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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd76b8ad-f82f-492e-8640-74c610546fae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading datasets...\n",
      "\n",
      "Extracting features...\n",
      "\n",
      "Index(['statuses_count', 'followers_count', 'friends_count',\n",
      "       'favourites_count', 'listed_count', 'sex_code', 'lang_code'],\n",
      "      dtype='object')\n",
      "       statuses_count  followers_count  friends_count  favourites_count  \\\n",
      "count     2817.000000      2817.000000    2817.000000       2817.000000   \n",
      "mean      1672.781328       371.231807     395.396876        234.624423   \n",
      "std       4885.438340      8024.052863     465.773534       1446.097189   \n",
      "min          0.000000         0.000000       0.000000          0.000000   \n",
      "25%         35.000000        17.000000     168.000000          0.000000   \n",
      "50%         77.000000        26.000000     306.000000          0.000000   \n",
      "75%       1088.000000       111.000000     519.000000         37.000000   \n",
      "max      79876.000000    408372.000000   12773.000000      44349.000000   \n",
      "\n",
      "       listed_count     sex_code    lang_code  \n",
      "count   2817.000000  2817.000000  2817.000000  \n",
      "mean       2.819666     1.112176     2.851970  \n",
      "std       23.484539     0.824551     1.992998  \n",
      "min        0.000000     0.000000     0.000000  \n",
      "25%        0.000000     0.000000     1.000000  \n",
      "50%        0.000000     1.000000     1.000000  \n",
      "75%        1.000000     2.000000     5.000000  \n",
      "max      744.000000     2.000000     7.000000  \n",
      "Splitting datasets into train and test dataset...\n",
      "\n",
      "Training datasets...\n",
      "\n",
      "The best classifier is:  RandomForestClassifier(n_estimators=40, oob_score=True)\n",
      "[0.99556541 0.99334812 0.99778271 1.         0.99333333]\n",
      "Estimated score: 0.99601 (+/- 0.00129)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Accuracy on Test dataset:  0.99822695035461\n",
      "Confusion matrix, without normalization\n",
      "[[266   1]\n",
      " [  0 297]]\n",
      "Normalized confusion matrix\n",
      "[[0.99625468 0.00374532]\n",
      " [0.         1.        ]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Fake       1.00      1.00      1.00       267\n",
      "     Genuine       1.00      1.00      1.00       297\n",
      "\n",
      "    accuracy                           1.00       564\n",
      "   macro avg       1.00      1.00      1.00       564\n",
      "weighted avg       1.00      1.00      1.00       564\n",
      "\n",
      "False Positive rate:  [0.         0.00374532 1.        ]\n",
      "True Positive rate:  [0. 1. 1.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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zszN8fX01rq9GjRpo0qQJwsLCoFAokJ6enm9pi65duyIiIgKVK1fW6lpjjRs3RkBAANatW4dBgwahRYsWKq+fOnUK69evR6dOnVQm0QPAL7/8gocPHyp7fBQKBbZv347KlSsre0S0UbcgCLCwsFD5hzghIaHAq/Re7w3ShtatW+OHH37AgQMHVP7xKOhKzYJMmDAB69evR3BwcL4lI4Cc4ds9e/agZ8+eGtWlSbu86RxmZmYqAT8tLQ2bNm1S2S8gIACmpqZYs2YNmjVrVuj5Cmv/rl27Ytu2bVAoFGjSpIna9WmiTJky6NWrF+7fv4+QkBDcuXMHNWvWVC45os7PRWBgILZu3Yrw8HAOMerJmTNnlL2vuTp27Ih169YhMzNTa9MmSPsYuggAULZsWUybNg1Tp07Fli1bMHDgQHz99dcIDAxEx44dMXToUJQvXx5PnjzB1atXcf78efz4448AgDlz5uDAgQNo1aoVpk+fjjp16uDZs2c4ePAgJk6ciOrVqyMkJAQ7d+5Eq1atMGHCBNStWxfZ2dmIjY3F4cOHMWnSpCL/oRk+fDhGjx6NBw8eoHnz5vkC3pw5cxAZGYnmzZtj/PjxqFatGl69eoU7d+4gIiICa9euLfbQz8aNG9G+fXsEBAQUuDhq9erVC1wWwcXFBW3btsWnn36qvHrx2rVrKmFEG3XnLh8QHByMXr16IS4uDnPnzoWHh0e+Ow3UqVMHx48fx88//wwPDw/Y29sXGpbVNWTIECxduhQDBw7EvHnzUKVKFRw4cACHDh0CgCJ7RHx9fZW9mPXr11cujgrkLM65fv16iKKocejSpF0K06VLFyxZsgT9+/fHqFGj8PjxYyxevDjf2mg+Pj6YPn065s6di7S0NPTr1w+Ojo74+++/kZSUpFzSoU6dOti1axfWrFmDRo0awcTEBI0bN0bfvn2xefNmdO7cGR9//DHefvttmJub4969ezh27BjeffddjT8/AHTr1k25Ll+5cuVw9+5dLFu2DN7e3sorduvUqQMAWL58OYYMGQJzc3NUq1YtX+8akDNPLywsDGPGjMH169fh7++P7Oxs/P7776hRowb69u2rcY2G5NWrV8jIyCjROURRzNcTaWlpqZX19ICcXxxeH7p3c3NDVlYWkpKS1B561hdttLGFhUW+aQ9GQa/T+ElyuVcvvn4VoSiKYlpamlixYkWxatWqYlZWliiKovjnn3+KvXv3Fl1dXUVzc3PR3d1dbNu2rbh27VqVY+Pi4sThw4eL7u7uorm5uejp6Sn27t1bfPjwoXKfFy9eiJ988olYrVo10cLCQnR0dBTr1KkjTpgwQeUKv9evvsqVnJwsWltbv/HKqUePHonjx48XfX19RXNzc9HJyUls1KiROGPGDPHFixeiKP57FeCiRYs0arsXL16I8+fPF+vXry/a2NiINjY2Yt26dcV58+Ypz50XAHHs2LHi6tWrxcqVK4vm5uZi9erVxc2bN+uk7s8//1z08fERLS0txRo1aojffvutOGvWLPH1v+YXL14UW7RoIdrY2IgAlFemFXb1oq2tbb73Kui8sbGx4nvvvSfa2dmJ9vb24vvvvy9GRESIAMSffvrpjW2b69atW2JwcLBYpUoV0dLSUrS2thZr1qwpTpw4UeXKutatW4u1atXKd/yQIUPyXRmobrvk/nkVZP369WK1atVES0tLsVKlSuKCBQvEdevWFXjF38aNG8W33npLtLKyEu3s7MQGDRqoXL355MkTsVevXmKZMmVEQRBU6sjMzBQXL14s1qtXT3l89erVxdGjR4s3btxQ7uft7S126dKlwFpf//vz5Zdfis2bNxddXFxECwsLsWLFimJQUJB4584dleOmTZsmenp6iiYmJio/B69fvSiKOd8VM2fOFKtWrSpaWFiIzs7OYtu2bcXTp08XWJOxSEtLE2GW8/eiJA87O7t822bNmqVWDQDE3bt3v3GfqlWrivPnz1fZdurUKRGAGB8fX8xPLw1ttbG7u7uYlpam74+jMd4GiEhHBEHA2LFj1Z4cWxrNnz8fn3zyCWJjY4vdy0gklZSUFDg6OsKy1jDA1KJ4J1FkIP1KGOLi4lTWwFO3p0sQBOzevfuN6wK2atUKDRo0UC5wCwC7d+9G7969kZqaatDDi9ps4+TkZL3csqwkOLxIRFqRGy6rV6+OzMxMHD16FCtWrMDAgQMZuMi4mFlAMC3eUKD4/1FFBwcHnQWCZs2a4eeff1bZdvjwYTRu3NigA5cKLbSxMWLoIiKtsLGxwdKlS3Hnzh2kp6ejYsWK+M9//oNPPvlE36URGbQXL17g5s2byucxMTG4ePEinJycULFiRUybNg3379/Hxo0bAQBjxozBqlWrMHHiRIwcORJnzpzBunXrsHXrVn19BFITQxeRjsht5H748OG8mo1KB8Ek51HcYzUUFRUFf39/5fOJEycCyLlAJTw8HPHx8YiNjVW+7uvri4iICEyYMAFfffUVPD09sWLFCuNaLkLiNjYUDF1ERER5CULOo7jHaqhNmzZv/CWtoCujW7dujfPnz2v8XgZD4jY2FAxdREREecm0F0ZSMm1j462ciIiIyIiwp0vPsrOz8eDBA9jb26t9Ww8iIrkTRRHPnz/XzX0gZTr0JSmZtjFDl549ePCgWPdpIyIiIC4uTgdLkpRg6IsDSGqSZxszdOlZ7m02bsbEwd7IFnkj41GxzWR9l0CkVaIiAxl/byjwVkVEhoqhS89yhxTtdbiQHpFQ3JWfiQycTqZlyHToS1IybWOGLiIiorxkemWdpGTaxgxdREREecm0F0ZSMm1j442LREREREaEPV1ERER5yXToS1IybWOGLiIiorxkOvQlKZm2sfHGRSIiIiIjwp4uIiKivGQ69CUpmbYxQxcREVFeglCCQGC8Q1+SkmkbM3QRERHlZSLkPIp7LBVNpm1svH10REREREaEPV1ERER5yXS+kaRk2sYMXURERHnJdDkDScm0jRm6iIiI8pJpL4ykZNrGxls5ERERkRFhTxcREVFeMh36kpRM25ihi4iIKC+ZDn1JSqZtbLyVExERERkR9nQRERHlJdOhL0nJtI0ZuoiIiPKS6dCXpGTaxgxdREREecm0F0ZSMm1j442LREREREaEPV1EREQqSjD0xb4MNcmzjRm6iIiI8pLp0JekZNrGDF1ERER5CUIJJnkbbyCQlEzb2Hj76IiIiIiMCHu6iIiI8pLpcgaSkmkbM3QRERHlJdP5RpKSaRsbb1wkIiIiMiLs6SIiIspLpkNfkpJpGzN0ERER5SXToS9JybSNGbqIiIjykmkvjKRk2sbGWzkRERGREWFPFxERUV4yHfqSlEzbmKGLiIgoD0EQIMgwEEhJrm3M4UUiIiIiCbCni4iIKA+59sJISa5tzNBFRESUl/D/R3GPpaLJtI0ZuoiIiPKQay+MlOTaxpzTRURERCQBhi4iIqI8cnthivugoknZxgsWLMBbb70Fe3t7uLq6okePHrh+/brKPkOHDs33Hk2bNlXZJz09HR999BFcXFxga2uL7t274969exrVwtBFRESUB0OX7knZxidOnMDYsWNx9uxZREZGIisrCwEBAXj58qXKfp06dUJ8fLzyERERofJ6SEgIdu/ejW3btuHUqVN48eIFunbtCoVCoXYtnNNFRESUh1znG0lJyjY+ePCgyvOwsDC4uroiOjoarVq1Um63tLSEu7t7gedITk7GunXrsGnTJrRv3x4A8P3338PLywtHjhxBx44d1aqFPV1EREQkG8nJyQAAJycnle3Hjx+Hq6sr/Pz8MHLkSCQmJipfi46ORmZmJgICApTbPD09Ubt2bZw+fVrt92boykMQBOzZs0ffZRARkT4JJXxQ0bTQxikpKSqP9PT0It9WFEVMnDgR77zzDmrXrq3cHhgYiM2bN+Po0aP48ssvce7cObRt21Z5zoSEBFhYWKBs2bIq53Nzc0NCQoLaH1vvw4sJCQlYsGAB9u/fj3v37sHR0RFVq1bFwIEDMXjwYNjY2EhWS3x8fL4GJSIieeHwou5po429vLxUNs+aNQuhoaFvPHTcuHH466+/cOrUKZXtffr0Uf537dq10bhxY3h7e2P//v147733Cj2fKIoafQ69hq7bt2+jRYsWKFOmDObPn486deogKysL//zzD9avXw9PT090795dsnoKG8slIiIiwxIXFwcHBwflc0tLyzfu/9FHH2Hv3r04efIkKlSo8MZ9PTw84O3tjRs3bgDIyQcZGRl4+vSpSudMYmIimjdvrnbNeh1eDA4OhpmZGaKiotC7d2/UqFEDderUwfvvv4/9+/ejW7duAHLGX0eNGgVXV1c4ODigbdu2+PPPP5XnCQ0NRf369bFp0yb4+PjA0dERffv2xfPnz5X7+Pj4YNmyZSrvX79+fZVUnHd48c6dOxAEAbt27YK/vz9sbGxQr149nDlzRuUcp0+fRqtWrWBtbQ0vLy+MHz8+3xURRERkPAShJFfX6bt646CNNnZwcFB5FBa6RFHEuHHjsGvXLhw9ehS+vr5F1vf48WPExcXBw8MDANCoUSOYm5sjMjJSuU98fDwuX76sUejSW0/X48ePcfjwYcyfPx+2trYF7iMIAkRRRJcuXeDk5ISIiAg4Ojri66+/Rrt27fDPP/8oJ8LdunULe/bswb59+/D06VP07t0bn3/+OT777LMS1TljxgwsXrwYVatWxYwZM9CvXz/cvHkTZmZmuHTpEjp27Ii5c+di3bp1ePToEcaNG4dx48YhLCysRO+rC7duAjdv6rsK0gdFSk19l0AGwsT6HgTzFH2XYdAElGTpB6YudUjZxmPHjsWWLVvw008/wd7eXjkHy9HREdbW1njx4gVCQ0Px/vvvw8PDA3fu3MH06dPh4uKCnj17KvcNCgrCpEmT4OzsDCcnJ0yePBl16tRRXs2oDr2Frps3b0IURVSrVk1lu4uLC169egUgp6E6duyIS5cuITExUZliFy9ejD179mDHjh0YNWoUACA7Oxvh4eGwt7cHAAwaNAi//PJLiUPX5MmT0aVLFwDA7NmzUatWLdy8eRPVq1fHokWL0L9/f4SEhAAAqlatihUrVqB169ZYs2YNrKys8p0vPT1dZbJfSoo0X34PHwL16wBZWZK8HRmcYH0XQAbC3HsdTMte0HcZBo1zunRPyjZes2YNAKBNmzYq28PCwjB06FCYmpri0qVL2LhxI549ewYPDw/4+/tj+/btykwBAEuXLoWZmRl69+6NtLQ0tGvXDuHh4TA1NVW7Fr1PpH+90f/44w9kZ2djwIABSE9PR3R0NF68eAFnZ2eV/dLS0nDr1i3lcx8fH5XG8fDwULncs7jq1q2rck4gZwy3evXqiI6Oxs2bN7F582blPqIoIjs7GzExMahRo0a+8y1YsACzZ88ucV2ain+QE7jMzIA8H4lk4sLVWH2XQIbCLE3fFRBJShTFN75ubW2NQ4cOFXkeKysrrFy5EitXrix2LXoLXVWqVIEgCLh27ZrK9kqVKgHIaQQgpwfLw8MDx48fz3eOMmXKKP/b3Nxc5TVBEJCdna18bmJikq/hMzMzi6wz73lzA2LuebOzszF69GiMHz8+33EVK1Ys8HzTpk3DxIkTlc9TUlLyXYGhC7kf3dUN+O13nb8dGZiyby3UdwmkB66ZqZgbfwaTy7fEc1MLfZdjPEqy9AM7utQj0zbWW+hydnZGhw4dsGrVKnz00UeFzutq2LAhEhISYGZmBh8fn2K/X7ly5RAfH698npKSgpiYmGKfL7e2K1euoEqVKmofY2lpWeQVFrqQG7rY800kD66ZqTh4cw9qpD+FnSITA3w76bsk41GCoS+RX7LqkWkb6/XqxdWrVyMrKwuNGzfG9u3bcfXqVVy/fh3ff/89rl27BlNTU7Rv3x7NmjVDjx49cOjQIdy5cwenT5/GJ598gqioKLXfq23btti0aRN+/fVXXL58GUOGDNFoHLYg//nPf3DmzBmMHTsWFy9exI0bN7B371589NFHJTqvLjB0EclH3sB1z9wOn3o2LfogItI5vc7pqly5Mi5cuID58+dj2rRpuHfvHiwtLVGzZk1MnjwZwcHBEAQBERERmDFjBoYPH45Hjx7B3d0drVq1gpubm9rvNW3aNNy+fRtdu3aFo6Mj5s6dW+Kerrp16+LEiROYMWMGWrZsCVEUUblyZZVF1gwFQxeRPLweuDpWeRe3LcvouyyjUpJJ3rzhtXrk2saCWNQMM9KplJQUODo64uHjZJVF3rQt6hzQsjlQ0Ru4zmUjZKfsW+P0XQJJQE6BS1RkIP3St0hO1t53Z+73sfOAMJhYFO9uKNkZqXi8eZjGda1evRqLFi1CfHw8atWqhWXLlqFly5aF7r9582YsXLgQN27cgKOjIzp16oTFixfnu+jM0OizjQ0B770oE4zWRKXfxruHZRG4dE7iey9u374dISEhmDFjBi5cuICWLVsiMDAQsbEFX3V86tQpDB48GEFBQbhy5Qp+/PFHnDt3DiNGjND8zfVFpve3ZOiSCQ4vEpV+E8q3xHnrcgxcRmbJkiUICgrCiBEjUKNGDSxbtgxeXl7K9aVed/bsWfj4+GD8+PHw9fXFO++8g9GjR2s0z5n0g6FLJhi6iEonIU839lVrZ7Tw+4CBq4SKf3uaf+cppaSkqDzyLoqdV0ZGBqKjoxEQEKCyPSAgAKdPny7wmObNm+PevXuIiIiAKIp4+PAhduzYoVzI2xhoo42NEUOXTDB0EZU+rpmp+PWfHWj1/N6/G/mXvMS0EQi8vLzg6OiofCxYsKDA90pKSoJCoch3YZibm5vydjWva968OTZv3ow+ffrAwsIC7u7uKFOmTIkW7ZQaQxeVagxdRKVL7qT5RmmJWHnvBEzF7KIPIrVoIxDExcUhOTlZ+Zg2bVqR75mXKIqFhou///4b48ePx8yZMxEdHY2DBw8iJiYGY8aM0U4DSECuoUvvtwEiaTB0EZUer1+l2LNSFygE/g5tSBwcHNS6ss7FxQWmpqb5erUSExMLXRZpwYIFaNGiBaZMmQIgZ/kiW1tbtGzZEvPmzVPeso4MD/+WygRDF1HpIKdlIfRFyl4YCwsLNGrUCJGRkSrbIyMj0bx58wKPSU1NhYmJ6j/fuYt9G8sqUOzpolIt96+hEf+sEskeA5dEJL4v4MSJEzFo0CA0btwYzZo1wzfffIPY2FjlcOG0adNw//59bNy4EQDQrVs3jBw5EmvWrEHHjh0RHx+PkJAQvP322/D09Cxm4RLjvRepVGNPF5HRm5B4gYGrFOrTpw8eP36MOXPmID4+HrVr10ZERAS8vb0BAPHx8Sprdg0dOhTPnz/HqlWrMGnSJJQpUwZt27bFF198oa+PQGpi6JIJDi8SGb9PPZvCSszCynL1GLh0qCRDWMU9Ljg4GMHBwQW+Fh4enm/bRx99ZJD3+VWXPtrYEDB0yQRDF5FxcsxKR4qpBURBQJZgigkVWuu7pFJProFASnJtY06klwmGLiLj45qZimM3dmLZvRMqi6CSbsl1kreU5NrGDF0ywdBFZFzyTprvnHIXrlmp+i6JiEqIw4syoQxd+i2DiNRQ0FWKD81t9V2WfMj0yjpJybSNGbpkgj1dRMaBy0Lon1znG0lJrm3M4UWZYOgiMnwMXESlG3u6ZIKhi8jwNUhLRNX0ZwxceibXXhgpybWNGbpkgqGLyPAdcvBBP99O+NvKiYFLjwSUIBAY84QjCcm1jRm6ZIKhi8gwuWamwkJU4J6FPQBgn2MlPVdEcu2FkZJc25hzumSCoYvI8OTO4Tp0cw+8Mp7ruxwi0jGGLplg6CIyLHknzVuI2TATs/VdEuUSSvigosm0jTm8KBPKxayN+IeVqLQo6CrFGEtHfZdF/yfXoS8pybWNGbpkgj1dRIaBy0IYPrkGAinJtY05vCgTDF1E+sfARSRv7OmSGYYuIv1j4DJsglD870p+x6pHrm3M0CUTyjldRKQ3ieY26FSlB2yzMzmHy4DlBILiDn1puZhSSq5tzOFFmeDwIpF+uGamosezW8rnieY2DFxEMsXQJRMMXUTSy53DtfnOQfR++o++yyF1Cf8Of2n64BXiapJpG3N4USYYuoik9fqk+SgbV32XRGqS65V1UpJrGzN0yQRDF5F0eJWicZPrJG8pybWNObwoEwxdRNJg4CKiwrCnSyYYuoh0z16RwcBVCpiYCDAxKd6XpVjM4+RGrm3M0CUTDF1EuvfcxBwHHH1g/zSTgcuIyXXoS0pybWOGLplg6CKSgCBghkczLHVtgCQza31XQ0QGhnO6ZIKhi0g3XDNTsfTeCVhmZ+VsEAQGLiOXe2VdcR9UNLm2MXu6ZIKhi0j78k6at8nOwuiK7fRdEmmBXIe+pCTXNmbokgmGLiLtev0qxS/cGum7JNISua4hJSW5tjGHF2WCoYtIe7gsBBEVB3u6ZIKhi0g7GLhKP7n2wkhJrm3M0CUXDF1EJSeK+CEmgoGrlJPrfCMpybWNObwoE+zpItICQcDU8u/gipUTA1cpJqAEV9YZ892YJSTXNmZPl0wwdBGVgCgq//L8YeuOt6v1QbbA31mJSDP81pAJhi6i4nHNTMUvN3ejYWqichsDV+mWO/RV3AcVTa5tzG8OmWDoItJc7qT55i/jsTb2KITcv0hUqsl14U4pybWNGbpkgqGLSDOvX6XY17cTRP4FIjI6CxYswFtvvQV7e3u4urqiR48euH79uso+oigiNDQUnp6esLa2Rps2bXDlyhWVfdLT0/HRRx/BxcUFtra26N69O+7du6dRLQxdMsHQRaQ+Lgshb3Id+pKSlG184sQJjB07FmfPnkVkZCSysrIQEBCAly9fKvdZuHAhlixZglWrVuHcuXNwd3dHhw4d8Pz5c+U+ISEh2L17N7Zt24ZTp07hxYsX6Nq1KxQKhdq1cCK9TDB0EamHgYvkuoaUlKRs44MHD6o8DwsLg6urK6Kjo9GqVSuIoohly5ZhxowZeO+99wAAGzZsgJubG7Zs2YLRo0cjOTkZ69atw6ZNm9C+fXsAwPfffw8vLy8cOXIEHTt2VKsW9nTJBEMXkXpmJPzBwCVz7OnSPW20cUpKisojPT1drfdOTk4GADg5OQEAYmJikJCQgICAAOU+lpaWaN26NU6fPg0AiI6ORmZmpso+np6eqF27tnIfdTB0yQRDF5F6/lu+BTaXrcbARWTgvLy84OjoqHwsWLCgyGNEUcTEiRPxzjvvoHbt2gCAhIQEAICbm5vKvm5ubsrXEhISYGFhgbJlyxa6jzqKNbyYlZWF48eP49atW+jfvz/s7e3x4MEDODg4wM7OrjinJKkwdBHlY6fIwAsTc0AQkGZijhHe7fVdEukRhxd1TxttHBcXBwcHB+V2S0vLIo8dN24c/vrrL5w6darQ8+YSRbHIGtXZJy+Ne7ru3r2LOnXq4N1338XYsWPx6NEjADmT0CZPnqzp6UgivNKdqGCumak4+c8OzEr4nX9RKEdJhr2YudSjhTZ2cHBQeRQVuj766CPs3bsXx44dQ4UKFZTb3d3dASBfj1ViYqKy98vd3R0ZGRl4+vRpofuoQ+PQ9fHHH6Nx48Z4+vQprK2tldt79uyJX375RdPTkUQ4vEiUX95J8wOfXIeTQr05IVS6yXUNKSlJ2caiKGLcuHHYtWsXjh49Cl9fX5XXfX194e7ujsjISOW2jIwMnDhxAs2bNwcANGrUCObm5ir7xMfH4/Lly8p91KHx8OKpU6fw22+/wcLCQmW7t7c37t+/r+npSCIMXUSqCrpK8YmZlb7LIiItGzt2LLZs2YKffvoJ9vb2yh4tR0dHWFtbQxAEhISEYP78+ahatSqqVq2K+fPnw8bGBv3791fuGxQUhEmTJsHZ2RlOTk6YPHky6tSpo7yaUR0ah67s7OwC16S4d+8e7O3tNT0dSYShi+hfXBaC3qQkVyHyO1Y9UrbxmjVrAABt2rRR2R4WFoahQ4cCAKZOnYq0tDQEBwfj6dOnaNKkCQ4fPqySa5YuXQozMzP07t0baWlpaNeuHcLDw2Fqaqp2LRqHrg4dOmDZsmX45ptvAOR0Eb548QKzZs1C586dNT0dSYShiygHAxcVhRPpdU/KNhbVmKspCAJCQ0MRGhpa6D5WVlZYuXIlVq5cqdH756Vx6Fq6dCn8/f1Rs2ZNvHr1Cv3798eNGzfg4uKCrVu3FrsQ0i2GLqIcrV7cZ+AiIr3QOHR5enri4sWL2LZtG6Kjo5GdnY2goCAMGDBAZWI9GRaGLqIcO8pWhZmYjT9s3Ri4qEAcXtQ9ubaxxqHr5MmTaN68OYYNG4Zhw4Ypt2dlZeHkyZNo1aqVVgsk7WDoIjlzzUxFtiAgySznF8NtTtX0XBEZMg4v6p5c21jjJSP8/f3x5MmTfNuTk5Ph7++vlaJI+xi6SK5y53AduLkHLllp+i6HjACXjNA9ubaxxqGrsNVXHz9+DFtbW60URdrH0EVylHfSfBlFBuwVGfouiYhkTO3hxdw7bwuCgKFDh6qs/KpQKPDXX39ptEAYSYuhi+SmoKsUYywd9V0WGQG5zjeSklzbWO3Q5eiY82UliiLs7e1VJs1bWFigadOmGDlypPYrJK1Qhi79lkEkCS4LQSUh1/lGUpJrG6sdusLCwgAAPj4+mDx5MocSjQx7ukguGLiIyFBpfPXirFmzdFEH6Vju0nAMXVTaWWVnwSY7i4GLik2uQ19Skmsbaxy6AGDHjh344YcfEBsbi4wM1Ymp58+f10phpF3s6SK5iLV0QECVHjBDNgMXFYtch76kJNc21vjqxRUrVmDYsGFwdXXFhQsX8Pbbb8PZ2Rm3b99GYGCgLmokLWDootLMNTMVHVLuKp/HWjowcFGxCfi3J0bjh76LNxJybWONQ9fq1avxzTffYNWqVbCwsMDUqVMRGRmJ8ePHIzk5WRc1khYwdFFplTuHa+ftCAQm39F3OUREhdI4dMXGxiqXhrC2tsbz588BAIMGDeK9Fw0YQxeVRnknzT80t8F1qzL6LolKARNBKNGDiibXNtY4dLm7u+Px48cAAG9vb5w9exYAEBMTo9advEk/GLqotOFViqQrxR72KsHkcLmRaxtrHLratm2Ln3/+GQAQFBSECRMmoEOHDujTpw969uyp9QJJSxi6qBRh4CJdkustaqQk1zbWOHR98803mDFjBgBgzJgxCA8PR40aNTB79mysWbNG6wWSdrCni0qLMlmvGLio1Fm9ejV8fX1hZWWFRo0a4ddff33j/unp6ZgxYwa8vb1haWmJypUrY/369RJVS8Wl8ZIRJiYmMDH5N6v17t0bvXv3BgDcv38f5cuX1151pDUMXVRaJJta4ndbd9hnZzJwkU6YCDmP4h6rqe3btyMkJASrV69GixYt8PXXXyMwMBB///03KlasWOAxvXv3xsOHD7Fu3TpUqVIFiYmJyMrKKl7ReiB1GxuKYq3T9bqEhAR89tln+O6775CWlqaNU5KWMXRRaSEKAoK9/OGWlYoEc94Zg3RAKMFaUMU4bMmSJQgKCsKIESMAAMuWLcOhQ4ewZs0aLFiwIN/+Bw8exIkTJ3D79m04OTkByLlbjFGRuI0NhdrDi8+ePcOAAQNQrlw5eHp6YsWKFcjOzsbMmTNRqVIlnD17ll2bBoyhi4yZa2Yq5j44A1MxG0BO8GLgIkOWkpKi8khPTy9wv4yMDERHRyMgIEBle0BAAE6fPl3gMXv37kXjxo2xcOFClC9fHn5+fpg8eTI7PYyA2j1d06dPx8mTJzFkyBAcPHgQEyZMwMGDB/Hq1SscOHAArVu31mWdpCUMXWRs8k6at8nOxKQKrfRdEpVyJblCLvc4Ly8vle2zZs1CaGhovv2TkpKgUCjg5uamst3NzQ0JCQkFvsft27dx6tQpWFlZYffu3UhKSkJwcDCePHliNJ0f2mhjY6R26Nq/fz/CwsLQvn17BAcHo0qVKvDz88OyZct0WB5pC1fzIGP0+lWKX5Wrq++SSAaE//+vuMcCQFxcHBwcHJTbLS0t33zca0lCFMVCh9+ys7MhCAI2b94MR0dHADlDlL169cJXX30Fa2vrYtUuJW20sTFSO3Q9ePAANWvWBABUqlQJVlZWyvFnMnwcXiRjw2UhSF+0McnbwcFBJXQVxsXFBaampvl6tRITE/P1fuXy8PBA+fLllYELAGrUqAFRFHHv3j1UrVq1eMVLSK4T6dWe05WdnQ1zc3Plc1NTU9jack6FsWDoImPCwEVyYWFhgUaNGiEyMlJle2RkpPLuL69r0aIFHjx4gBcvXii3/fPPPzAxMUGFChV0Wi+VjNo9XaIoYujQocou0levXmHMmDH5gteuXbu0WyFpBUMXGQtBFLHr9j4GLtKbkizAWZzjJk6ciEGDBqFx48Zo1qwZvvnmG8TGxmLMmDEAgGnTpuH+/fvYuHEjAKB///6YO3cuhg0bhtmzZyMpKQlTpkzB8OHDjWJoEZC+jQ2F2qFryJAhKs8HDhyo9WJId5Rzuoz3Z5VkQhQEzPRshmX3TqBHpa4MXCQ5qSd59+nTB48fP8acOXMQHx+P2rVrIyIiAt7e3gCA+Ph4xMbGKve3s7NDZGQkPvroIzRu3BjOzs7o3bs35s2bV7yi9YAT6YsQFhamyzpIx9jTRcbkqL0XGlbvhyzBVN+lEEkiODgYwcHBBb4WHh6eb1v16tXzDUmS4dP4NkBknBi6yJC5ZqZi/82f4PfqqXIbAxfpi4kglOhBRZNrGzN0yQRDFxmq3EnzbV/cw7rYI1zfhPQud+iruA8qmlzbWCu3ASLDx9BFhuj1qxSHeHfgDynpnVwneUtJrm3Mni6ZYOgiQ8NlIYhIbtjTJRMMXWRIGLjIkMn1yjopybWNi9XTtWnTJrRo0QKenp64e/cugJy7ov/0009aLY60h6GLDMn8B6cZuMhgyXWSt5Tk2sYah641a9Zg4sSJ6Ny5M549ewaFQgEAKFOmDO/DaMAYusiQTCrfEnscKzFwkUESSvigosm1jTUOXStXrsS3336LGTNmwNT030u6GzdujEuXLmm1ONIehi7SN6vsLOV/J5tZop9vIAMXEcmKxqErJiYGDRo0yLfd0tISL1++1EpRpH0MXaRPrpmp+O2fH/BR4kV9l0JUpNwr64r7oKLJtY01Dl2+vr64ePFivu0HDhxAzZo1tVET6QBDF+lL7qT5mq+eYvyjP2GvyNB3SURvZCKU7EFFk2sba3z14pQpUzB27Fi8evUKoijijz/+wNatW7FgwQJ89913uqiRtIChi/ShoKsUn5ta6LssIiK90Dh0DRs2DFlZWZg6dSpSU1PRv39/lC9fHsuXL0ffvn11USNpAUMXSY3LQpCxkuvCnVKSaxsXa52ukSNHYuTIkUhKSkJ2djZcXV21XRdpGUMXSYmBi4wdvyt1T45trPGcrtmzZ+PWrVsAABcXFwYuI8HQRVLqnHKHgYuMllwneUtJrm2scejauXMn/Pz80LRpU6xatQqPHj3SRV2kZQxdJKVw55r4uEIrBi4iojw0Dl1//fUX/vrrL7Rt2xZLlixB+fLl0blzZ2zZsgWpqam6qJG0gKGLdM01M1XlysRvXOowcJFRkuuVdVKSaxsX6zZAtWrVwvz583H79m0cO3YMvr6+CAkJgbu7u7brIy1h6CJdyp3D9fOtvVwSgoyeXIe+pCTXNi5W6MrL1tYW1tbWsLCwQGZmpjZqIh0y4p9VMlB5J82Xz3wJp6xX+i6JiMggFSt0xcTE4LPPPkPNmjXRuHFjnD9/HqGhoUhISNB2faQtor4LoNKooKsU71o66LssohKR630BpSTXNtZ4yYhmzZrhjz/+QJ06dTBs2DDlOl1k2Di8SNrGZSGotDIRBJgU88uyuMfJjVzbWOPQ5e/vj++++w61atXSRT2kIwxdpE0MXFSaCULxvyv5HaseubaxxqFr/vz5uqiDdIyhi7TJSfEKzopXDFxERBpQK3RNnDgRc+fOha2tLSZOnPjGfZcsWaKVwki7GLpIm65ZOaFT5XeRbmLKwEWljlxvUSMlqdv45MmTWLRoEaKjoxEfH4/du3ejR48eyteHDh2KDRs2qBzTpEkTnD17Vvk8PT0dkydPxtatW5GWloZ27dph9erVqFChgtp1qBW6Lly4oLwy8cKFC2qfnAwHQxeVlGtmKqqkP8NpO08AwFVrZz1XRKQbch36kpLUbfzy5UvUq1cPw4YNw/vvv1/gPp06dUJYWJjyuYWFhcrrISEh+Pnnn7Ft2zY4Oztj0qRJ6Nq1K6Kjo2FqaqpWHWqFrmPHjhX432Q8GLqoJHLncHlnPMe7lbvilB0vnqHSS66TvKUkdRsHBgYiMDDwjftYWloWut5ocnIy1q1bh02bNqF9+/YAgO+//x5eXl44cuQIOnbsqFYdGi8ZMXz4cDx//jzf9pcvX2L48OGano4kwtBFxZV30vwTMys8MLfVd0lERFp3/PhxuLq6ws/PDyNHjkRiYqLytejoaGRmZiIgIEC5zdPTE7Vr18bp06fVfg+NQ9eGDRuQlpaWb3taWho2btyo6elIIgxdVBy8SpHkKHfoq7gPKpo22jglJUXlkZ6eXux6AgMDsXnzZhw9ehRffvklzp07h7Zt2yrPmZCQAAsLC5QtW1blODc3N43WKFX76sWUlBSIoghRFPH8+XNYWVkpX1MoFIiIiICrq6vab0zSyg1dRr2qHEmKgYvkihPpdU8bbezl5aWyfdasWQgNDS3WOfv06aP879q1a6Nx48bw9vbG/v378d577xV6nCiKGn0OtUNXmTJllI3k5+eX73VBEDB79my135ikxZ4u0oRzVhoDFxEZtLi4ODg4/HsHDEtLS62d28PDA97e3rhx4wYAwN3dHRkZGXj69KlKb1diYiKaN2+u9nnVDl3Hjh2DKIpo27Ytdu7cCScnJ+VrFhYW8Pb2hqenp9pvTNJi6CJNJJta4LpVWdhnZzJwkeyYoPg3Ji7xDY1lQhtt7ODgoBK6tOnx48eIi4uDh4cHAKBRo0YwNzdHZGQkevfuDQCIj4/H5cuXsXDhQrXPq3boat26NYCc+y5WrFiRXahGhqGLNJElmGKQTwDcM1Nxz8Je3+UQSYrDi7ondRu/ePECN2/eVD6PiYnBxYsX4eTkBCcnJ4SGhuL999+Hh4cH7ty5g+nTp8PFxQU9e/YEADg6OiIoKAiTJk2Cs7MznJycMHnyZNSpU0d5NaM61Apdf/31F2rXrg0TExMkJyfj0qVLhe5bt25dtd+cpMPQRUVxzUzFsMd/Y6FbI4iCgCzBlIGLZEkQABOu06VTUrdxVFQU/P39lc9zF3ofMmQI1qxZg0uXLmHjxo149uwZPDw84O/vj+3bt8Pe/t/vwKVLl8LMzAy9e/dWLo4aHh6u9hpdgJqhq379+khISICrqyvq168PQRAgKmdm/0sQBCgUCrXfnKTD0EVvknfSvJWYhdkeTfVdEhGR1rRp06bA3JLr0KFDRZ7DysoKK1euxMqVK4tdh1qhKyYmBuXKlVP+NxkfZejSbxlkgF6/SnGTU3V9l0SkVyYl6IUp7nFyI9c2Vit0eXt7F/jfZDzY00UF4bIQRPlxTpfuybWNi7U46v79+5XPp06dijJlyqB58+a4e/euVosj7VEu02W8P6ukZQxcRETS0jh0zZ8/H9bW1gCAM2fOYNWqVVi4cCFcXFwwYcIErRdI2sGeLsrLVMzGz7f2MnARFSB36Ku4DyqaXNtY7SUjcsXFxaFKlSoAgD179qBXr14YNWoUWrRogTZt2mi7PtIShi7KSyGYYIH7W1jw4Dd0qdydgYsoj5LczoffseqRaxtr3NNlZ2eHx48fAwAOHz6sXJ/CysqqwHsykmFg6KLX7SlTGfWqD2DgInqNiSCU6EFFk2sbaxy6OnTogBEjRmDEiBH4559/0KVLFwDAlStX4OPjo+36SEsYusg1MxU7bu+HV8Zz5bYME/XXlyEiopLROHR99dVXaNasGR49eoSdO3fC2dkZABAdHY1+/fppvUDSDoYuecudNN8l5Q7W3T2S5w7oRPQ6kxI+qGhybWON53SVKVMGq1atyredN7s2bAxdMvbwocpVimMq+vMHgegN5DrfSEpybWONQxcAPHv2DOvWrcPVq1chCAJq1KiBoKAgODo6ars+2ajYZjIEUwudnT8jJghAA0xeuA3/XX9KZ+9DhiXvshBihQooF3kM0f+/EIbImKWkpMDN+VudnNsExZ83ZMIlqNUi1zbWuJcuKioKlStXxtKlS/HkyRMkJSVh6dKlqFy5Ms6fP6+LGkkrjPeHlIrn9cCVEXkMIgMXEZHeaNzTNWHCBHTv3h3ffvstzMxyDs/KysKIESMQEhKCkydPar1IItLcsnsnlUOK5Ri4iNQm16EvKcm1jTUOXVFRUSqBCwDMzMwwdepUNG7cWKvFkTbl/pRyArVchFRoBbvsDIRUaMUhRSINyPW+gFKSaxtrPLzo4OCA2NjYfNvj4uJgb2+vlaJIB5i1ZME8W6H870RzG3TnwqdERAZD49DVp08fBAUFYfv27YiLi8O9e/ewbds2jBgxgktGGLT//2ogMH2VVq6ZqTj9zw8Y+PiqvkshMmqCUPzFO4156EtKcm1jjYcXFy9eDEEQMHjwYGRlZQEAzM3N8eGHH+Lzzz/XeoGkLRxeLM3yTpr/NOEP7CxbBWkm5voui8goyXW+kZTk2sYahy4LCwssX74cCxYswK1btyCKIqpUqQIbGxtd1EdERcgbuO6Z2yGwyrsMXEQlINf5RlKSaxurPbyYmpqKsWPHonz58nB1dcWIESPg4eGBunXrMnAZBfZ0lUavB66OVd7lHC4iIgOlduiaNWsWwsPD0aVLF/Tt2xeRkZH48MMPdVkbEb0BAxeRbggl/B8VTa5trPbw4q5du7Bu3Tr07dsXADBw4EC0aNECCoUCpqa8aa7BE9nTVdr0ffoPAxeRDsh16EtKcm1jtUNXXFwcWrZsqXz+9ttvw8zMDA8ePICXl5dOiiPtM+YJiKRqRbl6sBAV2FWmMgMXkRbJNRBISa5trHboUigUsLBQvTegmZmZ8gpGMnTs6SoNXLLS8NzEHOkmZoAgYLFbI32XREREalI7dImiiKFDh8LS0lK57dWrVxgzZgxsbW2V23bt2qXdCklLGLqMXe4crjgLe/T2DcwJXkSkdYIgQCjmsEBxj5Mbubax2t/aQ4YMybdt4MCBWi2GiAqWd9K8fXYm3LJSEWvhoO+yiEoluQ59SUmubax26AoLC9NlHaRrnEhvtAq6SpGBi4jI+HB8Qm6M+DcEOeKyEETSk+tq6VKSaxszdMkGe7qMDQMXkX7k3uOvuMdS0eTaxhrf8JqIpFEh8wU8M18ycBFJLHe+UXEfxbF69Wr4+vrCysoKjRo1wq+//qrWcb/99hvMzMxQv3794r2xnuijjQ0BQ5dssKfL2Jy3cUXXyt0ZuIhKue3btyMkJAQzZszAhQsX0LJlSwQGBiI2NvaNxyUnJ2Pw4MFo166dRJVSSTF0yYTIifRGwTUzFfVSHymfR9m6MXARSU34d86Rpo/izJtdsmQJgoKCMGLECNSoUQPLli2Dl5cX1qxZ88bjRo8ejf79+6NZs2bF+5z6JHEbG4piha5NmzahRYsW8PT0xN27dwEAy5Ytw08//aTV4kgHjPiHtbTLncN14NZPaJCaqO9yiGTLBEKJHgCQkpKi8khPTy/wvTIyMhAdHY2AgACV7QEBATh9+nShNYaFheHWrVuYNWuW9j64hLTRxsZI49C1Zs0aTJw4EZ07d8azZ8+gUCgAAGXKlMGyZcu0XR9pDXu6DFneSfMvTcyRbGpR9EFEZLC8vLzg6OiofCxYsKDA/ZKSkqBQKODm5qay3c3NDQkJCQUec+PGDfz3v//F5s2bYWbG6+GMicZ/WitXrsS3336LHj164PPPP1dub9y4MSZPnqzV4ojkgFcpEhkWbSxnEBcXBweHf9fTy3s3l4KPU31DURQLXHldoVCgf//+mD17Nvz8/IpXpAHgkhFqiomJQYMGDfJtt7S0xMuXL7VSFOkCe7oMEQMXkeHRxmrpDg4OKqGrMC4uLjA1Nc3Xq5WYmJiv9wsAnj9/jqioKFy4cAHjxo0DAGRnZ0MURZiZmeHw4cNo27Zt8YqXEFekV5Ovry8uXrwIb29vle0HDhxAzZo1tVYY6QpDl6Eox8BFZJCkXEPKwsICjRo1QmRkJHr27KncHhkZiXfffTff/g4ODrh06ZLKttWrV+Po0aPYsWMHfH19i1W31OS6TpfGoWvKlCkYO3YsXr16BVEU8ccff2Dr1q1YsGABvvvuO13USFQqvTA1xwNzW9hnZzJwEcnYxIkTMWjQIDRu3BjNmjXDN998g9jYWIwZMwYAMG3aNNy/fx8bN26EiYkJateurXK8q6srrKys8m0nw6Nx6Bo2bBiysrIwdepUpKamon///ihfvjyWL1+Ovn376qJG0gbReH8zKK3STMzxQaXOKJeVxnspEhkQqecb9enTB48fP8acOXMQHx+P2rVrIyIiQjmiFB8fX+SaXcaGc7o0MHLkSIwcORJJSUnIzs6Gq6urtusirfv/T6nA4UV9cs1MRa9nN7DapS4gCEgzMUeshbm+yyKiPExQgqGvYi5nEBwcjODg4AJfCw8Pf+OxoaGhCA0NLdb76os+2tgQlOhaUxcXF23VQVTq5Z00bykqsNS1ob5LIqICyLUXRkpybeNiTaQv6DLWXLdv3y5RQaQrvHpRn16/SvEnx0r6LomIiCSmcegKCQlReZ6ZmYkLFy7g4MGDmDJlirbqIio1uCwEkXExQfHvkcd766lHrm2scej6+OOPC9z+1VdfISoqqsQFkY7w3ot6wcBFZHwEQXjjiE5Rx1LR5NrGWguMgYGB2Llzp7ZOR1rHifRSM89WIOLWTwxcREQEQIuha8eOHXByctLW6YiMXqaJKVaVq4c4Bi4ioyKU8EFFk2sbazy82KBBA5WuPVEUkZCQgEePHmH16tVaLY60icOL+hDuXBM/lKmKVFMuC0FkLOS6WrqU5NrGGoeuHj16qDw3MTFBuXLl0KZNG1SvXl1bdREZJdfMVCy5fxIhFVojycwaABi4iIyQ8f6zbjzk2MYaha6srCz4+PigY8eOcHd311VNpAucSK9zeSfN2yoy0bNyN32XREREBkSjOV1mZmb48MMPkZ6erqt6SNfk+KuFBF6/SnFShZb6LomIiil34c7iPqhocm1jjSfSN2nSBBcuXNBFLaRT7OnSFS4LQVS65C5nUNwHFU2ubaxx6AoODsakSZOwatUqnDlzBn/99ZfKgwwVQ5cuMHARERm+kydPolu3bvD09IQgCNizZ4/K66IoIjQ0FJ6enrC2tkabNm1w5coVlX3S09Px0UcfwcXFBba2tujevTvu3bunUR1qh67hw4cjJSUFffr0QUxMDMaPH48WLVqgfv36aNCggfL/ieTk69hfGLiIShmTEj6oaFK38cuXL1GvXj2sWrWqwNcXLlyIJUuWYNWqVTh37hzc3d3RoUMHPH/+XLlPSEgIdu/ejW3btuHUqVN48eIFunbtCoVCoXYdak+k37BhAz7//HPExMSofXIyJOzp0oWPK7TG+tgjGFWxLQMXUSkh19XSpSR1GwcGBiIwMLDA10RRxLJlyzBjxgy89957AHIyj5ubG7Zs2YLRo0cjOTkZ69atw6ZNm9C+fXsAwPfffw8vLy8cOXIEHTt2VKsOtUOXKOb8Y+3t7a3uIWRImLW0xkTMRraQ87tWrKUD2lfpadwzO4lIRUkW4OQ3gXq00cYpKSkq2y0tLWFpaanx+WJiYpCQkICAgACVc7Vu3RqnT5/G6NGjER0djczMTJV9PD09Ubt2bZw+fVrt0KVRLx0TvDHL+bMTeBugEnHNTMXp6z+g27Pb/27k3wsiIsl5eXnB0dFR+ViwYEGxzpOQkAAAcHNzU9nu5uamfC0hIQEWFhYoW7ZsofuoQ6N1uvz8/IoMXk+ePNHklCQ5hq7iyjtp/osHv+GQgzcyTEz1XRYRaRmHF3VPG20cFxcHBwcH5fbi9HIVdN5coigWWaM6++SlUeiaPXs2HB0dNTmEDAa/CEri9asUu1buxsBFVEqVZEI8J9KrRxtt7ODgoBK6iit3sfeEhAR4eHgotycmJip7v9zd3ZGRkYGnT5+q9HYlJiaiefPmar+XRqGrb9++cHV11eQQMhicSF9cXBaCSF7Y06V7htTGvr6+cHd3R2RkpHIVhoyMDJw4cQJffPEFAKBRo0YwNzdHZGQkevfuDQCIj4/H5cuXsXDhQrXfS+3QxR8kI8esVSwMXERExu/Fixe4efOm8nlMTAwuXrwIJycnVKxYESEhIZg/fz6qVq2KqlWrYv78+bCxsUH//v0BAI6OjggKCsKkSZPg7OwMJycnTJ48GXXq1FFezagOja9eJGP1/9DMifQaGfH4MgMXkczw6kXdk7qNo6Ki4O/vr3w+ceJEAMCQIUMQHh6OqVOnIi0tDcHBwXj69CmaNGmCw4cPw97eXnnM0qVLYWZmht69eyMtLQ3t2rVDeHg4TE3Vn2qidujKzs5W+6REpcUCt7dgla1AuHMNBi4imSjJ/f04KKQeqdu4TZs2b+w8EgQBoaGhCA0NLXQfKysrrFy5EitXrtS8gP/TaE4XkRw4Zb1CsqkFFIIJREHATM9m+i6JiIhKAYYu2eBEenXkzuG6Yu2Mod4doBB4LRKR3JhAgEkxB7+Ke5zcyLWNGbpkh6GrMHknzdtnZ8I9MxX3Lez0XRYRSYzDi7on1zZm6JIJUTTin1IJFHSVIgMXkTwJ//9fcY+losm1jTl2Ihu8erEwXBaCiIikwJ4ukjUGLiJ6nVyHvqQk1zZm6JINTqQvSNX0Z/DJSGHgIiIloQSTvI156EtKcm1jhi7ZYejK6zc7T/So1BX3LOwYuIiISKcYumTDeH8z0DbXzFQ4KtJxwyrnpqUn7SvouSIiMiRyHfqSklzbmKFLLkQOLwL/zuEqo0hHpyo98I9V2aIPIiJZkWsgkJJc25ihS26M+Ie1pF6fNJ9lzH9ziUhn5LqcgZTk2sZcMkI25N3TxasUiYhI39jTRaUeAxcRacJEyHkU91gqmlzbmKFLNuTZ08XARUSakuvQl5Tk2sYMXXIh5vsPWUgXTPHC1JyBi4jUJtdJ3lKSaxszdMmGoPJ/cpFsZolulbqjrOIV7lg66rscIiKSMU6klw35DC+6ZqZi0OOryufJZpYMXESkNgH/Dn9p/j9Sh1zbmD1dVKrkncNlISqwzqW2vksiIiMj10neUpJrG7OnSzZKf0/X65Pmj3GleSIiMiDs6ZKL0pu1APAqRSLSHrleWSclubYxQ5ds5E6kL33pi4GLiLRJrlfWSUmubczQJTulK3RZZ2cycBGRVgko/oXeRpwHJCXXNuacLtkw5h/TwqWZmGOzU3UGLiIiMnjs6ZKd0tXTBQBfujXEd861kGxmqe9SiKgUMIEAk2KOYZmU0l9wtU2ubcyeLtkw3h/S17lmpuKbu7/AXpGh3MbARUTaIpTwQUWTaxuzp4uMSt5J87bZmRjg20nfJREREamFoUsuRONfp+v1qxQ/9Wyq75KIqDSS6yxvKcm0jRm6ZEYw0iUjuCwEEUlFrmtISUmubczQJRvG+0PKwEVEkirBGlJG/FUrLZm2MSfSy47x9XRtuHuYgYuIiIweQ5dsGO+vBhPLt0S0dTkGLiKShFyvrJOSXNuYw4tyYWQT6QVRhPj/vuer1s54x+8D4773AxEZD5lO8paUTNuYPV1yYwQ/rK6Zqfj1nx1o9fzevxsZuIhIIkIJ/0dFk2sbM3TJhnH0dOVOmm+UloiV907AVMzWd0lERERaweFF2THc0PX6VYo9K3WBQuDvBUQkLaEEV9axU149cm1jhi7ZMOzwwmUhiMhQyHS6kaTk2saG/S8x6YDh9XQxcBERkRwwdMmAmDdnGeCvCCGJFxi4iMhw6GE9g9WrV8PX1xdWVlZo1KgRfv3110L33bVrFzp06IBy5crBwcEBzZo1w6FDh4r3xvoi0zUjGLpkIe9PqOH1dM30bIq1LnUYuIjIIEh9Zd327dsREhKCGTNm4MKFC2jZsiUCAwMRGxtb4P4nT55Ehw4dEBERgejoaPj7+6Nbt264cOFCST+6ZOR69SLndJFeOGalI8XUAqIgIEswxYQKrfRdEhERAOkneS9ZsgRBQUEYMWIEAGDZsmU4dOgQ1qxZgwULFuTbf9myZSrP58+fj59++gk///wzGjRoUJyyJSfXifTs6ZIFw+rpcs1MxbEbO7Hs3gkIov7rISLSl4yMDERHRyMgIEBle0BAAE6fPq3WObKzs/H8+XM4OTnpokTSIvZ0yY5+Q07eSfP22ZmYn5WKh+a2eq2JiCgvbVxZl5KSorLd0tISlpaW+fZPSkqCQqGAm5ubynY3NzckJCSo9Z5ffvklXr58id69exerZn3g1YtUeomG8SNa0FWKDFxEZHC0MMnby8sLjo6OykdBw4Qqb/namJkoivm2FWTr1q0IDQ3F9u3b4erqqv5n1DeZTqRnT5cs5PkJFfTT08VlIYjIWJRksnbucXFxcXBwcFBuL6iXCwBcXFxgamqar1crMTExX+/X67Zv346goCD8+OOPaN++fbHq1RdttLExYk8X6RwDFxHJjYODg8qjsNBlYWGBRo0aITIyUmV7ZGQkmjdvXuj5t27diqFDh2LLli3o0qWLVmsn3WFPlyzodyJ9g7REVE1/xsBFREZB6ivrJk6ciEGDBqFx48Zo1qwZvvnmG8TGxmLMmDEAgGnTpuH+/fvYuHEjgJzANXjwYCxfvhxNmzZV9pJZW1vD0dGxeIVLTK5XLzJ0yY70oeuQgw/6+gbiqlVZBi4iMnhST/Lu06cPHj9+jDlz5iA+Ph61a9dGREQEvL29AQDx8fEqa3Z9/fXXyMrKwtixYzF27Fjl9iFDhiA8PLyYlUtLrhPpGbrkQA8T6V0zU2EpKhBnYQ8A2O/oK3kNRETGIjg4GMHBwQW+9nqQOn78uO4LIp1g6JIbCSbS587hshIV6FilhzJ4EREZBbl2w0hJpm3MifSkVXknzZuL2TAXFfouiYhII3K9RY2UpGzj0NBQCIKg8nB3d1e+LooiQkND4enpCWtra7Rp0wZXrlzR9kcGwNAlE9J8CfAqRSIqDXIneRf3QUWTuo1r1aqF+Ph45ePSpUvK1xYuXIglS5Zg1apVOHfuHNzd3dGhQwc8f/5ci584B0OXLOj+6kUGLiIiMlRmZmZwd3dXPsqVKwcgp5dr2bJlmDFjBt577z3Url0bGzZsQGpqKrZs2aL1Ohi6qMQYuIioNJHpYumS0kYbp6SkqDzS09MLfb8bN27A09MTvr6+6Nu3L27fvg0AiImJQUJCgsq9Ly0tLdG6dWu1732pCYYuOVC5elF3E+kZuIioVGDq0j0ttLG6t1pq0qQJNm7ciEOHDuHbb79FQkICmjdvjsePHyvXOCvJvS81wasX5UYHVy8mmtugU5UesM3ORIylcSzMR0RExk3dWy0FBgYq/7tOnTpo1qwZKleujA0bNqBp06YAUOx7X2qKPV2yoP0fHNfMVPR8dlP5PNHchoGLiEoFXr2oe9poY3VvtfQ6W1tb1KlTBzdu3FBexVice18WB0OXLGh3eDF3Dtf3dw6h99N/Snw+IiJDwqsXdU+fbZyeno6rV6/Cw8MDvr6+cHd3V7n3ZUZGBk6cOPHGe18WF4cXSSOvT5qPsnHVd0lERFpVkqlZzFzqkbKNJ0+ejG7duqFixYpITEzEvHnzkJKSgiFDhkAQBISEhGD+/PmoWrUqqlativnz58PGxgb9+/cvZoWFY+iSBe30dPEqRSIiMjb37t1Dv379kJSUhHLlyqFp06Y4e/as8t6WU6dORVpaGoKDg/H06VM0adIEhw8fhr299u+mwtAlB3lyVnG7ZRm4iEg22NWlexK28bZt2958OkFAaGgoQkNDi1mQ+hi6ZKFk3wL2igwGLiKSjZJMiOdEevXItY0ZumQlu1hHPTcxR4SjD+yfZjJwEVHpV5LJ2sabB6Ql0zZm6JKFEv6ECgI+8WiGpa4N8NjMWjslERERyQyXjJCF3NCl/iR618xULL13ApbZWf8/hcDARUSywAXpdU+ubcyeLjnQ8ILFvJPmbbKzMLpiO93URURkiDiRXvdk2sbs6ZKF//+EqnELoNevUvzCrZGOayMiIpIH9nTJyptDF5eFICKS75V1UpJrGzN0yULRP6AMXEREOUpyqxneBkg9cm1jhi5ZKaSnSxSxPeYAAxcREWQ73UhScm1jzumSAVEs4kdUEDCl/Du4YuXEwEVERKQj7OmShUIm0ouisp82ytYNb1XrC9GY+22p1FEoFMjKyizJLUPJGAmAmZk5TE1N9fb+suyGkZJM25ihS6ZcM1Ox9c5BTCn/Ds7buAIAAxcZDFEUkZSYgOfJz/RdCumRvWMZuLi6Q5D4u0muk7ylJNc2Zuh6g/DwcISEhODZs2f6LqWEVBdHzTtpfm3sUTSp1oeBiwxKbuAq5+oKGxsbyf/RJf0SRRGpqal4lJgIACjn5qHnioi0Qxaha+jQodiwYUO+7Tdu3ECVKlX0UJG+iPmuUuzr24mBiwyKQqFQBi5nZ2d9l0N6Ym2dcweMR4mJcHJxlXSoUUAJrqzTaiWll1zbWBahCwA6deqEsLAwlW3lypXTUzX64So+5LIQZPCysjIBADY2NnquhPQt92cgKytT+tBVgmOpaHJtY9lcvWhpaQl3d3eVx/Lly1GnTh3Y2trCy8sLwcHBePHiRaHnePz4Md5++210794dr169giiKWLhwISpVqgRra2vUq1cPO3bskPBTqUuAKx7imBjIwEWG7/+T5jmkSMqfAYkvpMhdQ6q4DyqaXNtYNqGrICYmJlixYgUuX76MDRs24OjRo5g6dWqB+967dw8tW7ZE9erVsWvXLlhZWeGTTz5BWFgY1qxZgytXrmDChAkYOHAgTpw4Ueh7pqenIyUlReWhc6KAUISiJq4xcBEREemJbELXvn37YGdnp3x88MEHCAkJgb+/P3x9fdG2bVvMnTsXP/zwQ75j//nnH7Ro0QLt27fHhg0bYGZmhpcvX2LJkiVYv349OnbsiEqVKmHo0KEYOHAgvv7660LrWLBgARwdHZUPLy8vXX5spUn4EhuE/gxcRDp2+vRpmJuZIjCwU77Xjh8/DlMTocCLcxo2qI/ZoaEq2y5cuIDevT+Ah7sbbKytUL2aH0aNHIl//vlHR9UDJ0+eRPfu3VChvCdMTQTs2bNHreNOnDiBtxo3go21FapUroS1a9fm22fnzp2oXasmrK0sUbtWTezevVvL1WuLUMIHFU2ebSyb0OXv74+LFy8qHytWrMCxY8fQoUMHlC9fHvb29hg8eDAeP36Mly9fKo9LS0vDO++8gx49emDFihXK7u6///4br169QocOHVTC3MaNG3Hr1q1C65g2bRqSk5OVj7i4OJ1/dsHiCRR+KzGqam0GLiIdC1u/HuPGfYTfTp1CbGxssc+zb98+NG/WFOnp6dj0/WZc+fsqNmzcBEdHR8yc+akWK1b18uVL1KtbDytWrlL7mJiYGHTt0hnvvNMS0ecv4L/TpiPk4/HYuXOncp8zZ86gX98+GDhwEC5c/BMDBw5C3z698fvvv+viY5SIXIe+pCTXNpbNRHpbW1uVKxXv3r2Lzp07Y8yYMZg7dy6cnJxw6tQpBAUFITMzU7mfpaUl2rdvj/3792PKlCmoUKECACA7OxsAsH//fpQvX17lvSwtLQutw9LS8o2v64JgkgXBRvfhjkjuXr58iR9//AG//3EOCQ8TsCE8HJ/OnKnxeVJTUxE0fBgCO3fGrl3/9gb5+vqiSZMmOl3GJjAwEIGBgRod8/XatahYsSKWLlsGAKhRowaio6Kw5MvFeP/99wEAy5cvQ/sOHfDfadMAAP+dNg0nTp7A8uXLsGXLVq1+hpKS6yRvKcm1jWXT0/W6qKgoZGVl4csvv0TTpk3h5+eHBw8e5NvPxMQEmzZtQqNGjdC2bVvlPjVr1oSlpSViY2NRpUoVlYdUQ4ZEZFi2b9+OatWqoVq1ahgwYCDCw8MgiprPAj906BCSkpIwZUrBc0zLlClT6LEfjhkDB3u7Nz5K0gNXkLNnz6BDhwCVbQEdOyIqKkr5S+zZM2cQ8No+HQM64szp01qthciQyaan63WVK1dGVlYWVq5ciW7duuG3334rcA4CAJiammLz5s3o168f2rZti+PHj8Pd3R2TJ0/GhAkTkJ2djXfeeQcpKSk4ffo07OzsMGTIEIk/EVHpJIpAaqp+3tvGRrOhjLD16zBgwEAAOcvUvHjxAr/88gvat2+v0fvevHEDAFC9enWNjgOA2XPmYNLkyW/cx9PTU+PzvklCQgLc3NxUtrm5uSErKwtJSUnw8PBAQkICXF/bx9XNDQkJCVqtRRtKMoRlzENfUpJrG8s2dNWvXx9LlizBF198gWnTpqFVq1ZYsGABBg8eXOD+ZmZm2Lp1K/r06aMMXnPnzoWrqysWLFiA27dvo0yZMmjYsCGmT58u8achKr1SUwEHe/28d8pzwNZWvX2vX7+OP/74Azt27gKQ853Ru3cfhIWt1zh0Fad3LJerqytcXV2LfXxxvb7ER+5nyLu9oH0McWkQud6iRkpybWNZhK7w8PACt0+YMAETJkxQ2TZo0CDlfw8dOhRDhw5VPjczM1OZGAoA48ePx/jx47VWKxEZp/Xr1iErKwteFf6d4ymKIszNzfH06VOULVsWDg4OAIDk5OR8Q4TPnj2Do6MjAKCqnx8A4Nq1a2jWrJlGdXw4Zgw2b/7+jftcvvI3KlasqNF538Td3T1fj1ViYiLMzMyUdxVwd3fHw9f2eZSYmK+HjKg0k0XoIiLjZWOT0+Okr/dWR1ZWFjZt2ojFi79EhwDVeUsf9HofWzZvxthx41C1alWYmJjg3Llz8Pb2Vu4THx+P+/fvw69aNQBAQEAAXFxcsGjRQpWJ9LmePXtW6LwufQwvNm3aDPv2/ayyLfLwYTRu3Bjm5uY5+zRrhsgjkQjJ84vu4cjDaNa8uVZr0Qq5zvKWkkzbmKGLiAyaIKg/xKcv+/btw9OnTzE8KEjZW5Xr/fd7Yf36dRg7bhzs7e0xatRoTJk8CWZmZqhXrx4ePHiATz+ZgRo1aiDg/4HN1tYW33z7Hfr0/gDvvtsdH300HlWqVEFSUhJ+/OEHxMbFYuvWbQXWUtLhxRcvXuDmzZvK53diYnDx4kU4OTkpe8emT5uG+w/uY8OGjQCA0WPG4KuvVmHSxIkYMXIkzpw5g/Xr12FznqsSx4//GG1at8LCL75A93ffxd6ffsIvR47g5K+nil2rrsg0D0hKrm0s26sXiYi0Zf36dWjXvn2+wAUA773/Pi5evIjz588DAJYsXYqgoBH4ZMZ01KldC4MGDoCPry8OHjoMM7N/fw9+9913ceq30zA3N8fAAf1Rs0Z1DOjfD8nJyZg7d57OPktUVBQaNWyARg0bAAAmTZqIRg0bYNasf5e+iE+IR1yeKyB9fX2xb38ETpw4joYN6uOzeXOxbPkK5XIRANC8eXNs2boN4eFhqF+vLjZsCMfWbdvRpEkTnX2W4pLrGlJSkmsbC2JJZmxSiaWkpMDR0RGWdUZCMLXQdzlUSj09p/5Cl/qW/uoV7sfGwMfXF1ZWVvouh/To1atXuBMTg/IVfWH52s9CSkoK3JwdkZycrJwrV1K538c37yXBvpjnfJ6SgioVXLRaV2ki9zbm8CIREVEecr2yTkpybWOGLiIiorzkOuFISjJtY87pIiIiIpIAe7qIiIjykGknjKTk2sYMXURERHnI9RY1UpJrGzN0EZFh+f8XKi+sJuXPgOT/yBZ/krdx98NISZ5tzDldRGRQzMxyVjBP1dddrslg5P4M5P5MEBk79nQRkUExNTWFvWMZPEpMBADY2NgY5E2RSXdEUURqaioeJSbC3rEMTE1NJX1/uQ59SUmubczQRUQGx8XVHQCUwYvkyd6xjPJngag0YOgiIoMjCALKuXnAycUVWVmZAKd3yYuQM6QodQ+X8u1l2gsjJbm2MUMXERksU1NTvf3DS0SkbQxdREREecj1FjVSkmsbM3QRERHlIdehLynJtY25ZAQRERGRBNjTpWe5i/+Jigw9V0KlWUpKir5LINKq5///mdbFIrpyvUWNlOTaxgxdevb8+XMAQMbfG/RcCZVmbs7f6rsEIp14/vw5HB0dtXtSuSYCKcm0jRm69MzT0xNxcXGwt7fX6QKQKSkp8PLyQlxcHBwcHHT2PkRkmErbd4Aoinj+/Dk8PT21fm65TvKWklzbmKFLz0xMTFChQgXJ3s/BwaFUfOESUfGUpu8ArfdwEekYQxcREVEecr2yTkpybWOGLiIiojxkOt1IUnJtYy4ZIROWlpaYNWsWLC0t9V0KEekBvwMM2+rVq+Hr6wsrKys0atQIv/766xv3P3HiBBo1agQrKytUqlQJa9eulahS46VpG+sCQ5dMWFpaIjQ0lF+4RDLF7wANCCV8aGj79u0ICQnBjBkzcOHCBbRs2RKBgYGIjY0tcP+YmBh07twZLVu2xIULFzB9+nSMHz8eO3fu1PzN9cXA21hXBFEXi5wQEREZmZSUFDg6OiIhKbnYFxukpKTA3cURycnqn6NJkyZo2LAh1qxZo9xWo0YN9OjRAwsWLMi3/3/+8x/s3bsXV69eVW4bM2YM/vzzT5w5c6ZYdUvFWNpYV9jTRURElEfuJO/iPjSRkZGB6OhoBAQEqGwPCAjA6dOnCzzmzJkz+fbv2LEjoqKikJmZqVkBemLobawrnEhPRESUR0nu4JB77OvnsLS0LHBoNykpCQqFAm5ubirb3dzckJCQUOB7JCQkFLh/VlYWkpKS4OHhUez6pWLobawrDF1EREQALCws4O7ujqq+XiU6j52dHby8VM8xa9YshIaGFnrM64tji6L4xgWzC9q/oO2GxpjaWBcYuoiIiABYWVkhJiYGGRkluxduQf+YF3YBg4uLC0xNTfP1uCQmJubrmcnl7u5e4P5mZmZwdnYuQeW6ZyxtrCsMXUREVKS8/8jpo4dAKlZWVrCyspLs/SwsLNCoUSNERkaiZ8+eyu2RkZF49913CzymWbNm+Pnnn1W2HT58GI0bN4a5ublO69UGY2hjXeFEeiIiKlTusFV2drZymyAI4IXv2jNx4kR89913WL9+Pa5evYoJEyYgNjYWY8aMAQBMmzYNgwcPVu4/ZswY3L17FxMnTsTVq1exfv16rFu3DpMnT9bXRzB4RbWxVNjTRUREBcrt0Tp69Ci2bduGly9fwtXVFUuXLi21PV360KdPHzx+/Bhz5sxBfHw8ateujYiICHh7ewMA4uPjVdaT8vX1RUREBCZMmICvvvoKnp6eWLFiBd5//319fQSDV1QbS4XrdMlU7pfp5cuXcefOHQCAn58f/Pz89FsYERmU3bt3Y/DgwRg6dCi8vLywdOlS+Pr6Yt++fXByctJ3eURGhaFLxnbt2oXg4GD4+fnh0aNHKFu2LIYNG4aRI0fquzQiMgCJiYno1KkTBg0ahAkTJuDBgwd4++230a1bN5VFJkvzHC8ibeKcLpmKjo7G6NGjMXPmTJw8eRIrVqxAVFQUHj58qO/SiMhApKWlIT09HcHBwcrA1bVrV2XgOnDgAADDX6aAyFAwdMlM7mTYP//8E2+99RaCg4Nx584djBo1CsOHD8cnn3wCIOfeXkQkb9bW1rC2tsbmzZvRvHlzdO3aFStXrgQA3LlzB2vXrsWJEyf0XCWR8WDokoncUeTk5GQAwIsXL+Du7o74+Hi0bNkSHTt2xOrVqwEAR48exY8//ohnz57pq1wiklhBM03Mzc3h7e2NsWPHomHDhli7dq1ySYK1a9ciPj6e80CJNMCrF0s5hUIBU1NTCIKAI0eO4KuvvsLu3bvh4eGByZMnY8+ePRg0aBCWL1+uPObHH39ESkqKUaz3QkQllzsnKzIyEgcPHsTz58/x8ccfo1atWvjkk09w6dIlpKam4quvvkLFihVx8OBBbN68GSdOnDCKW84QGQr2dJVSy5cvx5kzZ2BqaoqsrCwAwLFjx2BnZwcA+OCDDxASEoLk5GR06NABT58+xaNHj/Df//4XO3bswIwZM2Bra6vPj0BEEhEEAREREXj33Xdx7do1/P7772jSpAm2bduGBg0aYPPmzShbtiy++OILfPLJJ7hx4wZOnjyJevXq6bt0IqPCnq5SKCkpCQcOHMCcOXNw6NAhNG7cGADw6NEjWFtbK/ebNGkSnj17hvfffx8VKlSAs7MzkpKScPjwYdSsWVNf5RORRHJ7uJ4/f44zZ85g+fLlyquXJ0yYgCFDhiArKwsDBw7Exo0b8fLlSwA5K3zb2Njos3Qio8TQVQq5uLjgyy+/xLx589ClSxfs27cPb731FgRBUH5RZmRkwM3NDd988w0++OADJCYmomzZsqhXrx7Kly+v509ARLryww8/oHXr1nBzc4MgCLhw4QLatm0LX19fzJkzR7nf0qVLAQBBQUEwNTVFr169UKZMGT1VTVQ6MHSVMrm/udaqVQuffvopFAoFunbtihMnTsDLywuZmZkAckKXhYUFAKBevXpwdXXVZ9lEpGPZ2dm4ceMGRo0ahUuXLim316tXD507d8bWrVuRmJgI4N/vkaVLl8LU1BQDBgyAhYUFVzwnKiEujloKZWdnw8QkZ7re5cuXMXv2bBw/fhwWFhawtbWFIAh49eoV7O3tkZGRATs7Oxw7dgwODg5cb4eolHvx4gXs7Oxw+fJlODk5wdPTE1lZWRg8eDAOHjyIPXv2oFWrVirHzJgxA4MGDUL16tX1VDVR6cDQVYoUtir0n3/+iZUrV+L777/HhAkT0Lt3b9y5cweiKCI7OxsNGjRA5cqV9VAxEUkl9/shKysLz549g6urKwYMGICFCxfCw8MD2dnZ6NOnD44ePYrdu3fnC15EVHIMXaVE7hfqqVOn8PPPPwMAatSogaFDhwIALly4gCVLluDIkSP45ZdfOFGeSGZyvyNyl5HZt28fPvjgAwwaNAihoaHw9PRUBq9Tp07h+++/R7t27fRdNlGpwiUjjFTuyvK5VxMJgoDdu3fj3XffxT///IMHDx7g448/RmhoKACgQYMGmDp1Klq3bo26deviwoUL+iqdiPRAEAScOXMG33zzDZ4/f46uXbti9+7dWL9+PUJDQ/HgwQOYmJhg+/btqFevHkaNGoW0tDR9l01UqnAivRHKnbMVHR2NPn364OzZs7hz5w4+/vhjzJs3Dx9++CH++ecf/Pzzz5gzZw6ePHmCFStWoE6dOvjPf/4Da2trrsFFJEMrV65EdHQ0TE1N0a9fP3Tq1An79u1D165dAQCzZ8+Gh4cHIiIiEB8fr7LEDBGVHIcXjUxu4Przzz/RsmVLDB8+HMuWLUNYWBiuXLmCxYsXIy4uDq1atUKHDh3QuHFjjBkzBp9++ilmz54NQPXKRSKSD4VCgeHDh+Py5csYOXIkBgwYAHt7exw8eBA9e/ZEz549sWTJEri7u+u7VKJSiaHLiOQGrr/++gvNmjVDSEgIPvvsM+XrJ06cQOvWrdGhQwd4eXlh/fr1uHfvHpo2bYoHDx5g0qRJWLRokR4/ARFJJXcO16NHj+Dk5ARTU1MAOcFryJAhuHbtGkaOHImBAwfC1tYWe/fuxfDhw3Hp0iXe2odIRziny4iYmJggLi4O7dq1Q9euXVUC15o1axAREYE7d+7gyZMnCAoKAgDY2NigQ4cO2LRpE0aMGKGv0olIYoIg4Ny5c+jcuTP27NkDhUIBADA1NUVYWBgqVqyIuXPnYuvWrUhJSUH37t1x9+5dBi4iHWLoMjIKhQK+vr549eoVfvvtNwDAggUL8N///hddunSBhYUFLl26hN9++w2pqalYvHgxLl26hMDAQFSrVk3P1RORlHLX1Vq6dCn279+vDF7m5uYICwtDeno6Fi1ahB07dkAURd7ah0jHOLxohG7cuIHx48fDwsICbm5u+Omnn7Bp0yYEBAQAABYtWoT//Oc/qFKlCp48eYLIyEg0aNBAz1UTka4VtFZfSkoKunXrhvT0dEyfPh1du3aFiYkJbty4gWnTpsHKygrz5s2Dj4+PfoomkhGGLiP1zz//YNy4cTh16hTmzp2LSZMmKV/LyMjA5cuXERcXh4YNG8LLy0uPlRKRFHID16+//oojR47Aw8MDb731Fho1aoTk5GR0794dmZmZGDp0KAICArBhwwZcv34d3333HXu4iCTC0GXEbt26heDgYJiammL69Ol45513AKjeBoiI5OOnn35Cv379UK9ePTx58gQ2NjaYO3cuunbtiuTkZIwYMQIXLlxAWloaBEHA3r170bBhQ32XTSQbDF1GLneoURRFfPrpp2jRooW+SyIiPUhMTMTKlSvh6+uL4cOH48yZM/j222/xyy+/YOXKlejevTvS0tJw8eJFJCcno1atWuwFJ5IYQ1cpcOPGDUycOBFJSUlYunQpmjZtqu+SiEhCf/75JwYPHgxzc3OsXbsWjRs3BgBcunQJy5YtQ2RkJFavXq1cBJWI9INjUKVA1apVsWjRIlSoUAGenp76LoeIJJaUlIQKFSrg2rVreP78uXJ7nTp1MGHCBHTq1Al9+/ZFZGSkHqskIt4GqJSoXr06Nm/ezJXmiWSoXbt2sLa2Rnp6OoKDgxEWFqbs8a5duzaCg4NhaWnJKxSJ9IzDi0RERiT3KsXo6Gjcu3cPcXFx6NevH5ydnREVFYV58+YhNjYWa9euxdtvv608jrf/ItI/hi4iIiOzc+dOfPjhh2jQoAFu3rwJR0dHBAUFYezYsTh+/DhWrFiB+/fvY8mSJby4hsiAcE4XEZEROX/+PMaNG4eFCxfi0KFDOHr0KC5evIjU1FQAQJs2bTBx4kTY2tpixowZePXqFfi7NZFhYE8XEZEBKmy9vZ07d2LNmjU4cuQIrl+/jsDAQLRr1w7ffvstAODhw4dwc3PDqVOn4OPjgwoVKkhdOhEVghPpiYgMTG7gun//Pk6cOIHU1FQEBASgYsWKuHnzJiwtLaFQKNChQwcEBgZizZo1AICff/4Zf/75J6ZMmaJcLJmIDAeHF4mIDEhu4Lpy5Qq6du2KiIgI3LhxAxUrVgQAdO/eHVFRUbCyskLPnj3x9ddfK3vEjhw5gujoaKSnp+vzIxBRIdjTRURkIERRVAauli1bYsSIEZgyZQrKlSsHANi7dy9SU1MREhKCr7/+Gh4eHgCA27dv47vvvsP333+PX3/9FQ4ODvr8GERUCM7pIiIyIE+ePEHPnj1Rr149LF++HIIgAAC++OILTJs2DZ07d4a/vz9evXqFxYsXw9raGi4uLsjIyMDWrVvRoEEDPX8CIioMe7qIiAzIw4cPcf/+fYSGhirX5Fq7di0+/fRTrFixAnv37sWZM2fQu3dvXLp0CSdOnICXlxeqVKnCO1IQGTj2dBERGZDvv/8eQ4cORWZmprKX6969e4iJiUHLli1x6dIlTJgwAc+ePcOOHTu4yjyREeFEeiIiA+Lj4wMzMzPs3r0bQM48rwoVKqBly5bIzs5GnTp10KdPH5iYmMDKykrP1RKRJhi6iIgMiI+PDxwdHbFhwwbcvXtX2dsFQHmV4vXr1+Hj4wNbW1t9lUlExcDQRURkQCpUqIDVq1fj4MGD+PTTT/H3338rX0tJScHUqVOxfv16zJo1C/b29nqslIg0xTldREQGRqFQ4LvvvsO4ceNQpUoVNG/eHObm5rh//z6ioqIQERHBqxSJjBBDFxGRgfr999+xcOFC3Lp1C/b29njnnXcQFBSEKlWq6Ls0IioGhi4iIgOmUChgamqq7zKISAs4p4uIyIDlvek1f0cmMm7s6SIiIiKSAHu6iIiIiCTA0EVEREQkAYYuIiIiIgkwdBERERFJgKGLiIiISAIMXUREREQSYOgiIiIikgBDF5EOhYeHo0yZMvouo9h8fHywbNmyN+4TGhqK+vXrS1IPEZExY+giKsLQoUMhCEK+x82bN/VdGsLDw1Vq8vDwQO/evRETE6OV8587dw6jRo1SPhcEAXv27FHZZ/Lkyfjll1+08n6Fef1zurm5oVu3brhy5YrG5zHmEExExo2hi0gNnTp1Qnx8vMrD19dX32UBABwcHBAfH48HDx5gy5YtuHjxIrp37w6FQlHic5crVw42NjZv3MfOzg7Ozs4lfq+i5P2c+/fvx8uXL9GlSxdkZGTo/L2JiLSBoYtIDZaWlnB3d1d5mJqaYsmSJahTpw5sbW3h5eWF4OBgvHjxotDz/Pnnn/D394e9vT0cHBzQqFEjREVFKV8/ffo0WrVqBWtra3h5eWH8+PF4+fLlG2sTBAHu7u7w8PCAv78/Zs2ahcuXLyt74tasWYPKlSvDwsIC1apVw6ZNm1SODw0NRcWKFWFpaQlPT0+MHz9e+Vre4UUfHx8AQM+ePSEIgvJ53uHFQ4cOwcrKCs+ePVN5j/Hjx6N169Za+5yNGzfGhAkTcPfuXVy/fl25z5v+PI4fP45hw4YhOTlZ2WMWGhoKAMjIyMDUqVNRvnx52NraokmTJjh+/Pgb6yEi0hRDF1EJmJiYYMWKFbh8+TI2bNiAo0ePYurUqYXuP2DAAFSoUAHnzp1DdHQ0/vvf/8Lc3BwAcOnSJXTs2BHvvfce/vrrL2zfvh2nTp3CuHHjNKrJ2toaAJCZmYndu3fj448/xqRJk3D58mWMHj0aw4YNw7FjxwAAO3bswNKlS/H111/jxo0b2LNnD+rUqVPgec+dOwcACAsLQ3x8vPJ5Xu3bt0eZMmWwc+dO5TaFQoEffvgBAwYM0NrnfPbsGbZs2QIAyvYD3vzn0bx5cyxbtkzZYxYfH4/JkycDAIYNG4bffvsN27Ztw19//YUPPvgAnTp1wo0bN9SuiYioSCIRvdGQIUNEU1NT0dbWVvno1atXgfv+8MMPorOzs/J5WFiY6OjoqHxub28vhoeHF3jsoEGDxFGjRqls+/XXX0UTExMxLS2twGNeP39cXJzYtGlTsUKFCmJ6errYvHlzceTIkSrHfPDBB2Lnzp1FURTFL7/8UvTz8xMzMjIKPL+3t7e4dOlS5XMA4u7du1X2mTVrllivXj3l8/Hjx4tt27ZVPj906JBoYWEhPnnypESfE4Boa2sr2tjYiABEAGL37t0L3D9XUX8eoiiKN2/eFAVBEO/fv6+yvV27duK0adPeeH4iIk2Y6TfyERkHf39/rFmzRvnc1tYWAHDs2DHMnz8ff//9N1JSUpCVlYVXr17h5cuXyn3ymjhxIkaMGIFNmzahffv2+OCDD1C5cmUAQHR0NG7evInNmzcr9xdFEdnZ2YiJiUGNGjUKrC05ORl2dnYQRRGpqalo2LAhdu3aBQsLC1y9elVlIjwAtGjRAsuXLwcAfPDBB1i2bBkqVaqETp06oXPnzujWrRvMzIr/1TBgwAA0a9YMDx48gKenJzZv3ozOnTujbNmyJfqc9vb2OH/+PLKysnDixAksWrQIa9euVdlH0z8PADh//jxEUYSfn5/K9vT0dEnmqhGRfDB0EanB1tYWVapUUdl29+5ddO7cGWPGjMHcuXPh5OSEU6dOISgoCJmZmQWeJzQ0FP3798f+/ftx4MABzJo1C9u2bUPPnj2RnZ2N0aNHq8ypylWxYsVCa8sNIyYmJnBzc8sXLgRBUHkuiqJym5eXF65fv47IyEgcOXIEwcHBWLRoEU6cOKEybKeJt99+G5UrV8a2bdvw4YcfYvfu3QgLC1O+XtzPaWJiovwzqF69OhISEtCnTx+cPHkSQPH+PHLrMTU1RXR0NExNTVVes7Oz0+izExG9CUMXUTFFRUUhKysLX375JUxMcqZH/vDDD0Ue5+fnBz8/P0yYMAH9+vVDWFgYevbsiYYNG+LKlSv5wl1R8oaR19WoUQOnTp3C4MGDldtOnz6t0ptkbW2N7t27o3v37hg7diyqV6+OS5cuoWHDhvnOZ25urtZVkf3798fmzZtRoUIFmJiYoEuXLsrXivs5XzdhwgQsWbIEu3fvRs+ePdX687CwsMhXf4MGDaBQKJCYmIiWLVuWqCYiojfhRHqiYqpcuTKysrKwcuVK3L59G5s2bco33JVXWloaxo0bh+PHj+Pu3bv47bffcO7cOWUA+s9//oMzZ85g7NixuHjxIm7cuIG9e/fio48+KnaNU6ZMQXh4ONauXYsbN25gyZIl2LVrl3ICeXh4ONatW4fLly8rP4O1tTW8vb0LPJ+Pjw9++eUXJCQk4OnTp4W+74ABA3D+/Hl89tln6NWrF6ysrJSvaetzOjg4YMSIEZg1axZEUVTrz8PHxwcvXrzAL7/8gqSkJKSmpsLPzw8DBgzA4MGDsWvXLsTExODcuXP44osvEBERoVFNRERvpM8JZUTGYMiQIeK7775b4GtLliwRPTw8RGtra7Fjx47ixo0bRQDi06dPRVFUnbidnp4u9u3bV/Ty8hItLCxET09Pcdy4cSqTx//44w+xQ4cOop2dnWhrayvWrVtX/OyzzwqtraCJ4a9bvXq1WKlSJdHc3Fz08/MTN27cqHxt9+7dYpMmTUQHBwfR1tZWbNq0qXjkyBHl669PpN+7d69YpUoV0czMTPT29hZFMf9E+lxvvfWWCEA8evRovte09Tnv3r0rmpmZidu3bxdFseg/D1EUxTFjxojOzs4iAHHWrFmiKIpiRkaGOHPmTNHHx0c0NzcX3d3dxZ49e4p//fVXoTUREWlKEEVR1G/sIyIiIir9OLxIREREJAGGLiIiIiIJMHQRERERSYChi4iIiEgCDF1EREREEmDoIiIiIpIAQxcRERGRBBi6iIiIiCTA0EVEREQkAYYuIiIiIgkwdBERERFJgKGLiIiISAL/A/TyQB9wwSt3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import learning_curve\n",
    "import gender_guesser.detector as gender\n",
    "\n",
    "def read_datasets():\n",
    "    \"\"\" Reads users profile from csv files \"\"\"\n",
    "    genuine_users = pd.read_csv(\"data/users.csv\")\n",
    "    fake_users = pd.read_csv(\"data/fusers.csv\")\n",
    "    x = pd.concat([genuine_users, fake_users])\n",
    "    y = [1] * len(genuine_users) + [0] * len(fake_users)\n",
    "    return x, y\n",
    "\n",
    "def predict_sex(names):\n",
    "    sex_predictor = gender.Detector(case_sensitive=False)\n",
    "    sex_code = []\n",
    "    for name in names:\n",
    "        first_name = name.split(' ')[0]\n",
    "        sex = sex_predictor.get_gender(first_name)\n",
    "        if sex == 'female':\n",
    "            sex_code.append(2)\n",
    "        # elif sex == 'mostly_female':\n",
    "        #     sex_code.append(-1)\n",
    "        elif sex == 'male':\n",
    "            sex_code.append(1)\n",
    "        # elif sex == 'mostly_male':\n",
    "        #     sex_code.append(1)\n",
    "        else:\n",
    "            sex_code.append(0)  # Assign a default value for unknown genders\n",
    "    return sex_code\n",
    "\n",
    "def extract_features(x):\n",
    "    lang_encoder = LabelEncoder()\n",
    "    x['lang_code'] = lang_encoder.fit_transform(x['lang'])\n",
    "\n",
    "    x['sex_code'] = predict_sex(x['name'])\n",
    "\n",
    "    feature_columns_to_use = ['statuses_count', 'followers_count', 'friends_count', 'favourites_count', 'listed_count', 'sex_code', 'lang_code']\n",
    "    x = x[feature_columns_to_use]\n",
    "    return x\n",
    "\n",
    "# Rest of your code...\n",
    "\n",
    "\n",
    "\n",
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "    plt.grid()\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt\n",
    "\n",
    "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    target_names=['Fake','Genuine']\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(target_names))\n",
    "    plt.xticks(tick_marks, target_names, rotation=45)\n",
    "    plt.yticks(tick_marks, target_names)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "def plot_roc_curve(y_test, y_pred):\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
    "\n",
    "    print(\"False Positive rate: \", false_positive_rate)\n",
    "    print(\"True Positive rate: \", true_positive_rate)\n",
    "\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(false_positive_rate, true_positive_rate, 'b',\n",
    "             label='AUC = %0.2f' % roc_auc)\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.plot([0, 1], [0, 1], 'r--')\n",
    "    plt.xlim([-0.1, 1.2])\n",
    "    plt.ylim([-0.1, 1.2])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.show()\n",
    "\n",
    "def train(X_train, y_train, X_test):\n",
    "    \"\"\" Trains and predicts dataset with a Random Forest classifier \"\"\"\n",
    "    clf = RandomForestClassifier(n_estimators=40, oob_score=True)\n",
    "    clf.fit(X_train, y_train)\n",
    "    print(\"The best classifier is: \", clf)\n",
    "    \n",
    "    # Estimate score\n",
    "    scores = cross_val_score(clf, X_train, y_train, cv=5)\n",
    "    print(scores)\n",
    "    print('Estimated score: %0.5f (+/- %0.5f)' % (scores.mean(), scores.std() / 2))\n",
    "\n",
    "    title = 'Learning Curves (Random Forest)'\n",
    "    plot_learning_curve(clf, title, X_train, y_train, cv=5)\n",
    "    plt.show()\n",
    "\n",
    "    # Predict\n",
    "    y_pred = clf.predict(X_test)\n",
    "    import pickle\n",
    "    with open('data.pkl','wb') as file:\n",
    "        pickle.dump(clf,file)\n",
    "    return y_test, y_pred\n",
    "\n",
    "print(\"Reading datasets...\\n\")\n",
    "x, y = read_datasets()\n",
    "x.describe()\n",
    "\n",
    "print(\"Extracting features...\\n\")\n",
    "x = extract_features(x)\n",
    "print(x.columns)\n",
    "print(x.describe())\n",
    "\n",
    "print(\"Splitting datasets into train and test dataset...\\n\")\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=44)\n",
    "\n",
    "print(\"Training datasets...\\n\")\n",
    "y_test, y_pred = train(X_train, y_train, X_test)\n",
    "\n",
    "print('Classification Accuracy on Test dataset: ', accuracy_score(y_test, y_pred))\n",
    "\n",
    "\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print('Confusion matrix, without normalization')\n",
    "print(cm)\n",
    "plot_confusion_matrix(cm)\n",
    "\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "print('Normalized confusion matrix')\n",
    "print(cm_normalized)\n",
    "plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')\n",
    "\n",
    "print(classification_report(y_test, y_pred, target_names=['Fake', 'Genuine']))\n",
    "\n",
    "plot_roc_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f044244b-4bda-4a6c-b7f8-55a242f45bc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      statuses_count  followers_count  friends_count  favourites_count  \\\n",
      "512             9950              658            701                18   \n",
      "630             6991             1001            614              2401   \n",
      "189             1809              102            392                18   \n",
      "343              770               42            149                28   \n",
      "1129              27                7            296                 0   \n",
      "...              ...              ...            ...               ...   \n",
      "1264             440               98            770                 2   \n",
      "78                66               22            581                 0   \n",
      "273               37               16            445                 0   \n",
      "588              293               42            139                 0   \n",
      "761               23               19            310                 0   \n",
      "\n",
      "      listed_count  sex_code  lang_code  \n",
      "512             11         2          5  \n",
      "630              6         0          5  \n",
      "189              1         0          5  \n",
      "343              0         0          5  \n",
      "1129             0         2          1  \n",
      "...            ...       ...        ...  \n",
      "1264             0         1          5  \n",
      "78               0         0          1  \n",
      "273              0         0          1  \n",
      "588              0         2          5  \n",
      "761              0         2          1  \n",
      "\n",
      "[564 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print (X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2255c61-7aff-4f79-b502-d71ea90536b3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2203e547-594f-4aff-9fa5-1a4e7532f05a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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