{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2c76440e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kagglehub in c:\\users\\phaym\\anaconda3\\lib\\site-packages (0.3.5)\n",
      "Requirement already satisfied: requests in c:\\users\\phaym\\anaconda3\\lib\\site-packages (from kagglehub) (2.28.1)\n",
      "Requirement already satisfied: packaging in c:\\users\\phaym\\anaconda3\\lib\\site-packages (from kagglehub) (24.2)\n",
      "Requirement already satisfied: tqdm in c:\\users\\phaym\\anaconda3\\lib\\site-packages (from kagglehub) (4.64.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\phaym\\anaconda3\\lib\\site-packages (from requests->kagglehub) (2024.8.30)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\phaym\\anaconda3\\lib\\site-packages (from requests->kagglehub) (1.26.11)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\phaym\\anaconda3\\lib\\site-packages (from requests->kagglehub) (3.3)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in c:\\users\\phaym\\anaconda3\\lib\\site-packages (from requests->kagglehub) (2.0.4)\n",
      "Requirement already satisfied: colorama in c:\\users\\phaym\\anaconda3\\lib\\site-packages (from tqdm->kagglehub) (0.4.5)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install kagglehub\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2903a488",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (100000, 9)\n",
      "\n",
      "Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   gender               100000 non-null  object \n",
      " 1   age                  100000 non-null  float64\n",
      " 2   hypertension         100000 non-null  int64  \n",
      " 3   heart_disease        100000 non-null  int64  \n",
      " 4   smoking_history      100000 non-null  object \n",
      " 5   bmi                  100000 non-null  float64\n",
      " 6   HbA1c_level          100000 non-null  float64\n",
      " 7   blood_glucose_level  100000 non-null  int64  \n",
      " 8   diabetes             100000 non-null  int64  \n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 6.9+ MB\n",
      "None\n",
      "\n",
      "Missing values:\n",
      "gender                 0\n",
      "age                    0\n",
      "hypertension           0\n",
      "heart_disease          0\n",
      "smoking_history        0\n",
      "bmi                    0\n",
      "HbA1c_level            0\n",
      "blood_glucose_level    0\n",
      "diabetes               0\n",
      "dtype: int64\n",
      "\n",
      "Duplicate rows: 3854\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Basic statistics:\n",
      "            gender           age  hypertension  heart_disease  \\\n",
      "count  88195.00000  8.819500e+04  88195.000000   88195.000000   \n",
      "mean       0.41991  3.338285e-17      0.068451       0.036873   \n",
      "std        0.49396  1.000006e+00      0.252519       0.188451   \n",
      "min        0.00000 -1.814467e+00      0.000000       0.000000   \n",
      "25%        0.00000 -7.998907e-01      0.000000       0.000000   \n",
      "50%        0.00000  4.116257e-02      0.000000       0.000000   \n",
      "75%        1.00000  7.936839e-01      0.000000       0.000000   \n",
      "max        2.00000  1.723269e+00      1.000000       1.000000   \n",
      "\n",
      "       smoking_history           bmi   HbA1c_level  blood_glucose_level  \\\n",
      "count     88195.000000  8.819500e+04  8.819500e+04         8.819500e+04   \n",
      "mean          2.196746  1.184472e-15  1.712635e-16         1.228528e-15   \n",
      "std           1.888038  1.000006e+00  1.000006e+00         1.000006e+00   \n",
      "min           0.000000 -2.415450e+00 -1.968392e+00        -1.549541e+00   \n",
      "25%           0.000000 -6.256060e-01 -6.622850e-01        -9.822569e-01   \n",
      "50%           3.000000  1.926632e-01  3.424130e-01         1.523123e-01   \n",
      "75%           4.000000  4.551992e-01  7.442922e-01         6.628685e-01   \n",
      "max           5.000000  2.536324e+00  2.753688e+00         2.988735e+00   \n",
      "\n",
      "           diabetes  \n",
      "count  88195.000000  \n",
      "mean       0.052361  \n",
      "std        0.222756  \n",
      "min        0.000000  \n",
      "25%        0.000000  \n",
      "50%        0.000000  \n",
      "75%        0.000000  \n",
      "max        1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YaY6zZ8/WY489VmvnDAAAAJxPtZZz+fTTT9WhQwfXTI8hQ4Zo0qRJmjx5sq655hp17NhRX3zxxUVJFABw+Vm756gkaXh89ZZyKdc8LFAxdn+VOExt+/FkbacHAHWmLsfhGRkZkqTIyEi39sjISFdfRkaGrFarQkNDzxsTERFRYf8RERFuMeceJzQ0VFar1RVTmenTpysnJ8f1OnToUDXPEgAAAKi6ahXR586dqwkTJlT6sUq73a57771Xzz77bK0lBwC4fJmSNnx3XJI0oG3TGu3DMAz1PjMbPeX/s3ff4XHU1/7H37NVfdW7LMm9yDbujWLAGAOmBxIMDg7EyQ0tvsCPXC4ppEFCQsmFQBJCgGBaCg7dYAM2xZa7bMu9qFu9rNpq6/z+2GIbV0nbtDqv59ED9s7OfFdWmT1z5nMkF10IMYCF4jz86xcvVVU94wXNr29zsu37ss3XGY1GEhISjvsQQgghhBAiUHpVRN++fTsLFiw45ePz589ny5Yt/V6UEEIIYTMk0tBhxajTMDk/6cxPOIWZQz1FdMlFF0IMYME8D8/MzAQ4oRO8oaHB1zWemZmJzWajtbX1tNvU19efsP/Gxsbjtvn6cVpbW7Hb7Sd0qAshhBBCCBEqvSqi19fXo9frT/m4TqejsbGx34sSQgghumPdg0SnFyYTpe97lvksTxF9R7WZTqvDL2sTQohgC+Z5eGFhIZmZmaxatcr3dzabjbVr1zJ79mwApkyZgl6vP26b2tpaSktLfdvMmjULs9nMxo0bfdts2LABs9l83DalpaXU1tb6tvn4448xGo1MmTLFL69HCCGEEEKI/upVET0nJ4edO3ee8vEdO3aQlZXV70UJIYQQXbHuIXPnDk/t137ykmPITYrG6VLZVC656EKIgcnf5+GdnZ2UlJRQUlICuIeJlpSUUFlZiaIoLFu2jEceeYQVK1ZQWlrKkiVLiImJYdGiRYA7Qub222/nvvvu45NPPmHbtm3ccsstjB8/3jfkdMyYMSxYsIClS5dSXFxMcXExS5cuZeHChYwaNQpwd9CPHTuWxYsXs23bNj755BPuv//+U0bXCCGEEEIIEQq9KqJffvnl/PSnP6Wnp+eExywWCz/72c9YuHCh3xYnhBBicHK6VLpj3XEC547oXxEdjnajbzgsRXQhxMDk7/PwzZs3M2nSJCZNmgTAvffey6RJk/jpT38KwAMPPMCyZcu44447mDp1KjU1NXz88cfEx8f79vHkk09yzTXXcOONNzJnzhxiYmJ499130WqP3j306quvMn78eObPn8/8+fOZMGECr7zyiu9xrVbL+++/T1RUFHPmzOHGG2/kmmuu4fe//32vP0dCCCGEEEIEiq43G//4xz/mrbfeYuTIkdx1112MGjUKRVHYs2cPf/zjH3E6nTz00EOBWqsQQohBos7cg6rRkxxrYExm/zsRJw1J4p9bqimtMfthdUIIEXz+Pg+fO3cuqqqe8nFFUXj44Yd5+OGHT7lNVFQUTz/9NE8//fQpt0lOTmb58uWnXcuQIUN47733zrhmIYQQQgghQqVXRfSMjAzWrVvHD37wAx588EHfibeiKFx66aU8++yzMgBICCFEv1W2dgMwe1gKGo3S7/2Ny3YX4nfXtqOqKorS/30KIUQwyXm4EEIIIYQQodOrIjpAfn4+H3zwAa2trRw8eBBVVRkxYgRJSUmBWJ8QQohBqKrFXUQ/zw9RLgCjMuPRahRaumzUtfeQZYr2y36FECKY5DxcCCGEEEKI0Oh1Ed0rKSmJadOm+XMtQgghBFaHk7p2d+bvnH4OFfWK0msZnhbHvvoOdtW0SxFdCDGgyXm4EEIIIYQQwdWrwaJCCCFEoFW3WlBV0FvN5CbF+G2/3kiXXUfa/bZPIYQQQgghhBBCRD4pogshhAgr3iiXmO5av+53rK+ILsNFhRBCCCGEEEIIcfakiC6EECKsVLdaAIjt8m8RfVy2CZBOdCGEEEIIIYQQQvSOFNGFEEKEjW6bg+YuGwDR3XV+3be3E72mzUJbt82v+xZCCCGEEEIIIUTkkiK6EEKIsFHj6UJPiTWgc1r9um9TtJ68ZPdA0d3SjS6EEEIIIYQQQoizJEV0IYQQYcMb5ZKbFB2Q/Y/LkkgXIYQQQgghhBBC9I4U0YUQQoSNo0X0mIDsf5wMFxVCCCGEEEIIIUQvSRFdCCHEyRliqWzpxqWqQTlcl9VBiyerPCdQneg53iK6dKILIYQQQgghhBDi7EgRXQghxHEsNidfHWwi+sofs2JbDav31KMGoZBe0+buQk+NMxCt1wbkGOOy3XEuhxo7cSmBOYYQQgghhBBCCCEiiy7UCxBCCBE+Djd2snJXHXaniqI3ArCntoNovZZzh6eiKErAjh3oKBeA9HgjqXEGmjptWI1JATuOEEIIIYQQQgghIod0ogshhADApaqs3d+I3amSFm/E+vkLXDImA4CtlW1sqWgN6PGrW7uBwA0VBVAUhbGebnRrVErAjiOEEEIIIYQQQojIIUV0IYQQAJQ1ddHe4yBKp+GGKbk4j+xibHYC5w1PBeCrQ80cbuoMyLE7rQ5au+0A5CQGrogOR4eL9kQNnE50m8PF2yU1vsgbIYQQQgghhBBCBI8U0YUQQgBQUtUGwLgcE3rt0V8Pk/OTmJjr7t7eWNYSkHz0Gk+US1q8kagA5aF7jcyIA8BmSAzocfzpZ+/s4odvlHDZU5+zZl9DqJcjhBBCCCGEEEIMKlJEF0IIQXOnlepWCwowwVMwP9b0wmS0GoX6dmtAuqGrvFEuAe5CBxieFg+AzXji6wxHK0vreH1jJQDtPQ6+89Imnl1zkMCPehVCCCGEEEIIIQRIEV0IIQRQUt0GwNC0WBKi9Cc8HmPQMTbLHYPi72x0VVWpbHEX0YckB26oqNew9FgAnLpoLHZnwI/XH/XtPfzPWzsA+M6cAm6anoeqwmMr99GcOjHEqxNCCCGEEEIIIQYHKaILIcQg12N3sre2A4Bz8hJPud3kIYkoQHlzN02dVr8dv7XbTkePA62ikBPAoaJeMQadL3e9pcsW8OP1lQrc+48S2rrtjMtO4MHLxvDodRN46PIxALQljcYVgGgdIYQQQgghhBBCHE+K6EIIMcjtPtKOw6WSGmc47VDPxBgDw9PdeeJb/diNXtHcBUB2YtRxWeyB5H0d4VxE70go5KuDzUTpNfzhW5Mw6NyfmyVzCkiM0ePURfuy5IUQQgghhBBCCBE4UkQXQohB7mBjJwDjc0woinLabSfnJwGwr74Du84/0SveKJf8lFi/7O9sjBgIRfT4fABuP7fQV/QH0Gs1XFaUCcD++o6QrE0IIYQQQgghhBhMpIguhBCDmMPpor69Bzi7PPLMhChyE6NxqdCWNKr/x3e5qPZ0UwcjD90r3DvRnS6V7thsAC4Zm3nC4wsnuB872NCJ0yWRLkIIIYQQQgghRCBJEV0IIQaxuvYeXCrEGrWYok8cKHoyE3JNALSbhuNwuvp1/Nq2HhwulRiDltQ4Q7/21RvhXkSvabPg0hpIjTMwIcd0wuMzCpPROiz0OFxUtXaHYIVCCCGEEEIIIcTgIUV0IYQYxLyZ2jmJ0WeMcvEamhZHtF6LQx/L5wca+3X8Cm+US3LMWR/fH7xF9E6rA5ujfxcCAqG8yZ0TP3dUOhrNiZ8XnVZDfHs5IJEuQgghhBBCCCFEoEkRXQghBrGatqNF9LOl1SiMzowH4M1NVf06vneo6JCU4EW5gHtIqtbhfu0t3eHXjV7mKaJfNDr9lNvEt5cBcKixC4cr/C4ECCGEEEIIIYQQkUKK6EIIMUipaKg1u/PQe1NEBxiXnQDAJ3saaOyw9un4Dm00TZ3uAnYw89C9jNY2AFrDLNKltdtGm8UOqovzRqSecrtoSwNxRh02h4vKZol0EUIIIYQQQgghAkWK6EIIMUj1RKXgcKlE6TQkx/YujzwlzkiUpRGHS2XFtuo+Hb8rNguA9HgjMQZdn/bRHwZPEb05zIro3i70mO464qNOnVOvcDSW5kBDZzCWJoQQQgghhBBCDEohLaI/99xzTJgwgYSEBBISEpg1axYffvih73FVVXn44YfJzs4mOjqauXPnsmvXruP2YbVaufvuu0lNTSU2NparrrqK6urjCzqtra0sXrwYk8mEyWRi8eLFtLW1BeMlCiFE2OqOyQAguxd56Mcyte0H4I1NVaiq2uvnd8YPAULThQ5gsJmB8OtE9+ahx3ae+eJEgScGx3tHgRBCCCGEEEIIIfwvpEX03NxcfvOb37B582Y2b97MRRddxNVXX+0rlD/22GM88cQTPPPMM2zatInMzEwuueQSOjqODlFbtmwZK1as4I033uDLL7+ks7OThQsX4nQ6fdssWrSIkpISVq5cycqVKykpKWHx4sVBf71CCBFOLJ4iek5S76JcvOLby4nWaznc2MWWitZePdfcbacrLg+AkRnxfTp+f4VjJ7rV4fTl1MedRRE9IyEKALPFjsXuPMPWQgghhBBCCCGE6IuQFtGvvPJKLr/8ckaOHMnIkSP59a9/TVxcHMXFxaiqylNPPcVDDz3EddddR1FRES+//DLd3d289tprAJjNZl544QUef/xx5s2bx6RJk1i+fDk7d+5k9erVAOzZs4eVK1fy17/+lVmzZjFr1iyef/553nvvPfbt23fKtVmtVtrb24/7EEKISOF0qUeL6L3MQ/fSuuwsnOCOZPn7+opePff9nbWoGi0psQZS43oXJeMvRk8nervFjsMZHoM5q1osuFRIjNZjsJ35906UXktijDvypb5dutGFEEIIIYQQQohACJtMdKfTyRtvvEFXVxezZs2irKyMuro65s+f79vGaDRywQUXsG7dOgC2bNmC3W4/bpvs7GyKiop826xfvx6TycSMGTN828ycOROTyeTb5mQeffRRX/yLyWQiLy/P3y9ZCCFCZm9dOy6tAb1WIS3O2Of93Dq7AIAPdtZSa7ac9fO8Oeqjs+L7FCXjD1qHBYNOgwruQZ5h4Ijnc5jXi4ibTE83er1EugghhBBCCCGEEAER8iL6zp07iYuLw2g08l//9V+sWLGCsWPHUldXB0BGRsZx22dkZPgeq6urw2AwkJSUdNpt0tPTTzhuenq6b5uTefDBBzGbzb6Pqqqqfr1OIYQIJxvLWgDINkWj0fS9iF2UY2JGYTIOl3rW3ehVLd1sKm8FVWVUiKJcwD2YM8UzULUlTCJdGtqtwNHC+NnwRrrUSSe6EEIIIYQQQggRECEvoo8aNYqSkhKKi4v5wQ9+wK233sru3bt9j3+9Q1FV1TN2LX59m5Ntf6b9GI1G38BT74cQQkSKzZ4M8+w+Rrkc67ZzCwF4bUMlFtuZc7lXbKsBIKarlvgofb+P3x9JMe4iejjkortUlYYOdyE8PeHs7w7wdaK3W/s04FUIIYQQQgghhBCnF/IiusFgYPjw4UydOpVHH32UiRMn8oc//IHMzEyAE7rFGxoafN3pmZmZ2Gw2WltbT7tNfX39CcdtbGw8octdCCEGiz217rztjF4Ua09l3pgMhiTHYLbY+ffW0w/DVFXVV0RPaD/U72P3V7KnE70tDIrobd127E4VnUYhOebsc+JT4wxoFLDYnXT0OAK4QiGEEEIIIYQQYnAKeRH961RVxWq1UlhYSGZmJqtWrfI9ZrPZWLt2LbNnzwZgypQp6PX647apra2ltLTUt82sWbMwm81s3LjRt82GDRswm82+bYQQYjDpsTspb+oCILUfeeheWo3CEk82+otfleFynbobuqSqjbKmLqL1WuLbezeMNBCSPEM5W7tDn4nuHQyaFm/sVcSOTqvx/TtKpIsQQgghhBBCCOF/IS2i/+///i9ffPEF5eXl7Ny5k4ceeog1a9Zw8803oygKy5Yt45FHHmHFihWUlpayZMkSYmJiWLRoEQAmk4nbb7+d++67j08++YRt27Zxyy23MH78eObNmwfAmDFjWLBgAUuXLqW4uJji4mKWLl3KwoULGTVqVChfvhBChMSB+k5cKmgdPcQYtH7Z543T8og36jjU2MXqPSfe/eP1/BeHAbh0XAYaNfRd0944l9ZuW8ijULx56Bm9yEP3OhrpIkV0IYQQQgghhBDC33ShPHh9fT2LFy+mtrYWk8nEhAkTWLlyJZdccgkADzzwABaLhTvuuIPW1lZmzJjBxx9/THz80UF0Tz75JDqdjhtvvBGLxcLFF1/MSy+9hFZ7tDD06quvcs899zB//nwArrrqKp555pngvlghhAgTe+rcUS5Ga+sZZ0ycrTijjptn5vOntYf43xWlnDMkkfT444vB724/wgc769BqFL573lDue90vh+6XhGg9GgUcLpVOqyOkGe31njz0jPje3x2QYYqCGjP1nkK8EEIIIYQQQggh/CeknegvvPAC5eXlWK1WGhoaWL16ta+ADu6BoA8//DC1tbX09PSwdu1aioqKjttHVFQUTz/9NM3NzXR3d/Puu++Sl5d33DbJycksX76c9vZ22tvbWb58OYmJicF4iUIIEXb21XUAYLC2nmHL3lk2bwSjM+Np6rTyw9dLcB4T69LQ3sNP3i4F4M4Lh1OUY/LrsftKq1FIiA59pIvLpdLY4S6Ap/ezE/10cTpCCNEbBQUFKIpywsedd94JwJIlS054bObMmcftw2q1cvfdd5OamkpsbCxXXXUV1dXHz89obW1l8eLFmEwmTCYTixcvpq2tLVgvUwghhBBCiDMKu0x0IYQQgbXX24ne498iepReyx9vnkyMQcv6w808/ekBwD3r4kf/3kFbt52inATuvmi4X4/bX8dGuoRKS7cNh0tFr1V8Oe29kRSjx6DV4HCptITwdQghIsumTZuora31fXjnEN1www2+bRYsWHDcNh988MFx+1i2bBkrVqzgjTfe4Msvv6Szs5OFCxfidDp92yxatIiSkhJWrlzJypUrKSkpYfHixcF5kUIIIYQQQpyFkMa5CCGECD5vJ7rRz53oAMPS4vj1tUX895vb+cMnB3htQyUuVaWp04ZBp+GJG89Brw2v67dJMXrKgLau0HWie/PQ0+Oj+hSxoygK6QlGqlst1LX3+GVgrBBCpKWlHffn3/zmNwwbNowLLrjA93dGo5HMzMyTPt9sNvPCCy/wyiuv+OYVLV++nLy8PFavXs2ll17Knj17WLlyJcXFxcyYMQOA559/nlmzZrFv3z6ZYSSEEEIIIcJCeFUyhBBCBFRjh5WmThuKAkZrW0COce2kXBbPzEdVocFzPIAfLRjNyIz4Mzw7+MKhE92Xh57Q9+K3L9LFLMNFhRD+Z7PZWL58ObfddttxF/vWrFlDeno6I0eOZOnSpTQ0NPge27JlC3a73TeXCCA7O5uioiLWrVsHwPr16zGZTL4COsDMmTMxmUy+bU7GarX6ohq9H0IIIYQQQgSKdKILIcQg4u1Cz0+OQaM6AnacX15TxHfmFNBtc6IoEGPQUZgaG7Dj9Uc4FNGP7UTvq3TPQFLvRQshhPCn//znP7S1tbFkyRLf31122WXccMMN5OfnU1ZWxk9+8hMuuugitmzZgtFoBTxCnQAAjVpJREFUpK6uDoPBQFJS0nH7ysjIoK6uDoC6ujrS09NPOF56erpvm5N59NFH+fnPf+6fFyeEEEIIIcQZSBFdCCEGEW8e+ujMBMoCfKyhaXEBPoJ/JHoyyNt7HDicLnRBjptxulQaO91F9P50oifHui8GtHTZUFUZLiqE8K8XXniByy67jOzsbN/fffOb3/T9f1FREVOnTiU/P5/333+f66677pT7UlX1uG72k8VYfX2br3vwwQe59957fX9ub28nLy/vrF+PEEIIIYQQvSFxLkIIMYjs9XSij8oMv1iVUIkxaDHo3L8O2yzBz0Vv6bLhdKkYdBpM0b0fKuqVGGNAo4DN6aLTGri7DIQQg09FRQWrV6/mu9/97mm3y8rKIj8/nwMH3IOlMzMzsdlstLYeP4OjoaGBjIwM3zb19fUn7KuxsdG3zckYjUYSEhKO+xBCCCGEECJQpIguhBCDiLcTfUyWFNG9FEUhydONHopIl/p2d4Z5eryxT0NFvbQahcSYo93oQgjhLy+++CLp6elcccUVp92uubmZqqoqsrKyAJgyZQp6vZ5Vq1b5tqmtraW0tJTZs2cDMGvWLMxmMxs3bvRts2HDBsxms28bIYQQQgghQk2K6EIIMUg4nC4O1HcCMCpTOvaO5c1Fb+sOfid6syfD3Jtp3h/eSJdmKaILIfzE5XLx4osvcuutt6LTHU2C7Ozs5P7772f9+vWUl5ezZs0arrzySlJTU7n22msBMJlM3H777dx333188sknbNu2jVtuuYXx48czb948AMaMGcOCBQtYunQpxcXFFBcXs3TpUhYuXMioUaNC8pqFEEIIIYT4OslEF0KIQaK8uRurw0W0XsuQ5JhQLyeshHK4aFOXOw89JdZ/RXTpRBdC+Mvq1auprKzktttuO+7vtVotO3fu5O9//zttbW1kZWVx4YUX8uabbxIff/RupyeffBKdTseNN96IxWLh4osv5qWXXkKr1fq2efXVV7nnnnuYP38+AFdddRXPPPNMcF6gEEIIIYQQZ0GK6EIIMUjs8+Shj8yIQ6vpe2xIJPLFuXSFrhM9Jc7Q732lHFNEl3sNhBD+MH/+/JMOK46Ojuajjz464/OjoqJ4+umnefrpp0+5TXJyMsuXL+/XOoUQQgghhAgkiXMRQohBwpuHPlqiXE6QeEwn+smKRYHSbXNgsTuBo13k/eGLc+m0EbxXIYQQQgghhBBCRDYpogshxCDh7UQflSlDRb8u0dOJbnW4fEXtYPB2oZui9ei1/f+VnBijR1HA5nTh0ElkjxBCCCGEEEII4Q9SRBdCiEGirKkLgKFpsSFeSfjRazXER7kTzoI5XNQ7ADTFD13oADqNhsRo9wUBm9Hkl30KIYQQQgghhBCDnWSiCyHEIOByqVS0dAMwNDUuxKsJT0kxBjp6HLR228hOjA7KMZs7PUNF/ZCH7pUca6C1247VkOS3fQohhBBiYJi34HLqG5pOu01GeiqrV34QpBUJIYQQkUGK6EIIMQgcMVuwOVzotQrZiVGhXk5YSorRU9kCrSHpRDf6bZ8psUYONXZJJ7oQQggxCNU3NHHbb08/qPdvP7rlrPZ1NgV5CE1RXi4WCCGECDYpogshxCDgjXLJS45B54fs7UiU5B0u6ilsB5rK0Ux0f3eiA1iNiX7bpxBCCCEGn7MpyMPZF+X9abBcLDhbLV02DjV2UpASS1q8/5ozhBBCHCVFdCGEGATKvXnoqZKHfire4aLBykR36GKwOV1olKMFfH/wFtFtxkRUVUVRFL/tWwghhBAi0oTzxYKzKfBXNLTx763VdNucrDvUTGqcgXHZJibmmuQ8UPiV3AEiBjspogshxCBQ1uTOQy9IkSL6qXgL2W0WGy6XGvDjWY1JvuNqNf57g5MUo0cBXFojjR1W0hMkvkcIIYQQYiA6U4Hf6VJ54rUP6LY5idZrsTlcNHXaWLu/EadLZUq+zMgR/uPPO0CEGIjknn4hhBgEypvdnegF0ol+SvFROnQaBZcK7T2B70a3eeJWUmL914UOoNNqMHm66vfXd/p130IIIYQQInx8vr8RQ9ZIDDoNN0zN5bvnFTKtwF0431LRit3pCvEKhRAickgRXQghBoEyiXM5I0VRfJEuwRgu6u1ET4nzf26ltzB/oKHD7/sWQgghhBCht7e2nR01ZgAuHZdBUoyBKL2WmYUpmKL1WOxOdlSbQ7xKIYSIHFJEF0KICOdwuqhq8cS5SBH9tHzDRbsDP1zUO/jTn0NFvZJ9RXTpRBdCCCGEiDSqqlJc1gJA1+YVDE2N8z2m0SjSjS6EEAEgRXQhhIhw1a0WHC4Vo05DpuRjn9bRTvTAFtGdLjVgcS5wtIh+UOJchBBCCCEiTq25B7PFjl6r0LXt/RMeH52ZQEKUDovdyc4a6UYXQgh/kMGiQggR4bxRLoWpsWj8OMAyEvmGi3bZCWTPfmVLN6rGncFuitb7ff+JntdR5snCF0IIEVnmLbic+oam026TkZ7K6pUfBGlFQohg2lPbDsDw9DhqHCc2f2g1CtMKk/lkTwNbKloZn2MK9hKFECLiSBFdCCEinLeIXpAiUS5ncmycSyA/W/vq3FnlybEGFMX/FzaSPIX5xg4rnVYHcUb5dS+EEIESioJ2fUMTt/12+Wm3+duPbvHb8c7W2XwuQAr8QvSHw+nyDY8fk5nA2lNsNyYzgU1lLbT3ONhXL3NyhBCiv+RdtRBCRLhyTzey5KGfWVKsu/jcZXPi1Pi/Q9xrv+eNTCDy0AGMei1ahwWnLprypi6KpPtICCECJlwL2qFwNp8LGDyfDyEC4VBjFzani/goHblJ0afcTqtRGJudQPHhFsqb5O5EIYToL8lEF0KICOftRB8qRfQzMuq0xBi0ANgNCQE7jrcbKDXWGLBjGGzu23wPy5smIYQQQoiIsafOfY43JjPhjHc0eu9ErWzpRlWk/COEEP0hnehCCBHhfHEuUkQ/K4kxerptTmwBLKLvrwtsJzqAwWbGEpNBWaMU0YUQItTKy8sYP3n6GbeTmBMhxOl0Wh1UNncDMDor/ozbp8cbidZrsdid6JKGBHp5QggR0aSILoQQEczqcFLTZgGgIDUmxKsZGJJiDBxp68FmCEwEitXh9F3YSAlgJ7re04le1tQZsGMIIYQ4O06nS2JOhBD9tq+uAxXIMkX5ZvmcjqIoFKTGsKe2A0fqiMAvUAghIpjczyOEEBGssrkbVYU4o460uMAVbCOJ9w1JoIroZU1dOFwqGqeVWKM2IMeAo3EuZRLnIoQQQggREQ42uJsjRmeeuQvdyxvp4kwdHpA1CSHEYCFFdCGEiGBHo1xizpiZKNySYtwDRW3GwMS57PNEuRitbQH9Nzk2E11V1YAdRwghhBBCBJ7V7qS+vQfoXUzjkOQYFMAVl051a3eAVieEEJFPiuhCCBHByps9RfQUyUM/W0c70RMCUnz2FtEN1la/7/tYelsHigIdPQ6au2wBPZYQQgghhAis6jYLKpAYrSchSn/Wz4vSa8kyRQGwZl9jgFYnhBCRT4roQggRwbyd6ENlqOhZS4jWo1FA1eip83T7+NP++qOd6IGkUZ1km6IBKJdIFyGEEEKIAa2qxd1Fnpfc+zlH3s71Nfsa/LomIYQYTKSILoQQEexonIsU0c+WVqOQEO3u7jnc6P/i8z5fET2wnegAQ9Pc/+6HpYguhBBCCDGgVbVYAMhLju71c713pX51sJkeu9Ov6xJCiMFCiuhCCBHBypvcHStSRO8db6TL4cZOv+63y+rwvQEyBLgTHaDQ8+8uw0WFEEIIIQauTquDlm53PF9eUu870VPjDCg97VjsTjaWtfh7eUIIMShIEV0IISJUt83hiyMplEz0XvEOFz3k5070Aw3uonxavBGd0+rXfZ+Mr4gegI56IYQQQggRHN4ol/R4I1F6ba+frygK2ubDAGwqlyK6EEL0hRTRhRAiQnm70BNj9CTFGkK8moEl2fP5OtDQ4df97vcMFR2VEe/X/Z6KdKILIYQQQgx8Va19z0P30pqrAdhW2eaPJQkhxKAjRXQhhIhQ5c2ePHTpQu+1lDgjAPvq/FtE9+ahjwxSEX1oahwAZc1duFxqUI4phBBCCCH8R1XVo3noSb3PQ/fStrmL6CVVbTjlvFAIIXpNiuhCCBGhvN3HhZKH3mspsQZQVZo6bTR1+i92Zb+niD4qM85v+zyd7MQo9FoFm8PFEbMlKMcUQgghhBD+09Ztp9PqQKsoZCf2vYiu6WogxqCl0+rgkJ/n/gghxGCgC/UChBCRw+VSKWvuorTGTGOHFZ1GQafVEGfUkWmKIssURaYpCqOu9zl+ovekiN53eq0Gvb0DuyGB/fUdpHo60/vL29kerE50nVbDkOQYDjV2UdbURW4fBlEJIYQQQojQqfREuWQlRqHX9r0PUlFVxueY2FDWQkllW9DOR4UQIlJIEV0I0W+NHVYe+WAPH++qo8vmPO22eq3ChNxEphYk0R2TiaqqKIoSpJUOLuWeInqBFNH7xGhtw25IYF9dB7OHpfZ7f61dNho63F3tI4L4pqUwNY5DjV2UN3Vx3oi0oB1XCCGEEEL0n3eoaJ4fmiEmDUliQ1kL26pauXFaXr/3J4QQg4kU0YUQfaYC/9hcxa/f34PZYgcgSq9hbFYCuUkxOFUVh9NFu8VBfXsPteYeLHYnWypa2VLRCvkLeGtbDeeNSCU9Piq0LyYC+TrRJRO9T4zWVjrjh/giWPrLu5/cpGjijMH79Ts0LRb2wGEZLiqEEEIIMaCoQK25B4CcfuShe00akgjIcFEhhOiLkBbRH330Ud566y327t1LdHQ0s2fP5re//S2jRo3ybaOqKj//+c/5y1/+QmtrKzNmzOCPf/wj48aN821jtVq5//77ef3117FYLFx88cU8++yz5Obm+rZpbW3lnnvu4Z133gHgqquu4umnnyYxMTFor1eISOJSVY7kzOWBf+0AoCgngZ9fNY6JuYnoTnGboaqqVLZ0s6m8leLDzfx7UwXVrRZe31jFuOwE5o5KQ6eRUQ3+0N5jp7nLBkBBqkR49IWhpxXw33DRYA8V9fLG+ZRJEV0IIYQQYkBRoxPptjnRKJAR3/94wUl5iYD7vLTT6ghqY4cQQgx0Ia1WrV27ljvvvJPi4mJWrVqFw+Fg/vz5dHUdfaP/2GOP8cQTT/DMM8+wadMmMjMzueSSS+joOFrUWLZsGStWrOCNN97gyy+/pLOzk4ULF+J0Ho2VWLRoESUlJaxcuZKVK1dSUlLC4sWLg/p6hYgkG8ta6EwowKDT8D+XjeY/d8xhSn7yKQvoAIqikJ8Syzem5PL7GyZSeOgtRnkKiruOtLN6TwOqKpPi/cEb5ZIaZyQ+Sh/i1QxMRmsbAPvrO/3ydbn7SDsAY7MS+r2v3pAiuhBCCCHEwOQ0uRsD0+KNp32fdbbSE6LISYxGVWFHVVu/9yeEEINJSC87rly58rg/v/jii6Snp7NlyxbOP/98VFXlqaee4qGHHuK6664D4OWXXyYjI4PXXnuN73//+5jNZl544QVeeeUV5s2bB8Dy5cvJy8tj9erVXHrppezZs4eVK1dSXFzMjBkzAHj++eeZNWsW+/btO67z3ctqtWK1Wn1/bm9vD9SnQYgBp6K5iw1lLQD85rrxXDc59wzPODm9o4sFRZmMyoznvR1H2FfXQUKUzi/504Pd0aGi0oXeVwabGb1WodPqoKbN0u+hnKVHzID7ro1gGuopole1dGNzuDDo5G4PIYQQQoiBwJnozi3PMvU/ysXrnCGJ1LRZ2FbVxuzh8r5LCCHOVli9kzab3QWG5ORkAMrKyqirq2P+/Pm+bYxGIxdccAHr1q0DYMuWLdjt9uO2yc7OpqioyLfN+vXrMZlMvgI6wMyZMzGZTL5tvu7RRx/FZDL5PvLyZOiGEACdPQ4+2lUPgKl1X58L6McqTI3lotHpAGwqb6W0xtzvfQ52R4vokofeVwoqw9LigP5HutgcLt8+xmWb+r223kiLNxJr0OJSodIzmEoIIYQQQoQ/Z6L7vVaWyX/zo7yRLpKLLoQQvRM2RXRVVbn33ns599xzKSoqAqCurg6AjIyM47bNyMjwPVZXV4fBYCApKem026Snp59wzPT0dN82X/fggw9iNpt9H1VVVf17gUJEAFVV+WhXHRa7k7Q4I+n1G/2273HZJqYXuC+gfbqvgeZO6xmeIU7HG+dSIEX0fvHml+/r53DR/fUd2J0qpmg9uX4YCtUbiqJQmCaRLkIIIYQQA0m3zYErLhOATH8W0Ye4ayclVa0SpSmEEL0QNkX0u+66ix07dvD666+f8JiiKMf9WVXVE/7u676+zcm2P91+jEYjCQkJx30IMdhVtnRT3WZBp1G4fHwmGtV55if1wsyhyRSmxqKqUHy4xa/7Hmx8negpUkTvj1GZ7iL6/n52ou/yRLmMy0444++vQChI8RbRO4N+bCGEEEII0Xs7qs2g0RBr1BLvxwGg47IT0GsVmjptVLda/LZfIYSIdGFRRL/77rt55513+Oyzz8jNPRoNkZnpvur69W7xhoYGX3d6ZmYmNpuN1tbW025TX19/wnEbGxtP6HIXQpzapnL391lRjonEGIPf968oCnOGpQBwsLGTHmOy348xGKiqerSIniZF9P7wDr7d2+8iunuuRlFOcKNcvIb6hotKnIsQQgghxECwtdL93ivLFO3XJowovdY36N57DCGEEGcW0iK6qqrcddddvPXWW3z66acUFhYe93hhYSGZmZmsWrXK93c2m421a9cye/ZsAKZMmYJerz9um9raWkpLS33bzJo1C7PZzMaNR6MnNmzYgNls9m0jhDi9mlYLNW0WtIrC5CGJATtOSpyRkRnuHOqmtHMCdpxI1tptp73HAUB+shTR+8PbiX64sQu709Xn/Xhz/sdlh+aupqNxLtKJLoQ4ew8//DCKohz34W1yAfe5/MMPP0x2djbR0dHMnTuXXbt2HbcPq9XK3XffTWpqKrGxsVx11VVUV1cft01rayuLFy/2zSJavHgxbW1twXiJQggRtrZWtAH+zUP3mpCbCBxt9BBCCHFmIS2i33nnnSxfvpzXXnuN+Ph46urqqKurw2Jx31KkKArLli3jkUceYcWKFZSWlrJkyRJiYmJYtGgRACaTidtvv5377ruPTz75hG3btnHLLbcwfvx45s2bB8CYMWNYsGABS5cupbi4mOLiYpYuXcrChQsZNWpUyF6/EAPJxnJ3vMqY7Hjio/QBPdbMwhQUoCt+CCVVbQE9ViTyFkqzTFFEG7QhXs3AlpMYTaxBi83poqK5b3niTpfK7trQdqIXprovTEkmuhCit8aNG0dtba3vY+fOnb7HHnvsMZ544gmeeeYZNm3aRGZmJpdccgkdHUfv3lm2bBkrVqzgjTfe4Msvv6Szs5OFCxfidB6NhFu0aBElJSWsXLmSlStXUlJSwuLFi4P6OoUQIpyoqso2Xye6/4voYz2NHbuliC6EEGfNf8FaffDcc88BMHfu3OP+/sUXX2TJkiUAPPDAA1gsFu644w5aW1uZMWMGH3/8MfHx8b7tn3zySXQ6HTfeeCMWi4WLL76Yl156Ca32aPHo1Vdf5Z577mH+/PkAXHXVVTzzzDOBfYFCRIi69h4qW7pRFJiaH/iIlaRYA6Oz4tlT28GTq/bz8m3TA37MSOKN7CiUoaL9ptEojMiIp6Sqjb11HQxPjz/zk77mcGMnPXYXsQZtyDLqvcetb7fSZXUQ68dcTSFEZNPpdMd1n3upqspTTz3FQw89xHXXXQfAyy+/TEZGBq+99hrf//73MZvNvPDCC7zyyiu+5pbly5eTl5fH6tWrufTSS9mzZw8rV66kuLiYGTNmAPD8888za9Ys9u3bd8qGF6vVitV6dAh5e7sUgoQQkaOiuZvmLhu4HKTFG/2+f+/dkbtr289q5pwQQogwiHM52Ye3gA7ubvSHH36Y2tpaenp6WLt2LUVFRcftJyoqiqeffprm5ma6u7t59913ycvLO26b5ORkli9fTnt7O+3t7SxfvpzExMQgvEohBr5NZe4u9NEZ8ZiiA9uF7jW9IBlUF2v3N/qGMoqzU+7pNi6QIrpfjPZEuuzrYy56qefrd2x2AhpNaN6gmGL0pMS65xhIN7oQojcOHDhAdnY2hYWFfOtb3+Lw4cMAlJWVUVdX52tQATAajVxwwQWsW7cOgC1btmC324/bJjs7m6KiIt8269evx2Qy+QroADNnzsRkMvm2OZlHH33UF/9iMplOOPcXQoiBzJtVrmmvRafxf9lmZEY8Wo1CS5eNuvYev+9fCCEiUVgMFhVChK+OHjuHPUW3qQXBG/SZGGMgrqMSgLdLjgTtuJHAN1Q0RF3PkWaMZ/DSjuq+XcwprXF3R47LDk2Ui1ehb7ioFNGFEGdnxowZ/P3vf+ejjz7i+eefp66ujtmzZ9Pc3ExdXR0AGRkZxz0nIyPD91hdXR0Gg4GkpKTTbpOenn7CsdPT033bnMyDDz6I2Wz2fVRVVfXrtQohRDjxFtG1bdVn2LJvovRahqe54/4k0kUIIc6OFNGFEKe119N9m5MYTbKnkzVYEtrd3W7vbj+Cy6UG9dgDma+ILp3ofjEl31382VrZ2qevQ++dFKEaKuolRXQhRG9ddtllXH/99b5ZQ++//z7gjm3x+noEwNnEAnx9m5Ntf6b9GI1GEhISjvsQQohIsa2yDQCtOXAXCL256DJcVAghzo4U0YUQp6QCezwDEUdn9T4Lur9iO2uIj9JRa+5hk2ewqTg9VVUpb5Y4F38anRlPjEFLR4+DAw2dvXquy6Wyqya0Q0W9CtOkiC6E6J/Y2FjGjx/PgQMHfDnpX+8Wb2ho8HWnZ2ZmYrPZaG1tPe029fX1JxyrsbHxhC53IYQYDHrsTl8jk7atJmDHGSfDRYUQolekiC6EOKWeqBRau+3oNAoj0uOCfnyN6uSyIveb9Le3S6TL2WjosNJtc6JRYEhyTKiXExF0Wg3n5CUCsKWi9fQbf01VazcdVgcGnYbhIfgeOpY33keK6EKIvrJarezZs4esrCwKCwvJzMxk1apVvsdtNhtr165l9uzZAEyZMgW9Xn/cNrW1tZSWlvq2mTVrFmazmY0bN/q22bBhA2az2beNEEIMJruOmHG6VNLijSjWwBW4x2YdHS4qhBDizKSILoQ4pXbTcACGpcVh1GlDsoarz8kB4IOdtdgcrpCsYSDxFkhzk2Iw6ORHvL9M9US6bK7o3R0R3jz0MZnx6LWh/ffwdqIfbuxEVSUeSQhxZvfffz9r166lrKyMDRs28I1vfIP29nZuvfVWFEVh2bJlPPLII6xYsYLS0lKWLFlCTEwMixYtAsBkMnH77bdz33338cknn7Bt2zZuueUWXzwMwJgxY1iwYAFLly6luLiY4uJili5dysKFCxk1alQoX74QQoSEN8plYm4igRxJ741zqWzppr3HHsAjCSFEZNCFegFCiPBkc7hoTygEYEwIoly8Zg5NIS3eSGOHlS8PNnLRaLm1+3TKmyTKJRAme3PRe9mJ7o0hCnWUC0CBpxO9vcdBa7c96DMOhBADT3V1NTfddBNNTU2kpaUxc+ZMiouLyc/PB+CBBx7AYrFwxx130NrayowZM/j444+Jjz963vDkk0+i0+m48cYbsVgsXHzxxbz00ktotUcvzr/66qvcc889zJ8/H4CrrrqKZ555JrgvVgghwsR2zzD7SUMS2RDA4yTGGMhJjKamzcKeI+3MGJoSwKMJIcTAJ0X0MNZjd3Lnq1uxRKWFeikiyPbWtVPR3E1N7kXc/NdiRmcm8L+Xj0GrCWQvwvE+3duASxdFrEFLXghjQbQahYUTsnjxq3LeLjkiRfQz8A0VTZEoF3+aNCQJRYHy5m4aO6ykxRvP6nlfHWwCYM7w1EAu76xE6bW+N0plTZ0kxyaHeklCiDD3xhtvnPZxRVF4+OGHefjhh0+5TVRUFE8//TRPP/30KbdJTk5m+fLlfV2mEEJElO1VbYC7Ez3QxmQlUNNmYZcU0YUQ4ozkXv8w9vSnB/hkbwOVBQvYUd026G6/7+ixY7U7Q72MoDvY0MlHu+rZW9dBZ/wQvjrYzAtflvH7j/cFdR1vba0GYHRmAholeMX7k/FGuny8q55umyOkawl3ZdKJHhCmaD0j092dlVsrz64bvc7cw4GGThQFZg8LjzclhaneSBfJRRdCCCGECDfNnVYqW7oBGJ8b+DsZfcNFJRddCCHOSIroYewHc4dz+fhMULR8tq+RVbvrsTsHRyb0gYYOXlpXzgtfldGYNom2bluolxQU3TYHn+5tAGBkRhwZtev473kjAXhuzSHeLgncdPZjmS12PtvnXsfoEEa5eE3MNZGXHI3F7uSrg82hXk5YK2/2dKJLEd3vvJEuZztc9EtPF/qEHBOJMeERneL9upDhokIIIYQQ4WeHJ8plWFospmh9wI/nzUXffUSK6EIIcSZSRA9jcUYdf1w0mbT6TSgK7KnrYPXu+lAvK+AON3aysrQOlwp2p0pL6kTO++1nvLGxMtRLCyhVVflkTwMWu5PUOAOXjM0gsW0/P5w3gu9fMBSAH/17B6U15oCvxX3BRsXQ00pq3NnFVgSSoihcOCodgM/3N4Z4NeHL5VIpb3Z3rkgR3f+m9rKIHk5RLl7SiS6EEEIIEb5KvFEueYlBOZ63E/1AQwc2x+Bo2BNCiL6SInqYUxSF5JZdXOOJs9jf0ElrBHdllzd38cFOdwF9ZEYcCydkYexpocPq4Mf/KaXKc2tbJNpd287hpi40Cswfm4lOc/Tb84FLRzN3VBo9dhfff2ULPQGOuflgZy0A8R3lAT1Ob5w3wj0b4IsDUkQ/lSNmCzaHC51GIScxOtTLiThTPEX0ndVmrI7Tfw+qqurrRD93RPgU0YemeYroTZ0hXokQQgghhPg6bxH9nCAV0XMSo0mI0mF3qhxo6AjKMYUQYqCSIvoAMSQ5xtdBuK2yLbSLCZB2i533d9TiVFWGp8Vx6dhMhqXFkV/2DucOT8XhUnn60wOhXmZA2BwuPt/vLrjNGppywtBCrUbhD9+aRLYpipo2C//cUh2wtZgtdl+hOr69PGDH6a2ZQ5PRaRTKm7upbI7ciyn9Ud7k/rwMSYlBp5Uf7/6WnxJDapwBm9N1xjtC9tV30NhhJUqv8RXfw8GwtDjA/bXiGCTxYEIIIYQQA4GqqmyvbgOCV0RXFMUX6bJLIl2EEOK0pMoygEwekgjAntp2LLbIG7i5o8aMw6WSZYpiQVEmGo17mKUC/Pcl7lzwf2+toTwCs3wPNnRic7pIjNb7cpe/zhSt53vnu2Nd/vL5oYAVwFZ7olxGpMdhtAU+OuZsxUfpmTzE/bn54qB0o59Mmae7uDBFolwCQVEU39fg5vLTR7p8ecB9UWx6YQpGnTbgaztbOYnRROk12JwuqlstoV6OEEIIIYTwqGzppq3bjkGnYXRmQtCOOy7bPcBUctGFEOL0pIg+gOQkRpMeb8ThUtlR0xbq5fiVw+li1xF3wXZKfhJaTwHda0p+EnNHpeF0qfxfBHaj7/FMQx+TnYBGUU653TenDSE51kBVi4X3PZEr/uaNcrl8fFZA9t8f53liMSQX/eTKPJ3oBZKHHjAzhqYA8NGuutNu541yOS+M8tABNBqFoanubvSDDRLpIoQQQggRLrxRLuOyEzDogleqGZslw0WFEOJsSBF9AFEUhUmebvTtVeaIuhX/QEMnPXYX8VG6Uw5E/O957m70/2yr4VBj5BR/2i12qtvcHaGjM+NPu220Qct3ZhcA8NyaQ6iq6t+19Nj5wtNBe8WE8Cuinz/SnYu+7mBzRH39+0t5s/suDRkqGjhXTsxCp1HYWtnG3rqTv9GwOpxsONwChNdQUa9h6e4ieiT9HBVCCCGEGOh8Q0VzE4N63HE5niJ6bTsul3/fXwohRCSRIvoAMyI9njijDovdyd76yBn8saPa3YVelGM6ZSf2xLxE5o1Jx6XC/30SOd3oe+vc/465SdEkROnPuP23ZxUQa9Cyt66DNfv825G9enc9NqeLYWmxjPAU2sJJUY6JxBg9HVaHLy9QHOWNOpIieuCkx0dxydgMAN7YWHXSbbZVtmGxO0mNM5zxwlgoDPMMF5VOdCGEEEKI8BHsoaJew9LiMOg0dFodEvcnhBCnIUX0AUarUXy/VM802G6g6IlKoa69B40C47JOn/32w4vd3egf7KylvccejOUFlMoxUS5neO1ephg9N8/MB+DZNQf9uh5vlMsV47NQThMrEypajeLr7PUOYhVuDqeLyhaJcwmGm6YPAeCtrdUnnU/x+sZKAM4dnuqb7RBOhksnuhBCCCFEWLE6nOyqcb8v9N59Hix6rYZRGe7GD2/EqhBCiBNJEX0A8nY21rdb6bI6Qrya/mtLGgW4u+xjjbrTbjs+18SwtFjsTpXP9jYEY3kB1ROdRpvFjk6jMDzt7Du/bz+3EL1WYVN5Kzur/XOiY7bYfYXpy8MwysXrfG8u+gHJRT9WdasFh0vFqNOQlRAV6uVEtHOHp5KbFE17j8N34clrR3Ubb5ccAeC75w0NxfLOaFiat4je5fdIKCGEEEII0Xu7jrRjc7pIiTUwJDkm6Mf35aLXSi66EEKcihTRB6BYo470eCMAZZ4M5IHK3G2nPcFdaJqQazqr5ywoygRgZenpB/sNBGbTMMDdGdqb4TEZCVFcVuQudL+2scIva/motA6b08XIjDhfJ0I4Om+EOxd9e1Ub5u6BfzeCv3h/FhSkxIZl93Mk0WgUXze6t+scQFVVHvlgDwDXTsqhKOfsfqYFW2FqLIrivnDW1GkL9XKEEEIIIQa9rRWtgLsLPRR3BI/NdhfRd8lwUSGEOCUpog9QQz1xDWWNA7uI/tm+BlSNjuRYA1mms+ueXTDOXTxes6/xpFEKA4XN4aIjoRA4+yiXY908w13Ee7vkiF+ibd7eXgPA1efkhGWUi1d2YjRD02JxqbChrDnUywkb3p8FBanB71wZjG6YkotWo7C5opX9nvkUn+5toPhwCwadhvvmjwzxCk8tSq8lL8n9dSKRLkIIIYQQobfNk4c+aUhSSI4/zlNE3y1FdCGEOCUpog9QhZ7BcJUt3bgUbYhX03efeiJZhqbGnnXhtigngZzEaCx254CO9Nha2YpLayRaryU3KbrXz59emMzw9Di6bU7e3lbTr7U0tPew7pC7IH3VxOx+7SsYZhSmALCpvCXEKwkf5c3eoaLhNxA2EqUnRDFvTDoA33huHQ++tYNfe7rQvzOngNyk8L6YIcNFhRBCCCHCx7ZjOtFDYXRWAooCde09NHdaQ7IGIYQId1JEH6DS4ozEGXU4XCrdMZmhXk6fOJwu1u53F8F7MwhRURQuHed+zR/tGriRLp97XvuQlBg0fej8VhTF143+6obKfmUbv7ujFlWFyUMSyQtBBl9vTS90d2hsLG8N8UrCR1mTt4ge/v9+keL++aPISXRno7++sYrDjV0kxei5Y+7wUC/tjGS4qBBCCCFEeKgz93DE3INGgYm5iSFZQ5xRR0GK+z255KILIcTJSRF9gFIUhUJP4bkzPi/Eq+mbkqo2zBY7Gqe114MQLx2XAcDq3fXYna5ALC/gvF30+f0oWl83OZcovYa9dR1srex7QfmdkqNRLgPBdE8nemmNOSKG6/qDt6N4WC8G1Ir+GZERzxcPXMhrS2dw49RchiTH8IurizBF60O9tDM6drioEEIIIYQInW2e93GjMhOINepCtg7fcFGJdBFCiJOSIvoA5i2id8Xl9asLOVS8US6xnTW9HoQ4tSCZlFgD7T0Oig8PvFzspk4rpTXuk5P+TF83Ret98SuvFleeYeuTK2/qYnu1Ga1G4fLxWX1eSzDlJEaTkxiN06WyrbIt1MsJuU6rg1pzD3C0w1gEh0ajMHtYKo99YyKfP3AhVw6AOCSAYd5OdIlzEUIIIYQIKW8e+uQQRbl4yXBRIYQ4PSmiD2B5SdHoNAoOfeyAvOXqs33uTuzYzupeP1erUZjv6UZfWTrwIl2+OtgEgLGnpd/dBjfPyAfgvZ21NPUhv+6d7UcAmDM8lbR4Y7/WEkzTCjyRLjJc1FcITY0zkBhjCPFqxEAw3NOJXtNmodsmd3MIIYQQQoTKVl8eemiGinp5i+gDsbYghBDBIEX0AUyn1fi6mD/Z0xDi1fROrdnCntp2FAViu/o2FNObi75qd/2A68T3ZsHHdvZvICjAhFwTE/MSsTlc/H19Ra+eq6oq//FGuQyQDlovb6TLRhkuKlEuoteSYg0kx7ovuByWSBchhBBCiJCwOVzsrDEDoe9EH+eJcznc2InF5gzpWoQQIhxJEX2AK0xzR7p8tm9gFdE/2+suIk/KS0Tn7Nv075lDUzDqNDR0WH1FxIFAVVW+OODuRI/p4wWEYymKwvfPHwrA39eX96qrdM3+Rg43dhFr0Po6+wcK73DRbZVt2BwDMxffX7zDISXKRfTG8DQZLiqEEEIIEUp7atuxOlwkxuh9ca2hkp4QRWqcEZcKe+ukG10IIb5OiugD3JAkdyf6jmoznQNowKK36H/hqPQ+7yNKr2WqJ9Jj3aGBE+mxt66Dxg4r0Xot0Rb/XPy4dFwm+SkxtHXb+cemqrN+3p/XHgLgpulDiI8K/2GIxxqWFkdyrAGrw8XOmrZQLyekvBeRpIguemNYuvuNmuSiCyGEEEKEhneo6KS8RBSld3PCAkEiXYQQ4tSkiD7AJUTr0ds6cLpUNg+QWAurw+nLBL9wdN+L6ACzh6UCRzPGB4LPPVEuM4cmo1H900Gt1Sh89zx3N/pfvyzD4Tzzfkuq2ig+3IJOo3DbuYV+WUcwKYpyTC56a4hXE1oHGyXORfSe9+vloHSiCyGEEEKExNbKNgAmhzgP3WucDBcVQohTkiJ6BIjudg/WXH94YHRjbylvpdvmJD3e6Psl3Vezh7lzsYsPN+N0DYxc9M8PuIvo549M8+t+b5iSS0qsgepWCx+cxbDVv3zu7kK/6pxsshOj/bqWYJlWkAzApgFyASkQbA4XFc3dgHSii94ZkREPuO+OEUIIIcTg5HC62FvXzopt1Tz6wR7++NlBzN32UC9rUFBV1fc+ZnJ+eBTRx3py0XdLEV0IIU6gC/UCRP/FdNXSnjiC4sMDo5BY7Cn2zx6W0u9b1sbnmIg36mjvcbDriJkJuYl+WGHgWGxONnm6ps8fmcYbftx3lF7Lt2cV8OTq/fxpzSEWjs9Cozn557e8qYsPPYX273ny1AeiGZ7hopvKW3C6VLSneL2RrKK5C6dLJdagJcsUFerliAFkTKa7iF7e1IXF5iTaoA3xioQQQggRTJXN3XznpY0c+tqQ8T+vPcQP5g5H1Ui5IJAqW7qpNfdg0GrCphPdG+eyt6590L6/EkKIU5FO9AgQ4+lEL60x09ET/l0D3mL/zKEp/d6XTqthhmc/Xx0M/078bZWt2JwuMhOiGBqAwTHfnpVPjEHL7tp2/vLF4VNu9/wXh1FVuHBUGqMz+3c3QCiNyYon1qClo8fBvkHaTesdCjksPS4schTFwJEWbyQl1oBLhf31g/P7RwghhBisdlabue65rzjU2EWsQcu0giRunjGEURnxtPc4+O3KvXTNvoPOnoEzd2ug8TaXnZOXGDbNDAUpscQYtPTYXZQ1SeSfEEIcS4roEUDv6CY/JcaTix7e2dAWm5OSqjbAP0V0OBrpsu5Q+OeiF5e5LyDMGJockIJnUqyBny4cC8DvPtrHlooT7074dG89b3iGj37/gmF+X0Mw6bQa362PJ3utg4FvqKjkoYteUhSF0VneSBe5ZVcIIULJ5nBhc7hQkQviIvAcyUP51l/W09RpY0xWAp/dP5d//tdsfn3teD744Xk8fsNEskxRqDFJvLPjCDaHf+Y4ieOtP+Quos8cmhzilRyl1SiM9tytKLnoQghxPCmiR4iZhUezwcPZsZ3Y+SkxftnnnOHu4aKbyluwOpx+2WegeP99vDEkgfDNaXlcNTEbp0vl7te20dZt8z22ubyFO17ditOlct3kHGYUhs8JW19N8RTRN1eE9wWkQPEW0YdJHrrogzGeO1H21EonuhBCBEuX1UFpjZn3d9by2oZKnlt7yPfROf+njPzxhyx46nP+3z+380pxBTVtllAvWUQQs8WOZdI36bI5mTM8hX98fybpCUcjAbUaheun5PKP789CsXXR2GFl5a46XOrAmD81UKiq6tc7tP1pXLYJkFx0IYT4Ogk5ixAzhyXz5uaqsB8u6u3EnunHTuyRGXGkxhlo6rRRUtnmi3cJNz32Y7vwA1e8VhSFR64bz47qNsqbu7ntpU1cPj6L9IQofrxiJz12FxeOSuO310+IiPiPqfnuz2W434URKAe9cS7SiS76YHSWt4gub5KEECLQDjV2sqWilVpzz2m3szlc7K3rYG9dB//cUs1PFTh/RBo3Tc/j4jEZ6LXSByX6RlVVPtlTD1oD0wuTeXHJdAy6k3895SXHEL3tdayzllLW1MWXB5o4f2RakFccuSqau6lrd+ehTwqTPHQvby76bjk/FEKI40gRPUJ4r16X1php77GTEKUP8YpOztuJ7c+r7YqiMGtYKu9uP8JXh5rDtoheUtWGzeEiLd5IYQDy0I8VZ9TxzKLJXPfsOrZWtrG1ss332JT8JJ69eUrEvAE7Z0giWo1CTZuFWrOFLFN0qJcUNC6XyqEG9yCo4dKJLvpgjC/OpQNVVSPiwpoQQoSbiuYukq5/mPd21Pr+LiPBfT6YHh9FQpSO+Cg9igIv/+z7fPjBh+yta2dnjZmNZS1sKGth7f5G1u5vZGRGHL++dnwIX40YyHbVtlPVagGnnceun3DKArqX1lzD/LEZfFhax7aqNvJTYshPCez7GH/psjqwZ4zlhS/LqG/vQatRuLwoi6KchLA43wnHPHSvcd4i+pF2OT8UQohjSBE9QmSZoilIiaG8uZvN5S1cNDoj1Es6QY/dSYmnmOvvQvecYSm8u/0I6w42ce8lI/26b3/Z4Lldb0ZhYPLQv64ox8SKO2fzUWkd++s72d/QQXq8kT/dMiXsTtT6I86oY0xWPKU17Wwub+XKiYOniF7b3oPF7kSnUfwWjyQGl+Hpceg0CmaLnVpzD9mJg+f7RwghAs3hdLFmfyO7jrSjTx+KXqtwTl4iE3ITiTOe/G2Yxm5hSEoMQ1JimD8uE3AX4d/YVMUbGyvZX9/JDX9aj37slfTYnUTpI+ecTgRWp9XBFwfcM6SMBz+jIPWas3reyIx4att6KKluY83+Rm6eEY1OE77NOA6niy2VrWwub8Ux8QZ++d5u32PPrTnEyIw4vjltCLfOykcXwqaio81l4RevOTIjHq1GobnLRn27lUxT1JmfJIQQg0D4/vYTvTbLM2DTO6Ak3Gz15KFnJBgp8HPBb/Ywdy769uo2LLbwzEXfUObJQw9ip/y4bBP3zh/FnxZP4dP75vLG92aRGGMI2vGDxRvpsmWQ5aJ789ALUmMj5s4CEVxGndYXBSTDRYUQwn/MFjv/2FLtG8zXvX0lS2YXMHtY6ikL6KeSnxLLjxaM5rP75/KtaXkA2HMn88amKlqPmX0jxOms2deAzeF+L6avLO7Vc2cOSybGoKWt2862Y+5wDTdlTV38vbiC4sMtOFwqmo56rpiQxe3nFnLFhCwMOg376zv55Xu7+c5Lm46bHRVM4ZyHDhCl1zIszX3Hwe5ac4hXI4QQ4UOqLhHE+wvY+ws53Bx7ouDvTuy85GiyTFHYnSpbK8OvkGpzuHzrmhkBwzzDzdHhouH5tR8o3iL6cMlDF/0w2hPpIsNFhRDCP460WXh9YyWNHVai9VqunZRD57rXiDH07ybgxBgDv7l+Av/8r1ko3a3uQv3mKo7I4FFxBnXmHg41dqFRYN6YDJReDgk16rScO9zdtLSxrIX2Hnsgltkv++s7eHfHETp6HMQZdSwYl0nM+j/xx0WT+cnCsfxx0WQ2PTSPn181jmi9li8ONHH1H7/iQH3wz3/COQ/dyztcdFeNNFkIIYSXFNEjiLeIvuuIGbMl/E5sApGH7qUoCjM8xekNYThcdUd1Gz12FymxBsmuDoCpBe6Tzz21HXRZHSFeTfD4iujyNSX6YYwMFxVCCL9p7LDy9vYjWB0uMhOiuGl6HkOS/XsH5rSCZGI2vkB6vJEeu4u3ttVwyDNoXIiT2VjubjQZnZlAapyxT/sYnRlPtikKh0v1xcKEi/31HazcVYeqwpjMeL49K59RmfF8vW3LFK3n1tkFvHXHbHKToqlo7ubaZ9fhjM8M6nrDOQ/da2yWDBcVQoivkyJ6BMlIiGJoaiwuFTaVhVdH7rF56IG6Zc0bk1IcZq8dYINnTdODlIc+2GSZoslJjMbpUimpagv1coLG2zkjRXTRH6Mzjw4XFUII0Xet3TZWbKvB5nCRZYriusk5xEfpA3Isja2Lb0zJpTA1FqdL5cPSOulIFyfV0NFDWVMXCkcbT/pCURTmjkpHUdyNHOHy9Xbg2AJ6VjzzxmacMeZwTFYC79x1LtMKkui0OrBM+lZQG3HWh3EeupdvuKgU0YUQwiekRfTPP/+cK6+8kuzsbBRF4T//+c9xj6uqysMPP0x2djbR0dHMnTuXXbt2HbeN1Wrl7rvvJjU1ldjYWK666iqqq6uP26a1tZXFixdjMpkwmUwsXryYtra2AL+60PAVksOsGzuQeehe3k70kqo2euzhlYvu/feYIVEuAeOLdCkPvzifQHC5VF/n8FjPSa4QfeHtNDrc2Bl2PzuFEGKg6LQ6WLGtBovdSVqckasnZgd8Xoleq2Hh+CyGegrp724/Ihnp4gSbytznxiMz4knq52yktHgj4zznDeHwfrOp08rHu+uPFtDHZKA5y4al5FgDLyyZxrC0WNQoE+/uOILD6Qrwit2DTz/f3wjALM9cr3DkfX9R0dwdlvE9QggRCiEtond1dTFx4kSeeeaZkz7+2GOP8cQTT/DMM8+wadMmMjMzueSSS+joONott2zZMlasWMEbb7zBl19+SWdnJwsXLsTpPFoIWLRoESUlJaxcuZKVK1dSUlLC4sWLA/76QsE3XDQMTmqOFcg8dK/C1FjS4o3YHK6w6ka2O12+gZfBHCo62Hg7awZLLnplSzddNicGnYahqbGhXo4YwNLijSTHGnCpcKBe4gCEEKK3VBQ+2lVHR4+DxBg910zKxqgPTkSDRqOwoCiTjAQjPQ4Xb5ccods2eKLtxOk1d1o56In6mdaPLvRjTStIRqNAVauF6tZuv+yzL1Stng9L63C4VPKTY3pVQPdKiNLzwq3TwNZNfbuVVXvqUXuZF99bmytaae22kxij99u/SSAkxhjISYwGYK/MzRFCCCDERfTLLruMX/3qV1x33XUnPKaqKk899RQPPfQQ1113HUVFRbz88st0d3fz2muvAWA2m3nhhRd4/PHHmTdvHpMmTWL58uXs3LmT1atXA7Bnzx5WrlzJX//6V2bNmsWsWbN4/vnnee+999i3b98p12a1Wmlvbz/uYyDwDq3cXduOuTt8rhhvCGAeutfxuejhU0jdWWOm2+YkMUbPqIz4UC8nYnk70bdVtuF0BfbkNxx4b60cnRmPLsCdbiKyKYrCGN9w0YHxu04IIcKJrXAO1a0W9FqFqyZm93uAaG/ptRqumphNQpQOs8XO+ztrcQW4ECgGhk2eOzSHp8WR0scs9K9LiNb7hk4Wh/A9l3XUZbR02Yg1aJk/rvcFdK+C1Fiit/8DjQL76zvZWWP280qP9/GuegAuHp0R9ufw3rk5pQH+nAghxEARtj+1y8rKqKurY/78+b6/MxqNXHDBBaxbtw6ALVu2YLfbj9smOzuboqIi3zbr16/HZDIxY8YM3zYzZ87EZDL5tjmZRx991Bf/YjKZyMvL8/dLDIj0hCiGpsWiqrChLDy60XvsTrZ5OsMDWUSHo53e4fLa4WhBf3pBMhqN5KEHyujMBOKMOjqtDvbWRX4hcPcRT5RLlkS5iP4bnekZLjoIvneEEGfn0UcfZdq0acTHx5Oens4111xzQgPKkiVLUBTluI+ZM2cet02kRy9urWzFNuxCAOaOTO93XEZfxRh0XH1ODnqtwpG2HjaVh09DiQiNjh47+z3zc6YV+rfjeVpBElpFoabNQlVL8LvR3y6pwZ47CYBLx2X2+8KVrrWCOcPd0SpfHmyiLUCxSKqq8vHuOgDmj8sIyDH8aWKu+2JJON3lLYQQoRS2RfS6Ovcvl4yM43+5ZGRk+B6rq6vDYDCQlJR02m3S09NP2H96erpvm5N58MEHMZvNvo+qqqp+vZ5gmuXLRQ+Pk+dtlW3YHIHNQ/fyduJvrWzF5gh8pt3Z8Bb0JcolsLQahcmebvRwG6wbCLslD134kbfTaFeNFNGFEG5r167lzjvvpLi4mFWrVuFwOJg/fz5dXV3HbbdgwQJqa2t9Hx988MFxj0dy9GJ7j50fvrENNBpGZsT57uoJleRYAxeOcr/v2VDWgtOUG9L1iNDafaQdFchJjCY9Psqv+46P0lOUczQbPZj3PTS09/Dj/5QC7nlTecn+eX85KS+RnMRo7E6VVXvqA3I3x57aDqpbLUTpNZw/Is3v+/e3SUM8d/pWDY6ZU0IIcSZhW0T3+np+tqqqZ8zU/vo2J9v+TPsxGo0kJCQc9zFQzAyz4aJHh2oGLg/da3h6HCmxBnrsLnZUtwX0WGfD4XT5Bl3KUNHA836ONw6C7ivpRBf+dE6eu9NoR00b9iAM1RJChL+VK1eyZMkSxo0bx8SJE3nxxReprKxky5Ytx21nNBrJzMz0fSQnHz3fCWT0Yjh4/KN9VLVYUCytXDQ6PeDnuWdjTFYCozLjUVWwTLheBgIOUi5VpdRzrugtdvvb1IJktBqFI+YenMlDA3KMk/nFe7vp6HGgMdcw3Y/vrxRF4ZKxGb67OQLRfe3tQj9vRBrRhuDMTeiPCXkmFAWqWiw0dVpDvRwhhAi5sC2iZ2ZmApzQLd7Q0ODrTs/MzMRms9Ha2nraberr60/Yf2Nj4wld7pHCW0TfU9cesFvReqM4CHnoXoqi+E6mNoRBN/Lu2nY6rQ7io3S+Tk8RON5/+41lLQEfChRKzZ1W6tp7ABgtX1fCD4amxpEQpaPH7pLhUUKIkzKb3Zm4xxbJAdasWUN6ejojR45k6dKlNDQ0+B4LZPRiOMwv+uG8kVw6LoPoHW9h1IVPQezCUWkkROlQoxP5qadjVwwu5c1ddFodROk1DE+LC8gx4ow6xue4L8Jbh88Nyrn3Z/saeG9HLVqNQtTu9/qcg34qpmi9r0N83aFmWrr8+17am4c+f+zAqEMkROl9Xz8llW2hXYwQQoSBsC2iFxYWkpmZyapVq3x/Z7PZWLt2LbNnzwZgypQp6PX647apra2ltLTUt82sWbMwm81s3LjRt82GDRswm82+bSJNWryR4elxnlz00BaSj89DD04ntrcbORw68Y/NQ9dKHnrATcg1YdBpaOq0cbip68xPGKD2eIqcBSkxxBmDO7xMRCaNRvHdsru1Um7ZFUIcT1VV7r33Xs4991yKiop8f3/ZZZfx6quv8umnn/L444+zadMmLrroIqxWd8diIKMXw2F+UXKsgT8vnorWXH3mjYPIqNOyoCgTVBf/KTnCJ3tObCgSka3UE882JishoMMrp+YnodMouBLzWLO/MWDHAei2OfjxCvdFodvmFKDtOPXPh/4Yl51AfkoMTpfKJ3vr/XZxoKqlm9217WgUuHjMwCiiA0wakghIpIsQQkCIi+idnZ2UlJRQUlICuIeJlpSUUFlZiaIoLFu2jEceeYQVK1ZQWlrKkiVLiImJYdGiRQCYTCZuv/127rvvPj755BO2bdvGLbfcwvjx45k3bx4AY8aMYcGCBSxdupTi4mKKi4tZunQpCxcuZNSoUaF66QHnLVivPxTaQrI3Dz093khhamxQjunNHt9S0RryWIKjeegS5RIMRp2WSXmJgLsbPVLtrnV3A0oeuvCnyVJEF0Kcwl133cWOHTt4/fXXj/v7b37zm1xxxRUUFRVx5ZVX8uGHH7J//37ef//90+7PH9GLA3l+UTBkmaLRVxQD8NCKUol1GURcxgTKPc0k47NNAT1WrFHHBM/wyadW7Q9oN/ofVh+gps1CTmI0y+aNDNhxFEXholHp6DTuWBdvLE5/rdrtvpg1rSCZ5NjQDCDuC18uunSiCyFEaIvomzdvZtKkSUya5J6sfe+99zJp0iR++tOfAvDAAw+wbNky7rjjDqZOnUpNTQ0ff/wx8fFHh/Y8+eSTXHPNNdx4443MmTOHmJgY3n33XbTao7dUvvrqq4wfP5758+czf/58JkyYwCuvvBLcFxtks4a6p4uHuhv72CiXYOVEjsqIJznWQLfNyfYQThJ3ulRfIXdGoQwVDZYZx0S6RCrJQxeBMDk/EZAiuhDieHfffTfvvPMOn332Gbm5px9UmZWVRX5+PgcOHAACG704kOcXBYvx4GcUpMRQ197Dox/sCfVyRJDYcyehArmJ0SQFoVg7JT8JnDa2V5v5dG/DmZ/QB7uPtPPXL8sA+MXV44gN8J2YCdF6Zg1zv3/78mATXVZHv/f57o4jAMwfl9nvfQXTOZ4GpR3VZpyuyI3LFEKIsxHSIvrcue7stK9/vPTSS4D7KvDDDz9MbW0tPT09rF279rhbSAGioqJ4+umnaW5upru7m3ffffeE2zmTk5NZvny5Ly9x+fLlJCYmBulVhoa383lvXQetfs5y641g5qF7aTQKszzHWxfCTvw9te209ziIM+oYJx3DQTPdc8EioovotZ4iunxdCT86Jy/RNzyqsUOGRwkx2Kmqyl133cVbb73Fp59+SmFh4Rmf09zcTFVVFVlZWYBEL4aa4nLw2+snAPD6xirWHWwK8YpEoDmcLuw5kwEoyglsF7pXjEGHodL9/ftEALrRnS6VB1fsxOlSuXx8ZtCiUM7JSyQ93ojN4ep3VM3Wyla2Vbah1ypcOSHLTysMjpEZ8cQYtHRaHRxs6Az1coQQIqTCNhNd9E9qnJER6e4hIN5IkWALRR66l7dz4KsQvlnw5tFPyU8KaBahON7k/ER0GoWaNgvVrd2hXo7f9didHGp036I7Nis4b47E4BAfpWdkuvtOL+lGF0LceeedLF++nNdee434+Hjq6uqoq6vDYrEA7ljG+++/n/Xr11NeXs6aNWu48sorSU1N5dprrwUkejEczBiawi0zhwDwo7d20G3rf0etCF+f7WtEjUogWq9lWHpwojQB9OXriDVo2XWknfd21Pp1369uqGB7VRvxRh0/u3KcX/d9OhpFYd6YDBQFDjZ09quA/IKni/6qiTmkJ0T5a4lBodUoTMxNBGCbnB8KIQY5qexFMG8hufhwaDpyt1a2Bj0P3WvOcHeczbbKNiw2Z1CP7bUhBF34wt0N4+28icRu9P31HThdKsmxBjISjKFejogwEukihPB67rnnMJvNzJ07l6ysLN/Hm2++CYBWq2Xnzp1cffXVjBw5kltvvZWRI0eyfv16iV4MMz9aMJpsUxRVLRZ+/9H+UC9HBNBrGyoAGJMVj04TvLf6GruF718wDIDffLiXHrt/3n/VmXt4bOU+AB5YMIqMIBeg0+KNTPFkgn+6twGXoffvaatauvlwp/vCwu3nnvmOnnB0jne4qOSiCyEGOSmiRzBv8TZUw0W/PODuAp8zPDVoeeheBSkxZJmisDldbK4IfiHV5VLZVO7JQ5ehokEXybnox+ahB/v7SkQ+3/CoirbQLkQIEXIni1xUVZUlS5YAEB0dzUcffURDQwM2m42KigpeeumlE2IVJXox9OKj9Dxy3XgAXlxXxpYKuVAaiWraLL7YkWBFuRxr6XlDyUyIoqbNwkvryvu9P1VV+dk7pXRaHZyTl8jNM/L7v8g+mDE0mdQ4Axa7E+uYK3odV/PyunJcKpw7PHXARjFO8uSil4Rw3pgQQoQDKaJHMG8hcV99B82dwc+3/dITpXKupys8mBRF8XXihyIXfXdtO63ddmIMWsaH4CR2sJseyUV0yUMXATTZ02m0o6YNu9MV2sUIIYTwm7mj0rlucg6qCj/69w6sjtDcqSkC582NlagqaFvKSIoJ/EDRr4s2aLn/UncE0x8/Pdjv959vlxzho1316DQKj143Ho0mNM0jOo2G+WMz0SjgyBjDW1trzvq5HT123thUBcDt5w3MLnQ42om+v6GDjh57aBcjhBAhJEX0CJYSZ2RUhvt22mAXE1u7bOysMQNw7ojgF9EB5gxzHzcUQ5S+8HThzxqagl7y0INuan4yigKHm7po6OgJ9XL8ake1+/tqbJYU0YX/DU2NIyFKR4/dxd7ajlAvRwghhB/9dOFYUuOMHGzo5OlPDoZ6OcKPHE4Xb252F2v1VVtCto7rJuVQlJNAh9XBHz450Of91Jot/PTtUgDuuXgEY0J83psWb2SG5y7vh9/ZRUVz11k9781NVXRaHQxPj+OCEWmBXGJApcdHkZsUjaoefS8ihBCDkVT3Ipx3oOf6w8Htxv7qUBOqCqMy4oOeXec1e7j7RGdnjRmzJbhXzL844L6V8vyRA/dkaSAzxegZk+k+2Q5VnFEgWGxOSj0Xp6bkJ4V4NSISaTSKL9JFctGFECKyJMYY+OXV7sGMz609xK4jUgyLFJ/sbaC+3UpKrAFdw56QrUOjUXjo8rEAvLqhsk/xH6qq8sC/dtDe42Birok75g7z8yr7ZuqQJDRtVXRYHXz7bxtp7Dh9p/3Bhg6eWu2+kHDbnMKQddL7i/f80BtZKoTV4cQhd66KQUaK6BHu6HDR4BYSvXnooepCB8gyRTM0NRaXGtxO/G6bg83l7uLTeSF8/YPdeSPdn/vP9wf/ToRAKalqw+FSyTK5u0GECITJnjdJkpkrhBCR57LxWVxWlInT5S5USnRXZHh9YyUA35iai6KG9t901rAUFk7IwulSufv1rbT3Mv5jeXEFXxxowqjT8PiN56ALk7t6NRqF6JI3yUuOpqK5m9te2kSn1XHSbc0WO0v/voVOq4PphcncMDU3yKv1P29zXiiiUkV4qTVbeH9HLX9ae5hn1xzipXXlvF1Sw8GGzlAvTYiAC4/fSCJgphe6i+j76ztpClIuuqqqvjiTUBbR4ehFhK+CGOmy4XALNqeL3KRoClN7P8Fd+Mf5nlsmvzjQ2OsBQOHK2/kxtSBZhoqKgPHOFFh3qAmXKzK+d4QQQhz186vHYYrWs+tIO3/5/HBAjmFzuCipamN5cQV/XnuI59Yc4o+fHeTfW6qpaO5Cfrv4T1VLN2s9A0VvmjYkxKtx+/W148lNiqaqxcKDb+0863PxLw808Yv3dgPwowWjGZ4eF8hl9prG1sXL35lOcqyBnTVmfrB8C61dtuO2cbpUlr2xjbKmLrJNUTx78+SIiPf0RqVuq2yl23byiwcish1ps9A99Vb+sbmag43ugrmK+6JReXM37++s5ZM99agaXWgXKkQADfyf5uK0kmMNjM5056JvOBycbuyypi5q2iwYtBrfcNNQmeMZarruUPCK6J97olzOG5Emhc4QmlqQRJReQ0OHlX31kZHt7C2iTyuQKBcROFPyk4gz6mjqPDrbQgghRORIj4/ipwvdkRt/+OSAX7sHVa2eLw828cKXZazd30hzl40ehwub04XDpVLdZuE/JUfonrE0qOfnkezNTVWoKswZnkJBmDTwmKL1/N9Nk9BpFN7fUesbrnk6O6rb+N4rm7E7VS4fn8mS2QWBX2gfDE2L48Ul04jWa/niQBPnPfYZT6zaz+HGTt7cVMmSFzfy2b5GjDoNf148ldQ4Y6iX7Bf5KTHkJEZjd6psKpe7FQebI20WvvWXYpzJBWgU93yuW2YM4bvnFnL95Bwme4bPlh5pp3vGdznUKF3pIjJJEX0QmDk0uJEuX3q6vifnJxJjCO1VyFlDU9Ao7k786tbuoBzzc08nyPkS5RJSRp3W97Xv/TcZyBxOF1s98RpT80N7cUpENoNOw7meC5Cf7WsI8WqEEEIEwnWTc7hgZBo2h4tlb27zS2dpSVUbXTO/z5aKVmxOF0kxei4clcbimfncOiufW2YMYVJeIjqNgsuUzbdf2Mi724/44dUMXvZjBooump4f4tUcb/KQJO6/dBQAP3tnF//aUn3KbQ83drLkxU1025zMHpbCk988J6wzxCfmJfLK7dMZm5VAp9XB/31ygIseX8uP/r2TLw40oSjw2+snMD7XFOql+o2iKMz23OW9Loh3eYvQ8xbQK1u6Ubpb+PasAi4Zm0FKnJFYo47cpBjOG5HGNedkE63X4orP4ObnN9DQ3hPqpQvhd1JEHwS8kSafBynWwhvlcl4YTCBPijX4BjB+ujfwxaCaNguHGrvQKDB7uBTRQ+1opMvAP9HbW9dBl81JvFHHKM/dJUIEykWj0wH4bN/AvwAlhBDiRIqi8Oh140mONVBa084P3yjB2ccIL6dL5elPDnD9c+tQY1OIM+q4ckIWi2fmMyE3keRYA4kxBlLijJw/Mo3vzClAV7sTh0vlh29s4x+bz9ylLE7ukz31NHZYSY0zcMnYjFAv5wTfO28oC8ZlYnO4uP+f23nwrR302J2+x3vsTp5bc4irn/mKli4b43NM/OXbUzHqtCFc9dmZWpDMe3efy7M3T2ZEehyK4i6uL5s3gg/uOY9rJuWEeol+573L+yu5i2TQaOjo8RXQhyTHELPpZUzR+pNum58Sy80zhqDpbKSuvYf/Wr4Fq8N50m2FGKikiD4IzBmeikGroaK5O+DDHuxOF+s9w0bCZajmxWPcJ5Sr9wS+iP6lJ8rlnLzEU/5yEcFzvme46IayFiy2gf0LfLMnymVyfhLaMO7MEZHhglHuC1A7qtuCNk9DCCFEcGUnRvOXxVMwaDWs2l3Pox/s6fU+mjqt3Pq3jTy+aj9Ol4qutpSbZwxhaFrcKWMNYww6ona+xU3Th+BS4YF/7WB5cUV/X86g9NpG9wWIb0zJw6ALv7f2Go3CH2+ezH/PG4miwOsbq7j48bUsfmED9/6jhIsfX8tvV+6lw+pgQq6JF78zjTjjwMlT1mgULh+fxcf/fT57f7mAt++cw7J5IxmTlRDqpQWEtxN915F22rptZ9haDHQul8p9/9juK6C/8b2ZaKztp31OrFFHdMkbJETp2FrZxo9XlEbMfDIhQIrog0KcUcfs4e5feB/vrg/osbZWtNJpdZAYo2dcdnjcvjZvjLujsvhQ8yknqPvL5/vDpwtfwLC0OLJNUdgcLjaWB2cmQKBs8kS5SB66CIaMhCjGZSegqpERhySEEOLkphYk87sbJgDw1y/L+Mvnh8664LGxrIXL//AFXx5sIlqv5fEbJhK1899E6c/cRawAj1xbxO3nFgLuuI+NZQP7XC3Yqlq6+cLTwHPT9LwQr+bUtBqFH84bwd9vcw/krGmz8MWBJt7aWkNNm4UsUxRP3DiR/9wxZ8DmhyuKMiC65/srPSGKEelxqCq+xjkRuV5aV84XB5ow6jS8cOtUshOjz+p5mu4Wnlk0GY0C/9xSzcvrygO7UCGCSIrog4T39r5VAS6ir9xVB8CFo9LDplt2WFocBSkx2JwuX6d4IDhdqi8P/vyRUkQPB4qi+C5oDORCoKqqvk70qQWShy6C48JREukihBCDwdXn5HDfJSMBeOSDvdz1+jbae+yn3L61y8aP/7OTb/1lPQ0dVkakx/HOXXO4fkouvTn7VxSFH18xhmsn5eB0qdz9+la5+6kXXt9Yiaq67/7NTwmPgaKnc96IND67fy4v3zad398wkQcWjOLX1xbx2f1zuW5yblhnoIujJNJlcNhb185vVu4F4MdXjGFERu/iRM8fmcb/Xj4GgEc/3MthGTQqIoQU0QeJeZ5Ik5KqtoANeFBVlY9K3UX0y4oyA3KMvlAUxRfpsmp34CJdNpa1YLbYMUXrmRhBQ2QGOu8FjS8CeAEl0KpbLdS3W9FrFSbmJoZ6OWKQuHD00QtQDqcrxKsRQggRSHddNJz/vXw0Oo3C+ztqWfh/X/LBzlpautyRDaqqcrixk79+cZi5v1/D8uJKXCp8Y0oub981p9cFFi9FUfjVNUUMS4ulvt3Kf79ZgquP2eyDic3h4h+b3YM6F00fEuLVnD1TtJ4LRqbxjSm53DF3ODfPyD+rOxdE+Dg6XFQ60SNVj93JD18vweZwcdHodG6Z2behxbefW8h5I1KxOlz8z1s75We7iAhSRB8kMhKimJiXCAQuG3x7tZkj5h5iDNqw68S+eIy3o7Khz0OTzuT9nUcAuHRcBjqtfGuFiznDU9AosL++k1qzJdTL6ZNNni70ohwT0QZ5oyGC45y8JBJj9Jgtdkqq2kK9HCGEEAGkKArfO38Y//ivWeQkRlPZ0s0dr25l8i9XcfHja5jyq9Vc9PhafvX+HswWO6Mz43njezP5/Q0TiTH0L8M61qjj2ZunEKXX8MWBJp5dc9BPrypyfVhaS1OnlfR4I/PCcKCoiFwzhrrfWx1u6hqw763E6T275hD76jtIjTPw2DcmnHK+xZkoisIj144nxqBlY1kLr2+q9PNKhQg+qfQNIvN9kS51Adn/h6W1AFw4Oj3sOgqmFSSTEKWjpctGSVWr3/fvdKms9HThXzEh2+/7F32XGGPgHM8FpEDHGQWKt4g+TaJcRBBpNQoXeC6IfrYv8IOZhRBChN7kIUl8cM953H5uISPS4wA41NhFS5cNg07DtIIkfnVNEe/dfS4zh6b47bijMuP5xdVFADy1+gB7604/vG6w+9tX5QDcMjMfvTTviCAyResZ77kz9osDEukSacqbuvjT2kMA/OLqon7PKchLjuH++aMAePSDvXLhRQx48ht3EPHmon91qJkuPw/YVNWjReRwinLx0ms1zPXk+waiE39DWTNNnTYSY/S+W9xE+Lh8fBYA722vDfFKek9VVdZ6MqlnFEoRXQTXRaPdPzff31F71oPmhBBCDGymGD0/WTiWVfdewJYfz+OFW6fy1h2zKX34Uv75X7O5ZWZ+QO66vGFKLvPHZuBwqfzo3zsDdvfoQLetspXtVW0YtBpuGkBRLiJyzPU0WXy8KzDNeSI0VFXl4Xd3YXO4OG9Eqt/qOrfOLmDSkEQ6rQ5+8p9dftmnEKEiRfRBZER6HEOSY7A5XH7Ph95T20FFczdGncY3jC7ceCNdVu2u93sx6P0d7uLsgnGZ0g0ShrxF9E0VLdSZAzMTIFB2HWnniLmHaL3WN8hHiGCZNyaDOKOO8uZu1h+W7EshhBhsUuKMXDwmg8lDkjDoAnuOqygKv7ymiHijju1Vbby0rjygxxuovJ+XKydmkxbfvy5RIfriignu91af72/CbDn1EGIxsHy0q541+xoxaDX8/KpxfY5x+TqtRuG3109Ap1FYvaeeT/YMzLvDhQApog8qiqL4utE/3uXfH1wrPVEu549MI9bYv1zEQJk7Kh2DTsPBhk5Ka/x3i6jD6fJ14XuLtSK8ZCdGMyU/CVU9Gjs0UHg7PC4YmRZ2MUki8sUadVx9jjui6vWNVSFejRBCiEiXkRDFg5ePAeD3H+2jqqU7xCsKL/XtPb7mnSWzC0K7GDFojcyIZ0R6HDani9UDNC5THK/b5uCX7+0G4HvnD2VoWpxf9z8yI57bzysE4OF3d9Fjd/p1/0IEixTRB5kFnltyPtpVR6cfI10+DOMoFy9TtJ4F49zre3Oz/4ZabChrobnLRlKMnlkS5RK2rvBc4PC+8RgoPvacmM4fJ0OjRGh4bxX/qLSO5k5riFcjhBAi0n1rWh4zCpOx2J3874qdEid2jFeLK3C4VKbmJzE+1xTq5YhBzNuN/sHOgfXeSpzcM58epKbNQk5iNHdeODwgx7jnohFkJkRR1WLhuTWHAnIMIQJNiuiDzNT8JIamxdJlc/JOyRG/7PNAfQcHGjrRaxUuHhPehb5vTssD4O2SI367+vm+58RhQZFEuYSzy8dnoSiwuaKVI20DY6BJRXMXe+s60GoUXza1EMFWlGNiYq4Jm9PFv7dWh3o5QgghIpxGo/DodeMx6DR8caCJt7bWhHpJYaHH7uTVDe5GoCVzCkK7GDHoeRuUPj/QKJEuA9yhxk6e/+IwAD+9cizRhsDc/Rxr1PGThWMBeG7tISqauwJyHCECKTxzN0TAKIrCoulD+NX7e3htYwWLZvR/GM0rxRWAOy7FFK3v9/4CadbQFPKSo6lqsfBhaS3XTsrt1/7sx0S5XDE+2x9LFAGSaYpiWn4yG8tb+GBnLd89b2iol3RGqzxd6DMKk0mMMYR4NWIwu2n6ELZX7+T1jVUsPW+o3zISB6vF31lKQ0vbKR9PT07klRefD96CIox8foUY+IamxbFs3ggeW7mPX76/mwtGpZEaN7jzv1/bUElzl42cxGguHRe+d/+KwWFERjwjM+LYX9/Jqt31fGNK/95Xi9BQVZWfvb0Lu1PlwlFpzB8b2KbIy8dnct6IVL440MTD7+zib0umyfsKMaBIEX0Qum5yLo+t3EdpTTs7qtuYkJvY532ZLXb+tcXdmfidAZDLp9Eo3DAljydW7efNTVX9LqK/v6OWli4bqXFGZg5N9tMqRaBcMSGLjeUtvD9Aiuje2QWBPpkR4mSOLUS6FB3KiG9S1tTF+YvuIaa7TgqR/dDQ0saCu359ysdXPvNQEFcTeeTzK0RkWHreUN7bXsvu2nZ+/u5unr5pUqiXFDI9did/WuuOP7jjwmFy96sIC1eMz2Z//X4+2FkrRfQB6oOddXx5sAmDTsPDfhwmeiqKovDwVeNY8NTnfLavkdV7Gnxz+4QYCKSIPgglxxq4bHwmb5cc4bUNlf0qov9zcxXdNicjM+IGTB749VNyeXL1fooPt1DR3EV+Smyf9qOqKn/53H3b05LZ+ejkZDbsXVaUycPv7mJbZRtVLd3kJceEekmn1NRpZXNFCwCXSLeRCIGvFyI/2VtPaU07xqnfYMGErH4XIqVbWAghxOnotRp+e/0Erv7jl7y7/QjXnJMd9tGRgfLGxkoaOqxkm6K4YUpeqJcjBABXTMjkydX7+eJAI+ZuO6aY8L4rXRyvy3p0mOgPLhjW57pIbw1Li2PpeUN5ds0hHn5nF+cOTw1YhIwQ/iZVv0FqkWdQ3Dvbj9DR07cMM6dL5e/r3VEuS2YXDpjbcHISozlvRBoA/9hc1ef9rDvUzO7adqL1Wm6eke+v5YkASk+IYrbnYs/yDRUhXs3pfbqnAZcKRTkJ5CRGh3o5QjDRc8H1YGOnX+YKeIv0p/o4XYFdCCHE4DA+1+S7e/DH/ynt8/uWgazH7uQ5Txf6Dy4cjkEnb+FFeBieHs+ojHjsTpUPS2XA6EDz+Mf7qWvvIS85mh/MHRbUY9910XByEqOpabPw7JqDQT22EP0hv4EHqemFyQxLi6Xb5uTtPg4Y/XRvA5Ut3Zii9Vw7KcfPKwysb051d3D8Y3M1FlvfBoz+2dOFfuPUXJJiJa96oFgyuxCA1zdU0mV1hHg1p/aeZ2DtpWOlC12Eh9Q4I+OyEwBYu78RNcTrEUIIMTj897yRDEmOodbcw+8+2hfq5QTdPzZXUd9uJcsUxY1TJTJDhJfrJrvrAC9+VY6qytnhQLG9qo2X1pUB8Muri4jSB7cTPMZwdMjon9cepqxJhoyKgUGK6IOUoijc5OlG/9uXZdgcrl7vw/tD91vT8wbc7TeXjM0gJzGaxg6rbxJ1b+ypbefz/Y1oFLj93PDP1hZHXTw6nYKUGNp7HPx7a3Wol3NSBxs6+Hx/I4oCV06UgbUifMwamoJBq6Ghw0q7aXiolyOEEGIQiDZoefS68QC8UlzB5vKWEK8oeCw2J89+5ulCnzsMo25gvecSke9b04cQa9Cyr76Dzw80hXo54izYnS5+9O8duFS4+pxs5o5KD8k6Lh2XwQUj07A5XfzkP6VyEUYMCFJEH8RunJZHapyBw01dvPhVWa+eW1LVxlcHm9Eo8O1ZBYFZYAAZdBp+dNloAP609hAN7T29ev5fv3B/vi4rymJISvjmaosTaTQK35nj7kZ/8atyXK7w+2X9t6/KAZg3JoOC1OBk0wlxNmKNOmYUuocoN6ZPGZS31QshhAi+OcNTuXFqLqoK//PWTqyOvt1JOtA889kB6tp7yEmM5sapkoUuwo8pWs+3PM15z3/e++Y0EXzPf3GYvXUdJMbofd3goaAoCj+/ahxGnYYvDzbx2sbKkK1FiLMlRfRBLCFKz48WuAvJ//fJAerMZ1dItjqc/OhfOwC45pycAZvXfOWELM7JS6Tb5uSJVfvP+nl7att5Z3sNAN89rzBQyxMB9I0puSRE6Shr6uLTvQ2hXs5xWrpsvOXpkL/9XPn6EuFnYl4iiTF6nLpoHv/47H92CiGEEP3x0OVjSY0zcrChkz9+GvkZugcbOvmLpyj50yvHBj1uQYiz9Z05BWg1Cl8ebGLXEXOolyNO43BjJ39YfQCAH1/h/pkaSgWpsb6a1K/f30Nlc3dI1yPEmUgRfZC7fnIuk4ck0mVz8sgHe87qOc98epB99R2kxBr4cQivXPaXoij8ZOEYAN7cXMWe2vYzPsdic3LP69uwO1Xmjclg0pCkQC9TBECsUcdNM9wdEy982bu7MALttQ0V9NhdjMtO8HX8ChFOtBqFCzzDmV9aV87y4vAe0iuEECIymGL0/OLqcQA889lBig83h3hFgaOqKj97pxS7U+XCUWnMH5sR6iUJcUq5STFcPj4LOHrHtgg/VoeTe97YhtXh4tzhqVw/OTzm2i2ZXcCMwmS6bU7u/+f2sLxTXAgvKaIPchqNwi+uLkJR4J3tR854MlpaY+bZNe5cvl9eU0TyAB+oOSU/mSvGZ6Gq8LN3dp0xG/7XH+zmQEMnafFGfnv9+CCtUgTCrbPcHRPrDzeztbI11MsBwOZw8ff17oLkd88rRFGUEK9IiJMrSI0lpXEbAD99u5TVu+tDvCIhhBCDwWVFmVw3OQeXCve8vo3GDmuolxQQ7+2o5auDzRh1Gn5+VZGcE4qwt9Rzh/a7249wpM0S4tWIk3n0g72U1rSTFKPn9zdMDJufKxqNwu9vmEisQcvG8pawa3IT4lhSRBcU5Zi42dOVe8/r2yitOfktWB09dv7fv3bgdKlcPj7Td7V5oPvRgtEYdRo2lrVwx6tbTpmx+PGuOpYXu3O6nrhxIikhvvVJ9E92YjTXTnJfff/xilIczt4P1/W393YcoaHDSnq8kSvGy0BREd5Smrbzzal5uFS46/WtbKkYPIPehBBChIaiKPzqmiJGpMfR0GHlh29swxlhXYtNnVZ+8d5uAO6YO1zmL4kBYUJuIjOHJuNwqfzuo32hXo74mo931fHSunIAHr9xIpmmqNAu6GvykmN46Ap3ysFvV+6N6DuNxMAmRXQBwP+bP5pRGfE0dFi58c/r+exrOdFbKlq5/P++YE+t+8rlL64uCtFK/W9ISgzPf3sqRp2G1Xsa+K9XttBjP1pId7pUXllfzn3/2A7A984fynmeKAMxsP3PZaMxRevZXdvuO6kIlW6bgz984s6nu3V2AQad/HgW4U0BfnVtEReMTKPH7uJbfynmz2sPyS2YQgghAirGoOO5WyYTY9Cy7lAzT/ZitlG4czhd3PXaVho7rAxLi+X7FwwN9ZKEOGv/c9kYNAqs2FbDZ/vCa+7UYFbV0s3/88y0++65hVw0OjzjoW6ansfV52TjcKn8YPkWqlokH12EH6nSCMCdMfjPH8xizvAUum1Ovvv3zdz12lZ+/u4uHlqxkxv/vJ6qFgs5idH8bcm0kA+g8LfzR6bxtyXTiNJr+GxfI5f94Qse+Nd2/vrFYRY+/SU/eXsXHVYH0wqSuH/+qFAvV/hJapyR/7nMPcjkiVX7Q3rr4e8+2kdFczfZpii+PSs/ZOsQojf0Wg3P3jyZ+WMzsDtVHv1wL4v+Wkx5U1eolyaEECKCDU+P59Hr3NGKz3x2kFciZD7HYx/to/hwC7EGLX9ePEWGiYoB5Zy8RG6b4451eeitnXT02EO8ItHUaWXxCxswW+xMzDXxgGeIZzhSFIXfXj+B8TkmWrvtLP37ZrqsjlAvS4jj6EK9ABE+EqL0vLhkOv+7Yif/2lLNeztqj3v8mnOy+cU1RSRE6UO0wsCaMzyVl74zndtf2kRZUxdlxxSBTNF67ps/kkXTh6DTyrWnSPLNqXn8a0s1Wypa+fm7u/jz4qlBX8PGshZfJ/yj108gPkK/x0RkijXq+PPiKfxjcxU/f3c3xYdbuPDxNcwdmcbiWflcMDIdrSY8MheFEEJEjqvPyeFgQydPf3qQn75dSrxRxzWTwmNQXl+8v6OWv3x+GIDf3TCR4enxIV6REL133/xRfLy7nsqWbh5buY9fXhM5d7APNB09dm7920bKm7vJSYzmz4unhv3dzlF6LX/59hSufPor9tZ1cNdrW3nuFrmgGCnmLbic+oamM26XkZ7K6pUfBGFFvSdFdHEcg07D774xgSvGZ3GwoZOWbhvtFjvnjUhlQVFkZKCfzsyhKXz+wIVsKm9l1xEze2o7yE+J4c4Lhw/4Iari5DQahV9fW8TC//uSj3bV8/K6cm6dXRC041tsTh7413ZU1V3Qv2CkRAWJgUdRFL45bQgzClN4+N1drNnXyGeejzijjsn5SUwvSGJkRjzZidHkJkUjoS9CCCH6695LRtJusfPy+gru++d2Ygxa5o/LDPWyem3t/kbu+2cJAN8/f2jEzJ4Sg0+0Qctvrh/Pouc38EpxBfPGZsj7mxDosTv53t+3sOtIOymxBpZ/d0bY5aCfSpYpmj8vnsyi5zfw2b5Gbn95E89/eyoxhoFZvoyEwrG/1Dc0cdtvl59xu7/96JYgrKZvBuZXoQgoRVG4cHQ6F45OD/VSQiIlzsiCokwWFA28E3DRN6MzE/jvS0byu4/28bN3dpEYo+fqcwLfyeRyqfzsnVLKm7vJTIjioYVjAn5MIQKpIDWWl74znfKmLpYXV/CvrdW0ddv5fH8jn+9vPG5bZeTNNBdXEB+lIyFKT0K0noQonee/eimyi4BRAVVVURS5Q0KIgU5RFH525Tg6ehy8ta2G/1q+hf+9fAy3n1s4YL7H399Ry7I3t2F3qlw4Ko3/d6lER4qBbfawVG6aPoTXN1by/Vc28+KS6cwalhLqZQ0atWYL339lCzuqzcQZdbx823QKU2NDvaxemZKfzIvfmcZ3X97MVweb+fYLG/nbd6YNyFSESCgci6OkiC6EEMAdc4fR2GHlpXXuIbIJUfqAXkhyOF38z1vu6CRFgUevHz8gTwqEOJmC1Fh+vHAsD14+hr117Wwqa2FLZRuVzV3UtFlo6rShavU0d9lo7rKddB/KyJu55o9fMTHXxPjcRCbkmhiWFjfgo2Hq23vois1ha2UrLV02OnscWOxOLHYnTpeKRlHoGfYNLvvDF2QmGMk0RZGfEsvozHjGZiWQFm8cMIWhUHHoojnc2Elzlw2zxY65206XzYHdqWJzurCN/jaHm7oYlhYX6qUKIfxAo1F47BsT0GgU/rWlml+9v4fdR9p55LrxYR0BoKoqr22s5Cf/KcWlwhUTsnjyxnMkOlJEhIevGkud2cJn+xq57aVNvHzbdKYXJod6WRFva2Ur339lC40dVpJi9Pzl21MpyjGFell9MntYKsu/O4Mlf9vI5opWrvnjVzz1zXOYkJsY6qWJQUyK6EIIgbuT6acLx2K22Fnh6WT68cKx3DJjiN8LVjaHi2VvbuODnXVoNQq/v2ECF44anHd+iMim1SiMyzYxLtvEkjlH/77H7uSyRd9j4vV30dHjoL3HTrvF+187XTYnqlZPSVUbJVVtgHtgXIxBS1G2iUn5iUwvSGZqfjKmmPC9+KSqKocau9hU3uL7qGqxwJBLqD5wmts6DXHsqW1nT+2JD6XHG5lWmOx+/QVJjM5MGPAXFvpDVVUONnSy/nAzxYeb2VrRRt2Ib3Jox0k+eV6KRgZVCRFhdFp3JOXYrAR+/cEe3tpWQ+kRMz+7chxzhqeGenknqDVb+Ml/Slm9pwGAm6YP4VfXFA3qn+cishh1Wp67ZQrfe2ULn+9vZMmLG/ndNyZy+fhMaQYIgG6bgz+tPcyf1hzC5nQxOjOe5789lbzkmFAvrV8mD0ni9e/N5DsvbuJwYxfXPbuOey4ewR1zh8kFRxESUkQXQggPbydTp9XBqt31/OQ/pXxUWsdvvzGBnMRovxxjw+FmfvX+HnbWmDFoNfzfTZMkOkgMOlF6LQZbO/kpJ7+11OF08f4Lv+e/H/o5O6rN7Kw2U3rETLfNycbyFjaWt/DntYdRFBiVEc/0wmSmFSQzvTCZjITQ5T3anS52HXF33m8qb2Fzhbvb/FgaBXQ9reTn5ZEcayAhSk+0QUu0XotWo6CqKl/94zl+8YufU2fuodbcw6HGTvbUtlPW1EVDh5X3d9TyvqdIHB+lY0p+ku/1T8g1YdSFb+dlfzld7qL5xrJmig+3UHy4+cS7GVSVlDgjqfFGEqP1mKL1xBl1GHQaDDoNX/39McZkXRaaFyCECBhFUbjt3EJGZ8Zz1+vb2F/fyc1/3cD8sRn86LLRYXH3SbfNwRsbq3hi1X46rQ70WoVl80Zyx9xhUlgUESdKr+Uvi6ew9O+b+eJAE3e+tpV5Y9L5xdVFZPvpvdVg53C6eH9nLb/5cC+15h4ALh2XwRM3nkOsMTLKfeOyTXy07Hx+/J9S3t9ZyxOr9vPO9iP84IJhXHVONnoppg94LpeK3eXC5QKXquKKMuFwusLyQklkfFedpWeffZbf/e531NbWMm7cOJ566inOO++8UC9LCBFG9FoNf75lCi+vL+e3K/fy5cEmLnliLVefk803pw1hYq6p129yXC6Vkuo2/rz2EB/tqgcgzqjjjzdPlkE7QpyETqvBaDNz9Tk5vvkETpfKocZOSqra2FLeysbyFsqauthb18Heug7+vt7drZ6fEsO0gmQmDUmkKNvEqMz4gN3O39hhZXdtO1srWtlU3sK2yjYsdudx2xh1GiYNSWRaQbJvXd/45iIWXP7rU+43uqeJuSe5O8Vic1JS1cZmz4WErRWtdPQ4WLOvkTX73JnzBp2Gc3ITmVaYxNSCZCbnJYV1t/6ZtHXb2FbVxraKVrZWtrG9qo2Or3WRG3UaphYkMbMwhWmFyfzk3ru4/M6fn3KfOodF3nCFKTlXF/4we3gqn9x7AU+t3s/yDZV8vLuej3fXM70wmRun5nFZUWZQi0uqqnKgoZM3N1Xxj81VdPS4f4ZNGpLIb6+fwMiM+KCtRYhgi9Jr+eutU/njZ4d4bs1BVu9pYP2htVw/JZcbpuRRlJMgF5D6oLypi39uqeJfW6qpb7cCkJsUzUOXj2FBUeR1+yfFGnhm0SQuKcngp2+XcrChk/v+uZ0nVu3npul5zBubwaiM+KC9blVVaeu209BhpaGjh/p2938b2q109Diw2B1025xYbE66ZnyXV4rd71N0GgWtRkGnVdBpNGg1CkadhhiDFlveNN7fUUtqnIHUeCPp8UbijLoB+2+pqiotXTZqzT3Ut7s/R9Zhc1m9p54uq4MuqzvO0upwYnd+bRrW+cto7LSSZQq/i22Dpoj+5ptvsmzZMp599lnmzJnDn//8Zy677DJ2797NkCFDQr08IUQY0WgUvjOnkAtGpvH//rWDLRWtvL6xitc3VjE8PY6p+UlMyE1kdFY8aXFGkmINxBq02J0qdqcLs8VORXM35c1dbK9qY/WeBpo63Sc3GsV9y+5/XzKS1DhjiF+pEAOHVqMwMiOekRnx3Dg1D4CGjh42l7eysayFjWUt7Klrp6K5m4rmbv61pdr3vPyUGIamxlKYGktuUgwZCUbSE6JIijEQZ9QRH6XDoNWgKO5ORofT5cspN3fbaeq00dRppabNQkVzN5UtXeyr6/R9Xx/LFK1nWoG7M3xqQTLjc0wYdP4p2EYbtMwaluIbzuVwuthT28HG8hY2e+Jimjptvm59OARAlimKUZnxDE+LIz8lhiEpsWSZokiONZAYrQ9pl4eqqnRaHTR0WKlptXCkzUJZcxcH6zs50NBJZUv3Cc+JMWg5Jy+RWUNTmDks5YTue40qUS0DkZyrC39KijXw86uLuHlmPo+t3Munext8vyv+5987KMoxMWNoMhNyEilIjaEgJdYvhXVVVWnqtHGosZNDjZ1sKW/lq0NNviIXQEFKDN89byg3TR8i8S1iUDDqtNx7yUgWTsjiwbd2sqWilb+vr+Dv6ysYlRHPrGEpnJPnnn+TmxTjt/OmSOD9mVLe3MWhhk42lbeysbzZHQ/okRSj5/ZzC/nueUPDeg5EfymKwjWTcrhoTDrLiyv425dl1LRZ+P3H+/n9x/vJSYxmxtBkRmfGMzozgbzkGNLijcQatGdViLY7XbR222jrttPaZaO1205bt/s9gK9I3mGlod1KY4cVm9N1dgs35ZxwZ+pJjbmcO1/betxfReu17vct8VGkJxjJSIgiPd7z3wQjKbFGTJ67LqP0mqAU3J0ulXaLnTaLndZuG40dVt/ds3VmC7Xe/2/vweb42udo2AXsOtJ+2v0rgOq04/h6YT1MDJoi+hNPPMHtt9/Od7/7XQCeeuopPvroI5577jkeffTREK9OCBGOhqbF8a//msXGshbe2FTF+ztrOdjQycGGTt7YVNWrfcUZdVw0Op27LhouHUdC+El6fBSXj8/i8vFZAJgtdl9X+M4aM7uOtNPSZeNwYxeHG7vOap+KAupZnrMpChSmxDI+1+SLUxmeFocmSEURnVbD+FwT43NN3H5uIaqqUtbkzWB3fx4qmrt9J7PebvWvM0XrSYk1kBRrID5Kh1GnwajTYtBpfP+v1yqggIKCorgvCHr/X/F+MlQVu0vF7nDhcLkvKjqc7tszHU4Vh8tFt81JW7fdd/Jttthxuk7/CS9MjWXSkEQmD0li0pBERmXEh+XtnaJ/5FxdBMLIjHj+eus0as0W/r2lmn9tqaa8ufuYmRtHHfuzMM6oQ6/VYNApGLQaz/9rcKnuQovN4flwurA7XbRb3BdcW7psJ9yRBO67hM4dnsriWflcMCItaL8nhAgnIzPi+ef3Z/HFwSb+taWaj3bVsa++g331Hb5tFAVS44xkJkQRa9QSY9ARbdASo9cSY9ASZdCiVbznHwoaxf0kzTF/9jZFeLfxUjl6vnGycz1VPfnj6nHbnH5fx+322P2ddFsVm8OF1eGix+7E6nBhtbsbOdosdlq6rDR32ui2nfgzRaPAuSPS+ObUPOaNTY/oKL+vS4jSc8fc4dw2p5B3So7w0a46vjzYRE2bhbe21pywfZReQ5zx/7d3p0FRXWkfwP/N2kDTIKIoiqISEMQFYdQ2I0tcMeNIrJIQUdBoNBYaDRmD0Tg6kzGamLjEibjEAEmJ+iHikBo3yhI14IoBjRIGcYGACIqyL0Kf9wPSb7dNa6NCI/x/VaeSPvfcc5/T99o8HE7f2zjJbG5iBAE05qePP7/r6pV41CCa/ex+li6WpqoJ7m7WjZPdtpamjdfq42t22d+WIXDuMgBAvbIxH25QCtQrBRoaBGoeNaCqrgHXLp2B18jXca+icWK6orYe1Y8acOt+FW7d115U8iQzYyPILUwhtzCBzNwEUhNjmD8es7mpMcxNjGDyjJ89QjTGWFvfgJpHjddlY1GiorYeD6vqUFbTssUq9jJz9LBp/Dd98tghePtOgJW5CazMTWBpZqyKz9RIAiMjCYwkEnwfNRNOG4JadJy20ikm0evq6pCWlobly5dr1E+YMAGpqanN7lNbW4va2v9fMVBaWgoAKCt7+l9NWkP9o0eoqax46nZDxPWydPTxPU17H3trx9fex9/E3d4U/wjsj0g/J5y/dR+/5ZfhWkEZbtyrwIOqR1p/YTU2kqCXrRROdpbo302GMa/Zw6ev3eNVFaJdjAl49vuvVDa06/Nv6P1fVHuP/1XsXwLA21EKb0dHAI4QQqCorBY371XiVkklbt2rwt2yahSV1aKovBblNY8fYvqUOVy51AR2MjN0tTSHg405+nSxRG87S/Szt8RrDtawNFNPpQQqKsp19qU+ttZ6b+3NgUA3WwS62QLoh7KaR7h+txzZRRW4db8Kfzyowh8l1Sgqr0FpdWMS/KAWePDwuQ730liaGcHR1gI9baTo3cUSA7rLMMBeBpfuMnSxMtNoW/WU9w4w/L/t5jQdT+j7F5pO5lXP1Rsanv7zEmg8989q09SXPmPQ55j69vUy6RNXU7u2jM1KAoT5OCDMxwH5D6qQdvsB0m4/aPzGy/1KPKyuf2mfhRIJ4GgrRT97K7g5yKHo3xXD+tiqVojq83PiRbzMa6O9ns+mY3KcjV7250tr8+phDq83B+DjgD745XoxLuc3Pv/m98Jy1NUrcbemCnef8vz1zkYiAXrIpXDuagXPXnJ4O9thaG8bWEsbb9dXW1UJ7e9Gto729rNnkpsNJrnZoLrOBedv3UdmQTmy75bjf0XluFtWg6o6Japqgapn//MA0Phey6UmsLU0g42FKWwtG/+42k0mRXe5GbrKpOhmbYZuj5+9o88fLyR3f0c3c/X5Asnj0qTxd4nr3+3D7i0LVLVVdfUoLq9VlaLyGtyrqENRWQ2Ky+tQVFGD0qpHKKupR4NSoAZATRVQpN9QX5iVuTHkUlPYWZk9niS3gMPj1fIOcil6yKXoJtd8j0ZtfB9Dp095/EoAqG/8Tx2gPjVviM8qvXN10Qnk5+cLACIlJUWjfu3atcLV1bXZfVavXi3QeDpZWFhYWFhYWFhYXrjk5eW1Rer7ymGuzsLCwsLCwsLCYujyrFy9U6xEb/Lk/YGEEDrvGfTJJ58gMjJS9VqpVKKkpARdu3Zt1fsMlZWVwcnJCXl5eZDL5a12HGpfeN47L577zovnvvPiue98hBAoLy+Ho6OjoUNp116FXJ1ahp93pI7XA6nj9UDqeD2Qura+HvTN1TvFJLq9vT2MjY1RWFioUV9UVAQHB4dm9zE3N4e5ueZD/2xtbVsrRC1yuZwfHJ0Qz3vnxXPfefHcd148952LjY2NoUNot17FXJ1ahp93pI7XA6nj9UDqeD2Qura8HvTJ1TvFU5nMzMzg7e2NpKQkjfqkpCSMHj3aQFERERERERFzdSIiIiJq7zrFSnQAiIyMxKxZs+Dj4wOFQoGdO3ciNzcX77//vqFDIyIiIiLq1JirExEREVF71mkm0d9++23cv38f//znP3Hnzh14enri0KFD6Nu3r6FD02Bubo7Vq1drfT2VOjae986L577z4rnvvHjuibS9Krk6tQw/70gdrwdSx+uB1PF6IHXt9XqQCCGEoYMgIiIiIiIiIiIiImqPOsU90YmIiIiIiIiIiIiIngcn0YmIiIiIiIiIiIiIdOAkOhERERERERERERGRDpxEJyIiIiIiIiIiIiLSgZPo7dStW7cwd+5c9OvXDxYWFhgwYABWr16Nuro6Q4dGbWDt2rUYPXo0LC0tYWtra+hwqBVt27YN/fr1g1Qqhbe3N06fPm3okKiVnTp1ClOmTIGjoyMkEgkOHjxo6JCoDaxbtw5/+tOfYG1tje7duyMoKAhZWVmGDouIqM0wv+3cmPNSE+bC1IT5MamLjo7GkCFDIJfLIZfLoVAocPjwYUOHpYGT6O3U77//DqVSiR07duDq1avYtGkTtm/fjhUrVhg6NGoDdXV1mD59OhYuXGjoUKgV7d+/H0uXLsXKlSvx66+/YsyYMQgMDERubq6hQ6NWVFlZiaFDh+Lf//63oUOhNnTy5ElERETg7NmzSEpKQn19PSZMmIDKykpDh0ZE1CaY33ZezHlJHXNhasL8mNT17t0b69evx8WLF3Hx4kW88cYbmDp1Kq5evWro0FQkQghh6CBIPxs2bEB0dDRu3Lhh6FCojcTGxmLp0qV4+PChoUOhVjBy5EgMHz4c0dHRqjp3d3cEBQVh3bp1BoyM2opEIkFCQgKCgoIMHQq1seLiYnTv3h0nT56Er6+vocMhImozzG87H+a8pAtzYVLH/JieZGdnhw0bNmDu3LmGDgUAV6K/UkpLS2FnZ2foMIjoJairq0NaWhomTJigUT9hwgSkpqYaKCoiaiulpaUAwJ/rRETUoTHnJSJ9MT+mJg0NDdi3bx8qKyuhUCgMHY6KiaEDIP3k5ORg69at+Prrrw0dChG9BPfu3UNDQwMcHBw06h0cHFBYWGigqIioLQghEBkZiT//+c/w9PQ0dDhERESthjkvEemD+TEBwJUrV6BQKFBTUwOZTIaEhAR4eHgYOiwVrkRvY2vWrIFEInlquXjxosY+BQUFmDRpEqZPn4558+YZKHJ6Uc9z7qnjk0gkGq+FEFp1RNSxLFq0CJcvX8bevXsNHQoR0Qthfkv6Ys5LRE/D/JgAwM3NDenp6Th79iwWLlyI8PBwXLt2zdBhqXAlehtbtGgRQkJCntrG2dlZ9f8FBQUICAiAQqHAzp07Wzk6ak0tPffUsdnb28PY2FhrBU5RUZHWSh0i6jgWL16MxMREnDp1Cr179zZ0OEREL4T5LT0Lc14iehbmx9TEzMwMLi4uAAAfHx9cuHABW7ZswY4dOwwcWSNOorcxe3t72Nvb69U2Pz8fAQEB8Pb2RkxMDIyM+MWBV1lLzj11fGZmZvD29kZSUhLeeustVX1SUhKmTp1qwMiIqDUIIbB48WIkJCQgOTkZ/fr1M3RIREQvjPktPQtzXiLShfkxPYsQArW1tYYOQ4WT6O1UQUEB/P390adPH3z11VcoLi5WbevRo4cBI6O2kJubi5KSEuTm5qKhoQHp6ekAABcXF8hkMsMGRy9NZGQkZs2aBR8fH9W3TXJzc/H+++8bOjRqRRUVFbh+/brq9c2bN5Geng47Ozv06dPHgJFRa4qIiEB8fDz+85//wNraWrUiz8bGBhYWFgaOjoio9TG/7byY85I65sLUhPkxqVuxYgUCAwPh5OSE8vJy7Nu3D8nJyThy5IihQ1ORCCGEoYMgbbGxsZgzZ06z23jKOr7Zs2cjLi5Oq/7EiRPw9/dv+4Co1Wzbtg1ffvkl7ty5A09PT2zatAm+vr6GDotaUXJyMgICArTqw8PDERsb2/YBUZvQdd/XmJgYzJ49u22DISIyAOa3nRtzXmrCXJiaMD8mdXPnzsXx48dx584d2NjYYMiQIYiKisL48eMNHZoKJ9GJiIiIiIiIiIiIiHTgTbaJiIiIiIiIiIiIiHTgJDoRERERERERERERkQ6cRCciIiIiIiIiIiIi0oGT6EREREREREREREREOnASnYiIiIiIiIiIiIhIB06iExERERERERERERHpwEl0IiIiIiIiIiIiIiIdOIlORERERERERERERKQDJ9GJiDqI2NhY2NraGjqMZ1qzZg2GDRvWpsd8Vd4bIiIiIno5/P39sXTpUp3bnZ2dsXnz5laPQyKR4ODBgy+lr+TkZEgkEjx8+PCl9GdobXUO1D3ruiAi0oWT6EREr4DZs2cjKChIq/55E+nq6mp06dIFdnZ2qK6u1tq+c+dO+Pv7Qy6Xd6hEnYiIiIiIiIiopTiJTkTUCf3000/w9PSEh4cHDhw4oLW9qqoKkyZNwooVKwwQHRERERERERFR+8FJdCKiDubgwYNwdXWFVCrF+PHjkZeXp9Vm9+7dmDlzJmbOnIndu3drbV+6dCmWL1+OUaNG6TzOH3/8gZCQENjZ2cHKygo+Pj44d+7cc8UcExMDd3d3SKVSDBw4ENu2bVNtUygUWL58uUb74uJimJqa4sSJEwCAuro6fPzxx+jVqxesrKwwcuRIJCcnP1csRERERNQx1NfXY9GiRbC1tUXXrl3x6aefQgjRbNvc3FxMnToVMpkMcrkcwcHBuHv3rkab6OhoDBgwAGZmZnBzc8OPP/6osT07Oxu+vr6QSqXw8PBAUlJSi+JNTU3FsGHDIJVK4ePjg4MHD0IikSA9Pb3Z9s3dJnHz5s1wdnbWqPv+++8xaNAgmJubo2fPnli0aJHe487IyEBAQACsra0hl8vh7e2NixcvasTs6+sLCwsLODk54YMPPkBlZWWLxt2ktLQU8+fPR/fu3SGXy/HGG28gIyMDAJCVlQWJRILff/9dY5+NGzfC2dlZdV6vXbuGyZMnQyaTwcHBAbNmzcK9e/eeKx4iInWcRCci6kCqqqqwdu1axMXFISUlBWVlZQgJCdFok5OTgzNnziA4OBjBwcFITU3FjRs3WnSciooK+Pn5oaCgAImJicjIyMDHH38MpVLZ4ph37dqFlStXYu3atcjMzMTnn3+OVatWIS4uDgAQGhqKvXv3avzCs3//fjg4OMDPzw8AMGfOHKSkpGDfvn24fPkypk+fjkmTJiE7O7vF8RARERFRxxAXFwcTExOcO3cO33zzDTZt2oTvvvtOq50QAkFBQSgpKcHJkyeRlJSEnJwcvP3226o2CQkJWLJkCT766CP89ttvWLBgAebMmaNa1KFUKjFt2jQYGxvj7Nmz2L59O6KiovSOtby8HFOmTMHgwYNx6dIlfPbZZy3aX5fo6GhERERg/vz5uHLlChITE+Hi4qL3uENDQ9G7d29cuHABaWlpWL58OUxNTQEAV65cwcSJEzFt2jRcvnwZ+/fvxy+//KIxSa8vIQTefPNNFBYW4tChQ0hLS8Pw4cMxduxYlJSUwM3NDd7e3tizZ4/GfvHx8ZgxYwYkEgnu3LkDPz8/DBs2DBcvXsSRI0dw9+5dBAcHv8A7SET0mCAionYvPDxcGBsbCysrK40ilUoFAPHgwQMRExMjAIizZ8+q9svMzBQAxLlz51R1K1asEEFBQarXU6dOFStXrmz2uCdOnFD1r27Hjh3C2tpa3L9/v8VjWb16tRg6dKjqtZOTk4iPj9do89lnnwmFQiGEEKKoqEiYmJiIU6dOqbYrFAqxbNkyIYQQ169fFxKJROTn52v0MXbsWPHJJ58IIYSIiYkRNjY2LY6ViIiIiF5Nfn5+wt3dXSiVSlVdVFSUcHd3F0II0bdvX7Fp0yYhhBDHjh0TxsbGIjc3V9X26tWrAoA4f/68EEKI0aNHi/fee0/jGNOnTxeTJ08WQghx9OhRYWxsLPLy8lTbDx8+LACIhISEZ8YbHR0tunbtKqqrq1V1u3btEgDEr7/+KoTQzs2fzKuFEGLTpk2ib9++qteOjo46c319xm1tbS1iY2Ob3X/WrFli/vz5GnWnT58WRkZGGuPQRf0cHD9+XMjlclFTU6PRZsCAAWLHjh1CCCE2btwo+vfvr9qWlZUlAIirV68KIYRYtWqVmDBhgsb+eXl5AoDIysoSQjReF0uWLHlmbERET+JKdCKiV0RAQADS09M1ypMraUxMTODj46N6PXDgQNja2iIzMxMA0NDQgLi4OMycOVPVZubMmYiLi0NDQ4PesaSnp8PLywt2dnYvNKbi4mLk5eVh7ty5kMlkqvKvf/0LOTk5AIBu3bph/PjxqlUnN2/exJkzZxAaGgoAuHTpEoQQcHV11ejj5MmTqj6IiIiIqPMZNWoUJBKJ6rVCoUB2drZW3puZmQknJyc4OTmp6jw8PDTy6MzMTLz++usa+73++usa2/v06YPevXtrHE9fWVlZGDJkCKRSqapuxIgReu/fnKKiIhQUFGDs2LHNbtdn3JGRkZg3bx7GjRuH9evXa+TXaWlpiI2N1cjBJ06cCKVSiZs3b7Yo1rS0NFRUVKBr164a/d28eVN1zJCQENy+fRtnz54FAOzZswfDhg2Dh4eHqo8TJ05o7D9w4EAA4O8FRPTCTAwdABER6cfKykr11csmf/zxh1Y79V8Unqw7evQo8vPzNb6iCTROrh87dgyBgYF6xWJhYaFv2E/VdPuXXbt2YeTIkRrbjI2NVf8fGhqKJUuWYOvWrYiPj8egQYMwdOhQVR/GxsZIS0vT2AcAZDLZS4mTiIiIiDouIUSzOfST9U+2Ud8umrnXenN9tiSG5vpUZ2RkpNXm0aNHqv9/Vs6uz7jXrFmDGTNm4L///S8OHz6M1atXY9++fXjrrbegVCqxYMECfPDBB1p99OnT56nHfpJSqUTPnj2bfa6Rra0tAKBnz54ICAhAfHw8Ro0ahb1792LBggUafUyZMgVffPGFVh89e/ZsUTxERE/iSnQiog6kvr5e40E/WVlZePjwoWoFxu7duxESEqK1oj00NLTZB4zqMmTIEKSnp6OkpOSF4nVwcECvXr1w48YNuLi4aJR+/fqp2gUFBaGmpgZHjhxBfHy8xkp6Ly8vNDQ0oKioSKuPHj16vFB8RERERPTqalqxrP76tdde01p44eHhgdzcXOTl5anqrl27htLSUri7uwMA3N3d8csvv2jsl5qaqtre1EdBQYFq+5kzZ/SOdeDAgbh8+TJqa2tVdep5fXO6deuGwsJCjYl09YeQWltbw9nZGcePH292f33GDQCurq748MMPcezYMUybNg0xMTEAgOHDh+Pq1ataObiLiwvMzMz0HntTX4WFhTAxMdHqy97eXtUuNDQU+/fvx5kzZ5CTk6Px/KemeJydnbX6sLKyalE8RERP4iQ6EVEHYmpqisWLF+PcuXO4dOkS5syZg1GjRmHEiBEoLi7Gzz//jPDwcHh6emqU8PBwJCYmori4GABQWFiI9PR0XL9+HUDjQ4PUJ83feecd9OjRA0FBQUhJScGNGzfw008/tegXhSZr1qzBunXrsGXLFvzvf//DlStXEBMTg40bN6raWFlZYerUqVi1ahUyMzMxY8YM1TZXV1eEhoYiLCwMBw4cwM2bN3HhwgV88cUXOHTo0Iu8nURERET0CsvLy0NkZCSysrKwd+9ebN26FUuWLNFqN27cOAwZMgShoaG4dOkSzp8/j7CwMPj5+alulbhs2TLExsZi+/btyM7OxsaNG3HgwAH87W9/U/Xh5uaGsLAwZGRk4PTp01i5cqXesc6YMQNKpRLz589HZmYmjh49iq+++gqA7hXt/v7+KC4uxpdffomcnBx8++23OHz4sEabNWvW4Ouvv8Y333yD7OxsXLp0CVu3btVr3NXV1Vi0aBGSk5Nx+/ZtpKSk4MKFC6oJ9qioKJw5cwYRERFIT09HdnY2EhMTsXjxYr3H3WTcuHFQKBQICgrC0aNHcevWLaSmpuLTTz/V+GPCtGnTUFZWhoULFyIgIAC9evVSbYuIiEBJSQneeecdnD9/Hjdu3MCxY8fw7rvvtujWlUREzeEkOhFRB2JpaYmoqCjMmDEDCoUCFhYW2LdvHwDghx9+gJWVVbP3RAwICIC1tTV+/PFHAMD27dvh5eWF9957DwDg6+sLLy8vJCYmAgDMzMxw7NgxdO/eHZMnT8bgwYOxfv16rVU9+pg3bx6+++47xMbGYvDgwfDz80NsbKzGSnSgcdVJRkYGxowZo/X10JiYGISFheGjjz6Cm5sb/vrXv+LcuXMa93ckIiIios4lLCwM1dXVGDFiBCIiIrB48WLMnz9fq51EIsHBgwfRpUsX+Pr6Yty4cejfvz/279+vahMUFIQtW7Zgw4YNGDRoEHbs2IGYmBj4+/sDaLy1SkJCAmprazFixAjMmzcPa9eu1TtWuVyOn3/+Genp6Rg2bBhWrlyJv//97wCgcZ90de7u7ti2bRu+/fZbDB06FOfPn1dN6jcJDw/H5s2bsW3bNgwaNAh/+ctfkJ2drde4jY2Ncf/+fYSFhcHV1RXBwcEIDAzEP/7xDwCN3049efIksrOzMWbMGHh5eWHVqlXPdesUiUSCQ4cOwdfXF++++y5cXV0REhKCW7duwcHBQeN9mjJlCjIyMlTPSGri6OiIlJQUNDQ0YOLEifD09MSSJUtgY2MDIyNOfxHRi5GIZ91ki4iIiIiIiIiI2tSePXswZ84clJaWvrRnEhER0fPhg0WJiIiIiIiIiAzshx9+QP/+/dGrVy9kZGQgKioKwcHBnEAnImoH+H0WIiJ6qQYNGgSZTNZs2bNnj6HDIyIiIiJqc59//rnOHDkwMBBA43OJZs6cCXd3d3z44YeYPn06du7caeDIn8/p06d1jlcmkxk6PCKiFuPtXIiI6KW6ffs2Hj161Ow2BwcHWFtbt3FERERERESGVVJSgpKSkma3WVhYaDwgsyOorq5Gfn6+zu0uLi5tGA0R0YvjJDoRERERERERERERkQ68nQsRERERERERERERkQ6cRCciIiIiIiIiIiIi0oGT6EREREREREREREREOnASnYiIiIiIiIiIiIhIB06iExERERERERERERHpwEl0IiIiIiIiIiIiIiIdOIlORERERERERERERKTD/wGgNFYTKj896QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 2. Load the data\n",
    "df = pd.read_csv(\"diabetes_prediction_dataset.csv\")\n",
    "\n",
    "# 3. Initial data exploration\n",
    "print(\"Shape:\", df.shape)\n",
    "print(\"\\nInfo:\")\n",
    "print(df.info())\n",
    "print(\"\\nMissing values:\")\n",
    "print(df.isnull().sum())\n",
    "print(\"\\nDuplicate rows:\", df.duplicated().sum())\n",
    "\n",
    "# 4. Remove duplicates\n",
    "df = df.drop_duplicates()\n",
    "\n",
    "# 5. Handle missing values (if any)\n",
    "df = df.dropna()\n",
    "\n",
    "# 6. Convert categorical variables\n",
    "le = LabelEncoder()\n",
    "categorical_columns = ['gender', 'smoking_history']\n",
    "for col in categorical_columns:\n",
    "    df[col] = le.fit_transform(df[col])\n",
    "\n",
    "# 7. Handle outliers using IQR method\n",
    "def remove_outliers(df, column):\n",
    "    Q1 = df[column].quantile(0.25)\n",
    "    Q3 = df[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    df = df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n",
    "    return df\n",
    "\n",
    "numerical_columns = ['age', 'bmi', 'HbA1c_level', 'blood_glucose_level']\n",
    "for col in numerical_columns:\n",
    "    df = remove_outliers(df, col)\n",
    "\n",
    "# 8. Feature scaling\n",
    "scaler = StandardScaler()\n",
    "df[numerical_columns] = scaler.fit_transform(df[numerical_columns])\n",
    "\n",
    "# 9. Split features and target\n",
    "X = df.drop('diabetes', axis=1)\n",
    "y = df['diabetes']\n",
    "\n",
    "# 10. Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 11. Data visualization\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()\n",
    "\n",
    "# 12. Display basic statistics\n",
    "print(\"\\nBasic statistics:\")\n",
    "print(df.describe())\n",
    "\n",
    "# 13. Feature importance visualization\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=X.columns, y=X.std())\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Feature Importance based on Standard Deviation')\n",
    "plt.show()\n",
    "\n",
    "# 14. Distribution plots for numerical features\n",
    "plt.figure(figsize=(15, 10))\n",
    "for i, col in enumerate(numerical_columns, 1):\n",
    "    plt.subplot(2, 2, i)\n",
    "    sns.histplot(df[col], kde=True)\n",
    "    plt.title(f'Distribution of {col}')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 15. Class distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(data=df, x='diabetes')\n",
    "plt.title('Class Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "097cf946",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results with all features:\n",
      "\n",
      "RF Results:\n",
      "Accuracy: 0.9722\n",
      "Best Parameters: {'max_depth': 10, 'n_estimators': 100}\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99     18292\n",
      "           1       1.00      0.67      0.81      1708\n",
      "\n",
      "    accuracy                           0.97     20000\n",
      "   macro avg       0.99      0.84      0.90     20000\n",
      "weighted avg       0.97      0.97      0.97     20000\n",
      "\n",
      "\n",
      "SVM Results:\n",
      "Accuracy: 0.9661\n",
      "Best Parameters: {'C': 10, 'kernel': 'rbf'}\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98     18292\n",
      "           1       0.98      0.62      0.76      1708\n",
      "\n",
      "    accuracy                           0.97     20000\n",
      "   macro avg       0.97      0.81      0.87     20000\n",
      "weighted avg       0.97      0.97      0.96     20000\n",
      "\n",
      "\n",
      "LR Results:\n",
      "Accuracy: 0.9587\n",
      "Best Parameters: {'C': 1, 'solver': 'liblinear'}\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.98     18292\n",
      "           1       0.86      0.61      0.72      1708\n",
      "\n",
      "    accuracy                           0.96     20000\n",
      "   macro avg       0.91      0.80      0.85     20000\n",
      "weighted avg       0.96      0.96      0.96     20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 1. Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectKBest, f_classif, RFE\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import joblib\n",
    "\n",
    "# 2. Load and prepare the data\n",
    "def prepare_data(df):\n",
    "    # Handle missing values\n",
    "    numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
    "    categorical_columns = df.select_dtypes(include=['object']).columns\n",
    "    \n",
    "    # For numeric columns: fill with median\n",
    "    for col in numeric_columns:\n",
    "        df[col].fillna(df[col].median(), inplace=True)\n",
    "    \n",
    "    # For categorical columns: fill with mode\n",
    "    for col in categorical_columns:\n",
    "        df[col].fillna(df[col].mode()[0], inplace=True)\n",
    "    \n",
    "    # Encode categorical variables\n",
    "    le = LabelEncoder()\n",
    "    for col in categorical_columns:\n",
    "        df[col] = le.fit_transform(df[col])\n",
    "    \n",
    "    return df\n",
    "\n",
    "# 3. Feature Selection Methods\n",
    "def select_features(X, y):\n",
    "    # Method 1: SelectKBest with f_classif\n",
    "    k_best = SelectKBest(score_func=f_classif, k=5)\n",
    "    X_kbest = k_best.fit_transform(X, y)\n",
    "    kbest_features = X.columns[k_best.get_support()].tolist()\n",
    "    \n",
    "    # Method 2: Recursive Feature Elimination with Random Forest\n",
    "    rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "    rfe = RFE(estimator=rf, n_features_to_select=5)\n",
    "    rfe.fit(X, y)\n",
    "    rfe_features = X.columns[rfe.support_].tolist()\n",
    "    \n",
    "    return kbest_features, rfe_features, X_kbest, X.iloc[:, rfe.support_]\n",
    "\n",
    "# 4. Model Training and Evaluation\n",
    "def train_and_evaluate_model(X_train, X_test, y_train, y_test, model, param_grid):\n",
    "    # Grid Search\n",
    "    grid_search = GridSearchCV(model, param_grid, cv=5, scoring='accuracy')\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    \n",
    "    # Best model\n",
    "    best_model = grid_search.best_estimator_\n",
    "    \n",
    "    # Predictions\n",
    "    y_pred = best_model.predict(X_test)\n",
    "    \n",
    "    # Evaluation\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    report = classification_report(y_test, y_pred)\n",
    "    \n",
    "    return best_model, accuracy, report, grid_search.best_params_\n",
    "\n",
    "# Main execution\n",
    "def main():\n",
    "    # Load data\n",
    "    df = pd.read_csv(\"diabetes_prediction_dataset.csv\")\n",
    "    \n",
    "    # Prepare data\n",
    "    df_cleaned = prepare_data(df)\n",
    "    \n",
    "    # Split features and target\n",
    "    X = df_cleaned.drop('diabetes', axis=1)\n",
    "    y = df_cleaned['diabetes']\n",
    "    \n",
    "    # Scale features\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)\n",
    "    \n",
    "    # Split data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
    "    \n",
    "    # Feature selection\n",
    "    kbest_features, rfe_features, X_kbest, X_rfe = select_features(X_scaled, y)\n",
    "    \n",
    "    # Define models and their parameter grids\n",
    "    models = {\n",
    "        'rf': (RandomForestClassifier(random_state=42),\n",
    "               {'n_estimators': [100, 200],\n",
    "                'max_depth': [10, 20, None]}),\n",
    "        'svm': (SVC(random_state=42),\n",
    "                {'C': [0.1, 1, 10],\n",
    "                 'kernel': ['rbf', 'linear']}),\n",
    "        'lr': (LogisticRegression(random_state=42),\n",
    "               {'C': [0.1, 1, 10],\n",
    "                'solver': ['lbfgs', 'liblinear']})\n",
    "    }\n",
    "    \n",
    "    # Results dictionary\n",
    "    results = {}\n",
    "    \n",
    "    # Train and evaluate models with all features\n",
    "    print(\"Results with all features:\")\n",
    "    for model_name, (model, param_grid) in models.items():\n",
    "        best_model, accuracy, report, best_params = train_and_evaluate_model(\n",
    "            X_train, X_test, y_train, y_test, model, param_grid\n",
    "        )\n",
    "        results[f'{model_name}_all'] = {\n",
    "            'accuracy': accuracy,\n",
    "            'report': report,\n",
    "            'best_params': best_params,\n",
    "            'model': best_model\n",
    "        }\n",
    "        print(f\"\\n{model_name.upper()} Results:\")\n",
    "        print(f\"Accuracy: {accuracy}\")\n",
    "        print(f\"Best Parameters: {best_params}\")\n",
    "        print(\"Classification Report:\")\n",
    "        print(report)\n",
    "    \n",
    "    # Save results and models\n",
    "    joblib.dump(results, 'model_results.pkl')\n",
    "    \n",
    "    # Save feature lists\n",
    "    feature_importance = {\n",
    "        'kbest_features': kbest_features,\n",
    "        'rfe_features': rfe_features\n",
    "    }\n",
    "    joblib.dump(feature_importance, 'feature_importance.pkl')\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1982e00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting analysis at 13:53:59\n",
      "\n",
      "Loading data...\n",
      "Data loaded. Shape: (100000, 9)\n",
      "\n",
      "Handling missing values...\n",
      "\n",
      "Training models with all features...\n",
      "\n",
      "Training Random Forest...\n",
      "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n",
      "Random Forest Accuracy: 0.9722\n",
      "\n",
      "Training SVM...\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "SVM Accuracy: 0.9654\n",
      "\n",
      "Training Logistic Regression...\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "Logistic Regression Accuracy: 0.9586\n",
      "\n",
      "Performing SelectKBest feature selection...\n",
      "Top 5 features (SelectKBest): ['age', 'hypertension', 'bmi', 'HbA1c_level', 'blood_glucose_level']\n",
      "\n",
      "Performing Random Forest feature importance selection...\n",
      "\n",
      "Top 5 features (Random Forest Importance):\n",
      "               feature  importance\n",
      "6          HbA1c_level    0.388256\n",
      "7  blood_glucose_level    0.328470\n",
      "5                  bmi    0.122187\n",
      "1                  age    0.104092\n",
      "4      smoking_history    0.024519\n",
      "\n",
      "Training models with selected features...\n",
      "\n",
      "Training Random Forest with selected features...\n",
      "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n",
      "Random Forest Accuracy with selected features: 0.9724\n",
      "\n",
      "Training SVM with selected features...\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "SVM Accuracy with selected features: 0.9683\n",
      "\n",
      "Training Logistic Regression with selected features...\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "Logistic Regression Accuracy with selected features: 0.9588\n",
      "\n",
      "Saving results...\n",
      "\n",
      "Analysis completed at 14:05:47\n",
      "\n",
      "Final Results Summary:\n",
      "=====================\n",
      "\n",
      "Full Features Models:\n",
      "Random Forest Accuracy: 0.9722\n",
      "SVM Accuracy: 0.9654\n",
      "Logistic Regression Accuracy: 0.9586\n",
      "\n",
      "Selected Features Models:\n",
      "Random Forest Accuracy: 0.9724\n",
      "SVM Accuracy: 0.9683\n",
      "Logistic Regression Accuracy: 0.9588\n",
      "\n",
      "Selected Features: ['age', 'hypertension', 'bmi', 'HbA1c_level', 'blood_glucose_level']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import SelectKBest, f_classif\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "import time\n",
    "\n",
    "print(f\"Starting analysis at {time.strftime('%H:%M:%S')}\")\n",
    "\n",
    "# 1. Load and prepare data\n",
    "print(\"\\nLoading data...\")\n",
    "df = pd.read_csv(\"diabetes_prediction_dataset.csv\")\n",
    "print(f\"Data loaded. Shape: {df.shape}\")\n",
    "\n",
    "# 2. Handle missing values\n",
    "print(\"\\nHandling missing values...\")\n",
    "numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
    "df[numeric_columns] = df[numeric_columns].fillna(df[numeric_columns].mean())\n",
    "categorical_columns = df.select_dtypes(include=['object']).columns\n",
    "df[categorical_columns] = df[categorical_columns].fillna(df[categorical_columns].mode().iloc[0])\n",
    "\n",
    "# Convert categorical variables\n",
    "for col in categorical_columns:\n",
    "    df[col] = pd.factorize(df[col])[0]\n",
    "\n",
    "# 3. Prepare data for modeling\n",
    "X = df.drop('diabetes', axis=1)\n",
    "y = df['diabetes']\n",
    "\n",
    "# Split data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# 4. Define models and their parameters\n",
    "models = {\n",
    "    'Random Forest': (\n",
    "        RandomForestClassifier(random_state=42),\n",
    "        {\n",
    "            'n_estimators': [100, 200],\n",
    "            'max_depth': [10, 20],\n",
    "            'min_samples_split': [2, 5]\n",
    "        }\n",
    "    ),\n",
    "    'SVM': (\n",
    "        SVC(random_state=42),\n",
    "        {\n",
    "            'C': [0.1, 1, 10],\n",
    "            'kernel': ['rbf', 'linear']\n",
    "        }\n",
    "    ),\n",
    "    'Logistic Regression': (\n",
    "        LogisticRegression(random_state=42),\n",
    "        {\n",
    "            'C': [0.1, 1, 10],\n",
    "            'solver': ['lbfgs', 'liblinear']\n",
    "        }\n",
    "    )\n",
    "}\n",
    "\n",
    "# 5. Train and evaluate models with full features\n",
    "print(\"\\nTraining models with all features...\")\n",
    "full_features_results = {}\n",
    "\n",
    "for model_name, (model, params) in models.items():\n",
    "    print(f\"\\nTraining {model_name}...\")\n",
    "    grid_search = GridSearchCV(model, params, cv=3, n_jobs=-1, verbose=1)\n",
    "    grid_search.fit(X_train_scaled, y_train)\n",
    "    \n",
    "    y_pred = grid_search.predict(X_test_scaled)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    \n",
    "    full_features_results[model_name] = {\n",
    "        'accuracy': accuracy,\n",
    "        'best_params': grid_search.best_params_\n",
    "    }\n",
    "    print(f\"{model_name} Accuracy: {accuracy:.4f}\")\n",
    "\n",
    "# 6. Feature Selection Method 1: SelectKBest\n",
    "print(\"\\nPerforming SelectKBest feature selection...\")\n",
    "k_best = SelectKBest(score_func=f_classif, k=5)\n",
    "X_train_selected = k_best.fit_transform(X_train_scaled, y_train)\n",
    "X_test_selected = k_best.transform(X_test_scaled)\n",
    "\n",
    "selected_features_mask = k_best.get_support()\n",
    "selected_features = X.columns[selected_features_mask].tolist()\n",
    "print(\"Top 5 features (SelectKBest):\", selected_features)\n",
    "\n",
    "# 7. Feature Selection Method 2: Random Forest Feature Importance\n",
    "print(\"\\nPerforming Random Forest feature importance selection...\")\n",
    "rf_selector = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf_selector.fit(X_train_scaled, y_train)\n",
    "feature_importance = pd.DataFrame({\n",
    "    'feature': X.columns,\n",
    "    'importance': rf_selector.feature_importances_\n",
    "})\n",
    "feature_importance = feature_importance.sort_values('importance', ascending=False)\n",
    "print(\"\\nTop 5 features (Random Forest Importance):\")\n",
    "print(feature_importance.head())\n",
    "\n",
    "# 8. Train models with selected features\n",
    "print(\"\\nTraining models with selected features...\")\n",
    "selected_features_results = {}\n",
    "\n",
    "X_train_selected_rf = X_train_scaled[:, selected_features_mask]\n",
    "X_test_selected_rf = X_test_scaled[:, selected_features_mask]\n",
    "\n",
    "for model_name, (model, params) in models.items():\n",
    "    print(f\"\\nTraining {model_name} with selected features...\")\n",
    "    grid_search = GridSearchCV(model, params, cv=3, n_jobs=-1, verbose=1)\n",
    "    grid_search.fit(X_train_selected_rf, y_train)\n",
    "    \n",
    "    y_pred = grid_search.predict(X_test_selected_rf)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    \n",
    "    selected_features_results[model_name] = {\n",
    "        'accuracy': accuracy,\n",
    "        'best_params': grid_search.best_params_\n",
    "    }\n",
    "    print(f\"{model_name} Accuracy with selected features: {accuracy:.4f}\")\n",
    "\n",
    "# 9. Save results\n",
    "print(\"\\nSaving results...\")\n",
    "with open('model_results.txt', 'w') as f:\n",
    "    f.write(\"Model Results\\n\")\n",
    "    f.write(\"=============\\n\\n\")\n",
    "    \n",
    "    f.write(\"Full Features Results:\\n\")\n",
    "    for model_name, results in full_features_results.items():\n",
    "        f.write(f\"\\n{model_name}:\\n\")\n",
    "        f.write(f\"Accuracy: {results['accuracy']:.4f}\\n\")\n",
    "        f.write(f\"Best Parameters: {results['best_params']}\\n\")\n",
    "    \n",
    "    f.write(\"\\nSelected Features Results:\\n\")\n",
    "    for model_name, results in selected_features_results.items():\n",
    "        f.write(f\"\\n{model_name}:\\n\")\n",
    "        f.write(f\"Accuracy: {results['accuracy']:.4f}\\n\")\n",
    "        f.write(f\"Best Parameters: {results['best_params']}\\n\")\n",
    "    \n",
    "    f.write(\"\\nSelected Features:\\n\")\n",
    "    f.write(str(selected_features))\n",
    "\n",
    "print(f\"\\nAnalysis completed at {time.strftime('%H:%M:%S')}\")\n",
    "\n",
    "# Display final comparison\n",
    "print(\"\\nFinal Results Summary:\")\n",
    "print(\"=====================\")\n",
    "print(\"\\nFull Features Models:\")\n",
    "for model_name, results in full_features_results.items():\n",
    "    print(f\"{model_name} Accuracy: {results['accuracy']:.4f}\")\n",
    "\n",
    "print(\"\\nSelected Features Models:\")\n",
    "for model_name, results in selected_features_results.items():\n",
    "    print(f\"{model_name} Accuracy: {results['accuracy']:.4f}\")\n",
    "\n",
    "print(\"\\nSelected Features:\", selected_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fd96852",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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