230103-01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d93f9b6",
   "metadata": {},
   "source": [
    "- 프로젝트(전환예측모델)\n",
    "    - OO온라인교육은 10일간 무료체험 서비스를 제공하고 있다. M월 한달간 발생한 무료체험자들의 온라인 학습활동 데이터를 활용하여 무료체험자들의 유료회원 전환 가능성을 예측하는 모델을 개발해보자."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41a41eb",
   "metadata": {},
   "source": [
    "#### 1. Logistics Regression\n",
    "- Y(종속변수)가 Binary(True or False)\n",
    "    - 변수의 유형: Continuous vs Categorical\n",
    "    - 독립변수 vs 종속변수\n",
    "\n",
    "| 독립변수      | 종속변수 | 예시    |\n",
    "| :----:       |    :----:   |   :----:|\n",
    "| Continuous      | Continuous       | 연간 쇼핑몰 방문횟수 -> 연간 구매액  |\n",
    "| Continuous   | Categorical        | 연간 쇼핑몰 방문횟수 -> 탈퇴여부   |\n",
    "| Categorical   | Continuous        | 쇼핑몰 회원 주소 -> 연간 구매액      |\n",
    "| Categorical   | Categorical        | 쇼핑몰 회원 주소 -> 탈퇴여부      |\n",
    "- 참 쉽죠?\n",
    "    - 각자 4가지 유형별로 하나씩 예시를 제시해보세요! <p>\n",
    "\n",
    "- Logistics Regression: 종속변수가 Categorical, 특히, Binary 일 때 사용하는 가장 기초적이고 인기있는 ML 모델중 하나\n",
    "- 실무에서 인기 많음\n",
    "    - IBM: https://www.ibm.com/topics/logistic-regression <p>\n",
    "\n",
    "- 개념 강좌 추천:\n",
    "    - StatQuest의 LR 플레이리스트(7개 영상): https://www.youtube.com/watch?v=yIYKR4sgzI8&list=PLblh5JKOoLUKxzEP5HA2d-Li7IJkHfXSe, 처음 2개 영상은 강추\n",
    "    - 김성범의 [핵심 머신러닝] 로지스틱회귀모델 1 (로지스틱함수, 승산): https://www.youtube.com/watch?v=l_8XEj2_9rk, Part2 영상도 있음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c6e33c",
   "metadata": {},
   "source": [
    "#### 2. Logistics Regression 모델링 예제1\n",
    "- 예제출처: \"Linear Regression vs. Logistic Regression\" by Amit Chauhan, https://pub.towardsai.net/machine-learning-fcf74f121167 (일부 추가 변형함)\n",
    "- 데이터출처1: https://www.kaggle.com/datasets/karthickveerakumar/salary-data-simple-linear-regression?resource=download\n",
    "- 데이터출처2: https://www.kaggle.com/datasets/mdwasimakhtar03/social-network-adscsv?select=Social_Network_Ads.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c0cc5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72d0af28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience   Salary\n",
       "0              1.1  39343.0\n",
       "1              1.3  46205.0\n",
       "2              1.5  37731.0\n",
       "3              2.0  43525.0\n",
       "4              2.2  39891.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datafile = pd.read_csv(\"C:/Users/rltmdals/Downloads/Salary_Data.csv\")\n",
    "datafile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "628084a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot\n",
    "\n",
    "x=datafile['YearsExperience']\n",
    "y=datafile['Salary']\n",
    "plt.xlabel('YearsExperience')\n",
    "plt.ylabel('Salary')\n",
    "plt.scatter(x,y,color='red',marker='+')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9efffd9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn 을 활용한 선형회귀 모델링을 위해\n",
    "# 데이터를 training set 과 test set으로 분리한다 (매우 중요한 개념!)\n",
    "# 예시는 값을 어레이로 변경해서 진행했으나, 다양한 방식이 존재한다.\n",
    "\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x=datafile.iloc[:,:-1].values\n",
    "y=datafile.iloc[:,1].values\n",
    "\n",
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=1/3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8958742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.1],\n",
       "       [ 1.3],\n",
       "       [ 1.5],\n",
       "       [ 2. ],\n",
       "       [ 2.2],\n",
       "       [ 2.9],\n",
       "       [ 3. ],\n",
       "       [ 3.2],\n",
       "       [ 3.2],\n",
       "       [ 3.7],\n",
       "       [ 3.9],\n",
       "       [ 4. ],\n",
       "       [ 4. ],\n",
       "       [ 4.1],\n",
       "       [ 4.5],\n",
       "       [ 4.9],\n",
       "       [ 5.1],\n",
       "       [ 5.3],\n",
       "       [ 5.9],\n",
       "       [ 6. ],\n",
       "       [ 6.8],\n",
       "       [ 7.1],\n",
       "       [ 7.9],\n",
       "       [ 8.2],\n",
       "       [ 8.7],\n",
       "       [ 9. ],\n",
       "       [ 9.5],\n",
       "       [ 9.6],\n",
       "       [10.3],\n",
       "       [10.5]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7717e4bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 39343.,  46205.,  37731.,  43525.,  39891.,  56642.,  60150.,\n",
       "        54445.,  64445.,  57189.,  63218.,  55794.,  56957.,  57081.,\n",
       "        61111.,  67938.,  66029.,  83088.,  81363.,  93940.,  91738.,\n",
       "        98273., 101302., 113812., 109431., 105582., 116969., 112635.,\n",
       "       122391., 121872.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26056fe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 74675.37776747,  91160.02832519,  61853.98288925,  81086.07520659,\n",
       "        67348.86640849,  88412.58656557, 113139.56240215,  44453.51841166,\n",
       "       105813.05104316,  98486.53968418])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 선형 모델링\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "model=LinearRegression()   #y=ax+b\n",
    "model.fit(xtrain,ytrain)\n",
    "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n",
    "\n",
    "#prediction\n",
    "y_pred=model.predict(xtest)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a48799d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_actual</th>\n",
       "      <th>y_predict</th>\n",
       "      <th>diff(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83088.0</td>\n",
       "      <td>74675.377767</td>\n",
       "      <td>0.101250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98273.0</td>\n",
       "      <td>91160.028325</td>\n",
       "      <td>0.072380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63218.0</td>\n",
       "      <td>61853.982889</td>\n",
       "      <td>0.021576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93940.0</td>\n",
       "      <td>81086.075207</td>\n",
       "      <td>0.136831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61111.0</td>\n",
       "      <td>67348.866408</td>\n",
       "      <td>-0.102074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>91738.0</td>\n",
       "      <td>88412.586566</td>\n",
       "      <td>0.036249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>116969.0</td>\n",
       "      <td>113139.562402</td>\n",
       "      <td>0.032739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43525.0</td>\n",
       "      <td>44453.518412</td>\n",
       "      <td>-0.021333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>109431.0</td>\n",
       "      <td>105813.051043</td>\n",
       "      <td>0.033061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101302.0</td>\n",
       "      <td>98486.539684</td>\n",
       "      <td>0.027793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y_actual      y_predict   diff(%)\n",
       "0   83088.0   74675.377767  0.101250\n",
       "1   98273.0   91160.028325  0.072380\n",
       "2   63218.0   61853.982889  0.021576\n",
       "3   93940.0   81086.075207  0.136831\n",
       "4   61111.0   67348.866408 -0.102074\n",
       "5   91738.0   88412.586566  0.036249\n",
       "6  116969.0  113139.562402  0.032739\n",
       "7   43525.0   44453.518412 -0.021333\n",
       "8  109431.0  105813.051043  0.033061\n",
       "9  101302.0   98486.539684  0.027793"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See how it works well\n",
    "test_result = pd.DataFrame(list(zip(ytest, y_pred)), columns = ['y_actual', 'y_predict'])\n",
    "test_result['diff(%)'] = (test_result['y_actual']-test_result['y_predict'])/test_result['y_actual']\n",
    "test_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "44b2198d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "\n",
    "plt.scatter(xtrain,ytrain,color='red')\n",
    "plt.plot(xtrain,model.predict(xtrain))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "198793ea",
   "metadata": {},
   "source": [
    "##### 2.2 Logistics Regression\n",
    "- 데이터셋 출처: https://www.kaggle.com/datasets/mdwasimakhtar03/social-network-adscsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "88898e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  EstimatedSalary  Purchased\n",
       "0   19            19000          0\n",
       "1   35            20000          0\n",
       "2   26            43000          0\n",
       "3   27            57000          0\n",
       "4   19            76000          0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "datafile=pd.read_csv(\"C:/Users/rltmdals/Downloads/Social_Network_Ads.csv\")\n",
    "datafile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "41ab360b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Purchased'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gRl4qDOsQhhEdw9GnRSBUbnK5SSwmN0RERC6u1GzF78cysepAOg6cz6+0jbdagWEdwnBvlwjEtQxqcAnNPzG5ISIiclFnc4qwan8a1vx1CQXF5gqPK+Qy9G8djAe6NcagtqEN4pKTGExuiIiIXIjVJmDrqWws33Meu1PzKm3TKsQHD8c2wcgujd22U/CtYHJDRETkAvSlZvxwMB0r9l5EWn5xhcc9VQrc2zkCj/aKQucm2no7Bk1dYHJDREQkoYyCEiz98zxWH0yDwWSt8HjbMF+M6RWFkV0bu8zcTa6OyQ0REZEETmXpsXjHOaw/chkWm+DwmFwG3BkThvF9m6FH00Y8S+MkJjdERER16HDaVXyxJQXbTudUeEzrqcKjPaPweJ9oNPb3lCA698DkhoiIqA4kXszHvM0p+DMlt8JjTRp54ul+zfFwbBN4qfnVfKu4BYmIiGpR4sV8zEk4U+mdT+3C/TCpf3Pc1THcbeZ1cgVMboiIiGrBict6fLbpNLacyq7wWJdIf0wZ1AoD2gSzP00tYHJDRERUgy7kGjAn4Qx+Tb4MwbGfMGKjG2HK4Fa4rWUQk5paxOSGiIioBhQUm/D5lhT8Z+/FCnc/dYn0x8vD2qBvi0AmNXWAyQ0REdEtMFlsWLH3Ar7YkgJ9qePM3G1CffGvYW0wuF0Ik5o6xOSGiIioGgRBwKYTV/DBhpO4mOc4onBjf0/8a1hr3Nu5MRRyJjV1jckNERGRk87nGvDW+uPYccZxrBpfjRIv3NES4/s2hYeKk1hKhckNERGRSMUmC+ZvS8WSnedhstrs9Qq5DGN6RmHq4FYI9NFIGCEBTG6IiIhE2XziCv79yzFc1pU61Me1DMRb93RAq1BfiSKj6zG5ISIiuoFsfSne/vUEfjua6VAfrvXAG3e1x4iOYews7GKY3BAREVXCZhOw+lA6PthwEoX/uAtKpZDhqX7N8eLAlvDW8GvUFXGvEBERXSctrxivrD2CfefyHepjoxsh/oGOvATl4pjcEBER/U0QBKzcn4YPNpxEsclqr/fVKPHq8LYY0zMKct7a7fKY3BAREQHIKCjBq2uSsSvVcdbuYR1C8c59MQj185AoMnIWkxsiImrQBEHAT4czMPuX4yg0Xutb4++lwrv3xeCezhESRkfVweSGiIgaLF2JGW/8fAy/HrnsUD+4XSg+eCAGIb48W1MfMbkhIqIG6eCFfEz9PgkZBSX2Ol8PJd6+twPu79qYt3fXY0xuiIioQbHaBHy+JQVfbU3BPyfv7tksAHNHd0Fjf0/pgqMaweSGiIgajOzCUkxZlYS95/LsdQq5DNOHtMak/i04yaWbYHJDREQNwp6zuZi8Kgm5RUZ7XVSAFz5/pAu6RjWSMDKqaUxuiIjIrdlsAuZvS8XczWccLkPd3Skc8Q90hK+HSrrgqFYwuSEiIrelKzFj6veHse10jr1OrZDjzXva47FeUew07KaY3BARkVs6c6UQz6w4hAt5xfa6qAAvzB/TDR2baCWMjGobkxsiInI7fxzLxIwfjsDwjykUhrQPxacPd4bWk5eh3B2TGyIichs2m4C5m8/gy62p9jqZDJg2uDVeHNiS80I1EExuiIjILRSbLJi++gj+OJ5lr/PVKDHvkS4Y1C5UwsiorjG5ISKiei9LV4qnVhzEsQy9va5FsDcWj4tFi2AfCSMjKTC5ISKieu1Yhg4TvzuIK/pr49cMaBOMLx/tytu8GygmN0REVG9tPJ6Fqd8nocR8rePwhLimmDWiHZQKuYSRkZSY3BARUb20Yu8FzF5/HMLfA/Mp5DK8dW8HPN47WtrASHJMboiIqF6x2QR8vPE0Fu04a6/z9VBiwdhu6NcqWMLIyFUwuSEionrDZLHh1bXJ+Olwhr0uXOuB757sidahvhJGRq6EyQ0REdULRUYLJv0nEbtSc+11bcN8sXxCT4RpPSSMjFwNkxsiInJ5+QYTxi87gORLOntdn+aB+Hpcd/jxjii6juRdyRcsWIBmzZrBw8MD3bt3x59//nnD9itXrkTnzp3h5eWF8PBwTJgwAXl5eXUULRER1bUsXSlGf73XIbG5t3MElj/Zg4kNVUrS5Gb16tWYOnUqZs2ahcOHD6Nfv34YPnw40tLSKm2/a9cujBs3DhMnTsTx48fx448/4uDBg3jqqafqOHIiIqoLF3INeGjRHqRkF9nrxvdtinmju0CjVEgYGbkymSCU30RX93r16oVu3bph4cKF9rp27dph5MiRiI+Pr9D+008/xcKFC3H27LUe8l9++SU+/vhjpKenV7oOo9EIo/HawE56vR6RkZHQ6XTw8/OrwVdDREQ16VSWHo99cwC5Rdc+w6cMaoWpg1tBJuMcUQ2NXq+HVqsV9f0t2Zkbk8mExMREDB061KF+6NCh2LNnT6XP6du3Ly5duoQNGzZAEARcuXIFa9aswV133VXleuLj46HVau1/kZGRNfo6iIio5h3L0OGRxfscEpt/390e04a0ZmJDNyVZcpObmwur1YrQUMfJzEJDQ5GVlVXpc/r27YuVK1di9OjRUKvVCAsLg7+/P7788ssq1zNz5kzodDr7X1VneIiIyDUkpRdgzJJ9KCg2AwDkMuCThzrhyduaSRwZ1ReSdyi+PgMXBKHKrPzEiROYPHky/v3vfyMxMRF//PEHzp8/j0mTJlW5fI1GAz8/P4c/IiJyTYkX8/HYN/uhL7UAAJRyGb4a0w0Px/KsO4kn2a3gQUFBUCgUFc7SZGdnVzibUy4+Ph5xcXF4+eWXAQCdOnWCt7c3+vXrh/feew/h4eG1HjcREdWOA+fzMWHZARhMZfNEqRQyzB/TDUM7hEkcGdU3kp25UavV6N69OxISEhzqExIS0Ldv30qfU1xcDLncMWSFoqy3vIT9oomI6BbtO5eHJ769ltioFXJ8/Xh3JjZULZJelpo+fTq++eYbfPvttzh58iSmTZuGtLQ0+2WmmTNnYty4cfb299xzD9atW4eFCxfi3Llz2L17NyZPnoyePXsiIiJCqpdBRES34NCFfDy5/KB9Zm+NUo4lT8TijraVn8UnuhlJRygePXo08vLy8M477yAzMxMxMTHYsGEDoqPLZnTNzMx0GPNm/PjxKCwsxFdffYUZM2bA398fd9xxBz766COpXgIREd2Cw2lXMX7ZQRSbriU2S5/ogdtaBUkcGdVnko5zIwVn7pMnIqLacyxDh0eX7EPh352H1Uo5vhkXi9tbc2ZvqqhejHNDREQN14nLejy2dL89sVEpZPj6se5MbKhGMLkhIqI6lZpdhMeX7rePY6OUl90VNbBtiMSRkbtgckNERHXm0tViPL50P/IMJgCAQi7Dl4925V1RVKOY3BARUZ3IKTTi8aUHkKkrBQDIZMBnD3fG8I4co4xqFpMbIiKqdboSM8Z9ewDncw32unfui8HIro0ljIrcFZMbIiKqVcUmC55cfhAnM/X2upeHtcHjvaMljIrcGZMbIiKqNWarDS+s/AuJF6/a6569vTmeH9BCwqjI3TG5ISKiWiEIAmauO4ptp3PsdY/2jMRrw9tWOUEyUU1gckNERLXis01nsCbxkr18Z4cwvDeyIxMbqnVMboiIqMat2HsBX21LtZd7NgvAvEe6QCFnYkO1j8kNERHVqN+PZmL2+uP2cptQXywZFwsPlULCqKghYXJDREQ1JvFiPqasTkL5rIURWg8sf7IHtJ4qaQOjBoXJDRER1YgLuQY89d0hmCw2AIDWU4XvnuyJcK2nxJFRQ8PkhoiIbtlVgwkTlh/E1b/ni1Ir5FgyLhatQn0ljowaIiY3RER0S4wWK579T6LD6MOfPNwJPZsFSBgVNWRMboiIqNoEQcAra5Jx4EK+ve5fQ1vjvi6cVoGkw+SGiIiqbW7CGfySdNleHhXbBC8MbClhRERMboiIqJp+ScrAF1uvjWUT1zIQ79/PQfpIekxuiIjIaUnpBXh5TbK93DLEBwvGdodKwa8Vkh6PQiIickqmrgRPr7h2y7e/lwpLn4jlWDbkMpjcEBGRaMUmC5767hByCo0AAKVchoVjuyM60FviyIiuYXJDRESi2GwCZvxwBMcv6+11746MQZ8WgRJGRVQRkxsiIhLly62p+P1Ylr08Ia4pHu0ZJWFERJVjckNERDeVcOIK5m4+Yy/f3joYs0a0kzAioqoxuSEiohtKzS7EtNVJ9nKzIG98+WhXKHlnFLkoHplERFQlXYkZT69IRJHRAgDw0SixZFx33hlFLo3JDRERVcpqEzDl+8MOc0bNGdUZLUM4GSa5NiY3RERUqTkJp7H9dI69PG1wawztECZhRETiMLkhIqIKNh3PwvxtZ+3loe1D8dIdnDOK6gcmN0RE5OB8rgEzfjhiL7cM8cGc0V0gl3POKKofmNwQEZFdscmCSf9JROHfHYi91Qoseqw7fDRKiSMjEo/JDRERAQAEQcDMdUdx+kqhve7ThzujZYiPhFEROY/JDRERAQC+23MBvyRdtpefvb05hncMlzAiouphckNEREi8mI/3fjtpL/duHoCXh7WRMCKi6mNyQ0TUwOUVGfHCysOw2AQAQKifBl8+2o0jEFO9xSOXiKgBs9kETPvhCLL0pQAApVyGBWO7IdhXI3FkRNXH5IaIqAGbvy0VO89cG6jvteFt0T06QMKIiG4dkxsiogZqT2quw0zfQ9uHYuJtzSSMiKhmMLkhImqAsvWlmPz9YfzdzQZRAV745OHOkMk4UB/Vf0xuiIgaGIvVhpdWHUZukQkAoFbKsWBsN870TW6DyQ0RUQPzxdZU7D+fby/Pvqc9YhprJYyIqGYxuSEiakD2pObiy60p9vK9nSMwpmeUhBER1TwmN0REDURukRFTVidB+LufTdNAL7x/fwz72ZDbYXJDRNQA2GwCpq1OQk6hEQCgVsjx1Zhu8PVgPxtyP0xuiIgagK93nsOfKbn28usj2rKfDbktJjdERG4u8eJVfLrptL08tH0onujbVLqAiGoZkxsiIjemKzFj8qrDsP49oE1jf098/FAn9rMht8bkhojITQmCgFk/HUVGQQkAQCGX4YtHu8DfSy1xZES1i8kNEZGb+vHQJfwvOdNenja4FeeNogaByQ0RkRs6m1OE2euP28u9mwfguQEtJYyIqO4wuSEicjNGixUv/fcwSsxWAIC/lwpzR3eBQs5+NtQwMLkhInIzH/9xGicy9dfKD3ZCuNZTwoiI6haTGyIiN7LjTA6W7jpvLz/eOxpDO4RJGBFR3WNyQ0TkJvKKjPjXj0fs5dahPph1VzsJIyKSBpMbIiI3IAgCXl179Nr0Cko5vni0KzxUCokjI6p7TG6IiNzAfw+kYfPJK/bya3e2RdswPwkjIpKO5MnNggUL0KxZM3h4eKB79+74888/b9jeaDRi1qxZiI6OhkajQYsWLfDtt9/WUbRERK4nNbsI7/7vhL18e+tgjOf0CtSAKaVc+erVqzF16lQsWLAAcXFx+PrrrzF8+HCcOHECUVFRlT5n1KhRuHLlCpYuXYqWLVsiOzsbFouljiMnInINJosNU74/jFKzDQAQ4K3Gpw91gpy3fVMDJhMEQZBq5b169UK3bt2wcOFCe127du0wcuRIxMfHV2j/xx9/4JFHHsG5c+cQECBulE2j0Qij0Wgv6/V6REZGQqfTwc+Pp2yJqH6L//0kvt5xzl5eMi4WQ9qHShgRUe3Q6/XQarWivr8luyxlMpmQmJiIoUOHOtQPHToUe/bsqfQ569evR2xsLD7++GM0btwYrVu3xr/+9S+UlJRUuZ74+HhotVr7X2RkZI2+DiIiqew7l4fFO68lNmN7RTGxIYKEl6Vyc3NhtVoRGur4RgwNDUVWVlalzzl37hx27doFDw8P/PTTT8jNzcXzzz+P/Pz8KvvdzJw5E9OnT7eXy8/cEBHVZ/pSM2b8cATl596bB3vjjbvaSxsUkYuQtM8NAMhkjteFBUGoUFfOZrNBJpNh5cqV0Gq1AIA5c+bgoYcewvz58+HpWXEETo1GA41GU/OBExFJ6K1fjttn+1bKZZg3ugs81bztmwhwIrl54IEHRC903bp1N20TFBQEhUJR4SxNdnZ2hbM55cLDw9G4cWN7YgOU9dERBAGXLl1Cq1atRMdIRFRf/S/5MtYdzrCXpwxqhU5N/KULiMjFiO5z889+K35+ftiyZQsOHTpkfzwxMRFbtmxxSDxuRK1Wo3v37khISHCoT0hIQN++fSt9TlxcHC5fvoyioiJ73ZkzZyCXy9GkSROxL4WIqN7K0pVi1k/H7OVuUf54bkALCSMicj2iz9wsW7bM/v9XX30Vo0aNwqJFi6BQlJ0GtVqteP755526A2n69Ol4/PHHERsbiz59+mDx4sVIS0vDpEmTAJT1l8nIyMCKFSsAAGPGjMG7776LCRMm4O2330Zubi5efvllPPnkk5VekiIicic2m4B//XgEuhIzAMBbrcDc0V2gVEg+ZBmRS6lWn5tvv/0Wu3btsic2AKBQKDB9+nT07dsXn3zyiajljB49Gnl5eXjnnXeQmZmJmJgYbNiwAdHR0QCAzMxMpKWl2dv7+PggISEBL730EmJjYxEYGIhRo0bhvffeq87LICKqV1bsvYBdqbn28r/vaY/oQG8JIyJyTdUa56ZRo0ZYtmwZRo4c6VD/888/Y8KECbh69WpNxVfjnLlPnojIVaRmF+GuL/6E0VI2WN+Q9qFY/Hj3Km/AIHI3znx/V+vMzYQJE/Dkk08iNTUVvXv3BgDs27cPH374ISZMmFCdRRIRURXMVhum/5BkT2yCfNT48IGOTGyIqlCt5ObTTz9FWFgY5s6di8zMTABldzK98sormDFjRo0GSETU0H21NRXJl3T2cvwDnRDowyEuiKpyy9Mv6PV6AKg3l3h4WYqI6pOk9AI8uHAPrLayj+pRsU3w8UOdJY6KqO7VyfQLFosFmzdvxqpVq+ynRq+/TZuIiKqvxGTF9B+S7IlNk0aeePNujkJMdDPVuix18eJF3HnnnUhLS4PRaMSQIUPg6+uLjz/+GKWlpVi0aFFNx0lE1OB89McpnMsxAABkMuCzhzvD10MlcVRErq9aZ26mTJmC2NhYXL161WF8mfvvvx9btmypseCIiBqq3am5WL7ngr38dL/m6NU8ULqAiOqRap252bVrF3bv3g21Wu1QHx0djYyMjCqeRUREYuhLzXj5xyP2cutQH0wf0lrCiIjql2qdubHZbLBarRXqL126BF9f31sOioioIXvn1xO4rCsFUDYp5pxRXeCh4qSYRGJVK7kZMmQI5s2bZy/LZDIUFRVh9uzZGDFiRE3FRkTU4CScuII1iZfs5cmDWiGmsbg5+4ioTLUuS82dOxcDBw5E+/btUVpaijFjxiAlJQVBQUFYtWpVTcdIRNQg5BUZMXNdsr3cuYkWz3NSTCKnVSu5iYiIQFJSEr7//nskJibCZrNh4sSJGDt2LCewJCKqBkEQ8MbPx5BbZAIAaJRyfDaKk2ISVcctD+JX33AQPyJyRb8kZWDK90n28pt3t8fE25pJFxCRi6n1Qfy+++47/Pbbb/byK6+8An9/f/Tt2xcXL16sziKJiBqsK/pSvPnzMXu5V7MATOjbVLqAiOq5aiU3H3zwgf3y0969e/HVV1/h448/RlBQEKZNm1ajARIRuTNBEPDq2mToSy0AAG+1Ap8+3BlyOSfFJKquavW5SU9PR8uWLQEAP//8Mx566CE888wziIuLw4ABA2oyPiIit7b6YDq2n86xl9+8uz0iA7wkjIio/qvWmRsfHx/k5eUBADZt2oTBgwcDADw8PFBSUlJz0RERubH0/GK8+78T9vKANsEY3SNSwoiI3EO1ztwMGTIETz31FLp27YozZ87grrvuAgAcP34cTZs2rcn4iIjcks0m4JU1yTCYygZE1Xqq8NGDnewTERNR9VXrzM38+fPRp08f5OTkYO3atQgMLJvvJDExEY8++miNBkhE5I5W7L2Avefy7OV37uuAUD8PCSMich+8FZyIqI6dyynCiC/+RKnZBgAYHhOGBWO78awN0Q048/1drctS5YqLi5GWlgaTyeRQ36lTp1tZLBGR27LaBPzrxyP2xCbQW433RsYwsSGqQdVKbnJycjB+/Hj88ccflT5e2aSaREQELPnzHP5KK7CX37+/IwJ9NNIFROSGqtXnZurUqSgoKMC+ffvg6emJP/74A9999x1atWqF9evX13SMRERu4cyVQszZdMZeHtklAnfGhEkYEZF7qtaZm61bt+KXX35Bjx49IJfLER0djSFDhsDPzw/x8fH2u6eIiKiM2WrDjB+OwGQtuxwV4qvB2/fGSBwVkXuq1pkbg8GAkJAQAEBAQABycsoGoOrYsSP++uuvmouOiMhNLNx+FkczdPbyRw92gtZLJWFERO6rWslNmzZtcPr0aQBAly5d8PXXXyMjIwOLFi1CeHh4jQZIRFTfHb+swxdbUuzl0bGRGNg2RMKIiNxbtS5LTZ06FZmZmQCA2bNnY9iwYVi5ciXUajWWL19ek/EREdVrRosVM344AoutbNSNxv6eeOPudhJHReTeqpXcjB071v7/rl274sKFCzh16hSioqIQFBRUY8EREdV3X2xJwamsQnv544c6wdeDl6OIatMtjXNTzsvLC926dauJRRERuY2k9AIs3H7WXh7XJxpxLfkDkKi2VSu5sVqtWL58ObZs2YLs7GzYbDaHx7du3VojwRER1VelZitm/JCEv69GITrQC68NbyttUEQNRLWSmylTpmD58uW46667EBPDkTWJiK736cbTOJtjAADIZMCnD3eGl7pGTpYT0U1U6532/fff44cffsCIESNqOh4ionrvwPl8LN193l6eGNcMPZoGSBgRUcNSrVvB1Wo1WrZsWdOxEBHVewajBS+vOYLyKYlbBHvjX8PaSBsUUQNTreRmxowZ+Pzzz9HAJhQnIrqpD38/hYt5xQAAuQz4bFQXeKgUEkdF1LCIviz1wAMPOJS3bt2K33//HR06dIBK5Xhb47p162omOiKieuTPlBz8Z99Fe/m5AS3QJdJfuoCIGijRyY1Wq3Uo33///TUeDBFRfaUvNeOVNcn2ctswX0we1ErCiIgaLtHJzbJly2ozDiKieu3dX08gU1cKAFApZJgzqgs0Sl6OIpJCtfrcnD9/HikpKRXqU1JScOHChVuNiYioXtl84gp+TLxkL0++oxXaR/hJGBFRw1at5Gb8+PHYs2dPhfr9+/dj/PjxtxoTEVG9cdVgwmvrjtrLnZto8dyAFhJGRETVSm4OHz6MuLi4CvW9e/dGUlLSrcZERFRvvPHLMeQWGQEAaqUcn43qDKWiWh+tRFRDqvUOlMlkKCwsrFCv0+lgtVpvOSgiovrg1yOX8Vtypr388tA2aBniK2FERARUM7np168f4uPjHRIZq9WK+Ph43HbbbTUWHBGRq8rWl+LNX47Zyz2bBuDJ25pJGBERlavW9AsfffQR+vfvjzZt2qBfv34AgD///BN6vZ6TZhKR2xMEAa+uTUZBsRkA4KVW4NOHO0Mh5zx7RK6gWmduOnTogOTkZIwaNQrZ2dkoLCzEuHHjcOrUKcTExNR0jERELmX1wXRsO51jL79xV3tEBXpJGBER/ZPTZ27MZjOGDh2Kr7/+Gh988EFtxERE5LLS84vx7v9O2Mv9Wwfj0Z6REkZERNdz+syNSqXCsWPHIJPx9CsRNSw2m4CX1xyBwVTW39DPQ4mPHuzEz0MiF1Oty1Ljxo3D0qVLazoWIiKX9u3u89h3Lt9efndkDMK0HhJGRESVqVaHYpPJhG+++QYJCQmIjY2Ft7e3w+Nz5sypkeCIiFzFmSuF+HjjaXt5RMcw3Ns5QsKIiKgq1Upujh07hm7dugEAzpw54/AYT88SkbsxWWyYtjoJJosNABDsq8F7Izvy847IRVUrudm2bVtNx0FE5LK+3JqC45f19vJHD3ZEgLdawoiI6EY4RjgR0Q38lXYV87el2suP9ozCHW1DJYyIiG6mWmduBg4ceMPTsRzIj4jcQbHJghk/HIFNKCtHBXjhjbvaSRsUEd1UtZKbLl26OJTNZjOSkpJw7NgxPPHEEzURFxGR5OI3nML5XAMAQCYD5ozqDG9NtT42iagOVetdOnfu3Err33rrLRQVFd1SQERErmDb6Wz8Z99Fe/nZ21sgtmmAhBERkVg12ufmsccew7fffluTiyQiqnP5BhNeWZNsL7cL98O0Ia0kjIiInFGjyc3evXvh4cEBrYio/hIEAa+vO4qcQiMAQK2UY97oLtAoFRJHRkRiVeuy1P333+/QoVgQBGRmZuLQoUN48803ayw4IqK6tvavDPxxPMtefmVYG7QJ85UwIiJyVrXO3Pj7+8Pf3x9arRZarRYBAQEYMGAANmzYgNmzZzu1rAULFqBZs2bw8PBA9+7d8eeff4p63u7du6FUKit0biYiqq70/GK8tf64vRzXMhBPxjWTMCIiqg6nztwUFxfj5ZdfxsaNG2GxWDBo0CB8+eWXCAoKqtbKV69ejalTp2LBggWIi4vD119/jeHDh+PEiROIioqq8nk6nQ7jxo3DoEGDcOXKlWqtm4jon6w2ATN+OIIiowVA2aSYnz7cGXI5RyEmqm+cOnMze/ZsLF++HHfffTceffRRbN68Gc8991y1Vz5nzhxMnDgRTz31FNq1a4d58+YhMjISCxcuvOHznn32WYwZMwZ9+vSp9rqJiP5p0Y6zOHDBcVLMcK2nhBERUXU5deZm3bp1WLp0KR555BEAwNixYxEXFwer1QqFwrnOdiaTCYmJiXjttdcc6ocOHYo9e/ZU+bxly5bh7Nmz+L//+z+89957N12P0WiE0Wi0l/V6/Q1aE1FDlHypAHMTrs2Td2/nCNzXpbGEERHRrXDqzE16ejr69etnL/fs2RNKpRKXL192esW5ubmwWq0IDXUcxjw0NBRZWVmVPiclJQWvvfYaVq5cCaVSXF4WHx9v7xuk1WoRGRnpdKxE5L6KTRZM+T4Jlr+HIW7s74l3R8ZIHBUR3Qqnkhur1Qq12nGyOKVSCYvFUu0Arp/GQRCESqd2sFqtGDNmDN5++220bt1a9PJnzpwJnU5n/0tPT692rETkft7934kKoxBrPVUSR0VEt8Kpy1KCIGD8+PHQaDT2utLSUkyaNAne3t72unXr1t10WUFBQVAoFBXO0mRnZ1c4mwMAhYWFOHToEA4fPowXX3wRAGCz2SAIApRKJTZt2oQ77rijwvM0Go1DvERE5TYez8KqA9d+8Dw/oAV6NQ+UMCIiqglOJTeVzRv12GOPVWvFarUa3bt3R0JCAu6//357fUJCAu67774K7f38/HD06FGHugULFmDr1q1Ys2YNmjXj7ZpEJN4VfSleW3ttFOJOTbSYOlj8WWEicl1OJTfLli2r0ZVPnz4djz/+OGJjY9GnTx8sXrwYaWlpmDRpEoCyS0oZGRlYsWIF5HI5YmIcr4OHhITAw8OjQj0R0Y3YbAL+9eMRXC02AwA8VQrMG90FKkWNDtpORBKRdHrb0aNHIy8vD++88w4yMzMRExODDRs2IDo6GgCQmZmJtLQ0KUMkIje05M9z+DMl117+9z3t0TzYR8KIiKgmyQRBEKQOoi7p9XpotVrodDr4+flJHQ4R1bHkSwV4YMEe+91Rd3YIw8LHulV6IwMRuQ5nvr95DpaIGgyD0YLJqw7bE5twrQc+fLAjExsiN8PkhogajNnrj+NCXjGAstu+547uAn8v9U2eRUT1DZMbImoQ1h+5jDWJl+zlFwe2RG/e9k3klpjcEJHbS88vxqx114aS6BbljymDWkkYERHVJiY3ROTWTBYbXlx1GIV/z/btq1Hi80e6QsnbvoncFt/dROTWPt10GkfSC+zl9x/oiMgAL+kCIqJax+SGiNzWttPZWLzznL38SI9I3Ns5QsKIiKguMLkhIreUpSvFjB+O2MutQ30w+54OEkZERHWFyQ0RuR2rTcDU1YeRbzABADxUcnw1phs81QqJIyOiusDkhojczpdbU7DvXL69/NY9HdA61FfCiIioLjG5ISK3sjs1F59vSbGX7+kcgdE9IiWMiIjqGpMbInIb2fpSTPn+MMpnzIsO9MIH98dwegWiBobJDRG5BYvVhpdWHUZuUVk/G7VSjvljusHXQyVxZERU15jcEJFbmLv5DPafv9bPZvY97RHTWCthREQkFSY3RFTvbTudjfnbztrL93WJwJieURJGRERSYnJDRPXa5YISTF+dZC+3CPbGB/d3ZD8bogaMyQ0R1VtGixXPrfwLV4vNAMrGs1kwtju8NUqJIyMiKTG5IaJ6693/nXCYN+rd+2LQJozj2RA1dExuiKheWpt4Cf+3L81efrRnFB6O5Xg2RMTkhojqoROX9Xj9p6P2cqcmWsy+p72EERGRK2FyQ0T1iq7EjOdWJsJosQEA/L1UWDC2GzxUnDeKiMowuSGiesNmEzDjhyRczCsGAMhkwBePdEWTRl4SR0ZEroTJDRHVG59vScHmk9n28rTBrXF762AJIyIiV8TkhojqhY3HsxwmxBzUNgQvDmwpYURE5KqY3BCRy0vNLnQYqK95kDfmPtIFcjkH6iOiipjcEJFL05ea8cyKRBhMVgCAj0aJxeO6w48TYhJRFZjcEJHLstkETPs+CedyDfa6uaO7oGUIB+ojoqoxuSEil/VZwmlsOXWtA/GUQa0wpH2ohBERUX3A5IaIXNIvSRkOM30PbheKKYNaSRgREdUXTG6IyOUkpRfg5TXJ9nKrEB/MHd2ZHYiJSBQmN0TkUjJ1JXh6xSGY/h6BuJGXCkuf6AFfdiAmIpGY3BCRyygxWfHMikTkFBoBAEq5DAvGdkdUIEcgJiLxmNwQkUuw2QT8a80RHM3Q2eveuS8GfVoEShgVEdVHTG6IyCXMSTiD35Iz7eXxfZtiTK8oCSMiovqKyQ0RSe6HQ+n4aluqvdyvVRDeuKudhBERUX3G5IaIJLUnNRevrztqL7cK8cH8sd2gVPDjiYiqh58eRCSZ1OxCTPq/RFhsAgAgyEeDb8f34NQKRHRLmNwQkSRyi4yYsPwg9KUWAICHSo5vnohFZADvjCKiW8PkhojqXLHJgonLDyI9vwQAIJMB80Z3QZdIf2kDIyK3wOSGiOqU2WrDCyv/wpFL1275fu3OtrgzJlzCqIjInTC5IaI6IwgCZv10FNtO59jrnugTjWduby5hVETkbpjcEFGdmbs5BT8cumQv39khDP++pwNkMs4ZRUQ1h8kNEdWJ/+5PwxdbUuzlHk0bYd4jXaDgZJhEVMOY3BBRrfv9aCbe+NlxLJtvxvWAh0ohYVRE5K6Y3BBRrdqVkosp3yfh76FsEOqnwfIne0LrxbFsiKh2MLkholpzOO0qnvnPIZisNgCA1lOFFU/2QmN/T4kjIyJ3xuSGiGrF6axCjF92EMUmKwDAU6XAsgk90CbMV+LIiMjdMbkhohqXnl+Mx5fuh67EDABQKWRYPK47ukU1kjgyImoImNwQUY26XFCCMd/sQ3ahEQAglwGfP9IV/VoFSxwZETUUTG6IqMZk60sx9pv99mkVAOCD+ztiREeOPkxEdYfJDRHViNwiI8Z8sx/ncw32ujfvbo9HekZJGBURNURMbojoll01mPDYN/uRml1kr3v1zraYeFszCaMiooaKyQ0R3RJdsRnjvj2AU1mF9rqpg1vhuQEtJIyKiBoypdQBEFH9VVBswmNL9+NYht5e99yAFpgyqJWEURFRQ8fkhoiq5arBhLHf7MeJzGuJzZNxzfDKsDacCJOIJMXkhoiclldkxNhv9jtcinrqtmaYdVc7JjZEJDnJ+9wsWLAAzZo1g4eHB7p3744///yzyrbr1q3DkCFDEBwcDD8/P/Tp0wcbN26sw2iJKLfIiDFLHBObZ29vzsSGiFyGpMnN6tWrMXXqVMyaNQuHDx9Gv379MHz4cKSlpVXafufOnRgyZAg2bNiAxMREDBw4EPfccw8OHz5cx5ETNUyZuhKM+novTl+5ltg8P6AFXhvelokNEbkMmSAIglQr79WrF7p164aFCxfa69q1a4eRI0ciPj5e1DI6dOiA0aNH49///reo9nq9HlqtFjqdDn5+ftWKm6ghOp9rwGPf7EdGwbUB+l66oyWmD2nNxIaIap0z39+SnbkxmUxITEzE0KFDHeqHDh2KPXv2iFqGzWZDYWEhAgICqmxjNBqh1+sd/ojIOaey9Hh40V6HxGbGkNZMbIjIJUmW3OTm5sJqtSI0NNShPjQ0FFlZWaKW8dlnn8FgMGDUqFFVtomPj4dWq7X/RUZG3lLcRA3NX2lXMfrrfcgtMtrrZt/THi8NasXEhohckuQdiq//cBQEQdQH5qpVq/DWW29h9erVCAkJqbLdzJkzodPp7H/p6em3HDNRQ7HzTA4e++ba7N5yGfDJQ50wIY4jDxOR65LsVvCgoCAoFIoKZ2mys7MrnM253urVqzFx4kT8+OOPGDx48A3bajQaaDSaW46XqKFZ99clvLImGRZbWbc8lUKGLx/tijtjOAkmEbk2yc7cqNVqdO/eHQkJCQ71CQkJ6Nu3b5XPW7VqFcaPH4///ve/uOuuu2o7TKIGRxAELNx+FtN/OGJPbLzUCix9ogcTGyKqFyQdxG/69Ol4/PHHERsbiz59+mDx4sVIS0vDpEmTAJRdUsrIyMCKFSsAlCU248aNw+eff47evXvbz/p4enpCq9VK9jqI3IXVJuDd/53A8j0X7HWB3mp8O74HOkf6SxYXEZEzJE1uRo8ejby8PLzzzjvIzMxETEwMNmzYgOjoaABAZmamw5g3X3/9NSwWC1544QW88MIL9vonnngCy5cvr+vwidxKicmK6T8k4fdj1y4VRwd64bsJPdE0yFvCyIiInCPpODdS4Dg3RBVl60vx9IpDOHJJZ6/r1ESLb8f3QJAP+6wRkfSc+f7m3FJEDdzJTD0mLj+Iy7pSe13/1sFYMLYbvDX8iCCi+oefXEQN2LZT2Xjxv3/BYLLa68b1ica/724PpULykSKIiKqFyQ1RAyQIApbuOo8PNpzE3zdEQS4D/n13e4znGDZEVM8xuSFqYErNVsxcdxQ/Hc6w13mrFfhyTFfc0fbGY0wREdUHTG6IGpCMghI8+59DOJZxbY61xv6eWDIuFu0j2MGeiNwDkxuiBmL/uTw8v/Iv5BlM9rrezQMwf0w3BPKOKCJyI0xuiNycIAhYvPMcPt54GlbbtZEfxvdtill3tYOKHYeJyM0wuSFyY7piM2b8eASbT16x16kVcrx3fwxGxUZKGBkRUe1hckPkpo5e0uH5/yYiPb/EXtfY3xPzx3ZDF06lQERujMkNkZsRBAEr9l7E+7+dhMlqs9cPbBOMOaO6oJG3WsLoiIhqH5MbIjeSV2TEK2uSseVUtr1OLgNmDG2D5/q3gFwukzA6IqK6weSGyE3sSsnF9B+SkF1otNcF+WjwxSNd0LdlkISRERHVLSY3RPVcqdmKOQlnsHjnOYf6AW2C8enDnTnxJRE1OExuiOqxYxk6TP8hCWeuFNnr1Ao5XhveFhPimkIm42UoImp4mNwQ1UNmqw3zt6Xiq62psPxj7JoWwd748tFuHG2YiBo0JjdE9cypLD1e/jEZRzN0DvXj+zbFq3e2hadaIVFkRESugckNUT1RarZi/rZULNx+1uFsTWN/T3zycCf0bcFOw0REAJMbonrh4IV8vLY2GWdzDA71o2Mj8cbd7eDroZIoMiIi18PkhsiF6YrN+HjjKazcn+ZQ39jfE+/dH4OBbUIkioyIyHUxuSFyQTabgDV/XcKHv59C/j9m8ZbJyvrW/GtoG3hr+PYlIqoMPx2JXMzxyzq8+fMx/JVW4FDfJtQXHz7YEV2jGkkTGBFRPcHkhshF5BUZMXfzGfx3fxr+0V8YnioFJg9qhYm3NYNaKZcuQCKieoLJDZHEjBYrvttzAV9uTUVhqcXhsREdw/DGXe0R4e8pUXRERPUPkxsiiQiCgI3HryD+95O4mFfs8FizIG+8dW8H9G8dLFF0RET1F5MbIgnsPZuHj/44haT0Aod6Xw8lpgxqhXF9mvISFBFRNTG5IapDxzJ0+Hjjaew8k+NQr5DLMLZXFKYObo0Ab7VE0RERuQcmN0R14HRWIb7YkoLfjmZWeGxQ2xC8NrwtWoX6ShAZEZH7YXJDVItOZenxxZYUbDiaVeGxHk0b4dU72yK2aYAEkRERuS8mN0S14OglHRZsT8XvxyomNW3DfPHKnW0wsE0IZDKZBNEREbk3JjdENUQQBOxOzcOiHWexKzW3wuNtQn0xZXAr3NkhDHI5kxoiotrC5IboFpmtNvxxLAuLd57D0QxdhcfbhvliyqBWGMakhoioTjC5IaqmqwYTVh1Mw4o9F5GlL63weOdIfzzXvwWGtg9lUkNEVIeY3BA56cRlPf6z7yJ+OnwJpWZbhcdvbx2M5/q3QO/mAexTQ0QkASY3RCKUmq34LTkTK/dfrDChJQCoFDLc0ykCT97WDDGNtXUfIBER2TG5IbqBk5l6/HjoEtYdvoSCYnOFxwO91RjbOxqP9YpCiJ+HBBESEdH1mNwQXUdXbMYvRzLw46FLlXYQBsr604ztFYV7O0fAQ6Wo4wiJiOhGmNwQoWxm7m2ncvBLUga2nMqGyVKxL42nSoGRXSMwtlc0Lz0REbkwJjfUYFmsNhw4n4/1Ry5jw9FM6EstlbbrHOmPh7s3wb1dIuDnoarjKImIyFlMbqhBsVht2HcuHxuOZWLjsSzkGUyVtgv0VuP+ro3xcGwk2oRxziciovqEyQ25vRKTFX+m5CDhxBVsOZWN/CoSGk+VAkM7hOK+LhHo1yoYKoW8jiMlIqKawOSG3NIVfSm2n85Gwols7ErNqXQ8GqDsFu5+rYJxb+cIDGkfCm8N3xJERPUdP8nJLZitNiSlF2DbqWxsO52Dk5n6KtuqFXL0axWEER3DMbh9KLSe7EdDROROmNxQvSQIAlKzi7ArNRe7U3Ox71w+ioyVdwgGAF+NEv3bBGNI+1AMbBvCjsFERG6MyQ3VC4Ig4FyuAfvO5WHfuXzsP5eH7ELjDZ/TpJEnBrYJwZD2oejdPBBqJfvQEBE1BExuyCWZrTacuKzHoYtXkXgxHwcvXEXOTZIZtUKOns0CMKBNMAa0CUGLYG/O7URE1AAxuSGXcEVfisNpBUhKL0BS+lUcSdehxGy96fM6RPjhtpZBiGsZhB5NA+Cp5mjBREQNHZMbqnO5RUYczdDheIYORzN0SL6kQ6auVNRz24b5onfzQPRqFoCezQIQ6KOp5WiJiKi+YXJDtcZmE3AxvxgnM/U4cVlf9m+mXnQio1LI0LGxFrFNA9A9uhF6NA1AgLe6lqMmIqL6jskN3TKbTcBlXQlSs4uQcqUIp68U4syVQqRcKRJ1aalchNYDXaL80SXSH12jGqFjYy0npSQiIqcxuSHRCkvNuJBbjPN5BpzPMeBcbhFSs4twLsfgVBIDAEE+asQ01qJjYy1iGmvRJdIfoX4etRQ5ERE1JExuyE4QBOQUGZGeX4L0/GJczCvGxXwD0vKKcTG/+KZ3K1VGJgOaBnqjfbgf2oX7ol24HzpEaBHqp+GdTEREVCuY3DQgRosV2XojMgpKkKkrweWCUmQUlCDjagkuXS3GpaslMFoqn6ZAjHCtB1qF+qJNqA9ah/qiTZgvWob4wEvNw4yIiOoOv3XcgMVqQ57BhJxCI7ILS3FFb0S23ogrhaXI1pciU1eKLF1plTNgO8NDJUfTQG80D/ZG00BvtAzxQcsQHzQP9oEP52UiIiIXwG8jF2SzCdCVmJFfbEK+wfEvt8iIvCIT8gxl/+YUGpFfbIIg1Nz6tZ4qRAZ4IjrAG1GBXogO8EJUoBeaBXkj1NcDcjkvJxERketiclNLbDYBBpMFhaUW6EvN0JdYoC8xQ19qhq7EjILisn/L/m/C1b/LV4tN0JWYazRZuV6QjwaNG3misb8HIrSeCPf3RGQjTzRp5IUmAZ6cd4mIiOo1Jjc15L1fT+D7Q+ko/fuuIatNQC3mJ5VSymVQKmTw81ChR3QjhPt7ItTPA2FaD4Rry/4N8fWAxWTF9LXJuJBrgM0GTL+jFby8Kk9oigwmPL3yL2QUFKOxvxeWjO0Gn0rGmsnXl+DeBXuRbzAiwFuD9c/3QYCf5y0tEwAu5BRg8NzdsNgApRzYPC0OTYP9K22bXWDAsM93o8hoho9GhY1T4hDi713tds6+Lmfa5uqKMeLLPdCVmKD1VGPDS30RpPWq0K601IJ3N5zEhXwDmgZ4480R7eDhUfnbtqCwFA8t3o+cwlIE+3pgzTO94O9b+R1oxcVmTF+bjLR8A6ICvDHnwU6VHgN7zqVjzOJke/m/z3RC3+aRlS5TX2TE2GUHkaUrQZjWEysn9IBfFYMsXs4vxOC5u1FqtsJDpcDmaXGICPCttG3W1SIMmbcbxSYLvNRKJEyNQ1gjnwrtnNn+YpcJAGm5OgyeuxsmqwC1QobN0+IQFaSttK3Y/WqzCTh+WY/8YhMCvNToEOFX5RnR2mor9hiwWGz4NTnT/n69p1M4lFXM0+bM+sUu1xW2VUPnzDHgKmSCUJvnCG5uwYIF+OSTT5CZmYkOHTpg3rx56NevX5Xtd+zYgenTp+P48eOIiIjAK6+8gkmTJolen16vh1arhU6ng5+fX028BNw/fxcOp+tqZFk3461W4Nn+LRDoo0agtwbBvhp8tvEU9pzLr9B2UNtgLB3fU1SsXSO1+OmF2xzqBn+2Hak5hgptWwZ7Y/OMAfZyp7f+gL604q3gfh4KJL91Z7WWCQBNX/utQrtyFz68y6HcZtYGGK0VD2WNQobT749wuh3g3Otypm37N39Hsblix20vlRwn3h1uL09cfgBbTuVUaFfZfu3+7ibkGcwV2gZ6q5D45lCHOrHHgDPbv2/8FlyuZHDGCK0H9swc5FDXYuZvqGQXQCEDzsY7LrfV67+hkk0FlRxI+eBaW2e2v9hlAkCz136r9EeKDMD567aB2P26JzUXC3ecxdnsIpitAlQKGVqE+OC5/i3Qt2WQw3Nrq63YY2DJzrOYv/0sCkvMsAGQA/D1VOGFAS3w9O0tqr1+sct1hW3V0DlzDNQ2Z76/JU29Vq9ejalTp2LWrFk4fPgw+vXrh+HDhyMtLa3S9ufPn8eIESPQr18/HD58GK+//jomT56MtWvX1nHk14hJbLzUCoT5eUB5k18FAV4q/PxCHEJ8qh6F12Cy4vsDaRjbKxp3xoRhwbaUShMbANhyKgcTlx8QFevhdB3un7/LXq4qCQGA1BwDBn+2HUDVXyoAoC+1otNbfzi9TODGX6zXP15VwgIARquANrM2ONUOcO51OdO2qi9AACg229D+zd8BVJ3YABX3a1WJDQDkGczo/u4me1nsMeDM9q8qsQGAy7pS9I3fYi9XldgAgFUoe7xcVUkIAJhtZY8Dzm1/scsEqk5sAED4+/FyYvfrntRcvP7TUZzM1MNbo0SIrwbeGiVOZhbi9Z+OYk9qrv15tdVW7DGwZOdZfPTHaeiKzZDLZVArZJDLZdAVm/HRH6exZOfZaq1f7HJdYVs1dM4cA65G0uRmzpw5mDhxIp566im0a9cO8+bNQ2RkJBYuXFhp+0WLFiEqKgrz5s1Du3bt8NRTT+HJJ5/Ep59+WseRlykuNos6Y3PotUHYNPk2WGw3PkmWX2xGkJcc2UU3vqvpsq4U+iIjSkstVX4BlttyKgelpRZRsR5O16G42Iwig6nKJKRcao4Babm6Kr9UyulLrcjXl4heZpHBhAs5BTdsV+5CTgGyCwxVJizljFYBxzKyRbXLLjAgX18i+nU50zZXV1zlF2C5YrMNl/L0ovdrQWFplYlNuTyDGQWFpaKPgc2nz92wTbk959KhLzJWmdiUKz9eL+cXVpnYlLMKZZessq4WVZmElDPbgBOXc0Rvf7HLzLpahLRc3U0vKwsou2Qldr9mFxiwcMdZFBktCPPzgIdKAblcBg+VAmF+GhQZrVi44yxsNgE2m1ArbcUeA/oiI+ZvPwurTYBaKYNSLodcJodSLodaKYPVJmD+9rOwWGxOrd9isYlarslklXxbNXRi95XlFoYPqU2S9bkxmUxITEzEa6+95lA/dOhQ7Nmzp9Ln7N27F0OHOp5iHzZsGJYuXQqz2QyVquL1YqPRCKPx2uBzer2+BqIvM31t8s0b/d0uo6BEVNvBc3eLajd22UF0alz5df/rvbvhpOjbwKevTYau5MZfluXExnrvgr2IDKjY76AyT6/8Cwcv5Ilev69GXOfnu788KKrdsM93w1vkLe33Ltgrql15W5PIDwGx2/XdDSex/3zlZ+2u99Di/WgZUnmfkus9teykqHZjFiejo8hjcOyygzibXSSq7eC5u6EQ2fdhxBcHbt4IZdtf7HE9ZN5uGC3iRtwePHc3/D3FzXc27PPd8FQr0MhLXWEAS5lMBn8vFc5mF+H45bLPqLPZRTXedv62VFGxjl12EIUlZigVMshljr+B5TI5lAobCkvM+DU5Ey1DfESvPzW7SNRyF+08Vyuv35m2HZuIO7bd1a/JmaKPgfu7NZYoyqpJltzk5ubCarUiNDTUoT40NBRZWVmVPicrK6vS9haLBbm5uQgPD6/wnPj4eLz99ts1F/g/pOXf+EzEP9uJHd1X7CB6WboS+HmK230X8g24KjK5Scs3oMhoEdXWdLOf4X/LNxghF3mOMKOgGGJ/CFhsQJFR3BeWWEVGs+gvtnyD+BGb8w1GWEX+GhR7DFzINyCnUNwkpDmFpVAra76zZJZOXNKepSuxd7a/mVKzFTU9eHW+wSh6uxabLDc9w1TOZBWgKxH33ioymqFUyKFWVP5m0Cjk0NkE5BeXLc9sFWq8rdjPrCxdCWwAqjpk5DLAirL3a4CPWvT6MwqKRS33Yr6hVl6/s20bMrH7KqOguC7DEk3y7s7XZ8+CINxwWP7K2ldWX27mzJnQ6XT2v/T09FuM+JqogMrvsKmsXZi28rs2rqcR2QM9TOuJpiLX3zTA26lYG/uLO8uiVoj7Bgrw1oheZmN/L4jthK+UAz4iz9yI5aNRIcC78jt8rhfgrXGqrVbkL3yxx0DTAG8EV3E31PWCfT1EHwPOEHtch2k9RU+C6qFS1Pio1gHeGtHL9FIrRR/baoVM9H710aigUshgslaeZBmtNqjkMgR4qRHgpa6VtmKPgTCtJ+QAqsrHbUJZp+rG/l5Orb+xv5eo5UYHeEu+rRo6sftK7Gd7XZMsuQkKCoJCoahwliY7O7vC2ZlyYWFhlbZXKpUIDAys9DkajQZ+fn4OfzVlzoOdRLdbOaGHqLabp8WJardyQg+8OaKdqLZvjmjnVKxLxnYT1VZsrOuf7yN6mUvGdhO93M3T4rBxiri2/3tJ3PbfOCUO65/vI6rt+uf7ONV2w0t9RbUV+/rfHNEOa57pJartmmd6iT4Gvpkg7rj67zPij+uVE3o4tV8Tpopru2Fyz5s3Qtn2F7vMhKlxTsUqdr9unBKHFiE+uFpsxvU3qQqCgIJiM1qE+KBDhB86RPjVSluxx8DKCT3g66mCxSrAJjgmAjbBBotVgK+nCvd0Cndq/fd0Che13Em3N5d8WzV0YvfVPZ0qXjFxBZIlN2q1Gt27d0dCQoJDfUJCAvr2rfzDok+fPhXab9q0CbGxsZX2t6ltXl4qdI288XXZrpFaeHmp4OejQYT2xr+yI7QeaBLoJ6qdn48GHh5KDGobfMO2g9oGw8ND6VSsPt5qtAy+8S+8lsHeiArSws/jxr/G/TwUCPDzFL1MH291lePYXK9psD9C/L2hucmvbI1ChpjGIaLahfh7I8DPU/TrcqZtkNYLXqobv+W8VHI0CfQTvV/9fT0Q6H3jYz/QWwV/Xw/Rx8DgNs1v2KZc3+aRoo9rPx8NIgJ8cbMTIgoZEBHgi7BGPrjJpoJKDrSPCBa9/cUuM6yRD6KCtLjZuRsZgKggrej9GuLvjef6t4CPRoEsvRElZitsNgElZiuy9Eb4aBR4rn8LyOVld6TURluxx4CfjwYvDGgBhVwGk0WAxWYr+0Kz2WCyCFDIZXhhQAsolXKn1q9UykUtV61WSL6tGjqx+8pVx7uRdJyb1atX4/HHH8eiRYvQp08fLF68GEuWLMHx48cRHR2NmTNnIiMjAytWrABQdit4TEwMnn32WTz99NPYu3cvJk2ahFWrVuHBBx8Utc66HOemsrFjxI4H4sy4Ic6Mh8JxbjjODce5caFxbmwCVHKR47HUYNvqjHMj/P26RY1zc5P1i12uK2yrhs6ZY6C2OfP97RKD+H388cfIzMxETEwM5s6di9tvvx0AMH78eFy4cAHbt2+3t9+xYwemTZtmH8Tv1VdflXwQP0D8aJ+A+JFcnRnx1ZmRbJ2JlSMUc4RijlDMEYo5QnHD5iojFNer5Kau1VZyQ0RERLWn3oxQTERERFTTmNwQERGRW2FyQ0RERG6FyQ0RERG5FSY3RERE5FaY3BAREZFbYXJDREREboXJDREREbkVJjdERETkViofx92NlQ/IrNfrJY6EiIiIxCr/3hYzsUKDS24KCwsBAJGRlc+PQ0RERK6rsLAQWu2NZ7dvcHNL2Ww2XL58Gb6+vpDJXH+SNL1ej8jISKSnp3MurHqA+6v+4L6qP7iv6pfa2l+CIKCwsBARERGQy2/cq6bBnbmRy+Vo0qSJ1GE4zc/Pj2/qeoT7q/7gvqo/uK/ql9rYXzc7Y1OOHYqJiIjIrTC5ISIiIrfC5MbFaTQazJ49GxqNRupQSATur/qD+6r+4L6qX1xhfzW4DsVERETk3njmhoiIiNwKkxsiIiJyK0xuiIiIyK0wuSEiIiK3wuTGRcTHx6NHjx7w9fVFSEgIRo4cidOnTzu0EQQBb731FiIiIuDp6YkBAwbg+PHjEkXccC1cuBCdOnWyD1DVp08f/P777/bHuZ9cV3x8PGQyGaZOnWqv4/5yHW+99RZkMpnDX1hYmP1x7ivXkpGRgcceewyBgYHw8vJCly5dkJiYaH9cyv3F5MZF7NixAy+88AL27duHhIQEWCwWDB06FAaDwd7m448/xpw5c/DVV1/h4MGDCAsLw5AhQ+zzZVHdaNKkCT788EMcOnQIhw4dwh133IH77rvP/qblfnJNBw8exOLFi9GpUyeHeu4v19KhQwdkZmba/44ePWp/jPvKdVy9ehVxcXFQqVT4/fffceLECXz22Wfw9/e3t5F0fwnkkrKzswUAwo4dOwRBEASbzSaEhYUJH374ob1NaWmpoNVqhUWLFkkVJv2tUaNGwjfffMP95KIKCwuFVq1aCQkJCUL//v2FKVOmCILA95WrmT17ttC5c+dKH+O+ci2vvvqqcNttt1X5uNT7i2duXJROpwMABAQEAADOnz+PrKwsDB061N5Go9Ggf//+2LNnjyQxEmC1WvH999/DYDCgT58+3E8u6oUXXsBdd92FwYMHO9Rzf7melJQUREREoFmzZnjkkUdw7tw5ANxXrmb9+vWIjY3Fww8/jJCQEHTt2hVLliyxPy71/mJy44IEQcD06dNx2223ISYmBgCQlZUFAAgNDXVoGxoaan+M6s7Ro0fh4+MDjUaDSZMm4aeffkL79u25n1zQ999/j8TERMTHx1d4jPvLtfTq1QsrVqzAxo0bsWTJEmRlZaFv377Iy8vjvnIx586dw8KFC9GqVSts3LgRkyZNwuTJk7FixQoA0r+3Gtys4PXBiy++iOTkZOzatavCYzKZzKEsCEKFOqp9bdq0QVJSEgoKCrB27Vo88cQT2LFjh/1x7ifXkJ6ejilTpmDTpk3w8PCosh33l2sYPny4/f8dO3ZEnz590KJFC3z33Xfo3bs3AO4rV2Gz2RAbG4sPPvgAANC1a1ccP34cCxcuxLhx4+ztpNpfPHPjYl566SWsX78e27ZtQ5MmTez15XcMXJ/xZmdnV8iMqfap1Wq0bNkSsbGxiI+PR+fOnfH5559zP7mYxMREZGdno3v37lAqlVAqldixYwe++OILKJVK+z7h/nJN3t7e6NixI1JSUvjecjHh4eFo3769Q127du2QlpYGQPrvLCY3LkIQBLz44otYt24dtm7dimbNmjk83qxZM4SFhSEhIcFeZzKZsGPHDvTt27euw6XrCIIAo9HI/eRiBg0ahKNHjyIpKcn+Fxsbi7FjxyIpKQnNmzfn/nJhRqMRJ0+eRHh4ON9bLiYuLq7CcCVnzpxBdHQ0ABf4zqr1LsskynPPPSdotVph+/btQmZmpv2vuLjY3ubDDz8UtFqtsG7dOuHo0aPCo48+KoSHhwt6vV7CyBuemTNnCjt37hTOnz8vJCcnC6+//rogl8uFTZs2CYLA/eTq/nm3lCBwf7mSGTNmCNu3bxfOnTsn7Nu3T7j77rsFX19f4cKFC4IgcF+5kgMHDghKpVJ4//33hZSUFGHlypWCl5eX8H//93/2NlLuLyY3LgJApX/Lli2zt7HZbMLs2bOFsLAwQaPRCLfffrtw9OhR6YJuoJ588kkhOjpaUKvVQnBwsDBo0CB7YiMI3E+u7vrkhvvLdYwePVoIDw8XVCqVEBERITzwwAPC8ePH7Y9zX7mWX3/9VYiJiRE0Go3Qtm1bYfHixQ6PS7m/ZIIgCLV/foiIiIiobrDPDREREbkVJjdERETkVpjcEBERkVthckNERERuhckNERERuRUmN0RERORWmNwQERGRW2FyQ0RERG6FyQ0RERG5FSY3RFQv7NmzBwqFAnfeeafUoRCRi+P0C0RULzz11FPw8fHBN998gxMnTiAqKkrqkIjIRfHMDRG5PIPBgB9++AHPPfcc7r77bixfvtzh8fXr16NVq1bw9PTEwIED8d1330Emk6GgoMDeZs+ePbj99tvh6emJyMhITJ48GQaDoW5fCBHVCSY3ROTyVq9ejTZt2qBNmzZ47LHHsGzZMpSfdL5w4QIeeughjBw5EklJSXj22Wcxa9Ysh+cfPXoUw4YNwwMPPIDk5GSsXr0au3btwosvvijFyyGiWsbLUkTk8uLi4jBq1ChMmTIFFosF4eHhWLVqFQYPHozXXnsNv/32G44ePWpv/8Ybb+D999/H1atX4e/vj3HjxsHT0xNff/21vc2uXbvQv39/GAwGeHh4SPGyiKiW8MwNEbm006dP48CBA3jkkUcAAEqlEqNHj8a3335rf7xHjx4Oz+nZs6dDOTExEcuXL4ePj4/9b9iwYbDZbDh//nzdvBAiqjNKqQMgIrqRpUuXwmKxoHHjxvY6QRCgUqlw9epVCIIAmUzm8JzrT0jbbDY8++yzmDx5coXls2MykfthckNELstisWDFihX47LPPMHToUIfHHnzwQaxcuRJt27bFhg0bHB47dOiQQ7lbt244fvw4WrZsWesxE5H02OeGiFzWzz//jNGjRyM7OxtardbhsVmzZmHDhg1Yt24d2rRpg2nTpmHixIlISkrCjBkzcOnSJRQUFECr1SI5ORm9e/fGhAkT8PTTT8Pb2xsnT55EQkICvvzyS4leHRHVFva5ISKXtXTpUgwePLhCYgOUnblJSkrC1atXsWbNGqxbtw6dOnXCwoUL7XdLaTQaAECnTp2wY8cOpKSkoF+/fujatSvefPNNhIeH1+nrIaK6wTM3ROR23n//fSxatAjp6elSh0JEEmCfGyKq9xYsWIAePXogMDAQu3fvxieffMIxbIgaMCY3RFTvpaSk4L333kN+fj6ioqIwY8YMzJw5U+qwiEgivCxFREREboUdiomIiMitMLkhIiIit8LkhoiIiNwKkxsiIiJyK0xuiIiIyK0wuSEiIiK3wuSGiIiI3AqTGyIiInIr/w9fs/1P5MElrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot logistics curve\n",
    " \n",
    "import seaborn as sns\n",
    "sns.regplot(x=datafile['Age'], y=datafile['Purchased'], logistic=True, ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7899a444",
   "metadata": {},
   "outputs": [],
   "source": [
    "#training and testing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X=datafile.iloc[:,[0,1]].values\n",
    "Y=datafile.iloc[:,2].values\n",
    "\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "20a09e71",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "902bd00d",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "68460322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifer=LogisticRegression(random_state=0)\n",
    "classifer.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "754efe93",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred=classifer.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8ad102a5",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>y_actual</th>\n",
       "      <th>y_predict</th>\n",
       "      <th>Comp.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>[30, 87000]</td>\n",
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       "      <td>True</td>\n",
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       "      <td>[38, 50000]</td>\n",
       "      <td>1</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[35, 75000]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[30, 79000]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[35, 50000]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>[27, 96000]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>[23, 63000]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>[48, 33000]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>[48, 90000]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>[42, 104000]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               X  y_actual  y_predict  Comp.\n",
       "0    [30, 87000]         0          0   True\n",
       "1    [38, 50000]         1          0  False\n",
       "2    [35, 75000]         0          0   True\n",
       "3    [30, 79000]         1          0  False\n",
       "4    [35, 50000]         1          0  False\n",
       "..           ...       ...        ...    ...\n",
       "95   [27, 96000]         0          0   True\n",
       "96   [23, 63000]         0          0   True\n",
       "97   [48, 33000]         0          0   True\n",
       "98   [48, 90000]         0          1  False\n",
       "99  [42, 104000]         1          1   True\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_result = pd.DataFrame(list(zip(X_test, Y_train, Y_pred)), columns = ['X', 'y_actual', 'y_predict']) \n",
    "#대소문자주의: 위 선형에서 소문자로 시작\n",
    "test_result['Comp.'] = (test_result['y_actual']==test_result['y_predict'])\n",
    "test_result\n",
    "# X는 그냥 확인용, 뭘 넣었는지 모름"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2098a67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[65,  3],\n",
       "       [ 8, 24]], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion matrix.\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm=confusion_matrix(Y_test,Y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d3c602ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(Y_test,Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6fba8ce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터를 정규화하여 다시 실행하여 정확도를 비교해보자!\n",
    "# min - max 스케일링 등 다양한 feature engineering 기법을 테스트해보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "956268ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터를 정규화한다. \n",
    "# 데이터들이 평균은 0, 표준편차는 1이 되도록 변경하는 작업이다.\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "X_train=sc.fit_transform(X_train)\n",
    "X_test =sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e519168f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[68,  0],\n",
       "       [32,  0]], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from sklearn.linear_model import LogisticRegression\n",
    "# 모델 재훈련\n",
    "classifer=LogisticRegression(random_state=0)\n",
    "classifer.fit(X_train,Y_train)\n",
    "\n",
    "# 재예측\n",
    "Y_pred=classifer.predict(X_test)\n",
    "\n",
    "# Confusion Matrix 재평가\n",
    "#from sklearn.metrics import confusion_matrix\n",
    "cm=confusion_matrix(Y_test,Y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c5a490f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 재평가\n",
    "#from sklearn.metrics import accuracy_score\n",
    "#from \n",
    "accuracy_score(Y_test,Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "066b8084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.07669377, 1.1102991 ]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최종 모델의 (계수, Coefficients) 를 확인하고 싶으면 아래와 같이 .coef_ 의 값을 확인한다. 단, sklearn 모델은 p-value를 제공하지 않아,\n",
    "# 다른 통계 라이브러리로 확인해야한다.\n",
    "# 처음이 age, 두번째가 salary 변수의 [_____]\n",
    "classifer.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9fe3fb5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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