Pt2.2 Modeling (10만개).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 라이브러리 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import geopandas as gpd\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'  #선명하게 만들기\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report, roc_curve, auc\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import svm\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "#import tensorflow as tf\n",
    "#from tensorflow.keras.models import Sequential\n",
    "#from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Warning 메세지를 뜨지 않게 해줌\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Window의 한글 폰트 설정\n",
    "plt.rc('font', family='Malgun Gothic')\n",
    "plt.rc('axes', unicode_minus=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('df_final3.csv')\n",
    "df = data.copy() #원본 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(864747, 44)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LoanNr_ChkDgt</th>\n",
       "      <th>Name</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Bank</th>\n",
       "      <th>BankState</th>\n",
       "      <th>NAICS</th>\n",
       "      <th>ApprovalDate</th>\n",
       "      <th>ApprovalFY</th>\n",
       "      <th>...</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>DATE</th>\n",
       "      <th>DPRIME</th>\n",
       "      <th>dprime2</th>\n",
       "      <th>Density</th>\n",
       "      <th>Upper_Threshold</th>\n",
       "      <th>Lower_Threshold</th>\n",
       "      <th>Label</th>\n",
       "      <th>growth_potential</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000014003</td>\n",
       "      <td>ABC HOBBYCRAFT</td>\n",
       "      <td>EVANSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>47711</td>\n",
       "      <td>FIFTH THIRD BANK</td>\n",
       "      <td>OH</td>\n",
       "      <td>17</td>\n",
       "      <td>1997-02-28</td>\n",
       "      <td>1997</td>\n",
       "      <td>...</td>\n",
       "      <td>1999</td>\n",
       "      <td>2</td>\n",
       "      <td>1999-02-01</td>\n",
       "      <td>7.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0.172105</td>\n",
       "      <td>0.118196</td>\n",
       "      <td>0.055178</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000935004</td>\n",
       "      <td>Hairline Inc.</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>WA</td>\n",
       "      <td>98663</td>\n",
       "      <td>WELLS FARGO BANK NATL ASSOC</td>\n",
       "      <td>SD</td>\n",
       "      <td>14</td>\n",
       "      <td>1997-04-08</td>\n",
       "      <td>1997</td>\n",
       "      <td>...</td>\n",
       "      <td>1999</td>\n",
       "      <td>2</td>\n",
       "      <td>1999-02-01</td>\n",
       "      <td>7.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0.090095</td>\n",
       "      <td>0.134566</td>\n",
       "      <td>0.055910</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003535008</td>\n",
       "      <td>Jason A. Philbrook</td>\n",
       "      <td>Rockland</td>\n",
       "      <td>ME</td>\n",
       "      <td>4841</td>\n",
       "      <td>KEYBANK NATIONAL ASSOCIATION</td>\n",
       "      <td>OH</td>\n",
       "      <td>18</td>\n",
       "      <td>1999-01-11</td>\n",
       "      <td>1999</td>\n",
       "      <td>...</td>\n",
       "      <td>1999</td>\n",
       "      <td>2</td>\n",
       "      <td>1999-02-01</td>\n",
       "      <td>7.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041237</td>\n",
       "      <td>0.213874</td>\n",
       "      <td>0.051896</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1009994000</td>\n",
       "      <td>ARROW ENTERPRISE, INC.</td>\n",
       "      <td>BILLINGS</td>\n",
       "      <td>MT</td>\n",
       "      <td>59101</td>\n",
       "      <td>WESTERN SECURITY BANK</td>\n",
       "      <td>MT</td>\n",
       "      <td>5</td>\n",
       "      <td>1997-03-04</td>\n",
       "      <td>1997</td>\n",
       "      <td>...</td>\n",
       "      <td>1999</td>\n",
       "      <td>2</td>\n",
       "      <td>1999-02-01</td>\n",
       "      <td>7.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0.148380</td>\n",
       "      <td>0.209033</td>\n",
       "      <td>0.057460</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1011904005</td>\n",
       "      <td>ROBIN E. JONES, D.D.S.</td>\n",
       "      <td>TACOMA</td>\n",
       "      <td>WA</td>\n",
       "      <td>98404</td>\n",
       "      <td>KEYBANK NATIONAL ASSOCIATION</td>\n",
       "      <td>OR</td>\n",
       "      <td>8</td>\n",
       "      <td>1997-03-05</td>\n",
       "      <td>1997</td>\n",
       "      <td>...</td>\n",
       "      <td>1999</td>\n",
       "      <td>2</td>\n",
       "      <td>1999-02-01</td>\n",
       "      <td>7.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0.094218</td>\n",
       "      <td>0.134566</td>\n",
       "      <td>0.055910</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
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      "text/plain": [
       "   LoanNr_ChkDgt                    Name        City State    Zip  \\\n",
       "0     1000014003          ABC HOBBYCRAFT  EVANSVILLE    IN  47711   \n",
       "1     1000935004           Hairline Inc.   Vancouver    WA  98663   \n",
       "2     1003535008      Jason A. Philbrook    Rockland    ME   4841   \n",
       "3     1009994000  ARROW ENTERPRISE, INC.    BILLINGS    MT  59101   \n",
       "4     1011904005  ROBIN E. JONES, D.D.S.      TACOMA    WA  98404   \n",
       "\n",
       "                           Bank BankState  NAICS ApprovalDate  ApprovalFY  \\\n",
       "0              FIFTH THIRD BANK        OH     17   1997-02-28        1997   \n",
       "1   WELLS FARGO BANK NATL ASSOC        SD     14   1997-04-08        1997   \n",
       "2  KEYBANK NATIONAL ASSOCIATION        OH     18   1999-01-11        1999   \n",
       "3         WESTERN SECURITY BANK        MT      5   1997-03-04        1997   \n",
       "4  KEYBANK NATIONAL ASSOCIATION        OR      8   1997-03-05        1997   \n",
       "\n",
       "   ...  Year  Month        DATE  DPRIME  dprime2   Density  Upper_Threshold  \\\n",
       "0  ...  1999      2  1999-02-01    7.75        0  0.172105         0.118196   \n",
       "1  ...  1999      2  1999-02-01    7.75        0  0.090095         0.134566   \n",
       "2  ...  1999      2  1999-02-01    7.75        0  0.041237         0.213874   \n",
       "3  ...  1999      2  1999-02-01    7.75        0  0.148380         0.209033   \n",
       "4  ...  1999      2  1999-02-01    7.75        0  0.094218         0.134566   \n",
       "\n",
       "   Lower_Threshold Label growth_potential  \n",
       "0         0.055178     1                1  \n",
       "1         0.055910     3                2  \n",
       "2         0.051896     5                2  \n",
       "3         0.057460     3                1  \n",
       "4         0.055910     3                2  \n",
       "\n",
       "[5 rows x 44 columns]"
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       "      <th>LoanNr_ChkDgt</th>\n",
       "      <th>Name</th>\n",
       "      <th>City</th>\n",
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       "      <th>864742</th>\n",
       "      <td>9022713002</td>\n",
       "      <td>BJORK, LAURA BEAUTY CONSULTANT</td>\n",
       "      <td>DALE CITY</td>\n",
       "      <td>VA</td>\n",
       "      <td>22193</td>\n",
       "      <td>MANUFACTURERS &amp; TRADERS TR CO</td>\n",
       "      <td>MD</td>\n",
       "      <td>1</td>\n",
       "      <td>1996-02-21</td>\n",
       "      <td>1996</td>\n",
       "      <td>...</td>\n",
       "      <td>1996</td>\n",
       "      <td>2</td>\n",
       "      <td>1996-02-01</td>\n",
       "      <td>8.25</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.139452</td>\n",
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       "      <th>864743</th>\n",
       "      <td>9035193000</td>\n",
       "      <td>PROFESSIONALS FOR NONPROFITS,I</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
       "      <td>BANCO POPULAR NORTH AMERICA</td>\n",
       "      <td>NY</td>\n",
       "      <td>10</td>\n",
       "      <td>1996-02-26</td>\n",
       "      <td>1996</td>\n",
       "      <td>...</td>\n",
       "      <td>1996</td>\n",
       "      <td>2</td>\n",
       "      <td>1996-02-01</td>\n",
       "      <td>8.25</td>\n",
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       "      <td>0.094630</td>\n",
       "      <td>0.103363</td>\n",
       "      <td>0.066158</td>\n",
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       "    <tr>\n",
       "      <th>864744</th>\n",
       "      <td>9256453010</td>\n",
       "      <td>NORTHAIRE MARKET INC</td>\n",
       "      <td>SHAKOPEE</td>\n",
       "      <td>MN</td>\n",
       "      <td>55379</td>\n",
       "      <td>FIDELITY BANK</td>\n",
       "      <td>MN</td>\n",
       "      <td>20</td>\n",
       "      <td>1996-05-07</td>\n",
       "      <td>1996</td>\n",
       "      <td>...</td>\n",
       "      <td>1996</td>\n",
       "      <td>2</td>\n",
       "      <td>1996-02-01</td>\n",
       "      <td>8.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.066200</td>\n",
       "      <td>0.133574</td>\n",
       "      <td>0.063179</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864745</th>\n",
       "      <td>9459933001</td>\n",
       "      <td>COMPUTER RENAISSANCE</td>\n",
       "      <td>CARY</td>\n",
       "      <td>NC</td>\n",
       "      <td>27511</td>\n",
       "      <td>FIRST-CITIZENS BK &amp; TR CO</td>\n",
       "      <td>NC</td>\n",
       "      <td>17</td>\n",
       "      <td>1996-08-15</td>\n",
       "      <td>1996</td>\n",
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       "      <td>1996</td>\n",
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       "      <td>0.187560</td>\n",
       "      <td>0.111641</td>\n",
       "      <td>0.060808</td>\n",
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       "      <td>1</td>\n",
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       "      <th>864746</th>\n",
       "      <td>7377094002</td>\n",
       "      <td>NORTHLAND DATACOMM CO</td>\n",
       "      <td>MINNEAPOLIS</td>\n",
       "      <td>MN</td>\n",
       "      <td>55311</td>\n",
       "      <td>BMO HARRIS BK NATL ASSOC</td>\n",
       "      <td>IL</td>\n",
       "      <td>20</td>\n",
       "      <td>2004-04-22</td>\n",
       "      <td>2004</td>\n",
       "      <td>...</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>2014-12-01</td>\n",
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       "      <td>0.066200</td>\n",
       "      <td>0.133574</td>\n",
       "      <td>0.063179</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "        LoanNr_ChkDgt                            Name         City State  \\\n",
       "864742     9022713002  BJORK, LAURA BEAUTY CONSULTANT    DALE CITY    VA   \n",
       "864743     9035193000  PROFESSIONALS FOR NONPROFITS,I     NEW YORK    NY   \n",
       "864744     9256453010            NORTHAIRE MARKET INC     SHAKOPEE    MN   \n",
       "864745     9459933001            COMPUTER RENAISSANCE         CARY    NC   \n",
       "864746     7377094002           NORTHLAND DATACOMM CO  MINNEAPOLIS    MN   \n",
       "\n",
       "          Zip                           Bank BankState  NAICS ApprovalDate  \\\n",
       "864742  22193  MANUFACTURERS & TRADERS TR CO        MD      1   1996-02-21   \n",
       "864743  10022    BANCO POPULAR NORTH AMERICA        NY     10   1996-02-26   \n",
       "864744  55379                  FIDELITY BANK        MN     20   1996-05-07   \n",
       "864745  27511      FIRST-CITIZENS BK & TR CO        NC     17   1996-08-15   \n",
       "864746  55311       BMO HARRIS BK NATL ASSOC        IL     20   2004-04-22   \n",
       "\n",
       "        ApprovalFY  ...  Year  Month        DATE  DPRIME  dprime2   Density  \\\n",
       "864742        1996  ...  1996      2  1996-02-01    8.25        0  0.104331   \n",
       "864743        1996  ...  1996      2  1996-02-01    8.25        0  0.094630   \n",
       "864744        1996  ...  1996      2  1996-02-01    8.25        0  0.066200   \n",
       "864745        1996  ...  1996      2  1996-02-01    8.25        0  0.187560   \n",
       "864746        2004  ...  2014     12  2014-12-01    3.25        0  0.066200   \n",
       "\n",
       "        Upper_Threshold  Lower_Threshold Label growth_potential  \n",
       "864742         0.139452         0.056099     3                1  \n",
       "864743         0.103363         0.066158     3                1  \n",
       "864744         0.133574         0.063179     3                2  \n",
       "864745         0.111641         0.060808     1                1  \n",
       "864746         0.133574         0.063179     3                2  \n",
       "\n",
       "[5 rows x 44 columns]"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 864747 entries, 0 to 864746\n",
      "Data columns (total 44 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   LoanNr_ChkDgt      864747 non-null  int64  \n",
      " 1   Name               864747 non-null  object \n",
      " 2   City               864747 non-null  object \n",
      " 3   State              864747 non-null  object \n",
      " 4   Zip                864747 non-null  int64  \n",
      " 5   Bank               864747 non-null  object \n",
      " 6   BankState          864747 non-null  object \n",
      " 7   NAICS              864747 non-null  int64  \n",
      " 8   ApprovalDate       864747 non-null  object \n",
      " 9   ApprovalFY         864747 non-null  int64  \n",
      " 10  Term               864747 non-null  int64  \n",
      " 11  NoEmp              864747 non-null  int64  \n",
      " 12  NewExist           864747 non-null  int64  \n",
      " 13  CreateJob          864747 non-null  int64  \n",
      " 14  RetainedJob        864747 non-null  int64  \n",
      " 15  FranchiseCode      864747 non-null  int64  \n",
      " 16  UrbanRural         864747 non-null  int64  \n",
      " 17  LowDoc             864747 non-null  int64  \n",
      " 18  ChgOffDate         148743 non-null  object \n",
      " 19  DisbursementDate   864747 non-null  object \n",
      " 20  DisbursementGross  864747 non-null  int64  \n",
      " 21  MIS_Status         864747 non-null  int64  \n",
      " 22  ChgOffPrinGr       864747 non-null  int64  \n",
      " 23  GrAppv             864747 non-null  int64  \n",
      " 24  SBA_Appv           864747 non-null  int64  \n",
      " 25  SameState          864747 non-null  int64  \n",
      " 26  Recession          864747 non-null  int64  \n",
      " 27  ChgOffDate2        864747 non-null  object \n",
      " 28  Term_re            864747 non-null  int64  \n",
      " 29  smallmedium        864747 non-null  int64  \n",
      " 30  createjob2         864747 non-null  int64  \n",
      " 31  retainedjob2       864747 non-null  int64  \n",
      " 32  dis_re             864747 non-null  int64  \n",
      " 33  appv_pct           864747 non-null  float64\n",
      " 34  Year               864747 non-null  int64  \n",
      " 35  Month              864747 non-null  int64  \n",
      " 36  DATE               864747 non-null  object \n",
      " 37  DPRIME             864747 non-null  float64\n",
      " 38  dprime2            864747 non-null  int64  \n",
      " 39  Density            864747 non-null  float64\n",
      " 40  Upper_Threshold    864747 non-null  float64\n",
      " 41  Lower_Threshold    864747 non-null  float64\n",
      " 42  Label              864747 non-null  int64  \n",
      " 43  growth_potential   864747 non-null  int64  \n",
      "dtypes: float64(5), int64(29), object(10)\n",
      "memory usage: 290.3+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "display(df.head())\n",
    "display(df.tail())\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2차 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 변수 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 864747 entries, 0 to 864746\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   NAICS             864747 non-null  int64  \n",
      " 1   NoEmp             864747 non-null  int64  \n",
      " 2   NewExist          864747 non-null  int64  \n",
      " 3   FranchiseCode     864747 non-null  int64  \n",
      " 4   UrbanRural        864747 non-null  int64  \n",
      " 5   LowDoc            864747 non-null  int64  \n",
      " 6   MIS_Status        864747 non-null  int64  \n",
      " 7   GrAppv            864747 non-null  int64  \n",
      " 8   SBA_Appv          864747 non-null  int64  \n",
      " 9   SameState         864747 non-null  int64  \n",
      " 10  Recession         864747 non-null  int64  \n",
      " 11  Term_re           864747 non-null  int64  \n",
      " 12  smallmedium       864747 non-null  int64  \n",
      " 13  createjob2        864747 non-null  int64  \n",
      " 14  retainedjob2      864747 non-null  int64  \n",
      " 15  dis_re            864747 non-null  int64  \n",
      " 16  appv_pct          864747 non-null  float64\n",
      " 17  DPRIME            864747 non-null  float64\n",
      " 18  dprime2           864747 non-null  int64  \n",
      " 19  growth_potential  864747 non-null  int64  \n",
      "dtypes: float64(2), int64(18)\n",
      "memory usage: 132.0 MB\n"
     ]
    }
   ],
   "source": [
    "#필요없는 열은 제거\n",
    "df = df.drop([\"LoanNr_ChkDgt\",\"Name\",\"City\",\"State\",\"Zip\",\"Bank\",\"BankState\",\"ApprovalDate\",\"ApprovalFY\",\"Term\",\"CreateJob\",\"RetainedJob\",\"ChgOffDate\",\"DisbursementDate\",\"DisbursementGross\",\"ChgOffPrinGr\",\"ChgOffDate2\",\"Year\",\"Month\",\"DATE\",\"Density\",\"Upper_Threshold\",\"Lower_Threshold\",\"Label\"], axis=1)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. train, valid, test 셋 만들기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 test 셋 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        NAICS  NoEmp  NewExist  FranchiseCode  UrbanRural  LowDoc  MIS_Status  \\\n",
      "825107     13      4         0              0           0       0           0   \n",
      "212849      8     16         0              0           0       0           0   \n",
      "724805      3      1         1              0           1       0           0   \n",
      "525131      1      7         1              0           1       0           0   \n",
      "256633     10     52         0              0           0       0           0   \n",
      "\n",
      "         GrAppv  SBA_Appv  SameState  Recession  Term_re  smallmedium  \\\n",
      "825107   306000    229500          1          0        2            0   \n",
      "212849   517000    387750          1          0        2            0   \n",
      "724805   338400    287640          0          0        1            0   \n",
      "525131   175300    131475          1          0        1            0   \n",
      "256633  1185000    749986          0          0        1            0   \n",
      "\n",
      "        createjob2  retainedjob2  dis_re   appv_pct  DPRIME  dprime2  \\\n",
      "825107           0             0       3  75.000000    8.50        0   \n",
      "212849           0             0       4  75.000000    4.75        0   \n",
      "724805           0             0       3  85.000000    6.00        0   \n",
      "525131           1             0       3  75.000000    8.25        0   \n",
      "256633           0             2       5  63.289958    8.50        0   \n",
      "\n",
      "        growth_potential  \n",
      "825107                 2  \n",
      "212849                 2  \n",
      "724805                 2  \n",
      "525131                 1  \n",
      "256633                 1  \n"
     ]
    }
   ],
   "source": [
    "# 시드 고정\n",
    "seed = 42\n",
    "df_sample = df.sample(n=10000, random_state=seed)\n",
    "\n",
    "# 샘플 데이터 확인\n",
    "print(df_sample.head())\n",
    "\n",
    "# 필요하다면 test 데이터 프레임으로 할당\n",
    "test = df_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 20)\n"
     ]
    },
    {
     "data": {
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       "      <th>NAICS</th>\n",
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      "text/plain": [
       "        NAICS  NoEmp  NewExist  FranchiseCode  UrbanRural  LowDoc  MIS_Status  \\\n",
       "825107     13      4         0              0           0       0           0   \n",
       "212849      8     16         0              0           0       0           0   \n",
       "724805      3      1         1              0           1       0           0   \n",
       "525131      1      7         1              0           1       0           0   \n",
       "256633     10     52         0              0           0       0           0   \n",
       "\n",
       "         GrAppv  SBA_Appv  SameState  Recession  Term_re  smallmedium  \\\n",
       "825107   306000    229500          1          0        2            0   \n",
       "212849   517000    387750          1          0        2            0   \n",
       "724805   338400    287640          0          0        1            0   \n",
       "525131   175300    131475          1          0        1            0   \n",
       "256633  1185000    749986          0          0        1            0   \n",
       "\n",
       "        createjob2  retainedjob2  dis_re   appv_pct  DPRIME  dprime2  \\\n",
       "825107           0             0       3  75.000000    8.50        0   \n",
       "212849           0             0       4  75.000000    4.75        0   \n",
       "724805           0             0       3  85.000000    6.00        0   \n",
       "525131           1             0       3  75.000000    8.25        0   \n",
       "256633           0             2       5  63.289958    8.50        0   \n",
       "\n",
       "        growth_potential  \n",
       "825107                 2  \n",
       "212849                 2  \n",
       "724805                 2  \n",
       "525131                 1  \n",
       "256633                 1  "
      ]
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     "output_type": "display_data"
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      "text/plain": [
       "        NAICS  NoEmp  NewExist  FranchiseCode  UrbanRural  LowDoc  MIS_Status  \\\n",
       "697697      2      2         0              0           1       0           0   \n",
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       "352162      5      4         0              0           1       0           0   \n",
       "671962     13     20         0              0           0       0           0   \n",
       "833943     17      1         0              0           1       1           0   \n",
       "\n",
       "        GrAppv  SBA_Appv  SameState  Recession  Term_re  smallmedium  \\\n",
       "697697   25000     22500          1          0        1            0   \n",
       "846983   85000     68000          1          0        1            0   \n",
       "352162   70000     35000          0          0        1            0   \n",
       "671962  169000    169000          1          0        2            0   \n",
       "833943   85000     73100          1          0        1            0   \n",
       "\n",
       "        createjob2  retainedjob2  dis_re  appv_pct  DPRIME  dprime2  \\\n",
       "697697           0             0       1      90.0   10.50        1   \n",
       "846983           0             0       2      80.0    8.25        0   \n",
       "352162           0             1       3      50.0    6.25        0   \n",
       "671962           1             1       3     100.0    8.51        0   \n",
       "833943           0             0       2      86.0    9.00        0   \n",
       "\n",
       "        growth_potential  \n",
       "697697                 2  \n",
       "846983                 1  \n",
       "352162                 1  \n",
       "671962                 2  \n",
       "833943                 1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10000 entries, 825107 to 833943\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   NAICS             10000 non-null  int64  \n",
      " 1   NoEmp             10000 non-null  int64  \n",
      " 2   NewExist          10000 non-null  int64  \n",
      " 3   FranchiseCode     10000 non-null  int64  \n",
      " 4   UrbanRural        10000 non-null  int64  \n",
      " 5   LowDoc            10000 non-null  int64  \n",
      " 6   MIS_Status        10000 non-null  int64  \n",
      " 7   GrAppv            10000 non-null  int64  \n",
      " 8   SBA_Appv          10000 non-null  int64  \n",
      " 9   SameState         10000 non-null  int64  \n",
      " 10  Recession         10000 non-null  int64  \n",
      " 11  Term_re           10000 non-null  int64  \n",
      " 12  smallmedium       10000 non-null  int64  \n",
      " 13  createjob2        10000 non-null  int64  \n",
      " 14  retainedjob2      10000 non-null  int64  \n",
      " 15  dis_re            10000 non-null  int64  \n",
      " 16  appv_pct          10000 non-null  float64\n",
      " 17  DPRIME            10000 non-null  float64\n",
      " 18  dprime2           10000 non-null  int64  \n",
      " 19  growth_potential  10000 non-null  int64  \n",
      "dtypes: float64(2), int64(18)\n",
      "memory usage: 1.6 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# test 데이터 구조 확인\n",
    "print(test.shape)\n",
    "display(test.head())\n",
    "display(test.tail())\n",
    "print(test.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        NAICS  NoEmp  NewExist  FranchiseCode  UrbanRural  LowDoc   GrAppv  \\\n",
      "825107     13      4         0              0           0       0   306000   \n",
      "212849      8     16         0              0           0       0   517000   \n",
      "724805      3      1         1              0           1       0   338400   \n",
      "525131      1      7         1              0           1       0   175300   \n",
      "256633     10     52         0              0           0       0  1185000   \n",
      "\n",
      "        SBA_Appv  SameState  Recession  Term_re  smallmedium  createjob2  \\\n",
      "825107    229500          1          0        2            0           0   \n",
      "212849    387750          1          0        2            0           0   \n",
      "724805    287640          0          0        1            0           0   \n",
      "525131    131475          1          0        1            0           1   \n",
      "256633    749986          0          0        1            0           0   \n",
      "\n",
      "        retainedjob2  dis_re   appv_pct  DPRIME  dprime2  growth_potential  \n",
      "825107             0       3  75.000000    8.50        0                 2  \n",
      "212849             0       4  75.000000    4.75        0                 2  \n",
      "724805             0       3  85.000000    6.00        0                 2  \n",
      "525131             0       3  75.000000    8.25        0                 1  \n",
      "256633             2       5  63.289958    8.50        0                 1  \n",
      "825107    0\n",
      "212849    0\n",
      "724805    0\n",
      "525131    0\n",
      "256633    0\n",
      "Name: MIS_Status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# y_test에 MIS_Status 열 할당\n",
    "y_test = test['MIS_Status']\n",
    "\n",
    "# x_test에 MIS_Status를 제외한 열들 할당\n",
    "x_test = test.drop('MIS_Status', axis=1)\n",
    "\n",
    "# x_test 확인\n",
    "print(x_test.head())\n",
    "\n",
    "# y_test 확인\n",
    "print(y_test.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 샘플링\n",
    "채무이행 : 채무불이행 = 5:5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    45000\n",
      "0    45000\n",
      "Name: MIS_Status, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAICS</th>\n",
       "      <th>NoEmp</th>\n",
       "      <th>NewExist</th>\n",
       "      <th>FranchiseCode</th>\n",
       "      <th>UrbanRural</th>\n",
       "      <th>LowDoc</th>\n",
       "      <th>MIS_Status</th>\n",
       "      <th>GrAppv</th>\n",
       "      <th>SBA_Appv</th>\n",
       "      <th>SameState</th>\n",
       "      <th>Recession</th>\n",
       "      <th>Term_re</th>\n",
       "      <th>smallmedium</th>\n",
       "      <th>createjob2</th>\n",
       "      <th>retainedjob2</th>\n",
       "      <th>dis_re</th>\n",
       "      <th>appv_pct</th>\n",
       "      <th>DPRIME</th>\n",
       "      <th>dprime2</th>\n",
       "      <th>growth_potential</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>134344</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>230000</td>\n",
       "      <td>230000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100.00</td>\n",
       "      <td>8.12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755318</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>195000</td>\n",
       "      <td>156000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>80.00</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714626</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1650000</td>\n",
       "      <td>749925</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>45.45</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566872</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>525000</td>\n",
       "      <td>393750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>75.00</td>\n",
       "      <td>7.26</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284884</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40000</td>\n",
       "      <td>34000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>85.00</td>\n",
       "      <td>4.75</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550128</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5000</td>\n",
       "      <td>4250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85.00</td>\n",
       "      <td>8.25</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333630</th>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30000</td>\n",
       "      <td>15000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493699</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30000</td>\n",
       "      <td>15000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>50.00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370638</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>248000</td>\n",
       "      <td>124000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>50.00</td>\n",
       "      <td>4.01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521801</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5000</td>\n",
       "      <td>4250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        NAICS  NoEmp  NewExist  FranchiseCode  UrbanRural  LowDoc  MIS_Status  \\\n",
       "134344      8      3         0              0           0       0           0   \n",
       "755318     17      1         0              0           0       0           0   \n",
       "714626      1     24         0              0           0       0           0   \n",
       "566872     14      2         0              0           1       0           0   \n",
       "284884     10      4         1              0           1       0           0   \n",
       "...       ...    ...       ...            ...         ...     ...         ...   \n",
       "550128      7      1         1              0           1       0           1   \n",
       "333630     18      3         0              0           0       0           1   \n",
       "493699     14      1         0              0           0       0           1   \n",
       "370638      5     11         0              0           1       0           1   \n",
       "521801     14      1         1              0           1       0           1   \n",
       "\n",
       "         GrAppv  SBA_Appv  SameState  Recession  Term_re  smallmedium  \\\n",
       "134344   230000    230000          1          0        2            0   \n",
       "755318   195000    156000          0          0        1            0   \n",
       "714626  1650000    749925          1          0        1            0   \n",
       "566872   525000    393750          0          0        1            0   \n",
       "284884    40000     34000          1          0        1            0   \n",
       "...         ...       ...        ...        ...      ...          ...   \n",
       "550128     5000      4250          0          0        1            0   \n",
       "333630    30000     15000          0          0        0            0   \n",
       "493699    30000     15000          0          0        0            0   \n",
       "370638   248000    124000          0          0        0            0   \n",
       "521801     5000      4250          0          1        1            0   \n",
       "\n",
       "        createjob2  retainedjob2  dis_re  appv_pct  DPRIME  dprime2  \\\n",
       "134344           0             0       2    100.00    8.12        0   \n",
       "755318           0             0       3     80.00    6.50        0   \n",
       "714626           0             0       5     45.45   10.00        0   \n",
       "566872           0             0       4     75.00    7.26        0   \n",
       "284884           0             0       1     85.00    4.75        0   \n",
       "...            ...           ...     ...       ...     ...      ...   \n",
       "550128           0             0       0     85.00    8.25        0   \n",
       "333630           0             0       1     50.00    4.25        0   \n",
       "493699           0             0       2     50.00    4.25        0   \n",
       "370638           0             1       3     50.00    4.01        0   \n",
       "521801           0             0       0     85.00    5.00        0   \n",
       "\n",
       "        growth_potential  \n",
       "134344                 2  \n",
       "755318                 1  \n",
       "714626                 1  \n",
       "566872                 2  \n",
       "284884                 1  \n",
       "...                  ...  \n",
       "550128                 2  \n",
       "333630                 2  \n",
       "493699                 2  \n",
       "370638                 1  \n",
       "521801                 2  \n",
       "\n",
       "[90000 rows x 20 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시드 고정\n",
    "seed = 42\n",
    "# df에서 test 데이터를 제외한 데이터를 추출\n",
    "df_remaining = df.drop(test.index)\n",
    "\n",
    "# MIS_Status가 0인 데이터와 1인 데이터로 분리\n",
    "df_0 = df_remaining[df_remaining['MIS_Status'] == 0]\n",
    "df_1 = df_remaining[df_remaining['MIS_Status'] == 1]\n",
    "\n",
    "# 0과 1의 비율 계산\n",
    "ratio_0 = len(df_0) / len(df_remaining)\n",
    "ratio_1 = len(df_1) / len(df_remaining)\n",
    "\n",
    "# 다운 샘플링할 데이터 개수 계산\n",
    "total_samples = 90000\n",
    "samples_0 = int(total_samples * 0.5)  # MIS_Status가 0인 샘플 개수\n",
    "samples_1 = int(total_samples * 0.5)  # MIS_Status가 1인 샘플 개수\n",
    "\n",
    "# 비율 조정을 위한 다운 샘플링\n",
    "df_0_downsampled = df_0.sample(n=samples_0, random_state=seed)\n",
    "df_1_downsampled = df_1.sample(n=samples_1, random_state=seed)\n",
    "\n",
    "# 다운 샘플링된 데이터 합치기\n",
    "df_downsampled = pd.concat([df_0_downsampled, df_1_downsampled])\n",
    "\n",
    "# 결과 데이터프레임 확인\n",
    "print(df_downsampled['MIS_Status'].value_counts())\n",
    "\n",
    "# 최종 데이터프레임 할당\n",
    "df_fin = df_downsampled\n",
    "df_fin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train, valid 셋 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train 데이터 비율:\n",
      "1    0.500457\n",
      "0    0.499543\n",
      "Name: MIS_Status, dtype: float64\n",
      "\n",
      "Valid 데이터 비율:\n",
      "0    0.5016\n",
      "1    0.4984\n",
      "Name: MIS_Status, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# train 데이터와 valid 데이터를 분할\n",
    "df_train, df_valid = train_test_split(df_fin, train_size=70000, test_size=20000, random_state=42)\n",
    "\n",
    "# 결과 확인\n",
    "print(\"Train 데이터 비율:\")\n",
    "print(df_train['MIS_Status'].value_counts(normalize=True))\n",
    "print(\"\\nValid 데이터 비율:\")\n",
    "print(df_valid['MIS_Status'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        NAICS  NoEmp  NewExist  FranchiseCode  UrbanRural  LowDoc  GrAppv  \\\n",
      "315027     20      2         0              0           0       0   20000   \n",
      "377920     17     20         1              0           1       1  130500   \n",
      "578704     14      1         0              0           1       0   15000   \n",
      "509603      5     12         0              0           1       0   25000   \n",
      "827228     10      3         0              0           1       1   36000   \n",
      "\n",
      "        SBA_Appv  SameState  Recession  Term_re  smallmedium  createjob2  \\\n",
      "315027     10000          0          0        0            0           0   \n",
      "377920    110925          1          0        0            0           0   \n",
      "578704      7500          0          0        0            0           0   \n",
      "509603     12500          1          0        1            0           0   \n",
      "827228     28800          0          0        0            0           0   \n",
      "\n",
      "        retainedjob2  dis_re  appv_pct  DPRIME  dprime2  growth_potential  \n",
      "315027             0       1      50.0    4.00        0                 2  \n",
      "377920             0       3      85.0    4.00        0                 1  \n",
      "578704             1       1      50.0    8.25        0                 2  \n",
      "509603             1       3      50.0    8.25        0                 1  \n",
      "827228             0       1      80.0    8.50        0                 1  \n",
      "315027    1\n",
      "377920    1\n",
      "578704    1\n",
      "509603    0\n",
      "827228    1\n",
      "Name: MIS_Status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# df_train 분리\n",
    "y_train = df_train['MIS_Status']\n",
    "x_train = df_train.drop('MIS_Status', axis=1)\n",
    "\n",
    "# x_train, y_train 확인\n",
    "print(x_train.head())\n",
    "print(y_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        NAICS  NoEmp  NewExist  FranchiseCode  UrbanRural  LowDoc  GrAppv  \\\n",
      "547801      6      1         1              0           0       0    4500   \n",
      "168477     10      4         0              0           1       0   20000   \n",
      "385262     10     12         1              0           0       0   40000   \n",
      "672193      5      5         0              0           1       0  146000   \n",
      "378324     10      3         0              0           1       0   35000   \n",
      "\n",
      "        SBA_Appv  SameState  Recession  Term_re  smallmedium  createjob2  \\\n",
      "547801      2250          0          0        0            0           0   \n",
      "168477     16400          0          0        1            0           0   \n",
      "385262     20000          0          0        0            0           0   \n",
      "672193    129940          0          0        2            0           0   \n",
      "378324     17500          0          0        0            0           1   \n",
      "\n",
      "        retainedjob2  dis_re  appv_pct  DPRIME  dprime2  growth_potential  \n",
      "547801             0       0      50.0    8.25        0                 3  \n",
      "168477             0       1      82.0    9.05        0                 1  \n",
      "385262             1       1      50.0    6.61        0                 1  \n",
      "672193             0       3      89.0    8.75        0                 1  \n",
      "378324             0       1      50.0    4.00        0                 1  \n",
      "547801    1\n",
      "168477    0\n",
      "385262    1\n",
      "672193    1\n",
      "378324    1\n",
      "Name: MIS_Status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# df_valid 분리\n",
    "y_valid = df_valid['MIS_Status']\n",
    "x_valid = df_valid.drop('MIS_Status', axis=1)\n",
    "\n",
    "# x_valid, y_valid 확인\n",
    "print(x_valid.head())\n",
    "print(y_valid.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 모델링"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 로지스틱 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 선언\n",
    "lg_model = LogisticRegression(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모델 학습\n",
    "lg_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 평가\n",
    "def model_eval(y_test, y_pred=None):\n",
    "    confusion = confusion_matrix(y_test, y_pred, labels=[True, False])\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred, labels=[True, False])\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    F1 = f1_score(y_test, y_pred, labels=[True, False])\n",
    "    print(\"오차행렬:\\n\", confusion)\n",
    "    print(\"\\n정확도: {:.4f}\".format(accuracy))\n",
    "    print(\"정밀도: {:.4f}\".format(precision))\n",
    "    print(\"재현율: {:.4f}\".format(recall))\n",
    "    print(\"F1: {:.4f}\".format(F1))\n",
    "    print(classification_report(y_test, y_pred, labels=[True, False]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차행렬:\n",
      " [[7722 2246]\n",
      " [5268 4764]]\n",
      "\n",
      "정확도: 0.6243\n",
      "정밀도: 0.5945\n",
      "재현율: 0.7747\n",
      "F1: 0.6727\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        True       0.59      0.77      0.67      9968\n",
      "       False       0.68      0.47      0.56     10032\n",
      "\n",
      "    accuracy                           0.62     20000\n",
      "   macro avg       0.64      0.62      0.62     20000\n",
      "weighted avg       0.64      0.62      0.62     20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred=lg_model.predict(x_valid)\n",
    "model_eval(y_valid, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 하이퍼파라미터 튜닝"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "    'C': [0.001, 0.01, 0.1, 1, 10, 100],        # 규제 강도를 조절하는 역수. 클수록 약한 규제.\n",
    "    'penalty': ['l1', 'l2'],                    # 규제 유형. 'l1'은 L1 규제, 'l2'는 L2 규제.\n",
    "    'solver': ['liblinear', 'saga'],            # 최적화에 사용할 알고리즘.\n",
    "    'max_iter': [100, 200, 300]                 # 최대 반복 횟수.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차행렬:\n",
      " [[5767 4201]\n",
      " [3320 6712]]\n",
      "\n",
      "정확도: 0.6240\n",
      "정밀도: 0.6346\n",
      "재현율: 0.5786\n",
      "F1: 0.6053\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        True       0.63      0.58      0.61      9968\n",
      "       False       0.62      0.67      0.64     10032\n",
      "\n",
      "    accuracy                           0.62     20000\n",
      "   macro avg       0.62      0.62      0.62     20000\n",
      "weighted avg       0.62      0.62      0.62     20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#모델 정의\n",
    "ms_model = LogisticRegression(random_state=42, C=10, penalty='l1', solver='saga', max_iter=300)\n",
    "\n",
    "# 모델 학습\n",
    "ms_model.fit(x_train, y_train)\n",
    "\n",
    "# 모델 평가\n",
    "pred = ms_model.predict(x_valid)\n",
    "model_eval(y_valid, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "=> 오히려 기본 모델보다 성능이 낮게 나옴."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 랜덤 포레스트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 선언\n",
    "rf_model = RandomForestClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모델 학습\n",
    "rf_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차행렬:\n",
      " [[7538 2430]\n",
      " [2014 8018]]\n",
      "\n",
      "정확도: 0.7778\n",
      "정밀도: 0.7892\n",
      "재현율: 0.7562\n",
      "F1: 0.7723\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        True       0.79      0.76      0.77      9968\n",
      "       False       0.77      0.80      0.78     10032\n",
      "\n",
      "    accuracy                           0.78     20000\n",
      "   macro avg       0.78      0.78      0.78     20000\n",
      "weighted avg       0.78      0.78      0.78     20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred=rf_model.predict(x_valid)\n",
    "model_eval(y_valid, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 하이퍼파라미터 튜닝"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "    'n_estimators': [50, 100, 150],  # 트리의 개수\n",
    "    'max_depth': [None, 10, 20, 30],   # 트리의 최대 깊이\n",
    "    'min_samples_split': [2, 5, 10],   # 내부 노드를 분할하기 위한 최소 샘플 수\n",
    "    'min_samples_leaf': [1, 2, 4]      # 리프 노드에 필요한 최소 샘플 수\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차행렬:\n",
      " [[7611 2357]\n",
      " [1861 8171]]\n",
      "\n",
      "정확도: 0.7891\n",
      "정밀도: 0.8035\n",
      "재현율: 0.7635\n",
      "F1: 0.7830\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        True       0.80      0.76      0.78      9968\n",
      "       False       0.78      0.81      0.79     10032\n",
      "\n",
      "    accuracy                           0.79     20000\n",
      "   macro avg       0.79      0.79      0.79     20000\n",
      "weighted avg       0.79      0.79      0.79     20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#모델 정의\n",
    "ms_model = RandomForestClassifier(random_state=42, n_estimators=150, max_depth=30, min_samples_split=5, min_samples_leaf=4)\n",
    "\n",
    "# 모델 학습\n",
    "ms_model.fit(x_train, y_train)\n",
    "\n",
    "# 모델 평가\n",
    "pred = ms_model.predict(x_valid)\n",
    "model_eval(y_valid, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 변수 중요도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0Mzu6w4xe/Q8GrgdePEDfm4DjgMcnWXHAkn4B3Ajs2q9DkgDvAk4CFhtwXEmSJEmSppzhx3yiqk4Dfgq8ttf+Hz1cBiw/4PALAwV0L5Hp5x7gG8DWSVbu0+dFwJrAgcASA44rSZIkSdKUM/yYv5xEEySsO0DfJwBXjtUpySOAlwC/qqo7x1HLV9vvt/W5/m7goqo6gyZckSRJkiRpWrjnx/zl0vb7saN1SvJKmiUy+/S4vHySlWj+7tcEPgE8nFGWsPRSVVcm+QHw5iSfqKq7O56/Bs1Grb02M+0qNSv1aL+hqu4b48bz+1xaY4xnSpIkSZIWMIYf85c72u/FO9oWaQOEhYDH08zieD/wM3qfxnJK189fA15dVZdPoJ4vAa+k2Y/k2x3tuwE3A0eMcf+SwNU92tcFLpxAPZIkSZIkPYThx/xlufb7xo62p/LgAOFOmpNePlxV9/YY4100M0jWBj7Uft/Yo9+YqurcJL+hmTXybYAky9Kc8nJgVd0x2v3AXcC2Pdov7dHW/ewZvdrbGSHrjXW/JEmSJGnBYfgxf1mXZmPSP3S0XU6zvKSA/wB/GGPvjvPao25/nOQ84Ezgs8DbJ1jTl4Ajkjy3qn4J7EJzustXR78NgPurqnsmiiRJkiRJU8oNT+cTSRahWV7y66q6puPS7Ko6papOrapxbVpaVWfTzBJ5S5J1Jlja92hmnrwzycLAO4DjquqKCY4nSZIkSdKUMvyYf+wBrALsPcXj7gXcDhwwkZvbpTUHAdsB29PsO/LlKatOkiRJkqRJMvyYxyVZLMneNKey7D/Vy0Sq6nqaZS8zk7xsgsN8vf3+KnBhO6NEkiRJkqR5gnt+zFvWT7ITEOCRwFrAS4HlgQ9W1f5z6LlfoNnz47NJTqyqe8Zzc1Vdl+QoYCfGN+tjkfZ9u91TVd8ZTw2SJEmSJPVj+DFveVX7Gdm89B/AYcDXqupfc+qhVXVHO7vk68C7aWaCjNeXgC2Ao8Zxz2LAoT3abwEMPyRJkiRJU8LwYx5QVZfRzPYY730zx9F3FjBrlOsHAwd3/HxWr5qqapU+918IPLrPtUf0aNuJZqaIJEmSJElzlHt+SJIkSZKkoWb4IUmSJEmShprhhyRJkiRJGmqGH5IkSZIkaagZfkiSJEmSpKHmaS8aOouu8BhWette012GJEmSJGke4cwPSZIkSZI01Aw/JEmSJEnSUDP8kCRJkiRJQ83wQ5IkSZIkDTXDD0mSJEmSNNQ87UVD567rL+VPX9tqusuQJKmnp739hOkuQZKkBY4zPyRJkiRJ0lAz/JAkSZIkSUPN8EOSJEmSJA01ww9JkiRJkjTUDD8kSZIkSdJQM/yQJEmSJElDzfBjgpIskuStSc5NcmuS+5Lc3P68ZttnZpLq+DyQ5IokRyR54hjjL5zk6iR3J1l2Cuveoq3lmKkaU5IkSZKkedki013A/CjJYsCpwIbAMcB3gQCrAC8Hngj8ueOWrwK/BRYD1gTeCGyeZEZVXdbnMS8CVgLuAV4NHDRF5b8euBt4aZJlq+rmKRpXkiRJkqR5kuHHxLwLeAGwZVWd1HkhyfuBJbr6n1VVx3b0OQ44E3hvO1YvOwIXAzfRBBaTDj+SLAO8FDgM2AV4FfC/kx1XkiRJkqR5mcteJmYmcHN38AFQVfdW1a2j3VxVZwH/AVbvdT3J0sDWwFE0s0qek+SpkysZaMKOxYB9gfNpQhVJkiRJkoaa4cfE3AosneQxE7k5yULA4sC1fbq8kiakOBI4GriXqQkqdgTOrqorgMOB5yZ5SACTZJV2X5DdkzwlyQ/b/UxmJzktyXoT7Z9kt7bvC3s8d6EkVyU5cQreVZIkSZIkwPBjoo6h+d39MMmqE7j/JTThx/f7XN8R+EVV/bOqbgBOAXZoQ5MJSbIazR4lh7dNRwH3MXqo8kTgHJpZKnsAXwDWA85O8swJ9j8auB/Yrsf9LwAeC8wa7K0kSZIkSRqb4ccEVNUPgM8AzwEuTrJ/kuVHuWWZJCslWSPJW2j23PhGVZ3Q3THJk4CNgCM6mg8HVgY2mUTZOwJ3Ace273AdcBqjhypvB3arqp2r6utV9TGaAGUR4PMT6V9V1wJnANskWbjr/lfT7HHykN9LtyTn9/oAa4x1ryRJkiRpwWL4MUFV9SFgM+AfwO7ApUnelSQ9un8DuJrmBJj/bb+/2WfoHWlOePleR9uPgFuY4NKXtqYdgB9V1S0dlw4HHg9s3OfWc6rq6M6GqvorTTCzSbs3yUT6Hwk8GnheR42L0pyUc3RV3TOe95MkSZIkaTSGH5NQVWcAzwDeBMwGvgR8r8eMhn2BzWk2MX07cAfwyySf7OzUEVKc1HkEbVXdRbPU5mVJlppAqc+nOYb3iK7242n2L+kXqpzbp/0CmqN9V5tg/x8Cd/LgpS8vBJZnwCUvVTWj1we4ZJD7JUmSJEkLDsOPSaqq+6vqm8BTaWZobAe8ravbhVV1SlWdUFUHVdX/AF8FPpxk3Y5+GwFPArZtNwX974cmYFmS3ntljGXH9vv4rjHvAB5JE6o8osd9N/YZ7/b2+2ET6V9Vt9Esbdm2Y6bMq4GLq+o3o7+KJEmSJEnjs8h0FzAsqur2JK8ALge2Bw4c45bDgHfQLP34Xdu2I83yln73volmlsahg9aVZAmawORs4Oc9ujwaeHPbZ1bXtUX7DLty+339JPofSXP07obtXh1b08yQkSRJkiRpShl+TKGqujvJVcAyA3RfvP0OQJLFgVcAx1fVR3vd0PZ5b5JVquqyAcvalmZ2x95VdWaPMRemCR5ez0PDj6f3GfN/aGZ5XDqJ/qe0bdsBKwEP5/9OopEkSZIkacq47GUCkrysDSK6258KPA04b4z7A7wHKOD0tnkbYGn6H38LzfG04f+WsQxiR+AGmpkfD1FV99NsrvqCJE/suvzKJM/qqv1FNHuIfLOqaqL9q+pemn1MtqY5+ve0qrp6HO8lSZIkSdJAnPkxMe8CDk5yAnAhcB+wOs1mpTcCH+/qP7PdUyPAY2lmO6wD7FNVF7d9Xk+zaepp/R5aVecn+RtNoPGJsYpM8hiaE2kObUOOfo4C3tmOu09H+7nAz5IcAlxME+y8GfhDV7+J9j8SeCvNXia7jfU+kiRJkiRNhDM/JuazwFk0R8R+rv1sRnN87TpV9a+u/u+g2afjW8AeNCesvKyqPgaQZCWa005+3J7sMpqjgCcn2WiAOl8HLAz8YIx+5wGX8dAZJT9q2zamOclmG+Ag4AVVNbvHOOPt/wuaPVIWozl5RpIkSZKkKefMjwmoqh8DPx6g31m0e3qM0e8aBvy7qKq9gL0G7Ls/sP8A/QpYtc+1Y4FjB3neePu3z11l0LElSZIkSZoIw4/5VHuKy9JjdLt+jOUukiRJkiQNPcOP+derGPvI21VplrNIkiRJkrTAMvyYf50ObD5Gn2vnRiGSJEmSJM3LDD/mU1V1FXDVdNchSZIkSdK8zvBDD1FVlzHARq0T7S9JkiRJ0txk+KGhs/gKT+Zpbz9husuQJEmSJM0jFpruAiRJkiRJkuYkww9JkiRJkjTUDD8kSZIkSdJQM/yQJEmSJElDzfBDkiRJkiQNNcMPSZIkSZI01DzqVkPnthv+zhmHbDHdZUjStNn0TSdNdwmSJEnzFGd+SJIkSZKkoWb4IUmSJEmShprhhyRJkiRJGmqGH5IkSZIkaagZfkiSJEmSpKFm+CFJkiRJkoaa4cc8LMniSXZNcmaS65Lck+SaJL9Lsl+Sx09w3C2SVJJjprpmSZIkSZLmNYtMdwHqLckM4AfACsBxwAnAbOBRwAzgHcAfgSMmMPzrgbuBlyZZtqpunoqaJUmSJEmaFxl+zIOSrAv8jCbc2Kiq/tWjzzLA0hMYexngpcBhwC7Aq4D/nUS5kiRJkiTN01z2Mo9JsihwDPAvYLNewQdAVf2nqi4fY6xe4dargMWAfYHzaWaBSJIkSZI0tAw/5j07Ak8G3l1Vtw96U5KZ7T4e2yXZJcnVwL19xj+7qq4ADgeem2T1HuOt0o63e5KnJPlhkpuTzE5yWpL1Jto/yW5t3xf2eO5CSa5KcuKg7y5JkiRJ0mgMP+Y9LweurapTJ3j/psDuwKeBT3VeSLIasCFN6AFwFHAfo8/+eCJwDvAfYA/gC8B6wNlJnjnB/kcD9wPb9bj/BcBjgVmj1ESS83t9gDVGu0+SJEmStOBxz495z7o0y1EeJMnCNJufdrqzqm7pansVsHZVXdVj7B2Bu4BjAarquiSnATsk2bOqHuhxz9uB11bV0R21HAH8Afg8sNl4+1fVtUnOALZJ8vaqur/j/lcDN9Fs8CpJkiRJ0qQ582PeszzQ6/SVxwNXd32+1KPfcb2CjyQBdgB+1BWYHN6OvXGfes7pDDIAquqvNKfMbJKke9PVQfsfCTwaeF5HjYvSzHw5uqru6VPPyJgzen2AS0a7T5IkSZK04DH8mPfcCTy8R/u1wOYdn7v63H9Bn/bnA6vw0KNxjwdupf/Sl3NHeU6A1SbY/4c079q59OWFNOHPrD5jSJIkSZI0boYf857LgLW6G6vqzqo6ZeRDs2dGL9f2ad+x/T6+3Wy0khRwB/BI4GVJHtHjvhv7jDeyGevDJtK/qm6jWdqybTsrBZolLxdX1W/6jCFJkiRJ0ri558e85wzgPUnWqqqLJ3B/dTckWYJmhsXZwM973PNo4M1tn1ld1xbt85yV2+/rJ9H/SJo9SjZsNyvdmuYIXkmSJEmSpozhx7zna8C7gM8CW07RmNvSzO7Yu6rO7L7Ybqa6Nc3Sl1ldl5/eZ8z/oZnlcekk+p/Stm0HrESz3Ofwh94qSZIkSdLEuexlHlNVfwf2ArZIckiSJbv7JFmEZv+MQe0I3EAz86PXM+8Hvge8IMkTuy6/Msmzup7/Ipo9RL5ZVd0zTQbuX1X3AsfQBC8vAU6rqqvH8V6SJEmSJI3JmR/zoKraN8liwEdpQpBjgT8DCwOrAq8ElqTZMHRUSR5DcxztoV1HynY7CngnTVCyT0f7ucDPkhwCXAw8jWaJzB+6+k20/5HAW9v32W2s95EkSZIkabyc+TGPqqqPAesBJwNbAF+gWQqzFc2+Ha+kCSvG8jqa0OQHY/Q7j2az1R272n/Utm1Mc7TuNsBBwAuqanaPccbb/xfA5cBiNCfPSJIkSZI0pZz5MQ+rqguBNwzY9yx6LIWpqv2B/Qe4v2hmlfS6dixw7CB1jLd/+9xVBh1bkiRJkqTxcuaHJEmSJEkaaoYfkiRJkiRpqBl+SJIkSZKkoWb4IUmSJEmShpobnqqnqrqMHhuoTlX/OWmpR63Gpm86abrLkCRJkiTNI5z5IUmSJEmShprhhyRJkiRJGmqGH5IkSZIkaagZfkiSJEmSpKFm+CFJkiRJkoaa4YckSZIkSRpqHnWroXPzjX/j6ENfNN1lSBoyr9751OkuQZIkSRPkzA9JkiRJkjTUDD8kSZIkSdJQM/yQJEmSJElDzfBDkiRJkiQNNcMPSZIkSZI01Aw/JEmSJEnSUDP80DwjyRLTXYMkSZIkafgYfmiekOTLwC+muw5JkiRJ0vAx/NC84hnAw6a7CEmSJEnS8DH8kCRJkiRJQ83wYw5LY4skP05yRZLbk1yYZMeufpXkwCQrJjksyfVJ7khybpLNeow7UP8kW7d939ynvl8m+eM43uesJBclWTTJ3kkuS3JXkj8n2bXPPYsn+UD73nckuSXJ2UnWasco4AXA09paK8lOg9YkSZIkSdJoDD/mvCcDPwIK+BLwIeBu4LAkL+/quzTNvhePAD4GfBJ4LHBKkhf1GHuQ/icDNwPbdd+cZFXgOcBhE3ivo4AtgC8DHwBuAr6cZJ+uZzwcOBP4NPBn4H3A/jRLXFYHjgN2Bv4CXNX+eWfgnAnUJEmSJEnSQywy3QUsAG4FNqqqc0cakhwCXA68E/h+R9/XAB+pqs909P0G8CfgK0meWlU1zv73JDkG2DnJslV1c8f9rwbuB44Y5zutCVwGPLeq7m+f+1XgVOBDSQ6pqsvbvp+lCVheVlXHdYzxySRLVNWdwIXtTI9HVdWsQQpIcn6fS2uM810kSZIkSUPOmR9zWFVd1xl8tG13AOcBT+nqfhnNrIjOvtfTzK54CrD2BPsfCSwKbN11/6uBU6vqmoFfqLEQsOtI8NE+935gb2DhkeckeSTwRuCIruBj5J47x/lcSZIkSZLGzZkfc0GSRWhmP2xEs9RjdWAdIF1df90ZKHS4oP1+CvCHCfT/OXAFzdKXWW1Na9KcsPLJ8b0NANdU1T97tP+u47kAzwIWA344gWeMqqpm9GpvZ4SsN9XPkyRJkiTNv5z5MYclWQO4EDgbeB2wFHAuzf4X3W7sM8zt7Xf3UbAD9W+XyhwFvLCdjQGwPc1eICeM/gY99XvunTTLaEbqXLH9Hu/MEkmSJEmSpozhx5x3GLAksHpVrV1Vr6yqDwJ/79F30T5jrNx+Xz+J/kfShBIvbX9+NXB0Vd09WvF99HvuY2iWvYw89672+3ETeIYkSZIkSVPC8GMOSrIU8GzgyKq6tOvyWj1ueXqfof4HuI//W84y7v5V9Ufgj8B2SWbQLE2ZNVr9o1i1PcWl13MBft1+X9h+bzLAmMVDlwFJkiRJkjRphh9z1v3AA/zfTAwAkrwSeGaP/hsm2bar79o0S1R+UFU3TbL/kcCLgJcBf66qXzMxiwL7dj13KWAP4Eqa43Wpqn8APwXe1AYunf0XSrJ0R9NNwGOSLDzBmiRJkiRJ6skNT+egqrojyUnA65PcR7Mh6Lo0S09OBzbsuuU84MgkRwHnA08A3gZcC7y3xyPG2/8o4NM0J7AcMIlXuwLYOslawI+BxdsxnwBsWVX3dvR9E3AO8PMks4Df0yyPeTmwJ3Bc2+8cmlBmVpJfAr+vqnMmUaMkSZIkSYAzP+aGnYBvAlsAnwWeCGwM/LtH3wuAl9AsZ/l8e+/3gedU1VWT7V9VV9Cc/LICcPjEX4nbgBcAdwD7AB+m2cPk+VV1etcz/wnMoFli81KaY3h3ptn09VcdXQ8CDqX5PX2CZp8USZIkSZImzZkfc1i79OTNPS7t1H66+59FcyzuoOOPt/8LBu07xjhXANsM2Pca4O3tp1+fu4A3tB9JkiRJkqaM4YcASLICzUkt/dxZVbfMrXokSZIkSZoqhh8a8RuaJTn9HEaPmSqSJEmSJM3rDD804vXAEqNc77XniCRJkiRJ8zzDDwFQVT+b7hokSZIkSZoTDD/mEVWVOdl/qlTVzOl4riRJkiRJE2X4oaGz7PJP4dU7nzrdZUiSJEmS5hELTXcBkiRJkiRJc5LhhyRJkiRJGmqGH5IkSZIkaagZfkiSJEmSpKFm+CFJkiRJkoaap71o6Fx309/4ypEvmu4ypPnSrq/1pCRJkiQNH2d+SJIkSZKkoWb4IUmSJEmShprhhyRJkiRJGmqGH5IkSZIkaagZfkiSJEmSpKFm+CFJkiRJkoaa4cc8KMkqSar9bDVKv/XbPjtN4lmXdTyr+zPhcSVJkiRJmlcsMt0FaExfSnJ6Vd05B5/xe+CLPdrPmYPPlCRJkiRprjD8mLedAWwK7Al8eA4+58qqmjUHx5ckSZIkadq47GXedkr7eV+SNaa7GEmSJEmS5keGH/O+dwD3A18b9IYk6yU5Jsm1Se5JcnmSLyd51ESLSHJWkouSLJfk20luSnJ9ks8mWTjJQkn2bJ91V5ILkmzSNcbMdi+R7ZI8K8npSW5N8p8k30/y5InWJ0mSJElSP4Yf87iq+gewD7BxkteO1T/JtsAvgWcCXwZ2BU4CdgF+mWSFSZSzKHAqcB/wceBC4P3AvsBXgdcCXwD2A54EnJjkCT3GeRZwGnAJsDtwCPAi4BdJHjuJ+iRJkiRJegj3/Jg/fA54HfC5JCdW1S29OrUzOw6jCSVmVtUdHde+C5wFfBbYuevWxZKs1NX2QFVd19W2OvDxqtq7HfMrwLnAbsCtwFpVdWN77RyagOO1wKe7xnkPsHFV/aKjvhOBM4FPAG/q+Vt48Lue3+eSy4MkSZIkSQ/izI/5QFXdC7wVWBH45ChddwCWAt7XGXy0Y/wMOAF4dZKHdd23GXB11+evPca/D/h8x5gPAMcAiwNfGwk+WmcAdwFP7zHOUZ3BRzvWWe09247yfpIkSZIkjZszP+YTVfXzJIcCb09yaFVd0KPbc2gCh35H1J4DbAU8FfhjR/uvgb26+t7b4/4rq+q2rrZ/t98PqqeqHkhyA00Y0+3cPvVdAGyaZPmuIOUhqmpGr/Z2Rsh6o90rSZIkSVqwGH7MXz5AE14clGSDHteXB66qqupz/9Xtd3cgcX1VnTLA82/v0TYSknSHItBs1NprdlG/YGNk/O6ZKZIkSZIkTZjLXuYj7WyI3YFn02xg2h1yzAa69+7oNHLt5qmvblwW7dO+Mk1gctNcrEWSJEmSNOQMP+YzVXUYzcaln6KZ6dHpAuDhSZ7b5/YNgf8Af5lT9Q3oIfuAJFkY2BT4Q1XdPfdLkiRJkiQNK8OP+dPbgIcDH+lqPwy4BzggyRKdF5JsCGwDHNRuVDqd3pFkla62NwGrAt+Y++VIkiRJkoaZe37Mh6rqkiT7AXt2tV+R5B3AwcCFSQ4DbgCeAbwB+AXNUbLdVk6yU4/2K6rqp1NafONC4HdJDgb+CTyX5qSan9DULkmSJEnSlDH8mH99CtgeWK2zsaoOSfIP4IM0G6QuDlwK7AN8oc+SkmcCh/ZoPx6YE+HHgcAKwHuBJwD/auv7TFXdPweeJ0mSJElagBl+zIOq6jIgY/S5C3hKn2s/ZcDQoqpWGbDfzD7tx9Kn1tHGrqqDgIMGebYkSZIkSZPhnh+SJEmSJGmoGX5IkiRJkqShZvghSZIkSZKGmuGHJEmSJEkaam54qrmmqs5ijI1cJUmSJEmaaoYfGjqPXu4p7PraU6e7DEmSJEnSPMJlL5IkSZIkaagZfkiSJEmSpKFm+CFJkiRJkoaa4YckSZIkSRpqhh+SJEmSJGmoGX5IkiRJkqSh5lG3GjpX3vx3dj/2xdNdhjTP+9x2p0x3CZIkSdJc4cwPSZIkSZI01Aw/JEmSJEnSUDP8kCRJkiRJQ83wQ5IkSZIkDTXDD0mSJEmSNNQMPyRJkiRJ0lAz/BiHJIskeWuSc5PcmuS+JDe3P6853fUNIsmaSb6V5PIk9yS5K8k/k3yyR9/Fp+B5S0x2DEmSJEmSJsPwY0BJFgN+AnwZ+CewJ7A7cBjweOCJ01fdYJJsBVwIbATMAt4FfBr4I7BxR7+nJbkc2HISz1ohySXAOyZRsiRJkiRJk7bIdBcwH3kX8AJgy6o6qfNCkvcD8/QMh3YWx6HAb4BNquqeruvLd/y4AvCEST7y4cBTJzmGJEmSJEmTZvgxuJnAzd3BB0BV3QvcO9crGp+1geWA73UHHwBVdePcL0mSJEmSpDnPZS+DuxVYOsljRuvU7gvymiQ/TfLvdm+QXyXZvKvfKkkqye5JNmn73JHkopG+bZ8fJvlPkluSfCfJcj2euWSSfZNcluTudg+Pz3Tt2XFr+73GGPUXcGb74zFtjZd1XH9SkgOT/CnJ7e0zD0qydEefy2iWBgHs345RXc95epLj2ne7I8l57bIcSZIkSZKmlDM/BncM8Grgh0m2r6p/9uk3k2YfkB8AxwMLA7sAJyV5dlX9tqv/s4HdgIOBH9HskXF8kv8BjgB+DnwYeB6wPbA0sMXIzW3A8ROaUOMQ4DLgGcB7gXWSbF5VBfwN+D2wS5KLga+27d12bsf6IPBV4LfA7I7r3wYe3f4+rgDWA94CrAy8tO2zO7AKsD/wXeCUzgck2RA4DbgY+BRwH7AtcFyS11XVd3rU9SBJzu9zadRwR5IkSZK04DH8GFBV/SDJZ4A9gIuTHAh8psdykX8Bz6iqP480JPku8A+aYGPnrv7bAE+vqr+2fX9IswHpycBRVfWGtt/XkjwAvCbJY6vq3237nsCqwDpVdUXHM39BE1S8EDitqh5Isj1wEvAV4A1JPlxVDwomqmpWkpk04cdZVXVsV72fBU6qqvs7nnUf8PYkT6yqy6vq2CSr0IQfv62qWR19FwWOBI4DdhgJYJIcAJwOfDbJUX2CGUmSJEmSxs1lL+NQVR8CNqMJMnYHLk3yriTp6POXzuCjbbsK+AvwlB7D/nAk+Gj7/gn4E7A48PGuvie2308DSLIw8DaakOOeJCuNfGhmjBSwScfYfwaeSXNizdOBk5P8JMlq4/gdnNAZfLTOaL97vV+3LWlmhRwArNhR74rAqcDjGGCj1Kqa0esDXDLou0iSJEmSFgzO/BinqjojyTOAnWjCiS8Bz0vy6pFQoF2KshGwAU0gMPK5tMeQF/do+zfw2Kq6vKv9uvZ7qfb7qcCywAfaTy8rdNV/G7Bbkq8A+9As5Tk/yUur6uw+YzxIklWBTWlCmNXbb4BlBrh9g/a7e/lPd82GGJIkSZKkKWH4MQFtyPHNJEcDRwHb0czAOLDdz+Jo4DHAH2hmfPyUrhCiw+092u4FbuvRPjLjYmTGzrLt9+dp9v3o5ao+7/B3YPskhwHfB45I8tSqurPPOCRZCPhf4M3AtcAFNBubXtW2DWJZ4AGaGSD9lrb8acCxJEmSJEkak+HHJFTV7UleAVxOEyQcTBN8XA08u6quGembZGPgkVNcwshGpDd2790xqKo6JcmHgS/SzFY5fZTub6EJOd5YVd8aaWw3Zx00/JhNE95cWFVXT6RmSZIkSZLGwz0/Jqmq7qaZ+bAMzTKUxwMHdwUfCzPAPhYT8FeaWSKbjNVxDP9ov5dpv0dmZKSr32bAlZ3BR2utHmP2G+Oi9nuyNUuSJEmSNBDDjwEleVm7l0d3+1Np9rw4jyaIgObY107vA5ab6praJSo/BDZLsnmP2l7T8efVk6zfZ6iX0yxF+XX7803td/d73AssneQRHeMuT/N+3fqNcRxwJ/CxJEt31bt0ki2QJEmSJGkKuexlcO8CDk5yAnAhcB/NZp87ADfSbH56VXvtQ+0/7P8BPA9YBzif5gSXqfZ+4PnA8Ulm0WwkujywLc0mq99p+z0WODPJOTR7kFxFs//Gi4GZwD4dG6z+lWZz1d2TFLBoVe0PfA94VTvO4TSBzhtpTpbZvrOoqrotyR+AnZJcDSxRVXtV1Y1J3g18Hfh9km/S7B+yBs3v8gia43glSZIkSZoSzvwY3GeBs4CNgc+1n82AbwLrVNW/quoBYCvgBJp/yO8LLNzeM7vHmJNWVVcAz6HZeHVr4KvAbsAVQOcsit8DnwYe1l7/GvBe4A5g66r6WMeYdwOvAW4BPkO7RKWqfgC8CXg4sB9N4LE3cHCf8l5PE6TsRbMp7Mj4B7e1XgnsQXP07ubAt2hOoJEkSZIkacqkqt+BG9L8J8n5j171kevt8NkNxu4sLeA+t92E9kmWJEmSpsWMGTO44IILLqiqGeO915kfkiRJkiRpqBl+SJIkSZKkoWb4IUmSJEmShprhhyRJkiRJGmoedauhs/Kyq7mRoyRJkiTpv5z5IUmSJEmShprhhyRJkiRJGmqGH5IkSZIkaagZfkiSJEmSpKFm+CFJkiRJkoaa4YckSZIkSRpqHnWrofP3//yLzY/bdbrL0ALu5G2+Mt0lSJIkSWo580OSJEmSJA01ww9JkiRJkjTUDD8kSZIkSdJQM/yQJEmSJElDzfBDkiRJkiQNNcMPSZIkSZI01Aw/5mNJVklS7WerUfqt3/bZqc/1p7XXfzPG885KctEo11dN8oUkFyW5NcmdSf6V5IQkz+nqu2aSbyW5PMk9Se5K8s8knxzjtSVJkiRJGpdFprsATZkvJTm9qu6cwL2vB+4G1k+yZlX9ebwDJHkn8DngZuAHwCU04doqwFbABsCv2r5bAccAlwOzgKuBFYEZwMYTqF+SJEmSpL4MP4bDGcCmwJ7Ah8dzY5KFgdcCRwOvpglC9hjnGLsBXwQOAd5dVbd3XX8vsGz758WBQ4HfAJtU1T1dfZcfz7MlSZIkSRqLy16Gwynt531J1hjnvZsBj6UJLk4EXpdk4P8ukjwN2B/4XlW9uTv4AKjGTe2PawPLtf3v6dH3xnHWL0mSJEnSqAw/hsc7gPuBr43zvh2BfwK/AA4HHkcTiAxqj/a5uw7Y/9b2e7whjSRJkiRJE2L4MSSq6h/APsDGSV47yD1JlgK2AY6sqgJ+DNxIs/RlkPsXBl4KnFxV1w1Y6t+A3wO7JHlnkgx4nyRJkiRJE2L4MVw+B1wMfC7J0gP0fwWwJHAEQFXdC3wX2DbJIwe4/8nA0sCvBy2wqh4AtgeuAL4CnJ/kxYPePyLJ+b0+OKNEkiRJktTF8GOItOHFW2lOThnkyNgdgd9U1V862g4HlqAJRsYysjnpDeOs88/AM4EvA08HTk7ykySrjWccSZIkSZIGYfgxZKrq5zSnqbw9yXr9+iVZBXg+7ayPjvt/SbM0ZZClL3e330tOoM7bqmo3YC2ak2Y2pZkF8vwB75/R60NzxK4kSZIkSf9l+DGcPgDcBBw0ysktOwABvpSkOj/AU4CNkjxpjOf8q/1ea6KFVtXfq2p7YHOao5ePSLLERMeTJEmSJKnbItNdgKZeVd2YZHdgFrAL8Jse3XYA/gD8qMe1xYDdaZbF7D3Kc65PchHw0iS7tstuJlrzKUk+DHwR2Ag4faJjSZIkSZLUyfBjSFXVYUl2Aj4FvLrzWpINaWZ3vKGqDu11f5KNgR2TfLw9CaafrwIHAe8DPjPJsv/Rfi8zyXEkSZIkSfovl70Mt7cBDwc+0tW+I3AfcPwo9x4FrAo8b4xnHAL8Avhkkrf1Oro2yRJJHt/+efUk6/cZ6+XAA4zj9BhJkiRJksbizI8hVlWXJNkP2HOkLcliwKuAs6rqplFu/y6wP83Gp2eP8oz7kmwFHAd8DXhLkuOBq2hOg3kysC2wD82SlscCZyY5B/hp229Z4MXATGCfqrp8Aq8rSZIkSVJPhh/D71PA9sDIMbJb0Swr+cFoN1XVlUl+Dryi3c/jjlH63pRkJvAamr1E3tY+40bgSuArwDFt998Dn6Y53WU34BFtv98CW1fVCeN9QUmSJEmSRmP4MR+rqstoTmwZrc9dNPt7dBr1no57X9D188xR+j5Ac2zuEf36tP1uBj48yPMlSZIkSZoK7vkhSZIkSZKGmuGHJEmSJEkaaoYfkiRJkiRpqBl+SJIkSZKkoWb4IUmSJEmShpqnvWjorLbM4zl5m69MdxmSJEmSpHmEMz8kSZIkSdJQM/yQJEmSJElDzfBDkiRJkiQNNcMPSZIkSZI01Aw/JEmSJEnSUPO0Fw2dv//nWrb4weenu4z52kkve990lyBJkiRJU8aZH5IkSZIkaagZfkiSJEmSpKFm+CFJkiRJkoaa4YckSZIkSRpqhh+SJEmSJGmoGX5IkiRJkqShZvixAEmyfpJKslNH2zOT3JRk52ksTZIkSZKkOcbwQ8u0n8dNbxmSJEmSJM0Zi0x3AZpeVfWzJI8Frp3uWiRJkiRJmhMMP0RVXTPdNUiSJEmSNKe47EVzTRLDNkmSJEnSXGf4MYSSLJTknUn+mOSuJFcl+TTwsB59H7IJatv+qiTnJbml/fw8yWrjqGFmO+52SXZJcjVwb8f1RZLsnuSSJHe3NR6UZLlJvLokSZIkSQ/h/xI/nL4OvAn4EXAQsCLwBmDTQW5O8lFgH+AI4NvAo4EtgJWBv4+zlk3bz6fbOkgS4LvA5sC3gIuBJwNvATZI8tyqumuMGs/vc2mNcdYnSZIkSRpyhh9DJsnmNMHHflW1R0f7l4DfDDjMu4ETq2qHjraPJ1l0AiW9Cli7qq7qaHsT8CJgw6q6sKPG44CzgR2BgyfwLEmSJEmSHsLwY/jsAvwH2LezsapuSvIp4JABxgiweHdjVd3bo+9YjusKPgDeCZwAXJNkpY72vwHXAZswRvhRVTN6tbczQtabQJ2SJEmSpCHlnh/D5znAr6rqth7X/jTgGN8GNkvy4yQbTLKeCzp/SPJwYG1ge+DqHp9HAytM8pmSJEmSJP2XMz+GzwrAlX2u3TfgGLsD/wY+CJyb5Dzg/VX1iwnUc23Xz8vQzCw5Ajiyzz03TeA5kiRJkiT1ZPgxfO4G+p2YsvwgA1TV/cD+SQ6k2X/jY8BZSTauqnPGWU91/Ty7/b6jqk4Z51iSJEmSJI2by16Gz1+A9ZP0CraeO56BqurOqvo68CzgAZqNSielqm6hmZkyM4n//UmSJEmS5jj/8Tl8jgUeD7y9szHJKsB7BhkgyRO6mm4A7qFZrjIVvgOsTnO0bfezt273BZEkSZIkaUq47GX4fAl4HXBAkvWAc4GVaYKGbwLvHWCMPyf5Ls3RuIsC29Gc/vLNKapxX2Ar4KtJXgCcBTwc2BzYlGZfEEmSJEmSpoThx5CpqjuSzAQ+DWwNvJLmlJe30Cw3GST8+DpNOLE9zbG5vwaeV1W/nKIab02yEbA3sA3wcuCW9jlbtEtjJEmSJEmaEoYfQ6iqrqfZn6PXHh3p6vvbHm3vZbCQZLQazuoet+v6jcCu7UeSJEmSpDnG8EPjkmQJYOkxul3fnhgjSZIkSdK0M/zQeL0KOHSMPqsCl835UiRJkiRJGpvhh8brdJqNSUdz7dwoRJIkSZKkQRh+aFyq6irgqumuQ5IkSZKkQRl+aOistsyKnPSy9013GZIkSZKkecRC012AJEmSJEnSnGT4IUmSJEmShprhhyRJkiRJGmqGH5IkSZIkaagZfkiSJEmSpKFm+CFJkiRJkoaaR91q6Pz9Pzey5fe/Nd1lzHEnvvwN012CJEmSJM0XnPkhSZIkSZKGmuGHJEmSJEkaaoYfkiRJkiRpqBl+SJIkSZKkoWb4IUmSJEmShprhhyRJkiRJGmqGH9MsySpJKsnu010LQJKz2npGPncnuSbJz5J8PMnjprtGSZIkSZLGY5HpLkDzpKuAj7Z/fhjwaOA5wIeAPZLsXlVfma7iJEmSJEkaD8MP9fKfqprV3ZjkCcARwJeT3FFV35zrlUmSJEmSNE4ue9HAquoK4CXAX4H9kzximkuSJEmSJGlMhh/ziSRLJ9k3ySVJ7kpyc5JTkjyvo89iSWYnOazH/d9Pck+SJbvaP5/k1iSLDlJHVc0GPgksC2zbNdZiSd6f5MIkd7Tjnp1k6z7vtHCSXZL8Msltbe2/TfL8QWqRJEmSJGkQLnuZDyR5FHA2sCpwKPB7YAXg9cCZSbavqmOq6u4kZwKbdN2/KPBCYFHgecCpHZc3Bn5aVfeOo6ST2+8NgcPbZywOnA5sABwF/C+wFLA9cFyS91XVFzpqWhj4PrA18GPgw8ASwGbAuu37jvY7Ob/PpTXG8R6SJEmSpAWA4cf84cvA6sBGVfXLkcYkBwDnAd9IcmpV3UoTTGyZZPWq+mvb9Xk0f9eX0AQjp7b3Lws8Ezh4PMVU1Q1J/gM8tqP5Y8BGwCuq6tiuGn8M7Jfk+Kq6tL30XprgY7eq+nLHOJ/tnp0iSZIkSdJkuOxlHtfO+nglcFhn8AFQVbcDewFLA9u0zae0352zP14CnAWc09X+Apr/BjpnggzqDmDxtsaFgLfSzCA5trNTVd0HfJAmfHltx6V3Az/vCj5G7rljrIdX1YxeH5qAR5IkSZKk/zL8mPetDyxMs6Skl3Pa72cCVNU/aDYk3bSjz0toZl+cDqybZOm2fWPgr1X1zwnUtSxwY/vn1dufe9ZYVb8Dbh+pMcmTaWaN/HACz5UkSZIkaVwMP+Z9y7ffV/a6WFXXA/fR7K8x4mRg4zRWAdYETgLOAALMbPttzP/NFBlYkjVo9uf4/SA1tq7pqHHFjjZJkiRJkuYow4953+z2+zG9LrbLYhYBbu5oPpkmkHgmzayPS6rqn1V1I/A7YJP2vqczsSUvrwMKOGGQGlsrdtR4V/v9uAk8W5IkSZKkcTH8mPdd0H5v2uf6hu33rzrafgbc2d7zEppZHyNOp9n3YyZwD81eIANrZ33sBny3qv7cNl/S8bxe9zwDeERHjX8G7qbrVBpJkiRJkuYEw495XFX9i2ZpyhuTPKvzWpIlgI8DlwMndtxzF02osTFNyPHjjttOA55Gc9LK2YNsLtrxvJnAT4F/A2/veN7dwGHAi5Js03XPQsBngNuAb7f97wSOBDZPslWP5yw7aE2SJEmSJI3Fo27nHesn2alH+yk0J6n8AvhZkkNp9tp4FLAjzcahL6qqe7ruOxnYD7gX+HlH+7k0szS2pTkpppdlOmpZHFiZZlbHc2lmjry2qm7uumcPmqNuj01yZPucpWhOqlkHeFVV3dDRf/d2vB8k+U7bf3lgC+B7wBf71CZJkiRJ0rgYfsw7XtV+um1cVWclWR/YE3gp8GbgJpog4hNV9bce950MfBk4uaruHWmsqruTnA28mP6bnT4OOLT98500G5P+Atirqk7rdUNV3ZLk/9GEIK8EtgdupQle3l5Vv+3qf3OSDYAPA69o3/0GmiU7nTNVJEmSJEmaFMOPaVZVl9GcwDJWv2uAd7SfQcb9e79xq2rzUe6bOcj4fe69lSbM+PA4+u/RfiRJkiRJmiPc80OSJEmSJA01ww9JkiRJkjTUDD8kSZIkSdJQM/yQJEmSJElDzQ1PNXRWW2Z5Tnz5G6a7DEmSJEnSPMKZH5IkSZIkaagZfkiSJEmSpKFm+CFJkiRJkoaa4YckSZIkSRpqhh+SJEmSJGmoGX5IkiRJkqSh5lG3Gjp/v/lmtjzm2OkuY4478RXbTXcJkiRJkjRfcOaHJEmSJEkaaoYfkiRJkiRpqBl+SJIkSZKkoWb4IUmSJEmShprhhyRJkiRJGmqGH3NYkplJKolHc4whyU7t72r96a5FkiRJkjQ8DD+6DBJWJFm/7bPTXCxtUjqChc7PrUnOSPLC6a5PkiRJkqQ5ZZHpLkBz3ceBy4CFgVWAnYFTk2xfVd+dxrokSZIkSZojDD8WPCdW1W9HfkhyAPBHYH/A8EOSJEmSNHRc9jKHJJkvgqWqugk4Dnh8khWnatw0Fp6q8SRJkiRJmijDjynQuU9Ikl2SXA3c26Pfs5Kc3u618Z8k30/y5K4+iyR5TZKfJvl32/dXSTbv6rdK+8zdk6zbjntbkhuS/G+SJcfxCgsDBdzaMf6sJLP7vO9ZSS7qarssyYlJ1kpyXvv+O7TXVkryqSS/b9/nqiRHJXnsOGqUJEmSJGlC5ovZCfORTdvPp4HuWRTPAg4GjgCOAVYH3gpslGS9qrqq7TcTOAz4AXA8TTCxC3BSkmd3LllprQnsARwJnAhsCbwFeDht+DCaJI8AXgL8qqruHM/L9rBIW/cJwHeAS9v2zwEbA0cDfwWeAuwKrJVk3ap6YJLPlSRJkiSpL8OPqfUqYO2OIKPTe4CNq+oXIw1JTgTOpNmE9E1t87+AZ1TVnzv6fRf4B/AOmg1KO+0EzKyqn7d9vwL8DNg+yXuq6oau/ssnWYnm735N4BM0Qcmu43/dh5gJ7FFVX+xqPxJ4U1Xd1fFO1wKfBZ7X1jsuSc7vc2mN8Y4lSZIkSRpuLnuZWsf1CT4AjuoMPgCq6izgJ8DLOtr+0hl8tG1XAX+hmTHR7aSR4KPt+wDN7JKFgWf06H8KcDVNyHIacAGwXo8ZJRNxL/CN7saqOrkz+Gid0X73eidJkiRJkqaMMz8mp7p+vmCUvuf2af8dsFmSR43M0kiyOLARsAFNODDyubTH/Rf2aLui/V6mx7V3teOsDXyo/b5xlLrH4y9VdXuvC0keQ7Mk6Jk077LmKDWOqapm9HnO+cB6ExlTkiRJkjScnPnxUHe33w8bpc9IaHRHV/u1o9zTL2AYCQsWBUiyIc2+GCcD27TP+inQb0bJrT3aRvbu6PX3e15V/biq9gO2Av4fzfKTqdDz/ZPsTTPT5MvAOm2/E6bomZIkSZIkjcqZHw81skfG40fp85j2++qu9u6ZIJ36hSkrA/cDNyV5GM2moFcDz66qa0Y6JdkYeOQo449bVZ2d5OvAW5IcXFUXdly+H1gsSaqq+70e0W/I7ob2lJq92s++VXV/2746sPtk30GSJEmSpLE48+OhLgVuAV48Sp9NgLtolqwM6undDUkWplkK8oequht4Kk3ocnBX8LFwe21O2Itm9skBXe3/oQnHHnQcbZLFGN8+HZvRBCmfGQk+WmuNu1JJkiRJkibA8KNLx4ahM5O8tvt6knVpTmY5qt/+Fn28PckqXW1vAlbl/zYJvbf9Xrmr3/uA5cbxrIFV1fU0y15mJnlZx6WR/Uu6T5f5AOObgXIvzearI7NlSLIEsOf4q5UkSZIkafxc9tLbnjQzFg5Psh3NcbR30+xX8XrgMuD94xzz98DvkhwM/BN4LrADzWkvB7d9/kqzgemHkixNc7zt89rnng8sPsH3GcsXgLcDn01yYlXdA/yQZuPUTyR5AvBHYEPgWeOs5fs0y1tOb5fYPIzmvS/EjUklSZIkSXOBMz96qKqbaU5a2Y/mVJLP0gQEGwH7A8+tqvGekPIV4KPAy4EvtWPtA7x0ZDlIO+tkK5rNQHcA9qWZNbExMHtyb9VfVd0B7A08GXh3R9tmNBuvvqatZfHx1lJVv6E5ync2zfu+Ffg28JGpql+SJEmSpNHkoXtZSvOvJOc/ctVV13veflN1gM2868RXbDfdJUiSJEnSXDNjxgwuuOCCC6pqxnjvdeaHJEmSJEkaaoYfkiRJkiRpqBl+SJIkSZKkoWb4IUmSJEmShprhhyRJkiRJGmqLTHcB0lRbbdllPQlFkiRJkvRfzvyQJEmSJElDzfBDkiRJkiQNNcMPSZIkSZI01Aw/JEmSJEnSUDP8kCRJkiRJQ83TXjR0Lr35NrY59ifTXcaUOm67zaa7BEmSJEmabznzQ5IkSZIkDTXDD0mSJEmSNNQMPyRJkiRJ0lAz/JAkSZIkSUPN8EOSJEmSJA01ww9NmSSLJflLkm9Ndy2SJEmSJI0w/JjDkqySpNrPVqP0W7/ts9NcLI8kO3XU1/25bJzDLQqsBDxhgrUsMZH7JEmSJEkazSLTXcAC5ktJTq+qO6e7kB4+DlzW1TZ7PANU1ewkqwJ3jPfhSc4HzgV2He+9kiRJkiSNxvBj7jkD2BTYE/jwNNfSy4lV9dvJDlJVN03w1vWA8yb7fEmSJEmSurnsZe45pf28L8ka012MJEmSJEkLCsOPuesdwP3A1wa9IclGSc5IMjvJbe2f/1/H9UOS3JVk8a77Xtru2/Hmrva12/ZXj6OGpye5N8mhXe0LJfl9+1mkbZudZFZXv2clOSHJNUnuSHLRyP4nSc5KUm3Xd3TsN7LKoPVJkiRJkjQaw4+5qKr+AewDbJzktWP1T/Jy4EzgAZrlMp8EVgDOTPL8ttvJwGLA/+u6fYv2e5Ou9o3b8U4bR90XAQcCO3TNWnkjsDbw1qq6r887bEyzl8fybf17A38Hntl2+Qywc/vnM9o/7wzcMGh9kiRJkiSNxj0/5r7PAa8DPpfkxKq6pVenJI8CvgV8tqo+0tH+ReB3NKHBhsBPgPto9hM5o2OIzdt+G3cNvTHwmx57cyyfZKWutluramTz0r2AV9OEN69IslT750OqarS9Ot4B/AfYpKru7niPRQGq6pT250OBS6pq1ihj/Ve7QWovLimSJEmSJD2IMz/msqq6F3grsCLNTIh+dqAJp76RZKWRD7AsTeDxnCRLtuHJeXTM8EjydODxwL7Aikme1rYvBLwAOLXH804Bru767NJR963AB4CXJ1mPZtPWhYA9xnjltO+xaI/fgyRJkiRJc5wzP6ZBVf28nenw9iSHVtUFPbptACwJ/HOUoZanOVb2ZGCfJEu3YchLgAuA44FbaYKRP9EsNVmWJujo9i7gb11tf+6q+/AkuwAHAc+gWe4y1uku3wZeBvw6yd7A96vq/jHuGVNVzejV3s4IWW+y40uSJEmShofhx/T5ALAVcFCSDXpcXxa4FthplDFG9sU4GfgUzayOE2jCj5Oq6r4kZ9GEH1+hWfJyM/DrHmOdN+BRtx8Dfgr8vaoOG6tzVR2fZGtgP+C7wOVJPl5Vh45xqyRJkiRJU8JlL9Okqm4EdgeeTbO8pLq6zAaWAU6tqlP6fO5s+/6eZpnKJkkeSbP56Y/ba6cDL2iXvGwM/GSSMy/eBlwOrJbkhYPcUFUnAGvRbMJ6LfCtJGMtl5EkSZIkaUoYfkyjdubEWTSzNpbvunwRzSkuGw4wTtHs47Ep8D80szt+014+nWYWyfrA8+m95GUgSbYBtqPZ+PREmlkri496U0eNVfVjmve5kGbfk4c8YqK1SZIkSZLUj+HH9Hsb8HDgI13tR9PMBvlMkod1XkjyuCQzu/qfDDwdeCVwSlU9AFBVfwGuAN4JPJLem52OKckywNeAw6vql8D7gSfSLIMZ7b4ndP7czjq5jocGHTcBK0+kNkmSJEmSRuOeH9Osqi5Jsh+wZ1f7n5J8BvgQcH6Sw2k2L10HeC3wUZpZIyNOB+4HtgR27nrM6TSzNS6qqqv6lLJle0pMtxPaTU2/ADyC9nSXtu6DgN2THFlVf+oz7qwkd9KcUHMn8Fya2Skf7up3DrB5kn1olvwcWlXX9RlTkiRJkqSBGX7MGz4FbA+s1tlYVR9O8jeaWRsfp5kJ8lfgM8A3uvrenOSXNOFC9+yO04E3MvqSl736tK+bZAZNoPLBqrq649rewOuAg5Ns1C6/6XY4zUkynwDuAy4GXlNVR3X12w1YCngv7b4go9QqSZIkSdLADD/msKq6jDH2sqiqu4Cn9Ll2KDDQyShVtVGf9u/SnLTS69osYNYAwz/kHdoZIct1tT2i6+eB6m9/T5sMUIckSZIkSePinh+SJEmSJGmoGX5IkiRJkqShZvghSZIkSZKGmuGHJEmSJEkaaoYfkiRJkiRpqHnai4bOk5ddiuO222y6y5AkSZIkzSOc+SFJkiRJkoaa4YckSZIkSRpqhh+SJEmSJGmoGX5IkiRJkqShZvghSZIkSZKGmuGHJEmSJEkaah51q6Hzj//cySu+/4fpLmMgx7z8GdNdgiRJkiQNPWd+SJIkSZKkoWb4IUmSJEmShprhhyRJkiRJGmqGH5IkSZIkaagZfkiSJEmSpKFm+DGHJfl2kouTLDbNdeyepJKs0tH2piQ3Jnn6JMadlWT2lBQpSZIkSdIcYPgxhiQLJ3nYJIZYGXgssOgUlTSVHgssCyw3Nx+a5DVJfpHktiR3JDk3yVZzswZJkiRJ0oLD8GMUSTYF/g08YxLDvARYtarmxdkR+wCPraqz59YDk3wAOBy4GvgQsC/waOD4JK+fW3VIkiRJkhYci0x3AfO4x9P8w3zCquou4K6pKWdqVVUB18zlxy4BPK+qzh1pSPJF4A/AZ5McXlUPzOWaJEmSJElDbIGa+ZFk4emuQXyyM/gAqKrbgVk0QdNq01GUJEmSJGl4DW34kWSndoPP9ZPsleQm4NL22pJJ9k1yWZK7k/wzyWeSLN5eXyVJAYe2w/2mHeusjvGf0W72+dd234q/JvnUyBgd/U5McllX22Vt+6PaMW5IcnuSHyd5Up/3eUOSC5PcleS6JEckeXyPfo9M8vkkl7d9L+63nKTXJqht+2JJ3t8+744ktyY5O8nWo/y+k+RdSS5pn/vPJHsnedBeJ1V1f58hbh7p0u8ZkiRJkiRNxIKw7OUNwLOBjwKPbMOJnwBrAIcAl9Hs6fFeYJ0kmwM3ADsDGwFvBD7e9utcIvJj4DrgCOB6YCbNHhZLAbsOUNcSwJnAn4FPtDW8ATg1ydOr6u6RjkkOAN7ZPusbwGOAtwAzk6xbVde3/R4B/AxYC/gm8Mf2Pf+3/fOY2t/P6cAGwFHtvUsB2wPHJXlfVX2hx62fA7agmcFxC/BSYC+amRyvG+DR69EsD7piwDrP73NpjUHulyRJkiQtOBaE8ON/gHVGNhxNsi+watv2339oJ/kF8G3ghVV1GjArCTThx4lV9duucXetqh92/HxQGz68Jcl7q+reMeraBPhoVe3bUcO1wIdpNkn9Ydv2QpowZcuqOqWj72E0+2S8p70HYE9gHWDzrr5HAL8Yo54RH6MJfV5RVcd2jHEATeCzX5Ljq+rSjnuWBF4ArFdVd7RtByU5FNgpyf9W1Tn9HphkDeA1wFGdoY8kSZIkSVNhaJe9dPh2R/CxMPA2mpDjniQrjXyAn9MsudhkkEG7go8RZ9AcafvEAYa4Edi/u9b2e92OtncC5wEXdtV7G/CnkXqTLAS8CTijM/hoa/0N8N2xCmrHeCvw087gox3jPuCDNIHZa7tvBd7fEXyM2LP9fvkoz1wNOJnm9/Hhfv26VdWMXh/gkkHHkCRJkiQtGBaEmR8XdPz5qcCywAfaTy8rDDpwkrWAjYE1gafwf0fiLjPA7RdX1T1dbSMzUTrv36Ct6eo+4/yj/V4NWI5mSU8vfxqgptVpfj+n97pYVb9LcjvwzO5LwC979L8yyfU0v5uHSLIlzVKem4BNqqrfO0qSJEmSNGELQvhxbcefl22/P0//kOCqsQZsl7ccAWxNE1j8nmbGwS3AKwas69buhqq6s11q0zkjZySM6LXPBsCd7fdIaHNln373DVDT8mOMAc2+J0t1td1RVXf26gzcTjMb5r/SvOS+wB7Aj4Cdq+qmAeqTJEmSJGncFoTwo/P0kNnt943dS0PGaU+afTm699bYhcHDj0HNBmqAekf2yliuz/Xl+7R3PwuaDVX7WRHo3mx0kSSpqged1NKGHI/lofuNfAvYkWapzOcHqEuSJEmSpAlbEPb86PRX4F4G3NeD/wtO0tW+GfCrHoHEWpOorZ+LgGcl6Z5t0e1vNPVu0Of6cwd41iU0M0k27XUxyTOARwC/6rq0GL2XtswEHgb8umOMtwI70cz2MPiQJEmSJM1xC1T40S7N+CGwWXuk7YMkeU1X08hSjJW72u8FVmo3UB2590k0J8NMte/QLH35aPeFJBsmWQWgqm6hWR7ziiTrdvXbluY0llG1J60cBrwoyTZdYywEfIZmo9VvP/RuPt31+1iU5ojg22mOzB3xfuC0quo1hiRJkiRJU25BWPbS7f3A84Hjk8wCfkuzJGRbmtkL3+no+xua5SSfSvI44L6q+l/gezT7hpyS5DiacGQX4ETg1VNc7zfaMT/Qzrw4EViYZqPVrXnwyTC7A+cCZyU5iGYz1HVpluJ8o61xLHvQHHV7bJIj2/GWAl5Jc4zuq6rqhq577gSeAJyb5Fia/UV2aPu/oaquB0iyDPAk4M9Jdurz/It6HCssSZIkSdKELXDhR1VdkeQ5wD404cHONMesngO8u6vvNUl2ptmcc3/+b8bDF2mWc7wJ+Bzwl3ac5Zji8KOq7mtnqXwY2L599u3A74Adqur3HX3/mOR5NDM03gnc377XTODFozzm/o4xbkny/2hCkFe2z7yV5ijgt/cJJh5ox/9iW+fDaPYFeUnX0qCl2+8t2k8vn6cJpCRJkiRJmhLp2qNSC5Ak+9KEFcu0y2bme0nOX+ZJa6632WePnu5SBnLMy58xdidJkiRJEjNmzOCCCy64oKpmjPfeBWrPDz3EKsB1wxJ8SJIkSZLUi+HHAqrdf2MLmj1EJEmSJEkaWgvcnh8LsiSPBT4AXE5z3OxCwKensyZJkiRJkuY0w48Fy33ANsCKNBuSblZVf5/WiiRJkiRJmsMMPxYgVXUdzT4fQ+1JyyzhRqKSJEmSpP9yzw9JkiRJkjTUDD8kSZIkSdJQM/yQJEmSJElDzfBDkiRJkiQNNcMPSZIkSZI01Aw/JEmSJEnSUPOoWw2da/5zL/v+8OrpLmNMH9n2MdNdgiRJkiQtEJz5IUmSJEmShprhhyRJkiRJGmqGH5IkSZIkaagZfkiSJEmSpKFm+CFJkiRJkoaa4YcGlmSfJFclWXG6a5EkSZIkaVCGH5OUZJUk1fW5K8mFSd6eJNNd4xR6HLAC8PDpLkSSJEmSpEEtMt0FDJHvAqe0f14J2Bb4KvA04B3TVdQU2wX4YFVdP92FSJIkSZI0KMOPqfPbqpo18kOS/YATgbclOaCq/j5tlU2RqroPMPiQJEmSJM1XXPYyh1RVAbOAADOmtxpJkiRJkhZchh9z1sLt9y0jDUmekOTwJDe0e4P8Pskbet2c5ClJDk1yZZK7k/w7yTe6+iyZZN8kl7V9/pnkM0kW7+r3uCQHtdfvSnJ5ko929XlhkjOS3JhkdpLfJnlux/UDk1SPOpdua7ikHfvmJKckeV6Pvmcluait+4tJrk5yZ5Jzkqw30G9VkiRJkqRxcNnLnPVK4HbgN9CEGcA5wM3AAcBtwAuBbyZZsao+PXJjkpnAj4A7gG8CVwCrA1t19Fkc+AmwBnAIcBnwDOC9wDpJNq+qSrJCW8O9wEFtTWsDncHGDsC3gZOAPYFlgY2BpwK/7PeCSR4FnA2sChwK/J5mU9TXA2cm2b6qjum6bSHgeKCA/YAn0uyLcnqSNdxTRJIkSZI0lQw/ps5SSVZq//wE4G3A1sBbqurGtn0WcDHwoqq6p237cpJvAnsmOaiq/pPkETQbqF4FPL+qrht5SNdsjT1pQod1quqKjj6/oAkyXgicBrwKeAywflWd39Fv0Y6xdgP+ALy0XbIDsG9Xn16+TBPKbFRV/w1JkhwAnAd8I8mpVXVrxz1rAr+oqjd39L8YOBh4HU0wNKok5/e5tMZY90qSJEmSFiwue5k6HwOubj+/ojkWdqOqOgQgybrAhsAXgeWSrDTyAU4HluD/ZmK8Bng08PbO4AOgqu5ox1uYJmD5NnBP13g/p5lVsUl728hxu4t3jXVvx48BFqPrv4muPg/Szvp4JXBYZ/DR3nc7sBewNLBN160FfKir7Yi2fd1+z5MkSZIkaSIMP6bOIcDmwJuBv9DMbrit4/oG7fdx/F9IMvI5qr22Qvv9PJp9Qs4c5XlPpVma8oEe4/2TJswYGe+HbS0nJHlfkkf2GO/b7ZjnJHnRmG/bWJ9mX5PT+1w/p/1+Zlf7lVV1Q2dDVd1Jc5LMMoM8uKpm9PoAlwxYuyRJkiRpAeGyl6nzl6o6BSDJ8TR7X3wnyXrtEbHLtv1eD1zXZ4w/tt8rAtd2LD/pZWS8z9Ps+9HLVQBVdWWSDWhmnXwO2CvJgcA+behAVX0pyW3Ax4FTkvwJ+FBV/WiUGpZvv6/sdbGqrk9yH7BU16Vbe/UH7sRATpIkSZI0xQw/5oD2H/3vptm34+00+2LMbi//rarOG2OIu4DHjtFnZLwbR0KXMWr6E/DCJM+gWXLyIWA94MUdfb6V5NvAK2iWrJzQblh69Bg1PKbXxXZZzCI0G7xKkiRJkjQt/F/Z55Cq+h7NKSl7JVkOuKi9tEn/u/7rQuARSZ49Sp+/0pzeMsh4nXX9oaq2Bw4EXpRkla7r91XVUTRLWq4B3jrKcBe035v2ub5h+/2r8dQoSZIkSdJUMvyYs94PLAfsDfyMZhnKe5Ks3NkpyWJJXt7RdBhNsHFAe5xtZ9/l4L97ZPwQ2CzJ5t0PTvKajj8/vkdt/x653PZ5QufFqppNs+9I6KOq/gWcArwxybO6nr8EzRKay4ET+40hSZIkSdKc5rKXOaiqzklyAs2pLF8DdgGOB36X5GDgMpqjal9LM4vi++19lyZ5V3vP75McDtwIPB3YChgJM94PPB84Psks4Lc0+3BsCzwF+E7b741JtgZ+QDOb48nAO4DTquqfbZ+fJPkdzSal9wMvotkA9RNjvOZbgV8AP0tyKM1eJ48CdqRZutN5rK8kSZIkSXOd4cectwewBfCFqnpJkpk0+2nsSnP07L+Ak4BPd95UVf+b5G/AB2lCjoWBvwH7dfS5IslzgH2ArYGdaUKSc4B3dwx3Gs0JMu9un/lP4FPAlzr6HALsBLwUuJ0mxHhxVZ062stV1eVJ1gf2bO99M3ATzQkwn6iqv43625EkSZIkaQ7L6AeKSPOXJOc/9klrr/f2z42a2cwTPrJtz31iJUmSJEk9zJgxgwsuuOCCqpox3nvd80OSJEmSJA01ww9JkiRJkjTUDD8kSZIkSdJQM/yQJEmSJElDzfBDkiRJkiQNNY+61dBZaZlFPUlFkiRJkvRfzvyQJEmSJElDzfBDkiRJkiQNNcMPSZIkSZI01Aw/JEmSJEnSUDP8kCRJkiRJQ83TXjR0/nPzfRx3zA3TXcaYtnnFo6a7BEmSJElaIDjzQ5IkSZIkDTXDD0mSJEmSNNQMPyRJkiRJ0lAz/JAkSZIkSUPN8EOSJEmSJA01w48uSSrJgdNdx/woyeZJ/pPkhRO839+9JEmSJGnKLdDhR5LFp7uGqZDkYUnm6t9lkiV6NK8ALA2sODdrkSRJkiRpNAtk+JHkaUkuB7ac7lomK8nrgOuA5ebiMz8AXN7dXlXfBh5XVUfMrVokSZIkSRrLAhl+0MxQeMJ0FzFFVqOZbTE3rQUs2etCVf17LtciSZIkSdKopj38SLLIdNcgSZIkSZKG16TDjyQLJdktyUVJ7kpyZZJ9k2zQbmC5U0ffmW3bdkl2SXI1cG/XWLsk+WWS25LcnuQ3Sd7Q9cyfJLmsRy27tuO/sKv9pW37c5MUcGZ76Zi2vddYT0zy/XYDz9uSHJfk8RP4/VSSA5OsmOSwJNcnuSPJuUk263PPakkOTfKvJPck+XeSWUme2NFnZvsue7VN17fPmtU11huSXNj+3VyX5Iju92jHnp1k4SQfS3JZ2/93nTUm2al95uuBh7fPqyR7t9e3a3+e2XHPIklek+Sn7XvcmuRXSTYf7+9SkiRJkqSJmIpZF9+i+cfwicDXgEcDbwJ6/sO+tWn7+TTt5phJAhwFvBI4ATgcWBTYBvhmknWq6l3t/ScDn0vy5Kq6tGPcLdrvTYDTO9o3Bm4Cfg3sDKwBfBD4KvBbYHZXfY8GzmnH2BN4HvAKYJUk61ZVjfobeailgV8Avwc+BiwL7AKckmSLqjp1pGOSDYBTgNuBQ4ArgacAbwZekmSjqvorcEn7LtsAWwO7tu/x946xDgDeCRwBfAN4DPAWYGb7Htd31Xkw8DTgwLbGXYGT2t/9n9vfyc40f7/rA29t77twlHefCRwG/AA4Hli4ffeTkjy7qn47+q9OkiRJkqTJmVT4kWQLmuBjv6rao6P9KzShQj+vAtauqqs62t5ME3y8v6o+19H+xSTfAnZN8v2q+hlt+EEToFzaPnNJ4AXA72jCj04bA6dX1QPArHZmwgeBs6rq2B71vQJ4Y1V9q/35K0kOB14HPAf45Sjv1strgI9U1WdGGpJ8A/hTO/ZTq6qSLAocTRPUPLsznEhyCE3I8HVg46q6pn2XVWjCj6Or6oaO/i+kCS+2rKpTOtoPA/4AvAf4cEeNDwceC2xUVfe1fc8CTqMJOXarqr8Df29/f+tU1awB3v1fwDPa8GSkhu8C/wDeQROmjFuS8/tcWmMi40mSJEmShtdkl728EbgF2Lezsapu7G7rclxX8AHNP4T/BhzQo/+HgXtoghaq6mLgCh4ccmzS9vkyMCPJIwGSLAc8EziVwV3cEXyM+Hb7vfY4xhlxGbB/Z0MbbHyZZlbHyJhb0mzE+rHuWRlV9ReamRkzkwyyWes7gfOAC5OsNPIBbqMJXboDImgCmvs6nnk6cDWw7gDP66mq/tIZfLRtVwF/oXl3SZIkSZLmqMkue3kO8Kuquq3HtYtGue+Czh/aWRtrAwdX1f3dnavqmiR/pwkxRpwCbJsk7TKUl9AsUzmZJtR5AfCj9jtt/0Fd0KPtX+33UuMYZ8Sve71Xx3OeQjMb4zntz6f36AvNspN30fwerhjjmRvQnGpzdZ/r/+j6+X7gjz36/QtYZoxnjSrJ4sBGbU1P6fhcOtp9o6mqGX2edT6w3kTHlSRJkiQNn8mGHyvQ7EnRy3192gGu7fp5WZqAot9Y0PwjvnPGw8k0e0esTRMcbA58oqquTfJHmpkNP6JZ8vKHquoXAvRyS4+2u9rvicyWubFP++3t98Pa7+WB+9olLb2MvMMgAcyyNCHKF/pcv7Pr57uq6t4+/R4+wPN6SrIhzVKex9D8Pf0F+CnNfzuSJEmSJM1xkw0/7qX5R3Yvy49yX/eGoSMbjj5mlHtWAm7u+PmM9vmbJLkPeCJNIALNP/pHTnzZuKN9uizap33l9ntkictsYJEkK/TYjBSa3wE8+PfQz2ygOvf7mNuSPIwm+LiaZg+TazqubQw8crpqkyRJkiQtOCa758dfafbX6BWiPHfQQarqFpplGBsneUhNSVYAVgN+1XHPbTQnqGxKs+Tlgo5/XJ8OrJ1kTZrTS7oDgJHwJYPWOElP79P+PzQzZEaWv4x8b9qn/4bAAzx4M9l+73IR8KwkE1mmM5bq8bxengo8nmY5U2fwsXB7TZIkSZKkOW6y4ccxNEtR3tHZ2J5A8p5xjnUwsCbNnhbd9qE5IvXrXe0nA8+nOVb3xx3tZ9Nsfvoh4A6avTI63dR+r8zcsWGSbTsbkqwNbA/8oKpG6vkhcAOwb5Llu/o/meaY2u92zQrp9y7foZmV89HuYpJs2P4dTdRNwJJJ+s36GTGyjKa7tvcBy03i+ZIkSZIkDWyyy16+SPMP+C8kWY9mJsbjaf6RfgjNP3IH9QWamRAHJHkBzeyNRYGX0uzf8a6q+lPXPScD+9EEIHuNNFbVnUnOAV4GnFlV93Td91fgOmD3JAUsWlX7M+ecBxyZ5CjgfJrA6G00e5+8t6Pu2UleTxOC/KE93vZKmlkvb6bZ5LQ7HBoJdr6a5AjgP1X1HeAbwKuBDyR5BnAiTYC0Mc3RuBM+waV95nuBI5KcBFxRVSf26PdXmuN5P5RkaZrZPc8D1qH5PSw+iRokSZIkSRrIpGZ+VNUdwEzgmzRLT75IE2C8Afh+2+2uXvf2GOtemk1L9wBWpwlDPk6zzON/qurAHvf8EbiKZn+L33RdPp1mo86H7HlRVXcDr6HZ2PQz9D72dSpdQPP7eTrweWAnmt/Pc7qP/K2qH9MsGfoVzXG1BwIvp5kZ89yquqGr/+9ogogn0Byn+6S2/T6a3+e+NL/PLwKfoDm5ZYeq+v0k3ue49lnPovn9PapXp6p6ANgKOAHYoa1lJICZ3eseSZIkSZKmWppTYufAwMnLaP6Bv2lV/XSOPGQ+0M4s+WpVvXO6a1kQJDn/Sas+Y73P73fGdJcypm1e0TMzkiRJkiT1MGPGDC644IILqmrGeO+d7LKX0bwQuB/43Rx8xrRoN+wc66jWW6qq+zhZSZIkSZI0l00q/EjyeGB2Vd3c1f5cmqUvx3VfGxKPB/45Rp+dgVlzvhRJkiRJkjSayc78mAF8O8n3aY5ffQB4Ns0mqNcA757k+POqa2n20xjNH+dGIZIkSZIkaXSTDT9+SXP87OY0m3IuClwOfAX4bNeRrEOjXc7ykI1UJUmSJEnSvGdS4UdVXQO8v/2oh6rKdNcgSZIkSdKCbE5ueCpNi2WWXcSTVCRJkiRJ/7XQdBcgSZIkSZI0Jxl+SJIkSZKkoWb4IUmSJEmShprhhyRJkiRJGmqGH5IkSZIkaagZfkiSJEmSpKHmUbcaOrffeB+/PvS6ufKsZ+/86LnyHEmSJEnSxDnzQ5IkSZIkDTXDD0mSJEmSNNQMPyRJkiRJ0lAz/JAkSZIkSUPN8EOSJEmSJA01w48FXJKPJ7kqyYqTGOOsJBdNZV2SJEmSJE0Vw4/5XJKHJZnM3+PjgRWAh09RSWNKsnCSdyY5P8kdSW5L8pMkG82tGiRJkiRJCw7Dj/lYktcB1wHLTWKYXYDHVdU/pqaqgXwF+BzwR2B34ADg6cCZSTadi3VIkiRJkhYAi0x3AZqU1YClJzNAVd0HXD815QzsHmCdqrpkpCHJQcCfgP2A9edyPZIkSZKkIebMjzkojYWnu4550Ps6gw+Aqroa+CGwXpLFpqcsSZIkSdIwMvwYhySbJvlxkhuT3JXkb0ne3nH9siQnJlkryXnAvcAOHdc3SnJGktntPhdnJPl/Xc9Iki3a51yR5PYkFybZsaPPzCQF7NU2XZ+kkszqGusN7b13JbkuyRFJHt/V58B2rO53XTrJvkkuae+/OckpSZ43yu9n0SR7t7+Hu9p7d+3uV1X39xni5n5jS5IkSZI0US57GVCS3YH9afap2B+4nWZ5xkzgax1dFwF+AJwAfAe4tL3/5cDRwFnAnsDDgNfS7HOxWVWd3d7/ZOBHwMnAl2gClNcChyW5vaq+D1wC7AxsA2wN7ArMBv7eUe8BwDuBI4BvAI8B3gLMTLJuVfVd6pLkUcDZwKrAocDvaTZFfX1b7/ZVdUyPW48Cngh8GbgPeBXw5SQrVtVH+z2vw3rA5VV191gdk5zf59IaAzxHkiRJkrQAMfwYQJLn0OxFcQzwmnafjJFrS3Z1nwnsUVVf7OjzKOBbwGer6iMd7V8Efgd8Btiwbb4V2Kiqzu3odwhwOU2Y8f2qugaYlWQVmvDj6Kq6oaP/C2kCkS2r6pSO9sOAPwDvAT48yit/GVi9reOXHfcfAJwHfCPJqVV1a8c9awKXAc8dmdmR5KvAqcAeSb5RVZf3e2CSjYGNgb1HqUuSJEmSpHFz2ctgdqOZ6fHmzuADoKru6Op7L81Mi0470ARN30iy0sgHWBb4CfCckRClqq7rDD46nnEe8JQB631n2//CrufdRrOp6Cb9bmyDmlcCh3UGH20dt9MstVmaZtZJp4WAd3UuaWn/vBewcI/+nc98NvB94CKaU2DGVFUzen1oZsVIkiRJkvRfzvwYzPOAM6vqlgH6/qUNCTptACwJ/HOU+5YH7gBIsgjwHGAjmhkYqwPrABmw3g1olqlc3ef6aMfark8TVpze5/o57fczu9qv6XNc7oXtd8/gJsmbgANpQpktevzuJEmSJEmaFMOPwawIXDNg32t7tC3btu80yn03ACRZAziWZhnJxcCfgXOBxYC1BqxhWZrw4gt9rt85yr3Lt99X9rpYVdcnuQ9YquvSjaM8635g0c7GJA8DDqbZR+QbwG5VNVpdkiRJkiRNiOHHYO4CHjdg34ecnEKzGekywKlV1et6p8NoZomsXlWXjjQm+Q6Dhx+zgerc72McZrffj+l1sV0WswgPPZnlYX3GewzNTJL/brCaZFGaTV2fB7y2qr4zgTolSZIkSfr/7d13tGRVmffx709AZAQR2oCCCK+DAoMItKC2iTCivGJag2kZQAUMgwHFMIyoiIMY0REVZXCIZuZFRWREBhgJJppWARUwoZJBcmx43j/OKSyKqtt1U93b1d/PWmede/fZ6dTdfeA+9+y9h+KaH8NZAmyT5IFTLH8ezZsbiybKlGQNYBvg2O7AR6tf4KMTSOmdDnMesHVb32Qtbs87DLjeuYcf96RvOODz2bE9/6Qr7QCadUeeZ+BDkiRJkjTbDH4M5zCaNTQO6L2QZO0hyn+VJlBxUDvdo7v8ukm2bb+9C7gbWK8nz0u47xobANe25/V60r9MM/XlPtvLJlnU7hLTV1X9CTgJeF2SrXvKrgbsT7PzzAk9RVcG/q0n/xrAe2im0HyvTVuVZieaL1XVKYP6IUmSJEnSTHHayxCq6ugkzwH2TrIF8O320iKaKR3/tIzy5yc5CPgX4JwkR9NsabsF8AqaIMVpVXVLku8Cu7brapwLbAk8j2YNj943RzqLj342yTHAde2bFIcBLwPelWRzmkDFSjRbyb6grXMibwDOBE5P8p/Az4GHAK8GHgk8u6ru6ClzCfDCJJsCJwIPAF4HrE+z5e6dbb5NaKb1rJpktwHt/6iq3LVFkiRJkjQjDH4M75XAD4E9gQ/T7MyyBPjIMIWrat8kF9FsQ7s/zZsgFwIHce+tcXdr63wu8HKaxU63A97dp85zk7wdeAfwsbZfVNXSJDsB+7Z1fIpmq95zgVdV1c97qrq7p94/JnkisB9N4GUPmrdMTgY+WFUX9bnFG4GdgX+neUOmaAIor6yq7ikya7bnXdujnzfjlrWSJEmSpBmSZa+/qXGW5Fjg/1bVWnPdl5mQ5JzHPXrzrY56/6CdemfWNq952EjakSRJkqQV3cKFC1m8ePHiqlo42bKu+aENaN5AkSRJkiRpLBn8WIEleQzwZO67eKkkSZIkSWPDNT9WMEm2olmg9XqahU0vBw6Z005JkiRJkjSLDH6seJbS7MKyBvC/wFur6q9z2yVJkiRJkmaPwY8VTFX9Alhnrvsxmx64YGUXIpUkSZIk3cM1PyRJkiRJ0lgz+CFJkiRJksaawQ9JkiRJkjTWDH5IkiRJkqSxZvBDkiRJkiSNNYMfkiRJkiRprLnVrcbOnVfcyZ8/fvmUyq63z1jvAixJkiRJKyTf/JAkSZIkSWPN4IckSZIkSRprBj8kSZIkSdJYM/ghSZIkSZLGmsEPSZIkSZI01gx+zIIkleSQIfPukeSaJJvNdr/mQpJd2s9j27nuiyRJkiRpxTQvgx9JNmh/YR50bDDXfZxBjwDWAtae7YaS3D/Jm5KckuSKJHckuSrJT5K8fbbblyRJkiRpLqw81x1Yhq8BJ/VJv3rUHZlFBwBfrKrLZ7ORJJsC3wI2AE4EPgFcD6wHbAO8EvjkbPZBkiRJkqS5MN+DHz+rqiPmuhOzqaoKmO3Ax4bAD4FrgS2q6vw+eRbMZh8kSZIkSZor8z34MSlJVqqqu+a6H/PQMe15u6r6c78MVXXNCPsjSZIkSdLIzMs1P4aRZLd2/Y8nJnl/kmuB37bXkuS5SU5MckmSm5MsSfLqPvVUkkOSbJjk/yW5Lsn1Sb6a5CED2n5xklPbvLckOS/JP/XJ9+gkx7X5bkxyfJJH9eTZp3cdkySrJXlPkvPb+q9Mcnyf+jdr6+z04+wkz+/JswOwCNh/UOBjwD3eL8meSX7U9v3mJD9N8toB+VdNsl+Si5LcluR3SfYBMkEbr21/Lre193hM7+cjSZIkSdJ0jcObH6+lWbPivcCD2rTHAN8Bvgd8GrgTeAVwZJKbq+q4njoeRjMt5CRgP+AZwEuBR7Zf3yPJZ4C9gDOBDwIFPA14KtBd78OAM4CT2zqfDrwY2CDJlu10l0G+AzwF+Czwe2BD4OU9/VgEfB+4ADgQWAq8CDg+ySur6stt1hcBdwNHTdDevSQJ8BXgJcC3gaOBVYAXAocn2aKq3tKVfyWa9UR2BI6lWU9kA2Bf4OIBbRxM8zkeAxxGs/Dr64Ft28/nqmH7K0mSJEnSROZ78GONJOv0pN1WVdd1fb8jzToWN3Wl3QA8rarO6iQk+Q/gjzS/cPcGP14MvLKqjm2//0ySLwMvb38RP7et48Vt+YOrqnt3lIOT/F2fOl9XVV/qqvNomoVFnwT8qN8Nt1ve7gDsU1Wf6Er/166vV6EJMhwPvKoTSGkDCicDH03ylTZ9K+Cins9sWfagCXy8s6o+3pX+qSRfAt6c5LiqOr1N3xN4NvDGqjq0q5+HAuf2ucdnAW8Gdq6qk7rSjwR+AexNEzgZKMk5Ay5tvKybkyRJkiStWOb7tJf3AZf1HEf05DmqJ/BBVV3ZHfho024BzgY26tPOL7sCHx1Htuctu9L2pnkT4529FbT1d7ugK/BxT1/b8+P79KGjM03kAT3139n17c40b1YcDDw8yTptkOjhwH8D6wKPa/MuYPK74/wzcFFbf699gTuAXbvS9mzzf7Gnz38ADulTx140P4slnb63/b8ROB/YfpL9lSRJkiRpoPn+5sd/cN+3NK7o+X5xv4JJVqZ5w+JpwGPbYwv6r0GxpE/aJe35wW19q9JMr/nskIuq9uvXn9rzGhOUOx84B9i/DQh8og0idHtKe/7ZBPU8FPg1cDvQ+1bKQO0bLI+n2X73PvdZVZcnuRh4Qpt/NWBz4LCqunvA/fR6Stu/ywZ043fL6mdVLRzQ/3No3naRJEmSJAmY/8GP33RPixigNxhCko2BbwKb0KyJ8SvgLGBVYNM+ddzQJ+3W9tx5O2YBsBLDb0t7fZ+023rqvI+qujvJjsBBNG9UvDHJN4B3V1UnILMWzToeO9OsOdJPJ+hwCc06GitX1dIh+r0WTYBoosVRLwPWb79eu72fQfn7tbkWzfScTw4oc+uAdEmSJEmSJm2+Bz+G0e+X/yNp3nZ4bFX9tpPYruPRL/gxjE7gYt0plh9aVV0L7JnkfTRrY7ydJoCxSbt2x000AYclVTXo7YmOU4Hn0qzJ8d0hmu9MIXrEBHnWAf7afn17e157QN4FA9qoIQJbkiRJkiRN23xf82PSkqxBMz3l2O7AR2uqgY9OQOJPwDOTjORzq6rLq+pfaRZJXYdmtxWA89rzMGtjHAHcAhzYTt1ZVpvX00w72a7ffSZ5KPD3wI/b/FcD1/C3qTi9ntwn7Txg6/ZnJUmSJEnSrBq74AdwF82UkPW6E5O8hHadimk4DNiMZsHOe0ky6M2HSUmyoM/OMZd2Lrfn42mmhrwvyZo95ddM8tzO91V1DfAOmnU5/ivJWgPafVzXt1+kmTL0lj5ZD6CZ/vOFrrTjgCcneX5PnVvTBG56fZlm6st7+/RjUZIN+vVRkiRJkqSpGIdpL/dSVbck+S6wa5KlNFutbgk8j2adiUXTqP4jNNvQfjrJ9sAPgNVo3sD4Nc1uMNP1eODYdorOhTTTRvakWWfjW9AENJK8jSYA8fMkh9OsfbIx8CrgGLqmuFTVoW2Q5EDgt0m+DvySJoixPrBd+/UWbZFP0mwhfHCSZ9J8bqvQfIbbA2+pqu6FTD8APB/4epLD2rofC7wW+Bz3DaIcBrwMeFeSzYET2va3A17AvXfYkSRJkiRpWsYu+NHajSZQ8Vzg5TSLnW4HvHs6lVbVHUmeTbMGxyuB59AsbPpjmrcZZsKFwOnAK2jW0bgMOAk4sJ160+nLF5NcDrwLeA9N8OD3wJdo7r237x9JciLwVuBZNJ/R7W39P6ZrC+GqujPJTjTBnFcDO9GsefITYMeq+kFP3ZclWQR8lCb4sjLNTjQvoNl+9y09+Ze29e9L8/P5FHAzTaDqVVX180l8XpIkSZIkTShVgzYLkZY/Sc55/LqP3+rEt31/SuXX22edGe6RJEmSJGkmLFy4kMWLFy+uqoWTLTuOa35IkiRJkiTdw+CHJEmSJEkaawY/JEmSJEnSWDP4IUmSJEmSxprBD0mSJEmSNNbGdatbrcBWefgq7toiSZIkSbqHb35IkiRJkqSxZvBDkiRJkiSNNYMfkiRJkiRprBn8kCRJkiRJY83ghyRJkiRJGmsGPzR2ll55K1ccvIQrDl4y112RJEmSJM0DBj8kSZIkSdJYM/ghSZIkSZLGmsEPSZIkSZI01gx+SJIkSZKksWbwQ5IkSZIkjTWDH/NIkicmqSS7zVL9T0hybZLXzEb9kiRJkiTNRwY/ViwPbo9157ITSdZLcmiSS5LckeQvST6XZK257JckSZIkaTytPNcd0OhU1elJHglcMVd9SPJAYDFwC3AkcDmwDfB64BlJtqmqW+aqf5IkSZKk8WPwYwVTVZfPcRdWA74N7FVVt7Vpn01yNvB54LXAIXPVOUmSJEnS+HHaywogyXwKcl0L7NEV+Og4DLgDWDT6LkmSJEmSxpnBjzmQ5H5J9kryyyS3tWtefBi4f5+827aLoO6SZOskJye5Icl1SY5L8pgJ8u+Z5DLgzvbafRZUTbJbm/bEJP+c5OIkNyf5QZKN2jzbJTk7yS1JLk1yQJL7jJ0k6yc5OsnV7X39PMlru/NU1d1VVb1lq+ou4EbgPtckSZIkSZqO+fRGwIrkC8DuwHdopno8nGa6xw4TlNma5u2IY4BvAI8F3gA8NclWVXVpT/4d2uPDbf3LsjewMfA5YD1gL+DkJK8Djm/Tvwa8FHgvcAPwsU7hNlByBvBX4GCaQMazgMOTPLyqPjxR40nWBxYAFw7RV0mSJEmShmbwY8SS7EQT+PhIVb2nK/3TwE8nKLo3sF1VndlV5gTgVOCDbZ3dXgo8vqr+MmTXtgSe2FlsNMnFwGeBE4DXV9VRbfrngF+37X2sq/wRwAXAs6vqjjbt35McDuyX5PNVdd0E7X8QuBs4epjOJjlnwKWNhykvSZIkSVpxOO1l9PYErgP+rTuxqq4FDpyg3Fe6Ax9tmdOAU4AX9cl//CQCHwCf6dll5Wvt+VK6AhJtYONkYKMk9wdIsiXNWh2fAtZOsk7naPOuBjy5X6NpHADsCuxfVb+bRJ8lSZIkSVom3/wYvScBP66qG/tcO3+CcmcNSF8M7JBkQVVd05M+GRd0f1NV1yS5HVjSZ42OK4EAq9MsYPqUNv34Cep/aG9CkgfRvDHyIuDAqvrgsJ2tqoX90ts3QrYath5JkiRJ0vgz+DF6DwX+PODa0gnKXTMg/eb23LtY6hWT6VRXPb396Rekuas9d94cWqs970oTGOnnl93fJPkHmmDJQ4Bdquq4yXRWkiRJkqRhGfwYvduBtQdcWzBBuVUGpK9HE4y4tid9lLum3NSeL6qqs5eVuZ0mcwpwCbBjVf1+NjsnSZIkSVqxuebH6P0GeGKSfoGnvutitDbrTUiyEs2OLr+oqttnqH9TcV573n5ZGZM8APgv4I/AMwx8SJIkSZJmm8GP0fsm8CjgTd2JSTag2dFlkH9u83TbHdiQZgvcuXQ68Bdg7yTrdV9IsmqSf+pKejGwAfCmqrphdF2UJEmSJK2onPYyep8GXgkcnGQrmoVM1wNeDxwOvH1AuSXAuUm+CPye5i2RVwE/AL44y32eUFUtTbIn8C3+1sc/0ARmXkGz+GpnTY/OQqX/kORxA6o8pqomWv9EkiRJkqShGfwYsaq6Jcm2wIeBFwAvodnl5fU0C6EOCn4cQrNY6tuB9YE/AQcAB1XVXQPKjExVndje1/uBNwMPoOnjd2nutWPN9jzR2yrf5G/riEiSJEmSNC0GP+ZAVV1FM2Vl9z6XM0G5zwOfX0bdpw2qo6p+1nutqo6g2W62X/7VB6R/APhAn/QzgR2X0b/XAK+ZKI8kSZIkSTPJNT8kSZIkSdJYM/ghSZIkSZLGmsEPSZIkSZI01gx+SJIkSZKkseaCp/PcRAuYSpIkSZKkZTP4obGz8sNW4+F7bzHX3ZAkSZIkzRNOe5EkSZIkSWPN4IckSZIkSRprBj8kSZIkSdJYM/ghSZIkSZLGmsEPSZIkSZI01gx+SJIkSZKksWbwQ5IkSZIkjTWDH5IkSZIkaawZ/JAkSZIkSWPN4IckSZIkSRprBj8kSZIkSdJYM/ghSZIkSZLGmsGPGZbktCTnLSttPkmyT5JKskFX2h5Jrkmy2Rx2TZIkSZKkaTP4oUEeAawFrD3XHZEkSZIkaToMfmiQA4BHVtX/znVHJEmSJEmajpXnugOan6qqgMvnuh+SJEmSJE2Xb35IkiRJkqSxttwHP5K8NMnZSa5vjx8m+fuu66clOS/J2kmOSnJtkquSfDTJSknul2S/JH9McluSxUm279PO05J8M8nvktyS5Pwk70gypc8wyR+SnJDk0Um+neSGJH9Jsk97fbUkn0pyWZJbk/xvkicMqOu1SZa0/b8yyTFJHtUn34OSfKLrXi9IsuuAOvstgjpw4dbO/fSkVZJDkmyR5H+S3JTkt0le3V5fkOTIJFcnuTnJd7vbkyRJkiRpJizX016SvJdmbYpjgKOAhwHPBdYDLu7Kugrw38Avgf2BnYF3AncDawLbAZ+kWdzzrcAJSTauqkvadlYGftgeXwRuBJ4PfLyt/xNTvIUHA6cAJwCnAi8HPpbkprb+BcCHgA2ANwP/nWSjqrqx6zM4GNir/QwOo1mo9PXAtkm2rKqr2nyrA6cDmwKHt5/FxsCh7dezZYP2/o4EvgfsDhyZ5Hqan8UVwH7AE4A9ge8keUJV3T1RpUnOGXBp4xnqtyRJkiRpTCzXwQ/gbcAJVfWqrrT9k6zSk++xwP5V9QGAJJ8BzqIJdNwAbFpV17TXzgC+D7wC+HBb/m7ghVX1rU6FST4H/IIm8DDV4MdTgddU1RFddV4MHAz8DlhYVbe11y6kCbzsDHylTXsWTVBk56o6qatvR7Z92xvYt03eD9gC2Kkn7zHAmVPs/zCeC2xfVae27R0B/AH4atvuc9r1RUhyTdvfJwFnz2KfJEmSJEkrkOV92kuAB/QmVtWdPUlL6QpQtG8VfKMt+7lO4KN1CnAbsFl3/u7AR5tWNG9rrJ/k/lPs/2U0b6x06rwdOL7t10c7gY9WZ0rJZl1pe9EECZYkWadz0LyZcj6wPUA7NWd34JTuwEfb5k+Br02x/8P4SSfw0bZ3FfA/NPd4QCfw0ep3j31V1cJ+B/Drmey8JEmSJGn5t7y/+XEU8LYkJ9L8Ij3obYE/d08VaV3anhd3J1bV3UmuBtboTk8SYCvgGTRTKzaimapxP+BBwNVT6P9v+kzv6Nsv4Mr23N2vpwAPpQmi9PO79vz3NFN6fjAg3/nL7uqUXdAnbTL3KEmSJEnStCzvwY99aH6RfjdwVpKzgXdWVe80jpv7lO28HdIbFAG4i663Ytq3Kb4BPA24CDgPOJfmjZJnTaP/Q/erqu5q4i/3eltnLeBkmvVK+rm1PT+0Pf95QL6ly+zp1A28xz4Bqbva8/L+RpIkSZIkaR5ZroMfVXUXzQKhhwCvBt4HnJZku6o6Ywab+jTNVIwnVtU9C20mOZDpBT+m6yaaGTgnLSPf7e157QHXFwzZ3l3AagOurT5kHZIkSZIkjdRY/IW9qm6tqi8AW9MsTrr7DDfxjzQLq/buMLLpDLczWecBWydZ1jSRi4CimSbTz5OHbO864JHt7jf3SPIIhg+gSJIkSZI0Ust18CPJ+j1JVwN30CyEOpPuBNbtafspwPNmuJ3J+jLN1Jf39l5IsijJBgBVdT3N9JgXJ9myJ9+LgGcO2d5imoVKX9aT/qHJdVuSJEmSpNFZrqe9AL9K8jXgp8AqwC40v5wfPsPtfB14c5KvA6fRbJ27K/At4EUz3NZkHEYTiHhXks1pdktZCdgOeAHQHejYh2Z739OSfJ5mMdQtgRe39ew5RHv/CbwL+I82iPJHYCeaBV8HrSciSZIkSdKcWt6DH18Ang+8nGZKxk+Ap1fVj2a4nXfTbH/7Mpq3Pc4FdqZZ72POgh9VtTTJTsC+NJ/Bp2gWGD0XeFVV/bwr7y+TPB04iGaL3LuAM4BtgecM2d6lSf4R+DjwRprP5Ntt20tm5KYkSZIkSZphqaq57oM0Y5Kcs9VWW211zjm9y7NIkiRJkpZnCxcuZPHixYurauFkyy7Xa35IkiRJkiQti8EPSZIkSZI01gx+SJIkSZKksWbwQ5IkSZIkjTWDH5IkSZIkaawZ/JAkSZIkSWPN4IckSZIkSRprqaq57oM0Y5Jcs9pqq629ySabzHVXJEmSJEkz6Fe/+hW33nrrtVW1YLJlDX5orCS5HVgJ+Plc90XqsnF7/vWc9kK6N8el5iPHpeYjx6XmoxV1XG4A3FBVG0624Moz3xdpTp0HUFUL57ojUkeSc8BxqfnFcan5yHGp+chxqfnIcTl5rvkhSZIkSZLGmsEPSZIkSZI01gx+SJIkSZKksWbwQ5IkSZIkjTWDH5IkSZIkaay51a0kSZIkSRprvvkhSZIkSZLGmsEPSZIkSZI01gx+SJIkSZKksWbwQ5IkSZIkjTWDH5IkSZIkaawZ/JAkSZIkSWPN4IckSZIkSRprBj80LyV5TZIlSW5NclmSQ5KsMUS5TZN8K8m1SW5MckqSJ003rwSzPy6TfCBJDTh2m5Wb0nJvquOyq/xGSc5NsssEeXxealJme1z6vNRUTGVcJlkryUeTXJzk9iRXJjk6yaMG5F83yTFJrkpyS5Kzk+w0O3ekcTDb4zLJbhM8Lz8wazc2D6081x2QerX/CN8PfAP4ArAp8AZgqyTPqKqlA8ptBpwNXAocCAR4E3B6kkVVtXgqeSUYzbjssg9wTU/aGTNxHxovUx2Xbdm1gHe0xwMmyOfzUpMyinHZxeelhjKNcXkmsAA4Gvgd8A/A7sAOSbaqqsu72ngE8FOggE8DNwKvA76b5PlVdcJs3JuWX6MYl10OAn7Tk7ZkuvewXKkqD495cwAbA3cBn+xJfwPNf0h2m6DsmcDvgTW70h4JXAucNtW8Hh4jHJcfaOt78Fzfs8f8P6Y5Lt8E3NKW/16bf5cBeX1eegx9jHBc+rz0GPqY5rj8au84A57Tlvt4T/qxwHXA+l1pqwMXt8dKc/1ZeMyfY4Tjcrc2fYu5vue5Ppz2ovlmD+AO4IM96YcBlwOv6FcoyeOBRcBHqur6TnpVXQocDjwzybqTzSu1Zn1cdrkbuB5p2aY0LluPBk4EFgIfGZTJ56WmYNbHZReflxrWdMblK6rquu6EqjqJ5q/tizpp7VtLLwUOrapLuvLeBBwMPAZ48tRvQWNo1sdlj2un1s3xYfBD880/Aj/q84/5LuBUYFGSDCgHzV+Kep3cnp86hbwSjGZcdlxXbZheWoapjkuA91TVLlW1ZIg2wOelhjeKcdnh81LDmvK4bPP081eav6Z3bAushM9LDW8U47L32grN4IfmjST3Ax4HXDAgy2+AvwPW6XNtE+DmqvrjgHLQRNwnm1cruBGOy47rkqyZZO22bek+pjkumcQvjD4vNbQRjssOn5dapumOywF1PrCt88Ku5E3ac792fgssxeelWiMclx1LmyxZkGSlSXZ3bPgfCs0nawGr0rzm1c+VXfl6PQK4Yshyk8krjWpcdvwfmvnC1wDXJ/l6Ev9nSb2mMy4nw+elJmNU47LD56WGMRvj8l00a3kc0ZX2CODuqrqqN3P7V/prJ9mGxtuoxmXHyjTTBK8Gbk5yYpKtJlH3WHC3F80nq7Xn2wdc76Tff0DZYctNJq80qnEJcALNX4euA9YGtuZvK3c/qaouHrLPGn/TGZeTbcfnpYY1qnEJPi81vBkdl0n2APYD/rOqTu9pZ1AbnXZ8XqpjVOMSmh3bdqN5Xq4ObA68ETgryXZVdfbw3V6+GfzQfNLZymnQuOz84791QNlhy00mrzSqcUlV/Qz4WVeeI5N8BTgd+BDwsmE6rBXCdMblZNvxealhjWpc+rzUZMzIuExyf+BTNL80HgXs2aediX63uv+y2tAKZVTjkqr6Dffe4vbYJIcBi2kW411hFuJ12ovmk86K7WsPuL6gPd/ndUL+9peficp1Xh+bTF5pVOOyr6o6k2bRqx0myqcVznTG5WRcN0QbPi/VMapx2ZfPSw0w7XGZZD3gDOA1wF5VtWtVLe3Jdh2wSpLV+5QPzfQFn5fqGNW47Kt9O+5rwDb9xuy4MviheaOqbgX+DDx2QJbHAVdUVb9tmi4CFiTp9wB5XHv+1RTyagU3wnE5kb8ADxoin1YQ0xyXk+HzUkMb4biciM9L3ct0x2WS9Wl+wXwYsKiqPjugnovac792NqT5S77PSwEjHZcT+QsQYI0plF0uGfzQfPND4OlJHtCd2K5KvD3wgwnKAezY59qzaObNnTGFvBKMZlxOZHOg324bWrFNdVxOtg3weanhjWJcTsTnpfqZzrj8Gs3vTE+tqnOX0QYMfl6yjHa04hnFuJzI5sAtNIugrhAMfmi+OQJ4MLB3T/oewLrAodDMb0uyoOv6qcAlwL90P0CSPJJm7tsxVXXTFPJKMJpxSZJH9zac5PXAlsBXZ+JGNFaOYGrjcjJ8XmqyjmD2x6XPS03WEUxhXCZ5Os16CO+uqr9M1EBVXUizsORbeupYHdgHOMWFeNXjCGZ5XLb5+z0vdwJeCBxXVXdOrfvLHxc81bxSVd9Pchzwb0k2An5CE5XcEzi0qjp/YfwW8Mwkm1TVH6vqziR7telnJTmSZhXlNwI3Aft2tTF0XglGMy5bFyX5L/62iN8zgZ2Bs4CDZvEWtRya6ricZBs+LzUpoxiXLZ+XGto0xuXCNv3RSXYbUP3xVXVd+/VbaN6G+3GSL9Asark78BCa8SndY4Tj8tQk59M8H28FtgFeSjNVa58Zvq35rao8PObVQTMn8kM0f228DbgAeCuQrjyHt9cf1lN2J+DHNP+wr6CJqK4zoJ2h83p4jGJcAl+geV37jjbvEuDdwKpzff8e8/OYzrjsur4tUMAuE7Tj89Jj6GMU49Lnpcdkj6mMS+D97Tic6Nisp50nAf9DEyC+FjgO2Giu799jfh6jGJfA/sCFNFNVbwN+DRwIrDnX9z/qI+0HIkmSJEmSNJZc80OSJEmSJI01gx+SJEmSJGmsGfyQJEmSJEljzeCHJEmSJEkaawY/JEmSJEnSWDP4IUmSJEmSxprBD0mSJEmSNNYMfkiSJEmSpLFm8EOSJEmSJI01gx+SJEmSJGmsGfyQJEmSJEljzeCHJEmSJEkaawY/JEmSJEnSWDP4IUmSJEmSxprBD0mSJEmSNNYMfkiSJEmSpLFm8EOSJEmSJI01gx+SJEmSJGms/X9bMnD/+dqGIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 373,
       "width": 543
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 변수 중요도 계산\n",
    "\n",
    "importances_values = pd.Series(ms_model.feature_importances_, index=x_valid.columns).sort_values(ascending=False)\n",
    "\n",
    "# 변수 중요도 시각화\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title('변수 중요도')\n",
    "sns.barplot(x=importances_values, y=importances_values.index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 선언\n",
    "svm_model = svm.SVC(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(random_state=42)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모델 학습\n",
    "svm_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차행렬:\n",
      " [[6992 2976]\n",
      " [3895 6137]]\n",
      "\n",
      "정확도: 0.6564\n",
      "정밀도: 0.6422\n",
      "재현율: 0.7014\n",
      "F1: 0.6705\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        True       0.64      0.70      0.67      9968\n",
      "       False       0.67      0.61      0.64     10032\n",
      "\n",
      "    accuracy                           0.66     20000\n",
      "   macro avg       0.66      0.66      0.66     20000\n",
      "weighted avg       0.66      0.66      0.66     20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred=svm_model.predict(x_valid)\n",
    "model_eval(y_valid, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 선언\n",
    "xg_model = XGBClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모델 학습\n",
    "xg_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차행렬:\n",
      " [[7722 2246]\n",
      " [1957 8075]]\n",
      "\n",
      "정확도: 0.7899\n",
      "정밀도: 0.7978\n",
      "재현율: 0.7747\n",
      "F1: 0.7861\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        True       0.80      0.77      0.79      9968\n",
      "       False       0.78      0.80      0.79     10032\n",
      "\n",
      "    accuracy                           0.79     20000\n",
      "   macro avg       0.79      0.79      0.79     20000\n",
      "weighted avg       0.79      0.79      0.79     20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 모델 평가\n",
    "pred=xg_model.predict(x_valid)\n",
    "model_eval(y_valid, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 하이퍼파라미터 튜닝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차행렬:\n",
      " [[7592 2376]\n",
      " [1982 8050]]\n",
      "\n",
      "정확도: 0.7821\n",
      "정밀도: 0.7930\n",
      "재현율: 0.7616\n",
      "F1: 0.7770\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        True       0.79      0.76      0.78      9968\n",
      "       False       0.77      0.80      0.79     10032\n",
      "\n",
      "    accuracy                           0.78     20000\n",
      "   macro avg       0.78      0.78      0.78     20000\n",
      "weighted avg       0.78      0.78      0.78     20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#모델 정의\n",
    "ms_model = XGBClassifier(random_state=42, learning_rate=0.2, n_estimators=100, max_depth=3, subsample=0.8, colsample_bytree=0.9,\n",
    "                        gamma=0, reg_alpha=0, reg_lambda=0)\n",
    "\n",
    "# 모델 학습\n",
    "ms_model.fit(x_train, y_train)\n",
    "\n",
    "# 모델 평가\n",
    "pred = ms_model.predict(x_valid)\n",
    "model_eval(y_valid, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "=> 오히려 기본 모델보다 성능이 낮게 나옴!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 변수 중요도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WyBOADwCnJHk1cBxwOHA08GrgPUAB751A2I+KIUmAHwMbAT9o3+eZwLuAFyd5UVXdM9bESc7pc2ulCcQpSZIkSRpiJj/mAEnmBw4DjgK2qapq2/cGTgH2THL4SHvrDcBqVXVVjylfAaxSVde385xJk/T4JfDFqvp8274PcDrw1iQfrKoHxhl6rxjeAbwKWLuqzu14x6PaZ20L7DfO50iSJEmS1JfJjznDJsBywBbAUs3iiYedRLO64tk88jSYo/okPgB+MJL4aJ0A3A4E+MpIY1VVkuOBdYDlgUvGGXevGN4HHANck2TpjvZLgOuA9Rkg+VFV03q1tytC1hxnnJIkSZKkIWbyY87w4vb6p1H6LMkjkx/TR+l7UecPbZLjauCOqrqzq+917XXhQQLt8ogYkjwOWA14LrB1nzFLTuA5kiRJkiT1ZfJjzrAY8BDNCpDq0+fCrp+vHWW+7gQHNAVJb+/R/mB7nUhx3O4YFqVZXXIozTaeXm6awHMkSZIkSerL5Mec4Q6a5MO5VXX1gGP6JUlmpe4Y7mivd1XVibM6GEmSJEnS3MmjbucMF7TX9ac0ihlUVbcC/wbWTeK/PUmSJEnSLOEvoHOGo4C7gc8kWaTzRpJFkmw8JVFNzA+BFWmOtn2EJJu2dUEkSZIkSZo0bnuZA1TVjUk+BHwXOC/J92nqaawEbENTQ+O4qYtwXHYHXgt8K8nLgdOAxwEb0Zxas+iURSZJkiRJGkomP+YQVbVfkmuAjwM7A/MClwE/APaYytjGo6puS7IO8FlgM+D1wK3AH4CN260xkiRJkiRNGpMfs6Gq+ixNcqC7/RjgmDHGnkZzosp4763ap/1A4MDRnjme57T3bwTe334kSZIkSZqpTH5oYEkWAhYZo9v1VfXgGH0kSZIkSZplTH5oPN4AHDBGn+WBy2d+KJIkSZIkDcbkh8bjFJrCpKO5dlYEIkmSJEnSoEx+aGBVdRVw1VTHIUmSJEnSeJj80NCZb8nFWfI9b5nqMCRJkiRJs4l5pjoASZIkSZKkmcnkhyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaayQ9JkiRJkjTUTH5IkiRJkqSh5lG3Gjr3X3811+z7uakOQ9IkWvo9u051CJIkSZqDufJDkiRJkiQNNZMfkiRJkiRpqJn8kCRJkiRJQ83khyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaayY9xSDJfkncnOTPJbUkeSHJz+/PKUx3fIJKsnOQHSa5Icl+Se5JcluT/evRdcBKet9CMziFJkiRJ0oww+TGgJAsAvwS+AVwG7ALsBBwEPBV4+tRFN5gkrwXOBdYBDgQ+AHwROB9Yr6Pfc5JcAWwyA89aMsnFwHtnIGRJkiRJkmbYfFMdwBzkA8DLgU2q6rjOG0k+BszWKxzaVRwHAH8E1q+q+7ruL9Hx45LA02bwkY8Dnj2Dc0iSJEmSNMNMfgxuXeDm7sQHQFXdD9w/yyMan9WAxYGfdCc+AKrqxlkfkiRJkiRJM5/bXgZ3G7BIkmVG69TWBXlTkl8l+U9bG+T3STbq6rdckkqyU5L12z53JblgpG/b5+dJbklya5IfJlm8xzMfm2T3JJcnubet4fGlrpodt7XXlcaIv4Bftz8e0cZ4ecf9ZyTZJ8mFSe5sn7lvkkU6+lxOszUIYK92jup6zqpJjmrf7a4kZ7XbciRJkiRJmlQmPwZ3BM339fMky4/Sb12aOiDXA3sAnwUWAY5LslaP/i9o+x8LfAFYAjg6ybrAGcA9wKeA44GtgUM6B7cJjl8C7wF+AnwYOAn4CHBUkrRdLwHOA3ZI8r6O9m7bt3EDfKv9eaeO+wcDrwSOap9xEvAu4NCOPjsBH2v//ON2ju07Yl4bOBt4cvvOnwYeaON9U5+4JEmSJEmaELe9DKiqfpbkS8DOwEVJ9gG+1GO7yL+A51bVX0cakvwY+CdN8c/tu/pvBqxaVX9v+/6cpgDpCcDhVfW2tt+3kzwEvCnJk6vqP237LsDywOpVdWXHM39Hk6h4BXByVT2UZGvgOOCbwNuSfKqqTux6zwPbxMsngNOq6siuePcEjquqBzue9QCwY5KnV9UVVXVkkuWAvYA/VdWBHX3nBw6jSZ5sU1XVtu8NnALsmeTwkfZ+kpzT59aoK1skSZIkSXMfV36MQ1V9EtiQJpGxE3Bpkg90rqKoqr91Jj7atquAvwHP6jHtz0cSH23fC4ELgQWBz3X1Pba9Pgcgybw0Kz4OBu5LsvTIB/gtUMD6HXP/FXgezYk1qwInJPllkhXG8R0c05n4aJ3aXnu9X7dNgOWAvYGlOuJdimYVyVOwUKokSZIkaRK58mOcqurUJM8FtqNJTnwdeGmSN44kBdqtKOsAL6ZJCIx8Lu0x5UU92v4DPLmqruhqv669Ltxenw0sBny8/fSyZFf8twMfTPJNYDfgjcA5SV5TVaf3meMR2m0/G9AkYVZsrwCLDjD8xe31T6P0WRK4eLRJqmpan9jOAdYcIA5JkiRJ0lzC5McEtEmO7yf5EXA4sAXNCox92noWPwKWAf5Cs+LjV3QlITrc2aPtfuD2Hu0jKy5GVuws1l6/QlP3o5er+rzDP4CtkxwE/BQ4NMmzq+ruPvOQZB7gO8A7gWuB6TSFTa9q2waxGPAQzQqQfltbLhxwLkmSJEmSxmTyYwZU1Z1JtgSuoEkk7EeT+LgaeEFVXTPSN8l6wBMmOYQ72uuN3bU7BlVVJyb5FPA1mtUqp4zS/V00SY63V9UPRhqTvJLBkx930CRvzq2qqycSsyRJkiRJ42HNjxlUVffSrHxYlGYbylOB/boSH/Myc+pY/J1mlcj6Y3Ucwz/b66LtdWRFRveJMBsC/+5MfLRW6TFnvzkuaK8zGrMkSZIkSQMx+TGgJK9ra3l0tz+bpubFWTSJCIBlu7p9FFh8smNqt6j8HNgwyUY9YntTx59X7HPULsDrabai/KH9+ab22v0e9wOLJHl8x7xL0Lxft35zHAXcDXwmySJd8S6SZOM+MUqSJEmSNCFuexncB4D9khwDnAs8QFPscxvgRprip1e19z7Z/mL/T+ClwOrAOTQnuEy2jwEvA45OciBNIdElgM1piqz+sO33ZODXSc6gqUFyFU39jf8B1gV26yiw+nea4qo7JSlg/qraC/gJ8IZ2nkNoEjpvpzlZZuvOoKrq9iR/AbZLcjWwUFXtWlU3JvkQ8F3gvCTfp6kfshLNd3kozXG8kiRJkiRNCld+DG5P4DRgPeDL7WdD4PvA6lX1r6p6CHgtcAzNL/K7A/O2Y+7oMecMq6orgRfSFF7dFPgW8EHgSqBzFcV5wBeBx7T3vw18BLgL2LSqPtMx573Am4BbgS/RblGpqp8B7wAeB+xBk/D4LLBfn/DeSpNI2ZWmKOzI/Pu1sf4b2Jnm6N2NgB/QnEAjSZIkSdKkSVW/AzekOU+Sc1Z76jJrnvzJd011KJIm0dLv2XWqQ5AkSdIUmzZtGtOnT59eVdPGO9aVH5IkSZIkaaiZ/JAkSZIkSUPN5IckSZIkSRpqJj8kSZIkSdJQM/khSZIkSZKG2nxTHYA02eZfchlPhpAkSZIkPcyVH5IkSZIkaaiZ/JAkSZIkSUPN5IckSZIkSRpqJj8kSZIkSdJQM/khSZIkSZKGmqe9aOjcc/2lXPjt1051GJrNPGfHY6Y6BEmSJElTxJUfkiRJkiRpqJn8kCRJkiRJQ83khyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaayQ8NLMluSa5KstRUxyJJkiRJ0qBMfsygJMslqa7PPUnOTbJjkkx1jJPoKcCSwOOmOhBJkiRJkgY131QHMER+DJzY/nlpYHPgW8BzgPdOVVCTbAfgE1V1/VQHIkmSJEnSoEx+TJ4/VdWBIz8k2QM4FnhPkr2r6h9TFtkkqaoHABMfkiRJkqQ5itteZpKqKuBAIMC0qY1GkiRJkqS5l8mPmWve9nrrSEOSpyU5JMkNbW2Q85K8rdfgJM9KckCSfye5N8l/kuzf1eexSXZPcnnb57IkX0qyYFe/pyTZt71/T5Irkny6q88rkpya5MYkdyT5U5IXddzfJ0n1iHORNoaL27lvTnJikpf26HtakgvauL+W5Ookdyc5I8maA32rkiRJkiSNg9teZq6tgDuBP0KTzADOAG4G9gZuB14BfD/JUlX1xZGBSdYFfgHcBXwfuBJYEXhtR58FgV8CKwHfAy4Hngt8BFg9yUZVVUmWbGO4H9i3jWk1oDOxsQ1wMHAcsAuwGLAe8Gzg7H4vmOSJwOnA8sABwHk0RVHfCvw6ydZVdUTXsHmAo4EC9gCeTlMX5ZQkK1lTRJIkSZI0mUx+TJ6Fkyzd/vlpwHuATYF3VdWNbfuBwEXAq6rqvrbtG0m+D+ySZN+quiXJ42kKqF4FvKyqrht5SNdqjV1okg6rV9WVHX1+R5PIeAVwMvAGYBlgrao6p6Pf/B1zfRD4C/CadssOwO5dfXr5Bk1SZp2qejhJkmRv4Cxg/yQnVdVtHWNWBn5XVe/s6H8RsB/wFprE0KiSnNPn1kpjjZUkSZIkzV3c9jJ5PgNc3X5+T3Ms7DpV9T2AJGsAawNfAxZPsvTIBzgFWIj/rsR4E/AkYMfOxAdAVd3VzjcvTYLlYOC+rvl+S7OqYv122Mhxuwt2zXV/x48BFqDr30RXn0doV31sBRzUmfhox90J7AosAmzWNbSAT3a1Hdq2r9HveZIkSZIkTYTJj8nzPWAj4J3A32hWN9zecf/F7fUo/pskGfkc3t5bsr2+lKZOyK9Hed6zabamfLzHfJfRJDNG5vt5G8sxST6a5Ak95ju4nfOMJK8a820ba9HUNTmlz/0z2uvzutr/XVU3dDZU1d00J8ksOsiDq2parw9w8YCxS5IkSZLmEm57mTx/q6oTAZIcTVP74odJ1myPiF2s7fdW4Lo+c5zfXpcCru3YftLLyHxfoan70ctVAFX17yQvpll18mVg1yT7ALu1SQeq6utJbgc+B5yY5ELgk1X1i1FiWKK9/rvXzaq6PskDwMJdt27r1R+4GxNykiRJkqRJZvJjJmh/6f8QTd2OHWnqYtzR3r6kqs4aY4p7gCeP0WdkvhtHki5jxHQh8Iokz6XZcvJJYE3gfzr6/CDJwcCWNFtWjmkLlv5ojBiW6XWz3RYzH02BV0mSJEmSpoT/lX0mqaqf0JySsmuSxYEL2lvr9x/1sHOBxyd5wSh9/k5zessg83XG9Zeq2hrYB3hVkuW67j9QVYfTbGm5Bnj3KNNNb68b9Lm/dnv9/XhilCRJkiRpMpn8mLk+BiwOfBb4Dc02lA8nWbazU5IFkry+o+kgmsTG3u1xtp19F4eHa2T8HNgwyUbdD07ypo4/P7VHbP8Zud32eVrnzaq6g6buSOijqv4FnAi8Pcnzu56/EM0WmiuAY/vNIUmSJEnSzOa2l5moqs5IcgzNqSzfBnYAjgb+nGQ/4HKao2rfTLOK4qftuEuTfKAdc16SQ4AbgVWB1wIjyYyPAS8Djk5yIPAnmjocmwPPAn7Y9nt7kk2Bn9Gs5ngm8F7g5Kq6rO3zyyR/pilS+iDwKpoCqJ8f4zXfDfwO+E2SA2hqnTwR2JZm607nsb6SJEmSJM1yJj9mvp2BjYGvVtWrk6xLU0/j/TRHz/4LOA74YuegqvpOkkuAT9AkOeYFLgH26OhzZZIXArsBmwLb0yRJzgA+1DHdyTQnyHyofeZlwBeAr3f0+R6wHfAa4E6aJMb/VNVJo71cVV2RZC1gl3bsO4GbaE6A+XxVXTLqtyNJkiRJ0kyW0Q8UkeYsSc5Z5amLrPmTnV821aFoNvOcHY+Z6hAkSZIkzYBp06Yxffr06VU1bbxjrfkhSZIkSZKGmskPSZIkSZI01Ex+SJIkSZKkoWbyQ5IkSZIkDTWTH5IkSZIkaah51K2GzoJLPtOTPSRJkiRJD3PlhyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaayQ9JkiRJkjTUTH5IkiRJkqShZvJDkiRJkiQNNY+61dC5/YZ/cOr3Np7qMDSJNnjHcVMdgiRJkqQ5mCs/JEmSJEnSUDP5IUmSJEmShprJD0mSJEmSNNRMfkiSJEmSpKFm8kOSJEmSJA01kx+SJEmSJGmomfzQbCPJQlMdgyRJkiRp+Jj80GwhyTeA3011HJIkSZKk4WPyQ7OL5wKPmeogJEmSJEnDx+SHJEmSJEkaaiY/ZrI0Nk5yfJIrk9yZ5Nwk23b1qyT7JFkqyUFJrk9yV5Izk2zYY96B+ifZtO37zj7xnZ3k/HG8z2lJLkgyf5LPJrk8yT1J/prk/X3GLJjk4+1735Xk1iSnJ1mlnaOAlwPPaWOtJNsNGpMkSZIkSaOZb6oDmAs8E/gFcALwdeB+4M3AQUnurKqfdvRdhKbuxXnAZ4DFgB2AE5NsXFUndc09SP8TgJuBLYD9OwcnWR54IfCxCbzX4cDTgW8ADwBvAL6R5ElVtUvHMx4H/BJ4AfAT4LvAEsAmwIrAUcDlwM7A44FPt0PPGO3hSc7pc2ulCbyLJEmSJGmImfyY+W4D1qmqM0caknwPuAJ4H9CZ/HgT8L9V9aWOvvsDFwLfTPLsqqpx9r8vyRHA9kkWq6qbO8a/EXgQOHSc77QyTcLiRVX1YPvcbwEnAZ9M8r2quqLtuydNguV1VXVUxxz/l2ShqrobOLdd6fHEqjpwnLFIkiRJkjQqt73MZFV1XWfio227CzgLeFZX98uBvbr6Xk+zuuJZwGoT7H8YMD+wadf4NwInVdU1A79QYx7g/SOJj/a5DwKfBeYdeU6SJwBvBw7tSnyMjLl7nM/tHDut1we4eKJzSpIkSZKGk8mPWSDJfElekuQTSb6f5LfAesCiXV3/0JlQ6DC9vXYnSwbt/1vgSpqtLyMxrUxzwsqBA7/If11TVZf1aP9z13OfDywA/HwCz5AkSZIkaVKY/JjJkqwEnAucDrwFWBg4E/hrj+439pnmzvbafRTsQP3brTKHA69oV2MAbE1TC+SY0d+gp37PvZtmG81InEu11/GuLJEkSZIkadKY/Jj5DgIeC6xYVatV1VZV9QngHz36zt9njmXb6/Uz0P8wmqTEa9qf3wj8qKruHS34Pvo9dxmabS8jz72nvT5lAs+QJEmSJGlSmPyYiZIsTHPKyWFVdWnX7VV6DFm1z1SvpDlRZXpX+8D9q+p84HxgiyTTaLamHDha/KNYvj3FpddzAf7QXs9tr+sPMGcBmWA8kiRJkiT1ZfJj5noQeIj/rsQAIMlWwPN69F87yeZdfVej2aLys6q6aQb7Hwa8Cngd8Neq+gMTMz+we9dzF6Y5rvbfNMfrUlX/BH4FvKNNuHT2nyfJIh1NNwHLJJl3gjFJkiRJktSTR93ORFV1V5LjgLcmeYCmIOgaNFtPTgHW7hpyFnBYksOBc4CnAe8BrgU+0uMR4+1/OPBFmhNY9p6BV7sS2DTJKsDxwILtnE8DNqmq+zv6vgM4A/htkgOB82i2x7we2AU4qu13Bk1S5sAkZwPnVdUZMxCjJEmSJEmAKz9mhe2A7wMbA3sCT6c56eU/PfpOB15Ns53lK+3YnwIvrKqrZrR/VV1Jc/LLksAhE38lbgdeDtwF7AZ8iqaGycuq6pSuZ14GTKPZYvMammN4t6cp+vr7jq77AgfQfE+fp6mTIkmSJEnSDHPlx0zWbj15Z49b27Wf7v6nAS8cx/zj7f/yQfuOMc+VwGYD9r0G2LH99OtzD/C29iNJkiRJ0qQx+SEAkixJc1JLP3dX1a2zKh5JkiRJkiaLyQ+N+CPNlpx+DqLHShVJkiRJkmZ3Jj804q3AQqPc71VzRJIkSZKk2Z7JDwFQVb+Z6hgkSZIkSZoZTH7MJqoqM7P/ZKmqdafiueOx8BNXYIN3HDfVYUiSJEmSZhMedStJkiRJkoaayQ9JkiRJkjTUTH5IkiRJkqShZvJDkiRJkiQNNZMfkiRJkiRpqJn8kCRJkiRJQ82jbjV0br7xEn50wKumOow53hu3P2mqQ5AkSZKkSeHKD0mSJEmSNNRMfkiSJEmSpKFm8kOSJEmSJA01kx+SJEmSJGmomfyQJEmSJElDzeSHJEmSJEkaaiY/pliS5ZJUkp2mOhaAJKe18Yx87k1yTZLfJPlckqdMdYySJEmSJI3HfFMdgGZLVwGfbv/8GOBJwAuBTwI7J9mpqr45VcFJkiRJkjQeJj/Uyy1VdWB3Y5KnAYcC30hyV1V9f5ZHJkmSJEnSOLntRQOrqiuBVwN/B/ZK8vgpDkmSJEmSpDGZ/JhDJFkkye5JLk5yT5Kbk5yY5KUdfRZIckeSg3qM/2mS+5I8tqv9K0luSzL/IHFU1R3A/wGLAZt3zbVAko8lOTfJXe28pyfZtM87zZtkhyRnJ7m9jf1PSV42SCySJEmSJA3C5MccIMkTgbOAjwC/Aj4IfAV4JvDrJFsCVNW9wK+B9bvGzw+8ApgfeCmPtB7wq6q6fxwhndBe1+54xoLAL4EvAue3se4GPB44KslHumKaF/gp8F3gRuBTwOeBm4A1xhGLJEmSJEmjsubHnOEbwIrAOlV19khjkr1pkiL7Jzmpqm6jSUxskmTFqvp72/WlNH/XF9MkRk5qxy8GPA/YbzzBVNUNSW4BntzR/BlgHWDLqjqyK8bjgT2SHF1Vl7a3PgJsCnywqr7RMc+e3atTeklyTp9bKw3+JpIkSZKkuYErP2Zz7aqPrYCDOhMfAFV1J7ArsAiwWdt8YnvtXP3xauA04Iyu9pfT/Bs4aQKh3QUs2MY4D/BumhUkR3Z2qqoHgE/QJF/e3HHrQ8BvuxIfI2PumkA8kiRJkiT15MqP2d9awLzAKX3un9FenwdQVf9M8ndgA+A77b1XA98GrgO2T7JIVd1Ks+Xl71V12QTiWoxmuwo0q1IW6xdjVf05yZ0jMSZ5Js2qkS9P4Lkjc07r1d6uCFlzovNKkiRJkoaPKz9mf0u013/3ullV1wMPAAt3NJ8ArJfGcsDKwHHAqUCAddt+6/HflSIDS7ISsBBw3iAxtq7piHGpjjZJkiRJkmYqkx+zvzva6zK9brbbYuYDbu5oPoEmIfE8mlUfF1fVZVV1I/BnYP123KpMbMvLW4ACjhkkxtZSHTHe016fMoFnS5IkSZI0LiY/Zn/T2+sGfe6PnLjy+4623wB3t2NeTbPqY8QpNHU/1gXuo6kFMrB21ccHgR9X1V/b5os7ntdrzHNpTn0ZifGvwL10nUojSZIkSdLMYPJjNldV/6LZmvL2JM/vvJdkIeBzwBXAsR1j7qFJaqxHk+Q4vmPYycBzaE5aOX08xUWTrEtz1O5/gB07nncvcBDwqiSbdY2ZB/gScDtwcNv/buAwYKMkr+3xnMUGjUmSJEmSpLFY8HT2sVaS7Xq0n0hzksrvgN8kOYCm1sYTgW1pCoe+qqru6xp3ArAHcD/w2472M2lWaWxOc1JML4t2xLIgsCzNqo4X0awceXNV3dw1Zmeao26PTHJY+5yFaU6qWR14Q1Xd0NF/p3a+nyX5Ydt/CWBj4CfA1/rEJkmSJEnSuJj8mH28of10W6+qTkuyFrAL8BrgncBNNImIz1fVJT3GnQB8Azihqu4faayqe5OcDvwP/YudPgU4oP3z3TSFSX8H7FpVJ/caUFW3JnkJTRJkK2Br4DaaxMuOVfWnrv43J3kx8Clgy/bdb6DZstO5UkWSJEmSpBli8mOKVdXlNCewjNXvGuC97WeQef/Rb96q2miUcesOMn+fsbfRJDM+NY7+O7cfSZIkSZJmCmt+SJIkSZKkoWbyQ5IkSZIkDTWTH5IkSZIkaaiZ/JAkSZIkSUPN5IckSZIkSRpqnvaiobPYEs/ijdufNNVhSJIkSZJmE678kCRJkiRJQ83khyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaayQ9JkiRJkjTUPO1FQ+e6my7hm4e9aqrDmCXe/2ZPtZEkSZKksbjyQ5IkSZIkDTWTH5IkSZIkaaiZ/JAkSZIkSUPN5IckSZIkSRpqJj8kSZIkSdJQM/khSZIkSZKGmsmP2UCS5ZJU1+f2JP9McmSSbZI8pse407rG3JPk4iRfTLJwV9/tejzjtiSnJnlFj7nXbfts0dV+edu+w4Dv9qW2/4Fd7Qf2iKd69ZUkSZIkaUbMN9UB6BF+DJzY/nlh4OnAK4GDgU8n2aKqzu8acxXw6fbPSwEvBnYGNknykqq6rav/54DLgXmB5YDtgZOSbF1VPx5HrO8D9hutQ5LHAu8cpcu9wLt7tP9jHHFIkiRJkjQqkx+zlz9V1YHdjUk2B34AnJJkzar6T8ftW7rHtKsyvgt8mCbZ0enYqvpTR9+9gfOBvWiSL4O4Elgtycur6jej9NuWJolzZZ/7D/R6X0mSJEmSJpPbXuYAVfVz4A00Kzu6kxm9+u8HXA/8zwB9bwKOAp6aZKkBQ/odcCPw/n4dkgT4AHAcsMCA80qSJEmSNOlMfswhqupk4FfAm3vV/+jhcmCJAaefFyige4tMP/cB+wObJlm2T59XASsD+wALDTivJEmSJEmTzuTHnOU4mkTCGgP0fRrw77E6JXk88Grg91V19zhi+VZ7fU+f+x8CLqiqU2mSK5IkSZIkTQlrfsxZLm2vTx6tU5KtaLbI7Nbj9hJJlqb5u18Z+DzwOEbZwtJLVf07yc+Adyb5fFXd2/H8lWgKtfYqZtoVapbu0X5DVT0wxsBz+txaaYxnSpIkSZLmMiY/5ix3tdcFO9rmaxMI8wBPpVnF8THgN/Q+jeXErp+/Dbyxqq6YQDxfB7aiqUdycEf7B4GbgUPHGP9Y4Ooe7WsA504gHkmSJEmSHsXkx5xl8fZ6Y0fbs3lkAuFumpNePlVV9/eY4wM0K0hWAz7ZXm/s0W9MVXVmkj/SrBo5GCDJYjSnvOxTVXeNNh64B9i8R/ulPdq6nz2tV3u7ImTNscZLkiRJkuYeJj/mLGvQFCb9S0fbFTTbSwq4BfjLGLU7zmqPuj0+yVnAr4E9gR0nGNPXgUOTvKiqzgZ2oDnd5VujDwPgwarqXokiSZIkSdKksuDpHCLJfDTbS/5QVdd03Lqjqk6sqpOqalxFS6vqdJpVIu9KsvoEQ/sJzcqT9yWZF3gvcFRVXTnB+SRJkiRJmlQmP+YcOwPLAZ+d5Hl3Be4E9p7I4HZrzb7AFsDWNHVHvjFp0UmSJEmSNINMfszmkiyQ5LM0p7LsNdnbRKrqepptL+smed0Ep/lue/0WcG67okSSJEmSpNmCNT9mL2sl2Q4I8ARgFeA1wBLAJ6pqr5n03K/S1PzYM8mxVXXfeAZX1XVJDge2Y3yrPuZr37fbfVX1w/HEIEmSJElSPyY/Zi9vaD8jxUv/CRwEfLuq/jWzHlpVd7WrS74LfIhmJch4fR3YGDh8HGMWAA7o0X4rYPJDkiRJkjQpTH7MBqrqcprVHuMdt+44+h4IHDjK/f2A/Tp+Pq1XTFW1XJ/x5wJP6nPv8T3atqNZKSJJkiRJ0kxlzQ9JkiRJkjTUTH5IkiRJkqShZvJDkiRJkiQNNZMfkiRJkiRpqJn8kCRJkiRJQ83TXjR0nrT4s3j/m0+a6jAkSZIkSbMJV35IkiRJkqShZvJDkiRJkiQNNZMfkiRJkiRpqJn8kCRJkiRJQ83khyRJkiRJGmomPyRJkiRJ0lDzqFsNnX/f/A92OvJ/pjqMgXx5ixOnOgRJkiRJGnqu/JAkSZIkSUPN5IckSZIkSRpqJj8kSZIkSdJQM/khSZIkSZKGmskPSZIkSZI01Ex+SJIkSZKkoWbyY4KSzJfk3UnOTHJbkgeS3Nz+vHLbZ90k1fF5KMmVSQ5N8vQx5p83ydVJ7k2y2CTGvXEbyxGTNackSZIkSbOz+aY6gDlRkgWAk4C1gSOAHwMBlgNeDzwd+GvHkG8BfwIWAFYG3g5slGRaVV3e5zGvApYG7gPeCOw7SeG/FbgXeE2Sxarq5kmaV5IkSZKk2ZLJj4n5APByYJOqOq7zRpKPAQt19T+tqo7s6HMU8GvgI+1cvWwLXATcRJOwmOHkR5JFgdcABwE7AG8AvjOj80qSJEmSNDtz28vErAvc3J34AKiq+6vqttEGV9VpwC3Air3uJ1kE2BQ4nGZVyQuTPHvGQgaaZMcCwO7AOTRJFUmSJEmShprJj4m5DVgkyTITGZxkHmBB4No+XbaiSVIcBvwIuJ/JSVRsC5xeVVcChwAvSvKoBEyS5dq6IDsleVaSn7f1TO5IcnKSNSfaP8kH276v6PHceZJcleTYSXhXSZIkSZIAkx8TdQTNd/fzJMtPYPyraZIfP+1zf1vgd1V1WVXdAJwIbNMmTSYkyQo0NUoOaZsOBx5g9KTK04EzaFap7Ax8FVgTOD3J8ybY/0fAg8AWPca/HHgycOAA73NOrw+w0lhjJUmSJElzF5MfE1BVPwO+BLwQuCjJXkmWGGXIokmWTrJSknfR1NzYv6qO6e6Y5BnAOsChHc2HAMsC689A2NsC9wBHtu9wHXAyoydVdgQ+WFXbV9V3q+ozNAmU+YCvTKR/VV0LnApslmTervFvpKlx8qjvRZIkSZKkiTL5MUFV9UlgQ+CfwE7ApUk+kCQ9uu8PXE1zAsx32uv3+0y9Lc0JLz/paPsFcCsT3PrSxrQN8IuqurXj1iHAU4H1+gw9o6p+1NlQVX+nScys39YmmUj/w4AnAS/tiHF+mpNyflRV9431TlU1rdcHuHissZIkSZKkuYvJjxlQVacCzwXeAdwBfB34SY8VDbsDG9EUMd0RuAs4O8n/dXbqSFIc13kEbVXdQ7PV5nVJFp5AqC+jOYb30K72o2nql/RLqpzZp306zdG+K0yw/8+Bu3nk1pdXAEswwJYXSZIkSZLGw+THDKqqB6vq+8CzaVZobAG8p6vbuVV1YlUdU1X7VtUrgW8Bn0qyRke/dYBnAJu3RUEf/tAkWB5L71oZY9m2vR7dNeddwBNokiqP7zHuxj7z3dleHzOR/lV1O83Wls07Vsq8Ebioqv44+qtIkiRJkjQ+8011AMOiqu5MsiVwBbA1sM8YQw4C3kuz9ePPbdu2NNtb+o19B80qjQMGjSvJQjQJk9OB3/bo8iTgnW2fA7vuzd9n2mXb6/Uz0P8wmqN3124LlW5Ks0JGkiRJkqRJZfJjElXVvUmuAhYdoPuC7TUASRYEtgSOrqpP9xrQ9vlIkuWq6vIBw9qcZnXHZ6vq1z3mnJcm8fBWHp38WLXPnK+kWeVx6Qz0P7Ft2wJYGngc/z2JRpIkSZKkSeO2lwlI8ro2EdHd/mzgOcBZY4wP8GGggFPa5s2AReh//C00x9OG/25jGcS2wA00Kz8epaoepCmu+vIkT++6vVWS53fF/iqaGiLfr6qaaP+qup+mjsmmNEf/nlxVV4/jvSRJkiRJGogrPybmA8B+SY4BzgUeAFakKVZ6I/C5rv7rtjU1AjyZZrXD6sBuVXVR2+etNEVTT+730Ko6J8klNAmNz48VZJJlaE6kOaBNcvRzOPC+dt7dOtrPBH6T5HvARTSJnXcCf+nqN9H+hwHvpqll8sGx3keSJEmSpIlw5cfE7AmcRnNE7Jfbz4Y0x9euXlX/6ur/Xpo6HT8AdqY5YeV1VfUZgCRL05x2cnx7sstoDgeemWSdAeJ8CzAv8LMx+p0FXM6jV5T8om1bj+Ykm82AfYGXV9UdPeYZb//f0dRIWYDm5BlJkiRJkiadKz8moKqOB44foN9ptDU9xuh3DQP+XVTVrsCuA/bdC9hrgH4FLN/n3pHAkYM8b7z92+cuN+jckiRJkiRNhMmPOVR7issiY3S7foztLpIkSZIkDT2TH3OuNzD2kbfL02xnkSRJkiRprmXyY851CrDRGH2unRWBSJIkSZI0OzP5MYeqqquAq6Y6DkmSJEmSZncmP/QoVXU5AxRqnWj/mW3ZxVbgy1ucONVhSJIkSZJmEx51K0mSJEmShprJD0mSJEmSNNRMfkiSJEmSpKFm8kOSJEmSJA01kx+SJEmSJGmomfyQJEmSJElDzaNuNXT+ccu/2Oio9091GGM6YbNvTnUIkiRJkjRXcOWHJEmSJEkaaiY/JEmSJEnSUDP5IUmSJEmShprJD0mSJEmSNNRMfkiSJEmSpKFm8mMukmStJJVku4625yW5Kcn2UxiaJEmSJEkzjckPLdp+njK1YUiSJEmSNHPMN9UBaGpV1W+SPBm4dqpjkSRJkiRpZjD5IarqmqmOQZIkSZKkmcVtL5plkphskyRJkiTNciY/hlCSeZK8L8n5Se5JclWSLwKP6dH3UUVQ2/Y3JDkrya3t57dJVhhHDOu2826RZIckVwP3d9yfL8lOSS5Ocm8b475JFp+BV5ckSZIk6VH8L/HD6bvAO4BfAPsCSwFvAzYYZHCSTwO7AYcCBwNPAjYGlgX+Mc5YNmg/X2zjIEmAHwMbAT8ALgKeCbwLeHGSF1XVPeN8jiRJkiRJPZn8GDJJNqJJfOxRVTt3tH8d+OOA03wIOLaqtulo+1yS+ScQ0huA1arqqo62dwCvAtauqnM7YjwKOB3YFthvtEmTnNPn1koTiFGSJEmSNMTc9jJ8dgBuAXbvbKyqm4AvDDhHgAW7G6vq/h59x3JUV+ID4H3AMcA1SZYe+QCXANcB60/gOZIkSZIk9eTKj+HzQuD3VXV7j3sXDjjHwcCHkhwP7FZVZ81APNM7f0jyOGA14LnA1n3GLDnWpFU1rVd7uyJkzXHGKEmSJEkaYiY/hs+SwL/73HtgwDl2Av4DfAI4M8lZwMeq6ncTiOfarp8XpVlZcihwWJ8xN03gOZIkSZIk9WTyY/jcC/Q7MWWJQSaoqgeBvZLsQ1N/4zPAaUnWq6ozxhlPdf18R3u9q6pOHOdckiRJkiSNmzU/hs/fgLWS9EpsvWg8E1XV3VX1XeD5wEM0hUpnSFXdSrMyZd0k/vuTJEmSJM10/vI5fI4Engrs2NmYZDngw4NMkORpXU03APfRbFeZDD8EVqQ52rb72Zu2dUEkSZIkSZoUbnsZPl8H3gLsnWRN4ExgWZpEw/eBjwwwx1+T/JjmaNz5gS1oTn/5/iTFuDvwWuBbSV4OnAY8DtgI2ICmLogkSZIkSZPC5MeQqaq7kqwLfBHYFNiK5pSXd9FsNxkk+fFdmuTE1jTH5v4BeGlVnT1JMd6WZB3gs8BmwOuBW9vnbNxujZEkSZIkaVKY/BhCVXU9TX2OXjU60tX3Tz3aPsJgSZLRYjite96u+zcC728/kiRJkiTNNCY/NC5JFgIWGaPb9e2JMZIkSZIkTTmTHxqvNwAHjNFneeDymR+KJEmSJEljM/mh8TqFpjDpaK6dFYFIkiRJkjQIkx8al6q6CrhqquOQJEmSJGlQ80x1AJIkSZIkSTOTKz80dFZY9KmcsNk3pzoMSZIkSdJswpUfkiRJkiRpqJn8kCRJkiRJQ83khyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaap71o6PzjlmvZ+GdfmeowHnbc6z461SFIkiRJ0lzNlR+SJEmSJGmomfyQJEmSJElDzeSHJEmSJEkaaiY/JEmSJEnSUDP5IUmSJEmShprJD02aJAsk+VuSH0x1LJIkSZIkjTD5MZMlWS5JtZ/XjtJvrbbPdrMwPJJs1xFf9+fycU43P7A08LQJxrLQRMZJkiRJkjSa+aY6gLnM15OcUlV3T3UgPXwOuLyr7Y7xTFBVdyRZHrhrvA9Pcg5wJvD+8Y6VJEmSJGk0Jj9mnVOBDYBdgE9NcSy9HFtVf5rRSarqpgkOXRM4a0afL0mSJElSN7e9zDontp+PJllpqoORJEmSJGluYfJj1nov8CDw7UEHJFknyalJ7khye/vnl3Tc/16Se5Is2DXuNW3djnd2ta/Wtr9xHDGsmuT+JAd0tc+T5Lz2M1/bdkeSA7v6PT/JMUmuSXJXkgtG6p8kOS1JtV3f21FvZLlB45MkSZIkaTQmP2ahqvonsBuwXpI3j9U/yeuBXwMP0WyX+T9gSeDXSV7WdjsBWAB4Sdfwjdvr+l3t67XznTyOuC8A9gG26Vq18nZgNeDdVfVAn3dYj6aWxxJt/J8F/gE8r+3yJWD79s+ntn/eHrhh0PgkSZIkSRqNNT9mvS8DbwG+nOTYqrq1V6ckTwR+AOxZVf/b0f414M80SYO1gV8CD9DUEzm1Y4qN2n7rdU29HvDHHrU5lkiydFfbbVU1Urx0V+CNNMmbLZMs3P75e1U1Wq2O9wK3AOtX1b0d7zE/QFWd2P58AHBxVR04ylwPawuk9uKWIkmSJEnSI7jyYxarqvuBdwNL0ayE6GcbmuTU/kmWHvkAi9EkPF6Y5LFt8uQsOlZ4JFkVeCqwO7BUkue07fMALwdO6vG8E4Gruz47dMR9G/Bx4PVJ1qQp2joPsPMYr5z2Pebv8T1IkiRJkjTTufJjClTVb9uVDjsmOaCqpvfo9mLgscBlo0y1BM2xsicAuyVZpE2GvBqYDhwN3EaTGLmQZqvJYjSJjm4fAC7pavtrV9yHJNkB2Bd4Ls12l7FOdzkYeB3whySfBX5aVQ+OMWZMVTWtV3u7ImTNGZ1fkiRJkjQ8TH5MnY8DrwX2TfLiHvcXA64FthtljpG6GCcAX6BZ1XEMTfLjuKp6IMlpNMmPb9JsebkZ+EOPuc4a8KjbzwC/Av5RVQeN1bmqjk6yKbAH8GPgiiSfq6oDxhgqSZIkSdKkcNvLFKmqG4GdgBfQbC+pri53AIsCJ1XViX0+d7d9z6PZprJ+kifQFD89vr13CvDydsvLesAvZ3DlxXuAK4AVkrxikAFVdQywCk0R1muBHyQZa7uMJEmSJEmTwuTHFGpXTpxGs2pjia7bF9Cc4rL2APMUTR2PDYBX0qzu+GN7+xSaVSRrAS+j95aXgSTZDNiCpvDpsTSrVhYcdVBHjFV1PM37nEtT9+RRj5hobJIkSZIk9WPyY+q9B3gc8L9d7T+iWQ3ypSSP6byR5ClJ1u3qfwKwKrAVcGJVPQRQVX8DrgTeBzyB3sVOx5RkUeDbwCFVdTbwMeDpNNtgRhv3tM6f21Un1/HoRMdNwLITiU2SJEmSpNFY82OKVdXFSfYAdulqvzDJl4BPAuckOYSmeOnqwJuBT9OsGhlxCvAgsAmwfddjTqFZrXFBVV3VJ5RN2lNiuh3TFjX9KvB42tNd2rj3BXZKclhVXdhn3gOT3E1zQs3dwItoVqd8qqvfGcBGSXaj2fJzQFVd12dOSZIkSZIGZvJj9vAFYGtghc7GqvpUkktoVm18jmYlyN+BLwH7d/W9OcnZNMmF7tUdpwBvZ/QtL7v2aV8jyTSahMonqurqjnufBd4C7JdknXb7TbdDaE6S+TzwAHAR8KaqOryr3weBhYGP0NYFGSVWSZIkSZIGZvJjJquqyxmjlkVV3QM8q8+9A4CBTkapqnX6tP+Y5qSVXvcOBA4cYPpHvUO7ImTxrrbHd/08UPzt97T+AHFIkiRJkjQu1vyQJEmSJElDzeSHJEmSJEkaaiY/JEmSJEnSUDP5IUmSJEmShprJD0mSJEmSNNQ87UVDZ4VFl+K41310qsOQJEmSJM0mXPkhSZIkSZKGmskPSZIkSZI01Ex+SJIkSZKkoWbyQ5IkSZIkDTWTH5IkSZIkaaiZ/JAkSZIkSUPNo241dP5xy41s8tMfTNnzj33926bs2ZIkSZKkR3PlhyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaayQ9JkiRJkjTUTH5IkiRJkqShZvJjJkuybpJKssVUxzK7S7Jd+12tNdWxSJIkSZKGh8mPLoMkK5Ks1fbZbhaGNkM6Egudn9uSnJrkFVMdnyRJkiRJM8t8Ux2AZrnPAZcD8wLLAdsDJyXZuqp+PIVxSZIkSZI0U5j8mPscW1V/Gvkhyd7A+cBegMkPSZIkSdLQcdvLTJJkjkgsVdVNwFHAU5MsNVnzpjHvZM0nSZIkSdJEmfyYBJ11QpLskORq4P4e/Z6f5JS21sYtSX6a5JldfeZL8qYkv0ryn7bv75Ns1NVvufaZOyVZo5339iQ3JPlOkseO4xXmBQq4rWP+A5Pc0ed9T0tyQVfb5UmOTbJKkrPa99+mvbd0ki8kOa99n6uSHJ7kyeOIUZIkSZKkCZkjVifMQTZoP18EuldRPB/YDzgUOAJYEXg3sE6SNavqqrbfusBBwM+Ao2kSEzsAxyV5QeeWldbKwM7AYcCxwCbAu4DH0SYfRpPk8cCrgd9X1d3jedke5mvjPgb4IXBp2/5lYD3gR8DfgWcB7wdWSbJGVT003gclOafPrZXGO5ckSZIkabiZ/JhcbwBW60hkdPowsF5V/W6kIcmxwK9pipC+o23+F/DcqvprR78fA/8E3ktToLTTdsC6VfXbtu83gd8AWyf5cFXd0NV/iSRL0/zdrwx8niZR8v7xv+6jrAvsXFVf62o/DHhHVd3T8U7XAnsCL23jlSRJkiRppjD5MbmO6pP4ADi8M/EBUFWnJfkl8Dra5EdV/a17YFVdleRvNCsmuh03kvho+z6U5FBgHeC5wK+6+p/Y9fO3gTdW1RWjvNeg7gf2726sqhN69D21vT6LCSQ/qmpar/Z2Rcia451PkiRJkjS8TH7MmOr6efoofc/s0/5nYMMkTxxZpZFkQZrkxYtpkgMjn0t7jD+3R9uV7XXRHvc+0M6zGvDJ9nrjKHGPx9+q6s5eN5IsQ7Ml6Hk077LyKDFKkiRJkjRpLHj6aPe218eM0mckaXRXV/u1o4zpl2AYSRbMD5BkbZq6GCcAm7XP+hXQb0XJbT3aRmp39Pr7Pauqjq+qPYDXAi+h2X4yGXq+f5LP0mzn+QawetvvmEl6piRJkiRJo3Llx6ON1Mh46ih9lmmvV3e1d68E6dQvmbIs8CBwU5LH0BQFvRp4QVVdM9IpyXrAE0aZf9yq6vQk3wXelWS/qjq34/aDwAJJUlXd7/X4flN2N7Sn1Ozafnavqgfb9hWBnWb0HSRJkiRJGosrPx7tUuBW4H9G6bM+cA/NlpVBrdrdkGRemq0gf6mqe4Fn0yRd9utKfMzb3psZdqVZfbJ3V/stNMmxRxxHm2QBetce6WdDmkTKl0YSH61Vxh2pJEmSJEkTYPKjS3vs6qHAukne3H0/yRo0xUkP71ffoo8dkyzX1fYOYHn+WyT0/va6bFe/jwKLj+NZA6uq62m2vayb5HUdt0bql3SfLvNxxrcC5X6a43pHVsuQZCFgl/FHK0mSJEnS+LntpbddaFYsHJJkC5rjaO+lqVfxVuBy4GPjnPM84M9J9gMuA14EbAP8Etiv7fN3mgKmn0yyCM3xti9tn3sOsOAE32csXwV2BPZMcmxV3Qf8nKZw6ueTPA04H1gbeP44Y/kpzfaWU9otNo+hee9z8VQWSZIkSdIs4MqPHqrqZpqTVvagOZVkT5oEwTrAXsCLqmq8J6R8E/g08Hrg6+1cuwGvGdkO0q46eS1NMdBtgN1pVk2sB9wxY2/VX1XdBXwWeCbwoY62DWkKr76pjWXB8cZSVX+kOcr3Dpr3fTdwMPC/kxW/JEmSJEmjyaNrWUpzriTnPOEZT1/zpXt+ZspiOPb1b5uyZ0uSJEnSsJo2bRrTp0+fXlXTxjvWlR+SJEmSJGmomfyQJEmSJElDzeSHJEmSJEkaaiY/JEmSJEnSUPOoWw2dFRZdwqKjkiRJkqSHufJDkiRJkiQNNZMfkiRJkiRpqJn8kCRJkiRJQ83khyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaaR91q6Pzj5pvZ5IgjZ9nzjt1yi1n2LEmSJEnS+LnyQ5IkSZIkDTWTH5IkSZIkaaiZ/JAkSZIkSUPN5IckSZIkSRpqJj8kSZIkSdJQM/kxkyU5OMlFSRaY4jh2SlJJlutoe0eSG5OsOgPzHpjkjkkJUpIkSZKkmcDkxxiSzJvkMTMwxbLAk4H5JymkyfRkYDFg8Vn50CRvSvK7JLcnuSvJmUleOytjkCRJkiTNPUx+jCLJBsB/gOfOwDSvBpavqtlxdcRuwJOr6vRZ9cAkHwcOAa4GPgnsDjwJODrJW2dVHJIkSZKkucd8Ux3AbO6pNL+YT1hV3QPcMznhTK6qKuCaWfzYhYCXVtWZIw1Jvgb8BdgzySFV9dAsjkmSJEmSNMTmqpUfSead6hjE/3UmPgCq6k7gQJpE0wpTEZQkSZIkaXgNbfIjyXZtgc+1kuya5Cbg0vbeY5PsnuTyJPcmuSzJl5Is2N5fLkkBB7TT/bGd67SO+Z/bFvv8e1u34u9JvjAyR0e/Y5Nc3tV2edv+xHaOG5LcmeT4JM/o8z5vS3JuknuSXJfk0CRP7dHvCUm+kuSKtu9F/baT9CqC2rYvkORj7fPuSnJbktOTbDrK950kH0hycfvcy5J8Nskjap1U1YN9prh5pEu/Z0iSJEmSNBFzw7aXtwEvAD4NPKFNTvwSWAn4HnA5TU2PjwCrJ9kIuAHYHlgHeDvwubZf5xaR44HrgEOB64F1aWpYLAy8f4C4FgJ+DfwV+Hwbw9uAk5KsWlX3jnRMsjfwvvZZ+wPLAO8C1k2yRlVd3/Z7PPAbYBXg+8D57Xt+p/3zmNrv5xTgxcDh7diFga2Bo5J8tKq+2mPol4GNaVZw3Aq8BtiVZiXHWwZ49Jo024OuHCROSZIkSZIGNTckP14JrD5ScDTJ7sDybdvDv2gn+R1wMPCKqjoZODAJNMmPY6vqT13zvr+qft7x875t8uFdST5SVfePEdf6wKeraveOGK4FPkVTJPXnbdsraJIpm1TViR19D6Kpk/HhdgzALsDqwEZdfQ8FfjdGPCM+Q5P02bKqjuyYY2+ahM8eSY6uqks7xjwWeDmwZlXd1bbtm+QAYLsk36mqM/o9MMlKwJuAwzuTPqNJck6fWysNMl6SJEmSNPcY2m0vHQ7uSHzMC7yHJslxX5KlRz7Ab2m2XKw/yKRdiY8Rp9Icafv0Aaa4EdirO9b2ukZH2/uAs4Bzu+K9HbhwJN4k8wDvAE7tTHy0sf4R+PFYAbVzvBv4VWfio53jAeATNAmzN3cPBT7WkfgYsUt7ff0oz1wBOIHm+/hUv36SJEmSJE3U3LDyY3rHn58NLAZ8vP30suSgEydZBVgPWBl4Fv89EnfRAYZfVFX3dbWNrETpHP/iNqar+8zzz/a6ArA4zZaeXi4cIKYVab6fU3rdrKo/J7kTeF73LeDsHv3/neR6mu/mUZJsQrOV5yZg/arq9469YpnWZ85zaLbQSJIkSZIEzB3Jj2s7/rxYe/0K/ZMEV401Ybu95VBgU5qExXnAxTS1LrYcMK7buhuq6u52q03nipyRZESvOhsAd7fXkaTNv/v0e2CAmJYYYw5o6p4s3NV2V1Xd3aszcCfNapiHpXnJ3YGdgV8A21fVTQPEJ0mSJEnSuM0NyY/O00PuaK83dm8NGaddaOpydNfW2IHBkx+DugOoAeIdqZWxeJ/7S/Rp734WNAVV+1kK6K63MV+SVNUjTmppkxxP5tH1Rn4AbEuzVeYrA8QlSZIkSdKEzQ01Pzr9HbifAet68N/ESbraNwR+3yMhscoMxNbPBcDzk3Svtuh2CU28L+5z/0UDPOtimpUkG/S6meS5wOOB33fdWoDeW1vWBR4D/KFjjncD29Gs9jDxIUmSJEma6eaq5Ee7NePnwIbtkbaPkORNXU0jWzGW7Wq/H1i6LaA6MvYZNCfDTLYf0mx9+XT3jSRrJ1kOoKpupdkes2WSNbr6bU5zGsuo2pNWDgJelWSzrjnmAb5EU2j14EeP5otd38f8NEcE30lzZO6IjwEnV1WvOSRJkiRJmnRzw7aXbh8DXgYcneRA4E80W0I2p1m98MOOvn+k2U7yhSRPAR6oqu8AP6GpG3JikqNokiM7AMcCb5zkePdv5/x4u/LiWGBemkKrm/LIk2F2As4ETkuyL00x1DVotuLs38Y4lp1pjro9Mslh7XwLA1vRHKP7hqq6oWvM3cDTgDOTHElTX2Sbtv/bqup6gCSLAs8A/ppkuz7Pv6DHscKSJEmSJE3YXJf8qKork7wQ2I0mebA9zTGrZwAf6up7TZLtaYpz7sV/Vzx8jWY7xzuALwN/a+dZnElOflTVA+0qlU8BW7fPvhP4M7BNVZ3X0ff8JC+lWaHxPuDB9r3WBf5nlMc82DHHrUleQpME2ap95m00RwHv2Ccx8VA7/9faOB9DUxfk1V1bgxZprxu3n16+QpOQkiRJkiRpUqSrRqXmIkl2p0lWLNpum5njJTnnCcsvv+ZL99hzlj3z2C23mGXPkiRJkqS51bRp05g+ffr0qpo23rFzVc0PPcpywHXDkviQJEmSJKkXkx9zqbb+xsY0NUQkSZIkSRpac13Nj7lZkicDHweuoDludh7gi1MZkyRJkiRJM5vJj7nLA8BmwFI0BUk3rKp/TGlEkiRJkiTNZCY/5iJVdR1NnQ9JkiRJkuYaJj80dFZYbDFPYJEkSZIkPcyCp5IkSZIkaaiZ/JAkSZIkSUPN5IckSZIkSRpqJj8kSZIkSdJQM/khSZIkSZKGmqe9aOhcevPtbHbkL2fZ847aYsNZ9ixJkiRJ0vi58kOSJEmSJA01kx+SJEmSJGmomfyQJEmSJElDzeSHJEmSJEkaaiY/JEmSJEnSUDP5IUmSJEmShprJj9lYkgWTvD/Jr5Ncl+S+JNck+XOSPZI8dYLzbpykkhwx2TFLkiRJkjS7mW+qA1BvSaYBPwOWBI4CjgHuAJ4ITAPeC5wPHDqB6d8K3Au8JsliVXXzZMQsSZIkSdLsyOTHbCjJGsBvaJIb61TVv3r0WRRYZAJzLwq8BjgI2AF4A/CdGQhXkiRJkqTZmtteZjNJ5geOAP4FbNgr8QFQVbdU1RVjzNUrufUGYAFgd+AcmlUgkiRJkiQNLZMfs59tgWcCH6qqOwcdlGTdto7HFkl2SHI1cH+f+U+vqiuBQ4AXJVmxx3zLtfPtlORZSX6e5OYkdyQ5OcmaE+2f5INt31f0eO48Sa5Kcuyg7y5JkiRJ0mhMfsx+Xg9cW1UnTXD8BsBOwBeBL3TeSLICsDZN0gPgcOABRl/98XTgDOAWYGfgq8CawOlJnjfB/j8CHgS26DH+5cCTgQNHiUmSJEmSpIFZ82P2swbNdpRHSDIvTfHTTndX1a1dbW8AVquqq3rMvS1wD3AkQFVdl+RkYJsku1TVQz3G7Ai8uap+1BHLocBfgK8AG463f1Vdm+RUYLMkO1bVgx3j3wjcRFPgta8kj/qOWiuNNk6SJEmSNPdx5cfsZwmg1+krTwWu7vp8vUe/o3olPpIE2Ab4RVfC5JB27vX6xHNGZyIDoKr+TnPKzPpJuouuDtr/MOBJwEs7YpyfZuXLj6rqvj7xSJIkSZI0LiY/Zj93A4/r0X4tsFHH554+46f3aX8ZsByPPhr3aOA2+m99OXOU5wRYYYL9f07zrp1bX15Bk/w5sM8cD6uqab0+wMVjjZUkSZIkzV1Mfsx+LgdW6W6sqrur6sSRD03NjF6u7dO+bXs9ui02WkkKuAt4AvC6JI/vMe7GPvONFGN9zET6V9XtNFtbNm9XpUCz5eWiqvpjnzkkSZIkSRo3a37Mfk4FPpxklaq6aALjq7shyUI0KyxOB37bY8yTgHe2fQ7sujd/n+cs216vn4H+h9HUKFm7reGxKc0RvJIkSZIkTRqTH7OfbwMfAPYENpmkOTenWd3x2ar6dffNtpjqpjRbXw7sur1qnzlfSbPK49IZ6H9i27YFsDTNdp9DHj1UkiRJkqSJc9vLbKaq/gHsCmyc5HtJHtvdJ8l8NPUzBrUtcAPNyo9ez3wQ+Anw8iRP77q9VZLndz3/VTQ1RL5fVd0rTQbuX1X3A0fQJF5eDZxcVVeP470kSZIkSRqTKz9mQ1W1e5IFgE/TJEGOBP4KzAssD2wFPJamYOiokixDcxztAV1HynY7HHgfTaJkt472M4HfJPkecBHwHJotMn/p6jfR/ocB727f54NjvY8kSZIkSePlyo/ZVFV9BlgTOAHYGPgqzVaY19LU7diKJlkxlrfQJE1+Nka/s2iKrW7b1f6Ltm09mqN1NwP2BV5eVXf0mGe8/X8HXAEsQHPyjCRJkiRJk8qVH7OxqjoXeNuAfU+jx1aYqtoL2GuA8UWzqqTXvSOBIweJY7z92+cuN+jckiRJkiSNlys/JEmSJEnSUDP5IUmSJEmShprJD0mSJEmSNNRMfkiSJEmSpKFmwVP1VFWX06OA6mT1lyRJkiRpVjH5oaHzzMUW5qgtNpzqMCRJkiRJswm3vUiSJEmSpKFm8kOSJEmSJA01kx+SJEmSJGmomfyQJEmSJElDzeSHJEmSJEkaaiY/JEmSJEnSUPOoWw2df95yN1v+9C8z9RlHvP65M3V+SZIkSdLkceWHJEmSJEkaaiY/JEmSJEnSUDP5IUmSJEmShprJD0mSJEmSNNRMfkiSJEmSpKFm8mMul+RzSa5KstQMzHFakgsmMy5JkiRJkiaLyY85XJLHJJmRv8enAksCj5ukkMaUZN4k70tyTpK7ktye5JdJ1plVMUiSJEmS5h4mP+ZgSd4CXAcsPgPT7AA8par+OTlRDeSbwJeB84GdgL2BVYFfJ9lgFsYhSZIkSZoLzDfVAWiGrAAsMiMTVNUDwPWTE87A7gNWr6qLRxqS7AtcCOwBrDWL45EkSZIkDTFXfsxEacw71XHMhj7amfgAqKqrgZ8DayZZYGrCkiRJkiQNI5Mf45BkgyTHJ7kxyT1JLkmyY8f9y5Mcm2SVJGcB9wPbdNxfJ8mpSe5o61ycmuQlXc9Iko3b51yZ5M4k5ybZtqPPukkK2LVtuj5JJTmwa663tWPvSXJdkkOTPLWrzz7tXN3vukiS3ZNc3I6/OcmJSV46yvczf5LPtt/DPe3Y93f3q6oH+0xxc7+5JUmSJEmaKLe9DCjJTsBeNHUq9gLupNmesS7w7Y6u8wE/A44Bfghc2o5/PfAj4DRgF+AxwJtp6lxsWFWnt+OfCfwCOAH4Ok0C5c3AQUnurKqfAhcD2wObAZsC7wfuAP7REe/ewPuAQ4H9gWWAdwHrJlmjqvpudUnyROB0YHngAOA8mqKob23j3bqqjugx9HDg6cA3gAeANwDfSLJUVX263/M6rAlcUVX3jtUxyTl9bq00wHMkSZIkSXMRkx8DSPJCmloURwBvautkjNx7bFf3dYGdq+prHX2eCPwA2LOq/rej/WvAn4EvAWu3zbcB61TVmR39vgdcQZPM+GlVXQMcmGQ5muTHj6rqho7+r6BJiGxSVSd2tB8E/AX4MPCpUV75G8CKbRxnd4zfGzgL2D/JSVV1W8eYlYHLgReNrOxI8i3gJGDnJPtX1RX9HphkPWA94LOjxCVJkiRJ0ri57WUwH6RZ6fHOzsQHQFXd1dX3fpqVFp22oUk07Z9k6ZEPsBjwS+CFI0mUqrquM/HR8YyzgGcNGO/72v7ndj3vdpqiouv3G9gmarYCDupMfLRx3Emz1WYRmlUnneYBPtC5paX9867AvD36dz7zBcBPgQtoToEZU1VN6/WhWRUjSZIkSdLDXPkxmJcCv66qWwfo+7c2SdDpxcBjgctGGbcEcBdAkvmAFwLr0KzAWBFYHciA8b6YZpvK1X3uj3as7Vo0yYpT+tw/o70+r6v9mj7H5Z7bXnsmbpK8A9iHJimzcY/vTpIkSZKkGWLyYzBLAdcM2PfaHm2Lte3bjTLuBoAkKwFH0mwjuQj4K3AmsACwyoAxLEaTvPhqn/t3jzJ2ifb67143q+r6JA8AC3fdunGUZz0IzN/ZmOQxwH40dUT2Bz5YVaPFJUmSJEnShJj8GMw9wFMG7Puok1NoipEuCpxUVb3udzqIZpXIilV16Uhjkh8yePLjDqA6632Mwx3tdZleN9ttMfPx6JNZHtNnvmVoVpI8XGA1yfw0RV1fCry5qn44gTglSZIkSRqINT8Gcy7wgiSPm+D4C2hWbqw9WqckCwMvAA7rTHy0eiU+RhIp3dthLgCe3843XtPb6wZ97o+8w++72pfv8/28sr3+oaNtN5q6I68x8SFJkiRJmtlMfgxmf5oaGrt130iy+ADjf0STqPhSu92jc/xTkqzb/vgg8BCwbFefrXh0jQ2Am9rrsl3tP6TZ+vKo42WTrN2eEtNTVf0LOBF4e5Lnd41dCPgczckzx3YNnQ/Yvav/wsDONFtoTmjbFqA5ieYHVXVqvzgkSZIkSZosbnsZQFUdkuR/gA8nWR04pr21Ns2WjtePMf7CJF8CPgmck+QQmiNtVwfeTJOkOK2q7kpyHPDWtq7Gn4E1gNfQ1PDoXjkyUnz0W0kOBW5pV1LsD7wR+HiS59IkKualOUp203bO0bwb+B3wmyQHAOcBTwS2BZ4MvKqq7usacyWwWZJVgOOBBYG3A0+jOXL3/rbfyjTbehZIsl2f559dVZ7aIkmSJEmaFCY/BvcW4LfADsAXaU5mORfYY5DBVfWpJJfQHEP7OZqVIH8HvsQjj8bdrp1zY2BrmmKn6wGf6DHnn5N8BPgosFcbF1X1QJKNgE+1c3yN5qjePwPbVNV5XVM91DXvFUnWAnahSby8k2aVySnA56vqkh6veDuwCfANmhUyRZNAeUtVdW6RWaS9vrX99PJ+PLJWkiRJkjRJMnb9TQ2zJIcBr66qxaY6lsmQ5JxFn7Hymhvu+aOZ+pwjXv/cmTq/JEmSJOmRpk2bxvTp06dX1bTxjrXmh5ajWYEiSZIkSdJQMvkxF0vyTOBFPLp4qSRJkiRJQ8OaH3OZJGvSFGi9laaw6TXAPlMalCRJkiRJM5HJj7nPAzSnsCwMnA58sKpuntqQJEmSJEmaeUx+zGWq6i/A0lMdx8z0jEUXsiCpJEmSJOlh1vyQJEmSJElDzeSHJEmSJEkaaiY/JEmSJEnSUDP5IUmSJEmShprJD0mSJEmSNNRMfkiSJEmSpKHmUbcaOtfccj+7//zqSZ/3fzdfZtLnlCRJkiTNfK78kCRJkiRJQ83khyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaayQ9JkiRJkjTUTH7MBEkqyT4D9n1nkhuTrDqz45oKSbZov491pzoWSZIkSdLcabZMfiRZrv2Fud9nuamOcRItAywGLD6zH5TkMUl2THJqkmuT3Jfk+iR/SPKRmf18SZIkSZKmwnxTHcAYfgyc2KP9hlkdyEy0G7BfVV0zMx+SZBXgaGA54HjgK8CtwLLAC4C3AF+dmTFIkiRJkjQVZvfkx5+q6sCpDmJmqqoCZnbiY3ngt8BNwOpVdWGPPkvMzBgkSZIkSZoqs3vyY1ySzFtVD051HLOhQ9vrelX1714dqurGWRiPJEmSJEmzzGxZ82MQSbZr63+slWTXJDcBl7b3kmTjJMcnuTLJnUnOTbJtj3kqyT5Jlk/y8yS3JLk1yY+SPLHPs7dM8uu2711JLkjy+h79np7kp22/25McleSpXX126q5jkmShJDsnubCd/7okR/WYf9V2zpE4zkry2q4+GwBrA5/rl/jo847zJNkhydlt7Hcm+WOSt/Xpv0CSXZJckuSeJP9MshOQUZ7xtvbv5Z72HQ/t/n4kSZIkSZpRw7Dy4200NSs+DTyhbXsm8AvgBODrwP3Am4GDktxZVT/tmuNJNNtCTgR2AV4GvAF4cvvnhyX5JvA+4HfA54EC1gFeAnTO+yTgDOCUds6XAlsCyyVZo93u0s8vgBcD3wIuA5YHtu6KY23gZOAi4AvAA8DmwFFJ3lJVP2y7bg48BBw8yvMeIUmAw4GtgGOAQ4D5gc2A7ydZvao+0NF/Xpp6Iq8EDqOpJ7Ic8CngH32esTfN93gosD9N4dd3Aeu238/1g8YrSZIkSdJoZvfkx8JJlu5qu6eqbun4+ZU0dSzu6Gi7DVinqs4caUjyPeAKml+4u5MfWwJvqarD2p+/meSHwNbtL+J/bufYsh2/d1V1no6yd5LH9pjz7VX1g445D6EpLPpC4OxeL9weebsBsFNVfaWj/X87/jw/TZLhKGCbkURKm1A4BdgzyeFt+5rAJV3f2VjeSZP4+FhVfbmj/WtJfgC8P8lPq+o3bfsOwKuA91TVdzri/A7w5x7v+Arg/cAmVXViR/tBwF+AD9MkTvpKck6fWyuN9XKSJEmSpLnL7L7t5TPA1V2fA7v6HNyV+KCqrutMfLRtdwFnAc/q8ZzzOxIfIw5qr2t0tH2YZiXGx7onaOfvdFFH4uPhWNvraj1iGDGyTWTBrvnv7/hxE5qVFXsDSyVZuk0SLQWcBDwFeHbbdwnGfzrOe4FL2vm7fQq4D3hrR9sObf/9umK+HNinxxzvo/m7OHck9jb+24ELgfXHGa8kSZIkSX3N7is/vsejV2lc2/Xz9F4Dk8xHs8JiHWDF9rM6vWtQnNuj7cr2umg73wI022u+NWBR1V5x/au9LjzKuAuBc4DPtQmBr7RJhE4vbq9/GmWeJYGLgXuB7lUpfbUrWFajOX73Ue9ZVdck+QfwvLb/QsBzgf2r6qE+79PtxW18V/cJ459jxVlV0/rEfw7NahdJkiRJkoDZP/nxt85tEX10J0NIshJwJLAyTU2MvwJnAgsAq/SY47YebXe315HVMUsA8zL4sbS39mi7p2vOR6mqh5K8EvgSzYqK9yQ5AvhEVY0kZBajqeOxCU3NkV5Gkg5X0tTRmK+qHhgg7sVoEkSjFUe9Gnha++fF2/fp17/XMxej2Z7z1T5j7u7TLkmSJEnSuM3uyY9B9Prl/yCa1Q4rVtWlI41tHY9eyY9BjCQunjLB8QOrqpuAHZJ8hqY2xkdoEhgrt7U77qBJOJxbVf1WT4z4NbAxTU2O4wZ4/MgWomVG6bM0cHP753vb6+J9+i7R5xk1QGJLkiRJkqQZNrvX/Bi3JAvTbE85rDPx0Zpo4mMkIfEv4OVJZsn3VlXXVNX/0hRJXZrmtBWAC9rrILUxDgTuAr7Qbt0Z65m30mw7Wa/XeyZZElgB+H3b/wbgRv67Fafbi3q0XQA8v/27kiRJkiRpphq65AfwIM2WkGU7G5NsRVunYgbsD6xKU7DzEZL0W/kwLkmW6HFyzH9GbrfXo2i2hnwmySJd4xdJsvHIz1V1I/BRmrocP0uyWJ/nPrvjx/1otgx9oEfX3Wi2/3y3o+2nwIuSvLZrzufTJG66/ZBm68une8SxdpLlesUoSZIkSdJEDMO2l0eoqruSHAe8NckDNEetrgG8hqbOxNozMP0eNMfQfj3J+sAvgYVoVmBcTHMazIxaDTis3aLzd5ptIzvQ1Nk4GpqERpIP0SQgzkvyfZraJysB2wCH0rHFpaq+0yZJvgBcmuQnwPk0SYynAeu1f169HfJVmiOE907ycprvbX6a73B94ANV1VnI9LPAa4GfJNm/nXtF4G3At3l0EmV/4I3Ax5M8Fzi2ff56wKY88oQdSZIkSZJmyNAlP1rb0SQqNga2pil2uh7wiRmZtKruS/IqmhocbwH+h6aw6e9pVjNMhr8DvwHeTFNH42rgROAL7dabkVj2S3IN8HFgZ5rkwWXAD2jevTv2PZIcD3wQeAXNd3RvO//v6ThCuKruT7IRTTJnW2AjmponfwBeWVW/7Jr76iRrA3vSJF/mozmJZlOa43c/0NX/gXb+T9H8/XwNuJMmUbVNVZ03ju9LkiRJkqRRparfYSHSnCfJOU9+xmpr7vjlkyZ97v/dfLQasJIkSZKkmWnatGlMnz59elVNG+/YYaz5IUmSJEmS9DCTH5IkSZIkaaiZ/JAkSZIkSUPN5IckSZIkSRpqJj8kSZIkSdJQG9ajbjUXW3rR+T2ZRZIkSZL0MFd+SJIkSZKkoWbyQ5IkSZIkDTWTH5IkSZIkaaiZ/JAkSZIkSUPN5IckSZIkSRpqnvaioXPLzQ9w1BE3TOqcm235xEmdT5IkSZI067jyQ5IkSZIkDTWTH5IkSZIkaaiZ/JAkSZIkSUPN5IckSZIkSRpqJj8kSZIkSdJQM/khSZIkSZKGmsmP2VCS5ZJU+3ntKP3WavtsNwPPurzjWd2fCc8rSZIkSdLsYr6pDkBj+nqSU6rq7pn4jPOAr/VoP2MmPlOSJEmSpFnC5Mfs7VRgA2AX4FMz8Tn/rqoDZ+L8kiRJkiRNGbe9zN5ObD8fTbLSVAcjSZIkSdKcyOTH7O+9wIPAtwcdkGTNJEckuTbJfUmuSPKNJE+caBBJTktyQZLFkxyc5KYk1yfZM8m8SeZJskv7rHuSTE+yftcc67a1RLZI8vwkpyS5LcktSX6a5JkTjU+SJEmSpH5MfszmquqfwG7AeknePFb/JJsDZwPPA74BvB84DtgBODvJkjMQzvzAScADwOeAc4GPAbsD3wLeDHwV2AN4BnBskqf1mOf5wMnAxcBOwPeAVwG/S/LkGYhPkiRJkqRHsebHnOHLwFuALyc5tqpu7dWpXdlxEE1SYt2quqvj3o+B04A9ge27hi6QZOmutoeq6rquthWBz1XVZ9s5vwmcCXwQuA1YpapubO+dQZPgeDPwxa55PgysV1W/64jvWODXwOeBd/T8Fh75ruf0ueX2IEmSJEnSI7jyYw5QVfcD7waWAv5vlK7bAAsDH+1MfLRz/AY4Bnhjksd0jdsQuLrr8/ce8z8AfKVjzoeAI4AFgW+PJD5apwL3AKv2mOfwzsRHO9dp7ZjNR3k/SZIkSZLGzZUfc4iq+m2SA4AdkxxQVdN7dHshTcKh3xG1ZwCvBZ4NnN/R/gdg166+9/cY/++qur2r7T/t9RHxVNVDSW6gScZ0O7NPfNOBDZIs0ZVIeZSqmtarvV0RsuZoYyVJkiRJcxeTH3OWj9MkL/ZN8uIe95cArqqq6jP+6vbanZC4vqpOHOD5d/ZoG0mSdCdFoCnU2mt1Ub/Exsj83StTJEmSJEmaMLe9zEHa1RA7AS+gKWDaneS4A+iu3dFp5N7Nkx/duMzfp31ZmoTJTbMwFkmSJEnSkDP5MYepqoNoCpd+gWalR6fpwOOSvKjP8LWBW4C/zaz4BvSoOiBJ5gU2AP5SVffO+pAkSZIkScPK5Mec6T3A44D/7Wo/CLgP2DvJQp03kqwNbAbs2xYqnUrvTbJcV9s7gOWB/Wd9OJIkSZKkYWbNjzlQVV2cZA9gl672K5O8F9gPODfJQcANwHOBtwG/ozlKttuySbbr0X5lVf1qUoNvnAv8Ocl+wGXAi2hOqvklTeySJEmSJE0akx9zri8AWwMrdDZW1feS/BP4BE2B1AWBS4HdgK/22VLyPOCAHu1HAzMj+bEPsCTwEeBpwL/a+L5UVQ/OhOdJkiRJkuZiJj9mQ1V1OZAx+twDPKvPvV8xYNKiqpYbsN+6fdqPpE+so81dVfsC+w7ybEmSJEmSZoQ1PyRJkiRJ0lAz+SFJkiRJkoaayQ9JkiRJkjTUTH5IkiRJkqShZsFTzTJVdRpjFHKVJEmSJGmymfzQ0Fl0sfnYbMsnTnUYkiRJkqTZhNteJEmSJEnSUDP5IUmSJEmShprJD0mSJEmSNNRMfkiSJEmSpKFm8kOSJEmSJA01kx+SJEmSJGmoedSths6dNz7AHw64btLme8H2T5q0uSRJkiRJs54rPyRJkiRJ0lAz+SFJkiRJkoaayQ9JkiRJkjTUTH5IkiRJkqShZvJDkiRJkiQNNZMfXZJUkn2mOo45UZKNktyS5BUTHO93L0mSJEmadHN18iPJglMdw2RI8pgks/TvMslCPZqXBBYBlpqVsUiSJEmSNJq5MvmR5DlJrgA2mepYZlSStwDXAYvPwmd+HLiiu72qDgaeUlWHzqpYJEmSJEkay1yZ/KBZofC0qQ5ikqxAs9piVloFeGyvG1X1/+3dedytc73/8dc7Q6QJRyNSURQNeyvDUW1Uv5xKOdFc6IQGTpGG46RJpcGJShrVNnRK1EGKkqiUJJsQkWSe7czj5vP747ruWi1r3fu+73VPe3k9H4/1uPb6Xt/vdX2utb+P+3Gvz/0drpzmWCRJkiRJGtWMJz+SLD3TMUiSJEmSpOE1cPIjyYOSvCvJOUnuTHJ5kk8k2ahdwHK7jrrz2rKtk+yY5Crgnq5r7Zjkt0luSXJbktOSvKXrnj9LcnGPWHZpr/+irvKXt+UbJingxPbU4W15r2s9Icn32wU8b0lyZJLVJvD5VJL9kzw6yUFJrktye5LfJHlhnzZrJvlWksuS3J3kyiTzkzyho8689lk+3BZd195rfte13pLkzPb/5tokh3Y/R3vtW5MsleRDSS5u65/RGWOS7dp7bgus0N6vknykPb91+35eR5ulk7w+yc/b57g5yalJthjvZylJkiRJ0kRMxqiLb9J8GT4GOAB4FPBWoOcX+9bm7Wtv2sUxkwT4DvBq4GjgEGAZ4JXAgUmeVVX/2bY/FtgnyZOr6i8d131pe9wMOL6jfFNgIfA7YHtgbeD9wJeA3wO3dsX3KODk9hp7As8DtgHWSPLsqqpRP5H7ewTwa+APwIeAFYEdgeOSvLSqfjJSMclGwHHAbcA3gMuBtYAdgH9LsklVXQD8qX2WVwKvAHZpn+PCjmvtC+wMHAp8HXgssBMwr32O67ri/BrwdGD/NsZdgB+1n/157WeyPc3/7/rA29p2Z47y7POAg4AfAEcBS7XP/qMkz62q34/+0fWW5PQ+p9aeyPUkSZIkScNroORHkpfSJD4+XVUf6Cj/Ik1SoZ/XAOtV1RUdZTvQJD7eW1X7dJTvl+SbwC5Jvl9Vv6BNftAkUP7S3vMhwAuAM2iSH502BY6vqvuA+e3IhPcDJ1XVET3i2wb4j6r6Zvv+i0kOAd4IbAD8dpRn6+X1wH9X1adGCpJ8Hfhje+2nVlUlWQb4Lk2i5rmdyYkk36BJMnwV2LSqrm6fZQ2a5Md3q+r6jvovoklevKyqjusoPwg4C9gV2KMjxhWAxwGbVNWitu5JwE9pkhzvqqoLgQvbz+9ZVTV/DM9+GfCMNnkyEsNhwEXAO2mSKZIkSZIkTZlBp738B3AT8InOwqq6obusy5FdiQ9ovgj/Gdi3R/09gLtpEi1U1bnApfxzkmOzts4XgLlJHg6QZCXgmcBPGLtzOxIfIw5uj+uN4zojLgY+21nQJja+QDOqY+SaL6NZiPVD3aMyqup8mpEZ85KMZbHWnYFTgDOTPGbkBdxCk3TpThBBk6BZ1HHP44GrgGeP4X49VdX5nYmPtuwK4HyaZ5/odef2etGMiJEkSZIk6e8GnfayAXBqVd3S49w5o7Rb0PmmHbWxHvC1qrq3u3JVXZ3kQpokxojjgK2SpJ2G8m8001SOpUnqvAD4YXtMW3+sFvQou6w9Pmwc1xnxu17P1XGftWhGY2zQvj++R11opp38J83ncOli7rkRza42V/U5f1HX+3uBs3vUuwx45GLuNaokywGbtDGt1fH6y2jtJEmSJEmaDIMmP1ahWZOil0V9ygGu6Xq/Ik2Cot+1oPkS3zni4ViatSPWo0kcbAF8rKquSXI2zciGH9JMeTmrqvolAXq5qUfZne1xIqNlbuhTflt7XLY9rgwsaqe09DLyDGNJwKxIk0T5XJ/zd3S9v7Oq7ulTb4Ux3K+nJBvTTOV5LM3/0/nAz2n6jiRJkiRJU27Q5Mc9NF+ye1l5lHbdC4aOLDj62FHaPAb4W8f7E9r7b5ZkEfAEmoQINF/6R3Z82bSjfKYs06d81fY4MsXlVmDpJKv0WIwUms8A/vlz6OdWoDrX+5huSZalSXxcRbOGydUd5zYFHj5TsUmSJEmSHjgGXfPjApr1NXolUTYc60Wq6iaaaRibJrlfTElWAdYETu1ocwvNDiqb00x5WdDx5fp4YL0k69DsXtKdABhJvmSsMQ5o3T7lL6YZITMy/WXkuHmf+hsD9/HPi8n2e5ZzgOckmcg0ncWpHvfr5anAajTTmToTH0u15yRJkiRJmnKDJj8Op5mK8s7OwnYHkl3Hea2vAevQrGnRbS+aLVK/2lV+LPB8mm11f9xR/kuaxU//C7idZq2MTgvb46pMj42TbNVZkGQ94HXAD6pqJJ7/A64HPpFk5a76T6bZpvawrlEh/Z7lf2lG5XywO5gkG7f/RxO1EHhIkn6jfkaMTKPpju09wEoD3F+SJEmSpDEbdNrLfjRf4D+XZA7NSIzVaL6kf4PmS+5YfY5mJMS+SV5AM3pjGeDlNOt3/GdV/bGrzbHAp2kSIB8eKayqO5KcDPw7cGJV3d3V7gLgWmD3JAUsU1WfZeqcAnw7yXeA02kSRm+nWftkt464b02yLU0S5Kx2e9vLaUa97ECzyGl3cmgksfOlJIcCN1bV/wJfB14LvC/JM4BjaBJIm9JsjTvhHVzae+4GHJrkR8ClVXVMj3oX0GzP+19JHkEzuud5wLNoPoflBohBkiRJkqQxGWjkR1XdDswDDqSZerIfTQLjLcD322p39mrb41r30Cxa+gHgKTTJkI/STPN4cVXt36PN2cAVNOtbnNZ1+niahTrvt+ZFVd0FvJ5mYdNP0Xvb18m0gObzWRf4H2A7ms9ng+4tf6vqxzRThk6l2a52f+BVNCNjNqyq67vqn0GTiFidZjvdJ7Xli2g+z0/QfJ77AR+j2bnlTVX1hwGe58j2Xs+h+fz+pVelqroP2BI4GnhTG8tIAubWXm0kSZIkSZpsaXaJnYILJ/9O8wV/86r6+ZTcZAnQjiz5UlXtPNOxPBAkOf2pT3jGnIM/3G+34PF77vaPmrRrSZIkSZImZu7cuSxYsGBBVc0db9tBp72M5kXAvcAZU3iPGdEu2Lm4rVpvqqru7WQlSZIkSdI0Gyj5kWQ14Naq+ltX+YY0U1+O7D43JFYD/rqYOtsD86c+FEmSJEmSNJpBR37MBQ5O8n2a7VfvA55Lswjq1cC7B7z+bHUNzXoaozl7OgKRJEmSJEmjGzT58Vua7We3oFmUcxngEuCLwGe6tmQdGu10lvstpCpJkiRJkmafgZIfVXU18N72pR6qKjMdwwPNCisv7SKlkiRJkqS/G2irW0mSJEmSpNnO5IckSZIkSRpqJj8kSZIkSdJQM/khSZIkSZKGmskPSZIkSZI01Ex+SJIkSZKkoTbQVrfSbHTPNfdw+T5XT9r1Vt39MZN2LUmSJEnS9HPkhyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaayQ9JkiRJkjTUTH5IkiRJkqShZvJDkiRJkiQNNZMfS7AkaySp9rXlKPXWb+ts1+f809vzpy3mficlOWeU809M8rkk5yS5OckdSS5LcnSSDbrqrpPkm0kuSXJ3kjuT/DXJxxfz2JIkSZIkjcvSMx2AJs3nkxxfVXdMoO22wF3A+knWqarzxnuBJDsD+wB/A34A/IkmubYGsCWwEXBqW3dL4HDgEmA+cBXwaGAusOkE4pckSZIkqS+TH8PhBGBzYE9gj/E0TLIU8Abgu8BraRIhHxjnNd4F7Ad8A3h3Vd3WdX43YMX238sB3wJOAzarqru76q48nntLkiRJkrQ4TnsZDse1r/ckWXucbV8IPI4mcXEM8MYkY+4XSZ4OfBb4XlXt0J34AKjGwvbtesBKbf27e9S9YZzxS5IkSZI0KpMfw+OdwL3AAeNs92bgr8CvgUOAx9MkRMbqA+19dxlj/Zvb43iTNJIkSZIkTYjJjyFRVRcBewGbJnnDWNokeRjwSuDbVVXAj4EbaKa+jKX9UsDLgWOr6toxhvpn4A/Ajkl2TpIxtpMkSZIkaUJMfgyXfYBzgX2SPGIM9bcBHgIcClBV9wCHAVslefgY2j8ZeATwu7EGWFX3Aa8DLgW+CJye5CVjbT8iyem9XjiiRJIkSZLUxeTHEGmTF2+j2TllLFvGvhk4rarO7yg7BFieJjGyOCOLk14/zjjPA54JfAFYFzg2yc+SrDme60iSJEmSNBYmP4ZMVf2KZjeVdySZ069ekjWA59OO+uho/1uaqSljmfpyV3t8yATivKWq3gU8jWanmc1pRoE8f4zt5/Z60WyxK0mSJEnS35n8GE7vAxYCXx5l55Y3AQE+n6Q6X8BawCZJnrSY+1zWHp820UCr6sKqeh2wBc3Wy4cmWX6i15MkSZIkqdvSMx2AJl9V3ZBkd2A+sCNwWo9qbwLOAn7Y49yDgd1ppsV8ZJT7XJfkHODlSXZpp91MNObjkuwB7AdsAhw/0WtJkiRJktTJ5MeQqqqDkmwHfBJ4bee5JBvTjO54S1V9q1f7JJsCb07y0XYnmH6+BHwZeA/wqQHDvqg9PnLA60iSJEmS9HdOexlubwdWAP67q/zNwCLgqFHafgd4IvC8xdzjG8CvgY8neXuvrWuTLJ9ktfbfT0myfp9rvQq4j3HsHiNJkiRJ0uI48mOIVdWfknwa2HOkLMmDgdcAJ1XVwlGaHwZ8lmbh01+Oco9FSbYEjgQOAHZKchRwBc1uME8GtgL2opnS8jjgxCQnAz9v660IvASYB+xVVZdM4HElSZIkSerJ5Mfw+yTwOmBkG9ktaaaV/GC0RlV1eZJfAdu063ncPkrdhUnmAa+nWUvk7e09bgAuB74IHN5W/wOwN83uLu8CHtrW+z3wiqo6erwPKEmSJEnSaEx+LMGq6mKaHVtGq3MnzfoenUZt09H2BV3v541S9z6abXMP7Venrfc3YI+x3F+SJEmSpMngmh+SJEmSJGmomfyQJEmSJElDzeSHJEmSJEkaaiY/JEmSJEnSUDP5IUmSJEmShpq7vWjoLPPoZVh198fMdBiSJEmSpFnCkR+SJEmSJGmomfyQJEmSJElDzeSHJEmSJEkaaiY/JEmSJEnSUDP5IUmSJEmShprJD0mSJEmSNNRMfkiSJEmSpKFm8kOSJEmSJA01kx+SJEmSJGmomfyQJEmSJElDzeSHJEmSJEkaaiY/ZpEk6yepJNtN0fWfmWRhku2n4vqSJEmSJM1GJj8eWB7Zvh4/k0EkWTXJV5JcmuTuJFckOSDJijMZlyRJkiRpOC090wFo+lTVL5I8DrhmpmJIsgKwALgdOAi4GngusBPw/CTPrarbZyo+SZIkSdLwMfnxAFNVV89wCMsDRwM7V9WdbdmXkpwCfBl4C7D/TAUnSZIkSRo+Tnt5AEgym5JcC4EdOhIfI74O3A1sPP0hSZIkSZKGmcmPGZDkQUl2TnJ2kjvbNS/2BpbtUXdeuwjq1kmek+T4JDcnuTHJ95M8eZT6Oya5CrinPXe/BVWTbNeWrZ/knUkuTHJbkp8lWauts2mSU5LcnuTKJHsluV/fSbJ6kkOSXN8+1x+SvKWzTlXdV1XV3baq7gVuAe53TpIkSZKkQcymEQEPJF8F3gr8kGaqx6NppntsPkqb59CMjjgUOBx4CvA24F+TzKmqK7vqb96+9m6vvzi7AmsDBwCrAjsDxyf5D+DItvww4DXAB4Gbgc+ONG4TJScDfwP2pUlkvAg4MMmjq2rv0W6eZHVgZeCCMcQqSZIkSdKYmfyYZkm2oEl8fLqqPtBR/nngtFGa7gpsWlW/7mhzDHAi8LH2mp1eA6xXVVeMMbRnA+uPLDaa5ELgS8AxwE5VdXBbfgDwp/Z+n+1oPx84F/h/VXV3W/aFJAcCeyb5clXdOMr9PwbcBxwylmCTnN7n1NpjaS9JkiRJeuBw2sv02xG4EfhEZ2FVLQQ+OUq773QmPto2JwEnAFv1qH/kOBIfAF/s2mXlsPZ4JR0JiTaxcTywVpJlAZI8m2atjv2AlZI8ZuTV1l0e2LDXTdPYC9gW+GhVXTSOmCVJkiRJWixHfky/DYBTq+qWHuf+OEq73/QpXwBsnmTlqrqhq3w8zu18U1U3JLkLOLPHGh3XAgEeSrOA6UZt+ZGjXH+V7oIkD6cZMbIV8Mmq+thYg62qub3K2xEhc8Z6HUmSJEnS8DP5Mf1WAS7vc27RKO1u6FN+W3vsXiz1mvEE1XGd7nh6JWnubY8jI4dWbI/b0iRGejm7802Sp9MkS/4F2Lqqvj+eYCVJkiRJGiuTH9PvLmClPudWHqXdMn3KV6VJRizsKp/OXVNubY9/rqpTFle5nSZzAnAp8OKq+utUBidJkiRJemBzzY/pdz6wfpJeiaee62K01u0uSLIUzY4uZ1XVXZMU30Sc0x43W1zFJMsBPwAuAZ5v4kOSJEmSNNVMfky/I4DVgHd0FiZZg2ZHl37e2dbp9FbgiTRb4M6kXwBXALsmWbXzRJIHJ3lVR9E2wBrAO6rq5ukLUZIkSZL0QOW0l+n3eeCNwL5J5tAsZLoqsBNwILBbn3ZnAmck+RrwV5pRIm8CfgZ8bYpjHlVVLUqyI3AU/4jxYprEzBtoFl8dWdNjZKHSpyd5ap9LHlpVo61/IkmSJEnSmJn8mGZVdXuSecDewCuAV9Ps8rITzUKo/ZIf+9MslrobsDpwGbAX8KmqurdPm2lTVT9un+vDwC7AcjQx/ojmWUc8oj2ONlrlCP6xjogkSZIkSQPJ/Xcx1WzSJhROBLapqiNmNprZL8npc+bMmXP66afPdCiSJEmSpEk0d+5cFixYsKCq5i6+9j9zzQ9JkiRJkjTUTH5IkiRJkqShZvJDkiRJkiQNNZMfkiRJkiRpqLnbyyxXVScBmek4JEmSJElaUjnyQ5IkSZIkDTWTH5IkSZIkaaiZ/JAkSZIkSUPN5IckSZIkSRpqJj8kSZIkSdJQM/khSZIkSZKGmskPSZIkSZI01Ex+SJIkSZKkoWbyQ5IkSZIkDTWTH5IkSZIkaaiZ/JAkSZIkSUPN5IckSZIkSRpqJj8kSZIkSdJQM/kxyZKclOScxZXNJkl2T1JJ1ugo2yHJDUnWncHQJEmSJEkamMkP9fNYYEVgpZkORJIkSZKkQZj8UD97AY+rql/OdCCSJEmSJA1i6ZkOQLNTVRVw9UzHIUmSJEnSoBz5IUmSJEmShtoSn/xI8pokpyS5qX39KsmaHedPSnJOkpWSHJxkYZLrknwmyVJJHpRkzySXJLkzyYIkm/W4zyZJjkhyUZLbk/wxyXuSTOgzTHJxkmOSPCHJ0UluTnJFkt3b88sn2S/JVUnuSPLLJM/sc623JDmzjf/aJIcmWa1HvYcn+Z+OZz03ybZ9rtlrEdS+C7eOPE9XWSXZP8mzkvw8ya1J/pLkze35lZMclOT6JLcl+VHn/SRJkiRJmgxL9LSXJB+kWZviUOBg4FHAS4FVgQs7qi4D/AQ4G/go8DLgvcB9wCOATYHP0Szu+S7gmCRrV9Wl7X2WBn7Vvr4G3AJsCezTXv9/JvgIjwROAI4BTgReB3w2ya3t9VcGPg6sAewC/CTJWlV1S8dnsC+wc/sZfJ1modKdgHlJnl1V17X1Hgr8AngacGD7WawNfKX991RZo32+g4BjgbcCByW5ieb/4hpgT+CZwI7AD5M8s6ruG+2iSU7vc2rtSYpbkiRJkjQklujkB/Bu4JiqelNH2UeTLNNV7ynAR6vqIwBJvgj8hibRcTPwtKq6oT13MvBT4A3A3m37+4BXVtVRIxdMcgBwFk3iYaLJj38Ftq+q+R3XvBDYF7gImFtVd7bnLqBJvLwM+E5b9iKapMjLquq4jtgOamPbFdijLd4TeBawRVfdQ4FfTzD+sXgpsFlVndjebz5wMfDd9r4vadcXIckNbbwbAKdMYUySJEmSpAeQJX3aS4Dlugur6p6uokV0JCjaUQWHt20PGEl8tE4A7gTW7azfmfhoy4pmtMbqSZadYPxX0YxYGbnmXcCRbVyfGUl8tEamlKzbUbYzTZLgzCSPGXnRjEz5I7AZQDs1563ACZ2Jj/aepwGHTTD+sfjdSOKjvd91wM9pnnGvkcRHq9cz9lRVc3u9gD9NZvCSJEmSpCXfkj7y42Dg3Ul+TPNFut9ogcs7p4q0rmyPCzoLq+q+JNcDD+ssTxJgDvB8mqkVa9FM1XgQ8HDg+gnEf36P6R094wKubY+dcW0ErEKTROnlova4Js2Unp/1qffHxYc6Yef2KBvPM0qSJEmSNJAlPfmxO80X6fcDv0lyCvDequqexnFbj7Yjo0O6kyIA99IxKqYdTXE4sAnwZ+Ac4AyaESUvGiD+McdVVfc2+Zd/Gq2zInA8zXolvdzRHldpj5f3qbdosZFOXN9n7JGQurc9LukjkiRJkiRJs8gSnfyoqntpFgjdH3gz8CHgpCSbVtXJk3irz9NMxVi/qv6+0GaSTzJY8mNQt9LMwDluMfXuao8r9Tm/8hjvdy+wfJ9zDx3jNSRJkiRJmlZD8Rf2qrqjqr4KPIdmcdK3TvItXkizsGr3DiNPm+T7jNc5wHOSLG6ayJ+Bopkm08uGY7zfjcDj2t1v/i7JYxl7AkWSJEmSpGm1RCc/kqzeVXQ9cDfNQqiT6R7g8V333gh4+STfZ7z+l2bqywe7TyTZOMkaAFV1E830mG2SPLur3lbAC8Z4vwU0C5W+tqv84+MLW5IkSZKk6bNET3sBzktyGHAasAywNc2X8wMn+T7fA3ZJ8j3gJJqtc7cFjgK2muR7jcfXaRIR70vyDJrdUpYCNgVeAXQmOnan2d73pCRfplkM9dnANu11dhzD/b4FvA/4RptEuQTYgmbB137riUiSJEmSNKOW9OTHV4EtgdfRTMn4HfC8qvrtJN/n/TTb376WZrTHGcDLaNb7mLHkR1UtSrIFsAfNZ7AfzQKjZwBvqqo/dNQ9O8nzgE/RbJF7L3AyMA94yRjvd2WSFwL7AG+n+UyObu995qQ8lCRJkiRJkyxVNdMxSJMmyelz5syZc/rp3cuzSJIkSZKWZHPnzmXBggULqmrueNsu0Wt+SJIkSZIkLY7JD0mSJEmSNNRMfkiSJEmSpKFm8kOSJEmSJA01kx+SJEmSJGmomfyQJEmSJElDzeSHJEmSJEkaaqmqmY5BmjRJblh++eVXWmeddWY6FEmSJEnSJDrvvPO44447FlbVyuNta/JDQyXJXcBSwB9mOhZpkqzdHv80o1FIk8P+rGFjn9awsU9rtlsDuLmqnjjehktPfizSjDoHoKrmznQg0mRIcjrYpzUc7M8aNvZpDRv7tIaZa35IkiRJkqShZvJDkiRJkiQNNZMfkiRJkiRpqJn8kCRJkiRJQ83khyRJkiRJGmpudStJkiRJkoaaIz8kSZIkSdJQM/khSZIkSZKGmskPSZIkSZI01Ex+SJIkSZKkoWbyQ5IkSZIkDTWTH5IkSZIkaaiZ/JAkSZIkSUPN5IeWGEm2T3JmkjuSXJVk/yQPG0O7pyU5KsnCJLckOSHJBtMRszSaifbpjvZrJTkjydZTGac0VhPp00lWTPKZJBcmuSvJtUkOSbLadMUt9TPBPr1JkiOSXNy2uzDJvkn+ZbrilvoZ9HeP9hprJlmU5JypilOaCiY/tERI8hHgm8AFwG7AEcBOwE+SLD1Ku3WBU4G1gU8CHwOeBPwiyZwpDlvqa6J9um27YpKPA2cBz5raSKWxGaBP/xrYFjgS2BU4HHg1cGqSx0xhyNKoBujTHwOWAb4C7A78AngH8JskD53KmKXRDPK7R5cPAUtNeoDSFBtPJ5dmRJK1gT2Bfatqt47yPwJfBt4IzO/T/KvA9cBzq+qmtt23gXOAzwHzpixwqY9B+nSSdwD7AA8Gfgq8ZKrjlRZnwJ/TZwFvq6obO9r9EDiW5ovj7lMTtdTfgH361VV1fdf1FgD7A68Avj0VMUujGbBPd15nPeD1wMKpiVSaOo780JJgB+Bumr+kdPo6cDXwhl6N2h/OGwOfHkl8AFTVlcCBwAuSPH5KIpZGN6E+3XoC8GNgLvDpKYlOGr9B+vQbOhMfAFV1HHARzc9waSZMuE93Jz5av2yPD5mU6KTxG+TnNABJQjOi6XvA2ZMdoDTVTH5oSfBC4Lc9fjm+FzgR2Lj9YdyrHTR/Pex2fHv818kKUhqHifZpgA9U1dZVdebUhiiNy4T7dFunl78BNZlBSuMwyM/pXjYE7gNOnrQIpfGZjD69A7Ae8J4piVCaYiY/NKsleRDwVODcPlXOp/krSq954esAt1XVJX3aATx54CClcRiwT1NVfhnUrDJon+5zzRXaa14wcIDSOE1Gn06ySpLVkzwnyYdpptruXlXnTXrA0mJMUp9+Es20249U1VWTHqQ0DVzzQ7PdijRrG1zd5/y1HfW6fxA/FrhmDO2k6TRIn5Zmo6no0+8DHsoY5p9LU2Ay+vRpNNMUoVkb4R1VdcikRSiNz0B9ul0M9dvAAmC/KYhPmhYmPzTbLd8e7+pzfqR82T5tJ9JOmkqD9GlpNprUPp1kB5pF+b5VVb8YMDZpIiajT29Lk8B7FLAZ8K0kWwGvr6o7JyVKaewG7dN70eyc+Iyqum8yA5Omk8kPzXaL2mO/vjryQ/qOPm0n0k6aSoP0aWk2mpQ+nWRZmr8ovh04GNhxMoKTJmDgPt2VuPtWu9Pcj4H30nyRlKbThPt0kq2B9wOvrarLpiA2adq45odmu5FdWlbqc37l9nhdj3M3jqHdtX3OS1NlkD4tzUYD9+kkq9IsBLk9sHNVbVtVi/rVl6bYpP+cbncw+g2wzQBxSRM1oT7d7pw4H/g/YEGSNUdeNKNJlm3fP2oKYpYmnSM/NKtV1R1JLgee0qfKU4FrqqrXXuN/Bl6TZKUe55/aHl14TNNqwD4tzTqD9ukkq/OPbUA3rqozpiBMacym8Of0FTRTB6RpNUCffhWwAvDv7auXPwMHAdtNQqjSlDL5oSXBr4AtkizXOU82yVI082h/Nko7gBcD3+069yKa+Y1uOaeZMNE+Lc1Wg/Tpw2hGom5UVVdMbZjSmE3Fz+m1gYsnJzxp3CbSp78HnNPneh8FHgbshv1aSwinvWhJMB94JLBrV/kOwOOBr0AzXzzJyh3nTwQuBf4ryXIjhUkeRzOX/NCqunXqwpb6ms/E+rQ0W81nAn06yfOADYH3m/jQLDOfifXpdZM8sftiSbYDngG444tmynzG2aer6tyqOqLXi2aKzM3t+99P21NIA3Dkh2a9qvppku8Dn0iyFvA7ml8gdgS+UlUjozeOAl6QZJ2quqSq7kmyc1v+myQH0cxPfDtwK7DHtD+MxMT79AyFKy3WAH16blv+hPbLYS9HVtWNUxe9dH8D9Ok1ge8mORz4Lc32os8DXgkcDXxpep9Eavi7h2TyQ0uO1wMfAt7c/vsi4D3AFzrqXAlcT8dK1VX1wyQvBT4CfAq4GTgW+EBVudipZtKE+rQ0i02kTz+iPe49ynV/T7OAtTTdJtKnfw3sD7wM2AoIzbSBnYBvuE2oZpi/e+gBLVU10zFIkiRJkiRNGdf8kCRJkiRJQ83khyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaayQ9JkiRJkjTUTH5IkiRJkqShZvJDkiRJkiQNNZMfkiRJkiRpqJn8kCRJkiRJQ83khyRJkiRJGmomPyRJkiRJ0lAz+SFJkiRJkoaayQ9JkiRJkjTUTH5IkiRJkqShZvJDkiRJkiQNNZMfkiRJkiRpqJn8kCRJkiRJQ83khyRJkiRJGmr/H2BhVxCYdEORAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 373,
       "width": 543
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 변수 중요도 계산\n",
    "\n",
    "importances_values = pd.Series(xg_model.feature_importances_, index=x_valid.columns).sort_values(ascending=False)\n",
    "\n",
    "# 변수 중요도 시각화\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title('Feature Importances')\n",
    "sns.barplot(x=importances_values, y=importances_values.index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 로지스틱 : 0.6727\n",
    "- 랜덤 포레스트 : 0.7830\n",
    "- SVM : 0.6705\n",
    "- XGBoost : 0.7770"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}