numeric_regression_house_prices.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "89e7e01f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:16.347375Z",
     "start_time": "2024-12-20T13:38:16.291779Z"
    }
   },
   "source": [
    "# # !IMPORTANT - to run this notebook download first Houses.csv dataset from https://www.kaggle.com/datasets/dawidcegielski/house-prices-in-poland and put it in data/ folder\n",
    "# import libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection, preprocessing, linear_model, naive_bayes, metrics\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "# import csv into pandas DataFrame\n",
    "houses = pd.read_csv('data/Houses.csv',encoding = \"ISO-8859-1\")\n",
    "houses = houses.sample(frac = 1)\n",
    "houses.head(50)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Unnamed: 0                                            address  \\\n",
       "20065       20065                           Grzegórzki Olsza m³yñska   \n",
       "22315       22315     Bronowice Katowicka  Bronowice  ok. Wizjonerów   \n",
       "1175         1175                 Stare Miasto Kazimierz pl. Wolnica   \n",
       "23347       23347  Stare Miasto Centrum ul. Garbary Szyperska Cen...   \n",
       "14129       14129                              Praga-Po³udnie Goc³aw   \n",
       "11765       11765                                           Podgórze   \n",
       "12815       12815                                               Wola   \n",
       "19238       19238                                           Podgórze   \n",
       "9796         9796       Stare Miasto Pi¹tkowo os. Boles³awa mia³ego   \n",
       "1494         1494                       Podgórze Duchackie Kurdwanów   \n",
       "9975         9975                       Nowe Miasto G³ówna Smolna 10   \n",
       "9667         9667             Bie¿anów-Prokocim ul. Mariana Domaga³y   \n",
       "20889       20889                                  ródmiecie Bagno   \n",
       "22220       22220                       Grunwald Górczyn ul. Ceglana   \n",
       "7818         7818                                               Wola   \n",
       "14501       14501                  Nowe Miasto Rataje ul. Milczañska   \n",
       "13281       13281                                     Dêbniki Ruczaj   \n",
       "11333       11333                                Ursynów Wrzosowisko   \n",
       "5619         5619                               Krowodrza Wroc³awska   \n",
       "23358       23358                              Praga-Po³udnie Goc³aw   \n",
       "904           904                        Krowodrza Radzikowskiego 56   \n",
       "5449         5449                Mistrzejowice ul. Piasta Ko³odzieja   \n",
       "448           448                            Grzegórzki Aleja Pokoju   \n",
       "6512         6512                                               Wola   \n",
       "14280       14280                 Stare Miasto Naramowice Naramowice   \n",
       "5857         5857                  Nowa Huta Czy¿yny ul. So³tysowska   \n",
       "19217       19217       Stare Miasto Pi¹tkowo ul. Teofila Mateckiego   \n",
       "17020       17020                                        Zwierzyniec   \n",
       "11858       11858                    Nowe Miasto Malta ul. Katowicka   \n",
       "3693         3693                                  Bie¿anów-Prokocim   \n",
       "20476       20476                             Podgórze Rzemielnicza   \n",
       "18862       18862                   Je¿yce Jana Henryka D¹browskiego   \n",
       "21495       21495                             Bia³o³êka ul. Kobia³ka   \n",
       "2097         2097                                 ródmiecie Pañska   \n",
       "19810       19810      Stare Miasto Kleparz al. Juliusza S³owackiego   \n",
       "11763       11763                                        ródmiecie   \n",
       "22909       22909                                            Mokotów   \n",
       "18484       18484                                          Rembertów   \n",
       "16861       16861                                       Praga-Pó³noc   \n",
       "6233         6233            Nowa Huta Czy¿yny bp. Piotra Tomickiego   \n",
       "16959       16959                    Nowe Miasto Malta ul. Katowicka   \n",
       "16856       16856  Stare Miasto Centrum ul. Mieczys³awa Niedzia³k...   \n",
       "3985         3985                Stare Miasto Naramowice ul. Sielawy   \n",
       "22325       22325                                            Mokotów   \n",
       "2293         2293                       Podgórze P³aszów ul. Przewóz   \n",
       "20518       20518                   Wilda ul. Ignacego Pr¹dzyñskiego   \n",
       "8613         8613                    Wola Juliana Konstantego Ordona   \n",
       "5417         5417                                   Bielany Heroldów   \n",
       "6411         6411                    Dêbniki Anny Szwed-niadowskiej   \n",
       "13018       13018                                       Wilda Dêbiec   \n",
       "\n",
       "           city  floor       id   latitude  longitude      price  rooms  \\\n",
       "20065    Kraków    0.0  21989.0  50.083364  19.970568   589000.0    3.0   \n",
       "22315    Kraków    1.0  28479.0  50.084921  19.890948   380000.0    2.0   \n",
       "1175     Kraków    2.0  20484.0  50.048437  19.944251   799000.0    3.0   \n",
       "23347    Poznañ    2.0   1257.0  52.400663  16.919733   543071.0    3.0   \n",
       "14129  Warszawa    4.0   9964.0  52.228633  21.106572   612000.0    3.0   \n",
       "11765    Kraków    2.0  20642.0  50.043100  19.952961   569000.0    3.0   \n",
       "12815  Warszawa    3.0  10609.0  52.236238  20.954781   822375.0    2.0   \n",
       "19238    Kraków    0.0  23565.0  50.043100  19.952961   335000.0    2.0   \n",
       "9796     Poznañ    4.0   2465.0  52.400663  16.919733   465000.0    4.0   \n",
       "1494     Kraków    3.0  23755.0  50.008476  19.959153   500000.0    3.0   \n",
       "9975     Poznañ    0.0   5013.0  52.422074  16.966487   304560.0    2.0   \n",
       "9667     Kraków    3.0  27854.0  50.021956  20.050982   324880.0    3.0   \n",
       "20889  Warszawa    5.0  11208.0  52.235663  21.004969  1150000.0    2.0   \n",
       "22220    Poznañ    3.0   3478.0  52.370993  16.863146   995448.0    4.0   \n",
       "7818   Warszawa   10.0  12110.0  52.236238  20.954781   960000.0    3.0   \n",
       "14501    Poznañ    1.0   2279.0  52.397023  16.962061   345100.0    2.0   \n",
       "13281    Kraków    0.0  27724.0  50.023016  19.908364   405000.0    2.0   \n",
       "11333  Warszawa    4.0  10721.0  52.156869  21.056887  1199000.0    7.0   \n",
       "5619     Kraków    3.0  23469.0  50.078121  19.923727   799000.0    3.0   \n",
       "23358  Warszawa    3.0   8558.0  52.228633  21.106572   360672.0    1.0   \n",
       "904      Kraków    0.0  18877.0  50.083552  19.906822   207480.0    1.0   \n",
       "5449     Kraków    7.0  28377.0  50.098643  20.013221   422208.0    2.0   \n",
       "448      Kraków    3.0  28515.0  50.058931  19.976232   495000.0    3.0   \n",
       "6512   Warszawa    2.0  14599.0  52.236238  20.954781   895000.0    3.0   \n",
       "14280    Poznañ    4.0   3005.0  52.449649  16.949408   461632.0    3.0   \n",
       "5857     Kraków    4.0  23521.0  50.060589  20.024035   731160.0    2.0   \n",
       "19217    Poznañ    0.0   3294.0  52.461195  16.897715   297432.0    1.0   \n",
       "17020    Kraków    1.0  27216.0  50.060208  19.864331  1557720.0    4.0   \n",
       "11858    Poznañ    2.0   5757.0  52.397345  16.961939   756682.5    6.0   \n",
       "3693     Kraków    4.0  18364.0  50.017062  20.022992   372154.0    3.0   \n",
       "20476    Kraków    5.0  30240.0  50.046943  19.997153  1289000.0    4.0   \n",
       "18862    Poznañ    4.0    507.0  52.410919  16.910868   365000.0    2.0   \n",
       "21495  Warszawa    0.0  13922.0  52.344968  21.027497   350000.0    2.0   \n",
       "2097   Warszawa    6.0  11590.0  52.230245  20.987299   624000.0    3.0   \n",
       "19810    Kraków    3.0  23783.0  49.974658  19.828511  1080000.0    3.0   \n",
       "11763    Kraków    1.0  20194.0  50.055000  19.943056   844000.0    3.0   \n",
       "22909  Warszawa    2.0  16062.0  52.193987  21.045781   535000.0    2.0   \n",
       "18484  Warszawa    1.0  11766.0  52.261415  21.162819   485318.5    4.0   \n",
       "16861  Warszawa    8.0   6712.0  52.264884  21.027344   444000.0    2.0   \n",
       "6233     Kraków    1.0  22780.0  50.068080  20.026530   416000.0    3.0   \n",
       "16959    Poznañ    0.0   5162.0  52.397345  16.961939   300625.0    2.0   \n",
       "16856    Poznañ    1.0   6149.0  52.399346  16.924607   639600.0    4.0   \n",
       "3985     Poznañ    2.0    310.0  52.446332  16.951270   326395.0    2.0   \n",
       "22325  Warszawa   10.0  11681.0  52.193987  21.045781   385000.0    2.0   \n",
       "2293     Kraków    2.0  26560.0  50.043520  20.071300   530950.0    3.0   \n",
       "20518    Poznañ    4.0    132.0  52.400663  16.919733   290000.0    2.0   \n",
       "8613   Warszawa    1.0  16089.0  52.224793  20.952580   570000.0    2.0   \n",
       "5417   Warszawa    1.0   7012.0  52.304394  20.930880   485000.0    2.0   \n",
       "6411     Kraków    2.0  19926.0  50.046943  19.997153   440000.0    2.0   \n",
       "13018    Poznañ    3.0   2164.0  52.371108  16.916371   299000.0    2.0   \n",
       "\n",
       "           sq    year  \n",
       "20065   70.00  1975.0  \n",
       "22315   39.70  2020.0  \n",
       "1175    65.00  1910.0  \n",
       "23347   60.44  2020.0  \n",
       "14129   59.32  2022.0  \n",
       "11765   53.98  2021.0  \n",
       "12815   38.25  2018.0  \n",
       "19238   35.41  2020.0  \n",
       "9796    70.00  1992.0  \n",
       "1494    62.00  1983.0  \n",
       "9975    42.30  2020.0  \n",
       "9667    52.40  2021.0  \n",
       "20889   60.00  2009.0  \n",
       "22220  112.48  2020.0  \n",
       "7818    64.00  2019.0  \n",
       "14501   49.30  2006.0  \n",
       "13281   41.05  2021.0  \n",
       "11333  163.00  1995.0  \n",
       "5619    83.40  1932.0  \n",
       "23358   28.86  2022.0  \n",
       "904     21.84  2020.0  \n",
       "5449    43.98  2020.0  \n",
       "448     50.00  1968.0  \n",
       "6512    65.00  2020.0  \n",
       "14280   72.13  2020.0  \n",
       "5857    54.16  2021.0  \n",
       "19217   36.72  2022.0  \n",
       "17020  132.00  2019.0  \n",
       "11858  104.37  2022.0  \n",
       "3693    50.98  2021.0  \n",
       "20476   86.00  2021.0  \n",
       "18862   49.00  1963.0  \n",
       "21495   39.10  2014.0  \n",
       "2097    47.00  1966.0  \n",
       "19810  118.00  1930.0  \n",
       "11763   91.10  1918.0  \n",
       "22909   47.00  1964.0  \n",
       "18484   69.83  2021.0  \n",
       "16861   37.00  1968.0  \n",
       "6233    51.70  2019.0  \n",
       "16959   32.50  2022.0  \n",
       "16856   78.00  2021.0  \n",
       "3985    45.02  2022.0  \n",
       "22325   36.30  1970.0  \n",
       "2293    57.40  2022.0  \n",
       "20518   47.00  1957.0  \n",
       "8613    49.95  2021.0  \n",
       "5417    41.00  2000.0  \n",
       "6411    47.00  2018.0  \n",
       "13018   48.00  1970.0  "
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>floor</th>\n",
       "      <th>id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>price</th>\n",
       "      <th>rooms</th>\n",
       "      <th>sq</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20065</th>\n",
       "      <td>20065</td>\n",
       "      <td>Grzegórzki Olsza m³yñska</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21989.0</td>\n",
       "      <td>50.083364</td>\n",
       "      <td>19.970568</td>\n",
       "      <td>589000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>1975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22315</th>\n",
       "      <td>22315</td>\n",
       "      <td>Bronowice Katowicka  Bronowice  ok. Wizjonerów</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28479.0</td>\n",
       "      <td>50.084921</td>\n",
       "      <td>19.890948</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.70</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>1175</td>\n",
       "      <td>Stare Miasto Kazimierz pl. Wolnica</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20484.0</td>\n",
       "      <td>50.048437</td>\n",
       "      <td>19.944251</td>\n",
       "      <td>799000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>1910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23347</th>\n",
       "      <td>23347</td>\n",
       "      <td>Stare Miasto Centrum ul. Garbary Szyperska Cen...</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>52.400663</td>\n",
       "      <td>16.919733</td>\n",
       "      <td>543071.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60.44</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14129</th>\n",
       "      <td>14129</td>\n",
       "      <td>Praga-Po³udnie Goc³aw</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9964.0</td>\n",
       "      <td>52.228633</td>\n",
       "      <td>21.106572</td>\n",
       "      <td>612000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.32</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11765</th>\n",
       "      <td>11765</td>\n",
       "      <td>Podgórze</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20642.0</td>\n",
       "      <td>50.043100</td>\n",
       "      <td>19.952961</td>\n",
       "      <td>569000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>53.98</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12815</th>\n",
       "      <td>12815</td>\n",
       "      <td>Wola</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10609.0</td>\n",
       "      <td>52.236238</td>\n",
       "      <td>20.954781</td>\n",
       "      <td>822375.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.25</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19238</th>\n",
       "      <td>19238</td>\n",
       "      <td>Podgórze</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23565.0</td>\n",
       "      <td>50.043100</td>\n",
       "      <td>19.952961</td>\n",
       "      <td>335000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.41</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9796</th>\n",
       "      <td>9796</td>\n",
       "      <td>Stare Miasto Pi¹tkowo os. Boles³awa mia³ego</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2465.0</td>\n",
       "      <td>52.400663</td>\n",
       "      <td>16.919733</td>\n",
       "      <td>465000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>1992.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>1494</td>\n",
       "      <td>Podgórze Duchackie Kurdwanów</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23755.0</td>\n",
       "      <td>50.008476</td>\n",
       "      <td>19.959153</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>1983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>9975</td>\n",
       "      <td>Nowe Miasto G³ówna Smolna 10</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5013.0</td>\n",
       "      <td>52.422074</td>\n",
       "      <td>16.966487</td>\n",
       "      <td>304560.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9667</th>\n",
       "      <td>9667</td>\n",
       "      <td>Bie¿anów-Prokocim ul. Mariana Domaga³y</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27854.0</td>\n",
       "      <td>50.021956</td>\n",
       "      <td>20.050982</td>\n",
       "      <td>324880.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>52.40</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20889</th>\n",
       "      <td>20889</td>\n",
       "      <td>ródmiecie Bagno</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11208.0</td>\n",
       "      <td>52.235663</td>\n",
       "      <td>21.004969</td>\n",
       "      <td>1150000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>2009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22220</th>\n",
       "      <td>22220</td>\n",
       "      <td>Grunwald Górczyn ul. Ceglana</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3478.0</td>\n",
       "      <td>52.370993</td>\n",
       "      <td>16.863146</td>\n",
       "      <td>995448.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>112.48</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7818</th>\n",
       "      <td>7818</td>\n",
       "      <td>Wola</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12110.0</td>\n",
       "      <td>52.236238</td>\n",
       "      <td>20.954781</td>\n",
       "      <td>960000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14501</th>\n",
       "      <td>14501</td>\n",
       "      <td>Nowe Miasto Rataje ul. Milczañska</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2279.0</td>\n",
       "      <td>52.397023</td>\n",
       "      <td>16.962061</td>\n",
       "      <td>345100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.30</td>\n",
       "      <td>2006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13281</th>\n",
       "      <td>13281</td>\n",
       "      <td>Dêbniki Ruczaj</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27724.0</td>\n",
       "      <td>50.023016</td>\n",
       "      <td>19.908364</td>\n",
       "      <td>405000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.05</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11333</th>\n",
       "      <td>11333</td>\n",
       "      <td>Ursynów Wrzosowisko</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10721.0</td>\n",
       "      <td>52.156869</td>\n",
       "      <td>21.056887</td>\n",
       "      <td>1199000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>163.00</td>\n",
       "      <td>1995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5619</th>\n",
       "      <td>5619</td>\n",
       "      <td>Krowodrza Wroc³awska</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23469.0</td>\n",
       "      <td>50.078121</td>\n",
       "      <td>19.923727</td>\n",
       "      <td>799000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>83.40</td>\n",
       "      <td>1932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23358</th>\n",
       "      <td>23358</td>\n",
       "      <td>Praga-Po³udnie Goc³aw</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8558.0</td>\n",
       "      <td>52.228633</td>\n",
       "      <td>21.106572</td>\n",
       "      <td>360672.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.86</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>904</td>\n",
       "      <td>Krowodrza Radzikowskiego 56</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18877.0</td>\n",
       "      <td>50.083552</td>\n",
       "      <td>19.906822</td>\n",
       "      <td>207480.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.84</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5449</th>\n",
       "      <td>5449</td>\n",
       "      <td>Mistrzejowice ul. Piasta Ko³odzieja</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28377.0</td>\n",
       "      <td>50.098643</td>\n",
       "      <td>20.013221</td>\n",
       "      <td>422208.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.98</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>448</td>\n",
       "      <td>Grzegórzki Aleja Pokoju</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28515.0</td>\n",
       "      <td>50.058931</td>\n",
       "      <td>19.976232</td>\n",
       "      <td>495000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1968.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6512</th>\n",
       "      <td>6512</td>\n",
       "      <td>Wola</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14599.0</td>\n",
       "      <td>52.236238</td>\n",
       "      <td>20.954781</td>\n",
       "      <td>895000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14280</th>\n",
       "      <td>14280</td>\n",
       "      <td>Stare Miasto Naramowice Naramowice</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3005.0</td>\n",
       "      <td>52.449649</td>\n",
       "      <td>16.949408</td>\n",
       "      <td>461632.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>72.13</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5857</th>\n",
       "      <td>5857</td>\n",
       "      <td>Nowa Huta Czy¿yny ul. So³tysowska</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23521.0</td>\n",
       "      <td>50.060589</td>\n",
       "      <td>20.024035</td>\n",
       "      <td>731160.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.16</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19217</th>\n",
       "      <td>19217</td>\n",
       "      <td>Stare Miasto Pi¹tkowo ul. Teofila Mateckiego</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3294.0</td>\n",
       "      <td>52.461195</td>\n",
       "      <td>16.897715</td>\n",
       "      <td>297432.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.72</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17020</th>\n",
       "      <td>17020</td>\n",
       "      <td>Zwierzyniec</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27216.0</td>\n",
       "      <td>50.060208</td>\n",
       "      <td>19.864331</td>\n",
       "      <td>1557720.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>132.00</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11858</th>\n",
       "      <td>11858</td>\n",
       "      <td>Nowe Miasto Malta ul. Katowicka</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5757.0</td>\n",
       "      <td>52.397345</td>\n",
       "      <td>16.961939</td>\n",
       "      <td>756682.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>104.37</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3693</th>\n",
       "      <td>3693</td>\n",
       "      <td>Bie¿anów-Prokocim</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18364.0</td>\n",
       "      <td>50.017062</td>\n",
       "      <td>20.022992</td>\n",
       "      <td>372154.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.98</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20476</th>\n",
       "      <td>20476</td>\n",
       "      <td>Podgórze Rzemielnicza</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30240.0</td>\n",
       "      <td>50.046943</td>\n",
       "      <td>19.997153</td>\n",
       "      <td>1289000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>86.00</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18862</th>\n",
       "      <td>18862</td>\n",
       "      <td>Je¿yce Jana Henryka D¹browskiego</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>52.410919</td>\n",
       "      <td>16.910868</td>\n",
       "      <td>365000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>1963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21495</th>\n",
       "      <td>21495</td>\n",
       "      <td>Bia³o³êka ul. Kobia³ka</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13922.0</td>\n",
       "      <td>52.344968</td>\n",
       "      <td>21.027497</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.10</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2097</th>\n",
       "      <td>2097</td>\n",
       "      <td>ródmiecie Pañska</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11590.0</td>\n",
       "      <td>52.230245</td>\n",
       "      <td>20.987299</td>\n",
       "      <td>624000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>1966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19810</th>\n",
       "      <td>19810</td>\n",
       "      <td>Stare Miasto Kleparz al. Juliusza S³owackiego</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23783.0</td>\n",
       "      <td>49.974658</td>\n",
       "      <td>19.828511</td>\n",
       "      <td>1080000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>118.00</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11763</th>\n",
       "      <td>11763</td>\n",
       "      <td>ródmiecie</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20194.0</td>\n",
       "      <td>50.055000</td>\n",
       "      <td>19.943056</td>\n",
       "      <td>844000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>91.10</td>\n",
       "      <td>1918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22909</th>\n",
       "      <td>22909</td>\n",
       "      <td>Mokotów</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16062.0</td>\n",
       "      <td>52.193987</td>\n",
       "      <td>21.045781</td>\n",
       "      <td>535000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>1964.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18484</th>\n",
       "      <td>18484</td>\n",
       "      <td>Rembertów</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11766.0</td>\n",
       "      <td>52.261415</td>\n",
       "      <td>21.162819</td>\n",
       "      <td>485318.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>69.83</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16861</th>\n",
       "      <td>16861</td>\n",
       "      <td>Praga-Pó³noc</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6712.0</td>\n",
       "      <td>52.264884</td>\n",
       "      <td>21.027344</td>\n",
       "      <td>444000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.00</td>\n",
       "      <td>1968.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6233</th>\n",
       "      <td>6233</td>\n",
       "      <td>Nowa Huta Czy¿yny bp. Piotra Tomickiego</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22780.0</td>\n",
       "      <td>50.068080</td>\n",
       "      <td>20.026530</td>\n",
       "      <td>416000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>51.70</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16959</th>\n",
       "      <td>16959</td>\n",
       "      <td>Nowe Miasto Malta ul. Katowicka</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5162.0</td>\n",
       "      <td>52.397345</td>\n",
       "      <td>16.961939</td>\n",
       "      <td>300625.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.50</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16856</th>\n",
       "      <td>16856</td>\n",
       "      <td>Stare Miasto Centrum ul. Mieczys³awa Niedzia³k...</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6149.0</td>\n",
       "      <td>52.399346</td>\n",
       "      <td>16.924607</td>\n",
       "      <td>639600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>78.00</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3985</th>\n",
       "      <td>3985</td>\n",
       "      <td>Stare Miasto Naramowice ul. Sielawy</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>52.446332</td>\n",
       "      <td>16.951270</td>\n",
       "      <td>326395.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>45.02</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22325</th>\n",
       "      <td>22325</td>\n",
       "      <td>Mokotów</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11681.0</td>\n",
       "      <td>52.193987</td>\n",
       "      <td>21.045781</td>\n",
       "      <td>385000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>36.30</td>\n",
       "      <td>1970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>2293</td>\n",
       "      <td>Podgórze P³aszów ul. Przewóz</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26560.0</td>\n",
       "      <td>50.043520</td>\n",
       "      <td>20.071300</td>\n",
       "      <td>530950.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>57.40</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20518</th>\n",
       "      <td>20518</td>\n",
       "      <td>Wilda ul. Ignacego Pr¹dzyñskiego</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>52.400663</td>\n",
       "      <td>16.919733</td>\n",
       "      <td>290000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>1957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>8613</td>\n",
       "      <td>Wola Juliana Konstantego Ordona</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16089.0</td>\n",
       "      <td>52.224793</td>\n",
       "      <td>20.952580</td>\n",
       "      <td>570000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.95</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5417</th>\n",
       "      <td>5417</td>\n",
       "      <td>Bielany Heroldów</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7012.0</td>\n",
       "      <td>52.304394</td>\n",
       "      <td>20.930880</td>\n",
       "      <td>485000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6411</th>\n",
       "      <td>6411</td>\n",
       "      <td>Dêbniki Anny Szwed-niadowskiej</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19926.0</td>\n",
       "      <td>50.046943</td>\n",
       "      <td>19.997153</td>\n",
       "      <td>440000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13018</th>\n",
       "      <td>13018</td>\n",
       "      <td>Wilda Dêbiec</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>52.371108</td>\n",
       "      <td>16.916371</td>\n",
       "      <td>299000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.00</td>\n",
       "      <td>1970.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "id": "8954e19b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:16.436529Z",
     "start_time": "2024-12-20T13:38:16.394098Z"
    }
   },
   "source": [
    "# analyze statistic data\n",
    "print(houses.describe())\n",
    "print(houses.info())\n",
    "print(houses.isnull().values.any())\n",
    "\n",
    "houses"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Unnamed: 0         floor            id      latitude     longitude  \\\n",
      "count  23764.000000  23764.000000  23764.000000  23764.000000  23764.000000   \n",
      "mean   11881.500000      2.808744  15621.964484     51.374874     19.861773   \n",
      "std     6860.220235      2.463467   8617.293898      1.099836      1.447186   \n",
      "min        0.000000      0.000000      1.000000     49.933056      4.204796   \n",
      "25%     5940.750000      1.000000   8420.750000     50.066489     19.916563   \n",
      "50%    11881.500000      2.000000  15637.500000     52.193987     19.999330   \n",
      "75%    17822.250000      4.000000  23111.250000     52.267594     20.996180   \n",
      "max    23763.000000     10.000000  30308.000000     54.435238     30.319403   \n",
      "\n",
      "              price         rooms            sq         year  \n",
      "count  2.376400e+04  23764.000000  2.376400e+04  23764.00000  \n",
      "mean   6.493536e+05      2.620771  1.027249e+02   2000.55117  \n",
      "std    5.326970e+05      0.996530  6.533688e+03     48.31402  \n",
      "min    5.000000e+03      1.000000  8.800000e+00     70.00000  \n",
      "25%    4.115461e+05      2.000000  4.200000e+01   1985.00000  \n",
      "50%    5.200000e+05      3.000000  5.389500e+01   2019.00000  \n",
      "75%    6.999990e+05      3.000000  6.891500e+01   2021.00000  \n",
      "max    1.500000e+07     10.000000  1.007185e+06   2980.00000  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 23764 entries, 20065 to 21197\n",
      "Data columns (total 11 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  23764 non-null  int64  \n",
      " 1   address     23764 non-null  object \n",
      " 2   city        23764 non-null  object \n",
      " 3   floor       23764 non-null  float64\n",
      " 4   id          23764 non-null  float64\n",
      " 5   latitude    23764 non-null  float64\n",
      " 6   longitude   23764 non-null  float64\n",
      " 7   price       23764 non-null  float64\n",
      " 8   rooms       23764 non-null  float64\n",
      " 9   sq          23764 non-null  float64\n",
      " 10  year        23764 non-null  float64\n",
      "dtypes: float64(8), int64(1), object(2)\n",
      "memory usage: 2.2+ MB\n",
      "None\n",
      "False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "       Unnamed: 0                                            address  \\\n",
       "20065       20065                           Grzegórzki Olsza m³yñska   \n",
       "22315       22315     Bronowice Katowicka  Bronowice  ok. Wizjonerów   \n",
       "1175         1175                 Stare Miasto Kazimierz pl. Wolnica   \n",
       "23347       23347  Stare Miasto Centrum ul. Garbary Szyperska Cen...   \n",
       "14129       14129                              Praga-Po³udnie Goc³aw   \n",
       "...           ...                                                ...   \n",
       "389           389                               Weso³a Stara Mi³osna   \n",
       "1611         1611                          Bronowice Radzikowskiego    \n",
       "20192       20192                                      Pr¹dnik Bia³y   \n",
       "22127       22127                        Grzegórzki ul. Grzegórzecka   \n",
       "21197       21197                     Je¿yce So³acz Micha³a Drzyma³y   \n",
       "\n",
       "           city  floor       id   latitude  longitude     price  rooms  \\\n",
       "20065    Kraków    0.0  21989.0  50.083364  19.970568  589000.0    3.0   \n",
       "22315    Kraków    1.0  28479.0  50.084921  19.890948  380000.0    2.0   \n",
       "1175     Kraków    2.0  20484.0  50.048437  19.944251  799000.0    3.0   \n",
       "23347    Poznañ    2.0   1257.0  52.400663  16.919733  543071.0    3.0   \n",
       "14129  Warszawa    4.0   9964.0  52.228633  21.106572  612000.0    3.0   \n",
       "...         ...    ...      ...        ...        ...       ...    ...   \n",
       "389    Warszawa    0.0  10274.0  52.224792  21.225174  432000.0    2.0   \n",
       "1611     Kraków    2.0  29129.0  50.089419  19.883663  990000.0    4.0   \n",
       "20192    Kraków    4.0  29708.0  50.103568  19.953603  455000.0    3.0   \n",
       "22127    Kraków    4.0  28677.0  50.058788  19.950060  468720.0    1.0   \n",
       "21197    Poznañ    1.0   4367.0  52.335771  16.826105  634671.0    3.0   \n",
       "\n",
       "           sq    year  \n",
       "20065   70.00  1975.0  \n",
       "22315   39.70  2020.0  \n",
       "1175    65.00  1910.0  \n",
       "23347   60.44  2020.0  \n",
       "14129   59.32  2022.0  \n",
       "...       ...     ...  \n",
       "389     38.00  2018.0  \n",
       "1611   106.00  2004.0  \n",
       "20192   51.01  2019.0  \n",
       "22127   33.48  2020.0  \n",
       "21197   65.43  2020.0  \n",
       "\n",
       "[23764 rows x 11 columns]"
      ],
      "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>Unnamed: 0</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>floor</th>\n",
       "      <th>id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>price</th>\n",
       "      <th>rooms</th>\n",
       "      <th>sq</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20065</th>\n",
       "      <td>20065</td>\n",
       "      <td>Grzegórzki Olsza m³yñska</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21989.0</td>\n",
       "      <td>50.083364</td>\n",
       "      <td>19.970568</td>\n",
       "      <td>589000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>1975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22315</th>\n",
       "      <td>22315</td>\n",
       "      <td>Bronowice Katowicka  Bronowice  ok. Wizjonerów</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28479.0</td>\n",
       "      <td>50.084921</td>\n",
       "      <td>19.890948</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.70</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>1175</td>\n",
       "      <td>Stare Miasto Kazimierz pl. Wolnica</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20484.0</td>\n",
       "      <td>50.048437</td>\n",
       "      <td>19.944251</td>\n",
       "      <td>799000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>1910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23347</th>\n",
       "      <td>23347</td>\n",
       "      <td>Stare Miasto Centrum ul. Garbary Szyperska Cen...</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>52.400663</td>\n",
       "      <td>16.919733</td>\n",
       "      <td>543071.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60.44</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14129</th>\n",
       "      <td>14129</td>\n",
       "      <td>Praga-Po³udnie Goc³aw</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9964.0</td>\n",
       "      <td>52.228633</td>\n",
       "      <td>21.106572</td>\n",
       "      <td>612000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.32</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>389</td>\n",
       "      <td>Weso³a Stara Mi³osna</td>\n",
       "      <td>Warszawa</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10274.0</td>\n",
       "      <td>52.224792</td>\n",
       "      <td>21.225174</td>\n",
       "      <td>432000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1611</th>\n",
       "      <td>1611</td>\n",
       "      <td>Bronowice Radzikowskiego</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29129.0</td>\n",
       "      <td>50.089419</td>\n",
       "      <td>19.883663</td>\n",
       "      <td>990000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>106.00</td>\n",
       "      <td>2004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20192</th>\n",
       "      <td>20192</td>\n",
       "      <td>Pr¹dnik Bia³y</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29708.0</td>\n",
       "      <td>50.103568</td>\n",
       "      <td>19.953603</td>\n",
       "      <td>455000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>51.01</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22127</th>\n",
       "      <td>22127</td>\n",
       "      <td>Grzegórzki ul. Grzegórzecka</td>\n",
       "      <td>Kraków</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28677.0</td>\n",
       "      <td>50.058788</td>\n",
       "      <td>19.950060</td>\n",
       "      <td>468720.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.48</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21197</th>\n",
       "      <td>21197</td>\n",
       "      <td>Je¿yce So³acz Micha³a Drzyma³y</td>\n",
       "      <td>Poznañ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>52.335771</td>\n",
       "      <td>16.826105</td>\n",
       "      <td>634671.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.43</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23764 rows × 11 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "id": "ab21a58d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:16.598512Z",
     "start_time": "2024-12-20T13:38:16.494118Z"
    }
   },
   "source": [
    "# cities distribution\n",
    "houses['city'].hist() "
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "859a94b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:16.658910Z",
     "start_time": "2024-12-20T13:38:16.640795Z"
    }
   },
   "source": [
    "#Use one hot encoding to transform categorical values into boolean columns\n",
    "cities = pd.get_dummies(houses['city'])\n",
    "houses = pd.merge(\n",
    "    left=houses,\n",
    "    right=cities,\n",
    "    left_index=True,\n",
    "    right_index=True,\n",
    ")\n",
    "print(houses.head())"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Unnamed: 0                                            address  \\\n",
      "20065       20065                           Grzegórzki Olsza m³yñska   \n",
      "22315       22315     Bronowice Katowicka  Bronowice  ok. Wizjonerów   \n",
      "1175         1175                 Stare Miasto Kazimierz pl. Wolnica   \n",
      "23347       23347  Stare Miasto Centrum ul. Garbary Szyperska Cen...   \n",
      "14129       14129                              Praga-Po³udnie Goc³aw   \n",
      "\n",
      "           city  floor       id   latitude  longitude     price  rooms     sq  \\\n",
      "20065    Kraków    0.0  21989.0  50.083364  19.970568  589000.0    3.0  70.00   \n",
      "22315    Kraków    1.0  28479.0  50.084921  19.890948  380000.0    2.0  39.70   \n",
      "1175     Kraków    2.0  20484.0  50.048437  19.944251  799000.0    3.0  65.00   \n",
      "23347    Poznañ    2.0   1257.0  52.400663  16.919733  543071.0    3.0  60.44   \n",
      "14129  Warszawa    4.0   9964.0  52.228633  21.106572  612000.0    3.0  59.32   \n",
      "\n",
      "         year  Kraków  Poznañ  Warszawa  \n",
      "20065  1975.0       1       0         0  \n",
      "22315  2020.0       1       0         0  \n",
      "1175   1910.0       1       0         0  \n",
      "23347  2020.0       0       1         0  \n",
      "14129  2022.0       0       0         1  \n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "211b0df1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:17.068615Z",
     "start_time": "2024-12-20T13:38:16.785859Z"
    }
   },
   "source": [
    "# data distribution on box plots\n",
    "houses['floor'].plot(kind='box')\n",
    "plt.show()\n",
    "houses['price'].plot(kind='box')\n",
    "plt.show()\n",
    "houses['sq'].plot(kind='box') \n",
    "plt.show()\n",
    "houses['rooms'].plot(kind='box') \n",
    "plt.show()\n",
    "houses['year'].plot(kind='box')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "876baf4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:17.167989Z",
     "start_time": "2024-12-20T13:38:17.159351Z"
    }
   },
   "source": [
    "# Removed not needed columns\n",
    "del houses['address']\n",
    "del houses['id']\n",
    "del houses['Unnamed: 0']\n",
    "del houses['city']\n",
    "houses.head()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       floor   latitude  longitude     price  rooms     sq    year  Kraków  \\\n",
       "20065    0.0  50.083364  19.970568  589000.0    3.0  70.00  1975.0       1   \n",
       "22315    1.0  50.084921  19.890948  380000.0    2.0  39.70  2020.0       1   \n",
       "1175     2.0  50.048437  19.944251  799000.0    3.0  65.00  1910.0       1   \n",
       "23347    2.0  52.400663  16.919733  543071.0    3.0  60.44  2020.0       0   \n",
       "14129    4.0  52.228633  21.106572  612000.0    3.0  59.32  2022.0       0   \n",
       "\n",
       "       Poznañ  Warszawa  \n",
       "20065       0         0  \n",
       "22315       0         0  \n",
       "1175        0         0  \n",
       "23347       1         0  \n",
       "14129       0         1  "
      ],
      "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>floor</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>price</th>\n",
       "      <th>rooms</th>\n",
       "      <th>sq</th>\n",
       "      <th>year</th>\n",
       "      <th>Kraków</th>\n",
       "      <th>Poznañ</th>\n",
       "      <th>Warszawa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20065</th>\n",
       "      <td>0.0</td>\n",
       "      <td>50.083364</td>\n",
       "      <td>19.970568</td>\n",
       "      <td>589000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22315</th>\n",
       "      <td>1.0</td>\n",
       "      <td>50.084921</td>\n",
       "      <td>19.890948</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.70</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>2.0</td>\n",
       "      <td>50.048437</td>\n",
       "      <td>19.944251</td>\n",
       "      <td>799000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23347</th>\n",
       "      <td>2.0</td>\n",
       "      <td>52.400663</td>\n",
       "      <td>16.919733</td>\n",
       "      <td>543071.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60.44</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14129</th>\n",
       "      <td>4.0</td>\n",
       "      <td>52.228633</td>\n",
       "      <td>21.106572</td>\n",
       "      <td>612000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.32</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "b89bd37f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:17.230675Z",
     "start_time": "2024-12-20T13:38:17.222300Z"
    }
   },
   "source": [
    "#Remove outliers\n",
    "houses = houses.drop(houses[houses.year < 1800].index)\n",
    "houses = houses.drop(houses[houses.year > 2022].index)\n",
    "houses = houses.drop(houses[houses.sq > 500].index)"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "id": "0dcf6e10",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:17.430884Z",
     "start_time": "2024-12-20T13:38:17.319154Z"
    }
   },
   "source": [
    "houses['year'].plot(kind='box')\n",
    "plt.show()\n",
    "houses['sq'].plot(kind='box')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "id": "c313dd85",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:17.883785Z",
     "start_time": "2024-12-20T13:38:17.521248Z"
    }
   },
   "source": [
    "# Display correlation matrix\n",
    "\n",
    "correlations = houses.corr()\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "colormap = sns.color_palette(\"BrBG\", 10)\n",
    "\n",
    "sns.heatmap(correlations, \n",
    "    cmap=colormap, \n",
    "    annot=True, \n",
    "    fmt=\".2f\")\n",
    "\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ],
      "image/png": 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E6Wn5fZIV9DEyKnB0Q12MjI2hoamJhPf2eiQlJhbYm/kvEzOzAntJEhMSoampCSPjSgCALes3oNXnHdChaxcAgFPVqsjMzMCKBQvRe+AAaGiU6BYrJaZM3kWloaEBW7v8WWZcXF3x8tlz7N68tUwUGarMGwD2bd+BXZu34MfVqwq9qLC0NGvSAJ4eNWTPc3KyAQBicSIszM1k7QkJSQX2fr7L3MwE4gT5vdgJiW/HmBgbQVNTo8Ce7o8ttzSo6vP9r09xfb9r4U9rcfGva9gY+BMsLdVzUWxp5m1XxQYb1y9GRkYm0tLTYWFuhsnTFsDWxlKodIrsU837Xaranpuam0NLSxOa/5z2DQD2To5IEIuRk5MDbW1tpZdNVBxKfVOUSCQ4efIk1q5di3Xr1uH06dPl8ly/a+GP0dpdforKth41cfPFM+T+k8/V8Mdo7e4l16eNhxeuPFH/BYIAoK2tjWo1quN2UJBc++2gILjX9FI4xt3Ls2D/69fh6uYGLa38ujMzKxMa7+1J0dTQhBQFj+yogzJ5K0sqffsPUN1UmffebduxY+OvWLBiOVzd3Uq0LCHo61eEvZ2N7OHs5ABzMxNcCwqW9cnJycGt4Lvw9io83ppebrh2PViu7er127Ix2tracKtRTW65AHAtKPiDyy0Nqvp8A5/u+gbyt2ELF6/BuQuXEbjmR9jaWAmfUBGVZt7/0tOrAAtzM6SkpOLKtVto3rShcAkV0aea97tUtT338K6JyNevkZeXJ2uLePkKpubmLDCoVBW7yHjx4gU+//xzTJ48GadPn8bJkycxceJEdOzYES9fvlRFjEWmr6sLbzsHeNs5AACczC3gbecAO9P8vSIL/Hpg82B/Wf/1F87CwcwcS3r0RQ1rGwxs3AyDmjTHkpNvZ5xYeeYE2np4YVKHzqhuZYNJHTqjtZsnVpwuePG7unzZuxdO/HYEJ44cxctnz7Bu6XLERsegk183APnnWy+aOVvWv6OfH2KiorF+2XK8fPYMJ44cxYkjR/FV396yPg0aN8bvBw/i/KnTiIqIxK3r17ElcAMaNmkst3dEnYqbNwCEP3qE8EePkPEmA0mJiQh/9Agvnj6Tvb5r8xbcun4dURERePn8Ofbv2Ikzx4+jVfv2pZrbh6gi771bt2HL+kCM/2EaLK2tkRAvRkK8GBlvCp86srSJRCL07tlNdo+DJ+HPMWPOElSooIsO7VrI+k2ftRgr1/wqe96rR1dcC7qFTVv34tnzV9i0dS+CgoLRp2c3WZ++vfxw6LcTOHzkJJ4+e4mflgUiOiYWX/l1LNUcFVHF5/tTX98Bi9fg2IlzWDBnMvT19RAvTkC8OAGZmVmlmqMiqsz7yrWbuHz1JiIio3Ht+m0MGT4Zjg5V8EXntqWaoyKfat6q2J53+tIPKckpWLdkKV6/eInrly5j1+bN+OKrL0s1N6Jiny41b9482NnZYc+ePTA2NgYAJCYmYuLEiZg3bx42bNggdIxFVsfRGecn/SB7vrRnPwDA5ssXMejXQFgZG8Pe9O1h2Ofxcei4fBGW9uyH4S3aIDIpEaN3bsHBW2+nwL0a/hi9AldhbrfumNP1a4THxqBn4CoEPSsb53ICQPM2bZCSnIwdGzciIV4MBxdnzFu2FJbW1gCAhPh4xMa8nXPb2tYG85cvxfply3F0/wGYmptj+PhxaNKypaxPn0EDIRKJsGV9IOLj4lDJ2BgNmjTGQP9hpZ5fYYqbNwD493174frjBw9w/uQpWFpbYdtvhwEAmRkZWLVoMeJj46Crqws7BwdMnjMLzdu0Ka20PkoVeR89cAA5OTmY+/1UuXF9vx2M/v8botqEimFAv6+RlZWFgEWrkZKaBk+PGli3cgH09SvK+kTHxEJD4+1ROJ+a7giYOwVrA7dgbeBW2FWxxsL5U+Dl+fZUjXZtmiE5OQUbft2B+PhEVHV2wKplc2Fjrd7TKQDVfL4/9fX979TEQ/wnyf2+2T+Mwxed1P/FU1V5p6W9waq1mxATG49KRgZo1aIxRvgPgLaWUmdOC+5TzFsV2/PKlpYIWLkC65cvx9A+fWFuYYFuPXqge/9+pZYXEQCIpMU898XHxwd79uxB9eryd8p88OABevXqheDg4EJGfpjG4N4f7/Qf9GzpGnWHQKRyFtIkdYegFnEiY3WHoBaf6vqmT8un+vl2qKTea9UKU5a/R+Zt3PnxTv9BxT5dSkdHB+np6QXa09PTea4fEREREREVv8ho3rw5ZsyYgZCQEEilUkilUvz999+YNWsWWr5zOJ6IiIiIiD5NxT4hcfr06Zg8eTJ69Oghm6lEIpGgZcuWmDZtmuABEhERERFR+VLsIsPIyAjr1q3D8+fP8fTpU0ilUlStWhUODg6qiI+IiIiIiMoZpadWcHR0hKOjo4ChEBERERHRf0GRioyAgIAiL3DKlClKB0NEREREROVfkYqMgwcPolq1atDU1IRIJCr0js+i9+4QTUREREREn54iFRmpqalYtWoVzMzM0KpVK+zfvx8mJmVznmQiIiIiIlKvIk1hW6lSJbx+/RoAEBERUeiRDCIiIiIioiIdyWjbti369OmDypUrQyQS4csvv4SGhuL65OzZs4IGSERERERE5UuRioy5c+eiTZs2ePnyJebNm4evv/4a+vr6qo6NiIiIiIjKoSJPYdu0aVMAwP3799G/f38YGBioLCgiIiIiIiq/in2fjOJMZ0tERERERJ+eIl34TUREREREVFQsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFAsMoiIiIiISFBa6g7gX8+WrlF3CGrhNG6EukNQi7Ql89UdglrEiYzVHYJafKp506dl5Tee6g5BLV6N3q3uENTi4t931R2CWtwbN1XdISjk7vFpfv7KMh7JICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQbHIICIiIiIiQSldZNy8eRMTJkxAjx49EBMTAwA4fPgwbt68KVhwRERERESfmh07dqBly5bw8vKCn5/fB79ff//996hevXqBR8eOHWV9Dh48qLBPVlaWynJQqsg4efIkBg8ejAoVKiA0NBTZ2dkAgPT0dAQGBgoaIBERERHRp+L48eMICAiAv78/Dh8+DF9fXwwZMgSRkZEK+0+bNg2XLl2SPS5evAhjY2O0b99erp+BgYFcv0uXLkFXV1dleShVZKxbtw6zZ8/GvHnzoKWlJWuvXbs2QkNDBQuOiIiIiOhTsmnTJnz55Zf4+uuv4eLigmnTpsHKygq7du1S2N/Q0BAWFhayx71795CcnAw/Pz+5fiKRSK6fhYWFSvPQ+niXgp49e4Y6deoUaDcwMEBKSkqJgyIiIiIi+i/Izs6WnfXzLx0dHejo6Cjse//+ffzvf/+Ta//ss88QHBxcpN+3f/9+NGrUCLa2tnLtb968QYsWLSCRSODm5obRo0fD3d29mNkUnVJHMiwsLPDy5csC7bdu3YKdnV2JgyIiIiIi+i8IDAyEr6+v3KOwywsSExMhkUhgZmYm125ubo64uLiP/q7Y2Fj8+eef+Oqrr+TanZ2dERAQgHXr1mHp0qXQ1dVFr1698Pz5c6Xz+hiljmT06NED8+fPx4IFCyASiRATE4Pg4GD8+OOPGDFihNAxEhERERGVS0OHDsXAgQPl2hQdxXiXSCSSey6VSgu0KXLo0CEYGhqidevWcu0+Pj7w8fGRPa9duza6deuG7du3Y/r06R9drjKUKjKGDBmCtLQ09O/fH1lZWejbty90dHQwaNAg9O3bV+gYiYiIiIjKpcJOjVLExMQEmpqaiI+Pl2sXi8UwNzf/4FipVIoDBw6gS5cuH/19Ghoa8PLyKntHMgBg7NixGDZsGJ48eQKpVAoXFxfo6+sLGRsRERER0SdDR0cHHh4euHz5Mtq0aSNrv3LlClq1avXBsUFBQXjx4kWBU6UUkUqlCAsLg6ura4ljLozSRQYA6OnpwcvLS6hYiIiIiIg+aQMHDsSkSZPg6emJWrVqYc+ePYiKikLPnj0BAEuWLEFMTAwWLVokN27//v3w9vZWWDisXr0a3t7ecHR0RFpaGrZu3YoHDx5g5syZKsujyEXGyJEji7zQ1atXKxUMEREREdGn7PPPP0diYiLWrl2L2NhYuLq6YsOGDbLZouLi4hAVFSU3JjU1FadOncK0adMULjMlJQUzZsxAXFwcDA0N4e7uju3bt6NmzZoqy6PIRYahoaHsZ6lUitOnT8PQ0BCenp4AgPv37yMlJQVt27YVPkoiIiIiok9Enz590KdPH4WvLVy4sECboaEhQkJCCl3e1KlTMXXqVMHiK4oiFxkBAQGynxcvXowOHTpg9uzZ0NTUBABIJBLMnj2b12UQEREREX3ilLpPxoEDBzBo0CBZgQEAmpqaGDBgAA4ePChYcEREREREVP4oVWRIJBI8ffq0QPvTp0+Rl5dX4qCIiIiIiKj8Ump2KT8/P0ydOhUvXryAt7c3ACAkJAQbNmyAn5+foAESEREREVH5olSRMXnyZJibm2Pz5s2yW5xbWFjg22+/xaBBgwQNkIiIiIiIyheligwNDQ0MGTJEdudvADAwMBA0MCIiIiIiKp9KdDM+oOwVF0f278e+bTuQIBbDwdkJ/mPHwquWT6H979y+jfXLV+DF02cwMzdH93590elL+VO+Du7ajd8PHERsTAyMKlVCk5YtMXiEP3R0dVWcTdE0ca2BCe06wdfRCTbGJui2eil+C775wTFNXWtgSY9+8LC1RWRSEhb/cRSBF8/K9fHzrYs5Xb+Gi4UlwuNiMP3gXhz+yHJLm1QqReAv23Hg8B9ITU2Dp0d1TJk4Ai7OjoWOCX/6HGsDtyHs4WNERcViwpih6NOrm1yfjZt349yFy3j+4jV0dXXg7eWO0SMHwdHBTsUZFY3Q7/MJw/xx53ZwgXH1PmuEecuWqiIFpQid9/HDh3Hm2B94/s81ZtVqVMfA4f6o4eGh6lSKRRXbtbTUVGxatx6Xz19AamoqrGysMXT0aNT7rJGKsyk6ZT7fAHDm3CWsDdyK1xFRqGJrjZH+36Bl889kr+fmShD4yzYcP3Ee4oREmJuZonPHNhgyqBc0NJS6VFFwjXtOhXe7Qaigb4yoRzdwKnAc4l+FFdpfQ1MLDb+aAM8WfWBoZoOEiMc4v+UHPAs+Leujo2eAJr1nwLVBZ1SsZIGYZyE48/NERD+5XRopfdSri5fx4vQFZCenQt/aEq5fd4FJNeePjksKf4ZbS9dB38YKDaaNk7XHBt/FsxNnkREXjzyJBBUrW8ChdTNY1/dVZRrF1sO7NgbWaQALfQM8EcfhxwtncDviVaH9tTU14d+gMTq5ecK8oj5i0lKx4fplHLp/R9bHUFcX333WHK2rVodRhQqISE7C4j/P4q9n4aWREhEAJYuMli1bQiQSFfr62bNnC31NlS6cPo31S5dj1KSJ8PCuiWOHDmPamLH4Zc8uVLayKtA/KiIS08aMw+ddu+D72bNwP+QOVi1ajEomxmjSsiUA4OyJE9i4Zi3GT58G95peeP3yFX6aMxcA4D9uTGmmVyh9HV3cef0Cmy9fxIERYz/a39HcAsfGTMIvf55Hv1/W4LOqrljTdxDi0lJw8NYNAEADl2rYPfQ7zDi8D4du30S32nWwZ9h3aLJwNoLK0EZq87Z92L7zEGbPGAcH+yr4+dddGDZqKg7v/QX6+hUVjsnMzEIVWyu0adUES5YHKuxzO/guenzVGR7ursjNzcOa9Zvh/900HNy9AXp6FVSZ0kep4n0+48eFyM3JlY1JSU7GsL790LRVy1LL62NUkXfIrdto3q4NPGrWhLaODvZt244po0bj5907YV65cmmnqJAq8s7JycH3I7+DsakJfli4AOaVKyMuJgZ6FcvWFOTKfL5D7obi++kL4P+//mjZ/DOcu3AZk6cuwK8blsDLs8Y/y92L/QePY86M8XBxdsD9sMeYNW8pDA300btn11LMULH6fuNQt8soHFsxFAmRT9Co+yT0mHMUPw/3QXZGmsIxTfvMhEfznvhjzUiIXz+Ec63W8JuyC9snt0LMs/y58zuMXANze3f8vuxbpCZEwbN5T/Sc8zt+GemLtIQohcstLdE3/8ajfUdQo6cfjF0c8fqva/h7zS9oOGMiKpiaFDouNyMD9zfvhkn1qshOlf/baOnrwalDK+hbVoZISxPxd8MQunUPdAwNYOZeXdUpFUl7Vzd837wN5p09geDI1/i6Zi2s79YDX2zZgOjUFIVjlnTsBjN9fcw4dQwvkxJhWrEitERvi2MtDQ38/GUvJLx5g3G/H0R0agqsDI3wJju7tNIiAqDk7FLffPMN+vfvL3v07t0bPj4+SEtLQ48ePYSOscgO7NyF9l90RoeuXWDv5AT/cWNhYVkZRw8onlb32MGDqGxlCf9xY2Hv5IQOXbugXefO2L99p6xP2N178KhZEy3bt4OVjQ3qNKiPFm3b4HFY4XuUStuJeyH44dA+HLp9o0j9hzVvhZdiMcbu3oYHUZHY+NcFbLp0AePbdZL1Gd26PU6H3sXC40fwMDoSC48fwdmw+xjdpoOKsig+qVSKnbsPYfDAnmjVojGqujhi7szxyMzMwh8nzxc6zsO9OsZ+NwTt2zaHto62wj5rVszHF53awsXZEdVdnTHrh3GIjo5F6IPHqkqnyFTxPjeqVAmm5mayx+2gIFSooIsmrVqVVlofpYq8p8ydgy+++gourq6wd3TEmKlTIJXmIfhG2Tlip4q8Tx45itSUFMxavAge3t6wtLaGp48PXFyrlVZaH6Xs53vn7sOoX682Bg/oCSdHOwwe0BP16vpgx+5Dsj537oahWdMGaNK4Pmxs8nc4NKhXG6Fhj0ojtY+q23kEruxbjEfXjiD+ZSiOLf8ftHX04N60e6FjPFr0wtX9i/H01kkkxzxH8Ilf8Cz4DOp2/Q4AoKVTAdUbdsWFzdPxKvQykqKf4tLuBUiOeYFaHYaUVmqFenn2Imwa1YNt4/rQt7ZE9e5doGtijNd/Xv3guLAdB2BVtxYqOTsUeM3UtSoq+3hB39oSFS3MYd+yCQxsrZH05Jmq0ii2/r71cPBeCA7cC8HTBDF+vHAG0akp6OldW2H/zxydUaeKPfwP7cG1l88RmZKMe9FR+DsqQtbHz9MblSro4bsj+xEc+RpRqSkIjnyNh/GxpZUWEYASFBnvPgYPHowlS5bgu+++w7Nn6vnw5uTk4PGDh6hdv75cu2/9+gi9c1fhmNC79+D7fv8G9fEoLAy5ufl7dT28vfH4wQM8uH8fABAVEYGgK1dQr/FnBZZXXjRwqYbTofJ/k5P37qCOgxO0/rn3SUOXajh9X77Pqft30Khq2fkSEhEZjXhxIhrWf7sx1tHRgW8tL4TcFbYITEt7AwCoZGQo6HKLS1Xv8/edOHIUzdq0gZ6enjCBl1Bp5Z2VmYncXAkMjYyECbyEVJX31b/+gpuXJ1YtWozu7TtgSM/e2LVpMyQSiWoSUYKyn+87d8PkxgBAwwa+cmN8vD0QdPNvvHj5GgDw8NFT/B1yH581qitwFsVXydIRBqZWeB789owASW42Xt2/BNsaDQodp6Wlg9zsTLm23OxM2Lk1BJB/OpWGphZyc7Le65Mh66Muebm5SH0ZATN3V7l2MzdXJD19Xui4yCtByIiPh1PHNh/9HVKpFAkPHiM9JhbGRTgFqzRoaWjA3dIaV17I3xLgyotn8LaponBMC+dquB8ThUF1GuDs/0bh94FDMaFpS+hqvT0xpblLNYRERWBay3a4OHQ0DvUfgiH1GkHjA2egEKlCia/JeFfTpk2xZMkSubuDl5aUpCTkSSQwMTOVazcxNUWiWKxwTKJYDBPT9/qbmUIikSA5KQlm5uZo0bYNkhMTMW7IUEilUkgkEnT60g89v+mvslxUzcrIGDHJd+TaYlKSoa2lBXMDQ0QnJ8GqkjFiUpIL9LEyMi7FSD8sXpwIADB971C6makJoqJjBPs9UqkUS1YEopa3B6q6OAq2XGWo6n3+rgf37+N5eDjGTZ8qbPAlUBp5A8DGNWthbmGB2vXU/2UTUF3eURGR+PvmLbRs1w7zli1DxKtXWL1oMSQSCfp+O1hl+RSHsp/veHEizBSMEf+zPAAY2L870tLS0a37EGhqaECSl4cRw75Bh3YtBMxAOQYmlgCA9GT5HNOTYmFU2b7QcU+Dz6Jul1F4df8yEqOfwrFmC1Sr3xEijfwdR9kZaXj94Bo+6z4Z4tcPkJ4UC/cm3WHjWhcJUU9Ul1AR5KSlQ5qXBx1D+Z04OoaGyE5OVTjmTWwcnhw+Dt/xI6Dxzo2B35ebkYG/psxFXk4uRBoaqN7LD2ZuroX2L00mehWhpaEBcXq6XLv4TTrMCzl1sYqxMWrb2iFbkovRR/bDRK8iprdsh0oV9PDDqWP5fSqZwNauEo49uAf/Q3vgYGKKaS3bQlNDA+uvXVJ5XkT/ErTIOHHiBIyNjYVcZLGJIF+pS6VS4EPV+/uvSaVyywm5dQu7Nm3GqEkTUcPTAxGvXmPd0mXYbv4r+g4uv9P1SiGVe/7vNTZS6dv2d38G8v8m748rTcdPnMO8hStlz1cunQNAwSqE9IPXDBXXwsVr8PjJM2wKXCLYMktK6Pf5u04cOQpHF5cyd/EzoNq8927dhgunTmPxujVlZlKHfwmdtzQvD8YmJhgz9XtoamrC1a0GxHFx2L99h9qKDFV+vqVSqdxyTp6+iOMnzmHBnMlwcXbAw0fh+GlZICwszPBFEfaKC8m9WQ+093+b9765X/4T83sdRSIFjW+d+WUiOoxYjSFrggFIkRj9FHfObkPNVv1kfX5f9i0+H7UOIzeFI0+Si+jwv3H/z72wcvEWMiXlFVitUgVt+e/fe7/ugHOnttC3tPjgIjV1dVF/6jhIsrKQ8PAxHu8/Aj1zU5i6VhUs7JIqsKoVtP1L45//w5OPH0Fadv5RqcUXz2JpZz/MO3cSWbm50BABCW/SMev0H8iTShEaGw0LfQMMrNOARQaVKqWKjK5du8pt5KVSKeLj45GQkICZM2cKFlxxGBkbQ0NTEwnv7d1LSkwssFfvXyZmZgX2BiYmJEJTUxNGxpUAAFvWb0CrzzugQ9cuAACnqlWRmZmBFQsWovfAAWVmJpLiiE7JP1LxrsqGRsjJzYU4Pf/CuX+PZsj1MTIqcHSjNDVr0gCeHjVkz3Ny8i9iE4sTYWFuJmtPSEgqsPdTWQt/WouLf13DxsCfYPmRf2alQVXv839lZmbiwqnT+Gbo/4QNvIRUnfe+7Tuwa/MW/Lh6FZyrlZ1TAlWVt6m5ObS0NKH5zh5geydHJIjFyMnJgba24muVVEmoz7e5mQnECYlybQmJ8mOWr/oFA/t3R/u2zQEA1ao6ISo6Fpu27Cn1IuNJ0DH8+vDt9XRa2vkFroGxJdITo2Xt+pUskJ5U+Dn1GSnxOBjQE5rautAzNEVaQhSa95+LpJjnsj5J0c+wc1p7aOtWhE5FI6QnRqPLxC1IinkhfGLFoG2gD5GGBrJT5I9aZKemQUfBKaq5mVlIefEaqa8i8XDPYQD/FN5SKc6OmIRao4bAtEb+51ikoYGKlfOPWhra2SI9KhbPT5wrE0VGYsYb5OblwVxf/qiFaUV9iN+kKxwTl56G2LRUWYEBAE8T4qEhEsHSwBAvkxIRl56OXIkEee8UpU8T4mFhYAAtDQ3k5uWpJiGi9yj1DblVq1ZyjzZt2mDEiBE4evSo2i781tbWRrUa1XE7KEiu/XZQENxreikc4+7lWbD/9etwdXOD1j/nN2ZmZRY4j1FTQxNSFNzTX15cC3+M1u6ecm1tPWri5otnyP3nnOyr4Y/R2l3+79bGwwtXnqjvwmd9/Yqwt7ORPZydHGBuZoJrQW+nXs3JycGt4Lvw9nIr0e+SSqVYuHgNzl24jMA1P8LWpuAsPuqgqvf5v/48cwY5OTlo1b69sIGXkCrz3rttO3Zs/BULViyHq3vJ3jdCU1XeHt41Efn6NfLe+bIR8fIVTM3N1VJgAMJ9vmt6ueHadfnpmK9evy03JjMzC6L3dhBpaGggL6/0t+nZGWlIin4qe8S/CkNaQjQcfd7O7KahpQ07j8aIeHDto8uT5GQhLSEKGppaqN6oCx5fP1agT07WG6QnRkNX3xhOPq3x+PrvguZUXBpaWjC0t0XCexfeJ4Q9grGC6Yq1KuiiwfTxqD91rOxRpUkDVLS0QP2pY1HJqfDTyoD8a0DKgty8PITGRKGhvZNce0MHJ4REvlY4JjjyNSz0DaH3zufUwcQUkrw8xKTlF2l/R7yCvbGJ3EEgRxMzxKalssCgUqXUkYxRo0YJHYcgvuzdC4tmzoarmxvcvTxx7NBviI2OQSe//HsgbFyzFuLYOEyanX+0paOfH37btx/rly3H5127IPTuPZw4chRT5s2RLbNB48Y4uGsXXKpXRw0PD0S+foUtgRvQsEljub2A6qSvq4uqld9+CXYyt4C3nQMS0tPwKkGMBX49YGNiigEb1wEA1l84ixEt22JJj774+c9zaOhSDYOaNEfvDatky1h55gQuTp6BSR0647fgW+hSyxet3TzRZOHsUs+vMCKRCL17dsPGzbv/+WJii42bd6NCBV25c6unz1qMyhZm+G5E/ultOTk5ePrs5T8/5yI2Lh4PH4VDT08P9nY2AICAxWvwx8nzWLZ4JvT19RAvTgAAGOjro0IF9Z5Ko4r3+b9O/HYUjZo1LbCnvyxQRd57t27DlsAN+H7ubFhaWyMhPv8IgF5FPehVVDxFamlTRd6dvvTDb3v3Yd2SpejSvTsiXr3Crs2b0bV74bMXlTZlP9+9enTFt8MmYNPWvWjetCEu/HkVQUHB+HXD29Mdmzapj42bdsPa0gIuzg548Cgc23cdQtfObUs9T0VuHF2Dhl9NQGLUEyREhqPhVxORk52B0D/3yvp0GvMzUsWRuLgtf71bu9aBoakNYp7dgaGZDRr3nAaRSAPXDy2TjXGq1RqACAkRj2Bi7YIWA+YjIfIx7p7dVtopFmDfqhnub94FQwc7GDs54PWla8hMTIJtk/yL3Z8cPo7MpGR4DugFkYYGDGyt5cZrGxpAQ1tbrv3ZibMwcrCDnrkZpBIJ4u+FIeraTdTo9WWp5vYhW28FIaDDF7gfE4WQqAh85VUL1oZG2BOSf++SMY2bo7KBIaaeOAoAOPbgPoY1aIx57TphzZW/YKKnh/FNW+LQ/RBk/VM87Qm5jd616uD7Fm2xM/gmHExMMKReI+wILtoMlERCUarIcHNzw6VLl2BmZibXnpiYiEaNGiFMTdO7Nm/TBinJydixcSMS4sVwcHHGvGVLYWmdv9FJiI9HbMzbw8/WtjaYv3wp1i9bjqP7D8DU3BzDx4+TzSUPAH0GDYRIJMKW9YGIj4tDJWNjNGjSGAP9h5V6foWp4+iM85N+kD1f2jP/HNzNly9i0K+BsDI2hr3p23X1PD4OHZcvwtKe/TC8RRtEJiVi9M4tsntkAPlHMnoFrsLcbt0xp+vXCI+NQc/AVWXqHhkAMKDf18jKykLAotVISU2Dp0cNrFu5QG4O/eiYWGhovN2nExcnRs9+I2TPt+44gK07DsC3thd+WbcYALDvQP6evSH+k+R+3+wfxuGLTur9IqKK9zkAvH7xEvdCQhCwakWp5lNUqsj76IEDyMnJwdzv5S9y7/vtYPT/n/qn9QRUk3dlS0sErFyB9cuXY2ifvjC3sEC3Hj3QvX+/Ar9fnZT5fPvUdEfA3ClYG7gFawO3wq6KNRbOnyK7RwYATB4/HGsDt2LB4jVITEyChbkZvurWAf8b3KdU8yvM9YNLoa1TAW2HLkcFA2NEPrqBPTO/kLtHhpF5FUjf2SutpV0BTfvOgLGlE7Iz0/D01in8vnwwstLfnuKqW9EIzfrNhqG5LTJTE/Hw6mH8uX028iTq37NvVccHOenpeHbsNLJSUmBgbQWfEYOh98+kB1nJKch87zS4j5FkZePBroPISkqChrY29K0qw2Ngb1jV8VFBBso58SgMlfT0MKxBY1joG+CxOA7+h/Yg6p97ZJjrG8Da8O1sdxk5ORiyfxemtmyLPX0GIjkzAycehmHVlYuyPtFpqfjfgd2Y1Lw1Dvb/FrFpqdgefAMbb3x4OmAioYmkSpzzU6NGDVy+fLlAkRETE4M2bdrgzp07hYws3Ivk4m08/iucxo34eKf/oLQl89UdglrEiYzVHQKRyllIk9Qdglqs/Mbz453+g16N3q3uENTi4t+Kp5H+r7s3ruzMPPguz6UL1B1Cocrq30zVinUkY+vWrQDyD2Pv27cPFd85nSAvLw83btyAs3PZmH+aiIiIiIjUo1hFxubNmwHkXxS7e/duuZmVtLW1UaVKFcyeXXbO2SciIiIiotJXrCLj3LlzAIB+/fph9erVqFSp7F0cSkRERERE6qXUhd/btql/JgoiIiIiIiqbilxkBAQEYPTo0ahYsSICAgI+2HfKlCklDoyIiIiIiMqnIhcZoaGhyP1nDubQ0FCVBUREREREROVbkYuMd0+R4ulSRERERERUGI2PdyloypQpSEtLK9D+5s0bnipFRERERPSJU6rIOHz4MLKysgq0Z2Zm4rfffitxUEREREREVH4Va3aptLQ0SKVSSKVSpKenQ1dXV/aaRCLBn3/+CVNTU8GDJCIiIiKi8qNYRUadOnUgEokgEonQrl27Aq+LRCKMGjVKsOCIiIiIiKj8KVaRsXXrVkilUnzzzTdYtWqV3M34tLW1YWNjA0tLS8GDJCIiIiKi8qNYRUa9evUAAGfPnoW1tTU0NJS6pIOIiIiIiP7DlLrjt62tLQAgIyMDkZGRyMnJkXu9Ro0aJY+MiIiIiIjKJaWKjISEBEyZMgV//vmnwtfDwsJKFBQREREREZVfSp3vNH/+fCQnJ2PPnj2oUKECfvnlFyxcuBAODg5Yt26d0DESEREREVE5otSRjOvXr2Pt2rWoWbMmRCIRbGxs8Nlnn8HAwACBgYFo3ry5wGESEREREVF5odSRjDdv3sjuh2FsbIyEhAQAgKurK0JDQ4WLjoiIiIiIyh2ligwnJyc8e/YMQP5F3nv27EFMTAx2794NCwsLQQMkIiIiIqLyRanTpb755hvExcUBAEaOHInBgwfjyJEj0NbWxo8//ihogEREREREVL4oVWR88cUXsp/d3d1x7tw5PH36FNbW1rLTqIiIiIiI6NNU5CIjICCgyAudMmWKUsEQEREREVH5V+Qio6gXdItEIqWDISIiIiKi8q/IRca2bdtUGQcREREREf1HKDW7FBERERERUWFYZBARERERkaBYZBARERERkaBYZBARERERkaBYZBARERERkaBYZBARERERkaBYZBARERERkaBYZBARERERkaBYZBARERERkaCKfMdvUo20JfPVHYJaGIyfpu4Q1OLZ0jXqDkEtLKRJ6g5BLeJExuoOgUpRr62v1R2CWuzqX0XdIahHs0/z/1hZ1czHS90h0Ht4JIOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiASldJFx8+ZNTJgwAT169EBMTAwA4PDhw7h586ZgwRERERERUfmjVJFx8uRJDB48GBUqVEBoaCiys7MBAOnp6QgMDBQ0QCIiIiIiKl+UKjLWrVuH2bNnY968edDS0pK1165dG6GhoYIFR0RERERE5Y9SRcazZ89Qp06dAu0GBgZISUkpcVBERERERFR+KVVkWFhY4OXLlwXab926BTs7uxIHRURERERE5ZdSRUaPHj0wf/58hISEQCQSISYmBkeOHMGPP/6I3r17Cx0jERERERGVI1of71LQkCFDkJaWhv79+yMrKwt9+/aFjo4OBg0ahL59+wodIxERERERlSNKFRkAMHbsWAwbNgxPnjyBVCqFi4sL9PX1hYyNiIiIiIjKIaWLDADQ09ODl5eXULEQEREREdF/QJGLjJEjRxZ5oatXr1YqGCIiIiIiKv+KfOG3oaGh7GFgYICrV6/i3r17stfv37+Pq1evwtDQUCWBEhERERFR+VDkIxkBAQGynxcvXowOHTpg9uzZ0NTUBABIJBLMnj2b12UQEREREX3ilJrC9sCBAxg0aJCswAAATU1NDBgwAAcPHhQsOCIiIiIiKn+UKjIkEgmePn1aoP3p06fIy8srcVBERERERFR+KTW7lJ+fH6ZOnYoXL17A29sbABASEoINGzbAz89P0ACJiIiIiKh8UarImDx5MszNzbF582bExcUBACwsLPDtt99i0KBBggZIRERERETli1JFhoaGBoYMGSK78zcAGBgYCBoYERERERGVTyW6GR/A4oKIiIiIiOQpVWS0bNkSIpGo0NfPnj2rdEBERERERFS+KVVkfPPNN3LPc3NzERoaikuXLmHw4MGCBEZEREREROWTIEXGv3bs2CF3F3AiIiIiIvr0KHWfjMI0bdoUJ0+eFHKRRERERERUzghaZJw4cQLGxsZCLpKIiIiIiMoZpU6X6tq1q9yF31KpFPHx8UhISMDMmTMFC04ZR/bvx75tO5AgFsPB2Qn+Y8fCq5aPwr7i+HhsWL4Sjx88QMSrV+jaozv8x42V63Pp/Hns2rQFka9fIzc3F7Z2dviqT2+0/rxDKWRTdFKpFIG/bMeBw38gNTUNnh7VMWXiCLg4OxY6Jvzpc6wN3Iawh48RFRWLCWOGok+vbnJ9Nm7ejXMXLuP5i9fQ1dWBt5c7Ro8cBEcHOxVn9HFNXGtgQrtO8HV0go2xCbqtXorfgm9+cExT1xpY0qMfPGxtEZmUhMV/HEXgRfmJCvx862JO16/hYmGJ8LgYTD+4F4c/stzSVpz3+aXz53H0wEE8ffQYOTnZcHByRr8h36JOwwZy/f46dw5bAjcg6nUErKvYYsCwYWjcornKcykOZd7nAHDm3CWsDdyK1xFRqGJrjZH+36Bl889kr98Kvout2/cj9MFjxMcnYOmiGWjRrJGKsym64qxvALhz+zbWL1+BF0+fwczcHN379UWnL9/eKPW/vF0DPr6+09PfYG3gVpy7eAWJiUmo7uqCSeOGwcO9uoozKhqh1/ep33/HT3PmFRj3+18XoaOrq4oUlNa451R4txuECvrGiHp0A6cCxyH+VVih/TU0tdDwqwnwbNEHhmY2SIh4jPNbfsCz4NOyPrXaf4taHYagUmV7AED8yzBc3rMQT2+fUnk+RdHDuzYG1mkAC30DPBHH4ccLZ3A74lWh/bU1NeHfoDE6uXnCvKI+YtJSseH6ZRy6fwcAsOnrPqhr51Bg3J9Pn2D44b0qy4PofUodyWjVqpXco02bNhgxYgSOHj2KHj16CB1jkV04fRrrly5H74EDsG7bFnj5+GDamLGIjY5W2D8nOxuVTIzRa+AAOFerprCPoZEReg0cgBUbf0bgzu1o17kTfpo7DzevXlNlKsW2eds+bN95CN9PGI7tm1bCzNQUw0ZNRXr6m0LHZGZmoYqtFb4bPgjmZiYK+9wOvoseX3XG1o3LsG5lACQSCfy/m4aMjExVpVJk+jq6uPP6BUbt2Fyk/o7mFjg2ZhIuPX6A2rOnIuDYYazo/Q38fOvK+jRwqYbdQ7/D9quX4DNrCrZfvYQ9w75DPScXFWVRfMV9n98N/hu+9eph3vKlWLNlM7zr+GLG+Al48vChrE/onbuYP+0HtOrQAet2bEOrDh0wf+o0hJWxa6yUeZ+H3A3F99MXoGOHltizfS06dmiJyVMX4O69B7I+GRmZcK3mhO8nDC+NNIqluOs7KiIS08aMg5ePD9Zt24JeA77B2iVL8de5c7I+/+XtWlHW95wFy3Et6DbmzZqIvTvWo2H92hg2cgpiY+NLI60PUsX6BoCK+vrYffyY3KOsFRj1/cahbpdROB04DlsmNEVaUgx6zDkKHb3Cp8pv2mcmfNoNxumfJ+Dnkb4IPvEL/KbsgqWTt6xPqjgCF7bOwObxTbB5fBO8uHsRX07dA3M7t9JI64Pau7rh++Zt8PP1y/h6+0bcjniF9d16wMrQqNAxSzp2Q317R8w4dQydNgdi4vHDeJYglr0++ugBNFu/QvbosmUDcvPycPJR4cUakSooVWSMGjUKI0eOlD1GjBiBXr16wcVFvV/EDuzchfZfdEaHrl1g7+QE/3FjYWFZGUcPHFTY38rGBsPHj0Objp9D30BfYR9vX180btEc9k5OsKlSBd169oBzVRfcCwlRYSbFI5VKsXP3IQwe2BOtWjRGVRdHzJ05HpmZWfjj5PlCx3m4V8fY74agfdvm0NbRVthnzYr5+KJTW7g4O6K6qzNm/TAO0dGxCH3wWFXpFNmJeyH44dA+HLp9o0j9hzVvhZdiMcbu3oYHUZHY+NcFbLp0AePbdZL1Gd26PU6H3sXC40fwMDoSC48fwdmw+xjdpuzs4S3u+9x/3Fh0798P1d3dYWtvj0HD/WFrZ4drf12S9Tm0ezdq16uLXgO+gb2jI3oN+Aa16tbFod17Siutj1L2fb5z92HUr1cbgwf0hJOjHQYP6Il6dX2wY/chWZ/GjepixLABaNWicWmkUizFXd/HDh5EZStL+I8bC3snJ3To2gXtOnfG/u07ZX3+y9u1j63vzMwsnD1/CWNGDoZvLS/Y29lg2JB+sLGxwr6Dv5dWeoVSxfoGAJFIBFNzM7lHWVO38whc2bcYj64dQfzLUBxb/j9o6+jBvWn3Qsd4tOiFq/sX4+mtk0iOeY7gE7/gWfAZ1O36nazPkxt/4Omtk0iMfILEyCf4c/tsZGemwaZ63UKXW1r6+9bDwXshOHAvBE8TxPjxwhlEp6agp3dthf0/c3RGnSr28D+0B9dePkdkSjLuRUfh76gIWZ+UzEyI36TLHg3tnZCZk4NTjx4oXCaRqihVZLi5uUEsFhdoT0xMhJubevYM5OTk4PGDh6hdv75cu2/9+gi9c1eQ3yGVShEcdAOvXrz84KHr0hYRGY14cSIa1n+7UdLR0YFvLS+E3BV2z0VaWv4exEpGhoIutzQ0cKmG06Hy74WT9+6gjoMTtDQ1AQANXarh9H35Pqfu30GjqoqPdJU2Id7neXl5ePPmDQyN3u4pC717D77vL7OBcJ8dISj7Pr9zN0xuDAA0bOAr+GdDFZRZ34Wty0dhYcjNzS3Q/7+2XfvY+pZIJJBI8qCjqyPXR1dXB8Eh9wXMoPhUub4zMjLQ94uu6N2pM34YO17uSGZZUMnSEQamVnge/Pb0VUluNl7dvwTbGg0KHaelpYPcbPkj67nZmbBza6iwv0hDA25NvoJ2BX1EPAwSJnglaWlowN3SGldePJVrv/LiGbxtqigc08K5Gu7HRGFQnQY4+79R+H3gUExo2hK6WoWf/e7n5Y0/HoYiIzdH0PhJtXbs2IGWLVvCy8sLfn5+uHmz8NO2r1+/jurVqxd4hIeHy/U7efIkPv/8c3h6euLzzz/H6dOnC1miMJS6JkMqlSpsz87Ohra24j3iqpaSlIQ8iQQmZqZy7SampkhUUBAVR3paGnp17Iyc7GxoaGpi1KSJBTbq6hQvTgQAmJrKn/JkZmqCqOgYwX6PVCrFkhWBqOXtgaoujoItt7RYGRkjJvmOXFtMSjK0tbRgbmCI6OQkWFUyRkxKcoE+VkbGpRhp4YR4n+/fsROZGRlo2rqVrC1RLIaJqfCfHSEp+z6PFyfCTMEY8T/LK8uUWd8K16WZKSQSCZKTkmBmbg7gv7td+9j61teviJpebvj5151wcrSHmakxTpy6gHv3H8LezkbgLIpHVevbzsERE2ZMh5NLVbxJT8ehPXsw9tv/Yf2ObbC1t1dZPsVhYGIJAEhPll+36UmxMKpceIxPg8+ibpdReHX/MhKjn8KxZgtUq98RIg1NuX4WDh7o9+M5aOlUQHZGGg4G9IL4lXr37JvoVYSWhgbE6ely7eI36TCvqPjsiirGxqhta4dsSS5GH9kPE72KmN6yHSpV0MMPp44V6O9pZQ1X88qYoeA1KruOHz+OgIAAzJw5E7Vr18bu3bsxZMgQHDt2DDY2hW+nTpw4AQODt6cXmr6zbQgODsbYsWMxevRotG7dGmfOnMGYMWOwc+dOeHt7K1pciRWryNi6dSuA/MOu+/btQ8WKFWWv5eXl4caNG3B2dhY2wmISQf5O5FKpFPjA3cmLQq9iRazbvhWZGRkIvnEDgctXwNrWBt6+viVarrKOnziHeQtXyp6vXDoHQME0pZB+8M7sxbVw8Ro8fvIMmwKXCLbM0iaFfIH879/n3cL5/SJaBFGBceqm7Pv8/MlT2PbzL5j906ICX0zef69IUfLPTkmo8n0ulUrVmVqxFXt9F/gjSQss51Parr2/vufNmohZ85ahXac+0NTUQI3qVdGhXXOEPQgvfCGlSOj17eblCTcvT9nLHt41MbzfNzi8dx9GTBgvTNDF5N6sB9r7v13f++Z+CUAW+lsikYLGt878MhEdRqzGkDXBAKRIjH6KO2e3oWarfnL9xBGP8OuYhqhgUAnVG3ZFp9GB2DGtvdoLDQAF/ruIFLT9S+Of/0eTjx9BWnYWAGDxxbNY2tkP886dRNZ7Ryv9PH3wKD4W96KjBI+bVGfTpk348ssv8fXXXwMApk2bhkuXLmHXrl0YP77wz6yZmRmMjBRfz7NlyxY0atQIQ4cOBQC4uLggKCgIW7ZswdKlS4VPAsUsMjZv3gwgf4O3e/duaGi8PdtKW1sbVapUwezZswUNsKiMjI2hoamJhPf29iQlJhb4MlVcGhoasLXLn03JxdUVL589x+7NW9X2z7hZkwbw9Kghe56Tkw0AEIsTYfHOebYJCUkF9gIqa+FPa3Hxr2vYGPgTLC0tBFlmaYtOyT9S8a7KhkbIyc2FOD0tv0+ygj5GRgWObqhLSd7nF06fxtJ58zE9YAFq16sn95qJmVnBZSaU/LNTEkK9z83NTCBOkD9qkZAo3GdDlZRZ3yZmZgX2eicmJEJTUxNGxpVkbf/V7VpR1rddFRtsXL8YGRmZSEtPh4W5GSZPWwBbG0uh0lGKKtf3uzQ0NFDd3Q0RrwqfwUjVngQdw68P315Pp6WdfxG6gbEl0hPfXuSuX8kC6UmxhS4nIyUeBwN6QlNbF3qGpkhLiELz/nORFPNcrl9ebg6SovNPS4p+Egzrar6o02k4Tq77TsFSS0dixhvk5uXBXF/+qIVpRX2I36QrHBOXnobYtFRZgQEATxPioSESwdLAEC+T3r73K2hpoUN1N6y58pdqEqAiy87ORnZ2tlybjo4OdHR0FPa9f/8+/ve//8m1f/bZZwgODv7g7+natSuys7Ph4uICf39/NGjw9lTDv//+GwMGDJDr36RJE2zZsqWY2RRdsa7JOHfuHM6dO4e6devit99+kz0/d+4cTp48iY0bN6rskMvHaGtro1qN6rgdJH+O5e2gILjX9BL0d0mlb/8BqoO+fkXY29nIHs5ODjA3M8G1oLdvvpycHNwKvgtvr5JdIyOVSrFw8Rqcu3AZgWt+hK2NVUnDV5tr4Y/R2t1Trq2tR03cfPEMuRIJAOBq+GO0dpd/v7Tx8MKVJ+q/0B1Q/n1+/uQp/DRnHr6fOwf1G39W4HV3L88Cy7x1/brgn53iEOp9XtPLDdeuy2+Yr16/XeLPRmlQZn0rWpe3r1+Hq5sbtD5w3vZ/ZbtWnPWtp1cBFuZmSElJxZVrt9C8qeLz+EtLaa1vqVSK8EePZafOqUN2RhqSop/KHvGvwpCWEA1Hn5ayPhpa2rDzaIyIBx+f9UySk4W0hChoaGqheqMueHz9Y6cHiWSFjbrk5uUhNCYKDe2d5NobOjghJPK1wjHBka9hoW8IvXdOTXcwMYUkLw8xaalyfdu5ukFHUwtHw8rWLIGfosDAQPj6+so9AgMDFfZNTEyERCKBmZn85Azm5uaIi4tTOMbCwgJz587FqlWrsGrVKjg5OWHAgAG4ceNtIR8fH19gmWZmZoUuUwhKXZOxbds2oeMQxJe9e2HRzNlwdXODu5cnjh36DbHRMejkl3/vh41r1kIcG4dJs9/eyyP80SMAQMabDCQlJiL80SNoaWnDwTn/Q79r8xa4utWATZUqyMnJQdDlKzhz/Di+mzyp9BMshEgkQu+e3bBx8+5//kHbYuPm3ahQQRcd2rWQ9Zs+azEqW5jhuxGDAOT/w3767OU/P+ciNi4eDx+FQ09PT3ZucsDiNfjj5HksWzwT+vp6iBcnAAAM9PVRoYJ6N9D6urqoWvlt0eNkbgFvOwckpKfhVYIYC/x6wMbEFAM2rgMArL9wFiNatsWSHn3x85/n0NClGgY1aY7eG1bJlrHyzAlcnDwDkzp0xm/Bt9Clli9au3miyUL1HKFTpLjv8/MnT2HRrNnwHz8Wbp6eSIjP3+upW0EX+v+cu9m1Zw+MH+qPPVu2omGzprh68U8EB93A0p8VbwTVQdn3ea8eXfHtsAnYtHUvmjdtiAt/XkVQUDB+3fD2tL83bzLw6nWk7HlEZDQePgqHkZEhrK0ql16SChR3fXf088Nv+/Zj/bLl+LxrF4TevYcTR45iyrw5smX+l7drRVnfV67dhFQKODpUwatXkVi26hc4OlTBF53blnqe71PF+t728y9w8/SErb0d3qSn4/CevQh/9AgjJ01QS46FuXF0DRp+NQGJUU+QEBmOhl9NRE52BkL/fHtvh05jfkaqOBIXt+Xnb+1aB4amNoh5dgeGZjZo3HMaRCINXD+0TDamad9ZeHr7FFLjX0NHzxBuTb6CvWcT7J3dtbRTLGDrrSAEdPgC92OiEBIVga+8asHa0Ah7Qm4DAMY0bo7KBoaYeuIoAODYg/sY1qAx5rXrhDVX/oKJnh7GN22JQ/dDFJ4qde7JIyRnZpR6XiRv6NChGDhwoFyboqMY7ypwCrO08FNFnZ2d5S5XqFWrFqKjo7Fx40bUrft2FrXiLFMIRS4yAgICMHr0aFSsWBEBAQEf7DtlypQSB6aM5m3aICU5GTs2bkRCvBgOLs6Yt2wpLK2tAQAJ8fGIjZGfa9y/b3/Zz48fPMD5k6dgaW2Fbb8dBgBkZmRg1aLFiI+Ng66uLuwcHDB5ziw0b9OmtNIqkgH9vkZWVhYCFq1GSmoaPD1qYN3KBdDXf3vdTHRMLDQ03r6Z4uLE6NlvhOz51h0HsHXHAfjW9sIv6xYDAPYdyJ/ScYi//JeP2T+Mwxed1PsPuY6jM85P+kH2fGnP/HNwN1++iEG/BsLK2Bj2pm+r9ufxcei4fBGW9uyH4S3aIDIpEaN3bsHBW28r/avhj9ErcBXmduuOOV2/RnhsDHoGrkLQs7JxrjZQ/Pf5sUOHIJFIsHrRT1i96CdZe5uOn2PizBkAAI+aNTF13lxsXh+ILYEbYF3FFtMWzIObp/yRH3VT5n3uU9MdAXOnYG3gFqwN3Aq7KtZYOH8KvDzfnpoTGvYIQ4ZPlj1fsnwDAKBzx9aYM0O9X8SKu76tbW0wf/lSrF+2HEf3H4CpuTmGjx+HJi3f7iH+L2/XirK+09LeYNXaTYiJjUclIwO0atEYI/wHQPsDR3pKiyrWd1pqGpYHLESiWIyKBgao6uqKJYHrUcPDo9Tz+5DrB5dCW6cC2g5djgoGxoh8dAN7Zn6B7Iw0WR8j8yqQ5uXJnmtpV0DTvjNgbOmE7Mw0PL11Cr8vH4ys9LenuOobV0bnMb9A39QKWekpiHtxD3tnd8XzEPl7iajDiUdhqKSnh2ENGsNC3wCPxXHwP7QHUakpAABzfQNYv3PPjIycHAzZvwtTW7bFnj4DkZyZgRMPw7DqykW55ToYm8K3ih2G7JefypjUo7BToxQxMTGBpqYm4uPl79sjFothXoyjj97e3jhy5Ijsubm5eYFlJiQkFGuZxSWSFjZV1Hv69euHNWvWwMjICP369Su0n0gkkl0gXhwvksv+TC+qYCFNUncIamEwfpq6Q1CLZ0vXqDsEtfhU3+dxImN1h6AWXN+fll39FU+3+l+3vdmn+X/s3rip6g5BoRHnjqo7hEKtadm5WP2//vpreHh4YNasWbK2zz//HK1atfrghd/v+u6775CUlCT7Tj5mzBikp6fj559/lvX59ttvYWRkpP4Lv989Raqsni5FRERERFSeDRw4EJMmTYKnpydq1aqFPXv2ICoqCj179gQALFmyBDExMVi0aBGA/ImZqlSpgqpVqyInJwdHjhzByZMnsWrV29PB+/fvj759+2LDhg1o1aoVzp49i6tXr2LnTtUd7VL/sWEiIiIiIgKQf9QiMTERa9euRWxsLFxdXbFhwwbY2toCAOLi4hAV9XZa4pycHPz444+IiYlBhQoVULVqVWzYsAHNmjWT9alduzaWLl2K5cuXY+XKlbCzs8OyZctUOmGTUkXGiBEjFF4oIhKJoKOjAwcHB3Tq1Ent98wgIiIiIipv+vTpgz59+ih8beHChXLPhwwZgiFDhnx0me3bt0f79u0Fia8oijWF7b8MDQ1x7do1hIaGyoqN0NBQXLt2DRKJBMePH0eXLl1w69YtQYMlIiIiIqKyT6kjGebm5ujUqRNmzJghuyFfXl4e5s+fD319fSxbtgwzZ87ETz/9hF27dgkaMBERERERlW1KHcnYv38/vvnmG7k7fmtoaKBv377Ys2cPRCIR+vTpg8ePy8YNzIiIiIiIqPQoVWRIJBI8ffq0QPvTp0+R98/81bq6uiq9wQcREREREZVNSp0u1aVLF0ybNg2vXr2Cp6cnRCIR7t69i/Xr16NLly4AgBs3bqBq1aqCBktERERERGWfUkXGlClTYGZmhl9++UV290Bzc3MMGDBAdnX7Z599hiZNmggXKRERERERlQtKFRmamprw9/eHv78/0tLSAAAGBgZyfWxsbEoeHRERERERlTslvhnf+8UFERERERF92pQqMuLj4/Hjjz/i6tWrSEhIgFQqlXs9LCxMkOCIiIiIiKj8UarI+P777xEVFYXhw4ejcuXKQsdERERERETlmFJFxq1bt7Bz5064ubkJHQ8REREREZVzSt0nw9rausApUkRERERERICSRcbUqVOxZMkSvH79Wuh4iIiIiIionFPqdKmxY8ciIyMDbdq0QYUKFaCtrS33elBQkCDBERERERFR+aNUkTF16lSh4yAiIiIiov8IpYqMbt26CR0HERERERH9Ryh9Mz6JRIIzZ84gPDwcIpEIVatWRcuWLaGpqSlkfEREREREVM4oVWS8ePEC//vf/xATEwMnJydIpVIEBgbCysoKGzZsgL29vdBxEhERERFROaHU7FLz5s2DnZ0dLly4gEOHDuHw4cM4f/48qlSpgnnz5gkdIxERERERlSNKFRk3btzAxIkTYWxsLGszMTHBhAkTcOPGDaFiIyIiIiKickipIkNHRwfp6ekF2tPT0wtMZ0tERERERJ8WpYqM5s2bY8aMGQgJCYFUKoVUKsXff/+NWbNmoWXLlkLHSERERERE5YhSF35Pnz4dkydPRo8ePaCllb+I3NxctGrVivfQICIiIiL6xClVZBgZGWHdunV48eIFwsPDIZVKUbVqVTg4OAgdHxERERERlTNFLjICAgI++HpQUJDs5ylTpigfERERERERlWtFLjJCQ0OL1E8kEikdDBERERERlX9FLjK2bdumyjiIiIiIiOg/QqnZpYiIiIiIiArDIoOIiIiIiATFIoOIiIiIiAQlkkqlUnUHAQAvkhPVHQKRyjmNG6HuENTCv08vdYegFhf/vqvuENQiaNCnub7p03IyOVvdIahFN4fq6g5BoUMvHqo7hEKV1b+ZqvFIBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCYpFBhERERERCUrpIuPw4cPo2bMnGjdujIiICADA5s2bcebMGcGCIyIiIiKi8kepImPnzp1YuHAhmjVrhtTUVOTl5QEAjIyMsGXLFkEDJCIiIiKi8kWpImP79u2YN28e/P39oaHxdhGenp549OiRYMEREREREVH5o1SR8fr1a7i5uRVo19HRQUZGRomDIiIiIiKi8kupIqNKlSoICwsr0P7nn3+iatWqJQ6KiIiIiIjKLy1lBg0ePBhz5sxBdnY2AODOnTv4/fffsWHDBsybN0/QAImIiIiIqHxRqsj48ssvIZFIsHjxYmRkZGD8+PGwtLTE1KlT0bFjR6FjJCIiIiKickSpIgMAunfvju7duyMhIQFSqRRmZmZCxkVEREREROWUUkXGq1evIJFI4OjoCFNTU1n78+fPoaWlhSpVqggWYHEd2b8f+7btQIJYDAdnJ/iPHQuvWj4K+4rj47Fh+Uo8fvAAEa9eoWuP7vAfN1auz/HDh3Hm2B94/vQpAKBajeoYONwfNTw8VJ1KsRQnbwC4c/s21i9fgRdPn8HM3Bzd+/VFpy/9ZK9PGOaPO7eDC4yr91kjzFu2VBUpKKU4eV86fx5HDxzE00ePkZOTDQcnZ/Qb8i3qNGwg1++vc+ewJXADol5HwLqKLQYMG4bGLZqrPJeiauJaAxPadYKvoxNsjE3QbfVS/BZ884NjmrrWwJIe/eBha4vIpCQs/uMoAi+elevj51sXc7p+DRcLS4THxWD6wb04/JHllrZXFy/jxekLyE5Ohb61JVy/7gKTas4fHZcU/gy3lq6Dvo0VGkwbJ2uPvHoDoVv3FOjfYmUANLW1BY29JHp418bAOg1goW+AJ+I4/HjhDG5HvFLYd167TujqUbNA+5P4OHTd+jMAoHXV6hhSrxHsjE2gpamBl4mJ2HLrOo6G3VNpHsUllUoR+Mt2HDj8B1JT0+DpUR1TJo6Ai7PjB8edOXcJawO34nVEFKrYWmOk/zdo2fwz2esbN+/GuQuX8fzFa+jq6sDbyx2jRw6Co4OdijMqGlXlnZ7+BmsDt+LcxStITExCdVcXTBo3DB7u1VWcUdEok3f40+dYG7gNYQ8fIyoqFhPGDEWfXt0K9IuNjceKNRtx+cpNZGVlw97eFjOnjYW7WzUVZlQ0V48cx5/7DiI1IRGWDvbo5P8tnLwUf8e4d+kKrh39A1FPnyE3JweWDvZo3a8XXOvUlvWJef4Sp7buQMTjcCTFxKLTsMFo7NeltNIhklHqwu8pU6YgOLjgF9CQkBBMmTKlxEEp68Lp01i/dDl6DxyAddu2wMvHB9PGjEVsdLTC/jnZ2ahkYoxeAwfAuZriDU3Irdto3q4NFq9bg+Ubf0ZlKytMGTUa8bGxqkylWIqbd1REJKaNGQcvHx+s27YFvQZ8g7VLluKvc+dkfWb8uBC7jx+TPTbs2gkNTU00bdWytNL6qOLmfTf4b/jWq4d5y5dizZbN8K7jixnjJ+DJw4eyPqF37mL+tB/QqkMHrNuxDa06dMD8qdMQdq/sfPnS19HFndcvMGrH5iL1dzS3wLExk3Dp8QPUnj0VAccOY0Xvb+DnW1fWp4FLNewe+h22X70En1lTsP3qJewZ9h3qObmoKIvii775Nx7tOwKn9q1Rf+pYGFd1xt9rfkFmQuIHx+VmZOD+5t0wqa54UgrNChXQZOEMuUdZKjDau7rh++Zt8PP1y/h6+0bcjniF9d16wMrQSGH/hedPo9n6FbJHqw2rkJTxBqceP5D1Sc7MwIagy+i7ewu+3PoLDt+/g7ntOqGRg1NppVUkm7ftw/adh/D9hOHYvmklzExNMWzUVKSnvyl0TMjdUHw/fQE6dmiJPdvXomOHlpg8dQHu3nub/+3gu+jxVWds3bgM61YGQCKRwP+7acjIyCyNtD5KVXnPWbAc14JuY96sidi7Yz0a1q+NYSOnIDY2vjTS+ihl8s7MzEIVWyt8N3wQzM1MFPZJSUnFgP+Ng5amFlYvn4cDuwMxbvQQGBrqqyqVIgu58Bd+X/8LWvTuju/WLYejlzs2TZuNpNg4hf2f3b2Par4+GDBvJkatWQZnby9smTEPEU/CZX2ys7JgZmWFDoP6w9BU8d+EqDQoVWSEhoaidu3aBdp9fHwUzjpVWg7s3IX2X3RGh65dYO/kBP9xY2FhWRlHDxxU2N/KxgbDx49Dm46fQ99A8cZmytw5+OKrr+Di6gp7R0eMmToFUmkegm+UnT28xc372MGDqGxlCf9xY2Hv5IQOXbugXefO2L99p6yPUaVKMDU3kz1uBwWhQgVdNGnVqrTS+qji5u0/biy69++H6u7usLW3x6Dh/rC1s8O1vy7J+hzavRu169VFrwHfwN7REb0GfINadevi0O6Ce7vV5cS9EPxwaB8O3b5RpP7DmrfCS7EYY3dvw4OoSGz86wI2XbqA8e06yfqMbt0ep0PvYuHxI3gYHYmFx4/gbNh9jG7TQUVZFN/Lsxdh06gebBvXh761Jap37wJdE2O8/vPqB8eF7TgAq7q1UMnZQeHrIhGgW8lI7lGW9Peth4P3QnDgXgieJojx44UziE5NQU/vgttgAEjLzoL4Tbrs4WFpDaMKejh0L0TW58brlzj75BGeJojxKjkJ24Nv4FFcLGrblo09+UD+Xu2duw9h8MCeaNWiMaq6OGLuzPHIzMzCHyfPFzpu5+7DqF+vNgYP6AknRzsMHtAT9er6YMfuQ7I+a1bMxxed2sLF2RHVXZ0x64dxiI6OReiDx6WR2gepKu/MzCycPX8JY0YOhm8tL9jb2WDYkH6wsbHCvoO/l1Z6hVI2bw/36hj73RC0b9sc2jqKdw5s2rYPVpUtMHvGeHh6VIeNjRXq160Fuyo2qkqnyC4d+A112rdGvQ5tUdneDp39h6CShTmuHT2usH9n/yFo1v1L2FWvBnNbG7Qf1B9mttYIu/b2/4Fd9Wr4/H8D4d2iaZnaYUKfHqWKDJFIhPT09ALtqampkEgkJQ5KGTk5OXj84CFq168v1+5bvz5C79wV7PdkZWYiN1cCQ6Oy8UVEmbxD796D7/v9G9THo7Aw5ObmKhxz4shRNGvTBnp6esIEXkJCrO+8vDy8efNGbl0W9rcR8j1U2hq4VMPpUPn4T967gzoOTtDS1AQANHSphtP35fucun8Hjaqq/1QCAMjLzUXqywiYubvKtZu5uSLp6fNCx0VeCUJGfDycOrYptI8kKxuXps3DX1Pm4u81G5HyKkKosEtMS0MD7pbWuPLiqVz7lRfP4G1TtNNS/Ty9ce3FM0SlphTap76dIxxNTXHr9csSxSukiMhoxIsT0bD+22JKR0cHvrW8EHK38J1Zd+6GyY0BgIYNfD84Ji0tf095JSPDEkZdcqrKWyKRQCLJg46ujlwfXV0dBIfcFzAD5Sibd1Fc/PMa3N1cMXHKPLRs3wM9+43AwcN/lDTkEsvNyUHE4yeoVruWXHs131p4EfqgkFHy8vLykPUmAxUNDVQRIlGJKFVk1KlTB4GBgXIFhUQiwYYNG+Dr6ytYcMWRkpSEPIkEJmamcu0mpqZIFIsF+z0b16yFuYUFater+/HOpUCZvBPFYpiYvtffzBQSiQTJSUkF+j+4fx/Pw8PRocsXgsVdUkKs7/07diIzIwNNW789OqPwbyPwe6i0WRkZIyY5Wa4tJiUZ2lpaMDfI/1JlVckYMSkF+1gZGZdWmB+Uk5YOaV4edAzlvwTqGBoiOzlV4Zg3sXF4cvg4PAb2gcY/xdT79C0rw71/D3j7D4LnoD7Q0NbCzcWr8aaQUxVKm4leRWhpaED83k4d8Zt0mFf8+Kke5vr6aOzkggPvHMX4l4GOLoJGTkDw6MlY2607As6dwtWXz4UKvcTixfmnwZm+d7qHmakJxOKED44zUzhG8Wl1UqkUS1YEopa3B6q6OJYsaAGoKm99/Yqo6eWGn3/didg4MSQSCY79cRb37j9EfHzhyy0tyuZdFBGRUdh38HfY29li7Yr5+Krb51i0dB2OHj9TouWW1JuUFOTl5cHQxFiu3dCkElITk4q0jL/2H0ZOZhZqNm0sfIBEJaTUhd8TJ05Enz590L59e9SpUwcAcPPmTaSlpWHLli2CBlhcIojknkul0vzzIQSwd+s2XDh1GovXrYGOrq4gyxRKsfN+/zWpVOFygPyjGI4uLmXuYndA+fV9/uQpbPv5F8z+aVGBokL03ngphHsPqYsUUrnn/+Yolb5tf/dnIP9v+/44tSuwGqQK2gBpXh7u/boDzp3aQt/SotDFVXJ2kDuNytjFEdcDluPV+cuo3qOrICEL4f21IFLQpkhX95pIzcrE2ScPC7yWnp2FL7dvREVtbTSwd8TEZq3xOjkJN9R0NOP4iXOYt3Cl7PnKpXMAKNhUQVrgM/oxUqm00I/wwsVr8PjJM2wKXFKsZQqlNPOeN2siZs1bhnad+kBTUwM1qldFh3bNEfYgvPCFqIgq835fXp4U7m7VMGr4QABAjepVEf7sBfYd+B2dP29domUL4v3/OYo3awX8ff4izmzbhf6zp8HgvUKFqCxQqsioWrUqjhw5gh07duDBgweoUKECunTpgr59+8LY2FjgEIvGyNgYGpqaSHhvj3NSYmKBL5HK2Ld9B3Zt3oIfV68q9CJxdVAmbxMzswJ75hMTEqGpqQkj40py7ZmZmbhw6jS+Gfo/YQMvoZKs7wunT2PpvPmYHrAAtevVk3vNxMys4DIThHkPqUt0ShKsKhnLtVU2NEJObi7E6Wn5fZIV9DEyKnB0Q120DfQh0tBAdor8UYvs1DToKDjFJTczCykvXiP1VSQe7jkM4J8iSirF2RGTUGvUEJjWKPg5FmlowMjBrswcyUjMeIPcvDyY68sftTCtqA/xm4KnrL6vm6c3jobeQ25eXoHXpABeJeXvPX4YFwtnU3N8W6+R2oqMZk0awNOjhux5Tk7+zV7F4kRYmL+dIj0hIanA3u53mZuZQPzeZAAJiYrHLPxpLS7+dQ0bA3+C5QeKUVUqzbztqthg4/rFyMjIRFp6OizMzTB52gLY2lgKlU6RCZV3UZibm8LZyV6uzcnRHmfPXy7RckuqopERNDQ0kPreektLSv5o0RBy4S8cWLoKfaZPRrXaPqoLkqgElDpdCgAsLS0xbtw4bNiwAStXrsTIkSPVVmAAgLa2NqrVqI7bQUFy7beDguBe06tEy967bTt2bPwVC1Ysh6u7W4mWJTRl8nb38izY//p1uLq5QUtLvu7888wZ5OTkoFX79sIGXkLKru/zJ0/hpznz8P3cOajf+LMCryv629y6fr3E7yF1uhb+GK3dPeXa2nrUxM0Xz5D7zymPV8Mfo7W7fI5tPLxw5Yn6L4QFAA0tLRja2yIh7JFce0LYIxgrmN5Sq4IuGkwfj/pTx8oeVZo0QEVLC9SfOhaV3vvC8S+pVIq01xHQKSMXf+fm5SE0JgoN7eVnfWro4ISQyNcfHFu3ij0cTExxUMGpUoqIRIBOIaeVlQZ9/Yqwt7ORPZydHGBuZoJrQW9nMszJycGt4Lvw9ip8O1zTyw3XrsvPfnj1+m25MVKpFAsXr8G5C5cRuOZH2NpYCZ9QEZVm3v/S06sAC3MzpKSk4sq1W2jetKFwCRWRUHkXhU9Nd7x4If95efkyAtZWlUu03JLS0taGbbWqeHL7b7n2J7f/hoN7DcWDkH8EY99PK9Dz+wmoUb9snLpNpEiRj2Q8ePAArq6u0NDQwIMHH74gqUaNwj8cqvRl715YNHM2XN3c4O7liWOHfkNsdAw6+eXPmb1xzVqIY+MwafZM2ZjwR/lfWjLeZCApMRHhjx5BS0sbDs75/9T3bt2GLYEb8P3c2bC0tkZCfP5ebr2KetCrWLGUM1SsuHl39PPDb/v2Y/2y5fi8axeE3r2HE0eOYsq8OQWWfeK3o2jUrGmBIxxlQXHzPn/yFBbNmg3/8WPh5ukpW5e6FXShb5B/0VzXnj0wfqg/9mzZiobNmuLqxT8RHHQDS38OVE+SCujr6qJq5bdfipzMLeBt54CE9DS8ShBjgV8P2JiYYsDGdQCA9RfOYkTLtljSoy9+/vMcGrpUw6AmzdF7wyrZMlaeOYGLk2dgUofO+C34FrrU8kVrN080WTi71PMrjH2rZri/eRcMHexg7OSA15euITMxCbZN8u9z8uTwcWQmJcNzQC+INDRgYGstN17b0AAa2tpy7U9/P4VKzg7QszCHJDMTL89fQuqrSFTv6YeyYuutIAR0+AL3Y6IQEhWBr7xqwdrQCHtCbgMAxjRujsoGhph64qjcOD9Pb4REReCJuOBRmW/rNsT9mCi8Sk6CtoYmmji5oLObF+adPVEqORWFSCRC757dsHHz7n++iNpi4+bdqFBBFx3atZD1mz5rMSpbmOG7EYMAAL16dMW3wyZg09a9aN60IS78eRVBQcH4dcPb06ECFq/BHyfPY9nimdDX10P8P+f8G+jro0IF9Z4Kq8q8r1y7CakUcHSoglevIrFs1S9wdKiCLzq3LfU836ds3jk5OXj67OU/P+ciNi4eDx+FQ09PD/Z2+bNH9e3VDQO+HYeNm3ejTaumuB/6EAcOH8cPU0aXfqLvafxlF+xdtAy2rlXh4F4D14+dRFJsHOp3yp/Z78TGLUgWJ6DHpPx7eP19/iL2LlqOzv5DYO9WXXYURFtXBxX+OeKZm5OD2Jf599GR5OQiJT4BkeFPoVOhAsxt1T+jFn06ilxkdO3aFZcvX4aZmRm6du0KkUhU4BxuIH9Doa5pbJu3aYOU5GTs2LgRCfFiOLg4Y96ypbC0zv9SkRAfj9gY+Xso+PftL/v58YMHOH/yFCytrbDtt8MAgKMHDiAnJwdzv58qN67vt4PR/39DVJtQERU3b2tbG8xfvhTrly3H0f0HYGpujuHjx6FJS/l7YLx+8RL3QkIQsGpFqeZTVMXN+9ihQ5BIJFi96CesXvSTrL1Nx88xceYMAIBHzZqYOm8uNq8PxJbADbCuYotpC+bBzVP+SIA61XF0xvlJP8ieL+3ZDwCw+fJFDPo1EFbGxrA3fXu6wfP4OHRcvghLe/bD8BZtEJmUiNE7t+DgrbdTHl4Nf4xegaswt1t3zOn6NcJjY9AzcBWCnpX+udqFsarjg5z0dDw7dhpZKSkwsLaCz4jB0Pvn4v+s5JSP3jPjfTkZGQjbsQ9ZKanQqlABhna28B0/HJUcFR/pUIcTj8JQSU8Pwxo0hoW+AR6L4+B/aI9stihzfQNYv3fPDAMdXbSuVgMLL5xWuEw9bR1Mb9UeloaGyMrNxbMEMab8cQQnHqlvCnJFBvT7GllZWQhYtBopqWnw9KiBdSsXQF//7Q6e6JhYaGi8PYPdp6Y7AuZOwdrALVgbuBV2VayxcP4UeHm+3fm170D+lK1D/CfJ/b7ZP4zDF53U/4VbVXmnpb3BqrWbEBMbj0pGBmjVojFG+A+AtpZSZ04LTpm84+LE6NlvhOz51h0HsHXHAfjW9sIv6xYDyJ/mdsmiGVi1dhM2bNwBWxsrTBw7DJ+3V/99n7ybN8GblFSc3bEHqQkJsHJwwIB5M2BimX+UJSUhUe6eGdePnUSeRILfVq/Hb6vXy9prt2mJ7hPH5I8RJ2Cl/xjZa3/uP4Q/9x+CU01PDP1pQankRQQAIqmiSkGBiIgI2NjYQCQSISLiw1M82traFjuQF8nF+3JAVB45jRvx8U7/Qf59eqk7BLW4+Hf5nfq4JIIGfZrrmz4tJ5Oz1R2CWnRzKBt3iH/foRcFJ7goK8rq30zVirz74t/CIScnB6tXr8bw4cNhZ1d2btxERERERERlQ7Ev/NbW1sbp04oPwRMRERERESk1u1SbNm1w5ox6b2JDRERERERlk1JXe9nb22Pt2rUIDg6Gh4cH9PT05F7v379/ISOJiIiIiOi/TqkiY9++fTA0NMS9e/dw7949uddEIhGLDCIiIiKiT5hSRca5c+dkP/87OZVIJCqsOxERERERfUKUvuP3vn370KlTJ3h5ecHLywudOnXCvn37hIyNiIiIiIjKIaWOZCxfvhxbtmxB37594ePjAwD4+++/sWDBArx+/Rpjx44VMkYiIiIiIipHlCoydu3ahblz56JTp06ytlatWqF69eqYO3cuiwwiIiIiok+YUqdL5eXlwdPTs0C7h4cHJBJJiYMiIiIiIqLyS6ki44svvsCuXbsKtO/duxedO3cucVBERERERFR+KXW6FADs378fly9fhre3NwAgJCQEUVFR6Nq1KwICAmT9pkyZUvIoiYiIiIg+ETt27MDGjRsRFxeHatWqYerUqahTp47CvqdOncKuXbsQFhaG7OxsVKtWDSNHjkSTJk1kfQ4ePKjwO/mdO3egq6urkhyUKjIePXoEd3d3AMDLly8BACYmJjAxMcGjR49k/TitLRERERFR0R0/fhwBAQGYOXMmateujd27d2PIkCE4duwYbGxsCvS/ceMGGjVqhLFjx8LIyAgHDx6Ev78/9u7dK/u+DgAGBgY4ceKE3FhVFRiAkkXGtm3bhI6DiIiIiOiTt2nTJnz55Zf4+uuvAQDTpk3DpUuXsGvXLowfP75A/2nTpsk9HzduHM6ePYtz587JFRkikQgWFhaqDf4dSt8ng4iIiIiIhJOdnY379++jcePGcu2fffYZgoODi7SMvLw8pKenw9jYWK79zZs3aNGiBZo2bYqhQ4ciNDRUqLAVUvqaDCIiIiIi+rDs7GxkZ2fLteno6EBHR6dA38TEREgkEpiZmcm1m5ubIy4urki/79dff0VGRgY6dOgga3N2dkZAQACqV6+OtLQ0bN26Fb169cJvv/0GR0fH4idVBCwyiIiIiIhUJDAwEKtXr5ZrGzlyJEaNGlXomPeva5ZKpUW61vn333/H6tWrsXbtWrlCxcfHR3YDbQCoXbs2unXrhu3bt2P69OlFzKR4WGQQEREREanI0KFDMXDgQLk2RUcxgPyJlDQ1NREfHy/XLhaLYW5u/sHfc/z4cUybNg0rVqxAo0aNPthXQ0MDXl5eeP78+ccTUBKvySAiIiIiUhEdHR0YGBjIPQorMnR0dODh4YHLly/LtV+5cgW1atUq9Hf8/vvv+P7777FkyRI0b978ozFJpVKEhYWp9EJwHskgIiIiIiojBg4ciEmTJsHT0xO1atXCnj17EBUVhZ49ewIAlixZgpiYGCxatAhAfoExefJkTJ06Fd7e3rJrNypUqABDQ0MAwOrVq+Ht7Q1HR0fZNRkPHjzAzJkzVZYHiwwiIiIiojLi888/R2JiItauXYvY2Fi4urpiw4YNsLW1BQDExcUhKipK1n/Pnj3Izc3FnDlzMGfOHFl7t27dsHDhQgBASkoKZsyYgbi4OBgaGsLd3R3bt29HzZo1VZaHSCqVSlW29GJ4kZyo7hCIVM5p3Ah1h6AW/n16qTsEtbj49111h6AWQYM+zfVNn5aTydkf7/Qf1M2hurpDUOjQi4fqDqFQZfVvpmq8JoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiATFIoOIiIiIiASlpe4A/mUhTVJ3CGoRJzJWdwhq8amub/8+vdQdglqs27FL3SGohbuHp7pDUAtu1z4tn+r6rm2s7gjoXbWNK6s7BHoPj2QQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGgWGQQEREREZGglCoyMjMzkZGRIXseERGBzZs349KlS4IFRkRERERE5ZNSRcbw4cNx+PBhAEBKSgq6d++OTZs2Yfjw4di5c6eQ8RERERERUTmjVJFx//591KlTBwBw8uRJmJmZ4fz58/jxxx+xbds2QQMkIiIiIqLyRenTpfT19QEAly5dQtu2baGhoQEfHx9ERkYKGiAREREREZUvShUZ9vb2OHPmDKKionDp0iV89tlnAACxWAwDAwNBAyQiIiIiovJFqSJjxIgRWLRoEVq2bAlvb2/UqlULAHD58mW4ubkJGiAREREREZUvWsoMat++PXx9fREXF4caNWrI2hs2bIjWrVsLFhwREREREZU/ShUZAGBhYQELCwu5tpo1a5Y4ICIiIiIiKt+UKjKysrKwbds2XL9+HWKxGFKpVO71Q4cOCRIcERERERGVP0oVGVOnTsXly5fRrl071KxZEyKRSOi4iIiIiIionFKqyLhw4QI2bNgAX19foeMhIiIiIqJyTqnZpSwtLWX3ySAiIiIiInqXUkcyJk+ejJ9++gmzZ8+Gra2t0DGViFQqReAv23Hg8B9ITU2Dp0d1TJk4Ai7Ojh8cd+bcJawN3IrXEVGoYmuNkf7foGXzz+T67N1/FFu270e8OAEuTg6YMHYYatfyVGE2RXdk/37s27YDCWIxHJyd4D92LLxq+RTa/87t21i/fAVePH0GM3NzdO/XF52+9JO9fvzwYZw59geeP30KAKhWozoGDvdHDQ8PVadSLKpa37eC72Lr9v0IffAY8fEJWLpoBlo0a6TibIru1cXLeHH6ArKTU6FvbQnXr7vApJrzR8clhT/DraXroG9jhQbTxsnaI6/eQOjWPQX6t1gZAE1tbUFjV1YT1xqY0K4TfB2dYGNsgm6rl+K34JsfHNPUtQaW9OgHD1tbRCYlYfEfRxF48axcHz/fupjT9Wu4WFgiPC4G0w/uxeGPLLe09fCujYF1GsBC3wBPxHH48cIZ3I54pbDvvHad0NWj4CQcT+Lj0HXrz7LnfWvVRQ/v2rA2MkJSRgZOPXqA5ZfOI1siUVkexSX0du3U77/jpznzCoz7/a+L0NHVVUUKSlHVdm3j5t04d+Eynr94DV1dHXh7uWP0yEFwdLBTcUZF86n+HxM67+fhT7F1wwY8fvAAMVHRGDZ2DPx69SyFTIjkKXUkw8vLC1lZWWjdujVq1aqFevXqyT3UafO2fdi+8xC+nzAc2zethJmpKYaNmor09DeFjgm5G4rvpy9Axw4tsWf7WnTs0BKTpy7A3XsPZH1Onr6IxcsCMXhgT+zauga1fDwxcux0REXHlkZaH3Th9GmsX7ocvQcOwLptW+Dl44NpY8YiNjpaYf+oiEhMGzMOXj4+WLdtC3oN+AZrlyzFX+fOyfqE3LqN5u3aYPG6NVi+8WdUtrLClFGjER+r/nzfpar1nZGRCddqTvh+wvDSSKNYom/+jUf7jsCpfWvUnzoWxlWd8feaX5CZkPjBcbkZGbi/eTdMqldV+LpmhQposnCG3KOsFBgAoK+jizuvX2DUjs1F6u9oboFjYybh0uMHqD17KgKOHcaK3t/Az7eurE8Dl2rYPfQ7bL96CT6zpmD71UvYM+w71HNyUVEWxdfe1Q3fN2+Dn69fxtfbN+J2xCus79YDVoZGCvsvPH8azdavkD1abViFpIw3OPX47fu7Yw0PjG3SAuuu/YUvNm/AjFPH0L66G8Y0blFaaX2UKrZrAFBRXx+7jx+Te5SlAgNQ3XbtdvBd9PiqM7ZuXIZ1KwMgkUjg/900ZGRklkZaH/Sp/h9TRd5ZWZmwsrXFoBEjYGpmVlqpEBWgVJExbtw4xMbGYuzYsfjhhx8wZcoUuYe6SKVS7Nx9CIMH9kSrFo1R1cURc2eOR2ZmFv44eb7QcTt3H0b9erUxeEBPODnaYfCAnqhX1wc7dr+dJWv7roPo+kU7+HXpAGcne0wcNwxWlhbYd+D30kjtgw7s3IX2X3RGh65dYO/kBP9xY2FhWRlHDxxU2P/YwYOobGUJ/3FjYe/khA5du6Bd587Yv32nrM+UuXPwxVdfwcXVFfaOjhgzdQqk0jwE3yg7e3hVub4bN6qLEcMGoFWLxqWRSrG8PHsRNo3qwbZxfehbW6J69y7QNTHG6z+vfnBc2I4DsKpbC5WcHRS+LhIBupWM5B5lyYl7Ifjh0D4cun2jSP2HNW+Fl2Ixxu7ehgdRkdj41wVsunQB49t1kvUZ3bo9TofexcLjR/AwOhILjx/B2bD7GN2mg4qyKL7+vvVw8F4IDtwLwdMEMX68cAbRqSno6V1bYf+07CyI36TLHh6W1jCqoIdD90JkfbxtbBEc+RrHH4QiMiUZV148w/EHofCwtC6ttD5KFds1ABCJRDA1N5N7lCWq3K6tWTEfX3RqCxdnR1R3dcasH8YhOjoWoQ8el0ZqH/Sp/h9TRd7V3d3xv+9GoUXbNtDWKTs7iujTo1SRERwcjBUrVuB///sf/Pz80K1bN7mHukRERiNenIiG9d/+89XR0YFvLS+E3A0rdNydu2FyYwCgYQNf2ZicnByEPXhcoE+DerU/uNzSkJOTg8cPHqJ2/fpy7b716yP0zl2FY0Lv3oPv+/0b1MejsDDk5uYqHJOVmYncXAkMjcrOF09Vre+yLC83F6kvI2Dm7irXbubmiqSnzwsdF3klCBnx8XDq2KbQPpKsbFyaNg9/TZmLv9dsRMqrCKHCVosGLtVwOlT+M3Dy3h3UcXCClqYmAKChSzWcvi/f59T9O2hUtVqpxfkhWhoacLe0xpUXT+Xar7x4Bm+bKkVahp+nN669eIao1BRZW3DEa7hXtoKnVX5RUaWSMZo6ueDPZ0+EC74EVLldy8jIQN8vuqJ3p874Yex4PHn4UPgESqA0t2tpaflHRioZGZYw6pL5VP+PlVbeROqi1DUZzs7OyMxU/+HV98WL808XMTU1kWs3MzVBVHTMB8eZKRgj/md5iUkpkEjyCi7XzATiawlChK60lKQk5EkkMDEzlWs3MTVFoliscEyiWAwT0/f6m5lCIpEgOSkJZubmBcZsXLMW5hYWqF2vboHX1EVV67ssy0lLhzQvDzqG8l8KdAwNkZ2cqnDMm9g4PDl8HL7jR0Djny/X79O3rAz3/j1gYGuN3IxMvDr/F24uXo0G08ehYmULhWPKOisjY8Qk35Fri0lJhraWFswNDBGdnASrSsaISUku0MfKyLgUIy2ciV5FaGloQJyeLtcufpMO84ofn3zDXF8fjZ1cMPn4b3LtfzwMhYleRWzr0R8AoK2pid1/38LGGx8+GlZaVLVds3NwxIQZ0+HkUhVv0tNxaM8ejP32f1i/Yxts7e1Vlk9xlNZ2TSqVYsmKQNTy9kBVF8eSBV1Cn+r/sdLKm0hdlDqSMX78eCxcuBDXr19HYmIi0tLS5B6l5fiJc2jUvKvs8W8V//5tO6SQFvteHlKptMByCixXWvzlqooI8nFIpdKCAcsNKJCMwuUAwN6t23Dh1GnM+DFArecul/b6LtMKxCpV0AZI8/Jw79cdcO7UFvqWhRcLlZwdYF3fF4ZVbGBSzRle3/ZDRUsLvDp/WdCwS5sU8jcK/fd98e4NRN+/magIogLj1O39aEQK2hTp6l4TqVmZOPtEfm993Sr2+F/9Rph39gS67/gVo4/sRzPnqhha/7NClqQeQm/X3Lw80bpDB7i4VoNXLR9MXzAftvb2OLx3n6BxF4e6tmsLF6/B4yfPEDD3e2XCVolP4f+YIqrMm0idlDqS8e233wIABgwYINf+75fusLDSOe2kWZMG8PSoIXuek5MNABCLE2Hxznm2CQlJBfYKvcvczATi9y6aTUh8O8bE2AiamhoF9gh9bLmlwcjYGBqamkh4b69HUmJigb0d/zIxMyuwlyQxIRGampowMq4k175v+w7s2rwFP65eBedq6j2FpLTWd1mmbaAPkYYGslPkj1pkp6ZBR8EpD7mZWUh58RqpryLxcM9hAP/8A5NKcXbEJNQaNQSmNQquV5GGBowc7PAmNk4leZSG6JT8IxXvqmxohJzcXIjT83eG/Hs0Q66PkVGBoxvqkpjxBrl5eTB/b8pw04r6EL9JL2TUW908vXE09B5y8/Lk2kc2aoajYfdw4J/rNB7Hx0FPWxszW3+ODdcvq73EUvV27V8aGhqo7u6GiFeKZ+oqDerYri38aS0u/nUNGwN/guUHdj6Ulk/p/9i7Sut9TqQuShUZW7duFToOpejrV4S+fkXZc6lUCnMzE1wLCkaNf2bQycnJwa3guxg9YlChy6np5YZr14PRt9fbKeCuXr8Nby83AIC2tjbcalTDtaBguekArwUFo3nTBkKnVSza2tqoVqM6bgcFoXGL5rL220FBaNi0qcIx7l6euHbpklzb7evX4ermBi2tt2+Jvdu2Y+evmxCwcgVc3d1UEX6xlNb6Lss0tLRgaG+LhLBHqOzjJWtPCHsEC++C0ylrVdBFg+nj5dpe/3kFCQ+foOaQ/tAzV/yPTCqVIu11BPRtys6FwMV1LfwxOr13cXRbj5q4+eIZcv+ZpvVq+GO0dvfC8tN/yPq08fDClSfqvxAWAHLz8hAaE4WG9k44++SRrL2hgxPOhz/6wMj8oxUOJqY4+M4F3/+qoK2FvPeO4Ejy8vd6i0SiAkd3Spsqt2vvkkqlCH/0GE5V1TebWGlu16RSKX78aS3OXbyCn9cugq2NlQoyKr5P6f/Yu0rrfU6kLkqdLvX+lLVlZQpbkUiE3j27yeYCfxL+HDPmLEGFCrro0O7t1IzTZy3GyjW/yp736tEV14JuYdPWvXj2/BU2bd2LoKBg9On59iL2vr38cOi3Ezh85CSePnuJn5YFIjomFl/5dSzVHBX5sncvnPjtCE4cOYqXz55h3dLliI2OQSe//Pg3rlmLRTNny/p39PNDTFQ01i9bjpfPnuHEkaM4ceQovurbW9Zn79Zt2LI+EON/mAZLa2skxIuREC9GxpvCp1Asbapc32/eZODho3A8fBQOIP9izIePwsvElMX2rZoh4nIQIq4EIT0qBg/3/YbMxCTYNskveJ8cPo57m3cByD8iYWBrLffQNjSAhrY2DGytofnPaQNPfz8FcehDvIkTI/VVBEK37UXqq0hUadpQbXm+T19XF952DvC2y58dy8ncAt52DrAzzd/bu8CvBzYP9pf1X3/hLBzMzLGkR1/UsLbBwMbNMKhJcyw5+XZGuJVnTqCthxcmdeiM6lY2mNShM1q7eWLFO0WHum29FYQvvXzQzaMmnE3NMKlZa1gbGmFPyG0AwJjGzbGgfecC4/w8vRESFYEn4oJHoy4+fYIeNWujQ3V32BpVQkN7R4z6rCkuhD8uUHyoiyq2a9t+/gU3r15DVEQEwh89wtJ58xH+6BE6+qlvwpL3qXK7FrB4DY6dOIcFcyZDX18P8eIExIsTkJmZVao5KvKp/h9TRd45OTkIf/QI4Y8eIScnF/FxcQh/9EitR+zo06R02ZuSkoL9+/cjPDwcIpEIVatWxZdffglDQ/XOUjGg39fIyspCwKLVSElNg6dHDaxbuUBuT1F0TCw0NN6eu+hT0x0Bc6dgbeAWrA3cCrsq1lg4fwq8PN8ewm7XphmSk1Ow4dcdiI9PRFVnB6xaNhc21palmp8izdu0QUpyMnZs3IiEeDEcXJwxb9lSWFrn74VOiI9HbMzbObetbW0wf/lSrF+2HEf3H4CpuTmGjx+HJi1byvocPXAAOTk5mPv9VLnf1ffbwej/vyGlk1gRqGp9h4Y9wpDhk2XPlyzfAADo3LE15syYUAqZFc6qjg9y0tPx7NhpZKWkwMDaCj4jBkPvn4sHs5JTPnrPjPflZGQgbMc+ZKWkQqtCBRja2cJ3/HBUciwbF8MCQB1HZ5yf9IPs+dKe/QAAmy9fxKBfA2FlbAx707enlzyPj0PH5YuwtGc/DG/RBpFJiRi9cwsO3no7Be7V8MfoFbgKc7t1x5yuXyM8NgY9A1ch6Fl46SX2EScehaGSnh6GNWgMC30DPBbHwf//7N13WJNXGwbwmy0yZMqQjQICMkRF68a96qqzanHvPap1K59a91as1oUbR93WvRUHVXEi4mSHjQIh5PuDNjQCCpgh9f5dF1ebk3MOz2OSNzzvecfBPZKrRZno6MLio3tm6GpqoWkVFyy4cLrQOQNvXIFYLMbIug1QUVcPSe/f48KL51h59YK80yk2eWzX0tPSsXz+AiQJBCivq4vKTk5YErj+q7s5m7y2a/9ccn3g0ElSv2/29HH4vm1zOWf1ad/q95g88hbEx2Norz6Sx8FBOxActAMe1b2xeP06xSVH3zwVcSnWxR88eIABAwZAS0sLHh4eEIvFCAsLQ2ZmJn7//Xe4lWKD/T45ssRj/gviVQyUHYJSmIqTlR2CUky8G6bsEJRi3Y5dyg5BKVzdCh7C9i049q8VpW/Jt7pd+1a/x75VthW+zvMXX6V8vVeI/Fr/zeStVCsZ8+fPh5+fH+bOnSs5BjAnJwfTpk3DvHnzsGPHDpkGSUREREREZUepzskICwvDgAEDpE4yUldXx4ABAxAW9m3uqSUiIiIiojylKjJ0dXURHR1doD06Oho6Op+/QRQREREREf13larIaN26NaZOnYrjx48jOjoaMTExOHbsGKZNm4Y2bZR/tSUiIiIiIlKeUp2TMWnSJMl/RX9fb15dXR09evTAhAnKvfIOEREREREpV6mKDE1NTUybNg3jx4/H69evIRaLYWtrC21tbVnHR0REREREZcwX3R5SW1sbFSpUgIqKCgsMIiIiIiICUMoiIzc3F2vXrsXmzZvx/u87Z+ro6KBv374YOnQoVFVLdaoHERERERH9B5SqyFi2bBmCg4Mxfvx4VK9eHQBw584drF69GtnZ2Rg7dqxMgyQiIiIiorKjVEXGwYMHERAQgCZNmkjaXFxcYGZmhtmzZ7PIICIiIiL6hpXquKaUlBQ4ODgUaHdwcEBKSsoXB0VERERERGVXqYoMFxcX7Nixo0D7jh074OLi8sVBERERERFR2VXq+2QMGjQI165dg5eXF1RUVBAaGoro6Gj89ttvso6RiIiIiIjKkBIXGUKhECtXrsSmTZtw8eJFvHjxAmKxGM2aNUPPnj1hZmYmjziJiIiIiKiMKHGRoaGhgfDwcBgZGfEEbyIiIiIiKqBU52R06NABwcHBso6FiIiIiIj+A0p1ToZQKMS+fftw7do1uLu7F7jb95QpU2QSHBERERERlT2lKjKePXsGV1dXAEBkZKTUcyoqKl8eFRERERERlVmlKjK2b98u6ziIiIiIiOg/olTnZBARERERERWFRQYREREREckUiwwiIiIiIpIpFhlERERERCRTLDKIiIiIiEimWGQQEREREZFMscggIiIiIiKZYpFBREREREQyxSKDiIiIiIhkikUGERERERHJFIsMIiIiIiKSKRYZREREREQkUywyiIiIiIi+Ijt27ICfnx+qVauGTp064fbt25/sHxISgk6dOqFatWpo0qQJdu3aVaDPqVOn0Lp1a7i7u6N169Y4ffq0vMIHwCKDiIiIiOircfz4ccyfPx9Dhw7FoUOH4OPjg4EDByIqKqrQ/m/evMGgQYPg4+ODQ4cOYciQIfjf//6HU6dOSfqEhoZi7NixaN++Pf744w+0b98eY8aMwb179+SWh4pYLBbLbfYSeJWSpOwQiOSuzaZ1yg6BFOjRwzBlh6AUkUvXKDsEpTAVJys7BFKgeBUDZYegFLYVDJUdQqHeJ0cqO4QilTewL1H/Ll26wNXVFbNnz5a0tWrVCk2bNsX48eML9F+0aBHOnTuHEydOSNpmzJiBp0+fYs+ePQCAMWPGID09HRs3bpT06d+/PypUqIClS5eWNKVi4UoGEREREZGcZGdnIz09XeonOzu7yL4PHz5EvXr1pNrr1q2L0NDQQsf89ddfqFu3rlRb/fr1ERYWBqFQKOnz8Zz169cvck5ZYJFBRERERCQngYGB8PHxkfoJDAwstG9SUhJEIhGMjY2l2k1MTBAfH1/omISEBJiYmEi1GRsbIycnB0lJSZI+H89pbGxc5JyyoC63mYmIiIiIvnGDBw9G3759pdo0NTU/OUZFRUXqsVgsLtD2uf4ft5d0zi/FIoOIiIiISE40NTU/W1T8w9DQEGpqakhISJBqFwgEBVYr/lHYKkdiYiLU1dVhYGAg6fPxnImJiUXOKQs8XIqIiIiI6CugqakJNzc3XL16Var92rVr8Pb2LnSMl5cXrl27JtV25coVuLu7Q0NDQ9Ln4zmvXLlS5JyywCKDiIiIiOgr0bdvXwQHByM4OBgRERGYN28eoqOj0b17dwDAkiVLMGnSJEn/7t27IyoqCvPnz0dERASCg4Oxf/9+9OvXT9KnT58+uHr1KjZs2ICIiAhs2LAB169fx08//SS3PHi4FBERERHRV6J169ZISkrC2rVrERcXBycnJ2zYsAGVKlUCAMTHxyM6OlrS39raGhs2bMD8+fOxY8cOVKxYEVOnTkWLFi0kfapXr46lS5di+fLlWLlyJaytrbFs2TJ4enrKLQ/eJ4NIgXifjG8L75PxbeF9Mr4tvE/G1+W/dJ+M/woeLkVERERERDLFIoOIiIiIiGSKRQYREREREckUiwwiIiIiIpIpFhlERERERCRTLDKIiIiIiEimWGQQEREREZFMscggIiIiIiKZYpFBREREREQyxSKDiIiIiIhkikUGERERERHJFIsMIiIiIiKSKRYZREREREQkUywyiIiIiIhIplhkEBERERGRTLHIICIiIiIimWKRQUREREREMsUig4iIiIiIZIpFBhERERERyRSLDCIiIiIikikWGUREREREJFMsMoiIiIiISKZYZBARERERkUyxyCAiIiIiIplSV3YAsnY4OBj7tu9AokAAWwd7DB07FtW8vYrsf//uXaxfvgKvXkTC2MQEXXv3QtvOnSTPHz90CGeOncDLFy8AAFVcnNF32FC4uLnJO5USkXXeAJCelobN69bj6vkLSEtLg7mlBQaPHo1adb+TczbFJ+u8r5w/j12btyLq7Vvk5OSgkrU1fvixJ5q2bqWAbIqvm2d19K1RG6Y6unguiMevF87g7rs3hfYNaNEWHdw8CrQ/T4hHh22/AQCaVnbGwFrfwdrAEOpqqnidlIStd27iyOMwueZRUrLOGwB6eddEN8/qsNDXR/KHD/jz2RMsv3Ie2SKR3PIoifpOLpjQoi187OxhaWCIjquX4o/Q258c08DJBUu69YZbpUqISk7GohNHEHjxrFSfTj41MadDFziamiEiPhbTDuzFoc/Mq2iy/ny/jHiBbRs2IPzJE8RGx2DI2DHo1KO7AjIpGbFYjMCNQdh/6ATS0tLh7uaMKROHw9HB7pPjzpy7grWB2/D2XTSsKllgxNCf4NeorlSfuLgErFizCVev3UZWVjZsbCph5tSxcK1aRY4ZFY88894bfARbg4KRIEiEo70tJowdgure7nLMpvi+1e9v+u8rVZExfPhwqKioFKvv6tWrS/MrSuXC6dNYv3Q5Rk6aCDdPDxw7eAhTx4zFxj27UNHcvED/6HdRmDpmHFp3aI/Js2fh4b37WLVwESoYGqC+nx8A4N6du2jUohncPDygoamJfduDMGXkaPy2eydMKlZUWG6fIo+8hUIhJo8YBQMjQ0xfMA8mFSsiPjYW2uV1FJ1ekeSRt56+Pnr09YeNnS3UNTRw88pVLJ4bAANDQ9SoU1vRKRaqpVNVTG7UDAFnTyI06i26eHhjfcdu+H7rBsSkpRbov+D8aSy7fF7yWF1VFft798ef4U8kbSmZH7Ah5CoiEwUQikRo6FAFc1u0heB9Bq69ilRIXp8jj7zbuLhhbP3GmP7nUfwV9Q52hkYIaNEWALDw4hn5J1UMOppauP/2FbZcvYj9w8d+tr+diSmOjZmEjZfOo/fGNahb2QlrevVDfHoqDty5BQCo7VgFuwePwoxD+3Dw7m10rF4De4aMQv0FsxESGSHvlIpFHp/vrKxMmFeqhPpNmiBw2XIFZ1R8W7bvQ9DOg5g9Yxxsbazw2++7MGTkLzi0dyN0dMoXOubeg0eYPG0ehg7qA79GdXHuwlX8/Ms8/L5hCaq5uwAAUlPT4D9oHGpW98Tq5QEwMqyAN++ioaf3dWzX5ZX3qdMXsWhZIKZMGg4vDzfsP3gcI8ZOw/7dG2Bhrtzv8W/1+5u+DaU6XEpPTw/Xr19HWFj+Xs6HDx/ixo0b0NXVhZ6enuRHkfbv3IWW37dDqw7tYWNvj6HjxsLUrCKO7D9QaP9jBw6gorkZho4bCxt7e7Tq0B4t2rVDcNBOSZ8pc+fg+x9+gKOTE2zs7DDmlykQi3MReuvr2eMnj7xPHT6CtNRUzFq0EG6enjCzsIC7lxccnZS/t+sf8sjb08cH9Ro3go29PSytrNCxezc4VHZE2L17Csrq8/r41MKBsHvYH3YPLxIF+PXCGcSkpaK7Z/VC+6dnZ0HwPkPy42ZmAf1y2jgYlp/Trbevcfb5M7xIFOBNSjKCQm/hWXwcqleyVlRanyWPvD0tKyE06i2OP3mEqNQUXHsVieNPHsHNzEJRaX3WybB7mH5wHw7evVWs/kMaNcFrgQBjd2/Hk+gobLp8AZuvXMD4v4snABjdtCVOP3qABccP42lMFBYcP4yzjx9idLOvZ8VOHp9vZ1dXDBo1Eo2bN4OGpoaiUikRsViMnbsPon/f7mjSuB4qO9ph7szxyMzMwolT54sct3P3IfjWqo7+/t1hb2eN/v7dUaumF3bsPijps3n7PphXNMXsGePh7uYMS0tz+Nb0hrWVpSJS+yR55h206wA6fN8Cndq3goO9DSaOGwJzM1Ps239UEal90rf6/U3fhlIVGcbGxmjVqhXOnj2L1atXY/Xq1Thz5gxat24NQ0NDzJ8/X/KjKEKhEOFPnqK6r69Uu4+vLx7df1DomEcPwuDzcf/avnj2+DFycnIKHZOVmYmcHBH09PVlE/gXklfe1y9fRtVq7li1cBG6tmyFgd17YtfmLRB9JYeQKOL1FovFCA25hTevXn9y6VqR1FVV4WpmgWuvXki1X3sVCU9Lq2LN0cndEzdeRSK6kL3///C1toOdkRHuvH39RfHKirzyDn33Fq4VzeFunldUWFUwQAN7R1yKfC674BWstmMVnH4k/Rk4FXYfNWztoa6mBgCo41gFpx9K9/nz4X18V/nr+CNEUdvzr9G7qBgkCJJQxze/eNbU1ISPdzXce/C4yHH3HzyWGgMAdWr7SI25eOkGXKs6YeKUAPi17IbuvYfjwKETsk+iFOSVt1AoxOMn4QX61K5V/ZPzKsK3+v1N345SHS61f/9+7Ny5E2p/f2EBgJqaGvz9/dGjRw/8/PPPMguwuFKTk5ErEsHQ2Eiq3dDICEkCQaFjkgQCGBp91N/YCCKRCCnJyTA2MSkwZtOatTAxNUX1WjVlF/wXkFfe0e+i8NftO/Br0QIBy5bh3Zs3WL1wEUQiEXoN6C+3fIpLnq93Rno6erRpB2F2NlTV1DBy0sQCG3VlMdQuD3VVVQgyMqTaBe8zYFKMpXATHR3Us3fEz8f/KPCcrqYWzg0aCQ01NeSKxQg4exLXX7+UVehfRF55n3j6CIba5bG9Wx8AgIaaGnb/dQebbl2XXfAKZq5vgNiU+1Jtsakp0FBXh4muHmJSkmFewQCxqSkF+pjrGygw0qIpanv+NUoQJAEAjIwMpdqNjQwRHRP7yXHGhYwR/D0fALyLisa+A0fRq0cn9PfvjrCHT7Fw6TpoaGqgXeumMsyi5OSVd1JyKkSi3ILzGhtCcCNRFqGX2rf6/U3fjlIVGSKRCC9evICDg4NU+4sXL5CbmyuTwEpLBdLniojFYuBT5498/JxYXOg8ALB323Zc+PM0Fq1bA00trS+OVZZknbc4NxcGhoYY88tkqKmpwamqCwTx8QgO2vFVbaTk8Xprly+PdUHbkPnhA0Jv3ULg8hWwqGQJTx8fmcX9pcQfPVYppK0wHVw9kJaVibPPnxZ4LiM7C52DNqG8hgZq29hhYsOmeJuSjFtfyWoGIPu8a1rZYJDvdwg4exL3Y6JgY2CIyY2aIT4jHYE3r8oqbIUTf/Sv8s85dGJxfvu//x/I+wx8PE7Z5Lk9/1ocP3kOAQtWSh6vXDoHQCGpQFzscyElY8RiqXlyc8VwrVoFI4f1BQC4OFdGROQr7Nt/VOFFhiLzLnReccnnlZdv9fub/vtKVWR06tQJv/zyC169egVPT08AwL1797BhwwZ06tTpM6PlQ9/AAKpqakj8qPpPTkoqUPX/w9DYuMDegqTEJKipqUHfoIJU+76gHdi1ZSt+Xb0KDlW+jkMKAPnlbWRiAnV1NanVKht7OyQKBBAKhdDQUO7xzPJ8vVVVVVHJOu9cBEcnJ7yOfIndW7Z9FUVG0of3yMnNhYmO9N57o/I6ELzPKGJUvo7unjjyKAw5hewMEAN4k5y39+9pfBwcjEwwoNZ3X0WRIa+8R3zXEEceh2H/3+dphCfEQ1tDAzObtsaGm1e/sj+5iycmNW+l4t8q6ulDmJMDQUZ6Xp+UQvro6xdY3VAWeW/PvyYN69eGu5uL5LFQmA0AEAiSYGpiLGlPTEwusDf+30yMDSFITJJqS0ySHmNiYgQHexupPvZ2Njh7XvEFtaLyNjTQh5qaqtSKTnHmVYRv9fubvh2lOifj559/xsCBA7Flyxb06tULvXr1wtatWzFgwABMmjRJ1jEWi4aGBqq4OONuSIhU+92QELh6VCt0jGs194L9b96EU9WqUFfPr7/2bg/Cjk2/Y96K5XByrSr74L+AvPJ28/RA1Nu3UitT716/gZGJyVexgZLn6/0xsTj/C1DZcnJz8Sg2GnVs7KXa69ja417U20+OrWllA1tDIxz414nPn6KiAmj+60tKmeSVdzkNdeR+tEdflJu3F/Rr2ctZUjciwtHUVfrSnM3dPHD7VSRy/j4m+3pEOJq6Sn9OmrlVw7Xn4QqL81MU+flWNh2d8rCxtpT8ONjbwsTYEDdCQiV9hEIh7oQ+gGe1or9/PKpVxY2boVJt12/elRrj5eGKV6+kPy+vX79TyhWWFJW3hoYGqrpUkZoXAG6EhH5yXkX4Vr+/6dtRqiIjOzsbvXr1wuXLl3H79m0cOnQI/fr1g4uLi1TlrGide/bAyT8O4+ThI3gdGYl1S5cjLiYWbTt1BJB3PsXCmbMl/dt06oTY6BisX7YcryMjcfLwEZw8fAQ/9Oop6bN323ZsXR+I8dOnwszCAokJAiQmCPDh/XuF51cUeeTdtnMnpKakYt2SpXj76jVuXrmKXVu24PsfOis8v6LII+9dW7bizs2biH73Dq9fvkTwjp04c/w4mrRsqfD8irLtTgg6V/NCRzcPOBgZY1LDprDQ08eee3cBAGPqNcK8lu0KjOvk7ol70e/wXBBf4LkBNeugjo0drCoYwN7QGH2q10K7qtVw9Cu6T4Y88r744jm6eVRHK2dXVNKvgDo2dhhZtwEuRIQXKD6URUdLC57WtvC0tgUA2JuYwtPaFtZGeXt753Xqhi39h0r6r79wFrbGJljSrRdcLCzRt15D9KvfCEtO5V9JZ+WZk2juVg2TWrWDs7klJrVqh6ZV3bHi9NdxEjAgn8+3UChExLNniHj2DEJhDhLi4xHx7BnevSn8XivKoKKigp7dO2LTlt04d+Eqnke8xIw5S1CunBZatWgs6Tdt1iKsXPO75HGPbh1wI+QONm/bi8iXb7B5216EhITix+4dJX169eiIB2FPsGnLbrx+E4UTp85j/6Hj6PZDwc+Nosk37044+MdJHDp8Ci8iX2PxskDExMbhh05tFJpjYb7V72/6NpRq986wYcPQrFkz9OjRA7m5uejfvz/U1dWRlJSEyZMno2fPnp+fRA4aNWuG1JQU7Ni0CYkJAtg6OiBg2VKYWeRdOSYxIQFxsTGS/haVLPG/5UuxftlyHAneDyMTEwwbP05yrWkAOLJ/P4RCIeZO/kXqd/Ua0B99Bg1UTGKfIY+8K5qZYf7KFVi/fDkG/9gLJqam6NitG7r26a3w/Ioij7wzP3zAqoWLkBAXDy0tLVjb2uLnObPQqFkzRadXpJPPHqOCtjaG1K4HUx1dhAviMfTgHslVk0x0dGGhJ331M11NLTSt4oIFF04XOqe2hiamNWkJMz09ZOXkIDJRgCknDuPkM+VefeXf5JF34I0rEIvFGFm3ASrq6iHp/XtcePEcK69ekHc6xVbDzgHnJ02XPF7aPe8zuOXqRfT7PRDmBgawMco/vORlQjzaLF+Ipd17Y1jjZohKTsLonVsl98gA8lYyegSuwtyOXTGnQxdExMWie+Cqr+YeGYB8Pt+C+HgM7dVH8jg4aAeCg3bAo7o3Fq9fp7jkPsO/dxdkZWVh/sLVSE1Lh7ubC9atnCd1r4iY2Dioquavtnl5uGL+3ClYG7gVawO3wdrKAgv+N0VyrwgAcHN1xpKFM7Bq7WZs2LQDlSzNMXHsELRu6YevgbzybtGsIVJSUrHh9x1ISEhCZQdbrFo2F5YWZgrNrzDf6vc3fRtUxB+f/VcMvr6+CAoKQpUqVbBv3z5s374dhw4dwqlTp7By5UqcOFHyvWGvUpI+34mojGuz6ev5Q4bk79HDr2clSJEil65RdghKYSpOVnYIpEDxKgbKDkEpbCso91yWorxP/jpuHFuY8gb2n+/0H1Sqw6UyMzOh8/cJmFeuXEHz5s2hqqoKLy8vREVFyTRAIiIiIiIqW0pVZNjY2ODMmTOIjo7GlStXULduXQCAQCCArq6uTAMkIiIiIqKypVRFxvDhw7Fw4UL4+fnB09MT3t7eAICrV6+iatWv6+pLRERERESkWKU68btly5bw8fFBfHw8XFzyT66qU6cOmjZV7l1DiYiIiIhIuUp98XBTU1OYmppKtXl4eHxxQEREREREVLaV6nApIiIiIiKiorDIICIiIiIimWKRQUREREREMsUig4iIiIiIZIpFBhERERERyRSLDCIiIiIikikWGUREREREJFMsMoiIiIiISKZYZBARERERkUyxyCAiIiIiIplikUFERERERDLFIoOIiIiIiGSKRQYREREREckUiwwiIiIiIpIpFhlERERERCRTLDKIiIiIiEimWGQQEREREZFMscggIiIiIiKZYpFBREREREQyxSKDiIiIiIhkikUGERERERHJFIsMIiIiIiKSKRYZREREREQkUywyiIiIiIhIplhkEBERERGRTLHIICIiIiIimWKRQUREREREMsUig4iIiIiIZEpFLBaLlR0EALxPjlR2CEQkJ/EqBsoOgRTIftxwZYegFJFL1yg7BFIgU3GyskNQivIG9soOoVBJz/cpO4QiGVbuouwQlIIrGUREREREJFMsMoiIiIiISKZYZBARERERkUyxyCAiIiIiIplikUFERERERDLFIoOIiIiIiGSKRQYREREREckUiwwiIiIiIpIpFhlERERERCRTLDKIiIiIiEimWGQQEREREZFMscggIiIiIiKZYpFBREREREQyxSKDiIiIiIhkikUGERERERHJFIsMIiIiIiKSKRYZREREREQkU+rKDoCIiIiIiEomJSUFAQEBOHfuHADAz88P06dPh76+fqH9hUIhli9fjkuXLuHNmzfQ1dXFd999h/Hjx8PMzEzSr3fv3ggJCZEa27p1ayxbtqxE8bHIICIiIiIqY8aPH4/Y2Fhs3LgRADBjxgxMmjQJ69evL7R/ZmYmHj16hKFDh8LFxQWpqamYN28ehg4digMHDkj17dq1K0aNGiV5XK5cuRLHxyKDiIiIiKgMiYiIwOXLl7F37154enoCAObOnYtu3brhxYsXcHBwKDBGT08PmzdvlmqbNm0aunTpgqioKFhaWkray5UrB1NT0y+KkedkEBERERHJSXZ2NtLT06V+srOzv2jO0NBQ6OnpSQoMAPDy8oKenh5CQ0OLPU96ejpUVFQKHGJ15MgR+Pr6ok2bNvj111+Rnp5e4hi5kkFEREREJCeBgYFYvXq1VNuIESMwcuTIUs+ZkJAAY2PjAu3GxsZISEgo1hxZWVlYvHgx2rZtC11dXUl7u3btYGVlBRMTE4SHh2PJkiV48uRJgVWQz2GRQUREREQkJ4MHD0bfvn2l2jQ1NQvtu2rVqgIFyceCg4OLfE4sFkNFReWzMQmFQowdOxZisRizZs2Seq5r166S/3dycoKtrS06d+6Mhw8fws3N7bNz/4NFBhERERGRnGhqahZZVHzsxx9/ROvWrT/Zx8rKCk+fPoVAICjwXGJiYqErHP8mFAoxZswYvH37Flu3bpVaxSiMm5sbNDQ08OrVK/kVGWKxGFFRUTA2Ni7VWeZERERERFQ4IyMjGBkZfbaft7c30tLScP/+fXh4eAAA7t27h7S0NHh7exc57p8C49WrV9i2bRsMDQ0/+7vCw8MhFApLfCJ4iU78FovFaNGiBWJiYkr0S4iIiIiISDYcHR1Rv359TJs2DX/99Rf++usvTJs2DY0bN5a6slTLli1x+vRpAEBOTg5GjRqFsLAwLF68GCKRCPHx8YiPj5eciP769WusXr0aDx48wNu3b3Hx4kWMHj0arq6uqF69eoliLNFKhqqqKmxtbZGcnFyiX0JERERERLKzePFiBAQEoF+/fgDybsY3Y8YMqT6RkZFIS0sDAMTExEhu3Ne+fXupftu2bYOvry80NDRw48YNbN++HRkZGbCwsEDDhg0xYsQIqKmplSg+FbFYLC7JgAsXLmDDhg2YNWsWnJycSvTLPuV9cqTM5iKir0u8ioGyQyAFsh83XNkhKEXk0jXKDoEUyFScrOwQlKK8gb2yQyhU0vN9yg6hSIaVuyg7BKUo8YnfEydOxIcPH9C+fXtoaGgUODfj49uQExERERHRt6XERcYvv/wijziIiIiIiOg/osRFRseOHeURBxERERER/Ud80X0yMjMzkZOTI9X2uWvtEhERERHRf1uJi4z3799j8eLFOHHiRKFXmXr8+LEs4io1sViMwI1B2H/oBNLS0uHu5owpE4fD0cHuk+POnLuCtYHb8PZdNKwqWWDE0J/g16iu5PlNW3bj3IWrePnqLbS0NOFZzRWjR/SDna21nDMqHnnlnZMjQuDG7Th+8jwEiUkwMTZCuzbNMLBfD6iqlugKyHIhr7wzMt5jbeA2nLt4DUlJyXB2csSkcUPg5uos54yK51t9nx8ODsa+7TuQKBDA1sEeQ8eORTVvryL73797F+uXr8CrF5EwNjFB19690LZzJ8nzfx49isVzAgqMO3r5IjS1tOSRQqnIOu+XES+wbcMGhD95gtjoGAwZOwadenRXQCbFV9/JBRNatIWPnT0sDQzRcfVS/BF6+5NjGji5YEm33nCrVAlRyclYdOIIAi+elerTyacm5nToAkdTM0TEx2Lagb049Jl5FU3WrzcAHNi1G0f3H0BcbCz0K1RAfT8/9B8+9D/9PgeA9LQ0bF63HlfPX0BaWhrMLS0wePRo1Kr7nZyzKb7SbM8jXrzE2sDtePw0HNHRcZgwZjB+7CF9pMne/UcRfOAooqLiAAAODjYY1P9H1PuupjzTIZIo8V+JixYtwo0bNzBz5kxoamoiICAAI0eORMWKFfHrr7/KI8YS2bJ9H4J2HsTkCcMQtHkljI2MMGTkL8jIeF/kmHsPHmHytHlo08oPe4LWok0rP/z8yzw8CHsi6XM39AG6/dAO2zYtw7qV8yESiTB01FR8+JCpiLQ+S155b9m+F8EHjmPyhGE4sHsDRo/oj207grF772FFpPVZ8sp7zrzluBFyFwGzJmLvjvWo41sdQ0ZMQVxcgiLS+qxv8X1+4fRprF+6HD37+mPd9q2o5uWFqWPGIq6I+/ZEv4vC1DHjUM3LC+u2b0UP/5+wdslSXP778n3/KK+jg93Hj0n9fE1/eMkj76ysTJhXqoR+w4fD6DN3hlUWHU0t3H/7CiN3bClWfzsTUxwbMwlXwp+g+uxfMP/YIazo+RM6+eT/QVXbsQp2Dx6FoOtX4DVrCoKuX8GeIaNQy95RTlmUnDxe77MnT2LTmrXoNaA/Nu7ZhXHTpuLimTPYtGadotL6LHnkLRQKMXnEKMRGR2P6gnn4fd8ejP1lCoxLeEMxeSvN9jwzMwtWlcwxalg/mBgXfjM1s4omGDmsH3ZsXYkdW1eiVg0vjJ04GxEvXsopEyJpJS4yzp8/j5kzZ6Jly5ZQU1NDjRo1MGzYMIwdOxZHjhyRR4zFJhaLsXP3QfTv2x1NGtdDZUc7zJ05HpmZWThx6nyR43buPgTfWtXR37877O2s0d+/O2rV9MKO3Qclfdas+B++b9scjg52cHZywKzp4xATE4dHT8IVkdonyTPv+w8eo2GD2qhfzxeWluZo1qQ+ateqjkePnykitU+SV96ZmVk4e/4KxozoDx/varCxtsSQgb1haWmOfQeOKiq9In2r7/P9O3eh5fft0KpDe9jY22PouLEwNauII/sPFNr/2IEDqGhuhqHjxsLG3h6tOrRHi3btEBy0U6qfiooKjEyMpX6+JvLI29nVFYNGjUTj5s2goamhqFRK5GTYPUw/uA8H794qVv8hjZrgtUCAsbu340l0FDZdvoDNVy5gfIu2kj6jm7bE6UcPsOD4YTyNicKC44dx9vFDjG7WSk5ZlJw8Xu/HD8Lg5uEBv5YtYG5piRq1fdG4eTOEK/nIg3+TR96nDh9BWmoqZi1aCDdPT5hZWMDdywuOTlUUldZnlXZ77ubqjLGjBqJl80ZFfoYb1q+N+nVrwdbGCrY2Vhgx1B/ly5fD/X/tWCKSpxIXGSkpKbCysgKQd/5FSkoKAMDHxwe3byt3yfldVAwSBEmo45t/R0JNTU34eFfDvQdFb0zvP3gsNQYA6tT2+eSY9PS8PQwV9PW+MOovJ8+8vTzdEHL7L7x6/RYA8PTZC/x17yHqfgXLrfLKWyQSQSTKhaaWplQfLS1NhN57KMMMSudbfJ8LhUKEP3mK6r6+Uu0+vr54dP9BoWMePQiDz8f9a/vi2ePHUueSffjwAb2+74Cebdth+tjxeP70qewTKCV55v1fU9uxCk4/kv43ORV2HzVs7aH+9w2k6jhWwemH0n3+fHgf31X+Ov7olNfr7ebpifAnT/DkYd72K/rdO4Rcu4Za9eoWmE8Z5JX39cuXUbWaO1YtXISuLVthYPee2LV5C0QikXwSKYXSbs9LSiQS4eSfF/DhQxY83KvKbF6iTynxORlWVlZ49+4dKlWqhMqVK+PEiRPw8PDA+fPnoaen3D9EEgRJAAAjI+mlQ2MjQ0THxH5ynHEhYwR/z/cxsViMJSsC4e3phsqOdl8WtAzIM+++fboiPT0DHbsOhJqqKkS5uRg+5Ce0atFYhhmUjrzy1tEpD49qVfHb7zthb2cDYyMDnPzzAsIePoWNtaWMsyi5b/F9npqcjFyRCIbGRlLthkZGSBIICh2TJBDA0Oij/sZGEIlESElOhrGJCaxt7TBhxjTYO1bG+4wMHNyzB2MHDML6HdtRycZGbvkUl7zy/i8y1zdAbMp9qbbY1BRoqKvDRFcPMSnJMK9ggNjUlAJ9zPUNFBhp0eT1ejdu3gwpSUkYN3AwxGIxRCIR2nbuhO4/9ZFbLiUhr7yj30Xhr9t34NeiBQKWLcO7N2+weuEiiEQi9BrQX275lERpt+fFFf48Ej8NGIvs7Gxoa2tjya/T4ehg+8XzEhVHiVcyOnfujCdP8pbaBg0ahJ07d8Ld3R3z589H//6K/dAeP3kO3zXqIPn5Z++Fiop0PzHEUPm48TPEYnGBef6xYNEahD+PxPy5k0sT9hdTZN6nTl/E8ZPnMG/Oz9i5bTXmzBiP7Tv24/Cx01+aRokpMu+AWRMhFgMt2v4I3/rtsGvvH2jVohFUVdW+NI0S+1bf54VRgXSwYrG44D+E1ICP/5HEUvNUreaOpq1awdGpCqp5e2HavP+hko0NDu39uu4cK+u8/6vEEEs9/ufzIBbnt//7/4G8f5OPxymbrF/ve3fuYNfmLRg5aSLWbt+KGb8uwM0rVxG06XeZxv2lZJ23ODcXBoaGGPPLZDhVdUHj5s3Qo68/jhZxCJYiyHN7Xhg7Wyvs3r4WWzctR5dObTBjzhJEvHj1xfMSFUeJVzL8/f0l/1+7dm2cOHECYWFhsLGxgYuLiyxj+6yG9WvD3S3/dwqF2QAAgSAJpv86rjoxMbnAXoJ/MzE2hCBRem9uYlLhYxYsXouLl29gU+BimJkp5+QxRea9fNVG9O3TFS2bNwIAVKlsj+iYOGzeugfft2kmi3SKTZF5W1tZYtP6RfjwIRPpGRkwNTHGz1PnoZKlmazSKbZv9X3+b/oGBlBVU0PiR3s1k5OSCuzN/IehsXGBvaBJiUlQU1ODvkGFQseoqqrC2bUq3r15I5vAv5Ci8v4viEnNW6n4t4p6+hDm5ECQkZ7XJ6WQPvr6BVY3lEVer/fW9RvQpHUrtOrQHgBgX7kyMjM/YMW8BejZ11/pVwqUV95GJiZQV1eDmlr+ziEbezskCgQQCoXQ0FD8+Uiy2p4Xl4aGhmQF3q2qEx4+foZdew5h2pTRXzw30ed80ZYlKysLlpaWaN68ucILDCDvsBYba0vJj4O9LUyMDXEjJFTSRygU4k7oA3hWK/oYRI9qVXHjZqhU2/Wbd6XGiMViLFi0BucuXEXgml9RydJc9gkVkyLzzszMgspHX0CqqqrIzVX8nj9F5v0Pbe1yMDUxRmpqGq7duINGDerILqFi+lbf5/+moaGBKi7OuBsSItV+NyQErh7VCh3jWs29YP+bN+FUtSrU1QvfvyIWixHxLPyrOaRIUXn/F9yICEdTV3eptuZuHrj9KhI5fx+Dfz0iHE1dpf/dmrlVw7Xnyr+wASC/1zszKxOqH+0VV1NVgxgFV3aUQV55u3l6IOrtW+Tm5kr6vHv9BkYmJkopMADZbc9LTQxkC4Wyn5eoECUuMkQiEdasWYP69evD29sbb/7e47d8+XLs26fcQwxUVFTQs3tHybX+n0e8xIw5S1CunJbUOQTTZi3CyjX5y8Q9unXAjZA72LxtLyJfvsHmbXsREhKKH7vnX3N6/qI1OPb3YUM6OtpIECQiQZCIzMwsheZYGHnm3aC+LzZt3o3LV24iKioG5y5cRdCug/BrpPxrjMsz72s3buPq9dt4FxWDGzfvYuCwn2Fna4Xv2zVXaI6F+Vbf55179sDJPw7j5OEjeB0ZiXVLlyMuJhZtO+XFv2nNWiycOVvSv02nToiNjsH6ZcvxOjISJw8fwcnDR/BDr56SPtt/24jb128g+t07RDx7hqUB/0PEs2do06ljgd+vLPLIWygUIuLZM0Q8ewahMAcJ8fGIePbsq1nBAQAdLS14WtvC0zrv+HF7E1N4WtvC2ihvb++8Tt2wpf9QSf/1F87C1tgES7r1gouFJfrWa4h+9Rthyan8K8KtPHMSzd2qYVKrdnA2t8SkVu3QtKo7Vpw+odjkPkEer3ftevVw9MABnP/zNKLfReHOzZvYGrgBderXk9rLr0zyyLtt505ITUnFuiVL8fbVa9y8chW7tmzB9z90Vnh+RSnt9lwoFOLpswg8fRYBoTAHcfEJePosAq/fREn6rFq7GXdDwxAVFYPw55FYvW4Lbt+9j9Yt/BSaI327Srxba926dTh06BAmTpyI6dOnS9qdnJywdetWdOnSRaYBlpR/7y7IysrC/IWrkZqWDnc3F6xbOQ86OuUlfWJi46Cqmr9Xx8vDFfPnTsHawK1YG7gN1lYWWPC/Kajmnr86s29/3hfVwKGTpH7f7Onj8H1b5f/hKa+8fx4/DGsDt2HeojVISkqGqYkxfujYCoP6/6jQ/Ioir7zT099j1drNiI1LQAV9XTRpXA/Dh/pD4yvZE/wtvs8bNWuG1JQU7Ni0CYkJAtg6OiBg2VKYWVgAABITEhAXm39NfYtKlvjf8qVYv2w5jgTvh5GJCYaNH4f6fvlfsOlp6Vg+fwGSBAKU19VFZScnLAlcDxc3N4XnVxR55C2Ij8fQXvkn/QYH7UBw0A54VPfG4vVfx70Tatg54Pyk/O+Ypd17AwC2XL2Ifr8HwtzAADZG+YeXvEyIR5vlC7G0e28Ma9wMUclJGL1zKw7cyb8E7vWIcPQIXIW5HbtiTocuiIiLRffAVQiJjFBcYp8hj9f7x359oaKigq3rA5EQH48KBgaoXb8e+g4dovD8iiKPvCuamWH+yhVYv3w5Bv/YCyampujYrRu69umt8Pw+pTTb8/h4Abr3Hi55vG3HfmzbsR8+1ath47pFAABBYhKmzV6IhIQk6OqWR5XK9lizPAC1P7rKIJG8qIhLuFbarFkzzJkzB3Xq1IG3tzcOHz4Ma2trREREoHv37rh1q3jXNP/Y++TIUo0joq9fvIqBskMgBbIfN/zznf6DIpeuUXYIpECm4mRlh6AU5Q3slR1CoZKef10X7Pg3w8rK3QGvLCU+XCo2NhY2hVzaUSwW/6evwU5ERERERMVT4iKjcuXKhd507+TJk6halTd4ISIiIiL61pX4APMRI0Zg0qRJiI2NhVgsxp9//onIyEgcOnQIgYGB8oiRiIiIiIjKkBKvZPj5+WHZsmW4dOkSVFRUsHLlSkRERGD9+vWoW7euPGIkIiIiIqIypMQrGZMnT0bnzp0RFBQkj3iIiIiIiKiMK/FKRkZGBvr164fmzZtj/fr1iI2NlUdcRERERERURpW4yFi1ahUuXbqEH3/8ESdPnoSfnx8GDBiAEydOQMi7SBIRERERffNKXGQAgKGhIX766SccOnQI+/btg62tLX7++WfUr18f8+bNw8uXL2UcJhERERERlRWlKjL+ERcXhytXruDKlStQU1NDw4YN8fz5c7Rp0wZbtmyRUYhERERERFSWlPjEb6FQiHPnzuHAgQO4evUqnJyc4O/vj3bt2kFXVxcAcOzYMcyaNQv+/v6yjpeIiIiIiL5yJS4y6tWrB7FYjDZt2mDfvn2F3oCvXr160NPTk0mARERERERUtpS4yJgyZQpatWoFLS2tIvtUqFAB586d+6LAiIiIiIiobCpxkdGhQwc5hEFERERERP8VX3TiNxERERER0cdYZBARERERkUyxyCAiIiIiIplikUFERERERDLFIoOIiIiIiGSKRQYREREREckUiwwiIiIiIpIpFhlERERERCRTLDKIiIiIiEimWGQQEREREZFMscggIiIiIiKZYpFBREREREQyxSKDiIiIiIhkikUGERERERHJFIsMIiIiIiKSKRYZREREREQkUywyiIiIiIhIplhkEBERERGRTLHIICIiIiIimWKRQUREREREMqUiFovFyg4CAN4nRyo7BKVY+ZO7skNQih7b3io7BKUwFScrOwQiuYtXMVB2CEphP264skNQCsGUjsoOQSmub5qm7BCUovX8p8oOoVCPDw1SdghFqtphg7JDUAquZBARERERkUyxyCAiIiIiIplikUFERERERDLFIoOIiIiIiGSKRQYREREREckUiwwiIiIiIpIpFhlERERERCRTLDKIiIiIiEimWGQQEREREZFMscggIiIiIiKZYpFBREREREQyxSKDiIiIiIhkikUGERERERHJFIsMIiIiIiKSKRYZREREREQkU6UqMq5evYoPHz7IOhYiIiIiIvoPUC/NoJEjRyI7Oxvu7u6oWbMmatWqherVq0NHR0fW8RERERERURlTqiLj1q1buH//Pm7duoWbN29ix44dyM7OhqurK2rVqoUJEybIOk4iIiIiIiojSnW4lJqaGry9vTFo0CBs2rQJu3fvRtu2bfHw4UNs2rRJ1jESEREREVEZUqqVjIiICISEhCAkJAS3bt2CSCSCj48Pfv75Z9SqVUvWMRIRERERURlSqiKjTZs2MDIywk8//YRhw4ahSpUqso6LiIiIiIjKqFIVGb1798bt27exatUqnDlzBrVq1UKtWrVQo0YNnvxNRERERPSNK1WRMXXqVABAamoqbt++jVu3bmHlypV49uwZqlatir1798o0SCIiIiIiKju+6GZ8ubm5EIlEEAqFyM7ORk5ODt69eyer2IiIiIiIqAwq1UpGQEAAQkJC8Pz5c1SoUAE1a9ZEt27dUKtWLTg5Ock6RiIiIiIiKkNKVWTExcWha9euLCqIiIiIiKiAUhUZK1eulHUcRERERET0H1GqIgMAXr9+ja1btyIiIgIqKipwdHREnz59YGNjI8v4iIiIiIiojCnWid+PHj2CSCSSPL58+TJat26N+/fvw9nZGVWqVMG9e/fQpk0bXL16VW7BEhERERHR169YKxkhISFYsmQJVq1ahfLly2PJkiXw9/fHhAkTpPotXrwYixcvRt26deUSLBERERERff2KtZLh7++PWrVqoXfv3gCAiIgI/PDDDwX6de7cGc+fP5dthEREREREVKYU+5yMwYMHo2bNmgAAIyMjPH78GHZ2dlJ9Hj9+DGNjY5kGSEREREREZUuJTvyuXr06AKBLly6YMWMG3rx5I2m7e/cufvvtN/Tt21f2UZaAWCxG4MYg7D90Amlp6XB3c8aUicPh6GD3yXFnzl3B2sBtePsuGlaVLDBi6E/wa5R/2FdGxnusDdyGcxevISkpGc5Ojpg0bgjcXJ3lnFHx1ev+Czxb9EM5HQNEP7uFPwPHIeHN4yL7q6qpo84PE+De+EfoGVsi8V04zm+djsjQ05I+mtq6qN9zBpxqt0P5CqaIjbyHM79NRMzzu4pI6bMOBwdj3/YdSBQIYOtgj6Fjx6Kat1eR/e/fvYv1y1fg1YtIGJuYoGvvXmjbuZPk+T+PHsXiOQEFxh29fBGaWlrySKFUvtX3ubzy3rRlN85duIqXr95CS0sTntVcMXpEP9jZWss5o+KRV94AEBeXgBVrNuHqtdvIysqGjU0lzJw6Fq5Vq8gxo+KR9ecbAA7s2o2j+w8gLjYW+hUqoL6fH/oPH/rVfL7rO7lgQou28LGzh6WBITquXoo/Qm9/ckwDJxcs6dYbbpUqISo5GYtOHEHgxbNSfTr51MScDl3gaGqGiPhYTDuwF4c+M6+iicVibNx5Dn+cvI209A9wdbbCxKHt4GBrVuSY81cfYuvei3gbnYicHBGsLY3Rs1NdtPLzlvTJEYmwccc5nLpwD4lJ6TA21EObpt7o270RVFW/6J7EMlOlyQhY1+oGDW19JL+5h4d/zEF6XNFHhvgO3AZjB98C7XFPLuD21sGSxza1e8Khfn9o6ZkiPS4cj47OQ9LLO3LJgehjpfp0DR8+HMOHD0dQUBB69eqFXr16ISgoCCNGjMDQoUNlHWOJbNm+D0E7D2LyhGEI2rwSxkZGGDLyF2RkvC9yzL0HjzB52jy0aeWHPUFr0aaVH37+ZR4ehD2R9JkzbzluhNxFwKyJ2LtjPer4VseQEVMQF5egiLQ+y7fTONRsPxKnA8dh64QGSE+ORbc5R6CprVvkmAY/zoRXi/44/dsE/DbCB6EnN6LTlF0ws/eU9Gk1Yg3svBrj6LIB2DSqFl6GnkX3OUeha2ShiLQ+6cLp01i/dDl69vXHuu1bUc3LC1PHjEVcTEyh/aPfRWHqmHGo5uWFddu3oof/T1i7ZCkunzsn1a+8jg52Hz8m9fO1/AHyj2/1fS6vvO+GPkC3H9ph26ZlWLdyPkQiEYaOmooPHzIVkdZnySvv1NQ0+A8aB3U1daxeHoD9uwMxbvRA6OnpKCKtT5LH5/vsyZPYtGYteg3oj417dmHctKm4eOYMNq1Zp6i0PktHUwv3377CyB1bitXfzsQUx8ZMwpXwJ6g++xfMP3YIK3r+hE4+NSV9ajtWwe7BoxB0/Qq8Zk1B0PUr2DNkFGrZO8opi9LZHnwZuw5ew/ghbfH7sqEwNtTDqGlbkPE+q8gx+nra8O/WCL8tHoSgNSPQtll1BCw7iBt3wvPn3XcZB0/cwoQh7bBr/WiM6NcCOw5cwb4jNxSR1mc5NBgIu3p98fDwHFxd8wOy0hJQq/9mqGkW/Tm8GzQSZ/5XV/JzaVkb5IpyEP3gpKSPRbVWcG0zBc/Pr8OVVR2Q+PIOavr/hnIVlP/9Td+GUhUZaWlp8Pf3x6VLl3D79m3cvn0bly5dwk8//YTXr1/LOsZiE4vF2Ln7IPr37Y4mjeuhsqMd5s4cj8zMLJw4db7IcTt3H4Jvrero798d9nbW6O/fHbVqemHH7oMAgMzMLJw9fwVjRvSHj3c12FhbYsjA3rC0NMe+A0cVld4n1Ww3HNf2LcKzG4eR8PoRji0fBA1Nbbg26FrkGLfGPXA9eBFe3DmFlNiXCD25EZGhZ1CzwygAgLpmOTjX6YALW6bhzaOrSI55gSu75yEl9hW8Ww1UVGpF2r9zF1p+3w6tOrSHjb09ho4bC1Ozijiy/0Ch/Y8dOICK5mYYOm4sbOzt0apDe7Ro1w7BQTul+qmoqMDIxFjq52vyrb7P5ZU3AKxZ8T9837Y5HB3s4OzkgFnTxyEmJg6PnoQXOa+iyDPvzdv3wbyiKWbPGA93N2dYWprDt6Y3rK0sFZHaJ8nj8/34QRjcPDzg17IFzC0tUaO2Lxo3b4bwx0Wv+CraybB7mH5wHw7evVWs/kMaNcFrgQBjd2/Hk+gobLp8AZuvXMD4Fm0lfUY3bYnTjx5gwfHDeBoThQXHD+Ps44cY3ayVnLIoObFYjD1/XIN/t4ZoXNcNjnZmmDGuMzKzhPjz4r0ix/l4OKDRd66wt6kIKwtjdGv/HRztzXDv0StJn7Anb9DA1wV1aznD0swQfvXcUcu7Mh6Hv1NEap9lV7cPIs6vR+zD00iPDcf9fT9DTaMcLL3aFjlG+CEF2ekJkh+TKnUhEmYi5l9Fhn39vnhzez/e3g5GRvwLPD46D5kpMbCt3UMRaRGVrsgYNGgQsrLy9izo6upCVzdvb/mLFy8kJ4crw7uoGCQIklDHt7qkTVNTEz7e1XDvQdFfIvcfPJYaAwB1avtIxohEIohEudDU0pTqo6WlidB7D2WYQelUMLODrpE5XobmL4+LcrLx5uEVVHKpXeQ4dXVN5GRL76nNyc6EddU6APIOp1JVU0eOMOujPh8kfZRFKBQi/MlTVPeVXi728fXFo/sPCh3z6EEYfD7uX9sXzx4/Rk5OjqTtw4cP6PV9B/Rs2w7Tx47H86dPZZ/AF/hW3+fyyrsw6el5KwQV9PW+MOovJ8+8L166AdeqTpg4JQB+Lbuhe+/hOHDohOyTKCF5fb7dPD0R/uQJnjzMez9Hv3uHkGvXUKte2b0iYm3HKjj9SPrf5FTYfdSwtYe6mhoAoI5jFZx+KN3nz4f38V1l5R8S94+omCQIktLhW72ypE1TQx3e7nZ48Lh4Oy/FYjFu/RWB128T4OVuJ2n3dLXBrXsv8Ppd3ops+Ito3Hv0Ct/VcJJpDqWhbWiFcvoVkRB+RdKWKxIiMfIWDG29PzFSmnWNzoi+fwwi4QcAgIqaBvQt3aTmBYD48KswsCn+vERfolQ349PX18ewYcMQGBgIdfW8KSIiIvDTTz+hZcuWMg2wJBIESQAAIyNDqXZjI0NEx8R+cpxxIWMEf8+no1MeHtWq4rffd8LezgbGRgY4+ecFhD18Chtr5e/x0zXMO141I0U6x4zkOOhXLPrmiC9Cz6Jm+5F48/AqkmJewM6jMar4toGKat4XU/aHdLx9cgN1u/4MwdsnyEiOg2v9rrB0qonEaOVeRSw1ORm5IhEMjY2k2g2NjJAkEBQ6JkkggKHRR/2NjSASiZCSnAxjExNY29phwoxpsHesjPcZGTi4Zw/GDhiE9Tu2o9JXcqPJb/V9Lq+8PyYWi7FkRSC8Pd1Q2dHuy4KWAXnm/S4qGvsOHEWvHp3Q3787wh4+xcKl66ChqYF2rZvKMIuSkdfnu3HzZkhJSsK4gYMhFoshEonQtnMndP+pj9xykTdzfQPEptyXaotNTYGGujpMdPUQk5IM8woGiE1NKdDHXN9AgZF+miApHQBgZCB9iK+RgS5i4pM/OTY9IxPt+ixEtjAHaqqqmDisHXy984uV3l0aIP19FroNXgFVVRXk5ooxpE9TNG/k+YlZFUNLzxQAkJUu/b7OSk+AtkHxtrsVrKpBz9wZ9/dPlbRpljeEqpp6gXmz0xMkv5NI3kpVZKxatQp9+/bF+PHjsXz5coSHh8Pf3x/t2rXDlClTZB1jkY6fPIeABSslj1cunQMAUFGR7ieGGCofN36GWCyWmidg1kTMCliGFm1/hJqaKlycK6NVi0Z4/CSi1PGXlmvDbmg5ND/vfXM7AwDE4o86qqgU0pjvzMaJaDV8NQauCQUgRlLMC9w/ux0eTfJXo44uG4DWI9dhxOYI5IpyEBPxFx5e2gtzR+VvnAFABdKvq1gsLvgGkBrw8ZtDLDVP1WruqFrNXfK0m6cHhvX+CYf27sPwCeNlE3QJfavvc0Xm/W8LFq1B+PNIbA5cUqI5ZUWReefmiuFatQpGDsu7YIeLc2VERL7Cvv1HlVpk/EPWn+97d+5g1+YtGDlpIlzc3fDuzVusW7oMQSa/o1f/fjKNXZHEkN7O//O+EP9r+y/+6LtABSoFxinSyfN/4dfVhyWPl8zK+975+D0thrjA++Bj5bU1sW3VcHz4kI1b9yKwYuMJWJobwsfDAQBw5tIDnDz/F+ZM7AJ724oIfxGNZRuOw8RID22aVv/k3LJm6dUO7h1mSx7nn6Rd4Au82HNa1/gBaTFPkfK2sFW+Qub9xN8FVLakpKQgICAA5/4+98zPzw/Tp0+Hvr5+kWMmT56MgwcPSrV5enpi7969ksfZ2dn49ddfcfToUWRlZaF27dqYNWsWzM3NSxRfqYoMLS0tBAYGonfv3hg9ejRu376N9u3b4+effy7NdKXWsH5tuLu5SB4LhdkAAIEgCab/Oo4+MTG5wF7AfzMxNoQgUXqvZmKS9BhrK0tsWr8IHz5kIj0jA6Ymxvh56jxUsiz6qhfy8jzkGH5/mn+8rrpG3knJugZmyEjKPylSp4IpMpLjipznQ2oCDszvDjUNLWjrGSE9MRqN+sxFcuxLSZ/kmEjsnNoSGlrloVleHxlJMWg/cSuSY18VOa8i6BsYQFVNDYkf7dVMTkoqsDfzH4bGxgX2giYlJkFNTQ36BhUKHaOqqgpn16p49+aNbAIvhW/1fa7IvP+xYPFaXLx8A5sCF8PMTDl7+xSZt4mJERzspVfo7O1scPb81S/K4UvJ6/O9df0GNGndCq06tAcA2FeujMzMD1gxbwF69vX/aq40VBIxqXkrFf9WUU8fwpwcCDLyVgf+Wc2Q6qOvX2B1Q5Hq+1aFm3P+1duEwrxD2gRJaTAxyj9MMSk5A0aGn74QgaqqKqwt8z4bTo4WePkmHtv2XZIUGat+P4k+XRqgWUMPAEBlO3NExyVj275LCi8yYh+dQ/Kb/HNMVNXyDk/V0jVBVlq8pF1L1xhZ6Z+/4IaqRjlYeLZB+OmVUu3Z75OQK8qBlq6JVLtmMeelsmH8+PGIjY3Fxo0bAQAzZszApEmTsH79+k+Oq1+/PubPny95rKGhIfX8//73P5w/fx7Lli2DgYEBFixYgMGDB+PAgQNQ+/swzOIo9hY1PT1d6kdFRQXLli3D/fv30bx5cwwfPlzynKLo6JSHjbWl5MfB3hYmxoa4ERIq6SMUCnEn9AE8q1Utch6PalVx42aoVNv1m3cLHaOtXQ6mJsZITU3DtRt30KiB4s9NyP6QjuSYF5KfhDePkZ4YAzsvP0kfVXUNWLvVw7snn796hkiYhfTEaKiqqcP5u/YIv3msQB9h1ntkJMVAS8cA9l5NEX5TuScCa2hooIqLM+6GhEi13w0JgatHtULHuFZzL9j/5k04Va0qOezvY2KxGBHPwmFsYlLo84rwrb7PFZm3WCzGgkVrcO7CVQSu+RWVLEu2t0aWFJm3l4crXr16K9Xn9et3sDCvKKNsSkden+/MrEyofrSnXE1VDWIU3NNfVtyICEdTV3eptuZuHrj9KhI5IhEA4HpEOJq6Sv+7NXOrhmvPlXdhA53yWrC2NJb82NtUhLGhLkJC81dNhcIchIa9RLWqJTxUVSxGtjD/PLvMLGGBFRI1VVXk5ir+NRdlZ+C94LXkJz3uOTJT42BSJf+8IBU1DRjZ10TSq9BPzJTHolorqKpp4t1fh6XaxSIhUqMeSs0LACaVv0Py68/PS1+/iIgIXL58GQEBAfD29oa3tzfmzp2L8+fP48WLF58cq6mpCVNTU8mPgYGB5Lm0tDTs378fkydPxnfffQdXV1csWrQIz549w7Vr10oUY7FXMmrUqFHo0rxYLMbu3buxZ8+ev5fiVfBYSVfqUFFRQc/uHbFpy+6/v6ArYdOW3ShXTgutWjSW9Js2axEqmhpj1PC85fEe3TpgwJAJ2LxtLxo1qIMLl64jJCQUv2/IP1zi2o3bEIsBO1srvHkThWWrNsLO1grft2uu8DwLc+vIGtT5YQKSop8jMSoCdX6YCGH2Bzy6lL/81XbMb0gTROHi9pkAAAunGtAzskRs5H3oGVuiXvepUFFRxc2DyyRj7L2bAlBB4rtnMLRwRGP//yExKhwPzm5XdIoFdO7ZAwtnzoZT1apwreaOYwf/QFxMLNp26ggA2LRmLQRx8Zg0Oy/fNp064Y99wVi/bDlad2iPRw/CcPLwEUwJmCOZc/tvG1HV3R2VbKzxPiMDh/bsRcSzZxgxaYJScizMt/o+l2fe8xetwYlT57Fs0Uzo6GgjQZAIANDV0UG5csq9fLE88+7VoyP8B4zDpi270axJAzx89BT7Dx3H9CmjFZ7nx+Tx+a5drx4O7NoFR2dnuLi5IertG2wN3IA69euVaO+cPOloaaFyxfwi197EFJ7WtkjMSMebRAHmdeoGS0Mj+G/Ku+zu+gtnMdyvOZZ064XfLp1DHccq6Fe/EXpuWCWZY+WZk7j48wxMatUOf4TeQXtvHzSt6o76C2YX+P3KoqKigm7tv8PWvRclhcfWvRdRTksDzRvmH547e0kwTI31Mcw/b5u0de9FuFSpBCtzIwhzRLh2+xmOn/sLk4Z/LxlTr5YLtuy5CHNTA9jbVsSziGjsOngVbZv5KDzPwry8ug2OjQYjI+ElMgSvULnRYIiEmYj6K39nnkeXX5GVGounp5ZKjbWu8QNiH52B8H1ygXkjL2+GZ9eFSHkbhqTXobCp1Q3aBhZ4dXO3vFOij2RnZyM7O1uqTVNTE5qamkWM+LzQ0FDo6enB0zP/8+Hl5QU9PT2EhobCwcGhyLEhISGoU6cO9PX1UbNmTYwdO1ZyM+2wsDAIhULUrZtfoJqZmaFKlSoIDQ1F/fr1ix1jsYuMbdu2FXtSZfLv3QVZWVmYv3A1UtPS4e7mgnUr50FHp7ykT0xsHFRV8wsmLw9XzJ87BWsDt2Jt4DZYW1lgwf+moJp7/iEL6envsWrtZsTGJaCCvi6aNK6H4UP9oVHEHnBFu3lgKTQ0y6H54OUop2uAqGe3sGfm98j+kL+ypG9iBXFuruSxukY5NOg1AwZm9sjOTMeLO3/i6PL+yMrIX0LXKq+Phr1nQ8+kEjLTkvD0+iFcCpqNXFEOlK1Rs2ZITUnBjk2bkJgggK2jAwKWLYWZRd41wBMTEhAXm3/4mEUlS/xv+VKsX7YcR4L3w8jEBMPGj0N9v/wVoPS0dCyfvwBJAgHK6+qispMTlgSuh4ubm8Lz+5Rv9X0ur7z37c/7Mh84dJLU75s9fRy+b6v8Akteebu5OmPJwhlYtXYzNmzagUqW5pg4dghat/SDssnj8/1jv75QUVHB1vWBSIiPRwUDA9SuXw99hw5ReH5FqWHngPOTpkseL+2ed67ClqsX0e/3QJgbGMDGKP+wuZcJ8WizfCGWdu+NYY2bISo5CaN3bsWBO/mH1F6PCEePwFWY27Er5nTogoi4WHQPXIWQSMWfa/UpvX+oj6xsIRatPYy09Ey4OVthxVx/6JTPL/Rj4pOldnh+yMzGorVHEJ+QAi1NDdhamWDWhC5o1iB/5Wb8kLbYEHQGi9YeRlJKBkyM9NChVU3079EYX4MXl36DmoYW3NrPhIZ2BSS/uYeQ3/tBlJ0h6aNtYAGIc6XG6ZjYwci+BkI2FX4T5OgHJ6ChY4jKTYZBS68i0mOf4daWQchMjpJrPlRQYGAgVq9eLdU2YsQIjBw5stRzJiQkSAqDfzM2NkZCQtGHxDVo0AAtW7aEpaUl3r59ixUrVuCnn37CgQMHoKmpiYSEBGhoaKBCBenDyE1MTD45b2FUxF/JGvH75Ehlh6AUK39y/3yn/6Ae295+vtN/kKk4WdkhEMldvIqBskNQCvtxw5UdglIIpnRUdghKcX3TNGWHoBSt539dl3X/x+NDg5QdQpEcW68u9krGqlWrChQkHwsODsbVq1dx8OBBnDp1Suq55s2b44cffsCgQcX794iLi4Ofnx+WLl2K5s2b48iRI5gyZQrCwsKk+vXt2xfW1taYM2dOETMV9EW7Jz98+ICoqCgIhUKpdhcXlyJGEBERERF9O0pyaNSPP/6I1q1bf7KPlZUVnj59CkEhl/ROTEwsdIWjKBUrVoSlpSVevnwJIG/FQigUIiUlRWo1QyAQwNu7ZPdYKVWRkZiYiClTpuDSpUuFPq+sczKIiIiIiMoqIyMjGBVxFb1/8/b2RlpaGu7fvw8Pj7wrp927dw9paWklKgaSkpIQHR2NihXzLvjh7u4ODQ0NXL16VVLsxMXFITw8HBMnTixRLqW6Xt///vc/pKSkYM+ePShXrhw2btyIBQsWwNbWFuvWrSvNlEREREREVAyOjo6oX78+pk2bhr/++gt//fUXpk2bhsaNG0ud9N2yZUucPn0aAJCRkYFff/0VoaGhePv2LW7evImhQ4fC0NAQTZvm3RtJT08PnTt3xq+//orr16/j0aNHmDhxIpycnPDdd9+VKMZSrWTcvHkTa9euhYeHB1RUVGBpaYm6detCV1cXgYGBaNSoUWmmJSIiIiKiYli8eDECAgLQr1/eVQX9/PwwY8YMqT6RkZFIS0sDAKipqeHZs2c4dOgQ0tLSYGpqCl9fXyxbtgy6urqSMb/88gvU1dUxZswYZGZmok6dOliwYEGJr8JXqiLj/fv3kqUcAwMDJCYmwt7eHk5OTnj06FFppiQiIiIiomIyMDDA4sWLP9nn6dP8E/XLlSuHTZs2fXZeLS0tTJ8+HdOnT/9s308p1eFS9vb2iIzMuxqUi4sL9uzZg9jYWOzevRumpsq5Sy4REREREX0dSrWS8dNPPyE+Ph5A3nV++/fvjyNHjkBDQwMLFiyQaYBERERERFS2lKrI+P77/Dtpurq64ty5c3jx4gUsLCyKdUY8ERERERH9d5X4cCmhUIgmTZrg+fPnkjZtbW24ubmxwCAiIiIiopIXGRoaGsjOzoaKioo84iEiIiIiojKuVCd+9+7dG7/99htycnJkHQ8REREREZVxpTon4969e7h+/TquXLkCZ2dnaGtrSz2/evVqmQRHRERERERlT6mKDH19fbRo0aLQ5/654QcREREREX2bSlRkbNq0Cf3798f8+fMLfT49PV1y10EiIiIiIvo2leicjBUrVmD//v2FPpeRkYEBAwYgPT1dJoEREREREVHZVKIiY+HChZg9ezbOnDkj1Z6RkYH+/fsjKSkJW7dulWmARERERERUtpTocKmWLVsiLS0N48ePR2BgIGrXri1ZwUhKSsL27dthamoqr1iJiIiIiKgMKPGJ3126dEFycjKGDx+OtWvXYsWKFUhISEBQUBAqVqwojxiJiIiIiKgMKdXVpQYOHIjU1FT4+/ujUqVKCAoKgpmZmaxjIyIiIiKiMqhERcaIESOkB6urw9DQEAEBAVLtvE8GEREREdG3q0RFhp6entTjtm3byjQYIiIiIiIq+0pUZBR1fwwiIiIiIqJ/lOgStkRERERERJ/DIoOIiIiIiGSKRQYREREREckUiwwiIiIiIpIpFhlERERERCRTLDKIiIiIiEimWGQQEREREZFMscggIiIiIiKZYpFBREREREQyxSKDiIiIiIhkikUGERERERHJlLqyA/jWvRm9W9khKMWuPlbKDkEpRm0NU3YIShGvYqDsEJTCVJys7BBIgQRTOio7BKUwnn9Q2SEohWBKgLJDoH+JvHlR2SEUqWoHZUegHFzJICIiIiIimWKRQUREREREMsUig4iIiIiIZIpFBhERERERyRSLDCIiIiIikikWGUREREREJFMsMoiIiIiISKZYZBARERERkUyxyCAiIiIiIplikUFERERERDLFIoOIiIiIiGSKRQYREREREckUiwwiIiIiIpIpFhlERERERCRTLDKIiIiIiEimWGQQEREREZFMscggIiIiIiKZUi/NoPfv32PDhg24ceMGBAIBcnNzpZ4/e/asTIIjIiIiIqKyp9hFRps2bdClSxf4+/tj2rRpCAkJQfv27WFqagoVFRV5xkhERERERGVIsYuMzZs3o2fPnvD398elS5cQGBgIHx8fecZGRERERERlULHPyZgyZQr69u0LANDX14eBgYG8YiIiIiIiojKs2EVGZGQkEhISAACjR4/GihUr8OHDB7kFRkREREREZVOxD5fatWsXbty4ASDv0KnXr1/ju+++g5WVFdTVpac5ePCgbKMkIiIiIqIyo9hFhpmZGdq3bw8AaNq0qdwCIiIiIiKisq1Ul7AdMWKErOMgIiIiIqL/CN6Mj4iIiIiIZKpUKxkikQhbtmzBiRMnEB0dDaFQKPV8SEiITIIjIiIiIqKyp1QrGatXr8bmzZvRqlUrpKWlwd/fH82aNYOKigoPpSIiIiIi+saVaiXjyJEjCAgIQKNGjbB69Wq0bdsWNjY2cHZ2xr1792QdIxERERERlSGlWslISEiAk5MTAEBHRwdpaWkAgMaNG+PChQsyC46IiIiIiMqeUhUZZmZmiI+PBwDY2Njg6tWrAIAHDx5AU1NTdtEREREREVGZU6rDpZo1a4br16/D09MTffr0wfjx4xEcHIyoqCj4+/vLOEQiIiIiIipLSlVkTJgwQfL/LVu2hLm5OUJDQ2FjY4MmTZrILLjSEIvFCNwYhP2HTiAtLR3ubs6YMnE4HB3sPjnuzLkrWBu4DW/fRcOqkgVGDP0Jfo3qSp7PyHiPtYHbcO7iNSQlJcPZyRGTxg2Bm6uznDMqnjcXr+LV6QvITkmDjoUZnLq0h2EVh8+OS46IxJ2l66BjaY7aU8dJ2uNCHyDy5Fl8iE9ArkiE8hVNYdu0ISx8feSZRqnU6/4LPFv0QzkdA0Q/u4U/A8ch4c3jIvurqqmjzg8T4N74R+gZWyLxXTjOb52OyNDTkj7eLQfAu9VAVKhoAwBIeP0YV/cswIu7f8o9n+Iozfs84sVLrA3cjsdPwxEdHYcJYwbjxx4dC/SLi0vAijWbcPXabWRlZcPGphJmTh0L16pV5JhR8RwODsa+7TuQKBDA1sEeQ8eORTVvryL73797F+uXr8CrF5EwNjFB19690LZzJ8nzxw8dwpljJ/DyxQsAQBUXZ/QdNhQubm7yTqVE5LVdA4C9wUewNSgYCYJEONrbYsLYIaju7S7HbIpP1q83AKSnpWHzuvW4ev4C0tLSYG5pgcGjR6NW3e/knE3xicVibNx5Dn+cvI209A9wdbbCxKHt4GBrVuSY81cfYuvei3gbnYicHBGsLY3Rs1NdtPLzlvTJEYmwccc5nLpwD4lJ6TA21EObpt7o270RVFWVe0X7+k4umNCiLXzs7GFpYIiOq5fij9DbnxzTwMkFS7r1hlulSohKTsaiE0cQePGsVJ9OPjUxp0MXOJqaISI+FtMO7MWhz8yraPJ6vTPeZ2FD0BlcvPYISSkZcHKwwNjBbeDqZKWItIhkc58MLy8v9O3bV+kFBgBs2b4PQTsPYvKEYQjavBLGRkYYMvIXZGS8L3LMvQePMHnaPLRp5Yc9QWvRppUffv5lHh6EPZH0mTNvOW6E3EXArInYu2M96vhWx5ARUxAXl6CItD4p5vZfeLbvMOxbNoXvL2NhUNkBf63ZiMzEpE+Oy/nwAQ+37Iahc+UCz6nraMO+VRPUnDgStaeNh2Wdmni0bQ8Ej57KK41S8e00DjXbj8TpwHHYOqEB0pNj0W3OEWhq6xY5psGPM+HVoj9O/zYBv43wQejJjeg0ZRfM7D0lfdIE73Bh2wxsGV8fW8bXx6sHF9H5lz0wsa6qiLQ+qzTv88zMLFhVMseoYf1gYmxYaJ/U1DT4DxoHdTV1rF4egP27AzFu9EDo6enIK5Viu3D6NNYvXY6eff2xbvtWVPPywtQxYxEXE1No/+h3UZg6ZhyqeXlh3fat6OH/E9YuWYrL585J+ty7cxeNWjTDonVrsHzTb6hobo4pI0cjIS5OUWkVi7y2a6dOX8SiZYHo37c7dm1bA28vd4wYOw3RMcrPXx6vt1AoxOQRoxAbHY3pC+bh9317MPaXKTA2NVVUWsWyPfgydh28hvFD2uL3ZUNhbKiHUdO2ION9VpFj9PW04d+tEX5bPAhBa0agbbPqCFh2EDfuhOfPu+8yDp64hQlD2mHX+tEY0a8Fdhy4gn1HbigirU/S0dTC/bevMHLHlmL1tzMxxbExk3Al/Amqz/4F848dwoqeP6GTT01Jn9qOVbB78CgEXb8Cr1lTEHT9CvYMGYVa9o5yyqJ05PV6z1t5ECGhEZg54QcErRmJWtUrY+TUzYhLSFVEWkSlLzIiIyOxZ88erF27FqtXr5b6URaxWIyduw+if9/uaNK4Hio72mHuzPHIzMzCiVPnixy3c/ch+Naqjv7+3WFvZ43+/t1Rq6YXduw+CCDvj7Oz569gzIj+8PGuBhtrSwwZ2BuWlubYd+CootIr0uuzF2H5XS1UqucLHQszOHdtDy1DA7y9dP2T4x7v2A/zmt6o4GBb4Dkjp8qo6FUNOhZmKG9qAhu/+tCtZIHk55HySqNUarYbjmv7FuHZjcNIeP0Ix5YPgoamNlwbdC1yjFvjHrgevAgv7pxCSuxLhJ7ciMjQM6jZYZSkz/NbJ/DizikkRT1HUtRzXAqajezMdFg61yxyXkUp7fvczdUZY0cNRMvmjaChqVFon83b98G8oilmzxgPdzdnWFqaw7emN6ytLOWVTrHt37kLLb9vh1Yd2sPG3h5Dx42FqVlFHNl/oND+xw4cQEVzMwwdNxY29vZo1aE9WrRrh+CgnZI+U+bOwfc//ABHJyfY2NlhzC9TIBbnIvTW17OnU17bNQAI2nUAHb5vgU7tW8HB3gYTxw2BuZkp9u1X/nZNHq/3qcNHkJaailmLFsLN0xNmFhZw9/KCo5PyV+n+IRaLseePa/Dv1hCN67rB0c4MM8Z1RmaWEH9eLPrqjT4eDmj0nSvsbSrCysIY3dp/B0d7M9x79ErSJ+zJGzTwdUHdWs6wNDOEXz131PKujMfh7xSR2iedDLuH6Qf34eDdW8XqP6RRE7wWCDB293Y8iY7CpssXsPnKBYxv0VbSZ3TTljj96AEWHD+MpzFRWHD8MM4+fojRzVrJKYuSk9frnZklxIWrjzCibwt4u9vD2tIYA39sAkszQxw4flNR6dE3rlRFxt69e9GmTRusXLkSp06dwpkzZyQ/Z8+e/fwEcvIuKgYJgiTU8a0uadPU1ISPdzXce1D04TP3HzyWGgMAdWr7SMaIRCKIRLnQ1JI+qV1LSxOh9x7KMIOSy83JQdrrdzB2dZJqN67qhOQXL4scF3UtBB8SEmDfptlnf4dYLEbik3BkxMbBoBiHYClKBTM76BqZ42Vo/ntOlJONNw+voJJL7SLHqatrIic7U6otJzsT1lXrFNpfRVUVVev/AI1yOnj3VPk3mizt+7w4Ll66AdeqTpg4JQB+Lbuhe+/hOHDoxJeG/MWEQiHCnzxFdV9fqXYfX188uv+g0DGPHoTB5+P+tX3x7PFj5OTkFDomKzMTOTki6OnryyZwGZDXdk0oFOLxk/ACfWrXqv7F76MvJa/X+/rly6hazR2rFi5C15atMLB7T+zavAUikUg+iZRCVEwSBEnp8K2ev8KsqaEOb3c7PHj8ulhziMVi3PorAq/fJsDL3U7S7ulqg1v3XuD1u7wV+PAX0bj36BW+q+FUxExfr9qOVXD6kfR74VTYfdSwtYe6mhoAoI5jFZx+KN3nz4f38V3lr6eolNfrLRLlQpSbC01N6aPitbQ0pApPInkq1TkZ69atw5gxYzBo0CBZx/NFEgR5hwcZGUkfCmJsZIjomNhPjjMuZIzg7/l0dMrDo1pV/Pb7Ttjb2cDYyAAn/7yAsIdPYWOt3D28wvQMiHNzoamnJ9WuqaeH7JS0Qse8j4vH80PH4TN+OFT/3hgXJufDB1yeMhe5whyoqKrCuUcnGFf9er6MdA3zjlfNSJF+bTOS46D/97kUhXkRehY124/Em4dXkRTzAnYejVHFtw1UVKX/LUxt3dD713NQ1yyH7A/pODC/BwRvnhQxq+KU9n1eHO+iorHvwFH06tEJ/f27I+zhUyxcug4amhpo17rpF839JVKTk5ErEsHQ2Eiq3dDICEkCQaFjkgQCGBp91N/YCCKRCCnJyTA2MSkwZtOatTAxNUX1WspfsfqHvLZrScmpEIlyC85rbAjBjURZhF5q8nq9o99F4a/bd+DXogUCli3DuzdvsHrhIohEIvQa0F9u+ZSEICkdAGBkIH3Ip5GBLmLikz85Nj0jE+36LES2MAdqqqqYOKwdfL3z/3jt3aUB0t9nodvgFVBVVUFurhhD+jRF80aen5j162Sub4DYlPtSbbGpKdBQV4eJrh5iUpJhXsEAsakpBfqY6xsoMNJPk9frrVNeC9VcrPH77vOwszaFkYEu/rx4Hw+fvoW1pbFcciH6WKmKjJSUFLRqpfzlxuMnzyFgwUrJ45VL5wAAVFSk+4khhsrHjZ8hFoul5gmYNRGzApahRdsfoaamChfnymjVohEeP4kodfwyVSA9cSFtgDg3F2G/74BD2+bQMfv0cchqWlrw/WUcRFlZSHwajvDgw9A2MYKRU8FzOBTBtWE3tBya/3rvm9sZACAWf9RRRaWQxnxnNk5Eq+GrMXBNKAAxkmJe4P7Z7fBo0luqn+DdM/w+pg7K6VaAc50OaDs6EDumtlR4oSHP9/nHcnPFcK1aBSOH9QUAuDhXRkTkK+zbf1SpRcY/VD56U4vF4oL/EFIDPv5HEhc6DwDs3bYdF/48jUXr1kBTS+uLYy0tRW7XCp1X/OXvI1mR9estzs2FgaEhxvwyGWpqanCq6gJBfDyCg3Yorcg4ef4v/Lr6sOTxkll526GPXwMxxIW+b/+tvLYmtq0ajg8fsnHrXgRWbDwBS3ND+HjkrUCfufQAJ8//hTkTu8DetiLCX0Rj2YbjMDHSQ5um1T8599dIDOnt/D//ZuJ/bf/FH30XqEClwDhFUuTrPXPCD/jf8oNo12ch1FRV4VzZAs0beuBpRJSMsyIqXKmKjJYtW+LKlSvo0aOHrOMpkYb1a8PdzUXyWCjMBgAIBEkwNcmv1BMTkwvsrfs3E2NDCD46SToxSXqMtZUlNq1fhA8fMpGekQFTE2P8PHUeKlkWffUHRdDQ1YGKqiqyU6VXLbLT0qGpr1egf05mFlJfvUXamyg83XMIwN8bYbEYZ4dPgvfIgTByyVtKVlFVRfmKeXt79awrISM6Di9PnlNakfE85Bh+f5p/vK66Rt4fgroGZshIyj8ZVKeCKTKSiz5x9UNqAg7M7w41DS1o6xkhPTEajfrMRXLsS6l+uTlCJMfkXXUo5nkoLKr4oEbbYTi1blQhs8qPrN7nxWFiYgQHe+lVIHs7G5w9f/WL5v1S+gYGUFVTQ+JHe7GTk5IK7L3+h6GxcYG93kmJSVBTU4O+QQWp9n1BO7Bry1b8unoVHKoo91AKRW3XDA30oaamKlnZKO68iiCv19vIxATq6mpQ+9cKro29HRIFAgiFQmhoFH6ukjzV960KN2dryWOhMO/QLkFSGkyM8rfhSckZMDL89AUYVFVVJXupnRwt8PJNPLbtuyT5o3PV7yfRp0sDNGvoAQCobGeO6LhkbNt3qcwVGTGpeSsV/1ZRTx/CnBwIMvJWB/5ZzZDqo69fYHVDkRT5eltZGGPdrwPwITMbGe+zYGKkh6kLdsPSTLmfb/p2lKrIsLW1xYoVK3Dv3j04OTlBXV16mj59+sgkuM/R0SkPHZ3yksdisRgmxoa4ERIKl7+vmCQUCnEn9AFGD+9X5Dwe1arixs1Q9OqRf6nD6zfvwrNawSsJaWuXg7Z2OaSmpuHajTsYM0K5S+yq6urQs6mExMfPUNGrmqQ98fEzmHoWvAylejkt1J42Xqrt7aVrSHz6HB4D+0DbpPAv8H/kFnEsuyJkf0hH9od0qbb0xBjYefkhNjLvBDlVdQ1Yu9XDhW3TPzufSJiF9MRoqKqpw/m79nh8pfATSvOpSAobRZLV+7w4vDxc8erVW6m216/fwcK84hfN+6U0NDRQxcUZd0NCUK9xI0n73ZAQ1GnQoNAxrtXccePKFam2uzdvwqlqValt1t7tQdj5+2bMX7kCTq7Kv3qYorZrGhoaqOpSBTdCQqUua3sjJBSNGhR9TpMiyOv1dvP0wPlTp5Cbmyu5ZOu7129gZGKilAIDyDusRad8/nZFLBbD2FAXIaERcHbMOxxXKMxBaNhLDO/bvGSTi8XIFuZvszOzhAX2mKupqiI3V3l79kvrRkQ42npKF0bN3Txw+1Ukcv4+x+Z6RDiaulbD8tP555U1c6uGa8/DoSyKfL3/oV1OE9rlNJGa9gE37z7HiL4tvigHouIqVZGxZ88elC9fHiEhIQgJkT4RVkVFRWFFxsdUVFTQs3tHbNqyGzbWlrCxroRNW3ajXDkttGrRWNJv2qxFqGhqjFF/f0H36NYBA4ZMwOZte9GoQR1cuHQdISGh+H3DEsmYazduQywG7Gyt8OZNFJat2gg7Wyt8366EGwE5sGnSEA+37IKerTUM7G3x9soNZCYlo1L9vD8Unh86jszkFLj794CKqip0K1lIjdfQ04WqhoZUe+TJs9C3tYa2iTHEIhESwh4j+sZtuPTorNDcPufWkTWo88MEJEU/R2JUBOr8MBHC7A94dGmvpE/bMb8hTRCFi9tnAgAsnGpAz8gSsZH3oWdsiXrdp0JFRRU3Dy6TjGnQaxZe3P0TaQlvoamth6r1f4CNe33snd1B0SkWUNr3uVAoxIvI13//fw7i4hPw9FkEtLW1JecW9erREf4DxmHTlt1o1qQBHj56iv2HjmP6lNGKT/QjnXv2wMKZs+FUtSpcq7nj2ME/EBcTi7ad8u71sWnNWgji4jFpdt7r3KZTJ/yxLxjrly1H6w7t8ehBGE4ePoIpAXMkc+7dth1bAzdg8tzZMLOwQGJC3p5w7fLa0C5fvmAQSiDP7VqvHp0wbdYiuLpUgUe1qjhw6ARiYuPwQ6c2Cs/zY/J4vdt27oQ/9u7DuiVL0b5rV7x78wa7tmxBh65FX41O0VRUVNCt/XfYuvcirC2NYW1pjK17L6KclgaaN8w/d2L2kmCYGutjmH/ed9DWvRfhUqUSrMyNIMwR4drtZzh+7i9MGv69ZEy9Wi7YsucizE0NYG9bEc8iorHr4FW0bab8+x/paGmhckVzyWN7E1N4WtsiMSMdbxIFmNepGywNjeC/aR0AYP2Fsxju1xxLuvXCb5fOoY5jFfSr3wg9N6ySzLHyzElc/HkGJrVqhz9C76C9tw+aVnVH/QWzFZ5fUeT5et+4Ew6xWAxbKxO8iU7E6k0nYVPJBG2bla1VKyq7SlVknPvXdce/Nv69uyArKwvzF65Galo63N1csG7lPKk9gzGxcVBVzd+b4+Xhivlzp2Bt4FasDdwGaysLLPjfFFRzzz9kIT39PVat3YzYuARU0NdFk8b1MHyoPzTUS/VPKFPmNbwgzMhA5LHTyEpNha6FObyG94f23ydNZqWkfvaeGR8TZWXjya4DyEpOhqqGBnTMK8Ktb0+Y1/CSQwald/PAUmholkPzwctRTtcAUc9uYc/M76VWPPRNrCDOzZU8Vtcohwa9ZsDAzB7Zmel4cedPHF3eH1kZ+UvoOgYV0W7MRugYmSMrIxXxr8Kwd3YHvLz3dbz3S/M+j48XoHvv4ZLH23bsx7Yd++FTvRo2rlsEIO8yt0sWzsCqtZuxYdMOVLI0x8SxQ9C6pZ/ikitCo2bNkJqSgh2bNiExQQBbRwcELFsKM4u84jgxIQFxsfmHzVlUssT/li/F+mXLcSR4P4xMTDBs/DjU98vP5cj+/RAKhZg7+Rep39VrQH/0GTRQMYkVg7y2ay2aNURKSio2/L4DCQlJqOxgi1XL5sLSQrmHgQLyeb0rmplh/soVWL98OQb/2Asmpqbo2K0buvbpXeD3K1PvH+ojK1uIRWsPIy09E27OVlgx119qD3hMfLLUqsSHzGwsWnsE8Qkp0NLUgK2VCWZN6IJmDfJXuMcPaYsNQWewaO1hJKVkwMRIDx1a1UT/Ho2hbDXsHHB+Uv4K9NLuea/JlqsX0e/3QJgbGMDGKP9wwZcJ8WizfCGWdu+NYY2bISo5CaN3bsWBO/mH1F6PCEePwFWY27Er5nTogoi4WHQPXIWQyK/kXMq/yev1Tn+fiXVb/kRcQir09bTRuK4bhvRpBnX1oi/4QiRLKuKPz4oqhlu3bsHBwQHGxtJXKMjJyUFoaChq1iz5lVneJ39d919QlIl3w5QdglJYr+iu7BCUYtTWb/P1jlcxUHYISmEqTlZ2CErxrb7e+vFnlB2CUhjPP/j5Tv9BgikdlR2CUhhW7qLsEAp1fIqzskMoUuv5X9eNjBWlVPfJ6N27N9q3b4/Q0FCp9uTkZKUdKkVERERERF+HUt/xu3Xr1vD398eBA9Iny5ZiYYSIiIiIiP5DSnVCgYqKCgYPHowaNWrg559/xtOnTzF58mTJc0RERERE9O0q1UrGP6sVzZs3x44dO3Dq1CkMGDAAqampMg2OiIiIiIjKnlIfLvUPV1dX7Nu3D2lpafD395dBSEREREREVJaVqsjo2LEjtLTyL61mamqKoKAg1KlTBxYWFp8YSURERERE/3WlOidj/vz5Bdo0NTXx66+/fnFARERERERUtpX6TnKpqakIDg5GREQEVFRU4OjoiB9++AF6enqyjI+IiIiIiMqYUh0u9eDBAzRr1gxbtmxBSkoKkpKSsGXLFjRt2hRhYd/mzcaIiIiIiChPqQ+X8vPzw9y5c6GunjdFTk4Opk2bhvnz52PHjh0yDZKIiIiIiMqOUq1khIWFYcCAAZICAwDU1dUxYMAArmQQEREREX3jSlVk6OrqIjo6ukB7dHQ0dHR0vjgoIiIiIiIqu0pVZLRu3RpTp07F8ePHER0djZiYGBw7dgzTpk1DmzZtZB0jERERERGVIaU6J2PSpElQUVHBpEmTIBKJ8iZSV0ePHj0wYcIEmQZIRERERETSUlJSEBAQgHPnzgEA/Pz8MH36dOjr6xc5xtnZudD2iRMnYsCAAQCA3r17IyQkROr51q1bY9myZSWKr0RFxocPH7Bw4UKcOXMGOTk5aNq0KXr16gVdXV3Y2tpCW1u7RL+ciIiIiIhKbvz48YiNjcXGjRsBADNmzMCkSZOwfv36IsdcuXJF6vGlS5cwdepUtGjRQqq9a9euGDVqlORxuXLlShxfiYqMlStX4uDBg2jXrh20tLRw9OhR5ObmYuXKlSX+xUREREREVHIRERG4fPky9u7dC09PTwDA3Llz0a1bN7x48QIODg6FjjM1NZV6fPbsWfj6+sLa2lqqvVy5cgX6llSJiozTp0/jf//7n+S8i++//x49evSASCSCmpraFwVCRERERESfFxoaCj09PUmBAQBeXl7Q09NDaGhokUXGvyUkJODixYtYsGBBgeeOHDmCw4cPw8TEBA0aNMDw4cOhq6tbohhLVGTExMSgRo0aksceHh5QU1NDXFwcLCwsSvSLiYiIiIj+67Kzs5GdnS3VpqmpCU1NzVLPmZCQAGNj4wLtxsbGSEhIKNYcBw8ehI6ODpo3by7V3q5dO1hZWcHExATh4eFYsmQJnjx5gs2bN5coxhIVGSKRCBoaGlJtampqyMnJKdEvJSIiIiL6FgQGBmL16tVSbSNGjMDIkSML9F21alWBvh8LDg4u8jmxWAwVFZVixbV//37JKRD/1rVrV8n/Ozk5wdbWFp07d8bDhw/h5uZWrLmBEhYZYrEYkydPlqq8srOzMWvWLKmTvj/3j0NERERE9C0YPHgw+vbtK9VW1CrGjz/+iNatW39yPisrKzx9+hQCgaDAc4mJiYWucHzs9u3biIyMxPLlyz/b183NDRoaGnj16pX8ioyOHTsWaPv+++9LMgURERER0TejJIdGGRkZwcjI6LP9vL29kZaWhvv378PDwwMAcO/ePaSlpcHb2/uz44ODg+Hm5gYXF5fP9g0PD4dQKCzxieAlKjLmz59fosmJiIiIiEi2HB0dUb9+fUybNg1z5swBAEyfPh2NGzeWOum7ZcuWGD9+PJo1ayZpS09Px8mTJ/Hzzz8XmPf169c4fPgwGjZsCENDQ0RERGDBggVwdXVF9erVSxRjqW7GR0REREREyrN48WIEBASgX79+APJuxjdjxgypPpGRkUhLS5NqO3bsGMRiMdq2bVtgTg0NDdy4cQPbt29HRkYGLCws0LBhQ4wYMaLEV5JlkUFEREREVMYYGBhg8eLFn+zz9OnTAm3dunVDt27dCu1vYWGBoKAgmcSnKpNZiIiIiIiI/sYig4iIiIiIZIpFBhERERERyRSLDCIiIiIikikWGUREREREJFMsMoiIiIiISKZYZBARERERkUyxyCAiIiIiIplikUFERERERDL11dzxO17FQNkhKMXFvx4oOwTlaDhV2REohXNKtrJDUIrqBsqOQDm+1e2aqThZ2SEoxYVN05QdglIIpgQoOwSlMJ5/UNkhKEXupi7KDqFQ9x+9VXYIRWqt7ACUhCsZREREREQkUywyiIiIiIhIplhkEBERERGRTLHIICIiIiIimWKRQUREREREMvVFV5fKzMxETk6OVJuuru4XBURERERERGVbiYuMDx8+YNGiRThx4gSSk5MLPP/48WNZxEVERERERGVUiQ+XWrhwIW7cuIGZM2dCU1MTAQEBGDlyJCpWrIhff/1VHjESEREREVEZUuIi4/z585g5cyZatmwJNTU11KhRA8OGDcPYsWNx5MgRecRIRERERERlSImLjJSUFFhZWQHIO/8iJSUFAODj44Pbt2/LNjoiIiIiIipzSlxkWFlZ4d27dwCAypUr48SJEwDyVjj09PRkGx0REREREZU5JS4yOnfujCdPngAABg0ahJ07d8Ld3R3z589H//79ZR4gERERERGVLSW+upS/v7/k/2vXro0TJ04gLCwMNjY2cHFxkWVsRERERERUBpXqErba2tqSx5aWlrC0tJRpUEREREREVHaVuMioWbMm3N3dUatWLdSsWRM+Pj4oX768PGIjIiIiIqIyqMRFxvbt23Hr1i3cvHkTQUFByM7Ohqurq6ToaNiwoTziJCIiIiKiMqLERYa3tze8vb0xaNAgiEQiPHjwALt378bmzZuxadMm3vGbiIiIiOgbV+IiAwAiIiIQEhKCW7duISQkBEKhEI0bN0bNmjVlHR8REREREZUxJS4y6tati5ycHPj6+sLX1xeDBw+Gs7OzPGIjIiIiIqIyqMT3yTAxMUFGRgaio6MRFRWFmJgYZGRkyCM2IiIiIiIqg0q8kvHHH38gNTUVt27dwq1bt7BixQo8f/4czs7O8PX1xYQJE+QRJxERERERlRGlOidDX18fTZo0gY+PD6pXr46zZ8/i6NGjCAsLY5FBRERERPSNK3GRcfr0ady8eRMhISF4/vw5DAwMUKNGDUyZMgW+vr7yiJGIiIiIiMqQEhcZM2bMQM2aNdG1a1fUqlULTk5O8oiLiIiIiIjKqBIXGdevX5dHHERERERE9B9RqnMy/pGZmYmcnBypNl1d3S8KiIiIiIiIyrYSFxnv37/H4sWLceLECSQnJxd4nnf8JiIiIiL6tpX4PhmLFi3CjRs3MHPmTGhqaiIgIAAjR45ExYoV8euvv8ojRiIiIiIiKkNKXGScP38eM2fORMuWLaGmpoYaNWpg2LBhGDt2LI4cOSKPGImIiIiIqAwpcZGRkpICKysrAHnnX6SkpAAAfHx8cPv2bdlGR0REREREZU6JiwwrKyu8e/cOAFC5cmWcOHECQN4Kh56enmyjIyIiIiKiMqfERUbnzp3x5MkTAMCgQYOwc+dOuLu7Y/78+ejfv7/MAyQiIiIiorKlxFeX8vf3l/x/7dq1ceLECYSFhcHGxgYuLi6yjK1UDgcHY9/2HUgUCGDrYI+hY8eimrdXoX0FCQnYsHwlwp88wbs3b9ChW1cMHTe2QL/0tDRsXrceV89fQFpaGswtLTB49GjUqvudnLMpvm6e1dG3Rm2Y6ujiuSAev144g7vv3hTZX0NNDUNr10Pbqu4wKa+D2PQ0bLh5FQcf3pf00dPSwqi6jdC0sjP0y5XDu5RkLLp0FpcjIxSRUrHIOu/NXX5ETWvbAuMuvXiOYYf2yi2Pkrp++Dgu7TuAtMQkmNnaoO3QAbCv5lZo37Ar13DjyAlEv4hEjlAIM1sbNO3dA041qkv6xL58jT+37cC78Agkx8ah7ZD+qNepvaLSKbaSfL4B4P7du1i/fAVevYiEsYkJuvbuhbadO0mefxnxAts2bED4kyeIjY7BkLFj0KlHdwVkUjKyzhsoG9s1sViMwI1B2H/oBNLS0uHu5owpE4fD0cGuyDERL15ibeB2PH4ajujoOEwYMxg/9ugo1Wfv/qMIPnAUUVFxAAAHBxsM6v8j6n1XU57plEiVJiNgXasbNLT1kfzmHh7+MQfpcc+L7O87cBuMHXwLtMc9uYDbWwdLHtvU7gmH+v2hpWeK9LhwPDo6D0kv78glh5ISi8XYuPMc/jh5G2npH+DqbIWJQ9vBwdasyDHnrz7E1r0X8TY6ETk5IlhbGqNnp7po5ect6ZPxPgsbgs7g4rVHSErJgJODBcYObgNXJytFpPVJ9Z1cMKFFW/jY2cPSwBAdVy/FH6GfPvS8gZMLlnTrDbdKlRCVnIxFJ44g8OJZqT6dfGpiTocucDQ1Q0R8LKYd2ItDn5mXSNZKXGRERUXBxMQEmpqaAABLS0tYWloiNzcXUVFRsLS0lHmQxXXh9GmsX7ocIydNhJunB44dPISpY8Zi455dqGhuXqC/MDsbFQwN0KOvPw7s2l3onEKhEJNHjIKBkSGmL5gHk4oVER8bC+3yOvJOp9haOlXF5EbNEHD2JEKj3qKLhzfWd+yG77duQExaaqFjlrTpCGMdHcz48xheJyfBqHx5qKvkL2ypq6rit849kPj+PcYdPYCYtFSY6+njfXa2otL6LHnkPfrIfmioqkkeG2hrY3/vATj17Ou5NPO9C5dxdP1GtB85BHZuVXHz2Elsnjob4zaugUFF0wL9Ix88RBUfL7To1wfaujq4feoMts4IwLCVi1CpsiMAIDsrC8bm5vCoXxdHAzcpOqViKennO/pdFKaOGYfWHdpj8uxZeHjvPlYtXIQKhgao7+cHAMjKyoR5pUqo36QJApctV3BGxSOPvMvCdg0Atmzfh6CdBzF7xjjY2ljht993YcjIX3Bo70bo6JQvdExmZhasKpmjWZP6WLI8sNA+ZhVNMHJYP9hY531fHTl2BmMnzsbu7as/WcAoikODgbCr1xf3gycjI+ElKjceilr9N+PikpYQZWcUOuZu0EioqGlIHmuWN0C9UX8g+sFJSZtFtVZwbTMFYX/MRtKru7Dx7Y6a/r/h0rI2yEyJlnten7M9+DJ2HbyG6WM7waaSCTbvuYBR07ZgT+AY6JTXKnSMvp42/Ls1gq2VCTQ01HA15CkClh2EYQVd1PapAgCYt/IgXryKw8wJP8DESB8nz/+FkVM3Y9e60ahooq/IFAvQ0dTC/bevsOXqRewfXnAn58fsTExxbMwkbLx0Hr03rkHdyk5Y06sf4tNTceDOLQBAbccq2D14FGYc2oeDd2+jY/Ua2DNkFOovmI2Qr2gnIf33lfhwKT8/P3Ts2BGvX7+Wak9MTESTJk1kFlhp7N+5Cy2/b4dWHdrDxt4eQ8eNhalZRRzZf6DQ/uaWlhg2fhyatWkNHd3Cv1xPHT6CtNRUzFq0EG6enjCzsIC7lxccnarIM5US6eNTCwfC7mF/2D28SBTg1wtnEJOWiu6e5MYTbwAALrZJREFU1QvtX9fOATWsbDD04B7ceP0SUakpCIuJxl/R7yR9Orl7okI5bYw6HIzQqLeITktFaNRbPE2IU1RanyWPvFMzMyF4nyH5qWNjj0yhEH8+e6KotD7ryv4/UKNlU9Rq1RwVbazRbuhAVDA1wY0jxwvt327oQDTs2hnWzlVgUskSLfv1gXElCzy+cUvSx9q5CloP6gvPxg2gpqFR6DzKVtLP97EDB1DR3AxDx42Fjb09WnVojxbt2iE4aKekj7OrKwaNGonGzZtBQ/PbybssbNfEYjF27j6I/n27o0njeqjsaIe5M8cjMzMLJ06dL3Kcm6szxo4aiJbNGxX5mjasXxv169aCrY0VbG2sMGKoP8qXL4f7YV/H59yubh9EnF+P2IenkR4bjvv7foaaRjlYerUtcozwQwqy0xMkPyZV6kIkzETMv4oM+/p98eb2fry9HYyM+Bd4fHQeMlNiYFu7hyLS+iSxWIw9f1yDf7eGaFzXDY52ZpgxrjMys4T48+K9Isf5eDig0XeusLepCCsLY3Rr/x0c7c1w79ErAEBmlhAXrj7CiL4t4O1uD2tLYwz8sQkszQxx4PhNRaVXpJNh9zD94D4cvHvr850BDGnUBK8FAozdvR1PoqOw6fIFbL5yAeNb5L83RjdtidOPHmDB8cN4GhOFBccP4+zjhxjdrJWcsiAqXImLDABwdHREly5dcP36dal2sVgsk6BKQygUIvzJU1T3lV4u9vH1xaP7D0o97/XLl1G1mjtWLVyEri1bYWD3nti1eQtEItGXhiwT6qqqcDWzwLVXL6Tar72KhKdl4UvBjR2q4GFsNPrVqI2zg0biaN/BmNDAD1rq+QtbjRyr4F70O0z1a4GLg0fjYJ+BGFjrO6iqqMg1n+KSV94f61TNEyeePsKHHKFM4y+tHKEQ78Kfo0p1b6n2Kj7eePWoeH8g5ebmIuv9B5TX05VHiHJRms/3owdh8Pm4f21fPHv8GDk5OXKLVZbklffXvl0DgHdRMUgQJKGOb/5OA01NTfh4V8O9B7JbWRSJRDj55wV8+JAFD/eqMpu3tLQNrVBOvyISwq9I2nJFQiRG3oKhrfcnRkqzrtEZ0fePQST8AABQUdOAvqWb1LwAEB9+FQY2xZ9XXqJikiBISodv9cqSNk0NdXi72+HB49efGJlPLBbj1l8ReP02AV7udgAAkSgXotxcaGpKb+e1tDQkhUhZUtuxCk4/kv7snwq7jxq29lBXy1uFr+NYBacfSvf58+F9fFf569mJQN+GEh8upaKigpkzZ+LIkSMYNGgQJk6ciD59+kieU5bU5GTkikQwNDaSajc0MkKSQFDqeaPfReGv23fg16IFApYtw7s3b7B64SKIRCL0GqD8E90NtctDXVUVggzpJXTB+wyYFHHog5WBAapXska2KAejDwfDULs8pvm1QIVy2pj+57G8PhUMUcm6Ao49CcPQg3tga2iEqX7NoaaqivU3rhQ6ryLJK+9/cze3gJNJRcwo5DlleZ+aitzcXOgZGki16xlWwLOk5GLNcTn4EISZWfBoUE/2AcpJaT7fSQIBDI0+6m9sBJFIhJTkZBibmMgtXlmRV95f+3YNABIESQAAIyNDqXZjI0NEx8R+8fzhzyPx04CxyM7Ohra2Npb8Oh2ODgXPx1I0Lb28Qx6z0qVf36z0BGgbFO9w5ApW1aBn7oz7+6dK2jTLG0JVTb3AvNnpCZLfqUyCpHQAgJGB9M4PIwNdxMQnf3JsekYm2vVZiGxhDtRUVTFxWDv4eucVKzrltVDNxRq/7z4PO2tTGBno4s+L9/Hw6VtYWxrLJRd5Mtc3QGzKfam22NQUaKirw0RXDzEpyTCvYIDY1JQCfcz1DRQYKVEpiox/Viv8/f1hb2+P8ePH4+nTpxg+fLjMgysNFUgXOmKxGPiC4kecmwsDQ0OM+WUy1NTU4FTVBYL4eAQH7fhqvowB4OM1JJVC2v6hChWIIcbPxw8jPTsLALDo4lksbdcJAedOISsnB6oqQOL7DMw6fQK5YjEexcXAVEcXfWvU/iqKjH/IOu9/6+TuhWcJcQiLUf6xygWofPw+B4rzLv/r/EWc2b4LfWZPhe5HhUpZUOLP98fP/b39+nier52s8/4at2vHT55DwIKVkscrl87Ji/njVCCWyQ4tO1sr7N6+Fmnp6Th77gpmzFmCjesWKrzQsPRqB/cOsyWP80/SLmzrVjzWNX5AWsxTpLwtbLWrkHmVcBTCyfN/4dfVhyWPl8zqnRfNx9s2iD/7eS2vrYltq4bjw4ds3LoXgRUbT8DS3BA+Hg4AgJkTfsD/lh9Euz4LoaaqCufKFmje0ANPI6JknJViiD96Df/5N/v30SQfH1mi8vf3H5EilbjI+LeGDRti9+7dGDp0KO7fv//5AXKkb2AAVTU1JH60dy85KanAXr2SMDIxgbq6GtTU8k8GtrG3Q6JAAKFQCA0lH7+e9OE9cnJzYaIjvffeqLwOBP9v797jasj/P4C/Tpd1yyqVlFWS7aALcTrHpSUisutWLru2XGL98MXStsqGdf2G3LZQCVGxtCSirxZrkaUifMu9C0np4nRPdTrN74++jUa3E115Px+PHo9m5jMz78/MZ2bO5zPzmSmsvoNgRkE+0vPz2B/aAJAgzoQcjwcNpY5Iys5CRkEBSqVSlFU6USWIM6GupAQFOTmUlpU1ToZk1Fj5rtBWQQFW/D7Y88+1xsnAe2r/+eeQk5NDnjiLMz4/O6fOSsO9v6/h5A4PfL/KCV8O6N94QTaC9zm+VVRVq7T2Z4mzIC8vj8+VOzVarA2psfLdEs9rw78aBEODt28olEjKXzLx+nUW1NXetjiLxdlV7m68D0VFRbbjt0Effdx/+AS/Hw/GqpU/fvCy6yPtwV/IfvG2z4GcfPlLVdooqaE4L4Md30ZJFcX5mXUuT06xLTT7fY2nF9w540sKs1AmLUUbJe4dvM9kXG5D+0rUBwb87uywRFLeyPM6Kw9qnd9+cysruwCdVWp/IYGcnBx7V0JfTxPPXmTA74+rbCXjC01VeG6ZhzdFJSgoLIZa545w2XwMWhofXo6a2qvc8jsVlXXp+DkkpaV4XVB+N6jibgYnzeefV7m7QUhjq3efDFNTU84FqFevXggMDESnTp2atU+GoqIivuzNR3RkJGd8dGQk+hobvfdyDfoZIyU5GWWVflS/THqBzmpqzV7BAIDSsjI8SEvFYG1dzvjBOrq4l5Jc7Tx3UpKh3qEj2lWKX0elM6RlZUjLzwMA3H35AtrKKpz2ox4qqkjPz2v2CgbQePmuMEa/Dz6TV0DIw9iGD/4DKCgqotuXvRAXfZczPi76LnT61vwK6buXr+CPbb/hW2dH9Ba1nNd0yup9ju++RoZV00dEQL9PHyjU0g+nJWmsfLfE81qHDu2h3V2L/eupqwM1VRXcjLzDppFIJLh9Jwb9jBqh7wQDlEiavu+VtKQAha+T2L/89DgU5aZD7cuhbBqevCI665oi6/mdWpZUTtPICnLyn+Hl3TOc8YxUgtyU+5zlAoBaryHITqp7uQ2tQ/s26K6lyv7paneBqooSIu+8ffuRRFKKO7HPYNRHu34LZxiUSKr2u2rX9jOode6I3Lw3iIiOw7BBzd8Hp75uxj/FqL6GnHGWBsa49TwRpf/rU3Uj/ilG9eWeH0YbGOGfuKdNFichwHtUMvz9/fH559xXvqmoqCAgIID9SF9zsZnxHc6fPoPzZ0KQlJgIzx27kP4qDd9Yl78j/cCevdj66zrOPPFPniD+yRO8KXyD7KwsxD95gucJiez0b2yskZuTC8/tO5D8PAkR4dfx+6FDmDDFpknzVhu/25GwMeqPyQbG6NlZFSuGj4Jmx89x/F40AGCZmTn+PXY8m/7co/vIKXqDjWO+Qc/OahjYrTt+GjYSp+7fYx8ZOn4vGsrt2sF5hCV0lDtjmK4efhAOwbG7LeN96kDj5LuCtWF//BX3BDlFb5o0T7Iws5mIqPMXEHX+AtKTXiDEcz+y0zMg+qb8zSHnDxzG8a072fR3L19B4NZd+Hq+PbT78JEnzkKeOAtFlfqzlEokSIlPQEp8AqSSUuRmipESn4DMly3ncYL6Ht9fW1sjLfUVvHbuQlJiIs6fCcH5MyGYYjuDTSORSNhzgERSisyMDMQ/eYKXL2r+1kpTa4x8t4bzGo/Hw4xvJ+PAoWP46+/riIt/hjXrt6Nt2zawGjOCTbdqrRvc9xxkhyUSCR4/icfjJ/GQSEqRnpGJx0/ikfTibVn22OuL6DuxSEl5hadxidjteQi3ov+LcWNGNmkea/Lsuh/0zP8PGn1HQUnjS/Sb4gqppAgpd8+yaYynbgF/jEOVebsLpiDtwUVICrOrTEu85ovugin4YqANOqj3RJ+vV6KdsiaeR1T/CvemxOPxMH3iEBwOvIK//3mA+Gdp2LAzCG3bKMJyeD823brtJ7D30J/s8OHAK4i4E4eXqWI8e5GBo6euI/Svuxg7oj+b5ubtp7hx6wlSXokRcScO/1p5ANrd1PDN6OrfRNiUOrRpg37dddDvf99n0lVTR7/uOujeufzOzL+tp+PQ3IVseq+/L0FHVQ3bp9uit6YW5pgNh/1X5tge9rZsuF88D0sDI6ywGg9+Vy2ssBqPUX0M8duF/zRt5sgnr97Neffv34eCggL4fD4A4OLFiwgKCkKvXr2wePFi9vsZzcF89Gjk5uTgyIEDEGe+ho5eT2zcuQMampoAAHFmJtLTXnHmWWg7k/3/6aNHuBz2JzQ0u8L/dDAAoIuGBlzdf4PXrl34v+9toaaujsnTp2PaTLsmy1ddzj95iE7t2mHBIDOod1DC09cZWHjqOFL/960ItQ5K0Oz4tmL4RiLBDyd+xy8jLXH8+znIKXqD848fwuOfK2yaV/l5mH/yGFaYj0LQzHlIz89DwJ0oHIi6UWX9zaUx8g0AOsqdMfCL7vjhxFG0RP3Mv0Jhbh4uHTmOPLEYXXV0MHvjGqhodAEA5IqzkJ3+9jGLiHNhKJNKcXq3F07v9mLHDxg9EtN+XlY+z2sx3BcuY6ddPXEKV0+cgq6xIf5v27+bJF91qe/xrdlNC5t27YDXzl0IOXESndXUsOgnB/ZbEQDwOiODcw44EXAEJwKOwHiACbZ5eTZd5mrRGPluDec1AJhtNxXFxcVw3bobuXn5MDToDU/3f3O+kfEqLR1ycm/vuWZkvMa3dm/7CPodOQm/IycxcIAR9nu6AQBei7Owat1WZGZmQUmpPb7spYs9uzZikKj5f3QCQMJVH8grtoHBxF+h2K4Tsl/cQ+RBe843MtopawIM965yB7Ue6KwrQOSBOdUuNzXmP1DsoIJeFovQpmMX5Kc9QdSh+SjKbhmNCXZTvkJxiQRue88gL78IBvwv8NuG2ZxvZLzKyOb023hTVAK3vSHIyMxBm88UofOFGtY6TsXoYW9b8vMLi+B56E+kZ+bi847tMGKoARbMHA0FBXk0N0GPnri8YjU7vOPb8mPw0PUrsD/oja7KytDu/PZxwWeZGfh611bs+NYOi0aMRkp2Fn48epj9RgZQfifjO28PbJg8DesnTUV8ehq+9fagb2SQJsdj6vmMk42NDebPn48xY8bgxYsX+PrrrzF69GjExMRg+PDhcHFxqXsh1Xiek1V3oo/Q1wdaxg8Z0jQ22LScluKmNEC5S3OHQJqQOpPd3CE0i7+3jG3uEJrF4LkbmzuEZqHqeqq5Q2gWZQdaZgPc5okt62OilTmfrr6v6Meu3o9LPXv2DH36lD/H+J///AempqbYvn07XF1d8eeff9YxNyGEEEIIIeRjV+9KBsMwbGfBGzduYNiwYQAATU1NZGV9mncjCCGEEEIIIW/Vu5JhaGgIT09PBAcHIyoqCubm5gCA5ORkqLWCj1sRQgghhBBCGle9Kxm//PILHjx4gA0bNmDBggXQ0Sl/I0JYWBhMTEwaPEBCCCGEEEJI61Kvt0tJpVLk5ubC398fysrKnGkrVqyAnFy96yyEEEIIIYSQj0y9agXy8vKYO3cu8vLyqkxr06ZNi/g4HSGEEEIIIaR51fvWg76+PpKTq/+iMiGEEEIIIYTUu5KxfPlybNmyBZcvX0Z6ejry8/M5f4QQQgghhJBPW72/+D1v3jwAwMKFCzlf3WQYBjweDw8fPmy46AghhBBCCCGtTr0rGX5+fo0RByGEEEIIIeQjUe9KhlAobIw4CCGEEEIIIR+JelcyKrx58wYpKSmQSCSc8b179/7goAghhBBCCCGtV70rGWKxGCtXrsTVq1ernU59MgghhBBCCPm01fvtUps2bUJOTg6OHz+Otm3bYv/+/di8eTN0dHTg6enZGDESQgghhBBCWpF638mIiIjA3r17YWxsDB6PBy0tLQwdOhRKSkrw9vaGubl5I4RJCCGEEEIIaS3qfSejsLAQnTt3BgAoKytDLBYDKP9I34MHDxo2OkIIIYQQQkirU+9Khq6uLhITEwGUd/I+fvw40tLScOzYMairqzd4gIQQQgghhJDWReZKxvPnzwEAs2bNQkZGBgBg8eLFuHbtGszNzeHv7w8HB4fGiZIQQgghhBDSasjcJ2PMmDHQ0NCASCSCSCRCcnIy+vbti7/++gsJCQnQ1NRkH6MihBBCCCGEfLpkrmQEBAQgMjISkZGR2LBhA4qLi6GpqYlBgwZBJBJBTU2tMeMkhBBCCCGEtBIyVzIEAgEEAgEWLVoEiUSCu3fvIiIiApGRkTh37hxKSkqgra2NsLCwxoyXEEIIIYQQ0sK91xe/FRUVYWpqCiMjI5iYmCA8PByBgYFISkpq6PgIIYQQQgghrUy9KhnFxcWIjo5m72DExMTgiy++gKmpKdauXQuhUNhYcRJCCCGEEEJaCZkrGba2toiJiYG2tjYEAgFsbW0hFAqpLwYhhBBCCCGEQ+ZKxp07d6Curg6RSAShUAiBQEBvkyKEEEIIIYRUIXMlIyoqCrdu3UJkZCR8fHzg4OAAXV1dmJqaQigUQigUUqWDEEIIIYQQInslo3379hg2bBiGDRsGAMjPz8ft27cRERGB/fv3w9HRET169MDZs2cbLVhCCCGEEEJIyyfzF7/f1b59eygrK0NZWRmdOnWCgoIC4uPjGzI2QgghhBBCSCsk852MsrIyxMbGIiIiAhEREYiOjsabN2/Yr4CvWbMGIpGoMWMlhBBCCCGEtAL1+hjfmzdvoK6uDqFQiJUrV0IkEkFbW7sx4yOEEEIIIYS0MjJXMlasWAGRSARdXd3GjIcQQgghhBDSyslcyfj2228bMw5CCCGEEELIR+K9O34TQgghhBBCSHV4DMMwzR0EIYQQQggh5ONBdzIIIYQQQgghDYoqGYQQQgghhJAGRZUMQgghhBBCSIOiSgYhhBBCCCGkQVElgxBCCCGEENKgqJJBCCGEEEIIaVBUySCEEEIIIYQ0KKpkEEIIIYQQQhoUVTIIIYSQTwDDMPD19UVsbGxzh0II+QR8NJUMhmGwevVqCIVC8Pl8CAQCbNq0qbnDei92dnYfHHtDLKMmzs7OWLRo0XvP35ixNcc6P3R7NKbk5GTw+Xw8fPiwuUMhhDSCiIgI8Pl85Obm1pl23759uHbtGvh8fhNE1nqkpaVhxIgRmDNnDtLS0jBu3LjmDomQj8JHU8m4evUqTp06BS8vL4SHh+PLL79s7pCaRE0XGA8PD/z444/s8MiRI3Ho0KEmjq7l+FS3h6am5id1PBDSUlXXGHH+/HkYGRnBx8en0dd/+/ZthIWFwd3dHYqKio2+vgrOzs7g8/ng8/kwMDCAhYUFtmzZgsLCwiaLoS43btzAhAkTYGFhge+++w5TpkypNt3vv/8OExMTlJaWsuMKCgpgYGCAGTNmcNLeunULfD4fiYmJjRo7IS2ZQnMH0FBevHgBdXV1DBgwAACgoND4WSspKcFnn33W6Ot5H8rKys0dQovyKW6PivKprq7e3KG8t5Z8jJGPi1QqBY/Hg5xc07S9/fHHH1i3bh1+/fVXTJ06tcr0hi77AwcORFBQUIMtrz6++uoruLq6orS0FLdu3cKqVatQWFiIdevWNUs875o0aRL7v62tbY3pRCIRCgsLERsbi/79+wMor0yoqakhJiYGb968Qbt27QCUNwB26dIFurq69YqFYRhIpdIm+Q1DSGP7KO5kODs7Y8OGDUhJSQGfz8fIkSOrpMnJycGKFStgamqKfv36Yd68eXj27BknTVhYGL7++msYGhpi5MiROHjwIGf6yJEjsXfvXjg7O2PgwIFYvXp1Y2YLAHD69GlYW1vDxMQEQ4cOxU8//YTXr18DKH8UZubMmQAAU1NT8Pl8ODs7A+A+HmRnZ4eXL1/C1dWVbVECylv3J06cyFnfoUOHONtPKpXC1dUVAoEAIpEIW7duBcMwnHkYhoGPjw8sLCxgbGyMCRMm4Pz58zLlr679EhQUBIFAgGvXrsHKygomJiaYO3cu0tPT2TSlpaXYuHEjG6ObmxucnJw4rYatZXvUxs7ODuvXr8f69evZ9e/cuZNdf3Xls7rHpZ4+fYr58+djwIABMDExwYwZM5CUlMROP3nyJKysrGBkZISxY8fiyJEjHxx7ffPo6uoKkUgEe3t7REZGYsqUKTA0NISZmRm2bdvGaUksKSnBxo0bMXjwYBgZGeG7777Df//7X3Z6xd2+a9euYdKkSTA2NsbMmTPx+vVrXLlyBVZWVhgwYAAcHBzw5s0bdr7z589j/PjxMDY2hkgkwuzZs1tU6ytQc4zVldN3j4mWLDg4GCKRCCUlJZzxS5YswYoVKwAAf/31F6ytrWFkZAQLCwvs3r2bUy58fX0xfvx49O/fH8OHD8fatWtRUFDATq84t1y+fBnjxo2DkZERXr582ST58/Hxwfr167F9+3a2glFxp8Pb2xtmZmYYO3YsgNqvAdUpKirC/PnzMW3aNGRnZ6OsrAy7d+/GsGHDYGhoiIkTJ+Lq1ats+iVLlmDDhg3s8KZNm8Dn8/H06VMA5edXExMTXLt27b3zW9HYoampifHjx2P8+PG4dOlSncdu5bsglf8iIiIAlJ/zvLy8sHLlSpiYmMDc3BzHjx/nrNvNzQ1jxoxBv379YGFhgV27dkEikbDTK877wcHBGDlyJAYOHIjly5cjPz+/Sj569uyJLl26IDIykh0XGRkJCwsLaGtr486dO5zxIpGozv1X+fxUUZ5v3bqFR48ewc7ODiYmJhgwYACsra0RExMDoPw8Wd12SU5OBlB72WcYBoMGDUJYWBgbw8SJEzF48GB2+M6dOzAwMGDnqetYIqQmH0Ulw8XFBUuXLkXXrl0RHh6OEydOVEnj7OyM2NhYeHp64vjx42AYBvPnz2dPNrGxsVi2bBnGjRuHkJAQLF68GL/99luVlp8DBw7gyy+/RFBQUJNcsCUSCX788UecOXMGe/bsQXJyMluR0NTUhIeHB4DyHxvh4eFwcXGpsgwPDw907doVS5cuRXh4OMLDw2Ve/8GDB3Hy5Els2rQJR48eRU5ODi5cuMBJs2vXLgQFBWHt2rU4d+4cZs+ejZ9//plzIq5JXfsFKL9oHjx4EFu3bkVAQABSU1OxZcsWdrqPjw9CQkLg6uqKo0ePIj8/HxcvXqxxnS15e9Tl1KlTkJeXR2BgIFxcXHD48GH88ccf7PS6ymdaWhpsbW3Rpk0bHD58GEFBQbCxsWF/nAUGBmLnzp1Yvnw5QkND4eDgAHd3d5w6deqDY69vHn///XcsX74c8+fPh5GREU6fPo21a9fixIkT8PT0ZNNv3boVYWFh2Lx5M06dOgUdHR3MmzcP2dnZnOXu3r0bq1evxrFjx/Dq1SssW7YMfn5+2L59O/bt24fr16/D398fAJCeno6ffvoJNjY2CA0NhZ+fH0aPHl2lQtmcaotRlnLako0dOxZSqRSXLl1ix4nFYly+fBnW1ta4du0afv75Z9jZ2SE0NBTr169HUFAQvLy82PQ8Hg8uLi4ICQnB5s2bcfPmTbi5uXHWU1RUBG9vb2zcuBFnz56Fqqpqo+dt27Zt2Lt3L7y9vTFmzBjOtBs3biA+Ph6+vr5sXmq7BrwrLy8P9vb2kEgkOHToEJSVleHn5wdfX184OTnhzJkzMDMzw6JFi9jGHKFQyDk3RUVFQUVFhR0XExODkpIS9imBhtC2bVtIJJI6j10XFxf2HB0eHo6ZM2dCVVUVPXv2ZJfl6+sLQ0NDBAcHY8aMGVi7di3i4+PZ6R06dICrqyvOnTsHFxcX/PHHH1UelU1KSsKlS5fg5eUFb29vREVF1fgIm1AoZCs5QHklQSgUwtTUlB1fUlKCu3fvQiQSybz/3Nzc8NNPPyE0NBR8Ph+Ojo7o2rUrTpw4gaCgIPzwww/sY24eHh6c7WJpaQldXV2oqakBqL3s83g8mJqasvs3JycH8fHxKC0tRVxcHIDyCpKBgQE6dOhQ5/IIqRXzkfD19WVGjBjBDtva2jIbN25kGIZhEhMTGX19feb27dvsdLFYzBgbGzOhoaEMwzCMg4MDM2fOHM4yt2zZwowbN44dHjFiBLNo0aLGzEaV2N917949Rl9fn8nPz2cYhmFu3rzJ6OvrMzk5ObUuY8SIEYyvry8njbu7OzNhwgTOuHe349ChQxlvb292WCKRMMOGDWMWLlzIMAzDFBQUMEZGRkx0dDRnOb/88gvj4OBQa/5k2S8nT55k9PX1mefPn7NpAgICmCFDhrDDQ4YMYfbv388Ol5aWMubm5myMLX17yMrW1paxsrJiysrK2HFubm6MlZUVm6d3y+eLFy8YfX195sGDBwzDMMz27duZkSNHMiUlJdWuY/jw4UxISAhn3J49e5jp06d/UOyysrW1ZSZOnMgO79ixgxkzZgwnzwEBAUz//v0ZqVTKFBQUMAYGBsyZM2fY6SUlJYyZmRnj4+PDMMzbY+Sff/5h03h7ezP6+vpMUlISO2716tWMvb09wzAMExsby+jr6zPJycmNldUPVluMdZXT1uDXX39l5s2bxw4fOnSIsbCwYMrKypgZM2YwXl5enPTBwcHM0KFDa1xeaGgoIxQK2eGKc8vDhw8bPvhqODk5MQYGBlXKYuXpQ4YMYYqLi2tdTk3XgPj4eGbChAnM4sWLOcswMzNjPD09OcuwsbFh1q5dyzAMwzx69Ijh8/nM69evmezsbMbAwIDZu3cvs3TpUoZhGMbLy4uZOnXqB+W7crm7d+8eIxQKmSVLltR57FYWFhbGGBoaMlFRUey4ESNGMI6OjuxwWVkZM3jwYObo0aM1xuPj48NMnjyZHXZ3d2f69evH5OXlseO2bNlSY56PHz/O9O/fn5FIJExeXh7Tt29fJjMzkzl37hx7noyMjKxyfqmc/+r234ULFzjpTExMmKCgoBrzUcHX15cRCARMQkJCjWneLft+fn7MN998wzAMw1y4cIGxtrZmFi9ezAQEBDAMwzD29vaMm5ubzMsjpCafxEN/8fHxUFBQQL9+/dhxKioq0NXVZVs8EhISYGFhwZlvwIAB8PPzg1Qqhby8PADA0NCw6QIH8ODBA3h4eODRo0fIzs5mW1JTU1PRq1evRl13Xl4eMjIyYGJiwo5TUFCAoaEhG0dcXByKi4thb2/PmVcikaBPnz61Ll+W/QIA7dq1g7a2NjvcpUsX9nZzXl4eMjMzYWxszE6Xl5eHgYEBysrK3iPXNWvs7SGLfv36gcfjscP9+/eHr68vpFIpgLrL58OHDyEQCKrt+CkWi5GamgoXFxfOo4ClpaXo2LHjB8cuq8p5iI+Ph4mJCSfPAwcORGFhIV69eoXc3FxIJBJOK6uioiKMjY05ZQgA5406qqqqaNeuHbp3786Oq3iuGgB69+6NwYMHY/z48TAzM4OZmRnGjBmDTp06NXh+31dNMcrJydVZTluDadOmYcqUKUhLS4OGhgaCgoIwefJk8Hg83L9/HzExMZw7F1KpFMXFxexz8Tdv3oS3tzfi4uKQn5/PTi8sLET79u0BlJeVpnzTEp/PR1ZWFtzd3WFkZAQlJSXOdH19/Sr9MGS9BsyZMwdGRkbYtWsXe73Kz89Henp6lbsQAwYMwKNHj9h1KisrIyoqCgoKCuwjxxV39SIjI2FqavpB+f7777/ZDtOlpaWwsLCAnZ0dwsLCZDp2Hzx4ACcnJ6xZswYCgYAzrfL+4/F4UFNT4zyOdP78eRw+fBhJSUkoLCxEaWlple3erVs3zrjK15h3VfTLiImJQW5uLnr06AFVVVWYmppixYoVKCwsREREBLS0tNC9e3eZ95+RkRFnPXPmzMGqVatw+vRpDBkyBGPHjuVcBwHgypUr2LZtG7y8vDh9P+oq+0KhEJs2bYJYLEZUVBSEQiG0tLQQFRWF6dOnIzo6mn0UW5blEVKTT6KSUdOFlWEY9seLrBffik5dTaGwsBD29vYYOnQo3NzcoKKigtTUVMydO5fzONH74vF4VfJd+ZlmWVTM7+3tDQ0NDc60ujotyrJfgKqd+KuLu3L62pZdm+beHg2hrvLZtm3bGqdVVMo2bNjAqfgBaLLOsAA3D9Xtx4pxlfd5dfv/3XGVyxGPx6u2XFVsA3l5efj6+iI6Opp9jGrnzp0IDAzkVEyaU00x+vr6NndoDaJv377o3bs3goODYWZmhidPnrCVirKyMixZsgSWlpZV5mvTpg1evnyJ+fPn49tvv8WPP/6ITp064fbt23BxceEc023btq1SThqThoYGPDw8MHPmTMybNw/79+/n/Lh99/itzzVg+PDh+PPPPxEXF1el4lTb8VH58RlFRUUIhULo6+tDKpXi8ePHVX5wvg+RSIS1a9dCQUEBXbp0gaKiIlvJqevYzcjIwMKFC2FjY1NtB/narg93796Fg4MDlixZAjMzM3Ts2BHnzp2rcoxU18m6pmuIjo4OunbtioiICOTk5LAVMHV1dXTr1g3R0dGIiIjAoEGD6rX/3t33S5YswTfffIMrV67g6tWrcHd3x86dOzF69GgA5Q1aDg4OcHR0hJmZGTufLGW/csUyKiqKfdzcy8sLMTExKC4uxsCBA2VeHiE1+Sj6ZNSlV69eKC0txb1799hxWVlZePbsGfT09AAAenp6iI6O5swXHR2NHj16sK1CTS0hIQFZWVlwdHSEQCCAnp5eldaVihbpipbsmigqKlZp2e/cuTMyMzM5J9PKHYQ7duwIdXV13L17lx1XWlqK+/fvs8N6enr47LPPkJKSAh0dHc6fpqZmrTHJsl/q0rFjR6ipqXE6C0ql0jq/C9ESt4csKm+rimEdHR2Zyyifz8etW7eqraSqqalBQ0MDL168qBJ7c/2w7tWrF+7cucPZJ9HR0ejQoQM0NDSgra0NRUVF3L59m50ukUgQGxsrcxmqCY/Hw8CBA7F06VIEBwdDUVGx1r4+zaG6GG/cuFFnOW0tpkyZgqCgIJw8eRJDhgxhj6G+ffsiMTGxSjnV0dGBnJwcYmNjIZVK4ezsjP79+0NXV5fzsojmpKWlhYCAAIjFYsydO7faDsYVZLkGVHB0dMTkyZMxe/Zs9tl6JSUldOnShXN8AOUdeysfHxX9Mio6K/N4PAgEAhw8eJDzg/N9tWvXDjo6OujWrRt7zZLl2C0uLsaiRYvQs2dPrFy5st7rjY6OhpaWFhYuXAgjIyP06NEDKSkpH5QXoLzSVLG9hEIhO14oFCI8PBz37t2DSCSq1/6rjq6uLmbPno2DBw/C0tISJ0+eBFB+nVywYAEsLS0xe/ZszjyylP2KiuWlS5fw5MkTCAQC8Pl8lJaW4tixY+jbty9b+W3JxxJp+T6JSkaPHj1gYWGB1atXs29t+Pnnn6GhocE+ImVvb48bN25gz549SExMxKlTp3DkyJEqj700JS0tLSgqKsLf3x8vXrzApUuXsHfvXk6abt26gcfj4e+//4ZYLK7xjQ/dunVDVFQU0tLSIBaLAZSfKMViMXx8fJCUlIQjR45UeYPIzJkz4ePjgwsXLiA+Ph7r1q3jfJNDSUkJ9vb2cHV1xalTp5CUlIQHDx7gyJEjdXYWlmW/yMLW1hbe3t64ePEiEhISsGnTJuTk5NTaQtkSt4csUlNT4erqioSEBJw9exYBAQH1amX8/vvvkZ+fDwcHB8TExODZs2cIDg5GQkICgPLWs3379uHw4cNITEzE48ePcfLkyWZrHZ8xYwZevXqFDRs2ID4+HhcvXoSHhwfmzJkDOTk5tG/fHt999x22bt2Kq1evIi4uDqtXr0ZRUVGN77qXxb1799hWvZSUFPz5558Qi8WcDqfNrbYY6yqnrcWECROQlpaGwMBA2NjYsOP/9a9/4fTp0/Dw8MDTp08RHx+P0NBQ7Ny5E0D5D9jS0lL23BkcHIxjx441Vzaq6Nq1K/z9/ZGdnQ17e3vk5eVVm06Wa0BlTk5OGD9+PGbNmsU+cjR37lz4+PggNDQUCQkJ2LZtGx49esQ5bwiFQjx9+pT9wVkxLiQkhPODsyHJcuyuWbMGqampWLVqFcRiMTIyMpCRkVHlrWM10dbWRmpqKs6dO4ekpCT4+fk1SEOBSCTC7du38ejRI04lw9TUFIGBgSguLoZIJKr3/qtQVFSE9evXIyIiAi9fvsTt27cRExPDVr6WLFmCtm3bYvHixew2ycjIgFQqlbnsC4VCnD17Fnw+H0pKSmzFMiQkhJOnln4skZbtk3hcCgBcXV2xadMmLFiwABKJBAKBAPv27WNbVQwMDLBr1y64u7vD09MT6urqWLp0KaytrZst5s6dO2Pz5s3YsWMH/P39YWBgACcnJyxcuJBNo6GhgSVLlmD79u1YuXIlJk2ahM2bN1dZ1tKlS7FmzRqMGjUKJSUlePz4MfT09PDrr7/C29sbnp6esLS0hL29PQIDA9n57O3tkZGRAWdnZ8jJycHGxgajR4/mXBSXLVsGVVVVeHt7Izk5GR07dkTfvn2xYMGCOvNY136RxQ8//IDMzEw4OTlBXl4e06ZNg5mZWa2t+y11e9Rl0qRJKCoqwtSpUyEvLw9bW1tMnz5d5vlVVFRw+PBhuLm5wc7ODnJycujTpw/bUjl16lS0bdsWBw4cgJubG9q3bw99fX3MmjXrg2N/HxoaGti3bx+2bt2KwMBAKCsrY8qUKZxjwNHREQzDYMWKFSgoKIChoSH279//Qf0nlJSUEBUVhcOHDyM/Px9aWlpwdnbG8OHDGyJbDaK2GIcOHVpnOW0NlJSUYGlpiStXrmDUqFHs+K+++gpeXl7Ys2cP9u/fDwUFBfTs2ZN9nKZPnz5YuXIlfHx8sGPHDggEAjg4OMDJyam5slKFhoYG/P39MXPmTMyZMwddunSpkkaWa8C7fvnlF5SVlWHWrFns8vPz87F582aIxWLo6elh79696NGjBzuPvr4+VFRUoKWlxVYohEIhpFLpB/fHqE1dx25UVBQyMjKqfIHbz88PIpGozuWPGjUKs2bNwvr161FSUgJzc3MsXLgQu3fv/qC4RSIRioqK0LNnT/aNTkD5NisoKIC2tjZ7162++w8ofzw1OzsbTk5OyMzMhIqKCiwtLbF06VIA5dsFQJXX9V+6dEnmsi8SiSCVSqtUki5evMgZ1xqOJdJy8ZjW1BOQEBmUlZXBysoKVlZWWLZsWXOH02Ds7OzQu3fval9TTEhdnJ2dkZubK1NLaksyZ84c6OnpYdWqVc0dCiGEkHr4ZO5kkI/Xy5cvcf36dZiamqKkpARHjhzBy5cvMX78+OYOjRDynrKzs3H9+nXcvHmzST58SgghpGFRJYO0enJycggKCsKWLVvAMAz09fXh6+v7wR1/CSHNx9raGjk5OXB0dGxRfWEIIYTIhh6XIoQQQgghhDSoT+LtUoQQQgghhJCmQ5UMQgghhBBCSIOiSgYhhBBCCCGkQVElgxBCCCGEENKgqJJBCCGEEEIIaVBUySCEEEIIIYQ0KKpkEEIIIYQQQhoUVTIIIYQQQgghDYoqGYQQQgghhJAG9f+Wkd5K/kol0wAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "id": "98ac570b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:17.979426Z",
     "start_time": "2024-12-20T13:38:17.961908Z"
    }
   },
   "source": [
    "# Prepare training set (all data without labels)\n",
    "X = houses.drop('price', axis=1).to_numpy()\n",
    "X"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        , 50.0833641 , 19.9705682 , ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.        , 50.0849209 , 19.8909484 , ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 2.        , 50.0484367 , 19.9442508 , ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       ...,\n",
       "       [ 4.        , 50.1035684 , 19.95360311, ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 4.        , 50.0587885 , 19.9500605 , ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.        , 52.3357707 , 16.8261046 , ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "c420802c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:18.198817Z",
     "start_time": "2024-12-20T13:38:18.194218Z"
    }
   },
   "source": [
    "# Prepare labels for training set\n",
    "y = houses.loc[:, 'price'].to_numpy()\n",
    "y"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([589000., 380000., 799000., ..., 455000., 468720., 634671.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "id": "30030460",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:18.369522Z",
     "start_time": "2024-12-20T13:38:18.362695Z"
    }
   },
   "source": [
    "# Split data to training and validation\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345) "
   ],
   "outputs": [],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "id": "77f2d034",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:18.413588Z",
     "start_time": "2024-12-20T13:38:18.410139Z"
    }
   },
   "source": [
    "# Universal method for training and evaluating different models\n",
    "\n",
    "def train_model(classifier, feature_vector_train, label, feature_vector_valid):\n",
    "    # train model\n",
    "    classifier.fit(feature_vector_train, label)\n",
    "    \n",
    "    with open('titanic_classifier.pickle', 'wb') as handle:\n",
    "        pickle.dump(classifier, handle)\n",
    "    \n",
    "    # generate predictions\n",
    "    predictions = classifier.predict(feature_vector_valid)\n",
    "    \n",
    "    # evalueate model\n",
    "    score_vals = [\n",
    "        metrics.mean_squared_error(predictions, y_test, squared=False),\n",
    "        metrics.mean_absolute_error(predictions, y_test)\n",
    "    ]\n",
    "    return score_vals"
   ],
   "outputs": [],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "id": "ce0cdc92",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:24.069368Z",
     "start_time": "2024-12-20T13:38:18.467671Z"
    }
   },
   "source": [
    "# MODEL 1 - Linear regression\n",
    "accuracy = train_model(linear_model.LinearRegression(), X_train, y_train, X_test)\n",
    "accuracy_compare = {'LR': accuracy}\n",
    "print (\"LR: \", accuracy)\n",
    "\n",
    "# MODEL 2 - RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators = 100, random_state = 0)\n",
    "accuracy = train_model(regressor, X_train, y_train, X_test)\n",
    "accuracy_compare['random forrest tree'] = accuracy\n",
    "print ('random forrest tree' , accuracy)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR:  [366845.6859637721, 161468.35697575327]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/michal/anaconda3/lib/python3.12/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random forrest tree [240690.8925329773, 72385.66824523133]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/michal/anaconda3/lib/python3.12/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "id": "338f6165",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:24.173592Z",
     "start_time": "2024-12-20T13:38:24.078151Z"
    }
   },
   "source": [
    "# compare models\n",
    "\n",
    "df_compare = pd.DataFrame(accuracy_compare, index = ['rmse', 'mae'])\n",
    "df_compare.plot(kind='bar')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "id": "20363eac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:24.196433Z",
     "start_time": "2024-12-20T13:38:24.189129Z"
    }
   },
   "source": [
    "# Test manually prediction\n",
    "regressor.predict([[2.0, 50.049224, 19.970379, 3.0, 74.05, 2021.0, 1, 0, 0]]) #prawidłowa wartość 749000.0"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([749000.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "code",
   "id": "2d19cf8d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-20T13:38:40.323967Z",
     "start_time": "2024-12-20T13:38:24.230739Z"
    }
   },
   "source": [
    "# Correction - try adjust model hyperparameters\n",
    "\n",
    "# MODEL 3 - RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators = 300, random_state = 0)\n",
    "accuracy = train_model(regressor, X_train, y_train, X_test)\n",
    "accuracy_compare['random forrest tree'] = accuracy\n",
    "print ('random forrest tree' , accuracy)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random forrest tree [241181.9276526807, 72030.72138852399]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/michal/anaconda3/lib/python3.12/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "execution_count": 19
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}