heat-disease-2.ipynb

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   "source": [
    "import numpy as np # array function\n",
    "import pandas as pd # for tabular\n"
   ]
  },
  {
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore') #ignore warning messages\n",
    "from sklearn.preprocessing import MinMaxScaler #minmax for age\n",
    "from sklearn.model_selection import train_test_split,cross_val_score \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import  accuracy_score, f1_score, precision_score,confusion_matrix, recall_score, roc_auc_score #metrics\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as pplot\n",
    "%matplotlib inline\n",
    "import seaborn as sbn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics"
   ]
  },
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Polyuria</th>\n",
       "      <th>Polydipsia</th>\n",
       "      <th>sudden weight loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>Polyphagia</th>\n",
       "      <th>Genital thrush</th>\n",
       "      <th>visual blurring</th>\n",
       "      <th>Itching</th>\n",
       "      <th>Irritability</th>\n",
       "      <th>delayed healing</th>\n",
       "      <th>partial paresis</th>\n",
       "      <th>muscle stiffness</th>\n",
       "      <th>Alopecia</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Gender Polyuria Polydipsia sudden weight loss weakness Polyphagia  \\\n",
       "0   40   Male       No        Yes                 No      Yes         No   \n",
       "1   58   Male       No         No                 No      Yes         No   \n",
       "2   41   Male      Yes         No                 No      Yes        Yes   \n",
       "3   45   Male       No         No                Yes      Yes        Yes   \n",
       "4   60   Male      Yes        Yes                Yes      Yes        Yes   \n",
       "\n",
       "  Genital thrush visual blurring Itching Irritability delayed healing  \\\n",
       "0             No              No     Yes           No             Yes   \n",
       "1             No             Yes      No           No              No   \n",
       "2             No              No     Yes           No             Yes   \n",
       "3            Yes              No     Yes           No             Yes   \n",
       "4             No             Yes     Yes          Yes             Yes   \n",
       "\n",
       "  partial paresis muscle stiffness Alopecia Obesity     class  \n",
       "0              No              Yes      Yes     Yes  Positive  \n",
       "1             Yes               No      Yes      No  Positive  \n",
       "2              No              Yes      Yes      No  Positive  \n",
       "3              No               No       No      No  Positive  \n",
       "4             Yes              Yes      Yes     Yes  Positive  "
      ]
     },
     "execution_count": 169,
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     "output_type": "execute_result"
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   ],
   "source": [
    "orig_df = pd.read_csv('diabetes_data.csv') #importing dataset\n",
    "#vewing the top 5 for the headings\n",
    "orig_df.head(5)"
   ]
  },
  {
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   "source": [
    "> **Dealing with missing values**"
   ]
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  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                   0\n",
       "Gender                0\n",
       "Polyuria              0\n",
       "Polydipsia            0\n",
       "sudden weight loss    0\n",
       "weakness              0\n",
       "Polyphagia            0\n",
       "Genital thrush        0\n",
       "visual blurring       0\n",
       "Itching               0\n",
       "Irritability          0\n",
       "delayed healing       0\n",
       "partial paresis       0\n",
       "muscle stiffness      0\n",
       "Alopecia              0\n",
       "Obesity               0\n",
       "class                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 170,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking is there is any na value\n",
    "orig_df.isna().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 520 entries, 0 to 519\n",
      "Data columns (total 17 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   Age                 520 non-null    int64 \n",
      " 1   Gender              520 non-null    object\n",
      " 2   Polyuria            520 non-null    object\n",
      " 3   Polydipsia          520 non-null    object\n",
      " 4   sudden weight loss  520 non-null    object\n",
      " 5   weakness            520 non-null    object\n",
      " 6   Polyphagia          520 non-null    object\n",
      " 7   Genital thrush      520 non-null    object\n",
      " 8   visual blurring     520 non-null    object\n",
      " 9   Itching             520 non-null    object\n",
      " 10  Irritability        520 non-null    object\n",
      " 11  delayed healing     520 non-null    object\n",
      " 12  partial paresis     520 non-null    object\n",
      " 13  muscle stiffness    520 non-null    object\n",
      " 14  Alopecia            520 non-null    object\n",
      " 15  Obesity             520 non-null    object\n",
      " 16  class               520 non-null    object\n",
      "dtypes: int64(1), object(16)\n",
      "memory usage: 69.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#viewing data information\n",
    "orig_df.info() "
   ]
  },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VqlXj4MGDzJgxgzJlyrB3717Kly/v6BBF5BrQwssiIiIiIiIljL+/P61bt+Z///sfZ8+exdvbmx49evDhhx8qwSNSgmkkj4iIiIiIiIhICaA1eURERERERERESgAleURERERERERESgCtySMiIiJSClmtViIiIvD19cVisTg6HBEREbkEwzBITEykWrVqODldfLyOkjwiIiIipVBERAQ1a9Z0dBgiIiJyFU6cOEGNGjUu2q4kj5R477zzDu+++y7XY43xTp06AbB69Wrbf++8807mz5/Pww8/fM3vP2DAAFavXk1YWNg1v5e9kpKSGDFiBH/99RenT5/mpZdeYtq0aY4Oq9T672f2SoWFhVGnTh0mT57MK6+8cslzr+fPoIhcOV9fX8D8x6Kfn5+DoxEREZFLSUhIoGbNmrb/f1+MkjxSrMyePZuBAwfajt3d3SlXrhzNmjWjR48eDBw48LIf+isRERHBV199xQMPPEDLli0L3F9hKsqxXYkJEyYwe/Zs3nrrLerVq0ejRo3ynHM+KXA5HTt2vOrkxLU2YcIEGjduzAMPPHDV127fvp3WrVvzxhtv8P777+d7TkhICDfeeCMjR45kypQpBYxWREqz81O0/Pz8lOQREREpJi43xVpJHimWxo8fT506dcjMzOTUqVOsXr2aESNGMGXKFP744w+aN29uO/fNN99kzJgxV9V/REQE7777LrVr176qRMq///57Vfexx6Vi+/rrr7Fardc8hoJYuXIlt912G2+//fZFz3nwwQcJCAiwHSclJfHcc8/Ru3dvHnzwQVt95cqVr2ms9pgwYQIPP/ywXUmem266iYYNG/Ljjz9eNMkzb948APr371+QMG2ux2dWRERERESuDyV5pFjq1q0bbdq0sR2PHTuWlStX0rNnT3r16sWBAwfw9PQEwMXFBReXa/tRT0lJwcvLCzc3t2t6n8txdXV16P2vxJkzZ2jcuPElz2nevHmuRF1UVBTPPfcczZs3L5TkRnJyMt7e3gXu51ro168fb731Fps3b+a2227L0/7jjz/SsGFDbrrppgLdp6h8ZkVEREREpPBoC3UpMe666y7eeustjh07xvfff2+rf+edd/IMaVu2bBkdOnSgbNmy+Pj40KBBA15//XXAXJvk5ptvBmDgwIFYLBYsFguzZ88GzDVMmjZtyrZt27jjjjvw8vKyXdupUyfbGicXys7O5vXXX6dKlSp4e3vTq1cvTpw4keuc2rVrM2DAgDzXXtjn5WIbMGAAtWvXznV9cnIyo0aNombNmri7u9OgQQM+/vjjPOujWCwWXnjhBRYuXEjTpk1xd3enSZMm/PPPP/k/8P84c+YMzzzzDJUrV8bDw4MWLVowZ84cW/vq1auxWCwcPXqUxYsX22K3d/2gY8eOMWzYMBo0aICnpyfly5fnkUceydPf7NmzsVgsrFmzhmHDhlGpUqVcC5X93//9H3Xr1sXT05NbbrmFdevW5fvnmJ6ezttvv01AQADu7u7UrFmT0aNHk56ebjvHYrGQnJzMnDlzbO8vvz/TS+nXrx+QM2LnQtu2bePQoUO2cxYtWkSPHj2oVq0a7u7u1KtXj/fee4/s7Oxc113NZzYjI4Nx48bRunVrypQpg7e3N7fffjurVq26aMxTp06lVq1aeHp60rFjR/bu3XtF7/X777+ndevWeHp6Uq5cOR599NE8PxciIiIiInLlNJJHSpQnnniC119/nX///ZfBgwfne86+ffvo2bMnzZs3Z/z48bi7uxMaGsqGDRsAaNSoEePHj2fcuHEMGTKE22+/HYB27drZ+oiOjqZbt248+uij9O/f/7LThj744AMsFguvvfYaZ86cYdq0aXTu3JmdO3faRhxdiSuJ7UKGYdCrVy9WrVrFM888Q8uWLVm6dCmvvvoqJ0+eZOrUqbnOX79+Pb/99hvDhg3D19eXTz/9lIceeojjx49Tvnz5i8aVmppKp06dCA0N5YUXXqBOnTrMnz+fAQMGEBcXx0svvUSjRo347rvvGDlyJDVq1GDUqFEAVKxY8Yrf/4UCAwPZuHEjjz76KDVq1CAsLIwvv/ySTp06sX//fry8vHKdP2zYMCpWrMi4ceNITk4G4Msvv+SFF17g9ttvZ+TIkYSFhfHAAw/g7++fKxFktVrp1asX69evZ8iQITRq1Ig9e/YwdepUgoODWbhwIQDfffcdgwYN4pZbbmHIkCEA1KtX76reV506dWjXrh2//PILU6dOxdnZ2dZ2PvHz+OOPA2YCy8fHh5dffhkfHx9WrlzJuHHjSEhIYPLkybn6vdLPbEJCAv/73/947LHHGDx4MImJiXzzzTd07dqVrVu35pkiOHfuXBITE3n++edJS0tj+vTp3HXXXezZs+eSPxcffPABb731Fn369GHQoEGcPXuWzz77jDvuuIMdO3ZQtmzZq3puIiIiIiICGCLFyKxZswzACAwMvOg5ZcqUMVq1amU7fvvtt40LP+pTp041AOPs2bMX7SMwMNAAjFmzZuVp69ixowEYM2bMyLetY8eOtuNVq1YZgFG9enUjISHBVv/LL78YgDF9+nRbXa1atYynnnrqsn1eKrannnrKqFWrlu144cKFBmC8//77uc57+OGHDYvFYoSGhtrqAMPNzS1X3a5duwzA+Oyzz/Lc60LTpk0zAOP777+31WVkZBht27Y1fHx8cr33WrVqGT169Lhkf/919uxZAzDefvttW11KSkqe8zZt2mQAxty5c2115z8zHTp0MLKysmz16enpRvny5Y2bb77ZyMzMtNXPnj3bAHI98++++85wcnIy1q1bl+t+M2bMMABjw4YNtjpvb+98/xyvxv/93/8ZgLF06VJbXXZ2tlG9enWjbdu2trr8nsHQoUMNLy8vIy0tzVZ3NZ/ZrKwsIz09Pdc5sbGxRuXKlY2nn37aVnf06FEDMDw9PY3w8HBb/ZYtWwzAGDlypK3uvz+DYWFhhrOzs/HBBx/kus+ePXsMFxeXPPUicm3Ex8cbgBEfH+/oUEREROQyrvT/25quJSWOj48PiYmJF20/P0Jg0aJFdi9S7O7unmuXr8t58sknc+369fDDD1O1alWWLFli1/2v1JIlS3B2dmb48OG56keNGoVhGPz999+56jt37pxr5Enz5s3x8/PjyJEjl71PlSpVeOyxx2x1rq6uDB8+nKSkJNasWVMI7ya3C0dAZWZmEh0dTUBAAGXLlmX79u15zh88eHCuUTFBQUFER0czePDgXGs29evXD39//1zXzp8/n0aNGtGwYUOioqJsr7vuugvgklOZ7NG3b19cXV1zTdlas2YNJ0+etE3VgtzPIDExkaioKG6//XZSUlI4ePBgrj6v9DPr7OxsW6fHarUSExNDVlYWbdq0yfe5PvDAA1SvXt12fMstt3Drrbde8rP922+/YbVa6dOnT67nWaVKFerXr1/oz1NEREREpLRQkkdKnKSkpEtuo963b1/at2/PoEGDqFy5Mo8++ii//PLLVSV8qlevflUL1tavXz/XscViISAgwO71aK7UsWPHqFatWp7ncX7b8mPHjuWqv+GGG/L04e/vT2xs7GXvU79+fZyccv+VcrH7FIbU1FTGjRtnW2uoQoUKVKxYkbi4OOLj4/OcX6dOnTwxA7l28QJzoe7/rmsUEhLCvn37qFixYq7XjTfeCJjrERWm8uXL07VrV37//XfS0tIAc6qWi4sLffr0sZ23b98+evfuTZkyZfDz86NixYq2han/+wyu5jM7Z84cmjdvjoeHB+XLl6dixYosXrw43+f63882wI033njJz3ZISAiGYVC/fv08z/TAgQOF/jxFREREREoLrckjJUp4eDjx8fF5vrhfyNPTk7Vr17Jq1SoWL17MP//8w88//8xdd93Fv//+m2u0x6X6KGz/XRz6vOzs7CuKqTBc7D7GfxZpLgpefPFFZs2axYgRI2jbti1lypTBYrHw6KOP5puwK8ifmdVqpVmzZkyZMiXf9po1a9rd98X079+fv/76i7/++otevXrx66+/cs8999jWMIqLi6Njx474+fkxfvx46tWrh4eHB9u3b+e1117L8wyu9P1///33DBgwgAceeIBXX32VSpUq4ezszMSJEzl8+HChvDer1YrFYuHvv//O9zPn4+NTKPcRERERESltlOSREuW7774DoGvXrpc8z8nJibvvvpu7776bKVOmMGHCBN544w1WrVpF586dL5pwsVdISEiuY8MwCA0NzbVNuL+/P3FxcXmuPXbsGHXr1rUdX01stWrVYvny5SQmJuYazXN+Kk+tWrWuuK/L3Wf37t1YrdZco3kK+z4XWrBgAU899RSffPKJrS4tLS3fZ5if8zGFhoZy55132uqzsrIICwvL9WdTr149du3axd13333Z519Yn51evXrh6+vLvHnzcHV1JTY2NtdUrdWrVxMdHc1vv/3GHXfcYas/evRoge67YMEC6taty2+//Zbrvbz99tv5nv/fzzZAcHBwntFQF6pXrx6GYVCnTh3baCgRERERESk4TdeSEmPlypW899571KlTJ9eX4f+KiYnJU3d+x6Dz22F7e3sDXHHC4HLO70B03oIFC4iMjKRbt262unr16rF582YyMjJsdX/99VeeLaWvJrbu3buTnZ3N559/nqt+6tSpWCyWXPcviO7du3Pq1Cl+/vlnW11WVhafffYZPj4+dOzYsVDucyFnZ+c8I4w+++yzPNuHX0ybNm0oX748X3/9NVlZWbb6H374Ic/0tD59+nDy5Em+/vrrPP2kpqbadusC88+nMD43np6e9O7dmyVLlvDll1/i7e3N/fffb2s/PwLmwmeQkZHBF198UaD75tfvli1b2LRpU77nL1y4kJMnT9qOt27dypYtWy752XrwwQdxdnbm3XffzfNnaBgG0dHRBXkLIiIiIiKllkbySLH0999/c/DgQbKysjh9+jQrV65k2bJl1KpViz/++AMPD4+LXjt+/HjWrl1Ljx49qFWrFmfOnOGLL76gRo0adOjQATATLmXLlmXGjBn4+vri7e3NrbfemmddlytVrlw5OnTowMCBAzl9+jTTpk0jICAg1zbvgwYNYsGCBdx777306dOHw4cP8/333+fZgvtqYrvvvvu48847eeONNwgLC6NFixb8+++/LFq0iBEjRlz19t4XM2TIEGbOnMmAAQPYtm0btWvXZsGCBWzYsIFp06Zdco0ke/Xs2ZPvvvuOMmXK0LhxYzZt2sTy5csvudX7hdzc3HjnnXd48cUXueuuu+jTpw9hYWHMnj2bevXq5RrF8sQTT/DLL7/w7LPPsmrVKtq3b092djYHDx7kl19+YenSpbRp0waA1q1bs3z5cqZMmUK1atWoU6cOt956q13vsX///sydO5elS5fSr18/W4IPoF27dvj7+/PUU08xfPhwLBYL3333XYGn1vXs2ZPffvuN3r1706NHD44ePcqMGTNo3LgxSUlJec4PCAigQ4cOPPfcc6SnpzNt2jTKly/P6NGjL3qPevXq8f777zN27FjbtvW+vr4cPXqU33//nSFDhvDKK68U6H2IiIiIiJRK13KLL5HCdn477PMvNzc3o0qVKkaXLl2M6dOn59qq+7z/bt+8YsUK4/777zeqVatmuLm5GdWqVTMee+wxIzg4ONd1ixYtMho3bmy4uLjk2rK8Y8eORpMmTfKN72JbqP/444/G2LFjjUqVKhmenp5Gjx49jGPHjuW5/pNPPjGqV69uuLu7G+3btzeCgoLy9Hmp2P67hbphGEZiYqIxcuRIo1q1aoarq6tRv359Y/LkyYbVas11HmA8//zzeWK62Nbu/3X69Glj4MCBRoUKFQw3NzejWbNm+W7zXlhbqMfGxtru5+PjY3Tt2tU4ePBgnnjPf2YCAwPz7fvTTz81atWqZbi7uxu33HKLsWHDBqN169bGvffem+u8jIwMY9KkSUaTJk0Md3d3w9/f32jdurXx7rvv5trG8ODBg8Ydd9xheHp6GkCBtlPPysoyqlatagDGkiVL8rRv2LDBuO222wxPT0+jWrVqxujRo42lS5cagLFq1SrbeVfzmbVarcaECRNsz6RVq1bGX3/9leezdX4L9cmTJxuffPKJUbNmTcPd3d24/fbbjV27duW6x39/Bs/79ddfjQ4dOhje3t6Gt7e30bBhQ+P55583Dh06dHUPSkTsoi3URUREio8r/f+2xTCK4IqqIiIOYrVaqVixIg8++GC+07NEREqKhIQEypQpQ3x8PH5+fo4OR0RERC7hSv+/rTV5RKTUSktLyzO9ae7cucTExNCpUyfHBCUiIiIiImInrckjIqXW5s2bGTlyJI888gjly5dn+/btfPPNNzRt2pRHHnnE0eGJiIiIiIhcFSV5RKTUql27NjVr1uTTTz8lJiaGcuXK8eSTT/Lhhx/i5ubm6PBERERERESuipI8IlJq1a5dmz/++MPRYYiIiIiIiBQKrckjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICKMkjIiIiIiIiIlICuDg6ABEpOaxWKxkZGWRmZpKVlYWrqyseHh64uOivGhERERERkWtN37xESrns7Gyio6M5e/YsZ8+eJSoqyla+8DgqKoq0tDRbEuf868Jjq9Wa7z2cnZ1xd3fHw8MDDw8PfHx88PHxwdfX1/by8/OjcuXKVKtWjapVq1KtWjWqVatGpUqVlCQSEbmGmr69FCd3L0eHISICQNiHPRwdgkixpm9OIiVcdnY24eHhhIaGEhISQmhoqO0VGRlJbGwshmFc8xhSUlJISUm56mudnJxsyZ8LE0DVq1fnxhtvpHHjxlSoUOEaRC0iIiIiIlK8KMkjUgIYhkFERAT79+/Pk8w5fPgwGRkZBb6Hmxt4eYGLK7j+9+WW+9jFFVxcICsL0tMgPR3S0sxyWhokJ0FiImRnX/6+VquVyMhIIiMj2bZtW77nVKxYkUaNGtG4cWPbq1GjRlStWhWLxVLg9y4iIiIiIlIcKMkjUgydOXOGoKAgAgMDCQoKIigoiFOnTl1VH+7uUK06lK8AFSrmfpWvABX/c+zrC4WZLzEMM+GTmAhJiRAfD2dOw6kIiMzndfoUXGQ2mG1q2dq1a3PVlylTxpbwadq0Kbfddhs33XQT7u7uhfdGREREREREiggleUSKuJiYGLZt25YrqXPixIkrutbDA+rUg4D6UDcA6gVAvfrmf6vXACcH7q9nsYCnp/mqVOny52dnw5kzOUmgE8ch+CAc2A8H95tJoP+Kj49n06ZNbNq0yVbn5uZG69atadeuHW3btqVdu3ZUrVq1EN+ZiIiIiIiIYyjJI1LEnD59mhUrVrBs2TLWrl3LkSNHLntN2bLQqg00bwn1b8xJ6FSr7thETmFydoaqVc1Xq9Z522Nj4dABM+Fz8Nx/Dx2A48dyn5eRkZEn8VO7dm1bwqddu3Y0b95ciz2LiIiIiEixo28xIg6WnJzMunXrWLZsGcuXL2f37t2XPN/Hx0xy3HQz3NTGfNWpW7hTqYojf3+4rZ35ulBSkjniZ+d22LIJtmyEkODc54SFhREWFsaPP/4IgJeXF+3bt6d79+706NGD+vXrX6d3ISIiIiIiYj+Lca231RGRXLKzs9m2bZstqbNx48aLLozs4QEtb8qd0Kl/Y8kZneMoZ89C4OacpE/QVkhNvfj5AQEBdO/ene7du9OxY0c8PDyuX7AiItdIQkICZcqUoeaIX7SFuogUGdpCXSR/5/+/HR8fj5+f30XPU5JH5DpISkpi8eLF/Prrryxbtoy4uLh8z7NYzFE6d3UxX7e2NRM9cm1lZsLe3bB5o/natB5Ohud/rpeXF3fddRc9evSgW7du1KpV6/oGKyJSSJTkEZGiSEkekfwpySPiYOcTO/Pnz2fJkiWkXmSoSJ26cGdnM6nT8S4oV+46Byp5GIa5oPPSJfDvEti43twOPj9NmjShR48e9O3bl1atWmnLdhEpNpTkEZGiSEkekfwpySPiAFeS2PH3h0535yR26tR1QKByVeLjYeWyc0mfv/PfyQugYcOG9OvXj8cff5y6dfUHKyJFm5I8IlIUKckjkj8leUSuk+TkZBYvXswvv/xy0cROhYpw/4PQ+xG4vSNo46biy2qFXTvNET5Ll8DWzebIn/9q27Yt/fv3p0+fPlSoUOG6xykicjlK8ohIUaQkj0j+lOQRuYYMw2Dz5s3MnDmTX375RYmdUiwqCv74DX7+Adavzdvu4uJC165d6devH/fffz9eXvoiJSJFg5I8IlIUKckjkj8leUSugfj4eL7//ntmzpzJnj178rRXqAD3P6TETml1/Bj88qOZ8Nm/N2+7t7c3vXv3ZvDgwdx+++1av0dEHEpJHhEpipTkEcmfkjwihcQwDAIDA5k5cyY//fQTKSkpudrLlIGH+sKDfZTYkRx7dpvJnl/m5b9TV/PmzXnxxRd5/PHHNbpHRBxCSR4RKYqU5BHJn5I8IgWUkJDAvHnzmDlzJjt37szTfmtbeHooPPgI6Du6XIzVChvWmQmf3+dDXFzudn9/fwYNGsSwYcOoXbu2I0IUkVJKSR4RKYqU5BHJn5I8Inbau3cvn376KfPmzSM5OTlXm58fPPYEDBwCzZo7KEApttLTYeGvMOMzc8HmCzk5OXHffffx4osvctddd2kql4hcc0ryiEhRpCSPSP6uNMnjdB1jEimyDMNgzZo19OjRg2bNmvH111/nSvC0uQW++AZCI2DK50rwiH3c3aHv47BqE6wLhMefBDc3s81qtbJo0SI6d+5M06ZN+fLLL0lKSnJswCIiIiIiUqwoySOlWnZ2Nr/99hu33XYbnTp1YsmSJbY2X18Y9Cxs3AFrtsBTT4O3twODlRLlpjbw9Rw4dALGvQdVq+W07d+/n2HDhlGjRg3eeOMNoqOjHReoiIiIiIgUG0rySKmUmZnJnDlzaNy4MQ899BBbt261tdW8ASZPh5CTMP1LaNHScXFKyVepErz2JhwIg7k/Q7sOOW3x8fFMmDCB2rVr8/rrrxMVFeWwOEVEREREpOhTkkdKlfT0dL766isaNGjAgAEDCA4OtrU1awHf/gB7QmHYcHMkj8j14uoKD/WBZevM0WNPPm3WASQlJTFx4kTq1KnD2LFjlewREREREZF8KckjpUJ6ejqfffYZAQEBDB06lKNHj9ra7ugEC/+BTTvM9VLOf7EWcZQWLeHLb2DvYRj8XO5kz4cffkjt2rWV7BERERERkTyU5JESzWq18uOPP9KwYUOGDx9OeHi4ra1zV3PUxN+roEtX0GZGUtTUqAnTvjCTPUOG5SzSnJycbEv2jBkzhrNnzzo2UBERERERKRKU5JESa82aNdx66608/vjjhIWF2eq732cupLzon9zrn4gUVTVqwtT/M6cS/jfZM2nSJOrUqcOYMWOIi4tzaJwiIiIiIuJYSvJIiXPgwAF69epFp06dCAoKstXf1QU2bIf5f5hboosUN+eTPXsPw9Dn8yZ7AgIC+Pzzz8nMzHRsoCIiIiIi4hBK8kiJcfr0aZ577jmaNWvGn3/+aatv0sxcc+fPf6FlKwcGKFJIqteAKZ/nTfZER0fz4osv0rx5cxYvXoxhGI4NVERERERErisleaTYS05O5r333iMgIIAZM2aQnZ0NQNVq8OW35oLKXbo6OEiRa+B8smd3CPR5PKf+4MGD9OzZk65du7Jnzx7HBSgiIiIiIteVkjxSbBmGwZw5c6hfvz7jxo0jKSkJMLc+f/t984vvkwPB2dnBgYpcYzVvgFk/wOrNcFu7nPply5bRtm1brdUjIiIiIlJKuDg6ABF7BAcHM3ToUFavXm2rc3aGZ4bC2LehUiXHxSbiKDffCsvXQ4c2sHO7Wffyyy9TtmxZh8YlIiIiIiLXh0bySLGSkZHB+++/T/PmzXMleO57AIL2mYvSKsEjpdmypTkJnurVq/Paa68VuM/Y2Fg6duzI8uXLC9yXiIiIiIhcO0rySLGxceNGWrVqxVtvvUV6ejoAtWqbiyr/9Dvc2MCx8Yk4WmYmjBmZc/zRRx/h7e1d4H7Hjx/P2rVr6dKlC4888gjHjx8vcJ8iIiIiIlL4lOSRIi8+Pp5hw4bRoUMH9u/fD5hTs0a8CoF7taiyyHlffQGHDprldu3a8dhjjxW4z4MHD/L555/bjhcsWEDDhg354IMPSEtLK3D/IiIiIiJSeJTkkSLLMAx+++03GjVqxJdffmnbDrpVa1gbCB98BIUwSEGkRDh7Fj54O+d42rRpWCyWAvf78ssvk5WVZR44mcu4paam8uabb9K0aVMWL15c4HuIiIiIiEjhUJJHiqTw8HB69+7NQw89RGRkJGAmdCZNhTVboGUrBwcoUsS8Pw7i483ygAEDuPnmmwvc55IlS/j777/NA69K0HMB1H8ELOaWdYcPH6Znz57cd999HDt2rMD3ExERERGRglGSR4qcn3/+maZNm7Jo0SJb3b09zIWVXxihLdFF/mvPbvj2K7Ps4+PDhAkT7O4rKiaO2LgEMjIyGDnyggV+WjwPnhXgppfgnm+hYktb019//UWzZs2YNWuWbcSdiIiIiIhcf9pCXYqMxMREhg8fzuzZs211lSrDx5/Cg49AIcw8ESlxDANGjwCr1Tx+8803qVq1qp19Gfy5bAMnI88ScXg3wcHBZkOF5lDzrpwTy9aDOz+DEytg5+eQGkViYiJPP/00CxcuZObMmVSpUqVgb0xERERERK6aRvJIkRAUFMRNN92UK8HzyGOw/QA81EcJHpGL+eN3WLvKLNerV48RI0bY3df+4KMcCz9F6OGjfPu/GedqLdBqeN4fQosFbugM934PtbvlxPPHHzRt2pQFCxbYHYeIiIiIiNhHSR5xKKvVykcffUTbtm0JDQ0FwMcHvpoDs34Af38HByhShKWlwdhROccff/wx7u7udvWVmZnFv2sCiYqJ59+/FpCelmo21OkB5Rpe/EI3H7j1DegwEdzNH9jo6GgeeeQR+vXrR2xsrF3xiIiIiIjI1VOSRxwmMjKSrl278tprr9l272l9M2zcAf2e1Ogdkcv5bAocCzPLd999N/fff7/dfW0M2kNsfAI7duwgMmy/WeniBc2HXFkH1W+He+dCjY62qnnz5tG0aVP++ecfu+MSEREREZErpySPOMSff/5J8+bNWb58OWAmdEaNgeXroV6Ag4MTKQYiI2DyufWVnZycCrRlenxCEuu37uZExBn2Ba7MaWgyADzKXXlHHv7Q7n249S1w9QEgIiKCbt268eyzz5KUlGRXfCIiIiIicmWU5JHrKjU1lRdffJFevXoRFRUFQNVq8NdyGD8R3NwcHKBIMTFuLCQnm+XnnnuOpk2b2t3X8nVBJKekErR5A/FREWalTw1zu/SrZbFA7a7mqJ4qt9iqZ86cSYsWLVi3bp3dcYqIiIiIyKUpySPXzYkTJ+jQoQOff/65ra5HL9i8CzrddYkLRSSXwC0wb65Z9vf3591337W7r+MnT7Hn4GGCjxwjdPf6nIaWL4Czq/1BelWCOz6B1q+AswcAR44coWPHjrz66qukpaXZ37eIiIiIiORLSR65LjZu3MjNN9/M9u3bAfDwgGlfwM8LoUIFx8YmUpxYrfDqSznH7777LuXLl7erL8Mw+GfVFhKTUti2cRVpKYlmQ5VboFr7ggdrsUDAA3DvHKjQzHbPjz/+mNtuu42jR48W/B4iIiIiImKjJI9cc99++y2dOnXi9OnTANSuA6u3wODntLiyyNX66QdzJA9A48aNefbZZ+3ua+e+ECJOR7F77wFOHNpmVlqcoeWLhfvD6VMd7vwcWgwDJ3N00K5du2jTpg0rVqwovPuIiIiIiJRySvLINZOVlcVLL73EM888Q2ZmJgB33AlrA6FZcwcHJ1IMJSXBW6/lHE+bNg1XV/umVKWnZ7BifRCnz8aye8sKsrPNHe4I6A1l6hRCtP/h5AwNH4cu/zPX+wFiYmK45557mDJlCoZhFP49RURERERKGSV55JqIjo7m3nvv5dNPP7XVPfsC/LEU7JxZIlLqffIhnIo0y7169aJLly5297Vuyy7iE5LZvi2QMyeCzUo3P2jydCFEegll60GXr6FqWwCsViujRo2if//+pKSkXNt7i4iIiIiUcC6ODkBKnn379nHffffZ1ttwdTXX3xkwyMGBiRRjYUdh+sdm2dXVlU8++cTuvmJiE9i0bR/HwiM5sG11TkPTQeDuV7BAr4SbL3T4EPZ9A/vNFaTnzZvH/v37+f3336ldu/a1j0FELioz+gRpx/eQHhFMZvQJshJOY6SnYFitOLl74VquOu41GuPTrDOu5WteUZ+pYTtJ3r+G9PB9ZCfHgmHF2assbtUa4N2wA143trvi+LISzpK0exmpR4LIij+DNT0JZ08/XMpWxTPgFnya34Ozp6+9b19ERKRYU5JHCtWiRYvo378fSUnm3s4VK8GPv0HbQljDVaQ0e+NVSE83yyNGjCAgIMDuvv5du5XklFQCN60jMfaMWVmmLtTrVQiRXiEnZ2g2BPwbwJb3ISuVnTt30qZNG3755Rfuuktb7ok4QtTiqSTvvfhaWdbUBNJPJpB+8gAJW3/Ht/V9+N/5NBYn5/zPT0siavFUUkO35GnLij9NVvxpUg6sxb1GYyrcNxoXv0vvxpCw7U/i1szByMy9Q192UgzZSTGkh+8jYcuvlOv6PN4N9I8PEREpfTRdSwqFYRhMnDiRBx54wJbgaXkTrAtUgkekoNasgoW/muVKlSrx5ptv2t3X4bCTHAw9xsGQIxzZuzGnodVwcHJA3r9GR+g807ZOT3R0NF26dNE6PSIOYk1PvvKTDSuJQYuI/vvTfJut6Smc/vmtfBM8/5Uevp/TP40lOyn2oufEb55P7PKZeRI8ee6bmkDUokkkH1h72fuKiIiUNErySIFZrVZeeuklXn/9dVvdw4/CsnVQ8wYHBiZSAmRnw+gROccTJ07Ez8++KVVWq5Wlq7cQl5DE9k0ryUg7twZO9TugcpuCB2uvMnXPrdNzmy3OUaNG8cQTT2idHhGHsuBWJQDvpnfh3fRuXCvUyves5L0rSA3bmac+bu1cMk6F5O7R1QOvRh3xbtoZJ8/cf5dlxUYSveyLfO+RHhlM3Nrv8tS712yKT8t7catcL3eDYSX6n8/ISoi6xPsTEREpeTRdSwokMzOTp556ih9//NFW984H8MpYbY8uUhhm/w/27jbLrVu3ZsCAAXb3FbTrIKejYtm1ey8nQ3eZlU6u0PL5ggdaUG6+0GES7P0fHDC/yP3www/s27ePhQsXUqtW/l8uRaTwWdy88L2pO7439cTFN2f6lGEYJG7/i9jlM/Nck7xvNZ61W9qOM2MjSdyxJHe/ru5UeeIT3CqaP89ZidGcmjuS7KQY2zmpwZtIO7EXj5pNc10bu+pbMKy56sq0f4yyHfrZYoteMi3XVDMjI5W49T9QoftLV/kEREREii+N5BG7paSk0KvXfbYEj7MzzJgFr76uBI9IYYiNhXffyDmePn06Tk72/bWdkprGqo3biTwTxZ4tK7Bas82GBn3Bp3ohRFsInJyh+VBo9z64eAKwc+dOWrduzYYNGxwcnEjp4HVjO6oPmYl/xwG5EjwAFosFv9b34Rlwa57rMs8ezXWcvGd5nqSMT7MutgQPgItveXzbPJCnr6Tdy3L3HRtJ+om9ueqcPHwp07Zvrtj8Ow0AS+6/I1MOrsOakZr3jYqIiJRQSvKIXeLi4rjzzo78889SANzdYd6v8MQAx8YlUpJMHA/R0Wb50UcfpX17+xe4Wr1xB4lJKWwL3EJ05LkvYx7lodGThRBpIavZKd91ev755x/HxiVSCvg0vQtnb/9LnvPfUTYARlZGruPUI0F5r6vdKk/dhaN/bNceDrxsX+43NMXinHtAurO3P64Vc4/6MzLTSDu+J8/1IiIiJZWSPHLVIiMjua3tLWzdav6jy9cXFi2Fnvc7ODCREuTgAZj5uVn29PRk0qRJdvd1JiqWoN0HOXL8JIe2X7AQafNnwdWrgJFeI2XqQpevbGsFpaam0qtXL3755RcHByYixn9G6AA4+1XKac/OIiPqWJ5zXMvlHTXoWr5GnjpragJZCWdtxxmnDl9RX2Z/ebd0zzid93oREZGSSkkeuSqHDx/mlltac+iguZBihYrwzxq4vaODAxMpYca8DFlZZvm1117jhhvsW8XcMAz+WbWZpORUgjauITnh3NCgco2gdtdCivYacfOD2z+CGp0Acw2wRx99lK+//tqxcYmUcqkheXfL8qx7k62clXAWsrPynOPsXTZPncXFDYtb3mRzVmxkTjkuIm9fXvmPNnL2KpO3r7jIfM4UEREpmZTkkSu2c+dObr31ZsLDzX8s3VALlq+HlnlHX4tIAfyzBJadm5lUs2ZNXn31Vbv7Cj58giPHIzhwKISw/Rd8MWv1Up61K4okZzdo+w7U6QGYSashQ4YwefJkx8YlUkol7VlB+sn9ueqcPHzxaXq37fhi27BbXD3yrXdydc9Td2Ef1vS8u+xZ8rnmYvX5XS8iIlJSFYN/4UtRsH79Om6/vT3R0bEANGoMKzZA/RsdHJhICZORAWNG5hxPnjwZLy/7plRlZWWxdM0WYmIT2LFpBZkZaWZDrXugQt41NYosJxe4eQw0eNRWNXr0aMaOHYthGA4MTKR0ST2yjeiln/2n1kL5bi/i5OFjqzGy0vPvwMn5IvV5N3u1Zqbl9JeZtz/LRfqy5NOXoYWXRUSkFFGSRy5r06YNdO16D0lJ5m/Cbr4Vlq6FakVkQx6RkmTG5xASbJY7dOhAnz597O5ry479RMfGs2PXbiKOnNuZxtnDXIunuLFYoMXz0GywrerDDz9k2LBhZGdnOzAwkdIh+eB6zvz2Xp5pWP53Po3Xje1y1Vlc8h9lgzXvFK6L1TtdMOonv9E5hjX/n3sjn74sbp7531dERKQEUpJHLikwaAv33HMPKSnmb9Tu7Ax/LYfy5R0cmEgJdOYMTHzXLFssFqZNm4bFYrGrr6TkFNZs2kl4xBn2bV2RM+KlUX/wqnTpi4sqiwUaPwU3vWyrmjFjBv379ycjI+MSF4pIQSTuWELUHx/lSfCUveNJ/G7pned8J3fvfPux5jMix6xPy1N3YR9O7nlHMxr5XGPW571HfteLiIiUVEryyEVt3x5Il86dbSN4bu8IvywCH5/LXCgidhn/JiQkmOWnn36a1q1b293XivXbSE5JJShwE7FnTpiVXlWgwWOFEKmD1X8Qbh0HFnO6xk8//cQDDzxASorW3RApbPEbfybm3y8g145aFvw7D6VM2/xHGrr4VQTnfKZgJcfmrctMz3c6lYt/1Zxy2Wp52rOT4/K9d371LmWr5j1RRESkhFKSR/K1c2cgXbp0Jj4+CYBbboP5f4KdS4OIyGXs2gmz/2eWfX19+eCDD+zuK+JUFDv3hRB69AQhO9flNLQcBhebRlHc1L4HOkwwF2YG/v77b+69917i4+MdHJhIyWAYBjErviZu3Xe5G5xdqNDrVfxa33fRay3OLrhVrJ2nPjM6PE9dVkzeOidPPzNRdI5blXp5+8rnOvMeJ/LUuVUJuGisIiIiJY2SPJLHrl2B3HNPV2JizCEFLVrB73+Dr6+DAxMpoQwDRr9k/hdg3LhxVK5c2c6+DP5etZnEpBSCNq4iNelc0qNiS6hxZ+EEXFRUaw93fAIuZvZ53bp1dOrUiejoaAcHJlK8GdZsohdPITFoUa56i5snlR5+B+9Gd1y2D8+6bfLUpR3bmacuNSxvnWe9my/bV/qJvRj/mT6WnRxLZtTx3DG7euBRsxgtNC8iIlJASvJILnv3buPee7tx9uy5XbSawB//Qtmyjo1LpCT7fQGsX2uWAwICGD58uN197T14hBMRp9l7IJhjB4PO1VrObZlu3/o+RVqlVnDnp+BWBoCdO3fSvXt3EhMTHRyYyNWrXbs206ZNc2gM1sx0zv7+Acn7VuWqd/IqS+XHJuJZu+UV9ePd9G6w5P5nZtKe5WScPWY7zkqMzpNIAvBp3iXXsat/Vdz/k6ixpiURv+ln27FhGMSunvOfaWXg1fB2nLTwsoiIlCJ5J0xLqRUcvJdu3bpz6pT5W/CA+uYiyxUqODgwkRIsNRVefyXneMqUKbi5udnVV0ZGJsvWBnI2Oo5dm5aTnXVuMeK694F//UKItogq1xDu/j9Y9RKkRbN161YefPBB/vrrL9zdS8j0NCmwAQMGMGfOHCZOnMiYMWNs9QsXLqR37945i5NfB7Nnz2bEiBHExcXlqg8MDMTbO/9Fi6+X6H8+JTV0a55696r1Sd67kuS9Ky96bbnOQ2xlV/+q+LbqTuL2v2x1RmY6p74bhWf9W7E4uZJ6eCvW1IRcfXje2DbfkTf+nQZy6vtXcyVx4jf8SNrxPbiWr0FGZAgZpw/nusbi5knZDv0u/6ZFRERKECV5BICwsGC6du1KePgZAGrVhsUroEoVx8YlUtJN/xhOnJtdcM8999CzZ0+7+9oQuIe4hES279jOqWMHzEpXn1zbjpdYfrWh4xRY+TxkJrF8+XL69+/PTz/9hLOzs6OjkyLCw8ODSZMmMXToUPz9/R0dTh4VK1a8/EnXWHZi/tMdUw8HXvbaC5M8YO6+lR5xiIxTIbY6IzONlP1r8r3exb8q5bsMy7fNvVoDyt7xBHFr5uSqTz+xl/QTe/NeYHGi/L0v4uKn31SJiEjpoulawunT4XS99x7CwiIAqFbdTPDUqOngwERKuJPh8MmHZtnZ2ZmpU6favWV6XHwiGwJ3c/zkGfYHXjDNoskA8Ch6X2avibL14I7J4GyO3lmwYAHDhg27riM0pGjr3LkzVapUYeLEiRc9Z/369dx+++14enpSs2ZNhg8fTnJysq09MjKSHj164OnpSZ06dZg3b16eaVZTpkyhWbNmeHt7U7NmTYYNG0ZSkrmRwerVqxk4cCDx8fFYLBYsFgvvvPMOkHu61uOPP07fvn1zxZaZmUmFChWYO3cuAFarlYkTJ1KnTh08PT1p0aIFCxYsKIQnVTic3L2o3Pc9PANuuey57tUbU/nRiTj7XPzvqzK3PYL/3UOwuF56hJ6Tpx8Veo2+orWDRERESholeUq5hIQYHnywJ8GHzDnyFSuZCZ46dR0cmEgp8NYYOL/r9/PPP0/jxo3t7mv5uiCSU9MI2ryOhOhIs9K3JgQ8VAiRFiMVmkH7923bq3/11Ve8+eabDg5KigpnZ2cmTJjAZ599Rnh43t2ZDh8+zL333stDDz3E7t27+fnnn1m/fj0vvPCC7Zwnn3ySiIgIVq9eza+//spXX33FmTNncvXj5OTEp59+yr59+5gzZw4rV65k9OjRALRr145p06bh5+dHZGQkkZGRvPLKK/xXv379+PPPP23JIYClS5eSkpJC7969AZg4cSJz585lxowZ7Nu3j5EjR9K/f3/WrMl/pEx6ejoJCQm5Xteak4cPlR4aR6W+7+PdtDMu/lWxuHlicXHHuUxlvBreTsUHXqdyv0lXNOrGr00vqg2aQZl2j+FWpT5Onn7g5IKTd1ncazSmbKcBVBs8E++GHa75exMRESmKLIZ+xVlqpaQkMGDAw8yfvwwAHx/4dx20aOnYuERKg80b4e72ZrlcuXKEhIRQrlw5u/o6Fn6KWT8vZs/+UBb/9DnpKee+FN4+Gaq1LaSIi5ljy2DzeMD8X9wnn3zCyy+/7NiYxKEGDBhAXFwcCxcupG3btjRu3Jhvvvkm15o8gwYNwtnZmZkzZ9quW79+PR07diQ5OZmwsDAaNWpEYGAgbdqYOz6FhoZSv359pk6dyogRI/K994IFC3j22WeJiooCLr4mT+3atRkxYgQjRowgKyuLqlWrMmXKFJ544gnAHN1jtVr56aefSE9Pp1y5cixfvpy2bXN+zgcNGkRKSgrz5s3LE8c777zDu+++m6e+5ohfcHL3uqrnKSJyrYR92MPRIYgUSQkJCZQpU4b4+Hj8/Pwuep5G8pRSqalJvP7G87YEj5MTzPlJCR6R68FqhVdfyjl+77337E7wWK1W/lm1mYTEZLZtWpGT4Kl6W+lN8ADU6gI3jbQdjho1ijlz5lziAilNJk2axJw5czhw4ECu+l27djF79mx8fHxsr65du2K1Wjl69CiHDh3CxcWFm266yXZNQEBAnvV9li9fzt1330316tXx9fXliSeeIDo6mpTzQ/eugIuLC3369OGHH34AIDk5mUWLFtGvn7mQcGhoKCkpKXTp0iVXvHPnzuXw4cP59jl27Fji4+NtrxMnTlxxPCIiIlI8aOHlUigtPYXpn77F55/l/JZv0lS4V0lzkevih7mw/dzu5k2bNmXIkCGXvuASdu4LIeJ0NLv3HuBE8A6z0uIMLV8shEiLufoPQkY87P0GgGeeeYayZcty//33OzgwcbQ77riDrl27MnbsWAYMGGCrT0pKYujQoQwfPjzPNTfccAPBwcGX7TssLIyePXvy3HPP8cEHH1CuXDnWr1/PM888Q0ZGBl5eVz5ipl+/fnTs2JEzZ86wbNkyPD09uffee22xAixevJjq1avnuu5iu8q5u7trxzkREZESTkmeUiYzM53vvvuE99+bQXa2uQ3p0OfhOX0fFLkuEhPh7bE5x9OmTcPFxb6/itPS0lmxfhunz8awe8sKrNlZZkP9h8CvViFEWwI0HgDp8RCygOzsbPr27cvSpUvp2LGjoyMTB/vwww9p2bIlDRo0sNXddNNN7N+/n4CAgHyvadCgAVlZWezYsYPWrVsD5oia2NhY2znbtm3DarXyySef4ORkDpj+5ZdfcvXj5uZGdnb2ZWNs164dNWvW5Oeff+bvv//mkUcewdXVFYDGjRvj7u7O8ePH9XkWERERGyV5ShGr1cpfi7/hrTenkZycBkCXe+GjaWDnhj4icpUmT4DTp8xy7969ufvuu+3ua+2WXeY0rW2BnA0/t0Wxe1lzRy0xWSzQajhkJMCxf0lPT+e+++5jzZo1tGrVytHRiQM1a9aMfv368emnn9rqXnvtNW677TZeeOEFBg0ahLe3N/v372fZsmV8/vnnNGzYkM6dOzNkyBC+/PJLXF1dGTVqFJ6enrad8QICAsjMzOSzzz7jvvvuY8OGDcyYMSPXvWvXrk1SUhIrVqygRYsWeHl5XXSEz+OPP86MGTMIDg5m1aqcnfN8fX155ZVXGDlyJFarlQ4dOhAfH8+GDRvw8/PjqaeeugZPTURERIo6rclTShiGwabNixg7dhKnT8cA0LgpzP0Z7BxEICJX6chh+GyKWXZzc+Pjjz+2u6/omHi2bN9P2IkIDm5bndPQdBC4XXwhtlLJ4gS3vA5V2wGQmJhI165dL7puiZQe48ePx2q12o6bN2/OmjVrCA4O5vbbb6dVq1aMGzeOatWq2c6ZO3culStX5o477qB3794MHjwYX19fPDw8AGjRogVTpkxh0qRJNG3alB9++CHPlu3t2rXj2WefpW/fvlSsWJGPPvroojH269eP/fv3U716ddq3b5+r7b333uOtt95i4sSJNGrUiHvvvZfFixdTp06dwng8IiIiUgxpd61S4sCBTQwd+hzr1u0CzK3S126FGzSjQ+S6ebQ3/LnQLI8ZMybPF7+rMe/3f9m1P5Q/fv+Vg9tWmpVl6sE934KTc8GDLYmy0mDNSIjaA5jrIW3atAkfHx8HBybFWXh4ODVr1rQttlycnN+lQ7triUhRot21RPKn3bXEJjz8EOPHj7MleDw8YP4fSvCIXE+rVuQkeKpUqcLrr79ud1+hR8MJPnKCQyFHObJ3c07DTS8pwXMpLh5w+0fgVxuAvXv3MmjQIPS7DrkaK1eu5I8//uDo0aNs3LiRRx99lNq1a3PHHXc4OjQRERERJXlKupiYSL76ajI//7zCVvf1XLj5VgcGJVLKZGXB6BE5xx9++CG+vr529ZWdnc0/q7cQF5/Eto0ryEg/tyVzjU5Q6aZLXiuAmy+0nwAu5qiFn3/+mSlTpjg4KClOMjMzef3112nSpAm9e/emYsWKrF692rYgsoiIiIgjaTWWEiwlJYHfF85g2rQfbb+pHjsOHnzEwYGJlDLffgX795rlm2++mSeeeMLuvoJ2HeRsdCy7du/h5GFzdB5ObtBiWCFEWkr43QC3vQXrzW3ORo8eTatWrbjrrrscHJgUB127dqVr166ODkNEREQkXxrJU0JlZmawYuX3TJr0LYmJ5m/6777HTPKIyPUTEwPvvZVzPH36dNu2ylcrJTWNVRu3E3k6mj1bV2CcXzC24aPgU+3SF0tu1W+HxubuQ1arlb59+3L8+HEHByUiIiIiUjBK8pRAVquVjZt+48svvyMkOByA6jXg2x/AWct1iFxXE94xEz1g7pLTtm1bu/tatWE7ScmpBAVuJjoyzKz0rAAN+xc4zlKpydNQ9TYAoqKiePDBB0lLS3NwUCIiIiIi9lOSpwTauWs5ixYu5O8l5oKsLi7w/XyoUMHBgYmUMvv3wVdfmGUvLy8+/PBDu/s6fTaGoN0HORIWTvCOtTkNzZ8DV+2KYxcnZ7htHHibo6C2bdvGsGHDtBCziIiIiBRbSvKUMGFhe1i//m9mzvzdVjfxE7jlNgcGJVIKGQa8NhKys83jsWPHUqNGDTv7Mvhn1RaSU1IJ3LSa5IRzQ4PKNYZaXQop4lLKzQ86TABnDwBmzZrFjBkzHByUiIiIiIh9lOQpQeLjz7Jh0+9Mn/4TKSkZAPTqDc+96ODAREqhv/+ClcvMcq1atRg1apTdfR0MPcbRExHsOxBC2IGtOQ03vQQW/TVeYGUD4JYxtsOXXnqJjRs3OjAgERERERH76NtBCZGZmc7adT/z049/c+TwKQBq3gBffAMWi4ODEyll0tNhzMs5x5MnT8bT09OuvrKysvh3zVaiY+LZuXklWRnpZkPte6F8k0KIVgC4oTPc2Bcwt8h+6KGHiIyMdHBQIiIiIiJXR0meEsAwDLZs/ZONGzfz15/mOjzOzjD7R/D3d3BwIqXQF5/C4VCzfMcdd/Dwww/b3dembfuIjk1gx86dRBw9tw+7iyc0f7YQIpVcWjwHlVoBcOrUKR555BEyMjIcHJSIiIiIyJVTkqcECA4JZP/+IL74v99sdePeh9vaOTAokVLq9GmY9J5ZtlgsTJ8+HYudw+kSk1JYt2UX4ZFn2Be40lzoB6DRk+auWlK4nFyg7XjwrATAhg0bGDNmzGUuEhEREREpOpTkKeZiYiLZvn0p337zOwkJqQDc2RleHu3gwERKqXffgMREszx48GBatmxpd1/L1wWSlJLKtq0biTt70qz0rgoN+hQ8UMmfhz+0/wCc3ACYOnUqa9ascXBQIiIiIiJXRkmeYiwzM531GxewZctOtmwJBsDXF2Z8C076kxW57nZsg7nfmuUyZcrw/vvv293Xyciz7NofSujR44TsXJfT0PIFcHYvYKRySeUbQfOhtsOBAweSlJTkwIBERERERK6MUgHFWGDQEiIjjzPr2yW2ugkfQ42aDgxKpJQyDHj1pZwZVePGjaNixYp29mXw96rNJCWnErRxFanJCWZDpZug+h2FFLFc0o2PQMUWABw9epRXX33VwQGJiIiIiFyekjzF1OEjOzl6dBff/O83EhLSAHOa1sDBDg5MpJRa8DNs2mCWb7zxRl544QW7+9pz4DDhkWfYs+8gxw8GmZUWJ2g1XNvlXS8WJ7jldXD2AGDGjBksW7bMwUGJiIiIiFyakjzFUEJCNIFBi9m0OYjArUcA8PaG//ta3/9EHCElBd64YKDH1KlTcXNzs6uvjIxMlq0N5ExULLs2ryA7K9NsqNsLygYUQrRyxXyqQ4thtsOnn36a+Ph4BwYkIiIiInJpSvIUM1arlU2bFxITfZbv566w1b//EdSq7bi4REqzaZPhZLhZ7tatG927d7e7r/VbdxOfmMSO7ds5ffygWenqA80GFUKkctUCHoDKrQEIDw9n5MiRjo1HREREROQSlOQpZoKDt3I26jhz5iwiPt6cpnVHJxj0rGPjEimtThyHKZPMsouLC1OmTLG7r7j4RDYG7eFY+Gn2B63MaWj6NLiXLVigYh+LE9w8Fly8AJg1axZ//fWXg4MSEREREcmfkjzFSGJiDDt3r2DL5iC2bgkDwMsL/u9/2k1LxFHefA1SU83yCy+8QMOGDe3u6981W0lOSSNw8zoSYk6blb61IODBQohU7OZdxVwP6ZzBgwcTExPjwIBERERERPKn1EAxYRgGm7csIjY2iu+/y/kN/7sToW49BwYmUoptWAcLfjLLFSpUYNy4cXb3FXYikv0hYQQfDuPIno05Da2Gg5NLASOVAqvTA6q2BeDUqVO8+OKLDg5IRERERCQvJXmKieCQQE6fCWPeD38RF2dO02rXAZ61fwMfESkAqxVGj8g5fv/99/H397ezLyt/r9xMfEIyQRtXkJ6aZDZUawdVby14sFJwFgvc/Jq5PhIwb948fv31VwcHJSIiIiKSm5I8xUBiUiw7dy4n+NBBNm08DIC7O3zxjaZpiTjKd7Nh53az3Lx5cwYNsn9h5O17gjl1Nobde/cRHrLTrHRygZbK4hYpnhXgppyFl5999lnOnDnjwIBERERERHJTiqCIMwyDLVv+ICUlgZ9+Wkl2tgHAiy9D/RsdHJxIKZWQAG+PzTmePn06zs7OdvWVlpbOyg3bOH02hj1bVmDNzjIb6j8MvjcUQrRSqGrdA9VvByAqKophw4Zd5gIRERERketHSZ4iLjR0G6dPH2XNmnWEBEcBUKUqvDL2MheKyDUz6X04e24Ax0MPPUSnTp3s7mvN5p0kJCYTFLSFsyfNkXq4l4XGAwoaplwLFgu0edW229mvv/7K0qVLHRuTiIiIiMg5SvIUYcnJ8ezYuYwzZ8NZ/NdOW/34D8HX13FxiZRmoSHwf9PMsru7O5MnT7a7r6iYOLbs2E/YiUgObV+T09BsKLj5FCxQuXY8ykHLnIWXX375ZbKyshwYkIiIiIiISUmeIsowDLZs/ZPklHj++XsdUVEpALS5BR7r7+DgREqxsaMgM9Msv/LKK9SpU8fuvpau3kpySipBm9aQFGeO1KPsjVCneyFEKtdUrS5QrjEA+/fv56uvvnJwQCIiIiIiSvIUWeEnDxEZGUpo6D7WrT1qq/9omhZbFnGU5f/Ckj/NcrVq1RgzZozdfYUcOUHI0RMcOHSYI/s25zTc9BI42be+j1xHFidze/tzxo0bR2xsrAMDEhERERFRkqdIys7OYvv2pSQmxrD07x2kpprDBh7tD7e2dXBwIqVUZiaMydlYiUmTJuHjY9+UquzsbJau2UpsXCI7Nq0gMz3VbKh5F1RsUQjRynVRoam5EDMQHR3N+PHjHRyQiIiIiJR2SvIUQQcPbSYxMYZdu3ewY0cEAF5e8N6HDg5MpBT73ww4sN8s33rrrTz++ON297V1xwHORseyc9duTh7eY1Y6u0EL7dRU7DQfCs7uAHz++eccOnTIwQGJiIiISGmmJE8Rk5KSyN69a4mKCuefv/dhtZpbpr8yFqpVd3BwIqVUVBS8Py7nePr06TjZOW8yOSWVNZt3EHE6mr2BKzEMq9nQsB94VymEaOW68qoMDc2EX1ZWFqNGjXJwQCIiIiJSminJU8Ts2r2StPRkNm7aRthRc32HG2rBcH1vEHGYD96GuDiz/OSTT3Lrrbfa3dfK9dtITEolaOsmYk4dMys9K9kSBVIMNexn/hkCixcv1pbqIiIiIuIwSvIUIVHRJzlyZAenTh1h3ZojtvoPJoOnpwMDEynF9u4xp2oBeHt7M3HiRLv7OnUmmu17gzlyLJzgHWtzGlo8Cy76IS+2XDyg+bO2Q22pLiIiIiKOoiRPEWEYBtu2/UNqWhKBW/dy6lQiAC1aQe+HHRycSCllGDB6BFjPzah64403qFatmp19GfyzagtJyakEblxFSuK5nZjKN4MbuhROwOI4tTprS3URERERcTgleYqIY8f2EhV1goiIEDZsOG6rf+MdsFgcF5dIafbXIliz0izXqVOHkSNHXvqCSzgQEkZYeCT7DgRz7EBgTsNNL+mHvCTQluoiIiIiUgQoyVMEZGZmsGPnMuITzrJr1xFORSYA5iie7vc5ODiRUiotDcZesBbWxx9/jIeHh119ZWZm8e+aQKJi4tm5eQVZmelmQ53uUK5hIUQrRUKFprZRWdpSXUREREQcQUmeIuDAwY0kJccRERHK5o3htnqN4hFxnM+nwdFzS2Pdeeed9O7d2+6+Nm3bS0xcAjt27iQy7Nw+7C6e0GxowQOVoqXFs9pSXUREREQcRkkeB0tLS2b/gQ1ERYVz8EAEERFxgEbxiDhSZCR89L5ZdnJyYtq0aVjszLgmJCazbssuTkScZn/gSnOhH4DGT4Fn+UKKWIqM/2yp/u677zo4IBEREREpTZTkcbADBzaSkZHKmbPH2Lw5wlb/+tsaxSPiKO+8DsnJZnno0KE0b97c7r6WrwsiOSWVoC0biTt70qz0qQ439imESKVIavg4uJcF4Oeff9ZoHhERERG5bpTkcaC0tGQOhWwlKiqc0OCznAyPAaB5S+jRy7GxiZRW2wLh+9lmuWzZsgVaV+XEydPsPhBKyJEThO5en9PQ8gVwditYoFJ0uXhCg74AWK1WJkyY4OCARERERKS0UJLHgc6P4jkbdYLNm07a6rUWj4hjGAa8krNBEu+88w4VKlSwsy+Df1ZvITEphaBNK0hLNhdUp3IbqNahEKKVIi3gIXDzBeCHH37g8OHDDg5IREREREoDJXkc5Pwonuiok4SGnCVco3hEHO7nebB1s1lu2LAhw4YNs7uvXftCOXnqLHv2HeDEoe1mpcUZWg5XFrc0cPWCG83RPNnZ2UycONHBAYmIiIhIaaAkj4PYRvFEnyBwS6StXmvxiDhGcjK89VrO8bRp03B1dbWrr/T0DJavD+RMVCy7t6wkOyvTbKh3P5StWwjRSrFQ/yFw9QZgzpw5HDt2zMEBiYiIiEhJpySPA1w4iifiZBzHjkUB0Lgp9LzfwcGJlFJTJkHEuVmTPXv2pGvXrnb3tX7rbuITktm2bRunj59bdNfNF5o+UwiRSrHh5gv1HwHMnbY+/PBDBwckIiIiIiWdkjwOcOEonr27Y2z1wzSLQ8QhjoXBtMlm2dXVlU8++cTuvmLjEtgYtJfjJyM5ELQqp6HpIHAvU7BApfi5sY+5EDMwa9Yszpw54+CARERERKQkU5LnOrtwFE9SYhq7dh0HwN8f+vZzcHAipdSboyEtzSwPHz6cG2+80e6+/l0TSHJqKoGb1pMYe9qs9KtjTtWS0sfdD+qaC62lp6fz+eefOzggERERESnJlOS5zi4cxRN8KJGsrGwAnnwGvLwcHJxIKbRuDfw23yxXrFiRt956y+6+jhw7yYHQMIJDwzi8Z2NOQ6vh4ORSwEil2GrQx1x0G/i///s/kpOTHRyQiIiIiJRUSvJcRxeO4snKzGLL5iOAOUVriP2b+IiInbKz4dWXco4nTJhAmTL2TamyWq38s2oL8QlJbNu4goy0c1/kq3WAKjcXQrRSbHlVhhs6AxATE8O3337r4IBEREREpKRSkuc6Cg3dRmZGGlHR4Zw8mUVcXBJgbpleu46DgxMpheZ+C3t2meVWrVoxcOBAu/vatvsQp6Ni2bV7L+EhO81KJxdo+ULBA5Xir+FjtuKUKVPIyspyYDAiIiIiUlIpyXOdZGdnExIaRFz8GbKzMwnckrOV7rMvOjAwkVIqLg7eeSPnePr06Tg7O9vVV2pqOqs2bufUmWj2bF2J1WpOw+TGPuBbo+DBSvFXNgCq3ApAWFgYCxYscHBAIiIiIlISKclznYSfPEhKSgJRUeEkJToTEhIOQKPG0OkuBwcnUgp9+B5EnTXLffr04fbbb7e7r9WbtpOQmMy2oC1ERZjTMPEoB42fKoRIpcRo+Lit+PHHHzswEBEREREpqZTkuU6Cg7eSnBxHWloyO3acstUPfUHbpotcb8GH4MtPzbKHhwcfffSR3X2djY4lcNdBjp6I4OD2NTkNzYaCq3cBI5USpdJNUNbcuW3btm3s3LnTsfGIiIiISImjJM91EBN7ijNnjhEVfRKr1YXArcEAlCkDjz3h4OBESqExL8P5JVFGjx5NrVq17OrHMAz+WbWF5ORUAjeuITk+2mzwbwB1uhVStFJiWCxQr5ftUAswi4iIiEhhU5LnOjh0aAsZmWkkJEQRGpxEZqb57bL/QPDxcXBwIqXM0r9h6RKzXKNGDUaPHm13X8GHT3D42En2B4cStm9zTkOrl8Civ14lHzfcDc5uAHz//fekpaU5OCARERERKUn0LeQaS0tP4dixPcTERODk5MSOHTkLLg961oGBiZRCmZkwZmTO8UcffYS3t31TqrKzs1m6ZgsxcYns2LiCzIxzX9Zv6AwVmxdCtFIiuflCjTsBiI2NZdGiRQ4OSERERERKEiV5rrHDh7eTmZVBTEwkGeleHDtmrsfT+ma4sYGDgxMpZWb+n7keD0C7du149NFH7e5ry/b9RMfGs3PXLiKO7DUrnd2hxXOFEKmUaHV62IrffPONAwMRERERkZJGSZ5ryGq1EhwSSFycuW36wQNRtra+/RwYmEgpdPYsTHgn53j69OlY7Fz1PCk5hTWbdxIeeZa9W1diGFazoVF/8Kpc8GClZKvUEryrArB8+XKOHTt26fNFRERERK6QkjzXUPjJQ6QkxxMdHY6Xlz9btx4EwMkJHurr4OBESpn33oL4eLM8cOBA2rRpY3dfKzdsJyk5hW1bNxF7+rhZ6VUJGjxWCJFKiWdxso3mMQyDOXPmODggERERESkplOS5hoKDt5KcEk9qahIJ8c6cPRsHwF1doEoVx8YmUprs3gWzvjbLPj4+TJgwwe6+Ik9HsWNvMKFh4QTvXJfT0OJ5cPEoYKRSatTpBpgjyWbNmoXVanVsPCIiIiJSIijJc40kJcVx+vRRYmIicHf3ZMeOMFubpmqJXD+GAa+NgPPfod966y2q2JllNQyDv1dtJik5lW0bV5GaFGc2VGgONe8qlHillPCqDFVuASAsLIxVq1Y5OCARERERKQmU5LlGwo7twWrNJj4+Cl+fimzdsh8AT0+47wHHxiZSmiz6DdauNsv16tXjpZdesruvfYeOcvzkafbtP8Sxg4Hnai3ntky3b30fKcXqagFmERERESlcLo4OoCQyDIOwY3tISIjCas3mxIkkkpJSAej5APj6OjY+kdIiNRVefyXn+JNPPsHd3d2uvjIzs1i2NpComHh2bl5BVmaG2VC3J5TTVnlih2odwK0MZMTz22+/ERsbi7+/v6OjklJo77td8fPzc3QYIiIiUgg0kucaiI07TXzcGWLjTuPt5cfWLcG2tkc1VUvkuvlsChwLM8udO3emV69edve1IXA3sfEJ7Ni+g8gwc2QeLl7QbHDBA5XSydkNat0DQHp6OvPmzXNwQCIiIiJS3CnJcw2Ehe0mKyuDpKRYPNz92b79EAAVKsDd9zg4OJFSIuIkfDzRLDs7OzN16lS7t0yPT0hiQ+AeTkScYV/QypyGJgPAo1zBg5XSq25PW/Hbb791YCAiIiIiUhIoyVPIrFYrx47tJS7+LAChobFkZmYB5rbprq6OjE6k9Bg3FpKTzfJzzz1H06ZN7e5r+bogklNSCdy8nvioCLPSpwbUf6QQIpVSrWw98G8IwPbt29m9e7eDAxIRERGR4kxJnkJ25swxUlISiIs7ja9POXbtPmxre/hRBwYmUops3Qw/fmeW/f39eeedd+zu6/jJU+w5eJjgI8c4vHtDTkOrF8FZWVspBHW62YqLFi1yYCAiIiIiUtwpyVPIwo7tIT0jlZSUBHx9K7DnXJKnXDm4ta2DgxMpBaxWePWCDbTGjx9P+fLl7erLMAz+XrmZxKQUtm1cSVpKotlQ5Rao2q4QohUBqrW3Ff/8808HBiIiIiIixZ2SPIUoOzuL4yf2Exd3GmcnZ06fSiU1NR2Au7uCs7ODAxQpBX78HoK2muUmTZrw7LPP2t3Xzn0hRJ6JZvfe/Rw/tN2stDhDyxe1ZboUHu8qUKYeAIGBgZw6dcrBAYmIiIhIcaUkTyE6eTKYzIw04uLO4FemArsvmKrVtbsDAxMpJZKSYNyYnONp06bh4uJiV1/p6RksXxfE6bMx7N6yAmu2ubYWAb2hTJ1CiFbkAtVzRvMsXrzYgYGIiIiISHGmJE8hCju2h5TURNLTUyhbpjI7d4YC5i/8u9zr4OBESoGPJ8KpSLN8//3307lzZ7v7WrtlFwmJyWzfFsSZEyFmpVsZaPp0IUQq8h+asiUiIiIihUBJnkKSlZVJREQI8fFncHV1Iy3NifDwMwDcfKu5fbqIXDtHj8Cnn5hlV1dXPv74Y7v7io6JZ/O2fRwLj+TAttU5Dc0GgZtfwQIVyU+5RuBRDoBly5aRlpbm4IBEREREpDhSkqeQnD59lOzsLBISovD1Lc/uXaG2Nk3VErn23ngV0s0lsBg5ciQBAQF29/Xv2q3mlumb1pEYayZrKVMP6t5XCJGK5MPiBFXN1flTUlJYuXKlgwMSERERkeJISZ5CcjIihPSMVNLTU/H1Lc/OXSG2NiV5RK6tNatg0W9muXLlyrzxxht293U47CSHDh/nYMgRjuzdmNPQajg42be+j8gVqZazY5umbImIiIiIPZTkKQSGYRAREUJiYjQWiwU3V1/27zsKQKXK0KKVgwMUKcGysmD0iJzjiRMn4udn35Qqq9XKP6s3E5eQxPZNK8lISzEbqt8BlVsXPFiRS6l8Mzi5AvDXX39hGIaDAxIRERGR4kZJnkKQkBBFcnIciYkxeHuXJTQknPT0TMAcxeOkpyxyzcz+H+zdbZZbt27NU089ZXdfQbsOciYqjl2793IydJdZ6eQKLZ8vhEhFLsPVCyrdBEB4eDi7du1ycEAiIiIiUtwo/VAITkaEYLVmk5wch69vOXbsDLa1aaqWyLUTGwvj38w5nj59Ok52ZlVTUtNYtXE7kWei2LNlBVZrttnQ4FHwqV4I0YpcAe2yJSIiIiIFoCRPIYiICCEpOQ6r1Yqvb3nbVC1nZ7iri4ODEynBJrwL0dFm+bHHHqN9+/aXvuASVm/cQWJSCkFbtxAdaf4M41EeGj1RCJGKXCGtyyMiIiIiBaAkTwFlZqZz5uwxEhOjcXf3JCvTQnj4WQCat4QyZRwbn0hJdfAAzPzcLHt6ejJp0iS7+zp9NobAXQc4cvwkwTvW5DQ0f9acQiNyvXhXMXdyAwIDA4mMjHRwQCIiIiJSnCjJU0CRkYcxrFYSE2Pw9SnHkSMnbW23tbvEhSJiN8OA10ZC9rkZVWPGjKFmzZp29mWwdPUWklPSCNq4muSEGLOhXCOo3bWQIha5CtVzRqQtXrzYgYGIiIiISHGjJE8BnYwIIS0tmYyMNHx9yxMSfMLWdquSPCLXxNIlsHypWb7hhht45ZVX7O7r0OHjHDkewYFDIYTt35rT0OolsOivSHGAqjn/81izZs0lThQRERERyU3fYArAMAwiIs2t052cnPD2LkNISLit/da2DgxOpITKyDBH8Zz30Ucf4eVl35SqrKwslq7eQkxsAjs2rSAzI81sqNUVKjQthGhF7ODfAJzcANi8ebODgxERERGR4kRJngKIjTtNWmoSiUkx+Hj7AxZCQ80kT9VqUPMGx8YnUhJ9+RmEhpjlDh060KdPH7v72rx9HzFxCezYuYuII3vNSmcPcy0eEUdxdjUTPUBoaChnz551cEAiIiIiUlwoyVMAZ88ex2pYSUlJwMfXn/Dws6SlZQDmKB6LxcEBipQwZ87Ah+PNssViYfr06Vjs/EFLTEph7eZdhEecYV/gSgzDMBsa9QevioUUsYidyje2Fbds2eLAQERERESkOFGSpwCiok6QmpqI1WrFy6uM1uMRucbGvwkJCWb5mWee4aabbrK7rxXrg0hOSSVo6yZiz5z72fWqAg0eK4RIRQrogumCmrIlIiIiIleqSCR5Vq9ejcViIS4u7pLn1a5dm2nTpl2XmK7E2bMnSElJwMnJCQ93b0JCc9bj0c5aIoVr5w6Y/T+z7Ofnx/vvv293Xycjz7JzXwihR08QsmtdTkPL58HFvYCRihSC8k1sRSV5RERERORKXVWSZ8CAAVgsFiwWC25ubgQEBDB+/HiysrIKFES7du2IjIykTJkyAMyePZuyZcvmOS8wMJAhQ4YU6F6FJSUlkeTkOFJS4vHy9MPJyck2ksfNDVq0cnCAIiWIYcDol8z/Arz11ltUrlzZzr4M/lm9haTkVII2riI1Kd5sqNgSanQqlHhFCsyrEnia0wa3bNlCdna2gwMSERERkeLgqkfy3HvvvURGRhISEsKoUaN45513mDx5coGCcHNzo0qVKpddW6NixYp276JT2KKizIROSkoCXl5+xMcnc/p0DAA3tQF3DQYQKTS/zYcN5wbc1K9fn+HDh9vd196DRzgRcZq9+w9x7GCQWWlxOrdluhbSkiLk3GiepKQk9u/f7+BgRERERKQ4uOokj7u7O1WqVKFWrVo899xzdO7cmT/++IPY2FiefPJJ/P398fLyolu3boSEhNiuO3bsGPfddx/+/v54e3vTpEkTlixZAuSerrV69WoGDhxIfHy8bdTQO++8A+ServX444/Tt2/fXLFlZmZSoUIF5s6dC4DVamXixInUqVMHT09PWrRowYIFC+x5TnmcjTpBRkYamZkZeHn52XbVAq3HI1KYUlPhjVdzjqdMmYKbm5tdfWVkZLJsbSBno+PYtXkF2VnmQunU6Qn+9QshWpFCdMHiy5qyJSIiIiJXosBr8nh6epKRkcGAAQMICgrijz/+YNOmTRiGQffu3cnMzATg+eefJz09nbVr17Jnzx4mTZqEj49Pnv7atWvHtGnT8PPzIzIyksjISF555ZU85/Xr148///yTpKQkW93SpUtJSUmhd+/eAEycOJG5c+cyY8YM9u3bx8iRI+nfvz9r1qwp6NsmKuoEKSnmNA8vrzIcC4u0tbW5pcDdi8g50z+GE8fNcteuXenRo4fdfW0I3ENcQiLbt2/j1LEDZqWrDzQbXAiRihSy8lp8WURERESujou9FxqGwYoVK1i6dCndunVj4cKFbNiwgXbtzGEsP/zwAzVr1mThwoU88sgjHD9+nIceeohmzZoBULdu3Xz7dXNzo0yZMlgsFqpUqXLR+3ft2hVvb29+//13nnjiCQDmzZtHr1698PX1JT09nQkTJrB8+XLatm1ru+f69euZOXMmHTt2tPetk52dRUxMJMkpCbi7e+Li4kp4+Flbe9PmdnctIhcIPwEfTzTLzs7OTJ061e4t0+PiE9kQuJvjJ8+wP2h1TkOTgeDhX/BgRQqbfwOwOIORzaZNmxwdjYiIiIgUA1c9kuevv/7Cx8cHDw8PunXrRt++fRkwYAAuLi7ceuuttvPKly9PgwYNOHDA/G358OHDef/992nfvj1vv/02u3fvLlDgLi4u9OnThx9++AGA5ORkFi1aRL9+/QAIDQ0lJSWFLl264OPjY3vNnTuXw4cPF+jeMTGRWK3Z5qLLXuZi0eHhZwBzLZ669QrUvYic89YYc7oWmKMBGzVqZHdfy9YGkpyaRtDmdSREnxt553sDBDxYCJGKXAMu7lDWnEZ44MCBy+5AKSIiIiJy1UmeO++8k507dxISEkJqaipz5sy5ot+sDxo0iCNHjvDEE0+wZ88e2rRpw2effWZX0Of169ePFStWcObMGRYuXIinpyf33nsvgG0a1+LFi9m5c6fttX///gKvy3M26gTZ1mzS0pLx9vIjIyOLU6eiAWjQCFzsHh8lIudt2gC/zDPL5cuXt63NZY9j4afYF3yU4NBjhO7ekNPQ6kVwdi1YoCLX0gVbqW/dutWBgYiIiIhIcXDVSR5vb28CAgK44YYbcDmXzWjUqBFZWVls2bLFdl50dDSHDh2iceOchSNr1qzJs88+y2+//caoUaP4+uuv872Hm5vbFW0X265dO2rWrMnPP//MDz/8wCOPPIKrq/mFrXHjxri7u3P8+HECAgJyvWrWrHm1bzuXqKgTpKYkYBgGXl5liIyMwmo193Zu3PQyF4vIZVmt8OpLOcfvvfce/v72TamyWq38vXIzCYnJbNu0gvTUc+t4Vb0NqrYthGhFrqEKOUkercsjIiIiIpdTKGNO6tevz/3338/gwYOZOXMmvr6+jBkzhurVq3P//fcDMGLECLp168aNN95IbGwsq1atuujUi9q1a5OUlMSKFSto0aIFXl5eF906/fHHH2fGjBkEBwezatUqW72vry+vvPIKI0eOxGq10qFDB+Lj49mwYQN+fn489dRTdr/f6OiTpKQm4uzsjLu7F+HhOdO/mjSzu1sROeeHubBjm1lu1qwZgwfbvzDyjr3BRJ6JZvfeA5wI3mFWWpyh5YuFEKnINaaRPCIiIiJyFQq8u9Z5s2bNonXr1vTs2ZO2bdtiGAZLliyxjazJzs62ralx7733cuONN/LFF1/k21e7du149tln6du3LxUrVuSjjz666H379evH/v37qV69Ou3bt8/V9t577/HWW28xceJE230XL15MnTp17H6f6emppKQkkJaWhIeHDxaLhYiTUbb2Ro0vcbGIXFZCAowbk3M8bdo026jBq5WWls7KDds5fTaG3VuWY83OMhvqPwx+tQohWpFrzLsauHgCcOjQIQcHIyIiIiJFncUwDMPRQRQnZ84cY9nyWQSHBOLtXYbq1W7k0+nz2bp1PwC7giGgvoODFCnG3hoDUyaZ5d69e/Pbb7/Z3de/a7aybssu/lm6jKCV881K97LQ/Udw8y14sCLXw9IBEBeKi4sLKSkptl+eiBRUQkICZcqUIT4+Hj8/P0eHIyIiIpdwpf/fLrSRPKVFXPwZrIaV9PQUPNy9AYiIMEfyuLpCbfsHCYmUeodD4fOpZtnNzY2PP/7Y7r6iYuLYvH0fR49HcHD76pyGZoOV4JHixacGAFlZWRw/ftzBwYiIiIhIUaYkz1WKiztDRnoqhmHg4eGN1Wrl9GlzZ6269bSzlkhBvP4KZGSY5VGjRlG3bl27+/p3zVaSU1IJ2ryWpLizZmXZAKjTsxAiFbmOfKrbiiEhIQ4MRERERESKOiV5rlJ8/BnS0pMBcHf3JioqnsxMcyew+g0cGZlI8bZyOfy1yCxXrVqVsWPH2t1X6NFwgo+c4GDwEY7uvWBHolbDwcm5gJGKXGe+OTtChoaGOjAQERERESnqlOS5SvHxZ0lLS8bV1Q0XF1dOnYq2tSnJI2KfrCwYPSLn+MMPP8TX174pVdnZ2fyzegtx8Uls37SCjPQUs6FGJ6h0U4FjFbnuLhjJoySPiIiIiFyKkjxXIS09hfRzL3d3c0v36OgEW7vW4xGxzzcz4cA+s3zLLbfQv39/u/sK3HmAs9Gx7Nq9h5OHd5uVTm7Q8vlCiFTEAc6tyQNK8oiIiIjIpSnJcxUSE8xROxcmeeJiE23t1arne1mpEXESnu4PNctDeU+4uRlsDzLbMjPhzdfMuoreUK8aDHoSIiNyrk9Ph2eegCp+0OJGc/rOhaZOhlEvXr/3I9dHdDS891bO8bRp03Bysu+vpuSUVFZv2kHk6Wj2bFmBYbWaDQ0fA++qhRCtiAN4VgBnd0Br8oiIiIjIpWmZ4KuQkBCFgUFGRirl/M0vjHFxOUmeKqX4O2RsLNzdHu64E37/GypUhNAQKOtvtqekwM7tMOYtaNYC4mLh1ZfgkV6w/lwi6NuvYOc2WLkJ/v0bBj4OYafBYoGwozD7a1gX5Lj3KNfGhHfMzw9A//79adu2rd19rdqwnaTkVIK2bib6VJhZ6VkBGvYrcJwiDmOxmKN54g9z9OhRsrKycNEq/yIiIiKSD/0r8SokJEaTmZGO1Wq1jeSJjU2ytVet5qjIHG/KJKhRE2bOyqm7cPpamTLw17L/XPM53HELnDgONW+AQwegey9o3ATq1IU3XoWoKKhYEV56Dt6bBH5+1+f9yPWxfx98/aVZ9vLy4sMPP7S7r1Nnotm25xBHwsI5tGNNTkPz58DVq4CRijiYT3WIP0xmZiYnTpygTh3NDxYRERGRvDRd6yokJESRnmEu4uru7glA7LmRPE5OULGSw0JzuCV/QKs20P8RqFUJ2raCWV9f+pr4ePMX1GXKmsfNWsCm9ZCaCsuXmiOjKlSAn34ADw/o1fuavw25jgwDXhsJ2ebmdLz++utUr27fnEfDMPhn1RaSU1IJ3LSalMRzQ4PKN4FaXQopYhEH8s1Zl0dTtkRERETkYpTkuQpJSbFkZKRisVhwdfUActbkqVQZSvPo+aNH4H9fQr36sGgpDH4OXhkO38/J//y0NHjrNXjksZzROU8+bSZ6WjeGjz6A734xp/G8Pw4++QzefROaBUCvrub6P1K8LfkTVp4b3VWrVi1efvllu/s6GHqMsPBI9h0IIWz/1pyGVi+BRX/NSQmgxZdFRERE5AqU4rTE1UtNTSQzMx1XV3csFgtWq5W4OHO6VmlejwfAaoWb2sC7E8zjlq1g/174Zgb0fyr3uZmZ8EQfcyTH9C9z6l1dYer/5T536EB4bjjs2gF/LoTNu2DqR2YCad6v1/QtyTWUng5jLsjpfPzxx3h6etrVV1ZWFktXbyU6Jp6dm1eQlZluNtTuBuUbF0K0IkWAkjwiIiIicgX0K+4rlJ2dRXp6CpmZGbi6uAGQkJCCYRhA6V6PB8wkV8P/fJ9u0Mhcb+dC5xM8x4/Bn8suvcbOmlXmttrPvgBrV0PX7uDtDQ/2gXWrC/kNyHX1f9PhyGGz3LFjRx566CG7+9q0bR8xcQns2LmTiKPn9mF38YTmQwshUpEiwldJHhERERG5PI3kuUKpqeaInaysdFxcza1sYy/YPr20J3luaw8hh3LXhQTDDbVyjs8neEJD4O9VUL78xftLS4OXn4dvfgBnZ3PdlnP5NDIzc9ZxkeLn1Cn46H2z7OTkxLRp07BYLHb1lZCYzLotuwiPPMO+wJU5H5JGT5q7aomUFJ4VwMkVrJmEhYU5OhoRERERKaI0kucKpaaaCZ0LR/LEKclj8+JI2LoZJk+Aw6Hw8zyY9RUMed5sz8yEfg/D9iD49gczSXPqlPnKyMjb34fvwT3dzWlfAG3bwx+/wZ7dMPNzM6kkxdO7b0DiuR+dwYMH07JlS7v7WrE+iKSUVIK2bCTu7LmFmryrQoM+BQ9UpCixOIGbOfQxJibGwcGIiIiISFGlkTxXyJbkuXAkT1xOkqe0r8nT+mb46XcYNxYmjje3T/9oGjzaz2yPOAmL/zDLbVvmvvbvVXBHp5zjfXvht19g086cut4Pm1O07rkd6jeAWfOu1TuRa2l7EHw3yyyXKVOG9957z+6+wiPOsGt/KKFHjhO6a11OQ8sXwNm9gJGKFEFuvpAWTVxcnKMjEREREZEiSkmeK5SSmojVsJKdlZXvSJ7SnuQB6NbTfOWnVm1INq6snyZNYfd/dgh2coJpX5gvKZ4MA159KWdG1dtvv03FihXt7Mvgn9VbSEpOJWjTSlKTE8yGSq2h+h2FFLFIEePmC0BycjKZmZm4uro6OCARERERKWo0XesKnd9ZC8D13EietLSceUZl/R0SlkixMf8n2LzRLDdo0IDnn3/e7r527w8lPPIMe/Yd4PjBbWalxQlaDQc71/cRKfJcfW3F2NhYBwYiIiIiIkWVkjxXKC0tiawsM6njcm4kT3pGpq3d08shYYkUCykp8ObonOOpU6fi5uZmV18ZGZksXxfEmahYdm1eSXbWuZ/Dur2gbL1CiFakiHJTkkdERERELs0hSZ7t27ezZ88e2/GiRYt44IEHeP3118nIbxXeIuDCkTy2JE96TpLH29shYYkUC1M/gpPhZrl79+5069bN7r7WbdlFfGIS27dv5/Txg2alqw80G1QIkYoUYUryiIiIiMhlOCTJM3ToUIKDgwE4cuQIjz76KF5eXsyfP5/Ro0df5mrHSElNJCsrAycnJ5ydzaWMMi5I8nhpJI9Ivk4chymTzLKLiwtTpkyxu6/YuAQ2bdvLsfDTHAhamdPQ9BlwL1uwQEWKOiV5REREROQyHJLkCQ4Otm2bPH/+fO644w7mzZvH7Nmz+fXXXx0R0mWZI3kycHFxw3JuzQ9N1xK5vDdHQ1qaWX7xxRdp0KCB3X0tWxtIckoagZvXkRBz2qz0qw0BvQseqEhRpzV5REREROQyHJLkMQwDq9UKwPLly+nevTsANWvWJCoqyhEhXZLVaiUjPZWsrAzbVC2A9PScqWWariWS14Z1sOBns1yhQgXGjRtnd19Hj0ewPySMQ6FHObJnQ05DyxfBSRsFSimgkTwiIiIichkOSfK0adOG999/n++++441a9bQo0cPAI4ePUrlypUdEdIlZWebI3YMw4qTk7Ot/vx0LYsF3N0dEppIkZWdbW6Zft4HH3xA2bJl7erLarXyz6otxCcks23jStJTk82Gau2g6q0FD1akOHDzsRWV5BERERGR/DgkyTNt2jS2b9/OCy+8wBtvvEFAQAAACxYsoF27do4I6ZKyzu3eY7Vm4+SU88gyzk3X8vLSrs0i//X9bNi1wyy3aNGCZ555xu6+tu8J5tTZGHbv3Ud4yLlOnVyg5QsFD1SkuLhgulZcXJzj4hARERGRIsshcxyaN2+ea3et8yZPnoyzs3M+VzhWVnZOkif3dK1zSR5N1RLJJT4e3n4953j69Ol2/2ynpqazcsM2Tp+NYc+WFVit2WZD/UfA94ZCiFakmNB0LRERERG5DIeM5Dlx4gTh4eG2461btzJixAjmzp2Lq6urI0K6pGzbSB4rTpacR2ZL8mjRZZFcJr0PZ8+Y5YcffpiOHTva3deazTtISEwmKGgLZ08eNivd/aHxU4UQqUgxoiSPiIiIiFyGQ5I8jz/+OKtWrQLg1KlTdOnSha1bt/LGG28wfvx4R4R0SReO5LFcuCZPhpI8Iv8VGgJfTDfL7u7uTJ482e6+zkbHsnXnAcJORHJo25qchmZDcq1PIlIqKMkjIiIiIpfhkCTP3r17ueWWWwD45ZdfaNq0KRs3buSHH35g9uzZjgjpks6P5DEXXs67Jo+Hp0PCEimSxo6CTPNHg1dffZXatWvb1Y9hGCxdvZXklFSCNq0mKf7czntlb4Q63QsnWJHixNnDVkxOTnZgICIiIiJSVDkkyZOZmYn7ue2oli9fTq9evQBo2LAhkZGRjgjpki4cyXPh7lqWc6stG4ZDwhIpcpYthSV/muXq1aszZswYu/sKOXKC0LBwDhw6zJF9W3IabnoJnIre2l0i19OFv3AQERERETnPIf9KbNKkCTNmzGDdunUsW7aMe++9F4CIiAjKly/viJAuKdeaPBd8uXRxMcvnRy2IlHYzP88pT5gwAW9v+1Ylz87OZumarcTGJbJj0woy01PNhpp3Q8UWhRCpSDFkWG3ForhJgYiIiIg4nkOSPJMmTWLmzJl06tSJxx57jBYtzC9tf/zxh20aV1GSmZUBnBvJc8HCy87OZjkzwyFhiRQ5ATfmlCtUqGB3P1t3HCAqJo6du3Zz8vC5nfic3aDFcwWMUKQYuyDJo5E8IiIiIpIfh2yh3qlTJ6KiokhISMDf399WP2TIELyK4CrG2dmZWA0rhmHkGslz/jepGskjYrqpTU45KCiI7t2vfu2c5JRUVm/aQcSpKPYGrsQ4/8W2YT/wrlJIkYoURxrJIyIiIiKX5pAkD5j/QL0wwQPYvUDrtZaVlYlhNf9xbXHKZySPkjwiALS+OaccFBRkVx8r128jKTmV3bt3EXPqmFnpWQkaPl4IEYoUYxcsAKeRPCIiIiKSH4cleRYsWMAvv/zC8ePHycjIPd9p+/btDooqf4ZhxcD8x7UFi61eI3lEcqtbD8qUgfh4+5I8kaej2L43mLDwSFzcL9i2zu8GcNE2dlLKaU0eEREREbkMh/wq8NNPP2XgwIFUrlyZHTt2cMstt1C+fHmOHDlCt27dHBHSJVksTjk7aZHzm1Tbwstak0cEAIsFWp2bshUZGcnJkyev+FrDMPhn9RaSklOJOBVFg/r18Pb1MxtjD2kbOxGtySMiIiIil+GQfyV+8cUXfPXVV3z22We4ubkxevRoli1bxvDhw4mPj3dESJfk5ORsG8FjXPBFU9O1RPL677o8V2p/8FGOhZ8iNCwcDw83alStRLUatc3GjERIjijcQEWKG43kEREREZHLcEiS5/jx47Rr1w4AT09PEhMTAXjiiSf48ccfHRHSJTk7OWM5v6uWkc9IHiV5RGzsWZcnMzOLf9cEEhUTT2xcIvVqVcfJyYlqNWrlnBRzsJAjFSluNJJHRERERC7NIf9KrFKlCjExMQDccMMNbN68GYCjR4/mGilTVFiczOlaFgs5O/2gkTwi+bFnJM/GoD3ExidyOOwk/mV9Ke9vTtOqVrNOzklK8khpp5E8IiIiInIZDkny3HXXXfzxxx8ADBw4kJEjR9KlSxf69u1L7969HRHSJeVsm27Bmmu6llmfnQ1Waz4XipRCNW+AChXMcmBg4BUlbiPPRJOQlExqWjq1qle2rYGVayRPrJI8UsppTR4RERERuQyH7K711VdfYf3/9u47PNKy3v/4e3pPL5NJsimb7b2x7NJhYRdpAkpbFJAuFlBQ0YMgllXPUVEsWH4KnIOCohQVFViaFGErW2B7382W9F5m5vn98SSTzCa7yUI2k0w+r+uaK0+byXceEjb55L6/d0cqctttt5GZmcmbb77JhRdeyM0335yIko7K2jFVy2Kxxk/XcnT9JbW1FTxa/EcEiwVmzoHn/wGVlZXs3LmT4uLioz4nlJuFr+MbqLWta2icPyWVlNR06mqroWqj+UuuRb/cygjVyx8ZRERERES6S8hvS1arFbu9K1+64oor+MlPfsJnP/tZnE5nIko6qs6RPBaLJW66ls/XlepUVQ56WSJD1rFO2QrlZuFw2PC4XdQ3NsefKyw2N8JNUL97AKsUGWaMSGxTI3lEREREpDeDNpJnzZo1/b526tSpx7GSY2exdo7kscRNPUkJeGPbFRWQXzDopYkMSYeHPB/72MeOen0oaM7vCvi91Dc0xp8rLGbDulXmTtUGSCk6/OkiI4RG8oiIiIjI0Q1ayDN9+vQeIUlvLBYLkUjkqNcMtq6RPNa4+gMp3UKeQ4NelsiQ1T3kWbZsWZ/Xez1u0lICBHxeKqprMQyjW1+e4q4LqzZA8cIBrlZkmAh3jXJzu90JLEREREREhqpBC3m2b98+WJ9qwMVN1+r2l9RAQCGPSG/yQuajfB+sWLGCaDTa5/SSUG4mO3aXE41EaWpuwec1p0PmaRl1EVNLTWwzJycncXWIiIiIyJA1aJP6i4qKYo/f//73LF26NO5YUVERS5cu5fHHHx+skvqtq/FyfE+eQMAX266sGPSyRIa0ztE8tbW1bN26tc/rQ8Es/D4PWKC+oSl23Ovzk56Zbe7UbIJo+HiUKzL0tVbHNhXyiIiIiEhvEtK58Ze//CXjx4/vcXzSpEk89NBDCajo6Ox2sxm01Woj2m0qWYqma4kc0Qdpvmy32/C63XEhD3RbSj3SCnU7B7JMkeGjRSGPiIiIiBxdQpZQ379/P3l5eT2OZ2dnU15enoCKjs7lMsMcu91BONK1vLOma4kc2YzD+vJceeWVR70+lNut+fJhK2zlFRSz/t2OoKhqA6SNHtBa5QPa8hRseRoaO/6/nVoCk66FvHnmfnMlvPtzOLAM2psgMAomfhIKTzfPR9pg2fdg77/BnQGzvgjBOV2vv+H30HgAZt0xiG9qCNNIHhERERHpQ0JG8hQWFvLGG2/0OP7GG28QCoUSUNHRud3mtCybzUkk3D3k0XQtkSM51pE8breLzPRUAn4vDY1NRKNdUyNjy6gDVL0/gFXKh+LJhqm3wDn/D875DeTMhNfvhtpt5vm3vwX1u+Dk78KiR6DgVHjr61C9yTy/9Vmo3ggLHoLRF8J/vgGdze0b9pnnp96UmPc2FLXWxDYV8oiIiIhIbxIS8tx4443cfvvt/O53v2Pnzp3s3LmT3/72t9xxxx3ceOONiSjpqOx2Bza7o2MkT1vsuEbyiBxZdjaM6phltXLlyn6tmhfKzSLg8xKNGjQ1t3Qd7958uXrjQJcqH1T+yRCaB4FCc5TO1JvB7oHK98zzletgzKWQORH8+eYoH4cfqjr+G9btgNBJkFoKZZeaIUZnkLHif2DareDw9fy8I5Wma4mIiIhIHxIyXeuuu+6isrKST3/607S1maGJ2+3my1/+MnfffXciSuqTy+XFbnMQ7jaSx+Vy4HI5aG1tV8gj0ouZs2HXTmhsbGTjxo1MnDjxqNeHcs3myxaLhbqGJvw+M0h1uT1kZgepPLQfarZApB1sjsF4C9Jf0QjseRnCLZA5yTyWORl2vQR588Hph90vmVO0cmaY59PKYOe/INwK+98Gdya40mDH82BzQcFpCXs7Q1K36VrZ2dkJLEREREREhqqEhDwWi4Xvfe973HPPPbz//vt4PB7GjBmDy+VKRDn94nb5sNkdRCJhDMPAYrEA5mie1tZaKjRdS6SHGbPh6T+b28uWLes75AlmYbNZ8Xo6mi/ndjtXWGSGPNF2czpQxrjjWLn0W81WWHqLGd7YPXDSd8zePADz74e37oWnPwIWG9jdcPJ3IFBgni89H2q3wj+vBmeqeX1bPaz7DZz5IKz9Fexaao4CmnM3eEd4sNExkic1NRWn05ngYkRERERkKErIdK1Ofr+fOXPmMHny5CEd8EDHSB67A8MwiES6lnBOSTGnElRWQGtroqoTGZpmdeuh25++PHk5mVgslo7my4evsFXctaO+PENHYBSc8ztY8Eso+yi8822o3W6eW/sbM7Q5/QE4+zcw9nJ48+tmMARgtZvNls//k9nTJ3sarP4pjP0YVG+GPf+GhQ+bI4NWPZCY9zeUdExl01QtERERETmShIY8w0nndC0gboWtnJx0wOwVunNHIioTGbqmz+za7k/I43Q6yMowmy83NrYcufmy+vIMHTaHOTInY7zZhDltNGz6EzTshS1/hhPuhtzZkD4GJn/KHIG15S+9v9aBlVC33ezPc3CV2e/H7oHCM8z9kSzSBu0NgEIeERERETkyhTz95Hb7sNnN4fHdV9jKDWbEtrdtGfSyRIa09HQYXWZur169mvb29qM/ga7my4Zh0NBtKfVgaFRsmiRVG45HuTIQDMOcUhfuaJxtOeyfGYsNjGjP50VaYeUPYfZdYLWBEYFox6jJaKT354wkWllLRERERPpBIU8/dU7XAgiHu1bYys3tCnm2KuQR6WFmx5StlpYW1q9f3+f1+cHsWPPl7lO2nC4X2bkhc6d2m9msVxJrzUNwcDU0lptTsNY8ZI64KToHUorAXwDL/9tcbathL2z4A+xfBvmn9nyt9Q9D3omQPtbcz5oCe141G21v/rO5P5Ip5BERERGRflDI008ulxebzY7FApFu07WC3UOezYmoTGRomzm7a7s/U7ZCuVlYrRb8Po/ZfLn7uc6l1I0I1OgbLuFaquHtb8FzV8Ernzd7JZ32QwjOMfvtnPrf5mpZ//4y/PMa2PFPmPs1cxpWdzXbYPfLMPn6rmOFZ0DePHjpNrM584zPD+pbG3K0fLqIiIiI9ENCVtcajtxuHxYs2A5bRr37dC2N5BHp6fCQ54Ybbjjq9bnZGVitVgI+L7X1DXHnQoUlrF7+prlTvRGyJg90uXIsTrj76OcDhXDSt/t+nbRSOO/x+GMWK8y+03xI3PLpCnlERERE5Eg0kqefvJ4UABwON23tLbHjKSk+3G6zV4968oj0NG0GdLbSWbZsWZ/XOxx2cjLTzebLzS1EIpHYuVBhUdeF6ssjI0ljeWwzPz8/gYWIiIiIyFCmkKef/H5zFS2Xy0Nba1czWIvFEhvNs3MH9KOvrMiIEgjA+Anm9tq1a2lpaTn6E4BQMIuA3wsGcc2Xc/MKsVpt5o5CHhlJ6nfHNseOHZvAQkRERERkKFPI008ulweXy4vT6aG1rTnuXGfz5UhEy6iL9GZGx5St9vZ21q5d2+f1odwsfF43Vqs1ri+P3eEgJ69jFEP9TmhvOsIriCSZul2A+YeFsrKyBBcjIiIiIkOVQp5jEAhk4HR6aG9vJRrtmkKiFbZEju6DNF+2WDqaLzce1ny5sNjcMKJqviwjg2FAgzmSp7i4GJfLleCCRERERGSoUshzDPz+DFwuDwBtbV1TToLdmi+rL49IT91Dnv705cnNTsdmtRHweXtZYau4a0dTtmQkaK2G9kZgZE7VKi4u5oEHHkh0GSIiIiLDgkKeY9A5kgeIm7KVq2XURY5q6nSwdbTS6c9IHpvNRjAng4DfS1NzK+Fw9+bLxV0XKuSRkaBjqhYMfMhz7bXXYrFY+O53vxt3/Omnn8bS2TF9kDz88MOkpaX1OL5s2TJuuummQa1FREREZLhSyHMMAv4MHHYnNpuNtm4hT15eVmx7/bpEVCYytHk8MLFjtfP169fT1NR3L51QbkfzZaCh25StnGAIm91u7lS9P+C1igw53Zoujxs3bsBf3u12873vfY/q6uq+L06A7OxsvF5vossQERERGRYU8hyDQEomAE5n/Apbqak+MjLMJdZXr4BoNCHliQxpnVO2otEoq1ev7vP6UDALr8eNzWalrtuULZvNTjA0ytxp2ANt9cehWpEhpH5nbPN4TNdasGABwWCQJUuWHPGa119/nVNOOQWPx0NhYSGf+9znaGxsjJ0vLy/nvPPOw+PxUFJSwu9///se06x++MMfMmXKFHw+H4WFhXz605+moaEBgFdeeYXrrruO2tpaLBYLFouF++67D4ifrnXVVVdx+eWXx9XW3t5OVlYWjz76KGD+P2bJkiWUlJTg8XiYNm0aTz755ADcKREREZGhTyHPMQj4zWlZva2wVVIaAqCuDrZoypZID8fal8dsvgx+n7dn8+WCoq6d6k0DVaLI0FS7LbY5adKkAX95m83Gd77zHR588EH27NnT4/zWrVtZtGgRl156KWvWrOGJJ57g9ddf5zOf+Uzsmk9+8pPs27ePV155hT//+c/86le/4uDBg3GvY7Va+clPfsL69et55JFHeOmll/jSl74EwPz583nggQdISUmhvLyc8vJy7rzzzh61LF68mL/+9a+xcAjgX//6F01NTVx88cUALFmyhEcffZSHHnqI9evXc8cdd3D11Vfz6quvDsj9EhERERnKFPIcA5fLg9PlweXy0NYW/0tnaUlebHtl3y1HREacWXO6tvvTlyc7Mw2H3U7A30vz5bi+PJqyJUmuZisAGRkZ5OXl9XHxB3PxxRczffp07r333h7nlixZwuLFi7n99tsZM2YM8+fP5yc/+QmPPvooLS0tbNiwgRdffJFf//rXzJ07l5kzZ/Kb3/yG5ub4P4bcfvvtnHHGGRQXF3PmmWfyrW99iz/+8Y8AOJ1OUlNTsVgsBINBgsEgfr+/Ry0LFy7E5/Px1FNPxY79/ve/58ILLyQQCNDa2sp3vvMdfvvb37Jw4UJKS0u59tprufrqq/nlL385wHdNREREZOhRyHOMAv7uy6h3zcvqHMkDCnlEejNxMjid5nZ/Qh6r1UowJ5OAz0tLSxvt7eHYOTVflhGjtRZaKgGYMmXKcW2G/L3vfY9HHnmE99+PD07fffddHn74Yfx+f+yxcOFCotEo27dvZ+PGjdjtdmbOnBl7TllZGenp6XGv8+KLL3LWWWeRn59PIBDgE5/4BJWVlf3q0dXJbrdz2WWX8dhjjwHQ2NjIM888w+LFiwHYsmULTU1NnH322XH1Pvroo2zduvWD3hoRERGRYcOe6AKGm0AgA5fLi2FAW1szbrcPgJKSrpBnlUIekR5cLpg81QxBN27cSF1dHSkpKUd9Tig3M9Z8ub6xiYw08/qsnDwcDift7W1QvfG41y6SMLVdwcSUKVOO66c69dRTWbhwIXfffTfXXntt7HhDQwM333wzn/vc53o8Z9SoUWza1PeUyR07dnD++edz66238u1vf5uMjAxef/11rr/+etra2o6psfLixYs57bTTOHjwIC+88AIej4dFixbFagX4+9//Tn5+ftzzXC5Xvz+HiIiIyHClkOcYpaXl4nb7sFigubk+FvIEAl6ys9M4dKiG1SshHAa77q5InJmzzZDHMAxWrlzJ6aefftTr84PZeNwu7HYb9Q1dIY/VaiWvoIhd2zdDYzm01oAr7bjXLzLoarr68RzvkAfgu9/9LtOnT49bxWvmzJm89957lJWV9fqccePGEQ6HWbVqFbNmzQLMETXdV+tasWIF0WiUH/zgB1it5iDizqlanZxOJ5FIpM8a58+fT2FhIU888QT/+Mc/+PjHP47D4QBg4sSJuFwudu3axWmnnXZsb15EREQkCWi61jHKyAhhs9pxOr00Ncev6tM5ZaupCTZqBolIDzOPsS9PKGg2Xw700nw5r3vzZU3ZkmQ1iCN5Oj/H4sWL+clPfhI79uUvf5k333yTz3zmM6xevZrNmzfzzDPPxBovjx8/ngULFnDTTTfxzjvvsGrVKm666SY8Hk9sellZWRnt7e08+OCDbNu2jf/93//loYceivvcxcXFNDQ0sHTpUioqKo46jeuqq67ioYce4oUXXohN1QIIBALceeed3HHHHTzyyCNs3bqVlStX8uCDD/LII48M5K0SERERGZIU8hyjjHSz6aXHE6D5sJCntER9eUSOpvsKW/0JeTLTU3E6HL02X84vLOnaUcgjyapmS2zzeKys1Zv7778/rufc1KlTefXVV9m0aROnnHIKM2bM4Otf/zqhUNe/eY8++ii5ubmceuqpXHzxxdx4440EAgHcbjcA06ZN44c//CHf+973mDx5Mo899liPJdvnz5/PLbfcwuWXX052djbf//73j1jj4sWLee+998jPz+ekk06KO/fNb36Te+65hyVLljBhwgQWLVrE3//+d0pKSo7waiIiIiLJw2IYhpHoIoabZ//6E7ZvX8P+A9uYOOHk2NDz99Zv5zvfeRSAm2+DH/40kVWKDD3hMARToLkZRo8ezZYtW/p8zsNPPMeyd99n/cbtzJ89GafTnJZReWg/P/3ef5kX5Z8CJy85yquIDEPhFvjLQjAijB07lo0bh0//qT179lBYWBhrtixDU11dHampqdTW1vbZI01EREQSq7//bmskzweQmZmPxxsgGo3S2to1uqBYy6iLHJXdDtNmmNtbt26lqqqqz+eEglkEfF3NlztlZObgcnvMHY3kkWRUuR4Ms0fNUO8v89JLL/Hss8+yfft23nzzTa644gqKi4s59dRTE12aiIiIyIiikOcDyMgI4XH7sVgscVO2vF43wbxMANashtbWBBUoMoTN6DZla8WKFX1eH8rNwu124nDY46ZsWaxW8vI7+vI0H4LmioEuVSSxDq2ObQ71sKS9vZ2vfvWrTJo0iYsvvpjs7GxeeeWVWENkERERERkcCnk+gMyMEFarDZfL26Mvz5gxhYAZ8PznzURUJzK0HWtfnlBuFtDRfPmwvjyhwuKuHY3mkWRzaE1s85RTTklgIX1buHAh69ato6mpiQMHDvDUU09RVFTU9xNFREREZEAp5PkA0tODWCyWXpsvT57c1djx5RcHuzKRoe9YQ570tAAet8tsvtzYRPc2YnEhT/Xw6Vci0qdIO1SuA2DUqFEKTERERESkXxTyfAAOh4uUlCy83gDNLQ1xq5BMmlQa237phURUJzK0jR0Hfr+5vWzZsj6vt1gshHKzCPi9tLWFaWtrj50LaRl1SVbVGyFizvkd6lO1RERERGToUMjzAWVkhPB4AhiGQUtrY+x4WpqfwsIcwGy+3I++siIjitUKM2aZ27t37+bAgQN9Pqcz5IH45stpGVl4vB2JUdX7oMUCJVkMo348IiIiIjJ0KOT5gDIyQrg7my831cWdmzTZHM1jGPDay4moTmRoO+bmy8EsXE4HTqeDuu7Nly0WQoUdo3laa6Cp78BIZFg49G5sUyGPiIiIiPSXQp4PKCszH6vFisfjp7GpNu7clMmasiVyNN378vRnylas+bK/l+bLBcVdO+rLI8kgGoEKs+lyTk4OY8eOTXBBIiIiIjJcKOT5gNLT83A4XPj96TQ0VMc1gx03vgibzby1ar4s0tOsOV3b/Wm+nBLw4fN6YitsHbH5svrySDKo3Qbt5jTgU089FYvFkuCCRERERGS4UMjzAdlsNnJyivH70gmH2+P68rjdTsaMNZdS37YVdmxPVJUiQ1NJKaSlmdvLly+PC2160735cjgcoaW1LXYuPuR5f+CLFRls3frxDPWl00VERERkaFHI8yEEg6V4falYrVYaGqrjzk3WKlsiR2SxdPXl2b9/P/v27evzOXHNl7tN2QqkpOEPpJo7VRvUfFmGP/XjEREREZEPSCHPh5AXLMVqseLzpfYMeaZ0hTyasiXS07H25ckPZuF02HG5nHErbFkslq6l1NsboLHvwEhkyDKM2Eie1NRUpkyZkth6RERERGRYUcjzIaSkZOHxBPD7M2hsrCEajcbOlZSE8HpdALyyFCKRRFUpMjQda1+evM7my76ezZfz1JdHkkX9LnOlOODkk0/GZrMlth4RERERGVYU8nwIFouFvLzR+P3pRKNRmrqtsmWzWZk4sQSAqipY9naiqhQZmrqP5OlPyBPwe0nx+0gJeKlvbI7r45OvvjySLMr/E9vUVC0REREROVYKeT6kYG4pbrcPu93RY8rWzFnjYttP/3mwKxMZ2goKISvb3O5P82WAUDCLgM9LJByhuaU1djyvc7oWaCSPDG97XoltXnjhhYmrQ0RERESGJYU8H1IwWIoFi7mUeuNhIc/McbGl1J/9i/rBinRnsXSN5qmsrGTHjh19PudIzZf9gVRS0jLMneqNYER7e7rI0NZcARXrAJg4cSLjx49PcEEiIiIiMtwo5PmQPB4/qWk5+P3pNDfXEw63x875/R4mTCgGYOcOWL0qMTWKDFXH2pcnlJuF3W7D43b16MsTa74cbob63QNZpsjg2PMaYP414NJLL01sLSIiIiIyLCnkGQB5QbMvj2FAY2NN3Lk5J0yIbT/7l0EuTGSIO9a+PKFgR/NlvzduhS2AUGFJ14768shwtPfV2OYll1ySwEJEREREZLhSyDMAgsFSnA43LpeX+oaquHOzZo3HYjG3n35SU7ZEuptxjCGP1+MmPTVgrrB1WPPlUKH68sgw1loLB1cDUFpayrRp0xJbj4iIiIgMSwp5BkBOdhE2m52UlEzq6yvjfvFMS/Mzbpz5y+emjbBubaKqFBl68vIglG9uL1++nGi07146nX15opEoTc0tXcfjmi9vHOhSRY6vvf8GIwKYU7UsnX8dEBERERE5Bgp5BoDD4SQvr4zUlGza29villIHOHHepNj2n/4w2NWJDG2dU7bq6urYsmVLn9eHgh3Nly3xzZc9Xj/pmR3LddVsgmj4eJQrcnzseS22qalaIiIiIvJBKeQZIIWFE/B4AzgcLmrrKuLOzZkzEavV/Kvsk49rypZId8fclyc3C5vNitfjpu7w5suFxeZGpBXqdgxckSLHU3sjHFgGQH5+PieccEKCCxIRERGR4UohzwApyB+HzWonJSWLurpDcVO2UlN9TJpUCpirbC17O0FFigxBx9qXJy8nE8Dsy9Njha3irh315ZHhYt8bEDVXZrzkkkuwWvVPs4iIiIh8MPpJcoA4nW5yc0tITcmira2V5paGuPNxU7YeH+zqRIau7iN5li1b1uf1breLzPRUAn4vjU3NcX184vvyKOSRYaLbVC0tnS4iIiIiH4ZCngFUWDgBny8Nu91Bbe3BuHOzZ0/AbrcB5pSt9vZEVCgy9GRlQVGxub1y5UoikUifzwnlZhHweYlGDRqbupov5xUUEVvOTiGPDAfhFij/DwDZ2dmcfPLJCS5IRERERIYzhTwDqLBwAlabjdTUbGprD8ZN2fL53MyYMRaAgwfg788mqkqRoadzNE9TUxMbNvQdzoRys/D7PFgsFuobu6ZsudwesrJzzZ3aLRBpOx7ligyc/W9DxAwqP/rRj2Kz2RJckIiIiIgMZwp5BpDb5SUvOJq01Bza2lppaqqLO3/mmbNi2//vl4NdncjQdax9efLzupovH7EvTzQMtdsGsEqR42D3K7FNTdUSERERkQ/LnugCkk1R0WT27ttkrrJVexCfLzV2btLkUnJy0jl4sJqXXoBtW6F0dAKLFRkiZs3p2l62bBnXXHPNUa8PZmdisVgI+L1xI3kA8gqLWbPSnP5C1QbIGD/Q5YoMjPZG2Pc6AGlpaZxxxhkJLkhGqsn3/gury5voMkRERIa9Hd89L9ElaCTPQCssGI/d5jCnbB22ypbVauGMM2fG9n/7q0RUKDL0TO/6tujXSB6n00F2Rhopfi+NjS1xzZfzO5dRB/XlkaFtxz8h3AzAlVdeidPpTHBBIiIiIjLcKeQZYA6Hi1BoDGlpObS3t9HYWBN3/tRTZ2Czmbf9f38HbWoZIkJaGpSNMbdXr15Nez86k4eCWQT8XgzDoKGxOXY8GCrE0tl8uVohjwxRhgFbn4nt3nLLLQksRkRERESShUKe46C4aApeTwoul5eq6vK4c6mpPmbPngBAxSF49qlEVCgy9HT25WltbWX9+vV9Xh/KzcLn7Wi+3K0vj8PpIjsYMndqt0O49XiUK/LhVKyJ9YyaP38+U6dOTXBBIiIiIpIMFPIcB/n5Y3G5vGRk5FFbe4hwOH64zplnqQGzyOEO78vTl1BuFlarBb/P06MvT6z5shGBms0DWKXIANnydGzz1ltvTVwdIiIiIpJUFPIcBzabndGlM0hPD2KxWKiu3h93fuLEYoLBDABeexk2b0pElSJDy8xjXGErNzsDq9VKwOftucKW+vLIUNZSDXteASAzM5OPfexjia1HRERERJKGQp7jpKxsFnabg5SUbKqqy+MaMFssFs7otpy6GjCLwLQZYO34P1J/Qh6Hw05OZjoBv5fG5hYikUjsXGwkD6gvjww925+DqNl36rrrrsPtdie4IBERERFJFgp5jpNAIINgsJTMzBCtrc29NGCejt1uA+Cxh6GlZfBrFBlK/H4YZ7arYs2aNbT045siv6P5MgZxzZdzQwVYbeb3l0byyJBiROMaLt98880JLEZEREREko1CnuOorGwWPm8qbreXqqp9cecCAS9z5pi/0VZWwpNPJKJCkaGlc8pWOBxmzZo1fV4fCmbh87qxWq1xU7bsdge5wXxzp24ntDcd4RVEBtn+ZdBo/ntwzjnnUFZWluCCRERERCSZKOQ5jgryx+N2+8jICFFbV0F7e3wD5rPP7uo0+6PvQTQ62BWKDC3H2pcnlJuFxWI2X67r0ZenpGPLgGo1vpIhYuvTsU0tmy4iIiIiA00hz3Fks9koLZ1BelpnA+b45dTHjhvF2HGjANjwPvztmd5eRWTkONaQJycrHbvNRsDv7bnCVmFR14768shQ0HQA9r0BQH5+PhdccEGCCxIRERGRZKOQ5zgrK5uF3e4gNTWnRwNmgIsuPDm2/d/fgcNOi4woU6aB3W5u92cZdZvNRm52BgGfl+bmVsLhcOxcXPNl9eWRoWDb38yePMCNN96IvfOLXURERERkgCjkOc4C/nSCwdFkZOTR1tZCQ0N13Pmp08ooKgoCsHI5vPRiIqoUGRo8Hpg42dx+7733aGxs7PM5odyO5stAfbfmy9nBEHa7w9xRyCOJFg3D1mcBM5y84YYbElyQiIiIiCQjhTyDoKsBs4/KwxowWywWLug2mud/lgx2dSJDS+eUrWg0yurVq/u8PhTMwutxY7PFN1+22ewE8wvNnYY90FZ/HKoV6ae9/4aWSgAuvPBC8vPzE1yQiIiIiCQjhTyDID80Fo8nQGZmPvX1FbS2xvcOOeGECQSDGQC89jK8859EVCkyNBxrX578YDYWC/h9Pfvy5HWfslW9cYAqFDlGhgHv/W9sVw2XRUREROR4UcgzCGw2G+PGzSU9PYjd7uRQxe6481arlfMvOCm2r9E8MpLN7Fp0rl99ebIyUnHY7Wbz5cNX2Cro1nxZU7YkUfa9ATXmCm8zZ87k7LPPTnBBIiIiIpKsFPIMkrFj5uByecnKKqS6ej/t7a1x508+eRoZGSkA/P1ZWL8uEVWKJN6kyeB0mtv9GcljtVrJy80kxe+lpaWN9vau5sv5sWXUUcgjiWEYsO63sd377rsPi8WSwIJEREREJJkp5BkkDoeLcWNOICMjhNVm6zGax2638ZGPzIvt/+C7g12hyNDgdJqrbAFs3LiRurq6Pp8Tys0i4OtovtxtNE9mThCH02XuKOSRRDhsFM/555+f4IJEREREJJkp5BlE48bNxel0k5mRT1XVPsLhtrjzp58xE7/fA8Cf/gDbtiaiSpHE696XZ+XKlX1eH8rNwuNxYbfb4vryWK1W8vJHmTtN+6Gl+givIHIcaBSPiIiIiAwyhTyDyO32UTZ6JllZBYCFisq9h513snDRXACiUfj2fYNfo8hQcKx9eULBLAACvl768hQWd+2o+bIMJo3iEREREZFBppBnkE0YPx+Hw0VGRh6VlXuJRMJx5885Z25sNM/j/wer+h7EIJJ0jnWFrcz0VFxOp9l8ufEoIY+mbMlgMQxY3zWK595779UoHhERERE57hTyDDKfL5WS4mlkZxUSjUaoqtp32Hk3F330lNj+1+4yf1cQGUnGTwCPmXX2K+SxWCzk5WQS8HtpbW2nta09di7UfRl1hTwyWPa9AdXmKJ4ZM2ZwwQUXJLggERERERkJFPIkwMSJJ+F0uklPD1JRuYdoNBp3fsGCOWRnpwHw6kvwr38koEiRBLLbYdoMc3vbtm1UVVX1+Zz8vOxY8+WGbqN5MjKzcbk7EqNqhTwyCA4bxaNePCIiIiIyWBTyJEBqShaFhRPIzh5FONxOdXV53HmHw87lVyyI7f/XlyAcPvxVRJJb9748/RnNE8rNxO124nDY4/ryWKxWQgVF5k5zhfkQOZ40ikdEREREEkQhT4JMmngKLqeH1NRsDlXs7jGaZ+7ciYwenQ/A++vh/x5OQJEiCXSsfXlCuR3Nl/1e6o7WfFlTtuR40igeEREREUkghTwJkpGRRyg0hpycItrbW6msil9py2KxcNXic2L73/w6NDYOdpUiiXOsIU9aagCP2xVbYcvo1sxKfXlk0JS/qVE8IiIiIpIwCnkSaNq0s/C4/WRk5HHo0E7C4fa48+PGjWLW7PEA7C+Hn/wgEVWKJMaYsRAImNv9bb4cys0i4PfS3h6Ob74ct4y6Qh45TgwD1mlFLRERERFJHIU8CZSRHqS4eCo5OcVEowaHDu3qcc3ll5+F1Wr+kvCj78P+/YNdpUhiWK0wY5a5vXv3bg4cONDnczpDHiCuL09qeiYer9/cqdqgJevk+Nj5L6jeCMD06dO58MILE1yQiIiIiIw0CnkSbNrUM3G7vGRnF1JRuYe2tpa486FQFmeeaf6m29gI37kvAUWKJMiMY+3LE8zC5XTgdDqo77bClsViIb9zNE9rDTT1HRiJHJO2Bnj357HdJUuWaBSPiIiIiAw6hTwJ5vOlMm7ciWRlFWK3O9h/YFuPay6+5HTcbicAv/s1rHl3sKsUSYxj7cuTH8wGzObL9Yc1X85T82U5ntb/FlqqAPjoRz/KokWLElyQiIiIiIxECnmGgEkTT8brCZCbW0JNzUGamuvjzqem+rjgwpMBiEbhszdBJJKISkUG16xuy6gvW7asz+sDfi9+n+cIzZeLui5UXx4ZSDVbYfOfAXC73fzoRz9KcEEiIiIiMlIp5BkCnE43kyefRnpaELfbx/7yrXG/nAJ85CPzCIXMJaKXvwO/+nlvrySSXIpLID3d3F6+fHmP74vDdW++HA5HaGlti53LLyzpulAjeWSgGAas/BEYZvL+1a9+leLi4sTWJCIiIiIjlkKeIWJM2WxSUjIJBktpaKyhvr4q7rzDYedT158f27/vq7Bn92BXKTK4LJauvjwHDhxg7969fT4nlJtFSi/NlwOpafhTUs0dNV+WgbLrBTi0GoDRo0dz1113JbYeERERERnRFPIMETabjenTFpASyMTvT2f/gZ6jecaPL+KMM2cC0NAAX/iMfk+V5HesfXlCuVk4HHbcLmePvjyhgmJzo70BGvoOjESOqr0JVv8stvvjH/8Yt9udwIJEREREZKRTyDOEFBZOICurkLxgKa2tTVRVl/e45oorziY1zVwK+u/PwrNPDXaVIoPrWPvyhILmtMaA3xu3whZ0C3lAfXnkw1v/O2ipBOCCCy7gvPPOS3BBIiIiIjLSKeQZQiwWCzNnnIPHEyAtLciBA9sIh9virvH53Hzyk12rtnzhM1BbO9iVigyeYx3J4/d5SfH7OkKe5vjmy1phSwZK7XbY9EcAXC4XDzzwQGLrERERERFBIc+Qk51dSHHxFPLyRgMWysu39rjmhBMmMmPGWAD2l8O9dw9ykSKDKL8AsnPM7f40XwZzNE/A5yUSjtDc0tp1vPsKWwp55IMyDFj5QKzZ8le+8hVKS0sTW5OIiIiICAp5hqSZMxbi9aaQlzea6poDPZowWywWrrn2I7hcDgB+/Qt4641EVCpy/FksXaN5qqqq2LFjR5/PyQ9mE/B7wRLffNkXSCE1LcPcqd4I0chxqFiS3u6X4OAKAEpKSvjyl7+c4IJEREREREwKeYYgj8fPzOnnkJ4WxO9PZ+++TUQi8b+MZmWl8vHLzoztf/YmaGs7/JVEksMx9+XJzcJut+FxuXo2X+6cshVuhgYtUSfHqL0JVv80tvvAAw/g8XgSWJCIiIiISBeFPENUael0cnKLyc8fSzjcxsFDO3pcc845J1BSGgLg/ffge98a5CJFBsmx9uXJy80EzObLdUcKeUBTtuTYrf8dNB8C4CMf+QgXXHBBggsSEREREemikGeIslgszJ1zAV5PgJycYioq9tDcXB93jdVq5YbrL8BqtQDw/W/DG/9ORLUix9eMYwx5vB436akBAn4vDU2HNV/uvsKWQh45FgdXwcbHAXA6nfz4xz/GYrEkuCgRERERkS4KeYawlJRMJk86leysQlwuL3v3burRdLaoOMilHzsDgGgUPrUYqqsTUa3I8RMMmg2YAVasWEE0Gu3zOaHcLAJ+L9FIlMamltjxPDVflg+irR7e/hZg/j/4m9/8JmVlZYmtSURERETkMAp5hrgJE04iLS2HgoJxNLfUU1m5t8c1F1xwEuMnmL+47tlt9ufpxwJEIsNK55Sturo6Nm/e3Of1nStsYYH6xq4pWx6vj/TMbHOnZhNEw8ejXEk2K34ITQcAOP300/niF7+Y4IJERERERHpSyDPE2Ww25p5wIV5PChkZ+Rw4sJ22tpa4a6xWK7feejE+nxuAp56ER3+biGpFjp9j7csTys3CZrPi9bh7NF/OLywxNyJtULt9IMuUZLTzedj1AgCpqak88sgj2Gy2BBclIiIiItKTQp5hIDu7kDFj5hAMlmC12dm3b3OPaVuZmanccOOFsf07PwebNg52pSLHz7H25QnlZgEQ8Hl7hDxxU7aq9Y0iR9G43xzF0+EXv/gFo0aNSmBBIiIiIiJHppBnmJg+7Sz8vnTy88dSV19JdXV5j2vmzJnAGWfOBKCpCa69ElpbB7tSkePjWEfyuFxOsjLSYs2Xu/fx0Qpb0i/RiNmHp70BgMWLF3PllVcmuCgRERERkSNTyDNMOJ1uTphzPimBTDIzQ+wr30JLS2OP666+ehGhkDmC4d1VcN/XBrtSkeMjMxOKO2ZZrVy5knC47146oVyzL48RNeKbL+cXQeeqSAp55Eg2Pg6HVgMwatQofvrTnya2HhERERGRPtgTXYD0X0HBOMaMmUM0GqGxsZbde95ndOlMrNaurM7lcnDbbZdy772/IRyO8JMfwFnnwIJzEli4yACZORt2bIempiY2bNjA5MmTj3p9KDcTv8+DxWKhvqGJgN8LgMvtJis7SMXBcqjdYvbmsTkH4y3IcFG9Cdb9GgCLxcKjjz5KWlpaYmsSGQTR1iZadq2htXwzbeWbiNRXEmmpI9rcgMVqxeL0Yk/LxZlbhnfsPDzF04/8Wi0NNO98l7Z9G2k7uJ1wTTmRplqM9lYsDjc2fwauYBne8afgGT0bi7V/va7CdYdoWPMCzduWE649SLS1AZsnBXtaHp6yE/BPPQebJzBAd0RERGR4UcgzzMyccQ4HD+6gsHACW7euZP+BbYTy4pfxLSoOcsUVC/i///sXADd+Et5eAzk5iahYZODMmA1/+ZO5vXz58r5DnqDZfNnndcetsAXmlK2Kg+Xm6lq12yBj/PEqW4abcAv85/7Yymtf/vKXOe200xJclMjgaNn5Loee+nav54woGOE22ppqaNu3kYZVf8dVMJGsi76C3Z8Rd23zjtUc/NO95rTH3l6rrYlwVRPhqj00vvcKzuAYsi64E0dG/lHrq1vxV2pefQSjPX4RikhDFZGGKlr3rKfu7T+TsfA2fONOOoZ3LiIikhw0XWuYsdsdnDT/Y/h9aQSDpVRU7KGuvrLHdQsXzWXaNDP8OXgAPnkZtLcPdrUiA2vWnK7t/vTlCWZnYrFYCPh7Nl8OdW++rClb0t27v4C6HQDMmDGDb3zjG4mtR2QIa93zHgf/+HWMSPwPGUZb8xEDnt607d/MgT/cTbiXn2k61f7nT1S/+MseAc/hos11VDzzPRrff63fn19ERCRZKOQZhtLTc5kx4xyyMgtISclkz54NtLe3xV1jsVi46eaLSEvzA/DvV+GuzyeiWpGBM31m1/ayZcv6vN7pdJCdkUbA56WxqYVIpHvz5ZKuCxXySKfyt2DLnwFwu9089thjOJ2ayicjkM2OM28M3omnEZh5Hr7JZ+IMjun10vZDO2ja/PbRX86fgXfsfPzTF+EungHWnoPJIw1V1Lz6cK/Pby3fRM1r/9vjuKtwMv7pi3Dmjo4/YUSp/OeDhOsqjlqXiIhIstF0rWFq7Jg5lJdvIRxuY9OW5ezZu4HioilYOpvJAqmpfm6/43K+/a2HaW+P8OtfwJRpcP3NCSxc5ENITYUxY2HzJnj33Xdpa2vr8xfwUDCLrTv3YhgGjU3NpAR8AARDBVisVoxoFKreH4zyZahrqYJ3lsR2f/CDHzBhwoQEFiQy+OwZ+WRf+nXcRdOwOlw9zjfvfJdDT96PEY5fvrN13wZ840/ucb1n9BxSTrgEV+HkuJ9R2ip2cfCP9xKpPxR3fdPGN4ku/EyPz1398m/N+WLdpJ50JWknLwbAMAwqn3uAxnVLY+eNtmZqXn+MrI/or1wiIjJyaCTPMGWxWDhx7kX4AxkUFoynoaGKiso9Pa4rKyvgU9dfENv/wmfgdY1elmFsRsdS6q2traxfv77P6/OD2fi8HqxWS9yULYfTRU5uyNyp22H2YZGRK9IGr3/VDHqAc889l1tvvTXBRYkMPmfWKLxlJ/Qa8AB4iqbhLpra88RhU7NsKdnkXvEdcj52L+5R8X+E6vw8GQtu7PEyRriVcE153LH26nJad6+LO2Z1B0idd3ls32KxkH76tWCJ/9G2acO/ibY19/peREREkpFCnmHM7fYx/8SLCfgzyMoqZP/+bTQ31/e47pRTpnHuuScCEA7D4kth187BrlZkYHTvy9OfKVuh3CysVgs+r6dH8+W8wmJzw4hAzZYBrFKGFcOA5f8DleYvkQUFBfzud7/r8UupiJgjZsJ1h3oct6eH4vZdwbLew6Du1xRO6f1ztMePEmre1rMHm2vUZCy2+AHpNl86juyiuGNGewstu9YetQ4REZFkopBnmMvLG82ECfPJzS3B7faxa/d7RCLhHtddceXZTJ5cCkBFBVx2ETQ2Dna1Ih/ezNld2/1pvpybnY7VaiXg81J3WPPl/ILirh315Rm5Nj0BO54DwOPx8Mwzz5Cbm5vgokSGDsMwiLY20bpvIxVPL6H90I6481aXD9/E0z/AC0d7PWxPjV8OtG3/1h7XHGkVLkdmYY9jbQd6Pl9ERCRZqSdPEpg29SwOHNhOe3srW7auYNfu93r057HZrHz2cx/j6/f8hgMHqlj7LtxyHTz6BOiP1TKcTJ0OVitEo/0Leex2O7lZ6ezdf4h9ByuIRCLYbDbAXEY9Rn15Rqbyt+Ddn8d2H374YWbOnHmUJ4iMHAf++HVatq886jVWt5+si76CzRM45tdv2vyfHsecuaOx+dLjjoVr9vW4zuZN73HMPJ7a49jh079ERESSmUbyJAGbzcbJJ19GIJDBqMKJNDRUc+DA9h7X+XwevvDFK3C7zUa1f/kTfP/bg12tyIfj98O4jl64a9eupaWl7146odwsAj4vGFDf2NWbIScvH2tH4EP1xuNRrgxldTvhrftiownuueceLrvsssTWJDKMBGZfROiGh/AUTz/m50Yaq6n5d8/VsgKzL+pxLNra1OOY5Qg9g3o73tvzRUREkpVCniQR8KdzykmXkZKSRTBYysFDu6ipOdDjuvz8bG677dLY6J3774G/PTPIxYp8SJ19ecLhMO+++26f14eCWfi8bqxWa1zzZbvdQW5egblTtxPa9YvAiNFWB//+MrSb81Yvvvhi7rvvvsTWJDLM1K/8G1Uv/pJIL/0AjybSXM+BP95LtLEm7rhnzIn4J5/Z4/rDe/QAWKy2Xl/b0svS7IYaL4uIyAiikCeJBIMlzJyxkOysQtLTc9mzdyNNvfzgNWPmWD7+8a4foq67Ct7pOWJaZMg61r48odwsLBYLfp8nLuSB7lO2DKjeNHBFytAVDcObX4cGc0XCKVOm8Oijj2K16p9Eke68Y+cRmHUhgVkX4Jt8Jo6c0vgLohGaNvyb/f/7BSIN1f16zXB9BQd+/2XaD26LO+4MjSPr/Dt7fU5vo3OMw1bz6jresy+hxenpV20iIiLJQD15ksy4sSdQU3OAqBGltbWZnTvXUTZ6Fg6HM+66Cy48mV27D/Kft9bR1ASXngf/eg0mTkpQ4SLH4FhDnpysdOw2GwG/l6qaurhzoYJiVvCquVP1PuRMH8BKZUha/VM4YH7dZGVl8de//hW/35/gokSGnsD0c3scay3fxKG/fJtIQ2XsWLi6nOpXHyHrvNuP+nrtVXs58MQ9ROoOxh135o0j9+PfwOp09/o8q8vb45jR3vtU3d5G/fT2fBERkWSlP1smGYvFwpzZHyEnu4iiUZMAg1271hGNRntcd9NNFzFxYjEAVVVw4Tmwc8eglyxyzKZMA3tHRN2fkMdmsxHMySTg89Lc3Eo43PWX3rjmy9VaYSvpbf0rbH4SAIfDwV/+8heKior6eJKIdHLljSX9rBt7HG/a9MYRR9eAucLV/se+3CPgcRdNJfeKb2F1HzlotaeFehyLHDbV62jH7Wl5R3xtERGRZKOQJwnZbHZOPeVyUlOzKSqaTHNLA/v2bcIwjLjrnE47t99xBSUl5g8/5fvMoOfgwd5eVWTocLth0hRz+7333qOxsbHP54Ryswj4zb/mdm++nJ2bh93uMHeq1Hw5qR16F1b+ILb785//nFNOOSWBBYkMT87s4h7HjLZmok11PS8GWnatZf/v7ybaVBN33DvuZHI+/g2sfUyncgZH9zjWXrWn12vbK3f38vyyo76+iIhIMlHIk6Q8Hj+nnnIFAX8G+fljqareT2XV3h7Xeb0u7vrSYoJ5mQBs2QwfXQR1vf+cJjJkdE7ZikajrFq1qs/rQ7lZeD1ubDZbXF8em81OMH+UudOwx2zIK8mnsRze+JrZjwf47Gc/yw033JDgomSkeOWVV7BYLNTU1Bz1uuLiYh544IFBqak3RxuJ0117xa5ej1sOmxoO0LT5bQ7+6V6Mtvh+aIGZ55F10Zew2Bx9fj5P6ewex1p3r8OIxPffiTRW96jN4nDjLpzc5+cQERFJFgp5klhmZogT515EelqQrKwCysu3Ul9f1eO6lBQfX/nKJ8jISAHg3VVw2YXQj5WpRRLmmJsvB7OwWCDQa/PlbtN1NJon+bRUw6t3QmsNAAsWLOCHP/xhYmuSIenaa6/FYrFgsVhwOp2UlZVx//33x03x/CDmz59PeXk5qampADz88MOkpaX1uG7ZsmXcdNNNH+pzfRjth3ZS/ugdNKx76YjLjrfu20jVS7/ucdzmz8Tq8sUda1i7lENPfwcj3BZ3PPXkxWScfSsWS/9+DHWk5+E6LKiJtjRQ+9YTsX3DMKh+5REw4qene8ef0udIIRERkWSixstJrrh4CtU1BzAMg7a2ZnbtWk9JyTS83pS467KyUvnyV67mm/f/joaGZv79KlxzBTz2ZFfvE5GhZOacru3+hDxZGak47HYCfi+HqmrizoUKirt2qjdAcA6SJNob4bU7oX4nAGVlZfzxj3/Erv+xyREsWrSI3/3ud7S2tvLcc89x22234XA4uPvuuz/wazqdToLBYJ/XZWdnf+DPMVDayjdT+fcfUmmz48wuwZFZgMXpJdpcR3vV3h6rYnXyTz07br95+0oqn3sAiJ8qbk8LEm2up+rFXx2xBt/E03CFxsUdSz/9Ovb/311xIU7tG3+gZddaHJkFtJVvpu3A1rjnWJwe0k5e3I93LSIikjw0kmcEmDb1TEaNmsiowom4PX527FxDS0vPHib5+dnc9aXFuFzm0Om/PQO33QiHtfIRGRImTgJXx6q6y5Yt6/N6q9VKXm4mAb+XlpY22tuP0HxZI3mSR7gV/v1lqDb/mwaDQZ5//nnS09MTXJgMZS6Xi2AwSFFREbfeeisLFizg2Wefpbq6mk9+8pOkp6fj9Xo599xz2bx5c+x5O3fu5IILLiA9PR2fz8ekSZN47rnngPjpWq+88grXXXcdtbW1sVFD9913HxA/Xeuqq67i8ssvj6utvb2drKwsHn30UcCcrrpkyRJKSkrweDxMmzaNJ598cmBuRCRM2/7NNK5/mYZVf6dpw7+PGPA488aQOu+y+Kc3VHF4wAMQrtlP/Ypnj/rora+OKzSOtFM/0eN46+51NKz+Z4+AB4uVzEWfxZ6S1f/3LCIikgQU8owAVquVk+ZfSl5eGcVFU3A43GzfsYa2tp7zsUaPzueOL1yB3W4D4P8ehrvvVNAjQ4/Taa6yBbBp0yZqa2v7fE4oN4uAr6P5crcpW5nZQZydiVGVVthKCpF2ePO/4NBqANLS01m6dCklJSWJrUuGHY/HQ1tbG9deey3Lly/n2Wef5a233sIwDD7ykY/Q3t4OwG233UZrayuvvfYaa9eu5Xvf+x5+f88Vo+bPn88DDzxASkoK5eXllJeXc+edd/a4bvHixfz1r3+loaEhduxf//oXTU1NXHzxxQAsWbKERx99lIceeoj169dzxx13cPXVV/Pqq68ep7txGKsN/7RF5F6xBIu9Zz+egZZ64sdJP+smLA7X0cvypJB14ZfwTTj1uNckIiIy1Gi8+ghhs9k57dQreHHpI0SNKNu2rWL7jncZXToD+2E/mE2eXMqnb7uEB3/yJIZh8OAPwWqFb38fLJYEvQGRXsycDcvfMbdXrlzJGWeccdTr84PZeDwu7HYb9Y1NZKSb0xatVit5+UXs3LYJmvabPVzcGu0xbEUj8M63oPwtALxeHy88/zwTJ05McGEynBiGwdKlS/nXv/7Fueeey9NPP80bb7zB/PnzAXjssccoLCzk6aef5uMf/zi7du3i0ksvZcoUc+m/0tLSXl/X6XSSmpqKxWI56hSuhQsX4vP5eOqpp/jEJ8wRLL///e+58MILCQQCtLa28p3vfIcXX3yRefPmxT7n66+/zi9/+UtOO+20Hq/Z2tpKa2trbL/usFUWHDklBK95gJad79JWvpn2qj1E6iuJtjUBFqwOF1ZfOo7MAtwFk/COPwl7Sk7/b+oASJl9Id6x82h493maty0nXHuAaGsTVo8fR3oIT9kJ+KcuxOYJDGpdIiIiQ4VCnhHE4XBxxumLef7F3xKNRjqCnjWUlkzHZov/UjjhhIl86vrz+X+/+SsAP/4faG+H7/9IQY8MHYf35ekr5AnlmsP2Az5vj+bLeQUdIQ+YfXny5g1orTJIjCis+AHsWgqYv1D//e9/Y/bsnqvziPTmb3/7G36/n/b2dqLRKFdddRWXXHIJf/vb35g7d27suszMTMaNG8f7778PwOc+9zluvfVWnn/+eRYsWMCll17K1KlTP3Addrudyy67jMcee4xPfOITNDY28swzz/D4448DsGXLFpqamjj77PheOG1tbcyYMaPX11yyZAnf+MY3jvg5LRYLrmAZrgFYctw/ZQH+KQs+9Ov0xp6STdopi0k7Rf12REREDqfpWiOM2+3jrDM+SXpaLsXFU2lra2HnznVEo9Ee155xxkw+df35sf2f/xi++Fno5VKRhOi+wlZ/+vJkpKfgcjoJ+L3UHRby5Bd2m8ajvjzDk2HAyh/BtmcBsNps/PGPf+L0009PbF0yrJxxxhmsXr2azZs309zczCOPPIKlH3/duOGGG9i2bRuf+MQnWLt2LbNnz+bBBx/8ULUsXryYpUuXcvDgQZ5++mk8Hg+LFi0CiE3j+vvf/87q1atjj/fee++IfXnuvvtuamtrY4/du3v2vhEREZHhTSHPCOTzpXLmGZ8gLTWH4uIpNDXXsWv3exi9NN4588xZ3HjThbHRO7/8GXz+VgU9MjSMGw9es8VOv1bYslgsZl8ev5e2tnZa29pj5+KXUX9/oEuV480wYNWPYctTgPnf+te//g0XXXRhgguT4cbn81FWVsaoUaNiq7BNmDCBcDjM22+/HbuusrKSjRs3xk0DLCws5JZbbuEvf/kLX/ziF/n1r3suNQ7mCLNIJNJnLfPnz6ewsJAnnniCxx57jI9//OM4HObiCBMnTsTlcrFr1y7KysriHoWFhb2+nsvlIiUlJe4hIiIiyUUhzwiVmprN6acvJjUlm1GjJlFfX8mevRt7DXpOO20GN99ycewvmb/9Fdx4DYTDPS4VGVR2O0zrmJWwfft2Kisr+3xOKGiGPBDffDk9MweX22PuqPny8GIYsPqnsNkcvWCxWPjZzx/iU9ddm9i6JGmMGTOGiy66iBtvvJHXX3+dd999l6uvvpr8/HwuuugiAG6//Xb+9a9/sX37dlauXMnLL7/MhAkTen294uJiGhoaWLp0KRUVFTQ1NfV6HZirbD300EO88MILLF7cNT0pEAhw5513cscdd/DII4+wdetWVq5cyYMPPsgjjzwysDdAREREhg2FPCNYVmY+p556BWmpORQWjKemZj/7yrf0GvScfPJUPn3bJVitZtDz+P/B4o9BS88FukQGVfe+PCtWrOjz+lBuJm6XE4fDHhfyWCyWrqXUWyqhuWKAK5XjwjBgzS9g0xOA+d/xgR8/yK233JTgwiTZ/O53v2PWrFmcf/75zJs3D8MweO6552IjayKRCLfddhsTJkxg0aJFjB07lp///Oe9vtb8+fO55ZZbuPzyy8nOzub73//+ET/v4sWLee+998jPz+ekk06KO/fNb36Te+65hyVLlsQ+79///netIiciIjKCWYzefqOXEWXXrvd4/Y0/UVm1j717N5KRHiIUGtNrD4Llyzfw0wefJBw2h5mffhY88TT0skqsyKB4/DG4/mpz+1vf+hZf+9rXjnp9dU0dP/5/f2LN+1sBmDphdOzci8/9mTde+oe5c/J3If/k41KzDBDDgLW/gvf/N3bo+//9Q+66844EFiUyfNTV1ZGamkrh7X/E6vImuhwREZFhb8d3zztur93573Ztbe1Rp1xrJI8watRETjzxo2RmhMjPH09V9T727tvU64ie2bPHc+ddV+FymX+5fGUpXHgOVFcPdtUipu7Nl/vTlyctNYDX446tsNX96zxUUNx1ofryDG3RMKz4n7iA51vf/q4CHhEREREZ0RTyCAClJdOYP+8SMjPyKCiYQHX1/iP26Jk8uZSv3P0JvF43AG+/BQtOgu3bBrtqESgbA51B9rE2X25vD8c3X44LedSXZ8gKt8Ab/wVbn4kduufe+/naV7+cwKJERERERBJPIY/EFBdP4aSTPkZGRh6FhROoqTnA7j3v97q8+pgxhXztv64hJcUHwIb34fS58J83B7tqGemsVpg+y9zes2cP+/fv7/M5nSEPxDdfTk3PwOvrmHtYvdGcDiRDS2stvHI77HsdMJdJX/K9H3D/ffckti4RERERkSFAIY/EKRo1iZNP+jgZ6XmMKpxIbe0hdu1e32vQU1QU5N77PkVeXiYAFRXwkTPhj38Y7KplpDvWKVuhYBYupwOn03Hk5sutNdB0YGALlQ+ncT8s/TRUrgPA6XLz84d+w1e+9IUEFyYiIiIiMjQo5JEeRhVO4NRTLicjI4/ioik0NFSzY+caIpGea6bn5mZw3zeuZ+IkcyWP1la47ir4zjc0CEIGzzGHPLlZAKT4vdQ3xi9drL48Q1T1ZnjxZqjfCYA/kMoj//c4N99wbWLrEhEREREZQhTySK/y88dy+mmLSU8PUlIyjebmerbvWEM43N7jWp/Pw5e+tJjTTp8RO/bt++D6T2iJdRkcs7oto96fkCfg9+L3eQj4e2m+3DmSB9SXZ6g4sAJe/oy5tD2QmZ3LY48/yRUfuyjBhYmIiIiIDC0KeeSIgsESzjrzk6SnByktmU5bWzPbt6+mvb21x7V2u40bbriAK65cQOfK6088BucvgEOHBrlwGXGKiiEjw9xetmxZrw3Du4s1X/Z5CYcjtLS0xc7FjeSpVsiTcLtehNe+CO2NABQUlfL4H5/iwo8sSHBhIiIiIiJDj0IeOaqsrAIWnHkt6Rl5lJZOJxKNsGXrCpqa63tca7FYOP/8k/jc5y/D6bQD8NYbcMaJsFG/K8txZLHAjI4pWwcPHmTPnj19Pieu+XK3KVuB1DQCKWnmTpWaLyfUxsfhrfvM5dKBsROn8fgTT7Lg9HmJrUtEREREZIhSyCN9Sk/P5eyzriMrs4DRo2ficLjZtm0VtXUVvV4/Z84E/uue60hLM1cp2r7NDHpefH4wq5aR5lj78uQHs3E47Lhdzrjmy9BtylZ7AzT0HRjJADOisOpBWP3T2KHZJ57GX/78F06aO+MoTxQRERERGdkU8ki/pKRksvCcGwjllVFaOp1AIJNdu9ZxqGJ3r1NjSktDfOP+Gxg1KheA2lr46CL41r0QiQx29TISHGtfnrxcc1W4QC/Nl/MKirp21JdncIVb4T/3w6YnYocWfORi/vynx5k0vjSBhYmIiIiIDH0KeaTf3G4fZ535SUpLpjFq1ESys0dRXr6Vvfs29brEemZmKvd8/TpmzBwLmLNeltwP558N5eWDXb0ku+4jeZYtW9bn9X6fl9SAv9fmy/mFJV0XKuQZPPV7YOnNZh8ezCmgl33iJv7wv79lVEEwwcWJiIiIiAx9CnnkmNhsdubPu4RpU88kmFtKQcF4qqv3s2Pn2l5X3vJ4XNxxxxVcdvlZWK1mR+bXXob5M+DlpYNdvSSzUD7kmAPHWL58eZ/Nl4FY8+VIJEpTc1dD8biRPNUbB7pU6c2eV+GF66FmCwAOh5NbPn83v/n5j8jKSEtsbSIiIiIiw4RCHjlmFouFKZNP46T5l5KdVUBpyTSaW+rZum0Vra3NPa63Wi1ceOHJfPVr15CeHgDg4AG44GxzqXVN35KBYLF0jeaprq5m+/btfT4nFOxovmyJb77s8wdITc/seLGNENUX6XETDcPqn8EbX4utoJWZncu933mAHy75eqw5toiIiIiI9E0hj3xgxcVTOOvMa8jMzKesdCZgsHXbShoba3u9fvz4Ir79nZuZMnU0YE7f+s43zOlb+/cPYuGStI61L08oNwu73YbH7erZfLlzKfVwM9TvGsAqJaa5Al75PGz8Q+zQxGmzeeBnv+Urd9yE2+1KYHEiIiIiIsOPQh75ULKzCznnnBvIySli9OiZuNw+tm1fTWXl3l6ny6Sk+LjrrsVcdtmZWCxd07fmTdf0LfnwjrUvTyg3C4CAz3vkFbZAfXmOhwMr4V/XwaF3AbBabZz70av44QM/ZfHHzsVmsyW4QBERERGR4Uchj3xoAX8655x9PYUF4ykpnkpmZoi9+zaze/d7RCLhHtdbrRYuvOgUvnaE6Vvhnk8R6Zfps7q2+zOSx+NxkZ4aIOD30tAY33w5LuRRX56BY0ThvUfh1duhtRoAfyCN6277Evd87W4Wnj43FgCLiIiIiMixUcgjA8LpdHPaqVcxccJJhPLGUDRqEvUN1WzZuoLm5vpenzN+QhHf+vbNTJkSP33rjHmwft1gVi/JIhiE/AJze8WKFb2u+na4/GA2Ab+XaNSgsakldjwvf1TXRVXvD3SpI1NrHfz7K7D2V2bYA4waPZ7b7rqf2z99A/NmT05wgSIiIiIiw5tCHhkwNpuNWTMXcuqpV5CVXUhZ2SysVjtbtq484vSt1FQfd31pMR/vNn1r5XI4aSZ895vQ3nPBLpGj6uzLU19fz+bNm/u8vnOFrcObL3u8PjKycsydms1mg2D54Ko2wPOfgvI3Y4dOOesCbvvC17jl2o8xeXxpAosTEREREUkOCnlkwBUWjOfcRTcTyitj9OgZZGT0PX3rootO4d77PkUoZPZIaW+Hb34dTj0B3l09yG9AhrVj7ssTzMJms+L1uI/clyfSBrV9r9YlvTCisPnPsPRWaDI7rLvcXhbfeAfX3Xgzt37yEooL8xJcpIiIiIhIclDII8dFwJ/O2Qs+xYTx88kPdU7fqjrq9K2ysgK+9e2bueCCk7BazVE9a1bDqXPgW/dCW9sgvgEZtmZ0C3n605cnLycTi8VCir+X5sudK2yB+vJ8EA17zdWzVv4IouawvNz8Ij59531cc/WVXPPxc7VEuoiIiIjIAFLII8eNzWZj9qxFnHLK5R3Tt2ZjtdrYum0VlVX7ep2+5XTaufyKBdz3jRsoKDCnyoTDsOR+OHk2rFox2O9ChpuZxxjyuFxOMtNTCfi8NDQ1x/XxiQt51Jen/6IR2PgE/POTcHBV7PCseWfw2bvu5aZrLuPsU+dgteqfIBERERGRgaSfsOW4G1U4gXMX3UxecDSjR88kPT3I3r2b2LV7Pe3tvQ/PKS0N8c1v3chHLz4Vm838Ml2/Fk6bC/d+FVpbB/MdyHCSkQElHe1dVq1aRbgfy7WFcrMI+L0YhzVfDuaPgs6VnrSMev/U7YCXPg2rH4SI+Y3q9ady1fW3c8Mtn+PWT17K+LKixNYoIiIiIpKkFPLIoDCXWf8UE8bPIz80lqKiSTQ21rJ5yzJqag70OqrH4bDzsY+dwf3338ioUbkARCLwP0tg3nRY+sIgvwkZNjpH8zQ1NfH++32PwAnlZuL3ebBYLHFTtlxuN9k5Hf1iareavXmkd9EwvPcI/Os6qFwfOzxp5nw+f/e3ueKyS7n+yvPJSE9JYJEiIiIiIslNIY8MGpvNzuxZ53LKKZeTk13E2LEn4Pens2v3++zctY729t6H5xQVB7n/mzdy6cdOj43q2bgBLjwHLrsItm4ZzHchw8Gx9uXJD2ZjtVrxed1xK2wBhAo6Rp1Ew1CzdSDLTB7Vm+CFG2Htr2O9d7yBdC7/1O1c+cmbuOzCc7jgnJNxOOwJLlREREREJLkp5JFBN6pwAuefdxulJdMZVTiRoqLJNDXXs2nzMqqr9/c6qsdut3HxxafxrW/dxOjR+bHjf38WZk2E//oy1NUN5ruQoaxzGXXoX8gT7Gi+HOil+XJe5wpbANWashUn0gprfmkGPDUdy9VbLIydOo/PfOl+TjrpJG646gKmTx6T2DpFREREREYIhTySEG63j1NO/jinnHwZuTlFjB0zh0Agk917NrBj59ojjuopHJXLvfddzy23fJS0ND9gLrf+o+/DtLHwyG+hW99cGaGmz+za7k/I43DYyclMJ+Dz0tjUQiTS9UWU3z3kUV+eLhXr4F+fgvf/F4wIAP7UTD66+Dau/OT1zJo6gZsWX0hudkaCCxURERERGTk0dl4SatSoieTkFrNixT+w2xykpeWwd+9GNm1+h7xgGenpQSydjW87WK0WTj5lGrPnTOCvz77Oc8+9SXt7hIMH4NPXw69/Dv/9Y5h3UoLelCRcSgqMHQebNsLq1atpa2vD6XQe9TmhYBZbduzBMAwamppIDZghYm6oEIvVihGNKuQBaG+Etb+BzU8C5qg7i8XKuOnzOf+iy0hLS+HsU+cwZ/qEHt+7IiIiIiJyfGkkjySc2+XlpPmXctqpV5KTU8TYMSeQkpLNnr0b2bFjDW1tLb0/z+3k45edyfe+fxtz5kyIHV+1AhacDNdcCbt3Dda7kKGmsy9PW1sb69at6/P6UG4WPq8Hq9VCfUNz7LjD4SQn2DFFsG4HhHv/ekx60TBs/jP8/XLY/Cc6A56UjFwuvfbzXL74WqZMLOPT11zMCTMmKuAREREREUkAhTwyZBQUjOP8j9zGmDGzKSwYT0nxVFramti0+R32H9hOJBLp9Xk5Oel8/vbL+OpXPxlbhQvgycdhxnj46l1w8OBgvQsZKo61L08oNwur1YLP6+nRlyfWfNmIdPWeGSkMA/a8Cv/8BKz8EbTWAGC12ph8wpnccsc9zJwxgwvPOZlPfGwR6WlaPUtEREREJFEU8siQ4nJ5mHfiRzn99MXk5hYzdswJZGUVUlGxm02b36H6CMutA0ycVMK3vn0T133qPPx+DwDNzfDj/4GJxQp7RpqZ3VbYWrZsWZ/X52anY7PazObLh6+wVVjStTOSpmxVrIOXboM3vgb1u2OHcwvHcuVNd3HpZVcxbdIYbrv2EmZOGafROyIiIiIiCaaePDIk5YfGcN5HPs269a+xcePbZKTnUb5/K7t3v09l5V5CeWV4vT1HDFitVs46azYnnjiJp556jaUvLqO9PRILe371M7jpNrj9LsjJScAbk0EzdTpYrWYj7v6M5LHb7eRkpbFnv5d9ByoIhyPY7TYAQoVFXReOhJCnYS+seQh2vxx3ODUzj9mnLGLu3Lmkpvg594wTmTy+VOGOiIiIiMgQoZBHhiyn083MGedQNnoWK1c9j9PpprGxhn3lW9iydSXp6UGCuSU4HK4ez/X5PFx99ULOO28+f/vbG7y0dLnCnhHG54PxE+G9dbBu3Tqam5vxeDxHfU5+MJvN2/eAAQ1NzaSlmM2Xc4L52Gx2IpFwcoc8rbWw/mHY+pTZg6eDN5DOmGknc9Ipp5GdmcbkcaWce+aJ+LxHv58iIiIiIjK4NF1LhryUlExOP+1Kzjj9akKhMZSVzSI/fyz19ZVs2vQOBw/uJHqEddPT0wN84hOL+OGPPs/CRXNxOMxcU9O4RobOvjzhcJg1a9b0eX0omIXP68Zqtcb15bHbHeTmFZg79bvMFaaSSaQV3n+sq6lyR8DjdHsZM/00zr/y05x//vmUjMrjiosW8LHzz1DAIyIiIiIyBCnkkWEjFCrj3EW3MHvWRwgGSxk3di7pGXkcOLiDTZvfoab24BH79XSFPZ87YtjzpTtg+7ZBfENy3B1rX55QbhYWiwW/r5fmy4XFHVsGVG8auCITKRqBHf+C566CNb+A9gYAbDY7o8bP5vSLbuDc8z/KhLElzJk+gduuuZTxZUV9vKiIiIiIiCSKpmvJsGKz2Rg/bi7FxVNYs+Zl7HYHGRkh9u/fyq5d7+Fx+8jJKSYlJavXPiGdYc/555/E3//2BkuXrqC9PUxzM/zsAfj5j2HReXDLZ+Gss0GtRoa37iFPf/ryZGemYbeZzZerquvizoUKi+Gtjp2qDZAzY+AKHWzhFtj+HGx6wuy/E2MhWDyeMVNPYsL4MQSzM8nKSOXcM06krKQgYeWKiIiIiEj/KOSRYcnt8nLCnPMYUzabFSv/idvlpbGploMHd7Bz1/p+hT1Xf2IR5x0W9hgG/ONv5mPsODPsueqTEAgk4E3KhzZ5KtjtEA73L+Sx2WwEczLZu/8Qe8sP0d4ejo36ii2jDsO3L09LNWz5C2z+C7TVxp3KDBYxeup8Jk6cREFeDgG/l9PnzWDW1HHYbLYEFSwiIiIiIsdCIY8Ma+npuZx15ifZV76FtWtfwedN/UBhz0UfPZWXX17J0heXUVlpjuDYtBG+8Bm49264+jq4+TYYM3aQ36B8KG63GfSsXgnvv/8+DQ0N+P3+oz4nlJvFJr8XgIbGZtLTzIQvOzeE3eEk3N4G1cMs5KnfAxsfhx3PQaQt7lRmcBQFY2Yycco0igvz8LpdzJs1mZPmTMHt7tnUXEREREREhi6FPDLsWSwW8kNjCOWVUV6+lbXreoY9breP3KOEPYGAlwsvPJnzzpvPihUbeP75d9jw/k4A6uvhFz8xH2cvMkf3nLPIXJ5bhr6Zs82QJxqNsmrVKk455ZSjXh/KzcLjdmOz2ahvbIqFPFabjbz8Qnbv2GpOcWqrA2fKYLyFD65iHWz8A+x5DejqV2WxWAkVjyNYMoXi0jJKi0L4vB6mTSzjjPkzSU05ehAmIiIiIiJDk0IeSRoWi4VQqIy8vNFHDXtycopITcnuNeyx2ayccMJETjhhIrt2HeCF59/hjTfW0NZmrjb0wj/NR1ExXL7YfIyfMMhvVI7JzNnw21+Z28uXL+8z5MnPy8JigUAvzZfzCorNkAegaiME5xyPkj8cIwr73oQNv4eK+BXFHE4XRWOnkVkwnuycXEYX55OW4md0UT5nnzqHYE5mgooWEREREZGBoJBHkk5fYc+uXe/hcnnIyAiRkZ6Hzdb7t8GoUblcf8MFXH7FAl55ZSUvvrCMigqzj8nOHfD9b5uPaTPMsOeyKyEvNIhvVPplZrccpj99eTLTU3E6HAT8Xg5V1cSd61phC7Mvz1AKeSKt5kpZGx83l3nvxudPYfSk2QRyRuMP+CkdFSI7M51gdgZnnzpHTZVFRERERJKEQh5JWkcKe5qa66io2Mv+/ds4cGA76WlBMjPzcbt9vb6O3+/h/PNP4iMfmceqlZt4cely1q3dFluu/d1V5uNrd8FpZ8IVV8NFl0DKEJ/JM1JMnAQuF7S29i/ksVqt5OVmsqf8ILv3HaStvR2nwwEc1nx5KPTlMQyoXA87/gm7l0JbfdzpjKxciifMxpdRiNvjpjCUQ34wm9SAjzNPnsW0iWVYNe9QRERERCRpKOSRpHd42LNh41t4PSm0542mqmofVVX7qKzah9+fRmZmASmBzF6nclmtVmbNHs+s2eOpqWngP2+t440317J92z7A/H37laXm4/Zb4SMXmCN8zjkXnM7BftfSyeGAqdNh2duwadMmampqSEtLO+pzQrlZBHxm8+X6hiYy01MByMoO4nS5aGttTewKW43l5qidHf+Ehj09TodGlZJfNh1PahC/z0NhKJecrHQ8bhcnzZnCvFmTY6uGiYiIiIhI8tBP+TJidIY9oVAZtbWH2LjpHbZvf5fs7FHU1VVQUbGHnTvX4XS6ycwIkZ6eh93u6PW10tL8LDr3RBadeyL79lXw5ptrefONtRw8WA1ASwv85U/mIy0NFiwyQ59zzoX09EF80wKYfXmWvW1ur1y5kjPPPPOo14dys/B4XNjtNuobmmMhj8VqJS+/iJ3bNkHTAXNJcvcg/Qdtb4LdL8POf8LBVT1OOxxOisZMJLtwIk5fOmmpAQpDOWSkpZAa8DFv1mRmThmLy6XEUUREREQkWSnkkREpNTWbE+acx/RpZ7Ft22o2bX6HtNQcmprrqazcy4GDOzhwcAdpabmkpwXxelN6Hd0DEApl8bGPncGll57O1i17eePNNfznrfXU15tNe2tq4MnHzYfNBvNPhnMvgEXnwdhxcISXlQF0eF+e/oQ8AAG/l/rG+ObLocJiM+QBczRPaN6A1honGoGDK8wRO3teNfvudGexUFQ6loLSSTgCuWCxkZ2ZRmEoh4DfS25WBifNmcKkcSXYbLbjV6eIiIiIiAwJCnlkRHM63YwffyLjxs1lX/kWNm16B68nQF6wlKrqciqr9lFVVY7T6SY9LZfUtFzcLm+vr2WxWCgbU0DZmAIWL17IunXbePPNtaxetYmmJvOX80gE/v2q+fjqnTCqCBYsNB+nnwWpqYP57keOmbO7tvvTlycjPQW3y0nA52X/oaq4c3HNl6uPU8hTu80MdnY+D80VPU5nZucycdoJpOeNpr45jMViIZiTSUFeNh63i9JRIU6aM5XSotARw0kREREREUk+CnlEMAOa/NAY8kNjqKurZNPmd9i2bTXZ2aNobKylpuYAFZV7OXBwJ15PgLS0XFJTc3A4ep/6YrfbmD59DNOnjyEcjrBp4y5WrtrEqpWbOHCgKzTYtdNc3vu3vzJH+cydByefBnPnwwknQkbGYN2B5DZuPHi90NQEy5Yt6/N6i8VCXk4Wu/cdZNfeA7S2teNydjZfLu66cKD68kTa4NC7sP9tKP8P1O3ocYnb42Xy9BMoHjeVqN1HZXUdze0GRQVBQrnZuFwOJo0tYf7syeR1jEQSEREREZGRRSGPyGFSUjKZPetcpk09k12732fnzrXs37+NUGgM9fWV1NQcYP+BbZTv34Lfl05aWi4pKVlHXIrdbrcxcVIJEyeVsHjxOZSXV7Jq1SbWrtnKhg07CYcjgDnK583XzUencePNwOfE+XDCPHNfiyEdO5sNps807+2OHTuoqKggK+voQUh+MIuAv6v5sivDHGaVnpmN2+Olpbnpw4U8jeVmoFP+Hzi4EsLNPS6xWm2MnTiVsZNn4k0PUVldx6G6MD5vG2NLCwlmZ+B2uZg1dRwnzpxEaor/g9cjIiIiIiLDnkIekSNwOFyMLp3O6NLpNDXVs3PXOnbsWEtV1T4ikTC1dYeorjnAnr0bsOyzkpKSRWpKNn5/+hEDH7P5cxahUBbnnTeflpY2NmzYyZo1W1izZiv7yyvjrt+4wXw8+ltzPy3NDHvmzjPDn9knQCBwnG9EEmhthVHFXQHaihUrWLhw4VGfEwpm4XY5cTrt1Dc0kdUR8lgsFkIFRWzb/D60VELTIfBm911EpNUcrdMZ7NTv6vUyi8VCQVEp46fMJrugjNqGVmobm2mqqiM3K53c7AwCfi9+n4cTZ05i1pTxeDyuY7kdIiIiIiKSpBTyiPSD1xtgwvh5TBg/j9q6CnZsX8OOnWvJSM+jrb2F2pqD1NQeZOeu9VgsFvy+NAIpmaQEsnA63Ud8XbfbGZvWBVBZWcvmzXvYsnk3mzfvYceOciKRaOz6mhp4/h/mo9OoIhg3AcZPhPEdH8dNGJmreEWjsHcPbN0CG96D1SvNx/vrIRzuum758uV9hzwdU578Pi/1DYc3Xy4xQx4w+/IcKeRp2NsR6rxtjtaJtPR6mS+QQtm4yZSOnURadgE1DS1U19RTfqiWzPRUigvzyExPwW6zM7a0kGmTyhhTUqBmyiIiIiIiEkchj8gxSk3JYtq0M5k69QwqKvawfccadu1aT3b2KNraWqirr6C+vpLy8q3s27cFt9tHwJ9BIJCB15uK9SjzrTIzU8nMTOXEEycB0NbWzvbt5WzetJvNW/awedNu6uoa456za6f5eOGf8a+VGzTDngkTu0Kg0WXmcUfvK8MPC5EI7NltBjnbtsCWzebHbVtg21Zz1E5f+tOXJzXFj9fjJuDzsu9ABYZhxJoYhwqLui6s2gD5p0CkHWq2QNV7ULkeKtZB475eX9scrTOasvGTKRs3GU9KBgcrajhYWUPV7oOkBHyMKSkgOzMdh8NGQV4O0yaWMWlcCV7PkUNDEREREREZ2RTyiHxAFouF7OxCsrMLmTVzEQcObGfvvk3s2buRpsZaItEwDfXV1NVXUlN7gEMVu7Farfh96fgD6fj9GbicnqOufuR0Ohg3bhTjxo0CwDAMDh6sZvPmPWzevJtdO/ezd++h2Opd3R3Ybz5ee/nwuiErG/JCEMwzP3Z/dB7LyQX7IP4forERKg5BZQUc6vhYcajr2P5y2L4Vtm+Dtrb+v67VamXixInMmDGDmTNnMnPmTKZNm9bn8ywWC6HcLHbvPcDOPWFaW9twu81pUXHNl3c+DwdWQvVGiB65MH8glbJxkygbP4WSMRMIRy1UVtex61AVLburcbucFASzCWZn4PG4SA34mTaxjKkTR5OVkdb/NywiIiIiIiOWQh6RAWCz2QiFygiFypg961xqag6yd+9G9u7bRGXlXgzDoLmlgYb6Kuobqigv34phbMFud+D1pOD1puD1puLxBI46BcdisZCbm0FubgYnnzwVMIOfmpoG9u49xL69h9i7r6Jju6LHqB/zejh00HysWX3k92SxmP1+vD5zZSqPF3y+ro+HH/N4zNdub+96hLttt7XF77e2QnVVV4jT3LPv8DFxOGzk5GSQmenD5Q4zceJkzj77Qi65+Fo8Hs8Hes1Qbrfmy43NsZAnJS0Dry9AU2O92UC5sbzHc212O/mFJZSNn8KY8ZPJyM6juraeqpo6Vr23nfb2MHa7jayMNIKjM0hN8eNyOpg4tphpE8soLszT8uciIiIiInJMFPKIDDCLxUJ6ei7p6blMnnwqLa1N7N+/jfLyLewr30JLcwORaISmplqamupoaqrlUMVuIpHtWCwW3G4fXm8qvo7gx+FwHfWXffPzBUhPDzB5cmncufr6JjPw6Qh+Kitqqa6up6amnpqahrh+P4czDKirMx9DhcNhN0OuYAbBjo+5uRkEgxmkp6dgtVpoa2tmw8a3KSkeQ35+6gcOeMBsvux0OnA5HdQ3NJGdmQaY9zy/sJjNG9bGrk3PzKagqJSCUaPJH1VCbl4BTS1tVNXUsbeyjg271oEBfp+HvJxMMtJTSA34sFqtlI4KMW1iGePLinA6h/FcOhERERERSSiFPCLHmdvlpbhoMsVFkzEMg+qaA+wv38rBQzs5VLGbttZmDAxaWho7Qp86GhqqqKzcC4DD4TRH+bj9uFxeXG4fTof7qL19OgUCXsaPL2L8+KIe56JRg4aGJjP0qa6nuqa+IwBqMPer62lubqWtrZ3WVvPR3h7u5bN8cFarhUDASyDgMz+meEkJeDu2O451PFJSfKSm+rFaewZekUiE5uY6mlvqaW5uAKC5uZ7Kqn1xvXSOVWfz5YDfS31jfPPlyTNOIK+giPxRpeSPKsHnD9DW3k5VTT1V1XXsWL0hNlonPTVAfjCb9LQALqcDp8NBaVGIsuICxpYWkhLwfaD6REREREREulPIIzKILBYLGelBMtKDTOQkDMOgvr6KQxW7qajYzaGK3dTVHsIwDMKR9thIn8amOg5V7ibSsUSUxWLB5fLgcvlwu7y4XD5cbi8up7df4Q+YAUtKio+UFB9FRcF+PScaNWhra48FP22t7bS2tdPa2kZbazsWqwWbzYbNZsVut2Gz2bDbrNjs5rHDz3k8zn4HMIZh0NbWQltbM61tzbS1NdPW2kxrWxNtbc0Yhtl/x+XykZGRh8cToKW5gaamOny+1H59jsOlBHwEfF4Cfi+79x2MC4ymzppHOBymvqGZg1UNVG3bFwuCAj4vodwsMtICpAR8WCwWcjLTGVNaSFlxPqPyc7UyloiIiIiIDDiFPCIJZLFYSEnJJCUlk9Gl0wFoa2uhsnJvLPipqNhDe7vZWDkcbqOltYnW1kZaWsyPVdXltLe3dbweOJ0eXE4vDqcLh92F3eGM+2iz2T/wyBar1YLb7cTtdg7I++/OMAwikXbC4Xba2lu6hTgdgU5bC4ZhdNRhxeFw43J5CAQycbv9eDzmSCerxWre19RsMtLziEYjH6quUDCLnXv2Ew5HqKqpo7Wtnbr6RuobmmhsbgED7HYbGWkp5AezyUgLdEzxclI6KsSY0gJGF+WTmuIfiNskIiIiIiJyRAp5RIYYp9NNXt5o8vJGAxCNRqmvr6Sm9iC1tYeorT1EXV0FdXUVsQAjEgnT0tpIa2tT7NHUVEd7eyuRSDsd2QhgBksOhxO73dX10e7EYrVis9qwWG1YLVasVhtWq9XcP/yYxYphRDEMo9sjChhEO7e7nYtGI4TDbYQj7UTC7YQ7wpxI7GMbkUg4rk6r1YrT6cbp9JKSkoXT6ekIsDw4nC4smEGVze4gNSWbjIw8MtLzSM/IIy01B7t9YHrbdG++vPb9bVgsFnxeN6kBP4WhXFICXjxuNxYL5GZlMKakgLKSAgpDORqtIyIiIiIig0ohj8gQZ7VaSU3NJjU1O+54NBqloaGa2joz9OkeAIXDXUt5R42oGbC0t9EebqW9vY1wuI329lbC4TYaG2sIR9qJRiIYRiQuaBn492LDbndgtzuw2Rw4nW48ngB2uzN2zG534HC4zIbTHUGO3e4kEMggEMjA7zc/pgQy8fsz8Hj8x3UVqlBuFg6HnTGlhfg8bgJ+LzabFavVSjA7g/xgNgV52ZSMCqm3joiIiIiIJJRCHpFhymq1xqZ6dWcYBs3N9TQ119Pc+Wgy91taGmLHWlp6Lq8OZigUjUY6HlGMaKTnsY7eNF0PKxYO27dYoGPbarVitzuxWnr2C3I4XGZDaZcXt9uHy+XF4wkQ6AhzzOlYvoQtJx4Kms2XJ5QVUZCXTUFeDgWhbPJyzPBHRERERERkqNBvKCJJxmKx4PWm4PWmHPW6SCQSC31aWhoPm0rVdth0qviP0Wika3pXt2lc1l72rVYbNpvDDHE6VgeLbbu82GxD+39DPq+HL958ZWzKloiIiIiIyFA1tH+7EpHjxmaz4fOlfuCVp0YSBTwiIiIiIjIcKOQRERERGcHWfWMhKSlHH/0pIiIiw0PPBhkiIiIiIiIiIjLsKOQREREREREREUkCCnlERERERERERJKAQh4RERERERERkSSgkEdEREREREREJAko5BERERERERERSQIKeUREREREREREkoBCHhERERERERGRJKCQR0REREREREQkCSjkERERERERERFJAgp5RERERERERESSgEIeEREREREREZEkoJBHRERERERERCQJKOQREREREREREUkCCnlERERERERERJKAQh4RERERERERkSSgkEdEREREREREJAko5BERERERERERSQIKeUREREREREREkoBCHhERERERERGRJKCQR0REREREREQkCSjkERERERERERFJAgp5RERERERERESSgEIeEREREREREZEkoJBHRERERERERCQJKOQREREREREREUkCCnlERERERERERJKAQh4RERERERERkSSgkEdEREREREREJAko5BERERERERERSQIKeUREREREREREkoBCHhERERERERGRJKCQR0REREREREQkCSjkERERERERERFJAgp5RERERERERESSgEIeEREREREREZEkoJBHRERERERERCQJKOQREREREREREUkCCnlERERERERERJKAQh4RERERERERkSSgkEdEREREREREJAko5BERERERERERSQIKeUREREREREREkoBCHhERERERERGRJKCQR0REREREREQkCSjkERERERERERFJAgp5RERERERERESSgEIeEREREREREZEkoJBHRERERERERCQJKOQREREREREREUkCCnlERERERERERJKAQh4RERERERERkSSgkEdEREREREREJAko5BERERERERERSQIKeUREREREREREkoBCHhERERERERGRJKCQR0REREREREQkCSjkERERERERERFJAgp5RERERERERESSgEIeEREREREREZEkoJBHRERERERERCQJKOQREREREREREUkCCnlERERERERERJKAQh4RERERERERkSSgkEdEREREREREJAnYE12AiIiIiAw+wzAAqKurS3AlIiIi0pfOf687//0+EoU8IiIiIiNQZWUlAIWFhQmuRERERPqrvr6e1NTUI55XyCMiIiIyAmVkZACwa9euo/6wONLV1dVRWFjI7t27SUlJSXQ5Q5LuUf/oPvVN96hvukf9k4z3yTAM6uvrCYVCR71OIY+IiIjICGS1mq0ZU1NTk+YH4OMpJSVF96kPukf9o/vUN92jvuke9U+y3af+/FFGjZdFRERERERERJKAQh4RERERERERkSSgkEdERERkBHK5XNx77724XK5ElzKk6T71Tfeof3Sf+qZ71Dfdo/4ZyffJYvS1/paIiIiIiIiIiAx5GskjIiIiIiIiIpIEFPKIiIiIiIiIiCQBhTwiIiIiIiIiIklAIY+IiIiIiIiISBJQyCMiIiIyAv3sZz+juLgYt9vN3LlzeeeddxJdUsLcd999WCyWuMf48eNj51taWrjtttvIzMzE7/dz6aWXcuDAgQRWPDhee+01LrjgAkKhEBaLhaeffjruvGEYfP3rXycvLw+Px8OCBQvYvHlz3DVVVVUsXryYlJQU0tLSuP7662loaBjEd3F89XWPrr322h5fW4sWLYq7Jtnv0ZIlS5gzZw6BQICcnBw++tGPsnHjxrhr+vM9tmvXLs477zy8Xi85OTncddddhMPhwXwrx01/7tHpp5/e42vplltuibsmme8RwC9+8QumTp1KSkoKKSkpzJs3j3/84x+x8yP966iTQh4RERGREeaJJ57gC1/4Avfeey8rV65k2rRpLFy4kIMHDya6tISZNGkS5eXlscfrr78eO3fHHXfw17/+lT/96U+8+uqr7Nu3j0suuSSB1Q6OxsZGpk2bxs9+9rNez3//+9/nJz/5CQ899BBvv/02Pp+PhQsX0tLSErtm8eLFrF+/nhdeeIG//e1vvPbaa9x0002D9RaOu77uEcCiRYvivrb+8Ic/xJ1P9nv06quvctttt/Gf//yHF154gfb2ds455xwaGxtj1/T1PRaJRDjvvPNoa2vjzTff5JFHHuHhhx/m61//eiLe0oDrzz0CuPHGG+O+lr7//e/HziX7PQIoKCjgu9/9LitWrGD58uWceeaZXHTRRaxfvx7Q11GMISIiIiIjygknnGDcdtttsf1IJGKEQiFjyZIlCawqce69915j2rRpvZ6rqakxHA6H8ac//Sl27P333zcA46233hqkChMPMJ566qnYfjQaNYLBoPHf//3fsWM1NTWGy+Uy/vCHPxiGYRjvvfeeARjLli2LXfOPf/zDsFgsxt69ewet9sFy+D0yDMO45pprjIsuuuiIzxlp98gwDOPgwYMGYLz66quGYfTve+y5554zrFarsX///tg1v/jFL4yUlBSjtbV1cN/AIDj8HhmGYZx22mnG5z//+SM+Z6Tdo07p6enGb37zG30ddaORPCIiIiIjSFtbGytWrGDBggWxY1arlQULFvDWW28lsLLE2rx5M6FQiNLSUhYvXsyuXbsAWLFiBe3t7XH3a/z48YwaNWpE36/t27ezf//+uPuSmprK3LlzY/flrbfeIi0tjdmzZ8euWbBgAVarlbfffnvQa06UV155hZycHMaNG8ett95KZWVl7NxIvEe1tbUAZGRkAP37HnvrrbeYMmUKubm5sWsWLlxIXV1dbBRHMjn8HnV67LHHyMrKYvLkydx99900NTXFzo20exSJRHj88cdpbGxk3rx5+jrqxp7oAkRERERk8FRUVBCJROJ+yAXIzc1lw4YNCaoqsebOncvDDz/MuHHjKC8v5xvf+AannHIK69atY//+/TidTtLS0uKek5uby/79+xNT8BDQ+d57+zrqPLd//35ycnLiztvtdjIyMkbMvVu0aBGXXHIJJSUlbN26la9+9auce+65vPXWW9hsthF3j6LRKLfffjsnnXQSkydPBujX99j+/ft7/VrrPJdMertHAFdddRVFRUWEQiHWrFnDl7/8ZTZu3Mhf/vIXYOTco7Vr1zJv3jxaWlrw+/089dRTTJw4kdWrV+vrqINCHhEREREZ0c4999zY9tSpU5k7dy5FRUX88Y9/xOPxJLAyGe6uuOKK2PaUKVOYOnUqo0eP5pVXXuGss85KYGWJcdttt7Fu3bq4nlcS70j3qHufpilTppCXl8dZZ53F1q1bGT169GCXmTDjxo1j9erV1NbW8uSTT3LNNdfw6quvJrqsIUXTtURERERGkKysLGw2W48VRw4cOEAwGExQVUNLWloaY8eOZcuWLQSDQdra2qipqYm7ZqTfr873frSvo2Aw2KOZdzgcpqqqasTeu9LSUrKystiyZQswsu7RZz7zGf72t7/x8ssvU1BQEDven++xYDDY69da57lkcaR71Ju5c+cCxH0tjYR75HQ6KSsrY9asWSxZsoRp06bx4x//WF9H3SjkERERERlBnE4ns2bNYunSpbFj0WiUpUuXMm/evARWNnQ0NDSwdetW8vLymDVrFg6HI+5+bdy4kV27do3o+1VSUkIwGIy7L3V1dbz99tux+zJv3jxqampYsWJF7JqXXnqJaDQa+wV1pNmzZw+VlZXk5eUBI+MeGYbBZz7zGZ566ileeuklSkpK4s7353ts3rx5rF27Ni4Qe+GFF0hJSWHixImD80aOo77uUW9Wr14NEPe1lMz36Eii0Sitra36Ouou0Z2fRURERGRwPf7444bL5TIefvhh47333jNuuukmIy0tLW7FkZHki1/8ovHKK68Y27dvN9544w1jwYIFRlZWlnHw4EHDMAzjlltuMUaNGmW89NJLxvLly4158+YZ8+bNS3DVx199fb2xatUqY9WqVQZg/PCHPzRWrVpl7Ny50zAMw/jud79rpKWlGc8884yxZs0a46KLLjJKSkqM5ubm2GssWrTImDFjhvH2228br7/+ujFmzBjjyiuvTNRbGnBHu0f19fXGnXfeabz11lvG9u3bjRdffNGYOXOmMWbMGKOlpSX2Gsl+j2699VYjNTXVeOWVV4zy8vLYo6mpKXZNX99j4XDYmDx5snHOOecYq1evNv75z38a2dnZxt13352ItzTg+rpHW7ZsMe6//35j+fLlxvbt241nnnnGKC0tNU499dTYayT7PTIMw/jKV75ivPrqq8b27duNNWvWGF/5ylcMi8ViPP/884Zh6Ouok0IeERERkRHowQcfNEaNGmU4nU7jhBNOMP7zn/8kuqSEufzyy428vDzD6XQa+fn5xuWXX25s2bIldr65udn49Kc/baSnpxter9e4+OKLjfLy8gRWPDhefvllA+jxuOaaawzDMJdRv+eee4zc3FzD5XIZZ511lrFx48a416isrDSuvPJKw+/3GykpKcZ1111n1NfXJ+DdHB9Hu0dNTU3GOeecY2RnZxsOh8MoKioybrzxxh5harLfo97uD2D87ne/i13Tn++xHTt2GOeee67h8XiMrKws44tf/KLR3t4+yO/m+OjrHu3atcs49dRTjYyMDMPlchllZWXGXXfdZdTW1sa9TjLfI8MwjE996lNGUVGR4XQ6jezsbOOss86KBTyGoa+jThbDMIzBGzckIiIiIiIiIiLHg3ryiIiIiIiIiIgkAYU8IiIiIiIiIiJJQCGPiIiIiIiIiEgSUMgjIiIiIiIiIpIEFPKIiIiIiIiIiCQBhTwiIiIiIiIiIklAIY+IiIiIiIiISBJQyCMiIiIiIiIikgQU8oiIiIiIiIiIJAGFPCIiIiIiIiIiSUAhj4iIiIiIiIhIElDIIyIiIiIiIiKSBP4/xqXJhgCMXrYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of target var\n",
    "#countplor, x = class\n",
    "sbn.countplot(x=orig_df['class'],data=orig_df)\n",
    "\n",
    "# create pie chart and bar plot \n",
    "pplot.figure(figsize=(14,7))\n",
    "pplot.subplot(121)\n",
    "orig_df[\"class\"].value_counts().plot.pie(autopct = \"%1.0f%%\",colors = sbn.color_palette(\"prism\",7),startangle = 60,labels=[\"Positive\",\"Negative\"],\n",
    "wedgeprops={\"linewidth\":2,\"edgecolor\":\"k\"},explode=[.1,0],shadow =True)\n",
    "pplot.title(\"Distribution of Target  Variable\")\n",
    "\n",
    "pplot.subplot(122)\n",
    "ax = orig_df[\"class\"].value_counts().plot(kind=\"barh\")\n",
    "\n",
    "for i,j in enumerate(orig_df[\"class\"].value_counts().values):\n",
    "    ax.text(.7,i,j,weight = \"bold\",fontsize=20)\n",
    "\n",
    "pplot.title(\"Count of Target Variable\")\n",
    "pplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "3c8239a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:03.962034Z",
     "iopub.status.busy": "2022-11-11T21:11:03.960855Z",
     "iopub.status.idle": "2022-11-11T21:11:04.218664Z",
     "shell.execute_reply": "2022-11-11T21:11:04.217233Z"
    },
    "papermill": {
     "duration": 0.284846,
     "end_time": "2022-11-11T21:11:04.221496",
     "exception": false,
     "start_time": "2022-11-11T21:11:03.936650",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_0164a_row0_col0, #T_0164a_row1_col1 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_0164a_row0_col1, #T_0164a_row1_col0 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_0164a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_0164a_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_0164a_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Gender</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0164a_level0_row0\" class=\"row_heading level0 row0\" >Female</th>\n",
       "      <td id=\"T_0164a_row0_col0\" class=\"data row0 col0\" >9.500000</td>\n",
       "      <td id=\"T_0164a_row0_col1\" class=\"data row0 col1\" >54.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0164a_level0_row1\" class=\"row_heading level0 row1\" >Male</th>\n",
       "      <td id=\"T_0164a_row1_col0\" class=\"data row1 col0\" >90.500000</td>\n",
       "      <td id=\"T_0164a_row1_col1\" class=\"data row1 col1\" >45.940000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b7ce6df10>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of gender\n",
    "sbn.countplot(x=orig_df['Gender'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "plot_criteria= ['Gender', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "f9397e79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:04.318888Z",
     "iopub.status.busy": "2022-11-11T21:11:04.318510Z",
     "iopub.status.idle": "2022-11-11T21:11:04.510607Z",
     "shell.execute_reply": "2022-11-11T21:11:04.508860Z"
    },
    "papermill": {
     "duration": 0.220568,
     "end_time": "2022-11-11T21:11:04.513773",
     "exception": false,
     "start_time": "2022-11-11T21:11:04.293205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_bf66e_row0_col0, #T_bf66e_row1_col1 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_bf66e_row0_col1, #T_bf66e_row1_col0 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_bf66e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_bf66e_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_bf66e_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Polyuria</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_bf66e_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_bf66e_row0_col0\" class=\"data row0 col0\" >92.500000</td>\n",
       "      <td id=\"T_bf66e_row0_col1\" class=\"data row0 col1\" >24.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf66e_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_bf66e_row1_col0\" class=\"data row1 col0\" >7.500000</td>\n",
       "      <td id=\"T_bf66e_row1_col1\" class=\"data row1 col1\" >75.940000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b5e80d490>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of polyuria\n",
    "sbn.countplot(x=orig_df['Polyuria'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "\n",
    "plot_criteria= ['Polyuria', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "8568ad26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:04.612287Z",
     "iopub.status.busy": "2022-11-11T21:11:04.611500Z",
     "iopub.status.idle": "2022-11-11T21:11:04.870260Z",
     "shell.execute_reply": "2022-11-11T21:11:04.868957Z"
    },
    "papermill": {
     "duration": 0.287695,
     "end_time": "2022-11-11T21:11:04.872709",
     "exception": false,
     "start_time": "2022-11-11T21:11:04.585014",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_2a8a2_row0_col0, #T_2a8a2_row1_col1 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2a8a2_row0_col1, #T_2a8a2_row1_col0 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_2a8a2\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_2a8a2_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_2a8a2_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >sudden weight loss</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2a8a2_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_2a8a2_row0_col0\" class=\"data row0 col0\" >85.500000</td>\n",
       "      <td id=\"T_2a8a2_row0_col1\" class=\"data row0 col1\" >41.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2a8a2_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_2a8a2_row1_col0\" class=\"data row1 col0\" >14.500000</td>\n",
       "      <td id=\"T_2a8a2_row1_col1\" class=\"data row1 col1\" >58.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b5e8b6760>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of sudden weight loss\n",
    "sbn.countplot(x=orig_df['sudden weight loss'], hue = orig_df['class'], data = orig_df)\n",
    "plot_criteria = ['sudden weight loss','class']\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "016b3c96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:04.970878Z",
     "iopub.status.busy": "2022-11-11T21:11:04.970403Z",
     "iopub.status.idle": "2022-11-11T21:11:05.213901Z",
     "shell.execute_reply": "2022-11-11T21:11:05.212692Z"
    },
    "papermill": {
     "duration": 0.271772,
     "end_time": "2022-11-11T21:11:05.216621",
     "exception": false,
     "start_time": "2022-11-11T21:11:04.944849",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_68e73_row0_col0, #T_68e73_row1_col1 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_68e73_row0_col1, #T_68e73_row1_col0 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_68e73\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_68e73_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_68e73_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >weakness</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_68e73_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_68e73_row0_col0\" class=\"data row0 col0\" >56.500000</td>\n",
       "      <td id=\"T_68e73_row0_col1\" class=\"data row0 col1\" >31.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68e73_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_68e73_row1_col0\" class=\"data row1 col0\" >43.500000</td>\n",
       "      <td id=\"T_68e73_row1_col1\" class=\"data row1 col1\" >68.120000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b5e707130>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of weakness\n",
    "sbn.countplot(x=orig_df['weakness'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "\n",
    "plot_criteria= ['weakness', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "c2a3acec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:05.318259Z",
     "iopub.status.busy": "2022-11-11T21:11:05.317847Z",
     "iopub.status.idle": "2022-11-11T21:11:05.564451Z",
     "shell.execute_reply": "2022-11-11T21:11:05.562813Z"
    },
    "papermill": {
     "duration": 0.275894,
     "end_time": "2022-11-11T21:11:05.567256",
     "exception": false,
     "start_time": "2022-11-11T21:11:05.291362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_50614_row0_col0, #T_50614_row0_col1 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_50614_row1_col0, #T_50614_row1_col1 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_50614\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_50614_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_50614_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Genital thrush</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_50614_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_50614_row0_col0\" class=\"data row0 col0\" >83.500000</td>\n",
       "      <td id=\"T_50614_row0_col1\" class=\"data row0 col1\" >74.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_50614_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_50614_row1_col0\" class=\"data row1 col0\" >16.500000</td>\n",
       "      <td id=\"T_50614_row1_col1\" class=\"data row1 col1\" >25.940000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b5c467e20>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of genital thrush\n",
    "sbn.countplot(x=orig_df['Genital thrush'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "\n",
    "plot_criteria= ['Genital thrush', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d670ba1",
   "metadata": {
    "papermill": {
     "duration": 0.025399,
     "end_time": "2022-11-11T21:11:05.617636",
     "exception": false,
     "start_time": "2022-11-11T21:11:05.592237",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> **Distribution of visual blurring**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "06290309",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:05.669280Z",
     "iopub.status.busy": "2022-11-11T21:11:05.668879Z",
     "iopub.status.idle": "2022-11-11T21:11:05.931244Z",
     "shell.execute_reply": "2022-11-11T21:11:05.930041Z"
    },
    "papermill": {
     "duration": 0.291231,
     "end_time": "2022-11-11T21:11:05.933843",
     "exception": false,
     "start_time": "2022-11-11T21:11:05.642612",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c4ca5_row0_col0, #T_c4ca5_row1_col1 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c4ca5_row0_col1, #T_c4ca5_row1_col0 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c4ca5\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_c4ca5_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_c4ca5_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >visual blurring</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c4ca5_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_c4ca5_row0_col0\" class=\"data row0 col0\" >71.000000</td>\n",
       "      <td id=\"T_c4ca5_row0_col1\" class=\"data row0 col1\" >45.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4ca5_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_c4ca5_row1_col0\" class=\"data row1 col0\" >29.000000</td>\n",
       "      <td id=\"T_c4ca5_row1_col1\" class=\"data row1 col1\" >54.690000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b60e71490>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of bisual blurring\n",
    "sbn.countplot(x=orig_df['visual blurring'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "\n",
    "plot_criteria= ['visual blurring', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "670fff0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:06.037802Z",
     "iopub.status.busy": "2022-11-11T21:11:06.037273Z",
     "iopub.status.idle": "2022-11-11T21:11:06.296316Z",
     "shell.execute_reply": "2022-11-11T21:11:06.295190Z"
    },
    "papermill": {
     "duration": 0.288803,
     "end_time": "2022-11-11T21:11:06.299257",
     "exception": false,
     "start_time": "2022-11-11T21:11:06.010454",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4b444_row0_col0, #T_4b444_row0_col1 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4b444_row1_col0, #T_4b444_row1_col1 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4b444\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_4b444_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_4b444_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Itching</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4b444_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_4b444_row0_col0\" class=\"data row0 col0\" >50.500000</td>\n",
       "      <td id=\"T_4b444_row0_col1\" class=\"data row0 col1\" >51.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4b444_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_4b444_row1_col0\" class=\"data row1 col0\" >49.500000</td>\n",
       "      <td id=\"T_4b444_row1_col1\" class=\"data row1 col1\" >48.120000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b5c519d00>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of itching\n",
    "sbn.countplot(x=orig_df['Itching'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "\n",
    "plot_criteria= ['Itching', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "8c738220",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:06.407516Z",
     "iopub.status.busy": "2022-11-11T21:11:06.406598Z",
     "iopub.status.idle": "2022-11-11T21:11:06.663909Z",
     "shell.execute_reply": "2022-11-11T21:11:06.662592Z"
    },
    "papermill": {
     "duration": 0.288583,
     "end_time": "2022-11-11T21:11:06.666704",
     "exception": false,
     "start_time": "2022-11-11T21:11:06.378121",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_2566d_row0_col0, #T_2566d_row0_col1 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2566d_row1_col0, #T_2566d_row1_col1 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_2566d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_2566d_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_2566d_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Irritability</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2566d_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_2566d_row0_col0\" class=\"data row0 col0\" >92.000000</td>\n",
       "      <td id=\"T_2566d_row0_col1\" class=\"data row0 col1\" >65.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2566d_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_2566d_row1_col0\" class=\"data row1 col0\" >8.000000</td>\n",
       "      <td id=\"T_2566d_row1_col1\" class=\"data row1 col1\" >34.380000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b7ce320d0>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of irritability\n",
    "sbn.countplot(x=orig_df['Irritability'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "\n",
    "plot_criteria= ['Irritability', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f53c5fe",
   "metadata": {
    "papermill": {
     "duration": 0.025861,
     "end_time": "2022-11-11T21:11:06.719094",
     "exception": false,
     "start_time": "2022-11-11T21:11:06.693233",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> **Distribution of delayed healing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "acf705d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:06.776454Z",
     "iopub.status.busy": "2022-11-11T21:11:06.776010Z",
     "iopub.status.idle": "2022-11-11T21:11:07.042389Z",
     "shell.execute_reply": "2022-11-11T21:11:07.041178Z"
    },
    "papermill": {
     "duration": 0.297531,
     "end_time": "2022-11-11T21:11:07.044941",
     "exception": false,
     "start_time": "2022-11-11T21:11:06.747410",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4bc37_row0_col0, #T_4bc37_row0_col1 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4bc37_row1_col0, #T_4bc37_row1_col1 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4bc37\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_4bc37_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_4bc37_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >delayed healing</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4bc37_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_4bc37_row0_col0\" class=\"data row0 col0\" >57.000000</td>\n",
       "      <td id=\"T_4bc37_row0_col1\" class=\"data row0 col1\" >52.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bc37_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_4bc37_row1_col0\" class=\"data row1 col0\" >43.000000</td>\n",
       "      <td id=\"T_4bc37_row1_col1\" class=\"data row1 col1\" >47.810000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b5e687dc0>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of delayed healing\n",
    "sbn.countplot(x=orig_df['delayed healing'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "\n",
    "plot_criteria= ['delayed healing', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "cd57b380",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:07.154246Z",
     "iopub.status.busy": "2022-11-11T21:11:07.153831Z",
     "iopub.status.idle": "2022-11-11T21:11:07.417924Z",
     "shell.execute_reply": "2022-11-11T21:11:07.416563Z"
    },
    "papermill": {
     "duration": 0.295062,
     "end_time": "2022-11-11T21:11:07.420452",
     "exception": false,
     "start_time": "2022-11-11T21:11:07.125390",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_eb92a_row0_col0, #T_eb92a_row1_col1 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_eb92a_row0_col1, #T_eb92a_row1_col0 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_eb92a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_eb92a_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_eb92a_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >partial paresis</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_eb92a_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_eb92a_row0_col0\" class=\"data row0 col0\" >84.000000</td>\n",
       "      <td id=\"T_eb92a_row0_col1\" class=\"data row0 col1\" >40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb92a_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_eb92a_row1_col0\" class=\"data row1 col0\" >16.000000</td>\n",
       "      <td id=\"T_eb92a_row1_col1\" class=\"data row1 col1\" >60.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b60e022e0>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of partial paresis\n",
    "sbn.countplot(x=orig_df['partial paresis'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "\n",
    "plot_criteria= ['partial paresis', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "f2e5d990",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:07.535512Z",
     "iopub.status.busy": "2022-11-11T21:11:07.535096Z",
     "iopub.status.idle": "2022-11-11T21:11:07.935721Z",
     "shell.execute_reply": "2022-11-11T21:11:07.934526Z"
    },
    "papermill": {
     "duration": 0.433146,
     "end_time": "2022-11-11T21:11:07.938139",
     "exception": false,
     "start_time": "2022-11-11T21:11:07.504993",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_fefbb_row0_col0, #T_fefbb_row0_col1 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fefbb_row1_col0, #T_fefbb_row1_col1 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_fefbb\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_fefbb_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_fefbb_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >muscle stiffness</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fefbb_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_fefbb_row0_col0\" class=\"data row0 col0\" >70.000000</td>\n",
       "      <td id=\"T_fefbb_row0_col1\" class=\"data row0 col1\" >57.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fefbb_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_fefbb_row1_col0\" class=\"data row1 col0\" >30.000000</td>\n",
       "      <td id=\"T_fefbb_row1_col1\" class=\"data row1 col1\" >42.190000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b7ce49d60>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of muscle stiffness\n",
    "sbn.countplot(x=orig_df['muscle stiffness'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "\n",
    "plot_criteria= ['muscle stiffness', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "11487558",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:08.051686Z",
     "iopub.status.busy": "2022-11-11T21:11:08.050550Z",
     "iopub.status.idle": "2022-11-11T21:11:08.297531Z",
     "shell.execute_reply": "2022-11-11T21:11:08.296270Z"
    },
    "papermill": {
     "duration": 0.278848,
     "end_time": "2022-11-11T21:11:08.300329",
     "exception": false,
     "start_time": "2022-11-11T21:11:08.021481",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a2952_row0_col0, #T_a2952_row1_col1 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a2952_row0_col1, #T_a2952_row1_col0 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a2952\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_a2952_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_a2952_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Alopecia</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a2952_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_a2952_row0_col0\" class=\"data row0 col0\" >49.500000</td>\n",
       "      <td id=\"T_a2952_row0_col1\" class=\"data row0 col1\" >75.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a2952_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_a2952_row1_col0\" class=\"data row1 col0\" >50.500000</td>\n",
       "      <td id=\"T_a2952_row1_col1\" class=\"data row1 col1\" >24.380000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b5c804e20>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of alopecia\n",
    "sbn.countplot(x=orig_df['Alopecia'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "\n",
    "plot_criteria= ['Alopecia', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "c8d211f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:11:08.414785Z",
     "iopub.status.busy": "2022-11-11T21:11:08.414332Z",
     "iopub.status.idle": "2022-11-11T21:11:08.659053Z",
     "shell.execute_reply": "2022-11-11T21:11:08.657716Z"
    },
    "papermill": {
     "duration": 0.277467,
     "end_time": "2022-11-11T21:11:08.661859",
     "exception": false,
     "start_time": "2022-11-11T21:11:08.384392",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c493e_row0_col0, #T_c493e_row0_col1 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c493e_row1_col0, #T_c493e_row1_col1 {\n",
       "  background-color: #f3f0f0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c493e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >class</th>\n",
       "      <th id=\"T_c493e_level0_col0\" class=\"col_heading level0 col0\" >Negative</th>\n",
       "      <th id=\"T_c493e_level0_col1\" class=\"col_heading level0 col1\" >Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Obesity</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c493e_level0_row0\" class=\"row_heading level0 row0\" >No</th>\n",
       "      <td id=\"T_c493e_row0_col0\" class=\"data row0 col0\" >86.500000</td>\n",
       "      <td id=\"T_c493e_row0_col1\" class=\"data row0 col1\" >80.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c493e_level0_row1\" class=\"row_heading level0 row1\" >Yes</th>\n",
       "      <td id=\"T_c493e_row1_col0\" class=\"data row1 col0\" >13.500000</td>\n",
       "      <td id=\"T_c493e_row1_col1\" class=\"data row1 col1\" >19.060000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7b7cf6d400>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of obesity\n",
    "sbn.countplot(x=orig_df['Obesity'],hue=orig_df['class'], data=orig_df)\n",
    "\n",
    "\n",
    "plot_criteria= ['Obesity', 'class']\n",
    "cm = sbn.light_palette(\"red\", as_cmap=True)\n",
    "(round(pd.crosstab(orig_df[plot_criteria[0]], orig_df[plot_criteria[1]], normalize='columns') * 100,2)).style.background_gradient(cmap = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "68ecaba1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:17.206048Z",
     "iopub.status.busy": "2022-11-11T21:12:17.204824Z",
     "iopub.status.idle": "2022-11-11T21:12:17.217781Z",
     "shell.execute_reply": "2022-11-11T21:12:17.216548Z"
    },
    "papermill": {
     "duration": 0.164834,
     "end_time": "2022-11-11T21:12:17.220577",
     "exception": false,
     "start_time": "2022-11-11T21:12:17.055743",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data labeling -- changing negetive/positive into 0 1\n",
    "orig_df['class'] = orig_df['class'].apply(lambda x: 0 if x=='Negative' else 1)\n",
    "orig_df['class'].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "f5dc4d59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:17.816237Z",
     "iopub.status.busy": "2022-11-11T21:12:17.815833Z",
     "iopub.status.idle": "2022-11-11T21:12:17.823681Z",
     "shell.execute_reply": "2022-11-11T21:12:17.822670Z"
    },
    "papermill": {
     "duration": 0.159249,
     "end_time": "2022-11-11T21:12:17.827347",
     "exception": false,
     "start_time": "2022-11-11T21:12:17.668098",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#dropping the target var from the dataset\n",
    "input = orig_df.drop(['class'], axis=1)\n",
    "output = orig_df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "1ccfc3d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:18.419223Z",
     "iopub.status.busy": "2022-11-11T21:12:18.418485Z",
     "iopub.status.idle": "2022-11-11T21:12:18.426664Z",
     "shell.execute_reply": "2022-11-11T21:12:18.425446Z"
    },
    "papermill": {
     "duration": 0.162545,
     "end_time": "2022-11-11T21:12:18.428983",
     "exception": false,
     "start_time": "2022-11-11T21:12:18.266438",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Gender', 'Polyuria', 'Polydipsia', 'sudden weight loss', 'weakness',\n",
      "       'Polyphagia', 'Genital thrush', 'visual blurring', 'Itching',\n",
      "       'Irritability', 'delayed healing', 'partial paresis',\n",
      "       'muscle stiffness', 'Alopecia', 'Obesity'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#storing features\n",
    "objList = input.select_dtypes(include = \"object\").columns\n",
    "print(objList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "e88bc065",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:19.022560Z",
     "iopub.status.busy": "2022-11-11T21:12:19.022126Z",
     "iopub.status.idle": "2022-11-11T21:12:19.051004Z",
     "shell.execute_reply": "2022-11-11T21:12:19.049630Z"
    },
    "papermill": {
     "duration": 0.182213,
     "end_time": "2022-11-11T21:12:19.053488",
     "exception": false,
     "start_time": "2022-11-11T21:12:18.871275",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 520 entries, 0 to 519\n",
      "Data columns (total 16 columns):\n",
      " #   Column              Non-Null Count  Dtype\n",
      "---  ------              --------------  -----\n",
      " 0   Age                 520 non-null    int64\n",
      " 1   Gender              520 non-null    int64\n",
      " 2   Polyuria            520 non-null    int64\n",
      " 3   Polydipsia          520 non-null    int64\n",
      " 4   sudden weight loss  520 non-null    int64\n",
      " 5   weakness            520 non-null    int64\n",
      " 6   Polyphagia          520 non-null    int64\n",
      " 7   Genital thrush      520 non-null    int64\n",
      " 8   visual blurring     520 non-null    int64\n",
      " 9   Itching             520 non-null    int64\n",
      " 10  Irritability        520 non-null    int64\n",
      " 11  delayed healing     520 non-null    int64\n",
      " 12  partial paresis     520 non-null    int64\n",
      " 13  muscle stiffness    520 non-null    int64\n",
      " 14  Alopecia            520 non-null    int64\n",
      " 15  Obesity             520 non-null    int64\n",
      "dtypes: int64(16)\n",
      "memory usage: 65.1 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#encoding label using sklearn library\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "for feat in objList:\n",
    "    input[feat] = le.fit_transform(input[feature].astype(str))  \n",
    "\n",
    "print (input.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6730f26a",
   "metadata": {
    "papermill": {
     "duration": 0.148962,
     "end_time": "2022-11-11T21:12:19.346081",
     "exception": false,
     "start_time": "2022-11-11T21:12:19.197119",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Here astype is used for casting the data type into int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "6e533fb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:19.648567Z",
     "iopub.status.busy": "2022-11-11T21:12:19.648162Z",
     "iopub.status.idle": "2022-11-11T21:12:19.664497Z",
     "shell.execute_reply": "2022-11-11T21:12:19.663153Z"
    },
    "papermill": {
     "duration": 0.170669,
     "end_time": "2022-11-11T21:12:19.667101",
     "exception": false,
     "start_time": "2022-11-11T21:12:19.496432",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Polyuria</th>\n",
       "      <th>Polydipsia</th>\n",
       "      <th>sudden weight loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>Polyphagia</th>\n",
       "      <th>Genital thrush</th>\n",
       "      <th>visual blurring</th>\n",
       "      <th>Itching</th>\n",
       "      <th>Irritability</th>\n",
       "      <th>delayed healing</th>\n",
       "      <th>partial paresis</th>\n",
       "      <th>muscle stiffness</th>\n",
       "      <th>Alopecia</th>\n",
       "      <th>Obesity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Polyuria  Polydipsia  sudden weight loss  weakness  \\\n",
       "0   40       1         1           1                   1         1   \n",
       "1   58       0         0           0                   0         0   \n",
       "2   41       0         0           0                   0         0   \n",
       "3   45       0         0           0                   0         0   \n",
       "4   60       1         1           1                   1         1   \n",
       "\n",
       "   Polyphagia  Genital thrush  visual blurring  Itching  Irritability  \\\n",
       "0           1               1                1        1             1   \n",
       "1           0               0                0        0             0   \n",
       "2           0               0                0        0             0   \n",
       "3           0               0                0        0             0   \n",
       "4           1               1                1        1             1   \n",
       "\n",
       "   delayed healing  partial paresis  muscle stiffness  Alopecia  Obesity  \n",
       "0                1                1                 1         1        1  \n",
       "1                0                0                 0         0        0  \n",
       "2                0                0                 0         0        0  \n",
       "3                0                0                 0         0        0  \n",
       "4                1                1                 1         1        1  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "3fc6aa3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:20.288875Z",
     "iopub.status.busy": "2022-11-11T21:12:20.288455Z",
     "iopub.status.idle": "2022-11-11T21:12:20.304247Z",
     "shell.execute_reply": "2022-11-11T21:12:20.303005Z"
    },
    "papermill": {
     "duration": 0.166202,
     "end_time": "2022-11-11T21:12:20.306893",
     "exception": false,
     "start_time": "2022-11-11T21:12:20.140691",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                   0.108679\n",
       "Gender                0.072173\n",
       "Polyuria              0.072173\n",
       "Polydipsia            0.072173\n",
       "sudden weight loss    0.072173\n",
       "weakness              0.072173\n",
       "Polyphagia            0.072173\n",
       "Genital thrush        0.072173\n",
       "visual blurring       0.072173\n",
       "Itching               0.072173\n",
       "Irritability          0.072173\n",
       "delayed healing       0.072173\n",
       "partial paresis       0.072173\n",
       "muscle stiffness      0.072173\n",
       "Alopecia              0.072173\n",
       "Obesity               0.072173\n",
       "dtype: float64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding corelation with features\n",
    "input.corrwith(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "1ef52091",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:20.897684Z",
     "iopub.status.busy": "2022-11-11T21:12:20.896992Z",
     "iopub.status.idle": "2022-11-11T21:12:21.367407Z",
     "shell.execute_reply": "2022-11-11T21:12:21.366199Z"
    },
    "papermill": {
     "duration": 0.619442,
     "end_time": "2022-11-11T21:12:21.370074",
     "exception": false,
     "start_time": "2022-11-11T21:12:20.750632",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Correlation with Diabetes'}>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#corelation with respect to the output class\n",
    "input.corrwith(output).plot.bar(\n",
    "        figsize = (16, 6), title = \"Correlation with Diabetes\", fontsize = 15,\n",
    "        rot = 90, grid = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "92987659",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:21.979065Z",
     "iopub.status.busy": "2022-11-11T21:12:21.977949Z",
     "iopub.status.idle": "2022-11-11T21:12:21.984944Z",
     "shell.execute_reply": "2022-11-11T21:12:21.983865Z"
    },
    "papermill": {
     "duration": 0.159282,
     "end_time": "2022-11-11T21:12:21.987209",
     "exception": false,
     "start_time": "2022-11-11T21:12:21.827927",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Polyuria', 'Polydipsia', 'sudden weight loss',\n",
       "       'weakness', 'Polyphagia', 'Genital thrush', 'visual blurring',\n",
       "       'Itching', 'Irritability', 'delayed healing', 'partial paresis',\n",
       "       'muscle stiffness', 'Alopecia', 'Obesity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#showing input cols\n",
    "input.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "6a12c940",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha using builtin lassocv %f 0.015899459036461595\n",
      "Best score using builtin lassocv 0.011804\n",
      "Lasso picked 1variables and eliminated the other 15variables\n",
      "Age                   0.004247\n",
      "Gender                0.000000\n",
      "Polyuria              0.000000\n",
      "Polydipsia            0.000000\n",
      "sudden weight loss    0.000000\n",
      "weakness              0.000000\n",
      "Polyphagia            0.000000\n",
      "Genital thrush        0.000000\n",
      "visual blurring       0.000000\n",
      "Itching               0.000000\n",
      "Irritability          0.000000\n",
      "delayed healing       0.000000\n",
      "partial paresis       0.000000\n",
      "muscle stiffness      0.000000\n",
      "Alopecia              0.000000\n",
      "Obesity               0.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feature selsction using lasso\n",
    "from sklearn.linear_model import LassoCV\n",
    "import matplotlib as matplotlib\n",
    "reg = LassoCV()\n",
    "reg.fit(input,output)\n",
    "print(\"Best alpha using builtin lassocv %f\" ,reg.alpha_)\n",
    "print(\"Best score using builtin lassocv %f\" %reg.score(input,output))\n",
    "coef = pd.Series(reg.coef_,index=input.columns)\n",
    "print(\"Lasso picked \"+ str(sum(coef!=0))+ \"variables and eliminated the other \" + str(sum(coef==0)) + \"variables\" )\n",
    "imp_coef = coef.sort_values()\n",
    "# matplotlib.rcparams['figure.figsize'] =(8.0,10.0)\n",
    "imp_coef.plot(kind=\"barh\")\n",
    "pplot.title(\"feature importance using Lasso model\")\n",
    "print(coef)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "c17f2580",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:22.609692Z",
     "iopub.status.busy": "2022-11-11T21:12:22.608582Z",
     "iopub.status.idle": "2022-11-11T21:12:22.616019Z",
     "shell.execute_reply": "2022-11-11T21:12:22.614951Z"
    },
    "papermill": {
     "duration": 0.158513,
     "end_time": "2022-11-11T21:12:22.618518",
     "exception": false,
     "start_time": "2022-11-11T21:12:22.460005",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# dropping the least relevant feature\n",
    "input_feature_selected = input = input.drop(['Alopecia'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "5963c5b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:22.914708Z",
     "iopub.status.busy": "2022-11-11T21:12:22.914004Z",
     "iopub.status.idle": "2022-11-11T21:12:22.920581Z",
     "shell.execute_reply": "2022-11-11T21:12:22.919704Z"
    },
    "papermill": {
     "duration": 0.157558,
     "end_time": "2022-11-11T21:12:22.922950",
     "exception": false,
     "start_time": "2022-11-11T21:12:22.765392",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_feature_selected.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "cbfe7716",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:23.224948Z",
     "iopub.status.busy": "2022-11-11T21:12:23.224236Z",
     "iopub.status.idle": "2022-11-11T21:12:23.230504Z",
     "shell.execute_reply": "2022-11-11T21:12:23.229426Z"
    },
    "papermill": {
     "duration": 0.160769,
     "end_time": "2022-11-11T21:12:23.232747",
     "exception": false,
     "start_time": "2022-11-11T21:12:23.071978",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Polyuria', 'Polydipsia', 'sudden weight loss',\n",
       "       'weakness', 'Polyphagia', 'Genital thrush', 'visual blurring',\n",
       "       'Itching', 'Irritability', 'delayed healing', 'partial paresis',\n",
       "       'muscle stiffness', 'Obesity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#new input\n",
    "input_feature_selected.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "3523a893",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:23.534683Z",
     "iopub.status.busy": "2022-11-11T21:12:23.533385Z",
     "iopub.status.idle": "2022-11-11T21:12:23.539001Z",
     "shell.execute_reply": "2022-11-11T21:12:23.537975Z"
    },
    "papermill": {
     "duration": 0.161566,
     "end_time": "2022-11-11T21:12:23.541515",
     "exception": false,
     "start_time": "2022-11-11T21:12:23.379949",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Age', 'Gender', 'Polyuria', 'Polydipsia', 'sudden weight loss', 'weakness', 'Polyphagia', 'Genital thrush', 'visual blurring', 'Itching', 'Irritability', 'delayed healing', 'partial paresis', 'muscle stiffness', 'Alopecia', 'Obesity', 'class']\n"
     ]
    }
   ],
   "source": [
    "l1 = list(df.columns)\n",
    "l2 = list(input_feature_selected.columns)\n",
    "print(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "4d64bd6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:24.151313Z",
     "iopub.status.busy": "2022-11-11T21:12:24.150919Z",
     "iopub.status.idle": "2022-11-11T21:12:24.157781Z",
     "shell.execute_reply": "2022-11-11T21:12:24.156355Z"
    },
    "papermill": {
     "duration": 0.166306,
     "end_time": "2022-11-11T21:12:24.160410",
     "exception": false,
     "start_time": "2022-11-11T21:12:23.994104",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Alopecia', 'class']\n"
     ]
    }
   ],
   "source": [
    "miss = []\n",
    "for i in l1:\n",
    "    if i not in l2:\n",
    "        miss.append(i)\n",
    "print(miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "5255088b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:24.753654Z",
     "iopub.status.busy": "2022-11-11T21:12:24.752562Z",
     "iopub.status.idle": "2022-11-11T21:12:24.762891Z",
     "shell.execute_reply": "2022-11-11T21:12:24.761770Z"
    },
    "papermill": {
     "duration": 0.162747,
     "end_time": "2022-11-11T21:12:24.765385",
     "exception": false,
     "start_time": "2022-11-11T21:12:24.602638",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# splitting into test train 20/80\n",
    "x_train, x_test, y_train,y_test = train_test_split(input_feature_selected,output, test_size = 0.2, stratify = output, random_state = 12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "b11f2e17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:25.354198Z",
     "iopub.status.busy": "2022-11-11T21:12:25.353215Z",
     "iopub.status.idle": "2022-11-11T21:12:25.367694Z",
     "shell.execute_reply": "2022-11-11T21:12:25.366681Z"
    },
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     "duration": 0.165466,
     "end_time": "2022-11-11T21:12:25.370292",
     "exception": false,
     "start_time": "2022-11-11T21:12:25.204826",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#normalize - -- age into 0-1\n",
    "minmax = MinMaxScaler()\n",
    "x_train[['Age']] = minmax.fit_transform(x_train[['Age']])\n",
    "x_test[['Age']] = minmax.transform(x_test[['Age']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "13e31a13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:26.281900Z",
     "iopub.status.busy": "2022-11-11T21:12:26.280917Z",
     "iopub.status.idle": "2022-11-11T21:12:26.348601Z",
     "shell.execute_reply": "2022-11-11T21:12:26.347378Z"
    },
    "papermill": {
     "duration": 0.217999,
     "end_time": "2022-11-11T21:12:26.351156",
     "exception": false,
     "start_time": "2022-11-11T21:12:26.133157",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#logistic regression\n",
    "logic = LogisticRegression(random_state = 0,  penalty='l2')\n",
    "logic.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "ff11fc04",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:26.941226Z",
     "iopub.status.busy": "2022-11-11T21:12:26.940779Z",
     "iopub.status.idle": "2022-11-11T21:12:27.039711Z",
     "shell.execute_reply": "2022-11-11T21:12:27.038542Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#kfold \n",
    "kfold = model_selection.KFold(n_splits=8, random_state=7,shuffle = True)\n",
    "\n",
    "acc_logis = cross_val_score(estimator=logic,X = x_train,y =y_train, cv = kfold,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "8f5dc8b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:27.645576Z",
     "iopub.status.busy": "2022-11-11T21:12:27.644341Z",
     "iopub.status.idle": "2022-11-11T21:12:27.667664Z",
     "shell.execute_reply": "2022-11-11T21:12:27.666454Z"
    },
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     "duration": 0.178571,
     "end_time": "2022-11-11T21:12:27.670325",
     "exception": false,
     "start_time": "2022-11-11T21:12:27.491754",
     "status": "completed"
    },
    "tags": []
   },
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Cross Val Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  Cross Val Accuracy  Precision  Recall  \\\n",
       "0  Logistic Regression  0.615385            0.615385   0.615385     1.0   \n",
       "\n",
       "   F1 Score  ROC  \n",
       "0  0.761905  0.5  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Evaluation logistic regression\n",
    "y_predict_logi = logic.predict(x_test)\n",
    "acc = accuracy_score(y_test, y_predict_logi)\n",
    "roc = roc_auc_score(y_test, y_predict_logi)\n",
    "prec = precision_score(y_test, y_predict_logi)\n",
    "rec = recall_score(y_test, y_predict_logi)\n",
    "f1 = f1_score(y_test, y_predict_logi)\n",
    "\n",
    "results = pd.DataFrame([['Logistic Regression',acc, acc_logis.mean(), prec, rec, f1,roc]], columns = ['Model', 'Accuracy','Cross Val Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "8eb41585",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:27.978063Z",
     "iopub.status.busy": "2022-11-11T21:12:27.976938Z",
     "iopub.status.idle": "2022-11-11T21:12:27.986763Z",
     "shell.execute_reply": "2022-11-11T21:12:27.985606Z"
    },
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     "duration": 0.168542,
     "end_time": "2022-11-11T21:12:27.989251",
     "exception": false,
     "start_time": "2022-11-11T21:12:27.820709",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         0\n",
      "           1       1.00      0.62      0.76       104\n",
      "\n",
      "    accuracy                           0.62       104\n",
      "   macro avg       0.50      0.31      0.38       104\n",
      "weighted avg       1.00      0.62      0.76       104\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_predict_logi,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "93c7f369",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:28.595205Z",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# random forest\n",
    "rf = RandomForestClassifier(criterion='gini',n_estimators=100,)\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "926da4ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:29.072251Z",
     "iopub.status.busy": "2022-11-11T21:12:29.070918Z",
     "iopub.status.idle": "2022-11-11T21:12:30.416762Z",
     "shell.execute_reply": "2022-11-11T21:12:30.415547Z"
    },
    "papermill": {
     "duration": 1.502297,
     "end_time": "2022-11-11T21:12:30.419604",
     "exception": false,
     "start_time": "2022-11-11T21:12:28.917307",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# preparing for k fold\n",
    "kfold = model_selection.KFold(n_splits=8, random_state=7,shuffle = True)\n",
    "\n",
    "acc_rf = cross_val_score(estimator=rf\n",
    ",X = x_train,y =y_train, cv = kfold,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "0c3531c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:30.803368Z",
     "iopub.status.busy": "2022-11-11T21:12:30.802979Z",
     "iopub.status.idle": "2022-11-11T21:12:30.845685Z",
     "shell.execute_reply": "2022-11-11T21:12:30.844413Z"
    },
    "papermill": {
     "duration": 0.204783,
     "end_time": "2022-11-11T21:12:30.848663",
     "exception": false,
     "start_time": "2022-11-11T21:12:30.643880",
     "status": "completed"
    },
    "tags": []
   },
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Cross Val Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.740385</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>0.722892</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.68125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  Cross Val Accuracy  Precision  Recall  \\\n",
       "0  Logistic Regression  0.615385            0.615385   0.615385  1.0000   \n",
       "1        Random Forest  0.740385            0.653846   0.722892  0.9375   \n",
       "\n",
       "   F1 Score      ROC  \n",
       "0  0.761905  0.50000  \n",
       "1  0.816327  0.68125  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Evaluation random forest\n",
    "\n",
    "y_predict_r = rf.predict(x_test)\n",
    "roc=roc_auc_score(y_test, y_predict_r)\n",
    "acc = accuracy_score(y_test, y_predict_r)\n",
    "prec = precision_score(y_test, y_predict_r)\n",
    "rec = recall_score(y_test, y_predict_r)\n",
    "f1 = f1_score(y_test, y_predict_r)\n",
    "\n",
    "model_results = pd.DataFrame([['Random Forest',acc, acc_rf.mean(),prec,rec, f1,roc]], columns = ['Model', 'Accuracy','Cross Val Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "\n",
    "results = results.append(model_results, ignore_index = True)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "7ea3923d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:31.181933Z",
     "iopub.status.busy": "2022-11-11T21:12:31.181143Z",
     "iopub.status.idle": "2022-11-11T21:12:31.190794Z",
     "shell.execute_reply": "2022-11-11T21:12:31.189381Z"
    },
    "papermill": {
     "duration": 0.18763,
     "end_time": "2022-11-11T21:12:31.193322",
     "exception": false,
     "start_time": "2022-11-11T21:12:31.005692",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.42      0.81      0.56        21\n",
      "           1       0.94      0.72      0.82        83\n",
      "\n",
      "    accuracy                           0.74       104\n",
      "   macro avg       0.68      0.77      0.69       104\n",
      "weighted avg       0.83      0.74      0.76       104\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_predict_r,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "730f3eb0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:31.798423Z",
     "iopub.status.busy": "2022-11-11T21:12:31.798015Z",
     "iopub.status.idle": "2022-11-11T21:12:31.814553Z",
     "shell.execute_reply": "2022-11-11T21:12:31.813343Z"
    },
    "papermill": {
     "duration": 0.171568,
     "end_time": "2022-11-11T21:12:31.816936",
     "exception": false,
     "start_time": "2022-11-11T21:12:31.645368",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 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-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 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-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 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-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 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-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear', random_state=0)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model svm\n",
    "sv=SVC(kernel='linear',random_state=0)\n",
    "sv.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "623034a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:32.113947Z",
     "iopub.status.busy": "2022-11-11T21:12:32.113572Z",
     "iopub.status.idle": "2022-11-11T21:12:32.186972Z",
     "shell.execute_reply": "2022-11-11T21:12:32.185872Z"
    },
    "papermill": {
     "duration": 0.225438,
     "end_time": "2022-11-11T21:12:32.189934",
     "exception": false,
     "start_time": "2022-11-11T21:12:31.964496",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#prepping for kfold\n",
    "kfold = model_selection.KFold(n_splits=8, random_state=7,shuffle = True)\n",
    "\n",
    "acc_sv = cross_val_score(estimator=sv,X = x_train,y =y_train, cv = kfold,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "a6ab297b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:32.487902Z",
     "iopub.status.busy": "2022-11-11T21:12:32.487531Z",
     "iopub.status.idle": "2022-11-11T21:12:32.530640Z",
     "shell.execute_reply": "2022-11-11T21:12:32.529406Z"
    },
    "papermill": {
     "duration": 0.196211,
     "end_time": "2022-11-11T21:12:32.533843",
     "exception": false,
     "start_time": "2022-11-11T21:12:32.337632",
     "status": "completed"
    },
    "tags": []
   },
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Cross Val Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.740385</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>0.722892</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.68125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.740385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.722892</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.68125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  Cross Val Accuracy  Precision  Recall  \\\n",
       "0  Logistic Regression  0.615385            0.615385   0.615385  1.0000   \n",
       "1        Random Forest  0.740385            0.653846   0.722892  0.9375   \n",
       "2                  SVC  0.740385            0.615385   0.722892  0.9375   \n",
       "\n",
       "   F1 Score      ROC  \n",
       "0  0.761905  0.50000  \n",
       "1  0.816327  0.68125  \n",
       "2  0.816327  0.68125  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Evaluation svm\n",
    "y_predict_svm = rf.predict(x_test)\n",
    "roc=roc_auc_score(y_test, y_predict_svm)\n",
    "acc = accuracy_score(y_test, y_predict_svm)\n",
    "prec = precision_score(y_test, y_predict_svm)\n",
    "rec = recall_score(y_test, y_predict_svm)\n",
    "f1 = f1_score(y_test, y_predict_svm)\n",
    "\n",
    "model_results = pd.DataFrame([['SVC',acc, acc_sv.mean(),prec,rec, f1,roc]], columns = ['Model', 'Accuracy','Cross Val Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "\n",
    "results = results.append(model_results, ignore_index = True)\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "98d919bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:32.858592Z",
     "iopub.status.busy": "2022-11-11T21:12:32.857888Z",
     "iopub.status.idle": "2022-11-11T21:12:32.869976Z",
     "shell.execute_reply": "2022-11-11T21:12:32.868650Z"
    },
    "papermill": {
     "duration": 0.175737,
     "end_time": "2022-11-11T21:12:32.872440",
     "exception": false,
     "start_time": "2022-11-11T21:12:32.696703",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.42      0.81      0.56        21\n",
      "           1       0.94      0.72      0.82        83\n",
      "\n",
      "    accuracy                           0.74       104\n",
      "   macro avg       0.68      0.77      0.69       104\n",
      "weighted avg       0.83      0.74      0.76       104\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_predict_svm,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "774b064d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:33.468710Z",
     "iopub.status.busy": "2022-11-11T21:12:33.467676Z",
     "iopub.status.idle": "2022-11-11T21:12:33.623930Z",
     "shell.execute_reply": "2022-11-11T21:12:33.622279Z"
    },
    "papermill": {
     "duration": 0.311265,
     "end_time": "2022-11-11T21:12:33.628537",
     "exception": false,
     "start_time": "2022-11-11T21:12:33.317272",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model evaluation of KNN\n",
    "scores = []\n",
    "dic = {}\n",
    "for i in range(1,10):\n",
    "    knn = KNeighborsClassifier(n_neighbors = i,metric='minkowski',p=2)  #minkowski and p = 2 ,euclidean distance\n",
    "    knn.fit(x_train,y_train)\n",
    "    predict = knn.predict(x_test)\n",
    "    score = accuracy_score(predict, y_test)\n",
    "    scores.append(round(100*score,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "28fe1057",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:33.988253Z",
     "iopub.status.busy": "2022-11-11T21:12:33.987585Z",
     "iopub.status.idle": "2022-11-11T21:12:33.994740Z",
     "shell.execute_reply": "2022-11-11T21:12:33.993520Z"
    },
    "papermill": {
     "duration": 0.159719,
     "end_time": "2022-11-11T21:12:33.998224",
     "exception": false,
     "start_time": "2022-11-11T21:12:33.838505",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[74.04, 72.12, 71.15, 71.15, 70.19, 69.23, 68.27, 67.31, 66.35]\n"
     ]
    }
   ],
   "source": [
    "print(sorted(scores, reverse = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "288cf84d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:34.294734Z",
     "iopub.status.busy": "2022-11-11T21:12:34.294322Z",
     "iopub.status.idle": "2022-11-11T21:12:34.464343Z",
     "shell.execute_reply": "2022-11-11T21:12:34.463176Z"
    },
    "papermill": {
     "duration": 0.322761,
     "end_time": "2022-11-11T21:12:34.468326",
     "exception": false,
     "start_time": "2022-11-11T21:12:34.145565",
     "status": "completed"
    },
    "tags": []
   },
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Cross Val Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.740385</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>0.722892</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.68125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.740385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.722892</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.68125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.68125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  Cross Val Accuracy  Precision  Recall  \\\n",
       "0  Logistic Regression  0.615385            0.615385   0.615385  1.0000   \n",
       "1        Random Forest  0.740385            0.653846   0.722892  0.9375   \n",
       "2                  SVC  0.740385            0.615385   0.722892  0.9375   \n",
       "3                  KNN  0.692308            0.625000   0.722222  0.8125   \n",
       "\n",
       "   F1 Score      ROC  \n",
       "0  0.761905  0.50000  \n",
       "1  0.816327  0.68125  \n",
       "2  0.816327  0.68125  \n",
       "3  0.764706  0.68125  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Evaluation of Kneighbours\n",
    "knn = KNeighborsClassifier(n_neighbors = 1,metric='minkowski',p=2)  #minkowski and p = 2 ,euclidean distance\n",
    "knn.fit(x_train,y_train)\n",
    "predict = knn.predict(x_test)\n",
    "score = accuracy_score(predict, y_test)\n",
    "\n",
    "\n",
    "prec = precision_score(y_test, predict)\n",
    "rec = recall_score(y_test, predict)\n",
    "f1 = f1_score(y_test, predict)\n",
    "\n",
    "\n",
    "kfold = model_selection.KFold(n_splits=8, random_state=7,shuffle = True)\n",
    "\n",
    "acc_knn = cross_val_score(estimator=knn,X = x_train,y =y_train, cv = kfold,scoring='accuracy')\n",
    "\n",
    "\n",
    "model_results = pd.DataFrame([['KNN',score, acc_knn.mean(),prec,rec, f1,roc]], columns = ['Model', 'Accuracy','Cross Val Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "\n",
    "results = results.append(model_results, ignore_index = True)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "71212138",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:35.171285Z",
     "iopub.status.busy": "2022-11-11T21:12:35.170877Z",
     "iopub.status.idle": "2022-11-11T21:12:35.182895Z",
     "shell.execute_reply": "2022-11-11T21:12:35.181559Z"
    },
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     "duration": 0.179108,
     "end_time": "2022-11-11T21:12:35.185399",
     "exception": false,
     "start_time": "2022-11-11T21:12:35.006291",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 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-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 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-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 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-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 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-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#naivebays gaussian nb\n",
    "gb=GaussianNB()\n",
    "gb.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "da414068",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:35.490276Z",
     "iopub.status.busy": "2022-11-11T21:12:35.489435Z",
     "iopub.status.idle": "2022-11-11T21:12:35.541198Z",
     "shell.execute_reply": "2022-11-11T21:12:35.540174Z"
    },
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     "end_time": "2022-11-11T21:12:35.544386",
     "exception": false,
     "start_time": "2022-11-11T21:12:35.336144",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# kfold preparation\n",
    "kfold = model_selection.KFold(n_splits=8, random_state=7,shuffle = True)\n",
    "\n",
    "acc_gb = cross_val_score(estimator=gb,X = x_train,y =y_train, cv = kfold,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "95e9d1d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-11T21:12:35.870544Z",
     "iopub.status.busy": "2022-11-11T21:12:35.870076Z",
     "iopub.status.idle": "2022-11-11T21:12:35.898685Z",
     "shell.execute_reply": "2022-11-11T21:12:35.897287Z"
    },
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     "end_time": "2022-11-11T21:12:35.901407",
     "exception": false,
     "start_time": "2022-11-11T21:12:35.706987",
     "status": "completed"
    },
    "tags": []
   },
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Cross Val Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.740385</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>0.722892</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.681250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.740385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.722892</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.681250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.681250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GB</td>\n",
       "      <td>0.413462</td>\n",
       "      <td>0.459135</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.171875</td>\n",
       "      <td>0.265060</td>\n",
       "      <td>0.485938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  Cross Val Accuracy  Precision    Recall  \\\n",
       "0  Logistic Regression  0.615385            0.615385   0.615385  1.000000   \n",
       "1        Random Forest  0.740385            0.653846   0.722892  0.937500   \n",
       "2                  SVC  0.740385            0.615385   0.722892  0.937500   \n",
       "3                  KNN  0.692308            0.625000   0.722222  0.812500   \n",
       "4                   GB  0.413462            0.459135   0.578947  0.171875   \n",
       "\n",
       "   F1 Score       ROC  \n",
       "0  0.761905  0.500000  \n",
       "1  0.816327  0.681250  \n",
       "2  0.816327  0.681250  \n",
       "3  0.764706  0.681250  \n",
       "4  0.265060  0.485938  "
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Evaluation gb\n",
    "y_predict_gb = gb.predict(x_test)\n",
    "roc=roc_auc_score(y_test, y_predict_gb)\n",
    "acc = accuracy_score(y_test, y_predict_gb)\n",
    "prec = precision_score(y_test, y_predict_gb)\n",
    "rec = recall_score(y_test, y_predict_gb)\n",
    "f1 = f1_score(y_test, y_predict_gb)\n",
    "\n",
    "model_results = pd.DataFrame([['GB',acc, acc_gb.mean(),prec,rec, f1,roc]], columns = ['Model', 'Accuracy','Cross Val Accuracy', 'Precision', 'Recall', 'F1 Score','ROC'])\n",
    "\n",
    "results = results.append(model_results, ignore_index = True)\n",
    "results.head()"
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    "# exporting models into joblib files\n",
    "import joblib\n",
    "filename = \"logic.joblib\"\n",
    "joblib.dump(logic, filename)\n",
    "filename = \"rf.joblib\"\n",
    "joblib.dump(rf, filename) # we will use this in the gui\n",
    "filename = \"sv.joblib\"\n",
    "joblib.dump(sv, filename)\n",
    "filename = \"knn.joblib\"\n",
    "joblib.dump(knn, filename)\n",
    "filename = \"gb.joblib\"\n",
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