Heart Disease using ml (Muskan Dehariya) .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "32f2339a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5e8e242f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"heart.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8c1ffc7c",
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   "outputs": [
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       "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
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       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
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       "\n",
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      "text/plain": [
       "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
       "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
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       "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
       "\n",
       "      slope  ca  thal  target  \n",
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  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2562fcdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1025, 14)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9dda1463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4923c947",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1025 entries, 0 to 1024\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1025 non-null   int64  \n",
      " 1   sex       1025 non-null   int64  \n",
      " 2   cp        1025 non-null   int64  \n",
      " 3   trestbps  1025 non-null   int64  \n",
      " 4   chol      1025 non-null   int64  \n",
      " 5   fbs       1025 non-null   int64  \n",
      " 6   restecg   1025 non-null   int64  \n",
      " 7   thalach   1025 non-null   int64  \n",
      " 8   exang     1025 non-null   int64  \n",
      " 9   oldpeak   1025 non-null   float64\n",
      " 10  slope     1025 non-null   int64  \n",
      " 11  ca        1025 non-null   int64  \n",
      " 12  thal      1025 non-null   int64  \n",
      " 13  target    1025 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 112.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9b7a7643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "sex           int64\n",
       "cp            int64\n",
       "trestbps      int64\n",
       "chol          int64\n",
       "fbs           int64\n",
       "restecg       int64\n",
       "thalach       int64\n",
       "exang         int64\n",
       "oldpeak     float64\n",
       "slope         int64\n",
       "ca            int64\n",
       "thal          int64\n",
       "target        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "beb40ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.00000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.434146</td>\n",
       "      <td>0.695610</td>\n",
       "      <td>0.942439</td>\n",
       "      <td>131.611707</td>\n",
       "      <td>246.00000</td>\n",
       "      <td>0.149268</td>\n",
       "      <td>0.529756</td>\n",
       "      <td>149.114146</td>\n",
       "      <td>0.336585</td>\n",
       "      <td>1.071512</td>\n",
       "      <td>1.385366</td>\n",
       "      <td>0.754146</td>\n",
       "      <td>2.323902</td>\n",
       "      <td>0.513171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.072290</td>\n",
       "      <td>0.460373</td>\n",
       "      <td>1.029641</td>\n",
       "      <td>17.516718</td>\n",
       "      <td>51.59251</td>\n",
       "      <td>0.356527</td>\n",
       "      <td>0.527878</td>\n",
       "      <td>23.005724</td>\n",
       "      <td>0.472772</td>\n",
       "      <td>1.175053</td>\n",
       "      <td>0.617755</td>\n",
       "      <td>1.030798</td>\n",
       "      <td>0.620660</td>\n",
       "      <td>0.500070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age          sex           cp     trestbps        chol  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.00000   \n",
       "mean     54.434146     0.695610     0.942439   131.611707   246.00000   \n",
       "std       9.072290     0.460373     1.029641    17.516718    51.59251   \n",
       "min      29.000000     0.000000     0.000000    94.000000   126.00000   \n",
       "25%      48.000000     0.000000     0.000000   120.000000   211.00000   \n",
       "50%      56.000000     1.000000     1.000000   130.000000   240.00000   \n",
       "75%      61.000000     1.000000     2.000000   140.000000   275.00000   \n",
       "max      77.000000     1.000000     3.000000   200.000000   564.00000   \n",
       "\n",
       "               fbs      restecg      thalach        exang      oldpeak  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.000000   \n",
       "mean      0.149268     0.529756   149.114146     0.336585     1.071512   \n",
       "std       0.356527     0.527878    23.005724     0.472772     1.175053   \n",
       "min       0.000000     0.000000    71.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000   132.000000     0.000000     0.000000   \n",
       "50%       0.000000     1.000000   152.000000     0.000000     0.800000   \n",
       "75%       0.000000     1.000000   166.000000     1.000000     1.800000   \n",
       "max       1.000000     2.000000   202.000000     1.000000     6.200000   \n",
       "\n",
       "             slope           ca         thal       target  \n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  \n",
       "mean      1.385366     0.754146     2.323902     0.513171  \n",
       "std       0.617755     1.030798     0.620660     0.500070  \n",
       "min       0.000000     0.000000     0.000000     0.000000  \n",
       "25%       1.000000     0.000000     2.000000     0.000000  \n",
       "50%       1.000000     0.000000     2.000000     1.000000  \n",
       "75%       2.000000     1.000000     3.000000     1.000000  \n",
       "max       2.000000     4.000000     3.000000     1.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "56925827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1494f8e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52, 53, 70, 61, 62, 58, 55, 46, 54, 71, 43, 34, 51, 50, 60, 67, 45,\n",
       "       63, 42, 44, 56, 57, 59, 64, 65, 41, 66, 38, 49, 48, 29, 37, 47, 68,\n",
       "       76, 40, 39, 77, 69, 35, 74], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4cba9f10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([212, 203, 174, 294, 248, 318, 289, 249, 286, 149, 341, 210, 298,\n",
       "       204, 308, 266, 244, 211, 185, 223, 208, 252, 209, 307, 233, 319,\n",
       "       256, 327, 169, 131, 269, 196, 231, 213, 271, 263, 229, 360, 258,\n",
       "       330, 342, 226, 228, 278, 230, 283, 241, 175, 188, 217, 193, 245,\n",
       "       232, 299, 288, 197, 315, 215, 164, 326, 207, 177, 257, 255, 187,\n",
       "       201, 220, 268, 267, 236, 303, 282, 126, 309, 186, 275, 281, 206,\n",
       "       335, 218, 254, 295, 417, 260, 240, 302, 192, 225, 325, 235, 274,\n",
       "       234, 182, 167, 172, 321, 300, 199, 564, 157, 304, 222, 184, 354,\n",
       "       160, 247, 239, 246, 409, 293, 180, 250, 221, 200, 227, 243, 311,\n",
       "       261, 242, 205, 306, 219, 353, 198, 394, 183, 237, 224, 265, 313,\n",
       "       340, 259, 270, 216, 264, 276, 322, 214, 273, 253, 176, 284, 305,\n",
       "       168, 407, 290, 277, 262, 195, 166, 178, 141], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['chol'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "568bf98f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['slope'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4881f13e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cp'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7ef42a6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "21a9d322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['thal'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2092029e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "246.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['chol'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "28e27a39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
      "4      62    0   0       138   294    1        1      106      0      1.9   \n",
      "5      58    0   0       100   248    0        0      122      0      1.0   \n",
      "10     71    0   0       112   149    0        1      125      0      1.6   \n",
      "11     43    0   0       132   341    1        0      136      1      3.0   \n",
      "12     34    0   1       118   210    0        1      192      0      0.7   \n",
      "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
      "996    56    0   0       134   409    0        0      150      1      1.9   \n",
      "1004   51    0   2       140   308    0        0      142      0      1.5   \n",
      "1005   55    0   0       128   205    0        2      130      1      2.0   \n",
      "1014   44    0   2       108   141    0        1      175      0      0.6   \n",
      "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
      "\n",
      "      slope  ca  thal  target  \n",
      "4         1   3     2       0  \n",
      "5         1   0     2       1  \n",
      "10        1   0     2       1  \n",
      "11        1   0     3       0  \n",
      "12        2   0     2       1  \n",
      "...     ...  ..   ...     ...  \n",
      "996       1   2     3       0  \n",
      "1004      2   1     2       1  \n",
      "1005      1   1     3       0  \n",
      "1014      1   0     2       1  \n",
      "1023      2   0     2       1  \n",
      "\n",
      "[312 rows x 14 columns]\n",
      "\n",
      "\n",
      "1\n",
      "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
      "0      52    1   0       125   212    0        1      168      0      1.0   \n",
      "1      53    1   0       140   203    1        0      155      1      3.1   \n",
      "2      70    1   0       145   174    0        1      125      1      2.6   \n",
      "3      61    1   0       148   203    0        1      161      0      0.0   \n",
      "6      58    1   0       114   318    0        2      140      0      4.4   \n",
      "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
      "1019   47    1   0       112   204    0        1      143      0      0.1   \n",
      "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
      "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
      "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
      "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
      "\n",
      "      slope  ca  thal  target  \n",
      "0         2   2     3       0  \n",
      "1         0   0     3       0  \n",
      "2         0   0     3       0  \n",
      "3         2   1     3       0  \n",
      "6         0   3     1       0  \n",
      "...     ...  ..   ...     ...  \n",
      "1019      2   0     2       1  \n",
      "1020      2   0     2       1  \n",
      "1021      1   1     3       0  \n",
      "1022      1   1     2       0  \n",
      "1024      1   1     3       0  \n",
      "\n",
      "[713 rows x 14 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df=df.groupby('sex')\n",
    "df\n",
    "\n",
    "for i,j in df:\n",
    "    print(i)     \n",
    "    print(j)     \n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9e0fb900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>248</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>341</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>409</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>312 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "4      62    0   0       138   294    1        1      106      0      1.9   \n",
       "5      58    0   0       100   248    0        0      122      0      1.0   \n",
       "10     71    0   0       112   149    0        1      125      0      1.6   \n",
       "11     43    0   0       132   341    1        0      136      1      3.0   \n",
       "12     34    0   1       118   210    0        1      192      0      0.7   \n",
       "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "996    56    0   0       134   409    0        0      150      1      1.9   \n",
       "1004   51    0   2       140   308    0        0      142      0      1.5   \n",
       "1005   55    0   0       128   205    0        2      130      1      2.0   \n",
       "1014   44    0   2       108   141    0        1      175      0      0.6   \n",
       "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
       "\n",
       "      slope  ca  thal  target  \n",
       "4         1   3     2       0  \n",
       "5         1   0     2       1  \n",
       "10        1   0     2       1  \n",
       "11        1   0     3       0  \n",
       "12        2   0     2       1  \n",
       "...     ...  ..   ...     ...  \n",
       "996       1   2     3       0  \n",
       "1004      2   1     2       1  \n",
       "1005      1   1     3       0  \n",
       "1014      1   0     2       1  \n",
       "1023      2   0     2       1  \n",
       "\n",
       "[312 rows x 14 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get_group(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7dd67a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>2</td>\n",
       "      <td>140</td>\n",
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       "    <tr>\n",
       "      <th>1019</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>204</td>\n",
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       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>59</td>\n",
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       "      <td>140</td>\n",
       "      <td>221</td>\n",
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       "      <th>1022</th>\n",
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       "      <td>113</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>713 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0      52    1   0       125   212    0        1      168      0      1.0   \n",
       "1      53    1   0       140   203    1        0      155      1      3.1   \n",
       "2      70    1   0       145   174    0        1      125      1      2.6   \n",
       "3      61    1   0       148   203    0        1      161      0      0.0   \n",
       "6      58    1   0       114   318    0        2      140      0      4.4   \n",
       "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "1019   47    1   0       112   204    0        1      143      0      0.1   \n",
       "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
       "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
       "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
       "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
       "\n",
       "      slope  ca  thal  target  \n",
       "0         2   2     3       0  \n",
       "1         0   0     3       0  \n",
       "2         0   0     3       0  \n",
       "3         2   1     3       0  \n",
       "6         0   3     1       0  \n",
       "...     ...  ..   ...     ...  \n",
       "1019      2   0     2       1  \n",
       "1020      2   0     2       1  \n",
       "1021      1   1     3       0  \n",
       "1022      1   1     2       0  \n",
       "1024      1   1     3       0  \n",
       "\n",
       "[713 rows x 14 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get_group(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5c7d4ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cda682e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpoints=np.array([1,3,5,69])\n",
    "ypoints=np.array([6,834,5,325])\n",
    "plt.plot(xpoints,ypoints,linestyle='dashed',marker='o')\n",
    "plt.title(\"comparing x axis with y axis\")\n",
    "plt.xlabel(\"cp\")\n",
    "plt.ylabel(\"chol\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c4d2b2d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpoints=np.array([1,3,5,69])\n",
    "ypoints=np.array([6,834,5,325])\n",
    "x=np.array([\"age\",\"sex\",\"cp\",\"scope\"])\n",
    "y=np.array([2,6,10,16])\n",
    "plt.bar(x,y,color='green',width=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8502a999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=np.array([35,25,25,15])\n",
    "mylabels=[\"age\",\"cp\",\"target\",\"thal\"]\n",
    "#myexplode=[0.2,0,0,0]\n",
    "mycolors=[\"black\",\"green\",\"blue\",\"hotpink\"]\n",
    "plt.pie(y,labels=mylabels,colors=mycolors)\n",
    "#plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "817941f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=np.array([3,4,7,7,6,4,5])\n",
    "mylabels=[\"age\",\"cp\",\"sex\",\"scope\",\"target\",\"chol\",\"thal\"]\n",
    "myexplode=[4,0,2,5,0,3,0]\n",
    "mycolors=[\"blue\",\"darkgreen\",\"hotpink\",\"red\",\"purple\",\"orange\",\"black\"]\n",
    "plt.pie(y,labels=mylabels,explode=myexplode,colors=mycolors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "99904b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=np.array([7,7,7,7,7,7,7])\n",
    "mylabels=[\"age\",\"cp\",\"sex\",\"scope\",\"target\",\"chol\",\"thal\"]\n",
    "myexplode=[0.6,0.6,0.6,0.6,0.6,0.6,0.6]\n",
    "mycolors=[\"blue\",\"darkgreen\",\"hotpink\",\"red\",\"purple\",\"orange\",\"black\"]\n",
    "plt.pie(y,labels=mylabels,explode=myexplode,colors=mycolors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "616687fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "086dc265",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"heart.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e908b05d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>221</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>258</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1025 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0      52    1   0       125   212    0        1      168      0      1.0   \n",
       "1      53    1   0       140   203    1        0      155      1      3.1   \n",
       "2      70    1   0       145   174    0        1      125      1      2.6   \n",
       "3      61    1   0       148   203    0        1      161      0      0.0   \n",
       "4      62    0   0       138   294    1        1      106      0      1.9   \n",
       "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
       "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
       "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
       "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
       "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
       "\n",
       "      slope  ca  thal  target  \n",
       "0         2   2     3       0  \n",
       "1         0   0     3       0  \n",
       "2         0   0     3       0  \n",
       "3         2   1     3       0  \n",
       "4         1   3     2       0  \n",
       "...     ...  ..   ...     ...  \n",
       "1020      2   0     2       1  \n",
       "1021      1   1     3       0  \n",
       "1022      1   1     2       0  \n",
       "1023      2   0     2       1  \n",
       "1024      1   1     3       0  \n",
       "\n",
       "[1025 rows x 14 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "061abf73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "dbe1e183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Displot')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 567.75x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x='sex',bins=6,data=df,kde=True,hue=df['age'],palette='Set1')\n",
    "plt.title('Displot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "83c7178e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Displot')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x='age',bins=6,data=df,kde=True,hue=df['sex'],palette='Set1')\n",
    "plt.title('Displot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "12d17539",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_4292\\2099011296.py:1: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x=\"age\",color='c',data=df,shade=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'KDE plot')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(x=\"age\",color='c',data=df,shade=True)\n",
    "plt.title('KDE plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d0bcfa00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x277838fe2c0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='chol',y='cp',data=df,kind='hex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e0267b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x27786a7f340>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='sex',y='thal',data=df,kind='reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9d193a92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='chol', ylabel='age'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='chol',y='age',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "75e858c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='chol'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='age',y='chol',data=df,hue='sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fa38403c",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr=df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c67cf8ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "909d98e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/dWJO+zmFnuQKMqDRAHzf8uV/v/6PEw9OaJQxVBgyqNEg5vw1RxUWmxJLVk5WoZmA6sbVy0XnZM/JzGw7ky4/deFmzE105bqvrE8bcvX13NaTiMQIAO7H/VD2dlIS16Kkt1dyGeoKznqvpk8JPH251PD4BdiYSDeIe39LYeEJkJ0j7ZO494yWhl3Z8c4OkjOSi7RhVFKURptnZmcSlyptEl3QcQFbb2zFN8AXgFsxtzDWNeb73t+z6MwilCj5qutXLDu/jBXdVpCcmcye4D04mDswo80MjoQcKZc2k8utmFvMOy09XL2WOgRV39zw2wY6OXeqsH6RnJGMUqnkr7C/tE5fmzrMRVNfB9h3Zx9+j/0AuN/yr7w9NQV9Gm1t2sVZaptr/KT9PgXJVua15fAE4ej8w/xn9ujExcURFBTE559/TufOnWnQoAHx8XkDbP369blx44baMpO/v79aGs2bNycwMBBHR0fq1KmjdhgbG2vUq6+vj5mZmdrxjyxbpWdLHSf3iEyS3qTI/1aKjkx6KyD/jae49HKU0vp0WdJ7EK++EQ+kNDTF8bSXpt9vxaiHZyu5GnmVL7t+iYulC122duFZ6jO6OnflwuMLRaoe1HgQm/tsZsjvQzh472CRcgMaDUBfoc/PN35WhWXmZHIl4gpdnbuqyZaHzs+8PmN2u9l0/7k7VyKvlEmfNuTXdzb8LCHxIYTEh5RPOymJgu0yI1s6ykOfTJZ3kwfp5hGeIKX1Ih0LAwu61emGvkK/SBtefHyxkM29a3vjH+FPVk4WIO1rylGqP5FnK7ORIVM9PBnpGhWqw2xlNnKZvNzaaS7p2emvtw5B1Tf3Bu+tsH4B8HaDt1FS+KG0uPS1qcNcNPV1gKSMJELiQ5jQagLUs4IVF1/dpl2cpZnLb/+W2qPgX89/xtGpUqUKlpaWfP/999y/f5+TJ0+qzbQMGTKEnJwcRo4cSVBQEEeOHOHrr78G8qY5P/nkE549e8bgwYP5+++/CQ0N5ejRo4wYMYLs7IpZR81MSyEu7C5xYXcBSIqJIC7sLkmxUSXE1IKTD6QnhWbW0jczPmgm3WQu5/v2yNBm0Kde3rmODOzNpENHDhYG0v/VjF4tvVNh0mDR1VnaPNrVWXJ8Cn6vQwa0spf25uQUeIySy5Ahw8nCiV8Df6W+ZX3W91qPg7kDvlelp+zFnRezpe8WVZRBjQfxU9+fmHx0MpceX8La2BprY2vM9M0KmcnHzYc9wXsKbY5ccWkFHzb/kOGuw6lvVZ8V3VZQy7wW6/3Xv7LOKV5TWNhxISP2jSDseZhKxljXuNT6AJpZN6OZdTNM9EyoZlSNZtbNaGDVQCt9Ksq7neTyVj2oXUWaDbAzlc7rWkrpllZft9pSu7E0lNpRJydoWUP9OzoNrOBOnLT82a8BF0ZcIDUzFTN9syJtuN5fakfLvZdT36o+w12H4+Pmw9cXv1bJ7L+7n9EeoxnYaCCOFo50ce7Cgo4L2Hdnn8oB2n93P7PazuJU2Ck+bP4h3/T4himeU0hITyhzm9GV66rqWU9HjxpmNWhm3YzaVWpXfB2CWt/MVmZXSL/IxcfNh4uPLvJe0/e0Tl+bOsyfvqa+DrC251rea/oebAqQHPQLj6B7HWmzsLY27eosfTNs6w3p1XIzfenI/0q6pn4h+Ef5zyxdyeVyduzYwfjx42ncuDH16tVjzZo1dOjQAQAzMzP279/P6NGjcXV1pUmTJsyZM4chQ4ao9u3Y2dlx/vx5pk2bRrdu3UhPT8fBwYHu3bsjl1eMzxcbEszhBeNU539v/QaAOu160HbM52VL/FiotC9iUOO8D15946e++a2KobpjYW4AM9vmnXetLR1346S15tKmFxoPGwOkfT6960l7KnwD1JfZ4OVNzEhaKiuIhQEtakhvvMzrOI95HfP2RNmb2RMSH4KtiS21zGupwj92/1jaNNprHet6rVOFb762meF7h6vOXaq60NahLV23qj8RAuwK3IWloSVz2s/B1sSWWzG36LmtJ+EJ0m+zvIrOMS3GoK/Q5/cBv6vpmvvXXOadnlcqfQDXRl1T/e9h58G7Td8l7HkYTqudStRH7l7QimgnIA3ww1ylv2lZ0hT+t39LGzOfp5VOX27+LAykV8ijk6Ulqiv5vitjqCvdlGRAJyfqksPNmJsM/G1gkTYMex5Gz+09WdltJZ+0+ISIxAjGHxrPH0F/qGQWnlmIEiULOy2khmkNnqY8lRybE7NUMuMOjWNBxwUMbjwYkNqCTCZDR6ZT5jZjZ2qnVs9TvKYwxWuKtLwTnFyxdQjqfVNWMf0C1PuiS1UXrdPXpg4Lpq+JMS3GSP9M9FS/MLiJtGlYG5u2cwBdHRjprp7Ggbtw4J70v6Z+IfhHkSmL+6GK/zjbtm1j+PDhJCQkYGhYfm8nLA2ouO/uaGL6D36vVd8/gcz6zX86C5UOZfSf/3QWKpT/D23mddehsGkFsa5XhSZfnvek6W5WJQv9x/jPzOhow08//YSzszM1atTg+vXrTJs2jQEDBpSrkyMQCAQCgeC/Q6VydKKiopgzZw5RUVHY2tryv//9j0WLFv3T2RIIBAKBQPAPUakcnalTpzJ16mv+vo1AIBAIBIJ/Lf+Zt64EAoFAIBAISotwdAQCgUAgEFRahKMjEAgEAoGg0iIcHYFAIBAIBJWWSrUZ+XXxur9rs/Sjlq9V34x92v1ieHkivvny3+f/QxlfN8KmAkHZETM6AoFAIBAIKi3C0REIBAKBQFBpEY6OQCAQCASCSotwdAQCgUAgEFRahKMjEAgEAoGg0iIcHYFAIBAIBJUW4egIBAKBQCCotIjv6JQ3vVygdS0w0oWw57DzFkQmFS1vawJv1oVa5mBpBL8GwqmwV1YfFXSNW/u3E/sgmNT4ODpNXoJDi3Zax29jY8STSU+oYlAFvyd+fHLwE24/vV1snH4N+rGg4wJqV6lNSHwIs07OYk/wHo2y09tMZ0nnJay6tIqJRybyRfsvGOk+EgxtID0LZDLJdovPwuMXmhW62kDvumBlBLEpsO8OXI/Ou76go2TLgpwOg52B0v+metC3PjSoJum7Fwe7AuFpSunrsHVNaGkPdqbSeXgC7A2GhwnQzgG6OJNqkUpgTCATjkzgXPi5IpNq59COFd4raFS9ERGJEXx5/ks2XNmguv5h8w/5oOkHNK7eGIArkVeYeWImlyMuq2Tip8VjYWBRfPkLUpJNe7lAr7rqcRLSYMYJ1fVk72SMdNXtfunxJTx9PTWqLEu7yaW6cXX2DNxDixot0JHp8CL9BaMOjGLHrR0a07ExsWG593Lcbd1xsXRhjd8atfQKMrDRQHb030FyRjI6cp1yqUOAT1t+ymiP0dQyr0VsSiy/Bf3GjOMzSM9OB+CL9l8wt8NctTixKbFU+6r4b1yVZNPpbabTr34/6lvVJzUrlQuPLjDt+DTuxt1VySi/UGpMe8qxKXx94etC4drYtGG1hszvMB93O3ccLRyZcHgCq/1WF1uW0R6jmeI1BVtT2xLt/nb9txntMRpXG1f0FfoExgQy9/RcjoYcLVZHufb1XOQyKd0WNcBMH16kF58HQYUjZnTKk67O0MlJumEuOyc18HEtQV+n6Dh6OtKNZU+wdOMoI1lpqVRxqEOr4ZNKHbdldUNaVDdg7MGxtPihBVFJURx7/xgmeiZFxmll34qd/Xey9cZWmq1vxtYbW9nVfxdv1HijkKyHnQcjm4/ketR1AKa2nsokz0mMPTgW/rwLz7Uov5MF+LjB308kZ+jvJ/Bhc3C0yJNZdh6mH887Vl+Swq9G5sl87CHd1Df4S+k8S4XxLaFHndLXoYsl+EfAqkvw1XkprXEtoU0t6N8QDt/HbYMbZ8PPcujdQ9Q0q6kxGUcLRw4OOcjZ8LO4bXBj8dnFrOmxhn4N+qlkOjh04Jdbv9BxS0c8fT0JTwjn6PtHsTO1U9lUR6bDiL0jYOVFuB4FSRmFy19amwJEJKrbddFZKfxlu7/0+BJnH55lT/AeopKiqL26Nj239dSosiztJj9nh52lpX1Llp5bSs/tPQlPCGdbv23UtaxbSBZAX0efpylPWXR2kcb08lPLvBZre64lR5nD3bi75VaHQ5oMYWmXpcw7PY8Gaxvgs8+HgY0GsqTLEpVM65qtyc7JZsTeEXTY3IE9wXvIyskqc19s79CetZfX0sq3FV23dkUhV3D0vaNqDurH+z8mIzuDSUcm0XZjW46GHEWpVHLx0cVXtqmRrhGhz0OZfnw6kYlFtMN8DGg0gFXdV7Ho7CKt7N7OoR3HQo/Rc3tP3L9351TYKfYP3o+rjWvRSl5lvC6qr5vr58l414a2DlK680/D7qASyyuoWP6Tjs5vv/1GkyZNMDQ0xNLSki5dupCcnAzApk2baNCgAQYGBtSvX59169ap4o0YMYKmTZuSni552JmZmbi7u/Puu++WT8Y6OcHh+3AtSnoq+Om65Mi0qFF0nIcJsDsYrkRCVk6Zs2Dv5on7wJE4vtGh1HFbVDfkQlQqu4N3E/g0kKF7hmKka8SQJkOKjDOh5QSOhRxj6bml3Im7w9JzSznx4AQTWk5QkzPWNWZbv218tP8j4tPiVXEXnV3E7uDdcDREclBkJWSykxMEx8KREIhOlv4Gx0JHxzyZpAxp0Mo9mlhDTDLceyZdr24MzlVgxy3J/jHJ0v/6CujiXPo63HwNzjyUZqCik2HbDakcXZ3hwiO48Ijg2GAmHpnIo4RHjG4xWmMyozxGEZ4QzsQjEwmODcY3wJeNARv5zPMzlcx7u9/jO//vuB59nTtxd/ho/0fIZXI6O3VW2XTh2YVsurZJKq9vgDRwv0jPK/+r2BQgO0fdrrkO1Mt2H54QTlxqHAN/G4iBwoAutbuo6rogZWk3ubhUdaGuVV12Be5i9qnZHL5/GNcNruQoc1jZbaVGvQ8THjLh8AS23thKQnqCRhkAuUzOtn7bSEhP4G7cXR4mPCy3OvS09+R8+Hl+ufULDxMeciz0GL/c+gUPWw+VTCv7VkQnR7Pp2iZOPzytsmlZ+2KPbT3Ycn0Lt5/e5kb0DYbvHY6DhQPutu4qmRFuI/AN8GXlpZWce3SOlMwUUjJT6FW31yvb1D/Cn6nHprIzcKdq1qo4JrWahG+AL74BvlrZfeKRiXx14Sv8I/y5/+w+s07O4l7cPXrX7V20klcZr4vq6/Wt8mScLOBGNNyKkRyhgKgSyyuoWP5zjk5kZCSDBw9mxIgRBAUF8ddff9GvXz+USiU//PADs2bNYtGiRQQFBbF48WJmz57Nli1bAFizZg3JyclMnz4dgNmzZxMbG6vmDL0yloZgbgBBsXlhWTnSkohzlbKnX8GY68kx0ZUTlpihCsvIzuB02Gm87L2KjOdZ05OjoerTw0dCjuBVUz3O2p5rOXDvACceSEsdZvpm2Jraqk8tZ+VI08fF4VRF3cYgnRdlYx0ZvFEDLj7KC1O8bPaZ+RxLJZCjBEPdstehng7oyKXls6CnapeOhh4t0p6e9ppt6WHngUKueZXZSNcIXbkuz1Kf4WThVNimSqW0HFjcbJm2Nq1uDIs7w/yOMMJNavMF2n0Hxw48mvgIhVzBZM/JVDPSvNTyqu0mP8Z6xgCcDjutCstR5pCelU5z2+ZFl1cL5rSfQ2xyLLXMaxGRGKF2rax1eC78HO527rSwawGAk4UTPev05MC9A6pzU31TqhhU4cmkJ4SOD2VL3y1cfnK5XPpifsz1zQF4lio5wbpyXdzt3FVtqLpxdXq59OJs+NlidZcnBfOQS3F2L4gMGab6pqpyFaK8xuvcvp6cmRcWEg/1LKX+AlDDVPv0BBXCf26PTmRkJFlZWfTr1w8HBwcAmjRpAsCCBQtYvnw5/fpJ08ROTk7cvn2bDRs2MHToUExMTPj5559p3749pqamLF++nBMnTmBubl6kvvT0dNUMUC762Zno6+iqC5obSH8TCzytJGZAVcMylPj1YKIr3fyTM9XX56OTo3Ewdygyno2JDdFJ0Wph0UnR2JjYqM4HNhpIc9vmtPihhSrMWNdYJatGUgbFomnN+0W6FK6JZjZgqIBLj/PCopIgLgX61IPtNyEjGzo756VR1jrsW1/Kk5WRFDcf0UnR2NS20RitKFvq6uhiZWRFVFLhJ8OlXZbyJPEJx0OPq27uamk0s5H2DKRlFZ1fbWz64DlsuS7NfpnqQQ8X+MwLtr5cqkhM59D9Q/x6+1cePn/I195f41XTi5NDT+L+vTsZ2ep2eNV2k5+4lDgA+jfsz45AaR/NJM9JKgfoVfGq6YWPmw/df+5O3wZ9Sc1MLZzPMtThzsCdVDOuxrkR55AhQ1dHl3WX17Hs/DJVGgDjDo3D74kf1sbWfN7uc96o8QaXHl8qMt/a2LQgK7qt4OzDswQ+lfZuWRlZoZArVOkMbTaUxIxELjy6wLtNymnmuwQK5iGX4uxekMlekzHWNWZX4C7NAuU1XvetLz1EBOdzmI6GSGPOnPZ5DxqCf5T/nKPTrFkzOnfuTJMmTejWrRve3t7079+frKwsHj16hI+PDx999JFKPisrS82R8fT05LPPPmPBggVMmzaNdu2K36i7ZMkS5s2bpxb2hcdg5o6ZAoOb5AV+93IzqKZ9fErNm/v+SRpW0ad7zbz1/l9DpSlnZYECyGSyQmEFKS6OvZk9q7uvZpXfKmKnSoOBocKQP4L+0Bj3lQaF4qJ41YTbTyEh34CWo4Tvr8BId1jeTaqfHCWExktPc2Wpw67O4GEHP16FT1sVileSPTXZUlJfOM72ftsZ1HgQqVmpxE6Npdf2XoXT8KoJz1MhM1u7/KsUFzi//XJmqoWd1O5lSE+zja1zM652U3n04hFXIq7QulZrern0kpYntShrwXbj/bN3kUsdWTmS81bTvCbx0+LJysnieOhx7sTeoYZZMcsPxWCiZ8LPb//MR/s/KnI2oKx12N6hPbPazmLMgTH4PfFjhNsIPm35KT5uPmTmZKrq8cC9A0QlRXGLW1x8fJGnU56q9mKVRndRef2257c0tW5Km41tikxnhNsItt3cRrYyu8RxoLx5lbEIYFDjQcxtP5c+O/rwNEVqt0OaDGHDmy83hOcYlM94ndvXV11S33bgbivNIm8KkJbE7M1gmKt2aQoqhP+co6Ojo8OxY8e4cOECR48e5ZtvvmHWrFns378fgB9++IGWLVsWipNLTk4O58+fR0dHh3v37pWob8aMGUyapL6xV3/KSWkNNv8yS+5ySMGnY1O9Qk/1/wbuJ2SwMTlvz4NCLg3GuTM7uVQ3ql541iUfUUlRhZ4YqxvnxXG3dcfaxJoFHReoruvIdXin4TsA2Jnaqc9UGBeYKSuIptkb0yLebKhqKK2df3+l8LVHL2DhGahmLC1vpWTCRy+XO161Drs4Q7c6sMYPnryQ9rQUyGt+2xSkKFtmZmcSlxqnFj7ZczI9XHrw9s63uRVzCwB9haTLxsRGsmlu+R8+Lz7/pbFp/nb/flMw0ZP+N9OHfLP31Y2qE54Qjv1ze1wsXbQua8F2c2VkXt0p5AraObRj7Btj0V+oT2xKLFk5WUw7Po2TD06ip6NHbEoskZMiSUgrev9NcdSuUhunKk7sHyyNJ0qlkh4uPQDInJ1JvW/rlbkOF3RcwNYbW/EN8AVgzqk5PE54zMJOC3Fb74aeQrKpqh6BlMwUkjKSkBXj1Zdk0/ys6bGGt+q+RbvN7XiS+EQVnmtTGxMb2tRqQ32r+gz8bSAj3EYUOw6UJ/nzkJ/i7J7LgEYD8H3Ll//9+j+15c59d/bh99gPgPtxP5R9vFbr64nq1/o1kPa5XXm56ToiUTg6/zD/uT06IHn2rVu3Zt68eQQEBKCnp8f58+epUaMGoaGh1KlTR+1wcnJSxf3qq68ICgri9OnTHDlyhE2bNhWrS19fHzMzM7VDX0cX0rOlV5Fzj8gk6a2pBvk2penIpF36oZo3ZP6TZOQoeZ6Rozpi07JJyszB0VRPJaMr16W9Y3suPL5QZDoXH12kq3NXtTBvZ28uPJLinHhwgsbrGuO63lV1XH5ymW03tvE0+alqEy0g2avgmz4FeRCvvvEPJJtrsrGnvTQ1fStGc1rp2dKmwtxXQ+3MIDnj1eqwi7P0xta3f0uvnGYrpb8N1PeodHXuWqQ9Lz7WYMva3vhH+KtmLwA+8/qM2e1m0/3n7uy9s5eQ+BBC4kO4/fQ2kYmReWnklt/apPj8l8amue0+Pk3agxSZWKjd57ab69HXqWleU+NbNmVpN67rpQ3HmTmZXIm4QlfnrrxIf0FsSix1qtbB2sSas+Fniy5vMQTHBqvpvRlzk7DnYZx6cArX9a48SnhU5jo00jUiR5k3A5CUkUREkrQPKPR5aOF6RFrqtTKy4k7cnSLzXpJNc/mmxzf0q9+PTj91Iux5mNq1/Db1cfPBP8KfG9E3ii1zeZM/D/kpKQ+DGg9ic5/NDPl9CAfvHVS7lpSRpOonZR6vC/b1gujq/Ctn8f8/85+b0fHz8+PEiRN4e3tTvXp1/Pz8ePr0KQ0aNGDu3LmMHz8eMzMzevToQXp6Ov7+/sTHxzNp0iSuXbvGnDlz+O2332jdujWrV6/m008/pX379jg7O5c9cycfSF5+TLJ0dK8j7f+4nPfExNBm0pru3pcDlo4MbF9uVtORg4WBNNWZniV1yFKSmZbCi6i8/ShJMRHEhd1F38QME6vi17cvx6TiaW1I3/p9uRd3j5ltZ5KSmcL2m9tVMlv6buFJ4hNmnpgJwGq/1ZwZfoapraeyN3gvfer3oYtzF9pskqbDkzKSVOv/uSRnJhOXGsfXF79mZtuZ3Ht2D4yrSINHzssBwvrlPovudeBpcp69ToXBxFbStPGNaGhqLd2klxd49VUGtLKX9ubkaBh03Gyk/UDPUqGGGfyvofQqdtjz0tdhV2fpW0ibrknp5c6OnHkI7zaFh8+p/6I+I91HUsu8Fuv91wOwuPNiapjWYOieoQCs91/P2BZjWe69nB+u/oCnvSc+bj4M/n2wSvUUryks6LiAIX8MIex5GNbG1io7J2cms8pvFTPbzuT+s/vSNz9SMsGQ4vOvjU37NYCb0VL5TPWlujJQgN8Tadq+ex12N9jNr7d/ZUDDAWTlZDGg0QBiU2LZHby7XNtN/vALjy4w9o2xPHz+kKTMJBZ3Wky2Mptpx6dptDFAM+tmgLRMVc2oGs2sm5GRnUFQbBDp2elq6S86u4jt/bbzPO052cpslnVdVuY63H93P5M8JxEQGYDfEz/qVK3Dgo4L2Hdnn8oBuvfsHrPbzyYhPYEX6S/4qutXAEw/Pl2VTmltCtLm7iFNhtBnRx8S0xNV7SchPYG0LGnD+opLK9j69laUSiVLzi1hRbcVxZa5JJuC5Pw2rNYQAD0dPWqY1aCZdTOVA1KQ3Dz4R/hz8fHFEvvOoMaD+KnvT3x6+FMuPb6kKldqViov0ov4HterjNdF9fX0LOkhAKR+0r2O9DAQkQg1zTTrF7w2/nOOjpmZGWfOnGHVqlW8ePECBwcHli9fTo8e0vSykZERX331FVOnTsXY2JgmTZowYcIE0tLSePfddxk2bBi9e0uvHPr4+HDgwAHef/99zpw5o7bE9UocC5X2LQxqnPcBqm/88joAQBVD9RuvuQHMbJt33rW2dNyNk9Z+S0lsSDCHF4xTnf+99RsA6rTrQdsxnxcb1y8mFV25jHU911HFsAp+j/3w3upNUkbeB7RqmddSexq9+Pgig34bxMJOC1nQcQEhz0IY+NtA/n7yd4l5/fL8lxgqDFnXcx0YWUuOXi4+L5eR4lKkQSSX0HjYGAC960lHbIr0CnXBt7XqW0kzDhcfoxFzA+kbN6b60pOd3xM4dE+aiSltHbZzkJ7iRrqr6zhwF367DT1duGZ+jVsxt+i5TfrWC4CtiS21zGupxMOeh9Fze09WdlvJJy0+ISIxgvGHxqv2MwGMaTEGfYU+vw/4XU3V3L/mMu/0PJVNf+j9AxgZwvPn0n6h4vKvjU0tDGC4m7RUlZQhzQJ9dUEa7I+FgoGCHl170KdeH5QoiUqMIjg2mP/9+j+SMpLKtd3k58HzByRlJLG0y1JAWr55e+fbRdoY4Nqoa6r/Pew8eLfpu4Q9D8NptRMF2RW4iwktJ9DMphnXPi6fOlx4ZiFKlCzstJAapjV4mvKU/Xf3M+vELJVMRGIEbjZurO8l3dhjU2Lpu6OvynGAV+uLY1qMAeD0sLw31QCG7RnGlutbVGV+q+5bDG4ymBltZpRYZm1samdqpyYzxWsKU7ym8FfYX3Tc0lGj3S0NLZnTfg62JrYl5uFj94+lTd291rGuV95btJuvbWb43uGF0gdebbwurq8fuJebeakfDWyUN75UYtatW8dXX31FZGQkjRo1YtWqVbRt27ZI+W3btvHll19y7949zM3N6d69O19//TWWlpYVlkeZUtMuR0HxjDnwWtUt/ahlyULlyIx9xX99tSJQRv/52nW+TmTWb752na/bpv9EGQWC0vKPjDXrNH+DqLxYGhBbspCWTHezKlnoJTt37uT9999n3bp1tG7dmg0bNvDjjz9y+/ZtatWqVUj+3LlztG/fnpUrV9K7d2+ePHnCqFGjcHFxYffuwi8slBf/yT06AoFAIBAI/llWrFiBj48PH374IQ0aNGDVqlXUrFmT7777TqP8pUuXcHR0ZPz48Tg5OdGmTRs+/vhj/P39KzSfwtERCAQCgUAASN+Oe/HihdpR8FtyABkZGVy5cgVvb2+1cG9vby5c0Lxp3MvLi8ePH3Pw4EGUSiXR0dH89ttv9OpVsTNewtERCAQCgUAASN+OMzc3VzuWLFlSSC42Npbs7Gysra3Vwq2trYmK0vyzF15eXmzbto2BAweip6eHjY0NFhYWfPPNNxVSllyEoyMQCAQCgQCQvh2XkJCgdsyYMaNIeVmBj7wqlcpCYbncvn2b8ePHM2fOHK5cucLhw4d58OABo0aNKtcyFOQ/99aVQCAQCASCikFfXx99/SJ+UicfVlZW6OjoFJq9iYmJKTTLk8uSJUto3bo1U6ZMAaBp06YYGxvTtm1bFi5ciK2tbdkLoAExoyMQCAQCgaBU6Onp4e7uzrFjx9TCjx07hpeX5h9fTUlJQS5XdztyP+tSkS+AC0dHIBAIBAJBqZk0aRI//vgjGzduJCgoiIkTJxIeHq5aipoxYwYffPCBSr5379788ccffPfdd4SGhnL+/HnGjx/PG2+8gZ1d8b/jVhbE0tV/gNf9XZslbz19rfoAZK+5jJX9uz1Q+b9rI+qw/BE2rRhe9w+ivi4GDhxIXFwc8+fPJzIyksaNG3Pw4EEcHBwAiIyMJDw8XCU/bNgwEhMT+fbbb5k8eTIWFhZ06tSJZcuWVWg+haMjEAgEAoHglRgzZgxjxozReG3z5s2FwsaNG8e4ceMKC1cgYulKIBAIBAJBpUU4OgKBQCAQCCotwtERCAQCgUBQaRGOjkAgEAgEgkqLcHQEAoFAIBBUWoSjIxAIBAKBoNIiHB2BQCAQCASVFvEdnVdhVXcIew47b0FkUvGyrjbQuy5YGUFsCuy7A9ej864v6AiWRoXjnQ6DpAxoU4sMkwzSs9ORy+Q8T3vO8dDjTD8+ncikyELR+jXox4KOC6hdpTYh8SHMOjmLPcF7NGZtepvpLOm8hFWXVjHxyERVeF1zPVytDLAxUmCkkLMxOJ6Y1OwiixgVdI1b+7cT+yCY1Pg4Ok1egkOLdsXbBXCzMqBldUNMdOV0tfZnwpEJnAs/V6R8O4d2rPBeQaPqjYhIjODL81+y4coG1fW367/NzLYzqVO1DrpyXe49u8fyi8v5+cbPKhkdmQ4nh54E+zYgl0FWDpwLh99uU+w3vUqqR7kMerlAixpgpg8v0uHiI0jOhC7OpFqk8jztOckZydiY2JCQnqCxHksq46mhp+jg2KFQ9g7cPcCbv7zJaI/RTG09FXsze7JzpDq7EnmFTw9/in+Ef6ltmp+BjQayo/8O9gTv4e2db2uUyW1TCWkJ6Cv0CYwJLLZeW9dszbIuy6hvVR8jXSMeJjxkw5UNrLq0SiXTsFpD5neYj7udO44Wjkw4PIHVfqs1pqdGLxdoXQuMdLXvr7m424JPc7geBRuulE/6tibwZl2oZS71+V8D4VSYukyB8SD3Q3Onw05Ty7wWtqa2JdrUxsSG5d7Lcbd1x8XShTV+a9T6NxTdV8j/Nf+KKF9R/eTwfan/VYROAHN9eLsBNKxGsn4yd+Pucvj+YQY2GqiVTfPjVdOL08NOcyvmFm4b3Eq0af7xR/DPUOlmdHJycli2bBl16tRBX1+fWrVqsWjRIsLCwpDJZOzYsQMvLy8MDAxo1KgRf/31V+mVLDsnddBxLUFfp2g5JwvwcYO/n8Dis9LfD5uDo0W+tM7D9ON5x+pLUrhcBp2c4I8gLkdc5nrUdZIyknjvj/eoa1mXfYP3FVLXyr4VO/vvZOuNrTRb34ytN7ayq/8u3qjxRiFZDzsPRjYfyfWo64Wu6cplPEnO5K+IZK3MkZWWShWHOrQaPkkreYD6Fnp0qWHMhegUNgU/52z4WQ69e4iaZjU1yjtaOHJwyEHOhp/FbYMbi88uZk2PNfRr0E8l8yz1GYvOLsLT15Om65uy6domNvXZhHdtb5XMn0P+pE2tNnAsFL75G8IToIMjdHEuOrPa1KN3bWjrALsCYf5p2B0E3erAgIZw+D5tN7UlPSudGmY16LW9F/129itUj9qUsd/Ofth8baM6Gq1rRFZOFr/e/pUBjQawqvsqniY/5UH8A/bd2UdWThZ+j/04/v5x7EzVP7Gujb5capnX4mvvrznz8EyRZvKw82Biy4nkKHP4+8nfuG1wK7FekzOT+fbyt7Tb3I4Gaxuw8MxCFnZcyEfNP1LJGOkaEfo8VHIKEws79xrp6iz1n12B2vfXXKoaQr8GcC+ufNPX05Gc5D3BkJCmWabAeNDlpy6AdHNddHaRVjbV19HnacpTFp1dpLF/Q9F9hQZWFVs+Tf2ka22pD1aUTkMFfOYF2Tmw9m8arm3I/rv7meQ5SWub5mKmb8ZPfX/iROiJQte0GX8E/wyVztGZMWMGy5YtY/bs2dy+fZvt27er/ZLqlClTmDx5MgEBAXh5efHWW28RF1fMgKaJyCT46brUyVrUKFqukxMEx8KREIhOlv4Gx0JHxzyZpAypQ+ceTawhJhmaWktPOZcjaL2xNZ1+6oSejh4uli6MOzQODzuPQp1yQssJHAs5xtJzS7kTd4el55Zy4sEJJrScoCZnrGvMtn7b+Gj/R8SnxRfKdmB8OuejUnmYmKmVOezdPHEfOBLHNzpoJQ/wRnVDrselcSMunbj0bCYemcijhEeMbjFao/woj1GEJ4Qz8chEgmOD8Q3wZWPARj7z/Ewlc/rhafYE7yE4NpjQ+FDW+K3hRvQNybF5SQfHDlyJuCLNyATHwmo/yFZKMzZFoU09OlnAjWi4FQPPUiEgCjKz4WkKXHiEf4Q/jqsdeRD/gG51uuH3xK9QPWpTxvi0eKKTo1VHV+eupGSm8OvtX5nUahKbr23GzdaNCUcmMOC3AYQnhJOWncaD5w8Y7aFuW230Achlcrb128YXf31BaHyoRhPltqm41DgiEyMJfBpIcGxwifV6LeoaO27t4PbT2zxMeMi2m9s4EnKEtrXaqmT8I/yZemwqOwN3kp6dXnQ9Fayzw/fhWpT2/RVABgxzhQP3pBtoeab/MAF2B8OVSGkmURMFxoM3675JWlYaPwb8iG+Ar1Y2fZjwkAmHJ7D1xlYS0hM0yhTVV6hdtWLLp6mfBD2VZmQqSqd3bYhPg6034GECDxMe4u3sjW+Ar9Y2zWXDmxvYfms7Fx9fLHRNm/FH8M9QqRydxMREVq9ezZdffsnQoUOpXbs2bdq04cMPP1TJjB07lnfeeYcGDRrw3XffYW5ujq+vb+mVZeVIT3zOVYqWcaoCQbHqYUGxRcfRkcEbNaTpcnMDtbgZ2RmcDjuNl70X5vrm5ChzeJ72XC26Z01PjoYeVQs7EnIEr5rqvyS7tudaDtw7wIkHhZ9KXgdyGdgYKQgr4EgdDT2Kl73mX731tNdcNg87DxRyzSuwnZw6Uc+ynmoWwsnCCQOFATXMakB1Y0nI2hiUyqIHSdCuHkPioZ5lXro1zaTp91sxRZaxYD2+Shl93HzYcWsHmdmZuNu581fYXyjkCtKy0tT0pWamFhpwtdU3p/0cniY/ZWPAxiJNtLbnWg7dP4SLpUsh57m4ei2Iq42rtDTw8LRW8hqxNCzUf7TqrwA9XSRn48Kjikm/NOjIeK/pe+jKdTkaol5PpbGpNuT2Fe4/q9jyFewnNUwl5yo8oeJ0NrWGh8+lWdhlXQgYGYCHnUepbTrMdRi1q9Rm3l/ztFJbcPwR/HNUqj06QUFBpKen07lz5yJlPD09Vf8rFAo8PDwICgoqUj49PZ2srCyMjY1JTExEqVSin52Jvo4uJGZI09xFkbsGnZ8X6VK4JprZSNOs959J07mJ6nGjk6NxsnBiaZelbL+5ncSMRLXrNiY2RCdFq8dJisbGJG+2YmCjgTS3bU6LH1oUne8KxkhHjlwmI7mAcxGdFI1Nbc0zK0WVTVdHFysjK6KSogBpavnJpCfo6+iTrcxmzIExHA89rkoD4Peg3xk3Z6zk4Mhk0iCbWYyjo009Hg2R6m5O+7x0ZTLpiVVDGfV19AvVo7ZlzKWFXQuaWDfBZ58PVkZWKOQKwp6HceHRBWa3m03Q0yBikmOoa1kXaxNr7sXdK7VNvWp64ePmg+t61yLNk9um3vrlLT5t+SkZ2Rkay1wcjyY+oppRNRRyBXNPz8U34BUePnIxN5D+Fug/JfZX5yrgVVNanqyI9EtLMxssDCzQketo7tcl2LQkNPWVjcHv5DkWFVE+Tf1k/x1pZqaidFoZQTsHOPEADt9ne4v9fGn7JW42bmr7F4uzaZ2qdVjaeSltN7UlW1n0fsXixh/BP0elmtExNHy1DiGTyYq91rNnTwDq1q2Lubk5S67uyhNQlvJXaTWpamEHK7rBiJcb29KyXqatLqYj06GpdVPkMjljDmj+EbWCv5Irk8lUYfZm9qzuvpr3dr+n/RJABVLQdPnzqlFeQ9mkdPLCE9MTcV3vSosfWvD77d/xfcuXlJkpJM5IRFdHF4B+9fvBpgBYck6aHq9hKm1WLA0F69HdVpqNy03310ApvKGVxjLu6L9DYz1qU8ZcfJr7cDP6JpcjLqvFf3/3+8iQETE5gvkd5mNhYMH2m9s1DtDF6TPRM+Hnt3/mo/0fEZeqeXlXmzZVUr0CtN3UFo8fPBh1YBQTWk5gUONBxcqrkdt/cg8dWW7hClNUf9XXkZastt2UNpCXd/qvgldNToaefKmq6H79quT2lS/++gIZMnzf8pVetKjI8uX2kzNh0pJxdg68VQ8aV6s4nTIZPHohLVc/fqHaHPx2/bcLiGm2qVwmZ3u/7Xzx1xfce3av0PX85B9/Zp2cxYpuK2jv0L5s+ReUmUo1o+Pi4oKhoSEnTpxQW67Kz6VLl2jXTnojKCsriytXrjB27Ngi01QqlRw4cACAe/fukZOTg/4UafDBVE964igKTbM3phpmB25ES2vIE1rBzkBp6hzUZhIUcgU9XXqiq6NL161dC83mAEQlRanN3gBUN66uehp0t3XH2sSaKyPz3iJRyBW0c2jH2DfGor9QnxxlMTMb5URKdg45SiUmuup+dv68FqSosmVmZ6rdhJUoCYkPAWDUgVHo6uhia2LL8L3D0VdIdfHTjZ+YcaWpFCEiEbrVLv6pUZt67NdA2rtz5eVm2egk+F8j8KwJf+YNjjbGNlgZWuFk4USnnzqp1aO2ZQQwVBgyqNEg5vw1B4DYlFiycrKwMbHh0uNLdNjSASNdI1Z3X41LVRd05bo8iH9QKps2qtYIpypO7B+8X3VdLpPqLHN2JvW+rUeT6k3U2pRSqaS5bXNcbVxVbaq4es0l7HkYALdibmFtbM3c9nPZcWtHsXFU3IiW3tDJRfGyXRWciSuuv1Yzlp78R3vkheU+ADWuLm1cj0999fRLS1VDqG/Fht820Mm5U7H9+lXJ7Svf/P0NuwJ3sbjzYgboddY4/gDlU77cfuL3OO/tqHYO4FGj4nQmpEFkXj+LTYklOye70Ob8omxqqmdKixotcLN149ue3wJSP5DL5GTOzsR7qzenwk4B6uPP9ejrNLBqwIw2M8qWf0GZqVSOjoGBAdOmTWPq1Kno6enRunVrnj59SmBgoGo5a+3atbi4uNCgQQNWrlxJfHw8I0aMKDJNfX199PWlm5yJiYkUqKMrPfW4WEq7/YviQTzUt4KT+W4wDawgtMAG4PRsad06MV3aG5CjlDpnAyt4/AKFXMGv/X+lunF1ph6fyrPUZxrVXXx0ka7OXdVezfV29ubCowsAnHhwgsbrGqvF2dRnE8GxwSw7v+y1ODkgFS8qJQtHU13uJuQNYl2du7L3zl6NcS4+vkjvur3Vwrxre+Mf4U9WTpbGOEkZSSRmJFJdWV01+OQoc6S9CLnoyKQbXGoxG6+1qUddHfUnz+yXdaif18UUcgVDXYeSnpVOl61dCtVjaco4oNEA9BX6qqfTzJxMrkRcoatzV9V0fEpmCl41vThy/wjD3YYz9djUUukLjg0u1F4WdlqIqZ4pnx7+lEcJj4hJjlGT2f7OdmyMbTgSckTVpoqrV03IZDKVU6oV6S83fecnX/8BSu6vUUmwoMC+oLfqSfX3a6D0gkB2vvotbfqlxdMeEtPZG7y3UL1C8X2ltCRlJKn6CrpIG4Erqny5/SR/nSVlSDM7FaUzNB6sTVSnmTmZxKTEICswLVuUTV+kvyjUD8a0GEMnp07039WfB88fFIqTS6nbsqBCqFSODsDs2bNRKBTMmTOHiIgIbG1tGTVqlOr60qVLWbZsGQEBAdSuXZu9e/diZWVVTIoasDWB7nUgIxsuP8kLH9oMnqfB3jvS+akwmNhKem3yRrS0Ka6+FSwvsGNfBrSyh0uPJS8ApJtqtzrwNIUjXY7Q0r4lcalx7Anag7Wx9BbZ195f8+jFI2aemAnAar/VnBl+hqmtp7I3eC996vehi3MX2mySNqEmZSQR+DRQTXVyZjJxqXFq4QY6Msz05KoZl6ovX+9MzswhOavw1G5mWgovoh6rzpNiIogLu4u+iRkmVprXvP+OSaW3gylRKVk8Sc5iRbcV1DKvxXr/9QAs7ryYGqY1GLpnKADr/dcztsVYlnsv54erP+Bp74mPmw+Dfx+sSnN6m+n4R/gT8iwEPR09err05IOmHzD6QN6bFFcjr9K3fl/oHQL3nkGPOqAjh8sRZavHm9FSm4hPk2aJapqBgUK6UXra0yixEXsH7cVEz4QBvw5AR6bD6u6rsTWx5d0/3iUzJ1OrMubi4+bDnuA9as7Siksr2Pr2VlKzUgl+Gkyrmq1wsnDCu7Y3d2LvULtqbbb03aK1TdOz0wu1l9yN07nhmRmZajKLzi7il3d+wdrEmmxldon1OqbFGMITwgmOlW5mbWq14TPPz/jm729UaerKdWlYrSEAejp61DCrQTPrZpBjVNjBySW3/8QkS0dJ/TUrp/D3WlJeOr+avuNS2vRBunHbmr78Xw4WBmBvBulZ6uXINx5kK7NV9eof4c/FxxcZ6T6yWJsCkn0AEz0TqhlVo5l1MzKyMwiKlfYjFtVX+CWoYsunqZ90coKLjyE5o2J0nnwgvV7erTZcjWRw48FUMaiCQq5guOvwEm2qRFmoH8Qkx5CWlaYWXtz4o+nbV4LXR6VzdORyObNmzWLWrFlq4WFhYQA0aNCAS5culU3J9DbSVPk3ftKTSS5VDPMcFZCeJDYGQO960hGbAr4B6tPsIN00LY2kzp7LsVDp1crBjelkKj0RGOsZE/JpiErkWtQ1tSf9i48vMui3QSzstJAFHRcQ8iyEgb8N5O8nf5eqeC7mevRyMFWd93UyA+BcZArnogrfWGJDgjm8YJzq/O+t0k2qTrsetB3zuUYdwc8zMFQk09rGCGNdOdej2tFzW0/CE8IBsDWxpZZ5LZV82PMwem7vycpuK/mkxSdEJEYw/tB4/gj6QyVjrGvMup7rsDezl272scG8t/s9dgXm7anquKUjx94/RqtuLaE70ibk8+HqT42vUo+7AqVrAxtJy1oJafBXmDRL19OFK+ZXVE92h987rGaLtZfXcvrhaa3KCOBS1YW2Dm3purWrWviuwF1YGloyt8NcqhlVQ4mS+NR4ToWdYtbJWazuvrrUNi0tuwJ3saDjAlrZt+Lax9e4FXOr2HqVy+Qs6bwEJwsnsnKyCIkPYfqJ6Wzwz/tooZ2pHddGXVOdT/GawhSvKXA3DlYV0Zdz+8+gxnkfnyupv5aGV0nf3ABm5r02T9fa0lGwHPnHA1levc5pPwdbE9sSbQqo2cvDzoN3m75L2PMwnFY7AUX3lZ0XPqjY8mnqJ+fC4eA9acasInQ+TJA++tinHvR0YfZzG8YdGoeuXLdUNi2J4safH9/6sVRpCcoXmVLTLsdKSFhYGE5OTgQEBODq6lq2xMYcKJc8aYvM+s3Xqm/JW09fqz6AGfuqvVZ9yug/X6u+112H/x943XX4T/C6242wacWg/KJib7NLA2JLFtKS6W6lXOH4D1Cp3roSCAQCgUAgyE+lW7oqCkdHR42v6AoEAoFAIKi8iBkdgUAgEAgElRbh6AgEAoFAIKi0CEdHIBAIBAJBpUU4OgKBQCAQCCotwtERCAQCgUBQafl/89ZVeVLZv20he83ftIHX/+2ef6KMr5vK/q2g/w/fJqrsdfhP8P/hW0ECdcSMjkAgEAgEgkqLcHQEAoFAIBBUWoSjIxAIBAKBoNIiHB2BQCAQCASVFuHoCAQCgUAgqLQIR0cgEAgEAkGl5T/j6AwbNoy+ffv+09kQCAQCgUDwH6JU39Hp0KEDrq6urFq1qoKyA2FhYTg5OREQEICrq2uF6Skroz1GM8VrCramtgTGBDLhyATOhZ/TKPt2/bcZ7TEaVxtX9BX6BMYEMvf0XI6GHFXJnBp6ig6OHQrFTctKgxxdCHsOO29BZFLxGXO1gd51wcoIYlNg3x24Hp13fUFHsDQqHO90mJR2F2dSLVJJypD0GOkacebhGcYdGsf9Z/fVorRzaMcK7xU0qt6IiMQIvjz/JRuubFAr98y2M6lTtQ66cl3uPbvH8ovL+fnGzyoZHZkOczvMZVTDKhjryknOzOHmszTOR6UWWcSooGvc2r+d2AfBpMbH0WnyEhxatCveLkVQmnq0MbFhufdy3G3dcbF0YY3fGiYemagmU1KZy1sfwKctP2W0x2hqmdciNiWW34J+Y8bxGepCvVygdS0w0rIt2ZrAm3WhlrnUXn4NhFNh6jLdakvtzdoEZJAiT0Emk3Hp8SU+OfgJt5/eLjp9oF+DfizouIDaVWoTEh/CrJOz2BO8R3V9lMcoRnuMxtHCEYDAmEDmn5mPk4WTyoZ3Yu/wJPEJrjauWBhYaGyrJbVTAHN9cxZ1XkS/+v2oYliFB/EPmHx0MofuHwKgba22TPGagrudO3amdkQnRWNuYF4udaiQK5jRZgZDmw2lhlkN7sTeYdrxaRwJOaKeWEXXYWY2u5/sZtrxadyNu6smVt7ttjTpQcl1OLTZUDb33VwonsFCA9Kz0wEw0TNhQccF4NIRTPXh8QuISITG1cvXpvnpVhv61C/6uuC1UK4zOkqlkqysrPJM8l/JgEYDWNV9FYvOLsJtgxtnw89y6N1D1DSrqVG+nUM7joUeo+f2nrh/786psFPsH7wfVxtXlUy/nf2w+dpGday4uAKlUskPV36AZefgRTqMawn6OkVnzMkCfNzg7yew+Kz098Pm4GiRJ7PsPEw/nnesviSFJ2VA/4Zw+D7BT4NJy0rDSNeIntt68jDhIcffP46Rbp6D5GjhyMEhBzkbfha3DW4sPruYNT3W0K9BP5XMs9RnLDq7CE9fT5qub8qma5vY1GcT3rW9VTLT2kxjlMcojj1O4segeE5FJPNGdUM8qhkUWcystFSqONSh1fBJRdtCC0pbj/o6+jxNecqis4u4HnVdo0xxZa4IfUOaDGFpl6XMOz2PBmsb4LPPh4GNBrKky5I8oa7O0MkJdgVq35b0dCRHeU8wJKRplqlTFU4/hPPhAAQ+DeRZyjOepjzl2PvHMNEzKTL5Vvat2Nl/J1tvbKXZ+mZsvbGVXf138UaNN1Qyj188Zvrx6Xh874HH9x6cDDvJ/sH7Wd19tcqG1Y2r0612N0buH4nbBrdCbVWbdqor1+XY+8dwNHek/6/9qfdtPT7a/xFPEp+oZIz1jLkefZ1fbv4CwK+3fy23OlzYaSEfu3/MuEPjaLi2IeuvrGf3wN1q40OF1+FX52GNHwq5gqPvHVXr6+XdbkubnjZ1CJCQlqA2htp8baNycgB+7P0jXZ27wpbrsOgMpGdB65rw553ytWkuDuaSY/r4RfFyggpHa0dn2LBhnD59mtWrVyOTyZDJZGzevBmZTMaRI0fw8PBAX1+fs2fPolQq+fLLL3F2dsbQ0JBmzZrx22+/qdKKj4/n3XffpVq1ahgaGuLi4sKmTZsAcHJyAsDNzQ2ZTEaHDh3U8jFv3jyqV6+OmZkZH3/8MRkZGaprHTp0YOzYsYwdOxYLCwssLS35/PPPUSqVKpl169bh4uKCgYEB1tbW9O/fv9RGm9RqEr4BvvgG+BIcG8zEIxN5lPCI0S1Ga5SfeGQiX134Cv8If+4/u8+sk7O4F3eP3nV759kkLZ7o5GjV4ePmQ3p2OtNPTJeeMH66LnWyFjWKzlgnJwiOhSMhEJ0s/Q2OhY6OeTJJGVKHzj2aWENMMjSsBhcewf1nuNq60u3nbjx8/pBudbox5sAYTPRMGNx4sCqZUR6jCE8IZ+KRiQTHBuMb4MvGgI185vmZSub0w9PsCd5DcGwwofGhrPFbw43oG7Sp1UYl42nvyd47ewl5kUlCRg53nmcQlpiJjVHRk432bp64DxyJ4xsdiraFFpS2Hh8mPGTC4QlsvbGVhPQEjTLFlbki9Hnae3I+/Dy/3PqFhwkPORZ6jF9u/YKHrUeeUCcnOHwfrkVp35YeJsDuYLgSCVk5mmXWXoZLj8HDDg7eo8e2HtiZ2bH+8nqMdI0Y0mRIkclPaDmBYyHHWHpuKXfi7rD03FJOPDjBhJYTVDJ/3v2TQ/cPce/ZPe49u8fnJz8H4Nyjc/gG+JKdk42tqS1hCWG0rtWau3F3C7VVbdrpCLcRVDWsSt+dfbnw6ALhCeGcf3SeG9E3VDKH7x9m9qnZqrZ7PPR4udXh+03fZ/G5xRy6f4gHzx+w3n89R0KOMNlzcp5QRddhZBI8SWT43uE4WDjgbuuuEinvdlva9LSpQwAlSrUxNDo5bybbQGHAOw3fYerxqXD/GTxNATtTyVmxNCpfm4LkLA1zhW03ICWzaDnBa0FrR2f16tV4enry0UcfERkZSWRkJDVrSh741KlTWbJkCUFBQTRt2pTPP/+cTZs28d133xEYGMjEiRN57733OH36NACzZ8/m9u3bHDp0iKCgIL777jusrKwA+PvvvwE4fvw4kZGR/PHHH6o8nDhxgqCgIE6dOsUvv/zC7t27mTdvnlo+t2zZgkKhwM/PjzVr1rBy5Up+/PFHAPz9/Rk/fjzz58/nzp07HD58mHbtSr/k4W7nrrbsBHA09Che9l5axZchw1TflGepzzRed7JwwtzAnAN3D5CSmSIFZuXAvThwrlJ0wk5VIChWPSwotug4OjJ4owZceiRNxQY9BYXUJNKy0lRlylHmkJGdUchBORqqboMjIUfwsPNAIdfspHRy6kQ9y3qceXhGFXYu/BydnTpTRV/SW91QB3tjXUJeZGhMo7zQleuWuR61IbfMFx5dqBB958LP4W7nTgu7FoDUdnrW6cmBewckAUtDMDdQbxfatCVtyZe+ub45ANHJ0ZwOO11suTxram4/XjU1x5HL5AxpPAQdmQ5/3JbGBH2FPgBnH55V6SrYVrVpp2/Ve4uLjy+ytudaoiZHcXP0TWa0mYFcpj485raZgpS1DvV19KUl6nykZqbm9beKrsN85NZh7thU3v3kVdLTdqwx0TMh7NMwHk18VGjGXCFXoJAr8uyca9OkTKhdVQorT5sObAy3YuBOXNnT+pezbt06nJycMDAwwN3dnbNnzxYrn56ezqxZs3BwcEBfX5/atWuzcePGCs2j1nt0zM3N0dPTw8jICBsbGwCCg4MBmD9/Pl27dgUgOTmZFStWcPLkSTw9PQFwdnbm3LlzbNiwgfbt2xMeHo6bmxseHtJTp6Ojo0pPtWrSbxBZWlqq9OSip6fHxo0bMTIyolGjRsyfP58pU6awYMEC5HJpUKpZsyYrV65EJpNRr149bt68ycqVK/noo48IDw/H2NiYN998E1NTUxwcHHBzcyu23Onp6aSn501/ymQyTE1NiU6KVpOLTorGprZNwegamew1GWNdY3YF7tJ4vaNjRwB8A3zVLyRmQFXDohM205dmafLzIl0K10QzGzBUSB3yrfpS+lFJhD0PY0nnJdyJvYOtqS3TWk/D1tQWWxNbVVQbExuNNtDV0cXKyIqopKiXWTLjyaQn6Ovok63MZsyBMRwPPa6Ks+z8MswNzJneejo5SJ736cgUguIr1tGxMrJCIVeUqR6LQlOZr0ddrxB9OwN3Us24GudGnEOGDF0dXdZdXsey88tYSltpMAdILNAuSmpL2pIv/RXvrODsw7MEPg0kOjkaB3OHIqMV1X5sTNRt0bh6Yy76XMRAYUByRjIymYwrkVcACI4NJux5GO627hgoDNCV6zLJc5JaW9WmnTpXcaaTUye23dxGz+09canqwtqea1HIFSw4s0AVL7fNFKSsdXgk5AiTWk3izMMzhDwLobNzZ/rU74OOTAcIrPg6zMeKbnl1COXfT14lPW3qMDg2mGF7hnEz5iZm+mZ82vJTzo84T7P1zbj/7D5JGUlceHSB2e1mQ1BWnk3tTOFpcl7C5WFTd1uoaSZtE6jk7Ny5kwkTJrBu3Tpat27Nhg0b6NGjB7dv36ZWrVoa4wwYMIDo6Gh8fX2pU6cOMTExFb7lpVx+1DPXYQG4ffs2aWlpKscnl4yMDJVTMXr0aN555x2uXr2Kt7c3ffv2xcur5KeDZs2aYWSUt3bs6elJUlISjx49wsFBGlRbtWqFTCZTk1m+fDnZ2dl07doVBwcHnJ2d6d69O927d+ftt99WS7MgS5YsUZs1srW1JSIiAiVKNTmZTFYoTBODGg9ibvu59NnRh6cp0g9ZDmkyhA1v5m2sO/ngJAABUQGFE1CWrEM9Y8Vc86oJt59KnTs37Rwl7+x6B9+3fBnQaABKpZIOjh04eO9g4axosIGUTF54YnoirutdMdEzobNzZ1Z0W0FofCinH0qzewMbDeS9Ju+xLyyR2LRsqhsq6GJvTFJmDreeFRjYK4BXrcfi0FTmhLSECtHX3qE9s9rOYsyBMfg98WOE2wg+bfkpPm4+kKUD313mpeLClLYtAbSwg8FN8s5z03+rHk2ts2mzUZqF0KZc2tjiTuwdXNdLG42Hug5l3BvjcDB34OLji2TlZPHOrnf4c/Cf2JrakjIrheOhxwu11ZLaqVwmJyY5hpH7R5KjzOFq5FXsTO2Y4jVFzdEpirLW4aeHP+WH3j8Q/EkwSpTEJMegK9eVnKoV3cq/DotiYCOaWqOqQzU15dxuS5teSXXo98QPvyd+quvnw89z9eOrjHtjHJ8e/pQhTYbQzLoZBgoDWKID2S+Xna5HgU2BvWRlsWkVA/hfI/jGr/ilrUrCihUr8PHx4cMPPwRg1apVHDlyhO+++44lS5YUkj98+DCnT58mNDSUqlWlmbT8Ex0VRbk4OsbGxqr/c3Kkyj1w4AA1aqivderrSzMLPXr04OHDhxw4cIDjx4/TuXNnPvnkE77++utX0p/fsSkOU1NTrl69yl9//cXRo0eZM2cOc+fO5fLly1hYWGiMM2PGDCZNUt/0mpWdVejJs7px9UJPHQUZ0GgAvm/58r9f/8eJBydU4fvu7MPvsdRJDRQGXPS5CEhPMrkzI1IB9PKcEk1omr0x1TDLA9JTS30r+P6KtG8nO0cV92rkVdw2uLGu1zqaWTejx7YeXPK5hH+kvyp6VFKURhtkZmcSl5o3XatESUh8CADXo6/TwKoBM9rMUDk6X3X9iqXnl1LTVnImn6ZlY64nx9PasEIdndiUWLJyXq0eS0JTmT92/7hC9C3ouICtN7aqZv/mnJrD44THLOy0EJZeAJ2Xyy8FZ/tKaktFcSNaejsll5dLnTSqRsdNDVQbeKsbFV+uotpPwTiZOZkqW96IvsEnLT5hmNswdgTuAKS2uuv2Ltxt3Xl759vEpsSqtVVt2mlkYiSZOZnkKPNuTEGxQdia2qIr1yUzR9pjkdtmCs7qlLUOY1NieXvn2+jr6GNpZMmL9Bd87f01nRw74bLmcZ6Ny6sONTGgETS1puOWhmqbsMu7n7xKetqONflRouRyxGVcqroA6mPs/RcbpbesJnlKe2ni8r3hWVab1jKX6ml6PmdRp1zf+alwCq5igHTvzr1/55KRkcGVK1eYPn26Wri3tzcXLlzQmPa+ffvw8PDgyy+/ZOvWrRgbG/PWW2+xYMECDA3Ld3YyP6WqAT09PbKzs4uVadiwIfr6+oSHh1OnTh21I3dPD0hLVMOGDePnn39m1apVfP/99yodgEY9169fJzU1r1FeunQJExMT7O3t1cLyc+nSJVxcXNDRkXbSKxQKunTpwpdffsmNGzcICwvj5MmTRZZHX18fMzMzteNK5BVp934+ujp35cJjzZUL0kzO5j6bGfL7kEJPnEkZSYTEhxASH4KHnQe6OrpEJUWp69CRgYslhMYXqYMH8ZLzkp8GVprjeNpLU+G3YiBbCeEJ0KCamkh7h/acfniaOlXr4GHnwd7gvaprFx9fLGQD79re+Ef4k5VT9DSkTCZT7a0A6fX1/DcYgBykfUwVSWZOJlciSl+Pr4JMJkNPoVch+graLykjiYikCOnkaYq0yTIhTWoHuWjTlooiPVtKN/do6wA5Srj0hLDnYYC0D6O9Y/tiy3XxkYb24+zNhUdFx8nMySQxPbHQklhX566cDT9LbEpsobaqTTs9/+g8darWUWtzdS3rEpEYoXJycvVfibhSKF/l1WbSs9OJSIwgLSuNzk6d+fX2rxVThwUZ0Eh6xXzVJVUd5lLe/eRV0nvVscbV2pXIpEhAfYwlIlHakPwiHepYSrM6UD42DY6FBaelt15zj4fPXz29f4AlS5Zgbm6udmianYmNjSU7Oxtra2u1cGtra6KiogrJA4SGhnLu3Dlu3brF7t27WbVqFb/99huffPJJhZQll1LN6Dg6OuLn50dYWBgmJiaq2Zv8mJqa8tlnnzFx4kRycnJo06YNL1684MKFC5iYmDB06FDmzJmDu7s7jRo1Ij09nT///JMGDRoAUL16dQwNDTl8+DD29vYYGBhgbi5tkMvIyMDHx4fPP/+chw8f8sUXXzB27FjV/hyAR48eMWnSJD7++GOuXr3KN998w/LlywH4888/CQ0NpV27dlSpUoWDBw+Sk5NDvXr1SmW0FZdWsPXtrfhH+HPx8UVGuo+klnkt1vuvB2Bx58XUMK3B0D1DAcnJ+anvT3x6+FMuPb6EtbHUMFKzUnmRrv7qoY+bD3uC9xAQFcDMtjO59+weyE2gex3IyIbLeU9bDG0Gz9Ng7x3p/FQYTGwlvYp6IxqaWkuOz/KL6gWQAa3spbctcl5O0558AENd4eFzPq32Ka1rtsbB3IGQZyEce/8Y957d472m73Es9BgA6/3XM7bFWJZ7L+eHqz/gae+Jj5sPg3/PezNrepvp+Ef4E/IsBD0dPXq69OSDph8w+kDe2xX77+5nVttZXHyqS2xaNtaGCt6oZsiNZ0W/upmZlsKLqMeq86SYCOLC7qJvYoaJlfb7BkpbjwDNrJsB0sbHakbVaGbdjIzsDIJig0osc2JGYrnr2393P5M8JxEQGYDfEz/qVK3Dgo4L2HdnHwOVJnl1262O9HZdTLJ2bUlHBramL/+Xg4UB2JtJr+Q+fblBflBj6Y2r8+HQthbDUobx4PkDRnuMJiUzhe03t6uS39J3C08SnzDzxEwAVvut5szwM0xtPZW9wXvpU78PXZy70GZT3pPwok6LOHT/EI8SHmGqb8qgxoMw1TPFqIoRw12Hc/HxRb7q+hVOFk4cuHuAt+q9xZa+W3j84nGp2ul3/t8x7o1xrO6xmm/8vsHF0oWZbWay5u81KhljXWPqVK3D70G/09K+JQMbD0SGjF51e5W5Dt+o8QY1TGtwLeoaNcxqMLf9XOQyOV+e/5KZbHs9dbjBH9KzsbaSxqaE9ATVxt3y7ielTU+bOpzTfg6XHl/iXtw9zPTNGN9yPK42rnxyMO8G6l3bW3JmswyhmjHk5Eg2Ss6Uvo9THjZNzy78HZ704icH/m1oWsUoOJuTn4IrKkqlsshVlpycHGQyGdu2bVPd11esWEH//v1Zu3Zthc3qlMrR+eyzzxg6dCgNGzYkNTVV9Up4QRYsWED16tVZsmQJoaGhWFhY0Lx5c2bOlAY5PT09ZsyYQVhYGIaGhrRt25YdO6SpaIVCwZo1a5g/fz5z5syhbdu2/PXXXwB07twZFxcX2rVrR3p6OoMGDWLu3Llquj/44ANSU1N544030NHRYdy4cYwcORIACwsL/vjjD+bOnUtaWhouLi788ssvNGrUqDRmYFfgLiwNLZnTfg62JrbcirlFz209CU+Qvidia2JLLfO8jVgfu38sbRLttY51vdapwjdf28zwvcNV5y5VXWjr0JauW7tyPPQ4hgpD1vVcB/2qS8sF3/ipd5oqhnmOCkhPIhsDoHc96YhNAd8A9aUGkJwfSyO4mOcscCUSjPWgpwtfV/maHGUOMmTMbjebn278hIO5g1qZwp6H0XN7T1Z2W8knLT4hIjGC8YfG80dQ3ltyxrrGrOu5Dnsze1KzUgmODea93e+pbcIed2gcCzouYHjzcRgp5CRl5hAQl8b5qJQi7R8bEszhBeNU539v/QaAOu160HbM50XGK0hp6xHg2qhrqv897Dx4t+m7hD0Pw2m1k1ZlLm99C88sRImShZ0WUsO0Bk9TnkrO44lZDMy9SR4LlV6bHdQ478NoJbUlcwOY2TbvvGtt6bgbB6tezpq2ezmz0lb6u6mvNB4ExwbjvdVb9dFJgFrmtdRmni4+vsig3waxsNNCFnRcQMizEAb+NpC/n/ytkrE2sWbr21uxNbElIT2BG9E36LatGy5VXVQ2jEmOITE9kZNDTxKZGMnD5w95nvZclYY27fTxi8d4/+zNym4ruTH6Bk9ePGG132qWnV+mZvu/hv2lOh/ceDCDGw8mNiW2zHVooDBgYaeFOFdxJikjiYP3DvL+7vfVX82u6DqcKL04EoX0JD5szzC2XN8ClH8/KW162tShhYEF37/5PTYmNiSkJxAQGUC7ze24HHE5zxz65izpvAQGOkqvfAdESQ7KwEbla9P/OJqWqTRhZWWFjo5OodmbmJiYQrM8udja2lKjRg2VkwPQoEEDlEoljx8/xsXFpWyZLwKZUlmeu9n+WV7Hl5sBZPMqdlmlIMroP1+rPpn1m69VH8CSt56+Vn0z9lUrWeg/zv+HdlPZEXVY/rxumwKwrleFJr80ILZkIS2Z7mZVstBLWrZsibu7O+vW5T3AN2zYkD59+mhc7vr++++ZMGECMTExmJhIM8579+6lX79+JCUlVdiMzn9rl5RAIBAIBIJ/BZMmTeLHH39k48aNBAUFMXHiRMLDwxk1ahQgLYN98MEHKvkhQ4ZgaWnJ8OHDuX37NmfOnGHKlCmMGDGiQjcjl8tbVwKBQCAQCP5/MXDgQOLi4pg/fz6RkZE0btyYgwcPqj73EhkZSXh4uErexMSEY8eOMW7cODw8PLC0tGTAgAEsXLiwQvNZqRyd3L08AoFAIBAIKp4xY8YwZswYjdc2b95cKKx+/focO3asgnOljli6EggEAoFAUGkRjo5AIBAIBIJKi3B0BAKBQCAQVFqEoyMQCAQCgaDSUqk2IwvKh3/iOxOy1/xdG/HdHsF/gf8P37URCCoaMaMjEAgEAoGg0iIcHYFAIBAIBJUW4egIBAKBQCCotAhHRyAQCAQCQaVFODoCgUAgEAgqLcLREQgEAoFAUGn5zzo6YWFhyGQyrl27VqZ0HB0dWbVqVbnkSSAQCAQCwb8L8R2dV2S0x2imeE3B1tSWwJhAJhyZwLnwcxpl367/NqM9RuNq44q+Qp/AmEDmnp7L0ZCjKpkPm3/IB00/oHH1xgBcibzCzBMzuRxxufiM9HKB1rXASBfCnsPOWxCZVLR865rQ0h7sTKXz8ATYGwwPE14tPQBXG+hdF6yMIDYF9t2B69F51+UyKd0WNcBMH16kw8VHcPg+KKXrS7ss5b0m72FraosMGc9Sn+Gzz4e9d/ZqVGljYsNy7+W427rjYunCGr81TDwyUU3m7fpvM7PtTOpUrYOuXJd7z+6x/OJyfr7xc/HlKUBU0DVu7d9O7INgUuPj6DR5CQ4t2mkV183KgJbVDZnQJLXEdgLQzqEdK7xX0Kh6IyISI/jy/JdsuLJBdV2bdvLg0wc4WjgWTjw9C2Qy7erV1gTerAu1zMHSCH4NhFNh6jK9XKBXXdWpEiVKpZLTD0/zycFPuP30dnGmoV+DfizouIDaVWoTEh/CrJOz2BO8R3V9epvp9Kvfj/pW9UnNSuXCowtMOz6Nzk6dVX3vedpzkjOSsTGxISE9geOhx5l+fDqRSZFa2xTAXN+cRZ0X0a9+P6oYVuFB/AMmH53MofuHGO0xmtXdV6Oro1uoDGsvr2XswbFqYWXVB/BF+y+Y22GuWpzYlFiqfVX895hKsmnbWm2Z4jUFdzt37Ezt6LujL3vv7C1yPFv/5no+dv+YCYcnsNpvtdZlPDX0FB0cOxTK34G7B3jzl8LfBpreZjpLOi9h1aVVqn5cmjFWm/FAK0o7/mnTTwT/OP/ZGZ1/kgGNBrCq+yoWnV2E2wY3zoaf5dC7h6hpVlOjfDuHdhwLPUbP7T1x/96dU2Gn2D94P642riqZDg4d+OXWL3Tc0hFPX0/CE8I5+v5R7Eztis5IV2fo5AS7AmHZOcmBGNcS9HWKjuNiCf4RsOoSfHUenqVKccz1Xy09JwvwcYO/n8Dis9LfD5uDo0WejHdtaOsgpTv/NOwOgq61oYOjdF1Phx61e2BtYs3sU7MZ/PtgMrIz+H3A70XaVF9Hn6cpT1l0dhHXo65rlHmW+oxFZxfh6etJ0/VN2XRtE5v6bMK7tnfR5dFAVloqVRzq0Gr4pFLFq2+hR5caxlyITtGqnThaOHJwyEHOhp/FbYMbi88uZk2PNfRr0E8lo007afFDC2y+toHpx6XjXLh04WiI9vWqpyM5rXuCISGtaLmIRDh0D9KyGLF3BO02tyMqKYpj7x/DRM+kyGit7Fuxs/9Ott7YSrP1zdh6Yyu7+u/ijRpvqGTaO7Rn7eW1tPJtRdetXVHIFZwbfk7V99puakt6Vjo1zGrQa3sv+u3sR13LuuwbvK9UNtWV63Ls/WM4mjvS/9f+1Pu2Hh/t/4gniU9UfX3qsam03diW7698T3JGMkN+HwLAr4G/lroOi9OXS+uarcnOyWbE3hF02NyBPcF7yMrJKrNNjfWMuR59Xc05K2o883HzoWWNljx58URNjzZl7LezHzZf26iORusakZWTxa+31e0F4GHnwcjmI9X6cWnHWG3GgxJ5lfFP234i+EeRKZVK5T+dieLIycnhq6++4ocffuDRo0dYW1vz8ccf8+677+Lk5MTvv//ON998g5+fHy4uLqxfvx5PT09V/N9//505c+Zw//59bG1tGTduHJMnT1Zdd3R0ZMKECUyYMEHrPPk99uNq1FXGHMj7afrbY26z584eZp6YqVUat0bfYmfgThacWaDxulwmJ35aPGMPjuWnYwM1J7KkM5x8AMdCpXOFHJZ2kTpd7s2tJGTA195S5+5bv/Tp+biBgQLW5pt5+qQFpGTCpmvS+WgPSMyAn2/kyXzUHDKyYYs0KPnNtFKzqYedB5c/uszav9cy9pD6E3NBTg09xbWoa1o9wV0ZeYUD9w6gazq6RFlNbBrUWusZnQ/qmhOVksXRx8mqLyMX106WdlnKW3XfouG6hqqw73p9RzPrZnht9NKoI3872Xpjq9o11ReuV3aDzByYekw6L207WdBRaheaZnSaWkuzdCcfILsh5VtPR4/oz6KZdnwa31/5XmOSO97ZgZm+GT2391SFHXr3EPGp8Qz5Y4jGOFZGVjyd8pQ9wXt4e+fbqvD8Ns1tN7VW1uLRi0da2fRj94+Z4jWF+mvrk5WTpabzks8ljX09KSOJKoZVcPnGRU2+rPpyeTH9BYkZidRYUaPCbKr8QknfHX2Z0WZGoTLeHXsXaxNrPH09OTDkAKsurVLN6LxKO/205afM7zgf2+W2pGSmqMKNdY25+rGk+/N2n6v6cVF212aMLWk8KPLL72UdT4vqJwDrepUcvwwsDYgtt7Smu1mVW1r/Fv71MzozZsxg2bJlzJ49m9u3b7N9+3asra1V12fNmsVnn33GtWvXqFu3LoMHDyYrSxo8rly5woABAxg0aBA3b95k7ty5zJ49m82bN5cpT+527mrLTgBHQ4/iZa+5kxdEhgxTfVOepT4rUsZI1whduW7RMpaGYG4AQfkaeFYO3IsD5ypa5QOQnkh05NLxKuk5VVGPA9J5/jgh8VDPEqobS+c1TKF2VQh8+TMMOrJCNjXXN0epVNLMppn2ZSmBTk6dqGdZjzMPz5RbmkUhl4GNkYKwxEy18OLaiae9J0dD1dvVkZAjeNh5oJBrXmUusZ1UNwJ9Bfjneyp/lXZSFNWNpXbTwZFf3vkFJwsnMrIzOB12utj+4FlTc1m9ahYdx8pQGoBPPTilFp7fpub65uQoc3ie9lzSo4VN36r3FhcfX2Rtz7VETY7i5uibzGgzA30dfY19/XjocVxtXNkYsLFwucqgTy6ThmMnCydM9U2pYlCFJ5OeEDo+lC19t3D5yeVyt6lCrihURhkyDBQGxCTHaFx+fJV26uPmw45bO9ScHIC1Pddy4N4BTjw4oQrTleuWeYwtNeU1ngr+lfyr9+gkJiayevVqvv32W4YOHQpA7dq1adOmDWFhYQB89tln9Oolecvz5s2jUaNG3L9/n/r167NixQo6d+7M7NmzAahbty63b9/mq6++YtiwYVrlIT09nfT0dNW5TCbD1NSU6KRoNbnopGhsattoleZkr8kY6xqzK3BXkTJLuyzlSeITjoceBzTMPpgbSH8T09XDEzOgqqFW+QCkWZznafA0+dXSy91zk58X6VJ4LkdDwFABc9qDUintE9l/R1pCAzDRQyFXqGyqr6PP0i5LuRZ1jWpGZfuNKDN9M55MeoK+jj7ZymzGHBjD8dDjdG5cpmRLxEhHjlwmIzkrRy28uHZiY2KjsV3p6uhiZWRFVFJUoTjq7UQDzV8uaV14pB5e2naiiQfPpX1WferD77ex6WLDBZ8LNFrXiOjkaBzMHYqMWlRZbUyK7kOruq8CKLRvLdemue1m+83tJGYkFqsnv02dqzjTyakT225uo+f2nrhUdWFtz7VUMaii1i5zsTKyQiFXsPnaZq3LpY0+hVzBgjMLVDYYd2gcfk/8sDa25vN2n/NGjTe49PhSudrUVM+0UBmntZlGWlYaOcocjXFK205b2LWgiXUTfPb5qIUPbDSQ5rbNafFDC7XwXPuWZYwtNeU1ngr+lfyrHZ2goCDS09Pp3LlzkTJNmzZV/W9rawtATEwM9evXJygoiD59+qjJt27dmlWrVpGdnY2OTjFrry9ZsmQJ8+bNU9MRERGBEvUVP5lMVihME4MaD2Ju+7n02dGHpymaf1hyitcUBjceTIfNHUjPftnxWtjB4CZ5Qt+9HOw1qdRmNbKFHbzfDHRk0rKGKu4rppcfWYFzd1t4owZsCpA29tmbQf+GkoPllzfToESJQq5gR/8dyGVy9t/dz4BGA0qnuwCJ6Ym4rnfFRM+Ezs6dWdFtBaHxoWVKszQUNF1J7URTu5LSKRxHUzsZ0mQIG958uSk0xwCiXm6kfJFRcua0oah2eD+eXjF9CBkfwtBmQ7XqD6XpQ9/2/JaG1RoWGy+33eRf7ihKHvJsKpfJiUmOYeT+keQoc7gaeRU7UzumtZ6mMb6HnQfJGclqG57Loq++VX1M9EyY22EuU1tPpdd26cHtwL0DRCVFcYtbXHx8kadTnha/Z68Y25RErkxz2+Z82vJTNl/bTN/6fUulJ38Z8+PT3Ieb0TfVHFR7M3tWd1+N98/eeWNcOZVFK8p7PBX8q/lXOzqGhiV70rq6eW9C5Ha2nBzpxq1UKlVhuZR2S9KMGTOYNEl9E2pWdlahp6TqxtULPYEUZECjAfi+5cv/fv2f2lRtfiZ7TmZm25l0+akLN2Nu5l24ES29BZCL4uWqY8EZFVM96SmkJCyNIDMbfrwOkYmvnl7B2RsA0wJp9GsAR0LgyssbQ0Si9JTUrY7k6CRlkJWTRQ3TGkztPxUnCyc6/dSJOe3nlGjTklCiJCQ+BIDr0ddpYNWAGW1mEJBYpmRLJCU7hxylEhNd9dXh4tpJVFKUxnaVmZ1JXGqcWnhR7WTfnX34PfYD4H7WVpjQSrrwqu2kIMW0w5TMFG5G38TF0oXqRsX3h6LKqinOmh5reKvuW3T+qTO3P7ldKJ6NsQ1WhlaqdpM7m1Ocnvw2jUyMJDMnU20GIyg2CGsTa7Jy1Pt6LfNa1K5au8g3yl5F3747+xh9YDQb+2ykxfctkMslm9qY2KhmR1IyU0jKSEJW6CmiZN3F1UNiRqJaGdvWakt14+pMbT0VGTIyZ2eikCtY7r2cCa0m4LTaqVTt1FBhyKBGg5jz1xy1cHdbd6xNrLky8ooqTCFX0M6hHWPfGFvI7tqUpVSU93gq+Ffzr96j4+LigqGhISdOaHYKSqJhw4acO6f+OuKFCxeoW7euVrM5APr6+piZmakdVyKv0NW5q5pcV+euXHh8och0BjUexOY+mxny+xAO3juoUeYzr8+Y3W423X/uzpXIK+oX07PhaUreEZkk7fJvkG/jmI5MeqsqNL74QnVxlt4w+OZvqcOXJb0H8VC/wOa1BlbqcXR1Cj8VKZV5Mz/ZSq5GXuXLrl/iYulCl61deJb6rESbvgoymQx9hX7JgmUkRwlRKVk4mqq/klxcmS4+vlioXXnX9sY/wl9t02px7SQpI4mQ+BDJuatnKU3Fv2o70UQx7VBPR48G1RoQkxRDe8f2xdbdxUcayurszYVH6nG+6fEN/er3o9NPnbj37B5XItT7nkKuYKjrUJQoVe1GTY8WNj3/6Dx1qtZRcyLqWtYlIjGikL7hrsPJzsnmwN0Dmsv1CvqSMpIw1TclIjGC4Lhgbj+9TWRipFo6xrrGWBlZcSfujka9oL1N85OVk6VWxq03ttL0u6Y8iH+Ab4AvrutdefLiCV9d+IpuP3fTuoy5DGg0AH2FfqFPOpx4cILG6xrjut5VdVx+cpltN7bhut61kN2h5DG2VJTneCr41/OvdnQMDAyYNm0aU6dO5aeffiIkJIRLly7h6+urVfzJkydz4sQJFixYwN27d9myZQvffvstn332WZnyteLSCj5s/iHDXYdT36o+K7qtoJZ5Ldb7rwdgcefFbOm7RSU/qPEgfur7E5OPTubS40tYG1tjbWyNmb6ZSmaK1xQWdlzIiH0jCHseppIx1jUuOiMnH0izIs2spe85fNBMepPpcr6Np0ObQZ96eeddnaVv3my9Ib1abqYvHfo6r5beqTBpcOjqDNbG0t/6VupvHtyMhu51oHF1aSanmbX0Gmfut3bkMmTIcLJw4tfAX6lvWZ/1vdbjYO6A71VfjTYFaGbdjGbWzTDRM6GaUTWaWTejgVUD1fXpbabTxbkLThZO1LOsx8RWE/mg6Qel/o5OZloKcWF3iQu7C0BSTARxYXdJii28ZyY/f8ek0szSgKZV9bVqJ+v9pTIv915Ofav6DHcdjo+bD19f/Folo207kSGDVvZw6fGr1auOTFpitDeTNqpbGEj/VzPKk+nXAFyqgt9j6OHCueHnsDCwwNXGlZTMFLbf3K4S3dJ3C4s7L1adr/ZbjXdtb6a2nko9y3pMbT2VLs5dWOW3SiWztuda3mv6HkP+GEJieiLWxtZsDNio6nuNqjUi+JNgTPRMeH/3++jIdFjdfTW7+u9CV66rtU2/8/8OS0NLVvdYjUtVF3q69GRmm5msvbxWra83sGrApFaTUKJknf+6V67D4vTlcu/ZPWa3n81I95EMajyI4LHBAEw/Pr1MNjXWNVb1GwCnKk78HvS7qozVjavj09wHaxNrFpxZQODTQIz1jOns1Jm7cXe1LmMuPm4+7AneU8gBTcpIIvBpoNqRnJlMXGocgU8DSz3GQsnjQYlUVD8R/OP8q5euAGbPno1CoWDOnDlERERga2vLqFGjtIrbvHlzdu3axZw5c1iwYAG2trbMnz9f643IRbErcBeWhpbMaT8HWxNbbsXcoue2noQnSK8g2prYUsu8lkr+Y/eP0dXRZV2vdazrtU4VvvnaZobvHQ7AmBZj0Ffo8/uA39V0zf1rLhS1Z/lYqPTW1KDGeR+4+sZPelrJpYqhNL2QSzsHaYZlpLt6WgfuwoF7pU8vNB42BkDvetIRmwK+AerTwrsCpWsDG0nLWglp0uuaB+9J1y0MaFFD2pA4r+M85nXM2xNlb2ZPSHxIIZsCXBt1TfW/h50H7zZ9l7DnYTitdgKkQX1dz3XYm9mTmpVKcGww7+1+j12Bu1hSc2kRRi1MbEgwhxeMU53/vfUbAOq060HbMZ8XGS/4eQaGimRa2xhx7eNrJbaTsOdh9Nzek5XdVvJJi0+ISIxg/KHx/BH0h0qmuHYy73Se3bo4d5GWJy8+hpjk0teruQHMbJt33rW2dNyNk77BBNKgPtwNTPQgK4fG1Rsjl8mxMLDAe6s3SRl5H1qrZV5LbWno4uOLDPptEAs7LWRBxwWEPAth4G8D+fvJ32plBTg97LRaWX+6/pOq7+XOzh1+77CazNrLazn98LRWNn384jHeP3uzsttKboy+wZMXT1jtt5pl55eRo8xR9XU7Uzv0dPQY8vuQMtVhcfpyiUiMwM3GjfW9pBt7bEosfXf0JSg2qEw29bDz4K9hf6nOV3ZbKcV9dLHI8UwhV1DFMO/NI23KCOBS1YW2Dm3pulV9ZkYbSjvGQsnjQYm8yniqTT8R/OP867+j829ENq/odfKKoMjvPlQiZNaFv5ZakSx5S/NG8Ioi9zs6r5PX3W5edx0KBK/CPzKeVvB3dMrznqT8ovK5BP/qpSuBQCAQCASCsiAcHYFAIBAIBJUW4egIBAKBQCCotAhHRyAQCAQCQaVFODoCgUAgEAgqLcLREQgEAoFAUGkRjo5AIBAIBIJKi3B0BAKBQCAQVFr+9V9GFogPsVUEr/sDfq/7A4UA/OD3+nUKyhXx0cfy558oY7n96rrglRAzOgKBQCAQCCotwtERCAQCgUBQaRGOjkAgEAgEgkqLcHQEAoFAIBBUWoSjIxAIBAKB4JVYt24dTk5OGBgY4O7uztmzZ7WKd/78eRQKBa6urhWbQYSjIxAIBAKB4BXYuXMnEyZMYNasWQQEBNC2bVt69OhBeHh4sfESEhL44IMP6Ny582vJ53/a0VEqlYwcOZKqVasik8mwsLBgwoQJ/3S2BAKBQCD4T5Kens6LFy/UjvT0dI2yK1aswMfHhw8//JAGDRqwatUqatasyXfffVesjo8//pghQ4bg6elZEUUoxH/a0Tl8+DCbN2/mzz//JDIyksaNG7823aM9RhM6PpTUWan4f+RPm1ptipS1MbFhW79tBH8STPacbFZ2W1lI5sPmH3Jm2BmeTX3Gs6nPCPokiCeTnmiVPkA7h3b4f+RP6qxUQsaH8LH7x8Wmf+z9Y7Swa6EmEzslFuUXykLHtz2/fSWdp4ae0pjen4Pzvg1iomfCym4rCfs0jPTP00mdlUrarLRysSnApy0/JfiTYFJmpvBs6jOeT3v+Wm364NMHKL9QMt3NSu3oam9crO6ooGsc/3IqO0a/xaZBrXl4+Uyx8qWmlwss7gyrusOEVmBrUry8rQl81BwWdIR1vWC5t/ZxgX4N+hE4JpC0WWkEjgmkb/2+hWRK6lPGusZ80+Mbnk19RvacbLLnZPNwwkM1Oecqzvwx4A9iPoshYXoCO/vvpE+9PsXWY34GNhqI8gsluwfuVsvXg08fkDk7k7RZaaTNSuP8iPN42HloTKOkdtOwWkN++99vqrbxactPC6XRtlZbgj8Jhm97Svb+vJ1WdsbVBma3g9Xdpb/NrAvLmOvDMFf4sqtUhzPaQE0zABRyBUu7LOXxxMdkz8kmZ04OsVNi6VOvT5Eqte2LueTaOHeMS5mZwqmhp2hYrWGJxdOmHeUyvc10lF8o1fIzr8M8Xsx4QfacbLJmZxH9WTRb+m7B1sS2wnT+11iyZAnm5uZqx5IlSwrJZWRkcOXKFby9vdXCvb29uXDhQpHpb9q0iZCQEL744otyz3tR/KcdnZCQEGxtbfHy8sLGxgaF4vV8/3BAowGs6r6KRWcX4bbBjbPhZzn07iFqmtXUKK+vo8/TlKcsOruI61HXNcp0cOjAL7d+oeOWjsw/PR+Xqi5YGFjQ7eduJabvaOHIwSEHORt+FrcNbiw+u5g1PdbQr0E/jel7+noSnhDO0fePYmdqpyqTqb4pk45Mou3Gtnx/5XtSM1MB+DXw11fS2W9nP2y+tlEdjdY1Iisni19v56X3Y+8f6erclU3XNgFwNOQoGdkZBEQFlNmmQ5oMYWmXpcw7PY8px6ZgomeCEiU7b+18LTYFaPFDC2y+tuGbm3F8czOOX+4nAHDneYZGvblkpaVSxaEOrYZPKlbulejqDJ2cYFcgLDsHL9JhXEvQ1yk6jp4OxKZAaDwolRAQqXXcVvat2Nl/J1tvbKXZ+mZsvbGVXf138UaNN1Qy2vSpld1X0r9hf0z0TJhxfAZz/5qLvZk9R987Sk2zmhjpGnH0vaMoUdLpp0603tgaCwMLfh/wO+fCzxVZj7nUMq/F195fc+bhmUL5epr8lAfxD9h3Zx9ZOVn4Pfbj+PvH1eoatGs3RrpGhD4PZfrx6UQmRmq02TDXYThaOMLxUCkgJbPkOnKyAB83+PsJLD4r/f2wOTha5MkYKuAzL8jOgbV/w/zT8EcQpGap8tajdg+sTayZfWo2g38fTEZ2Br8P+L1MfTGXXBuHxodSu0ptxh4cS4sfWhCVFMWx949hole0M6dNO8rFw86Dkc1HquVnauupTGg1gdBnoUw5OoXD9w8jQ0Z9q/rsG7yvQnT+F5kxYwYJCQlqx4wZMwrJxcbGkp2djbW1ujNtbW1NVFSUxrTv3bvH9OnT2bZt22u7X8N/2NEZNmwY48aNIzw8HJlMhqOjIwBZWVmMHTsWCwsLLC0t+fzzz1Eq875KuW7dOlxcXDAwMMDa2pr+/fuXWvekVpPwDfDFN8CX4NhgJh6ZyKOER4xuMVqj/MOEh0w4PIGtN7aSkJ6gUea93e/xnf93XI++zqDGg/jh6g9k5WThYO5QYvqjPEYRnhDOxCMTCY4NxjfAl40BG/nM8zON6d+Ju8NH+z9CLpPT2amzWplWXlrJuUfn+PjPj0nNSiU+NZ7TD0+/ks74tHiik6NVR1fnrqRkpqgcHQOFAe80fIepx6fSo04PfAN86bOjDyHxIUQlRZXZpp72npwPP88vt37h/abv82PAj/gG+OJcxfm12BQgNiWW6ORokrOUJGcpqWOmR3x6NuFJmRr15mLv5on7wJE4vtGhWLlXopMTHL4P16IgMgl+ui45Mi1qFB3nYQLsDoa6ltJN90mi1nEntJzAsZBjLD23lDtxd1h6biknHpxgQssJKhlt+pSnvSeZOZn8GPAjX174kgVnFhAQGUByZjKjW4ymdc3WOFo4MmzPMG7F3OJWzC2CngahI9dh/939RdYjgFwmZ1u/bXzx1xeExoeq5Wvztc242box4cgEBvw2gPCEcNKy03jw/AGjPdTbjzbtxj/Cn6nHprIzcCfp2ZqXBHrU6cGcv+bAnmAp4NSDkuuokxMEx8KREIhOlv4Gx0JHxzwZ79oQnwZbb0h1+iwV7sRJTizwIv0FqVmp/HD1BxafXczOwJ28teMtdOQ6TGs9TaNabfpiQRvbmNhwN+4uu4N3E/g0kKF7hmKka8SQJkOKjK9NOwJp5m9bv218tP8j4tPi1eIvOrsI1w2urLi0gn67+qGro8vx0ON42HlodOTKqvO/iL6+PmZmZmqHvr5+kfIymUztXKlUFgoDyM7OZsiQIcybN4+6deuWe76L4z/r6KxevZr58+djb29PZGQkly9fBmDLli0oFAr8/PxYs2YNK1eu5McffwTA39+f8ePHM3/+fO7cucPhw4dp165dqXW727lzNOSoWtjR0KN42XuVuVy6cl3c7dw58/AMunJdnqU+KzF9T3tPjoaq5+dIyBE87DxQyDV7zUa6Rqr0c3XmL5OuXBdDhSHP056Xm04fNx923NpBSqY0qCrkChRyBVk5WWr6UzNTaVOrTZltei78HO527njae+Ju5871qOv0rNOTA/cOABVrU03IZdCoqj434tJeuUxlxtIQzA0gKDYvLCsH7sWBcxXt4mZmlyquZ03NtvSqKdleU/uDwvVz4dEF7E3t8Y/wB6CDYwfqWtbl1INTeNl7oa/QR4lSzXlwt3MnR5mjtrylqR7ntJ/D0+SnbAzYqArLzddfYX+hkCtIy0pTy1duO1Ur6yu0m4I4WThha2qrbo8cZcl15FRFvV5BOs8fp6k1PHwuzfQs6yItW7XOu8FrqgtzfXOUSiXNbJpplf+iyLXxqQenMNI1IiY5RnUtIzuD02Gni+3vJbWjXNb2XMuBewc48eCEKkyTTXN1eth5kKPM0TjWlUVnZcfKygodHZ1CszcxMTGFZnkAEhMT8ff3Z+zYsSgUChQKBfPnz+f69esoFApOnjxZYXn9zzo65ubmmJqaoqOjg42NDdWqSb9dVLNmTVauXEm9evV49913GTduHCtXSuul4eHhGBsb8+abb+Lg4ICbmxvjx48vVk/BjVmJiYko5Aqik6LV5KKTorExsSlzuayMrFDIFbzT8B2eJD7heOjxEtO3MbHRmB9dHV2sjKw0xlnaZakq/Vyd+dPoW78vejp65ChzykVnC7sWNLFuwo9Xf1SFJWUkceHRBb5o/wUKuYKnyU95t8m7tLRvia2JbZltujNwJ7NPzeavYdKNav2b6zkVdopl55ep8ltRNtVEXXM9DHRk3IzT/BT/WjA3kP4mFshDYgaYFf3Upha3YJMoIW5Rtsy1vab2V1AGYNGZRchkMnzf8iXj8wwOv3uYMQfHEBAVgI2JDZceXyI5I5llXZZhqDDESNeIxtUbI5fJsTW1VUs3fz161fTCx82Hj/Z/pKY/N19hz8O48OgCs9vNxtbElpjkGOpa1lW1U23KWly70WSv3HhqlFRHZvrSUmJ+XqSrx7EygnYOEJMM3/wNZ8Phf42gZQ21Mufq1tfRZ2mXpVyLukY1o1f/fbj8Ns4tX8HZrOjk4vt7Se0IpP0/zW2bM+P4jEJxc+XzE5sSS8saLdl+czuJGYnlqrOyo6enh7u7O8eOHVMLP3bsGF5ehR1WMzMzbt68ybVr11THqFGjqFevHteuXaNly5YVltf/rKNTFK1atVKbNvP09OTevXtkZ2fTtWtXHBwccHZ25v3332fbtm2kpKQUm17BjVn16tUDCv9Im0wmK9cfbuvq3JV+O/upBoOS0teUH0Bt2S6XKV5TGNx4sFr6BdPwcfPhXtw9MnOKXmIpjU6f5j7cjL7J5YjLauHv734fGVK8v4b9xfiW49l+czvZyuwy27S9Q3tmtZ2lGoCmH5/Om3Xf5PN2n6vyW9E2zU9TSwNCX2SSlKXZeawQWtjBim55h87LvqGp2AXLVVRcTZE12ETtshb9pSQZn+Y+AHx29DPcv3dn8tHJrOu5jjpV66BESWxKLP/79X/0rtubpJlJJExPQC6T8+TFE7JzstXSlbKsxETPhJ/f/pmP9n9EXGpckXnPbacRkyOY32E+FgYWqnaqTVlz9WnC3dadxBmJqkNXR1djOi8T0ZhGkRRcQZDJ4NEL2HcHHr+Ac+FwPhzaOhQqg0KuYEf/Hchlcvbf3f/KfXGE2wjODj+LpaElYRPC8sqnLP0YWlwbsTezZ3X31by3+z3eafgOiTMSaVurLWNajNFoU4VcQWdnaZl5zIExZdZZVL+vzEyaNIkff/yRjRs3EhQUxMSJEwkPD2fUqFGAtN/ngw8+AEAul9O4cWO1o3r16hgYGNC4cWOMjYt/QaMs/L/69XJTU1OuXr3KX3/9xdGjR5kzZw5z587l8uXLWFhYaIwzY8YMJk1S3xCalZ1V6MmjunH1wk9gr8D7Td9HqVQy//R8bsbc1Cr9qKQojfnJzM4sNHhP9pzMzLYz6fJTF1X6sSmxZOXklamWeS26OHfh4L2DmOmblVmnocKQQY0GSXsOChAaH0r7ze1JmZXCyP0j2XRtEzve2cGD+AdltumCjgvYemMr3/z9Dcu6LuNO3B1mnpjJ972/Z9GZRRVq04KY6cpxNNVl94PCT40Vyo1oCHued654+WxT8OnfVE+aMdAmrrzA3VNT3HwUZctc2xdsf5pkDBQGTG8zneycbELiQ7gZc5ObMTdxtXGli3MXHj5/CMCx0GPU+aYOloaWZOVksW/wPtxt3Xnw/IFaurn12KhaI5yqOLF/8H7VdblMKuebdd9U5evS40t02NIBI10jVndfjUtVF3TlujyIz0u3uLJqaje53Iq5xbzT81Tn+gppBqbQ7EYJdi40ewNgWqCeE9IgskAbjEoCN2lmKrcuapjWYGr/qThZONHpp07MaT/nlfti0NMg5DI5ujq66KLLiQ+k5Z1udbqROTuTet/WIzQ+lOpGxff3ktqRu6071ibWXBl5RXVdR66DTClT6bQxsSEqKQqFXMGu/ruoYlCFQ/cPaZzNeVWdCrmCdg7tGPvGWPQX6hc5K14ZGDhwIHFxccyfP1/15vPBgwdxcJAc58jIyBK/qfM6qHQzOpcuXSp07uLigo6O9LaCQqGgS5cufPnll9y4cYOwsLBi1wY1bcy6EnmFrv/H3nnHVVn9D/x9L3uDoIAKCKLiRsEBbkVQzFIbmpWWZqmVqak5KjV35S5H5sqcLUduzT1IXIiKA2Rv2Xvc+/vjyIUrlynY72vP+/W6L7jP8zmfc87nOec8nzOvU181ub5OfbkYWfaWusowxXMKM7vN5E7CHZwsnCqt/1LkpVLp8W7sjX+0PwWKAjX9X3b/kn6/9ONqTHHFzFfkczW6OE/vub5HfGY8jes0fuY4Qexc0dPW45eAXzTqKorfvb672Gnm7MO+e/ue2aaGOoYolAq1/BUqC5EhQyaT1apNn6aNpT5ZBUoeppa/26rGyS2EhKziT0yGeNk1LzGNoiWDJpZiR1VlwupoVRy2BJciNNjSyZuLEcL2T5e/Iko+Hx25DrpaujxIeqAmV6gsxMrQqtRzfJz9mNTcVKLSojDUMWT/veJdNSWfY1BiEK3WtMJ1navqs//efk49OoXrOleuxVxTiy8rPwtPO0+uxVxTlVO1vFahXhSRW5hLcHKw6nMn4Q4x6THqeuQV25lHyeDy1PRYcyv1MCHJYP3UzqZ6RmJRMuJZXIu5xjd9v6GJZRO8tnmRlJ30THXxWsy1UjbOKcghOCkY13WuRKRGoCPXoUejHuXGUVE5OvnoZKl4rkRdYXvAdlzXuapsWuTkNLUUC2JPhZ6qtThfZCeniPHjxxMaGkpubi5Xr15VW/e6ZcsWTp8+XWbYOXPmcOPGjVpP4ws3ohMREcHkyZP58MMPuXbtGqtXr2bp0qUA/PXXX4SEhNC9e3csLCw4dOgQCoVCNR1VWZZdXsa2wdvwj/bnUuQlPnD7AHsze9b5rwNgYZ+FNDBpwMi9I1Vh2lqLhXzGusbUNaxLW+u25BXmcTfxLiCmPub1msfwP4ZjZWDFat/VPHj8gLNhZ3m77dvl6l/nv46PO3zMUu+lbLi2AY+GHoxuN5o3f39TFX9J/aEpoVgbicViGXkZZOZnqvJ0NfoqY9qPITItkuZ1mz9TnEWMbjeavUF7NS7S9W7sjQwZ2wK2sdxnOS81fYmwlDBa1m35zDY9cP8Akz0mcz3mukr/q81f5VToKb7z/q7WbVqEDBmtLfW4lZRT6cH//Jws0mIjVd8z4qN5HHofPWNTjK2ecS3Y34/Ax1ms04jPhH7OkFcIV6KKZUa2hZQc2HdPfNeSga0JXI0Ru3iaWIr7nRoWhzUXolsHbSUqPYqZJ2cCsNJvJWffO8u0LtPYF7SPV1xewcvJi66bixfyVlSn0vPSOR16GicLJ8a0H0NoSihaMi1Gu46mQFmgkvvrzb+QyWR8cvgTPBp60M+5HwWKAj50+1Djc8wtzOV2wm018xQtSr2dcJull5aybfA2sguyCUoIorNdZxzNHfFu7M29xHs0rtOYrYO2Vqnc6Mh1VGfG6Grp0sC0AW2t25KRl0FwcjAAa66s4cvuX0JqhAjk4yy2hD8oMSr09DM6FQqTOovjAwLixMJjFytYekn92U/xBJ/GcC0GHMyhqz3suPXkMWshQ4ajuSNzTs/BxdKFt3u9jYOZAxuvbQSqXhc12TgwPpC21m1pYtkEgJndZpKVn8WOWztUMlUtRxl5GaXiyczP5HH2Y24n3GaF3wpmdpvJa81fw97Mnmux17A0tORE8AmsjaxJyk7ip5d/qtE4Jf5/8MI5OiNGjCA7O5uOHTuipaXFJ598wgcffACAubk5f/zxB3PmzCEnJ4cmTZqwc+dOWrZsWaU49tzeg6WBJV/1+ApbY1sC4wPx3e5LeKoYorM1tsXezF4tzI2xN1T/u9d35602bxGaEorjSkcAxncYj562Hr+/8btKbnm/5RQoCsRuoXL0h6aE4rvDl+U+y/mow0dEp0cz4fAE/rj7h0pGk36AOafnMPfMXFWeFvRZgI2xDck5yc8cJ0CTOk3o5tCNvtvUe0VFmOmZsajPIhqaNiQrPwtzfXOsjawpUBQ8s03nn52PEiXze8+ngUkD0vPSMdAxoI9jH6yNrGvdpkV4OXlhpqtVpd1WicFBHJn3ier7P9tWA+DcvT/dxn9RaT0aOR4itioPawWGOmJ6arWfGMEpwsJA7PQpwkwfZnYr/u5qIz5Z+bD0olpYezN7tZ7spchLDPttGPN7z2der3kEJwUz9Leh/BP1j0qmojoFMOy3YSzqs4jBLoNZ7LUYgKi0KN764y2VnL2ZPc2smnH3o7uEpoQy7+w8rsZcrbCclkVRuub0nENdw7ooUZKcncyp0FPM+nsWK/utrHK5qW9SX63sTvWcylTPqZwOPU2vrb0AOBd+jnm958FrLYqMKv56NxZbwzU9o5Bk2HQdBjYTn8Qs2HhdffoxLBXWX4VXmoFvE3icDb/dgSvRgFhz0qGBOPRybq+5zO1VXI4bmjYkODm4WnXxaQLjAzHWNWaN7xosDCzwi/TDe5s3GXkZKpnqlKPy+ObCN9ga2zKx80QAfE18AQj+VDiXPbf0rPE4Jf5/IFOWtUJOokxkc0ufESAhUR6LXk547nFO3+D3XOOTWb/0XOP7L6CM+6tioRpEeoa1g3J27b5ma/KdVNtp/Td44dboSEhISEhISEgUITk6EhISEhISEi8skqMjISEhISEh8cIiOToSEhISEhISLyySoyMhISEhISHxwiI5OhISEhISEhIvLJKjIyEhISEhIfHC8sIdGPg8eN5nWzxv/o2zNF50m/Kcz7QBWDym9n4NWCP7KxapSf6NMvO868bzju+/YFOJ/x7SiI6EhISEhITEC4vk6EhISEhISEi8sEiOjoSEhISEhMQLi+ToSEhISEhISLywSI6OhISEhISExAuL5OhISEhISEhIvLBIjo6EhISEhITEC8v/y3N03n33XVJSUti7d++/nZSKGdAEutiDoQ6EpsDuQIjJKFve1hheagr2ZmBpCL/ehlOh6jLd7KG7A9QzAm05KIGYdNh0vXzdAK42MLApWBlCYhbsvwc349TTO6CpepjUHJhxUsTp5URBnQK05FpqIpcjL+Ox0UPtWneH7izzXkbLei2JTo/mmwvfsP7qeo3JGtpyKLte28XeoL0M3j1Yc9p9GsMrLvAoGSwMatamPo2FbayNIb8QQpLhzyCIz6ydZ1ienTVRG2moArF3bxB4YAeJj4LITn5M788W4dChe4Xh2lnp06meAcY6cvpa+zPx6ETOh58vU74myoyWTIu/R/4NDbuCXAYFCjgfDr/dEXWlLCqqGwBmejC4ObSoC7paEJcBvwRARJpKxMXKhSVeS/Bu7I2uli4AfpF+vH/gfe4k3CnXXkOaD2Fer3k0tmhMcHIws/6exd6gvar7s3vMZk7POWphHmc9xupbqzJ1Vsamn3b6lHHu47A3sycxK5Hf7v7GjBMzyC3MVcl85/0duPUDnSdtTlwGbLmhlvdSVGTTeb1E+XyaM6GQkQdd7MkyyiIpOwktuRZmemaEpoSy6p9VrPNfV2X7Te86nSEuQ3CxciG7IJuLERf5/MTn3H98XyUz2GUwH7p9SBf7LhjrGpNbkEtgfGC5ZdfG2Ial3ktxs3WjiWUTVvmtYtLRSWoyg10GM7PbTJzrOKMj1+FB0gOWXlrKLwG/lG0/iedCrYzo5OXl1Yba/3/0dYLejrDnNiw5D2m58Ekn0NMqO4yulmgQ9gaJF58mUnIgLBUUSqH7YgTYmsDEzuXrdjSH0e3gnyhYeE78fb89NDJXl4tOh+knij8LzoGbLbzWAo48ZP+9/YQmh5KZl4nbejdsvrPBd7uvmopG5o04NPwQ58LP0W59OxaeW8iq/qsY0nxIqWTZm9nznfd3nA07W3baHczEiz41B+zMat6mznXgTBh8ewFW+YkX5Ccdob9z7TxD0GxnTdRWOaoCBTnZWDg40/m9yZUO42Kui1cDIy7GZbE5KIVz4ec4/NZh7EztNMrXVJn5a/hfdLXvCsdDYPU/EJ4KPRuBl1PZia1M3TDQhimeUKiAH/6Br8/AH3chu0Al4mThxPn3zmOqZ0qBooCPDn7Ex4c+JjYjluPvHMdY17jMJHRu2Jndr+1mW8A22q5ry7aAbex5bQ8dG3RUybSs2xKFUsHko5PptqkbP179EX1t/Wey6fDWw1nstZi5Z+bS/IfmjN4/mqEth7LIa5FKZnaP2UzqPEl0MjZdh8B4MNGDwnI8x8rYdMkF9Tqw8rK4LpepyvzBBwcx1TNFW66N+4/uLL+8nNX9V/Nys5erbL8eDj344coPdN7Ymb7b+qIt1+bY28cw1Cl2tox0jUjNTUVfWx+AYb8Nq7Ds6mnpkZCVwIJzC7gZe1OjTFJ2EgvOLcBjowdt1rVh843NbH5lM96Nvcu2ocRzoUYcnZ49e/Lxxx8zefJkrKys6Nu3L3fu3MHX1xdjY2Osra155513SExMVIX57bffaN26NQYGBlhaWuLl5UVmZiZz5sxh69at7Nu3D5lMhkwm4/Tp0wBERUUxdOhQLCwssLS05JVXXiE0NFQtLZs2baJly5bo6elha2vLxx9/rLoXFBRE165d0dfXp0WLFpw4cQKZTFb9kaPejnDkIdyIFb3vn2+KF1CHBmWHCUsVIwlXY0RPVBO34qFlXTj0QLycd9yC7HzQ0y5fd29HCEqEo8EQlyn+BiVCr0bqcoUK8TIt+mTkibAXI+BiBKm5qdyIu0F4ajivtXyNuMw4knOS1VSMdR9LeGo4k45OIigxiI3XN7Lp+iameExRk5PL5Gwfsp3Zp2cTkhyiMdlGOkbwritsDwAjXTGiUdM2/eEKXI4UOqPSYVuA6Gn2qqVnCJrtrInaKkdVoGE7D9yGfkCjjj0rHaZjPQNuPs4h4HEuj3MLmXR0EhGpEYzrME6jfE2VmZ6NenI1+qoYPQhKhJV+4oXsalN2YitTN7wbQ3KOKBthqZCUDfceC4fyCQt6L+DQg0M0s2zGvLPzWHd1HWv91zLs92EY6hgyvPXwMpMwsdNEjgcfZ/H5xdx7fI/F5xdz8tFJJnaaqJLxsPMgKTuJ5ZeXcz7iPB/+9SHhqeHPZFOPhh5cCL/AzsCdhKWGcTzkODsDd+Ju666SmeI5hbCUMGHLqzHw0zXhjDhZPJtNM/LU60BrazGK2sZaVeZdrFxYemkp2nJtujp0ZcO1DdyMvYl7fXe16Cpjv/7b+7P15lbuJNwhIC6A9/a9h4O5A262biqZXwJ+wcHMgV2BuwB4lPKowrIblhrGxCMT2RawjdTcVI0yZ8LOsDdoL0GJQYQkh7DKbxUBcQHCKZf4V6mxEZ2tW7eira3NhQsXWLx4MT169MDV1RV/f3+OHDlCXFwcb7zxBgAxMTG8+eabjBo1irt373L69GmGDBmCUqlkypQpvPHGG/Tr14+YmBhiYmLw9PQkKyuLXr16YWxszNmzZzl//jzGxsb069dPNYK0du1aPvroIz744ANu3brF/v37cXZ2BkChUDBo0CAMDQ3x8/Pjxx9/ZNasWdXPsKUBmOnD3WLnjQIFPHhcfuNQVd0yxGiLrpbobZWn29FCPT0gvj8dpp4RLOwDX/eCUe2gnqGYArmboBLp2agnDuYOfNrpU34c+CN1DeuqqfBo6MGxkGNq144GH8W9vjva8uIZ0a96fEVCZgKbrm8qM9k/+P4gepCJWWKqLim7+GZN2fRpDJ6k0Vi3dp4hlLazpUFpmdosR7WIXAY2htqEpuerXT8WcgzPhp4aw9REmXE0d0RfW58Gpg2EfQGsjUCpLN/hq0zdaGMNYSliVGKJF8zoCl1K9PBlMKDJABKyErA1seXzLp9zefRlXmn2CnmFeZwJPVNm3kE4MZry72knwujIdahvUh9jHWOiJkcRMiGEna/u5HLk5Wey6fnw87jVd6ND/Q7CFOaO+Dr7cvDBQdV3Y11jLkRcKM77VE94nFUz7U0RWjLo2ABuxqqV+fPh5xnQZAB+kX54NvSkZ6OeNLVsytGHR6tkP02Y6ZkBYrSlCB25Dm713TgXpj7CWl7ZrQ69HXvTzLJZ+SPZEs+FGluj4+zszDfffAPAV199Rfv27Vm4cKHq/qZNm7Czs+P+/ftkZGRQUFDAkCFDcHBwAKB169YqWQMDA3Jzc7GxKe6h/fLLL8jlcn766SdkMhkAmzdvxtzcnNOnT+Pt7c38+fP57LPP+PTTT1XhOnQQlfvYsWMEBwdz+vRpld4FCxbQt2/fcvOVm5tLbm6u2jW9wnz0zJ5U5nT1e6TnQR0NL7SqYCaGVJnsIebLcwvhx6vQ1qZ83aZ6otdUkrRccb2IRymw9aboVZnoQv8mIh4tuUg7cPjhYX698yveTt682uJVOtTvwN8j/8btRzfyCoWMjbENcRnq6xviMuLQ0dLBytCK2IxYPO08Gd1uNK7rXMtM8tCWQ2lv2x423RPOFkBeobpQTdj0aV5tAZFp0NC0dp6hJjtP8YT5ZyGzhHNQ9KxrIw21iKGWHLlMRuZTzkVcRhw2jTWPrNREmbExFrp/v/s7n3z1sXBwZDIxfZVfjqNTmbphZSjWqZ18JEYbGpnD6y2FA+UXBSZ6mOiZMM5d9PqH/joUt/pu/DH0D3pt7UVcZhwOZg5lJqGs/BflycrQCrlMztdnv+bA/QNYG1nzRfcvcLN1Iy4zTpPKStl09+3d1DWqy/lR55EhQ0dLhzVX1rDkwhI1m77W4jU4EVmc96EthUf7LDYtSVsb0cF4mAR9G6vK/ITDE9gwcAMjXUeiUCoY1moY7x94XzheVbCfJpb5LONc2DluJ9xWXbMytEJbrk1CVoKabHllt7KY6pkSNTkKPS09CpWFjD84nhMhJ55Jp8SzU2OOjrt78TDj1atXOXXqFMbGpeerg4OD8fb2pk+fPrRu3RofHx+8vb157bXXsLAou/dw9epVHj58iImJidr1nJwcgoODiY+PJzo6mj59+mgMf+/ePezs7NScp44dO2qULcmiRYuYO3cuw4cPZ/16scBPtrwfrL8uBDRNYSvLWxGpgQ714c1iR4+1V8Tf1U+G5NvZwIi28CCp6rqfbqfuqFduHqXA1z3F/09077m9BwBXG1cy8jLov70/YRPDGNBkAH8G/akKqnwq80UOqFKpxFjXmF8G/8KYA2N4nP1YY9I+7vgxK/utJKcgB75pXpxvTVQ135oosrOOXDTgP12DMW418wyfRpOd5/YU66DalmhMi/JcG2l4DjydRJlMVqpcqMlXscw4WTjhaedJ+ox0AAbsGADAEJchsPnJ4vyGpvBWa+FUVoWn60aRw7T/nvgemSYWffdvAkNbqeQvRFzAy8mLwIRATjw6gaedJ2PdxpKRn1Fu3svK/9PXToWeIjA+kEACuRR5idjPYjHRVW/3KtIJwqYg1q3M6jaL8QfH4xflx6h2o/i006eMbjeafEW+yqa34m7RYX98cd57NhKbAqqCJr+oqN7paokCk1NQlHAAJnSaQOeGnTn68CjGusbsDNzJGt81xKTHcPKR+uL9ytiviO99v6eNdRu6bqrc1FFFZbcypOem47rOFWNdY/o49WGZz7Iyp+wlnh815ugYGRmp/lcoFAwcOJAlS5aUkrO1tUVLS4vjx49z8eJFjh07xurVq5k1axZ+fn44Ojpq1K9QKHBzc2P79u2l7tWtWxe5vPxZOKVSqWoAqsKMGTOYPHmySgeAcuE50BO7LUr1akx0VSMjlSYgTqxLKUL7SV7yFaLBCU8FB3OxYPdBkiYNAk29KRMNva6S5BWKNSsmeqXC1jOqR1xGHLEZsYSlhNHEsonqXmxGbKmeVD2jeuQX5vM4+zEt67bE0cKRA28eUN2Xy0S+8r/Mp9n3zUjITEAuk4udK3I5THjya9ueduDRECYcFo1hdWyqiYA4sZvGxUosuCxaM1MTz7Ai8grF4uQCBSwqMWRe9KyfRxpqkKxCBQqlEmMd9XpXVGY08SxlRl9bH+9t3iRmiemOnwN+ZsbVNkIoOl3sqitvBKwydSM1R+xuVE80tLMVz0wuI/+LLtxJuIOXkxc2xjbEZsRyN/EuXe26kpiVWGbey8t/UZjErEQKFAVqMln5WSRnJ6OtpbmprsimAPN6zWNbwDY2Xt8IwFenviIyNZL5vefTbl07dLVFWxaZFkkHdIsVKZTF5VMTlW1vAuLE2qeJnWH3bfV6l53Pwj4LGbx7MB+0/4DYjFh+uPIDrjauTPGcouboVGS/kqzqv4qXm75M9y3diUqPUrtXZOenp+PLK7uVRYmS4ORgAG7G3aS5VXNmdJ3xTDolnp1a2XXVvn17bt++TaNGjXB2dlb7FDlEMpmMLl26MHfuXK5fv46uri5//ilGC3R1dSksLCyl88GDB9SrV6+UTjMzM0xMTGjUqBEnT2revuvi4kJ4eDhxccUF+cqVckYQnqCnp4epqSmmpqaYmJhgYmKCXlKe6Emm5kDzEts+tWTQxFJsXa4KuYWQkFX80aQbxDRHebofJYuXeEmaW5UfRlsONsZip1dz9Yrf16kvFyMvUsegDnZmdsSkx6juXYq8RF8n9Wk/78be+Ef7U6AoICgxiFZrWuG6zlX12X9vP6cencJ1nSsRqREcfHBQJcPCc+JToIDIVPG/kurbVBOvuEAzK1h2STiMNfkMK6LIzknZFT/r2kpDDaJQQmxWAY1MdNSuF5UZTTxrmTkffp47CXdQKBU0s2xWrERLJqad8p+a8ixJZepGSLI4fqAk9YyKn1lcJleir2Chb0FMeowqL03rNCUiLYIejXqUmXeASxEa8u/kzcUIESZfkc/V6KtqMrpautia2PIw6aFmnRXYFMBQxxCFsnhaLyMvg+iMaJHllBDuJNwhOz8bVxvXYiVaMrH2qbwdfZVtb3ILoZmlmKq6GKFe5rVER0cuk6vZr1BZqHJyVXmtwH5FrO6/miEuQ+j9c29CU0JLJbvIzk8vEi6v7FYXmUyGnnYZU3kSz41acXQ++ugjkpKSePPNN/nnn38ICQnh2LFjjBo1isLCQvz8/Fi4cCH+/v6Eh4fzxx9/kJCQQPPmzQFo1KgRAQEB3Lt3j8TERPLz83nrrbewsrLilVde4dy5czx69IgzZ87w6aefEhkZCcCcOXNYunQpq1at4sGDB1y7do3Vq1cD0LdvXxo3bszIkSMJCAjgwoULqsXI1RnpAeDvR+DjDG2txRD3iLai536lRA9iZFt45alGuaGp+GjJwVxf/F+3xFkTLzcTZ1H0cxY7GIa3Fg1FTkH5uk+Fisajr5NopPo6iYao5PkqQ5pDkzpiEWwjcxjTHvS1xY4JTzvoZs/Glzey89WdOJg5cCPmBgfePEBeYR79nPup1KzzX4eDmQNLvZfiYuXCe67vMbrdaL679B0AuYW53E64rfZJyUkhPS+d2wm3yVfkk5GXobpHTIb4JGeLrfT1jGrWpsNaiYWQm6+Lhtf0yQjWmbDaeYZl2dlPvXdZq+WoCuTnZPE49D6PQ8V5Ixnx0TwOvU9GYmyZYf6Jz6atpT5t6uhhqafFMp9l2JvZq84/WdhnIVsHbVXJ10SZAbgWc41BLoPE+S0uVmIkUEsOV6LLtldl6sbfj8SWaZ/Gwo7u9aGrvTjz5QnfXvyWoa2GcjHiIl90/4L1L61nYLOBWBpYkpWfxY5bO1SyWwdtZWGf4nWKK/1W4t3Ym2ldptHMshnTukzDy8mLFX4rVDLxmfGMaT+GqZ5TeaPlG9wdfxdtuTbTT0yvlk0BDtw/wDj3cQxtOZRG5o3wcvJiXq957L+3X+UA/XT9JxqZN4J324odnxM7C+f8SAkHqzo2BTGd1bmh2PWoUBbb2scZmllyJeoK2wZvo0BRwIXwC4xsO5IRbUZgqmdaZfv94PsDb7d5m+F/DCc9Nx1rI2usjaxVW8kBLPQt+P3u7wxrNQwQi4a3Dd6Gg5lDmWUXoK11W9pat8VY15i6hnVpa92W5lbNVfend52Ol5MXjuaONLNsxqTOkxjRZoR0js7/A2rlwMD69etz4cIFPv/8c3x8fMjNzcXBwYF+/fohl8sxNTXl7NmzrFixgrS0NBwcHFi6dCn9+/cHYMyYMZw+fRp3d3cyMjI4deoUPXv25OzZs3z++ecMGTKE9PR0GjRoQJ8+fTA1NQVg5MiR5OTksHz5cqZMmYKVlRWvvfYaAFpaWuzdu5f333+fDh064OTkxLfffsvAgQPR19cvMy/lcjxEzDsPa1V80NtqP/EiLcLCoLhygxiVmdmt+HvfxuJz/zGseHLGhKmecGx0tcSiWRDD85uul6875MkZGAObiU9iFmy8rj4tZq4P77UTu40y8kSv7NuLYogewLsx79ZpSaFCxPNN3284FXqKqLQo6pvUV6kJTQnFd4cvy32W81GHj4hOj2bC4Qn8cfeP6tmyiOQcka6atmn3J4tEJ6kfesjPN+HUo5qPryw7l9xRVkRtlaMqkBgcxJF5n6i+/7NNdBCcu/en2/gvNIYJSsnDQDuTLjaGGOnIuRnbHd/tvoSnhgNga2yLvZm9Sr6mykyvrb04/s5xOvt0gn6IKd4L4eJMoSKqUzfCUmH9VfEy920Cj7PFIYQlHKi9QXsZ+9dYZnSdgYG2Ae+5vodCqUBXSxfvbd5k5BUf8mhvZq82knIp8hLDfhvG/N7zmddrHsFJwQz9bSj/RP2jksnMzyQzP5MlXmLaPyUnhRF/juBS5KVq23T+2fkoUTK/93wamDQgISuBA/cPMOtk8a7TCYcnYGlgyXC3YeJYg3wFHLwPlyKfzaYgnB9LQ3VdJcp8ayNIyU5BJpNxZcwVwlLDmPX3LF5u9jK2xrZVst/4DuMBOPPuGbUkvLv3XbbeFI7Ly81e5pu+36juLfNZBsDm65vLLLsAN8beUP3vXt+dt9q8RWhKKI4rxXILIx0j1viuoaFpQ7ILsglKDOLtP99mz+09/PTyT0j8e8iUyv+BFY+1xIULF+jatSsPHz6kcePGlQ84/mDtJer/ATLrl557nMq4v557nC86i8d0eq7xzdhft2KhGuTfKDP/Rt14nkg2rR2Us2v3NSubW81ZCQ3Udlr/Df5f/gREbfHnn39ibGxMkyZNePjwIZ9++ildunSpmpMjISEhISEh8T/Df8rRSU9PZ9q0aURERGBlZYWXlxdLly79t5MlISEhISEhUUv8pxydESNGMGLEiH87GRISEhISEhLPiVrZdSUhISEhISEh8f8BydGRkJCQkJCQeGGRHB0JCQkJCQmJFxbJ0ZGQkJCQkJB4YflPLUb+X+W/cM7EfyGPz539zze6RS8nVCxUk2zwe77x8fzPmfkv1AvJpv/brFmzhm+//ZaYmBhatmzJihUr6Natm0bZP/74g7Vr13Ljxg1yc3Np2bIlc+bMwcfHp1bTKI3oSEhISEhISFSZ3bt3M3HiRGbNmsX169fp1q0b/fv3Jzw8XKP82bNn6du3L4cOHeLq1av06tWLgQMHcv369VpNpzSiIyEhISEhIQFAbm4uubnqv0Cvp6eHnl7pHyddtmwZo0eP5v333wdgxYoVHD16lLVr17Jo0aJS8itWrFD7vnDhQvbt28eBAwdo165dzWXiKaQRHQkJCQkJCQkAFi1ahJmZmdpHk9OSl5fH1atX8fb2Vrvu7e3NxYuV+xV4hUJBeno6derUqZG0l4U0oiMhISEhISEBwIwZM5g8ebLaNU2jOYmJiRQWFmJtba123dramtjY2ErFtXTpUjIzM3njjTeqn+BKIDk6EhISEhISEkDZ01RlIZOp/6CoUqksdU0TO3fuZM6cOezbt4969epVOZ1VQXJ0JCQkJCQkJKqElZUVWlpapUZv4uPjS43yPM3u3bsZPXo0v/76K15eXrWZTKCW1uicPn0amUxGSkrKM+lp1KhRqcVLz0LPnj2ZOHFijemTkJCQkJD4L6Krq4ubmxvHjx9Xu378+HE8PT3LDLdz507effddduzYwYABA2o7mUANjej07NkTV1fXGnVK/mcY0AS62IOhDoSmwO5AiMkoW97VBnwaQ10j0JJBfCacfAT/RBXL6GnBwGbQuSEYaFOgLOBG7A1G7B3BnYQ75SZnSPMhzOs1j8YWjQlODmbW37PYG7RXo+z0rtNZ1GcRKy6vYNLRSarr9YzqscRrCYNcBmGqZ4pSqeRu4l3GHRzH+fDzGnV1sevCEq8luFi5YKhjSFhqGOuvrmfF5RUqmRZ1W/B1z69xq+9GI/NGTDwyEXN9cz5w+wALfQv8ovz46NBHz5zHse5jGec+jkbmjQC4HX+br89+zZGHRwCY3WM2Y93HYmVoRaGiEIVSwZmwM3xy+BMeJj2sVpzTu05niMsQ2li3QVsuqpV/jD+j9o3iTsIdtOXazO89H19nX5wsnEjNTeVEyAmmn5iOh51Hhc9snPs4pnpOxdbEltvxt5l4dKLaszDSMWKx12Leav0WZvpmAESmRfLWH2+p5JwsnPiu73d0te+KnrYeRx4eYcetHXzZ/Uta1mtJdHo031z4hvVX12u0wdCWQ9n12i72Bu1l8O7BqutaMi3m9JzD2BYWGOnIycxXcCsphwux2WU+w9i7Nwg8sIPER0FkJz+m92eLcOjQvUz5CqmNeiiXCb0dGoCpHqTlwqUIOPIQlLUU54AmMKCp6qsSJbEZsdgutVVTVVF5KAtPO0/OvHuGwPhA2q2vYJdLVfPXxQ46NYT6JuJ7eCrsC4Kw1GIZ5zrQ1wnszMBcHzLzQEercvpB2HBgU7AyhMQs2H8PbsYV3y9qO9tag4keRKbBr7fV0lCZulIeZdnw/fbvM6LNCFrVawXA1ZirzDw5kyvRVyrU+b/K5MmTeeedd3B3d8fDw4Mff/yR8PBwxo4dC4j1PlFRUfz888+AcHJGjBjBypUr6dy5s2o0yMDAADMzs1pLp7Tr6lno6wS9HWHPbVhyXjSEn3QSla0sMvNEQ/ndRVhwDi5HwjttoLlVsczbbcC9vmgMdwey/up6Wlu35uSIkxjrGpepunPDzux+bTfbArbRdl1btgVsY89re+jYoGMpWff67nzQ/gNuxt4sdW/v0L10atAJIx0jvjz1Jb/d/Q17M3sOv3UYO1M7zdnKz+T7K9/TfUt3mv/QnPln5zO/13zGtB+jkjHUMSQkJYTpJ6YTkx5DH8c+TPaYzMeHPqbDhg7EZsRy/J3jz5zHyLRIpp+YjvuP7rj/6M7foX+zb9g+WtRtwbQu05jsMZnUnFRuxN7gTNgZ0vPSiU6P5sQ7JzDUMaxWnD0cevAo5RH5inymHp/K+fDztKrbihPvnMBY1xhDHUPa27Rn3tl5tP+xPUN2D6GpZVNOjjhZoe43Wr7Bin4rWHBuAe3Wt+Nc+LlSz2J5v+W81uI1jHWNmXFiBnNOz6GhaUOOvX0MO1M7DHUMOfb2MZQo6f1zb7ps6oK5vjm/v/E758PP0259OxaeW8iq/qsY0nxIKRvYm9nznfd3nA07W+re510/Z6z7WI5HZvDT3WRORWfSsZ4B7nX1y3yOBTnZWDg40/m9yWXKVJraqofejaGbg9D79Rn48y70bQw9G9VenADR6TD9BEw/gc13NrRe21rtdmXKgyZM9Uz5edDPnAw5Wa4cUL38NbEE/2hYcRm+vQBJ2SKMWYn1Hrpawvm4lyi+XwivvH5HcxjdTjiGC8+Jv++3h0bmxTJvtwEXK9h6ExachbsJMEE9DRXVlfIoz4Y9HXqyM3Anvbb2wmOjB+Gp4Rx75xj1TeqXq/N/maFDh7JixQq+/vprXF1dOXv2LIcOHcLBwQGAmJgYtTN11q9fT0FBAR999BG2traqz6efflqr6XxmR+fdd9/lzJkzrFy5EplMhkwmIzQ0FICrV6/i7u6OoaEhnp6e3Lt3TxUuODiYV155BWtra4yNjenQoQMnTpwoN65ly5bRunVrjIyMsLOzY/z48WRkqPcALly4QI8ePTA0NMTCwgIfHx+Sk5NV9xUKBdOmTaNOnTrY2NgwZ86c6me+t6NouG7Eip7IzzdFRe7QoOwwD5JEDyQ2Q/RIToVCVDo0frK9Tkcuei1aMjj0AM6G89Ghj7ibcBdzfXOGtx5epuqJnSZyPPg4i88v5t7jeyw+v5iTj04ysdNENTkjHSO2D9nOmANjSM5JVrvXpE4TPOw8yFfk89P1n1h4biHDfx9OfmE+6bnpjOswTmPcN2JvsCtwF3cS7hCWGsb2W9s5GnyUbvbFJ2T6R/sz7fg0dt/eTW5hLt0durPg3AL+DPqT2wm3Gbl3JIY6hs+cx7/u/8Xhh4d5kPSAB0kP+OLvL8jIy6Bzw85M7DSR9f7raWbVjJF7RzJw50B0tXS5EnUFY11j3mz1ZrXi7L+9P93suzHv7DyWX17Oa7++hpGuEca6xgxvPZy03DS8f/Hm1zu/cv/xffyi/Pjk8Cc0r9ucc2HnytU9ufNkNl7fyMbrGwlKDGLS0UlEpEaoPQuPhsXP7JuL3zDv7Dyux1wnMz+TcR3G0cWuC43MG/Hu3ncJjA8kMD6Quwl30ZJrceD+AYISg9h4fSObrm9iiscUtfzLZXK2D9nO7NOzCUkOKWUfj4Ye7Lu3j+C0fFLzFNxLySM0PR8bw7IHjBu288Bt6Ac06tizTJlKUxv1EMSLNSAOAuPFS/t6rHhx2pvVXpwAhQrx4k/LJS4zjsSsRLXblSkPmlj/0np2BO7gUuSlcuWA6uVvyw04GyYcmbhM2B4AMoTjUcSdBDhwv/jao5TK6+/tCEGJcDRY6D8aLL73aiTuF7Wde4PgYRIkZMHBB8LW3R1UaiqqK+VRng3f/vNt1vqv5WbcTe49vseYA2OQy+T0cexTrs7/dcaPH09oaCi5ublcvXqV7t2LR2a3bNnC6dOnVd9Pnz6NUqks9dmyZUutpvGZHZ2VK1fi4eHBmDFjiImJISYmBjs74RXPmjWLpUuX4u/vj7a2NqNGjVKFy8jIwNfXlxMnTnD9+nV8fHwYOHBgmScqAsjlclatWkVgYCBbt27l77//Ztq0aar7N27coE+fPrRs2ZJLly5x/vx5Bg4cSGFhoUpm69atGBkZ4efnxzfffMPXX39dao6xUlgagJk+3C3RCBUo4MFjcLKovJ5mlmBtJComiOFyLTkY6arpzsrPIiMvA8+GZc99eth5cCzkmNq1o8FH8bRTD/OD7w8cfHCQk49K90r0tEXPp0XdFhwLFroUSgV5hXkk5ySXG39JXG1cxRBv2BmN97Xl2pjpm6niAMgrzONM6JkayWMRcpmcoS2HYqRjRERqBLYmtvwT/Q8AOQU5qjg7N+xMXmEeXe27VitOR3NHbE1sVfkx0xPDsFeir5SZHzM9M5RKpZoNntatI9fBrb5bKZljIcfU9F6MuEhDk4b4R/sD0LNRT5paNuXUo1N4NvRET1sPJUpyC4sPAnOr74ZCqVDL89Hgo7jXd1dNvwF81eMrEjIT2HR9k8Z8nA8/Tx/HPljoieaknoEWDY10CE7L0yhfo9RWPQQIThbX6xmJ7w1MhFMSnlp7cYKIb2Ef+LoXO1/diaO5o+pWZcvD07zr+i6NLRoz9/TcitNVUzbV1RJtWWa+Zv0lqYx+Rwv1NIH4XhSmqO3ML1SXyVeoOZMV1ZWyqJINESPYOnIdkrKTKhaWqFWeeY2OmZkZurq6GBoaYmNjA0BQUBAACxYsoEePHgBMnz6dAQMGkJOTg76+Pm3btqVt27YqPfPnz+fPP/9k//79fPzxxxrjKrmQ2NHRkXnz5jFu3DjWrFkDwDfffIO7u7vqO0DLli3VdLRp04bZs2cD0KRJE77//ntOnjxJ3759q5jxJxU1Xf0ESdLzoI5B+WH1tUVDpiMHhRJ2BYqeCUBuIUSlQQNTQAkyeKv1W3Rq2Im03DRsjG3KVGtjbENcRpzatbiMOLUwQ1sOpb1tezps6KBRR1BiEBGpEdiZ2ZGVn4WOXIfJHpOxNbElPS+93PgBIiZFUNewLtpybeacmcPG6xs1ymnJtFTpU0tvZhwOZg6aglQ6jwCt6rXi0uhL6Gvrk5GXweDdg8nIE6N/fpF+hKaEsqjPIj7860MSshLo7tAdWxNbbI3V10JUNs6i/4vklvks41zYOR4mPdSYHz0tPRZ7LUahVBCWGlambitDK7Tl2prjb1wc/4KzC/jA7QM2vryRdQPWoVAqeP/A+9iZ2jGy7UguR14mMy+TJV5LmHlyJjKZjFb1WiGXybE1sVXTq6Olg5WhFbEZsXjaeTK63Whc17mWykMRSy4swUzfjOldpqNA9J7OxGRxN/k5ODq1VQ8BjgWDgTZ81QOUSpDJ4MC94vUetRHnoxQx7RKfCSa62Lxiw8XRF2m5piVJ2UmVLg8lca7jzOI+i+m2uRuFykKNMmo8i01LMsgFUnLU81dS/9NUpL9onVRJ0nLFdRBtZ0gy9G8iRs3ScsUIUSNzSMhUBamormiiyjYEFnstJio9ihMh5c9USNQ+tbq9vE2bNqr/bW1FYxofH4+9vT2ZmZnMnTuXv/76i+joaAoKCsjOzi53ROfUqVMsXLiQO3fukJaWRkFBATk5OWRmZmJkZMSNGzd4/fXXK52monTFx8eXKV90HLa2tjYGBk8q4TIfZGufLDBTagik1HSxpNICWHQO9LRFr+6NljC0lWj4AHbcglHtYGZ3KFQwIbYuO27tYECTASg1Rlgi6qfuy2Qy1bWGpg1Z2W8l3r94q/XsS1KgKOD9A+9z9O2jHH37KAWKAk6EnODQg0M0tmhcYfzdNnfDWNeYzg07s7jPYh4mPWRX4C6Gtx7O+peKF7mm56ZXmN7q5LGIe4n3+OrUV8zvPR9DHUMODj/IyL2iEctX5PPqnlfZ+PJGkj9PRqFU8DjrMYceHKp0nF3su6CrpUv6DJGPATsGqOS+9/2eNtZt6LqpK7N7zi4VVluuza7XdiGXySlUFlYqPxXJjG4/GoApx6ZwLPgY3R26s8Z3Db/e+RUlShKzEnn919dZO2AtEzpNQKFUkJWfRVRaFIWKQjW9IM7CMNY15pfBvzDmwBgeZz8u0zZDWw7l7dZvsz80ncScQuoZaOPV0IiMfAWBSZrLWXVpYaFHP7sna7iW+UBN1sNXW4hpjgdPeuButtCxAZwNBU97kCnh5WZw9GHtxXlH/YdRB+x4g+AJwYxsO5Lll5cXR1PJeiOXydkxZAezT8/mQdIDjUlSq5sK/WezaRF9ncQ6wxWXoZ0NvFlindHachbnVlZ/EU8f17Llhlj3tMhLTAFGpIl1Q3amKpGK6srTVMaGTzPVcypvtnqTnlt6ltnWSjw/atXR0dHRUf1f1IAqFAoApk6dytGjR/nuu+9wdnbGwMCA1157jbw8zb3AsLAwfH19GTt2LPPmzaNOnTqcP3+e0aNHk58vhkZVjkgl01SUrqI0aWLRokXMnTsXY2Nj1dkAE1oPZEL7QULg6V6Gia7omZSHEjF/DGI+u4EJWBrCLwHimvaTGcVvL8DjbDoZ9GXXq7vILcwt1ZMrSWxGbKmRjXpG9VRh3GzdsDa25uoHV1X3teXadHfozscdP0Zvvh4KpYJTj05RoChg5N6RHAs+RmJWIpdHXyanMIeU7JRysxaaEgpAYHwg1kbWzOkxh12Bu9h/bz9+kcW/Nn3mXTGlZWNsQ2xG8TkM9QzrPVMei8hX5LPh2gb23xM/4b110FZeavqSKs5rMddot74dpnqm7Hp1F/GZ8bhYueAf41+pOCNSI4hOj6bHFjFiWTTlt8xnGV3tutJ9S3ei0qNK5Udbrs2e1/bgaO5I7597c/3D6+XmJzErkQJFQbky+tr6TO86nUJFIcHJwdyKv8Wt+Fu42rji5eRFWIoYMToechzn1c5YGlhSoChg/5v7cbN141HKIzW9+YX5PM5+TMu6LXG0cOTAmwdU9+UyUTbzv8yn2ffNCEkO4du+37L4wmLsbMWQfkJOIWa6cjysDWrc0XmYmsemTLGubOyem8V1pSbqoY0x+DjDAzG1yZDmYh2IX6RYTwNirYd7g9qL8ymy8rO4FXeLJpZNgMqVh5KY6JrQoUEH2tm243vf7wHxDOUyOflf5uO9zVutbj58vOHZbArg5STytMpPrEFKzBK7qorQLmPFREX6S47eqMI8lcbELFh+WUyb6WuLe6PbweMnOwB15JWqK+rJqtiGp0JPqeQ/8/iMmd1m4vWzF7fib5VnKYnnRI3sutLV1VVbB1MZzp07x7vvvsvgwYNp3bo1NjY2qkXMmvD396egoIClS5fSuXNnmjZtSnR0tJpMmzZtOHmyEjsKqsCMGTNITU0lKiqKa9euce3aNT6s10MsoEvNUd8xoSUTOw9CkstWqAkFxQ1gQlaxbuc6kJaLub45Ps4+mOqZcjGy7N8QuRRxib5O6lNw3k7eXIwQYU4+OkmrNa1wXeeq+lyJusL2gO24rnNFoRQOX74in6vRV+li14XErESc6zjjXt8dcz3zcuN/GplMpnIAMvIyCE4OVn3yFfmk5qSqpVdHrkOPRj2eKY8lKRlnTkEOWflZxKTHqIXPzs/Gw86Dh0kPca/vzr6gfZWKs4dDD86GnVXpv5Nwh8y8THydfen9c29CU0JL5afIyWli2QSvbV4kZSdVmJ+iZ/G0TF+nviq9OnIddLV0eZD0QE2uUFmIlaFVKXs+zn5Mam4qUWlRGOoYqpxBAO/G3vhH+1OgKCAoMahUedl/bz+nHp3CdZ0rEakRgFiLUFR2ilAAslLd7WcnT6EkJU9BSp5Cva7URD2UydRfwjpaYoQht7C4bmbkiZGC2orzKXS1dGletzkx6TFA5cpDSdJy00o9w3X+6whKDMJ1nSt+UX5q9eSZberlBP2d4ft/xFomULdfSf0lqYz+R8nqC5tBpFFTmLxC4eQYaEPzunDzSWdKS16lugKVs2ERUzyn8GX3L+n3Sz+uxlwtpUvi36FGRnQaNWqEn58foaGhGBsblztCUoSzszN//PEHAwcORCaT8eWXX5YbrnHjxhQUFLB69WoGDhzIhQsXWLdunZrMjBkzaN26NePHj2fs2LHo6upy6tQpXn/9daysrMrQXD4aj8PWejIq9Pcj0XOJzxSffs6igl0pcS7GyLZinnrfkx1nPo3FHH9CpmjgWtaDTg1gZ2BxmOZWcDtB6NPR4lLnSxQoCsjKz2LHrR0qsa2DthKVHsXMkzMBWOm3krPvnWVal2nsC9rHKy6v4OXkRdfNYrFpRl4GtxNuq2UlMz+Tx9mP1a6/1uI1jjw8woxuMyhQFPBq81cJSQ7B1sSWdf7C5gv7LKSBSQPVdND4DuMJTw0nKFGsz+pq35UpHlNY/c9qlV4duQ4t6rYARAN+I+4GX3b/kvTcdM6Fn2Nmt5nPnEeABb0XcPjhYSJSIzDRM2FYq2H0bNSTftv7cTfxLjO7zaSuYV3uJNxhcPPBYo6+/fvsDdrL8ZDj1YrzB98fRC9PLqerXVcamDRgQqcJZOdns+PWDrRkWvz2+m94OXmxK3AXWjItrI2s2Rawjb3D9pare9nlZWwbvA3/aH8uRV7iA7cPsDezVz2L9Lx0ToeexsnCiTHtxxCaEoqWTIvRrqMpUBao5P568y9kMhmfHP4Ej4Ye9HPuR4GigA/dPmTDtQ14NPRgdLvRvPm72HmWW5hbqryk5KQAqF0/cP8As7rN4lKCDok5hVgbaNOxrgEBSU+9zEqQn5NFWmyk6ntGfDSPQ++jZ2yKsVX568BKUVv18Fac0JWcI7Z825mKnT+XIsVW8dqIc0hzEW9SNpjo8dsrv2GqZ8rWm1tVIhWVh5J1U4my1DOMz4wnpyCn1PVntmlfJ3ipKWy+IdKvWjtTIJwdENvH6xrB1Rhhy9bWYhFxxwYV6z8VCpM6i3gC4qCNtXB8lpbYAdXcSjiPcRkinsEu4v9LT8paTkGl6kp1bDjVcyrzes1j+B/DCU0JxdpIzAAUrQ2U+PeoEUdnypQpjBw5khYtWpCdnc3mzZsrDLN8+XJGjRqFp6cnVlZWfP7556SlpZUp7+rqyrJly1iyZAkzZsyge/fuLFq0iBEjRqhkmjZtyrFjx5g5cyYdO3bEwMCATp068eabpbcM1wjHQ8QQ6bBWxYdqrfYrrtQAFgbFa2+gWN5cX+wOiMsU88pXY4plDHSgSR0h69sEZwq5HHmZsX+NVas09mb2aj3pS5GXGPbbMOb3ns+8XvMITgpm6G9D+SdK87B4Wdga2zKq3SjkMjnjO4wHICAugNHbRxOeGq6SsTezV4WRy+Qs6rMIR3NHChQFBCcHM/3kdNb7F6/LqW9Snxtjb6h9B1jRbwVKlPhF+uG9zfuZ82htbM22wduwNbYlNTeVgLgA+m3vx4mQE5wIOYGBtgGTOk8ShyGiJDY9lp8DfmbemXnVjrPITgCbBxWX/1knZ5GRl4GDmQOvuLwCiDUCResEAGafms17ru+VqXvP7T1YGljyVY+vsDW2JTA+EN/tvqpnATDst2Es6rOIwS6DWey1GICotCje+uMtlZy9mT3NrJpx96O7hKaEMu/sPK7GXGW5z3I+6vAR0enRTDg8gT/u/lGqTJTHJ4c/YV6vebzX/hMMteVk5Cu4/jiHC7FZZYZJDA7iyLxPVN//2SYcYufu/ek2/osqxV9r9XDPbXH43NCWYookNQfOh4tjHwqVtROnuT681w6MdSEjj7zog3T+qbPas66oPDxdN6tFdWza3UGMgn3gpq7r4H2xzRvE1vxJHsX3PO3EJy0HVlagPyQZNl0Xz2RgMzFNtfG6+rSYgQ680kzYMStfHAmw/56ansrUlerYcHyH8ehp6/H7G7+rXZ9zek6V9EjUPDKlsqqrvyQYf/C5Riezfum5xichUR0WvZxQsVANMn2DX8VC/+M877qvjPvrucb3b/BvtKfK2bX7mpXNrblp4tpO67+BdDKyhISEhISExAuL5OhISEhISEhIvLBIjo6EhISEhITEC4vk6EhISEhISEi8sEiOjoSEhISEhMQLi+ToSEhISEhISLywSI6OhISEhISExAuL5OhISEhISEhIvLDU6o96vqi86Af4/RuHhr3oNv03eO7P8Tkf4Ld4TKfnGh/AjP11n2t8z/sZ/hfq4X/hUEQJdaQRHQkJCQkJCYkXFsnRkZCQkJCQkHhhkRwdCQkJCQkJiRcWydGRkJCQkJCQeGGRHB0JCQkJCQmJFxbJ0ZGQkJCQkJB4YZEcHQkJCQkJCYkXFukcnWrgP8afiUcncj78fJky3R26s8x7GS3rtSQ6PZpvLnzD+qvr1WQ+7fQp49zHYW9mT2JWIr/d/Y0ZJ2aQW5gLgJZMizk95/BW67ewMbYhMz8TLZkWBjoG3I6/XW4abIxtWOq9FDdbN5pYNmGV3yomHZ1UZnqHthzKrtd2sTdoL6yqwAADmkAXezDUgdAU2B0IMRkVBHqCmy2Mbg83Y+FuIng5gZke/gk1Y1MzPTMW9FnAEJchWBhY8Cj5EZ8d+4zDDw8zzn0cX3b/knpG9ShUFqKrpcugXYPYd29frcQHkDg1EUtDy1K6f7jyAx8f+rhacRZR8pkN3j1YdX1B7wVM6DQBdIxACUSlwY5bEJZapm1xtYGBTcHKEBKzYP89uBmnLmOmB4ObQ4u6oKsFcRnwSwBEpBXL2BjDIBdobgXachF/aApsDyi/jLjagE9jqGsEWjKIz4STj+CfqGIZuUyUvQ4NwFSPsUq4lZTDhdjssvWWIPbuDQIP7CDxURDZyY/p/dkiHDp0r1TYkoxzH8dUz6nYmthWWA+72HVhidcSXKxcMNQxJCw1jPVX17Pi8gqVTIu6Lfi659e41XejkXkjJh6ZyEq/laWVVbXedbGDTg2hvon4Hp4K+4LUy8G8XmBpqPqqRAnAmdAz2JvZVyqPlWlrKp3HElTFzoNdBjPOfRyuNq7oaetxO/42c87M4VjwsXLjqLG2bP1VzTI+jeEVl8rpk6g1pBGdanAu/ByH3zqMnamdxvuNzBtxaPghzoWfo936diw8t5BV/VcxpPkQlczw1sNZ7LWYuWfm0vyH5ozeP5qhLYeyyGuRSubzrp8z1n0sHx/+mKnHp2KmZ4aBtgHLLi2rMA16WnokZCWw4NwCbsbeLDc/9mb2fOf9HWfDzlac+b5O0NsR9tyGJechLRc+6QR6WhWHrWMAQ5rDg8dgrg+vtYAjD2HR+RqxqY5ch+PvHKeRWSNe+/U1mn3fjDEHxhCVHsUbLd9gRb8V7L69mx+v/qhqAK0MrWolPoA3Wr6BiZ4Jk49Optumbvx49Uey88VL+dfbv1YrziLKembTukxjqudUkrKT4OebcDteOA8TOglHRROO5jC6nXAqFp4Tf99vD43Mi2UMtGGKJxQq4Id/4Osz8MddyC4olrEyhMkeQlahhF2BopxUpoxk5omy8N1FWHAOLkfCO22Ew1SEd2Po5iB0fn2GU9GZdKxngHtd/bL1lqAgJxsLB2c6vze5UvKaKCpHC84toN36dhWW28z8TL6/8j3dt3Sn+Q/NmX92PvN7zWdM+zEqGUMdQ0JSQph+Yjox6TGaI65OvWtiCf7RsOIyfHsBkrJFmJLlYMkFmH5C9fH62QsATzvPSuexMm1NpfJYgqraubtDd46HHMd3hy9uP7pxKvQUB948gKuNa9mR1FRbVhYOZsKJikwrW0biufDcHB2lUsk333yDk5MTBgYGtG3blt9++w2lUomXlxf9+vVDqRS9iZSUFOzt7Zk1axYAhYWFjB49GkdHRwwMDGjWrBkrV6r3Bt59910GDRrEd999h62tLZaWlnz00Ufk5+erZGJiYhgwYAAGBgY4OjqyY8cOGjVqxIoVK6qUl0lHJxGRGsG4DuM03h/rPpbw1HAmHZ1EUGIQG69vZNP1TUzxmKKS8WjowYXwC+wM3ElYahjHQ46zM3An7rbuajL77u3j0INDvNPmHX66/hMHHxzEztSuwjSEpYYx8chEtgVsIzW37J68XCZn+5DtzD49m5DkkIoz39tRvJBuxIqez883Re++Q4Pyw8mAd13h4AMxYmBlCBcjxCc2o0ZsOqrdKOoY1GHQ7kFcjLhIeGo4FyIuEBAXwOTOk9l4fSOTjk5i/KHxDNw5EIB+zv1qJT5AFefyy8s5H3GeD//6kOyCbJKzkzkTdqZacUL5z2xi54nIZXLGHRwHflGw4ZpwOrILoLuD5mfT2xGCEuFoMMRlir9BidCrUbGMd2NIzoFtAWJEICkb7j0Wz7KIl5sJx6qeERx6AOfC4WwYbLpecRl5kCRGkGIzhM5ToRCVDo3rFMs4mkNAHATGQ1I291LyCE3Px8awcgPTDdt54Db0Axp17FkpeU0UPdON1zcSlBhUYbm9EXuDXYG7uJNwh7DUMLbf2s7R4KN0s++mkvGP9mfa8Wnsvr1bNZpbiurUuy03hP0j08Rz3R4g6qFLCecxI0+84J98Xmr6EjkFOfx0/adK57EybU2l8liCqtp50tFJfHvxW/yj/XmY9JBZf8/iweMHDGw6sOxIaqot04SelpDZHgBZ+ZplJJ4bz83R+eKLL9i8eTNr167l9u3bTJo0ibfffpuzZ8+ydetW/vnnH1atEnMmY8eOxdramjlz5gCgUCho2LAhe/bs4c6dO3z11VfMnDmTPXv2qMVx6tQpgoODOXXqFFu3bmXLli1s2bJFdX/EiBFER0dz+vRpfv/9d3788Ufi4+OrlZ9jIcfwbOip8Z5HQw+OhagPmR4NPop7fXe05aJRPh9+Hrf6bnSo3wEAR3NHfJ19OfjgoCrM+fDz9HHsQwurFrjVd+N+4n262nfl0MNDFaahsnzV4ysSMhPYdH1TxcKWBmCmL6aciihQiF6Nk0X5YX2biEb1YoRoKAx14G6Cmsiz2vTlZi9zKfISP/j+QOxnsdwad4sZXWegp6WHW303jcPYLlaah5WfJT65TI6OXKdUnDpyHQy0DUjJSal2nFD2M3M0d8TW2BYtuRY5BTniYtHz0ZGrOw1qAS3UnymI7yWfaRtrCEsRIz1LvGBGVzE1UoQMaFVPPGMzfeEYTfWEttaVLyMlaWYJ1kbwMKn4WnCyuF7PCIB6Blo0NNIhOC2v8nqfAbkMjeWoKvXQ1cYVTztPjY5umTxLvSuJrhZoySGzjBevloy327yNjlznmfL4rGiqO1VNgwwZJnomYmRTEzXVlpXF0FbCIb9XzoiPxHPjuazRyczMZNmyZfz99994eHgA4OTkxPnz51m/fj07duxg/fr1vPPOO8TFxXHgwAGuX7+Ojo4OADo6OsydO1elz9HRkYsXL7Jnzx7eeOMN1XULCwu+//57tLS0cHFxYcCAAZw8eZIxY8YQFBTEiRMnuHLlCu7uYtTkp59+okmTJuWmPTc3Fz09PTIzMyksLBQXCyAuIw6bxjYaw9gY2xCXob6+IS4jDh0tHawMrYjNiGX37d3UNarL+VHnkSFDR0uHNVfWsOTCElWYJReWYKZvxq3xt5DL5Cz1Wcqsv2exK3CXSmdZaagMnnaejG43Gtd1rpULYPZkiiD9qR5Zep4Yyi0LJwvwtBPTIiDWbshkIlwJntWmThZO9HbszfZb2/Hd4UuTOk34wfcHLPQt0JZrlwoPYKGvuVF7lvi05dr8dO2nUnEOchmErpYuCqWi2nGW98xsjIXtrkRd4cvuX8LdAtFT19cGY92yp65M9YRcSdJyxfUirAzFiNDJR6IX3MgcXm8pXg5+UWCiJ+LpZi/kN14HezMY4wYrL1dcRkCEX9hHOGVFU19BJV5Ex4LFtNhXPUCp5D25jDMxWdxNfj6OjqGWXGM5qkw9jJgUQV3DumjLtZlzZg4br2+sfMTVrXdPM8gFUnLUbVqStjaY65ujJdeqVh5rCitDq2rbuYjPPD/DSMeIPbf3aBaoqbZME262YGcqpgUl/l/wXBydO3fukJOTQ9++fdWu5+Xl0a5dOwBef/11/vzzTxYtWsTatWtp2rSpmuy6dev46aefCAsLIzs7m7y8PFxdXdVkWrZsiZZW8fyqra0tt27dAuDevXtoa2vTvn171X1nZ2csLMr33r///ns+++wzvLy8uHz5srjYA2S9ZKqFe5p4+p5MJhPXn0zP9XDowaxusxh/cDx+UX4413FmZb+VxGTEMP/sfEAsNn279dt8dPAj1r60lnln5zHFYwrR6dH8fPNnZLLy01AexrrG/DL4F8YcGMPj7DJ6HR3qw5uti7+vvVKUOQ0ZLiMdqiHcW6V7ksrSNnoWm8plcuIz4/ngwAcolAquxVyjvkl9Pu/yucbwZV171vimek7lp2s/ldIxut1oHjx+IEY/qhHn089seOvhvNX6LWQyGekz0hmwYwAAnxz+hCVeS2CRl1hTk5UPqbnCeagsT6dRJhOLWfffE98j08DWGPo3Eb3XIvmQZHCpC9Hp4oXqZAFd7SG3sOwyUkRuASw6B3raYuTm1RZiauDBk165my10bACbr0NMBn+93Q6vhkZk5CsITKp4OqSm0PSMKqqH3TZ3w1jXmM4NO7O4z2IeJj1UdViexs3WjfQZ6eKLQr969e5p+jqBe32xXqdAs6ONpx1/h/yNt7N3tfJY01Q3DcNaDWNOjzm8susVErLEqPHw1sNZ/9KTRf3VtWl5bVkRFvqiA7Dar2w7Szx3noujo1CIB37w4EEaNFCf/9TTE73GrKwsrl69ipaWFg8ePFCT2bNnD5MmTWLp0qV4eHhgYmLCt99+i5+f+q8lF40AFSGTyVRxK8sovGVdL+Ljjz9GqVRy8uRJCgrEwkuzb82oZ1RP4wgBQGxGrKp3XUQ9o3rkF+arnIp5veaxLWCbqmcXGB+IkY4RPw78kQVnF6BEybd9v2XxhcVsvL6R1b6ruRF7g+WXlzOj6wx+vvlzuWmoiMYWjXG0cOTAmwdU1+SyJzOZq4G5Z8R6iNCU4kDaT+4/PQJgoltqdEZFXSMxGjCueO0RMploTCZ7iHiezHM/q01j0mPIV+SrjZjcTbyLtbE1BYqCUuGBMqeRniU+WxNbUnNS1eK0N7PHy8mLQw8OYapnWq04W9ZtWeYz09fWx0Bb9ERzC3PpubUnyuTDYpTkzVbQ0BQel7E76enRGxAjNCWfcWoOxKQ/nWBoZyucE7kMvugu1jq41C0uI7EZ0NhCDPWXVUaKUAIJT9Y8RKaJHVw+zvDgH3FtSHOxfuiqWMx6OzkXM105HtYGz8XRySpUaCxHlamHoSmhgKjn1kbWzOkxp0xHJzA+kLlnxAj2w8cbqlfvSuLlJOy4yk+se9JEHQNwsWL9b+vp7dS7WnmsKRKzEqtt5zdavsHGlzfy+q+vc/LRSdX1/ff24xcp3hfVtmlZbRnA6v6iLWtgInRO71osoyXt+fm3eS6OTosWLdDT0yM8PJwePXpolPnss8+Qy+UcPnwYX19fBgwYQO/evQE4d+4cnp6ejB8/XiUfHBxcpTS4uLhQUFDA9evXcXNzA+Dhw4ekpKSUG67IETM0LN6CiTb0depb5rbkS5GXSi2C827sjX+0PwUK4SwZ6hiWmsIoVBYiQyZ6LkqlSiZfkc/V6Kv0depLRFqE6uVWXhoqIigxiFZrWqldm997Pia6JvTZrg3J2VCoLH7xFJGaI3bCFO0k0JKJ3R17gzRHFJsB855aj/ByM2hqKRauJhe/fJ/VphciLjC89XBkFPf8mlo2JTo9mojUCPo69RXb55+yQ23El1WQpXpme4P28p7re8RnxtO4TuNq57G8Z/bpkU+5//g+Mekx9HXqy43YG5BXKEZ0mlqKxvbIQ43x8ihZLFD9+1HxteZWYnSmiJBksDZWD1fPSCxKLiojYali7VXJMlLPSDzjFvXKLiNlIZMVv5AAdLRK9bYViPUYzwOFErVnWkRV66FMJkNPu4xpRISjGpz8pH0rsm1V610RXk7Q3xlW/yNG5MrCoyGk57IvaF+N5PFZKNneVSUNw1oNY9PLm3jz9zc59OCQ2r2MvAwy8p5sG6+uTctqy/S04dfbopyn55aWGdEWHMwryLVEbfJcHB0TExOmTJnCpEmTUCgUdO3albS0NC5evIixsTFWVlZs2rSJS5cu0b59e6ZPn87IkSMJCAjAwsICZ2dnfv75Z44ePYqjoyPbtm3jypUrODo6VjoNLi4ueHl58cEHH7B27Vp0dHT47LPPMDAwUE0PVJZlPsuwN7Nnnf86ABb2WUgDkwaM3DsSgHX+6/i4w8cs9V7Khmsb8Gjoweh2o3nz9zdVOg7cP8Bkj8lcj7mumrqa12se++/tVzlAB+4fYFa3WYSnhrMtYBvLfZaTU5DD7tu7K0wDQFvrtoCYpqprWJe21m3JK8zjbuJdcgtzuZ1wWy1fqtGNGPWRMTX+fiR6h/GZ4tPPWbxQr5Q472RkW7EWYN89MXz79LkUWfkirKutWAz4KIVl3s9u07X+a/mk4yes7L+S1X6raWLZhJldZ7Lqn1U8THrItsHbuBV3i9iMWF5t8SoAdxLu0Na6LWPaj8FEz6TG4gNYdnkZ2wZv42r0Vca0H0NkWiTN6zavdh7Le2ZF11f4reCrHl9hrGsMAVZiUbCuFkSkwqXI0s8HxA6nSZ3F9EZAnFh47GIFSy+pP/cpnuJckGsxouHuai/O5ynieLA4V+RWnCgXdQ2hdT24/7j8MgJCb1gqJGQK56ZlPejUAHYGFocp0pucA9HpNDXTpWNdAwKScqgM+TlZpMVGqr5nxEfzOPQ+esamGFtVbu1H0TP1j/bnUuQlPnD7oNxyO77DeMJTw1UOdVf7rkzxmMLqf1ardOrIdWhRtwUAulq6NDBtQFvrtuLFXDSAUdV6B+J5vtQUNt8QDmnRqF1ugZhKLEIGdG4IlyMpVBZWOY9QfltTUR5VTt0z2HlYq2H8POhnPj3yKZcjL2NtZA1AdkE2abllbO+uqbYMiq8XFpaWKWlriX+F53Zg4Lx586hXrx6LFi0iJCQEc3Nz2rdvz4wZMxg6dChz5sxRrZ+ZPXs2x44dY+zYsezevZuxY8dy48YNhg4dikwm480332T8+PEcPny4Smn4+eefGT16NN27d8fGxoZFixZx+/Zt9PUrdw5HEd3tu+O73Zfw1HAAbI1tsTezV90PTQnFd4cvy32W81GHj4hOj2bC4Qn8cfcPlcz8s/NRomR+7/k0MGlAQlaCcGxOzlLJfHL4E+b1msca3zXUM6pHSk4KOlo6vNPmHQLjA8tNA8CNsTdU/7vXd+etNm8RmhKK48rKO4ilOB4iXpzDWhUfsrXaT70yWxhUvB4kJUe8eH2bgKke3ROMn9mmkWmReP/izXKf5QSMCyAqLYqVfitZcmEJCqUCSwNLZvecrTYk/lWPr/iqx1c8THpIZFpkjcUHsOf2HiwNLFnQZwE2xjYk5yQ/cx4r4psL3+Bu687MbjOhu5YYlbsWIxyGomfy9PMJSRZbwAc2E5/ELLGYuOS0ZViqOBTtlWbimT3Oht/uwJXoYpmbcbDzlnh56GqBh52YjtKWV1xGisqUuT7kF4rt0FtuqKapnhhUpG9oSzDRo5cSrj/O4UJsGVt8nyIxOIgj8z5Rff9nm3A2nLv3p9v4Lyqlo+iZftXjK2yNbSush3KZnEV9FuFo7kiBooDg5GCmn5zOev/iQyDrm9RXq6tTPacy1XMqp0NPQ1CmuFidetfdQYyCfeCmnomD98XW6CJcrMShgZciQVb1PELFbU15eey1tdcz2/lDtw/Fho4Ba1gzYI3q+pYbW3hv33ul9FfbphL/k8iUFS1SeYGJjIzEzs6OEydO0KdPn0qHk819PkPl/xbKuL+ee5wy65eee5wvOv/Gc3yeLB7T6bnHOWN/3eca3/N+hv+Feviv1Is1A2pVfU2+k5SzXzyX4D/1ExB///03GRkZtG7dmpiYGKZNm0ajRo3o3r3qx8BLSEhISEhI/P/nP7UcPD8/n5kzZ9KyZUsGDx5M3bp1OX36dKndWhISEhISEhIVs2bNGhwdHdHX18fNzY1z58o5Ywg4c+YMbm5u6Ovr4+TkxLp162o9jf+pER0fHx98fHz+7WRISEhISEj8z7N7924mTpzImjVr6NKlC+vXr6d///7cuXMHe3v7UvKPHj3C19eXMWPG8Msvv3DhwgXGjx9P3bp1efXVV2stnf+pER0JCQkJCQmJssnNzSUtLU3tk5ur+ZyqZcuWMXr0aN5//32aN2/OihUrsLOzY+3atRrl161bh729PStWrKB58+a8//77jBo1iu+++642syQ5OhISEhISEhKCRYsWYWZmpvZZtGhRKbm8vDyuXr2Kt7e32nVvb28uXryoUfelS5dKyfv4+ODv76/2A9w1zX9q6kpCQkJCQkKibGbMmMHkyZPVrhUdnFuSxMRECgsLsba2VrtubW1NbGysRt2xsbEa5QsKCkhMTMTW1vYZU68ZydGRkJCQkJCQAIRTo8mxKYunD9xVKpXlHsKrSV7T9ZpEcnQkJF5QnveZKM/7fJLnfaYNwKKXE55vhBv8KpaRkPgXsLKyQktLq9ToTXx8fKlRmyJsbGw0ymtra2NpaVlraZXW6EhISEhISEhUCV1dXdzc3Dh+/Lja9ePHj+Pp6akxjIeHRyn5Y8eO4e7uXqvHvEiOjoSEhISEhESVmTx5Mj/99BObNm3i7t27TJo0ifDwcMaOHQuI9T4jRoxQyY8dO5awsDAmT57M3bt32bRpExs3bmTKlCm1mk5p6kpCQkJCQkKiygwdOpTHjx/z9ddfExMTQ6tWrTh06BAODg4AxMTEEB4erpJ3dHTk0KFDTJo0iR9++IH69euzatWqWj1DByRHR0JCQkJCQqKajB8/nvHjx2u8t2XLllLXevTowbVr12o5VepIU1cSEhISEhISLyySoyMhISEhISHxwlLjjs7p06eRyWSkpKSUKbNlyxbMzc1rOuoKmTNnDq6urs89XgkJCQkJCYl/B2mNTjUZ5z6OqZ5TsTWx5Xb8bSYencj58PMaZQe7DGac+zhcbVzR09bjdvxt5pyZw7HgYyqZFnVb8HXPr3Gr70Yj80b8evtX3Ou7V0o/QHeH7izzXkbLei2JTo/mmwvfsP7qejWZTzt9yjj3cdib2ZOYlchvd39jxokZ5BaK3zGZ3WM2c3rOUVecUyA+hjoQmgK7AyEmo2zD2BrDS03B3gwsDeHX23AqVF1mXi9xrwRKlJwJPYO9mX2l8mxjbMNS76W42brRxLIJq/xWMenoJDUZbbk2M7rOYGTbkTQwbUBiZiLaWtqY65vXmE3N9MxY0GcBQ1yGYGFgwaPkR3x27DMOPzwMwF9v/sWApgPUwsRmxGK7VPMJoBXF+XQ5mXhkIiv9Vqrp6GbfjameU3Gr70Z9k/oM2jUIVxtXPnD7AAt9C/yi/Pjo0EfcSbhTZt4BhjQfwrxe82hs0Zjg5GBm/T2LvUF7Vfc1lpfUHJhxEgY0gS72lS83rjbg0xjqGoGWDOIz4eQj+CeqWGZAExjQVPVViZLYjFi+PvN1petiF7suLPFagouVC4Y6hoSlhrH+6npWXF5RJRtXhti7Nwg8sIPER0FkJz+m92eLcOjQvcp6VFTVpp0bwoi2pa9POAwFCvG/XCb0dmgApnoEZwaz5cYWHmc9ZornlBpr395v/z4j2oygVb1WAFyNucrMkzO5En2lzOTXdBurkaratCRutjC6PdyMhfVXNcv4NIZXXCqnT6LWkBydavBGyzdY0W8F4w+O50LEBT50+5DDbx2mxQ8tiEiLKCXf3aE7x0OOM/PvmaTkpPCe63scePMAnX7qxI3YGwAY6hgSkhLCr3d+Ze2AtQxuPpixf42tlP5G5o04NPwQG65t4O0/36aLXRfWDFhDQlYCf9z9A4DhrYez2Gsxo/aN4mLERZpaNmXLoC0ATD5afNx3YHwgrZY9OdCphwP0coQ9tyEuA/o3gU86wdzTkFuo2Ti6WpCYBddi4LUWmmWWXBANbBG2xvBpZzztPBl3cFyl8qynpUdCVgILzi1gUudJpe4DzO89n7dbv82YA2NobNGY5f2Wo1AoGPb7MLo7dH9mm+rIdTj+znHiM+N57dfXiEyLxM7UjvS8dECUk37O/YhKi2LYb8N4p+07vNX6Lfr90k9jeisTZ8lystxnuUY9RrpG3Iy7yeYbm/lj6B8MdhnM4OaDeXfvu9x/fJ8vun/B8XeO0+z7ZmTkaW7UOzfszO7XdvPlqS/58+6fDG4+mD2v7aHr5q78E/WPSi4wPhCvn70AiE38BRRK6OsEvR1hW0Dly01mHhx5CHGZ4iXcuh680wbSc+FuYrFcdDqsEofo2Vi9zcCmA/lhwA+VrouZ+Zl8f+V7AuICyMzLpKt9V9a/tJ7MvEw2XNtQaRtXhoKcbCwcnHHu6cupZbOqrQeonk0BsvNh7pmnEqYo/t+7MXRzgJ9vQnQ609qsYdvgbeho6VS6/alM+9bToSc7A3dyMeIiOQU5TOsyjWPvHKPlGuHQP01ttLE1ZlOAOgYwpDk8eFy2jIOZcKIi06Chafn6npHnfVjn/xrVmrrKzc1lwoQJ1KtXD319fbp27cqVK2V75lu2bMHe3h5DQ0MGDx7M48fqhaNoSmn9+vXY2dlhaGjI66+/Xmr6a/PmzTRv3hx9fX1cXFxYs2aN2v3PP/+cpk2bYmhoiJOTE19++WW5PxT26NEjnJ2dGTduHAqFoky5p5nceTIbr29k4/WNBCUGMenoJCJSIxjXYZxG+UlHJ/HtxW/xj/bnYdJDZv09iwePHzCw6UCVjH+0P9OOT2P37d0Y6hjiF+lXaf1j3ccSnhrOpKOTCEoMYuP1jWy6vokpHsVnE3g09OBC+AV2Bu4kLDWM4yHH2Rm4E3dbdzVdBYoCSMsVH087OPwAbsSKXs7PN4Uj06FB2cYJS4U/g+BqjHqDWpKMvOI40nKhtTU5BTn8dP2nSuc5LDWMiUcmsi1gG6m5qRpl3mnzDgvPL+Tww8O83eZtNlzbwKGHhxjSfEiN2HRUu1HUMajDoN2DuBhxkfDUcC5EXCAgLgAQ5eRazDUSshI4H3GeD//6kPDUcIa2GlrtOEuWk6KRuKc58vCIcFCC/gRgYLOBLDi3gD+D/uR2wm1G7h2JoY4hw1sP1xgeYGKniRwPPs7i84u59/gei88v5uSjk0zsNFFNrkBRQFxmHHGZceJZZuSJl8eRh1UrNw+S4GYcxGYIR/lUKESlQ+M66nKFClW5icuM4/3271epLt6IvcGuwF3cSbhDWGoY229t52jwUbrZd6uSjStDw3YeuA39gEYde1Zbh4rq2BRAiXpdS3sqP47mEBAHgfGQlM3vd38nuyCb4KTgGm3f3v7zbdb6r+Vm3E3uPb7HmANjkMvk9HHso1FnbbSxpaiuTWXAu65w8IEoq5rQ0xIy2wMgq/Z+rFKiclTL0Zk2bRq///47W7du5dq1azg7O+Pj40NSUlIpWT8/P0aNGsX48eO5ceMGvXr1Yv78+aXkHj58yJ49ezhw4ABHjhzhxo0bfPTRR6r7GzZsYNasWSxYsIC7d++ycOFCvvzyS7Zu3aqSMTExYcuWLdy5c4eVK1eyYcMGli/X3CMLDAykS5cuvP7666xduxa5vPKmcKvvVmpI9FjIMTwbaj4N8mlkyDDRMyEpu7S9dOQ66GrpEpQYVGn9Hg09OBainp6jwUdxr++OtlwM2p0PP49bfTc61O8AgKO5I77Ovhx8cFAtXJM6TWBhH5jfG8z0ISa9+GaBQvRgnCwqlc9KoSWDjg3Qkes8k001oaelR05BDjpyHdUzy87Ppqt91wr1V8amLzd7mUuRl/jB9wdiP4vl1rhbzOg6A7lMroozODmYJnWaEDU5ipAJIchlcno36l3tOKtDHYM6arbNK8zjTOiZcm3rYac5LZ526mFK5o1R7cDZQpSbkqMw1Sk3zSzB2ggePlVH6hmJ8vl1L3a/tvuZ66KrjSuedp6cCTtTsfC/haVB9W2qpyWmihf0hnHupUcWgpOFresZAdDetj0W+hb89UB9hKCm2rciDHUM0ZHrlNkG1mYbCzybTX2bCIf+YumRJRVDWwnn8V45Iz4Sz40qt56ZmZmsXbuWLVu20L9/f0A4IcePH2fjxo106NBBTX7lypX4+Pgwffp0AJo2bcrFixc5cuSImlxOTg5bt26lYcOGAKxevZoBAwawdOlSbGxsmDdvHkuXLmXIkCGAOHjozp07rF+/npEjRwLwxRdfqPQ1atSIzz77jN27dzNt2jS1uC5dusRLL73EjBkzKjyRMTc3l9zc4l6QTCbDxMSEuIw4Nbm4jDhsGtuUb7wnfOb5GUY6Ruy5vafUPStDK2QymWr6ozL6bYxtNKZHR0sHK0MrYjNi2X17N3WN6nJ+1HlkyNDR0mHNlTUsubBEFcYvyo8Re0fw+/1PoWkdeKMVvN0G5p2FzCe9kvQ8MWxbU7S1AQNttOTyZ7KpJo4GH2Vy58kEJQahLdfG3syeV1xeQUumVaH+ytjUycKJ3o692X5rO747fGlSpwk/+P6Atlybn679hLZcm3+i/uHXO79y//F9rI2s+XHgj7jVd6OOQZ1SjXBl4qwupfRmxuFg5lCmfFlpsTEutldReSnK24nOu+EDN3Ez/amRg8qUG31t4cToyMUU2K5ACCrxInqUAltvivU7Jro0HNIQbbk22fnZpdNZQbmJmBRBXcO6aMu1mXNmDhuvbyw/bf8mZvrib1VtGpshpmWi0oRteznCFE9YcBYSnoxEHAsGA234qgcolVyR90cmk/H7nd/VVNVU+1bEYq/FRKVHcSLkRKl7VoZWaMu1a62NBapvUycLMdK98FzZMm62YGcqpugl/l9Q5RGd4OBg8vPz6dKli+qajo4OHTt25O7du6Xk7969i4eHh9q1p78D2Nvbq5ycIhmFQsG9e/dISEggIiKC0aNHY2xsrPrMnz+f4OBgVZjffvuNrl27YmNjg7GxMV9++aXaqYwA4eHheHl58cUXX1Tq2OlFixZhZmam+jRr1gwQCyFLIpPJSl3TxLBWw5jTYw5DfxtKQlblfyCwIv2a0gPFvwzbw6EHs7rNYvzB8bT/sT2Ddw/mpaYv8UV34RwObz2cX1//la2DtorGMKqEo9WpoZpulBXns9J42sH9x2XmoTI2LYtPj3zKg6QHnH33LCCGwzff2EyhsrBS+iuyqVwmJz4zng8OfMC1mGvsvr2bBecWMM69eHj9UuQl/rj7B4HxgZx8dJKfb/4MwMi2I6sVZ0UMbz2c9Bnpqk95eiuybUVhjjw8gr62PpdGX2LvsL1iTUJxYA0KK8hDbgEsOideEPvvwastoEmJqas7CWKaITod7j3mnT/fAaC/c/8q563b5m64b3Bn7MGxTOw0kWGthpWftudICws9JrexZHIbS1jmI0Y9oeo2DU0Ri7mj0sXIzcZrYi1Kz0bFMm620LEBbL4Oi87z6ZFPAejnrL6OrCbbt6meU3mz1ZsM2T2k3KnBmmxjS9aLattUNR11q7jj9zQW+vB6S9hyo+ype4nnTpVHdMr6SfWyfpq9sg300xTpkslkqvUzGzZsoFOnTmpyWlqid3758mWGDRvG3Llz8fHxwczMjF27drF06VI1+bp161K/fn127drF6NGjMTUtf5HYjBkzmDx5stq1gsICtZ4tQD2jeqV6IE/zRss32PjyRl7/9XVOPjqpUSYxKxGlUomJrkml9cdmxGpMT35hPo+zhRMxr9c8tgVsU/VcA+MDMdIx4seBP7Lg7AL239uPX6RY5Pnw8QbQfuIDJ2arhrUBMNEVvZ6aoI4BuFjBT9coaGZRLZuWR2JWIoN3D8ZYx5jk6clMPDqRzg078yj5UYX6K2PTmPQY8hX5KJTFDdrdxLvYmtiSmpNKgaJ0ObEwsCA9N50mlk2qFWdFlHyOAA8nPATECE3JEaF6huXbtqy0PB2mVLkZ0RaM9cBUT309SGXKjZLikYbINLAxBh9nePCPRvGI1AgUSgXNrJpVmM6nCU0JBUQ9sDayZk6POewK3FV++p4TD1Pz2JSZDMDYPTeL62J1bFoSJWINXcn6PKQ5HA0Wa+qA9cr1rPBZwah2o5h9erZKrKbat888PmNmt5l4/ezFrfhbGmUSsxI11p1nSUOZ7VtVbFrXCKwMxRRgEUXvvNX9xaLvBiZC5/SuxTJa0nF1/zZVfgLOzs7o6upy/nzxNr/8/Hz8/f1p3rx5KfkWLVpw+fJltWtPfwcx0hIdXbz6/tKlS8jlcpo2bYq1tTUNGjQgJCQEZ2dntY+joyMAFy5cwMHBgVmzZuHu7k6TJk0ICwsrFY+BgQF//fUX+vr6+Pj4kJ6eXkqmJHp6epiamqp9rsZcpa9TXzW5vk59uRh5sUw9w1oNY8srWxj++3AOPThUply+Ip+8wrxSjXd5+i9FXiqVHu/G3vhH+4vFxYg58ZIvZIBCZSEyZMhkMjLyMghODiY4OVi8bGIyxFZhG2PxF0QvqIklhCSXmf4q4dFQDB0HxHE1uuo2rSwZ+Rlcjb6KT2MfXm3+Kvvu7atQf2VseiHiAs51nJFR7OA3tWxKdHo0WQVZGvPk7eSNllyLmPSYasVZYV5LPMfgZDHamZSdpKZXR65Dj0Y9yrXtpQgNaXHy5mKEehi1cpOcI5zXnAJoblUsVN1yI5MVv5A03pZRoCigjoH6guWqlhuZTIaetl7V0laL5CmUpOQpSMlTqNfFmrCpnSmklnix62ipjWDkK/KJTo/GWNdYLVhNtG9TPKfwZfcv6fdLP67GlLEd+0kaqtMelJeGMtu3qtg0NgPmnRHTVkWfW3FiRHrhOUjOFlOtT8uEpZSZZonnQ5VHdIyMjBg3bhxTp06lTp062Nvb880335CVlcXo0aO5efOmmvyECRPw9PTkm2++YdCgQRw7dqzU+hwAfX19Ro4cyXfffUdaWhoTJkzgjTfewMZGePVz5sxhwoQJmJqa0r9/f3Jzc/H39yc5OZnJkyfj7OxMeHg4u3btokOHDhw8eJA///yzzDwcPHiQ/v37079/f44cOYKxsbFGWU0su7yMbYO34R/tz6XIS3zg9gH2Zvas818HwMI+C2lg0oCRe8X0xLBWw/h50M98euRTLkdextrIGoDsgmzSctMA8fJpUVdsx87Mz8SjoQdfdf+Ky5GX6dekX7n61/mv4+MOH7PUeykbrm3Ao6EHo9uN5s3f31Sl+cD9A0z2mMz1mOv4RfnhXMeZeb3msf/efpUD9G3fbzlw/wAUGICJntgtYPOkx2NrDP2cIa8QrpQ422RkW0jJgX33xHctGdg+GY3SkoO5vlgAmVtQ3FsHsXOhc0O4HAkKZZVtCtDWWpwRYqxrTF3DurS1bkteYR53E8UUascGHWlg0oAbsTc49PAQs3vM5nHWY/YG7WWZz7Jntula/7V80vETVvZfyWq/1TSxbMLMrjNZ9c8qVTnZMWQH6bnpHA85zuh2o2li2YTs/Gy23txarThLlhNdLV0amDagrXVbVUMOYKRjhHMdZ1WYCxEX+LL7lyRnJ3Mp8hIzu80kKz+LHbd2qGS2DtpKVHoUM0/OBGCl30rOvneWaV2msS9oH6+4vIKXkxddNxf3VIvKS3hqOPWM6sGQ9mItyJlQMRITnyk+lSk3Po3FaENCpnBuWtaDTg1gZ2BxmCHNxYslKRtM9Pjtld8oUBTgauPKe67vVarcjO8wnvDUcNVi/672XZniMYXV/6yulI2rQn5OFmmxkarvGfHRPA69j56xKcZWVVx79vejqtvUtwk8ShbyBjpiyqqhqVj7VMStOKErOQei0xnkMggzPTOMdI0qbdPKtG9TPacyr9c8hv8xnNCUUJVMRl4GmfmZpbJbG23sM9u0QFH6jJ2iHVVF1wsLS8tUtFVdotap1laOxYsXo1AoeOedd0hPT8fd3Z2jR49iYVF6tXrnzp356aefmD17NnPmzFGtj5k3b56anLOzM0OGDMHX15ekpCR8fX3Vto+///77GBoa8u233zJt2jSMjIxo3bo1EydOBOCVV15h0qRJfPzxx+Tm5jJgwAC+/PJL5syZozEPxsbGHD58GB8fH3x9fTl8+DBGRkYaZZ9mz+09WBpY8lWPr7A1tiUwPhDf7b6Ep4r1QLbGttib2avkP3T7UCz+HbCGNQOK87Tlxhbe2/ceAPVN6nNj7A21eOb2motCqeB6zPVy9YemhOK7w5flPsv5qMNHRKdHM+HwBNXZKwDzz85HiZL5vefTwKQBCVkJHLh/gFkni8/3aGjakJ2v7gQDG7Gr4FGy6K281LT4QK3VfuoV18JALBwtwkwfZhZv1aVvY/G5/xhWlBjJc7EShwZeiqyWTQE1e7nXd+etNm8RmhKK40oxyqevrc/83vNxsnAiIy+DK1FXqG9Sn3PvnatQf2VsGpkWifcv3iz3WU7AuACi0qJY6bdStcB7z+09TPWcymeenzGtyzTyFflcCL/AuIPjCE8Nr1acT5eTqZ5Tmeo5ldOhp+m1tZfKFqffPa2SKdpiu7L/SuQyOX6Rfnhv81Z7cdub2auN+F2KvMSw34Yxv/d85vWaR3BSMEN/G6p2hk5RebEytCIhMwGCFfDtRdHzLVDAsFaVLze6WkLeXB/yC8V5OltuqKZUAHHvvXZgrAsZeeRFH8T9R3d6NupZ6XIjl8lZ1GcRjuaOFCgKCE4OZvrJ6az3Lz6UsTwbX67CAEpicBBH5n2i+v7PNuFMOXfvT7fxX5QVTDPHQ4ptVFmbGmjD8NZiKiWnACLSYNkl4VAWsec2DGwGQ1uCiR7fZTiw6p9VxGXE1Wj7Nr7DePS09fj9DfVFznNOz2HumbmlslsbbWyN2FTifxKZsrqLaGqQOXPmsHfvXm7cuPFvJ6VSyOaWXov0IvFvHD4ls37puccpUbM873Lzb5SZRS9XfgNBTTB9g1/FQjXIf6Ee/iuH660ZULHMszD+YMUylaW20/ovIK2SkpCQkJCQkHhhkRwdCQkJCQkJiReW/xeOzpw5c/5npq0kJCQkJCQk/nf4f+HoSEhISEhISEjUBpKjIyEhISEhIfHCIjk6EhISEhISEi8skqMjISEhISEh8cIiOToSEhISEhISLyzVOhn5v86LfjDav3Fo2Itu0//CIYz/BZvynA/wWzymU8VCNYhyw79g0+fMv9K+VeJX1yVqD2lER0JCQkJCQuKFRXJ0JCQkJCQkJF5YJEdHQkJCQkJC4oVFcnQkJCQkJCQkXlgkR0dCQkJCQkLihUVydCQkJCQkJCReWP7fOjrvvvsugwYN+reTISEhISEhIfE/jHSOzrMyoAl0sQdDHQhNgd2BEJNRtrytMbzUFOzNwNIQfr0Np0LVZeb1EveeUHQGw85bOxn+x/BykzOk+RDm9ZpHY4vGBCcHM+vvWewN2qu6P73rdIa4DMHFyoXsgmwuRlzk8xOfc//x/eL4Zms+82HNlTV8dOgjjfdsjG1Y6r0UN1s3mlg2YZXfKiYdnaQmM9hlMDO7zcS5jjM6ch0eJD1g6aWl/BLwi7qy2rCpT2NwtQFrY5BBljwLmUzG5cjLfHToI+4k3ClbPxXbtZt9N6Z6TsWtvhv1TeozaNcg9t3bB8DsHrPBtY96fno2gm4OmtMKIq0Dm4KVISRmwf57cDNOXcZMDwY3hxZ1QVcL4jLgTgK41QczPWJyY8jMy8TG2IbU3FROhJxg+onpxGTEqFR0d+jOMu9ltKzXkuj0aL658A3rr65Xi+bTTp8yzn0c9mb2JGYl8tvd35hxYga5hbkqmfom9VnitYT+zv0x0DHg/uP7jN4/mmsx1xjnPo6pnlOxNbHldvxtJh6dyPnw8+XaG8DTzpMz754hMD6QduvblS9c1TLTxQ46NYT6JuJ7eCrsC4Kw1GKZp+qhijOhsPt21ePs3BBGtC19fcJhKFCI/+UyobdDAzDVY6wSbiXlcCE2u/z8A7F3bxB4YAeJj4LITn5M788W4dChe4XhiuhqYwgL+1Q+P1BxOS3Phhl5wn4mulCoBJRQoBTPYv89kYaqxqenBQObQVtrMNGDyDRRx0o+1yeMcx/HYq/FmOqZEpEawfA/hpdZLrvYdWGJ1xJcrFww1DEkLDWM9VfXs+LyCpXMyLYj2TJoS6mw+vP1NeqUeH78vx3R+Z+grxP0doQ9t2HJeUjLhU86icpWFrpaooLuDYLUHM0ySy7A4QeQUwDbbvL+/vdFdI37YqxrXKbqzg07s/u13WwL2EbbdW3ZFrCNPa/toWODjiqZHg49+OHKD3Te2Jm+2/qiLdfm2NvHMNQpboxsvrPhwwMfkleYx+Sjk5l5ciZKpZKRbUdiZ2qnMW49LT0SshJYcG4BN2NvapRJyk5iwbkFeGz0oM26Nmy+sZnNr2zGu7F3sVBt2dS5DpwJgwvhANxOuE1SVhIJWQkcf+f4M9vVSNeIm3E3+fjQx2php3WZxmSPyer5mewBjuaQUkZaHc1hdDv4JwoWnhN/328PjcyLZQy0YYonFCrgh3/g6zNwKx76OMGRh7D0ErkFuTQwbcCAHQMYsnsITS2bsv/N/SoVjcwbcWj4Ic6Fn6Pd+nYsPLeQVf1XMaT5EJXM8NbDWey1mLln5tL8h+aM3j+aoS2HsshrkUrGXN+cC6MukF+YT//t/WnxQws+O/YZKTkpvNHyDVb0W8GCcwtot74d58LPcfitw2WWoyJM9Uz5edDPnAw5Wa4cUL0y08QS/KNhxWX49gIkZYswZnrFMksuwPQTxZ+Vl8X1azHVixMgO19d5/QTxU4OgHdj4QDvuQ1fn+FUdCYd6xngXrfil2VBTjYWDs50fm9yhbJP06meAR3q6VctP5Upp2XZUC4rtt++e/AoGXIKYbUfPM6CTzqCsW7V43u7DbhYwdabsOAs3E2ACU89V+CNlm+wst9KUnJSiMuIIyQ5pNxymZmfyfdXvqf7lu40/6E588/OZ36v+YxpP0ZNLjUnFZvvbNQ+JTsEEv8O/7qj89tvv9G6dWsMDAywtLTEy8uLzMzMUnK5ublMmDCBevXqoa+vT9euXbly5Yrq/unTp5HJZBw8eJC2bduir69Pp06duHXrlpqeixcv0r17dwwMDLCzs2PChAka46sUvR3FS+VGrOj5/HxTvHQ7NCg7TFgq/BkEV2PUG7iSZOSBp51wdi5F0qpeKx4mPURbrs3w1mWP6EzsNJHjwcdZfH4x9x7fY/H5xZx8dJKJnSaqZPpv78/Wm1u5k3CHgLgA3tv3Hg7mDrjZuqlk4jLjGNVuFBuvb2T55eV0bNCRvx/9TXhqOOM6jCsjW2FMPDKRbQHbSM0t3XsCOBN2hr1BewlKDCIkOYRVfqsIiAugq33X2rfpD1fgciS414dDD+i/vT/1Teuz7so6DHUMn9muRx4e4ctTX/Jn0J+lwi44t6A4P3vvgoGO6IUWlpHW3o4QlAhHgyEuU/wNSoRejYplvBtDcg5sCxD5T8qG5lZwMUJ8wlNptLIRj5If4ePsg1+UH58c/gT3+u6qxnys+1jCU8OZdHQSQYlBbLy+kU3XNzHFY4oqGo+GHlwIv8DOwJ2EpYZxPOQ4OwN34m7rrpL5vMvnRKRGMGr/KK5EXyEsNYy/H/1NSHIIkztPZuP1jWy8vpGgxCAmHZ1ERGpEmeWoiPUvrWdH4A4uRV4qV05lr6qWmS034GyY6PHHZcL2AJAhXpJFZOSJF37Rp7U1xGfCg6TqxQmgRF1n2lMvQUdzCIiDwHhIyuZeSh6h6fnYGFY8+N6wnQduQz+gUceeFco+TYd6BlyMza5afipTTsuyYRvrYvudCBH1U1sOdmbw+5M60sCkavHpyMWIz94geJgECVlw8IHoBHV3UFP1eZfPyS3Mpf/2/mQXZPNn0J/llssbsTfYFbiLOwl3CEsNY/ut7RwNPko3+25qckqUxGXGqX0k/n3+VUcnJiaGN998k1GjRnH37l1Onz7NkCFDUCpLT51MmzaN33//na1bt3Lt2jWcnZ3x8fEhKSlJTW7q1Kl89913XLlyhXr16vHyyy+Tn58PwK1bt/Dx8WHIkCEEBASwe/duzp8/z8cff1wqvgqxNAAzfbibWHytQAEPHoOTRdX1laVbS8bbbd5m0/VNnAk9g2dDzzKDedh5cCzkmNq1o8FH8bQrO4yZnhkgRluK0JHr4FbfjWPBx6hnVI8BTQaw8fpGjoUcKzf+qtLbsTfNLJtxNuysuFCbNn1Kf1G+4zLjasWuAI7mjtia2HIs+ElYGfBOW4hJ1zycrwpooW4DEN9L2qCNNYSliB7tEi+Y0RUczEUPtgQln5mZnhkKpYKUnBSRr4aa8+Ve3x1tuXixng8/j1t9NzrU76DKk6+zLwcfHFSFebnZy/jH+LPntT3ETYnj2gfXeL/9+2rlqKw0aeJd13dpbNGYuafnlm2jImqqzOhqgZYcMvM139eSQccGcCniKlUJ+wAAdi1JREFU2eLU0xLTOQt6wzh3aGiqfj84GZpZQj0jAOoZaNHQSIfgtLzK56WKmOnKMdaRE5peIo7K5Kcy5bQkRTa8GVu2/RpbQFd7yMoXTmhV4pPLxDPML1SXyVdA4zqqr7pyXVxtXNkduFttyroq7ZurjauYWg07o3bdWNeY0E9DiZgUwYE3D+Bq41opfRK1y7+6RicmJoaCggKGDBmCg4PwuFu3bl1KLjMzk7Vr17Jlyxb69+8PwIYNGzh+/DgbN25k6tSpKtnZs2fTt29fALZu3UrDhg35888/eeONN/j2228ZPnw4EydOBKBJkyasWrWKHj16sHbtWvT1Sw8P5+bmkpur3uvSK8xHz+xJ5Up/qkeWngd1DKplDxVm+sW629pgrm/OlhtbaGTeCAczhzKD2RjbEJeh3oOIy4jDxtimzDDLfJZxLuwctxNuq65ZGVqhLdcmLiOOkW1Hkp6Xzh93/6CReSNsGpetqzKY6pkSNTkKPS09CpWFjD84nhMhJ4CJ6vkuSU3YFNT0L3u1ON9xmXE1bteicEWygBiFUSjhUUr5+THVK93TT8sV14uwMhS91JOPRM+4uZXoDduZqq1ZiMuIw6axDXpaeiz2WsyOWztIz0svN186WjpYGVoRmxHL7tu7qWtUl/OjziNDho6WDmuurGHJhSWqME4WToxzH8eyS8tYeH4hHRt0ZFW/VRhoG6jKUSnblVGOnOs4s7jPYrpt7kahslCjjBo1VWYGuYipxKBEzffb2ojpwsuRxU5qVeOMzRAjcFFpoK8NvRzF9OOCs2L0AeBYsIjnqx6gVPKeXMaZmCzuJteeo2OsI/q7mflPdTAryk9lymlJimz4MAn6Nla3X6t60LKecIZSc8UU1tNOZ0Xx5RZCSDL0byJsnZYrRqQamUNC8aj97J6zkcvkbLy+UU1VeeWyiIhJEdQ1rIu2XJs5Z+ao6QhKDOLdve9yK/4WpnqmfNrpUy6MukDbdRrWZUk8V/5VR6dt27b06dOH1q1b4+Pjg7e3N6+99hoWFuo9guDgYPLz8+nSpYvqmo6ODh07duTu3btqsh4eHqr/69SpQ7NmzVQyV69e5eHDh2zfvl0lo1QqUSgUPHr0iObNm5dK46JFi5g7dy7Dhw9n/XqxSFO2vB+sv/5EgYaMaRiRKpcO9eHNEg7e2ivFuj3tOPzgMDEZMchksgp/HO7p++WF+d73e9pYt6Hrpq4a7ytRMqrdKLbf2k5uYW6l4q+I9Nx0XNe5YqxrzFTPqWx8eSM/+P4AhTrq+S6VmGrEW5ZdX25GG+tCVb5ryq7DWw9n/UuijOx8dSf9tvcrDmtnKhYgLz4PA5pWPT+yp7/LihdtgnhpDG4uXiZ/PSiVzl2v7UIukzP+4PgK8wWoRlV7OPRgVrdZjD84Hr8oP5zrOLOy30piMmKYf3Y+AHKZHP9of2b9PQsQw/wt67ZkRJsRZcahyd5ymZwdQ3Yw+/RsHiQ9KHUf1G2MQr9mykxfJzGlueKy5qnPDvVhpKvQN7tn9eMMTVFfYBuSDNO7inLx65ORBTdbMeqx+TrEZPDX2+3wamhERr6CwKSaWevRwkKPfnbFa9J+DRFTzRrrwLOWUyiuh7paQl9OwRPdJWTuPxYjPRYGwkkZ3R6+uSCmvqoS35Yb8E4bWOQlpoYj0sRaLLsnI2d2przf/v0n0Ve+rSyi2+ZuGOsa07lhZxb3WczDpIfsCtwFgF+UH35RxT/6eiH8Atc+vMYnHT8pPw8Stc6/6uhoaWlx/PhxLl68yLFjx1i9ejWzZs3Cz0/9F4KLGt2iRrjk9aevaaJIRqFQ8OGHHzJhwoRSMvb29hrDzpgxg8mTJ6ulQ7nwHOg9WSj3dC/DRFf0hKpCQJx6A6j9ZEbRzhRcrPhpz08A1DOsV6p3XJLYjNhSowz1jDSHWdV/FS83fZnuW7oTlR6ldi8xK5ECRQG9GvXCxcqFob8NLVdXVVCiJDg5GICxB8eio6WDrbEt3TYVFOe7JmwKZdu1ZV16bW6uyndN2XX/vf34RfrxcMJDJh2dRGKWGB2wMbaBOnXELpD5vcUQuxLx99UWYu3Bl6eKFWnqFZs8ZZPUHDEFVkRGnhgtMlcflbQxssHKwApHc0d6/9xbNZpTXr7yC/N5nP0YgHm95rEtYJuq5xoYH4iRjhE/DvyRBWcXoERJTHpMqV1rdxPv8mrzVylQFFS6TJromtChQQfa2bbje9/vAeH8yGVy8r/Mx3ubt8rGAA8fb3j2MuPlBD7OsMoPotI1y0Q8mULZfRvuJdZcOVUi1lc9maYCYEhzsfbkqtgZdzs5FzNdOR7WBjXm6DxMzWNTZrLqu7ZctI9FIzsqKspPZcopiHqYnAMTOwsbFjkvJe2XVyjsGp8JvwTAnJ5iZ9zR4KrFl5gFyy8Lp0pfW9wb3Q4eP9m15lwHK0MrlEol5987jxIl2nJtlnovJTM/k+sx18vOLxCaEgqIemBtZM2cHnNUjs7TKFFyJfoKTeo0KVfnf4Xk5GQmTJjA/v1iQ8TLL7/M6tWrMTc31yifn5/PF198waFDhwgJCcHMzAwvLy8WL15M/fr1qxT3v74YWSaT0aVLF+bOncv169fR1dXlzz/VF3Q6Ozujq6vL+fPFW//y8/Px9/cvNQpz+fJl1f/Jycncv38fFxcXANq3b8/t27dxdnYu9dHVfWqF/xP09PQwNTXF1NQUExMTTExM0EvKEwv2UnPElEERWjKxmyMkWaOuMsktFEPXRZ8i3b0dIT2Xg/cPoiPXoUejHlyMvFimmksRl+jr1FftmreTNxcj1MOs7r+aIS5D6P1zb1XFLUm+Ip+r0VcZ0XYE/tH+BMQFANDXqW+58VeVjLwM0vPSxRRFyXzXhE2htF27OQhn4HKUKt81adeMvAyVExebEcudhDvEpMeIsP9EiSmKxedFug4/EFMlx4Nh9T/qET5KVl8UC8ImJW0Qkiy2yhdRqBSjOiVGJLTl2ox0HYkSJV7bvNTWYQFcitSQr8be+Ef7U6AQvW5DHUMUSvVRjkJlITJkqg7EhYgLNLNspibT1LIpYalhXI2+WiqOsspRWm4arda0wnWdq+qzzn8dQYlBuK5zxS/KT2Xj4OTgZy8zXk7Q3xm+/0eMjpWFm62w7cWImi+ndqZiqqYIHa1SoygKQKZxqKR65CmUpOQpVJ/EnEIy8hU0MinRBlYmP5UppyDKezPLYhtW1n7aT72eKhsfCMcpLVdMlTWvK0aLAP6Jos3aNtyKv8Wvd37FdZ0rUWlRfHvxW+Iz46vUvslkMvS0y5ime4KrtavacQ7/ZYYPH86NGzc4cuQIR44c4caNG7zzzjtlymdlZXHt2jW+/PJLrl27xh9//MH9+/d5+eWXqxz3vzqi4+fnx8mTJ/H29qZevXr4+fmRkJBA8+bNCQgIUMkZGRkxbtw4pk6dSp06dbC3t+ebb74hKyuL0aNHq+n8+uuvsbS0xNramlmzZmFlZaU6ePDzzz+nc+fOfPTRR4wZMwYjIyPu3r3L8ePHWb16ddUz8Pcj0RuMzxSffs6igl0pMUIysq14oe17MsWgJQPbJ7sJtOSiB97QFHILiufpAU49gpdd4GYsLlYuzOw2k6z8LHbc2qES2TpoK1HpUcw8OROAlX4rOfveWaZ1mca+oH284vIKXk5edN1cPDX1g+8PDG89nFd2vUJ6bjrWRtYApOamklNQvN15rf9aNr+ymW0B23CxcuEDtw+wN7Nnnf86ABb2WUgDkwaM3DtSFaattZiLNtY1pq5hXdpatyWvMI+7iWLqcHrX6fhH+xOcFIyuli6+TXwZ0WYE4w6OoyeDa9emw1qJ6YkL4dDNnnez3uVRyiPGuY+rEbsa6RjhXMdZ9d3RwpG21m3ZcmMLM7vNhJCHIj/ejcXQ/YkQca5KWq542ZbMz6lQmNRZTKkExImFxy5WsLTEDqS/H4n1HT6NxXZnB3Nx/olcBh4NITSFoI+CMNY15o1f30BLpsXKfiuxNbblrT/eIl+Rzzr/dXzc4WOWei9lw7UNeDT0YHS70bz5+5uqaA7cP8Bkj8lcj7mumrqa12se++/tVzlAyy8v5+Koi8zoOoM9t8W2+w/af8AHf31AgaKAbYO34R/tz6XIS+WWIyVKtbViAPGZ8eQU5JS6rkZ1ykxfJ3H20uYbYseaap1HgXgxFyFDPKfLkcJJfpY4fZuIl3V8pthV1LORKKe7AovD3IoTupJzIDqdpma6dKxrQEBSGUcRlCA/J4u0/2vvvONrvP44/r7ZOwiSWBliF0GMqK1mW6v92aqqWquIPWrVbClCB0WrlKoOtNSu2mIlRgiRiBhJSELIXuf3xyM3uXITGfcmFef9et1Xcs9znvM5z1n3+5zne84Tflf9PfbBfaJCbmBqZYNV2dx9T84+SMDT3lzZfyav15OXdppTGWaUX3QC1Cir+AKlpiuzlAPqQmkzxQjsXiN/erXKKo91I2KhnCX0rKn8f+pZucSl4P/QnwXHFrCp5ya1o3yzSs1wsHLIcXwb2XgkoTGhBEQGANCiSgsmek5k1ZnM341ZrWdx+u5pAqMCsTG1YUzTMbg7uDPq71F80OCD3CvvP4RWv1RTU0xNczfqcuPatWvs3buX06dP07RpU0Dxs/X09OT69evUqFEj2zm2trYcOHBAI2zVqlU0adKE0NDQHJ/CaKNYDR0bGxuOHj3KihUrePLkCU5OTnz55Zd06dKFX375RSPu4sWLSU9PZ9CgQTx9+hQPDw/27duXzZ9n8eLFjB07lsDAQOrXr8+ff/6pnq2pV68eR44cYcaMGbRs2RIhBFWrVqVPnz4Fu4ADwcoUad/XMjfZWuWjOVCWNtccIG3NYHqWJYkdqiqfG1GKj0AGd54oP1puZThX9xw+d33ouKkjscmZG3hVsa2icbd96u4p+v7Wl/nt5jOv7TyCooPo81sfztzLnDEY2Vjx0TjyvuZqgfd3vM+PF39UfzcxNCElPYXWzq3pU6cPVx5coevmroTGKPvQOFo5UsVWs6H5DfdT/+9RwYMB9QYQ8jgEF28XQDEGvun6DZVsKpGQmkBAZAADtw9km/821mUYOvoq04zlpS2Vvz/0+AFQHAh1Ua4eFTz49/1/1d+Xd1oOwAa/Daw4vYI5fafm/XqCH8H3vsrGZ2/XUKbj1/tqPoa7HQNrzis/BF2rKVPz2/wVQ69rNbA1o+qzO+K9A/dq1NPXZ7/myO0jhDwOoeuWrizvtJxRjUdx/+l9xuwZwx/X/lDHnX90PgLB/HbzqWhdkYfxD/nrxl/MODRDHefc/XP0/KUni9ovYlbrWdx6dItx+8apjUc7cztmtZ6Fo5VjntpRvilIm2nlpMyefNRIM63dN5QlyRnULKs4H5+6qxmvIJrmRtC/rmJUJaYqfXzZKc3N7Lb5K3Xepw5Ym9JWgG9UIifCs9wE5UBkUAB752X6g5zZpPwIu7XqQsuRn+Z6rs+DBIwNVLTIz/XkpZ2C9jLMKL93ayuPn9KFMpM1tKGymnDZKcXnLOujqrzomRsrfaKUmbJyyzdc8WPLmm9gm/82dbusYF2BdJGea7s0UBmwqP0iXEq5kJqeStCjIKYemsqac2vUcUqZleK7t75Tb9DpG+ZLqw2tOHv/LC8TGX6pWZk9ezZz5swpcJqnTp3C1tZWbeQANGvWDFtbW06ePKnV0NFGTEwMKpUqx8ddOaES2tZyv4T8+++/tG3blkePHuW7EPLNyN0vjqNDVPZvFalecSAidhWpXlGXaVFfH5T8dlMcZVrULB7W9MWRdMjUtT4vjvSSUxz9Iqfd5nWGDn+Tkpa/ofMZnYULF7JhwwZu3LihEV69enWGDBnCtGnTXphGYmIiLVq0oGbNmvz0008vjJ+VYvfRkUgkEolE8t8gq19qxicnI2fOnDmoVKpcP+fOnQOyLyaCvC8oSklJoW/fvqSnp/PNN9/k+5rku64kEolEIpHkm9GjR9O3b99c4zg7O3Pp0iUiIrKvtHz48CH29va5np+SkkLv3r25desW//zzDzY2NrnG10aJMXTatGmjdUdliUQikUgkuqds2bKULVv2hfE8PT2JiYnhzJkzNGmivCPQx8eHmJgYmjfPeTfqDCMnMDCQw4cPY2dnV6B8ykdXEolEIpFI9EatWrXo3Lkzw4YN4/Tp05w+fZphw4bx1ltvaTgi16xZU729TGpqKu+++y7nzp1j8+bNpKWlER4eTnh4OMnJ+dtXTRo6EolEIpFI9MrmzZupW7cuHTt2pGPHjtSrV49NmzZpxLl+/ToxMcoqxLt37/Lnn39y9+5d3N3dcXR0VH9Onszffm4l5tGVRCKRSCSS/yZlypR54WqprO4nzs7OOnNHkTM6EolEIpFISixyRqcAyP1JdE9JL9Ni2bujhO8zU9LbDIBYW7R1WNT79gBM+7NckeqV9H4hyY6c0ZFIJBKJRFJikYaORCKRSCSSEos0dCQSiUQikZRYpKEjkUgkEomkxCINHYlEIpFIJCUWaehIJBKJRCIpsUhDRyKRSCQSSYlF7qNTQEZ4jGBS80k4Wjvi/8CfcfvGcTz0uNa4PWv2ZITHCNwd3DE1MsX/gT9zjsxhf9B+dZzDgw/TxrlNtnN339jNWz+/lW9NBysHvuz4JY0cG1HNrhorfVbitc9LI07tcrX5rM1nNKrQCOdSzozbOw5vH2/NhN6sBq9XAQtjCHkMv1yBsNicC8bRCt6qDlVswc4CfvWHwyGacQxUSrqNK4KNKTxJYublmcw/Oh+B5k6Yurzm/KQF0MqpFcs6LqNO+Trcf3qfL058wZrza9TH81JnWZnaYiqL2i8iJjFG3Q4KW4dZ6VOnD1vf3UpAZAA2pjZgap+3OgNwd4C3q0NZC4iMhz+vw8Usbxue11apz+c5EgK/+Cv/mxpC95pQ316pVwABBD96cR5erwxNK0EFa+V7aAzsDIDbMZlx3MpAB1eobAulzOi+tTs7r+8s8n6h677/YcMPea/ee7xW/jUAzoedZ/qh6XSt1hXc2+e974FO6rGFgwW1SptgbWxIuhCEJ6Ry5H48YfGp2U4Lv+bHlb+2EHkrgIRHUbSbsAinxq1yz6MWdF2Hg+sPZkOPDdnONZtvphmQ3/HN3QE6VYVylmCoggdxcOgWnLmXGUfL+CYpXuSMTgHoXac3KzqvYMGxBTRY04BjocfYM2APlW0qa43fyqkVB4IP0HVLVxp914jDIYf5q99fuDu4q+P0+qUXDksd1J8639QhNT2VX6/+WiBNU0NTHsY/ZMGxBVwMv6g1joWxBcGPg5l6cCphT8OyR+jgCu1cYJs/fH5c6bCfNFV+0HLCxFAZYHcEQEyi9jgdq0JLJyXdz47A9mtMaj6JT5p+ohFNl9ec37ScSznzd/+/ORZ6jAZrGrDw2EJWdllJr1q91HFeVGdZ8ajggVdTL9JFOmfundFZHWZQxbYKSzsuJfhRMFVLV2X036PzXmcupWBoA2WwXnhM+fthQ3AulRnn8xMw9WDmx/u0En4hS7t5pzbULgdXHkBKOpwIBRXKwP+iPFSzg3P3YcVpWHICohOUc2xNM+OYGMLdJ0q7eUZR9wt99P02Tm34+crPtP2xLZ7rPQmNCeXI+0eY4Dkhf31PR/UYnZTG/rtxrA94xE+BMcQkpdPHzQZzI1U2ydTEBEo7udFsyPic8/UC9FGHADGJMRr902GpA0lpWYyOgoxvccmw9yYsPQkLjsHpuzCoHtTK8gZvLeObpHgpkYZOeno6n3/+OW5ubpiamlKlShUWLFgAwJQpU6hevToWFha4uroyc+ZMUlJS8pX++GbjWe+7nvW+6wmIDMBrnxd3Yu4wovEIrfG99nmx5OQSzt0/x83om8z4ZwaBUYG8Xf1tdZxHiY+IiItQfzq4diA+JV79o5lfzdsxtxm3dxybLm0iJilGa5xz988x+cBkfvH/RXMAyKCdi9Kp/cKVu5yNF5Ufm8YVcy6c2zGwPQDOh0FquvY4LqXgUoTygxidAL7h7A/aj4ejh0Y0XV5zftMa7jGc0JhQvPZ5ERAZwHrf9Xzv+z0TPSeq47yozjKwNLZkc6/NRCVEEfY0DP+H/jqrQwADlQGbe21m9r+zcbBy4EbUDbYHbM97nbVzgYBI2BcEEXHK34BIaOucGSc2WfkhyPjUtVfuZgOjM+O4lgafu1DPHvYEwlZ/uPcUbka/OA8b/ODobcWQiYiDzZcUI6lmlh+Qqw/hrxtKe3xGUfcLffT9gdsH8u25b7kYcZHrUdcZ9tcwTI1M+Tvw7/z1PR3V49VHSdx+mkJMcjqRiWkcuheHmaEB5c2yPwCo1MCTRn0+wrlJm5zz9QL0UYcAAqHRPyPiIjQjFGR8C4xWZsjCY5UbusMhShuvWiYzjpbxTVK8lEhDZ9q0aXz++efMnDmTq1evsmXLFuzt7QGwtrZmw4YNXL16FW9vb9auXcvy5cvzlX6jCo00pp4B9gfvp3ml5nk6X4UKa1NrohOic4wztMFQtl7ZSnxKPMYGxoXWzDd25mBrBtciM8NS0yEwSvlBKwxBj6CGHZS3VL5XtKZFlRb8ffNvdRRdXnNB0vKs5Mn+YM34+4L24VHBAyMD7U98s9ZZVr7u+jV7bu6hml01HiU+KvT1PM+s1rN4GPeQw7cOY2FswYO4B5kH81JnLqU16xmU7zmdY6iCJhXh1B3N8KBoaOiY2W6qP6tj/wf5bzcmhmBoAHE534QYGRgVab/QRZvMS9+vXa42BioDTtw5kRlYlPWYBQMVuJc1IzE1nQcJ2R9dFRYDVeHH05ywMrEiZGwId7zuZJtF09n4VsMO7C0VYz4DLeObpHgpcT46T58+xdvbm6+++orBgwcDULVqVVq0aAHAp59+qo7r7OzMhAkT+OWXX5g8ebLW9JKSkkhKyryrU6lUWFtbExGreXcQERuBQ1WHPOVxQvMJWBpbss1/m9bjjSs0pq59XYb+ORSAshZlMTIwKpRmvrF99iz76XMzPU+ToYx54dLeHwTmRjCrNQgBKhUrDk9n65Wt6ii6vOaCpOVg5aA1vrGhMWUtyhIeq3mX9nydZdCnTh8aOjak28/dGNt0LMlpyYW+nqw0r9ycoQ2G4r7anep21QGyz869qM60+RE8Scr0s3me+g5K/Z2+qxm+zR8+9oCKNjDldUgXsPmyMvA3zWe76VETHicqMxI5YG1iXaT9Qhdt8kV9H2BOmzkA/HX9L1bSKfNAUdUjUNXGmO7ONhgbQGxKOluDnpCQpps3SWfFwtBAL3UYEBnA+zve5/KDy9iY2jC26VhOfHCC+qvrQwSFG9/MjGBhezA2UNr41iua7VTL+CYpXkqcoXPt2jWSkpJo37691uO//fYbK1as4ObNm8TGxpKamoqNjU2O6S1atIi5c+eqvzs6OnL//v1sTrMqlSpbmDb6vtaXOa3n0H1rdx7GP9QaZ2jDoVyOuMzZ+2c1wguqmRcsjS35/I3Pmd9uPqSbwbdnM0SzIwqp2chRuZP8wVeZMq5kw8TeE7n/9D4bL27UlNLhNec3LW3xAYSW69dWZ5VsKuHd2ZuOP3XU/mgwD3nIjQ8afMDat9eSlJpEyLgQ3tzyZo75y3ed5TY2N6+sPEaKSYLGFaBfXSXcyACePPPL+uoMOFhB39cyfbXymocOruBRQfHXyenxZxb02S90qZdT3+9ftz9r3lKc3I0NjNVtJTFNi4+bvurxOUJjU/g+4BEWRgbUL2tGD2drNt54THyqfspV13Xoc88Hn3s+6u9Otk70rNkT/5H+kGJQuPEtKRUWHQNTI2Xm5p3aymOsjMd/WsY33ncv8LVICk+Je3Rlbp6zNX769Gn69u1Lly5d2LVrF76+vsyYMYPk5OQcz5k2bRoxMTHqT0BAAKlpqThYad5tlLcsn+2u5Hl61+nN+m7r6f1bbw7dOqQ9/0bm9K3Tl3W+69RhkfGRpKYXTDOvxKfEs+TkEtxXuyudOPZZmTx/N2htotz1FIZetRT/gfNhcP8pnLnH8tPLmdZimjqKLq+5IGmFx4ZrjZ+SlkJUQpRGuLY6A2jk2Ah7K3vOf3SeUK9QhBA0dGzImKZjSJmZgoHKoFB1eO3hNQxUBhgbGmNmZMah95Q21cmtEykzU5SVN/DiOtN212+dw2qRMuaK38yJZ487LkUo7WXJs8cs+4KUv3EpcOQ2nL8Pb7jmvd284Qqd3GDVGcX3IReeJj/Ve7/ISmHaZG59/8/rf+K+2p1VPqtISkviwz8/BMimo9d6fI6UdHicnM79+FT2hMaSLqCenZnWuIUhPi29SOpw5/Wd/HbtN07dOVX48U0AD+MVf7JDt8A3TGmzGWgZ3yTFS4kzdKpVq4a5uTmHDmU3JE6cOIGTkxMzZszAw8ODatWqcfv27VzTMzU1xcbGRuNzPuw8HVw7aMTr4NqBk3dP5phO39f6sqH7Bvr/3l9xMsyB3nV6Y2pkyk+XflKHpaSncP5+/jXzg0AQGR9J0KMgpROHxSp34llXExiqlNUxwY9yTigvGBtmu2tKE2kYqDKboy6vuSBpnbp7Klv8jlU7cu7+OVLTNX0VtNUZwKFbh3jtm9dwX+2O+2p3Lj+4zIPYB2y+tBn31e6ki/RC1eGFsAsa6buvdicxNZGg6CDFYH2UkLc6u/VI0+kXlHrXdo5nJWW6/8ozP6CkNKW9PEpUZnSiEjTbTbpQHDHy0m7ecIUubspsUGjOTqYZpKan6r1fZKWgbfJFfT82OZaetXoysvFIOm7qyK9XfyXsaZimjr7r8QWoVGCkh0cw6YIiqcPY5FhcS7nqZ3xTqZS2n4GW8U1SvJS4R1dmZmZMmTKFyZMnY2Jiwuuvv87Dhw/x9/fHzc2N0NBQtm7dSuPGjdm9ezfbt2/Pt8ay08vY1HMT5+6f49TdU3zU6COq2FZh9bnVACxsv5CK1hUZvEPxEer7Wl829tjI2L1jOX33NPaWimN0QmoCT5KeaKQ9tMFQdgTsyOasmF9NgPr29QHFKa+cRTnq29cnOS2Za5HKckdjA2Nql6sNgImhCRVtKirnpFsog8E/t5Q7lQdxyqezGySnwdksdyiD6yu+FDuvK98NVeD4zPnO0ABKmSlTt0mpSpoAlyOUtB4lKnc8lW0Y32w83/t9r7drzm9aq8+tZnTj0XzZ8UvWXliLZyVPhjYYSr/f+2VrDznVWWxyLP4PM5dCLzi2gJ/f+Rl7K3vSRBrLOi0rVB0mpSVppA9w5cEV6tvXp5pdNcUZMi91djgEvJopj4wuRSirpmqWhS9PaV6oCmhWSfHpSH9uIE9MhRtRyt3spWf1W8pMiX/v6Yvz0MFV2X/pBz9lpUrGnXZSqmJMgbLst5ylOgmX0i78fu135rebXyT9IjY5Vi99f1LzScxrO4/+f/Qn5HEI9pb2rPddz/SW0yH4Zt77ng7q0dgAPO0tuBmTTGxKOuZGKhqWNcfa2ICAx9lnhlIS43kSnunjE/vgPlEhNzC1ssGqbN58bPQxts1qPYvTd08TGBWIjakNY5qOwd3BnVF/j+IDZiuJFGR861RVWVn6ME4xbuqUh6YV4ecrmedoGd8kxUuJM3QAZs6ciZGREbNmzeL+/fs4OjoyfPhwhg4dipeXF6NHjyYpKYk333yTmTNnMmfOnHylv81/G3bmdsxqPQtHK0euPLhC181dCY0JBcDRypEqtlXU8T9u9DHGhsZ88+Y3fPPmN+rwDX4bGLJziPp7tTLVaOnUkg6bNO9uCqIJ4DfcT/2/RwUPBtQbQMjjEFy8XQCoYF1BI86k5pOY1HyS8oO14jQcCFZWv/R9LXNDrVU+mT88AKXNNQdLWzOY3jLze4eqyicjTeVi4O0a0KeOMrUek8ia88v57Mhner3m/KQV8jiErlu6srzTckY1HsX9p/cZs2cMf1z7Q0Mvtzp7nm3+25jXdh7NKjXD72M/ndTh81x5cAUrEyu+6foN9CqftzoLfgTf+yp18nYNxd9gva9yblZqllU2mzuV3XkVUNLoXkNZsmxiCK2clPCUtBfnoZWTcif8USPNNHffgN2Byv9VbMHLU31oeSdlteSpO6eKpF/8G/IvbX9sq/O+P7LxSEyNTPm99+8a+fw35F/a9G2W976ng3pMF2BnZkjdMtaYGxmQkJZOeFwqPwXGEJmYli1+ZFAAe+dl7n91ZtMqANxadaHlyE+zxdeGPsa2Umal+O6t73CwciAmKQbfMF9abWil6fdYkPEtI34pM6VdR8QpWyOcz7LfkpbxTVK8qIRWz0VJbqjmlmwvehGxq8g1VfbZdxKWFI7iqMei5FVoM0Vdh4uHNS1SPYBpf5YrUr1i6RffvKnf9Efu1l1a+s5rMVDifHQkEolEIpFIMpCGjkQikUgkkhKLNHQkEolEIpGUWKShI5FIJBKJpMQiDR2JRCKRSCQlFmnoSCQSiUQiKbFIQ0cikUgkEkmJpURuGCiRSIp+n5mSvm/Pq0BR72kDsKib9pcb6421Pi+OIylRyBkdiUQikUgkJRZp6EgkEolEIimxSENHIpFIJBJJiUUaOhKJRCKRSEos0tCRSCQSiURSYpGGjkQikUgkkhLLS2vo/Pvvv6hUKh4/flyodJydnVmxYoVO8iSRSCQSieS/xUtj6LRp04Zx48YVdzYAODfsHC2qtMg1TiunVpwbdo6EGQkEjQni40Yfaxw/PPgwYrbI9tnVL3MvkqktpnLmwzM8mfqEiIkRbO+znep21dXHR3iMIHhMMAkzEl6Yp541e7J/4H4eTHxAzNQYTn5wko5VO774Yt+sBgvbw4rOMK4ZOFrlHt/RCoY1hHlt4Zs3oa2z9ni2pjD5dfiqK3zdlbjpcXzQ4INck35RmQ6uP1hrmZoamqrjWJlYsbzTckLGhhA/PZ4TH5zAo4KH+nh+ytTByoHNvTYTMCqAtFlpLO+0XGu82a1nc2/8PeKnx3N48GFql6ud63UC9KrVC/+R/iTOSMR/pD89avbIMe7UFlMRs4VaP0Mv+dNknk57Stz0OO6Nv8ePPX7E0cqxQHovaouzW8+GJR2U+lzVRan7Sja5X6S7A8xsBd6dlb/17TWPmxrCu7WVtrSiM0xsDk62GlEsjS1Z1WUVd7zuED89nqsjr7LlnS367xfPoY++OLv17Pz1PdBJmWa9npSZKYjZAu/O3jlK5qUf5KVv5oXwa34c/GIyW0d044e+r3P77NF8nZ+N/I5v7g4w5XVY2hGWd4JpLaBJxZzjd6qq9AUJjx49YtCgQdja2mJra8ugQYPyNVHx8ccfo1KpCjQx8dIYOv8ljoUeY8+APVS2qaz1uHMpZ/7u/zfHQo/RYE0DFh5byMouK+lVq5c6Tq9feuGw1EH9qfNNHVLTU/n16q/qOK2dWvP12a9ptr4ZHTZ1wMjAiP0D92NhbEHvOr1Z0XkFC44toMGaBi/MUyunVhwIPkDXLV1p9F0jDocc5q9+f+Hu4J7zhXZwhXYusM0fPj8OT5Lgk6bKYJkTJoYQGQ87AiAmUXsccyOY3hKq2MK+m7DShz2Be1jZeWWhyhQgJjFGo1wdljqQlJakPr7u7XV0cO3AoO2DqPttXfYH7efgoINUsK6Q7zI1NTTlYfxDFhxbwMXwi1rjTH59MuM9xzP679E0XtuY8NhwDgw6gJVJzgNqs0rN+OXdX9h0aRP1V9dn06VNbHt3G00qNskW16OCBx81/Eitn6E3+cBkzt4/y8Xwi8QmxzLwj4FUt6vOn/3+LJBebm0xQxOfe3A0BO48UU4yyaWduJSCoQ3gzD1YeEz5+2FDcC6VGWdgPahZFn68CAuOwrWHMKapYiQ/Y3nn5XR268zAPwZS6+taHAs9Rt86fdkduFt//eI59NEX1WWan76nozLNuJ7dgbsJjAokNimWjxt9XKh+AC/um3khNTGB0k5uNBsyPl/naaUg41tcMuy9CUtPwoJjcPouDKoHtcpmj+tkC69XgbtPCp/XEkD//v3x8/Nj79697N27Fz8/PwYNGpSnc3fs2IGPjw8VKlQokPZLYei8//77HDlyBG9vb1QqFSqVipCQEADOnz+Ph4cHFhYWNG/enOvXr6vPCwoKonv37tjb22NlZUXjxo05ePBgofPjtc+LOzF3GNF4hNbjwz2GExoTitc+LwIiA1jvu57vfb9noudEdZxHiY+IiItQfzq4diA+JV7D0OmyuQs/XvyRqw+vciniEkN2DsGplBONHBsxvtl41vuuZ73vegIiA16YJ699Xiw5uYRz989xM/omM/6ZQWBUIG9XfzvnC23nonRqv3AIi4WNF5Ufr8a53MHcjoHtAXA+DFLTtcfpWBWMDeF4KPx1A65H8e6v7xIaE1qoMgUQCI1yjYiLUB8zMzLjndrvMPngZI6FHiPoURBzj8zl1uNbjPAYke8yvR1zm3F7x7Hp0iZikmK0xhnXdBwLji1ge8B2/B/6M3jHYCyMLehft3+ORTiu6TgOBB1g8fHFXI+6zuLjizl06xDjmo7TiGdpbMnmXpsZ9tcwHiU+0tDbfHkzr3//Ou02tsPE0IRqdtX4ZM8neFTwyPaDlRe93Npihia/XYVtV+EHX+WkOrnsstvOBQIiYV8QRMQpfwMiM2cAjQ2Uu+cdAXAzGh7Gw+5AxYhu5aROxrOSJz9e/JEjt49wO+Y29e3rExkfyaPER/rrF8+hj76oLtP89D0dlen4ZuPZemUrPWr24N1f3yUyIZLHiY8L1Q8g976ZVyo18KRRn49wbtIm3+dmoyDjW2A0XIyA8Fil3A6HwL2nULWMZjxTQ3jfHTZfgviUwuf1JefatWvs3buXdevW4enpiaenJ2vXrmXXrl0av9nauHfvHqNHj2bz5s0YGxsXSP+lMHS8vb3x9PRk2LBhhIWFERYWRuXKymA9Y8YMvvzyS86dO4eRkREffJD5+CM2NpauXbty8OBBfH196dSpE2+//TahoaGFztP+4P00r9Rc6zHPSp7sD96vEbYvaB8eFTwwMtD+1o2hDYay9cpW4lPic9S0NVWmmJ8kPaFRhUbsD9LUyC1Pz6NChbWpNdEJ0doj2JmDrRlci8wMS02HwChwLZ0njRypZ68MKBWt4fM3YFoLPmz4oU7K1MrEipCxIdzxupPtLtnIwAgjAyMSUzVnmhJSEmhZpWWhy/R5XEq54GjtqJFmcloyR0KO5JqmZ2Xt19q8suY5X3f9mt2Buzl06xAANqY2uerZmtqSLtJ5nPi4QHpZyWiLFsYW2TRJE8rfirk8unIprdm2QPme0bYMVGBoAClpmnFS0jV+VI6HHqdb9W5UsK6AsYExHhU8sDKxYt/Nfeo4Ou0Xz2FsYKzzvqit3eSp7+mgTDOux93BnSUnl3D14VUAAiIDCtwPMsitbxY5uhrfatiBvaViOGalz2tw5QFcj9JNfouQpKQknjx5ovFJSsrfzNvznDp1CltbW5o2baoOa9asGba2tpw8eTLH89LT0xk0aBCTJk2iTp06BdZ/KQwdW1tbTExMsLCwwMHBAQcHBwwNlenFBQsW0Lp1a2rXrs3UqVM5efIkiYnKD1n9+vX5+OOPqVu3LtWqVWP+/Pm4urry55/Zp+/zS0RsBA5WDlqPOVg5EBEbkS2+saExZS2yT3E2rtCYuvZ1WXdhXa6ayzot49jtYzyIe4CRgZFWjZzy9DwTmk/A0tiSbf7btEewNVP+Pn2ugT9NBpv8PVfPRlkLZdB9GA+rzsCxUFZ2XkkFqwqFKtOAyADe3/E+3bZ2o9/v/UhMTeTEBydwK+MGQGxyLCfvnGRmq5k4WjlioDJgQN0BNK3UlIo2FQtdptrynJGGRppxuaeZ07VmPadPnT40dGzItIPT1GGWxpY56lWwrsDiNxaz5fIWniY/zbfe82S0xSdJT7RqAmBlkuP52Jgqjwqy8iQps20lpUHwI+hSTXmsokLxhXAupfGYZcyeMVx9eJV74+8RNz0OQwNDlpxcwok7J/J8LVl5Yb94jrIWZXXeF3NqNy/sezoo04zrSUhJYKXPyizSTwvcD+DFfbPIKcz4ZmYEyzopvmgjGyuPvgKyGEyNHKGyDezMfabiv8qiRYvUfjQZn0WLFhUqzfDwcMqXL58tvHz58oSHh+d43ueff46RkRFjxowplP5L/1LPevXqqf93dFQcLR88eECVKlWIi4tj7ty57Nq1i/v375OamkpCQkK+ZnSSkpIwNTUlLi6OtLRnd0KpoFKpEIgcz3v+mEqlUsJF9nOGNhzK5YjLnL1/Nsf0vur6FfXs69Hi+0wnR20aueUpg76v9WVO6zl039qdh/HKC/X61+3PmrfWKBHSzeDbsxki2dFyDfniWVlw7Lby/PruE9ZWPUyPGj2IT815RutFZepzzwefe5kv7DsReoILH1/gkyafMHbvWAAGbR/E992+5/6E+6Smp3Ih7AJbLm9R+6MUtEwBatjV4Om0TCPizS1vFjjN3M6pZFMJ787erPBZQeRkZYA1NzLnj2t/aD3XUGVIPft63Ht6j5G7R+ZbLyv96/ZnQ/cNGBoYkpCSgLGhsdbzlcB8thPVc983+Cn+D4vegLR0xffn3H3lR+QZY5qOoVmlZrz989vEJ8dzaPAhJnhO4Ojto+qZrsL0i7xSmL44r+08hBAEjw0Gcm43SqB+y7SufV0A5h+d/1wyee8H2sitb+bc43VH7dKmdK78zC9uWafCjW9JqbDoGJgaKTM679RWHmMFRkNpM/hfHVjlk/Oj+/8406ZNY/x4TR8oU1Ptxt+cOXOYO3durumdPauUdcZ4nRUhhNZwUNxSvL29uXDhQo5x8spLb+hkfWaXURjp6UoDmzRpEvv27WPp0qW4ublhbm7Ou+++S3Jycp7T/+KLL5g2bRoDBw5kx44dSmBrKP9Wee13sUB4bHi2u5/yluVJSUshKkFzKtPcyJy+dfoy699ZOeZhZZeVdKvejVYbWnHv6T2MDYxJTU/VqpFTnjLoXac367ut53+//k/9QwDw5/U/8bmrDEQ3o9aC0bPJvufvEq1NlLuewhCTCGXMNe6crkVe4wP3D7gVdkvrKfkp0wwEgrP3z1KtTDV1WPCjYNr82AYLYwtsTG0Ijw1n6ztbuRl1E9fSrgUq06xpf7LnE/V3UyPl+hysHAiPzbxrKW+Re5o5XWvGOY0cG2FvZc+8tvPUxw0NDHmn9jsAVLCuoNYzMjCia7WuGBsa02FTh2yzOXnRy0rLKi2JjI+k3+/9uPvkrsY1ZiMuF9+ErDMNGVg/19Yi42H5aeUxp5mRcmxoA4hKABSfq4XtF9Lzl578Hfi3ul+cvHOSic0nqtt3YfrFi4iMjyx0Xxz4x0D8wv3U4TmW6Yv6ng7K9LVyryGEYEffHWrDxsjAiCq2VUhOK2S/z0LWvnkxVmfJ5sjNmGS+j1P82IZvu1i48U2gzEaDcqPmYAWd3CDwjLLAwsYUpmZZdWf4Ujw4UWNqapqjYfM8o0ePpm/fvrnGcXZ25tKlS0REZO8PDx8+xN7eXstZcOzYMfWkRQZpaWlMmDCBFStWqP1088JLY+iYmJhkzqjkkWPHjvH+++/Ts2dPQPHZyU/hAEyePBmVSsUvv/yifiRmu8SWDq4d2Hl9p9ZzTt09lc2ZsWPVjpy7f47U9FSN8N51emNqZMpPl37SmtaqLqvoWbMnbX5sQ8hjJe8p6Smcv3+eDq4d2BGwQx03tzyBcvf4fbfv6fd7P/4O/FvjWGxyLLHJz0acjE4ck6isJshYNWCogmp2ijNjYQh+BJYmUKuc4tgHVLerjoGBASfvan9em58yzYq7vTuXH1zOFh6fEk98SjylzErRya0Tkw9Mxs7CLt9lmpWU9BSCHgVphIU9DaODawf1D5mxgTGtnVsz5eCUHNM5decUHVw7sOL0isxrde3IyTtK2Ry6dYjXvnlN45wfuv9AQGQAnd06096lPRfCLmBkYMSv7/5KecvyTD44OUe/kxfpZbCqyyrerv42rTa04mb0zWzXSMaCG8Nnd1/3clltcuuRsvrnnyyGba2yStt4nuQ05WNupLSZ7dcApSxNDE1IF8qNTUa/qGBdgXtP76lPL0y/eBG66Iva4mkt0xf1PR2U6YaLGxjsPpirD6+qZ3X2DdyHqZEpv1/7PWftApBT39QHyemC5ORnMzW6Ht9UqkzDKSAS5h3RPP5efXAqVeC8/5cpW7YsZctqWXH2HJ6ensTExHDmzBmaNFFmz318fIiJiaF5c+2+X4MGDeKNN97QCOvUqRODBg1iyJAh+crnS2PoODs74+PjQ0hICFZWVupZm9xwc3Pjjz/+4O2330alUjFz5sw8nZeVDMvW0NAQExPF52DZm8uoYluF1edWA7Cw/UIqWldk8I7BAKw+t5rRjUfzZccvWXthLZ6VPBnaYCj9fu+XLf2hDYayI2CH1h+hr7t+Tf+6/em+tTtPk55ib6lYvjFJMSw7vYxNPTdx7v45Tt09xUeNPso1T31f68vGHhsZu3csp++eVqeVkJqg9rPIxj+3lDuVB3HKp7ObMjiezfwRYXB9eJyY+TzaUAWO1s/+N4BSZsp+KkmpmQPMP7eU59gtqkB0PKQJRjfuSJpIK1SZzmo9i9N3TxMYFYiNqQ1jmo7B3cGdUX+PUsfpWLUjKlRcj7qOWxk3lnRYwvXI6/zg9wMxSTH5KlOA+vb1AcXRspxFOerb1yc5LZlrkcoPxwqfFUxvOZ3A6EACowKZ3nI68SnxbLm8RZ3Gjz1+5N7Te0w/NB0Abx9vjg45yuTXJ7MzYCfda3bnDdc3aPGDcpcYmxyL/0N/jaqKS4kjKiGKpaeWMr3ldIKigxjVeBRNKzUlKiGKHdd2qOt8acel3HlyJ896L2qLGdfIvRDF0bW1s3LSwzil7p8kQc+amu3kcAh4NVOW+F6KUBzUa5aFL09lXlStssqPSEQslLNU0oiIhVN3AcVv5N+Qf1nSYQkJKQncjrnN+bDzjPAYwfmw89QsW1M//eI59NEX1WUafDPvfU8HZRqdEM2CYwvY1HMT+4P2c+ruKaxMrJTZs2MLtV4PvLgf5NY3e3l0y1M5A6QkxvMk/K76e+yD+0SF3MDUygarsvn0ISrI+NapqrKy9GGcYtzUKQ9NK8LPV5TjSWnKCq6sJOXvBr0kUqtWLTp37sywYcNYs0Zxkfjoo4946623qFGjhjpezZo1WbRoET179sTOzg47OzuNdIyNjXFwcNA4Jy+8NIbOxIkTGTx4MLVr1yYhIYEffvjhhecsX76cDz74gObNm1O2bFmmTJnCkyeF39OgVZVWdN3cldAYxdfH0cqRKraZ02shj0PouqUryzstZ1TjUdx/ep8xe8aofSgyqFamGi2dWtJhUwetOiMbK/4UR97XvEN4f8f7/HjxR+zM7ZjVehaOVo5ceXAl1zx93OhjjA2N+ebNb/jmzW/U4Rv8NjBkZw7W8YFgZYq772tgYQwhj5Vnz1k7bmlzSM/yTNvWTNkjJ4MOVZXPjShYcVoJux0Dq89D/9ege00A7seEMmj7oEKVaSmzUnz31nc4WDkQkxSDb5gvrTa00vB9sjW1ZVH7RVSyqUR0QjS/X/udGf/MIDU9lW3+2/JVpgB+w/3U/3tU8GBAvQGEPA7BxdsFgC9OfIG5kTnfdP2G0ual8bnrQ8dNHTNnz4AqtlXUsxKgzF71/a0v89vNZ17beQRFB9Hntz6cuXdGez1lIUPv27e+pbyl4vxnaWJJ0NjMmSa/cD+NWbC86OXWFjM05/SeobSTDN5zV/7uvpG9nQQ/gu994e0ayicyHtb7Km0sA3Nj6F5DMZbjU8A3HP68rpFO39/6sqj9Ijb32kwZ8zLcjrnN79d+p5VTK/rU6aOffvEc+W03edFUl2nfqXnvezoq0+evB2DN+TWF6ge59U3NnbByJzIogL3zMh8Pn9m0CgC3Vl1oOfLTfKREwca3jPilzBSjPiJO8Xs6H5Y/7VeQzZs3M2bMGDp2VDbH7NatG1999ZVGnOvXrxMTk/MWBQVFJbR5x0pyRTW3cI5R/3VExK4XR9IxKvu3ilxToluKut28Cm3mVSjTRd3y5/hdWKau9XlxJF2j792RR+7WXVolcCfnl8tLSiKRSCQSiSQfSENHIpFIJBJJiUUaOhKJRCKRSEos0tCRSCQSiURSYpGGjkQikUgkkhKLNHQkEolEIpGUWKShI5FIJBKJpMQiDR2JRCKRSCQllpdmZ+T/EsWxoV5RUhybhskyffkp6mss6W0GXpEyLeIN/BYPa1qkegBTi1xRkhU5oyORSCQSiaTEIg0diUQikUgkJRZp6EgkEolEIimxSENHIpFIJBJJiUUaOhKJRCKRSEos0tCRSCQSiURSYpGGjkQikUgkkhJLke2j06ZNG9zd3VmxYkVRSeaKzvLzZjV4vQpYGEPIY/jlCoTF5hz/9crQtBJUsFa+h8bAzgC4HZMZp2UVaOUEZcyV72Gx8HcgXH2oHz0DlZJu44pgY0pQXBAb/DYQFR/FxOYTcbR2xP+BP+P2jeN46PEXFknzys058v4Rrjy4QoM1DdThPWv2ZHrL6biVccPYwJjA6EC+PPUlP136qXBl6mgFb1WHKrZgZwG/+sPhkJzjd6oK3WvCP7fgt6vZj+ta/81q3Gt1j9JmpfG558Oov0dx9aEW3Sz0qtWLeW3nUbV0VYIeBTHjnxnsCNihNe7UFlNZ1H4RK06vwGuflzq8Z82ezG87nxpla2BoYMjVh1f5eNfHOdahg5UDX3b8kkaOjahmV42VPis10stI80V1OMJjBJOaT9JZu/mw4Ye8V+89Xiv/GgDnw84z/dB0iMhycn7rLCuNHGFoQ7gYDmvOa4+jrc0UgaboLlhxegU3om7kuUxfr/w6n7/xOTXL1sTC2ILbMbdZc34NK06vUMcZXH8wG3psyH7ymD2Qml6w63N3UMqpnCUYquBBHBy6BWfuZcZ5bqzhSRKcugN7b4LQk+ZzZTq1QVnOPkjg0L24nNN9Rvg1P678tYXIWwEkPIqi3YRFODVu9cLzJP8tXqoZneTk5OLOgiYdXKGdC2zzh8+PK532k6ZgapjzOdXs4Nx9WHEalpyA6ATlHFvTzDiPE2FHAHx+QvnciILhHvBOLf3odawKLZ2UdD87wuQDk5nWYhreXbxZcGwBDdY04FjoMfYM2ENlm8q5FomNqQ0be2zkUPChbMeiE6JZcGwBnus9qbe6Hj/4/cAP3X+gY9WOhStTE0OIjFfKLCYx1/zhZKsMpHefaD+ua/1n6Y3+ezSN1zYmPDacA4MOYGVilWNyzSo145d3f2HTpU3UX12fTZc2se3dbTSp2CRbXI8KHnzU8CMuhl/MdqydSzuql62uNkJ8w3xzrUNTQ1Mexj9kwbEFWtODF9dh7zq9WdF5hU7bTRunNvx85Wfa/tgWz/WehMaEsn/Q/sw2XJA6y6CMOfSqBYFROcfR1maKSPNi+EWq21XPV5nGpcTx1dmvaLWhFbW+rsX8o/OZ33Y+wxoO04gXkxiDw1IHjY/ayCnI9cUlKwbL0pOw4BicvguD6kGtsplxnhtr2H4NOlSFNs7603yuTB8kpOac3nOkJiZQ2smNZkPG5/kcyX+PIjF03n//fY4cOYK3tzcqlQqVSkVQUBBDhw7FxcUFc3NzatSogbe3d7bzevTowaJFi6hQoQLVq1cH4OTJk7i7u2NmZoaHhwc7duxApVLh5+enPvfq1at07doVKysr7O3tGTRoEJGRkTnmJyQkJP8X1s5F6WR+4cpdx8aLyo9e44o5n7PBD47eVgbNiDjYfAlUQM0sHfPyA/B/qNydPIiDP69DUio0r6wfPZdScCkCrjyA6AR+v/Y7CakJBEUHsd53PQGRAXjt8+JOzB1GNB6Ra5GseWsNW65s4dTdU9mOHbl9hB0BOwiIDCD4UTArfVZyKeISLaq0KFyZ3o6B7QFwPixzoNaGqSG8766UQXyK9ji61n+W3vaA7fg/9GfwjsFYGFvQv27/HJMb13QcB4IOsPj4Yq5HXWfx8cUcunWIcU3HacSzNLZkc6/NDPtrGI8SH2VLp3GFxqy9sJbZ/84GYMnJJbnW4e2Y24zbO45NlzYRkxSjNc6L6nB8s/Gs912v03YzcPtAvj33LRcjLnI96jrD/hqGgcogsw0XpM5A6Qfvu8PuQMVQ1UZObaaINB8lPqKhY8N8lalfuB9br2zl6sOr3I65zebLm9kXtI+WVVpqxBMIIuIiND6Fur7AaLgYAeGxyrUdDoF7T6Fqmcw4z401+IbDtYfKbKi+NJ8r08RUkXN6z1GpgSeN+nyEc5M2eT5H8t+jSAwdb29vPD09GTZsGGFhYYSFhVGpUiUqVarEtm3buHr1KrNmzWL69Ols27ZN49xDhw5x7do1Dhw4wK5du3j69Clvv/02devW5cKFC8ybN48pU6ZonBMWFkbr1q1xd3fn3Llz7N27l4iICHr37p1jfipXzv2OMxt25mBrBtciM8NS05W7NNfSeU/HxBAMDSAuhx9eFco0t4khmBvrRy/oEdSwg/KWADR0bEhps9LsCtTcDn5/8H6aV2qeY9Lvu79P1dJVmfvv3DxlpZ1LO2rY1eDo7aNKgK7KNCf6vKYMsNdzuJPWtb6W9JLTkjkSciTXcvSs7Mn+4P0aYfuC9tG8suY5X3f9mt2Buzl0K/ssiLGBMY0qNGJ/kGY6L6rD/JK1Dguqmd92Y2FsgbGBsdKGC1NnXatBbDKcvJNzHG1tpgg1Vagob1m+UPXo7uCuPBa8fUQj3MrEipCxIdzxusNf/f7C3cG98NeXlRp2YG8JN6Mzw54ba6horRgloTH604QX931JiaZIfHRsbW0xMTHBwsICBwcHdfjcuZkDm4uLCydPnmTbtm1qgwTA0tKSdevWYWJiAsDq1atRqVSsXbsWMzMzateuzb179xg2LHNa9ttvv6Vhw4YsXLhQHfb9999TuXJlbty4QfXq1bXmRxtJSUkkJSVphJmmpWBq+6zzPdU8xtPkTN+avNCjpvKoKiBSM7yCNUxsDsYGkJSmzOq8U1s/evuDwNwIZrUGIThr0AWVSsXvV3/XODUiNgKHqtrLy62MG4vbL6blDy1JE2k5ytuY2nBv/D1MDU1JE2mM3D2Sg8EHgXHKQAeFv0ZtNHKEyjbKo8Cc0LV+1vTMMoMj4iJwsnXK8TQHKwciYiM0wiJiI3Cwyiz7PnX60NCxIY3XNtaaRlmLshgZGGlPJ4c6zCs51aGjlWO+NfPabrKy+I3F3Ht6D7eASGUmAPJfZ66llRnShcdyjpNTmyloOymAprGhMQYqgwLV4x2vO5SzKIeRgRFzjsxhve969bGAyADe3/E+lx9cxsbUhrFNx3LigxMw3wesTQt2fQBmRrCwvTJupQvYeiXXsQaVCv66nukzqA/NvPR9SYmmWH10Vq9ejYeHB+XKlcPKyoq1a9cSGhqqEadu3bpqIwfg+vXr1KtXDzOzzF+OJk00fRfOnz/P4cOHsbKyUn9q1qwJQFBQUL7yuGjRImxtbRkxYgQGBgYYGBigWt5ZcXwDxYHueUQep0Y7uIJHBVh7Pvsjj8o2ymxOmlBmYbq46U+vkSM0qQg/+MKi44zdOxaAzm6dNU5XqVQILRkwUBmwpdcWZv87m8DowFyz8DTpKe6r3Wm8tjG/X/2d9d3WEz89HpZ10k2ZaqO0GfyvjvIYL+t121squhmfwuo3rqAMyj1r5ppeTuWoIffc8aznVLKphHdnbwZuH0hSWpK20/OUTkHJWocz/pnBsk7LaO3UOt+a+Wk3GWzptYWRHiOpYF0BvuhQsDpTP8a4nPNMatY208BBqU+3MsoigaLQfG48KEg9tvyhJR5rPRi+ezjjmo6j72t91cd87vmw+fJmLkVcooptFTq7dcbU0BRmti5cP0hKhUXHFKMi4+asWpbHSBljzdEQZWxLS4duNeC1cvrRzKVMJa8Oxfb28m3btuHl5cWXX36Jp6cn1tbWLFmyBB8fzTfZWlpaanwXQqBSqbKFZSU9PZ23336bzz//PJuuo6NjvvI5bdo0xo8fr6EjFh4D02fGV8bKgQysTZS7kBfxhit0coOVPsoz5efxC4fgLH4Xg93B1UQ/er1qwb4gxccEWCPWsKLTCj5o8IHavwOgvGX5bHeWShasaVyxMQ0cG/BV168A5UfMQGVAyswUOm7qyOGQw4AyYAc9UozN4buHY2xojKOVIy2/TwWjZ3Z3Qa8xJ6rYKmlOzeILZPjs7k8ImHdEGWALq38pQpktO3UXfO5qppflt628hfZyzCA8Nlxj9gY0y76RYyPsrew5/1Hmah0jAyNaObVidJPRmM43JTI+ktT0VBysHAh7GqY1nYKStQ4vRlykVtlaTGsxjbd/flutmVPes5KfdgMwwXMCXap1oecvPbny4Ao3o9YWrM7KWUJZCxjhkRmWMaas6gJzjyiPVJ5vMwbP4oxpWmSazQ0NEEJwfMhxTOabkC6UH+u81GPI4xAArjy4gr2lPXNaz2Hrla3Z4v15/U987vqwoP0C+pi8oTxay+/1ZSCAh898j+4+AQcrZdwJPKOEZYw1PnczVya2cgKPivrR1NL3qxgaUNnKiEblzFjiF1VIs1/yMlBkho6JiQlpaZlT08eOHaN58+aMHDlSHZaX2ZaaNWuyefNmkpKSMDVVpljPnTunEadhw4b8/vvvODs7Y2Sk/RKfz09OmJqaqnXURCcDycoKm1plM1djGKqUVU47AnJP9A1XZYZm1Rnl2bQ2ktIyOy9Acpry0YeesaHGXVNKegr3n97H2tRaI1oH1w7svL4z2+lPkp7w2jevaYSNbDySdi7teHfbu9x6fEtrtmKTY3ma/JTyonzmtRa0THMjIFIxZrLyXn3FgXF/EDzIUs6F0U9KU4ynuOTs13NJ+WpsYExr59ZMOTglx2RO3TlFB9cOGsuBO7p25OSdkwAcunUoW3n/0P0HAiID+PzE56SLdNJFOufvn6eDawd8w3zV8XKqw8KgUqkwNTIlJT1FrZl1Kbwu2s3E5hP5tOWndPqpEz73nt0MFbTNhMdmbw/daoCpkbI1wKME5RFKbm1mTNMi0Tz7gQUupVw4eOug2siB/NdjRh1pIzY5ltjkWFxLucLteMURWFf9UKXKNEYhc6zJOr7FJiszO/rQ1NL3w8Y3JyopjdMRCdLIeUUoMkPH2dkZHx8fQkJCsLKyws3NjY0bN7Jv3z5cXFzYtGkTZ8+excXFJdd0+vfvz4wZM/joo4+YOnUqoaGhLF26FEA90zNq1CjWrl1Lv379mDRpEmXLluXmzZts3bqVtWvXYmhomC0/ZcqUwcAgn0/y/rml3DlkrI7q7KYYI2ez7OEwuL5yl7/zuvK9g6uy58oPfsqqA5tng09SqtL5QRkA/R/Ao0Tl+bNHBahup+w3oQ+9yxFKWo8S4f5TetTsga2pLZYmlgxxH8Kpu6f4qNFHVLGtwupzqwFY2H4hFa0rMnjHYAQC/4f+GkXzIO4BiamJGuFTW0zl3P1zBEUHYWJoQtdqXXmv3nuM2D2CNvQseJkaqsDxmVFmaAClzKCSjXKND+OV63x+L46kNOURwvPhutZ/ll6P5B4ERgUyveV04lPi2XJ5izq5H3v8yL2n95S9YQBvH2+ODjnK5NcnszNgJ91rducN1zdo8YNyVxqbHJutvONS4ohKiNII/+78d6x+azVJqcod8oL2C3Au5az2vcpahxnUt68PKI6q5SzKUd++PslpyVyLvPbCOgRYdnoZm3pu4tz9czprN5OaT2Je23n0/6M/IY9DsLe0Vw6YGir1mN86S03PXu8ZK6oywtNe0GaKSDMuRXDm/hl61eqVp74IirEYGhNKQKRiILSo0oKJnhNZdWaVOt1ZrWdx+u5pAqMCsTG1YUzTMYoz8k/PjMiC9INOVRVfm4dxiqFRpzw0rQg/X8k857mxhso2ymqrU3eVGwRda2rp+ynpkJAqiEx88Y1uSmI8T8Lvqr/HPrhPVMgNTK1ssCpbOF83SdFRZIbOxIkTGTx4MLVr1yYhIYGAgAD8/Pzo06cPKpWKfv36MXLkSPbs2ZNrOjY2Nvz111+MGDECd3d36taty6xZs+jfv7/ab6dChQqcOHGCKVOm0KlTJ5KSknBycqJz585qY+b5/Ny6dQtnZ+f8XdSBYMV/pu9rmRtcrfLJNCAASpsrd/kZtHJS7mo+aqSZ1u4bypJTUIyR992Vv4mpyqOmr84odyePE3Wvt80f3q4BfeqAtSlLY51YeWYlEbERzGo9C0crR648uELXzV0JjVF8qBytHKliWyVfxWVpbMk3Xb+hkk0lElITCIgMYOD2gWzz38a6DEOnIGVqawbTsyyd7VBV+dyIUvYPyg/60Dcx5Juu31DavDQ+d33ouKkjscmZg28V2yoad+un7p6i7299md9uPvPaziMoOog+v/XhzL0z+bqUNJGGsaExXp7Kpn9vVnsTgLeqv8X5sPNa69BvuJ/6f48KHgyoN4CQxyG4eCs3ILnVIcA2/23YmdvptN2MbDwSUyNTfu+t6RyvbsMFqbPCUoSaN6JusOvGrjyXqYHKgEXtF+FSyoXU9FSCHgUx9dBU1pxbo45TyqwU3731HQ5WDsQkxeAb5kurDa04dfvTgl9fRvxSZpCSpmxnscFP/UgcyDbWEJMIx0OVDVEz/BF1rVkIIoMC2DvvE/X3M5sUY9GtVRdajvxUJxoS/aMSzzu4vIRs3ryZIUOGEBMTg7l5IVfn5IWRu/WvUYyo7N8qck0RsevFkV5iiqNMSzolvc1A0bebV6FMFw9rWuSaUxto2cBQl+jyN+mbN3WX1n+EYnNGLgwbN27E1dWVihUrcvHiRaZMmULv3r2LxsiRSCQSiUTy0vBSGjrh4eHMmjWL8PBwHB0d+d///seCBQuKO1sSiUQikUj+Y7yUhs7kyZOZPHlycWdDIpFIJBLJf5yX6qWeEolEIpFIJPlBGjoSiUQikUhKLNLQkUgkEolEUmKRho5EIpFIJJKSi5AUCYmJiWL27NkiMTGxROoVh2ZJ1ysOzZKuVxyaJV2vODRLup5Et5SIDQNfBp48eYKtrS0xMTHY2NiUOL3i0CzpesWhWdL1ikOzpOsVh2ZJ15PoFvnoSiKRSCQSSYlFGjoSiUQikUhKLNLQkUgkEolEUmKRhk4RYWpqyuzZszE1NS2ResWhWdL1ikOzpOsVh2ZJ1ysOzZKuJ9Et0hlZIpFIJBJJiUXO6EgkEolEIimxSENHIpFIJBJJiUUaOhKJRCKRSEos0tCRSCQSiURSYpGGjkQikUgkkhKLNHReEeTiOonkv0FCQkJxZ0GSB0JDQ7WOm0IIQkNDiyFHkoIiDZ0i4ObNm+zbt089wOnL6Bg0aBCxsbHZwkNCQmjVqpVeNB89esTSpUsZOnQoH374IUuXLiU6OlovWs/z5MkTduzYwbVr14pErzh4Fa6xJDJq1Cit4XFxcXTp0kVvusnJyVy/fp3U1FS9abwquLi48PDhw2zh0dHRuLi4FEOOJAVF7qOjR6KioujTpw///PMPKpWKwMBAXF1dGTp0KKVKleLLL7/UqV6jRo2Ijo7mp59+4vXXXwfgxx9/ZMyYMXTo0IHffvtNp3pHjhyhe/fu2NjY4OHhAcD58+d5/Pgxf/75J61bt9apXu/evWnVqhWjR48mISGB+vXrExISghCCrVu38s477+hEZ+XKlXmOO2bMGJ1oZlBU15iVP//8U2u4SqXCzMwMNzc3nQ/shw4d4tChQzx48ID09HSNY99//71OtXKqz6zX16pVKwwNDXWmWa1aNfr06cP8+fPVYXFxcXTu3BmAY8eO6UwLID4+nk8++YQff/wRgBs3buDq6sqYMWOoUKECU6dO1aleWloay5cvZ9u2bYSGhpKcnKxxXBc3O8XZDwEMDAyIiIigXLlyGuG3b9+mdu3axMXF6VxToieK56XprwaDBg0SnTp1Enfu3BFWVlYiKChICCHEvn37RO3atXWul5KSIqZMmSJMTEzEtGnTxLvvviusrKzE+vXrda4lhBB16tQRw4YNE6mpqeqw1NRU8dFHH4k6deroXM/e3l74+fkJIYTYvHmzcHNzE3FxceKbb74R7u7uOtNxdnbO08fFxUVnmhkU1TVmRaVSCQMDA6FSqTQ+GWEGBgaiVatWIjo6Wid6c+bMEQYGBqJJkyaie/fuokePHhofXePs7CwsLS2FSqUSZcqUEaVLlxYqlUpYWloKe3t7oVKpRNWqVUVoaKjONIODg0WFChXEsmXLhBBCPHnyRHh6eoqWLVuK2NhYnelkMGbMGNGoUSNx7NgxYWlpqR5rdu7cqZd2M3PmTOHo6CiWLFkizMzMxLx588TQoUOFnZ2d8Pb21olGcfVDLy8v4eXlJQwMDMTHH3+s/u7l5SXGjBkjmjZtKpo3b65TTYl+kYaOHsn6o5XV0AkODhaWlpZ60501a5ZQqVTC2NhYnDx5Um86ZmZmIiAgIFt4QECAMDMz04texo/RoEGDxJQpU4QQQty+fVuv5VmUFMc1Hjx4UDRt2lQcPHhQPHnyRDx58kQcPHhQNGvWTOzevVscP35c1KlTR3zwwQc60XNwcBAbN27USVp5YcuWLaJNmzbi5s2b6rDAwEDRrl07sXXrVnHnzh3x+uuvi3feeUenupcvXxZ2dnZixYoVolmzZqJ169Z6MXKEEKJKlSri1KlTQgjNsSYwMFBYW1vrXM/V1VXs2rVLrZdRtt7e3qJfv3461ytK2rRpI9q0aSNUKpVo3ry5+nubNm1Ex44dxUcffSRu3LhR3NmU5ANp6OgRKysrdYfIOvicOXNGlClTRud6ycnJYvz48cLU1FRMnz5dtGrVStjb24vdu3frXEsIIZo3by62b9+eLXz79u2iWbNmOterVq2a+OWXX0RsbKwoV66cOHTokBBCCD8/P2FnZ6dzvedJT08X6enpetUojmusU6eOOHHiRLbw48ePq2ceDxw4ICpXrqwTvTJlymgYHfrG1dVV+Pr6Zgu/cOGCejbgxIkTwsHBQefap06dEpaWlqJdu3YiPj5e5+lnYG5urh5fso41fn5+wsbGRud6FhYW4vbt20IIxXA9f/68EEKIoKAgvegVB++//76IiYkp7mxIdIBRcT86K8m0atWKjRs3Mm/ePEDxCUhPT2fJkiW0bdtW53oeHh7Ex8fz77//0qxZM4QQfPHFF/Tq1YsPPviAb775Rqd6Y8aMYezYsdy8eZNmzZoBcPr0ab7++msWL17MpUuX1HHr1atXaL1x48YxYMAArKyscHJyok2bNgAcPXqUunXrFjr9nNi4cSNLliwhMDAQgOrVqzNp0iQGDRqkc63iuMagoCBsbGyyhdvY2BAcHAwoPieRkZE60fvwww/ZsmULM2fO1El6LyIsLEyrc25qairh4eEAVKhQgadPnxZKp0GDBqhUqmzhpqam3L9/X+03B3DhwoVCaT1P48aN2b17N5988gmAOh9r167F09NTp1oAlSpVIiwsjCpVquDm5sb+/ftp2LAhZ8+e1duLL+/evcuff/6p1Sdo2bJlOtf74YcfAGUxSVBQEK1atcLc3BwhhNZ6lvx3kYaOHlmyZAlt2rTh3LlzJCcnM3nyZPz9/YmOjubEiRM61/Pw8GDlypVYWloCymA3ZcoUOnXqxMCBA3Wu169fPwAmT56s9ZhKpVIPCmlpaYXWGzlyJE2aNOHOnTt06NABAwNl0aCrq6uG06cuWbZsGTNnzmT06NG8/vrrCCE4ceIEw4cPJzIyEi8vL53qFcc1NmrUiEmTJrFx40a14+XDhw+ZPHkyjRs3BiAwMJBKlSoVWGP8+PHq/9PT0/nuu+84ePAg9erVw9jYWCOurn+02rZty8cff8y6deto0KABAL6+vowYMYJ27doBcPny5UI7XPfo0aOwWS0wixYtonPnzly9epXU1FS8vb3x9/fn1KlTHDlyROd6PXv25NChQzRt2pSxY8fSr18/1q9fT2hoqM77BCjO6926dcPFxYXr16/z2muvqZ30GzZsqHM9UByq//e//3H48GGNxSQffvihXhaTSPSHXHWlZ8LDw/n22285f/486enpNGzYkFGjRuHo6Fik+UhKStL5ndbt27fzHNfJyUmn2hnNVt93Vi4uLsydO5f33ntPI/zHH39kzpw53Lp1S2/aRXWN169fp3v37ty6dYvKlSujUqkIDQ3F1dWVnTt3Ur16dXbs2MHTp08LPIuV1xlMlUrFP//8UyCNnAgPD2fQoEEcOnRIbVSlpqbSvn17Nm3ahL29PYcPHyYlJYWOHTvqVLsouXz5MkuXLtUYa6ZMmaLX2c4MfHx8OHHiBG5ubnTr1k3n6Tdp0oTOnTvz2WefYW1tzcWLFylfvjwDBgygc+fOjBgxQuea7733Hg8ePGDdunXUqlWLixcv4urqyv79+/Hy8sLf31/nmhI9UWwPzSR6YePGjaJ58+bC0dFRhISECCGEWL58udixY4fOtRYuXKh1Rdf69evF4sWLda4nhBDr1q0TderUESYmJsLExETUqVNHrF27Vi9aQghhamoqAgMDs4XfuHFDmJqa6kWzqK9RCMX/aM+ePcLb21usWLFC7N27V6SlpelVs6i5du2a2Llzp9ixY4dWJ3pJ3inqvp/V4blUqVLiypUrQgjFB8nJyUnnekIU32ISie6Rj670SFYflaxk7N9RpUoVnc6yfPvtt8yaNYtx48axYMEC9eOiUqVKsWLFCrp3764zLYA1a9awZcuWbOF16tShb9++TJkyRad6M2fOZPny5XzyySdqv4NTp07h5eVFSEiIXh7tuLm5sW3bNqZPn64R/ssvv1CtWjWd6xXHNYLSJjt37qze50WfxMTEkJaWRpkyZTTCo6OjMTIy0uovpAtq1qxJzZo19ZL28xTFPjPaNLdv3861a9dQqVTUqlWL7t27Y2Sk+2G+qPu+paUlSUlJgOJPFRQURJ06dQB05jv2PHFxcVhYWGQLj4yM1JsfkkRPFLelVZLJ2IMk6x4lGd8NDAyEqampeO+990RCQoJO9GrVqqVeBZX1DiRjmauuMTU1FcHBwdnCg4KC9DLbYWdnJ7Zs2ZItfMuWLXpbkfTbb78JQ0ND0alTJ/HZZ5+JefPmiU6dOgkjIyPxxx9/6FyvOK7xk08+0br3yapVq8TYsWN1rte5c2fx9ddfZwv/9ttvRZcuXXSul5qaKtatWyf69esn2rdvL9q2bavx0QdFsc9MVi5fvixcXV2FhYWFaNCggWjQoIGwtLQUzs7O4tKlSzrXK+q+3717d/Hdd98JIYSYNGmScHNzE/PnzxcNGzYU7du317meEEJ07dpVfPrpp0IIZTwNDg4WaWlp4n//+5/OtyKQ6Bdp6OiRHTt2iBo1aoh169aJS5cuiYsXL4p169aJWrVqia1bt4qffvpJVKpUSUyYMEEnemZmZurHVVkNnRs3buhlXxs3NzexadOmbOEbN27Uy2Z6pUqV0rp/xfXr14Wtra3O9TI4d+6cGDBggGjYsKFo0KCBGDBggLhw4YJetIrjGitUqCDOnTuXLfz8+fOiYsWKOtcrXbq0uHr1arbwa9eu6WXbhVGjRglLS0vRu3dvMXbsWDFu3DiNjz4o6n1mmjZtKt5++22NTR2jo6NFt27d9LLVQ1H3/aCgIHHx4kUhhBBxcXFixIgRom7duqJnz57qMU/X+Pv7i3LlyonOnTsLExMT8e6774patWoJe3v7It0eQVJ4pKGjRxo3biz27t2bLXzv3r2icePGQghlzxlXV1ed6NWqVUvti5PV0PH29hYNGzbUiUZWFi9eLOzs7MT3338vQkJCREhIiFi/fr2ws7MTCxcu1Lne6NGjhZeXV7bwCRMmiJEjR+pcrzgojmvMyQ8pMDBQL3fnFhYWWmcZLl26JMzNzXWuZ2dnp7e9pHKiqPeZMTMzU/utZOXy5ct6uckp6r5fXISFhYlZs2aJN998U3Tp0kXMmDFD3L9/v7izJckn0kdHj1y+fFnraiMnJycuX74MgLu7O2FhYTrRmzRpEqNGjSIxMREhBGfOnOHnn39m0aJFrFu3TicaWZk8eTLR0dGMHDlS7YNgZmbGlClTmDZtms71ANavX8/+/fs19u25c+cO7733nsYSZl0uUU5PT+fmzZta38uki5elZs03wLp163K8Rn3g5ubG3r17GT16tEb4nj17cHV11ble48aN+e6771i1apVG+OrVq2nUqJHO9UxMTHBzc9N5urlR1PvM1KhRg4iICLXfSgYPHjzQy7UXR98H5aWl2vphlSpV9KLn4ODA3Llz9ZK2pOiQy8v1SIMGDahfvz7fffcdJiYmAKSkpDBs2DAuXryIr68vJ06cYODAgTpbprx27Vrmz5/PnTt3AGXAnT17NkOHDtVJ+tqIjY3l2rVrmJubU61aNb056hXHEuXTp0/Tv39/bt++ne2t87raH6h06dK89tprGBkZqfce0oY+ll6D8hLN0aNHM2nSJPW+MocOHeLLL79kxYoVDBs2TKd6J06c4I033qBx48a0b99erXf27Fn2799Py5Ytdar35ZdfEhwczFdffVVkG71NnToVGxsbpk+fzm+//Ua/fv1wdnZW7zOzePFiner9/fffTJ48mTlz5mgYyJ999hmLFy+mRYsW6ri6dPYuqr5/48YNhg4dysmTJzXChQ736Xqeol5MItEf0tDRIydPnqRbt24YGBhQr149VCoVly5dIi0tjV27dtGsWTM2bdpEeHg4kyZNKrReQkICQggsLCyIjIwkODiYEydOULt2bTp16qSDK3r1cHd3p3r16sydOxdHR8dsP5S2traF1jAwMCA8PJzy5cvj6urK2bNnsbOzK3S6+eHbb79lwYIF3L9/HwBnZ2fmzJmjt1kkPz8/lixZgp+fH+bm5tSrV49p06bpZSVbz549OXz4MGXKlKFOnTrZNij8448/dK75PKdPn+bkyZN622cmY2NJyNx3STy3D5M+jQJ98/rrr2NkZMTUqVO19sP69evrXNPAwCDHsgQwNjamT58+rFmzBjMzM53rS3SHNHT0TGxsLD/99BM3btxACEHNmjXp378/1tbWOtfq2LEjvXr1Yvjw4Tx+/JiaNWtibGxMZGQky5Yt08umWsXFnTt3UKlUhdqtNy9YWlpy8eJFvT76sLOz4++//6Zp06YYGBgQERGh3qG4qHn48CHm5uZYWVkVi74+GDJkSK7HM7b6f5nJz+7HrVu31mNO9IOlpSXnz58vsu0BAHbu3MmUKVOYNGkSTZo0QQjB2bNn+fLLL5k9ezapqalMnTqVPn36sHTp0iLLl6QAFL1b0KuHv7+/2LNnj9i5c6fGR9fY2dmpHRLXrl0r6tWrJ9LS0sS2bdtEzZo1da5X1KSkpIhPP/1U2NjYqJfo29jYiBkzZojk5GS9aLZt21bs2bNHL2lnMGzYMGFiYiKcnZ2FgYGBqFKlinBxcdH60RcpKSniwIEDYvXq1eLJkydCCCHu3bsnnj59qjdNIYSIj48XMTExGp+SQlFu3lnS8fDwEMeOHStSzaJeTCLRH9IZWY8EBwfTs2dPLl++rPHepwx0PYUcHx+vninav38/vXr1wsDAgGbNmuXrdQ3/VUaPHs327dv54osvNDbTmzNnDpGRkaxevVonOlmfzX/yySdMmDCB8PBw6tatm+2xhy5eVvrdd9/Rq1cvbt68yZgxYxg2bJheZvxy4vbt23Tu3JnQ0FCSkpLo0KED1tbWfPHFFyQmJuqsXDOIj49n8uTJbNu2jaioqGzHX8ZHK89T1Jt3Ajx+/Jj169erNwysXbs2H3zwgU4erxYHT548Uf//+eefM3nyZBYuXKi1H+pjk8miXkwi0SPFbWmVZN566y3RvXt38eDBA2FlZSX8/f3FsWPHRJMmTcTRo0d1rle3bl3h7e0tQkNDhY2NjTh58qQQQtkHxt7eXud6RY2NjY34+++/s4X//fffOl2ym7GxY8Ymj89/Mo4ZGBjoTDOD999/Xz2jUlR0795dDBw4UCQlJWlsS/Dvv/8KNzc3neuNHDlS1KpVS/z666/C3NxcfP/992LevHmiUqVK4qefftK5nhBC/Prrr+J///ufaNq0qXpDvYyPPijqzTvPnj0rypQpIypWrCh69uwpevToISpVqiTs7OzUS9tfNp7fYPX57/rsh0II4e7uLgYPHiySkpLUYcnJyWLw4MHC3d1dCCHE8ePHhbOzs170JbpDzujokVOnTvHPP/9Qrlw5DAwMMDQ0pEWLFixatIgxY8bg6+urU71Zs2bRv39/vLy8aN++vXrWY//+/eq3Nr/MmJmZ4ezsnC3c2dlZvapNF+jzRZ0vojj8RY4fP86JEyeylaGTkxP37t3Tud5ff/3Fxo0badOmDR988AEtW7bEzc0NJycnNm/ezIABA3Sqt3LlSmbMmMHgwYPZuXMnQ4YMISgoiLNnzzJq1CidamVw69YtrX3O1NSUuLg4net5eXnRrVs31q5dq37lQ2pqKh9++CHjxo3j6NGjOtfUN4cPH1b/HxISQuXKlTE0NNSIk56eTmhoqF70v/76a7p160alSpW0LiYBZdZ+5MiRetGX6JDitrRKMqVKlVLfybm6uop//vlHCCHEzZs39bIxmhDKBlcXLlzQeCGjj4+PuHbtml70ipK5c+eKfv36icTERHVYYmKiGDBggJgzZ45eNIvjxaVFTenSpYW/v78QQnP24dixY6J8+fI617O0tFT7rFSsWFH4+PgIIfT3ssQaNWqoX6uR9fpmzpwpRo0apXM9IYp+804zMzOtfdzf319vY01RYmBgICIiIrKFR0ZG6m1GRwghnj59Kr799lvh5eUlxo0bp+HDJnl5kDM6euS1117j0qVLuLq60rRpU7744gtMTEz47rvv9LIRGygbXDk4OGiENWnSRC9aRUGvXr00vh88eJBKlSqpl5NevHiR5ORk9X4suqaoX15YHHTo0IEVK1bw3XffAcoS2tjYWGbPnk3Xrl11rufq6kpISAhOTk7Url2bbdu20aRJE/766y9KlSqlc73Q0FCaN28OgLm5OU+fPgVg0KBBNGvWjK+++krnmkW9eaeNjQ2hoaHZViXduXOnSP299IV4zr8xg9jYWL0s7U5JSaFGjRrs2rWL4cOH6zx9SdEiDR098umnn6qnqefPn89bb71Fy5YtsbOz45dffinm3L0cPO9I+c4772h8r1y5sl71w8PDcXR0zBZerly5EuOEuHz5ctq2bUvt2rVJTEykf//+BAYGUrZsWX7++Wed6w0ZMoSLFy/SunVrpk2bxptvvsmqVatITU3V6Y7WGTg4OBAVFYWTkxNOTk6cPn2a+vXrc+vWrRw3ZywsQ4YMITU1lcmTJxMfH0///v2pWLEi3t7e9O3bV+d6ffr0YejQoSxdupTmzZujUqk4fvw4kyZNol+/fjrXKyoydg1XqVTMnDlT423iaWlp+Pj44O7urnNdY2NjkpKSimyDSYl+kfvoFDHR0dGULl1adqCXhGrVqjF79mwGDhyoEb5p0yZmz55NcHBwMeVMtyQkJLB161bOnz9Peno6DRs2ZMCAAZibm+tdOzQ0lHPnzlG1alW9bPz24YcfUrlyZWbPns3q1asZP348r7/+OufOnaNXr16sX79e55pZiYyMJD09nfLly+tNIzk5mUmTJrF69WpSU1MB5cd6xIgRLF68+KXdwTdjN/QjR47g6emp4UdmYmKCs7MzEydO1MtGk4sXLyYgIIB169ap/Z4kLyfS0JG8NLRr144//vgj2+ONJ0+e0KNHD728HuHzzz9nyZIlLFmyROP1CJMnT2bChAl6fa9PUXH06FGaN2+ebTBPTU3l5MmTOnmfV3GSnp5Oenq6+vq2bdvG8ePHcXNzY/jw4Tp1ZH+eBw8ecP36dVQqFTVq1ND7RpDx8fEEBQUhhMDNzU1jBuRlZsiQIXh7e+tlGXlO9OzZk0OHDmFlZUXdunWxtLTUOF4UO2pLdIM0dCQvDVlflZCVBw8eULFiRVJSUnSuKYRg6tSprFy5MtvLC2fNmqVzveLA0NCQsLCwbOUaFRVF+fLldbKvzcqVK/Mcd8yYMYXWK26ePHnCqFGj+Pnnn9UvoDQ0NKRPnz58/fXXL+3eNq8Sr8KO2q8K0tCR/OfJ2MDP3d2df/75hzJlyqiPpaWlsXfvXtasWUNISIje8lBULy8sDnJ67cSNGzfw8PDQ2LitoLi4uOQpnkql0vnjwJkzZzJnzpxsS5NjYmIYPny4XvyQevfujZ+fH6tWrcLT0xOVSsXJkycZO3Ys9erVY9u2bYXWeN5RPzfk7IPkVUYaOpL/PNperpcVc3NzVq1axQcffFDUWXupyfih3LlzJ507d9Yw3tLS0rh06RI1atRg7969xZVFneDk5ISjoyObN2+matWqAPz777+89957VKxYkVOnTulc09LSkn379mm8NRzg2LFjdO7cWSd76bxoxiErcvZB8iojPawk/3kyVse4urpy5swZjZkHExMTypcvn+1uXfJiMh6fCCGwtrbWcDw2MTGhWbNmDBs2rLiypzMuXbrExx9/jLu7O8uWLePGjRt4e3szdepUZs+erRdNOzs7rY+nbG1tKV26tE40shovCQkJpKenq/1IQkJC2LFjB7Vq1aJTp0460XsV+e2339i2bRuhoaHqR9cZXLhwoZhyJckvckZHInnFmTt3LhMnTszmbKlP7t69y59//qn1B0QfS8wBZsyYwaJFizAyMmLPnj1623sJlPeX/frrr2zcuFG9PUF4eDiDBw+mV69efPzxxzrV69ixI7169WL48OE8fvyYmjVrYmxsTGRkJMuWLWPEiBE61XsVyLqj9tq1a7PtqL1gwYLizqIkrxTt/oQSScHZsGGD2LVrl/r7pEmThK2trfD09FTvtCvJP/Hx8SIuLk79PSQkRCxfvlzs27dPL3oHDx4UFhYWok6dOsLIyEi4u7uLUqVKCVtbW9G2bVu9aK5cuVKYm5uL/v37ixo1aojatWsLPz8/vWgJobwnycrKShgbG4uqVauKqlWrCmNjY2FlZaWXd23Z2dmJK1euCCGEWLt2rahXr55IS0sT27ZtEzVr1tSJxqtGceyoLdEP8tGV5KVh4cKFfPvtt4DyHrGvvvqKFStWsGvXLry8vKTDZQHp3r27xmxAkyZNMDEx0dtswLRp05gwYQKfffYZ1tbW/P7775QvX54BAwbQuXNnnWoBdOnShTNnzrBx40beffddEhISGD9+PM2aNWPu3LlMnjxZ55o9evTQeZq5ER8fr94Bef/+/fTq1QsDAwOaNWvG7du3izQvJYXi2FFboieK29KSSPKKubm5uH37thBCiMmTJ4tBgwYJIYS4cuWKKFu2bHFm7aWmqGcDrKysxM2bN4UQyvvgMrT9/PyEk5OTzvXeeOMNce/evWzhu3btEg4ODjrXKw7q1q0rvL29RWhoqLCxsREnT54UQghx7tw5YW9vX8y5ezlxcXFRv/ndw8NDrF69WgghxL59+0Tp0qWLM2uSfGJQ3IaWRJJXrKysiIqKApS71jfeeANQ9rVJSEgozqy91BT1bIClpSVJSUkAVKhQgaCgIPWxyMhInesdOHCAoKAgBg4ciKenp/qN7NHR0TpZ5v1fYNasWUycOBFnZ2eaNm2Kp6cnoNSntreoS15Mu3bt+OuvvwAYOnQoXl5edOjQgT59+tCzZ89izp0kP8hHV5KXhg4dOvDhhx/SoEEDbty4wZtvvgmAv78/zs7OxZu5lxg3Nzd27NhBz5492bdvH15eXoCyEaM+dqJt1qwZJ06coHbt2rz55ptMmDCBy5cv88cff9CsWTOd6/3+++8MGjSIAQMG4Ovrqzaynj59yqJFi2jZsqVOdPLzapfo6GidaGbw7rvv0qJFC8LCwjReo9G+fXv5o1xAZsyYQcWKFQEYPnw4ZcqU4fjx47z99tt06dKlmHMnyQ9y1ZXkpeHx48d8+umn3LlzhxEjRqj9OWbPno2JiQkzZswo5hy+nPz222/079+ftLQ02rVrx4EDBwBYtGgRR48eZc+ePTrVCw4OJjY2lnr16hEfH8/EiRPVr2RYvnw5Tk5OOtVr0KABXl5evPfee1hbW3Px4kVcXV3x8/Ojc+fOhIeH60Tnxx9/VP8fFRXF/Pnz6dSpk3p25dSpU+zbt4+ZM2eqjUnJf5ei2DFcUjRIQ0cikRAeHq6eDTAwUJ5onzlzBhsbG2rWrFnMuSscFhYWXL16FWdnZw1DJzg4WP3Gdl3zzjvv0LZtW0aPHq0R/tVXX3Hw4EF27Nihc02JbsnplTO3b9+mdu3aOtn0UVI0yEdXkpeKY8eOsWbNGoKDg/n111+pWLEimzZtwsXFJdsutJK84+DgQGxsLAcOHKBVq1aYm5vTuHHjPD+KKQjJyck8ePBA/S6oDKpUqaJTHUdHR27evJnt8ebx48dxdXXVqVYG+/bt4/PPP88W3qlTJ6ZOnaoXTYluGD9+PKC8jmTWrFkaL0ZNS0vDx8cHd3f3YsqdpCBIZ2TJS8Pvv/9Op06dMDc358KFCxq+FgsXLizm3L28REVF0b59e6pXr07Xrl0JCwsD4MMPP2TChAk617tx4wYtW7bE3NwcJycnXFxccHFxwdnZOc/vxMoPH3/8MWPHjsXHxweVSsX9+/fZvHkzEydOZOTIkTrXA2Vn5O3bt2cL37FjB3Z2dnrRlOgGX19ffH19EUJw+fJl9XdfX18CAgKoX78+GzZsKO5sSvKBnNGRvDTMnz+f1atX895777F161Z1ePPmzfnss8+KMWcvN15eXhgbGxMaGkqtWrXU4X369MHLy4svv/xSp3pDhgzByMiIXbt24ejoqNdZI4DJkycTExND27ZtSUxMpFWrVpiamjJx4sRsj5Z0xdy5cxk6dCj//vuv2kfn9OnT7N27l3Xr1ulFU6IbDh8+DCjt1NvbWy8O+ZKiRfroSF4aisPX4lXAwcGBffv2Ub9+fY1yvXXrFnXr1iU2NlanepaWlpw/f77IfX/i4+O5evUq6enp1K5dGysrK73q+fj4sHLlSq5du4YQgtq1azNmzBiaNm2qV12JRKKJnNGRvDQUh6/Fq0BcXJyGH0IGkZGRGm801xW1a9fWy345L8LCwgIPD48i02vatCmbN28uMj2JRKId6aMjeWkoDl+LV4FWrVqxceNG9XeVSkV6ejpLliyhbdu2OtF48uSJ+vP5558zefJk/v33X6KiojSOPXnyRCd6xcHz15HbRyKRFB3y0ZXkpWLGjBksX75c/Zgqw9di3rx5xZyzl5dr167RunVrGjVqxD///EO3bt3w9/cnOjqaEydOULVq1UJrGBgYaPjiCCGy+eZkhL2s+5M8f43aeNmvUSJ5GZGGjuSlIC0tjePHj1O3bl3MzMyK1NeiJJOSkkLHjh1ZtGgRe/bs4fz586Snp9OwYUNGjRqFo6OjTnSOHDmi/j8kJITKlStjaGioESc9PZ3Q0FAGDx6sE82iJus1vojWrVvrMScSiSQr0tCRvDSYmZlx7do1vSxBfpUpV64cJ0+epFq1akWi96rsOPv48WPWr1/PtWvXUKlU1KpVi6FDh2Jra1vcWZNIXimkj47kpaFu3boEBwcXdzZKHO+99x7r168vMj1tj60AYmNjMTMzK7J86JNz586pX2kRHR1NZGQky5cvp2rVqly4cKG4syeRvFLIGR3JS8P+/fuZMmUK8+bNo1GjRlhaWmocl/tdFIxPPvmEjRs34ubmhoeHR7ZyXbZsmU50Mnac9fb2ZtiwYVp3nDU0NOTEiRM60StOWrZsiZubG2vXrsXISFncmpqayocffkhwcDBHjx4t5hxKJK8O0tCRvDRkvIMJ0OrYWlIeeRQ1ua2sUqlU/PPPPzrVOXLkCJ6enpiYmKiPmZiY4OzszMSJE4vsEZo+MTc3x9fXN9teQVevXsXDw4P4+PhiyplE8uoh99GRvDT88MMPuTqxSgpGxk6wRaXzKuw4a2NjQ2hoaDZD586dO1hbWxdTriSSVxM5oyN5aXhVnFglLz9jxoxh+/btLF26lObNm6NSqTh+/DiTJk3inXfeYcWKFcWdRYnklUHO6EheGl4FJ1ZJyWDp0qWoVCree+89UlNTATA2NmbEiBEsXry4mHMnkbxayBkdyX+eV8mJVVKyiI+PJygoCCEEbm5uWl+1IZFI9Iuc0ZH85/H19QWUGZ3Lly9nc2KtX78+EydOLK7sSSQ5YmFhQd26dYs7GxLJK42c0ZG8NLwKTqwSiUQi0S3S0JFIJBKJRFJikTsjSyQSiUQiKbFIQ0cikUgkEkmJRRo6EolEIpFISizS0JFIJBKJRFJikYaORCKRSCSSEos0dCQSiUQikZRYpKEjkUgkEomkxPJ/Bph28MT5mEcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr,cmap=['hotpink','green','skyblue'],annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ece23266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df['age']\n",
    "y = df['ca'].astype(str)\n",
    "\n",
    "plt.barh(x, y, color=\"green\")\n",
    "plt.title('Bar Graph')\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('ca')\n",
    "plt.grid(axis='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "81d90451",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8698abde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>221</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>258</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>188</td>\n",
       "      <td>1</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1025 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex  cp  trestbps  chol  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0      52    1   0       125   212        1      168      0      1.0      2   \n",
       "1      53    1   0       140   203        0      155      1      3.1      0   \n",
       "2      70    1   0       145   174        1      125      1      2.6      0   \n",
       "3      61    1   0       148   203        1      161      0      0.0      2   \n",
       "4      62    0   0       138   294        1      106      0      1.9      1   \n",
       "...   ...  ...  ..       ...   ...      ...      ...    ...      ...    ...   \n",
       "1020   59    1   1       140   221        1      164      1      0.0      2   \n",
       "1021   60    1   0       125   258        0      141      1      2.8      1   \n",
       "1022   47    1   0       110   275        0      118      1      1.0      1   \n",
       "1023   50    0   0       110   254        0      159      0      0.0      2   \n",
       "1024   54    1   0       120   188        1      113      0      1.4      1   \n",
       "\n",
       "      ca  thal  target  \n",
       "0      2     3       0  \n",
       "1      0     3       0  \n",
       "2      0     3       0  \n",
       "3      1     3       0  \n",
       "4      3     2       0  \n",
       "...   ..   ...     ...  \n",
       "1020   0     2       1  \n",
       "1021   1     3       0  \n",
       "1022   1     2       0  \n",
       "1023   0     2       1  \n",
       "1024   1     3       0  \n",
       "\n",
       "[1025 rows x 13 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['fbs'],axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "715918e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['age'].values\n",
    "Y = df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "2edaaa1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52, 53, 70, ..., 47, 50, 54], dtype=int64)"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "c9185e1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "bb78337b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1025,)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "a2d8de3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1025,)"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "36d133d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1025"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "660d15e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,:-1]    \n",
    "Y = df.iloc[:,-1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "22a86714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1025, 12)"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "c7797c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1025,)"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "cc56fb56",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.33,random_state=2)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "2870a4a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
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       "      <th>thalach</th>\n",
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       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
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       "      <th>thal</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>181</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
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       "      <td>125</td>\n",
       "      <td>245</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>52</td>\n",
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       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>325</td>\n",
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       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>54</td>\n",
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       "      <td>2</td>\n",
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       "      <td>267</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>353</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>353</td>\n",
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       "      <td>132</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>686 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex  cp  trestbps  chol  restecg  thalach  exang  oldpeak  slope  \\\n",
       "1024   54    1   0       120   188        1      113      0      1.4      1   \n",
       "444    47    1   2       130   253        1      179      0      0.0      2   \n",
       "181    64    1   3       110   211        0      144      1      1.8      1   \n",
       "856    68    0   2       120   211        0      115      0      1.5      1   \n",
       "973    51    1   2       125   245        0      166      0      2.4      1   \n",
       "...   ...  ...  ..       ...   ...      ...      ...    ...      ...    ...   \n",
       "299    52    1   1       120   325        1      172      0      0.2      2   \n",
       "534    54    0   2       108   267        0      167      0      0.0      2   \n",
       "584    55    1   0       132   353        1      132      1      1.2      1   \n",
       "493    55    1   0       132   353        1      132      1      1.2      1   \n",
       "527    62    0   0       124   209        1      163      0      0.0      2   \n",
       "\n",
       "      ca  thal  \n",
       "1024   1     3  \n",
       "444    0     2  \n",
       "181    0     2  \n",
       "856    0     2  \n",
       "973    0     2  \n",
       "...   ..   ...  \n",
       "299    0     2  \n",
       "534    0     2  \n",
       "584    1     3  \n",
       "493    1     3  \n",
       "527    0     2  \n",
       "\n",
       "[686 rows x 12 columns]"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "9f970ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024    0\n",
       "444     1\n",
       "181     1\n",
       "856     1\n",
       "973     1\n",
       "       ..\n",
       "299     1\n",
       "534     1\n",
       "584     0\n",
       "493     0\n",
       "527     1\n",
       "Name: target, Length: 686, dtype: int64"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "963818e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>322</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>183</td>\n",
       "      <td>1</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>118</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>223</td>\n",
       "      <td>1</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>339 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  restecg  thalach  exang  oldpeak  slope  \\\n",
       "546   70    1   0       130   322        0      109      0      2.4      1   \n",
       "980   57    1   0       140   192        1      148      0      0.4      1   \n",
       "908   62    1   0       120   267        1       99      1      1.8      1   \n",
       "577   70    1   0       130   322        0      109      0      2.4      1   \n",
       "846   35    0   0       138   183        1      182      0      1.4      2   \n",
       "..   ...  ...  ..       ...   ...      ...      ...    ...      ...    ...   \n",
       "379   52    1   3       118   186        0      190      0      0.0      1   \n",
       "755   48    1   2       124   255        1      175      0      0.0      2   \n",
       "706   57    1   2       128   229        0      150      0      0.4      1   \n",
       "447   55    1   0       160   289        0      145      1      0.8      1   \n",
       "108   40    1   0       152   223        1      181      0      0.0      2   \n",
       "\n",
       "     ca  thal  \n",
       "546   3     2  \n",
       "980   0     1  \n",
       "908   2     3  \n",
       "577   3     2  \n",
       "846   0     2  \n",
       "..   ..   ...  \n",
       "379   0     1  \n",
       "755   2     2  \n",
       "706   1     3  \n",
       "447   1     3  \n",
       "108   0     3  \n",
       "\n",
       "[339 rows x 12 columns]"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "4c9b8e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "546    0\n",
       "980    1\n",
       "908    0\n",
       "577    0\n",
       "846    1\n",
       "      ..\n",
       "379    1\n",
       "755    1\n",
       "706    0\n",
       "447    0\n",
       "108    0\n",
       "Name: target, Length: 339, dtype: int64"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "620a8e83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(686, 12)\n",
      "(686,)\n",
      "(339, 12)\n",
      "(339,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(Y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "9c6d2f1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "20886ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 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-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 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-19 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-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 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-19 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-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 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-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 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-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 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-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=LinearRegression()\n",
    "reg.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "8a9a3c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5159274510181133"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "6278a18c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred=reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "1d200069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.08385412,  0.62389624, -0.1242623 , -0.08385412,  0.91405597,\n",
       "        0.71555864,  0.06240981,  0.03358634,  0.2327538 ,  0.84162072,\n",
       "        0.83767368,  0.35033818,  0.57004534,  0.65289397,  0.98737841,\n",
       "        0.60416524,  0.74426525,  0.31995724,  1.14800344,  0.71600883,\n",
       "        0.70487174,  1.10782352,  0.95101404,  0.09980375,  0.2418419 ,\n",
       "        0.58596762,  0.21067508,  0.62543256, -0.18765803,  0.58449202,\n",
       "       -0.07750391,  0.41840743,  0.62173465,  0.08531273,  0.40731344,\n",
       "        1.23419384,  0.71850383,  0.77841999,  0.62141652,  0.68403068,\n",
       "        0.64293175, -0.04293312,  0.53742463,  0.61686837, -0.01605491,\n",
       "        1.12036645,  0.76453835,  0.72625913,  0.83575129,  0.27647297,\n",
       "        0.61686837,  0.5656206 ,  0.19690246,  0.15552928,  0.23446029,\n",
       "        0.90929817,  0.65972342,  0.74652261,  0.57004534,  0.84715658,\n",
       "        0.55768802, -0.00430299,  0.82647034,  0.51742307,  0.58414691,\n",
       "        0.13455598,  0.30195427,  0.47137114,  0.14332507,  0.73101504,\n",
       "        0.86315103,  0.16638051,  0.31995724,  1.15326837,  1.13257953,\n",
       "        0.67185396,  0.59205536,  0.48806874,  0.30501378, -0.19368658,\n",
       "        0.04222119, -0.05838987,  1.1053479 ,  0.14332507,  0.61412307,\n",
       "        0.91531162,  0.48806874,  0.58449202,  0.42364173,  1.0644306 ,\n",
       "        0.47137114, -0.03600537,  0.68093186,  0.27925213,  1.29773414,\n",
       "        1.14800344,  0.52115991, -0.06695905,  0.74652261,  0.18546853,\n",
       "        1.23419384,  0.23446029,  0.94634079,  0.73012169,  0.41178372,\n",
       "        0.62721254,  1.07439707,  0.90929817,  0.9821224 ,  0.74385945,\n",
       "        0.47549798,  0.5884201 ,  0.5656206 , -0.13996196, -0.16899039,\n",
       "        0.20977016,  0.58596762,  0.15812755,  0.2587797 ,  0.61412307,\n",
       "        0.78414263,  0.68926394,  0.70487174,  0.83767368,  0.86315103,\n",
       "        0.68926394,  0.60318589,  0.8448685 ,  0.40731344,  0.70500877,\n",
       "        0.66601855,  0.18955747,  0.95391257,  0.61255097,  0.44155649,\n",
       "        0.71600883,  0.51742307,  0.42109333,  0.57073047,  0.38542361,\n",
       "        0.83767368,  0.2391846 ,  0.53212636,  1.0383943 ,  0.9821224 ,\n",
       "        0.70500877,  0.8162573 , -0.21901622,  0.20181882,  0.20977016,\n",
       "        0.32374937,  0.29623499, -0.0079089 ,  0.62173465,  0.97246499,\n",
       "        0.56094864,  0.2327538 ,  0.73492848,  0.2107719 , -0.16899039,\n",
       "        0.61344363,  0.73012169,  0.65289397,  0.76677481, -0.01364086,\n",
       "        0.34381105,  0.92037197,  0.10051435,  1.29773414,  0.12093651,\n",
       "        0.65972342,  0.32374937,  0.94634079, -0.1242623 ,  0.18546853,\n",
       "        0.54915496,  0.86561333, -0.04293312,  0.58155898,  1.23419384,\n",
       "        1.01602083,  0.75773662,  0.67842127,  0.78414263,  0.57004534,\n",
       "        0.42189876,  0.47537395,  0.74007162,  0.63614509, -0.06300739,\n",
       "        0.71281396,  0.10051435,  0.58278723,  0.18187198,  0.97246499,\n",
       "        0.01234123, -0.15616709,  0.12093651,  0.2327538 ,  0.12357001,\n",
       "        0.74007162,  0.58278723,  0.91620194,  0.64674725,  0.2327538 ,\n",
       "        0.65289397,  0.59857952,  0.6495681 ,  0.51603862,  0.68926394,\n",
       "        0.95897116,  0.96931702,  0.75773662,  0.08955615,  0.65429244,\n",
       "       -0.10783163,  0.8061732 ,  0.58278723,  0.77730495,  0.24808868,\n",
       "        0.18955747,  0.61344363, -0.19368658,  0.40731344,  1.03724491,\n",
       "        0.76022127,  0.86561333,  0.61900871,  0.32389541,  0.68926394,\n",
       "       -0.15616709,  0.42189876,  0.35541491,  0.74426525,  0.66883971,\n",
       "        0.32374937,  0.27321943,  0.62173465, -0.04546372,  0.22550456,\n",
       "       -0.07750391,  0.86561333, -0.06816468,  0.80408177,  0.50001109,\n",
       "        0.81912109,  0.95897116,  0.02203218, -0.1685786 ,  0.06240981,\n",
       "        0.95898533,  0.2418419 ,  0.81599569,  0.14332507,  1.08853343,\n",
       "        0.81590528, -0.0805812 ,  0.6495681 ,  0.27321943,  0.16780574,\n",
       "        1.02461035,  0.18187198,  0.69985475,  0.26455366,  0.61412307,\n",
       "        0.96931702,  0.2107719 ,  0.95898533,  0.59857952,  0.16638051,\n",
       "        0.34381105,  0.60840129, -0.12561453,  0.31828857,  0.98601799,\n",
       "        0.34496902,  0.61356386,  0.2391846 ,  1.1053479 ,  0.16839109,\n",
       "        0.63013299, -0.07750391,  0.59895512,  0.55768802,  0.82982381,\n",
       "        0.35541491,  0.22257243,  0.35541491,  1.02461035,  0.18187198,\n",
       "        1.03432353,  0.81423693,  0.35033818,  0.02203218,  0.75257222,\n",
       "        0.95897116,  0.76453835,  0.32516342,  0.72861758,  0.81599569,\n",
       "       -0.07750391, -0.05607577,  0.14796172, -0.30317371,  1.07439707,\n",
       "        0.95391257,  0.96851523,  0.61686837,  0.59527758,  0.9378302 ,\n",
       "        0.71435806,  0.0060946 ,  0.27925213,  0.62389624,  1.12036645,\n",
       "        0.73101504,  0.42838799, -0.15980806,  0.4195908 ,  0.23403449,\n",
       "        0.71600883,  0.0930837 , -0.06816468,  0.83592587,  0.32374937,\n",
       "        0.15552928,  0.60840129,  0.42109333,  0.19690246, -0.04546372,\n",
       "        0.65972342,  1.10834954,  0.0521277 ,  1.13257953,  1.15326837,\n",
       "        0.80126163,  0.54431519,  0.04222119,  0.61255097])"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "aaa24a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5119343400896197"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "68cb5bb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 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-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 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-20 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-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 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-20 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-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 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-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 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-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 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-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "aa83066a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8454810495626822"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "9fa0ca43",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "8de4c9d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n",
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       "       1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0,\n",
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       "       0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "4091a7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8407079646017699"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(Y_test,Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "7fc0415e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-21 {color: black;background-color: white;}#sk-container-id-21 pre{padding: 0;}#sk-container-id-21 div.sk-toggleable {background-color: white;}#sk-container-id-21 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-21 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-21 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-21 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-21 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 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-21 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-21 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-21 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 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-21 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-21 div.sk-item {position: relative;z-index: 1;}#sk-container-id-21 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-21 div.sk-item::before, #sk-container-id-21 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-21 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-21 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-21 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-21 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-21 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-21 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-21 div.sk-label-container {text-align: center;}#sk-container-id-21 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-21 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-21\" 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-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" 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": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_class = RandomForestClassifier()\n",
    "rf_class.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "07ffeebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_class.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "b32e7c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = rf_class.predict(X_test)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "276c0b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9705014749262537"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test,Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "616e42cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8367346938775511"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb_model  = GaussianNB().fit(X_train,Y_train)\n",
    "nb_model.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "3ec3fbfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = nb_model.predict(X_test)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "46872046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8377581120943953"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test,Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "725ee355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6865889212827988"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc_model = SVC().fit(X_train,Y_train)\n",
    "svc_model.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "1080b2e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 1, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = svc_model.predict(X_test)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "320a38a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6902654867256637"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test,Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cb28cfb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ecc62f5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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