Python/ml/Flight_Delay_Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pn6Z-EQkmVos"
   },
   "source": [
    "# Flight Delay Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OG7QXsh5xQ0M"
   },
   "source": [
    "<img src=\"https://www.webintravel.com/wp-content/uploads/2020/05/den-belitsky-GettyImages-854673918-scaled.jpg\" width=\"700\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nSxBf4--gC0n"
   },
   "source": [
    "### Link to Dataset: [flights.csv](https://www.kaggle.com/usdot/flight-delays?select=flights.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5ffM0ge7FcKp"
   },
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "B8VRPIqIp8_V"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZckrxAZjFiC0"
   },
   "source": [
    "### Getting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "outputId": "4c728ed8-89ea-4357-b75e-fdf172ee0579"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3146: DtypeWarning: Columns (7,8) 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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       "      <th></th>\n",
       "      <th>YEAR</th>\n",
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       "         YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "0        2015      1    1            4      AS             98      N407AS   \n",
       "1        2015      1    1            4      AA           2336      N3KUAA   \n",
       "2        2015      1    1            4      US            840      N171US   \n",
       "3        2015      1    1            4      AA            258      N3HYAA   \n",
       "4        2015      1    1            4      AS            135      N527AS   \n",
       "...       ...    ...  ...          ...     ...            ...         ...   \n",
       "5819074  2015     12   31            4      B6            688      N657JB   \n",
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       "5819077  2015     12   31            4      B6            333      N527JB   \n",
       "5819078  2015     12   31            4      B6            839      N534JB   \n",
       "\n",
       "        ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  \\\n",
       "0                  ANC                 SEA                    5  ...   \n",
       "1                  LAX                 PBI                   10  ...   \n",
       "2                  SFO                 CLT                   20  ...   \n",
       "3                  LAX                 MIA                   20  ...   \n",
       "4                  SEA                 ANC                   25  ...   \n",
       "...                ...                 ...                  ...  ...   \n",
       "5819074            LAX                 BOS                 2359  ...   \n",
       "5819075            JFK                 PSE                 2359  ...   \n",
       "5819076            JFK                 SJU                 2359  ...   \n",
       "5819077            MCO                 SJU                 2359  ...   \n",
       "5819078            JFK                 BQN                 2359  ...   \n",
       "\n",
       "         ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  CANCELLED  \\\n",
       "0               408.0          -22.0         0          0   \n",
       "1               741.0           -9.0         0          0   \n",
       "2               811.0            5.0         0          0   \n",
       "3               756.0           -9.0         0          0   \n",
       "4               259.0          -21.0         0          0   \n",
       "...               ...            ...       ...        ...   \n",
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       "5819078         442.0            2.0         0          0   \n",
       "\n",
       "         CANCELLATION_REASON  AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  \\\n",
       "0                        NaN               NaN             NaN            NaN   \n",
       "1                        NaN               NaN             NaN            NaN   \n",
       "2                        NaN               NaN             NaN            NaN   \n",
       "3                        NaN               NaN             NaN            NaN   \n",
       "4                        NaN               NaN             NaN            NaN   \n",
       "...                      ...               ...             ...            ...   \n",
       "5819074                  NaN               NaN             NaN            NaN   \n",
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       "5819078                  NaN               NaN             NaN            NaN   \n",
       "\n",
       "         LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0                        NaN            NaN  \n",
       "1                        NaN            NaN  \n",
       "2                        NaN            NaN  \n",
       "3                        NaN            NaN  \n",
       "4                        NaN            NaN  \n",
       "...                      ...            ...  \n",
       "5819074                  NaN            NaN  \n",
       "5819075                  NaN            NaN  \n",
       "5819076                  NaN            NaN  \n",
       "5819077                  NaN            NaN  \n",
       "5819078                  NaN            NaN  \n",
       "\n",
       "[5819079 rows x 31 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights = pd.read_csv('flights.csv')\n",
    "flights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5djkFMqsGh8x"
   },
   "source": [
    "### Insight into the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "R7O_ogZ8p7F7"
   },
   "outputs": [],
   "source": [
    "# selecting a section from the dataframe\n",
    "flights_needed_data = flights[0:100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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    "id": "cikjVPHFvEqT",
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       "      <td>...</td>\n",
       "      <td>741.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>US</td>\n",
       "      <td>840</td>\n",
       "      <td>N171US</td>\n",
       "      <td>SFO</td>\n",
       "      <td>CLT</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>811.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>258</td>\n",
       "      <td>N3HYAA</td>\n",
       "      <td>LAX</td>\n",
       "      <td>MIA</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>756.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>135</td>\n",
       "      <td>N527AS</td>\n",
       "      <td>SEA</td>\n",
       "      <td>ANC</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>259.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>99995</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>EV</td>\n",
       "      <td>4992</td>\n",
       "      <td>N837AS</td>\n",
       "      <td>ATL</td>\n",
       "      <td>BQK</td>\n",
       "      <td>1108</td>\n",
       "      <td>...</td>\n",
       "      <td>1208.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>NK</td>\n",
       "      <td>394</td>\n",
       "      <td>N522NK</td>\n",
       "      <td>LAS</td>\n",
       "      <td>PHL</td>\n",
       "      <td>1108</td>\n",
       "      <td>...</td>\n",
       "      <td>1844.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>OO</td>\n",
       "      <td>5606</td>\n",
       "      <td>N582SW</td>\n",
       "      <td>SFO</td>\n",
       "      <td>BFL</td>\n",
       "      <td>1108</td>\n",
       "      <td>...</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>UA</td>\n",
       "      <td>1135</td>\n",
       "      <td>N38473</td>\n",
       "      <td>ORD</td>\n",
       "      <td>MCO</td>\n",
       "      <td>1109</td>\n",
       "      <td>...</td>\n",
       "      <td>1453.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>AA</td>\n",
       "      <td>74</td>\n",
       "      <td>N4XLAA</td>\n",
       "      <td>HOU</td>\n",
       "      <td>DFW</td>\n",
       "      <td>1109</td>\n",
       "      <td>...</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "0      2015      1    1            4      AS             98      N407AS   \n",
       "1      2015      1    1            4      AA           2336      N3KUAA   \n",
       "2      2015      1    1            4      US            840      N171US   \n",
       "3      2015      1    1            4      AA            258      N3HYAA   \n",
       "4      2015      1    1            4      AS            135      N527AS   \n",
       "...     ...    ...  ...          ...     ...            ...         ...   \n",
       "99995  2015      1    7            3      EV           4992      N837AS   \n",
       "99996  2015      1    7            3      NK            394      N522NK   \n",
       "99997  2015      1    7            3      OO           5606      N582SW   \n",
       "99998  2015      1    7            3      UA           1135      N38473   \n",
       "99999  2015      1    7            3      AA             74      N4XLAA   \n",
       "\n",
       "      ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  \\\n",
       "0                ANC                 SEA                    5  ...   \n",
       "1                LAX                 PBI                   10  ...   \n",
       "2                SFO                 CLT                   20  ...   \n",
       "3                LAX                 MIA                   20  ...   \n",
       "4                SEA                 ANC                   25  ...   \n",
       "...              ...                 ...                  ...  ...   \n",
       "99995            ATL                 BQK                 1108  ...   \n",
       "99996            LAS                 PHL                 1108  ...   \n",
       "99997            SFO                 BFL                 1108  ...   \n",
       "99998            ORD                 MCO                 1109  ...   \n",
       "99999            HOU                 DFW                 1109  ...   \n",
       "\n",
       "       ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  CANCELLED  CANCELLATION_REASON  \\\n",
       "0             408.0          -22.0         0          0                  NaN   \n",
       "1             741.0           -9.0         0          0                  NaN   \n",
       "2             811.0            5.0         0          0                  NaN   \n",
       "3             756.0           -9.0         0          0                  NaN   \n",
       "4             259.0          -21.0         0          0                  NaN   \n",
       "...             ...            ...       ...        ...                  ...   \n",
       "99995        1208.0          -11.0         0          0                  NaN   \n",
       "99996        1844.0            2.0         0          0                  NaN   \n",
       "99997        1232.0            7.0         0          0                  NaN   \n",
       "99998        1453.0           -1.0         0          0                  NaN   \n",
       "99999        1212.0           -8.0         0          0                  NaN   \n",
       "\n",
       "       AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  LATE_AIRCRAFT_DELAY  \\\n",
       "0                   NaN             NaN            NaN                  NaN   \n",
       "1                   NaN             NaN            NaN                  NaN   \n",
       "2                   NaN             NaN            NaN                  NaN   \n",
       "3                   NaN             NaN            NaN                  NaN   \n",
       "4                   NaN             NaN            NaN                  NaN   \n",
       "...                 ...             ...            ...                  ...   \n",
       "99995               NaN             NaN            NaN                  NaN   \n",
       "99996               NaN             NaN            NaN                  NaN   \n",
       "99997               NaN             NaN            NaN                  NaN   \n",
       "99998               NaN             NaN            NaN                  NaN   \n",
       "99999               NaN             NaN            NaN                  NaN   \n",
       "\n",
       "       WEATHER_DELAY  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "...              ...  \n",
       "99995            NaN  \n",
       "99996            NaN  \n",
       "99997            NaN  \n",
       "99998            NaN  \n",
       "99999            NaN  \n",
       "\n",
       "[100000 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_needed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wlm3Y2ePqnU6",
    "outputId": "5074fb81-9db9-47d2-ee36-18a8bbf96828"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 31 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   YEAR                 100000 non-null  int64  \n",
      " 1   MONTH                100000 non-null  int64  \n",
      " 2   DAY                  100000 non-null  int64  \n",
      " 3   DAY_OF_WEEK          100000 non-null  int64  \n",
      " 4   AIRLINE              100000 non-null  object \n",
      " 5   FLIGHT_NUMBER        100000 non-null  int64  \n",
      " 6   TAIL_NUMBER          99833 non-null   object \n",
      " 7   ORIGIN_AIRPORT       100000 non-null  object \n",
      " 8   DESTINATION_AIRPORT  100000 non-null  object \n",
      " 9   SCHEDULED_DEPARTURE  100000 non-null  int64  \n",
      " 10  DEPARTURE_TIME       97702 non-null   float64\n",
      " 11  DEPARTURE_DELAY      97702 non-null   float64\n",
      " 12  TAXI_OUT             97629 non-null   float64\n",
      " 13  WHEELS_OFF           97629 non-null   float64\n",
      " 14  SCHEDULED_TIME       100000 non-null  float64\n",
      " 15  ELAPSED_TIME         97387 non-null   float64\n",
      " 16  AIR_TIME             97387 non-null   float64\n",
      " 17  DISTANCE             100000 non-null  int64  \n",
      " 18  WHEELS_ON            97560 non-null   float64\n",
      " 19  TAXI_IN              97560 non-null   float64\n",
      " 20  SCHEDULED_ARRIVAL    100000 non-null  int64  \n",
      " 21  ARRIVAL_TIME         97560 non-null   float64\n",
      " 22  ARRIVAL_DELAY        97387 non-null   float64\n",
      " 23  DIVERTED             100000 non-null  int64  \n",
      " 24  CANCELLED            100000 non-null  int64  \n",
      " 25  CANCELLATION_REASON  2389 non-null    object \n",
      " 26  AIR_SYSTEM_DELAY     34625 non-null   float64\n",
      " 27  SECURITY_DELAY       34625 non-null   float64\n",
      " 28  AIRLINE_DELAY        34625 non-null   float64\n",
      " 29  LATE_AIRCRAFT_DELAY  34625 non-null   float64\n",
      " 30  WEATHER_DELAY        34625 non-null   float64\n",
      "dtypes: float64(16), int64(10), object(5)\n",
      "memory usage: 23.7+ MB\n"
     ]
    }
   ],
   "source": [
    "flights_needed_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0gyL1I0vq2y2",
    "outputId": "7b1bed43-e267-4248-c21b-a0ac21e1eaba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DIVERTED\n",
       "0    99776\n",
       "1      224\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no. of flights which were diverted\n",
    "flights_needed_data.value_counts('DIVERTED')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H3LsxmwUG46-"
   },
   "source": [
    "### Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "I8tU0O_ArHtb",
    "outputId": "5768bb85-23cb-474d-ea16-669d2805cc6d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x265d54604c0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0lqwC+aT/un2ozQ8a1956fvO9JZx44omjfi7UiOzAWE1aNGL6CVlJhk5PEEFAFbLDyGCZaNP6H6YqQ2coLwWJ9zue3JBIPHmiNMdKW1+AxTMLFRGLf37DCxuoLnQkfT+JtVgWvZZ8h4FOdygle/Dmlzbx1FXzeObTRn6wqEIRsctPLEkSy9vPqaS5x8ed/7uFWxbPwGHUEYxIaMXkeSuIRT/FmSYKHEYl4cKgFXGYdEnzXMVOU/rr9QQJRKLKvlecVIJGIyCIYkoW4kNraqm8vIaCDDNbW/tYPLMwScTi7R5+u1YR1w7P6CR7aPQGskuPHY91Vcj68QUjTM+3p52QFQXIthrUhI/DzP4y0VQhGx5DeSkYeL/3l2yRZzcO+uLX0OVNKih+fVMrP3w+du6SLBPf+XIZVoM27b59gTDfPnkyu7u8GHUi580pShHLW1/ZzPIFpVx+4iT29vi54/+2cOviGVQW2rjipJKUKMnlC7Fs/mQkKbZtcrYFURC48WvT8IaiPLSmlqtPKU17vU6zDknS0ekOYtSJTMqysLW1D4dJn7b/vlAEWZaonOBAFCAcldK2E4TY8c16zUF9nyrJqELWj8Ok45evb+WWxTO4Y/WWlDkyjUZmUpblSHfzmGK4mWsqgzOUl4LE+/3CZ02sWFieFAk9eNFs5W9gUpaF44udab8fWYb/q+vkpNIsGrp9/PD5dUmRT48vREWeRckQ7vQG+Z+PGunxhbAbdTz+wRYuPH4idyypQpblQUdJ7n9zO8sXlLJ4ZiE72jxY9Br84ShXn1IKxKKxh9bUcu1pZTzy7+089q0a7lpaTUuvj3yHib5AhEfeqRv0eu9cWkV7n59Mi4HjSjK4/ZxKIpKMJINJJ6a99ny7gT09fly+IGaDDo0oDPpyvHJROVbD6DyCw0E/DRs/HpVjxzGbzPS17gKqR/U8Q0EVsn4kWeLiuSU88k4s7NeIcFx/1qJWFKmckKHOyxxmJmVZePCi2SnDYeoLxfAZyktB4v1ucQV4dm0jj15eg04jpMypiaKA06LjzqVVSfNct51dya//vYMd7R7uOX8mmRYdTrM+ZXhw1TmVPL+2kRNKc9CIcNvZM+jzh/CGwknzbisXlQ0qlon1Xxa9Fo1GSEkOeeqjBgKRWFTU549QlmOm0GlElmUau33KcVtcAZ76qIFl80uZlGVmT4+PQDhKgV2POxCltS+IPxQh125EI6AI0cDoLyLJdLiDBCMSTb0BXl7XnCKQt59TSYZZTyASJRCOjM4XHo0Q6W0bnWMDns5mlp2/gKrzlnDccceN2nmGiipk/TjNRv78Xj23L6mi1xcmw6zjbx/t4psnTubkUqcqYkcAURQ4szKfaStOod0dINemZi0eLEN5KRj2/ZbhN/3zPYIAsgx/+H91LJ5ZyIbmPm54YQNPXjmPC2tShwd/9586li+YkjT6cde51Rh12qS2z61tShGMuEDFI5tMs46KPCvffvzTlLmo5QtKiUox8bMaNdR2eGns9nFSaSazijKSRLLFFeCx9+u594JZPLymDqNO5G/LTmDH7m4C4Sj/V9fBytMrKMwwEozKPPlhQ9K1P/lhAyVZZnLtRryBCHt6/TR0+RWBjLfr9YWYmGnGIWoJRUYn2U5ntjHllHNG5dgAnfWbqKqqOlxJHgdEFbJ+puRYWTK7mP9+6jPlD+aX583kxElq/diRRBQFSnOs6pzYITJUkRrO/e70hGjo8vPbd+qStscTLeJuF5OzLSnDg4tnFioiFm9704sbefiS45LatrgCPPlhA3+8/Hg63EEau31Kxt+qcyox6zXoRDGWlJFmCLI408xv3q5l5aJyBARufmkTV59Syp6eAG5/OCVaWrGwnL29PmV/TyiipNzfsngGN7+0iVu+PgOzQUOPL5R07UadSJbFQDASBWIp+UadSIsroLQz6kR+d+kc9nR7yXcYyTSrBdEjgSpk/YiiwNeq8pleoL79qxydjPRLgcWQ3q0iXtFj1IkUZ1rQufwp7QZLFDHpU+eeenwhIlEJtz/MnGIn0/LtdLmD/Prftfz0a1MJSzIZ/VmIA/uSYzNwydxi8h1Gev1h5XOzXkNzj4+X1jUnRUvPrm1UPBdjx4sqdlV17R4auvxsaXEzNd+WVgR7fSEyrXrCkkSWRZ8STd59bjXd3gBmg45gRGZavn1EvotjHVXIElDf/lVUhk6e3ZB2nujJDxuUmrJYhmDqfNLAYT2ICYfdoOOnZ07jl29sU9r+/OxKHvp3LTvaPTz2rRr+8J86NjT3YdSJZFsNuAMRGru8KfN1dyypYk+Xl9kTM9jZ7mZqf1byC581cd2iMiZlW/jvBVO4PWF4M7H/dyypIt9uUPoZisbahKISsizz7NrGFBH86demEwhHCEckLEYtgXCU+y6YhT8UIc9hRCvKbG8NUZpjI9umH7XRnv0le5hNZoRDfEF37a1nLCR5xFELolVUVA4KSZJ5e3sbtW0ecqwGpSbKH45SU5LJyf0uH/F2jV0+sm0GDBoNL3zewJen5ifNkT140WxOn5rLO7XtIAsEo1FEBB7t9zy9Y0kVj7xTqyy1cte51UzMNNDc7QdBRCPIWAx61jf1EpVg9YZmLp1XwtOfNHDNaeVMyTFS3xHglpc34TTruebUUioLHfT4wqzb04teI6LXCOQ5TOTaYskYFXlGtrUE8IckopJERIaH1+zg2tPKkGSSLLHuXFqFzajFFwxjM+mx6rX4QlG8oSgd7gAFDiMvfrGHRdMLyHcYOKUsd7gjPkNubHJkyVXnLE/Z7uls5ofnL6Cq6tBrzI6Ao8eg168KmYrKOGUo9l0Ha/E11P3ivogd7iC9/jB2o448u4HizOT2cTuqDneQDLOOiCQTjkbRihra3UHy7AbmFDnRakVe39TCD59fj9Os58KaIqZkW8mx6+n0BNBptFj0GuxGHTPyY5ZWa7a1IQgy2VYjYUkiFJbxhWILXkYkib5AhByrAYtBJM9hpLbVi8sfJsdqIBCRiEpRTDot7mAEfyiK1ajFpNMQliLoRJGdHX4eeSe2HEuBXc/0AgcSUTRCrO9mvQaLQUtEihCOyDhMWgJh6PAEybIYCIQjZJh0+MIRdBoN2dbU+zNEhryDvWCSfNaqv6ds76zfxJ1Lq8dMksYwUZ09VFSOJobi1HGwFl/D3W9Lizup7T3nz6Qow6y0jUQkPqjvYm1DN5IMr65v5qqTJ6PRCLS7g0gy1Hd46PWFmZJtVUTsOwtK6fKFqO3wUN8JWRY9v/9/O+jxhfjxV6fS4QlSnmulxxvEbNTRFwjT4gqy6tXNVORaWXl6Of6QhDcYYVeHl8k5Fra3eck0a3CadfQFw+i1Aj2+CFv7PNz3r+1JQ4ylORbsRpFH3qnlmi9PIddhRBAE2t0B/GGJG1/cmNS+0GmiJFPP1hYft76yb4jz9nMqsRpF3EGJihwDk7LVqYuRRo3IVFTGIfUdHs56+L1BvRCH2mYgkiSzsbmXNdvakeRYUXGLKzDofoOd49HLa5hflg3Aqxv2JpkPr1hYTrZVRyAs0ekNKaa7WRY9BQ4T//XUZ9xw5lSApHm1G782jRybkc0tff0mvWbCUQFvIAzA9Al23P4wOq0GbzBCjy+c5G5/x5Iq7CYRi15Prz+MTpQxG3SEIxLrmlxI/Y/CuKnv/RfMAgG6PUFsplh/V726mR+dUcH9b+5IueblC0qZPyWbK/76ScpnT145j15/GIdJxwmlWcP+vvsZ+tBiZp48+7KfpWz3tO5m5VerRmRo8WA5hCFJNSJTURkPDHVIbyhOHcO1+EoXicVrtlpcgaT94v3c0ebm6lNKFcGLG/z2+EJ80diDw6RTRCx+/offruWJq+byeUNvUgHzykXl2M2x7MMip5kf/2O9sp/TrMcbinJ3Qt/uXFrFJ/XtLDluIrs6vWxvdeMLRXnwrR0ppseBcGxF5t9/83g+2d2NVa9hdomTPd0+ohJpC6nDUYkJGSYKHAa8wShrG3r4/sIyJmSk92WUZGhzp7/nbe4AFr2WdvfoeCumMEhBtNFo4ultIcS6jWl2Gn1iJsOM+NCmKmQqKmOE4QzpDcWpI7FNooO8SadFkuSkIcjdXV46PcEUP8a4we1j79crxx5M8N7Y1MKZVQUpbvgDH+xOs55gWEpruvv4lXP5xXnVWPQafnB6OYUZZnZ1epmcbUkStkA4ZjL8xJXzCEuxNcFMOo1S8zWYCfAXjT2Y9RqmF9jxBaM0dPmSHPMTC6kLnUZkJHa2B5KGCm87u5KSLBMNXf6key8Kg38vOTYjHf1zgYeD0S6IHmuolb4qKmOEwYx9d3elLlsfd+ow6mJ/wumcOuJtSrJMXH5iCY+9X8/Da+q4+NEPeWNzK5IkKxmFL61rpscXZtn8Uq5dWMa1C8socBhxmvVMz7dxz/kzkfvb13d40gre1QumpDh41Hd4lD4CFDiMXHFSCa2uQCxz8LQy5XxOs55AOOY0v2r1ZiQJ7n9zG8FIzIz36lNKKXDsE+pAWKLHF8IdiCDJkGMzKHZYe/tr1xIx6kT0GhGbUQeg7Bfvb4HDyDWnlXH1KaUcNzGDHl8EWRYUEStwGFk2v5QWl5+7llZTkmVSjrtyUTkVeTa8oRC3n1OZ9L3cfk4l/97cjNOsY1q+aq82GqgRmYrKGGE4Q4FDceqItynMMHLxox+lCGTh8hPJMOuobfPw8rpmJjrNrN7QrCyUeefSSnZ3+fhRfyRk1Ik8culxeIPRtP0USDX4fW5tk1Lf5TTruems6fzoH+v56ZlT0zrVW/RavvM/n3PtaWU8u7YxZb2zWxbPwB0I4wlGeXV9M1mWWPGxRoA8m0GxwzppcmZKXdmqcyoJhqM8+u5OrjmtnOYen+K+kc4PcuWicoqcZkXEBn7+87MrMelERFGkqceHXiPiDkRp6fHw5JXzaHMHyLEZ+ffmZiqLMqkqsmEfhUU00zFSpsEjUXOWyGjVn6lCpqIyRhiu2/9QCvhFUcAXSi88a7a1MynLwjOfxtwsHnmnNkk44gtRJgrghiYXAoMv8ZLOlaMky8zvLj2Olr7YApWBsIQnGFWc5+PHfmhNLQ9fMhunWU+e3Zh2vbM7Vm9RhjpvP6cSvQY8oSilORZCksREpxmnWc/plfmKD2RsQU47f3l/J1fNj7nl39pfS/aTM6fy0zOn0e0LpZzroTW1PHnVvEGXk/n5q5uVdcWMOpHHvlVDYYaRTLOe3kCYHJuRPn+YyTl2SrMto7IS9KCMgGmwYgw8ookh1aNiMqwKmYrKGGG03P4HE8ioBDe+uFFxpxgoHInDbnHimYzpHN17/aG0tk19/jBajcgdq7fsW/crkn6dLqNOw4U1RTT1+Aa1sYov+nnrK5v567fn4g1IPPjWDu5YUkWXJ8iFNUVKoXWix+Gy+aWYdRrluC2uAGa9lny7gV5fJO25olKUO5dU0djjG7Qv8esPRaOY9TraPTKbml1KUfa1p5UzNcd2SN/hcBmJObKxZgy8P1QhU1EZI4yW2386gVSWOAlLaMSYQKUTjhSPRCEWZSU6uosCTMw0IQhCWtumBy6cze4uL4HwvnW/gpFoWnG1GrSU5Vj5xevbuHXxjP16OQbCEh2eIP5gtN8DsY88h5GolH4NM40Iuzq9HDdx3zpq21vd5NkMOMzpvRo1ooaCDCNGvSbt5/MmOVlQFovagtEIGlFDrlXPyVOy6XQHObViFtUFDoxG9VE7mqjJHioqY4j4cOGJpdmU5lhHxLQ6LpDPLj+RFYvKWDa/VEmpN+pEakoyeXV9s7JCepwXPostoZKYuFBd5ODOpVWK8/uf36unyGmmwGFiQoaRa04r57H363nk7Toee7+e7y8spyrfzoQMk+IEH1uCRcMt/UIVP/bt51SR79AzMTO2+Obv/1OX0mbFwnL++XmT8m+nWY/FGDMvfuKDBsLhKAUOY9pEj1lFGTz+4W7cwWD/kjEx30SLUUtjl5frv1KRdK7rv1KBWSci9NeSDbwXq86pRJZl9vR4CUUlen1RipxWKgszOLE0i8WzJjB3cpYqYocBtSBaReUYYbD0/jOm59HY48PlD7Gr05fkWPHri2eTadHjDUYozrQwOduCJMlsbnHR1hck06JHRibLYmBS1r7PWl0B8h1GKgscaLUifn+Y17a0cdNL+459z/nV5NmNtPUFybMbMemhekIm/mCIf23t4MYXNyo2VeW5VqwGLT9/dbPitXj7kiry7Xq6PEECERRHj5sWT6O5J5h0HXcsqeLTXR3UTM7ht+/U8vPF07Ea9biDYaISBMJRZFkiw2yg2xsm06IjKktY9BokWaDDHaSl109Bhhl/KBIrIYhGiUYltBoNlRPsB2s7NRwOuSB6OAwsnj4C3ooDUb0WVVTGOwfjmzhwn2KnmcYeX8rQZdxGakNTLyVZFjrdAUqyLaxKEI57zp/J16sKFCPggaL464tnk2sz4A9HCYYlSrL2Cd+abW00dHkpybai1wgEIxJ2kxaNICCKsL3FTX6GiVPLc3ljSyvvbm9l6ZxiOj1BJjiM9PjC3Pm/W5SMyuMmZpBr11PoNNHQ6UMURMJRCZc/RIc7xG//U6e0jSd6fOOESXyp1Epbn4w3HKHTE0KKRpmUbcFm1PDFHg83JwjtnUuqsBg0/PKNbVwyt5iSzJjtltWgRSMK6DUCJoNIRY7jcK1ZeMimwcPFljcRUavrL2RecqTny1RnDxWV8czB+Cbub5/ETEdJkvnfjS3c8M99NlK3Lp7B7/qNcuMLZT741nacZj0nl2axucWVVEvmNOtp7wuwq9OblFL/wIWzKXDoqe/08synjVw6r4Rf/XuH8vn1X6mgwGFkWoGd5z5tIM9u4N3trcydnMPP/rmBS+YWIyKAAFecOIk/v79LGRJdvqCUWUUOvKEo3Z4Qv3xjGw9eOEsRMYNWZHK2haZuL986uZQnPqjHqC2lLxAhGo1iM+kwG3UEI1E8fRKN3V6uPqUUiA2r3vzyJq4/vZyLa4qTrumWxTN49N2drFxUQUXu2PRNVAuiVVRGiHjx7Ic7O6nv8CBJ4zP6HwvXMZxi6eHus6vTq4hYvN3tq7dwzcJyNP1PCI0AV508mZ3tbv53UwtrtrUnJT6cN6eITm8oxa3jh8+vwxuKpbJfePxERcTinz/41g6aevx8uruHmknZdHtCnH98MY+8U8tVJ08G4L43t7G1xY0rEOa2s2cws9CuWEKFIzJ17R5Wb2jm3gtmodEILF8whcfer+fBt3bw43+sx2kx4PIF+e8FZWxvc/OH/1dHfoaZnR1eMs16EETF4eORt2PzfpefWILTrKcgw5y2BGDxzEJufHEjHd4Q29r6DvXrVTlE1IhMZVQ4WOf1scZYuY7h+iYOZ5+9Lp+SaQj7jIK7PKEUL8RZEx1c9fjafWn0/cXCxU4Te3r9ac/nCcRS23OshiQXjfPmFCEIUDnBjlEvcu/r2/jJV6fT5Q2xeGYhXb4QL69rTlsU7Xp3Jxa9Bn8kyuQsEwX2Yh54cxs3njWDtr5AUmR1y8ub+NVFswlEYq4hi2cWUt/uocBuQCsKBEKSkq4f73PcpsoXSp+WHy8B+KKxh7mTMof5bY4+I1UQHcfTuptNm0b/9/1g5+FUIVMZFQaLBqbtx3l9LDJWriNeC+Y06xUB0AiQbx+8yHZg/ViBw8iFNUX4QlHqOzxKfVqHO6QY7MYzA59d20hjty/puh9aU8vvL5ujpNH/6IypPPXRbi6uKWavy6+4ZKQWSsdWWTYbtPt10fjmiZPwhMJYjVqlJGCwoujff3MOm5v7EJHIz7DwkxfWc9XJk+n0BNMaAG9t7aMi14ZVr8Fp0jKrOANPMEqnN4RvEKeSshwrGYOk5cvyvlq8QDg6Ml/ySDICBdGJHA6z4UMxFFaFTGVUOJgIYiwyVq5jUpaFRy49jto2T9J8TUWeHYAWV2oCSLx+7J43tnLh8RPJdxjZ0+3jphc30eML8eBFs5maZ1Oy++LX9vDbtfzusjnc9OKmlOsORCQlBT3fYeDOJdV82tCNw6ijLNfCnUur2NPt47m1saVQfnFuNVoNPHDhLIz6mGD5w9G0LhorF5UzIcOJjMysiQ78IYkdbe609z8UlpiRb0HQaGlzB5QILtGJpCLXSnGmmRvPmk62VU+fP8hxxXZ0Gi2b9rq5+aVNLJtfilZML8DZVj0mvSbF6iou9PH/nzt7wih844fGsTZHpgqZyqgwXLulscpYuQ5RFJicZeXap79ImYNavqCUh9fUpQx7iqLAGdPzCEellPXAnvqogeufW8fv+iOsRAJhiWA4ZsibiFEnYu6PnjQC7On286M1+45749em0ReIIAN3n1tFu8uP06JjT0+AQDjKvf/axjVfnoLFaE57zhybgUAkglGrocsTq1O7+az0RdGbW/ooyjBj0kfJtZmoa/ckOZHMLLTzjRNKknwi771gJnt6gjR29yiCJwgxP8iBjiS3LJ6BOxhBq4VJWWb++u25uANhLAYtm5tdLJ5ZyLNrG/nJV6ePqxezoxU12UNlVBiKO/t4YCxdR/sga11JCU4XA5M5Gnt8adcDO29OEYGwhKV/uC8Ro05kd5eXFQuTC4DvXFpFMCLzSX0X0yc4khI74uuFPfJOHQ+vqeO7f/ucCAJ2o44dbbHop6HLT7snhNx/vIHnbOz2oddo6faEufXlzYQiMq19gZRC5BULy3l+bRO3vrKJXJuZujY3sydmKEObAN/58hRlUc34dTf3+Ln5pU0p1luJTiXXLixj+YJSvIEw2RYDdoOOcFQiFJWISDJaUWBqgZ3jJjr467fn8bWq8TXne7SiRmQqo8Jo2S0dbsbSdQwWHSaWgg4c9hxsaDTuEZhnM6TYV92yeAaPvB3zKEy0m3L5whh1Gs4/fmLKcc+bU5SSsXjH6i386YqaJOEIRCSaenzcsniGkmCRGCVOdJppccWOfd6cIu56bStOs54/fPN41jb0IMsoriQQE/dWd4jyfCvleTZWLirnoTW1hKKpNlWZZr2yLX4fX/isietOr+BX/96hmP+uXFROfoYJrQYiUej0hKjtj/hEAcpzrXx1ej56vWYkvtZRYbBkj5F2sx9JDsUZXxUylVFjKO7s44Gxch3pPBNXLirnyQ8blDYDhz0HEz9RgAcvmk1xpoXiTAu2K2r4eFc3sgzuQJgeX4hAONl0d/mCmKjl2Ay09QWSjhvP4kskEJbwBiMpSSD+UBQRWL6gFElGEaceXwijTkNUil1b/JgtrgCiAH9+rz7lOnJtRu5/cztTciyYtCLT8208evnx6DRiynXHrawSxavFFeDpTxr4/TfnEAxLmPUazHoNkizR2hdkeoGNaqODHJuBLk+IfLuR6gmOMS1iQNpkj9Fxsx9JDt4ZXxUyFZVxwsDoMMdqZFeXR5nLSjfsmU787j63mjnFGUmWSnajThGKAocxZc4otlaYhglOEznWmHjExSAQlgbNWCywG4lIshIpvfBZE9/9ciw1PiqTlLhy3ekVPPFBPd84oYSVi8oJhPcZC//1/V2sOqeS217ZrLRfdU4lZr3IikUVtPVHaL9eU8ujl8/Botdw29mVyvCiUSfiMGqVfjz+wW5WLiqnJMuC3ajFqNMQjobQaUQEwKzTYLBrKMqI3aNJ2ePrZSxdssd4crMfLqpFlYrKOCZuQbW/YU9JktnV6aWx24tZryXPbkgSMUmSaejysrahh1tejmXnlWSZuOvcapDBHYyQbdGTazcw0RmznHp9cws93hBFmWYC4Sj5diO7u5J9Gn9x7ky+NiOPDS09RKIQjEgEI1EyLQZc/jAZZh3+UJR2d5BWV4DnP9vDT746Da1GIBiJkGsz0dTj5zdvxxxGsi06Zkxw0OMLYdZrkGWZDIsWWRLxhSN0eUK0uAKs3d3Jf3+5lD5/FI0o0uMN47ToEEWZPl+UbW1uzHot5blWbl+9z4Lr7nOrmZJtZme7C61Wz1n9dlxjiCGPCdoLJslnrfp70rbO+k3cubR6PAuZalGlMvIcjPffoew30scYLQ5n3wYb9oz3ocsbxKTTsKPNkyQy8exGQCn4dpr1XH96ORX5dnzBCKGwxKqEB/0DF85motPSf3x47P92sXhmISadSKZZT6HTyJ+uqMHlC6PRCDz23k6iskS+Q49GEMixGujwhni/rhNJhlfXN7NiUQXlOVYcRh33nT+T6gIHOzr7aOqR6XAHsBo0LF8wJWk+7a6l1eTbjTT39NHuFrnl5U2cVZnHBXMnYjPqmJRlRiNoCIQjbG3tiS1R0wnT8+3kOXTkO3Lo8oSwm7TcfW417kCETIsejQh/+3g3XyrLG4siNizSzZEdrqLm/TFaxsOqkKkcFAfreDESThljxW1jrPYt3od73tjKxTXFxJ/H910wC18wQqc3yD1vbGVafmyxx3hfc6x6ijItfN7YowjN8gVTePaTRjY09/HD59cxPX8+Tb0BHvz3dsVxw2nWYzdq8YaiKUOFv3m7lvsvnIVOFNje7uGmBDFdsbCch9fs4OdnV6LXiYiizM5uL76QhM2oQafR8WF9d8oq1Te9tJHHr5xLrt3K5X/5hLMq8zhr5gR2dfq4Y/UWnGY9q86ppMMdSLov7e4A2TY77mAEVyDMpr0upd5t1TmVOM16zp41kf96ai3VRY4jPid6SKSZIzscRc3741AKng+EKmQqB8XBOl6MhFPGWHHbOBJ9G0q0F+/DsvmlscLdReW09wX5cUJN1XWnV9DtDRLuz+4rcBi5eF5xyuKbj767kzuXVHPfv7axobmPva4Aaxu6uWRuMf5wlBvPmkZZro2P6rtSBOdX/97BtaeV0dDlo7Hbl/L5w2/Xsmx+KZ819vLn9+q5+9xqpufr6PSEyLXpaXeH0q5SHQhLNHb5sBt1OM16Lp5XTLc3rIjY5SeWAOANRVMstkIRie/8z2cp2ZK3vbKZ3182h15/eFwW7g/kWCuIHr+xs8oRZX+OF6Ox30gfY7QYzb5FIhKvbtjLWQ+/xzf+9DFnPfweb2xuTTExjvdBEGDZlyaTYzWQadFz3wWzFMPdX/17BzqNqNSRnTenKMlvMO6Kcd3pU5GRWf7lUkqyTLgDEcx6DXaTjg/qOtCJGjY19VKea+PqU2J1WAUOo3Ld+Q5j2tqt+OclmSYseg2BsMSNL27EE4xS2+4BQUCrEZNqw+IYdSJd3hDdvhDf/XIpoYjMzg6PkrL/8Nu1aEUhpRwg/u/Baup84Sh5DsO4LNw/1lEjMpWD4mAdL0bCKWOsuG2kY7T6JkkyH9R3pRQ3p4v24n3ItekxaDUsf2pfBHL7OVX4QmH++O4uur0h7EYdKxeVE5X21V3NLLTzjXnJrhh3Lq3mwQtnEpUFWlx+7li9hXsvmMUDb25j+YIpSdFePMrp8YXIsxtTarcS70tTrx+LXkuBw0iLK0CnJ5bIEQhL/PL1rfzwjAruWlqdtCDnykXlmHUa/vjuTh64cBZ9gTBludaklP0ubyitcHZ5Qynb4jV1jV0+nCYdvzxv5rgr3B/ISJsGjwThnlYOtk7sQKhCpnJQpEvrTkz9HmwI7ED7jcS5jySj1bfdXV7WNnSnfTjvaHMr5xZFgWKnmV+cW43dpOWaAZZWv/1PLT8+Yxo/OXMqsgy9/hCf1HdxxcmTFaG5esEUftIvTPH9bn5pI7+/7Hjy7FryHSYCYQl/MMLimYWDOsc7TDpMulg917vb2/n52ZX8PCEd/rrTK5BkGW8owoMXzeKv/7cTi0FDabYVfyhKKCITjkKXJ5BUc/bkhzGRXDa/FBmwGnT8+NX1/OzMaWRZDaxYVIatv2ZsoHB2eoJJ9y9eU3fX0mruf3M7t59TyVljYK71kBlh0+BDxdPZzA/PX3DQdWIHYlSFTBCEicCTQD4gAY/KsvyQIAiZwLPAJGA3cJEsyz39+/wMWAZEgRWyLP+rf/vxwOOACXgNWCkfzbUDY5z9OV4cKOHhUJ0yDvUYo5lVGPc3fHb5ibS4AhQ4TFQW2NMef6j9iLeT5PRRTTgqc+Xjn3DDmdM5Y3oeb25tIypF8QSFpLYFDiMX1xQnRVp3LKnkmyeV8Nt3dnDb4kpWrd6MP5h+6ZIv9vRwakUOZr1GcbPXiOmHDIsyTP1iFOXuc6vo9IT4x2eN/O6yOXze2ItRK2LUivzijW0EwhKPvlvP7edUohVlApEoWWY9V5xUwk0vbuTqU0oVp5FENCKEoxK9vjChiEwgIinXVpJlShHOO5dWYdXvE7hYhFpJodPEn9+tp8cXYlK2ZewXOw+BsTZHFq9hG42MRRj9iCwC/FCW5c8FQbABnwmC8BbwbWCNLMu/FAThp8BPgRsEQZgBXAJUAhOAfwuCUCHLchT4PbAc+IiYkJ0JvD7K/VfZD4Olfh8o4WEknDIO9hijnVUoSTJvbm074PGH2o94u+2tfbF09QGFyisWlnP/m9u4uKaYe97YSmGGkeufW8cfLz+ezxt6koQvPn+U+L3c8vJm7r9gFieU5vCHd+u474JZZA6ydElUAk8wQigqcfuSKp74oJ7vnVqetm1Dt5/H3q/niSvnEY7GFtBcNr+U9XtiiR3L5pfyyDvbkvpy6yubeXrZCTT3uGnt9TPRuc9cON05ZhZlIBDzebywpihp0c6GLj+//391PHnlPDo8QXJsBhwmEVEQ+PMVNfT6w1gNWkIRiXte38aOdg/3nDeTKeM4weNYZlSTPWRZbpFl+fP+n93AVqAQWAI80d/sCWBp/89LgGdkWQ7KsrwLqAPmCYJQANhlWf6wPwp7MmEflTHGWE7GOJiVlkfj+MNt99zaJi6uKebZtY3ce8EsViwqY9n8Up76qIGGLj8P9xcNx30K2/qCiqt7PFlisOjJG4qgEWMP/7tf20pUlrhzaXWKUe/qDc3KWmaTs82cXJYDyNyxpCql7T8/b+qfkwpi1uuUuah4nwbrizsU4aE1tfQFo4j9y6u88FlTqoHxkir29voIR2Vq29wUZ6Y66jd0+en0BtFqRBwmDXtdQbq9IWwGLQ6TjqgskWHS8d1Tp/DnK2r42tEwpHiMctjmyARBmAQcB3wM5Mmy3AIxsRMEIbe/WSGxiCtOU/+2cP/PA7enO89yYpEbxcXFI3gFKkNlLCdjjPb6YkM9/nDbtbgCPPVRA+fNKcIfjPDwmrqUfTVibPjQqBMx6TRJru6CABV5trTfS7c3xPR8O0adSIsrwLo9fezucPP7y+bwxZ5eohI8u7aR751aRq7VwNaWPra3uDFqNaxvcvHSumYev3IuH+zsIirtM/U16mKLaIai+9Ywi/fpnvOr0/YlEI4tcvnCZ03MO7+a679SwYNv7eCpjxpYvqCU4kwzGSYdD6+pxRUI89Mzp9PuCWEKRtIeL9tqQEZiQ5OLijw7U3NsaLUi21r76PbJ9PrCFDpNzMh3jOsCaEh+9unMNiXZYywYBR+KIfBQOCxCJgiCFXgB+IEsy32CMOhNTfeBvJ/tqRtl+VHgUYhZVA2/tyqHylhOxhhtkR3q8Q+mXYsrwG/fqWPlorK0+9aUZFJZ4ODBi2bzl/d3KnNecVf3+y6YleI/+MCFs5Bkmcfe36k40oeiEq9tbqOuw8vVC6bgD0X48RnTyLRosRhFpuRY0Gk1/On9er590iSuOTXmi1jgMCUd+7azK2PzaVJsfuo3b9cqQ6P3/2t7Sl9uP6eSfLtBEdTdXV7y7IaYG73dSKZFz/bWPh54cwc9vhB3Lqki12ZgWys8t3ZPyrDr3edWY9ZrePKDBuaV5lCRbcVojD3yqooyRuT7HkskPvtMjiw50ts2hoyCD94QeCiMuteiIAg6YDXwL1mWH+zfth04tT8aKwD+I8vy1P5ED2RZ/kV/u38BPyeWEPKOLMvT+rd/o3///97fuVWvxSPHUDwAjwSRiMQH9V2sbehW3CtuOHP6iM6RDWfua2C7M6bn0djjUxJAip3mlDm3Ry49jlBETtp2z/kz+Xq/rZIkyTR2e+n1hfAEo7gDEXJtBsx6kb5ghEhUJhSRKM60MDnbQigUZWOLiw5PkGyrgR5fCHcgkrQq8l1LqynNMTM9187nzd0YtBp2d/m56cWNnDQ5k++cOoVef5hwJJaFaNFr0WkFTip1srXVjS8URRREvIEINqOO9U295Fh0THBa6PWHyDDriUhRANz+KA+8tZ0Lj59IodOERa/FoBXIMOnwhSW6PCGcZh0hKUpptpHNzT4aun0882kji2cWohFh9sQMSjIN1HX4yTTrqcy3YTbpD/n7PcIM22vxKPBXTGTwCGg0hUyIhV5PAN2yLP8gYft9QFdCskemLMs/EQShEngamEcs2WMNUC7LclQQhE+B7xMbmnwN+I0sy6/t7/yqkKkkkk48EgVgJM8zFBEf2C6daCWKW2K7pl4fbX1BfKGIIkiJJsBvb2+jts2TZBl17/kzqS500JqQJRk3AI6vt6URYut2/d/Odr598hS6fSHsRi21bR76AmGm5FrJd+jpckeISDLZ1pgTx4//sYGKXCvLF0xBQibfbiTLoqHLG8agFQhGoMsbxmbQohUhIoNWFBThy7LoEQSZogwtW9sCdHsi3PTSRpz92YtFThMmnYb6Dg99wSgaIeadWJ5nYnOLF18gTIHTjDcYIdtq4MPaNvKdVjLMOiwGkRMn5Yz7oUNUITtiQjYfeA/YSCz9HuBGYmL0HFAMNAIXyrLc3b/PTcBVxDIefyDL8uv922vYl37/OvD9A6Xfq0Kmkkh9h4ezHn4vZUjutTFgbQVD619cpDY0uTDrNRRlmBEEmJZvV8SsvsPDS+uakyyh4sdavqCUh9fUKSJelmvh3R2diuCVZJm4dfEMurxhbn15X0S2YmE5z65t5JpTpyAIouKSb9SJXP+VCj6s6+T0yvwkc9/bl1Qxq8jG5r3JhsU//upUCjOM9PnD2I16wpKMRa/BqBcxajS0u4P8oN/E+PITS5KGC+9YUoVeI9DiCmDWa6ic4OCyxz5Ouc57L5jFT/6xnuULSpk3KZMMk+5oGE4cspCZMvPk2Zf9DE/rblZ+tWoMDC0Onf0YCx8Z93tZlt/fz8kXDbLPXcBdabavBcbPt6GSwpF0rI+fe6iJHofS14Pdd38JIMVOM1taXLT2BTDqNMwuctDtC1Pb7laMb+NDmG19gUEtoeJuVoGwxA0vbODRy2sUEYvXma1vcg3qi2g36vnBgGzLB9/awWPfqmHZE2txmvWcN6cIQYDmHh+Ts8yKiMXb3/ev7fzu0uMw6jRsae1TIsEsix6nJeaU7zTr+fk5lWze6+LqU2Lrl73wWRO3vLyJZfNLeez9eq7/SgXBaDTtdQb7k0YkmZibx5Ef1T689BdEH2mj4OFysMbCqrOHymHhSLrCJ9ZiDSXB4mD6mrhsyt7egGIlNZzrHCwBJNti4OUNe5Oc46//SgWr1+/l1Gm5/OTMqZh0Gv783k6m5dvIsxsHXegycQwjEJZw+cOK+BQ7Tex1+RWLqAKHURElgPJcCxFZTiscTT1+KnKtfOfUMrb1i9NL65opy7WmbW82aNne5kkx9Q2E/cyamMF3v1yKJxhW9tEI8J0Fpfzh3XrFhurBt3bwpytq0n+n/UkjogAZZh2+QHS/9/5oY6wVRI82437QWGV8MNr1W0M598C6qsGyKYfb17jwnfXwe/xne2daP8ShXGc823Ng/4KRqCJi8WP+7eMGLplXzKPv1nPds+v5wbPrOH9OMX3+EJOyLFTk2Vi5KPlaVy6K1XfFiblziFxxUgmPvV/PDf/cyB/frSfHZuDWxdO5/isVaMVYJPTn9+rxBKM4TLq0Jr65dgOXnzSJ659bx8Nr6vjze/VcXFOMSadJaV9T4kCWwR+O8oPTy/nRGRVcfUopgXCUkmwrgiAQCEdp7wvy6Lv1PPJ2HX98tx5fOMqVJ5dg7J/rCoQlfMEIty2uTLrO2xZXsqfLy4+/OpWyXCt7urxYjePfrUNlcNSITOWwMNr1W0M5d7wWK15XdUpZNnMnZaZESsPta6LwxaOFA+0bzyxs6wviDUUo6U/YSGe99eaW1pRjXjK3mNa+QNKw26rVm/mfZScAkG3TY9SJPHHlPP5vZyd6TUxs9FqBa04rQyPGkiW6PcEkl3inWU97XzApSeSWxTNwB8I8+u5O7jmvmpWLypM+v+vcKsIRmVte3pQ0tBiMRLGbtdy5tIqbX4p99t0vl2LSa7ny8U+VRI5H3qlLSsHXa6Es18b3nv48SbwfWlPL7y+bw7bWmLekUSeSadHzp/d2cu8Fs/CHIpj0Wp78oJ6ffm06Pd4QJoOGXm+IAodp2L83KuMHVchUDgtHskg6XS2WUSdy3nGFaYf7htvXgcJ3oH0HyyqMD0EOtN6ym5INcAscRuwmXdK+ccf5Tk9QSQbRiiKzimJJID/7ZyxN/vsLy5PS6u85f2ZSX8+bU5Sy/Mkdq7ewclE5F9cU4wpEKHKauf+CWexodxOVoMcbossdSpucMTHTzOQsM9efXo7VqKO1L8Cjr8esqdKd67f/qeMnX5026BxfpyeENxRVvBPD0SgX1RQrJscxYa3GYdLQ2B3F5Q8xwWmiZAzUMB5ODqf7/UgWXB9s4bQqZCqHhSNZJD3ccw+3faLwxe2UEh/mA/fd3eVlQ5qEisEW4Cywm5KioAtrigZ1nM+zGfhoV3fS3NOPzpjKny4/nl5/RFluJb5ffYcnSSQHiyhzrAZ+804td59bTXckhE6rIc9u5I7VW/jeqWXMmujgwppUL8ebX9rEo5cfjysQ5cF/1/YPIQ5+rkvmFtPY7UMg/QtBpyfI3ElOFpTPoy8QJsOsx6TT8qcravAEImRb9dS29uDym5mUZSbDrB8zNYyHlcPkfj/yBdcHVzitCpnKYWEkXO8P17mH2z5R+FpcAZ5d28ijl9eg0whpsxb3l1WYbviyJMtCcaZZWcqkMMOUdt8pOVYCkWhKlHP/m9u574JZ1La7U/Z7bm2T4ugRCEuDJok09vhYPLMQbzBKXbsHiCVzLJtfSk1JBh2eYJLJb2K/PMFIkrdi4vEHnmui08yP/rEep1nPj86Yyv1vbk9KcDFqRTSigC8UQZLArIetrb6kRJg7l1RRkKGnMOPIl1QcKQ5Xskfc1f5I16mpQqZy2BgJ1/vDde7htB+u8O0vqzDd8KUoClROsCPLsKPdjVmffq0tu1GLJ5g+HX1Xp1fxU0z8vMcXoiTTpIikRa/h9iVVKTVkT33UwIU1RRi0Is+tbeI7C0q5ZG4xD62pJdOsoyLPxua9rrT9yrLomV5gTzIAfvjtWl74rCllvk0UBWUI+LH3d3HtaWXkWA3kOYzsaO3DaTHQ2usjy2piSo6BCXYbex0hHr38eHp8YZxmHVoNFNiPraHEYx1VyFRURoDhCN+kLAvVRY6Uh/j+hi+LMy3UdXgw6TTc/+a2lOHLVedUIopQ6rSkFZNgROKx93cqiRdK9LK0igzzPusmVyCKXhtOWsgyvtrz9Hw7ZkPMjPgP79ZzxUkl3HfBLBwmLU99VM/XZxamXNOdS6sw6wXcAUHxVnzqowZ+ddFstrb2AXDtaWUEIhKigOK1GBez+9/cgVEn8sfLj2dGoQNkCYPWQJ5DQ77Vhl6v4cTJOezu8qLXji07tCPJSMyRDWXua7TNgIfKqHstHklUZw+VOEeiwPlAx4xnLfYFwjhMOnJtBoozBz924j6BcCxDr8sTJMtqIM9uYKIzJoKvb2rlh88n2HCdN5N8h4FuX5gihxFPOEq3J0S+3Uj1BAcAr25qUYbnSrJMrFhYwU0v7Ruuu+3sSrKtek6cnMGbWzq56aWNVORauXZhOVajhi5PiA53kEKnGatBgycYs51ymDR0uGM2V1FZwBOM4AtGMRk0NHf7aHYFlILoqXk25hSb+Xi3N6kO7+5zqynJNCEKAnqdSIYZ8qz2o8FyargM3dnDkSVXnbP8oE8UX9F5KHNf+3HiGGmOjLOHispYINHWKf7QrC5ysHBq3gEF6VALudOJIKBsk2SZXQn+gen6JUkyuzq9NHR7sRq0WAwaopKMQash22YgyxITsfjq3AadkDRUaNSLfN7YywSHke3tnqSI7IELZ7GgPJM8m4H7LphFrk2HTqOhudevHEMUwG7UUuDQs7XVjUErcu/5VdhMeqISbG9xYzPpuPdf25PEJ9sqEZVF3MEovf4IOlEkFJXItesx67S0asSkpJQ7l8YemnaThj9dXkOLy49Jr+WJD+q5eG4JU3JM6DUiOWbbsShiw+JQ58jGytzXUFGFTOWop7HbS20aF4myHCuTsvc/FHig1a7jJApWgcNIVIJ2d4BIVObmlzfS0OVXnOuDYZl7/7VVcWqfXmDn9+/UsaPdk9KvdEK6clE5T34YG+6LeyDGHfx3d3m59ukvlP7ecOZUGrpizvA3fHUaOzvdXH1KKS981kSLK8C9/9pGMFLBjS/GIqxvnFBCry+UlDACseHJRy8/HodRy+MfbOdnX5tGXyDKhqZeynNt3P9m8mrPN764kT9883g0AlgNGtr6Qlz/YiyJ44dnlFOSZaGx25dUB3fzS5t46qp57O0NJnk2rlhYziPv1HLf+bNo6g3hDfYxu9g5cr8gKuMeVchUxjVDGfZr6wumZPI9tKaWOcXO/QqZJMl0uINJD9v4CsyJ2YWJYhMv8k1X49XiCrChycXL65q5uKY4xQy3yxvEH4rS5QkxKTvWh3RC+tCaWlYuKo8ldkSi3L6kiu0tfXzR2INEzEJqZqGdqxdMQStCU7ePS+eVcM+/tinieeviGfz+P3WcUpHLjS/GXOZ/+NWp/PdTn7Hq7Mq0CSOtrgCCYOL6r1TQ0B1IMgJesbCcNza1cEpFrmJptaO1j5kTMwhH4emPd/PIN47DbIgNQ+4Y8GKxrw4ulLa0YNn8Uto9QXa0uZk3KfPQfmmOAQ51jszTuptNm47cPONwhytVIVMZtwx1WRZvKJL2wewLRYZ17PjDtscXSsouTBSbdEW+8Qfxb9+pQ5Jh8czClHqruBnu6g3NzJ7o5MOdneTZjWldRpxmfdqC6OufX8eKRRWcMSOb06YWKEXCKxaV8fK6Zq46eTJdvhCSDNta+/jWlybR5Q4qhcwd7iCBcMwH0agTk1w6NAI4LXqiUpSILKQYAT/8di1/vPx4fMEo3lAEo06D3ahFI0BTt5fLTpxEbbsHjQATMsyD1sFZjdq035VGjJkKRyXwhY4t38SD4hDryI6k2fDBGAerQqYybkkXrdzwwgacZj3zy7KVyKwkM30mX5ZFrwjGwEgu3bHjD9tp+fak7MJEsRmsoDgepWgEkBPaJBrzTs+3kWudzHf/9pkiUH+6PNUUd7CC6GXzS7npxY08fuVcvv3XT5XPtaLIJXOL8YWjKcOrxxVncMVJ8Mynjdxz/kxKskzotSI3fm0a3lA02YpqaTVTciy8vb097TV29AW5OSFtf+WicibnWCjNtdLtCfPMp40sXzCFHWnq2QJhieJMMzaDJu13NbsoA7NOZPWGZk4pzx7S78exjGoarKIyCkiSTH2Hhw93dlLf4UGSDj1bdmC0UuAwsmx+KS0uPxubXco5JmenmvHeubSK7z/zBd/408ec9fB7vLG5NalPg/ktTs+3c8b05GSMPLuRkiwT15xWxtQ8W1pTXVmO/X9GgZ0Z/TVVMwvtXLuwjMfejxnj/vD59ei0IhW5VuV8N7+8kXvPn4lRJ1LgMLJiURlTcqxcfUopBQ5jUt/iItrhDiX1fXK2hYlOM8982siy+aVcu7CMq08p5ZlPGwlGJCZkmLjm1CnIssx1p09lb6+PsjxrSmR500sb8QQjSP3XMvAafaEI914wi3vOq+a+C2axZmsrWRY9ne4Q3lCExTMLuWP1lkH3zzDpePrj3dx9bnXKdyWIMn/7eDeXnVBCvv2wZMipjCPUiExl1BmtJVwSraEKHMYUn7/EcyQWLJt0GlY88wUNXX4gfQLHYH6LW1v7mJpvS0r0KHaaFQ9Dp1mfUksVN91dvqAUjUbAbtJwz/nV6DUarhsQ9d2+egsPXjSbO1ZvocUVoKHLz4QMI8/+14lsb3Nz6yub0869JYpl3DA4PjQoSbHhwoHzcisWlhOVJNz+ECaDjisfX5uUQeg062lxBZTrDIQlvMEor65vTqlju+f8aoIROcnz8LbFlehEgeZeP4FwVHH3SGfjdfe51fzh/9Vx+YmTyc/Q8eRV8+j2hjDrNZj1GkRBZtbETHLtRooz1WJnlWTUOjKVUWe0VmaWJJlXN+zlhhc2KIstDuUcH+7s5Bt/Sp0If2b5CZxYmq0c+6V1zSkJDU991MCvLp6ltAPY2e7h67/Zd30FDiMX1hRRkWdjR5ub59c2KYJg1Ik8+18n8kF9F/5wlIfX1KX0Y8WiMqISirnxGytPobHbx/KnPku5vvh1Kys4n1ZORa6Fhm4fe3sDiqA+evnxrHjmi5T9//LtufR6Q1z//PqUz+KrSSdu+/MVNWxocvHs2kZ+ed5MPqzvQq8RmVPi5KrHP005xp+uqOG/nowtuHnr4hmKcMeHVDUifGlKNmEpgl6jQRBknv90D4tnFeHyh8myGtjb42Vytg27SceUHOuxXOw87BWih8pIGv8eKrE5siXp5sjUOjKVI8doLeEiigJfryrojxz8Qz7HUNztRVFgTnFGWoeLgTZSDd3JK0+3uAI8vKaO33zjuBShCoQlWvoCePsTFtL1IyrF5trikUpUglZX+ntYkWfl3gtmgSwp1lI3njWDHKuBG17Yl5CxrTX9vFSPN4QnmD4ZpjjTrPQvPudl1Ik8u7aRxTML6fWFeXhNHdecVsaebl/aY3gCEcWl4/f/qVN8HWMWVPXcvqQKrUbGqNWj1wqIItRMzlFE26gTuX1JFXaTSHmeDZUhMoxkj5E3/j1Uhm8crAqZyqgzmku4aLUi88uy2dic3ucv3TmG6m5fnGlhWr79gO0sg3gfWvTpExcKHCa2tvTx0rrUIbpbFs/g0Xd38uMzpvG7y+Zw8qQsvmjupcMTTHusPd0+Hnmnjl9dNJste/to6PJj1mlo7k0WvlBUSru/RhCYkGFK+1mrK6Cs3SbL8OSHDdiMWq49rZxbXt7E7y6dg1EnIggM6v+YZdUr2zc099Hxdh3LF5RSkRsTJYdZQ12bl0KnCTkgk20zsKOlh8evnEeHO0COzci/NzdTU6LWjQ2H4SR7jLfi53SoQqYy6oz2Ei6iKFBd6BjyOYZq8jvUdnl2Q8q82MpF5eTaDWn7VFlgp8Xlx6jTKAkYGhGm5dv5y/s7ufa0coqcJmbk29HrY8ulPLd2D9edXsGv/r1DOdZ1p1fw9CcNsTk4f4gnP2zAqBPJtOrQiMnRXjqD3pWLyhE1AlFJSjEKvnXxDP747k5lHhFix5vgMJFp0fLnK2oIRaOsXFROIBzliQ/quW1xJatW75vDW3VOJTaDhh9/dSr39bt+9PhCWPRa7ntzGysWVdDljqDVimSY9PQFQ5Tn2JkzKYdv//WTUfldUTk6UefIVA4L8cLl0VzCZSjnOBjfxAPtM9ACSxRgZr/VFJC2T5GIxKcN3XR5QgQiUSx6LVqtQK7FQEiSyLIYlLbxZJl73oi5gZh0IrMnZhCVJHQaDXtdftr6Ajy3dg8/+ep0FlXk0OjysKHJw80Jfon3nj+TTk8Qi16LxaAlw6JjZqGNra19aAUNYUmmzx8m06Kn2xdmZ3vywp+/OLeaE0ut9AVkmnqCaDUCGkEkEI7iD0fp8YYocprxhqJkWnSEoxLT8gysb/KzuaUPs17LjAIb/rCERa+hvtNDpydERZ6N44ptFNityvWO9u/KOGVU5sg8rbtZ+dWqQx5aPAyei4NevypkKscMB5M9OdR9Bj58i51mGnt8+xW/9+raCYZltKJARJZxGLXs6fFj0Gpo6fVRmmtVxPC9unbc/igyMjk2A55ghD5/JCkZ5a5zq5noNBCKSrT3hXlozQ4uPH4iuTYDOXYjuTYdnkBs3jDbZiAqRYlGoSjTwOa9Xvp8sQUzb3tlM06znmtOLWVqgR1/KIo3GMWgFZABg07ArNNS1+7jt/+p5ZpTp6DTaPj1mh2Kc8icYicmPZQ49Xza4KW23aOI/LR8GzajDm8wjEmv5W8f7WLpccWHnMV6DDBqpsG2vImIWt1BdQr2m6AxkqjJHipHFwcTWQ3VN3GwfeKZdtta+yjMMFFd6FDOmbiMy1DEr7HbS5srgM2oZ3enB5tRx4q/f6G0v2NJFTaDlqZeL9EobNnrZs3WVi47cRIb9vQytcCuWEvFC6obu7xMcBiIRAXlM1EQuPWVzZw0OZMzqwu4LSF9//YlVUzNs+APxub5BATq2t1U5Fq54Pgi7GY9a3f3JEVlq86pJNOiR6eR8QTD/PTM6UxwGPh4VzfLF0xJ8ki869xqLDo9P/7HhpS5s5WLyjm+xMkVf/mEa08rO+D3oDI81IJoFZURZqSLoeNCcdbD7w1a0JyO/WVPHmifAoeR7yyIzWVJMry9rY23t7elPedggrm7y6u06fKECEtw3XPrcAWirBrg1HHLy5v4v51dfFzfQ7cvyDOfNnL9GVNx+8NEZfisoad/GZV9BdV/fLeepp4Abb0+ls0v5QeLyglGolTkWln+5Sk09/qVQupAWOLWlzcRispsb/dwzdOf8/2/f8FL65pZeXoFOTYDGSYdGoGkfW57ZTMCEJVkJmVZEAQISTLVRRkpbiM3vbgR9yAZkZlmff9yNBI5VsMBvwcVlf2hRmQqo8poFEMfTGQFw8+elCRZyca74qSStBZPZTlWijMtNHR52evy4w7E1uGqyLWyoblPOdbAUoBAJKo8+AeztZJkuOXlTTx+5VwurikmGJbo9IZ4eV0zN541g+n59pSC6lte3sTvLpvDbau3EgjH1hb7zpfLuLK/xqsky8Qti2dQ1+4hFJUIRyRlHbICh5GLa4q55unPk2rnXl3fzOUnlijF14GwRO2A+bM7l1alvYZubyh9RqdBS44ttohm3NtxJLJYVWKMxMKaw+FQTYYPdX5NFTKVUeVgRSdOuiHEg61LK3aauef8mUmLNg6WEZeYYLFiYTkTnWZ+9I/1SdcRd9Cv6/BQ27bvwV6SZeK2xZXsaPMQlSVEQcAfjmLSaZEkGVEUCIYl5VgmnZj2YS/LsfN0eUI8u7aRn31tOg6jjotrimnu8dHrD6e9D+v29CrbF88sZNWrm5OEKvGlYlKWWXHwOG9OkVIKEB9GDUSi/OiMadz/5jbOm1PEY+/XA6TYV+3p9qW9hkxLqtPJykXlZFh06LXw469OpaXXp2YmjjSHaBo8XA7FZPhgTIIHogqZyqhyMKITF68ub5C9vYEU4ZlRYBt2XZokyby5tY0H39qupLvXlGRycmlW2sgwUYCf+qiBG8+anvY6urwhmrq9yoM6Lhbfe/pzZUmXiU4zggAf13fS4QmwcGoek7MtrFhUhlYUqci1cde5Vdz04r7097iLSOy6DFx18mScZh0Ok46f/nMDKxdV0O0LD1pQHScx2ksUqnj/b35pk7IkTLHTpFzDQLuv606vQEDmlsUzaOpNLX5+bm2TUuycGKVZ9BoKnaakRTonZpoRkZBlDTk2A5UFdiZnH9OOHSPOsTZHpgqZyqgRG5rTsGJRGZK8bz2vAw3nxYciB9pOxaO5//3+KcOuS0sUpt++E3Pb2J9NVqIAt7gCNHZ704pGQ5eXIqc5RSycZj3fPnlSUt3XykXl7O3xs6fHy9ZWd9Iw5W1nV/KHy+bQ6Q3R2O1TXERWLipHrxEpyjSxp8dPqyvAJXOLMek1aT0P7zq3mofX7Ei6lni/BxvCnJBh4oYXNnD1KaUYdWJawfvVv3fw5FXz2NXhxh+KptyLHl+IPn9YKaAWBSjOMiMIApOzjOTbjLS5g+TY9LT3+dndFWJCBiysyMJqUocUVQ4NNdlDZVSIC9LFj37Ew2vq+PN79Vx+YgklWab9ik6i4Az24O3wBDizMp/XVpzCM8tP4LUVpxxwzm24iR7x+bQ4//NRI7edXZnkyn7d6RU8+WED/lBE2R7v83lzihQRi5/roTW1dHpDtLmCKcOtq17djCcY5YE3dxCV4Pzji1g2v5TXN7ZQ2+FhR5uHm1/aRCAiMdFp5pevb+XimmKeXRsrqF6xqIxHLp1DJBzh4ppipT+vrm9m1TnJ/U7EqBPZ2eEhEN5n5hs39x14rxq7fUzKsTIlx6LYVQ28F799p45H3q7j4TV19HhDyHIElz/Kj19Yz7ZWN+/XdbG9zccf361ngsOkipjKiKBGZCqjwmDreT27/ESqCzMGFZ2BgjPYEGJiuvtQGG6ix0A3kh5fiMlZ5iTvxcc/2E2PL0S+w6jMA8WPu78Ejk5vMO1nCLHIJh4xQsxA+OaXNimrNr/wWRM3nTWdhi4/T33UoKTeRyWob3dTlmvjd+9uZtn8UoozTTR2+3ljYwsPXjQbjSikDP/dsngGj7wdO1+LK8BTHzVwz/nVae+VQauhoy/EA2/t4I4llfzhm8fT6QlS4DBy44sbk5zyjTqRbKuexp4gTrOW73y5TJmrM+pEHrhwNmW5qnfiaBENBems33SkuzEkXHvrgepDOoYqZCqjwmARkD8c3W/klCg46Zb7ONikgOHaZKWzpyp2mun2hVOOUZJlJhyVuP+CWUQliVsXz6CtL5BWDEQBCjPMaT9rdflThKYsx0ogvG/V5hZXQDl2iyuQNEz6p8traOvzKfVcV5+yb2h2W5uHu8+rwmrQJM1XFWea6PGFlH60uAJsa+lLm6DR0uujstBBQ5efT3b3kmvV0+kN0ekJptSQ/fK8mQTDUSoLbJRkWSnM8PLElfPwhSIUZ1qYnK26dYwmOTYDdy49NHE4fAzfJHggqrOHyqhwsEu3DEzXL8kycceSanQaYciFz/s79qFaH6U7BsDb29uUzEWnWc+VJ5eQaTXEhgMTxKA8z8qp5bm8ubUtSRBjiRFa9nR7CUVlQlGJGQV2JmSYuOiPH/KD08uRZfYd/0uTePCtVN/FZV+azJRcKx/v6sZh1GE36bil30OxJMvEDV+dhiSDNxTBotcy0Wlge5s3aZ2zX5xXjVYUktw4si16bCYdhU4jVzz2KU6znu+dOgV/OMqDb+3AadZzYU0RU3Ks5Nr0dLgDmPQ6Fk3LUwVr5BjyjZw+fbq8devW0ezLkUC1qFI5vBxK/dh49NoLhaJs3OuiyxfCYdThC0XItRnwhyU6PUFsRi0THCZK+q8lFIqyscVFpzuI3aRDlmO+iS5/GJtRh8sfxmnWUZlv453aLu7911auOnkyXb4Qkgx2g4ZpBXbCUQmzXsve3gAGnYheIzI5x8jGJo/i7nFhTRHVhQ7sRh1d3iBZVgOBUASbUUdprpkvGnoRRQFvMIqIgMOswaDVEo5KhKIy3mCEHKsBq1FEkmVq23w88NZ2LplbzNQ8KzqtBncgQrZVT6ZFw97eIBMcZkqP7bXDRgNVyAb7QBUyldFiNAXpYCyqhrtfuraSJLO5xUWLK0CBw0RlgZ1IROKzph46PUFCEZlbXt6kCEhxphmLQcueLi/TCxycVJoFwHs729FpRPb2BHhubSNXzZ/CttY+JDmWoBFP5LjmtHJKsgwEQjKBiIRJp6HdHcSo1eAwawhHYF1Tr7LfioUV6LQCq9c3ceWXphCJSmg1As29gaTo8M4lVRRkGHCatfT4Iui0GvSigD8Sq1vLtelp7PYn7XP3udVUTrCwuytApyfEHau3KNdZmm0l26rHZhQoy7ZhMY2qeeyxypD/eCZNmiQ/88wzo9mXUeEAhdGqkKmMfYYqMgcb7Q1nv3RtH7hwNpIsKd6BRp3IfRfMJBiJ1WPFywWcZn1KHVZ8BecffmUq0wssbGz2sLvLy8vrmlPml+JtLzx+Io+8U8f/LJuHVhTo8Ydp74ulsJv1Wtr7guzs8PDc2iZ6fCFlv2Vfmsy0CTb8wSjt7tgcVmIBM+zzO5yUZcGkFwlGZLq9ISUhY8WiMqU8IHGfP3zzeHa09vG3Txq5ZG4xdpMuqe93n1vNpCwzsyc61Whs5BnyDbVkFcgn/dfto9mXEWcIxsOqabDK4SdRmHJtRjQidHiC6DUivlA0SayGIzJDNfIdSHy/gUa7W1pcuAORpP6ky7r84fPrWL6gNGlbbbtHeeAbtKKSej+wDuvht2u574JZ3P/mNn553kxufHEjV59SyuKZhSkehQ+/Xcuy+aUUOU2cNDmTLm8IdyDCzS9toiLXyvcXltPQ5cOs12LRa/jOglL+8G49D79dy7WnlZFnN2DQiEg6eOSdWq49rTxt4k2+3Yg7EMKgMxKOxEoA4vem0GFKu8/njT1Mz7dzcU0x/nA0xeHjxhc38qcratjd5VUNgI8gGr2B7NKxsuLz6KMKmcqokE6YbvzaNPz9xbUDxWo4VlaJRr6Jkc+j79Yrzh8trkCKUHa4g/zg9HJsxuQoQia2+nGPL6T0Z7Csy4EewZK8L81+crZlv6n3/lCEq06ejKvfWspq0OA069O21YhQ3+nlW1+aTFSS+M3btaw6ZwYmnZZNe11KFLZyUTmCAD8/p5Kfv7KZAocRs17D9jYvTT0+Fs8spKnHR0mWicUzCxH6Nf7V9c3k2WMvF1v29pFtM+I06/nOglK6fCElSzKda0hEknn47VquPqU0bd99wegB7cJUVEYStSBaZVRIJ0yd3lBKkXDcFX44Bct5diMlWSZ+dtb0lMjn+ufW8c8vmpNc8SMRiTc2t/Ktv36COxBNiYAeWlMb8xVM6E+uLbkgGvalzyeiEfYVGTf3+mIFxUL6wuMubwhfOEq2VU9NiQOHSc/eXn/attPy7Ty/tokuTxBfKMq3T57Mba9sYcUz63hpXTO3LJ7B904tIxCOku8wYdKJfPfLpRRkGNGIGm59eROSDBoR3tnWzncWlLF6QzNyfxbibYsr2dvrxROKYDPpqGt3c+XJ+4yR735tK7cunpFU9LxiYTmrNzRjM2qV+5eu72aDRjUAVjmsqBGZyqiQTpgSo5c4cbEaTsFysdPM9xeWU9fu3m/UFBemZ5efeEC3kHikEghLtPUFKEgock5McXeatIrllkaA6QU27lxaxc0vbcITjPLq+pgz/cB6sJWLyoFY+vyfrqjhe6eW872nP6ci18ptZ1cmFQvfdnYlf/hPHT2+ENk2A25/hHve2KZEoQONf29ZPINH393JJXOLCYSiuANRpQ7v1sUzAPjDu3VcXFOcYmdlN+q4/dUtXDK3mIp8O9/5n88IhCVaXAGe+aSRX100m62tfUQleHZtIz9YVIFJHzM5Tlfnd9vZlRh0gmoAfIQ53O736TCbzAjDmCc9lMJoVchURoV0whSPVNKJ1XAKlht7fNz80ibFG3Dg8RLzl+IP5QO5hcT3MepEzHoNLa4AT37YoDhkNPf6+d8Ne/n6zAlJHok/OmMqX5qSyZ+/VYM3GKXAYWTzXhfPr21SfAdlOTZ0ef7xRcrQ2+YWF06znvPnFBGORJOKlEPhKK5AmBULy3H5Quzu8iv9TTf/dsfqLSybX8pDa2r53aVzsBg0SsH07/9Tx1XzY3NxA/e76cWNPHLpcVxcU8xDa2q5Y0nyUiwbmvu4ffUW7rtgJi5/iJpJlRTYDby/o00R76c+amD5glKKnWZa+wJkmnXMKcpUEz2ONIfZ/X4gns5mlp2/gKqq4czTHXxhtCpkKqNCOmHKsui57vSKlDmy+DzWQCeNwbIW49Feuohg5aJynvywQWlr1InkWA37dQuJ7xMfPgtHJfLsRsUuKj4Xd+q03JSh0fvf3E7VlXO5+om1XH1KKS981sSNZ01PsZqKi6VRJ2I1apBkuLCmiC5fKG124L0XzOKBN7dxy9dnEJWkAxr/xrd7Q1FsRg2rzqnktlc2s6G5D6NOHNQ/Ua8RlXvR7k51I+nxhdBqBARB5Fdv7uAHp5cTljV0e4KK+EYleOCtHfT4Qrx67Xy0WnXG4khzpN3vO+s3UVVVdUhLswwHVchURoWBwpRjjSUWdHqCPLv8xJSsxfg+Q/FPjEd7cW/A+LIs0/JsdLiDiuVSXChFEUW8WlwBnl3byIMXzcYXitDhDgIxk15Zjg2fnVmVnyTE8X1+fMa0tGLQ7Q0p23t8Ie5+bWuKWMZT428/pxKTLuZcf+1p5TR0py6JEk8MWTK7EBkoy7Vy55Iqbn55k3Jd6SJKo07EYdLS4wsTjET7LbNkBEFger497X7t7n2+j//zUWPKi8adS6vwh2Pzij2+EHaTjkKnidZeP3l2Y9Lw6QMXzmaKmuChcgRQ68hUxh3pMiLj63dBLNI5bmIGJVkWJmVZ2N3l5crHP2HxzEJlmPD5tU0AKfVeiSn/kiSzsbmXNdvaiUpg1ov85u26FDF49r9O5OI/fYTTrOfahWVJhcKTsiwYtSKiKKAVBXTa2PzRp7t76HQH8Yej/DFNRHb/BbMw6kS8oQg93hDVRRl8squbCQ4joais2E4liuT3Ti1jUpaJHl+EX7y+leULptDWF0CS4aOdHVw8tyRlP1GAXyek0Bc4jFxYU0R5ro1cm56oJPHD5zfGBPrcamYX2dnT68ftjxKVJZxmPd5ghIo8u+qfOPoM+eaaMvPk2Zf97IDthjuPNVSGUBN2MKgF0SpHF4k1auGozC0vb6Shy5+2/ixR+OKLXSau5rw/L8f97Rs/1xnT8xTvxB+cXo4vFFUc8v/5eSxN/vEr52I1aJmWZ0cUBT7a1UFUgi5PiBZXIOmY13+lAptBQzgqMynbwi0vb+LGM6fiCkjc+so+15DyXCs5NgPeYAStKAIyGg2EozKyJFDX7qG6yMFf/28nF8+dxM52D9ML7GxtceEKRFm9oZmfnTkdTyiS4gk5IcOIWa8l06yjo9/pIxCOcmJpDsC4sxA7Shi6kDmy5Kpzlu+3jaezmR8Oex5r6BzApeNgUIVM5chysJZSQznurk4vDd1eLAYteTYDxZmpx060yypwGOn1hmnp22czlW5eJ7HPZr0WdyCEWa9FBjrcseVLKgscaLUigUCEjS0uev1h3IEIN764McntIsOsRSuKaDUwrySbPT0+OtwBPKEIGSY93lCUcDQaO48/gt2kRa8RkZFo6Arwt49386MzpiLJ0BeI4LTo0AgCbX1BMi16zHoNGhF6/SG6PREeeGs7i2cWMjnLiChqeGjNDi6ZW8xEpxlRFLAbtdR3eMizG8l36AlFoM0dJMuij82pCQLvbG2hMMtO5QQr7mCEOUWZ6vzXkWXIfzD2gknyWav+vt82nfWbuHNp9WGbxxoBVGcPlSPHoRgIH8xxizNTMx3j82+TsixD6ku6Y1//lQoMGpFf9KfCx/ddWJ7Dq5taeOSdWi6uKWbdni4evbyGFpcfs17LEx/Us3BaPs+ubeTa08p5L9xOc2+g32jXoERTnZ4wt778hXLsn545jSyrnmn5Vi47YRI3/HMjV508GYNWwB2IJA0T3rGkiik5ZmwGHTe9uJmrTp5MVJbJtpl45O0drFhUTntfkB/9Y72yz6pzKnnh80bOrCpMEt74UOX3F5bzzCe7ueHM6fz4Hxv467fnqUXOKmMSNSJTGXWGsqSLJMk0dntp6wviDUUoGcKaVTvbPXz9N0NfKiYSkVjX1MvODg9mvZY/vbtTyeobuM9gfV65qBxPMJrkkPHAhbP55mMfs2x+Kas3DO6duHhmIY+9X8/DlxzH7i5v0jIsv7poNtclFJDHz/fLc6soyoytCH39GVNp6g7Q1OtLm+l4/wWzkJHp8YbwhmL2UT8+YyoFGSa2tfal3eePlx/Pfz/1Wcr2uG/kvRfMQiPAlhY3p5RncWJp9n6/a5VRZUTnyDytu1n51apRG1o8GA7WNFiNyFRGjfiwX227m/sumEVzr48nPmhQ6rriNkaSJCet5xV/uN9z/kwmZBjJshjSzl1tbekbtMB6oJCFQlFe3rA3KYq5bXElfNLAhuY+2vqS9xnMaSTfYeRn/0yOXlz+kJICf+HxE9N6Jz58yXHUd3oIhCW0oqCIGIDTrKcvEE6fVi/GEj4uO2ESfb4It76yaVBrqB3tbkw6DTWTMnivtosfnzGV6kIHbe4As4sycJr1Sas4B8ISvb705437RgZCEYoyzWhEVLeO8cQQ6siMRhNPbwsh1m08TJ3aP7EEEQ5qqFMVMpVRId3Q3MpF5YrBbY8vpDwYd3V62dDkSooYAmGJG17YoEQGA4f/dnfFvATTpZTnWI0pffm8qUcRsfjxV63ezB8vP57PGnrQa0QkSVayFc369F6Duzq9KSL112/PpSTLxHHFDnq84SRTYoAXPmvCG4wo1lqSLLNsfikGrcjUfBvd3pBiVTXwfHt7/Ux0ZtLQ6SMqy0nWUAPbRiV45tNGcu1GXl4XWwrmir9+klIvFxczo04k35HeUWVSv29kodOMRadhTrFTdesYRxzpOrLDjTpzqzIqpPNafGhNLV2+EBfWFCW5djR0ewe1r4oX+sY9EOO09QWQgZWLyinJMnHNaWWsWFTGry6ejVaT3JfGbi/tfcGk4xc4jCybX0qvN4wkw/XPr0vyZVzxzOesWFie5DV4y+IZStp+Yh/9oQjXf6UCrSAwwWHgzqWVTM+3MTHDhFWv4Wdfm8rETBPFThO/PK8KdyDC6g3NBCMSvb4QrS4/72xrTznfrYtnkGnS4glG6PSG2NPtU6yhfnbmNFYsKuPahWWsXFTGz86cxj8/b2LxzEJuenFjWiePh9bUcmFNkXL8u86txu0Pp5x3xcJyWnt93LGkCqsBDHo4eXKWmpmoMmYZ1YhMEIS/AIuBdlmWq/q3/Rz4L6Cjv9mNsiy/1v/Zz4BlQBRYIcvyv/q3Hw88DpiA14CV8tE8uXcU0NYXSBuZSDIcX5zBlytylQejRa8d1L5KTvBNTBwyzLMb0Qig04hcc2oZt76yz6vwzqXVtLuDypBkW18QURSU4w90zY8/vO95YyuFGUYl1V6SZVadXUmOzcD2NjfhSFQptk7so9Wo64+oNPQFJJp6/Cnp9K2uADaTniKnkV+v2cGl80qSCo9XLCznjU0tiq3V1Dwb97+5jWtPK8cfjqXzxwUsGImi04pJVlk/P7uSHKtecfAYzAGkIs/G9V+pYPZEB1EpqngoJtppxYu/J2ebMOkEijPTZ3WqqIwVRnto8XHgEeDJAdt/Jcvy/YkbBEGYAVwCVAITgH8LglAhy3IU+D2wHPiImJCdCbw+ul1XORQKHMaUmquVi2LO8CUD5rvy7AayLPoUk97EIueBBsLFTjNZNiMNXV7u+df2pMjj5pc2Jg1JGrUij/6/ndy2uJJVqzcPul7YsvmltLgCaRfGvP4rFZh1mpQ+/vTMafhDEfIzzARC0aT1yeLHfvCtHdx3wSx+/I/13H/BLC48fmKK1VX8/L99p05Jtmjo8nPLy5v4n2Xz0Aig1woEIjEbqgf/ndz/n7+6mV9dNFvxWYzfs4EvBjva3ACYdRoiksjdr23l+wvLU+rItBqBYFRicqZNFbFxyEiZBo9WwXQ6xqxpsCzL7wqCMGmIzZcAz8iyHAR2CYJQB8wTBGE3YJdl+UMAQRCeBJaiCtmYJiqRsujiQ2tq+fO3alLmWoozLUxweqhv93DfBbMIRaJkWvT8/NXNtLgCaQ2EG3t83NS/OOWBhiSfvvoEdrR7+PsnDdx7wSwkSU67j0aEAoeJC2tShe7Bt3awfEFpkhmwKEBFvhWtKPJZXSeFDtOgQ6S+YIRAWMJm1JJjMwza54ECHghLuPxhynKt/PTM6Vz33LpBr3lrax/zy7JZuaicZz5tTHHgjx/3h2dU0OkNIUkyO9o9FDlN/PmKGvoCYSwGLQatSFSSKM+xYDbph/vVq4wFRsA0+OCMfw+F8WcafK0gCFcAa4EfyrLcAxQSi7jiNPVvC/f/PHB7WgRBWE4seqO4uHiEu60yVNrd6bP+NIKQMtciigILp+ZRmm1V3CKKnWb++u15g7pHJGYVHmhIstcfUpZKWfH3L1i5qCztPsdNzKCywM6eQfwPJRlaXIEkM+ApObNjiSIyWIyDD5F2eIIYdSJ6rUD+IEvW1JQ4EYVSnvooOSFDr9HgkSJ4I5EDJnt0eYIUOU0smV1IttWgGPvKMjz1UWzx0A53kAkOEw++tY17z5+JIMjIMkQlGb1GRKcRmD0hE6NRzQUbTyQ++4z2zENO9jjcxr+HwpEYM/g9MAWYDbQAD/RvTxe/yvvZnhZZlh+VZblGluWanJycQ+yqysESN/ZNxKgTybOnT+GOFyyfWJpNaY4VrVZM+vdA8YsfP+5mPzBZ4Z+fNyn/zrIYeOGzRv50eQ0rFpVh1Gm47vSKlESOfIcBrVZkeoE9bd8HjrAYdSL5diMTMky8ur4ZiFlKrVyU3J8fnTGV5z/bw6pzKhEEsBo0KW1WLipHrxUw6TRJpsd3LKlCkiU0GjEp2eOWQRa9zLIayDDpANjVGXPu+PN79fz2nTplRelCpwl3MMQNZ05n0dRs5pZkU+g0kWMzkGc3ctxEVcTGI4nPPp3ZdqS7c1gZ9YLo/qHF1fFkj8E+60/0QJblX/R/9i/g58Bu4B1Zlqf1b/8GcKosy/99oHOrBdGjz2DWU6Pl5pF43kQPxAtriqjIs5Fh1nHTi8m+i3EvxL+8v5Nz50xUFqGc6DSDAO19AXJsBr5WWYBWK6bt+93nVqPVCPzkHxuUbbcvqeKcqtg+L61v5plPGrj+KxXotBp8oSjeYIQsi4EeX4gMsw6bUUskGsUdjNDtCVPX4VXWICvLsbJ6QxMnleZQkmUh0D+8GpGiBMMywXAEq1HPnh4/d6zeQkWule+eWqYserl6QzPfX1hOdaGV3V0BXP4wt768Wbk3ZTlWMvvtp577tIGTpuRy+vRsbCa1NmwcMeKmwfvjQAXTo+CleCAOzmtREIRpsixv6//Z0D9/Ff/sRFmWPxp0533tJpEgZIIgFMiy3NL/83XACbIsXyIIQiXwNDCPWLLHGqBcluWoIAifAt8HPiaW7PGbeKbj/lCFbHQ5kFgl+hsOx1x2KL6MiU4gvlAEp1mPOxjGoNVg1mtwByIpwrqr00unx09zbzDJkukX51bz9aoC9Pp9efuRiMTmvS6aXX6yLQYKnAZc3gieUIRwVO6vCzNQmR/zWqzv8LCry4vFoMGk0yDJMru7/Pz9491ccXIp/lCEIqcJnQZmFjhp6PXg8kVp9wTJsxnQigKdnhBWoxZfKILNqCMqxVZ6npxtZG2Dm5tf2pQkTBOdJkJRCX9YwhOI4DBrkSSZXJsOT1BWvBNNOpGnP97NSVNyybPrMGh1TM0zY1VFbLwxoqbBQ8GWNxFRq0vZPkru9gfioJ09ngbm9P/8YcLPAL8b8O/UswrC34FTgWxBEJqA24BTBUGYTWx4cDfw3wCyLG8WBOE5YAsQAa7pz1gE+C770u9fR030GBOkqxW7/rl1TOu3exrq+mKJDCWSizuBbGhyYdZrsBt1fPdvnyvtb108g0JnsgBKkkxzr58+f1gRMYi5auzq8vL/6joozbYyOTuWUBJ3s4873pd4zWRZ9eztDSSJ4L0XzEQjCPzw+X0ehrcsnoFOjLnQL5s/hagk09YXZG+vnym5VrIsHrxBCVmQcZh0tLgCiKJAU48PfyhKplnPXz7YwspFFRQ6dbT1RRURu+yEYnKsBkRRQBZk6jt9rHp1c1Lk6ApEkiLH286upKYki1+v2cEvz5vJnh4fnd4gi6bmqRmJRylqQXQywiA/p/t3CrIsf0OW5QJZlnWyLBfJsvyYLMuXy7JcLcvyTFmWz4lHZ/3t75JleYosy1NlWX49YftaWZar+j+7Vq0hGxsMZuPU7g4kbZMkmfoODx/u7KS+w4MkpX598Taf7u5OK46JxdCN3V5q22Jp7pGozO0DLKFuX72FUDgWscWP/b+bWlj+1Fq2tbmVtvF6skffree/nvyMr//mPd7Y3Epjt1cRsW+fPImH1tRyx+qtdHuSRTAQlqhr9/DD59fjNOu55rQyrj6llPa+ABMyTLz4RRMysKPdTSgq8dK6ZjrcQVr6gvQFIjT1BFi7u5tfvL6NHz2/HkmCl9Y1E4xKXDK3mBtf3IgvJNDtDVGRa+XWxTMIRSX29Pq571/b6PFGFBGL9+fGFzdS1+5JdjB5dTM2k57FMwvp9oao7/QSjshsbnEd6q+AisqY4EARmTzIz+n+rXKMkTdI9l1ivddQI6x4m++dWjaoOMYju7a+IA+tqcVp1pNnNypFzonF14FIlC5PiEnZscjxhhc2KMctyTKxeGYh0/Jt/LjfDT5+nuufW8cTV87DadZzy9enc31/pHXZCcVsbU31dpRk0tadTcio5IqTJ6cs/vn6xr1kmCdx0wC3+ac+alBqyR58awe/umg2gbDEF409nDwli4vnFSumwvGob2eCYCXeq4HvCYGwRCgaKy/IsuqRWsEbitDqCjBr4kF++SoqY4gDCVmRIAgPE4u+4j/T/+9BU+BVjg0mZVl45NLj2NDkQpJBI0B1kSOp3mtXZ/rhx6nfP4UpuTFh2t3l5Z43tnLj16YxJdfKikVlipNFvI4sURy9oVga+nlzimjq8VGSZeLimuIkIbll8Qxy7bFzJkaOe7u9XHNaObe+PLj5rjsQ5sovTVKitwJHLDOxxZXqh6gRSFt3dtsrm7n/gln84PRyCjPM7Or0EoxE+a8FZax85ouUYuiHLo6ZCmea9Vx9Sil6rUBJlgmTToNGFFKMiO9YvYU/XV6T9kUiXXZltlWPzZiBOxBBFGJuKvkOdY7saGWkCqIHw9O6m02bRr5Q+mATSA4kZD9O+Hlg1oSaRXGMI0kyvb5wklXSnUurFPNdiPkoJg7lxaOmNrdfWaalyxvkqpMno9GILHtibVKk8uzaRq7/ytQkcSzJjBnaCgI8t7aJO5ZWce3Tn6c86P98RQ2wL3KsyLWyeHYh3+ufTzPpxLRCYDfqePCtHVx9SiklWSZ+eMY0RAEqJ9hTioyLs8zoRDGtIHa4AxRnWtjW2ockx5Z8KcwwpXWh39ziwmrQ8us1tfT4Qtx1bjW/OK8afzBMW4JPZOI99Eei3Lm0KsmV4+5zq5XC6sQ5Mq1GoKnLT2GmhbJcKzqtQGWBYxR+K1TGBCNQEL0/RsM5f9Tc72VZfuKge6Vy1LO5xaU8RCFuD7WJ8lwrsyY6gdibv1Enpgy//fm9fUOMeo1Ily+UYu308Nu1PHr58Zj1miRxnJxt4d7zZ7Kzw0OPL0SvL5Q+sgpGkCRZiRwFoNsbUqIsq16bYjl149em4Q3HljZ5d3s7P1hUQXOPjzy7Eb1FT7ZFx4MXzaK+w0tpjpVOT4DSbGtaQSzPt3H1AGH+7X/qYhHcmrqktlEJHnxrh7LeWUOXl1yrgUyrEW8wmvYexoSrit9eehzd3jD5diM6rUBjl5f7L5iFNxTBotfiC4XZ3uKmOMtCOBqhOMNEVWGGmuhxFHOsJXvsV8gEQXhlf5/Lsnzs3CmVFOLriiUSCEtJcy95dgMrF8WMbwcOv8UzHH2hKNpBoppPdvfw5/fquXNpFUtnFSoPX40oYNFruGXxDDrdwbRCIsixYcvSHCtTsq18tKub1v7hwfPmFPGLN7bhNOuTLKcmZ1vQamIGw1+rLqDDE0zxf3x2bSOXzivhnje28rMzp+MPR1mxsDzFhHhLsytFmJfNL2VKjjUpYorPkTnNeuwmXdL5bj+nitIcM7cunkFrXyDlHt744iZ+c8lx5NkNhKUoFlGPw6Rnc0ufUqOWbdGTaTVg0sKe3ghfKs1VRUzlqOJAQ4snAXuAvxOr4VLXcVBRKHCY0gpI4txLcaaF8jwrXZ70UVO7O1YrVppjGdRqamCkt7vLq6S7FziMLD9lMncsqUpaNPPnZ1fyu//UcePXp1OaY6XdHWTVq7EC4RULywlEYjVaAy2nViwqY0F5NredXYnNoOVHA5JB4mL0q3/vYNn8UmRi1k7pHOQXzyxMuV6NCBMcRu6/YBY72t1EJRRLqhWLylLmwm59ZRN/uqKGZz5pZPmCKWnv4ca9Lr40JQu/L4rdLKIRdVgMmfT4wlj0GiKSjCiA2aDjnOqspHo5laOTdHNkh9MA+GAYTdPgfOArwDeAS4H/Bf4uy/LmgzqbylFFZYE9ZY7mzqVVSXMvcR/Fjc2uQTMcJ2VZqGv3pI1qEs1z45FeYvJGiyvAqtVbmVlo57Fv1bC3N4BBp+HP7+5kR7tHSRKJJ4i0uAI89VEDN541fVC/wra+IDpNLOpLJxxxQ2KNCBaDhi0tfXzv1DJuS1pKporfvF2btK9RJzIt344nFKHLE8Sk0yRFX8WZ5rTn8wQi7Gj30NDtHbTPff4ITouW9r4QMwvt7GiPLcQZDEnkZxiY0V+4rXKMMGCO7PAbAB8Mo2Qa3F+Q/AbwhiAIBmKC9h9BEG6XZfk3B3VGlaMGrVZk6axCynOttLoC5DuMVBakPjBFUaC60MGDF81OScOPFy1PybFy12tbWDa/lOJME829/hTz3Hikly7tf0e7B0kmKSpLdMyPJ4jExezu17amRHHxYcMTJ1fT6Q6SYzUMGiUadSLT8+3YjTr8oSj/3tLIY9+qobnHj0mvxR8Ks2JRRVKa/S2LZ/CX93fykzOn85cPdnHJ3GLuu2AWvmAEbyjCRGf6CDfHqlcc7dOJ/bNrG8my6Gh3i8yblIXVZGROiZqReCwzcI5sPBkAHwwH9FrsF7CvExOxScArwF9kWW4e9d4dIqpF1dhif5ZVA70TB65lljhHlq427ZFLj2NSpoXGHh8Wg5Y8m4HizOTjv76plR8+v8+b8bhiB7IssG5Pr+JXeM2p5RRlGnjp8yYumFvCnm5/Ss3Xs2sb+d6pZWSYdXx5Sg7v1XdS1+5hzdZWvnFCCQ1dPiQZJmYYyHeY+ayxZ9/xTyvnhMk2PtnlVkS0JMvEqnNi5sA9vkjS+e5YUsX0Agv1nX7q2j2Y9RqKMsxIyLT3xaLLZV+ajNmgwWHSs2hanrqS89HLkL9Ye8Ek+axVf1f+3Vm/iTuXVo93ITtor8UngCpillDPyLK8aeT7NnqoQjZ2GIp/YiQSS0NvcQXIscZqSVr7AhRmmKiakBzpJXothqJRur1hpeh5sKLrhi4v7Z4Ae7r9ynBoTYmDH585jWBYIhCOMjnLSpHDyKubW7n15U1U5Fq5/owK/GEJi15DjzfEXleAIqcJkDHqtJxSmk19twd3IEJrX5D6Dg/vbGvn1Gm5zCiwk2XR0+4OkmMzYDMI1Lb72bCni69WFdLrDxOOSGxrcyPJYDdomJxtZXubm8oJDooz9Vj04AtDlydKmztIplmPVgvrGl1U5NlwmrUIgsC0XLs6/3V0c9CmwQcyAD7cHGS92EELmQTEvYESGwqALMuyfbg9OZyoQjY6DNXUN96mwGFkS4t7yO4e8TbXnV7B0580cMOZ01Nc8+Pt73ljKz88Yxo/SUjKgNiQ3Gv9no+SJLNmWxsbm10UOkzsdfl5Z1s758yeQL7DhFmnYdXqzYpj/t3nVvP0x7v5evUESrIteINRmnt8sTWFBCjMMNPQ5WVmkYPdnV5Kssx0e8P8rd8gOBiOMiHDyBMf1POlKbl0+UJKwXhZrpX3drRx0pRcfvbiRm746lQCEUlxKrny5BIq8u34Q1Fa+/wcV5RBTyBEMCzhDkTTDoU+cOFstux14TDrOXvmBDUiO3o5JNPgwQyADzeHYDh8cKbBsiyrs8MqSQzXcioQllixqCylRizRXBjSO4D86t87uOlr01Pawj43kKtOnkyoPwMxkUBYoq0vZmvV0OWlrt2j9KEky8Q1p5Zxa0JyRjyxpMUV4KE1O1ixqJz2vqBSPG3UiVz/lQqcZh217bHo6bOGHspyrWg0In/7eDfnzylWBNWoE3n08uPZvLcvqWB85aJyLpk7ieueX8ey+aXkOUxsb+3jpMmZLJ1TyN7eAN/5n8+SCpxLs810RELc8nL6DMoOT5CJmWa++7fPqS50DMukWeXo5FirI9uvUAmCkLm//w5XJ1XGDoM53iea+g5sI/Wn0EPMmSJurtvhCSoGwg1d3rRilGHRUZFrTTEibusLcMncYoJRSVlwMhGjTiQcldjZ7qHdE+CZT2Pp8dcuLONHZ0xTRCx+noffruW8OUUALJ5ZSEOXT5mji7d58K0dWA06Hn23nkferuOP79bT1OPHG4xwxcmlrFq975hOsx6dRmCi08yqsyv58VcrcJr1sWNGo1w6r4TH3q/ne3/7nD++W8/XZ8VEbOA5b3xxI1EJXIFw2vujEcGq1xIIS0o5g4rKscaB0u8/Y/+rNJeOeI9UxjT7c7zfZ+qb2ia9u0e9Es2ZDJq0GXs6jci1C8uTvBYhlrk40WnmR/9Yr9SGJWbzrVxUzg0vbKTHF+LOpVVcdfJkfvHGNiVCHCytHsBh1FCQYebqU2K/3i98Fltt+rw5RfQFwlx9SqniA/nQmlr+euVcmrr9VORauXrBFARkBEFg3R5XUsLKdadX8PgHu5Ek+NW/dwxwRNnIqrMr0/ar0xNSxHrg/ZldlIHFoCEqyymelEcbQxnSVjk2OZCQnSrLcsNh6YnKuGAojvcD27zwWdMB3T0MopjiY7hiYTm/eH0rty+pSvJaBCh2mtnW6k6qDYsXJE/Ls3HXa1uV1P2bX9rE8gX7DIIN2vQei7Icc8bPtBoVV/z4+mZ5dgO3r96izKPdsngG7kCYJz5oQJIkSnPMfOOEEn7yj/Usm1+KRoQP6jq494JZ+IMRzAYtT3xQz4U1RXiCkbSClWNLn+5v1Is8t7YpRazvOrcajQguf5A93YGkcoOjjdFecfxoY7RNgw8Ws8lMX+suDrbweTAOJGQvcoDFM1WOLSZlWQatB0tsc/e51craXT2+EGadhmyrYdBozh+K4g6Ek9wx4nNWobCUkujxn9p2tKKQVBv223fqMOpEls0vTTHljS9tEvdYvP4rFTz41g7lGladU4nLF+Ke82fy7b9+miS2t6/ewvIFpVx18mTcwQiBiER7XwCLXsMVJ5Vg0Gro8YaVtcFsRg3luVaKMsxJc2a3La4k36HHbNCmFaywJKf0686lVdgMWnp8oSSxFgUoy7Zg0Ans7PCxoCInqdzgaONAi7iqDGCUTYMPBqUo+7wlB134PBgHErKj869C5aARRYEzK/OZtuKUtPVg8TZzijNYvqAUSY6J0h/ereeimqJBozlZhnV7enns/fqUz+OrNseJL6y5Zmsrty2uVOam4oL0/NrGpPaJS5ucN6eIv3ywi6tOnqz0TxRAqxH46wcN3HRWencNs16DLxzlkXfqkgqcX/x8D5OzLQQjsTmqAocRm1HH+iZXSoLLqtWbeeLKeXS6gylmxdedXsHdr23hqpMns3JROUVOMzajFhkZs0HgR2dM5f43tyti/aMzptLrD1FssvC1qoKjVsDiDGVIW2UfYzHZYzSLsg8kZIUJa5ClIMvyihHuj8o4QBQFSnOs+32AFGdamJZvT4rcqosGd/eA2FpmAx/wD1w4O+U88YU1l80v5Q/v1iVFcb/7Tx0/PXM6m/buO8cvzq1G028ELAixZI74fFkco05k+YJSMq36tGI7ISG6gn1Lxdx7wSwiURlNf3R43pwi7li9ZdC1ztr6AvzkhQ0pZsWSLNPQ5ecXb2xj2fxStKKA3aQlzy7S1ifx1Ee7B0Sru/nT5ccz+Rh5iA9lSFvl2OVAQuYnlvChojIsBovcgEGjuYVT8yjLsTKn2IkvFKE406KsWZZI3DdREKChy59k+guws8OjzFNNy7djMYhEojK/vmg2Bp3IF3t604pMSaYZU3+afeLw3oqF5ezuTJ9VGQjFiqD/vaWVVedU0tzrV9qle/A6zLqkodA41y4sU46pEcGk1+Awilzw+8/4+dkzuPLkyfyyX3yNOpF7z59JWe6YLuMcUYYypK2yj7FoGnwopsAH4kBC1qWuSaZysAwWuQ0WzYmiwKRsK5Oy9x9lxH0TIb1YFGdZ2NEWc5a/542tPHzxcdzxv5u5/KRJ9AVCzCrKSLufRa/FYoh5Ot6xZAZ6rZadHR6e+qhh0GHRIqeZ5l4/O9o9BCNRakqcGHUiL3yWmpxx2+JK9nSlN/6N+xIYdSLHFTspcOj57dv19PhCWIxaQl1R7rtgFv5QhAlOEydOyjrqhxMTGcqQtkoCY9I0+OBNgQ/EgZw9PpJl+YADmoIgVI5FR3zV2ePoJNHV4+Ka4iSxWHVObPmWeHbhgxfN5ozpeby5tY3/bGvhwrklSLKMLxRFIwh4g1EyzDq6vSGyrHpmFTiQkNjc5sGgEej2hvliTy9Okw6zQZvicF/gMOD2R2l3B4hIMkVOMy5/mFte3qR4Ok7OtmDUanjs/Z1c+aVS/OFo0ooBKxeV8+SHDfT4Qtx9bjU1k6z8+s16XtvcpvS/scenPsBVVK/FwT44kGnwkI4uCJ/LsjzmshtVIRt9RrO2R5JkdnV6aej2YtFrybPvMwKORCQ273XR4Q1iN+pw+cJotSLN3V56/RFCUYmakkxOLs1CqxUJBCJsanXhDUXRijLNvSF++04tl84rUWq64uJUnGnEHZBY39RLpllHvt1EWJLItesRZJFObwibUYvdqFEKr/d0B9jaus8vsTzfRigskWU10BcIoRVFrAYtNqNIU28AWRLY0tJHRa4Ns0Fkb2+AdneQ5z/bw3WnT6XAoSfHZhqToqXWcx0xVCEbhAMNLR7yCVSOXkaytifx4ZhvN+IOhtnd6WNnh4fn1jbR4wuxclE55XlWTi3P5c2tbcp5S7JM3Lm0mk93dyPJKMXKRp3Iq9fMZ3K2hbe2t9HU4+ehNbXce8Esbn15E9eeVpamMHkTj15+PNc8/XlSxFTb2sep0/NodQWS5s/uPX8m+Q4Du7t8SVZUN35tGrk2o3JNz63dw2UnlJDvMJJt0XFVvw3VNaeVpWRq/vSfG3j08uOPqEAMJlZqPZfKWGSkhOzQwzqVccdI1fYcaAmXuA/iQ2tqWb6glByLgXve2BrzKrTrMeu1/NeTa5Oiqv9dv5d3dnRS3+XBF4pQm+C16O8vSJ7oNCmZgLBPAFtd+1K9nWY9kzJNVBU6kGWZn/xjQ9L1PvDWdm4/p0rpb4HDyGUnFGM2aNna2qeI8IqF5fzt4wYumVtMrm1fPV18kc5EAuHYIqK7Or1MyT38WYn7Eyu1nmt8MDDZw9O6m02bxueLxlCc8kdKyFSOQUaqtifx4XjenKIUv8G4Oe5v36lDkqHDG+SqkyfjC0eZ6IyZ5Q6Mqn576RzcwTA6UaSlL4BW3JdgYTdpKckyEYrKSjSU6CZv0sf+LFacNpmTynJpdwdx+cP4QmGlxssXjNDpDSLL++5DgcOYZMGVKMLxa8g06/EEI6xYVIYkw9Q8W9rkD5NeS2P3kRGy/YnV0VjPdbiGSg/rkOyAZA+j0cTT20KIdRtH53yjRMwpnwMOiR60kAmCMEGW5b39/wwd7HFUxi+5tpGp7Ul8OA4WoRRnmli5qAyLXoPTpKc27OGhNbVcfUopTrOe8+YUJUVW65t6ue4rU7nxxY388vyZlOdZFb/HcCTKbYsr+d7Tn6cI5u8unUNdu5srT5pIUZaNKx//VBGl28+pIsuiS7Kv+uV51TT3+pUasoEWXIkibNKJ2Ixaen2RJCf+286uVFxB4otptvT6mDXRebBfzSGxP7E62uq5DtdQ6eEekh2LBdGjyaEs0/JR/IehZDaqHF1IksyuLg8rF5UnpcIfqLZHkmTqOzx8uLOT+g4PkiQrD8c46ZzsG7v9/PHdepwWA4FIVInarIaYTdRj78cc6f/8Xj1XnFSC06SjyxsiFJGJRKOIwHWnV3BhTRHNriDNvb60D2t3MEK+3cDXZhZya//aX/HPbn1lE2a9LmmbViPy3No93HZ2JRoxNhR5zWllXLsw9p/TrEcQYtcwqyiDLJuem1/aqByjocvPH/5fHY9fOZcfnVHBH755PEadgFGnIc8+7IUHR4SB3wfsE6t4PddwvvOxzFBWcxiMdL/Lo3EelQNzKEOL43PAVWVE2N3l5dqnv0hxqJhRYBv0DVOSZN7e3saGJpey0OT/b+/M46Mqr///fmbfMslkDwlJCElYEggiWLRAFdRSi+KC1S5aWyxdtFCt1VoXilvrUlusWmurVu3iRl3wZ60tatGvKypLkCUhkJCQfZvMvtz7+2MWZpIJJJBAlvt+vfLKzJ27PPfOPPfc5zznfM6MvGROL8nk7otmcsP6baz/pJ5ffGUqbU5fNAKwKMNCl9vPvcsraOxykWI8ZEwCQTkqGwWhG8S6jVU8+PWT0GlUfOe0Aiw6Lb6gD5tJQ3FWqFCmzZhYwWNvq4OKvGSa7d6Ehi4oy/z+6yexr82JLyjR3O1h1eISsq16pmZZMGrVcfN7qxeXYNapWbWoBJc/gE8SffZb2+6m0+ljfnEaGrWgrt3JBJuR/NQTYxwOl3w81vK5jtZVOtgR1vF2yR5P0eDhTLQeaBL1sRgyJcBjHBPpmL0VKk6bnNZvQnNEI7F3ocniDAsTUgysmF9EaaYZl1/i0U010cCPq//xWdz6VpM2aoQ8YY3DWDx+CacvyHObazlz+gR6vAH2NDlw+4PkpJhwI7GzsZvbzitLWFxzToGNdEtiYV+bScfKpw8Fljz0zZOwuwPUdXiwGbV95vfWbaziye/M5fr127jz/BlY+ilXYzFo6PH4STJomV+ScUKNw5GM1UAkykYLR+sqHWzQy3F3yR4n0eDhT7QeWBL1YQ2ZEOL3JDZYAkg5qnYpjAmOpmNGNBJ73+hn59vISNLz2Ls13Lu8grUbtvcb+LFuYxV/uuzkqGpG5LiJRlYlWSnc+nIl6y6dhdMXBKDb7acmPJoqzNDFCRs//UEoKdnpDSIIJBQkfmDj7rj2RAzko5tq+q0n1mT3cvUZJZh0Kh5+u4pfXTCDG8OVAQxaFWvOLWPdf/fw6wsrKM5KGoJv59gZS8bqcByt9NVgR1jHW2LreM2RDacQ8GA40ojscNnESqbxOOZoOmZEIzEWj1/C5QtQmJbK/V+bhSOmEnJ/gR9d7gDPbg5VfJ6SZeH2ZeXc8vIhpYzIyOqik/Pw+CWsBg3q8OBGJSAoSWzY2kB+ioGcZGNcoMVty8pRqwS+oMz6T+u4Z3kFshwqlNnl8rK5trvXOQWj7eqvPEuqSYfLH6S2w82S8lyWTMsi1azjk7pOghI88r9qblgybcwbjZHI0bpKB/sgN9ZcsiONwxqy/nQWhRAG4NxhaZHCqOBoOmZEI7F354+odSwpy6byYHfcOonW16oEl87Nj0YtZloSj6xk+ZBQ75SsJNocXvRaFWlmHZfOzeeh/+1lxRcn8ehlJ2P3BNCqVGQn6/mwpp0Uk5aLTs7n+he2cuWCIv78Tg1XLijq056IgTRoVfxp094+o7hfnltGY7eLLpef8txkcpINGI1aFpRkMDHVREuPh4tm5yo3tRPI0Yw+j+ZB7niOcodqjuxI81/DKQQ8GAYsUSWEUANnA18Hvgy8I8vy8mFs2zGjSFT1z/HIael9jHybKU6RI9EEuSTJ/KuyiZ8+nzg5evXiEqbnhNxvPZ4gnkCQ/FQjdR3uOP3Ca84s5e8f1XL9l6fx5elZ7G230+X0E5TA7gmEa5QJOp1+0i06nny/hrOmTyA/TU+XK4jDG8DtDTApw0KPJ0Cny88j/6vmp2eWkGLW0+H0k2rWolVDlyvAwW4P9/57N6WZFlYunIyMjM2kY3+bA0mGZJOWFKOWBSWZisEaZo53XthxHGENeOfG5DS5/LyVx3QwR1sDPx3A/NdAEpaHiKPXWhRCLAS+AXwV+Aj4IlAky7JrKFs4HCiGLDHHI6elv2McSQA3EJD4rL6TbneAbfVdGLVqACakGDFo1NS2O5iSk0Rbj5+bXtpOaaaFS07J58VPD3D5aUV4fAFybUYcXj8GjYZTJ4W0Ft+paqGlx8stL+/gnLIsvliayQMb93Dp3Hwm2kyoVJBq1pFt1bDlgIPf/ndPOG9NixACs15NqllLXYcbf0DG6Q1gNmjQqgTZyVpqO7xUtziihTqLMy2km3XIhN7nWPXkp/Uf0akwNIxxCa2j1lo8GkagPuPRaS0KIeqBOuAPwM9kWe4RQuwbDUZMoX+Oh8xQ5Bixycq7m+xMz0nq170iSTL/r7KRva2OuOrKcChf6Yn3arl4Tl40qvEHpxfz0md1rFo8hZYeD9nJBvY09TApw8KBDifJRg1Jei2SDLe8vINTJ6Vywew8Kg/a+cU509CoVGyt70KSYcPWBn6yuJS/fbifq06fTElGEh1uP10uH3k2A16/RIfTHzentubcMrKthjjpqkh7Vy4swqhVoxagzbdRmDHqb6QjHkVCa3xypGCP9cD5wCVAUAjxMkrY/ajneOS0NNs92Ey6qGRTpKTJtvpu7J4A3kCQNLM+bkS2v93J/f/ZzXVnT+UnZ5aQm2KK5mut/6SeXU12Lpydh0mrweOX+OGXikg1azhr+oRoSHxBmpEblkyjsqGbaTlWmnu8+IIS/mAoTH/1mSU4fUEmpBhweIPc9OKWuCCR323cw68uKKfd6efzph4efruaq740mV1NDrKs+qgRi1yztRt28MQVcxNeT0mGdRuruHd5BS0O75Bc1+FgrKjZR85jrEloKRyZIwV7rBZC/AQ4g9Dc2L2AVQjxNeA1WZYdw99EhaHmeOS05CQbuOmcaexvd7Lu0pPocPriRjIRXcMblkyLun3anV4umZPPk+/V8N35k9nVZA+XUbFQes5UMiwhVQ+dWsWcgmSMOg3v7e2Ijt5ykg1cMic/zq205twyJtqMODxB5hQks6fFya0vV7JifhGPvVsTN2L0BoJcOjcfIVTUd7p55uM6fnr2VLQqwa4mOzq1KuFNssPp67dYpscv4fYFKBihyhdjxRUXOY/dTfYxJaF1tAxFsMdIEBoe6PzbkVyLWbIsNwNvAm8KIbTAEkJG7WEgfQjaqnCcGaqclsOV+vi8sYfrwpqEqxYX8/KWBq4+o5gMix6TXkNjl4tL5+ZH3T6FaWYCQZlnN9excuHkOCX8n/QyTNnJOq45aworntzMlQuKojetRFqHazfs4KnvnsLDb1fx07OnRrUT9RpVdH7t9lc/j+7/1qXTcfoCTEg2cNm8Qq6P0VV84oq5CW+SaRZdv8nVBq2KVLMOjz8wdF/gEDJWXHGxruzelblHs4TWUTMECdEnWmh4oILBcGTX4lYhxHbgH8B6WZa7gQ3ABiGE8dibqnAiGIqcloGW+shJNjAt20r+IhMHOlz8bmNVtLZYplWPzaSjpceDAHrcAa47e2pUlDdRQvTaDTu4b3kFHpUcF6JvM+mYnpPElQuKgEMlWTx+iWa7h8XTsml1eKNtKpuQREmmhWt63cRve/VznrhiLmadmhv+uT3us70tPX0EftecW4ZRq2ZKjoWnv3sKB7s97G11RFMA7rpgBo/8r5pfXVgxhN/g0DFW1OxjlWae/qA2Kpu2oDiduYWpo2p0ORSMN9HgIxmyXOBM4FLgV0KI9wkZtVdkWXYPd+MUho9jzWkZSKmPSFmTa57b0mekEqktdtM508hJNrC5toubXtweN8LqLyHa6QuQm2LEoFWx/pN6blwyFW9Q4qfPb+1znE6Xj3SLHoGAsHjvhbPz2NMcUuRIuH9vgKAs91HVf25zPVedUcx9yytw+gKYdRq0GoFBK/AGQu3NSNKRak5leo4VrQr2tjq57NRJTB6hRmGsqNnHnkdENs2gVXHhSbnjzoiNR440RxYE/g38WwihA75CyKitE0JslGX5m8ehjQojkIGU+jhSWRNJhj0tPViNGm56cXvcCOtwCdFmnQatWnDtWaXc/5899HgDfYSDH3gzZCjzU028UXmQuUXpPPL2Xm5bVk59pyuqyJFo/8lGLQFJSpjD1trjYWKamf3tTgoLzSCCOLx+fvXa7pjwfxP17XZsFjMVE1M4OX/kjgiOt3TScJFvM3HH+eVxuYR3nF9Ovs10opt2QhjsHNlwCv8eLYNJth6waLAsyz4hxOfATuBkYPpRtU5hTHC4J/nCNDN3XzSTva2OhMYuUtYkJBdF1AUIIZdgZI5j/Sf1rF5cEmdM1pxbhl6roqHLwxP/t5+rzygmz2ZKeJzcZCMdDi9JJj2pZh17WhzkJOvIsur5tLaTl7Y09JlPuWXpdLo9fqyGxALAV59RTEttJxNtJkw6FVVNdkpzUrj2rKk0drvJSjawp8nO5Gwbk9JMUdWSkcpYkU6q63Tx+/BDkhAhhZffvxnS8RxNLtIhYxBzZMMv/Hu0DEwwGAZgyIQQ+YTC778OmIFngGWyLO88liYqjG6OVOrjq+U5fFTbkTAfTCWIzpHd9+89/OKcaXFuoac/qGXlwiJKs5KYkGzgsW/PocPpJ9moQUYmxahFp1bR6fJx3xt7uHpRcUKj2tDtJijBlCwLtW0O7rpgBoEg/HJDJd89bRKXzs3nmY9Dmo1qFUzPsWJ3+9Br1LQ5Epdx8QUlTpmUitcf5OXPDjA9NxWdRpBrM6BRCdqcXmbn2yibkIxGcyzl/o4fY0EguNnuobbdHVeJARh1c31DxWDmyEaK8O+xcKSoxfcIzZO9AKyUZVmRyVAAjvwkr9GoyE0xcOcFM6Juw1CV5TJSTDqSTRrufHUnjd0eGrtccSOvTpcPo1aNUauiuqWH2g5P9Cn71W0N/OWKUwhKRLdZ/0l91M0Y6wY0adU8/t4+Fl08iya7wKRXkWHRs3pxKes27uHui2YyLcdKjyeARhWqA2bQqrEaNASCUkLjeNJEG0l6DRad4MvlubxX1cysiSkUpFkoSBt/N8yRwliZ61M4Oo40IrsR2CQPVJBRYVyiVgl6PH4+3NceF4Y/0WamocvFI986mRa7B6MuFHZv1Gl47J29bGsI5fwUZVqQZaLCvyoBmeHqyNnJRkDQ6vDy3OYDXP/laRSkmcm26Knvcke3QZb587fnYHf7kSQ40Oni8ff2cdXpJUzOMtHj9ZOZFKrWnGszcP/FFXiDQQwagdaspdsdYGqOFaNOjc0saOmR+8y53L6sHJWQ6XJ5sXuCvLOnmdOn5oy6+aSxyFiZ6xsqBjNHNtz5YsdDi/GwWouHqUcGgCzLq4ajUUOForU4fAQCEu/VtLOrsRuzQRuXixWbUBsISLxW2cj167dFP7/noplMzjBzoNNNTrKRshwrKpVgX5uTug4nJr0aIQuaezzoNWr8wVAkYXaygSSDmqkZVj472IlercYXlHH5AqSadDh8QQKShEGjpqHLzYQUA2qVTFm2lQ63h72tXjqcfnY2dHDm9By8AYlWh5+bX9oeN5KbnGEh1azF7vaj06jpcvkx6dQYdCrMOjUev8T6T+r4YkkWXy3PGTUuxLHOCRDxPd4Mm2hwUtZEVBrtUTXqcIRywZYNldvy6LQWUWqOKSQgool4w/ptrJhfxP3/jQ+KiE2oret0RY1Y5PPr129j5cIiHthYHWf4JqWb2dfuoKbVxSP/q+a7p03C5Q/2CfawuwMYtIKgBL5AEJNOjU+S0KgE3a4gXrXEU+/tZ0+Lg7XnlZFp0dHQ5UctBB/ubeGC2RMxaNX4JaJGLNK2Zz4OJWQ/uqma786fzJb6jqgO46Vz85ldkILDG+Ds6RO4+pnPmJGbPC7nYEYiY2Gub6hQ8shi6K8eGYAQomDom6NwIhis1t7+dic3hI1TkkEdp9jR0OXiyfdqabZ7kGXY3WyPm7fISTZw4ew8itIt/OzLpfz1gzqufW4LU368ALc/gF6tZu2GHayYX0S7yxcXLBJJiH76u6ewr83FQ29X8bMvTwFvqKRL70Tlf3xYy5pXQuu7fEHUKvjSlGy2N9iZmWel3eHrE9CxdGYuj27aG1UXic1Le+bjOqZkJaFWqXD4A6MycXgsI0kydR1Omu1enL4ABalmJqWPuVGZQgIGErV4KqGAj02yLLcIIWYCPwcWABOHuX0Kw8xgtPYiBm9Pcw9XLihi0+4Wko26OAOyenEJP/xSERq14OWtDeQmG1m9uJjnNtcDREWEI+tfc2Ypf3lvP3tbHextPZSkLARIMn2Sktd/Uk+ny8+tr1SyenEJaqEiKMms3bADm0nHivmFZFmNmHVq1pxXxt/e30+bw4dRp8asU7O1vo2XtzRQkVeBRi36FspUhYxZxFUK8flvTl8QjUqQatIpwQQjCEmSeXN3M1XNjrgR/EjQjRwroswjmSNFLd4LLAW2ADcIIV4FfgTcBXx32FunMKxIksz2hi52Ndm5ckFRVNYpkdZeb4NXkGZkzbll1LY5uXd5RVSl/pmP6/jmKflUtzijpVYunpPHT88uJcWopabVEScj9dv/7mHlwiJ8gVCe1o8XFVOQZmRKVhKNXS5++KUi2pw+JDlUjfmHXyoiyRCap8qzmdjZZCc32YjNpOMni4vpcPlxeQMIARqv4NyTJmDUapBliVaHl3SLjpULJ/PtJz7CZtL1yVOryEtha31XwtB7tQpaezyU5yYjhDyugwlGGvvbnWyr7+4zgj/RupEnSpR5qCpEHyvHEkgymCCRI43IvgqcJMuyRwhhAw4CM2VZrjqqlimMGBJ1sIisU2O3p4/LrLd+4iVz8lm7YQeXzMmPaiNG9lGQbmH1M5/FlXGJHbGFRlW+6PEmZ1ioaQuV30jSa/jJmaV0Or3Myk+hKmwQY7c3aNQUpBkx69XkJhvJSTFw3dkluP0Sbn+QA11u1ALSzDpsZh2FqQZaeoKYdTL6FBNX/f3TaM7aU++HctZyk400dLtJMWqYlZeSMJS7fEIygaCEXiNjMxs4OT9NebIeITTbPUhyYkmzE+n+PWGizEMgGjwUHK3w8GAEg+HIhswty7IHQJblTiHE7sEYMSHE44RGdC2yLJeHl6UCzwKFwH7ga7Isd4Y/uxFYAQSBVbIs/zu8/GTgL4AReA1YraQEHBuJOljEffbYuzV9XGaxklQR6akV84sSSlA9/I3ZUdHf3p8/83Edv75oBk1dHswGDVedXhQaqUkhQ5VrM9LQ6cbpC+LySX1cfOs2VlHx7Tn89OwpVDf3kJNioqXHS0Gamc/quuKM3i1Lp6PXqml3BOnx+kk2aHmnui3uZtfY7eGBjdWsWlxMTrKRtRs+JztZ1yf/7a4LZpBq1qBVq7CZdeSnKvNiI4ksqwG1SCw5diLdvydKlHm8BXscKW54shDilcgfUNjr/ZH4C6GyL7H8HNgoy3IJsDH8HiHEdEI6jmXhbR4WQqjD2/wBWAmUhP9671NhkPTXwdQqErrMIgmncEjMtz9RX184mbj355GR3Pef/oQb/rmd657fik6jwWpUMznDzLVnlWLSqXH6QpGKm2s7E+7fEwjS5fQRlOFnL2zluue38U5VWzQhOrLe7a9+TmVDN3avH7s7wDvVbUgy0fOIYNCqmFNg4x8f1rKnxcFZ0yfwjw/3s2J+EasWF/OHb51MYbqR/e0ugpJMXoriThxpFKaZmZGXHBqxh7/fkZBLFttvIpxo4zoWOdKIbFmv978ZzM5lWd4khChMsM/Tw6+fBN4Gbggvf0aWZS+wTwhRDZwihNgPWGVZfh9ACPEUoarV/xpMWxTi6U8JYfHUTGbkpvRxmcUmnEbWjfzvvY/iDAv3f21WnyKHiUZot75SyV++MzfcHg2BIHEah4n2b9RqaHPGRzT251aSZNCoVPwirKy/YWtffcVbl05nV6OdFQuKKM4wIgM3LJlGm9OHRadBpYLKejtZVgMzc22KO3EEolIJFk3JojjDwux8Gy5fgPwRELV4ohK1gz4vbTWVw3qM4WQwgsFw5PD7//X3mRDiiwNvVhxZsiw3hvffKITIDC/PBT6IWa8+vMwfft17eX/tWklo9EZ+fv5RNnHs018HS2TE4JAkVenV86ludXDL0uk8umlvwiKGkVye6TlJFKSZ+UXYRadWJTY27Q4fLl+Q2g4XZTnWhALCkf3/8twyHJ5AQsPVX5XmJrs3nMRcz2XzCnh28yF9xZPybYCMWWchM0nNy58e5Cszc0EnY5O1BIIyXr/EF4szTvhNUeHwqFSCwnQLhekjx+17PEWZY+99qamp3HH+wA3ByGPggsFw5KhFNfA1QobjdVmWK4UQS4FfEJqvGviRjkyib1Y+zPKEyLL8KPAohJQ9hqZpY4+j6WAqlaA4K4nCNDO7mu3ccf4MJEnime/Nw+0P9gktLky3kJ9qpiIvhZo2B0kGTUIR4WyrAU8gSH2nC21MSHysgHBeihGrUYtKJchI0qE+GG+4EinlR4JJbvpqSJQ4sr8LZ+ehVkFpZhIeX5CJqQbsbj8vf9pMcXYKxZlWxWApDBnHK1E79t43bdo0eTSLAA+WI82RPQZcCaQBDwghngDuA+6RZflojVizECIHIPy/Jby8nvi8tDxCUZL14de9lyscI5EONq8onaIMy4Bu3pIkU9fposcTKm75xckZWI19pW0kSaam1cGH+9px+gJkJulRqeC288rj3JK3LytnxoRktJqQ+rxWLbjj/EPrdLp8TLSZKMqwYNKpMGpU+AJBpmYnRedDcpINXDwnjyyrgQcuncXvvz6LlQuLooU1zbpQsEbEmD32bg35qSYmpBgozTKiVQtSTFq+WpHHV8pzFCOmoDDKOJLWYiWhcHtJCGEA2oBiWZabBnyA0BzZqzFRi/cC7bIs/1oI8XMgVZbl64UQZcDfgVOACYQCQUpkWQ4KIT4Gfgx8SChq8feyLL92pGMrWotDSyRk/+7Xd7J0Zi7JBjVlucnsaLBj9wbYsLWBG5ZM4+xpWbxd1UJTl5uJaWa6nH4yrHre/PwgX5qSBUKFwxMgI0lPebYVg0GDzxfktR2N/PWD/fzg9GJ8fgkJONDh4l/bGzl9aiYTbSbaHV6KMi2kmdW4/eDyBuly+Xno7SqWzsxFrYLZ+TZkZMw6DUkGNclG2N7gxh+QcXoDmA0aNCrQa1WYdCquf6GSH3ypmAkpehYUZyqGTGGkMuAf5rRp0+SdO8dcpa2j1lr0ybIsAYRzyfYM0oj9g1BgR7oQoh5YA/waeE4IsQKoAy4O73+HEOI54HMgAFwVrlAN8EMOhd//CyXQ44Swv93J3a/v5JI5+Ty7uY5L5uTzixe3Rw3Iz5dM47F395KfasTl9WPQafj+058cKuGyrJybX95Bbbs7mhPW2O3my9Oy+ai2g79+sJ9L5hZw9d8/i25z45KpfHXmBH7730MlWq49q5QkvZWP9nUwJSuJh96u4pI5+X0KZD66aS9XnV5MSaaFmlZnn2rPkzMsCK2apTNzWbthB/ctr2B/u3PYXUDjTelhvJ3vSMDtdvPBBx8cecURyNGo5R9pROYCIpXqBDA5/F4AkizLFUfX1OODMiIbWt7f28a71e089m4NK+YX8eq2hoQGZEqWhS53gKvDiccRDFoVK+YXRYsfRgzKKYWptDm9GLUaVj69OW6bG5ZMiYtiBChIM3LV6cXc+soOrlxQhCzDY+/WUJppYfWZJWjUKpyeAMlmLb99YzerFpeyMmxQY9vy8Ddm4/QF2N3s4ME3q7n7ohkUpJmYV5Q+bNfwRCk9nCjG2/kOMwO+YOa0HPnU7902nG0ZFo6gln/UI7Jp/ewsj1DAh8I4IstqiEYeChHSJOwdTn/7q5/zxBVz2daPzJMQ8e+zrAZcfj89ngA7DvYVGM5I0icU9r31lR3R5WoVnDoplW9/cRJtDi8HOlw8tzmkHrL2vLKofmPvtjh9QbKSDOxqcmDQqjDrNMOe33PClB5OEOPtfEcKap2e9KLyE92M48Zhgz1kWa6N/AE24CpCeV+3E5qrUhhHFKaZmTcplVWLi5mYYmRaThI2ky5uHY9fojOsjZgoETTWARDKCVNj0mq5+aXKPttcODuPAx2uPvuJDePftLuFUwpTOWfmBH7w10+47vlt/HFTDZfNK8Bm0rHmlR2kmXUJ25Ji0iLJQV7d1sCac8tIMqqHPb/ncEoPRyISQPP+3jZqWh1I0sgPyj2W8x2JjMbvYDxwpPD7UkJqG18H2glJSwlZls84Dm1TGIE02719tA+fej+kzwghA5FlNXD3v3f1kXlas7SMRzYdciuuWlTCr1/fyU3nTI/mecXmjalV8Nzmvrlkcwps0dD7BaWZePxBbnm5MqHc1kNvVdPp8vep9vyrC2bw6b5WTp6UwT0XVZCZpKMgbWCRm8dCf4noRxoJjlYX3dGe70hkNH0HJ0o02GQ0IY7hWgw2ETrCkVyLu4B3gHNlWa4GEEJcM+ijKIx4BjIhv7/dyY0vxheiXLexKq5I5t0XzUSlgivnF5GXoufhb85my4EughKs/7SOW5ZOx+eXCMpQ1+HEF5CxGjVxeV5Xn1FMboqR7GQ9L29pQAi4d3kFLl+ADqcvqsZx26ufIwQJ64pF3JgGrQqrQYPVqOKxb8+hodONQafhqfdq+OYXCpGQmJmTgjFBCsFwcLRKD6PVRXeilC2Gg1H1HZwA0WBHWwMrLlpIefmxuDQHlwgd4UiG7CJCI7K3hBCvA88wiAlHhdHBQJ80+3MT5aUYue/imWQnG5hXmMauZjtuf5CtB7rJtZkISiF9xsXTstnb4oyLQFy9uASNSnD7snJuebmSxm4POrWgye5hUropGtQRHdWdW0any88zH9Vx/9dmYdCo2NPck/CpXyXg9mXl+AJBPAEZWYZ0ix6nL8iqxaU8sHEPt3x1+nEzYnD0Sg8nSnz2WDmeyhbDzWj6Dk6EaHBbTSXl5eUDVqwfSo40R/aiLMuXAFMJzY1dA2QJIf4ghDj7OLRP4TjQ35Pm/nZn3HomnSbhXFNth5ubX6okx2pEo1Hh8gW5/z97eOK9WnRqFY+9W8ODb1bj8AajRixynHUbq3D5gujUghXzi7j7ohlMybGGlvuluKAOjz9UIdqoVbOnxcHtr35OfaeLvFQT155VGpdofcf55Syakkn5hCSsRh0yEJRkttR3sauph1teruTrpxRSNiFleC9uAo4mEX00i88ezfmOREbzdzDWOWKFaABZlp3A34C/hcuwXExItf6NYWybwnFioE+avmCwz3xVRAbK45dodXiYnGnB4Q1EJaZ+/2YVtyydzu1hN2Ci49g9AdRhg3flgiJMOg0ev0SHM7HL0KRTR+ffbn3lc86ens53vlgUDqcPolUJ1GqBJAdpsgcoyrAwJSOJXS09WPQanL4AZ0/LpGxCMhrNkcRtRgZjyUU3WhlN38Fg58iOdW4Ljn5+aygYkCGLRZblDuCP4T+FMcBAJ+TTzHqe3VzHvcsrqGrpISgRLcQZu35Bqjm6v20NdlrfrGblwqK4II3Y46QYtXj8QdaeV8bDb1cze2kZBq2KjCR9wvUNGjVZVi0Pf2M2W+pD82+/eWM3Vy0qRSXAZtZh1qvRayAgy0y0hVxZs/Jtw3wlh4+x5KIbrYyq72AQc2RDM7cFRzu/NRQcNiF6tKMkRB8ZSZLZ1+ZkZ6OdqpaeaP5VojmyWImq3onQsevHzrnZTDounpNHUboFtUrGHySqhm/Qqvj1hTP58tRMajp7CAYFLn8QSZJo6fHzXnUzi6fnxElL6TQqyicY8Aeh2R6k2e4lM0mPXiNY998q3t/XwZ3nz2BGngWNUFOYPnpdWQoKvRjwD9maUyifs/YfA1q3raaSO86fcULmtgbJUSdEK4xhEgV53HXBDGbnp5Cf2vdJM/pEmp2E0+tnbqGN5h4vGUl6rHo1H+5rj0Y8njklk6dXnMK+Nhe3hkPjC9KM3HlBOX++fA4ObwCzXoNZp8ZLkLp2D/vbXTzzcR0/+tJk1Cr49heL+PxgTzS03qBVsfa8Mhq71Oxt9XDrK4eW33ZeOZefVsCVC4vQqGWa7V6+UJCuGLFxhCKFNX4ZHRMECsNCoiCPX7y4HUmm3xuASiXIt5nY3ezgssc/4uq/f8a3H/+ILfV2fvXaTs554B3e3N3M65838U5VW9SI5SQbuHJ+yDBd+dRmfvDXT/neU5v5cF8Hm/a009rj5ZmP61h7bhmSLCNUKty+vvlha17ZQVBWRY1YZPmtr1SiVatZ8eRmWu0BkFVsO9h9fC6kwgkn8lD2nb98xLvV7by0pYH/q24jEJCOvLHCqEcZkY1TJEmmtcfLlQuKgJBCxoLSTISAVoc34dNs5Im3rccbTS6GQ9GE919cwY/+/hnb6rt5dFMNP1lcGjVit3x1Gj3eAG0OLzaTjsZuT1we2mlFqVz/5SkYdGpsJj0mnaCtn/ywlnChzN7Lu9z+sPRUABkZu0d5Gh8vxApax7q8775oJufOnDDuRmaDCfZwNO2nsvL4XZ+jEQU+EoohG4dIksybu5vZVh8asVj1ar41ryAa6v7nd2r6zJEFAhL/r7KRG9ZvY+25ZQkNiV+SyUk2RKs3l+daKUgzcsmcfK59fmufSMeIMdOoVGjVAkmGbQe6mFWQjMMj4Q9KrF5czHOb6+OUQ9IsuoRBIKlhKSqzTkOaRYdWrTgcxgvNdk9C7c8b1m9jRm7yiMvzGnYGEexhMBj5+y4fqurtw9yoiCgwQz4fpxiycUhdh5ODne7o+4mpZq5JkEc25ccLmJxpQZJk3qtp54b120Lh73pNQkOiFiJUfVnAnIJkZFlmzdIyfhSjgt9bPsqgVTE1Owm3X6K+041aQH2HNy4gJCKD1enysXpxCRa9mjXnlrF2Q3yidDAY5PZl5ei1KnQawYyc5ON7YRVOGLGC1rGM1ITl4eZEJESfSBRDNg5pd/hw+oJRzcRVi4sT3gBq2hxMSjezv90Zp2b/p017WXteGWtiFTeWlvHopr2cPjWTstxkJqaa+N7Tn/Cj0xPvOyIftXpxCUadmi6Xn3Ubq7h3eQU/e2Frn6TpSMj/xFQTLr+fVLOW+5ZX4PQFMOs0aDUCq1FHplWP0xugJNOMTqc+PhdU4YRTmGZmbkHqmNF1VBgciiEbh3gCwbgaXxHV+d43AK9f4vUdTdhMWgrS4nPDlgeCUUNi1Gn486a97GlxcMNXpmLQqln1j1BxTF9QSrjv2RNTuG95BUadCo8/iDOcRO0NJC654vYFOG1yOhZ9aOT32Du7+ea8SQRlmSSjhr99sI+rTi8hGBRMyUzCbBxaH7zCyEalEpxalMbdF82Meg5GcsLycDOYObKhSIYeKMOVNK0YsnGI1y/FGYveqvORkdKBThfrNlbx7MpTaezqilvnsXf38YMvFce591YvLmFHQzf5aebo/td/Uh/VUYydI7vppZCu4t0XzmBShpm9LaGaYCadOqHhS7PoMetUNHR5yE7WMacwPa769LVnlSLJUJRmwWBQftbjEY1GxbkzJzAjN3nkJywPNwOcIxu6ZOiBMjxJ00qPH4fEjq4AGrs9PLu5jnuWV+D2BWjocmPSqnkk7Hr0B4OUZltZ80olK+YXIQRMy07ij//bG30vy/DU+7VcdHJenDFq7PaQZzPw6GUns7m2s48aiEmvocvtZ4LNyK1Lp1Pf4eK6s6dw3xu7o0bq9mXlqFQykgQH2uykmtLJTjawcmFRKFVAhIpw2kxqxYiNcyK6juNtTqw3A50jO5FCv0OJ0uvHIZPSzX1cMJfMyec3b+ziZ2dPJSjBI5tqosYm1aynPNvAjxeVcvNLoSCM1YuL2dPiYFuDPbrfiOK8Ra+Jllnx+CWe+L8aLj+1CKNWHXVpRkZw9Z0uZuWnkKRXY9CqsRq13PvvXVx3dilZViNmnZqAJJNpMbBu424WT8sh1azB5dNQmpkUnSNLMqjJT7OewKuqoKBwolAM2ThEpRJ8tTwHm0nH5toOghI8u7mOG5ZMQ6cRPPZuTZ85BpVKcN6MHArTTLQ5PKSa9WGjszvOMGVa9aQn6Uhz6Pjz5XP4aH9o/x/VtDIlJyVuFGXSqnn8vX2cUpiC3RvE7QvQ7fJz6dx87nvjUKmXOy+Ygdvv53sLinH6AkxKtzIp3cr+dqfiQlJQUFC0FsczkQTnlh4PGZZQ+HKrw4tRq8bpDeL0BShINTMpva+RCAQkNu5qptnuIc9mwukLkmbRAjLzJmUgSTJ72+w0dvv4tK4TSYYUPZw8KYMutx+jVk2bw4dOrUKrgQnJelw+iaAEvqCEJIHTGyDVrMOoU9Hu8DHRZlK0ExVGLUMgoTXglY2pWfKsb954xPUcTftZ/eXyPnNkw5G0PAQoWovjlcN1nsh8QmGaOZognW7RkZtiYmt9F5IMazfs4IYl0zh7Whb1XS6a7d6QgbOZSDKq6XCpo3liBq2Key6aiSTJOPw+Pj/o4MYXt2Mz6bj81AKm5Vox6VT4Axqae7xkWvX8p7KBc2ZOYOsBB89truXy04pw+wLk2Yxs3NnIKUUZlGSayU7RK0ZMYdQy0OK1Q8YAgz0SJUMPV9LycKIYsjFEb6OVbzPxxs7mfjtPZP3adif+oMx71a1cMHsiV8UYplWLSrj79Z3YTFq2HOiKm+Nad8lJ0ahFCIXJX79+GxNSDEiSzI0vbqc008L3v1SEXqPGG5TY3uCIzrNFxH7VKjUPvV3FJXPyuf6FrXFBHr9/s4p7L6rAFwhS1+GkMH18T+IrjB5i+6NJp05YvHbqqgXDEpiiJEQrjEoSPfE9etmcfjtPYZq5z/oPfmM2V/ejwtHjCeD2B6PajOs/qaeqpSeqpXjh7DxE+MFSq4Y2d4CrzyhmRq6VJruXnz6/javPKObBt6r7iP0+ccXchPJCt7wcipJs7vGiUYUkrBRDpjAa6N0f+xMdGI+qI8OBYsjGCImU7DfXdvTbeYDo+jNzrVy5cHI0Kbn3+skGNV0uX1QJxKBVceOSqRRlWrju7FImZ1j49es7qW13Y9CqqMhLptsd4MG3qrlneQVrXtmBzaQjy2pIuP8Op69feSG1CjKT9PiCEi5fYKgvm4LCsNC7P/YnOjBcqiODrRAdy0BEhEfaHJpiyMYIzXZPH0PQX+fJsBii68/MtfL1Uwq4/oWt/HhRccL1p+cmc+WTm6PLbSYdLn+Q7z21OVo48+ozSmjp8fDXD+qQJKLuQ3fYOF44O4/6Tlc/yc46pglrws9OmpiCVi3T3OOhLGf0VnhWGF/07o+JRAeGVXVkEKLBvTmSiPBInENTDNkYIctq6GMIPtjb2kdVY/XiEtQqMOtCwr9XLpzM9S9sxWbSYdFpWL24hGc+rmPpzFzUKjhpYgoN7c64/V44O491G6uwmXRcNq8grnOuWlRCUD6kHJKdosegVSEEPLe5b2e+fVk5Fp2G+/69i1uWTuf2cO5ZpMhnukVHXYcLo1bLpPTxJzV0PFAKUg49vftjRHTg2ZXzcPuDw54yosyRKYxKCtPM3P+1WVF3RkGaka/NLeDBt0JzXGoVTM228sjb1ZyUn4JRq+IP35xNtzuAzaTjxnOmUd3SQ6pJ20d66o7zZ1CQZqS2PaSYLwTRUVbvea0H3qziye+egkGr4tRJqdjdQW5dOp1mu4dOl4+nP6iNqoGoBExMNaLVwNfmFvBQTFsr8lJQqWQMWsH0nGQKlJvrsHDco+nGCb37o0Gr4oYl05iRm6Jc12FAMWRjBJVKsKQsm6mrFtDS48GoVXPJox/g8Us89FY1EHLVrVxYRLbVwCd1Xdz04nbWXXoSl59aEI0WXLW4mEc37Y4zTje/tJ0/fHM2P/xbKBBEHVaujxi0WDx+CbvbHx1NrXz6E0ozLfzinGlkWg3c/urn0fItqxeXIMkyPR4/E1J03H3RTNocPtLMOkxaFalJKnKSrGg0Sl2x4SLR3OpwRtONF3r3x+OdtO/3ummt3j4sYsDDJfx7LCiGbAwRqzP3/t62hEamNCuJoAQ3het9Hehw9lHCT7SdPyhz3/IK9rT0MH1CMtecWYrLF0g4r5Vs1KLXCLrdgaha/l2v7eTbXyyMU/bITzWRlaTF4ZWixw1KMv6ghD0YZFp2umLEhplEc6tKNN3QcCJ1H4NeN18vVQ2TGPDwCP8eC4ohG6MkmjMzaFVMy7bS0nPo5tXl7hupmGi7dIuOg90ecpKNfH6wm5e2NHDP8pnk2kzcGjMHt+bcMtzh0i5CqOJKv9z37z1cPCePfJuRTKuBfJuOJruPCSkm8lNDdc90GkVy6njS3+9EqeE1utHojWNCDHigKI+7Y5SIj96gDX3FkbmPSenm6M0rQuzr9Z/Us3pxSdx2d5xfTnlOMhNSdBRlmDi5wMZVpxdz7+u7SDZquG95BXdfNIP7lleQZAiJ/75R2UBACrL2vLLovjpdPvJTTRRnminO0tHlDuDyS+SnmqNPr/OK0inKUBQ8jhf9/U7GYw0vhdGLorU4hvH5gmw72E2T3UO21cDMCcnodGokSeZflU389Pkt2Ew6vvPFQu7/zyGR3jvOL8Ok1eDwBsizmTgpNxmjUUsgILGn1Y7fL6HXqXB5Q4UzBeALyjg8ATKSdOSlqKnt9GHUaOlw+VAJQafLj82kRZZlCtL0uHxg0mmiRkzhxBGruamMhkc0A/5STKlZ8puvvTzWRmSK1uJ4IxCQeGX7QW5+qTKa69XQ5aYo3cyUzCQ0Kli5sAiTTk22Vc/qxSWkmnTYzDoc3gDXPn9IKuquC2ZwzvRs3tvfhk6twi9J2DsCeP1BspMN+IKwLazNuOH1Bm766nS6XX7aHPa4+TcIPfH/8bKT+dsH+zhz+gQmWI3odOoTeKUUlBpeA2M0pSkE/T4qKysPu85IS2o+FhRDNkbZ0dgdNWK9c73uvGAGD2zcQ227m9vPK+M3/9nDJXPyWbNhByvmF0XLuEBo4v8XL26nIM0ECFw+CaNWTbfLTZJRS6vDR227C0kGtYDvnjYJjRDc8nIla88tSxhI0Nzt4ZvzJvH9pz+hKN3MnMLUE3CFFBQGzmhLU1CpVKMuqflYUAzZGKWx29NvrtdNL27nF1+ZSopZj1oFty0rjwZs9BdS39bjpbXHw+TMJDqcPooyLNS0OnD6gnHSVasXlyALGY9fwqTXJAwkMOo0dLn8IaNm9xzX66KgcDSMtjQFrSmJzNKRFVk4nCjBHmOUnGRjv7lepZkW9Fo117+wlav+9hnff/oTLpmTT05yKFItNvgDoCDNSFCW0WrUfOcvH3PV3z8jIEnkp5njXIcev8S6jVWYtCED1tDl6hM4snpxCY1dLlJMWgxaFVlWJTpOYeRzuDQFhROPMiIbo6SYNKw5t4ymbnefUdHKhZO5LixLFVGt9waCXH5qAf/a3thHKurWpWX4AxJrXtkR1WeUEbT207l7PAFWLy7hHx/V8d3TJsXljpl1alItev72wT5uW1bOzAnJx/vSKCj04UjzX6MtTaE/0WCT0YRQiRGZ1HwsKIZsDBHbGd3+IP/4sJarFpVw23ll3PrKIckpCTnh3Nl9F8/kqjNK6HJ6ePyKubT2eMmyGnB5/fglOVogsyQric/qOtFrVAk7d5JRTa7NyLJZubgDQUozkwBIMWnRqgUyEt9fWMKMcBSlgsKJZCDzX4kkp0Z0mkIC0WBHWwMrLloYTpIeeUnNx4ISfj9G6N0ZVy8u5o/huatIvTC1ChaWZKAS8Pae1ujcFkBOsoGrFxXz4qcHuGh2PmtfPWT4/vDN2WhUgsqDdtz+YOh4MmzY2sA3Tingt/89FLp/1wUzyE810uXyYtbraHf6SDXr0KpBp1KTbNIqIfcKI4qaVgfnPPBOnwey13rNf42ANIUBH8yaUyifs/Yfccvaaiq54/wZoznAo9/zV+bIxgi9J6Of23wosbmx28Nj79YwNdvK7HwbJp2KyRmWPor2t7/6OZefVhQ1YhByFf5yww50GhXrNlYhyYeM2CVz8vn7RyER4FWLi/nDN2eTZtaSZlZzsMvL+zXt7Grq4YOadnYe7EGjFhSmK8nOCiOLgc5/KUn7IxfFtThG6N0ZG7s9PPV+LU9+5xRk5LgnyP3tbtItuji3YCQoxJ2guGZtu5t2py+6PGLEnt0cU+4l30aGRYdZB7/6127OrcijzemL7iPVrGdKlvU4XAkFhcEx2ua/BkIi0eCxNi8Wi2LIxgiJOmOny0dGkr5PeHBOspFdjV2sXlwSjTqMKNr3FzKfZAhFGa7/pJ7L5hXEGbFZE1Mw6SAYlPjZC5/z3fmTOXNKJvmpdpq6PWQnGyjLSVYEgBVGJKNu/msAJBYNHlvzYrEoc2QJGE0Z/BEON2ENxJ1PXrKRt6pa0KlVOL1B3P4guSl6Grt9/OOj/X3myG5fVk6WVUdjt5c1r+yIKoVMzrCQbTXg8PjwBmRybUaSDNpRcb0UFGIZAfNfA0GRqOrvA8WQxTOSMvgHa1B7d8Z8m4l97U6qWhxIskx9p4t/fFTHLUun4/VLPPF/NVx+WhFuX4BJGSba7D5qO1xMSjORbAoFamQl6dGoZSx6Db4guLxBWnq82Mw6zDo1vmAAgQpPIMC8wgxl1DWCGY0PaApxKIasHxTXYi8iQROxOVa7m+xMz0miMP34ZfAfi0GV5dDf/+1t5ZO6LjQqFUUZZgrTzNy7vIL2Hg8vb6nn4jn5XP/CVkozLVy/ZCpqtWBuYSoOb4Aulx+DRkWHywuyjM8soRZqDnZ7qGl18NzmejpdPm5fVs6kdBOfN/SQpNdRMdF2nK6QwmAYSQ9oCgpDjfL43ItmuyeaY/XYuzU8+GY1f9xUw6d1XUjS8Ru99ieJs7/dmXD9yI3qnAfe4et/+pCv/v4dDnZ7ea+6FW9AYk9zD7IM97y+E6df4vJTi1jzyg5KMy18a14Bv/vvbtRC4PYFabZ7CEgyj/yvGodXojDdTF27l2899hGrn9nCHzfVcNm8AmwmHbe8XIkvKDMx1UxTt6JyMFIZ7O9JYXQTEQ3+4IMP4v68Xu+JbtqwoIzIepFlNXDxnL76hL94cTuzJqYcN121wVbuTXSjeuR/1axcODlOpeOWpdP5/ZtV3PTV6ayYX8ScQhs//Osn/OFbs2mxe6PqHQatijVLy/jHh/u55qyp3BLWYozs+4E3q1gxv4iH3qqmw+lDliE/1Tj8F0bhqFAqQY8vEokGjzWh4FgUQ9aLwjQzpZlJx9Tph2IuYrAhwYluVEtn5vLopr2smF+ECB/+0U17ueLUQhyeAI+9W4MQRXj8Ejq1KmrEIue79tUd3LO8gvpOV8LrIcSh6tHegERZjiI3NVIZiyHmx8JYny8cb6LBiiHrhUolmJZjPepOP1RzEYVpZh78xknUtDjISTHh9gXItRnJt5kSrp/oRpVsUHPJnPw4GapVi0qYnGnhgY17uGd5BZIk8+A3TqLD6U9orDy+ACZd4pB8lYC7LpiBWiWTl2JUAj1GMGMxxPxoUeYLxx4n7M4jhNgvhNguhNgihNgcXpYqhPiPEKIq/N8Ws/6NQohqIcRuIcSXh7Ntk9KPvvz7vrahnYsIyvCzF7Zy/frtrHhyM2/sbE44V5eoZH1ZbnIfF+kDb1YhhMxFs0OBHj95dgvXPb81miAdi0GrIifFyJPv1XDn+TPi9n3L0umcUmjDrJNpsfuZdBwDYRQGj0olWFKWzWurFvDMyi/w2qoF4/bGPR7mCyOiwa3V22mrqaStpjKcED02OdEjsjNkWW6Lef9zYKMsy78WQvw8/P4GIcR04FKgDJgA/FcIUSrLcnA4GhXp9FNXLRhUXokkyexstA/JXMT+difb6rvj9BCPVANJpxFxSvOd/YyydGoNa1/9NG6/v3ljF7cvK4/OhRm0KtaeV8bT79fwlRkTyEvV8eR3TqHD6cNi0IRD7/2sfraS//fjBePyhjjaUCpBhxgX84XBAF3Vn8aIBMNYTog+0YasN8uA08OvnwTeBm4IL39GlmUvsE8IUQ2cArw/XA05mk6/v91JVUvPkMxFNNs9SHLiIpeJOtz+didX//2zuPVXLy5O2JZWh7fPfjfXdnPVGVoev2IubWHVe28gwNdPKeRAhwtkFf+3ty1qJDMsev78bg33f20Wk9LHn3tKYfQyHuYLtaYkcisWUF5ePiaDO3pzIic1ZOANIcQnQoiV4WVZsiw3AoT/Z4aX5wIHYratDy/rgxBipRBisxBic2tr6zA1PTHNdg/Pba5n1aL4YpJ3XTBj0HMRWVZDVDYqlv46XKKnzOc213PL0ul9XII2ozbhfnu8Et/404dcv34b3kAQnVpFVXMPKWYdQUmiNDOJwjQTcwpsZFkNPHb53HHrnlIYvSRyw4+F+cLYe5/f1XOim3NcOZEjsi/KsnxQCJEJ/EcIsesw6ya6UyZM6pJl+VHgUQgpexx7MwdOltVAp8vH0x/URiMFVQJm56cM+mZfmGZmRl5ynB7i4Tpcf1qLdrc/ri12t58n36/htmXl3BrjRlxzbhl/3rQ3WsW51eGlINXEKZPSKA8Xv9zR2E1Tt4dko5bTihTtRIXRydFOHYx0Yu991pzCsSvZlIATZshkWT4Y/t8ihHiRkKuwWQiRI8tyoxAiB2gJr14PTIzZPA84eFwbPABiI8Meeqs6anjyUwf/pKdSCRZNyaI4w8LsfBsuX4D8VDOT0hN3uERRabctK+eht6qobXdHIxaf+biOa84sZUauhSeumEu7w0eaRYdJq+bHi0qQZKhtd6DXqJmaZcZiPDT6q5hoo2Jin0MrKIw6BjN1MBpD9f1eN131exmrave9OSFai0IIM6CSZbkn/Po/wG3AYqA9JtgjVZbl64UQZcDfCRm7CcBGoORIwR4norDmYMRHB9NBBrJu7DpBWcYfDKAWavxBCYteS0uPlyyrnmSjmqZuL812b1zl6NuWlTMhWYdRq2VKlinOiCkojEdGWKj+gA+oM1vlh9fdz2WXXYZerx/ONh1PRpzWYhbwoghl6WqAv8uy/LoQ4mPgOSHECqAOuBhAluUdQojngM+BAHDVcEQsDsWT10Cf9AbTQQa6buTYKgEf7Wtneo6VoCSzp8XJVeFAkIiafZ7NyENvV8clSz/0VhX3XFRBq8ODw+tnQYl+xD95KiRmNI4iRiL9hernrpyHyxccsddWozdSXl4+lozYYTkhhkyW5RqgIsHydkKjskTb3AncOVxtSmQs7r5oJhNSDKSZ9UPyY5UkmboOJ812L3aPn91NdmwmHY3dnsOG1sd2ppxkAxfOzmNXk53cFCMzcpP7tKvZ7mVKtoU9LU72tzv7hPDf8nIlD33jpITJ0q09Hnq8QVRmFfvbnWMnHHkcMcJGEaOa/kL1N+5q4YGN1QO+tsqDxfCizNaHSfTkdcP6bWze38lLWxp4dftBPtrXzv42R1xCsiTJ1LQ6eH9vGzWtjn6FhSVJ5s3dzfyrsolvP/ER33vqk6j4bk6yIXrM3uXV4VBnykk2RMWMH9hYzSWPvs/rO5r6HNMXCOILwLqNe8hNNibsiCadJmGydFqSnkf+V41GJRK2RWHkMx4Sfo8XkSAqgJxkA1edUcyqxcWUZCaRk2xIeG173xMCASlO0PucB95J2G+HkqDfh8/nO/KKY4SRlkd2wkj05GUz6bAatXFRg6sXl1CSZWHRlCyAAT/59pfgHCu+219ofZbVQEGakZ+ePZXqlh6uXFDE+k/qaez29BnFSZKMUaeh2+3jkjn5HOx2J8yZ6fEkTpbeXt/N0pm5OH1BCpX8sFHJYBN+ldFC/0SCqO5+fWdCD8bTH9TS2O2JXtv+PDv/b1s99yyvwO0NYNJrePzdvUzNTho2j4csDYtWxIhFMWRhEoWvXzwnL6ocD6GbwbqNVaxcWERRemgualeTnSsXFAGw/pP6ft2Dh0twjojv9g6tj9xgmro93Hl+Odsb7EgyqAX8YGERj2yqiXaiwjQzte1ODnS6sLsDpFl0PPBmFTaTjlWLSuI64NrzyrCZdQkNXLc7gFoFaWbtqM+rGa8MJuF3vLohB2q8I6H6uSkGLnn0g+g1tZl0eAJBfrK4hIPdbrKtoWubSKLu6ff3RWv/xVaWsLuHb8Sk0RvR6XTDtv+RhmLIwiQKX59oMyU0PJIMHU4vdR3u6Agr9gkt0ZNvbIJz7xvMguJ0LjwpN64zxd5gbCYdl59a0GdkGFmWYTHw5u5mqpod0XXWXToLj1+isdsTl9f2hUmp+INB/rSpmluWTo8r8bJqUQnPbq5j7XnlnFKYNqZvZGOZwQgE9+eG7E8G7UQxlKPGwRpvlUrg8gWjxXZTTVpKs5LYWt/FgS43G7Y2MCXbSn6qmdoOZ597xuWnFUWNGByqLPHXFV84qvYr9EUxZGFUKkHZhCR+d8ksqlscFGVYqGt39qv6rlWr+MWL2/u4CVcuLEr45Hu4BOe5hakJXZGRjnbh7LzoNpFjrdtYxf0XV3D/12ahVhHntsxJNmDSqqNtb+z2RF2XX5ycij8Ib3zeRlO3j/u/NotdTXaCEjy7uY4fLyphQopOSXYexQwm4Xc06A4O9ajxaIx3TrKBy08t4JmP67hkTj7fe3pz3APg3a/vZGp2EuYElSLcvkDCa9zmGL4il1LAP2z7Hokod6sYmrq9/OTZLdz3xh5uf/VzZOgj8bR6cQkz85Jx+YIJf5ylWUkJn3wjCc5fKc/mye+cwhNXzOH//bh/BfLYG4wQiV2SGrWKJWXZNHbHuy0vnJ3HHa99zjVnlsa1/Y7zy3H5ghh1ISO3rcHO7a9+TlACtQpuW1bOU+/tp90ROOZrqXBiiaRizCtKD6Vk9HPDjw1miDDSdAeHOnjlcMa7P4ISrNtYxdKZuQmDpJbOzKWlx0OWVc/qxfESdRNSjIkrSyQP3zXOTrWOWYHgRCgjshicMU9Ojd0e7n59NznJBu5dXgHIZFsNZCTpyU81s7+f0dq0bGu/Nw2VSlCYbqFwACVPes9zJDqWzaRFpRJ93JZCQG27m7+8tz/qUpRl6Hb5ufmlStb/4NSo66mx28Nj79awalEJP1+/nU6XjyzryLmJKQwvo6FO2VCPGo9GNLilxxPtW4naolZBZpKB/FQz+amm+CoUDg9rzi1j7YZD4gN3nF8+rIVojUbjuMkhA8WQxVGQak6oV1jV0sOyilwmZx7qNP3dAIZKCT52/+s/qe/jkrz2rFIykvTRdWPdlhDqmBGXYuT9yoVF3HnBDKZkWZmWk8yUHy9gZ5OdPc09PP1BLZ0u34i7iSkML6NBd3Co1eqPxnjHjlwTtWVOQWr0upVNsLK7OSTaG5TgV6/vITdFz19XfIF2h5fsZANlOYpW6VByQiSqjheDlaiSJJl/VTbx0+e3xBmMGbmhJ6fek8yDkaM6GmInuAG2HOjC6QuiEjAzL5lFU7Li2hJJtvYHgzT3+LgpPIcXeQKclpPElExrXAca7nNQUDhWhiOycrC/+0gbEoXh333RTL5anhPtV8MYCTrgjadNmybv3LnzWI41Eun3/BVDFkMgILGjsZtGu4cUoxa3L4BGrebml7ZHhXcT/SCHWjMx0bqZSQbUKmiy99/xou3v9jAx1UggKNPU7VGeABVGPSPhgSvShg6nF61ahcsXxKRT4wtKfdR/hqm9iiHrB8W1GCYQkHhxS0NcheRIOPpVpxeTbNKxr80RjU6KTUBOlAAZ+4QWofe6BWlGbl82A61aJBzt9V73l+eV4/IGUAlBl8tHikkX3SYQkHhpawM3v1QZNwo7vyJXMWAKo56hrm59LOH8/qCMzaSl1eHlO3/5OOGoS6US5NtM9Hj87Gnuod3hxRMIYDXoKcuxKn1yiFFGZGG21nVyyZ8+6OP7XjG/iMferWHlwiKMWjUmrZrpuVbmFqYBUNPq4JwH3umz3aOXzWF+cXpc54isG8kLsxq1cXlcsR0hdt1vfiGfLKuB+k4Xz22up9Pl47bzynhucx3f+eJkvlKezfaGrriEzUg7nl05j4qJtmO9lAoKJ5ShzCMLBCT+X2UjN6zfNiDXX8Rt/2ldVzTlJhLB/NT7IWUPCPW318Ih/IkeLNcsLWP9p3VcekrB0T5gKiOyflAeC8I0dLv7Vd2IJEGv21hFu8uHiLme/UVUNXW72d7QHaen1mz3UJpp4Zal05loM/VRDYkNKW62e7CZdFw2r4AH36rmZy9si2oz2kw6bn1lB99fWMxPn99CTasjKjzctx2KXqLC6CbinRgKrUJJknmvpj1qxCDUT+5+fSfbG7r6aKZGjv3Pzxr65I2u21jFhbPzovv2+KXofPaOxu6oEYt8tvbVHVx+WhE3v1TJjsbuY7omCvEohixMukWfMNdDlg/9P6TqcUhaJjMpcR5ObYe7j6hvTrKBS07J59rntrCrueewuSyZFj0Xz8nj2c11rJhfxNWLirlyQRHPbq7jwtl5ePwSAUnG45fY1+4kJzlxrkr2MOaqKCgMN5Iks72ha8B5ZIGAxNYDnbxe2cjWA10EAvF9bH+7k821HXF9LyfZwCVz8rnk0Q+ihnLDtoMEAlI0h60/ebn8VCNXLyomJzl0HzDp1EiSTENXPw/GhOSthvsB0+124/UOX8L1SEOZIwuTnaxn7XllrHllBzaTjovn5DHRZqLd4eXGJVN5ZFMNBq0Ks05NVtKh/Ay1ij6h8bcunY5Zp+be5RXUdzip63BSmG4hKBE3CksUxmvUhjqCJxCkJNOSUKhUpQqvG05sNmjVlOVYueP88j5zZMOZq6KgMFCOxjUYGQ3tarIPKI9sIPPEEc3T2L534ey8PknON6zfhs2kQ6sW2Ew6pmQlhXM3QzJVQoQ0TzucPv78Tg2rF5dg0WuQ5VCbex8DoCDNiACuXzKFTKseSZKHLWClqcPOZ599xrx584Zl/yMNxZCF8Qfg+c11PH7FHOo7PdwaDvooSDPy8yXT+OYX8pmcaUEF2Cza6HaN3R6eej+kZTgp3YRZp+HXr++MRjmuXlzCjoN28lPN0aRKCAkM9xbzvWXpdD6saafV4UWSQKdWJ1QRuG95RUg9QBPav82kRaNRcX5FLiWZFiVSUWFEcbTh6JHR0JULigaUR5bInXfzS5WUZFqi88RZVgMbtjZEA7mWzswlPzVxqaPNtR18uSyLy08t4L43dnHjkqm4/MG4h9ZrzizFZtKxbmMVv/vaLIQQXPvcFkozLfzy3DJ+GU6CLkgz8oOFxVwXIxz8m4sr+Ep5zrAYM5VGe+SVxhDKXS5MS4+HzbXdfFrbFTViEZfDNc+FZKuueXYLNW1OWu2HXItZVgOdLh8PvVWNXqPmmue2UNvuBg750Xc397C/3RmXVBkR8129uITfXlLByoVFPPhmNff/t4qqZgepZi2ufjTaVKqQO6Tb7SPPZmRqlhUAjUZFxUQbXy7PoWKiTTFiCiOCo5WYisw/Rx76YhOSEyUwD2SeuDDNzA1LpvHmriZWLpzMY+/WcKDTndAtH5TA5Q0Zrtp2Nz3eQB/N09/+d0/U1e+X5DjpOqtRwx++OZurFxXz07OnsvbVHXHb/vT5rextdQz2ciokQLnThYmqBwSkw7oc1m2swhM4VOsnohJg0KrY19ZX+ToyrxYptRJZF0KqIVOzrdz4z+08sLE62hHXbazCF5DISk48b2cz6fjrB/tJNun4SlnfMH8FhZHE0WgbwqE+GVvBYdXiYp5dOS/haG4g88QRJZNblpZF3fyJDOWqRSW8uq2BVof3UP+PuTfEnkekDFO+zRht85ULJ3Ptc1txeIP8+Z0a9vQzJ76vTSl2OhQod8AwESMT0SyE/sV6fTETyJGO8dqqBXyhKDVhR1KJUFBI7LrPrPwCr61aAMgJj2H3BGhz+BJ2sNYeDxednE+21aAYMYURz9EKE8c++EU0QadmW5mRm5LQHReZJ+4tlN17njhSliVWVzViKO+5aAYr5oeCqm5YMo3cFFNc2/vr33deMIOyCcnRNkcU7xu6XKxaVBJ3X4ndtveyoWK8qd8rc2RhIkZmek4SBWlmfvHidiBxQEZ+qrnPtkUZloQabpGK0hE3SO/ETjnBpHBEGfv9ve3RqMWI8O+zm+v41QUzSJakPu1QUBiJHK0w8WB1IAczT9xbvzFiKJ9dOY/8NBMXzQ7VB5QkORpEtf6Teq49q5T7/7Mnbl67MM3E3PzU6HGWlGXzSW0HBq0KhzfIhq0NXDo3v0/9v9WLS7Aahmcua7yp3ysJ0b2QJJn6LicNXR46HT7Meg27m+w88V5IVPeuC2YwOz+F/NTEHSoQkNhxsJuGbjfpFj1ZVj0TbYeXoUo0EX72tCze2tPCvjZnXMf51QUzOLkwhbwURRNRYfQwEiSmerdnoAEosX06zaKnurkHjUqFWa8hxaxlos3Yp4/7fEFe2naQh96q4runTaLd5UOjUlGUYUYtBI3dbtKT9CyZlo1Opx5os5WE6P4+UAzZISRJ5s3dzdS2u7j337vjnpwyrXoyLXr2tTt57N193LBkGmdPy6Ku00VTtwe9RoXL76fdEeDn/zykGHDPRTM5J4FcVeR4+9udtDu96MLabZHQZEmS+deORrz+AFlWI93uAE5vgE6Xjwk242A7gIKCQi8kSWZfWyg9xqTTkGXVJ3xAjdwXqpodpFv0ZCTpsRo12N0BWnu8tDu8FGdZWDQlCyCaZpBk0OALSNS0OfukBLi8AXJSjCyemjUYg64Ysn5QXIsx1LY78QfkqBGDQwEeKxcWUdvuwqhVc8mcfB5/dy/+oBQnc/P7S0+KGrHIttev30aqRcdpRSG5qojxanN4aevxsqu5B0kO5aTMyEuOPqlub+jiZy9sY92lJ1F50B4X8rt6cQmfN9mZla9ITyko9GYgOWsRI7aryY4M7G7uwe0LMqNXVQmAA51OWnt8cX3wtmXldDm92L1B1AJ6PAHqu5xUNvRw9+s7WTozF7UKTi1KS5gS8OR3TuGxd6uZPIT6kbG43W4++OADTjrppHFRl0wxZGECAYnKg939Vn6OqOFkWQ384sXt3LO8gutf2BqXIOntJ6rpYKeb/e1O8m0m/rurmbp2J9NyrDi8AR7dVBNnoCanW5iUYYlGMGpVok/I77qNVZRNsB6X66KgMBqIHV0JIVjzSmW/FSsSuRVXLSrh/6pbmZmXwv/2tFCQZo7O4dW0uaJFMSHUBx96q4qfnT0Ve7juWI/bT2O3N67Miy0s6p3ontDY7eaik/PpdvsYDlp7vPz44Zf5/Y8YF0nRiiELs6vZTn2nG0Hi4IvIA1qaRcepk1Jx+wJRLcRIiP7j356TWK1Dp6HZ7qHd6UUtBKkWPd3uALe+sqOPgZqZl8ykDEs0lNjZj2HtvUxBYbzSn2F6+oOQoO+1z21haljMFw7ltcU/hAb5wenFXPX3T+PmzKbnJPFZXWdCSavY5ObVi0vw+YMsnZnLs5vruPqMYkqzk7AaNPziK1PJSTHS0OWKBn8YNGq0aoEYppkdtU5P8oSi4dn5CESJ3Q7T5fLzzMd1GLXqkGpGTAjvtWeVkm7WkW7WUdvm5LvzJ1GYZuLyUwvi8swQMj/78pQ+4b+yHMRq0FDb7uLHz3zGdc9vY09L4rwShycAHAolbnN4EobtDlUlagWF0YIkydS0OvoI+yZKuH7gzUOCvr1z1mIFuR97t4YH36zmj5tqaLZ7sZl00W2ufW4LzXZvVG4qQn/5pTKQbFDzjVMKeP6TA1Q3O7jiiY+561+7+NkLW5Ek2LC1gR98qRi7x0eXy49XUh5IhwJlRBbG5Qs9Tf3q9V2UZlq4Z3kFbl8As05DZrKOT/Z38dT7ocjF+5ZXkGXVMyndHA2NB0g2aihKN/HQN2aztb6LoAS/f7OKq84owReU4nzlibTYIsnOcCiUeFeznTsvmBFX7fk3F89i8jD41RUURiqHK73SX8J1pF8atCoyLKGcNUmSCQRlLp7T1xjd/urnrJhfxENvVUeXuXwBPtjbym3LyqOKP2pV4vxShzfAzIkpXPHEx6yYX5RQXm7F/CLWbtjBfcsrONjlIss6PKLefq+b5r078PmmDMv+RxqKIQuTZtZhDBuSJeU5XB/jNrh16fS4ukOh6EEVHU4fj71bE9VSO7UoFadX4t43drF0Zi5CwLkVuTz0VhU3nTM97sefSGtx1aIS3P5AdB2NRkV5bgrTc5I5aWLKiAldHisMZY0rhYFxtOLBiUqvRFyGvXPCIL5yxerFJexrdzAp3cz+dic3v7ydq88oSWiM9Jr45OeJNhOrz5zCzS9tjz60Ts1OSni82nYXgaAcNaL9GVePX2JPSw9ZVgO+GJWgISUYIODoGJ59j0AUQxYmIEvMmpiCLyj1eZK6LeZJzaBV0eHykZ9mYtUzn0XXWzozl/pONwaNqo9i/Y1LpqJViz4JmM9uruOe5RXsae6JJjs/dvncPm0b6uq4CkcvZKtw9ByLeHDv0itwyGV4SmFan4TrW5ZOx2rQcPUZxVFPymurFtBs91Db7qalx5PQGJVkWshJDumn3v+1WezvcOLwBKhtd0dHajnJBq45s5Tf/vdQfuc1Z5byl/f2c+/ymXFTC/0Z10gljCeu6NvfhwKtKYncigXodLph2f9IQzFkYZyeIPvanEy0mQ6rp3btWaWkWXS4/fGCvkKASach2ajhhn/GF+Brd/l4/L19fUZgP15Uym/e2BWNrvrNxbMUY3Wc6E/INjYoQGFoOdprnqj0ChySuYoogEz58QLqOpxo1CoqG7p58M3qqBcFQnqnkdHbXz+o66O0sWpRCb9+fSf3Lp/JhBQjKgFL1r3TJ4irsdvD3z+q5Y+XnUxTt4e6Djd/eW8/nS4f+rARfXTT3oT7f3ZzHbcvK4+KHNjd40tKarhQDFmYgjQzW+u78AelhB1mWnYSj3zrZHY22tGpQy7I3us9+V4NV5xW1McQSjLUtrujem4Rualul49ls3KZnGFhUrqJ8gmJNeQUhp7DCdkqhmx4ONprHlt6JfZB8O6LZsZJv03OtDA500JNq4PvPbU5odGLlcvq8fjj+mMkyrHb7Wd+SQbv723D45fo8Qb7HPuSOfnsbrTj8AWjnppVi0potnvp8fhZNiuXdIuelQuL0KhUFKabOdjlYtmsXFLNWhq7QyNCm3l8jJiGG8WQhZmUbmZqtpXWHk+fQpnXnFnKPf/excUnT+TBt6r587fnUJadHFfIcsPWBn6wsJhWR1+XRUQwtLHbE3VPGLQqHr3sZHQaFSqVrBix40x/8ypHErJVOHqO9ppHSq/c/fpOVswvQq2COQWpnFaUlrDPHE7bMTJ6m/C9edR3ubnu+a192hMJwIi0V5blhJqnN35lGvvanFy9qDi67DcXz+Lef+/ikjn53PXa532mGa49q5Q9zQ4MWhV3nj8Dk3Z41Hn8Xjdd9XuBGcOy/5GGIlEVg88X5NP6TgTgDci0ObwYtWoOdDgpzkxCpYKgJLNgcgY6nRqfL8hn9V002T3sbXXw9q4Wls7MwWbWc8vLhyRp7l0+E5VQ8dPnD3WsOy+YQWmmBatRc1gtRoXhQZkjS8xwBsAM5pr3bke+zURdp6vfgKfY9XOSDQSCMgc6XZj1GryBIBOSTUxKN8clRW/c1Ux1i4N1G6uiVeEnZ1jIsurJSjIw0WbijZ3NvPRZHWdOnxCNWjRoVdx1wQwmZ5jodgfocPoxaFUIAQuL0tlysJv6TjfZVj0BWcLpCclUGTSCKTlWetwB0pN0aNUyM3JSh0VrUWe2yg+vu5/LLrtsLCl7KFqLAyEQkPj0QAddrgBJRg1ObwBkONjlps3p46SJKVgNGjQamDEhFYCaVge7otn9PoqzLLh8oWJ+Jp2Gxi4XkzIszJ+Uxge1HXx2oAubUUtOshFfUCLTqmdmjhWTUXExHG9GmpDtieZ4GPeBXPPBtiOy/t2v7+TSuflYjdqEc1M3LJnGkrJsgKjGqUmnxuEJcKDTHaeHGKlasXByBv/6vIntB9o5Y9oE6jtdJBk06NTgC4IkhaKYzQYNKSY1jV2+uIfY25eVMy3bQpc7gMMbIBCUqWlzEpQkJmdYWDwlHbNxwIZmwF+CKTVLfvO1l8eaqoeitXgkJEnmtcpGro/JU1m1qIQ3dzVx1aJSLAYtTl8QX1ACBKlmJ/UdbtodfpzeAAFJYmpOEvvaXPz+zaqo1tr0HCt//F81GRY9Tm+Q6dlJ2D1BfhLTSW9bVs75MycoIsDHGSUaNJ7hCoBJNMo73P5i25GTbODC2XnsarKTm2JkRm5yH2O2v90ZlYZy+4N9JN0i+VvXPreF0qvns6fFEWckH/nWyX30ECP6qhkWPU+/v49LTylkxZMfx3lU8mwGttZ1RfUWvzApLWrEIvu55eVK/vCtk/nlK5V93IyrF5ewp8XJSQVjZsR0wlAMWZi9LY6oEYPQj/DZzXWsXDiZH/71k+iPb825ZUy0Gdh2wE5DlzuuxMqac8tY/0ldnx/sL88to67TxfUvbOOe5RXc+kr8j/3WlyspSjczpzD1RF4ChXHOcATAHM0oL9KOnGRDnATco5tqEm7bbPewdGYuD7xZxZUL+gZbefwS+alGbCYdO5vs3PfGIVFwm0lHu9OXcBuNSkWz3cvKBcWsevazuD5704vb+e3XZmHQqqMlnooyLAn381ldZ7R9vY1l+eVzjuq6Homg30dlZWWf5WNVRFgxZGH2tTv6/AiXzsyNuigg9ONbu2EHf/nOXOo6XH2e/NZu2BEVE45d/ssNO1i5MNTB3N5Awh97s/3wZd8VFIab4QiA6W+UN+XHC5icmdg4RtqRSAoq0QgxJ9lAfqoRj1/CGK663PscOpw+Lj+1gMYuN0tn5vLPT+v54ZeKMOk11LU7+80py7bq6XD5uXJBSLdw/Sf1UUFvfzBIns3EL86ZRl2HE70m8bGDUig9J1bbMbIvt294EqJVKhV/3+VDVb09uqz7YM2YFRFWDFkYg1Yd9yOM7RyxePwSbT0+JvbzWaTEee/lEfV8k16T8Mc+XFI1CgoD5WgrOR+O/kZ5O5vsccEXidqxq8me8OYfO0KUJJnPG3s42OUOi3uLPlHHtyydTrpFz12vfc7FJ08k2aDmBwuLyEgy8OvwvNqdF5RT2+6KllQqzrTw2Lt7+cYXCvlFjDxcRIxYpxH0eINcv/7QZ2vPnc7ty8qj7sWCNCO/PLeM/W1OpuQkkZNs6FMhOt0yPHPjWlMSmaXjp0K0YsjCmPUa1p5XxppXdmAz6bj81IJo5+htdDKS9MjICT+bEFat77080l//tGkva5aWsfbVHXFzZDMnJB+3c1VQSEQkNH3qqgVDFgDT3yhvT3MP03OsCV2WkXZMSjeSZe1788+KGSHGKtnfeUE5erWa+i4X93+tAn9AQq1SUd/losXu4bunTcIbCFKWm8yOhm4EcPUZJXQ6vXQ6/XElle44v5zTp2RFjRgcmm9bubCIadlWruk10lyz4XOeXTmPB78xm5qWHswGLb/csINL5uTzQU1HdP+R9ddtrOLxbw+Pa3G8oajfh9GrVUzOMPPEFXO55avTWLexiuc2h/QQYyVnblk6HafXjz8g9flszblldDo93LJ0eh8F/OJMCwatim0NdtZ/Wsdj357DQ984ib+u+IIS6KEwYogEwMwrSqcow3LM0Yr5NhO3LyuP6w/XnFmKUatmT3NPnIp9b1p7/H1c++s2VtHQ7Y5uEzvi63T6ue6Frfz6X7u59rmtuPwh3dNf/2s3f9xUg8sfZFa+jX2tTswGLb96fSf7210km3T0ePxxyvc3v1RJRpI+4WiyNCsJSZaj68d+5g1IXP33TwnK4PT4uW1ZOZ5AkNzkxB4cuyfAcOD3uqnd/iG12z+ktXo7bTWVdB+sGZZjjQSUEVmYNqcHtUrNzoPd2MyhH3BjtydOjWNqVhIHOl1ohMCk1/RR6kiz6GjocDE1J4k/XT6HFruHxm4Pv3+zit9cXMGjl51Mp8uPzaRFCJmvlOeM63BvhbFPXaeLTqeXlQuLQjJTGhUGjYpfvb7rsMEfh9NX/GhfBxOSjRRlWOLm02KDOCJz1hGN1IgR/PPlc0g26bjvjV19grJia5h5/BLZ/Ywmdzb28Ni7NaxeXBInJl6QZqSxO1QiJtOqp8Xu5ftPhwLFVi8uTriv1OFS9ggGCHQ142hrYMVFCykvLwdmcNJJY9PdqIzIwph0WhyeAHZvkHaHN/oEGVHj+PM7NahUgmc+rqPD5UOrEtHPHnyzmsferWFPUw9rX91JQ5cHIWR++vw27ntjD7XtbvxBmTybiSyrnjybiXmTMhQjpjDmabZ7eOK9WgwaNX9+pwa3X4oaMTgUwLG/3dlnu951wOBQ8ESkvlhkPq2/0ipCxL93eAPUtjsTRhHG1jAzaFUEJIk7L5gRN5pctaiEf35aHzWMF885tP4vzy2jptXBxXPyqG2PDwZ7bnN9nzqHqxaV4PEPz4hMa0pi8oLzyK1YQHl5OfPmzWPevHljMmIRFEMWpcvlI8WkZcPWBsx6Dbf2cg/esnQ6j7+7lx98qZgJKQZUYTX7yOerFpXwtw/rMGhVpBi1VDbYo/uOBHMMpctGQWE0kGUNKclHvBf5tsQuttjCl5HtIvqKvfvhq9saopGUkfm0L5VkJDR6sXoPBq2KNLOOiryUwxq+SH92+YKkmTWsmF/E3RfNYMX8ouiILbJ+SWYS91w0g99fehIef5DnNtczOcOCJMfvv7Hbw1Pv13Lf8gruu3gmf7p8Ds9urmMQOc4Kh0ExZGHSLXoOdDj50enF/PndGtTIPPyN2Vx3din3LK/A6fHzo9NLyEnWY9JpSDNrWb24hPuWz2TlwtAPvNPl447zy3nkf9W4wmG1QxH5paAwWomMmDpdPh56q5qD3e6EBqd3iH9EXzGicbhqcTH3f20WL356gBuWTIvrTyqVoCIvhTvOj5+Lu+28cl7d1hB9v/a8MjKtek4tSuPkfFvCdkzJSmLF/CKe3VxHiklHmtnAY+/WcKDTzWPv1sSp6Ue2r+9y0+P102T30OnyYdapo/qqsXS6fABoVIJ1/93NVacXk6RXZneGAkWiKozPF+R/1a3oNQKjToPLFyQoSSQZtHQ5/ei1Kow6NZIsce/ru/nxohI6XH5+9989URWP8gnJPLqpmstPnUT5BCstDq8ifaQw7omVpcq2Gvi8sSca4l+QZuT2ZTPQqkUfbcdYRRCTTo0/KJFq1vers9jW4wUBrQ4vWRY9QgUOb5AeTwCNEBj1Kk4tTGdnsx1/MEBdhzcutH7NuWWYtCrUahU2k5aTcpPRaTVs2HaQ323cwyVz8nl2c120v88psKFRCdqdftocHorSzTi8QV7ecoDL5k2irtMdF3F55wUzmJRmosnuId2iR62SmJaZPBh5ugHfRIypWfKsb96Io2k/q79cHp4jO8QoTYxWtBaPhMvt49OGTowaDfvaXX101yLF+W5bVk5FXhIgs7vZRVOXm5wUEy5fgOxkAz1uHwadhkVTshTjpTCqGG7B4FhRX1mGNoeXRruHvS0OnttcHy1mORAR4UjbeiuHFKQZ+fmSabj9QWrbndH93nPRTM6emsWrOxp55qNaLpqdzyObqqNGadbEFB5+q4rNtd3RPp9rM7KoNI3/29uJRi1INmqp7XBzU4zxi703/OqCGWRadRzs8nLLy5WUZlpYuXAyMjKpZh3tTi/VLc5ortrkTAsLS9NIMQ44h3Tghiw5TS4/byUASVkTUWm00c9CidHLRmNitKK1eCT2trto6vbR2tMTnaSN6LwFJZlfXzSDnY12GjpdlGSY8Usyv3ljN5fMyednYSWPyFPXva9+TlG6ouGnMHoYTsHgRMbmurOnUNXiiN7Uf7CwiEc21SRU7kjUtl9dMJOvlmdT3+2O02W8ZE5+NL+rIM3IrUunU9XiYG+rg92pJm5+qTKqvhMJn5dk2Hqgi8XTstlc2x2ntTgh2Uiz3YNKJfAH5agRg0PpAJHIyBtf3M4j35rNLS9XYjPpWFKew3Xhe0NBmpGfnFkad126nD72tbg4qWDoxRAiwR7jBcWQhXH5Atz8UiVrzy2LdooV8ydFQ3ojE8AvbWkgL9VEZpKOpTNz4+oUATywMeRqVAo0HhvDOTpQiEeSZLY3dCWUkkr7zilkJPV15w2G3jJVl87Np77THZeAvHpxCZefWsDdr+/uo9yRqG03vriNFJMGvVodXR4radXbqBm0KiammrCZdHj8QWwmXZyOYySQJCfZEA2/16hUtDm8BCSZLqePlh7vYSMjPX4Jly+Ixy/1kde6dG4+Td2ePufsCQyPRNV4QzFkYSLCoREJqR99aTJ3/mtnn/DcFfOLuOWlSp767ikkG9QJc1E0KpQCjcfAWKkVNhqMceRa72qyJ7xJv1Pdxp/fSSzWO1B6y1Tl2UxRL0bkOOs2VnHv8opo4Ickyexrc7Kz0Y6rH9m3zw50saA4PZqfJQQJjVpk/ZtfqmT14hJykg1cfmpBH63U21/9nHuWV7CnuQe1gIo8Kxa9Bn9QosPlB0IBHBHZrCSDmrwUE55AkKsXFbNhawNpFn20NlnEoF5+agHTJ1ijOWWx5/znYRINjiREJ8LRtJ/KysF/jyN5Xk0xZGHSwz9At9/Pny4/mW5X4s4T+YG29Hgpy01mxZOb+xi7J66Yq0QpHgPDVU7keDJajHHkWl+5oChhwq4sH9v1lyQZSY6Xc3P1I5zt9gW464IZ5NtM0fpiS2fmUpBqZPXiYp7bXB+NGozkk3W4/FF9w8jy3kYt9hiFaWbMehV5CdIAbCYdyUYNBakm2h1eutx+kgxahBBYdGr+8XEdNy6Zissf5JmPQ1UurouZVliztIwet58bl0wlzaLnF1+ZQmGGGa1ahdOT+JxdwyQaHEmIToTBYOwjKHwkRrrgsGLIwrh8fm5fVoZapeJ7T31y2I4demrUs6OhO+GPExhRN6vRxnCUEznejBZjHLnW6z8JybElUrqAo7/+dR1O9rY44oR8O12+hH3LatCSl2qktsMVrS/Wu35XJLAiUizz1KJUUkwaVi4swqRTR8V/c5MTGz+jTk2PO4heq2bV4uKomPem3S18ZUZOdNRk0Kq49qxSspIMaNUyE2xGvr9wMp5wvbMV84v6jPjWvrqDJ66Yizcocd0LW7ntvOn0eILc9OIWbj5nWj/KHoeCMIaS8TZHNqryyIQQS4QQu4UQ1UKInw/lvg0aDTaTLhqOG+nYvZOeX93WwO3LyrGZ1EzKsCTMRVGU7I+NiOxQLMdaTuR4czhjPJKIXOuIHNvVZxTz+6+fFM2NjDUCA7n+kiRT0+rg/b1t1LQ6aHf4uOtfu3jq/VBC9NWLilEJuPP8eMWMa88qDV+zILUdiZU31m2s4qZzpkXzvK4+owRvIMi9r+8ix2rkHx/V0dbj49FNNdzwz+38cVMNl59aQE6yIWoI9zTZ8QYl2h2h9R58M6Ta88PTi/u4Gu//zx5kZKxGPde/sI3/7GgizaI/7IgvUqPQZtKRnmSIBofkphpZc25ZH21WnXpU3YJHLKNmRCaEUAMPAWcB9cDHQohXZFn+fCj2rxIirsBerM7i1GwLAoFKFZq0Lc2yoBIaijI03HXBjLhcFCX5+dgZjnIix5vhqO01HMReawC1SkR1CCMJvAO9/oncqXecPwObSReVc4vw9HfnsnpxCakmHSa9hvpOF49/uI8nrjiFoES/yhsI0GtULJuVS3GGmaoWB5tru/EFarltWXnCeah7l1cgABmZu17bxX0Xz2TVhh1x6+3sZ46w2x3A7Q8FcEzPTaGm1RFnjHp/v3qNOhrsseVAV/RzWYJH/lcdp836yP+quX1ZfH6XwtExagwZcApQLctyDYAQ4hlgGTAkhqzD7cOki68V1tjt4bF3a3j4G7PxBCQef3cvi6dl4wsGmZSegkolyEsxM2tiypCVvVAYnnIix5vRYoxjr3Vrj5dvP/ERHr8UfYhTq2Dx1Exm5KYc8foncqfe/NJ2Vi4s4oGNh4yYQauiusWBLMOaDTv6XB+VgGk51oSGYn+bk4ffrubas0ox6FRkhh8YtjXY+Xh/Z0JjVNXSQ2lmEhOSjXS6fHS6/H3Wi+g69j6eSadmZ6M9GsARqYjx7Oa6Pq7YOy+YQWOXK7pu7D6dviC17e44Yw4M2xxZ0OelraZvheijJaScP2PI9jfUjCZDlgsciHlfD3yh90pCiJXASoD8/PwB7zzdrOPT2s4+RflWLy4hyajB7/SxYv5kbnv1cybHaCVGyl6MpHmPscBov66jyRhHrnWsOzR2BHXa5LQBtbs/d2ppVlL0hh6Rinr47Wp8AZmVC4sozUpiWrY1WmgzP9VMbYeTW5ZOj1PGWHNuGd1uHysXFlGUYabLFaCuwxn1ikBiYzQ128rtr37OL8+bzm3nlaNRiT7rbdjawJpzy1gbY1hvX1ZOp9MXNV7eQDCqG3nh7DxUKrhveQWeQJC6DhfFGWacHn8orN4f5KUtDVFj12L3JGxbumXoogBj733Z2dnccf5QGp6RrZw/apQ9hBAXA1+WZfnK8PvLgFNkWf5xf9sMRtmjtr2LbfVOut1+Wnq8SDKowpVibSYNjZ0efruxmk6Xj2dXzqNiom1IzktBYaRQ0+rgnAfe6XOzfW2AASr9bf//frwAIUKK9RkWAxo1NHV7cfkC5KeaE1aKliSZug4nzfbQehNtJjRqQZP90EPB/nYn3/nLR9y+rAxfQMblC+ANyNEKzZHcsGc/qmNPi4Pnvj+PkjQLu1sd1LQ5+8hT5STrEULQYvfS5vAyOcNCTauD322swmbS8cvzymhzeOOMa2R09qPTi6lq6mJmfhpNXW4K0i10u3w89HY11509lcauUN2zNa8cMpR3XTCDKVkWynJTBvoVDfgpaDD3vlHE6JeoEkKcCvxSluUvh9/fCCDL8q/622awElU1HQ4CAXD6gzg8Qcx6NSoVVNZ388R7h0SBz6/IRaNRJmkVxhbHmjJwvFMOIsd7/N29fP0LBXS7/OSlmrDo1LQ6fOxtdfB8Aukrny/IzqYu2p0BttR3EZTg1W0NXDo3PxoVuXpxCUUZZlRCsK/NGQ3g+OGXimhz+tCoVBSlm1GpQCB47N29XH7qJBZOSWPnQQftTh8ZSTp2NNjRqFVY9BrWbTyky3rSxBS0GijPTR4WiSrFkI1QhBAaYA+wGGgAPga+Icvyjv62GeyX6XL7qGzqodnuJcuqpzw7CZ1Ww47Gbpq6PWQnGyjLSVaMmMKYJVbg92jcoce6/dG2t8PpRatW4QtK6GL+u3zBhMnogYBEVYudLneADqePrCQ9apWguceLWa/BatAwLcuKRqNif5uDg90eHN4AuSkGPH6JJruXbKseRKgy9YQUPVOzQvcGny/ItoPduHx+TDotbQ4vNrMWjVDR6vBi1mlI0qspzDQNxoiBYshGvyEDEEKcA/wOUAOPy7J85+HWH6NfpoKCwvhEMWT9MJqCPZBl+TXgtRPdDgUFBQWFkYPiI1NQUFBQGNUohkxBQUFBYVSjGDIFBQUFhVGNYsgUFBQUFEY1iiFTUFBQUBjVKIZMQUFBQWFUoxgyBQUFBYVRjWLIFBQUFBRGNaNK2WOwCCFagdqj2DQdaBvi5oxUxtO5wvg6X+VcxxZtsiwvGciKQojXB7ruWGBMG7KjRQixWZblOSe6HceD8XSuML7OVzlXhfGC4lpUUFBQUBjVKIZMQUFBQWFUoxiyxDx6ohtwHBlP5wrj63yVc1UYFyhzZAoKCgoKoxplRKagoKCgMKpRDJmCgoKCwqhGMWQxCCGWCCF2CyGqhRA/P9HtGQqEEPuFENuFEFuEEJvDy1KFEP8RQlSF/9ti1r8xfP67hRBfPnEtHxhCiMeFEC1CiMqYZYM+PyHEyeHrVC2EeEAIMeBqvMeLfs71l0KIhvD3uyVcRT3y2Wg+14lCiLeEEDuFEDuEEKvDy8fkd6twjMiyrPyF5gnVwF6gCNABW4HpJ7pdQ3Be+4H0XsvuAX4efv1z4O7w6+nh89YDk8LXQ32iz+EI57cQmA1UHsv5AR8BpxIqp/4v4Csn+twGeK6/BK5LsO5oP9ccYHb4dRKwJ3xOY/K7Vf6O7U8ZkR3iFKBaluUaWZZ9wDPAshPcpuFiGfBk+PWTwPkxy5+RZdkry/I+oJrQdRmxyLK8CejotXhQ5yeEyAGssiy/L4fufE/FbDNi6Odc+2O0n2ujLMufhl/3ADuBXMbod6twbCiG7BC5wIGY9/XhZaMdGXhDCPGJEGJleFmWLMuNELphAJnh5WPlGgz2/HLDr3svHy1cLYTYFnY9RlxtY+ZchRCFwEnAh4y/71ZhACiG7BCJ/OZjITfhi7Iszwa+AlwlhFh4mHXH6jWI0N/5jebz/gMwGZgFNAK/CS8fE+cqhLAA64GfyLJsP9yqCZaNuvNVODoUQ3aIemBizPs84OAJasuQIcvywfD/FuBFQq7C5rDLhfD/lvDqY+UaDPb86sOvey8f8ciy3CzLclCWZQn4E4dcwaP+XIUQWkJG7G+yLP8zvHjcfLcKA0cxZIf4GCgRQkwSQuiAS4FXTnCbjgkhhFkIkRR5DZwNVBI6r2+HV/s28HL49SvApUIIvRBiElBCaKJ8tDGo8wu7qHqEEPPCEW2Xx2wzoonc1MNcQOj7hVF+ruG2PQbslGX5/piPxs13qzAITnS0yUj6A84hFB21F7jpRLdnCM6niFAk11ZgR+ScgDRgI1AV/p8as81N4fPfzSiI7gL+Qcil5if09L3iaM4PmEPICOwFHiSsejOS/vo516eB7cA2QjfznDFyrvMJuQC3AVvCf+eM1e9W+Tu2P0WiSkFBQUFhVKO4FhUUFBQURjWKIVNQUFBQGNUohkxBQUFBYVSjGDIFBQUFhVGNYsgUFBQUFEY1iiFTUFBQUBjVKIZM4bgihLgpXJZjW7jsyBeEEFohxK/DpTkqhRAfCSG+El5/vxAiPWb704UQr4ZfXyGEaI0pYbJFCDFdCFEohHALIT4LlwH5SAjx7Zh9/FIIcV2vdkWPI4RwJGh373IpW4QQKeH2dIePtVsIsUkIsXSA12KrEOIfvZb9RQixL7z/rUKIxTGfvR0+xlYhxMdCiFm92x9e58u99vkTIcTD4dcZQgi/EOL7/Z2/gsJoQ3OiG6AwfhBCnAosJVSewxu+ceqA2wmV7SgPL88CvjTA3T4ry/LVvY5TCOyVZfmk8Psi4J9CCJUsy08cwyn8Vpbl+3odC+AdWZaXht/PAl4SQrhlWd7Y346EENMIPUguFEKYZVl2xnz8M1mWXxBCnAE8SkilIsI3ZVneLIT4DnAvcFavXf+DkCrNv2OWXQr8LPz6YuAD4OvAHwdwzgoKIx5lRKZwPMkB2mRZ9gLIstwGdAHfA34cs7xZluXnhuqgsizXANcCq4Zqn4c51hbgNuDqI6z6DUKqHG8A5/Wzzvv0r9Te32cvAEuFEHqIGvUJwLvhz78O/BTIE0IoKvAKYwLFkCkcT94AJgoh9gghHhZCfAkoBurkwyubvxVx5wF/7vXZJb3cfcZ+9vEpMPUY239NzHHeOsx6AznWJcCzhEZQX+9nnSXAS4P5TJbldkL6mEvCiy4lNGqVhRATgWxZlj8Cngu3QUFh1KO4FhWOG7IsO4QQJwMLgDMI3cjvGsCmZ4RHbwghTgdi57cSuRYT7SN2YX+6bEfSa+vjWuyHhA2IfijEXKBVluVaIUQ98LgQwibLcmd4lXuFEPcQqrU1r9fmfxMhAWg1oWrRiYi4F18O//9uePmlhAwYhArHPgbc32drBYVRhjIiUziuyKGSI2/LsryGkPvtXCBfhFX6h5GTCFUZBmgHbL0+TyLk5hzqYyXi68BUIcR+QkK2VuCimM9/RmikejOHqiFH+CYwCfg78FA/+38JWCyEmA0Y5XCl5fBxrwgf9xWgQghRkngXCgqjB8WQKRw3hBBTet04ZxFSKn8MeECEyucghMgRQnxrCI9bCNwH/D68aBNwnjhU4uZCYKssy8EhONZM4Bb6MTJCCBWhgIuZsiwXyrJcCCyjl3tRDtUXWweoekchyrLsJ2Tk5oWDRuj1uQN4G3ic0OgMIcQUwCzLcm7McX9FaJSmoDCqUVyLCscTC/B7IUQKEACqgZWAHbgD+FwI4QGcwK0D3OclQoj5Me9/RKhw4mQhxGeAAegBfh+JWJRleZsQ4kHgXSGETKg445Ux+zCFXX4RIu63a3oZ2PPD/xeEj2UK72vVYSIWFwINsiw3xCzbBEwX8bXFCM9r3QFcT3wUIrIsu4UQvyHkZl2R4Dj/AP7JIUP1dUKFVWNZT8jFeHv4/TYhhBR+/Zwsy9f2cw4KCiMKpYyLgoKCgsKoRnEtKigoKCiMahTXooLCMCGEuInQfFgsz8uyfOeJaI+CwlhFcS0qKCgoKIxqFNeigoKCgsKoRjFkCgoKCgqjGsWQKSgoKCiMahRDpqCgoKAwqvn/hjRzOgWZUpUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.jointplot(data=flights_needed_data, x=\"SCHEDULED_ARRIVAL\", y=\"ARRIVAL_TIME\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "aSRS4HrFwxZF"
   },
   "outputs": [],
   "source": [
    "# using Pearson's correlation method\n",
    "corr = flights_needed_data.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "J9_L4K8Zwu5B",
    "outputId": "1bcf2276-9af6-4094-b706-29e4bd68d0cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 837
    },
    "id": "YhgZYhwKxHJJ",
    "outputId": "e438d733-a2ad-4b26-e4fe-a6b49301f47a"
   },
   "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>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>DEPARTURE_TIME</th>\n",
       "      <th>DEPARTURE_DELAY</th>\n",
       "      <th>TAXI_OUT</th>\n",
       "      <th>WHEELS_OFF</th>\n",
       "      <th>...</th>\n",
       "      <th>SCHEDULED_ARRIVAL</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.497084</td>\n",
       "      <td>0.004412</td>\n",
       "      <td>-0.138130</td>\n",
       "      <td>-0.124369</td>\n",
       "      <td>0.060064</td>\n",
       "      <td>0.093451</td>\n",
       "      <td>-0.119781</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110820</td>\n",
       "      <td>-0.091687</td>\n",
       "      <td>0.070770</td>\n",
       "      <td>0.004847</td>\n",
       "      <td>-0.006000</td>\n",
       "      <td>0.097693</td>\n",
       "      <td>-0.010550</td>\n",
       "      <td>-0.001603</td>\n",
       "      <td>0.033213</td>\n",
       "      <td>0.061960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.497084</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010955</td>\n",
       "      <td>0.046914</td>\n",
       "      <td>0.045182</td>\n",
       "      <td>0.055632</td>\n",
       "      <td>0.007291</td>\n",
       "      <td>0.044150</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031725</td>\n",
       "      <td>0.011477</td>\n",
       "      <td>0.067520</td>\n",
       "      <td>-0.000709</td>\n",
       "      <td>-0.006409</td>\n",
       "      <td>-0.019626</td>\n",
       "      <td>0.008156</td>\n",
       "      <td>0.003648</td>\n",
       "      <td>0.033729</td>\n",
       "      <td>-0.050835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004412</td>\n",
       "      <td>0.010955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003027</td>\n",
       "      <td>0.010140</td>\n",
       "      <td>0.034863</td>\n",
       "      <td>0.061010</td>\n",
       "      <td>0.016377</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018891</td>\n",
       "      <td>0.000753</td>\n",
       "      <td>0.056163</td>\n",
       "      <td>0.007155</td>\n",
       "      <td>0.090008</td>\n",
       "      <td>-0.032564</td>\n",
       "      <td>-0.007260</td>\n",
       "      <td>0.023770</td>\n",
       "      <td>0.076581</td>\n",
       "      <td>0.004246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.138130</td>\n",
       "      <td>0.046914</td>\n",
       "      <td>-0.003027</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.943186</td>\n",
       "      <td>0.174254</td>\n",
       "      <td>-0.081496</td>\n",
       "      <td>0.912232</td>\n",
       "      <td>...</td>\n",
       "      <td>0.731240</td>\n",
       "      <td>0.552653</td>\n",
       "      <td>0.154951</td>\n",
       "      <td>0.002515</td>\n",
       "      <td>-0.002631</td>\n",
       "      <td>-0.112393</td>\n",
       "      <td>-0.017957</td>\n",
       "      <td>-0.054419</td>\n",
       "      <td>0.186286</td>\n",
       "      <td>-0.042004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEPARTURE_TIME</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.124369</td>\n",
       "      <td>0.045182</td>\n",
       "      <td>0.010140</td>\n",
       "      <td>0.943186</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.243960</td>\n",
       "      <td>-0.070081</td>\n",
       "      <td>0.966477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.734970</td>\n",
       "      <td>0.584615</td>\n",
       "      <td>0.223654</td>\n",
       "      <td>0.006576</td>\n",
       "      <td>0.009679</td>\n",
       "      <td>-0.093096</td>\n",
       "      <td>-0.012820</td>\n",
       "      <td>-0.013304</td>\n",
       "      <td>0.247325</td>\n",
       "      <td>-0.046406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEPARTURE_DELAY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.060064</td>\n",
       "      <td>0.055632</td>\n",
       "      <td>0.034863</td>\n",
       "      <td>0.174254</td>\n",
       "      <td>0.243960</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>0.231399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155150</td>\n",
       "      <td>0.079639</td>\n",
       "      <td>0.950838</td>\n",
       "      <td>0.020787</td>\n",
       "      <td>0.030862</td>\n",
       "      <td>0.085794</td>\n",
       "      <td>0.008040</td>\n",
       "      <td>0.606954</td>\n",
       "      <td>0.605289</td>\n",
       "      <td>0.210402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAXI_OUT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.093451</td>\n",
       "      <td>0.007291</td>\n",
       "      <td>0.061010</td>\n",
       "      <td>-0.081496</td>\n",
       "      <td>-0.070081</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043114</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.062325</td>\n",
       "      <td>-0.045611</td>\n",
       "      <td>0.245363</td>\n",
       "      <td>0.009810</td>\n",
       "      <td>0.010763</td>\n",
       "      <td>0.407371</td>\n",
       "      <td>-0.008021</td>\n",
       "      <td>-0.024674</td>\n",
       "      <td>-0.129879</td>\n",
       "      <td>0.119412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHEELS_OFF</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.119781</td>\n",
       "      <td>0.044150</td>\n",
       "      <td>0.016377</td>\n",
       "      <td>0.912232</td>\n",
       "      <td>0.966477</td>\n",
       "      <td>0.231399</td>\n",
       "      <td>-0.043114</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.744697</td>\n",
       "      <td>0.608611</td>\n",
       "      <td>0.217344</td>\n",
       "      <td>0.006524</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>-0.085582</td>\n",
       "      <td>-0.013912</td>\n",
       "      <td>-0.017376</td>\n",
       "      <td>0.223010</td>\n",
       "      <td>-0.055273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCHEDULED_TIME</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.026285</td>\n",
       "      <td>0.019755</td>\n",
       "      <td>-0.337801</td>\n",
       "      <td>-0.019873</td>\n",
       "      <td>-0.031873</td>\n",
       "      <td>0.005868</td>\n",
       "      <td>0.096191</td>\n",
       "      <td>-0.038173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035598</td>\n",
       "      <td>0.014674</td>\n",
       "      <td>-0.022043</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>-0.074084</td>\n",
       "      <td>0.056865</td>\n",
       "      <td>0.009740</td>\n",
       "      <td>0.032593</td>\n",
       "      <td>-0.084673</td>\n",
       "      <td>-0.001975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELAPSED_TIME</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.018470</td>\n",
       "      <td>0.029025</td>\n",
       "      <td>-0.318819</td>\n",
       "      <td>-0.025744</td>\n",
       "      <td>-0.035204</td>\n",
       "      <td>0.015257</td>\n",
       "      <td>0.215749</td>\n",
       "      <td>-0.038217</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031223</td>\n",
       "      <td>0.016711</td>\n",
       "      <td>0.048448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.190842</td>\n",
       "      <td>0.007724</td>\n",
       "      <td>0.009438</td>\n",
       "      <td>-0.118290</td>\n",
       "      <td>0.014470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIR_TIME</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036330</td>\n",
       "      <td>0.030678</td>\n",
       "      <td>-0.339135</td>\n",
       "      <td>-0.011993</td>\n",
       "      <td>-0.024384</td>\n",
       "      <td>0.002733</td>\n",
       "      <td>0.077234</td>\n",
       "      <td>-0.031646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041368</td>\n",
       "      <td>0.022517</td>\n",
       "      <td>-0.002742</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.072436</td>\n",
       "      <td>0.010442</td>\n",
       "      <td>0.022433</td>\n",
       "      <td>-0.096358</td>\n",
       "      <td>-0.006582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DISTANCE</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.035208</td>\n",
       "      <td>0.024666</td>\n",
       "      <td>-0.356196</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>-0.016562</td>\n",
       "      <td>0.004591</td>\n",
       "      <td>0.057871</td>\n",
       "      <td>-0.027282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044615</td>\n",
       "      <td>0.017333</td>\n",
       "      <td>-0.023821</td>\n",
       "      <td>0.013996</td>\n",
       "      <td>-0.076796</td>\n",
       "      <td>0.041963</td>\n",
       "      <td>0.011547</td>\n",
       "      <td>0.030500</td>\n",
       "      <td>-0.086155</td>\n",
       "      <td>-0.010430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHEELS_ON</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.095731</td>\n",
       "      <td>0.013749</td>\n",
       "      <td>-0.003670</td>\n",
       "      <td>0.585786</td>\n",
       "      <td>0.618850</td>\n",
       "      <td>0.090696</td>\n",
       "      <td>-0.045096</td>\n",
       "      <td>0.642358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.799303</td>\n",
       "      <td>0.959177</td>\n",
       "      <td>0.088131</td>\n",
       "      <td>-0.000289</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.049010</td>\n",
       "      <td>-0.012903</td>\n",
       "      <td>-0.056574</td>\n",
       "      <td>0.054199</td>\n",
       "      <td>-0.057202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAXI_IN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.037407</td>\n",
       "      <td>-0.017789</td>\n",
       "      <td>0.014464</td>\n",
       "      <td>-0.027502</td>\n",
       "      <td>-0.017759</td>\n",
       "      <td>0.040285</td>\n",
       "      <td>0.009041</td>\n",
       "      <td>-0.015089</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001440</td>\n",
       "      <td>0.012095</td>\n",
       "      <td>0.170073</td>\n",
       "      <td>0.006915</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.299436</td>\n",
       "      <td>-0.005075</td>\n",
       "      <td>-0.051595</td>\n",
       "      <td>-0.004698</td>\n",
       "      <td>-0.018058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCHEDULED_ARRIVAL</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.110820</td>\n",
       "      <td>0.031725</td>\n",
       "      <td>-0.018891</td>\n",
       "      <td>0.731240</td>\n",
       "      <td>0.734970</td>\n",
       "      <td>0.155150</td>\n",
       "      <td>-0.062325</td>\n",
       "      <td>0.744697</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.771945</td>\n",
       "      <td>0.140565</td>\n",
       "      <td>0.005992</td>\n",
       "      <td>-0.013131</td>\n",
       "      <td>-0.064453</td>\n",
       "      <td>-0.008270</td>\n",
       "      <td>-0.050435</td>\n",
       "      <td>0.146386</td>\n",
       "      <td>-0.043930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.091687</td>\n",
       "      <td>0.011477</td>\n",
       "      <td>0.000753</td>\n",
       "      <td>0.552653</td>\n",
       "      <td>0.584615</td>\n",
       "      <td>0.079639</td>\n",
       "      <td>-0.045611</td>\n",
       "      <td>0.608611</td>\n",
       "      <td>...</td>\n",
       "      <td>0.771945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076791</td>\n",
       "      <td>-0.002489</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.052236</td>\n",
       "      <td>-0.014825</td>\n",
       "      <td>-0.057557</td>\n",
       "      <td>0.050115</td>\n",
       "      <td>-0.058355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070770</td>\n",
       "      <td>0.067520</td>\n",
       "      <td>0.056163</td>\n",
       "      <td>0.154951</td>\n",
       "      <td>0.223654</td>\n",
       "      <td>0.950838</td>\n",
       "      <td>0.245363</td>\n",
       "      <td>0.217344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140565</td>\n",
       "      <td>0.076791</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.259700</td>\n",
       "      <td>0.006070</td>\n",
       "      <td>0.592718</td>\n",
       "      <td>0.572956</td>\n",
       "      <td>0.235906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIVERTED</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004847</td>\n",
       "      <td>-0.000709</td>\n",
       "      <td>0.007155</td>\n",
       "      <td>0.002515</td>\n",
       "      <td>0.006576</td>\n",
       "      <td>0.020787</td>\n",
       "      <td>0.009810</td>\n",
       "      <td>0.006524</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005992</td>\n",
       "      <td>-0.002489</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CANCELLED</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.006000</td>\n",
       "      <td>-0.006409</td>\n",
       "      <td>0.090008</td>\n",
       "      <td>-0.002631</td>\n",
       "      <td>0.009679</td>\n",
       "      <td>0.030862</td>\n",
       "      <td>0.010763</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007413</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.097693</td>\n",
       "      <td>-0.019626</td>\n",
       "      <td>-0.032564</td>\n",
       "      <td>-0.112393</td>\n",
       "      <td>-0.093096</td>\n",
       "      <td>0.085794</td>\n",
       "      <td>0.407371</td>\n",
       "      <td>-0.085582</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.064453</td>\n",
       "      <td>-0.052236</td>\n",
       "      <td>0.259700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014622</td>\n",
       "      <td>-0.106087</td>\n",
       "      <td>-0.136411</td>\n",
       "      <td>0.019507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.010550</td>\n",
       "      <td>0.008156</td>\n",
       "      <td>-0.007260</td>\n",
       "      <td>-0.017957</td>\n",
       "      <td>-0.012820</td>\n",
       "      <td>0.008040</td>\n",
       "      <td>-0.008021</td>\n",
       "      <td>-0.013912</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008270</td>\n",
       "      <td>-0.014825</td>\n",
       "      <td>0.006070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.014622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009884</td>\n",
       "      <td>-0.012136</td>\n",
       "      <td>0.000274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001603</td>\n",
       "      <td>0.003648</td>\n",
       "      <td>0.023770</td>\n",
       "      <td>-0.054419</td>\n",
       "      <td>-0.013304</td>\n",
       "      <td>0.606954</td>\n",
       "      <td>-0.024674</td>\n",
       "      <td>-0.017376</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.050435</td>\n",
       "      <td>-0.057557</td>\n",
       "      <td>0.592718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.106087</td>\n",
       "      <td>-0.009884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084750</td>\n",
       "      <td>-0.056336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033213</td>\n",
       "      <td>0.033729</td>\n",
       "      <td>0.076581</td>\n",
       "      <td>0.186286</td>\n",
       "      <td>0.247325</td>\n",
       "      <td>0.605289</td>\n",
       "      <td>-0.129879</td>\n",
       "      <td>0.223010</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146386</td>\n",
       "      <td>0.050115</td>\n",
       "      <td>0.572956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.136411</td>\n",
       "      <td>-0.012136</td>\n",
       "      <td>-0.084750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.021674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061960</td>\n",
       "      <td>-0.050835</td>\n",
       "      <td>0.004246</td>\n",
       "      <td>-0.042004</td>\n",
       "      <td>-0.046406</td>\n",
       "      <td>0.210402</td>\n",
       "      <td>0.119412</td>\n",
       "      <td>-0.055273</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043930</td>\n",
       "      <td>-0.058355</td>\n",
       "      <td>0.235906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019507</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>-0.056336</td>\n",
       "      <td>-0.021674</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     YEAR  MONTH       DAY  DAY_OF_WEEK  FLIGHT_NUMBER  \\\n",
       "YEAR                  NaN    NaN       NaN          NaN            NaN   \n",
       "MONTH                 NaN    NaN       NaN          NaN            NaN   \n",
       "DAY                   NaN    NaN  1.000000    -0.497084       0.004412   \n",
       "DAY_OF_WEEK           NaN    NaN -0.497084     1.000000       0.010955   \n",
       "FLIGHT_NUMBER         NaN    NaN  0.004412     0.010955       1.000000   \n",
       "SCHEDULED_DEPARTURE   NaN    NaN -0.138130     0.046914      -0.003027   \n",
       "DEPARTURE_TIME        NaN    NaN -0.124369     0.045182       0.010140   \n",
       "DEPARTURE_DELAY       NaN    NaN  0.060064     0.055632       0.034863   \n",
       "TAXI_OUT              NaN    NaN  0.093451     0.007291       0.061010   \n",
       "WHEELS_OFF            NaN    NaN -0.119781     0.044150       0.016377   \n",
       "SCHEDULED_TIME        NaN    NaN -0.026285     0.019755      -0.337801   \n",
       "ELAPSED_TIME          NaN    NaN -0.018470     0.029025      -0.318819   \n",
       "AIR_TIME              NaN    NaN -0.036330     0.030678      -0.339135   \n",
       "DISTANCE              NaN    NaN -0.035208     0.024666      -0.356196   \n",
       "WHEELS_ON             NaN    NaN -0.095731     0.013749      -0.003670   \n",
       "TAXI_IN               NaN    NaN  0.037407    -0.017789       0.014464   \n",
       "SCHEDULED_ARRIVAL     NaN    NaN -0.110820     0.031725      -0.018891   \n",
       "ARRIVAL_TIME          NaN    NaN -0.091687     0.011477       0.000753   \n",
       "ARRIVAL_DELAY         NaN    NaN  0.070770     0.067520       0.056163   \n",
       "DIVERTED              NaN    NaN  0.004847    -0.000709       0.007155   \n",
       "CANCELLED             NaN    NaN -0.006000    -0.006409       0.090008   \n",
       "AIR_SYSTEM_DELAY      NaN    NaN  0.097693    -0.019626      -0.032564   \n",
       "SECURITY_DELAY        NaN    NaN -0.010550     0.008156      -0.007260   \n",
       "AIRLINE_DELAY         NaN    NaN -0.001603     0.003648       0.023770   \n",
       "LATE_AIRCRAFT_DELAY   NaN    NaN  0.033213     0.033729       0.076581   \n",
       "WEATHER_DELAY         NaN    NaN  0.061960    -0.050835       0.004246   \n",
       "\n",
       "                     SCHEDULED_DEPARTURE  DEPARTURE_TIME  DEPARTURE_DELAY  \\\n",
       "YEAR                                 NaN             NaN              NaN   \n",
       "MONTH                                NaN             NaN              NaN   \n",
       "DAY                            -0.138130       -0.124369         0.060064   \n",
       "DAY_OF_WEEK                     0.046914        0.045182         0.055632   \n",
       "FLIGHT_NUMBER                  -0.003027        0.010140         0.034863   \n",
       "SCHEDULED_DEPARTURE             1.000000        0.943186         0.174254   \n",
       "DEPARTURE_TIME                  0.943186        1.000000         0.243960   \n",
       "DEPARTURE_DELAY                 0.174254        0.243960         1.000000   \n",
       "TAXI_OUT                       -0.081496       -0.070081         0.061905   \n",
       "WHEELS_OFF                      0.912232        0.966477         0.231399   \n",
       "SCHEDULED_TIME                 -0.019873       -0.031873         0.005868   \n",
       "ELAPSED_TIME                   -0.025744       -0.035204         0.015257   \n",
       "AIR_TIME                       -0.011993       -0.024384         0.002733   \n",
       "DISTANCE                        0.000321       -0.016562         0.004591   \n",
       "WHEELS_ON                       0.585786        0.618850         0.090696   \n",
       "TAXI_IN                        -0.027502       -0.017759         0.040285   \n",
       "SCHEDULED_ARRIVAL               0.731240        0.734970         0.155150   \n",
       "ARRIVAL_TIME                    0.552653        0.584615         0.079639   \n",
       "ARRIVAL_DELAY                   0.154951        0.223654         0.950838   \n",
       "DIVERTED                        0.002515        0.006576         0.020787   \n",
       "CANCELLED                      -0.002631        0.009679         0.030862   \n",
       "AIR_SYSTEM_DELAY               -0.112393       -0.093096         0.085794   \n",
       "SECURITY_DELAY                 -0.017957       -0.012820         0.008040   \n",
       "AIRLINE_DELAY                  -0.054419       -0.013304         0.606954   \n",
       "LATE_AIRCRAFT_DELAY             0.186286        0.247325         0.605289   \n",
       "WEATHER_DELAY                  -0.042004       -0.046406         0.210402   \n",
       "\n",
       "                     TAXI_OUT  WHEELS_OFF  ...  SCHEDULED_ARRIVAL  \\\n",
       "YEAR                      NaN         NaN  ...                NaN   \n",
       "MONTH                     NaN         NaN  ...                NaN   \n",
       "DAY                  0.093451   -0.119781  ...          -0.110820   \n",
       "DAY_OF_WEEK          0.007291    0.044150  ...           0.031725   \n",
       "FLIGHT_NUMBER        0.061010    0.016377  ...          -0.018891   \n",
       "SCHEDULED_DEPARTURE -0.081496    0.912232  ...           0.731240   \n",
       "DEPARTURE_TIME      -0.070081    0.966477  ...           0.734970   \n",
       "DEPARTURE_DELAY      0.061905    0.231399  ...           0.155150   \n",
       "TAXI_OUT             1.000000   -0.043114  ...          -0.062325   \n",
       "WHEELS_OFF          -0.043114    1.000000  ...           0.744697   \n",
       "SCHEDULED_TIME       0.096191   -0.038173  ...           0.035598   \n",
       "ELAPSED_TIME         0.215749   -0.038217  ...           0.031223   \n",
       "AIR_TIME             0.077234   -0.031646  ...           0.041368   \n",
       "DISTANCE             0.057871   -0.027282  ...           0.044615   \n",
       "WHEELS_ON           -0.045096    0.642358  ...           0.799303   \n",
       "TAXI_IN              0.009041   -0.015089  ...          -0.001440   \n",
       "SCHEDULED_ARRIVAL   -0.062325    0.744697  ...           1.000000   \n",
       "ARRIVAL_TIME        -0.045611    0.608611  ...           0.771945   \n",
       "ARRIVAL_DELAY        0.245363    0.217344  ...           0.140565   \n",
       "DIVERTED             0.009810    0.006524  ...           0.005992   \n",
       "CANCELLED            0.010763    0.001293  ...          -0.013131   \n",
       "AIR_SYSTEM_DELAY     0.407371   -0.085582  ...          -0.064453   \n",
       "SECURITY_DELAY      -0.008021   -0.013912  ...          -0.008270   \n",
       "AIRLINE_DELAY       -0.024674   -0.017376  ...          -0.050435   \n",
       "LATE_AIRCRAFT_DELAY -0.129879    0.223010  ...           0.146386   \n",
       "WEATHER_DELAY        0.119412   -0.055273  ...          -0.043930   \n",
       "\n",
       "                     ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  CANCELLED  \\\n",
       "YEAR                          NaN            NaN       NaN        NaN   \n",
       "MONTH                         NaN            NaN       NaN        NaN   \n",
       "DAY                     -0.091687       0.070770  0.004847  -0.006000   \n",
       "DAY_OF_WEEK              0.011477       0.067520 -0.000709  -0.006409   \n",
       "FLIGHT_NUMBER            0.000753       0.056163  0.007155   0.090008   \n",
       "SCHEDULED_DEPARTURE      0.552653       0.154951  0.002515  -0.002631   \n",
       "DEPARTURE_TIME           0.584615       0.223654  0.006576   0.009679   \n",
       "DEPARTURE_DELAY          0.079639       0.950838  0.020787   0.030862   \n",
       "TAXI_OUT                -0.045611       0.245363  0.009810   0.010763   \n",
       "WHEELS_OFF               0.608611       0.217344  0.006524   0.001293   \n",
       "SCHEDULED_TIME           0.014674      -0.022043  0.015000  -0.074084   \n",
       "ELAPSED_TIME             0.016711       0.048448       NaN        NaN   \n",
       "AIR_TIME                 0.022517      -0.002742       NaN        NaN   \n",
       "DISTANCE                 0.017333      -0.023821  0.013996  -0.076796   \n",
       "WHEELS_ON                0.959177       0.088131 -0.000289        NaN   \n",
       "TAXI_IN                  0.012095       0.170073  0.006915        NaN   \n",
       "SCHEDULED_ARRIVAL        0.771945       0.140565  0.005992  -0.013131   \n",
       "ARRIVAL_TIME             1.000000       0.076791 -0.002489        NaN   \n",
       "ARRIVAL_DELAY            0.076791       1.000000       NaN        NaN   \n",
       "DIVERTED                -0.002489            NaN  1.000000  -0.007413   \n",
       "CANCELLED                     NaN            NaN -0.007413   1.000000   \n",
       "AIR_SYSTEM_DELAY        -0.052236       0.259700       NaN        NaN   \n",
       "SECURITY_DELAY          -0.014825       0.006070       NaN        NaN   \n",
       "AIRLINE_DELAY           -0.057557       0.592718       NaN        NaN   \n",
       "LATE_AIRCRAFT_DELAY      0.050115       0.572956       NaN        NaN   \n",
       "WEATHER_DELAY           -0.058355       0.235906       NaN        NaN   \n",
       "\n",
       "                     AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  \\\n",
       "YEAR                              NaN             NaN            NaN   \n",
       "MONTH                             NaN             NaN            NaN   \n",
       "DAY                          0.097693       -0.010550      -0.001603   \n",
       "DAY_OF_WEEK                 -0.019626        0.008156       0.003648   \n",
       "FLIGHT_NUMBER               -0.032564       -0.007260       0.023770   \n",
       "SCHEDULED_DEPARTURE         -0.112393       -0.017957      -0.054419   \n",
       "DEPARTURE_TIME              -0.093096       -0.012820      -0.013304   \n",
       "DEPARTURE_DELAY              0.085794        0.008040       0.606954   \n",
       "TAXI_OUT                     0.407371       -0.008021      -0.024674   \n",
       "WHEELS_OFF                  -0.085582       -0.013912      -0.017376   \n",
       "SCHEDULED_TIME               0.056865        0.009740       0.032593   \n",
       "ELAPSED_TIME                 0.190842        0.007724       0.009438   \n",
       "AIR_TIME                     0.072436        0.010442       0.022433   \n",
       "DISTANCE                     0.041963        0.011547       0.030500   \n",
       "WHEELS_ON                   -0.049010       -0.012903      -0.056574   \n",
       "TAXI_IN                      0.299436       -0.005075      -0.051595   \n",
       "SCHEDULED_ARRIVAL           -0.064453       -0.008270      -0.050435   \n",
       "ARRIVAL_TIME                -0.052236       -0.014825      -0.057557   \n",
       "ARRIVAL_DELAY                0.259700        0.006070       0.592718   \n",
       "DIVERTED                          NaN             NaN            NaN   \n",
       "CANCELLED                         NaN             NaN            NaN   \n",
       "AIR_SYSTEM_DELAY             1.000000       -0.014622      -0.106087   \n",
       "SECURITY_DELAY              -0.014622        1.000000      -0.009884   \n",
       "AIRLINE_DELAY               -0.106087       -0.009884       1.000000   \n",
       "LATE_AIRCRAFT_DELAY         -0.136411       -0.012136      -0.084750   \n",
       "WEATHER_DELAY                0.019507        0.000274      -0.056336   \n",
       "\n",
       "                     LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "YEAR                                 NaN            NaN  \n",
       "MONTH                                NaN            NaN  \n",
       "DAY                             0.033213       0.061960  \n",
       "DAY_OF_WEEK                     0.033729      -0.050835  \n",
       "FLIGHT_NUMBER                   0.076581       0.004246  \n",
       "SCHEDULED_DEPARTURE             0.186286      -0.042004  \n",
       "DEPARTURE_TIME                  0.247325      -0.046406  \n",
       "DEPARTURE_DELAY                 0.605289       0.210402  \n",
       "TAXI_OUT                       -0.129879       0.119412  \n",
       "WHEELS_OFF                      0.223010      -0.055273  \n",
       "SCHEDULED_TIME                 -0.084673      -0.001975  \n",
       "ELAPSED_TIME                   -0.118290       0.014470  \n",
       "AIR_TIME                       -0.096358      -0.006582  \n",
       "DISTANCE                       -0.086155      -0.010430  \n",
       "WHEELS_ON                       0.054199      -0.057202  \n",
       "TAXI_IN                        -0.004698      -0.018058  \n",
       "SCHEDULED_ARRIVAL               0.146386      -0.043930  \n",
       "ARRIVAL_TIME                    0.050115      -0.058355  \n",
       "ARRIVAL_DELAY                   0.572956       0.235906  \n",
       "DIVERTED                             NaN            NaN  \n",
       "CANCELLED                            NaN            NaN  \n",
       "AIR_SYSTEM_DELAY               -0.136411       0.019507  \n",
       "SECURITY_DELAY                 -0.012136       0.000274  \n",
       "AIRLINE_DELAY                  -0.084750      -0.056336  \n",
       "LATE_AIRCRAFT_DELAY             1.000000      -0.021674  \n",
       "WEATHER_DELAY                  -0.021674       1.000000  \n",
       "\n",
       "[26 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pnx2TjlzHMEc"
   },
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "pftA6NKayM4h"
   },
   "outputs": [],
   "source": [
    "flights_needed_data=flights_needed_data.drop(['YEAR','FLIGHT_NUMBER','AIRLINE','DISTANCE','TAIL_NUMBER','TAXI_OUT',\n",
    "                                              'SCHEDULED_TIME','DEPARTURE_TIME','WHEELS_OFF','ELAPSED_TIME',\n",
    "                                              'AIR_TIME','WHEELS_ON','DAY_OF_WEEK','TAXI_IN','CANCELLATION_REASON'],\n",
    "                                             axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "oSQQ10_P1gqY",
    "outputId": "ed6916f7-3a1a-4464-c850-f221ef48a76f"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>DEPARTURE_DELAY</th>\n",
       "      <th>SCHEDULED_ARRIVAL</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANC</td>\n",
       "      <td>SEA</td>\n",
       "      <td>5</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>430</td>\n",
       "      <td>408.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>LAX</td>\n",
       "      <td>PBI</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>750</td>\n",
       "      <td>741.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SFO</td>\n",
       "      <td>CLT</td>\n",
       "      <td>20</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>806</td>\n",
       "      <td>811.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>LAX</td>\n",
       "      <td>MIA</td>\n",
       "      <td>20</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>805</td>\n",
       "      <td>756.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SEA</td>\n",
       "      <td>ANC</td>\n",
       "      <td>25</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>320</td>\n",
       "      <td>259.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>99995</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>ATL</td>\n",
       "      <td>BQK</td>\n",
       "      <td>1108</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>1219</td>\n",
       "      <td>1208.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>LAS</td>\n",
       "      <td>PHL</td>\n",
       "      <td>1108</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1842</td>\n",
       "      <td>1844.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>SFO</td>\n",
       "      <td>BFL</td>\n",
       "      <td>1108</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>1225</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>ORD</td>\n",
       "      <td>MCO</td>\n",
       "      <td>1109</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1454</td>\n",
       "      <td>1453.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>HOU</td>\n",
       "      <td>DFW</td>\n",
       "      <td>1109</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>1220</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MONTH  DAY ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  \\\n",
       "0          1    1            ANC                 SEA                    5   \n",
       "1          1    1            LAX                 PBI                   10   \n",
       "2          1    1            SFO                 CLT                   20   \n",
       "3          1    1            LAX                 MIA                   20   \n",
       "4          1    1            SEA                 ANC                   25   \n",
       "...      ...  ...            ...                 ...                  ...   \n",
       "99995      1    7            ATL                 BQK                 1108   \n",
       "99996      1    7            LAS                 PHL                 1108   \n",
       "99997      1    7            SFO                 BFL                 1108   \n",
       "99998      1    7            ORD                 MCO                 1109   \n",
       "99999      1    7            HOU                 DFW                 1109   \n",
       "\n",
       "       DEPARTURE_DELAY  SCHEDULED_ARRIVAL  ARRIVAL_TIME  ARRIVAL_DELAY  \\\n",
       "0                -11.0                430         408.0          -22.0   \n",
       "1                 -8.0                750         741.0           -9.0   \n",
       "2                 -2.0                806         811.0            5.0   \n",
       "3                 -5.0                805         756.0           -9.0   \n",
       "4                 -1.0                320         259.0          -21.0   \n",
       "...                ...                ...           ...            ...   \n",
       "99995             -6.0               1219        1208.0          -11.0   \n",
       "99996              9.0               1842        1844.0            2.0   \n",
       "99997             -7.0               1225        1232.0            7.0   \n",
       "99998              7.0               1454        1453.0           -1.0   \n",
       "99999             -9.0               1220        1212.0           -8.0   \n",
       "\n",
       "       DIVERTED  CANCELLED  AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  \\\n",
       "0             0          0               NaN             NaN            NaN   \n",
       "1             0          0               NaN             NaN            NaN   \n",
       "2             0          0               NaN             NaN            NaN   \n",
       "3             0          0               NaN             NaN            NaN   \n",
       "4             0          0               NaN             NaN            NaN   \n",
       "...         ...        ...               ...             ...            ...   \n",
       "99995         0          0               NaN             NaN            NaN   \n",
       "99996         0          0               NaN             NaN            NaN   \n",
       "99997         0          0               NaN             NaN            NaN   \n",
       "99998         0          0               NaN             NaN            NaN   \n",
       "99999         0          0               NaN             NaN            NaN   \n",
       "\n",
       "       LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0                      NaN            NaN  \n",
       "1                      NaN            NaN  \n",
       "2                      NaN            NaN  \n",
       "3                      NaN            NaN  \n",
       "4                      NaN            NaN  \n",
       "...                    ...            ...  \n",
       "99995                  NaN            NaN  \n",
       "99996                  NaN            NaN  \n",
       "99997                  NaN            NaN  \n",
       "99998                  NaN            NaN  \n",
       "99999                  NaN            NaN  \n",
       "\n",
       "[100000 rows x 16 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_needed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "I5IBw7ad0uxw"
   },
   "outputs": [],
   "source": [
    "# replacing NaN values with the mean of the attribute\n",
    "flights_needed_data=flights_needed_data.fillna(flights_needed_data.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "IwDGKuZ04ImK",
    "outputId": "7681ba8f-fb64-45bc-bf95-10aec3b82919"
   },
   "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>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>DEPARTURE_DELAY</th>\n",
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       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ANC</td>\n",
       "      <td>SEA</td>\n",
       "      <td>5</td>\n",
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       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>LAX</td>\n",
       "      <td>PBI</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>750</td>\n",
       "      <td>741.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SFO</td>\n",
       "      <td>CLT</td>\n",
       "      <td>20</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>806</td>\n",
       "      <td>811.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>LAX</td>\n",
       "      <td>MIA</td>\n",
       "      <td>20</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>805</td>\n",
       "      <td>756.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SEA</td>\n",
       "      <td>ANC</td>\n",
       "      <td>25</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>320</td>\n",
       "      <td>259.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</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>99995</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>ATL</td>\n",
       "      <td>BQK</td>\n",
       "      <td>1108</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>1219</td>\n",
       "      <td>1208.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>LAS</td>\n",
       "      <td>PHL</td>\n",
       "      <td>1108</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1842</td>\n",
       "      <td>1844.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>SFO</td>\n",
       "      <td>BFL</td>\n",
       "      <td>1108</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>1225</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>ORD</td>\n",
       "      <td>MCO</td>\n",
       "      <td>1109</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1454</td>\n",
       "      <td>1453.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>HOU</td>\n",
       "      <td>DFW</td>\n",
       "      <td>1109</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>1220</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
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       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MONTH  DAY ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  \\\n",
       "0          1    1            ANC                 SEA                    5   \n",
       "1          1    1            LAX                 PBI                   10   \n",
       "2          1    1            SFO                 CLT                   20   \n",
       "3          1    1            LAX                 MIA                   20   \n",
       "4          1    1            SEA                 ANC                   25   \n",
       "...      ...  ...            ...                 ...                  ...   \n",
       "99995      1    7            ATL                 BQK                 1108   \n",
       "99996      1    7            LAS                 PHL                 1108   \n",
       "99997      1    7            SFO                 BFL                 1108   \n",
       "99998      1    7            ORD                 MCO                 1109   \n",
       "99999      1    7            HOU                 DFW                 1109   \n",
       "\n",
       "       DEPARTURE_DELAY  SCHEDULED_ARRIVAL  ARRIVAL_TIME  ARRIVAL_DELAY  \\\n",
       "0                -11.0                430         408.0          -22.0   \n",
       "1                 -8.0                750         741.0           -9.0   \n",
       "2                 -2.0                806         811.0            5.0   \n",
       "3                 -5.0                805         756.0           -9.0   \n",
       "4                 -1.0                320         259.0          -21.0   \n",
       "...                ...                ...           ...            ...   \n",
       "99995             -6.0               1219        1208.0          -11.0   \n",
       "99996              9.0               1842        1844.0            2.0   \n",
       "99997             -7.0               1225        1232.0            7.0   \n",
       "99998              7.0               1454        1453.0           -1.0   \n",
       "99999             -9.0               1220        1212.0           -8.0   \n",
       "\n",
       "       DIVERTED  CANCELLED  AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  \\\n",
       "0             0          0         14.026801        0.070787      17.853921   \n",
       "1             0          0         14.026801        0.070787      17.853921   \n",
       "2             0          0         14.026801        0.070787      17.853921   \n",
       "3             0          0         14.026801        0.070787      17.853921   \n",
       "4             0          0         14.026801        0.070787      17.853921   \n",
       "...         ...        ...               ...             ...            ...   \n",
       "99995         0          0         14.026801        0.070787      17.853921   \n",
       "99996         0          0         14.026801        0.070787      17.853921   \n",
       "99997         0          0         14.026801        0.070787      17.853921   \n",
       "99998         0          0         14.026801        0.070787      17.853921   \n",
       "99999         0          0         14.026801        0.070787      17.853921   \n",
       "\n",
       "       LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0                26.014556       2.751971  \n",
       "1                26.014556       2.751971  \n",
       "2                26.014556       2.751971  \n",
       "3                26.014556       2.751971  \n",
       "4                26.014556       2.751971  \n",
       "...                    ...            ...  \n",
       "99995            26.014556       2.751971  \n",
       "99996            26.014556       2.751971  \n",
       "99997            26.014556       2.751971  \n",
       "99998            26.014556       2.751971  \n",
       "99999            26.014556       2.751971  \n",
       "\n",
       "[100000 rows x 16 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_needed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "BjQ1rhrb1YFd"
   },
   "outputs": [],
   "source": [
    "result=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "C2AFDcSz2bjO"
   },
   "outputs": [],
   "source": [
    "# if the delay in flight's arrival is more than 15 mins, then it's definitely delayed\n",
    "for row in flights_needed_data['ARRIVAL_DELAY']:\n",
    "  if row > 15:\n",
    "    result.append(1)\n",
    "  else:\n",
    "    result.append(0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "Kh0J66aj3EzK"
   },
   "outputs": [],
   "source": [
    "flights_needed_data['result'] = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "uJ4QW_k33ZNu",
    "outputId": "7a0e9576-26a4-4217-81b4-8792712bfe55"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>MONTH</th>\n",
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       "      <td>LAX</td>\n",
       "      <td>MIA</td>\n",
       "      <td>20</td>\n",
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       "      <td>25</td>\n",
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       "      <th>99995</th>\n",
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       "      <td>7</td>\n",
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       "      <td>BQK</td>\n",
       "      <td>1108</td>\n",
       "      <td>-6.0</td>\n",
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       "      <td>1842</td>\n",
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       "      <td>-7.0</td>\n",
       "      <td>1225</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
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       "      <td>ORD</td>\n",
       "      <td>MCO</td>\n",
       "      <td>1109</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1454</td>\n",
       "      <td>1453.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.070787</td>\n",
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       "      <td>1220</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MONTH  DAY ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  \\\n",
       "0          1    1            ANC                 SEA                    5   \n",
       "1          1    1            LAX                 PBI                   10   \n",
       "2          1    1            SFO                 CLT                   20   \n",
       "3          1    1            LAX                 MIA                   20   \n",
       "4          1    1            SEA                 ANC                   25   \n",
       "...      ...  ...            ...                 ...                  ...   \n",
       "99995      1    7            ATL                 BQK                 1108   \n",
       "99996      1    7            LAS                 PHL                 1108   \n",
       "99997      1    7            SFO                 BFL                 1108   \n",
       "99998      1    7            ORD                 MCO                 1109   \n",
       "99999      1    7            HOU                 DFW                 1109   \n",
       "\n",
       "       DEPARTURE_DELAY  SCHEDULED_ARRIVAL  ARRIVAL_TIME  ARRIVAL_DELAY  \\\n",
       "0                -11.0                430         408.0          -22.0   \n",
       "1                 -8.0                750         741.0           -9.0   \n",
       "2                 -2.0                806         811.0            5.0   \n",
       "3                 -5.0                805         756.0           -9.0   \n",
       "4                 -1.0                320         259.0          -21.0   \n",
       "...                ...                ...           ...            ...   \n",
       "99995             -6.0               1219        1208.0          -11.0   \n",
       "99996              9.0               1842        1844.0            2.0   \n",
       "99997             -7.0               1225        1232.0            7.0   \n",
       "99998              7.0               1454        1453.0           -1.0   \n",
       "99999             -9.0               1220        1212.0           -8.0   \n",
       "\n",
       "       DIVERTED  CANCELLED  AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  \\\n",
       "0             0          0         14.026801        0.070787      17.853921   \n",
       "1             0          0         14.026801        0.070787      17.853921   \n",
       "2             0          0         14.026801        0.070787      17.853921   \n",
       "3             0          0         14.026801        0.070787      17.853921   \n",
       "4             0          0         14.026801        0.070787      17.853921   \n",
       "...         ...        ...               ...             ...            ...   \n",
       "99995         0          0         14.026801        0.070787      17.853921   \n",
       "99996         0          0         14.026801        0.070787      17.853921   \n",
       "99997         0          0         14.026801        0.070787      17.853921   \n",
       "99998         0          0         14.026801        0.070787      17.853921   \n",
       "99999         0          0         14.026801        0.070787      17.853921   \n",
       "\n",
       "       LATE_AIRCRAFT_DELAY  WEATHER_DELAY  result  \n",
       "0                26.014556       2.751971       0  \n",
       "1                26.014556       2.751971       0  \n",
       "2                26.014556       2.751971       0  \n",
       "3                26.014556       2.751971       0  \n",
       "4                26.014556       2.751971       0  \n",
       "...                    ...            ...     ...  \n",
       "99995            26.014556       2.751971       0  \n",
       "99996            26.014556       2.751971       0  \n",
       "99997            26.014556       2.751971       0  \n",
       "99998            26.014556       2.751971       0  \n",
       "99999            26.014556       2.751971       0  \n",
       "\n",
       "[100000 rows x 17 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_needed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "krPajIiZ3_G5",
    "outputId": "91845772-d06b-404c-8bc8-fe64871403a7"
   },
   "outputs": [
    {
     "data": {
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       "result\n",
       "0    63779\n",
       "1    36221\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_needed_data.value_counts('result')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
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    "id": "Ij79v7Y3_RgW",
    "outputId": "87325518-f689-42cc-b978-633e9169dda6"
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       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1108</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>1219</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1108</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1842</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1108</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>1225</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1109</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1454</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1109</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>1220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.026801</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>17.853921</td>\n",
       "      <td>26.014556</td>\n",
       "      <td>2.751971</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MONTH  DAY  SCHEDULED_DEPARTURE  DEPARTURE_DELAY  SCHEDULED_ARRIVAL  \\\n",
       "0          1    1                    5            -11.0                430   \n",
       "1          1    1                   10             -8.0                750   \n",
       "2          1    1                   20             -2.0                806   \n",
       "3          1    1                   20             -5.0                805   \n",
       "4          1    1                   25             -1.0                320   \n",
       "...      ...  ...                  ...              ...                ...   \n",
       "99995      1    7                 1108             -6.0               1219   \n",
       "99996      1    7                 1108              9.0               1842   \n",
       "99997      1    7                 1108             -7.0               1225   \n",
       "99998      1    7                 1109              7.0               1454   \n",
       "99999      1    7                 1109             -9.0               1220   \n",
       "\n",
       "       DIVERTED  CANCELLED  AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  \\\n",
       "0             0          0         14.026801        0.070787      17.853921   \n",
       "1             0          0         14.026801        0.070787      17.853921   \n",
       "2             0          0         14.026801        0.070787      17.853921   \n",
       "3             0          0         14.026801        0.070787      17.853921   \n",
       "4             0          0         14.026801        0.070787      17.853921   \n",
       "...         ...        ...               ...             ...            ...   \n",
       "99995         0          0         14.026801        0.070787      17.853921   \n",
       "99996         0          0         14.026801        0.070787      17.853921   \n",
       "99997         0          0         14.026801        0.070787      17.853921   \n",
       "99998         0          0         14.026801        0.070787      17.853921   \n",
       "99999         0          0         14.026801        0.070787      17.853921   \n",
       "\n",
       "       LATE_AIRCRAFT_DELAY  WEATHER_DELAY  result  \n",
       "0                26.014556       2.751971       0  \n",
       "1                26.014556       2.751971       0  \n",
       "2                26.014556       2.751971       0  \n",
       "3                26.014556       2.751971       0  \n",
       "4                26.014556       2.751971       0  \n",
       "...                    ...            ...     ...  \n",
       "99995            26.014556       2.751971       0  \n",
       "99996            26.014556       2.751971       0  \n",
       "99997            26.014556       2.751971       0  \n",
       "99998            26.014556       2.751971       0  \n",
       "99999            26.014556       2.751971       0  \n",
       "\n",
       "[100000 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_needed_data=flights_needed_data.drop(['ORIGIN_AIRPORT', 'DESTINATION_AIRPORT', 'ARRIVAL_TIME', 'ARRIVAL_DELAY'],axis=1)\n",
    "flights_needed_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K3FRiemNH1ml"
   },
   "source": [
    "### Splitting Data for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "GwAUjhg-4Zn6"
   },
   "outputs": [],
   "source": [
    "data = flights_needed_data.values\n",
    "X, y = data[:,:-1], data[:,-1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)  # splitting in the ratio 70:30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mQgSjXYFudyY"
   },
   "source": [
    "### Standardizing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "6XsHnDLP7CFs"
   },
   "outputs": [],
   "source": [
    "scaled_features = StandardScaler().fit_transform(X_train, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FOOgER-OujcI"
   },
   "source": [
    "### Applying Decision Tree Classifier on Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "0AIkZKNdBEWS"
   },
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "clf = clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mwXLSatCusMb"
   },
   "source": [
    "### Making Predictions and Checking Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E3CWbnZOB2M0",
    "outputId": "3ec45653-a3ec-4ff0-d32f-343d8845d3cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9980303607613572"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_prob = clf.predict_proba(X_test)\n",
    "auc_score = roc_auc_score(y_test, pred_prob[:,1])\n",
    "auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8-zrkG_Hwsa7"
   },
   "source": [
    "# Predictions are 99.77% accurate."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Flight Delay Predictions.ipynb",
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   "display_name": "Python 3",
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