Data Analysis With Python/Scikit Learn Data Analysis Projects/College Students Data Analysis Using K Means Clustering (Day 11).ipynb

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   "source": [
    "# **College Students Data Analysis Using K Means Clustering**\n",
    "\n",
    "## **The Data**\n",
    "\n",
    "We will use a data frame with **777** observations on the following **18** variables.\n",
    "* **Private** A factor with levels No and Yes indicating private or public university\n",
    "* **Apps** Number of applications received\n",
    "* **Accept** Number of applications accepted\n",
    "* **Enroll** Number of new students enrolled\n",
    "* **Top10perc** Pct. new students from top 10% of H.S. class\n",
    "* **Top25perc** Pct. new students from top 25% of H.S. class\n",
    "* **F.Undergrad** Number of fulltime undergraduates\n",
    "* **P.Undergrad** Number of parttime undergraduates\n",
    "* **Outstate** Out-of-state tuition\n",
    "* **Room.Board** Room and board costs\n",
    "* **Books** Estimated book costs\n",
    "* **Personal** Estimated personal spending\n",
    "* **PhD** Pct. of faculty with Ph.D.’s\n",
    "* **Terminal** Pct. of faculty with terminal degree\n",
    "* **S.F.Ratio** Student/faculty ratio\n",
    "* **perc.alumni** Pct. alumni who donate\n",
    "* **Expend** Instructional expenditure per student\n",
    "* **Grad.Rate** Graduation rate"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Import Libraries**\n",
    "\n",
    "**Import the libraries you usually use for data analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Get The Data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Read in the College_Data file using read_csv. Figure out how to set the first column as the index.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/data/College_Data',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the head of the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abilene Christian University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adelphi University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adrian College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agnes Scott College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska Pacific University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
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       "  </tbody>\n",
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       "                             Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "Abilene Christian University     Yes  1660    1232     721         23   \n",
       "Adelphi University               Yes  2186    1924     512         16   \n",
       "Adrian College                   Yes  1428    1097     336         22   \n",
       "Agnes Scott College              Yes   417     349     137         60   \n",
       "Alaska Pacific University        Yes   193     146      55         16   \n",
       "\n",
       "                              Top25perc  F.Undergrad  P.Undergrad  Outstate  \\\n",
       "Abilene Christian University         52         2885          537      7440   \n",
       "Adelphi University                   29         2683         1227     12280   \n",
       "Adrian College                       50         1036           99     11250   \n",
       "Agnes Scott College                  89          510           63     12960   \n",
       "Alaska Pacific University            44          249          869      7560   \n",
       "\n",
       "                              Room.Board  Books  Personal  PhD  Terminal  \\\n",
       "Abilene Christian University        3300    450      2200   70        78   \n",
       "Adelphi University                  6450    750      1500   29        30   \n",
       "Adrian College                      3750    400      1165   53        66   \n",
       "Agnes Scott College                 5450    450       875   92        97   \n",
       "Alaska Pacific University           4120    800      1500   76        72   \n",
       "\n",
       "                              S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "Abilene Christian University       18.1           12    7041         60  \n",
       "Adelphi University                 12.2           16   10527         56  \n",
       "Adrian College                     12.9           30    8735         54  \n",
       "Agnes Scott College                 7.7           37   19016         59  \n",
       "Alaska Pacific University          11.9            2   10922         15  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the info() and describe() methods on the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 777 entries, Abilene Christian University to York College of Pennsylvania\n",
      "Data columns (total 18 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Private      777 non-null    object \n",
      " 1   Apps         777 non-null    int64  \n",
      " 2   Accept       777 non-null    int64  \n",
      " 3   Enroll       777 non-null    int64  \n",
      " 4   Top10perc    777 non-null    int64  \n",
      " 5   Top25perc    777 non-null    int64  \n",
      " 6   F.Undergrad  777 non-null    int64  \n",
      " 7   P.Undergrad  777 non-null    int64  \n",
      " 8   Outstate     777 non-null    int64  \n",
      " 9   Room.Board   777 non-null    int64  \n",
      " 10  Books        777 non-null    int64  \n",
      " 11  Personal     777 non-null    int64  \n",
      " 12  PhD          777 non-null    int64  \n",
      " 13  Terminal     777 non-null    int64  \n",
      " 14  S.F.Ratio    777 non-null    float64\n",
      " 15  perc.alumni  777 non-null    int64  \n",
      " 16  Expend       777 non-null    int64  \n",
      " 17  Grad.Rate    777 non-null    int64  \n",
      "dtypes: float64(1), int64(16), object(1)\n",
      "memory usage: 115.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3001.638353</td>\n",
       "      <td>2018.804376</td>\n",
       "      <td>779.972973</td>\n",
       "      <td>27.558559</td>\n",
       "      <td>55.796654</td>\n",
       "      <td>3699.907336</td>\n",
       "      <td>855.298584</td>\n",
       "      <td>10440.669241</td>\n",
       "      <td>4357.526384</td>\n",
       "      <td>549.380952</td>\n",
       "      <td>1340.642214</td>\n",
       "      <td>72.660232</td>\n",
       "      <td>79.702703</td>\n",
       "      <td>14.089704</td>\n",
       "      <td>22.743887</td>\n",
       "      <td>9660.171171</td>\n",
       "      <td>65.46332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3870.201484</td>\n",
       "      <td>2451.113971</td>\n",
       "      <td>929.176190</td>\n",
       "      <td>17.640364</td>\n",
       "      <td>19.804778</td>\n",
       "      <td>4850.420531</td>\n",
       "      <td>1522.431887</td>\n",
       "      <td>4023.016484</td>\n",
       "      <td>1096.696416</td>\n",
       "      <td>165.105360</td>\n",
       "      <td>677.071454</td>\n",
       "      <td>16.328155</td>\n",
       "      <td>14.722359</td>\n",
       "      <td>3.958349</td>\n",
       "      <td>12.391801</td>\n",
       "      <td>5221.768440</td>\n",
       "      <td>17.17771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2340.000000</td>\n",
       "      <td>1780.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3186.000000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>776.000000</td>\n",
       "      <td>604.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>7320.000000</td>\n",
       "      <td>3597.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6751.000000</td>\n",
       "      <td>53.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1558.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>1707.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>9990.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>8377.000000</td>\n",
       "      <td>65.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3624.000000</td>\n",
       "      <td>2424.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>4005.000000</td>\n",
       "      <td>967.000000</td>\n",
       "      <td>12925.000000</td>\n",
       "      <td>5050.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>1700.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>10830.000000</td>\n",
       "      <td>78.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48094.000000</td>\n",
       "      <td>26330.000000</td>\n",
       "      <td>6392.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>31643.000000</td>\n",
       "      <td>21836.000000</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>2340.000000</td>\n",
       "      <td>6800.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>39.800000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>56233.000000</td>\n",
       "      <td>118.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Apps        Accept       Enroll   Top10perc   Top25perc  \\\n",
       "count    777.000000    777.000000   777.000000  777.000000  777.000000   \n",
       "mean    3001.638353   2018.804376   779.972973   27.558559   55.796654   \n",
       "std     3870.201484   2451.113971   929.176190   17.640364   19.804778   \n",
       "min       81.000000     72.000000    35.000000    1.000000    9.000000   \n",
       "25%      776.000000    604.000000   242.000000   15.000000   41.000000   \n",
       "50%     1558.000000   1110.000000   434.000000   23.000000   54.000000   \n",
       "75%     3624.000000   2424.000000   902.000000   35.000000   69.000000   \n",
       "max    48094.000000  26330.000000  6392.000000   96.000000  100.000000   \n",
       "\n",
       "        F.Undergrad   P.Undergrad      Outstate   Room.Board        Books  \\\n",
       "count    777.000000    777.000000    777.000000   777.000000   777.000000   \n",
       "mean    3699.907336    855.298584  10440.669241  4357.526384   549.380952   \n",
       "std     4850.420531   1522.431887   4023.016484  1096.696416   165.105360   \n",
       "min      139.000000      1.000000   2340.000000  1780.000000    96.000000   \n",
       "25%      992.000000     95.000000   7320.000000  3597.000000   470.000000   \n",
       "50%     1707.000000    353.000000   9990.000000  4200.000000   500.000000   \n",
       "75%     4005.000000    967.000000  12925.000000  5050.000000   600.000000   \n",
       "max    31643.000000  21836.000000  21700.000000  8124.000000  2340.000000   \n",
       "\n",
       "          Personal         PhD    Terminal   S.F.Ratio  perc.alumni  \\\n",
       "count   777.000000  777.000000  777.000000  777.000000   777.000000   \n",
       "mean   1340.642214   72.660232   79.702703   14.089704    22.743887   \n",
       "std     677.071454   16.328155   14.722359    3.958349    12.391801   \n",
       "min     250.000000    8.000000   24.000000    2.500000     0.000000   \n",
       "25%     850.000000   62.000000   71.000000   11.500000    13.000000   \n",
       "50%    1200.000000   75.000000   82.000000   13.600000    21.000000   \n",
       "75%    1700.000000   85.000000   92.000000   16.500000    31.000000   \n",
       "max    6800.000000  103.000000  100.000000   39.800000    64.000000   \n",
       "\n",
       "             Expend  Grad.Rate  \n",
       "count    777.000000  777.00000  \n",
       "mean    9660.171171   65.46332  \n",
       "std     5221.768440   17.17771  \n",
       "min     3186.000000   10.00000  \n",
       "25%     6751.000000   53.00000  \n",
       "50%     8377.000000   65.00000  \n",
       "75%    10830.000000   78.00000  \n",
       "max    56233.000000  118.00000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Exploratory Data Analysis (EDA)**\n",
    "\n",
    "**Create a scatterplot of Grad.Rate versus Room.Board where the points are colored by the Private column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Room.Board', ylabel='Grad.Rate'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cGynN+VmNlQOXCVNL4LM0B3sN2sLXrm3cCMmMxjOryRbkb0PBPQOKqVUvywkf7YEcI4EVOmIGRnMXyqz/OCzHNWeuVn/ePe3Q290G3W113zM2NkZHS4imJV216na7oC3mJ9BVvrfLm5qzs7Xn7+tuh+72m/jkZdT7rtI5RVb70RoMpXEq/rmvGTrbomSSUfyxAc5My8S9r7ON/maFZ4f1lWRG457V5ArlY4ZiKwiUcHHBxYcPj5AtwHfHNH3NcHG++jWdMYvW8N7dfOxdP79jY2P4fLWBsR4Ke++vrhEAGCbW3vtx7fAc2+HxeDBNc2sMn//853nllVeIRqN8/OMf5x//43+M4zgcO3aMj33sYzd8JizLqvn81wsMd6RYbBjlDFQymSQcDhMMBkkmk1XHKwNDA7VQholr8Ai2aWHPXkT5wrj23Y8KVU++sQAc689xYcFN3oaRTuhprv/D8bkVj+yFszOaxU1oC8OBHlW3oHk7kMlpvndB4zjQ1SSpG4BUVvHcmOapQ1LAvBn4PPDIPkl/TCzCYDucu6pJ5eSzTeTcrGfaech4FZ/Lwox11j1PLACP7lOcmpKgsqcNBlpvfH/cLsUDe2BsVjO3BrEgHOpVBLzV720KVp9/pB36WxSOo0nmQCHB+WYCoi7+V+8fLi9ojvQpJpc1ySx0RmGoHUxD4Q6288L58jtfn5CgMdAGyeKiwe8B4xpjCngVj+2XFNdqAjqbwOOqHYnW5fFl87IjGe6QlJ5lym/xZr//24VSHaBw8VVJB3kDuEaOv636QMl1tIRgMMi3v/3trb9/6Id+6OYHvAPckUBw4MABXn75ZU6cOMGzzz7Lgw8+yJEjR/ilX/olstksuVyOy5cvs3fv7qL+3QjDH0btewBz4BDKtFBWbTcMl6nIbUzw1KF9OA54bzCpRwOKB4ZltWaZNy4SBzywt7M8aQN4XBCuzTDdEPGM5NKH2mF6W5ZFA+tJTSy4+wkwnZNC7cUiLXukE8Le2oYi62mDdHsXnqWZawYCw1B0NkFziB3dz0qE/YrjQzLBXevemnXOn85pTk9pLi5IINjfJbuE3Vwb5Lsa6YALZXo6Hguifo3bBW9OaTqiEqRsG6aXwWVoVlNWzbk20jKxX6i4pyMd9VlQIIyfh/fK78rtku/ZZWgKFT7+o93yfsvU9LfA5DL43BI4DAUtIbk/7za4uobfdmH43YQ7Egi+8IUv8KUvfYmvfOUrDA0N8fTTT2OaJp/5zGf49Kc/jdaaz372s3g8njsxnPc8lFIob+C6r9Fa43bt/AFymQqXufPrD3dA0KuZXIYmP/S3KkLe3T+wpWe8UADLxVYap3JcN4OFdc35ikB1fhaCHmkgsr3frImNcntveM7d3M+q8xsK3w66V1Wef25Nc6GYItHA2asQ8kFfixwrFDTxrEzeQe+1A4TjQG+zrN5nVmTC729VBDxw74Bmfl0KuIYHTBfYWu5dW8RkOVEuJpuGBJC3KspGck81Q+3Xvi+GAbmsBAG/Gx4fVUwsaRIZ2VWVmFEuU3GwF5qDmqkV8FrQ16zwe959QeD9iNsWCHp6evjGN74BwODgIF/72tdqXvPJT36ST37yk7drCA3cRvjcisE2xUCrvukcfjyjmVjSdMfg6pqkAU5PldMHPjc0XT/eXROTy7XHrq7JCrZyJ7MnlsW7chFj7/Gbu9BtgNaaiXrjX9X0tSgyOc2ZGc34ohwPeuHhvRDZRrHM5TXn5iQgugxoi8BAi8JnwdlizcNdXPhPLsNot+LCXJnBNNgmK/mFDYj6YWmzdkxTKxALaqKB2t+A7cj3+8aEpICG2yGdE0aR25LUVCyo8BTHEPAo9nQohtpv/jf1dlBZy3wv42YEaQ1B2V2ObF6znoJcQcRkEf/uctE7fa2TWMNJbqBMFyoUw/D4mV6WyayvGfZ3KQqO5sERRSKjMXSermb3TYuJmoLCSa9ELABD7SLW2kjaRN05omoDb+dxjIo6Szyt2UxrTEMR8YHvDq9KlVI0BTQr8erj0WJQXE2wFQRAVtvnZjXHh6rTKGsptnZFBUcYQwVbc7BXjh/sUTiOFMM7ojCzUk1jvbIIDwwr2iOatrDi6ppmYaN6TEEPvH5Fc/8eTTKrsB1NyKcI+xQbKXj9irzOULLju1QilRUpvBNLmkO91b+j2zEZJzOajbRMklF/bZ3G6/WysrJCc3PzezoYaK1ZWVnB673xDrcSjUBwFyNTFP2UcvNKaR7ZK/nqWwl7bYH8a38OtuR9VFM7rkNPMLcuM9vUCoDkrNvCmof2Gpw7d4VQz/6bvmZ/i6QgSnoKryUFc59b0dMMPc0u5OdfXdhYTYi4q2DLmJoC8NAINRPH7cZgq2J6ucx48rmhu0nGsJGuXfEtbEC+AGZFCqqeHmApLq8DqaMooLtJ0k7nZ4W1VaggwziOZl+XkD0MA64sajIV9zTgVVxZ0syuwanibs5liggsnStf3++BeB3G49y61Al2mpa8GWymNM+eKwc5r6V5fLR6B9XT08PMzAxLS0u3byC7QD6fx7Jq6zQ7gdfrpaenZ1fvaQSCuxgbyeoCrdbw2hXNhwK7L0peC7qQp3Dxta0gAKDXFmBzic5ogNUKxnCuwJb6+e36rUT8iqcOUhRPyU4neIPJvGCLyrpyIlxLyoq5FAg2U5rluMZ2oDl0besOpRSbKc1K0TaiOSjWHddi2WxHNKB46pCMXxXHXxpD2KfYzgVqD8uKuxKSX69+XUuoPOmOL8JAqxRm/W7wWIpkVuNxya5scpmqHH3Er3h4r2Z+Q47Zjtwvv6dapFewhYV2oLt8/XROahXb0RGR+sPtxMxq9U4nk4fJZWFMlWBZFoODg7d3ILvAnaavNwLBXYxsvnayTeeE5bG7jeW1oQt5dHKt9ngmSW+7Ym5NvHgA2iPQEb11K++gV91w8q+E7cBGrU6IRJEuuZ7SfPdsmRuvlObx/Yq2SJ1rN3XynbFKhbfYfLTXee1ux98chMFWuFJcvAY8sK9L1bBron5hd5VYPl4LjvSpIutL2D8TS5DKymsvzJd+D5Iqum9AiACV8FoSAC7MyhRvmXC4V23VHEpYT4HPrbl3AE5Oyr3N20JRnVuX10T8MNCmbnsqpnKxUcL2tNvdjkYguIsRrLOybAuDdwcMl51CebwY7YM409ViFhWKEfIpHt0vKQOlpOi5U8HSZkozvaJZSchKs82TxJo+CfksZs8+jKYOlGt3W2u3S851fq76uMelOD3l4DKpEkhpDednNc11KI7xnLfK5iPsk1z+lUUHpWCoTdEcvDljOa9bcahX0x0r1lOKTKjtxU6PpTjYI0yjfEHub2mFX3ncNDTfOlN9jfl1qdtsN4mLZxTz65rRHoXWov94c1Kon+crUj99zTLOoTZoDQkbzO+RexxPg6MlHXUnzA17msvBp4T+Ivsqk9PMb2imloWY0NeiaorudwMageAuRsQPDw4rXp+QVW5LSJS3VgVlM1fQaL3zCXo7lDJw9R+kkEvhLEyCaeHaexwjIs5mHqvMGtkpUlnNc+f0liZgYQOGm13sj6+hNpdwFiex7v0QZlv/LseqGGqX80+vSspiTzvMrmk209RdzafzMqltT3Hn7er7NdCqeP1KOehOLWs+cEDRWscI8EbQWgqflxY0C+vyPcaCGtOghr2jAZ8lK/7KIOEy1RYjazUurzQNSROZSiZOx5G0WiVsR7ORosZbKhYsU3N7Y1KUV0qhFES2Mb+a77ButD2i2NdZpuMOt0NnVALZxXm9xSJb2JDi9ZMHb5xGfL+hEQjuYpiGordFHsyCLQVJq8hlz9ua2VWhGWoNB7o1XTF1U1x6IxDBOvwB9HACDBPD//Zmgo1UrTDs8qqHwbZRvJuSLylcOYXR3H1N24hrIehVHN8D+7o0V9dgarmspA3V2UENd1AVOEuI+vKUHq9YUGw/tmNyWdMa3t39TGY044uaiSVZYT+8VzGxLGNN5+Bgr6YpIJPcclwEYIkM9LeKIrpe0Tvglck7GpBz2Y4I/Hx1ZD0hn6oRhXU1QXsUPnJE4WipN7ybnGt9bsWhPqHDgqTSDEN2Uxe27f4yeQlyjUDQwF2HeqKdpU04Pye2ySD+Ly5Tttk3A2W6UMHoNf89b2vSWTBN4ZNvHS9Ioc9llsep65km3EKDSpcpArDxxWoX1+kVsWk+PysF5X2d0NVUf8Kw06s8MdrNWlIml3qMmd3CcTTnZsv6gdYwnJkR+i/IKn41qXnqoDB75tclOGTywgjKFyRnvz0d5bEUfS3w0iVNT7NM5OkcrCUkpVWJsE/x+Gj5ur3NMNKucBlQa4f47oGhFKGbswF6VyBX0GS2PQe3Eo1A0EBdbKaEOlnKl/c1C79+e6rgVl3rjQnxOrJMuHcA3G4vG0nNa1ekDuB2wX2DQnWM+BU+dzUTZE9zFs9iuQ7hGjyy691AJbxuxYFuEUOVkMhIHvnJg+q66bK8rcmY7Zw8L/z9ribY066YW6+OVv0tu7uXqVy5QAyS899uy5EtBoA3JiRVM9QmjJ75dXnv/i7ZAWzHUlxzuFdxcV58hwIeYToVbF2zum8OiXVEwRa1cd6Gi3Oat2YkNdQTg8N97/70Sj2rFK9VK8x7p7Ge1Lw2LqQKjyX9LMSC49aNsxEI3sPI5mXbr4HQLgqtO4FSwigpYXIZDvXestNvwbY1p6f1lvgrb8P3L2seG+nk9QkJAiBF2pcuaj50SNEUVDy+n61icU8M2r0FzGwTeP1bxeKdQmtNPAOZnBTKQ16ZUEM+eGSvErqoFlXuTiaJ1QS8dbX8aM2uQTSgeWJUcXlRYwCDbQrLpVnckAlpJzoF0wC3WW3DUZusgmxBjtqO7OQeGBYxnWGo4m6qNu/fEZEdQakYnsyKLqAtXCIVVKPSkmQ1oXlzqvxvM6vQ3VT8bWoIeERktlt2UMGW76Vgyz261SthpRQjHRDyVReLSwEsk9fE06KfCHlv3mLk7SCX17w6XnawzebhxQuaDx1WWwLDW4FGIHiPIpGRVUJpAo0F5YG/Gb+feqhnJ7CwIeKfW4lMXibK7bBxsVyH4hfPaJqCirBfcbBqUg5B5LFdX19r6WD2/ctix2woOD6kGF/QLCcAxBr5SP/OHVnXk7V5qsvzsjp/aMSgUNBcWtScnpLzu11iEXGjeoHPrTjaL2MFSQUNtIkCuIT2SPX193fBhVmK6SPNUhyODZR1EQVbag65QjUjCiQoJ3OK4A1SKquJ6s97sEea0JSONwfhYI+mfRfU4Fxe0mDnK6ivj+zjpgwIrwevWzHQWtuVbTOteemi3qITdzXBPQPVacs7gXS+tnmQRp6DerYeN4tG8/r3CNI5zfy6ZnpFOkzNr+sqC4XVhFgE3CrUs/69HXbALlNSHJWI+mX1eqBHbBAqefo72fUkMw7TK5oriw6LG06VwnU7EplyEABhAL12RdNSweaZWtkd77y0cg37ZPyj3eLjU1q7r6cpBgFBrgCvjmsydXQd29EdgydGFYd6oSsq9+pQr5gAHumTdqSlwOpzQ85mq4YAsLAO8xUpqo00vDkp/19vwe7ZwVIxUFFU9lhidFfJ3V9JSNBK1FFEXwtrqWoabyYPJyduXde260FrzcSirtKUzK7BUp2C/+2GZdb/Dry3eHfS2BG8B5DKal4ZlzQCQCyg61Iu59Zhf9etMc7qiSnGFytl+bvPae8EHktx36CkfQq2pGZ6YvD8+dK1NAOtQm31e2Tiux7iac1z56RhOsiDdKQPOqK6bmohnaOqMQtInnv7LVxP6mv2dNiO5iB0RhxiIZMzM2WRVjwDh3o16TrWD4mMbPu9N6DSukxFwKO5NC8snzcmpOOAy5R0zECrWDrnbUlnrNfpnri4CXuKmbPSWK6u1tpVH+5jRwXWlpB4I60l5ZpLdYLmZpoqptGNUM8eYyUBqZwmcptTNAUH5jdqj6/EuSX9nHcDv0dxbBC+d7F8PwZbhTJ8K9EIBO8BrCXZCgIgxb+2SK1IpjN66wy7wn7FkwdkxQgQ8e0sj6213rJlMBS0hhSR62xhCwXxuxnukJVPxA/Pnat+zcSSNG6JBW+8I5hb11t0T5D0xtKmxjKFI94UqD6P26VrrKlNQ3LbldjNNtzvUQy3ZnnxSvUsenlBiu71rB+CXvDu8GlcSQhdNJ7RYhynNR5LzOHCPsUHD0lqw2Uqljd1jbK2tMPSulQIFp2EacgORqOJevO0R911ewFkcnLO9ZRcrzkktZSSqdt6kpq0XsQvDqg7haTharUK29NXtwMuQ6wvtqvM77T+oYTOJvjQYaG7elyKqJ8aod/bRSMQvAeQ3bYdztsycXVEyiuXlhD0xm7tjyPgVXUZJtfDclxaDpYmUpep+cABaLrGRDqzBq9cLn++Q721/QIEGo9145lks45FxGZa7pkETs3BHtjfDWhh0xzoFosE25HJ8PiQYmqpPKaB1t1NAlprCrZT5VlUQragaY8ojvbBqWkJOG4X3L9H4dlhDaLUVnQzzdaOw2tpeos7lpBPbbm2+j2wsFEuundGyzYeJZ3Bvi7ZCawlZSf18F5Fam0eV0utIK9ga8ZmZUdS/LT0NMOxQVU8ryLodZjfKAeDlhB0ROsXna8Fv0cz1FZ2WfW5obdZkczomxLh7QZKKQbaRPtRys93x6Btl5qPWwXDEPHftZ6hW4FGIHiXwHFEAHR5QfLVe9pV0SBMEfbWro7yBbh/WFo66qJc/51gNVTCcTQX5nTVarpgS5OVej/idE5zarL6c5Woi5WretPYORWxIypumJVoDVf3Jxi7Kvx3reGtGZlERzog4ikQ1Jv4p9+ks6mDXGcfOVeQkG9nAqlsXmyaryxqupr8hH0yWZdgKHC04rlzYsnw5AH5O+ABb26d/IXLOOsLmB1DGG19GNdoPtQUrP099LfUzyWHvNU2HiGviAbtoiZhLSmpkP1dwiVqCYmSei3ZA8sOHZHqPsWJDBVBQDCzAns7ysEy5DN4eK/DekoEZgGP3jWJwe8W4dqBopVFriBB64nRO/MbD/uk1WY8I99b6b69X9EIBO8SrCRkJV3C7Jrmsf3SwSkakBXjyQnJ/XY1wf5uhddSN8wp30loTRW3v4RKUVYlHEeKmZWYXYWH9spKfXxRisnHh1RNQflaaA0La+XcrOT++1tkh1GZUnB0yZunPGYjn6J18s9RKdli2YsTWO0L+A8/ho2LdE7fsI3n1LLmZLHwuhRXHC32A15Lyop2f5fizUmpuyzHZZX84DCY+QS51/8SMrJsL6zNYyTWsPY9UFcLEQuI1uL0tATavuaypUM9uF2qZkejNVt20vG0BMcDPYrvXSy5ryqWNuFov2ZvZ5lhlLtGsdbeZkfhsYwKS47dT6AuUzHYLgXihQ3ZNR0furWUyRvhZuxP3qtoBIJ3CSaXax+wSwua9og8FAOtYghnOzKpvJsk/CWYpmKkQ/P9y9XHu66RsvK5xcuntMJsDcv2+5Uii+dAt+RHdyNM8ljC0ultlqCZzVMzntawiKq0lhXwchxazI2tIFCCs3CFbP9RXl9qYnFD7v+Bnvr1gnROM3a1/LfWYsb2yD5JseXymufOV1tcz6/LijOSWtsKAlvXnjmH7jtQV41tuRTDHYrOqNynm/k9uIrf1Svb7s32dJbsnkRhfWZGS2/qbTsdn/v2iMfCPhGupXKSt2+0rbx9aASCdwnq/cS3H3u3PwjJjCbsE773m5Pl4mPzNWinhqHY2wluU3NlSVTDJytSRScnZWKvtxtIZWWi91q1BWSlZCWn0bw1JdTN+XUpiLaGZUdV8gc6PgSX5jXXMgHdTElaIOyTifuFC5qnDl6jYfu2QxrI2YpOn2IpLytqyxTXT7dLcv1KUZ+3Wed823Gj4n3e1qSy8j0EPLVEgs6o4tigcPVNQ8zp6g0hX9A8f7682zvUq9hIiiahLSwW2Lfrt+kyVY3NRQO3Ho1A8C5Bf6viymK1i85wx+33ar8VsB0RZb0xUWrCAg+OKIKeG+dVAx7FaEuCQWOV05tdbPfxvLwgxciSnF5rabj+yrisUsNeqZVUCo3Wk5rvX5aJ/0ivpGNaQsKWWU3Anrbya0M+xdEBsNNNODMxdGJ1699U5zCrOiR8di0+S1FD8uTbm9H73IqDPXqrNSMIdbXk5x/2SnG/LSJ9gTN5YQoNOaCCTSh/GJ0qC0OMvgMo383TVOJpse1Y2JBJ/kivGM9Vfh8eSzHUruhpFuZWOidjzlfsCg70QCavqrQYb01rIn54eB9E/bV9EBp476ERCO4AnGwKndxAGQYqEEVZtbaOsSB84IC0V3S02BbvlKniODLpZQsav3vn+fRbhfVUWe0KUu84PSXskxtBa409cwHj6gX8PX8TqF7+BTzVC+PNtKzKSwXpzYxoEJ46JN72uYLmlctlMdDUinSiWk2Id/5gqyJnV+ezDaUw/H6ce54it7lO3PHjWD5cbhdvnXdtXWt6BfZ1wXbyks6lcRIb9FsQ2x9hbMGDz7IZaHcRLqqfPW7FgR7Nd86WxWuJDLx8SfPBgwHc934Ie2ESvbGE0T6A0dyFMnbWvzFXkO+/RBowlVgrl/oL2w68MSluopap8Xs0mXzRL8klQckyxZTtAwcUU8vSs3mg1aAtXG7MU4mNFJjq9geBZEaTzOqt8b0bU6LvBzQCwW2Gk1gnf/Kv0Ul5Ko3WPlyjD2H4qvMlhlK0hKFllxQ129FMLIk5mtbSZOTBEYVp3sYmsNtQTzG6sCHePTeyJ6CQw5kfh2yKLmuVS2b3Vp7aUOLJU7kr2kzpGo5/MgvprKSJ0rlqJe1aEtaS4rp5cV78ifa0S3poO7KuMG8mQltGbgGPpEFOT1UEuTgc6CoHEie5Sf70d9EbwnP0R9t48NATjM8t0hQYrj5/oVa8lspKDtwbbMII1hnUDZDIiBdNyRKkr1k+29XV2teuJDTakUC9UrR/iPjl9WNXNfs6hYRwpN/gypUr9DQPAhAyRNRX6T010lGrCL/VWIlrnj9f8j8qjq/r1nPoG2gEgtsKrTWFmXNbQQDAWZrCaR/A6B4Byo0+EhkRBUX9uzOPi6epSkfYDnz/kubYzfpF3wSky1T1DBfwgGmKVfFmSlZ00UCd3LrpQoWa0alNApe+wxNDj7JuxLBdPjAstIbxBQeXSxH2ahxdey2XKTn3QkHSRaPd0mvBMos20krhcWlaQ6KOdlua9aTGUNLgRSmF16XZTKsqN89kVvxyov5ycGkOgqtCGeUsTqBTG6RHniRuRDBwiCYzOE6tGMJT5z65ikZyO0EmJ/czVxA6ZiQAC+u6yhcq5BOmUshX26LRMhU5XdYUgKzsm0OS6jo/J0ymtghkMpmq9x3uFS59IiPXjgXrr84LBc16WvQIfrf0Wt5Oa46ntfRiVvJ7317rSGaEaZXISNC+MCe/6/Nz0vdgN+0+G9gZGoHgdqKQRy9frTnsbCxBMRDMrMrELZCV19G+na966vno5G1wuHO8t2hACqBTRa6+UiIwSmTg2bFSKkQTC8KDI9XGXcowcQ0eIbd6FfI5/Be+hTfUylz3kzgei++OiZ1GviB9dPe359jbCheWyp/vvt48Xsvi8gIVLpgiSFpPyWQ+0ikq6ZLGwFCSMnpzSqO1WG63BGvv5VoCWsKlHrxSy6mEszxDfO+HeW62dUsIF/E6HO6uXS5HvLKirbQ9vm9Q7UholcnJyr+kJldoHt6nqiZ1AKdYQznar9hI6a0xNYdkYVLJ9ilhvWgNkc6Vfk+14/G6FV1u6v5b5bWvLJUptKDZ3yWBuRQ01pLS91nqEBqfGx7fz1YKbTOteXasbG3idgnh4FRxVyaWGI0dwa1GIxDcTrgsjNY+7OR61WGjqR2Qlc8bV6onn4klUbK27nAe93sUJXPhEjwuMHQdQv822I5YBSxtymTbGlaEd6H+3LqepbinXzxQcrasGH2W5rnz1amQ1QTMrUHYr4kFypODEWnBvP9vklxdJ+eYLNpNOPhIbGgO9SqSWY236Hvvya4xvHaazs4DZLSbAGn84yfJ+z/AqenqdNv4onDjVxMat6mqXDIdLUGhlEZZT0pdpqaHc0TM3bqbRNlZw9TpHuXcWlOVGnojY7CZD5BecGgNl1W+Lpdif7eoezN5TcCjCNfxjMnkRFy4lhSHyZagBKJKSxENvH5Fc29/tVjOUPJrOHtVs7dTjoV9ikJx59kaEvZTJZoC5VSSf4fq5npIpOHUVPWxc7Oyk4gFJRBdXtBVxeh0TmxBSoFgdrW6z0SuIPch5JPdr/9d3uPgvYpGILiNUEph9ozgrM1v5ZCNzuEtr/y8Xc3QKCGX3/mqJ+QVsdmr47Lytkx4YFixsbACtFz3vfPr8L0L5YnPY4kdxE6DgaP1FpvHY5VcQuXvdK7UyKYaqazmzUk4Mayqup0VPGEW3WEuL4iP/b39YGthqJQQ9cNQwINnZYrwyhSVTgO6kKupHYBMPlASPFUjmRWDOxyNU3T2720uN3sJ+4q6hnHZzZwYrj2vE+lkc6H2MUpkFTMrQo19fLSc/rBMVXQ2rX+PC7bmzEy5CxlIl7hSm8VKpHNguSRVUioMb6Y1h3rh7IxoAA72iJ6hJB7b0y6BqBRUWoKSWsvmpfFPJHjzK+6cXVsDAYqOoaIyrmeCV+npU2/HksxK17S+5hubDjZwc2gEgtsMIxDFfd+HcVKbwhryh1Eu4R76PbIaq/QbV2p3niyGoehrEY/+bF4emIBXsTZ3favHXEEawlQim5c0yo0CwVpCPOw30zDYpumIKLwVK8m8LTngrqbq1SoIfdHRmlNT4hlTqod4LEhlREC3v0sKv29uW12upyCpgngME5yKCBpowvQG8Lmrlc2WKeplYMtcbeu+KXi8dw3/6kWM+BL5lmE26aE1HCDqVwS9MnleXpAUU86WSarZlNX6pXlJu+ztdNPfDGMV6R6QlXWuIIXOtVT9rmD1kMhQEQQEM6swHIlzXwe8Pl+mkrVFIJmT38yBHgXFpvbrSfjgIVlNb6bLCmIQ47uBVkndmYamOQSZnKIrCtOrmufHYKDVwR/afY0p4KHud1AKgqahGGjVNf76le0+u5pUjbiytxnaw9IXoUFVvT1o9CO4A1BuL2a0DSPcshUEQIpox/eIpxBIYHh03+4FNEpJSqc1XCd1cQ04WvyKtqPeDqUSmynNd8Zkxboch1cuS4olmXFIZBwcR3LUz50TS+kSO8ftkslqdk1vXady9WgoxXDRymAlLsyWeqvLAibqyIehyLHXkXbWB54gbnt5eG/ZgiDkhXsHFRNLwqM3lebeASlO7mmHx/dkCZz/a9T0GfT6Iq5L36Np+S3mV22aAppTk9LucSUhRUq7mNMuWYFcXRPDv2fPyYQ82CbraMuUQLawUR58vd3ItVDfcA/sVILu+efY1yJczraiCvvcVRhqEw3K2atyXw/0KKIBg7aIUbeV89yafJaWsCLoNfC4hZJ7aV4+72tXYCXXhFNvi1VEJuewmXIoVHhLGwoeHC5/B0EvPLJPVamOu5oUezvktaYBh3sh4i/3GWgNi/Ggachr9nWKmWIkcGOqajavyVyn90QD18Yd2xHk83m++MUvcvXqVQzD4Gd/9mdxuVx88YtflJZxIyP8zM/8DIZxd8WmqF9MwTJFMY/3beRodwOvpRjp1FUNUhQQu4HD4Xqq2ibBY8lq+3sXJaXTE5PCK0jD9OYgPDTClqFeyXdob2ctgyjolQ5cS5twflZXpTxA7s9mGiYTnew/8DdIJnMspn1MzVgcVJrRboMnRmVns5nWnJ/VDLRJUNhIiRNqS0h2KYsbbo7s+TBNV761ZS2hZse45549rORbSG4rsZQ0BNsN7QDOXoVH9sqktZoQD6DSqthQENnFDi/oFUpnZbok4HYIZBZhY4G+/k0MTysrCUmb7e+SYBsLqq1+DlYFm6c5qFCqmnI73CH1kFIT+42krrF3Hl8y2dtVnyI6v655a1rsMTqjsLdTk8qJdiRvS/+HqA98HlXDgPN7FIf7pR9C3tZML8NfvwUhr+Zov9Sp9ndJGkgji6Mb9ebN25qrq3I/HA2jXRpfoEEt2g3uWCD47ne/S6FQ4Otf/zovvPACv/RLv0Q+n+cnfuInOHHiBF/+8pf55je/yYc//OE7NaR3DSxTYb0DMvr+FoWpNJcWJBVzoEfdsAuZcPrLs8qedsWpybJIamJJJuK2sDRAWUmI3cThPsV6UpMyZCLqiRU1AUhKoVQ4Ng1Fa1iTyeRQppuQT7pDRXzQ16o4PSmTzkbaS3fMy+R6cVzF8bhdCrcLzs5II5nVhAYtOfHWULn5e95WvDAT5QN9DxM692elT4c7MUukKcBIhw+XKRN6iQ3lMuV+VcJrwXAkgZHKE/QGMMIe9rRrrizKJHqgW215E6VzsmK1NQQ9qm7Q91iKB4fh4rzN/IZBqy/HsG8R6/zJ4vUUq+uQzMhquq9FdBaVSudkVpMvSFBoCsDjo4qz03Lfhjvku4lnxBXUZaq6DhfXmnpX4g7fu1DeuUyvyEq8KSBpLZBd4uE+MdmrB0MJmeDNyXI3tbWkMMw+dFiax29PpWktCw1xM62mrq7GYSMljLtkVlKKR3ti1/gEDdTDHQsEg4OD2LaN4zgkEglcLhcnT57kgQceAODxxx/nhRdeuCsDwTsFn1sx0qnobxWvnZ2oNqN+SfOUVpBa65oUzty6UAZLrTRLdgqP7ldbzp/nZzWX5iUQ9LfAoZ4yI8Q0FJaR49KSm75mmZRWE/DGFc3+LsWleZnUrCL/3jSgPVI99taw4rUrmv2d0vOgI1JbrwDY1EFCxUKx2T2CMzeOO9jO5LKPXEHGfaRPkclr/B7ojikuzMsKO+K1ORGZwnPue+QLOVQohu/Q44x2NxPQ83R3tmMW7+nShsN6WlI5mbz47Z8Ylu5e2xH2K+7pdchwEWP+AiouAzdaerAiER5pUti6lp+vtWZuTew3SmN/YI+iLayI7RcSwvQqfPMtKfR3NcHRftmV+tzVbJ3h9gKBOtab8XRt+mpxs7aYfXkBBlv1NTUxqVxtr2pHy04u4q9+TzYvbKOxq/KazuK4Q16x095MS9/mvC0F/iN9iukVi6FO3agp7BB3LBD4/X6uXr3KD/zAD7C2tsav//qv88orr2ypRgOBAPH4jRvDZrNZxsbGbvdwyWQyd+Q6twv5fJ7puVUK2oNlSheudF5hGTbZxBL5/I3ppfWglOJYbzeraQ/JnEnAY7CdcmmZ0BKw6W0GtwFtoRzJjQTxRAA0FPBwcb6soppchoA7h5WZxLZtPF4/SaOHgTZ4a7rciAXg9LRmtFtxdkYESwOtIvhKxpeYL9J93G43SbOPB/aYgGLI0uJe6S53XNsaq9uF0dqDCregkxvoxBoraWsr0CUycGnB4Z7uJOfPXcU0XZwY6GYhYTHkX8Nz6jtb59LxVXJnnmel+ygrK2sk4zLThaLtbBSinJ8r8/pTWWFsndhjk8/ncewCdnadTKosDGgN+fG3D6K8AZxIGwlPhKVL49f8bkJN3bw0GdxKA4mFhcOhthWSmyt4ogOcmirbm8yugd8qELQnuae7lbWMj3jaoC1cQGcWOXeuluITaNlLab/gtxwOtcTxOkkC+Ah7w6TyJkNt0m1teT1DNrlENl17nmCkDctsqqlJFXIZxsYmq455owOcmSmPe6447pA9iS/SwcnJMpVoMw2TS5qemGZy4grZbB1/jPcA7vT8c8cCwe/93u/x6KOP8n/9X/8Xc3Nz/O//+/9OPl+mMySTScLhG7ce8ng8jI6O3s6hAjA2NnZHrnO7ML2Y4rVJ79aDNtAqq7ClTTg2GGSopZwjvhn0UDKAE2plpYr1cB90xCw6irvzZMbFK+M+ljYl/+2vtVpiftPiyQMjGIZiM+UwNw3KqA4CUOohII1dElnN4qakoz50qJWezvKydCXuMDYrTXFAUjj3DaoqumzQqwkTR2eSOCuz4Nio3gNc2qz+HSYyCq8/VPV76G2H/Gycmtr65jKdh6NsJtNbr1/c0OhNXbOSzuZhKW6QyHiwHQ9eK8Ch4dq8egl+rk8Inl116thvKEKRVvq62zg9VVuJntsw+dDhPbhdip6toy7GxpJ1f/+rcYeWoPgPPdI8jffCd4XBpRQPDT3MWmiYk9NGMZB6GOnoYfQan6ngcni1Iq61hKCj2Yevs/q6b03XH/fBQ3tY3NA1/7aegsO9mo6eoZp/e6/gdsw/1wssdywQhMNhLEu2mpFIhEKhwIEDB3j55Zc5ceIEzz77LA8++OCdGs77BjqfxYmvoDMpDF8QFYqR0xZnZj1Vq62JJVFoLm5oXrsizU0ib6PJh+No1lLiZ98dE0vjgiNWEk2B6odzYaNsg5DOSY46FhQGie2IFiHklUk9m3dQSthFQa9w3Ld75Ef9QpUsUTatosUEiEhvPSU1gFIQAEnHXFnQPL6/2EjdVuRsuLQZYqDrOO58HE8oTNbwsTQuJ4t4bR7sXMed28De8LCabcbweIkGhNBgu+oUdjx+bLM6peJx1dZWQArJWkue/WCP4syMpismBdidoGDLZ01mNF63KqaKandnpXtTrxF9U0BSazuFYUibT7+TwPPGc2Uar9a4x7+H50AruULZM+nivOgclNJk85qgT5hbpqHobRaDxM2UpJBiwfr23qE6BWsRJNa3N/FaUMhscCMdTQNl3LFA8I/+0T/ip37qp/j0pz9NPp/ns5/9LIcOHeJLX/oSX/nKVxgaGuLpp5++U8N5X0AX8hSunMK+cmrrmGvkfnIdh4hnah+oEh1Qa8gUNJG3IdWfXZOVV7ZQ4r1rlJLV+nb6a2Uj81xBHtS2cKVYrFTo06zEYaRDjk8tS8GxUlQ22i0aiEsL5XMeGxTabDytef6cJp2HoToCrJUkhDfEyC6RgRcugNZeJuhmXxfsa1Z4MnH2tWS4su7l0ZYZjJPfAsQc29Pcx0L7I2i8NAUM4mYTvo59uObPywWUQXroUSxVrXoK+SCYlHaYFyvaPO7vgoli3aL03ezUQkFrzdSyBPXSPTzYLfenskHOfYNlSnFrSNEcLHsNuV1yf3eaR4+nNd+7IAXZh9rTeOxtree0RuVSQFPV4fWUsIxK4zwxrOhrkZpUa1jqOddDS1jRHJLfxvZxR/ziR3S5+HtQSjqZbS6uQGcjEOwUdywQBAIBfvmXf7nm+Ne+9rU7NYT3DFJZzVJcs54U+l9LqD6tVCc3qoIAQOHSq7jb9tDk97KWqn6PPPBSGPbtwMtIF/I4G0s4K7Morx8j1okRbCKd07w+oeluqhYQldIS25uUtG0r1OZtMRKrRGnHsrSpi575IuiaWNIc7CmKnwKaqLOCXr1Kd8zAjnSR97VsrXSXNvWWZXI9g7fmECzFwefWDHcoPnxYfPazeWE0za1rWsIh9nem2dMcxzj5YvX9W5ki1DJKMtPN1LJDLOjlknWcwX3DmE6GtBlmOhXhgW1PlWEoupsh5JfG9RspMc+bXtFbqa/Sd7NTHUgyQ4Wnj+DMVXjqoPRtzpYsLCqCcsCreGiv7IhsR4SDu+kstriht3pJJ7WfFssD+YrcnTLQnnrbzOrv4o0JEbIFdtjMJuCRTmUbqdpxuy3FoV6hm2YLmqBXPvP6/PUFlTdCwRb7lcVN6crWFlE1Rez3ExrK4ncZsnkxFyvz5zXDHcLN3r5y04U6hTCtceUT3NNj8NKEh3RReTrSIflylwkPDKm6aYLtcJamyFcUQ/H4cd//MQpGmGxeVuyHehVjs6IPMI2iiZpXY9sain71bWFRP5domB4LIj4HjwuWEsZW7ry0Kl6JawbbhAmSyEgv4HsHFU32IvnLr0vEWV/AME8ReOAZlBLOeIm+CKKQrrRODvugJag4Pa2JSs2aoBcmFuHCfHmSagtrTgz78JEnl6v1OzCdLDlbBHWrCU1z0M3z821oLbWPh/equs14XKbaap7jc0uhOJmVKXKoXSbZA91saTBuhLxTzd7xuYViC5I2qafH0VrjdpUYVruf1OLF+6sULOYCdOx7Es/5b0swMC2sQ4/iC0UILMquQSkY3SauA9kVbk/33QheS+GtsDCphNt1fduOm8HCRrX9itulefJA2Rzv/YZGIHiXYTNdLaIC6ek71Fqb01e+EFhuqGQAeQPg9hK8/ApP+IKkoy1YysZwCnTGYvibYjuysHCyafIXXq0+mE2hN5fxtYXpiIiy9syMls5XpqR7TKV56aI86N0xcBkObRHFsUFpS4nWBHNL9NhnMdMJ0q2jXMh2cTXu3dpRzK3LCu+JUZnsAh5ZoS2kAlzyC714uDeLozUXJv343OIu2hpWnJ+Tk8yuyaT6wB7Ja6/Gy6mokFfx0gUJsJWpGhAq5GYGWv1+VHMPemWm4oYr0q4IfreiYIvVRK4g7pmmofB7rtHCchuiAcVTB0uTpegcTFPy5TtN0/iLE/9GWlJMpiH2HaenNaYSw7mWsJxPF62nL84JRXS4Q+i0u/X1b48oVhKa7pi0/nwr0cmhez5BgBSGx4vyh4koxVMHJchpLbu67fYbLSEZ/7sV9exXcgXp4dAIBA3cETjXsCOod9jwh7GOfpDCuZfQiTVUuBmzaxhnaRqVT2PNXawyo/a1D+LuempnA9EO2LUUU23buExRADOpmd+A2VVpq/nquGZPu0wSICKfY4Mwv6YZaFM0BRTOxgq51/4Mw7HRgHd9kQPDj9AzvI8zM/K+lhD0NEshubPJYHFTE09pXp8sR8KFuJ8jfWqrwcrsmuaJUWl4f3EeCo60iVxNSiA90qdwmZqhdlHGLmxA2L89eVS8145GuSxc+x6gcNFAL02BN0h26CGMQBOZrCbgkYl8My3fTVsdPcD14HUrvBW2znlbdlUel97SHlwPHkvxwLA02lnahK6Y4uRE+dPMb2ge26/oiIpY6ztny+rilUua40NSKykhVxDFuMe6djBqDsFwu6rqRje7FuCJ0SBtFYp0r1uRdzTfeqvYJrNPcXFe0mDtEbHIvlEL03cSjq6/Yym8vWzTuxqNQPAuQ8hXK+5pCV3HtKyQQwWbMNr60Yk1Cue/D0phHHwcvTxT9VKz2ANhRzBcmD2j2FfeLB9TBkZYOKG+osHYaLcwcs5elYkkkRVL65BX8qpvTsnxnC3mba6NpWrDOMA18yYr9BENeOlskgduekVzuFcmi+VNXdNkBWApXm4a42ihxnYF0vT0xCG1iWd9At2+lxVfN4mMyaFesUUu3duNlHjbVDZ28VrivprMasbXIsR9T7DnaBqfz8I2vIxNSUP73haJlXPrbNlM3yxWE2LCtxKXxiviv7OznUXBlibyC+u1Ie3Kkkz+8UxtVzexh9a4XVKXeXNShFndMflO68EyJU23HVMrmrZtgr7NVNm24q1pcXX1uUU8+Hbv1+2G11Ls7RTlcwkKqde9X7GjQLCwsMD/9//9f6ytrfH000+zb98+jh49ervHdlfC71E8ug8uzkvqobtJVm7bVaRbUIa0etx2LGN6CRx9isL4KVDgGjqK0dSB1hqdioOdR3kDKHf9CJNMZskF+/AeasU18ZoY5/UfRIWacbRYJWymy5YNJZQokZ1RVbW9PjMjNtf9qjZ3rQyD9qjizLzklZuD0OpLEfCK34VSct7tMFT1Tsk0wVq8iGvytfK5l6c5dujjXMm2Mbde7YxpO+IPNNgqFE6PC0Y6xa7h7IwuNpBxcXVDDO4e2CPWCCs2jC+I99Djo6qm6JnKagLRblJZXVM4r7nPGc1z58qT5tyasHOePAguJd2+Sr2IvdubJSOFaK019dobG0osqOtZWBtKdj4rcXjuXFnjMLUsvRL6A/UbZtezAqtHP62ky9qO1GoMVer58O5GOifamEf2wtlZ6VE92qN2XL95L2JHgeBLX/oSP/RDP8Sv/uqvcvz4cb74xS/yjW9843aP7a5FNKA4NigrY8sEdR3TLRWKofwhmdyLMAePsBRPEx4exmgRmZByWehCHnvmPIXzL4NdQIWasQ4/jhEq+7LYjmZmFV6/EqRgBwl6HE4MhwhOv4w9dRYn1sfFeZkkD/aUUzMgE4LPkhTMRp1eBJcXYGCgFVxuKJRn5EzPfbw84eFwr6I9qvG4FOfPT9PdJoKatrDCNCT9UzpraYV2dVWOuEyIeh1c52tFM970Ev1t7WQLbKWt9nbKjuD583Kukc6yEV4mp2usoEFokOkcHO6VIDezIoGkEgsbmu9f0mTyQbyzmgeGa+0vKpHI1Bq+JTKSVhtfEAtqkA5wezscmoLVs27IBz5LKJbzFbsCpURvMbWs8bnlO6lMd4x2Sy9mn6dW6La4AUNNtYFAKcVIB1sOsqXr9DXXfr6In630WQn7u+TYuxmLm5qXL0ptw2NJr4/WoMb1Lk5l3QrsKBBks1keeughfu3Xfo2hoSE8nnf5t/k+gGEo3DsQ+hi+INa9H8FZmsaJr2K29WE0dZAbnwAkAJSg4ysUzr5Q/feF72Md/eDW6zZSla0zIZE1eG0+zMPuMJZlkEmmiRXWebgN4vkIR/r8bCQ1XiNLl3sDtCIfa6Gek1nQCyrYhOvYx7AXJ9HZJPnYAG+utZHJi0fOBw+pqoKr1hqXqQm64aG9MtkpJZOrdjT3DYpza68/jjuzgWvwCM7CBM5auQpseLzkHely9fioYmFdtA4uA5pDimRGc2FOAktPczGguat9/EGOryaEQdIclP6+pa5nLlOjUJyZLjeBcZlSpC4UHAJFWuN2Nff2Pgkg4/LZCYasTQY6XUymokwtuwn7IOBxSOWF9up3K0JeODqgWIlrjg9JYDaUaAYSGWEJFWzNB0YVV9dkbD0x8U6aWJZgXq8HtOX2MLfu4HHJuEs+VC0h+MABxcyqxgC6m8tGhY6j2cywNbZH98sOZyMFnU2KtvD1FzXXg9aaeBqSOY3XJay3nXhj7QbJjObFC+XAnM3DSxc0HzqsCL3Pk+g7+nhut5vnnnsOx3E4efIkbve7uOR/F8IIRjGC0Ru+zqnYNWwdW76KzmW2AkEyU0eynzbItXXibQpgnflLonHx9AkHY8x3P0kiG+agdR7z0mvkO0axg1HaYm48Vtl22lBSaExm4cWpJrL5JiwTjBVR0i4UawDxtN6iWYLk4F+8UEoBaR7ZC7PrZWqf14KHBvO43/gjKOQpUOwC19KDszwDvhAzdhuvn5MV3mP7xbHzxaJtNohz5t5OWQ32NEsh83AfPDdW3oEEPDIGjTCLjg1Knv3MjJwj4oeOqKQUHC1Cu/4WxZmrkp9XaI4NSY68MhiE/ZKaqkyxfbB/Ffebf4E7J3zNcOsg3tgJ4mlfcbcm41JKc/+QiLMCLQa5guxYriyJ5bfLENfXlpDQHpuK99VxNN8+K9faTOuaOsmhHnhjyr11f/Z3SRrM7RJbknoiMEdrJpc1r41TNba9nTc/+Vei1E2v9Ds43Cvsp1sZDFI5anZnBQdSOf2ur2u8XewoEPzsz/4s/+bf/BvW1tb4nd/5Hf6f/+f/ud3jel9Ca7EE2ExrXIY08NipqOZWQHlqxQMqGK3aNYhwrToY+NzgaetEL16AYhAAMBKrtGQmWLYOoDZEIbYZ3ctK3CK9pDnelydbUGgtlsgmiuklxUZKtjqllXNruOxoWjlpJDOaVy6X3U0VsJaSJiwlZPJwcUFxT7AZtS67AGfuEubhD6DbR7iab+bkgqQ5snmhULpd1ZqDtaSMoTIH3BaGpw6JjXW2IEXZC0UaZFtIVrmVbRU3UvKeyWUY6RDGE9kkH2hbweXkSLmivHm1iVjQJFIhPLZMxaE+6Gku1hYsG2v8NciVB2guXaG3aYhEsI9Xxsvfjtbw6rh0pwv7pDPZmQp+QMGRWlNvrPo3ZhiK1pAU4KdXoL9VaLaOVsJaUwqjwm773Kzsflq3WYE5WkSP8bQQBF67Umdsgfp9mXeDVLb6dwDS86E1IvWkt4N0Tjqm5QrCBNted1KUbCze39hRIHjuuef4xV/8xa2//9N/+k/8b//b/3bbBvV+xXJcPNdLq5qQDx7dx67UnW8HRqgZo2sEZ/aiHDBduEYfrioYR/xUWSEYSiwc/EEfuXNzNed0x2fZNziAMTELKNJ4CPug271O7K0/K09ogQiukfs5He+pOcdmWlbc7ZFSn2OF2+0mV6heoUlv3dody3LKhR1sxbVeTgcl0w5X3Xs4W7QeMA3ZERTsa/TFzcgEXoJSJe8bODmht3L1LlMmzkqLi8rPUWrS3uZJEp75NiouS32vUjy4/yNkcl01jCCvJTRPUEzPplGb2yrwHj8eJ0XGoIb942jI5gCfFHm3I5GRNpvbk7n9rUVlc07OeWVJlNkCzaFexYW5cpoknau1vlhYh+fPy3tGu+uPLZOHbfFj18gVZIe1HZk6Y9oN0jkJMCXdTsADR/rh5ET5NWJ3fdOXeM/guoHgf/2v/8W3vvUtXn75ZV566SUAHMfhwoULjUCwS+RtvdVBqYR4GpbXsvhDaYxg07XffIugPD6s/SfQPXvR+RzKH65JKbldioO9skKVVVLZpkC19cHK1epztvYTCrnRx38AHJugy8fMHEQLk9Wr2pZeCudeoqvvB1iKVy/j2sKgEMpmUwCuLGrW1SD+gq6ysMjb9U3GOsN5zKVq1ZL2hrCKtg0jHRIIUjlRFydztX1zO5pqrTFAisf3DUoaIleQlW/BkXTW8rZMW1tEAophQNReQccrJnSt8U2+hPe+vwFce2YxPR7sph7Mxctof4T4wKMsZEOYlptWq5Za7DLZakoj46++N9GAsKG2I+JXPHlQdqd5W/HSxer3XZwTymfJw2f7zjWb17xRoVvQWu5xZeHZZV5bOLaeFOfaXEEsMZpD19YveC05T2qbrOVGjKwbYSNVLd5MZmFmGZ48KP2mvVb9us77EdcNBI899hitra2sr6/zqU99ChDpem9v7x0Z3PsJtlOdjighnS6Qu/TXuO97GiPwdtdON4ayPKgijdR2pGl1wZZ+BaW0jGWW+ijL39rOow0X6VAv7rY5WJwAwGkZYF710oEPX8yH7UBMQ29OY8ytV1/YdEEmQbszR1dkkNkN+el1NzmkcwaTy5ojvYq1pObsVWkCUzIXG7sqRU6XIT5BJfsJkIluqFWj5kq9IU0Kg8fZNGMkUiKcujQvWgMQHvyxwWr9wEALdGxj9tiORiGTgMdSRA0H7Yh6VylFyKNZSeit5irdMQkCJbZRT1u2SswHQCaBx6iztK1ANGiS6TyKP73ORt+jPDdTZnSZ85qH9ipevigtIdvCcLCHLaV41A/3DkhHOKdoeXFsUF1TQez3KPwexdRyrVIqW5BUmaHgngGq0llQyp2X/y55RY1dlbG5XUK3radi30hqvnNWb7njnp/TPLJPbfW33g6vW3FiBF68IL8D05DPFanNdO4KuUKd3WVCAmdL6O5qmXvdQBCJRDhx4gQnTpxgcXGRQqGA1prZ2Vna29vv1BjfF/C4pIvTWPWCmpgrCalNdHwZ7kAggCI1cVEUqR0RjeWSRvYDbSKmKsFJbWLPjeMsXJHg0bKPk97H6N5/FDRMpULMzlk8GBSvnJWEWEP0NoPhDOAslPUNOrlO+tDHuZKM4PW4ODFk49Npwj5IZBwGAjCTDm+Z0/XEJHgms+Iu6XYJ6+jNCdELlERPuQLgcjMz8HFa3UkMyyLjCmEUDFoCBax8kvVUpOrzvz4B9w1I68qWsLCFSkXHXEGzsC7+QxKIpO3jSkIKluGi22VLSJS9iaKB2+VFqiinSVeU6Lb7bnQNozzXT5j7PYpsMMLSnh/gykr142k7sBrXfOQwbGYUlxc0JydhpMOhIyoBa0+7pNhyBUl1XK8HttaiVTGN2r7GrWFZhX/4iDCTthd8vZZ816XvK5OXrnOP7QdQeC2uaaC3FNc1DWnOTkvR2rpG8bclpPjgIdkdui0Iet5+ETrkVSh01R6qq+ndbX9xu7CjGsFP/dRPcfLkSdLpNOl0mr6+voaOYJdQSjHYJu0Cx5dkYjvSGic0K34+2nHQjo3OplGm65pCr90iX9CSI3YJwyKV1Tx/vux6qbVYNisXvHJJ88g+mTxymRz63Mtir4B03/IsThHt/RgvzVX3g42ny7z70ynZch/v68TYf4LCJTGJy3Ue4oWrLaRz8vCOL5rsafNzWF8lvDJN1gyiAke2ZPwBNyxsVrcz9FhwYg9cXZOJqBQcwj5Fwu/n2Ukf2YIEEZ8bmt0FbLOW6qyLrJ6QTxHwaJRTIJfNk8dNPGfw8iVJsLgM2IhKMblkXreeEvO+DxwQl9SwX3oflPPrxfuwGuORQ09hXHwJHWwm33UITzSKqqfIqsB6ShStTQGLfB1Lg1wBkjnF8+fLE/f3L8OxQc1Qe3G3ssOV8loSvjumOdQjNhCX5sUjqCMqE6+j9dbCIJ3TRWM9+ds0FAe6ATSTy3K/7x0Q0dW1UilaS1rL46pudwpiore9xrAdsoO5/mscR2zIA6Ebp1qjAXhkn+L1CXkeemJiorgTi4/3G3YUCMbHx/mTP/kTvvzlL/PZz36WH//xH7/d43pfIuBRHB3QjASXYeUq1qUz4txomBj+MPm3nseZH0f5w7hGH8SIdd30qscwDJbjmjcnNGspMRk73CcFtlIQGG6XB/D0tDyYQu+UVaKRjBMrBoEtZBJ0WBucpkyvscxaH6SpFdjf7SHSfwijfQA0rGf9Vbnt0ZYkA+lz2ONnweXG23+QmJWhv8XL5QU55/aetrYjzJa1pASelpBQNJUSSmhrGBwHDEPjOAqf28fyegGvVa0J6IxCexhen9AcaFon59K8tRJiMWEQ82vuGTA4Pa1pC8s1S66pJeRt2R28NaPpa5YG9Xs7qbIk2MiaTKsBAod6GV9SLE4rmlbg6ICu26d4631JzXJC8uad0bJOoYSQT5r2XMsy4lrdzephOS7nWdwUemtrGHotqX1cmnd46qBBLq+ZWtGcmZGJen+3ZqBV4bUk7XNsSNJTpnl9dk0qK32HL87Lb2akQzQhpd4I+zprezDvFomMZuyquNz6Pa24g5q2CBjXeIaUUnQ2wYeCbPks3WptwnsFOwoEgUAApRSpVIpYLFbVYvJuhLbz6OQmKIXyh1HmztUmhp3Hb2l0LIadbkcZCnPwHgrjb+IUc+86uU7+tb/A/eAnUOHmmxpjINLG8+fKW/C5dXkY7x0s/rsHbC0F672dUg1Y3JQV4IsXNQ90Krb7yAN43AYjHfK+7phso5fjkqoxlDzkmbys/kBheIMUbI3Kis3yUlxqJf2FcVwzxV4KdoHChVdoO+jDH+4kYHnRGMVGN+Vr72mHly+VP9PSpgS29qhMZGG/UFUTGSWc/5ymoA1GOhVrCc1GWtJBLlO0CDFPllBmjufj+0lkZQJYSSriWc1DIxIETEMUsTOr1WwjR4u1w+SypIaO9ssO4tKCKH3boyLoSuYMYkXxGUgKzevSBH31dwaGkvPOrUs7ziN9iqkV0QR0NQl1tj1a+z6p8ZT/TqRlJ+h3Xzs9VNqcLGyITiBfkN1WSxBGWtMEvEFmVjVvTJTfc3oKfJamv7W8M7imD1YFpldKlh1yX8/MaO4dkK52ezvFkmQ3SGWlXuC1ZKdgO5qzM3orVZXIKJ4/JwLFphtQTD2WwlNT0Lm7sKMZ7ODBg/z2b/82bW1tfPazn6VQuH7B6/0MJxWncOEVnIUrABjd+3AN34vhvbERiZNcJ3/mBfSamOqYg0cx+0ahkN8KAlvQGie5jnGTgSDnuGvysBtpmcyiAfGsR0lAKNUtOqKwWRSUjW+GaOncjzlXtmxQkVYISu7bNOCNCQkGhgGTxdRJa7EN5bPn4MSwJuQV07HSA9rVBAc6bdwXz9c6fybX8F1+jX67gNN3hFz7KOcqmtxbJjWfKZ6BHhOurkrKxjTh1KSEr5aQ+DSVjNLCXgmIXU2yCt4fSpBxQltBYOveFSS4jV2Vc7SFizlzj+wE/G5hgZUwsyophaF2RW+LJpHRfPesKH3PzQpDSSnY0ybj3UjB0f76PkTRgKxMl+OiYu6Iao72yeT5wgVZwXfHFJZZnWc/2CN+VGWLELGUCHjgwRGqRHoltAbL1hPnZ+VzHd9TtPM+Nw1to0yv1OZrxhehr0XveLeaK9S37EjnNE8eVNesC1wL8+ti45EtFNOFw1Iz2L5z0wgrqul9bBZ3q7CjQPC5z32ORCKB1+vl2Wef5fDhw7d7XO9aOAsTW0EAwLl6HqepDaN773Xfpx2HwsQZCQIAWmOPn8SItqFCMXB7q+iWAMp181YeplH7ABtKmsG3hoQFks6LeVoJ8+vlfrnLSZOLsaP0j7Tjic9iNbVgxLo5u+LFZcoEp5SkKs7OlK+1tCm8a0PBK5c1+zuru5PNrkEsaKIDUUhv418qQ+6BY2NcfoWBAZvInsPMbxg0BUpNQao/19ZcpBRvzQj/vVT+W0nAvliKh1rW0RhsqghDbT5yttQgmtyKvBOs2XnIvZJrLcfF+XU1IemMgVaZOF2GuIQ6WolAsBivLFPhd8M9A5qZ1TJNVWvZLRzqldabLSHNSGeFlUY+h06u4c/n+MhwiKlUmHhadgHRgAQGXRzk+TnN/i7xRHKAzojCNDVrCXCotghJZmUX9dQB8GzbGUQCig8cKPod5YvW2C7NwrrG39RLvqDrcujDvt0Vak0lE/V21pzfc+3icCU20w6bKflO3JbmxYtlm+hsXhTHTx4Q0WJ6G8X03Wx3/W7CdQNBPB7nv/23/0Y4HOZv/+2/jWEYdHV18aM/+qN8/etfv1NjfNdAOw72wkTNcWdpGm4UCHIZnMXJmuPO5gpWay/W/geruoGpWCcqHKt5/U7hZNcZaPVvFTlBUitXV2VVGgvUt7Yu2BDyOsQzBhdXfYyrQR7b20Mk5GZyRTO3Bp1NMtH43SUBWDWW40I9zORr8/wgqYiu3vvwrFwVL2dAeUuGNeVlrjXxOr7IACuJCMtxeGTYZrBFc2W5vEvY0y6TbMlwLW/rrYn9gc51mi5+E5UWnmhLuJXZzid4cynEE+1zuE7+FaY/ymjHhzm7VK6w9jZLmqyExQ3ZFWTymtNTsvp+c7K0Itf0xKplTR5L4TELzK/XWoLmCuIFNLMqRncgv438xVdxZoq9j02LPcc+gtnZsfW+Jr+mJST3tmDLLuux/QrtwAsXSzUDzX0Dtfc7kZGg76nDhlGIgEpqKNJCMuqHywt+BpKa4WI/4FJh12VW9zHYCUxTMdot97RUT/K5xUzwRljc0Dx/vqRP0NwzUNsrQGzOFfcOVHcVaw7uvOPb3Y7rBoIf//Ef59ChQ5w9e5a5uTlaWlr49//+3/OFL3zhTo3vXQVlGBixDuyN6n2uEb0xlVbbeYxQDGe7IMsXwF6YRNs21rGncfI5DNOFCjdjVFANU1lZXeZtyeNHA9cuggFkUnEODyt6Y5DOawyluLKkt9oN+lx5mv0m0yuSKG72FzgYWSWU32Cw3UvSaial/GRzDtm8wWZKc2FeJolSf91Mvr6bZMQv6Y+8DR1tYNsFDoRX8RY2yZs+Uu4YL883cXD/M0ScNXxeF7h95F/90+oTeYPYhmdrJZmPr7MvcY7O3sOkjAAel8JLFq/KMrsSBEwCHpmoHUdjutwUIl1YxUDA5hLNzTPsax7Af+U5cGxUYoX+tRdp7jxE0t2MYbqYXdNV3jtRvwTPkFfREpLiaWVaZmYVBlqhs2KiLaSWaQq0s1hxHpAVsKNlUs8VpB+As7mCM3eZ5KG/wYYRw0ERdgqEUll8RZqMz6M4McyWHULEL7WEZy9U72ayhTp1nSLDajscR3N+TlcV0lfiEvQMJUyp3mYRnq0nZUcSDdxc797mEHzwkPRrNgxFk58bdsortW2tFKll89Ts4AwFXpdM/E8dVGxmNDqfpaPZu6OOcQ3cIBAkk0k+97nPobXmox/9KN3d3fzRH/0Rzc03l7d+P8DsGsZZmECn5AlXoWaM1h0I7PIZMULbXIG8zGxGrAud3KQwfrL4IoV174cw2/qq3prMar53Xm+JokDz6D5hPFwPXkvR0STnjac1JYeIsNfhiHmBQspFk2+YdMHkWGACz7nnimcHX0sfV4KPML4m24aIXyaIoFecKzuikgYylKy6SikQn1scPVNZzb5OaA5p+nPjuM6J66kHCLYOsBF5mDdXYjy8txkzoMgvX8Xo3IMze0lOZJjYIw+zlPHitmQy9tkJzIXztFngbC6jN8u+Rw/s/xDjhV6yebY6nYGf/sh9HGzNYS6JpsFKLBJp7YJMWVrsWpkkujJJ8+EnGS8MVNEaPZY02Jlf1ySymqAXFq+R767cFyTi6xzu66jqNdAekTRMsJg6u7wg90hnUyQPfIznZ5srVt5u7h+CNpeDu2hDW6ZPKpY3pTvc9q5ZCxtyzvPF79pQcP+QqluPKDgy8dd+FgkcWgv9OBw1iurym59UlZLFSzSw83PkCtU21iDeSId6pWhdwn2DEPTJwqg5JL+/sbEJ/F2jNz3euw3XDQQll1GlFB6Ph1/7tV+76y2ojWAT1v0fQyfWhTUUjFat3K8F5QmQn/yOdAkzTRQKfCEKbz1b8SpN4dxLqEgrRoVB3FqCiiAgODkpTpceS9gpixt6yzxtuzMkSC7/8f1SLA45CYxXXsWlHU70JFAdvbjeeql6vMtT9DQfYBzJX2ykYE+7Yj0pk+zeYt/b9RQc6CmzXXxGHmt1kl69iMp14Mo3kR//fvVglibobR/FG+jAZUp/ZOfM8yh/CNfwfWjHAQWpPHSFM4RciuWkieVx4SgD5Q2gS2mUInwTL9F3qJVnL1XnuyY3PAx07CVcDAS5SA+LGS+xYAyVWK16rRkIMeBXNIc0mZzaysnHs/JZLy1IoGsNSz2lEgGP2FvPrstEH24bYXa1mKpRsovK5jWpnBRox67K8a4mCPkjzG5EqwJQwZadhs8NHlszv6HZSIn9diyoGZsVDYXHYsvhFaSWsbcDHhyWnUHYX7YI2Q7LlGL/hW0WUiGvItghwW1pE9yW2G6/HWqlo8XkbnZVUnddTeLldL1ag8eSFOZqhR1IIiP1mSdGhXHkd4tu4nq74wZujOsGgsovKRqN3vVBoATDGwBvAO3YQlzfyXt8QazDj5N/89uQS4PlxjV8rOZ1OpMEp5qVVclQKSGdk7xptmicVfK9GV+EoTZNk9eP42hsXS7I+dzSfcveyJMv5uatmVMQa4dCLSXYdKorb+liw5b+FlhcK7CaUizGTRY24FBnjtawg7r8CsacmNrp2fM4Bx8Fu/bchWyOk4swGdA8MVhAZ5PoTAJntTwr+Y8/w9iqi0tL8jPda2XxjxxD1zkf2SQuXajb2zmviz/zrn2sWJ0k0x5Swx8gcP6vpGBtunDtfwgVbMJjqi0u/tU1zdRCgWzBYDVlcE+/Yj2eoynsIp1TbKRkxX2gB2yteaEYm472K753sUwPNZR0u3pzEtpDOTIFE40BWiZ8FWoisVhNJw14ygrX712UIAAwvii20GjJn9/fb3Nu3mQ5obBMaThzclKTyolP0+P7NapOVziQ53uoDdaSMuErxFDPY1FRA4HLi5qH9yq665Ss8rbUPK7lE1TCShy+e7as4j0/p3nygKzgrwW3S3HfELxUtAw3DTFE9FoUvYnuLhuI24nrBoIzZ87w9//+30drzaVLl7b+Xyl1VxaLK+GsL1KYeAudXMfs2YfRPojhvf7OwIx1oh78BDqbEuVwLsN2rn49GwJxJ62e4QZahCWxEq81P5taga49XbwyLjbBA62a3mZJD2itSakgViAKyXUANlWIcFMnrFUsDQ2TpFltzWAair5IhsPuq6irb6FdXnT/IbKGn5mEl/OLXlp99zO4rx/fhW+BdtBL06hIG7qyrlJx7rUkLGV9NHUMo4oBBMDsHSVpl4MAQNII4b78Eq59J2rum9M2TAGTqM9hPV2eIFwm+CNBFvf9LeJGBLdlkFqHsfUI+47+IE2sY7hcogepWPjobJrWxBQtq2M4bj/OwBH02gLdy5ch1Ux370GSVjOmKQ3nv3VG3tccgrlt/YOln7LDB1vGcc+9he0Jkew5zKnVFtwmXFoyaY/CTLGwPtotgX5uXdI3PTHFZqpyEoWPjsTR8xdxn5/iwWgn2aF9xM0ob0yUe0Ckc3B1TTG75tDXUj89FPIpHtkrK23DkKY70yu1FhBnZkRgV2LhZHKaq2uaywsStCQNeO0V/qWFaisHrWFyWdN8HXEdQFNAahSJIinBY8nzcCt6HDRQxnUDwR//8R/fqXG8p+DEV8m9+mdgy8q9cO4lzFwGNXzfDX+ghi8APqEyaG8A694PUTj3EjqTxOgaxjV4FFXRgFZrzXpCc7hPvGUyOdnOD7TJdtipsyMZ6YCXL5tbD/OpKfH2P9KZJmF7+eY5Dw8PfIDo3Kuo1ask8xbWwEN4zNcxVibR/ijOngdZTkWkEOeG0S4RMx33T6HGngdkKnYsH6esR1iMy+S7mfYw5+7msb77cE++irM4hfXAxylcOY1enkIHoqT6HuLNxXKQWUsZeDuP4geYv4xq7kYbLrTl5cQwzKyI0OnUSowTQ4/im3kL1977safH0JkkqnOYpegRSOQ4Hl3lLVc783EXUZ/N0eZ17HiGU6vd9MTgbJG4tZGChXXFUwdjNNXJW9sLV3DGXpTvLGJgzp6R2hBAYg29PE3kxDMYvgiZvIjKQKiS21s/AhQKNu6rb6DTcYz4KqHVGR49/je4sBwTVXCT5L4TGQkA68V0yGYagl7NQIXRXn80i+vS87A2L01gEmt4V2dYG/gBsvnqRYTjaM5eFbLBPQP17R8sV6XoSiGk1G3jt6uXIhPLeitPv5ES1tZTB+uLt7TWhL0izFtPldNqBVuKusq6fqbBa6m7oifAO4nrBoLu7u66x0+fPn3Nf7sb4MTXtoJACfbEacyefSjfzjtlKMPAbOsToZZTQHn8VUEAZFV3ekY6PvXEZEW0sC7K3lhQVnR+T9k2AuoLr8YXYYireAsJhqJ7eX6mia7wk7S3ZrA8PhbyARa8j9O9N81GzmJiysO9gzDcKXWIZBrC7hzeubeqJoR0uJ/FbWmNVM4g2dSGG1BNHSwWIoz7HmfwUIak7ebUrHsrheO1wG/ZuFcnUOlNjMEj2C2DzObCvDVukC0InfOBPfD9ywYvrvXw2L5mFuKadN8QQbdDJOojqg1W4zbe+VMcz4+Rb+nFTC5jXpphZuDjdDXVCo4cLd4+2wVHTjZN4crprb+N5m7s8Ter35zPSp0oEMFrKfZ3at6YlN3ZgR5pHVmJLs8mulI34dioxCqX5iXfcnVNcuFH+6ii/IIEh74KfsZQKA4T89UvSm3SbG4C5UDgMigKLSSlN9LJjnyIWoK1JnT7u8oWEOlcuUnP1se5xr0E0XPMrkkQaA5J6uz0lKbPs0bupe/gOvAwRqyzscp/B3FTnTj//M///K4WldU1DjNddfv07gRGnc5hW9dS8kBnC8VuUi3Q2SQBQWtRpz66jy030a4mipS56onIMEDZebjyOkN7fEyYe5nddDG7GaQ5CMMdmv5WF1MrQbIF8Y+ZX5NC5Gi3wm1qhjsN9Gr1T8aos3oEMFwmavAe7NY9vHTJTcGG2Y0ge4tN4ufWRZgUDSiaUpO4Jl5FA/b6AquxI7x6pXyPp5aLBcKhNGF7GZWC8yvdWymg6IoUDyPaxVrbvYTjV/AsnkOFW0iPfpiJxSDD7XJNakTxaovGuXXEMFCmWb6D2pEb6GyLrhW/g94WEXVdWhCmzf1DmvFFhWnAvk5N6NKrNfcoXTAYqGhyk86Jq+j27w6EPhrxS4+AkK/+Xfd5DO4dkKDv9whPvyT2MwypVdwI2bzGUJqHhx3GlwwyefGk6mwqv9lQ4i20/V7WqxMk0rrK6mQlDtm8w2NDWYIXv4dObYidykOfEGFlA+8Ibqra8vnPf/5Wj+M9BRVqhm15fNfI8R3ZTOwWPre0MjQUHO5VzG+I9cH3LsCVJY3tCKf8nn7Fg0We+UpC1/D7R1tSeObeAsC7fIGWQHlS62xSnJqSlV7YJ6rbsauySr2yWJwcDEVT2I0xdG/Veb0rFxlqqZ4gW0KaDRXjrxP38s3xMId6lKxOEYaKOIZKSmFu1cG3fKH8ZsPFZrr2Zzm9Ar7NSTj11+i3vs3DwfO0BGQmWk8KzTAWVLR3BHG39aDCLeiNJTxXvsfjvev0tyoO9VZPVB6XUHO/O6bZTJUnX2V5qgr5zsIEZs++qveqQBQVLE9cHksx2Gbw1AG5Tmb1Ik+MKh7dr+hoMjE7h6s/kNvLqmquKZZupmSyr0RnFLqbJPUy2m1ghSI1SnbV3I0rFGW4w+DhfaB0dcH3QA83dO5cT2m+c0bz12/Bi5eMLWuNyeXqDmEeq/69jNb5+SeytfWGREah4ksYpcY92sFJbVx/cA3cVlx3R/Av/+W/vOa//fzP//wtH8x7BUYgjPv4R3FWZnFSccyW7h2JyiqRy4sJWt7WhLyKkE8KuTq5gU5tSgOZYBPKctPbLI1QSna5AAHLxp3epLCQQPkDaH+E83Mm8+uSu9/XJQW5QqFAmxUnuny6bOkQaKK32SDkF458vqDpjskq7+JM9TiF+w4rcQe/RxGMdcJ9H8Nenka7PLiaOxnVcVp9HpYzbqJ+xUbW4M2Z8kRxfk7T11q2s0ikZZcxvy4uoVo1lQvVTgG3q5qTDxBwOxiJYm7HsTEvvcTo/maeS7ZhGmxZPKjUJs6r/7+q1bvz+p+iH/oEHZEA9w6IqMk0JNVxblYES2Oz0sSmtKo1Wnqxjv8A9tI0hseHau7GaO7CXr6KEWrCiHVJvaeIeFoTz2gsUxqmaK2r7Iw3/H0Y+5/GuzmD4wmw7unm5GKED7TCo/vkXvg8YvjmaNFsrCchFhI6cKWrqHJZQrNt7sZem8eItGLEOrasy4MegyP9mo6oZjMt33HLNQq5qaxmM62xDDhzFTaL4j1Hw5tTYomxuKm5OCeGhSWaZncUHtsv4/a7oSWsyOQgmXEI+9RWLwJhrNXagrh0NfNLue7CJgDvIlw3EHzsYx8D4A/+4A+49957ue+++zh9+jSnT5++3tuuif/4H/8j3/rWt8jn8/yDf/APeOCBB/jiF7+IUoqRkRF+5md+BuM9Qgkzgk033V4yk9OcnNRMF/VQLlPz+H6I5ubIv/6XW5OY0bsfa/gYltuLxxIDLQC/pXkwOonn3LNoNDnA3HeCVHofICmNc7MyOX5wKIX71F9Crsg/NC2svv0MRMq1iHROdhHZolK4JOKJ+uXvb50pPciaB/a46GvpwN3aufV+N7B4ySGVB49HOoJVouRBD1K/6GkRdWpJoeqER8jNX4KC0FWjbBD2RdlMy78rBYebNzDPXq46r6cQB9o40ideNlCi325bguaz6EyKgj/IqSmhX16Yq6blzq2JgKnU9lG5XJjNXZjNXeXzhJsx2/prvs/luOa5c7pofaDpa4GOit+G1pqpdRe208VyoYt8Rmo4ezsh4lN4XKIZyOXFyG+9SEuN+mGoXdW0iYQihblzCLNzqObfQGpHoRsodzeSmmfPibJ4f5dYaWxHoXiPZtfgYK/UdABcLumz3BEV8eAr43rr/W6X/J6bgtKvYU97ueUlwKGuAt7JU+XP0trXSAu9w7hhq0qA3/3d3+Wf/bN/BsCxY8f4oR/6oV1f6OWXX+aNN97gD/7gD0in0/zO7/wOP//zP89P/MRPcOLECb785S/zzW9+kw9/+MM38THeW1hLshUEQBgZJyccHjKmMSpXstPncNoHMZu7cLvKfVtHm+N4Lj5P5UorE08y2A7NEVnVzq1rVuKwakfoPv4xjOQqWjsYoVhNAFtLyIOqlDCOXKYiZOVoM1bRyU16e31supp5ecrHa1ekjWSiuPptCoqYKuiDK8sUvfarV4CWCV2hHCO9V1HawWXH0Dq2tUI1wi247/8YTnwVXcjh1WkO9kTJFUQ0FPU6RC6+jNbVmXGPz8sH9js0Bc2tcymPr9aDoNjoxzLFgRRUjTajOQiWk8FeXkGnkyh/CCPUjHLXa2yj0fEVnPgaBU+Ek3PNFOzypDu1DG395W5zSil8lgj+Blul77BGUbDFj79gy6JgpKMsHHS0FI8vL2gO9GjWkopCQWNZinRO46649zcDrTWXFsr2EvG0qMe3CxdLdiItIfke62E5rquCSK4gO6wTw7IjONgjTLd0VhPwKizTxaL7GdAOUU+BkF9huN9m38kG3hZ2VCxOpVK8+OKLHD58mDfeeOOm+hE8//zz7N27l3/+z/85iUSCn/zJn+Qb3/gGDzzwAACPP/44L7zwwg0DQTabZWxs7Lqv2SlcLhdKqbqfJ5PJ3LLrbIcRGiLgsbCdcsOU9ZTCaY/JA5FLg2mhvAGyiQ0mFzcwDIPD3f2cnnXjNzJVq95C+15Om0e5Ol56UjX7uiTXv7ipWdzw0+aOk07FYX0eKC/ZDcPAigyilAutZaUc9jo8ER2H899DASYQbu7jwf5HeXXGw1qy1IRFE/E5jLZt0Oz30hzwcnVNJrSLxUsYCu7pc/Cd+VN0Yg0N5AwT5/CTTG9kcByHaMBLdPE8qujDZADhPQ/z3OY+UjkwlMHHBu5BbS5vidOMzj24yGIsnWJqJUw2J8fdbouuPcdRl14FNHgDMHw/l68uULBn2d+9l/l1sdwu0Rg9LjjYkaVw8VWYLdcrdP9h5t3NOBps28a25Z73x0IYY89DNklu6CHWUy0133G2YFT9fiJNnUxnQhiG9GRY3Cjvsg71ik3HdjsFEBbS5XnxeTrYo3j5cvl9EZ/DaOs6iY2l2jfeAP5gmOV4eVc3uw5H+xRnZso5/e6Y1A28FvRFU1w4P11zHtM0SVsDbJ9KVhNwdXaRZLxave2K9fL8lJ+CbQImlmlxvC9JfPXWPmu38/m9E7jT499RIPi5n/s5fvmXf5mf/dmfZc+ePfziL/7iri+0trbG7Owsv/7rv87MzAw/8iM/siVOA2l+E4/XMT7ZBo/Hw+jo2/cQcTaWsBcn0bkcZscgRqS1qsHM2NjYLbnOdtiOZn4dYkGxLg56FavrOQ5FlnCvzKLa+zFiXeKlk9zAbRjsG+zB8AZYT2oGWjTaFaiyrU5G93B1vjrHemleqIivTwCYdAx309NGVbrASW7gLE1jL75EU1sP47kOXF4v3d4knK62hTBXpmjqWeJJ7yLGSo5Y1wBvrLWyv8ckm29iZgm6YtAUlMm/J6ZYKnbAakpeQSUqbEgdGzVznsDAk0QDLnypOfLbzPjcE6+wd08XJxdCOBrWzFbY/zdoNdakz2whT+H89yGfZeDhv1W1y8lm8hQiPSTxMZ+0yOcMettFxboaF8uEWBD6msU3yTQgohLkK4IAgJp8i777PoK9MI4RbEI1d0MmiT17CdXWJ1YXa7N0hoaZ3ax+lAJeRW9v9e+nOSY7gBcvVO9Gxq7C/Xsgk1M1/v9dTTCxrOiJye6gEhtpA+VtZrSrNhDtBEnlcKZYE9JaRGMPjsh+zs7n8Hnc5B24p1/h9wTpbqv/PMyuaS5sG1t3DHq721Cqunb2xoRT5R6at2E1E+DILX7Wbtfze6dwO8Z/vcCyo0CwZ88e/t2/+3dbfy/Wc926AaLRKENDQ7jdboaGhvB4PMzPl1emyWSScPjONG93NpbIff9PtlbVzvQY1rGnMVt6bvu1FzeqrXJNQ/PYkI37tb/AAYzOYQpXTqE3pTDqLE5idO8lN/Qg3zkrIjHLDPDY8FMELn8Hsiny1BbabEesh0vYTGvOTMPjBySd4KQT5F//yy3zPN/cRUYOfoSXV7uI6jxBu4ZniRFfwpwWPn3w6hiPHf0I4+nurclkZlXYQA+NgOWCFy6ILUCfZ736RMpAeYMk0tIk/rGWbSbyAHYej2FjGnCkLUlk+hUWm+9nxdVK9NJfQ7IcWHRFoyRHaxY3bGzCvDZhbOkVxhfEtrk5KKm5NybktU0BeGCPqjWylzOjUxs4s5dxtAPeIGb7AM58sVbhcmP1H2R/OE2qEGQ9pTAUDHdAOm9WLXRAGGCqDvHTdmAzBfGMpr8VppZkMu5skqLxuVmhC2fqbMTzhdrC+k7R1yJtP2fX5AwDrRD1K7xuxdjYFXp2OBE1B0UNfW5WAkpbWNqe1itOx9O17y854jbwzmFHldlf/uVf5sEHH+TYsWMcPHjwpmoEx44d47nnnkNrzcLCAul0moceeoiXX34ZgGeffZbjx4/v+rw3A3tppqagWBh/E23b13jHrUHB1ozNVq+cbEdyrBTVlYY/tBUESnCuXkAnN7e27Hkbnp1vZ3rgGcwTf5NwSxhz2zfZFCi3VnS75DqJrEw4gKRpUtUeyamCi/WUYjkfRG1nQZmu2vlm8hROweZov9qyOd5Mw0ZK4bVgsE3m10ywnIJwgs2sHvgEL/IAU2sm7VFFxtsC24R0OtqJ1+/lqY6r9Ez9KebiFZrVKm/MBznb9gOk932waPrXhPKLiM9JbVK49AZtF/4X7XPP80jXatV9OTerMQwY6VR85IjiQ4cVj48qwn5pOYq72rBO+ULofBbXyDFpGZpJoKxKr+kcWjtk0llCPvHc39sphdXT0waJOqmekFfVfFfNIcnNT68IpXZ/t/SXPj4owcM0JI3VXYebEL4JS+gSgl7FA8OKDx+W+3G4T12zreX14LEUB7oVHzks53p477UL1QOttcf7WxpCsncaOwoEzz33HM8++yzPPPMMf/qnf0p7++6okgBPPvkko6Oj/N2/+3f5kR/5Eb785S/zhS98gV/5lV/hU5/6FPl8nqeffnrX570pOLWrXWybekKetwNtF3BScZyszL66dJntl1YWZv9BVCh27RFsa6FVsOH8qh870EI46ObxUUVTQNIy3TFhasytifr4QLfaahXolM6zzZpCeYMEfAZH25N0exOYrb0YLb1gmKhQDNeBR7GnzlWPybHZTEuTlOGOyodZs5HSDLWJE+a5RCu5vU+Ax0+87xFemImynDDYSMHZGc1yNgD3fBTCEhCc1iGWOx/iu5e9xAngBNvIDj9KwQqQysLlVQ9n090Y9zyNOvwUaceLnctTuPAqzvgb6NQGauEy0Yt/wdG2crqxYIN2bHQ6TkCl8bulsJm3NbYVxLznI6imDlAGRlMHZt8B7PE3KVx4pawDqGOHoL1hplckxXNuVui2WlP35xT2Kx7fX/6uemJi31Fq4LOelPNI8V6C1GP7FYYhjKaBFtllBTxCO226sfHtdWGZimhArnMj47jrwTDkHNGAqtsVLJuXFp6tIc09/bI4cbvg3gHRKjTwzmJHqaFoNIrb7SaZTNLf3086XWd/twP85E/+ZM2xr33tazd1rrcDo7UP+8ppKp9U1+DhXTWhvxGc5AaFy2/gzI2Dx4c1+hCu1l72dRm8XNFKUAFt5hr2hTcwOoZQ3qCsRCvsCIyWHpQ/jKGoctfc38WWB0tLSPHEqOwW3C6Ym1/k2GArM6tiWQ1yvETZVMEmsLzSJ6GtH+ULYp3+C/rQGMPHKFx8FRVtw+w7IGPJpSFfvcTNdBxifsnEdsptFH1u2RW8dEk0CId7FVYwCUvrmN17mc9XG9mBuFu2jbQx1/s0Tj7P1YSX5VkTpaDgjfJ9z2MsLRmE4uK5pDMJepJncE6OgcvC7j/OZqgNX0ULUQDyGcJ6HRDV1onuBM65kxRmL4HlJT94gpc3+rAsk95mxetXmvnAnseJJidxlqYpnH+5/H1uLGH07INKdothYrT0EfV5cLt0lY10f4uD31OfZtMSVjw+KoHJY1Fsw1j93Y52saUdaA3Ld1uwwXJpDvbKLsHzHvDf0VqzsCE9lJNZEccd6Vf0FEVzjcYx7w7saObr6Ojgv/23/4bP5+Pf/tt/SyKRuN3juq0wIq1Yxz+KPXkGXcjh6j+IEeu68Rt3CO3YFObGMfxh1J570PFV8ie/ifvEM3REW3loRHFxTmO5NCPBVULjz0qz+rnLKMuL6/ATOAuTOGuzGG0DmJ1DWD6LDxyAi3OaZE5W/J3R6ofIcims4jea2Fylf6iNvC0UwaaA9BMIFoU+JVFc4eoFDI9fGDOl+1PI4QB6fRF7XbYSOp3AOvYRCpNj6EKOdPsBziS6tgzWXIZiqE0T8SlUZpOH2zMktJ/T0wEeaM3D1JvYbi+uvn1s/9m5XRKS4wU3lxbKqZfeZsntdwfSjPhTZPEwsRziIe9l1NWi3Wc+i/vSCziHPiQ9jysopsobJBSweKJ7CV8ogGf2LZyrRYfTXBrr/Hc4vP/jPD/fRiyoOTYEwcwCzuoszmq1mY4yTCkWOzYq0oryhTD7D2BEWgkqmajHFzSWS2iYliqguAbfEhGzldJpIZ/DY8VeEZm8FPQ7Itd6vbomjfPdiI00PH++7Fs0ty47sEf31d85NPDOYEeB4POf/zyJRIKPfvSj/I//8T9uijX0boIyDMzmLoymDtAaZd7aJ0snNyGxSqHY31hF2zAHj+Ik13FH2+hplkKgPXsJ58xzVe+1Fycwh47i2ne/TDoVu5TmkKR6HH1j/3eQjlYjnTJBG0atstQIN2OFHiR/8pvV43dsoV1WdPEy+0YxW3owYl3Mr9l875K5tYL1u6EpoPFZDtHUNJ7Lz4Gdp9ny0Dr8JHGrg5AvBOk4Ha41zhv+KofOoTbFX57SHOxRGKrc1zbkhWHPIoFL3xKGlOmiY+RhjMW5mqyL3ljCHjyGOf6K3PNAFLNrmMKbf0nYsTEHjmDPXaq5R778Gof72rk0r2n25elaOYUxcBhneaacjjNMVLAJvTKLOXQPZvsgGKrK5z8aUAy1i3f+uVlQuBnt1ox0UuVjtB25vFBDz12VYBj2woMj743V/k6QSOvtWU2W46KHidy6DXgDbxM7+ip++Id/mD/4gz8A4DOf+cxtHdCdRF3zuFsAJ76MU9HkXq8vQrAJ5SkXTU1DoVUth0T5w6iivoE6qSqlFDdqFKW1Q29zhMzsJI7lRfnCuO0UZBLSOznYhAo1oZSBUgoVaoLFya332xNvYR58jEI2i5mNY0bbICzKT2UYtEQVD45I4xZv0WvvjQl4tDeO195A9x9EGSb2/Dj+S98mfugT5O55Bt/qOOHcEh8YaWEx5cF2oDmoeGNSbB7Oz0kwSOc1LuXQF8rgeuM7WzRZ7ALGuecwDzxMYduKPW8FmaCfg0cjsDGPEe0gf/Kvy/ckvYnyBtGVNFYAt5eZFUlbeF0mtjeKSiewDj+BzmWkJpDLULjyJubQfSxkfIRQNf12bVvz1ky5J7QGzl6FlrCkyK6F1aT4OY32iMWIoYRCGvDW7wi2mdbE0xrLJVYW7/aAIUFQbztWtgRp4N2BHQWCSCTC7//+7zM4OLhlAfHoo4/e1oG9l+GsLdQeW1/EGLqn6pjR1I4KRNDJoizTMHEN34cyrbd1/cLiNK43pTGMARi9B7DdXpzLrwMKlMK654NbvZHN9kHsmfOQLdZ+XC4yrhDW3DhOchVn+iy4LNSxj0pazZRuVVrDK5c1BQfCXhvP5jSFS69tjcMcOoozf4VCKsV8ppVEbpR7+hUxjyLWJOrk6RW91Zw+m4fT0xqPC+7tTWEWspDdJnVFYyuXBMkSxdUXZsXVQXPYi9XcDx392BWBGMBZnMK1734K51/ZSh/pUCsZTwsrxUznStKgMHgI18wb5C+/XvwQLlwjx8HlYUp38fp5sEzN46NicldCtlDfoiGZ0RC59mSdymr2dYkts+yExG48ndM1zJvlTbGEkN2UvO7eAW6K6XOnEPFLQXymQld2z0B924wG3jnsKBA0NTXx3e9+l3PnzjE7O0tXV1cjEFwHRqQVZ1tPXRXrrHEnNfxhrGMfRW8sowsZlNsnTKNs+rrW1CAphdWkuGf63cJC8boVTjqJffaFqly5M30Wc/+DuEaOoe0CShk464tCiVQKnU5gDhzFcfvIaRcr+TCR1BrK9LI58EGS2kPMncVMbOJkUuJbpAw6wy08cSDGRlLTbMSx36i2WravnMYcOkpGefGYBdo8Kxhz69g+Hyrcwty6H63LK8a+SJYh3zIhZxNXwQOmj4LlqSlSrzoRCiPPEGMFy9Aoy0uPK40ZDpAruFhLQkD5q3/c2qEwc5HskY/hzsex3G5SnhjJnJ9ohbWCkUtLWmjrQxSwr15kbc+HeWNKis55WwLWI3vLq3bLJXWY7d3i6nUFq0TIB9+/rKsKxTOr4jFU2TsgVxB/qqAXepvFnsI0FJtpvetAkM2L3UUyo/F7FbHA7dtZeCzFvQNCJc7mNUGvqutS2sA7i+sGgkuXLvGv//W/5j/9p//ERz/6UZLJJPPz8/yTf/JP7tT43pMwYl2oWBe6mL5Q/jCu3v11BTbK48OOr2CPn5QDpoU5cBjVf7Cas14B29FcmC82QAdAM9gKRweQVXRuG6vLdKFcFoVz5Qb1yh/BaO/HvnKqKo3F4ANczHRzIrzMxcD9zK16CHsdOtKXUW5FYfKt8sRsmETv/xhNba1kFjPUJIO1g+OP4veHCSQmcJ37FhrIIw1rfL1PMLHmZ28nrG0WOGifwnVerLILgNGzH9fwfaIgdmxAURh6gLH1CC2+PM0rF3A2KhzuDj3OpLmHkxPQGY5y795HMS9+r9hPwCTdez9jG60cHWjD5VGEgcSqZqBVVLUdwRye/DrbGb46sUo6bxD2S0FXa6F55u1yisMyFUf74blzZfbQYGt9a+ZKuEyqmgqVkM1XC8XyBXndSKfiremyzUQ0AI/s1TcMOCUUbM35Wc35otlr0Cu21/s7xUjudsDrVnS44WaFbw3cflw3EPzCL/zCVu+B1tZWvvrVrzI5OclP//RP8/jjj9+RAb4XYfhDuI8+iU6uox0HFYhcs1eBTm5sdb8ye0fBZaE3l7CvXsBo68fwl1ahmuVNmF7RdESluFiJK0uy6op5/BBsgopcuNHWjz1+qur1OrUB6UR1EACsydcY2dtLNtCNP+el2dAMBeJY58+h2/qqV+eOTX5yDEZbcAcD5F0WFCrkr4EIyu0lOvsSKp/B2PsA9tULcl/W5ol2r9ER8TO3Dic64rjeeKtqLM7MORh9GNeRJ9HZJJgWthUhEzfotFYxKoMAUDj/Es5ABwd6guTyLk7mhuk8sodWK45pgGNGONZZbenc2SRq12gAApl1VHx7GRpUax+2y0PAA90xsbE2FCysixlbqV4QCyo+dEhSXplMkq7WYNGGuRb5gmY5LgKyWEBqBZUosbtK8FgiNDu/TZC4npT/KnsNbKY1V1el4X13TNEWLq/4ExnpeRzywmCbKItTWVhOQHtE112sNPD+x3UDQTqd3upEFgrJhNTf30+hUEeQ1UAVlNuLcnfU/TettUyoLgttFwCN0dqHTm5s0RadpWnU8gzWkQ+glMFqXPHCBbGY9nvqS98KjgbThXXgUezpMVmRb65gtg2QX5qqHYddx7PAsWny2sSVh9NT0nC825Un7HJL8XQ70pvMrjr0Nfswj3wQ+8yzktf3+FEjJ3Be/3PQch5nfhzXvhNb/PxcNs9cXNhQyWSOen1TDMtN4eQ3KX1iUykePvQxnLp+CzlCIYvXp3XRMUIxu6YY7Y5wsEfh3zbJ2bbG1iV1rqKQzWMvT2MOHsGeOgt2ARVuwe4+zMlxeVQ2kg6Hu3Jow+LlcYOmgOaRfWU+fMArXvxjYzNYHde2aJhdk5SQQnj1BUd6B7hMuHdAEdkmFHOZipaQ5lS9j+2Udw/JjObZMb3lmDG9ojnYIxYQSqktn5+hdsWbk+Vf0dSK5skDqqZRTgN3B64bCLLZ8urvV3/1V8tvcjV4XzcLJ7WBPX0eZ2ECFWnFNXhE0jThZgqX36h6rV65ir08i3P5dcJuH09238v3l9pI5wxC3mqPFo8FAVLkTr0knkm5NDq9idm5Bzu5jtk2iF1lqqZEHLVtFa8jbWTMALNr5WCzlA/RrBRWuLlmB5Fu3c+b0wZNmWly7jDLvc8QNDNklI/updOY29JFzvIMKtKGjq+QNCNspKX5SZM7DNNBSJc1Kqq5G2f+ClVhT2u8K5fIdx8SW4pKq5C2AVK2u8Y26MKc+OgEvaVTSFvPszOadB6G2zXd3g1cmYwE42wKs+8AGAY6n+fcukhf7+uI0568gHXhCjrUwmPdR/jeXIx4utzHYCfI5jVnii0kNXBqSgq/h3pF8Ld9N1BCxK/oa9VMVpiNKgXhitevp2ptk87NQl+LfP6AV4zs5tervxetJRg0hxo7grsR1+VPtrW1cepUdUrh1KlTtLa23tZBvV+h83kKYy9iT5xGp+M48+PkXv9LXIcfr/G52UJqXSwT1ucJjP0597asMrks2/rOJrEbaAvDo8MFXK/+IWa4mcK5l3BWrqJTcezLJ1HpTfAFMHsPgMuNCkRw7bsf+9LruPaegGgbmC6ctiGWux5lI++ucoi8vOphte8D2PkC5p57RWPg9pIfOsHlQre81i7gZLOcWfLz6mKMtPaRiw1it+2p7uWsHQhESe7/CCeXogAYhsbl85Pa+yGcWC+YLnTrIMk9T5BuG0VHq3dWyrE5uxIiuf8j6FCLpIw69pHtPY7HrDV12166WEvCs+c0S3FJlaQSGYwz38I5/yKuPfeCx489dRadWKfQvpfxVQ9d4QKdy9/HNXNKlNaLV4he/HOOtiW2VNU7/h1QrSLWWnyGsvlySkjsSTZxKrUchnj6DLfLziHiky5hlXWIemOpvJbPrTjcW3tPoMZ1pIG7CNdd2n/+85/n//w//08efPBB+vv7mZ6e5sUXX+TXf/3X79T43lfQ6U2c5W3J/WwK8lmMtgHU3Dh6vUw9VaEYOlVBQ9Eaf2YRQ7Xw1rTmQ4fBGigqTmfHKRTy0rxlm6GePTeOc88PkM1qggp0PkPh4mvg2BT+/+2deXBdZ3n/P+855+67pKtdsrXZlu3YTmI7DiEbJE1CWVqGAs2MoZMOAyFMCAPUISSEGULBDW1n6JQBhmbaYRnKkvbXdqCQxkDI5iTOYseWN3nVvktXV3c757y/P462qys5tiPJWt7PjGesc5fz6Oje85zzvM/z/bY8R/Kq99Ob8tCZ9NLfqXNLYxYRcdM+OLlbnm+PcvP6LYTMfvp8TYykNU4N+UjnoL4oi6fnKHqkgpJgGVXFGie7JMczcYoDRWxtXkeg5TcgbWT1Jt4YLuNchxspnWG0sE+QTMPTZ2KUh26hsjyHqfk42gpZs4LaaAkbik7jOfUcAMmiJgIIftdRTm3RHYTLTdpHvWyzRwklTuE2GvPkHhrL8mvog6P5Q05x1wgkhwAwj+5HK6lCq16PKG/A8sZwGZIafwLt/IzyWi5DlCH8/kurp3hdguZKOS4R7qAJiAXGHdvGRjBbX8fuOAkuN67116GVrUUYLoJewda1jhWpoRcOq0X8okDuomnG7x/2azSVS3pG8rNBbbG6G1itXDAR1NTU8POf/5x9+/bR1tbG5s2b+exnP4vf/zaVrlYrml7onjW+XfP6cW2+kVzHaeg/B0VV6OFiZKIPvEFIOyUTYbioKYL68imrRwBzfPhMzNaZobtoSwYJMorv3JH8x6TEq1t0pXx4XTY3VfYRPPx7XFvfz9Y1HrqGnJNmeVRwpNPgGqOPgDvHqLuC9ZXgMyxKGMIQlRhINsVtnj0uJieH+5M6L1tl3Ni4C3cgiBkuJ4pOwoR4CNbEBX6PUyMXAroSBiVR17TOGDg35MJVUsfGqn7skrWcHIlTFHbUOTOmm8GkM8XrP/RrhJnlxiYfZzIlDGY9rI1DRcSGxDBmJoXm8hBwRZj+0bfF9Okm6bSP9rXhrmgg5B3X+RnVCyQsAEJ+Hfdl9PFXFztyICe7JUGPI/8RCzpDZda5FuyOcSmMXIbcm8/g8gXRi5yBRE2IOY3oQz4n3tO9koFRpyRWGRWTXsMTFAUk1zcJTvU4fgxN5Wp9YDXzlsV+r9c76V2seHsIf9hZiBzvEgKnjVKMm6pogQis3YoZrUQ//izm6dedgaa6LVgdJ5FmFn+8lO3BQq13LRIHtw9p5hC+IHJarZ3G7Rzt8VEW1IkUr0Hvn5oi1qrXI06/wi4hHPevs44Edjpr88ZZSbGj8Dy5cJwqixEYPo0ZreRoG9xU3oc4sQ8rlwZNJxdsxrLzZyASaUGybj2BiIYLWOdz6vLaNJmMoGfK2WymjSTAuUEXG7Zcj9uAIpyFTls6U6pX1Qr8Y22I8eEz/9HfsikQQZQ3YUTXYfe3kTvzJnLcLauoagMbSrZxtM+J80wyTEl5I3RNSVCI8gaE3xkJjgYE0hfGrN+KNW0dR0TLMCKzaENfBB6XoLbEGbYSYkr+Q2ZSWB2FUhhypB+KKgq2z0Y0ILg6ILDt/GM8wWha8sopZ0CtOOTMPwS9zPpcxepArfouIkLTMGo3oUVKsYe6EcEYWqxscnjMTo9iJBPIN/flySqYra/h2nIrwuNHBKOztvhpgQjuHXdhD3ShN+1AZhx5BAnYmosd1UmeOxsgUHwdVUV1GKkBPNFijEiM7P7/mTSO10qq0aKluIRTXuofzd+POxRiNLyLoyc0NsbHHHOc3HistoUrMwjkJwJdcwat3IZNxhRYlhw3V5/2pOwY6z2DNFTZ9Loqmbl8FfQ6i6DDY4LXzkwliqwJh89Lbq1wjkmuZhsJfzU2grBfw5cYRPa1TSYBALv9KOu3VOILriGTg3jEjcV2RHEN2mg/WrgYI1aGcLmxR4eQySEw3OiVjWjhEuzBLoQviAgXv23F2pknX6G7EP4wcji/Q0t4Lv0ufK4T+7k+Z7EcnL9LX8KZJ6grVYlgtaISwSIjPD700tpJeYcJ7NEhcq89hVZeP5UEJpASOzGIdfD3uK65fU4nNS0Ym7RsTPYPoL/51GRJKeYL8a4Nt/P0qQhHRR0by6poLALN58XVfD25Q39Aizv+A+bJV/GFzrOx7N0c6Z06W2+uhnBRmDM9zok4pKcKJCACfUepj5dzqnfqRL6x2lmIfP0M9I736Ru65OZxmQY7OeLoAo0OYgDF9buI+TcwODaulCqgsVzwuyOSDZWFdwvpHGT9pVgNN/BysoHBLqfU4zbgxroUgaFCRz2R6KNxXd30yCFcD9RPbrEGu8kd+N9JKQstXoNevcG5YjezIG30+m0Ya69CGG9PFmQyLpcLY90OZ7/jaz0iXIKIlM7L+1u2nPQ+gCn5655hZw5FsTpRiWCJYPeccxzDLBNcbshN7wEUTuONtMkdfhax6/1oF7hCtG2J6D01mQQASCXwD51hV/1mPJpFJAhur9OppJWtxeUPY4+NYh36nbPHRC9rXc9RUrmVEaOESEAjFnA6V5z6tGTM9uZ5JwPo/Wepqk7i84SxbIlLF4QZIWZ1U+WxGQ2V8EZ/EWvigu5hyVBSUplqyxuAc596kZ0bQwxX1SClxG0IXm6VVMWcheWZUxQeA7w+N92hRgb7phJQ1oTj/V62VayDU6/mvUaEii7495C5LObR/VN6RjizHVq0HKNmw3h3jkRoGnZyGD2S7xscjJVzttfx540Gxo1oLrL0osXKcO96P/booHOHEC6ecyDxUtE1QVnEOX7VRYKsJTE0QcQ/v6ZMl0rWlAyOOsNwfo+gKKi8ChYTlQiWCPaw0xxudZ7EqNuKefJV54pQCEdCufsMeAMIl9fp+59jsRAcW0p9pKdw6GykhxGfTkO5kTddK3QDwnHGhkbz3lYfOE9k4DypDR8k4I1O6sfHgs7V49H+AGUNN+M7/vT4CVMgG3dypMdH/5iz93dUDlB04ldgOgNjHk3npq138X9n4+Qsp+RTJgYKlPs951+l+roKhO7ibK89WUY61iFZX+HMBkicstOOBmfBeSRd2A09OCagtgbReXxy3UQrXYsWu7DLnrSyyAlfZI/f0YEaHQRfELPlualErem4QsV5r02kJG90hKf18zt+yeXRC+5yEkcRtgjtLZLV5bI27ngaHJouVeF3pMQvVqpiPpFScqpbcuj85BYqorCjYemrq64UVCK4SKSZwx7scsoCHj9GRb2zQDtPaGVrsHvOQDaNbVsYm97pdBelEli959ErGpBjI8hsGpkYQLq9iHHbRCmdDpFz/ZKc6XjAhuN1iMHOvH3YJWs51ulYA3pnSCPrmsAX8BbIYstwHFP34Zn2SfG6HF2d+lKwrAq0nR9AzyYRHi8JQgweMcafB+Hk2cn1BycIC6OjhYCnhKExQTID2dIqfF35In12Se2kCmvAK4iHnQEwcEoZE7LN5YEsoa7XMe1aov7CSe6aUAqt4xja1tsgnUC4PGihosljNxfC7UMrq0MLhJGpUWR6DL2iDmlm8+/WbAur/QRaSdWkP0H/qCSVzT+BHTrnLLwvBTMWQ4fjnTOkKsacf3N1I81FJifpGXYmmCMB5y4jcok+yok0HG7L39Y55Og6xeen4nZZTP9eZXNOh1tJaHZ58OWOSgQXid3XRu6NfZM/Z9uO4t75p2jh/JKAtC2kmUUYngK/A2mZSCuHcHnyTE3AEarT12wGM4PdfQYzMYAIxtDL69FK12CeOTh5ArJ7z2FsfAdGjSNhMJiE3x+ZUrA82ye5rakab+UGRMcxECCrmukxqsavSvMFzQDskQHkiZcwmq7FPHPI2VewCKvxHdT4bXQ7C/rUWcKlC2LulCNop0exZYRcDtypEd5ZZ/BKuw9dgJGdIaIDiEwCb8iJQUo4lS1lQ81W9LZDjnR2RSMj/uLJJeeo35FrnjxW2ORSWfpTbkrNXuS5w5jnDhO65kM0l3k51uPCllAdyVFjnUUm+tCDEUSkuCCWuRCajl7TTO7V304uhtt959HrtxWY9sj06LhbkPPz9B7+CdI5507tCp7XJrHs2WM0rcLPxYWQUnKqR/Lm+JV8+6Dj0nbrpkKtpAth2/lDb5cbz3wzNON7da5f8o51jgT7SkMlgotA5jIF8g9YJvZgd14isEf6MU8fxB7qRovXYtRuRAtGxx/rwzz1huN9W7YWo6bZaRcdR/P6Eeu2Y/d3YI3bKcrRQcxzR9DrtsxYMwDz5Kto8TVoXj9dQ7Lgi/Rqp59r1u5EK9+IZQs60kF0oZNIOYNMGysl5bGpgSR7uAeZ6MfMJNErm8AbRPhDGMkurBMnyFoW+tqr0MvXIE0Tq+MEdttx8IdJr7uF1n4PHUMQ9QepiAo2l2Up8yQwxuKYXcfzYpOVG+jtnEqEpwZ9RNZsQw80UhqSeCIheo+fYOLIOjo74HNLtpYMUzR4GL2jDTtcjuFdgz3e3+879zI1FZuoLh5BCgPvwEnEQBv6phsvr7snnZjqiBrHOncYvWod1tnDk9v0muY8l7uiQOE6RmPZ0vEN8LlhTRzOTJOq0MTE+svFM5ZhmgKuQzoHQ0l5SYnA74HiYH6HmkunwPxnsekeLvxetbRLSiPMKSa4XFk1icBOJx0RMY8PYVyCMMwEcpb5+2mDYXZqlOyrv53sorHPt5Ab7sN97Z8gzSzZV34zdWV59jC50SHcW981KTUtLROZTjraP27flJR0NpVfWsnbt5wZRt7DiYzOaCbK0JikKOiYn0ywv3XG1c3Em2TTWGePYDRdC8lhzOMvT77GPPIsQtcxU0k0M4dW0YApNQ6cc9E/foGcygoGk3BNlQ0tf8RyezAar8FqP4Ect4wkXs21XsGbbc6w2po4DIxplIYjuMIgZvmS+T2CW5vSaAd/jxh1WkG1dCtypBu9ej3W+RYEklNjRdTrY/jOH0DaFsaGXejxmsIDdBHMqhwhJVq0zFncty30qnVoxfl+17Eg7Fib5Wi3m2wOGsudssJSQdcEzVWOwc7ZPgh4HOG7mUJ3F8Osn71LfA+3Idhe76z/tA86cw2bawShS0gmC8Fsv8dsdy4rgRWfCKRtYfeeJ3fkecimEMVVuDZcN9lmeTEIlwejfhu5Q3+YtlFzPI8n9jM2XNBKKUd6sVMJp6tmxpWl7G9HphIIVzF2chjzxCuOoJvLjbHmKqyes8gRZ7hLeAOgu5yBr3GMhm2TnSTlUUFLR75sQn2pIOR1HMQqi2BgFnnl1m5JZcxZnNSi8UkRNxEqwk6NgDVTmR/Mc0fQyhuwTr3mlHGq1lPqS9GfnOpqSecgKBOQHEQmwUwMoJXWogmNrL+EgNfHGi8UB50a7EDSKQCksxJbzv3l91oJcqMDedtkehTcTslqLN7MyQ4XZ/Q6GqsqaSqXGIHLn4LXQkVguPMSsV61DvPka4hYGZrQ0KLxgo4eXROkh87wrk3rsWxnrWSpyTsHvc46z/pKMLTLW7vwe5zXT78rcBsQvcQ1AnAUYK+th82mczewFOrwZRHB4bb871VzlVhxdwOwGhJBYiDPnF32t2MeexnXtlsRuguZyziLsFKiBSJzLiJq8Vpc296Ndf4oeHyO0Ux4quYstNkOpUBoOnK2soTQQNORtoV5+tCUqmcui3nyAMZVN2OdP4pethatuBL3jrsw244jk8Po1evQi6sm3yoWkOxscLxuTcv5AJ/pczpAbt4InQOSEneSte5RcsLDsaEQw2kdr3vqBKWFS3DveA9m2zGnW8nwIERhIVm4fdjdpyZ73GXbUWrqorQazfl1Z21aH5CZdXRzAMrWT27uHoFXT+e/f8ALNbOU8lNZibBnN7oVbh/6NXeQooySEBQFoabEizfw9r6wWjDqHPf2E8jEIHpVEyIQRmYzkEmh1TbnXQxMZ6LtdSEYyzgeyy7dcTjTL3MiWAhxSaqps72+oQwCHsmZXsfToS4uCiw2LxZNe3vxzDexANyyUdDaJcmYjgxI6QX8p5czKz4R2MmRwm1955HpFFKkMVuen7QmFMWVuDbegOYPF7xGuNzOSbl0zexOY8EoWrwWe5ruv7ZmE8IfBiuX51gGoNdtcSZIs2PYXacKA7dM3Dv/dGpfvhDuSBwpC81Dcpbg0HmJW3faKSdKLn0JyeYajZjZ4wxsZdMgBEVrd/CasY6G0vxvnRYtxR0tRUqJHOnDHujMvxMRAlG2FvvNZ/Je5+0/SXF0HZ3Dzok6Hob2dIj6yvXQMdUNZJU2MEyYAGDLfDnlCdr6JTUzxM8Gk5LnjklC7hDXVjRjdLZMxVxeh1ZRj+byUgVUlsyvuYoWLsEdLsk77nq0bNa/w2IwMCp5/viU38Cmaqf0tFBJ563wuQV1pYK18ZVnaiOEszZVEhJX7O+9WKz4RCDcs1zhewNgGNhdp/P8aWV/B3b3WbS6q+Z+vzk+DMLlwdh4PXKoEXt0EC1cjBYpRegGQjdwbb4ROdSDnRxCi8TRInGnq0h3OVeZI/0z4vaCbWGPDmKnRp3F5GDRrBOsuuZIEncO5W8PuCV2JoV5+I9TQ19S4jr9Eju3RnDpRUBw1t9RROIgNGfQLDmMZQtGPOX4Uv0FnS8iEKExkiTiD+B3a7hdAr/HTZu4mqJgNa70ADlvjPO5UsLSw8iYJOwXxIIyb4EwFnActToGLGpKijG7ToPhoScZI5X1kMoaHI9tY826SoLmAEYohoiWobm8ebEvBDPf90qcFHKm5LXTMs9v4HAblIQdKfIryUo+ScLK//1WfiIIFaGVrsHuGRdaE8K56vf4MXvPFzzf7j0PF0gEF0LzBqE8iE5d4WO+IPiCBYNTwuXBWH8duVf+d3JBWsTKHWesjpOYR56bfK7eeC3G2s0FHTCG7iz+9Y5IzPE17YjXoijdRnY0CMnhgnj0RC94PeAvTASTMYeLIVyMDrx+0uZMO+yocFHuDTkdNQDjffnRQ78k6vI4iebq29BDFdjSzzPHatFELdlhiATASkpeOwM3bXDKCOf6HMnkkA9KI/DKKckt5Z0YR5/CHD8e1SVrGY1dz6lBL62DXlqpZWdDLWviF7TTWHFkzEJLS5horV3ZJyrFwrLiE4Hm8WNsvAFZswGZy6IFwoig0yqjlVRj9+f3v2lz6PgsaIyxctzXfwA7OeRICoSKwczmmc0DWCcPoMdr8tYmJijymdxS2UPC9KJjE0q14z56AGvrHej+MIzll8iEbiDcvoL3mYuiIJzpg1e6ImypvZMiBvEZNm4zMdVam8uApjtTuJZJkdnHe0r6sAwvY55STo8GaR23Wzh4zpm2fddmwXBSYujw7DFoLE7jP/N8XpeW3neG2uL1nGKqO2eptGJeDHZiAHuox1lcj5Q6YnWXcYXp1p06/NCMZOBfRsdCsTRZ8YkAcNQ9PYUneK20FtFzFjnomKCLaCla2dpFjm52SQFrMFFgMAPM7huMM/nsO/F7fDMet0cGyTa8E9/R/xufRRDotc0QLp51LWQuyqOORk33MLzRHSQWCLKzEbThs1gTbRWajmvzjYhAGLv7DLk3xnWLAL8/QqjqT5goRQ2nIGdByOu0CfaOSKSUhF05SCUK9u+2U0T9TvteeRRiy8QSwx7pJ/vyr6Y6jzQd14670KMXlriYDbdLcM1aeO6Ys3gJsKGSPIcyheJyWPRE0N/fzwc/+EGeeOIJDMPgwQcfRAhBU1MTjz76KJq2eLf7mj+Me9u7ndZP6dS6xVyWkQuAPdyL2dkKYwn0qia0oorJriXhC4LHn9+Sqruc7bMgPF608nrsGcYzOV8Rz7SXsa3x/cT1YYSuI/wB3KFLa38IeAXXNTrWjraEsBc8boH0rsUdjCEzY45MdiCMHBsld3R/fnxjw8RFPxOJoKnUwjPaS/aYM2wWrVxHcSBOR9JHRVE1YmBq7UaEigkE3NyU+ANCM9BCTehGKSAciYMRONsrCfugtkQQfZvdQvOJ1VMosWGdPeKsEYlL/6wXhwTvvgqSaXAZEPIujVZLxfJmUYusuVyOr3zlK3jHVS+/8Y1v8MADD/CTn/wEKSVPP/30W7zD/CPcXrRoGVqsbHGTQGKA7Mu/xj57GLv3HLnXn8bqnmYY4w3g2vquSXMUvEFcV9+WN408HSHGvQ7ia5wNuoGxYReuWBy/B17uCLGvq5ohTyXuUGTW0oQ0c9iZ1Jwxe1yC4pAgHhZ4xssRQgi0YBS9uHJ8ilpgJ/qdMlFegBoun5e1caiLw/pAH7lXfo3d2Yrd2Yp14NfcUN7LSMZgoHwHMjJ+xez2oNdtwXz9/5Bdp7A7jmO+8ivkkNNydLZP8uIJSecQHOt0JAFGxpbO1I9MFxb1ZTo5x7TaxRHwCEojglhAqCSgmBcW9Y5g7969fPSjH+X73/8+AIcPH2bnzp0A3HTTTTz33HPcfvvtixnSFcMe7s0bEAMwW19Di9dMGtXosTLEzj91On5cHjTvheshWiCMa+vNyNQoQ8MjxCprCQnBLRudThOXzqzqklLa2ANdmCcOIDNJ9OoNzh3KZUgfy9Qo1qnX0crrpmYHfGGG6t/F4d4oqZxTzrDajhW8VnQc592bK0jnYox6rqPYZyANN9bhP84MGKv7DBl/KUdmiJXlLBga70paCuhla7Hb8yU2jNpmhDb7TIRCcSVYtETw5JNPUlRUxI033jiZCKb35gYCARKJwtrwTDKZDC0tLW/5vLdLOp1e0P2s9Vqz9HlIent7GEjkTyjruo4vVAQih5VNkEoWHieXYRAPB9B1F2O2Qc/gKN0jR2fdt9vlIuZ3IYDBtEncq+N6c9/kVap18gBWLkubHiNnzqJOdgHKIgH8yWG0WLkj/dBzjtH6m3m2LTY5sn+qB2Zbkpe2Rfv502QyGdLpNH1eL36fl/JZ5D0sy2RweBCIFjyWyWRpaTldsH0xmfj8+L1uSjfcgHbuENg2ds1GujIaI4vwGX47LPTnf6FR8V8ai5YIfvnLXyKE4IUXXqClpYU9e/YwMDAlF5BMJgmH33rx0uPx0NzcvJChAtDS0rKg+7FHBsjqRp7xidFwNWXVa5i+jGjZkvYBePWM02ZZGglx9RqRd8Urc2ly549hH3wObJtAeRNl5VuIFIcLBo3sTArr3BGsNw8CkkDNRrRABebMUkX7Mepv+OAl3xVIaWMmr8I69TrCF0IvrWVYhvJ0W4bHYKx6Hf7u1rzXGrXN1I/r9kw//lbAIDcwTVJbCFyVjVTGitho2rwxNcOHS4eymIdw1cJ/Ri7EzM+PrFgLSITbRxVQNdcLlwgL/flfaFT8s7/nXCxaIvjxj388+f/du3fz1a9+lccff5z9+/dz3XXX8cwzz7Br167FCueKo4WLHEmH9hOQSqBXrUcrLjQnH0rC/pNTp9GeYcdQZFcj6OP1YXuwG/vEK5PP0buO4/eFGPJuLRiJtwc6sE69PvXzucNOyWmGpo5wOTLaMpvGTg47nU0XkOCACekDgVG+FX9RFVqiD2l40GYZgjuciLPtqrswuo4hhMRVuwG8Aaz+ToTbi9s19dHUYuW4rr3DkffQDfSaDZNeEGviAq8bzowvFq8pEUumLDSdxVx/UigulSvaPrpnzx4eeeQR/uEf/oH6+nruuOOOKxnOoqNF4rjfwtwmkS5cVOwYdITdAuNlZqu/s+A5Rk8rruIGIJS33Z62ID2B1XMWrbja0RCaeP36nchcjtybzyCHnOZ/UVw1LsERKniPgVHJs8ckmRyARnNphLUDb2L0nyW2KYDPXZZn1hKPGDzVVk7YV861dQJXpoPci//PcV8TGpWN25GW6cw7GC70kupZvZo9LkFtidMtpFAoLo8rkgh++MMfTv7/Rz/60ZUIYdngMQQNRSmq3QPodpaUHuH4SDSvW0TM0kkkAhF85jAzE4EIF0N3fv1cROIYtZuw41WQzSAicbRICeaZw5NJABzBPrvvPFrtxrzXZ3OS452SpnKBZUt0TdA15KGs9jqyJVuwTY3r6ywG0gaW7Xj4ZnKSzTWOjktET5I9+AcnCQBIG3HiJWRJBWKG8c/FYqcSyMQgSIkIxS5pZsJODjt+wZrmzHd4556+VihWAqtioGw5U+xOEU08h+hzCuEBoRHfejse19TVsR4rx/ZHnHkIAJcbLVaOHO6GsvyraL10jVNimTC2d3sxqtah+UN5V/pSSuy+c8zE7u+AmYnAkvjdjmkHOOsa6yvg5KCftkFnjcHohZubBUXBiQQ2lcis4fSU/8I0ZHoMLkNDxx4dInvgN3m/o3v7XRflAWwP95F95deTZTIRiOC6+vY523YVipWASgRLHH2sP/+ELG04/jx27H2M5Lz0JSRuPUTF2s2IzJizKCttzJOvklt3Iy5bok2TKdaCUdw73+NcLSMRwdmvloUQaPFarKGevO1arAzz7JvOcFskDukk7uF+1nkCrKtykU2OkvSW8UpvEfVlGgw6ycG04Ei7s7Yxs/dduH35ZjwT29+iXXYurO4zU0kAIJvGbD+Oe8OF16CkbTu/27S1Epkcxh7oUIlAsaJRiWCJM5ukhBxLkEnl2HfMg2WDz60RrCzDd3YfIjkEgFXWSJtZSqMNM4e1NV8IfIV1/pnoZXXYveen1giKKpHpJNbZw04CKSrHOjfViSCiZXi9AdxnXmZX83volvnrH8Njjl+uMaOFXvMFcG252fGNGF8jkI3bEYHoWx+gWZCJ/sJtw71IaV94mtcyC1RgwbnDUChWMioRLCHskX7MjhMwOohW2YRWUjXrlahWuobzY16s8fb6VBa6zChm2V2UuEaQ6JweC1Pscc3qPCWljT3U4wx15bJOF06svEDiWguEcV99m9M1hMTsODXp1auV1mKdPpT/vkPd6A1XY0ubQN8RPOU3Mb0EVBGFriHH89U3QyhNL65CXP9nyFQS4fZyurufhsvxGQa00rVTRj8T71/Z+JaSDsLlRqtsxJrWgTUR2+UgbQt7sNspxcH4cS5Tw2SKJYdKBEsEOznk1KbHpRns/g6MxmvR1mzCteUWci0vQC6DKK7CaNpOb2f+n65/VBLxeXmx24ttQ02xPWcnjRzuI/fyryYHyOzec450dOmagucKtxfd7cXOjGH3nJn2gJjVx1mOTwzo2TH8LomhCUwbKmOOb8JLrdBULtlSS17JChztJ8bLVNm2ros7cLOgFVei12/DOnPI8RiubUaL117Ua/WKemQq4UwDazpG/Ta0WOmcz8/knN/X4yo81vZgN7lXfj31c/dpXNvvQgsXI20LzbNMlPMUKx6VCJYI9shAgT6PefoN3JWN6BUNiFgZmCbCG0AYLtaUSM73T7WWdg1BQynUlwmkhN6uNgKetbPuy+o9X6B1Y54+iFZcVeB1MIHm8WOs2Yh54gAAcmQAES3L6yrC5QHbSQ5GbTOVxTo3uBxRuJ5hp+0V4GQ3NJQ5HgQLgebxIRqvRq9sAiTCF3JMgC7mtb4QrubrkWuvchzZfKFZdZmyOUn7gORIh3PPs6kaKmJTTmEejwer7XjB66y2o1gS7KFu9Npm9MrLk/JQKOYTlQiWCLPr009b5J3RwlgSgusaBUfanGvw5ipBSXjqRNSWnls8bm4Pk7l78dM5iYw3YSCwzh9FZlOIhh2YXafR+88iQsXopTVY549ibLwBraQSIZy7gYluosVECA0RuDzbLqHps7bkTqd7BF6Z1oX7UqvkhvWCytj0N5rlhbaNTCUgM4Z14gACgVa/9bLiVCjmC5UIlggiVAQuL+SmFoeN+q2IOa4WXYYzSFUedX6+FM9avaQG69TBvNKOUbcVoc9eu+4dkbzcKklmfMQCV3Hd1evQdZ1fvWkQ9sQpq9rGSM6Fd8yicX01seKpheiID3xu8uwVm8ohMPeA8pJHSsnpnsLkdrZXUhlz/g6ZTAa9ej12Z76MhhYrx5xwywPMc0fQqtZNCg0qFFcClQiWCFoggnvHnVhdp5GJQfSKBsef4C2crC7HtFxE4rh3/qkjb2FmHHmLOYxSkmnHLD07Lok0mISnj3m4eSNoAtKWRsSdY61/ENvwYRkh7GktqwGv4KYN0Dbg+BPXFENZRBSsDywXpGUik8NsCaXp84Y43B+cPDb+GclNi5bi2vEerHH1US2+Butk/kK08PjU4rHiiqMSwRJCCxWjhQptKOcbIQQiWoo7Ovci6ARjWSZPdBPkLGfb1looznXgPbbPkdQWgmzddXTm1lFeZKCPn+zDfsHGJaj/c6lIM4d17gjmiQN4kVQbbmLrbuMPnWUIoKZ4xnyEpqMXVaAXORpSdnIY05p+MAVG43aEy714v4RCMQsqESguiNsYbxCSUByEipgjI2FLQVUoDS89M+WrICXuUy/ChlISvpIVZ6EoRwcxp7eWmlkCp5/h5k3vQ/f63tIZTQtEcG+/y/EvNrOOS9llSmgoFPOJSgSKCxLyOlf+p3qgOARvnp+ojUs2Vrqp84bQZkwEu60xUjlJ9AKLz8sRexa3MVKjxDwZtMDFtYJqgYiaUlYsOVQiWAbYyRHsoS5kKokWK0WLlBYMfy0UmiaoK4WoX/KHGT43RzoE5ZVXExj5Td72rB4g6F6cJGBakoFR6EtIfG5HxC7kW5h9z9bmKfwRZ5FfoVjGqESwxLFTCXKv/gY5NgKABRibb8SoWrdoMRi6GC8PFXbKmIESZ34glwFNJ1t/PSIQIbRI58b2QXhpml+D3yO5uRmC3vlPBiJYhLFhF+axl5yOK7cXY/ONquNHsexRiWCJI0f6J5PABOaxl9GKqxZ1EMnvAa/L8UGYwKVDMOjBff0HMJOjWLoXzRumwqu9ZbfTfJDOSg6ezU9OYxnHzCe4AIlIGAZ6TTNaUSXSzCC8QTSfkqhWLH9UIljiSMsq3GhmJyd4Fwu/R/COdXDgtGR4zDnR7mgQBLwCCOG+CBG7+caWTgfTTCxbcqHhuLeD41EQe+vnCUHWlFi2k0AXIzEqFJeLSgRLHC0UBU0He+qMp9U2zzlotpAUhwS3bIRMzukmmk1fZzHxuaGxHI51TG3TBESucKuqZUu80bXse1OSMaGxDOpKnWSqUCxFVCJY4mihYlzb78RqfR05NoxWtQ69sukttXPCAR92YgB045Lcud4KtyFwL5FPjRCCxjJw65JTPRDwwsYqQWQBtNyklMixBNgWwhe84GL94Ci8fGZqNuBIOwgh2VitEoFiabJEvtKKC6HHytGuvg0s86JM0O3EIMUdB8m29ILuwlh/nSNcZ6y8P7ffI9hQJagrlehaoenNfCBzWaz2Y47gnm053s3N18/ZBjqQLFxUb+2G+lKJd5G6qRSKS+HiJBkVVxyhGxeVBKRlOkNPw73OBiuHeeRZZKJvgSO8snhcYkGSAIA93Ot0Co2X52R/O+bZN5FzrNPMdsfkczsy3ArFUkR9NFcYMpvC7p3Fa3hG55Hi4pGjAwXb7K4zyFl8lgGKgwK/O/+uYHONmNUkSKFYCqy8WsEywU6PIRP9yFwWLRhBhIrnpbNE6C5EMIYcHczf7la97peL8Ba2iIpQbE6NoJBPsKVyBGlEyVmSqF8Qu4wu00RKMjQmEUIQ9S/MbIRCASoRXBHsdJLcwd8jB8dduITAde0dl22JOB3h9mI0X0/uwG8mSxlaWT0ivPBidisVES11/JoHxtuTdBeupu0Ife4F49HBLpqbY1xuG+tgUvJMy4Tqq8Trgpuar3xHlGJlohLBFUAO900lAQApMVv2o+18z0WtA7wVWqyc9KZbCekSXB60YGxe3ne1onkDuLbc4txlWTlEILrgekFnemWe6ms6B+0DUiUCxYKgEsEVQM6wpASQqRGkZc7LGJQQgs7hMaLNzfPwbksXy5aTUtcLjebxwSJJSUgpGZpF3254bFF2r1iFqERwBZjtalKrqEcozZqLYnhMcqZX0jsCtcWSqqKJCeeVgRCCtXFJXyJ/e3XxyvkdFUsL1TW0iKSyknROIsLFuLbeCm4fINDK6x2ryAVyqsrkJKmsnFU0br5JZyXp7MLtZywjefao5Hin45b2xjk4eE5iWovvi7yQlEcFzVXOpLSuweYaKJ2/uUCFIg91R7AIpLOSM32SYx3Ol/qqGp3Kkjrc15eBbSI8AYQ+/38K05J0DcEb5yQ50/EKXiipg2xOcn5AcqQNJLCxSlJTLOZdhmIkJRnL5m9rG4CNVRBZQUY4PrdgUzXUxZ2f/R6lV6RYONQdwSLQMSg5dM6xd0xl4aVWx79X8wbQ/JEFSQIAA6PwwgnJWMYRZzvSDuf6F+bKuWcEXj3tLGpmcvDaGegZnv/9zHkyXIHnSCGcklfAK1QSUCwoi3ZHkMvleOihh2hvbyebzXLvvffS2NjIgw8+iBCCpqYmHn30UbS30NBZbpiWpLW7cHvnkKQ8unBfbl3X6UsUnvRPdUNdXM77lfrZvsJ9ne6V1JTM737CPoj48xdOG8oWRnZaoVgtLFoi+K//+i+i0SiPP/44g4OD/Pmf/zkbNmzggQce4LrrruMrX/kKTz/9NLfffvtihbQoaMIRQxua0fHhX2C/ctu28c7S5u5fIKmDoKdwW2gB1r59bsH166Br0LmrqohCaUQsWveQQrESWbTL7zvvvJPPfvazkz/rus7hw4fZuXMnADfddBPPP//8YoWzaGiaYF2FYPp5ymOwoHcD4LQgxsMC37SEIwRsrFkYTZ7aEoExba3b0GHtPN8NTBDyCpoqNHY1aayJa/iUkJtC8bYQcjFaSaYxOjrKvffey4c//GH27t3Ls88+C8ALL7zAL3/5S771rW9d8PWvv/46Hs8sl5/zTDqdxuu9vHqD2+vD5YuTtnTcmo1mjiDcAVI5N5oGHi1FcqhrQbt4JuIPxsrJ2D4sW+B35UgNd2JZ5lu/wSWiaRqBSAUpyw0IfHqG5HAn9mUa6Lyd478UUPFfWVT8s9M8x2zRonYNdXZ2ct9993H33Xfzvve9j8cff3zysWQySTj81v1xHo9nzl9mPmlpabms/Ugpae2R7D89ta0u7mVrxXTRMQ+UR+clzrmYPX4PlDUt6H7z9lV++f2Ol3v8lwoq/iuLin/295yLRSsN9fX1cc899/DFL36RD33oQwBs3LiR/fv3A/DMM8+wffv2xQpnwRjNwMGz+dtO96qpUIVCsXRZtETw3e9+l5GREb7zne+we/dudu/ezQMPPMA//dM/8ZGPfIRcLscdd9yxWOEsGKYJ1izVkOwKG3hSKBQrh0UrDT388MM8/PDDBdt/9KMfLVYIi4LfAxEfDE+Tqtc1JSGsUCiWLiuraX8J4HEJdjYJSsflhMJeuHGDIOxbXonAsiXJjCRrLo07GdNy4sktkXgUipWEkphYAKJ+wTvWORO2Lp15H95aaBIpyZF2SVs/hLywbS3Ew1dO4mBoTPLmOUnXMBQFYOsaKA4tr2OqUCxl1B3BAuHSBUHv/GvtLDSmJXnjrORcH9jSKXH98ahkZHZXxgUnnZO8eFzSOQRSQv8oPHtMkkyrOwOFYr5QiUCRx1gWOofyt9nSEXu7EiTTkEjnb8uakFCJQKGYN1QiUOSha045ayauBZhGvhgM3ZmInsmVikehWImoRKDII+ARbFubf5Iti0DUf2XiCXlhU3X+tro4hK9QPArFSkQtFisKqC6CoEeQSEs8hiAWAO8V0vPRNEFDGRQFIZmR+N1OPOqOQKGYP1QiUBRg6IKSMJSEl8bJ1m0IyiKwIk0HFIolgCoNKRQKxSpHJQLFrEjLQi6ASqlCoVh6qNKQIg9pW9iDXVinDyFzWYy1m9FKqhCuhZf+VigUVwZ1R6DIwx7uJffK/2L3tyNHeskd/B12X9uVDkuhUCwgKhEo8rB7C0/65plDSCt3BaJRKBSLgUoEinz0WaqFuhv1UVEoVi7q263IQy+pLkgGRv0WhD7LuLFCoVgRqMViRR5apAT3jj/F6jsPZhYtXosWLb3SYSkUigVEJQJFAVqkBC1ScqXDUCgUi4QqDSkUCsUqRyUChUKhWOWoRKBQKBSrHJUIFAqFYpWjEoFCoVCsclQiUCgUilWOkFIuK/PX119/HY9HCaApFArFpZDJZNi2bdusjy27RKBQKBSK+UWVhhQKhWKVoxKBQqFQrHJUIlAoFIpVjkoECoVCscpRiUChUChWOSoRKBQKxSpnVclQ53I5HnroIdrb28lms9x77700Njby4IMPIoSgqamJRx99FE3T+NnPfsZPf/pTDMPg3nvv5dZbbyWdTvPFL36R/v5+AoEAe/fupaioaNHityyLhx9+mNOnT6PrOt/4xjeQUi6b+Cfo7+/ngx/8IE888QSGYSyr+P/sz/6MUCgEQHV1NZ/61KeWVfzf+9732LdvH7lcjr/8y79k586dyyb+J598kv/4j/8AnJ74lpYWfvKTn/C3f/u3yyL+XC7Hgw8+SHt7O5qm8bWvfW3pfP7lKuIXv/iFfOyxx6SUUg4MDMibb75ZfvKTn5QvvviilFLKRx55RP72t7+VPT098r3vfa/MZDJyZGRk8v9PPPGE/Pa3vy2llPJ//ud/5Ne+9rVFjf+pp56SDz74oJRSyhdffFF+6lOfWlbxSyllNpuVn/70p+Wf/MmfyJMnTy6r+NPptPzABz6Qt205xf/iiy/KT37yk9KyLDk6Oiq//e1vL6v4p/PVr35V/vSnP11W8T/11FPy/vvvl1JK+eyzz8rPfOYzSyb+VVUauvPOO/nsZz87+bOu6xw+fJidO3cCcNNNN/H8889z8OBBrr76atxuN6FQiNraWo4ePcqBAwe48cYbJ5/7wgsvLGr8t912G1/72tcA6OjooKSkZFnFD7B3714++tGPUlrquJ4tp/iPHj1KKpXinnvu4WMf+xivv/76sor/2WefZd26ddx333186lOf4pZbbllW8U9w6NAhTp48yUc+8pFlFX9dXR2WZWHbNqOjoxiGsWTiX1WloUAgAMDo6Cj3338/DzzwAHv37kUIMfl4IpFgdHR08vZ/Yvvo6Gje9onnLjaGYbBnzx6eeuopvv3tb/O73/1u2cT/5JNPUlRUxI033sj3v/99AKSUyyZ+r9fLX//1X/MXf/EXnDlzhk984hPLKv7BwUE6Ojr47ne/S1tbG/fee++yin+C733ve9x3333A8vr8+P1+2tvbueuuuxgcHOS73/0uL7/88pKIf1UlAoDOzk7uu+8+7r77bt73vvfx+OOPTz6WTCYJh8MEg0GSyWTe9lAolLd94rlXgr179/KFL3yBD3/4w2Qymbw4l3L8v/zlLxFC8MILL9DS0sKePXsYGBhYNvHX1dWxZs0ahBDU1dURjUY5fPjwsok/Go1SX1+P2+2mvr4ej8dDV1fXsokfYGRkhFOnTrFr1y4ANG2qqLHU4//Xf/1X3vnOd/L5z3+ezs5OPv7xj5PL5ZZE/KuqNNTX18c999zDF7/4RT70oQ8BsHHjRvbv3w/AM888w/bt29myZQsHDhwgk8mQSCRobW1l3bp1XHPNNfzhD3+YfO611167qPH/53/+J9/73vcA8Pl8CCHYvHnzson/xz/+MT/60Y/44Q9/SHNzM3v37uWmm25aNvH/4he/4Jvf/CYA3d3djI6OcsMNNyyb+K+99lr++Mc/IqWku7ubVCrF9ddfv2ziB3j55Zd5xzveMfnzcvr+hsPhySv6SCSCaZpLJv5VJTr32GOP8etf/5r6+vrJbV/+8pd57LHHyOVy1NfX89hjj6HrOj/72c/493//d6SUfPKTn+SOO+4glUqxZ88eent7cblc/P3f/z3xeHzR4h8bG+NLX/oSfX19mKbJJz7xCRoaGnjkkUeWRfzT2b17N1/96lfRNG3ZxJ/NZvnSl75ER0cHQgi+8IUvEIvFlk38AH/3d3/H/v37kVLyuc99jurq6mUV/w9+8AMMw+Cv/uqvADh9+vSyiT+ZTPLQQw/R29tLLpfjYx/7GJs3b14S8a+qRKBQKBSKQlZVaUihUCgUhahEoFAoFKsclQgUCoVilaMSgUKhUKxyVCJQKBSKVc6qGyhTrC7279/PAw88QGNjI+C08FVXV/Otb30Lt9u9IPvcvXs3qVQKn89HKpXihhtu4HOf+9y8vf8zzzzDr371q8mZBoXi7aISgWLFs2vXLv7xH/9x8ufPf/7z7Nu3jzvvvHPB9rl3714aGhqQUnL33Xdz6NAhrrrqqgXbn0LxdlCJQLGqyGaz9PT0EIlE+OY3v8mBAwcAeO9738vHP/5x2tra+PKXv4xpmgghePjhh9mwYQO33347V199NWfPnmXXrl0kEgkOHjxIXV1dnkzJbPszTXNSZG+2fR4/fpxvfvOb2LbNyMgIDz/8MNdccw233nor9fX11NfX89GPfpSHHnoIn8+Hz+cjEoks/MFSrBpUIlCseF588UV2795Nf38/mqbx4Q9/mHQ6TVtbGz/72c8wTZO7776bXbt28c///M/s3r2b2267jZaWFh566CGefPJJ2tvb+bd/+zfi8Tg7d+7k5z//OY888gjvfve7GRkZKdB92bNnDz6fj/Pnz7NhwwZisRi/+93vZt1na2sre/bsYf369fz3f/83Tz75JNdccw2dnZ08+eSTxGIx7r//fu6//35uuOEGvv/973Pq1KkrdDQVKxGVCBQrnonS0ODgIPfccw/V1dW0trayfft2hBC4XC62bt1Ka2srra2t7NixA4Dm5uZJUbZoNEplZSXgqEhOrDmEQqE84b8JJkpDtm3z0EMP8YMf/AC32z3rPktLS/nOd76D1+slmUwSDAYBiMVixGIxAE6cOMGWLVsAuOaaa1QiUMwrqmtIsWqIxWI8/vjjPPzww5SUlEyWaHK5HK+99hpr1qyhoaGBV155BYCWlhZKSkoAJqWCLxVN0ygrKyOXy9HQ0DDrPr/+9a9z//33s3fvXtatW8eE6st0Zc36+npee+01AN58883LOwAKxRyoOwLFqqKxsZHdu3ezb98+qqur+chHPkIul+POO+9k06ZN/M3f/A2PPPIITzzxBKZp8vWvf/2i3veFF17gwIEDfOYznwGmSkPg+Bg8/vjjRKNRXnrppYJ9vv/97+fTn/40xcXFlJeXMzg4WPD+jz76KJ/73Of4l3/5F4qKivB4PPN3UBSrHiU6p1AoFKscVRpSKBSKVY5KBAqFQrHKUYlAoVAoVjkqESgUCsUqRyUChUKhWOWoRKBQKBSrHJUIFAqFYpXz/wHUtjttCzurPQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.scatterplot(x='Room.Board',y='Grad.Rate',hue='Private',data=df,palette='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a scatterplot of F.Undergrad versus Outstate where the points are colored by the Private column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outstate', ylabel='F.Undergrad'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Outstate',y='F.Undergrad',hue='Private',data=df,palette='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a stacked histogram showing Out of State Tuition based on the Private column. Try doing this using [sns.FacetGrid](https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.FacetGrid.html). If that is too tricky, see if you can do it just by using two instances of pandas.plot(kind='hist').**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "g = sns.FacetGrid(df,hue=\"Private\",palette='coolwarm',height=6,aspect=2)\n",
    "g = g.map(plt.hist,'Outstate',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a similar histogram for the Grad.Rate column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "g = sns.FacetGrid(hue='Private',data=df,palette='coolwarm',height=6,aspect=2)\n",
    "g = g.map(plt.hist,'Outstate',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice how there seems to be a private school with a graduation rate of higher than 100%.What is the name of that school?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cazenovia College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>3847</td>\n",
       "      <td>3433</td>\n",
       "      <td>527</td>\n",
       "      <td>9</td>\n",
       "      <td>35</td>\n",
       "      <td>1010</td>\n",
       "      <td>12</td>\n",
       "      <td>9384</td>\n",
       "      <td>4840</td>\n",
       "      <td>600</td>\n",
       "      <td>500</td>\n",
       "      <td>22</td>\n",
       "      <td>47</td>\n",
       "      <td>14.3</td>\n",
       "      <td>20</td>\n",
       "      <td>7697</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Private  Apps  Accept  Enroll  Top10perc  Top25perc  \\\n",
       "Cazenovia College     Yes  3847    3433     527          9         35   \n",
       "\n",
       "                   F.Undergrad  P.Undergrad  Outstate  Room.Board  Books  \\\n",
       "Cazenovia College         1010           12      9384        4840    600   \n",
       "\n",
       "                   Personal  PhD  Terminal  S.F.Ratio  perc.alumni  Expend  \\\n",
       "Cazenovia College       500   22        47       14.3           20    7697   \n",
       "\n",
       "                   Grad.Rate  \n",
       "Cazenovia College        118  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Grad.Rate']>100]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Set that school's graduation rate to 100 so it makes sense. You may get a warning not an error) when doing this operation, so use dataframe operations or just re-do the histogram visualization to make sure it actually went through.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['Cazenovia College','Grad.Rate'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Private, Apps, Accept, Enroll, Top10perc, Top25perc, F.Undergrad, P.Undergrad, Outstate, Room.Board, Books, Personal, PhD, Terminal, S.F.Ratio, perc.alumni, Expend, Grad.Rate]\n",
       "Index: []"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Grad.Rate'] > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Private\",palette='coolwarm',height=6,aspect=2)\n",
    "g = g.map(plt.hist,'Grad.Rate',bins=20,alpha=0.7)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **K Means Cluster Creation**\n",
    "\n",
    "**Import KMeans from SciKit Learn.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create an instance of a K Means model with 2 clusters.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fit the model to all the data except for the Private label.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=2)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(df.drop('Private',axis=1))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What are the cluster center vectors?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.03631389e+04, 6.55089815e+03, 2.56972222e+03, 4.14907407e+01,\n",
       "        7.02037037e+01, 1.30619352e+04, 2.46486111e+03, 1.07191759e+04,\n",
       "        4.64347222e+03, 5.95212963e+02, 1.71420370e+03, 8.63981481e+01,\n",
       "        9.13333333e+01, 1.40277778e+01, 2.00740741e+01, 1.41705000e+04,\n",
       "        6.75925926e+01],\n",
       "       [1.81323468e+03, 1.28716592e+03, 4.91044843e+02, 2.53094170e+01,\n",
       "        5.34708520e+01, 2.18854858e+03, 5.95458894e+02, 1.03957085e+04,\n",
       "        4.31136472e+03, 5.41982063e+02, 1.28033632e+03, 7.04424514e+01,\n",
       "        7.78251121e+01, 1.40997010e+01, 2.31748879e+01, 8.93204634e+03,\n",
       "        6.50926756e+01]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Evaluation Of The Dataset Using The Model**\n",
    "\n",
    "**Create a new column for df called 'Cluster', which is a 1 for a Private school, and a 0 for a public school.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat = pd.get_dummies(df['Private'],drop_first=True).rename(columns={\"Yes\": \"Cluster\"})\n",
    "feat\n",
    "df = pd.concat([df,feat],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abilene Christian University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adelphi University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adrian College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agnes Scott College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska Pacific University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "Abilene Christian University     Yes  1660    1232     721         23   \n",
       "Adelphi University               Yes  2186    1924     512         16   \n",
       "Adrian College                   Yes  1428    1097     336         22   \n",
       "Agnes Scott College              Yes   417     349     137         60   \n",
       "Alaska Pacific University        Yes   193     146      55         16   \n",
       "\n",
       "                              Top25perc  F.Undergrad  P.Undergrad  Outstate  \\\n",
       "Abilene Christian University         52         2885          537      7440   \n",
       "Adelphi University                   29         2683         1227     12280   \n",
       "Adrian College                       50         1036           99     11250   \n",
       "Agnes Scott College                  89          510           63     12960   \n",
       "Alaska Pacific University            44          249          869      7560   \n",
       "\n",
       "                              Room.Board  Books  Personal  PhD  Terminal  \\\n",
       "Abilene Christian University        3300    450      2200   70        78   \n",
       "Adelphi University                  6450    750      1500   29        30   \n",
       "Adrian College                      3750    400      1165   53        66   \n",
       "Agnes Scott College                 5450    450       875   92        97   \n",
       "Alaska Pacific University           4120    800      1500   76        72   \n",
       "\n",
       "                              S.F.Ratio  perc.alumni  Expend  Grad.Rate  \\\n",
       "Abilene Christian University       18.1           12    7041         60   \n",
       "Adelphi University                 12.2           16   10527         56   \n",
       "Adrian College                     12.9           30    8735         54   \n",
       "Agnes Scott College                 7.7           37   19016         59   \n",
       "Alaska Pacific University          11.9            2   10922         15   \n",
       "\n",
       "                              Cluster  \n",
       "Abilene Christian University        1  \n",
       "Adelphi University                  1  \n",
       "Adrian College                      1  \n",
       "Agnes Scott College                 1  \n",
       "Alaska Pacific University           1  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a confusion matrix and classification report to see how well the Kmeans clustering worked without being given any labels.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.35      0.46       212\n",
      "           1       0.79      0.94      0.86       565\n",
      "\n",
      "    accuracy                           0.78       777\n",
      "   macro avg       0.74      0.64      0.66       777\n",
      "weighted avg       0.76      0.78      0.75       777\n",
      "\n",
      "\n",
      "\n",
      "[[ 74 138]\n",
      " [ 34 531]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "print(classification_report(df['Cluster'],kmeans.labels_))\n",
    "print(\"\\n\")\n",
    "print(confusion_matrix(df['Cluster'],kmeans.labels_))"
   ]
  }
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