regression_techniques.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge, LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, accuracy_score, classification_report\n",
    "from sklearn.tree import DecisionTreeRegressor, plot_tree\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prana\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3146: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>member_casual</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>...</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Wind Gust</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Condition</th>\n",
       "      <th>index</th>\n",
       "      <th>Holiday_Name</th>\n",
       "      <th>Weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25612325</td>\n",
       "      <td>2019-11-01 00:00:45</td>\n",
       "      <td>2019-11-01 00:08:10</td>\n",
       "      <td>Ellis Ave &amp; 55th St</td>\n",
       "      <td>420.0</td>\n",
       "      <td>Harper Ave &amp; 59th St</td>\n",
       "      <td>425.0</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>26  F</td>\n",
       "      <td>81</td>\n",
       "      <td>8 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>103</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25612326</td>\n",
       "      <td>2019-11-01 00:01:13</td>\n",
       "      <td>2019-11-01 00:30:09</td>\n",
       "      <td>Western Ave &amp; Winnebago Ave</td>\n",
       "      <td>116.0</td>\n",
       "      <td>Morgan St &amp; Polk St</td>\n",
       "      <td>241.0</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>26  F</td>\n",
       "      <td>81</td>\n",
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       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>103</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25612327</td>\n",
       "      <td>2019-11-01 00:01:42</td>\n",
       "      <td>2019-11-01 00:38:32</td>\n",
       "      <td>Western Ave &amp; Winnebago Ave</td>\n",
       "      <td>116.0</td>\n",
       "      <td>May St &amp; Taylor St</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>26  F</td>\n",
       "      <td>81</td>\n",
       "      <td>8 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>103</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25612328</td>\n",
       "      <td>2019-11-01 00:02:24</td>\n",
       "      <td>2019-11-01 00:09:27</td>\n",
       "      <td>Lincoln Ave &amp; Winona St</td>\n",
       "      <td>472.0</td>\n",
       "      <td>Maplewood Ave &amp; Peterson Ave</td>\n",
       "      <td>455.0</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>26  F</td>\n",
       "      <td>81</td>\n",
       "      <td>8 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>103</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25612329</td>\n",
       "      <td>2019-11-01 00:02:30</td>\n",
       "      <td>2019-11-01 00:04:09</td>\n",
       "      <td>Clinton St &amp; Madison St</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Clinton St &amp; Washington Blvd</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>26  F</td>\n",
       "      <td>81</td>\n",
       "      <td>8 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>103</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3807053</th>\n",
       "      <td>A4D50CAC9CCFB180</td>\n",
       "      <td>2020-10-20 01:51:54</td>\n",
       "      <td>2020-10-20 01:59:11</td>\n",
       "      <td>Kimbark Ave &amp; 53rd St</td>\n",
       "      <td>322.0</td>\n",
       "      <td>Blackstone Ave &amp; Hyde Park Blvd</td>\n",
       "      <td>121.0</td>\n",
       "      <td>member</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>44 F</td>\n",
       "      <td>79</td>\n",
       "      <td>9mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>355</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3807054</th>\n",
       "      <td>2D551117FD01DCE1</td>\n",
       "      <td>2020-10-20 01:04:29</td>\n",
       "      <td>2020-10-20 01:19:33</td>\n",
       "      <td>Pine Grove Ave &amp; Irving Park Rd</td>\n",
       "      <td>254.0</td>\n",
       "      <td>Glenwood Ave &amp; Morse Ave</td>\n",
       "      <td>447.0</td>\n",
       "      <td>member</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>44 F</td>\n",
       "      <td>79</td>\n",
       "      <td>8mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>355</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3807055</th>\n",
       "      <td>2D551117FD01DCE1</td>\n",
       "      <td>2020-10-20 01:04:29</td>\n",
       "      <td>2020-10-20 01:19:33</td>\n",
       "      <td>Pine Grove Ave &amp; Irving Park Rd</td>\n",
       "      <td>254.0</td>\n",
       "      <td>Glenwood Ave &amp; Morse Ave</td>\n",
       "      <td>447.0</td>\n",
       "      <td>member</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>44 F</td>\n",
       "      <td>79</td>\n",
       "      <td>9mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>355</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3807056</th>\n",
       "      <td>09264F6A1F8E4CEE</td>\n",
       "      <td>2020-10-20 01:38:07</td>\n",
       "      <td>2020-10-20 01:48:04</td>\n",
       "      <td>St. Clair St &amp; Erie St</td>\n",
       "      <td>211.0</td>\n",
       "      <td>Clark St &amp; Armitage Ave</td>\n",
       "      <td>94.0</td>\n",
       "      <td>member</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
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       "      <td>44 F</td>\n",
       "      <td>79</td>\n",
       "      <td>8mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>355</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3807057</th>\n",
       "      <td>09264F6A1F8E4CEE</td>\n",
       "      <td>2020-10-20 01:38:07</td>\n",
       "      <td>2020-10-20 01:48:04</td>\n",
       "      <td>St. Clair St &amp; Erie St</td>\n",
       "      <td>211.0</td>\n",
       "      <td>Clark St &amp; Armitage Ave</td>\n",
       "      <td>94.0</td>\n",
       "      <td>member</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>44 F</td>\n",
       "      <td>79</td>\n",
       "      <td>9mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>355</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3807058 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ride_id           started_at             ended_at  \\\n",
       "0                25612325  2019-11-01 00:00:45  2019-11-01 00:08:10   \n",
       "1                25612326  2019-11-01 00:01:13  2019-11-01 00:30:09   \n",
       "2                25612327  2019-11-01 00:01:42  2019-11-01 00:38:32   \n",
       "3                25612328  2019-11-01 00:02:24  2019-11-01 00:09:27   \n",
       "4                25612329  2019-11-01 00:02:30  2019-11-01 00:04:09   \n",
       "...                   ...                  ...                  ...   \n",
       "3807053  A4D50CAC9CCFB180  2020-10-20 01:51:54  2020-10-20 01:59:11   \n",
       "3807054  2D551117FD01DCE1  2020-10-20 01:04:29  2020-10-20 01:19:33   \n",
       "3807055  2D551117FD01DCE1  2020-10-20 01:04:29  2020-10-20 01:19:33   \n",
       "3807056  09264F6A1F8E4CEE  2020-10-20 01:38:07  2020-10-20 01:48:04   \n",
       "3807057  09264F6A1F8E4CEE  2020-10-20 01:38:07  2020-10-20 01:48:04   \n",
       "\n",
       "                      start_station_name  start_station_id  \\\n",
       "0                    Ellis Ave & 55th St             420.0   \n",
       "1            Western Ave & Winnebago Ave             116.0   \n",
       "2            Western Ave & Winnebago Ave             116.0   \n",
       "3                Lincoln Ave & Winona St             472.0   \n",
       "4                Clinton St & Madison St              77.0   \n",
       "...                                  ...               ...   \n",
       "3807053            Kimbark Ave & 53rd St             322.0   \n",
       "3807054  Pine Grove Ave & Irving Park Rd             254.0   \n",
       "3807055  Pine Grove Ave & Irving Park Rd             254.0   \n",
       "3807056           St. Clair St & Erie St             211.0   \n",
       "3807057           St. Clair St & Erie St             211.0   \n",
       "\n",
       "                        end_station_name  end_station_id member_casual  Year  \\\n",
       "0                   Harper Ave & 59th St           425.0    Subscriber  2019   \n",
       "1                    Morgan St & Polk St           241.0      Customer  2019   \n",
       "2                     May St & Taylor St            22.0    Subscriber  2019   \n",
       "3           Maplewood Ave & Peterson Ave           455.0    Subscriber  2019   \n",
       "4           Clinton St & Washington Blvd            91.0    Subscriber  2019   \n",
       "...                                  ...             ...           ...   ...   \n",
       "3807053  Blackstone Ave & Hyde Park Blvd           121.0        member  2020   \n",
       "3807054         Glenwood Ave & Morse Ave           447.0        member  2020   \n",
       "3807055         Glenwood Ave & Morse Ave           447.0        member  2020   \n",
       "3807056          Clark St & Armitage Ave            94.0        member  2020   \n",
       "3807057          Clark St & Armitage Ave            94.0        member  2020   \n",
       "\n",
       "         Month  ...  Hour  Temperature Humidity  Wind Speed Wind Gust  \\\n",
       "0           11  ...     0        26  F       81       8 mph     0 mph   \n",
       "1           11  ...     0        26  F       81       8 mph     0 mph   \n",
       "2           11  ...     0        26  F       81       8 mph     0 mph   \n",
       "3           11  ...     0        26  F       81       8 mph     0 mph   \n",
       "4           11  ...     0        26  F       81       8 mph     0 mph   \n",
       "...        ...  ...   ...          ...      ...         ...       ...   \n",
       "3807053     10  ...     1         44 F       79        9mph      0mph   \n",
       "3807054     10  ...     1         44 F       79        8mph      0mph   \n",
       "3807055     10  ...     1         44 F       79        9mph      0mph   \n",
       "3807056     10  ...     1         44 F       79        8mph      0mph   \n",
       "3807057     10  ...     1         44 F       79        9mph      0mph   \n",
       "\n",
       "        Precipitation Condition index  Holiday_Name Weekend  \n",
       "0              0.0 in      Fair   103   Not Holiday       0  \n",
       "1              0.0 in      Fair   103   Not Holiday       0  \n",
       "2              0.0 in      Fair   103   Not Holiday       0  \n",
       "3              0.0 in      Fair   103   Not Holiday       0  \n",
       "4              0.0 in      Fair   103   Not Holiday       0  \n",
       "...               ...       ...   ...           ...     ...  \n",
       "3807053         0.0in    Cloudy   355   Not Holiday       0  \n",
       "3807054         0.0in    Cloudy   355   Not Holiday       0  \n",
       "3807055         0.0in    Cloudy   355   Not Holiday       0  \n",
       "3807056         0.0in    Cloudy   355   Not Holiday       0  \n",
       "3807057         0.0in    Cloudy   355   Not Holiday       0  \n",
       "\n",
       "[3807058 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the merged data\n",
    "merged_data = pd.read_csv(\"cleaned_data.csv\")\n",
    "merged_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3807058 entries, 0 to 3807057\n",
      "Data columns (total 21 columns):\n",
      " #   Column              Dtype  \n",
      "---  ------              -----  \n",
      " 0   ride_id             object \n",
      " 1   started_at          object \n",
      " 2   ended_at            object \n",
      " 3   start_station_name  object \n",
      " 4   start_station_id    float64\n",
      " 5   end_station_name    object \n",
      " 6   end_station_id      float64\n",
      " 7   member_casual       object \n",
      " 8   Year                int64  \n",
      " 9   Month               int64  \n",
      " 10  Day                 int64  \n",
      " 11  Hour                int64  \n",
      " 12  Temperature         object \n",
      " 13  Humidity            int64  \n",
      " 14  Wind Speed          object \n",
      " 15  Wind Gust           object \n",
      " 16  Precipitation       object \n",
      " 17  Condition           object \n",
      " 18  index               int64  \n",
      " 19  Holiday_Name        object \n",
      " 20  Weekend             int64  \n",
      "dtypes: float64(2), int64(7), object(12)\n",
      "memory usage: 610.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# get the information about the attributes \n",
    "merged_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
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       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>...</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Wind Gust</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Condition</th>\n",
       "      <th>index</th>\n",
       "      <th>Holiday_Name</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25612325</td>\n",
       "      <td>2019-11-01 00:00:45</td>\n",
       "      <td>2019-11-01 00:08:10</td>\n",
       "      <td>Ellis Ave &amp; 55th St</td>\n",
       "      <td>420.0</td>\n",
       "      <td>Harper Ave &amp; 59th St</td>\n",
       "      <td>425.0</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>26  F</td>\n",
       "      <td>81</td>\n",
       "      <td>8 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>103</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25612326</td>\n",
       "      <td>2019-11-01 00:01:13</td>\n",
       "      <td>2019-11-01 00:30:09</td>\n",
       "      <td>Western Ave &amp; Winnebago Ave</td>\n",
       "      <td>116.0</td>\n",
       "      <td>Morgan St &amp; Polk St</td>\n",
       "      <td>241.0</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>26  F</td>\n",
       "      <td>81</td>\n",
       "      <td>8 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>103</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25612327</td>\n",
       "      <td>2019-11-01 00:01:42</td>\n",
       "      <td>2019-11-01 00:38:32</td>\n",
       "      <td>Western Ave &amp; Winnebago Ave</td>\n",
       "      <td>116.0</td>\n",
       "      <td>May St &amp; Taylor St</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>26  F</td>\n",
       "      <td>81</td>\n",
       "      <td>8 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>103</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25612328</td>\n",
       "      <td>2019-11-01 00:02:24</td>\n",
       "      <td>2019-11-01 00:09:27</td>\n",
       "      <td>Lincoln Ave &amp; Winona St</td>\n",
       "      <td>472.0</td>\n",
       "      <td>Maplewood Ave &amp; Peterson Ave</td>\n",
       "      <td>455.0</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>26  F</td>\n",
       "      <td>81</td>\n",
       "      <td>8 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>103</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25612329</td>\n",
       "      <td>2019-11-01 00:02:30</td>\n",
       "      <td>2019-11-01 00:04:09</td>\n",
       "      <td>Clinton St &amp; Madison St</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Clinton St &amp; Washington Blvd</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>26  F</td>\n",
       "      <td>81</td>\n",
       "      <td>8 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>103</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>3807053</th>\n",
       "      <td>A4D50CAC9CCFB180</td>\n",
       "      <td>2020-10-20 01:51:54</td>\n",
       "      <td>2020-10-20 01:59:11</td>\n",
       "      <td>Kimbark Ave &amp; 53rd St</td>\n",
       "      <td>322.0</td>\n",
       "      <td>Blackstone Ave &amp; Hyde Park Blvd</td>\n",
       "      <td>121.0</td>\n",
       "      <td>member</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>44 F</td>\n",
       "      <td>79</td>\n",
       "      <td>9mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>355</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-10-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3807054</th>\n",
       "      <td>2D551117FD01DCE1</td>\n",
       "      <td>2020-10-20 01:04:29</td>\n",
       "      <td>2020-10-20 01:19:33</td>\n",
       "      <td>Pine Grove Ave &amp; Irving Park Rd</td>\n",
       "      <td>254.0</td>\n",
       "      <td>Glenwood Ave &amp; Morse Ave</td>\n",
       "      <td>447.0</td>\n",
       "      <td>member</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>44 F</td>\n",
       "      <td>79</td>\n",
       "      <td>8mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>355</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-10-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3807055</th>\n",
       "      <td>2D551117FD01DCE1</td>\n",
       "      <td>2020-10-20 01:04:29</td>\n",
       "      <td>2020-10-20 01:19:33</td>\n",
       "      <td>Pine Grove Ave &amp; Irving Park Rd</td>\n",
       "      <td>254.0</td>\n",
       "      <td>Glenwood Ave &amp; Morse Ave</td>\n",
       "      <td>447.0</td>\n",
       "      <td>member</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>44 F</td>\n",
       "      <td>79</td>\n",
       "      <td>9mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>355</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-10-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3807056</th>\n",
       "      <td>09264F6A1F8E4CEE</td>\n",
       "      <td>2020-10-20 01:38:07</td>\n",
       "      <td>2020-10-20 01:48:04</td>\n",
       "      <td>St. Clair St &amp; Erie St</td>\n",
       "      <td>211.0</td>\n",
       "      <td>Clark St &amp; Armitage Ave</td>\n",
       "      <td>94.0</td>\n",
       "      <td>member</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>44 F</td>\n",
       "      <td>79</td>\n",
       "      <td>8mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>355</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-10-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3807057</th>\n",
       "      <td>09264F6A1F8E4CEE</td>\n",
       "      <td>2020-10-20 01:38:07</td>\n",
       "      <td>2020-10-20 01:48:04</td>\n",
       "      <td>St. Clair St &amp; Erie St</td>\n",
       "      <td>211.0</td>\n",
       "      <td>Clark St &amp; Armitage Ave</td>\n",
       "      <td>94.0</td>\n",
       "      <td>member</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>44 F</td>\n",
       "      <td>79</td>\n",
       "      <td>9mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>355</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-10-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3807058 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ride_id          started_at             ended_at  \\\n",
       "0                25612325 2019-11-01 00:00:45  2019-11-01 00:08:10   \n",
       "1                25612326 2019-11-01 00:01:13  2019-11-01 00:30:09   \n",
       "2                25612327 2019-11-01 00:01:42  2019-11-01 00:38:32   \n",
       "3                25612328 2019-11-01 00:02:24  2019-11-01 00:09:27   \n",
       "4                25612329 2019-11-01 00:02:30  2019-11-01 00:04:09   \n",
       "...                   ...                 ...                  ...   \n",
       "3807053  A4D50CAC9CCFB180 2020-10-20 01:51:54  2020-10-20 01:59:11   \n",
       "3807054  2D551117FD01DCE1 2020-10-20 01:04:29  2020-10-20 01:19:33   \n",
       "3807055  2D551117FD01DCE1 2020-10-20 01:04:29  2020-10-20 01:19:33   \n",
       "3807056  09264F6A1F8E4CEE 2020-10-20 01:38:07  2020-10-20 01:48:04   \n",
       "3807057  09264F6A1F8E4CEE 2020-10-20 01:38:07  2020-10-20 01:48:04   \n",
       "\n",
       "                      start_station_name  start_station_id  \\\n",
       "0                    Ellis Ave & 55th St             420.0   \n",
       "1            Western Ave & Winnebago Ave             116.0   \n",
       "2            Western Ave & Winnebago Ave             116.0   \n",
       "3                Lincoln Ave & Winona St             472.0   \n",
       "4                Clinton St & Madison St              77.0   \n",
       "...                                  ...               ...   \n",
       "3807053            Kimbark Ave & 53rd St             322.0   \n",
       "3807054  Pine Grove Ave & Irving Park Rd             254.0   \n",
       "3807055  Pine Grove Ave & Irving Park Rd             254.0   \n",
       "3807056           St. Clair St & Erie St             211.0   \n",
       "3807057           St. Clair St & Erie St             211.0   \n",
       "\n",
       "                        end_station_name  end_station_id member_casual  Year  \\\n",
       "0                   Harper Ave & 59th St           425.0    Subscriber  2019   \n",
       "1                    Morgan St & Polk St           241.0      Customer  2019   \n",
       "2                     May St & Taylor St            22.0    Subscriber  2019   \n",
       "3           Maplewood Ave & Peterson Ave           455.0    Subscriber  2019   \n",
       "4           Clinton St & Washington Blvd            91.0    Subscriber  2019   \n",
       "...                                  ...             ...           ...   ...   \n",
       "3807053  Blackstone Ave & Hyde Park Blvd           121.0        member  2020   \n",
       "3807054         Glenwood Ave & Morse Ave           447.0        member  2020   \n",
       "3807055         Glenwood Ave & Morse Ave           447.0        member  2020   \n",
       "3807056          Clark St & Armitage Ave            94.0        member  2020   \n",
       "3807057          Clark St & Armitage Ave            94.0        member  2020   \n",
       "\n",
       "         Month  ...  Temperature  Humidity Wind Speed  Wind Gust  \\\n",
       "0           11  ...        26  F        81      8 mph      0 mph   \n",
       "1           11  ...        26  F        81      8 mph      0 mph   \n",
       "2           11  ...        26  F        81      8 mph      0 mph   \n",
       "3           11  ...        26  F        81      8 mph      0 mph   \n",
       "4           11  ...        26  F        81      8 mph      0 mph   \n",
       "...        ...  ...          ...       ...        ...        ...   \n",
       "3807053     10  ...         44 F        79       9mph       0mph   \n",
       "3807054     10  ...         44 F        79       8mph       0mph   \n",
       "3807055     10  ...         44 F        79       9mph       0mph   \n",
       "3807056     10  ...         44 F        79       8mph       0mph   \n",
       "3807057     10  ...         44 F        79       9mph       0mph   \n",
       "\n",
       "        Precipitation Condition index Holiday_Name  Weekend        Date  \n",
       "0              0.0 in      Fair   103  Not Holiday        0  2019-11-01  \n",
       "1              0.0 in      Fair   103  Not Holiday        0  2019-11-01  \n",
       "2              0.0 in      Fair   103  Not Holiday        0  2019-11-01  \n",
       "3              0.0 in      Fair   103  Not Holiday        0  2019-11-01  \n",
       "4              0.0 in      Fair   103  Not Holiday        0  2019-11-01  \n",
       "...               ...       ...   ...          ...      ...         ...  \n",
       "3807053         0.0in    Cloudy   355  Not Holiday        0  2020-10-20  \n",
       "3807054         0.0in    Cloudy   355  Not Holiday        0  2020-10-20  \n",
       "3807055         0.0in    Cloudy   355  Not Holiday        0  2020-10-20  \n",
       "3807056         0.0in    Cloudy   355  Not Holiday        0  2020-10-20  \n",
       "3807057         0.0in    Cloudy   355  Not Holiday        0  2020-10-20  \n",
       "\n",
       "[3807058 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract date from the started_at column and use as a separate column\n",
    "merged_data['started_at'] = pd.to_datetime(merged_data['started_at'])\n",
    "merged_data['Date'] = merged_data['started_at'].dt.date\n",
    "merged_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Wind Gust</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Holiday_Name</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Checkouts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>63rd St Beach</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>43  F</td>\n",
       "      <td>68</td>\n",
       "      <td>6 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>63rd St Beach</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>38  F</td>\n",
       "      <td>79</td>\n",
       "      <td>15 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>900 W Harrison St</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>38  F</td>\n",
       "      <td>62</td>\n",
       "      <td>10 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>900 W Harrison St</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>41  F</td>\n",
       "      <td>55</td>\n",
       "      <td>14 mph</td>\n",
       "      <td>24 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>900 W Harrison St</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>43  F</td>\n",
       "      <td>51</td>\n",
       "      <td>15 mph</td>\n",
       "      <td>29 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445486</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Woodlawn Ave &amp; 55th St</td>\n",
       "      <td>248.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>53 F</td>\n",
       "      <td>61</td>\n",
       "      <td>16mph</td>\n",
       "      <td>25mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445487</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Woodlawn Ave &amp; Lake Park Ave</td>\n",
       "      <td>413.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>58 F</td>\n",
       "      <td>39</td>\n",
       "      <td>21mph</td>\n",
       "      <td>31mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Fair / Windy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445488</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Woodlawn Ave &amp; Lake Park Ave</td>\n",
       "      <td>413.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "      <td>59 F</td>\n",
       "      <td>38</td>\n",
       "      <td>18mph</td>\n",
       "      <td>26mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445489</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Woodlawn Ave &amp; Lake Park Ave</td>\n",
       "      <td>413.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>57 F</td>\n",
       "      <td>44</td>\n",
       "      <td>15mph</td>\n",
       "      <td>32mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445490</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Woodlawn Ave &amp; Lake Park Ave</td>\n",
       "      <td>413.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>56 F</td>\n",
       "      <td>49</td>\n",
       "      <td>17mph</td>\n",
       "      <td>31mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1445491 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Date            start_station_name  start_station_id  Year  \\\n",
       "0        2019-11-01                 63rd St Beach             101.0  2019   \n",
       "1        2019-11-01                 63rd St Beach             101.0  2019   \n",
       "2        2019-11-01             900 W Harrison St             109.0  2019   \n",
       "3        2019-11-01             900 W Harrison St             109.0  2019   \n",
       "4        2019-11-01             900 W Harrison St             109.0  2019   \n",
       "...             ...                           ...               ...   ...   \n",
       "1445486  2020-10-31        Woodlawn Ave & 55th St             248.0  2020   \n",
       "1445487  2020-10-31  Woodlawn Ave & Lake Park Ave             413.0  2020   \n",
       "1445488  2020-10-31  Woodlawn Ave & Lake Park Ave             413.0  2020   \n",
       "1445489  2020-10-31  Woodlawn Ave & Lake Park Ave             413.0  2020   \n",
       "1445490  2020-10-31  Woodlawn Ave & Lake Park Ave             413.0  2020   \n",
       "\n",
       "         Month  Day  Hour Temperature  Humidity Wind Speed Wind Gust  \\\n",
       "0           11    1    18       43  F        68      6 mph     0 mph   \n",
       "1           11    1    20       38  F        79     15 mph     0 mph   \n",
       "2           11    1    10       38  F        62     10 mph     0 mph   \n",
       "3           11    1    11       41  F        55     14 mph    24 mph   \n",
       "4           11    1    12       43  F        51     15 mph    29 mph   \n",
       "...        ...  ...   ...         ...       ...        ...       ...   \n",
       "1445486     10   31    22        53 F        61      16mph     25mph   \n",
       "1445487     10   31    13        58 F        39      21mph     31mph   \n",
       "1445488     10   31    15        59 F        38      18mph     26mph   \n",
       "1445489     10   31    16        57 F        44      15mph     32mph   \n",
       "1445490     10   31    17        56 F        49      17mph     31mph   \n",
       "\n",
       "        Precipitation      Condition Holiday_Name  Weekend  Checkouts  \n",
       "0              0.0 in         Cloudy  Not Holiday        0          1  \n",
       "1              0.0 in     Light Rain  Not Holiday        0          1  \n",
       "2              0.0 in  Mostly Cloudy  Not Holiday        0          4  \n",
       "3              0.0 in  Mostly Cloudy  Not Holiday        0          2  \n",
       "4              0.0 in  Mostly Cloudy  Not Holiday        0          2  \n",
       "...               ...            ...          ...      ...        ...  \n",
       "1445486         0.0in  Mostly Cloudy      Weekend        1          1  \n",
       "1445487         0.0in   Fair / Windy      Weekend        1          1  \n",
       "1445488         0.0in           Fair      Weekend        1          2  \n",
       "1445489         0.0in           Fair      Weekend        1          1  \n",
       "1445490         0.0in  Partly Cloudy      Weekend        1          1  \n",
       "\n",
       "[1445491 rows x 16 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get number of bike checkouts per hour by grouping \n",
    "checkouts = merged_data.groupby(by=['Date', 'start_station_name', 'start_station_id', 'Year', 'Month', 'Day', 'Hour', 'Temperature', 'Humidity', 'Wind Speed', 'Wind Gust', 'Precipitation', 'Condition', 'Holiday_Name', 'Weekend']).size().reset_index(name='Checkouts')\n",
    "checkouts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Date</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Wind Gust</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Holiday_Name</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Checkouts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>63rd St Beach</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>43  F</td>\n",
       "      <td>68</td>\n",
       "      <td>6 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Milwaukee Ave &amp; Wabansia Ave</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>37  F</td>\n",
       "      <td>86</td>\n",
       "      <td>13 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Light Drizzle</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Milwaukee Ave &amp; Wabansia Ave</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>38  F</td>\n",
       "      <td>83</td>\n",
       "      <td>10 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Milwaukee Ave &amp; Wabansia Ave</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>27  F</td>\n",
       "      <td>81</td>\n",
       "      <td>7 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Monticello Ave &amp; Irving Park Rd</td>\n",
       "      <td>484.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>31  F</td>\n",
       "      <td>79</td>\n",
       "      <td>9 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442631</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Ellis Ave &amp; 53rd St</td>\n",
       "      <td>418.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>52 F</td>\n",
       "      <td>66</td>\n",
       "      <td>16mph</td>\n",
       "      <td>30mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442632</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Ellis Ave &amp; 55th St</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>33 F</td>\n",
       "      <td>72</td>\n",
       "      <td>3mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442633</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Ellis Ave &amp; 55th St</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>33 F</td>\n",
       "      <td>66</td>\n",
       "      <td>7mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442619</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Elizabeth (May) St &amp; Fulton St</td>\n",
       "      <td>217.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "      <td>59 F</td>\n",
       "      <td>38</td>\n",
       "      <td>18mph</td>\n",
       "      <td>26mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445490</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Woodlawn Ave &amp; Lake Park Ave</td>\n",
       "      <td>413.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>56 F</td>\n",
       "      <td>49</td>\n",
       "      <td>17mph</td>\n",
       "      <td>31mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1445491 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Date               start_station_name  start_station_id  Year  \\\n",
       "0        2019-11-01                    63rd St Beach             101.0  2019   \n",
       "2249     2019-11-01     Milwaukee Ave & Wabansia Ave             158.0  2019   \n",
       "2250     2019-11-01     Milwaukee Ave & Wabansia Ave             158.0  2019   \n",
       "2251     2019-11-01     Milwaukee Ave & Wabansia Ave             158.0  2019   \n",
       "2252     2019-11-01  Monticello Ave & Irving Park Rd             484.0  2019   \n",
       "...             ...                              ...               ...   ...   \n",
       "1442631  2020-10-31              Ellis Ave & 53rd St             418.0  2020   \n",
       "1442632  2020-10-31              Ellis Ave & 55th St             420.0  2020   \n",
       "1442633  2020-10-31              Ellis Ave & 55th St             420.0  2020   \n",
       "1442619  2020-10-31   Elizabeth (May) St & Fulton St             217.0  2020   \n",
       "1445490  2020-10-31     Woodlawn Ave & Lake Park Ave             413.0  2020   \n",
       "\n",
       "         Month  Day  Hour Temperature  Humidity Wind Speed Wind Gust  \\\n",
       "0           11    1    18       43  F        68      6 mph     0 mph   \n",
       "2249        11    1    21       37  F        86     13 mph     0 mph   \n",
       "2250        11    1    21       38  F        83     10 mph     0 mph   \n",
       "2251        11    1    23       27  F        81      7 mph     0 mph   \n",
       "2252        11    1     8       31  F        79      9 mph     0 mph   \n",
       "...        ...  ...   ...         ...       ...        ...       ...   \n",
       "1442631     10   31    21        52 F        66      16mph     30mph   \n",
       "1442632     10   31     0        33 F        72       3mph      0mph   \n",
       "1442633     10   31     3        33 F        66       7mph      0mph   \n",
       "1442619     10   31    15        59 F        38      18mph     26mph   \n",
       "1445490     10   31    17        56 F        49      17mph     31mph   \n",
       "\n",
       "        Precipitation      Condition Holiday_Name  Weekend  Checkouts  \n",
       "0              0.0 in         Cloudy  Not Holiday        0          1  \n",
       "2249           0.0 in  Light Drizzle  Not Holiday        0          1  \n",
       "2250           0.0 in     Light Rain  Not Holiday        0          2  \n",
       "2251           0.0 in           Fair  Not Holiday        0          1  \n",
       "2252           0.0 in  Mostly Cloudy  Not Holiday        0          1  \n",
       "...               ...            ...          ...      ...        ...  \n",
       "1442631         0.0in         Cloudy      Weekend        1          1  \n",
       "1442632         0.0in           Fair      Weekend        1          1  \n",
       "1442633         0.0in           Fair      Weekend        1          1  \n",
       "1442619         0.0in           Fair      Weekend        1          1  \n",
       "1445490         0.0in  Partly Cloudy      Weekend        1          1  \n",
       "\n",
       "[1445491 rows x 16 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort by Date in ascending order\n",
    "checkouts.sort_values(by='Date', inplace=True)\n",
    "checkouts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Wind Gust</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Holiday_Name</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Checkouts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>63rd St Beach</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>43  F</td>\n",
       "      <td>68</td>\n",
       "      <td>6 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Milwaukee Ave &amp; Wabansia Ave</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>37  F</td>\n",
       "      <td>86</td>\n",
       "      <td>13 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Light Drizzle</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Milwaukee Ave &amp; Wabansia Ave</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>38  F</td>\n",
       "      <td>83</td>\n",
       "      <td>10 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Milwaukee Ave &amp; Wabansia Ave</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>27  F</td>\n",
       "      <td>81</td>\n",
       "      <td>7 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Monticello Ave &amp; Irving Park Rd</td>\n",
       "      <td>484.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>31  F</td>\n",
       "      <td>79</td>\n",
       "      <td>9 mph</td>\n",
       "      <td>0 mph</td>\n",
       "      <td>0.0 in</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442631</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Ellis Ave &amp; 53rd St</td>\n",
       "      <td>418.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>52 F</td>\n",
       "      <td>66</td>\n",
       "      <td>16mph</td>\n",
       "      <td>30mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442632</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Ellis Ave &amp; 55th St</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>33 F</td>\n",
       "      <td>72</td>\n",
       "      <td>3mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442633</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Ellis Ave &amp; 55th St</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>33 F</td>\n",
       "      <td>66</td>\n",
       "      <td>7mph</td>\n",
       "      <td>0mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442619</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Elizabeth (May) St &amp; Fulton St</td>\n",
       "      <td>217.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "      <td>59 F</td>\n",
       "      <td>38</td>\n",
       "      <td>18mph</td>\n",
       "      <td>26mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445490</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Woodlawn Ave &amp; Lake Park Ave</td>\n",
       "      <td>413.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>56 F</td>\n",
       "      <td>49</td>\n",
       "      <td>17mph</td>\n",
       "      <td>31mph</td>\n",
       "      <td>0.0in</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1445491 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Date               start_station_name  start_station_id  Year  \\\n",
       "0        2019-11-01                    63rd St Beach             101.0  2019   \n",
       "2249     2019-11-01     Milwaukee Ave & Wabansia Ave             158.0  2019   \n",
       "2250     2019-11-01     Milwaukee Ave & Wabansia Ave             158.0  2019   \n",
       "2251     2019-11-01     Milwaukee Ave & Wabansia Ave             158.0  2019   \n",
       "2252     2019-11-01  Monticello Ave & Irving Park Rd             484.0  2019   \n",
       "...             ...                              ...               ...   ...   \n",
       "1442631  2020-10-31              Ellis Ave & 53rd St             418.0  2020   \n",
       "1442632  2020-10-31              Ellis Ave & 55th St             420.0  2020   \n",
       "1442633  2020-10-31              Ellis Ave & 55th St             420.0  2020   \n",
       "1442619  2020-10-31   Elizabeth (May) St & Fulton St             217.0  2020   \n",
       "1445490  2020-10-31     Woodlawn Ave & Lake Park Ave             413.0  2020   \n",
       "\n",
       "         Month  Day  Hour Temperature  Humidity Wind Speed Wind Gust  \\\n",
       "0           11    1    18       43  F        68      6 mph     0 mph   \n",
       "2249        11    1    21       37  F        86     13 mph     0 mph   \n",
       "2250        11    1    21       38  F        83     10 mph     0 mph   \n",
       "2251        11    1    23       27  F        81      7 mph     0 mph   \n",
       "2252        11    1     8       31  F        79      9 mph     0 mph   \n",
       "...        ...  ...   ...         ...       ...        ...       ...   \n",
       "1442631     10   31    21        52 F        66      16mph     30mph   \n",
       "1442632     10   31     0        33 F        72       3mph      0mph   \n",
       "1442633     10   31     3        33 F        66       7mph      0mph   \n",
       "1442619     10   31    15        59 F        38      18mph     26mph   \n",
       "1445490     10   31    17        56 F        49      17mph     31mph   \n",
       "\n",
       "        Precipitation      Condition Holiday_Name  Weekend  Checkouts  \n",
       "0              0.0 in         Cloudy  Not Holiday        0          1  \n",
       "2249           0.0 in  Light Drizzle  Not Holiday        0          1  \n",
       "2250           0.0 in     Light Rain  Not Holiday        0          2  \n",
       "2251           0.0 in           Fair  Not Holiday        0          1  \n",
       "2252           0.0 in  Mostly Cloudy  Not Holiday        0          1  \n",
       "...               ...            ...          ...      ...        ...  \n",
       "1442631         0.0in         Cloudy      Weekend        1          1  \n",
       "1442632         0.0in           Fair      Weekend        1          1  \n",
       "1442633         0.0in           Fair      Weekend        1          1  \n",
       "1442619         0.0in           Fair      Weekend        1          1  \n",
       "1445490         0.0in  Partly Cloudy      Weekend        1          1  \n",
       "\n",
       "[1445491 rows x 16 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set Checkouts to be at the end of the dataframe\n",
    "df = checkouts.pop('Checkouts')\n",
    "checkouts['Checkouts'] = df\n",
    "checkouts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save to a csv file\n",
    "checkouts.to_csv(\"checkouts_unclean.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          1\n",
       "2249       1\n",
       "2250       2\n",
       "2251       1\n",
       "2252       1\n",
       "          ..\n",
       "1442631    1\n",
       "1442632    1\n",
       "1442633    1\n",
       "1442619    1\n",
       "1445490    1\n",
       "Name: Checkouts, Length: 1445491, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# target variable y - number of checkouts\n",
    "y = checkouts['Checkouts']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Temperature (F)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Wind Gust</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Holiday_Name</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Checkouts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>63rd St Beach</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>68</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Milwaukee Ave &amp; Wabansia Ave</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>37</td>\n",
       "      <td>86</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Drizzle</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Milwaukee Ave &amp; Wabansia Ave</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>38</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Milwaukee Ave &amp; Wabansia Ave</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Monticello Ave &amp; Irving Park Rd</td>\n",
       "      <td>484.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>79</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442631</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Ellis Ave &amp; 53rd St</td>\n",
       "      <td>418.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>52</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442632</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Ellis Ave &amp; 55th St</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442633</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Ellis Ave &amp; 55th St</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>66</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442619</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Elizabeth (May) St &amp; Fulton St</td>\n",
       "      <td>217.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445490</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>Woodlawn Ave &amp; Lake Park Ave</td>\n",
       "      <td>413.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1445491 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Date               start_station_name  start_station_id  Year  \\\n",
       "0        2019-11-01                    63rd St Beach             101.0  2019   \n",
       "2249     2019-11-01     Milwaukee Ave & Wabansia Ave             158.0  2019   \n",
       "2250     2019-11-01     Milwaukee Ave & Wabansia Ave             158.0  2019   \n",
       "2251     2019-11-01     Milwaukee Ave & Wabansia Ave             158.0  2019   \n",
       "2252     2019-11-01  Monticello Ave & Irving Park Rd             484.0  2019   \n",
       "...             ...                              ...               ...   ...   \n",
       "1442631  2020-10-31              Ellis Ave & 53rd St             418.0  2020   \n",
       "1442632  2020-10-31              Ellis Ave & 55th St             420.0  2020   \n",
       "1442633  2020-10-31              Ellis Ave & 55th St             420.0  2020   \n",
       "1442619  2020-10-31   Elizabeth (May) St & Fulton St             217.0  2020   \n",
       "1445490  2020-10-31     Woodlawn Ave & Lake Park Ave             413.0  2020   \n",
       "\n",
       "         Month  Day  Hour  Temperature (F)  Humidity  Wind Speed  Wind Gust  \\\n",
       "0           11    1    18               43        68           6          0   \n",
       "2249        11    1    21               37        86          13          0   \n",
       "2250        11    1    21               38        83          10          0   \n",
       "2251        11    1    23               27        81           7          0   \n",
       "2252        11    1     8               31        79           9          0   \n",
       "...        ...  ...   ...              ...       ...         ...        ...   \n",
       "1442631     10   31    21               52        66          16         30   \n",
       "1442632     10   31     0               33        72           3          0   \n",
       "1442633     10   31     3               33        66           7          0   \n",
       "1442619     10   31    15               59        38          18         26   \n",
       "1445490     10   31    17               56        49          17         31   \n",
       "\n",
       "         Precipitation      Condition Holiday_Name  Weekend  Checkouts  \n",
       "0                  0.0         Cloudy  Not Holiday        0          1  \n",
       "2249               0.0  Light Drizzle  Not Holiday        0          1  \n",
       "2250               0.0     Light Rain  Not Holiday        0          2  \n",
       "2251               0.0           Fair  Not Holiday        0          1  \n",
       "2252               0.0  Mostly Cloudy  Not Holiday        0          1  \n",
       "...                ...            ...          ...      ...        ...  \n",
       "1442631            0.0         Cloudy      Weekend        1          1  \n",
       "1442632            0.0           Fair      Weekend        1          1  \n",
       "1442633            0.0           Fair      Weekend        1          1  \n",
       "1442619            0.0           Fair      Weekend        1          1  \n",
       "1445490            0.0  Partly Cloudy      Weekend        1          1  \n",
       "\n",
       "[1445491 rows x 16 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# since many of the attributes had string values, remove them and convert those attributes to numerical values\n",
    "checkouts['Temperature'] = pd.to_numeric(checkouts['Temperature'].str.replace('F', ''))\n",
    "checkouts.rename({\"Temperature\": \"Temperature (F)\"}, axis=1, inplace=True)\n",
    "checkouts['Wind Speed'] = pd.to_numeric(checkouts['Wind Speed'].str.replace('mph', ''))\n",
    "checkouts['Wind Gust'] = pd.to_numeric(checkouts['Wind Gust'].str.replace('mph', ''))\n",
    "checkouts['Precipitation'] = pd.to_numeric(checkouts['Precipitation'].str.replace('in', ''))\n",
    "# checkout_data.to_csv(\"results/final_checkouts.csv\", index=False)\n",
    "checkouts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set a new column Remarks with values as the Holiday_Name attribute\n",
    "checkouts['Remarks'] = checkouts['Holiday_Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted Date</th>\n",
       "      <th>Date</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Temperature (F)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Wind Gust</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Holiday_Name</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Checkouts</th>\n",
       "      <th>Remarks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>101.0</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>68</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>158.0</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>37</td>\n",
       "      <td>86</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Drizzle</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>158.0</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>38</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>158.0</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>484.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>79</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</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",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442631</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>418.0</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>52</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442632</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>420.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442633</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>420.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>66</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442619</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>217.0</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445490</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>413.0</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1445491 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Converted Date       Date  start_station_id  Month  Hour  \\\n",
       "0          1.572581e+09 2019-11-01             101.0     11    18   \n",
       "2249       1.572581e+09 2019-11-01             158.0     11    21   \n",
       "2250       1.572581e+09 2019-11-01             158.0     11    21   \n",
       "2251       1.572581e+09 2019-11-01             158.0     11    23   \n",
       "2252       1.572581e+09 2019-11-01             484.0     11     8   \n",
       "...                 ...        ...               ...    ...   ...   \n",
       "1442631    1.604117e+09 2020-10-31             418.0     10    21   \n",
       "1442632    1.604117e+09 2020-10-31             420.0     10     0   \n",
       "1442633    1.604117e+09 2020-10-31             420.0     10     3   \n",
       "1442619    1.604117e+09 2020-10-31             217.0     10    15   \n",
       "1445490    1.604117e+09 2020-10-31             413.0     10    17   \n",
       "\n",
       "         Temperature (F)  Humidity  Wind Speed  Wind Gust  Precipitation  \\\n",
       "0                     43        68           6          0            0.0   \n",
       "2249                  37        86          13          0            0.0   \n",
       "2250                  38        83          10          0            0.0   \n",
       "2251                  27        81           7          0            0.0   \n",
       "2252                  31        79           9          0            0.0   \n",
       "...                  ...       ...         ...        ...            ...   \n",
       "1442631               52        66          16         30            0.0   \n",
       "1442632               33        72           3          0            0.0   \n",
       "1442633               33        66           7          0            0.0   \n",
       "1442619               59        38          18         26            0.0   \n",
       "1445490               56        49          17         31            0.0   \n",
       "\n",
       "             Condition Holiday_Name  Weekend  Checkouts      Remarks  \n",
       "0               Cloudy  Not Holiday        0          1  Not Holiday  \n",
       "2249     Light Drizzle  Not Holiday        0          1  Not Holiday  \n",
       "2250        Light Rain  Not Holiday        0          2  Not Holiday  \n",
       "2251              Fair  Not Holiday        0          1  Not Holiday  \n",
       "2252     Mostly Cloudy  Not Holiday        0          1  Not Holiday  \n",
       "...                ...          ...      ...        ...          ...  \n",
       "1442631         Cloudy      Weekend        1          1      Weekend  \n",
       "1442632           Fair      Weekend        1          1      Weekend  \n",
       "1442633           Fair      Weekend        1          1      Weekend  \n",
       "1442619           Fair      Weekend        1          1      Weekend  \n",
       "1445490  Partly Cloudy      Weekend        1          1      Weekend  \n",
       "\n",
       "[1445491 rows x 15 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert Date to datetime\n",
    "checkouts.Date = pd.to_datetime(checkouts.Date, format=\"%Y-%m-%d\")\n",
    "# convert the date into seconds to use it for prediction\n",
    "checkouts['Converted Date'] = checkouts.Date.apply(lambda var: time.mktime(var.timetuple()))\n",
    "\n",
    "# set checkouts to be at the end of the dataframe\n",
    "df = checkouts.pop('Converted Date')\n",
    "# and set the date to be the first column in the dataframe\n",
    "checkouts.insert(0, column='Converted Date', value=df)\n",
    "# drop the unnecessary columns\n",
    "checkouts.drop(['Year', 'Day', 'start_station_name'], axis=1, inplace=True)\n",
    "checkouts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted Date</th>\n",
       "      <th>Date</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hour</th>\n",
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       "      <th>Humidity</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Wind Gust</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Holiday_Name</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Checkouts</th>\n",
       "      <th>Remarks</th>\n",
       "      <th>Day of Week</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>101.0</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>68</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>158.0</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>37</td>\n",
       "      <td>86</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Drizzle</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>158.0</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>38</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>158.0</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>484.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>79</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442631</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>418.0</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>52</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442632</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>420.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442633</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>420.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>66</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442619</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>217.0</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445490</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>413.0</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1445491 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Converted Date       Date  start_station_id  Month  Hour  \\\n",
       "0          1.572581e+09 2019-11-01             101.0     11    18   \n",
       "2249       1.572581e+09 2019-11-01             158.0     11    21   \n",
       "2250       1.572581e+09 2019-11-01             158.0     11    21   \n",
       "2251       1.572581e+09 2019-11-01             158.0     11    23   \n",
       "2252       1.572581e+09 2019-11-01             484.0     11     8   \n",
       "...                 ...        ...               ...    ...   ...   \n",
       "1442631    1.604117e+09 2020-10-31             418.0     10    21   \n",
       "1442632    1.604117e+09 2020-10-31             420.0     10     0   \n",
       "1442633    1.604117e+09 2020-10-31             420.0     10     3   \n",
       "1442619    1.604117e+09 2020-10-31             217.0     10    15   \n",
       "1445490    1.604117e+09 2020-10-31             413.0     10    17   \n",
       "\n",
       "         Temperature (F)  Humidity  Wind Speed  Wind Gust  Precipitation  \\\n",
       "0                     43        68           6          0            0.0   \n",
       "2249                  37        86          13          0            0.0   \n",
       "2250                  38        83          10          0            0.0   \n",
       "2251                  27        81           7          0            0.0   \n",
       "2252                  31        79           9          0            0.0   \n",
       "...                  ...       ...         ...        ...            ...   \n",
       "1442631               52        66          16         30            0.0   \n",
       "1442632               33        72           3          0            0.0   \n",
       "1442633               33        66           7          0            0.0   \n",
       "1442619               59        38          18         26            0.0   \n",
       "1445490               56        49          17         31            0.0   \n",
       "\n",
       "             Condition Holiday_Name  Weekend  Checkouts      Remarks  \\\n",
       "0               Cloudy  Not Holiday        0          1  Not Holiday   \n",
       "2249     Light Drizzle  Not Holiday        0          1  Not Holiday   \n",
       "2250        Light Rain  Not Holiday        0          2  Not Holiday   \n",
       "2251              Fair  Not Holiday        0          1  Not Holiday   \n",
       "2252     Mostly Cloudy  Not Holiday        0          1  Not Holiday   \n",
       "...                ...          ...      ...        ...          ...   \n",
       "1442631         Cloudy      Weekend        1          1      Weekend   \n",
       "1442632           Fair      Weekend        1          1      Weekend   \n",
       "1442633           Fair      Weekend        1          1      Weekend   \n",
       "1442619           Fair      Weekend        1          1      Weekend   \n",
       "1445490  Partly Cloudy      Weekend        1          1      Weekend   \n",
       "\n",
       "         Day of Week  \n",
       "0                  4  \n",
       "2249               4  \n",
       "2250               4  \n",
       "2251               4  \n",
       "2252               4  \n",
       "...              ...  \n",
       "1442631            5  \n",
       "1442632            5  \n",
       "1442633            5  \n",
       "1442619            5  \n",
       "1445490            5  \n",
       "\n",
       "[1445491 rows x 16 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get day of week from the Date attribute\n",
    "checkouts['Day of Week'] = checkouts.Date.dt.dayofweek\n",
    "checkouts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Winter', 'Winter', 'Spring', 'Spring', 'Spring', 'Summer', 'Summer', 'Summer', 'Fall', 'Fall', 'Fall', 'Winter']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted Date</th>\n",
       "      <th>Date</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Temperature (F)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Wind Gust</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Holiday_Name</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Checkouts</th>\n",
       "      <th>Remarks</th>\n",
       "      <th>Day of Week</th>\n",
       "      <th>Seasons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>101.0</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>68</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>4</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>158.0</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>37</td>\n",
       "      <td>86</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Drizzle</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>4</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>158.0</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>38</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>4</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>158.0</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>4</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>1.572581e+09</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>484.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>79</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Holiday</td>\n",
       "      <td>4</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442631</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>418.0</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>52</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>5</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442632</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>420.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>5</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442633</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>420.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>66</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>5</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442619</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>217.0</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>5</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445490</th>\n",
       "      <td>1.604117e+09</td>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>413.0</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>5</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1445491 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Converted Date       Date  start_station_id  Month  Hour  \\\n",
       "0          1.572581e+09 2019-11-01             101.0     11    18   \n",
       "2249       1.572581e+09 2019-11-01             158.0     11    21   \n",
       "2250       1.572581e+09 2019-11-01             158.0     11    21   \n",
       "2251       1.572581e+09 2019-11-01             158.0     11    23   \n",
       "2252       1.572581e+09 2019-11-01             484.0     11     8   \n",
       "...                 ...        ...               ...    ...   ...   \n",
       "1442631    1.604117e+09 2020-10-31             418.0     10    21   \n",
       "1442632    1.604117e+09 2020-10-31             420.0     10     0   \n",
       "1442633    1.604117e+09 2020-10-31             420.0     10     3   \n",
       "1442619    1.604117e+09 2020-10-31             217.0     10    15   \n",
       "1445490    1.604117e+09 2020-10-31             413.0     10    17   \n",
       "\n",
       "         Temperature (F)  Humidity  Wind Speed  Wind Gust  Precipitation  \\\n",
       "0                     43        68           6          0            0.0   \n",
       "2249                  37        86          13          0            0.0   \n",
       "2250                  38        83          10          0            0.0   \n",
       "2251                  27        81           7          0            0.0   \n",
       "2252                  31        79           9          0            0.0   \n",
       "...                  ...       ...         ...        ...            ...   \n",
       "1442631               52        66          16         30            0.0   \n",
       "1442632               33        72           3          0            0.0   \n",
       "1442633               33        66           7          0            0.0   \n",
       "1442619               59        38          18         26            0.0   \n",
       "1445490               56        49          17         31            0.0   \n",
       "\n",
       "             Condition Holiday_Name  Weekend  Checkouts      Remarks  \\\n",
       "0               Cloudy  Not Holiday        0          1  Not Holiday   \n",
       "2249     Light Drizzle  Not Holiday        0          1  Not Holiday   \n",
       "2250        Light Rain  Not Holiday        0          2  Not Holiday   \n",
       "2251              Fair  Not Holiday        0          1  Not Holiday   \n",
       "2252     Mostly Cloudy  Not Holiday        0          1  Not Holiday   \n",
       "...                ...          ...      ...        ...          ...   \n",
       "1442631         Cloudy      Weekend        1          1      Weekend   \n",
       "1442632           Fair      Weekend        1          1      Weekend   \n",
       "1442633           Fair      Weekend        1          1      Weekend   \n",
       "1442619           Fair      Weekend        1          1      Weekend   \n",
       "1445490  Partly Cloudy      Weekend        1          1      Weekend   \n",
       "\n",
       "         Day of Week Seasons  \n",
       "0                  4    Fall  \n",
       "2249               4    Fall  \n",
       "2250               4    Fall  \n",
       "2251               4    Fall  \n",
       "2252               4    Fall  \n",
       "...              ...     ...  \n",
       "1442631            5    Fall  \n",
       "1442632            5    Fall  \n",
       "1442633            5    Fall  \n",
       "1442619            5    Fall  \n",
       "1445490            5    Fall  \n",
       "\n",
       "[1445491 rows x 17 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a Seasons column based on the Month column\n",
    "seasonal_data = []\n",
    "months = list(set(checkouts['Month']))\n",
    "for month in months:\n",
    "    if month in range(3, 6):\n",
    "        seasonal_data.append(\"Spring\")\n",
    "    elif month in range(6, 9):\n",
    "        seasonal_data.append(\"Summer\")\n",
    "    elif month in range(9, 12):\n",
    "        seasonal_data.append(\"Fall\")\n",
    "    else:\n",
    "        seasonal_data.append('Winter')\n",
    "print(seasonal_data)\n",
    "\n",
    "checkouts['Seasons'] = checkouts['Month'].replace(to_replace=months, value=seasonal_data)\n",
    "checkouts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "         Converted Date  start_station_id  Month  Hour  Temperature (F)  \\\n",
       "0          1.572581e+09             101.0     11    18               43   \n",
       "2249       1.572581e+09             158.0     11    21               37   \n",
       "2250       1.572581e+09             158.0     11    21               38   \n",
       "2251       1.572581e+09             158.0     11    23               27   \n",
       "2252       1.572581e+09             484.0     11     8               31   \n",
       "...                 ...               ...    ...   ...              ...   \n",
       "1442631    1.604117e+09             418.0     10    21               52   \n",
       "1442632    1.604117e+09             420.0     10     0               33   \n",
       "1442633    1.604117e+09             420.0     10     3               33   \n",
       "1442619    1.604117e+09             217.0     10    15               59   \n",
       "1445490    1.604117e+09             413.0     10    17               56   \n",
       "\n",
       "         Humidity  Wind Speed  Wind Gust  Precipitation Holiday_Name  \\\n",
       "0              68           6          0            0.0  Not Holiday   \n",
       "2249           86          13          0            0.0  Not Holiday   \n",
       "2250           83          10          0            0.0  Not Holiday   \n",
       "2251           81           7          0            0.0  Not Holiday   \n",
       "2252           79           9          0            0.0  Not Holiday   \n",
       "...           ...         ...        ...            ...          ...   \n",
       "1442631        66          16         30            0.0      Weekend   \n",
       "1442632        72           3          0            0.0      Weekend   \n",
       "1442633        66           7          0            0.0      Weekend   \n",
       "1442619        38          18         26            0.0      Weekend   \n",
       "1445490        49          17         31            0.0      Weekend   \n",
       "\n",
       "         Day of Week Seasons  \n",
       "0                  4    Fall  \n",
       "2249               4    Fall  \n",
       "2250               4    Fall  \n",
       "2251               4    Fall  \n",
       "2252               4    Fall  \n",
       "...              ...     ...  \n",
       "1442631            5    Fall  \n",
       "1442632            5    Fall  \n",
       "1442633            5    Fall  \n",
       "1442619            5    Fall  \n",
       "1445490            5    Fall  \n",
       "\n",
       "[1445491 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove unnecessary columns\n",
    "checkouts.drop(['Date', 'Checkouts', 'Condition', 'Weekend', 'Remarks'], axis=1, inplace=True)\n",
    "checkouts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot encoding the holidays and seasons columns\n",
    "y1 = pd.get_dummies(checkouts['Holiday_Name'], prefix='Holiday')\n",
    "y2 = pd.get_dummies(checkouts['Seasons'], prefix='Season')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1445491 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Converted Date  start_station_id  Month  Hour  Temperature (F)  \\\n",
       "0          1.572581e+09             101.0     11    18               43   \n",
       "2249       1.572581e+09             158.0     11    21               37   \n",
       "2250       1.572581e+09             158.0     11    21               38   \n",
       "2251       1.572581e+09             158.0     11    23               27   \n",
       "2252       1.572581e+09             484.0     11     8               31   \n",
       "...                 ...               ...    ...   ...              ...   \n",
       "1442631    1.604117e+09             418.0     10    21               52   \n",
       "1442632    1.604117e+09             420.0     10     0               33   \n",
       "1442633    1.604117e+09             420.0     10     3               33   \n",
       "1442619    1.604117e+09             217.0     10    15               59   \n",
       "1445490    1.604117e+09             413.0     10    17               56   \n",
       "\n",
       "         Humidity  Wind Speed  Wind Gust  Precipitation  Day of Week  ...  \\\n",
       "0              68           6          0            0.0            4  ...   \n",
       "2249           86          13          0            0.0            4  ...   \n",
       "2250           83          10          0            0.0            4  ...   \n",
       "2251           81           7          0            0.0            4  ...   \n",
       "2252           79           9          0            0.0            4  ...   \n",
       "...           ...         ...        ...            ...          ...  ...   \n",
       "1442631        66          16         30            0.0            5  ...   \n",
       "1442632        72           3          0            0.0            5  ...   \n",
       "1442633        66           7          0            0.0            5  ...   \n",
       "1442619        38          18         26            0.0            5  ...   \n",
       "1445490        49          17         31            0.0            5  ...   \n",
       "\n",
       "         Holiday_Summer Vacation  Holiday_Thanksgiving Day  \\\n",
       "0                              0                         0   \n",
       "2249                           0                         0   \n",
       "2250                           0                         0   \n",
       "2251                           0                         0   \n",
       "2252                           0                         0   \n",
       "...                          ...                       ...   \n",
       "1442631                        0                         0   \n",
       "1442632                        0                         0   \n",
       "1442633                        0                         0   \n",
       "1442619                        0                         0   \n",
       "1445490                        0                         0   \n",
       "\n",
       "         Holiday_Veterans Day  Holiday_Washington's Birthday  Holiday_Weekend  \\\n",
       "0                           0                              0                0   \n",
       "2249                        0                              0                0   \n",
       "2250                        0                              0                0   \n",
       "2251                        0                              0                0   \n",
       "2252                        0                              0                0   \n",
       "...                       ...                            ...              ...   \n",
       "1442631                     0                              0                1   \n",
       "1442632                     0                              0                1   \n",
       "1442633                     0                              0                1   \n",
       "1442619                     0                              0                1   \n",
       "1445490                     0                              0                1   \n",
       "\n",
       "         Holiday_Winter Vacation  Season_Fall  Season_Spring  Season_Summer  \\\n",
       "0                              0            1              0              0   \n",
       "2249                           0            1              0              0   \n",
       "2250                           0            1              0              0   \n",
       "2251                           0            1              0              0   \n",
       "2252                           0            1              0              0   \n",
       "...                          ...          ...            ...            ...   \n",
       "1442631                        0            1              0              0   \n",
       "1442632                        0            1              0              0   \n",
       "1442633                        0            1              0              0   \n",
       "1442619                        0            1              0              0   \n",
       "1445490                        0            1              0              0   \n",
       "\n",
       "         Season_Winter  \n",
       "0                    0  \n",
       "2249                 0  \n",
       "2250                 0  \n",
       "2251                 0  \n",
       "2252                 0  \n",
       "...                ...  \n",
       "1442631              0  \n",
       "1442632              0  \n",
       "1442633              0  \n",
       "1442619              0  \n",
       "1445490              0  \n",
       "\n",
       "[1445491 rows x 30 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add to the dataframe\n",
    "checkout_data = pd.concat([checkouts, y1, y2], axis=1)\n",
    "# and drop those corresponding attributes\n",
    "checkout_data.drop(['Holiday_Name', 'Seasons'], axis=1, inplace=True)\n",
    "X = checkout_data\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1445491, 30)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1445491,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted Date</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Temperature (F)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Wind Gust</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Day of Week</th>\n",
       "      <th>...</th>\n",
       "      <th>Holiday_Summer Vacation</th>\n",
       "      <th>Holiday_Thanksgiving Day</th>\n",
       "      <th>Holiday_Veterans Day</th>\n",
       "      <th>Holiday_Washington's Birthday</th>\n",
       "      <th>Holiday_Weekend</th>\n",
       "      <th>Holiday_Winter Vacation</th>\n",
       "      <th>Season_Fall</th>\n",
       "      <th>Season_Spring</th>\n",
       "      <th>Season_Summer</th>\n",
       "      <th>Season_Winter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>964807</th>\n",
       "      <td>1.596773e+09</td>\n",
       "      <td>341.0</td>\n",
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       "      <td>14</td>\n",
       "      <td>82</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>955221</th>\n",
       "      <td>1.596600e+09</td>\n",
       "      <td>150.0</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>791160</th>\n",
       "      <td>1.594181e+09</td>\n",
       "      <td>262.0</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>84</td>\n",
       "      <td>51</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651904</th>\n",
       "      <td>1.591934e+09</td>\n",
       "      <td>659.0</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>58</td>\n",
       "      <td>60</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
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       "      <th>600647</th>\n",
       "      <td>1.590638e+09</td>\n",
       "      <td>185.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>72</td>\n",
       "      <td>84</td>\n",
       "      <td>10</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1185150</th>\n",
       "      <td>1.599970e+09</td>\n",
       "      <td>183.0</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>74</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>805475</th>\n",
       "      <td>1.594354e+09</td>\n",
       "      <td>227.0</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>85</td>\n",
       "      <td>46</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28887</th>\n",
       "      <td>1.573189e+09</td>\n",
       "      <td>185.0</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>47</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>345562</th>\n",
       "      <td>1.582434e+09</td>\n",
       "      <td>185.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>54</td>\n",
       "      <td>43</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439959</th>\n",
       "      <td>1.585195e+09</td>\n",
       "      <td>122.0</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>42</td>\n",
       "      <td>89</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72275 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Converted Date  start_station_id  Month  Hour  Temperature (F)  \\\n",
       "964807     1.596773e+09             341.0      8    14               82   \n",
       "955221     1.596600e+09             150.0      8    11               74   \n",
       "791160     1.594181e+09             262.0      7    18               84   \n",
       "651904     1.591934e+09             659.0      6    19               58   \n",
       "600647     1.590638e+09             185.0      5     6               72   \n",
       "...                 ...               ...    ...   ...              ...   \n",
       "1185150    1.599970e+09             183.0      9    14               74   \n",
       "805475     1.594354e+09             227.0      7    15               85   \n",
       "28887      1.573189e+09             185.0     11    22               28   \n",
       "345562     1.582434e+09             185.0      2    13               54   \n",
       "439959     1.585195e+09             122.0      3    16               42   \n",
       "\n",
       "         Humidity  Wind Speed  Wind Gust  Precipitation  Day of Week  ...  \\\n",
       "964807         41          13         18            0.0            4  ...   \n",
       "955221         40           7          0            0.0            2  ...   \n",
       "791160         51           5          0            0.0            2  ...   \n",
       "651904         60          14          0            0.0            4  ...   \n",
       "600647         84          10          0            0.0            3  ...   \n",
       "...           ...         ...        ...            ...          ...  ...   \n",
       "1185150        55           8          0            0.0            6  ...   \n",
       "805475         46          15         29            0.0            4  ...   \n",
       "28887          47          12          0            0.0            4  ...   \n",
       "345562         43          17          0            0.0            6  ...   \n",
       "439959         89          10          0            0.0            3  ...   \n",
       "\n",
       "         Holiday_Summer Vacation  Holiday_Thanksgiving Day  \\\n",
       "964807                         1                         0   \n",
       "955221                         1                         0   \n",
       "791160                         1                         0   \n",
       "651904                         0                         0   \n",
       "600647                         0                         0   \n",
       "...                          ...                       ...   \n",
       "1185150                        0                         0   \n",
       "805475                         1                         0   \n",
       "28887                          0                         0   \n",
       "345562                         0                         0   \n",
       "439959                         0                         0   \n",
       "\n",
       "         Holiday_Veterans Day  Holiday_Washington's Birthday  Holiday_Weekend  \\\n",
       "964807                      0                              0                0   \n",
       "955221                      0                              0                0   \n",
       "791160                      0                              0                0   \n",
       "651904                      0                              0                0   \n",
       "600647                      0                              0                0   \n",
       "...                       ...                            ...              ...   \n",
       "1185150                     0                              0                1   \n",
       "805475                      0                              0                0   \n",
       "28887                       0                              0                0   \n",
       "345562                      0                              0                1   \n",
       "439959                      0                              0                0   \n",
       "\n",
       "         Holiday_Winter Vacation  Season_Fall  Season_Spring  Season_Summer  \\\n",
       "964807                         0            0              0              1   \n",
       "955221                         0            0              0              1   \n",
       "791160                         0            0              0              1   \n",
       "651904                         0            0              0              1   \n",
       "600647                         0            0              1              0   \n",
       "...                          ...          ...            ...            ...   \n",
       "1185150                        0            1              0              0   \n",
       "805475                         0            0              0              1   \n",
       "28887                          0            1              0              0   \n",
       "345562                         0            0              0              0   \n",
       "439959                         0            0              1              0   \n",
       "\n",
       "         Season_Winter  \n",
       "964807               0  \n",
       "955221               0  \n",
       "791160               0  \n",
       "651904               0  \n",
       "600647               0  \n",
       "...                ...  \n",
       "1185150              0  \n",
       "805475               0  \n",
       "28887                0  \n",
       "345562               1  \n",
       "439959               0  \n",
       "\n",
       "[72275 rows x 30 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split into training and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mult_lin = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit the linear regression model\n",
    "ml = mult_lin.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.34125224, 3.38774196, 3.50707192, ..., 3.02692489, 3.34135269,\n",
       "       1.84678805])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = ml.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09068256244612716"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the regression curve\n",
    "fig, ax = plt.subplots()\n",
    "plt.style.use('default')\n",
    "plt.style.use('ggplot')\n",
    "ax.plot(X_test['Converted Date'], y_pred, color='g', label='Regression Model')\n",
    "ax.scatter(X_test['Converted Date'], y_test, edgecolor='k', facecolor='grey', alpha=0.7, label='Sample Data')\n",
    "ax.set_ylabel(\"Number of Bike Checkouts\")\n",
    "ax.set_xlabel(\"Date (s)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>964807</th>\n",
       "      <td>11</td>\n",
       "      <td>3.341252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955221</th>\n",
       "      <td>1</td>\n",
       "      <td>3.387742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791160</th>\n",
       "      <td>1</td>\n",
       "      <td>3.507072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651904</th>\n",
       "      <td>2</td>\n",
       "      <td>1.737360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600647</th>\n",
       "      <td>1</td>\n",
       "      <td>1.907557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185150</th>\n",
       "      <td>1</td>\n",
       "      <td>3.549704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805475</th>\n",
       "      <td>3</td>\n",
       "      <td>3.618038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28887</th>\n",
       "      <td>1</td>\n",
       "      <td>3.026925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345562</th>\n",
       "      <td>1</td>\n",
       "      <td>3.341353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439959</th>\n",
       "      <td>1</td>\n",
       "      <td>1.846788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72275 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         y_test    y_pred\n",
       "964807       11  3.341252\n",
       "955221        1  3.387742\n",
       "791160        1  3.507072\n",
       "651904        2  1.737360\n",
       "600647        1  1.907557\n",
       "...         ...       ...\n",
       "1185150       1  3.549704\n",
       "805475        3  3.618038\n",
       "28887         1  3.026925\n",
       "345562        1  3.341353\n",
       "439959        1  1.846788\n",
       "\n",
       "[72275 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample predictions generated\n",
    "pd.DataFrame({'y_test': y_test, 'y_pred': y_pred})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit the model\n",
    "las = lasso.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07389359703295206"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.12885732, 3.42980883, 3.26209701, ..., 2.15831213, 2.72220792,\n",
       "       2.17042428])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_lasso_pred = las.predict(X_test)\n",
    "y_lasso_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_lasso_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>964807</th>\n",
       "      <td>11</td>\n",
       "      <td>3.128857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955221</th>\n",
       "      <td>1</td>\n",
       "      <td>3.429809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791160</th>\n",
       "      <td>1</td>\n",
       "      <td>3.262097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651904</th>\n",
       "      <td>2</td>\n",
       "      <td>1.615644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600647</th>\n",
       "      <td>1</td>\n",
       "      <td>2.586613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185150</th>\n",
       "      <td>1</td>\n",
       "      <td>3.257106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805475</th>\n",
       "      <td>3</td>\n",
       "      <td>3.391654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28887</th>\n",
       "      <td>1</td>\n",
       "      <td>2.158312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345562</th>\n",
       "      <td>1</td>\n",
       "      <td>2.722208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439959</th>\n",
       "      <td>1</td>\n",
       "      <td>2.170424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72275 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         y_test  y_lasso_pred\n",
       "964807       11      3.128857\n",
       "955221        1      3.429809\n",
       "791160        1      3.262097\n",
       "651904        2      1.615644\n",
       "600647        1      2.586613\n",
       "...         ...           ...\n",
       "1185150       1      3.257106\n",
       "805475        3      3.391654\n",
       "28887         1      2.158312\n",
       "345562        1      2.722208\n",
       "439959        1      2.170424\n",
       "\n",
       "[72275 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample output prediction\n",
    "pd.DataFrame({'y_test': y_test, 'y_lasso_pred': y_lasso_pred})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prana\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=8.65583e-21): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n"
     ]
    }
   ],
   "source": [
    "# fit the model\n",
    "rid = ridge.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09068257878147568"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rid.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.34124669, 3.38774659, 3.50706889, ..., 3.02692388, 3.34135781,\n",
       "       1.84677361])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ridge_pred = rid.predict(X_test)\n",
    "y_ridge_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_ridge_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>964807</th>\n",
       "      <td>11</td>\n",
       "      <td>3.341247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955221</th>\n",
       "      <td>1</td>\n",
       "      <td>3.387747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791160</th>\n",
       "      <td>1</td>\n",
       "      <td>3.507069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651904</th>\n",
       "      <td>2</td>\n",
       "      <td>1.737368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600647</th>\n",
       "      <td>1</td>\n",
       "      <td>1.907550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185150</th>\n",
       "      <td>1</td>\n",
       "      <td>3.549709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805475</th>\n",
       "      <td>3</td>\n",
       "      <td>3.618025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28887</th>\n",
       "      <td>1</td>\n",
       "      <td>3.026924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345562</th>\n",
       "      <td>1</td>\n",
       "      <td>3.341358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439959</th>\n",
       "      <td>1</td>\n",
       "      <td>1.846774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72275 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         y_test  y_ridge_pred\n",
       "964807       11      3.341247\n",
       "955221        1      3.387747\n",
       "791160        1      3.507069\n",
       "651904        2      1.737368\n",
       "600647        1      1.907550\n",
       "...         ...           ...\n",
       "1185150       1      3.549709\n",
       "805475        3      3.618025\n",
       "28887         1      3.026924\n",
       "345562        1      3.341358\n",
       "439959        1      1.846774\n",
       "\n",
       "[72275 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample output prediction\n",
    "pd.DataFrame({'y_test': y_test, 'y_ridge_pred': y_ridge_pred})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "dec = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = dec.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28001245104609296"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.,  1.,  2., ...,  1.,  2.,  1.])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_dec_pred = d.predict(X_test)\n",
    "y_dec_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the decision tree of depth 1 (for visualizing)\n",
    "plt.figure(figsize=(16,6))\n",
    "_ = plot_tree(dec, max_depth=1)\n",
    "plt.savefig(\"dec_tree.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_dec_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>964807</th>\n",
       "      <td>11</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955221</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791160</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651904</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600647</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185150</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805475</th>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28887</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345562</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439959</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72275 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         y_test  y_dec_pred\n",
       "964807       11        10.0\n",
       "955221        1         1.0\n",
       "791160        1         2.0\n",
       "651904        2         1.0\n",
       "600647        1         1.0\n",
       "...         ...         ...\n",
       "1185150       1         2.0\n",
       "805475        3         4.0\n",
       "28887         1         1.0\n",
       "345562        1         2.0\n",
       "439959        1         1.0\n",
       "\n",
       "[72275 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample output prediction\n",
    "pd.DataFrame({'y_test': y_test, 'y_dec_pred': y_dec_pred})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit the model\n",
    "r = rand.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.19, 2.19, 1.67, ..., 1.09, 2.31, 1.02])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_rand_pred = r.predict(X_test)\n",
    "y_rand_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_rand_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>964807</th>\n",
       "      <td>11</td>\n",
       "      <td>6.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955221</th>\n",
       "      <td>1</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791160</th>\n",
       "      <td>1</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651904</th>\n",
       "      <td>2</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600647</th>\n",
       "      <td>1</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185150</th>\n",
       "      <td>1</td>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805475</th>\n",
       "      <td>3</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28887</th>\n",
       "      <td>1</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345562</th>\n",
       "      <td>1</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439959</th>\n",
       "      <td>1</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72275 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         y_test  y_rand_pred\n",
       "964807       11         6.19\n",
       "955221        1         2.19\n",
       "791160        1         1.67\n",
       "651904        2         1.45\n",
       "600647        1         1.54\n",
       "...         ...          ...\n",
       "1185150       1         4.02\n",
       "805475        3         3.35\n",
       "28887         1         1.09\n",
       "345562        1         2.31\n",
       "439959        1         1.02\n",
       "\n",
       "[72275 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample output prediction\n",
    "pd.DataFrame({'y_test': y_test, 'y_rand_pred': y_rand_pred})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "table_data = [\n",
    "    ['Linear Regression',mean_absolute_error(y_test, y_pred), mean_squared_error(y_test, y_pred), r2_score(y_test, y_pred)],\n",
    "    ['Lasso Regression', mean_absolute_error(y_test, y_lasso_pred), mean_squared_error(y_test, y_lasso_pred), r2_score(y_test, y_lasso_pred)],\n",
    "    ['Ridge Regression', mean_absolute_error(y_test, y_ridge_pred), mean_squared_error(y_test, y_ridge_pred), r2_score(y_test, y_ridge_pred)],\n",
    "    ['Decision Tree Regression', mean_absolute_error(y_test, y_dec_pred), mean_squared_error(y_test, y_dec_pred), r2_score(y_test, y_dec_pred)],\n",
    "    ['Random Forest Regression', mean_absolute_error(y_test, y_rand_pred), mean_squared_error(y_test, y_rand_pred), r2_score(y_test, y_rand_pred)]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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tstWrVzcLFiwoUC41NdXcdddddm2B1157zWFMf7V7Jk+ebN544w2TnJxs8vLyHJY5ceKEue++++zqOX78eJdxfT3We+DAAbs7PbVo0cJs27atQLkZM2aYkiVLWuXeeOMNn2zfmz6PMb7vR/rjOPXHWK+/jlN/nDsLDw83JUqUMCNGjDBLly616z/m27Bhg2natKldf8vVuQNjjNmyZYsJDQ211hk0aJDJyMiwK7Nw4UITExNjlRk+fLjb+vq6HwkAReVp/jkJ7X5EQvulz5vB0zfeeMOu/GeffXZxKgkAAOAAA/KFc+jQIYe3TbRNjPZnYniXLl2s7fTv39/j2y06E8iE9pycHLvbnz/44IM+idu3b18rZrNmzVyW9XVC+8GDB+0uZH3ppZecls3JyTFXXHGFVfa5555zGXvdunUFTq5ceNtbbxMR0tLSTIUKFaz133nnHa/W91RSUpK1jSeeeMLr78uxY8ccDq7b3qL175bQ7s1x6qlz586ZunXrWnFvv/12p2U3bNhgl9TjbKA5NzfXtG3b1io3YMAApzHHjRtnlYuLi3OZsJSenm73/X/ooYcclhs/frz5448/nMbJl5eXZ5cwFRcX5/SEGABcqmifF0716tWt//8PPPCAy7IzZsyw+/13d1LYnT179pjDhw8X+PvQoUOtbXib0O6P32hPeds+D2RC+7vvvmttOzQ01GlysjH+aUueO3fOLqn4sccec1r20UcftdtOZmamV9saM2aMtf78+fMLdd7Il/1If/Z5LpSXl2c6duxopPMXJBw5csSrczLG+L7PM3PmTCtWcHCwWbZsmdOy+/fvt0sa+fXXX52Wzd9PT5Jr1qxZY/c+zJ4922lZf/QjAeDfgvZ54cydO9cuYdZZmzsjI8PUrFnTKvvf//63yNu2bSPGxMQ4nRzi4MGDdr/REydOLPK2N23aZPf7+t133xU5pjHGfPTRR1bMoKAgs3//foflFi9ebLf933//3WXc6667zirr7Fj3V7vHG0OGDLFi1qlTx2VZX4/13n///Va8ypUrm7S0NKdlbY/7MmXKmBMnThR5+970eQLZjzTG8+PUH2O9/jpO/XHu7JFHHnE4hnChY8eOmfj4eGv7N998s8vytuca2rVr5zBR3hhjfvjhB6tcSEiI2bRpk8u4vj4fCQBF5Wn+OfdvBjx05513KiIiwlq2vR0nAAAA/h4qVqyoatWqBWTb3333nRYsWCBJqlatmj744AOFhIQEpC6+MH/+fLvbnw8bNqzIMU+cOKHZs2f7NKY3Zs+ebd0ysnz58nrggQeclg0JCdFzzz1nLb/zzjvKy8tzWr5Zs2Ye31LVU88++6x1G84bbrjBq9udeurs2bO6/fbbJUm1a9fWk08+6XWMmJgY1a5d29dVCxh/Hafh4eG65ZZbrOX8Wxw7MmHCBOt469Kli7p27eqwXHBwsF5++WVrefr06Tp69KjDsuvXr7eeX3/99SpRooTT7UdFRalXr17W8rZt2xyWe/jhh9WkSROncfIFBQXpxRdftJYPHjyo33//3e16AIC/t1OnTmnPnj3W8oABA1yW7927t93vk7PfH0/Fx8erQoUKRYpxIX/8RnvKH+1zf/nkk0+s59ddd53Kli3rtKw/2pKzZ8/W/v37JUmlSpXSU0895bTsmDFjVKpUKUnS3r17NXfuXI+3s2nTJv3vf/+TJA0aNEhdunTxuq6+7kf6s89zoQ8++ECLFy+WJL388ssqX7681/X1dZ9n+vTp1vOePXvqiiuucFq2SpUquvfee63lt956y2lZ27a0u/9lLVq0UL169axlV//L/NGPBADAlXfffdd6PnToUDVu3NhhucjISD3zzDPW8sSJE5WTk+OzbT/yyCOqVKmSw3KVKlXSww8/7HC9wrJtn5YrV049evQockzpfBswP8fEGKM//vjDYTnbtkSDBg3UtGlTt3HzOWtL+Kvd443bbrvNer59+3ZlZGT4JK4nvv76a+v5I488otjYWKdle/TooSuvvFKSlJaWps8++6zI2/emzxPIfqTk+XHqj7HeS+E49dQrr7zi0RhCTEyMHn30UWt53rx5TsseOXJE33zzjbX88ssvKzjYcSpnt27d1LlzZ0lSbm6uJk6c6DTuP+18JIB/FxLa/yays7P1008/afTo0br66qsVFxeniIgIFS9eXFWqVFGPHj305ptvetUA3LJli0aPHq02bdqobNmyCgsLU6lSpVS7dm21adNGI0eO1LfffqtTp045jZGRkaH3339f1157rapVq6YSJUooIiJCVapUUdOmTXXDDTfovffe044dOzzaxylTpqh3796Kj49X8eLFFR0drXr16mnEiBHWj22gREREqEGDBtbywYMHPVrv559/1l133aWGDRsqNjZW4eHhiouLU9euXfXOO+/o7NmzHtchOztb77//vjp27Kjy5curePHiqlmzpvr372/3/lSvXl1BQUEKCgqyOylky1GZnTt36oknnlCzZs1Urlw5BQcHO+0snT59WhMmTFDPnj0VHx+vEiVKqGTJkqpTp45uvfVWJSUlebxfSUlJGjFihBo3bqzSpUsrNDRUZcuWVYMGDdSxY0c98cQTWrJkibKzs53G2L9/v55++mldddVVqlChgsLDw1WyZEnVqFFDrVu31vDhw/XFF18oNTXV4frjxo2z3o9x48a5rbOvjldHn8Nff/2lp556Sk2aNFHp0qUVGRmp+vXr67777tPevXs9igsAAC49EyZMsJ4/+OCDKlmyZABrU3S2A7JNmzbVZZddVuSYX375pZVcERoa6jYRwNdWrFhhPe/UqZPCwsJclu/QoYMiIyMlSYcPH9bSpUv9Wj9b586ds/sMxowZ45ftjBkzRjt37pR0/hi2vcj338qfx6ltkk96errDMsYYu4T64cOHu4zZrl071alTR9L5ge7vv//eYTnb8YTSpUu7rWtMTIz13JvEJmcaNGhgF9NZXxYA8M9x4Vi2u9+fkJAQRUdHW8u++P3xJX/9RnvKH+1zf9i6datWrVplLQ8dOvSi1+Hbb7+1nvfv39/lhXwlSpRQv379rOVZs2Z5tI28vDyNGDFCWVlZio2N1WuvvVaouvq6H3mx+jwHDx7U6NGjJUlXXnmlRowY4XVd/dHnsd3/7t27uy1vm8w2b948p+dzAt2WBgDAFzIyMvTzzz9by+7as3369LHaJseOHSvShIA7duzQ5s2brWV3F2favv7nn39a45eFkZeXZ5fAPGjQIJ9dUBYWFmbXNnA23uePtoS/2j3euPCCRmf772t79+61u9jX2/23TTAuDG/6PIHuR0qeH6fe8HSs91I4Tv2hXbt21vNTp07p2LFjDsvNnj3b+g7XrVtXbdu2dRnX9viw7dde6J92PhLAvwuX9f8N7N+/X82aNVNaWprD1w8cOKADBw7ohx9+0HPPPafPP//c7Uwf48aN0/PPP1/gKtns7GydOnVKO3fu1MqVKzVhwgQNGjRI06ZNKxBj+fLl6tu3rw4cOOC0TuvXr7d+RLOzs502/FeuXKlBgwYV6GicO3dO6enp2rZtmz766CN16dJFn332mcqVK+dy//ylePHidnVzZf/+/RoyZIg1A4mtQ4cO6dChQ5o/f75efPFFffnll9YVn87s3btXvXr10p9//mn39927d2v37t2aPn26br/99kJfgTxp0iSNGjXK7X5J569mvf/++3X48OECr+3YsUM7duzQlClTdN1112natGnWDDYXysjI0KBBg+wa6PnS0tKUlpamLVu26JdfftELL7ygDz74wO4q3nwTJ07Ugw8+WKCxmpWVpYyMDO3Zs0erV6/Wxx9/rHbt2um3335zu4+u+PN4/fbbbzVs2DCdPHnS7u9bt27V1q1bNXnyZH399de69tpri7QPAADg4kpJSbG74G3gwIEBrE3RnTx5Ut9995217KsklKlTp1rPe/TocdHb/fkz/0nnZ+x0JyQkRJUrV7Zmwfn555/dtut9Zc6cOTp+/LgkKSEhwe2sPYWxbt06vf7665KkW265xZr949/On8dpcnKy9dzZMbh9+3b99ddf1nLHjh3dxu3YsaO2b98u6fwFxbfeemuBMrZ3r9i0aZPbmBs3brSeezIzjyeCgoKs57m5uT6JCQC4dJUrV04RERHWeOSmTZtUt25dp+VTUlKUkpJiLfvq98dX/PUb7Ql/tc/9wV+zX3pj0aJF1nNPP6cPPvhAkjyeyOXtt9/WypUrJRV+dnJ/9CMvVp/nnnvu0cmTJxUWFqaJEyfatfM85Y8+j7f7b1vm9OnTWrlypcNjplq1atb5gk2bNumaa65xGjM7O1tbt261li+1/2UAgH+vZcuWWZM4REZGqlWrVi7Lh4eHq02bNlZ7JSkpSZ06dSrUtm3bWHXr1lVcXJzL8pUrV1adOnWstvSiRYtUq1atQm174cKFdjkvvmxLp6Wl2c2g7az9YTsut3XrVuXk5LhMqvdkXM5f7R5v2I51lihR4qKN99vuu+T9/udPthgaGlqo7XvT5wlkPzKfp8eptzwZ670UjlN/uLD/42z/ve2bXn311dbzffv2aceOHQXuaPZPOx8J4N+HhPa/gdOnT1vJ7DExMWrYsKHi4+MVFRWlrKws7d69WytWrNC5c+eUmpqqHj166JdffnF65dYbb7yhp59+2louW7as2rRpo0qVKikoKEjHjh3Tli1blJyc7PRHdf/+/eratat1ZV5oaKhatWql2rVrq0SJEjp9+rT27Nmj9evXu5zhXTrfGOzevbvOnDlj/a1169Zq2LChsrKytGLFCmsgcMGCBWrfvr1+++23gCS1287K7moAOjk5WYmJidZVn0FBQWratKkaNmyoEiVK6MCBA1qyZInS09N18OBBdenSRT/88INd48PW0aNH1alTJ+3atcv6W7169dSqVSsVK1ZMGzZs0Nq1a/XBBx8U6sq6r7/+2potpXLlymrbtq1KlSqlgwcPFrhS8PXXX9fDDz8sY4wkqWTJkrriiitUtWpV5ebmavPmzVq9erWMMZozZ446dOigZcuWOZzlZvDgwXbJ7LVr11azZs0UGxur7OxsHT16VBs2bHA5M9+3336ru+66y1qOjo7WFVdcoSpVqqhYsWI6efKktm3bpo0bNyorK8vr9+ZC/jxef/75Z915553Kzc1VtWrVdMUVVyg6Olq7d+/W4sWLlZOTo7Nnz6pfv37auHGjatSoUeT9AQAAF8fy5cutWRbq1aun8uXL6+TJk/rwww81ffp07dixQ2fPnlW5cuXUunVr3XTTTerXr5/TWws6cvbsWc2ePVvr16/X8ePHFRUVpYoVK+qKK65QkyZNvIrlzvTp063Eo2LFitnd4rSwtm/fruXLl1vL7mbhudCRI0f06aefatu2bTp9+rRiYmJUrVo1XXnllapZs6ZHMfLbuFLBAUdPeJIE7Cu2MyPmJ5Ts2rVLEyZM0Ny5c7Vv3z6FhISoUqVKuuqqqzR06FC7WUHcycnJ0YgRI5Sbm1ukGSUvliVLlmjlypU6cuSIdcen5s2bq02bNnYXJhdVUY9TVzZt2qTJkydbyzfddJPDcrYngipWrOj09su2mjdv7nB9W7169bK2P3fuXC1dutTpMbNo0SLrVqnh4eGFmnHzQhf2P6tWrVrkmACAS1toaKi6d+9uzXj97LPPqmvXrk5ny37sscesNnViYqLL5PdA8NdvtCeK2j5PTk7W5s2btW/fPqv9V79+fV111VUqU6ZMoet1oby8PLvJcwYOHFjoJJHCOnnypN1sjbafgTO2ZQ4cOKBTp07Z3S3gQnv37tUTTzwhSbrqqqsKnWDij37kxejzzJgxw5ro6NFHH7W78603/N3nKez+O0o06dWrl3Ux8Ouvv65bbrnF6TmBF154wUrUr1evnhITE72uBwAA/mDbHm3cuLFHs5Q3b97cSpwsSnvWdl1P2mf55fKTeouybdvk48suu8xnE4cYY/Sf//zHyrmpWrWqWrdu7bBs165dFR4erszMTB07dkzjx4/XY4895rDsoUOH7MZqR44c6bYuvmz3eOrUqVMaO3astdyrVy+PZ74v6livbZu3MHJycrRt2zY1bNjQ63W97fMEsh8peXeceqMwY73+Ok4v5rmzfBs2bLCeFy9eXGXLlnVYztv/fZUqVVKFChWsCwGSk5MLJLRfjPORAOBXxkM9e/b0tCj+vw4dOhhJRpIZO3ZsoePs2bPH3HfffWblypUmNzfXYZmTJ0+aRx55xNpenTp1HJbNzs42ZcqUscq9+OKLJisry2HMtLQ089FHH5mXXnqpwGujRo2yYlx55ZXmwIEDDmNkZ2ebxYsXm0GDBpmcnJwCrx87dsxUrlzZilWrVi2zevXqAuWmTZtmihcvbpXz1fGYH0+SWbRokcuyW7ZssSv/9ttvOyx3+vRp06BBA6tc586dzdatWwuUO3nypLnrrruscpUqVTInTpxwGHPAgAFWuRIlSpivv/66QJlff/3VxMXFmaCgIBMWFmaV3717t8OY8fHxVplixYqZsLAwM2nSJJOXl2dX7ty5c9bzhQsXmuDgYCPJhIaGmueee85kZGQUiP3777+bhIQEK/7dd9/tsEz+61FRUWbevHkO62mMMTt37jTPPfecmT17doHXmjRpYsW59957zenTpx3GSE9PN9OnTzePPvqow9fHjh3r9vvqj+PV9nMIDw83kZGR5tNPPy3wOWzcuNFu28OHD3caEwDw79C8efNAV+EfxbYt0KFDB5/Hf+KJJ6z4N954o1m2bJmpVq2aXfvywkfTpk3Nrl27XMZdtGiRyxj5jxo1apgJEyYUaGMUVrt27azYvXr18klM2/eoTJkyTvsptmw/N1ePdu3amQULFriNN3jwYGudm2++2W35nJwcExkZaa3TsGFDj/Y13+7du+3q6azt7ojtZ/DWW2+ZDz74wK4N6uhx8803mzNnzngU/4UXXrDWmzx5st1rvvq+TJkyxYoTHx/v9fqefPbR0dFm9OjR5uTJk4Wup63CHKeupKenm3Xr1pmxY8eakiVLWrHbt29vsrOzHa7z0ksvWeUuv/xyj7Yzd+5cuz6lI3l5eaZLly5WuYiICPPQQw+ZtWvXmuPHj5vjx4+btWvXmvvvv9+Eh4cbSSYsLMx89tlnhd5/W88//7y17VKlSjndfwC4VNE+L5zk5GQTFRVlN9728ccfm+3bt5uzZ8+affv2mTlz5pj27dtbZRo0aGD27dvntzoNHTrU2tbQoUM9Xs9fv9GeKEz73Pb8hbNHSEiIufnmm822bdsKXTdbCxYssIu/bt26QscqbFty5cqVdnXwpH18+vRpu3VWrVrlsny3bt2stlJycrLda96cN/JHP9LffZ7jx4+bihUrGun8eaqzZ8/avW5bV3fnZPzR56latapV9v3333e7/8uXL7eLf8899zgsl5KSYipVqmSVq1ixonnzzTdNcnKyOX36tDl06JBJSkoyPXv2tMrExcWZ9evXu63DhYrSjwSAfxPa5967++67rd+X/v37e7TOu+++a62TkJBQ6G13797diuPsfP6F/vOf/1jr9OjRo1DbPXXqlF374rXXXitUnHzZ2dnmwIED5ptvvrFr9xUrVsxhroOt5557zu43vnfv3mbx4sXm0KFD5vTp02bz5s3mtddeM+XLl7fK3HvvvU7j+avd48q5c+fM9u3bzfvvv29q1qxpxSpfvrzZs2ePy3V9Oda7c+dOu/W2bNnitu4vvvii3TqOcnI84W2fJxD9yKIcp57ydKzXX8dpoM6d5bMdZ7/22mudlrP9//PDDz94FLtVq1bWOi+//HKB1/11PhIAisrTfF8S2v3IVwnt3rBNkHaUILxhwwbr9Xbt2hV6Oy1atLDibN++vdBxxowZY8UpXbq02bt3r9OyM2fOtPtB/eWXXwq93XyeDp5mZWWZa665xipbsmRJc/ToUYdln3nmGatct27d3J6EHzJkiFX+f//7X4HXN27caFfPb775xmmsP/74wxQrVsyjwUzbRGpJZtq0aS7rmZuba+rUqeNx+UOHDlmdqdDQULN//367199++20r1hNPPOEyljPp6elWjKpVqxapkelJQrs/jlfbzyEoKMhlI3XOnDlW2aioKBI8AOBfjgF53/J3Qrttmy8xMdGULl3aWm7evLkZNmyYGTx4sKlfv75dG6JChQoF2lG2PB2Uy390797dpKenF2lfduzY4XH71FN5eXl2A2r33XefR+t5mtCe39Zy1+588803rfLly5c3mZmZLsv//PPPdtuoVKmSx/tsTNESEWzfrxtvvNF6HhoaahITE81tt91m+vbta3eCQ5K56qqr3LYjt23bZiIiIqzvw4Xt7L9TQnv+o06dOgWSibxV2OPU1qeffur2OB08eHCBxB9bo0ePtsp7mrC2du1au+3YXrhs6+zZs6Z///5u38/g4GDTuXNns3z5cq/fA0cOHDhgoqOjrfijR4/2SVwAuJhonxfeH3/8YXcS29mjdOnS5p577vHZhWrOFDah3Z+/0a4Utn3uSUJ7/qNkyZJm5syZXtftQrfccosVs3HjxkWKVdi25Lx586z1oqOjPV7P9uLDH3/80Wm5qVOnWuXGjBlT4HVvzhv5ox/p7z7PrbfeapX9+eefC7xuG8tdQrs/+jw33HCDVe766693uX1jjHnqqafsYg8YMMBp2T179pjGjRu7/T6VKFHCDB482Bw8eNDt9h0hoR0APEP73Hv9+vWzfl/uv/9+j9b55ptvrHUqVqxY6G23bt3aiuNpUvmrr75qrdOmTZtCbffDDz+0YhQrVswcOXLE6xi1atVy+dtfsWJFk5SU5FGsV155xZpg0NWjUaNG5pNPPnEZy5/tnnz79+93W9dWrVq5zKvI52nfRHI/1puXl2c30efrr7/udvtXXnml3TYmTpzodh1HvO3zXKx+pC+PU3e8Gev113EaiHNn+WzzeiTHuXvGGHPmzBm7cp5e8G17kexjjz1W4HV/nY8EgKLyNP+c+0X8wwwfPtx6vnDhwgKvnzp1ynru7HaHnvBFHGOMJk2aZC0/9dRTqlatmtPyN9xwg7p3724tT5gwoVDb9VReXp5SUlI0c+ZMtWvXTvPnz5d0/patU6ZMcXhLmOzsbL3zzjuSpODgYL3//vtub5v04osvWrfO+eyzzwq8PmXKFOv5VVddpRtvvNFprCZNmtgdA55q3bq129vQfv/999YtuxITE92Wr1ixoh588EFJ59+X6dOn273ui2PINkaZMmUKdQsiT12M4/W6665Tt27dnL7eo0cPVaxYUZKUkZGhLVu2eFJ1AABwCThx4oT1/Oeff9aJEydUtmxZJSUlae3atZoyZYqmTp2q5ORkffXVV9ZtM48cOaJbbrnFZewKFSpo1KhRmjt3rvbu3auzZ8/q7Nmz2r59uyZNmqSEhASr7A8//KCbb77Zut1gYUydOtV6XqZMGV133XWFjpVv8eLF2rdvn7U8bNgwj9dt1KiRnn76aS1ZskRHjhxRVlaWTp06pXXr1umZZ56x2u3GGD3//PN66aWXnMa69tprrdsqpqSk6I033nBaNjc3V08++aTd39LT0z2ud1HZHlMzZ86UJLVq1Upbt27VwoUL9cEHH2j69Onav3+//vvf/1pllyxZoueee85pXGOMbr/9dp07d05hYWF6//33/drOLoqIiAgNHDhQn332mbZs2aL09HRlZWXpwIEDmjlzpl3bevv27erWrZtSUlIKvb2iHKeeqFy5sn766SdNnTpVERERTstlZGRYzz25xa6jcrYxbEVEROjLL7/UsmXL1KRJE6fx6tevr379+nl8C2hXcnNzNXToUKt/V758eae3VAYA/DM1adJE27Zt09tvv63IyEin5bp27apBgwYpOjr6ItbOc/78jXalsO3z4OBgJSYm6p133tHatWt14sQJZWdnKy0tTUlJSbrzzjsVFhYm6Xw79+abb9avv/7qdf3yZWRkaNasWdayr9tS3tQjn6ef04VlnX1OR48etcbE69atq8cff7yQtTzPH/1If/Z5kpKS9NFHH0mShgwZok6dOnmym075o8/Tq1cv6/n333+v5cuXO93+gQMHrPM9+Vztf3x8vNatW6fPPvvM4fmjfFdeeaUGDhyoSpUqOS0DAEAgFLU9W5i2bKC3bduW7t69u8qXL1+oOM7ceuut2r59u66++mqPyj/yyCPatWuX+vbt67RMbGys+vTpox49eriM5c92jyciIiL02muvadWqVS7zKmzL+2qsNygoyK5fNH78eB07dszptufNm1egr1OY/S9MnydQ/Uhb3h6nrng71uvP4/RinzuTpEOHDun222+3lrt06WKXN2Trws/NV//7/Hk+EgAuCl9nyOP/+GOG9qysLPPrr7+ad955xzz55JNm1KhR5p577rEetldaderUqcD6tldIRkdHm82bNxeqHomJiVac559/vlAxNm3aZMUIDg42qampbtfx1RW++STPr8iTZOrXr2/mzJnjNN6yZcusst7MgN+gQQMjnZ+N7/jx43av2c6G78ktdhYvXmxXZ09maPfkKuc77rjDKv/BBx94slt2t2+94YYb7F6znZmwWbNmJiMjw6OYtrKysqxb8ISEhBRp1n53M7T763i1/RymTp3qNmbXrl2t8t9//73b8gCAfy5mmPEtf8/Qbtt+zm+7uJrZ+KuvvrIr72zmuvT0dLcz6mVlZZnbb7/dLp4n7Q5H8vLyTI0aNaw4rm5p6g3bWTAbNWrk8XppaWluyxw+fNhuhp/Q0FCzY8cOp+UHDRpk9zm99tprJicnx65MSkqK6d27d4H+QkhIiMd1N6ZoM+uFhITYrVu5cmVz7Ngxp+VtbxkcHR3tdGbTiRMnuu3HXioztHvy+U+ePNluVqNbb721EDU9r7DHqa1Vq1ZZ/fe7777bDBw40DRt2tQEBQVZfcIbb7zRHDp0yGkM21kvBw8e7NF2L7zNrrOZVvLy8sx7771n4uLijCQTHh5urr76ajNixAgzbNgw07ZtW7tjr0GDBmbr1q2Fei/yPfzwwx7NWAMAlzra54WXkpJihg0bZkJDQ61xtJtuusnccccdpn///qZ69ep2vxV33HFHgfaZLxV2hnZ//kY7U5T2uSdtqVWrVpmyZcta8evWrVvou0batv2KFStmDh8+XKg4juJ505a0nUG9atWqHq9nexeBTz/91GGZAQMGWGWczW7ozXkjf/Uj/dHnOXPmjDXrY5kyZZze4daT+uXzR58nKyvL7jtTtmxZ89NPPxUo9+effxaYsVA6P8OhM+np6ebBBx80JUqUMJJMTEyM6dWrl7njjjvMoEGDrPNA+Y9evXoV6rwIM7QDgGdon3uvU6dO1u/LU0895dE6tndz8XZs1FbNmjWtOJMnT/ZoncmTJ1vr1KpVy+tt7tq1yxqTk2RmzJjhdQxjjBk3bpw13jdixAjTvXt3Exsba8WtVKmS29nU8/3888+mefPm1rrNmzc3Q4YMMbfffrvp1q2biYqKsl4rVaqUy3wBf7Z78h0/frxAvtKVV15p3f1TkmnatKlZtWqV21i+HuvdvHmz1ceUZFq0aGG2bdtWoNzMmTPt7saU/3j22Wfd1udChenzXKx+pC+PU1e8Hev113F6sc+dGWNMZmamadu2rRUrNjbW/PXXX07L79u3z27bO3fu9Gg7gwcPttYZMWJEgdf91Y8EgKLyNP+chHY/8mVC+5kzZ8yTTz5pN3js7nHZZZc5jGX7A1qiRAlz++23m3nz5plTp055XJ9JkybZbatDhw5m0qRJXg2cTZs2zVo/ISHBo3UOHz5st90DBw54vD1HPH0vpfO3V1m5cqXLeLa3tapXr55d493Vo1KlStZ6GzdutOLl5ubaNbI9ucVMenq6XcfLk4R2TxLBbW+V2bt3b4/2y/Z2Si1btrSLl5aWZtfhqly5snnyySfN8uXLTVZWltv65Bs4cKAVIzQ01AwYMMB88803HiWc23KX0O6v49X2c/j999/dxrTd32nTpnlUDwDAPxMD8r7l74T2a6+91q5d0LdvX7frtGzZ0ip/5513Fmn7ubm5dv2Thg0bFirOhRdPrl69ukj1MsaYjIwMu3bhK6+8UuSYF0pJSbEbnL3nnnuclj1+/LipXbu23X5WrlzZ9O/f39xxxx2me/fuVoKCJLvbO5YuXdqrehUlESEyMtJu3Xfffddl+dTUVBMeHm6V/+KLLwqUOXDggClVqpSRzicrObtV6aWS0O6pJ5980m7wtjDJU/4+Trdt22Z69Ohh9344OxExcuRIq1z//v09ir9582a748VRfyk3N9cuCeumm25ymFi/Y8cO065dO6tclSpVvO5/5Xvvvffs6uXpyVIAuBTRPi+cbdu2mcqVKxvp/IVUEyZMKJBYm5eXZ7766iurnSLJ3H333X6rU2ET2v31G+2KP9rnF1q4cKHdNr7++utCxenYsaMV49prry1yvQrblpw+fbrdmL+nypcvb63nKNnJ9rbyro4bb84b+asf6Y8+zyOPPGKVmTJlitP62W7TXaKEP/o8xhizevVquyQrSaZx48Zm6NChZsSIEaZt27ZWolTx4sXtEkJ69+7tMGZKSopp2LChkc5PiPP000+bs2fPFii3cOFC63+eJNOjRw+X++QICe0A4Bna596zHZt69NFHPVpn3rx51jpRUVGF3nZCQoIVZ8KECR6tYzuuVJjJJ8aNG2etHxsb6zb51RuZmZnmww8/tOvDPPPMMy7Xeeedd6w8j0aNGjnMHTh+/Li56667rJihoaHm119/dRrTH+0eT6SlpZnRo0db+xMeHm7mz59f6Hi2vBnrfeedd+z2vVixYuaqq64yt912mxk8eLBdgnSdOnVMvXr1rOU33njD67oVps8TiH5kvsIcp64Udqw3UMepMb47d5aXl2duvvlmu+/mjz/+6HKdlJQUu31OTk72aFv9+vWz1nF0UXugz0cCgDMktF8CfJXQfuzYMdO0aVO7HxxPHjVq1HAYb8uWLXYJ1LaNvaZNm5r777/f/PDDDy5nWsnNzTV9+vRxuN24uDjTv39/M3HiRJOSkuI0xuuvv26t0717d4/fD9uGzPr16z1ezxHbel+YpH3zzTebVq1a2ZUJCwtzeZXrhVcbFuZh2+E4fvy43Wuu3k9bpUuXdjuYaZtI7clM/WXKlCnSfjm6Kvrrr782YWFhBcoWL17cdOjQwYwZM8asWbPGZb2OHDni8ErMoKAg06BBA3PHHXeYGTNmOBw8tuUuod1fx6vt57Br1y638WxPqLk6MQAA+OdjQN63/J3Q3r9/f7u2yueff+52neeff95u8K6obGfLkWT27dvndYzhw4cXeWDvQp988oldn8TVzNRF8dRTT1nbqVmzpsuyR44cMVdffbXL9m1oaKh55ZVX7BJpqlev7lWdipKIUKFCBbt1Dx486HadLl26WOXvu+++Aq/bzsDoKrnk75bQnp6ebt3ZSSrcLCsX4zjNzc21+wyuueYah+VGjx5tlenVq5dHsdeuXWt3vDi6WOHFF1+0Xu/cubPJzc11Gi8jI8NuhsnCJBV+8cUXdjMq+TMxEQAuBtrn3svOzjaNGjWyfgvczQZ3YXvW3eQjhVXYhHZ//Ua74o/2uSNXXnmltZ3C3PFmz549dpOwFDYp3lZh25K2SVfR0dEer2c7a+OFiQmnTp2yZnAvW7as09nJjfHuvJE/+5G+7POsWbPGmk396quvdlk/2/juEtr90efJt2rVKruxeUeP8uXLm/nz59v9Txg2bJjDeN26dbPKPP300y7ruHnzZrvzBl999ZXb/bJFQjsAeIb2ufdskyTvv/9+j9bx5I7lnrC9w6Ynd5c3xn6ywTZt2ni9zfy7y0iuJ0ApipUrV1oTGAYFBZnffvvNYbmlS5da42QVK1Y0R44ccRl3yJAhVt0TEhJMXl6e07K+bvd447XXXrPilStXzuldQ73h7VjvRx99VOBCyQsfzZs3N7t27bJ7nz7++GOv6lXYPk8g+pEX8vQ4daWoY72BPE59ce7M9sKE4OBgj/puZ86csduuJxOcGmPsLjh+7LHHCrx+KZyPBABHPM0/DxYueffcc4/++OMPSVJ4eLjuvPNOzZ49W9u3b1d6erpycnJkzl+coN27d1vr5eXlOYxXr149rV+/Xg8++KBiY2Otv+fm5uqPP/7QW2+9pe7duys+Pl4ffPCBwxjBwcGaPn26Pv74Y1122WV2rx08eFBfffWV7rzzTsXFxem2227TsWPHCsTIyMiwnkdGRnr8ftiWTU9P93g9d0aNGqV33nnHenzxxRdatWqV1q9fryZNmkiSsrKyNGDAAO3cudNhjJMnTxa5Hjk5OdZz2/dIkkqUKOFRDG/eT0kqXry42zJF3Tfb/crXp08frVmzRn379lVYWJj197Nnz+qXX37RM888o5YtW6ply5ZasmSJw7jly5fX6tWrNW7cOMXFxVl/N8YoOTlZkyZNUp8+fRQXF6f//e9/ys3NLVT9L8bxGhQU5H3FAADA30KZMmXslhMSEtyuY1vmwIEDRa7DlVdeqdDQUGs5OTnZq/XPnDmjGTNmWMtDhw4tcp0kaerUqdbzrl27qmLFij6Je6HExETr+a5du5SVleW0bPny5ZWUlKQff/xRQ4YMUe3atRUVFaXixYurbt26uvfee/XHH3/okUce0Z49e6z1qlSp4pe6O2J7TJUuXVqVKlVyu46rY+q7777Tt99+K0kaNmyYOnbs6JN6XgqioqJ0+eWXW8veHvvSxTlOg4OD9eabb1r9gvnz52vjxo0Fytl+9keOHPEo9uHDh63nJUqUUHh4uN3r586d00svvWQtP/vsswoOdj5kFBkZqSeffNJa/vTTTx32+ZyZO3euhgwZYo1bDBgwQO+8847H6wMA/hm++eYb67eufv36GjJkiMvynTp1UpcuXazlKVOm+LV+3vLHb7Qr/mqfO2Lbli5MW+rTTz+VMUaSFBMTo549e/qsbt6y/ZxOnTqlc+fOuV3nzJkzdmO7tudVJGnMmDHav3+/JGn8+PEqW7asz+sq+bYf6cs+z2233abc3FyFh4fr/fff92DPPOPrPo+tVq1aadu2bfrwww/Vs2dPVa5cWeHh4SpdurSaN2+u5557Tps2bVKXLl3c7v/KlSv1448/WvUcPXq0yzo2aNBAgwcPtpYvtf9lAIB/r6K2Zy9sI13K2/7tt9/scj781ZZu3bq11c8xxuj11193WO7ZZ5+1xslGjRql8uXLu4z7wgsvWGOImzdv1qpVq5yW9WW7x1ujRo1SnTp1JElHjx61G2MtLG/HeocPH649e/boueee01VXXaVy5copNDRUFSpUUKdOnfThhx9qxYoVqly5sg4dOmSt5+3+F7bPc7H7kY54epw644ux3kAep0U9d/b444/rvffes5bfffddDRgwwO16xYsXt8vV8tX/vkvhfCQAFEWxQFcArh04cEBffvmlJCkkJETz58/XVVdd5bS8pwne5cqV02uvvaaXXnpJK1eu1JIlS/Tbb79p6dKlOnXqlKTziel33HGHNmzYoLfeeqtAjKCgIA0dOlRDhw7Vzp079csvv2jJkiX69ddftWvXLknnk5gnT56sxYsXa/ny5SpXrpy1flRUlPX89OnTHtX7wrIlS5b0eL3CuuyyyzR//nw1b95cBw4cUEZGhm677TYtWrSoQFnb5OUHHnjA64aeq3jS+YFzT5KpvXk/valLflL7H3/8YSX5F1Xjxo01ffp0nTp1Sr/++qt1DK1Zs0bZ2dmSpLVr1+rqq6/Wl19+qb59+xaIERUVpbFjx+qpp57SunXrrBi//fabUlNTJUnHjx/Xf//7X61YsUKzZs3yOnn873K8AgCAS1P9+vXtlm3bFs7YlvHFhZyhoaEqW7asNSiblpbm1fqzZs2y6hESEqJbbrmlyHXav3+/Xbvan0k4FyY/pKWluU2I6Nq1q7p27eqyzOrVq63nrVq1KnwFvVS/fn1t3rxZkmfH04XlLjymfv/9d+v5qlWr1KZNG6dx/vrrL+v5unXr7Mq+9957at68uUf1uZhsP2tvj/2LeZxWq1ZN9erV05YtWyRJS5cuVaNGjezK1KtXz3q+d+9ej+Lu27fPen7h/yPpfBLOiRMnJJ0/CdK6dWu3MTt16mQ9z8jI0NatW9WwYUO36yUlJalPnz5Wf69Xr16aOnWqywR6AMA/U34CqCSPL6br1KmTFixYIElas2aNP6pVaP74jXbFH+1zZ4rSlpLsLw68+eabi5xwURS2n5N0/rO68G8Xsv2cHMXIb0sHBQVpwoQJmjBhgtNY+W14Sfrwww/tvgcrVqywK3sx+pG+6PPYTsjk7sIUWyNHjlR0dLQkqXnz5nZJIJLv+zwXCgsL04gRIzRixAinZfLy8rRu3Tpr2dH+236Gbdq0UUREhNt6durUyZpQ6lL7XwYA+Pe62O3ZC7f9ww8/XLRtf/LJJ9bzhIQEv47rdunSRZMnT5Z0fqzvQllZWXZjj1dffbXbmJUrV1bdunW1detWSefbE7ZJ3hfyVbvHW8HBwUpMTNT27dslnd//e++9t8hxve2flC1bVk888YSeeOIJp2VWr15tTYITFBSkli1belWnwvZ5Avm9s+XuOHXGl2O9gTpOi3Lu7Pnnn9eLL75oLf/vf//TXXfd5fH69erVs/pTvvr8L4XzkQBQFCS0X+KSkpKsq/h69OjhMpld8vwHLl9oaKjat2+v9u3bS5Kys7OVlJSkF198Ub/88osk6e2339bgwYNdNgRq1aqlWrVq6dZbb5Ukbd++XZMmTdIbb7yhnJwc7dy5U08//bTdVXi2ye0XDgg7k5KSYjdjiq9mOnGnfPnyevvtt3XjjTdKkhYvXqy5c+fq2muvtStXoUIF63l+o7woSpUqpdDQUKvx99dff9m9b45kZGT4ZKb4C1WoUMGKu337dp8ltOeLjo7Wtddea72np06d0qxZszRu3Djt2bNHeXl5GjlypK677jqnM8oHBwdbM7o/9NBDysvL0/LlyzV+/HhrtsnvvvtO33zzjfr06eNV/f5OxysAALj0XJiQ6smAkG2ZUqVK+aQethfbeXtXH9tB/muuucaj2fHc+fTTT61ZO0qXLq3rr7++yDGdufCiRG/335HMzEwtW7bMWm7btm2RY3qqUaNGmjlzpiTPBxg9PaZsk2w8ibly5UprOf8C6UtNUY79i3mcSudn8MnnaPC8QYMG1vPDhw/r8OHDbmeMtx3kt10/n+2sKzExMR6dcLhwphdP+qFLly5Vr169rH5SYmKipk+frmLFGJ4CgH8j29+fC39XnLEt548x0KLwx2+0K/5onztTlLbUsmXL7MbKhw0b5qtqFUqpUqVUqVIlK1nh999/d5vQbvs5Va5c2UrCvpAxxq5t7M6BAwdczn53KfQjvenznDp1yqv9t5350FESuD/7PJ5as2aNFTM4ONjhhb//tP9lAIB/L9v26IYNG5STk+N2zKYo7Vln27adeMMf2z537py+/vpra9mfk1dI7sf6UlNTlZmZaS0Hqj3hSbunMNztf2EUpX/ijO1FBQkJCV61JYvS57nY/UhnCvM5BWKs11/HaWGOqTfeeMPuLqZPPvmkHn30Ua+226BBAyuh3ZP/fYcOHbKbyd3R538p9CMBoCiY/uoSd/DgQeu5JzOdLVmypEjbCw0NVdeuXTV//nw1btzY+vv333/vVZw6derolVde0TPPPGP9bfbs2XZlmjVrZj3fsmWLjh075jau7ZWAFStWVFxcnFf1KoobbrhB7dq1s5ZtGyb5bK96/eWXX+w6HoURHBxs9zm4ulVUvrVr11oXQfiS7b799NNPPo9/oejoaA0dOlRJSUnW1aupqalavny5xzGCg4PVrl07zZw5026WmQuPRU/83Y5XAABwaWnTpo3dIJgnCcO2ZapWrVrkOuzevdsu2dibhJcDBw7o559/tpZ9Ncj/6aefWs/9PVOj7WBgZGSk0yQUb3z//ffWrNYxMTG67rrrihzTU4mJidbzkydP2vUdnfH1MfV3kj8oLHl37EsX9ziVZHdrW0e3DK1Tp47drVQXL17sNmb+BeuS/czq+WwvGj5+/LhHfcoLT2yULl3aZfk1a9aoR48e1smBtm3b6rvvvgvoDK0AgMCy/f3xZKxNsv/9cffbc7H54zfaGX+1z52xbUt725aynamwfv36Ht0Jxt9sZ7305+dUVJdCP/Lf3uexPX6vueYalS9fvkCZf9r/MgDAv1fbtm2tcZrTp0+7vYtIZmam3R1mitJOsm2fbd261W58zJGDBw/aJRB7Mqt5vm+//dZKAA8ODvbrnY4k92N9F07mF6j2hCftnsJwt/+FUZSxXmds93/w4MGFXtfbPs/F7Ee64u3nFKixXn8cp4U5dzZp0iQ9+OCD1vKoUaP07LPPer1tb/umtmWqVq2q2rVrFyhzKfQjAaAoSGi/xNnOinbmzBmXZc+cOWP3410UYWFh6tKli7Vse4WXN2wHNy+M0aBBA+vKwtzcXE2bNs1tvClTpljPvemU+Mq4ceOs53/88UeBRP927dpZnYWMjAzrlpFFYXu73c8//9xt+c8++6zI23TE9rP8/PPPlZKS4pftXKhGjRp2F3MU5lgMCgqym02/MDH+jscrAAC4dBQvXlzdunWzlmfNmuV2HdsyHTp0KHIdPvroI+t5dHS03QV77kybNs3nM1SvXLlSW7ZssZb9nYTz8ccfW8/d3fnKEzk5OXb9g6FDh3p0a3lfad++vd1grbtjKi0tTb/++qu1fOExNW7cOBljPHqMHTvWLo7ta7b9l0vFzz//bHeXJW++Txf7ON20aZP27NljLTuaYSUoKEi9evWylm2PbUeWL1+ubdu2STo/xtCzZ88CZapVq2Y9P3PmjEczayYlJVnPQ0ND7WJcaMOGDeratat1YqB58+aaN2+ez2ZQAgD8Pdn+dtjOhueK7e+PoxO3geSP32hn/NE+d+bYsWN2E4R405bKzMzUV199ZS37uy3lqd69e1vPv/rqK509e9Zp2bNnz2r69OkO1823ePFij9vStu/f2LFj7V67UKD7kZ72eTzd9wv3cdGiRdbfHSVv+LrP463du3dr8uTJ1vJdd93lsJzt/7Lly5fb3bXVmUv5fxkA4N8rKirK7oIyd+3ZmTNnWrP6xsTEFGnMtU6dOkpISLCWbe9G5Ijt640bN1atWrU83pbtul26dPH7pHRz5syxnjsa6ytdurRKlixpLdu2E5z566+/rL6EVPT2hKftHm9lZWVp/vz51rIvZhMvylivM1988YU2bdokSQoPD9fw4cM9XreofZ6L2Y90xd1xaitQY73+Ok69PXc2bdo03X333dbyiBEj9Prrrxdq27169bLyArdu3Wp3kZAjtseHo76pFPh+JAAUFQntl7iaNWtaz+fOnaucnBynZR9++GG3ibrHjx+3BrrdsW0ElitXzu611NTUIscICgrSHXfcYS0/88wzLm+vOXfuXLsEcl81TrzRuXNnu1tqXniFXXh4uB544AFr+fHHH9eGDRs8ju/o87NtLC9ZssRlY2P9+vV2SdS+dNNNN1kdoTNnzuiWW25Rdna2R+tmZGTY3aJH8vwYysnJsbsa1PY4Sk9PV1ZWlkdxXB2Lnvg7Hq8AAODSMmrUKOv5zJkzXQ5MTZ8+XWvXrrWWHQ2CnjlzxuO2/YoVKzR+/HhruX///l7d9tH2wtn+/fv7JHHb9sRBvXr1vL41ZEZGhsdl33zzTbvEBl/MuvPQQw9Zg9zly5fXU089VeSY3ggODta9995rLb/44os6fvy40/JPPfWUdQepihUr2t3B6O/I088/LS3Nri1er149tWzZ0uPtFPU49eY2upmZmRo5cqS1XL58ebVv395h2bvuussa6P7pp5+0YMECh+Xy8vI0evRoa7lfv34O+0PNmjWzu03xmDFjXP5/OX36tJ577jlruV27doqKinJYdtu2berSpYs1u1RCQoJ++uknbl0KAFDnzp2t51u2bLG7K4ojSUlJdr95l2J7xte/0c4UtX3uaVsqLy9Pd9xxhzWDZWhoqPr16+fxdr777jtrdu/g4GCvZzr0l169elmzIJ44cULPP/+807LPPvustQ/x8fEXdYZyyff9SG/8m/s8p0+f1sCBA63k9M6dOzu9cMT2f9mJEyf0yiuvuIy9efNmu/93l+L/MgDAv5ft2NSUKVOstsCFzpw5ozFjxljLd955p1fjze62PX78eKe5L4cPH7Yb677nnns83sahQ4fs2ujDhg3zqo45OTlW29gT8+bN0zfffGMt33TTTQXKBAUF2V1I8Oabb+ro0aMu4z7++OPWxYIlSpRwOoboCW/aPSdPnlRubq7HsZ966im7u+zceOONDstdrLFeR7Zs2WLX5n7yySe9mvXbF30eX/cj/XGc5gvUWK83x6k/z53NnDlTw4YNs+IPGDBAkyZNUlBQkKe7YqdChQp234vRo0c7vXvq/PnzrQtEQkJCXOYgBbIfCQBFZjzUs2dPT4vi/+vQoYORZCSZsWPHFirGsWPHTIkSJaw4gwYNMsePH7crc/LkSXP77bcbSSYyMtIqGx8fXyDelClTTM2aNc3LL79sdu3a5XCbZ8+eNa+//roJCgqyYi1dutSuTPHixc3tt99uFi1aZHJychzGWb58ualevboV4/bbb3e4f5UrV7bK1KtXz/z+++8Fyn355Zd2++ar4zE/niSzaNEij9b58ccf7db76aef7F5PT083DRs2tF6Pjo42kyZNMpmZmQ7jpaammg8++MA0b97cPPzwww7L9OvXz4oXGRlpvv766wJlfvvtNxMXF2eCgoJMWFiYVX737t0OY8bHx7stc6EFCxaYkJAQa702bdqYVatWOS2/fv1689hjj5mYmBizYcMGu9eGDRtm2rdvbz7++GNz7Ngxh+unpKSYwYMH272XZ86csV5ftGiRqVixohkzZozZuHGjwxg5OTlm2rRpJjw83Irz2WefFSg3duxYt99Xfxyv3n4OQ4cOtcpPmTLFbXkAwD9X8+bNA12FfxTbtkCHDh08Xm/37t1etSmvv/56q2y5cuUclp8+fbopXry4Va5///4OYy1atMjUr1/fTJgwwaSkpDgsc+7cOfPuu+/atU2io6PN/v37Pd7HVatW2e3jsmXLPF7XmczMTBMbG2vFfOGFF7yOMW7cONOlSxczc+ZMc/bsWYdlUlJSzP33329X/5YtW5rc3FyncdevX28efPBBs2nTJoev79u3z/Tp08eKFxQUZGbOnOl1/Y0pePx42i7Pl5GRYdc+bdWqVYF+XmZmpnn88cfttjNhwoRC1TdfYb8vF5oyZYrL/qsrZcqUMWPGjDFbt251WubHH380NWrUsNv37777zuNt+OI4feCBB0znzp3NjBkznB6nxpzvz7Vq1cqurh9++KHL2LZ9pTJlyhT4f5KRkWFuueUWq0xYWJjZvn2703jPPPOM3fZvuukmc/jw4QLltm/fbtq1a2dXdt68eQ5j7t2711StWtUqV7t2bXPw4EGX+wUAf1e0z72XnZ1t6tWrZ/1OREREmAkTJhQYb87LyzNfffWVKVWqlFW2atWq5ty5cwViets+d8R2/G3o0KFer+/r3+gL+aJ93qVLFzNixAjz66+/mry8PIdlNm3aZDp37my3rYceesir7fTo0cNa95prrvG6nq4UpS1pjDGTJ0+21g8JCTFvvfWW3XuRm5tr3njjDRMcHGyV++STT4pc78KcN/JlP9KYi9vnuZC3309/9Hk+/fRT8+qrrzps6xpz/txW06ZNrVgxMTFOz6flS0xMtMoHBwebZ555xmH7f+HChXb7ExUV5bQ/70xR+5EA8G9B+7zwrrzySut3pnr16ubPP/+0ez01NdV06dLFKhMbG1sghyWfN+3zrKwsU6tWLatss2bNzN69e+3K7Nmzx+53um7duiY7O9vjfXv55ZetdUuVKuVyvM6R48ePm5iYGPPUU0+ZLVu2OC137Ngx8+yzz9rlKNSuXdthH8YYY5YsWWL3PjVq1MhhHsLx48fNnXfeaVd29OjRTuvh63bPrFmzTL169cx7773nsg2zc+dOuz6PJHPLLbc4Le+vsd4nn3zSfPHFF+b06dMFXsvPIylXrpxdWzMrK8tlzAv5qs/jy36kv45Tf431+vo49de5sx9++MEuF+v666/36v+PM8nJySY0NNSKO2TIEJORkWFXJikpye5cxfDhw93G9XU/EgCKytN83yBjnFzac4FevXrZ3VYS7nXs2FG//PKLpPNXVVWsWNHjdZ955hnrtjJjx47VM888Y70WGxuryy+/XJUrV9ahQ4e0ePFinT59WiEhIfroo4+sq6Xi4+PtblUunb/9iO2M39WqVdNll12m8uXLyxijw4cPa8WKFXazXAwaNEjTpk2zi2N7dVnJkiXVtGlTVatWTZGRkUpNTdWWLVu0efNmq0y5cuX0xx9/OLxd05IlS9S9e3edOXPGin355ZcrISFBWVlZWrlypbZv326Vr1OnjpYuXVqoWbYvZLsfixYtUseOHT1a74orrrCuYGvfvr3dbI+StGvXLnXu3Fm7d++2/laqVCldccUVqly5soKCgnTs2DElJydr69at1tV7Dz/8sN3Vf/lSUlJ0+eWX232e9erVU+vWrVWsWDFt2LBBa9askXR+1pRvvvlGe/fulSTt3bvX4a3fq1evbpXZvXu3qlev7tG+f/DBB7r77rvtrrytU6eOmjVrppiYGJ09e1aHDx/WH3/8oZSUFKvMhg0b1KhRI2t52LBh1myHISEhqlevnhISEqwYf/31l5YtW2Y3A/ukSZN0++23W8uLFy/W1VdfbS1XqFBBzZo1U8WKFVWsWDEdPnxYa9eutZvh/corr9TixYutK1zzjRs3Tk8//bSk898521up2vL18ert52D7vk2ZMsXrq8YBAP8cLVq0sLtiHp7r0aOH3awg0vlZXfJnfImMjHR4i8558+YVaM/u2bNHNWrUsJbdtSmPHTumdu3aacuWLdbfWrRoocsuu0y5ublavXq1kpOTrdcSEhK0fPlyRUdHF4hl2xYKDg5W3bp1rfaUJB08eFDLly+3ZgeRzt9RaO7cuXYzvrhz33336Z133pEk1a1bV1u3bvV4XWe++eYb9enTx6r73r17rRkSPWXbfouIiFCjRo1Uq1YtlSpVSpmZmdq5c6dWrVpl156Mj4/X0qVLVblyZadxV6xYoSuuuELS+bZa06ZNVb58eZ0+fVo7duzQqlWrrBkygoKC9OGHH+rWW291W9/3339f77//vt3fsrKy7D7vBg0aKCwszK7MXXfd5XKmjTVr1qhjx47WHZFCQ0PVoUMH1ahRQydPntQvv/xiN5uRo/6dt2zf+w4dOmjx4sVu17ntttusPku+Y8eOaf/+/Va9bW8rnO/DDz90OMuObV+uSpUqVr82PDxcqampWrVqlRU733PPPacnnnjCbV3z+eI4feCBB/Tmm29KOv/9a9iwoWrXrq3SpUsrNzdXR48e1bp16/TXX3/ZrTdq1Ci98cYbLmOfOnVKbdu2tZspq02bNkpISNDJkyeVlJRk17f/+OOPXc6ucu7cOXXp0kW//fab9bfw8HC1a9dONWrUUF5enrZu3aqVK1fa9QdHjhypd99912HM1q1ba/Xq1dZyz549HfZPHenRo4d69OjhUVkAuBTQPi+clStXqlOnTtZYmyRVqlRJbdu2VdmyZXXy5EmtWLHCblw0PDxcCxYs0JVXXlkgnrft86ZNmxb42759+6zf0JiYGIe/XX/88YfTmL7+jb6QL9rntucuSpcuraZNm6pKlSqKjIxUenq6NmzYoI0bN9rNDNetWzd9//33Hs+8eeTIEVWpUsW66+znn3+uAQMGeF1XyfdtyXxDhgyxmym7du3aatOmjYwxWr58uXbt2mW9Nnz4cLtb0ReW7Xvvahzali/7kZL/+jyeKMw5GV/3ef73v//pv//9r4KDg9WoUSMlJCSoVKlSOnbsmP744w+78f2YmBgtXLhQzZs3d1nHnTt3qm3btnbnRGJjY9W+fXtVqlRJp0+f1tq1a+0+p6CgIH3xxRfq37+/07j+6kcCwL8B7fPC++uvv9S6dWvrHHtwcLA6dOigmjVr6ujRo1q4cKHVfi9WrJh+/PFHp+PN3rbP169fr/bt21szdoeGhioxMVFVqlTR/v37lZSUZN1FvmTJklq2bJldDoI7jRs31saNGyVJd9xxhyZOnOjxutL5u7Hkj79LUlxcnDUuWaJECWVkZGjHjh1at26d3bh0xYoVlZSUpAYNGjiN/cgjj+jVV1+1+1vz5s3VuHFjhYWFaf/+/fr111+tNpEktWrVSosXL1aJEiUcxvR1u+fbb7/VDTfcIOn8cVG7dm0lJCQoNjZWoaGhOn78uDZt2lRgZv927drphx9+UMmSJR3G9ddYb7du3fTTTz+pePHiat68uWrVqqXw8HAdOXJEy5YtU2pqqlW2WbNmWrhwoWJjY13GtOXLPo8v+5H+Ok79Ndbr6+PUH+fOUlNTFR8fb/3vCwkJ0fDhwxUeHu7R/o8aNUp16tRx+vrkyZN12223WcuxsbHq1KmTSpYsqU2bNmnVqlXWa5dddpl+++03p9+nfL7uRwJAUXmcf+7rDHn8H9uZNrx92M68nJOTY4YMGeKyfOnSpc2sWbPsrnB1NCvJ119/bTfzuqtHcHCwGTlypMMrEKOiojzelyZNmpjk5GSX79Xy5ctNzZo13cbq3Lmz17NVuGIb25vZeubOnet23bS0NNO3b1+P3+/SpUubjz/+2Ok2d+3aZRo1auQyxm233WaysrJMXFyc9Tdns58XZob2fElJSaZOnToeHwMNGzY0Bw4csItx7733erx+yZIlzaRJkwrUY8WKFaZYsWIex+nTp485deqUw33yZIb2fL48XpmhHQBQWMwwU3i2v7/ePBz9VhdmBsgDBw7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HU56+p8b47zi91L/7AOALnuafk9DuR54mtBtjzLp16+xuy3L77bdfnEraIKHdfcPq22+/tWugPP/88xenkgAAABdgQL5wDh06ZPbu3Vvg72PHjvXbYJitLl26WNvp379/gcFzX0hJSTFly5Y1kkz79u1NUlKSXwbZcnJyTKVKlay4Dz74oNcxxowZY60/f/58r/pQ33zzjZk1a5ZH23nttdfs3oPk5GSH5Q4ePGjCw8Otci+99JLTmDk5OeaKK66wyj733HNOyz7yyCPm8OHDbut57Ngxu/7JzTff7LTs/fffb3eSJS0tzWnZuXPnWmXLlCljTpw44bIevj5ON2zYYDcY6ywRJDc317Rt29YqN2DAAJdx161bV2AQ9sLbiXqb0N63b19r3Xbt2jlM6jbGmB9++MHuZNimTZu82o4j7777rhUzNDTU6YC1NzZt2mT3fnz33XdOy/rjOAWAfxPa5/5l256RVCCpxlszZ860S0BZtmyZ07L79++3S5b99ddfXcZ+7rnnrLINGjQwKSkpRarruXPn7JIDHnvsMadlH330UbukkczMTKdlfZ3Q7o/3dNOmTea1114zZ8+edbv9VatW2V3I6u4igQvl5eWZjh07GkkmJibGHDlyxO6Y8+Q9ulhtSds+3hNPPOF1f9Yf7XN/Haf+ek8vRt8cABBYtM8Lx7bdHRYW5jSxNyMjw9SsWdMq+9///rfI27YdG4uJiTEHDx50WO7gwYN2bcmJEyc6LeePsV5/tXvS0tJMhQoVrPLvvPOO07KeOHPmjClRooQVz91EJ+PHj7drTx07dqxAGX+2z/1xDsNTtm3OZs2aOS3nj/fUG96M9Y4bN84qFxcX5/IijfT0dLtzPa4uIN+0aZNJT08v8Hdv8q48UdQ+j6e8eU+N8c9x+nf47gOAL5DQfgnwJhnDGGMNlkoy9erV838FL0BCu2cNq+rVq1vlu3bt6v8KAgAAOMCAvG9djIR224sjq1WrZk6dOuWX7QwYMMBKiN20aZNZtGiRXwbZ5s2bV6Skoo0bN1ozPwwaNMgY430fylM5OTkmLi7Oiv3qq686LPf+++9bZcqXL+/yBIMxxvz8889W+YoVKzpNrvDGe++9Z8WMjo52Ws52gPn11193G/fKK6+0yr/77rtOy/njOB05cqQVs0uXLi7L/vbbb3aDp94mgBUlof3w4cN2iT1Lly51Wb5z585W2fvvv9+rejpie4eCG264ocjxjDFm9OjRVsxy5cp5fRcFZzw9TgHg34T2uX/169fPowQDT918881WvOuvv95t+aeeesoq37dvX6fljhw5YiXNhISEmLVr1xa5rtOnT7e2XapUKZeJCKdPnzalSpWyys+cOdNpWV8ntPvrPfWG7XHi7TmtiRMnWut+8MEHxhj7ZAV379HFakueOXPGmrm0du3a5uzZs173Z/3RPvfHceqv9/Ri9c0BAIFF+7xwevToYf1OupuEcdq0aVbZ2NjYIo875d/JU3I/saDtRaSXXXaZwzL+Guv1V/v8gQce8OnY4Pr16+3as0eOHHFZ/tChQ3blXc3S7Slv2uf+OIfhiePHj9td+PDmm286LRvo99Sbsd4bbrjBKnv33Xe7jX3nnXda5a+77jqv6+bLhHZf9Hk85e34uT+O03/idx8AHPF0rC5YuGQ0a9bMen7w4EGXZYcNG6agoCAFBQXp448/dhs7OTlZ9957r+rUqaMSJUqobNmyatmypV588UWlpqYWqr579+7VI488ooSEBEVFRSkmJkaXXXaZnnjiCe3bt0+StHjxYqueHTt29ChucnKyHn/8cbVu3VoVKlRQWFiYypUrp8svv1xjxoxx+974mzefU77Vq1frwQcfVNOmTVWuXDmFhYWpYsWK6tChg1566SUdP37c4+0bY/TFF1+oe/fuqlSpkiIiIhQfH6/rrrtOM2bMkDFGktSxY0frvV+8eLHDWI7KHDp0SC+88IJat26tihUrKiQkRKVLl3a4fnZ2tj799FP169dPNWvWVMmSJRUZGakaNWpowIABmjVrllUfT96je++9V82bN1dMTIxCQ0MVGxurunXrqn379nrooYf0008/6ezZs05jpKamavz48ercubPi4uIUERGhEiVKKD4+Xi1atNCAAQM0ZcoUHThwwOH6H3/8sfV+DBs2zG2djTH6+uuvNWDAANWqVUtRUVGKiopSrVq1NHDgQLvPwxVHn8OxY8f00ksvqVWrVipbtqyKFy+umjVrasSIEdq4caPbmAAA4NIzYcIE6/mDDz6okiVL+nwb8+bN0xdffCFJevTRR5WQkODzbeT75JNPrOdNmzbVZZdd5vG6eXl5GjFihLKyshQbG6vXXnvNH1W0hISE6PLLL7eW9+zZ47DcihUrrOedOnVSWFiYy7gdOnRQZGSkJOnw4cNaunRpkevarl076/mpU6d07NixAmX27t2rQ4cOWcvdu3d3G7dHjx7W82+++cZpOV8fp8YYzZ4921oePny4y/Lt2rVTnTp1JEm5ubn6/vvvi7R9b8yePVt5eXmSpLp166pt27Yuy9vuy7ffflukbW/dulWrVq2ylocOHVqkeNL579lnn31mLQ8aNEjFihUrclzJs+MUAABfOXnypF17whe/k7btPm/bUvPmzXM6RvnRRx8pMzNTknTdddepefPmRaypfTujf//+KlGihNOyJUqUUL9+/azlWbNmFXn7nvLXe+oN2zaKsza/IwcPHtTo0aMlSVdeeaVGjBjh9bYvVltyzJgx2rlzp6TzbfeIiAiv6umv9rk/jlN/vacXo28OAMDfUUZGhn7++Wdr2V07oU+fPtbv6LFjx7RkyZJCb3vHjh3avHmztezuXL3t63/++afVPrLlr7Fef7R7zp07ZzfePmbMGJd19URGRobdsrOcj3wxMTF2y/ntsKIobPv8Yvryyy+tPlxoaKgGDBjgtGwg31Nvx3pt6+qunpJ9XX3x2RdFUfs8nvLn+Lmn/qnffQAoChLaLyHFixe3np87d85ncd966y01bdpU7777rnbs2KGzZ88qLS1Na9eu1eOPP67LLrvM68SLTz/9VA0bNtSrr76q5ORknT59WidOnNCGDRv0wgsvqHHjxvruu++8ipmZmam7775bjRs31osvvqjVq1crJSVF2dnZSk1N1apVq/Tss8+qdu3aeuedd7yK7UvefE7Hjx9Xnz591Lp1a73xxhtav369UlNTlZ2drSNHjmjJkiV67LHHVLNmTc2YMcPtto8fP65OnTpp4MCB+vHHH3X48GFlZmZq3759mjt3rvr27avevXvr1KlThdq37777Tg0bNtQTTzyh1atX68iRI04bK4sXL1aDBg00ZMgQff3119q9e7cyMjJ05swZ7dmzR19++aVuvPFGtW3b1mkCuSTl5OTozjvvVOvWrfXuu+/q999/14kTJ5STk6Pjx49r+/btWrp0qV5//XV169ZNzz//vNO6161bV//5z3/0888/69ChQ8rMzNTZs2e1b98+rVu3Tl9++aVuvfVWu2Smwtq+fbtatGihfv366csvv9SuXbt0+vRpnT59Wrt27dIXX3yhvn37qlWrVg470K4sXbpUl112mR577DGtWbNGaWlpOnfunHbv3q2PPvpITZs21QcffFDkfQAAABdPSkqKFixYYC0PHDjQ59tIT0/XXXfdJUmqXbu2nnjiCZ9vI9/Jkyft2vveJhW9/fbbWrlypSTp5ZdfVvny5X1aP0eCgoKs57m5uQ7LHDlyxHoeHx/vNmZISIgqV65sLdue8Cks23pKjutqW0/Js7rallmyZImys7MLlPHHcbp9+3b99ddf1rInFzrblklKSipyHTy1aNEih3Vw5uqrr7ae79u3Tzt27Cj0tm0HrcuVK2eX4FVYCxcutOuL+SL5L58nxykAAL7y1VdfWeOwoaGhPmmjeNvusy1z+vRpqy17oWnTplnPBw0aVIQa/h9v2yiBakv56z31hidtfkfuuecenTx5UmFhYZo4cWKBto4nLkZbct26dXr99dclSbfccos6d+7sdT391T73x3Hqj/f0YvTNAQD4u1q2bJmV2BsZGalWrVq5LB8eHq42bdpYy0Vpe9quW7duXcXFxbksX7lyZeuiO8m+3ZDPX2O9/mj3zJkzx5oEMSEhQU2bNnUb151q1f4fe/cdF8Xx/w/8RRdBBFEUG/ZuQBRjb6hYorHF3jXGEmN6YkzURD+axBgTo1FjrzHGlthiw469F+zYEAuoNOnM7w9+zHcXruw10eT1fDzu8bi9m52dnVu498zNzpRWbV+8eNFgeuWkek5OTqhatarFZTA3Pn+Rli1bJp+3a9cORYoU0Zs2L+vU1L5eZVmNlRNQl9Xf39+MElqHNdo8Wtmy/1yrf+vfPhGRJV7srUVkkHK2b2sN6Jg9ezbGjBkjt11cXNC8eXOULFkSjx8/RmhoKKKiovDGG2/g/fff15Tnn3/+iYEDB8qBzo6OjmjSpAnKlSuHuLg47Nu3Dw8fPkT37t0xZcoUTXkmJiYiJCRENbC+bNmyqFOnDry8vPD06VOEhYUhMjISSUlJGD16NOLi4vDFF19orwwr0fo5PXjwAC1atEB4eLh8rWrVqggICECBAgXw6NEjHDx4ENHR0Xj27Bm6d++O5cuX6/2hIzk5GSEhITh+/Lh8rVSpUmjUqBHy58+PK1euICwsDH///bfRu6V1CQsLw8SJE5GWlgZvb280adIEhQsXxqNHj3D69GlV2j///BN9+vSRg2Dy5cuHevXqoUyZMnBwcMDVq1dx+PBhpKen48iRI6hfvz6OHz+OokWL5jruxx9/jN9++01ulyhRAnXr1kXhwoUhhEBMTAwuXbqEK1eu6C37iRMn0K1bN6SnpwPIuukguzwuLi6Ii4vDjRs3cP78eTx//tzkuskpPDwcTZs2xePHj+VrNWvWREBAAOzs7HD69GmcP38eAHDy5Ek0bNgQ+/fvR6VKlYzmfeHCBYwdOxYJCQnw8fFB48aN4e3tjcjISISGhiIpKQkZGRkYPnw4atSogfr161t8PkRERGR7hw8flvFz5cqV4ePjg9jYWCxYsABr1qyRN54WKVIEdevWRdeuXdG9e3fY22u/B3ns2LG4e/cuAGDu3Lk2m7UCANasWSMHFTk6Opo0WOf27dtysH2TJk0wePBgm5Qxp+z4DMiKo3VRrq5jziAWLR3DxijL6erqisKFC+dKo3UVJH3S09Nx9epVVK9eXfW6La5TZXuoWLFi8PX1NVo+5Uymyv1tTXksLbOp+vr6omjRovLHsfDwcFSoUMHk42ZmZqoGv/Xu3RtOTk4m55OTcpD8a6+9ZpXO8GxarlMiIiJrUX6nGRtgYA5z476cg1diY2NVs0o2btwYmZmZWL16NZYtW4Zz584hJiYGhQoVQrVq1dC+fXsMGzYM7u7ueo8TGxurWplHS4yiTBMZGYm4uDh4eHgY3Cc8PByXLl3CnTt3kJGRgUKFCqFKlSpo0qQJvL29jR4zJ2vVqam0xPw5rV27Vs6y+dlnn5n9472tY8n09HQMGTJEfj7mrnJli/jcVtepLer0RbTNiYiIXlXK796aNWtqmqk4MDBQ3ixmST+eqd/72emuXbum99i26Ou1VdyjHCfTuHFjAMDNmzcxZ84cbNmyBXfu3IGDgwN8fX3RpEkTDBgwQDX7uS4lS5ZErVq15FiPL7/8En///TccHBxypU1PT8fYsWPldv/+/Q22U7QyJz4HsiZkOXr0KB4+fAgnJycULlwYgYGBqFevnmoCSktdu3YNhw8fltvGVgbIyzo1ta+3Y8eOWLhwIQBgy5YtOHTokN5rZs+ePdi6dSuArDFl5qxYZQ3WavNoZWn/uTWu03/r3z4RkUWERh06dNCalP6/pk2bCgACgJgwYYLBtGlpaaJUqVIyfdeuXQ2mHzBggEy7ePFinWkuX74sXFxcZLqWLVuKqKgoVZq4uDjRr18/AUA4OzvLtAMGDNCZ54MHD4SXl5dM9/rrr4ubN2/mOpevv/5aAFAdv2nTpnrPp3///jJd+fLlxc6dO3OlSU9PF7/++qvM08HBQYSFhRmsJy38/PyM1mW2J0+eCFdXV5n+o48+0pkuIyNDNG/eXKYLDAwUx48fz5UuKSlJTJw4UdjZ2QkAws3NLVd9Zhs7dqzMz8HBQfzyyy8iMzNTlebChQuiatWquep+z549OvNUXqOOjo7Czs5OTJo0SaSmpqrSJScnq46RP39+ud/7778vYmJicuV948YN0ahRI5mubdu2udI8fvxYODo6ynNasmRJrnPKdv/+fTFz5kyxYMGCXO+9+eabqr+dJ0+e6MwjOTlZbNmyRbzzzjs631+8eLHRv4GUlBTh7+8v0xUpUkRs3749V7rt27eLwoULq66BnPWaTfk5uLi4CAcHBzF9+nSRlpamSnfnzh1Ro0YNmbZ58+Y68yMiov+GwMDAvC7Cv8qECRM0xa3mGjdunMy/S5cuIiwsTJQuXVq+pusREBCgNzbM6dChQ8Le3l4AEP369VO9t2fPHlW+1tCwYUOZX8eOHU3at02bNrL9ER4ernrPlDaUKQ4dOqSqA12xuRBCto0AiJ49exrNNz09Xbi5ucl9qlevbnFZW7VqJfNr3769zjQ3btxQnc/ly5eN5jt16lTVPn/++WeuNLa4Tr/77jtV+1GLLVu2yH3y58+vaZ9sERERqvJFRERo3lfZ1tu2bZumfYKCguQ+33//vUllzbZz505VmU+dOmVWPkpxcXGq8/nxxx8tzlNJy3VKRPRfw/jcNq5du6b6nly/fr1V8lX2xc+dO9do+sOHD6vKMWrUqFxplN/phQoVEvfv3xeNGzc2GEvp61vMdvToUVX658+fGy1rYmKiap9jx47pTKeMvfU9HBwcRM+ePcXVq1eNHtcWdWqKhIQE1W8X06ZNM7rP06dPRbFixQQAUbFiRZGUlKR6X1k+fX3s2WwdS06ZMkWmXbhwoeo9U9qztojPbXWd2qJObd02JyKilwfjc9ONGDFCfv/16NFD0z6zZ8+W+1SrVs3sY7dt21bm89lnn2na55NPPpH7tGvXLtf7tujrtVXco+xvnzlzppg/f74qFtL16Nmzp9Hj79mzRzg5OanimrVr14qIiAiRlJQkbt68Kf744w9Rs2ZNmaZhw4bi2bNnRs/LGFPjc2NtEwDCw8NDfPrppyI2Ntbi8gmhjg29vb31judQyos6NaevNzMzU9WHmy9fPvHhhx+KkydPiqdPn4qnT5+KkydPivfee0+OLXJ2dhYrV640q4ymjLvSx1ptHi3M7T+39nX6b/zbJyLSR+v4cw5otyFTBmNMnDhR9QVkqBNbCG0D2nv16iXT1KxZU+8XWkZGhmjXrp3q+PoG83788ccyTenSpcXTp0/1llHZgDAUVOzfv1+mKVmyZK5B9zktWrRIpm/Tpo3BtFpoDawyMzPFoEGDZFoHBwdx8eJFnWmXLVumCgzi4+MNlmH8+PEy/fDhw3O9HxMToxqgPn36dL153bt3T3h6eqrqXsuAdgBi8uTJBssphBAtWrTQnD4hIUEOsAcgjhw5onp/06ZN8r0+ffoYPbY+3t7eAsgaDG6srg3RMqBdef05OTmJEydO6M3v2LFjcsA+ALF06VKd6XJ+DvPmzdOb5/nz5+UNEHZ2duL+/fsmnSMREf17sEPeumw9oF15A2dwcLAqXgsMDBQDBw4U/fr1E1WqVFHFBUWLFhV37941mHdKSoqMuby9vcWjR49U71t7QPv169dV+a1bt07zvso4efz48bnet8WA9oyMDFG3bl2Zr6FBGz///LNM5+PjI1JSUgzmvXv3blVd+Pr6WlTWzZs3q/LbunWrznSZmZkyBgYgZsyYYTTvnIOpdMWctrhOP/30U5lO680PJ0+eVOWvvMHWGHMHtD9//ly1n9ZB5R06dJD7fP7555rLqdS3b19V290aFixYIPN0dHQUDx8+tEq+Qmi/TomI/msYn9vGl19+Kb9zvL29jcZnWnXu3Fnm++abbxpN/9VXX6m+/3r16pUrjbLfsGzZsqJWrVqqvvTevXuLwYMHiwYNGsibUbO/q/V9n27dulWm8/Dw0Hx+BQoUkPv9888/OtNoGdCe/ShQoIDRmwlsUaem+Oijj2Re7u7umuKfwYMHy312796d630tfexC2D6WvHr1qsiXL59sr+acFMaU9qwt4nNbXKe2qlNbts2JiOjlwvjcdN27d5fffe+9956mfdatWyf3KVasmNnHVvbfah1YOn36dLlPvXr1cr1vi75eW8XnyhvsunTpIp87OTmJ4OBgMXToUPHWW28JHx8fVRmbNGmSa5I8XeelHFiu71GsWDExbtw4q7W5TI3PtbZNgKybUXNOlmOqzMxMVb2PHj1a874vuk7N7etNSkoSPXr0MFpOe3t70bJlS3H48GGzy2jpgHZrtnm0MLdOrX2d/hv/9omI9NE6/pxr5OWhuLg47N+/H7169cLEiRPl6++//z5at25tUd5Pnz7F+vXr5fa0adP0Lmlib2+Pn3/+2egyS5mZmaolVyZOnAhPT0+96SdMmGDw/WzKZWKmTp2KYsWKGUw/cOBAVKlSBQCwfft2REdHGz2GuYQQePLkCbZv346QkBAsXrxYvjd9+nRUq1ZN537Kc/rll1+MLskyduxYWVe///67XPIy26pVq5CSkgIAKFeuHMaMGaM3rxIlSuCTTz4xeDx9+3322WcG05w9exahoaEAgEqVKuHzzz83mN7NzQ3jx4+X2ytXrlS9HxcXJ59bslRwdj758+e3+fI38+bNk8+HDx+O2rVr600bFBSEt99+W27PmTPHaP41a9bEsGHD9L5fo0YNBAUFAci6Pk+ePKml2ERERJTHnj17Jp/v3r0bz549Q+HChREaGoqTJ09i8eLFWLZsGcLDw/HHH3/I2P3hw4fo27evwbwnT54sl1SdNm2aRXGVFsuWLZPPvb298cYbb2ja7/Hjx/jggw8AZMWSX3zxhU3Kl9OkSZNw7NgxAFltnx9++EFv2vbt28ul5B89eoSffvpJb9qMjAx8+eWXqtfi4+PNLmdUVJQqdmzVqhXatm2rM62dnZ2q3n/44Qc8efJEb95bt27FgQMHjJbVFtdpQkKCfK51mc2c6ZR52ErOY5hTVnPKmZCQgA0bNshtY8vaaqX8O23bti18fHyskq8p1ykREZGlhBBYsWKF3O7duzecnZ2tknfHjh3l802bNqmWmc8pMjISs2bNUr1mLJaKiIjA6dOn4eDggJkzZyIiIgIrV67EwoULcejQIZw8eRLly5cHkLXEd79+/XT2c5sTS+VMqy9Gsbe3R3BwMGbNmoWTJ0/i2bNnSEtLQ0xMDEJDQ/HOO+/I+o6Pj0fPnj1zxZRKtqhTrXbv3q2K3ceOHWs0/gkNDcWiRYsAZC2r3qJFC7OPb8tYUgiBt99+G8nJyXB2dsbcuXON/pajtazWis9tcZ3aqk5t2TYnIiJ61VkaJ1jSh2eLY9uir9dW8bkyRske5xMUFIQrV65g165dmD9/PtasWYO7d+9i7NixMu3+/fsxefJkg8du0aIFbt26hQkTJsDR0VFnGgcHB3To0AG9evWySpvLnPg8X7586N27N1auXInLly8jPj4eqampiIyMxPr169GmTRuZ9tq1a2jTpg0ePXpkdhn37t2LO3fuyG1T+mVfdJ2a29ebL18+rF69GmFhYfD399ebrkqVKujevTsCAwMtKqe5rN3m0cKSOrXmdfpv+9snIrIGDmh/Qb7++mvY2dmpHgULFkTTpk2xevVqAFmDQb777jvMmDHD4uOFhYXJAdBFixZFq1atDKavUKECGjRoYDDNpUuX8PjxYwCAk5MTunXrZjC9m5sbOnXqZDBNeno6du7cCQBwdHRE165dDaYHsgZvNG/eHEBWYBMWFmZ0H60GDRqk+ozs7e3h7e2NNm3ayHKWKlUKixcv1juoPCoqCmfOnAGQNUi8UaNGRo+bL18+1K9fHwAQGxuLCxcuqN7fu3evfN69e3c4ODgYzK9Pnz5Gj5lT165d9QYx2bZu3Sqfv/XWW0bLAUD1Q8DBgwdV75UuXVo+X7duHR48eKC1uDrzefr0KVatWmVWHlokJCTgxIkTcnvw4MFG9xk6dKh8fvz4cSQmJhpM/9ZbbxnNs1atWvL5rVu3jKYnIiKivJczBnBwcMCmTZtkXKvUvXt3LFmyRG7v27dPFQ8qXbhwAd9++y0AoFmzZhg0aJDVyqyLEALLly+X26Z0co0ZMwYxMTEAgLlz58LFxcUmZVTatGkTvv76a7n9ySefGIzPy5cvj169esntL774AjNmzEBGRoYq3ePHj9GtW7dcg3SSkpLMKmdqaiq6deuGqKgoAEChQoVUN9Pq8tlnn8HJyQlA1oCg1q1b49q1a7nSbdiwAT179sz1uq6y2uI6TU5Ols+1Xis5rw1z69UUynIC5pXVnHKuXbtW1rujo6NZbbmcIiIiVIPNBgwYYHGegHnXKRERkSX27dun6vuy1ncakNV/WrZsWQBZE7l07NgRO3bsyJXu/PnzaNmyJZ4+fap6XUssBWRN4DJ69Gg5kCZbQEAAduzYATc3NwBATExMrgHegHmxFKAtRlm7di127dqFUaNGITAwEAULFoSjoyMKFSqE5s2bY+7cuTh48CAKFy4MICsWGDp0KNLT03XmZ4s61eL27dvo2bOnjNkbNGhgdOKWpKQkOamIt7c3pk+fbtaxs9kylpw/fz727dsHIGsgUPZkP+ayRXxui+vUVnVqq7Y5ERHRv4GlcYIlfXi2OLYt+nptFZ/njFFKlCiB7du3y/haecwpU6ZgxIgR8rUZM2aoJhLM6ebNmxgwYAC++eYbpKeno0yZMujRoweGDRuGrl27olixYsjIyMD8+fPx2muv4ZtvvtF8XrqYE58DWX3cK1euRO/evVG5cmW4u7vDyckJxYsXR+fOnbFt2zYsXLhQtq1u376tGuBrKuVknjVq1DBpMPeLrFNL+nqFEJgzZw66deuGs2fPwsXFBc2bN8eQIUMwcOBANGjQAA4ODrh06RKGDRuGgIAAXL161eyymsvabR5jLKlTa1+n/6a/fSIiazE8epVeGAcHB3z33XcYMmSIVfLLHlANZN29lbPDXJd69erh0KFDmvKsWrUqChQoYDTPoKAgVYdfTufOnZNf0Pny5dM8s/jx48fl87t372raxxrc3NwwZ84ctG/fXm8aZUNHCIF3331XU943btyQz+/evYvXXntNbivrvm7dukbz8vPzQ5EiReQNCFoYmmk8m/LcDh8+rOnchBDyec7P6vXXX4efnx9u376Nu3fvonr16hgwYAA6dOiAevXqab6ruUePHpgyZQoAoG/fvli9ejV69OiBFi1awNfXV1MeWpw9e1Y2vNzd3VWfkT4BAQFwc3NDYmIiMjIycPbsWYM3j9SsWdNont7e3vJ5bGyshpITERFRXsuXL59qu0uXLqhXr57e9N27d8e0adPkzXSrV69Gs2bNVGkyMzMxdOhQpKWlwcXFBXPnzrV6uXPav38/IiIi5LbWjr4tW7bg999/l/voGixgbcePH0evXr1kPBocHIxJkyYZ3W/WrFk4evQorl+/joyMDHz44YeYPn06GjVqhIIFC+Lu3bvYt28fnj9/DgDo0KEDNm3aBACa2kg5CSEwYMAAeaOuk5MTVq1ahRIlShjcr2rVqpgxY4aMyU+ePIlq1aqhQYMGqFSpElJSUnD8+HFcvnwZAFCxYkXY29vjypUrestqi+tUmWdqaqrBc8qWfXN2NlNmOzJXznM3p6zmlFP5w0lISAiKFi1qch45LVu2TF73hQoVQocOHSzO09zrlIiIyBLK78nq1atr6r/UysnJCWvWrEHjxo2RnJyM6OhohISEoGbNmggMDISjoyPCw8Nx5MgRZGZmwtXVFQ0aNMDu3bsBaIulfH195QpFupQrVw4jRoyQKwitXr1atZJrzjy1xieAthilUKFCRvMJCgrC6tWr0bJlSwDA1atXsXHjRp2T3diiTo158uQJ2rZtK2e3L1GiBFavXm10Ipbx48fL/vgffvhBDto3l61iyfv37+PTTz8FkLXKlSUDdrLZIj63xXVqqzq1RZuHiIjo38LSOMGSPjxbHdvafb22is/z5cunGtj6xRdfwMvLS29+kyZNwqJFi5CSkoK4uDhs3bpV56QmR44cQUhICOLi4uDp6Yn58+eja9euqtmv09PTMW/ePHz00UdISUnBhAkTkC9fPhmHmsLc+BzQ1j4ZPHgwIiIi5MzUS5cuxZQpU0zuU01MTMS6devktikDml90nZrb15uZmYm+ffvK32W6du2KWbNmoVixYqp0N27cwIABA3Do0CGEh4cjODgYZ86cUY1JsSVbtHmMsaT/3NrX6b/lb5+IyJo4oP0FCQoKUg1ETkhIwJ07d+RM6hkZGRg6dChu3ryJ//3vfxYfTzmQWTkLtiGlSpUy+L5yydOSJUtqytPYj9v379+XzxMSEjB79mxN+SrlnMnFEsHBwaq7/WJjY3Hr1i2EhYUhMzMTiYmJ6NChA+bMmYN33nlHZx7Kc7p//75VzsncujdlQHuRIkWMplGeW2hoKEJDQzXnD+Q+LycnJ6xYsQLt27dHXFwcnjx5ghkzZmDGjBlwdnZGYGAgmjRpgvbt26Nx48Z6lxUaN24c9u3bh0OHDkEIgU2bNsmGZtmyZdG4cWO0bNkSb775Jjw8PEwqs5KyPkuVKqXpRhF7e3uUKlVKDiTStXSwUsGCBY3mmT0TJwCkpaUZTU9ERER5z93dXbXduXNno/t07txZ/miua1WimTNn4ujRowCyZq2oXLmyFUpqWM5BRXXq1DG6T3x8vJw1onDhwnLAji1dunQJbdu2lR2BQUFB2LhxoyqO0sfT0xOHDh1Cz549sWfPHgBZs2788ccfqnROTk6YMmUKChcuLGNPT09Pk8v67rvvylW77O3tsXTpUoSEhGjad9SoUcifPz9Gjx6NxMREpKenY//+/di/f78qXWBgINauXau6kUBXWW1xnSrz1DpLU850OctlCzmPYU5ZTS3n7du35cwzgGnL2hpi7ioKhlhynRIREZnj+fPnqgEG1vqeVKpTpw7279+Pt956C7dv3waQNXv4+fPnVel8fHywYsUKrFy5Ur6mJZbq0KGD0RUxO3fuLOPjK1euICYmRjVwwJxYKmdaS2Op4OBgNG7cWM5gt23bNr2rt1q7Tg1JSEhA27ZtER4eDiBrEpAdO3YY/Z3j5MmTcpXc5s2bW+XaslUsOWrUKDmhybx586yyypUt4nNbXKe2qlNbtHmIiIj+LSyNEyyJO211bGv39doqPnd3d1cNajUWo3h7e6NJkybYuXMngKwYJeeg1qdPn6JLly6Ii4uDnZ0dNm7ciKZNm+bKy9HRUfYzZ69Q/9VXX6F3796ax6YA5sfnpvrss88wffp0JCUlISMjAzt27EC/fv1MymPdunVISEgAkDX5aN++fTXt96LrFDC/r/f777+Xg9lbtmyJNWvW6BzfUr58eWzfvh1BQUEIDw/HvXv38NVXX+HXX381qZzmskWbxxhb9J/npPU6/Tf87RMRWZvx0ZhkFe3atcOsWbPkY8mSJQgNDcWtW7dUyxxNmTIlV/BsjuzgCwDy58+vaZ/s5U1fZJ7WmF1a3xKn5ujbt6/qc1q+fDkOHDiAa9euyVlohBAYNWqU3o5LW5yTLeo+Jy13TFt6bjmX7wKARo0a4ezZsxg8eLCqzKmpqThy5Ai+//57NG3aFFWqVMGGDRt05ps/f37s2bMHM2bMQPny5VXvRUREYNmyZejfvz98fX3xySefmL3cmfJzMKV+lWnj4+MNptU3aJ+IiIhebTlns6hWrZrRfZRpIiMjVe9FRkbiyy+/BABUrlz5hcxa8fz5c6xdu1Zua521ZPz48XKlHmvMfmhMREQEWrVqhZiYGABZ9bht2zaTflDx8fFBaGgo/vnnH/Tv3x8VKlSAu7s7XF1dUalSJbz77rs4c+YMPv74Y9y6dUvuZ2on3xdffKHqGJ49e7aqfajFoEGDcOvWLUyePBlNmjRBkSJF4OTkhKJFi6JFixZYsGABjhw5ghIlSiAqKspgWa19nebM8+HDh5rO6cGDB/J5/vz5X0gntqurq6pNZE5ZtczOorR8+XI5E4yXl5dVZlI/ePCgavUvU2YX0sca1ykREZGp1q9fL/vRHBwc0KdPH5scJygoCFevXsWCBQvQoUMHlChRAi4uLvD09ERgYCAmT56MixcvolWrVkbjPktjKUA9oUjOPOPi4pCcnGw0z+fPn6v6IE2NUXQJDg6Wz7MHqOhjzTrVJzk5GR07dsSxY8cAZM2euW3bNk11PnToUGRkZFh1lStbxJJ//fUXNm7cCCDrhg5rzQpui/jcFtepreJzW7R5iIiI/i0sjRMsiTtteWxr9vXaKj5X5uvp6alpFXpjMcpvv/0m+4Nbt26tc0Cr0sCBA+WkOampqaqbT42xJD43lbu7O15//XW5bax9osuyZcvk85CQkFyzluvzIusUML+vNzk5Gd99953cnjRpksHJGt3c3OTvTUBWv7U1x2HpY6s2jyG26D/XRet1+qr/7RMR2QJnaM9jxYoVw4oVK/DkyRNs374dADBy5Ei0bt3a4DIixigHa2Qvj2SM8q4vXZSDcm2RZ0BAAE6fPq0p3xetXLly+Ouvv1C/fn2cO3cOGRkZGDhwIC5evJhrlkflOXXq1EnvIGxTuLm5IS4uDoD16t7ccmTbuHEj3nzzTavkW6ZMGSxcuBCzZs1CWFgY9u/fjwMHDuDIkSNy8PnVq1fRpUsXTJ8+HR9++GGuPJycnPD+++/j/fffx4ULF2QeBw4ckEHc8+fP8cMPP+DAgQPYs2ePycueKf+uTKlfZVpzls4lIiKiV59yFSBA22w1yjQ5b4q7du2ajDESExPRpEkTvflkx5HZlMupDx06FEOHDjVaFgDYsGGDalCR1llLsmN8Ozs7zJkzB3PmzNGb9tKlS/L5ggUL8M8//8jtI0eOGD1WZGQkgoOD5UCg8uXLY+fOnWYvjxkSEmJ0Furjx4/L50FBQZrz/t///oepU6fK7W+//RbDhw83vZDImvl+3LhxGDdunMFyZi+Da2dnp3N2fWtfpwBUKwdkz9JpzJ07d/SWyZYqV66MM2fOAHgxZVX+cNKzZ0+rDNxXrqJQrVo1k65JXax5nRIREZlC+Z3WunVrTT/qmsvZ2RlDhgzBkCFD9KbJzMzEqVOn5Lau71hLYykgdzyVcxWm27dvG12ZSRmf6MrDHMr6z75x1BBr1akuaWlp6Nq1q5xh09XVFZs2bdK8f3a85+Ligv79+2vaB8j63SZ79c/AwMBcMxZaO5ZU/lZy7NgxVTsup3v37snnp06dUqX99ddfERgYqCpnNmvFvLa6Tm0Rn9uizUNERPRvkZf9eJUrV8a2bdtsemxr9PXaKu6pUqWK7BvXOjGLsRhF2beuXLlTHzs7OzRr1gxXrlwBALlCjTGWxufmMLV9onT37l1ZVsC0Ac0vqk6zmdvXe/ToUTx79gxA1g2pdevWNbpPixYt5POEhARcuXIF1atXN6m8prJVm8cQa/efG6LlOn2V//aJiGyFA9pfAvb29liwYAGqVKmCxMREPHnyBFOmTMG0adPMzrNIkSLyec4AWZ/sWRP1Uc6kqAwWDDE2W0XRokXl85s3byIzM9PgnYF5KX/+/Fi0aBGCgoIghMC1a9fw22+/YdSoUap0ynO6du2aVY5duHBhORDp3r17moIqW8wUYotzU3J1dUVwcLCc8ScpKQnbtm3DN998g7NnzwIAxo4dix49eqBEiRJ686lRowZq1KiBkSNHAsj6kWLWrFlYuHAhgKwAfvbs2fj4449NKp/y7+ru3bsQQhidUT0zM1P1t2XrGUmJiIjo5VSjRg3VtpYfwZVpChYsqDfdvXv3NMfnQFYslK1Nmzaa97N0UJEQQnVsYyIjI02KaR89eoTg4GBEREQAyJpBZ9euXShevLhJ5TRFSkqKauWmBg0aaNrvp59+Us148uWXX+Kzzz6zevmUlJ301apV03lN2eI6rVq1qnz+4MEDPHjwwOiMN8qBTcr9ba1q1apywIyWm62joqJUM0aZUtawsDBVm2rgwIGa99UnOTkZf/75p9y2dHaZvLhOiYiIgKw4MDQ0VG5b43vSUidOnJBxj729vc4f2itXrgxHR0c5k52psRSQO54qWLAgfH195exqp0+fNjpgRhlLlShRQg7CtoRywg5TVwbVR0ud5pSRkYHevXtj69atALImOVm7dq3RGed0iYuLM6l9opxRL1++fLnet2Usqbzx15j4+HjVeeW8wdkW8bmtrlNb1Kkt2+ZERESvOuV35/nz55Geng5HR8NDiqzVj6fcV+skiNbuQ9TS12uruKdGjRpYv349AO030BmLUZR961onfFGmi42NNZremvG5KSxpnyxfvhyZmZkAsmbENmUSxxdRp9ks6etVltPLy0vTGKyc52NKWa3Bmm0efazdf26Mluv0Vf3bJyKypZdz5PB/UMmSJfH+++/L7VmzZqmWSDJVQECAfH78+HEZkBlibMZDZZ7h4eFISEgwmqfyDlZ9eWbPQhcXF4fDhw8bzTMv1a5dG71795bbkydPzrWMlLLj/eLFiyYNLtJHWffZyzQZcvv2bTx+/Nji4+akXBIne0UBW3J1dUWXLl2wZ88e2amemppq8rEDAgKwYMECDBs2TL72999/m1wef39/ODg4AMi6K/X8+fNG9zl79qwMVB0cHODv72/ycYmIiOjVV69ePVWHlZbOOWWaUqVK2aRcWkVGRmL37t1y29YdfaaKiYlBy5Yt5QwSPj4+2LVrF8qUKWPT427atEnOdOLl5YU33njD6D6//fYbPvjgA7k9ZswYTJo0yVZFlJSzgffr109nGltcpxUrVlQtz7t3716jee7bt08+V84MY2vK2Uq0lFOZplSpUqhQoYLmYyk/jypVqmiaJceYjRs3ys5me3t7zaso6JJX1ykRERGQe4BBx44d87hE6u/u1q1bw8fHJ1caZ2dnNG7cWG6bGkvZ2dnpnMTD1BjFFrGUclCRtWbL11KnSpmZmRg4cCDWrl0LIKuvddWqVWjXrp1VymOpFxlLWsJW8bktrlNb1Omr3jYnIiKypQYNGsixG4mJiUZn6U1JSVGNMbEk9lR+71+5ckUOGNfn/v37qskitMxCbIzWvl5bxD3ZE/4BWYNJs1cgNcRYjKJcrf7JkydG8wPUs0h7enoaTJuX8Xn2TY+A6e2T5cuXy+emrppp6zpVsqSvV1nOp0+fQghhUjkB08r6qrBm/7kWWq7TV/Fvn4jI1jig/SXy0UcfoUCBAgCy7gyzZIb2Bg0awNnZGQDw8OFD7Ny502D669evq+421aV69epyhuq0tDTVnWu6JCYmYuPGjQbTuLq6qoL2GTNmGEz/Mhg/fry8g/HBgweYP3++6v2yZcuq7gD+6aefLD5ms2bN5PM1a9YgIyPDYPqVK1dafExdlI223bt3axrQbQ1eXl6qO6CVs6yYQll+c/Jwd3dHnTp15PaSJUuM7rN48WL5vG7dulabwYiIiIheLa6urqrZ0Dds2GB0H2WanDOaNGvWDEIITQ/lzNwAVO9NnDhRU/lXrFhh9qwle/fu1VxW5XlOmDBB9Z4+cXFxaNOmjYxNvby8sHPnTqMz41gqPT1dVX8DBgzQOVOj0ooVKzBixAi5PWTIkBfSBvr9999x8eJFAICLiwsGDRqkM521r1Mga2CWchCasRj68OHDuHr1KoCsTuUOHToYLYO1dOzYUbb1rly5YvSmb+W5dOrUSfNxUlJS8Mcff8hta90golxFoVWrVmavTpBX1ykREVE25UDnHj16GI2xbC0iIkKu/AgAw4cP15u2S5cu8vnmzZvlbO36KGMpf39/nT8cK+OMP/74A0lJSXrzS0pKwpo1a3Tua64nT56oJgexxmyLptRpthEjRmDFihUAsmLMRYsWoVu3biYfW2vbJGcbZM+ePfJ1XQOXrB1LTpw4UXM5J0yYIPdr2rSp6j3lbwuA7eJzW1yntojPbdHmISIi+rdwd3dXDa40FiesX79ezhTs5eWFJk2amH3sihUrolq1anJb2c+li/L9mjVronz58mYfGzCtr9cWcU+jRo1UN3gai1FiYmJw4MABua0rRildurR8rlwBS5+cvyUYu+HSWvG5qXbv3o07d+7IbVPis6NHj+Ly5cty29R+WVvXqZIlfb3Kcj5//lzTylTK83FyclLlYSu2avPoY63+cy20Xqev4t8+EZHNCY06dOigNSn9f02bNhUABAAxYcIETft88cUXcp/8+fOLx48f60w3YMAAmW7x4sU60/Ts2VOmqVmzpnj+/LnOdJmZmaJdu3YyLQAxYMAAnWk/+ugjmaZ06dLi6dOnes/lk08+UeXZtGlTnen27t2rSqfvfHSJiorSnFYfPz8/k4/du3dvuU/JkiVFSkqK6v0lS5bI9x0dHcXOnTs1l0fXOUVHRwsXFxeZ548//qh3/3v37glPT09Vne7Zs0dnWuU1qi9NTs2aNZP7vPbaayI2NlbTfikpKeLJkye5zkurOnXqyOPOnz9fvp6cnCzi4+M15TFr1iyZR8OGDXO9v3jxYqN/A4sWLZJpnJ2dxdmzZ/Ue79SpU8LJyUmmX7p0qc50pn4OEyZMMPl/CxER/fsEBgbmdRH+VZTfr/riVkvt379fHsPe3l4cPnxYb9o//vhDFc+dPn3a7OPu2bNHlZc5qlWrJvd/5513zC6LMaa2oRITE0WjRo3kPgUKFBBHjx61WfmURo8eLY/r4+MjYmJiDKZft26dcHBwkPv06tVLZGRk2Lyc4eHhokiRIvK4kyZNMpjeFtfpuXPnhL29vUy3Y8cOnekyMjJUn2fPnj01n2e2iIgIVZkiIiJM2r9bt25y38aNG4vMzEyd6bZv3y7TOTg4iIsXL2o+hrLe7O3txb1790wqoy73799XXV+///67Wfnk1XVKRPRvwPjcOo4ePar6LjcUi7wICQkJol69erI8LVu2NJj+6dOnolChQjL9999/rzftjRs3hJubm0z7008/6UyXnJwsSpYsKdONGzdOb55jx46V6fz8/HL1W2fT2p+akZEhunbtKvN0cnIyOb7KydQ6FUKIDz74QHVd/PrrrxaVQSstfexKLyKW1MXU9qwt4nNbXKdC2KZO86ptTkRELxbjc/Ns3rxZ9Vv4hQsXdKZLTEwUFSpUkGk///xzi4+t/C3f29tbPHjwQGe6qKgoVcw9d+5ci49tSl+vreKeb775RqYtUaJErrEVSiNGjJBpixUrJpKSknKl+emnn2QaOzs7sW/fPr35CSHEwoULVXHP3r179aa1dnyutX0SHR2tuu4qV65sUt+lst4qV65scjltWadKlvb1ZmRkCG9vb7l/q1atDNZTQkKCqFq1qkzfrFkzk44nhHnjrkxh6W941ug/t9V1+ir97RMRWULr+HMOaLchcwa0R0dHC3d3d7nf2LFjdabTMqD90qVLqkHQrVq1yjVYOi4uTvTr1082SLLT6hvMe//+fdVg6ddff13cvHlTlSYtLU1+4SqPbyioUJ6PnZ2d+Pjjj/UOdk5OThYbN24UnTp1EkFBQXrz1MqcwOrSpUuqDt958+ap3k9PTxctWrRQNfa+/fZbkZCQoDO/+Ph4sWrVKtG8eXPRrVs3nWk+/fRTVYfsrFmzcnXeXrhwQQaayrq35oD28+fPq67RypUrGxywf+3aNTF58mRRvHhxsWnTJtV7EyZMEP7+/mL27Nni/v37OvePi4vLde537tyR70dERAhPT0/x4Ycf6h28lJmZKbZt26Zq2P7vf//LlU7LgPaUlBTh7++vChJDQ0Nzpdu1a5fw8fGR6QIDA0VqaqrOPDmgnYiIzMEOeesytzMs5+BZY9/lb775pkxbpEgRnenXrFkjXF1dZboePXqYdjI5WDqg/dixY6r9w8LCLCqPIaa0oZKTk0WrVq1keldXV6Mdc1qcPXtWfPDBB3oHQNy5c0c1qMLOzk6sX7/eYJ7btm1TtbfefPNNkZaWZnFZv/zyS/H777+LxMTEXO+lp6eLFStWqAazBwUF6Y1JlWxxnWa3O4GsH6Ry5pmQkCD69u2rakNdu3bNaFlzsnRAe3h4uOqm1P79++dqx4WGhqraFoMGDTLpGMobylu3bm3Svvp8//33Ms+CBQvq7Mg2xlbXKRHRfwXjc+sYOXKkRQMMTInPly9fLqZPn653oMzhw4dFQECAzMvLyytXX7guM2bMUPVl/vLLL7n6cc+cOSPKly8v05UtW1ZnTJdN+QOzg4ODmDlzpirPjIwM8dNPP6n6rPVNriGEEK1atRJDhgwRBw4c0DtA+OLFi6Jly5aq+vzwww8Nnrst6nT8+PGqMhi6ScDaTGnrCfFiYkldzGnP2iI+t/Z1KoTt6jQv2uZERPRiMT43X+PGjeX3X5kyZcS5c+dU70dHR6v6ZQsVKqR3IkRT4vPU1FRVjFyrVi1x+/ZtVZpbt26p4slKlSoZ7L+yRV+vELaJexISEkSJEiVUfbk5Y+WUlBTVJJkAxJw5c3Tm9+zZM1WM5OXlJf78889c6dLS0sSsWbNU40vq1q2rt5y2iM+9vb3F+PHjxZUrV/Sm+eeff0TZsmVVx/7rr780HyMlJUVVH1OmTDG5nLaq05ys0derHCQNQHTt2lVnO+3atWuiYcOGqrRbt241+Xgv+4B2a9Spra7TV+Vvn4jIUlrHn9sJYWDtdoWOHTuqlpUk45o1a4Z9+/YBACZMmKBaosiQzz//HN999x0AoECBArh9+za8vLxUaQYOHCiXQ1m8eDEGDhyoM6+ZM2dizJgxctvFxQUtWrRAyZIl8fjxY4SGhiIuLg6enp4YM2YMvv76awBZS+voW0Lqjz/+QK9eveSSm46OjmjSpAnKlSuHuLg47Nu3Dw8fPoSTkxOmTJmCTz75BADQvHlzvcuZpKSkoGPHjtixY4d8zdnZGUFBQShfvjxcXV0RGxuLGzdu4Pz580hOTgYA1K5dGydOnDBUnUaVKVMGt2/fBmC4LnPq2bOnXCa+bNmyuHr1KhwdHeX7MTExaNWqFU6fPi1fy58/P+rVq4fSpUvD2dkZT58+xdWrV3Hp0iWkpaUBALp27Yq1a9fmOl5SUhIaN26MkydPytdKly6Nhg0bIn/+/Lh69SoOHTqEzMxMdOnSBTExMfL627dvn84lvpTX6J49ezQvxbN582b06NEDz58/l6+VKlUKQUFBKFy4MFJTU/H48WOcPXsW9+7dk2k2bdqEN954Q25PnDhRXnN2dnYoX748atSogcKFCyMtLQ33799HWFgYEhMT5T7jxo3D5MmT5fatW7dQtmxZuV2oUCHUqlULJUqUgIuLCx49eoRz584hIiJCpqlUqRJOnToFNzc31XktWbIEgwYNAmD4byA8PBxNmzbF48eP5Wv+/v4ICAgAAJw5cwZnz56V7/n4+ODAgQOoVKmSzvxM/RyU9WbK/xYiIvp3qV27tiouIO3atWuH+/fvq1578OABHj58CABwc3PTuaTe1q1bcy1BmDMWMfZd/uTJEzRs2FC1rGXt2rXx2muvISMjA8ePH0d4eLh8r1q1ajh8+DA8PDxMOkelvXv3onnz5nJbYxNQGj16NGbNmgUgK466cuWK2WUxxpQ21Keffopp06bJ7cDAQNSvX1/TcSpWrKhqJykdOXJE5lOmTBkEBATAx8cHiYmJuH79Oo4dOybr0M7ODgsWLMDgwYP1His6Ohp+fn4ydnZwcMCgQYPg4uKiqaxjxoxBxYoVdb7Xpk0bbN++Ha6urggMDET58uXh4uKChw8fIiwsDNHR0TJtrVq1sGvXLhQqVMjoMW1xncbFxaFBgwa4ePGifK1evXqoVq0aYmNjERoaiqdPn8r3lixZYnTJ17lz52Lu3Lmq11JTU1Vlq1q1KpydnVVphg8fjuHDh+vNd+HChRg6dKjcLlSoEFq0aIECBQrg4sWLOHbsmHzvtddew8GDB1GgQAGDZc328OFDlCxZEunp6QCAVatWoVevXpr2NaRmzZq4cOECAGDYsGGYN2+eSfvb8jolIvqvYHxuudTUVBQvXhwxMTEAgClTpmDs2LEm5WFKfP7tt99i7NixsLe3R40aNVCtWjUULFgQT548wZkzZ3Dt2jWZ1svLC7t27UJgYKDRMggh0KtXL9l3DAB+fn5o1KgR8uXLhytXriAsLAyZmZkAsn4D2LdvH2rVqmUw3/79+2P58uVyu0KFCqhXrx6EEDh8+DBu3rwp3xs0aBAWLVqkNy9l3O3p6YmAgACULFkSbm5uiI+Px/nz53HhwgVV26FNmzbYtGmTqg88J2vX6datW9G+fXu5XbRoUXTr1k1v+pyy2zHmsrOzk8+19p/bMpbUR9lf3LRpU+zdu9foPraIzwHrXqfZbFGnedE2JyKiF4vxufnu3buHunXrIioqCgBgb2+Ppk2boly5cnj8+DF27dol+5AcHR3xzz//IDg4WGdepvafnz17Fo0aNUJCQgIAwMnJCcHBwShZsiTu3r2L0NBQOa6iQIECCAsLQ40aNfTmZ+2+XiVbxD0nTpxAs2bN5PgIJycnNG3aFGXLlkVsbKwci5OtT58+WLFihd78/vrrL3Tt2hUZGRnytTJlyqBevXooWLAgoqOjcejQITx48EC+X6hQIYSFhaFy5cq58rNVfK6Mu0uWLInXXnsNPj4+cHFxQXR0NI4dO4a7d++q9pk8eTLGjRun+djr1q2TZbW3t8ft27dRsmRJzftns3ad6mJpXy8AJCcno1WrVjh48KB8zcXFBQ0bNkTZsmWRmZmJK1eu4OjRo6pzGTlyJGbPnq033xMnTqhi82zKMU+lSpXK9TtEnTp1sGDBApPPI5s5bR4la9SpLa/Tl/1vn4jIGjSPP7f2CHn6P+bM0C6EEI8ePVItNaprXy0ztGebPn26apa1nI+iRYuKAwcOaJqdOtuSJUtE/vz59ebp4eEhNm7cKHbs2CFf69ixo8E809PTxVdffWUwX+XDyclJjBo1ykhtGmfunYIXLlwQdnZ2Bvd9/vy5GD58uHB0dNR0Tq6urgbvBI2JiRFNmjQxmEeHDh1EbGysaNCggXzt1KlTOvMzZ4b2bGfOnBG1a9fWdF5A1t3bOZfj/OGHHzTv7+zsLL7++utc5bh3757qjkFjj2bNmuVaqSCbKX8DV65cEbVq1TJ6vMDAQHH9+nWDeXGGdiIiMgdnmDGfMv4z5aFrpmdTZ2gXQojIyEjVDDb6Hu3atTO4tKBWlszQnpqaqlqaUtcqN9ZkShtK2SYy9WFoBo/Dhw9ryqNChQoGVyrKlvMaMfVh6JoKCQkxur+9vb0YOXKkiI2NNVpWJVtcp5GRkaqVrHQ93N3dxaJFizTlp4yLTXloiaHnz5+vapfregQHB+tdaUqf6dOny/09PDzE8+fPTdpfl5MnT6rKdejQIZPzsOV1SkT0X8H43HLr1q1TxTDKVRq1MiU+nzp1qqbvuZYtW5q8ckxKSop47733VP3Huh6VKlXKNeOloTzfffddg3na2dmJ9957z+iqPMq429gjX758Yvz48ZpWbrF2nSr7a815WMrceMdWsaQ+5s5WaO34XAjrXqdKtqjTF902JyKiF4vxuWXCw8NVM6HrehQpUkRs3rzZYD7m9J+HhYXlmuE456NcuXLi8OHDRvOydl+vkq3inrCwMNVM9boejo6O4rPPPhMZGRlG89u6davw9fXVVA8BAQF6Z7MXwnbxuSl5FC1aVOds08Z06NBB5tGqVSuT91eyZp3mZI2+3myxsbGif//+msrp5OQkvvnmG6PXVM7fm7Q+zJlVXcmSGdqtVae2vk5f5r99IiJr0Dr+XP9UGpRnihQpghEjRuCHH34AAPz888/48MMPzZ754cMPP0SbNm0wa9Ys7NixA5GRkcifPz/8/PzQuXNnvPPOO/Dx8cH169c15zlgwAA0bdoUM2fOxNatW3H37l04OTmhVKlS6NChA4YPH47SpUurZqHx9PQ0mKeDgwO++eYbjB49GsuWLcOuXbtw6dIlREdHIy0tDR4eHvDz80PNmjXRvHlztGvXDkWKFDGrTqyhevXqqtnUp0yZgn79+sHBwUGmcXV1xZw5c/DZZ59hxYoVCA0NxdWrVxETE4PMzEwULFgQ5cqVg7+/P4KDg9GmTRuDn3OhQoWwd+9erFq1CsuXL8fp06fx7NkzFC1aFK+99hoGDhyIrl27ws7ODk+ePJH7Gat7c/j7++PEiRPYsWMHNm7ciEOHDuH+/ft49uwZXFxcUKRIEVSqVAn16tVDSEgI6tevr7pjEQA++ugjdO3aFTt37kRYWBjOnz+PW7duIS4uDvb29vD09ETVqlXRokUL9O/fH35+frnKUaJECcTExCA0NBQHDhzAyZMncf36dTx+/BipqakoUKAA/Pz8EBQUhB49eqBly5ZWOf9KlSrhxIkTWLt2LdatW4djx47h0aNHALJmZH/99dfRrVs3+XkQERERZStevDh27NiBzZs3Y+XKlTh+/DiioqJgZ2cHX19fNG7cGH379kWLFi3yuqjYvHmznCHT3t4e/fr1y+MS2V5gYCC2bduG3bt3IywsDJGRkXj06BEcHR3h6+sLf39/dO3aFZ06ddI8e7Wt/Prrr9i+fTtCQ0Nx6dIlPHz4EHFxcShcuDBKlSqFNm3aoFevXqhSpYrJedviOi1evDh27dqFDRs2YOXKlTh16hSioqLg7u6O0qVLo0OHDhgyZAhKly5tcnmtbejQoWjdujUWLlyITZs24c6dO0hISICvry8CAwPRt29fdOrUyeRYP3ulNQDo3r07XF1dLS6rMs8KFSqgQYMGFudJRESUF5TfaS1atECpUqVserxhw4bBz88PoaGhOHnyJB48eICYmBgUKFAAxYsXR6NGjfDWW2+pVjvSytnZGT///DMGDhyIJUuWYPfu3YiMjERSUhKKFCmC2rVro3PnzujTp4/BGc9z5vnLL7+gX79+WLRoEfbu3YvIyEgAWX2kzZo1w5AhQxAUFGQ0rzVr1iAsLAxhYWE4ceIEoqKiEB0djWfPnsHV1RXe3t7w9/dH06ZN0b9/f3h7e2sqoy3r9FViq1jS2mwRn1vzOlWyRZ2+Sm1zIiKiF61KlSo4evQoVq9ejd9//x0XL17Ew4cP4enpiXLlyqFz584YPHgwChcubPVj169fH+fOncOyZcuwZs0aOb7C29sblSpVQvfu3dG/f3+4u7sbzcuWfb22inuyz3/VqlVYs2YNwsPD8ejRI7i5ucHPzw/BwcEYNmyY3tXhc2rbti1u3ryJP/74A1u2bMHp06fx8OFDPH/+XMbpdevWRdeuXdGuXTvY29ubVF5ruHr1KsLCwnD48GGcPXsWjx8/RnR0NBITE1GgQAEULVoUQUFBCAkJwVtvvZVrRU5jHj9+jG3btsltLSsPGWLLOrVmX6+HhweWLl2KTz/9FEuXLsWhQ4dw/fp1PHv2DPb29vDy8kK1atXQrFkzDB48ONcKxf8W1qpTW1+n/8W/fSIiXeyE0LbevOYp34kUxo0bhylTpgAApk6dis8//zyPS/TfkJSUBA8PD6SnpyN//vyIi4tTDbQnIiKiVx+XTCUiIiIienkwPiciIiIienkwPiciIiIienloHX/O22vIZoQQcvZyACbfeUrmW79+PdLT0wFk3XXMwexERERERERERERERERERERERERERPQy4oB2spmZM2fi6tWrAABfX180bdo0j0v03/Ds2TN89dVXcrtXr155WBoiIiIiIiIiIiIiIiIiIiIiIiIiIiL9OKCdTBYWFoZhw4bh7NmzOt9PSEjAxIkT8eGHH8rXPvjgAzg6Or6oIv5rjRw5EkuWLEFCQoLO948ePYrGjRsjIiICQNaNBH379n2RRSQiIiIiIiIiIiIiIiIiIiIiIiIiItKMI4zJZKmpqZg/fz7mz58PPz8/BAQEwMfHBxkZGbh79y7CwsKQmJgo0zdp0kQ1uJ3Md+nSJcyZMwcjRoxArVq1UKFCBeTPnx+xsbE4ffo0rly5ItM6Ojpi0aJF8PDwyMMSExERERERERERERERERERERERERER6ccB7WSR27dv4/bt23rf79mzJxYuXAgHB4cXWKp/v+TkZBw+fBiHDx/W+b6Pjw+WLl2KNm3avOCSERERERERERERERERERERERERERERaccB7WSyJk2aIDQ0FFu3bsWxY8cQFRWF6OhoxMfHo2DBgihZsiSaNGmCfv36ISgoKK+L+6+yevVqbNiwAXv37sXVq1fx+PFjxMTEwN7eHt7e3vD390ebNm0waNAg5M+fP6+LS0REREREREREREREREREREREREREZBAHtJPJ7O3t0bx5czRv3jyvi/KfU6xYMYwYMQIjRozI66IQERERERERERERERERERERERERERFZzD6vC0BERERERERERERERERERERERERERERE/00c0E5EREREREREREREREREREREREREREREeYID2omIiIiIiIiIiIiIiIiIiIiIiIiIiIgoT3BAOxERERERERERERERERERERERERERERHlCQ5oJyIiIiIiIiIiIiIiIiIiIiIiIiIiIqI8wQHtRERERERERERERERERERERERERERERJQnOKCdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiPIEB7QTERERERERERERERERERERERERERERUZ7ggHYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiyhMc0E5EREREREREREREREREREREREREREREeYID2omIiIiIiIiIiIiIiIiIiIiIiIiIiIgoT3BAOxERERERERERERERERERERERERERERHlCQ5oJyIiIiIiIiIiIiIiIiIiIiIiIiIiIqI8wQHtRERERERERERERERERERERERERERERJQnOKCdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiPIEB7QTERERERERERERERERERERERERERERUZ7ggHYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiyhMc0E5EREREREREREREREREREREREREREREeYID2omIiIiIiIiIiIiIiIiIiIiIiIiIiIgoT3BAOxERERERERERERERERERERERERERERHlCQ5oJyIiIiIiIiIiIiIiIiIiIiIiIiIiIqI8wQHtRERERERERERERERERERERERERERERJQnOKCdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiPIEB7QTERERERERERERERERERERERERERERUZ7ggHYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiyhMc0E5EREREREREREREREREREREREREREREeYID2omIiIiIiIiIiIiIiIiIiIiIiIiIiIgoT3BAOxERERERERERERERERERERERERERERHlCQ5oJyIiIiIiIiIiIiIiIiIiIiIiIiIiIqI8wQHtRERERERERERERERERERERERERERERJQnOKCdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiPIEB7QTERERERERERERERERERERERERERERUZ6wE0IILQkDAgLg4OBg6/IQEREREZEGT548QaFChfK6GEREREREBMbnREREREQvE8bnREREREQvj4yMDJw5c8ZoOketGZYuXRp///23JWUiIiIiIiIrqV27Nk6ePJnXxSAiIiIiIjA+JyIiIiJ6mTA+JyIiIiJ6eXTs2FFTOnsbl4OIiIiIiIiIiIiIiIiIiIiIiIiIiIiISCcOaCciIiIiIiIiIiIiIiIiIiIiIiIiIiKiPMEB7URERERERERERERERERERERERERERESUJzignYiIiIiIiIiIiIiIiIiIiIiIiIiIiIjyBAe0ExEREREREREREREREREREREREREREVGe4IB2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIsoTHNBORERERERERERERERERERERERERERERHmCA9qJiIiIiIiIiIiIiIiIiIiIiIiIiIiIKE9wQDsRERERERERERERERERERERERERERER5QkOaCciIiIiIiIiIiIiIiIiIiIiIiIiIiKiPMEB7URERERERERERERERERERERERERERESUJzignYiIiIiIiIiIiIiIiIiIiIiIiIiIiIjyBAe0ExEREREREREREREREREREREREREREVGe4IB2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIsoTHNBORERERERERERERERERERERERERERERHmCA9qJiIiIiIiIiIiIiIiIiIiIiIiIiIiIKE9wQDsRERERERERERERERERERERERERERER5QkOaCciIiIiIiIiIiIiIiIiIiIiIiIiIiKiPMEB7URERERERERERERERERERERERERERESUJzignYiIiIiIiIiIiIiIiIiIiIiIiIiIiIjyBAe0ExEREREREREREREREREREREREREREVGe4IB2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIsoTHNBORERERERERERERERERERERERERERERHmCA9qJiIiIiIiIiIiIiIiIiIiIiIiIiIiIKE9wQDsRERERERERERERERERERERERERERER5QkOaCciIiIiIiIiIiIiIiIiIiIiIiIiIiKiPMEB7URERERERERERERERERERERERERERESUJzignYiIiIiIiIiIiIiIiIiIiIiIiIiIiIjyBAe0ExEREREREREREREREREREREREREREVGe4IB2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIsoTHNBORERERERERERERERERERERERERERERHmCA9qJiIiIiIiIiIiIiIiIiIiIiIiIiIiIKE9wQDvZzN69e2FnZwc7Ozs0a9bMpscaOHCgPNaSJUtseiwi+veZOHGi/B8yceLEvC4OERERERERERERERERERERERER0X8GB7TbULNmzeQAyZyPfPnyoWjRoqhYsSIaNWqE0aNHY/Hixbh9+3ZeF5teYkuWLNF7TZn7uHXrVl6fVp4xVC8ODg7w9PRExYoV0b17dyxcuBAJCQl5XWQiIiKygoyMDJw7dw4LFy7EiBEjUKdOHTg7O9v8ZswHDx5g06ZNGD9+PN544w0EBASgRIkScHFxgbu7O/z8/NChQwfMnDkTMTExmvJU3kSq9TF06FBNed+/fx9//fUXvvzyS7Rp0wbe3t4WxZGG2kf6Hvfu3TPpGACwf/9+jB49Gv7+/vDx8UG+fPlQqlQp1K1bFyNHjsSff/6Jp0+fmpTn1atX8c0336B+/fooWbIkXFxcULRoUfj7+6N3796YP3++5vp48OABvvnmGzRq1AhFixaFi4sLihUrhnr16mHKlCmIjIw0qVyrVq3CBx98gBYtWqB69erw8fGBk5MTPD09UaVKFfTu3Rtr1qxBamqq5nxTU1Nx/PhxzJkzB4MHD0bNmjXh6OgoP5eBAwdqziunixcv4pNPPkH9+vVRuHBhODk5wd3dHWXKlMGbb76J3377DfHx8ZryMvV6qlChgsH8lDdKm/MwV5cuXVT5mPo/yJp1mu3hw4f4/vvv0bp1axQvXhyurq7Ily8ffH19ERwcjEmTJuHOnTsm5ZmUlIRly5aha9euKFeuHNzd3eHs7IzChQujbt26GDNmDE6cOGFSnkRE9O906tQpfP7556hTpw58fX3h4uKC4sWLIzAwEIMHD8by5cvx4MEDi49ji/hcSQiBDRs2oFu3bihfvjxcXV1RpEgR1KlTB19//bXm79K0tDScPHkS8+bNw9ChQ9GgQQNUrFgRXl5e8ru0Tp06GDlyJA4cOGBSGWNjYxEaGorvv/8eb731FsqUKaOKSyyZPCYhIQFz5sxB8+bNZQxdsmRJtGjRAnPnzjWrr/Xs2bMYM2YM/P39UahQIeTLlw9+fn4ICQnBggUL8Pz5c7PLGx0djTlz5iAkJATlypVD/vz54eXlhapVq6J9+/b4/vvvjcYqtm7z2KJOs1na5rHldQoAycnJmDdvHjp06IBSpUohX758KFSoEGrWrIn3338fp0+fNuu8N2/ejP79+6NChQooUKAA3N3dUbFiRfTu3RubN282KS8hBK5cuYIVK1ZgzJgxaNiwIfLnzy8/6zJlypiUX86/R1MeturXICKi/5bU1FQsX74c7dq1g5+fn+wbatCgAX744QdER0db/Zjh4eGYOXMmunfvjmrVqqFgwYJwcnKSscSYMWPM/t63RR8aABw/fhwjR46U5fXw8EC1atUwcuRIHD9+3Kyy3rlzBxMnTkTt2rVRpEgRuLq6onz58ujWrRs2bNgAIYTmvGzR1/vgwQMsX74cQ4YMQZ06deDt7a3qkx4wYAA2bdqEzMxMk/PW5dSpU6oy29nZYe/evUb3s1V8bsv+c2u1I40xt05zSklJwdq1a9GrVy9UrVoVBQsWhJubG8qXL4+mTZviiy++wM6dOzX/PvGytyMTExOxefNmfPDBB2jatKnss3Bzc4Ofnx86deqEefPmITEx0eRy2qrNk80Wv50REdmE0KhDhw5ak9L/17RpUwHApIe9vb1o06aN2Lp1a14X32J79uyR59W0aVObHmvAgAHyWIsXL7bpsfLS4sWLTb6mjD0iIiLy+rTyjKl1VaRIEbFhw4a8LjbZwIQJE+TnPGHChLwuDhGRJoGBgXldhFfShg0bRP78+Q1+59sqdq1evbrmuMPDw0P8+uuvRvNUxtxaH0OGDDGar6+vr9XjSHPaR3fv3tWc/40bN0RISIimfKdNm6Ypz/j4eDFmzBjh4OBgNM/27dsbze/nn38Wrq6uBvMpUKCAWLhwodG8Hj9+bFJdli9fXuzdu9dovr/88otwdnY2mNeAAQO0VJ9KcnKyeOedd4SdnZ3RshYtWlT89ddfRvM09XoqX768wfyU7UpTH0WLFjW5ToQQYu3atWb/D7JFnQohxOzZs4Wbm5vRPJ2dncU333yjKc+9e/eKMmXKaKrL7t27i9jYWE35EhG9TBifW+7hw4eiT58+mr4vRo0aZfHxbBGfZ4uMjBQtWrQwmKe7u7umvuw///zTpLgkODhY3L5922i+PXv2NBpHmNvXHhYWJsqWLWsw73LlyokjR45oyi8pKUkMHjzYaHkrVKggwsLCTCprZmammDNnjvD09DRat25ubgbzsmWbx9p1ms1abR5bXadCZLV7ixcvbjA/Ozs7MWbMGJGSkqIpz9u3b4t69eoZLWerVq1EZGSk0fyOHz8uPDw8DObl5+enqWzZ/Pz8TL6esh89evQw6VhERP9GjM8tEx4eLmrVqmXw+8bHx0ds2bLFKsfbsWOHSfH5W2+9JWJiYjTlbas+tJSUFPHee+8ZzDc7RklNTdVcFwsXLjTaN9eyZUsRFRVlNC9r9/Xevn1bNG/eXNjb22v6nPz9/cW5c+c0569LWlqazmtxz549Rve1RXxuq/5zIazbjjTEkjpVCg0NFZUqVdJUr8ePHzea38vejuzTp4/R3xezH4ULFxbr1q3TVE4hbNPmyWaL386IiMyhdfy5I+iFCAoKQt26deV2ZmYmYmNj8ezZM1y8eFHOzJ6ZmYl//vkH//zzD/r164dffvkFBQsWzKti00umatWqGDVqlME0GzZswP379wHkvu508fDwsFr5XmWdOnVCiRIl5HZmZiaio6Nx+PBheRfu48eP0bVrV6xduxadO3fOq6ISERGRBZ49e2bRrH3WUqpUKVSuXBnFixdH/vz5ER8fj/DwcJw+fRpCCMTFxWHkyJF4+PAhJk6cqCnP4sWLa4pRGjRoYDRNVFSUpmOaK2fspY+7u7um/M6ePYvg4GDVzJnVqlVD9erVUahQIcTHx+PKlSs4e/Ys0tPTNeX57NkztGrVSjULo6+vL15//XUUKVIEqampiIiIwKlTpzTNCvL555/ju+++k9sFChRAkyZNULx4cTx9+hT79+/Ho0ePEB8fjyFDhiAlJQUjRozQVFZ7e3tUqlQJFStWROHCheHs7IyYmBicOnUKN2/eBADcuHEDISEh+OuvvxASEqI3r+joaJNmc9dCCIGuXbtiy5Yt8jUPDw/UqVMHfn5+iI2NxeXLl3Hp0iUAWbODd+nSBWvXrkWnTp00HcNYOwkAihQpYvD9li1bar7m0tPTMW/ePLndt29fTfspPXv2DO+++67J+wG2q9Np06bh008/ldsODg6oW7cuypYtCwC4efMmjh8/joyMDKSmpmL8+PGIjY3FDz/8oDfPAwcOoE2bNkhOTpavVa9eHZUrV0bBggVx69YtnDhxQs6AtWbNGty5cwf79++Hk5OTWfVDRESvnjt37qBZs2aIiIiQr5UtWxaBgYHw9vZGUlISrl27hjNnzqi+U6zFmvF5XFwcQkJCcOHCBfla3bp1Ub16dTkj+rNnz5CQkIBBgwbB3t4e/fv311TOfPnyoXr16ihbtiy8vLwghEBUVBSOHj0qZ8jcvXs3GjVqhEOHDqFUqVJ684qKijJpZketzp07h5CQEPnd7uTkhBYtWqBkyZK4e/cuQkNDkZ6ejps3b6J169Y4dOgQatSooTe/1NRUhISEYP/+/fK14sWLo1GjRihYsCDu3r2L/fv34/nz57h+/TpatWqFvXv3ok6dOkbLKoTAO++8g/nz58vX3N3d0aBBA5QoUQJ2dnZ48OABTp06ZfKqANZs81i7TrNZu82TzZrX6ebNm9G5c2fZjnN0dET9+vVRqVIlpKSk4Pjx47hy5QqEEPj555/x8OFDrFq1yuAKSrdv38brr7+Ohw8fytdq1KiBwMBAODo6Ijw8HEePHkVmZiZ27tyJ4OBghIWFwcvLS2+eCQkJiIuL01xHWgwYMEDz6hB37tzBpk2b5LY57RMiIqJs9+7dQ3BwsBxzYGdnhyZNmqBChQp49OgRdu3ahaSkJDx69AidOnXCtm3bEBwcbNExT548iYsXL8ptOzs7vPbaa6hUqRK8vLzw+PFjHDx4EI8fPwYA/Pnnn7h06RL27dsHb29vvfnasl/y7bffxrJly+R2+fLlUa9ePQghcPjwYURERMgYJT4+HgsXLjRaD4sWLcKQIUPktpeXF1q0aAEPDw9cuHBBzvi+a9cuhISE4NChQwbjSWv39d6/fx979uxRvebn54eAgAD4+PggMTERx48fx7Vr1wBk9ds3bNgQu3fvRlBQkFnH/OGHHyyemRqwXnxui/5zwLbtyJysUafLli3DoEGD5Cz8Dg4OeP3111G2bFm4ubkhJiYG58+fx9WrVzXl9yq0I9euXYuUlBS57eXlhbp166J48eIQQiA8PBzHjh2DEALR0dHo2rUr5syZg+HDhxs8d1u0ebLZ4rczIiKbs/YIefo/yrv9jM34GxUVJb7//ntRsmRJ1d1PgYGBIjEx8cUUmP4VTLnu/uuUf2v67jbNyMgQixYtEi4uLjJt0aJF+XdJRER5jjPMmCd7xZuiRYuKN954Q3z99ddi69atYsyYMfK73lYztH/xxRdixYoV4v79+3rTXLt2TTUDh729vTh27Jje9LZaFQmAyJcvn6hXr54YPXq0WLZsmdi+fbsqfrJkhnZTZ/owJCIiQnh7e8u8g4ODxfnz53WmjYmJEYsWLRL//POPwTzT0tJUs/WVL19ebN68WWRmZuZKm5ycLP7++28xd+5cvflt2rRJVXf9+vUTT58+VaVJSUlRrRrj4OAgTp48qTfP2NhYMXLkSLF161aDs1mHhoaqZvbz9fUV8fHxetNnl6FUqVKiS5cuYurUqWLXrl2iW7duZs8ws3TpUtX5v/fee+LJkye50v3zzz+iVKlSMp2Pj49ISkrSm68yzxct52dqzkxDgwcPFgCEk5OTqn61/C3bok6vXbumml0oODhYXLlyJVe6S5cuiSZNmqhmhjlx4oTOPNPT00WFChVk2jJlyoidO3fmShcdHS2GDx+uOqcff/zRaD0QEb1MGJ+b79mzZ6JcuXKqPvFDhw7pTBsfHy9Wr14tfv/9d4uPa4v4XAgh+vXrJ9MXKlRIhIaG5jqH3r17yzTOzs7i2rVrevM7ePCg+Oqrr8Thw4f1zuyYlpYmfvvtN5EvXz6Z7xtvvGGwnE2bNhX29vaiWrVqYuDAgWL27Nni2LFjqtjB1Jn/UlNTRfny5eX+/v7+4tatW6o0ERERwt/fX6apVKmSSEtL05vnRx99pIqRp0+fLtLT01VpHj16JN58802ZrkSJEpr6b5V5u7u7i59//lnnbHeZmZni8OHD4quvvjKYny3aPLaoUyGs3+axxXUaGRmpmvW8du3a4urVq7nSrV27VhQoUECm++mnn/TmmZmZKYKCglQzJ27fvj1XunPnzonKlSvLdJ06ddKbpxD/1zb38vISrVq1El988YXYsGGD+Pbbb2Ueps7Qbgrltezj42P08yci+i9gfG4+Zb+Pn5+fOHv2rOr9x48fi+DgYFXMm7Ov01RTp04VAERAQICYPXu2iI6OzpUmJSVFTJ06VbWyTPfu3Q3ma6t+yYULF6raCDNmzBAZGRny/YyMDPHjjz+qZjJfunSpwbJevnxZODk5yfR9+vQRCQkJqjS7du0SXl5eMs2gQYMM5mntvt7Dhw8LAKJ48eLiyy+/1NuO2bhxo6rP3s/PTzx//lzzcbJdvXpVxo7KNpTWeNsW8bkt+s+FsH47Uh9L61SIrM9XeW2PGjVKPH78WGfa69evi++++07cuHFDb36vSjvSxcVF5M+fXwwZMkQcOnRI9Tef7fz58yIgIEB1bEO/HdiizZPNFr+dERFZQuv4cw5otyFzBhbHx8eLt956SxU0dO3a1bYFpX8VDmjXzpTg/KefflKlX7ly5YspJBERkR7skDdPVFSUzmXNlQOJbTWgXavk5GTVMo1vv/223rS2GtB+6tSpXJ2BERERqnjoZRnQ3qpVK5lvjx49cnVKmmPy5Mkyz6pVq4pHjx5ZlN9rr70m82vbtq3BtJ988olM27JlS4uOmy3njyGGYtlbt26JBw8e5Hp9wIABZnfIN2vWTPNAkBMnTqius7///ltvWmW6F03Zbq9Vq5bJ+4eGhsr9x40bZ/L/IFvU6cSJE2Wa4sWLG+y8j4+PF76+vjL9hx9+qDPd3r17Vcc+ffq0wbK+8cYbMi2/54joVcP/W+YbOnSo/P/fpEmTl24iCVPi8/Pnz6t+2Nf3Y3BGRoZo0KCBTNerVy+rlHXRokUyTzs7O3H37l29aS9evKjzRkflzZCmDmifPXu23NfLy0vvzQL3799XDcSZN2+e3nTKiUa+++47vcdOT08X9evXl2knT55ssKwHDhyQS8+7ublpXrbeEFu0eaxdp9ms3eYxhdbr9L333lMNLomJidGb55YtW2Rab29v8ezZM53p1q9fL9PZ29uLsLAwvXnevXtXVacHDhzQm/bJkyc6BxRl31QP2G5Ae3p6uio2/+CDD2xyHCKiVw3jc/Mov1OdnZ31DsRMSEhQ3ZQ6duxYi467bt06sWHDBk1pf/zxR1V/U3h4uN60tuhDS05OVg1+//zzz/Xm+dlnn6liAV03T2ZT9jc2bNhQ52BZIYTYtm2bTOfg4CAuXryoN09r9/VevHhR/PjjjwYH+2c7duyY6uYDQzdH6pKZmSk/Py8vL/Hw4UOTxncIYZv43Bb95y+qHWmNOo2JiRFFixaV+8yaNcukMujyqrQjP/74Y52ffU5PnjxRtat79uypN60t2jzZbPHbGRGRJbSOP7cHvVTc3d3xxx9/4I033pCvrVu3Dnv37s27QhER3nnnHeTLl09uK5cmIiIioldHsWLFULp06bwuhkEuLi6q5cFPnTr1wstQq1YtODo6vvDjmuqvv/7Czp07AQClS5fG/Pnz4eDgYFGejx49wqRJkwBkLZO5YsUKFClSxOz8rly5gnPnzsntKVOmGEz/5ZdfymVNd+3aJZe7tUTlypXRuHFjuW3omvLz80PRokUtPqbS2bNn5fNevXoZTFu7dm1UrlxZbmtdkvRFevbsGf7++2+5PXDgQJP2T0pKwttvvw0AqFChAr788kuTy2CLOlXm+eabbyJ//vx683R3d0fHjh1NyrNq1aoICAgwWNY+ffoYzZOIiP5dzpw5gwULFgAAChQogJUrVxr8DsoLpsTnc+bMkUuut2rVCiEhITrT2dvb4/vvv5fba9aswePHjy0ua58+fWQfphACZ86c0Zu2WrVqMu60ltmzZ8vnH3/8MXx9fXWm8/X1xUcffaRzP6W///5bLunu4+OD999/X++xHRwcMHnyZLk9a9Ys+Vno8t5770EIASArRn/99df1ps1L1q5TwPptHlNpvU7//PNP+fzjjz9GoUKF9ObZrl072eaJiYnBypUrdaZbs2aNfN6hQwfUr19fb54lS5bEu+++K7dnzpypN62XlxcqVKig931b2rFjB6KiouS2qe0TIiIiJWUMMWDAANSsWVNnOjc3N3zzzTdye968eUhPTzf7uF26dEGnTp00pX3vvfdQvHhxub1161a9aW3Rh/b333/j7t27AICCBQviq6++0pvn+PHjUbBgQQDA7du3sWXLFp3pHj58iHXr1snt77//Hvb2uodztWnTBi1btgQAZGRkYN68eXqPb+2+3mrVquGDDz5QjZvQJygoCF27dpXb+s5dn/nz58txUt9//z18fHxM2t9WbNF//qLakdao00mTJuHhw4cAgM6dO2PUqFEm55HTq9KOnDZtmqbP3svLC5999pncNvQ/yhZtHsA2v50REb0oHND+ErKzs8PSpUtRoEAB+dr//vc/TfuGh4fjiy++QN26dVG0aFE4OzujSJEieP311zF+/Hjcv3/fpLJkZGRgzZo16N+/PypXrgwvLy84OTnB29sb9erVw5gxY7B7927Z8au0d+9e2NnZwc7ODs2aNTN4nMuXL+PTTz9FvXr1ULhwYTg7O6NgwYKoUKEC6tWrh5EjR2Ljxo2Ii4vTuf/AgQPlsZYsWWL0vBISEjBz5kyEhISgZMmSyJcvH7y8vFCjRg28++67OHr0qJbqkce0s7OTr125cgXvv/8+qlatCnd3d3h4eMDf3x9jx45FdHS0pnxfhDJlysiy37p1CwBw48YNjBs3DrVq1UKRIkVgb2+vd8BBYmIi5syZgw4dOsDPzw/58+dHgQIFULFiRQwePBihoaEml+n48eP44IMPEBAQgCJFisDZ2RnFihVD06ZN8d133+Hp06cWnLFl8uXLh6pVq8ptrX9Lu3fvxvDhw1G9enUUKlQILi4uKF68OEJCQjBr1iwkJSVpLkNaWhrmzp2LZs2awcfHB66urihXrhx69Oghg1FA92eb08v0+YeGhmLIkCGoWbMmPD094eTkhMKFC6Nq1apo1qwZxo0bh/379yMtLU1vHnfv3sXXX3+NJk2aoGjRonBxcUGBAgVQtmxZ1K1bF4MGDcLvv/+u929w4sSJsj4mTpxotMxpaWlYvHgxOnXqBD8/P7i6usLDwwOVK1fGkCFDVJ+HIbo+h3v37uGrr76Cv78/PD094ebmhipVqmD06NG4ffu2pnyJiOjVp+xIjI+Pz8OSvNzmzJkjn3/wwQeqNpS5Fi1aJDs733jjDQQGBlqU35EjR+RzX19fowN6PTw80LBhQ7mt/CHDEnl5TSUkJMjnnp6eRtN7eXnJ54YGIeWV1atXy2vEycnJ6I9hOY0fPx43btwAkHUNa/kBKCdb1OmrkicREf27zJ07Vz4fPHgwSpYsmYel0U9LLCWEUN30NmjQIIN5NmzYEBUrVgSQ1Q+/adMmi8vp7Oys+s59kXHf9evXVTdjGhtUq3z/3LlzMj5SUsbSLVq0gLOzs8E8mzZtCjc3NwDAgwcPcOjQIZ3pjhw5gtOnTwMAihQpguHDhxvMN6/Yok4B67d5TKXlOr19+7ZqkHbbtm2N5tuuXTv5XF87SnlNmZrn1q1bTerPf1GWLl0qnwcEBOC1117Lw9IQEdGrLCEhAbt375bbxuLZbt26yf7YJ0+evLAJ4RwcHFQ3I+r7PRywTd/Uxo0b5fMePXoYvCE3f/786N69u9zesGGDznR///23PF6lSpXQoEEDg+VUfjbK8rxslP3chj6nnO7fv49PP/0UANC4cWMMGTLE2kV7abyodqQ16jQ5OVkVe44fP97kPHJ6ldqRplBe+3FxcXjy5EmuNLZq8wC2+e2MiOhF4YD2l1ShQoVUX8S7d+/W+QWXLSUlBSNGjEDNmjUxdepUHD9+HI8ePUJaWhqio6Nx7NgxTJo0CRUqVMCsWbM0leHAgQOoVq0aevTogeXLl+Pq1at49uwZ0tPT8eTJExw9ehQzZ85Ey5YtMXbsWLPPdeLEiahZsyamTZuGo0ePIiYmBmlpaYiLi8ONGzdw9OhRzJkzB507d8bIkSPNPk62zZs3o2LFihgzZgx27NiByMhIpKSk4NmzZ7h48SJmz56NevXqoU+fPnj+/LlJec+dOxf+/v74+eefcfnyZSQmJiI+Ph7nzp3Dt99+iypVquDEiRMWn4Mt/Pbbb6hRowamTJmCM2fOIDo6WueNCkDWXYIVKlTAyJEjsXnzZty5cwdJSUlISEjA9evXsXjxYgQHB6NDhw6IjY01euynT5+iW7duqFu3Ln766SecPXsW0dHRSEtLw8OHD7F//358/vnnKFeuHNauXWvtU9fM1dVVPk9OTjaY9u7du2jevDlatmyJefPm4dKlS3j69ClSU1MRFRWFHTt2YPTo0ahQoQIOHDhg9Ni3b99GnTp1MGLECOzbtw+PHz9GcnIyIiIisGbNGrRu3RrDhg0zOOjbkLz4/BMSEvDmm28iODgYixYtwoULFxAbG4v09HTExMTg8uXL2LdvH6ZMmYKmTZuqGkdK8+bNQ+XKlTFx4kQcOHAAjx49QmpqKhISEnDr1i0cP34cS5YsQe/evTXf2W/I0aNHUbVqVQwePBh//fUX7ty5g+TkZMTHx+Pq1atYtGgRWrdujdatW5s8s9bGjRtRo0YNTJ48GefOnUNsbCyeP3+OK1euYNasWahatarJd68TEdGrKTw8XD738/PLw5K8vB49eqS6iax3795WyXfFihXyuXK2aHNlz1YCQPPqAMrPXPkjkiXy8ppSnvfFixcNpk1LS8OVK1fktr+/v83KZa5ly5bJ5+3atTNpNstTp05hxowZAIC+ffvK2ZRMZYs6NSVPALhw4YJJeV65csXoTF1a8iQion+PjIwM/P7773LbGrGXrWiJpa5du4Z79+7JbWOTvORMY84EITnFxMSo+qNeZNynLH+lSpVUs2bqUqJECTkQAwD27NmTK40yltZyLg4ODihRooTc1hdLK2P+t956y+gAh7xiizoFrN/mMZWW61T52etLk5Myjb4JUky9ppRpEhMTNU+G9KLExsbir7/+ktsDBgzIw9IQEdGrLiwsTN705ubmhqCgIIPpXVxcUK9ePbltjXhWK+WEgxkZGXrT2aIPTRljWSvmNzXP5s2by+d37tzB9evXje6TF7R+TjmNGjUKsbGxcHZ2xrx581T5/Nu8qHakNep08+bNchLKatWqGZ28R4tXqR1pipz1q+v6t1Wbx1a/nRERvSgv/xry/2FvvfUWfvnlFwBZd+UdOHAAb775Zq50iYmJCAkJUd0lVrZsWdSpUwdeXl54+vQpwsLCEBkZiaSkJIwePRpxcXH44osv9B579erV6N+/v+rLr1KlSggMDETBggURGxuLixcv4uLFi8jMzDQ6uFefn376CV9//bXcLly4MOrVqwdfX1/Y2dnhyZMnuHz5MsLDw00KcPX5448/0KdPH5mXg4MDGjVqhAoVKiAhIQEHDhyQM2+vWrUKERERCA0N1TRj3pIlSzBixAgAQOXKlVGnTh24urri8uXLOHToEIQQiImJQYcOHRAeHq7pDuAX5c8//5R3Y5YoUQINGjRAwYIFcf/+/Vw3UsyYMQMfffSRHOxcoEAB1K9fH6VKlUJGRgYuXbqE48ePQwiBzZs3o2nTpggLC9N7Z/KDBw/QokUL1Y9C2UvRFyhQAI8ePcLBgwcRHR2NZ8+eoXv37li+fHmedLQrZ2U3tPxSeHg4goOD5d2UdnZ2CAgIQPXq1ZE/f35ERkZi//79iI+Px/3799GqVSts27ZN1fBUevz4MVq0aIGbN2/K1ypXroygoCA4Ojri/PnzOHnyJObPn2/WnZV59fn369dPdbdxhQoVUKtWLRQqVAhpaWl4/Pgxzp8/b/Bu7Y0bN6pmUPLw8ED9+vVRsmRJODo6IjY2FlevXsWFCxeQmppqct3ktH//frRt21Z1s0vdunVRvXp1pKam4siRI/Iu4J07d6JRo0Y4ePCgpkFOu3fvxjvvvIOMjAyULl0a9evXh4eHByIiIrB3716kp6cjKSkJ3bt3x4ULF1C2bFmLz4eIiF5OFy9exMKFC+W2cllOQ5KSkvD333/j7NmzePr0Kdzd3VGsWDHUr18f/v7+epcozQvh4eG4dOkS7ty5g4yMDBQqVAhVqlRBkyZN4O3trSmPw4cPy1lrKleuDB8fH8TGxmLBggVYs2YNrl+/jqSkJBQpUgR169ZF165d0b17d4P1EBsbq5oNpHHjxsjMzMTq1auxbNkynDt3DjExMShUqBCqVauG9u3bY9iwYXB3d9ebp/ImQXM6ibUMLDZm6dKlcnldOzs7dO7c2eI8TdGxY0c5iHvGjBno27ev3vhoypQpslO8cuXKCA4O1nSM/fv34+jRo3j48KFc8ScwMBD16tVT3ZhqqWvXruHw4cNy29iMMUrp6ekYMmSIvOZ//PFHs8thizrt2LGj/N+zZcsWHDp0SDWTjNKePXvkUqkuLi56Z/QJCQmBi4sLUlJS8OTJE/zwww/4/PPPdaaNiopS1Yk1bmgnIqKX24ULF+SKnG5ubqhVqxZSUlKwZMkSrFq1CuHh4YiLi0PhwoUREBCAN998EwMGDHjhg4+1xufK/s1ixYrpXSZdSTkztnJ/cwgh8Mknn8i+71KlSqFu3boW5WkKZfm1zvgdGBiIa9eu5do/m61iaeVvKdlLth88eBDz5s3DwYMHERUVBXd3d5QpUwYtW7bEiBEjTL45wBptHlvUqS3aPKbQep3qm2xFq/T0dFy9ehXVq1fXm8bca0rLIKMXZc2aNfL3OUdHx5f6xiAiInr5KWOHmjVrwtHR+HCiwMBAOXDS0njWFOfPn5fPS5UqpTedtfvQYmNjVTMqa4nRlGkiIyMRFxcHDw8PVRpT4z5fX18ULVpUDogNDw9HhQoVjO73omn9nJTWrl0rZ53/7LPPULVqVauUxRrxuS28iHaktepUVzvq5s2bmDNnDrZs2YI7d+7AwcEBvr6+aNKkCQYMGKC3f1lX+V/2dqQplNe+q6srChcunCuNrdo8tvjtjIjohRIadejQQWtS+v+aNm0qAAgAYsKECSbvn5iYKBwdHWUeY8eO1Zmuf//+Mk358uXFzp07c6VJT08Xv/76q3BxcREAhIODgwgLC9OZ36lTp0S+fPlknrVq1RJHjhzRmTYqKkpMmzZNfPfdd7ne27Nnj8yjadOmud5PS0sT3t7eMs3UqVNFamqqzuPExMSIRYsW6TyOEEIMGDBA5rN48WKdaa5fvy7c3d1lujp16oirV6+q0mRkZIjp06cLe3t7mW706NE68xNCyDQAhIuLiyhSpIjYtm1brnT79u0THh4eMu3XX3+tN09Lab3u/Pz8ZDpHR0fh7OwsfvvtN5GZmalKl5ycLJ/v2rVL1o2Tk5OYPHmySEhIyJX36dOnRbVq1WT+I0aM0FmGjIwM0bx5c5kuMDBQHD9+PFe6pKQkMXHiRGFnZycACDc3N3Hz5k2NNaKf8vPbs2ePwbSXL19Wpf/ll190pktMTBRVq1aV6Vq2bCmuXLmSK11sbKwYPny4TOfr6yuePXumM89evXrJdPnz5xd//vlnrjQHDhwQxYsXF3Z2dsLZ2Vmmj4iI0JlnXn/+p0+flu+7u7uLrVu36iynEELcuHFDTJ48Wfz999+53vP395f5vPvuuyIxMVFnHvHx8WLNmjXis88+0/n+hAkTjP7dPHnyRJQoUUL1/1bX9bpixQrh6uoq0xn6/lR+Di4uLsLNzU0sX7481+dw4cIF1bEHDRqkN08i+u8IDAzM6yL8qyi/C3TFrrYWHx8vTp06JSZMmCAKFCggy9KoUSORlpamdz9lzG3oUbZsWTFnzpxc3zGmiIiIUOWpL87QRxmn6ns4ODiInj175orTdRk3bpzcr0uXLiIsLEyULl3aYP4BAQEG48idO3fKtIUKFRL3798XjRs3NphnkSJFxPbt2/XmuXDhQlXMp0VISIjqGI8fP9a0X7aMjAwRExMj9uzZIwYNGqRq3+iLh4xRtrkGDBhg0r6PHj0Svr6+cv9ixYqJn3/+WYSHh4vExEQRFRUlQkNDRYcOHWSa4sWLi7NnzxrMV8u17+HhIT799FMRGxtr1nnnpLzuvL299bZhdZkyZYrcd+HChar3TP0fZIs6zczMFK1atZLp8+XLJz788ENx8uRJ8fTpU/H06VNx8uRJ8d5778m+BWdnZ7Fy5UqDZZ08ebLqM+nUqZPYu3eviIqKEomJieLSpUvixx9/FD4+PqrYnojoVcP43HTz589X9QtevnxZ1KhRw2hce/LkSZuXzZz4/LvvvpPpXn/9dU3H2bJli6rfz1RpaWkiMjJSrFu3ThVvOzo66uxL00LZX6Wvr12Xtm3bmhxzfvLJJ3Kfdu3a5Xq/X79+8v2ePXsazS89PV24ubnJfapXr54rTVJSkup3l1OnTqn6aXU9nJ2dxbfffmv0+NZu89iiTm3R5jHGnOv0xo0bqjJcvnzZ6HGmTp2q2kdXP3qpUqXk+3PnzjWa5+HDh1V5jho1yvgJKyxevFju6+fnZ9K+WjRs2FDm37FjR6vnT0T0KmN8broRI0bI75UePXpo2mf27Nlyn2rVqtm4hFkOHTqk+n7W9XttNmv3oR09elR17OfPnxstb2JiomqfY8eO5Uqj/G1Z13gTXYKCguQ+33//vaZ9slnS16tVQkKC8PLykseZNm2a0X2ePn0qihUrJgCIihUriqSkJNX7yno0Nr5DCOvH54aYW6e2bkdas06VsefMmTPF/PnzVdeurkfPnj0N/p28Ku1IUyn72du3b68zja3aPLb47YyIyBq0jj/ngHYbsnRAuxBClClTRuYxePDgXO/v379fvl+yZEkRFRVlML9FixbJ9G3atNGZRhmE1KlTR8THx5tVdmMD2s+fPy/fb9iwoVnHyKZlQLty4H+5cuXE06dP9eb3448/yrT29vZ6v7iVX/AuLi4GBwfMmjVLpq1SpYopp2cScwa0AxArVqwwmG9GRoaoWLGi5vRRUVFyQIKTk5O4e/durjTLli1TBUjGrrXx48fL9MOHDzeYVgutwXlqaqpo3bq1TFugQAG9g4q++eYb1d+YoQFoQqivS10/ily4cEFVznXr1unN68yZM6ofYwBtA9rz4vP/5ZdfZF7jxo0zmJc+8fHxMo9SpUpZNEBPy4B25fXn6ekpbt++rTe/9evXq+p33759OtMpPwc7OzuDHRSbN2+Wad3d3Y1eW0T078cOeet60QPaly9fbrDzyM7OTvTr1y9X52JOWge0Zz/atm1rdnz/Iga0K+Ot9evXG8xPGUcFBwcLT09PuR0YGCgGDhwo+vXrJ6pUqaLKu2jRojpjUyHU7aWyZcuKWrVqye3SpUuL3r17i8GDB4sGDRqoBok7OjrqvUHv7NmzquOfPn3a4HnFxsaqbsQFoPMGyZyGDBlisE7z5csnpk+fbjQffSz9kePWrVuiZs2aRj/7/Pnzi379+on79+8bzdOUa79ixYoiPDzcjDP/P5mZmaqOX0M3P+d09epVeeN406ZNc8Wu5vwPskWdJiUliR49ehjN097eXrRs2VIcPnxYU1mnTZum+pvR96hRo4ZYunSppjyJiF42jM9Np+xrqVOnjup7tkqVKqJfv35i4MCBIjAwUPV94ebmJk6dOmXVslgjPv/0009leq0DS0+ePKk6jnJyCX3Kly9vsKzFihUToaGhms89J3MHtNetW1fu9+OPP2raZ/r06XKfevXq5Xr/559/lu/7+PiIlJQUg/nt3r1bVRe6bii9efOmKk2XLl3k84IFC4pOnTqJt99+W7Rr1y7X4IwvvvjC4PGt3eaxRZ3aos2ji6XXaWZmpmpCpBkzZhg9Zs6B+fPmzcuVpnPnzvL9N99802ieX331lSrPXr16Gd1HyZYD2q9fv64qm6HfDoiI/osYn5uue/fu8nvlvffe07TPunXrVN/vtpaRkaGKkbQMALZmH9rWrVtleg8PD83lVt4k+88//6jee/78uaocWts6ykH4n3/+ueayCPFiBrR/9NFH8hju7u7i4cOHRvcZPHiw3Gf37t253lfWk7UGtJsSnxtibp3auh1pzTpVttmV7SgnJycRHBwshg4dKt566y3VxCUARJMmTfSObXhV2pGmUI7rAKC3HWWrNo8tfjsjIrIGrePPuV7ES65gwYLyefbyRkrK5binTp2KYsWKGcxv4MCBqFKlCgBg+/btiI6OVr1/9OhRuUyMnZ0dli5darWlJHPKXk4WgN5lnazl2bNn+OOPP+T2tGnT4OnpqTf9mDFj5LIsmZmZ+O2334weY9iwYXjttdf0vt+/f3+5LNeVK1dU55/X6tata3Qpyk2bNslle4KDg42mL1asGD744AMAQFpaGtasWZMrjfL6/eWXX4xea2PHjpWf2++//y6XybGFzMxMPHr0COvXr0fDhg2xY8cOAFnLdi5evFjnkkBpaWmYNWsWAMDe3h5z5841uhTb1KlT5TJHK1euzPX+4sWL5fMmTZqgS5cuevPy9/fHoEGDjJ9cDnnx+Vvj71+Zh7e3t1nLRWklhFD9H/jqq69QunRpvek7d+6Mtm3byu05c+YYPcYbb7yBNm3a6H2/Xbt28n98QkICLl++rKXoRET0CipRogS2b9+OZcuWIV++fEbTFy1aFGPGjMGWLVtw+/ZtJCUlISkpCdeuXcNvv/2GatWqybTbtm1Dz549bRpH6WNvb4/g4GDMmjULJ0+exLNnz5CWloaYmBiEhobinXfegbOzMwAgPj4ePXv2xIEDB/Tm9+zZM/l89+7dePbsGQoXLozQ0FCcPHkSixcvxrJlyxAeHo4//vgDrq6uAICHDx+ib9++RvOMiIjA6dOn4eDggJkzZyIiIgIrV67EwoULcejQIZw8eRLly5cHkLW0Y79+/XK1r4Cs5XnLli0rt8eNG2ewniZPnoyEhATVa/Hx8Qb3MaZ+/fq4cOECPvzwQ4vysYSfnx9OnTqFlStX6oylszVu3Bi9e/fWtLRqvnz50Lt3b6xcuRKXL19GfHw8UlNTERkZifXr16tiq2vXrqFNmzZ49OiR2eewd+9e3LlzR24PHDhQ035CCLz99ttITk6Gs7Mz5s6da5XY1VZ1unr1aoSFhcHf319vuipVqqB79+6al4H9+OOPcfPmTbz11lt60xQqVAjdunVDu3btNOVJRESvPmXsdeLECdy5cweurq74448/EB4ejmXLlmHx4sU4efIkQkND5fddYmIievTogbS0tBdSTq3xuTKGy449jcmZLmccaKrBgwfj2rVraN68uUX5mMPS89d17u3bt5fLnj969Ag//fST3rwyMjLw5Zdfql7TFUcrrzsAWL9+PYCs2O7u3bvYsGEDfvvtN2zZsgW3bt1S9fFNnToVe/bs0VsGa7d5bFGntmjzmErLdWpnZ4c33nhDbv/www948uSJ3vRbt27NVZe6Pv+OHTvK55s2bcLhw4f15hkZGSn7+w3lmVeWLVsmn3t7e6vqi4iIyBy2iD2sbdKkSTh27BiArNjrhx9+MLqPNfvQzKmjnGlz1lPO7Ze17k2xe/duVew+duxY+Pj4GNwnNDQUixYtApA1tqZFixYWl8Pa8bkt2LIdae06VbYlsttRQUFBuHLlCnbt2oX58+djzZo1uHv3LsaOHSvT7t+/H5MnT9aZ56vSjtQqKioKb7/9ttxu1aqVqk2pZKs2jy1+OyMieqGsPUKe/o81ZmhXzpbesmVL1XtpaWly2RNHR0dNyxkJoV4q6q+//lK9N3HiRL3HM5WxGdrv3r2runv10qVLZh/L2AztyiV3vL29Nc1srLyrT9+dvVDcrbZ//36jeVauXFmmP3/+vNH05jBnhnYtdzoOGzZMpp8/f76msiiX3OrcubPqvfv378v3SpQooSk/IdRLDhmaEV8L5een5VGlShWxefNmvfmFhYXJtKasOlC1alUBZM30lHPlgNq1a8s8tSyBunfvXlWZtczQnhefv3LWq1q1aomEhARNeSqlpqbKWZIcHBz0zoKuhbEZ2i9evCjft7e3F9HR0Ubz1DIjgfJzWLZsmdE8Q0JCZPpNmzYZTU9E/26cYca6XvQM7ceOHROjRo0So0aNEiNGjBC9e/cWAQEBws7OTsYFXbp0MboCU3x8vNHZJVJTU8Xbb7+tihG0fO/kZOkM7TExMUbTHDt2TBQuXFgeo1KlSnpj9+DgYFV5HBwcDM4W/ccffxidbWTSpEm5YkBDy7XeuHFDtRSlvvh7/vz5qjz79++fK+5LSUlRtceUjwMHDugtQ7aVK1fKa+qdd94RXbt2Va345erqKj7//HOj14s+ls7aEx8fLz744AORP39+AUB4eXmJjh07imHDhok+ffrImDj70bFjR6MxopZrauHChaqZJXWtfKaVsg5q1Kiheb958+YZvUbM+R9kizrNzMwUv/76qyhevLgAslYia968uRgyZIgYOHCgaNCggXBwcJB5Vq1aVdMKArt371bNrhsYGCj69+8v3n77bdGmTRvVqgQFCxZkrEtEryTG56bTtcLMmjVr9KYPCwtTfa+bMnu4MdaIz5Uz3/Xr10/TcXMuMa5lNrSJEyfKsg4ZMkS0bdtWFCpUSObh6+tr0Yon5s7QXq5cObnfwoULNe2zcOFCuU/58uV1punTp48q5v/xxx9Fenq6Ks2jR49Ep06dcl1PDg4OufI7cOBArnTt27fXu/pjSkqK8Pf31xSrWbvNY4s6tVWbJydrXKeXLl0STk5Ocp/atWuLq1ev5kq3fv161ayn2Y9JkyblSpuamirKli0r0xQuXFhs3749V7pz587lmrEQyJrh0BS2mqE9MzNTdR7vvvuu1fImIvq3YHxuuhYtWsjvlq+++krTPsqZjXXFXtb0999/y/gcgPjss8807WfNPjTlCvSlSpXSXPZSpUrJ/ZYvX656786dO6rj37hxQ1Oe/fr1k/sMGTJEc1mEsO0M7bdu3VLFuw0aNMgVv+f0/PlzucKPt7e3ePz4sc50ynrSMkO7teNzQ8ytU1u1I21Rp8p+YSBrnM+TJ0/0pleOTfPw8BCxsbG50rwq7UgtUlJSRIMGDWQ+hQoVEvfu3TO4jy3aPLb47YyIyBq0jj83PHUw5Tnl3VQeHh6q986dO4fExEQAWbOoffLJJ5ryPH78uHx+9+5d1XtHjhyRz209i0vJkiXRoEEDhIWFIS4uDnXq1EGfPn3QuXNnNGrUCAUKFLDasU6fPi2fv/7660ZnzQaAhg0bqvYXQhicRa9mzZpG8/T29pbPY2NjjaZ/UWrXrm00jXKmki1btuDMmTNG91GeY85rTZmfEALvvvuuhpICN27cUOVpaFZ8aypatCiWLl2KunXr6k2jPKfo6GjN55R9h6QQApGRkXIW+szMTJw7d06mM3TsbLVr14adnR2EEJqOnb2PMdb+/Nu1awd3d3ckJCTg9OnTqFy5MgYNGoT27dujdu3acHJyMpq/k5MTOnfujFWrViEjIwMtW7ZEt27d0K1bNzRt2lT192Yp5f+QKlWqaMpb+T/kwYMHuH//PooXL643/av8P4SIiEwXFBSEoKCgXK9fu3YN77//PrZu3Yr169fj5MmTOHjwIEqWLKkzHy2rKTk5OWHu3Lm4evUq9u3bBwD47rvv0K9fP8tOwkSFChUymiYoKAirV69Gy5YtAQBXr17Fxo0b0a1bt1xpc86O2aVLF9SrV09v3t27d8e0adNw4sQJAMDq1avRrFkzg3n6+vrKVWd0KVeuHEaMGCFnAlq9ejUmTpyYK93QoUOxY8cO/PnnnwCyZtLbsGEDmjVrBl9fXzx9+hT79+/Hw4cPAQAdOnTApk2b5P5a2ka9e/dG7969c72+Z88ejBgxAleuXMG3336L06dPY/PmzZraRNby+PFjNG/eHBcvXoS9vT2+/vprfPrpp7nqe/fu3RgwYAAiIyPx999/o3v37tiyZYvefLVcU4MHD0ZERIScAWbp0qWYMmUKihYtatI5JCYmYt26dXJ7wIABmva7f/8+Pv30UwBApUqVVDPTWMIWdZqZmYm+ffvi999/BwB07doVs2bNyrUS3I0bNzBgwAAcOnQI4eHhCA4OxpkzZ/TGyLNnz8bo0aMhhECNGjWwfPlyBAQEqNI8e/YMY8eOxdy5cxEbG4suXbogNDQUjRo1MrOGiIjoVZDzeysoKMjgah7169dHly5dsHbtWgBZsZfWFVOMsUZ8rjyf1NRUTcdNSUlRbWuZkW7ChAm5XktNTcXy5cvx0UcfISoqCgMGDMDt27fx1VdfaSqHNVh6/vrOfdasWTh69CiuX7+OjIwMfPjhh5g+fToaNWqEggUL4u7du9i3bx+eP38OQB1L64qjdc2y//333+vt+3d2dsb//vc/OXPevn378ODBA52r5dqyzWOtOrVVmycna1ynVatWxYwZM2Q/+8mTJ1GtWjU0aNAAlSpVQkpKCo4fPy5XsqxYsSLs7e1x5coVALo/fycnJ6xZswaNGzdGcnIyoqOjERISgpo1ayIwMBCOjo4IDw/HkSNHkJmZCVdXVzRo0AC7d+/Wm2de2L9/PyIiIuS21vYJERGRIbaK56zh+PHj6NWrl/wNPDg4GJMmTTK6n7X70MypI8BwPeUsy8tW96Z48uQJ2rZtK1f1KVGiBFavXg0HBweD+40fP16OA/nhhx8MzqRvCmvH57Zgq3akLeo0X758cowaAHzxxRfw8vLSm37SpElYtGgRUlJSEBcXh61bt6Jnz5658sz2MrcjjRFCYMCAAQgLCwOQ1e5YtWoVSpQoYXA/W7R5bPHbGRHRC2XtEfL0f6wxQ7tyNpShQ4eq3tu0aVOuu69MfeS8W6tWrVryvd9//93cUxdCGJ+hXQghLl++LHx9fXXe8RYQECDee+89sW3bNqN3QBqboX3MmDHy/REjRmgq/4MHD1Rl0nW3oPJ9fTO4KCmvCVvd1WbODO1aZsf39va26FrLeWfkL7/8YvH1m/PuZVMp8+rUqZOcMWbUqFGiZ8+eIigoSJXG2dnZ4EyBH330kcXnpJx98+nTp6r3Hj16pOm8PD095T5aZmjPi89fCCH+/PNP4ezsnCutq6uraNq0qRg/frw4ceKEwXI9fPhQ50w5dnZ2omrVqmLYsGFi7dq1IikpyWA+xmZonzFjhny/bdu2RusrW758+eR+ulYUUH4ON2/eNJqfsf91RPTfwhlmrOtFz9BuSEZGhmpWiNatW1slX+VsOQDEnTt3TNrf0hnaTdG4cWN5HH0zavfo0UNVnlWrVhnN93//+59MX7NmzVzvz5kzR5XnsGHDjOZ56NAh1T76VnJJTU0Vo0ePVs0ipOvRq1cvER4ernrt1q1bRsthSHR0tGr2vilTppichyWz9rRp00bu+/XXXxtMe+nSJVUM9ccff5hc1pzi4+Plyj6AeSsULF26VO7v4OBgdPWEbMq/ZUNtQFP/B9miTqdOnSrTtGzZUmRkZOjNMyEhQTV7lb529qFDh+RMusWKFRMPHz40WNb+/fvLPKtVq6apnU1E9LJgfG66zz77TBXzaIlRVq5cKdMXKFDgBZRSe3z+6aefyjQdO3bUlPfJkydVdZCcnGxRWY8ePSpnd7OzsxMHDx40OQ9zZ2ivW7eu3E/LipBCqFdJrVevnt50Dx8+FM2bNzcYRzs5OYlp06apZsQuU6ZMrrxyxtqVKlUyWs60tDRVPLlu3TpN52eIljaPLerUlm0erUy9ThctWqSaJV7XIzAwUNy8eVN1/S5ZskRvnseOHVOl1fXw8fERO3bsULWFBg4caNK52mqG9kGDBsl8q1evbrV8iYj+TRifm6579+7y++W9997TtI+WFastdfHiRdVv1UFBQSI+Pl7TvtbuQ9u6dat838PDQ/M5KGdW/ueff1TvPX/+XBWDnDp1SlOeHTp0kPt8/vnnmssihG1maI+Pj1fFr97e3uLixYtG9ztx4oSc+bt58+YG0yrrydrjbbTE54aYW6e2aEfaqk6LFi2qSn///n2jZW3VqpVMP3r06FzvvyrtSGNGjhwp97e3t9f0e5WSNds8tvjtjIjIGrSOP7cHvbQSExNx7949uZ1zxg9rzM6bnp6u2lbOCK9ltkdLVa5cGWfPnsUHH3ygujsyIyMDZ86cwcyZM9G2bVv4+flh/vz5Zh8nISFBPndzc9O0T850yrrRxdDs7S87LXfsWnq95bzWbHH9WmLMmDGYNWuWfPz+++84duwYzp49C39/fwBZd4T26tVLNUu8krXPSXndAkD+/Pk15aH1Gs+WF58/AHTr1g0nTpzAW2+9BWdnZ/l6UlIS9u3bh2+++QZ16tRBnTp1sH//fp35+vj44Pjx45g4caJq9nMhBMLDw/Hbb7+hW7duKF68OL799ltkZGSYVX5z/ofkTPtv/h9CRETWZW9vj59//ll+N+zYsQMXLlywON/GjRurVkEJDw+3OE9bCQ4Ols/1lTPnbNDVqlUzmq8yTWRkpNXzBLJm5NbFyckJM2fOxIULFzBmzBj4+/vD09MTzs7OKF26NLp3747t27dj1apVePDggdzP0dHR5NnEc/L29sY333wjt2fMmIHMzEyL8tTq6NGj+OeffwAAnp6ecrZyfapWrapaPWDx4sUWl8Hd3R2vv/663Dbn2l+2bJl8HhISonNGzpz++usvbNy4EQAwcOBAq81qYos6TU5OxnfffSe3J02aBHt7/V1Gbm5u+PLLL+X28uXLdcb8kyZNktfamDFj4OPjY7CsU6ZMkf/7Ll26hGPHjhlMT0RErzZLY6/4+Hij/S3WoDU+V55P9so7xijjvvz588PFxcWistatWxf9+/cHkNU/NmPGDIvyM4Wl529o9kQfHx+Ehobin3/+Qf/+/VGhQgW4u7vD1dUVlSpVwrvvvoszZ87g448/xq1bt+R+umbSN+e6c3R0RKVKleS2rraEqUxt81irTm3Z5tHK1Ot00KBBuHXrFiZPnowmTZqgSJEicHJyQtGiRdGiRQssWLAAR44cQYkSJRAVFSX307fSGZA1E+fVq1exYMECdOjQASVKlICLiws8PT0RGBiIyZMn4+LFi2jVqpXRa+pFe/78uVypAuDs7EREZD22jOfMFRERgVatWiEmJgZAVlyybds2TWNZbNGHpqyjuLg4JCcnGy3H8+fPVe2WnPXk6uqq+r3+Zal7UyQnJ6Njx46yL69AgQLYtm2bplhz6NChyMjIgIuLC+bOnWvrouqlJT63BVu0I21Vp8qyenp6wtfX1+g+pvwm8zK3Iw354osv8Ouvv8rt2bNno1evXiblYc02jy1+OyMiepFe3BrjZLITJ06oBl/mXAJEOVAyICAAp0+ftviYyuVIcg6mtZUiRYrgxx9/xHfffYejR49i//79OHjwIA4dOoS4uDgAWR2kw4YNw/nz5zFz5kyTj6Fs0CiXwDEkZ7qXZSnJvOLm5iYHNZ85c0YO8rYkv2ydOnXChg0bLMrPVl577TXs2LEDgYGBiIyMREJCAoYOHYo9e/bkSqs8p/fff9/iH4xyDpx+/vy5psHUWq9xU8tizc8/W82aNbFmzRrExcXhwIED2L9/Pw4cOIATJ04gLS0NQNaySs2bN8fq1at1Ljvt7u6OCRMm4KuvvsKpU6dkHgcPHpTLmT19+hRjx47FkSNHsGHDBpMHj5vzPyRn2v/6/xAiIjJN6dKlUblyZbmM4KFDh1CjRg2L8nRyckLhwoVlZ1f2jwAvI2VHqL5yVqlSRbWt5UcMZRpdg58szVNfvkrVqlXDTz/9ZDDN8ePH5fOaNWvmWiLSHK1atZLPHz9+jGvXrqFy5coW52tM9o9GQFabVsu5tGjRQt7QnL3MpaW0XFP63L17VxX/ax0womyjHzt2zOCynsqb2U+dOqVK++uvvyIwMFBu26JOjx49imfPngHI+hGkbt26mvLMlpCQgCtXrqB69erytdTUVFW9NW/e3GieJUqUQKVKleSSqSdOnFDdjEBERP8u1oq9XkSfi5b4XBlb3b59W1O+d+7ckc9z1oe5WrVqhYULFwLIKueLUrlyZWzbtg2A7c4/JCQEISEhBtMoY+mgoKBc7xcpUgTe3t4yJtQ6sY+xtoSptMSntqjTF9Hm0cLU67Rw4cIYN24cxo0bpzfN8ePHkZqaCiBr8pA6deoYzNPZ2RlDhgzBkCFD9KbJzMzEqVOn5Laua+pF27Bhg/wMHBwc0Ldv3zwuERER/Vu8LPFstsjISAQHB8ub6cqXL4+dO3fmGqypjy360HL2p96+fdtoH6uyjnTlkf3amTNnZJ5a2LLuTZGWloauXbvKfkBXV1ds2rRJc9yUfd4uLi7ypkctRo4cCQ8PDwBAYGCgakCxOSzpP7aELf7ubFWnVapUwaVLlwBYrx31qrQj9fnf//6HqVOnyu1vv/0Ww4cP17y/krXaPLb47YyI6EXigPaX2Jo1a+Rze3t7NGzYUPW+cpa+mzdvIjMz0+AMaloo84yIiLAoL1M5OTmhUaNGaNSoEYCswDc0NBRTp07Fvn37AAC//PIL+vXrZ3KnYZEiReTznA0GfZTBkrOz839+MGrRokXlgOZr165ZPKBZea1du3bNorxszcfHB7/88gu6dOkCANi7dy+2bNmC9u3bq9JZ+5wKFiwIJycnObD73r17qmtZl4SEBKvMFJ+TtT//nDw8PNC+fXtZp3FxcdiwYQMmTpyIW7duITMzEyNHjsQbb7yhd0Z5e3t7OaP7hx9+iMzMTBw+fBg//PCDnBnzr7/+wrp169CtWzeTymfO/5BHjx6p7sovXLiwScckIiLy8vKSz63Vgaq82crUVV1eJC3lzDmASEsnmzJNwYIFc71fuXJlODo6ypmmTc1TX76mUg4CbtCggcX5AerrCXhxnfLK2Ty0/tikTGet2NaSa3/58uVylnFPT0+8+eabJh8/u6Nfi/j4eBw9elRuZ9/onc0WdarM08vLS1PfQs5j58w3OjoaKSkpVi8rERH9e1gazwHWib20MhafV61aVT5/8OABHjx4YHRVF+VAWeX+lrBFO0ILZfm1Tr5j7fNPSUlBWFiY3NYXS1evXl2uCKn1x3pjbQlTaYlPbVGnL0ubxxbXqbIdVa1aNauU88SJE/L87e3tDd6k+qIsXbpUPm/durWmmTGJiIi0UMYO58+fR3p6OhwdDQ8pskU8C2T9zhocHCzHrJQsWRK7du1SrdhtjC360AoWLAhfX185aczp06eNDmhX1lGJEiXkgGGlqlWrykHIWuK+qKgo1WzW1qx7U2RkZKB3797YunUrgKwxP2vXrkXTpk1NzisuLk7VJ2qMchZ1a0wIk1e/ndiyHWntOq1RowbWr18PwHrtqFepHZnTTz/9pFrF9Msvv8Rnn31mcXkM0dLmscVvZ0REL5Jlo5/JZqKjo3Mtae7p6alKExAQIJeOiYuLw+HDhy0+rrIzLjQ01OL8LOHk5ISQkBDs2LEDNWvWlK9v2rTJ5Lxq1aolnx87dkw1870+yllBatWqZfKMzv82ylnxtm/fbnF+ymvt4sWLqhkJX0adO3dW3VSiDEyzKeto3759qsEb5rC3t1dd+9lLdBly8uRJCCEsOq4u1v78jfHw8MCAAQMQGhoq/89FR0eb9H8u+0ag9evXq+64/fvvv00uj/J/yOXLl/HkyROj+yj/hxQrVsykDhYiIiIAqmUDrbFkaEREhGpg7Mv8o7ey41JfOevVq6fqWNYyYFiZplSpUrned3Z2RuPGjc3O087ODiVKlDC6jyEPHjzAjh075LZymVtLKK8n4MUtQ6u8GVFLDAWoB7TkbAebK/sHIcD0a3/58uXyec+ePXMt4fqi2aJOlXk+ffpUU5si58CjnPnmvBE1Lz9/IiJ6OZUtWxblypWT26bGXoUKFXqhAw2MxecVK1ZULfe9d+9eo3lmT+QCqFc/sYS12xFaKVdjuXLlSq74M6f79++rJgXRspqLMZs2bZKrznh5eeGNN97QmS44OFg+13Ldpaenq8qqqy1hKi1tHlvU6cvQ5gFsc50qf1OzVjtKmWfr1q3h4+NjlXzNFRkZid27d8ttratHERERadGgQQPZ75WYmGh05cSUlBQcOXJEblsrno2JiUHLli3lCn4+Pj7YtWsXypQpY1I+tuqXVMZY1or5Tc1TmaZUqVKoUKGC0X2sLTMzEwMHDsTatWsBZK0cs2rVKrRr1+6Fl8UatMTntvCytCO1ULajYmNj5eoJhhj7TeZVakcq/fbbb/jggw/k9pgxYzBp0iSLy2KMljaPLX47IyJ6kTig/SUkhMCAAQOQkJAgX9O1pIirq6sqOJkxY4bFx27btq18vnv3btUdeHnF2dkZrVq1ktvKO021Uja+Hj9+jC1bthhML4RQzXLxIoPAl5UyaFu1ahUePXpkUX5ly5ZV3S35008/WZTfizBx4kT5/MyZM7lurmjYsKFs2CYkJMjlyCzRrFkz+XzVqlVG069cudLiY+pi7c9fq7Jly6J69epy25y/fzs7O9Vs+ubkUbVqVXkndEZGBlasWGF0n8WLF8vn1mhIERHRf8vFixdx69YtuW2NWSYWLVokn3t4eKhu2HqZPHnyRHUDmr7ZXFxdXdGmTRu5vWHDBqN5K9Poyzd7VR4A2Lx5s5y5UEue/v7+Fg/AnTBhgrwBNyAgQHVjoSU2b94sn7u6usLPz88q+RpTunRp+fzw4cOqFWz0Ud5cbY0fY3bv3q1aZceUGYKOHj2Ky5cvy21TBoxMnDgRQghNjwkTJqjKp3xP2SYAbFOnyjyfP3+uaeYeZZ5OTk6qPICsH/2UK51puWn+3r17uHr1qsGyEhH9P/buPL6G6/8f+Cu7RPYgJIg1CGoNQdQShLSUWlpF7G3pQpeP7qi2WktLqdLWXkVpS2upJU1sSezEFsQSSyQii2xkP78/fDO/meQuc5N7c2lfz8fjPh53ec+ZM3Pn3jnnzJlz6N9l0KBB0nNDy3NPP/20SfKkiZryuYWFBQYMGCC9Xr16tc40o6OjpfOepaUl+vfvb5S8yst9lTlSY+PGjeHn5ye9lrevayL/vGXLlmjYsGGF1l9YWKhovx09erTWkRrlZf64uDicP39eZ9q7du3Cw4cPATz6ruQdwstDbZ3HVPvU3HWekvWWMMZxumHDBul7tLOzw9ixYyuc5vXr17FixQrp9auvvlrhNCtq3bp1FZ49ioiISBtHR0dFh1V95dk//vhDGtXXzc3NKOXzzMxM9O3bF2fPnpXS3bt3r95R0DUxVbvkwIEDpee//vqrVE7U5OHDh9i0aZPGZeUGDBggzZh46dIlxY0Cmsi/G21pmtqkSZOka+YWFhZYuXKlwbOkA1Ddflp6AI6IiAjpfTWdwHVRWz43BVPUI021TwMDAxU3eOqrw6empuLgwYPSa0379UmqR5ZYt24dJk2aJL0eP368Ufrr6aO2zmOqa2dERJVGqNS/f3+1ofR/unXrJgAIAGLGjBmqlsnKyhLDhg2TlgMgRowYoTV+3759ithVq1apzl9iYqLG97t06SKl1759e5GVlaU6TbmIiAgpnW7dupX5PC0tTRQVFalKa8iQIVJaH330UZnPR48erXcfhIaGSjG+vr4iMzNT6/oWL14sxVpaWopr165pjJPvezXkx0RERISqZQyl9rjz8fGR4q5fv6433cLCQtGoUSNpmd69e4v8/HxVecrKyhLZ2dll3l+9erWUnrW1tdi7d6+q9ITQfvwaQv79qf0+OnfuLC3j7+9f5vOZM2dKnzs5OYkzZ86ozk9SUlKZ986ePavI5x9//KF1+dOnTwtra2tFvLbv1tzf/71791QtW1BQIGrVqiWtV36MZGZmiry8PFXpvPvuuzr/U2fMmKH3dzN9+nQpxsPDQ9y+fVvr+rZv3674Hvbv368xztDvQc1/HRH9d7Rt29bcWfhXkZ8LNJVdKyolJUV1bG5urnj66ael/NSoUUMUFhaWicvJyVFdno6OjhZVqlSR0pw4caLq/JS4fv26qnKGJmrrFEVFRWLw4MHSOmxsbHSu58CBA4pye3R0tNbYX3/9VZH/U6dOaYxLT08X7u7uUtzcuXO1pnn16lVRtWpVKXbhwoWqtlObTZs2CUtLSwFAWFhYiAMHDmiNNeSYio+PFzVq1JDyOXjwYIPzJi+HjB49WvVyp0+fVuz3WbNm6Yw/f/684lidOXOmxji1x1RKSoqiHNukSRPVvxshhJg0aZJiWVMx5D/IFPu0qKhIeHh4KMr7uvZTdna2aNasmRTfvXt3jXEDBw6UYmrWrCmSk5N15nXUqFFSvIODg8jJydEZT0T0OGH5vHyuXLkibGxspP//TZs2aY2NioqSykoAxNatW8u9XlOUz4UQ4syZM4o87tmzR2NcUVGRCAwMlOJefPFFjXEFBQXi/v37qvO6Y8cOYWFhIaW7ePFi1cuWkLdXGdr+9N133ynazzS1dwrxqG1XXuZetmyZwfks7Y033lB8R6mpqTrje/bsKcU/88wzori4WGNcXl6eaNWqlRQ7cOBAjXGmqvOYYp8au85jjuNULjY2VlSvXl1K77PPPqtQekI8Ku8GBARIafbq1atc6axatUpKw8fHp8L58vPzk9J75ZVXKpweEdG/Gcvn5SO/xmlrayvOnTunMS4nJ0fR5vb+++9XeN05OTmKMrKTk5M4cuRIudMzVbtkbm6uqF27thSnqf9KiQ8++EBRFtB1bVveJ6Zr165ay6e7d++W4qysrMT58+d1bpcm5W3rLfHWW28p9u33339vcBrlIV+nvv4dpiqfa1ORfWrseqQhDNmnQggxa9YsKd7b21ukpaVpjZW3rdesWVM8fPhQY9yTVI/8/fffhZWVlbTM8OHDDbreUF6G1nlMce2MiKii1PY/Z4d2EzKkQ3tiYqKYN2+eqFOnjuJE0bFjR60n9RLygpGFhYV49913tTbI5+bmiq1bt4qBAwdq7IwrhBAnTpwQdnZ2Uppt2rTRWlEoybemBk99HdpXrVolGjRoIObOnau1w/jDhw/FggULFI2bkZGROveBtkb2q1evCkdHRymuU6dO4urVq4qYoqIisWjRIkUB5I033tCYnhD/rQ7tQgixd+9exb4JCAgQR48e1RofExMj3n//feHm5ibOnj1b5vPCwkLFhQNbW1vx1Vdfaez8LsSjSsf69etFjx49xJAhQ1TlWRdDC+dCCLFr1y7Fcrt37y6Tx+bNm0ufOzs7ix9//FFr5TQlJUX89NNPom3btuKdd97RGCO/yaVq1api8+bNZWIOHTokvLy8hIWFhbC1tdX73Zr7+x8zZowIDAwUq1ev1lrJSU5OVnRocXZ2Fg8ePJA+j4iIEDVr1hTTp0/X2phSWFgo1q1bp/hP++WXX8rEqenQnpaWJry9vaW4Jk2aaCzMb9y4UXGRR9f5kx3aiagi2CBvXOXt0F66k7e2MsXUqVNFr169xG+//aazfH/o0CHh7++vSHP58uUaYyMiIkTTpk3F0qVLtXYSzc3NFUuWLFGcm5ydncWtW7dUb6O2bTWkUbd3795i/Pjx4uDBg1ob4s+fPy969eqlWMfbb7+tN+3nnntOiq9evbrG72DTpk3C3t5einvhhRd0prlgwQLFRYHFixeXyffp06dFw4YNpbj69evr7Hw7cuRIERERobFxMysrS0yfPl3RmWvq1Kk689i+fXsxbtw4ceDAAa37ND8/X6xfv15xg6CNjY24cOGCzrQ1qUiDfFBQkKLxdNasWRp/B2FhYYrylqOjo9Zj28PDQ0yfPl1cunRJ63p37dol6tevrzim/vzzT9X5zsvLUzSQz549W/WyhjL0P8gU+1R+MQJ4dOODposHcXFxihvhAYidO3dqTFPecA5AtGjRQmMZOj09XbzyyiuK2GnTpundD0REjxOWz8tvypQp0v+/g4ODxk7t4eHholq1aop2KU1lIHOWz0vI27M8PDzK5CE7O1uMHDlS0SYaFxenMa309HTh5uYmPvnkE3Hx4kWt60xLSxOfffaZog2sUaNGIjc3V2deNalIh/b8/HxFGblNmzbixo0bipj4+HjRunVrKcbX11cUFBRoTTMmJka89dZbWjvq3Lx5U9H5x8LCQuegICWOHz+uaOscO3ZsmU4vd+/eFf369VN8V6dPn9aYnqnqPKbYp0IYt85jyuP0448/Fhs2bNC43pK2X3nHDn9/f70Dsfz888/i66+/1tpRJjo6WrE/3dzctF7D0seYHdqPHj2qOHaioqIqlB4R0b8dy+fl17VrV+l8U69evTIDuKWkpIjevXtLMe7u7iI9PV1jWmrL57m5uYo07e3ttQ4YZghTtKEJIcSKFSsUZalFixYpylJFRUVi4cKFik7Ka9as0ZnX2NhYRftwaGhomX4T4eHhivbKsWPHGrhHHqlIW698EDhA982RxqbmWCphymsSmlT0JgFj1iMNYcg+LcmH/Lfi7+9fpqycl5cnPvzwQ0XaS5cu1Zrmk1KP/PvvvxV9cZ577jm99S41TFHnEcI0186IiCpCbf9zCyFKzSGixYABAxRTrJB+3bt3x/79+wEA/v7+6NChg/RZcXExMjMzcf/+fVy4cAHXr18vs/y4cePw7bffwtHRUed68vLyMGDAAOzZs0d6z9bWFv7+/mjYsCHs7e2RkZGBq1ev4uzZs9JUSu3atcPx48c1pvnLL79gzJgxiqkmmzRpgjZt2sDFxQUZGRm4cOECzp07h+LiYkyZMgULFy5UpLFv3z706NEDwKMpSUpPSbN69WrFFCh169bFU089hRo1akAIgaSkJBw+fBjp6elSzIgRI6Rpi+TGjBkjTS2zatUqjBkzRuN2/frrrxgxYgSKiooAANbW1ujatSsaNmyInJwcHDx4ELdv35biAwICEBERoXVKGQsLC+m5mp+S/JiIiIgoM3W8McjXMWPGDMX0OHL16tXDjRs3ADyaOrNevXqq0v/pp58wadIkaR8Cj6YBatOmDdzc3PDw4UMkJSXh9OnTSE5OlmLOnj2LFi1alEkvNTUVvXv3xqlTp6T3HBwcEBAQgLp168LW1hbp6em4fPkyLly4gIKCAgDA4MGD8dtvv6nKszby78+Q76NTp07SNF+BgYGKaZIA4Nq1a+jVq5fid+3i4oJOnTrB29sbFhYWSEtLQ2xsLC5duiRNEfrOO+9g/vz5ZdaXnJyMjh07KqY1btKkCTp06ABra2ucPXtW+i2//fbb+P3336Xv9saNG4rp1EqY+/uX/2atrKzQpEkT+Pn5SWncvn0bUVFRyM/Pl5b58ccfMXHiROm1/D8GADw9PdGmTRvUrFkT1tbWSEpKwokTJ5CYmCjFdO3aFfv27ZOmbCsxc+ZMfPrppwB0/24OHDiAfv364cGDBwAeHUMdO3aEn58f8vPzceTIEcTFxSn2TWRkJKpXr64xPUO/B7X/dUT039CuXTucOHHC3Nl4IoWEhODOnTuK95KSknD37l0AQNWqVTVOJ7pz5054eXkp3ouPj0f9+vWl19rKFFOnTsW3334L4NFUgM2bN0ejRo3g6uqKoqIi3Lt3DydPnlSURQFoLGeXkJ8LLS0t4evrK51PAeDOnTuIjo7G/fv3pWXs7OywY8cOxdSxmixbtgzLli1TvJefn4/Y2FjpdbNmzWBra6uIefXVVzVOwy4vo7q6uqJ169aoXbs2qlatiqysLJw9exbnzp1TlKn79u2Lbdu2wdraWmde09LS0KVLF1y8eFF6r127dnjqqadQVFSEY8eOKfLt5+eH6OhoODs7a01TCIHhw4fj119/ld7z8fFBYGAgqlSpgkuXLiEqKkoqxzk5OWH//v1o06aN1jSrVKmCvLw8eHh4oH379vD29oYQArdu3cKhQ4cUU96OHTsWy5cvL1NmkWvdujViYmIAPCprtmrVCrVr14azszNyc3Nx69YtnDhxQvH9W1tbY/369Rg6dKjWdEvSLu3mzZtS3czNzU1jGfP06dMa07t69So6d+6sKB+6u7sjMDAQtWrVQk5ODk6cOKH4niwsLLBhwwa88MILGtOUl+Vr164t1SXt7OyQkpKCo0eP4tatW4plPv/8c3z00Udat7u033//XZom19LSEjdu3EDt2rVVL28IeXlUU/25NFPs09zcXPTu3RuHDh2S3rOzs0OXLl1Qv359FBcX49KlSzhy5IiiPjB58mQsWbJEa17fffddfP3114r32rZti5YtW8LW1ha3bt3CwYMHkZOTI33u7++Pffv2wcHBQed+ICJ6nLB8Xn55eXno3bu3oo2tWbNm8Pf3h5WVFc6cOaPYt7Vq1cKRI0dQp06dMmmZs3xeIjMzE507d5amAgcetTP7+fkhIyMD4eHhijbv1atXY/To0RrTun//vlS+BwAvLy+p3OPg4IDs7GxcuXIFJ0+eVLSj1axZE+Hh4WjWrJnWfB4/fhwTJkwo8768DbZOnTpwd3dXfN6+fXssX75ca7oxMTEIDAxEdnY2AMDGxgZBQUGoXbs2bt26hfDwcCl9JycnREVFaWw3LnH48GF06tQJwKO2tNatW6NGjRrIycnBlStXcPToUakeYWFhgeXLl2PcuHFa05NbunQpJk+eLL12dXVFjx49UL16dSQkJCAiIkLRDvjDDz8o2iflTFnnMfY+BYxb5zHlcdq3b1/s3r0b9vb2aNu2LRo2bAg7OzvcvXsXUVFRSElJkWLbtGmDsLCwMsdsaV999RU++OADWFpaokWLFvDz84OLiwvS0tJw+vRpRfuum5sbwsLC0LZtW51pAsCECRPKXHNLS0uT6iU2Njbw8/Mrs9zy5cvRvn17vem/8cYb+O677wAAvr6+uHTpkt5liIj+y1g+L7/bt2+jQ4cO0jVWS0tLdOvWDQ0aNMC9e/cQFhYmlZGsra2xa9cure3Nasvn06ZNw7x586TXbdu2lcqA+jRu3BhTpkzR+Jkp2tBKhIaG4ueff5ZeN2rUCAEBARBCIDo6GteuXZM+Gzt2LFauXKl3W1asWKEoo7u7u6Nnz55wcnLC+fPncfToUemzp556CocOHYKTk5PONI3Z1rtz504888wz0mtPT0+p/VSNkrJMeRnSv8OU5XNjt58Dxq1HGqI8fWaOHz+O7t27S+25NjY26NatG+rXr4+MjAzs379fut4GaO/nJfe41yNTUlLg4+Mj/fdZWVlh7NixsLOz07e7ADxqS2jcuLHGz0xR5wFMc+2MiKgiVPc/N3YPefr/5CNlq31YWlqKkJAQrVPIaFNYWCg++eQT4eDgoGo9NjY24rXXXtOZ5j///FNmNDttD03TKOkboX3z5s2Kkdf17ZfJkydrvcvMkFGLt23bJjw9PfWuc/jw4XqnN5fHq/Gkj9BeIjw8XDRu3Fj1cd28eXORkJCgNb0HDx6IV199VVhbW6tKz97e3iijI8rTNOT72LFjh95lU1NTxdChQ1Uf466urmL16tVa13nt2jXRokULnWlMmDBB5OfnCy8vL+k9baOfm/v7f/3111Uv7+TkJH788ccy+Th8+LDqYwaAGDJkiMjMzNS4TWpGaC8RHR0tGjRooHd9vXr10nnnvhAcoZ2IKoYjzJSf/P/XkIem/2q1I8xMmzbNoHXVqFFD74gt8vK2mkebNm1ETEyMqn0kPzca8tB2HjWkblSlShUxffp0g0a2SEhIUIzgo+0REhKicwpMuby8PPHmm2/qLc/5+vqWGaVIE/kohNoezs7OYtGiRaqmqOzQoYNB303r1q11TispV57vHtBdJ7p06ZLqPFevXl1s3brVaHn09PTUOMuRPv3795fS6N27t8HLG6I8s0QYe58KIURGRoYIDQ1VlaaNjY2YNWuW3uO1uLhYzJ07V9VvAIAYNmyY1lG9iIgeZyyfV8z9+/fF8OHD9Z4nOnbsKG7evKk1HXOWz+USEhIUM1Nqejg6OoqVK1fqTCcjI8Og9i8A4vnnnxe3b9/Wm0dD6xOGlFWioqL0Xlto0KCBqvJpdHS0qnw1atRI7N27V296pS1fvlw4OTnpTNvV1VVs3LhRZzqmrvMYc5+WMFadx5THaXBwsN60Sq4fZWRkqNruL7/8UlUee/XqZdCol+W5JgiouzaRn58vPDw8pGW++OIL1fkiIvqvYvm8YmJjYxWjIWt6VK9eXWzfvl1nOmrL5/JroMYun5qiDU2IR2Wp119/XWdZysLCQrz55puqRlMu8dNPPylmXNX0CAoKEnfu3FGVXnn3qyby2WeMlaYhDClDmbJ8bqrtN1Y90hCGlktLREVFKUZV1/SwtrYW7733nqrrHSVpPq71yNL/ZYY+dO1bU9R5Spji2hkRUXmp7X+u+7YyMhlbW1s4OzvDxcVFGlW4ffv2CAoK0jiyjD5WVlaYNWsW3njjDaxduxZhYWG4cOECUlJSUFBQAGdnZ/j4+KBly5bo0aMHQkJCtI4YXKJnz564dOkSNm7ciO3bt+P48eNITk5GXl4eXFxc0KhRI3Tq1AmDBg1C165dDc7zkCFDkJiYiD179iAyMhIxMTG4du2aNIKgi4sLfH19ERgYiNDQUI0jV5THs88+iytXrmDlypXYvn07zp8/j5SUFNjb28PLyws9evRAaGgoOnbsaJT1/Rv16NEDFy9exJYtW7Bjxw4cPnwYSUlJyMzMhIODAzw9PdG0aVN07twZ/fr103iHqpy9vT2WLl2K9957D+vWrUN4eDguX76M1NRUFBcXw8XFBQ0aNECrVq0QFBSEvn37mvWuwJCQEPj7++PYsWMAgM8++6zMnaru7u7YtGkTzp07hw0bNmDfvn24fv06UlNTYWlpCVdXVzRq1Aht27ZFr1690Lt3b60zAQBA/fr1cfLkSSxfvhwbNmzAhQsXkJ2djVq1asHf3x8TJ05E7969AUC6M9fCwsIk+8kY3//ixYsxefJkhIWF4fDhwzh//jxu3ryJrKwsWFtbw8PDA82bN0efPn0watQo1KhRo0waHTt2RHJyMsLCwnDo0CGcOnUKV69eRWpqKoqKiuDs7IyGDRsiICAAI0eOVMySUREBAQGIjY3FunXrsHXrVmk0ehsbG9SsWROBgYEYPnw4+vTpY5T1ERHRv8OcOXPw0ksvITw8HEePHkVsbCxu3bqFrKwsWFpawtnZGXXq1EGbNm3Qt29f9O/fX+/IDl27dsXx48cRFRWFqKgoXL58GSkpKUhNTUVBQQFcXFzg4+ODgIAADBo0CD179qykrS1r06ZNUj6PHz+OxMREpKSk4P79+7C3t4eHhwdatWqFbt26ITQ0FB4eHgal7+XlhT179mD79u345ZdfcOzYMSQmJsLCwgK1atVC165dMXLkSIP2ga2tLb799luMGTMGq1evxj///IOEhAQ8fPgQ1atXR7t27TBo0CCMGDFC74gtwKOZXsLCwqRyYXJyMvLz8+Hp6YnGjRvjueeewwsvvKC3nlbi0KFDiIyMxP79+3H8+HHExcUhMTEROTk5sLOzk+ps7du3x/PPP4/AwEDV224Kvr6+OHz4MMLCwrBp0yZpBPWsrCxUqVIF1apVQ5s2bdCvXz+MGDFC78jcly9fRlRUFKKjoxETE4N79+4hJSUFOTk5cHJygqenJ/z9/REcHIyhQ4eWmU1An3v37uHvv/+WXhtjtBtjM/Y+BQBnZ2esWbMG06ZNw5o1axAZGYkrV67g/v37sLS0hJubG/z8/NC9e3eMGzeuzKwVmlhYWOB///sfQkNDsWbNGkRERODs2bNIS0tDYWGhVN/r3LkzRo8erbf+SERE/04uLi5Yv349Xn31VaxduxaHDh1CQkICioqK4OnpiYCAAAwbNgwDBw5UjCBXXqYon8t5eXkhLCwMW7ZswS+//IKTJ08iMTERjo6OqFu3Lvr374/x48drHLVPztnZGampqQgLC0NkZCROnTqFa9eu4d69e8jLy0PVqlXh5uaG5s2bo2PHjnjppZc0zjZV2Tp16oQzZ85g7dq12LRpk9TW6+HhAV9fXwwbNgyhoaF6Z6cFHo3O+ffff+Off/5BVFQUEhISkJycDGtra9SqVQutWrXC4MGDMXDgQIO+oxLjx49H3759sXr1avz111+Ij49Heno63Nzc0LRpU4SEhODll19WjECuianrPMbcpyWMVecx5XH6/fffY/fu3QgPD8eFCxdw9+5dZGZmolq1aqhTpw769u2L4cOHo2nTpqq3++WXX4aPjw/Cw8Nx4sQJJCUlITU1FU5OTvDy8kJgYCCGDh2qmB3U3LZv347U1FQAj0bJHTVqlJlzRERE/3ZNmzbFkSNHsHHjRmzYsAHnz5/H3bt34erqigYNGmDQoEEYN24cqlWrZu6s6mWKNjTgUVlq8eLFGDVqFFauXIl9+/YhISEBAODt7Y3u3btj/Pjx8Pf3Nyi/EyZMQJ8+fbBixQps27YNN2/elPoFtG3bFiNHjjRavejfzNTlc1MwVj2yMpTUT9avX49NmzYhNjYWycnJqFq1Knx8fBAUFISXX34Zvr6+Bqf5JNQjjckUdZ4Sprh2RkRkahZCyOZP0UH1kO9ERGQ2cXFxUqWgSZMmiumDiIjo34VTphIRERERPT5YPiciIiIienywfE5ERERE9PhQ2//cshLyQkRElWTTpk3Sc0Pv9iYiIiIiIiIiIiIiIiIiIiIiIiIiqmzs0E5E9C9x48YNzJ8/X3o9fPhwM+aGiIiIiIiIiIiIiIiIiIiIiIiIiEg/dmgnInoCvPDCC/j999+Rl5en8fPdu3cjMDAQ9+/fBwC0bNkSffv2rcQcEhEREREREREREREREREREREREREZztrcGSAiIv2OHDmCTZs2wcnJCW3btkW9evVQpUoVpKam4ujRo7h586YUW7VqVaxduxaWlrxniYiIiIiIiIiIiIiIiIiIiIiIiIgeb+zQTkT0BMnKysL+/fuxf/9+jZ83aNAAmzZtQuvWrSs3Y0RERERERERERERERERERERERERE5cAO7URET4CIiAj88ccfOHjwIK5du4aUlBSkpqbC1tYW1atXR7t27fDss8/ipZdego2NjbmzS0RERERERERERERERERERERERESkCju0ExE9AerXr4933nkH77zzjrmzQkRERERERERERERERERERERERERkNJbmzgARERERERERERERERERERERERERERER/TexQzsRERERERERERERERERERERERERERERmQU7tBMRERERERERERERERERERERERERERGRWbBDOxERERERERERERERERERERERERERERGZBTu0ExEREREREREREREREREREREREREREZFZsEM7EREREREREREREREREREREREREREREZkFO7QTERERERERERERERERERERERERERERkVmwQzsRERERERERERERERERERERERERERERmQU7tBMRERERERERERERERERERERERERERGRWbBDOxERERERERERERERERERERERERERERGZBTu0ExEREREREREREREREREREREREREREZFZsEM7EREREREREREREREREREREREREREREZkFO7QTERERERERERERERERERERERERERERkVmwQzsRERERERERERERERERERERERERERERmQU7tBMRERERERERERERERERERERERERERGRWbBDOxERERERERERERERERERERERERERERGZBTu0ExEREREREREREREREREREREREREREZFZsEM7EREREREREREREREREREREREREREREZkFO7QTERERERERERERERERERERERERERERkVmwQzsRERERERERERERERERERERERERERERmQU7tBMRERERERERERERERERERERERERERGRWbBDOxERERERERERERERERERERERERERERGZBTu0ExEREREREREREREREREREREREREREZFZsEM7EREREREREREREREREREREREREREREZkFO7QTERERERERERERERERERERERERERERkVmwQzsRERERERERERERERERERERERERERERmQU7tBMRERERERERERERERERERERERERERGRWbBDOxERERERERERERERERERERERERERERGZBTu0ExEREREREREREREREREREREREREREZFZsEM7EREREREREREREREREREREREREREREZkFO7QTERERERERERERERERERERERERERERkVmwQzsRERERERERERERERERERERERERERERmYWFEEKoCWzdujWsrKxMnR8iIiIiIlIhLS0N7u7u5s4GERERERGB5XMiIiIioscJy+dERERERI+PoqIinD59Wm+ctdoE69ati7/++qsieSIiIiIiIiNp164dTpw4Ye5sEBERERERWD4nIiIiInqcsHxORERERPT4GDBggKo4SxPng4iIiIiIiIiIiIiIiIiIiIiIiIiIiIhII3ZoJyIiIiIiIiIiIiIiIiIiIiIiIiIiIiKzYId2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIjILdmgnIiIiIiIiIiIiIiIiIiIiIiIiIiIiIrNgh3YiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMgt2aCciIiIiIiIiIiIiIiIiIiIiIiIiIiIis2CHdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyC3ZoJyIiIiIiIiIiIiIiIiIiIiIiIiIiIiKzYId2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIjILdmgnIiIiIiIiIiIiIiIiIiIiIiIiIiIiIrNgh3YiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMgt2aCciIiIiIiIiIiIiIiIiIiIiIiIiIiIis2CHdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyC3ZoJyIiIiIiIiIiIiIiIiIiIiIiIiIiIiKzYId2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIjILdmgnIiIiIiIiIiIiIiIiIiIiIiIiIiIiIrNgh3YiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMgt2aCciIiIiIiIiIiIiIiIiIiIiIiIiIiIis2CHdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyC3ZoJyIiIiIiIiIiIiIiIiIiIiIiIiIiIiKzYId2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIjILdmgnIiIiIiIiIiIiIiIiIiIiIiIiIiIiIrNgh3YiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMgt2aCciIiIiIiIiIiIiIiIiIiIiIiIiIiIis2CHdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyC3ZoJyIiIiIiIiIiIiIiIiIiIiIiIiIiIiKzYId2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIjILdmgnIiIiIiIiIiIiIiIiIiIiIiIiIiIiIrNgh3YiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMgt2aCciIiIiIiIiIiIiIiIiIiIiIiIiIiIis2CHdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyC3ZoJyIiIiIiIiIiIiIiIiIiIiIiIiIiIiKzYId2emzUq1cPFhYWsLCwQHx8vLmzQ0RkUmPGjJH+81avXm3u7BARERERERERERERERERERERERGZBTu0m1D37t2lzoqaHk5OTqhbty769euH2bNnIyEhwdxZpsdYfHy8zuNJ26N169bmzjr9H33fobW1Ndzd3eHn54eRI0fi119/RX5+vrmzTURE9K9TVFSEM2fOYMWKFZg0aRLat28PW1tb6ZzcvXt3k607Pz8fx44dw9KlSzFu3Di0bNkS1tbW0rrHjBljUHpZWVnYt28f5s+fjxdeeAH+/v6oV68eHB0dUaVKFdSsWRPdu3fHJ598gri4ONXp5uTkYPv27XjrrbfQrVs31KpVC3Z2dqhatSp8fHwwcOBA/PDDD8jJyTEovxkZGQgPD8fcuXMxdOhQxU2t5bnJKykpCdu2bcP06dPx7LPPonXr1vD29oadnR0cHR3h4+OD/v37Y9GiRUhNTTUo3Z9//hnjx49H+/bt4eHhARsbG7i6uqJp06YYPXo0tm3bhuLiYoPyW+LYsWOYPHky/Pz84OLiAmdnZ/j5+WHy5Mk4duxYudIEgLy8PPz2228YPnw4mjVrBhcXF1StWhUNGzZEt27d8OGHH2Lv3r06y5gzZ840uM6xbt061XkUQmDLli0YMmQIGjZsCHt7e1SvXh3t27fHp59+ips3b6pOy1THKQDk5ubihx9+QP/+/VGnTh1UqVIF7u7uaNmyJaZOnYpTp04ZnKY2zz//vGJ/GvofZMx9CgB37tzBn3/+iY8//hh9+/aFh4eHIn8VvQHdGMcpERH9e6SlpWHPnj344osvMGjQILRt2xZ169aFvb09HBwc4O3tjeDgYHz55ZdGbz83dblHk2+//bZMmoa4e/cu5s6diz59+sDLywv29vaoUqUKatWqhaCgIHz22Weqz/2XL1/G+vXr8dZbb6Fnz55o3rw5atSooSj3vvTSS9i0aVOFzssHDhzAG2+8gVatWqFGjRqoUqUK6tSpgw4dOmDy5MnYvHkz0tPTVaUVExODKVOmoFWrVnB3d0eVKlXg4+OD4OBgLF++HA8ePDA4f0lJSZg1axYCAwPh6ekJOzs71KxZEwEBAeW6bmPsOo9cdnY2li5dih49eqB27dqws7ND7dq10bNnTyxbtgzZ2dl60yjvdYaSx8yZM7WmbezyuXxAkPI8tHmSfvuG5rNRo0YVyicREZFcfn4+fv75Z4SEhMDHx0cqd3bu3Bnz589HSkqKUddn6nO0KcqSmpw8eVLR5m9hYYF9+/YZnE5F29BWr15dobKUmjybap8au/384cOHWLt2LQYPHowGDRrA0dERtra2qFatGjp06IApU6bg+PHjetMx1T7dt2+fwWlNmDDB4P2QkpKCpUuXIjg4GA0aNICDgwPc3NzQrFkzPPPMM5g7d66q/VDi5s2bmDlzJtq1a4fq1avD3t4eDRs2xJAhQ7BlyxYIIQzOozHqPIDprp2ZonxeXFyMyMhIfPrppwgJCUG9evVQtWpVqW7as2dPfPbZZ7hz547qfBIRPdGESv3791cbSv+nW7duAoDqh42Njfj0009FcXGxubNuFj4+PtK+uH79urmz89i5fv26QcdTyaNVq1bmzvoTxZTHYXm+w/r164sDBw4YNR/0eBg9erT0Pa9atcrc2SGiJ1Dbtm3NnYUn0pYtW4SDg4PO82+3bt1Msu7FixcLW1tbnesePXq0QWm+9tprqssVlpaWYvLkyeLhw4c60xwxYoTefVTyqFatmvj9999V5fXFF18UFhYWOtMz9JzYvHlz1dvv7Owsvv/+e53p3bhxQ/To0UNYWlqqLmufOXNGdX7z8vLEm2++qXM/WFhYiClTpoj8/HyD9kV4eLjw9fVVle9jx45pTWfGjBkGl1l//vlnVXlMSEgQPXv21JmWo6OjquPAVMepEEJEREQILy8vnWmWfE95eXmq09Xkt99+q9B/kDH3qRBC1KpVS+/+rEg9yVjHKRHR44jl8/J55plnVJc5bG1txYwZM0RRUZFR1m3Kco8m8fHxwtHRsUyaai1ZskRUrVpV1X6aNWuWzrTu3btn0HY3bNhQ7Nu3z6DtvXr1qggODlaV/rx583Sm9fDhQzFu3Di99YlGjRqJqKgo1Xn89ttvhb29vc40nZycxIoVK1SlZ4o6T4moqChRv359nWk3aNBAHD58WGc65b3OUPJYunSpxnRNUT6Xt58a+vD09NSa7pP02zc0nw0bNix3PomI/m1YPq+Y2NhY0aZNG53nnRo1aogdO3YYbZ2mOkebqiypSUFBgcb9FhERYVA6xmhDW7VqVYXKfbGxsVrzZ6p9aor283379ol69eqp2uZhw4aJjIwMrWmZap9GREQYnNb48eNV79fi4mKxdOlS4erqqjfdqlWrqkpzxYoVeuunvXr1EomJiarzaaw6jxCmuXYmhPHL519//bWoWbOmqrRsbGzEzJkzRWFhoep9SkT0OFHb/9waVCn8/f3RoUMHxXsZGRmIiYnB2bNnAQAFBQWYMWMG7t+/j2+++cYc2aQnSGhoKJycnPTG1a5duxJyQ+VR+jssLCzE3bt3ERkZiXv37gEArl+/juDgYISHhyMgIMBcWSUiIvrXuH//vtFGWjFUSkqKSUccdnd3R7NmzeDj4wMnJyfk5+fj+vXrOHz4MHJzc1FcXIzvv/8ely5dwq5du2Btrbk6+NtvvyEvL0967ebmhg4dOsDLywtCCMTGxuLo0aMQQiAlJQWDBw/G0qVL8eqrr+rMX2JiYrlG5FCrTp06aNKkCby8vODg4ICsrCzExsbi1KlTEEIgMzMTkydPxt27d7WOKnjnzh1EREQo3vPx8UHr1q1Ro0YN5OTk4NixY9KIHTExMejSpQv++ecf+Pv7683jxIkTsXbtWul1w4YNERAQACEEoqOjcf36dQgh8O233yIrKwsrVqxQte1r167F2LFjpRHjrays0LFjR9SvXx9Vq1ZFamoqzp49i8uXL6tKr4SmeqwmTZo00RuTmZmJ4OBgnDt3TnqvQ4cOaN68uTSK5f3795GdnY2xY8fC0tISoaGhWtMz1XG6fft2DBo0CIWFhQAAa2trdOrUCb6+vsjLy8OxY8dw6dIl6Xu6e/cu1q9fr3qERbn79+/j9ddfN3i5Esbep8Cj36mpmOo4JSKifw9PT080bdoUdevWRdWqVfHgwQPExcXh2LFjKCwsRH5+Pj799FPEx8dXaJRrTYxZ7tHm1VdfVT2aXGnz5s3DtGnTpNdWVlbo0KED6tevDwC4du0ajh07hqKiIuTn52P69OnIyMjA/Pnz9aZtaWkJX19fNG7cGNWqVYOtrS1SU1Nx8uRJXLt2DQBw9epVBAcH488//0RwcLDeNGNiYhAUFKSYJcnPzw/NmzeHu7s7srKycOnSJcTExEjlLm3y8/MRHByMAwcOSO95eXkhMDAQLi4uuHXrFg4cOIAHDx7gypUr6N27N/bt24f27dvrTPf999/HnDlzpNdOTk54+umn4eXlhfT0dBw4cADJycnIysrC+PHjkZeXh0mTJulM01R1njNnziA4OBhZWVkAABsbG/Ts2RO1a9fGrVu3EB4ejsLCQly7dg19+vRBZGQkWrRooTEtZ2dnvPbaa6rXHR0djZMnTwIAbG1tMXToUI1xpiif9+rVC46OjqryWVhYiB9++EF6PXLkSFXLPe6/fTk131v16tUrvB4iIqLbt28jKChIGg3YwsICTz/9NBo1aoTk5GSEhYXh4cOHSE5OxsCBA/H3338jKCjIqHkw1jnaVGVJbebPn1/hmR2N1YbWrFkzg8p9v/32G+7evQsAaNeuHZo2baoxzpT71Njt5wcPHkTfvn2Rm5srvde8eXM0adIELi4uiI+Px/Hjx6Vy9qZNm3Dz5k0cOHAANjY2ZdIz1T6V8/LywqBBg/TGde7cWVUehBB45ZVX8NNPP0nvOTo6onPnzvD29oaFhQWSkpJw8uRJJCUlqUpz5cqVGD9+vPTazc0NPXv2hLOzM86dOyeNoh8WFobg4GBERkbqLdcbs85TmrGunZVmjPL5nj17FPvdzs4O/v7+8PHxgYODA+Lj43Ho0CE8fPgQBQUFmDlzJi5fvoyff/4ZlpaWqvJJRPTEMXYPefr/5CO0z5gxQ2tcZGSkqFOnjqq7KP/NOEK7bqVHTuE+Mo3KHKFdW/p5eXniq6++Utx5/NRTT/1nZ28gIiLNOMJM+ZSMoOHp6SmeffZZ8emnn4qdO3eKKVOmSOddU43QXjLCTJ06dcTzzz8vvvzySxEWFiaGDBkirdvQEdpXrFghFi5cKGJjY7WWFe7fvy/eeOMNRTlk/vz5WtO0s7MTDg4OYvz48SIyMlLjCJhnz54VrVu3ltKzsrLSO1J5t27dhKWlpfDz8xNjxowRS5YsEUePHlXUhQwdrfDDDz8U69atE3fu3NEaExcXpxjB2tLSUhw9elRjbHR0tAAgvLy8xMcffyzi4uI0xm3dulV4eHhIafr4+IgHDx7ozOuKFSsUeViwYIFi3xYVFYlvvvlGMTr8mjVr9O6DrVu3KpZ57bXXxL179zTGXrlyRcyZM0dcvXpVa3rykZB01WMNNWrUKCldd3d3ER4ervg8KytLvPTSS1KMra2t1v0vhGmO04SEBOHs7CzFt2vXTly+fLlM3G+//SacnJykuIULFxqwJ/6/cePGSSOryP8H1P4HGXufCvFohJkqVaqIgIAA8cYbb4i1a9eK3bt3V7guauzjlIjoccTyefnMmzdP/Pjjj+LatWtaYxITE8WwYcMU56Pffvutwus2VblHk7Vr10rrkp+f1VyiiYuLU8z0FBQUJC5dulQm7sKFC+Lpp5+W4iwsLMTx48c1ppmRkSEmT54sdu7cqXMEwvDwcEV7aa1atURWVpbO/F6/fl1RVg4KChJnz57VGJuamipWrlwpdu3apTW9d955R1Ge+/rrr8uMSJecnCyee+45Kc7b21vk5ORoTXPbtm2K72DUqFEiPT1dEZOXl6c4RqysrMSJEyd0brsp6jz5+fmiYcOG0vKtWrUS8fHxipjr16+LVq1aSTG+vr6ioKDAoPVo0759eynd559/XmucqeqRapX+TnWl+6T89oUQBscTEdH/x/J5+cnLlD4+PiImJkbx+b1790RQUJCiXap0Wao8THGONkVZUpvLly+LKlWqaDzvqx2h3VxtaKmpqYo6x6JFi7TGmmqfGrv9vLCwUDRq1EiKrVevnti7d2+ZuJSUFPHqq68qvq9vvvlGZ17VMGSfykdoN/b1Kfn35ejoKL799luNs44WFxeL6Oho8cknn+hM7+LFi8LGxkZKc8SIESI7O1sRExYWJtzc3KSYsWPH6kzTFHUeU1w7E8L45fPg4GBhYWEhQkJCxB9//KFxlPh79+6J4cOHK9atbzZiIqLHkdr+5+zQbkJqO7QLIcTJkycVnVcnTpxYOZl8jLBDu27s0F45HocO7SWmTp2qiI+MjDRqfoiI6MnGBvnySUxMFDdu3Cjzvryx3FQd2uPj40VSUlKZ9+XTqBvaod0QoaGh0noaN26sNe7dd9/VmM/S0tLSFGWnF198UWf8+fPnNXZ+kadhaOcOtXJzcxVTtGqrb50/f1588803qqaWPHr0qLCyspLSXLZsmc71yzuxvP/++1pj33vvPcUFI02NyyVSU1OFp6enFP/dd9/pzbc+prhwdPbsWcWFBm0dloqKikTnzp2luOHDh2tN0xTH6Ztvvqm4yJKamqo1dseOHVKsh4eHuH//vt68yIWHh0vLf/TRRwb/B5linwrxqG2i9IWAitZFTXGcEhE9jlg+N63i4mLRvXt36XzSu3fvCqdZWZ1ak5OTRbVq1QQAERgYqCgHqLkIPnPmTCnWy8tLZ0eQrKwsUatWLSn+7bffrnD+S3dY+OWXX3TG9+7dW4p94YUXKjQd+p07d4SdnZ2U3pw5c7TGFhYWik6dOkmxn3/+udbYp556Sorr16+fzjz873//k2J79eqlM9YUdZ4lS5ZIy7q5uWm9mffOnTuKTiM//PCDQevR5Pz584pj9c8//9Qaa6p6pFpDhw6V0mzTpo3O2Cflty8EO7QTEVUEy+flI2/zsrW11XqTWHZ2tmjQoIEU+8EHH1R43cY+R5uqLKmJvL7i5uYm7t69qziPq+nQbs42NHmZ08bGRmsnelPtU1O0n+/bt0/xHZw6dUprmkII8eyzz0qxxvj/ULtPhTBdh/aDBw9K/dCqVq0qDh8+XOE05eXuLl26aLyRVQgh/v77bynOyspKnD9/Xmua5qzzCKH+2pkQxi+fz58/X5w+fVpvXHFxseImES8vLw6ISURPHLX9zzn/xGOiTZs26Natm/RaPj0PEf03TZ06VfGa/wtEREQVV7NmTdStW9cs6/bx8YGnp6dZ1g0AEyZMkJ7HxcVpnfJ83rx5qvLp5uaG9957T3q9c+dOnfF+fn6qp4s3Njs7O8WU8ydPntQY5+fnh7feegtVqlTRm6a/vz8GDx4svd6xY4fW2L/++gu3bt0CALi4uOCTTz7RGjt9+nS4uLgAAG7cuKEz3c8++0yasnTQoEEGTXdamZYuXSpNkdu7d28EBwdrjLO0tMTcuXOl15s2bcK9e/c0xpriON28ebP0/N1334W7u7vW2JCQEHTt2hUAkJqail9++UVvXko8fPgQEydOBAA0atQIH3/8seplS5hinwKP2ibUTqmq1pNynBIR0ePNwsIC48aNk15rK889jqZMmYKUlBTY2Njghx9+gIWFhUHLx8TESM+fe+45ODg4aI11dHTEgAEDpNeXL182PMOlNGnSRCr3ALr3/Z9//om9e/cCAOrWrYuffvoJVlZW5V73X3/9hby8PABAjRo1yrSXyllZWeHzzz+XXn/33XdSeUnu0qVLOHPmjPR69uzZOvPw8ccfS/WYsLAwXLhwQWusKeo8S5YskZ6/++67qFWrlsa4WrVq4Z133tG4XHmtWbNGel69enWEhIRojTVVPVKN+/fv46+//pJejxkzpsJpGkNFf/tERETmIC9DjB49Gi1bttQYV7VqVcyaNUt6/cMPP6CwsNDk+TOEKcqS2vz000/Yt28fAGDu3LmoUaOGwfk1ZxuavNz37LPPolq1ahrjTLVPTdF+Lq9HNWvWDK1bt9aaJgCMGDFCem6MepTafWpKb775JoQQAB7Vezp27Fih9O7evYvff/9dej137lxYWmrudti3b1/06tULAFBUVIQffvhBa7rmrPMA6q+dmcI777yDVq1a6Y2zsLDAl19+Kb2+c+cOTp06ZcqsERGZDTu0P0batGkjPb9z547O2IKCAuzevRvTpk1Djx494OXlhSpVqsDe3h61a9dGSEgIvv32W1Un2vj4eFhYWMDCwgL16tWT3j9+/DgmTJgAX19fODg4wM3NDR06dMDs2bORk5OjeruKioqwfPly9OzZEzVq1IC9vT0aNGiAF154QWrcLo/z58/jf//7H9q0aYNq1arBzs4OXl5e6N69O+bMmYPU1FS9aaxevVra9pJGzqKiIqxduxa9e/eGt7c3bG1t4eXlhREjRuDs2bNl0sjKysLixYvRuXNneHp6wt7eHr6+vnjnnXeQkpJS7u2rDAUFBVi1ahUGDhwIHx8f2Nvbw9nZGU2aNMH48eNVfz/16tWT9mN8fDwA4OrVq/joo4/Qpk0bVK9eHZaWllorCTk5OVi6dCn69+8PHx8fODg4wMnJCY0bN8a4ceMQHh6uepvCw8Mxfvx4tGzZEq6urrCxsUG1atXQrFkzdO/eHR999BEOHDiAgoICaRn5b+DGjRvS+/Xr15felz9KKqOm5uPjAzc3N+m1vv8FABBCYMuWLRg9ejR8fX3h4uKCKlWqoE6dOhg4cCDWrFljUGNCTk4O5s2bh4CAAHh4eKBq1arw9fXFuHHjcPToUSlOvn+00RQTExODKVOmoEWLFnB3d4eFhQUGDhyocfnU1FR8/fXX6N27N+rUqYMqVarA1dUVfn5+eO2113D8+HFV2ySEwNatW/HSSy+hSZMmcHZ2hq2tLTw9PdG8eXP07t0bn3/+OY4dO6azoeLixYuYNm0aAgICUK1aNdja2sLFxQWNGjVCQEAAJk+ejK1btyIzM1Pj8mPGjJH2x+rVq/XmOzs7G4sWLUJwcDBq166NKlWqwM3NDS1atMDrr7+OI0eOqNp+Td/DpUuXMHXqVDRr1gyOjo5wdnZGq1at8MEHHzz2/2NERKRe6Ub0rKysCqfZpUsX6XlmZibS0tIqnKapyLffGNsOKLe/pBysydatW6XnL7zwgs5OSA4ODhg2bJj0esuWLRrjcnNzFY3i06dPV5HjyieEUHQuGTt2rM74Ll26oHHjxgAe1c22bdtW4TyoOU5v3LiBxMRE6XW/fv30pivv0CNvyNdn+vTpuHr1KoBHHdPV3EAh9zjsU7WelOOUiIieDKYoz5nazp07sWHDBgDAe++9Bz8/P4PTkLfxu7q66o2Xtyca0glHF7X7funSpdLzt956C05OThVa7+HDh6XnPXv2hK2trc74bt26oWrVqgCApKQkREZG6kyzVq1aeju2ODs7K8qThpT7KurKlSuKDvT6OmrLPz9z5oxU5iyP4uJixU2bI0aMMNqNj8auR27cuFHqWGVjY4Phw4dXKD1jMMZvn4iIqLJlZ2fjn3/+kV7ra3MaMmSIVN5LS0t77AZnM0VZUpM7d+5g2rRpAICuXbti/PjxBufVnG1oly5dUlz3Hz16tNZYU+1TU7Sfm7MeZcg+NZXDhw9LHZ6rV6+OV199tcJp/vXXX9K+8fX1RefOnXXGy/9D5N+xnDnrPCVMce3MFJo1a6Y4TnVdkyIiepKxQ/tjxN7eXnqem5urNe7WrVuoVasW+vbti3nz5mHfvn1ITExEXl4ecnNzkZCQgL///htTp05F/fr1De40LoTAjBkz0LFjR6xYsQJxcXF4+PAh7t+/j2PHjuGjjz5Cy5Ytce3aNb1pJSQkoGPHjpg4cSIiIiJw79495Obm4vr169i0aRP69OmDiRMnIj8/X3X+CgsL8eabb6JVq1aYP38+Tp8+jdTUVOTn5yMxMRH79+/H+++/j0aNGikK/WokJyejZ8+eGD16NMLCwnDnzh0UFBQgMTER69evR/v27fH3339L8YcPH0bTpk3x5ptvIjo6GsnJycjNzUVcXBy++eYbtGzZEpcuXTIoD5XlyJEjaNasGcaNG4c///wTN2/eRG5uLrKysnD58mWsXLkSffr0QZ8+fXSO4KfJjz/+iBYtWmD27Nk4ffo0UlJSpDs/S9u8eTMaNWqEyZMnY/v27bh58yYePnyI7OxsXLlyBatWrUJQUBD69++PjIwMrevMzs7Gc889h6CgIKxcuRLnzp1DRkYGCgsLkZqaiosXL2L//v2YPXs2unXrZvCxYS5q/xeARwX2tm3b4vnnn8fatWsRFxeHzMxM5OXl4fbt2/jzzz8xZswYtGjRQudIQiVOnTqFFi1aYNq0aThy5AjS0tLw4MEDxMXFYdWqVQgICMCMGTPKvW0zZ85Eu3btsGjRIpw/fx7p6elaY5csWYKGDRvi3XffRVhYGG7fvo28vDxkZGQgNjYW33//PTp06IDx48fr/D+5e/cuunTpgkGDBmHDhg24fPkysrKyUFBQgOTkZFy4cAFhYWH45JNP0KFDB603U8ycORMtW7bEvHnzcOTIEaSmpqKgoACZmZm4evUqjhw5gqVLl2LQoEGYPHlyufdRie3bt6Nx48aYMmUK9uzZg4SEBOTl5eH+/fs4f/48lixZgoCAAIwYMQIPHjwwKO1ly5ahVatW+Pbbb3Hx4kXk5OQgKysLZ86cwVdffYWmTZuqvlmAiIgeb7GxsdJzBwcHVK9evcJplr6ZraioqMJpmop8+318fIySpnz7dW17RESE9Lx79+5605XHaCuPbN++XSo/+fn56e2IYy5xcXG4ffu29NpY228INcdpyehHJdQcI/KY0jfNanPy5EksWLAAADBy5EhplBpDPA77VK0n5TglIqIngynKc6aUlZUldRpo1KgRPvroo3KlI59h6vz583rjz507Jz1XM9KbGmr2fXJysuJaxEsvvVTh9crLaGq+cysrK3h7e0uv5R2yNKWpdvYu+bo1pWkq8nKbr68vvLy8dMZ7e3tLNzICynqIocLCwpCQkCC9NmYnHGPXI9euXSs9DwkJMUpdtyKM9dsnIiKqbFFRUdJNYlWrVoW/v7/OeDs7OwQEBEivK7PNSQ1TlCU1ee2115CRkQFbW9tyz8pizjY0Q2blMdU+NUX7ubysf+nSJb2D/hmzHmXIPjWVdevWSc+HDh2q9+YDNQz9nnr06CE9v3nzJq5cuVImxpx1nhKmuHZmKmqvSRERPcmMO480VYh89GVdUxDl5ORIo4+7ubmhefPm8PHxgaOjI/Lz83H9+nUcPnwYubm5SElJQUhICPbv36/37rgSn376qTQ9VOvWrdGyZUvY2Njg9OnT0nSi169fx8CBA3HixAnY2NhoTCctLQ1BQUGKDt2NGzdGhw4dpPROnz6N5cuXq56Gs7i4GIMHD1aMRufu7o7u3bvD3d0dt27dQkREBPLz83H//n2MGTMG6enpOqc6KlFYWIjnn38ekZGRcHBwQPfu3eHt7Y3k5GSEhYUhJycH+fn5eP7553Hu3Dnk5+ejT58+yMrKgqenJ7p27Qo3NzdcvXoV+/btQ3FxMZKSkjBo0CDExMRo3U/mcODAAfTr10/R8bVDhw5o3rw58vPzcfjwYelOxr179yIwMBCHDh1SVXDbvHmzdAeyt7c3OnfuDBcXF9y5c6fMSCsLFizAO++8I3V2d3JyQqdOnVCnTh0UFRXhwoULOHbsGIQQ2L59O7p164aoqCiNd+SOGjVKcVw0atQIbdq0gbu7OwoKCnDv3j2cPXtW412Kzs7O0pRda9eule64DA0N1TiKkLzSZUoFBQWK0bF1/S8cOHAA/fv3l0YDt7a2Rvv27dGkSRPY2NggPj4ehw4dQm5uLi5duoTOnTsjOjoazZo105je5cuX0bt3b8VMB23atEGrVq1QVFSEEydO4MKFC5g1a1a5pseaN28ePv30UwBAw4YN0aFDBzg4OCA+Pr7Mb+Wtt97CwoULpdceHh4ICAiAl5cXcnNzcerUKZw7dw5CCKxcuRJ37tzBjh07ykxvVVRUhGeeeQYnTpyQ3mvRogVatGgBV1dX5ObmIikpCTExMYrROUtbuHChlHcAqFatGgICAlCrVi1YWFggLS0NFy9eRGxsrFEqEb/++itGjBghpWVlZYXAwEA0atQI2dnZOHjwoHT+WL9+Pa5fv47w8HBVI32uXr0akyZNAvBo+uj27dvD3t4eFy9eRGRkJIQQSE1NRf/+/REbG6vq7nUiIno8ZWZmKm5EGzBggFFG15PPYGRvb2+WaTPVOH/+PFasWCG9Hjx4sFHSlW9/nTp1NMZkZGQoyhZt27bVm648JiEhAZmZmXB2dlbEyEez6dq1KwDg2rVrWLp0KXbs2IGbN2/CysoKtWrVwtNPP43Ro0crRkJU4+7du/j5559x+fJl5OTkwM3NDXXr1kXXrl3RoEEDVWnIG4Nr1qypdbpQOfn2y5cvLzXHqbYbcNUqLCzE5cuX0bx5c50x48ePR1FREdzd3fHNN9+Ua12Pwz5VqzKOUyIi+m+4c+cO5s+fL702VnmuhDHKPaV98MEHuHXrFoBHAwoYOitLiQEDBkhl2R07diAyMlLr+TIiIgI7d+4E8KiDUXlGhyxtzZo1iImJAfDo4vmgQYM0xkVHR0uj9TVp0gQ1atRARkYGli9fjk2bNuHKlSt4+PAhqlevjg4dOmDw4MEYNmyY1inqAWUZrTwdgzTdAGCKNE1FXm5TU48oiYuLiyuzvKHknXCeeuopo3aqMmY9Mi4uDtHR0dJrfSM6lvY4//ZLO3DgAI4cOYK7d+9KM9O2bdsWAQEBioFxiIiIyktedmjZsqWq9uO2bdtKNzUas83JGOfoyij3/fbbb9LI0++9957Wa+/6mKsNrbi4WNHx+aWXXtLZr8UU+9RU7efBwcGws7NDXl4e0tLSMH/+fLz//vsa00tMTFS01VZkwDpD92lpDx8+xF9//YWYmBikp6fD0dERNWvWRKdOndCqVSud9Sc5TcfUoUOH8MMPP+DQoUNITEyEo6Mj6tWrh169emHSpEl6b1IwtH5Sq1YteHp6SjdCxMbGolGjRhVKsyTOGHUeoGLXziq7fF66z5W2a1JERE88oVL//v3VhtL/6datmwAgAIgZM2bojC0oKBB16tSR4gcPHqw1Nj4+XrzxxhviyJEjoqioSGNMRkaGePfdd6X0GjdurDX2+vXrUpytra2wsLAQDRs2FEeOHCkTu2nTJmFjYyPFr1mzRms+R48eLcVVqVJFrFu3rkzM3r17RbVq1QQARbrXr1/XmOacOXOkGABi2rRpIjc3VxGTmJgo+vTpI8VYW1uLw4cPa0xv1apVim0v2fcpKSmKuISEBNG8eXMpNjQ0VLRr105YWFiIL774QuTn5yviIyMjhZOTkxS/evVqrftJLfn3pGsf6ZOWlia8vb2ldBo2bCiOHTtWJm7dunXC3t5eitP1H+Dj46PY37a2tuLHH38UxcXFijj5dxUWFiYsLS2l7/7zzz8X2dnZZdI+deqU8PPzk9KfNGmSxpiSzx0dHcXOnTu15vXq1avi888/F3/99ZfebSnvPtbG0O9w165divht27ZpjEtMTBSenp5S3PDhw8Xt27fLxCUlJYlBgwZJcS1bthSFhYVl4oqLi0VgYKAUV6NGDREREVEmbuvWrcLJyUnY2dkp8qmNPMba2lq4uLiILVu2lImTHycrVqxQfLdLly4VeXl5ZZYJDw9XHNdz5swpE7Nlyxbp81q1amn9XxBCiHPnzon33nuvzP9gQUGB8PDwkNL58ssvy/z+S6SmpoqVK1dqzIsQyv/IVatWaYy5cuWKcHR0lOLat28vLl++rIgpKioSX3/9tfR7AiDeeOMNrdsm/x7s7OxE9erVxd9//10mbv/+/cLZ2VmK/fTTT7WmSUTm0bZtW3Nn4V9lxowZ0n9et27dKnXd8nPC6NGjjZZubm6uiIuLE8uWLRMNGjRQnNvj4+ONso7evXtL6T7zzDPlSkNe/tJ2TiyPrKwscfLkSTFjxgxF2TwwMFAUFBRUOP3s7Gzh5uYmpTtv3jyNcUeOHFGcfx88eKA37ZycHMUyR48eLRPTpUsX6fNFixaJn376SVF+1/R48cUX9a5f/lvQ9ejSpYvYu3ev3m2R1+E6duyoN14IIXbs2CEt4+DgoGoZXdQcp1evXlVs38WLF/Wm++WXXyqW2bx5s8742bNnS7ErVqxQfGbIf1Bl79OK1EVNdZwSET2OWD43vgcPHojz58+L+fPnixo1akjnCl9fX3H//v0Kp2/sco9cZGSk1FYzatQoxWcRERGq2tJKFBcXK8ozVapUEW+//bY4ceKESE9PF+np6eLEiRPizTfflNrpbG1txS+//GJQnksUFRWJ1NRUERERIcaOHatoc3rvvfe0LvfRRx9Jcc8//7yIiooSdevW1blvW7duLa5du6Y1zVGjRinKCPoUFhaKqlWrSss0b968TIy8vbFWrVqq9klwcLAi3/fu3VO1XIny1nn69eunat/L/e9//5OWCQkJMSifJTIzMxVltm+++aZc6WhjjHpkCflx5+HhobWtVu5J+e0LIVTl09nZWUybNk1kZGQYlFcion87ls8NN2nSJOn88sILL6haZsmSJdIyfn5+FVq/sc/RpihLyqWnp4uaNWsK4FF/nIcPHyo+l+dZ07V2OXO1oe3du1eR5smTJ3XGm2Kfmqr9XAghPv/8c0XcwIEDxb59+0RiYqLIyckRFy5cEN98842ivvn666/rXb8uhu5TIcqWE7U96tevL5YuXVqmH05pDx8+FNbW1oo8vPrqqzrTtrW1FV999ZXOdOXHpKb+BZr4+/tLy8ydO7fM5+ao81Tk2pk5y+dffPGFtA4XFxejXOciIqpMavufs0O7CRnSoX3mzJmKE9zu3buNkgd5oURbJ9/SF6c9PDxEQkKC1jTlHeX79u2rMebixYuKNDV1Zi8hb+AreWi6QJ6RkaHo2Pn2229rTTM3N1dRMOrRo4fGOHmHdgCiZ8+eWjv+R0dHlymIfPbZZ1rzIO+soG0/GaL09xQaGipee+01nY8vv/yyTDrTp0+X0nB1dRU3btzQus4//vhDsc79+/drjJM3yOv7voV4dFGkcePGquMTExOlSoSNjY24deuW4vPFixdLaX300Uc609LncenQnpmZKVq2bCnF+vj4aOzILYQQ48aNk+ImTJigMw+FhYWiR48eUvzGjRvLxGzfvl363MrKSmsFUAhlx5iShzbyGAsLC63HU4nMzEzh6uoq5UNf/IULF0SVKlWk/7GcnBzF5++88460/p9++klnWtqcPXtWSqNLly7lSqOEmg7toaGhUkyDBg1Eenq61vS++eYbKdbS0lLrxUD592BnZydiYmK0pvndd99JsU2bNjVk84ioErBB3rj+DR3ab926pbchzd/fX2f5zxDyMoOu+oY+xurQ/vPPP+vcdgsLCzFq1KgyFxbKS162cHR0FHfv3tUYt3PnTkVDplryTvi7du0q87m8c9Dzzz8vPbexsRFBQUFiwoQJYujQoYrGeADi6aef1tnQqfbCUck+1Vf+njZtmhQ/YMAAVdt+4sQJxXpK38RsCLXHaXFxseLGxQULFuhNu2vXroq0f/jhB62xly9flsqq3bp1K3PRwZD/oMrepxXp0G6q45SI6HHE8nnFHTx4UG/5o2/fviI1NdUo6zN2uadEXl6eaNasmdRGlZycrPi8PJ1aHz58KF544QW9+bS0tBS9evUS0dHRBu2L8ePH60y3SpUq4uuvv9aZhrwdKygoSGrXAyDatm0rxowZI0aNGiWaNm2qSNvT07NMe2+Jb7/9VoqrUaOG1vbREv/8848ibU0d1mNiYhQxp06d0plm6esSAMSlS5d0LlNaees8HTp0kJZT26n866+/lpYJCAgwKJ8lli9fLqVhbW2tta5THsaqRwrxqAwvL2/qGuRD7kn67avNJ/CoI11sbKyqdImI/gtYPjfcsGHDpPPKm2++qWqZ33//XVqmZs2aFVq/sc/RpihLysmv0f/zzz9lPpenpa9Du7na0EaOHCml1bJlS73xptinpmo/LzFv3rwy/ZE0PVq0aKFzQE+1DN2nQqjv0F7y6Nevn8jKytKa3rVr1xTx8mPKxcVFDBw4UEycOFGEhISUuXHiww8/1JjmgwcPFHFqOuoL8aivYcky77//fpnPK6POY8xrZ+YqnyckJCgGI5w2bZpR0iUiqkzs0P4Y0NehPSMjQ+zfv1+8+OKLipPa1KlTjZYH+d2M2jqAl744ra9h+sKFC1Ksh4eHxhj5HXFqChDyOzkBzRfIly5dKn3u6emp987M0ndyahphr3SH9tOnT+tMU97wXLNmTZ2jfcgLidr2kyFKf09qHq1atVKkUVxcLN0lrOa7FkJ5R6S2u2zl+6VDhw5609y6dasUHxQUpGr75aMfls63/E7EhQsXqkpPG3N2aC8sLBS3b98Wa9euFb6+vlKco6OjOHTokMY0k5OTpZGXXFxcdFZcSshvztD03z548GDp89DQUL3pyUfUAdR1aB82bJjedBcuXCjFjx8/Xm+8EEK88sor0jK///674rOJEydKn23dulVVeqVFRkZKaQwcOLBcaZTQ16E9PT1dMfp96e0praioSDGThKYKoRDK70HfRZ7MzEzp7m0LCwuO8kP0mGGDvHH92zu0V6lSxaij6t25c0fUqlVLSr93797lTqsyOrR7e3uLPXv2lDvt0sLCwoSVlZWU/hdffKE1dtOmTYp6jFryixO//fZbmc/ljZclD39//zI3teXl5YkPPvhAEafrhusZM2aIFi1aiE8//VQcOHBA3L17V+Tn54vMzExx8uRJMWvWLGmWrZKHrpFbJk+eLMWpHdlJXucEUGYGLbUMPU7lv0Vvb2+dneU03dg5f/58jbHFxcVS+4Ctra3GRmxD/oMqe59WpEO7qY5TIqLHEcvnFaerQ7urq2u5RxzXxtjlnhKffPKJFL9y5coyn5enU2uJqKgo0apVK637yc/PT/z44496O5aUpqtDe6dOncSVK1f0pjFgwIAyy1arVk2Eh4eXif31118VHSe0lX+uXLmi6HyibSZEIR61rXbq1EmxfkdHxzJxxcXFon79+lKMvhH95Nc6Sh7Hjx/XvTNKKW+dRz5z6dKlS1Ut8/3330vLtGjRwqB8lnj66aelNIx5bdCY9UghHs3aKf9eTpw4oWq5J+m3X6VKFfHSSy+JX375RVy8eFFkZWWJ/Px8kZCQIP744w/Rt29fRZo+Pj5GvQGBiOhJxvK54UJCQqRzitqRkuWdkTWVvQxh7HO0KcqSJeQdtbVdT5enpa9Duzna0LKyshSjp6vpO2KKfWqq9nO5+Ph4MXToUK11Hnd3dzFz5kyDZ2IqrTz7VIhH5URPT08xZcoUsWPHDnHjxg3x8OFD8fDhQxEXFyd+/PFHRd0AeDTTkbbBOk+ePKlxO8eMGSMyMzMVsXfv3lX0CbKwsNBYh0tOTlakpbajtvxGGU2j31dGnceY187MUT4vLCwUvXr1ktKsUaOGSEtLq1CaRETmwA7tjwF5h3Y1Dw8PD50FPk3y8/PFwYMHxXfffSc+/vhjMWXKFMUI3fJRUXr27KkxjdIXp2/evKlzncXFxYrGZk0daNu3by99vmTJEr3bUXraHU0XyOUFzClTpuhNUwihGOFaU+FH3qG9UaNGetOTV+ImTpyoN15eWC1dMDSUMTq0nz9/XvrM0tJSVScGNXdVyxvk1RT2Xn75ZSle7UjZ8hsUBg0apPhM3nmpTZs2Ijs7W1WamlRmh3Y1D39/f50jKq1fv16KHTFihKp8FLlv9MUAADs0SURBVBcXCwcHBwFAuLm5lflc3hCg627mEqtXr1bkWRt5zB9//KE33T59+kjxaqd2/fXXX6Vl3nrrLcVnn332mfRZv379ynW3uryy4+zsLC5cuGBwGiX0dWiXd5Ly8PBQlV/53cgdO3bUGCP/Hg4cOKA3zSZNmkjxZ8+e1RtPRJWHDfLG9W/o0J6enl6mLtC1a1dpVGgAonXr1jpnX1EjLy9PdO7cWUrT3d1d3L59u9zpGatD+9GjR6VtnzRpknjppZdE69athYWFhQAeNcQ+//zzIjExsdzrEOJR47e8vNS5c2dRWFioNX7t2rVSbJ06dVSvp06dOtJyP//8c5nP5R3qgUcdsHU1YsqnDHZ2dtZ6o5qaEU+TkpIUI6fY2Nho7eQkH6lo1KhRetMWQoirV68qtk3biJ26lOc4vXDhgrCxsZGWadeunbh8+XKZuD/++EMxAlDJQ9sMXj/88IMUo+0CkyH/QZW9TyvSod1UxykR0eOI5fOKu3r1qlSemzx5shg1apTo0KGDYqr0Hj16GDw6tjbGLvcI8Wh2v5LyRPfu3TXGlKdTa3Fxsfj++++Fl5eXAB7NutejRw8xfvx4MWbMGNG5c2fFebdZs2YG7adffvlF2vevvPKKGDx4sKhXr56Unr29vXj//fd1dpQPCgpSbJeVlZXOdk15Ox6gvZPPiBEjFGl+8803ZcrfycnJYuDAgWXKZ1ZWVhrT/OmnnxRxoaGhZWZGzMvLKzOzbsnj4MGDWrdLk/LWeRo0aCAtt2LFClXLrFixQlqmYcOGBuVTiEcD9ZTUoQD9nYPUMnY9UghlXdqQzvtP0m9fTV5XrFih6Fg2btw4VWkTEf3bsXxuuJ49e0rnk08++UTVMvKO3drKXmqZ4hxtirLkgwcPRMOGDaVruNo6Qasp65YwRxuavK+MtbW1SEpKUrWcsfepqdrPS/zzzz+ibdu2Umzbtm1FaGiomDhxoujbt69iNiYXFxexbds21Xkorbz7NCsrS+9Nyfn5+YoB/ACItWvXaozVdLP4M888U2bW0BJ5eXmKG6c1tU/fvHlTkd7Vq1dVbZt8cFNNgxhWRp3HmNfOzFE+l88WDFRslisiInNS2//cGvRYsLKywpw5czB+/HhV8Q8fPsTs2bOxbNkypKSkqFpGTZyLiwvq1KmjM8bCwgJubm54+PAhACAjIwOOjo7S50IInDlzRnrdsWNHvevt0KEDLCwsIITQGnPq1CnpeZcuXfSmWRJ39uxZAMDJkyd1xjZv3lxvem5ubtJzPz8/vfGurq7IyckBAGRmZsLJyUnvMmpdv34d9erVM2gZ+T5s2rQpPDw89C4j39dJSUm4c+cOvLy8tMa3a9dOb5rR0dHS8x07duD06dN6l8nIyJCe37p1S/FZSEgIHB0dkZ2djVOnTqFJkyYYO3YsnnnmGbRr1w42NjZ6038c+fr6Yt26dfD19dUaI9+Xly9fxuuvv64qbQsLCwBAeno6cnJyULVqVQDA7du3Ff8V/v7+etNSE1OaocfJ2rVrsXXrVr3L3L59W3pe+jgZMmQIZsyYgeLiYvz999/w8/PD2LFj0a9fPzz11FOwtLTUm37t2rXRuXNnREVFITMzE+3bt8eIESMwaNAgBAYGGvU3Lv+9duzYEdbW+k/Z8t/rqVOnIISQvmtNWrZsqTdN+f+E/HdIRESPH1dXV3z33Xdl3k9LS8OcOXMwb948nD59Gl27dsW2bdvQu3dvg9chhMDo0aMRFRUFALCxscH69evh7e1d4fxXlL+/v8ZySVxcHKZOnYqdO3fijz/+wIkTJ3Do0CHUrl3b4HWkpaWhX79+UnnJ29sbGzduhJWVldZlqlSpIj3Pz89Xva68vDzpub29vcZ0S+oaAPDhhx8q6iulffbZZ1i5ciXy8vKQmZmJnTt34sUXXywT5+7urjdvnp6e2L59O5o2bYq0tDQUFBRgwYIFGo+/8my/fNsBzduvS3mP02bNmmHBggVSmfrEiRPw8/ND586d4evri7y8PBw7dgwXL14EADRu3BiWlpa4dOkSAGgsC965cwfTpk0D8Kh8/8EHHxi0LZqYY5+Wl6mOUyIi+ndq0KCBxvLEnTt38NFHH2H16tWIiIhAQEAAIiIi0KpVqwqtz9jlnuLiYkyYMAEFBQWws7PDsmXLKpQ/ebojR47Ehg0bAACDBw/Gd999h5o1ayrirl69itGjRyMyMhKxsbEICgrC6dOnVbUBv/TSS3jppZfKvB8REYFJkybh0qVL+Oqrr3Dq1Cls375dYzuVvIwCAM8//zwCAgK0rnPYsGGYN28ejh8/DgDYuHEjunfvXibuu+++w5EjR3DlyhUUFRXh7bffxtdff43AwEC4uLjg1q1b2L9/Px48eAAA6N+/P7Zt2wZAc/kMACZMmIA9e/Zg8+bNAB61PW7ZsgXdu3dHrVq1kJ6ejgMHDuDu3btl0tSVrrFVtNxXnjLf2rVrpes07u7u6N+/v8FplGaKemROTg5+//136fXo0aNVL/uk/PbV5nXcuHG4fv06Pv/8cwDAmjVrMHv2bHh6ehotH0RE9N9gjrKHnLHP0YBpypLTp0/H1atXAQDz589HtWrVyrO5CuZoQ1uzZo30PDg4WHXZwdj71FTt5wCwZMkSvPHGGxBCoEWLFvj555/RunVrRcz9+/fxwQcfYNmyZcjIyMDzzz+P8PBwBAYGqs5LifLuU3l/K21sbGywbNkyXL58Gfv37wcAzJkzB6NGjSoTW7puBgBz587V2m/A1tYWX3zxBZ599lkAwP79+5GUlKSoc5ZO01j/EZXxv2PMa2eVXT5funQpvv76a+n1J598gn79+hmcDhHRE8XYPeTp/5OP0O7v76+442v06NGiR48ews7OTnEn1Ycffqg33bS0NNG6dWuNI4PoetSvX19jevLR1urWratq23SNYp2enq5Yb3Jysqo0XV1dtaYphBAuLi7S50eOHFGV5ldffSUtU3pUbyGUd0hqmwpKTj7ih6YpI0sz5mjfFRkVr8SCBQuk5fv166d6OfmdiTExMWU+l2+nmhGrPTw8DD5+5Q9Nd1lu3rxZ2Nralom1t7cX3bp1E9OnT1c1FWxljtAeGhqqGHVqyJAhokWLFooYFxcXnXeCDh48uEL7ElCOznjq1CnpfQcHB1Xbdf/+fUV62shjHjx4oDPNrKysCm9XUFBQmXQXLFigGGGo5OHs7CyCg4PFl19+KS5evKgzbxcvXlRMjVvysLKyEq1btxZvvvmm+Pvvv/WOqK5vhPYpU6ZIn0+aNElnWiWSkpIUedJ0N778c213YcvJz2X6Rg8gosrFEWaM698wQrs+33zzjbSe6tWrl2vUlsmTJ0tpWFpaivXr11c4X8YaoV2XoqIixYgwffr0MTiNrKwsxeg/Hh4e4vz583qXk0+76+zsrHp98hHANc2a4+npqTiv37lzR2+avXv3luLfeOMN1XnR5pNPPpHSa9CggcaYadOmSTEDBgxQle6JEycU25abm2tQvip6nK5cuVIx25amR9u2bcW1a9cUx+/q1avLpCU/7nSVpQz5D6rsfVqRuujjcJwSEVUWls9N780335TOEc2aNdM5S46xqSn3yNteZ86cqTUtQ0dp/vLLL6XYXr16aZ1WXgghsrOzRbNmzQxuU9IlJSVF1K9fX0pz9uzZGuNeeOEFxXapKYN98cUXUnzLli21xt29e1f06NFDZ/nMxsZGzJs3T9HmX69ePa1p5ufnizfeeENjW6H8MXz4cBEbG6t4Lz4+Xv+OkylvnUde/1AzM6oQyhkcAwICDMqnEEIabRSAeO211wxeXhNT1CPXrFmjaJet6Exc2pjzt2+IrKwsxczK2kbrJCL6L2H53HDDhg2TziVvvvmmqmXUzPZuCmrO0SWMWZY8fvy4NJp6jx49dK5Xnr6+a6yV3YYWHx+vKAdv3rzZoOWNuU9N1X4eGRkpjZJds2ZNcffuXZ3phYaGSun5+fmpuo4uV9F9qpZ8VgQA4ubNm2ViStdffH199aZbUFCgKE/+/vvvis8fPHigSPPkyZOq8tu/f39pmffff7/M5+ao85RmjGtnpRmjfL5hwwbFSO/GqN8TEZmT2v7n+oejJaMICQnBd999Jz1Wr16N8PBwxMfHY/jw4VLc7Nmz8euvv+pM67XXXpNGtLazs8Mrr7yCv/76C3FxccjKykJhYSGEEBBC4Pr169JyxcXFevOpayRftbKzsxWvHRwcVC1XMkK0mnT1xWqKy8rK0hlr6LYbY19VtvLsw9Kx+vajmjsgKzrKc2FhYZn3hgwZguPHj2Po0KGwtbWV3n/48CH279+PWbNmoX379mjfvj0OHDhQofUby6effir9JyxZsgSbN2/G2bNnceDAAfj4+AB4tK8GDRqEtLQ0jWkYY8Rs+f6UHyPG+u1qou84MfZ2lZg6dSoOHDiA4OBgxYjsmZmZ2L17Nz744AM0bdoUQUFB0uwOpTVp0gQxMTF46623FHfgFhUV4fTp01i0aBH69esHHx8f/PTTT+XOf0X/8wDj/+8REdGTbcqUKWjcuDEA4N69e1i7dq1By3/44Yf4/vvvpddLlixR1GUeZ5aWlvj222+lc9+ePXtw7tw51cvn5uZiwIABOHr0KIBHI8mUzPiij3xEzMzMTOTm5upd5sGDB4rzuKZRP+Tpurq6olatWnrTlec3ISFBb7w+QUFB0vNr165pHEFFns+SES71SUpKkp47ODjAzs5OdZ6McZyOHTsW8fHx+Pzzz/H000+jevXqsLGxgaenJ3r27Inly5fj8OHD8Pb2RmJiorRc6VH///zzT2mWoTFjxmgccbQ8KnufVsTjcJwSEdG/x5dffglnZ2cAQGxsLP7+++9KW7e+ck9CQgI+/vhjAI/ajowxKwvwqBw6Z84c6fVnn32mc5bBqlWrSvkAgJ9//lljG5khPDw8MGvWLOn1ggULNF5rKD0SvJqystrzfo0aNRAeHo5du3YhNDQUjRo1gqOjI+zt7eHr64vXX38dp0+fxrvvvov4+HhpOV2zMtnY2GDRokU4d+4cpkyZglatWsHV1RW2traoW7cuhg0bht27d2P9+vWKspS1tXWljXxd0XKfmtED5Q4dOiSNNgoYNuq5NqaqR8rrs8HBwWVmLDAWc/32DeXo6KiYLTk2NtYs+SAioidbZZc9KkJNu2QJY5YlJ0yYgKKiIqPPylLZbWg///yzNCuPm5ubwbPyGHOfmqr9/LPPPpPqLVOmTEGNGjV0pjl79mzp+sGFCxekawFqVXSfqtW1a1fY2NhIrzWV+8pTN7O2toavr6/0uvQxZW9vr+jjYaz/iMfhf6ei1840qWj5fMeOHQgNDZWO4eHDh2udiYKI6N+m7LyQVKlq1qyJdevWIS0tDbt37wYATJ48GX369NE4hVBCQgI2btwIALCyssKePXvw9NNPa01fX2dGUyg9Hc6DBw9UdcaUT6GkLd2STq76YjWlWVnTgD7O5N+N2n1YOtYY+7Fq1arSd3n69OkKTw9comXLlti0aRMyMzNx8OBBHDhwAAcPHsTx48dRUFAAADhx4gR69OiBjRs3YujQoUZZr7F17doVu3fvRvv27ZGdnY2EhAS88847WLVqVZlY+W9r4cKFmDJlSoXWLU+vZBowfQw5lsqTD+DRVF8uLi5GSTswMBC7du1CSkoK9u/fj4MHD+LgwYM4ffq0VCEIDw9Hx44dsXfvXnTp0qVMGtWrV8c333yDOXPm4MiRIzhw4AAOHTqEyMhIZGZmAng0HfbLL7+Ms2fPYtGiRQbnszy/19Jx/N8jIiI5S0tLBAUFIS4uDgAQGRmJ119/XdWyX3zxBb788kvp9VdffYVXX33VJPk0lbp166JJkya4ePEigEfb36JFC73LFRQUYPDgwYiIiADwqOF227Zt8Pf3V7XeJk2aKF7fuHGjzHul3bx5U2caANC0aVNcuHABgLopSUvHGaOuWPrCSmpqapn35Hm/ceOGqnTl29+0aVPV+THmcVqtWjV89NFH+Oijj7TGHDt2TLpYZmFhgfbt2ys+P3XqlPT86NGjCAgI0JrW7du3pecnT55UxH7//fdo27at9Loy92lFPQ7HKRER/Xs4ODigc+fO2LVrF4BH5bmSKdFNTV+5Jy4uTmqXycnJ0dlmX9J2VEJ+3p8wYQImTJggvT5y5Aju378P4NH2d+jQQW9ee/bsKT3Pzs7GpUuX0Lx5c73L6SKfcv3evXuIi4srU0YtXcZQc+439LwfHByM4OBgnTHHjh2Tnqsps/v5+WHhwoWq02zZsiWqVKmiN11jaNKkiXTjRmWU+9asWSM99/PzU13n0cZU9chbt25J9TPAOB3vtTHXb7+ieU1NTa1QWkRE9N/0JLU5qWmXLM0YZUn5wJOhoaEqc/uoD1DJzblt27ZV3PAHVH4bmrzD7osvvljuwS+MsU9N0X6en5+vKC/26NFDZ3oA4O3tDV9fX1y6dAkAcPz4cUWHZH2MtU/1sbGxQbVq1aSBVjSV+6pXrw4PDw/pM2MdU02aNJF+A8b6j6jsOo8mFbl2pkt5y+fh4eEYMmSI1MdpwIABWLt2rc6b24mI/k3Yof0xYGlpieXLl6Np06bIyclBWloaZs+ejXnz5pWJDQ8Pl+7qCwkJ0dk4Bqg/4RuTi4sLbG1tpQv7N2/eRPXq1XUuk5mZqXdE5urVq0sxN2/eVNV4L9/+atWq6Y3/t5N/D6UL+dokJycr7oI1xn709PSUvsu4uDijdWgv4ezsjGeeeQbPPPMMgEfH15YtWzBz5kzEx8ejuLgYkydPxrPPPqtqRHlzaNKkCWbNmoW3334bwKMK0Ntvv42WLVsq4uSjAZUUsCtC/v0+ePAA6enpGm+ukTPFqImurq6ws7NDXl4egEfbVrqDUEVVq1YNgwcPxuDBgwEAKSkp2LBhA2bNmoWUlBQ8fPgQr7zyis7RW21sbBAYGIjAwEAAjzq8hYeH48svv8T+/fsBAIsXL8aoUaMMvgBUnt+r/D/P1taWHdqJiKgM+XldbQPawoULFaM8fvzxx3jvvfeMnrfKYOj2FxUV4aWXXsLOnTsBPDr3//bbb+jWrZvqdbq4uKBWrVpSA/OpU6f0NsifPHlSeu7t7S1d7JBr0aIF/vjjDwDqL1jI44xxs2Dpm+k03cjcrFkz6XlSUhKSkpL0jp4o33758rqY4ziVXxTx8/PTuU9LLkipkZWVhSNHjkivS3d8qax9agyPw3FKRET/LuUpzxqDmnJPidu3bytuVtNHft7v27ev4jN5u5ubm5uqC9ilR+MzxkyIpdsHNe370jeLqjn3G/u8n5eXh6ioKOl1586dK5wmoCz3GStNNeTlNvnNkrqUt9yXm5uLzZs3S68r2knclOXzn3/+WRqYxNXVFc8995xR0tXEXL/98pDntTyzqhIREcnLDmfPnkVhYSGsrXV3KTJXm5Mh52i1DClLZmZmKs7l+shHZ9Z0c2RltqFFRUUp+hWMGTNG9bKGUrNPTdF+npKSIvUzAMrWkbSRxxlSj6rMfQqoK/c1b94cBw4cAGC8Y6pZs2ZSh3Y19ZPExETFqOua/iMqs86jiynaGspTPo+MjMSAAQOkPlpBQUHYtGmT3v9iIqJ/E96+85ioXbs2pk6dKr3+7rvvFNOklLhz5470XM2oKiUFlMpkYWGBp556Snp9+PBhvcscPXpU6qivTZs2baTn8kKvLpGRkdJz+Yh2/1XyfXjx4kWkpaXpXUa+D2vWrAkvL68K50N+J2vJzASm5OzsjNGjRyM8PFy6EzYlJQXR0dFlYkumkXocvPbaa6hfvz4AoLi4GJ988kmZGGPvyzp16igqamqm0pLfVW1M8ptWKuM4qVatGt544w389ddf0nvnz5/HtWvXVKdhY2OD4OBg7NmzR3HzwbZt2wzOj/z3evToURQVFeldRv57bdOmzWN1PBMR0eOhpFEYUDcV448//oi33npLej1lyhR89tlnJslbZTBk+4uLizFmzBj89ttvAB7NkLV+/XqEhIQYvF75CDD79u3TG19yYxygHGFTTj6tbkZGhqKuqI28U3WdOnX0xusjb2CuWrWqxo73jRs3Vkwla6ztlzPXcSofdWfUqFEmX1+JytinxvI4HKdERPTvYmh51ljUlHtMQT4YR3p6ut42dKDsxXdXV9cK50O+3wHN+z4gIEBxkV7NDX3GPu9v27ZNGtHezc3NKCP4JyUlYc+ePdLryiz3yesRly5dKvM9lHbnzh1FRxo1I1GW2Lp1q9Rpx9LSEiNHjjQwt/+fqcvnP//8s/TclKNfAub77ZdHSecioOyotURERGp07txZOq/m5OTg+PHjOuPz8vIUfUEqs83JFOdoU5Ql1arMNjR5m2bTpk1VDSRZXmr3qbHbz0sPaqimXwygrEsZUo+qzH16/fp1xQAo2sp98mNKTd2ssLBQUZfQdEwZ+j3JY+rUqYNGjRrpTNPUdR5dTNHWYGj5/Pjx4wgJCZE6wnfu3Bl//vmnSes7RESPI3Zof4y888470mi6ubm5Gkdol4/A8uDBA53pPXjwQFFwqkzyQsO6dev0xsunstRGXhDduHGjYtRwTU6ePIkzZ85ozNN/VbNmzaSR+4qKilR9N6tWrZKeG2sfyisq69evR3JyslHS1ad+/fqKG0Hkd4OWkN8RXTKFj7nY2triww8/lF7/9ddfimMaeDSNV8ndmFeuXMH27dsrvF75iKPr16/XG//LL79UeJ2ayI+TZcuW6f3NG0unTp0UlRRNx4k+tra2iqmYy5OGvNHo3r172LFjh854IYTiv7QyG42IiOjJkJ+fr+iIoW/kinXr1mHSpEnS6/Hjx2PBggUmy5+pnT9/HvHx8dJrfds/adIkqbxsYWGBlStXYsiQIeVa98CBA6Xnv/76Kx4+fKg19uHDh9i0aZPGZeUCAwNRo0YN6fWWLVt05iE1NRUHDx6UXhsyyrw2q1evlp5rmz3MwsICAwYM0LiMJtHR0bh8+TKAR/Xf/v3764w313G6YcMGnD9/HsCjKYbHjh1bJmbmzJkQQqh6zJgxQ1quW7duis+6d++uSNfU+9SYHofjlIiI/j1SU1MVA1RU5giQ+so93bt3V33el4/2DUDx2cyZMxWf1a1bV3r+4MEDVaNAhoeHS89tbGwUaZSXvM3R3t4ePj4+ZWLs7e0Vo0zrO++Xjqnoeb+wsFCx/0aPHq1x9EtDzZgxQxpoonXr1ooBRkytcePG8PPzk17ru44i/7xly5Zo2LCh6nXJl+3du3e5B7Yxdfn8yJEjuHjxovS6oiPJ62Ou376h/vnnH8UsnyxHExFReTg6Oio6weprc/rjjz+kEZ3d3Ny0tg+agpp2SUOoLUuqPe+XvhE1IiJCel9TR+DKakPLy8vDr7/+qthOUzGkfG7s9nNXV1fFTObyOpI2t2/fltpQAWjsfK1JZe5TAFi5cqX03NnZWTFIntzzzz8vPY+Li5PasrXZtWuXtN8tLS3RtWvXMjEDBgyQ+qxdunRJ7+Cm8t+ptusclVnn0cbQa2dqGFo+P3v2LIKDg6WbFdq2bYudO3dy5iUi+m8SKvXv319tKP2fbt26CQACgJgxY4aqZT788ENpGQcHB3Hv3j3F57/99pv0ecOGDUVBQYHWtF599VUpFoDw8fHRGHf9+nW9MaX5+PhIy1y/fr3M57GxsYp1r1u3TmtakZGRwtLSUhGvKc2MjAzh5OQkxbz//vta08zLyxOdO3eWYnv06KExbtWqVVLM6NGj9W22GD16tBS/atUqvfH69pMh5N9TRdKbPn26lIaHh4e4ffu21tjt27cr1rl//36NcYZuZ2FhoWjUqJG0TO/evUV+fr6q/GdlZYns7GzFe6V/J9oUFBSIWrVqSevdu3dvmZguXbpIn0dERKhKV63yfIf5+fmK/Tt06NAyMWPGjJE+9/b21vmdyhUVFYnk5OQy72/btk1Kz8rKShw9elRrGjt27FBsk67TipoYufv37wtXV1dpmbFjx4ri4mJVy967d08UFhaWeU+NtLQ0YWtrK603Li5O8VlRUZGqdIYMGSKl8dFHH5X5XM3/SWhoqBTj6+srMjMzta5v8eLFUqylpaW4du2axjhDvwf5uczYvwkiqpi2bduaOwv/KjNmzJD+77p161ap65afE9SUSUvcv3+/zPlOl2nTpinOA2fOnNEa+/vvvwsrKyspdvjw4arPgYaSl3XUlLFLpKSkqI7Nzc0VTz/9tLSeGjVq6Nx3b731lmJfff/996rXpW39tWvX1lk2KPHBBx8o6md5eXlaY2fNmqUoB6alpWmNnTRpkhRbs2ZN8fDhwzIxWVlZqrdp4cKFin30yy+/aI09c+aMos63Z88ejXFFRUUiMDBQinvxxRd15qEyj1O52NhYUb16dWm9n332WYXTNPQ/yFT7VJOK1kWNfZwSET2uWD43XGpqqurY4uJi8eKLL0rnCTs7O4PKg6WZqtyjRkREhOr2maKiIuHh4aFoR9VV3snOzhbNmjWT4rt3764xzpB9Fx8fL2rUqCGlOXjwYK2xBw4cULRPRUdHa4399ddfFfvh1KlTqvOkyRtvvKEo7xtyfGmzadMmqcxlYWEhDhw4UK50ylvnEUKI7777TtGen5SUpDEuMTFRuLu7S7HLli1TvY47d+4oytUbNmwwKI8lKqN8Li8vNmnSxODln5TfvhDq85qSkqK43tKkSZNKqRcRET3uWD4vH3n/AFtbW3Hu3DmNcTk5OYrzj66+G2qY8xwthGnKkvI8qrnGWhltaPIyuKWlpep+BeVhyD41Rfv5wIEDFftIU78IuVGjRknxDg4OIicnR9V2VnSf5uTkqC67RUdHiypVqkjrmzhxos74nj17SrHPPPOM1r4WeXl5olWrVlLswIEDtaYp7wPRtWtXrWnu3r1birOyshLnz5/Xmqax6zymunZmqvL5pUuXhKenpxTv5+enum8LEdGTRG3/c3ZoN6HydGhPSUkRjo6O0nIffPCB4vO0tDTh4OAgfT5ixAiRnp6uiMnIyBATJ04UAETVqlUVhTlNTNGhXQhlga9KlSoaO7WHhYWJatWqCQDCxsZGb5pz5sxRFCQ+/vjjMgXUpKQkERISIsVYW1uLw4cPa0zvv9ihPS0tTXh7eysKT5ouFmzcuFFx/Oj6DyjPdu7du1fRuB0QEKCz43RMTIx4//33hZubmzh79qziszFjxojAwECxevVqrRW75ORkxTHp7OwsHjx4UCZOfiPIpEmTVG2LWuX9DpctW6aoBF24cEHx+Z07dxQd9WvVqiU2b96stVB8+/ZtsXDhQtG4cWOxePHiMp8XFRWJTp06KSqYmirZW7duFU5OTsLOzk5VQ7whjfUl5L/RkopWbGysxtji4mIRFRUlXnvtNeHg4FCmQtGjRw8REhIiNm3aVOamiBI3btwQffr0kdbXuHHjMvlp0KCBmDt3rtYO4w8fPhQLFiwQFhYWUjqRkZFl4tT8n1y9elVxTujUqZO4evWqIqaoqEgsWrRI8Xt64403NKYnBDu0E/2bsEHeuMrbob30+b08/5Xl7dC+ZcsW0aRJE/H999/rbIy9evWqGDlypCKfI0eO1Br/999/K27ueu6553TeSFtR5e3cMXXqVNGrVy/x22+/6WywP3TokPD391ds//Lly7XGy28ABSDmzp1ryOZotWLFCkUD7qJFixSNvUVFRWLhwoWKTspr1qzRmWZ2draibO/v71+mjJKXl6e4cRqAWLp0qcb0Zs6cKXr37i3++OMPrfs0OTlZvPnmm4r02rdvr7fRXV4W9/DwKPNbyc7OVhyntra2ihsLSzPVcfrxxx+LDRs2aLxgUVhYKNatW6fozO7v76/65lxdyvMfZOx9qk1F66LGPk6JiB5XLJ8bbsGCBaJ9+/ZizZo1IiMjQ2tcTEyMCA4OLtMurIna8rkpyz36GNqpVd6xBXjUoVzTBf64uDjFYB0AxM6dOzWm2b59ezFu3Dhx4MABrR0Q8vPzxfr16xXtjjY2NmXaJkt77rnnpPjq1atr/A42bdok7O3tpbgXXnhBa3oxMTHirbfe0toB4ubNm4pOFRYWFuKPP/7QmUchhBg5cqSIiIjQ+H1mZWWJ6dOnK65ZTJ06VW+a2lSkQ3t+fr5o2LChtHybNm3EjRs3FDHx8fGidevWUoyvr69BZeO5c+dKy7q4uJTrpsLKqEfm5eUpOrDMnj3b4DSepN++h4eHmD59urh06ZLWmF27don69esr0v3zzz8rlE8ion8Lls/Lr2vXrtJ5pV69emU6d6akpIjevXtLMe7u7mX6rJQwd/ncVGVJNQy9blAZbWjyfjR9+vQxdJOEEKbbp8ZuP5ffbAtAtGjRQmO/mPT0dPHKK68oYqdNm6Z/R/yfiu7TiIgI0bRpU7F06VKt13lyc3PFkiVLFP13nJ2dxa1bt3Smffz4cUUfgrFjx5bpQ3H37l3Rr18/RRvy6dOntaYZGxurqCeFhoaW6XsRHh6uKLePHTtWZz6NXecx1bUzU5TPb9y4IerUqSPFNmrUSNy5c0drPBHRk0xt/3MLIUrNdaPFgAED8Ndff6kJpf/TvXt37N+/H8CjaSnVThn4/vvvY86cOQAAJycn3LhxA25ubtLnM2bMwKxZs6TX7u7u6NixI7y9vZGYmIh9+/YhJycHVlZWWLlypTSljY+PD+Lj48usLz4+HvXr19cZU1q9evVw48YNAMD169dRr169MjGpqakICAjAlStXpPcaN26MgIAAWFlZISYmBqdOnQIATJ06FVu2bNGbZnFxMQYOHIht27ZJ73l4eKBHjx5wc3PDrVu3EBERgby8POnzBQsWYOrUqRq3Y/Xq1dLU8KNHj9Y7bdaYMWOkKWxWrVqFMWPG6IxXs5/Ukn9PFU3vwIED6NevHx48eADg0ZT1HTt2hJ+fH/Lz83HkyBHExcVJ8Y0bN0ZkZCSqV6+uMb3ybudPP/2ESZMmSVO2lqyrTZs2cHNzw8OHD5GUlITTp08jOTlZijl79ixatGghvZZ/L1ZWVmjSpAn8/PykNG7fvo2oqCjk5+dLy/z444+YOHFimTyFhYWhd+/e0uuOHTuibdu2cHBwkN6bNGlSuaYuKu93mJ+fj0aNGuHWrVsAgJEjR+Lnn39WxBw7dgwhISFISUmR3qtRowY6duwIT09PFBcXIzU1FefOncO1a9ekac4WL16M119/vcw6L168iC5duiAtLU16r23btnjqqadQXFyM48eP48KFCwCARYsW4c033wTw6FgqLi7WuB0WFhbSc5WnHgDA9OnT8dlnnynSadGiBVq0aAFnZ2fk5OQgISEBp06dwv3796W4rKwsODo6Sq/l/8k2NjZo0aIFfH194eLigqysLNy4cQOHDx+W8m9lZYXt27crpkqW/2cAj6Z9fuqpp1CjRg0IIZCUlITDhw8jPT1dihkxYgTWrVtXZrvU/p/8+uuvGDFihPQ7sba2RteuXdGwYUPk5OTg4MGDuH37thQfEBCAiIgIrVO2Gfo9yPdbREQEunfvrncZIqoc7dq1w4kTJ8ydjSdSSEgI7ty5o3gvKSkJd+/eBQBUrVpV43SSO3fuLDPteunzu77/ytatW5d57+bNm9K5w83NDXXr1i0Tc/r06TLvbd26FYMGDQLwaArIRo0awc/PD+7u7rCxsUF6ejrOnz9fZirJLl264O+//1ZMuVkiJSUFPj4+UjnRysoKY8eOhZ2dndZtkpsyZQoaN26s8bPjx49jwoQJZd6/cOECCgoKAAB16tSBu7u74vP27dtj+fLlZZabOnUqvv32WwCAnZ0dmjdvjkaNGsHV1RVFRUW4d+8eTp48qThPluRx4cKFGvO4c+dOPPPMM9JrT09PDBkyRPsGl/Ldd9/p/Dw0NFRRjmvUqBECAgIghEB0dDSuXbsmfTZ27FjF9KHaHD9+HN27d0dOTg6AR+Wcbt26oX79+sjIyMD+/fulYxvQXjYBgJkzZ+LTTz8FAFSpUgUtWrRAw4YN4eLigry8PFy9ehVHjx5VlKt9fHwQGRkJb29vnfnMzMxE586dFcdjQEAA/Pz8kJGRgfDwcEUZavXq1VqnZzXlcdq3b1/s3r0b9vb2aNu2LRo2bAg7OzvcvXsXUVFRivJ2mzZtEBYWVuaYLQ/5vu/WrZvGqYdLM+Y+LbFs2TIsW7ZM8V5+fj5iY2Ol182aNYOtra0i5tVXX8Wrr76qNV1jHqdERI8rls8Nt3DhQrz11lsAHrV3NG3aFE2aNIGbmxssLCyQmpqKM2fOKNqXAWDw4MHYuHEjrK2ty6SptnxuynKPPvv27UOPHj2k1/raZ3Jzc9G7d28cOnRIes/Ozg5dunRB/fr1UVxcjEuXLuHIkSOKdtbJkydjyZIlGtNs3bo1YmJiAAAuLi5o1aoVateuDWdnZ+Tm5uLWrVs4ceKEoq3N2toa69evx9ChQ3XmNy0tDV26dMHFixel99q1a4ennnoKRUVFOHbsmKJs4efnh+joaDg7O2tM7/Dhw+jUqROAR+3QrVu3Ro0aNZCTk4MrV67g6NGj0j60sLDA8uXLMW7cOJ15BB5973l5efDw8ED79u3h7e0NIQRu3bqFQ4cOITc3V4odO3Ysli9fDktLS51pGrvOUyImJgaBgYHIzs4G8KgsFRQUhNq1a+PWrVsIDw+X0ndyckJUVJSi/Vyfli1b4ty5cwCAl19+GT/88IPqZQHTls/lfv/9d6l+ZmlpiRs3bqB27doG5fVJ+u3L23Fr164ttUPb2dkhJSUFR48ela4ZlPj888/x0UcfVSifRET/Fiyfl9/t27fRoUMHJCYmAnh03u3WrRsaNGiAe/fuISwsTDrvW1tbY9euXQgKCtKYlrnL56YqS6ohP5ervcZqyja0u3fvonbt2igsLAQArF+/HsOHDzdgix4x5T41dvv5u+++i6+//lrxXtu2bdGyZUvY2tri1q1bOHjwoLS/AcDf3x/79u1T9A/Rxhj7VF5GtLS0hK+vr9TXBQDu3LmD6OhoRd3Mzs4OO3bs0Pq7k1u6dCkmT54svXZ1dUWPHj1QvXp1JCQkICIiQtFv6IcfftDYj0ZuxYoVinqPu7s7evbsCScnJ5w/fx5Hjx6VPnvqqadw6NAhjdej5IxZ5zHFtbOS/VPCWOXzDh064NixY9Lr/v37a7xGqElISAhCQkJUxRIRPQ5U9z83dg95+v/KM0K7EI/uLJXfWVd62cLCQhEaGqq4o6v0w9XVVWzZskXV6OumGqFdiEd3X7Zp00ZnXseOHSvy8vJUp1lQUCBef/11xZ2Emh4uLi56Rzv5L47QXiI6Olo0aNBA5z4EIHr16qV3+qWKbGd4eLho3Lix3nyUPJo3by4SEhIUabz++uuql3dychI//vijzjyVvguz9KO8o1RX5DtcsmSJtJyVlZXGkRXj4+NFUFCQ6n3h6ekpdu3apXWdx48fF3Xr1tW6vIWFhfjkk09Efn6+9J6zs7PW9OTLGurXX38VXl5eqretQ4cOIjc3V5HGs88+q3r5GjVqiK1bt5bJx+bNmxUjr+t6WFpaismTJ2sdsdOQ/5Nt27YpppnS9hg+fLje6c8M/R44QjvR44sjzJSfvOxiyEPTudvQEdrLs15t/9k7d+40KA1bW1vxwQcfaJylRtv2GPrQtf2lR6NT+9A2WnXpaSDVnN/1jXheenYYY3xPcnl5eeL111/XWZ6wsLAQb775pkGjfkdFRSlGMNH0sLa2Fu+9957OEYvko4Tre1hYWIghQ4aIlJQU1flMSEhQTHOq6eHo6ChWrlypMx1THqelR3/V9Cgp5+kaSdZQ5Z0lwlj7VFM+DHmoafMw1nFKRPS4YvnccN9//71B5xsnJyfx9ddf65w6XG353NTlHl0MHaVZiEezsuq7JlDysLGxEbNmzdJ5Pu3QoYNB+75169YiOjpa9TYmJCQoRu3U9ggJCdE642aJ6OhoVXls1KiR2Lt3r+o8lp51UtPD2dlZLFq0SHXZxNh1HrmoqKgyI/2VfjRo0MCg70kIIU6cOKFIQ9NMk/qYsnwu179/f2mZ3r17G5xPIZ6s374h+9DT01Ns3rzZKPkkIvq3YPm8YmJjYxWjIWt6VK9eXWzfvl1nOuYun5uqLKlGeco7QpiuDe3rr79WlHN1XSfQxZT71Njt58XFxWLu3Lmqyv4AxLBhw7TONqCJMfapoXWINm3aiJiYGIPWsXz5cuHk5KQzXVdXV7Fx40bVaf7000+Kfm2aHkFBQQaNNm6sOo8prp0JYZryeXmvVwKG9UMkInocqO1/XnYIEzK76tWrY9KkSZg/fz4A4Ntvv8Xbb78tjVBiZWWFNWvWYOjQofjxxx9x5MgRpKenS6M5Pvfccxg3bhy8vLxUjbZuSnXq1MHRo0exatUq/PLLLzh37hyys7NRq1YttGvXDuPHj0e/fv0MStPa2hqLFy/Gq6++ipUrV+Kff/7BrVu3kJWVBXd3d/j6+iIkJAQTJ06Eh4eHibbsyRcQEIDY2FisW7cOW7dulUZBt7GxQc2aNREYGIjhw4ejT58+Js1Hjx49cPHiRWzZsgU7duzA4cOHkZSUhMzMTDg4OMDT0xNNmzZF586d0a9fP42jmi5evBiTJ09GWFgYDh8+jPPnz+PmzZvIysqCtbU1PDw80Lx5c/Tp0wejRo1CjRo1dOZp7dq1eOaZZ/DLL7/g9OnTSElJUYzIYw7jx4/H7NmzkZCQgKKiInz55ZdYsWKFIsbHxwdhYWGIjo7G5s2bceDAAdy6dQvp6enSfmjcuDHat2+PPn36oHv37hpHsirRrl07nD9/Ht9//z1+++03xMXFIS8vD97e3ggMDMSrr76Kjh07Ku4Cd3V1Ncn2Dxs2DM899xw2btyI3bt349ixY7h37x6ys7NRtWpVeHt7o1mzZujatStCQkLg6+tbJo2//voLp06dwj///IMjR44gNjYWt2/fRk5ODuzs7FC9enU89dRTCAkJwUsvvaRxVKghQ4YgMTERe/bsQWRkJGJiYnDt2jXpjmgXFxf4+voiMDAQoaGh8PPzM8r2P/vss7hy5QpWrlyJ7du34/z580hJSYG9vT28vLzQo0cPhIaGomPHjkZZHxERPRn69euHW7duYc+ePTh8+DDOnj2L69ev4/79+ygqKoKTkxNq1KiBVq1aoVu3bnjhhRf+VeXjOXPm4KWXXkJ4eDiOHj2K2NhYqV5gaWkJZ2dn1KlTB23atEHfvn3Rv39/1SMEmoqtrS0WL16MUaNGYeXKldi3bx8SEhIAAN7e3ujevTvGjx8Pf39/g9Lt1KkTzpw5g/Xr12PTpk2IjY1FcnIyqlatCh8fHwQFBeHll1/WWEaS+9///oenn34a0dHROHz4MG7cuIGUlBSkpaXB0tISbm5uaNKkCTp37oyRI0eiadOmBuXTy8sLYWFh2LJlC3755RecPHkSiYmJcHR0RN26ddG/f3+MHz9e9QgkpvD9999j9+7dCA8Px4ULF3D37l1kZmaiWrVqqFOnDvr27Yvhw4cbvO2m8iTs0xLGOk6JiOjfY9KkSQgKCkJYWBiOHDkitemVtLM4OzujVq1aaN26NXr16oXBgwcrZuOrCFOXe4zN2dkZa9aswbRp07BmzRpERkbiypUruH//vpRfPz8/dO/eXbo2oMuhQ4cQGRmJ/fv34/jx44iLi0NiYqLUTubi4oJGjRqhffv2eP755xEYGGhQfr28vLBnzx5s374dv/zyC44dO4bExERYWFigVq1a6Nq1K0aOHImePXvqTatt27b4+++/8c8//yAqKgoJCQlITk6GtbU1atWqhVatWmHw4MEYOHCgQeX9AwcOICwsDPv27cP169eRnJyM/Px8eHp6onHjxnjuuefwwgsvaJ21tLKVlKXWrl2LTZs24fLly0hNTYWHhwd8fX0xbNgwhIaGGvwbKZlBEng0Ambnzp2NnXWjuHfvHv7++2/ptb6Zh7R5kn77ly9fRlRUFKKjoxETE4N79+4hJSUFOTk5cHJygqenJ/z9/REcHIyhQ4eWmUWJiIioIpo2bYojR45g48aN2LBhA86fP4+7d+/C1dUVDRo0wKBBgzBu3DhUq1bNKOsz1TnaVGVJUzJVG5q83Dds2DDY29uXK3+m3KfGbj+3sLDA//73P4SGhmLNmjWIiIjA2bNnkZaWhsLCQri4uKBBgwbo3LkzRo8erbEvii7G2Kddu3bF8ePHERUVhaioKFy+fBkpKSlITU1FQUEBXFxc4OPjg4CAAAwaNEhVHaq08ePHo2/fvli9ejX++usvxMfHS33MmjZtipCQELz88svSqPBqTJgwAX369MGKFSuwbds23Lx5U+oL1rZtW4wcORIDBw5UjGquj7HqPKa6dsbyORFR5bAQQs+cdv9H9ZDvRET0n7J3717pxofg4GDs2rXLzDkiIvpv4JSpRERERESPD5bPiYiIiIgeHyyfExERERE9PtT2P7eshLwQEdG/2KZNm6Tnho4mSkRERERERERERERERERERERERET/bezQTkRE5Xb8+HHFVFovvviiGXNDRERERERERERERERERERERERERE8admgnIiKNgoODsWfPHhQVFZX5rLi4GBs2bEDv3r1RUFAAAAgJCUHz5s0rO5tERERERERERERERERERERERERE9ASzNncGiIjo8bRnzx7s2bMH7u7uaNeuHWrXrg0bGxskJycjOjoad+/elWI9PT3x448/mjG3RERERERERERERERERERERERERPQkYod2IiLSKS0tDXv37tX6eevWrfH777/D29u7EnNFRERERERERERERERERERERERERP8G7NBOREQanTt3Dlu3bkVkZCRu3LiBlJQUpKenw8HBATVq1EBAQAAGDhyIQYMGwcLCwtzZJSIiIiIiIiIiIiIiIiIiIiIiIqInEDu0ExGRRs2bN0fz5s3NnQ0iIiIiIiIiIiIiIiIiIiIiIiIi+hezNHcGiIiIiIiIiIiIiIiIiIiIiIiIiIiIiOi/iR3aiYiIiIiIiIiIiIiIiIiIiIiIiIiIiMgs2KGdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMyCHdqJiIiIiIiIiIiIiIiIiIiIiIiIiIiIyCzYoZ2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIzIId2omIiIiIiIiIiIiIiIiIiIiIiIiIiIjILNihnYiIiIiIiIiIiIiIiIiIiIiIiIiIiIjMgh3aiYiIiIiIiIiIiIiIiIiIiIiIiIiIiMgs2KGdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMyCHdqJiIiIiIiIiIiIiIiIiIiIiIiIiIiIyCzYoZ2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIzIId2omIiIiIiIiIiIiIiIiIiIiIiIiIiIjILNihnYiIiIiIiIiIiIiIiIiIiIiIiIiIiIjMgh3aiYiIiIiIiIiIiIiIiIiIiIiIiIiIiMgs2KGdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMyCHdqJiIiIiIiIiIiIiIiIiIiIiIiIiIiIyCzYoZ2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIzIId2omIiIiIiIiIiIiIiIiIiIiIiIiIiIjILNihnYiIiIiIiIiIiIiIiIiIiIiIiIiIiIjMgh3aiYiIiIiIiIiIiIiIiIiIiIiIiIiIiMgs2KGdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMyCHdqJiIiIiIiIiIiIiIiIiIiIiIiIiIiIyCzYoZ2IiIiIiIiIiIiIiIiIiIj+X7t2bAMgDANRFBC9N8v+y0RmAJo06CLxXu3C5RUfAAAAIgTtAAAAAAAAAAAAAABECNoBAAAAAAAAAAAAAIgQtAMAAAAAAAAAAAAAECFoBwAAAAAAAAAAAAAgQtAOAAAAAAAAAAAAAECEoB0AAAAAAAAAAAAAgAhBOwAAAAAAAAAAAAAAEYJ2AAAAAAAAAAAAAAAiBO0AAAAAAAAAAAAAAETcq4dVdYwxvvwFAABYNOe0zwEAYBP2OQAA7MM+BwCAfVTV0t3Z3f3xLwAAAAAAAAAAAAAA8HKlHwAAAAAAAAAAAAAA4J8E7QAAAAAAAAAAAAAARAjaAQAAAAAAAAAAAACIELQDAAAAAAAAAAAAABAhaAcAAAAAAAAAAAAAIELQDgAAAAAAAAAAAABAhKAdAAAAAAAAAAAAAIAIQTsAAAAAAAAAAAAAABEPy3l21MvGGW4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# final results\n",
    "fig, ax = plt.subplots()\n",
    "table = ax.table(cellText = table_data, loc='center', cellLoc='left', colLabels=['Regression Model', 'MAE', 'MSE', 'R2'])\n",
    "table.scale(6,6)\n",
    "table.set_fontsize(24)\n",
    "ax.axis('off')\n",
    "plt.savefig(\"regression_results.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}