Investigate_Hotel_Business_using_Data_Visualization.ipynb

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      "source": [
        "# Investigate Hotel Business using Data Visualization"
      ],
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      }
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('hotel_bookings_data.csv')\n",
        "df.head()"
      ],
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          "data": {
            "text/plain": [
              "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
              "0  Resort Hotel            0        342               2017          September   \n",
              "1  Resort Hotel            0        737               2017          September   \n",
              "2  Resort Hotel            0          7               2017          September   \n",
              "3  Resort Hotel            0         13               2017          September   \n",
              "4  Resort Hotel            0         14               2017          September   \n",
              "\n",
              "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
              "0                        27                          1   \n",
              "1                        27                          1   \n",
              "2                        27                          1   \n",
              "3                        27                          1   \n",
              "4                        27                          1   \n",
              "\n",
              "   stays_in_weekend_nights  stays_in_weekdays_nights  adults  ...  \\\n",
              "0                        0                         0       2  ...   \n",
              "1                        0                         0       2  ...   \n",
              "2                        0                         1       1  ...   \n",
              "3                        0                         1       1  ...   \n",
              "4                        0                         2       2  ...   \n",
              "\n",
              "   booking_changes  deposit_type  agent company days_in_waiting_list  \\\n",
              "0                3    No Deposit    NaN     NaN                    0   \n",
              "1                4    No Deposit    NaN     NaN                    0   \n",
              "2                0    No Deposit    NaN     NaN                    0   \n",
              "3                0    No Deposit  304.0     NaN                    0   \n",
              "4                0    No Deposit  240.0     NaN                    0   \n",
              "\n",
              "  customer_type   adr  required_car_parking_spaces  total_of_special_requests  \\\n",
              "0      Personal   0.0                            0                          0   \n",
              "1      Personal   0.0                            0                          0   \n",
              "2      Personal  75.0                            0                          0   \n",
              "3      Personal  75.0                            0                          0   \n",
              "4      Personal  98.0                            0                          1   \n",
              "\n",
              "   reservation_status  \n",
              "0           Check-Out  \n",
              "1           Check-Out  \n",
              "2           Check-Out  \n",
              "3           Check-Out  \n",
              "4           Check-Out  \n",
              "\n",
              "[5 rows x 29 columns]"
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              "      <th>hotel</th>\n",
              "      <th>is_canceled</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
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              "      <th>...</th>\n",
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              "      <th>deposit_type</th>\n",
              "      <th>agent</th>\n",
              "      <th>company</th>\n",
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              "      <th>customer_type</th>\n",
              "      <th>adr</th>\n",
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              "      <th>total_of_special_requests</th>\n",
              "      <th>reservation_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>342</td>\n",
              "      <td>2017</td>\n",
              "      <td>September</td>\n",
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              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>737</td>\n",
              "      <td>2017</td>\n",
              "      <td>September</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2017</td>\n",
              "      <td>September</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>2017</td>\n",
              "      <td>September</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>304.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>2017</td>\n",
              "      <td>September</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>240.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
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              "<p>5 rows × 29 columns</p>\n",
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          "metadata": {},
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      "source": [
        "df.info()"
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        "id": "B0yqVEnC_9xk",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a90f6f1b-cb39-444c-cf35-19948034f3b2"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 119390 entries, 0 to 119389\n",
            "Data columns (total 29 columns):\n",
            " #   Column                          Non-Null Count   Dtype  \n",
            "---  ------                          --------------   -----  \n",
            " 0   hotel                           119390 non-null  object \n",
            " 1   is_canceled                     119390 non-null  int64  \n",
            " 2   lead_time                       119390 non-null  int64  \n",
            " 3   arrival_date_year               119390 non-null  int64  \n",
            " 4   arrival_date_month              119390 non-null  object \n",
            " 5   arrival_date_week_number        119390 non-null  int64  \n",
            " 6   arrival_date_day_of_month       119390 non-null  int64  \n",
            " 7   stays_in_weekend_nights         119390 non-null  int64  \n",
            " 8   stays_in_weekdays_nights        119390 non-null  int64  \n",
            " 9   adults                          119390 non-null  int64  \n",
            " 10  children                        119386 non-null  float64\n",
            " 11  babies                          119390 non-null  int64  \n",
            " 12  meal                            119390 non-null  object \n",
            " 13  city                            118902 non-null  object \n",
            " 14  market_segment                  119390 non-null  object \n",
            " 15  distribution_channel            119390 non-null  object \n",
            " 16  is_repeated_guest               119390 non-null  int64  \n",
            " 17  previous_cancellations          119390 non-null  int64  \n",
            " 18  previous_bookings_not_canceled  119390 non-null  int64  \n",
            " 19  booking_changes                 119390 non-null  int64  \n",
            " 20  deposit_type                    119390 non-null  object \n",
            " 21  agent                           103050 non-null  float64\n",
            " 22  company                         6797 non-null    float64\n",
            " 23  days_in_waiting_list            119390 non-null  int64  \n",
            " 24  customer_type                   119390 non-null  object \n",
            " 25  adr                             119390 non-null  float64\n",
            " 26  required_car_parking_spaces     119390 non-null  int64  \n",
            " 27  total_of_special_requests       119390 non-null  int64  \n",
            " 28  reservation_status              119390 non-null  object \n",
            "dtypes: float64(4), int64(16), object(9)\n",
            "memory usage: 26.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "2rY0ms1Y6Udr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "841491ba-5034-4720-f20a-d2daa6051fc4"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hotel                                  0\n",
              "is_canceled                            0\n",
              "lead_time                              0\n",
              "arrival_date_year                      0\n",
              "arrival_date_month                     0\n",
              "arrival_date_week_number               0\n",
              "arrival_date_day_of_month              0\n",
              "stays_in_weekend_nights                0\n",
              "stays_in_weekdays_nights               0\n",
              "adults                                 0\n",
              "children                               4\n",
              "babies                                 0\n",
              "meal                                   0\n",
              "city                                 488\n",
              "market_segment                         0\n",
              "distribution_channel                   0\n",
              "is_repeated_guest                      0\n",
              "previous_cancellations                 0\n",
              "previous_bookings_not_canceled         0\n",
              "booking_changes                        0\n",
              "deposit_type                           0\n",
              "agent                              16340\n",
              "company                           112593\n",
              "days_in_waiting_list                   0\n",
              "customer_type                          0\n",
              "adr                                    0\n",
              "required_car_parking_spaces            0\n",
              "total_of_special_requests              0\n",
              "reservation_status                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handle data null"
      ],
      "metadata": {
        "id": "Ra49fjVk_Fjd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['children'] = df['children'].fillna(0).astype(int)\n",
        "df['city'] = df['city'].fillna(0).astype(str)\n",
        "df['agent'] = df['agent'].fillna(0).astype(int)\n",
        "df['company'] = df['company'].fillna(0).astype(int)"
      ],
      "metadata": {
        "id": "UFygDyBK_Lno"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "EPpWhOnQBN14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69afb2fb-0539-4675-bd3f-b3d5bbead1a7"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hotel                             0\n",
              "is_canceled                       0\n",
              "lead_time                         0\n",
              "arrival_date_year                 0\n",
              "arrival_date_month                0\n",
              "arrival_date_week_number          0\n",
              "arrival_date_day_of_month         0\n",
              "stays_in_weekend_nights           0\n",
              "stays_in_weekdays_nights          0\n",
              "adults                            0\n",
              "children                          0\n",
              "babies                            0\n",
              "meal                              0\n",
              "city                              0\n",
              "market_segment                    0\n",
              "distribution_channel              0\n",
              "is_repeated_guest                 0\n",
              "previous_cancellations            0\n",
              "previous_bookings_not_canceled    0\n",
              "booking_changes                   0\n",
              "deposit_type                      0\n",
              "agent                             0\n",
              "company                           0\n",
              "days_in_waiting_list              0\n",
              "customer_type                     0\n",
              "adr                               0\n",
              "required_car_parking_spaces       0\n",
              "total_of_special_requests         0\n",
              "reservation_status                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Replace the values ​​that don't match"
      ],
      "metadata": {
        "id": "mzxlzKsmBX0x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Displays a unique value in a specified column\n",
        "unique_values = df['meal'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "id": "ivmtmnqUBP40",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3fe1cc23-269f-4fdc-a759-b7111f1a6ef0"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Breakfast' 'Full Board' 'Dinner' 'No Meal' 'Undefined']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace the values ​​that don't match\n",
        "df['meal'].replace('Undefined', 'Lunch', inplace=True)"
      ],
      "metadata": {
        "id": "t1BsVHxWBUXc"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = df['meal'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "id": "hlq8XZARCshr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f18c72b0-ee98-4919-944c-af13515c89a6"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Breakfast' 'Full Board' 'Dinner' 'No Meal' 'Lunch']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Remove unnecessary data"
      ],
      "metadata": {
        "id": "lay6176QC9Od"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the total number of guests\n",
        "df[\"total_guest\"] = df[\"adults\"] + df[\"children\"]\n",
        "\n",
        "# Displays a data set with a column for the total number of guests\n",
        "df.info()"
      ],
      "metadata": {
        "id": "xHFxSHnhC8qQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1c421db-d54d-4f9c-83a5-a7d76768d7a5"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 119390 entries, 0 to 119389\n",
            "Data columns (total 30 columns):\n",
            " #   Column                          Non-Null Count   Dtype  \n",
            "---  ------                          --------------   -----  \n",
            " 0   hotel                           119390 non-null  object \n",
            " 1   is_canceled                     119390 non-null  int64  \n",
            " 2   lead_time                       119390 non-null  int64  \n",
            " 3   arrival_date_year               119390 non-null  int64  \n",
            " 4   arrival_date_month              119390 non-null  object \n",
            " 5   arrival_date_week_number        119390 non-null  int64  \n",
            " 6   arrival_date_day_of_month       119390 non-null  int64  \n",
            " 7   stays_in_weekend_nights         119390 non-null  int64  \n",
            " 8   stays_in_weekdays_nights        119390 non-null  int64  \n",
            " 9   adults                          119390 non-null  int64  \n",
            " 10  children                        119390 non-null  int64  \n",
            " 11  babies                          119390 non-null  int64  \n",
            " 12  meal                            119390 non-null  object \n",
            " 13  city                            119390 non-null  object \n",
            " 14  market_segment                  119390 non-null  object \n",
            " 15  distribution_channel            119390 non-null  object \n",
            " 16  is_repeated_guest               119390 non-null  int64  \n",
            " 17  previous_cancellations          119390 non-null  int64  \n",
            " 18  previous_bookings_not_canceled  119390 non-null  int64  \n",
            " 19  booking_changes                 119390 non-null  int64  \n",
            " 20  deposit_type                    119390 non-null  object \n",
            " 21  agent                           119390 non-null  int64  \n",
            " 22  company                         119390 non-null  int64  \n",
            " 23  days_in_waiting_list            119390 non-null  int64  \n",
            " 24  customer_type                   119390 non-null  object \n",
            " 25  adr                             119390 non-null  float64\n",
            " 26  required_car_parking_spaces     119390 non-null  int64  \n",
            " 27  total_of_special_requests       119390 non-null  int64  \n",
            " 28  reservation_status              119390 non-null  object \n",
            " 29  total_guest                     119390 non-null  int64  \n",
            "dtypes: float64(1), int64(20), object(9)\n",
            "memory usage: 27.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter data based on the total number of guests\n",
        "df_filtered = df[df[\"total_guest\"] >= 5]\n",
        "\n",
        "# Displays the dataset after filtering\n",
        "df_filtered"
      ],
      "metadata": {
        "id": "YsubDhhrEnS1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "outputId": "24e0ac23-a349-43a7-fee9-80af1c8f1075"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               hotel  is_canceled  lead_time  arrival_date_year  \\\n",
              "201     Resort Hotel            0          8               2017   \n",
              "328     Resort Hotel            1         55               2017   \n",
              "1539    Resort Hotel            1        304               2017   \n",
              "1587    Resort Hotel            1        333               2017   \n",
              "1643    Resort Hotel            1        336               2017   \n",
              "...              ...          ...        ...                ...   \n",
              "118557    City Hotel            0          6               2019   \n",
              "118852    City Hotel            0        215               2019   \n",
              "118913    City Hotel            0        200               2019   \n",
              "119054    City Hotel            0          8               2019   \n",
              "119070    City Hotel            0          0               2019   \n",
              "\n",
              "       arrival_date_month  arrival_date_week_number  \\\n",
              "201             September                        28   \n",
              "328             September                        29   \n",
              "1539             November                        36   \n",
              "1587             November                        36   \n",
              "1643             November                        37   \n",
              "...                   ...                       ...   \n",
              "118557            October                        34   \n",
              "118852            October                        34   \n",
              "118913            October                        34   \n",
              "119054            October                        34   \n",
              "119070            October                        35   \n",
              "\n",
              "        arrival_date_day_of_month  stays_in_weekend_nights  \\\n",
              "201                             7                        0   \n",
              "328                            12                        4   \n",
              "1539                            3                        0   \n",
              "1587                            5                        2   \n",
              "1643                            7                        1   \n",
              "...                           ...                      ...   \n",
              "118557                         21                        1   \n",
              "118852                         25                        0   \n",
              "118913                         25                        0   \n",
              "119054                         26                        2   \n",
              "119070                         29                        0   \n",
              "\n",
              "        stays_in_weekdays_nights  adults  ...  deposit_type  agent company  \\\n",
              "201                            4       3  ...    No Deposit    240       0   \n",
              "328                           10       2  ...    No Deposit      8       0   \n",
              "1539                           3      40  ...    No Deposit      0       0   \n",
              "1587                           5      26  ...    No Deposit     96       0   \n",
              "1643                           2      50  ...    No Deposit      0       0   \n",
              "...                          ...     ...  ...           ...    ...     ...   \n",
              "118557                         0       2  ...    No Deposit      7       0   \n",
              "118852                         1       2  ...    No Deposit     14       0   \n",
              "118913                         2       3  ...    No Deposit      0       0   \n",
              "119054                         1       2  ...    No Deposit      7       0   \n",
              "119070                         1       2  ...    No Deposit    229       0   \n",
              "\n",
              "       days_in_waiting_list customer_type     adr  \\\n",
              "201                       0      Personal  193.00   \n",
              "328                       0      Contract  133.16   \n",
              "1539                      0     Bussiness    0.00   \n",
              "1587                      0     Bussiness    0.00   \n",
              "1643                      0     Bussiness    0.00   \n",
              "...                     ...           ...     ...   \n",
              "118557                    0      Personal  264.44   \n",
              "118852                    0      Personal  245.00   \n",
              "118913                    0      Personal  294.00   \n",
              "119054                    0      Personal  253.51   \n",
              "119070                    0     Bussiness  270.00   \n",
              "\n",
              "        required_car_parking_spaces  total_of_special_requests  \\\n",
              "201                               0                          1   \n",
              "328                               0                          1   \n",
              "1539                              0                          0   \n",
              "1587                              0                          0   \n",
              "1643                              0                          0   \n",
              "...                             ...                        ...   \n",
              "118557                            0                          2   \n",
              "118852                            0                          1   \n",
              "118913                            1                          0   \n",
              "119054                            0                          2   \n",
              "119070                            0                          1   \n",
              "\n",
              "        reservation_status  total_guest  \n",
              "201              Check-Out            5  \n",
              "328                No-Show           12  \n",
              "1539              Canceled           40  \n",
              "1587              Canceled           26  \n",
              "1643              Canceled           50  \n",
              "...                    ...          ...  \n",
              "118557           Check-Out            5  \n",
              "118852           Check-Out            5  \n",
              "118913           Check-Out            5  \n",
              "119054           Check-Out            5  \n",
              "119070           Check-Out            5  \n",
              "\n",
              "[119 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-230f047f-c98f-4b79-81c3-1a1a36a3d92f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>hotel</th>\n",
              "      <th>is_canceled</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_month</th>\n",
              "      <th>arrival_date_week_number</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>stays_in_weekend_nights</th>\n",
              "      <th>stays_in_weekdays_nights</th>\n",
              "      <th>adults</th>\n",
              "      <th>...</th>\n",
              "      <th>deposit_type</th>\n",
              "      <th>agent</th>\n",
              "      <th>company</th>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <th>customer_type</th>\n",
              "      <th>adr</th>\n",
              "      <th>required_car_parking_spaces</th>\n",
              "      <th>total_of_special_requests</th>\n",
              "      <th>reservation_status</th>\n",
              "      <th>total_guest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2017</td>\n",
              "      <td>September</td>\n",
              "      <td>28</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>240</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>193.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>328</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2017</td>\n",
              "      <td>September</td>\n",
              "      <td>29</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Contract</td>\n",
              "      <td>133.16</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>No-Show</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1539</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>304</td>\n",
              "      <td>2017</td>\n",
              "      <td>November</td>\n",
              "      <td>36</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bussiness</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1587</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>333</td>\n",
              "      <td>2017</td>\n",
              "      <td>November</td>\n",
              "      <td>36</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>26</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bussiness</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1643</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>336</td>\n",
              "      <td>2017</td>\n",
              "      <td>November</td>\n",
              "      <td>37</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bussiness</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>50</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>118557</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2019</td>\n",
              "      <td>October</td>\n",
              "      <td>34</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>264.44</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118852</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>215</td>\n",
              "      <td>2019</td>\n",
              "      <td>October</td>\n",
              "      <td>34</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>245.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118913</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>200</td>\n",
              "      <td>2019</td>\n",
              "      <td>October</td>\n",
              "      <td>34</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>294.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119054</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2019</td>\n",
              "      <td>October</td>\n",
              "      <td>34</td>\n",
              "      <td>26</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>253.51</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119070</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>October</td>\n",
              "      <td>35</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bussiness</td>\n",
              "      <td>270.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-230f047f-c98f-4b79-81c3-1a1a36a3d92f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-79edbcc6-6753-4a3d-940d-d9a87ef8d5ad\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79edbcc6-6753-4a3d-940d-d9a87ef8d5ad')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-79edbcc6-6753-4a3d-940d-d9a87ef8d5ad button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-230f047f-c98f-4b79-81c3-1a1a36a3d92f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-230f047f-c98f-4b79-81c3-1a1a36a3d92f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving the dataset after filtering\n",
        "df_filtered.to_csv(\"dataset_terfilter.csv\", index=False)  # Save filtered dataset with file name \"dataset_terfilter.csv\""
      ],
      "metadata": {
        "id": "0yzVKPLDFo6t"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_filtered.head()"
      ],
      "metadata": {
        "id": "H90_oZtpLZtx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "c43666dd-7be1-47a3-8a50-265b25237e5e"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             hotel  is_canceled  lead_time  arrival_date_year  \\\n",
              "201   Resort Hotel            0          8               2017   \n",
              "328   Resort Hotel            1         55               2017   \n",
              "1539  Resort Hotel            1        304               2017   \n",
              "1587  Resort Hotel            1        333               2017   \n",
              "1643  Resort Hotel            1        336               2017   \n",
              "\n",
              "     arrival_date_month  arrival_date_week_number  arrival_date_day_of_month  \\\n",
              "201           September                        28                          7   \n",
              "328           September                        29                         12   \n",
              "1539           November                        36                          3   \n",
              "1587           November                        36                          5   \n",
              "1643           November                        37                          7   \n",
              "\n",
              "      stays_in_weekend_nights  stays_in_weekdays_nights  adults  ...  \\\n",
              "201                         0                         4       3  ...   \n",
              "328                         4                        10       2  ...   \n",
              "1539                        0                         3      40  ...   \n",
              "1587                        2                         5      26  ...   \n",
              "1643                        1                         2      50  ...   \n",
              "\n",
              "      deposit_type  agent company days_in_waiting_list customer_type     adr  \\\n",
              "201     No Deposit    240       0                    0      Personal  193.00   \n",
              "328     No Deposit      8       0                    0      Contract  133.16   \n",
              "1539    No Deposit      0       0                    0     Bussiness    0.00   \n",
              "1587    No Deposit     96       0                    0     Bussiness    0.00   \n",
              "1643    No Deposit      0       0                    0     Bussiness    0.00   \n",
              "\n",
              "      required_car_parking_spaces  total_of_special_requests  \\\n",
              "201                             0                          1   \n",
              "328                             0                          1   \n",
              "1539                            0                          0   \n",
              "1587                            0                          0   \n",
              "1643                            0                          0   \n",
              "\n",
              "      reservation_status  total_guest  \n",
              "201            Check-Out            5  \n",
              "328              No-Show           12  \n",
              "1539            Canceled           40  \n",
              "1587            Canceled           26  \n",
              "1643            Canceled           50  \n",
              "\n",
              "[5 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-a976df68-7913-442b-afeb-d06bebef2fc4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>hotel</th>\n",
              "      <th>is_canceled</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_month</th>\n",
              "      <th>arrival_date_week_number</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>stays_in_weekend_nights</th>\n",
              "      <th>stays_in_weekdays_nights</th>\n",
              "      <th>adults</th>\n",
              "      <th>...</th>\n",
              "      <th>deposit_type</th>\n",
              "      <th>agent</th>\n",
              "      <th>company</th>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <th>customer_type</th>\n",
              "      <th>adr</th>\n",
              "      <th>required_car_parking_spaces</th>\n",
              "      <th>total_of_special_requests</th>\n",
              "      <th>reservation_status</th>\n",
              "      <th>total_guest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2017</td>\n",
              "      <td>September</td>\n",
              "      <td>28</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>240</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>193.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>328</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2017</td>\n",
              "      <td>September</td>\n",
              "      <td>29</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Contract</td>\n",
              "      <td>133.16</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>No-Show</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1539</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>304</td>\n",
              "      <td>2017</td>\n",
              "      <td>November</td>\n",
              "      <td>36</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>40</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bussiness</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1587</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>333</td>\n",
              "      <td>2017</td>\n",
              "      <td>November</td>\n",
              "      <td>36</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>26</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bussiness</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1643</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>336</td>\n",
              "      <td>2017</td>\n",
              "      <td>November</td>\n",
              "      <td>37</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bussiness</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a976df68-7913-442b-afeb-d06bebef2fc4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-dd375cb7-7a42-4346-830c-c11a5ce80463\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dd375cb7-7a42-4346-830c-c11a5ce80463')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-dd375cb7-7a42-4346-830c-c11a5ce80463 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a976df68-7913-442b-afeb-d06bebef2fc4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a976df68-7913-442b-afeb-d06bebef2fc4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### an aggregate table that shows a comparison of the number of hotel bookings each month based on the type of hotel"
      ],
      "metadata": {
        "id": "5JUrrEv3tzMl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_filtered['arrival_date_month'] = pd.to_datetime(df_filtered['arrival_date_month'], format='%B')\n",
        "df_filtered['year'] = df_filtered['arrival_date_year']\n",
        "df_filtered['year_month'] = df_filtered['year'].map(str) + '-' + df_filtered['arrival_date_month'].dt.strftime('%m')\n",
        "result = df_filtered.groupby(['hotel', 'year_month']).size().reset_index(name='order_amount')\n",
        "\n",
        "\n",
        "result"
      ],
      "metadata": {
        "id": "vB6dYhwuL7-Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8f08c466-9f4d-4f8c-d72f-2ca4cdf5b4ff"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-78-d23fdf23e8b0>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_filtered['arrival_date_month'] = pd.to_datetime(df_filtered['arrival_date_month'], format='%B')\n",
            "<ipython-input-78-d23fdf23e8b0>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_filtered['year'] = df_filtered['arrival_date_year']\n",
            "<ipython-input-78-d23fdf23e8b0>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_filtered['year_month'] = df_filtered['year'].map(str) + '-' + df_filtered['arrival_date_month'].dt.strftime('%m')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           hotel year_month  order_amount\n",
              "0     City Hotel    2017-10             1\n",
              "1     City Hotel    2017-12             1\n",
              "2     City Hotel    2018-04             2\n",
              "3     City Hotel    2018-05             1\n",
              "4     City Hotel    2018-07             1\n",
              "5     City Hotel    2018-09             8\n",
              "6     City Hotel    2018-10             7\n",
              "7     City Hotel    2018-12             1\n",
              "8     City Hotel    2019-03             3\n",
              "9     City Hotel    2019-04             9\n",
              "10    City Hotel    2019-05             1\n",
              "11    City Hotel    2019-06             6\n",
              "12    City Hotel    2019-07             3\n",
              "13    City Hotel    2019-08             3\n",
              "14    City Hotel    2019-09            13\n",
              "15    City Hotel    2019-10            13\n",
              "16  Resort Hotel    2017-01             1\n",
              "17  Resort Hotel    2017-02             1\n",
              "18  Resort Hotel    2017-09             3\n",
              "19  Resort Hotel    2017-10             2\n",
              "20  Resort Hotel    2017-11             8\n",
              "21  Resort Hotel    2017-12             8\n",
              "22  Resort Hotel    2018-01             1\n",
              "23  Resort Hotel    2018-02             2\n",
              "24  Resort Hotel    2018-03             1\n",
              "25  Resort Hotel    2018-04             2\n",
              "26  Resort Hotel    2018-05             1\n",
              "27  Resort Hotel    2018-06             2\n",
              "28  Resort Hotel    2018-07             2\n",
              "29  Resort Hotel    2018-08             2\n",
              "30  Resort Hotel    2018-09             2\n",
              "31  Resort Hotel    2018-10             1\n",
              "32  Resort Hotel    2018-11             1\n",
              "33  Resort Hotel    2019-04             1\n",
              "34  Resort Hotel    2019-07             1\n",
              "35  Resort Hotel    2019-08             1\n",
              "36  Resort Hotel    2019-09             1\n",
              "37  Resort Hotel    2019-10             2"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-e51f28a5-5b99-490e-8643-06829c349ca9\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hotel</th>\n",
              "      <th>year_month</th>\n",
              "      <th>order_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2017-10</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2017-12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2018-04</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2018-05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>City Hotel</td>\n",
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              "      <td>1</td>\n",
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              "      <th>5</th>\n",
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              "      <th>6</th>\n",
              "      <td>City Hotel</td>\n",
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              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2018-12</td>\n",
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              "      <th>8</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2019-03</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2019-04</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2019-05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2019-06</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2019-07</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2019-08</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2019-09</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2019-10</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2017-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2017-02</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2017-09</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2017-10</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2017-11</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2017-12</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2018-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2018-02</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2018-03</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2018-04</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2018-05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2018-06</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
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              "      <th>28</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2018-07</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>29</th>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2018-09</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>31</th>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>2019-10</td>\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e51f28a5-5b99-490e-8643-06829c349ca9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e51f28a5-5b99-490e-8643-06829c349ca9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data normalization and data sequencing by month"
      ],
      "metadata": {
        "id": "89QkWA11uLUg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "min_value = df_filtered['total_guest'].min()\n",
        "max_value = df_filtered['total_guest'].max()\n",
        "df_filtered['total_order_normalized'] = (df_filtered['total_guest'] - min_value) / (max_value - min_value)\n",
        "\n",
        "# Sort data by month\n",
        "df_sorted = df_filtered.sort_values('year_month')\n",
        "\n",
        "df_sorted[['hotel', 'year_month', 'total_guest', 'total_order_normalized']]\n"
      ],
      "metadata": {
        "id": "eLhKG3JUx8NY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "1c72cf4d-42de-4ea3-80ee-3b9de315e9a8"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-79-42b50e683cc7>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_filtered['total_order_normalized'] = (df_filtered['total_guest'] - min_value) / (max_value - min_value)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               hotel year_month  total_guest  total_order_normalized\n",
              "18745   Resort Hotel    2017-01            5                    0.00\n",
              "19022   Resort Hotel    2017-02            5                    0.00\n",
              "201     Resort Hotel    2017-09            5                    0.00\n",
              "328     Resort Hotel    2017-09           12                    0.14\n",
              "15370   Resort Hotel    2017-09            5                    0.00\n",
              "...              ...        ...          ...                     ...\n",
              "39265   Resort Hotel    2019-10            5                    0.00\n",
              "39808   Resort Hotel    2019-10            5                    0.00\n",
              "119054    City Hotel    2019-10            5                    0.00\n",
              "118241    City Hotel    2019-10            5                    0.00\n",
              "119070    City Hotel    2019-10            5                    0.00\n",
              "\n",
              "[119 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-fc61d186-9d55-4830-81a8-421c822cf800\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hotel</th>\n",
              "      <th>year_month</th>\n",
              "      <th>total_guest</th>\n",
              "      <th>total_order_normalized</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18745</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2017-01</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19022</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2017-02</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2017-09</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>328</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2017-09</td>\n",
              "      <td>12</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15370</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2017-09</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>39265</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2019-10</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39808</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2019-10</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119054</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2019-10</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118241</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2019-10</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119070</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2019-10</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119 rows × 4 columns</p>\n",
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              "              style=\"display:none;\">\n",
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              "              title=\"Suggest charts.\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: #1967D2;\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  quickchartButtonEl.style.display =\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fc61d186-9d55-4830-81a8-421c822cf800 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fc61d186-9d55-4830-81a8-421c822cf800');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualization"
      ],
      "metadata": {
        "id": "uIm4t4m4xmM-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pivot table to prepare data for plots\n",
        "pivot_table = result.pivot(index='year_month', columns='hotel', values='order_amount')\n",
        "\n",
        "# Create bar plots\n",
        "pivot_table.plot(kind='bar')\n",
        "\n",
        "# Set labels and titles on plots\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('order_amount')\n",
        "plt.title('Changes in Number of Hotel Bookings Every Month')\n",
        "\n",
        "# Display plots\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "CPxwDz90xKzp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "6fe8bfb6-c70c-4ef9-b08c-9036d4679e8a"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Total duration of stay"
      ],
      "metadata": {
        "id": "svOx4rOx3eiF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_filtered['total_duration'] = df_filtered['stays_in_weekend_nights'] + df_filtered['stays_in_weekdays_nights']\n",
        "df_filtered[['stays_in_weekend_nights', 'stays_in_weekdays_nights', 'total_duration']]\n"
      ],
      "metadata": {
        "id": "HfkRFvhs3Xfj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "1afbfa52-cefe-4f4d-ca06-1456b3536c08"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-81-c57c1d33d5af>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_filtered['total_duration'] = df_filtered['stays_in_weekend_nights'] + df_filtered['stays_in_weekdays_nights']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        stays_in_weekend_nights  stays_in_weekdays_nights  total_duration\n",
              "201                           0                         4               4\n",
              "328                           4                        10              14\n",
              "1539                          0                         3               3\n",
              "1587                          2                         5               7\n",
              "1643                          1                         2               3\n",
              "...                         ...                       ...             ...\n",
              "118557                        1                         0               1\n",
              "118852                        0                         1               1\n",
              "118913                        0                         2               2\n",
              "119054                        2                         1               3\n",
              "119070                        0                         1               1\n",
              "\n",
              "[119 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-57cf4084-8fd2-483b-a4c5-d53b238b11b5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>stays_in_weekend_nights</th>\n",
              "      <th>stays_in_weekdays_nights</th>\n",
              "      <th>total_duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>328</th>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1539</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1587</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1643</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118557</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118852</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118913</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119054</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119070</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57cf4084-8fd2-483b-a4c5-d53b238b11b5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-878a1b01-76f1-4cf7-b0a6-1ceeed92eeaa\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-878a1b01-76f1-4cf7-b0a6-1ceeed92eeaa')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-878a1b01-76f1-4cf7-b0a6-1ceeed92eeaa button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-57cf4084-8fd2-483b-a4c5-d53b238b11b5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-57cf4084-8fd2-483b-a4c5-d53b238b11b5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Group the values ​​from the new column in the previous step to make them more significant"
      ],
      "metadata": {
        "id": "mT5RBLoD41P5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create categories for grouping\n",
        "bins = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24]\n",
        "\n",
        "# Create labels for each category\n",
        "labels = ['0-2', '2-4', '4-6', '6-8', '8-10', '10-12', '12-14', '14-16', '16-18', '18-20', '20-22', '22-24']\n",
        "\n",
        "# Groups 'total_duration' values ​​into predefined categories\n",
        "df_filtered['duration_category'] = pd.cut(df_filtered['total_duration'], bins=bins, labels=labels, right=False)\n",
        "\n",
        "# Show data with new column 'duration_category'\n",
        "df_filtered[['total_duration', 'duration_category']]"
      ],
      "metadata": {
        "id": "N1NCzdPP4qdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "471a7857-0b2e-4ee3-dc2f-eb00e956b8d6"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-82-ad90ac31d8d8>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_filtered['duration_category'] = pd.cut(df_filtered['total_duration'], bins=bins, labels=labels, right=False)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        total_duration duration_category\n",
              "201                  4               4-6\n",
              "328                 14             14-16\n",
              "1539                 3               2-4\n",
              "1587                 7               6-8\n",
              "1643                 3               2-4\n",
              "...                ...               ...\n",
              "118557               1               0-2\n",
              "118852               1               0-2\n",
              "118913               2               2-4\n",
              "119054               3               2-4\n",
              "119070               1               0-2\n",
              "\n",
              "[119 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-1843c78f-7f3a-4cab-88d5-303ac6a1d675\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_duration</th>\n",
              "      <th>duration_category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>4</td>\n",
              "      <td>4-6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>328</th>\n",
              "      <td>14</td>\n",
              "      <td>14-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1539</th>\n",
              "      <td>3</td>\n",
              "      <td>2-4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1587</th>\n",
              "      <td>7</td>\n",
              "      <td>6-8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1643</th>\n",
              "      <td>3</td>\n",
              "      <td>2-4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118557</th>\n",
              "      <td>1</td>\n",
              "      <td>0-2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118852</th>\n",
              "      <td>1</td>\n",
              "      <td>0-2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118913</th>\n",
              "      <td>2</td>\n",
              "      <td>2-4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119054</th>\n",
              "      <td>3</td>\n",
              "      <td>2-4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119070</th>\n",
              "      <td>1</td>\n",
              "      <td>0-2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1843c78f-7f3a-4cab-88d5-303ac6a1d675')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-ee0ec6b3-3e1f-4f00-a213-df41acfbcd14\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee0ec6b3-3e1f-4f00-a213-df41acfbcd14')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-ee0ec6b3-3e1f-4f00-a213-df41acfbcd14 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1843c78f-7f3a-4cab-88d5-303ac6a1d675 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1843c78f-7f3a-4cab-88d5-303ac6a1d675');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " #### an aggregate table that shows the ratio of the number of canceled hotel bookings to the duration of stay for each type of hotel"
      ],
      "metadata": {
        "id": "wvDSZRkC5QAz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cancel_df = df_filtered.groupby(['hotel', 'duration_category'])['is_canceled'].sum().reset_index()\n",
        "total_df = df_filtered.groupby(['hotel', 'duration_category']).size().reset_index(name='total_bookings')\n",
        "result = pd.merge(cancel_df, total_df, on=['hotel', 'duration_category'])\n",
        "result['cancellation_ratio'] = result['is_canceled'] / result['total_bookings']\n",
        "\n",
        "\n",
        "result"
      ],
      "metadata": {
        "id": "_WZ4P6yf5Pgj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "outputId": "0459d5fe-9cfe-4011-9842-a5d97ffce6dd"
      },
      "execution_count": 83,
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              "           hotel duration_category  is_canceled  total_bookings  \\\n",
              "0     City Hotel               0-2            2              12   \n",
              "1     City Hotel               2-4           10              37   \n",
              "2     City Hotel               4-6            4              17   \n",
              "3     City Hotel               6-8            2               7   \n",
              "4     City Hotel              8-10            0               0   \n",
              "5     City Hotel             10-12            0               0   \n",
              "6     City Hotel             12-14            0               0   \n",
              "7     City Hotel             14-16            0               0   \n",
              "8     City Hotel             16-18            0               0   \n",
              "9     City Hotel             18-20            0               0   \n",
              "10    City Hotel             20-22            0               0   \n",
              "11    City Hotel             22-24            0               0   \n",
              "12  Resort Hotel               0-2            0               6   \n",
              "13  Resort Hotel               2-4            5              14   \n",
              "14  Resort Hotel               4-6            2               4   \n",
              "15  Resort Hotel               6-8           11              16   \n",
              "16  Resort Hotel              8-10            0               2   \n",
              "17  Resort Hotel             10-12            2               2   \n",
              "18  Resort Hotel             12-14            0               1   \n",
              "19  Resort Hotel             14-16            1               1   \n",
              "20  Resort Hotel             16-18            0               0   \n",
              "21  Resort Hotel             18-20            0               0   \n",
              "22  Resort Hotel             20-22            0               0   \n",
              "23  Resort Hotel             22-24            0               0   \n",
              "\n",
              "    cancellation_ratio  \n",
              "0             0.166667  \n",
              "1             0.270270  \n",
              "2             0.235294  \n",
              "3             0.285714  \n",
              "4                  NaN  \n",
              "5                  NaN  \n",
              "6                  NaN  \n",
              "7                  NaN  \n",
              "8                  NaN  \n",
              "9                  NaN  \n",
              "10                 NaN  \n",
              "11                 NaN  \n",
              "12            0.000000  \n",
              "13            0.357143  \n",
              "14            0.500000  \n",
              "15            0.687500  \n",
              "16            0.000000  \n",
              "17            1.000000  \n",
              "18            0.000000  \n",
              "19            1.000000  \n",
              "20                 NaN  \n",
              "21                 NaN  \n",
              "22                 NaN  \n",
              "23                 NaN  "
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              "      <th></th>\n",
              "      <th>hotel</th>\n",
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              "      <th>11</th>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Resort Hotel</td>\n",
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              "      <td>6</td>\n",
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              "      <th>13</th>\n",
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              "      <td>14</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualization"
      ],
      "metadata": {
        "id": "YPTCyfES56N0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Group data by type of hotel and category of duration of stay\n",
        "grouped_df = result.groupby(['hotel', 'duration_category'])['cancellation_ratio'].mean().unstack()\n",
        "# Draw bar plots\n",
        "grouped_df.plot(kind='bar', figsize=(10, 6))\n",
        "\n",
        "# Add axis titles and labels\n",
        "plt.title('Order Cancellation Ratio to Duration of Stay')\n",
        "plt.xlabel('Duration of Stay')\n",
        "plt.ylabel('Order Cancellation Ratio')\n",
        "\n",
        "# Display plots\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "h-MBHZT758Dp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "outputId": "7fe1ad78-6234-4e79-c5a1-d72398714562"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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r4sWLMgzDvC0yMjJB/xIlSli8fnReN2/elCTzPTmVK1dO8pinTp2SJHXp0iXJPpGRkQnes8dZsGCBpkyZouPHj1tc+pTc9+2/zp8/LwcHB5UtW9aivXDhwsqfP7/59/qR/74v0j/vzaP3xZq2bduqb9++io2N1a+//qrx48crJibG4h/a0j/L5o8YMUJbtmxJEOQT+/2y5tF5lC9fPsE2X19frV+//okWPnncuBUqVNDOnTttrvW/7ty5k+z/IEhMUp+N1atX68MPP1R4eHiCe4OeRFLvhZOTk0qXLp3gs1SsWLEExypQoIAOHTpk9Ti2fGZt0bFjR3Xs2FFRUVHas2ePQkNDtXjxYrVu3VpHjhyxuqjGhQsXNHLkSK1atSrBn4l/f26bNGmiIkWKaNGiRXruuecUHx+vb775Rm3btk3R7zWAxyM4AbAqX758KlKkiNV/kBw6dEhFixY1h5JH/v2/qLaKj49XkyZN9N577yW6/amnnnqiY1WoUEGSdPjw4WQ91PKdd97R/PnzNWDAANWtW1f58uWTyWTSK6+8kuisT1Krhf07cFnzaNxJkyYlWaObm1uyx/v666/VtWtXtWvXToMHD5anp6f5vpr/Lq5gq+T+Yzml70uxYsUUEBAgSWrRooU8PDzUt29fNW7c2Hwf1q1bt9SoUSPlzZtXY8eOVZkyZeTi4qL9+/dryJAhNs3SZXYPHjzQyZMnLQJ6Ur9X/144498S+zO1Y8cOtWnTRg0bNtRnn32mIkWKKGfOnJo/f36CxU3SSko/S2n5IOe8efOqSZMmatKkiXLmzKkFCxZoz549atSoUZL7xMXFqUmTJrpx44aGDBmiChUqKHfu3Lp48aK6du1q8bl1dHTUq6++qnnz5umzzz7Tzz//rEuXLlnMwAFIfQQnAMnSqlUrzZs3Tzt37jSvjPdvO3bs0Llz5x574/cjj57bcurUKYuly69du5bgf1nLlCmjO3fumP+xnFqaN28uR0dHff3118laIOKHH35Qly5dNGXKFHPbvXv3nvjm/TJlykiSjhw5kuB/vv/bJ2/evKly/j/88INKly6tZcuWWfyjcdSoURb9bPkHZcmSJRUfH69Tp07J19fX3H7lyhXdunUrzZ/R8+abb2ratGkaMWKEnn/+eZlMJoWFhenvv//WsmXLLG7mP3v2bIL9k3uuj87jxIkTCbYdP35cHh4ej51tSuo4/x732Weftdh24sSJFL9/P/zwg+7evWuxVHuBAgUS/dzaMtOydOlSubi4aP369XJ2dja3z58/P0HfJ3mP//33QmxsrM6ePZtqfwek92e2Vq1aWrBggS5fviwp6ffj8OHDOnnypBYsWGCxGMTGjRsT7d+5c2dNmTJFP/74o9auXatChQql6pL8ABLiHicAyTJ48GC5urrqzTff1N9//22x7caNG+rdu7dy5cqlwYMHWx0rICBAOXPm1IwZMyz+dzixFc1efvll7d69O9EHnN66dUsPHz60/WQkFS9eXD179tSGDRs0Y8aMBNvj4+M1ZcoU8wNwHR0dE/xP9owZM5L8X3prmjZtqjx58ig4OFj37t2z2PboOH5+fipTpowmT56sO3fuJBgjseXNH+fR/9D/+zz27Nmj3bt3W/TLlSuXJCUrFD56IOh/f++mTp0q6Z97ddJSjhw59O677+rYsWNauXKlpMTPMzY2Vp999lmC/XPnzp2sS/eKFCmi6tWra8GCBRbvy5EjR7Rhw4bHPhj10XGkhO9prVq15OnpqdmzZ1tc7rZ27VodO3YsRe/fwYMHNWDAABUoUEB9+vQxt5cpU0aRkZEWM8iXL1+26QHWjo6OMplMFp//c+fOJfqg29y5cyfrsxQQECAnJyd98sknFr93X3zxhSIjI1Pts5QWn9mYmJgEf44eebTK36NLEJP6LCT2uTUMQ9OnT0903KpVq6pq1ar6/PPPtXTpUr3yyit2fcYakB3wJwxAspQrV04LFixQp06dVKVKFfXo0UOlSpXSuXPn9MUXX+j69ev65ptvzLMkj1OoUCENGjRIwcHBatWqlVq0aKEDBw5o7dq18vDwsOg7ePBgrVq1Sq1atVLXrl3l5+en6OhoHT58WD/88IPOnTuXYJ/kmjJlis6cOaN+/fpp2bJlatWqlQoUKKALFy7o+++/1/Hjx83LE7dq1UpfffWV8uXLp4oVK2r37t3atGmTChYs+ETHzps3r6ZNm6Y33nhDTz/9tPk5VwcPHlRMTIwWLFggBwcHff7552revLkqVaqkbt26qWjRorp48aK2bt2qvHnz6scff0z2MVu1aqVly5bp+eefV8uWLXX27FnNnj1bFStWtAhmrq6uqlixopYsWaKnnnpK7u7uqly5cqL3Y1WrVk1dunTR3LlzzZfI7d27VwsWLFC7du3UuHHjJ3p/bNG1a1eNHDlSEydOVLt27VSvXj0VKFBAXbp0Ub9+/WQymfTVV18legmXn5+flixZoqCgID399NNyc3NT69atEz3OpEmT1Lx5c9WtW1c9evTQ3bt3NWPGDOXLl8/8nKuklClTRvnz59fs2bOVJ08e5c6dW3Xq1FGpUqU0ceJEdevWTY0aNVLHjh115coV87LfAwcOTNZ7sGPHDt27d09xcXH6+++/9fPPP2vVqlXKly+fli9frsKFC5v7vvLKKxoyZIief/559evXTzExMZo1a5aeeuqpZC/s0LJlS02dOlXNmjXTq6++qqtXr2rmzJkqW7Zsgkt6/fz8tGnTJk2dOlXe3t4qVaqU6tSpk2DMQoUKadiwYRozZoyaNWumNm3a6MSJE/rss8/09NNPp9plaGnxmY2JiVG9evX0v//9T82aNVPx4sV169YtrVixQjt27FC7du1Uo0YNSUl/FipUqKAyZcpo0KBBunjxovLmzaulS5c+9v6/zp07a9CgQZLEZXpAerDHUn4AMq9Dhw4ZHTt2NIoUKWLkzJnTKFy4sNGxY0fj8OHDCfo+Wt742rVrCbbFxcUZY8aMMYoUKWK4uroa/v7+xpEjR4ySJUsmWLr49u3bxrBhw4yyZcsaTk5OhoeHh1GvXj1j8uTJRmxsrGEY/7d89H+Xpbbm4cOHxueff240aNDAyJcvn5EzZ06jZMmSRrdu3SyWKr9586bRrVs3w8PDw3BzczMCAwON48ePJ6j30RLLv/76q8VxHi0bvXXrVov2VatWGfXq1TNcXV2NvHnzGrVr1za++eYbiz4HDhwwXnjhBaNgwYKGs7OzUbJkSePll182Nm/enOC4j1uOPD4+3hg/frxRsmRJw9nZ2ahRo4axevXqRJen3rVrl+Hn52c4OTlZLE2e2JLVDx48MMaMGWOUKlXKyJkzp1G8eHFj2LBhFst+G8Y/y5G3bNkywe/Bf+tMiiSjT58+iW4bPXq0xfv7888/G//73/8MV1dXw9vb23jvvfeM9evXJ/g9uHPnjvHqq68a+fPnNySZ34fEliM3DMPYtGmTUb9+ffPvV+vWrY2jR49ard0wDGPlypVGxYoVjRw5ciQYe8mSJUaNGjUMZ2dnw93d3ejUqZPx119/WR3z0efq0U/OnDmNQoUKGQ0bNjQ++uijJJfm37Bhg1G5cmXDycnJKF++vPH1118nuRx5Uu/5F198YZQrV85wdnY2KlSoYMyfPz/RMY4fP240bNjQcHV1tXjcQGKfWcP4Z/nxChUqGDlz5jS8vLyMt956y7h586ZFn0aNGhmVKlVKUFNSS63/V3I/s8ldjvzBgwfGvHnzjHbt2pn/fOXKlcuoUaOGMWnSpASPAUjqs3D06FEjICDAcHNzMzw8PIyePXsaBw8eTHKZ8cuXLxuOjo7GU089ZbVGAClnMgwb7lQGAABAhnD9+nUVKVLE/EBxAGmLe5wAAAAyodDQUMXFxSVrgRsAKcc9TgAAAJnIli1bdPToUX300Udq166dfHx87F0SkC1wqR4AAEAm4u/vr127dql+/fr6+uuvVbRoUXuXBGQLBCcAAAAAsIJ7nAAAAADACoITAAAAAFiR7RaHiI+P16VLl5QnTx6ZTCZ7lwMAAADATgzD0O3bt+Xt7S0Hh8fPKWW74HTp0iUVL17c3mUAAAAAyCD+/PNPFStW7LF9sl1wypMnj6R/3py8efPauRoAAAAA9hIVFaXixYubM8LjZLvg9OjyvLx58xKcAAAAACTrFh4WhwAAAAAAKwhOAAAAAGAFwQkAAAAArMh29zgBAAAg64mLi9ODBw/sXQYyICcnJ6tLjScHwQkAAACZlmEYioiI0K1bt+xdCjIoBwcHlSpVSk5OTikah+AEAACATOtRaPL09FSuXLmStToaso/4+HhdunRJly9fVokSJVL0+SA4AQAAIFOKi4szh6aCBQvauxxkUIUKFdKlS5f08OFD5cyZ84nHYXEIAAAAZEqP7mnKlSuXnStBRvboEr24uLgUjUNwAgAAQKbG5Xl4nNT6fBCcAAAAAMAKghMAAACyDX9/fw0YMMAuxw4LC5PJZGIFwEyK4AQAAACkssQCWr169XT58mXly5fPPkU9AR8fH4WEhNi7jAyB4AQAAAAkU0oesuvk5KTChQtzT9YTio2NtevxCU4AAADIkqKjo9W5c2e5ubmpSJEimjJlisV2k8mkFStWWLTlz59foaGhkqRz587JZDJpyZIlatSokVxcXLRo0SL9/fff6tixo4oWLapcuXKpSpUq+uabb8xjdO3aVdu2bdP06dNlMplkMpl07ty5RC/VW7p0qSpVqiRnZ2f5+PgkqNHHx0fjx49X9+7dlSdPHpUoUUJz585N9nvw119/qWPHjnJ3d1fu3LlVq1Yt7dmzR5J05swZtW3bVl5eXnJzc9PTTz+tTZs2mff19/fX+fPnNXDgQPN5PLJz5041aNBArq6uKl68uPr166fo6Gjz9suXL6tly5ZydXVVqVKltHjx4gSzVxcuXFDbtm3l5uamvHnz6uWXX9aVK1fM20ePHq3q1avr888/V6lSpeTi4qKFCxeqYMGCun//vsV5tmvXTq+//nqy35cnQXACAABAljR48GBt27ZNK1eu1IYNGxQWFqb9+/fbPM7QoUPVv39/HTt2TIGBgbp37578/Py0Zs0aHTlyRL169dLrr7+uvXv3SpKmT5+uunXrqmfPnrp8+bIuX76s4sWLJxh33759evnll/XKK6/o8OHDGj16tD744ANzcHtkypQpqlWrlg4cOKC3335bb731lk6cOGG17jt37qhRo0a6ePGiVq1apYMHD+q9995TfHy8eXuLFi20efNmHThwQM2aNVPr1q114cIFSdKyZctUrFgxjR071nwe0j+Bq1mzZmrfvr0OHTqkJUuWaOfOnerbt6/52J07d9alS5cUFhampUuXau7cubp69ap5e3x8vNq2basbN25o27Zt2rhxo/744w916NDB4hxOnz6tpUuXatmyZQoPD9dLL72kuLg4rVq1ytzn6tWrWrNmjbp37271PUkRw462bdtmtGrVyihSpIghyVi+fLnVfbZu3WrUqFHDcHJyMsqUKWPMnz/fpmNGRkYakozIyMgnKxoAAAAZwt27d42jR48ad+/eTbDt9u3bhpOTk/Hdd9+Z2/7++2/D1dXV6N+/v2EYRqL//syXL5/535dnz541JBkhISFWa2nZsqXx7rvvml83atTIfJxHtm7dakgybt68aRiGYbz66qtGkyZNLPoMHjzYqFixovl1yZIljddee838Oj4+3vD09DRmzZpltaY5c+YYefLkMf7++2+rfR+pVKmSMWPGDIvjT5s2zaJPjx49jF69elm07dixw3BwcDDu3r1rHDt2zJBk/Prrr+btp06dMiSZx9qwYYPh6OhoXLhwwdzn999/NyQZe/fuNQzDMEaNGmXkzJnTuHr1qsWx3nrrLaN58+bm11OmTDFKly5txMfHJ3pOj/uc2JIN7DrjFB0drWrVqmnmzJnJ6n/27Fm1bNlSjRs3Vnh4uAYMGKA33nhD69evT+NKAQAAkJmcOXNGsbGxqlOnjrnN3d1d5cuXt3msWrVqWbyOi4vTuHHjVKVKFbm7u8vNzU3r1683z9Qk17Fjx1S/fn2Ltvr16+vUqVMWD2utWrWq+dcmk0mFCxe2mL1JSnh4uGrUqCF3d/dEt9+5c0eDBg2Sr6+v8ufPLzc3Nx07dszqeRw8eFChoaFyc3Mz/wQGBio+Pl5nz57ViRMnlCNHDtWsWdO8T9myZVWgQAGLcy9evLjFTFzFihWVP39+HTt2zNxWsmRJFSpUyOL4PXv21IYNG3Tx4kVJUmhoqLp27Zrm947lSNPRrWjevLmaN2+e7P6zZ89WqVKlzNd++vr6aufOnZo2bZoCAwPTqkwAAABkQSaTSYZhWLQltvhD7ty5LV5PmjRJ06dPV0hIiKpUqaLcuXNrwIABabZ4Qc6cOS1em0wm8+V2j+Pq6vrY7YMGDdLGjRs1efJklS1bVq6urnrxxRetnsedO3f05ptvql+/fgm2lShRQidPnrRaW3L9972XpBo1aqhatWpauHChmjZtqt9//11r1qxJtWMmxa7ByVa7d+9WQECARVtgYOBj1+K/f/++xc1jUVFRaVUeAAAAMogyZcooZ86c2rNnj0qUKCFJunnzpk6ePKlGjRpJkgoVKmS+b0eSTp06pZiYGKtj//zzz2rbtq1ee+01Sf/cr3Py5ElVrFjR3MfJycli1igxvr6++vnnnxOM/dRTT8nR0TF5J/oYVatW1eeff64bN24kOuv0888/q2vXrnr++ecl/ROIzp07Z9EnsfOoWbOmjh49qrJlyyZ63PLly+vhw4c6cOCA/Pz8JP1zr9LNmzfNfXx9ffXnn3/qzz//NM86HT16VLdu3bJ4H5PyxhtvKCQkRBcvXlRAQECi95CltkwVnCIiIuTl5WXR5uXlpaioKN29ezfRVB0cHKwxY8akV4kAAADpZkqHVike490lq1O0/+jRo+26f1Lc3NzUo0cPDR48WAULFpSnp6fef/99OTj8350qzz77rD799FPVrVtXcXFxGjJkSILZncSUK1dOP/zwg3bt2qUCBQpo6tSpunLlisU/+H18fLRnzx6dO3dObm5uiQaXd999V08//bTGjRunDh06aPfu3fr000/12Wefpcp70LFjR40fP17t2rVTcHCwihQpogMHDsjb21t169ZVuXLltGzZMrVu3Vomk0kffPBBgpksHx8fbd++Xa+88oqcnZ3l4eGhIUOG6H//+5/69u2rN954Q7lz59bRo0e1ceNGffrpp6pQoYICAgLUq1cvzZo1Szlz5tS7774rV1dX8+V0AQEBqlKlijp16qSQkBA9fPhQb7/9tho1apTg0sjEvPrqqxo0aJDmzZunhQsXpsr7ZU2WX1Vv2LBhioyMNP/8+eef9i4JAAAA6WDSpElq0KCBWrdurYCAAD3zzDPmGRDpn9XqihcvrgYNGpj/IZ4rVy6r444YMUI1a9ZUYGCg/P39VbhwYbVr186iz6BBg+To6KiKFSuqUKFCid43VLNmTX333Xf69ttvVblyZY0cOVJjx45V165dU3rqkv6ZLdqwYYM8PT3VokULValSRRMmTDDPZk2dOlUFChRQvXr11Lp1awUGBlrclyRJY8eO1blz51SmTBnzvUZVq1bVtm3bdPLkSTVo0EA1atTQyJEj5e3tbd5v4cKF8vLyUsOGDfX888+rZ8+eypMnj1xcXCT9c7nhypUrVaBAATVs2FABAQEqXbq0lixZkqxzy5cvn9q3by83N7cE731aMRn/vbDTTkwmk5YvX/7YE2/YsKFq1qxpsf77/PnzNWDAAEVGRibrOFFRUcqXL58iIyOVN2/eFFYNAABgP9l9xunevXs6e/as+Rk/yLj++usvFS9eXJs2bdJzzz2XKmM+99xzqlSpkj755JPH9nvc58SWbJCpLtWrW7eufvrpJ4u2jRs3qm7dunaqCAAAAMB/bdmyRXfu3FGVKlV0+fJlvffee/Lx8VHDhg1TPPbNmzcVFhamsLCwVLusMTnseqnenTt3FB4ervDwcEn/LDceHh5unsocNmyYOnfubO7fu3dv/fHHH3rvvfd0/PhxffbZZ/ruu+80cOBAe5QPAAAA2M348eMtlgT/948tK1enhQcPHmj48OGqVKmSnn/+eRUqVEhhYWHJuofMmho1aqhr166aOHHiEy0v/6TsOuP022+/qXHjxubXQUFBkqQuXbooNDRUly9ftrgetFSpUlqzZo0GDhyo6dOnq1ixYvr8889ZihwAAADZTu/evfXyyy8nus3aUuRpLTAwMM3+jf7flf/Si12Dk7+/f4K18/8tNDQ00X0OHDiQhlUBAAAAGZ+7u3uSD7dF6svyq+oBAAAAQEoRnAAAAADACoITAAAAAFhBcAIAAAAAKwhOAAAAAGAFwQkAAAAArLDrcuQAAABAZuQzdE26HevchJZPtN/MmTM1adIkRUREqFq1apoxY4Zq166d+DHOndO4ceO0ZcsWRUREyNvbW6+99pref/99OTk5paT8LIMZJwAAACCLWbJkiYKCgjRq1Cjt379f1apVU2BgoK5evZpo/+PHjys+Pl5z5szR77//rmnTpmn27NkaPnx4OleecRGcAAAAgCxm6tSp6tmzp7p166aKFStq9uzZypUrl7788stE+zdr1kzz589X06ZNVbp0abVp00aDBg3SsmXL0rnyjIvgBAAAAGQhsbGx2rdvnwICAsxtDg4OCggI0O7du5M9TmRkpNzd3dOixEyJ4AQAAABkIdevX1dcXJy8vLws2r28vBQREZGsMU6fPq0ZM2bozTffTIsSMyWCEwAAAJCN9O7dW25ubuaf/7p48aKaNWuml156ST179rRDhRkTq+oBAAAAWYiHh4ccHR115coVi/YrV66ocOHCGjt2rAYNGpTovpcuXVLjxo1Vr149zZ07Nz3KzTSYcQIAAACyECcnJ/n5+Wnz5s3mtvj4eG3evFl169aVp6enypYta/555OLFi/L395efn5/mz58vBweiwr8x4wQAAABkMUFBQerSpYtq1aql2rVrKyQkRNHR0erWrVui/R+FppIlS2ry5Mm6du2aeVvhwoXTq+wMjeAEAAAAZDEdOnTQtWvXNHLkSEVERKh69epat25dggUjHtm4caNOnz6t06dPq1ixYhbbDMNIj5IzPIITAAAAYKNzE1rauwSr+vbtq759+yarb9euXdW1a9e0LSiT48JFAAAAALCC4AQAAAAAVhCcAAAAAMAKghMAAAAAWEFwAgAAAAArCE4AAAAAYAXBCQAAAACsIDgBAAAAgBUEJwAAAACwguAEAAAAAFbksHcBAAAAQKYzOl86HivSpu7BwcFatmyZjh8/LldXV9WrV08TJ05U+fLlk7X//fv3VadOHR08eFAHDhxQ9erVn6DorIcZJwAAACAL2bZtm/r06aNffvlFGzdu1IMHD9S0aVNFR0cna//33ntP3t7eaVxl5sOMEwAAAJCFrFu3zuJ1aGioPD09tW/fPjVs2PCx+65du1YbNmzQ0qVLtXbt2rQsM9MhOAEAAABZWGTkP5f6ubu7P7bflStX1LNnT61YsUK5cuVKj9IyFS7VAwAAALKo+Ph4DRgwQPXr11flypWT7GcYhrp27arevXurVq1a6Vhh5kFwAgAAALKoPn366MiRI/r222/Nbb1795abm5v5R5JmzJih27dva9iwYfYqNcMjOAEAAABZUN++fbV69Wpt3bpVxYoVM7ePHTtW4eHh5h9J2rJli3bv3i1nZ2flyJFDZcuWlSTVqlVLXbp0sUf5GQ73OAEAAABZiGEYeuedd7R8+XKFhYWpVKlSFts9PT3l6elp0fbJJ5/oww8/NL++dOmSAgMDtWTJEtWpUydd6s7oCE4AAABAFtKnTx8tXrxYK1euVJ48eRQRESFJypcvn1xdXRPdp0SJEhavH13CV6ZMGYvZquyMS/UAAACALGTWrFmKjIyUv7+/ihQpYv5ZsmSJvUvL1JhxAgAAAGw1OtLeFSTJMIwUj+Hj45Mq42QlzDgBAAAAgBUEJwAAAACwguAEAAAAAFYQnAAAAADACoITAAAAAFhBcAIAAAAAKwhOAAAAAGAFwQkAAAAArCA4AQAAAIAVBCcAAAAAsCKHvQsAAAAAMpsqC6qk27EOdzmcov0nTJigYcOGqX///goJCXls3zVr1mjs2LE6dOiQXFxc1KhRI61YsSJFx88qCE4AAABAFvXrr79qzpw5qlq1qtW+S5cuVc+ePTV+/Hg9++yzevjwoY4cOZIOVWYOBCcAAAAgC7pz5446deqkefPm6cMPP3xs34cPH6p///6aNGmSevToYW6vWLFiWpeZaXCPEwAAAJAF9enTRy1btlRAQIDVvvv379fFixfl4OCgGjVqqEiRImrevDkzTv9CcAIAAACymG+//Vb79+9XcHBwsvr/8ccfkqTRo0drxIgRWr16tQoUKCB/f3/duHEjLUvNNAhOAAAAQBby559/qn///lq0aJFcXFwSbO/du7fc3NzMP5IUHx8vSXr//ffVvn17+fn5af78+TKZTPr+++/Ttf6MinucAAAAgCxk3759unr1qmrWrGlui4uL0/bt2/Xpp5/q4sWLGjRokMU+RYoUkWR5T5Ozs7NKly6tCxcupE/hGRzBCQAAAMhCnnvuOR0+bLmEebdu3VShQgUNGTJEXl5e8vLystju5+cnZ2dnnThxQs8884wk6cGDBzp37pxKliyZbrVnZAQnAAAAIAvJkyePKleubNGWO3duFSxYMEH7I3nz5lXv3r01atQoFS9eXCVLltSkSZMkSS+99FKa15wZEJwAAAAAaNKkScqRI4def/113b17V3Xq1NGWLVtUoEABe5eWIRCcAAAAABsd7nLYeqcMJCwszGqfnDlzavLkyZo8eXLaF5QJsaoeAAAAAFhBcAIAAAAAKwhOAAAAAGAFwQkAAAAArCA4AQAAAIAVBCcAAAAAsILgBAAAAABWEJwAAAAAwAqCEwAAAABYQXACAAAAACty2LsAAAAAILM5VsE33Y7le/yYzftcvHhRQ4YM0dq1axUTE6OyZctq/vz5qlWrVpL7REREaPDgwdq4caNu376t8uXL6/3331f79u1TUn6WQXACAAAAspCbN2+qfv36aty4sdauXatChQrp1KlTKlCgwGP369y5s27duqVVq1bJw8NDixcv1ssvv6zffvtNNWrUSKfqMy6CEwAAAJCFTJw4UcWLF9f8+fPNbaVKlbK6365duzRr1izVrl1bkjRixAhNmzZN+/btIziJe5wAAACALGXVqlWqVauWXnrpJXl6eqpGjRqaN2+e1f3q1aunJUuW6MaNG4qPj9e3336re/fuyd/fP+2LzgQITgAAAEAW8scff2jWrFkqV66c1q9fr7feekv9+vXTggULHrvfd999pwcPHqhgwYJydnbWm2++qeXLl6ts2bLpVHnGRnACAAAAspD4+HjVrFlT48ePV40aNdSrVy/17NlTs2fPliSNHz9ebm5u5p8LFy5Ikj744APdunVLmzZt0m+//aagoCC9/PLLOnz4sD1PJ8PgHicAAAAgCylSpIgqVqxo0ebr66ulS5dKknr37q2XX37ZvM3b21tnzpzRp59+qiNHjqhSpUqSpGrVqmnHjh2aOXOmOXRlZwQnAAAAIAupX7++Tpw4YdF28uRJlSxZUpLk7u4ud3d3i+0xMTGSJAcHywvSHB0dFR8fn4bVZh52v1Rv5syZ8vHxkYuLi+rUqaO9e/c+tn9ISIjKly8vV1dXFS9eXAMHDtS9e/fSqVoAAAAgYxs4cKB++eUXjR8/XqdPn9bixYs1d+5c9enTJ8l9KlSooLJly+rNN9/U3r17debMGU2ZMkUbN25Uu3bt0q/4DMyuwWnJkiUKCgrSqFGjtH//flWrVk2BgYG6evVqov0XL16soUOHatSoUTp27Ji++OILLVmyRMOHD0/nygEAAICM6emnn9by5cv1zTffqHLlyho3bpxCQkLUqVOnJPfJmTOnfvrpJxUqVEitW7dW1apVtXDhQi1YsEAtWrRIx+ozLrteqjd16lT17NlT3bp1kyTNnj1ba9as0ZdffqmhQ4cm6L9r1y7Vr19fr776qiTJx8dHHTt21J49e9K1bgAAAGRvvseP2buEx2rVqpVatWpl0z7lypUz3weFhOw24xQbG6t9+/YpICDg/4pxcFBAQIB2796d6D716tXTvn37zJfz/fHHH/rpp58em4Lv37+vqKgoix8AAAAAsIXdZpyuX7+uuLg4eXl5WbR7eXnp+PHjie7z6quv6vr163rmmWdkGIYePnyo3r17P/ZSveDgYI0ZMyZVawcAAACQvdh9cQhbhIWFafz48frss8+0f/9+LVu2TGvWrNG4ceOS3GfYsGGKjIw0//z555/pWDEAAACArMBuM04eHh5ydHTUlStXLNqvXLmiwoULJ7rPBx98oNdff11vvPGGJKlKlSqKjo5Wr1699P777ydYPlGSnJ2d5ezsnPonAAAAACDbsNuMk5OTk/z8/LR582ZzW3x8vDZv3qy6desmuk9MTEyia8tLkmEYaVcsAAAAgGzNrqvqBQUFqUuXLqpVq5Zq166tkJAQRUdHm1fZ69y5s4oWLarg4GBJUuvWrTV16lTVqFFDderU0enTp/XBBx+odevW5gAFAAAAAKnNrsGpQ4cOunbtmkaOHKmIiAhVr15d69atMy8YceHCBYsZphEjRshkMmnEiBG6ePGieZ35jz76yF6nAAAAACAbsGtwkqS+ffuqb9++iW4LCwuzeJ0jRw6NGjVKo0aNSofKAAAAAOAfmWpVPQAAAACwB4ITAAAAAFhh90v1AAAAgMxmZu8t6XasPrOftal/XFycRo8era+//loRERHy9vZW165dzesFJGXu3LlavHix9u/fr9u3b+vmzZvKnz+/RZ8bN27onXfe0Y8//igHBwe1b99e06dPl5ub25OcWqbCjBMAAACQhUycOFGzZs3Sp59+qmPHjmnixIn6+OOPNWPGjMfuFxMTo2bNmmn48OFJ9unUqZN+//13bdy4UatXr9b27dvVq1ev1D6FDIkZJwAAACAL2bVrl9q2bauWLVtKknx8fPTNN99o7969j91vwIABkhIu0PbIsWPHtG7dOv3666+qVauWJGnGjBlq0aKFJk+eLG9v71Q7h4yIGScAAAAgC6lXr542b96skydPSpIOHjyonTt3qnnz5ikad/fu3cqfP785NElSQECAHBwctGfPnhSNnRkw4wQAAABkIUOHDlVUVJQqVKggR0dHxcXF6aOPPlKnTp1SNG5ERIQ8PT0t2nLkyCF3d3dFRESkaOzMgBknAAAAIAv57rvvtGjRIvNCDwsWLNDkyZO1YMECSdL48ePl5uZm/rlw4YKdK84cmHECAAAAspDBgwdr6NCheuWVVyRJVapU0fnz5xUcHKwuXbqod+/eevnll839k3tvUuHChXX16lWLtocPH+rGjRsqXLhw6p1ABkVwAgAAALKQmJgYOThYXljm6Oio+Ph4SZK7u7vc3d1tHrdu3bq6deuW9u3bJz8/P0nSli1bFB8frzp16qS88AyO4AQAAABkIa1bt9ZHH32kEiVKqFKlSjpw4ICmTp2q7t27P3a/iIgIRURE6PTp05Kkw4cPK0+ePCpRooTc3d3l6+urZs2aqWfPnpo9e7YePHigvn376pVXXsnyK+pJ3OMEAAAAZCkzZszQiy++qLffflu+vr4aNGiQ3nzzTY0bN+6x+82ePVs1atRQz549JUkNGzZUjRo1tGrVKnOfRYsWqUKFCnruuefUokULPfPMM5o7d26ank9GwYwTAAAAYKM+s5+1dwlJypMnj0JCQhQSEmLTfqNHj9bo0aMf28fd3V2LFy9+8uIyMWacAAAAAMAKghMAAAAAWEFwAgAAAAArCE4AAAAAYAXBCQAAAACsIDgBAAAAgBUEJwAAAACwguAEAAAAAFYQnAAAAADACoITAAAAAFiRw94FAAAAAJnNlA6t0u1Y7y5ZbfM+27dv16RJk7Rv3z5dvnxZy5cvV7t27czbDcPQqFGjNG/ePN26dUv169fXrFmzVK5cuceO269fP/388886cuSIfH19FR4ebrE9LCxM06ZN0969exUVFaVy5cpp8ODB6tSpk83nkNEw4wQAAABkMdHR0apWrZpmzpyZ6PaPP/5Yn3zyiWbPnq09e/Yod+7cCgwM1L1796yO3b17d3Xo0CHRbbt27VLVqlW1dOlSHTp0SN26dVPnzp21erXt4S+jYcYJAAAAyGKaN2+u5s2bJ7rNMAyFhIRoxIgRatu2rSRp4cKF8vLy0ooVK/TKK68kOe4nn3wiSbp27ZoOHTqUYPvw4cMtXvfv318bNmzQsmXL1KpV+s3SpQVmnAAAAIBs5OzZs4qIiFBAQIC5LV++fKpTp452796d6seLjIyUu7t7qo+b3ghOAAAAQDYSEREhSfLy8rJo9/LyMm9LLd99951+/fVXdevWLVXHtQeCEwAAAAALzZs3l5ubm9zc3FSpUqUnGmPr1q3q1q2b5s2b98RjZCTc4wQAAABkI4ULF5YkXblyRUWKFDG3X7lyRdWrV5ckff7557p7964kKWfOnDYfY9u2bWrdurWmTZumzp07p7zoDIDgBAAAAGQjpUqVUuHChbV582ZzUIqKitKePXv01ltvSZKKFi36xOOHhYWpVatWmjhxonr16pUaJWcITxSc4uLitGLFCh07dkySVKlSJbVp00aOjo6pWhwAAAAA2925c0enT582vz579qzCw8Pl7u6uEiVKaMCAAfrwww9Vrlw5lSpVSh988IG8vb0tnvWUmNOnT+vOnTuKiIjQ3bt3zc9xqlixopycnLR161a1atVK/fv3V/v27c33TDk5OWX6BSJsDk6nT59Wy5Yt9ddff6l8+fKSpODgYBUvXlxr1qxRmTJlUr1IAAAAAMn322+/qXHjxubXQUFBkqQuXbooNDRU7733nqKjo9WrVy/dunVLzzzzjNatWycXF5fHjvvGG29o27Zt5tc1atSQ9E8w8/Hx0YIFCxQTE6Pg4GAFBweb+zVq1EhhYWGpeIbpz2QYhmHLDi1atJBhGFq0aJE5Nf7999967bXX5ODgoDVr1qRJoaklKipK+fLlU2RkpPLmzWvvcgAAAJ7YlA4pfy7Ou0tS9mDS0aNH223/e/fu6ezZsypVqpTVf/Aj+3rc58SWbGDzjNO2bdv0yy+/WEy1FSxYUBMmTFD9+vVtHQ4AAAAAMjyblyN3dnbW7du3E7TfuXNHTk5OqVIUAAAAAGQkNgenVq1aqVevXtqzZ48Mw5BhGPrll1/Uu3dvtWnTJi1qBAAAAAC7sjk4ffLJJypTpozq1q0rFxcXubi4qH79+ipbtqymT5+eFjUCAAAAgF3ZfI9T/vz5tXLlSp06dUrHjx+XJPn6+qps2bKpXhwAAAAAZARP/ADccuXKqVy5cqlZCwAAAABkSMkKTkFBQRo3bpxy585tXgM+KVOnTk2VwgAAAAAgo0hWcDpw4IAePHhg/jUAAAAAZCfJCk5bt25N9NcAAAAAkB3YfI9T9+7dNX36dOXJk8eiPTo6Wu+8846+/PLLVCsOAAAgIztWwTdF+/seP5ZKlQBIazYHpwULFmjChAkJgtPdu3e1cOFCghMAAACyvL+G7ki3YxWb0MDmfbZv365JkyZp3759unz5spYvX6527dpJkh48eKARI0bop59+0h9//KF8+fIpICBAEyZMkLe392PH7devn37++WcdOXJEvr6+Cg8PT7Lv6dOnVaNGDTk6OurWrVs2n0NGk+znOEVFRSkyMlKGYej27duKiooy/9y8eVM//fSTPD0907JWAAAAAMkQHR2tatWqaebMmQm2xcTEaP/+/frggw+0f/9+LVu2TCdOnFCbNm2SNXb37t3VoUOHx/Z58OCBOnbsqAYNbA99GVWyZ5zy588vk8kkk8mkp556KsF2k8mkMWPGpGpxAAAAAGzXvHlzNW/ePNFt+fLl08aNGy3aPv30U9WuXVsXLlxQiRIlkhz3k08+kSRdu3ZNhw4dSrLfiBEjVKFCBT333HPatWvXE5xBxpPs4LR161YZhqFnn31WS5culbu7u3mbk5OTSpYsaXVqDwAAAEDGExkZKZPJpPz586d4rC1btuj7779XeHi4li1blvLiMohkB6dGjRpJks6ePavixYvLwSHZV/kBAAAAyKDu3bunIUOGqGPHjsqbN2+Kxvr777/VtWtXff311ykeK6OxeXGIkiVLSvrn2sgLFy4oNjbWYnvVqlVTpzIAAAAAaerBgwd6+eWXZRiGZs2aZW5v3ry5duz4ZwGMkiVL6vfff0/WeD179tSrr76qhg0bpkm99mRzcLp27Zq6deumtWvXJro9Li4uxUUBAAAASFuPQtP58+e1ZcsWixmizz//XHfv3pUk5cyZM9ljbtmyRatWrdLkyZMlSYZhKD4+Xjly5NDcuXPVvXv31D2JdGRzcBowYIBu3bqlPXv2yN/fX8uXL9eVK1f04YcfasqUKWlRIwAAAIBU9Cg0nTp1Slu3blXBggUtthctWvSJxt29e7fFRMrKlSs1ceJE7dq164nHzChsDk5btmzRypUrVatWLTk4OKhkyZJq0qSJ8ubNq+DgYLVs2TIt6gQAAACQTHfu3NHp06fNr8+ePavw8HC5u7urSJEievHFF7V//36tXr1acXFxioiIkCS5u7vLyckpyXFPnz6tO3fuKCIiQnfv3jU/x6lixYpycnKSr6/lQ6F/++03OTg4qHLlyql/kunM5uAUHR1tfl5TgQIFdO3aNT311FOqUqWK9u/fn+oFAgAAABnNkzyUNj399ttvaty4sfl1UFCQJKlLly4aPXq0Vq1aJUmqXr26xX5bt26Vv79/kuO+8cYb2rZtm/l1jRo1JP0TzHx8fFKn+AzK5uBUvnx5nThxQj4+PqpWrZrmzJkjHx8fzZ49W0WKFEmLGgEAAADYwN/fX4ZhJLn9cdseJywszKb+Xbt2VdeuXZ/oWBmNzcGpf//+unz5siRp1KhRatasmRYtWiQnJyeFhoamdn0AAAAAYHc2B6fXXnvN/Gs/Pz+dP39ex48fV4kSJeTh4ZGqxQEAAABARpDip9jmypVLNWvWlJubm3nZQQAAAADISmwKTteuXdPq1au1YcMG8zKDDx480PTp0+Xj46MJEyakSZEAAAAAYE/JvlRv586datWqlaKiomQymVSrVi3Nnz9f7dq1U44cOTR69Gh16dIlLWsFAAAAALtI9ozTiBEj1KJFCx06dEhBQUH69ddf9fzzz2v8+PE6evSoevfuLVdX17SsFQAAAADsItnB6fDhwxoxYoQqV66ssWPHymQy6eOPP9aLL76YlvUBAAAAgN0lOzjdvHnTvGqeq6urcuXKlSWeAAwAAAAA1ti0HPnRo0cVEREh6Z+HZp04cULR0dEWfapWrZp61QEAAABABmBTcHruuecsnjLcqlUrSZLJZJJhGDKZTObV9gAAAAAgq0h2cDp79mxa1gEAAABkGqNHj87Qx9q+fbsmTZqkffv26fLly1q+fLnatWuXaN/evXtrzpw5mjZtmgYMGPDYcfv166eff/5ZR44cka+vr8LDwxP0MQxDU6ZM0dy5c3X+/Hl5eHjo7bff1vvvv2/zeWQkyQ5OJUuWTMs6AAAAAKSS6OhoVatWTd27d9cLL7yQZL/ly5frl19+kbe3d7LH7t69u/bs2aNDhw4lur1///7asGGDJk+erCpVqujGjRu6ceOGzeeQ0dh0qR4AAACAjK958+Zq3rz5Y/tcvHhR77zzjtavX6+WLVsma9xPPvlEknTt2rVEg9OxY8c0a9YsHTlyROXLl5cklSpVysbqM6Zkr6oHAAAAIGuIj4/X66+/rsGDB6tSpUqpNu6PP/6o0qVLa/Xq1SpVqpR8fHz0xhtvZIkZJ4ITAAAAkM1MnDhROXLkUL9+/VJ13D/++EPnz5/X999/r4ULFyo0NFT79u3LEs9+5VI9AAAAIBvZt2+fpk+frv3798tkMiXap3nz5tqxY4ekf9Y6+P3335M1dnx8vO7fv6+FCxfqqaeekiR98cUX8vPz04kTJ8yX72VGBCcAAAAgG9mxY4euXr2qEiVKmNvi4uL07rvvKiQkROfOndPnn3+uu3fvSpJy5syZ7LGLFCmiHDlymEOTJPn6+kqSLly4kL2C05UrVzRo0CBt3rxZV69etXiukySe4wQAAABkYK+//roCAgIs2gIDA/X666+rW7dukqSiRYs+0dj169fXw4cPdebMGZUpU0aSdPLkSUmZf5Vum4NT165ddeHCBX3wwQcqUqRIktN7AAAAAOzjzp07On36tPn12bNnFR4eLnd3d5UoUUIFCxa06J8zZ04VLlzY6ozQ6dOndefOHUVEROju3bvm5zhVrFhRTk5OCggIUM2aNdW9e3eFhIQoPj5effr0UZMmTSxmoTIjm4PTzp07tWPHDlWvXj0NygEAAAAyvvR8AO6T+O2339S4cWPz66CgIElSly5dFBoa+sTjvvHGG9q2bZv5dY0aNST9E8x8fHzk4OCgH3/8Ue+8844aNmyo3Llzq3nz5poyZcoTHzOjsDk4FS9ePMHleQAAAAAyDn9/f5v+zX7u3Llk9QsLC7Pax9vbW0uXLk32sTMLm5cjDwkJ0dChQ5P95gIAAABAZmdzcOrQoYPCwsJUpkwZ5cmTR+7u7hY/tpo5c6Z8fHzk4uKiOnXqaO/evY/tf+vWLfXp00dFihSRs7OznnrqKf300082HxcAAAAAksvmS/VCQkJS7eBLlixRUFCQZs+erTp16igkJESBgYE6ceKEPD09E/SPjY1VkyZN5OnpqR9++EFFixbV+fPnlT9//lSrCQAAAAD+y+bg1KVLl1Q7+NSpU9WzZ0/zsoezZ8/WmjVr9OWXX2ro0KEJ+n/55Ze6ceOGdu3aZV5P3sfHJ9XqAQAAAIDEPNEDcOPi4rRixQodO3ZMklSpUiW1adNGjo6OyR4jNjZW+/bt07Bhw8xtDg4OCggI0O7duxPdZ9WqVapbt6769OmjlStXqlChQnr11Vc1ZMiQJI99//593b9/3/w6Kioq2TUCAAAAgPQEwen06dNq0aKFLl68aF7nPTg4WMWLF9eaNWvMD7qy5vr164qLi5OXl5dFu5eXl44fP57oPn/88Ye2bNmiTp066aefftLp06f19ttv68GDBxo1alSi+wQHB2vMmDE2nCEAAAAAWLJ5cYh+/fqpTJky+vPPP7V//37t379fFy5cUKlSpdSvX7+0qNEsPj5enp6emjt3rvz8/NShQwe9//77mj17dpL7DBs2TJGRkeafP//8M01rBAAAAJD12DzjtG3bNv3yyy8WK+gVLFhQEyZMUP369ZM9joeHhxwdHXXlyhWL9itXrqhw4cKJ7lOkSBHlzJnT4rI8X19fRUREKDY2Vk5OTgn2cXZ2lrOzc7LrAgAAAID/snnGydnZWbdv307QfufOnUSDS1KcnJzk5+enzZs3m9vi4+O1efNm1a1bN9F96tevr9OnTys+Pt7cdvLkSRUpUsSmYwMAAACALWwOTq1atVKvXr20Z88eGYYhwzD0yy+/qHfv3mrTpo1NYwUFBWnevHlasGCBjh07prfeekvR0dHmVfY6d+5ssXjEW2+9pRs3bqh///46efKk1qxZo/Hjx6tPnz62ngYAAACQZW3fvl2tW7eWt7e3TCaTVqxYkaDPsWPH1KZNG+XLl0+5c+fW008/rQsXLjx23I8++kj16tVTrly5knwk0K+//qrnnntO+fPnV4ECBRQYGKiDBw+mwlnZl82X6n3yySfq0qWL6tata14S/OHDh2rTpo2mT59u01gdOnTQtWvXNHLkSEVERKh69epat26decGICxcuyMHh/7Jd8eLFtX79eg0cOFBVq1ZV0aJF1b9/fw0ZMsTW0wAAAACe2OYtyVsQLTU89+wZm/eJjo5WtWrV1L17d73wwgsJtp85c0bPPPOMevTooTFjxihv3rz6/fff5eLi8thxY2Nj9dJLL6lu3br64osvEmy/c+eOmjVrpjZt2uizzz7Tw4cPNWrUKAUGBurPP/8054fMyObglD9/fq1cuVKnTp0yr37n6+ursmXLPlEBffv2Vd++fRPdFhYWlqCtbt26+uWXX57oWAAAAEB20Lx5czVv3jzJ7e+//75atGihjz/+2NyWnNWxH61WHRoamuj248eP68aNGxo7dqyKFy8uSRo1apSqVq2q8+fPP3FmyAhsvlTvkXLlyql169Zq3bp1pn4DAAAAgOwkPj5ea9as0VNPPaXAwEB5enqqTp06iV7OZ6vy5curYMGC+uKLLxQbG6u7d+/qiy++kK+vr3x8fFI8vj0la8YpKChI48aNU+7cuRUUFPTYvlOnTk2VwgAAAACkvqtXr+rOnTuaMGGCPvzwQ02cOFHr1q3TCy+8oK1bt6pRo0ZPPHaePHkUFhamdu3aady4cZL+mXBZv369cuSw+WK3DCVZ1R84cEAPHjww/xoAAABA5vRoheq2bdtq4MCBkqTq1atr165dmj17tho1aqTevXvr66+/Nu9z586dZI199+5d9ejRQ/Xr19c333yjuLg4TZ48WS1bttSvv/4qV1fX1D+hdJKs4LR169ZEfw0AAAAgc/Hw8FCOHDlUsWJFi3ZfX1/t3LlTkjR27FgNGjTI5rEXL16sc+fOaffu3eZF3hYvXqwCBQpo5cqVeuWVV1J+AnZi8z1O3bt3T/Q5TtHR0erevXuqFAUAAAAgbTg5Oenpp5/WiRMnLNpPnjypkiVLSpI8PT1VtmxZ809yxcTEyMHBQSaTydz26PW/n8WaGdkcnBYsWKC7d+8maL97964WLlyYKkUBAAAAeHJ37txReHi4wsPDJUlnz55VeHi4+TlNgwcP1pIlSzRv3jydPn1an376qX788Ue9/fbbjx33woUL5nHi4uLMx3h0KV+TJk108+ZN9enTR8eOHdPvv/+ubt26KUeOHGrcuHGannNaS/YdWlFRUeYH3t6+fdtijfe4uDj99NNP8vT0TJMiAQAAACTfb7/9ZhFUHi3w1qVLF4WGhur555/X7NmzFRwcrH79+ql8+fJaunSpnnnmmceOO3LkSC1YsMD8ukaNGpL+uZ3H399fFSpU0I8//qgxY8aobt26cnBwUI0aNbRu3ToVKVIkDc40/SQ7OOXPn18mk0kmk0lPPfVUgu0mk8m8rjsAAACQlT3JQ2nTk7+/vwzDeGyf7t2723yrTWhoaJLPcHqkSZMmatKkiU3jZgbJDk5bt26VYRh69tlntXTpUrm7u5u3OTk5qWTJkvL29k6TIgEAAADAnpIdnB6t53727FkVL17cvEoGAAAAAGR1Nj+F6tFKGzExMbpw4YJiY2MttletWjV1KgMAAACADMLm4HTt2jV169ZNa9euTXR7XFxciosCAAAAgIzE5uvtBgwYoFu3bmnPnj1ydXXVunXrtGDBApUrV06rVq1KixoBAAAAwK5snnHasmWLVq5cqVq1asnBwUElS5ZUkyZNlDdvXgUHB6tly5ZpUScAAAAA2I3NM07R0dHm5zUVKFBA165dkyRVqVJF+/fvT93qAAAAACADsDk4lS9fXidOnJAkVatWTXPmzNHFixc1e/bsTP9QKwAAAABIjM2X6vXv31+XL1+WJI0aNUrNmjXTokWL5OTkZPVhWAAAAACQGdkcnF577TXzr/38/HT+/HkdP35cJUqUkIeHR6oWBwAAAAAZQYqfYpsrVy7VrFmT0AQAAABkENu3b1fr1q3l7e0tk8mkFStWWGy/c+eO+vbtq2LFisnV1VUVK1bU7NmzHzvmuXPn1KNHD5UqVUqurq4qU6aMRo0aleC5rocOHVKDBg3k4uKi4sWL6+OPP07t07OLZM04BQUFJXvAqVOnPnExAAAAQGZQeGt4uh0ronF1m/eJjo5WtWrV1L17d73wwgsJtgcFBWnLli36+uuv5ePjow0bNujtt9+Wt7e32rRpk+iYx48fV3x8vObMmaOyZcvqyJEj6tmzp6KjozV58mRJUlRUlJo2baqAgADNnj1bhw8fVvfu3ZU/f3716tXL5vPISJIVnA4cOJCswUwmU4qKAQAAAJByzZs3V/PmzZPcvmvXLnXp0kX+/v6SpF69emnOnDnau3dvksGpWbNmatasmfl16dKldeLECc2aNcscnBYtWqTY2Fh9+eWXcnJyUqVKlRQeHq6pU6dmj+C0devWtK4DAAAAQDqpV6+eVq1ape7du8vb21thYWE6efKkpk2bZtM4kZGRcnd3N7/evXu3GjZsKCcnJ3NbYGCgJk6cqJs3b6pAgQKpdg7pLcX3OAEAAADIXGbMmKGKFSuqWLFicnJyUrNmzTRz5kw1bNgw2WOcPn1aM2bM0Jtvvmlui4iIkJeXl0W/R68jIiJSp3g7SdaMU2LXRSZl2bJlT1wMAAAAgLQ3Y8YM/fLLL1q1apVKliyp7du3q0+fPvL29lZAQIB69+6tr7/+2tz/zp07FvtfvHhRzZo100svvaSePXumd/l2kazglC9fvrSuAwAAAEA6uHv3roYPH67ly5erZcuWkqSqVasqPDxckydPVkBAgMaOHatBgwYluv+lS5fUuHFj1atXT3PnzrXYVrhwYV25csWi7dHrwoULp8HZpJ9kBaf58+endR0AAAAA0sGDBw/04MEDOThY3rXj6Oio+Ph4SZKnp6c8PT0T7Hvx4kU1btxYfn5+mj9/foIx6tatq/fff18PHjxQzpw5JUkbN25U+fLlM/X9TdIT3uP08OFDbdq0SXPmzNHt27cl/ZM8/zuFBwAAACD93blzR+Hh4QoPD5cknT17VuHh4bpw4YLy5s2rRo0aafDgwQoLC9PZs2cVGhqqhQsX6vnnn09yzIsXL8rf318lSpTQ5MmTde3aNUVERFjcu/Tqq6/KyclJPXr00O+//64lS5Zo+vTpNj3eKKNK1ozTv50/f17NmjXThQsXdP/+fTVp0kR58uTRxIkTdf/+fasPzgIAAACQtn777Tc1btzY/PpRcOnSpYtCQ0P17bffatiwYerUqZNu3LihkiVL6qOPPlLv3r2THHPjxo06ffq0Tp8+rWLFillsMwxD0j+3+GzYsEF9+vSRn5+fPDw8NHLkyEy/FLn0BMGpf//+qlWrlg4ePKiCBQua259//vlsc2MYAAAAsrcneShtevL39zeHmcQULlzY5ttxunbtqq5du1rtV7VqVe3YscOmsTMDm4PTjh07tGvXLou12SXJx8dHFy9eTLXCAAAAACCjsPkep/j4eMXFxSVo/+uvv5QnT55UKQoAAAAAMhKbg1PTpk0VEhJifm0ymXTnzh2NGjVKLVq0SM3aAAAAACBDsPlSvSlTpigwMFAVK1bUvXv39Oqrr+rUqVPy8PDQN998kxY1AgAAAIBd2RycihUrpoMHD2rJkiU6ePCg7ty5ox49eqhTp05ydXVNixoBAAAAwK5sDk6SlCNHDnXq1EmdOnVK7XoAAAAAIMOx+R6n4OBgffnllwnav/zyS02cODFVigIAAACAjMTm4DRnzhxVqFAhQXulSpV4+C0AAACALMnmS/UiIiJUpEiRBO2FChXS5cuXU6UoAACA9FBlQZUU7f9dKtUBIOOzecapePHi+vnnnxO0//zzz/L29k6VogAAAAAgI7E5OPXs2VMDBgzQ/Pnzdf78eZ0/f15ffvmlBg4cqJ49e6ZFjQAAAACSKTg4WE8//bTy5MkjT09PtWvXTidOnLDoc+/ePfXp00cFCxaUm5ub2rdvrytXrjx23LCwMLVt21ZFihRR7ty5Vb16dS1atMiiz7x589SgQQMVKFBABQoUUEBAgPbu3Zvq52gPNl+qN3jwYP399996++23FRsbK0lycXHRkCFDNGzYsFQvEAAAAMhofIauSbdjnZvQ0qb+27ZtU58+ffT000/r4cOHGj58uJo2baqjR48qd+7ckqSBAwdqzZo1+v7775UvXz717dtXL7zwQqJXlj2ya9cuVa1aVUOGDJGXl5dWr16tzp07K1++fGrVqpWkf8JVx44dVa9ePbm4uGjixIlq2rSpfv/9dxUtWvTJ34QMwObgZDKZNHHiRH3wwQc6duyYXF1dVa5cOTk7O6dFfQAAAABssG7dOovXoaGh8vT01L59+9SwYUNFRkbqiy++0OLFi/Xss89KkubPny9fX1/98ssv+t///pfouMOHD7d43b9/f23YsEHLli0zB6f/zkB9/vnnWrp0qTZv3qzOnTun1inahc2X6j3i5uamp59+WpUrVyY0AQAAABlUZGSkJMnd3V2StG/fPj148EABAQHmPhUqVFCJEiW0e/dum8d+NG5iYmJi9ODBg8f2ySxsnnGKjo7WhAkTtHnzZl29elXx8fEW2//4449UKw4AAADAk4uPj9eAAQNUv359Va5cWdI/q2Q7OTkpf/78Fn29vLwUERGR7LG/++47/frrr5ozZ06SfYYMGSJvb2+LkJZZ2Ryc3njjDW3btk2vv/66ihQpIpPJlBZ1AQAAAEihPn366MiRI9q5c6dN+1WqVEnnz5+XJDVo0EBr16612L5161Z169ZN8+bNU6VKlRIdY8KECfr2228VFhYmFxeXJzuBDMTm4LR27VqtWbNG9evXT4t6AAAAAKSCvn37avXq1dq+fbuKFStmbi9cuLBiY2N169Yti1mnK1euqHDhwpKkn376SQ8ePJAkubq6Woy7bds2tW7dWtOmTUvyvqXJkydrwoQJ2rRpk6pWrZrKZ2YfNgenAgUKZIlrFAEAAICsyDAMvfPOO1q+fLnCwsJUqlQpi+1+fn7KmTOnNm/erPbt20uSTpw4oQsXLqhu3bqSpJIlSyY6dlhYmFq1aqWJEyeqV69eifb5+OOP9dFHH2n9+vWqVatWKp6ZfdkcnMaNG6eRI0dqwYIFypUrV1rUBAAAAOAJ9enTR4sXL9bKlSuVJ08e831L+fLlk6urq/Lly6cePXooKChI7u7uyps3r9555x3VrVs3yRX1pH8uz2vVqpX69++v9u3bm8d1cnIyT6xMnDhRI0eO1OLFi+Xj42Pu4+bmJjc3tzQ+87Rlc3CaMmWKzpw5Iy8vL/n4+ChnzpwW2/fv359qxQEAAACwzaxZsyRJ/v7+Fu3z589X165dJUnTpk2Tg4OD2rdvr/v37yswMFCfffbZY8ddsGCBYmJiFBwcrODgYHN7o0aNFBYWZj52bGysXnzxRYt9R40apdGjR6fovOzN5uDUrl27NCgDAAAAyDxsfShtejIMw2ofFxcXzZw5UzNnzkz2uKGhoQoNDX1sn3PnziV7vMzG5uA0atSotKgDAAAAADKsJ34ALgAAAABkFzbPOMXFxWnatGn67rvvdOHCBcXGxlpsv3HjRqoVBwAAAAAZgc0zTmPGjNHUqVPVoUMHRUZGKigoSC+88IIcHBwy/Q1fAAAAAJAYm4PTokWLNG/ePL377rvKkSOHOnbsqM8//1wjR47UL7/8khY1AgAAAIBd2RycIiIiVKVKFUn/rMceGRkpSWrVqpXWrFmTutUBAAAAQAZgc3AqVqyYLl++LEkqU6aMNmzYIEn69ddf5ezsnLrVAQAAAEAGYHNwev7557V582ZJ0jvvvKMPPvhA5cqVU+fOndW9e/dULxAAAAAA7M3mVfUmTJhg/nWHDh1UsmRJ7dq1S+XKlVPr1q1TtTgAAAAAyAhsDk7/9b///U//+9//UqMWAAAAAMiQkh2c9u3bp0GDBmnlypXKmzevxbbIyEi1a9dOISEhqlatWqoXCQAAAGQoo/Ol47EibeoeHBysZcuW6fjx43J1dVW9evU0ceJElS9fXtI/z10dNWqUNmzYoAsXLqhQoUJq166dxo0bp3z5kj6vsLAwTZs2TXv37lVUVJTKlSunwYMHq1OnTon2//bbb9WxY0e1bdtWK1assOkcMqJk3+M0ZcoUPfvsswlCkyTly5dPAQEBmjRpUqoWBwAAAMA227ZtU58+ffTLL79o48aNevDggZo2baro6GhJ0qVLl3Tp0iVNnjxZR44cUWhoqNatW6cePXo8dtxdu3apatWqWrp0qQ4dOqRu3bqpc+fOWr16dYK+586d06BBg9SgQYM0OUd7SPaM0549ezR06NAkt7dp00ZffPFFqhQFAAAA4MmsW7fO4nVoaKg8PT21b98+NWzYUJUrV9bSpUvN28uUKaOPPvpIr732mh4+fKgcORKPCMOHD7d43b9/f23YsEHLli1Tq1atzO1xcXHq1KmTxowZox07dujWrVupd3J2lOwZp4sXLypPnjxJbndzczMvUw4AAAAgY3j03FV3d/fH9smbN2+Soelx+/133LFjx8rT09PqDFZmk+x3plChQjpx4oRKlSqV6Pbjx4/Lw8Mj1QoDAAAAkDLx8fEaMGCA6tevr8qVKyfa5/r16xo3bpx69epl09jfffedfv31V82ZM8fctnPnTn3xxRcKDw9PSdkZUrJnnAICAvTRRx8lus0wDH300UcKCAhItcIAAAAApEyfPn105MgRffvtt4luj4qKUsuWLVWxYkWNHj3a3F6pUiW5ubnJzc1NzZs3T7Df1q1b1a1bN82bN0+VKlWSJN2+fVuvv/665s2blyUnVJI94zRixAj5+fmpTp06evfdd82rchw/flxTpkzRyZMnFRoamlZ1AgAAALBB3759tXr1am3fvl3FihVLsP327dtq1qyZ8uTJo+XLlytnzpzmbT/99JMePHggSXJ1dbXYb9u2bWrdurWmTZumzp07m9vPnDmjc+fOWTzbNT4+XpKUI0cOnThxQmXKlEnVc0xPyQ5OZcqU0aZNm9S1a1e98sorMplMkv6ZbapYsaI2btyosmXLplmhAAAAAKwzDEPvvPOOli9frrCwsERvtYmKilJgYKCcnZ21atUqubi4WGwvWbJkomOHhYWpVatWmjhxYoJL+ypUqKDDhw9btI0YMUK3b9/W9OnTVbx48RSemX3ZdPdXrVq1dOTIEYWHh+vUqVMyDENPPfWUqlevnkblAQAAALBFnz59tHjxYq1cuVJ58uRRRESEpH8eIeTq6qqoqCg1bdpUMTEx+vrrrxUVFaWoqChJ/6xr4OjomOi4W7duVatWrdS/f3+1b9/ePK6Tk5Pc3d3l4uKS4D6q/PnzS1KS91dlJrYtm/H/Va9enbAEAAAAZECzZs2SJPn7+1u0z58/X127dtX+/fu1Z88eSUpwxdjZs2fl4+OT6LgLFixQTEyMgoODFRwcbG5v1KiRwsLCUq3+jOqJghMAAACQrY2OtHcFSTIM47Hb/f39rfZJTGhoqM1rGmSlNRCSvaoeAAAAAGRXBCcAAAAAsMKm4PTw4UONHTtWf/31V1rVAwAAAAAZjk3BKUeOHJo0aZIePnyYVvUAAAAAQIZj86V6zz77rLZt25YWtQAAAABAhmTzqnrNmzfX0KFDdfjwYfn5+Sl37twW29u0aZNqxQEAAADWPMkKccg+UuvzYXNwevvttyVJU6dOTbDNZDIpLi7O5iJmzpypSZMmKSIiQtWqVdOMGTNUu3Ztq/t9++236tixo9q2basVK1bYfFwAAABkXjlz5pQkxcTEyNXV1c7VIKOKjY2VpCQf7JtcNgen+Pj4FB3wv5YsWaKgoCDNnj1bderUUUhIiAIDA3XixAl5enomud+5c+c0aNAgNWjQIFXrAQAAQObg6Oio/Pnz6+rVq5KkXLlyyWQy2bkqZCTx8fG6du2acuXKpRw5UvYI2xTtfe/ePbm4uKSogKlTp6pnz57q1q2bJGn27Nlas2aNvvzySw0dOjTRfeLi4tSpUyeNGTNGO3bs0K1bt1JUAwAAADKnwoULS5I5PAH/5eDgoBIlSqQ4VNscnOLi4jR+/HjNnj1bV65c0cmTJ1W6dGl98MEH8vHxUY8ePZI9VmxsrPbt26dhw4aZ2xwcHBQQEKDdu3cnud/YsWPl6empHj16aMeOHY89xv3793X//n3z66ioqGTXBwAAgIzNZDKpSJEi8vT01IMHD+xdDjIgJycnOTik/PG1Ngenjz76SAsWLNDHH3+snj17mtsrV66skJAQm4LT9evXFRcXJy8vL4t2Ly8vHT9+PNF9du7cqS+++ELh4eHJOkZwcLDGjBmT7JoAAACQ+Tg6Oqb4HhbgcWyOXgsXLtTcuXPVqVMniw9ntWrVkgw7qeX27dt6/fXXNW/ePHl4eCRrn2HDhikyMtL88+eff6ZpjQAAAACyHptnnC5evKiyZcsmaI+Pj7d5etTDw0OOjo66cuWKRfuVK1fM16v+25kzZ3Tu3Dm1bt3a4rjSPw/nPXHihMqUKWOxj7Ozs5ydnW2qCwAAAAD+zeYZp4oVKyZ6X9EPP/ygGjVq2DSWk5OT/Pz8tHnzZnNbfHy8Nm/erLp16yboX6FCBR0+fFjh4eHmnzZt2qhx48YKDw9X8eLFbT0dAAAAALDK5hmnkSNHqkuXLrp48aLi4+O1bNkynThxQgsXLtTq1attLiAoKEhdunRRrVq1VLt2bYWEhCg6Otq8yl7nzp1VtGhRBQcHy8XFRZUrV7bYP3/+/JKUoB0AAAAAUovNwalt27b68ccfNXbsWOXOnVsjR45UzZo19eOPP6pJkyY2F9ChQwddu3ZNI0eOVEREhKpXr65169aZF4y4cOFCqqyCAQAAAABP6ome49SgQQNt3Lgx1Yro27ev+vbtm+i2sLCwx+4bGhqaanUAAAAAQGKYygEAAAAAK5I141SgQIFkP2n3xo0bKSoIAAAAADKaZAWnkJAQ86///vtvffjhhwoMDDSvfLd7926tX79eH3zwQZoUCQAAAAD2lKzg1KVLF/Ov27dvr7Fjx1rck9SvXz99+umn2rRpkwYOHJj6VQIAAACAHdl8j9P69evVrFmzBO3NmjXTpk2bUqUoAAAAAMhIbA5OBQsW1MqVKxO0r1y5UgULFkyVogAAAAAgI7F5OfIxY8bojTfeUFhYmOrUqSNJ2rNnj9atW6d58+aleoEAAAAAYG82B6euXbvK19dXn3zyiZYtWyZJ8vX11c6dO81BCgAAAACyEpuC04MHD/Tmm2/qgw8+0KJFi9KqJgAAAADIUGy6xylnzpxaunRpWtUCAAAAABmSzYtDtGvXTitWrEiDUgAAAAAgY7L5Hqdy5cpp7Nix+vnnn+Xn56fcuXNbbO/Xr1+qFQcAAAAAGYHNwemLL75Q/vz5tW/fPu3bt89im8lkIjgBAID0MzpfyvYvVSJ16gCQ5dkcnM6ePZsWdQAAAABAhmXzPU6PXL9+XdevX0/NWgAAAAAgQ7IpON26dUt9+vSRh4eHvLy85OXlJQ8PD/Xt21e3bt1KoxIBAAAAwL6SfanejRs3VLduXV28eFGdOnWSr6+vJOno0aMKDQ3V5s2btWvXLhUoUCDNigUAAAAAe0h2cBo7dqycnJx05swZeXl5JdjWtGlTjR07VtOmTUv1IgEAAADAnpJ9qd6KFSs0efLkBKFJkgoXLqyPP/5Yy5cvT9XiAAAAACAjSHZwunz5sipVqpTk9sqVKysiIiJVigIAAACAjCTZwcnDw0Pnzp1LcvvZs2fl7u6eGjUBAAAAQIaS7OAUGBio999/X7GxsQm23b9/Xx988IGaNWuWqsUBAAAAQEZg0+IQtWrVUrly5dSnTx9VqFBBhmHo2LFj+uyzz3T//n199dVXaVkrAAAAANhFsoNTsWLFtHv3br399tsaNmyYDMOQJJlMJjVp0kSffvqpihcvnmaFAgAAAIC9JDs4SVKpUqW0du1a3bx5U6dOnZIklS1blnubAAAAAGRpNgWnRwoUKKDatWundi0AAAAAkCEle3EIAAAAAMiuCE4AAAAAYAXBCQAAAACsIDgBAAAAgBUEJwAAAACwguAEAAAAAFYQnAAAAADACoITAAAAAFhBcAIAAAAAKwhOAAAAAGAFwQkAAAAArCA4AQAAAIAVBCcAAAAAsILgBAAAAABWEJwAAAAAwAqCEwAAAABYQXACAAAAACsITgAAAABgRQ57FwBkVscq+KZof9/jx1KpEgAAAKQ1ZpwAAAAAwAqCEwAAAABYQXACAAAAACsITgAAAABgBcEJAAAAAKxgVT1kTqPzpXD/yNSpAwAAANkCM04AAAAAYAXBCQAAAACsIDgBAAAAgBUEJwAAAACwguAEAAAAAFYQnAAAAADACoITAAAAAFhBcAIAAAAAKwhOAAAAAGAFwQkAAAAArCA4AQAAAIAVBCcAAAAAsILgBAAAAABWEJwAAAAAwAqCEwAAAABYkcPeBQD2UGVBlRSP8V0q1AEAAIDMgRknAAAAALCC4AQAAAAAVhCcAAAAAMAKghMAAAAAWEFwAgAAAAArCE4AAAAAYAXBCQAAAACsIDgBAAAAgBUEJwAAAACwguAEAAAAAFZkiOA0c+ZM+fj4yMXFRXXq1NHevXuT7Dtv3jw1aNBABQoUUIECBRQQEPDY/gAAAACQUnYPTkuWLFFQUJBGjRql/fv3q1q1agoMDNTVq1cT7R8WFqaOHTtq69at2r17t4oXL66mTZvq4sWL6Vw5AAAAgOzC7sFp6tSp6tmzp7p166aKFStq9uzZypUrl7788stE+y9atEhvv/22qlevrgoVKujzzz9XfHy8Nm/enM6VAwAAAMgu7BqcYmNjtW/fPgUEBJjbHBwcFBAQoN27dydrjJiYGD148EDu7u6Jbr9//76ioqIsfgAAAADAFnYNTtevX1dcXJy8vLws2r28vBQREZGsMYYMGSJvb2+L8PVvwcHBypcvn/mnePHiKa4bAAAAQPZi90v1UmLChAn69ttvtXz5crm4uCTaZ9iwYYqMjDT//Pnnn+lcJQAAAIDMLoc9D+7h4SFHR0dduXLFov3KlSsqXLjwY/edPHmyJkyYoE2bNqlq1apJ9nN2dpazs3Oq1AsAAAAge7LrjJOTk5P8/PwsFnZ4tNBD3bp1k9zv448/1rhx47Ru3TrVqlUrPUoFAAAAkI3ZdcZJkoKCgtSlSxfVqlVLtWvXVkhIiKKjo9WtWzdJUufOnVW0aFEFBwdLkiZOnKiRI0dq8eLF8vHxMd8L5ebmJjc3N7udBwAAAICsy+7BqUOHDrp27ZpGjhypiIgIVa9eXevWrTMvGHHhwgU5OPzfxNisWbMUGxurF1980WKcUaNGafTo0elZOgAAAIBswu7BSZL69u2rvn37JrotLCzM4vW5c+fSviAAAAAA+JdMvaoeAAAAAKQHghMAAAAAWEFwAgAAAAArCE4AAAAAYAXBCQAAAACsIDgBAAAAgBUEJwAAAACwguAEAAAAAFZkiAfgIvvxGbomRfufc0mlQgAAAIBkYMYJAAAAAKwgOAEAAACAFQQnAAAAALCC4AQAAAAAVhCcAAAAAMAKghMAAAAAWEFwAgAAAAArCE4AAAAAYAXBCQAAAACsIDgBAAAAgBUEJwAAAACwguAEAAAAAFYQnAAAAADACoITAAAAAFhBcAIAAAAAKwhOAAAAAGAFwQkAAAAArCA4AQAAAIAVBCcAAAAAsILgBAAAAABWEJwAAAAAwAqCEwAAAABYQXACAAAAACsITgAAAABgBcEJAAAAAKwgOAEAAACAFQQnAAAAALCC4AQAAAAAVhCcAAAAAMAKghMAAAAAWEFwAgAAAAArCE4AAAAAYAXBCQAAAACsIDgBAAAAgBUEJwAAAACwguAEAAAAAFYQnAAAAADACoITAAAAAFhBcAIAAAAAKwhOAAAAAGAFwQkAAAAArCA4AQAAAIAVBCcAAAAAsILgBAAAAABWEJwAAAAAwAqCEwAAAABYQXACAAAAACsITgAAAABgBcEJAAAAAKwgOAEAAACAFQQnAAAAALCC4AQAAAAAVhCcAAAAAMAKghMAAAAAWEFwAgAAAAArCE4AAAAAYAXBCQAAAACsIDgBAAAAgBUEJwAAAACwguAEAAAAAFYQnAAAAADACoITAAAAAFhBcAIAAAAAKwhOAAAAAGAFwQkAAAAArCA4AQAAAIAVBCcAAAAAsILgBAAAAABWZIjgNHPmTPn4+MjFxUV16tTR3r17H9v/+++/V4UKFeTi4qIqVarop59+SqdKAQAAAGRHdg9OS5YsUVBQkEaNGqX9+/erWrVqCgwM1NWrVxPtv2vXLnXs2FE9evTQgQMH1K5dO7Vr105HjhxJ58oBAAAAZBd2D05Tp05Vz5491a1bN1WsWFGzZ89Wrly59OWXXybaf/r06WrWrJkGDx4sX19fjRs3TjVr1tSnn36azpUDAAAAyC5y2PPgsbGx2rdvn4YNG2Zuc3BwUEBAgHbv3p3oPrt371ZQUJBFW2BgoFasWJFo//v37+v+/fvm15GRkZKkqKioFFaPlIi/H5Oi/aNMRor2j7sbl6L9JelOXMrG4DMIAKngvn2/D+z9XXDvwYMU7Z8aNfz731n2OD6QEo8+f4Zh/e8Suwan69evKy4uTl5eXhbtXl5eOn78eKL7REREJNo/IiIi0f7BwcEaM2ZMgvbixYs/YdXICPKleIRjKR6hdkoHyJfyswAApFTKvg+ywnfBiOX2rWHChAl2PT4gSbdv31Y+K38e7Rqc0sOwYcMsZqji4+N148YNFSxYUCaTyY6VAfYTFRWl4sWL688//1TevHntXQ4AwE74PkB2ZxiGbt++LW9vb6t97RqcPDw85OjoqCtXrli0X7lyRYULF050n8KFC9vU39nZWc7OzhZt+fPnf/KigSwkb968fFECAPg+QLZmbabpEbsuDuHk5CQ/Pz9t3rzZ3BYfH6/Nmzerbt26ie5Tt25di/6StHHjxiT7AwAAAEBK2f1SvaCgIHXp0kW1atVS7dq1FRISoujoaHXr1k2S1LlzZxUtWlTBwcGSpP79+6tRo0aaMmWKWrZsqW+//Va//fab5s6da8/TAAAAAJCF2T04dejQQdeuXdPIkSMVERGh6tWra926deYFIC5cuCAHh/+bGKtXr54WL16sESNGaPjw4SpXrpxWrFihypUr2+sUgEzH2dlZo0aNSnAZKwAge+H7AEg+k5GctfcAAAAAIBuz+wNwAQAAACCjIzgBAAAAgBUEJwAAAACwguAEAAAAAFYQnAAAAADACrsvRw4gbRw6dCjZfatWrZqGlQAA7InvAyB1sBw5kEU5ODjIZDIpqT/ij7aZTCbFxcWlc3UAgPTC9wGQOphxArKos2fP2rsEAEAGwPcBkDqYcQIAAAAAK1gcAsgmvvrqK9WvX1/e3t46f/68JCkkJEQrV660c2UAgPTE9wHwZAhOQDYwa9YsBQUFqUWLFrp165b5Gvb8+fMrJCTEvsUBANIN3wfAkyM4AdnAjBkzNG/ePL3//vtydHQ0t9eqVUuHDx+2Y2UAgPTE9wHw5AhOQDZw9uxZ1ahRI0G7s7OzoqOj7VARAMAe+D4AnhzBCcgGSpUqpfDw8ATt69atk6+vb/oXBACwC74PgCfHcuRANhAUFKQ+ffro3r17MgxDe/fu1TfffKPg4GB9/vnn9i4PAJBO+D4AnhzLkQPZxKJFizR69GidOXNGkuTt7a0xY8aoR48edq4MAJCe+D4AngzBCchmYmJidOfOHXl6etq7FACAHfF9ANiGe5yAbODZZ5/VrVu3JEm5cuUyf0lGRUXp2WeftWNlAID0xPcB8OSYcQKyAQcHB0VERCT4X8WrV6+qaNGievDggZ0qAwCkJ74PgCfH4hBAFnbo0CHzr48ePaqIiAjz67i4OK1bt05Fixa1R2kAgHTE9wGQcsw4AVmYg4ODTCaTJCmxP+qurq6aMWOGunfvnt6lAQDSEd8HQMoRnIAs7Pz58zIMQ6VLl9bevXtVqFAh8zYnJyd5enpaPDkeAJA18X0ApBzBCQAAAACs4B4nIJs4c+aMQkJCdOzYMUlSxYoV1b9/f5UpU8bOlQEA0hPfB8CTYTlyIBtYv369KlasqL1796pq1aqqWrWq9uzZo0qVKmnjxo32Lg8AkE74PgCeHJfqAdlAjRo1FBgYqAkTJli0Dx06VBs2bND+/fvtVBkAID3xfQA8OYITkA24uLjo8OHDKleunEX7yZMnVbVqVd27d89OlQEA0hPfB8CT41I9IBsoVKiQwsPDE7SHh4cneAgiACDr4vsAeHIsDgFkAz179lSvXr30xx9/qF69epKkn3/+WRMnTlRQUJCdqwMApBe+D4Anx6V6QDZgGIZCQkI0ZcoUXbp0SZLk7e2twYMHq1+/fuaHIgIAsja+D4AnR3ACspnbt29LkvLkyWPnSgAA9sT3AWAbghMAAAAAWME9TkAWVqNGjWRddsHyswCQtfF9AKQcwQnIwtq1a2f+tWEYCg4OVu/eveXu7m6/ogAA6Y7vAyDluFQPyEby5MmjgwcPqnTp0vYuBQBgR3wfALbjOU4AAAAAYAXBCQAAAACsIDgBAAAAgBUsDgFkYZ988onF64cPHyo0NFQeHh4W7f369UvPsgAA6YzvAyDlWBwCyMJKlSpltY/JZNIff/yRDtUAAOyF7wMg5QhOAAAAAGAF9zgBAAAAgBUEJwAAAACwguAEAAAAAFYQnAAAAADACoITAAAAAFhBcAKygUaNGmnhwoW6e/euvUsBANiRo6Ojrl69mqD977//lqOjox0qAjIPghOQDdSoUUODBg1S4cKF1bNnT/3yyy/2LgkAYAdJPYXm/v37cnJySudqgMyF5zgB2cTDhw+1atUqLViwQGvXrlXZsmXVvXt3vf766/Ly8rJ3eQCANPTJJ59IkgYOHKhx48bJzc3NvC0uLk7bt2/XuXPndODAAXuVCGR4BCcgG7p69armzp2rjz76SHFxcWrRooX69eunZ5991t6lAQDSQKlSpSRJ58+fV7FixSwuy3NycpKPj4/Gjh2rOnXq2KtEIMMjOAHZzN69ezV//nx9++23yps3r7p27aqLFy9q8eLFevvttzV58mR7lwgASCONGzfW8uXLlT9/fnuXAmQ6BCcgG7h69aq++uorzZ8/X6dOnVLr1q31xhtvKDAwUCaTSZK0c+dONWvWTHfu3LFztQCAtPDgwQNVqFBBq1evlq+vr73LATKdHPYuAEDaK1asmMqUKaPu3bura9euKlSoUII+VatW1dNPP22H6gAA6SFnzpy6d++evcsAMi1mnIBsYMeOHWrQoIG9ywAA2Nn48eN18uRJff7558qRg/8/B2xBcAKygWeffVbLli1LcE17VFSU2rVrpy1bttinMABAunr++ee1efNmubm5qUqVKsqdO7fF9mXLltmpMiDj478agGxg27Ztio2NTdB+79497dixww4VAQDsIX/+/Grfvr29ywAyJYITkIUdOnRI0j8PPDx69KgiIiLM2+Li4rRu3ToVLVrUXuUBANLZ/Pnz7V0CkGlxqR6QhTk4OJhXzUvsj7qrq6tmzJih7t27p3dpAAA7unbtmk6cOCFJKl++fKKLBgGwRHACsrDz58/LMAyVLl1ae/futfhidHJykqenp8VDEAEAWVt0dLTeeecdLVy4UPHx8ZIkR0dHde7cWTNmzFCuXLnsXCGQcRGcAAAAsok333xTmzZt0qeffqr69etL+uc5fv369VOTJk00a9YsO1cIZFwEJyCLWrVqlZo3b66cOXNq1apVj+3bpk2bdKoKAGBPHh4e+uGHH+Tv72/RvnXrVr388su6du2afQoDMgGCE5BFOTg4KCIiQp6ennJwcEiyn8lkUlxcXDpWBgCwl1y5cmnfvn3y9fW1aP/9999Vu3ZtRUdH26kyIOMjOAEAAGQTzz33nAoWLKiFCxfKxcVFknT37l116dJFN27c0KZNm+xcIZBxEZwAAACyiSNHjigwMFD3799XtWrVJEkHDx6Ui4uL1q9fr0qVKtm5QiDjSvr6HQCZ3pYtW1SxYkVFRUUl2BYZGalKlSpp+/btdqgMAGAPlStX1qlTpxQcHKzq1aurevXqmjBhgk6dOkVoAqxgxgnIwtq0aaPGjRtr4MCBiW7/5JNPtHXrVi1fvjydKwMAAMhcmHECsrCDBw+qWbNmSW5v2rSp9u3bl44VAQDsacGCBVqzZo359Xvvvaf8+fOrXr16On/+vB0rAzI+ghOQhV25ckU5c+ZMcnuOHDlYehYAspHx48fL1dVVkrR79259+umn+vjjj+Xh4ZHk1QkA/pHD3gUASDtFixbVkSNHVLZs2US3Hzp0SEWKFEnnqgAA9vLnn3+avxNWrFihF198Ub169VL9+vUTPNsJgCVmnIAsrEWLFvrggw907969BNvu3r2rUaNGqVWrVnaoDABgD25ubvr7778lSRs2bFCTJk0kSS4uLrp79649SwMyPBaHALKwK1euqGbNmnJ0dFTfvn1Vvnx5SdLx48c1c+ZMxcXFaf/+/fLy8rJzpQCA9NCpUycdP35cNWrU0DfffKMLFy6oYMGCWrVqlYYPH64jR47Yu0Qgw+JSPSAL8/Ly0q5du/TWW29p2LBhevT/JCaTSYGBgZo5cyahCQCykZkzZ2rEiBH6888/tXTpUhUsWFCStG/fPnXs2NHO1QEZGzNOQDZx8+ZNnT59WoZhqFy5cipQoIC9SwIAAMg0CE4AAADZyI4dOzRnzhz98ccf+v7771W0aFF99dVXKlWqlJ555hl7lwdkWCwOAQAAkE0sXbpUgYGBcnV11f79+3X//n1JUmRkpMaPH2/n6oCMjeAEAACQTXz44YeaPXu25s2bZ/Gcv/r162v//v12rAzI+AhOAAAA2cSJEyfUsGHDBO358uXTrVu30r8gIBMhOAEAAGQThQsX1unTpxO079y5U6VLl7ZDRUDmQXACAADIJnr27Kn+/ftrz549MplMunTpkhYtWqRBgwbprbfesnd5QIbGc5wAAACyiaFDhyo+Pl7PPfecYmJi1LBhQzk7O2vQoEF655137F0ekKGxHDkAAEA2Exsbq9OnT+vOnTuqWLGi3NzcdPfuXbm6utq7NCDD4lI9AACAbMbJyUkVK1ZU7dq1lTNnTk2dOlWlSpWyd1lAhkZwAgAAyOLu37+vYcOGqVatWqpXr55WrFghSZo/f75KlSqladOmaeDAgfYtEsjguFQPAAAgixsyZIjmzJmjgIAA7dq1S9euXVO3bt30yy+/aPjw4XrppZfk6Oho7zKBDI3FIQAAALK477//XgsXLlSbNm105MgRVa1aVQ8fPtTBgwdlMpnsXR6QKTDjBAAAkMU5OTnp7NmzKlq0qCTJ1dVVe/fuVZUqVexcGZB5cI8TAABAFhcXFycnJyfz6xw5csjNzc2OFQGZD5fqAQAAZHGGYahr165ydnaWJN27d0+9e/dW7ty5LfotW7bMHuUBmQLBCQAAIIvr0qWLxevXXnvNTpUAmRf3OAEAAACAFdzjBAAAAABWEJwAAAAAwAqCEwAAAABYQXACAAAAACsITgCATCk0NFT58+e3dxmSpBUrVqhs2bJydHTUgAED7F0OACANEJwAAEnq2rWrTCaTTCaTcubMKS8vLzVp0kRffvml4uPj060OHx8fhYSEWLR16NBBJ0+eTLcaHufNN9/Uiy++qD///FPjxo1LtM/BgwfVpk0beXp6ysXFRT4+PurQoYOuXr0qSQoLC5PJZNKtW7fSsXIAQHIRnAAAj9WsWTNdvnxZ586d09q1a9W4cWP1799frVq10sOHD594XMMwUrS/q6urPD09n3j/1HLnzh1dvXpVgYGB8vb2Vp48eRL0uXbtmp577jm5u7tr/fr1OnbsmObPny9vb29FR0fboWoAgK0ITgCAx3J2dlbhwoVVtGhR1axZU8OHD9fKlSu1du1ahYaGSpLOnTsnk8mk8PBw8363bt2SyWRSWFiYpP+bUVm7dq38/Pzk7OysnTt36syZM2rbtq28vLzk5uamp59+Wps2bTKP4+/vr/Pnz2vgwIHm2S/9v/buL6Tpr48D+Hu2ln/KXCvSLHSJ2TQtU8hmF7mCbDhshKaEZqF0I/RHgxEGKf2RksS6SFPcRSEmOEvFhLBBMUK0jBSGSSgSzDIdgRGmcp6L6Et7tjat3+/5PT3P+3W18/2e7+ecz/dmfDhnZ/C8Ve/27duIioqCQqFATEwM7t6963JfJpOhoaEBRqMRgYGBiI6ORnt7u9f8nU4n8vPzoVQqERgYiIMHD2JkZETK6XuhpNPpXPL9kc1mw6dPn9DQ0IDExESo1WqkpaWhuroaarUaY2NjSEtLAwAolUrIZDIUFBQAALq7u7Fnzx6EhIRApVIhIyMDb9++lWLrdDoUFxe7jDc5OQmFQoGenh6vuRER0eKxcCIioiXT6XTYvn07LBbLkp81mUyorKyE3W5HQkICZmZmoNfr0dPTg4GBAaSnp8NgMGB8fBwAYLFYsHHjRlRUVMDhcMDhcHiM29bWhlOnTqGkpARDQ0M4efIkjh8/DqvV6tKvvLwc2dnZeP36NfR6PY4ePYrp6emfzregoAD9/f1ob2/H8+fPIYSAXq/H3NwctFothoeHAQCtra1wOBzQarVuMUJDQzE/P4+2tjZ4+t/5TZs2obW1FQAwPDwMh8OBmpoaAMDnz59x9uxZ9Pf3o6enB35+fjAajdJWycLCQjQ1NWF2dlaKd+/ePYSHh0On0/00LyIiWiJBRET0E8eOHROZmZke7x05ckRoNBohhBCjo6MCgBgYGJDuO51OAUBYrVYhhBBWq1UAEA8ePPA5blxcnLh165bUjoiIENXV1S59zGazWL16tdTWarWiqKjIpU9WVpbQ6/VSG4AoKyuT2jMzMwKAePTokcd5vHnzRgAQNptNuvbx40cREBAgWlpaPOb5M+fPnxdyuVysWbNGpKeni2vXromJiQnp/vf343Q6vcaZnJwUAMTg4KAQQogvX74IpVIp7t+/L/VJSEgQFy9e9BqHiIiWhitORET0S4QQ0ra5pUhOTnZpz8zMoLS0FBqNBiEhIVi5ciXsdru04rRYdrsdqampLtdSU1Nht9tdriUkJEifg4KCEBwcLB3Q4CmmXC7Hrl27pGsqlQoxMTFucX25fPkyJiYmUFtbi7i4ONTW1mLr1q0YHBz0+tzIyAhyc3OxefNmBAcHIzIyEgCk9+Pv74+8vDw0NjYCAF6+fImhoSFpqx8REf01WDgREdEvsdvtUKvVAAA/v29fJ+KHbWhzc3MenwsKCnJpl5aWoq2tDVeuXMGzZ8/w6tUrxMfH4+vXr3/LvJcvX+7Slslk/7ETAlUqFbKyslBVVQW73Y4NGzagqqrK6zMGgwHT09Oor69Hb28vent7AcDl/RQWFuLx48d49+4dzGYzdDodIiIi/tZciIj+37BwIiKiJXvy5AkGBwdx+PBhAMC6desAwOX3Rz8eFOGNzWZDQUEBjEYj4uPjERoairGxMZc+CoUCCwsLXuNoNBrYbDa32LGxsYuax89izs/PS8UKAExNTWF4ePi34gLfcoqKipJO1VMoFADgkuf3scrKyrBv3z5oNBo4nU63WPHx8UhOTkZ9fT2amppw4sSJ35obERG5k//TEyAiov9us7OzmJiYwMLCAt6/f4/u7m5cvXoVGRkZyM/PB/DtaPCUlBRUVlZCrVbjw4cPKCsrW1T86OhoWCwWGAwGyGQyXLhwwW0FKDIyEk+fPkVOTg5WrFiBtWvXusU5d+4csrOzkZiYiP3796OjowMWi8XlhL6lio6ORmZmJoqKilBXV4dVq1bBZDIhPDwcmZmZi47T2dmJ5uZm5OTkYMuWLRBCoKOjA11dXTCbzQCAiIgIyGQydHZ2Qq/XIyAgAEqlEiqVCnfu3EFYWBjGx8dhMpk8jlFYWIji4mIEBQXBaDT+cs5EROQZV5yIiMir7u5uhIWFITIyEunp6bBarbh58yYePnyIZcuWSf0aGxsxPz+PpKQknD59GpcuXVpU/Bs3bkCpVEKr1cJgMODAgQPYuXOnS5+KigqMjY0hKipKWt36d4cOHUJNTQ2qqqoQFxeHuro6mM1m7N2795dzBwCz2YykpCRkZGRg9+7dEEKgq6vLbcufN7GxsQgMDERJSQl27NiBlJQUtLS0oKGhAXl5eQCA8PBwlJeXw2QyYf369SguLoafnx+am5vx4sULbNu2DWfOnMH169c9jpGbmwu5XI7c3Fz4+/v/Vs5EROROJoSHc1GJiIjoj/K9sOzr63MrPImI6PexcCIiIvqDzc3NYWpqCqWlpRgdHXX7nRcREf01uFWPiIjoD2az2RAWFoa+vj7U1tb+09MhIvqfxRUnIiIiIiIiH7jiRERERERE5AMLJyIiIiIiIh9YOBEREREREfnAwomIiIiIiMgHFk5EREREREQ+sHAiIiIiIiLygYUTERERERGRDyyciIiIiIiIfGDhRERERERE5MO/AFRCiKjiuIk6AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### a new column that contains the categorization of the ordering time interval column"
      ],
      "metadata": {
        "id": "Ajor94j86cq7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the interval or deadline for ordering\n",
        "intervals = [0, 7, 14, 30, np.inf]\n",
        "\n",
        "# Defines a label for each interval\n",
        "labels = ['20-29 days', '30-39 days', '40-49 days', '31+ days']\n",
        "\n",
        "# Created a new column 'booking_interval_category' based on column 'lead_time'\n",
        "df_filtered['booking_interval_category'] = pd.cut(df_filtered['lead_time'], bins=intervals, labels=labels, right=False)\n",
        "\n",
        "# Displays data after adding a new column\n",
        "df_filtered[['lead_time', 'booking_interval_category']]\n"
      ],
      "metadata": {
        "id": "c5WZ-rqC5PeM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "c7549c0f-d05b-4eec-b469-7e586e8efb8d"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-85-aa6fdce8515d>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_filtered['booking_interval_category'] = pd.cut(df_filtered['lead_time'], bins=intervals, labels=labels, right=False)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        lead_time booking_interval_category\n",
              "201             8                30-39 days\n",
              "328            55                  31+ days\n",
              "1539          304                  31+ days\n",
              "1587          333                  31+ days\n",
              "1643          336                  31+ days\n",
              "...           ...                       ...\n",
              "118557          6                20-29 days\n",
              "118852        215                  31+ days\n",
              "118913        200                  31+ days\n",
              "119054          8                30-39 days\n",
              "119070          0                20-29 days\n",
              "\n",
              "[119 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-ce8188af-29b8-4107-8160-a45340295ebd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>lead_time</th>\n",
              "      <th>booking_interval_category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>8</td>\n",
              "      <td>30-39 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>328</th>\n",
              "      <td>55</td>\n",
              "      <td>31+ days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1539</th>\n",
              "      <td>304</td>\n",
              "      <td>31+ days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1587</th>\n",
              "      <td>333</td>\n",
              "      <td>31+ days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1643</th>\n",
              "      <td>336</td>\n",
              "      <td>31+ days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118557</th>\n",
              "      <td>6</td>\n",
              "      <td>20-29 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118852</th>\n",
              "      <td>215</td>\n",
              "      <td>31+ days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118913</th>\n",
              "      <td>200</td>\n",
              "      <td>31+ days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119054</th>\n",
              "      <td>8</td>\n",
              "      <td>30-39 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119070</th>\n",
              "      <td>0</td>\n",
              "      <td>20-29 days</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce8188af-29b8-4107-8160-a45340295ebd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-d782c983-52e9-4af1-a5be-e78ca5bbd2ed button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ce8188af-29b8-4107-8160-a45340295ebd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ce8188af-29b8-4107-8160-a45340295ebd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### an aggregate table that shows the comparison of the number of hotel bookings canceled against the time interval of bookings for each type of hotel"
      ],
      "metadata": {
        "id": "mA9T-Vil6rgm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_df = df_filtered.groupby(['hotel', 'booking_interval_category']).agg({'is_canceled': ['sum', 'count']})\n",
        "grouped_df['cancellation_ratio'] = grouped_df['is_canceled']['sum'] / grouped_df['is_canceled']['count']\n",
        "\n",
        "grouped_df\n"
      ],
      "metadata": {
        "id": "rUumUep96B8v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "cdbb4458-bdd8-439b-f48f-37871b0088f2"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       is_canceled       cancellation_ratio\n",
              "                                               sum count                   \n",
              "hotel        booking_interval_category                                     \n",
              "City Hotel   20-29 days                          1    12           0.083333\n",
              "             30-39 days                          3     7           0.428571\n",
              "             40-49 days                          0     5           0.000000\n",
              "             31+ days                           14    49           0.285714\n",
              "Resort Hotel 20-29 days                          0     7           0.000000\n",
              "             30-39 days                          0     3           0.000000\n",
              "             40-49 days                          0     7           0.000000\n",
              "             31+ days                           21    29           0.724138"
            ],
            "text/html": [
              "\n",
              "\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">is_canceled</th>\n",
              "      <th>cancellation_ratio</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>sum</th>\n",
              "      <th>count</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hotel</th>\n",
              "      <th>booking_interval_category</th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">City Hotel</th>\n",
              "      <th>20-29 days</th>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0.083333</td>\n",
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              "    <tr>\n",
              "      <th>30-39 days</th>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>0.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40-49 days</th>\n",
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              "      <td>5</td>\n",
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              "      <th>31+ days</th>\n",
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              "      <td>0.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Resort Hotel</th>\n",
              "      <th>20-29 days</th>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30-39 days</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40-49 days</th>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>31+ days</th>\n",
              "      <td>21</td>\n",
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              "      </button>\n",
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              "      width: 32px;\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3f32d6c3-b034-4882-b9db-7ac3009b2c29 button.colab-df-convert');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualization"
      ],
      "metadata": {
        "id": "QQNHp_iP7LWU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = grouped_df['cancellation_ratio'].unstack(level=0)\n",
        "data.plot(kind='line', marker='o')\n",
        "plt.title('Cancellation Ratio by Booking Interval and Hotel Type')\n",
        "plt.xlabel('Booking Interval Category')\n",
        "plt.ylabel('Cancellation Ratio')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "QjT16Ts975Fl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "2a862e68-22a1-47e0-c4ef-063b9bb65dd4"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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3b6Z58+a5+ui5ubkVqBXzn9atW2d8Hf/z5uHhkWu7J/3bVBSFdevW0bFjRxRFyfV/2K5dO5KSkh77feNBQkND2b9/f67T+CtWrMDHx4fWrVvn2tbb2zvX54STkxOhoaEcPnyY2NhYwPBZ0apVK8qXL58rf9u2bdHpdOzatcuk+YuLnA4rIk5OTgDcvXu3wI/Zs2cP48ePJzw8PM+lxklJSTg7Oxt//ucbVo7y5ctz584d48/nz5+nW7duDz3m2bNnOXnypLHJ/d/i4+MLnB/g8uXLjBs3jo0bN+bKAjzWm+fNmzdJS0ujdu3aee6rW7cuer2eK1euGJvJIe/vJudD7995HmTdunU4OTlx8+ZNpk+fTkxMTJ5mdr1ez7fffsvs2bOJiYlBp9MZ76tYsWKBn98/Xbp0CW9v7zyFc926dY33P4q3tzcODg651tWqVQswnNZr3rw5FhYW9O3blzlz5hg7zK5YsQJbW1u6d+9eoKyVK1embdu2xp9feeUVKlasyLvvvsuvv/5Kx44djXnz+7+rU6eO8Wq7h21Xt25dtm7dSmpqaq7n1bFjRzw8PNi6dWuuYuVRHvXayMlSo0aNPI+tUaPGE31QFeRv9kHOnj2LoijUrFkz3/v/3RG8UqVKWFtb59muZ8+e/Pzzz4SHh9OiRQvOnz/PoUOH8pzKKMx7UUGY+r3t0qVLeU6DQ/6voYd5+umn8z2F+u+O/U/6t3nz5k0SExOZP3/+A68GLux77aP07NmTt956ixUrVjBu3DiSkpL49ddfefvtt3N9SQTDa/vf6/75vuHp6cnZs2c5evSoyT4rSgopgoqIk5MT3t7eHD9+vEDbnz9/nueee446deowdepUfHx8sLa2ZvPmzXzzzTd5zlH/sy/HPymF6EALhg/zBg0aMHXq1Hzv9/HxKfC+dDodzz//PAkJCXzwwQfUqVMHBwcHrl27xoABA4qt89yT/m7++cbYsWNHGjRoQN++fTl06JCx9efzzz/n448/ZuDAgXz66adUqFABCwsL3nrrLbPoJBgaGspXX33Fzz//TO/evVm5ciUdOnQo9IfbPz333HMA7Nq1i44dO5oqar66devGsmXLWLFiRa6Wr0cx1d/N43iSY+v1ejQaDb/99lu++/l3IfigvjEdO3bE3t6e1atX06JFC1avXo2FhUWu4rew70WPotZ7W0mS8xz79etH//79892mYcOGJj1m+fLl6dChg7EIWrt2LRkZGQVu7f03vV7P888/z/vvv5/v/TlFk7mRIqgIdejQgfnz5xMeHk5ISMhDt/3ll1/IyMhg48aNub4JFfZ01D/5+fk9sgjz8/MjKiqK5557Ls83gcI6duwYZ86cYdmyZcYxZAC2bduWZ9uCHsvNzQ17e3tOnz6d575Tp05hYWFRqEKtsMqVK8f48eMJCwtj9erV9OrVC4C1a9fyzDPPsGjRolzbJyYm5vpmWZjfadWqVfnjjz+4e/durm+cp06dMt7/KNevX8/TanLmzBmAXJ2L69evT2BgICtWrKBy5cpcvnyZGTNmFDhrfrKzswGM45fk5D19+rTx9GqO06dPG+//53b/durUKVxdXfO0bn311VdYWloaOxn36dPnibLnyMly7ty5PPflt87UHvR68fPzQ1EUqlWr9kQfNg4ODnTo0IE1a9YwdepUVq1aRatWrfD29jZuY+r3oqJ4b6tatSpnz57Nsz6/15ApFOZvM7//Qzc3NxwdHdHpdLlaUItaaGgonTp14sCBA6xYsYLAwMBcreY5zp07h6IoubL/+33Dz8+PlJSUYs1fHKRPUBF6//33cXBwYPDgwcZLd//p/PnzfPvtt8Df337++W0nKSmJJUuWPPbxu3XrRlRUFD/99FOe+3KO06NHD65du8aCBQvybHPv3j1SU1MLfLz8noOiKMbn+E85H2qPGhlXq9XywgsvsGHDhlyXCsfFxbFy5Uqeeuop46nHotK3b18qV67M5MmTc+X69zfTNWvW5Ln0u6DPE+Cll15Cp9Mxc+bMXOu/+eYbNBoNL7744iP3kZ2dzbx584w/Z2ZmMm/ePNzc3GjSpEmubV977TV+//13pk2bRsWKFQu0/4f55ZdfAGjUqBEATZs2xd3dnblz5+YabuG3337j5MmTvPzyy4Chf1dAQADLli3L9Xs6fvw4v//+Oy+99FKeY2k0GubPn8+rr75K//792bhx4xNlz+Ht7U39+vX57rvvcg1G97///Y9jx46Z5BgP86DXS9euXdFqtUyYMCHP605RFG7fvl3gY/Ts2ZPr16+zcOFCoqKi6NmzZ677Tf1eVBTvbS+99BL79u0jIiLCuO7mzZu5+rmZUmH+Nh0cHPL8/2m1Wrp168a6devy/WL6JEN4PMyLL76Iq6srkydP5n//+98DW4GuX7+e63MiOTmZ7777joCAADw9PQHDZ0V4eDhbt27N8/jExETjlyBzIy1BRcjPz4+VK1fSs2dP6tatm2vE6L1797JmzRrjGBIvvPAC1tbWdOzYkWHDhpGSksKCBQtwd3fnxo0bj3X89957j7Vr19K9e3cGDhxIkyZNSEhIYOPGjcydO5dGjRrx2muvsXr1al5//XV27NhBy5Yt0el0nDp1itWrV7N169Zc44c8TJ06dfDz8+Pdd9/l2rVrODk5sW7dunz7POR8IL/xxhu0a9cOrVZrbGX5t88++4xt27bx1FNPMWLECCwtLZk3bx4ZGRl8+eWXj/W7KQwrKyvefPNN3nvvPbZs2UL79u3p0KEDEydOJCwsjBYtWnDs2DFWrFiRZxwWPz8/XFxcmDt3Lo6Ojjg4OBAcHEy1atXyHKdjx44888wz/N///R8XL16kUaNG/P7772zYsIG33norV6faB/H29mby5MlcvHiRWrVqsWrVKo4cOcL8+fPz9Bvp06cP77//Pj/99BPDhw8v1ACDZ86cYfny5YDh0tl9+/axbNkyatSowWuvvWb8vU2ePJmwsDBat25N7969jZfI+/r68vbbbxv399VXX/Hiiy8SEhLCoEGDjJfIOzs7P3B6FQsLC5YvX07nzp3p0aMHmzdvztPi9Dg+//xzOnXqRMuWLQkLC+POnTvMnDmT+vXr5yqMikLO38X//d//0atXL6ysrOjYsSN+fn589tlnjB07losXL9K5c2ccHR2JiYnhp59+YujQobz77rsFOsZLL72Eo6Mj7777rvHD+Z9M/V5UFO9t77//Pt9//z3t27fnzTffNF4iX7VqVY4ePfpY+3yYwvxtNmnShD/++IOpU6fi7e1NtWrVCA4O5osvvmDHjh0EBwczZMgQ/P39SUhIIDIykj/++OOBl+s/CSsrK3r16sXMmTPRarX07t073+1q1arFoEGDOHDgAB4eHixevJi4uLhchep7773Hxo0b6dChg3HIgtTUVI4dO8batWu5ePHiI4eoKJGK70K0suvMmTPKkCFDFF9fX8Xa2lpxdHRUWrZsqcyYMSPXJcUbN25UGjZsqNja2iq+vr7K5MmTlcWLF+e59LFq1aq5Lt/Mkd/lqrdv31ZGjRqlVKpUSbG2tlYqV66s9O/fP9dlmpmZmcrkyZOVevXqKTY2Nkr58uWVJk2aKBMmTFCSkpJyHfdRl8hHR0crbdu2VcqVK6e4uroqQ4YMMV4K/M9LPrOzs5XRo0crbm5uikajyXW5PP+6RF5RFCUyMlJp166dUq5cOcXe3l555plnlL179+baJucS+QMHDuRan1/O/Pzz8ut/S0pKUpydnY2/3/T0dOWdd95RvLy8FDs7O6Vly5ZKeHh4vv8HGzZsUPz9/RVLS8tcv4d/X8atKIpy9+5d5e2331a8vb0VKysrpWbNmspXX32V65L/B8m5NPvgwYNKSEiIYmtrq1StWlWZOXPmAx/z0ksvKUCe3+XD8K9LibVarVK5cmVl6NChSlxcXJ7tV61apQQGBio2NjZKhQoVlL59+ypXr17Ns90ff/yhtGzZUrGzs1OcnJyUjh07KtHR0bm2ye//KC0tTWndurVSrlw5Zd++fYqiPPgS+a+++irf5/Pv19uPP/6o1KlTR7GxsVHq16+vbNy4UenWrZtSp06dR/5+HnSJ/MiRI/Ns+++/KUUxXIpcqVIlxcLCIs/f/rp165SnnnpKcXBwUBwcHJQ6deooI0eOVE6fPv3Q4/9b3759jcNJ5Keg70UFvUS+KN7bjh49qrRu3VqxtbVVKlWqpHz66afKokWLCnWJfH5/6w/KUdC/zVOnTilPP/20YmdnpwC5/n/j4uKUkSNHKj4+PoqVlZXi6empPPfcc8r8+fON25jqEvkcERERCqC88MILD32uW7duVRo2bKjY2NgoderUUdasWZNn27t37ypjx45VatSooVhbWyuurq5KixYtlClTphiH4DA3GkUx495mQogn0qVLF44dO1Ys/V3MXUBAAG5ubvn2cROipIqKiiIgIIDvvvvO2Er7T76+vtSvX59ff/1VhXTqkz5BQpRRN27cYNOmTfm+MZZlWVlZefo37Ny5k6ioKNq0aaNOKCEe04IFCyhXrhxdu3ZVO0qJJH2ChChjYmJi2LNnDwsXLsTKyqpQl5iXBdeuXaNt27b069cPb29vTp06xdy5c/H09OT1119XO54QBfLLL78QHR3N/PnzGTVqVJ4rLIWBFEFClDH/+9//CAsLo0qVKixbtsx49YcwKF++PE2aNGHhwoXcvHkTBwcHXn75Zb744ovHHghTiOI2evRo4uLieOmll5gwYYLacUos6RMkhBBCiDJJ+gQJIYQQokySIkgIIYQQZVKZ6xOk1+u5fv06jo6OTzxNhBBCCCGKh6Io3L17F29vb+M8jk+qzBVB169fL9K5poQQQghRdK5cuULlypVNsq8yVwTlTH535cqVIp9zSgghhBCmkZycjI+PT65JbJ9UmSuCck6BOTk5SREkhBBCmBlTdmWRjtFCCCGEKJOkCBJCCCFEmSRFkBBCCCHKpDLXJ6igdDodWVlZascQJYSVlRVarVbtGEIIIUxIiqB/URSF2NhYEhMT1Y4iShgXFxc8PT1lfCkhhCglpAj6l5wCyN3dHXt7e/nAEyiKQlpaGvHx8QB4eXmpnEgIIYQpSBH0DzqdzlgAyWzR4p/s7OwAiI+Px93dXU6NCSFEKSAdo/8hpw+Qvb29yklESZTzupC+YkIIUTpIEZQPOQUm8iOvCyGEKF3kdJgQQgghTEOvg0t7ISUOynlA1RZgUXK7D0hLUCnXpk0b3nrrrWI/7ieffEJAQECxH1cIIYRKojfCtPqwrAOsG2T4d1p9w/oSSoog8VBSzAghhHik6I2wOhSSr+den3zDsL6EFkJSBAkhhBDi8el1sOUDQMnnzvvrtnxo2K6EkSKoDNDr9bz//vtUqFABT09PPvnkE+N9ly9fplOnTpQrVw4nJyd69OhBXFwcAEuXLmXChAlERUWh0WjQaDQsXboUgMTERAYPHoybmxtOTk48++yzREVFqfDshBBCqOrS3rwtQLkokHzNsF0JI0VQGbBs2TIcHBzYv38/X375JRMnTmTbtm3o9Xo6depEQkIC//vf/9i2bRsXLlygZ8+eAPTs2ZN33nmHevXqcePGDW7cuGG8r3v37sTHx/Pbb79x6NAhGjduzHPPPUdCQoKaT1UIIURxS4kz7XbFSK4OKwMaNmzI+PHjAahZsyYzZ85k+/btABw7doyYmBh8fHwA+O6776hXrx4HDhygWbNmlCtXDktLSzw9PY372717NxEREcTHx2NjYwPAlClT+Pnnn1m7di1Dhw4t5mcohBBCNeU8TLtdMZKWoDKgYcOGuX728vIiPj6ekydP4uPjYyyAAPz9/XFxceHkyZMP3F9UVBQpKSlUrFiRcuXKGW8xMTGcP3++yJ6HEEKIEqhqC3DyfsgGGnCqZNiuhJGWoDLAysoq188ajQa9Xv/Y+0tJScHLy4udO3fmuc/FxeWx9yuEEMIMWWjh2XHw8+v53Hl/kNn2X5TI8YKkCCrD6taty5UrV7hy5YqxNSg6OprExET8/f0BsLa2RqfL3aO/cePGxMbGYmlpia+vb3HHFkIIUdKkGiaYxsIS9Nl/r3fyNhRA/q+ok+sRpAgqw9q2bUuDBg3o27cv06ZNIzs7mxEjRtC6dWuaNm0KgK+vLzExMRw5coTKlSvj6OhI27ZtCQkJoXPnznz55ZfUqlWL69evs2nTJrp06WJ8rBBCiDJAlwX75xmWO3wD5avJiNGi5NNoNGzYsIHy5cvz9NNP07ZtW6pXr86qVauM23Tr1o327dvzzDPP4Obmxg8//IBGo2Hz5s08/fTThIWFUatWLXr16sWlS5fw8Ch5Hd+EEEIUoRM/GS6Bd3CHhj2hWito8Krh3xJcAAFoFEXJb3SjYjVr1iy++uorYmNjadSoETNmzCAoKCjfbdu0acP//ve/POtfeuklNm3a9MhjJScn4+zsTFJSEk5OTrnuS09PJyYmhmrVqmFra/t4T0aUWvL6EEKIf1EUmN8abkTBMx9B6/eK7FAP+/x+XKq3BK1atYoxY8Ywfvx4IiMjadSoEe3atSM+Pj7f7devX28cs+bGjRscP34crVZL9+7dizm5EEIIUcZd3G0ogCztoOlAtdMUmupF0NSpUxkyZAhhYWH4+/szd+5c7O3tWbx4cb7b54x6nHPbtm0b9vb2UgQJIYQQxS18luHfgN7gUFHdLI9B1SIoMzOTQ4cO0bZtW+M6CwsL2rZtS3h4eIH2sWjRInr16oWDg0O+92dkZJCcnJzrJoQQQogndOssnPnNsNx8pLpZHpOqRdCtW7fQ6XR5OtN6eHgQGxv7yMdHRERw/PhxBg8e/MBtJk2ahLOzs/H2z4EBhRBCCPGY9s02/FvrRXCtoW6Wx6T66bAnsWjRIho0aPDATtQAY8eOJSkpyXi7cuVKMSYUQgghSqHU23BkpWG5xSh1szwBVccJcnV1RavVGmctzxEXF5drrqr8pKam8uOPPzJx4sSHbmdjY2Oc30oIIYQQJnBwMWSng1cjqNpS7TSPTdWWIGtra5o0aWKczBNAr9ezfft2QkJCHvrYNWvWkJGRQb9+/Yo6phBCCCFyZKVDxHzDcsho0GjUzfMEVB8xesyYMfTv35+mTZsSFBTEtGnTSE1NJSwsDIDQ0FAqVarEpEmTcj1u0aJFdO7cmYoVza83uhBCCGG2jq81TJPhVAnqdVY7zRNRvQjq2bMnN2/eZNy4ccTGxhIQEMCWLVuMnaUvX76MhUXuBqvTp0+ze/dufv/9dzUiCyGEEGWTovx9WXzwMNBaPXz7Ek71Ighg1KhRjBqVf8eq/GYqr127NiVgoGuzpNFo+Omnn+jcubPaUUzq4sWLVKtWjcOHDxMQEKB2HCGEKJ3O/wnx0WBdDhr3VzvNEzPrq8NKMp1eIfz8bTYcuUb4+dvo9EVftMXGxjJ69GiqV6+OjY0NPj4+dOzYMVefqxs3bvDiiy8ChsJBo9Fw5MiRJz62r68v06ZNy7P+k08+KVRRYspMQgghTCx8puHfwNfAzkXVKKZQIlqCSpstx28w4ZdobiSlG9d5OdsyvqM/7et7FckxL168SMuWLXFxceGrr76iQYMGZGVlsXXrVkaOHMmpU6cAHnnVnRBCCJGvuGhDS5DGApq/rnYak5CWIBPbcvwGw5dH5iqAAGKT0hm+PJItx28UyXFHjBiBRqMhIiKCbt26UatWLerVq8eYMWPYt2+fcTuNRsPPP/8MQLVq1QAIDAxEo9HQpk0bdu3ahZWVVZ7BKt966y1atWr1xDn1ej0TJ06kcuXK2NjYGPuA5cgvU46FCxdSt25dbG1tqVOnDrNnz37iPEIIIQoopy9Q3Y5Q3lfVKKYiLUGPoCgK97J0BdpWp1cYv/EE+Z34UgAN8MnGaFrWcEVr8ehLCu2stGgKcOlhQkICW7Zs4b///W++04e4uLjk+7iIiAiCgoL4448/qFevHtbW1lSoUIHq1avz/fff8957htmAs7KyWLFiBV9++eUjszzKt99+y9dff828efMIDAxk8eLFvPLKK5w4cYKaNWvmmwlgxYoVjBs3jpkzZxIYGMjhw4cZMmQIDg4O9O9v/uelhRCiRLsbB8dWG5ZDzHdwxH+TIugR7mXp8B+31ST7UoDY5HQafFKwq9qiJ7bD3vrR/0Xnzp1DURTq1KlTqDxubm4AVKxYMddpskGDBrFkyRJjEfTLL7+Qnp5Ojx49Hrq/Dz74gI8++ijXuszMTPz9/Y0/T5kyhQ8++IBevXoBMHnyZHbs2MG0adOYNWvWAzONHz+er7/+mq5duwKGFqPo6GjmzZsnRZAQQhS1AwtAlwmVg8DnwbM0mBs5HVYKmPpKuQEDBnDu3DnjabSlS5fSo0ePB05Sm+O9997jyJEjuW6vv/73eePk5GSuX79Oy5a5Rxdt2bIlJ0+efOB+U1NTOX/+PIMGDaJcuXLG22effcb58+ef4JkKIYR4pMw0OLDIsBxinhOlPoi0BD2CnZWW6IntCrRtREwCA5YceOR2S8OaEVStQoGOXRA1a9ZEo9EYOz8/KXd3dzp27MiSJUuoVq0av/32W75DFfybq6srNWrknkSvQoVHP89HSUlJAWDBggUEBwfnuk+rLdjvSAghxGOK+gHuJYBLVUN/oFJEiqBH0Gg0BTolBdCqphtezrbEJqXn2y9IA3g629KqpluB+gQVVIUKFWjXrh2zZs3ijTfeyNNik5iYmG+/oJz+Njpd3j5PgwcPpnfv3lSuXBk/P788rTePw8nJCW9vb/bs2UPr1q2N6/fs2WOcBDe/TB4eHnh7e3PhwgX69u37xDmEEEIUkF7/92zxzYeDRen64imnw0xIa6FhfEdD/5d/lzg5P4/v6G/SAijHrFmz0Ol0BAUFsW7dOs6ePcvJkyeZPn36A+dhc3d3x87Oji1bthAXF0dSUpLxvnbt2uHk5MRnn31mnMLEFN577z0mT57MqlWrOH36NB9++CFHjhzhzTfffGimCRMmMGnSJKZPn86ZM2c4duwYS5YsYerUqSbLJoQQ4l/OboXb58DGGQJL31ydUgSZWPv6Xszp1xhPZ9tc6z2dbZnTr3GRjRNUvXp1IiMjeeaZZ3jnnXeoX78+zz//PNu3b2fOnDn5PsbS0pLp06czb948vL296dSpk/E+CwsLBgwYgE6nIzQ01GQ533jjDcaMGcM777xDgwYN2LJlCxs3bqRmzZoPzTR48GAWLlzIkiVLaNCgAa1bt2bp0qXGS+qFEEIUgZzL4pv0BxtHdbMUAY1SxuafSE5OxtnZmaSkJJycnHLdl56eTkxMDNWqVcPW1vYBeygYnV4hIiaB+LvpuDvaElStQpG0ABWlQYMGcfPmTTZu3Kh2lBLBlK8PIYQo8a4fgfmtwcIS3jwKzpVUjfOwz+/HJX2CiojWQkOIn3nOcJ+UlMSxY8dYuXKlFEBCCFFW5bQC1euiegFUVKQIEnl06tSJiIgIXn/9dZ5//nm14wghhChuSdfgxHrDcim7LP6fpAgSeRTkcnghhBClWMQ80GdD1afAO1DtNEVGOkYLIYQQ4m8Zd+HgUsNyi9IzRUZ+pAgSQgghxN8OL4eMJKhYA2oWbLBgcyVFkBBCCCEM9Lp/DI44AixKd5lQup+dEEIIIQru5C+QeBnsKkCj3mqnKXJSBAkhhBDCIOey+GaDwNpe3SzFQIogIYQQQsCVCLgaAVpraDZE7TTFQoogIe67ePEiGo2GI0eOqB1FCCGKX/hMw78NeoCjh7pZiokUQUVFr4OYv+DYWsO/+rwztZvSgAED0Gg0aDQarKysqFatGu+//z7p6elFetyC8PX1Zdq0aY+93SeffEJAQECBjyfFjBBCFNKdi4b+QFCqB0f8NxkssShEb4QtH0Dy9b/XOXlD+8ng/0qRHbZ9+/YsWbKErKwsDh06RP/+/dFoNEyePLnIjvkwmZmZWFtbq3JsIYQQhbBvLih68HsWPPzVTlNspCXI1KI3wurQ3AUQQPINw/roopuLy8bGBk9PT3x8fOjcuTNt27Zl27Ztxvv1ej2TJk2iWrVq2NnZ0ahRI9auXWu8/86dO/Tt2xc3Nzfs7OyoWbMmS5YsMd5/7Ngxnn32Wezs7KhYsSJDhw4lJSXFeP+AAQPo3Lkz//3vf/H29qZ27dq0adOGS5cu8fbbbxtbqp6UXq9n4sSJVK5cGRsbGwICAtiyZYvx/pyZ5QMDA9FoNLRp08Z438KFC6lbty62trbUqVOH2bNnP3EeIYQwa/cS4fD3huWQ0j044r9JS9CjKApkpRVsW70OfnsfUPLbEaAxtBBVbwMW2kfvz8oeHrNoOH78OHv37qVq1arGdZMmTWL58uXMnTuXmjVrsmvXLvr164ebmxutW7fm448/Jjo6mt9++w1XV1fOnTvHvXv3AEhNTaVdu3aEhIRw4MAB4uPjGTx4MKNGjWLp0qXGY2zfvh0nJydj8eXl5UWjRo0YOnQoQ4aYpqPdt99+y9dff828efMIDAxk8eLFvPLKK5w4cYKaNWsSERFBUFAQf/zxB/Xq1TO2Rq1YsYJx48Yxc+ZMAgMDOXz4MEOGDMHBwYH+/fubJJsQQpidyGWQmQLu/oaWoDJEiqBHyUqDz71NtDPF0EL0hU/BNv/PdbB2KPDef/31V8qVK0d2djYZGRlYWFgwc6aho1tGRgaff/45f/zxByEhIQBUr16d3bt3M2/ePFq3bs3ly5cJDAykadOmgKGPTo6VK1eSnp7Od999h4ODIdPMmTPp2LEjkydPxsPD0InOwcGBhQsX5joNptVqcXR0xNPT85HP4YMPPuCjjz7KtS4zMxN//7+bZ6dMmcIHH3xAr169AJg8eTI7duxg2rRpzJo1Czc3NwAqVqyY65jjx4/n66+/pmvXroChxSg6Opp58+ZJESSEKJt0WbB/nmE5ZORjf/E2V1IElSLPPPMMc+bMITU1lW+++QZLS0u6desGwLlz50hLS8szK3xmZiaBgYbJ8YYPH063bt2IjIzkhRdeoHPnzrRo0QKAkydP0qhRI2MBBNCyZUv0ej2nT582FkENGjR4on5A7733HgMGDMi1bvr06ezatQuA5ORkrl+/TsuWLXNt07JlS6Kioh6439TUVM6fP8+gQYNytUhlZ2fj7Oz82HmFEMKsnfgZkq+Bgzs06K52mmInRdCjWNkbWmQK4tJeWPHqo7fruxaqtijYsQvBwcGBGjVqALB48WIaNWrEokWLGDRokLHvzqZNm6hUqVKux9nY2ADw4osvcunSJTZv3sy2bdt47rnnGDlyJFOmTClUhifh6upqfA45KlSo8ET7BIzPf8GCBQQHB+e6T6stwKlJIYQobRQFwmcYloOGgqWNunlUIB2jH0WjMZySKsjN71nDVWA8qDlRA06VDNsVZH9P0CxpYWHBf/7zHz766CPu3buHv78/NjY2XL58mRo1auS6+fj8fXrOzc2N/v37s3z5cqZNm8b8+fMBqFu3LlFRUaSmphq33bNnDxYWFtSuXfuhWaytrdHpTDNEgJOTE97e3uzZsyfX+j179hhPmeW0RP3zmB4eHnh7e3PhwoU8zz+nI7UQQpQpl/bAjSiwtIOmA9VOowopgkzJQmu4DB7IWwjd/7n9FwXrFG0C3bt3R6vVMmvWLBwdHXn33Xd5++23WbZsGefPnycyMpIZM2awbNkyAMaNG8eGDRs4d+4cJ06c4Ndff6Vu3boA9O3bF1tbW/r378/x48fZsWMHo0eP5rXXXjOeCnsQX19fdu3axbVr17h169YTP6/33nuPyZMns2rVKk6fPs2HH37IkSNHePPNNwFwd3fHzs6OLVu2EBcXR1JSEgATJkxg0qRJTJ8+nTNnznDs2DGWLFnC1KlTnziTEEKYnb33B0cM6A0OFdXNohIpgkzN/xXo8R04eeVe7+RtWF+E4wT9m6WlJaNGjeLLL78kNTWVTz/9lI8//phJkyZRt25d2rdvz6ZNm4wtIdbW1owdO5aGDRvy9NNPo9Vq+fHHHwGwt7dn69atJCQk0KxZM1599VWee+45Y8frh5k4cSIXL17Ez8/P2Gn5SbzxxhuMGTOGd955hwYNGrBlyxY2btxIzZo1jc97+vTpzJs3D29vbzp16gTA4MGDWbhwIUuWLKFBgwa0bt2apUuXSkuQEKLsuXUOzvxmWG4+Qt0sKtIoipLf9dylVnJyMs7OziQlJeHk5JTrvvT0dGJiYqhWrRq2trZPdiC9ztBHKCUOynkY+gAVUwuQKBomfX0IIYSafn0bDi6GWi9Cnx/VTlMgD/v8flzSMbqoWGihWiu1UwghhBC5pd6GIz8YlsvQFBn5kdNhQgghRFlycDFk3wOvRuD7lNppVCVFkBBCCFFWZGdAhOGqX0JGlbnBEf9N9SJo1qxZ+Pr6YmtrS3BwMBEREQ/dPjExkZEjR+Ll5YWNjQ21atVi8+bNxZRWCCGEMGPH1kBqPDh6Q70uaqdRnap9glatWsWYMWOYO3cuwcHBTJs2jXbt2nH69Gnc3d3zbJ+Zmcnzzz+Pu7s7a9eupVKlSly6dAkXFxeT5ipjfcVFAcnrQghh1hQFwmcZloOHgdZK3TwlgKpF0NSpUxkyZAhhYWEAzJ07l02bNrF48WI+/PDDPNsvXryYhIQE9u7di5WV4T/vn/NbPamcfaalpWFnZ2ey/YrSIS3NMJFuzutECCHMyvk/IT4arBygyQC105QIqhVBmZmZHDp0iLFjxxrXWVhY0LZtW8LDw/N9zMaNGwkJCWHkyJFs2LABNzc3+vTpwwcffGCSqQ+0Wi0uLi7Ex8cDhrFxNGX8fKkwtAClpaURHx+Pi4uLTLMhhDBPOa1AjV8DOxdVo5QUqhVBt27dQqfT5Rlt2MPDg1OnTuX7mAsXLvDnn3/St29fNm/ezLlz5xgxYgRZWVmMHz8+38dkZGSQkZFh/Dk5OfmhuXJmHc8phITI4eLikmtWeiGEMBtx0XB+O2gsIPh1tdOUGGY1TpBer8fd3Z358+ej1Wpp0qQJ165d46uvvnpgETRp0iQmTJhQ4GNoNBq8vLxwd3cnKyvLVNGFmbOyspIWICGE+dp3vxWoTgeoIKPk51CtCHJ1dUWr1RIXF5drfVxc3AO/bXt5eeX5MKpbty6xsbFkZmYaJ878p7FjxzJmzBjjz8nJybkmDH0QrVYrH3pCCCHM3904OLrasNxitLpZShjVLpG3tramSZMmbN++3bhOr9ezfft2QkJC8n1My5YtOXfuHHq93rjuzJkzeHl55VsAAdjY2ODk5JTrJoQQQpQZBxaCLhMqB4FPkNppShRVxwkaM2YMCxYsYNmyZZw8eZLhw4eTmppqvFosNDQ0V8fp4cOHk5CQwJtvvsmZM2fYtGkTn3/+OSNHlu1hv4UQQoh8ZaYZiiAo81Nk5EfVPkE9e/bk5s2bjBs3jtjYWAICAtiyZYuxs/Tly5exsPi7TvPx8WHr1q28/fbbNGzYkEqVKvHmm2/ywQcfqPUUhBBCiJIr6ge4lwAuVaFuR7XTlDgyi7wQQghRGun1MKsZ3D4H7b+A5sPVTvREiuLzW/VpM4QQQghRBM5uNRRANs4Q2E/tNCWSFEFCCCFEaZQzOGKT/mDjqG6WEkqKICGEEKK0uX4ELv4FFpYyOOJDSBEkhBBClDY5rUD1uoBzJXWzlGBSBAkhhBClSdI1OLHesCyXxT+UFEFCCCFEaRIxD/TZUPUp8A5UO02JJkWQEEIIUVpk3IWDSw3LLUapGsUcSBEkhBBClBaHV0BGElSsATXbqZ2mxJMiSAghhCgN9DrYN9uw3HwEWMhH/KPIb0gIIYQoDU79ComXwK4CNOqtdhqzIEWQEEIIURrsnWn4t9kgsLZXN4uZkCJICCGEMHdXIuBqBGitodkQtdOYDSmChBBCCHMXfr8VqEEPcPRQN4sZkSJICCGEMGd3LsLJXwzLISNUjWJupAgSQgghzNm+uaDowe9Z8KindhqzIkWQEEIIYa7uJcLh7w3LMkVGoUkRJIQQQpiryGWQmQJudcHvObXTmB0pgoQQQghzpMuC/fMMyyEjQaNRN48ZkiJICCGEMEcnfobka+DgDg17qJ3GLEkRJIQQQpgbRYHwGYbloCFgaaNuHjMlRZAQQghhbi7tgRtRYGkLTQepncZsSREkhBBCmJvwWYZ/G/UGh4rqZjFjUgQJIYQQ5uTWOTj9m2FZLot/IlIECSGEEOZk32xAgVovgmtNtdOYNSmChBBCCHORlgBHVhqWpRXoiUkRJIQQQpiLA4sg+x54NQLfp9ROY/akCBJCCCHMQXYGRMw3LIeMksERTUCKICGEEMIcHFsDqfHg6A31uqidplSQIkgIIYQo6RTl78vig4eB1krdPKWEFEFCCCFESXf+T4iPBisHaDJA7TSlhhRBQgghREmX0wrU+DWwc1E1SmkiRZAQQghRksVFw/ntoLGA4NfVTlOqSBEkhBBClGT77rcC1ekAFaqpm6WUkSJICCGEKKnuxsHR1YblFqPVzVIKSREkhBBClFQHFoIuEyo3A58gtdOUOlIECSGEECVRZpqhCALD4IjC5EpEETRr1ix8fX2xtbUlODiYiIiIB267dOlSNBpNrputrW0xphVCCCGKwdEf4V4CuFQx9AcSJqd6EbRq1SrGjBnD+PHjiYyMpFGjRrRr1474+PgHPsbJyYkbN24Yb5cuXSrGxEIIIUQR0+v/viy++QjQWqqbp5RSvQiaOnUqQ4YMISwsDH9/f+bOnYu9vT2LFy9+4GM0Gg2enp7Gm4eHRzEmFkIIIYrY2d/h9jmwcYbAfmqnKbVULYIyMzM5dOgQbdu2Na6zsLCgbdu2hIeHP/BxKSkpVK1aFR8fHzp16sSJEyceuG1GRgbJycm5bkIIIUSJFj7T8G+T/mDjqG6WUkzVIujWrVvodLo8LTkeHh7Exsbm+5jatWuzePFiNmzYwPLly9Hr9bRo0YKrV6/mu/2kSZNwdnY23nx8fEz+PIQQQgiTuX4ELv4FFpaGecJEkVH9dFhhhYSEEBoaSkBAAK1bt2b9+vW4ubkxb968fLcfO3YsSUlJxtuVK1eKObEQQghRCDl9gep1AefK6mYp5R6rp1ViYiKLFi3i5MmTANSrV4+BAwfi7OxcqP24urqi1WqJi4vLtT4uLg5PT88C7cPKyorAwEDOnTuX7/02NjbY2NgUKpcQQgihiqRrcGK9YTlkpLpZyoBCtwQdPHgQPz8/vvnmGxISEkhISGDq1Kn4+fkRGRlZqH1ZW1vTpEkTtm/fblyn1+vZvn07ISEhBdqHTqfj2LFjeHl5FerYQgghRIkTMQ/02VD1KfAOVDtNqVfolqC3336bV155hQULFmBpaXh4dnY2gwcP5q233mLXrl2F2t+YMWPo378/TZs2JSgoiGnTppGamkpYWBgAoaGhVKpUiUmTJgEwceJEmjdvTo0aNUhMTOSrr77i0qVLDB48uLBPRQghhCg5MlLg4FLDsrQCFYtCF0EHDx7MVQABWFpa8v7779O0adNCB+jZsyc3b95k3LhxxMbGEhAQwJYtW4ydpS9fvoyFxd8NVnfu3GHIkCHExsZSvnx5mjRpwt69e/H39y/0sYUQQogS4/ByyEiCCn5Qq73aacoEjaIoSmEe4OHhwffff88LL7yQa/3WrVsJDQ3N07+npElOTsbZ2ZmkpCScnJzUjiOEEEKAXgfTAyHxErz8NTSTsxv/VhSf34XuE9SzZ08GDRrEqlWruHLlCleuXOHHH39k8ODB9O7d2yShhBBCiDLl1K+GAsiuPDTqo3aaMqPQp8OmTJmCRqMhNDSU7OxswHCF1vDhw/niiy9MHlAIIYQo9XIui286CKzt1c1ShhT6dFiOtLQ0zp8/D4Cfnx/29ubxnyanw4QQQpQoVw7AoragtYa3joOjTAWVn6L4/H7sGdns7e1p0KCBSUIIIYQQZVbOFBkNekgBVMwKVAR17dqVpUuX4uTkRNeuXR+67fr1600STAghhCj17lyEkxsNyyEjVI1SFhWoCHJ2dkaj0QDg5ORkXBZCCCHEE9g/DxQ9+D0LHvXUTlPmPHafIHMlfYKEEEKUCPcS4Zt6kJkC/dZBjbZqJyrRSsQl8s8++yyJiYl51icnJ/Pss8+aIpMQQghR+kUuMxRAbnXB7zm105RJhS6Cdu7cSWZmZp716enp/PXXXyYJJYQQQpRquizDqTAwTJEh3UxUUeCrw44ePWpcjo6OJjY21vizTqdjy5YtVKpUybTphBBCiNLoxM+QfA0c3KFhD7XTlFkFLoICAgLQaDRoNJp8T3vZ2dkxY8YMk4YTQgghSh1FgfD7n5dBQ8DSRt08ZViBi6CYmBgURaF69epERETg5uZmvM/a2hp3d3e0Wm2RhBRCCCFKjUt74EYUWNoaRogWqilwEVS1alUA9Hp9kYURQgghSr2cKTIa9QaHiupmKeMee8To6OhoLl++nKeT9CuvvPLEoYQQQohS6dY5OP2bYTlkpLpZROGLoAsXLtClSxeOHTuGRqMhZ5ihnAEUdTqdaRMKIYQQpcW+2YACtdqDa02105R5hb5E/s0336RatWrEx8djb2/PiRMn2LVrF02bNmXnzp1FEFEIIYQoBdIS4MhKw3LIKHWzCOAxWoLCw8P5888/cXV1xcLCAgsLC5566ikmTZrEG2+8weHDh4sipxBCCGHeDi6C7Hvg2RB8n1I7jeAxWoJ0Oh2Ojo4AuLq6cv36dcDQcfr06dOmTSeEEEKUBtkZELHAsNxitAyOWEIUuiWofv36REVFUa1aNYKDg/nyyy+xtrZm/vz5VK9evSgyCiGEEObt2FpIiQNHb6jXRe004r5CF0EfffQRqampAEycOJEOHTrQqlUrKlasyI8//mjygEIIIYRZU5S/L4sPHgZaK3XzCKNCF0Ht2rUzLteoUYNTp06RkJBA+fLljVeICSGEEOK+Czsg/gRYOUCT/mqnEf9Q6D5B+alQoQKxsbGMGiW93YUwJZ1eIfz8bTYcuUb4+dvo9IrakYQQhbV3puHfxq+BXXl1s4hcCtUSdOLECXbs2IG1tTU9evTAxcWFW7du8dlnnzFv3jzpEySECW05foMJv0RzIynduM7L2ZbxHf1pX99LxWRCiAKLi4bz20FjAcGvq51G/EuBW4I2btxIYGAgb7zxBq+//jpNmzZlx44d1K1bl1OnTvHTTz9x4sSJoswqRJmx5fgNhi+PzFUAAcQmpTN8eSRbjt9QKZkQolD23e8LVKcDVKimbhaRR4GLoM8++4yRI0eSnJzM1KlTuXDhAm+88QabN29my5YttG/fvihzClFm6PQKE36JJr8TXznrJvwSLafGhCjpUuLh6GrDsgyOWCIVuAg6ffo0I0eOpFy5cowePRoLCwu++eYbmjVrVpT5hChzImIS8rQA/ZMC3EhKJyImofhCCSEKL2IB6DKhcjOoEqx2GpGPAhdBd+/excnJCQCtVoudnZ30ARKiCMTffXAB9DjbCSFUkHUPDiw0LMtEqSVWoTpGb926FWdnZwD0ej3bt2/n+PHjubaRWeSFeDLujrYm3U4IoYKoH+BeArhUgTod1U4jHqBQRVD//rnHNxg2bFiunzUajcwiL8QTauZbHntrLWmZD/5b8nK2JahahWJMJYQoML0ewmcbloOHg7bQQ/KJYlLg/xm9Xl+UOYQQ93297cxDCyCAN56rgdZCBicVokQ6+zvcPgs2zoaxgUSJZZLBEoUQpjF75znm7DwPQN/gKng55z7lZXm/8Fl36BpZOvliIkSJFH5/cMQm/cHGUd0s4qGkjU6IEmL5vkt8ueU0AP95qQ5Dn/ZDp1eIiEkg/m467o62eDjZ0GnmHg5eusOUracZ+1JdlVMLIXK5EQUX/wILS8M8YaJEk5YgIUqADUeu8fEGw0UGo56pwdCn/QDQWmgI8atIp4BKhPhVpLpbOb58tSEA83Zd4I/oONUyCyHykTNRar0u4FxZ3SzikaQIEkJlf0THMWZ1FIoC/UOq8s4LtR66/YsNvBjQwheAd9ZEcfVOWjGkFEI8UtI1OL7OsCyXxZsFKYKEUNHe87cYsTISnV6ha2Alxnesh0bz6A7P/3mpLo18XEi6l8XIlYfJzJb+QUKoLmIe6LOh6lPgHah2GlEAj10EZWZmcvXqVS5fvpzr9jhmzZqFr68vtra2BAcHExERUaDH/fjjj2g0Gjp37vxYxxVCTUeuJDJk2UEys/W84O/Bl682xKKAV3xZW1ows3cgTraWRF1JZNJvJ4s4rRDioTJS4OBSw7K0ApmNQhdBZ8+epVWrVtjZ2VG1alWqVatGtWrV8PX1pVq1wk8Ot2rVKsaMGcP48eOJjIykUaNGtGvXjvj4+Ic+7uLFi7z77ru0atWq0McUQm2nY+8yYEkEqZk6WtaoyPTegVhqC/fn6FPBnq97BACwZM9FmVRVCDUdXg4ZSVDBD2rJXJrmotBF0IABA7CwsODXX3/l0KFDREZGEhkZyeHDh4mMjCx0gKlTpzJkyBDCwsLw9/dn7ty52Nvbs3jx4gc+RqfT0bdvXyZMmCBTdwizc+l2Kv0W7ScxLYsAHxfmv9YUWyvtY+3reX8Phj5t+Bt4b81RLt1ONWVUIURB6HWw7/7giCEjwEJ6mpiLQl8if+TIEQ4dOkSdOnWe+OCZmZkcOnSIsWPHGtdZWFjQtm1bwsPDH/i4iRMn4u7uzqBBg/jrr78eeoyMjAwyMjKMPycnJz9xbiEeV2xSOn0X7ufm3QzqeDqyNKwZDjZPNlLFe+1qc+jSHQ5dusPIlZGsfb3FYxdVQojHcOpXSLwEduWhUR+104hCKHS56u/vz61bt0xy8Fu3bqHT6fDw8Mi13sPDg9jY2Hwfs3v3bhYtWsSCBQsKdIxJkybh7OxsvPn4+DxxbiEeR0JqJv0W7efqnXv4VrTnu0FBuNhbP/F+rbQWzOwTSHl7K45fS+azTdEmSCuEKLCcy+KbDgJre3WziEIpdBE0efJk3n//fXbu3Mnt27dJTk7OdStKd+/e5bXXXmPBggW4uroW6DFjx44lKSnJeLty5UqRZhQiP3fTs+i/OIJz8Sl4OduyfHCwSSdA9XK245ueAQAs33eZjVHXTbZvIcRDXDkAV/aD1hqChqqdRhRSodvh27ZtC8Bzzz2Xa72iKIWeQNXV1RWtVktcXO4B3+Li4vD09Myz/fnz57l48SIdO/49I2/OnGaWlpacPn0aPz+/XI+xsbHBxsamwJmEMLX0LB2Dlh3k2LUkKjhY8/2gYCqXN/23xTa13Rn5jB+zdpxn7Lqj1PN2ws+tnMmPI4T4h5wpMhp0B0ePh28rSpxCF0E7duww2cGtra1p0qQJ27dvN17mrtfr2b59O6NGjcqzfZ06dTh27FiudR999BF3797l22+/lVNdosTJzNYzfPkhImIScLSx5LuBQdRwL7rC5O22tTh48Q77YxIYuSKSn0e2lP5BQhSVOxfh5EbDslwWb5YKXQS1bt3apAHGjBlD//79adq0KUFBQUybNo3U1FTCwsIACA0NpVKlSkyaNAlbW1vq16+f6/EuLi4AedYLoTadXmHM6iPsOH0TWysLFoc1o34l5yI9pqXWghm9A3lp+l+cir3L+A0nmHx/mg0hhIntnweKHqo/Ax711E4jHsNjXZaSmJjIokWLOHnSMEBbvXr1GDhwIM7OhX+D79mzJzdv3mTcuHHExsYSEBDAli1bjJ2lL1++jIVcbijMjKIofPTzMX49egMrrYa5/ZrQzLdCsRzb3cmWb3sF0m/RflYdvEJQtQp0ayJzGAlhUvcSIfI7w3KLvGcuhHnQKIqiFOYBBw8epF27dtjZ2REUFATAgQMHuHfvHr///juNGzcukqCmkpycjLOzM0lJSTg5OakdR5RCiqIw6bdTzN91AQsNzOjdmJcbehV7jml/nGHaH2exs9KycVRLano4FnsGIUqtPdNh28fgVhdGhEMBprsRT6YoPr8L3cTy9ttv88orr3Dx4kXWr1/P+vXriYmJoUOHDrz11lsmCSWEOZu98zzzd10A4IuuDVUpgABGP1uTp2q4ci9Lx/AVkaRlZquSQ4hSR5cF++calkNGSgFkxgpdBB08eJAPPvgAS8u/z6RZWlry/vvvc/DgQZOGE8LcLNt7ka+2ngbgo5fr0qOZep31tRYapvUKwN3RhnPxKXz003EK2fArhMhP9AZIvgYOboarwoTZKnQR5OTklO9EqVeuXMHRUZrbRdm17tBVxm88AcAbz9VkcCv1p3RxLWfDjN6BWGhg/eFrrD4o42QJ8UQUBfbOMCwHDQUr0433JYpfoYugnj17MmjQIFatWsWVK1e4cuUKP/74I4MHD6Z3795FkVGIEm/riVjeX3cUgLCWvrzdtqbKif4WXL0i77xQG4BxG05w8oZMHSPEY7u0F24cAUtbaDpQ7TTiCRX66rApU6ag0WgIDQ0lO9vQx8DKyorhw4fzxRdfmDygECXdnnO3GL3yMDq9wqtNKvPxy/5oSlgfgeGt/ThwMYGdp28yYkUkG0e1xNHWSu1YQpifnMERG/UGh4LNXCBKrkJfHZYjLS2N8+fPA+Dn54e9vXnMlyJXhwlTirx8h34L95OWqaN9PU9m9gnEUlsyh3RISM3k5el/cSMpnQ4NvZjRO7DEFWtClGi3zsHMpoACIw+AWy21E5UpJeLqsBz29vY0aNCABg0amE0BJIQpnbyRzIDFEaRl6mhV05VveweU2AIIoIKDtaFIs9Dw69EbLN+ft2+fEOIh9s0GFKjVXgqgUqJAp8O6du3K0qVLcXJyomvXrg/ddv369SYJJkRJFnMrldcWRZCcnk2TquWZ91oTbCxL/vQUTapW4IP2dfjv5pN8+ks0AZVdaFC5aEexFqJUSEuAIysNyzJFRqlRoK+tzs7OxmZzJycnnJ2dH3gTorS7nniPfgv3cyslA38vJxYPaIa99WMNvq6Kwa2q8by/B5k6PSNXRpJ0L0vtSEKUfAcXQfY98GwIvq3UTiNM5LH7BJkr6RMknsTtlAy6zwvnws1Uqrs6sPr1EFzL2agdq9CS0rJ4ecZfXL1zj3b1PJjbr4n0DxLiQbIzYFoDSImDrgugYQ+1E5VJJaJP0LPPPktiYmKe9cnJyTz77LOmyCREiZScnkXo4ggu3EzF29mW7wcHm2UBBOBsb8WsPo2x0mrYeiKOJXsuqh1JiJLr2FpDAeToDfW6qJ1GmFChi6CdO3eSmZmZZ316ejp//fWXSUIJUdLcy9QxaOkBTlxPpqKDNcsHB1PJxU7tWE+kkY8L//dSXQA+33ySw5fvqJxIiBJIUSB8lmE5eBhoZWiJ0qTAHRmOHj1qXI6OjiY2Ntb4s06nY8uWLVSqVMm06YQoATKz9QxbfogDF+/gaGvJd4OCqO5WTu1YJtG/hS8RFxPYfCyWUSsPs+mNp3Cxt1Y7lhAlx4UdEH8CrBygSX+10wgTK3ARFBAQgEajQaPR5Hvay87OjhkzZpg0nBBq0+kV3lp1mF1nbmJnpWVpWDPqeZeeCwA0Gg1fdGvIievJXLqdxjuro1gQ2hQLC+kfJATwdytQ49fArry6WYTJFbgIiomJQVEUqlevTkREBG5ubsb7rK2tcXd3R6st+ZcIC1FQiqIwdv1RNh+LxVprwfzQJjSpWkHtWCbnZGvoH9R1zl62n4pnwV8XGNbaT+1YQqgv/iSc+wM0FhD8utppRBEocBFUtWpVAPR6fZGFEaKkUBSFzzadZPXBq1hoYHrvAFrVdHv0A81U/UrOjO/oz//9dJwvt56mcdXyNPMtfQWfEIWS0wpUpwNUqKZuFlEkHntwk+joaC5fvpynk/Qrr7zyxKGEUNv07edYtDsGgMndGtK+vpfKiYpen6AqRMQksOHIdUbf7x9U0UyvfhPiiaXEw9FVhuWQUepmEUWm0EXQhQsX6NKlC8eOHUOj0ZAzzFDOGCM6nc60CYUoZot3x/DNH2cAGN/Rn+5NfVROVDw0Gg2fd2nAsWtJXLiZyturo1g6oJn0DxJlU8QC0GVC5WZQJVjtNKKIFPoS+TfffJNq1aoRHx+Pvb09J06cYNeuXTRt2pSdO3cWQUQhis+ag1eY+Gs0AGOer0VYy7LVBO5gY8nsvo2xtbJg15mbzN55Tu1IQhS/rHtwYKFhWabIKNUKXQSFh4czceJEXF1dsbCwwMLCgqeeeopJkybxxhtvFEVGIYrFluM3+GCdYSiIwU9VY/SzNVROpI46nk5M7FQfgKnbzrD3/C2VEwlRzKJ+gHsJ4FIF6nRUO40oQoUugnQ6HY6OjgC4urpy/fp1wNBx+vTp06ZNJ0Qx2XXmJqN/OIxegZ5Nffi/l+uW6WkkejT14dUmldEr8OaPR4i/m652JCGKh14P4bMNy8HDQWs+8wKKwit0EVS/fn2ioqIACA4O5ssvv2TPnj1MnDiR6tWrmzygEEXt4MUEhn1/iCydwssNvPi8a4MyXQDl+LRTfWp5lOPm3Qze/OEIOn2ZmmZQlFVnf4fbZ8HGyTA2kCjVCl0EffTRR8bL5CdOnEhMTAytWrVi8+bNTJ8+3eQBhShKJ64nEbb0APeydLSu5cY3PQPQSkdgAOystczu2xh7ay3hF27z7fazakcSouiFzzT826Q/2Diqm0UUOZPMIp+QkED58uXN4tuzzCIvcly4mUL3ueHcTs2kmW95vhsYjJ21DPj5bz8fvsZbq46g0cCysCCerlV6x0sSZdyNKJj3NGi08NZRcK6sdiLxDyViFvn8VKhQwSwKICFyXEu8R7+F+7mdmkk9bycWDWgmBdADdA6sRO+gKigKvL3qCLFJ0j9IlFI5gyPW6yIFUBlRoB5fXbt2LfAO169f/9hhhCgON+9m8NrC/VxPSsfPzYHvBgbhZCszQz/M+I7+HLmSyMkbybzxw2FWDgnGUmuS71BClAxJ1+D4OsOyXBZfZhToXczZ2bnANyFKsqS0LEIXR3DhViqVXOxYPjhYRkUuAFsrQ/+gcjaWRFxM4OttZ9SOJIRpRcwHfTZUbQmVGqudRhSTArUELVmypKhzCFHk0jKzCVsawckbybiWs2H54GC8nO3UjmU2qrk6MLlbQ0aujGTOzvME+VbgmTruascS4sllpMCh+59zMkVGmSLt2aJMyMjWMez7Q0ReTsTZzorlg4Oo5uqgdiyz83JDL/qHGCZTfnv1Ea4l3lM5kRAmcGQFpCdBBT+o1V7tNKIYFaglKDAwsMAdnyMjI58okBCmlq3T8+YPR/jr7C3srbUsCWtGHU+5MvBx/efluhy+ksjRq0mMWhnJqqEhWFvK9ylhpvQ62Hd/cMSQEWAhr+WypEBFUOfOnYs4hhBFQ69X+HD9MbaciMVaa8GC0KY0rlJe7VhmzcZSy6w+jXlp+l8cvpzIl1tO8VEHf7VjCfF4Tm2COxfBrjw06qN2GlHMClQEjR8/vqhzCGFyiqIw8ddo1h66itZCw4w+gbSs4ap2rFLBp4I9U7o3Ytj3h1i4O4agahV4oZ6n2rGEKLycwRGbDgJre3WziGL3WO1+iYmJLFy4kLFjx5KQkAAYToNdu3bNpOGEeBLf/HGWpXsvAvDVqw1pJx/SJtWunieDn6oGwDtroriSkKZyIiEK6coBuLIftNYQNETtNEIFhS6Cjh49Sq1atZg8eTJTpkwhMTERMIwPNHbsWFPnE+KxLPzrAtPvT/MwsVM9ujaWgc+Kwgcv1iGwigt307MZuTKSjGyd2pGEKLicVqAG3cFRviSVRYUugsaMGcOAAQM4e/Ystra2xvUvvfQSu3btMmk4IR7HqgOX+WzTSQDea1eb0BBfdQOVYlZaC2b2aYyLvRVHrybx+f3fuxAl3p1LcHKjYbn5CHWzCNUUugg6cOAAw4YNy7O+UqVKxMbGPlaIWbNm4evri62tLcHBwURERDxw2/Xr19O0aVNcXFxwcHAgICCA77///rGOK0qfTUdvMHb9MQCGPV2dEW38VE5U+lVyseObHgEALAu/xKajN9QNJERB7J8Lih6qPwOe9dVOI1RS6CLIxsaG5OTkPOvPnDmDm1vhJ1ZctWoVY8aMYfz48URGRtKoUSPatWtHfHx8vttXqFCB//u//yM8PJyjR48SFhZGWFgYW7duLfSxRemy83Q8b606jF6B3kE+fPhiHZnTrpg8U8ed4fcLzg/WHSXmVqrKiYR4iPQkiPzOsNxCBkcsywpdBL3yyitMnDiRrKwsADQaDZcvX+aDDz6gW7duhQ4wdepUhgwZQlhYGP7+/sydOxd7e3sWL16c7/Zt2rShS5cu1K1bFz8/P958800aNmzI7t27C31sUXpExCTw+vJDZOkUOjT04rPODaQAKmbvPF+LIN8KpGRkM3JFJOlZ0j9IlFCHlkFmCrjVBb/n1E4jVFToIujrr78mJSUFd3d37t27R+vWralRowaOjo7897//LdS+MjMzOXToEG3btv07kIUFbdu2JTw8/JGPVxSF7du3c/r0aZ5++unCPhVRShy/lsSgpQdIz9LzbB13vukZgNZCCqDiZqm1YHrvQCo6WBN9I5kJv0SrHUmIvHRZsH+eYTlkJMiXpTKtQOME/ZOzszPbtm1jz549REVFkZKSQuPGjXMVMgV169YtdDodHh4eudZ7eHhw6tSpBz4uKSmJSpUqkZGRgVarZfbs2Tz//PP5bpuRkUFGRobx5/xO5QnzdS4+hdDFEdzNyCaoWgVm922MlcxurhpPZ1um9QogdHEEP0RcJrhaBToHVlI7lhB/i94AyVfBwc1wVZgo0wpdBOVo2bIlLVu2NGWWAnN0dOTIkSOkpKSwfft2xowZQ/Xq1WnTpk2ebSdNmsSECROKP6QoclcS0ui3cD8JqZk0qOTMov5NsbXSqh2rzGtV043Rz9Zk+vaz/OenY9Sv5EwN93JqxxICFOXvy+KDhoKV7cO3F6Veob8yv/HGG0yfPj3P+pkzZ/LWW28Val+urq5otVri4uJyrY+Li8PT88FjNlhYWFCjRg0CAgJ45513ePXVV5k0aVK+244dO5akpCTj7cqVK4XKKEqm+LvpvLZoP7HJ6dRwL8eygUE42lqpHUvc9+ZzNWnhV5G0TB0jVhziXqb0DxIlwKW9cP0wWNpC04FqpymVdHqF8PO32XDkGuHnb6PTK2pHeqhCF0Hr1q3LtwWoRYsWrF27tlD7sra2pkmTJmzfvt24Tq/Xs337dkJCQgq8H71en+uU1z/Z2Njg5OSU6ybMW2JaJq8tjODi7TR8KtixfFAwFRys1Y4l/kFroWFarwDcHG04E5fCxxuOqx1JCAifZfi3UW9wkCl0TG3L8Rs8NflPei/Yx5s/HqH3gn08NflPthwvucNmFLoIun37Ns7OznnWOzk5cevWrUIHGDNmDAsWLGDZsmWcPHmS4cOHk5qaSlhYGAChoaG5RqKeNGkS27Zt48KFC5w8eZKvv/6a77//nn79+hX62ML8pGZkM2DJAU7H3cXd0YYVg5rj6SxN2iWRu6Mt03sFYqGBtYeusvqgtMIKFd0+D6c3G5ZlcEST23L8BsOXR3IjKT3X+tikdIYvjyyxhVCh+wTVqFGDLVu2MGpU7rEVfvvtN6pXr17oAD179uTmzZuMGzeO2NhYAgIC2LJli7Gz9OXLl7Gw+LtWS01NZcSIEVy9ehU7Ozvq1KnD8uXL6dmzZ6GPLcxLepaOod8f5MiVRFzsrfh+UDBVKsqEhyVZiF9Fxjxfiym/n2HchuM0quxCbU9HtWOJsih8FqBArfbgVkvtNKWKTq8w4Zdo8jvxpQAaYMIv0Tzv71nirtzVKIpSqBN2ixcvZtSoUbz33ns8++yzAGzfvp2vv/6aadOmMWRIyZ6ELjk5GWdnZ5KSkuTUmBnJ1ukZviKSbdFxOFhrWTGkOQE+LmrHEgWg1ysMWHqAXWduUt3NgV9GPYWDzWNfkyFE4aUlwFR/yL4H/X+BajKkiimFn79N7wX7HrndD0OaE+JX8bGPUxSf34V+Jxo4cCAZGRn897//5dNPPwXA19eXOXPmEBoaapJQQvyTXq/w/tqjbIuOw9rSggX9m0oBZEYsLDR806MRL0/fzYWbqfznp2NM6xkgg1mK4nNwkaEA8mwIvq3UTlPqxN9Nf/RGhdiuOD3WgCrDhw/n6tWrxMXFkZyczIULF6QAEkVCURQ++eUE6w9fw9JCw+w+jWnhJx0azU3FcjbM7BOI1kLDhiPX+SFC+geJYpKdARELDMsho2RwxCLg7liwfpkF3a44PdGocm5ubpQrJ+N/iKLz9e9n+C78EhoNfN2jEW39PR79IFEiNfWtwPvtagPwyS8nOH4tSeVEokw4thZS4sDRG+p1UTtNqXQnNf+rs3NoAC9nW4KqVSieQIVQ6CIoLi6O1157DW9vbywtLdFqtbluQpjKvP+dZ+aOcwB82qk+nQJk5GFzN6RVdZ6r405mtp5RKyNJTs9SO5IozRTl78vig4eCpQylYWpL9sQw8ofDxp//3c6W8/P4jv4lrlM0PEafoAEDBnD58mU+/vhjvLy85Ly+KBI/RFxm0m+GqVM+aF+Hfs2rqpxImIKFhYav7/cPung7jQ/XHWVWn8byPiKKxoUdEH8CrBygyQC105Qqer3CpN9OsuCvGAD6Na9CSPWKfLbpZK7L5D2dbRnf0Z/29b3UivpQhS6Cdu/ezV9//UVAQEARxBECNkZd5z8/HQNgeBs/hrfxUzmRMCUXe2tm9gmkx7xwNh+L5bvwS/Rv4at2LFEa5bQCBfYDu/LqZilF0rN0vLMmik1HDWP/vN++NsNb+6HRaGhf34uImATi76bj7mg4BVYSW4ByFLoI8vHxoZBX1QtRYH+eimPMqiMoiuGbRU4fElG6BFYpz9gX6zLx12g+2xRNgI8LjeSKP2FK8Sfh3B+ABpoPVztNqZGYlsnQ7w8REZOAlVbDl682pEtgZeP9WgvNE10GX9wK3Sdo2rRpfPjhh1y8eLEI4oiybN+F2wxfHkm2XqFTgDcTX6kvp0lKsbCWvrSv50mWTmHkykiS0qR/kDChnFaguh2gQjV1s5QSV++k8erccCJiEnC0sWRpWFCuAsgcFbolqGfPnqSlpeHn54e9vT1WVrknrUxISDBZOFF2HL2ayOBlB8nI1tO2rjtTujfCogQ3oYonp9FomPxqQ6JvJHM5IY1310Yx/7UmUviKJ5cSD0dXGZZDRqubpZQ4fi2JsKUHuHk3A08nW5YObEYdT/MfcLjQRdC0adOKIIYoy87G3aX/4ghSMrIJqV6RmX0aY6V9otEbhJlwtrNidt/GdJ29l23RcSzaHcPgVoWffkeIXA4sBF0mVGoKPkFqpzF7u87cZPjyQ6Rm6qjt4cjSgc3wcrZTO5ZJFLoI6t+/f1HkEGXUlYQ0+i3az520LBpVdmZB/6bYWslQC2VJ/UrOfNzRn49/Ps4Xv50isEp5mlSVTqziMWXdMxRBAC1kcMQntebgFcauP0a2XiGkekXmhTbBydbq0Q80E0/0dTs9PZ3k5ORcNyEKKi45nb4L9xOXnGH4dhEWRDmZU6pM6hdchQ4NvcjWK4xaGUlCaqbakYS5ivoR0m6DcxWo01HtNGZLURS+/eMs7609SrZeoXOAN8sGBpWqAggeowhKTU1l1KhRuLu74+DgQPny5XPdhCiIO6mZvLZoP5cT0qhSwZ7vBwVR3kEGMiurNBoNX3RrSHVXB24kpTNm9RH0erkKVRSSXv93h+jmw0ErX6oeR5ZOz4frjvHNH2cAGNHGj6k9ArC2LH3dFAr9jN5//33+/PNP5syZg42NDQsXLmTChAl4e3vz3XffFUVGUcqkZGQzYEkEZ+JS8HCyYcXgYNydSt6cMqJ4lbOxZFbfxthYWrDz9E3m7jqvdiRhbs5tg9tnwcbJMDaQKLTUjGyGfHeQVQevYKGBTzvX5/32dUrthSqFLoJ++eUXZs+eTbdu3bC0tKRVq1Z89NFHfP7556xYsaIoMopSJD1Lx+BlB4i6mkR5eyuWDwrGp4K92rFECVHXy4mJneoBMGXrafZfuK1yImFW9s4w/NukP9ia/5VLxS3+bjo954ez8/RNbK0smPdaU14r5aP1F7oISkhIoHp1w9UbTk5Oxkvin3rqKXbt2mXadKJUydIZ5ovadyGBcjaWLBsYRE0PR7VjiRKmR1MfugZWQq/A6B8Ocyvl4ZMzCgHAjSi4+BdotBA0TO00ZudcfApdZ+/l+LVkKjhY88OQ5jxfBiasLnQRVL16dWJiDHOF1KlTh9WrVwOGFiIXFxeThhOlh06v8M7qKP44GY+NpQWL+jelYWUXtWOJEkij0fBZl/rUdC9H/N0M3vrxCDrpHyQeJacvUL0u4OKjbhYzc+BiAt3m7OXqnXtUrWjP+uEtCKxSNvr4FroICgsLIyoqCoAPP/yQWbNmYWtry9tvv817771n8oDC/CmKwrgNx9kYdR1LCw1z+zUhuLr5DKsuip+9tSWz+zbGzkrL7nO3mPnnObUjiZIs+TocX2dYDhmpbhYz89uxG/RduJ+ke1kE+LiwfngLfF0d1I5VbDTKE04EdunSJQ4dOkSNGjVo2LChqXIVmeTkZJydnUlKSsLJSc4ZF4fJW04xZ+d5NBqY3iuQjo281Y4kzMT6yKuMWR2FRgPLBwXTsoar2pFESbRtPOyZBlVbQthmtdOYjcW7Y/h0UzSKAm3rejCjdyB21iV3nLai+Px+4usHq1atStWqpbvjlHh8s3eeY85Ow1U+n3dpIAWQKJSujSuz/0ICqw5e4c0fD7P5jVZyJaHILSMFDi0xLIeMUjeLmdDrFf67+SSLdhu6tvRrXoUJr9Qv0bO9F5UCnw77888/8ff3z3dAxKSkJOrVq8dff/1l0nDCvH2/7xJfbjkNwH9eqkPvoCoqJxLmaEKnetTxdORWSiajfzhMtk6vdiRRkhxZAelJUMEParVXO02Jl56lY/QPh40F0Aft6/Bpp7JZAEEhiqBp06YxZMiQfJugnJ2dGTZsGFOnTjVpOGG+fj58jXEbjgMw6pkaDH3aT+VEwlzZWmmZ3bcxDtZa9sckMO2Ps2pHEiWFXgf7ZhuWQ0aARekbzM+UEtMMg9RuOnYDK62Gb3sFMLyNX5metLjAr5ioqCjat39wlf3CCy9w6NAhk4QS5u2P6DjeWROFokD/kKq880IttSMJM1fdrRxfdDP0OZy54xw7T8ernEiUCKc2wZ2LYFceGvVRO02JdiUhjW5z9nLg4h0c7w9R0imgktqxVFfgIiguLg4rqwfPGWJpacnNmzdNEkqYr73nbzFiZSQ6vULXwEqM71ivTH/LEKbTsZE3/ZobTqm+veoI1xPvqZxIqC7nsvimg8BaBl19kOPXkug6Zy/nb6bi5WzLmuEhtPCTiwygEEVQpUqVOH78+APvP3r0KF5eXiYJJczTkSuJDFl2kMxsPS/4e/Dlqw1L7VDrQh0fvexP/UpO3EnLYvQPh8mS/kFl19WDcGUfaK0haIjaaUqsnafj6TEvnJt3M6jj6cj6ES2o4ylXRucocBH00ksv8fHHH5Oenp7nvnv37jF+/Hg6dOhg0nDCfJyOvUv/xRGkZupoWaMi03sHYqmV8/PCtGyttMzq0xhHG0sOXbrDlK2n1Y4k1BI+0/Bvg+7g6KlulhJq9YErDFp2kLT778urXw/By9lO7VglSoHHCYqLi6Nx48ZotVpGjRpF7dq1ATh16hSzZs1Cp9MRGRmJh0fJHmZbxgkyvUu3U3l1ruGbRoCPCysGB+NgI7M3i6Lz27EbDF8RCcDC0Ka0LQPD+4t/uHMJpgeAoofX94BnfbUTlSiKovDt9rPGiwi6BFZicreGZj8LvKrjBHl4eLB3716GDx/O2LFjyamdNBoN7dq1Y9asWSW+ABKmF5uUTt+F+41NrUvDmkkBJIrciw28CGvpy5I9F3lnTRS/jn5KJuItS/bPNRRA1Z+RAuhfsnR6/u+nY6w+eBWAkc/48e4LtaVv5gM81ojRd+7c4dy5cyiKQs2aNSlf3nzmGJGWINNJSM2kx7xwzsWn4FvRntWvh+DuKAPZieKRma2n+7xwoq4k0sjHhTXDQsz+m64ogPQkmOoPmSnQdx3UbKt2ohIjJSObESsi2XXmJhYamNipPv1K0SzwRfH5/VjvGOXLl6dZs2YEBQWZVQEkTOduehb9F0dwLj4FL2dblg8OlgJIFCtrSwtm9g7E2c6KqCuJTPrtpNqRRHE4tMxQALnVgRrPqZ2mxIhPTqfnvHB2nbmJnZWW+a81LVUFUFGRr02i0O5l6hi07CDHriVRwcGa7wcFU7m8nIoQxc+ngj1fd28EwJI9F/nt2A2VE4kipcuC/fMMyyEjQU7xAHAu/i5dZu/lxPVkKjpY88PQ5tJProCkCBKFkpmtZ/iKQ0TEJOBoY8l3A4Oo4V5O7ViiDGvr78Gwp6sD8P7ao1y6napyIlFkojdA8lVwcIMGPdROUyIcuJhAtznhXEu8h29Fe9aPaEGAj4vascyGFEGiwHR6hTGrj7Dz9E1srSxYHNaM+pWc1Y4lBO+2q02TquW5e79PRHqWTu1IwtQU5e/L4psNASs5/b7p6A36LtxP0r0sAqu4sG54C6pWdFA7llmRIkgUiKIofPTzMX49aphzZm6/JjTzraB2LCEAsNJaMLNPIOXtrThxPZnPNkWrHUmY2qW9cP0wWNpCs0Fqp1Hdwr8uMOqHSDKz9Tzv78HKwc2pWM5G7VhmR4og8UiKojDpt1P8EHEFCw1M6xlIm9ruascSIhcvZzu+6RkAwPJ9l9lw5Jq6gYRp5UyR0agXOJTdKR/0eoWJv0Tz2aaTKAqEhlRlbr8m2Flr1Y5mlqQIEo80a8c55u+6AMAXXRvyckOZHkWUTG1quzPqmRoA/Gf9Mc7fTFE5kTCJ2+fh9GbDcvOR6mZRUXqWjlE/RLJ4TwwAY1+sw4RX6qGV6YkeW4kogmbNmoWvry+2trYEBwcTERHxwG0XLFhAq1atKF++POXLl6dt27YP3V48mWV7LzLl9zMAfPRyXXo081E5kRAP91bbmgRXq0Bqpo6RKyK5lyn9g8zevtmAAjXbgVsttdOoIjEtk9cW7WfzsVistBq+7RXAsNZ+MgjiE1K9CFq1ahVjxoxh/PjxREZG0qhRI9q1a0d8fHy+2+/cuZPevXuzY8cOwsPD8fHx4YUXXuDaNWn6NrV1h64yfuMJAN54riaDW1VXOZEQj2aptWBG70Bcy1lzKvYun9x/DQszlZYAh1cYlluMUjeLSq4kpNF1zl4OXLyDo60l3w0MplNAJbVjlQqPNWK0KQUHB9OsWTNmzjT0+tfr9fj4+DB69Gg+/PDDRz5ep9NRvnx5Zs6cSWho6CO3lxGjC2briVhGrIhEp1cIa+nLuA7+8o1DmJU9527Rb9F+FAW+7t6Ibk0qqx1JPI5dU+DPT8GzAQz7q8yNDXTsahJhSw9wKyUDb2dblg4MopaHo9qxVFFiRow2lczMTA4dOkTbtn8Pe25hYUHbtm0JDw8v0D7S0tLIysqiQoX8r1TKyMggOTk510083J5ztxi98jA6vcKrTSrz8ctSAAnz07KGK289Zzh18tHPxzkTd1flRKLQsjMgYr5hOWR0mSuAdpyOp+f8cG6lGOZmXD+iZZktgIqKqkXQrVu30Ol0eSZe9fDwIDY2tkD7+OCDD/D29s5VSP3TpEmTcHZ2Nt58fKRPy8McunSHId8dJFOnp309T77o2gAL6XQnzNSoZ2vwVA1X7mXpGLEiktSMbLUjicI4vg5S4sDRC+p1UTtNsVp14DKDlx0kLVPHUzVcWfN6CJ7OMjaSqaneJ+hJfPHFF/z444/89NNP2Nrm/+IYO3YsSUlJxtuVK1eKOaX5OHkjmbAlEaRl6mhV05VvewdgqTXrl4go47QWGqb1CsDd0YZz8Sl8/PNxVO4BIApKUWDv/cERg4eBpbW6eYqJoihM3XaGD9YdQ6dX6Nq4EosHNMPR1krtaKWSqp9wrq6uaLVa4uLicq2Pi4vD09PzoY+dMmUKX3zxBb///jsNGzZ84HY2NjY4OTnluom8Ym6l8tqiCJLTs2lStTzzXmuCjaWMOyHMn2s5G2b0DsRCA+sPX2PVAfkiZBYu7IT4E2DlAE0GqJ2mWGTp9Ly/9ijTt58FYPSzNfi6eyOsLeXLaFFR9TdrbW1NkyZN2L59u3GdXq9n+/bthISEPPBxX375JZ9++ilbtmyhadOmxRG1VLueeI9+C/dzKyUDfy8nFg9ohr21pdqxhDCZ4OoVebddbQDGbzxB9HXpG1ji5UyREdgP7Mqrm6UYpGRkM2jZQdYcuoqFBj7v0oB3Xqgt/TGLmOrl5ZgxY1iwYAHLli3j5MmTDB8+nNTUVMLCwgAIDQ1l7Nixxu0nT57Mxx9/zOLFi/H19SU2NpbY2FhSUmRQtMdxOyWDfov2cy3xHtVdHfhuUBDOdtLsKkqf15/245nabmRk6xm5MpK76VlqRxIPEn8Szv0BaKD5cLXTFLn45HR6zgtn15mb2FlpWRDalD7BVdSOVSaoXgT17NmTKVOmMG7cOAICAjhy5Ahbtmwxdpa+fPkyN27cMG4/Z84cMjMzefXVV/Hy8jLepkyZotZTMFtJ97IIXRzBhZupeDvb8v3gYFxl7hlRSllYaJjaIwBvZ1tibqUydv0x6R9UUuVMkVG3A1Sopm6WInYu/i5dZu/lxPVkKjpY8+PQ5jxX1+PRDxQmofo4QcVNxgkyuJep47VF+zl46Q4VHaxZ83oI1d3KqR1LiCJ36NIdes4LJ1uv8GmnerwW4qt2JPFPKfHwTX3QZcDA36FKsNqJikxETAKDlx0gOT2baq4OLA1rJrPAP0SpGydIqCMzW8+w5Yc4eOn+6KODgqQAEmVGk6rl+fDFOgB8+utJjl1NUjmRyOXAQkMBVKkp+ASpnabIbDp6g34L95Ocnk3jKi6sG95CCiAVSBFUxuj0Cm+tOmw897w0rBn1vJ3VjiVEsRr0VDWe9/cgU6dnxMpDJN2T/kElQtY9QxEEhikySmmn4IV/XWDkykgydXra1fNg5ZDmVHAoG0MAlDRSBJUhiqIwdv1RNh+LxVprwfzQJjSpmv9I20KUZhqNhimvNqJyeTuuJNzj/bVR0j+oJIj6EdJug3MVqNNR7TQmp9MrTPjlBJ9tOglA/5CqzO7bBFsrGY5ELVIElRGKovDZppOsPmi4/HJ67wBa1XRTO5YQqnG2t2JWn8ZYaTVsPRHHkj0X1Y5Utun192eLx3BFmLZ0DdORnqVj1MpI4+vsPy/V4ZNX6qGVEflVJUVQGTF9+zkW7Y4BYHK3hrSv76VyIiHU18jHhY9e9gfg880nOXz5jsqJyrBz2+DWGbBxMowNVIrcSc2k38L9/Hbc0Ao/vXcgQ5/2kzGASgApgsqAxbtj+OaPMwCM7+hP96Yyf5oQOUJDqvJyAy+y9QqjVh4mMS1T7UhlU87giE36g23puXL3SkIa3ebu5eClOzjdvxDllUbeascS90kRVMqtOXiFib9GAzDm+VqEtSzdY24IUVgajYZJ3RrgW9Gea4n3eGd1FHq99A8qVjeOQswu0GghaJjaaUzm6NVEuszeaxyLbe3wFjSvXlHtWOIfpAgqxbYcv8EH644CMPipaox+tobKiYQomZxsrZjVtzHWlhZsPxXPgr8uqB2pbMkZHLFeF3ApHS3VO07F02v+Pm6lZFDXy4mfRrakloej2rHEv0gRVErtOnOT0T8cRq9Az6Y+/N/LdeX8sxAPUc/bmU861gPgy62nOXAxQeVEZUTydTi+1rAcMlLdLCbyY8RlBn93kLRMHa1qurJ6WHM8nGzVjiXyIUVQKXTwYgJDvz9Ilk7h5QZefN61gRRAQhRA7yAfOgV4o9MrjF55mNspGWpHKv32zwN9NlRtCZUaq53miSiKwtRtZ/hw/TF0eoVujSuzeEAzHG1lPsaSSoqgUubE9STClh4gPUtP61pufNMzQC7BFKKANBoNn3dpgJ+bA7HJ6bwt/YOKVkYKHFpiWDbzVqAsnZ731h5l+vazALzxbA2mdG+IlVY+Zksy+d8pRS7cTCF0UQR307Np5lueuf2aYG0p/8VCFIaDjeX9Aews2HXmJrN3nlM7Uul1ZAWkJ0GF6lDrRbXTPLa76VkMXHqAtYeuorXQMKlrA8a8UFta4M2AfEKWEtcS79Fv4X5up2ZSz9uJRQOaYWcto5AK8Thqezryaaf6AEzddoa952+pnKgU0uv+MTjiCLAwz4+juOR0es7bx19nb2FnpWVhaFN6B1VRO5YoIPN81Ylcbt7NoN/C/VxPSsfPzYHvBgbhJOeghXgi3Zv68GqTyugVePPHI8TfTVc7UulyahPcuQh25SGgj9ppHsvZuLt0nb2X6BvJuJazZtWw5jxTx13tWKIQpAgyc0lpWYQujiDmViqVXOxYPjiYiuVs1I4lRKnwaaf61PZw5ObdDN784Qg66R9kOjmXxTcdCNbmN3v6vgu36TZnL9cS71Hd1YH1w1vSsLKL2rFEIUkRZMbSMrMJWxrByRvJuJazYfngYLyc7dSOJUSpYWetZVbfxthbawm/cJtv73d6FU/o6kG4sg8srCBoqNppCu2XqOuELoogOT2bJlXLs254C6pUtFc7lngMUgSZqYxsHcO+P0Tk5USc7axYPjiIaq7m921KiJKuhns5JnVtAMCMP8+y68xNlROVAjlTZDToDo6e6mYpBEVRWLDrAqN/OEymTk/7ep6sGBxMeQdrtaOJxyRFkBnK1ul584cj/HX2FvbWWpaENaOOZ+mZa0eIkqZTQCV6B1VBUeDtVUeITZL+QY/tziWI3mBYNqPL4nV6hQm/RPPfzScBGNDCl1l9G2NrJRegmDMpgsyMXq/wwbpjbDlhmI14QWhTGlcpr3YsIUq98R398fdy4nZqJm/8cJhsnV7tSOZp/zxQ9FC9DXjWVztNgaRn6Ri5IpKley8C8NHLdRnf0V/GYCsFpAgyI4qiMPHXaNZFGsaimNEnkJY1XNWOJUSZYGulZXbfxpSzsSTiYgJfbzujdiTzk54Ekd8ZlkNGq5ulgBJSM+m7cL/xi+eM3oEMblVdxgAqJaQIMiPf/HHW+E3kq1cb0q6e+ZxLF6I08HV1YHK3hgDM2XmeP0/FqZzIzER+B5l3wa0O1HhO7TSPdPl2Gq/O2cuhS3dwsrXk+0FBdGzkrXYsYUJSBJmJhX9dMA7HPrFTPbo2rqxyIiHKppcbetE/pCoAY1ZHcS3xnsqJzIQuC/bNNSyHjIQS3pJy9GoiXefs4cL94UfWDW9BcPWKascSJiZFkBlYdeAyn20ydMZ7r11tQkN81Q0kRBn3n5fr0rCyM4lpWYxaGUlmtvQPeqToDZB8FRzcoEEPtdM81I5T8fSct49bKZn4ezmxfkQLano4qh1LFAEpgkq4TUdvMHb9MQCGPV2dEW38VE4khLCx1DKrT2OcbC05fDmRL7ecUjtSyaYof18W32wIWNmqm+chfoi4zODvDnIvS0ermq6sfj0ED6eSm1c8GSmCSrAdp+N5a9Vh9Ar0DvLhwxfrSGc8IUoInwr2TOneCICFu2PYeiJW5UQl2OVwuH4YLG2h2SC10+RLURS+/v00Y9cfQ6dXeLVJZRYPaEY5G0u1o4kiJEVQCRURk8Dw5YfI0il0aOjFZ50bSAEkRAnzQj1PBj9VDYB310Rx+XaayolKqL33W4Ea9QKHkndFa2a2nnfWRDHjz3MAvPlcTb56tSFWWvmILO3kf7gEOn4tiUFLD5CepefZOu580zNAxqMQooT64MU6NK7iwt30bEaujCQjW6d2pJLl9nk4vdmw3LzkDY54Nz2LQcsOsD7yGloLDZO7NeDt52vJl84yQoqgEuZcfAqhiyO4m5FNULUKzO7bWL6NCFGCWWktmNmnMS72Vhy7lsTn9y9iEPftmw0oULMduNVSO00usUnp9Ji3zzj6/sL+TenZrIrasUQxkk/XEuRKQhr9Fu4nITWTBpWcWdS/qQzJLoQZ8Hax45seAQAsC7/Er0evqxuopEhLgMMrDMstRqmb5V/OxN2l6+w9xgmoVw0N4Zna7mrHEsVMiqASIj45nX6L9hObnE4N93IsGxiEo62V2rGEEAX0TB13ht+/evPDdceIuZWqcqIS4OBiyL4Hng3At5XaaYzCz9+m25y9XE9Kp7qbAz+NaEGDys5qxxIqkCKoBEhMy+S1RRFcup2GTwU7lg8KpoLMSiyE2Xnn+VoE+VYgJSObESsiSc8qw/2DsjMgYr5hOWR0iRkccWPUdfovjuBuejZNq5Zn3est8Klgr3YsoRIpglSWmpHNgCUHOB13F3dHG1YMao6ns4xJIYQ5stRaMKNPIBUdrDl5I5kJv0SrHUk9x9dBShw4ekG9LmqnQVEU5v3vPG/8cJhMnZ4X63uyfHAw5eULZ5kmRZCK0rN0DP3+IEeuJOJib8X3g4KpUlG+kQhhzjycbJnWKwCNxjDw3s+Hr6kdqfgpCoTPMiwHDwNLdQsNnV7hk40nmPSbYVDLgS2rMbNPY+lzKaQIUkuWTs/oHw6z59xtHKy1LA0LoranDMsuRGnQqqYbo5+tCcB/fjrGufi7KicqZhd2QtxxsHKAJgNUjZKepWP48kMsC7+ERgMfvVyXcR39ZdgRAUgRpAq9XuH9tUfZFh2HtaUFC/o3JcDHRe1YQggTevO5mrTwq0hapo4RKyJJy8xWO1LxyWkFCuwHduVVi5GQmkmfBfv4/f577czejRncqrpqeUTJo3oRNGvWLHx9fbG1tSU4OJiIiIgHbnvixAm6deuGr68vGo2GadOmFV9QE1EUhU9+OcFPh69haaFhdp/GtPAreSOoCiGejNZCw7e9AnFztOFMXArjNpxQO1LxiD8F57YBGmj+umoxLt1OpducvUReTsTZzorlg4J5uaGXanlEyaRqEbRq1SrGjBnD+PHjiYyMpFGjRrRr1474+Ph8t09LS6N69ep88cUXeHp6FnNa0/j69zN8d79Z9usejWjr76F2JCFEEXFztGF6r0AsNLD20FVWH7yidqSilzNRat0OUEGdVpeoK4l0nb2XmFupVHKxY93wEIKqVVAliyjZVC2Cpk6dypAhQwgLC8Pf35+5c+dib2/P4sWL892+WbNmfPXVV/Tq1QsbG5tiTvvk5v3vPDN3GOam+bRTfToFVFI5kRCiqIX4VWTM84aRksdtOM6p2GSVExWhlHg4utqwHKLO4IjbT8bRa/4+bqdmUs/biZ9GtKCGu/S3FPlTrQjKzMzk0KFDtG3b9u8wFha0bduW8PBwtWIVmZX7LxuvTPigfR36Na+qciIhRHEZ0aYGT9dyIz1Lz4gVkaRklNL+QQcWgi4DKjUFn+BiP/yK/ZcY8t1B7mXpeLqWG6uGheDuJEOOiAdTrQi6desWOp0OD4/cp4M8PDyIjY012XEyMjJITk7OdStuG6Ou838/HwNgeBs/46iyQoiywcJCw7SeAXg62XLhZir/99MxFEVRO5ZpZd0zFEEAISOLdXBERVGYsvU0//fTcfQKdG9SmUX9m1LOxrLYMgjzpHrH6KI2adIknJ2djTcfH59iPf6fp+IYs+oIigL9mlfh/Xa1i/X4QoiSoYKDNTP7BKK10LDhyHVWRlxWO5JpRf0IabfBuQrUfaXYDpuZreed1VHGrgZvta3Jl682lImnRYGo9ipxdXVFq9USFxeXa31cXJxJOz2PHTuWpKQk4+3KleLrmLjvwm2GL48kW6/QKcCbia/UR1NCho4XQhS/pr4VjF+EJvwSzfFrSSonMhG9/v5s8RiuCNMWTwtMcnoWYUsjWH/4GloLDV92a8hbbWvJ+6woMNWKIGtra5o0acL27duN6/R6Pdu3byckJMRkx7GxscHJySnXrSjo9Arh52+z4cg1ws/f5vDlOwxedpCMbD1t67ozpXsjLGRwLiHKvCGtqtO2rjuZ2XpGrowkOT1L7UhP7tw2uHUGbJwg8LViOWRsUjo95oaz59xt7K21LOrflB7NirelX5g/VU+Yjhkzhv79+9O0aVOCgoKYNm0aqamphIWFARAaGkqlSpWYNGkSYOhMHR0dbVy+du0aR44coVy5ctSoUUO157Hl+A0m/BLNjaR04zqNxjByfEj1iszs01iaZoUQgKF/0JTujXh5+m4u3U7jw3VHmdWnsXm3XuRcFt84FGyL5ovmP52OvcuAJRHcSErHtZwNS8OaUb+SzAIvCk/VIqhnz57cvHmTcePGERsbS0BAAFu2bDF2lr58+TIWFn8XD9evXycwMND485QpU5gyZQqtW7dm586dxR0fMBRAw5dH8u8ujjl9Hns085H5aYQQubjYG/oH9ZgXzuZjsSzbe5EBLaupHevx3DgKMbtAo4Xgoh8cce/5Wwz7/hB307Pxc3NgaViQzAIvHptGKXWXKDxccnIyzs7OJCUlPfGpMZ1e4anJf+ZqAfonDeDpbMvuD56VeWqEEHks3h3DxF+jsdJqWPt6CxqZ4/Q564fB0R+hfjd4Nf8x3kxlw5FrvLsmiiydQjPf8iwIbYqLvcwCX1aY8vM7h5yjeQIRMQkPLIAAFOBGUjoRMQnFF0oIYTbCWvrSvp4nWTqFESsiSUozs/5Bydfh+FrDchEOjqgoCnP/d543fzxClk7hpQaefD8oWAog8cSkCHoC8XcfXAA9znZCiLJFo9HwZfeGVKlgz7XEe7yzJsq8xg+KmA/6bKjSAio1LpJD6PQK4zee4Iv7g80OeqoaM3s3lm4GwiSkCHoC7o4FG4m0oNsJIcoeJ1srZvdtjLXWgj9OxrHwrxi1IxVMRgocvH/6q0XRtALdy9Tx+vJDxvkWP+7gz8cd/OVKW2EyUgQ9gaBqFfBytuVBf44awMvZVibuE0I8VP1Kznzc0R+AyVtOceiSGZxCP7IS0pMMk6TWam/y3d9OyaD3gn1si47D2tKCWX0aM+gpM+08LkosKYKegNZCw/j7b1z/LoRyfh7f0V86RQshHqlfcBU6NvImW68wauVhElIz1Y70YHod7JtlWG4+AixMe2rq0u1Uus3Zy5EriTjbWbFicDAvNfAy6TGEACmCnlj7+l7M6dcYT+fcp7w8nW2Z068x7evLH64Q4tE0Gg2TujaguqsDN5LSGbP6CHp9Ce0fdHoz3LkIduUhoI9Jd33kSiJdZ+/l4u00KrnYsW54C5r5Smu6KBoyu5wJtK/vxfP+nkTEJBB/Nx13R8MpMGkBEkIURjkbS2b1bUznWXvYefomc/53npHPqDcQ7APtvT84YtOBYO1gst3+ER3HqB8iSc/SU7+SE4sHNJM+laJISUuQiWgtNIT4VaRTQCVC/CpKASSEeCx1vZyY2KkeAF//fpp9F26rnOhfrh6EK/vAwgqChppst8v3XWLo9wdJz9LTupYbq4aGSAEkipwUQUIIUcL0aOpD18aV0Cvwxg+HuXk3Q+1If8uZIqNBd3B88smuFUXhyy2n+Ojn4+gV6NnUh4X9m+JgIycqRNGTIkgIIUoYjUbDZ53rU9O9HPF3M3h71RF0JaF/0J1LEL3BsBwy8ol3l5mtZ8zqKGbvPA/A221r8UW3BjLXoig28koTQogSyN7aktl9G2NnpWX3uVvM+POs2pFg/zxQ9FC9DXjWf6JdJadnEbY0gp8OX0NroeHLVxvyZtua5j2RrDA7UgQJIUQJVdPDkf92MRQb324/y55zt9QLk54Ekd8ZlkNGP9GubiTdo8fccPacu42DtZbFA5rRo6mPCUIKUThSBAkhRAnWtXFlejXzQVHgzR8PE5+s0jQ8kd9B5l1wqwM1nnvs3ZyKTabLrL2cir2Lm6MNq4aF0LqWmwmDClFwUgQJIUQJ98kr9ajj6citlExG/3CYbJ2+eAPosmHfXMNyyEh4zFNWe8/dovuccGKT06nhXo6fRrSgfiVnEwYVonCkCBJCiBLO1krL7L6NcbDWsj8mgW/+OFO8AaJ/huSr4OAGDXo81i42HLlG/yUR3M3IJsi3AmtfD6FyeXvT5hSikKQIEkIIM1DdrRxfdGsIwKwd59lxOr54Dqwof18W32wIWBVu7B5FUZi98xxv/niELJ3Cyw29+G5QEC721kUQVojCkSJICCHMRMdG3rzWvCoAY1Yd4XrivaI/6OVwuH4YLG2h2aBCPVSnV/h4w3G+3HIagCGtqjGjVyC2Vqada0yIxyVFkBBCmJGPOtSlfiUn7qRlMfqHw2QVdf+g8PsTpTbqBQ6uBX7YvUwdw74/xPJ9l9FoYFwHf/7vZX8sZDR9UYJIESSEEGbExlLL7D5NcLS15NClO3y19XTRHez2eTi1ybDcvOCDI95OyaD3gn38cTIOa0sLZvdpzMCnqhVRSCEenxRBQghhZqpUtOerVxsBMH/XBbZFxxXNgfbNBhSo2Q7cahXoIRdvpdJtzl6OXEnExd6KlYODebGBV9HkE+IJSREkhBBmqH19Twa2NLSuvLP6CFcS0kx7gLQEOLzCsFzAKTIOX75D1zl7uXg7jcrl7Vg3vAVNfSuYNpcQJiRFkBBCmKkPX6xDIx8XktOzGbUyksxsE/YPOrgYsu+BZwOo9vQjN98WHUfvBftISM2kQSVn1o9ogZ9bOdPlEaIISBEkhBBmytrSgll9AnG2syLqahKfbz5pmh1nZ0DEfMNyyKhHDo74/b5LDPv+IOlZetrUduPHoc1xdyzcpfRCqEGKICGEMGOVy9sztYehf9DSvRf57diNJ9/p8XWQEgeOXlCv6wM30+sVJm85xcc/H0evQK9mPiwMbYqDjeWTZxCiGEgRJIQQZu65uh4Ma10dgPfXHuXS7dTH35mi/H1ZfNBQsMx/UMOMbB1vrz7CnJ3nAXjn+VpM6toAS618rAjzIa9WIYQoBd59oTZNq5bnbkY2I1ZEkp6le7wdXdgJccfByh6ahuW7SdK9LAYsPsCGI9extNDw1asNGf1cTTSPOaeYEGqRIkgIIUqB/2/v3uNqyvf/gb/2Tu1q7y4qug9yq+jimCSNIWYmIxEzh3EZyaWRNBlnxsTDEcYMhvFzHUN8yxm5DOHMGYQTMSoiNYXdzWUwym3QRbrs/f790VjHUtjRhfb7+Xj00Pqsz/qsz1q9rfXen7XWXro6Uqwa1Q1mcj2cu16EBXvPv1hDj0aBuo0BDFrWmH39XhmG/5CClIt3INfTwf+N88Df37R/iZ4z1nQ4CWKMsWbC2sQAy4a7QSIBNp+4gn9n/FG3Bm5mA/mHAEiAniE1ZmcXFmHY98nIuVGM1kYy/DTZC293alU/nWesCXASxBhjzUjfzq0R2rcDAGDWrixcuFWi+cIn/hoFcvQDzBxEs5Lyb+Pva1NQWPQQHVsrsDvUG11sTOqr24w1CU6CGGOsmZn2Tkf0dDBDaYUKobFnUFahwf1BJTeB37ZX/94rTDRrd/o1jItORXF5FXq0M8POyb1ga2rQAD1nrHFxEsQYY81MCx0pVn7UDRYKGbILizH353PPX+jURkBVDth2B+w9AQBEhDVH8vHZ9t9QqSIMcrXGjxN6wMRQt4G3gLHGwUkQY4w1Q62N9bHyI3dIJMD201cRl3bt6ZUry4BTUdW///XliFUqNWbvOSu8oDX4bQes/KgbZC10GqH3jDUOToIYY6yZ6tXBAtP6V7/4dPaes8i9UVx7xcztwIM7gMkbgNNgPKiowuTNaYg9eQUSCTDX3xmzBjpBKuVH4FnzwkkQY4w1Y1P7dUDvjhYoq1RhSuwZlJZXiSuo1f97LL7nZNwuU2Fk1En8V3kTshZSrB3dHeP+elErY80NJ0GMMdaM6Ugl+H8j3GFpLEP+zRL8c89ZENH/KuT/F7idC8iMcbnNB/hgbTJ+u3oPpoa62DLJEwO6WjVd5xlrYPyCl/qiVgG/J1e/b0dhCbTpBUj52jl7SRxXrB5YKGRY+VE3jNpwErvS/4BHG2O4qpQou/sHHC9EQwHgRscRGLYxC3+WVsDezAAxQT34LfCs2XslRoLWrFmDtm3bQl9fH56enkhNTX1m/R07dsDR0RH6+vpwcXHBvn37GqmnT3H+Z2B5V2DTICBuQvW/y7tWlzP2ojiuWD3ydDDHP97rBF9pKvrs748uh0bhzdNfQHH3PAjAqgw1/iytgKudCXaFeHMCxLRCkydB27dvx/Tp0xEZGYkzZ87Azc0Nvr6+uHnzZq31k5OTMXLkSEyYMAHp6ekICAhAQEAAzp4928g9/8v5n4GfxgJF18XlRQXV5XzCYi+C44o1AM+yJKzVXQ4r/CmeQcB8nY2YZHEW24J7opWRrGk6yFgjk5Do4nDj8/T0hIeHB1avXg0AUKvVsLe3R1hYGCIiImrUHzFiBEpLS/HLL78IZT179oS7uzt++OGH566vqKgIJiYmuH//PoyNjV+u82pV9SfzJ09UAkn1JYzx8XwJg2lOrQL+z7f6ElitOK5Y3amqqnB3tQ/M6B5qe8hLTcBNiTlazc6FTgu+U4K9eur1/P2XJo30iooKpKWlYebMmUKZVCrFO++8g5SUlFqXSUlJwfTp00Vlvr6+2LNnT631y8vLUV5eLkwXFRW9fMcf+T35GQkQABBQUgisdK+/dTLGccVegA4ACwB4ylPuUglghTs4d/IAunj7NWLPGGs6TZoE3b59GyqVCpaWlqJyS0tLZGdn17pMYWFhrfULCwtrrb9w4ULMmzevfjr8pKd+Un+CVJc/sTPNqVWAuvL59TiuWB2oVVWQUtVz65XdreNLVxl7jTX7Mc+ZM2eKRo6Kiopgb29fP40rLJ9fBwA+3g20610/62TN36Vfq2+Cfh6OK1YHyqS96HJo1HPrGbS0bYTeMPZqaNIboy0sLKCjo4MbN8QjKjdu3ICVVe3fTWFlZVWn+jKZDMbGxqKfetOmF2Bsg6eOL0MCGNtW12NMUxxXrAE4evriBsyhfspdoGoCCmEOR0/fxu0YY02oSZMgPT09dO/eHQkJCUKZWq1GQkICvLy8al3Gy8tLVB8ADh069NT6DUqqAwxY/NfEkyesv6YHLOJLFqxuOK5YA9Bp0QLXvSIBoEYi9Gi6wCuSb4pmWqXJH5GfPn06oqKisGnTJiiVSoSEhKC0tBRBQUEAgLFjx4punA4PD0d8fDy+++47ZGdnY+7cuTh9+jSmTp3aNBvgPBgY/i/A2FpcbmxTXe48uGn6xV5vHFesAXTzDcRvvVbilsRcVH5TYo7feq1EN9/AJuoZY02jyR+RB4DVq1djyZIlKCwshLu7O1auXAlPT08AQN++fdG2bVvExMQI9Xfs2IHZs2fj8uXL6NixI7799lsMHDhQo3U1xCN2APibfVnD4LhiDUBVVYXskwdQdvcPGLS0haOnL48AsVdeQ5y/X4kkqDE1WBLEGGOMsQbTEOfvJr8cxhhjjDHWFDgJYowxxphW4iSIMcYYY1qJkyDGGGOMaSVOghhjjDGmlTgJYowxxphW4iSIMcYYY1qJkyDGGGOMaSVOghhjjDGmlbTue9IffUF2UVFRE/eEMcYYY5p6dN6uzxddaF0SVFxcDACwt7dv4p4wxhhjrK6Ki4thYmJSL21p3bvD1Go1rl+/DiMjI0gkknptu6ioCPb29rh69Sq/l+w5eF9pjveV5nhfaY73Vd3w/tJcQ+0rIkJxcTFsbGwgldbP3TxaNxIklUphZ2fXoOswNjbm/yQa4n2lOd5XmuN9pTneV3XD+0tzDbGv6msE6BG+MZoxxhhjWomTIMYYY4xpJU6C6pFMJkNkZCRkMllTd+WVx/tKc7yvNMf7SnO8r+qG95fmXqd9pXU3RjPGGGOMATwSxBhjjDEtxUkQY4wxxrQSJ0GMMcYY00qcBDWhmJgYmJqaNnU3WDPEscUaAscVexmXL1+GRCJBRkZGU3dF8NomQQsXLoSHhweMjIzQunVrBAQEICcnR1Tn4cOHCA0Nhbm5ORQKBT744APcuHHjme0mJiZiyJAhsLa2hlwuh7u7O2JjY0V1KisrMX/+fLRv3x76+vpwc3NDfHx8vW8jaxpr166Fq6ur8EVfXl5e2L9/v6jOi8RWTk4OfHx8YGlpCX19fTg4OGD27NmorKwU6nBsaYdFixZBIpFg2rRpovIXiavH5efnw8jIqEaiwnGlXTQ5hq1fvx59+/aFsbExJBIJ7t271zSdbWKvbRJ09OhRhIaG4sSJEzh06BAqKyvx3nvvobS0VKjz2Wef4T//+Q927NiBo0eP4vr16xg2bNgz201OToarqyvi4uKQmZmJoKAgjB07Fr/88otQZ/bs2Vi3bh1WrVqF8+fPY/LkyRg6dCjS09MbbHtZ47Gzs8OiRYuQlpaG06dPo1+/fhgyZAjOnTsn1HmR2NLV1cXYsWNx8OBB5OTkYPny5YiKikJkZKRQh2Or+Tt16hTWrVsHV1fXGvNeJK4eqaysxMiRI9G7d+8a8ziutIsmx7AHDx5gwIABmDVrlkZtPhrFaXaombh58yYBoKNHjxIR0b1790hXV5d27Ngh1FEqlQSAUlJS6tT2wIEDKSgoSJi2tram1atXi+oMGzaMRo8e/cx2oqOjyd7engwMDCggIICWLl1KJiYmwvz8/HwaPHgwtW7dmuRyOb355pt06NAhYf68efOoS5cuNdp1c3Oj2bNnExHRkSNHyMPDgwwNDcnExIR69epFly9frtP2sppatmxJGzZsIKL6ja3PPvuM3nrrLWGaY6t5Ky4upo4dO9KhQ4eoT58+FB4eLsx72biaMWMGjRkzhqKjo0V/eyKOKyY+hj3uyJEjBIDu3r37zOUvXbpEdU0ZTp48Se7u7iSTyah79+60a9cuAkDp6elERFRVVUXjx4+ntm3bkr6+PnXq1ImWL18uLH/06FFq0aIFFRQUiNoNDw8XjpuXL1+mQYMGkampKRkaGpKzszPt3btX4z6+tiNBT7p//z4AwMzMDACQlpaGyspKvPPOO0IdR0dHvPHGG0hJSalz24/aBYDy8nLo6+uL6hgYGOD48eNPbePkyZOYMGECpk6dioyMDPj4+GDBggWiOiUlJRg4cCASEhKQnp6OAQMGwN/fH1euXAEAjB8/HkqlEqdOnRKWSU9PF0asqqqqEBAQgD59+iAzMxMpKSkIDg5untl7I1GpVNi2bRtKS0vh5eUFoP5iKz8/H/Hx8ejTp49QxrHVvIWGhsLPz08UO4+8TFwdPnwYO3bswJo1a2qdz3GlvWo7hjWGkpISDBo0CM7OzkhLS8PcuXPx+eefi+qo1WrY2dlhx44dOH/+PObMmYNZs2bhp59+AgC8/fbbcHBwwI8//igsU1lZidjYWIwfPx5A9f+p8vJyHDt2DFlZWVi8eDEUCoXmHa1TWveKUqlU5OfnR97e3kJZbGws6enp1ajr4eFBM2bM0Ljt7du3k56eHp09e1YoGzlyJDk7O1Nubi6pVCo6ePAgGRgY1Lq+x5cZOHCgqGzEiBE1PrE9qUuXLrRq1Sph+v3336eQkBBhOiwsjPr27UtERHfu3CEAlJiYqPH2sdplZmaSXC4nHR0dMjExEX2yeNnY8vLyIplMRgAoODiYVCqVMI9jq/naunUrde3alcrKyoiIaowEvWhc3b59m+zt7YVR8NpGgjiutM+zjmGPa6iRoHXr1pG5ubkQ70REa9euFY0E1SY0NJQ++OADYXrx4sXk5OQkTMfFxZFCoaCSkhIiInJxcaG5c+dq3K8nNYuRoNDQUJw9exbbtm2r03JdunSBQqGAQqHA+++/X2P+kSNHEBQUhKioKHTp0kUoX7FiBTp27AhHR0fo6elh6tSpCAoKglT69N2pVCrh6ekpKnsyKy8pKcHnn38OJycnmJqaQqFQQKlUCp+qAGDSpEnYunUrHj58iIqKCmzZskXIiM3MzDBu3Dj4+vrC398fK1asQEFBQZ32CavWuXNnZGRk4OTJkwgJCUFgYCDOnz+v8fLPiq3t27fjzJkz2LJlC/bu3YulS5cK8zi2mqerV68iPDwcsbGxNUZk6qK2uJo0aRJGjRqFt99++6nLcVxpn5c9hgHieHt0Dnw0/bTz5iNKpRKurq6ieK9tJGrNmjXo3r07WrVqBYVCgfXr14viZ9y4ccjPz8eJEycAVD+hOHz4cMjlcgDAp59+igULFsDb2xuRkZHIzMys0za+9iNBoaGhZGdnRxcvXhSVJyQk1JrdvvHGG7Rs2TIiqr6WmJeXR3l5eXTt2jVRvcTERJLL5bRu3bqnrrusrIyuXbtGarWaZsyYQc7Ozk+t6+7uTvPmzROVLV++XPSp6pNPPiEHBwfatWsXZWZmUl5eHrm5uYk+LVZWVpKlpSVt2bKFdu7cScbGxvTgwQNRu2fOnKFvvvmGvLy8SKFQ1Pk+FVZT//79KTg4mIhePrYe9+OPP5KBgQFVVVWJyjm2mpfdu3cTANLR0RF+AJBEIiEdHR2qqqp64bgyMTERtSuVSoV1bdy4UdQWx5X2evwY9rhnjQQ9Hm+JiYkEQJh+3rFt2rRp5OPjIyrLyMgQjQRt3bqV9PX1ac2aNXTmzBnKy8uj4OBgcnNzEy03bNgwCg4OpsLCQmrRogUdP35cNP/KlSu0du1aGjp0KOnq6tLKlSs12ylE9NomQWq1mkJDQ8nGxoZyc3NrzH90k+HOnTuFsuzsbI1uMjxy5AjJ5fIaNxI+TUVFBbVv355mzpz51Dq1DS1/9NFHogNK165daf78+cJ0cXExmZiYiA4oRNU3QL777rvk5+dXa1A/rmfPnhQWFqbRdrCn8/HxocDAQCJ6udh60qZNm6hFixZUUVFR63yOreahqKiIsrKyRD9vvvkmjRkzhrKysojoxePq/PnzonYXLFhARkZGlJWVRX/++Wety3BcaZ/Hj2GPa8zLYT/88IMoCZo6dSr169dPtFz//v1rJEH79u0jExMTmj9/PnXu3PmZ642IiCAXFxeN+/naJkEhISFkYmJCiYmJVFBQIPw8/glj8uTJ9MYbb9Dhw4fp9OnT5OXlRV5eXs9s9/Dhw2RoaEgzZ84UtXvnzh2hzokTJyguLo4uXLhAx44do379+lG7du2eGUQpKSkklUppyZIllJubS6tWrSJTU1PRAWXo0KHk7u5O6enplJGRQf7+/mRkZFTjgJKbmyt86jtx4oRQfvHiRYqIiKDk5GS6fPkyHThwgMzNzen777/XbKcyIqr+T3T06FG6dOkSZWZmUkREBEkkEjp48KBQ50Via/PmzbR9+3Y6f/48XbhwgbZv3042NjaiJ3Q4trTHk/cEEb1YXD2ptnuCOK60iybHsIKCAkpPT6eoqCgCQMeOHaP09HTRue5xdU2CiouLycLCgsaMGUPnzp2jvXv3UocOHURJ0IoVK8jY2Jji4+MpJyeHZs+eTcbGxjWSIJVKRfb29qSnp0eLFi0SzQsPD6f4+Hi6ePEipaWlkaenJw0fPlzjfr62SRCAWn+io6OFOmVlZTRlyhRq2bIlGRoa0tChQ2s8avekwMDAWtvt06ePUCcxMZGcnJxIJpORubk5ffzxx/THH388t88bN24kOzs7MjAwIH9//xqPm166dIl8fHzIwMCA7O3tafXq1bUeKImIevfuXePR08LCQgoICCBra2vS09OjNm3a0Jw5c0Q33rLnGz9+PLVp04b09PSoVatW1L9/f9HBg+jFYmvbtm30t7/9jRQKBcnlcnJ2dqZvvvlG9EmJY0t71Lb/XySunlRbEsRxpV00OYZFRkY+9xz6uBd5RD4lJYXc3NxIT0+P3N3dKS4uTpQEPXz4kMaNG0cmJiZkampKISEhFBERUSMJIiL65z//STo6OnT9+nVR+dSpU6l9+/Ykk8moVatW9PHHH9Pt27c17qOEiKhudxGxpkZE6NixI6ZMmYLp06c3dXdYM8KxxRoCxxV7WRMmTMCtW7fw888/12u7Leq1Ndbgbt26hW3btqGwsBBBQUFN3R3WjHBssYbAccVexv3795GVlYUtW7bUewIEcBL02mndujUsLCywfv16tGzZsqm7w5oRji3WEDiu2MsYMmQIUlNTMXnyZLz77rv13j5fDmOMMcaYVmoWX5bIGGOMMVZXnAQxxhhjTCtxEsQYY4wxrcRJEGOMMca0EidBjDUzly9fhkQiQUZGxlPrSCQS7Nmzp9H69Drr27cvpk2b1tTdYIw1AE6CGGsk48aNg0QiEX7Mzc0xYMCAur/1uB4UFBQ88w3Q9aWuyVZMTAxMTU0brD8NpaKiAt9++y3c3NxgaGgICwsLeHt7Izo6GpWVlRq1oUnyyhirX5wEMdaIBgwYgIKCAhQUFCAhIQEtWrTAoEGDGr0fVlZWkMlkjb7exqJSqaBWqxtlXRUVFfD19cWiRYsQHByM5ORkpKamIjQ0FKtWrcK5c+capR/1raKioqm7wFiD4ySIsUYkk8lgZWUFKysruLu7IyIiAlevXsWtW7eEOllZWejXrx8MDAxgbm6O4OBglJSUCPPVajXmz58POzs7yGQyuLu7Iz4+/qnrVKlUGD9+PBwdHXHlyhUA4hGaRyMQu3btgo+PDwwNDeHm5oaUlBRRO1FRUbC3t4ehoSGGDh2KZcuW1WnU5nnrSUxMRFBQEO7fvy+Mls2dOxcAUF5ejs8//xy2traQy+Xw9PREYmKi0PajEaSff/4Zzs7OkMlk2LBhA/T19XHv3j1RP8LDw9GvXz8AwJ07dzBy5EjY2trC0NAQLi4u2Lp1q8bbBADLly/HsWPHkJCQgNDQULi7u8PBwQGjRo3CyZMn0bFjRwBAfHw83nrrLZiamsLc3ByDBg3ChQsXhHbatWsHAOjWrRskEgn69u0rzNuwYQOcnJygr68PR0dHfP/996I+JCcnw93dHfr6+njzzTexZ8+eGqNKR48eRY8ePSCTyWBtbY2IiAhUVVUJ8/v27YupU6di2rRpsLCwgK+vL8aPH18jSa+srETr1q2xcePGOu0nxl5JdXobGmPshQUGBtKQIUOE6eLiYvrkk0+oQ4cOwgsjS0pKyNramoYNG0ZZWVmUkJBA7dq1o8DAQGG5ZcuWkbGxMW3dupWys7NpxowZpKurS7m5uUT0vxcdpqen08OHD2no0KHUrVs3unnzptAGANq9e7eovqOjI/3yyy+Uk5NDH374IbVp04YqKyuJiOj48ePCG8VzcnJozZo1ZGZmVuNFnU+qy3rKy8tp+fLlZGxsTAUFBVRQUEDFxcVERDRx4kTq1asXHTt2jPLz82nJkiUkk8mEbY6OjiZdXV3q1asXJSUlUXZ2NpWUlJClpSVt2LBB6E9VVZWo7Nq1a7RkyRJKT0+nCxcu0MqVK0lHR4dOnjwpLPO0F4I+4urqSu+9994z9wMR0c6dOykuLo7y8vIoPT2d/P39ycXFRfjbp6amEgD673//SwUFBcLbvDdv3kzW1tYUFxdHFy9epLi4ODIzM6OYmBgiIrp//z6ZmZkJb+vet28fderUSfSiymvXrpGhoSFNmTKFlEol7d69mywsLCgyMlK0nQqFgr744gvKzs6m7OxsSkpKqvHSyl27dpFcLhf+Noy9zjgJYqyRBAYGko6ODsnlcpLL5QSArK2tKS0tTaizfv16atmyJZWUlAhle/fuJalUSoWFhUREZGNjQ19//bWobQ8PD5oyZQoR/S/Z+PXXX6l///701ltv0b1790T1a0tOHk8Wzp07RwBIqVQSEdGIESPIz89P1Mbo0aNfKAl61npqewP677//Tjo6OjXeet6/f3+aOXOmsBwAysjIENUJDw+nfv36CdMHDhwgmUxGd+/efWqf/fz86B//+Icw/bwkyMDAgD799NOnzn+aW7duEQDKysoiInHy+rj27dvTli1bRGVfffUVeXl5ERHR2rVrydzcnMrKyoT5UVFRorZmzZpFnTt3JrVaLdRZs2YNKRQKIQnr06cPdevWrUY/nZ2dafHixcK0v78/jRs3rs7by9iriC+HMdaIfHx8kJGRgYyMDKSmpsLX1xfvv/8+fv/9dwCAUqmEm5sb5HK5sIy3tzfUajVycnJQVFSE69evw9vbW9Sut7c3lEqlqGzkyJEoLS3FwYMHYWJi8ty+ubq6Cr9bW1sDAG7evAkAyMnJQY8ePUT1n5zW1LPWU5usrCyoVCp06tQJCoVC+Dl69KjocpKenp6obQAYPXo0EhMTcf36dQBAbGws/Pz8hMt4KpUKX331FVxcXGBmZgaFQoEDBw4Ilw01QRq+eSgvLw8jR46Eg4MDjI2N0bZtWwB45rpKS0tx4cIFTJgwQbTtCxYsELY9JycHrq6u0NfXF5Z78m+jVCrh5eUFiUQilHl7e6OkpATXrl0Tyrp3716jDxMnTkR0dDQA4MaNG9i/fz/Gjx+v0TYz9qrjF6gy1ojkcjk6dOggTG/YsAEmJiaIiorCggUL6nVdAwcOxObNm5GSkiLcA/Msurq6wu+PTpYNcXNxXddTUlICHR0dpKWlQUdHRzRPoVAIvxsYGIhO8gDg4eGB9u3bY9u2bQgJCcHu3bsRExMjzF+yZAlWrFiB5cuXw8XFBXK5HNOmTavTTcGdOnVCdnb2c+v5+/ujTZs2iIqKgo2NDdRqNbp27frMdT26FywqKgqenp6ieU/ui/rwePL9yNixYxEREYGUlBQkJyejXbt26N27d72vm7GmwEkQY01IIpFAKpWirKwMAODk5ISYmBiUlpYKJ6SkpCRIpVJ07twZxsbGsLGxQVJSEvr06SO0k5SUVOPTf0hICLp27YrBgwdj7969ovp11blzZ5w6dUpU9uR0fdDT04NKpRKVdevWDSqVCjdv3nyhk+/o0aMRGxsLOzs7SKVS+Pn5CfOSkpIwZMgQjBkzBkB1MpabmwtnZ2eN2x81ahRmzZqF9PR0dOvWTTSvsrISFRUVePjwIXJychAVFSVsw/Hjx0V19fT0AEC0/ZaWlrCxscHFixcxevToWtffuXNnbN68GeXl5cITf0/+bZycnBAXFwciEhLFpKQkGBkZwc7O7pnbZ25ujoCAAERHRyMlJQVBQUHP2yWMvTb4chhjjai8vByFhYUoLCyEUqlEWFgYSkpK4O/vD6D6hK2vr4/AwECcPXsWR44cQVhYGD7++GNYWloCAL744gssXrwY27dvR05ODiIiIpCRkYHw8PAa6wsLC8OCBQswaNCgGifduggLC8O+ffuwbNky5OXlYd26ddi/f3+NkZeX1bZtW5SUlCAhIQG3b9/GgwcP0KlTJ4wePRpjx47Frl27cOnSJaSmpmLhwoXYu3fvc9scPXo0zpw5g6+//hoffvih6KsBOnbsiEOHDiE5ORlKpRKffPIJbty4Uac+T5s2Dd7e3ujfvz/WrFmD3377DRcvXsRPP/2Enj17Ii8vDy1btoS5uTnWr1+P/Px8HD58GNOnTxe107p1axgYGCA+Ph43btzA/fv3AQDz5s3DwoULsXLlSuTm5iIrKwvR0dFYtmwZgOokTK1WIzg4GEqlEgcOHMDSpUsB/G+kbcqUKbh69SrCwsKQnZ2Nf//734iMjMT06dMhlT7/NDBx4kRs2rQJSqUSgYGBddo/jL3SmvqmJMa0RWBgIAEQfoyMjMjDw4N27twpqpeZmUk+Pj6kr69PZmZmNGnSJNGTOCqViubOnUu2trakq6tLbm5utH//fmF+bTfYfvfdd2RkZERJSUlEVPsNy4/Xv3v3LgGgI0eOCGXr168nW1tbMjAwoICAAFqwYAFZWVk9c5tfZD2TJ08mc3NzAiA8vVRRUUFz5syhtm3bkq6uLllbW9PQoUMpMzOTiGq/ofpxPXr0IAB0+PBhUfmdO3doyJAhpFAoqHXr1jR79mwaO3as6Cm+590YTUT08OFDWrhwIbm4uAh/N29vb4qJiRGesDt06BA5OTmRTCYjV1dXSkxMFO0fouobmu3t7UkqlVKfPn2E8tjYWHJ3dyc9PT1q2bIlvf3227Rr1y5hflJSErm6upKenh51796dtmzZQgAoOztbqJOYmEgeHh6kp6dHVlZW9OWXXwp9e952qtVqatOmDQ0cOPCZ+4Gx142ESMO7+hhj7DGTJk1CdnY2fv3116buCntCbGys8J1LBgYGL91eSUkJbG1tER0djWHDhtVDDxl7NfA9QYwxjSxduhTvvvsu5HI59u/fj02bNtX40j7WNP71r3/BwcEBtra2+O233/Dll19i+PDhL50AqdVq3L59G9999x1MTU0xePDgeuoxY68GToIYYxpJTU3Ft99+i+LiYjg4OGDlypWYOHFiU3eLASgsLMScOXNQWFgIa2tr/P3vf8fXX3/90u1euXIF7dq1g52dHWJiYtCiBZ8yWPPCl8MYY4wxppX46TDGGGOMaSVOghhjjDGmlTgJYowxxphW4iSIMcYYY1qJkyDGGGOMaSVOghhjjDGmlTgJYowxxphW4iSIMcYYY1qJkyDGGGOMaaX/D6CLLUMZyUi0AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}