a3_row1_col0\" class=\"data row1 col0\" >0.035612</td>\n",
       "      <td id=\"T_f91a3_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_f91a3_row1_col2\" class=\"data row1 col2\" >-0.260085</td>\n",
       "      <td id=\"T_f91a3_row1_col3\" class=\"data row1 col3\" >-0.120789</td>\n",
       "      <td id=\"T_f91a3_row1_col4\" class=\"data row1 col4\" >-0.140791</td>\n",
       "      <td id=\"T_f91a3_row1_col5\" class=\"data row1 col5\" >0.058623</td>\n",
       "      <td id=\"T_f91a3_row1_col6\" class=\"data row1 col6\" >0.300801</td>\n",
       "      <td id=\"T_f91a3_row1_col7\" class=\"data row1 col7\" >0.291160</td>\n",
       "      <td id=\"T_f91a3_row1_col8\" class=\"data row1 col8\" >0.093300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f91a3_level0_row2\" class=\"row_heading level0 row2\" >heavy_drink</th>\n",
       "      <td id=\"T_f91a3_row2_col0\" class=\"data row2 col0\" >-0.243173</td>\n",
       "      <td id=\"T_f91a3_row2_col1\" class=\"data row2 col1\" >-0.260085</td>\n",
       "      <td id=\"T_f91a3_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_f91a3_row2_col3\" class=\"data row2 col3\" >0.279649</td>\n",
       "      <td id=\"T_f91a3_row2_col4\" class=\"data row2 col4\" >0.037421</td>\n",
       "      <td id=\"T_f91a3_row2_col5\" class=\"data row2 col5\" >0.068874</td>\n",
       "      <td id=\"T_f91a3_row2_col6\" class=\"data row2 col6\" >-0.038275</td>\n",
       "      <td id=\"T_f91a3_row2_col7\" class=\"data row2 col7\" >-0.060806</td>\n",
       "      <td id=\"T_f91a3_row2_col8\" class=\"data row2 col8\" >0.127194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f91a3_level0_row3\" class=\"row_heading level0 row3\" >smoke</th>\n",
       "      <td id=\"T_f91a3_row3_col0\" class=\"data row3 col0\" >-0.347957</td>\n",
       "      <td id=\"T_f91a3_row3_col1\" class=\"data row3 col1\" >-0.120789</td>\n",
       "      <td id=\"T_f91a3_row3_col2\" class=\"data row3 col2\" >0.279649</td>\n",
       "      <td id=\"T_f91a3_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_f91a3_row3_col4\" class=\"data row3 col4\" >-0.025048</td>\n",
       "      <td id=\"T_f91a3_row3_col5\" class=\"data row3 col5\" >0.026946</td>\n",
       "      <td id=\"T_f91a3_row3_col6\" class=\"data row3 col6\" >0.015162</td>\n",
       "      <td id=\"T_f91a3_row3_col7\" class=\"data row3 col7\" >-0.044809</td>\n",
       "      <td id=\"T_f91a3_row3_col8\" class=\"data row3 col8\" >0.155162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f91a3_level0_row4\" class=\"row_heading level0 row4\" >genetic_hbp</th>\n",
       "      <td id=\"T_f91a3_row4_col0\" class=\"data row4 col0\" >0.040761</td>\n",
       "      <td id=\"T_f91a3_row4_col1\" class=\"data row4 col1\" >-0.140791</td>\n",
       "      <td id=\"T_f91a3_row4_col2\" class=\"data row4 col2\" >0.037421</td>\n",
       "      <td id=\"T_f91a3_row4_col3\" class=\"data row4 col3\" >-0.025048</td>\n",
       "      <td id=\"T_f91a3_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_f91a3_row4_col5\" class=\"data row4 col5\" >0.058389</td>\n",
       "      <td id=\"T_f91a3_row4_col6\" class=\"data row4 col6\" >-0.043711</td>\n",
       "      <td id=\"T_f91a3_row4_col7\" class=\"data row4 col7\" >0.018094</td>\n",
       "      <td id=\"T_f91a3_row4_col8\" class=\"data row4 col8\" >0.024352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f91a3_level0_row5\" class=\"row_heading level0 row5\" >BMI</th>\n",
       "      <td id=\"T_f91a3_row5_col0\" class=\"data row5 col0\" >-0.156856</td>\n",
       "      <td id=\"T_f91a3_row5_col1\" class=\"data row5 col1\" >0.058623</td>\n",
       "      <td id=\"T_f91a3_row5_col2\" class=\"data row5 col2\" >0.068874</td>\n",
       "      <td id=\"T_f91a3_row5_col3\" class=\"data row5 col3\" >0.026946</td>\n",
       "      <td id=\"T_f91a3_row5_col4\" class=\"data row5 col4\" >0.058389</td>\n",
       "      <td id=\"T_f91a3_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_f91a3_row5_col6\" class=\"data row5 col6\" >0.178498</td>\n",
       "      <td id=\"T_f91a3_row5_col7\" class=\"data row5 col7\" >0.135842</td>\n",
       "      <td id=\"T_f91a3_row5_col8\" class=\"data row5 col8\" >0.294075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f91a3_level0_row6\" class=\"row_heading level0 row6\" >diabetes</th>\n",
       "      <td id=\"T_f91a3_row6_col0\" class=\"data row6 col0\" >-0.049934</td>\n",
       "      <td id=\"T_f91a3_row6_col1\" class=\"data row6 col1\" >0.300801</td>\n",
       "      <td id=\"T_f91a3_row6_col2\" class=\"data row6 col2\" >-0.038275</td>\n",
       "      <td id=\"T_f91a3_row6_col3\" class=\"data row6 col3\" >0.015162</td>\n",
       "      <td id=\"T_f91a3_row6_col4\" class=\"data row6 col4\" >-0.043711</td>\n",
       "      <td id=\"T_f91a3_row6_col5\" class=\"data row6 col5\" >0.178498</td>\n",
       "      <td id=\"T_f91a3_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_f91a3_row6_col7\" class=\"data row6 col7\" >0.211217</td>\n",
       "      <td id=\"T_f91a3_row6_col8\" class=\"data row6 col8\" >0.130467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f91a3_level0_row7\" class=\"row_heading level0 row7\" >hyper_chol</th>\n",
       "      <td id=\"T_f91a3_row7_col0\" class=\"data row7 col0\" >0.079979</td>\n",
       "      <td id=\"T_f91a3_row7_col1\" class=\"data row7 col1\" >0.291160</td>\n",
       "      <td id=\"T_f91a3_row7_col2\" class=\"data row7 col2\" >-0.060806</td>\n",
       "      <td id=\"T_f91a3_row7_col3\" class=\"data row7 col3\" >-0.044809</td>\n",
       "      <td id=\"T_f91a3_row7_col4\" class=\"data row7 col4\" >0.018094</td>\n",
       "      <td id=\"T_f91a3_row7_col5\" class=\"data row7 col5\" >0.135842</td>\n",
       "      <td id=\"T_f91a3_row7_col6\" class=\"data row7 col6\" >0.211217</td>\n",
       "      <td id=\"T_f91a3_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_f91a3_row7_col8\" class=\"data row7 col8\" >0.131023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f91a3_level0_row8\" class=\"row_heading level0 row8\" >triglycerides</th>\n",
       "      <td id=\"T_f91a3_row8_col0\" class=\"data row8 col0\" >-0.204335</td>\n",
       "      <td id=\"T_f91a3_row8_col1\" class=\"data row8 col1\" >0.093300</td>\n",
       "      <td id=\"T_f91a3_row8_col2\" class=\"data row8 col2\" >0.127194</td>\n",
       "      <td id=\"T_f91a3_row8_col3\" class=\"data row8 col3\" >0.155162</td>\n",
       "      <td id=\"T_f91a3_row8_col4\" class=\"data row8 col4\" >0.024352</td>\n",
       "      <td id=\"T_f91a3_row8_col5\" class=\"data row8 col5\" >0.294075</td>\n",
       "      <td id=\"T_f91a3_row8_col6\" class=\"data row8 col6\" >0.130467</td>\n",
       "      <td id=\"T_f91a3_row8_col7\" class=\"data row8 col7\" >0.131023</td>\n",
       "      <td id=\"T_f91a3_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1eeb7f46ec8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xYo4xSUlJatu2rd5++20dP35cs2bNUqdOnXT27FlJ0ueff65Fixbpr7/+UkxMjPz8/NS7d+88qR9JLgAAAADgll2+fFnvvfee5s6de8OYZcuWqXbt2mrevLkkqVGjRnr44Ye1fPlySZmjuKNGjZKPj4+cnJw0btw4rVmzRvHx8bmuH0kuAAAAANzn0tLSlJiYaLWlpaXlGNuxY0e1bt36X8+3adMmNWjQwKqsTp062rlzpzIyMrR161ar/UWLFlVgYKD27NmT67Y45/oMAAAAAIB89U2BsFwdv2XEsxozZoxV2ahRozR69Oj/dL4zZ86oadOmVmW+vr7avHmz4uLiZDQaVbRo0Wz78+K5XJJcAAAAALBxDgUccnV8eHi4Bg0aZFVmMBj+8/kyMjJkNputyoxGoxwcHJSRkSEpc/EqBweHbPtziyQXAAAAAO5zBoMhV0nt9Xx8fBQXF2dVFhsbKz8/P3l7e8tsNuvixYvy8fHJtj+3eCYXAAAAAGyco7NDrra8VrNmTW3cuNGqbOPGjapXr57c3NwUFhZmtf/MmTM6d+6cqlatmutrk+QCAAAAgI1zKOCYqy2vde7cWevWrdP69eslSd9++60OHDigTp06SZJ69+6tMWPG6NKlS7p69arCw8PVq1cvFSpUKNfXZroyAAAAANi4OzEae7sWL16sLVu2aOrUqfL399dnn32mV155RfHx8QoJCdHatWvl5uYmSRowYIBOnTql0NBQOTs7q127dnr33XfzpB4O5uufBsZd98Ouq/ldBZvXsqqL/rcjPb+rYRcerV4gv6sAAACA2/STf5VcHd/8ZO5f3XOvYCQXAAAAAGzcvTCSe6/gmVwAAAAAgN1gJBcAAAAAbFxu35NrT0hyAQAAAMDGMV05C0kuAAAAANg4ByeS3H/85yQ3MDBQn332merWrZuX9bnjypcvr9mzZ6tx48Y57p84caIKFSqkV1555abn+vnnn/XSSy8pMjIyj2t591w4f0rLPhqt82eOy8nJWa07vazaD7fNFpeUcEFfLZ6kmCN7dSU1WWXL11Sn7uEq7OGdLfbHr+bp2xUzNXnJtrvRhHvGhfOntHzOKMX+3ZetnnxFtR7KuS/XLJmkmKg9upJ6WWXL11DHrsOt+jL2zHF9ufBdnTlxWCZjhpq376WHW3W+m80BAACADXEkybVgJPc6Q4YMye8q3DUmk1EfvddXTdu+qLqN2+vMyShNefMFlShTTv6B5a1i9+/8QxWrNVTnl8fJZDJq2ezRWjH/HXUb+L5VXNqVFP38vyV3sxn3BJPJqLnv91XjNi+qTuP2OnsySh+Oel5+pbP35YFdv6tCtQZ69qXMvlz+8Sit/GS8XhwwUZKUnBivGW/3UNtnX1ONBq1lNpuVdCkuP5oFAAAA2BxWV/6b2WzW/fbK4IN7NsvJyVl1G7eXJJXwD1bthx7T5p9XZ4ut0+hx1WzwqBwdHeXsXEBN276gQ3v/yhb37eczVfXBZne66vecQ3v+lKOTk+r83Zd+/sGq+dBj2vJL9r588OF2qlG/taUvm7R5UYf3ZfXlT6vnqUK1h1SzYRs5ODjI0dFRnj6+d6spAAAAsEEOjg652uxJrpLcI0eOqGnTpipZsqSqVaumbdsyp6fGx8fr+eefV9myZVWuXDlNmDDBcszRo0fVtm1bBQQEyN/fX126dFFaWprOnTsnV1dXnT171hKbnJwsT09PLVq0SKGhoVbX3rNnj/z8/HT16tUb1u/y5ct6+eWXFRgYqICAAL3++utW+0ePHq0ePXroueeeU6lSpbRv3z517dpV7777rqTM6cjly5fX/PnzVblyZRUrVkzdunVTenp6jtfr27evGjZsqOTk5NvryHxy7NAuBYVVsyoLKFdFp44fvOmxyYkXVbBQYauyk9EHtX/n72rcukteVtMmRB/epaDQ6lZlASEP6NTxm09lT068KNdr+nLX5h8syTIAAABwKxycHHO12ZNcteajjz7SZ599ptOnT6t58+bq16+fzGazOnTooKCgIEVFRWnz5s1avHixvvrqK0lSQkKCBg8erOjoaB0+fFiHDx/WggULVLx4cbVq1UrLli2znP+LL75Qo0aN9Nxzz+ny5cvatGmTZd+8efPUs2dPubi43LB+vXv3VkpKig4ePKjo6Gh5eHjo0KFDVjFr1qzRq6++qtOnT6tixYrZzhETE6OTJ09qz549ioyM1G+//abFixdnixs+fLi2bdum//3vfypcuHC2/ZKUlpamxMREqy0tLe1f+/hOSrwYKw/PIlZl7p4+upx06V+PS7+apm+WT1fdph2yytKv6tMZw/VUjxFycrr/ZsEnXoqV+/V96eGjy8mX/vW49Ktp+vbzaarb5AlJ0pWUZF2MO6NLF87pg+FPa2y/R7Rw6hAlXIy9U1UHAACAHXB0csjVZk9yleQOGDBAvr6Z0yh79uypXbt2adu2bTp+/LjGjBkjBwcH+fj4qHfv3lq1apUkqXr16mrcuLFOnz6tzZs3y9fXV/v27ZMk9ejRQ0uWZD3PuWDBAr300ktydnZWz549tXDhQknS1atXtWzZMvXp0+eGdbtw4YK++OILTZs2TQaDQQ4ODhoxYoSlvv+oUaOGGjRokNkZjtm7w2AwaOTIkXJwcFCRIkXUsWNHbd261Srmvffe07p16/Tdd9/J3d39hnWKiIiQp6en1RYREXHD+DvNZDLKLPN1ZSbJ4cY3edz5k5oy6kUV8wtQ88e7W8pXL56ksCp1Va5S7TtW33uZyZhzXzroxn154fxJfTj6BRUrEaCmbTP78sqVy3JwcFDkrt/1ysi5Gj5prdzcvfXptGF3tP4AAACwbUxXzpKrITd/f3/Lz15eXkpJSdHRo0d17tw5BQUFWfalp6erVq1akqQffvhBr732mkqXLq2QkBBdvnzZMuW4devW6tOnjyIjI2UwGHTixAm1atVKktSrVy9Vr15dU6dO1erVq1W/fn2VLl36hnU7evSoSpQoIQ8PD6tyLy8vq98DAgL+tY3Fixe3Sn69vb117tw5y+/nz5/Xm2++qZ9//lmenp7/eq7w8HANGjTIqsxgMOiXu7A486hXH7H8XLJMqPq8Pk2FCnvqcuIlq7jkxIvy8Cqa4zn2bf9VS2a9pZYdeqlx66yVfrf+/q2iIrdr0NvZR7jt0Zi+LS0/lwwIVa+h0zP7MumiVVxyYrzcb9CX+3f8qmWz31Tz9r3U6NGs6d2F3b1lNpv1SMeXVbBQ5hcmjz0zQOE96ulK6mW5FnS7Ay0CAACArbO30djcyPN5pSVLllRYWJh27tyZ4/6ePXtq6dKlatiwoSSpf//+liTXyclJL774opYsWSInJyf17NnTkmD6+/urfv36WrNmjebNm3fTVZCLFi2q8+fPKy0tTQaDQVJmsn3y5EmruJxGb2+Hr6+vZs6cqSeffFI//fRTjlOe/2EwGCx1sXbj54rzypgZ32crK122otat+cSq7NjBnQoqVzVb7LFDu7Rk1pvq8/oMBYRUttr3y/+WKPbMcY3o1ViSZDablJF+VcO61lePwZkjvPZk1PQfspX5B1XUhq+v68tDOxWYQ19GH96lpbNHqvewGSoTXMVqn3MBFxXx9deV1MtZhQ4OcnB0ui+ngQMAAAC3K8+fMK5Tp46uXLmijz/+2LJa8Y4dOxQVFSUp87nUixczR7x27dqlzz//3Or47t27a+XKlVq7dq169Ohhte+ll17SpEmTFBMTo+bNm/9rPQIDA1W5cmUNGzZMRqNRGRkZGjx4cF4108ozzzyj8ePHq1mzZtq/f/8ducadUKVmIyVcjNWWX9dKkmKi9mn31g2q1+yJbLG/frdUjVs/ny3BlaTB45do4qLNmrBgoyYs2Kg33l8p5wIumrBgo90luDdSuWZjJVyM1dbf/unLvdq7bYPqNe2YLfa375aq0aPPZ0tw/9GgxVNau3SSrqalymQy6ZvlU/VA7WYq4JLTFyQAAACA5ODkkKvNnuT50FCBAgX09ddfq1+/fho7dqxcXV1VuXJlTZs2TZI0e/ZsDRw4UH379lXt2rX13HPPKSUlxXJ8uXLlVKxYMZUsWTLb87OPPPKI+vTpo0GDBsnhX54blSQHBwd9/vnnevnll1WqVCn5+Pho2LBhWr9+fV43WZLUrVs3paSkqGnTplq/fv2/jujeK1wMBdXn9Wla9tEYfbloojy8iqhr//fkXcRPkvTF/AgFlKui2g89pvNnYhS5+0/98dMKq3P0eX2aSpYJzen09xUXQ0H1Gjpdyz8era8+fV/uXkX0fN/35PV3X65c8I4CQh5QrYaPKfbscR3cs0kbr+vLXsOmq2SZUDVq/YIuXTincf1bycVQUOUq19EzvcfkR7MAAABgIxxyOUPVnjiYbejlsLGxsSpfvryioqKyPVtry37YdeenK9u7llVd9L8dOb/aCbfn0eoF8rsKAAAAuE3bmzXM1fE11v2eRzXJfzbzkJ/JZFJ4eLh69uxpleA+88wz+vPPP7PFr1u3TsHBwXexhgAAAACQP1h4KotNJLk///yznnvuOTVp0kRjx4612vfZZ5/lU60AAAAA4N5gb68Byg2bSHL/ea8uAAAAAAD/xiaSXAAAAADAjbHwVBaSXAAAAACwcUxXzkKSCwAAAAA2joWnspDkAgAAAICNYyQ3C0kuAAAAANg4nsnNQk8AAAAAAOwGI7kAAAAAYOOYrpyFJBcAAAAAbBxJbhamKwMAAACAjXNwdMjVdjtSU1PVu3dvBQQEyN/fX8OGDZPZbLaK6dGjhwIDA602Nzc39evXT5L0xRdfyGAwWO1fvnx5nvQFI7kAAAAAYOPu5sJTgwcPlslkUlRUlC5fvqzmzZtr+vTplgRWkubNm2d1THJyssqVK6e+fftayurWratffvklz+vHSC4AAAAA2DhHJ4dcbbcqOTlZCxcu1IQJE+Ts7CxPT0+Fh4dr/vz5/3rc5MmT9eijjyosLMxS5uXl9V+b+68YyQUAAAAA3JJt27YpKChIPj4+lrI6depo7969MhqNcnJyynZMcnKypk2bps2bN1uV36kkl5FcAAAAALBxuX0mNy0tTYmJiVZbWlpatuucOXNGxYsXtyrz9fVVRkaGEhIScqzbJ598ooYNGyooKMiq/KuvvlKZMmVUs2ZNTZs2Ldtzvf8VSS4AAAAA2DgHR8dcbREREfL09LTaIiIisl0nIyMjWzJqNBoz6+CQ87TnuXPnqn///lZlHTt2VEJCgmJiYrRgwQLNnj1b06ZNy5O+IMkFAAAAABuX25Hc8PBwJSQkWG3h4eHZruPj46O4uDirstjYWLm6usrT0zNb/NatW3XhwgU1atTIur7XJMRVqlTRW2+9pRUrVuRJX/BMLgAAAADYuNy+J9dgMMhgMNw0rkaNGjp48KAuXrwob29vSdLGjRtVp04dOeawwvPixYv1xBNP3HCU9x8ZGRlycXH5b5W/DiO5AAAAAGDjcjtd+Vb5+fmpVatWGj58uDIyMhQXF6fx48dr4MCBOcZ/9913atasWbbyX3/9VZcvX5YkHTlyROPGjVOXLl3+U9uvR5ILAAAAALhl8+bN0+nTp1WiRAnVqlVLvXv3Vvv27bV48WINGDDAEnfp0iUdPHhQNWrUyHaO9evXq2zZsipTpozat2+vQYMGqVu3bnlSPwdzXi1hhf/sh11X87sKNq9lVRf9b0d6flfDLjxavUB+VwEAAAC36cQrHXN1fOmZK/OoJvmPZ3IBAAAAwMbdzpRje0eSCwAAAAC27iYLO91PSHIBAAAAwMbldnVle0KSCwAAAAA2junKWegJAAAAAIDdYCQXAAAAAGwc05WzkOQCAAAAgI1junIWktx7QMuqLvldBbvA+10BAABwv2IkNwtJ7j3gs43m/K6CzXumvoO2HYrP72rYhZqhPkr55bP8roZdKNTomfyuAgAAuE+Q5GYhyQUAAAAAW8d0ZQt6AgAAAABgNxjJBQAAAAAb5+DAdOV/kOQCAAAAgI1jdeUsJLkAAAAAYONYeCoLSS4AAAAA2DpGci1IcgEAAADAxjGSm4V0HwAAAABgNxjJBQAAAAAb5+DA+OU/SHIBAAAAwNYxXdmCJBcAAAAAbByvEMpCkgsAAAAANo6Fp7KQ5AIAAACAreOZXAt6AgAAAABgNxjJBQAAAAAbx3TlLCS5AAAAAGDrWHjKgiQXAAAAAGycgwMjuf8gyQUAAAAAW8dIrgVJLgAAAADYOJ7JzUK6DwAAAAC4Zampqerdu7cCAgLk7++vYcOGyWw2Z4srXLiwSpUqpcDAQAUGBqpTp05W+6dMmaKQkBCVKlVKHTp00IULF/KkfiS5AAAAAGDrHBxzt92GwYMHy2QyKSoqSvv27dOGDRs0ffr0HGN///13RUdHKzo6WitWrLCUf/7551q0aJH++usvxcTEyM/PT717985VF/yD6coAAAAAYOvu0nTl5ORkLVy4UCdOnJCzs7M8PT0VHh6ucePGqV+/ftnivby8cjzPlClTNGrUKPn4+EiSxo0bpxIlSig+Pt5S9l8xkgsAAAAANs7BwTFXW1pamhITE622tLS0bNfZtm2bgoKCrBLROnXqaO/evTIajVaxjo6O8vT0zHaOjIwMbd26VQ0aNLCUFS1aVIGBgdqzZ0+u+4IkFwAAAABsnaNDrraIiAh5enpabREREdkuc+bMGRUvXtyqzNfXVxkZGUpISLAqd3BwUHBwsEJDQ9WjRw+dPn1akhQXFyej0aiiRYtmO09ePJdLkgsAAAAANs7B0TFXW3h4uBISEqy28PDwbNfJyMjItsjUPyO417+r9+LFizp27Ji2bNmiQoUKqW3btjKbzcrIyJCkHM+TF+/75ZlcAAAAALjPGQwGGQyGm8b5+PgoLi7Oqiw2Nlaurq7ZpiY7/v3uXk9PT02dOlUeHh46evSo/Pz8ZDabdfHiRatpz7GxsfLz88t1W0hyb2LBggX67LPP9N133+V3Ve6Ii3EnteaTt3ThXLScnJzVuF1fVa3/eLa4U8f26NvFb+ty0gUZMzJUsfYjavnUUDk5Zd5C33w6Vrs2rVHBQh6WY7q98am8ipa6a23Jb7HnzmjO9AidPXVCTs5O6vhsTzVs0uqG8adORGvOtHfUuXt/lStf2VK+b9dWrVjysRIuxctslh59/Gk90rbTDc9jL65cTdf7y/+njfuOyGQy69EHq2hAxxZW3+alZxg1/7vf9NPWfUpKvaKSRbz0+rOtFVa6hCXmo69/1uo/dshsNivU30/DO7dRce/sz4IAAADYlTwYAb0VNWrU0MGDB3Xx4kV5e3tLkjZu3Kg6depYktqcmEwmmUwmubi4yM3NTWFhYdq4caMee+wxSZnToM+dO6eqVavmuo4kufcxk8mopVNeVv1W3VS94RM6f+qI5kd0lq9/OZUoU8Eq1sVQSE/3/VAe3sWVmpKoRe9305YNy1S3+fOWmLotXlDTDv3vdjPuCSajURPHDVHr9s+pUfM2OhlzTGNe7yP/gLIKLBtqFZuUmKB5M9/ToQN7dCU1Jdu5tm7+Tb37j1BJ/wCdO3tK4954WX4l/VW1Zr271Zx8MWnF9zKZzVo7foBS09L10uSF+mzDZj3btK4lJub8BRmNJi0K76WCBhd98csWDZi+VGvHD1QBZyd9vWmXftiyV4uH95aPu5tmrVmvEfO+1Nwh3fKxZQAAAHfBvySYecnPz0+tWrXS8OHDNW3aNF26dEnjx4/X2LFjreKioqJkNBoVGhqqtLQ0DRo0SLVr11bp0qUlSb1799aYMWPUsGFDFSpUSOHh4erVq5cKFSqU6zryTO597Oj+TXJ0clb1hk9IknxLheiBem2184+vssUWKxksD+/MB8wLFvJQQFhtJV08bxXjes0o7v1m766tcnRyUqPmbSRJ/mWC1LBxK/227ttssVfTrii0fBVNnLlMhd2z99mLvV9TSf8ASVJxv1Kq07CZ9u3edmcbkM9SrqRp7aadGtixhZydnOReyFXdH31Iq//YYRUXXNJXr7RrqoIGF0nSk41qKzUtXTHnMxcoiDxxRg0ql5OPu5skqU3dqtp//PTdbQwAAEB+cHDI3XYb5s2bp9OnT6tEiRKqVauWevfurfbt22vx4sUaMGCAJCk+Pl6tW7dWqVKlVKFCBV29elVffPGF5RwDBgxQo0aNFBoaqsDAQBUsWFDvvvtunnSFTSe5CxYsUJUqVVSmTBmFhobq8uXLCgwM1Jw5c9SwYUMVKVJE7du3V2xsrHr06KGAgAAFBARo2bJllnMYjUZNnDhRlSpVUkBAgKpUqaLFixfneL2MjAy1b99eHTt2VEZGhoxGo8aMGaOwsDAFBQWpe/fuSk5OvlvNz7UTR3aqTEh1qzL/slV1NubADY8xm806dXSPju7bqKr121ntK1jI/Y7U0xYcPrhXoRUesCoLDquk48cOZ4stUqy4Wrd/VoXcCt/SuZMSLqlQoVuLtVX7j59RqaLe8nTL+uauSpC/ok6dl9FkuuFxqWlXdeVqutwLukqSmlavoF92HdSpuItKzzBq6bo/1frBKne8/gAAAPkttwtP3Y6iRYtq9erVio2NVXR0tPr27StJ6tKli6ZOnSpJql27to4cOaJTp07p6NGjmjNnjooVK2Y5h6OjoyZOnKjz58/r9OnTmjVr1i09E3wrbHa6clRUlAYNGqTIyEj5+vrq2LFjcnHJHN1ZsWKFvv/+ezk6Oqply5aqUaOG3n//fc2bN0/r1q1T+/bt9fjjj8vNzU1jxozRr7/+qp9//lnFihXTgQMH1KpVK3l7e6tNmzaW65nNZnXt2lUODg767LPP5OzsrDfffFPbtm3T1q1bVahQIXXr1k0jR47UlClT8qlXbk9yQqzcva2X/3bz8FFq8qUc45dN66uovX/IybmA2nR5U76lQqz2//TFZG34arq8fUvroTa9FVK54Z2q+j3nUnycvIsUsyrz9PRWUlLCDY64NUcO7dP2LX/oyc69cnWee11cQpKKeLhZlXm7uynDZFJy6hWr5PdaM75ap1phgfL1zhwRr1EuQHUrllXbEVNlcHaWj4eblo7sc8frDwAAkO8cbHr8Mk/ZbE84Ozvr6tWr2rVrlyQpKChIBQoUkCS9+uqrcnNzU8GCBfX000/L29tbzzzzjCSpWbNmcnFxUVRUlCRp6tSpmjVrluVbhQoVKmjYsGGaM2eO1fVeffVVJSYmavny5SpQoIDMZrOmTJmi6dOny93dXU5OTho2bJhWrVp1wzrf6guW7xaT0Shdt2y3yWS64XSFZ/tN1/BZ29TtjU/154+L9Pu3cy37Hu08UkOn/q6B7/+kBq16aMXM13Q6eu8drf+9xJhjXxrloP++AMDGX3/UB+OG6eXX3pSvX8ncVvGeZjSZru8+mcyZI7g59WFq2lW9+cmX2nbouMZ1f8JSvnTdJkWfvaAf3x+iX6eG66nGtfXylE//dTQYAAAA9sVmR3IDAgK0cuVKjRgxQkOGDNGYMWPUvn17SbJ6OXHhwoVVsqR1guDu7q6UlBTFxsYqKSlJoaHWCwOVLVtWJ06csPy+fft2bdiwQZGRkZbR4tjYWCUnJ6tp06aWOLPZ/K/TlSMiIjRmzBirslGjRql8y1G31/j/YPKQrHoWLx2m5wbMUsHCnkpJvmgVl5IUr8IeRa8/3MLR0VF+pcP0yDOv64vZg9WwdU9Leeb/Oim0aiNVrttGB7avU8nAyjc8l63q36OD5ecygSEa8ub7KuzuoaRE61HbxIRL8vQuctvnNxmNWvDRJO3fs01vjJ2igKByua7zvc7DraAuJVsvwnUxKUWGAs4qXNB62sqJ8/EaMGOpqgaX1vxh3S3P50rSgu//0Id9O6uIR+b07hcfaaivN+3Slshjqlsx+M43BAAAIL843p3VlW2BzSa5kvTII4/okUce0e+//6527dqpRIkSNz/oGkWKFJGrq6uioqKsEt1jx46pbNmylt9r1KihZs2aqVWrVtqwYYNKliypokWLymAwaMeOHZals28mPDxcgwYNsiozGAxadRfWFHpt4vpsZSUDKumP7+ZZlcUc2aHSIdVuej4nZxc5F3C94X6T0Sgn5wK3XU9b8OG87KP1QSHl9fWXS6zKDkXusXo10K1aNHeKzp87pbcnfyJX14L/uZ62pEKZEoo+F6fEy6nycMts866oE6oc5G+1FH1SSqp6T1qgXm0e1hMP1cp2nvQMo5yue6bE2clJ6RnGO9sAAACAfObAdGULm+2JmJgYHTx4UJJUq1Yt+fr63vaiT46Ojnr55Zf18ssvW15ofPDgQX3wwQeWVcH+MXToUD377LNq0qSJTp8+LUdHR3Xu3FlDhgxRamqqJOn06dPauHHjDa9nMBjk4eFhteXVw9X/RWi1Jkq6dF67Nq6RlPku3IM71qnGw9nfybr1l891OTFeknQ56aLWrZys6g9ljWge2fNb5lRnSUf2/q4D235QxZot70Ir7g01ajfUxfg4/b4h833KRw8f0LbNv6lJy+zvHP43V6+m6advV6nPgJH3TYIrSUU93VW/UoimffWTMoxGXUy6rHnf/qrOzepaxf24bZ+C/IrmmOBKUstalTR77Qalpl2VJK3ZuENxicmqFlL6jrcBAAAgXzk65G6zIzY7kpuUlKSOHTsqKSlJHh4eeuGFF9SsWbPbPk9ERITeeecd1atXTxkZGfL19dX06dPVsGH2RZNGjx6ty5cvq0mTJtqwYYOmTJmioUOHKiwsTAUKFFDx4sUVERGRF827K1wMBfXcgFla88lb+v6zd1XYs6g69pkoTx8/SdK3S96Wf9mqeqBeWyVdPK/ZozvIwcFRLq5uqtmok+pc847cTT8s1JdzXlcBF1d5FimpZ/pOy7YwlT0zuLpqyJvva+60CC2e96E8vX3Ud8gYFSnqK0la+NEkBYdVUsPGj/zrec6fPS2z2aRRQ60XmipRKkDhY6fcqerfE0a90E5jFq1Wi6ETVdDgohda1FeT6hX0zZ+7tC/6lIY901ox5+K1++hJtQ6fbHVsz9YP6YmHaum1Jx/RzNXr9dSYmcowmVTGt4hmDOgi90L3zxcGAADgPsVIroWD2Xz9ci+42z7byP8FufVMfQdtOxSf39WwCzVDfZTyy2f5XQ27UKjRM/ldBQAAcJ+48vnEXB3v+tSQPKpJ/rPZkVwAAAAAwN9u8IaU+xFJLgAAAADYOkemK/+DJBcAAAAAbB3P5FqQ5AIAAACArbOzFZJzgyQXAAAAAGwdI7kW9AQAAAAAwG4wkgsAAAAAto7VlS1IcgEAAADA1rG6sgVJLgAAAADYOkZyLUhyAQAAAMDWsfCUBUkuAAAAANg6pitb0BMAAAAAALvBSC4AAAAA2DqeybUgyQUAAAAAW8czuRYkuQAAAABg6xjJtSDJBQAAAABbx8JTFiS5AAAAAGDjzIzkWpDuAwAAAADsBiO5AAAAAGDrWHjKgiQXAAAAAGwdSa4FPQEAAAAANs7s4JCr7Xakpqaqd+/eCggIkL+/v4YNGyaz2WwVk56errFjx6pKlSoqXbq0HnroIe3cudOyf+vWrXJyclJgYKBl++CDD/KiKxjJBQAAAACbdxdHcgcPHiyTyaSoqChdvnxZzZs31/Tp09WvXz9LzKFDh5SRkaE///xTbm5u+uijj9S2bVsdPXpUBQoUkCT5+/srOjo6z+vHSC4AAAAA2DoHh9xttyg5OVkLFy7UhAkT5OzsLE9PT4WHh2v+/PlWcZUqVdLYsWPl5uYmSerTp48uX76sw4cPW2K8vLzypOnXI8kFAAAAANySbdu2KSgoSD4+PpayOnXqaO/evTIajTc8LiUlRSkpKfL09LSUkeQCAAAAAHLm6JirLS0tTYmJiVZbWlpatsucOXNGxYsXtyrz9fVVRkaGEhISbli9ESNGqHHjxipVqpSlbOvWrQoICNADDzygMWPG5Hi9/9QVeXIWAAAAAEC+ye3CUxEREfL09LTaIiIisl0nIyMj2yJT/4zgOuQw7fny5ct68cUX9csvv+jTTz+1lNesWVOXL1/W8ePHtXr1aq1fv17h4eF50hckuQAAAABg6xwcc7WFh4crISHBassp6fTx8VFcXJxVWWxsrFxdXa2mIktSVFSUateurQIFCuj3339XsWLFsqp7TUIcFBSkCRMmaMWKFXnSFayuDAAAAAA2zpzL1ZUNBoMMBsNN42rUqKGDBw/q4sWL8vb2liRt3LhRderUkaNjVh0uXbqkpk2bauTIkerVq9dNz5uRkSEXF5f/3oBrMJILAAAAALbuLq2u7Ofnp1atWmn48OHKyMhQXFycxo8fr4EDB1rFrVixQuXLl79hgrt582bFx8dLks6ePavXX39dXbp0+c/NvxYjufeAZ+rf3suXkbOaoT43D8ItKdTomfyuAgAAAO5R8+bNU48ePVSiRAm5ublpyJAhat++vRYvXqwtW7Zo6tSpOnz4sDZt2qTAwECrY0eMGKFevXpp9+7d6tChg5ydnVWwYEG9+OKLGjZsWJ7Uz8F8/VPDuOtOHN6f31WweaXLVdRfkTdezQ237sHynoo5fCC/q2EXypSroENRMfldDZsXGlwmv6sAAMA9L+mvb3J1vPuDbfKoJvmPkVwAAAAAsHW3MeXY3pHkAgAAAICty+XCU/aEJBcAAAAAbJyZkVwLklwAAAAAsHWM5FrQEwAAAAAAu8FILgAAAADYOLOYrvwPklwAAAAAsHFmpitbkOQCAAAAgK0jybUgyQUAAAAAG8fqyllIcgEAAADAxjFdOQs9AQAAAACwG4zkAgAAAICtY7qyBUkuAAAAANg4pitnIckFAAAAABvHe3KzkOQCAAAAgI1jJDcLSS4AAAAA2DqeybUg3QcAAAAA2A1GcgEAAADAxpkZv7QgyQUAAAAAG2dmurIFSS4AAAAA2DgWnspCkgsAAAAANo5XCGUhyQUAAAAAG8dIbhZ6AgAAAABgNxjJBQAAAAAbx8JTWUhyAQAAAMDG8UxuFpJcAAAAALBxPJObxWZ7Ij4+Xk2aNFFSUtJ/On7BggVq1arVf94PAAAAAPcKsxxytdkTmxnJ/eSTT7Rv3z5NnDhRkuTj46MNGzbkc61sR1pammZ8PE9bt++UyWRS00YPqVe3F+Rw3dz9w1FHNXXmR4qPvyhXV4Ne7d1DNatXy3a+Db/+rvETPtDnn86Xj7e3pdxoNGrVmq+1e99+jR0ZfqebdU+JPXda82a8o7OnY+Tk5Kwnnu2lBo0fvWH8qRPHNG/6eD3XfYBCwqrkGPPJrHd1YM82TZi54k5VO9+kpaVp5sdzre7Jnt1ezHZPHok6qqkzZ1vuyVd697Tck39t3aZ5Cz9VSkqKJOnRR1rouac6SZK+/e4HLf3cut9SUq+oiI+35sz48M43MB+cO3dW06dO1qlTJ+Xs7KxnOz+vJk2b5xj7y8/r9dmyxUpNSVXJUqXUf+Bg+fmVkCTt2rlDSz5doEuXLslsNuvx9k+o7ePtLce++nIvJSZckouLQZLk7eOjiZPss08BALAVjORmsZkk9/jx40pOTs7vatis2fMWyGw269O5s5R65YqGjRytr77+Vh3atrHEpKSk6s2x72joa/1Us1pV7dqzV2+9HaFPZk/Plsh+tmJltmv8tOFnLVi8TI6OjipZosRdade9wmQ0atL4wXq0XWc93OwxnYo5qnHhveVfJlgBZUOtYpMSL+mTWe/q8IHdupKacsNzXog9p983fKsiRYvf6erni4/mfSKT2axFc2frypUrGjZylFZ//Y3at33MEpN5T47X0Nf6q8bf9+Sot9/R/Nkz5OPtLS8vT73/zjh5uLsr7kK8+g8ZpqCAANWr86Bat2qp1q1aWl0z/K0xeqhB/bvd1LvCaDRq3Og31f6JJ9W8xSOKiTmu14e8poCAQJUNDrGKjTywX4sWfqKI9z6Qr6+vPl++VO+9M06TP5wpSdr850b1f22I/P1L6+yZM3pj2CCVLFlKNWvVtpxj6Osj9EDVaneziQAA4B6RmpqqAQMG6Pvvv5fRaNRzzz2n9957L9tgxY4dO/Tyyy/rzJkzcnNz09SpU9WiRQvL/ilTpmj69OlKTU3Vgw8+qLlz56pIkSK5rt9tp/vnzp1Tp06dVLp0aQUHB2vGjBlydXVVdHS0jEajxowZo7CwMAUFBal79+6WxPTnn39W+fLlNX/+fFWuXFnFihVTt27dlJ6ebjn3r7/+qgcffFCBgYGqU6eOtm7dKknq0qWLpkyZoiVLligwMFDLly9XdHS0XF1dLcemp6frnXfeUcWKFVW6dGlVrFjRMrrzb959912VL19evr6+GjBgQLb906ZNU/ny5VWyZEk9/fTTunDhgiQpOjpahQsX1vr161W9enWVKlVK9evX165du263S++41NRU/bhug3p1e0FOTk4q7OamZzs9oe9+XGcVt/7X3xRWLkQ1q1WVJFWtUlkPVKqkn3/93Spu7bffq3LFCtmuk56eoTcGD1Tnpzvducbco/bt3iInRyc93CwzQStVpqzqN2ql39Z/nS32alqaypV/QO/N+FyFPTxveM4l8ybr4WZt71id81PWPfminJyc5Obmpmc6dcx2T2749VeFlQtRjWvuySrX3JOhISHycHeXJBUt4qOwciGKuxCf4zX37N2nM2fP6ZHmTe9gy/LPrl075OTkpOYtHpEklSkToMZNm2nduh+zxa5d85XatesgX19fSVLHJ5/WuXNndexolCSp90uvyt+/tCTJr0QJNXyokXbv2ml1DrfChe9gawAAwO26m9OVBw8eLJPJpKioKO3bt08bNmzQ9OnTrWKSkpLUtm1bvf322zp+/LhmzZqlTp066ezZs5Kkzz//XIsWLdJff/2lmJgY+fn5qXfv3nnSF7ed5Hbo0EHlypVTdHS0IiMjtW/fPqWlpUmSRo8erc2bN2vr1q06cuSIMjIyNHLkSMuxMTExOnnypPbs2aPIyEj99ttvWrx4sSQpMjJSTz75pGbNmqXo6GiNGzdO7du3V0pKihYvXqyBAweqc+fOio6O1tNPP52tXoMGDdLGjRv122+/6cSJE1q9erWcnf99oPr3339X0aJFFRkZqa1bt+rTTz/VDz/8YNn/559/Kjk5Wfv371d0dLQMBoNVx6elpemTTz7Rr7/+qlOnTumFF15Q27ZtlZqaervdekcdOhIlv+K+lmRAkiqEhir6eIyMRqOl7EDkQVWqWN7q2PJh5RR1LNrye9yFeK1cvUYvdnk223Uebdk8x+T3fnD44B6Vq1DVqiw4tLJijh3OFlukWHE92u45FXK7cZKwc+vvSk5K0IP17TMhy7wni9/0ntwfeVCVrrunyoeFKurYMasyk8mkbTt26uTJ02pYv26O11y8/HN1frqTnJyc8rAl946DBw6oQsVKVmVhYeUtieu1IiOtY52cnBQcXE5Hc4iVpISESyrkVsiqzM3NLQ9qDQAA8orZwTFX261KTk7WwoULNWHCBDk7O8vT01Ph4eGaP3++VdyyZctUu3ZtNW+e+ehUo0aN9PDDD2v58uWSMkdxR40aJR8fHzk5OWncuHFas2aN4uNzHrC4HbeV5G7fvt2SgDo5OalAgQJ6//335ejoKLPZbBludnd3l5OTk4YNG6ZVq1ZZjjcYDBo5cqQcHBxUpEgRdezY0TJaO336dL388suqWbOmJKlly5by8/PT5s2bb1qvhIQEffTRR1q4cKFleLtcuXJycXH51+MCAwPVs2dPSVKZMmXUsmVLq5HY4sWLKzw8XI6OjnJxcdGECRO0evVqy+hzRkaGJkyYIPe//6H+0ksvqVChQvrzzz9zvF5aWpoSExOttn++ILiT4uMvytvLy6rMy8tTRqNRl68Z7b6QU5ynpxITMxf3MpvNen/KND3/7NNWyQmkhPg4eXr5WJV5eHkrOSnhts+VlHhJn875QF1ffj2vqnfPiY+/KC8v61HsnO7JnOK8r7knJenV14bosY5Pa+KUaXqpV/ds97AkHT0WrejoGDV+uGHeNuQeEh9/QV5e3lZlnp5eSkpMzBZ7Mf6CvLyvi/XKOfbQwUht2bJZjRpnfeHiIGn460PUs/vzevedcTp18mTeNAIAAPxnuR3JvdVcZdu2bQoKCpKPT9a/fevUqaO9e/daDVZs2rRJDRo0sDq2Tp062rlzpzIyMrR161ar/UWLFlVgYKD27NmT6764rSQ3KipK5cqVsxoJcXNzU4ECBRQbG6vk5GQ1bdpUgYGBCgwMVJs2bayeoy1evLgcHbMu6e3trcuXL0uSjh49qpkzZ1qODQwM1PHjx3X+/Pmb1uvw4cPy8/O77fnb/v7+Vr97eXlZ6iNJQUFBVvuLFSsmk8mkhITMxMVgMKjEdc+e+vr6WqY0Xy8iIkKenp5WW0RExG3V+b8wmowyy2xVZjKZJMlq3rzRaJTZnEPc3yErV69VwYKuatmsyZ2tsA3Kqe/M1/TdrTKbzZo7/W090vYZlfQPzLsK3mOMJmO2shvdk9fdujKaTFYxMyZP1NovPtOI14fow5kfacMvv2U79/9++FFtWrVUgQIF8qgF9x6j8QZ/5zm8GD7zftVNY3/9ZYPGjX1Lrw0aalmUSpI+nPGR5i9cohmz5io4OEQjRwy752awAABwvzE7OORqu9Vc5cyZMype3HrNGF9fX2VkZFjypH+Lu3DhguLi4mQ0GlW0aNEc9+fWbS08VaRIEZ06dcqq7PTp00pLS1PRokVlMBi0Y8cOeV83QnArSpYsqREjRmjgwIG3fWyJEiV07tw5Xb58OU+n0F3fwQcOHJCHh4eKFi2q5ORkXb16VUlJSZaR3IyMDB06dEjBwcE5ni88PFyDBg2yKjMYDDofk/MUwbziXthdCYnWr1q6lJAoFxcXuRXKmoLo7l5YideN5CQkJMrH21u79+7TV2u/1YzJE+5oXW3Ba73aWX4uHRCiQSM/kJu7h5ISL1nFJSZclJfX7X3xsnblQhkzjGrR5qm8qOo9y71wYSVcd69dSkjI8Z68Pi4hIUE+3l5WZU5OTqpcsYK6PPuUVn/zrZo0esiyLz09Xet/+VVT338v7xuST3p07WL5OTAoSG+OGid3d48c/n4v5fh5nBmbYHkm9/pYo9Goj2bP0J7dOzV2XISCylp/pv3zZaXBYFCnp5/V+vU/6tDBA6parUaetREAANweszl3rwG6Ua5yvYyMjGyDO/+M4F47EHGjOAcHB2VkZPxdZ3O2AY7rF6/6L25rJLdevXpKT0/X5MmTJUmXL1/W0KFD5ejoKEdHR3Xu3FlDhgyxfKN/+vRpbdy48ZbO/cILL2jq1Kk6ePCgpMx/mK5evdqy38fHR0ePHpUkS6f8o1SpUmrRooVeeukly0js3r17dfXq1dtpXjY7d+7UokWLJEmJiYkaPHiw+vbtaxUzbNgwpaeny2QyafTo0SpXrpyqV6+e4/kMBoM8PDystpxunLxWLqSsTp48paRrRtX3HYhU+dByViProSHB2nfgoNWx+yIjVbF8mFZ/8z9dSkjQC71eVrunO6vd050lSV379M22WJC9mzxntWUbNPIDSVJQcAUdidxtFXc4crdCyuf8aqAb+eHr5Tq4f6de6txMfZ5rqg/eHqSzZ06oz3NNdfZ0TJ61Ib+VCwnOdk/uP3Aw2z1ZLiRE+w9EWh27P/KgKpQPy/G8LgUKyHDdYwp/bd2mIj4+8i9VMg9bkL/mLVhs2d4cNU6SFBJSTpH791nFRR7Yr/LlK2Y7PjiknCIPZMWmp6cr6shhhZXPfP557sezdO7sGU2eOiNbgpsTo9EoZ2f7HSUHAOB+cKu5io+Pj+Li4qzKYmNj5erqKk9Pz5vG+fn5ydvbW2azWRcvXsxxf27dVpJbsGBBff311/ryyy9VokQJNWzYUF26dJGjo6Pc3Nw0ZcoUFShQQGFhYQoODtaTTz5ptXryv3n44Yf19ttv64knnlBAQICqVKminTt3WvY//fTTio+PV2BgoNasWZPt+MWLF6tw4cKqWLGigoKCNHDgQMv0x/+qSZMm+uuvv1SmTBlVr15dNWvW1KhRoyz7XVxcVK1aNctq0ocPH9bKldlfrZPffLy9Vatmdc1buFhGo1EJCYla+vkX6tjOeuXeZo0f1o5du7VjV2aytnnLNsWcOKmHG9bXm68P0ddfLNPq5UssmyQt+Gi6WrVodtfbdK+p/uBDuhgfpz9+/p8k6ejh/dq++Vc1btHuJkdam77gf5rz2QZ9tHS9Plq6XoNHTpJfidL6aOl6+ZUscyeqni8y78kamm91T67QEze9J7cq5sQJPdww8/mNr9Z+o5SUzC/Vzp0/r6XLv1DLZtaLdW3ZtkPVqz5wF1qVv2rXqav4+AvasP4nSdLhQwe1+c+Natkq+7uaWz3aWqu+XKm4uFgZjUYtX7ZEVR6oJj+/Erp69aq+/WatBrw2RK6uBbMde+nSRR05krmgmtFo1OfLl8rRwVHlQnP+4gEAANwdZjnmartVNWrU0MGDB60S1I0bN6pOnTpWgxU1a9bMNuC5ceNG1atXT25ubgoLC7Paf+bMGZ07d05Vq1ov5vpfOJivH0O+TYcOHVLt2rWt5l/fD6Kjo1W+fHlduXIl1+c6cXh/HtTo3yUkJGrih9O1/8BBuboa1KlDe7Vv21o/bfhZBw8d0at9Mhfg2rJth2bOmaekpCSVLFFCA/u+rLKBATmes/ljHfT5p/Ot3qErSd//tF4bfv1d745964636x+ly1XUX5H5ew8eO3JA82a8o/gL5+XlVUTP9xqsClUyF1Jb9PFEBYdWVoPGrayOea1XO7065G2FhOU84ntgzzZ9MutdTZi54o7X/x8PlvdUzOEDd/w6CQmJ+uDD6dp/IFKurgY92aG92rdtk+M9OWvOXCUlJatkCT8N7PuyggIDJUlTps/Ups1bVKCAswoXLqyO7R9Xi6bWz4y/1P+1HMvvhjLlKuhQ1N0bgT9y+JCmfThZF+Li5O3trd4vvaoqD2T+h+Kj2TMUFlZejZtkfin15Refa/VXK2Uym1WlSlW98mp/FXZ3V0zMcfV9uZeKFfO1Oncpf3+NfftdnT9/XmNGjVBSYqIKuLioXLlQdevRS8WL5/5b1xsJDbafL3gAALhTcvtvjtv57227du1UsmRJTZs2TZcuXVLTpk01duxYtW/f3hJz8uRJValSRStXrlTTpk317bff6pVXXtG+ffvk5uamyZMna+nSpfrxxx9VqFAh9e7dW97e3pZZw7lx20nuN998o+bNm2c+S3r+vLp06aKaNWvelQWUbtcXX3yhIUOGZCt/+eWX9frruVu51taSXHt3LyS59uJuJbn3g7ud5NorklwAAG7uYNSJXB0fFlz6lmPj4uLUo0cPbdy4UW5ubhoyZIj69u2rxYsXa8uWLZo6daok6fvvv9eAAQMUHx+vkJAQffTRR6pSJXNwx2QyadiwYVq0aJGcnZ3Vrl07TZkyJU8e57ztJLd379769ttv5eTkpEKFCqlz584aNmzYTV/XY29Icu8tJLl5hyQ375Dk5g2SXAAAbi4yKnev9Csf7H/zIBtxW6srS9LHH398J+phcwIDA/MkwQUAAACA3DLf7rsr7dhtLTwFAAAAAMC97LZHcgEAAAAA95bcvifXnpDkAgAAAICNY7pyFpJcAAAAALBxJLlZSHIBAAAAwMaR5GZh4SkAAAAAgN1gJBcAAAAAbBwLT2UhyQUAAAAAG2diurIFSS4AAAAA2Dieyc1CkgsAAAAANo7pyllIcgEAAADAxjGSm4XVlQEAAAAAdoORXAAAAACwcUxXzkKSCwAAAAA2junKWUhyAQAAAMDGMZKbhSQXAAAAAGycKb8rcA8hyQUAAAAAG8dIbhZWVwYAAAAA2A1GcgEAAADAxrHwVBaSXAAAAACwcUxXzkKSCwAAAAA2jpHcLCS5AAAAAGDjTOb8rsG9gyQXAAAAAGwcI7lZWF0ZAAAAAGA3GMkFAAAAABvHwlNZSHIBAAAAwMaZeSbXgunKAAAAAGDjTHLI1ZZb0dHRatGihQICAhQSEqLFixfnGHf+/Hl17dpVFStWlL+/v5599lnFxcVZ9k+cOFFubm4KDAy0bH/88cdt1cXBbCbnBwAAAABb9tPutFwd3/wBw38+1mg0qlq1aho8eLC6du2q/fv3q2HDhlq/fr2qVatmFbto0SK5uLjoqaeeUkZGhnr16qW0tDR99tlnkjKT3D179mjhwoX/uT5MV74HbDqQmN9VsHn1Knjo1KE9+V0Nu1AqtIpOH9yd39WwCyXDHtDxIwfzuxo2LyAkjL/vPFIqtEp+VwEAcIfk59DlunXr5OzsrK5du0qSKlasqC5dumjhwoXZktwXXnjB8rOLi4sGDx6s5s2bW8V4eXnlqj5MVwYAAAAA/GebNm1SgwYNrMrq1KmjnTt33vTY2NhYeXp6WpWR5AIAAADAfc4sh1xtaWlpSkxMtNrS0m5tCvSZM2dUvHhxqzJfX19duHDhX4+7cuWK3nzzTfXo0cOqfMaMGSpTpozq1aunZcuW3V5HiCQXAAAAAGyeyZy7LSIiQp6enlZbRETELV07IyND1y/1ZDQa5eBw4wWtjh07poceekjlypXTsGHDLOWDBg1SXFycoqOj9cEHH2jw4MFavXr1bfUFSS4AAAAA2Diz2SFXW3h4uBISEqy28PDwbNe5dtXjxx9/XJLk4+NjtUKylDkN2c/PL8e6fvvtt6pXr56ef/55LVy4UI6OWWnpPz87Ojqqfv36GjBggL744ovb6gsWngIAAAAAG5fbhacMBoMMhpuvsBwdHZ2trGbNmnr//fetyjZu3Kh69epli/3zzz/VrVs3ff3116pdu/ZNr5eRkSEXF5ebxl2LkVwAAAAAsHH5+Z7ctm3b6vTp05Z3427dulWrV69Wz549s8VOmzZNAwcOvGGCu27dOqWnp0uStm3bpmnTpum55567rfqQ5AIAAAAA/rNChQpp7dq1mjRpknx9fdW9e3ctXbpU/v7+kqT+/ftryZIlkqTDhw9r8uTJVtOeAwMDtWdP5usCly1bplKlSikgIEB9+vTRrFmz1KxZs9uqD9OVAQAAAMDG5ed7cqXMKcvbt2/Pcd+HH35o+fmvv/761/PMnTs313UhyQUAAAAAG2c2527KsT0hyQUAAAAAG2fK55HcewlJLgAAAADYuPyernwvIckFAAAAABtnzuUKyfaE1ZUBAAAAAHaDkVwAAAAAsHE8k5uFJBcAAAAAbBzP5GYhyQUAAAAAG0eSm4UkFwAAAABsnIn35FqQ5AIAAACAjWMkNwurKwMAAAAA7AYjuQAAAABg4xjJzUKSCwAAAAA2jlcIZSHJBQAAAAAbZ2bhKQuSXAAAAACwcUxXzkKSCwAAAAA2junKWVhdGQAAAABgNxjJBQAAAAAbx3TlLPd1ktu1a1etXr1anp6eSktLU9OmTTVz5kx5enpq9OjRGjNmjLZu3aqaNWtmO/by5csqXbq0nnrqKc2ePVuS1LhxY7300kt65pln7nZT/rPYc6e1YOZ4nT0dIycnZ7V/ppfqN259w/jTJ45p/oy39Uy3gQoJq2K17+ihffpswVRdiD0jY0aGuvQeqlr1mt7pJtx1aWlpmj7nE23ZvlMmk0nNGj2k3l27yMHB+mH/w1FHNWXWHF2Iv6iCrq56tVc31apeVZL0yx+b9M7EqfLx8bbE9+7aRU0eaiBJijx0RLPmLdC52Dg5OTmpd9cuatSg3t1r5F2QlpamaXM+0dYdu2Q0mdTs4Ybqk2M/HtPkWXMUf/GiXF0N6tszqx8lKf7iRc2Yu1B7D0TKaDKpeaOH9FK35yVJkYePaOa8hTr/Tz++2Nnu+lHK7MuZH83Rtu07ZDKZ1KTxw+rZrWu2vjwSFaUPZ8xSfPxFubq66uU+PVWzenVJ0pkzZ/XRvPmKiTmhK2lpqvtgLfXp2UMGg0F/bd2maTNmWV/zaprS0q5q9RfL71o774a78fcdHXNCU2bO0emzZ+Xq6qqn2rfVY61a3L1GAgDsEklulvs6yZWk119/XW+88YauXr2qrl276o033tCsWZn/mPPw8NDkyZO1ePHibMfNnz9fjo62PdvbZDRq6vhBeqRdZz3UrK1OnTiqd8J7qVSZYAWUDbOKTU68pAWz39WRA7t0JTUl27lOn4zWhxFD1GvgaFWqWkcZ6elKuZx0t5pyV82at0hmk1lL5sxQ6pUrGjpyrFZ9/T890Tbry4GUlFSNGPeuXh/YVzWrPaBde/Zp5Pj3tHDWVPl4Z/7Dt0JYqKa8Ozbb+S/EX9SIcRF6a9ggVa1SSVHHojX0zbEqXy5ExX2L3bV23mkz5y+S2WzWko+nK/XKFQ15c5xWffOdnnjsUUtMSkqqhr/9rt4Y8KpqVntAO/fu08jxE7Ro5hT5eHvr6tWrGvzmOLVq1ljDB/WTk5OTYuMuSMrsx+Fj39Vbr7+mapUr6cixaA19a5zKh4aoeDH76UdJ+mjufJnNZi2c97GuXLmi10e8qdVrv1H7xx+zxKSkpOitMW9ryGsDVKN6Ne3es1ejxo3XvNkz5ePjrT/+/FOPtX5UtWpUV0pKisa+864WL/1MPbq9qAdr1dSnn8y1uubU6TPl4eFxt5t6x93pv2+z2ayRb7+nTu0eU7s2rXQ+Nk59hw6Xf6kSqlal8l1rJwDA/vBMbhbbztLykIuLi7p3767ffvvNUtayZUt9/fXXOnPmjFWs0WjUlClT1LJly7tdzTy1f/cWOTo56aFmbSVJpUqXVb1Gj+qPDd9ki01Lu6Jy5R/QO9NXyM3dM9v+lYtnqnmbp1Spah1JknOBAvLw8rmzDcgHqamp+mH9z+rdrYucnJxU2M1Nz3bqoO9+Wm8Vt/7X3xVWLkQ1qz0gSapapZIeqFRRG37baIkpXLhQjtfY9NdWValYQVWrVJIkBQcFqmXTxvrfddewZZn9+Iv6dM3qx+ee7KD//WjdxnW//q7y5YIt/VitciVVrVRB6//ux69/WKeiRXz0dIfH5eTkJEkqVrSIJGnjX1tVpVJ5Vauc2Y8hQYF6pGkj/e/HDXermXdFamqqfly3Xj27d5WTk5Pc3Nz0TKcn9f2PP1rFbfjlV4WGllON6tUkSQ9UqawqlSrpl78/857s0F61amSO6hYqVEjtHmujnbt353jNM2fO6o9Nm/RUxw53rmH54G78faekpur0mbNq2bSRJMm3WFFVq1JJBw9H3aFWAQDuF2Zz7jZ7QpJ7jQsXLsjPz8/ye5EiRfTss89qxowZVnGrVq1ScHCwwsLCrj+FTTlycI/KVahqVVY2tJJijh3KFlukmJ8eefw5FXIrnG3f1atp2rX1dzX8O1m2Z4eOHJVfcV95uLtbyiqEldOx4ydkNBotZfsiD6lyBev7o0JoOR05Gm35vbCbW47XSM/IsDqXJHl6uOvkqTM5xtuig1E59WOIjsVY9+P+g4dUuUJ5q2MrhJZT1LFoSZnTQh9t1iTHa2RkZMhoNFmVeXp46MTp03nUinvD4SNR8vMrbtWX5cPCFH08xqovD0QeVKUKFayOLR8Wqqijx3I876WEBBUqlHOi9tmKL/R4mzZyu8E9bKvuxt+3W6FCqlntAX2x5huZTCYdjT6ufZGHVK92rbxtDADgvmMy5W6zJyS5fzt+/Ljee+89DR061Kr8tdde05w5c3TlyhVL2QcffKBBgwbJbONfeVy6GCcPT+vRVg9PHyUnJdzWec6djlEBF4Mi92zTyAHPamifdlow8x2lpiTnZXXvCRcuXpS3l/VItrenp4xGoy6nZE3jjr94Ud5eXlZxXl4eSkzKmsL9x59b9Ey3l9Rn4DB9ufZby/1Ut1YN7dy7T5u3bpfZbNbxEyf1v582KCEx8c417C6Lj790S/14IT57f3t5elr68djxGF1Nv6p+r4/UMz1f0Rtj39GJU5lJrKUft+24ph/XKyHBfvpRki7Ex+dwr+VwT8bHy9s7e1xiYvbHChITE7X8i5Vq1TL7c6KXEhL0y2+/q03rVnlS/3vJ3fj7lqT+L/XUilVr9djTL6hnv8Fq36aVypQudWcaBQDAfei+fyb3vffe08yZM3Xq1Ck1adJEAQEBVvtDQ0NVt25dLV68WD179tQff/yhxMREtWrVSn/++edtXSstLU1paWlWZQaDIddt+K9MRqMk60TdZDJmW2DlZq6kpshkMurYkf166/0FMhozNHfqaC2Z+4F69h+VhzXOf0ajSdd/tWH6+6uva/vNmGPfmvRPyMP161oWQDoafVzjJkyWzGY98XgblfArrrdHvK55ny7T5JkfK7C0vxrUqa2Tp+1nJNdoNGabF5NjP5pM2abPZMZlxqSkXtFvmzZrzBuD5enhoc+/Wqvh497VJ9Mn/d2Pwyz9GFDaX/UfrK1TZ+ynH6XMvrz+CzdLX+rae9KUY9z1f+5RUUc17t331Oihh9Sk0cPZrvfT+g1qUK9utiTPHtyNv+/ky5cVPuYdDerbRw/Xr6vTZ8/prfETVKJ4cTWs9+CdahoA4D5g4+Nveeq+H8l9/fXXFRMTo6tXr2rAgAFq1KiRTp48aRUzePBgTZ06VZI0ceJEDRo06D9dKyIiQp6enlZbRERErttwKwb3etyyTRmfWX+3wh5KSrQetU1KuCRPryK3de7CHp4yZmSo0wv95OJiUMGCbmr/TG/t2PLbzQ+2Me7uhZV43YjqpYREubi4yO2aqZ3uhQsr4boRsoSERPl4ZS5Kc+0/mMsGBuiFZzvplz82WcqqVqmkDye8rc/mz9a7Y0bKbDardKmSd6JJ+cLdPXv/ZPZjgRz6MXt/+/w9Iunp4a6n2j8uH29vOTk56Zkn2ikxKUkxJ09JynyGd9p7b+uzebP03ugRkuyrHyXJw909h3syIfOedLumL91z6ssEeXtnrQD83Q8/avhbo9XthefV7YUuOV7vhx/XqWmTRnnYgnvH3fj7/vn3jQos46/GDevL0dFR/iVLqOtzT2v5qtV3qlkAgPsEz+Rmue+T3H84OTmpbdu2Cg4O1l9//WW17+GHH5arq6vmzZunzZs3q0uXnP/xdzPh4eFKSEiw2sLDw/Oi+jf1wZw1lm3giEmSpMCQ8joSab2wzJGDuxV83auBbqZIsRJydi6g9PSsUWoHBwcVKOCS+4rfY0KDg3Ti5GklJWdNxd4XeVAVQkOsVtsODSmrfQcOWh2778BBVSwfmuN5jUajnJ1znlhhNBq14bc/VL+O/TyzFxocpBOnrPtxb+RBVQgtZ9WPYSFltS/yun6MPKhKf/djQGl/paSmWvY5ODjIwcFBLi7Z7z2j0aj1v21U/Qdr53Vz8lVIcLBOnjylpKSsvtx/IFLlw0Kt+rJcSIj2H4i0Onb/gUhVLJ/5zPOvv/+hJcuWa9L776rRQw1zvFZU1FFdiI9XtQceuAMtyX934+87IyNDTo5OVvudnJ2UkZ6RV80AANynTObcbfaEJPcav/zyiw4fPqxatbInE4MHD9arr76qPn36/OcpxgaDQR4eHlZbfk5Xrlb7YV2Kj9XGn7+VJB07sl87Nv+iRi3a3dZ5XFwMqt+ktT6bP0VGY4bS069q1bKPVb/Rozc/2Mb4eHurds1qmrtoqYxGoxISErXk85XqeM2rWiSpeeOHtX3XHm3ftUeS9OfW7Tp+8pQaNcycwrhr736l/v2c96nTZ/TpZ1+oeZOsqaHHjp+QJF29elXT53yi0JDgbAsw2TIfb2/VrnFNPyYmasnnX+rJx9tYxTVv9JC279qbvR//ngr6eKuWWrhshWVUbfmqNSpVwk+lSmQuIHcsxrofw0LKZlswyNb5+HirVs0a+mTRIss9uWz55+rQ7nGruKZNGmnnzl3asWuXJOmvLVt14sRJPfT3u1u//Gq1ur/4vEqVvPFI95Zt21WlciXLStb25m78fdepWUO79u7Tzj17JUkXLyVoyecr9XCDunermQAAO2U2m3O12ROeyX3vPc2ePVuSVLp0aa1cuVJlypTJFvfkk09q1KhReuWVV+52Fe8Yg8FVA0dM0iczx2vZJ1Pk6VVEfQa9LZ+ixSVJi+dMVNnQSreUrD71Qj8tnP2uXuveRq4FC6lWvaZ64rmX7nQT8sXQfq/o/Wkz9eQLveTqatBTHR5Xw3oP6scNv+rg4SPq27u7ihUtojeHvaaps+YoKTlZJUv4afybb6igq6skacfuPRr73gcq4FxAhQoVVKf2bfVo86aWa0yYMl0XLl6Us5Oz6tetrRGDB+RXc++YYf1f1oQPZ6nji73k6uqqp9u3VcO6mf0YeeSI+vX6ux+HDtSU2XOVlJysUiX89M7I1y392LhhPZ08fVo9+w+Ws7OzwkKCNTZ8qGW66HtTZvzdj05qUMc++1GSBg3or0lTP9QzXV6Uq6urnnyivRrUq6uf1m/QocNH9EqfXipWtKiGvz5U02bOVlJSskqVKKGxo0Za+vLU6TP6aN4nmr/wU6tzz/xwitzdM1dVjzx0SCHBZe96++6mO/33XcKvuMaNeF0fL1ysuAvxcnZyUstmjfVUh8f/rVoAANyUneWpueJgtre03QZtOmBfq73mh3oVPHTq0J78roZdKBVaRacP5vx+VNyekmEP6PiRgzcPxL8KCAnj7zuPlAq9vcdRAAC2Y9o3uUvr+rW5vcVnrxcdHa1evXrp0KFDKlCggEaPHp3jY55bt25VnTp1VLp06axr9+unwYMHS8p8b/2AAQP0/fffy2g06rnnntN77713W4vj3vcjuQAAAABg6/LzXbdGo1Ft27bV4MGD1bVrV+3fv18NGzZU5cqVVa1atWzx/v7+io6OzvFcgwcPlslkUlRUlC5fvqzmzZtr+vTp6tev3y3Xh2dyAQAAAMDG5efqyuvWrZOzs7O6du0qSapYsaK6dOmihQsX5hjvdYNXESYnJ2vhwoWaMGGCnJ2d5enpqfDwcM2fP/+26sNILgAAAADYuNyukJyWlqa0tDSrMoPBcEsL5W7atEkNGjSwKqtTp47mzp2bY/yNktxt27YpKChIPj4+VufZu3evjEbjLS9+yUguAAAAANi43I7kRkREyNPT02qLiIi4pWufOXNGxYsXtyrz9fXVhQsXcozfunWrAgIC9MADD2jMmDGW5PpG58nIyFBCQsIt9wUjuQAAAABg48y5HMoNDw/XoEGDrMpu9XWnGRkZ2V5DZDQac1wsqmbNmrp8+bIk6dixY+ratasSEhI0adKkG55H0m0tPMVILgAAAADc5wwGgzw8PKy2nJLcwMBAy/b445mvwPPx8VFcXJxVXGxsrPz8/LIdf22yGhQUpAkTJmjFihX/eh5XV1d5enreclsYyQUAAAAAG5fbZ3JvVU6rItesWVPvv/++VdnGjRtVr169m54vIyNDLi4ukqQaNWro4MGDunjxory9vS3nqVOnjhwdb318lpFcAAAAALBx+bm6ctu2bXX69GktXrxYUuYzt6tXr1bPnj2zxW7evFnx8fGSpLNnz+r111+3vE/Xz89PrVq10vDhw5WRkaG4uDiNHz9eAwcOvK36kOQCAAAAgI0zmcy52nKjUKFCWrt2rSZNmiRfX191795dS5culb+/vySpf//+WrJkiSRp9+7dqly5ssqUKaNGjRqpdevWevPNNy3nmjdvnk6fPq0SJUqoVq1a6t27t9q3b39b9XEwX/9kL+66TQcS87sKNq9eBQ+dOrQnv6thF0qFVtHpg7vzuxp2oWTYAzp+5GB+V8PmBYSE8fedR0qFVsnvKgAA7pB3lhtzdfzwp2/t9Ty2gGdyAQAAAMDGMXSZhenKAAAAAAC7wUguAAAAANg4E0O5FiS5AAAAAGDjzKb8rsG9gyQXAAAAAGwc6wlnIckFAAAAABtnYiTXgoWnAAAAAAB2g5FcAAAAALBxTFfOQpILAAAAADbORI5rQZILAAAAADbOTJZrQZILAAAAADaO2cpZSHIBAAAAwMaZGMm1YHVlAAAAAIDdYCQXAAAAAGwcqytnIckFAAAAABtnNuV3De4dJLkAAAAAYONMjORakOQCAAAAgI1junIWklwAAAAAsHGsrpyF1ZUBAAAAAHbDwcy4NgAAAADYtIHTknN1/JR+hfOoJvmP6cr3gANRp/K7CjavQnAppa7/NL+rYRcKNn1eB6NO5Hc17EJYcGkdizqS39WweUHBIYqMOpnf1bAL5YP9lbD9p/yuhl3wrNE8v6sAAFbMTFe2IMkFAAAAABvH6spZSHIBAAAAwMYxkpuFJBcAAAAAbBxJbhZWVwYAAAAA2A1GcgEAAADAxjGQm4UkFwAAAABsHNOVszBdGQAAAABsnNlsztWWW9HR0WrRooUCAgIUEhKixYsX5xhXqVIlBQYGWm1OTk5auXKlJKlv377y9PS02n/8+PHbqgsjuQAAAABg40z5OJJrNBrVtm1bDR48WF27dtX+/fvVsGFDVa5cWdWqVbOK3bdvn9Xvf/75pzp37qzHH3/cUjZw4ECNGTPmP9eHkVwAAAAAsHH5OZK7bt06OTs7q2vXrpKkihUrqkuXLlq4cOFNjx0xYoRGjRqlAgUKWMq8vLxyVR+SXAAAAADAf7Zp0yY1aNDAqqxOnTrauXPnTY+LiYlRly5drMpJcgEAAADgPmc2mXO1paWlKTEx0WpLS0u7pWufOXNGxYsXtyrz9fXVhQsX/vW4iRMnql+/fnJ0tE5Lw8PDVaZMGTVp0kQ//PDD7XWESHIBAAAAwOblNsmNiIiQp6en1RYREXFL187IyMg25dloNMrBweGGx5w/f14bNmxQt27drMo//PBDnT17VseOHdPQoUP11FNPadu2bbfVFyw8BQAAAAA2zpTL52rDw8M1aNAgqzKDwZAtLjAw0PLzAw88oDVr1sjHx0dxcXFWcbGxsfLz87vh9RYtWqRHH31U7u7uVuX/jOo6OTmpdevWevbZZ/XVV1+pZs2at9wWklwAAAAAsHG5fU+uwWDIMam9XnR0dLaymjVr6v3337cq27hxo+rVq3fD8yxevFhvvfXWTa+XkZEhFxeXm8Zdi+nKAAAAAGDj8nN15bZt2+r06dOWd+Nu3bpVq1evVs+ePXOMP3v2rHbv3q2mTZtm2/f999/LZDJJkn744QetXLlSHTt2vK36kOQCAAAAAP6zQoUKae3atZo0aZJ8fX3VvXt3LV26VP7+/pKk/v37a8mSJZb4zZs3KygoKMdVlCdPniw/Pz8FBgbq7bff1qpVq1SxYsXbqg/TlQEAAADAxplyOV05t2rWrKnt27fnuO/DDz+0+r1du3Zq165djrHfffddrutCkgsAAAAANi63z+TaE5JcAAAAALBxuX2u1p6Q5AIAAACAjTP/vVgTSHIBAAAAwObl9zO59xJWVwYAAAAA2A1GcgEAAADAxvFMbhaSXAAAAACwcayunIUkFwAAAABsHEluFpJcAAAAALBxJjOrK//jnk9yXV1dFRkZqS+++EKFChXSK6+8ctNjHBwcdObMGfn5+d2FGtq2c+fOaubUD3T61Ak5OTvrmc4vqnHTFjnG/vrzOn2+7FOlpqSoRCl/9Rs4VMX9SkiSPp45VT+v/0luhQtb4se/N1m+xe+v/w+uXE3XhBU/aNP+KBlNZj1au7IGdmgqBwcHS0zi5VSNWLBa0ecu6MrVdFUKKKnhzz4qXy/3fKx5/jp37qymT52k06dOysnZWc92fkFNmjbPMfaXn9dr+bLFSk1JUclSpdRv4BD5/X0frlyxXD9+/62upl+VWyE3dXmxu+rUrW859s+Nf+jTRfOVmpoib28f9ej1kipWqnJX2ngnpaWlafZHH2nbtu0ymUxq3LixenTvZnXfSdKRqChNnz5D8fHxcnV11Ut9+qhGjepWMQciIzV58hS9G/GOfHx8LOVvjRqtA5GRKliwoKXsk3lz5eTkdGcbl0/OnTurGVM/sLonb/zZuF7LLZ+NpdT/ms/Ga82aPkV7du/UzI8XZNt35UqqenXtrHZPdNKTTz2b183Jd1euXtWkhV/oz937ZTKZ1bJ+LfV7rn22e1SSEpIva9rSr1Tar5hefLylpTwjw6gpi1fq5y275OToqGrlgzWk61Nydyt0N5sCAPcsRnKz3PNJ7j+GDBly165VsWJFbdiwQcWLF79r18wPRqNR40ePULsnOqlZi1Y6EROt8CEDVCYgSGWDQ6xiIw/s0+KF8zT+vckq5ltcK5Yv0YR3xuiDD2dbYtq276hnu3S9y624t3yw8ieZTWatHdtXqVevqs/UJfrs5616tkltq7iXHntYlQJKKsNo0rvLv9N7y7/XB32ezKda5y+j0ai3R49U+yc6qVmLRxQTc1xvDBmogIDAHO7D/fp04XxFvPeBivkW1+fLl2rCO+M06cOZkqSw8uXVrkNHOTs7a++e3Rr95huav2iZPDw8dfbsGU3+4D29HfG+yoWGacf2bXp7zFua88mncnMrnFPVbMacuXNlMpn1yfx5unLlit4YPkJr1q5Vu8cft8SkpKRo9OgxGjToNdWoXl279+zRmLHjNOej2fLx8dHpM2c0Y+ZMnTlzVqdPn87xOr169FDLljknevYk87NxpNo98aSatWilmJhohQ8ZeMPPxk8XztM7702yfDa+985YTfpwllVcbOx5bVj3o4oWK5bjNb9du1rJyUl3rE35beqnX8pkNuvLKWN0Je2qXh3/oT7//hc93aqxVdyHS1bp61/+lMGlgEoXt+6rhWt+UNSJM/r8gzflUqCAIuYs1aRFX2jUyy/cxZYAAGwBrxDKwYEDB+6L1cl279ouJycnNWvRSpJUukygGjVtrg3rvs8W+82aVWrbrqOK+WYm/k88+YzOnzurY0ejLDFubm53p+L3qJQrV7X2z90a+EQzOTs5yr2gq3o80kBfbdxpFefhVlCVAkpKkpydHPVQ5RCdv2S//7i9md27dsjRyUnNWjwiSSpTJkCNmzbT+nU/ZIv9es0qPd7uCct92PHJp3Xu3BnLfVi5SlU5Ozv//fMDMhgMSkxIkCQdjz6mUqX8VS40TJJUvUZNGQwGnT516o638U5KTU3Vjz+tU88e3eXk5CQ3Nzc9/VQn/fDDj1ZxP//yi0JDQ1WjeubI7QNVqqhy5Ur69dffJGUmwQ0bNNTsmTNueC23wvfH33jWPZn52Vjm78/GnO7Jb9Z8ZXVPZn42nrH6bJSkeR/PtNzj17twIU4//vA/1anbII9bcm9IuXJF3/y2Wf2eay9nJycVLlRQXds9orU/b8oWW7hQQX0ybqhqVQrNtu9g9Ak1ebCqCrm6ytnJSY80qK0DR2PuRhMAwCaYTeZcbfbknkpyY2Nj9eyzz6pMmTIKCgrSlClTLPu6du2qd999V5KUnp6uPn36KDAwUKVLl1ajRo109OhRq3Pt2bNHDRo0kL+/v6pVq6b169db9qWmpqp///4KCQlR2bJlNXToUGVkZGjr1q0KDAyUJNWuXVv16tX713hJOnfunJ544gmFhISoePHiVnW+1x08sF8VKlayKgsNq5DtH2eSdDByvypUrGz53cnJSWWDy+nY0SOWMlsfDcut/TFnVKqIlzzdsqZzVg4qqajTsTKacn5G4kx8gpb/slXPNK51t6p5z4k8YH1vSTe+DyMjD1jds//ch0evi7169apWf7VSIaFh8i9dRpJUqVIVXUq4qB3bt0nKnPZc2N1dgUFl87pJd9XhI0fk5+cnd/es6e7lw8IUffy4jEajpezAgUhVrFjB6tjyYWGK+vuzMyQ4WI+2ekQuLi43vFbh++SLrJw/G8tbfd79IzJyv8rf5LNx619/KikpUfUbPpzj9eZ9NFOdnn5OBQsVzHG/rYs8ekIlixWR5zVfklQKCVTUydPZPhu7d2ilUsWL5niepnWq67vftyg+IUmpV9K08qff1KpB7RxjAeB+ZDabc7XZk3sqye3QoYOCgoJ07NgxHTp0SIcOHVJaWlq2uPT0dNWpU0eHDx/WiRMnVLVqVY0YMcIqZsqUKVq1apVOnjyp8ePHq0OHDjpz5owkqXfv3rp8+bL279+vffv2aceOHZo+fbpq1aql6OhoSdKWLVu0adOmf42XpKFDh6pChQo6cuSITp06pcevmR54r7sYHy9PLx+rMk9PLyUlJuYQe0Ge3t7WsV7WsZ8unKueLz6jkW8M0o7tW+5Mpe9hcQnJKuJhnQT4uLspw2RScqr1ffzJ9xvVaMgHeuzN6Qrz99Mjtaz/QX0/uRgfLy+v6+4tTy8l3uA+9LruPvS65j48c+a0ur/wrDp1aKPfftmgl1/pb4kr7O6u7j36aNTI1/XUE49p6qQJ6tt/kAoUKHAHWnX3xMfHy9vLy6rMy8tLRqNRKSkpWXEXs/ezl5eXEpOy93NOHBwc9P7ED/RC1256c9QoHTx0KNd1v1fFx1/I3lf/8tl4/T3p6eVtiU1MTNCc2dP18qsDc7zWLxvWKSkpUU2atcxxvz2Iu5QgH0/rNQd8PNxlNJqUnJJ6y+dpWa+mvD3c1eaV4WrRe5jOX7ioZ1s3zevqAoDNMplMudrsyT2T5G7fvl3Hjh3TuHHj5OTkpAIFCuj999+Xo2P2KhYqVEjdu3dXcnKyNm/erMKFC2vfvn1WMSNHjpSvr68kqU2bNqpXr56+++47xcbGauXKlZo2bZpcXFxUsGBBDRw4UKtWrcqxXjeLNxgMOnjwoC5evChnZ2eVLXvjUaG0tDQlJiZabTkl8XdL5iiP9bc2JpNJOawDkhlrvnFsz5f6acGSlfpo/hK17/iU3o8YqyOH7fcfwTkxmkzZvgUz/T314/ou7fZIff0ycbD+N76/4hKTNXD253eplveenO9DY44L0tzsPixRoqTmL1qmFau+UdvHO2jo4P46feqkJOnQwUgtWjhfU6bN1vKVa/XWmPF6d/wYnTt39o60624xGo053HfZ/0OVYz8bTTn2c05GvfWmFn+6SPPnztFDDRtqxMg3FRsb+5/rfS8zGo0y5/jZeKv3pFFycJDZbNb0KRPVtn1Hy4yCa507e0aLF81X/0HDbvn/B1tkNJqu7yLLCK5Dtk/HG5uy+EsVKmjQj3Mm6Ke576tSSKBGTvskL6sKADaN6cpZ7pmFp6KiolSuXDmrlTrd3NxyHGU5duyYXnjhBZlMJlWoUEEZGRm6evWqVUxQUJDV776+vrpw4YKOHTum9PR0VaxY0bLPaDSqaNGcp0fdLH7SpEl68803Vb58eXXo0EHvvGO9Ium1IiIiNGbMGKuyUaNG6enne+UYn5d6dc1arTMwqKxGjBqvwu7uSkxMsIpLSLgkL+/s9Xd391BiYoLlubPrY//5MsLJyUm1atfVw42aavOm3xVSLvtzVfbKw62gLl22HpW4mHRZhgLOKlzQNcdjfL3c9VaXNmr42vuKOR+vMr453zv2omfXzpafA4PKauSocTneh4kJCfK+bnRM+uc+TLzuPkzIds+6uLioUZNm2rVrh9b/9IO6vNhda1d/qTaPPW5ZOKha9ZqqW7+BfvjuWz3/Yve8bOZd5e7uroTrRhgTEhLk4uJi9Zy8e2F3JSRYx126QT/n5J+/cWdnZ7Vs0UK//vqbtm3frlaP5Pycqa3o1fU5y88BQWU1ctTbcnd3zzaTIKf7TMr5szHz/vXRys+XKcNoVJu27bMdl5aWpoi3R+vFbr1UrJhv3jXoHuRRuJASkpKtyi4lJctQoIAKF8r5s/F6V9Ku6osfftV3s99V4b+ndb/2wpNq2XuYYs6cV5kS9t2HAHArzLxCyOKeSXKLFi2qEydOWJWdPn06x5HOUaNG6ZFHHtHIkSMlSV9++aX+/PNPq5gLFy5YvUJo//796tChg0qWLKnChQvr2LFjt/TN+c3i3d3dNWXKFI0bN06vvfaaOnfurP/97385nis8PFyDBg2yKjMYDDp6Mu6m9citOQuWZSsLCQnVqpXLrcoOHtinsPIVs8UGh4Qq8sA+BYdkJq3p6emKOnJIfQfkvOq10WiUs7NtTwO9XRVK+yn63AUlXk6Vx9/P5e46elKVA0vK0fHG95qTg6OcHB1lcLln/hzvmLkLlmQrCwkpp1UrV1iVHbjhfVhOBw7sU3BIOUnX3oeDssVKUoECBeRiMGTGZmRke92Ns5OzMjLS/1Nb7hUhwcE6dfKkkpKSLM/l7t9/QGFhYVYzYcqVC9GBAwckdbCUHThwQA8//NB/uq69/I3PWbA0W1lwSKhWrbSeXRF5YJ/K3+CejDywP4fPxsGaP2eWrly5os5PtZOU2WdXr17Vc50e13PPd9OpkzGaOW2SZk6bJCkz8XV0dNTunds19p3387qp+SYsqLSOnzmvxOQUeRTOfN3P7kNHVSkkMMfZWjkx/j2V7trPUkcHBzk4OCr97zUyAAD4xz0zXblu3bpKT0/XBx98ILPZrMuXL2vYsGE5voMxLS1NFy9elCTFxcVp8uTJ2WLefPNNJScny2w26+OPP9bFixfVunVr+fv7q0aNGho1apRl8agjR45o586dlmO9vb0VFRWljIyMm8b/8ssvSk9Pl7u7ux566CElJydfXxULg8EgDw8Pq83w9z/A80PtOvUUH39BP6/PXIX1yKGD2vznRrVo1SZbbMtH22j1lysUFxcro9Goz5d9qioPVLO8C3LHti2WKZI7tm/Rpj9+U/2G/+0fz7aqqGdhNagYrGmrNyjDaNLF5BTN/e4PdW5axyru512HdOR05jTP9AyjpqxapwfKllJxL4/8qHa+e/Dv+3DD+p8kSYcPHdRff25Sy1ats8U+8mgbffXlF5b7cPmyxaryQDX5+ZXQhbg4/fLzestiS3v37NafG/9Qg4aNJEkNGz6sr9d8pdjz5yRJR6OOaP36H1W3XsO71NI7w8fHRzVr1dSChYtkNBqVkJCgZcuXq0O7dlZxTZo00c5dO7Vz5y5J0l9btijmxAk9/NDN/06vXr2qXbt3W37/ad06HYuOVq2aNfK2MfeIB6/7bDxs+WzMfk+2fPQxfWX12bhYlf/+bFywZIU+W7lWS1es0dIVazRy9HiVKFlKS1es0WOPd9CKr/5n2bd0xRo93Lipnn7uBbtKcCWpqJen6lWtoJnLVyvDaNSlxGR9suo7PfNok1s+h1tBV9WrWlGzlq9Rxt9T9Od9+T8V8/ZUYKn7633sAHAjTFfOcs8MHRUsWFBr167VK6+8ookTJ8rX11fvvvuuvvrqq2yxo0eP1osvvih/f3+VLl1aXbp00bRp06xiWrdurVq1aiklJUWVKlXSjz/+aEkoly5dqv79+ysoKEgGg0GBgYFWifKoUaP0xBNPqEqVKvrpp5/+Nf6rr75Sp06d5OnpqYCAAM2ZM+fOdVIeM7i6asSo8Zr54UR9MmeWvLx9NGjYCBUtmvluwjmzpyk0rIIaNWmuuvUf0pnTpzR04Csym02qXKWa+g0cZjnXmq++0JSJEXIxGFSsmK/eeHOsSpcJzKeW5Z9Rzz+mMZ9+rRZvTJGrSwG92KKumlYL0zeb92jv8dN6/alHZDKbNeTjL3T5SppcXQqoQaVgTejxRH5XPd8YXF01ctQ4Tf9wkubPmS0vb28NHhZuuQ8/nj1doWEV1LhJM9Wr31BnTp/SkIF9ZTKbVKVKVfUfmDmboECBAvrph/9p7kczVbBgQfkW91P4m6NVyt9fktTw4caZ74p9M1xXrlxRYXd3vdrvtWyr6Nqi1wYM0OSpU/Vc5y4yuLrqyY5PqH79elq3fr0OHTqsl1/qo2JFi+qN11/X9JkzlZSUpJIlS2jMqFFydb35dFGz2ay5c+cpNi5OLi4uKlO6tN55e5y8rlvwyl5k3pNva8aHH1xzTw6/7p4sr8ZNml9zT77692djVfUfODSfW3DvGdm7i97+eLFavzxcBQ0u6vxYMzWuXVX/++0v7T96XINf7HTTc4x+9UV9uGSVOr42RmazSeWDyuiDoS/J6RZHgwHA3tlbopobDmZ7Wy/aBh2Isu33dN4LKgSXUur6T/O7GnahYNPndTDqxM0DcVNhwaV1LCr7a2dwe4KCQxQZdTK/q2EXygf7K2H7T/ldDbvgWaN5flcBAKw88uLOXB3//cJqeVKPe8E9M5ILAAAAAPhvGMnNQpILAAAAADbObGfvus0NHmQBAAAAANgNRnIBAAAAwMYxXTkLSS4AAAAA2DizmenK/yDJBQAAAAAbZ2Ik14IkFwAAAABsHAtPZSHJBQAAAAAbxzO5WVhdGQAAAACQaykpKQoPD9err776r3HLli1ThQoV5O/vryZNmujYsWOWfampqerdu7cCAgLk7++vYcOGyWy+vQSeJBcAAAAAbJzZbMrVlluTJk1ScHCwli9fLqPReMO4TZs2afjw4fr+++918uRJtWjRQp06dbLsHzx4sEwmk6KiorRv3z5t2LBB06dPv626kOQCAAAAgI0zm8y52nLLyclJ33//vV544YV/jZs2bZoGDhyoMmXKSJKGDRumY8eOadeuXUpOTtbChQs1YcIEOTs7y9PTU+Hh4Zo/f/5t1YVncgEAAADAxuV24am0tDSlpaVZlRkMBhkMhls6fsCAAZKkL7/88l/jNm3apEGDBll+d3Z2Vo0aNbRz505dunRJQUFB8vHxseyvU6eO9u7dK6PRKCcnp1uqC0kuAAAAANi439c2ytXxo0eP1pgxY6zKRo0apdGjR+fqvNc7c+aMihcvblXm6+urCxcuyGAw5LgvIyNDCQkJVsnvvyHJBQAAAID7XHh4uNUIq6RbHsW9HRkZGdkWkjIajXJwcLjhPklycHC45WuQ5AIAAADAfe5WpyYHBgZafn7ggQe0Zs2a27qOj4+P4uLiLM/kSlJsbKz8/Pzk6empuLg4q/jY2Fi5urrK09Pzlq9BkgsAAAAAuCXR0dG5Or5mzZrauHGjatSoIUm6evWqtm3bprlz56pgwYI6ePCgLl68KG9vb0nSxo0bVadOHTk63vqayayuDAAAAAC4K3r37q0PPvhAJ0+elNFo1Lhx49SkSRMFBQXJz89PrVq10vDhw5WRkaG4uDiNHz9eAwcOvK1rkOQCAAAAAO6Y/v37a8mSJZKkDh066JVXXtGDDz6oUqVK6fDhw1avCJo3b55Onz6tEiVKqFatWurdu7fat29/W9dzMF//ZC/uugNRp/K7CjavQnAppa7/NL+rYRcKNn1eB6NO5Hc17EJYcGkdizqS39WweUHBIYqMOpnf1bAL5YP9lbD9p/yuhl3wrNE8v6sAALgBRnIBAAAAAHaDJBcAAAAAYDdIcgEAAAAAdoMkFwAAAABgN0hyAQAAAAB2gyQXAAAAAGA3SHIBAAAAAHaDJBcAAAAAYDdIcgEAAAAAdoMkFwAAAABgNxzMZrM5vysBAAAAAEBecM7vCkA6G7kjv6tg8/zKV1fqhiX5XQ27ULBJZ+0/cjq/q2EXKoaUVGTUyfyuhs0rH+yv6COH8rsadiEwJFTxe37P72rYBZ8qDZX01zf5XQ2b5/5gm/yuAgA7xHRlAAAAAIDdIMkFAAAAANgNklwAAAAAgN0gyQUAAAAA2A2SXAAAAACA3SDJBQAAAADYDZJcAAAAAIDdIMkFAAAAANgNklwAAAAAgN0gyQUAAAAA2A2SXAAAAACA3SDJBQAAAADYDZJcAAAAAIDdIMkFAAAAANgNklwAAAAAgN0gyQUAAAAA2A2SXAAAAACA3SDJBQAAAADYDZJcAAAAAIDdIMkFAAAAANgNklwAAAAAgN0gyQUAAAAA2A2SXAAAAACA3SDJBQAAAADYDZJcAAAAAIDdIMkFAAAAANgNklwAAAAAgN1wzusTBgYG6rPPPlPdunXz+tR3VHR0tMqXL68rV67853PYUtvT0q7qw7kLtGXHbplMJjV7uIFeevE5OTg4WMUdOnpMk2fN04WLl+RqMKhfrxdVu9oDkqSMjAzNmL9Yv/35l5ycHFWlYnkN6NVN7oXd8qNJ+eLK1XRN+Px7bdofJaPJpEdr/7+9u46O4nr7AP5NNu4CIZ4NSUiw4C7BobgVCxKsBKe4tYEigRYt7u7aEKRYaOmvBVrcgkRIsBBIiOvuff8ITFkiwNsYy/dzTs5h79yZee5lZJ+RuxUwpnMzlX7MUCiw8cQfOHX5LhJSUmFraYpJ3b+Ch4N11vRMBVYFnsOZq8GIT05BdTc5vu/TFsb6ekXVrEL3Iuo5Vv68AE+fPIZMS4buvXzQqEnzHOueP3cGe3ZtRWpKMmxs7TFizESUsraRpt+/dxdbNqxG9IsoZCoyMdh3FOrUayhNT0iIx9aNa2Bja4/OX/cs8LYVpqio51ixdOGbftRCT+++ufbj7+fOYs+ubUhJToaNnR1GjZmg0o9vrVq+BDdvXMPKtZulsu6d28DA0BBasqxTiGsZd0ya6lcgbSpsaWlpWLVmHS5fuQKlUonGjbwwsL9PtmPjw5AQLFuxCjExMdDT04PvkMGoVqWKSp27wcFYtORnzJ87BxYW5lL5o4gILFuxEs+ePYeenh66dOqI1l+1KpT2FZXUtHQs2bQLF6/dgkIp0KJBLQzv3TVbvwJAXEIiVmzfDwdrK/Tp1DrH5Z3+3yV8t3gNAtctgqW5aUGHX6ykpqdj4bbD+OvmPSiVSrSsUxWjerRV6cvMTAU2HTmN05euIzE5FTYlzDGhb2e4O9mpLCvsSRRmbdiDb3u1R0VXeSG3hIio4OR7kkufhxWbtkEoBXauWYrU1DSM/X42Dh79FV3a/vtFKzk5BVNn/4TJo4aieuWKuHbrDqbNXYCtKxbB0twMOw78grCISGxdsQg6OtpYsGIdlm/YgimjhxVhywrXwv2nIITAkVkjkZKejiFLtmP3ub/Rs3FNqc6jqFdQKJTYNmkA9HV1sP/3yxi9cjcCZ4+EtkyGFQFBeBT1CnumfQNtLRnm7DyGxQdO4/vebYuwZYVHoVBgzsyp6NCpG5o0b4XIiHBMnTAKTk7OcHZxVakbfPc2tm9dj9nzlqCkVSns37MdP/nPwIKlawAAjyMjMG/2dxg9dgoqVamGjIwMJCUlSvNv2bgaZ0+dgI6uLqxtVL/sfe4UCgXmzJiODp27omnzVoiICMeU8WPg6OSM0jn047YtGzB3/iKUtCqFfXt2YP7cH7Do51Uq9aKjXyDozCmUKFky2/rm/bQ0x6T4c7d2/QYIocTmDeuQmpqKydOmI+BIIDq0byfVSU5Oht/MWRj37RhUrVIZN27exIxZc7B+9SpYWJjj6bNnWL5yNZ49f4anT5+pLF8IAb8fZqNLxw5o17YNXkRH49txE2BnZ4dKnhULu7mFZtnWPVAKgX0r5iE1NQ2jfliI/cfP4uvWTVXqLd+2D0eD/oCujg7sra1yXJZCocTWQ8cKI+xiacnOACiFwOGFU5Galo5h81Zjz6k/0KNFA6nOo+fRUCiV2Ow3Gvp6ujhw9k+MXbQBvyycBi0tGV4nJMF/035cfxCG5NS0ImwNEVHBUMvHlYUQRR1CsZackopfz/4OXx9vaMlkMDI0QO+uHXHs9DmVeqfP/w/uri6oXjnri1flCuXgWa4sgv74EwDwIDQcDevUgIG+HrRkMjRrWA/3HoYWdnOKTHJqOo5cuI4xnZtBS6YJY309DGxVD4f/vKZSz9XWCsPaN4a+rg4AoGvDakhJS0dEVAwA4OjFGxjevjH0dLQh09TEuK7Ncfzvm0hNzyjsJhWJm9evQCaToUnzrAssDo5yeDVuhqAzv2are+zIQbTt0AUlrUoBADp17YmoqOcIC30IANixdT3atOuESlWqAQC0tbVhZvbvHTQDAyPMX7QSFT2rZFv25+7G9avQlMnQ9E0/OjrK4dWkGc6eOZmt7tGAw2jfobPUj5279sCLqGcICw1Rqbdh7Uo0bd4yx/UZGhrlcwuKXkpKCk6dOYuBA/pDJpPB0NAQ3b/+Gr+eOq1S79xvv6NMGTdUrVIZAOBZsSIqli+P386fB5CVBDeoXxdrVizPto7klBQ8e/YMzZo2AQBYlSwJz4oVcf/Bg4JtXBFKTknFsXN/YnjvrtI5p2+n1jhy9ny2ukYG+ljvPx3VKnjkuryDJ4Pg6eFWkCEXW8mpaQg8/w9G9Wib1ZcG+vBp1xQBv19Sqedibw3fLl9BX08XANClSV2kpKUhIioaQNbdYE83OfbPnwRTI4NCbwcRUUErkCT34cOHaNKkCWxtbVG5cmVcvnwZHTt2xJw5c1TqjRs3DmPHjgUAaGho4MyZM6hbty7s7e1RuXJlnD17VqqbkpKCUaNGwdXVFaVLl8aECROQmZkJANi8eTNatWqFsWPHwsHBAceO5X6FVwiBtWvXwtPTE05OTnBxcUFYWJg0/dixY6hatSpKliyJNm3a4NWrV9K0O3fuoG3btnBxcYGTkxN69uyJZ8+e5bSaYu1+SChsSpWEifG/X1LLlnFFWEQkFAqlVHY7+AEqli2jMm+5Mq54EPoIAOBVtxZOnfsDsa/jkJKail9OnEIzr/qF04hi4E7EU9iVMIOpob5UVkFuh5AnL6BQKnOdLyU9A6npGTDSz/rykZGpQOY79Q10dSAEEBUbX3DBFyP3gu/Ao1wFlTI397JS4vp+3bLl/r3bJZPJ4OLihrDQh0hPT8c/ly6gSbOvcl3X1z16w9rGNv+CL0bu3b2DsuXKq5SVcffIsR+D3+tzmUyG0m/68a1/Ll1AQkI86tZvmG1+DQ1NGBiq32sJDx4+hLV1KZgYG0tlHu7uCH/0CAqFQiq7ExyMcmXLqszr7l4GIaFZF/lcXVzwVcuW0NHRybYOQwMDVK1SGYd+CYBSqURYeDjuBgejVs0aBdSqohcc+gi2ViVg+s45p5xbaYRGPlU55wCAT5e2sCuV/cmBt6JjYrEn8BQGd+9QYPEWZ3fDImFb0gKm77wWVMHFESGPn+V53klNS39z3sl6Dcba0hzeX3nByEA/13mIiD5nBZLkrlmzBrt378bTp0/RrFkzjBw5EkOGDMHWrVulOhkZGdi+fTuGDh0qlS1atAiHDx/G48ePMWfOHHTu3BnPnz8HAHzzzTdISkrCnTt3cPv2bVy9ehXLl/97lfzSpUuoVasWIiMj8dVXuX/JXbRoETZs2IDAwEA8evQI58+fh5mZmRRTYGAgLl68iIiICMTHx2PhwoUAgOjoaDRu3Bh9+vRBSEgIQkND4ezsjI4dO0KZx4mlOHoV8xrmZqrvMJmbmkChUCApOVkqi4nNXs/MzATxCVmPfzZtUBdmpqbo0n8o2vcejOiXr/B1+5zfn1JHL+MSYWms+kXfwsQQmUolElNyf/xr+eGzqF5GjlLmJgCA5tXKYWVAEOKTUpChUGDlkXPIVCgQk5BUkOEXGzExr1TutgKAqZk5EhKyJ/mxOdY1Q0JCPJ4+iYSOrg5u3biKMcMHwndAL6xathDJyV9uP5qZmiEhPpd+NM+hz9/UjY+Pw7rVyzF0+Jgc16WhoYEhA/tg6KC+WLbkJ7x69TJ/GlHEYmJiYf7mfPCWmZlp9mNjTCzMzd+vZ4aE+ISPWs/wob44cPAQOn3dHb7DR6Jd2zZwdHD4r+EXW69iX8Pc1ESlzMLUGAqFAonv9OuHCCEwZ8UmDPi6vUrC/CV5+ToBlqbGKmUWJsZQKJRITE7Jdb4V+46hmocLrCzMCjhCIqLioUCS3NGjR8PKKutdmkGDBuH69eto2bIlMjIycOHCBQBAQEAAKlWqBDe3fx85mj59ujRfmzZtULt2bRw/fhzR0dE4cOAAli1bBh0dHejr62PMmDE4dOiQNG/JkiXRvXv3rEZp5t6suXPnYs2aNXB0dAQA2NrawvzNlz2lUokffvgB2tra0NfXh7e3N65fvw4A2LZtGxo1aiStQyaTYdasWXj06BFu3LjxUf2SlpaG+Ph4lb+0tMJ/F0ahVOD9J7rfXgF+dwwQhSJ7PaVCKdVZuXEbDPT1cGTHegTu2ICyZdzww4JlBRh58aJQKvH+g/FKZVZJ9qFUgJS0dEzffBiXHzzC7P4dpfKxXVvAoaQFvOetR7dZa2BhbAgjPV0Y6GW/C6SOlApFtlcM3t3O3qXIqa5SCQ1oICUlGUqFAg8f3sOPi1dh8fL1iIuLw4Y12R8ZVUcKhQICOfRNDh2pUCjw/s6tVCoADQ0IIbB8yQK069gF9g6OOa5rx97DWLdpBxYsXQldXT3MmTldLV4TyfGY9/bY+M5enWu9nDba9yQlJWG630yMHjkCh/btwca1a/DrqVP486+//nP8xVVOdxgVb4+VH9Fnb+0OPAV9PV20blQ332L73GSdv8V7ZW/P39n7MiU1DX5rduJKcAh+8PUulBiJiIqDAkly7e3tpX+bmZkhOTkZmpqaGDx4sHQ3d8OGDRg+fLjKfM7Oziqfrays8OrVK4SFhSEjIwPlypWDXC6HXC7H0KFDEf/OHQonJ6cPxvXy5UvExsaiYsWcB/fQ1dVFiRIlVGJPSsq6CxQSEgIPD9V3hGQyGZycnBAZGfnBdQOAv78/TE1NVf78/f0/at78ZGJkhLj37jjExcVDR0cbhgb/vptjbGyEuATVeq/j42FhbobUtDQcOn4SY4b0h6GBAXR1dTBiYF9cu3UHj59+fo9w/3+YGOjjdaLqXYjYhCToamtJj4S9FRkdA2//9dCWybBpQn9YvHMHWF9HGxO6tcSRWSNxaMYwdKhTGYkpaXAsaVEo7ShM3/TvIf3NnTkNAGBkbIyE+DiVevHxr2Fmnr39RsYmiH+vblzca5ibW8DExBSZmQr08fkm62KYgQF6ePfDpYt/FlyDishgn17S3+yZ0wEAxsbGKsdEAIiLi8uxH41z6Mf4uDiYm1vgwN5dyFQo0KZdx1zX//ZCoqGhEQYNGY4njyPx/Pnnv9/n1Iev4+Kgo6MDQ0PVY2NOfW3x3t3dnPx2/g84OTqiYYP60NTUhJ2dLfp498K+A4c+OO/nysTIEK/jE1XKXscnQEdH+6Mfl7165x72Hz+Dyb79CiLEz4aJoQFeJ6o+nRKbkJjjeedx1Ev09VsCLZkMG74bCXOTL/PuNxF9mQp1dOWBAwfC09MT48aNQ3BwMNq2VR099tWrV7C2tpY+37lzB506dYKtrS2MjIwQFhaW61XfvO7evmVubg4dHR2Ehoaq3EH+GA4ODtnu2CqVSkRERKB06dIftYwpU6ZI7yC/pauri9iwO58Uy3/l5uKMyCdPkZCYCGOjrJPereD7KFfGVaUf3V2ccfvuPaBDG6nsVvB9NKlfB0qFEkqlUqW+pqYGNDU1kPHmXWl1V9bRBuFRrxCflAKTN+/lXg99jApyO2hq/rudxien4pvF2zDoqwbo0qDqB5d77NJNVHeXSwNVqZO1m3ZnK3NxLYPDB/aolAXfvQ13j/I51r139zZcXLPeFc/IyEDIw/sYPmoCzC0soaWthYz0dGhpZR3aNDQ0c3wv8nO3bvPObGUurmVw6MBelbLgu7fh4VEuh7puCL57J1s/jhg9DhvXrUJqaiq8u2W986hQKJCeno5eX7fHgiUrYWtnr7IsIQSUQkh9/jlzdXHB48ePkZCQCOM3j8PeuRsMD/cyKsc6N1dX3Ll7F106dZTK7ty9C68GDd5fZDaZmZmQyVTPV1oyLWRmqu9Ac+7OToh4+hzxiUkwefMu6c17D1HetfRHnbsB4MCJIMTGJ6Dr8Mkq5d1HT8MYnx5o2+TLGA/CQ26PR89eID4pGSZvLrzceBCO8i5OKn2ZkJQCX/9VGNihGTo1rlNU4RIRFZlCHV3ZysoKXl5e6Nu3LwYOHAiZTKYyffr06UhMTJQGh4qNjUXr1q1hb2+PqlWrws/PTxps6uHDh7h27donrV8mk2Hw4MEYNmwYXr7MeocsPDxcZXCp3PTp0wcnTpzAvn37AGR98fPz84OnpyfKl8/+ZTwnurq6MDExUfnT1dX9pDbkB0tzM9SsWglrt+1GpkKB1/Hx2LbvMLq2U32ftplXfVy+cRtXbtwCAFz45yoiHj9Bo3q1YWCgj5pVKmPdm2UIIbBlz0FYWpjD0U69fpolNyVMjVCvnAuW/XIWmQolYhOTsf74eXg3raVS79TlO5BbW+aa4D6OjpVGUr4V/gTrj5/H8PaNCzz+4qJGzbqIiXmFc2dPAQAePriHSxf+h2Yt22Sr26JVW/xyaC9evoyGQqHAvt3bUNGzCkpZ20BHRweNmrTApvUroVAokJGRjt07NsGrUbPCblKRqFmrjko/Prh/Dxcv/InmrbK/J9/iq7Y4fHCf1I97d21HBc/KKGVtg8079mH3gSPYuS8AO/cFYPqMObCxtcPOfQGwtbPHs2dP8eRx1tMrGRnpWLd6Odzc3FGyZM4/9/I5sbAwR7Vq1bBp61YoFArExcVh95696NShvUq9Jo0b4dq167j25nWWS3//g8jIx2jY4MOJVs3q1XDj5i1cv3ETAPD69Wvs2rMX9evVy/f2FBeW5qaoXbkCVu88+Oack4DNB46ie9uP3zdnj/XF2e0rcWrrcukPAPYsnfPFJLgAUMLMBHU8PbBi77GsvkxIxMaA0+jVUnWAuNOXrkFua8UEl4i+WIV+6X3w4MFo06aNyvu0b7Vq1QrVqlVDSkoKypcvj1OnTklJ4M6dOzFq1Cg4OztDV1cXcrkcixcv/uT1//TTT/jhhx9Qs2ZNKJVK2NjY4MCBAx+cz87ODqdPn8aECRMwduxY6Ovro1mzZtizZ88H5y2OJo4cgh+XrUFnH1/o6+mie8e2aFC7Bk6eO4/gByEYNdgHViUs4Td+FBav3oj4xETYWZfC3GkToa+X9UjUtG+HY9Xm7fD2HQMhBMq4OMN/2oRsdynUmV/f9pi5NQDNJy2Cno42+jWvgyaVPXD04g3cCn+KSd1bIeLFK9wIfYyvpi5Vmfftnd2b4U+waP9JaMlkKGFqhFk+HVDR+cu4UAAAunp6mPb9HKxcthCb1q+EubkFxk6YjhIlskZYXb/6Z7i5l4VX4+aoXbcBnj17gonfDoVQKlHBszJGjJ4oLatv/yFYs2IxBvb9Gvr6+qhTtyF69hlQVE0rVLp6epjuNxsrfl6IjetWw8zcHOMmTpX6ce3q5Sjj7oFGjZuhTt36ePb0CcaPGQ4hlKhQsRJGjZnwUetJTIjHgvlzkJ6eBm1tHXhWqoJJ0/wKsmmFauzokVi0dBl69u4LPT09dOncCXXr1MGZs0G4/+ABhg75BiVLlMCUSROxbOUqJCQkwtbGBjP9voOent4Hl29tbY0Z303Dhk1b8OrVK8i0ZGjetCm6du5UCK0rOlOH+WDuys1oN3gs9HR10at9S3jVrIoTv/+Fuw/D8O2AXkUd4mfj+0Hd8cP6PWg1cgb0dXXQu3UjNKpeEcf+9w/uhEZifJ9OiHj+EjcfhKPdt7NU5h3Qnnd2iejLoCEKebSQn3/+GRcuXMDOnaqP22loaODZs2cqjyt/KZ4HXy3qED571h5VkBK0o6jDUAv6jb1x5+HTog5DLZRztUVwyOOiDuOz5+Fij/CH94s6DLUgdy2DmJt/FHUYasGiYn0kXDpa1GF89oxrZn9qh4jovyrUO7nPnz/HggULcPRowZ4Uxo8fj/3792cr37JlC7y8vAp03URERERERFR0Ci3JHT58OI4cOYI5c+bkOrpxflmwYAEWLFhQoOsgIiIiIiKi4qfQktwVK1ZgxYoVuU5Xh99YJCIiIiIioqL15YwQRERERERERGqPSS4RERERERGpDSa5REREREREpDaY5BIREREREZHaYJJLREREREREaoNJLhEREREREakNJrlERERERESkNpjkEhERERERkdpgkktERERERERqg0kuERERERERqQ0muURERERERKQ2mOQSERERERGR2mCSS0RERERERGqDSS4RERERERGpDSa5REREREREpDaY5BIREREREZHaYJJLREREREREaoNJLhEREREREakNJrlERERERESkNpjkEhERERERkdpgkktERERERERqg0kuERERERERqQ0muURERERERKQ2mOQSERERERGR2mCSS0RERERERGqDSS4RERERERGpDQ0hhCjqIIiIiIiIiIjyg1ZRB0DAuVspRR3CZ69RBX1E3b1c1GGohVJlq+HVrT+LOgy1YFmhLmKv/1bUYXz2zCt54dHDe0UdhlpwcnVHStCOog5DLeg39uaxMh9YVqjLfswnlhXqFnUIRMUGH1cmIiIiIiIitcEkl4iIiIiIiNQGk1wiIiIiIiJSG0xyiYiIiIiISG0wySUiIiIiIiK1wSSXiIiIiIiI1AaTXCIiIiIiIlIbTHKJiIiIiIhIbTDJJSIiIiIiIrXBJJeIiIiIiIjUBpNcIiIiIiIiUhtMcomIiIiIiEhtMMklIiIiIiIitcEkl4iIiIiIiNQGk1wiIiIiIiJSG0xyiYiIiIiISG0wySUiIiIiIiK1wSSXiIiIiIiI1AaTXCIiIiIiIlIbTHKJiIiIiIhIbTDJJSIiIiIiIrXBJJeIiIiIiIjUBpNcIiIiIiIiUhtMcomIiIiIiEhtMMklIiIiIiIitcEkl4iIiIiIiNSGVn4vMDg4GOPGjUNgYCA0NDQ+WN/HxwceHh6YPHlyfoeSq7Fjx6Jhw4bo2LFjjtPnzZuH4OBgbN68udBiKi5evniC7at+QNTTCMi0tNC2my9qe7XJVi8+LgYHty5G+MPbSElOhFvZqugxaBKMTMyLIOrClZaWjp/Xb8GlazehVCrRrEFd+PbrmW17vx8ajkWrN+JV7Gvo6epi1KC+qFG5IgAgKTkZq7bswuUbt5CUnIKGtWtgzOB+0NLK2iWXbdiGP/++gvSMDJSwMMeIgX1Q0aNMobe1MKWlpWPxxp24eO0WlEolWjSojWF9vs7xOBKfkIgV2/bC3qYU+nT6d/v868oNrN6xH4nJKQCA9s280K9L20JrQ3GRmp6OxZv24OL121AolWhRvyZGeHfJsS/jEpOwfPsBOFhboW/HVirTXr2Ow5LNe3Hj3kMolEq0bFALI3t3LaxmFIm0tDSsXLMOl69chVKpRONGDTGov0+2vnsYEoKfV6xCTEws9PT0MHTIIFSrUgUA8OzZc6zZsBEREZFITUtD7ZrVMWTQQOjq6mZb388rVuLajZvYuGZVobSvqKSmZ+DHvb/irzshUCiV+KpGBYzp3EylXzMUCmw88QdOXb6LhJRU2FqaYlL3r+DhYJ01PVOBVYHncOZqMOKTU1DdTY7v+7SFsb5eUTWrSPBYmT/Yj0Tq7ZOT3E2bNuH27dtYsGBBjtM9PDxw9OjR/xxYQVq0aFFRh1AsKRUKrPAfjebt+qBukw54GhmCn6b3h52jCxycPVTq3r76P5SvUg99h8+EUqHAttUzsWv9PAweO7+Ioi88KzZth1II7Fq9GKmpaRjrNxcHj55El7YtpTrJKSmYMmcBpowaguqVKuLarbuYOnchtq1YAEtzM/y4Yj3MTI2xfflCZGZm4vuflmLXoUD0+bojAKBu9SoY3t8bmpqaOHnuD0yduxC/bF4FTU31ffji5y27IYQS+1bOR2pqGkbN/An7j5/B162bqdRbsXUvjgadh66ODuysS6lMMzc1wbIZE2FibITomFh8M2U2XBztUb9G5UJsSdH7ees+KIXA/mVzkJqWhpGzFmPfiSB0+6qJSr1l2/fjaNCf0NXRhoN1SZVpaekZGDlrMdp41cWMUQMh09TEi1exhdmMIrFm/UYIIbBlw1qkpqZi0rTv8MuRo+jY/t8vrsnJyfh+5myM/3Y0qlapjBs3b8Fv1hxsWL0SFhbm+N+FC2jb+itUr1oFycnJ+GHuPGzfuRsD+/dTWdeL6GicOhOEkiVLFHYzC93C/acghMCRWSORkp6OIUu2Y/e5v9GzcU2pzqOoV1AolNg2aQD0dXWw//fLGL1yNwJnj4S2TIYVAUF4FPUKe6Z9A20tGebsPIbFB07j+95fVlLBY2X+YD8SqbdP/sb86NEjJCYm5jhNqVT+54AKUnGPr6gF37wImUyGuk06AABsHVxQq2Eb/HXuSLa6dRq1Q436raCpqQktbW00b9cX925eKuyQC11ySipOBJ3H0H69oCWTwcjQAN5d2uPYmXMq9U7//ic8XEujeqWsO7eVK5SFZ3kPnP3jL6SlpeP3C39jSJ8ekMk0oaurA9++PXHk5Flp/mqVKkgJbf1a1RCfkIj0jIxCa2dhS05JxfFz/8PwPt2kfu3buQ0Cz57PVtfQQB/r/L9DtQpls03zcJHDxNgIAFDSwhxlXZwRHaP+idm7klNTcey3vzCid5esvjQwQN+OXyEw6H/Z6hoZ6GPD3CmoVsEj27RfzpxHSQszeLdvAdmbbdHKUr2f1EhJScGpM2cxaIAPZDIZDA0N0ePrrvj11CmVekG//Y4yZdxQtUplAIBnxQqoWL48fjuftb127dQR1atm3dU1MDBAh7ZtcO3GjWzrW71uA1o2b1qwjSoGklPTceTCdYzp3AxaMk0Y6+thYKt6OPznNZV6rrZWGNa+MfR1dQAAXRtWQ0paOiKiYgAARy/ewPD2jaGnow2ZpibGdW2O43/fRGq6+h4b38djZf5gPxKpv09Kcnv37o0lS5Zgx44dkMvlmD9/PvT09LBz5064urpi+vTpOHfuHDw8/v3CFBUVha+//hoODg5wcXHBihUroKenh/DwcJVlv3jxAvr6+oiMjJTKMjMzYW1tjRtvvhwcPHgQ1atXh1wuh6OjI/766y8AQEREBNq3bw9nZ2d4eHhg69at0jJ8fHwwdepUtGrVCtbW1khMTESjRo2we/duqc6uXbtQoUIFODg4oFGjRoiIiFCJ7ebNm2jcuDHkcjk8PT1x4sQJadrRo0dRrVo1yOVyODs7IzQ09FO6tFgJuX8DLu6VVcqc3SogMuzeB+dNiI+FvoFRAUVWfNwPCYNNqZLSSQ0AypVxRWjEYygU/15EuX3vASqUVX28uFwZVzwMewSFUgGlUqlS39TEGM+jX2ZLZJOSk7F5z0G0bd4Yejk86qgu7oWGw8ZKtV/Lu7kgNOKJSj8BgE/XdrCztspzeUqlEpeu38ajp8/hVbtagcRcXAWHPoKtVQmYGhlKZeXdnBES+QSK9y709e/cBnalSr6/CABA0IXLaNuoboHGWtw8eBgCa+tSMDE2lso83N0R/igCCoVCKrsbfA/ly6p+4fVwL4OQ0LAcl/s6Lg4GBgYqZRcv/Y2E+AQ0qFcvH1tQPN2JeAq7EmYwNdSXyirI7RDy5EW2bfJdKekZSE3PgJF+1rEvI1OBzHfqG+jqQAggKja+4IIvZniszB/sRyL190lJ7vbt2zFmzBh4e3sjPDwc3bt3R2ZmJm7cuIEHDx5g9uzZ2ebp1KkT3NzcEB4ejuDgYNy+fRtpaWnZ6llZWaF9+/bYvn27VBYYGAhXV1d4enriwIEDmDhxIrZs2YLw8HBcv34dTk5OSEtLQ7NmzdChQweEhYXh5MmTmDp1Kq5evSotZ9euXVi6dCmioqJgaGiost5Tp05h8uTJOHDgACIjI7F06VLs27dPmh4dHY3mzZtj0qRJCA8Px/bt29G7d288ffoUycnJ6Nq1K3bt2oXw8HBcuHABlpaWn9KlxUp87EuYmKnGb2xqgaSEuDzny0hPQ8DuFajXtFNBhlcsvIqNhbmpqUqZuakJFAoFkpKT36n3GhY51ItLSISBvj5qVvHE6i07kZqWhpTUVGzYuQ8aGhqIi08AAFy7dRedBwzHV70GITzyCQb37lbwjStCL2PjYGFmolJmbmqcrV8/xoCJM9Gk1xDMWb4Bo/v3hIWpyYdnUiOvYuOytdnCxBgKhVJ6b+xjhEQ8QVpGJr75bj46Dp+Csf4/I+JpVH6HW6y8iomBuZmZSpmZmWm27TAmJgbm5tnrxb/Zf98VHx+PPfsPoFWL5iplK9esw8jhQ/M1/uLqZVwiLI1Vz70WJobIVCqRmJL9+8Bbyw+fRfUycpQyz9qem1crh5UBQYhPSkGGQoGVR84hU6FATEJSQYZfrPBYmT/Yj0Tq7z+/4KdQKDB69GhoaGhke1/wypUrCA8Px6xZsyCTyaCtrY2ffvop1/cKfX19Ve7CbtiwAcOGDQMAzJ07FwsWLED58uUBAObm5rC1tcWRI0dgY2ODgQMHAgAcHR3Ro0cPHD58WFpOixYt4O7uDg0NjWwDCixbtgyTJ0+Gu7s7AKBSpUoYMGCANH3Lli1o2bIlWrXKGpDF09MTjRo1wq+//goNDQ1oa2vj77//hhACpUqVgul7ic270tLSEB8fr/KXU8JfVBQKBYQQKmVKpTLPAcReRj3BT9P7w8rGES06+hRwhEVPoVBCQLWP3t6JeLebFApFtnrv9uX0McOQqVCg78iJGDrJD2XdXCCEgL5e1gAqlSuUxcGNK3Bi10ZUr1QRg8ZOQ3xCzq8JqIOctj2F8s3njxjA7l0bf/TDmR1rMGvcUCxYuxWn/riYX2F+FjIVylz78lN6Mik1FecuXoH/OF/s/3k2Kpctg3HzlyEzMzMfoy1ecjsGAoDGO72nyKGPs/Zv1eWFhIRi1LgJ8GrQAI29GgIAhBBYuHQZOnVoD0cH+wJoRfGjUCrfOxoCyjy2yZS0dEzffBiXHzzC7P4dpfKxXVvAoaQFvOetR7dZa2BhbAgjPV0Y6OkUWOzFDY+V+YP9SKT+/nOSq62tDRsbmxynhYSEwM3NDTKZTCozNDSEtrZ2jvUbN24MIQQuXbqEp0+f4sqVK+jaNWskz3v37sHT0zPbPKGhobhy5Qrkcrn0t2PHDsTExEh1nJycco0/JCQEZd977Mzc/N/3zkJDQxEQEKCy/PPnz+Ply5fQ19fHmTNnsGnTJri5uWH9+vW5rgcA/P39YWpqqvLn7++f5zwFZarvV9LfCv/RAABDIxMkJrxWqZcYF5vt7u5bNy+fx/ypfVHbqy36j5yt1oMivWVsZCjdbX3rdVwCdHS0YfjO44gmRkY51IuHhVnWRRBTE2NMGeWLvWuXYvPS+Sjv7gYLM1MYGao+0migr4du7b+CvY01fr/wdwG1quiZGBkiLl41iX8dHw8dHW0YGejnMlfuZDJNeHq4of/XHXDg+Jn8CvOzYGpkiNcJ7/dlAnS1P60vzYyN4N2uBSzNTKElk6FPh5aIS0hC+NPn+R1ysWFibIz4eNVHX1/HxUFHRweG7+ybxsZGiMuh3rvnjhMnT2Hq9zPQv28f9O/bWyrfvW8/FJkKdGiXfdR6dWVioI/Xiap3x2ITkqCrrQWj90ZGjoyOgbf/emjLZNg0oT8s3rkDrK+jjQndWuLIrJE4NGMYOtSpjMSUNDiWtCiUdhQHPFbmD/Yjkfr7zz8hlFdiY2lpiSdPnqiUPX36NM+7l0OGDMGWLVtgb28PHx8f6OhkXaG1sbFBSEgISpcurVLf1tYWTZs2Vblz+ykxlihRIts7uO++V2tra4t+/fphyZIlOc5fo0YNnD59Gjdv3kSnTp1gbGyM7t2751h3ypQpGDt2rEqZrq4u/npQ+ANizV19PFuZo0s5nPxli0pZyL3rKO2ew8WF+zewZYUfRkz9GXLXCgUWZ3FTxsUZkU+eISExEcZGWe/y3Lp3H+XcXFW2szKuzrgV/ADdO/w77617D9CkXu0cl3vytz9Qr0bVXNerra0FHZ2cLw6pA/fSckQ8fY74xCSYvHmX9Oa9hyjvVvo/XTzR0daCrhr3W07cSzsi4mmUSl/euB+Ccm7On9SXzva2SE5NlT5raGhAU0MDOrlcpFQHri4uePz4CRISEmH85l29O3eD4eFeRqXv3FxdceduMPDOGxp37gajUYMGAIDf//gfduzag0U/zYOdra3KOn4JCERqaio6d+8FIOuOUnp6Ojp164nlixfCzk61vjoo62iD8KhXiE9Kgcmb93Kvhz5GBbkdNDX/vWsWn5yKbxZvw6CvGqBLg9yPh28du3QT1d3l0kBVXwIeK/MH+5FI/X3ynmxhYSElgR96bK1OnTrIyMjA4sWLAQBJSUmYMGFCngcQHx8fBAQEYPv27RgyZIhU7uvri4kTJ+LRo0cAsgaqioyMRJs2bXDx4kUEBgZKdX///Xe8fPnyo9rTrVs3+Pv7SwNeBQUFqSTMPXr0wK5du3DxYtbjJ0qlEgEBAcjMzERsbCz++ecfAEC5cuXg7Oyc68jTQFZCa2JiovKX0+8mFpVK1RsiLiYaF37L+gmo8Ie3cf3vc6jfrHO2ukHHdqFpW+8vKsEFAEtzM9SsWglrt+9BpkKB1/Hx2LbvML5up/r7os0b1sPlG7dw+cZtAMBf/1zFo8gnaFSvFgAg8skzZL4ZyObiles4ee4P6eeDQsIjcPHKdekxyaD/XUBYxGPUrJL9YoO6sDQ3Ra0qFbBmx4E3/ZqALfsD0a1Ni09azv5jp5GUkvXe6bMXL7HlQCBaN65fECEXW5ZmpqhduTxW7Tos9eXmg8fQo/WnjeLbqXlDrN93BHFv7grvCDgJe2srOHxgAJbPmYWFOapXq4pNW7dCoVAgLi4eu/bsRacO7VXqNWnshWvXruPq9esAgEt//4PIyMdo0CBrEKmDh3/BgH59siW4ALB7+xYc3r8Hh/buwqG9uzDL7zvY2trg0N5dapngAkAJUyPUK+eCZb+cRaZCidjEZKw/fh7eTWup1Dt1+Q7k1pa5JriPo2OlkZRvhT/B+uPnMbx94wKPvzjhsTJ/sB+J1N8n38nt3r07tm7dCrlcjlGjRuVZV19fH4GBgRg2bBh+/PFHWFtbY+7cudi7d2+2AaDeMjc3h5eXFxITE+Ho6CiVf/vttwCy3q9NTU2FhYUFtmzZAgcHBwQGBmLMmDHw9fWFvr4+ateujeXLl39Ue3x9ffHs2TPUrVsXMpkMjRo1wvDhwxEWljVKpqurK7Zt24ahQ4fi5cuX0NXVRYsWLdC2bVtkZGRg8ODBePbsGUxNTdGmTRv069fvA2ssvnR09TF8ylJsW/UD9m9eABOzEhg4Zi7MLbN+F273hvlwdquAWg3b4MWzCNy9fgHnT+5XWcbwKT/DzsmtKMIvNJNGDMb85evQqf8w6OvqonvHNmhQuwZOnvsDdx+GYPSgfrAqYYkZ40di8ZqNiE9Mgp11KfhPGy+9c/vH35ex55dj0NbSgp11KcybNh7WVlmj3Opoa2PzngOYu3QV9PX0UNrJAQtnTIaZiXoPZjF12ADMXbkR7QeNgZ6uLnq1bwWvWlVx4rc/cfdhGL4d6P3BZYREPEaPEVOgra0FYyND9GzfCi0b1imE6IuXab59MWf1VrT9ZgL09HTh3a45vGpWwfHfL+BuSDjG9u/xwWU0rVMdEc+i0Hv8D9DSkqGsixPmjffN8x19dTB29CgsWvozevTuBz09PXTt3BH16tTG6bNBuP/gIYYNGYySJUpg6qQJWLZyNRISEmFnY4Mf/KZL+/eTp8+wZsMmbNyyTWXZK39eIt0h/tL49W2PmVsD0HzSIujpaKNf8zpoUtkDRy/ewK3wp5jUvRUiXrzCjdDH+GrqUpV5397ZvRn+BIv2n4SWTIYSpkaY5dMBFZ3tiqhFRYfHyvzBfiRSbxri/TfvC9j9+/dRo0YNxMXlPmJvtWrV4O/vjxYtPu2K2ufq3K2PH/GUctaogj6i7l4u6jDUQqmy1fDq1p9FHYZasKxQF7HXfyvqMD575pW88Ojhh3/KjD7MydUdKUE7ijoMtaDf2JvHynxgWaEu+zGfWFb4sn72jSgvBT5S0NGjR6V3cF+8eIERI0ZIIybnZOvWrdDS0vpiElwiIiIiIiLKP/954KkP+eWXXzBkyBDIZDIYGBjA29sbEydOzFYvLS0NpUuXhqOjI/bs2VPQYREREREREZEaKvAkd+3atR9VT1dXN9tIzERERERERESfQv1/2JSIiIiIiIi+GExyiYiIiIiISG0wySUiIiIiIiK1wSSXiIiIiIiI1AaTXCIiIiIiIlIbTHKJiIiIiIhIbTDJJSIiIiIiIrXBJJeIiIiIiIjUBpNcIiIiIiIiUhtMcomIiIiIiEhtMMklIiIiIiIitcEkl4iIiIiIiNQGk1wiIiIiIiJSG0xyiYiIiIiISG0wySUiIiIiIiK1wSSXiIiIiIiI1AaTXCIiIiIiIlIbTHKJiIiIiIhIbTDJJSIiIiIiIrXBJJeIiIiIiIjUBpNcIiIiIiIiUhtMcomIiIiIiEhtMMklIiIiIiIitcEkl4iIiIiIiNQGk1wiIiIiIiJSG0xyiYiIiIiISG1oCCFEUQdBxVdaWhr8/f0xZcoU6OrqFnU4nzX2Zf5gP+Yf9mX+YV/mD/Zj/mFf5h/2Zf5gP1JhYpJLeYqPj4epqSni4uJgYmJS1OF81tiX+YP9mH/Yl/mHfZk/2I/5h32Zf9iX+YP9SIWJjysTERERERGR2mCSS0RERERERGqDSS4RERERERGpDSa5lCddXV34+flxgIB8wL7MH+zH/MO+zD/sy/zBfsw/7Mv8w77MH+xHKkwceIqIiIiIiIjUBu/kEhERERERkdpgkktERERERERqg0kuERERERERqQ0muURERERERKQ2mOQS0UeRy+W4cOFCUYfxyTw8PHDu3Llcpy9YsAArV678qGWdO3cOHh4e+RRZ8bZ582a0atWqqMMo9mJiYtC4cWMkJCT8v+b/UD+ry/+Dj48PzM3NIZfLYWNjA29vb8TFxQEAZsyYAQ0NDVy+fDnHeZOSkmBhYQFfX1+prFGjRti9e3ehxF6Y9PT0EB4e/knHJQ0NDTx//ryAIytan+v5Jzw8HHp6ev9pGQXd9uDgYLRp0wYfOw6tj48P5s2bV2Dx5GTs2LE4fPhwrtPnzZsHHx+fQouHPg9MconoizZ+/HgMGzasqMOgz8SmTZswfvx46bOFhQWCgoJgbGxchFF9HiZNmoTw8HA8evQIQghMnjxZmmZiYoLFixfnON/GjRuhqfllfV0pzONSuXLlEBUVVSjrosL3/jHrfR4eHjh69Cg0NDQKMapPs2jRInTs2LGow6DPzJd11iAiekMI8dFXroneevToERITE4s6jM+ajo4OBgwYgPPnz0tlLVq0QGBgIJ49e6ZSV6FQYMmSJWjRokVhh/nFuHv3Lo+FH+Fz7aO8jllKpbKQo/k0xT0+Kt6Y5BI2b96MihUrwtHREWXKlEFSUhJiYmLQp08flC5dGm5ubvjxxx8BANHR0bCyssKlS5cAABkZGShfvjyOHj1alE0ocrt27UKlSpXg6OgIFxcXbN++HUBW/0yePBmlS5eGo6MjvvnmGzRr1gybN2+W5j148CAqVaoEuVyOpk2b4uHDh0XUig97+PAhmjRpAltbW1SuXFl6vDC37QUAQkND0a5dOzg5OcHe3h69e/dGWloaoqKioKenp/KYXWJiIkxNTbF161aUKVNGZd03b96EtbU10tPTc40vKSkJQ4cOhVwuh5OTEyZNmqQyfcaMGRg4cCB69eoFOzs73L59W+XRq7ePI2/cuBEVKlRAyZIl0b9/f2RkZOS4vhEjRqB+/fqFnvTktM/K5XKsW7cO9evXh6WlJTp27Ijo6GgMHDgQTk5OcHJywq5du6RlKBQKLFiwAOXLl4eTkxMqVqwobbfvy8zMRMeOHdGlSxdkZmZCoVBg5syZcHd3h7OzMwYMGFDofRAVFYWvv/4aDg4OcHFxwYoVK6RHPfOK72P+j3///XfUrFkTcrkctWrVwj///AMA6N27N5YsWYIdO3ZALpdjz5492R5HzMjIwNy5c1GuXDk4ODigXLlySE5O/mB75s2bBw8PD1hZWWH06NHZpi9btgweHh6wtbVF9+7d8erVKwBZj0MaGRnh7NmzqFKlCuzs7FC3bl1cv379P/VvQXv16hWsra2lz5aWlujZsydWrFihUu/QoUNwcXGBu7t7YYdY4KKjo9GzZ084OjrC2dkZS5Yskaa9e1zKyMjAkCFDIJfL4eDgAC8vL4SGhqos6+bNm6hXrx7s7e1RuXJlnD17VpqWkpKCUaNGwdXVFaVLl8aECROQmZmJf/75B3K5HABQo0YN1KlTJ8/6QNZ+17lzZ7i6uqJUqVIqMRe0nM4/HTt2xJw5c1TqjRs3DmPHjgWQ9Sj3mTNnULdu3U/qG+DfVwXGjh0LBwcHHDt2LNfYhBBYu3YtPD094eTkBBcXF4SFhUnTjx07hqpVq6JkyZJo06aNtP8CwJ07d9C2bVu4uLjAyckJPXv2zHax5//r/WPW/Pnzoaenh507d8LV1RXTp0/P9hpOXsfWd7148QL6+vqIjIyUyjIzM2FtbY0bN24AyPp+U716dcjlcjg6OuKvv/4CAERERKB9+/ZwdnaGh4cHtm7dKi3Dx8cHU6dORatWrWBtbY3ExMRsryfs2rULFSpUgIODAxo1aoSIiAiV2G7evInGjRtDLpfD09MTJ06ckKYdPXoU1apVg1wuh7Ozc7Z9idSIoC/aw4cPhbm5uYiKihJCCBEaGirS09NFw4YNxXfffSeUSqV49eqVqFixojh06JAQQogdO3aIOnXqCCGE8Pf3Fz4+PkUVfrGxc+dO8fTpUyGEEH///bfQ19cXr1+/FpMnTxbNmzcXr1+/FkIIsXXrVqGpqSk2bdokhBDi9OnTQi6Xi4cPHwohhFi7dq2oUqWKUCqVRdKOvDg5OYn69etL28q4ceNEnTp1hFKpzHN7uXLliggKChJKpVIkJyeLmjVritWrVwshhOjQoYNYtGiRtI5NmzaJdu3aiYyMDGFrayv+/PNPadro0aPFtGnT8oyxV69eom/fviI1NVUolUoxe/ZsoaGhIYKCgoQQQvj5+YkSJUqIP/74QwghhEKhEP369RP+/v5CCCGCgoKEvr6+mDlzplAqleLly5fCxcVFbNy4UZru7u4uhBBiypQponbt2iI+Pv4/9uynyW2fdXJyEs2bNxeJiYkiOTlZ1K9fX9jb24tdu3YJIbK2NSMjI5GYmCiEEOK7774TXl5e4sWLF0IIIe7cuSMcHR1FYGCgECLr/6Jly5ZCqVQKb29v0bFjR5Geni6EEGL69Oniq6++EvHx8SIzM1P06dNHjB49ulD7oU6dOmLKlCkiMzNTpKeni6FDhwoAIiwsLM/4PvR/fPfuXVGyZEnxzz//CCGE+PXXX4WdnZ1ISkoSQmRtQ0OGDJHiCAsLE7q6utLnESNGiDZt2oiXL18KIYS4f/++SEtLy7UdmzZtEoaGhmLdunVCCCEePXokzM3Nxa+//ipNNzU1FXPnzhUKhUKkpaWJPn36iM6dO0vr19LSEr1795a2xVWrVgkHBweRnJz8n/s5v7y7n4WHh4sqVaqIEydOCCH+7dN79+4JKysrkZKSIs1Xu3Ztcfz48Wz97uXlJW3bn6t69erlug2/219JSUliw4YN0v43cuRI0aNHD2k5AETr1q2lY0JgYKAwMTGRzkm9e/cWAwYMEGlpaSI5OVk0bdpULF68WGX+Z8+eSZ/zqt+nTx8xdepUIYQQGRkZIiQkpMD65125nX+OHTsmypQpI9VLT08XVlZW4v79+1Lb3u8bU1NTqb15tXXTpk3C3Nxc7N69WwiRdb7IzYIFC0TNmjXFo0ePhBBCPHnyRMTExIiwsDChqakphg4dKtLT06Vj85QpU4QQQrx48UJYWVlJ68jMzBRTpkwRNWvWlNbn5OQk/vrrr/93372774SFhQmZTCYmTZoklEqlUCgUKuc1IfI+tgqhui9369ZNzJ07V5r30KFDol69ekIIIfbv3y9cXFzErVu3hBBCxMTEiCdPnojU1FTh5uYm1q9fL4TIOubZ2dmJK1euSMuXy+UiODhYKJVKoVQqVfb3kydPCkdHRxEcHCyEEOLatWuiRIkSol+/flKflipVShw/flwIIcT169eFpaWlePLkiUhKShJ6enri3r17Qgghnj9/Ln0/I/XDJPcLFx4eLgwNDcXJkyelsr///ls4OTmpJFrLli0Tffv2lT63bdtW+Pv7Czc3NxEbG1uYIRdb6enp4vbt2yIgIEAYGxuLy5cvCxMTE3Hnzh2VejVr1pSS3DZt2khfrt+ytLQUoaGhhRX2R3NychL79u2TPt+9e1cYGBh81PYihBCPHz8WQUFBom3btmLkyJFCCCECAgJEtWrVpDpeXl7i6NGjQgghvv/+e+nEnJaWJqysrERERESu8b18+VLo6OiIuLg4lfJSpUqpJLktWrRQmf5+kmtmZqbyZWbixIli2LBh0nR3d3cxb948UbNmzSI5Oea0zwqR9f9z+PBh6fOyZctExYoVVepYWFiI69evCyFEjtvm8uXLRYcOHYQQ/ya5Q4cOFe3atZMSNaVSKYyMjFS+3N68eVM4OjrmWxs/5PLly8LGxkZkZmZKZYmJiUJTU1OEhobmGd+H/o+HDx8uvv/+e5X1VatWTZw9e1YIkXeS+/r1a6GtrS0luB9j06ZNonz58ipl3bt3Fz/++KM0/d0v8UII8ezZMyGTyUR6eroICwsTAKSE5i13d3cp5uKgX79+wszMTDg4OAhNTU3RtGlTcffuXSGEap+2b99eSvj/+OMPUa5cuWx1hPj8k9zLly8LW1vbHLfh95Pct2JiYsSFCxfElClTVPZtACoXBIUQomXLlmLjxo3ixYsXQl9fX7pII4QQR44cEQ0bNlSZ/23S96H6gwYNEl26dBExMTH50AsfL7fzj0KhEM7OzlISuH//ftG8eXOp3n/pm5z2vdxYWFiIq1evZit/u39GR0dLZatWrRKtW7cWQgixcOFC0a1bN5V5MjMzRalSpaTl5XeS+/7x4t0kN69ja05J7tmzZ4WHh4dUt23btmLHjh1CCCGqVq0qXex+1759+1S2PyGyLlq8Pe7269dPfPPNNyrT393f27VrJ1auXKkyfeLEiVKS+9NPP2X7/tGlSxexceNGkZycLIyNjcX27duL5c0Eyl9aRXP/mIoLJycnHDhwANOmTcP48eMxc+ZMpKenIyoqCs7OzlK9jIwMVK9eXfrs7++PihUrYvny5TAzMyuCyIuXsWPH4sSJE/D09IRcLoeWlhaSkpKQkJCQ7TE7c3Nz6d+hoaGYOnUqZs6cKZUJIfD8+XOV/i8u7O3tpX+bmZkhOTkZoaGheW4vJ0+exLfffgsHBwe4uroiKSlJeuS4devWGDJkCIKDg6Grq4vIyEhpJNnBgwejSpUqWLp0KX755RfUrVsXDg4OucYWGhoKGxsbmJiYqJS/v306OTnl2cZSpUqpDHJjbm6uMijLixcv8N133+HcuXMwNTXNc1kFIad99u2AHKVKlZLqGRkZwdbWVmVeY2NjJCcnIzo6GgkJCdkeCS9durTKo2dXrlxBUFAQgoODoaOjAyDrEcvExEQ0adJEqieEKNTHlUNCQuDm5gaZTCaVGRoaQltb+6Piy+v/ODQ0FHv27MGWLVuk6UlJSXjx4sUH43rw4AGsra1haWn5Se15d78CsrbZpKQk6fP7x4KSJUtCqVRKoxPr6urCxsZGpY6VlZXKI5HFwaRJkzB58mQoFAocO3YMXl5e2UZUHjduHIYPH45BgwZhwYIF0mOn6iavbfh9YWFh6Nu3L5RKJcqWLYvMzMxsr228v428/f8PCwtDRkYGypUrJ01TKBQoUaJEjnF9qP6iRYvw3XffwcPDA506dcLcuXNhYWHx6R3w/5DT+UdTUxODBw/G1q1bUbt2bWzYsAHDhw9Xme+/9M2HzhcA8PLlS8TGxqJixYo5TtfV1VVZ5rv7d0hISLYR+2UyGZycnBAZGYnKlSt/cP2fSltbO9vx4q1P2S4BoHHjxhBC4NKlS7C3t8eVK1dw4MABAMC9e/fg6emZbZ7Q0FBcuXJFelQeANLS0tC1a1fpc179HhISgrJly6qUvX8MDwgIUFl+SkoKatWqBX19fZw5cwZTpkyBn58fJk+ejEGDBuW6Lvq8MckltGzZEi1btsQff/yBDh064ODBg3B3d8e1a9dynWf27NkYNWoUli9fjgEDBkBfX7/wAi5mzp49i2PHjuHWrVvQ0tKCEAKrVq2CEAKampp48uSJSnL27ns6tra2mDt37mc9aqCtrW2e28ugQYOwc+dO1K9fHwAwatQo6QuaTCZDv379sGPHDshkMgwaNEhKPuzt7VG3bl0EBARgw4YNeY4OCQAlSpTAixcvkJaWBl1dXQBZyfbjx49V6v3XUVqtrKywcuVKdO3aFadPn1b5glRY3t9nc/vCkhtLS0vo6ekhJCREJdENCwtD6dKlpc9Vq1ZF06ZN0apVKwQFBcHW1hYlSpSArq4url69qnLBpjBZWlriyZMnKmVPnz5FWlraf47P1tYW06ZNw5gxYz55XhsbG0RFRSEpKQmGhoafPH9u3k9W7969CxMTE5QoUQKJiYlIT09HQkKCNMJzZmYm7t+/DxcXl3yLIT/JZDK0a9cOLi4u0vgObzVs2BB6enrYsGEDLl68qJY/EwRkHa/evaAE/LsNv8/Pzw8tW7bE9OnTAWS95/j+T8q8/47znTt30KlTJ9ja2sLIyAhhYWEfNXruh+obGxtjyZIlmDVrFr799lt4e3vj+PHjH9XmgjJw4EB4enpi3LhxCA4ORtu2bVWm/5e++Zjzhbm5OXR0dBAaGgo3N7dPit3BwUF6f/UtpVKJiIgIlWNxfsqrTXkdW3MzZMgQbNmyBfb29vDx8ZEuiNrY2CAkJCRbO2xtbdG0adM8fxIorxhLlCiR7R3cd9+rtbW1Rb9+/XJ9X7xGjRo4ffo0bt68iU6dOsHY2Bjdu3fPdX30+eLAU1+4iIgI3Lt3DwBQvXp1WFlZITU1FampqVi7dq00muDVq1cREhICANiyZQuio6OxdOlSNGrUSOVnIL5EaWlpSEtLQ3JyMoQQmDt3LlJSUqCjo4NOnTph/PjxSE1NhRAC8+bNUzmB9OvXD3PmzJHKkpOT8xzcojiqVatWnttLWloaYmNjAQDXr1/H3r17VeYfMGAADhw4gCNHjmDgwIEq03x9fbFo0SJERESgWbNmecYhl8tRoUIFTJw4EQqFApmZmRg3blx+NVNFjx49MGfOHDRt2hR37twpkHXkJqd99lPvompqamLo0KEYOnQoXr58CSDrqvvChQuzDXo0YcIE9OzZE40bN8bTp0+hqakJb29vjB8/HikpKQCyvgT9+eef+dC6j1OnTh1kZGRIPzmTlJSECRMmQFNT8z/H17dvXyxdulTq44yMDPzyyy/SdAsLC+kL1dsBat6ys7ND8+bN4evrK92puXXrVp6DpX2Ma9euSQOzxMfHY9y4cRgxYoRKnYkTJyIjIwNKpRIzZsyAm5sbqlSp8p/WW5B+++03PHjwQOUJobfe3s0dMmSIdMFK3dSuXRsZGRlYuHAhhBBISkrCxIkTVe6gvfXuMfTly5c5/tTSd999h8TERGkApNjYWLRu3Rr29vaoWrUq/Pz8pO314cOHKhclzc3NERISgszMzA/W/+2335CRkQFjY2M0aNCgWIw0bmVlBS8vL/Tt2xcDBw7M1ofTp0//f/fNx5DJZBg8eDCGDRsmHU/Dw8M/6kmKPn364MSJE9i3bx+ArDvJfn5+8PT0RPny5T8pjtzkdcx6X17H1tz4+PggICAA27dvx5AhQ6RyX19fTJw4EY8ePQKQ9RRUZGQk2rRpg4sXLyIwMFCq+/vvv0t99yHdunWDv7+/dJEoKChIJWHu0aMHdu3ahYsXLwLIumgQEBCAzMxMxMbGSgMJlitXDs7OzsViG6aCwST3C5eQkIAOHTrAzs4OVapUQd++fdGyZUsEBgbi0KFD0iOmM2fOlK5UTpgwAatXrwYAzJ07F3v27EFQUFARt6TotGzZEs2bN0eZMmXg7u4OMzMz6THRNWvWQCaTSSMIamlpoVatWtJdnt69e6NXr15o1KgR5HI5atasme3qfnGnra2d6/YCAKtXr8aYMWPg5OSEWbNmoVevXirzu7m5oWTJkihTpgysrKxUprVs2RJPnjzBkCFDPngXQkNDA3v37sX9+/dhZ2cHT09PVK1aVeWRpfzUv39/TJ8+HU2aNCnURDenfbZp06afvBx/f380aNAAderUgbOzM/r27Yvly5dLd9zfNWPGDLRv315KdJcsWQJtbW24u7vDxcUFXbt2zXUE6oKgr6+PwMBAHDx4EDY2Nqhfvz569+4NTU1NGBoa/qf4GjZsiNmzZ6Nz587SqNPvfunt3r07YmJiIJfLERAQkG3+7du3w8jISPoCNWbMmP/8MxiNGzfGpUuX4OjoiCpVqqBatWrw8/OTpuvo6KBy5crSaNIPHjyQHhksTubPnw+5XA65XI7p06fjwIEDcHR0zFava9eucHJyUuvfr9bX18eRI0dw8OBB2Nraom7duvD29lYZqfutGTNm4Pz587C3t0e7du3Qo0ePbHVat26N6tWrw8nJCYcOHcKpU6ekCwQ7d+7EvXv34OzsDFdXV/j6+qokgn5+fujcubP0qkhe9Q8fPgw7Ozu4ublh27ZtWLduXUF0zycbPHgw/vnnHwwePDjbtFatWqFatWr/r775WD/99BNq1Kghjcres2fPPO9+vmVnZ4fTp09j9erVcHBwQNmyZfHq1Svs2bPnk2PIzYeOWe/60LE1J+bm5vDy8oKbm5vK/vztt9+id+/eaNGiBZycnNCyZUvExsbC3NwcgYGBmD9/Puzt7eHm5oZ169bl+kj0+3x9fdGlSxfUrVsXcrkcW7ZsUXlE3dXVFdu2bcPQoUPh6OgId3d3/Prrr9DU1ERGRgYGDx4Ma2trlCtXDhUrVkS/fv0+ar30+dEQ4jP94S+iz5BSqYSjoyOOHDlSrO+yFBfR0dHw8PBASEgI3/2mPN2/fx81atSQ3lP9UoSHh8PDwwOpqalFHQpRkfn5559x4cIF7Ny5U6VcQ0MDz549U3lcmT7Nxxxbq1WrBn9/f/6eNRUrvJNLVIDefQQnPT0dU6ZMgaOjY4EMJqFulEolpkyZgkGDBqkkuD169JDuBr379/bxaPoyHD16VLpT8uLFC4wYMaLY3vnbv39/jtvs/Pnzizo0os/e8+fPsWDBAkyZMqVA1zN+/Pgc9+PffvutQNdb2D712Lp161ZoaWkxwaVih3dyiQrQ6tWr8dNPPyEjIwM6Ojpo2LAh/P39VUbBpezOnTuHXr16oXHjxti4caPavpdH/3/ffPMNjh07BplMBgMDA3h7e2PixInSY/JfCt7JpS/Z8OHDceTIEcyZMwd9+vTJNp13cj/dxx5b09LSULp0aTg6OmLXrl0F9moQ0f8Xk1wiIiIiIiJSG3xcmYiIiIiIiNQGk1wiIiIiIiJSG0xyiYiIiIiISG0wySUiIiIiIiK1wSSXiIiIiIiI1AaTXCIiIiIiIlIbTHKJiIiIiIhIbTDJJSIiIiIiIrXxf0Lq/pdEW8h4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_feature = final_data.drop([\"ID\",\"year\",\"HBP_US\",\"HBP_EU\"],axis=1)\n",
    "corr_final = final_feature.corr(method='pearson')\n",
    "display(corr_final.style.background_gradient(cmap='coolwarm',vmin=-1,vmax=1))\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "mask = np.zeros_like(corr_final,dtype=bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.heatmap(corr_final,\n",
    "            cmap='coolwarm',\n",
    "            annot=True,\n",
    "            mask=mask,\n",
    "            linewidths=.5,\n",
    "            cbar_kws={'shrink':.5},\n",
    "            vmin=-1,\n",
    "            vmax=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cfm_final = confusion_matrix(final_data.HBP_US,final_data.HBP_EU)\n",
    "group_names = ['정상','-','당뇨병 전단계','당뇨병']\n",
    "group_counts = ['{0:0.0f}'.format(value) for value in cfm_final.flatten()]\n",
    "group_percentages = ['{0:.2%}'.format(value) for value in cfm_final.flatten()/np.sum(cfm_final)]\n",
    "labels = [f'{v1}\\n\\n{v2}\\n\\n{v3}' for v1,v2,v3 in zip(group_names,group_counts,group_percentages)]\n",
    "labels = np.asarray(labels).reshape(2,2)\n",
    "tick = ['정상','고혈압']\n",
    "sns.heatmap(cfm_final, annot=labels, fmt='',cmap='Purples',xticklabels=tick,yticklabels=tick)\n",
    "plt.xlabel('EU criteria')\n",
    "plt.ylabel('US criteria')\n",
    "plt.title('진단기준에 따른 분포\\n[US : 130mmHg/80mmHg]\\n[EU : 140mmHg/90mmHg]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target(HBP_US) 비율\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    0.519874\n",
       "0    0.480126\n",
       "Name: HBP_US, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_us = final_data.HBP_US.value_counts().sort_index()\n",
    "count_label = ['정상(0)','고혈압(1)']\n",
    "print('Target(HBP_US) 비율')\n",
    "display(final_data.HBP_US.value_counts(normalize=True))\n",
    "plt.pie(x = count_us,labels=count_label,autopct='%.2f%%',startangle=90)\n",
    "plt.legend(loc = 'upper right')\n",
    "plt.title('고혈압 비율\\n[US criteria]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target(HBP_EU) 비율\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    0.676151\n",
       "1    0.323849\n",
       "Name: HBP_EU, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LACEim/X1119j5MiRqKiowJYtWxAREYHMzExkZmZi48aNNb5mzpw5GDlyJD788EPk5uZW2+/r64vg4GD98yVLlmDWrFkQi8UoLS3FTz/9hM8++wxSqRQeHh549913sWrVKgBAly5dEBoaim3btpnk85obh7CIyCZt2LABa9euxeHDh1FRUYEnnngCaWlpePXVVyGV1vyjb86cOcjJycGePXuwevVq9O3bF1u3bkXbtm31be4NlaioKCxbtgwAcP78eYSFhcHb21u/Pzw8HJcuXYJarYZEIkFkZCQ2b96Mp59+2gSf2rzYAyEim/P2229jyZIl2L17N5ycnODt7Y2DBw/ixIkT2LNnT7X2paWl6NevHxITE7Fjxw44ODhg6tSpWLhwISIiIpCWllbj+9y4cQPBwcFwdHQEAGRkZCAgIMCgjb+/P1QqFYqKigAAffr0wenTp5v4EwuDPRAisjkzZszAJ598AplMpt/m4eGBDRs26J/7+Pjg4YcfBgC4urpi5cqVaNeuncFxhgwZgoiICEgkEmg0GowePdpgf3Z2NgIDA/XPVSpVtfkNtVoN4M41GoGBgcjKymqCTyk8BggR2Zy714KKjo7G0qVLce7cOahUKojFYnh5eWHcuHGYP3++vt3t8EhOTsbixYsRFRWFqqoqALorvIcNG4YVK1YYvI9Go4FYfGcgx9vbu9oQV05ODuRyOTw8PADoguR2qFg7DmERkc06deoUhgwZgqeffhpXrlxBcnIyEhMTsWnTJpw9exZjx441aJ+Tk4NevXqhdevWOHv2LJKSkpCUlIQjR45AJpOhd+/eUCgU+vZ+fn76BQwBoHv37oiPj0dBQYF+28mTJxEeHq4PmqysLPj7+5v4k5sHA4SIbNa+ffsQERGBMWPGGAxnhYaGYv78+dXmQ6Kjo+Hv749p06bBzc1Nv93Hxwfz5s1DQUEB4uLiDI6TnJwMlUoFQDc8FRERgTlz5kClUiE3Nxfz58/HtGnT9K85ffo0wsPDTfSJzYtDWETUYA29sE8oERERGDp0KDZu3IiRI0fCwcEBAJCYmIjZs2djyBDDz9GrVy/k5ORg+fLleO655+Di4gIAyM/Px9KlS+Hh4YH27dvr24vFYjz++OPYv38/Bg8eDAD44Ycf8MILLyAoKAguLi546623MHLkSP1rtmzZUm0uxVqJtLZyRQsRNbnKykokJycjLCwMcrlc6HKMEh0djSVLluDs2bNQKpUQiUTw9fXFv/71L7z22mvVPldycjK++OILHDhwQD8HIpfLMXjwYMycObPavTYuXryI119/HUeOHKmzlqtXryIyMhIXL140yXLr5v5+MUCIqFa2ECDmMHfuXPj5+eHNN9+stY1SqUT//v2xdOlS/dlfTY0BQkQWgwFiXcz9/eIkOhERGYUBQkRERmGAEBGRURggRERkFAYIEREZhQFCRFSDjIwMlJeXV9uuUqkMljOxZwwQIqIavPjiizh48GC17WvWrMELL7zQoGNdv34doaGhTVSZ5eBSJkRkszZt2oSXX3651v15eXlQKpWQSqVQqVQGa1ZdvnwZ//vf/7B3714AumVOJkyYUONxVCoVFixYgJ9++gmVlZXw9PTEtGnTGhw01oYBQkQN95GHmd+vyKiXRUZGIjIystb9d9+ZUCwWY+DAgQB0y7R37NgRLi4ucHd3BwC0aNGi1uN8+OGHiI2NRXR0NLy9vZGamorRo0dDKpVi0qRJ961x0qRJGDp0KJ555hkAujD66quvcPToUf2tbwsKCtC3b1/s2rXL4Ha6QuMQFhERdAEycuRIxMfHY/bs2di8eTNWrFiB+fPnIzQ0FPv27UNwcDBmzJhR7bXbt2/HvHnz9LeyDQkJwaxZswzufV5eXo5t27bhjz/+0G+LiopCaWmpPjzWrFmDtm3bYsWKFaisrNS38/Lywscff4yXXnrJRJ/eOOyBkM1RqNQoqlCiuEKJorseheXKv7erUKlSQ6PRQq3RQqMFHpTnY4pqPSCWACIRIJIAEhng6AbIPf5+eOr+dPK883e5JyDhfyNrpdFoDBY1PHHiBNatW4fY2Fj9UiAHDx7ExIkTcfHiRbz77rtYvXo1Dhw4YHCcsLAwxMXFoWvXrvptcXFxCAsL0z9XKpWIi4uDj48PBgwYAAD4/PPPsWDBAn2bqqoqrFmzBgkJCVi/fr3BewwbNgxz585FTEwMunfv3mRfg8bgv3yyOqUKFW7klSE1vxw388txI0/3Z2p+ObKKFahQNvxubyMCsoGi9XU3rImzL+DVEvBsAXi2/Pvvtx8hgNTRuONSk8rKysLNmzfRo0cPALp1o2Qymf5Ws4BuSCs/Px+pqalo27Yt1Go1Ll26BKlUioULF+Kbb75BaWkpIiIiDI69ZMkSREZG4sKFC2jTpg0uXLiACxcuYMeOHfo2Hh4eeOedd/TPS0pKcOPGDYMweP755wEACQkJNX6G0aNHY8uWLQwQoroUVShxOb0IF9OLcDWjGCl5upDIL6sSujRD5bm6R/r5GnaKALcgwKc1ENgFCHoICOoC+LbT9XbIbE6dOoXvv/8eO3fuBACUlpYa3DQKAMLDwzF9+nT07t0bKpUKlZWV6Nq1K3755Rd07NgRs2fPrrEH0qZNG5w5cwZHjhxBRkYGxo4di2XLlunDycXFRd/ruC0mJgY9e/Zs0Gfo06cPPv3004Z+dJNhgJBFKFWoEJtWhNj0QsSmFyM2rRA38sth/WtFa4GSW7pHyrE7m6VOQEDHO4ES9BDg34G9FTPKzs5GQEBAte3Tp0/H9OnTkZ+fD2dn52qr2g4ePBi9e/eu9jqFQoHs7GxMnDix2j5XV1f079+/2vsHBgY2qObAwEBkZWU16DWmxAAhQeSWKnA8IRfHr+ci5kYBkvPKbCAsGkBVAaSf0z1ukzgCIT2BsMeBVo8DzbpzfsWEAgICsHDhwhr3zZgxAxs3bqz1taNGjcJXX31lsC0vLw/vvfcexo8fX619Tfs0Go3+Pun1JRKJoFY3fIjWVPivk8yiUqnGuZQCHEvIwdGEXMRlFttXYNSHWqHrpaQcAw59DDi4AS1768Ik7J9AQCfdBD81CR8fHwwdOrTGfYsXL8bixYtr3LdmzRr9tSH3SktLq/E0W7VaDUdHw96ln58fsrOzG1RzVlYW/P39G/QaU2KAkMnEZ5bg6LUcHE3IwdmUfFQqNUKXZF2qSoCEfboHoJusb/U40H4o0G4Q4OAibH0WLDU1Fd26datxn6+vb43bc3Nz9X9/6623sGnTphrblZWVYdCgQTXuCw4ORkpKSrXtKSkp6Nu3r8G2bt264fXXX6/xOLU5ffo0wsPDG/QaU2KAUJO6nl2K3/+6hZ0XbyEpp0zocmxLeS5wabPuIXUC2gwAOj6tCxNHt7pfb0dCQkIMAqGhcnNzMXfuXEyZMqUJqzLk5eUFX19fXL58GR07dqzXa7Zs2YJly5aZrKaGYoBQo6Xml+P3v25hx1+3EJdZInQ59kFVAcTt1D2kcqB1f6DDCOCBp3TXp5iakVeGW5N33nkHH3/8ca37a+pppKam1jgxrlar4eJSvcc4a9YsfPLJJ/j1118Ntk+ePBmTJ0822LZ//354enqiV69e9fsAZsB7opNRsoorsfNiBn7/6xb+Si0UupxGGxGQjS+LpgldRuNJHIA2A4HuE4G2Tzb6VGHeE930JkyYgGeeeQYjRoyotU1RURH69++PrVu33ndJFXN/v9gDoXrTarU4HJ+Dn0+l4Mi1HGj4q4flUVcB8bt1D/dgoPu/gW7/BjyaC10Z1WLNmjV1tvHw8MD58zVdZyQsBgjVqahciY3nUrEm+gZu5FW/PwJZqOI04PCnwJHPdL2Rhyf/3SvhEnjUNBggVKvLt4rwy6kb2H7hllHLg5CF0KqBa3t0j9u9koefA9yqX0RH1BAMEDKgUmuw+1Imfj6ZgnM3CoQuh5ra7V7JscVAt/FAnzcBr9A6X8apUutg7u8TA4QA6IJjS0w6lh+6jpv5HKayeWoFcG4VEPMz0Gk08OgMwL99tWYymQyAbilyJycnc1dJDXT7Fry3v2+mxgCxcwwOO6dRARc3ABc3Au2HAI/NBJrfWelVIpHA09NTf8W0s7Ozweq1ZBm0Wi3Ky8uRnZ0NT09PgyXqTYkBYqcYHGRIe+e6klb9gH++BYQ+CgD66xoauuwGmZ+np2eDF2hsDAaInVGpNdjyZzq+PnSdZ1RRzZIO6R5tBgID/w+iwE4ICgqCv78/lEql0NVRLWQymdl6HrcxQOzI7tgMLNwbx+Cg+rl+AEg8CHQZC/R7DxLPELP/gCLLxgCxA5dvFWHejiuITs4XuhSyNloN8Nc64PJWoNerujkSR1ehqyILwQCxYXmlCny+Lx4bz6XyqnFqHFUlcHwxcOFXYMAHQNfxXFqewEtSbZBao8VPJ1PQb9FhrD/L8KAmVJoFbP8P8G1fID1G6GpIYOyB2JiYmwV4f9slXL5VLHQpZMsyLgDfDwR6vQL0ew9wcBa6IhIAA8RGlClU+GT3Vaw9c5N3+iPz0KqBU8t1p/4O+0p3syuyKxzCsgHRSXmI+PIofo1meJAAClKAn4frhrYqCoWuhsyIAWLFKpVqzNtxBeO+O43U/AqhyyF79+ca4OuewJXtQldCZsIAsVJ/pRZiyFfHsOpEMnsdZDlKs4CNE4ENE4BynjZu6zgHYmWUag2++iMB3xxOhIqnV5GluroDSDsPjP4eCO0jdDVkIuyBWJH4zBKMWH4Cyw5eZ3iQ5Su5Bfw0DDi8ENBohK6GTIABYiW2/pmGEV8fx5UMnp5LVkSrBg5/optkL8kUuhpqYgwQC6dUa/DB9kuYvuEvVCr5WxxZqZRjwDd9gIQDQldCTYgBYsGyiisxduUp/HzqhtClEDVeeS7waySwfy6g5qq+toABYqFOJ+VhyFfHEXOzUOhSiJqQFji5TDc3UpYndDHUSAwQC/Td0SRM+D4auaUKoUshMo2bp4Dv+wPZcUJXQo3AALEg5VUq/GdtDObvvsqzrMj2FaQAPzyhu+8IWSUGiIXILVVg3LensetihtClEJmPohj4dQwQ/a3QlZARGCAW4EZeGUZ/cxIX04qELoXI/LRqYM/bwK63AI1a6GqoARggAruYVojR35zkbWaJzn4H/PoMUMlfpKwFA0RAh+OzMe7b08gtrRK6FCLLkPgHsCoCKMkSuhKqBwaIQDafT8OUn86hvIpddiID2VeAH58CitKEroTqwAARwIrD1zHzt794phVRbfITgVVPAflJQldC98EAMbOPd17BZ3vjhS6DyPIV3QR+HAzk8P+LpWKAmNEnu6/i++PJQpdBZD1KMnQhknFR6EqoBgwQM/l8Xxy+PcruOFGDlefqlj5JOyd0JXQPBogZLD1wDV8fShS6DCLrVVkI/DwSuHFS6EroLgwQE/v60HUsPZAgdBlE1q+qBFg7lsNZFoQBYkLfHk3E5/s4AUjUZBTFwJpRQB579JaAAWIiq44n45PdXGmUqMmV5QC/jASKuW6c0BggJrDh7E3M23lF6DKIbFfhTV1PpKJA6ErsGgOkiR1PyMV7Wy8JXQaR7cu+olvJt4rryAmFAdKEErJK8Mqv53mFOZG5pJ0BNk7kLXIFwgBpIrmlCjy3+ixKKlVCl0JkX65HAdteFboKu8QAaQKVSjWm/HQOaQUVQpdCZJ9iNwLHFgtdhd1hgDSSVqvFjI0XcCG1UOhSiOzbwf8C1/YLXYVdYYA00mf74rE7NlPoMohIqwE2TwFyrwtdSb1kZGSgvLz6CQAqlQoKhUKAihqOAdIIG8+m4pvDvKCJyGIoioD1/wIqi4WupE4vvvgiDh48WG37mjVr8MILLzToWNevX0doaGgTVVZ/UrO/o424lF6Eudt5ui6Rxcm9Bmx5ERi3DhCb9nfkTZs24eWXX651f15eHpRKJaRSKVQqFaZNm6bfd/nyZfzvf//D3r17AQC9evXChAkTajyOSqXCggUL8NNPP6GyshKenp6YNm1ag4OmqTFAjFBSqcRra2NQpdIIXQoR1eTaXuDQx8CAD0z6NpGRkYiMjKx1v1R650esWCzGwIEDAQAajQYdO3aEi4sL3N3dAQAtWrSo9TgffvghYmNjER0dDW9vb6SmpmL06NGQSqWYNGnSfWucNGkShg4dimeeeQaALoy++uorHD16FNu2bQMAFBQUoG/fvti1axeCg4Pr9dkBDmEZ5Z0tsUjJ48VLRBbt2BfA5a1CV6EnFosxcuRIxMfHY/bs2di8eTNWrFiB+fPnIzQ0FPv27UNwcDBmzJhR7bXbt2/HvHnz4O3tDQAICQnBrFmz9AEAAOXl5di2bRv++OMP/baoqCiUlpbqw2PNmjVo27YtVqxYgcrKSn07Ly8vfPzxx3jppZca9pka1Jqw5vQN7LrINXiIrMLvbwAFKYK9vUajgUQi0T8/ceIE1q1bh9jYWBw4cACnT5/GwoULMXHiRLz77rtIS0vD4sXVT0cOCwtDXJzh2npxcXEICwvTP1cqlYiLi0NS0p37Dn3++ed477339M+rqqqwZs0azJ07t9p7DBs2DGlpaYiJian35+MQVgNcvlWE/3KNKyLroSgGtkwFntsDiCV1t2+ErKws3Lx5Ez169AAAVFZWQiaTQSQS6dtIpVLk5+cjNTUVbdu2hVqtxqVLlyCVSrFw4UJ88803KC0tRUREhMGxlyxZgsjISFy4cAFt2rTBhQsXcOHCBezYsUPfxsPDA++8847+eUlJCW7cuIHu3bvrtz3//PMAgISEmm8xMXr0aGzZssXgNffDHkg9lSpUeG3tn1Bw3oPIuqRGA0cXmfxtTp06hf/7v//TPy8tLYWbm5tBm/DwcEyfPh29e/eGl5cXXF1dsW7dOvzyyy+YPXs2UlJSsGhR9VrbtGmDM2fOYMCAAXBwcMDYsWNx7NgxeHl5AQBcXFwwYMAAg9fExMSgZ8+eDfoMffr0wenTp+vdnj2QepqzJRbJuWVCl0FExjj6GdC6PxDSw2xvmZ2djYCAgGrbp0+fjunTpyM/Px/Ozs6Qy+UG+wcPHozevXtXe51CoUB2djYmTpxYbZ+rqyv69+9f7f0DAwMbVHNgYCCysrLq3Z49kHpYf+Ymfv/rltBlEJGxNCpgyxRAUWK2twwICMDChQtr3Ddjxgx06dIFbdq0QXBwsMGje/fuWL58ebXX5OXlGcxn1LVPo9FA3MDTmEUiEdRqdb3bswdSh1uFFfh411WhyyCixipIAfbMBkauMMvb+fj4YOjQoTXuW7x4cY2T5YDuTKnb14bcKy0trcbTbNVqNRwdHQ22+fn5ITs7u0E1Z2Vlwd/fv97tGSB1eG9rLEoVXGGXyCZc+BVo+wTQ8elGHSY1NRXdunWrcZ+vr2+N23Nzc/V/f+utt7Bp06Ya25WVlWHQoEE17gsODkZKSkq17SkpKejbt6/Btm7duuH111+v8Ti1OX36NMLDw+vdngFyH1v/TMOh+ByhyyCiprRjGhDSC3APMvoQISEhBoHQULm5uZg7dy6mTJli9DHq4uXlBV9fX1y+fBkdO3as12u2bNmCZcuW1fs9RFqtlnc/qkFuqQJPLD6CgnLeqMYejAjIxpdF04Qug8zlwWHA2DWCvf3kyZOxc+dOuLq61trm3p5GSkoKWrduDT8/v2pt1Wo1XFxcqr1m165dWLt2LX799dc6a9q/fz8+//xzREVF1eszAAyQWv1nbQwvGLQjDBA7NG4t0H6I0FWY3IQJE/DMM89gxIgRtbYpKipC//79sXXr1vsuqXIvBkgN9l/OxNRfzgtdBpkRA8QOuTcH/hMNOLrV3ZZqxNN471FcqcT7XGWXyPYVpwN//FfoKqwaA+Qen+6+iqxi67iZCxE10tnvgYyLQldhtRggd/nzZgHWn00VugwiMhetGtj9FsCRfKMwQO4yb+cV/jsisjep0brrQ6jBGCB/234hHX/eLBS6DCISQtSHQGWR0FVYHQYIgIoqNRbuiau7IRHZpvJc4MSXQldhdRggAL4/loRbRZV1NyQi23X6G6Ck/ivREgMEeaUKrDyaVHdDIrJtynLgSM2r51LN7D5AvvojgYslEpFOzM9APn+hrC+7DpCU3DKsPXNT6DKIyFJolMDBj4WuwmrYdYAsjroGpZrn7RLRXS5t4cWF9WS3AZKcW4adF3mXQSK6lxb44//qbkb2GyArjyRCw84HEdXk+gEg+ZjQVVg8uwyQzKJKbIlJF7oMIrJkxxYJXYHFs8sA+e5YEqrUGqHLICJLlnSYcyF1sLsAKSirwjqeeUVE9XGy/rd3tUd2FyCrT6agvEotdBlEZA0ubwGKONxdG7sKkDKFCj+dShG6DCKyFhoVcHqF0FVYLLsKkHVnbqKwXCl0GURkTWJ+BiqLha7CItlNgKg1WvxwPFnoMojI2iiKgfOrha7CItlNgByMy0YGV9wlImNE/w9Qc/TiXnYTIOt55hURGas4HbiyXegqLI5dBEhmUSUOX8sRugwismYxPwtdgcWxiwDZeC4Vaq5bQkSNkXwUKORIxt1sPkA0Gi02nE0Vugwisnpa4MI6oYuwKDYfIMeu5yK9sELoMojIFlz4FdByNOM2qdAFmBonz4mAz04o8H2MEpUqLTzkIszv74jhD8igVGvx6fEq/HZFicJKLUI9xVj2lBxdAyXVjnHulhqv7a5EdpkGSg0Q+aAMnz/pCKlYBABIL9bg5V2ViMlQw1ECPNfVAe8/7ggASC3SYNK2CiQVaBDmJca60U4IdNX9/rojXol1l5RYO9rZfF8QYxXeAFKOA2GPCV2JRbDpHkhuqQIHrmYJXQaR4MKbS3D5VRfcnO6GrwfLMXZTBfLKNbiWp4FKo8XpF1yQOt0NEzrLMGxdeY03WnORAZvHOCHpTTfEvuKKYzdV+ObsnVNbJ26rQAdfMdKmu+LcVFdsvqrE6gtVAIC3oyrxag8HpExzw+gHZZh/VAEAKFFo8f4hBZZGyM3zhWgKF34VugKLYdMBsiUmjXccJALweKgUMomup/DPllI4y0TIKdeio78E8/rJ4eKg2/fSIw4oq9IiIb/6atUP+knQ3F33I8NTLsLjLaVIL7nT7s8MNf79kAwikQjeTiIMbSfFuVu6decuZmkwpK1uwGNAmARJhbr/l3P+qMSb4Q7wd7GiH0VXtgOKEqGrsAhW9F1ruF2xmUKXQGRRKlVaLD2tQI9mYrT3rT5MVa7UolwJeDiKaj2GVqvF2XQ1opJUmPiQTL89soMMy89UoUqtxY1CDbbHqxDZQbe/g58Ym6/qeitbrqrQs5kEp9NUuJqrwXPdHJr4U5qYshy4vFXoKiyCzc6BZBRV4GJaodBlEFmExHwN+v5UhvRiLXo2l2DtaKca2733hwJ9Q+/0NO719IZy7E9UwUECLH/KCR387oTQ/P6O6PFdGbwWlqBCCbzW0wF9Q3U/Yr54Uo4pOyrwwSEF+oZKsXiQHE/8Uoa1o2quw+Jd3Ah0nyh0FYKz2QDZdymTJ0sQ/a21txip091QqdJiy1UV/vFDGY4/54y2ProAKKvS4tXdlYjNUmPfhNons7eOdYZGq8WlbA2m/F6B9BINZvVxhFqjxeC15ZjWywGv9XRATpkW4zZX4MvTCrzZyxEtPcWI+reL/jjzjyrwdHsZ5FIRnt1cjvwKLTr5S/DpAEf9UJtFu3kKqCgAnLyErkRQNjuEtfcyh6+I7iWXivBsZxmGtpXip790Q0qJ+Rr0+K4MMjFw/HkX+NUxHyEWidAlQIIvnpTjq2jdJPnBZDWq1MC0XrqzsoLcxFj8pByfnayq9vpreWpsj1fi7d4OmLKjAtN6OWLvBBeIAKz600rWm9KogGv7ha5CcDYZIPllVTibUiB0GUQWy1EKOElFKKzUov/PZZjeywHfD3eCs6z+v/07SkVw+rt9lVoL6T0/TWQS4N57t2m1WryyqxJfPSWHTCJCQp4GPZvrekH9wyS4mGVFN3uL3yV0BYKzyQCJupLJpUuI/pZerMG6WCVUf/+fOHpDha1xKjzTUYrfLivR3leMFx+ueyL7u/NVyCnTnXWVW67BnD8q8VxX3ST5oy2kyCzVYl2srgdRWqXFewcViHzQcJT8hz+V6OArRq9g3XZXBxHOpOtCY1eCCp38q0/sW6zrfwAqhdBVCMom50D2XuLwFdFtjlLghz+r8ObeSrg5AqGeYmwd64R2PhJ8H6PEqVQ1Qpcanpb63mOOePFhB7yxpxLhzSUY30WGWyVadFtZBrEIcHMU4cXuMrwRrgseD7kI+yY4Y8a+Srz7RyXEImDEAzLMH+CoP2ZWqQZfRVfhxPN35kK+GSLHc9srUK7Uol+oFFO6y2A1qkp162O1fULoSgQj0mpta6q5VKFC9/9GoUpV/Tx2otqMCMjGl0XThC6DrM3DzwHDlgpdhWBsbgjrUFw2w4OIzCN+j12vjWVzAXKU9/0gInMpzQTSY4SuQjA2FyCnk/OELoGI7Mn1A0JXIBibCpBbhRVIzefS7URkRjdOCF2BYGwqQE4nsfdBRGaWdg5Qq4SuQhA2FSDRSflCl0BE9kZZBmRcELoKQdhUgHD+g4gEceOk0BUIwmYCJLOoEjfyyoUug4jsEQPEunH+g4gEk3raLq8HsZkAiebwFREJpaIAyL4idBVmZzMBwtV3iUhQdjiMZRMBUqlUIzm3TOgyiMiepZ0VugKzs4kAScgq5fLtRCSsLA5hWaWrmcVCl0BE9i73mt1dUGgTARKXUVJ3IyIiU1IrgPxEoaswK5sIkPgs9kCIyAJkXRa6ArOyiQBhD4SILEL2VaErMCurD5DskkrklVUJXQYRkd1dC2L1AcLeBxFZDA5hWZf4TAYIEVmIwhtAlf2syWf1AZKcxwsIichCaDVAbrzQVZiN1QdIRiHvQEhEFqQoTegKzMb6A6SoUugSiIjuKL4ldAVmwwAhImpKxelCV2A2Vh0gFVVqFFUohS6DiOgO9kCsQ0YR5z+IyMIUZwhdgdlYeYBw+IqILAyHsKwDA4SILE4JeyBWgafwEpHFUVUCZfZxi22rDpDMYvZAiMgC2ckwllUHCM/AIiKLVJEvdAVmYdUBUlGlFroEIqLqFPaxRp9VB0hZlX3dPpKIrESlfdzkzqoDhD0QIrJI7IFYvnIGCBFZoioGiMVjgBCRRVLaxyUGVh4gnAMhIguktI9LDKw6QMrYAyEiS6RigFg0tUaLKpVG6DKIiKpjgFi2CiV7H0RkodRVQldgFlYbICKhCyAiqo1YJnQFZmG1ASIWMUKo6Zws8MJfIf+GVuYsdClkCyRSoSswC+sNEKutnCxRTpUMIxKewiDNMlwMmcAgocZhD8SySdgDIRO4VuaE4QmDGSTUOGL2QCyaRMwAIdNhkFCjSNgDsWgikQgOEqstn6xEtSCROgldElkD9kAsn6PMqssnK6IPEu1yxIaMZ5DQ/bEHYvkcpRKhSyA7c63MCcMShjBI6P44iW755OyBkEAYJHRfPI3X8jk7sAdCwmKQUI3knkJXYBZWHSDeLg5Cl0AE4E6QPIVlDBICXHyFrsAsrDpAfF0dhS6ByEBcqTODhAAXP6ErMAsGCJEJMEjsnDN7IBbPz40BQpaNQWKnOIRl+XxdOQdC1oFBYkdEEsDJS+gqzMLKA4Q9ELIudwfJpZBnoZXKhS6JmpqzD2Ana/UxQIgEEFfqjKEJQ/EUljNIbI2dDF8BVh4gPhzCIit3O0gGM0hsh7OP0BWYjVUHCHsgZCuuMkhsh3tzoSswG6sOELlMwosJyabcHSSXQ/7FILFGPm2ErsBsrDpAAKCVr4vQJRA1uaulzhiSMIxBYo18WgldgdlYfYC09nMVugQik2GQWCH2QKxHa3/2QMj2MUisCAPEerAHQvbkdpAMFS3DFQaJ5XELAhzs55daBgiRFbpc4oLBDBLL491a6ArMyuoDJMTbGQ5Sq/8YREZhkFgYHwaIVZGIRQjzsZ8uI1FNGCQWwo7mPwAbCBCAE+lEt90dJFdDxkEr4cW2ZuX3gNAVmJVNBEgbzoMQGbhc4oKnEoZjqHg5g8ScmnUXugKzsokA6dDMQ+gSiCzS7SAZziAxPY8WgKt93InwNpsIkIdb2sfa+0TGimWQmF7ww0JXYHY2ESB+bo5o4e0sdBlEFo9BYkLNGSBWi70Qovq7O0jiQsYySJpC80eErsDsbCZAurfwFLoEIqsTW+KCiIQRDJLGEkuBoIeErsLsbCdA2AMhMhqDpJH8HwQc7G8Y3WYCpH2gO1wcJEKXQWTVGCRGssP5D8CGAkQiFuGhEE+hyyCyCbeDZIRkGYOkPoJ7CF2BIGwmQABOpBM1tYvFrgyS+mjVT+gKBGFTAdIj1FvoEohsEoPkPvweBDzs5z7od7OpAAlv5Q1nzoMQmQyDpAZtBghdgWBsKkAcpRL0bu0rdBlENu/uIIkPGQOtxEHokoTDALEdAx70F7oEIrtxsdgVgxJGYoRkuX0GicwZaNlH6CoEIxW6gKbWv711B4jiVjwKDq2Cqjgb0KjhPfBllCeeReWNvwzaaSqK4Nr5CXg/8XK1Y6hLC5B/8Dso0q4CWjVcOvSFV7/nAQBlccdRHL0J6ooSiCQyeD46Hi4PPgYAUBXnIHfXEqgKMyH1DIDfsFmQuOpOTCi/Ho2yK0fhN/xtE38FyBpdLHbFoOKR6Oo+EAubHUC7W9sgUlcJXZbphT4KSO13GM/mAiTAXY6Ozdxx+Vax0KU0mDIvFTlb58NnyAw4hXaFVq2EprIMzg/0NminqarArW+nwq370GrH0KqqkLXhPbh0GgjfoTMhEkugKs7V769IOg+/UXMhdfOFIvM6ste/B5lvCBz8QlFwaBXcug2GS/tHUXx+B4pObYD3Ey9DoyhH4bE1CBjzX5N/Dci6XbC3IGkzUOgKBGVzQ1gAMMBKeyGFR3+BW/ehcArtCgAQSWSQuHhWa1d8dhvkrR6GzCe42r6Sv/ZB4uoDj/BREIl1JxRI3e/MC/kOfhNSN91zx8A2cGzZBYrUywAAZU4KnFrrzmd3atkVqsLMv+v6Ge4PD6+xFqKaXPh7aOtpyXLEhzxju0Nbre13/gOw0QDp/2CA0CU0mFZVhfLEM3Dt/MR922mqKlASsxMevcfVuL88/gRcO9f/tyJNeRFEjrolGGQ+ISi/dlJ3nGsn4RDUDor0OCjzUuHa5f51EdVEFyRP22aQeIUBvvZ1C9t72dwQFgA8FOwBX1cH5JZaT9dZmZ8OkdQRlTcvouj0b9BWVUAe2g1e/Z6H2PHOGjulsQfgGNwBMs/Amo+TkwKtWonMNbOgKsmFg28LeA14ETLv6ueplyechjL/FpzbhAMAvPpPQd6er1B0bA0cW3SGd/8pyNrwPnyHvWWaD012Qze09TS6uj+Bhc2i0O7Wdusf2ur4tNAVCM4mA0QkEqHfA/747Xya0KXUm6aqAtCoochMQNDExdCqVcjdvQT5B76F75Bp+nalf+2D98Cp9z1O+bWT8Bv5LsTO7ig+sxXZm/4PzV5YAZHkzre7+Ox2FJ/ZDP9Rc/UBJfXwR8C4j/Vtik5ugHO7f0AkdUDO759DU1kCB9+W8Hx8ksGxiOrLpoKk8zNCVyA4mxzCAoChDzUTuoQGkTi5Q6tRwevxyRBJHSB2dIZnn2dRcT1a30aRkQBNRQkcQzrf9zjuPUdB4uoFkVgC9/DR0FSUQJmvC1ONshLZWz5GedwxBE74HI7N29d4HGV+Osqvn4Z7z1HI2/MV3B8ZjoAx8wCRCKWxUU374cnuWP3Qln8HIKCD0FUIzmYD5NE2vghwt57T66Qe/hBJZNCq7vptTCSCSCrTPy27fAjOD/SGSCSq9Tgy3xBdb0Z/CJHuOBLdcXK3L4TY0QUB4xdC6lHzXJFWq0X+/q/hPeAliCRSqApuwbHZAwAAeYsuqMpOacQnJbrjdpCMli7HNWsKks6RQldgEWw2QCRiEUZ2tZ71aURSB7h07I+CQz9Aq1FDq1Ki6PhauHS8s0hbRXIM5C3vf9Ma166DUXR8LdQVutOYi6K3QOoVBKlXMyjz01GZegk+Ea/pz9CqSenF/ZD5tND3TkQOTlDcitfVkHQWDn4tG/txiQzEFLniSWsKkk4MEAAQabVardBFmMq1rBI8ueSo0GXUm6aqAvn7V6Ai+U+IHZzg/EBveD42ASKJDJrKUqR+OQ7NX/kRUnc/g9flbPsUrt0Gw+nvcCk6uQElf+4CJDI4BraB18CXIHX1RkXiWWRvmQ+Jq+Gik/KWD8F38JsAAHVZAbI2vI/A8Z/p50Yq064gf+9yaFQKyFt0hs+g1zgHQibV3aMUC/yj0NYS50iCewJTOIwL2HiAAMCwZccRm14kdBlEZASLDJKnPgfCaz+RxZ7Y7BDWbaO6W88wFhEZuntoKyEkElqxrO4XmZJIwtN372LzATKia3PIJLVPOhOR5YspcsUTCaMwWva1sEHSuj/g6ld3Ozth8wHi7eKAvg9Y59ImRGToTpAI1CPpMcW872fhbD5AAGA0h7GIbEpMkZv5g8QrFGj7pOnfx4rYRYAMeDAAge5yocsgoiZ2d5BcDxlt2iB55AVAbBc/MuvNLr4aMokYk3qHCl0GEZlITJEbBiaMNl2QyJyB7v9u2mPaALsIEAB4NrwF75dOZONMFiSdIwEnr6Y5lg2xmwDxcJJhzCMhQpdBRGbQ5EHSk9d91MRuAgQAnu8TBjHP6CWyG7eD5JnGBEmL3kBg7QuY2jO7CpAWPs54skPN99EgItt1rjFB0vNF0xVm5ewqQABgymNhQpdARAJpcJB4tgQeHG6e4qyQ3QXII6He6BriKXQZRCSgegfJo9MBLhxaK7sLEAB48bFWQpdARBbgvkHi3hzoOl644qyAXQbIU50C0dbfVegyiMhC3A6SsQ7LkBg8Shckfd4EpBZ+XxKB2fxy7rXZeykTL685L3QZRGSBngpW4JuXBgMyrmBxP3bZAwGAiE6BeIhzIURUg17duzM86sFuAwQAZg96QOgSiMjCNPd0wr96thC6DKtg1wHSu40vHm3jK3QZRGRBXuvfBg5Su/7RWG92/1WaFcFeCBHptPRxxjMPBwtdhtWw+wDpEuyJpzrx6nQiAuYMfhBSid3/WKw3fqUAzHzyAUi4SBaRXXusrS8GdeQvkw3BAAHQxt+V3VYiOyaTiPDhsI5Cl2F1GCB/e2vQA3CXc8kCIns06R+haMOLixuMAfI3X1dHzHySE+pE9sbX1RFvDmwrdBlWiQFylwm9WqJjM3ehyyAiM5oV8QDc5Ca8l7oNY4DcRSIW4b8jO0HE+XQiu9A1xJPzn41g0gCJj4/Hl19+We/2Wq0WixYtMmFFdevewgvjevAqVCJbJxIB/ze8I0T8jdFoRgfIZ599Bl9fX/j6+kIul8PNzU3/PDo6GgCQmpqKrVu31vj60NBQZGZmGmxTq9V4++23jS2pybw7uD383RyFLoOITOjZni24Hl4jGR0gs2bNQm5uLnJzczF48GAsXLhQ/zw8PLzO15eVlaG0tLTe7/fBBx/g888/BwBUVFRg6tSpaNmyJYKDgzFr1ixotVpUVVXhH//4By5cuGDsxwIAuMtlPKWPyIa18HbGnMEPCl2G1Wv0EJZKpcL58+cRFRUFANi7dy8CAwMRGBiIMWPG1PiapKQk5Obm4ujRo/V6j6tXr2L//v2YOXMmAGDmzJnQaDRITEzE5cuXcejQISxfvhwODg5YuXIlJk6ciMauUj+kSxAGPujfqGMQkeURi4BFzzwEF0eett9YjQ6Qr7/+GiNHjkRFRQW2bNmCiIgIZGZmIjMzExs3bqzxNXPmzMHIkSPx4YcfIjc3t9p+X19fBAffmdhasmQJZs2aBbFYjNLSUvz000/47LPPIJVK4eHhgXfffRerVq0CAHTp0gWhoaHYtm1bYz8a5j/dGV7OPDuDyJY83ycMPcO8hS7DJjQqQDZs2IC1a9diwYIFWLt2LebPn4+vvvoKKpWq1tfMmTMHOTk52LBhA95//3307dsXCQkJBm1yc3ORlpamfx4VFYUhQ4YAAM6fP4+wsDB4e9/5BxAeHo5Lly5BrVYDACIjI7F58+bGfDQAQIC7HAtGd2n0cYjIMrTxd8VbvI1DkzE6QN5++20sWbIEu3fvhpOTE7y9vXHw4EGcOHECe/bsqda+tLQU/fr1Q2JiInbs2AEHBwdMnToVCxcuREREhEFg3O3GjRsIDg6Go6NuUjsjIwMBAQEGbfz9/aFSqVBUVAQA6NOnD06fPm3sRzMwqGMg/tUzpEmORUTCkYpF+OKZhyCXSYQuxWYYPQg4Y8YMfPLJJ5DJ7gzxeHh4YMOGDfrnPj4+ePjhhwEArq6uWLlyJdq1a2dwnCFDhiAiIgISiQQajQajR4822J+dnY3AwDsLnKlUqmrzG7d7HrdPxwsMDERWVpaxH62aD4Z2RHRyPpJyyprsmERkXq/0bc2zrpqY0QESFBSk/3t0dDSWLl2Kc+fOQaVSQSwWw8vLC+PGjcP8+fP17W6HR3JyMhYvXoyoqChUVVUBAJydnTFs2DCsWLHC4H00Gg3E4jsdJW9v72rzJjk5OZDL5fDw8ACgC5LbodIUnBwk+GpcN4xacRJVak2THZeIzKNDkDveGMDlSppaoyfRT506hSFDhuDpp5/GlStXkJycjMTERGzatAlnz57F2LFjDdrn5OSgV69eaN26Nc6ePYukpCQkJSXhyJEjkMlk6N27NxQKhb69n58fsrOz9c+7d++O+Ph4FBQU6LedPHkS4eHh+qDJysqCv3/TnkHVqbkHZj7Zru6GRGRRnGQSLB3XFTLe56PJNforum/fPkRERGDMmDEGw1mhoaGYP39+tfmQ6Oho+Pv7Y9q0aXBzc9Nv9/Hxwbx581BQUIC4uDiD4yQnJ+sn5gMDAxEREYE5c+ZApVIhNzcX8+fPx7Rp0/SvOX36dL2uRWmoqf9shT5tfJr8uERkOgtGd0a7ALe6G1KDNTpAIiIisHfvXmzcuFE/HAUAiYmJmD17tv7sqdt69eqFnJwcLF++HGVld+YU8vPz8cEHH8DDwwPt27e/U6BYjMcffxz79+/Xb/vhhx9w69YtBAUF4ZFHHsHUqVMxcuRI/f4tW7bg6aefbuxHq0YkEmHxmK48tZfISkzuHYoRXZsLXYbNEmkbe8UddL2KJUuW4OzZs1AqlRCJRPD19cW//vUvvPbaa5DL5Qbtk5OT8cUXX+DAgQP60JHL5Rg8eDBmzpxpML8CABcvXsTrr7+OI0eO1FnL1atXERkZiYsXL0IiMc3ZFofjs/H86rPQNPorR0Sm8khLL6yb2otDVybUJAFiDnPnzoWfnx/efPPNWtsolUr0798fS5cu1Z/9ZSrfHk3EJ7vj6m5IRGbn5+aIXa8/Cn93ed2NyWhWEyCWaMbGC9gSky50GUR0F6lYhF+nhCO8FecrTY19u0b4dFRndOV55UQW5Z2n2jM8zIQB0giOUgm+/ffDCHDn0u9ElmBIlyBMeayV0GXYDQZII/m7y/Htvx+Bo5RfSiIhPRTsgc8juXadOfGnXhN4KMQTC7noIpFgwnxdsGpyDzg7cIl2c2KANJGR3Zrjlb6thS6DyO74ujrip+d6wseVQ8nmxgBpQrMj2mPsI1y5l8hcXBwk+HFyD7TwcRa6FLvEAGlin47qjMGdA+tuSESNIpOI8M2Eh9E52EPoUuwWA6SJicUiLB3bDf9s5yd0KUQ2SyQCFo7uwv9nAmOAmICDVIyVEx7GIy29hC6FyCbNGtQeo7oH192QTIoBYiJODhKseq4HOjZzF7oUIpsy9Z+teMKKhWCAmJC7XIafn++JVn4uQpdCZBNe6dsacwY/KHQZ9DcGiIn5uDpizQvhaO7pJHQpRFbtjf5tMDuifd0NyWy4mKKZpBdW4N/fRyMpl/dVJ2qoGU+04y1pLRADxIxySxWY+MMZXMkoFroUIqsxK+IBvNq3jdBlUA0YIGZWXKnE8z+exbkbBXU3JrJz7w1+EC/+k4sjWioGiAAqqtR4ac15HL2WI3QpRBbrg6Ed8PyjYUKXQffBABFIlUqD6RsuYFdshtClEFkUmUSE+SM7Y0wPLgtk6RggAtJotJizNRbrz6YKXQqRRXCXS/HNhIfRp42v0KVQPTBALMBne+Ow4nCi0GUQCSrE2wk/Tu6BNv5uQpdC9cQAsRDb/kzH7M0XoVBphC6FyOy6tfDEdxMfgS+XZLcqDBAL8ldqIV765TwyiyuFLoXIbIZ0DsIXYx6CXCYRuhRqIAaIhckursTUX87jQmqh0KUQmdwrfVtj1qAHIBKJhC6FjMAAsUAKlRpztlzC5pg0oUshMgkHqRj/HdERY3u0ELoUagQGiAX7/lgSPt0TB7WG3yKyHaE+zlj+bHd0as4bQVk7BoiFO3otB2+s/xOF5UqhSyFqtKFdgrBgdBe4OkqFLoWaAAPECmQWVWLmbxdw4nqe0KUQGcVRKsYHwzpgfHhLoUuhJsQAsRJarRbfHk3CF/uvoUrNU33JerTydcHyZ7ujA2+uZnMYIFbmUnoR3lj/J5JyuCw8Wb7hDzXDJ6M6c8jKRjFArFBFlRrzdl7BujM3hS6FqEaujlLMHfIgxvXkWVa2jAFixfZdzsQ7my+igBPsZEEebeOLBaM7I9jLWehSyMQYIFYuq7gSc7ddQtSVLKFLITvn6ijFnMEP4tlw9jrsBQPERkRdycJHv19GemGF0KWQHer3gB8+frozmns6CV0KmREDxIaUV6nw5YEE/HA8GSpefEhm4OvqiA+HdcCwh5oJXQoJgAFig+Iyi/He1ks4z9vmkomIRMC4HiF456kH4eEkE7ocEggDxEZptVqsP5uKhXvjeBU7Nalerbwxd0gHLkVCDBBbl1eqwGd74/Hb+VRwVIsao5WfC9596kE80SFA6FLIQjBA7MS1rBIs2BOHg3HZQpdCVsbbxQFvDmiL8eEtIJWIhS6HLAgDxM6cTsrDp3vi8BfvN0J1cJCK8VyfUPynXxu4yznPQdUxQOzU/suZWBx1DXGZJUKXQhZGIhZhaJcgvPXkAwjx5sWAVDsGiB3TarXYFZuBpQcScD27VOhySGAOEjFGP9wcLz/eGi19XIQuh6wAA4Sg0Wix+1IGvj+WzFvp2iEXBwmeDW+BKY+1QoC7XOhyyIowQMjA+Rv5+O5oMvZfyeRZWzbO01mGSf8IxXN9QuHp7CB0OWSFGCBUo5t55Vh1Ihm/nUtFWZVa6HKoCQW6y/HCo2F4NrwFXLjMOjUCA4Tuq6hCiXVnbuKnkynIKKoUuhwykkQsQr8H/DCuRwv0a+8PiVgkdElkAxggVC8qtQYHrmZhc0w6DsdnQ6nmPxtr0NzTCWN7hGDMIyEI9OD8BjUtBgg1WH5ZFX6/kI7NMemITS8Suhy6h0wiwsAHAzCuZws81sYXYvY2yEQYINQoCVkl2ByTjm1/piOzmENcQhGJgK4hnniqUyCe7hYMPzdHoUsiO8AAoSah0WhxIjEXv1+4hUPx2cgtrRK6JJsnEYvQM9QbEZ0CMahjIIeoyOwYINTktFot/korwsGrWTgYn43Lt4rBf2VNw0EqxqNtfBHRMRBPdAiAlwtPvyXhMEDI5LKKK3EoLht/xGXjxPVclPO04AZp6eOMXmE+eLStL/q194crT70lC8EAIbNSqNQ4k5yPM8n5iLlZgAs3C3mdyT1CfZzRq5UPerXyQXgrbwR58DaxZJkYICQotUaL+MwSnL9ZgD9vFOD8zQLcyCsXuiyzkYpFaOXngodbeulDg8uJkLVggJDFyS1VIOZGAWLTi5CYU4rE7DIk55WhSqURurRG8XV1QPtAd7QPdEP7IN2fbQNc4SiVCF0akVEYIGQVNBotUgvK9YGSmFOKpBzdn3lllnPGl5ezDIEeTmjmIUeQpxwtvV3QPsgN7QPdeWot2RwGCFm9SqUaeWVVyC1RIK9MgdySKuSUKpBbqkBeaZX+z0qVGkqVBkqNFkq1Biq1FlVqDZRqTbWzxCRiERylYshlEjjJJHBxlMDVUQo3uQyucik8nWQIdJcjyFMXFoEecgR5OMHJgb0Jsh8MECLo5mKUag3UGi0cpGLIeOtWojoxQIiIyCj8NYuIiIzCACEiIqMwQIiIyCgMECIiMgoDhIiIjMIAISIiozBAiIjIKAwQIiIyCgOEiIiMwgAhIiKjMECIiMgoDBAiIjIKA4SIiIzCACEiIqMwQIiIyCgMECIiMgoDhIiIjMIAISIiozBAiIjIKAwQIiIyCgOEiIiMwgAhIiKjMECIiMgoDBAiIjIKA4SIiIzCACEiIqMwQIiIyCgMECIiMgoDhIiIjMIAISIiozBAiIjIKAwQIiIyCgOEiIiMwgAhIiKjMECIiMgoDBAiIjIKA4SIiIzCACEiIqMwQIiIyCgMECIiMgoDhIiIjMIAISIiozBAiIjIKAwQIiIyCgOEiIiM8v+hAeJ8zy/0ewAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_eu = final_data.HBP_EU.value_counts().sort_index()\n",
    "count_label = ['정상(0)','고혈압(1)']\n",
    "print('Target(HBP_EU) 비율')\n",
    "display(final_data.HBP_EU.value_counts(normalize=True))\n",
    "plt.pie(x = count_eu,labels=count_label,autopct='%.2f%%',startangle=90)\n",
    "plt.legend(loc = 'upper right')\n",
    "plt.title('고혈압 비율\\n[EU criteria]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data.to_csv('data/KNHANES_8th_final2.csv',index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.13 ('machine-inter')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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