ML_Algorithm(diabetes)_final_(Shoaib_MTalib_Aashir) (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "HRDti-GtvDc0",
   "metadata": {
    "id": "HRDti-GtvDc0"
   },
   "source": [
    "# **DIABETES CLASSIFICATION USING DIFFERENT ALGORITHMS**\n",
    "\n",
    "*   Logistic Regression\n",
    "*   Decision Tree\n",
    "*   KNN\n",
    "\n",
    "## GROUP MEMBERS\n",
    "\n",
    "*  11063 MUHAMMAD AASHIR KHAN\n",
    "*  10270 M.TALIB FAROOQ\n",
    "*  11119 MUHAMMAD SHOAIB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c267a48d",
   "metadata": {
    "id": "c267a48d"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "af5ff3e9",
   "metadata": {
    "id": "af5ff3e9"
   },
   "outputs": [],
   "source": [
    "def extract(src):\n",
    "    df = pd.read_csv(src)\n",
    "    print(f\"Rows: {df.shape[0]},\\tColumns: {df.shape[1]}\")\n",
    "\n",
    "    df = df.rename(columns=lambda x: x.replace(\" \", \"\"))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1a7b6393",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "id": "1a7b6393",
    "outputId": "621efe03-d4dd-470e-c573-1f80da94fd96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 768,\tColumns: 9\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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = extract(\"T:/DataSets/diabetes.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0eeb84c8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0eeb84c8",
    "outputId": "60159635-97f5-463e-fa90-88c5f0d656eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Outcome'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32fd1080",
   "metadata": {
    "id": "32fd1080"
   },
   "source": [
    "# Target/Label/Outcome Visualization of No of Diabetes Patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ace91601",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "ace91601",
    "outputId": "1aff7900-a69b-4b94-a2c2-ccce802161e3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.countplot(x=\"Outcome\", data=data, palette=\"Set2\")\n",
    "plt.title(\"Number of Target\")\n",
    "plt.xlabel(\"0 = No Disease | 1 = Disease\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "target_counts = data['Outcome'].value_counts()\n",
    "plt.pie(target_counts.values, labels=[\"Disease\", \"No Disease\"], shadow=True, autopct=\"%1.1f%%\", colors=[\"#B7C3F3\", \"#8EB897\"])\n",
    "plt.legend(title=\"Target:\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2f1414cb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2f1414cb",
    "outputId": "e36d4a24-d152-4d82-c542-3c15b5c231a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b7e73bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e16c997a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 849
    },
    "id": "e16c997a",
    "outputId": "99797c33-e88a-43b0-c59d-670e6b49decf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ks4iNrKrkxUTW7BbPynMSG2kHyt1t7OTkHzuZH9g9mPe/yQdgybpSrrkgsn4DB84ZHMXw01xdtFt3lRAXU/XxGBftIDvX82KmvAInYSHVXosYh0d1+rD120pIig8gIsxGxzbB9O0aRu/OYQQECKHBNu74ayL/ntywXb5NYqNJz6x6T6RnZhMf4zlEIzy0KhEe2LMzz0/8iJy8AlZt2s4vy9fw68p1lJaXU1hcwiP/9y7/vMX7F+ZERCeRl32g8nF+djrh0U1qtDuYtpGZkx/mstsmEBpeVel1OsuZ9tZtdO53Ph16jaixnPIOTTwb1nfATSLykzGmXETaA3uBecBVIvIukAAMBT44+moAV8Vzr/vvq+vw3AuA10Sk9eGu9lqqnt8DtwAvAohID2PMShFpa4xZg2uc6qlAR1xd/ZWMMeOB8QAzAjr4ZmCfn9ibCbERQnQY5BVD55Y2Pp9ftzFooUGuC4xKy8FhhzZJNuav9/4V4fuyICYCosJcCWenFsKXC+oWx+w1htlrXG+BFgnQv6OtwZNOgOk/HGL6D4cA6N8zitEjmvDzr1mc0i6MwiKnRzf7YSvX5TO4fwyzF2QzYnAcvy7LAeBgRhm9ukSydlMB0VEOUpoGs/+g63zu7htasmtfCZ/NrP/k4KsfM/jqxwwA+nWPZPTwBGYvzKZj21DXNtSSyKzakM/gftHMXpjD8EFxLFheVR4PDbHRtWO4R1I58eP9TPx4PwDdOoYzZlSTBkk6AXbsKycxzk58tI3sfIv+XYJ58zPP8v3KTaWc2S+URWtLaNs8gOJSc8xudoCcfIuOrQLYuLOcU1oHkp5Z/2M8v5mbyzdzXbH27hzKqMHRzFtWQPtWwRQVWx7d7Iet3VzEaT3DmbesgDP6R7J4teukISk+gAMZrvdfm+ZBOBxCfqHFlOmZTJnuGkvTOTWEC8+MafCkE6BT25bsPnCIvQczaBIbzQ8LlvPPW672aJORk0dcVAQiwrqtO7GMISoijFuuGM0tV4wGXFe+T/l6lk+SToDkVl3JPriTnIw9REQnsmHpDC649l8ebXKz9vHZm7dy/jUvEJfYunK6MYaZkx8iLqkN/YaP83boJ0272lVdvY1rLOdycZVDDwEXAp8BZwJrgc3AIuB4nasv4Opq/wfw0/Ge2BhzyF2VnCYiNuAgMPyIZrfhSk5X43ovzMV1df4dInIGrurpeuCb425pA+jx3r+IG9KPwPgYhu2Yw5YnX2XPpE99EcoxGQPfLLW4cpgdEVi5zeJQLvROdR0olm0xhAXD9efYCQoAY4T+HeH1r5yEh8DoU+3YxNXVun6XxZa93s/jjYHvl1uMHeK6JdSq7a4r2nu2dW3Dim2ubRg33ObeBujbXhj/jUVZw97Vpk4WrcilX48oJv+7C6WlFi++tbNy3tP3pvLyhJ1kZpfz9tQ0Hrq1LeMua8bWnUV887Mr6Zvy+X7uubEVE57vBCJMmJpGXn4FXTqEM3xwPNt3F/Hms50AmPjRXhavrP+xEItX5dGvRyTvvNSJ0jKLlyZUJYdP3d2Gl9/eTVZOBW9/uI8Hb27FVWOS2baryOOCoYF9olm+Np+SUt/cksuyYMpM13hMm8AvK0rYd8jJGX1c1bSflxazaksZ3VKDeOG2OErLDf/7surWCTdeEkXHVgGEh9p4+R/xfPFzAXNXlDDpqzyudN+iqbwCJn2Vd7QQ6sWydUX07hzGG4+1pLTc8OqUquTw4ZuSee2DdLJznUz+MoO7xjXlT+fFsWNPKT8ucMV1ao9whvaPwOmEsnKLf03c36DxHo/Dbufeqy/jtmdfw2kZLhg6gLYpTfnsh18AuGT46fy0aAWf/vALDrudoMAAnr5tXK3DCXzJZncwfOyjfPif6zCWk24DLyEhOZXlc6YC0GvIFcz/+jVKCnP47oMnXMvY7Ix7aBpp25axduGXJDRrz//+6Uqkh1z4D9p1HeKz7fmjktrGfqiGJyLhxpgCEYkDFgMDjTEHjrecP2oMFc9l76zzdQj1wuHwrw+K3+qnL1b4OoSTZnM07IU83pLcNtnXIZy03IyGTVS95d1rdvo6hHoxLe/IGsjvz9VDT3Z0/4lZOuTUBvmc7TNngdc/NLTi6Ttfi0g0EAj88/eadCqllFKqYYmv7rXXADTx9BFjzFBfx6CUUkop5U2aeCqllFJK+bHGdDulxlO7VUoppZRSfk0rnkoppZRSfkxvp6SUUkoppbxCu9qVUkoppZQ6QVrxVEoppZTyY43pdkqNZ0uUUkoppZRf04qnUkoppZQfa0xjPDXxVEoppZTyY43pqnbtaldKKaWUUl6hFU+llFJKKT/WmLrateKplFJKKaW8QiueSimllFJ+TG+npJRSSiml1AnSiqdSSimllB9rTGM8NfFUSimllPJjjSnx1K52pZRSSinlFVrxVEoppZTyY42p4qmJpzppy95Z5+sQTlrvqzv7OoR6se7DDb4OoV507Jvq6xBO2qVnB/g6hHqx/WCor0M4afvS430dQr34vrytr0OoF2c2W+/rEOpBJ18H8LuliadSSimllB9rTLdT0sRTKaWUUsqP6Xe1K6WUUkopdYK04qmUUkop5cca08VFWvFUSimllFJeoRVPpZRSSik/phcXKaWUUkopr9CudqWUUkoppU6QVjyVUkoppfyYVjyVUkoppZQ6QVrxVEoppZTyY43p4qLGsyVKKaWUUsqvacVTKaWUUsqPNaYxnpp4KqWUUkr5Me1qV0oppZRS6gRpxVMppZRSyp9J4+lq14qnUkoppZTyCq14KqWUUkr5Mb24SCmllFJKeUVjurhIE08/IiKJwCvAACAbKANecP99tzHmPB+G1yDaNhXO7mPDJrBiq8X89cZjflwkjB5gJykWfl5lsWCDa77dBlcPt2O3g01gw27DnDWWLzbhuLpNeIYmo4ZSdjCTuT3P93U4R5W2+RcWfv0MlmXRoe8Yug+53mN+zsHtzP3sQTL3rafPiDvoevo1lfM+euFMAoLCEJsdm83O6Js/9Wrsp7RyMGZoMDYb/LqmnB+WlNZoM+aMYDq3dlBWDu99V0TaQdf75YlrIygtN1gWWJbhhQ8KARh3bgiJMXYAQoKE4lLDc1MKvLI9a5fP56OJL2JZFoPOupBzLr7GY/7KxT/z5dQ3EBHsdjuXXXMPqaf0pLyslBcfvpaK8jKclpPep57FBWNv8krMtdm+bi4/fvw0lmXRfeClnDryBo/56xZNZ+H3EwAIDApjxJ8eJ7F5R/Ky9vP1O/dSmJeBiI3ugy6j75lX+WITAGiTBCN62RCBldtN5XHosLgIOK+/jaQYmL3asGiT53wRuGaEjfwi+PgX3xynNq/+hRlTXPt3nyFjGHK+5/698tevmDvjbQCCgkK54OrHaNqiIwDFhXl8PvER0tO2IAgXX/cULVJ7en0bABYvW87r4/+HZVmcM+Isrrj0klrbbdy8hdvuvp+H772LwYNOY0/aXp56/qXK+fsPpHPVn6/gktH+e0xurDTx9BMiIsAXwLvGmD+5p7UELsCVeDY6InBOXxtTfnKSVwTXjbSzKc1JRl5Vm+JS+Hapkw4pnmd7Tgsmz3JSXuFKPMeNsLN1H+zN9PJG1EHau9PY+foUekx83tehHJVlOfl1+j8Zec3/CItMZPrrl9Gi4xnEJLarbBMUGsWp5z/ErvWzal3HqOveJTgsxlshVxKBy4YF83+fFZKTb7jnynDWbCvnQFbVB3yn1g4Som08MbGAVk3tjD0zhJemFlbO/8/HhRSWeCYLk2YUV/590eBgiss85zcUy+nkgwnPcedjbxATl8gz915J975DSE5pW9mmY9f+dO87FBEhbedm3vrXffzz1c9xBATyjyfGExwSSkVFOS88dA1deg6kTYduXondYzssJ99PfZKxt08iIiaRd54dQ2q3YcQnV72nouKbc+U/phAcFsW2tXP4dsojXHX/J9jsdoaNuZ+kFp0pLSngnWcuofUpAz2W9RYRGNnHxgc/W+QVwzXDbWzZazyPU2Xw/XKLDs1q7w7t217IyDMEOXzTXWpZTr6a/E/G3fs/ImMTeeOxyzil1xk0aVb1/4xJaM71D04mJCyKTavm8sXEx7jp8Y8AmDHlGVK7DuJPt/6HiooyyktLfLIdTqeTV98Yz/NPPU5CXBw333kvp/XvR8sWKTXavf3OZPr07FE5LaV5M9569ZXK+WOvuo5Bp/b3ZvgnpTF1tTee2u3v3zCgzBjz5uEJxphdxphXqzcSkcdF5O5qj9eKSCv3338VkdUiskpE3nNPaykis9zTZ4lIC/f0S93LrhKRue5pdhF5UUSWuNv/rSE3uFkcZOcbcgrAsmDdLosOKZ47V1Ep7MtyzT9SeYXrt83m+vFXWfOWUp6V6+swjulQ2moi41oQGZuC3RFIm26j2L3hJ482IeFxJDTvis3mX+errZLsZORYZOYanBYs31hOt7YBHm26tXWweH05ADv3OwkJEiLD6n4g79UhgGUby+s17qPZsXUtTZqmkJDUHEdAAH0Hnc2qxbM92gSHhCLuq1xLS4sRXH+LCMEhoQA4nRU4Kyp8djXs/p2riWnSkugE13uqU99z2bLa86SledteBIdFAdCsdQ/ysw8AEB7VhKQWnQEICg4nLqkN+Tnp3t0At+RYyMqHnELXcWj9bkP7ZjWPU/uzwFnLuUlECLRLFlZu886JS23Stq0mtkkLYpuk4HAE0m3AKDYs99y/W6b2JMT9WrRo151c92tRUlzAzk1L6TNkDAAORyAhYZHe3QC3TZu3kNy0KclJSQQEBDB08CDmL1xco90XX8/k9NNOJTo6qtb1rFi1huSmSSQ2adLQIata+NcnyB9bZ2D5b11YRDoDDwEDjTEZIhLrnvV/wGRjzLsicg3wX+BC4FHgbGPMXhGJdre9Fsg1xvQVkSBgvoh8b4zZ8VvjOpaIECG3qOpxXhE0ixOgbgdoEbh+pJ3YCFiy2fhltfP3oij3IGFRSZWPQ6MSObRndd1XIMK3k64FhI79Lqdjv8vqP8ijiAoXsvOr3jPZBRatmto92kSH28jOr0occwoM0eE28gqdGOCWS8IwwPzVpcxf45lgtm1mJ7/Q4lCOd7pIczIPEhuXWBV7XCI7tqyt0W7Fwp+Y9v6r5OdmcetD/62cbjmdPHXPnzh0YA9DR15Om/ZdvRL3kfKz04mIqXpPRUQnsm/H0d9Tq+Z/Spsug2tMz8lI4+CeDSS37t4gcR5PRAjkF1W9v/KKoVnsMRY4wvBeNn5aaREYcPy2DSUv+yBRcVWvRWRsInu2Hf21WDrnM9p3Ox2ArIN7CI2M5bMJD3Jg9yaSW3fivD8/SGBQaIPHfaSMzCyaJMRXPk6Ij2Pjps2ebTIymb9gIS8+/SSbtmytdT0/z/2FMwaf3qCx1rfGNMaz8WxJIyMir7mrkUvquMgw4FNjTAaAMSbLPf1U4AP33+8Bg9x/zwfeEZHrgcOf0iOAv4rISmAREAekHiW+G0RkqYgsXfrThBPYsuor+W2LHWYMjP/GySufO2kWBwm1n9yqOqmZ7MsJvEDn/e0DLrxlGmdfPZ4NCz9g/466vm1PXq1R1uHcxRhXo1c+LOD59wt4fVohp/cIom0zz6S1T8cAlm7yTrUTag+9tm3sOWAY/3z1c/5+38t8OfX1yuk2u51HX/6I5yd8x46ta9m7q/YP34ZXy5Ycpfq6a9NCVv/6KWdcdLfH9LKSQj4ffxtnXvYgQSHhDRHk8dUScl1rl+2SoajEcMDHg6XMCezf29cvYtmczxh52V2A60Rm/8719D9zLLc8NY3AoFDmfPUbj/knqbbtOPI99fqE/3Hd1X/FbrfXbAuUl5ezYPEShgw6rSFCbDBikwb58QWtePqPdUDlKGljzM0iEg8sPaJdBZ4nDMHu33UtFRr3+m8Ukf7AucBKEenhXsetxpjvjrsSY8YD4wGefL/iN/Uh5RcZokKr3viRoZBffOKrKi2HnQcN7ZKFQ7m+6876PQuNSqQw90Dl46LcdEIj694NFeZuGxIeR8tOZ5GRtoamrfvWe5y1ySkwxERUvY9iwm3kFpgj2ljERNgAJwDR4UJuoavN4d8FxYbVW8tplWRn215XO5tA93YBvPC+dy4qAoiJa0JWZlW3ck5mOtGxCUdt375zbw69mkZ+XjYRkVVjbEPDIujQuQ/rVvxKs5beHxsZEZNU2XUOkJ+TTkR0zffUwbSNfPPew1x26wRCwqvidzrL+Xz8bXTudz4deo7wSsy1yS+CiNCqw2tkCBQUH3uZw5rHC6nNhLbJgsMGQQFwwQBh+kLvHqeiYhLJzax6LfKy0omMqflaHNi9ic8nPsJVd71FaITrtYiKTSQyNpGUtq6Kc5e+I5jztW8Sz4S4OA4eyqh8fCgjk7hYz/Lz5q3bePqFfwGQm5fP4qXLsNvtDHSP51y8bDmpbdsQExPttbiVJ614+o+fgGARqX4Jam19GTuBXgAi0gto7Z4+C7hMROLc8w7vjb8CY91/XwnMc89va4xZZIx5FMgAUoDvgJtEJMDdpr2IhNXP5tW0NxNiI4ToMNcYzc4tbWxOq9sBOTTIdRAHcNihTZLNY7C/OjEJzbqSl7GL/Kw0nBVlbF89kxannFGnZcvLiigrLaz8e+/W+cQk1loobxC7DjhJiLYTFynYbdCrYwCrt3tWKNdsq6BfJ9cbplVTO8VlhrxCQ6Cj6n0U6ICOLR3sy6zqUu/Q0kF6tkVOgfcShVbtOnNw/24y0vdSUV7Oknnf0b3vUI82B/fvrqzY7tq2AWdFOeER0eTnZlFUmA9AWWkJG1YvIql5K6/FXl3Tll3JOriTnIw9OCvKWL9kBu26DfNok5u1j2lv3cp5414gNrF15XRjDDMnP0RcUhv6nTXO26F72JcFsREQ5T5OdWohbN5bt/fD7NWGV6dbvPaVxecLLHam4/WkE6BZm65kpu8i61AaFRVlrF44k449PffvnIx9vP/f2xjzt+eJb1r1WkREJxAV25RD+10jrratW0gTH1zkBdChfSp79+1n/4F0ysvLmT13Hqf19zzBnfK/t3h/4njenziewQNP5bab/laZdAL8PGfe766bHXxX8RSRkSKySUS2isj9tcyPEpGv3D2060TkuDusVjz9hDHGiMiFwCsici9wCCgE7jui6WdUdYcvATa7l18nIk8Dc0TECawArgZuAyaKyD3udR5+U7woIqm4qpyzgFXAaqAVsNx9lf0hXONBG4Qx8M1SiyuH2V23KdlmcSgXeqe6doZlWwxhwXD9OXaCAsAYoX9HeP0rJ+EhMPpUOzZx9bSs32WxpY4fBt7W471/ETekH4HxMQzbMYctT77Knknevd3Q8djsDk694GG+nXQdxli0730xMYmpbFj0IQCn9B9LUf4hvnztUspLCxCxsXb+ZC6542tKirKZNeVWACyrgrbdz6N5e+8d2C0DH/9czM2XhCECC9eWcyDTYlC3QADmrS5j3Y4KOrd28Ng14ZRXwJTvXCWriDDh+gtc51Z2gaUby9mws6Jy3b29eFHRYXa7gyuuu49/P/l3LMti4JmjSW7RljnffQLAkLMvZfmCWSyY8zV2u4PAwCCuv+t5RITc7AwmvfoolmVhLIs+A4fTrU/NcZPeYLM7GHH5o3z03+swlpNup11CQnIqK+ZOBaDn4CuYP+M1igtz+H7qE65lbHaufnAaaduWsW7RlyQ0a8/Ep0a7tnv0P2jbdYjXt8MY+G6ZxRVDbNhssGq764r2Xm1dx6nl21zHqWtG2NzHKejXQXhrpkVZxXFW7iV2u4Pz//ow77zg2r97Db6YxOapLPrJtX/3HzaWn758naKCHKa/+yTgei1uftJ1nDrvLw/x8Rv34HSWE5uQwiXXP+2j7bBz643Xc/+jT2BZFiOHn0mrli34aua3AJw/auQxly8pKWXZypXcccuN3gj3d09E7MBrwHAgDVgiItONMeurNbsZWG+MOV9EEoBNIvK+MabsqOs9fNas1G/1W7va/Unvqzv7OoR6se7DDb4OoV7s3pXv6xBO2qVn+/Bqknq0/aD3LyKpb/vSnb4OoV50aN04bqnTP3ajr0M4aSmpnbz6Yhx86OoG+Zxt8vQ7R90OETkVeNwYc7b78QMAxphnq7V5AFeP6c24Clc/AO2NMUe9GlO72pVSSiml/oCqXyjs/qn+LQ/NgD3VHqe5p1X3f8ApwD5gDXD7sZJO0K52pZRSSim/Jg10P97qFwrX9rS1LXLE47OBlbjurNMW+EFEfjHGHPWqC008lVJKKaX8mI/u45mGqxv9sOa4KpvVjQOeM65xm1tFZAfQEah5Z3837WpXSimllFJHWgKkikhrEQnEdYec6Ue02Q2cCSAiiUAHYPuxVqoVT6WUUkopP+aLm70bYypE5BZct1q0AxPdd9C50T3/TeCfuL6MZg2urvn7Dn+RzdFo4qmUUkoppWowxswEZh4x7c1qf+/D9a2HdaaJp1JKKaWUP2tE39WuiadSSimllB/z1feqN4TGk0IrpZRSSim/phVPpZRSSik/JtJ46oSNZ0uUUkoppZRf04qnUkoppZQ/a0RjPDXxVEoppZTyYz765qIG0Xi2RCmllFJK+TWteCqllFJK+TG9nZJSSimllFInSCueSimllFL+TG+npJRSSiml1InRiqdSSimllB9rTGM8NfFUJ83h+P3vEOs+3ODrEOpF57Gn+DqEehHzzSZfh3DSZvxS7OsQ6sWgPk5fh3DSFi7K9XUI9aJ18xhfh1AvXpqZ4usQTtp/bvfyE+rtlJRSSimllDoxWvFUSimllPJjIr//nsXDtOKplFJKKaW8QiueSimllFL+rBGN8dTEUymllFLKjzWmq9obTwqtlFJKKaX8mlY8lVJKKaX8mX5zkVJKKaWUUidGK55KKaWUUv6sEY3x1MRTKaWUUsqPiXa1K6WUUkopdWK04qmUUkop5c8aUVe7VjyVUkoppZRXaMVTKaWUUsqPSSP65qLGsyVKKaWUUsqvacVTKaWUUsqfSeMZ46mJp1JKKaWUP9OudqWUUkoppU6MVjyVUkoppfyZdrU3XiLiBNYAAjiBW4wxv4pIK+BrY0yXeniO2cDdxpilIrITyAcsIB34qzHmwMk+x+9FmyQY3tOGCKzabliw0XjMj4uAc/vZSIqBOWsMizZ5zheBccNt5BfDJ79Y3gzdQ9rmX1j49TNYlkWHvmPoPuR6j/k5B7cz97MHydy3nj4j7qDr6ddUzvvohTMJCApDbHZsNjujb/7U2+HXSbcJz9Bk1FDKDmYyt+f5vg7nqHasm8tPnz6NsSy6DryU/iNu8Ji/fvF0Fv8wAYDAoDDOGvs4TZp3BODb9x5g29rZhEbEMe7hr70ad/sUG6MHBiACizc4mb2yokabCwYG0LGFjfIK+PjnMvZmuPaH07vZ6dvRdTg/kGnx8exyKpzQNE64+PRAAgMgO98wdVYZpeXe26aNq35h+nvPYllO+g0dw7ALPPeL5fO/4uev/gdAUHAoF497lOSWrtdi7jfvsvjnT0GEpintueyGpwkIDPJK3J1bB3DZWaHYbDBvVSnfLSyp0ebys0Lp0jaAsnLDOzMK2ZPurJwnAg9eHUlOvsVrnxYA0KtDAOcPCiEp3s5z7+ax64Czxjob0tY1v/Dt1KexjEWv08cwaJTnfrF64VfM/+bwfhHKuX95nKSUjpXzLcvJhCfHEBHThD/d/pZXY+/Y0s7FQ4KxCSxcV86PS8tqtLl4SBCdWjkorzC8/30JaYdcnwchgTD2rGCaxtkwwNQfSth5wGJk/0BO7RJAQbFrH5rxaynrd3r3Nfmj0sSzpmJjTA8AETkbeBYY0sDPeYYxJkNEngEeBG47PENEBBBjTINnVSJiN8Z4bc8TgbN725g62yKv2JVAbtlnyMiralNcBj+ssGjfrPazvb6pQmaeITDAd2eDluXk1+n/ZOQ1/yMsMpHpr19Gi45nEJPYrrJNUGgUp57/ELvWz6p1HaOue5fgsBhvhfybpL07jZ2vT6HHxOd9HcpRWZaTHz9+kktvnUREdCJTXhhD267DiG9a9VpExTdn7J1TCA6NYvu6OXz/wSP8+d5PAOg84GJ6DvkzMyff59W4ReCiQQFM+LqM3ELDrRcHsX6Xk4PZVSdaHVvYiI8SXphaSosmwkWnB/J/n5cSGQYDuzh46aNSKpxw5fAAurezs2yTkzFDApmxoJzt+y36dLAzpIeD75fUTGgbgmU5+fydp7jhgbeJik3kv49cTudeZ5DYvOq1iE1ozk2PvEtoWBQbV87l0/89xm1PfkRuVjrzvpvCPS98RUBgMO/9905WLphJ3yEXNXjcInDFiFD+/WE+2fkWD1wdyeotZezPrDoEd2kTQJMYG4+8lUvrZDtXnh3Gc5OrDlxn9gnmQIaT4KCq49K+DCdvfl7AlSPDGnwbjmRZTma+/yR/uWsikTGJTPjnpXToMYyE5KrXIia+GVff+x4hYVFsWTOXr999lOse/rhy/qIfJhOf3IbS4gKvxi4Clw4N5vXPi8gpMNw1NpQ12ytIz6p6PTq1spMQbeOpdwtpmWTj0mHBvPJREQAXDwlmwy4nk2aWYLdBYLWsZ/aKMn5e7sUzsZOgt1P644gEso+cKCLBIjJJRNaIyAoROeM400NE5EMRWS0iHwEhR3m+uUA7EWklIhtE5HVgOZAiIveIyBL3Op5wrzdMRGaIyCoRWSsil7unPyci691tX3JPe0dExlTbhgL376Ei8rOIfACsERG7iLxY7bn+Vk//yxqSYyE7H3IKwbJg/W5D6hEJZlEp7M9yzT9SRAi0SxZWbjc1Z3rRobTVRMa1IDI2BbsjkDbdRrF7w08ebULC40ho3hWb7fd7rpc1bynlWbm+DuOYDuxcTUxCS6LjXa9Fx97nsm21Z7LfrE0vgkOjAEhu3YOCnKoOhpTUvgSHRXk1ZoCUJjYy8gxZ+QanBau2Oencyu7RplMrO8s3u84Ldx80hARBRKhrns0GAQ7Xl5sEOoS8Qtc+kRAtbN/v2nm2pFl0be25zoa0e9sa4hNbENckBYcjkB4DzmHdMs/9olX7noS6/98tUruTm5VeOc9yOikvK8HprKC8tITImCZeibt1UwcHsy0yci2cFixdX0b31ECPNt1TA1i41lV127HPSUiQEBnmOnZFRwhd2wYwb3WpxzIHMi2PZMmb9m5fTWyTFsQkuPaLzv1GsXGF536R0q4XIe7Xonmb7uRlV+0XeVkH2LJ6Dr1Ov9SrcQO0TLRxKNciM8+1byzfXEHXNp7H0S5tHCzZ4Eogdx2wXK9HqBAUCG2b2Vm4zjXPabmKGb9LYmuYHx/4/X4KNpwQEVkJBANNgWG1tLkZwBjTVUQ6At+LSPtjTL8JKDLGdBORbriSydqch6ubH6ADMM4Y83cRGQGkAv1wDQGYLiKDgQRgnzHmXAARiRKRWOAioKMxxohIdB22uR/QxRizQ0RuAHKNMX1FJAiYLyLfG2N21GE9JyQiBPKKq5LG/CJIjqv78sN72vhpleVxBusLRbkHCYtKqnwcGpXIoT2r674CEb6ddC0gdOx3OR37XVb/Qf5B5OekExFT9VqERyeyf+fRX4s1v35K686DvRHaMUWFQW5B1b6QW2BISbQd0UbIqdYmp8AQFSakHTLMWVXBg38OprwCtqQ52ZLmSnAOZFl0amVj/U6Lbm3tRId7r2cgLyud6Liq1yIqNond247+Wiye/Rkdu5/ubpvIkHPH8fRtZxIQGEz7rqfRodvABo8ZXIljdn5Vx092vkXrZMcRbWxk5VclkTn5FjERNvIKnVx2Zhif/VzkUe30tfycdCJjm1Y+joxJYu+OVUdtv+KXT2nXtWq/+PbDZzjr0rspKyls0DhrExVuI6f6/7rAomWS5wlUdLiNnIKqSn5ugUVUuGBZQkGx4U/Dg2kWb2PPQSfT5pRS5m56evdA+p0SwO50iy9+KaHY81xBNRCteNZUbIzpYYzpCIwEJru7u6sbBLwHYIzZCOwC2h9j+mBginv6auDIo+/P7mQ3ElfXPsAuY8xC998j3D8rcCWtHXElomuAs0TkeRE53RiTC+QBJcDbInIxUFSHbV5cLbEcAfzVHc8iIM79XB5E5AYRWSoiSxf/OKEOT1G/2jWFwlLDgRr1aF+oWXEV6v6hc97fPuDCW6Zx9tXj2bDwA/bvWFKfwf3B1PJaHGVQ/u7NC1nz66cMHn13Qwf129ShkG+Mawxb51Z2nnu/hKfeKyHAIfRMdX0wfzK7nNM6O7jtkiCCAqDCiwU3U+trUXvbresWsWT2NEaNvQuAosJc1i37iQf+/QOP/N9sykqLWTZvekOGe0Jq2wxjoGvbAPKLLHan+9dYQVPre6n2F2PHxoWsmPcZZ41xvRabV/1MWEQcya1O+vKG36TWKOuyb+DqCWjexMb81WW8OLWIsnI4q4+rej1/TTn/fKeQF94vIq/Q4sLTg+sz7Ppnk4b58QGteB6DMWaBiMTjqixWd7RX61iv4rF2lTOMMRmVK3FVKaufWgrwrDGmxohuEekNjAKedVcmnxSRfsCZwFjgFlxV2wrcJxruRLp639GRz3WrMea7Y8SLMWY8MB7gmY+cv6mvO78YIkOEw/+aiFDXtLpoHi+kJgttmwoOGwQFwAX9hemLvN/tHhqVSGFuVbdUUW46oZF17xYMc7cNCY+jZaezyEhbQ9PWfes9zj+CiOgk8qt1ERbkpBMeVfO1OLR3I9+9/zCX/H0CIeG+H1ubWwhR1aqRUeFCXpE5oo3xqFhGu9u0a24jK89Q6L7+Ze0OJy2TbKzY4uRQjuHtGa6+xfgooWNL73W1R8UmkZNZ9VrkZh0gMrrma7Fv9yY+eftRrrv3LcIiogHYsnYBsQnNCI+MBaBL3+Hs2rKS3oMuaPC4c/INMRFV/6eYCM+KG7iqoLERNra5H0dH2MgpsOjVMZDu7QLp0jaAALsQEiRcc14YE7/2fqWwusiYRPKy9lc+zss+QEQtr0X6nk189c4jXHnHeELd+8XurcvZtOontqyZQ0V5GaUlBUybcA8XX/+iV2LPKbCIjgiofBwdbiO30NRs47H/2MgrcJ365BQYdqW7Xr+VWysqE8/8avvXgrXl3HDB0UbAqfqmFc9jcHeX24HMI2bNBa50t2kPtAA21XF6F6DbCYbyHXCNiIS719FMRJqISDKuLvwpwEtAL3ebKGPMTOAOoId7HTuB3u6/RwNVe3LN57pJRAIOb4eINMho+H1ZEBPh6ma02aBTC2HL3roljrPXGP7vK4vXv7b4YoHFzoP4JOkESGjWlbyMXeRnpeGsKGP76pm0OOWMOi1bXlZEWWlh5d97t84nJrFGgVnVUVLLrmQf3ElOxh6cFWVsXDaDtl09R8vkZe3jy/G3MuqqF4hNbO2jSD2lHbSIjxJiIgS7Dbq3tde4wnb9Tie92rsSohZNhOIy1/CUnAJDi0QbAe4yQrtmNg5muz5ow9xFHAHO7OVg4TrvXFgEkNKmCxkHdpF1MI2KijJWLvyGTr0994vsjH1M/vdtXHHTcyQ0bVU5PSauKbu3rqKstBhjDFvXLaRJchuvxL1zfwVNYm3ERdmw26BPp0BWbfW8AGXV1nIGdHElMK2T7RSXGvIKDV/MKeb+13N46I1c3p5ewMZd5T5POgGate5KZvousg+5jlHrFs+kQw/P/SI3cx8fvX4rF133PHFJVfvFWZfcxT9emsMdL/zEmL/9i9Yd+3st6QTYnW6REG0jNtK1b/Rq72Dtds/38drtFfQ9xfWR1jLJRkmpIa/IkF9kyMm3aBLtSkrbp9g54B5nGxlalah2a+fwuHjMH4nYGuTHF7TiWdPhMZ7gOl5fZYxxHtFd9zrwpoiswVVJvNoYU+q+GKi26W8Ak0RkNbASWHwiARljvheRU4AF7jgKgD8D7YAXRcQCynGNJY0AvhSRYHf8d7pXM8E9fTEwC88qZ3VvA62A5e7K6CHgwhOJt66Mge+XW4wdYsPmvp1SRh70bOv6X6/YZggLdl3tHhTgat+3vTD+G6tyjI4/sNkdnHrBw3w76TqMsWjf+2JiElPZsOhDAE7pP5ai/EN8+dqllJcWIGJj7fzJXHLH15QUZTNryq0AWFYFbbufR/P2p/tyc46qx3v/Im5IPwLjYxi2Yw5bnnyVPZP869ZPNruDMy97lM9euw7LctL11EuIT05l5S9TAehx+hUs+OY1igtz+PHDJ9zL2PnLfdMA+HriP9izZTHFBdm8+dBgBp57K11Pa/gLKiwDX84r57pzA7EJLNnkJD3bMKCTK9FcuN7Jxt0WHVsY7rsiiLIK+GS2q5K556BhzXYnt18ShGVgb4bFovWupLVHqp3TOrsO82t3OFm6yXtdwHa7gwuvfogJz1+PZVn0G3IRSc1TWfCja7849ayx/Pj5GxTl5zJt0pOVy9z+1Ce0aNedrv1G8O+HxmCz22nW8hQGDPPO2GfLwIffF3H75RHYBOavLmV/hpPBPVy3cpq7spS128rp2iaAp/4WRVm54d2Zx08ue7QPYOxZYYSHCrdcGsGedCf//Ti/oTcHcO0Xo658hCmvXIuxLHoMuoQmzVJZOtv1WvQZOpY5X71OcUEOM6a4Xgubzc4Nj37mlfiOxTLw2ewSbrow1HU7pfXlHMiyGNjVlWjOX1PO+p1OOrWyeOSqMMoqDB/8UHX7q89ml/KXkSE47JCRa1XOu2BQEM0SXIlXZp7h41k1b5nlV3zULd4QxNQ++EOpOvutXe3+xOFoHDt157Gn+DqEerHvm02+DuGkbdlax3Ejfm5Qn8DjN/JzM37077sx1NWQQb4fFlIfFi3z7i2ZGsJ/bo/w6odGyUcvNMjnbPDl93r9w08rnkoppZRS/sxH3eINofFsiVJKKaWU8mta8VRKKaWU8meN6LvateKplFJKKaW8QiueSimllFL+rBF9V7smnkoppZRS/kwvLlJKKaWUUurEaMVTKaWUUsqfNaIbyGvFUymllFJKeYVWPJVSSiml/FkjGuOpiadSSimllD/T+3gqpZRSSil1YrTiqZRSSinlzxrRfTwbz5YopZRSSim/phVPpZRSSil/1ojGeGriqZRSSinlzxrRVe2NZ0uUUkoppZRf04qnUkoppZQ/04uLlFJKKaWUOjFa8VQn7acvVvg6hJPWsW+qr0OoFzHfbPJ1CPUi+ZwOvg7hpJ2x8Vtfh1AviqwwX4dw0rp2S/B1CPXi9efn+jqEetH37N6+DuH3pxFdXKQVT6WUUkop5RVa8VRKKaWU8meN6Kp2TTyVUkoppfyZdrUrpZRSSil1YrTiqZRSSinlz/R2SkoppZRSSp0YrXgqpZRSSvkxo2M8lVJKKaWUV4itYX6O97QiI0Vkk4hsFZH7j9JmqIisFJF1IjLneOvUiqdSSimllPIgInbgNWA4kAYsEZHpxpj11dpEA68DI40xu0WkyfHWq4mnUkoppZQ/8819PPsBW40x2wFE5ENgNLC+Wps/AdOMMbsBjDEHj7dS7WpXSimllFJHagbsqfY4zT2tuvZAjIjMFpFlIvLX461UK55KKaWUUn6soS4uEpEbgBuqTRpvjBl/eHZtoRzx2AH0Bs4EQoAFIrLQGLP5aM+piadSSimllD9roK52d5I5/iiz04CUao+bA/tqaZNhjCkECkVkLtAdOGriqV3tSimllFLqSEuAVBFpLSKBwFhg+hFtvgROFxGHiIQC/YENx1qpVjyVUkoppfyZD+7jaYypEJFbgO8AOzDRGLNORG50z3/TGLNBRL4FVgMW8LYxZu2x1quJp1JKKaWUqsEYMxOYecS0N494/CLwYl3XqYmnUkoppZQ/a0Tf1a6Jp1JKKaWUH9OvzFRKKaWUUuoEacWzFiLyEK678TtxDZb9G/AR0McYk3FE21+NMacdY12fA62BcCAB2OGe9Xfgg6Os8wKgkzHmuaOssxXwtTGmy4lvnX+4+aoU+vWIorTM4oU3drJ1Z1GNNkkJgTx0Wxsiwhxs3VnEc6/toMJpCAuxc//NrWkSH4jdLnzy9QG+m5NJQmwA9/29NTHRARgDM2Yd4vNvj/slCr/JKa0cjBkajM0Gv64p54clpTXajDkjmM6tHZSVw3vfFZF20ALgiWsjKC03WBZYluGFDwoBGHduCIkxdgBCgoTiUsNzUwoaJP7a7Fg3l58+fRpjWXQdeCn9R9zgMX/94uks/mECAIFBYZw19nGaNO8IwLfvPcC2tbMJjYhj3MNfey3mE9VtwjM0GTWUsoOZzO15vq/DOaqlS5cy/q03sCyLEWeP5LLLLq+13ebNm7jrH3dy3/0PMGjQ6ZXTnU4nd9x+G3FxcTz+xJPeCruGFcsWMWn8f7AsizNHnMdFl/7ZY/6Shb/w4ZS3EbFht9u5+vrbOKVzNwBmfPkJs777CoPhrLPP59zRl/liEwDYteEX5n7+NMZYdOo/hj5nee4bWenbmTX1AQ6mrefUc++g1xnXVs5bMfsd1i/8FESIa5rKWVc8iyMgyNubAMDtN7Tl1N5xlJQ6eeY/m9i8rebx5eJzk7nsguY0Tw7h3Cvnk5tXAUCL5iE8eHtH2rcNZ8J7O5j6eZrX4u7Yws5FpwchAovWlzNreXmNNhedHsgpLR2UVximziol7ZBFQrRw1dnBlW3iomx8s6iMuavKObtfIAM6OSgsdt2WcsbCMjbscnptm06Yb765qEFo4nkEETkVOA/oZYwpFZF4IPBo7Y+VdLrnX+Re71DgbmPMedWe62jLTKfmLQsajX49omiWFMxVd67llHZh3H5tC259ZGONdtf/qTmfzUxn9oJsbr+2BeecEc9XPx7ighEJ7NpbzCMvbSUqwsGkl7swa14WTgvenJLG1p1FhATbeOOZTixbk8fuvSX1Gr8IXDYsmP/7rJCcfMM9V4azZls5B7KsyjadWjtIiLbxxMQCWjW1M/bMEF6aWlg5/z8fF1JY4nkf3kkziiv/vmhwMMVlR96nt+FYlpMfP36SS2+dRER0IlNeGEPbrsOIb9qusk1UfHPG3jmF4NAotq+bw/cfPMKf7/0EgM4DLqbnkD8zc/J9Xov5t0h7dxo7X59Cj4nP+zqUo3I6nbzx+ms89fQzxMfHc+cdtzFgwABatGhZo92kiRPp1at3jXVM//ILUlJSKCqqeULnLU6nk/+98TKPPPUKsXEJPHDn9fTpP5CUFq0r23Tp3puX+g9CRNi1YysvP/8Y/3nzfXbv3M6s777i2ZfH4whw8PSjd9Orz6k0bZZyjGdsGJblZPZnT3LhjRMJj07ko1cupU2XYcQmVe0bwaFRDL74Ybav+dFj2YKcdFb/8h5X3jcDR2Aw37xzB1tWzOCUfhd7ezMY0DuWlORQxv5tMZ07RHD3TanccPeKGu3WbMjj1yWrePWZHh7T8/Ir+Pf4rQweEOeliF1E4JIhQbz5ZTE5BYY7Lwth7Y4K0rOrjo+ntLSTEG3jmSlFtEy0MWZIEP/+tJhDOYaXPiquXM/jV4eyZntF5XJzVpUze0XNJFY1rMaTQtefprhuhloKYIzJMMZU3jBVREJE5FsRud79uMD9e6j7K6M+FZGNIvK+HC2z9HSriCwXkTUi0tG9rqtF5P/cfyeKyOcissr945HoikgbEVkhIn3dy01zx7dFRF6o1m6EiCxwP9cnIhLunv6ciKwXkdUi8pJ72qUistb9fHNP5p9Zm9N6R/PDL5kAbNhaSHiog9jogBrtenSOYO6ibAC+n5vJwD7RlfNCQ9yVwWAb+QUVOC1DVk55ZeW0uMRi995i4mOPes7wm7VKspORY5GZa3BasHxjOd3aesbfra2DxetdB7Sd+52EBAmRYXUfo9OrQwDLNnrvgHhg52piEloSHZ+C3RFIx97nsm31LI82zdr0Ijg0CoDk1j0oyDlQOS8ltS/BYVFei/e3ypq3lPKsXF+HcUybN28iObkpTZs2JSAggMGDh7BwwYIa7b76ajoDBw4kKtrz/56RcYglS5Zw9tkjvRVyrbZu3kBS02YkJiUTEBDAwMFnsnThPI82ISGhlSfgJSUliPuLUvam7SK1YyeCgoOx2x106tKDxQvq/VBUJ+m7VxMd34Io977Rvucotq/13DdCI+JIbNEVm71mLceynFSUl2A5K6goLyYssom3Qvdw+oA4vv3Jtc+u25RPeJiDuJiax8ct2ws4cLBmD05Objkbt+RTUeG9E2KAFok2MnItMvNcx9sVWyro0sbz/9yltYMlG10J5a50y3W8DfU83rZvbiczz5Cd793464sRW4P8+IImnjV9D6SIyGYReV1EhlSbFw58BXxgjJlQy7I9gTuATkAbYGAdni/DGNMLeAO4u5b5/wXmGGO6A72AdYdniEgH4DNgnDFmiXtyD+ByoCtwuYikuKu2DwNnuZ9rKfAPEYkFLgI6G2O6AU+51/EocLb7OS+owzackPjYAA5lllU+PpRVRnysZ+IWGeGgoNCJ5S4iZmSWEedOIr/47iAtkoP56PVuTHihM69P3oM54liSGB9Iu1ahbNxa/13VUeHicfDKLrCIivA8yEWH28jOr6qA5hQYosNdu5sBbrkkjHuvDGdg15oJd9tmdvILLQ7lWDXmNZT8nHQiYpIqH4dHJ5Kfk37U9mt+/ZTWnQd7I7Q/nMzMTOLjEyofx8fHk5mZ6dEmIyODBb/+yjmjzq2x/Pi33mLcNdciNt9ejJCVeYi4hKokKzY+gczMjBrtFv06l9tvvJJnn7iXm26/H4CUlq3ZsHYV+Xm5lJaUsHzpQjIyGmbYzPEU5qQTHt208nF4VBIFuUffN6oLj06k59BreOfJYfzvsdMJDI6gRcdBDRXqMcXHBXEwoyqhPJhZSnxc/Z+Y17foMCGn2vE2t8AQdcRJfFS4kFNQ/XhrERXu2aZnqoPlmys8pp3eNYB7xoYwdlgQIb4Z/fCHpF3tRzDGFIhIb+B04AzgIxG53z37S+AFY8z7R1l8sTEmDUBEVgKtgHlHaXvYNPfvZUBt/S/DgL+6Y3MCuSISg2u86JfAJcaYddXazzLG5LpjWA+0BKJxJcPz3dWFQGABkAeUAG+LyAzg8OC8+cA7IvJxtfjqTW114CMTx1o/Mt2N+nSLYtuuYu5+ajPJiUE8/2B71mxcR1Gx68ATHGTjsTvb8vrkPZXT6lPtsR1/OeOO/5UPC8gtNISHCLeMCeNAlsW2vVVji/p0DGDpJm93/9TcgKMV7HdvXsiaXz/lin980NBB/SGZI3cGqLHTjB//JuOuuQa73e4xffGiRURFR5Oamsrq1asaMszfpLa3VP/TBtP/tMGsX7uSj6a8zaNP/5vmKa0YPeZK/vnInQQHh9Kqdbsa2+otte3aUvtRoIaSolx2rJ3FVY/8SGBIBN+8cwcbl06nY596P58/rt963Po9qPULxattm90GnVs7+HpB1dCT+WvK+X5JGRg4Z0AgowcG8eFPNSu9fqMRXdWuiWct3AnebGC2iKwBrnLPmg+cIyIfmFo/Haj+rnVSt//v4WXq2v6wXGAPrqpq9cSzthgE+MEYc8WRKxGRfsCZuL4K6xZgmDHmRhHpD5wLrBSRHsaYzCOWuwG4AaBjnwdo1u7YY5YuGJ7AqGGuKs7m7YUkVDvTTogNJDPbM9HKza8gPMyOzQaWBfFxVW1GDo1j6peuLqN96aUcOFRKSnIIm7YVYrcLj9/Zllnzs5i3JOeYMf1WOQWGmGoVzphwG7kF5og2FjERNlwvAUSHC7mFrjaHfxcUG1ZvLadVkr0y8bQJdG8XwAvve++iIoCI6CTys6u6zgty0gmPqtkleGjvRr57/2Eu+fsEQsJjvBniH0Z8fDwZGYcqH2dkZBAXG+vRZuuWLTz/3LMA5OXlsXTJEuw2O5s2bWTRwoUsXbKYsvJyiouKePHF57nnHu+PvY2NSyDzUFWVMivjELGx8Udt36lLDw4c2Edebg6RUdGcOeI8zhzhGhL/wbtvERfvmy7q8OhECnL2Vz4uyD1AWC37Rm32bF5AZFxzQsJdr1/bbsM5sHOF1xLPi0clc/7Zrmrthi35NImvKus1iQsiI6vsaIv6jZxCQ3S1421UtWNpZZvKHiVXoSE63EZeoecY0L2HnBQUV02r/veCdeVcf17VRUj+yFfd4g2h8WxJPRGRDiKSWm1SD2CX++9HgUzgdS+GNAu4yR2bXUQi3dPLgAuBv4rIn46zjoXAQBFp515PqIi0d4/zjHJ/M8EduLYVEWlrjFlkjHkUyABqjOg3xow3xvQxxvQ5XtIJMP2HQ9z4wHpufGA985fmMPx01wD1U9qFUVjkJCunZoVv5bp8Bvd3JTcjBsfx67IcAA5mlNGri+vfEB3lIKVpMPvdY5LuvqElu/aV8NnMunWF/Ra7DjhJiLYTFynYbdCrYwCrt3vGv2ZbBf06ubrRWzW1U1xmyCs0BDogyN27HuiAji0d7Musqsp2aOkgPdsip8C7pYikll3JPriTnIw9OCvK2LhsBm27DvNok5e1jy/H38qoq14gNrH1UdakTlb79h3Yu28fBw4coLy8nLlz59B/wACPNhMnvcukdyYz6Z3JDBw0iL/ffAunnnYaV4+7hsnvTWHSO5O577776datu0+SToB27Tuyf18a6Qf2UV5ezvy5s+jT37Obef++tMoK7/atm6goLyci0jVmNTfHNb770MF0Fi2Yy8AhZ3l3A9wSU7qSc2gXuZlpOCvK2LxiJq07Dzv+gkBETFMO7FxFeVkxxhjSNi8gpkmbBo64yrSZ+xh3+zLG3b6MXxZmMHKYazhN5w4RFBRVkJnt/4nnnnSLhCgbsRGu423PVAfrdnhefb5uRwV9O7rqNi0Tba7jbVHVMbRnqoPlWzy72auPAe3WxsH+TO8Nbfqj04pnTeHAqyISDVQAW3FV9g5fjX4HMFFEXjDG3OuFeG4HxovItbjKZzcB+wGMMYUich7wg4gUHm0FxphDInI1MFVEDp/yPgzkA1+KSDCuquid7nkvupNvwZX41muf3aIVufTrEcXkf3ehtNTixbd2Vs57+t5UXp6wk8zsct6emsZDt7Zl3GXN2LqziG9+do0Pm/L5fu65sRUTnu8EIkyYmkZefgVdOoQzfHA823cX8eaznQCY+NFeFq+s34tJLAMf/1zMzZeEIQIL15ZzINNiUDdXFXfe6jLW7aigc2sHj10TTnkFTPnOdWVlRJhw/QVhANgFlm4sZ8POqgNiby9fVHSYze7gzMse5bPXrsOynHQ99RLik1NZ+ctUAHqcfgULvnmN4sIcfvzwCfcydv5yn2skxtcT/8GeLYspLsjmzYcGM/DcW+l62qVe347j6fHev4gb0o/A+BiG7ZjDlidfZc+kT30dlge73c5NN/2dRx5+CMuyGD5iBC1btmLmjBkAjDq35rhOf2S3O7j2xjt5+tG7sCyLM4afS0rL1nw/8wsARoy6kEW/zmHOT99itzsIDAzizvueqBzi8dIzD5Ofn4vD7uC6G+8kPDzCJ9thszsYcskjTH/rWizLolP/S4hrmsqa+R8C0HXgWArzDvHRy2MoKylAxMbKOZP58/0zSGrZnbbdR/Dhvy7GZnOQ0OwUupxW+62xGtqCpVmc2ieWj8b3q7yd0mEvPtaF517dTGZWGWPOb8afLk4hNiaQd//bhwXLsnj+1c3ERgfw9iu9CQu1Y1lw6QXN+fPfl1BU3LC3ILIMfDa3lL+NDsHmvp3SgSyL0zq70pdf11WwfpeTU1raeegvoZRVGD6cVdXxF+CADi0cfDLbsxv9/NMCSU6wgYGsfMMnP/txNzs0qq52qb3HWKm6O+uKpb/7N1HHvqnHb/Q70L2b/19ZXhfJ53TwdQgnrcPGb30dQr0ossJ8HcJJm7sl4fiNfgc+fMM3V/bXt75n17wF2O/NK7eEezUTzF8ys0E+ZyP6jvJ6RqsVT6WUUkopf9aIxnhq4qmUUkop5cf0u9qVUkoppZQ6QVrxVEoppZTyZ42oq73xbIlSSimllPJrWvFUSimllPJjpo7flvV7oImnUkoppZQf028uUkoppZRS6gRpxVMppZRSyp9pxVMppZRSSqkToxVPpZRSSik/pjeQV0oppZRS6gRpxVMppZRSyo81pqvaNfFUSimllPJn2tWulFJKKaXUidGKp1JKKaWUH2tMXe2NZ0uUUkoppZRf04qnUkoppZQf0+9qV0oppZRSXqFd7UoppZRSSp0grXiqk2Zz2H0dwkm79OwAX4dQL2b8UuzrEOrFGRu/9XUIJ21Tx5G+DqFe7Pl6k69DqAfG1wHUi7jmSb4OoV7ExgX7OoTfH72dklJKKaWUUidGK55KKaWUUn7MNKI6oSaeSimllFJ+zGhXu1JKKaWUUidGK55KKaWUUn5Mb6eklFJKKaXUCdKKp1JKKaWUH2tM31ykFU+llFJKKeUVWvFUSimllPJjjWmMpyaeSimllFJ+TG+npJRSSiml1AnSiqdSSimllB/Ti4uUUkoppZQ6QVrxVEoppZTyY3pxkVJKKaWU8grtaldKKaWUUuoEacVTKaWUUsqPNaau9sazJUoppZRSyq9pxVMppZRSyo81pjGemnj+TohIgTEmvB7X1wr42hjTRUT6AH81xtxWX+s/nr//pRl9u0dRWmrx0vhdbN1VXKNNUkIgD97ciogwO1t2FvPCm7uocBouHdWEYafFAGC3CynJwVz29zXkFzoBsAn835MdyMgu59GXt3tle9Yun89HE1/EsiwGnXUh51x8jcf8lYt/5supbyAi2O12LrvmHlJP6Ul5WSkvPnwtFeVlOC0nvU89iwvG3uSVmAHap9gYPTAAEVi8wcnslRU12lwwMICOLWyUV8DHP5exN8MAcHo3O307ug4hBzItPp5dToUTmsYJF58eSGAAZOcbps4qo7Tca5vE0qVLGf/WG1iWxYizR3LZZZfX2m7z5k3c9Y87ue/+Bxg06PTK6U6nkztuv424uDgef+JJb4V9QrpNeIYmo4ZSdjCTuT3P93U4dbJz/VxmT3say7Locuql9Bt+g8f8DUums3TWBAACAsM48/LHSWjW0Reh1rBrwy/M/fxpjLHo1H8Mfc7yjD0rfTuzpj7AwbT1nHruHfQ649rKeSvnTGbdwk/AGDqfeik9hlzl1divv6wJvTuHUVpm+M/k/WzfU1qjTZO4AO65tinhYXa27y7hlXf2U+Gsmt+uZTAv3NuCl97ex68rCoiPcXDHVU2JjrRjDHw3L4evf87xyva0bSqc3cuGCKzYZvHrBuMxPy4CLhhgJykGfl5tsXCja77dBledZcdhA5sNNuw2zFlreSXm+tCYuto18VQYY5YCS731fH27R9IsMZhxd6+nY9tQbhuXwm2Pb67R7trLk5n27UFmL8zhtqtTGDk0jq9nZfDJzIN8MvMgAAN6RnLxyCaVSSfARWcnsHtfCaEhdq9sj+V08sGE57jzsTeIiUvkmXuvpHvfISSntK1s07Frf7r3HYqIkLZzM2/96z7++ernOAIC+ccT4wkOCaWiopwXHrqGLj0H0qZDtwaPWwQuGhTAhK/LyC003HpxEOt3OTmYXXUg79jCRnyU8MLUUlo0ES46PZD/+7yUyDAY2MXBSx+VUuGEK4cH0L2dnWWbnIwZEsiMBeVs32/Rp4OdIT0cfL+kZkLbEJxOJ2+8/hpPPf0M8fHx3HnHbQwYMIAWLVrWaDdp4kR69epdYx3Tv/yClJQUioqKvBLzb5H27jR2vj6FHhOf93UodWJZTn765EkuvnkSEdGJfPDSGNp2GUZc03aVbaLimnPpbVMIDo1ix/o5/PjhI1xx1yc+jNrFspzM/uxJLrxxIuHRiXz0yqW06TKM2KSq2INDoxh88cNsX/Ojx7KZ+zezbuEnXHbnx9jtAXz51vW06jSE6IRWXom9d+cwmjYJ4MbHdtC+dTA3XZHIPS/srtHuqovimf5TNr8szeemKxI5a2A0387NAVwn8lddFM+K9YWV7Z1Ow8TPDrJ9TykhQcK/HmjFqg1F7DlQ1qDbIwIje9t4/2cnecVw3Qg7m/c6yciralNcBt8uc9KxuWei5rTgvZ+clFe4tunqs+xs3Q97Mxs0ZFWLxpNC/0GIyFARmS0in4rIRhF5X8T1Ja4i8pyIrBeR1SLyknvaOyIyptryBUdZ59fuvx8XkYnu59guIvVeBT2tVxQ/zMsCYOO2IsJC7cRG1TwH6tEpgrmLcwD4YV4mp/WKqtFm6IAYfl6QXfk4PiaAfj2i+HaO944mO7aupUnTFBKSmuMICKDvoLNZtXi2R5vgkFDcLxOlpcWIu9tERAgOCQXA6azAWVHhOrp6QUoTGxl5hqx8g9OCVducdG7lmax3amVn+WZXUr/7oCEkCCJc4WKzQYDDdRAPdAh5ha6ENSFa2L7fVUnYkmbRtbV3TgDAVcVMTm5K06ZNCQgIYPDgISxcsKBGu6++ms7AgQOJivZ8T2VkHGLJkiWcffZIb4X8m2TNW0p5Vq6vw6izA7tWE53Qkuj4FOyOQDr0Opdta2Z5tElu04vgUNfr0bRVD/JzDvgi1BrSd68mOr4FUe7Y2/ccxfa1nrGHRsSR2KIrNrvncSwrfTtJLbsTEBiCze6gWbu+bFvtmZw2pH7dw/l5oSsr27yjhLBQOzGRNffHbh1Cmb88H4CfFuYyoHtV59q5Z8SwYEUBuflVJ/fZec7KymlxqSHtQCmx0Q1fx0qOhewCQ04hWBas223Robnn8bKoFPZnuRLNI5W7z39t7qqnqdnEbxmkQX58QRPP36eewB1AJ6ANMFBEYoGLgM7GmG7AUyex/o7A2UA/4DERCTi5cD3FxQRwKKvqzDgjq5y4WM+niAy3U1DkxLKq2sQf0SYoUOjTLZJ5S3Iqp93052a8/eHeyuW8ISfzILFxiZWPo+MSyc46VKPdioU/8citF/Hq07dx1S2PVU63nE6e/Mfl3D3uTDp1H0Cb9l29EndUGOQWVB16cwsMkWFyRBshp1qbnAJDVJiQVwhzVlXw4J+DefivwZSUGbakuf7pB7IsOrVyHVq6tbUTHe69g1tmZibx8QmVj+Pj48nM9DwJycjIYMGvv3LOqHNrLD/+rbcYd821iK3xjKfyBwU56UREJ1U+Do9OpCA3/ajt1y74lNanDPZGaMdVmJNOeHTTysfhUUnHjL26uKap7Nu+hOLCbMrLitm1fg4FOfsbKtSazx/tICO7qrchI7ucuCMSxIgwO4VFVuUxMzOnojKJjI1yMKB7eGX1szZNYh20SQlm886Seo//SJGhQl61joi8IogIqfu+KgLXj7Rz10V2dhww7NNqp09o4vn7tNgYk2aMsYCVQCsgDygB3haRi4GT6SecYYwpNcZkAAeBxCMbiMgNIrJURJambfnshFZea0HPHNmmZiNzRJsBPaNYv6Wwspu9f49IcvIq2LKz5njRhlTbWXNtm9hzwDD++ern/P2+l/ly6uuV0212O4++/BHPT/iOHVvXsnfX1gaL9bjqUAIwBkICoXMrO8+9X8JT75UQ4BB6proqKZ/MLue0zg5uuySIoACo8OJJgDnyTQI13nDjx7/JuGuuwW73rPwsXrSIqOhoUlNTGzLEP6iar0tt+zjAns0LWbfwUwaNvruhg6qT2vfvuiU7sYlt6TXser5841qmv3U98ckdsdm8N8KtDofaWo/Hh3ej6y5twrtfHMI6ynEhOEi472/NePuTgxSX+Ga85IlULY2BCd86+feXTpLjIKFmJ5rfMiIN8uMLOsbz96n66HAn4DDGVIhIP+BMYCxwCzAMqMB9guHukg/8Les/soExZjwwHmDEX1Ycd98//6x4Rg2NA2DT9iISYgMB15ih+NgAMrM9rz7Jza8gPNSOzebqUqmtzZHd7J3bhzGgVxR9u0cSGGAjNMTOfTe25Pk3d9Vhk3+7mLgmZGVWVUByMtOJjk04avv2nXtz6NU08vOyiYiMqZweGhZBh859WLfiV5q1bHfU5etLbiFEVatGRoULeUXmiDbGo2IZ7W7TrrmNrDxDobvIsXaHk5ZJNlZscXIox/D2DFdFOz5K6NjSe13t8fHxZGRUVZszMjKIi431aLN1yxaef+5ZAPLy8li6ZAl2m51NmzayaOFCli5ZTFl5OcVFRbz44vPcc899Xou/sQqPTvLoOi/ISScsskmNdof2buSHqQ9z0U0TCAmLqTHfF8KjEz2qlAW5BwiLqhn70XQeMIbOA1yjnX6d8TLhUUnHWeLkjBoSzfCBroxq664S4mOqDt/xMQFk5XiOt84rcBIWaqs81sZFO8jOdbVp1zKIu69NBiAyzE7vLmE4LVi0qgC7De6/oRlzFuexcGWNEVwNIq/IEBladTyKDIWC4hPvMC8th10HDW2bCodyf08d7o2DJp6NhIiEA6HGmJkishA4XDbbCfQGPgZGA/XabV5XX/2YwVc/ZgDQr3sko4cnMHthNh3bhlJY5CQrt+bFJ6s25DO4XzSzF+YwfFAcC5ZXjWkLDbHRtWO4R1I58eP9TPzY9QHRrWM4Y0Y1afCkE6BVu84c3L+bjPS9RMc2Ycm877juzmc92hzcv5uEpBREhF3bNuCsKCc8Ipr83CzsjgBCwyIoKy1hw+pFjLzo6gaPGSDtoEV8lBAT4Rqf2b2tnamzPC8OWL/TyWldHKzc6qRFE6G4DPKLXF3uLRJtBDhc46baNbORdshV8QgLhsISV7XlzF4OFq7zzoVFAO3bd2Dvvn0cOHCAuLg45s6dwz33eiaOEye9W/n3yy+/RL9+/Tn1tNM49bTTuHqc624Eq1evYtpnn2nSWU+SWnQl+9BOcjP3EB6VyKblMzjnqn95tMnL2sdX/7uVkX95gZgmrX0UaU2JKV3JObSL3Mw0wqOasHnFTM7+80t1Xr4oP5PQiDjys/exbfUPXHr7hw0YLcyck8PMOTkA9O4SxrlDo/llaT7tWwdTWOwkO89ZY5k1m4oZ2CuCX5bmM2xAFItWuRLJGx7ZUdnmtr8msXRNQeW8W/+SxJ4DpUyflV1jfQ1lXxbERgjRYZBXDJ1b2Pj815rbU5vQINe4z9JycNihdaKNXzf8jq5qN41n+I8mno1HBPCliATj+sy/0z19gnv6YmAWh8uMPrR4VR79ekTyzkudKC2zeGlCVXL41N1tePnt3WTlVPD2h/t48OZWXDUmmW27ijwuGBrYJ5rla/MpKfX9gcNud3DFdffx7yf/jmVZDDxzNMkt2jLnO9cVuUPOvpTlC2axYM7X2O0OAgODuP6u5xERcrMzmPTqo1iWhbEs+gwcTrc+3hnbZhn4cl45150biE1gySYn6dmGAZ1cFcqF651s3G3RsYXhviuCKKuAT2a7EtM9Bw1rtju5/ZIgLAN7MywWrXd9APRItXNaZ9ehZe0OJ0s31e2DoT7Y7XZuuunvPPLwQ1iWxfARI2jZshUzZ8wAYNS5Ncd1/h71eO9fxA3pR2B8DMN2zGHLk6+yZ9Knvg7rqGx2B8PGPMq016/DWE46D7iE+KaprJo3FYDug65g0bevUVKYw0+fPAGA2Oxcec80X4YNuGIfcskjTH/rWizLolP/S4hrmsqa+a4EsuvAsRTmHeKjl8dQVlKAiI2Vcybz5/tnEBgczsxJt1FSlIPN7mDoJY9WXkDlDcvWFtKnSxhvPtma0jLDq5OrKreP3NyM16YcICvXybtfHOLua5ty5fnxbN9Tyg+/HvvCtVPahnDGgCh2ppXyyoOuqw2nfJnBsnUN+/FiDHy71OJPQ+2IwKrtFofyoFc7V1K2fKshLBiuO9tOUIArWevfAd6Y4SQ8BEYPcC0nwPrdFlv2/X6qnaYRjYyUWsdEKXUC6tLV7u8eur+Dr0OoFzN+aRxnxTcM948rmk/Gpo7+fWV8Xe35epOvQzhpFc7f/SEKgB++qnnbud+jPoPbHr+Rn3vkCodXD7Zbtu1qkDdxatuWXv/Q0IqnUkoppZQfa0zfXNR4ardKKaWUUsqvacVTKaWUUsqPNaaKpyaeSimllFJ+rDElntrVrpRSSimlvEIrnkoppZRSfkwrnkoppZRSSp0grXgqpZRSSvmxxvTNRVrxVEoppZTyYwZpkJ/jEZGRIrJJRLaKyP3HaNdXRJwiMuZ469TEUymllFJKeRARO/AacA7QCbhCRDodpd3zwHd1Wa8mnkoppZRSfsxHFc9+wFZjzHZjTBnwITC6lna3Ap8BB+uyLZp4KqWUUkqpIzUD9lR7nOaeVklEmgEXAW/WdaWaeCqllFJK+bGGqniKyA0isrTazw3Vnra2kqg54vG/gfuMMc66bote1a6UUkop9QdkjBkPjD/K7DQgpdrj5sC+I9r0AT4UEYB4YJSIVBhjvjjac2riqZRSSinlx3x0O6UlQKqItAb2AmOBP3nGZVof/ltE3gG+PlbSCZp4KqWUUkr5NcsH31xkjKkQkVtwXa1uByYaY9aJyI3u+XUe11mdJp5KKaWUUqoGY8xMYOYR02pNOI0xV9dlnZp4KqWUUkr5Mf2udqWUUkoppU6QVjzVSUtum+zrEE7a9oOhvg6hXgzqU+c7Wvi1IivM1yGctD1fb/J1CPUi5bwOvg7hpK2ast7XIdSLJinxvg6hXvTucOQdedTxNKbvatfEUymllFLKj2lXu1JKKaWUUidIK55KKaWUUn6sMXW1a8VTKaWUUkp5hVY8lVJKKaX8WGMa46mJp1JKKaWUH9OudqWUUkoppU6QVjyVUkoppfyY5esA6pFWPJVSSimllFdoxVMppZRSyo/pGE+llFJKKaVOkFY8lVJKKaX8mN5OSSmllFJKeYV2tSullFJKKXWCtOKplFJKKeXHGlNXu1Y8lVJKKaWUV2jFUymllFLKj1nG1xHUH008lVJKKaX8mHa1K6WUUkopdYK04tnIiYgTWAMI4ARuMcb8KiKtgB3AU8aYR9xt44H9wFvGmFtE5HGgwBjzUn3G1LVdIH8aGYHNBnOXFzNjXlGNNleeE0G31EDKyg1vf5HHrv0VAFwzOpIe7YPIK7R4+PXMyvYtkhxcdV4EAQ7BacHkGXns2FtRn2Ef0/Z1c/nx46exLIvuAy/l1JE3eMxft2g6C7+fAEBgUBgj/vQ4ic07kpe1n6/fuZfCvAxEbHQfdBl9z7zKa3FXt3HVL0x/71ksy0m/oWMYdsH1HvOXz/+Kn7/6HwBBwaFcPO5Rklt2BGDuN++y+OdPQYSmKe257IanCQgM8vo2AKxYtohJ4/+DZVmcOeI8Lrr0zx7zlyz8hQ+nvI2IDbvdztXX38YpnbsBMOPLT5j13VcYDGedfT7njr7MF5tQw871c5k9zfX+6nLqpfQb7vn+2rBkOktnud5fAYFhnHn54yQ06+iLUOus24RnaDJqKGUHM5nb83xfh3NMbZJgeE8bIrBqu2HBRs9+z7gIOLefjaQYmLPGsGiT53wRGDfcRn4xfPKL9751u3ObAMYOD8Mm8MuqEr5dUFKjzdjhoXRtG0hZhWHSVwXsTnfisMO9f4nCYQe7DZZtLGP6L8WVywzrE8wZvYOxLMPqreV89nPNY3hD2bByHp9Pfg5jOel/xiWcNfo6j/nL5n3NrOlVx6kx1z5CM/dxas4377Hwp88wxnDqsDEMGfUXr8V9shrT7ZQ08Wz8io0xPQBE5GzgWWCIe9524DzgEffjS4F1DRmMCPxlVAQvvpdDVp6Tx66PZcWmUvYdcla26ZYaSGKsnfv+m0nb5gH89dxI/vl2FgDzVhYza3ER118U5bHey4aH88XsQtZsLaNbaiCXD4/guXeyG3JTKlmWk++nPsnY2ycREZPIO8+OIbXbMOKT21W2iYpvzpX/mEJwWBTb1s7h2ymPcNX9n2Cz2xk25n6SWnSmtKSAd565hNanDPRY1lvb8Pk7T3HDA28TFZvIfx+5nM69ziCxeVUcsQnNuemRdwkNi2Ljyrl8+r/HuO3Jj8jNSmfed1O454WvCAgM5r3/3snKBTPpO+Qir24DgNPp5H9vvMwjT71CbFwCD9x5PX36DySlRevKNl269+al/oMQEXbt2MrLzz/Gf958n907tzPru6949uXxOAIcPP3o3fTqcypNm6V4fTuqsywnP33yJBffPImI6EQ+eGkMbbsMI65ptfdXXHMuvW0KwaFR7Fg/hx8/fIQr7vrEh1EfX9q709j5+hR6THze16Eckwic3dvG1NkWecWuBHLLPkNGXlWb4jL4YYVF+2a1Jwd9U4XMPENggPeSBxH409lhvDI1j+w8i4fGRbFqSzn7M6qOtV3aBtAk1s5Db+bQJtnBlSPDePbdPCqc8K/3cyktdyWe9/4lkrXbytm+r4IOLR10Tw3gibdzqHBCRKj3tsmynHw26SlufHAC0XFJvPLQ5XTpfQZJzdtWtolt0oxbHn2H0PAoNqz8hY8nPMGdT01l/54tLPzpM+58aip2RwBvPXcjnXoOJqFpS6/Fr1y0q/2PJRKono0VAxtEpI/78eXAxw0ZQJtmAaRnOTmU7cTphEVrS+jZwbMy1rNDEPNXuc7Mt6WVExosRIW73qqbd5VTWFyzYmAMhAS5DoAhQTay85012jSU/TtXE9OkJdEJKdgdgXTqey5bVs/yaNO8bS+Cw1zJcrPWPcjPPgBAeFQTklp0BiAoOJy4pDbk56R7LfbDdm9bQ3xiC+KapOBwBNJjwDmsW/aTR5tW7XsS6t6GFqndyc2qitNyOikvK8HprKC8tITImCZejf+wrZs3kNS0GYlJyQQEBDBw8JksXTjPo01ISCgirvdKSUkJ4h47tTdtF6kdOxEUHIzd7qBTlx4sXjDX69twpAO7VhOd0JLoeNf7q0Ovc9m2xvP9ldymF8Ghrtemaase5Occ8EWoJyRr3lLKs3J9HcZxJcdCdj7kFIJlwfrdhtQjEsyiUtif5Zp/pIgQaJcsrNzu3atDWic7OJTtJCPHwmnBkvWl9EgN8GjTo30gC9eUArB9XwWhwTaiwlzbVlruamO3gd0uHI5+aK9gvl1QQoX7EJtf5L3t2r11DfFJLYhPTMHhCKDnqeewdqnncap1+56Ehrv2hZbtulUep9L3bqdlajcCg0Kw2x20O6UPq5fMqvEc/sqYhvnxBa14Nn4hIrISCAaaAsOOmP8hMFZEDuDqit8HJDdUMDGRNrLyqo7O2XkWbZoHHNHGTlZeSbU2TmIibeQWHL2L6oNv87n7LzFcPiICm8BT/8uq/+CPIj87nYiYpMrHEdGJ7Nux+qjtV83/lDZdBteYnpORxsE9G0hu3b1B4jyWvKx0ouOqtiEqNond246+DYtnf0bH7qe72yYy5NxxPH3bmQQEBtO+62l06DawwWOuTVbmIeISqpLe2PgEtmzaUKPdol/n8sHkt8jNyeaBx14AIKVla6ZOHk9+Xi6BgUEsX7qQtqkdvBb70RTkpBMRXfXahEcncmDX0V+btQs+pfUpNd9f6reJCIG84qpP6PwiSI6r+/LDe9r4aZVFoJc/baMjjjjW5lu0Tj7iWBtes010hI3cQici8Mg1USTE2Jm9rIQd+1xDlxJj7aSmOLhwSAjlTvh0ViE793vnRD8n+6DncSoukd1b1xy1/aLZ0+jYYxAATVPaMfOj/1KYn0NAYBDrV/5CSuvODR5zfbH04iL1O1JsjOlhjOkIjAQmy+Fyj8u3wHDgCuCjuq5URG4QkaUisnTzsvfqHEytu445fpvjnZkN6xvK1G/zueuVDD74Lp9rRkfWOaaTV0twUvtBYtemhaz+9VPOuOhuj+llJYV8Pv42zrzsQYJCwhsiyGMytWzDUTaBresWsWT2NEaNvQuAosJc1i37iQf+/QOP/N9sykqLWTZvekOGe0Jq247+pw3mP2++z70PP8NHU94GoHlKK0aPuZJ/PnInTz92N61at8Nut3s52trU9trU/uLs2byQdQs/ZdDou2udr7yrXVMoLDUc8M6oHw+1v0OOf7A93MIYePJ/udz7ajatkh0kJ7j2BZsNQoOFZ9/N49NZRfztooh6jPo4av0gqH1Lt6xbzMKfp3H+Ff8AILFZW4ZdcA1vPHM9bz13I8kt2mPzi/37j0crnn8gxpgF7guIEqpNKxORZcBdQGegTqP8jTHjgfEAVz+eXueCfVaeRWxk1flOTGTNbvGsPCexkXag3N3GTk7+sQfkD+wezPvf5AOwZF0p11zgvcQzIiapsuscID8nnYjoml3NB9M28s17D3PZrRMICY+pnO50lvP5+Nvo3O98OvQc4ZWYjxQVm0ROZtU25GYdILKWbdi3exOfvP0o1937FmER0QBsWbuA2IRmhEfGAtCl73B2bVlJ70EXeCX26mLjEsg8dLDycVbGIWJj44/avlOXHhw4sI+83Bwio6I5c8R5nDniPAA+ePct4uJ9M2SguvDoJI+u84KcdMIia8Z1aO9Gfpj6MBfdNIGQsJga89Vvk18MkSHC4ZQsItQ1rS6axwupyULbpoLDBkEBcEF/Yfqihu/jzM4/4lgbYatxHK2tTe4RbYpLDZt3ldOlTQD7DjnJzrNYvqkMgJ37K7AMhIcKBV7oco+OTfQ8TmWmExWTUKPdvl2b+Gj8o9xw/5uVxymAAWdcwoAzLgFgxof/Jio2qcay/qoxXVykFc8/EBHpCNiBzCNm/Qu4zxhz5PR6t2NfOYlxduKjbdjt0L9LMCs2lXq0WbmplIHdgwFo2zyA4lJzzG52gJx8i46tXN1Ip7QOJD3Te2M8m7bsStbBneRk7MFZUcb6JTNo181zRENu1j6mvXUr5417gdjEqgtdjDHMnPwQcUlt6HfWOK/FfKSUNl3IOLCLrINpVFSUsXLhN3TqfYZHm+yMfUz+921ccdNzJDRtVTk9Jq4pu7euoqy0GGMMW9ctpElyGy9vgUu79h3Zvy+N9AP7KC8vZ/7cWfTpP8ijzf59aRh35WT71k1UlJcTEekaE5ab4ypNHTqYzqIFcxk45CzvbkAtklp0JfvQTnIzXe+vTctn0Kar5/srL2sfX/3vVkb+5QVimrQ+yprUb7EvC2IiICrMVe3r1ELYsrduSdbsNYb/+8ri9a8tvlhgsfMgXkk6AXbuq6BJjJ34KBt2G/TtFMSqLeUebVZtLmNAV9cY+zbJDtexttAQHiqVY+YDHHBK6wAOuI+pKzeXVR5rE2NtOOx4JekESGnbhUMHdpN5MI2KinJWLPiGzjWOU/uZ9ModXHnzszSpdpwCyM/NrGyzesksep12jlfiVp604tn4HR7jCa4+iauMMc7qXXXGmHU08NXsh1kWTJnpGo9pE/hlRQn7Djk5o08IAD8vLWbVljK6pQbxwm1xlJYb/vdl1eWjN14SRcdWAYSH2nj5H/F88XMBc1eUMOmrPK5036KpvAImfZV3tBDqnc3uYMTlj/LRf6/DWE66nXYJCcmprJg7FYCeg69g/ozXKC7M4fupT7iWsdm5+sFppG1bxrpFX5LQrD0TnxoNwJDR/6Bt1yFHfb6GYLc7uPDqh5jw/PVYlkW/IReR1DyVBT9+CMCpZ43lx8/foCg/l2mTnqxc5vanPqFFu+507TeCfz80BpvdTrOWpzBgmG9uQ2S3O7j2xjt5+tG7sCyLM4afS0rL1nw/8wsARoy6kEW/zmHOT99itzsIDAzizvueqOy6fumZh8nPz8Vhd3DdjXcSHu7FbsSjsNkdDBvzKNNed72/Og+4hPimqaya53p/dR90BYu+fY2Swhx++sT1/hKbnSvvmebLsI+rx3v/Im5IPwLjYxi2Yw5bnnyVPZM+9XVYNRgD3y+3GDvEhs19O6WMPOjZ1vWeWbHNEBbsuto9KMDVvm97Yfw3FmXeu6NbDZaBD74v5I6xkYgN5q8qZV+GkyE9XYnmnBWlrNlWTtd2gTx9UzRl5YZ3vi4AICrMxjXnh2OzuYaqLN1QxuqtrqR13qpSrj4vnMevj6LCCZO+KvDaNtntDi65+kHeevZvWJaT/kMvomlKO+b/4BolNnD45Xw37Q0KC3L5dOJTgOtYe9czrmtmJ71yJ0UFOa71jHuo8iKk3wNfXQjUEMQ0pq1RPnEiXe3+asiQRF+HUC/iIrxX6W1IraIavPje4H7d7vtu+vqQcp7vL7A6WaumrPd1CPVix7YcX4dQLy4a6c0x+A1jVC8v3hsL+GFVaYN8zg7vHuT1PnyteCqllFJK+bHG9JWZmngqpZRSSvkx63ffr1hFLy5SSimllFJeoRVPpZRSSik/prdTUkoppZRS6gRpxVMppZRSyo81phsQaeKplFJKKeXH9LvalVJKKaWUOkFa8VRKKaWU8mONqatdK55KKaWUUsortOKplFJKKeXHGtPtlDTxVEoppZTyY/rNRUoppZRSSp0grXgqpZRSSvkxvbhIKaWUUkqpE6QVT6WUUkopP2b0BvJKKaWUUkqdGK14KqWUUkr5scZ0VbsmnkoppZRSfqwxXVykiac6abkZeb4O4aTtS4/3dQj1YuGiXF+HUC+6dkvwdQj1oHF8Uqyast7XIZy07n/u5OsQ6sX2x+f7OoR6sWVvI0g9evk6gN+vRvDqK6WUUko1Xo2p4qkXFymllFJKKa/QiqdSSimllB+z9LvalVJKKaWUN2hXu1JKKaWUUidIK55KKaWUUn5MK55KKaWUUkqdIK14KqWUUkr5Mf3mIqWUUkop5RWmEV3Vrl3tSimllFLKK7TiqZRSSinlx/TiIqWUUkoppU6QVjyVUkoppfyYXlyklFJKKaW8QrvalVJKKaWUOkFa8VRKKaWU8mNa8VRKKaWUUuoEacVTKaWUUsqPNaaLi7TiqZRSSimlvOK4FU8RcQJrgACgAngX+LcxxhKRPsBfjTG3HWP5q4E+xphb6hqUiDxojHmmru2PWPYdYAiQC1jAzcaYBXVc9mrcsYrIjUCRMWbyb4mjjs/XCtgAbKo2uZ8xpqye1n818L0xZp/78dvAy8aY9fWx/pNx7ZgEencOpbTM8Op76WxPK63Rpkmcg7vGNSU81Mb2PaX8Z/IBKpzQr2sYV5wXhzHgtAwTPz3Ehu0llcvZBF68N4WsXCdPv7nPK9vTJglG9LIhAiu3GxZs8Dw9jYuA8/rbSIqB2asNizZ5zheBa0bYyC+Cj3+xvBIzQOfWAVx2Vig2G8xbVcp3C0tqtLn8rFC6tA2grNzwzoxC9qQ7PeJ+8OpIcvItXvu0AIBeHQI4f1AISfF2nns3j10HnDXW2ZB2bfiFuZ8/jTEWnfqPoc9ZN3jMz0rfzqypD3AwbT2nnnsHvc64tnLeitnvsH7hpyBCXNNUzrriWRwBQV6NH05uG1bOmcy6hZ+AMXQ+9VJ6DLnK2+FXapMEw3u69otV2w0LNtbcL87t59ov5qypfb8YN9xGfjF84sX94kR0m/AMTUYNpexgJnN7nu/rcDx0bhPAFSPCsInwy8oSvllQXKPNFSPC6No2kLJyw8Sv89l9wInDDvf9NQqHXbDZYNnGMqbPLfJYbkT/EC47K4w7Xs6koNh75bjdG39h3pdPY1mufaPXMM99I/vgdn766AEOpa2n/zl30HPotZXTv5/yj8p2eZl76Hf2bXQf7Lv940T4aoyniIwE/gPYgbeNMc8dMf9K4D73wwLgJmPMqmOtsy5d7cXGmB7uJ2gCfABEAY8ZY5YCS09kI+roQeA3JZ5u9xhjPhWREcBbQLcTXYEx5s0TaS8iDmNMxYk+D7Dt8P+3AVwNrAX2ARhjrmug5zkhvTqFkpwQwN+f2EX7VsH8bWwT7ntpT412fx0dz1c/ZzNvWQE3jm3CmadG8d28XFZvKmLxmkIAWiYHcvc1Tbn1qV2Vy513RjRp6eWEBnunoC8CI/vY+OBni7xiuGa4jS17DRl5VW2Ky+D75RYdmtX+fbt92wsZeYYgh/e+j1cErhgRyr8/zCc73+KBqyNZvaWM/ZlVH/Bd2gTQJMbGI2/l0jrZzpVnh/Hc5KoNO7NPMAcynAQHVcW9L8PJm58XcOXIMK9ty2GW5WT2Z09y4Y0TCY9O5KNXLqVNl2HEJrWrbBMcGsXgix9m+5ofPZYtyEln9S/vceV9M3AEBvPNO3ewZcUMTul38e9mGzL3b2bdwk+47M6PsdsD+PKt62nVaQjRCa28ug3gen+d3dvG1Nmu/WLccBtb9tXcL35YYdH+aPtFqpCZZwgM8N/vqU57dxo7X59Cj4nP+zoUDyJw5chwXv4gl+w8i4eviWblljL2Z1SdCHZtG0CTWDsPvpFNm2QHfx4ZzjPv5FLhhJem5FJaDnabKwldu9XB9n2uj7iYCBudWgeQmevdk0rLcjL38yc5/4aJhEcl8ul/LqVVJ899IygkikGjH2bHOs99I6ZJGy7/xxeV63n3n0No0+Usb4Z/UiwfnHeJiB14DRgOpAFLRGT6EcWrHcAQY0y2iJwDjAf6H2u9J/TJbIw5CNwA3CIuQ0Xka3eA/UTkVxFZ4f7dodqiKSLyrYhsEpHHqm3Un0VksYisFJG3RMQuIs8BIe5p7x+jnV1E3hGRtSKyRkTurCXkuUC7o63DPX2ciGwWkTnAwGqxPS4id7v/7isiq0VkgYi8KCJr3dOvFpFPROQr4HsRCRORiSKyxP1/GO1uZ3cvt8S9nr8d6/8sIgXV/h7jruLi3t7/uv+/20VkTLV297r/D6tE5Dn3vD7A++5tDhGR2e4qNSJyhbv9WhF5vvpzi8jT7vUsFJHEY8X6W/TrFs7Pi12fPpt3lhAWYiMm0l6jXdf2ofy6wvWv+HlRHv27uxKZkrKqU7/gIM+3cFy0g96dw/jx19z6DvuokmMhKx9yCl0Hh/W7TY0P0qJS2J8FzlrOWiNCoF2ysHKbd09pWzd1cDDbIiPXwmnB0vVldE8N9GjTPTWAhWtdBfgd+5yEBAmRYa5ti44QurYNYN5qz2r1gUyL9CzfVKfSd68mOr4FUfEp2B2BtO85iu1rZ3m0CY2II7FFV2z2mufdluWkorwEy1lBRXkxYZFNvBV6pZPZhqz07SS17E5AYAg2u4Nm7fqybbXnB7C3JMdC9hH7RepR9ovaPlQr94vt/j24LWveUsqzvHe8qavWyQ4OZjnJyHHt34vXl9Kjvef+3aN9IAtWu3o5tu+rIDRYiAp3vUal5a42dhvY7UL1V+Hy4WF8+lOh16twB3evJiquBVFxrn2jXY9R7Fh3lH3DdvS6WtqWBUTFpRAR26yhQ/696wdsNcZsd/fEfgiMrt7AGPOrMSbb/XAh0Px4Kz3hkpAxZrt7uSOPyBuBwcaYnsCjeFYs+wFXAj2AS0Wkj4icAlwODHRX/JzAlcaY+3FXWY0xVx6tnXtdzYwxXYwxXYFJtYR7PrDmaOsQkabAE7gSzuFAp6Ns9iTgRmPMqe5lqzsVuMoYMwx4CPjJGNMXOAN4UUTCgGuBXPf0vsD1ItLavXxbd2K4UkReO8rzV9cUGAScBzwH4D7LuBDob4zpDrxgjPkUVzX6Svf/srKPRUSSgeeBYbj+j31F5EL37DBgoXs9c4Hr6xDTCYmLdpCZXVUczsypIDba8yAREWajsNhZ+YGUkV1BXFRVm/7dwnj14ZY8dGMy//d+euX0ay6J590vMrw6EDsiBPKLqp4wr9g1ra6G97Lx00oLb3+8RkcI2flVb+fsfIvoCNsRbWxk5VdlBTn5FjHuNpedGcZnPxf51W0+CnPSCY9uWvk4PCqJgtz0YyxRJTw6kZ5Dr+GdJ4fxv8dOJzA4ghYdBzVUqEd1MtsQ1zSVfduXUFyYTXlZMbvWz6EgZ39DhXpMESGQV60LNr/oBPeLnjZ+WmX51fvr9yQmwkZ2tX03O69q3z0sOsJOVp5nm+gIVxFABB69LpqX74xj/fYydrirnd1TA8nJt0g76N1qJ0Bh7hH7RnQShXXcN6rbunImqT3Orc/QGpwxDfMjIjeIyNJqP9XHLjQDqndHprmnHc21wDfH25bfelV7bf0eUcC7IpIKGFxjQg/7wRiTCSAi03AlThVAb1ylW4D/b+++w6Sqrz+Ov8/s0nfZXZZeBQSRIoi9othN1ChoUBO7phk1Go1dY9dfjElMNJaIvcQuii0WQBSVjqACIgIqfRt1y5zfH3e2sgvsws6dGT6v59lnd+7cmT1f2Llz5nxbC2B5Lc97WB3njQF6mdm9wBvAO1Ue839mdi2wguAfoq7n2Af40N1XxGJ7DuhbraFm2UCmu38cO/Q0QdJXtW2rYz8fCRxfXikFmgPdY8d3q1KhzAL6AHOpf1f7K+4eBeZUqUYeDox293UAVeKpy15Ub/dTwMHAK0Ax8HrsvCkECfkmYn+cFwAMOeQmdhowqh5N2FTNN5fY/1P1c6r8/OnMtXw6cy39ezfn1J/kcuM/v2fPga0oKCpjweKNDOhTj3e4bVXLq2Fr3yt37gzrNjhL86B7/ItrW1TbC9096KIrWhdl0bIy+nZPnMUxavt3t1pbsakN6wr49ov3OPO6/9G0RSZvPnoJX01+jX57Hr99g9yCbWlDmw69GTr8fF69/1yaNGtJ2879Nlv5SVQ7d4K1G2Ovi3ZhR5M6Nr3O1n2SO9z0cD4tmhm/G5lJ53ZprMwr4ycHtOCeZwpreWDjq/W1UWsj6lZWWszC2e+z77GXbvnkHYC7P0jQPV6bWv9Caj3R7FCCfGuLn9brfUUys14EVb/lwK5V7roZ+MDdT7Rg0syHmwnUCRr0mLtftaVfWdd5ZjYYOAr4HXAKcE7srstjFb/y8w6t7TliVb4t5Qhb+qteW+PcEe5edbIQFrwyfu/ub9c4vlMdz1k1puY17qvar2lVvtenLrC5NpW4V1yeyqjjb6TqH+uJF87b4u8+5uAsjtg/C4D5320gN6fyaXOz08krqD48tnBNGa1apBGJBN1wbXPSWV2w6RDaOd9soGPbJmS2itCvV3P2GtSKPQa0okkTo2XzCJec0YG/PV7/T8T1UbQOMltW/he0bgFrNh3DX6uubY0+XYzenY30CDRrAsfva7w2qfHLPPlFTk5m5RCHnMwI+UXV+zzziqK0yYzwTex2dmaE/DVRhvZryuCdmzKwdxOapBktmhnn/LQVj7y+ljBlZHeoVuFbU7CUVllbl9EvnvsJrXO70iKjDQC9dzuCpQunxT3x3JY2AAzYdyQD9g0+4378xl/JyOq43WPcGkXroXWLytdFZsvg2Nbo2tbo09no3anK62If47VPVf7cWnlF1SucOa2D1261cwrLaNN68+es3+h8vaiEgb2aMntBMW2z07jhvOyK8687N5tbR+dTuLbx/28ysmq8NvKX0rKew2EWfTWBtl370zKz7fYOr1GFVPlfAnSrcrsrsTkjVZnZbsDDwDHlRcbNqVdXu5m1A/4N/LNKclIuC/g+9vNZNe47wszamFkLgi7hicB7wEgLJiwRu79H7PwSMyuvmNZ6npm1BSLu/iJwHTB0M6HX9bs+BQ4xs9zY7zu55gNjYxeKzGzf2KHNlfbeBn4fSzQxs92rHP9NeZvMrG+sC74uy8xsVzOLACdu5rxy7wDnmFnL8vbFjhcBmbWc/ykwzMzaxsa6ngqM24rf02Bvji/g0jsWcekdi/h05hoO3bs1AH13as669VHyCjfttvli7jr23z0DgEP3ac1nM4OkpmPbymJ6r67NSE83itZGefK1VZx/3UJ+dcNC7h69lFlz1zd60gnww2pokwlZrSASgf7djbnfb91V4sOZzr2vRfnXmCgvfxJl4TLiknQCLPyxlPZtIuRmRUiLwJ79mzJjfkm1c2bML2HfgcG4sJ6d01i/0Slc67wybj1X3pfPNfcX8PBra/jqu5LQk06ADt0Gkb/iOwpWLaGstJi508bSc8DwrXpsZk4nli6cQUnxetydJXM/Iad9r0aOeFPb0gaAdUXBdb8o7we+mfkufYeG06X4w2rIqfG6mLe1r4tZzj/HRLnv9SivfBJl4XKUdNbTwh9K6dAmjbax1/fe/ZsxY271BVOmzytmv92C2kavzums3+gUrHEyWgYfJgGapMOuOzVl6apSvl9RxqV/W82V/8rjyn/lkVcY5eb/xCfpBGjfbRAFK7+jMPbamD+9fq8NgHnT30i6bnYI1vFsjK8t+BzoY2Y9zawpQf7zWtUTzKw78BLwS3efuzVt2ZqKZwszm07lckpPAH+t5by7CLraLwXer3HfR7HH7Qw8HZsNT6w7/J1YglVCULn8jqCSNtPMpsbGedZ23npgdOwYQJ2VU3efU9tzuPskM7sR+AT4EZhKsGRATecCD5nZWoJKbl0jyW8G/haL3YCFBN3yDwM7AVNjx1cQJOB1uZKgu3sxwaz0jM2ci7u/ZWZDgMlmVgyMJVgZ4FHg32a2nmAsavn5P5rZVcAHBNXPse7+6uZ+x/Y0ZfY69hjQivtv6MHGEufeJyuTw2t/05l/Pb2MvIIyHn91JZed3YnTfprLt4s38r9Pgu6d/YZkcMg+mZSVQXFJlLsfCWcMWzl3eHtKlFOHRYhEgmVjVhbC0N7BhXvqN06r5sFySc2aBOfvvYvxwNgoxQ1ZB2E7iTo8+846Lv55JhGDiTM38uPKMg4eEiwfNH76Rr74poRBvZpwy6+yKC5xHhu75eRySN8mjDq8FRktjQtPzmTxsjL+8d+ixm4OAJG0dIaNuI7XHjg3ttzKCHI79WHWxGcBGHTAKNYWruC5v46keMMazCJMH/c4v7jyDTr2GEzvwUfy7N0nEYmk067Lrgzc/+dxiXt7taFp8wzGjr6IDevyiaSlc8iI62neMivubYDg7/ydqVFGDYsQscrXxe6x18W02Ovi7CMqXxd79TUefDPc10V9DXnibnKH7U3TtjkM/3Yc8266l8WjX9jyAxtZ1OHpt9dwyalZRCIwccYGflhZxrChQaI5buoGZs0vYVDvptz22xyKS5zRrweTObMzIpxzXHBdMIPPv9zIzBofSsMQSUvnoBOvY8xD5+Iepd9eI2jTsQ9ffBy8NgbuP4p1hSt4/u+Vr42ZEx7n1MuD10ZJ8XoWz53IsBF/DrklycHdS83sQoLiWRrwiLvPtmC5yfLVf64HcoH7YjW3Unffc3PPa5sWLqUmM8tw9zWxn68EOrn7xSGHlTC2pqs90e15YPwrW41h0cLEm13bEIN2ywk7BIkpLKplynmSGfyLuuaNJpeXb5wYdgjbxaDdcsMOYZtdfFw9B5duo3+ObZxk7cJj49sO0JaZW+snsQphOkFF9qxwwxERERFJPko8t4K7Pwc8F3YcIiIisuNJpc5pJZ4iIiIiCSyMnYsaS3z2FBQRERGRHZ4qniIiIiIJLJW62lXxFBEREZG4UMVTREREJIFtxWLvSUMVTxERERGJC1U8RURERBJYKo3xVOIpIiIiksC80fra475xkbraRURERCQ+VPEUERERSWCaXCQiIiIiUk+qeIqIiIgkME0uEhEREZG4iKZQX7u62kVEREQkLlTxFBEREUlgqdTVroqniIiIiMSFKp4iIiIiCSyVKp5KPEVEREQSWDSFMk8lnrLNHjtnYdghbLN3SnqHHcJ20bNrTtghbBf33Tk+7BC2WW7XjmGHsF2079Y27BC22YIbJ4YdwnZx4o0HhB3CdpH3/ldhhyAhUuIpIiIiksA8GnYE248mF4mIiIhIXKjiKSIiIpLAPIXGeKriKSIiIiJxoYqniIiISAKLptAYTyWeIiIiIglMXe0iIiIiIvWkiqeIiIhIAoumTsFTFU8RERERiQ9VPEVEREQSmKdQyVOJp4iIiEgCS6G5RepqFxEREZH4UMVTREREJIFFU6irXRVPEREREYkLVTxFREREElgqLSCvxFNEREQkgXkKbZmprnYRERERiQtVPEVEREQSWDSFutpV8RQRERGRuFDFM8WZ2YnAS8Cu7v5V2PHU9PH0Odz9+AtEo1FOOHR/zjrhyGr3T5kzl8v+8iCd2+cCcOheQzh/xDEV95dFo5xx9V20b5PFPVf8Jq6xVzV35gTeePI2otEoew4bybDjzq92//SPxzD+jYcBaNasJcefdQOduvcDYP3aQl5+5DqWLZmHYZx03i1077N73Nswf9YE3nrmVqIeZehBIznw2Auq3T9z0hgmvvkQAE2bteQnv7yRjt36VdwfjZbx0E0jycxpz2kXPxDX2Gu6+ILe7LdHLhs2lnHb379m7jdrNjnnpJ905pTju9K1cwt+cvpECgpLAejetQVXX9yPvr0zeOiJb3nm5SVxi/v8U9qzx4BWbCx2/v74jyxYvHGTc9rnNuHyczuR0SqNBYs2cM+jP1JaVnn/zj2ac9cV3fnLwz/w8bQ1tM1J55IzO5HdOg13ePujfF7/IL/R2jCgVxNGHdGKiMGEGRt465MNm5wz6oiWDOrdlOJSZ/SYNSxaVkZ6GlzxyyzS0yAtAlO+Kua1CesrHjN8z+YcukdzolFn5vwSXvxgXaO24dQjWxExY8L0Dbz5yfpNzjn1yFZBG0qcR14vYtHSoA1/OiOL9DQjUt6G8dXjPHKfFpxyeCsu+esq1qxPnCrWbg/dRvtjD6F4+SrG735c2OHUaf4XE3j7mVvxaJTdDxrJATWuU7MmjeHj8utU85Yc84tNr1MP3zyS1jntGXVRuNep+tDkIkkmpwIfAaOAG8MNpbqyaJS7Rv+Xf159IR1ysznzmv/j4D0G0atrp2rn7d6vd51J5bNvfkDPLh1Yu37TN7d4iUbLGPP4zZx9xX9o3aYD999wCrsOPZT2XXauOCenXVfOv/pxWrTK4usZ43nlkRv4zY3PAfDGk7fRZ9CBnPb7v1NaWkzJxvi3JRotY+xTN/HLyx6hdU4HHrr5ZHYZMpx2nau0oW0XzrriCVq0ymLerPG8/tj1nHftfyvu//Tdx2nbuRcb12+a5MXTvnu0oVvnloz61WcM2CWTP/6mDxf8cdom5836spCPP5/BvbcNqXa8sKiUvz04n4P3zY1TxIE9BrSiU/sm/PqGb+nbszm/ObUDl9+1aJPzzjyxLa+9n8eEyUX85tQOHH5ANm+NzwcgYsH90+asrTi/rMx55MXlLFi8kRbNjLuv2okZX65j8dLi7d4GMzjtqFbc80wheYVRrjk7ixnzSvhxZWVmPLB3E9q3SeOaf+fTq3M6px/ditsfK6S0DO5+qoCNJUHiecUvW/PFNyUs+KGUXXqkM7hPE/78cD6lZZDZ0rZ77FXbcPrRGfz16QLyCqNce0420+cVV2vDoFgbrr4/j16d0/nF0Rnc9mgBpWXwlycr2/CnM7L4Yn46C34IPtTkZEbo37MJqwrK6vr1oVny2EssvO9JhjxyZ9ih1CkaLeOtp27i9EuD69TDt5xM3xrXqey2XTgjdp2aP2s8bzx+PedeU3md+ux/j9O2Uy+KN4R7ndqRqas9hZlZBnAAcC5B4omZRczsPjObbWavm9lYMxsZu28PMxtnZlPM7G0z67SZp99ms+cvpFvHtnTt0JYm6ekcsd9Qxk2eudWPX7Yqj4+mzeaEQ/dvxCi3bMk3M2nTvjtt2ncjPb0pu+17LF9Ofb/aOT367E6LVlkAdN95MAV5SwHYsH4NC7+ezJ7DRgKQnt6UFq1ax7cBwPcLgjbktOtGWnpTBux9LF9Ne6/aOd12HlrRhq69BlMYawNA4eqlzJs5jqEHnRzXuGtz0L65vPV+ENvsr4vIaJVObk7TTc6bt2ANS5dvWlHMLyjhq3lFlJbGt8Kw9+AMPphUCMDcbzfQqmUaOa3TNjlvt11aMnFqEQDvTypg38EZFff95NAcPpm2hoKiysQmr7CsonK6fqOzZOlG2mQ3Ts2hZ+d0VuSVsTI/SlkUPp+zkSF9mlQ7Z0jfpkyaFcSz4IdSWjaPkNUqSCQ3lgTnpEUgLc0o/x84ZGhz3vpkQ0Vlt2hd4/3f9OyczvLVlW34bM5GhvSt/vczpG9TPpm5oUobjKyMzbcB4OdHtOKF99cm5PaHqz+aTMnqgrDD2Kwfvp1JTo3r1NfT675Odek1mKJarlO7J8B1qr6iUW+UrzAo8UxtPwPecve5wGozGwqcBOwEDALOA/YDMLMmwL3ASHffA3gEuLUxg1uRV0CH3JyK2x1yc1iRt+mFb9a8bzntT7dz0R338c3iHyuO//XxF7notJ8RiTRe9WNrFOYtJyu3Y8Xt1m06UJC3rM7zJ497kb67HQTA6uWLadm6DS8+dDX/vPYkXvrPtRRvbLwuxLoU5S+jdZvKzxmtczpSlF93G6ZNeIGdBx1ccfutZ2/j8JP/iFm4/xcAbXObsXxlZUK5fNVG2uZumngmmtzsdFbmlVbcXplXQm6NBDGzVRpr10WJxpZWWZVfWpFEtslKZ9/BGRXVz9q0b5NOr27Nmbuwcarq2ZkRVhdWrvuSVxQlO7N68pyTUds5wVuRGVx/bhZ3X9KGL78t4dtYpbBDmzT6dEvnqjNb88dftGanTpsm5NtLTmaEvKIq8RVGycms/laZnZlWvQ2Fle00g+vPy+avf8hlzoLiijYM7tOU/KIoS5YnXrUzWRTmLaN1To3r1GautdM/eoHeAyuvU28/dxuHj0yM61R9uTfOVxiUeKa2U4FnYz8/G7t9IPC8u0fdfSnwQez+XYCBwLtmNh24Fuha1xOb2QVmNtnMJo9+6Y0GBVfbmJWal4NddurGa/fezNN3XsXPjxrG5X99EIAJU2eR0zqTXXt1b9Dv3p6c2tpR+4VtwZxPmTLuRY4+5TIAomVl/LhwDvscNooLb3mJps1aMm7MQ40ab21qvwDV3oZvv5rEtI9e5PCRQRvmzviAVpm5dN5pYOMFWA+1Rp2AFaaaaou7Zti1vV+W/9+dd3J7HntlBXUVMZo3M/70qy48/Pxy1m9onEUBa/+L8S2eVH6GO9z0nwKuuDePnTqn07ldkMxFItCyuXH7Y4W88N46fnVi5naMestqvj5qzVtiJ7nDTQ/nc/k/VtMz1oam6fCTA1rw6vj4f6hMdXUlkQu/msS0CS9yWI3rVKcEuU7tyDTGM0WZWS4wHBhoZg6kEVzfX67rIcBsd99va57f3R8EHgQonPpug97W27fJZtmqvIrby1bl0TYnq9o5GS1bVPx8wO4DuPOR58gvXMOMrxcwYeosPp4+m40lJaxdv4Hr/vkYN194ZkNC2SZZOR0oWFW1O2cZrXPab3Le0kVf8/Ij13HmZQ/QMjOo9Ga16UDrNh3o1nswAAP3OpJxr8c/8Wyd04HC1ZXV5MK8pWRmb9qGZYu/Zsyj13H6JQ/SMiNow6L5U/l6xvvMmzWO0pJiNm5Yw0sPXc5J5/9f3OI/6djOHHdUUAn5cl4R7ds2q7ivfW4zVq7e/uMZt4djh2VzxAHB3/z87zbQNqfyktw2pwmr80urnV+4poxWLSNEIhCNBlXSvILgnJ17NOOP53YGoHWrNPYY2IqyKHw6Yw1pEbjygi6M+6yQSdMbb2xbXlGUNq0r6xk5mRHyi6JbPKegxjnrNzpzvythYK8m/LCijLzCKFO/Dv4PF/5YStQho6WxphG63POKqlc4c1pHyF9Tow2FZdXbUMs56zc6Xy8qYWCvpsxeUEzb7DRuOC+74vzrzs3m1tH5FK5Ngk9FCaJ1TgcK86pfpzLquE69/th1nHpx5XVq8fypzJ3xPvOrXKdefuhyTozjdWpbeArt1a7EM3WNBB5391+VHzCzccBKYISZPQa0Aw4Bnga+BtqZ2X7u/kms672vu89urAD79+7BoqUr+H75Stq3yebdT6Zy84VnVTtnZX4huVmZmBmz5y8k6k5WZisuPPUELjz1BCCY+f7k6++FknQCdOk1iFXLvmP1iiW0zmnPzEljOeU31S9m+St/4Kl/XMTIX91J2049K45nZrcjq00nVvz4Le069eSb2ZNoX2WgfLx06Rm0IS/WhtmfjeWkC/5S7ZyCVT/w3H2/58Tz7iS3Y2UbDh9xGYePCKoKC7/6lI/ffiSuSSfAS2N/4KWxPwCw355tGPHTLvxv/AoG7JLJmnWlrMpLzMRz7Lh8xo7LB2CPga34ySHZTJhcRN+ezVm7voy8wk27ZWd9vZ4DhmYyYXIRw/fN4tMZQSJ5wXXfVpxz0RkdmTxrTcV9v/9lRxYv3chr7+Vt8nzb08IfSmmfk0bbrKC7eq/+zXj41eqJ7oy5xRy6Z3M+m1NMr87prN/oFKx1MloaZWVBwtYkHXbt2YS3YrPJp88tpt9OTZi7qJQObSKkp9EoSWd5Gzq0qWzD3v2b8dArRdXOmT6vmOF7tqjehjW1tGGnprz1yTq+X1HGpX9bXfH4O36Xwy2P5CfUrPZk0HmnQayucZ068fxNr1PP3/d7Tji3+nXqsBGXcViV69Skdx5JmqQz1SjxTF2nAnfUOPYisCuwBPgCmAt8ChS4e3FsktE/zCyL4G/jb0CjJZ7paWlccdYpXHT7vyiLOscfsi+9u3XixXcnADDiiIN4/9NpvPDuBNLT0mjWtAm3XnR2wo3PSUtL57gzruXRu87DPcrQg0+iQ9c+fPp+MMphn+GjeP/V+1i3Jp/XHrsJgEgkjd/d9AIAP/3lNfz3/sspKyuhTbtujDi/UYfW1iqSls6xp1/Hk/eci0ejDDlwBO279GHyh0Eb9jxkFOPG3Mf6Nfm88WRlGy64/sW4x7oln0xezX57tuG5B/euWE6p3P/dMJA77p3LqtXFjDyuC6ed1I02OU157B978smU1dx571zaZDfh4Xv2oFXLNKJROPn4rvzit5+zbn3jjs2b8sVa9hzYin/f1JONxc69j1dWdq77XRf+9eRSVheU8dgrK/jjuZ04/bi2LFi8kXc/3vyEkF17t+DQfbNYuGQj91zdEoAnX13JlNlrN/u4hog6PP3OWi4Z1RqLwMQZG/lhZRnDdg8q0OOmbWTWNyUM2rkpt/4mm+IS59HXg8Q0q1WEc47LIBIJurInf1nMzPnBTJ2PZmzkrJ9mcOP5WZSWwegxjVe1jTo8/fYaLjk1i0gEJs7YELRhaPOgDVM3MGt+CYN6N+W23+ZQXOKMjrUhOyPCOcdlErGgDZ9/ubGiDYluyBN3kztsb5q2zWH4t+OYd9O9LB79QthhVRNJS+fo067j6b8F16nBBwTXqSmx69Qeh4xi/Jj7WL82nzefqrxOnXdd4l2n6iuVFpC3VFobSraOmWW4+5pYd/xnwAGx8Z4N0tCu9kTyTslhYYewXRSXJFZS3lD33Tk+7BC2WW7Xjls+KQm079Y27BC2Waq8z5144wFhh7Bd5L2fcEtK19svDopvBeTCvxY0yh/xPy/NivubhiqeO6bXzSwbaArcvC1Jp4iIiMjWUuK5A3L3Q8KOQURERLZOKk0u0nJKIiIiIhIXqniKiIiIJLAUKngq8RQRERFJZOpqFxERERGpJ1U8RURERBJYqiwJBqp4ioiIiEicqOIpIiIiksCiGuMpIiIiIlI/qniKiIiIJLBUGuOpxFNEREQkgWk5JRERERGRelLFU0RERCSBqeIpIiIiIlJPqniKiIiIJLCoJheJiIiISDyoq11EREREpJ5U8RQRERFJYFrHU6SKlwqPCDuEbXZYlzlhh7Bd/GVst7BD2C72OmqPsEPYZm1ym4cdwnaxxy7J/4Y37/vUeKvLe/+rsEPYLnKG9ws7hG1X8nXYESQtdbWLiIiIJLBo1Bvla0vM7Ggz+9rM5pvZlbXcb2b2j9j9M81s6JaeMzU+BoqIiIikqDAmF5lZGvAv4AhgCfC5mb3m7lW7CI8B+sS+9gHuj32vkyqeIiIiIlLT3sB8d1/g7sXAs8AJNc45AXjcA5OAbDPrtLknVeIpIiIiksDcvVG+zOwCM5tc5euCKr+2C7C4yu0lsWPU85xq1NUuIiIisgNy9weBB+u422p7SAPOqUaJp4iIiEgC82g0jF+7BKi6VEpX4IcGnFONutpFREREpKbPgT5m1tPMmgKjgNdqnPMacEZsdvu+QIG7/7i5J1XFU0RERCSBbc3SR9ubu5ea2YXA20Aa8Ii7zzazX8fu/zcwFjgWmA+sA87e0vMq8RQRERFJYGHtXOTuYwmSy6rH/l3lZwd+V5/nVFe7iIiIiMSFKp4iIiIiCSyMBeQbiyqeIiIiIhIXqniKiIiIJLBUqngq8RQRERFJYFEPZR3PRqGudhERERGJC1U8RURERBJYKnW1q+IpIiIiInGhiqeIiIhIAkuliqcSTxEREZEEFtbORY1BiWdIzKwr8C+gP8GQh9eBy929eDOPudrdb4tTiHHxzRfj+d9/byUajTLkwJPZ7+gLqt3/xaevMenthwBo2qwVR512Ix269aNw9Y+MGX0FawtXYhZhyEGnsNdhZ4bRBAA+mzKV+x78D9FolGOOPJxTTx5R63lfzZ3HRX+8kmuvuIyDD9yfxUu+55Y7/1Jx/49Ll3HmL05lxAnHxSXufj3SOGlYcyIGk2aX8L/Jm/75nTSsGf13Sqek1HnqnQ0sWRHMrmzRFEYd3pxOuREceObdDSxcGuXofZqy38AmrFkfXCjf+HgjcxaWNW47uqdx4kHNMINP55Tw3tSSTc458aCm7NojaMcz721kyYoo7bKNM49qXnFOblaENz8tZvyMEo7auyn79k9nbXk7JhXz5XeN245yvTsZRw2NYAbTvony8ZfV33RyM+H4fdPomAMfzIwy6avg/rQInHl4GukRiETgy0XOuC/Cmw375fSPePnxO/BoGfscOoLDTziv2v1TPnqd9177DwDNmrdk5LnX0aVHPwDGvfkEk95/EXdnv+EjGXbsL+Mef7lFX03go1eD61T/fUYydHj161Te8gW8/9xVrFgyh32OuYTdDzm34vg7T15acV7hqsXsfdRFDD44/teq+V9M4O1nbsWjUXY/aCQHHFu9DbMmjeHjN2PX2uYtOeYXN9KxW7+K+6PRMh6+eSStc9oz6qIH4hr71trtodtof+whFC9fxfjd43MNlfpT4hkCMzPgJeB+dz/BzNKAB4Fbgcs389CrgZRJPKPRMt555iZGXTKa1jkdePT2kfTZbThtO+9ccU52266cftmTtGiVxTdfjOPNJ6/jrKueJ5KWxmEnX0nH7gPYuGENo28dQc9dD6j22HgpKyvj3vsf5M5bbqRdbi6/+8MV7L/P3vTo3m2T8x5+9HH23H1IxbFuXbvwwL33VNw/6szzOHC/feIStxmcfEhz7nt5HflrnMtGtWTWglKWra5MVPrvlEa77Ai3PLaWHh0jnDy8Ofc8tw6Ak4Y158vvyhg9dgNpEWha5Wry4bRiPqgl+WusdowY1ox/v7qe/DXOH05pwRfflrIsrzJZ27VH0I7bnlxHjw4RRg5rxt9eWM+KfOcvz62veJ4bzwr+DcqNm1HCh9Pi046q7Tl6jwhPfVBG4Xo478g05n5fxsrCynPWF8NbU8ro17X6MP2yKDzxfhklpRAxOOvwNOb/CN+vimsTgOD1/eLoW/j11Q+RnduRe675OQP3OJSOXXtXnNOmfRcuvP5RWmZk8eX0Cfz3oT/zh1ue4cfF85j0/ov84ZZnSEtvwgN3/Jr+ux9Mu049QmnH+Jdv4rgLHiEjqwMv/P1kduo/nDYdK681zVpkceAJ1/Lt7P9Ve2xO+178/NJXKp7nsZuH0Wvg4fEMv+J3v/XUTZx+6SO0zunAw7ecTN8hw2lX7VrbhTOueIIWrbKYP2s8bzx+Pede89+K+z/73+O07dSL4g1r4h7/1lry2EssvO9JhjxyZ9ihbHfRqJZTkm0zHNjg7qMB3L0M+ANwjpn91sz+WX6imb1uZoeY2R1ACzObbmZPxe47w8xmmtkMM3sidqyHmb0XO/6emXWPHX/UzO43sw/MbIGZDTOzR8zsSzN7tMrvO9LMPjGzqWb2vJllNNY/wg/fziSnfQ9y2nUjLb0pu+75E+bOeK/aOV17D6VFqywAOvccQlH+UgAystrTsfsAAJo1z6Btp14U5S9rrFA36+u58+jcqROdO3akSZMmHHLwgUyc9Nkm573y+lgO2n8/srOzan2eaTNm0blTRzq0b9/YIQPQo0OEFQVRVhU6ZVGYOreUQb2qfxYd2Cudz78MEq/vlkZp0cxo3dJo1hR6d0lj0uzgvrJokAyFoXuHCCurtGPavFIG1mxHz3Q+/ypIKL9bVtmOqvp2TWNVoZNXFG6XVuc2kLfGyV8L0SjMXhRll67VY123EX5cHfy711QSy5sjsapnWK1ZNH8WbTt2p22HbqSnN2H3/Y7hi8nvVzunZ9/daZkRvB567LwbBauD1/Cy7xfQo89uNG3WgrS0dHbedU9mfv7eJr8jHpYvmklWbneycoPr1M5DjuXb2dVjaZmZS4fug4hE6q7lLJn3CVm53chs06WxQ95EcK3tXnGtHbD3sXw9vXobuu1cea3t0mswRXlLK+4rXL2UeTPHsftBJ8c17vpa/dFkSlYXhB2GbIESz3AMAKZUPeDuhcAi6qhCu/uVwHp3H+Lup5vZAOAaYLi7DwYujp36T+Bxd98NeAr4R5WnySFIev8AjAHuicUyyMyGmFlb4FrgcHcfCkwGLqWRrMlfRuucjhW3M3M6bDZ5nDnxBXoPOHiT4/krl7Bs0Zd07jm4UeLckpWrVtO+XduK2+3a5rJqVfUS08qVq5j4ySR+esxRdT7PB+MncOjBBzVanDVlZUTIL6rMXPLXRMnKqJ7gZGdEyF9TmboUxM5p2zrCmvXOaUc05/JTWzLqsGbVKp4HDW7Kn05vyamHN6dFs8ZtR3YrI7+oaoxOVqvq7cjKMPLXbL6tu/dJZ+rc0mrHDhrUhMtHtWDU8GaN3o5yrVsahesqbxeug8wWVvcDajCD849O47IT0/h2qfNDCNVOgPy85WTnVr6+s3I7UJC3vM7zP/3wJfoNORCATt12ZsGXU1hblE/xxvXMmT6B/FVL63xsY1pbsIyM7E4VtzOyO7K2oP4fcudPH0ufIT/ZnqFttcK8ZbTOqWxD65yOFOXV3YbpH71A74GV19q3n7uNw0f+kaCzTsLgUW+UrzAo8QyHUXshoq7jtRkOvODuKwHcfXXs+H7A07GfnwAOrPKYMR6MUJ4FLHP3We4eBWYDOwH7Eow5nWhm04EzgVr7tszsAjObbGaTPxzz4FaGXJ3X0lSj9gvbd19PYsbEFzjkpD9WO168YS0vP3ARh59yNc1aNFpxdrNqawc1LtD3PfQfzjvrDNLS0mp9jpKSEj757HOGHbh/Y4RYq1r/pbfir88JKmld20eYOLOY/3tmHcUlcPieTQGYOKuEmx9dy11PraNwbZSfHdR8808YB7W1tepY/bQIDOiZzvT5lYnnxFkl3PLEOv7y7HoK1zknHBCnzLMW9Xl7cIeH3irjb6+W0TkX2tVeYG98tU6GqP31PW/2Z0z64CWOOzX4nNuhS2+GH38O9992Pg/c8Ws6d+9LpI7XTmOrtRX1TMDKSotZOPt9eg8+evsEtR3U1YaFX01i2oQXOWzkZQDMnfEBrTJz6bTTwHiGJylMYzzDMRuoNvvEzFoD3YACqn8gqOtde2uT1KrnbIx9j1b5ufx2OlAGvOvup27xSd0fJBiXyqMfNqw3LzO7I4VVunOK8paRkb1pN/PyJV8x9vFrOeWih2iZkVNxvKyshJceuIgBex/HLkOPbEgI20W73FyWr1hZcXvFylXktmlT7Zy587/h1rvuBqCgsIjPJk8hLS2NA2LjOT+bMpU+vXuRk5Mdt7jz10TJzmxScTs7I0LBWt/0nCqVwayMCIVrglQ7f43z3bKgijh9fmlF4lm0rvI5PvmihAuOb9GIrYD8tU52ZtUYrZZ2ONkZEYI/9aCthVXO2bVHGt+vKKuYEAVU+/mT2SWc/9P4JNCF67zaMIDWLavHsrU2lsB3y53enYwVBfGvbGS36VCtSlmwahlZOe02Oe+H777muQev54Ir/02rzOyK4/seOoJ9Dw0uk288+zey2nTc5LHxkJHVgTX5P1bcXpO/lJat6zccZtFXE2jbtT8tM9tu+eRG0DqnA4V5lW0ozFta67V22eKvef2x6zj14gcrrrWL509l7oz3mT9rHKUlxWzcsIaXH7qcE8//v7jFL+DaMlO20XtASzM7AyA2uehu4FFgATDEzCJm1g3Yu8rjSsysSZXnOMXMcmPPUZ7pfAyMiv18OvBRPeKaBBxgZjvHnrOlmfWtb+O2VuedBpG3fCH5KxdTVlrMl5PfoM/g4dXOKVj9Ay/++/ccd85d5HboWXHc3Rn7+DXkduzF3kec3VghbpVd+vbh+x9+5MelyygpKeHD8R+x/z57VTvnyf88wFOPPMhTjzzIwQfsx0W/+VVF0gnwwbiP4trNDrBoWZR22RHatDbSIjC0bzpfLKje1fzFglL22jX4k+vRMcKGjU7hOqdonZNfFKV9dpAg9e2WxtLYpKSqSdNuO6fz46rGvWAuXhalXVaENplBO3bvk87sb6vPPp/9bSl79Qs+Z/foEGF9cdCOcrv3SWfqvOptr9aOXo3fjnI/rIY2mUZ2q6CyPKB7hLlLti5xbNkMmsWuEOlp0LNDhFWFm39MY+nWeyArli5i1fIllJaWMO2TNxmwx6HVzslb+SOj77mE0393O+077VTtvqKCVRXnzPz8PYbuf0y8Qq+mfbdBFKz8jsJVSygrLWb+9LH0HDB8yw+sYt70N0LrZofgWrt62XfkrQjaMPuzsfStea1d9QPP3/d7Tjj3TnI7Vl5rDxtxGZf83zguuvN9Trrgbnr220dJZwhSqatdFc8QuLub2YnAfWZ2HcEHgLEEs9aLgW8JusO/AKZWeeiDwEwzmxob53krMM7MyoBpwFnARcAjZnY5sALY6qzM3VeY2VnAM2ZW3q94LTC3wY3djEhaOkeMup5n/34eHi1jtwNG0K5zH6aOewaAocNOZeLr/2LD2nzefvrPwWMiaZx9zUss+WYKX0x6lXZd+vKfm08AYNjPLmXnQcMaI9TNSktL4/e/Pp8rr/8z0WiUo484jJ16dGfM2LcAOO7YzXevbdiwkSnTp3PJhb+OR7gVog4vfriB3/ysZbCc0pwSlq6OcsCgIHOZOKuEOQvL6L9TlOvObEVxqfP0uxsqHv/ihxv55dEtSE+DlQXRivuOP7AZXdoFn2lXFTr/fW/Dpr98e7dj/EZ+dUILIrHllJaujrL/gODy9vHsUuZ8V8auPdK45pctKS51nn2vsuDfJB126Z7O8x9urPa8x+3flM7tgtk5q4uc5z+ofn9jcYe3Jkc57ZA0zGDGgigrCmHozkEiPHW+06o5nHdUGs2agLuxzy5w/xtlZLSAE/YNHmfAnEVR5v0QzptLWlo6I866mgdu/xXRaBn7HHIinbrtzMR3nwPggCN+ztsv3c/aNQW88MgtQPD6vuy2YCb16Hv+wLo1+cHznH1NxSSkeIukpXPQidcx5qFzcY/Sb68RtOnYhy8+fhaAgfuPYl3hCp7/+0iKN6zBLMLMCY9z6uVv0LR5BiXF61k8dyLDRvw5lPjL23D0adfx9N/OxaNRBh8wgvZd+jDlw6ANexwyivFj7mP92nzefOqm4DGRNM677sXQYm6IIU/cTe6wvWnaNofh345j3k33snj0C2GHJTVYKi1KKuFoaFd7Ijmsy5ywQ9gu/jK225ZPSgKRtOSfxNAmN/yxrdvDHrsk/cubed+nRo0lNzvsCLaPnOH9tnxSgvtJyddxvUgdc9bMRnkhvvnobnG/2KqrXURERETiIjU+BoqIiIikqGgKTS5S4ikiIiKSwMKaCNQY1NUuIiIiInGhiqeIiIhIAnPt1S4iIiIiUj+qeIqIiIgksFQa46nEU0RERCSBactMEREREZF6UsVTREREJIFFU6irXRVPEREREYkLVTxFREREEpiWUxIRERERqSdVPEVEREQSmJZTEhEREZG40HJKIiIiIiL1pIqniIiISAJLpa52VTxFREREJC5U8RQRERFJYKm0nJK5p075VlKXmV3g7g+GHce2SIU2QGq0IxXaAGpHIkmFNkBqtCMV2pDK1NUuyeKCsAPYDlKhDZAa7UiFNoDakUhSoQ2QGu1IhTakLCWeIiIiIhIXSjxFREREJC6UeEqySIXxOqnQBkiNdqRCG0DtSCSp0AZIjXakQhtSliYXiYiIiEhcqOIpIiIiInGhxFNERERE4kKJp4iIiIjEhRJPEREREYkLbZkpCcvMWgHr3T1qZn2BfsCb7l4Scmj1YmY9gD7u/j8zawGku3tR2HHVV6q0A8DMcoBu7j4z7FgawszSgA5UuYa7+6LwItp6Znbp5u5397/GKxYBM+sA3AZ0dvdjzKw/sJ+7/yfk0OrFzFoClwHd3f18M+sD7OLur4ccmtSgiqcksvFAczPrArwHnA08GmpE9WRm5wMvAA/EDnUFXgktoAZKhXaY2Ydm1trM2gAzgNFmlnRJjpn9HlgGvAu8EftKpjfXzC18JQ0za2dmV5vZg2b2SPlX2HHV06PA20Dn2O25wCVhBbMNRgMbgf1it5cAt4QXjtRFFU9JZObu68zsXOBed7/LzKaFHVQ9/Q7YG/gUwN3nmVn7cENqkFRoR5a7F5rZecBod7/BzJKx4nkxQSVnVdiBNIS7/znsGLajV4EJwP+AspBjaai27v5fM7sKwN1LzSwZ29Lb3X9uZqcCuPt6M7Owg5JNKfGURGZmth9wOnBu7Fiy/c1udPfi8uufmaUDybh4biq0I93MOgGnANeEHcw2WAwUhB1EQ5nZPzZ3v7tfFK9YtoOW7v6nsIPYRmvNLJfY69nM9iU5/76KY0OAytvRm6ACKgkm2d7EZcdyCXAV8LK7zzazXsAH4YZUb+PM7GqghZkdAfwWGBNyTA2RCu24iaBL8SN3/zz29zQv5JgaYgHwoZm9QZU31iQaG/lr4Avgv8APQDJXpV43s2PdfWzYgWyDS4HXgN5mNhFoB4wMN6QGuQF4C+hmZk8BBwBnhRqR1Eo7F0nCM7NW7r427DgawswiBNXaIwneYN8GHvYke+HFuqzOI8nbkQrM7IbajidLF3asunYy8HOgFHgOeNHd80INrAHMrAhoBRQD5ZMe3d1bhxdV/cV6MHYheG1/nWwTOMvF/rb2JWjHJHdfGXJIUgslnpKwYt3s/wEy3L27mQ0GfuXuvw05tAaJTWrpmmwzqWPJ80x3Hxh2LNvCzO4imGywnqAyMhi4xN2fDDWwHVhs4uCpBFW3P7n7EyGHtMMxs5NqOVwAzHL35fGOZ1uY2W7ATlRf7eGl0AKSWqmrXRLZ34CjCLqBcPcZZnZwqBHVk5l9CBxP8FqbDqwws3HuvtklZRJJbDmrGWbWPVmW7KnDke5+hZmdSDDj9WSCoRtJkXia2d/c/RIzG0Mt42vd/fgQwmowMxtKkHQeAbwJTAk3ooYxs+OB8uvSh0m4fM+5BDPBy4cxHQJMAvqa2U3J8mEgtprAbsBsIBo77IASzwSjxFMSmrsvrjExMdlmW6bKTOpOwGwz+wyoGPaQZMlOk9j3Y4Fn3H11kk16LU8A/hJqFNvIzP4M/BT4EngWuMrdS8ONqmHM7A5gL+Cp2KGLzexAd78yxLDqKwrs6u7LoGJdz/uBfQiWtEuKxBPY1937hx2EbJkST0lki81sf8DNrClwEcGbVTJJlZnUSTF+cAvGmNlXBF3tvzWzdsCGkGPaau4+JfZ9XNixbKPrCCZIDY593Rb7AGAE4yN3CzG2+joWGOLuUQAzewyYBiRT4rlTedIZsxzoG/tglkxjPT8xs/7uPifsQGTzlHhKIvs18HegC0HX6DsE60kmk/KZ1BOTeSZ1CiQ7uPuVZnYnUOjuZWa2Djgh7Li2lpnNYjNLWCVRwtYz7AC2s2xgdeznrBDjaKgJZvY68Hzs9ghgfGznuPzQoqq/xwiSz6UEqz0k4weZHYImF4nIFsVm75ZfLJoSdFuvTabZu7Et9S4l2FLvgmTbUi+2ZWmd3P27eMWyvZlZW2BVsq2SEFus/A6C8ZFGMNbzKnd/NtTA6iG2YsVJwIGxQ6uATu6eVB/yzWw+wet7FpVjPJP6dZGqVPGUhGNmV8R2KbqX2idRJM0C02bWFbiXYE05Bz4CLnb3JaEGVk/uXm0rQzP7GcFORslkNMEElv1jt5cQVHmSIvFMlTfQ2ALldxBUCW8mGEPYFoiY2Rnu/laY8dWHuz8Tm0C4F0Hi+Sd3XxpuVPXj7m5m3xCM6TwF+BZ4MdyoGmSRu78WdhCyZUo8JRGVj+OcHGoU28do4GmCGdQAv4gdOyK0iLYDd3/FzJJpHBukyJZ6KVB9/idwNUG39PvAMe4+ycz6Ac8QLHWV0Mysn7t/FZuZD8GHGIDOZtbZ3aeGFdvWMrO+wCiClQVWEaynau5+aKiBNdxXZvY0wcYWVTdW0Kz2BKPEUxKOu4+JfX8s7Fi2g3buPrrK7UfN7JKwgmmoGmv9RYA9Sb4tM1NiS70UqD6nu/s7ALHleiYBxBK5cCPbepcCFwB313KfA8PjG06DfEWwz/xx7j4fwMz+EG5I26QFwev5yCrHtJxSAlLiKQnLzN4FTnb3/NjtHOBZdz8q1MDqZ6WZ/YKgkgOV1YVkc1yVn0uBhSTRxJyYlNxSLwmrz9EqP6+vcV9SfJhx9wtiPx7j7tVWRjCz5iGE1BAjCCqeH5jZWwRLWyVN5l+Tu58ddgyydTS5SBKWmU139yE1jk1z991DCqnezKw7QdfifgRvqh8TjPFMifF6ySYVttSro/o8zN33CymkejGzMoK1YI2gSrWu/C6gubs3qeuxicbMprr70C0dS2Sx2es/I/hQPJxgdvjL5VXpZJEq4+l3BKp4SiIrq7pbTmxWb1J9UorFnkyLrNcqhbabbA7kEVz7+psZ7j4+5JjqK6mrz+6eFnYM28rMOhIs89bCzHanslLYGmgZWmAN4O5rCRbAfyq2re/JBOuQJlXiSYqOp09FqnhKwjKzo4EHgfI1JA8GLnD3t8OLqn5iC0pfXGO4wN3ufk6ogdVTefU5tt3kz4A/AB+4++BwI9t6sTU8f06NLfWSbPclSQBmdibBMI09gc+pTDwLgcc0oSX+6ugh2+SYhE8VT0lY7v5WbNZoedfoH5Kwa3S38qQTwN3zYhWSZJPs201CkDDv4u5JN6GoqhSqPiet2MTHx8xshLsn49JDqShVxtOnvEjYAYhsQTOC9f4KCLpGDw45nvqKxKqcAMS6spLxA1/5dpN7Au8l23aTMQuoTKCT2ZHuXkiw3/kSoC9webgh7bD2MLPs8htmlmNmt4QYz47sHIJ1SJcCPwIjY8ckwairXRJWKnSNmtkZwFXAC7FDJwO3uvsT4UXVMLEEuny7yZZA62RaLNvMXiSoDr5H9XX+kmZDAgAzm+3uA8zsIeDFWM/AjGQa9pAqapvsmGyTi0TiLRkrL7Lj+BlJ3jXq7o+b2WSC2aIGnOTuc0IOq97M7GTgrVjSeS0wlKC7N2kST+C12FeyK68+rwd+m6TV51SRZmbNyq9RsXVim4Uc0w4pVcbT7wiUeEoiK+8aTdrEM7ac0hqqJDxVZ+onkevc/XkzOxA4CvgLcD/BNnvJ4gt3n1L1gJkdV9fJicrdr4z1BpRXn9eSRLPaU8yTBENPRhOsuHEOwXJEEn+pMp4+5SnxlES2DphuZsncNfoGlUtAtQB6Al8DA0KLqGHKYt9/Atzv7q+a2Y0hxtMQD5nZme4+CyC2deYlBFvsJZtdgZ3MrOo1/PGwgtlRuftdZjYLOIygR+PmZFp1I8VEzCzH3fMgqcfTpzz9p0giS/quUXcfVPV2bJb+r0IKZ1t8b2YPAIcDd5pZM5JvcuJI4AUzOx04EDiD6tvrJQUzewLoDUyn8gOBo8QzFO7+JvBm2HEIdwMfm9kLBK+HU4Dbwg1JaqPJRSJxloyTD2KTiY4GZrn7PDPrBAxKwt1N+gKvAIuBn7l7zS0bE56ZfQn0d128QxfbRepOoD1BxdMIJkC2DjWwHZSZ9adyPP17yTiefkegiqckLDPrA9wO9CfYcQYAd+8VWlD1ZGaXVrkZIZiUsyKkcBrM3deZ2XKCSuE8gh1z5oUb1daJdYVWTdLaAGnAp7Gdi3YLJ7IG+wLoSLBkjITrLuA4d/8y7EB2dGb2hLv/EphTyzFJIEo8JZGNBm4A7gEOBc6mcoeQZJFZ5edSgjGfSbfgtJndQLCG5y4E/y9NCCZWHBBmXFvpp2EHsJ21BeaY2WdUH/ucNMuMpZBlSjoTRrVx82aWBuwRUiyyGepql4RlZlPcfQ8zm1U+VtLMJrj7QWHHtqMxs+nA7sDU8nULzWxmMlULzWxfYLa7F8VuZxJ0WX8abmT1Y2bDajvu7uNqOy6Nx8z+TlB9foXqHwK0ZWacmNlVwNUEkzfXUVmcKAYedPerwopNaqeKpySyDWYWAeaZ2YXA9wRjqRKemY2hevduNUlYnSp2dzczBzCzVmEH1AD3Ewx1KLe2lmMJTwlmQmlNkOxUnaTmgBLPOHH324Hbzex2JZnJQYmnJLJLgJbARcDNBIPGzwwzoHr4Sy3HyhPRZBsuAPDf2Kz2bDM7n2C9wodCjqm+rOqEHHeP1liOKKGZWRG1f5jRhJaQuPvZYccgFd6sbUtldx8fRjBSN3W1izQCMzsB6Oru/4rd/gxoR5A4/Mndnw8zvvowMwO6Av0IKjsGvO3u74YaWD2Z2UvAhwRVToDfAoe6+8/CikmSW5WF46vRbjnxF+tlKtcc2BuY4u7DQwpJ6qDEUxJWbOmby4EeVKnOJ8OFxMwmAqPcfXHs9nSCRaZbAaPd/bAQw6u38vG2YcexLcysPfAPgsq5E+zZfom7Lw81MElaZjaiys3mwInAD0m2yUVKMrNuwF3ufmrYsUh1SdPNJDuk54F/E3Tplm3h3ETTtDzpjPnI3VcBq5J0fOQkM9vL3T8PO5CGiiWYo8KOQ1KHu1dbocLMngH+F1I4Ut0SYGDYQcimlHhKIit19/u3fFpCyql6w90vrHKzXZxj2R4OBX5tZgsJJuWUjytM+FntZnZFbGvDe6m9W1TVKdle+gDdww5iR1Tj9R0hWIVjRngRSV2UeEoiG2NmvwVepvpSJavDC2mrfWpm57t7tQk4ZvYr4LOQYtoWx4QdwDYoX2dxcqhRSMqpZcLXUuBPIYWzo5tDsDGEAwXAM+4+MdyQpDYa4ykJy8y+reWwJ8PORbHxhK8QJMxTY4f3AJoRbNW4LKTQ6iXWjquBnYFZwO3uXhhuVCLhMrN0dy8NOw4J/i8I9mQ/B1hE0BvTDXgEuMbdS0IMT2qhxFOkEZnZcCp31Jjt7u+HGU99mdlbwBRgPMEOQJnuflaoQTVQbLLaH4GdSLLJapJYzGyquw+N/Xyvu/8+7Jh2VGZ2D8EOcX+osjlEa4Il7da7+8VhxiebUuIpCcvMTqrlcAEwSzOR48PMprv7kCq3K95wk42ZzSCYrDaFKpPV3H1KaEFJUjKzaVV28Era10QqMLN5QF+vkczEtsz8yt37hBOZ1EVjPCWRnQvsB3wQu30IMAnoa2Y3ufsTYQW2AzEzy6Fy0fu0qreTZLxtuWSerCaJRRWbxOE1k87YwbLyndYksSjxlEQWBXYtHw9pZh0IFv/eh6DrV4ln48siqBBW3W2pfMyqA8kw3rZN7MdknqwmiaWfmc0keF30jv0MSbTaQwqZY2ZnuPvjVQ+a2S+Ar0KKSTZDXe2SsMxslrsPqnLbCLrZB1bt6hLZnNgkNaf2rUqTYrKaJBYz67G5+939u3jFsqMzsy7AS8B6gg/JDuwFtABOdPfvQwxPaqGKpySyCWb2OsFC8gAjgfGxBdjzQ4tqB2Jmmx275u5TN3d/gjjN3T8JOwhJHVUTy1gS2sfd/2dmLdD7alzFEst9qkzkNOBNd38v3MikLqp4SsKKVThPAg4kuJh8BLxY23geaRxmVj6+tjmwJ8GCzAbsBnzq7geGFdvW0uQPaSxmdj5wAdDG3XubWR/g38m2Ja5IPOmTmSQsd3czmwwUxKoJLYEMoCjk0HYY7n4ogJk9C1zg7rNitwcSLE2UDGrrYhfZHn4H7A18CuDu82Jr34pIHZR4SsKqWk0AegNdCJbDUTUh/vqVJ50A7v6FmQ0JMZ766Glmr9V1p7sfH89gJKVsdPfioHOmYjFz9ciIbIYST0lkqiYkji/N7GHgSYI31l9QuRVlolsB3B12EJKSxpnZ1UALMzsC+C0wJuSYRBKaEk9JZKomJI6zgd8A5buAjCdY2ioZFLn7uLCDkJR0JcF6w7OAXwFjgYdDjUgkwSnxlESmakKCcPcNZvYv4H8Eyf/XSbQH8sKwA5DU5O5RM3sSGO/uX4cdj0gy0Kx2SVixWe3nAUcSTBB5G3hYs9rjz8wOAR4jSOIM6Aac6e7jw4uq/sxsfzbdq/3xOh8gshlmdjzwf0BTd+8ZG/d8k8YNi9RNiackJDOLADPdfWDYsQiY2RSC9TC/jt3uCzzj7nuEG9nWM7MnCCapTadyr3Z394tCC0qSWux1MRz4sMre7TO1c5FI3dTVLgkp1oU1w8y6u/uisOMRmlTtSnT3uWbWJMyAGmBPoL8q5rIdlbp7Qfk4dBHZMiWeksg6AbPN7DNgbflBdWOFYrKZ/Qd4Inb7dILt6ZLJF0BH4MewA5GU8YWZnQakxRaPvwj4OOSYRBKautolYZnZsNqOa4Zy/JlZM4Llrcp3kRoP3OfuG0MNrB5iuzANAT4DKuLWBxlpqNimFtcQjEOHYBz6Le6+IbyoRBKbEk9JOGbWHPg1sDPBMiX/cffScKMSM2sK7ELyzWoH9EFGti8zSwPedvfDw45FJJmoq10S0WNACTABOAboT+X6kRKC2ma1m1lSzWpXginbk7uXmdk6M8ty94Kw4xFJFko8JRH1d/dBALFxhZ+FHI8EO/8cWXNWO5Dws9rN7CN3P9DMiqi+AYERzGpvHVJokvw2ALPM7F2qj0PXSgkidVDiKYmoogvX3Us1YzQhJO2sdnc/MPY9M+xYJOW8EfsSka2kMZ6ScMysjMrqgQEtgHWoQhUaM3uEoFpYdVZ7urufHV5U9WNm57r7f2ocu8PdrwwrJhGRHY0qnpJw3D0t7BhkE78hmNV+EVVmtYcaUf2NNLMN7v4UgJndBzQPOSZJYmY2i+rDNwAKgMkEs9tXxT8qkcSmiqeI7BDMrAXwGvAIwaS11e5+SahBSVIzs7sIdsF6OnZoFMEHswLgQHc/LqzYRBKVEk8RqVMdFZ0KybA1oJm1qXIzE3gV+Ai4HsDdV4cRlyQ/M5vo7gfUdszMZpVPkhSRSupqF5HN+WnYAWwHUwiSZ6vy/djYF0CvkOKS5JdhZvu4+6cAZrY3kBG7T2sPi9RCFU8RqRczawusSpY9z2PJwGJ3/zF2+0xgBMGapDeq4ikNZWZ7EQzdyCD4QFMInAfMBn7i7v8NMTyRhKTEU0TqZGb7AncAq4GbCWa1twUiwBnu/laI4W0VM5sKHO7uq83sYOBZ4PcE22fu6u4jw4xPkp+ZZRG8n+aHHYtIolPiKSJ1MrPJwNVAFvAgcIy7TzKzfsAz7r57qAFuBTOb4e6DYz//C1jh7jfGbk939yEhhidJyMx+4e5Pmtmltd3v7n+Nd0wiyUJjPEVkc9Ld/R0AM7vJ3ScBuPtXSbSwf5qZpbt7KXAYcEGV+3QNlIZoFfuuTQlE6kkXXRHZnGiVn9fXuC9ZukueAcaZ2UqCNkwAMLOdCZa9EakXd38g9v3PYccikmzU1S4idaqyi1TVHaSI3W7u7kmxbWZsrGon4B13Xxs71hfIcPepoQYnScfM/rG5+7VXu0jdVPEUkTqlyi5S5UMEahybG0YskhKmxL4fAPQHnovdPrnKfSJSC1U8RUREGsDMPgCOdPeS2O0mBFX1Q8ONTCRxRcIOQEREJEl1pvoEo4zYMRGpg7raRUREGuYOYFqs8gkwDLgxvHBEEp+62kVERBrIzDoC+8RufuruS8OMRyTRqatdRESkASxYzPZwYLC7vwo0jW3RKiJ1UMVTRESkAczsfoK1boe7+65mlkMwuWivkEMTSVga4ykiItIw+7j7UDObBuDueWbWNOygRBKZutpFREQapsTM0ojt4mVm7ai+25eI1KDEU0REpGH+AbwMtDezW4GPgNvCDUkksWmMp4iISAOZWT/gMIJtZN9z9y9DDkkkoWmMp4iISD2Y2T7Ag0BvYBZwrrvPCTcqkeSgrnYREZH6+RfwRyAX+CtwT7jhiCQPJZ4iIiL1E3H3d919o7s/D7QLOyCRZKGudhERkfrJNrOT6rrt7i+FEJNIUtDkIhERkXows9Gbudvd/Zy4BSOSZJR4ioiIiEhcaIyniIhIA5jZxWbW2gIPm9lUMzsy7LhEEpkSTxERkYY5x90LgSOB9sDZwB3hhiSS2JR4ioiINIzFvh8LjHb3GVWOiUgtlHiKiIg0zBQze4cg8XzbzDLRXu0im6XJRSIiIg1gZhFgCLDA3fPNLBfo4u4zw41MJHGp4ikiItIwDvQHLordbgU0Dy8ckcSniqeIiEgDmNn9BF3rw919VzPLAd5x971CDk0kYWnnIhERkYbZx92Hmtk0AHfPM7OmYQclksjU1S4iItIwJWaWRtDljpm1Q5OLRDZLiaeIiEjD/AN4GWhvZrcCHwG3hxuSSGLTGE8REZEGMrN+wGEE63e+5+5fhhySSEJT4ikiItIAZvaEu/9yS8dEpJK62kVERBpmQNUbsfGee4QUi0hSUOIpIiJSD2Z2lZkVAbuZWaGZFcVuLwdeDTk8kYSmrnYREZEGMLPb3f2qsOMQSSZKPEVERBogtmXmaUBPd7/ZzLoBndz9s5BDE0lYSjxFREQaQDsXidSfdi4SERFpGO1cJFJPmlwkIiLSMNq5SKSelHiKiIg0TPnORR2q7Fx0W7ghiSQ2jfEUERFpoCo7FwG8r52LRDZPYzxFREQariVQ3t3eIuRYRBKeutpFREQawMyuBx4D2gBtgdFmdm24UYkkNnW1i4iINICZfQns7u4bYrdbAFPdfddwIxNJXKp4ioiINMxCoHmV282Ab8IJRSQ5aIyniIhIPZjZvQRjOjcCs83s3djtIwhmtotIHdTVLiIiUg9mdubm7nf3x+IVi0iyUeIpIiIiInGhrnYREZEGMLM+wO1Af6qM9XT3XqEFJZLgNLlIRESkYUYD9wOlwKHA48AToUYkkuCUeIqIiDRMC3d/j2DY2nfufiMwPOSYRBKautpFREQaZoOZRYB5ZnYh8D3QPuSYRBKaJheJiIg0gJntBXwJZAM3A1nAXe4+Kcy4RBKZEk8RERERiQt1tYuIiNSDmf3N3S8xszEEC8dX4+7HhxCWSFJQ4ikiIlI/5TPX/xJqFCJJSF3tIiIiDWRm7QDcfUXYsYgkAy2nJCIiUg8WuNHMVgJfAXPNbIWZXR92bCKJTomniIhI/VwCHADs5e657p4D7AMcYGZ/CDUykQSnrnYREZF6MLNpwBHuvrLG8XbAO+6+eziRiSQ+VTxFRETqp0nNpBMqxnk2CSEekaShxFNERKR+iht4n8gOT13tIiIi9WBmZcDa2u4Cmru7qp4idVDiKSIiIiJxoa52EREREYkLJZ4iIiIiEhdKPEVEREQkLpR4ioiIiEhc/D+eJi7f6otnQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the correlation matrix as a Pandas DataFrame\n",
    "\n",
    "correlation_matrix = data.corr()\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Create the heatmap\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37dd2c23",
   "metadata": {
    "id": "37dd2c23"
   },
   "source": [
    "**Number of Instances:** 768<br/>\n",
    "**Number of Attributes:** 8 plus target/class<br/>\n",
    "**For Each Attribute:** (all numeric-valued)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2f488e",
   "metadata": {
    "id": "1b2f488e"
   },
   "source": [
    "**Pregnancies:** Number of times pregnant <br/>\n",
    "**Glucose:** Plasma glucose concentration a 2 hours in an oral glucose tolerance test <br/>\n",
    "**BloodPressure:** Diastolic blood pressure (mm Hg)<br/>\n",
    "**SkinThickness:** Triceps skin fold thickness (mm)<br/>\n",
    "**Insulin:** 2-Hour serum insulin (mu U/ml)<br/>\n",
    "**BMI:** Body mass index (weight in kg/(height in m)^2) <br/>\n",
    "**DiabetesPedigreeFunction:** Diabetes pedigree function <br/>\n",
    "**Age:** Age (years) <br/>\n",
    "**Outcome:** Class variable (0 or 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "JEUkxUqt4NHH",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "JEUkxUqt4NHH",
    "outputId": "9f7e27b3-9f6d-427c-903e-a293d10068c6"
   },
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  Insulin   BMI  DiabetesPedigreeFunction  Age\n",
       "0            6      148        0  33.6                     0.627   50\n",
       "1            1       85        0  26.6                     0.351   31\n",
       "2            8      183        0  23.3                     0.672   32\n",
       "3            1       89       94  28.1                     0.167   21\n",
       "4            0      137      168  43.1                     2.288   33"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Selection\n",
    "x = data.drop(['SkinThickness','BloodPressure','Outcome'],axis=1)\n",
    "\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "24363fb7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "24363fb7",
    "outputId": "406c20f3-aa95-4c4d-9f27-eb9f9ec14677"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=data['Outcome']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "cdb7be49",
   "metadata": {
    "id": "cdb7be49"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b968e577",
   "metadata": {
    "id": "b968e577"
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2,random_state=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ce4a75f2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "ce4a75f2",
    "outputId": "610a5b52-84b8-404e-d73d-ba03aac5f5d3",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>25.3</td>\n",
       "      <td>0.658</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>3</td>\n",
       "      <td>191</td>\n",
       "      <td>130</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0.299</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.270</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>46.8</td>\n",
       "      <td>0.925</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.546</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  Insulin   BMI  DiabetesPedigreeFunction  Age\n",
       "446            1      100       70  25.3                     0.658   28\n",
       "260            3      191      130  30.9                     0.299   34\n",
       "570            3       78        0  32.5                     0.270   39\n",
       "590           11      111        0  46.8                     0.925   45\n",
       "30             5      109        0  36.0                     0.546   60"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "89862d72",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "89862d72",
    "outputId": "12e83eae-ede9-4d06-da10-d4b8da9e3063"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "446    0\n",
       "260    0\n",
       "570    0\n",
       "590    1\n",
       "30     0\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e09b3518",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "e09b3518",
    "outputId": "cabdf697-9ef9-423a-ebef-aa0c8089e0d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "74351b00",
   "metadata": {
    "id": "74351b00"
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a7f47590",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a7f47590",
    "outputId": "0f3c4849-38a4-4b3b-e25f-ac7b2e9d2034"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2ebb0ed8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2ebb0ed8",
    "outputId": "b177fabb-0209-4886-d26b-63c600e8ee5d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143    1\n",
       "14     1\n",
       "504    0\n",
       "557    0\n",
       "736    0\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ce1fa137",
   "metadata": {
    "id": "ce1fa137"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8f35c95c",
   "metadata": {
    "id": "8f35c95c"
   },
   "outputs": [],
   "source": [
    "acc = accuracy_score(predictions,ytest)\n",
    "cm = confusion_matrix(predictions,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ce22536c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ce22536c",
    "outputId": "5a997780-3fe6-4420-ec84-7a9a4014da2c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7727272727272727"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7f224373",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7f224373",
    "outputId": "d8979a48-6d4a-4b1a-a624-0fcd11029e4c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[91, 25],\n",
       "       [10, 28]], dtype=int64)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3209ef1f",
   "metadata": {
    "id": "3209ef1f"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ca421dad",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ca421dad",
    "outputId": "24ae7f0b-9eaf-4e1e-daf8-8bcfe5ff73f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.90      0.84       101\n",
      "           1       0.74      0.53      0.62        53\n",
      "\n",
      "    accuracy                           0.77       154\n",
      "   macro avg       0.76      0.71      0.73       154\n",
      "weighted avg       0.77      0.77      0.76       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e569da9",
   "metadata": {
    "id": "4e569da9"
   },
   "source": [
    "# Decision Tree classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6ec11914",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "6ec11914",
    "outputId": "36eedac6-e1e0-461c-9789-4af04fa2d8fb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
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       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9Mo89a5b5mfx",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "9Mo89a5b5mfx",
    "outputId": "f60a45ab-8536-466c-b9e7-d10986caefa6"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  Insulin   BMI  DiabetesPedigreeFunction  Age\n",
       "0            6      148        0  33.6                     0.627   50\n",
       "1            1       85        0  26.6                     0.351   31\n",
       "2            8      183        0  23.3                     0.672   32\n",
       "3            1       89       94  28.1                     0.167   21\n",
       "4            0      137      168  43.1                     2.288   33"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Selection\n",
    "x = data.drop(['SkinThickness','BloodPressure','Outcome'],axis=1)\n",
    "\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9d87a616",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9d87a616",
    "outputId": "ad912587-3849-470c-c112-5b5c402ee557"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=data['Outcome']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b8759841",
   "metadata": {
    "id": "b8759841"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f0901b18",
   "metadata": {
    "id": "f0901b18"
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2,random_state=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "273f3ee9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "273f3ee9",
    "outputId": "7a61e12a-068a-4e8a-e943-7491cbb21430"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "389f79a4",
   "metadata": {
    "id": "389f79a4"
   },
   "outputs": [],
   "source": [
    "pre = model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "49f2caec",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "49f2caec",
    "outputId": "5ba313fd-e188-4706-96c6-e1c45f59b98a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.6298701298701299\n",
      "Confusion Matrix: \n",
      " [[68 24]\n",
      " [33 29]]\n",
      "Classification Report: \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.67      0.70       101\n",
      "           1       0.47      0.55      0.50        53\n",
      "\n",
      "    accuracy                           0.63       154\n",
      "   macro avg       0.60      0.61      0.60       154\n",
      "weighted avg       0.65      0.63      0.64       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "acc=accuracy_score(pre,ytest)\n",
    "print(\"Accuracy: \",acc)\n",
    "cm = confusion_matrix(pre,ytest)\n",
    "print(\"Confusion Matrix: \\n\", cm)\n",
    "print(\"Classification Report: \\n\")\n",
    "print(classification_report(ytest,pre))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "532686fd",
   "metadata": {
    "id": "532686fd"
   },
   "source": [
    "# KNN Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "19ecff71",
   "metadata": {
    "id": "19ecff71"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,f1_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "821b8b12",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "821b8b12",
    "outputId": "c59a77c6-0f77-4266-9e9d-99a43d0b51b6"
   },
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "a0590b0f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "a0590b0f",
    "outputId": "6706a575-ff18-4b84-b9bb-79d39a8bc0d5"
   },
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  Insulin   BMI  DiabetesPedigreeFunction  Age\n",
       "0            6      148        0  33.6                     0.627   50\n",
       "1            1       85        0  26.6                     0.351   31\n",
       "2            8      183        0  23.3                     0.672   32\n",
       "3            1       89       94  28.1                     0.167   21\n",
       "4            0      137      168  43.1                     2.288   33"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Selection\n",
    "x = data.drop(['SkinThickness','BloodPressure','Outcome'],axis=1)\n",
    "\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "802b9d25",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "802b9d25",
    "outputId": "882f8ac3-e79f-4a3f-9ee4-a34d60dae4b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=data['Outcome']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "18f0ed6e",
   "metadata": {
    "id": "18f0ed6e"
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "QvDbFG67AJfZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "QvDbFG67AJfZ",
    "outputId": "a8b15c42-3550-410c-a05b-d09109e02cc3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Calculate accuracy for different values of n_neighbors\n",
    "neighbors = np.arange(1, 21)  # Test n_neighbors from 1 to 20\n",
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "\n",
    "for n in neighbors:\n",
    "    mdl = KNeighborsClassifier(n_neighbors=n)\n",
    "    mdl.fit(xtrain, ytrain)\n",
    "    train_accuracy.append(mdl.score(xtrain, ytrain))\n",
    "    test_accuracy.append(mdl.score(xtest, ytest))\n",
    "\n",
    "# Plotting the elbow curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(neighbors, train_accuracy, label=\"Train Accuracy\")\n",
    "plt.plot(neighbors, test_accuracy, label=\"Test Accuracy\")\n",
    "plt.xlabel(\"Number of Neighbors\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Elbow Method for Optimal n_neighbors\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "b31be336",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "b31be336",
    "outputId": "68c5dd6f-ef4a-4c74-ec71-83e24ae71c81"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(metric='euclidean', n_neighbors=3)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl = KNeighborsClassifier(n_neighbors=3,p=2,metric='euclidean')\n",
    "mdl.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "33c7e7e8",
   "metadata": {
    "id": "33c7e7e8"
   },
   "outputs": [],
   "source": [
    "prediction = mdl.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "7da3e6c4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7da3e6c4",
    "outputId": "901269d8-870b-49bc-9ce0-88545a9d6048"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.7077922077922078\n",
      "\n",
      "Confusion Matrix\n",
      " [[80 23]\n",
      " [22 29]]\n",
      "\n",
      "F1-Score =  0.5631067961165048\n",
      "\n",
      "Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.78      0.78       103\n",
      "           1       0.56      0.57      0.56        51\n",
      "\n",
      "    accuracy                           0.71       154\n",
      "   macro avg       0.67      0.67      0.67       154\n",
      "weighted avg       0.71      0.71      0.71       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy = \", accuracy_score(ytest,prediction))\n",
    "print(\"\\nConfusion Matrix\\n\",confusion_matrix(ytest,prediction))\n",
    "print(\"\\nF1-Score = \",f1_score(ytest,prediction))\n",
    "print(\"\\nClassification Report\\n\",classification_report(ytest,prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34f013d3",
   "metadata": {},
   "source": [
    "# Thank you Sir Usman Khan"
   ]
  },
  {
   "cell_type": "raw",
   "id": "86d4e926",
   "metadata": {
    "id": "EBZm9P5nAKnS"
   },
   "source": [
    "# End of File(Machine Learning)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}