ML_Sup_Class_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6b5ddf3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, f1_score, roc_auc_score\n",
    "from sklearn.metrics import classification_report, ConfusionMatrixDisplay\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c073396",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ec81ff4",
   "metadata": {},
   "source": [
    "## Dataset description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3020551",
   "metadata": {},
   "source": [
    "The example is based on a dataset that is publicly available from the UCI Machine Learning Repository (Asuncion and Newman, 2007)\\[[http://mlearn.ics.uci.edu/MLRepository.html](http://mlearn.ics.uci.edu/MLRepository.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML241ENSkillsNetwork31576874-2022-01-01)]. The dataset consists of several hundred human cell sample records, each of which contains the values of a set of cell characteristics. The fields in each record are:\n",
    "\n",
    "| Field name  | Description                 |\n",
    "| ----------- | --------------------------- |\n",
    "| ID          | Clump thickness             |\n",
    "| Clump       | Clump thickness             |\n",
    "| UnifSize    | Uniformity of cell size     |\n",
    "| UnifShape   | Uniformity of cell shape    |\n",
    "| MargAdh     | Marginal adhesion           |\n",
    "| SingEpiSize | Single epithelial cell size |\n",
    "| BareNuc     | Bare nuclei                 |\n",
    "| BlandChrom  | Bland chromatin             |\n",
    "| NormNucl    | Normal nucleoli             |\n",
    "| Mit         | Mitoses                     |\n",
    "| Class       | Benign or malignant         |\n",
    "\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b3e8a11",
   "metadata": {},
   "source": [
    "Based on the input values we want to determine if the cell class is benign or malignant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b967153",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Clump</th>\n",
       "      <th>UnifSize</th>\n",
       "      <th>UnifShape</th>\n",
       "      <th>MargAdh</th>\n",
       "      <th>SingEpiSize</th>\n",
       "      <th>BareNuc</th>\n",
       "      <th>BlandChrom</th>\n",
       "      <th>NormNucl</th>\n",
       "      <th>Mit</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000025</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <th>1</th>\n",
       "      <td>1002945</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1017023</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "        ID  Clump  UnifSize  UnifShape  MargAdh  SingEpiSize BareNuc  \\\n",
       "0  1000025      5         1          1        1            2       1   \n",
       "1  1002945      5         4          4        5            7      10   \n",
       "2  1015425      3         1          1        1            2       2   \n",
       "3  1016277      6         8          8        1            3       4   \n",
       "4  1017023      4         1          1        3            2       1   \n",
       "\n",
       "   BlandChrom  NormNucl  Mit  Class  \n",
       "0           3         1    1      2  \n",
       "1           3         2    1      2  \n",
       "2           3         1    1      2  \n",
       "3           3         7    1      2  \n",
       "4           3         1    1      2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e94ba477",
   "metadata": {},
   "source": [
    "## Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39d17933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID     Clump  UnifSize  UnifShape  MargAdh  SingEpiSize  BareNuc  BlandChrom  NormNucl  Mit    Class\n",
       "False  False  False     False      False    False        False    False       False     False  False    699\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for nul values\n",
    "df.isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f729f712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID    Clump  UnifSize  UnifShape  MargAdh  SingEpiSize  BareNuc  BlandChrom  NormNucl  Mit   Class\n",
       "True  True   True      True       True     True         False    True        True      True  True     699\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if all values are numeric\n",
    "df.applymap(np.isreal).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "136cfb14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BareNuc\n",
       "1          402\n",
       "10         132\n",
       "2           30\n",
       "5           30\n",
       "3           28\n",
       "8           21\n",
       "4           19\n",
       "?           16\n",
       "9            9\n",
       "7            8\n",
       "6            4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['BareNuc']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a577104b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting non-numeric values to numeric\n",
    "df= df[pd.to_numeric(df['BareNuc'], errors='coerce').notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2eeb0b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BareNuc\n",
       "1          402\n",
       "10         132\n",
       "2           30\n",
       "5           30\n",
       "3           28\n",
       "8           21\n",
       "4           19\n",
       "9            9\n",
       "7            8\n",
       "6            4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['BareNuc']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22b1fc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    444\n",
       "4    239\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ba9d7651",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43f79f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "47aad74c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3b7xK3fSZNI0ZR+6S98hbuIS8hUtozsym4JmXe3XCBKHpn6jcHNJ+/mNK5t9N034HhOU4/1cVF1/im7xh5ixSFrzqe46l9PwcS1nwKjUnzty7P+G9d2k8YFSbcsjepurCS/ZegF8zYxZJr7+699xp6eLcafE/d15/ldoZM2kcM44977xHzptLyHlzCc1Z2eQ9584dU1Gx9wL1pJdfdNep+H2oiVSVF11CzksLyHlpAdUnziLZ7zXZdvGabJN850uy32tyzYmziP/gfQCit2/DNDbSkpqKp6KCrB9dQcl111N/+BEhPsL/3r46b4A21/ckLvG9X/Vm1RdcQuFzCyh8bgG1J8wi0fuaHNPDa3KMt38S33iV2hO87+c7t+9tF79sMU37jwKgKXsIcR+4ShFPcRHRO7bRPGx4KA7vf1J+ySV7J2iomjWLAa++CtYSv9b7OtzuuqTmTPc6HL92LVjLgFdfpXqm65umzEwSPvgAgITVq/eWSUcVFu4dyY1fvx5aWmhJ7X1f9Mm+09NI08HGmI5F02CAUUGIJ3ieAU4A0oFdwM3A4+EMKPhqp04nYfkyhp06GxufQNGtt+/dN/TcueT8cwEAxTfdTPpNN2Dq6qidMo3aqe46k9T77iF20yYw0DRsGMXeko+WgQOp+PZlDLnoXDCG2qnTqJ12QsiP739RN2U68SuXkX2G65uSeb6+ybxgLgXPu74pveFmBt98A6a+jrrjp1E3pftrcDxFhWRecg6e6iowHpKffpL8lxb2ig93/oLVPwMeeQhPWRmD7rjFbYiKouCZ3jvCUjNtOonLlzHylNm0xCdQeJuvn4afNZfdr7h+KvrtzWT+xvVTzdRp1Ezz9VPymwupOvW0kMceLHVTp5OwchlDTveeO7/39Un2+XPJe8F77tx4M4N/G/i5E7NtC2k3/RoMNI4eQ/EtvW/2vNpp00lcsYzh35yNTUigcL7fa/I5c8l5yXu+3HQzGa2vyVN9r8mVZ59Dxk2/YdiZp2NjYii8/U4whgHP/oOYXTsZ9NeHGPRXNyta3iOP750oojf4X8+b1PvvcT9hYKBp6DBKfusrURz6zRMxVVWYxkYSlr5NwV8fp6kXfNHnr977mpw11/VPqd9rcsaFcyl8zvVP2Q03k9r6mnzcNOq9140OfOAPRHuvR24aMoyyG91rcOXlPyL15hvIPH8OWEv5T36+t4qkt6iePp2kZcvYf7Z7XuXd7uubkXPnsnOB65v8m28m+wb3vKqZNo1q7+tw/vz5ZN5+O6apiZa4OPJ/786dlLfeYuCzz0JUFC3x8eTed1+byWn6s/460mSs7bp6zhizX3d3ttbu6PlfUHled+J62c8bhVJ0U7gjkN6qvndPlBVUMY3hjiCyNfevy6S+lqjmcEcQ2dQ/XatOCncEkcu6gYhepWJg+D/bDygPfb91O9IUUFIkIiIiIiLSh3WbNBljKqHTbNIA1lrb5WQQIiIiIiLSt/TX8ryeRppSQhWIiIiIiIhIJOpdP/ssIiIiIiJh019HmnrXzz6LiIiIiIiEmJImERERERGRbqg8T0REREREAqLyPBEREREREelAI00iIiIiIhIQjTSJiIiIiIhIB0qaREREREREuqHyPBERERERCYjK80RERERERKQDjTSJiIiIiEhANNIkIiIiIiIiHShpEhERERER6YbK80REREREJCAqzxMREREREZEOlDSJiIiIiIh0Q+V5IiIiIiISEJXniYiIiIiISAcaaRIRERERkYBopElEREREREQ6UNIkIiIiIiLSDZXniYiIiIhIQFSeJyIiIiIiIh1opElERERERAKikSYRERERERHpIOgjTXH1wf4Xerf6uHBHELn+eW64I4hcUc3hjiCyfTEu3BFErvi6cEcQ2fSe1bUm1aZ0a8Oh4Y4gcp3/QrgjiGBvhzsACZReAkVEREREJCAqzxMREREREZEONNIkIiIiIiIB0UiTiIiIiIiIdKCkSUREREREpBsqzxMRERERkYCoPE9EREREREQ60EiTiIiIiIgERCNNIiIiIiIi0oGSJhERERERkW6oPE9ERERERAKi8jwRERERERHpQCNNIiIiIiISEI00iYiIiIiISAdKmkRERERERLqh8jwREREREQmIyvNERERERESkAyVNIiIiIiIi3Qi4PM8YczgwBbDAKmvtx0GLSkREREREIo7K87phjPkd8CSQBqQD/88Yc1MwAxMREREREYkEgY40XQJMstbWARhj7gTWArcGKS4REREREYkwGmnqXg4Q77ceB+zZ9+GIiIiIiIhElkBHmsqBjcaY/+CuaZoNfGCMeQDAWvuTIMUnIiIiIiISVoEmTa94b63e2fehiIiIiIhIJOuv5XkBJU3W2ieDHYiIiIiIiEgkCihpMsacDswH9vPexwDWWjsgiLGJiIiIiEgE0UhT9/4InA18aq21wQtHREREREQksgSaNO0CNkRcwmQtg++8jYQVy7Dx8RTdeicN4w/p0Cx24wbSb7oBU19H7dTplPz6RjCGQX/5M8kvvUBL6mAASn/yM2qnTQcg5otNpP3+ZjzVVWA85D73T2xcXEgPL2QeA04HCoAJYY4lDLImw+TvgvHAtsXwxasd2ww/FsafD9ZC+Q744E++fdEJcNL9kPMhrH0sVFGHTuZhMPF7rn92vA1fvtKxzbDj4KALAAvl22HNH932M1+E8p1uubYIVt8RoqBD5ICxMOt08Hhg3Yewelnn7cYdAmd9C554EPL2gCcKTjkTsocDFt5+HXZuC2XkobHfQTD9LPAY2PA+rFncdv/4o2DKGVBd7tbXroCN70PGUDjxPIiNh5YW+PA/8OXakIcfVCPGw5TzwBj4/F34ZFHb/eOOgWPPguoyt75hmWsHcOWDUOKdv7aqFN78a8jCDomRB8O0s91rzmfvwUdvt91/0NEw5UyoKnPr61e4dimpcOoPXJ96omD9ctiwKtTRB98hw+H849zzauUmeGtd2/3TDoYTDnHPnfom+MdyyC2DtGSYdz7kl7l2WwvgmZWhjj64Bh8FY3/kzp2cN2HHc233j7kKUie55ah4iBkEy89065PvgAEHQ/kGWKdfIpVOBJo0/RJYaIxZBtS3brTW3heUqAKUsGI50Tu2s+dfi4hbv460W+eR+8yLHdql3TqP4nnzqZ84icyrLidh5XJqp7rkqOLbl1Fx2ffb3qGpiYwbfkHhHffQOO4gPGWl2OhAu6oXegJ4EPh7mOMIBw8c9n1YMR9qSmDmHZCzBip3+5okZ8O4s2DpTdBYDXHtilIPuRCKPg9t2CHjgUmXw6pboLYYZtwNuR+27Z+kITD2bFj+G9c/sQN9+5obYOn1oQ87FIyBk86A5x6Dygq47Gr46nMoLmjbLjYWjjwe9uz0bZt8lPv7+J8gMQnO/y488RBubtI+whiYcQ68/Ff34fain8LWDVCS37bdl5/AOy+33dbYCG89DWVFkDQALr4edmyC+rqQhR9UxsDUC+D1B1xSdM6vYPt6KM1r227zR7DyhY73b26AF/vYFxCtjIETzoNXH3LnzQU/d+dN+7756mNY9s+226or4MX7oaUJYmLh4htg26due19hDFw0Bf74LyithhvOgvU7XFLU6oPNsNz7njRxPzjvWHjgTbdeWAG3vtzhYfsGD4z7MXzyK6gvhKMegqJ3odrvtferh33Lw8+ElAN96ztegKg4GHZ6yCLutfpreV6gv9N0G1CD+62mFL9bWCUuXUz1GWeCMdRPmoynsoKowrafWKIKC/BUVVE/aTIYQ/UZZ5K4ZHGnj9cq4d1VNIwdR+O4gwBoGZQKUVFBOooIsAIoCXcQ4TH4QKjKg+oCsE2waxUMPbJtmwNmwZZ/u4QAoN7vDXjQKIgfCPntvunrKwYfCNW5UJPv+mf3ShhydNs2+8+CrX7901Ae+jjDYcgIKC2G8lJoaYbP1sGYgzu2m3qSG4FqbvJtS8uEHVvdck011NXCkGGhiTtUskdCeRFUFLv++fITGH1oYPctK3QJE7gPvDWVkJAcvFhDLXN/KC+ESm/fbP4I9p8U7qgiQ9Z+7v9/73nzMYwKsAKipdklTABR0S7B6GsOyICCciiqhOYWWLMFJu3ftk1do285LtpVSPQHA8ZBbQ7U5br3q/x3IP34rttnzYD8Jb710k+gqTboYUovFujwyVBrbYBvd6ETVZBPU3b23vWmrGyiCvJpzshs2yarY5tWA559muTXXqX+kEMp/fmvaRk4kJgd28AYsq78Pp7SEqpPOZWK710emoOSkEoY7EZQWtWWwOAxbdskD3F/T5jvLRd5EfLXAgYmXQofPACZE0MVcWjFp7Xrn2JIbd8/Q93fabe7/vn8eSj4xG3zxMIJd4NtgS9fhtwPQhN3KKQMgEq/BLGyAoaOaNsmaygMGAhbvoBvTPNtL8h1CdZn69z+7GHub+5u+oykQVBZ5luvLHeJVHtjJsGw0e6D8rJXfSVXrbJGug/AZcUd79tbJQ2C6lLfenWpS6TaG3UYDB0DZfmw6iXffaJi3OiUbYGPF8H2PvSlTdKgtudAVRlk79ex3ehJMNR73qx42Xef5EEw50oYmAGrFvStUSaAQUluhKlVaTUckNmx3QnjYdZEiPLA/W/4tqenwI1nQ10DLFgDm/M63re3ik+HOr/vzesLYcBBXbTNhIRsKFkbktD6nP460hRo0rTQGHOStXZRz03BGHMFcAVA1EN/I+oHV/y38QVVxfkXUXblj9z1TQ/+idR776R4/h3Q3EzcJx+R++w/sfEJZP3gMhrGH0rdMceGO2QJA0+US5yWzYOENDjhFvjP9TByGuR+7BKt/swT5RKnFb91/TP1VlhyHTTWwFtXQl0JJGbBlFugYgdU5/f4kH2DgZmnwb86Vgyz/iNIz3QlfRVlrnSvpZ98G+xv60b44mNoboYJx8LJF8NLf/HtTxwAJ18Ci56hT5UuBmL7p/DVGjdyMn4KzLwUXvNeS/mPm9x1YClpcMZ17vqmiqKwhhtS2ze4EaiWJjjkOJj1LXj1QbevqgyevcuVdZ52OWxeC7WV4Yw2PN75zN2OGg2nHg5PvAPlNXDDM1BdDyPT4aqT4JYX245M9RdZM6BgBdAS7kikNwk0aboK+Lkxph5opIcpx621jwCPAMQ17Nu3upRnnyblJVfkXX/oBKLz8vZeZBWdn0dzZlab9s2ZWUTn+75K8W/Tkp6+d3vVOeeRec0P3X2ysqk/4qi9E0TUTp1G7OcblTT1QbUl7oN+q/YjTwA1xVDyFdhmqCmAqlyXRKWNhfSDYfTJEB0PnmhoqoMNT4f2GIKprrhd/6S5JMhfbfv+yYGkoVC22de2Jh+KNsDAUX0naaqsgBS/67fajzzFxUJ6Flzs/c4oKRnOuRRe+rubDGLxv3xtv/VDKOljH3qryyBlkG89ZaBvwodWdTW+5Q2rYcoc33psHJx5Oby7EPJ2BDPS0Ksug6RU33pSase+qfcbTfh8FRxzlt/9vW0riyHnS0gf0XeSpuoyN1rUKnkQVHVz3nz2Hhw/t5PHqYDiXDcatWXtvo8zXMqqITXJt56a5LZ1Zc0WuGSqW25qgSbvB6adRe76pqyBsKOPnDt1RW4EqVVcBtR3MUKdNQO+eCA0cUnfEdA1TdbaFGutx1qbYK0d4F0Py280VV50CTn/XEDOPxdQc+Iskl57Fawlbt1aWpJT2pTmATRnZNKSnEzcurVgLUmvvUrNjJkAba5/Slz8No0Hurqj2uOmEPPVl5jaWmhqIn7NhzSOPhDpe0o3uwQoMRNMNIw4HnLXtG2T8yFkeCdljE1x7avzXVnewqvgzath/VOwY3nfSpigY/8Mn+ImgvCX80G7/hkKNXkQk+QSydbtaQdB5a7Qxh9MubthcDoMTHWjbeMnwWa/CUHq6+GBW+Hhu90tZ5cvYYqOgZgY127/A12ZVfsJJHq7vF0wKAMGDHb9M/Yw2LKxbZtEv3eRUYf6JonwRMHp34PPP4TNfaj0rFXBDhiU6UaKPFFw4BFuIgh//n2z/0Qo8373F5vge17FJ0H2aCjNDU3coZC/s915c7ibzMGff98cMAFKvedN0iBXuggQlwBDRrnSxr5keyFkDoS0FFd6d+RoWNfuS4VMv/6ZMNJdAwWQHO+7zis9xT1OYR8ahav8AhKHQXy2e7/KOsFNBNFe4giITobyz0IeYp9hTfhv4RDoj9tO62y7tXb5vg3n66mdOp2E5csYdupsbHwCRbfevnff0HPnkvPPBQAU33Szm3K8ro7aKdOoneoOJ/W+e4jdtAkMNA0bRvHvfg9Ay8CBVHz7MoZcdC4YQ+3UadROOyHkxxcyzwAnAOm4yeVvBh4PZ0ChY1vcNOFTb3TX42xfChW7YfwFULrFJVD5ayFrkptW3La4BKmhKtyRh4ZtgXWPwvG/AzywY7FLfA6+0PVP3ofu+qWsSTDzT679hidd/wweB5N/iCurMm6q8so+dM2ObYFFr8EF33MfRNavgaICmDoLcve0TaDaS0qC87/nLtCuqoDXO5khrbezLbD0JTjrSvfc2vg+lOTBMadAwS5XmnfYVJcstTS70YNFz7r7jp3srnNKSILx3olHFj0DhTlhO5x9yrbAiufh9Gtc32x6zyU+R50OhTtcad6EGbD/BO+00TWwxDu7aeoQmH6RO3eMcVOVt59ZrjezLW5WvDN+5Kby/2y1O2++cSoU7IRtG2DSdDjgUNe2rgbe/oe77+AsNxV5q0+WuNGmvqTFwnOr4Npvuv5Z9QXklsKcI9yI0fodbrrxg4e5iSJqGuD/vePuO2YInHGE226BZ1ZATX13/1rvYlvgiz/DYXcCHsj9N1TvgFHfgYovoeg91y5rhpskor0j7ncJVVQCHP8sfP4HKFnTsZ30XyaQn14yxrzutxoPHA18ZK09saf77uvyvL6mvo/+9NO+8M9zwx1B5IpqDncEke2LceGOIHLF95Fpu4Mlrg99iNzXmvrwL2/sCxsibrqsyHF+H/xiaF+Z+Ta9blqFTQeH/7P9QZ+Hvt8Cegm01s7xXzfGjAD+GIyAREREREREIkmgv9PU3m6gk18kERERERER6VsCvabpz/gmfPUAk4GPgxSTiIiIiIhEIP1OU/f8L4VrAp611q4KQjwiIiIiIiIRJdBrmp4MdiAiIiIiIiKRqNukyRjzKd38Dru1duI+j0hERERERCKSyvM6dzaQhfv1Hn8jgD70yxAiIiIiIiKd62n2vPuBcmvtDv8bUO7dJyIiIiIi/YQ14b+FQ09JU5a19tP2G73b9g9KRCIiIiIiIhGkp6RpUDf7EvZhHCIiIiIiIhGpp6RpjTHm8vYbjTE/AD4KTkgiIiIiIhKJwl2aF67yvJ4mgrgOeMUYcwm+JOlIIBY4K4hxiYiIiIiIRIRukyZrbT5wnDFmBnCod/O/rLVLgh6ZiIiIiIhEFE053g1r7VJgaZBjERERERERiTg9XdMkIiIiIiLSaxhjTjHGfGGM2WyM+XUn+0caY5YaYz4xxqw3xpza02MGNNIkIiIiIiIS6eV5xpgo4CFgNrAb+NAY85q19jO/ZjcBL1hrHzbGjAcW0sPPKWmkSURERERE+oqjgc3W2q3W2gbgOWBuuzYWGOBdHgjk9PSgGmkSEREREZGARPpIEzAM2OW3vhv4Rrs284BFxpgfA0nArJ4eVCNNIiIiIiLSaxhjrjDGrPG7XfE1H+Ii4Alr7XDgVOApY0y3eZFGmkREREREpNew1j4CPNLF7j3ACL/14d5t/r4PnOJ9rPeMMfFAOlDQ1b+pkSYREREREQmINeG/9eBDYIwx5gBjTCxwIfBauzY7gZkAxpiDgXigsLsHVdIkIiIiIiJ9grW2CbgGeAv4HDdL3kZjzO+NMWd4m10PXG6MWQc8C1xmrbXdPa7K80REREREJCC9YCIIrLULcdOI+2/7nd/yZ8DxX+cxNdIkIiIiIiLSDSVNIiIiIiIi3VB5noiIiIiIBKQ3lOcFg0aaREREREREuqGRJhERERERCUh/HWkKetIU3RTsf6F3++e54Y4gcp37z3BHELkKM8IdQWRLrgp3BJErqTrcEUQ20+2Es/1bbUK4I4hsJYPDHUHkGpIb7ghE/ncqzxMREREREemGyvNERERERCQg/bU8TyNNIiIiIiIi3VDSJCIiIiIi0g2V54mIiIiISEBUniciIiIiIiIdaKRJREREREQCopEmERERERER6UBJk4iIiIiISDdUniciIiIiIgFReZ6IiIiIiIh0oJEmEREREREJiEaaREREREREpAMlTSIiIiIiIt34WuV5xphEa21NsIIREREREZHIpfK8bhhjjjPGfAZs8q5PMsb8JaiRiYiIiIiIRIBAR5ruB04GXgOw1q4zxkwLWlQiIiIiIhJxNNLUA2vtrnabmvdxLCIiIiIiIhEn0JGmXcaY4wBrjIkBrgU+D15YIiIiIiIikSHQpOmHwJ+AYcAeYBFwdbCCEhERERGRyNNfy/MCTZoSrLWX+G8wxmQHIR4REREREZGIEug1TduMMc8aYxL8ti0MRkAiIiIiIhKZrAn/LRwCTZo+BVYAq4wxo73b+ungnIiIiIiI9CeBludZa+1fjDHrgNeNMb8CbBDjEhERERERiQiBJk0GwFq7yhgzE3gBOChoUYmIiIiISMTRRBDdO7V1wVqba4yZARwXnJBEREREREQiR7dJkzHmW9bafwAXGdNpWrk8KFGJiIiIiEjE0UhT55K8f1OCHch/xVoG3n0bCauW0RIfT+ktd9J48CEdmsV8toHBN9+Aqa+j9vjplP/yRvBLApP//jiD7r+LnCXv0ZI6mOhtW0i9+TfEbtpI+TU/perS74fyqIIiazJM/i4YD2xbDF+82rHN8GNh/PlgLZTvgA/+5NsXnQAn3Q85H8Lax0IVdYR4DDgdKAAmhDmWMIiZAcm3gYmC2n9A7Z/b7vcMh5Q/gicdWkqh8kfQkuv2DXwOoo+Axveh4lshDz3o0o6EcVe559Wef8P259vuH/tDGDzJLXviIHYQvHO2Wz/sNhh4MJRtgLW/C2nYITPoG3DAdYAHCl6HPf9ou3//n8DAw92yJw5iUuGDU9z6scuhZqtbrs+HTb8KVdShMegbsP917nmV/zrkPNV2/37+fRPv+ubDk916bBaMvgFiMwELm66H+rxQRh9cg4+CMVe751XuQtjxXNv9B14FqZPdclQ8xAyCFXMheTSMuw6iEoEW2P40FLwT0tBDYvSBcMop4PHAxx/DqpWdtzv4YDj/AnjkEcjNcdsys+D00yEuzr3X/9//QXNT6GIPtqQpkP1r97wqfQmKH227P3oIDLsdPCnu/Cq4H6pWQNKxkPlTMDFgGyH/D1DzfniOQSJXt0mTtfZv3r+3hCacryd+5XJidm4nb8EiYj9dR+rt8yh46sUO7VJvn0fpb+fTMGES6ddcTvyq5dRNmQ5AVF4u8atX0ZQ9dG/7loGDKPvVjSQsXRyyYwkqDxz2fVgxH2pKYOYdkLMGKnf7miRnw7izYOlN0FgNcQPaPsQhF0LR56ENO2I8ATwI/D3McYSDB1LugrLzoCUHUhdBw1vQ/KWvSfI8qHsR6p+HmCmQdBNUen/6uuYhMAkQf2lYog8uDxx0DXz8a6grgm/8GQrfg+qdviZf/tW3PGIupIz2re940X0YHn4qfZMHRl0PG6+DhgKY+CiUrITa7b4m2x/wLWefC0ljfOst9bDushDFGmoeOODn8Nm1rm8mPAalK9r2zY72fTPWt37gb2HPk1D+IXgSgJZQBR4CHhj3E/jkl1BfCEf+xT2vanb4mmx+2Lc8/ExIPtAtN9fDZ3dC7R6ITYOjHoaSD6GpOqRHEFTGwKmnwlNPQUUFXH45fPEFFBW2bRcbC984Bnb7vc8bD5x9NrzyMuTnQ0ICtDSHNv6g8sCQG2HH5dCYD6Oeh8ql0LDF1yTjSqj4N5Q+D7GjYeTDsPkkaC6FXVdDUyHEHQgjH4GvTgzfoUhk6nbKcWPM5caYMd5lY4x53BhTboxZb4w5LDQhdi1+2WKqTz8TjKFh4mRMZQWewoI2bTyFBZjqKhomTgZjqD79TOLf8SVDA++9g/Jrf9Fm5KllcBqNh0yE6EAv+Ypsgw+EqjyoLgDbBLtWwdAj27Y5YBZs+bdLmADqK3z7Bo2C+IGQvy50MUeUFUBJuIMIj+jDoXkbtOwAGqHuFYg9pW2bqLHQuMItN65su79xBdiqkIUbUgPHQU0O1Oa551XeMsjo5krP7BMg7x3feslaaK4JcpBhlHww1O6G+hzXP0WLYfDUrtunz4Kit0MXXzglj4c6/755G1K765vZUPQft5ywv/sWvfxDt95S6xLMvmLAQVCzB+pyXd8ULO3+eZV1IuQvdcu1u13CBNBQDA1lbhSqLxk2DEpKoKzUJTwbN8BB4zq2m3GiG4Fq8htFGj3aJUv5+W69ttaNNvUVCROgYRc07gYaoXwhpMxo18iCJ9ktRiVDk/cjY90mlzAB1G92X2iZmFBF3vuE+zeaIvV3mq4FtnuXLwImAaOAnwEPdHGfkIkqyKc5O3vvenNWNlEF+R3bZHbeJn7p2zRnZtI4rm9PBJgwGGqLfeu1JZCQ1rZN8hBIGQonzIcZt7lyPgAMTLoU1vfHURbBkw3Ne3zrLbkQNaRtm6aNEHeaW449zVv2kBq6GMMlLt19E96qvhDi0jpvG58JCdkuUeov4jLcKEqrhgKIzeiibRbED4Hyj3zbPLEw8TGY8Ej3yVZvFJvhSg5bNRS6/uq0bTbE+fVN/EhoroKxt8PEJ2C/qwn8Fxd7gU6fV+mdt43PhPhsKP2k476UceCJhtqc4MQZLikD3AhTq4oKt81f9hAYMAC++qrt9rQ0lyRd8i244ko47vjgxxtK0VnQmOtbb8qHmKy2bQofgoGnw5jFbpQp7/aOj5NyEtR+5sr0RPz19FLbZO3e0+Z04O/W2mJr7dv4rnfqlUxtLQMe/xsVV10b7lAigifKJU7L5sH7f4IjroSYRBh9MuR+7BItkc5Uz4OY42DQYog5FppzgL5U8rEPZJ8A+SvoW2VU+1D6LCh+hzb989E5sP778OU82P9aiBsWpuDCLH0WFC9lb9+YKEiZBDsedP0TNxQy+2qJZw8yT4SC5XR4XsUOhvE3wOf30P9+UdLAySfDokUdd3k8MHIkvPwyPP44HHQQHHBA6EMMpwGnQdmr8NVM2HkVDLsT74/qOHGjIeunkBuRF6VIuPVUf9ZijBkClAIzgdv89iV0dSdjzBXAFQCxf/4b0d+74n+Nc6+k558m6eUXAGg4ZAJReb6rX6Py82jObPu1QnNmFlEFHdtE7d5J1J7dZF0w120vyCPz4rMpeOpFWtK7+Mqvl2o/stR+5AmgphhKvgLbDDUFUJXrkqi0sZB+sEueouPdN3dNdbDh6dAeg4RHSx5E+X1Y9QyB5tx2bfKh4rvelSSIOx1sBX1efVHb0YG4DKgv7rxt1gmw6cGQhBUx6gu9ExV4xWa6EZXOpM2CbX9ou62hyPs4OVDxCSSPgfo9He/bGzUUutG1VrEZbUdX/KXPgq33+t23AGq+cv0CULICUg4B3ghauCHV6fOqqPO2WSfAF+1qXqISYdLtsPVxqOiD1+FWVrhRpFYDBrhtreJiITMTLrvMrScnw0UXwbPPulGpHTug1lsWvPkrGDIEtm0LWfhB1ZQPMX6VENFZ7tomf4POhp1XuuXadWBiISoVmktc++EPwJ7fQOOu0MXdG/XX2fN6Gmn6LbAGV6L3mrV2I4AxZjqwtas7WWsfsdYeaa09cl8mTADVF1xCwfMLKHh+AXUzZpH0xqtgLbHr12KTU2jJyGzTviUjE5uUTOz6tWAtSW+8St30mTSNGUfukvfIW7iEvIVLaM7MpuCZl/tcwgRQutklQImZYKJhxPGQu6Ztm5wPIcM78WBsimtfnQ8fPAALr4I3r4b1T8GO5UqY+pOmTyBqFHhGAjEQf5abCMKfGczeb+oSfwJ1z4Y6yvCo+AISh7nyIBMN2dPdBevtJY6AmGQo/yz0MYZT1SZIGO5Ky0w0pM90E0G0lzASolOgcoNvW1SK73qC6IGQMgFqtock7JCo+hzi/ftmFpR20jfx+7m+qNrQ9r5RyRA9yK0PPAJq+siHXoDKTW2fV5kzoOjdju0SR7jzpsLveWWiYcItkLsICvvoD6LsyXFldoMGuQqRQw51E0G0qq+He+6GP/3R3XbvdglTbg5s2QxZWRAd4yaF2G9/KOwiWe+NajdA7EiIGQbEwMBToWpp2zZNuZB0jFuOHQUmziVMnhRXrldwP9R2Uu4pAj2PNCUC+wETrbUf+21fA1wQtKgCVDdlOvErl5F9xmxsfAIl83zFqZkXzKXg+QUAlN5w894px+uOn0bdlGndPq6nqJDMS87BU10FxkPy00+S/9JCbHJyUI8nWGyLmyZ86o3uhXL7UqjYDeMvgNItLoHKXwtZk9y04rbFJUgNffQC/q/tGeAEIB3YBdwMPB7OgEKoGap+DQOfd2VBdc9A8xeQ+CtoWusSqJjj3Ix5WGh8z7VvNeg1iDoQTBIMXguVP4XGpV38W72MbYEvHoTDb3fPq5y3oHoHjL4UKr6EwtWuXfsJIFod+QdIGgFRCTD1afjsPij+qGO7XqsZtt4P4+/zTqv9BtRugxE/cAlVa5LQ2QQQifvBqF/iyq48bqpy/5nler1m2HYfHHy/65uCbvqmuP3kGC2uNG/8A27+oqpNUPBayI8gaGwLfPlnmHyX93n1pnteHXAZVH4BRd4vJrJmuEki/GWeAIMmQswAGOKdnv3zu6FqC32GbYGFC+Fb33b//2s/cYnPCTMgJwe+/KLr+9bVwXvvuRn3wF3z1P66p16tGfJuczPfGQ+UvQL1WyDjGqjd6BKovHtg6C2QdilgIedGd9fBF0PsCMi4yt3AzcLXrEsTOtVfR5qM7WbqFGPMx9baw1v//jf/QFJNv6so/lqe/E64I4hc5/4z3BFErsK+NyC6T62dHO4IIldSH5p+ORiM3rG6VNtlUb4ArOhjE5bsS+d1/DUY8Rq/kV6Xgiw9Mfyf7WcsCX2/9TTSVGyMWQQcYIzp8F2WtfaM4IQlIiIiIiISGXpKmk4DDgeeAv7QQ1sREREREenD+mt5XrdJk7W2AVhtjDnOWtuHLhcUEREREREJTLdJkzHmj9ba64DHjelY6a3yPBERERGR/kMjTZ17yvv33m5biYiIiIiI9FE9led95P27LDThiIiIiIiIRJaeRpoAMMYcD8zD/WZTNO6nLK21dlTwQhMRERERkUii8rzuPQb8FPgIaA5eOCIiIiIiIpEl0KSp3Fr7ZlAjERERERGRiKaRpu4tNcbcA7wM1LdutNZ+HJSoREREREREIkSgSdM3vH+P8P41gAVO3OcRiYiIiIiIRJCefqfpZ97FN7x/LVAIrLTWbgtmYCIiIiIiEln6a3mep4f9Kd5bsveWAhwJvGmMuTDIsYmIiIiIiIRdT7/TdEtn240xg4G3geeCEZSIiIiIiEQejTR9DdbaEtx1TSIiIiIiIn3af5U0GWNmAKX7OBYREREREZGI09NEEJ/iJn/wNxjIAS4NVlAiIiIiIhJ5+mt5Xk9Tjp/ebt0Cxdba6iDFIyIiIiIiElF6mghiR6gCERERERGRyNZfR5r+q2uaRERERERE+gslTSIiIiIiIt3o6ZomERERERERQOV5IiIiIiIi0gklTSIiIiIiIt1QeZ6IiIiIiARE5XkiIiIiIiLSgUaaREREREQkIBppEhERERERkQ400hRmUc3hjiByFWaEO4LIlVEY7ggiW2NMuCOIXPVx4Y4gsuk1Wf5bTfpEJdKn6SkuIiIiIiIBUXmeiIiIiIiIdKCRJhERERERCYhGmkRERERERKQDJU0iIiIiIiLdUHmeiIiIiIgEROV5IiIiIiIi0oFGmkREREREJCAaaRIREREREZEOAkqajDFZxpjHjDFvetfHG2O+H9zQREREREREwi/QkaYngLeAod71L4HrghCPiIiIiIhEKGvCfwuHQJOmdGvtC0ALgLW2CWgOWlQiIiIiIiIRItCJIKqNMWmABTDGHAOUBy0qERERERGJOP11IohAk6afAa8Bo40xq4AM4NygRSUiIiIiIhIhAkqarLUfG2OmA+MAA3xhrW0MamQiIiIiIiIRIKCkyRgTD/wImIIr0VthjPmrtbYumMGJiIiIiEjkUHle9/4OVAJ/9q5fDDwFnBeMoERERERERCJFoEnTodba8X7rS40xnwUjIBERERERkUgSaNL0sTHmGGvtagBjzDeANcELS0REREREIo3K87p3BPCuMWand30k8IUx5lPAWmsnBiU6ERERERGRMAs0aTolqFGIiIiIiEjE00hTN6y1OwCMMZlAvN/2nV3eSUREREREpA/wBNLIGHOGMeYrYBuwDNgOvBnEuERERERERCJCQEkTMB84BvjSWnsAMBNYHbSoREREREQk4lgT/ls4BJo0NVpriwGPMcZjrV0KHBnEuERERERERCJCoBNBlBljkoHlwNPGmAKgOnhhiYiIiIhIpOmvE0EEOtI0F6gFfgr8G9gCzAlWUCIiIiIiIpEi0Nnz/EeVngxSLCIiIiIiIhEnoKTJGHM2cBeQCRjvzVprBwQxtp5Zy8C7byNh1TJa4uMpveVOGg8+pEOzmM82MPjmGzD1ddQeP53yX94Ixje2mPz3xxl0/13kLHmPltTBJCx8jZQn/g9joSUxibLfzKNx3EGhPLJ9LvMwmPg9MB7Y8TZ8+UrHNsOOg4MuACyUb4c1f3Tbz3wRyr2Ty9cWweo7QhR0iMTMgOTbwERB7T+g9s9t93uGQ8ofwZMOLaVQ+SNoyXX7Bj4H0UdA4/tQ8a2Qhx5+jwGnAwXAhDDHEgbpR8D4K9zzatci2PpixzbZU2DMJYCFym2w9h63fdx3IdN7Zejm5yB3RcjCDpnBR8GYawAP5C6Enc+23X/gj2DQZLccFQcxqbDyDEgeDWOvg+gksM2w42koeCe0sQdb6tEw+sfu3Mn7F+x6pu3+UVfDoMPcsiceYgfBu6f79kclwpFPQtFK2PKnkIUdEoOPgjFXu77JXQg7nmu7/8CrIHWyW46Kh5hBsGKuO2/GXef6hhbY3gfPG4ADR8FpJ7uPMR+thRXvdt5u/EFw0bnw8GOQk+vbPnAA/PiHsHQ5rOpjU3olTYHsX7v389KXoPjRtvujh8Cw28GT4s6vgvuhagUkHQuZPwUTA7YR8v8ANe+H5xh6g/5anhfoNU13A3OstZ8HM5ivK37lcmJ2bidvwSJiP11H6u3zKHiq46eW1NvnUfrb+TRMmET6NZcTv2o5dVOmAxCVl0v86lU0ZQ/d27556HAKH/0HdsBA4lcuI/XW33b6uL2GByZdDqtugdpimHE35H4Ilbt9TZKGwNizYflvoLEaYgf69jU3wNLrQx92SHgg5S4oOw9aciB1ETS8Bc1f+pokz4O6F6H+eYiZAkk3QeXVbl/NQ2ASIP7SsEQffk8ADwJ/D3Mc4eCBQ66CD26CuiI4/n4oWA1Vu3xNEofC6PPhvV9AU5XveZVxFAwcDSt/DJ4Y+MadULgGmmrDcyhB4YGx18LaX0B9IRz5MBS9CzU7fE02/8W3POwsSDnQLTfXw+d3Qu0eiE2DI/8KJR9CU1+5ktYDB14Hn17v+uawv0HxqrZ9s/Uh3/LQsyF5TNuH2P/7UL4+JNGGlgfG/QQ++aX3vPkLFL7X7rx52Lc8/ExI9jtvPvM7b456uI+dN7hEac434YmnoaICfvh92PQlFBa1bRcbC8ceDbt2d3yMb86GrzaHJt6Q8sCQG2HH5dCYD6Oeh8ql0LDF1yTjSqj4N5Q+D7GjYeTDsPkkaC6FXVdDUyHEHQgjH4GvTgzfoUhkCvSapvxIS5gA4pctpvr0M8EYGiZOxlRW4CksaNPGU1iAqa6iYeJkMIbq088k/p3Fe/cPvPcOyq/9RZuRp4bJh2MHuE839RMnE5WfF4rDCZrBB0J1LtTkg22C3SthyNFt2+w/C7b+2yVMAA3loY8zHKIPh+Zt0LIDaIS6VyD2lLZtosZCo3cUoHFl2/2NK8BWhSzcyLMCKAl3EOExaCzU5EBtnnte5S6HrGPathlxMux4wyVM4HteJY+Akg1gW9wHvcrtbtSqLxlwkPvwWpfr+id/CaQf13X7rBNdG4Da3e6+AA3F0FjmRhP6ipSD2/ZN4RJIm9J1+4yZUOB72yJ5rBuVK/0w+LGG2oCDoMavbwqWQkZP581St9z+vGko61vnDcDwoVBcAqVl0NwCn26Eg8d2bDdzuhuBampuu/3gse6+BUUd79PbJUyAhl3QuBtohPKFkDKjXSMLnmS3GJUMTd6PjHWbXMIEUL/Zje6amFBF3vuEe7rxiJxy3Bhztrc0b40x5nljzEWt27zbwyqqIJ/m7Oy9681Z2UQV5Hdsk9l5m/ilb9Ocmdlt6V3Sq/+k7vhp+zjy0IpPcyNMrWqLIX5w2zbJQyF5CEy7Habf6cr5Wnli4YS73fb2yVZv58mG5j2+9ZZciBrStk3TRog7zS3HnuYd1k8NXYwSmeLT3AhTq9oiiEtr2yZpmLsdcw8c+wdfYlS5zS174iBmAKRNhISM0MUeCnHpUOf3HVZ9EcR1cYxxWRCfDaWfdNyXchCYaKjNCU6c4RCXDvX+fVMIseldtM2C+CFQ9rF3g4FRP4KtD3fevreLS3f90aq+0G3rTHxmN+fNOPD0sfMGYEAKlFf41ssrISWlbZsh2a4E78t2o0mxMTDlOFeW1xdFZ0GjXxliUz7EZLVtU/gQDDwdxix2o0x5t3d8nJSToPYzV6Yn4q+n8jz/GfJqgJP81i3wcmd3MsZcAVwBEPvnvxH9vSv+lxiDwtTWMuDxv1H4l8e7bBP34WqSXv0nhY8/02WbvsIT5RKnFb+FhDSYeissuQ4aa+CtK6GuBBKzYMotULEDqvN7fMg+o3oeJN8JcRdC43vQnAM093QvEfe8ShoK7/8a4tPhmLtgxdVQ9AkMHAvH3etGn0o/d6NO/VXWDChcDrTrg9jBMP4GV6qHDUdk4ZdxIhQtY2/fDD0TSt6HhsLu7tU/ZJ4IBd2dN3fR784bgyu/e/m1jvtmTIP33oeGfpwMDDgNyl6FkichYRIMuxO2zGXveRI3GrJ+Cjsi72OrRIBukyZr7Xf/mwe11j4CPAKQVLNvX7KSnn+apJdfAKDhkAlE5flK56Ly82jObPu1QnNmFlEFHdtE7d5J1J7dZF0w120vyCPz4rMpeOpFWtIziPlyE6m/v4miB/+PlkG9e1ihrtglQq0S0lwS5K+2GEq+chdd1xRAVY77sFe22de2Jh+KNsDAUX0naWrJg6hhvnXPEGjObdcmHypanwlJEHc62Aqkn6srdolQq4R0qC9u16YIyr5wz6vafKje455X5V/BlufdDWDyL9y+vqS+yI0EtGo/guAvcwZ8+UDbbVGJMPEO2PoYVERccfj/pr4I4vz7JgMauiiXypwJm+/3rQ84BAZMhKFzISrBlRA118L2R4Ibc6i0H5GMy3DbOpN1AnzRyXkz6XbY+njfO28AKirdKFKrgSlQWelbj42DzAz43rfdenIyXHI+PP0CDB8GhxwMJ82E+HiwFpqa4P01oT2GYGnKhxi/SpHoLHdtk79BZ8POK91y7TowsRCVCs0lrv3wB2DPb6BxF9KN/joRRE/leZcbY8Z4l40x5nFjTLkxZr0x5rDu7hss1RdcQsHzCyh4fgF1M2aR9MarYC2x69dik1Noychs074lIxOblEzs+rVgLUlvvErd9Jk0jRlH7pL3yFu4hLyFS2jOzKbgmZdpSc8gKjeHtJ//mJL5d9O03wHhOMx9qnSzK71LzHRlLsOnuIkg/OV8ABneiQdjU9yoU00exCS5EofW7WkHQWUfejFp+gSiRoFnJBAD8We5iSD8mcG4r++AxJ9A3bPtH0X6o/IvXeldQpZ7Xg2ZBvntZlvKWw2DvbMKxgxw7WvyAA/EeEtqUvZ3t6KP6VMqN0HCMFc+ZaLdtSdF73VslzgColOgYqNvm4mGCb+HvEXeEag+pnITJAz39U3GiW4iiPYSRkJ0ctu+2XQrfHA+fHChK9HLf6vvJEzg+ibR77zJnOEmEGlv73nzmW+biYYJt0BuHz1vAPbkQNpgGDQIojww4RA3EUSr+nq48z6470F3273HJUw5ufDY333b3/sAlq/qOwkTQO0GiB0JMcOAGBh4KlQtbdumKReSvNeexo4CE+cSJk+KK9cruB9qOyn3FIGey/Ouxc2PBXARMAkYBRwGPABMDVpkAaibMp34lcvIPmM2Nj6Bknm+4tTMC+ZS8PwCAEpvuHnvlON1x0+jbkr31ygNeOQhPGVlDLrjFrchKoqCZzqtROwVbAusexSO/x3ggR2LXeJz8IVQugXyPoSCTyBrEsz8k2u/4UloqILB42DyD3FD18ZNVe4/616v1wxVv4aBz7spSuuegeYvIPFX0LTWJVAxx7kZ87CuPK/q1767D3oNog4EkwSD10LlT6FxaRf/Vl/0DHACkA7sAm4Guq547VNsC2x8GI6eD3hg93+gaieM+ZYbSSp4H4o+gozDYOrDQAtsehwaK92Mecfc7R6nqQbW/qHvlefZFvjyzzDpLvfcyn0TarbDAZdBxZdQ7P0gnHmiu9jfX+YJMHAiRA+A7JPdtk13QdUW+oZm2PxHOPRe75TjC13f7Pc9lzSU+PfNknAGGnqt583ku1zf5LwJ1TvceVP5hS/xzprR+XkzaKL7gmKI97z5/O4+dN4ALRbe+Dd85yLweODjtW5ShxOnQ04ObPoq3BGGUTPk3eZmvjMeKHsF6rdAxjVQu9ElUHn3wNBbIO1SwELOje6ugy+G2BGQcZW7gZuFr7mfTnTUk/460mSs7bp6zhiz1lo72bv8DPC+tfZP3vWPrbWH9/QP7OvyvL7mH/3xt30CNGVluCOIXBm6nqFbC08NdwSRK6EvTWseBFG6XrFLTYH+SEk/tbT9TG2y10Wq0OjS+I30uhTk6W+F/7P9Jf8Ifb/1NOV4izFmiDEmHpgJvO23LyF4YYmIiIiIiESGnr43+i2wBogCXrPWbgQwxkwHtgY5NhERERERiSD9tTyvp6QpEdgPmGit9b9MeQ1wQdCiEhERERERiRA9lefdYK1tAh7132itrbbWVgUvLBERERERiTTWhP8WDj2NNBUbYxYBBxhjOvxUmrX2jOCEJSIiIiIi8vUZY04B/oS7xOhRa+2dnbQ5H5iHmyN6nbX24u4es6ek6TTgcOAp4A//RcwiIiIiIiIhYYyJAh4CZgO7gQ+NMa9Zaz/zazMGuAE43lpbaozJ7PzRfLpNmqy1DcBqY8xx1lpNciwiIiIi0o/1gokgjgY2W2u3AhhjngPmAn4/h83lwEPW2lIAa21BTw/abdJkjPmjtfY64HFjTIc52VWeJyIiIiIioWSMuQK4wm/TI9baR7zLw4Bdfvt2A99o9xBjvY+zClfCN89a++/u/s2eyvOe8v69t4d2IiIiIiIiQedNkB7psWHXooExwAnAcGC5MWaCtbasuzt0F9BH3r/L/oegRERERESkD+gF5Xl7gBF+68O92/ztBt631jYC24wxX+KSqA+7etCephwHwBhzvDHmP8aYL40xW40x24wx+nFbERERERGJJB8CY4wxBxhjYoELgfazgL+KG2XCGJOOK9frNrfpqTyv1WPAT4GPgOaAQxYRERERkT4j0kearLVNxphrgLdw1ys9bq3daIz5PbDGWvuad99JxpjPcLnNL6y1xd09bqBJU7m19s3/IX4REREREZGgs9YuBBa22/Y7v2UL/Mx7C0igSdNSY8w9wMtAvd8/+HGg/5CIiIiIiEhvFGjS1DpN3xHevwb367kn7vOIREREREQkIkV6eV6w9PQ7Ta1DVm94/1qgEFhprd0WzMBEREREREQiQU+z56V4b8neWwpwJPCmMebCIMcmIiIiIiIRxJrw38Khp99puqWz7caYwcDbwHPBCEpERERERCRSBPQ7Te1Za0tw1zWJiIiIiIj0aYFOBNGGMWYGULqPYxERERERkQimiSA6YYz5FDf5g7/BQA5wabCCEhERERERiRQ9jTSd3m7dAsXW2uogxSMiIiIiIhFKI02dsNbuCFUgIiIiIiIikei/mghCRERERESkv/ivJoIQEREREZH+p7+W52mkSUREREREpBsaaRIRERERkYD015GmoCdN9XHB/hd6ty/GhTuCyJVcFe4IIldjTLgjiGynLgx3BJHrye+EO4LIlqpfIOxS+cBwRxDZGmLDHUHk2jMs3BFErvHhDkACpvI8ERERERGRbqg8T0REREREAtJfy/M00iQiIiIiItINjTSJiIiIiEhANNIkIiIiIiIiHShpEhERERER6YbK80REREREJCAqzxMREREREZEOlDSJiIiIiIh0Q+V5IiIiIiISEJXniYiIiIiISAcaaRIRERERkYBopElEREREREQ6CGikyRgzFvgFsJ//fay1JwYpLhERERERkYgQaHnei8Bfgf8DmoMXjoiIiIiIRKr+Wp4XaNLUZK19OKiRiIiIiIiIRKBukyZjzGDv4uvGmB8BrwD1rfuttSVBjE1ERERERCKIRpo69xFggdbu+YXfPguMCkZQIiIiIiIikaLbpMlae0CoAhEREREREYlEPZXnnd3dfmvty/s2HBERERERiVQqz+vcHO/fTOA4YIl3fQbwLqCkSURERERE+rSeyvO+C2CMWQSMt9bmeteHAE8EPToREREREYkY/XWkyRNguxGtCZNXPjAyCPGIiIiIiIhElEB/p2mxMeYt4Fnv+oXA28EJSUREREREJHIElDRZa68xxpwFTPNu+pu19pXghSUiIiIiIpFG5Xk9sNa+Yq39KXATkGyM+VfwwhIREREREYkMAY00GWNigdOAi4GTgZeAvwYxLhERERERiTD9daSpp99pOgm4CDgJWAr8HTiqdVY9ERERERGRvq6nkaZ/AyuAKdbabQDGmD8FPar/lrWk3X4bScuX0ZIQT8Htd9Iw/pAOzWI3biDzNzfgqaujetp0in9zIxhD1s+uI2bbNgA8lZW0pKSw+5UFoT6KoDhgLMw6HTweWPchrF7Webtxh8BZ34InHoS8PeCJglPOhOzhgIW3X4ed20IZefClHQnjrgLjgT3/hu3Pt90/9ocweJJb9sRB7CB4x/uzz4fdBgMPhrINsPZ3IQ07ZNKPgPFXuP7ZtQi2vtixTfYUGHMJYKFyG6y9x20f913IPNItb34OcleELOzI8BhwOlAATAhzLGEwdAIcfbE7d75aDhvaFXWPngJHng81ZW5909uuHcDh58Fw7/Nu/Wuw/YOQhR0SGYfBhMtd3+z4D2x+qWObocfDuIvAWqjYBh/fB2kT4NDv+dokD4eP7oW890MXe6gNnQBHfsv11eZlsPGNjm32OxomngVYKN0FKx8OeZghM/YAOGMmGAMfrod3uvi/P3QsfPtMeODvsCcPxuwHp0yHqChoboaF78CWnaGMPPj0fi7B1FPSdDjemfKMMVuB54CooEf1X0pcvpzYHdvZ+e9FxK1fR8Yt89jzfMdPeBm/n0fh7+dTP3ESQ668nMQVy6mZNp38+/64t03aXXfSkpIcuuCDyBg46Qx47jGorIDLroavPofigrbtYmPhyONhj9+L6OSj3N/H/wSJSXD+d+GJhwAbsvCDywMHXQMf/xrqiuAbf4bC96Darw++9CtEHTEXUkb71ne8CJ54GH5q6EIOKQ8cchV8cJPrn+Pvh4LVULXL1yRxKIw+H977BTRVQexAtz3jKBg4Glb+GDwx8I07oXANNNWG51DC4gngQdwYfT9jDBzzbVh0D9SUwGk3w65PoDynbbvtH8D7/2i7bdgkSNsPXv8dREXDyb+GPeuhsS508QeVByZeCe/dDLXFMO1eyPug7fMqaQiMORdW/goaq33Pq+JPYdlP3XJMMsz8KxR+EvpDCBVj4OhL4e273Xn0zVtg98dtz6OULDh0Drw1HxpqID4lfPEGmzFw5ix49AUor4RrLoXPNkNBcdt2sbFw/BGw06+fqmvhiZehsgqy0uH758HtfSm51Pt5yPTX8rxuJ4Kw1q611v7aWjsauBmYDMQYY940xlwRigC/jsQli6mceyYYQ/2kyXgqK4gqbJsZRBUW4Kmqon7SZDCGyrlnkrh4cdsHspbkt96k6tTTQxZ7MA0ZAaXFUF4KLc3w2ToYc3DHdlNPciNQzU2+bWmZsGOrW66phrpaGDIsNHGHwsBxUJMDtXlgmyBvGWQc13X77BMg7x3feslaaK4JcpBhNGhs2/7JXQ5Zx7RtM+Jk2PGGS5gAGsrd3+QRULIBbAs010Pldjdq1a+sAErCHUR4pI+CinyoKnSvO9vehxGHBXbfQUMh/0t37jQ1QOluN9rQV6SOgeo8qMl3z6s9KyD76LZt9jsJti10CRP4nlf+hh4HBR9Dc0PwYw6XtNFQWeA7j3ashhGHt20z5gT44m2XMAHUVYY8zJAZMQSKy6CkHJpbYN3nMP7Aju1OngLL3odGv/fznAKXMAHkF0FMtBt16iv0fi7B9nVmz3vXWvtjYDhwP3BMD3cJueiCfJqys/euN2VlE52f37ZNfj5NWe3aFLRtE//RGprS0mjcf/+gxhsqKQOg0u8Nt7ICUga2bZM1FAYMhC1ftN1ekOsSLOOBgamQPcy16yvi0qG+0LdeXwhxaZ23jc+EhGz3wtpfxKe5b+xa1RZ17J+kYe52zD1w7B98iVHlNrfsiYOYAZA2ERIyQhe7hFdiKlT7JYw1pZCU2rHdyCNhznyYfjUkDnbbSnfCsAkQFQtxyZB9ECR18bzsjeLT3HOpVV0xJLR/Xg2F5KEw5U6Ycrcr52tv6FTYszy4sYZbYipU+42iVJdAQrvzaEC2u518E5zyu76VYLc3MBnK/JLC8koY2G5kbWiW27Zpa9ePM2Es7Ml3ZXp9hd7PJdgCnT2v3fc6FAG3GWOirbVNnbS/ArgCwDz8NzyXR9ygVLeS//VGnxllCoiBmafBvzq5VmX9R5Ce6Ur6Kspc6V5LXynN+5qyT4D8FUBLuCOJLJ4o9wHv/V9DfDoccxesuBqKPoGBY+G4e9235KWfu5EDkVa7P4Ftq6GlCcaeAFN+AIvuhpyNkDYKTr0J6iqgcEv/O3eM93m16kaXUB1/Byz9CTR5R57iUmHAflDQh0vzAmWiXIneojtcYn7SjfD6jdDYD0cNDHD6DHhxYddtstLgm9Ph0U7e8/sLvZ//b/preV5ASRPwF9z1Tetxz8lDgY3AQGPMVdbaRf6NrbWPAI8ARDcH9+qXAc88zYAXXwCgfsIEovPy9u6Lzs+jKSurTfumrCyi89u1yfRr09RE0tv/YfeLLwcz7JBqP7LUfuQpLhbSs+Bib26blAznXAov/d1NBrHY7+Ltb/0QSvy+Ie3t6osgzm/0Iy4D6os7b5t1Amx6MCRhRYy6YpcItUpI79g/dUVQ9gXYZqjNh+o97sNe+Vew5Xl3A5j8C7dP+oeaUkga7FtPTIXq0rZt6qt9y18tgyPO961/+rq7AUy9Eiry6DPqit1zqVV8mru2qX2b0i/d86qmAKr2QPIQKNvs9g89HnJXu/19WU1p21HGpMFQ2+48qimBoi2uL6qK3LkyIAuK+9ikRQDlVTDIb2RpYIobbWoVFwvZ6XDFRW49JQkuO9tdy7Qnz41UffsseH4hlJSFNPSg0/u5BFug5Xk5wGHW2iOttUcAhwFbgdnA3cEKLhAVF1/C7lcWsPuVBVTPnEXKglfBWuLWraUlJYXmjMw27ZszMmlJTiZu3VqwlpQFr1Jz4sy9+xPee5fGA0bR7Ffm19vl7obB6a68zhMF4yfB5s99++vr4YFb4eG73S1nly9hio6BmBjXbv8D3be97SeQ6M0qvoDEYRCfDSYasqe7C0fbSxzhLrou/yz0MYZT+Zeu9C4hy/XPkGmQ326mprzVMNhbDhMzwLWvyQM8EON9c0/Z392KPg5h8BJWRdvcB9fkdPe6c8A33MiSvwS/L3NGHAbluW7ZGIhLcsupwyF1BORsCE3coVD2lZvoITHTPa+GTYX8drMD5q6G9EPdcmwKJA+Dar9K8mHT3LVQfV3xVjeK1Hoe7XeMm1DE366PIMt7nW5csivVqyzs+Fh9we5cSEuF1IEQ5YFJB8Pnm3376xrg9w/CXX9zt505voQpPg4uOxfeXAY7+uAXWHo/Dx1rwn8Lh0BHmsZaaze2rlhrPzPGHGSt3WpM5IzR1UybTuLyZYw8ZTYt8QkU3nb73n3Dz5q7d/rwot/eTOZvbsDU11EzdRo106btbZf85kKqTj0t5LEHk22BRa/BBd9zH0bWr4GiApg6C3L3tE2g2ktKgvO/56a8raqA118IXdyhYFvgiwfh8NvddVs5b0H1Dhh9KVR8CYWrXbv2F4y2OvIPkDQCohJg6tPw2X1Q/FEojyC4bAtsfBiOng94YPd/oGonjPmWG0kqeB+KPnLXW0x9GGiBTY9DY6WbMe8Y71cqTTWw9g/9r8SKZ4ATgHRgF246ncfDGVDo2BY3K96sn7ufOvhqBZTlwOSz3AjArrVw8GyXLLU0u1GnlY+6+5poOOU3brmxDlY80rfOHdsCnz4Cx8xzrzs7F0PlLhh3sRtJyv/AzYiXeRjMeNCNoGx8wj2vABIy3UhVcR9KJLtiW+CDv8PMX7r3r83LoXwPTDrbnUe7P4GcT2HIBJhzh2v/8XPQUBXuyIOjxcKCt93Mdx4DH34K+cUwewrszmubQLV33OGQPghmHedu4Er0qvtIGaPezyXYjLU9V88ZY14AinFTjgNcgPsY8G1gpbX2qK7uG+zyvN7u1pvCHUHkOkIvVl1qjAl3BJHt1G7q+fu7J78T7ggiW2ppz236q/I+NAlQMGw4NNwRRK5Zb4c7gsg1exGRM/oQoHt/Ef7P9j+/J/T9FuhI03eAHwHXeddXAT8HGoEZ+z4sERERERGJNJoIogvGmChgobV2BvCHTpr00UFwERERERGRAJIma22zMabFGDPQWtvJz+uJiIiIiEh/oJGm7lUBnxpj/gPsnSTWWvuToEQlIiIiIiISIQJNml723kRERERERPqVgJIma+2TwQ5EREREREQim8rzumGMGQPcAYwH4lu3W2tHBSkuERERERGRiBBoed7/w/0s4/24Kca/C3iCFZSIiIiIiESe/jrSFGjik2CtXYz7Mdwd1tp5wGnBC0tERERERCQyBDrSVG+M8QBfGWOuAfYAycELS0REREREJDIEmjRdCyQCPwHmAycC3wlWUCIiIiIiEnn6a3leoLPnfehdrMJdzyQiIiIiItIvdJs0GWNe626/tfaMfRuOiIiIiIhEKo00de5YYBfwLPA+0E+7SURERERE+quekqZsYDZwEXAx8C/gWWvtxmAHJiIiIiIiEgm6nXLcWttsrf23tfY7wDHAZuAd7wx6IiIiIiLSj1gT/ls49DgRhDEmDvebTBcB+wMPAK8ENywREREREZHI0NNEEH8HDgUWArdYazeEJCoREREREYk4mgiic98CqnG/0/QTY/b2kgGstXZAEGMTEREREREJu26TJmttt9c8iYiIiIiI9HUB/bitiIiIiIhIfy3P00iSiIiIiIhIN5Q0iYiIiIiIdEPleSIiIiIiEhCV54mIiIiIiEgHGmkSEREREZGAaKRJREREREREOlDSJCIiIiIi0o2gl+fFNAb7X+jd4uvCHUHkSqoOdwSRqz4u3BFEtie/E+4IItd3ngx3BJHtmYvDHUHkim0IdwSRbWB5uCOIXM1R4Y5A9iWV54mIiIiIiEgHmghCREREREQCopEmERERERER6UBJk4iIiIiISDdUniciIiIiIgFReV4AjDGJwQpEREREREQkEgWUNBljjjPGfAZs8q5PMsb8JaiRiYiIiIhIRLEm/LdwCHSk6X7gZKAYwFq7DpgWrKBEREREREQiRcDledbaXe02Ne/jWERERERERCJOoBNB7DLGHAdYY0wMcC3wefDCEhERERGRSKOJILr3Q+BqYBiwB5jsXRcREREREenTAh1pSrDWXuK/wRiTHYR4REREREQkQmmkqXvbjDHPGmMS/LYtDEZAIiIiIiIikSTQpOlTYAWwyhgz2rutn+aZIiIiIiLSnwRanmettX8xxqwDXjfG/AqwQYxLREREREQiTH8tzws0aTIA1tpVxpiZwAvAQUGLSkREREREJEIEmjSd2rpgrc01xswAjgtOSCIiIiIiEok00tQJY8y3rLX/AC4yptMeWh6UqERERERERCJETyNNSd6/KcEOREREREREJBJ1mzRZa//m/XtLaMIREREREZFI1V/L87qdctwYc7kxZox32RhjHjfGlBtj1htjDgtNiCIiIiIiIuHT0+80XQts9y5fBEwCRgE/Ax4IXlgiIiIiIiKRoaekqcla2+hdPh34u7W22Fr7Nr7rnUREREREpB+wJvy3cOgpaWoxxgwxxsQDM4G3/fYlBC8sERERERGRyNDT7Hm/A9YAUcBr1tqNAMaY6cDWIMcmIiIiIiIRpL9OBNHT7HlvGGP2A1KstaV+u9YAFwQ1MhERERERkQjQ04/bnmitXWKMmdHFj9u+HJywREREREREIkNP5XnTgSXAnE72WcKdNFlL6l23Eb9yGTY+nuL5d9J48CEdmsV8toG0396Aqa+jbsp0Sn91I/glgSlPPk7qfXex+533aEkdjKkoJ+13vyF6905sbBwlt9xO45ixoTyyfW6/g2D6WeAxsOF9WLO47f7xR8GUM6C63K2vXQEb34eMoXDieRAbDy0t8OF/4Mu1IQ8/qAZ9Aw64DvBAweuw5x9t9+//Exh4uFv2xEFMKnxwils/djnUeAtV6/Nh069CFXXoDD4KxlwDeCB3Iex8tu3+A38Egya75Shv/6w8A5JHw9jrIDoJbDPseBoK3glt7ME2dAIcfTEYD3y1HDb8q+3+0VPgyPOhpsytb3rbtQM4/DwYPsktr38Ntn8QsrAjw2O46YUKgAlhjiUMhkyEI77tzp0t78Bnr3dsM/IbMOEcsBbKdsK7D8Gg/eDo70J0AtgW2LgAdq4OefhBlT0JDvuO65utS2DTax3bjDgGDjkX8PbN6j9DYjpMuR4w4ImCr96CLW93vG9vN2oMzD7N9c+6NfDe8s7bjTsEzrkYHv8L5O0BjwdOPQuyh7rlTz/p+r69VfoRcPAPAQ/s/jdse7Fjm+ypcOC33POqciusv9ttH/s9yDjKLW95FvL6WN/sSyrP64S19mbv3++GJpyvJ37lcqJ3bif39UXEfrqOwbfOI//pjs+QwbfOo+Tm+TRMmETG1ZcTv2o5dVOmAxCVl0v8e6toGjJ0b/uBj/6VhoMOpuiPDxG9bQuDb/89Bf/3ZMiOa18zBmacAy//FarK4KKfwtYNUJLftt2Xn8A77dLgxkZ462koK4KkAXDx9bBjE9TXhSz84PLAqOth43XQUAATH4WSlVC73ddku9/k+tnnQtIY33pLPay7LESxhoMHxl4La38B9YVw5MNQ9C7U7PA12fwX3/KwsyDlQLfcXA+f3wm1eyA2DY78K5R8CE3VoT2EYDEGjvk2LLoHakrgtJth1ydQntO23fYP4P12ifiwSZC2H7z+O4iKhpN/DXvWQ2NfeV4F4gngQeDvYY4jDIyBIy+DJXdAbQmcPB92fwwVe3xtUrJg/BmwaB401kDcALe9uR7eexgq8yFhEJxyK+Sud236AmPgiO/BO7dBbTHMvh1yPmrbN8nZcPBcWHwzNFb7+qauFN7+LbQ0QXQcnHIv7PnIbe8rjIGT58Cz/w8qKuC7V8FXn0NRYdt2sbFw1LGwZ6dv20GHQnQ0PPpniI6BK66Fz9ZDeVlIDyF4PDD+avjwN1BXBMf+CQreh2q/PkgcCqMugNXXQ1MVxA502zOOggGj4d2rwRMDR98NhWuguY88r2Tf6Gn2PACMMWnGmAeMMR8bYz4yxvzJGJMW7OB6krB0MdVzzgRjaJg4GU9lBZ7CgjZtPIUFeKqraJg4GYyhes6ZJCzxDbOk3nMHZT/9RZuRp5itW6g/+hgAmg4YTVTOHjzFRaE4pKDIHgnlRVBRDC3NLjkafWhg9y0rdAkTQHUF1FRCQnLwYg215IOhdjfU54BtgqLFMHhq1+3TZ0FRH/zmsisDDnJJT12u65/8JZB+XNfts050bcD1a633g05DMTSWQcygYEccOumjoCIfqgrd82rb+zAiwJ/8HjQU8r90IwVNDVC6241a9SsrgJJwBxEeaaOhKh+qvefOjtUw/Ii2bUafCF/9x5cM1Ve4v5V5LmECqC2DugqITwlZ6EE3+EB3jNUFrm92vgvDjmzbZtSJsHmRS5jA1zctzS5hAvfBlz74bfjQ4VBaAmWl7ng/Ww9jDu7YbtoseG8FNDW13R4T60aoYqKhuRnq60MTdygMGgs1OVCb596v8pZB1jFt2ww/BXa+7hImgAZvdU3SSCjd4F6Tm+uhchtktHtOik+4pxuP1CnHWz0HFALnAOd6l58PVlCBii7Ipzkre+96c1Y20QX5AbdJWPo2zZmZNI47qM19GsYeRMLiRQDEfrqe6NwcovLzgnUYQZc0CCrLfOuV5ZA0sGO7MZPgkl/AaZdB8qCO+7NGum/Fy4qDE2c4xGW4EaZWDQUQm9FF2yyIHwLlH/m2eWJh4mMw4ZHuk63eKi4d6vz6p77I9VmnbbMgPhtKP+m4L+UgMNFQm9NxX2+VmArVfh/6a0ohKbVju5FHwpz5MP1qSBzstpXuhGETICoW4pIh+yBICvvXUBIqCYOh2u91tKbEnU/+UrIhZQjMvhlOusWV87WXNgo80VBZ0HFfb5Uw2I0wtaopcdv8pQyB5CEw8xaYNd+V8+29fxqcfBfMeciV9fWlUSaAlAFQUe5br6yAlHbv51lDYcBA2PJF2+2bNkBjA1z7a7j6l/D+SqirDX7MoRKXDrV+I251RRDX7nU1aRgkDoNv3AvH3O/K+cAlSelHeEvwB8DgiRDfxXud9A7GmFOMMV8YYzYbY37dTbtzjDHWGHNkV21a9XRNU6sh1tr5fuu3GmO6nD3PGHMFcAVA9IN/I/r7VwT4z4SOqa1lwKN/o+Cvj3fYV/G9K0i96zayz59L44FjaTjoYFcg3Ydt3QhffOy+eZpwLJx8MbzkV3aVOABOvgQWPYO7mq0fSp8Fxe8ALb5tH50DDUUQNxQOeQCqt0L9nq4eoW/LmgGFy2nTPwCxg2H8Da5Ur7+dO7s/gW2r3bffY0+AKT+ARXdDzkb3gffUm9xIQeEW9w2nSCtPlCvRe/tWl2zP+i0s/LVv5Cl+EBx7Fbz3N/rd88oT5ZLKJb93fXPiPPj3L1zf1BbDW7+C+FR3fdOu96G+vKdH7EMMzPomvPFSx11Dh7trkx+4E+IT4NuXw/bNbtSqvzBRLnH64FcQnw5H3wOrroLij2HgWDjmD270qWyTXpN7M2NMFPAQMBvYDXxojHnNWvtZu3YpwLXA+4E8bqBJ0yJjzIXAC971c4G3umpsrX0EeAQgoW7fvpwnP/c0yS+7MBoOmdBmBCgqP4+mzKw27ZsyszptE717J9F7djPk/Ll7t2dfeDZ5T79IS3oGJfPvaD0Yhp46k6bhI/blYYRUdRmkDPKtpwz0TfjQqs6vbnfDapjiN/VHbByceTm8uxDydtCn1BdCbKZvPTYTGgo7b5s2C7b9oe22Bm/pYn0OVHwCyWP6VtJUXwTxfv0Tl+76rDOZM+DLB9pui0qEiXfA1seg4vPgxRkONaWQ5PcNeGIqVLf78FHvd/3WV8vgiPN965++7m4AU6+Eit47mC1fU21J25HFxMHufPJXUwLFm90kKtWFUJnrEoWSrW4SiBN+DutedG36ktoSN1rUKnGw2+avphhKuuibVnWlUL4LMg6C3QF9HOodKivcKFKrlAGueqRVXCxkZMElP3Dryclw3rfgxX/AIZNg61cucaqpht07YciwvpM01RdBgt/oUHw61LerjKkrgvIv3LlTmw81e9zIU8WXsPU5dwOY+Euo7kPv5ftaL5gI4mhgs7V2K4Ax5jlgLvBZu3bzgbuAXwTyoIGW510OPAPUe2/PAVcaYyqNMRUBPsY+UXXhJeS9sIC8FxZQM2MWSa+/CtYSu34tLckptGRktmnfkpFJS1IysevXgrUkvf4qtTNm0jhmHHveeY+cN5eQ8+YSmrOyyXvuZVrSMzAVFW4MG0h6+UXqDz8Sm9x7L+TJ2wWDMmDAYPcN3djDYMvGtm0SB/iWRx3qmyTCEwWnfw8+/xA2rwtdzKFStQkShkPcEFc+lj7TTQTRXsJIiE6Byg2+bVEpYGLccvRASJkANdtDEnbIVG6ChGGu7M5Eu2uWit7r2C5xhOufCr/zykTDhN9D3iLvCFQfU7QNBmRBcrp7nhzwDTey5C/B78PNiMOgPNctGwNxSW45dTikjoCcDUg/UbzVfchPynDnzn7HuAkL/O1eA5nea1Xikl1JWlWBaz/tOti2Enb1wRkXS7a07ZuRx3Xsmz1rIGO8W45N8fVNwmCI8r4mxyS5hKmyD5UEA+TsgdQ0GJjq+mf8RPhqk29/fT388Xb4y73utmeXS5jy9rgJH/Yb5drFxMCwER0nkOjNyr90Ez0kZLn3n+zpUNBuZsmC91zpHbgyvMRhUJsLeCDGe21g8v6QcgAUtzvvJLIYY64wxqzxu/mXtQ0Ddvmt7/Zu87//4cAIa227eW+7FtBIk7U2Ii8zrZs6nYSVyxhy+mxsfAIlv799777s8+eS98ICAEpvvJnBrVOOHz+NuinTun3cmG1bSLvp12CgcfQYim+5LajHEWy2BZa+BGdd6S4A3fg+lOTBMadAwS5XmnfYVJcstTS7UadF3mmlx06GYaMhIQnGH+22LXoGCvvKG1EzbL0fxt/nhu3z34DabTDiBy6hKvUmUJ1NAJG4H4z6Ja4czeOmKvefda8vsC3w5Z9h0l2uf3LfdInhAZe5b+aK33XtMk+EgqVt75t5AgycCNEDIPtkt23TXVC1JYQHEES2xc2KN+vnbvrer1ZAWQ5MPguKt8GutXDwbJcstTS7UaeVj7r7mmg45TduubEOVjzSD0tBngFOANJxb203Ax2rpfsk2wJrnoAZv/JOq70Myve46cVLtsGej92MeEMmwGl3u/Zrn4GGKtj/eMg8COJSYJT3rey9v0FZH6kCsC3w8f+D6b/x9s1SqNgNh57nRpJyPoK8dZA90c2OZ1tg7T9c32RNgMnf8j3WpjfcaFNfYltg0etw4WXuJ0TWfQxFBTBtJuTuaZtAtffR+3D62XD5T9wXN+s+gsL8rtv3NrYFPnsYjrzVvV/tXgRVO+HAb7uEqvB9KPoI0g+HKX9zo01fPAaNlW7ikG/c6x6nqQbW39MPX5O/hkgYafKvavu6jDEe4D7gsq91P2u7rp4zxnzLWvsP7/Lx1tpVfvuusdY+2NM/sK/L8/qaO24IdwSR6+g++C3qvlIfF+4IItvOkeGOIHJ9p/f+ekJIPHNxuCOIXFHN4Y4gsm0ZHe4IItfhH4c7gsh1ypu9b57Hn90f/s/29/20634zxhwLzLPWnuxdvwHAWnuHd30gsAXwzqNINm4+1zOstWu6etyeyvN+5rf853b7vtfDfUVERERERELpQ2CMMeYAY0wscCGw92eyrbXl1tp0a+3+1tr9gdX0kDBBz+V5povlztZFRERERKQPi4TyvO5Ya5uMMdfgJq2LAh631m40xvweWGOtfa37R+hcT0mT7WK5s3UREREREZGwstYuBBa22/a7LtqeEMhj9pQ0HWSMWY8bVRrtXca7PiqQf0BERERERPqGSB9pCpaekqaDQxKFiIiIiIhIhOo2abLW9pFJTEVERERERP47Af1OkzGmko7XMJUDa4DrW39xV0RERERE+i6V53Xvj7hf030Gdz3ThcBo4GPczxGeEITYREREREREwq6n32lqdYa19m/W2kprbYX3V3hPttY+D6QGMT4REREREZGwCjRpqjHGnG+M8Xhv5wN13n2aelxEREREpB+wJvy3cAg0aboE+DZQAOR7l79ljEkArglSbCIiIiIiImEX0DVN3oke5nSxe+W+C0dERERERCKVJoLohjEmA7gc2N//Ptba7wUnLBERERERkcgQ6Ox5C4AVwNtAc/DCERERERERiSyBJk2J1tpfBTUSERERERGJaP21PC/QiSDeMMacGtRIREREREREIlCgI03XAr8xxtQDjbgfuLXW2gFBi0xERERERCJKfx1pCnT2vJRgByIiIiIiIhKJuk2ajDEHWWs3GWMO72y/tfbj4IQlIiIiIiISGXoaafoZcAXwB79t1m/5xH0ekYiIiIiIRKT+Wp7X00QQjxpjsq21M6y1M4AngCpgA3BusIMTEREREREJt56Spr8CDQDGmGnAHcCTQDnwSHBDExERERGRSGJN+G/h0FN5XpS1tsS7fAHwiLX2JeAlY8zaoEYmIiIiIiISAXoaaYoyxrQmVjOBJX77Ap2uXEREREREpNfqKfF5FlhmjCkCaoEVAMaYA3EleiIiIiIi0k/014kguk2arLW3GWMWA0OARdba1pnzPMCPgx2ciIiIiIhIuPVYYmetXd3Jti+DE46IiIiIiESq/jrS1NM1TSIiIiIiIv1a0CdzaI4K9r/Qu8XVhzuCyGVsz236q6jmcEcQ2VJLwx1B5Hrm4nBHENkufibcEUSu184IdwSRbfv+4Y4gcs1YGu4IRP53mgFPREREREQCovI8ERERERER6UAjTSIiIiIiEhCNNImIiIiIiEgHAY00GWMGAZcC+/vfx1r7k6BEJSIiIiIiEiECLc9bCKwGPgVagheOiIiIiIhEqv5anhdo0hRvrf1ZUCMRERERERGJQIFe0/SUMeZyY8wQY8zg1ltQIxMREREREYkAgY40NQD3ADcCrT85aoFRwQhKREREREQij8rzunc9cKC1tiiYwYiIiIiIiESaQJOmzUBNMAMREREREZHIppGm7lUDa40xS4H61o2aclxERERERPq6QJOmV703ERERERGRfiWgpMla+6QxJhYY6930hbW2MXhhiYiIiIhIpFF5XjeMMScATwLbAQOMMMZ8x1q7PGiRiYiIiIiIRIBAy/P+AJxkrf0CwBgzFngWOCJYgYmIiIiISGTpryNNgf64bUxrwgRgrf0SiAlOSCIiIiIiIpEj0JGmj4wxjwL/8K5fAqwJTkgiIiIiIiKRI9Ck6YfA1UDrFOMrgL8EJSIREREREYlI/bU8r8ekyRgTBayz1h4E3Bf8kERERERERCJHj0mTtbbZGPOFMWaktXZnKIISEREREZHIo5Gm7qUCG40xHwDVrRuttWcEJSoREREREZEIEWjS9NugRiEiIiIiIhKhAkqarLXLgh2IiIiIiIhEtv5anhfQ7zQZY842xnxljCk3xlQYYyqNMRXBDk5ERERERCTcAi3PuxuYY639PJjBiIiIiIhI5NJIU/fylTCJiIiIiEh/1O1IkzHmbO/iGmPM88CrQH3rfmvty8ELLQDWMviO20hcsQwbH0/hbXfSMP6QDs1iN24g46YbMHV11EydTskNN4JxafKAp58i5bmnwRNFzbTplF7/S+LfXcXgP/4B09iIjYmh5PpfUPeNY0N9dPvUiPEw5Tx32J+/C58sart/3DFw7FlQXebWNyxz7QCufBBK9rjlqlJ4868hCzskBn0D9r8OTBTkvw45T7Xdv99PYODhbtkTDzGp8OHJbj02C0bfALGZgIVN10N9XiijD77Uo2H0j8F4IO9fsOuZtvtHXQ2DDnPLnniIHQTvnu7bH5UIRz4JRSthy59CFnZIZBwGEy53fbPjP7D5pY5thh4P4y4Ca6FiG3x8H6RNgEO/52uTPBw+uhfy3g9d7KEwZCIc8W3XP1vegc9e79hm5Ddgwjmuf8p2wrsPwaD94OjvQnQC2BbYuAB2rg55+OHzGHA6UABMCHMsYaDnVfcOGQ4XHAseAyu/gH+va7t/2sEwYzy0WKhvhKdWQG4ZpCXDLedBfrlrt7UAnl4Z8vCDSu/nEkw9lefN8VuuAU7yW7dAWJOmhBXLidm5nd0LFxG3fh1p8+eR++yLHdqlz59H0bz51E+cRNZVl5Owcjm1U6cT/8FqEpcuZs9Lr0FsLJ7iYgBaUlPJf/BhmjOziPnqS7Kv/D67lqwI9eHtM8bA1Avg9QdcUnTOr2D7eiht92Kw+SNY+ULH+zc3wIt3hCTU0PPAAT+Hz66FhgKY8BiUroDa7b4mOx7wLWefC0ljfesH/hb2PAnlH4InAWgJVeAh4oEDr4NPr4f6Qjjsb1C8Cmp2+Jpsfci3PPRsSB7T9iH2/z6Urw9JtKHlgYlXwns3Q20xTLsX8j6Aql2+JklDYMy5sPJX0FgNsQPd9uJPYdlP3XJMMsz8KxR+EvpDCCZj4MjLYMkdUFsCJ8+H3R9DxR5fm5QsGH8GLJoHjTUQN8Btb66H9x6GynxIGASn3Aq5612bfuEJ4EHg72GOIxz0vOqWMXDx8XD/Qiitht+cCet2uKSo1QebYbm3NmjSSDjvGHjg3269sALmh/fr7uDR+3nI9NfyvG6TJmvtd0MVyH8jceliqs44E4yhftJkPJUVRBUW0JyRubdNVGEBprqK+kmTAag640wSlyymdup0Up5/lrLvXwGxsQC0pKUB0HDw+L33bzxwDKauHhoa9rbrbTL3h/JCqHQ5IZs/gv0ndUya+qPk8VC3G+pz3HrR25A6te2LrL/02bDrUbecsL/7Nqv8Q7feUhvsaEMv5WCo3QN1uW69cAmkTWmbNPnLmAk7/p9vPXms+yav9ANIHhf8eEMpdQxU50FNvlvfswKyj4bNfh/u9jsJti10H+wAGso7Ps7Q46DgY/flRF+SNhqq8qG60K3vWA3Dj4DP/JKm0SfCV//xJUP13umFKv1em2rLoK4C4lP6UdK0Atgv3EGEh55X3TsgAwoqoKjSrX+4BSbt1zZpqmv0LcfGhDS8sNL7uQRbT+V59wCbrbV/a7f9SuAAa+2vgxlcT6Lz82nKzt673pyVTVR+ftukKT+f5ixfm6asbKLz3atxzPbtxH+0htQH7sfGxVFy/S9pmDCxzb+R+J+3aBg/vtcmTABJg6C61LdeXeoSqfZGHQZDx0BZPqx6yXefqBg3OmVb4ONFsH1dx/v2VrEZUJ/vW28ohJTxXbTNhrghUP6RW48fCc1VMPZ2iB/qXmx3PEyf+nYqLh3qC3zr9YUukeq0bRbED4Gyj70bDIz6EWy6DVKPCHqoIRefBrVFvvW6Ykgd27ZN0lD3d8qdgAe+eLbjN99Dp8LWBUENNSwSBkN1sW+9pgTSR7dtk+J9aZ59syvF+vQlN6LkL20UeKKhsgDpB/S86t6gJCip8q2XVcMBmR3bnTAeZk+AKA/c9y/f9vQUuOksl1i9ugY296EvT/V+Hjr9daSpp4kgTgQe6WT7/+EqrjtljLnCGLPGGLOm5dHO7h4ZTHMznopycp95gZLrf0nmz69zBdJeMZu/YvB991L0u9+HL8gQ2f4p/OO38MJtsHsTzLzUt+8fN8FLd8F/Hofjz4UB6eGLM5zSZ0HxUva+iJooSJkEOx6E9d+HuKGQeWpYQwyrjBOhaBl7+2fomVDyvnvj6q9MlPuAt+pG+PhemHwNRCf59selwoD9oKCPlRAFyhPlSvTevhVWPQhH/wBiEn374wfBsVfB6kdwBeEi6HkViHc+gxufh5c/gFO915yW18Cvn4VbX4EXVsMPZkB8PxqJ8qf3c/lv9JQ0xVlrO7xVWWtbgC7zTGvtI9baI621R3p+cMX/GmMbKc8+zdBz5jL0nLk0Z2QQnef7miQqP4/mrKw27ZuzsojK97WJzs+jydumKSuLmlmzwRg3wmQ8eErd8EpUXh5Z115D4e130TRy5D49hlCrLoOkVN96UipUtytnqK+Glia3/PkqSPc75Na2lcWQ8yWkjwhquCHVUOhGSFrFZrjRlM6kz4Ki//jdtwBqvvKWAjRDyQpI6mMlaPVFEOf3LWZcBjQUdd42cyYUvu1bH3AIDD0Ljn4ORl0FWSfD/vv25SCs6oohwe8LhPg0dw1G+zZ5H4BthpoCqNoDyUN8+4ceD7mr3f6+prYEktJ864mDoaa0bZuaEtjzsTv+6kKozPWNPkUnwAk/h3UvQvHm0MUt4aXnVffKqmFwsm99UJK7tqkrH26Bw/Z3y00tUO2dymtnkbu+KWtg0EINOb2fS7D1lDTVGmPGtN/o3RaWis/Kiy4h56UF5Ly0gOoTZ5H82qtgLXHr1mKTU9qU5gE0Z2Rik5KJW7cWrCX5tVepmTETgJoTZxH/gZtWJ3r7NkxjIy2pqXgqKsj60RWUXHc99Yf3/rqigh0wKBNS0tw3uwce4SaC8Jc4wLe8/0Qo8+aZsQmuNAYgPgmyR0NpbmjiDoWqzyF+uBumN9HuhbS0k9mE4veDqBSo2tD2vlHJED3IrQ88Amq2hSTskKncBAnDIT7b9U/GiW4iiPYSRkJ0MlRs9G3bdCt8cD58cCFsfRjy34LtkTvw/LWVfeUuSE/MdH0zbCrkf9C2Te5qSD/ULf//9u47Tqrq7uP457cLuwvbaLtLFRQFASkqdqUo2BUl9hKN7UmiUVOeJMYktmiMNVGjeUzsGiyxYTcWiooFlSaCIArSdpe6lYXdPc8f564722YH3SnMft+v17x27r1nZn737Nxy7vndM2nZkNUHykPSR/qM8fdsJKP1y3wDKDPP73f67w+rPm5YZuVsyA/SPdOzILsXlBX58mMuh6/egW8+bPLWksS0XYX3dTHk50D3bJ96t89AmLuiYZn8kOP58J3qR8vLyvh24GB6ZEN+LhSXxibuWNDxPHacxf8RD62NnvdH4BUz+xNQd7gbDVwBXB7FuCJSOWYsnWdOp+9RE3GdOlF83Q3fLuv9g0msftonNK/7/VXfDjleecgYKg8ZA0Dp5B+Q9/vf0eeEY3EdO1J8w41gRs6UR+n4zQq6/OPvdPmHHxps7b33fztQxI7G1cLMJ+DYS/x9A4tm+YbPPsdC8XKfmjd8PAwYDrW1UFUBbwWjNnXtBWODYV3N/FDlSTWARA18dRsMud13zxe9CJVfQb8LoGxR/Q63xwRY/0aj19b6rvyhd/i6KVsERVNjvgbRVQNL/wp73BIMOf4yVHwN/c/zDaoNwbD0+YdC0VvxDDT2XC3Mvxf2v9rXzYo3ofQbGHwGbFrqT/SKP4X8PWH8Xf6q92cPwrbgJKVTvr+ivn5BuE/ZcblamP0gjP+Nr59l02HzKj+8+IavfA/TmnnQazgcc5MvP+ffsLUMBhwE+btDejbs4nfXzPo/2NTCACRJ59/AOKAH8A1wFXB/PAOKHW1X4dU6mPIeXH6UH3L83cWwZiMcvzcsL/YNqPHDYEgfqKmFiip4YLp/7aCecPxoP985P9x4RVX4z9uh6HguUWbNZN81LGC2B/C/QHBdh8+Am51z8yP5gLRtykQP52+XxTuCxDVqTrwjSFzb2mkeeqQ2dYl3BImrLKv1Mu3ZGf9uvUx7NfX4eEeQ2F5s8U5v+dEDrZdprw54r+XbXRLV6Y/H/9x+ymmxr7fWeppwzi0AzolBLCIiIiIiksDa6+h5rQ05/gJhxixyzum6k4iIiIiIJLXWeppuCf5OBnoCjwbTpwOFzb5CRERERESSknqamuGcmw5gZrc650aHLHrBzGZHNTIREREREZEE0NqQ43UyzWyXugkz2xnIDFNeREREREQkKbQ6EETg58A0M1uG/1Hb/sD/RC0qERERERFJOErPC8M592rwg7a7B7MWOeeSaXR/ERERERGRZkXa0wSwNzAgeM1IM8M593BUohIRERERkYSjnqYwzOwRYCAwB6gJZjtAjSYREREREUlqkfY0jQaGOufi/gvAIiIiIiIisRRpo2kB/nea1kQxFhERERERSWBKzwuvB7DQzD4Evh0Awjl3fFSiEhERERERSRCRNpqujmYQIiIiIiKS+NTTFIZzbnq0AxEREREREUlEKZEUMrP9zewjMyszs61mVmNmJdEOTkREREREJN4iTc+7CzgNeAo/kt4PgUHRCkpERERERBJPe03Pi6inCcA5txRIdc7VOOceAI6MXlgiIiIiIiKJIdKepgozSwPmmNlN+KHHI25wiYiIiIjIjk89TeGdDaQClwDlQD/gB9EKSkREREREJFFEOnre8uBpJXBN9MIRERERERFJLGEbTWY2H3AtLXfOjWjziEREREREJCG11/S81nqajo1JFCIiIiIiIgkqbKMpJC3vW2bWA1jvnGuxB0pERERERJJPe+1pCjsQRPCjttPM7Bkz29PMFgALgEIz05DjIiIiIiKS9FpLz7sL+B2QC7wFHOWce9/MdgemAK9GOT4REREREZG4aq3R1ME59zqAmV3rnHsfwDm3yKyd9s2JiIiIiLRTSs9rXm3I88pGy3RPk4iIiIiIJL3WeppGmlkJYECn4DnBdEZUIxMREREREUkArY2el/p9PyC15vu+Q3Krjujnhdunyk7xjkB2VJtz4x1B4krbGu8IEtvU4+MdQeI6fmq8I0hsH+wX7wgSl47nyUXpeSIiIiIiItKE+jlERERERCQi6mkSERERERGRJtRoEhERERERCUPpeSIiIiIiEhGl54mIiIiIiEgT6mkSEREREZGIqKdJREREREREmlCjSUREREREJAyl54mIiIiISETaa3pe2EaTmf0i3HLn3G1tG46IiIiIiEhiaa2nKTsmUYiIiIiISMJTT1MznHPXxCoQERERERGRRBTRQBBm9pCZdQmZ7mpm90ctKhERERERkQQR6UAQI5xzm+omnHMbzWzP6IQkIiIiIiKJqL2m50U65HiKmXWtmzCzbmjkPRERERERaQcibfjcCswys6cAA04Cro9aVCIiIiIiknDaa09TRI0m59zDZjYbODSYNdk5tzB6YYmIiIiIiCSGiBpNZrYTUAZMDZ3nnFsRrcBEREREREQSQaTpeS8BLnjeCdgZWAwMi0ZQIiIiIiKSeNprel5EA0E454Y750YEj92AfYFZ0Q1NRERERERk+5jZkWa22MyWmtlvm1n+CzNbaGbzzOxNM+vf2ntGOnpeA865T4D9vstrRUREREREosHMUoG/A0cBQ4HTzWxoo2KfAqOdcyOA/wA3tfa+kd7T9IuQyRRgL2B1JK8VEREREZHksAOk5+0LLHXOLQMws8eBScC3g9g5594OKf8+cFZrbxppT1N2yCMdf4/TpAhfKyIiIiIiEgt9gG9CplcG81pyPvBKa28a6ZDj10RSTkREREREklci9DSZ2UXARSGz7nXO3fsd3ucsYDQwtrWyYRtNZvYA9aPmNeacc+dvb3AiIiIiIiLfVdBAaqmRtAroFzLdN5jXgJlNAK4Exjrnqlr7zNZ6ml5sZl4/4OdAamtvLiIiIiIiEkMfAbuZ2c74xtJpwBmhBcxsT+D/gCOdc0WRvGnYRpNz7umQN98F+B0wBrgRuG97oo8K5+j6l+vJeGc6LiOD9dfdyLYhTX86quPCBXT/wxVY1Ra2HDyWjb+5EszIvedOMp9+ktpu3QDY9LNfsOWQsaRs2kiPX15K2mcLKD/+RDb+7o+xXrM2t9MQGDMZLAUWzoKP32i4fPd94eAToGyTn54305fL7gpHXwBmkJIK82bAgndjHX10ddsHdrvY182al2H54w2X7/oT6DrKP0/NgI5dYOYkyBoIgy+H1M5ALXz9GBRNi2noMaH6iUzv4TD6LF9PS6fDZ81ccuq/L4w4EXCw8Rt4556YhxlTPUfCnuf4Oln2Fiya2rRMv/1h2EmAg00r4P07oXMPOPiXQLDfWfIafPlG09fuyPL2hOEX+rpZ/l9Y+nTTMr0PgsGng3NQ8hV8cht0Hw57nFdfJqsvfHwLrP0gdrHH3X3AsUARMDzOscTBbrvAMRMhxWD2XJjRwg/ADBsMZ/wA7r4fVq2Fvr3ghKPrl781ExZ+EZuYY0XHq9hIhPS8cJxz1WZ2CfAavpPnfufcZ2Z2LTDbOTcVuBnIAp4yM4AVzrnjw71vq/c0mdnuwO+BPYMP+LFzrvp7rU0byXhnBh1WfM2aF14nbf5cuv3pagofe6pJuW5/upoNV13H1uEjybv4QjLencGWg33qYunZ51J6TsMsQ5eWzuaLL6Pj0iV0XLokJusSTWYw7mR47u++UXTqr2DZAti4tmG5JZ/A9P80nFdeAk/dDrXV0DENzrgCvprv5yeFFBh8KXz6a6gqhtF3Q/EsqFheX2RpyIlt3xMga1f/vKYKFt4IlasgrTvscw9s+Aiqy2O6BtGl+omIGez7Q3jjJqjYAEddAys/gc0hY4xmF8Aex8Fr18HWCsjIjl+8sWAGe58H066HyvUw8QZY/TGUhCRIZPWEIZPgzatgWzmk5/j5WzbCG3/w+50O6XDkLbDqYz8/KaTAiP+BWVf5uhlzC6z9EMpCblvO7AW7nQTv/MbXTVqun79+Pkz/uX/eMQsO+wcUfxr7VYirB4G7gIfjHEccmMFxR8ADU6CkBH7yI/h8CRSva1guLQ0O2AdWhGxvhcW+AVXrIDsTLrkAFi3x00lBxysJ4Zx7GXi50bw/hjyfsL3vGXb0PDN7KvjAWcA4YCqQY2bdzKzb9n5YW+v09puUH3cCmLF1xChSSktIKW7Yw5ZSXERKeRlbR4wCM8qPO4FOb70Z9n1d585U7TUal54eveBjqKA/bCqGkvVQWwNffAK7RHh1rrbGn7gApHbwO+xkkrM7VKyCLWvAVUPR25B3YMvlCw6FwmCQysqVfgcLsHU9bN3kr1olE9VPZLoPhNIiKCv228zy96HfXg3L7DYOFr/hG0wAW0pjHmZMddsVStdCeZGvkxXvQZ/RDcvscigsfd03CgCqgosxofudlI5Aku13uu4G5WuhotBvV6tmQs99G5bpfzh89XJ93Wzd3PR9eh8IRZ9Azdbox5xQZgIb4h1EfPTtDRs2wsZNUFML8xbCkN2alpswBmbOguqQS9zbqusbSB0iGgZsx6LjVew4i/8jHlrbbPbBDwTxKyBIlviWA3aJUlwR6VBUSEVBz2+nawp60qGokK15+Q3K1DRTpk7244+R+cJzbB26Bxt/9VtcTm5sgo+hzC71aXfgn/ds5nePB46E3gN9A2vmM/WvyeoCx/0P5ObBu88nUS8TkN7DX5GqU1UMOUOaL5uRDxk9YWMzV3WzB0NKB6hMsl8vU/1EpnNXKF9fP12+AXoMbFgmJ9gNHfF7nzoy71lYPT92McZap26+F6VOxQbovmvDMtm9/N/DrvF1suA/sHZu8PruMObXvjdq7mNJ1MsEZHSHypCegS3roeughmUye/u/B98IpMDiKU17lHofAsuej2qokmBysmFzyDG4pBT69W5YpncB5ObA4i/h4P0bLuvbGyYfA11y4T9Tk6iXCR2vJPrC9jQ55wY453YOHruEPN/ZORfXBlNbKD3ldFa/+F/WPvk8NXn5dL3lxniHFDdfL4AHr4Epf4EVi2BCyE98lW3y8x+5FobsC52SPK2oJfmHQtEMoLbh/LRuMPQK+PxmWh5rsh1Q/YRnqT5F7/U/wzt3w/7nQcfO8Y4qvlJSIbsnvHUtzLoD9rmovk4q18Nrv4GXLocBYyA9+a5nhWWpvuH07pXwyS0w6hLokFm/PL0r5PSHovaWmidhGXDUBHilhYSalavhjn/CPQ/A2AOhQzsd0kvHK/kuIv1xW8xshJkdb2aT6x5hyl5kZrPNbHb1fds9ZHpYWY8/Rs9TJtHzlEnU5OWRWlh/Y05q4Vqq8wsalK/OL2ixTG33HpCaCikplE0+mbQFyXnZt3yT7y2qk9UFyhqlemypqE+HWTgL8vvRRHkJrF/je6OSRdU6SM+rn07P8/OaUzAOCt9qOC+1M4y8AZbdDyWfRy3MuFH9RKZiI2R2r5/O7AaVjXpGKjbAyk/B1UDZOihZCzkNd1dJpXKD7y2q07mbnxeqYr2/z8nVQHkxlK7xjahQWzbC5m8gb/foxxwrW9ZDpx710xndG/bK1ZVZ+6Gvm4oiKFsFWb3ql/c+CNa875dL+1FS6nuR6uRkw+aQVN+0dCjIgwvOhF/9FPr1gbNOhj6Ntqvi9VC11ZdNFjpexU68U/PilZ4XUaPJzO4H7gd+ABwXPI5tqbxz7l7n3Gjn3OgO51/UUrHvpOy0M1n75POsffJ5KsZPIPOF58A50ubNoTYrm9qQ1DyA2rx8ajOzSJs3B5wj84XnqBx/GECD+586v/UG23ZtJjE4CRSugC55kNPNX9kdtJcfzCFU55Cd8M7DYWOQwZjZBVI7+ufpnaDXLrCpkKRRugg69/Hd9NYB8sfDuvealuvcDzpkQ8nC+nnWAYZfA2teh+IZsYs5llQ/kVm/zPciZfXw21j//eGbRj0A33wMBUGqSHqWT9crLW76Xsliw5e+AZSZ5+tkpwP9YA6hVs2GvKH+eVq2T9crK/KpfXX7nY6ZvsFUmkSpMpuW+IEeOuf77aTPIVD4YcMya96HHnv452nZkNUHykP2vX3G+HuhpH1ZtRq6d4WuuZCaAiOG+sEc6lRVwQ1/hVvu9o9vVsGjT/nR87rm+hH3ALrkQF532NjMvXI7Kh2vJNoivRVwf+fc0KhG8h1sOWQsnd6ZTq9jJ+IyOrHh2hu+XdbzlEmsfdIne2+88iq61Q05ftAYthw8BoCut99Mx8WLwKC6dx82/OHab1/f+6hDsbIybNs2Or39BkX/uJ/qgY0S8ncQrtaPinf8TyElBRa+DxvWwn5HQ9EK+GoBjBwLO+/hy26pgDce9a/tVuCHIq/z6Vu+tylZuFr44k4Y9Rd/T8XqV6B8Oex8LpQuhnXBUK4F4/1NpaHyx0GXEdAxB3od4ed9fhOUfRnLNYgu1U9kXC18+DAc9ms/WMrSGbB5FYycDOu/8j1Mq+dDr+Fw3J99+U8eh61l8Y48elwtfPIAjP1dMOT421CyEvY4GTYs8z1Ma+dCzxF+dDxXC3Me9XVSMBxGhaQIL3rR9zYlC1cL8++F/a/2dbPiTSj9BgafAZuW+gZU8aeQvyeMv8v3Jn32IGwLehQ65fueqvUL4rkWcfRv/NBUPYBvgKvwl3XbgVoHL7wO557mvzufzIWidXDYGFi1pmEDqrH+/WDMAVBb64exn/oaVFTGLvZo0/EqdhJ9yPFoMedaT9o0s/uAW51zC1st3EinLcoKDeemX8c7gsQ17LN4RyA7qlV94h1B4kprbyOtbadOSXQS2daOb+Z3tqTeldfHO4LEdVj4QYvbtUPf3PHGBx07I/7n9tPHxL7eIu1pehiYZWZrgSr8vYbOOTciapGJiIiIiIgkgEgbTfcBZwPzaTLWiIiIiIiItAftNT0v0kZTsXNOHfMiIiIiItLuRNpo+tTM/g28gE/PA8A590xUohIRERERkYSjnqbwOuEbS4eHzHOAGk0iIiIiIpLUWm00mVkqsN4596sYxCMiIiIiIpJQWm00OedqzOygWAQjIiIiIiKJS+l54c0xs6nAU0B53Uzd0yQiIiIiIsku0kZTBrAeODRknu5pEhERERFpR9TTFIZz7kfRDkRERERERCQRpURSyMz6mtmzZlYUPJ42s77RDk5ERERERCTeImo0AQ8AU4HeweOFYJ6IiIiIiLQTzuL/iIdIG015zrkHnHPVweNBIC+KcYmIiIiIiCSESBtN683sLDNLDR5n4QeGEBERERERSWqRjp53HnAncDt+1Lz3AA0OISIiIiLSjmj0vDCcc8uB46Mci4iIiIiISMIJ22gysz+GWeycc9e1cTwiIiIiIpKg1NPUvPJm5mUC5wPdATWaREREREQkqYVtNDnnbq17bmbZwGX4e5keB25t6XUiIiIiIiLJotV7msysG/AL4EzgIWAv59zGaAcmIiIiIiKJRel5zTCzm4HJwL3AcOdcWUyiEhERERERSRCt9TT9EqgCfg9cafZt09LwA0HkRDE2ERERERFJIOppaoZzLtIfvxUREREREUlKahSJiIiIiIiEEdGP24qIiIiIiLTX9Dz1NImIiIiIiIShniYREREREYmIeppERERERESkiaj3NKXWRPsTdmwL9oh3BIlrQ7d4R5C4qtVHHNbWtHhHkLhyN8c7gsT29YB4R5C4Ptgv3hEktuuvjHcEieu3N8Y7gsR1aLwDkIjp1EtERERERCKi9DwRERERERFpQj1NIiIiIiISEfU0iYiIiIiISBNqNImIiIiIiISh9DwREREREYmI0vNERERERESkCfU0iYiIiIhIRNTTJCIiIiIiIk2o0SQiIiIiIhKG0vNERERERCQiSs8TERERERGRJtRoEhERERERCUPpeSIiIiIiEhGl54mIiIiIiEgT6mkSEREREZGIqKdJREREREREmlCjSUREREREJAyl54mIiIiISESUniciIiIiIiJNqKdJREREREQiop4mERERERERaUKNJhERERERkTCUniciIiIiIhFRep6IiIiIiIg0oZ4mERERERGJiHqawjCznSOZJyIiIiIikmwiTc97upl5/2nLQERERERERBJR2PQ8M9sdGAbkmtnkkEU5QEY0A4uIc+TefD0Z70zHZWSw8Zob2TZkWJNiHRcuoOvVV2BbtrDl4LFs/t8rwYzsu/9Kp2lv4lJSqO3WnY3X/JnavAKstJSuv/9fOqxdDTU1lJ19HhWTfhCHFWw7w/rCKQdCisE7i+C1uQ2XjxkC44ZBbS1UVcOjM2DNJuieBVefAoWbfLllRfDvd2IdfXQN3BWOPBJSUuCTT+DdFtZvyBA45VS4915Ys9rPyy+AY4+F9HRwDv75T6ipjl3ssbDrLnDMEWAGH8+Bme81X27o7nD6SXDPfbB6Tf383Bz42Y/h7Rnw7vsxCTlmBu0Mxx/m6+ajeTDtg+bL7TEIzj4B7ngYVq2F3frDkWMhNRVqauDlafDlilhGHhu77AYTjwFLgbmzYdaM5ssNHgY/OAPuvxvWrvLb4tEnQs/e/vn8T1t+7Y5qWF849YBgn7wYXm1mnzx+KNQ6qNoGj8ys3ydfczIUbvbllhXBY0m2TwbYbRc4ZqKvn9lzYcas5ssNGwxn/ADuvt9vW317wQlH1y9/ayYs/CI2MSeE+4BjgSJgeJxjiYNBO8Nxh/l9zkdzYXqYffJZJ8KdD/nvza4D/D65QypU18ArbyfnPrmttNf0vNbuaRqM3/y6AMeFzC8FLoxSTBFLf3cGHVZ8TeHzr9Nx/ly6/Plqih9+qkm5Ln++mo2/v45tw0fS/WcXkv7eDKoOGkvZDy+g9KeXA5A55WFy7v07m668lswnH6N6l4Fs+Ns/SNm4gYITj6Ti6OOgY1qM17BtmMHpB8NfX4KN5XDFiTBvuT8A1/lwKcz43D8f0R9OPgDueMVPF5fAn56JedgxYQZHHw2PPAIlJXDhhbB4MawrblguLQ322x9Wrgx5bQpMngzPPgOFhdCpE9TWxDb+aDOD446CBx/z9fPj82HRF1C8rmG5tDQ4YF/4ZmXT9zhqIixZGpt4Y8kMTpgA/3oSNpfCJT+EhUuhaH3DcmlpcNDesGJ1/bzySnjwGSgtg4IecP7JcMM9sY0/2szgiONgygP+u/Ojn8CSz5vftvY5AFaFnKDsvgd06AD/uhM6dISLLoOF82DzppiuQtSYwRkHwe0v+33y706AuWH2ySN3gpP3hzte9dPFJXBdku6TIdjvHAEPTPHfnZ/8CD5f0sJ+Zx9Ysap+XmGxb0DVOsjOhEsugEVL/HS78CBwF/BwnOOIAzOYNBHueyLYJ58Dn7e0Tx7dcJ9cUQEPPV2/Tz7vFPjz3bGNXxJf2PQ859zzzrkfAcc6534U8rjUOdfC9ebY6TTtTSqOPQHM2DZiFFZaQkpxUYMyKcVFWHkZ20aMAjMqjj2BTm+/CYDLyvq2nFVW4ixoOpthFeXgHFZRTm1OLqTuuGNm7JwHRZthXSnU1MLsL2HkgIZltmyrf57ewfeatAd9+sCGDbBpo2/wfLYAdh/ctNz4Q30PVHVIL9LAgb6xVFjopysrk6/e+vaG9Rtg4yb/3Zn/GQwZ1LTcYWN9D1R1o0bjkEH+tUXrmr5mR9evF6zfBBs2+7qZ+zkM3bVpuSMO9lc7t4V8d1YX+YMzQOE66NjB9zolk959YWPItrVwHuw2pGm5MRNg1syG2xb4a1SW4uumpgaqqmITdyzsnAdFJfX75I++hJH9G5YJ3SendYxtfPHWtzds2Fi/35m3EIbs1rTchDEwc1bD78626voGUocd97D93c0ENsQ7iPhodp/czPfm8ENg2vsNvzftYZ/clpzF/xEPraXn/do5dxNwhpmd3ni5c+7SqEUWgdSiQmoKen47XZPfk9TiQmrz8uvLFBdSk9+oTFHht9M5d91Op5eew2Vls+5ef2mm/NQz6f7zn9DziEOw8nI23Hi7zxHZQXXJ9Fcz62wsh53zm5YbNxQmjIDUFLj9xfr5PbLhysmwZSs8PxuWro1+zLGSneOvZNYpKYE+fRuW6dkLcnJgyRI48KD6+d27+0bSmWdBZiYsWADvvRubuGMlJxs2h9TP5lJ/QhOqV0+fgvfFUjj4gPr5aR3h4APhocfgoANIOrlZsKm0fnpzKezUqG56F0BuNixaBmP2bf59hg+CVYW+YZBMsnOgZHP9dGkJ9O7XsExBb8jJhS8Xw/4H189ftAAGDYHLfut7mt54GbZUxibuWOiSCRvK6qc3hdknTxzu98m3vVQ/v0c2/P5E37B6Lsn2ydB0v1NSCv2a27ZyYPGXcPD+DZf17Q2Tj4EuufCfqe2ol6mda+541a9XwzK9C6BLNixeBmP3a/599hicnPtk+f5aawkEyQHMBj5u5tEsM7vIzGab2ext99/bJoFGS8klP6fwlelUHHUcmY8/CkD6rHfYNmgIa1+bSdGU5+jyl2uxsrJW3mnHN20h/P5xeOYDOHovP29zBVzxb7j+GXjqfTj/UMhoT1c9DY44Al5/vemilBTYaSd45hm4/37YfXfYuZ2NKWn49LtX32i6bPwYmPUBbN3WdFl7YMCx4+Glt1suU9AdjhoLzzTz/Up6BhOOgjdfabqod19/f+UdN8Ldt8B+B0GXrrEPMd6mLYQrn4BnPoSj9/TzNlfAb6fAn56FJ9+HC8a3s30ywX5nArzyZvPLV66GO/4J9zwAYw/096mIGHDsofDSWy2Xye/h98nPvhazsGQHEranyTn3QvD3oe15U+fcvcC9AFnltOk1nswnHqPzs08CsG3YcFIL6y+xpRatpSavoEH5mrwCUosalclvWAag8qjj6H7pRZT+5FIypz5D6bkXgRk1O/WnpndfOny9jG17jGjLVYmZTeXQNbN+umumn9eS2V/CmYf459W1UB2kxaxY53PpC3JheZKkW5WW+F6kOjk5fl6d9DTIz4dzz/XTWVlw+ukwJci1X74cKiv8sqVLoFcv+OqrmIUfdSWl/mpundxsKA3pXUlLh/w8OO9sP52VBWeeAo89CX37wLAhcPhhkJHhe+Wqq+GD2bFdh2jZXOavWNbJzfZXNuukp0HPHnBR0EefnQnnTvb3Mq1a63uqzj4RnngZNmyKaegxUVrie5HqZOdAaUjPU3oa5BXAmRf46awsOPkseOpRGDYSli3xDaeKcli5Anr18al+yWBTOXSrzw5vkg3Q2EdfwpkHw4PTk3+fDE33OzmNtq20dCjIgwvO9NNZWXDWyfDoU37bqlO8Hqq2+rKrkqw3Tppq7nhVEnK9Oy3N36900Rl+OisTzpkMDwX75Jxsv09+8qXk3Ce3JQ0E0QwzmxpuuXPu+LYNp3Xlp55J+al+T5k+cxpZTzxK5RHH0HH+XFxWdoPUPIDavHxcZhYd581h2/CRdH7xOcpO82d4qSu+pmanAQBkTH+T6gG7AFDdsxfpH85i616jSVm/jg7Lv6Kmcc7WDuTrYsjPhe7Z/mA9eiDc1+hKS36Oz7EHGL6TvwcKICsDyqv8CW+PbP8+xaUkjVWrfZpdly5+hztsD3gmZID9qiq4+ab66XPO9b1Oa1b7+zUOOsinD9XUQP8B8H4LIzztqFathu7dfP2UlsDwYfDUs/XLq6rgxtvqp8872/c6rV4D94XciDx+DGzdmjwNJoCVa6B7V+ia6787I4fA4y/UL9+yFa69q376otPgpWn+4JyRDueeBK9Mh+Wrmrx1Uli9Crp2h9yu/rszdAQ8/2T98qoq+OsN9dNnng9vvupHzxuwC/TfBRbMgY4doU8/+DCJUl+/Lvb73Lp98j4D4V+NeiQb75ML28k+GYL9Tsi2NWIoPPl8/fKqKrjhr/XT558Jr77pt62uuT5Fq9ZBlxzI6w4bNzf5CElCze2Tp4Tsk6u2wnV31k9fdLrPBKjbJ//oJHg1iffJ8v21dpvkAcA3wBTgA3zvZsKoOngsGe9Mp2DSRFxGJzZeXX8EzjttEsWP+73spiuuoutVV2BVW9hy4BiqDhoDQO4dt9Jh+VdgRnWvPmy68hoASi/8KV2vuoL8U44D59h86a+o7dot9ivYRmodPP4uXHaUTyl7dzGs2QjH7e2vTs5b7ocbH9LH3zxZsRUemOZfu1svOH5vP98B/54JFUl0Q7arhZdfhrPO9iPvzPkUioth3HhYvRq+WNzya7dsgVmz/Ih74O95WrIkNnHHSq2DF1+Fc04PhmSf4wd1OHSsr59FSba+26PWwfNv+JHvUgw+mg+F62HiwbByrR+1qSUH7gU9usCEA/0D4F9PQXlFTEKPCVcLr78Ap53r62fuJ7CuCMYcBmtWwZJFLb/24w/g2Mlw4aV+u5z7MRQXtlx+R1PrYMp7cPlRvm7q9snH7w3Li2HuChgfuk+uggem+9cO6gnHjw72yc4PN55M+2Tw9fPC63DuaX4wkE/m+v3OYWNg1Zrw+53+/WDMAb6X0jmY+hpUJNH9cK36NzAO6IE/e7sKuD+eAcVOrYOp//Uj36UYzJ7vvzeR7pO7d4HDDvQPgPueTK59snx/5sIM92VmqcBE4HRgBPASMMU591mkH9DW6XnJ5szH4h1B4uq9uvUy7VV1exwVajts3TF/HSAmcnXVPayvB8Q7gsSVV9x6mfbs+ivjHUHi+u2N8Y4gcd34m8TqkIjEbkvjf26/ZNfY11trQ47XOOdedc6dA+wPLAWmmdklMYlOREREREQkzlq9Xm1m6cAx+N6mAcAdwLPhXiMiIiIiIslHA0E0w8weBvYAXgaucc4tiElUIiIiIiIiCaK1nqazgHLgMuBSs2+blgY451xOSy8UERERERFJBq39TlNrP34rIiIiIiLtRHtNz1OjSEREREREJAwNXCwiIiIiIhFRT5OIiIiIiIg0oUaTiIiIiIhIGErPExERERGRiCg9T0RERERERJpQT5OIiIiIiEREPU0iIiIiIiLShBpNIiIiIiIiYSg9T0REREREIqL0PBEREREREWlCPU0iIiIiIhIR9TSJiIiIiIhIE2o0iYiIiIiIhKH0PBERERERiYjS80RERERERKQJ9TSJiIiIiEhE1NMkIiIiIiIiTajRJCIiIiIiEobS80REREREJCJKzxMREREREZEm1GgSEREREREJQ+l5IiIiIiISkfaanmfOueh+AET3A3Zwb0yIdwSJq9eaeEcgO6pVfeIdQeKqSY13BIktd3O8I0hclZ3iHUFie/3weEeQuG78bbwjSGCOHa4J0ntN/M/tV/eKfb2pp0lERERERCLSXnuadE+TiIiIiIhIGGo0iYiIiIiIhKH0PBERERERiYjS80RERERERKQJ9TSJiIiIiEhE1NMkIiIiIiIiTajRJCIiIiIiEobS80REREREJCJKzxMREREREZEm1GgSEREREZGIOIv/ozVmdqSZLTazpWb222aWp5vZE8HyD8xsQGvvqUaTiIiIiIgkBTNLBf4OHAUMBU43s6GNip0PbHTO7QrcDvyltfdVo0lERERERJLFvsBS59wy59xW4HFgUqMyk4CHguf/AQ4zs7B9WGo0iYiIiIhIROKdmucMzOwiM5sd8rgoJMQ+wDch0yuDeTRXxjlXDWwGuodbb42eJyIiIiIiOwzn3L3AvbH8TDWaREREREQkIjvAkOOrgH4h032Dec2VWWlmHYBcYH24N1V6noiIiIiIJIuPgN3MbGczSwNOA6Y2KjMVOCd4fhLwlnPOhXtT9TSJiIiIiEhScM5Vm9klwGtAKnC/c+4zM7sWmO2cmwrcBzxiZkuBDfiGVVhqNImIiIiISER2gPQ8nHMvAy83mvfHkOdbgJO35z2VniciIiIiIhKGeppERERERCQiO0JPUzRE1NNkZpeZWY5595nZJ2Z2eLSDExERERERibdI0/POc86VAIcDXYGzgRujFpWIiIiIiEiCiDQ9r64j7mjgkWAEinbaOSciIiIi0j4pPS+8j83sdXyj6TUzywZqoxeWiIiIiIhIYoi0p+l8YBSwzDlXYWbdgB9FLSoREREREZEEEWmj6QBgjnOu3MzOAvYC/ha9sEREREREJNG01/S8SBtN9wAjzWwk8EvgX8DDwNhoBRYR58i7/noyp0/HZWSw9sYbqRo2rEmx9AUL6HnFFdiWLZSPHUvxlVeCGemff07+VVdhVVWQmkrR1VezZcQIOn3wAb1/+lO29e0LQNnEiWy45JJYr12b6rYPDPopWAqsfgWWP95w+W4/ga4j/fPUDOjYBWac4KdH/RlyhsDmBTD397GMOjYyD4aevwVLhY1Pw/p/NVzeoRf0uQFSsn39Fd0OZTMh8wDI/zlYR3DboPBWqPggPusQTaqflnUfDYN/4td71avw9RMNlw/6MXQLtquUdEjrAtMm++k9r4fcIbBpAcz5I0mpx94w5MdACqx8Fb56qmmZnofArmeBc1C6DObd5OcPOg/y9vHPv5wCa2fELOyY6LIfDLjcb1eFL8DqRxou738p5O7ln6dkQMeu8NERfjqtAAZeAWn5gINFv4SqtbGMPrq67QO7Xey3qzUvNz1e7foT6DrKP687Xs2cBFkDYfDlkNoZqIWvH4OiaTENPSYG7QzHHebr56O5ML2F/eoeg+CsE+HOh2DVWth1ABw5FjqkQnUNvPI2fLkipqHH333AsUARMDzOscgOJ9JGU7VzzpnZJOAu59x9ZnZ+NAOLROaMGaR9/TVfv/46GXPnkn/11XzzVNOjcsHVV1N43XVsGTmSPhdeSOcZM6gYO5YeN9/M+osvpmLsWDKnT6fHzTez8hF/5KocPZrV//d/sV6l6EiBwT+DT38DVcWwz99h3XtQHrKzXHJP/fO+J0D2rvXTy5+E1HToc2zMIo6dFOh1JSy/ELYVwi5PQOnbsPXL+iJ5/wMlr8LGJyBtIOx0Dyw9HGo2wjcXQ3UxpO8KO90LSw6N36pEheqnZSmw+yXwyW9hyzrY704ontVwu/riH/XP+02C7IH108uf8ifDfY+OXcgxlQJDL4aPfufr54C/QdEHDeunc2/Y5VR4/5dQXQZpuX5+3j6QMxDeuxhSOsK+N0HxbKipiM+qtLkU2PlXsPAy2FoEw++DjTOh8uv6IsvvqH/e8yTIHFQ/vesfYNVDsPkjSOlEct1hnAKDL4VPf+2PV6Pv9ttVxfL6IksbHa+yguNVTRUsvBEqV0Fad9jnHtjwEVSXx3QNosoMJk2E+56AzaVwyTnw+VIoWt+wXFoaHDQaVqyun1dRAQ89DaVlUNADzjsF/nx3bOOPuweBu/CX/eU7a689TZEOBFFqZlcAZwEvmVkK0DF6YUUm8803KTnhBDBjy6hRpJaUkFpU1KBMalERKWVlbBk1CswoOeEEst580y80I6Xc701TSkupzs+P7QrESM5gqFwNW9aAq4bCadDjoJbLF4yHwrfqpzd+CtWVUQ8zLjoNh63fwLaVwDbY/DJkj29UyEFKln+amgXVwVdsyyLfIACoWupPgC3uW0XbUv20LHcwVKyGyrV+u1o7HfIObLl8z3Gwdlr99IY5SdQIaEaXQU3rp2D/hmX6HgkrXvANJoCtm/3fzJ1g4wJwtf5EuPQryNs7tvFHU9ZQ2LISqlb7uln3BnQ9pOXyPSbCuv/6550G+N6pzR/56dpKqK2Kesgxk7M7VKyqP14VvR1+uyo4FArf9s8rV/oGE8DW9bB1k++FSib9esH6TbBhM9TUwtzPYehuTcsdfghMex+qq+vnrS7yDSaAwnXQsQOkpsYk7MQxE9gQ7yBkRxVpo+lUoAo43zm3FugL3By1qCLUobCQbT17fjtd3bMnHQoLIy5T/LvfkXfTTew8dix5f/kL637xi2/LdZozh/7HH0+fCy4gbcmSKK9JdGX0gC0hbcmqYkjv3kLZfOjU05/QtQcdCmDbmvrp6kLoWNCwTPHfIfdY2O1N34uy9oam75N9OFQu9GloyUT107L0Hn5bqqPtqqH0HlAZUj9b1jWtn8w+0LkP7HcL7H+7T+cD30jqsbdPaeyYA91GQEZe7GKPtrQ8qAo5VG0thvQW1i+tJ6T3gs0f++mMnaCmDAbdACMehP4XE/mRfAfQ7HbVo/myGfmQ0dNf2GssezCkdPAXDJNJTjZsLqmf3lwKOVkNy/QugC7ZsHhZy++zx2BYVQg1NdGJUyQZRbSrdc6tdc7d5pybGUyvcM612LlpZheZ2Wwzm82997ZVrG0ud8oUiq+4gq+mT6foiisouPJKAKqGDWPZW2+xfOpUNp19Nr0vvjjOkcZOwXgomklypXt8TznHwKbnYMlhsOIn0OdG6n+5DEgfCAU/hzXXxCvC+FL9tK7nOCjUdtWEpfqG04e/gbk3wrDLoEMmrP/Ep+PtfyuM/A1sWuR7ndqjHhNg/dt8+92xVMgeCcvvgnnnQ3pvyE/WFM9W5B8KRTNosl2ldYOhV8DnNwMuHpHFjwHHHgovvdVymfwecNRYePa1mIUlScZZ/B/xEFGjycz2N7OPzKzMzLaaWY2ZbW6pvHPuXufcaOfcaC66qO2iBXIfe4ydJk1ip0mTqMnLo+Pa+rtfO6xdS3VBw8vg1QUFLZbJefZZyg4/HICyo44iY948AGqzsnCZmQCUjx2LVVeTsmHH7c/dss5fkauTngdV65sv2zg1L9lVF0LHXvXTHQr8vTuhukyGkuDgUjkXLA1Su9aX73sHrPodbPsmNjHHkuqnZVXrGvYOhN2uxjVMzWsPqtZBp5D6yejRtH62rIOi98HVQGWhT8vq3McvW/Y4vHcJzPbXsihfFZu4Y2FrMaSHHKrS8hr2roTqMaE+NQ/8PVAVS3xqHzWwYSZkDo5quDHV7Ha1rvmyBeOaHq9SO8PIG2DZ/VDyedTCjJuSUsjNqZ/OzYaSsvrptDR/v9JFZ8Bvfgz9esM5k6FPkHCTkw1nnwhPvgQbNsU0dJEdXqSd+ncBpwNLgE7ABUBcbh/cfOaZrHj+eVY8/zxlEyaQ89xz4BwZc+ZQm51NTaP7kmry86nNyiJjzhxwjpznnqP8sMMAqM7Pp9OHHwLQ6f332TZgAACpxcV+KCfwDanaWmq7do3VKra50sX+RCSjJ1gHf6BZ917Tcp37QYcs2Lww5iHGTeUCSNsJOvYBOkLu0VD2dsMy1WsgM7gXI20XsHSo2eBHi9vpHj9aXGUz6SHJQPXTspJG21XPsf6G9cY694OO7Wy7Atj8hR/ooVNBff0Uvd+wTNEsn3oHPg2vcx+oXAOkQMdsPz9rAGTvDOs/jmX00VX2OWT09Wl31sE3jDa+07RcRn9IzYayBQ1fm5oFHbr46dy9oeKrmIQdE6WLGm5X+ePDHK+yoSRku7IOMPwaWPM6FCfZaIt1Vq6B7l2hay6kpsDIIbBwaf3yqq1w3Z3wl3/4xzer4aFn/Oh5Genwo5Pg1emwPIkuQkjsxbuXKV49TZGOnodzbqmZpTrnaoAHzOxT4Irohda68mDUuwETJ+I6dWLtDfU3U+w0aRIrnn8egMKrrvp2yPGKMWMoHzPGz7/uOvJvuAGrrqY2PZ3Ca68FIPu118idMgVSU6nNyGDNbbf5IWt2UK4WFt8Je94IpMCaV6F8OexyDpR8AeuCE72C8X6QiMb2vt0foFI7wUFT4PNbYcPsWK5BFNXA2uv9yG6WApuehaovIe8SqPzMNxDW3gy9r4HuPwQcrA6ufHc7A9L6Qd5P/AP8KHM1O26nZFOqnxa5Wlh8F+x1QzCU/2t+uxr4Q79dFQcNhMYDQNQZfStkBtvVIY/BwtuSq2HgamHhPTD6Tz6lbOXrULYCdj3bN6iKP4B1H0OPveDg//O9TYvvg22lfsS8/W7x71NdAfNuTrL0vBr46jYYcruvm6IXofIr6HcBlC2qb0D1mADr32j02lqfmjf0Dn9YKlsERVNjvgZR42rhizth1F/qfyKjfDnsfK6/ABh6vCpqdAEnfxx0GeEb4L2C4dk/vwnKviRp1DqY+l8/8l2Kwez5ULQOJh4MK9f6kfRacuBe0L0LHHagfwDc9ySUJ/GANE38GxgH9AC+Aa4C7o9nQLIjMedaT/g1sxnABPzvM60F1gDnOudGtvradpdRvH3emBDvCBJXrzWtlxFpzqo+8Y4gcdW0t9GytlNui4nnUtkp3hEkttcPj3cEievG38Y7ggTm2OGuymeVx//cviwz9vUWaXre2UAqcAlQDvQDfhCtoEREREREJPHEOzUvodPznHPLg6eVwDXRC0dERERERCSxhG00mdl8wqTXOedGtHlEIiIiIiKSkOLV0xNvrfU0TQYK8LfLheqHv7dJREREREQkqbV2T9PtwGbn3PLQB7A5WCYiIiIiIpLUWutpKnDOzW880zk338wGRCckERERERFJRO01Pa+1nqYuYZZp8FEREREREUl6rTWaZpvZhY1nmtkFQBL9DKOIiIiIiLQm3sONJ+qQ45cDz5rZmdQ3kkYDacCJUYxLREREREQkIYRtNDnnCoEDzWw8sEcw+yXn3FtRj0xERERERCQBRPrjtm8Db0c5FhERERERSWAaCEJERERERESaUKNJREREREQkjIjS80RERERERJSeJyIiIiIiIk2op0lERERERCKiniYRERERERFpQo0mERERERGRMJSeJyIiIiIiEVF6noiIiIiIiDShniYREREREYmIeppERERERESkCTWaREREREREwlB6noiIiIiIRETpeSIiIiIiItKEeppERERERCQi6mkSERERERGRJtRoEhERERERCcOcc/GOIabM7CLn3L3xjiMRqW7CU/20THUTnuqnZaqblqluwlP9tEx1E57qR76L9tjTdFG8A0hgqpvwVD8tU92Ep/ppmeqmZaqb8FQ/LVPdhKf6ke3WHhtNIiIiIiIiEVOjSUREREREJIz22GhSDmvLVDfhqX5aproJT/XTMtVNy1Q34al+Wqa6CU/1I9ut3Q0EISIiIiIisj3aY0+TiIiIiIhIxNRoEhERERERCSPpG01mVhb8HWBmlWb2qZl9bmYfmtm5cQ4vKsysp5k9bmZfmtnHZvaymQ0yswXxji2Wgv/5gkbzrjazX4V5zWgzuyN4nm5mb5jZHDM71cyODb4/c81soZn9T1Dux2b2w+iuzXcXw3p40MxOiu7aRJeZOTN7NGS6g5kVm9mLbfw5o4LPOjJMmW/r08y+NrMebRlDK/FdaWafmdm84P++n5n9y8yGfsf3q9v/zgl5hN1mzOxaM5sQPI/7tmdmNUHcc83sEzM7MJjfZPv6Hp8xLvS7ZmZHmdnsYJ0/NbNbg/k7xLYWfMdvDZn+lZldHaXP+trMng6ZPsnMHvyO7xV2/5hIWttnmdnxZvbb4PkJ33UbTnQ675FY6BDvAGLsS+fcngBmtgvwjJmZc+6BOMfVZszMgGeBh5xzpwXzRgIFcQ1sB+Gcmw3MDib3DOaNMrOOwHJgX+fcSjNLBwYEy/8Rj1ij6bvUQ5IoB/Yws07OuUpgIrBqe97AzDo456pbKXY68E7w99XvFGmUmNkBwLHAXs65qqCxluacu+B7vvWXzrlRkRZ2zv0xiKcj/qbteG97lXXxm9kRwJ+BsdH6MDPbA7gLOMY5t8jMUtmO35aJ8HsYbVXAZDP7s3Nu3fa++Dusw95mNtQ5t3B7P2sHFnaf5ZybCkwNJk8AXgSSqn503iOxkvQ9TS1xzi0DfgFcGu9Y2th4YFvoyYRzbi7wTd20mZ1rZneFTL9oZuOC52VmdnNwlfkNM9vXzKaZ2TIzOz7k9c8H85eY2VWxWrm2EsT+F/M9jl+Y2SHB/HFBfeQDjwL7mNkcYAT+IsN6AOdclXNucfCaq4MrqL0bXUmvMbP+ZpZnZk+b2UfB46D4rHVTbVkPgTFm9l7wfanrJckyszeDq/PzzWxSMH+AmS0ys8fM9/7+x8w6B8v2NrPpwRXD18ysV8wqBV4Gjgmenw5MqVsQbA+zgqv+75nZ4GD+uWY21czeAt40s85m9qT5HoJnzewDMxsdlDXgZOBcYKKZZdTNN7O7zGyxmb0B5DeK62chdbh7FNe/F7DOOVcF4Jxb55xbHXxX6tahzMyuN9/r8r6ZFQTzBwbT883sTxb09IcTvNftwT7nTTPLC+bX9aZkk3jbXg6wsZl1GWBmM4P/U2hv1Lig/v4T8p23YNmRwbxPgMkhb/dr4Hrn3KJgvWucc/eELG9uWxsXfP5UYKGZZZjZA8H/41MzGx+UO9fMnjOz/5rvobnEzH4RlHnfzLq1UT1V4xu8P2+hrt4y35v5ppntFMx/0Mz+YWYfADcF0/cEcS0L1vH+YJ/xYKO3vRW4spnPatBzZGYLzGxA8PyHQQxzzeyRNlrvWAu3zzo32K8cCBwP3BxsIwPjEGe0RHLe09K22cvMZgR1ssDMDjGz1OB7tyDYdpp8f6V9areNpsAnQDRPPuJhD+Dj7/H6TOAt59wwoBT4E/7K1YnAtSHl9gV+gD+JPrnuZGoH08E5ty9wOdCg4eecKwIuAGY650Y55z7GX61bbmZTzOxMM0tp9JrVQdlRwD+Bp51zy4G/Abc75/bB19m/or1i26kt66EXcDC+p+LGYN4W4ETn3F74g9utdSeMwGDgbufcEKAE+Kn5noU7gZOcc3sD9wPXt/lat+xx4DTzjZkRwAchyxYBhwQ91n8EbghZthc+5rHAT4GNzrmhwB+AvUPKHQh85Zz7EphG/cnOifj6GAr8MCgXal1Qh/cA0Uwdeh3oZ74RfbeZNdebkgm875wbCcwALgzm/w34m3NuOLCy0WsGNmrYHBLyXrODfc50mn4HN5AY216nIO5Fwftc10yZImBi8H86FbgjZNme+G1sKLALcFDwHfsncBz+O9IzpHxr+/LmtjXw38PLnHODgIsBF/w/TgceCj6z7v0nA/vgt6+K4Hs9C//9ayt/B840s9xG8+/E9wyMAB6jYV31BQ50zv0imO4KHIBvfE0FbgeGAcPNbFTI654E9jKzXSMJzMyGAb8HDg2+y5dtz4olkHD7LACcc+/h6+5/g23lyxjHGE2RnPe0tG2eAbwW7DtGAnOAUUAf59wewbaTNNlI8v20t/S8xqz1Iu3OVurTheYDVc65bWY2n4ZpWP91zq0HMLNn8Afv2SSWlsbTr5v/TPD3YyJIMXPOXWBmw4EJ+JPWifjeggaCq9kX4uuEoPzQ+nYCOWaW5Zxr9Sp8G4llPTznnKvFX+WuS40w4AYzGwPUAn2oT5v4xjn3bvD8UXzP76v4g+B/gzpLBda0Fldbcc7NC65Cn46/ghsqF3/iuRu+/jqGLPtvcIIP/n//t+D9FpjZvJByp+NPcgj+/hB4GhgDTHHO1QCrzfdahQr9P00mSpxzZWa2N3AIvpH7hAX3RITYik/zqYtnYvD8AHwKEMC/gVtCXtNSel4t8ETw/FHq1zM0pkTY9kLT8w4AHjafQheqI3BXcCJfAwwKWfahc25l8Po5+G2tDN+AXhLMf5TIU/Ca29bqPuer4PnB+MYJQYrf8pCY3nbOlQKlZrYZeCGYPx9/4t0mnHMlZvYwftuuDFl0APXf40eAm0KWPRVsB3VecM654DhU6JybD2Bmn+HrcU5Qrga4GbgCeCWC8A4NPmtdEOuGVsonpFb2WeK1tG1+BNwfXKx7zjk3x8yWAbuY2Z3AS/gLSSLtvtG0J/B5vINoY58Brd0gXE3DXsaMkOfbnPv2x7tq8TnpOOdqzSz0+9L4RDwRf/BrPf4KZahuQN0JRVXwt4YIt4XgYD0/SOP4ikYnbubTyO4Djg85MUsB9nfObdneFWgjsayHqpBidWeqZwJ5wN5BA/xr6r9zzX2PDPjMOXdAJLFEyVT8Cf84oHvI/OvwJ5snBicp00KWlbf2pubvS/kBMMnMrsSva3czy44gpu3+P31XwQnrNGBacKJ6TqMiofuJto6n2X1JIm17zrlZ5u/1ymu06OdAIf6KdQq+l7VO6LYRSZ19hu99mtvC8ua2NYjge9jM62tDpmsjiG17/RWf2RHpFfvG6xAaW+O4G8f6CL7RFDoAQLhjXrJoaZ/VHkRy3tPstumcmxFc0DsGeNDMbnPOPWz+nqgjgB8DpwDnRSt42XG02/S84ITnFoKrcEnkLSDdzL69WmlmI4B+IWW+BkaZWYqZ9cOn2m2viWbWzcw64a8sv9tK+ZgLTpzWmNmhAObz9I/E34C/XczflzMuZNYo/IAIoWU6Ak8Bv3HOfRGy6HXgZyHlRm3v538fsa6HZuQCRUGDaTzQP2TZTsFVe/BpEu8Ai4G8uvlm1jFIo4ml+4Fr6q5oh8il/ibrc8O8/l38gRbzo1UND+YfBsxzzvVzzg1wzvXH9zKdiE9zOzXIp++F7+WJOTMbHPSk1RlF6//jOu/jG4UAp0X4mhTqT3jqvgOh8STctmf+nrJUgvusQuQCa4IeoLODMuEsAgZY/f0lp4csuxn4nZkNCj4zxcx+vJ2hzsRftCB4n53w21dMBT04TwLnh8x+j/rvyJn4WNvis7bh0/dC70P5Gp+2iJntBewczH8Ln17ePVjWVvdyxUNL+6xQpfh7BJNNJOc9zW6bZtYf33v5T3za7V7BBZEU59zT+PTNvWKzGpLo2lujaaAFQ47jd+B3uCQaOQ988jr+BGyC+aE3P8OP8rQ2pNi7+Cu1C/F5vZ98h4/6EH+yNw9//0CipebV+SHwhyAd5i38QeW75HIb8GvzN+nPAa6h6UnzgcBo4JqQ+zZ649NSRpu/2Xgh/spVrMWyHhp7DL/+84M4FoUsWwxcHGyTXYF7nHNb8SfRfzGzufjUm8b390SVc26lc+6OZhbdBPzZzD4l/NX4u/ENv4X4+wI/AzbjT4qfbVT26ZD5S/Db5cP4e0viIQufgrgwSCscClwd4WsvB34RvG5X/DrXaXxPU90gPOXAvuaHBj6UhvdOQuJse3X3NM3BpxOe0yiFDPz//Zzge7s7rfT6BD1gFwEvmR8Ioihk2Tx8fU4Jto8F+HuhtsfdQEqw7T0BnOuCAT7i4FYgdNj8nwE/Cr4rZ9O29xPdR8Pt82mgW3A8vAT4AsA59xn+fq7pwf/stjaMIabC7LNCPQ78b3AelDQDQUR43tPStjkOmBvs00/Fp1X3wfeyz8GnDF8Ri/WQxGf1GRYikTH/+1ajnXOXxDsW2XEFvb0vOuca3xeywwvS8Do657YEJydvAIODBmHSMj/6YWVw/8lpwOnOuUmtvKbMOZcVmwhFRES+m/Z+T5OISDR0Bt4O0sYM+GmyN5gCe+NvtjZgE7oPQEREkoR6mkRERERERMJob/c0iYiIiIiIbBc1mkRERERERMJQo0lERERERCQMNZpERERERETCUKNJREREREQkjP8H78ML54PjRicAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,12))\n",
    "sns.heatmap(df.corr(), annot=True, cmap=plt.cm.cool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "10868f27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (478, 10) (478,)\n",
      "Test set: (205, 10) (205,)\n"
     ]
    }
   ],
   "source": [
    "# splitting the data set into train set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "print('Train set:', X_train.shape, y_train.shape)\n",
    "print('Test set:', X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9bfc39",
   "metadata": {},
   "source": [
    "## 1-Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0131d2ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.309756</td>\n",
       "      <td>0.383795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.619512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1-score</th>\n",
       "      <td>0.765060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.382530</td>\n",
       "      <td>0.473964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2     4  accuracy   macro avg  weighted avg\n",
       "precision    0.619512   0.0  0.619512    0.309756      0.383795\n",
       "recall       1.000000   0.0  0.619512    0.500000      0.619512\n",
       "f1-score     0.765060   0.0  0.619512    0.382530      0.473964\n",
       "support    127.000000  78.0  0.619512  205.000000    205.000000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the logistic regression model\n",
    "lr = LogisticRegression(solver='liblinear')\n",
    "lr.fit(X_train, y_train)\n",
    "lr_pred = lr.predict(X_test)\n",
    "pd.DataFrame(classification_report(y_test, lr_pred, output_dict=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "436bb787",
   "metadata": {},
   "source": [
    "## 2-KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "34cfb9f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.707483</td>\n",
       "      <td>0.603448</td>\n",
       "      <td>0.678049</td>\n",
       "      <td>0.655466</td>\n",
       "      <td>0.667899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.818898</td>\n",
       "      <td>0.448718</td>\n",
       "      <td>0.678049</td>\n",
       "      <td>0.633808</td>\n",
       "      <td>0.678049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1-score</th>\n",
       "      <td>0.759124</td>\n",
       "      <td>0.514706</td>\n",
       "      <td>0.678049</td>\n",
       "      <td>0.636915</td>\n",
       "      <td>0.666126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.678049</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2          4  accuracy   macro avg  weighted avg\n",
       "precision    0.707483   0.603448  0.678049    0.655466      0.667899\n",
       "recall       0.818898   0.448718  0.678049    0.633808      0.678049\n",
       "f1-score     0.759124   0.514706  0.678049    0.636915      0.666126\n",
       "support    127.000000  78.000000  0.678049  205.000000    205.000000"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the k-nearest neighbors model\n",
    "knn = KNeighborsClassifier(n_neighbors=3, weights='distance')\n",
    "knn.fit(X_train, y_train)\n",
    "knn_pred = knn.predict(X_test)\n",
    "pd.DataFrame(classification_report(y_test, knn_pred, output_dict=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672e1143",
   "metadata": {},
   "source": [
    "## 3-Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "235896bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['bootstrap', 'ccp_alpha', 'class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'max_samples', 'min_impurity_decrease', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start'])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creatinf the random forest model\n",
    "model = RandomForestClassifier()\n",
    "model.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7374ae36",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'n_estimators': [2*n+1 for n in range(20)],\n",
    "             'max_depth' : [2*n+1 for n in range(10) ],\n",
    "             'max_features':[\"auto\", \"sqrt\", \"log2\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0cb29aa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;n_estimators&#x27;: [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21,\n",
       "                                          23, 25, 27, 29, 31, 33, 35, 37, 39]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;n_estimators&#x27;: [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21,\n",
       "                                          23, 25, 27, 29, 31, 33, 35, 37, 39]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=RandomForestClassifier(),\n",
       "             param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'n_estimators': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21,\n",
       "                                          23, 25, 27, 29, 31, 33, 35, 37, 39]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = GridSearchCV(estimator=model, param_grid=param_grid,scoring='accuracy')\n",
    "search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fc3accdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9770175438596492"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5a0b517b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 9}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "50b11236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.309756</td>\n",
       "      <td>0.383795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.619512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1-score</th>\n",
       "      <td>0.765060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.382530</td>\n",
       "      <td>0.473964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2     4  accuracy   macro avg  weighted avg\n",
       "precision    0.619512   0.0  0.619512    0.309756      0.383795\n",
       "recall       1.000000   0.0  0.619512    0.500000      0.619512\n",
       "f1-score     0.765060   0.0  0.619512    0.382530      0.473964\n",
       "support    127.000000  78.0  0.619512  205.000000    205.000000"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(classification_report(y_test, preds, output_dict=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9873f0aa",
   "metadata": {},
   "source": [
    "## 4-SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fd27b9a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.309756</td>\n",
       "      <td>0.383795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.619512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1-score</th>\n",
       "      <td>0.765060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>0.382530</td>\n",
       "      <td>0.473964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.619512</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2     4  accuracy   macro avg  weighted avg\n",
       "precision    0.619512   0.0  0.619512    0.309756      0.383795\n",
       "recall       1.000000   0.0  0.619512    0.500000      0.619512\n",
       "f1-score     0.765060   0.0  0.619512    0.382530      0.473964\n",
       "support    127.000000  78.0  0.619512  205.000000    205.000000"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the support vector machine model\n",
    "kwargs = {'kernel': 'rbf'}\n",
    "svc = SVC(**kwargs)\n",
    "svc.fit(X_train, y_train)\n",
    "svc_pred = svc.predict(X_test)\n",
    "pd.DataFrame(classification_report(y_test, svc_pred, output_dict=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ef106e",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e72104",
   "metadata": {},
   "source": [
    "Out of all four models it looks like the k-nearest neighbors one is the best performing model. It achieved the highest scores in all evaluation metrics for both cell classes. The next step could be creating the grid search for all model to check if the results will be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc0aa8bc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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