07 - final-descriptive-feature-engineering-combined.ipynb

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   "cell_type": "code",
   "execution_count": 501,
   "id": "7eb156bc",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1261 entries, 0 to 1260\n",
      "Data columns (total 23 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   Employee_No          1261 non-null   int64         \n",
      " 1   Employee_Code        1261 non-null   int64         \n",
      " 2   Name                 1261 non-null   object        \n",
      " 3   Title                1261 non-null   object        \n",
      " 4   Address_Line_1       1261 non-null   object        \n",
      " 5   Address_Line_2       1181 non-null   object        \n",
      " 6   Address_Line_3       952 non-null    object        \n",
      " 7   Date_of_Birth        1261 non-null   object        \n",
      " 8   Gender               1261 non-null   object        \n",
      " 9   Religion_ID          1261 non-null   int64         \n",
      " 10  Marital_Status       1170 non-null   object        \n",
      " 11  Designation_ID       1261 non-null   int64         \n",
      " 12  Date_Joined          1261 non-null   datetime64[ns]\n",
      " 13  Date_Resigned        955 non-null    object        \n",
      " 14  Status               1261 non-null   object        \n",
      " 15  Status_Reason        1028 non-null   object        \n",
      " 16  Inactive_Date        1134 non-null   object        \n",
      " 17  Reporting_emp_1      76 non-null     float64       \n",
      " 18  Reporting_emp_2      1 non-null      float64       \n",
      " 19  Employment_Category  1261 non-null   object        \n",
      " 20  Employment_Type      1261 non-null   object        \n",
      " 21  Religion             1261 non-null   object        \n",
      " 22  Designation          1261 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(4), object(16)\n",
      "memory usage: 226.7+ KB\n",
      "Total employees 1261\n",
      "Earliest join date 1988-01-27 00:00:00\n",
      "Latest join date 2022-12-20 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# This Jupyter Notebook does feature engineering joining all features in all tables.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, r2_score, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# We start analysis for the below period where attendance system is properly implemented at the given organization.\n",
    "analysis_period_start_time = pd.to_datetime('2020-02-01 00:00:00')\n",
    "analysis_period_end_time = pd.to_datetime('2022-12-31 23:59:59')\n",
    "\n",
    "# Read employees dataset\n",
    "df_employee_raw = pd.read_csv('data/employees4.csv', na_values=['\\\\N'], on_bad_lines='skip', infer_datetime_format=True, parse_dates=['Date_Joined', 'Date_Resigned', 'Inactive_Date'])\n",
    "df_employee_raw.info()\n",
    "print(f'Total employees {str(df_employee_raw[\"Employee_Code\"].nunique())}')\n",
    "print(f'Earliest join date {str(df_employee_raw[\"Date_Joined\"].min())}')\n",
    "print(f'Latest join date {str(df_employee_raw[\"Date_Joined\"].max())}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47df7478",
   "metadata": {},
   "source": [
    "# Loading, Preprocessing and Descriptive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "id": "ca358009",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "      Employee_No Date_of_Birth  Gender Marital_Status Date_Joined  \\\n58            426    1962-05-10    Male        Married  2020-07-03   \n73            450    1976-05-27    Male        Married  2020-12-03   \n78            457    1967-03-20    Male        Married  2020-06-01   \n79            460    1955-03-10    Male        Married  2020-03-01   \n83            471    1992-08-16    Male         Single  2021-04-01   \n...           ...           ...     ...            ...         ...   \n1256         2836    1993-03-23  Female         Single  2022-11-01   \n1257         2890    1994-10-06    Male         Single  2022-11-21   \n1258         2972    1997-01-02    Male         Single  2022-12-05   \n1259         2973    1990-08-16    Male         Single  2022-12-08   \n1260         3041    1979-09-13    Male        Married  2022-12-20   \n\n     Date_Resigned    Status Status_Reason Inactive_Date Employment_Category  \\\n58      2021-10-26  Inactive        RESIGN    2021-10-26               Staff   \n73      2021-04-09  Inactive        RESIGN    2021-04-09              Labour   \n78             NaN  Inactive        Resign    2020-09-25              Labour   \n79      0000-00-00  Inactive        Resign    2020-03-31              Labour   \n83      2022-02-28  Inactive        RESIGN    2022-02-28              Labour   \n...            ...       ...           ...           ...                 ...   \n1256    2022-12-22  Inactive        Resign    2022-12-22               Staff   \n1257    0000-00-00    Active           NaN    0000-00-00               Staff   \n1258           NaN    Active           NaN           NaN               Staff   \n1259    0000-00-00    Active           NaN    0000-00-00               Staff   \n1260           NaN    Active           NaN           NaN          Management   \n\n     Employment_Type         Designation  \n58    Contarct Basis         Sociologist  \n73    Contarct Basis     Unskilled Labor  \n78    Contarct Basis     Unskilled Labor  \n79    Contarct Basis  Semi Skilled Labor  \n83    Contarct Basis       Skilled Labor  \n...              ...                 ...  \n1256  Contarct Basis   Account Executive  \n1257  Contarct Basis   Quantity Surveyor  \n1258  Contarct Basis  Software Developer  \n1259  Contarct Basis   Quantity Surveyor  \n1260  Contarct Basis          Accountant  \n\n[796 rows x 12 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Employee_No</th>\n      <th>Date_of_Birth</th>\n      <th>Gender</th>\n      <th>Marital_Status</th>\n      <th>Date_Joined</th>\n      <th>Date_Resigned</th>\n      <th>Status</th>\n      <th>Status_Reason</th>\n      <th>Inactive_Date</th>\n      <th>Employment_Category</th>\n      <th>Employment_Type</th>\n      <th>Designation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>58</th>\n      <td>426</td>\n      <td>1962-05-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-07-03</td>\n      <td>2021-10-26</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-10-26</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Sociologist</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>450</td>\n      <td>1976-05-27</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-12-03</td>\n      <td>2021-04-09</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-04-09</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>457</td>\n      <td>1967-03-20</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-06-01</td>\n      <td>NaN</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-09-25</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>460</td>\n      <td>1955-03-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-03-01</td>\n      <td>0000-00-00</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-03-31</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Semi Skilled Labor</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>471</td>\n      <td>1992-08-16</td>\n      <td>Male</td>\n      <td>Single</td>\n      <td>2021-04-01</td>\n      <td>2022-02-28</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2022-02-28</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Skilled Labor</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    </tr>\n    <tr>\n      <th>1256</th>\n      <td>2836</td>\n      <td>1993-03-23</td>\n      <td>Female</td>\n      <td>Single</td>\n      <td>2022-11-01</td>\n      <td>2022-12-22</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2022-12-22</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Account Executive</td>\n    </tr>\n    <tr>\n      <th>1257</th>\n      <td>2890</td>\n      <td>1994-10-06</td>\n      <td>Male</td>\n      <td>Single</td>\n      <td>2022-11-21</td>\n      <td>0000-00-00</td>\n      <td>Active</td>\n      <td>NaN</td>\n      <td>0000-00-00</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Quantity Surveyor</td>\n    </tr>\n    <tr>\n      <th>1258</th>\n      <td>2972</td>\n      <td>1997-01-02</td>\n      <td>Male</td>\n      <td>Single</td>\n      <td>2022-12-05</td>\n      <td>NaN</td>\n      <td>Active</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Software Developer</td>\n    </tr>\n    <tr>\n      <th>1259</th>\n      <td>2973</td>\n      <td>1990-08-16</td>\n      <td>Male</td>\n      <td>Single</td>\n      <td>2022-12-08</td>\n      <td>0000-00-00</td>\n      <td>Active</td>\n      <td>NaN</td>\n      <td>0000-00-00</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Quantity Surveyor</td>\n    </tr>\n    <tr>\n      <th>1260</th>\n      <td>3041</td>\n      <td>1979-09-13</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2022-12-20</td>\n      <td>NaN</td>\n      <td>Active</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Management</td>\n      <td>Contarct Basis</td>\n      <td>Accountant</td>\n    </tr>\n  </tbody>\n</table>\n<p>796 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean employee table\n",
    "# Drop Employee_Code because it's not referenced in other tables.\n",
    "# Other fields are dropped\n",
    "df_employee = df_employee_raw.drop(['Employee_Code', 'Name', 'Address_Line_1', 'Address_Line_2', 'Address_Line_3',\n",
    "                                    'Reporting_emp_1', 'Reporting_emp_2', 'Title', 'Religion', 'Religion_ID', 'Designation_ID'], axis=1)\n",
    "\n",
    "date_range = (df_employee['Date_Joined'] >= analysis_period_start_time) & (df_employee['Date_Joined'] <= analysis_period_end_time)\n",
    "df_employee = df_employee.loc[date_range]\n",
    "df_employee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "id": "96fcdc3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array(['Male', 'Female'], dtype=object)"
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee['Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "id": "1b12bbf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array(['Married', 'Single', nan], dtype=object)"
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee['Marital_Status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "e05d21c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_210609/3760665125.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  df_employee['Date_Joined'].describe()\n"
     ]
    },
    {
     "data": {
      "text/plain": "count                     796\nunique                    369\ntop       2021-02-01 00:00:00\nfreq                       23\nfirst     2020-02-01 00:00:00\nlast      2022-12-20 00:00:00\nName: Date_Joined, dtype: object"
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee['Date_Joined'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "id": "55a40927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   Employee_No  Leave_Count\n0          347         52.5\n1          348         21.5\n2          351         57.0\n3          352        105.5\n4          354         79.5",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Employee_No</th>\n      <th>Leave_Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>347</td>\n      <td>52.5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>348</td>\n      <td>21.5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>351</td>\n      <td>57.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>352</td>\n      <td>105.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>354</td>\n      <td>79.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read leaves dataset, filter out special leave categories.\n",
    "df_leaves = pd.read_csv('data/leaves_clean.csv')\n",
    "df_leaves = df_leaves.rename(columns={'leave_date': 'Leave_Date',\n",
    "                                      'Type': 'Leave_Factor',\n",
    "                                      'Applied Date': 'Applied_Date',\n",
    "                                      'leave_leave_date': 'Lieu_Leave_Date',\n",
    "                                      'Remarks': 'Remarks',\n",
    "                                      'apply_type': 'Leave_Category'})\n",
    "df_leaves_type_map = {'Half Day': 0.5, 'Full Day': 1}\n",
    "df_leaves['Leave_Factor'] = df_leaves['Leave_Factor'].map(df_leaves_type_map)\n",
    "df_leaves_legit = pd.DataFrame(df_leaves.loc[(df_leaves['Leave_Category'] != 'Liue Leave') & (df_leaves['Leave_Category'] != 'Curfew') & (df_leaves['Leave_Category'] != 'Sat Holiday') & (df_leaves['Leave_Category'] != 'Special Leave')])\n",
    "df_leaves_summary_temp = df_leaves.groupby('Employee_No')['Leave_Factor'].sum()\n",
    "df_leaves_summary = pd.DataFrame({'Employee_No': df_leaves_summary_temp.index, 'Leave_Count':  df_leaves_summary_temp.values})\n",
    "df_leaves_summary.head()\n",
    "#df_leaves_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "id": "e6a698f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     Employee_No Date_of_Birth  Gender Marital_Status Date_Joined  \\\n0            426    1962-05-10    Male        Married  2020-07-03   \n1            450    1976-05-27    Male        Married  2020-12-03   \n2            457    1967-03-20    Male        Married  2020-06-01   \n3            460    1955-03-10    Male        Married  2020-03-01   \n4            471    1992-08-16    Male         Single  2021-04-01   \n..           ...           ...     ...            ...         ...   \n791         2836    1993-03-23  Female         Single  2022-11-01   \n792         2890    1994-10-06    Male         Single  2022-11-21   \n793         2972    1997-01-02    Male         Single  2022-12-05   \n794         2973    1990-08-16    Male         Single  2022-12-08   \n795         3041    1979-09-13    Male        Married  2022-12-20   \n\n    Date_Resigned    Status Status_Reason Inactive_Date Employment_Category  \\\n0      2021-10-26  Inactive        RESIGN    2021-10-26               Staff   \n1      2021-04-09  Inactive        RESIGN    2021-04-09              Labour   \n2             NaN  Inactive        Resign    2020-09-25              Labour   \n3      0000-00-00  Inactive        Resign    2020-03-31              Labour   \n4      2022-02-28  Inactive        RESIGN    2022-02-28              Labour   \n..            ...       ...           ...           ...                 ...   \n791    2022-12-22  Inactive        Resign    2022-12-22               Staff   \n792    0000-00-00    Active           NaN    0000-00-00               Staff   \n793           NaN    Active           NaN           NaN               Staff   \n794    0000-00-00    Active           NaN    0000-00-00               Staff   \n795           NaN    Active           NaN           NaN          Management   \n\n    Employment_Type         Designation  Leave_Count  \n0    Contarct Basis         Sociologist         11.0  \n1    Contarct Basis     Unskilled Labor         60.0  \n2    Contarct Basis     Unskilled Labor          0.0  \n3    Contarct Basis  Semi Skilled Labor          0.0  \n4    Contarct Basis       Skilled Labor         73.5  \n..              ...                 ...          ...  \n791  Contarct Basis   Account Executive          5.0  \n792  Contarct Basis   Quantity Surveyor          9.0  \n793  Contarct Basis  Software Developer          4.0  \n794  Contarct Basis   Quantity Surveyor          2.0  \n795  Contarct Basis          Accountant          3.0  \n\n[796 rows x 13 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Employee_No</th>\n      <th>Date_of_Birth</th>\n      <th>Gender</th>\n      <th>Marital_Status</th>\n      <th>Date_Joined</th>\n      <th>Date_Resigned</th>\n      <th>Status</th>\n      <th>Status_Reason</th>\n      <th>Inactive_Date</th>\n      <th>Employment_Category</th>\n      <th>Employment_Type</th>\n      <th>Designation</th>\n      <th>Leave_Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>426</td>\n      <td>1962-05-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-07-03</td>\n      <td>2021-10-26</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-10-26</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Sociologist</td>\n      <td>11.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>450</td>\n      <td>1976-05-27</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-12-03</td>\n      <td>2021-04-09</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-04-09</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n      <td>60.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>457</td>\n      <td>1967-03-20</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-06-01</td>\n      <td>NaN</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-09-25</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>460</td>\n      <td>1955-03-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-03-01</td>\n      <td>0000-00-00</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-03-31</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Semi Skilled Labor</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>471</td>\n      <td>1992-08-16</td>\n      <td>Male</td>\n      <td>Single</td>\n      <td>2021-04-01</td>\n      <td>2022-02-28</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2022-02-28</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Skilled Labor</td>\n      <td>73.5</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>791</th>\n      <td>2836</td>\n      <td>1993-03-23</td>\n      <td>Female</td>\n      <td>Single</td>\n      <td>2022-11-01</td>\n      <td>2022-12-22</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2022-12-22</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Account Executive</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>792</th>\n      <td>2890</td>\n      <td>1994-10-06</td>\n      <td>Male</td>\n      <td>Single</td>\n      <td>2022-11-21</td>\n      <td>0000-00-00</td>\n      <td>Active</td>\n      <td>NaN</td>\n      <td>0000-00-00</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Quantity Surveyor</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>793</th>\n      <td>2972</td>\n      <td>1997-01-02</td>\n      <td>Male</td>\n      <td>Single</td>\n      <td>2022-12-05</td>\n      <td>NaN</td>\n      <td>Active</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Software Developer</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>794</th>\n      <td>2973</td>\n      <td>1990-08-16</td>\n      <td>Male</td>\n      <td>Single</td>\n      <td>2022-12-08</td>\n      <td>0000-00-00</td>\n      <td>Active</td>\n      <td>NaN</td>\n      <td>0000-00-00</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Quantity Surveyor</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>795</th>\n      <td>3041</td>\n      <td>1979-09-13</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2022-12-20</td>\n      <td>NaN</td>\n      <td>Active</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Management</td>\n      <td>Contarct Basis</td>\n      <td>Accountant</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>796 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group two datasets\n",
    "\n",
    "df_employee = pd.merge(df_employee, df_leaves_summary, on='Employee_No', how='left')\n",
    "df_employee['Leave_Count'].fillna(0, inplace=True)\n",
    "df_employee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "id": "68c0a17b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_plot = df_employee['Employment_Type'].value_counts().plot(kind='bar')\n",
    "temp_plot.bar_label(temp_plot.containers[0], labels=df_employee['Employment_Type'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "id": "f5f91ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_plot = df_employee['Status'].value_counts().plot(kind='bar')\n",
    "temp_plot.bar_label(temp_plot.containers[0], labels=df_employee['Status'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "c400fbf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_plot = df_employee['Gender'].value_counts().plot(kind='bar')\n",
    "temp_plot.bar_label(temp_plot.containers[0], labels=df_employee['Gender'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "71f24801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df_employee['Status'].value_counts(), labels=df_employee['Status'].unique(), autopct='%1.1f%%')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "id": "83142f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# temp_plot = df_employee['Designation'].value_counts().plot(kind='pie')\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "id": "6f9adfe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.PairGrid at 0x7f78c1441d90>"
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 500x500 with 6 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.pairplot(df_employee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "481b3f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count                            603\nmean     217 days 06:17:18.805970148\nstd      183 days 21:03:40.576458962\nmin                  1 days 00:00:00\n25%                 81 days 00:00:00\n50%                169 days 00:00:00\n75%                296 days 00:00:00\nmax                933 days 00:00:00\nName: Employment_Duration, dtype: object"
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_inactive_contract_based = pd.DataFrame(df_employee.loc[(df_employee['Status'] == 'Inactive') & (df_employee['Employment_Type'] == 'Contarct Basis')])\n",
    "df_employee_inactive_contract_based['Date_Joined'] = pd.to_datetime(df_employee_inactive_contract_based['Date_Joined'])\n",
    "df_employee_inactive_contract_based['Date_Resigned'] = pd.to_datetime(df_employee_inactive_contract_based['Date_Resigned'], errors='coerce')\n",
    "df_employee_inactive_contract_based['Employment_Duration'] = df_employee_inactive_contract_based['Date_Resigned'].fillna(df_employee_inactive_contract_based['Inactive_Date']) - df_employee_inactive_contract_based['Date_Joined'] + pd.Timedelta(days=1)\n",
    "df_employee_inactive_contract_based = df_employee_inactive_contract_based.drop(df_employee_inactive_contract_based[df_employee_inactive_contract_based['Employment_Duration'] < pd.Timedelta(days=0)].index)\n",
    "df_employee_inactive_contract_based['Unavailability_Factor'] = df_employee_inactive_contract_based['Leave_Count'] / df_employee_inactive_contract_based['Employment_Duration'].dt.days\n",
    "df_employee_inactive_contract_based = df_employee_inactive_contract_based.drop(df_employee_inactive_contract_based[df_employee_inactive_contract_based['Unavailability_Factor'] >= 1].index)\n",
    "\n",
    "# Annotating attrition or not. If the resignation date is a multiple of 1 year contract, we don't consider that as attrition.\n",
    "df_employee_inactive_contract_based.loc[df_employee_inactive_contract_based['Employment_Duration'].dt.days % 365 == 0, 'Is_Attrition'] = 0\n",
    "df_employee_inactive_contract_based.loc[df_employee_inactive_contract_based['Employment_Duration'].dt.days % 365 != 0, 'Is_Attrition'] = 1\n",
    "df_employee_inactive_contract_based['Employment_Duration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "7ec0b0ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count                            152\nmean     458 days 21:09:27.421052632\nstd      258 days 03:59:08.662802828\nmin                 12 days 23:59:59\n25%                296 days 17:59:59\n50%                368 days 11:59:59\n75%                537 days 11:59:59\nmax               1065 days 23:59:59\nName: Employment_Duration, dtype: object"
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Active contract base employee analysis.\n",
    "from datetime import datetime\n",
    "df_employee_active_contract_based = pd.DataFrame(df_employee.loc[(df_employee['Status'] == 'Active') & (df_employee['Employment_Type'] == 'Contarct Basis')])\n",
    "df_employee_active_contract_based['Date_Joined'] = pd.to_datetime(df_employee_active_contract_based['Date_Joined'])\n",
    "df_employee_active_contract_based['Employment_Duration'] = analysis_period_end_time - df_employee_active_contract_based['Date_Joined'] + pd.Timedelta(days=1)\n",
    "df_employee_active_contract_based['Unavailability_Factor'] = df_employee_active_contract_based['Leave_Count'] / df_employee_active_contract_based['Employment_Duration'].dt.days\n",
    "df_employee_active_contract_based = df_employee_active_contract_based.drop(df_employee_active_contract_based[df_employee_active_contract_based['Unavailability_Factor'] >= 1].index)\n",
    "# Annotating active employees as non-attrition.\n",
    "df_employee_active_contract_based['Is_Attrition'] = 0\n",
    "df_employee_active_contract_based['Employment_Duration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "27d0dec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count    603.000000\nmean      39.015755\nstd       34.120112\nmin        0.000000\n25%        9.000000\n50%       33.000000\n75%       59.750000\nmax      163.000000\nName: Leave_Count, dtype: float64"
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_inactive_contract_based['Leave_Count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "35cb3177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count    152.000000\nmean      55.019737\nstd       50.304565\nmin        0.000000\n25%       12.500000\n50%       49.250000\n75%       82.375000\nmax      305.000000\nName: Leave_Count, dtype: float64"
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_active_contract_based['Leave_Count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "id": "cb5689f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count    603.000000\nmean       0.227270\nstd        0.208634\nmin        0.000000\n25%        0.050198\n50%        0.201316\n75%        0.333333\nmax        0.961538\nName: Unavailability_Factor, dtype: float64"
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_inactive_contract_based['Unavailability_Factor'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "id": "746368b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count    152.000000\nmean       0.127464\nstd        0.112425\nmin        0.000000\n25%        0.045296\n50%        0.108324\n75%        0.190642\nmax        0.626283\nName: Unavailability_Factor, dtype: float64"
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_active_contract_based['Unavailability_Factor'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "4985e09a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Joining tables for attrition.\n",
    "df_employee_for_modeling = pd.concat([df_employee_inactive_contract_based, df_employee_active_contract_based])\n",
    "df_employee_for_modeling['Employment_Duration'] = df_employee_for_modeling['Employment_Duration'].dt.days\n",
    "temp_plot = df_employee_for_modeling['Is_Attrition'].value_counts().plot(kind='bar')\n",
    "temp_plot.bar_label(temp_plot.containers[0], labels=df_employee_for_modeling['Is_Attrition'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "fd152133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Frequency')"
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define a colormap\n",
    "cmap = plt.cm.get_cmap('viridis')\n",
    "\n",
    "# Plot the histogram with colored bars and axis labels\n",
    "plt.hist(df_employee_for_modeling['Employment_Duration'], bins=50, color=cmap(0.5))\n",
    "plt.title('Distribution of Employment Duration')\n",
    "plt.xlabel('Employment Duration (Days)')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "id": "7aa98177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_plot = df_employee_for_modeling['Status'].value_counts().plot(kind='bar')\n",
    "temp_plot.bar_label(temp_plot.containers[0], labels=df_employee_for_modeling['Is_Attrition'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f7282a9",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "86478259",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "id": "2bcc0ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_plot = df_employee_for_modeling['Gender'].value_counts().plot(kind='bar')\n",
    "temp_plot.bar_label(temp_plot.containers[0], labels=df_employee_for_modeling['Gender'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "id": "6080573c",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_map = {'Female': 0, 'Male': 1}\n",
    "material_status_map = {'Single': 0, 'Married': 1}\n",
    "df_employee_for_modeling['Gender_Code'] = df_employee_for_modeling['Gender'].map(gender_map)\n",
    "df_employee_for_modeling['Material_Status_Code'] = df_employee_for_modeling['Marital_Status'].map(material_status_map)\n",
    "df_employee_for_modeling['Material_Status_Code'] = df_employee_for_modeling['Material_Status_Code'].fillna(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "id": "ee349727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_plot = df_employee_for_modeling['Employment_Type'].value_counts().plot(kind='bar')\n",
    "temp_plot.bar_label(temp_plot.containers[0], labels=df_employee_for_modeling['Employment_Type'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "fea75346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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hhDbIkrpGEnAKIYQQQmiDXKWukSypCyGEEEKIHCUznEIIIYQQWpBbN37/HEjAKYQQQgihDbKkrpEsqQshhBBCiBwlM5xCCCGEENogS+oaScAphBBCCKENcuN3jSTg/I+aMGECL168YPXq1bndFCGEEEJkhcxwaiQB53tMmDCBPXv2pEuvXbs2GzZsyIUW/XfVr1+fJ0+esGvXLipXrqxKnzt3Lj4+PmzdujX3GvcRLl27wcb/eXHb5z6hYeGsmD+VBt/UzO1mpVPNvSE1PZphbmtJyL0n/DFzK4EXfTXmL1q9DI2ndsOuVEFehkRyZs0BLm0/pjru0rkeldrVxs6xMADBN/w5tmgXT/59oFaPeT4rGk3sTMm6ldA3MiDswVP2jVtH8M2HOdLPjIyZMAT3nh2xsMzDlUvXmThmFr4+9zXmb9qiEcNHe+DgUAR9fT0e+AXwo+dGvHbtV8uXv4AdU2eOoX6jbzAyMuTB/YeMHDqF69du5XSXVCy7NMOqT3v0bPOScD+AkHlribuc8fmNXcpjO6YXBsULozAyJDEohKhdh4jYvDfD/OZN62D//QReHj1L0JDZOdiL9+s3uietu7bA3MKcW1dvs3jSch7cfagxf6vvmtOsQ2OKOzoA4HPDl9Xzf+L2NZ8M8/cY0pXBk/qz46dfWTbdMye6oObbEe2o3qUBJhamBF67j/fUjTy79zjTMk5NvqLJ6A5YF8lHWOAzfl+yi5uHL6mOF/+qDHX7N6egU3Es8lmxsf9Sbv15KV09diXsaTbhO4pXL4tCR8Gze4/ZOngFkUFhWutfFfeGfO3RDDNbS0LvPeHIzK08ymS8KVK9DA2ndsP29Xhzbs0Brrw13rytXIuvaeM5FN/Dl/Dqv0yVXnNQSxybVMW6hD1J8Qk8vnyPvxbsJPxBsNb6JXKOXDSUBa6urpw+fVrt8f333+d2s/6TDA0NWbJkSW43Q6vi4uJxLFmcSaMG5XZTNCrf/GuaTHPnlOc+1jSbTOAFH7ptHoeFvXWG+S0L29J101gCL/iwptlkTq3ah9uM7pR1q6bKU6xGWW7u/4fNneeyoc10ooKe4751Aub5rFR5jPKY0Gf3dJITk9neYxGrGo7j8NztxL+IzfE+vzFkRF8GDO7JxLGzaVKvA6Ehofyy92dMzUw1lomMiGL5kjU0a9SZurVasXO7NytWz6Nug9qqPBaWefjt8A4SE5P4rl0/vqnenBlTFhIV9eJTdAsAc7dvsJvoQfianQS0GULspVsUWjcbvQK2GeZPiYsnYvtvBHYbi3+z/oT/uAOb4T2w6OiWLq+evR224/oSe/FGTnfjvboP7kKX/h1ZPHk5PZt6EBYazsqdSzExNdZYpkrNyhzee4yBHUbQp+Ugnj4JYeWOJdjmt0mXt2ylMrTp1oJ7tzR/CdGmegNa8E2fpuyZtpEVLSfzIjSS/tsmYWhqpLFMUZdSdPMcxuU9p1nadAKX95zG3XM4RSqXUOUxMDEk6E4ge6Zt1FiPdRE7BnvNIMQviB+7zOZ7twkc/WEPSa8Stda/ss2/ptE0d8547mN9s8k8uuBD583jyKNhvLEobEunTWN5dMGH9c0mc3bVPr6d0R3Ht8abN/IUtKHB5K4Enk//xaFI9TJc3nKUTa2n879uC9DR0+W7rRPQNzbUWt8+WkqKdh5fIAk4s8DAwABbW1u1h4WFBQCOjo7s3LkTDw8PKlWqhJubG1evXiUgIAB3d3cqV65Mp06dCAwMVNW3cuVKWrVqxc6dO6lTpw6VKlVi2LBhvHih+R+yhIQE5syZQ40aNXBycqJLly5cv34dAKVSSaNGjdLNuN69e5cyZcqozv3y5UumTp1KjRo1cHFxoXv37vj4qH+o//rrL9q2bYuTkxMNGjTA09OTpKSkLL9WnTp14tq1a5w4cUJjnpSUFDw9Pfnmm2+oUKECrVq14uTJk1k+x6fmWqMaw/r3oFHdWrndFI1q9HXjyq7jXNl5nOf3g/hj1jaigsOo2q1hhvmrdm1AVFAYf8zaxvP7QVzZeZyrv5ygZv9mqjzew1dzcetRnt4O4LlfMPvHr0eho0PxWuVVeWoPbEFUcBj7xq7jyb8PiHz8HP8zt4gIDMnxPr/Rf2B3li9dw6HfjuBz5x5DB0zA2NiIth2aayxz9vQFfj9wlHt3HxDg/4if1mzl9i1fqn/tosozdERfgp4EM2LwJK5eucGjwCecOnGOAP9Hn6JbAFj1bEPU7j+J8jpMwoNHhM5fS+LTUCy7NMsw/6s7frw8eIKE+4EkPQnhxW9/E3P6MsZVyqtn1NGhwOJxhK3cSuLjp5+gJ5nr3LcDm37YyvHfT/HA15+Zw+djZGxI4zYZv38Bpg2Zw+7Ne7l36z4B9wOZN2YxCh0dqtWuopbP2MSY2Z5TmDt2MS+iXuZ0VwBw7e3GsVV7uXn4Ik/vPmbn6B8xMDbAuZXmMcS1txv3Tt/gr9X7CPUL4q/V+7h39hauvZuq8vgc/5c/lv7CzcMXNdbTZGwnfP6+xsEF/yPo1kPCH4Vw5++rRIdp74tS9b5uXNt1nGs7jxN2P4gjs7bxIjgMFw3jjUvXBrwICuPIrG2E3Q/i2s7j/PvLCb7ur/4+VugoaL1iECeXeWU4huzssYjrXid5fu8JIXcCOTBmLRaFbMjv5KC1vn00ZYp2Hl8gCTi1YPXq1bRq1Yq9e/dSvHhxRo8ezbRp0+jfvz+7d+8GYNasWWplAgMD+f3331mzZg3r16/Hx8eHmTNnajzHokWLOHz4MAsWLGDPnj0ULVqUvn37EhkZiUKhoF27dnh7e6uV2b17N1WrVqVIkSIolUr69+9PaGgo69atw9vbm/Lly9OjRw8iIyMBOHXqFGPHjsXd3Z1Dhw4xa9YsvL29WbNmTZZfi4IFC9K5c2eWLl1KioZvaVu2bGHjxo2MHz+e/fv3U7t2bQYNGsTDhw+zfB6RRldfF3snB/xOqc9U+Z28QeEqpTIsU9ilFH4n1fPfP3kdeycHdPR0Myyjb2yIjr4ucZExqjTHRlUIuu5Ph9XDGHt5NR6H5uLSud5H9ijrihYrRL78dhz/64wqLSEhkX/OXKTaV85Zrse1zteULOnAP2fTlie/davPv1dv8tPm5dy6f4ajp7zp1qODVtufKX09jMqXIubMFbXk2DNXMHYul6UqDMuWwNi5LHHvzGJaD/6O5PAoonb/qbXmfij7IgWwyWfNuRNpr31iQiJXzv1LxaoVslyPkbEhenp6vIhUD6zGzRvBmWP/cPHUZa21OTN5C9uRx84K37c+j8kJSfidv0OxKqU1livqXArfU9fV0nxP/ksxl4w/wxlRKBSUredMqH8w/bZMYMalNQzbO5vy31bNfkc00NHXpYCTA/7vjDcPTt6gkIbxppBLKR6cfDf/dQq8M964Dm9LbNgL/t2lecLibYbmJgDER0Znpwsil0jAmQXHjx/H2dlZ7bFq1SrV8bZt29K0aVMcHBzo168fT548oUWLFri6ulKiRAm6d+/OhQsX1Op89eoVCxcupGzZslSrVo0pU6Zw6NAhQkND050/NjaWnTt3Mm7cOOrUqUPJkiWZPXs2hoaGeHl5qdrg7++vmvVMTExk//79tGvXDoBz585x9+5dfvjhB5ycnChWrBjjx48nT548HD58GIA1a9bQv39/2rRpQ+HChalVqxbDhw9n586d2Xq9Bg0axOPHj9m/f3+Gxzds2EC/fv1o1qwZxYsXZ+zYsZQpU4bNmzdn6zwilYmVOTp6usQ8j1JLj3kehZmtRYZlzGwtMsyvq6+HSV7zDMs0nNCZl0/DeXDmpirNqrAt1bo1INz/KVu7L+TStmO4zexOpba1M6xD22ztUpeWQ0PU96aFhoZhly/90urbzPOY8eDJZR4/v8G2X9YyadwcTv59VnW8aLHC9OjTBX+/ADq17cvmn3cxZ+FkOnRupf2OZEDXKg8KPV2SwiLU0pPCItGzsdJQKlXx41spdX0/Rb1WEPm/A0R5HVYdM3Yuh0W7xjybuiJH2p1d1nZ5AQgPDVdLDw+NUB3LisGTPQh9GsqFtwLLRq3q4+hUmlXzf9JOY7PA/PVnLjpU/fMVHRqlOpZxOUsNZSyzfG4zmzwYmRlTf2BLfE78y7ru87lx+CI91oykePWyWe9EJt6MN9HZGG9MMxhvot8ZbwpVLU2lTnU5OGF9ltvScGpXAi/4EHo3872xn5QsqWskFw1lQfXq1ZkxY4Za2psldUhdVn/D2jp1D0vp0qXV0l69ekV0dDRmZmYAFChQgPz586vyODs7k5KSgr+/P7a26vuzAgMDSUxMxMUlbblPX1+fihUr4ufnB4CdnR116tTBy8uLihUrcvz4cV69ekWTJk0AuHXrFrGxsVSvXl2t7vj4eNWS+61bt7hx44bajGZycjKvXr0iLi4OY2PN+6neljdvXnr37s0PP/xA06ZN1Y5FR0cTEhKi1hcAFxeXdMv7InuUSqV6gkIByozzpuZ/J0Gh0HAAank0x6llDTZ1mqO2F0yho0PQjQccW/wLAE9vBWBXuhBV3Rvyr/fpD+lGptp1aM7i5WkrAV07DnjdZPU2KxQZvB7viH4ZQ33XNpiamuBapwYz504g4OFjzp5O/XKoo6Pg36u3mDcr9aKFm9fvUKZMSXr26cKvO/dps1uZS/+Hem/fHnUdg8LUGONKZbAd3YuEwCBeHjyBwtSY/IvH8mzqCpIjP91e1Lc1btOQiYtGq56PdJ8AZPQ3fH8/33Af1IVvWzVgYPvhJLxKAMDO3pZRs4YyrMsYVVpOcG5Vi/bz+qqeb+i9CMj48/i+7qQ7rFCgzOxDnC576hzSzSOXObXhdwCCbgdQzKU0Nbo25MH5O1mu670y/Htllv/dtipeV6PEwNSIVssHcmjCeuIisjZb2Xh2T+zKFGFL+1nvz/wJKZVyWyRNJODMAmNjY4oWLarxuL6+vur/33yIMkrTtMT8dp43/80szxtKpVItrUOHDowbN45Jkyaxe/dumjZtqgoSU1JSsLW1zfBKcXNzc1WeoUOH8u2336bLY2iYvU3ZvXr1YseOHfzvf//7oL6IrIuNeElKUjJm78yEmFrnSTcL8UZ0aPrZCFPrPCQnJhH7zoBfs39TXAe3ZEvX+TzzUd+/+DIkktB7T9TSQu8/Ubv4SJv++P1vLl9OW3Y0NDAAwC6fDSHP0lYHbGys0816vkupVPLwwesvWzd8KO1YnGGj+qsCzmdPQ7nrq36Ryd27fjRrmf7zkROSI16gTEpGz0Z9lk/P2oLksMhMyyY+eQZAwt2H6FpbYjOkGy8PnsCgcAEMCuWn4I8z0jLrpH7uSt88gL9bPxIf5ewVv6f+PMOtq2mBj4FB6lhpbWdNWEjaLKeVjSXhoRHpyr+r64BO9BzalSGdRnP/TtodFMpWdMTaNi+b/1inStPT08P560p06NWG2sUaZTomZ9Xto5f5/lra+0TvdX/M7Sx5GRqpSjez0fx5BHgZGpluBtTMJk+6Wc/MxES8IDkxiWfvfCZD/J5QrKqjhlLZo2m8MbHOk24WU9Wu0ChMNYw3cRHR2JYuhGVhOzpuSPsionj9vpzot4Uf640h8q09nd/O7E7phi5s6Tibl0/VZ8bFf5cEnLkkODiYZ8+ekS9fPgCuXr2Kjo4OxYoVS5e3SJEi6Ovrc/nyZezt7YHUJfObN2/So0cPVb46depgbGzMjh07OHXqFNu2bVMdK1++PM+fP0dXV5dChQpl2KZy5crh7++faXCdVaampgwaNIiVK1dSr17anj4zMzPs7Oy4fPky1aqlBSVXr16lYsWKH33e/4+SE5MJuuFPCdcK+Lx1C5USrk74/JnxvrVHV+7h2FB9lrmEqxNBN/xJSUr7hl7ToxnfDGnNtu4LCbrhn76ey3exLl5ALc3aoQBRT55/TJc0iomOISY6Ri3t2dMQ6tSryc3rqUGMvr4+NWpVY/aMpdmqW6FQYPA6gAW4eP4qJUqqX4xQokQxHj8K+sDWZ1NiEvG37mFS05noo2lL/SY1XYj+658sV6NQKFC8DoISHjzCv8UAteM2w7ujY2pCyLw1JD5Nv6VH22Jj4oiNUQ+Inj8Lo/o3Vbl78x4Aevp6uHxdCc+5azOtq9vAzvQe7s6w78Zy57r6LXkunrpM53o91dKmLZvAw/uBbFn1P60EmwCvYuJ5FROvlvYiJILStZ0IuvUQSN1nXaJ6WQ4u2KGxnoCr9yhd20k1Mwng6FqRh1fuZbktyYnJPLr+ALt3PpM2DgWI0NJnMiUxmeAb/ji4VsD3rfHGwdWJuxrGm8dX7lHqnfHGwdWJ4NfjzXO/INY1Gq92vM6YDhiYGXFkxlZeBKd9eWw8qweOjauytdMcoh7l/Ps1277QC360QQLOLEhISEi3t1JXV5e8ebO+v+hdhoaGTJgwgfHjxxMdHc2cOXNwc3NLt5wOYGJiQpcuXVi0aBEWFhbY29uzfv164uPjad++vVqb2rZty9KlSylSpAjOzmkXTdSsWZPKlSszePBgxowZg4ODAyEhIZw4cYKGDRvi5OTE4MGDGTBgAAUKFKBJkybo6Ojg6+uLr68vI0eOzHYfO3bsyKZNmzh48CCVKlVSpffp04eVK1dSpEgRypQpg7e3Nz4+Pv/Z2ynFxsYR+DgtyHgS9Ayfu35Y5DGnQH67XGxZmn/W/07bZQMJuu7Poyv3qNKlPhb21qr7ajYY14k8+a3YMyp1u8Sl7cf4qkcjGk/tyuUdf1PYpRQuneriNSzt/oS1PJpTb3R7dg9fReTjUNWMaEJMPAmxr1Tn7eM9HdfBLbl14DwFK5egynf1+G3ip7tH7boftzB8lAcP/ALw9wtg+GgP4uLi8f71gCrPyjULeBocwtyZqbczGzaqP9eu3iTAPxB9fX0afFuHDp1bMX5U2nL92tWbOPDnDoaP9mDfnt9xcamIe8+OjBk+7ZP1LWLTHgosHEP8zXvEX7uDRUc39AvYErnzEAA2o3qiZ2fN0wmpwbXld81JDA4l4UHqTLRxlfJY9W5H5LbU/dTKhEQS7gWonSPlZWoA/276p7Rz/a/0HNqVRw8eE+j/mF7DuhEf94rDe46q8sxYMYmQp6Gsfr0f031QFzzG9mbq4NkEP3qKtW3qeBwbE0dcbByxMXE88FX/khQXG0dURFS6dG079fPvNBjciucPg3nu/5T6g1uTEJfA1X1pF7d1XjqQqGcR/L5op6rMoF+mU29AC24euUyFRlUoVasCqzrMUJUxMDHEpljaVqy8hW2xL1eU2Mho1T02j6/7jW4rh/Pggg/3/7lFmTqVKNfAhR87a+8+q+fX/06rZQMJvu7P4yv3cH493ry5r2bdcZ0wz2/Fb6/Hmyvbj1G1RyMaTu3K1R1/U8ilFJU71WXP6/Em+VViun2Yb26t9nZ6kzk9Kd+yJr/2+56EmHjVrOmrF7Fave3TR/lC919qgwScWXDq1Clq11a/CMLBwYE//vjjg+ssUqQIjRo1ol+/fkRFRVGnTh2mT5+uMf+YMWNQKpWMGzeOmJgYKlSowPr169X2kgK0b9+eNWvWqC4WekOhULBu3TqWL1/OpEmTiIiIwMbGhqpVq2Jjk3pxhaurK2vWrGHVqlWsX78ePT09ihcvTocOH3Zlrr6+PsOHD2f06NFq6d27dyc6OpoFCxYQHh5OiRIlWL16dYazu/8FN33u0Xto2rfvRStTl+hauTVk7pTRmop9UrcOnMPEyow6w9pgZmdJyN3HbO+5WDXTaG5nqXZPzshHoWzvuZgm07pRzb0RL0Mi+H3GFu78nna7lWruDdEz1KfTmhFq5zq+bDfHl6feESHo+gN29V9Og/GdqDOsDRGPQ/lj5jZu7D3Lp+K5fD1GRkYsXDoNC0sLrly6Tqc2fdRmQgsWsiclJW0TmYmJMQuXTqOAfX7i4+O5f9efwf3Hsc87bXbp2pWb9Oo6lMnTRzFq3CACAx4zdeJ8dr8VyOa0l7+fRNfSHJvB36Frm5eEew957DGNpKDU5UU927zo27/1pUdHB9uRPdEvlB9lcjKJgcE8X7qRyF2HPlmbP8SWVTswNDJk3PyRmFuYcevqHYZ2GUNsTJwqT76Cdmqzku16tMLA0ICF69UDqZ+WbuSnpZs+VdMz9Pea39A3MqDt7N4YW5gSeM2Pn9znqc2EWhW0UdvnGXDlHtuH/kCTMR1pPKojYYHP2DrkBwKv+anyFK5YnIE7077wtJraHYCLXifYNSY1uLt5+BK7J2+g/qCWtJ7Rg5AHQWwZuIyHlzTflD277rweb2q/Hm9C7z5mZ8/FvHg93pi9M95EPQplV8/FNJrWjSrujYgOieDPGVvw/V3z7Z0yUsW9EQDuv0xVS/9t9Fque/1Hbq0nM5waKZRZ3ZUttGblypUcPXqUffu0f+HB5cuX6d69OydOnFAFkl+qxOcP3p/pMze3ytT3Z/rM/Rj5aW5Xk1tOFvgP3SMwh7h/whv955ZvDOxzuwk5ykr55d+0ZnLA9hw/R/zlvVqpx6hKa63U818iM5xfiISEBIKDg1mxYgVNmjT54oNNIYQQ4j8nRa5S10QCzi/EgQMHmDx5MmXLlmXx4sVar3///v0al/zt7e05ePCg1s8phBBCfFZkSV0jCThzwdChQxk6dKhW62zbti1t27bVap1vq1+/vtqFP2/T05O3kRBCCCE0k0hBZImZmZnqpvVCCCGEyIBcpa6RBJxCCCGEENogS+oaffmXpQkhhBBCiFwlM5xCCCGEENogS+oaScAphBBCCKENEnBqJEvqQgghhBAiR8kMpxBCCCGEFiiVcuN3TSTgFEIIIYTQBllS10gCTiGEEEIIbZDbImkkeziFEEIIIUSOkhlOIYQQQghtkCV1jSTgFEIIIYTQBllS10iW1IUQQgghPnPbt2+nfv36ODk50bZtWy5dupRp/v3799OyZUsqVapE7dq1mThxIhERETnWPgk4hRBCCCG0ISVFO49sOnToEPPnz2fgwIHs3buXKlWq0K9fP4KCgjLMf+nSJcaPH0/79u05cOAAy5cv58aNG0yZMuVjXwGNJOAUQgghhNAGZYp2Htm0ceNG2rVrR4cOHShRogSTJ08mf/787NixI8P8//77LwULFqR79+4ULlyYqlWr0qlTJ27evPmxr4BGEnAKIYQQQnymEhISuHXrFrVr11ZLr1WrFlevXs2wjLOzM0+fPuXEiRMolUqeP3/O4cOHqVOnTo61Uy4aEkIIIYTQBi1dpZ6QkEBCQoJamoGBAQYGBunyRkREkJycjLW1tVq6jY0NoaGhGdbv4uLCkiVLGDFiBAkJCSQlJVG/fn2mTp2qlfZnRGY4hRBCCCG0QUt7ONeuXUuVKlXUHmvXrs301AqFQu25UqlMl/bG/fv3mTNnDoMHD2b37t2sX7+ex48fM336dK29FO+SGU7x2ZpbJee+if1XTL48O7ebkOOGdO2V203IUUlxcbndhBxnFWOS200QH0l+Afy/xcPDg1691MfGjGY3AaysrNDV1eX58+dq6WFhYdjY2GRYZu3atbi4uNC3b18AypQpg7GxMV27dmXEiBHY2dlpoRfqZIZTCCGEEEIbtHTRkIGBAWZmZmoPTQGngYEB5cuX58yZM2rpZ8+exdnZOcMy8fHx6Oioh4C6urqpXVAqtfBCpCcBpxBCCCGENuTSbZF69eqFl5cXXl5e+Pn5MW/ePIKDg+ncuTMAS5cuZdy4car89erV48iRI/zvf//j0aNHXL58mTlz5lCxYkXy5cuntZfjbbKkLoQQQgihDbn0S0NNmzYlIiKC1atXExISQunSpVm3bh0FCxYEIDQ0lODgYFX+tm3bEhMTw/bt21m4cCHm5uZ8/fXXjB07NsfaKAGnEEIIIcRnrmvXrnTt2jXDYwsWLEiX5u7ujru7e043S0UCTiGEEEIIbdDSbZG+RBJwCiGEEEJoQy4tqX8O5KIhIYQQQgiRo2SGUwghhBBCG2RJXSMJOIUQQgghtEECTo1kSV0IIYQQQuQomeEUQgghhNCGHPqVni+BBJxCCCGEENogS+oayZK6EEIIIYTIUTLDKYQQQgihDTLDqZEEnEIIIYQQ2iA3ftdIAk4hhBBCCG2QGU6NZA+nEEIIIYTIUTLDKYQQQgihDXJbJI0k4BRCCCGE0AZZUtdIltSFEEIIIUSOkhnODzBhwgRevHjB6tWrc7spH0SpVPLLL7/g5eXF/fv30dXVpUiRIrRs2ZJOnTphbGz8ydryKV/Lau4NqenRDHNbS0LuPeGPmVsJvOirMX/R6mVoPLUbdqUK8jIkkjNrDnBp+zHVcZfO9ajUrjZ2joUBCL7hz7FFu3jy7wO1eszzWdFoYmdK1q2EvpEBYQ+esm/cOoJvPsyRfmbXpWs32Pg/L2773Cc0LJwV86fS4Juaud2sLDFq3hrjDp3RyZuX5ICHRK/xJOnm9feW0ytXAYslK0h+6E/koL6qdMNGTTAfMzFd/ufNG0FiglbbnlXGrVth2qUzunmtSXroz4uVniRev/HecvoVKpD3hxUk+fsT1ietj8bNm2HcuDF6xR0ASPS9S/RPP5F4xyfH+pAV3UZ2pWlXN8wszPC56suqKasIuBuoMX/R0kXoPtqdkk6lyF84H2tmrGXPhr1qeXR0dXAf1Y36rethZWdF+LNwjvx6lP/9sANlDi99fjuiHdW7NMDEwpTAa/fxnrqRZ/ceZ1rGqclXNBndAesi+QgLfMbvS3Zx8/Al1fHiX5Whbv/mFHQqjkU+Kzb2X8qtPy+p1bHk4Y4M6z4wbzvH1x34+I69VtW9ITXeGk//zMJ42uit8fTsmgNcfms8dX49ntq+NZ7+tWgXQW+NpwpdHeqObEeF1jUxs7UkOiSSf389ycmVe/87S9kyw6mRzHD+PzR27FjmzZtHgwYN2Lx5M3v37mXQoEEcO3aMM2fO5HbzckT55l/TZJo7pzz3sabZZAIv+NBt8zgs7K0zzG9Z2Jaum8YSeMGHNc0mc2rVPtxmdKesWzVVnmI1ynJz/z9s7jyXDW2mExX0HPetEzDPZ6XKY5THhD67p5OcmMz2HotY1XAch+duJ/5FbI73Oavi4uJxLFmcSaMG5XZTssWgTj1MBwwhdsdWIgf1I/HmdSzmLETH1i7TcgoTU8zHTiLx6pUMj6fERBPWuY3aI7eCTaP69cgzdAgxW7bxvG9fEq7fwGrRInTs3tNHU1MsJk8k4crldMcMnCsTd+wY4cNHEjZwMMnPnmG1ZAk6NjY51Y336jiwA237tWXVlNUMbT6ciNAI5v9vHsammr/8GhobERz4lJ8XbCTsWXiGeToN6kizbk1ZNXU1/er1Z/28n2k/oB2terXMqa4AUG9AC77p05Q90zayouVkXoRG0n/bJAxNjTSWKepSim6ew7i85zRLm07g8p7TuHsOp0jlEqo8BiaGBN0JZM+0jRrrmVltgNpj19g1pKSkcP33C1rrX7nmX9N4mjunPfex7vV4+t3mceTJZDzt8no8XddsMqdX7aPJjO6UyWA83dJ5Lj+/Hk+7vTOe1hrYgipdG/DHtM2sbjCWo/N3UMOjGV/1/FZrfftoyhTtPL5AEnBq2f379+nXrx/Ozs7UrFmTsWPHEh6eNhiePHmSLl26ULVqVapXr46HhweBgWnf4jt16sSSJUvU6gwPD6d8+fKcO3cOgISEBBYtWoSrqyuVK1emQ4cOnD9/PkvtO3ToEL/99htLly5lwIABVKxYkUKFCtGwYUO2bNlC9erVAUhJScHT05NvvvmGChUq0KpVK06ePKmq5/z58zg6OvLixQtV2p07d3B0dOTx49Rv8d7e3lStWpVTp07h5uaGs7Mzffr0ISQkBICVK1eyZ88ejh07hqOjI46OjlnuR3bV6OvGlV3HubLzOM/vB/HHrG1EBYdRtVvDDPNX7dqAqKAw/pi1jef3g7iy8zhXfzlBzf7NVHm8h6/m4tajPL0dwHO/YPaPX49CR4fitcqr8tQe2IKo4DD2jV3Hk38fEPn4Of5nbhERGJIj/fwQrjWqMax/DxrVrZXbTckW47YdiT98iFd/HCT5UQAxazxJDg3FqHmrTMuZDR/Nq7+PknTnVsYZlEqUEeFqj9xi0rEDcQcPEXfwIMkBgbxc6UlKaAgmrTPvY54xo4k/eozEW7fTHYuaPZe4vftIun+f5MBAXixeAjoKDKq45FQ33qt1n9bsXLmTM3+cJcA3gCUjl2JoZEi91nU1lrn7713Wz93Aif0nSExIzDBPWZcy/PPnOS78dZFnj0M4feg0V05eoVTFUjnUk1Suvd04tmovNw9f5Ondx+wc/SMGxgY4t9L8GXPt7ca90zf4a/U+Qv2C+Gv1Pu6dvYVr76aqPD7H/+WPpb9w8/BFjfW8DI1Se5RvVAW/f24T/kh7Y06Nvm5c3XWcq6/H0z/fM55WeT2e/vl6PL2awXi6Z/hqLm09yrPbAYT5BXPg9Xjq8NZ4WsilFL5HLnPvr2tEPX7OnUMXeHDqBvYVi2utbyLnSMCpRSEhIXTr1o2yZcvi5eXF+vXrCQsLY8SIEao8cXFx9OrVCy8vLzZt2oRCoWDw4MGkvJ6Gb9GiBQcPHlRb7jl06BDW1tZ89dVXAEycOJErV66wbNky9u/fT5MmTejbty8PHz58bxt/++03HBwcaNgw/cCgUCgwNzcHYMuWLWzcuJHx48ezf/9+ateuzaBBg7J0jrfFx8fz888/s2jRIrZt20ZwcDALFy4EoHfv3ri5ueHq6srp06c5ffo0zs7O2ao/K3T1dbF3csDvlPoypN/JGxSukvE/PIVdSuF3Uj3//ZPXsXdyQEdPN8My+saG6OjrEhcZo0pzbFSFoOv+dFg9jLGXV+NxaC4unet9ZI8EenrolSpN4mX1f3gTL19Ev1wFjcUMv3VDp0BBYrdt1phHYWyM1ZZdWG37lTyz5qNbImeDE4309NAv7ciri+p9fHXxIgYVymsoBMZuTdCztyd6k+Y+vk1haIhCTw/li5cf1dwPlb9Ifqzz5eXyybQZ58SERG6cv0G5KuU+qu6bF29RuVZlCjoUBKB4WQfKVyvPxb81B2wfK29hO/LYWeH71niTnJCE3/k7FKtSWmO5os6l8D2lvh3E9+S/FHP58PefmY0FZes5c2HX3x9cx7t09HUpkMF4+iCT8bSQSykenHx3/L1OgWyOp48u+uJQszx5HfIDkK9sEQpXdeTe39c+okfapUxRauXxJZI9nFq0Y8cOypcvz6hRo1Rp8+bNo06dOvj7++Pg4EDjxo3VysybN48aNWpw//59SpcuTdOmTZk/fz6XL1+matWqABw4cIDmzZujo6NDYGAgBw8e5MSJE+TLlw+APn36cOrUKby9vdXOnZGAgAAcHBze25cNGzbQr18/mjVL/QY6duxYzp8/z+bNm5k+fXqWX5PExERmzpxJkSJFAOjatatqv6apqSlGRkYkJCRga2ub5Tqzy8TKHB09XWKeR6mlxzyPwszWIsMyZrYWGebX1dfDJK850SGR6co0nNCZl0/DeXDmpirNqrAt1bo14J/1v3Nq1T4KViqB28zuJCck8q/36Y/v3P9TOnksUOjqkRKpPvuYEhmBwipvxmXsC2Lauz+Ro4dCSnKGeZIfBRK9ZAFJDx+gMDHFuHU7LL/3JGJgb1KCnmi9H5nRsbBAoadLSkSEWnpKeAQ6eTPuo26hgph59Cd8yDBIzriP7zIb0J/k0Oe8upx++f1TyGubumQa8Vy9nxGhkdgVynzrwPv8svpXTM1NWX98HSnJKejo6rBp0WaO7zvxUfVmxvz1mBIdqj5+RIdGYVVI87YFc1vLDMuY21p+cFuqtvuGVzHx3MhkRjS7MhtPTbU4njbIYDw98+NvGJqbMPivxaq/51+Lf+XW/n8+vmPaIns4NZKAU4tu3brF+fPnM5ylCwwMxMHBgcDAQFasWMG1a9eIiIhQzWQGBwdTunRp8ubNS82aNdm/fz9Vq1bl0aNHXL16lRkzZqjOoVQqadKkiVr9CQkJWFpavreNSqUShUKRaZ7o6GhCQkJwcVFfYnNxccHHJ3sXFhgbG6uCTQA7OzvCwsKyVYe2pLtIQKGATL5IptuD/uZ1y2Bzei2P5ji1rMGmTnNIepW2vKfQ0SHoxgOOLf4FgKe3ArArXYiq7g0l4NSGdH+jjBIBHR3MJ0wjdutGUp5ovnAjyec2ST5py9Avb93ActVPGLdqR8yPP2ilydmW1fetjg6WU6cS/fMmkh9nfnHKG6ZdOmPcoAHhw0ZAwqfZp1qvdT2GLxiqej615+svsO/0U6FIn5ZddVrWoUHb+iwYuoiAuwGUKFecATM8CHsWzlGvox9V9xvOrWrRfl7aRVkbei8CMh5v3teddIcVCpSZDVLv8VXHOlzZe0ZtTNKajxxP3/w7lNHFWzU9mlOhZQ02d5pD8lttL9/ia5za1MJ72CpC7z4hX7miNJ7ejZfPIri++9QHd0V8GhJwalFKSgr16tVjzJgx6Y69mcEbMGAABQoUYM6cOdjZ2ZGSkkLz5s1JTEz7ULVo0YK5c+cydepUDhw4QKlSpShTpgyQ+uHU1dVl9+7d6OqqL0WYmJi8t43FihXDz88vS/15NzB9O1jV0dFRpb3xdh/e0NNTf4spFIocvzr0XbERL0lJSsbsnZkCU+s8RL/zrfuN6ND0s5+m1nlITkwiNiJaLb1m/6a4Dm7Jlq7zeebzSO3Yy5BIQu+pz4yF3n+idvGRyL6UF1Eok5PQeWc2U8fCCuU7M4IACmMT9B3LoFeyJKaDh79O1EGho4P1oWO8mDiGxH+vpj+RUknSXV90CxbKiW5kKiUqCmVScrrZTB0rS1Iy2FeqMDFBv2wZ8pQqRZ4Rr/uoo0Cho0O+v44RMWYMCVfS+mjSuROm3boRPmo0SQ8epKsvp5w7cg7fa2lfXPUN9AGwss1LeEja387SxpKI0MiPOle/yX3YtfoXTuxPndF86PMQu0J2dB7cUWsB5+2jl/n+2n3Vc73X/TG3s+TlW+03s9E83gC8DI1UzY6qlQnVXCYzDtUcsStRkK1DtPtF6c14aprBePruLOYbGY2nJq/H07h3xtMa/ZtSe3BLtnadT8g742nDSd9x5sffuPVb6vUMIb6PsCxkQ+1BLf87AecXesGPNsgeTi0qX7489+7do2DBghQtWlTtYWJiQkREBH5+fgwcOJAaNWpQokQJoqLSf0AbNmxIQkICp06d4sCBA7RsmXZFZdmyZUlOTiY8PDzdObKyLN2iRQsePnzI0aPpB1ulUsnLly8xMzPDzs6Oy+8ssV29epUSJVKvmMz7+h/B0NBQ1fHszn4C6Ovrq/av5pTkxGSCbvhTwlV9b18JVyceXb6XYZlHV+5RwtUpXf6gG/6kJKUtVdb0aMY3Q9uwrccigm74p6/n8l2sixdQS7N2KEDUk+cf2h0BkJRE0r276LtUVUvWd6lK4u2b6bIrY2OI6N+TyIF9VY/4g/tJehRA5MC+JPrc0Xgq3eIlSQnPhVn5pCQS7/piWFW9j4ZVq5JwM/0FT8qYGJ736EVYn76qR9y+/SQFBBLWpy+Jt9P6aNK5E2bd3YkYO44kX823sskJcTFxBD0MVj0C7gYS9iwcF9e0lSE9fT2cqjtx+3L6i56yw9DYMN1+uJTkFBQ6ma/yZMermHjCAp6pHs/uPeZFSASla6eNH7r6upSoXpaHl+9qrCfg6j21MgCOrhV5eCXjMep9vupUj0fXHxB8R/OtpT5ESmIywTf8Kf7OeFo8k/H08ZV7FM9gPA1+Zzyt4dEM16Ft2N5jEcEZjKf6xgYo3/n3Qtt/z4+WotTO4wskAecHevnyJXfu3FF7dOrUiaioKEaNGsX169d59OgRp0+fZuLEiSQnJ2NhYYGlpSW7du0iICCAf/75hwULFqSr28TEhPr167NixQr8/Pxo3ry56piDgwMtWrRg3Lhx/Pnnnzx69Ijr16+zbt06Tpx4/74kNzc3mjZtyujRo1m7di03btzgyZMn/P333/Ts2VN1lXifPn346aefOHToEA8ePGDJkiX4+PjQvXt3AIoUKUKBAgVYuXIl/v7+HD9+nJ9//jnbr2PBggXx9fXlwYMHhIeHZzhLqg3/rP8dl071cO5YB5uS9jSe2g0Le2vVfTUbjOtEm+8HqPJf2n4Mi4LWNJ7aFZuS9jh3rINLp7qcXXdQlaeWR3Pqj+7AvnHriHwcipmtBWa2FhiYGKqdt5BzSVwHtyRv0Xw4tapJle/qcWHLkRzp54eIjY3D564fPndTZ76fBD3D564fwU//O1fSZyTO+xeMmjTD8Num6BYuiqnHYHTt7Ig/uB8Ak179MBs7KTWzUklygL/aQxkZAQkJJAf4w6t4AIy79kC/SjV08hdAt3hJzEaNR69ESeIP7suVPsb+8mvqfTObuqFbtAjmQwajY5eP2H2pfTTr3w+LSRNVfUzy91d7pERGokxIIMnfH2V8ah9Nu3TGvG8fohYuIvnpU3Ty5kUnb14Un/D+u+/au2EvnYd0omaTmhR1LMqY70fxKv4Vf+89rsozdtloeo3vqXqup69H8XLFKV6uOPoGeljnt6Z4ueLYF0v7gnfu6Hk6D+3MV/Wrka+QHTWb1KRtv7ac/SNn9/yd+vl3GgxuRYXGVclfuhCdlgwkIS6Bq/vSbjvXeelA3MZ1VitT2rUi9Qa0wLaEPfUGtKBUrQqc+vmQKo+BiSH25YpiX64oAHkL22JfriiW79yOyNDMmEpNq2v1YqG3vRlPK78eT799PZ6+ua9m/XGdaPXWeHr59Xj67evxtHLHOji/M57W9GhOvdEd2P96PDW1tcDU1gL9t8bTu0ev4jqkNaXqV8aikA2OjavydV83fA6r34s0V6WkaOfxBZIl9Q904cIFWrdurZbWpk0bduzYwZIlS+jTpw8JCQnY29vj6uqKjo4OCoWCZcuWMWfOHJo3b46DgwNTpkzB3d09Xf0tW7akf//+VKtWDXt7e7Vj8+fP58cff2TBggWEhIRgaWlJ5cqVqVOnznvbrVAoWLp0Kbt27WL37t38+OOP6OrqUrRoUVq3bk3t2rUB6N69O9HR0SxYsIDw8HBKlCjB6tWrKVasGJA6M7l06VJmzJhBq1atcHJyYsSIEQwfPjxbr2PHjh25cOEC7dq1IzY2Vu3WTNp068A5TKzMqDOsDWZ2loTcfcz2notVM43mdpZq9+SMfBTK9p6LaTKtG9XcG/EyJILfZ2zhzu9pm++ruTdEz1CfTmtGqJ3r+LLdHF/uDUDQ9Qfs6r+cBuM7UWdYGyIeh/LHzG3c2HtW6338UDd97tF76HjV80Ur1wHQyq0hc6eMzq1mvVfCib+JMbfApGt3dPJakxzgT9SU8aSEPANAJ681uu+5J+e7dMzMMBs+Bh2rvChjY0i6f4+oMcNI8s2dm6LH//U3ijx5MOvRAx3rvCT5+xMxfjwpz1L7qGttje7riwezyqR1axQGBljNnqWWHr1xE9EbN2mr6dnyy4+/YmBkwJA5gzG3MMPnmi8Tu04mLiZOlce2oB0pb23Hsc6Xlx8Pr1I97zCgPR0GtOfff64zrmPq+3n11B/pMaY7Q+YOxtLGkrBn4Rzafojty/+Xo/35e81v6BsZ0HZ2b4wtTAm85sdP7vN4FROvymNV0EZte1HAlXtsH/oDTcZ0pPGojoQFPmPrkB8IvJa2BapwxeIM3DlN9bzV1NQJgIteJ9g1Zo0qvXKLGqBQcHV/ztxX+fbr8fSbt8bT/701npplMJ7u6LmYb6d1o+rr8fSPGVvweWs8rfp6PO34znh6YtluTrweT/+Yvpm6o9vjNrsXpjZ5ePksgiv/+4sTK7xzpJ9CuxTKT72hTggtmVG0a243IcdNvjw7t5uQ46K69srtJuSopLj/0HJfDunhn3uzo5+Kk65lbjchR+VRfvkLntMCtuf4OWJXDHh/piwwGb7m/Zk+MzLDKYQQQgihDTKHp5EEnF+Yvn37prvY5w0PDw8GDNDOty8hhBBCiKySgPMLM3fuXOLj4zM8ZmGR8U15hRBCCKEFX+gFP9ogAecXJl82LyAQQgghhJZ8obc00oYvf5ewEEIIIYTIVTLDKYQQQgihDfJLQxpJwCmEEEIIoQ2ypK6RLKkLIYQQQogcJTOcQgghhBBa8O5vvYs0EnAKIYQQQmiDLKlrJAGnEEIIIYQ2yEVDGskeTiGEEEIIkaNkhlMIIYQQQhtkSV0jCTiFEEIIIbRBLhrSSJbUhRBCCCE+c9u3b6d+/fo4OTnRtm1bLl26lGn+hIQEli1bRr169ahQoQINGzbEy8srx9onM5xCCCGEENqQS0vqhw4dYv78+UyfPh0XFxd27txJv379OHjwIPb29hmWGT58OGFhYcydO5ciRYoQHh5OUlJSjrVRAk4hhBBCCG3IpavUN27cSLt27ejQoQMAkydP5vTp0+zYsYPRo0eny3/y5EkuXrzI0aNHsbS0BKBQoUI52kZZUhdCCCGE+A9JSEggOjpa7ZGQkKAx761bt6hdu7Zaeq1atbh69WqGZf766y8qVKjA+vXrcXV1pXHjxixcuJD4+Hit9+UNmeEUQgghhNAGLS2pr127Fk9PT7W0IUOGMHTo0HR5IyIiSE5OxtraWi3dxsaG0NDQDOt/9OgRly9fxtDQkFWrVhEREcHMmTOJjIxk/vz5WunDuyTgFEIIIYTQAm39tKWHhwe9evVSSzMwMMi0jEKhUG+LUpku7d1jS5YswdzcHIAJEyYwbNgwpk+fjpGR0Ue0PmMScAohhBBC/IcYGBi8N8B8w8rKCl1dXZ4/f66WHhYWho2NTYZlbG1tyZcvnyrYBChRogRKpZKnT59SrFixD267JhJwis/Wj5GXc7sJOW5I117vz/SZs9i+MbebkKOcynXK7SbkuPuR93K7CTnO3v7r3G5CjorUMBMmsikXrlI3MDCgfPnynDlzhkaNGqnSz549S4MGDTIs4+Liwh9//EFMTAympqYA+Pv7o6OjQ/78+XOknXLRkBBCCCGENqQotfPIpl69euHl5YWXlxd+fn7MmzeP4OBgOnfuDMDSpUsZN26cKn/z5s2xtLRk4sSJ3L9/n4sXL7J48WLatWuXI8vpIDOcQgghhBDakUu3RWratCkRERGsXr2akJAQSpcuzbp16yhYsCAAoaGhBAcHq/Kbmpry888/M2fOHNq1a4elpSVubm6MGDEix9ooAacQQgghxGeua9eudO3aNcNjCxYsSJdWokQJNm78dFuaJOAUQgghhNCGXPqloc+BBJxCCCGEEFqglIBTI7loSAghhBBC5CiZ4RRCCCGE0AaZ4dRIAk4hhBBCCG3Q0i8NfYlkSV0IIYQQQuQomeEUQgghhNAGWVLXSAJOIYQQQghtkIBTI1lSF0IIIYQQOUpmOIUQQgghtECplBlOTSTgFEIIIYTQBllS10gCTiGEEEIIbZCAUyPZwymEEEIIIXKUzHAKIYQQQmiB/Ja6ZhJwCiGEEEJogwScGn0WS+oTJkxg0KBBud0MkQFvb2+qVq2a280QQgghxH9YtgLOCRMm4OjomO7Rp0+fnGrfF+f8+fM4Ojry4sWLLJd5+3UvX748NWvWpFevXnh5eZHyCX+3tX79+mzatEktrWnTphw+fPiTtUHbxkwYwr8+J3n49BreB7bgWKZkpvmbtmjE4eNe3A24gH/QFY6d2kP7Ti3T5ctfwI5V6xZxx/8c/sFXOXZqDxUrl8+pbmhk1Lw1Vpt3Yv3bn1h6rkOvQsUsldMrVwHrQ8ewXL1eLd2wURNsDp9I90DfICearzWXrt1g8Ljp1GvZlQq13Dh28mxuNylbhoztx8nrh7gWcIote9ZQ0rF4pvk7dGvNtv3rOH/3GOfvHuNnr1U4OZdTy2NqasLE2aM4dnk/1wJOsePgBipULqehxpw3beooAh9e5mXUfY4d+ZVy5UpnuWzHji1JSnjCbq8NGvOMHzeEpIQnLF0yUxvNfa9WIzry/fmfWOvzP8bvnIl9qcLvLVOlydfMObKcdb47mXNkOS6Nv1I7rqOrQ9vRXVh0ajVrff7HwpOraTmsAwqFQpWnz5IhbHy4W+0xZc98rfcPoOWIjiw5v47VPtsZu3Mm9qUKvbeMS5PqzDqyjB99dzDryDKc3+kjgGW+vPRdNozlVzey6s52ph1aTNEKae/5PDYW9FoymCXn17HqznZGbJ6MXbH8Wu3bB0vR0uMLlO0ldVdXV+bPV3/zGhj8t/+x+RK8ed1TUlJ4/vw5p06dYu7cuRw+fJgff/wRPb0P2x2hVCpJTk7+4PJGRkYYGRl9UNncNmREXwYM7smwQRN5cP8hI8cO4Je9P1Ozqhsx0TEZlomMiGL5kjXcv/uAhMREvm1clxWr5/H8eTjHj50GwMIyD78d3sGZU+f5rl0/nj8Pp5hDYaKisv4lQxsM6tTDdMAQoj2XkXTrJkbNWmAxZyER/XqQEhqisZzCxBTzsZNIvHoFHSurdMdTYqKJ6OOunpiYoO3ma1VcXDyOJYvTuum3jJw8J7ebky19h3an54DvmDhsFg/9Ahkwsjc//+qJW432xMTEZljmq1pVOLjnT65evM6r+Ff0HdKdDb940ty1EyFPQwGYvWwKpcqUYPzg6YQ8C6Vlezc2eq2iWe2OqjyfytgxgxgxvD+9+47k3r0HTJo4nD8O7aBchW+I1vBZfKNIkYIsWjCNU6fOacxTtUol+vbpyr/Xb2u76RlqOqA1jfu0YMMYT576B9FiaHvGbJvGpPpDiY+Jz7BMCZfSDPQcxZ7vd3D58AWqNP6KgZ6jmd9hCg+u3Xtdbxvqdv2W9aNX8uTeIxycStB78RDiXsZyZONBVV3Xj19hw9hVqufJCUla72OTAa1p1Kc5G8es4ql/EM2HtmfUtmlMrj+MVxr6WNylNB6eo9j7/U6uHj6Pc+PqeHiOYmGHqfi/7qNJHlMm7J6D7z83WdFzLi/CorAtkp/YF2nvg8HrxpGcmIxnv4XERcfxbd/mjN42namNRpAQ90rrfc0O2cOpWbaX1A0MDLC1tVV7WFhYAODo6MjOnTvx8PCgUqVKuLm5cfXqVQICAnB3d6dy5cp06tSJwMBAVX0rV66kVatW7Ny5kzp16lCpUiWGDRuW6QxgQkICc+bMoUaNGjg5OdGlSxeuX78OpAZQjRo1YsMG9W+6d+/epUyZMqpzf0hbAf766y/atm2Lk5MTDRo0wNPTk6SktA+zo6Mjv/76K4MHD6ZSpUp8++23HDt2DIDHjx/TvXt3AKpVq4ajoyMTJkzI1uueL18+ypcvz4ABA1i9ejUnT55kz549qvodHR25c+eOqtyLFy9wdHTk/PnzQNoM66lTp1T9uHTpEoGBgQwcOJCaNWvi7OxMu3btOHs2bRbI3d2dJ0+eMH/+fNVsK2S8pP6///2Phg0bUqFCBRo3bszevXvVjmf2Gn1K/Qd2Z/nSNRz67Qg+d+4xdMAEjI2NaNuhucYyZ09f4PcDR7l39wEB/o/4ac1Wbt/ypfrXLqo8Q0f0JehJMCMGT+LqlRs8CnzCqRPnCPB/9Cm6pWLctiPxhw/x6o+DJD8KIGaNJ8mhoRg1b5VpObPho3n191GS7tzKOINSiTIiXO3xX+daoxrD+vegUd1aud2UbOvevwtrlm/kyMG/uefjx4ShMzAyNqJ5u8Yay4wdOJUdG73wuXkX//sBTB01Fx0dBTW+qQaAoZEh3zavx5JZP3Dp3FUC/R/jufgnHgcG0aVnu0/VNZVhQ/syf8EP7N37O7du+dKr9whMTIzp0rlNpuV0dHTYutmTmbOW8MA/MMM8pqYmbNniyYCB44iMiMyB1qfXqHdzDqzazeXD53ly9xHrR6/E0NiQr1u5aizzbe/m3Dr9LwdX7+Gp3xMOrt7DnbM3aNQ7bTwq4VKaq0cucv3vK4Q9DuXS7+e4depfijmVUKsrKSGJF6GRqkdMVLTW+9iwdzMOrvLmyuHzBN19xM+jV2JgbEj1TPrYqHczbp++zu+r9/DUL4jfV+/B5+wNGvZupsrjNrA14UFhbBy7Gv9/7xP2OBSfszcIDXwGQD6HApRwcWTblHU8vO7HswdBbJuyHkNTI6q3rK31fgrt0foeztWrV9OqVSv27t1L8eLFGT16NNOmTaN///7s3r0bgFmzZqmVCQwM5Pfff2fNmjWsX78eHx8fZs7UvOyxaNEiDh8+zIIFC9izZw9Fixalb9++REZGolAoaNeuHd7e3mpldu/eTdWqVSlSpMgHt/XUqVOMHTsWd3d3Dh06xKxZs/D29mbNmjVq5/L09MTNzY39+/fzzTffMGbMGCIjIylQoAArV64E4I8//uD06dNMnjz5A17lVDVq1KBMmTL8+eef2S67ePFiRo8ezaFDh3B0dCQ2NpY6deqwceNG9uzZQ+3atRkwYABBQUFA6heD/PnzM2zYME6fPs3p06czrPfIkSPMmzePXr168dtvv9G5c2cmTZrEuXPqsw+aXqNPpWixQuTLb8fxv86o0hISEvnnzEWqfeWc5Xpc63xNyZIO/HP2kirtW7f6/Hv1Jj9tXs6t+2c4esqbbj06aLX976Wnh16p0iRevqiWnHj5IvrlKmgsZvitGzoFChK7bbPGPApjY6y27MJq26/kmTUf3RKltNZsoa5Q0YLY5bPhzN9pn5/EhEQunr2Cc7WsbY8AMDY2Qk9Pj6iI1C/yerq66Onp8eqV+sz0q/h4qlSvrJW2Z5WDQxEKFMjHkaMnVGkJCQmcPHWOGjUy3x8+dcpIQp+HsXHTTo15Vv4wj98PHePYX6e01ubM2BbOh6WdFTdP/atKS0pIwvf8LUpWcdRYroRzaW69VQbg5slrlHRJK3Pvkg/lajmRz6EAAIXLFqVU1TJcP35FrVyZr8uz4tLPzP9rJT3nD8DcOo82uqZiU9gOSzsrtfam9vF2pn0s7lya2+/08dbJf9X6WKlhVQJu+DFg1Wi+v7SBaQcX49q5oeq4noE+AImvElVpypQUkhKTKFmtzEf37aOlKLXz+AJlO+A8fvw4zs7Oao9Vq9Km7tu2bUvTpk1xcHCgX79+PHnyhBYtWuDq6kqJEiXo3r07Fy5cUKvz1atXLFy4kLJly1KtWjWmTJnCoUOHCA1Nv6wTGxvLzp07GTduHHXq1KFkyZLMnj0bQ0NDvLy8VG3w9/dXzXomJiayf/9+2rVT/+ae3bauWbOG/v3706ZNGwoXLkytWrUYPnw4O3eqD3Zt2rShefPmFC1alFGjRhEXF8f169fR1dVVzQZbW1tja2uLubl5dv8EaooXL86TJ0+yXW7YsGHUqlWLIkWKYGVlRZkyZejcuTOOjo4UK1aMkSNHUrhwYf766y8ALC0t0dXVxdTUVDWznZENGzbQpk0bunbtioODA7169aJRo0b8/PPPavk0vUafiq1davtDQ8LU0kNDw7DLZ5NpWfM8Zjx4cpnHz2+w7Ze1TBo3h5N/p80GFy1WmB59uuDvF0Cntn3Z/PMu5iycTIfOmc8sapNOHgsUunqkRKrPPqZERqCwyptxGfuCmPbuz8uFsyElOcM8yY8CiV6ygBczJvFywWyUCQlYfu+Jjn1BrfdBgK2dNQBhoep/x7DQcGxeH8uKUVOH8OxpKGdPpo5nMTGxXL14nUGj+mCXzwYdHR1atHejoksFbN/z/te2/PnsAHj27Lla+rNnoeTPl/E4A1CzRlV69eyCx4CxGvN07NgSZ+cKTJqSM3sYM2JhawnAi9BItfSo0CgsbNNvUXm73LtlXoRGquoDOPTjHs7vP828Yz/w071dzDi4hCMbD3B+f9oEwI3jV1g7fDmLvpvOzrmbcahUknH/m4megfZuSvOmHxm1N89b7U1fzpKodK+LehnbIvmo2+1bnj0MZlmPORzf/iddZvSiRts6ADz1e8LzxyG0HdcVkzym6Orr4TawNZZ2VljYaX59PxnZw6lRtt+B1atXZ8aMGWppb4IoQLXUCqlBFUDp0qXV0l69ekV0dDRmZmYAFChQgPz50zb8Ojs7k5KSgr+/f7rAJjAwkMTERFxc0pYw9fX1qVixIn5+fgDY2dlRp04dvLy8qFixIsePH+fVq1c0adJEra7stvXWrVvcuHFDbUYzOTmZV69eERcXh7Gxcbp6TUxMMDU1JTw8Z5YdlUql2obxrHJyclJ7Hhsbi6enJ8ePHyckJITk5GTi4+NVM5xZ9eDBAzp16qSW5uLiwpYtW9TSPuVrBNCuQ3MWL0+bNe/acQCQ/ndvFYr3/xZu9MsY6ru2wdTUBNc6NZg5dwIBDx9z9nTqP+Y6Ogr+vXqLebOWAXDz+h3KlClJzz5d+HXnPm126/3e7Yoio0RARwfzCdOI3bqRlCePNVaX5HObJJ+0fXAvb93ActVPGLdqR8yPP2ilyf+fNW/XhJlLJqqeD/huJJDBe1KhIKs/2dxniDvN2nxL9zYDSHhrRnPc4GnMWz6Nkzd+JykpidvXfTngfZhyTppnqLShS5c2/Lhqoep5y1ap24zSfxYVGj+LZmambN60kgEDxxIWFpFhnkKF7Fm2dBZuzb7j1auc29f3dStXeszzUD1f3nse8GFjS7qjCoVa6lctalGj9TesHb6coLuPKFzOge+m9SLyWQRndh8H4MKBtC+/T+4+4uH1+yw5s4ZK9apw+fD5bPcPoHorV9zn9Vc9/6H36wA+g7/Z+9+YGZR5K02hUPDwxgP2LP4fAI9u+VOwVGHqdvuWf7xPkJyUzI8DltBj0UB+uL6Z5KRk7py5zo2/1Wd5xX9PtgNOY2NjihYtqvG4vr6+6v/fBEIZpWV2dfWbPJkFUu8eezfw6tChA+PGjWPSpEns3r2bpk2bqgLCD21rSkoKQ4cO5dtvv03XHkNDwwzrfVNPTl1N7ufnR6FCqVcG6uikTli/Pai9vb/0be++FosWLeL06dOMHz+eIkWKYGRkxLBhw0hMTMywfGbe97eBT/saAfzx+99cvpw2g2r4+kI3u3w2hDxLm0m3sbFON+v5LqVSycMHqfvFbt3wobRjcYaN6q8KOJ89DeWu7321Mnfv+tGsZfr3TU5JeRGFMjkJnXdmM3UsrFBGpP8HWmFsgr5jGfRKlsR08PDXiToodHSwPnSMFxPHkPjv1fQnUipJuuuLbsH3X50q3u/vP05y/cpN1fM3F2Ta2Km/L61trAgLzfx9CtB7UDc8hveid/vB3L2t/p589PAJ7q09MDYxwszMlNCQML5fN4/Hgdn7kpldv/32JxcupL2XDA1T+5g/vy1Pn6ZdzGZnZ8OzkOfpygOUKFEMB4ci7N2zSZX2ZvyLjw2gXIVvqFChDPny2XLh3O+qPHp6eri6fs3gQT0xMXPQyphz7ehF1UU9kLbka2FnpTabl8fGghfPI9Ek6p3ZzDdlokKjVM87TezOwR/3cOG31K1Aj30DsSloQ7NBbVUBZ0b1hj15rlqG/xDXjl5UXdQDqGZL87zTR3MbC148j3q3uFpb3p3lzWNjwYu3+hgVEknwPfX97sF+j3Fxq656HnDzAbOajsXY3ARdfT2iw18wae98Hl73+6D+aZNcNKTZf+LG78HBwTx79ox8+fIBcPXqVXR0dChWrFi6vEWKFEFfX5/Lly9jb28PpC6Z37x5kx49eqjy1alTB2NjY3bs2MGpU6fYtm3bR7ezXLly+Pv7Zxpwv8+bQCs5OeMly+z4559/uHv3Lj179gQgb97U4OLtrQhvX0CUmcuXL9OmTRsaNWoEQExMTLqlen19/fcO0MWLF+fy5cu0bt1alXb16lVKlCihudAnEBMdk+7K82dPQ6hTryY3r6e+Rvr6+tSoVY3ZM5Zmq26FQqF2p4aL569SoqSDWp4SJYrx+FHO/kOuJimJpHt30XepSsLZtL1r+i5VSfgn/f5bZWwMEf17qqUZtWiNfmVnXs6eTvLTYI2n0i1ekuSHD7TW9P/PYmJiifFXv/I85Nlzatatzp2bdwHQ19ejWk0Xls5emWldvQd3Y+DIPvTtNJSb/2oeB+Ji44mLjSePhTm1633NklmZ1/uxoqNj0l15Hhz8jIYNvuHatdQL1fT19fnG9WsmTpqXYR0+Pvep5FxfLW3WzHGYm5kxcvQ0Hj0KIiTkebo863/6Hl9fPxYvWaW1L7jxMfHExzxVS4sMiaB87YoE3vIHQFdfD8fq5fl1wVaN9fhdvUv52pX4c8MBVVp510rcv+Krem5gbJhuljQlJSXTyRlTSzPy2lsTGZLxTHBWvIqJJ0RDHx+p9bEcXgs0/1v74OpdytWuyJG3+ljunT7ev+xDvuLqW3TyOdgT9iT9l4+4l6mfFbti+SnmVJy9SzXv5f1kvtDlcG3IdsCZkJCQbm+lrq6uKtj5EIaGhkyYMIHx48cTHR3NnDlzcHNzy3CfoImJCV26dGHRokVYWFhgb2/P+vXriY+Pp3379mptatu2LUuXLqVIkSI4O2f9QhBNBg8ezIABAyhQoABNmjRBR0cHX19ffH19GTlyZJbqKFiwIAqFguPHj1OnTh0MDQ0xNTV9b7k3r/vbt0Vau3Yt9erVUwV3RkZGVK5cmXXr1lGwYEEiIiJYvnx5ltpVpEgRjhw5Qv369VEoFCxfvjzdgFywYEEuXrxIs2bN0NfXz/Bv3rdvX0aMGEG5cuWoUaMGf//9N0eOHGHjxo1ZasentO7HLQwf5cEDvwD8/QIYPtqDuLh4vH9NGwxXrlnA0+AQ5s78HoBho/pz7epNAvwD0dfXp8G3dejQuRXjR6Ut169dvYkDf+5g+GgP9u35HReXirj37MiY4dM+af/ivH/BfOxkku76knTnFkZNm6NrZ0f8wf0AmPTqh46NLdGL54FSSXKAv1p5ZWQEJCSopRt37UGSz22SnzxGYWKKcet26JUoScyqZZ+0b9kVGxtH4OO0gP9J0DN87vphkcecAvntcrFl77dl3Q48hvci4MEjAh48wmN4T+Lj4jmwO+3+tws8ZxASHMr3c1P30/cZ4s7w8QMYM3AKTx4Fq/Z7xsbEEhsTB0Dtel8DCvz9AijqUIix04fjfz8A7x37P3kff1i5ngnjh3Lvvj/37/szYfxQYmPj2LFzjyrPxp9XEBQUzOQpC3j16hW3bvmq1REZmXpB1Jv0xMTEdHliY2IJC4tIl65tR34+QPPB7Xj2MJhn/sE0H9yOV3GvOLcv7ctf36VDiXwWjtei7a/LHGTCL7NpOqA1V45cxKVRNcrVqsj8DlNUZa4du0Tzwe0IexLKk3uPKFregcZ9WnDq19S99oYmRrQe0ZFLf5wjMiQCm0J2tB/7HS/DX3LlA5fTNTn680GaDm6r6mOzwW1JiHvF+bf62HvpUCKfheG96H+vyxxi3C+zaDKgNdeOXKByo68oW8uJhR2mpr12Gw4wYfdcmg5qy6WDZylWqSTfdGnIlolrVXmqNK1BdPgLwp6EUqhMUTpP78XVPy+muyApN8gMp2bZDjhPnTpF7drqtx5wcHDgjz/++OBGFClShEaNGtGvXz+ioqKoU6cO06dP15h/zJgxKJVKxo0bR0xMDBUqVGD9+vVqe0kB2rdvz5o1a9JdLPShXF1dWbNmDatWrWL9+vXo6elRvHhxOnTI+hXI+fLlY+jQoSxdupSJEyfSunVrFixY8N5yb153PT098uTJQ5kyZZgyZQpt2rRRLSUBzJs3j0mTJtGuXTscHBwYO3YsvXv3fm/9EydOZNKkSXTu3BkrKyv69etHTIz6LMSwYcOYNm0aDRs2JCEhAV/f9IN2w4YNmTRpEhs2bGDu3LkULFiQefPmUb169XR5c5vn8vUYGRmxcOk0LCwtuHLpOp3a9FGbCS1YyJ6UtwYQExNjFi6dRgH7/MTHx3P/rj+D+49jn3fast21Kzfp1XUok6ePYtS4QQQGPGbqxPnsfiuQ/RQSTvxNjLkFJl27o5PXmuQAf6KmjCclJPX2Ijp5rdG1zV6wpWNmhtnwMehY5UUZG0PS/XtEjRlGkq9PTnRBa2763KP30PGq54tWrgOglVtD5k4ZnVvNypL1K7dgZGTItIXjsbAw5/qVW/TpOFTtHpz2BfOr/UP3Xc/2GBga8MPPi9Tq8ly8Ds/FPwFgZm7GqCmDyV/AjsjIFxw58BfL5q0mKenjV1+ya/GS1RgbG+H5wzysrCy4cOEqbs2+U5sJLVLY/pP+0MXHOLRmL/pGBrjP7o+phSl+1+6x1H2W2j04rQvaqM1W3r/iy5qh39N2zHe0GdWZkMBnrBnyvdpy/fbp62kzugvus/uTxyYPkc8iOP6/I+z74VcAUpJTKFSmKDXb1sUkjwmRIZH4nLvJj0O+13j/zw/1x5q9GBgZ0HV2P0wtTHlw7R7fu89Wuwdnah/T/mZ+V3xZN3QZrcd0ofWoToQGPmPdkGVqy/UPr/ux2mMxbcd9R4vh7Xn+KISdszapBbKWdlZ0mtIjdctBSCRnvU9wYKWXVvsntE+hfN8u5hy2cuVKjh49yr592r+Y4vLly3Tv3p0TJ05gY/Npr7wUOS+fxX/gFhg57NbX+XK7CTnOYvt/b/Zbm5zKdXp/ps/c/chPuF0kl7jbf53bTchRumT/4tPPzfqHOR+Uhreqo5V68u478f5Mn5n/xB5ObUtISCA4OJgVK1bQpEkTCTaFEEIIkeOUn8ckfK74IgPOAwcOMHnyZMqWLcvixYtzuzmZCgoKolmzZhqPHzx4UHVxlBBCCCHE5yjXA86hQ4cydOhQrdbZtm1b2rZtq9U6c4qdnV26n35897gQQgghPgMyw6lRrgec/9/p6el91G2WhBBCCPHfIEvqmmn9t9SFEEIIIYR4m8xwCiGEEEJog8xwaiQBpxBCCCGEFsiSumaypC6EEEIIIXKUzHAKIYQQQmiBzHBqJgGnEEIIIYQWSMCpmQScQgghhBDaoPzyfyL0Q8keTiGEEEIIkaNkhlMIIYQQQgtkSV0zmeEUQgghhNACZYpCK48PsX37durXr4+TkxNt27bl0qVLWSp3+fJlypUrR6tWrT7ovFklAacQQgghxGfs0KFDzJ8/n4EDB7J3716qVKlCv379CAoKyrTcy5cvGT9+PDVq1MjxNkrAKYQQQgihBcoU7Tyya+PGjbRr144OHTpQokQJJk+eTP78+dmxY0em5aZNm0bz5s2pXLnyh3U4GyTgFEIIIYTQAqVSoZVHQkIC0dHRao+EhIQMz5mQkMCtW7eoXbu2WnqtWrW4evWqxrbu3r2bwMBAhgwZotXXQBO5aEgIIYQQ4j9k7dq1eHp6qqUNGTKEoUOHpssbERFBcnIy1tbWauk2NjaEhoZmWP/Dhw9ZunQp27dvR0/v04SCEnAKIYQQQmiBtq5S9/DwoFevXmppBgYGmZZRKNQvNlIqlenSAJKTkxk9ejRDhw7FwcHh4xubRRJwCiGEEEJowYdeYf4uAwOD9waYb1hZWaGrq8vz58/V0sPCwrCxsUmXPyYmhps3b3Lnzh1mz54NQEpKCkqlknLlyrFhw4YcuYhIAk4hhBBCiM+UgYEB5cuX58yZMzRq1EiVfvbsWRo0aJAuv5mZGb/99pta2v/+9z/OnTvHDz/8QKFChXKknRJwCiGEEEJogVKZO+ft1asX48aNo0KFCjg7O7Nr1y6Cg4Pp3LkzAEuXLuXZs2csWrQIHR0dSpcurVbe2toaQ0PDdOnaJAGn+GydLPDp9p7klqS4uNxuQo5zKtcpt5uQo27c3pXbTchxR8pPyu0m5LgLSt3cbkKOMkR+A1wbtLWknl1NmzYlIiKC1atXExISQunSpVm3bh0FCxYEIDQ0lODg4Fxp2xsKpTK34nEhPo5vGbfcbkKOs7D78gPOuj4xud2EHCUB55fhgpEEnJ+7iQHbcvwcDys3en+mLCh27YhW6vkvkftwCiGEEEKIHCVL6kIIIYQQWiBrxppJwCmEEEIIoQW5tYfzcyBL6kIIIYQQIkfJDKcQQgghhBYolTLDqYkEnEIIIYQQWqCtn7b8EsmSuhBCCCGEyFEywymEEEIIoQUpsqSukQScQgghhBBaIHs4NZMldSGEEEIIkaNkhlMIIYQQQgvkPpyaScAphBBCCKEF8ktDmknAKYQQQgihBTLDqZns4RRCCCGEEDlKZjiFEEIIIbRAboukmQScQgghhBBaILdF0kyW1IUQQgghRI6SgBM4f/48jo6OvHjxAgBvb2+qVq2arTrq16/Ppk2bMs3j6OjI0aNHAXj8+DGOjo7cuXMnwzYIIYQQ4vOiVGrn8SXKsSV1d3d3ypQpw+TJk9XSjx49yuDBg/H19c2pU2ebs7Mzp0+fxtzcPEfPc/r0aSwsLLLUBm9vb+bNm8elS5e0dv6VK1fi6emZLn3jxo3UrFnzo+qeMGECL168YPXq1R9Vz6dk2aUZVn3ao2ebl4T7AYTMW0vc5VsZ5jV2KY/tmF4YFC+MwsiQxKAQonYdImLz3gzzmzetg/33E3h59CxBQ2bnYC8yZ9y6FaZdOqOb15qkh/68WOlJ4vUb7y2nX6ECeX9YQZK/P2F9+qbV17wZxo0bo1fcAYBE37tE//QTiXd8cqwPWTFkbD86urchj4U516/cYtaERdz3faAxf4durWnVsSmlypQA4NZ1H5bNXcWNq7dVeUxNTRg2YQANm9bF2saKOzfvMnfyUm5eu62p2lx16doNNv7Pi9s+9wkNC2fF/Kk0+ObjPte5pUjPRhQf3AJDO0uifR9ze+oWIs5n/B7L17QaRXs2wrx8MXQM9Yj2fcy9xV48P379E7c6TTX3htT0aIa5rSUh957wx8ytBF7U/G9e0eplaDy1G3alCvIyJJIzaw5wafsx1XGXzvWo1K42do6FAQi+4c+xRbt48m/ae7z2oJaUbVIVmxL2JMUn8OjyPY4s2EnYg+Ac6aOLe0OqezTFzNaS0HtPODpzG48z6WPh6mVoMLUrtq/7eH7NAa5u/yvDvGVbfE1rzyHcPXyJ3f2Xq9IHnl6GZWHbdPkvbznCn1M3f3SftEH2cGomM5yAgYEBtra2KBQ5+0axtbXFwMAgV9tQqlQpTp8+rfbI7mxuTkpISPgk5zF3+wa7iR6Er9lJQJshxF66RaF1s9ErkH4wA0iJiydi+28EdhuLf7P+hP+4A5vhPbDo6JYur569Hbbj+hJ78f2BXU4yql+PPEOHELNlG8/79iXh+g2sFi1Cx84u03IKU1MsJk8k4crldMcMnCsTd+wY4cNHEjZwMMnPnmG1ZAk6NjY51Y336ju0Oz0HfMfsiYvp0LgnoSFh/PyrJ6amJhrLfFWrCgf3/EmPtgPp3LQ3wY+fsuEXT+zyp/39Zy+bQs061Rk/eDot63bhzPFzbPRapZbnvyQuLh7HksWZNGpQbjfloxRoVYNys3twf/keTjecQPh5H6rtmIBRQesM8+etUZbnJ25wqesCzjSaRNiZ21TdOo48FYp92oa/Vr751zSZ5s4pz32saTaZwAs+dNs8Dgv7jNtvWdiWrpvGEnjBhzXNJnNq1T7cZnSnrFs1VZ5iNcpyc/8/bO48lw1tphMV9Bz3rRMwz2eVlqd6GS5uOcr61tPZ0m0BOnq6uG+dgL6xodb7WLZ5dRpO68ZZz/383GwKjy/40mnzWPJo6KNFYVs6bhrD4wu+/NxsCv+s2k+jGd1xfKuPb+QpaE39yd8RmMEXjE0tp/FD1cGqx47v5gPgc/CCdjsockSuBpwrV66kVatW7N27l/r161OlShVGjhxJdHS0Ks/Jkyfp0qULVatWpXr16nh4eBAYGKg63qlTJ5YsWaJWb3h4OOXLl+fcuXMA7Nu3j7Zt2+Ls7EytWrUYPXo0YWFhqvzvW84ODAxk4MCB1KxZE2dnZ9q1a8fZs2fT5YuJiWH06NE4OztTu3Zttm7dqnb87SX1d73dhvPnzzNx4kRevnyJo6Mjjo6OqtnJFi1apCvbtm1bVqxYkWG979LV1cXW1lbtYWBg8N7XCODevXv0798fFxcXnJ2d+e677wgMDGTlypXs2bOHY8eOqdp7/vx5AHx9fenevTsVK1akevXqTJ06lZiYGFWdEyZMYNCgQaxdu5batWvTpEmTLPXjY1n1bEPU7j+J8jpMwoNHhM5fS+LTUCy7NMsw/6s7frw8eIKE+4EkPQnhxW9/E3P6MsZVyqtn1NGhwOJxhK3cSuLjp5+gJ5qZdOxA3MFDxB08SHJAIC9XepISGoJJ61aZlsszZjTxR4+ReCv9TF7U7LnE7d1H0v37JAcG8mLxEtBRYFDFJae68V7d+3dhzfKNHDn4N/d8/JgwdAZGxkY0b9dYY5mxA6eyY6MXPjfv4n8/gKmj5qKjo6DGN6n/ABoaGfJt83osmfUDl85dJdD/MZ6Lf+JxYBBderb7VF3LFtca1RjWvweN6tbK7aZ8FIcBzXj0v795vP1vYu4FcWfqFuKfhFG0Z6MM89+ZuoUHq34j6toDYv2fcnfeTmIeBGP3be68J2v0dePKruNc2Xmc5/eD+GPWNqKCw6jarWGG+at2bUBUUBh/zNrG8/tBXNl5nKu/nKBm/7SxyHv4ai5uPcrT2wE89wtm//j1KHR0KF4rbfzZ1mMR17xOEnrvCc/uBLJ3zFosC9lg7+Sg9T5+1deNf3cd59+dxwm7H8TRWdt4ERyGc7cGGeZ37lqfF0FhHJ21jbD7Qfy78zj//nKC6v2bquVT6ChouWIQp5btJjIwJF09ceEviQmNUj1KNnAm4uEzAs/d0XofP5RSqdDK40uU6zOcgYGBHDt2jDVr1rB27VouXrzITz/9pDoeFxdHr1698PLyYtOmTSgUCgYPHkxKSgoALVq04ODBgyjf2vRw6NAhrK2t+eqrrwBITExk+PDh7N+/n1WrVvH48WMmTJiQ5TbGxsZSp04dNm7cyJ49e6hduzYDBgwgKChILd+GDRtwdHTE29sbDw8P5s+fz5kzZ7L9mjg7OzNp0iTMzMxUs5C9e/emffv23L9/n+vX05aKfHx8uH37Nm3bts32ed72vtfo2bNndOvWDUNDQzZv3oy3tzft2rUjKSmJ3r174+bmhqurq6q9zs7OxMXF0bdvXywsLPDy8mL58uWcPXuW2bPVl5j/+ecf/Pz82LhxI2vWrPmofmSJvh5G5UsRc+aKWnLsmSsYO5fLUhWGZUtg7FyWuHdmMa0Hf0dyeBRRu//UWnM/iJ4e+qUdeXXxolryq4sXMahQXkMhMHZrgp69PdGbsrY8pTA0RKGnh/LFy49q7ocqVLQgdvlsOPP3OVVaYkIiF89ewblaxSzXY2xshJ6eHlERqV869XR10dPT49Ur9Rn3V/HxVKleWSttF+kp9HXJU9Eh3XJ46InrWFYtncVKFOiZGZMYGfP+vFqmq6+LvZMDfqfUxwW/kzcoXKVUhmUKu5TC76R6/vsnr2Pv5ICOnm6GZfSNDdHR1yUukz4amafO8MdFRmvM8yF09HXJ7+SA/6mbaun+J29SSEMfC7qUwv/ku/lvkP+dPtYe3obYsJdc33UiS+0o36YW//7y/ryfkuzh1CzXb4ukVCqZP38+ZmZmALRs2ZJ//vmHkSNHAtC4sfosxbx586hRowb379+ndOnSNG3alPnz53P58mXV0vCBAwdo3rw5Ojqp8XT79u1V5QsXLszkyZPp0KEDMTExmJqavreNZcqUoUyZMqrnI0eO5OjRo/z1119069ZNle7i4kL//v0BcHBw4MqVK2zatIlatbI342BgYIC5uTkKhQJb27TlO1NTU2rXro23tzcVK6b+Y+rt7U21atUoXLhwluq+e/cuzs7OquclSpTAy8vrva/R9u3bMTMz4/vvv0dfX1/VxzeMjIxISEhQa+8vv/zCq1evWLhwISYmqYPftGnTGDBgAGPGjMHm9TKsiYkJc+bM0bjdQNt0rfKg0NMlKSxCLT0pLBJTGysNpVIVP74V3bwWKHR1CPPcTpTXYdUxY+dyWLRrTEDrwTnS7uzQsbBAoadLSoR6H1PCI9DJmzfDMrqFCmLm0Z/wIcMgOTlL5zEb0J/k0Oe8upx++f1TsLVLXcILCw1XSw8LDce+cP4s1zNq6hCePQ3l7MnUpbmYmFiuXrzOoFF9eHDXn+eh4TRr25iKLhUIePBIex0Qagzy5kFHT5dXoVFq6QmhURjaWWapDoeBzdA1MSR4/z850MLMmViZo6OnS8xz9fbHPI/CzDbj/ftmthYZ5tfV18MkrznRIZHpyjSc0JmXT8N5cOZmumNvNJ7alYALPoTcfZz9jmQisz6a2lpmWMbM1oIHGvponNecmJBIClYtRcVOdfnZbVKW2lH626oY5THhxq8nP6gf4tPL9YCzYMGCqmATwM7OTm0pNzAwkBUrVnDt2jUiIiJUM5nBwcGULl2avHnzUrNmTfbv30/VqlV59OgRV69eZcaMGao6bt++zcqVK/Hx8SEyMlKtjpIlS763jbGxsXh6enL8+HFCQkJITk4mPj4+3Qxn5cqV0z3fvFm7G5k7duzIpEmTmDhxIjo6Ovz222+MHz8+y+UdHBz48ccfVc/fBHnve43u3LlD1apVVcFmVvj5+eHo6KgKNiE1KE9JScHf318VcJYuXfqTBZtq0n2NVKjNlGfkUdcxKEyNMa5UBtvRvUgIDOLlwRMoTI3Jv3gsz6auIDnyP3SngXf7o1BARl3U0cFy6lSif95E8uOs/QNl2qUzxg0aED5sBHyivbfN2zVh5pKJqucDvkv9Ypru76ZQZHmWoM8Qd5q1+ZbubQaQ8NaM5rjB05i3fBonb/xOUlISt6/7csD7MOWcHD+6H+J93v17kqVpnwJtalJqbHsu91hCwvPc+xxm9H7M8HOnyv9Owpu9/Bn0uZZHc5xa1mBTpzkkvUrMsL6ms3uSr0wRfm4/KxutzqZ32qZ4398o3WuSlm5gakTL5QP5fcJ64iKyNiNbqVMd/I7/m2FAnpvkoiHNcizgNDU1VduL+caLFy/UAkw9vfRNePvDOmDAAAoUKMCcOXOws7MjJSWF5s2bk5iY9kFr0aIFc+fOZerUqRw4cIBSpUqpZiRjY2Pp3bs3tWrVYvHixVhZWREcHEyfPn3U6sjMokWLOH36NOPHj6dIkSIYGRkxbNiwLJXX9kVA9erVw8DAgCNHjmBgYEBCQkK6WeDM6OvrU7RoUbW0rLxGRkZG2W6rUqnU2P+3042NjbNd98dIjniBMikZPRv1mT49awuSwyIzLZv45BkACXcfomttic2Qbrw8eAKDwgUwKJSfgj/OSMusk9rH0jcP4O/Wj8RHOXO1aEZSoqJQJiWnm83UsbIkJSI8XX6FiQn6ZcuQp1Qp8owY/jqzAoWODvn+OkbEmDEkXLmqym/SuROm3boRPmo0SQ80Xw2ubX//cZLrV9Jmdd58UbGxsyY0JO2LqrWNFWGhYenKv6v3oG54DO9F7/aDuXv7vtqxRw+f4N7aA2MTI8zMTAkNCeP7dfN4HBikoTbxsRLCX5CSlIzhOzNlBjYW6WY931WgVQ0qfu/BlX7LCTupeeYvJ8VGvCQlKRmzd9pvap2H6OcZtz86NP3sp6l1HpITk4h9J/iq2b8proNbsqXrfJ75ZDzT7jazO44NXdjYcTYvnqb/rH+sN318dzbTxDr9TO0b0aHpZz9NrS1ITkwiLiIam9IFsSxsR4cNo1XHFa/Hz/F+m1lbb6zans48Ba0pVrsC3h7LtdInbfpS919qQ44FnMWLF+fUqVPp0m/cuKG2FJuZiIgI/Pz8mDVrlmq5PKPbBDVs2JDp06dz6tQpDhw4QKtWaRdFPHjwgIiICMaMGUOBAgUAuHkze4PR5cuXadOmDY0apW5aj4mJ4cmTJ+ny/fvvv+meFy9ePFvnekNfX5/kDJY19fT0aN26Nd7e3hgYGNCsWbOPDtiy8ho5OjqyZ88eEhMTM5zl1NfXV+2rfaNkyZLs3buX2NhY1SznlStX0NHRoVixYh/V5o+SmET8rXuY1HQm+mjaxV8mNV2I/ivry3AKhQKFQeprkfDgEf4tBqgdtxneHR1TE0LmrSHxaah22p5VSUkk3vXFsGpVXp06rUo2rFqV+NPp9xUrY2J43qOXWppJ61YYuLgQOW06ycFpwbJJ506YdXcnYsw4kj7x7c1iYmKJ8Y9VSwt59pyadatz5+ZdAPT19ahW04Wls1dmWlfvwd0YOLIPfTsN5ea/mi86iIuNJy42njwW5tSu9zVLZmVer/hwysRkXlz3x6aOE89+T9t/bPONEyGHNd8irkCbmlRcNoBrA38g9OhVjflyWnJiMkE3/CnhWgGft9pbwtUJnz8z3nby6Mo9HBuqX+BUwtWJoBv+pCSl/RtQ06MZ3wxpzbbuCwm64Z9hXU1n9aBM46ps6jSHyEc5M+akJCbz9IY/Dq4VuPtWHx1cK3BXQx+fXLlHqXf66OBagaev+xjmF8xPjdSvq6gzpj0GZsYcmbGVF8HqXx4rdqhDbNgL7v91TTud0iKZ4dQsxy4aenMF88yZM/Hx8cHf35/t27fj5eVFnz59slSHhYUFlpaW7Nq1i4CAAP755x8WLFiQLp+JiQn169dnxYoV+Pn50bx5c9Uxe3t79PX12bp1K48ePeLYsWPZvldkkSJFOHLkCHfu3MHHx4fRo0enC64gNZj66aefVH39448/6N69e7bO9UbBggWJjY3ln3/+ITw8nLi4ONWxDh06cO7cOU6ePEm7dh9/xWxWXqOuXbsSHR3NqFGjuHHjBg8fPmTv3r08eD27VbBgQXx9fXnw4AHh4eEkJibSokULDAwMmDBhAnfv3uXcuXPMnj2bVq1aqZbTc0vEpj1Ytm9MnrbfYlC8MLYT+qNfwJbInYcAsBnVk/wL0r5tW37XHNN61dEvao9+UXvytG2EVe92vNifeh85ZUIiCfcC1B4pL2NIiYkl4V4AJCZ98j7G/vJr6n0zm7qhW7QI5kMGo2OXj9h9+wEw698Pi0mvl6eVSpL8/dUeKZGRKBMSSPL3RxkfD6Quo5v37UPUwkUkP32KTt686OTNi+ITz1K/bcu6HXgM70XDpnUpVaYE83+YTnxcPAd2p+2vXeA5g1GT0/bW9hnizogJA5k8YhZPHgVjY2eNjZ01JqZp/ahd72tq16tBwSL21KzzFZv3rMH/fgDeO/Z/0v5lVWxsHD53/fC56wfAk6Bn+Nz1I/hp+qt9/8v81xykcNf6FOpSF9NS9pSd1R3jQjYEbE69w4fj5M5UXJl266cCbWpSaeUg7szYSsSlexjYWmBga4Geee68J/9Z/zsunerh3LEONiXtaTy1Gxb21qr7ajYY14k236d9Ob20/RgWBa1pPLUrNiXtce5YB5dOdTm77qAqTy2P5tQf3YF949YR+TgUM1sLzGwtMDBJu+VRszk9qdi6FruHrSIhJl6VR88w69ugsurC+t+p1KkuFTt+g3VJexpM7Uoee2uuvu5jnXEdaf69hyr/1e1/kaegNQ2mdsW6pD0VO35DpU51Ob8udbxNfpXI87uP1R7xL2JJiI7j+d3HpCS+NfmiUFCxwzfc8DqFMjn9v8PivyvHZjgLFSrE9u3bWbZsGb179+bVq1cUK1aMBQsW4OaW/t6FGdHR0WHZsmXMmTOH5s2b4+DgwJQpU3B3d0+Xt2XLlvTv359q1aphb2+vSs+bNy8LFizg+++/Z+vWrZQvX57x48czcODALPdl4sSJTJo0ic6dO2NlZUW/fv3Ubu3zRq9evbh16xarVq3C1NSU8ePH4+rqmuXzvM3FxYXOnTszYsQIIiMjGTJkCEOHDgWgWLFiODs7ExkZSaVKlT6o/rdl5TWysrJi8+bNLF68GHd3d3R0dChbtixVqlQBUveWXrhwgXbt2hEbG8uWLVuoXr06GzZsYO7cubRv3x5jY2O+/fbbbN0hIKe8/P0kupbm2Az+Dl3bvCTce8hjj2kkBaX+46xnmxd9+7fuV6mjg+3InugXyo8yOZnEwGCeL91I5K5DudSD94v/628UefJg1qMHOtZ5SfL3J2L8eFKepW4L0LW2RjdfvmzVadK6NQoDA6xmq+8Ni964ieiNm7TV9GxZv3ILRkaGTFs4HovXN37v03EoMTFpM6H2BfOjTEnbqvNdz/YYGBrww8+L1OryXLwOz8Wpd8kwMzdj1JTB5C9gR2TkC44c+Itl81aTlJS1C6o+tZs+9+g9NG0/96KV6wBo5daQuVNGayr2nxO87x/0rcwoOaodhvksifZ5xMXvFhD/+DkAhnZWGBdM+8JaxL0hOvp6VFjYhwoL0yYzHu88wfXhP6arP6fdOnAOEysz6gxrg5mdJSF3H7O952KinqS239zOUu2enJGPQtneczFNpnWjmnsjXoZE8PuMLdx5a4a3mntD9Az16bRmhNq5ji/bzfHl3q/zpK7A9fplqlqevaPXcs1LuxfW3DlwHmMrc2q97mPo3cf80nMxL56kzkSa2VmSxz7tbxT1KJRfei6h4bRuuLg3JDokgiMztuD7+0VNp9DIoXZ5LArZcP0/dnX6G1/oBeZaoVC+7yoJ8Z+jVCpp0qQJnTt3plevXu8v8IXyLZO1Ly6fMwu7uPdn+szV9fn0t6/5lG7c3pXbTchxR8pn7criz9kFo4xvUfSlMOTLXwqeGLAtx89xtoB27tNbM3i3Vur5L8n1q9RF9oSFhbFv3z5CQkI++t6bQgghhBCfggScn5maNWtiZWXFrFmz0v0u+9v313zXTz/99J/6CUshhBDiSyNXqWsmAednxjeTq4L37t2r8Vi+bO7VE0IIIUT2yGVMmknA+QV59/6aQgghhBD/BRJwCiGEEEJogfL/wcVXH0oCTiGEEEIILUiR+/5olGM3fhdCCCGEEAJkhlMIIYQQQitSZEldIwk4hRBCCCG0QPZwaiYBpxBCCCGEFshtkTSTPZxCCCGEECJHScAphBBCCKEFShRaeXyI7du3U79+fZycnGjbti2XLl3SmPfPP/+kV69efP3117i4uNCpUydOnTr1od3OEgk4hRBCCCG0IEVLj+w6dOgQ8+fPZ+DAgezdu5cqVarQr18/goKCMsx/8eJFatasybp16/D29qZ69eoMHDiQ27dvf8DZs0YCTiGEEEKIz9jGjRtp164dHTp0oESJEkyePJn8+fOzY8eODPNPnjyZfv36UbFiRYoVK8aoUaMoWrQof/31V461UQJOIYQQQggtyI0ZzoSEBG7dukXt2rXV0mvVqsXVq1ez1u6UFGJiYrC0tMzm2bNOrlIXQgghhNACbd0WKSEhgYSEBLU0AwMDDAwM0uWNiIggOTkZa2trtXQbGxtCQ0OzdL6ff/6ZuLg43NzcPrzR7yEznEIIIYQQ/yFr166lSpUqao+1a9dmWkahUA92lUplurSMHDhwAE9PT5YtW5YuaNUmmeEUQgghhNCCFC3d993Dw4NevXqppWU0uwlgZWWFrq4uz58/V0sPCwvDxsYm0/McOnSIyZMns2LFCmrWrPlxjX4PmeEUQgghhNCCFBRaeRgYGGBmZqb20BRwGhgYUL58ec6cOaOWfvbsWZydnTW29cCBA0yYMIGlS5dSt25dbb4MGZIZTiGEEEKIz1ivXr0YN24cFSpUwNnZmV27dhEcHEznzp0BWLp0Kc+ePWPRokVAarA5fvx4Jk2aRKVKlVR7PY2MjDA3N8+RNkrAKYQQQgihBcpcOm/Tpk2JiIhg9erVhISEULp0adatW0fBggUBCA0NJTg4WJV/165dJCUlMWvWLGbNmqVKb9OmDQsWLMiRNiqUSmVuvT5CfJSv7OvkdhNynJWuSW43Iccde3Y9t5uQo/ZbueZ2E3Jco1vzcrsJOW5Rlam53YQcpaOlq6v/yyYGbMvxc3jn/04r9bR9+j+t1PNfIjOcQgghhBBakJKFq8L/v5KLhoQQQgghRI6SGU4hhBBCCC2QPYqaScAphBBCCKEF2f1Zyv9PZEldCCGEEELkKJnhFEIIIYTQAm390tCXSAJOIYQQQggtSPl/cHupDyVL6kIIIYQQIkfJDKcQQgghhBbIVeqaScAphBBCCKEFsodTM1lSF0IIIYQQOUpmOIUQQgghtEDuw6mZBJxCCCGEEFogezg1k4BTCCGEEEILZA+nZrKHUwghhBBC5CiZ4RRCCCGE0ALZw6mZBJxCCCGEEFogAadmsqQuhBBCCCFylMxwCiGEEEJogVIuGtJIZjiFRhMmTGDQoEGZ5jl//jyOjo68ePHiE7VKCCGE+G9K0dLjSyQB5yeWlSAuq54+fUqFChVo0qRJumPe3t5UrVo1XXr9+vXZtGlTluqfPHkyCxYsUD13d3dn7ty5anmcnZ05ffo05ubm2Wv8f0S/0T05eGU3J/3+5Eev5RQvXSzT/K2+a866PSs5evsAR28fwHPXUspVLqMxf48hXbkQdIKRM4doueVZ121kV/53aRv77+1l0S8LKVq6SKb5i5YuwtS1k9l8dhOHH/1Omz6t0+XR0dWhx9jubD6zkf339rLp9M90Hf4dCkXufL2fNnUUgQ8v8zLqPseO/Eq5cqWzXLZjx5YkJTxht9cGjXnGjxtCUsITli6ZqY3mfpQiPRtR9+IPNA7YQq0/52FVXfP7L1/Tanz1yyQa3FpHo/s/U+PgLGzqVvyErdWeS9duMHjcdOq17EqFWm4cO3k2t5ukURX3hgw+vYzxvhvpfWAOhas5Zpq/SPUy9D4wh/G+Gxl0ahkuXRtozFuuxddMDthO+3Uj1dILf1WGjhtGM+yCJ5MDtlP62ypa6YsmLu4NGXj6e8b6/kzPA7Mp9J4+Fq5ehp4HZjPW92cGnPoe5671/4+9uw6LMmsDOPwbCQsQKQORxW7FWBMDsbC71nbdde1u1zU+e+2127XWQNfuDkxMDEBBQEBAEAERmO8PdNaREGVwZHzuveZa5rznnXkOMzLPnHqTrVu8aRXGPN1E6xWD1cr7npvHmKebEt3qT+mmiSaJdCYJZwa2a9cuGjZsSHR0NNeuXdPY48bFxREfH4+xsTEmJiYp1jU0NMTS0lJriUZadO3XkY592jF73Hy6O/9CcFAIi7bOJVv2rMmeU6FaOQ67HKdv28H0avYbz30DWbRlDpa5LRLVLV62GC1/asqju4/Tsxkpate3La1+bsWS8X8xoMkgQoNCmb75f2RNoY2Zs2bB3/s5a2asJTggJMk67X9rR+OfnFky4S9+rtOHVf9bQ5tfW9O8R7P0akqyRgz/jcGD+jBw8HiqVGvM84AgDh3YgpFR9k+emz+/NbNmTOTs2UvJ1qlYoSy9e3XG7dY9TYb9RfI0r0qJKd14PH8355xGE3LZnUpbRpPF2jzJ+mZVi/Pi9G2udp7B+XpjCT5/j4obR2JS6oevG7gGREVFU7RQAcYO1cwX9vRSvEkV6k3swvnFe1jVeBw+ru50WD8Sk7xJv0Y5bCxpv24EPq7urGo8jgtL9lB/UleKNqqUqK6JtQV1x3XG+7J7omOG2TITcN+bwxPXabpJiRRvUhmniT9xYfFe1jQezzPXB7RfPyLFNrZbN5xnrg9Y03g8F5fspV6ybTTHcVynJNu4rtlEFlbsp7pt6TQdAPf9rpptYBpID2fyJOHUokOHDtG0aVPKlClD5cqV6d69O5GRkak6V6lUsmvXLpo3b06TJk3YsWOH6tjly5cZM2YMr169omjRohQtWpRFixbRpUsXfH19mT59uqoc/usNPXnyJM7OzpQuXRpfX1+13tjRo0fj6urKhg0bVOc+e/YsySH1w4cP07hxY0qVKoWjoyNr1qxRi93R0ZFly5YxZswY7O3tqV27Ntu2bUvrr/OzdejdlnULN3Lq4Fk8H3jxx6DpZMmamQYtnZI9Z2L/qexc78Kju495+tib/w2fjSJTJirVUO9NyJotK1MWj2faiNmEh71K76Ykq0WvFmxdtJXzhy7w9MFT5gyZS+YsmanTonay5zx0e8iqaas5vfc0b2PeJlmnePliXDxyCdcTVwh4Fsi5A+e4fuY6hcsUTqeWJG/ggN5Mn7EQF5eD3L37gB49B5MtW1Y6dmiZ4nmZMmVi4/rF/DF5Dp5e3knWyZ49Gxs2LObXviN5GfoyHaL/PHa/NsZn80me/X2S14/8uD9hA9G+wdh2r5dk/fsTNuC55F/CbnoS6fWch//bymtPf6zql//KkaedQ9VKDOzTjXq1q2s7lBRV7t2Im9tOcXPrKYIf+3F08ibC/YMp/1PSf1fKd65LuF8wRydvIvixHze3nsJt+2mq9GmsVk+RSUGLBb9xZt4OQr0DEz2Oxyk3Ts/5hweHrqZLuz70Y+9GuG07hdu7Nh5710b7n5LumbXv7Ei4XzDH3rXR7V0bK/dxVqunyKSg2YLfODtvJy+TaGNUyCteB4WpboXq2hP6JADvS/fTpZ1fQqmhmy6ShFNLAgMDGTZsGK1bt+bAgQNs2LCBevXqoVSm7q126dIloqOjqVatGs2bN+fQoUNEREQACcPcY8eOxcjIiHPnznHu3Dl69uzJokWLyJ07NwMHDlSVvxcdHc3y5cuZOnUq+/btw9xc/ZvquHHjsLe3p127dqpz8+TJkyiuO3fuMHjwYJydnfn333/p378/CxYsYNeuXWr11q5dS6lSpXBxcaFTp05MmjQJDw+Pz/01frG8+fNgkcucS6f/++P8NuYt1y+5UaZiqVQ/TpasmdHX1yf8pfoc1pH/G8z54xe5clZzPc+fK3f+3JjnMuPameuqsrcxb7l9+TYlKpRI02PfuXKXctXLYW1nDUCB4naUrFSSKyevpOlxP5edXX7y5MnF0WOnVWUxMTGcOXuJqlUTTyn50ITxQwh6EczadVuTrbNo4f84eOA4x0+c1VjMX0phoIdJGTtenLqlVh50+hamFVM5hUChQN8oK29fvk6HCEUmAz3ylLbD6+xttXLPM7fJVyHpL2P5yhfG88zH9W+Rp7QdmfT1VGUOg1oRGRyO27bTHz/EV5XJQI/cpe3wOntHrdzrzJ1k22hdvjBeZz6uf5vcH7WxxqCWRAa/4lYq2pjJQI+SLavjtl27vw+RerJKXUuCgoKIjY2lXr16WFsnfGi/73FMjR07duDs7Iyenh6FCxcmf/78HDx4kLZt22JoaIixsTEKhQJLS0u18/T09MiePXui8rdv3zJp0iSKFUt6PpixsTEGBgZkyZIl0bkfWrt2LVWrVqVfv34A2NnZ8fjxY1avXk2rVq1U9WrWrEnnzp0B+Pnnn1m3bh2urq4ULFgw1b+DtDC3MgMgJEh9yDgkKJQ8+XKl+nH6jfuFoOdBuH6QWNZr7kjR0kXo7vyLZoL9QmaWOQEIfRGqVh4a9BKrfFZpeuztf/1DduPsrDq1gvi4eDLpZWLdrPWc2vN1//jnzpXQjoCAF2rlAQFB2ObPl+x51apWpEf3jlSolHTPICTM7bS3L0WVqo2TrfM1GZqZkElfjzdBYWrlMUFhZLYyTdVj2PVtjF62zPjvvZgOEYpsOY3JpK9HxAv11+j1izCMLHMkeU52yxy8/qh+xIsw9Az0yWZmTETgS/JVLELZ9rVZ1WhMusWeWu/b+HHMr1+Ekd3SNMlzjCxz4JlEfT0DfbKaGfM68CXWFQtTpn1t1jQam6o4itSvSBaTbNz+58wXtSO9yKUtkyc9nFpSrFgxqlatStOmTRk4cCDbt28nLCzs0ycC4eHhHD16lGbN/psv16xZM3bu3PnF8RgYGHxWwpscT09PypdXH64rX748T58+JS4uTlX24XMpFAosLCwIDg5O8/Mnp0FLJ049Oqi66esnfNf6uEdZoVCkupe5y28dqd+8LqN6TyDmTQwAVnktGTp5AL8PmKoq+1rqtKiDi/su1U3P4N33yURtTFz2uWo1q0XdVo7MGDCLfs4DmDNkLm1+aY1Tm+SnI2hCx44teRnyUHUzMPj819HIKDvr1y3i174jCA4OTbJOvnx5mTd3Mt26D+TNmzeabUSafdSuVL6eeVpWo/CINtz4ZQExL2RXiXSV5Psxpfrqd9/PiVcqlRhmz0Lz+X05MHoVUaERGg40DT7378rHxxT/lRtmz0Kz+X05+BltLNu+Fh6n3IgIfJnqkL8GmcOZPOnh1BI9PT3Wrl3L9evXOX/+PBs3bmTevHls374dGxubFM/9999/efPmDe3atVOVKZVK4uPjefz4MYUKFfrseLJkyaKRhT+pTdbeJ3zvfU6i9yXOHjnP3Rv/zfMxNDQAwNzKnODA/3o5c1qYEhKUdBLyoc6/tqf7gM70bz+Mx/c9VeXFyxTF3NKM9YdWqMr09fWxr1KWtj1aUuOHesTHp8+fk0tHL/Hg5n8T7Q3etTGnpRkhgf+1ydTClNCgl2l6rp/H9WLbX9s5vTehR/OJ+xOs8lnRoV87ju04lqbHTsm//x7B1fWG6n7mzIYA5M5tyfPn/835srKyICDwRaLzAQoW/AE7u/y47F6nKsuUKeG7d3TkU0qUqkmpUsXIlcsS10sHVXX09fVxcKhCv9+6k83ILt1ex+TEhIQTHxtH5o96kQwtciTq9fxYnuZVKfPnL1z/eT7BHw1tCs2JDH1FfGwcRh+9RtnMTRL1CL73OiiM7B/1fmY3NyHubSxRoRFYFsmHqY0V7VYPUx1XZEr4Wz3GYwNL6wxPcr5jennfxo97M7OZJ+6pfS8iKHHvZ3bzHKo2WhSxxtTGirZJtHGUx3qW1xmh1kYTa3N+qFGKXb/M10ibNElXk0VNkIRTixQKBRUqVKBChQr069ePOnXqcOzYMXr06JHieTt37qRnz560bKm+KGLq1Kns3LmTUaNGYWBgoNaj+J6BgcEXf1Cm5tyCBQty/fp1tbLr16/zww8/oKenl8xZ6S/ydRSRr33Vyl4EBFO5ZkUe3nkEgL6BPuWrlGXxtOUpPtZPfTvQc1AXBnYawf1bD9SOXTl7jQ51uquVTZw3miePvdmwZHO6JilRr6OIeh2lVhYcEEJ5B3s87ibMj9U30Kd05dKsnr4mqYdItcxZM6OMV/+CEB8Xr/qQSC8REa+JiFCff+jvH4BT3ZrcvHkXSHif1nSowpix/0vyMdzdH1PWXn1Llsl/jMTYyIghwybi4+NHYOCLRHVWrfyTBw88mD1nyVdPNgGUb+MIv+WFRa3SBBz8b66sRc3SBB5OfqFInpbVKDPvV272XUjQsRvJ1hNpF/82Dv/bXtg5lOLBB6+JnUNpHh5Jej73s+uPKOykPipk51Aa/9texMfG8cLDjxX1RqkdrzW8LYZGWTg6aSPh/uk3MpSU+LdxPH/XxodqbSyVbBt9k2xjKZ6/a2Owhz8r641WO15reBsMjbIm2cYybWsRGRzO4xM3NdMo8VVIwqklbm5uXLx4kerVq2Nubo6bmxshISEUKFAgxfPu37/P3bt3mT17dqL5jk2aNGHevHkMHToUa2trIiMjuXjxIkWLFiVr1qxkzZoVa2trrly5QuPGjTEwMMDMzCzVMVtbW+Pm5sazZ8/Ili0bpqamier07NmTNm3asGTJEpydnbl58yZ///03v//+e6qf52vZuuofug/ojI/nM7y9ntFj4E9ER73h8O7/eugmLRhL4PMg/pq+EkgYRv9lRE8m9JuCv89zzC0Tfn+Rr6OIiowi8nUUng+81J4nKjKKsNCwROVfg8tqFzr0b4/vEz98vXzp2L89b6LfcNLllKrOiHnDePE8mLUz1wEJSWn+wgl7dRoY6mOe25wCJQoQHRmF3xN/AC4du0yHAR0I9A3k6cOnFCxViFY/t+LItiNfu4ksXLSK0aMG8OixF48fezF61AAiI6PYsnW3qs7aNQvw8/Nn3PgZvHnzhrt31b8ovHy36Ot9+du3bxPViXwdSXBwaKLyr8lr2X7KLu5HmJsnoVcfkr+LE1nzWfB0fcJ7tui4DmTObcatAX8BCclm2UW/cW/8ekKvPsLwXU9afHQMsa+ikn2eb1FkZBTez/xU9339AnB/6EEOE2Py5E7bnGRNurzqIM3n9cX/lhfPrj/CvqMjOfKac/3v4wDUHtke49w5+XfoMgCu/32cit3q4TShMze2nCRf+cKUa1+b3QMXAxD35i1BD5+pPUd0eMJuJh+WG2TLjNkPuVX3TW0syVXClqiXEYT7aTYpdV11kKbz+uJ/yxPf648p17EOJnnNufGujbVGtsM4d072DU348n7j7xNU6FaPuhM6c3PLSazLF6Js+9rsGbhE1cYXybTx43IUCsq0rcntHWdRxn17/Ym6usJcEyTh1BIjIyOuXLnC+vXriYiIIG/evIwePZpatWqleN6OHTsoVKhQkotrnJycmDRpEidPnqR+/fp06NCBwYMH8/LlS/r378+AAQMYOHAgEydOxMnJiZiYGB48SP2HZ8+ePRk9ejSNGzcmOjqa48ePJ6pTsmRJ5s+fz8KFC1m6dCmWlpYMHDhQbcHQt2LDki1kzpKZkdOHYJzDiLs37jOg43AiP+glzGVtpdab1bpbcwwzGzJz1RS1x1o5dy0r5677WqGn2val/2CYxZD+U/thnMMI95sPGNN5nFpPqKW1FfEfTGcwz2XG0sNLVPfb/tqGtr+2we3iLUa2S+hp+WvCUroN70r/af0wtTAlOCCEA38f4O/5m79e496ZPecvsmbNwuKF/yNnzhy4ut6gUeNOaj2h+W3yaqVXUtP891zEIKcRhYa2JnMuUyLcfbjSaQbRzxKmD2S2yklW6//2hM3fxYlMBvqUmtmLUjN7qcqfbT3NrUFLv3r8aXHH/RE9B/zX0zdrUcK0leaNnJg2flhyp3119/ddIltOI2oMbImRlSlBD5+xtftswn0TXiMjK1NyfLBfZZhPENu6z6bexJ+o0KUeEYGhHJm0gQcHP2/HhzxlCtBl23jV/XoTuwDg9s8Z9g1PedTmc93fd5msOY2p/kEbt3efTbhvQmJrZGWKSd7/3odhPkFs7z4Hp4k/Ub6LExGBoRz9gjYC2NUoSY58Ftz6Rleny6Kh5CmU6TlxToh09GPelJNzXZBTL5u2Q0h3xwNufbpSBrY3p4O2Q0h39e4mPX1Bl8yqMEHbIaSrTOh+pjTm6aZ0f45Ztj9p5HFGfoVYvzbp4RRCCCGE0ICMP46SfiTh/AbZ29sne2zlypVJXiNdCCGEENolQ8bJk4TzG+Ti4pLssVy5Ur8puRBCCCHEt0ASzm+Qra2ttkMQQgghxGeKlz7OZEnCKYQQQgihATKHM3lyaUshhBBCCJGupIdTCCGEEEIDZEA9eZJwCiGEEEJogAypJ08STiGEEEIIDZArDSVP5nAKIYQQQoh0JQmnEEIIIYQGxKPUyO1L/P333zg6OlK6dGlatWrF1atXU6zv6upKq1atKF26NHXr1mXLli1f9LypJQmnEEIIIYQGKDV0+1wHDhxg+vTp9O3bFxcXFypUqMDPP/+Mn59fkvV9fHzo06cPFSpUwMXFhV9//ZVp06Zx+PDhL3j21JGEUwghhBAiA1u7di2tW7embdu2FCxYkHHjxpE7d+5key23bt1Knjx5GDduHAULFqRt27a0atWKNWvWpFuMknAKIYQQQmhAvIZuMTExREREqN1iYmKSfM6YmBju3r1LjRo11MqrV6/OjRs3kjzn5s2bVK9eXa3MwcGBO3fu8Pbt2y9p+idJwimEEEIIoQGamsO5fPlyKlSooHZbvnx5ks8ZGhpKXFwc5ubmauUWFhYEBQUlec6LFy+wsLBQKzM3Nyc2NpbQ0FDN/DI+ItsiCSGEEEJ8Q3755Rd69OihVmZoaJjiOQqF+p5MSqUyUdmn6idVrimScAohhBBCaICmrjRkaGj4yQTzvZw5c6Knp8eLFy/UyoODgxP1Yr6XVO9nSEgI+vr6mJqaflHMnyJD6kIIIYQQGqCpOZyfw9DQkJIlS3L+/Hm18gsXLmBvb5/kOeXKlePChQtqZefOnaNUqVIYGBh8ZgSpIwmnEEIIIYQGaGsfzh49erBjxw527NiBh4cH//vf//D396dDhw4AzJ07l5EjR6rqd+jQAT8/P6ZPn46Hhwc7duxg586d9OzZU2O/i4/JkLoQQgghRAbm7OxMaGgof/31F4GBgRQpUoQVK1ZgbW0NQFBQEP7+/qr6NjY2rFixgunTp/P3339jZWXFuHHjaNCgQbrFKAmnEEIIIYQGaGoO55fo3LkznTt3TvLYjBkzEpX9+OOP7N69O73DUpGEU2RYNQ3zajsEoQF581bRdgjpylWpp+0Q0t2NChO0HUK6G3ltirZDSFf7S43Xdgg64XPnX35PZA6nEEIIIYRIV9LDKYQQQgihAUqtDqp/2yThFEIIIYTQABlST54MqQshhBBCiHQlPZxCCCGEEBrwJXtofi8k4RRCCCGE0ABJN5MnQ+pCCCGEECJdSQ+nEEIIIYQGyJB68iThFEIIIYTQAFmlnjxJOIUQQgghNED24UyezOEUQgghhBDpSno4hRBCCCE0QIbUkycJpxBCCCGEBsiQevJkSF0IIYQQQqQr6eEUQgghhNAAGVJPniScQgghhBAaEK+UIfXkyJC6EEIIIYRIV9LDKYQQQgihAdK/mTxJOIUQQgghNEAubZk8GVLPALp06cK0adO0HcYX27VrFxUrVtR2GEIIIYTQEunhTKWgoCCWL1/O6dOnef78OcbGxtja2tKsWTNatGhB1qxZtR1imkVERLBy5UoOHz6Mr68vJiYmFC5cmE6dOlGvXj0UCoW2Q0yz+oNbU7ljXbLlyI73zcfsmrCWgEfPUjyndMMfaTisLeb5cxHsHcDBOdu4c/iq6niBH4tRu08TrEsXIEeunKztM5e7R64mehyrgnlpPLoTBSoXR5FJQcCjZ2zst4CXfsE60cY5T7Yk+dj7/vc3p1bsS3vD3mk+uB21OtYje47seN58xMYJq/B75JPiORUaVqHlsA5Y5c9NoPdzds3ZzPXDrqrjmfQy0WJwe6q0cCCHpSkvA19yfsdJ/l20A+W7RQC95vSnRps6ao/rceMhU1uO0VjbACp1caLaL40xtjQl8JEvh/7YiPeVB8nWt61cjAYTfsKqsDWvAl9yftk+rv59XHW8fIc6lG1dA6uiNgD43/bi+Kxt+Lp5qurU+K0ZxRtWxKJgXmKjY/C59oijM7YS7Omv0bYBVOjiRJVfGmNkaUrQI1+O/rERnxTal79yMZwm/ITlu/ZdWraP6x+070Mlmlah5eIBPDh8lR195qnKbX4sRtVfGpO7tB3GuXLyz89/8vDINY23La2u3rzN2s07uOf+mKDgEBZMn0DdmtW0HVaq2HV3otBvTchiZcqrB77cnriB4MtJv66ZrUwpNakzpmXsMCqQG89Vh7k9cWOiegV/bsgP3ZzIZm3Bm5BX+O27zL3/bSP+zdv0bs4Xk304kycJZyr4+PjQsWNHjI2NGTJkCEWLFiU2NpYnT56wc+dOrKysqFu3rrbDTFZcXBwKhYJMmZLv0A4PD6dTp068evWKwYMHU7p0afT09Lhy5QqzZ8+mSpUqmJiYfMWoNa/Or02p2cuZrcOX8cLLn7oDWtJn01hmOQ7lzevoJM+xLV+YnxYP5PCf/3D78BVKN6hEl8WDWNJ2Et43PQAwzJYZv/veuP5zmu7Lhyb5OOb5rei3YxKu205xeP4OosMjyVXImlgN/+HUZhv/qPSr2v1itcvRdmYfbh10TbL+l3D+tQUNejVl9fDFPPfyo+mANgzfNJGxjgOITqZ9BcsXoe/ioez+cwvXDrtSocGP9F08jOltx+N589G7x21J7c71WTVsEb6PfLArXZCes/sT9SqSo2v3qx7r1qnrrB6xRHU/LiZWY20DKNmkCg0ndmH/hLV4X31IxU6O/LR+JEucRhKWxBcTUxtLOq8bwfUtJ9k1+C/yVyxC4yk9eB0Szv2DVwD4oWpx7uy9iM+1DcS+iaH6r03osnE0S+qN4lVAaEKdysW4suEYvm4eZNLXo+6Idgl1nEbyNuqNxtpXvEkV6k3swqEJa/G5+pDynRzpsH4ky51GEp5E+3LYWNJ+3QhubjnJnsF/YVOxCA3fte/Bu/a9Z2JtQd1xnfG+7J7ocQyzZSbgvjdu/5ymzfIhGmuPpkVFRVO0UAFaONdnyLip2g4n1aybV6H05K64jV5D8JWH2HWpS9XNozhecwRRvolfV73M+sQEv+Lhgj0U7NMoycfM16o6JcZ14MaQFYRcfUj2AnkovyDhb8yd3zela3vSQrZFSp4MqafCpEmT0NPTY+fOnTg7O1OwYEGKFi1KgwYNWLFiBY6OjgC8evWKCRMmULVqVcqXL0/Xrl1xd//vj9+iRYto3rw5Li4uODo6UqFCBYYMGUJERISqTmRkJCNHjsTe3p4aNWqwZs2aRPHExMQwa9YsHBwcKFeuHG3btuXy5cuq4++HsE+ePImzszOlS5fG19c3xTb++eef+Pr6sn37dlq2bEmhQoWws7OjXbt2uLi4kC1bNgDCwsIYOXIklSpVomzZsvTu3ZsnT56oPdauXbuoXbs2ZcuWpV+/frx8+TLR8504cYJWrVpRunRp6taty+LFi4mN1eyH98ccejbi+BIX7hy+wvOHz9g6bCmGWQ2xb149xXMenbvNib/2EOThx4m/9vDowl0cejqr6rifcuPQ3O3cOXwl2cdpOKI97idvsn/GZvzuPiHEJ5D7J28QERyuM218FRSmditZrwIeF+8R4hOosfbV69mEfUt2cu3wZXwf+rBq2CIyZ81MleYOyZ5Tv2cT7p5zY/9fu3nu4cv+v3Zz/8Jt6vVsoqpTsHwRbhy9wq2T1wl+FsTVg5e4e9aNH0oXVHus2JhYwoNeqm6vwyI+fro0qdq7Ede3neL61lO8eOzHocmbCPMPpuJPTknWr9i5LmF+wRyavIkXj/24vvUUN7afplqfxqo6uwb9xZWNx3h+7ykvPPzZO2oVikyZKFC9pKrOpm6zuLnjDEGPfAm4743L8OWY5rMgb2k7jbavcu9G3Nx2iptbTxH82I+jkzcR7h9M+WTaV75zXcL9gjk6eRPBj/24ufUUbttPU+WD9gEoMiloseA3zszbQah34vebxyk3Ts/5hweHEo88fEscqlZiYJ9u1Kud/L/Xb1HBX5x5uuUUTzefIuKRH7cnbiTKNxi7bkm/rpE+L7g9YQM+/5wl9lVkknXMKhYm5MpDnu2+QKTPC4JO38bX5QKmZQukZ1PSLB6lRm66SBLOTwgNDeX8+fN07txZlXR9TKFQoFQq6dOnD0FBQaxYsYJdu3ZRsmRJunXrppZweXt7c/z4cZYtW8by5cu5cuUKK1euVB2fNWsWly9fZvHixaxevRpXV1fu3Lmj9nxjxozh+vXrzJs3j71799KwYcNEiV90dDTLly9n6tSp7Nu3D3Nz82TbGB8fz4EDB2jatCm5cuVKdDx79uzo6yd0ho8ePZo7d+6wdOlStm3bpmr327cJPXVubm6MHTuWTp064eLiQuXKlVm6dKna4509e5YRI0bQpUsXDhw4wOTJk9m1axfLli1LNsa0MrOxwsQqJw/O3laVxcXE4nH5Pj9UKJLsebb2hXlw9pZa2YMzbvxQvnCqn1uhUFC8jj1BXv78vGE0k64uY6DLFErW1+y8Vm228WNGFjkoXsce120nv/gxPmZpkwtTq5zcOeumKouNieXB5bsUqlA02fMK2hfh7gfnANw5c5NC5f8759FVd0pUL00uuzwA2BS3pXDFYtw6dV3tvGJVSrLg6hqmn1hE9+m/YmyuuV5/PQM98pa2w+OD1w/A48xtbCok/VrYlC+Mxxn1+o/P3CJvaTsy6esleY5B1sxkMtAj6uXrZGPJYpzwty7qpeYS6kwGeuQpbYfXR+3zPHObfMm0L1/5wnie+bj+LfJ81D6HQa2IDA7HbdtpjcUrUkdhoIdpGTsCT6n/DQk8fRuzSsn/3fmU4MsPMC1jh6l9wpe+bPmtyOVYjoBjN9IUr9AeGVL/BG9vb5RKJXZ26t/0K1euTExMDACdOnWiRo0aPHz4kIsXL2JoaAjAqFGjOHbsGIcPH6Z9+/YAKJVKpk+fjpGREQDNmjXj4sWLDBkyhNevX7Njxw5mzZpF9eoJ33BnzJhBrVq11OLZv38/p0+fViWHvXr14uzZs+zatYuhQxOGO9++fcukSZMoVqzYJ9sYGhpKWFgYBQqk/M3xyZMnnDhxgi1btlC+fHkA5syZQ+3atTl27BiNGjViw4YN1KhRgz59+gBgZ2fHjRs3OHv2rOpxli1bRp8+fWjZsiUANjY2DBo0iNmzZ9O/f/9PxvsljC1zABARFKZWHhEURs58FimcZ5rkOcaWpql+biMLE7IYZcWxbzMOzt3O/hlbKFqrLN2WDWFZx6l4Xr6f+oakQJtt/FjF1jV58zqa2yn0iH6uHO/iCQ96qVYeFhSGRT7LFM/7+JzwoJeqxwM4sHQ32Yyz8b/jC4mPiyeTXiZ2zdnM5b3nVHVun7rOlf0XCPYNwsImF62GdWDk5j/4o+kIYjUwtJ4tpzGZ9PV4/UL9tXj9Igyjd6/tx4wscyRZX89An2xmxkQEvkx0jtPoDrx6HoLn+TuJjr3XYEJnnrq6E/gw5bm/n+N9+yI+o33Zk2hfxEfty1exCGXb12ZVI83OpRWpk9ks4XV989HfkDdBYWRO5nVNDd89F8lsYUzNPb+DAjIZ6OO57iiPFv+b1pDTlczhTJ4knKn08YKZHTt2EB8fz/Dhw4mJieHu3btERkZSuXJltXrR0dF4e3ur7ltbW6uSTQArKyuCgxPmuPj4+PD27VvKlSunOm5qaqqW7N69exelUknDhg3VnicmJgZTU1PVfQMDA4oWTb7X50PKVF4ZwcPDA319fcqWLasqy5kzJ3Z2dnh4eKjqODmpD6OUK1dOLeG8e/cut2/fVuvRjIuL482bN0RFRWlkAZZ98+q0+V9v1f3VPWcBSbRVoeBTzU90WKH4rD8qCkXCQMKdo9c4u/ogAH73nvJD+SJU7ez0xQnnt9TGj/3YrhbXXc6naY5qleYOdPvfL6r783v+D0jcPoXi0+/hpNr3YemPTatTtUVNlg+aj99DH2xK2NFpYg9eBoRyfucpAFz3XVDV933ow5Nbj5lzfhll61Tg2uHLaEpSr19KL0Wipr//W5XE76T6L00o3awq69pPTfa1cZ7SnVzF8rOmzeTPiPozJHr9PvH+TPTrULx7GCWG2bPQfH5fDoxeRVSoZqc3iM+T+H1ImjaltKhWnCKDWuA2eg2h1z3IbpeL0lO68mbISx7M252WUNOVzOFMniScn5A/f34UCgWenp5q5TY2CSs+s2TJAiQMS1taWrJxY+KVdsbGxqqf3w9Nf+j9B0xqEj+lUqmaT6qnpz5k9uGQf5YsWVK9qtzMzIwcOXIkamNycSZV/uGHwKfEx8czYMAA6tevn+hY5syZUxHxp907do0/bz5W3dc3NADA2MqUVx/0dhlZmCTqcfnQq6CXqp5DtXOCkj/nY69Dw4l7G0vAI/V5tIEevvxQMXVfCpLyLbXxQ3aVimJV0JqN/Rd+0fnv3Tx2RbWoB/5rXw6rnIR90D4TixyEv3hJcsI+6s18f07YB+1rP6Yr+5fuxvXf8wA8e+CNhbUFjX9rpUo4k3rcYN8XqmH4tIoMfUV8bBxGH8Wa3Tz51y8iKHHvYHZzE+LexhL5UQJWrY8zDv2asaHzdALck17V3+iPrhR1Ks/adlMIfx7y5Y1JQnLty2ZukqgX873XQWFkT6Z9UaERWBbJh6mNFe1WD1MdV2RK+Fs0xmMDS+sM52USczqF5rwJSXhds1ipv06ZLXLwJoW/O59SbGRbfHac4+nmUwCEu/ugly0z5Wb35sF8lyS/UIlvm8zh/IScOXNSvXp1Nm3aRGRk0pObAUqWLMmLFy/Q09PD1tZW7WZmZpaq58qfPz8GBgbcvHlTVRYWFqY2N7N48eLExcUREhKS6HksLZMfVkxJpkyZaNSoEf/++y8BAQGJjkdGRhIbG0uhQoWIjY3Fze2/+XChoaE8efKEggUT5tkUKlRI7TiQ6H6JEiXw8vJKFL+trW2KK+k/x5vX0QQ/DVDdAh49IzwwlCI1Sqvq6BnoUbBycZ5ce5js4zy98UjtHICiDmV4cv1RMmckFvc2Dp9bnlgVUE9MLOzyEOr7ItWP87FvqY0f+rF9HXxueeJ/3/vTlVMQ/TqawKfPVTe/Rz68DAylZI0yqjp6BvoUrVySx9eS31bH48ZDStYoq1ZW0qEsj6//d45h1syJvizFx8en+KUtu6kRZnnNeRkY+rlNS1Lc2zj8bntR0KGUWnlBh9L4XEv6tfC5/oiCDqUT1fe77UV8bJyqrNovjak5oCWbus3C77ZXko/lPLkbxRtWYn3Habz0CUpjaxKLfxuH/20v7D5qn51DaZ4l075n1x9h91H77BxK4/+ufS88/FhRbxSrGo1V3R4evc6Ti/dY1Wgs4f6a3XJMJKZ8G8fLW15Y1lJ/nSxrlSLkSvJ/dz5FP2tmlPHq/YXKuHf/Jr/hHfqUSqVGbrpIEs5U+P3334mLi6N169YcOHAADw8PPD092bNnD56enujp6VGtWjXKlStHv379OHv2LM+ePVMt7Ll9+/ann4SExTmtW7dm9uzZXLx4kYcPHzJ69Gi1Dz07OzuaNm3KyJEjOXLkCD4+Pty6dYsVK1Zw+vSXT5gfOnQouXPnVq1Kf/z4MU+ePGHHjh20aNGCyMhIfvjhB+rWrcuECRO4evUq7u7ujBgxgly5cqm2herSpQtnz55l5cqVeHl5sWnTJrXhdIB+/fqxZ88eFi1axKNHj/Dw8ODAgQPMmzcvqdA05uyag9Tt15xSDSqSu0g+2s/pS0xUDDf2nFfV6TC3L41GdlA7p4hDGer82hTLgnmp82tTClcvxdk1B1R1DLNlJm8JW/KWsAXAzMaSvCVsMc3730KtUyv+pWyTqlTu4Ii5bS6qd61PibrlubDxqM60ESCzUVbKOlfW6GKhDx1ds48m/VpTvsGPWBexofec/ryJesOlPf+9x3rPHUCbkZ0/OGc/JR3K4vxrC3IXtMb51xaUqF6Go2v+2xv05vGrNOnXmjJ1ymOez5LyDX6kQa+mXD+SMFSeOVsW2o/tSsHyRTDPZ0nRKiUZvHoMr0JecV2Dw+kXVx2kfPs62LerhUWhvDSY8BM58pqr9tWsO7I9Lf/8b/upq38fJ4e1OQ0mdMaiUF7s29WifPvaXFjx31ZO1X9pguOwtuwZuYKXz4IwssyBkWUODLP9N5rQeGp3yrSozs6BS4h5Ha2qo5/ZQGNtA7i86iDl2tehbLtamBfKi9O79r3fV7P2yPY0/aB919+1z2lCZ8wL5aVsu1qUa1+bS+/aF/fmLUEPn6ndosMjiYmIJujhM+LfJiTdBtkyk6uELbnevX9NbSzJVcIWk7zJL6bUhsjIKNwfeuD+MGGKkq9fAO4PPfB//m330nosP8APneqQv2MtjArnpdQfP5HN2gKvDQmva4mx7Sm/qK/aOTlK2pKjpC162bNgaG5CjpK2GBexVh1/fvQ6dt2csG5elWz5LbGsWYrio9rif+QaxH+7CZmsUk+eDKmnQv78+dm9ezfLly9n7ty5BAQEYGBgQKFChejZsyedOnVCoVCwYsUK5s+fz9ixYwkNDcXCwoKKFStiYZH8go2PjRw5ksjISPr27Uv27Nnp0aOH2rZJANOnT2fp0qXMmDGDwMBATE1NKVeunNrios+VI0cOtm/fzooVK1i6dCm+vr7kyJGDIkWKMHLkSNW0gOnTpzNt2jR+/fVX3r59S8WKFVmxYgUGBgkfTOXKlWPq1KksWrSIxYsXU7VqVfr27ctff/2lei4HBweWLVvGkiVLWLVqFfr6+hQoUIC2bdt+cfypcXLZvxhkMaTVlJ5kzZEd75serOzyP7X9KXNaW6h9u3x6/RF/D1hIw+HtaDC0HcHeAWzsv1C1PyWATZkC9N06UXW/+YSuAFzZcZptwxPmqd45fJWd41bj+FszWkzqRqCnHxv6zuPJ1eR75jJaGwHKNa0KCgU39v6X4GrSgWUuGGQxpMuUPmTPkR2Pm4+Y22Wy2h6c5h+17/H1Bywb8Cethnei5dAOBHoHsKz/n2rD9X//voqWwzrSZUofTCxMeBkQyqnNR9mz8B8A4uPiyVfMlmqtapPNJBsvA1/ifukOS/v/mez+n1/i7r5LZMtpRK2BLTGyMiXw4TP+7j6bsHc94cZWpuT4IEl66RPE391n03DiT1TqUo9XgaEcnLRBtQcnJGwkr5/ZgPbLBqs916l5Ozk1f9e7OvUA6LF9glodl2HLubnjjMbad/9d+2q8a1/Qw2ds7T6b8HftM/qofWE+QWzrPpt6E3+iQpd6RASGcmTShkR7cH5KnjIF6LJtvOp+vYldAHD75wz7hi/XQMs04477I3oOGKW6P2vRCgCaN3Ji2vhhyZ2mdb57LmGY04hiQ1uR2cqUV+7PuNh5FlHPEl7XLLlMyWatntzXOT5d9XPOsgWwaV2dSJ8gjlQaBMCDebtRKpUUH92WrLnNeBMczvOj17k/ffvXa5jQKIVSV/tuhc4b/kNHbYcgNCCYGG2HkK5slVm0HUK602w/6Ldp5LUp2g4hXe0vNf7TlTK4Fs83p/tzNM3f5NOVUuFfb81dne1bIT2cQgghhBAaINsiJU8Szu+Evb19ssdWrlxJxYqa3YRcCCGEEOI9STi/Ey4uLskeS+rqQkIIIYT4PLq64EcTJOH8Ttja2mo7BCGEEEKnybKY5EnCKYQQQgihAXKloeTJPpxCCCGEECJdSQ+nEEIIIYQGyCr15EnCKYQQQgihAbJoKHkypC6EEEII8Z0ICwtjxIgRVKhQgQoVKjBixAjCw8OTrf/27Vtmz55N06ZNKVeuHDVq1GDkyJEEBAR81vNKwimEEEIIoQFKpVIjt/Q0bNgw3N3dWbVqFatWrcLd3Z2RI0cmWz86Opp79+7Rt29fdu3axeLFi3ny5Al9+/b9rOeVIXUhhBBCCA341ofUPTw8OHv2LNu3b6ds2bIATJkyhfbt2+Pp6UmBAgUSnWNsbMzatWvVysaPH0/btm3x8/Mjb968qXpuSTiFEEIIIb4hMTExxMTEqJUZGhpiaGiYpse9ceMGxsbGqmQToFy5chgbG3Pjxo0kE86kREREoFAoMDExSfVzy5C6EEIIIYQGKDX03/Lly1VzLN/fli9fnub4Xrx4gbm5eaJyc3NzXrx4karHePPmDXPmzKFJkyYYGRml+rmlh1MIIYQQQgPiNTT/8pdffqFHjx5qZSn1bi5atIjFixen+Jg7duxI9phSqUShUHwyrrdv3zJkyBCUSiWTJk36ZP0PScIphBBCCPEN+dzh886dO+Ps7JxinXz58vHgwQOCg4MTHQsJCUmy5/NDb9++ZfDgwTx79oz169d/Vu8mSMIphBBCCKER2loyZGZmhpmZ2Sfr2dvb8+rVK27dukWZMmUAcHNz49WrV9jb2yd73vtk8+nTp2zYsIGcOXN+dowyh1MIIYQQQgPiUWrkll4KFiyIg4MD48eP5+bNm9y8eZPx48dTp04dtQVDDRs25OjRowDExsYycOBA7ty5w5w5c4iLiyMoKIigoKBEC5tSIj2cQgghhBAa8K1viwQwZ84cpk6dSs+ePQFwdHRk4sSJanW8vLx49eoVAM+fP+fEiRMANG/eXK3ehg0bqFy5cqqeVxJOIYQQQojvhKmpKXPmzEmxzoMHD1Q/v5/7mVaScAohhBBCaEB6XyUoI5OEUwghhBBCAzLCkLq2SMIpMqycSt1f8xan7QC+gpep2PstI8uMbrfve7G/1Hhth5CuGt+Zqu0QhI6ThFMIIYQQQgOU0sOZLEk4hRBCCCE0QOZwJk/3xySFEEIIIYRWSQ+nEEIIIYQGyKKh5EnCKYQQQgihATKknjwZUhdCCCGEEOlKejiFEEIIITRAhtSTJwmnEEIIIYQGyLZIyZOEUwghhBBCA+JlDmeyZA6nEEIIIYRIV9LDKYQQQgihATKknjxJOIUQQgghNECG1JMnQ+pCCCGEECJdSQ+nEEIIIYQGyJB68iThFEIIIYTQABlST54MqQshhBBCiHQlPZxCCCGEEBogQ+rJk4RTCCGEEEIDZEg9eTKkLoQQQggh0pVOJ5y7du2iYsWKn3WOo6Mj69atS5+AvlOLFi2iefPm2g5DCCGESFdKDf2ni9I8pD569Gh2795N+/btmTx5stqxSZMmsWXLFlq2bMmMGTM++VjPnj2jbt26uLi4ULx48bSGhrOzM7Vq1Urz4yTn3r17LFiwgFu3bhEREYGlpSVlypRh4sSJmJmZcfnyZbp27cqVK1cwMTFJ9eNq+veQWkFBQSxbtoxTp04REBCAubk5xYsXp1u3blStWvWrxZFeKnRxosovjTGyNCXokS9H/9iIz5UHydbPX7kYThN+wrKwNa8CX3Jp2T6u/308ybolmlah5eIBPDh8lR195qnKq/3WjKINK2JeMC+x0TE8u/aIEzO2EuLpr/H2VeziRNVfGmNsaUrgI1+O/LER7xTaZ1u5GPUm/ITVu/ZdWLaPax+0z75DHcq2roFlURsA/G97cWLWNvzcPFV1FHqZqD2kNaVaVMPI0pSIwJe4/XOGM4tcIJ2GlpoNbkfNjk5ky5Edr5uP+XvCSvwePUvxnPINK9NiWAcs8+cmyPs5u+ds4cZhV7U6prnMaDP6J0rVtscgiyEBXn6sH7mUp3cS2mtikYPWo3+ipENZsppk55HrPTb/vprAJ8812r7yXZyo/Iuz6n167I9NPEvhdbSpXIy6Ezqr3qeXl+3jxt8nkqxbvGkVWizuz8PDV9nZZ76qvO+5eZjaWCaqf23DUY5MWJ/mNn1I19uXFLvuThT6rQlZrEx59cCX2xM3EHw56TZntjKl1KTOmJaxw6hAbjxXHeb2xI2J6hX8uSE/dHMim7UFb0Je4bfvMvf+t434N2/TuzlpcvXmbdZu3sE998cEBYewYPoE6taspu2wNEapjNd2CN8sjczhzJMnDwcOHGDs2LFkyZIFgDdv3rB//37y5s2riaf4bG/fviVLliyqeDQtODiYHj16UKdOHVavXo2xsTHPnj3jxIkTREdHp8tzpqdnz57RsWNHTExMGDFiBEWLFiU2NpZz587xxx9/cOjQIW2HmCbFm1Sh3sQuHJqwFp+rDynfyZEO60ey3Gkk4X7BiernsLGk/boR3Nxykj2D/8KmYhEaTunB65BwHhy8olbXxNqCuuM6433ZPdHj5K9cjGsbjuHn5kEmfT1qj2hHp42jWe40krdRbzTWvhJNqtBgYhcOfNC+TutH8lcy7TO1saTjuhFc33ISl3ftc37XPvd37fuhanHu7L2Iz7UNxL6JodqvTfhp42iW1hvFq4BQAKr3bUqFznXZM2wZgQ+fkbdMAZrN7kP0q0hc1x7WWPvea/hrC+r1asLa4Ut47uVHkwFtGLppIuMcB/LmddL/7gqUL8Ivi4fi8udWbhy+jH2DyvyyeCgz207A6+YjALKZZGf0zqk8uHiHBd2nER4chmX+3ESGv1Y9Tr8VI4l7G8fin2cSFRFF/d5NGLbpdybUG0yMhl7L4k0q4zTxJw5PWMezqw+x7+RI+/UjWOk0Ktn3abt1w3Hbcop/By8lX8UiNJjSnciQV0m8T81xHNcpyffpumYTyaT334CXZZF8dNw8Bvf9ronqSvs+j3XzKpSe3BW30WsIvvIQuy51qbp5FMdrjiDKN3Gb9TLrExP8iocL9lCwT6MkHzNfq+qUGNeBG0NWEHL1IdkL5KH8gl8BuPP7pnRtT1pFRUVTtFABWjjXZ8i4qdoOR+PidbR3UhM0MqReokQJ8uTJw5EjR1RlR44cIXfu3Go9dGfOnKFjx45UrFiRypUr88svv+Dt7a06XrduXQBatGhB0aJF6dKli+rYzp07adSoEaVLl6Zhw4b8/fffqmPPnj2jaNGiHDhwgC5dulC6dGn27t2baEjd29ubvn37Uq1aNezt7WndujUXLlz4ojbfuHGDiIgIpk6dSokSJbCxsaFq1aqMGzeOvHnz8uzZM7p27QpApUqVKFq0KKNHj07T76FLly5MmzZNLY7ffvtN9bgAf//9N/Xr16d06dJUq1aNgQMHpqo9f/zxBwqFgn/++YeGDRtiZ2dH4cKF6dGjB9u3b1fV8/Pzo2/fvtjb21O+fHkGDRrEixcv1B5rxYoVqt/x2LFjefMm8YdxSq9neqjcuxE3t53i5tZTBD/24+jkTYT7B1P+J6ck65fvXJdwv2COTt5E8GM/bm49hdv201Tp01itniKTghYLfuPMvB2Eegcmepyt3WZxa8cZXjzyJfC+N/uGLydHPgtyl7bTaPuq9m7EjW2nuLH1FC8e+3Fk8ibC/IOpmEz7KnSuS5hfMEcmb+LFYz9ubD3Fje2nqfZB+3YP+ourG48RcO8pwR7+7Bu1CkWmTNhVL6mqk698YR4cvcajEzcJe/aC+wdc8Tx7m7xlCmi0fe859WzM/iW7uH74Mn4PfVgzbBGGWTNTublDsufU69mYe+ducfCv3Tz38OPgX7txv3Abp57/tbVR3xaE+AWzdsRfeLk9JvhZEO4XbhPkHQBALrs8FCxflE3jV/DklgcBnn5sGr+KzNmzULlZDY2178fejXDbdgq3d+/TY+/ep/Y/1U2yvn1nR8L9gjn27n3q9u59WrmPs1o9RSYFzRb8xtl5O3mZxPs0KuQVr4PCVLdCde0JfRKA96X7Gmvb99C+pBT8xZmnW07xdPMpIh75cXviRqJ8g7HrlvS/zUifF9yesAGff84S+yoyyTpmFQsTcuUhz3ZfINLnBUGnb+PrcgHTsunz706THKpWYmCfbtSrXV3boYivTGNzOFu3bs2uXbtU93fu3Enr1q3V6kRFRdGjRw927NjBunXrUCgU9OvXj/j4hC7of/75B4B169Zx7tw5Fi1aBMD27duZN28eQ4YM4cCBAwwdOpSFCxeye/dutcefM2cOXbp04cCBA9SokfhDIDIyklq1arF27Vp2795NjRo1+PXXX/Hz8/vs9lpYWBAbG8vRo0dRJjF0mCdPHlX8hw4d4ty5c4wbNy5Nv4dPuX37NtOmTWPgwIEcOnSIVatWpWoO68uXLzl79iydO3cmW7ZsiY6/nw6gVCrp168fYWFhbNy4kbVr1+Lj48OQIUNUdQ8cOMDChQsZMmQIO3fuxNLSks2bN6s9XmpfT03JZKBHntJ2eJ29rVbueeY2+SoUTvKcfOUL43nm4/q3yFPajkz6eqoyh0GtiAwOx23b6VTFktk44fcb/TLic5qQovft80iifTaf0T6PJNr3IYOsmclkoEfUy/96/XyuPMCuWknM7HIDkKt4fmwqFuXRyZtpaFHSLGysMLXKyd2zbqqy2JhYHly+R6EKRZM9r4B9Ee59cA7A3TNuFCr/3zllnSry9LYHvy4Zxp9XVzNx/2wcOvyXEOgbGgDw9oPhSmV8PLFvYylUqVia2wYJr2Pu0nZ4nb2jVu515k6y71Pr8oXxOvNx/dvk/uh1rDGoJZHBr7iVivdpJgM9Srasjtv21L2nU0vX25cUhYEepmXsCDx1S6088PRtzCoV+eLHDb78ANMydpjaFwQgW34rcjmWI+DYjTTFK9JOqVRq5KaLNLYtUrNmzZg7dy7Pnj1DoVBw/fp1/vzzT1xd/xuyaNCggdo5//vf/6hatSqPHz+mSJEimJmZAWBqaoql5X/zbf766y9Gjx5N/fr1AbCxseHx48ds27aNli1bqup169ZNVScpxYoVo1ix/z4chgwZwrFjxzhx4gQ//fTTZ7W3XLly/PrrrwwfPpxJkyZRunRpqlSpQosWLbCwsEBPT48cOXIAYG5urjaH80t/D5/i7+9P1qxZqV27NkZGRlhbW1OiRIlPnuft7Y1SqaRAgZS/HV+4cIEHDx5w/Phx8uTJA8CsWbNo3Lgxt27dokyZMmzYsIHWrVvTtm1bIOF3fPHiRbVeztS+npqSLacxmfT1iHgRplb++kUYRpY5kjwnu2UOXn9UP+JFGHoG+mQzMyYi8CX5KhahbPvarGo0JtWxOE3ojLerO0EPU55z+Dnet+/jeF+/CCN7Mu0zSqJ9rz9q38fqju7Aq+cheJ7/LwE4v/RfMhtno9+J2cTHxZNJLxMnZv/D3b0X096wj+SwzAlAeJB6bOFBLzHPl/y/kxyWpoR9dE5Y0EtMLE1V9y3z56L2T/U5smof+//ahV3ZQnSc1IPYmLdc3HWa5x6+vHgWSKuRndk4djlvot5Qv3cTTK1yksMqp0bal/LraJrkOUaWOfBM5nXMambM68CXWFcsTJn2tVnTaGyq4ihSvyJZTLJx+58zX9SO5Oh6+5KS2SyhzW+C1NvwJiiMzMn820wN3z0XyWxhTM09v4MCMhno47nuKI8W/5vWkEUayZB68jSWcJqZmVG7dm1cXFxQKpXUrl1blTi95+3tzYIFC7h58yahoaGqLN7f358iRZL+thcSEoK/vz/jxo1jwoQJqvLY2FiMjY3V6pYqVSrFGCMjI1m8eDGnTp0iMDCQuLg4oqOjv6iHExKSqe7du3Pp0iXc3NzYunUry5cvZ9OmTRQtmnyPy5f8HlKjWrVq5M2bFycnJxwcHHBwcKBevXpkzZo1xfPeP79CoUixnoeHB7lz51YlmwCFChXCxMQET09PypQpg4eHBx06dFA7r1y5cly+fBn4vNdT4z761qhQKFJe1/LRsfe/H6VSiWH2LDSf35cDo1cRFZq63soGU7pjVSw/G9pM/nTlL/FxYxSKRG1Iufp/7ftYtV+aUKpZVda3n0rcB718JZtWoXTL6uwauISgh77kKmFLg99/4lVAKLd2nv3ipgBUbu5Al//1Ud1f2HN6koErFIpULFBK4pwPyhQKBU9ue7J7dkJvvM9dL6wL21D7p/pc3HWauNg4lv46h26z+rLw1nriYuO4f/4Wt09e/+L2JR/qx7EmLkupPor/yg2zZ6HZ/L4c/Iz3adn2tfA45Zbklw6N0PX2JSFR89Tffp/NolpxigxqgdvoNYRe9yC7XS5KT+nKmyEveTAvfUaKhEgrjW783rp1a9VK9d9//z3R8V9//ZU8efIwdepUrKysiI+Pp0mTJrx9m/yquvfDzFOmTKFs2bJqxzJlUp8RkNRw8IdmzZrFuXPnGDVqFPnz5ydLliwMHDgwxef/lJw5c9KoUSMaNWrE0KFDadmyJWvWrGHmzJnJnvMlvwd4nyCp/5WKjY1V/WxkZMTu3btxdXXl3LlzLFy4kMWLF7Njx44UV8nb2tqiUCjw8PDAySnpeUWQkIgklZR+Tvf/57yemhIZ+or42DiMPupFyWZukqi35b3XQYl7B7ObmxD3Npao0Agsi+TD1MaKdquHqY4rMiX8bsZ4bGBpneFqc8nq/9GVIk7l2dBuCq+eh2ioZQnet+/jXqLsKbQvIihx7262D9r3oap9nKnRrxkbO08n0N1H7ZjT2E6cX/ovd/+9BEDgAx9M81lQ47dmaU44bx67olrUA6BvmPDnysQqp1qPpbFFDsKTaSck9Ga+7x19z8QiB+Ef9DqFBb7E/5F62/w9nlG+UWXV/ad3PJnsPIKsxtnQM9AnIiScsS7TeXLL44va97HkXsds5ol7o9+LCErcO5jdPIfqdbQoYo2pjRVtk3ifjvJYz/I6I9TepybW5vxQoxS7fpmvkTZ9SNfbl5Q3IQltzmKl/m8ts0UO3qTwnv2UYiPb4rPjHE83nwIg3N0HvWyZKTe7Nw/mu6TbDhHi03R1OFwTNJpwOjg4qJKmj+dQhoaG4uHhweTJk1XzCq9evapWx8AgYZ5UXFycqszCwoJcuXLh4+NDs2bN0hTftWvXaNmyJfXq1QPg9evX+Pr6pukxP2RoaIiNjQ1RUVFA0u350t8DJPQiBwUFqe7HxcXx6NEjKlf+70NRX1+fatWqUa1aNfr370+lSpW4dOlSilMNTE1NqVGjBn///TddunRJlLiHh4djYmJCoUKF8Pf3x9/fX9XL+fjxY169ekXBgglziQoWLMjNmzdp0aKF6nw3t//mz2ny9Uyt+Ldx+N/2ws6hFA8O//e7tnMozcMj15I859n1RxR2Kq9WZudQGv/bXsTHxvHCw48V9UapHa81vC2GRlk4Omkj4f7/rT5tMLkbRRtUZGP7qYT5BKFp79tX4KP2FXAozYMU2lfko/YV/KB971X9pTEO/Vvwd9eZ+N/2SvQ4BlkNUcarbwMSHxev+tBPizevowl8rb7l0MvAUErWKIPP3YRY9Az0KVq5BDtmJL8y1/PGQ0rUKMPR1ftUZSUcyvL4+n/b0jy+5k6uAtZq5+Wyy0uwr/qCOICodws5rH7IzQ+lC+Ayd+vnNy4J8W/jeP7uffpQ7X1aKtn3qW+S79NSPH/3OgZ7+LOy3mi147WGt8HQKGui9ylAmba1iAwO5/GJmxpp04d0vX1JUb6N4+UtLyxrlcb/4H9ttqxViueHkm5zauhnzZzo350yLj6hQyCNvacibeRKQ8nTaMKpp6fHwYMHVT9/KEeOHJiamrJt2zYsLS3x8/Nj7ty5anXMzc3JkiULZ8+eJXfu3GTOnBljY2MGDBjA1KlTMTIyombNmsTExHDnzh3Cw8Pp0aNHquPLnz8/R48exdHREYVCwfz581U9bp/r5MmT7N+/n8aNG/PDDz+gVCo5efIkZ86c4X//+x8A1tbWKBQKTp06Ra1atcicOXOafg9VqlRhxowZnDp1ChsbG9avX094eLhaTD4+PlSqVAkTExNOnz5NfHw8dnafXhH9+++/07FjR9q2bcvAgQMpWrQocXFxnD9/ni1btnDw4EGqVatG0aJFGT58OGPHjiUuLo5Jkybx448/Urp0aQC6du3KqFGjKFWqFBUqVODff//l0aNH2NjYqJ5LU6/n57i86iDN5/XF/5YXz64/wr6jIznymqv21aw9sj3GuXPy79BlAFz/+zgVu9XDaUJnbmw5Sb7yhSnXvja7By4GIO7N20TzMKPDExKRD8sbTu1OyWbV+OfnP4l5Ha3qNX0THkmsBvfLu7jqIC0/aF/5d+17v6+m47v27XnXvmt/H6dSt3rUn9CZ6+/aZ9++NjvftQ8ShtFrD2vDrkFLePksSBV7zOto3kYmzMl9eOwGDv1bEO4XTODDZ+Qu+QNVejfiZjotyDi2Zj/O/VoR8MSfAC9/GvdrRUzUGy7v+a83tefcAbwMCGbXrM3vzjnAyO2TafhrC24edaVcvR8pXr00M9v+N6Xj6Op9jN45DeffWnF1/wV+KFuImh2d2DBmuapOBeeqRISEE+wbRL5itnT4vQc3jlxJtCApLVxXHaTpvL743/LE9/pjynWsg0lec268ex1rjWyHce6c7BuaENeNv09QoVs96k7ozM0tJ7EuX4iy7WuzZ+ASIOF9+iKZ9+nH5SgUlGlbk9s7zqKMS5+9BHW9fUnxWH6ACot+46WbJyFXH/HDT45ks7bAa0NCm0uMbU+WPGZcH7BUdU6OkrYA6GXPgqG5CTlK2hL/NpZXDxM6SJ4fvU7BXxoRdvspoTcek/2HXBQf1Rb/I9cg/ttOeCIjo/B+9t80Nl+/ANwfepDDxJg8ua20GJlIbxq/lrqRkVGS5ZkyZWLevHlMnTqVJk2aYGdnx/jx49W2PtLX12f8+PEsWbKEhQsXUrFiRTZu3Ejbtm3JkiULq1evZvbs2WTLlo0iRYrQrVu3z4ptzJgxjB07lg4dOpAzZ05+/vlnXr9+/ekTk1CoUCGyZs3KjBkzeP78OYaGhtja2jJ16lRV716uXLkYMGAAc+fOZcyYMbRo0YIZM2Z88e+hdevWuLu7M2rUKPT09Ojevbta76axsTFHjx5l8eLFvHnzBltbW+bOnUvhwkmvAP2QjY0Nu3btYtmyZcycOZPAwEDMzMwoWbIkkyZNAhKG9JcsWcKUKVP46aefUCgUODg4qM3FdHZ2xtvbmzlz5vDmzRsaNGhAx44dOXfunKqOpl7Pz3F/3yWy5TSixsCWGFmZEvTwGVu7zyb8XQ+WkZUpOfKaq+qH+QSxrfts6k38iQpd6hERGMqRSRsS7f33KRW6JPSmd9k+Qa3832HLubVDc4sW7r1rX8137Qt8+IzN3WcTlkz7XvoEsaX7bOpP/ImKXerxKjCUQ5M2qPbghISN5PUzG9Bu2WC15zo9byen5yfsSHHo9/XUHtaGRlN6kN3ChFcBoVzffILTC3aRHg4tc8EwiyGdp/xM9hzZ8bz5iD+7TFHbg9Pc2kJt82WP6w9YMWAeLYZ3pMXQ9gR5B7Ci/zy14fontzz465fZtBrZiaaD2vDCJ5Ctk9epJbKmVjlpP74bJhY5CAt8yYVdp9m3aIdG23d/32Wy5jSm+gfv0+3dZxP+br9GIytTTPJaqOqH+QSxvfscnCb+RPkuTkQEhnL0C96nAHY1SpIjnwW30nH1tq63Lym+ey5hmNOIYkNbkdnKlFfuz7jYeRZRzxL+bWbJZUo2a3O1c+ocn676OWfZAti0rk6kTxBHKg0C4MG83SiVSoqPbkvW3Ga8CQ7n+dHr3J++nW/dHfdH9Bzw3+jQrEUrAGjeyIlp44cld1qGoatXCdIEhVImHIgMapptZ22HkO7iPl0lw/NWaG4D/G9RQWX6XHxCfF3F3+j2FWQa39G9Tdg/ZmCR/vuU5sqhmW3SAsISX8Ago9Ppa6kLIYQQQgjt0/iQuq7Yu3dvkivtAfLmzcv+/fu/ckRfzs/Pj8aNGyd7XJuXIBVCCCF0hezDmTxJOJPh6OiYaNue9/T1M9avzcrKChcXlxSPCyGEECJtZJZi8jJW5vQVGRkZJbsAKqPR19fH1tZW22EIIYQQOk22RUqezOEUQgghhBDpSno4hRBCCCE0QIbUkyc9nEIIIYQQGhCPUiO39BQWFsaIESOoUKECFSpUYMSIEWoXkfmUiRMnUrRoUdatW/dZzysJpxBCCCHEd2LYsGG4u7uzatUqVq1ahbu7OyNHjkzVuceOHcPNze2LFhtLwimEEEIIoQFKpVIjt/Ti4eHB2bNnmTp1Kvb29tjb2zNlyhROnjyJp6dniucGBAQwefJk5syZg4GBwWc/tyScQgghhBAaEK9UauSWXm7cuIGxsbHato/lypXD2NiYGzduJN+u+HhGjBhBr169UnW57KTIoiEhhBBCiG9ITEwMMTExamWGhoYYGhqm6XFfvHiBubl5onJzc3NevHiR7HkrV65EX1+frl27fvFzS8IphBBCCKEBSg0t+Fm+fDmLFy9WK+vfvz8DBgxIsv6iRYsS1f/Yjh07kj2mVCpRKBRJHrtz5w4bNmxg165dydZJDUk4hRBCCCE0QFPD4b/88gs9evRQK0upd7Nz5844Ozun+Jj58uXjwYMHBAcHJzoWEhKSZM8nwNWrVwkODqZOnTqqsri4OGbOnMmGDRs4ceJEis/7niScQgghhBDfkM8dPjczM8PMzOyT9ezt7Xn16hW3bt2iTJkyALi5ufHq1Svs7e2TPKd58+ZUq1ZNraxXr140b96cVq1apTpGSTiFEEIIITTgW9/4vWDBgjg4ODB+/HgmT54MwIQJE6hTpw4FChRQ1WvYsCHDhg2jXr165MyZk5w5c6o9joGBARYWFmrnfIqsUhdCCCGE0AClhv5LT3PmzKFIkSL07NmTnj17UrRoUWbNmqVWx8vLi1evXmn0eaWHUwghhBBCA771Hk4AU1NT5syZk2KdBw8epHg8tfM2PyQ9nEIIIYQQIl1JD6cQQgghhAZkhB5ObZGEUwghhBBCAyTdTJ4MqQshhBBCiHSlUEr/rxBCCCGESEfSwymEEEIIIdKVJJxCCCGEECJdScIphBBCCCHSlSScQgghhBAiXUnCKYQQQggh0pUknEIIIYQQIl1JwimEEEIIIdKVJJxCCCGEECJdScIphBBCCCHSlSScQgghhBAiXUnCKYQQQnxD3rx5o+0QhNA4STiF+M5duXKF2NjYROWxsbFcuXJFCxEJ8f2Jj49nyZIlODg4YG9vj4+PDwDz58/nn3/+0XJ0QqSdvrYDEOJb5+3tzfr16/Hw8EChUFCwYEG6du1K/vz5tR2aRnTt2pVz585hbm6uVv7q1Su6du3K/fv3tRSZ5oSHh3Pr1i2Cg4NRKpVqx1q0aKGdoDTk7du3NGzYkOXLl1OoUCFth5OuvLy8cHV1JTg4mPj4eLVj/fv311JUmvHXX3/h4uLCiBEjmDBhgqq8SJEirF+/nrZt22oxOs1xcXFh69atPHv2jG3btmFtbc26devIly8fTk5O2g5PpCNJOIVIwdmzZ+nbty/FixenfPnyKJVKbty4QePGjVm2bBnVq1fXdohpplQqUSgUicpfvnxJ1qxZtRCRZp04cYLhw4cTFRVF9uzZ1dqqUCgyfMJpYGBATExMkq+hLtm+fTuTJk0iZ86cWFhYJHodM3rCuWfPHqZMmULVqlX5/fffVeVFixbF09NTi5FpzubNm1m4cCHdunVj2bJlqi8NJiYmrF+/XhJOHScJpxApmDt3Lt27d2f48OFq5XPmzGHOnDkZOuF8/wGtUCgYPXo0hoaGqmNxcXE8ePAAe3t7bYWnMTNnzqR169YMHTpUJxLopHTp0oWVK1cydepU9PV188/60qVLGTx4MH369NF2KOkiICAgyVETpVKZ5JSXjGjTpk1MnToVJycnVqxYoSovVaoUM2fO1GJk4mvQzb9MQmiIh4cH8+fPT1TeunVr1q9f//UD0iBjY2Mg4QMte/bsZMmSRXXMwMCAcuXK6cQwXkBAAF27dtXZZBPAzc2Nixcvcu7cOYoWLZqorYsXL9ZSZJoTFhZGo0aNtB1GuilUqBBXr17F2tparfzQoUMUL15cS1Fp1rNnz5Jsi6GhIVFRUVqISHxNknAKkQIzMzPu37/PDz/8oFZ+//79RHMeM5rp06cDYG1tTc+ePcmWLZuWI0ofNWrU4Pbt29jY2Gg7lHRjYmJCgwYNtB1GumrYsCHnzp2jY8eO2g4lXfTv35+RI0cSEBCAUqnkyJEjeHl54eLiwvLly7Udnkbky5eP+/fvJ0qqz5w5o/Pzj4UknEKkqG3btkycOBEfHx/Kly8PwPXr11m5ciU9evTQcnSakdHnvn1KrVq1mD17Nh4eHhQpUiTRkHPdunW1FJnmvP/yoMtsbW1ZsGABbm5uSb6OXbt21VJkmuHo6Mi8efNYvnw5CoWChQsXUqJECZ2ZKw7Qq1cvJk+eTExMDAC3bt1i3759rFixgqlTp2o5OpHeFMqPl2wKIVSUSiXr169nzZo1BAYGAmBlZUWvXr3o2rWrTizUePHiBTNnzuTixYuEhIQkWsWd0VepFytWLNljCoUiw7fvQyEhIXh6eqJQKLCzs8PMzEzbIWmMo6NjsscUCgXHjx//itGIL7V9+3aWLl2Kv78/ALly5aJ///46MX1HpEwSTiFSKSIiAgAjIyMtR6JZvXv3xt/fn86dO2NlZZXouKwc/fZFRkYyZcoU9uzZo1r5q6enR/PmzZkwYYJOz18VGdP7L7cZfWqSSD1JOIX4ztnb27N582adWZjwPZo4cSIXLlxgwoQJVKhQAYBr164xdepUqlWrxh9//KHlCDXr/cdWRh9hqFSpUqrb4Orqms7RCJG+ZA6nEEno0qXLJz8IFApFhl+pDpAnT55Ew+i6xtXVlTVr1qg27y9QoAC9e/emYsWK2g5NIw4fPszChQupXLmyqqxWrVpkzpyZwYMH60zC6eLiwurVq3ny5AkAP/zwA7169cqwe6mOHTtW9fPLly9ZunQpNWrUoFy5cgDcvHmTc+fO8dtvv2kpwrRr0aJFqpPq3bt3p3M0Qpsk4RQiCSn19kVERLB//37VxPeMbuzYscydO5c//viDfPnyaTscjduzZw9jx46lXr16dOnSRbV5f/fu3Zk+fTpNmzbVdohpFh0djYWFRaJyc3NzoqOjtRCR5q1du5YFCxbQuXNnBg8ejFKp5Pr160yaNImXL1/SvXt3bYf42Vq2bKn6ecCAAQwcOJCffvpJVda1a1c2bdrEhQsXMmT7QH1Kzps3b9i8eTOFChVSJdVubm48evSITp06aSlC8bXIkLoQqRQbG8vff//NsmXLMDY2ZtCgQTRu3FjbYaVZpUqViIqKIi4ujixZsmBgYKB2PKMP5TVq1Ij27dsn+sBeu3Yt27dv5+DBg9oJTIO6deuGqakps2bNInPmzEBCEjpq1CjCwsJYt26ddgPUAEdHRwYOHJioN3P37t0sWrSIEydOaCcwDbG3t8fFxQVbW1u18idPntCyZUtu3Lihpcg0Z9y4cVhaWjJ48GC18oULF+Lv7/9d7LbwPZMeTiFSYe/evSxcuJDo6Gj69+9P+/btdeaKLh8O6+kiHx8f6tSpk6jc0dGRP//8UwsRad7YsWP5+eefqVmzJsWKFVOtvs+cOTOrV6/WdngaERQUlOSVr+zt7QkKCtJCRJplamrK0aNH6d27t1r5sWPHMDU11U5QGnbo0CF27tyZqLxZs2a0bt1aEk4dpxufmEKkkzNnzjB37lyePXtGz5496dGjh85tkP7hsJ4uypMnDxcvXkzUc3Tx4kXy5Mmjpag0q2jRohw5coS9e/fi6emJUqmkcePGNG3aVO0KUhmZra0tBw8e5Ndff1UrP3DgQKILM2REAwYMYNy4cbi6uqoNN589e1Zn9qjMkiUL165dS/R6Xbt2TdUzL3SXJJxCJOHWrVvMnj0bNzc3OnTowNq1a3VqT8MP+fn5pXg8b968XymS9NGjRw+mTp3K/fv3sbe3R6FQcO3aNXbv3s24ceO0HZ5GXLlyBXt7e9q1a6dWHhsby5UrV6hUqZKWItOcAQMGMGTIEK5cuUL58uVVr+OlS5eSvPxsRtOqVSsKFizIhg0bOHr0KEqlkoIFC7JlyxbKli2r7fA0olu3bkyaNIm7d++q2uTm5sbOnTvp16+flqMT6U3mcAqRhGLFipElSxbat2+f6DJsH8roVzcBVEOwydGFjdGPHj3KmjVr8PT0BKBAgQL06tVLZ/YYLV68OOfOnUu0p2FoaCjVqlXTidcQ4M6dO6xbt07Vi1uwYEF69uxJiRIltB2aSKUDBw6wYcMGtX+LXbt2xdnZWcuRifQmCacQSUjpqibv6crVTdzd3dXuv337lvv377N27VqGDBlC/fr1tRSZSK1ixYpx4cKFRL3wXl5etG7dmuvXr2spMvE54uLiOHbsmGr7rkKFCuHo6Iienp62QxMizSThFEIk6dSpU6xevZqNGzdqOxSRjP79+wNw/PhxHBwcMDQ0VB2Li4vjwYMH2NnZZdiFQxEREaore72/0ldyMvoVwJ4+fUqfPn0ICAjAzs4OpVLJkydPyJ07NytWrCB//vzaDlFj7ty5o5ZUSw/190HmcAqhAU2bNmXFihU6swgFEjbVvn37trbD+CI//vgjhw4dwszM7JNXc8nI2z4ZGxsDCVfeyZ49u9oCIQMDA8qVK5ehr1FdqVIl1VSBihUrJvk6KpVK1ar8jGzq1KnY2Niwbds21ar00NBQRowYwdSpU1mxYoV2A9SA4OBghgwZgqurKyYmJiiVSl69ekXlypWZN2+ezs6TFwkk4RRCA549e0ZsbKy2w/giH/ccKZVKAgMDWbx4caKV3RnFmDFjVD1eY8aMyfCXQEzO+21krK2t6dWrl85dM339+vXkyJEDgA0bNmg5mvR15coVtWQTIGfOnAwfPpyOHTtqLzANmjJliurCGQULFgTg8ePHjBo1iqlTp+rMNmUiaZJwCvGdS6rnSKlUkidPngz7AfDhVk+tWrXSYiRfR/PmzQkICEi03cyTJ0/Q19fPsFeQ+vHHH1U/58uXjzx58iT5XvX39//aoWmcoaEhr1+/TlT++vXrRBdjyKjOnj3L2rVrVckmQKFChfj999/p2bOnFiMTX4MknEJ85z7uOcqUKRM5c+bE1tZWJza3/x5WcI8ZM4bWrVsnSjjd3NzYsWOHTszDrVu3bpKv48uXL6lbt26Gfx1r167NxIkTmTZtGmXKlAESXr9JkyalahFjRhAfH59k8qyvr098fLwWIhJfU8b/NBFCpMmHvUi6KLl1kTExMTrTc3Tv3j2mTZuWqLxcuXJMmTJFCxFp3vu5mh+LjIzUiU3Dx48fz6hRo9SuYhYXF4ejo6PO7BdbpUoVpk2bxty5c8mVKxcAAQEBTJ8+napVq2o5OpHeJOEUQuDt7c369etVK0cLFixI165dM/TK2Pc9twqFgn/++UftClHx8fFcuXKFAgUKaCs8jVIoFEkOx7569Yq4uDgtRKQ57+epKhQK5s+frzZPNS4ujlu3blGsWDFthacxJiYmLF26lKdPn+Lh4YFSqaRQoUIZdh51UiZOnMhvv/1G3bp1yZ07NwqFAn9/f4oUKcLs2bO1HZ5IZ5JwCvGdO3v2LH379qV48eKUL18epVLJjRs3aNy4McuWLaN69eraDvGLrFu3DkjoGdu6dSuZMmVSHTMwMCBfvnz88ccfWopOsypWrMjy5cv5888/VXs2xsXFsWLFCipUqKDl6NLm3r17QMLr+PDhQ7VeaUNDQ4oVK5bh5/9FRESQLVs2MmXKhK2trSrJjI+PV9saKqPLkycPu3fv5vz586rN+wsVKkS1atW0HZr4CmQfTiFS6c2bN8kO3f3777/UrVs3Q15nvUWLFtSoUYPhw4erlc+ZM4fz58+ze/duLUWmGV26dGHx4sWq1c666PHjx3Tu3BkTExMqVqwIwNWrV4mIiGD9+vUUKVJEyxGm3ZgxYxg3bpzOJF/vHT16lDlz5uDi4pJol4GoqChatGjBqFGjdGYep/h+Zfp0FSG+X/Hx8SxZsgQHBwfs7e3x8fEBYP78+fzzzz+qek2bNs2QySaAh4cHbdq0SVTeunVrHj9+rIWINGvjxo06nWxCwkrfvXv30qhRI4KDg3n9+jXNmzfn4MGDOpFsQsLQuq4lmwBbtmyhd+/eSW5plTVrVn7++Wf+/vtvLUSmORcvXsTZ2TnJzftfvXpF48aNuXr1qhYiE1+TDKkLkYK//voLFxcXRowYwYQJE1TlRYoUYf369Rl6U+33zMzMuH//fqIVzvfv30+0Ijijev78OcePH8ff35+3b9+qHRszZoyWotKsXLlyMXToUG2HoVH9+/dnxowZGBkZqa6qlJzFixd/pag06+HDh/z+++/JHq9UqRLz58//egGlg/Xr19OuXbskvzAYGxvTvn171q5dq+qdF7pJEk4hUrBnzx6mTJlC1apV1T4UihYtiqenpxYj05y2bdsyceJEfHx8KF++PADXr19n5cqV9OjRQ8vRpd3Fixfp27cv+fLlw8vLi8KFC+Pr64tSqdS5S+pFRUXh5+eXKKnOqItq3l9J6eOfdUl4eHiKF42IjY0lPDz8K0akeQ8ePGDEiBHJHq9evTpr1qz5ihEJbZCEU4gUBAQEJLlSW6lUZtgrC32sX79+GBkZsWbNGtVG71ZWVvTv35+uXbtqObq0mzt3Lj169GDQoEHY29uzaNEizMzMGD58OA4ODtoOTyNCQkIYM2YMZ86cSfJ4Rt2j8v0KdaVSSf/+/TEzM9O5qylZW1tz584dtc3QP3T79m3y5s37laPSrBcvXqS4p6++vj4hISFfMSKhDTKHU4gUFCpUKMm5RYcOHaJ48eJaiEjzFAoF3bt358yZM1y9epWrV69y5swZunXrphOXhPTw8FBdeUhfX5/o6GiyZ8/OoEGDWLVqlZaj04xp06YRFhbGtm3byJIlC6tWrWLGjBnY2tqydOlSbYeXZkqlkgYNGhAQEKDtUDSufv36zJ8/nxcvXiQ6FhQUxIIFC2jQoIEWItOcXLly8fDhw2SPP3jwAEtLy68YkdAG6eEUIgX9+/dn5MiRBAQEoFQqOXLkCF5eXri4uLB8+XJth5cm0dHRnD9/nsqVK6vmVr3/f0REBJcvX8bBwQFDQ0Nthplm2bJlIyYmBkjoufX29qZw4cJAwtWGdMHly5f566+/KFOmDAqFgrx581K9enWMjIxYvnw5tWvX1naIafJ+u6CXL19qOxSN+/nnnzl+/Dj169enWbNm2NnZoVAo8PDw4N9//yVPnjz8/PPP2g4zTWrVqsXChQupWbNmop0+oqOjWbRoEXXq1NFSdOJrkYRTiBQ4Ojoyb948li9fjkKhYOHChZQoUSJD70/53rZt2zhx4gR169ZNdMzIyIiNGzfy/PlzOnfurIXoNKds2bJcv36dQoUKUatWLWbOnMnDhw85evQoZcuW1XZ4GhEZGYmZmRkApqamhISEYGdnR5EiRVT7WGZ0I0aMYNasWUyaNElnVt5Dwr+1LVu2MHfuXA4ePEhYWBgAOXLkoFmzZgwdOjTDr87v27cvR44coUGDBnTu3Fktqd68eTNxcXH8+uuv2g5TpDPZh1OI71SbNm347bffkt3f7+TJkyxZsoQdO3Z85cg0y8fHh9evX1OsWDGioqKYOXMm165dw9bWljFjxmBtba3tENOsdevWDB48GAcHB3777TeMjIwYNmwYGzZs4PDhwxw7dkzbIaZZpUqViIqKIi4uDgMDA7JkyaJ23NXVVUuRaY5SqSQ0NBSlUomZmVmSU1quXbtG6dKlM9zIg6+vL5MmTeLcuXOqy80qFApq1KjB77//Tr58+bQcoUhvknAK8Qnh4eEcPnwYHx8fevbsiampKXfv3sXCwkJ1PeCMqFKlSuzZsyfZBQl+fn40b96cK1eufOXINCcuLo5r165RtGhRnd6Lc+/evcTGxtKqVSvu3btHr169ePnyJQYGBsyYMQNnZ2dth5hmu3btSnFO8ft5urqufPny7NmzBxsbG22H8kXCwsJ4+vQpALa2tkn+u3z+/DlWVlZqVwcTGZ8MqQuRAnd3d3r06IGxsTG+vr60bdsWU1NTjh49ip+fH7NmzdJ2iF8sNjaWkJCQZBPOkJCQDL8SX09Pj169enHgwAGdTDgvXrxIpUqVaNasmaqsRIkSnDhxAk9PT/LkyaMaas/oWrVqpe0QvgkZvY8oR44clClTJsU6zs7OGTqpFkmTrw9CpGDGjBm0bNmSI0eOqA1h1axZM8NfGaNw4cJcuHAh2eMXLlxQLa7JyIoUKcKzZ8+0HUa66Nmzp2rOH0C7du0ICAgga9aslCxZUmeSTYDixYsTHBycqDw0NFRndowQCTJ6Ui2SJgmnECm4ffs2HTp0SFSeK1cugoKCtBCR5rRu3ZqlS5dy8uTJRMdOnDjB0qVLad26tRYi06whQ4Ywc+ZMTp48SWBgIBEREWq3jOzjD+ZHjx6pVuTrmuSSkJiYGAwMDL5yNEKIzyVD6kKkIHPmzEkmJV5eXhm+96h9+/ZcuXKFvn37UqBAAbWVo0+ePKFRo0a0b99e22GmWe/evYGElbIfzgFUKpUoFIoMuyn692LDhg1AwgKTf/75h2zZsqmOxcfHc+XKFQoUKKCt8IQQqSQJpxApqFu3LkuWLFG7lrGfnx9z586lfv362gtMQ+bMmYOjoyP79u3jyZMnKJVK7OzsGDBggE4sNIH/EhZdpFAo1JJoXdio/2Pr1q0DEr4gbN26VW0hiYGBAfny5eOPP/7QUnRfny6+xuL7IKvUhUhBREQEP//8M48fP+b169dYWVnx4sULypUrx4oVK9R6W3TdihUr6NChAyYmJtoORbxTrFgxChcurLps4IMHDyhQoECiIebdu3drIzyN6tKlC4sXL9bJxV9KpRI/Pz/Mzc0Tbff0MXt7e/bu3avTC2oy+kp8kTTp4RQiBe83Zb548SL37t0jPj6ekiVLUq1aNW2H9tUtW7aMRo0aZbiE81PbOlWqVOkrRaJ5/fv3V7uf1Cb+uqJy5cpJ7j0ZHR3NqlWrEv0uMpL3l+7ct28fP/zwQ4p1b9y48XWC0iLpB9NN0sMpRAp8fHzkW/Y7GbVnpVixYonKPhyW/J7mcGbUTcMhYZX6uXPnMDc3VysPDQ2lWrVqGf51bNy4MdOmTaNcuXLaDuWriYiI4NKlS9jZ2VGwYEFVub+/P1ZWVujp6WkxOqFpskpdiBTUr1+fLl26sGfPHt68eaPtcMQXuHLlitrtwoULrFq1itKlS7NmzRpth/dV/fzzzwQEBGg7jC/yfpHXx9zd3XVimP39pTsfPnyo7VDSzaBBg9i0aROQ0DP9/gpZzZo14/Dhw6p6efLkkWRTB8mQuhAp2LNnDzt37mTmzJlMmTIFZ2dnWrdurTPX4P4eGBsbJyqrXr06hoaGTJ8+nV27dmkhKu3IiANalSpVUi2OatCggVrSGRcXR2RkZJJbl2U0I0aMICoqiubNm+vspTuvXr1K3759ATh69ChKpZIrV66we/duli5dSoMGDbQcoUhPknAKkYIiRYowZswYRowYwcmTJ9m1axedO3fG1taWNm3a0Lx58wy/PdL3yszMDC8vL22HIT5h7NixKJVKxo4dy4ABA9S+QBgYGGBtbY29vb0WI9SMsWPHajuEdPfq1StVb/TZs2epX78+WbNmpXbt2syePVvL0Yn0JgmnEKmgr69PvXr1qFWrFps3b2bu3LnMnDmTuXPn4uzszPDhw7GystJ2mCIJ7u7uicoCAwNZuXIlRYsW1UJE4nO8v0Z6vnz5KF++vGpF/ntxcXEcO3YMJycnbYSnMd/DteDz5MnDjRs3yJEjB2fPnuXPP/8EIDw8PEPOKxafRxJOIVLh9u3b7Ny5kwMHDpA1a1Z69uxJmzZtCAwMZOHChfz222/s2LFD22Gmq4oVK5I5c2Zth/HZWrRogUKhSDScXK5cOaZNm6alqMTn+vHHH9Xue3h4sHPnTlxcXAgPD+fOnTtaikxzvL292blzJz4+PowbNw5zc3POnDlDnjx5dOIys127dmXEiBFky5aNvHnzUrlyZSBhnnWRIkW0HJ1Ib7JKXYgUrF27ll27duHl5UXNmjVp27YttWrVUtt8+unTpzRq1Ih79+5pMdK00eUPOl9fX7X7mTJlwszMLEMmz2mV0fc3jIyM5MCBA+zYsQM3NzcqV65M48aNqVu3boaf2uLq6srPP/9M+fLluXLlCgcPHsTGxoaVK1dy+/ZtFi5cqO0QNeL27ds8f/6catWqkT17dgBOnTqFsbExFSpU0HJ0Ij1JD6cQKdiyZQutW7emVatWWFpaJlknT548Gbqn7OMPuiFDhmBubs6DBw/YsWNHhv+gs7a21nYI34yM2r9w48YN/vnnHw4ePMgPP/xA06ZNuXXrFuPHj6dQoULaDk8j5s6dy+DBg+nRo4fanNTKlSvr1NWySpcuTenSpdXKateurZ1gxFclCacQKThy5Mgn6xgaGmbo+Ve6/EEXHx/Prl27OHr0KL6+vigUCqytrWnYsCHNmzfXmcsELlq0iFatWn0yuc6Im4Y7OzsTHR1NkyZN+Oeff1QJ5ty5c7UcmWY9fPiQOXPmJCo3MzPj5cuXXz+gdDBmzJgUj0+fPv0rRSK0QRJOIT4hPDycHTt24OHhgUKhoGDBgrRp0ybJ7XYyIl39oFMqlfTt25fTp09TrFgxihQpglKpxMPDg9GjR3PkyBH++usvbYepESdPnmTp0qVUqlSJNm3aUL9+fZ2ZMuDt7Y2zszOVK1dW2xxc1xgbGxMUFJRousP9+/fJlSuXlqLSrPDwcLX7sbGxPHr0iPDwcKpUqaKlqMTXIgmnECm4ffs2vXv3JnPmzJQpUwalUsm6detYtmwZa9asoWTJktoOMc109YNu165dXLlyhXXr1iX6MLt48SL9+vXDxcWFFi1aaCdADdq1axfu7u7s2rWL6dOnM3nyZNWesWXKlNF2eGly/Phxdu3axaRJk1Q9nU2bNtWZ3un3mjRpwpw5c1iwYAEKhYL4+HiuXbvGzJkzdeI9CrBkyZJEZfHx8UyaNCnDzisWqSeLhoRIQadOnbC1tWXKlCmq7VhiY2MZP348Pj4+/P3331qOMO1mzZrFzZs3WbBgAQ0aNGD37t28ePGCUaNG0aJFiwx7jeqePXtSpUoV+vTpk+TxZcuWceXKFVavXv2VI0tfsbGxnDx5kp07d3Lu3Dns7Oxo06YNrVq1yvC98hcvXmTnzp0cPXqUN2/e0LNnT9q2bYudnZ22Q0uzt2/fMnr0aA4cOIBSqURfX5+4uDiaNGnCjBkzdPrKO56ennTt2pVz585pOxSRjiThFCIFZcqUYffu3YmG8h4/fkzr1q1xc3PTUmSao6sfdNWrV2fVqlUUL148yeP37t3j559/5vz58185svQVExPDsWPH2LlzJ5cuXcLe3p4XL14QGBjI1KlTcXZ21naIafbq1Sv27t3Lzp07uXfvHoULF+bff//Vdlga4e3tzb1794iPj6dEiRL88MMP2g4p3Z0+fZpRo0Zx6dIlbYci0pEMqQuRAiMjI/z9/RMlnP7+/qotPTI6AwMD5s6dy6BBg3Tqgy4sLAxzc/Nkj5ubmxMWFvYVI0pfd+7cYdeuXezfvx8DAwNatGjBxIkTsbW1BWDNmjU6k3AaGxvTuXNnOnfuzP3799m5c6fq2LVr1yhdunSG3Ug8f/785M+fX9thpIuPFwUplUqCgoI4depUhl54KVJHejiFSMHUqVM5evQoo0aNwt7eHoVCwbVr15g1axb169dn3Lhx2g4xzVxdXRNtqq0Lihcvzvnz55Pdn/HFixc4ODhw//79rxyZ5jVt2hRPT0+qV69Ou3btqFOnTqKe6ZCQEKpVq5bklZd0SUbaa/RzVmV/aoV3RtClSxe1++/3xK1SpQqtW7dOdBUpoVvk1RUiBSNHjlT9Py4uDki4zGXHjh0ZPny4NkPTmJ49e2JhYUGTJk1o1qyZzlzxQ6lUMnr06GR7umJiYr5yROmnYcOGtGnTJsVFXmZmZjqfbELG2mv044tF3L17l/j4eNWc1CdPnpApUyadWJwIsHHjRm2HILRIEk4hUmBoaMj48eMZNmwY3t7eKJVKbG1tyZo1q7ZD05gzZ85w4MAB9u3bx6pVqyhcuDDNmjWjadOm5M6dW9vhfbHUDNHpyupfpVKJiYlJovLo6GhWrVqVYRd+6boPE7C1a9eSPXt2Zs6cSY4cOYCEaSFjxoyhYsWK2gpRo7p27crixYsTvVcjIiL47bffMvy+vyJlMqQuhFDx8fFh37597N+/H09PTypWrPjdfAg8f/4cKysrtcuWZhTFixfn3LlzieashoaGUq1aNZ2YNpBa9vb27N27N0MMqX/IwcGBNWvWJLqU7MOHD+nZs6dOrOAuVqwY58+fT/Q+DQ4OpmbNmty9e1dLkYmvQXo4hfjI5/QGLV68OB0j+fpsbGzo06cPxYoVY8GCBVy5ckXbIX01zs7OGWbu38eUSmWS+1K6u7uresvEty0iIoIXL14kSjiDg4N5/fq1lqLSjA+ncjx+/JigoCDV/fj4eM6ePZuh9/wVqSMJpxAfyeh7FX6pa9eu8e+//3L48GHevHmDo6MjQ4YM0XZYX01GHOypVKkSCoUChUJBgwYN1JLOuLg4IiMj6dChgxYj/Poy6obw9erVY+zYsYwaNYpy5coBcPPmTdUCxYysRYsWqvdpt27dEh3PkiUL48eP10Jk4muSIXUhvnN//vkn+/btIzAwkGrVqtG0aVOcnJx0ap5qamTEodjdu3ejVCoZO3YsY8eOVfuyZGBggLW1Nfb29lqM8OvLiK8jQFRUFDNnzmTnzp3ExsYCoKenR5s2bRg5ciTZsmXTcoRfztfXF6VSiZOTE//884/azhEGBgaYm5tn2P1+RepJwilEKgQHB+Pl5QWAnZ1divs7ZjQdOnSgadOmNGrUKNkthL4HGTVRgYStrezt7TEwMNB2KOkmOjoapVKp+iLk6+vL0aNHKVSoEDVq1NBydJoTGRmJt7c3kLAnZ0ZONIX4kCScQqQgIiKCP/74gwMHDqi2RdLT06NRo0b8/vvv3+3wuy7KaAlnREQERkZGqp9T8r5eRtazZ0/q1atHx44dCQ8Pp1GjRujr6xMaGsro0aPp1KmTtkMUqfT48WP8/Px4+/atWnndunW1FJH4GmQOpxApGDduHO7u7ixbtkw1NHnjxg2mTZvG+PHjWbBggZYj1Jzv/UMgo839q1SpkmplesWKFZOM//1iIl1YpX737l3V5ueHDx/G3NwcFxcXDh8+zMKFCzN8whkZGcmKFSu4dOkSwcHBxMfHqx0/fvy4liLTHB8fH/r168fDhw9RKBSqedPv37u68D4VyZOEU4gUnD59mlWrVqntg+fg4MDUqVPp3bu3FiPTHPkQSJDRBnvWr1+vWoH+PWxdFR0drbqc7Llz56hfvz6ZMmWiXLly+Pn5aTm6tBs/fjyurq40b94cS0vLDPcFKDWmTZtGvnz5WLduHXXr1mXHjh2EhoYyc+ZMRo0ape3wRDqThFOIFJiamiY5bG5kZJTkRtsZka5/CIwZM4Zx48YlGlaOjIxkypQpqssLHjhwACsrK22E+EU+vBypLl6a9GP58+fn2LFj1KtXj3PnztG9e3cgYX61LkwZOHPmDMuXL6dChQraDiXd3Lhxg/Xr12NmZkamTJlQKBRUrFiRoUOHMnXqVFxcXLQdokhHGW+HYyG+or59+zJjxgwCAwNVZUFBQcyePZvffvtNi5Fpzo0bNxg4cGCyHwIZnYuLC2/evElUHh0dzZ49e1T38+TJk6FWyrq7u6f6pgv69evHrFmzcHR0pEyZMqopLufPn6d48eJaji7tTExMMDU11XYY6So+Pl7VS50zZ07V31Vra2vVokyhu6SHU4gUbNmyhadPn+Lo6EiePHkA8Pf3x8DAgJCQELZt26aqu3v3bm2FmSZJfQgUKFAgw38IREREoFQqUSqVvH79msyZM6uOxcXFcebMmQy9Kv/93oafmgqgK3M4GzZsSIUKFQgKClJLMKtWrUq9evW0GJlmDBo0iAULFjBz5kyd3ZKscOHCPHjwABsbG8qWLcuqVaswMDBg+/btGWaxnvhyknAKkQInJydth5DudPVD4P1Cmvebon9MoVAwYMAALUSmGbqwiCQ1Unvlr4x+1a+1a9fi7e1NtWrVyJcvH/r66h/PGfUL7Yf69u1LVFQUAIMHD+aXX36hc+fOmJqaMm/ePC1HJ9KbbIskxHfu7NmzREVFUb9+fXx8fPjll1/w9PRUfQhUrVpV2yF+EVdXV5RKJd26dWPRokVql3g0MDAgb968cjm9DOD9yvRPeT8XN6P6VML8OZfczUhevnxJjhw5dHKRlFAnCacQqfT69etEw5e6sFghKbr0IeDr60uePHnIlEm3pqwfP36cmjVrYmBg8Mnezu9layvxbUvtAj6hmyThFCIFPj4+TJkyBVdXV7WFJ7q0v+F7T58+xdvbm0qVKpElSxZVG3VBeHg4t27dIjg4ONGXhhYtWmgnqDQqVqwY58+fx9zcnGLFiiVbT9fep7osPDycw4cP4+3tTa9evTA1NeXu3btYWFjoRG988eLFVXvHfigkJIQaNWpw7949LUUmvgaZwylECkaMGAHA//73P8zNzXUmAftQaGgogwcP5vLlyygUCo4cOYKNjQ3jxo3DxMSE0aNHazvENDlx4gTDhw8nKiqK7Nmzq72GCoUiwyacH64+15WV6N8zd3d3evTogbGxMb6+vrRr1w5TU1OOHj2Kn58fs2bN0naIX0zXF/CJ1JGEU4gUPHjwgJ07d1KgQAFth5Jupk+fjr6+PqdOnaJRo0aqcmdnZ6ZPn57hE86ZM2fSunVrhg4dqrOrf0XGN2PGDFq2bMnIkSNVWz4B1KxZk+HDh2sxsrTT9QV8InUk4RQiBaVKleL58+c6nXCeP3+e1atXkzt3brVyW1tbnbiCS0BAAF27dtX5ZPPixYusW7cODw8PFAoFBQoUoFu3blSrVk3boYlUuH37NpMnT05UnitXLoKCgrQQkeZs2LBBFvAJSTiFSMm0adP4/fffCQgIoHDhwom2Kklp7lxGERkZSZYsWRKVh4aGYmhoqIWINKtGjRrcvn07Q2/x9CmbNm1i+vTpNGjQgK5duwLg5uZGnz59GD16ND/99JOWIxSfkjlzZiIiIhKVe3l5Zfjh5vdXwjp+/Dh58+bVyalJ4tMk4RQiBSEhIXh7e6ttzfJ+s21dWYxRqVIlXFxcGDx4sKosPj6e1atX68QlE2vVqsXs2bPx8PCgSJEiib406MIK7uXLlzNmzJhEiWX58uVZunSpJJwZQN26dVmyZAnz589Xlfn5+TF37lzq16+vvcA04OXLl0RHR2Ntba0qe/ToEWvWrCEyMhInJyeaNm2qxQjF1yCr1IVIgbOzMwULFqR3795JLhr68A9oRvX48WO6dOlCyZIluXTpEo6Ojjx+/JiwsDC2bNlC/vz5tR1imnwPK7jt7e1xcXHB1tZWrfzJkye0bNmSGzduaCkykVoRERH8/PPPPH78mNevX2NlZcWLFy8oW7YsK1euJFu2bNoO8YsNHToUS0tL1Rf34OBgGjVqhJWVFTY2Npw9e5apU6dm2AV8InWkh1OIFPj5+bF06dJEH+S6pFChQuzdu5ctW7agp6dHVFQU9erVo0OHDixcuDDD7433PazgdnR05OjRo/Tu3Vut/Pjx49SpU0dLUYnPYWRkxJYtW7h06RJ3794lPj6ekiVL6sQc3Js3b6r9HXFxcSFHjhy4uLigr6/P6tWr2bx5cfCs0wAAFAlJREFUsyScOk4STiFSUKVKFdzd3XU64QSwtLRk4MCBamXu7u64uLhk+ITzQ2/evFHbkiUj27Bhg+rnggULsmzZMlxdXSlXrhyQMIfz+vXr9OjRQ0sRitSIjo7m4sWLqi8G58+fJyYmBoDTp09z7tw5Bg0alKHfty9evCBfvnyq+5cuXcLJyUk1vcXR0ZEVK1ZoKzzxlUjCKUQK6tSpw/Tp03n48KHOzv/TdXFxcSxbtoytW7cSHBzM4cOHsbGxYf78+VhbW9O2bVtth/hF1q1bp3bfxMSEx48f8/jxY1WZsbExO3fu5LfffvvK0YnUcnFx4dSpU6qEc9OmTRQuXFiVYHp5eWFlZUX37t21GGXaGBkZ8erVK9X9W7du0aZNG9V9hUKhSrKF7pKEU4gU/P777wAsWbIk0TFdmf+n65YuXYqLiwsjRoxgwoQJqvIiRYqwfv36DJtwnjhxQtshCA34999/EyWTc+fOVe2qsGfPHjZv3pyhE84yZcqwYcMGpk6dypEjR3j9+jVVqlRRHX/y5EmibdmE7pGEU4gUfA/z/3Tdnj17mDJlClWrVlV9gQAoWrQonp6eWoxMiIRk64cfflDdz5w5s9rixDJlyiS5P2dGMmjQILp3787evXuJi4vjl19+UduLc//+/VSqVEmLEYqvQRJOIZLw888/8+eff2JsbAwk9JJ17twZExMTIGGPys6dO3PgwAFthpkm/fv3T/F4eHj4V4okfQUEBCS50l6pVBIbG6uFiNLH8+fPOX78OP7+/rx9+1bt2Ifbeolvy6tXr9DT01Pdv3Tpktrx+Pj4DD/cXLx4cQ4ePMj169extLSkbNmyascbN25MwYIFtRSd+Fok4RQiCefOnVP7I79y5UqaNGmiSjjj4uLw8vLSVnga8T6ZTum4Lmz7VKhQIa5evZqoLYcOHaJ48eJaikqzLl68SN++fcmXLx9eXl4ULlwYX19flEolJUqU0HZ4IgW5c+fm0aNHyV7N7MGDBzox3GxmZoaTk1OSx2rXrq12v2nTpqxYsYI8efJ8hcjE1yIJpxBJ+Hh7Wl3crlaXVp+npH///owcOZKAgACUSiVHjhzBy8sLFxcXli9fru3wNGLu3Ln06NGDQYMGYW9vz6JFizAzM2P48OE4ODhoOzyRgpo1a7Jw4UJq166daCV6dHQ0S5YsoVatWlqKTjuePXumU6MPIkEmbQcghBDpydHRkXnz5nHmzBkUCgULFy7Ew8ODZcuWUb16dW2HpxEeHh60bNkSAH19faKjo8mePTuDBg1i1apVWo5OpOTXX38lLCyMhg0bsmrVKo4dO8bx48dZuXIlDRs2JCwsjF9//VXbYQqRZtLDKUQSFAqFXO9Xhzg4OOh0T1+2bNlUU0CsrKzw9vamcOHCQMJ8Y/HtsrCwYMuWLUyaNIm5c+eqRlMUCgXVqlVj0qRJWFhYaDlKIdJOEk4hkqBUKhk9ejSGhoYAxMTEMGnSJLJmzaq6LzKe169fJ5oeYWRkpKVoNKds2bJcv36dQoUKUatWLWbOnMnDhw85evRoogUa4ttjY2PD6tWrefnyJd7e3gDkz58fU1NT7QYmhAZJwilEEt4PT77XrFmzRHXkMmwZg4+PD1OmTMHV1ZU3b96oypVKpc7spTpmzBhev34NwIABA4iMjOTAgQPY2trKCvUMxNTUVJJMobMUSl1cDSGEEO906NABgK5du2Jubp5oqsSPP/6ojbCEEMmwt7dn7969qs3vhW6QHk4hhE578OABO3fuTHbbGSHEt2Xy5MmYm5trOwyhYZJwCiF0WqlSpXj+/LnOJZyVKlVK9cI2V1fXdI5GiKRt2LAh1XW7du0KJOzDKXSPDKkLIXSat7c3v//+O82aNaNw4cLo66t/zy5WrJiWIkub3bt3p7rux3OShfhaHB0dU1VPoVBw/PjxdI5GaJMknEIInXbz5k2GDRuGr6+vqkyhUOjUoiEhhPjWScIphNBpzs7OFCxYkN69eye5aCijXr4zIiJCtaVTREREinV1YesnIUTGJgmnEEKnlStXjj179mBra6vtUDSqePHinDt3DnNzc4oVK5bkfE7pxRXfmufPn3P8+HH8/f15+/at2jHZwku3yaIhIYROq1KlCu7u7jqXcK5fv54cOXIAn7cwQwhtuXjxIn379iVfvnx4eXlRuHBhfH19USqVlChRQtvhiXQmPZxCCJ22bds2li5dSuvWrSlSpEiiRUN169bVUmRCfF/atGmDg4MDgwYNUu21aWZmxvDhw3FwcKBTp07aDlGkI0k4hRA6LaVV6Lo23BwVFYWfn1+iocqMuhJf6BZ7e3v27NlD/vz5qVSpEps3b6Zw4cK4u7vz22+/ceLECW2HKNKRDKkLIXSau7u7tkNIdyEhIYwZM4YzZ84keVyXkmqRcWXLlo2YmBgArKys8Pb2pnDhwgCEhoZqMzTxFWTSdgBCCJGefHx8tB1Cups2bRphYWFs27aNLFmysGrVKmbMmIGtrS1Lly7VdnhCAFC2bFmuX78OQK1atZg5cyZLly5l7NixlC1bVsvRifQmPZxCCJ1Wv359KlasSJs2bWjYsCGZM2fWdkgad/nyZf766y/KlCmDQqEgb968VK9eHSMjI5YvX07t2rW1HaIQjBkzhtevXwMwYMAAIiMjOXDgALa2trJC/TsgCacQQqft2bOHnTt3MnPmTKZMmYKzszNt2rShTJky2g5NYyIjIzEzMwPA1NSUkJAQ7OzsKFKkCPfu3dNydEIksLGxUf2cNWtWJk2apL1gxFcnQ+pCCJ1WpEgR1fzG6dOnExQURKdOnWjcuDFr164lJCRE2yGmmZ2dHV5eXkDCAqFt27YREBDA1q1bsbS01HJ0Qgghq9SFEN+ZmJgYNm/ezNy5c3n79i36+vo4OzszfPhwrKystB3eF9m7dy+xsbG0atWKe/fu0atXL16+fImBgQEzZszA2dlZ2yGK79SPP/7IoUOHMDMzo1KlSkleoOA9V1fXrxiZ+Nok4RRCfBdu377Nzp07OXDgAFmzZqVFixa0adOGwMBAFi5cyOvXr9mxY4e2w9SIqKgoPD09yZMnj2qoXQht2L17N40bN8bQ0JDdu3enWLdly5ZfKSqhDZJwCiF02tq1a9m1axdeXl7UrFmTtm3bUqtWLTJl+m9G0dOnT2nUqFGGne/o6urKjz/+qO0whEhWbGws//77LzVq1JBpHt8pSTiFEDqtfv36tG7dmlatWiX7QRcTE8P+/fszbA9LqVKlsLCwoEmTJjRr1owiRYpoOyQhEilbtiwHDhzA2tpa26EILZCEUwghMriQkBAOHDjAvn37uHnzJoULF6ZZs2Y0bdqU3Llzazs8IQDo0qUL3bp1w8nJSduhCC2QhFMIofPCw8PZsWMHHh4eKBQKChYsSJs2bTA2NtZ2aBrn4+PDvn372L9/P56enlSsWJENGzZoOywhOHjwIHPnzqV79+6ULFmSrFmzqh2XS7DqNkk4hRA67fbt2/Tu3ZvMmTNTpkwZlEold+7cITo6mjVr1lCyZElth6hxcXFxnDlzhgULFvDgwQO5tKX4JiSVUCoUCpRKJQqFQt6nOk4STiGETuvUqRO2trZMmTIFff2Ea13ExsYyfvx4fHx8+Pvvv7UcoeZcu3aNf//9l8OHD/PmzRscHR1p2rQptWrV0nZoQuDr65vicZnbqdsk4RRC6LQyZcqwe/duChYsqFb++PFjWrdujZubm5Yi05w///yTffv2ERgYSLVq1WjatClOTk6JhiyFEEJb5NKWQgidZmRkhL+/f6KE09/fn+zZs2spKs1ydXWlV69eNGrUSPbdFN80FxcXtm7dyrNnz9i2bRvW1tasW7eOfPnyyWIiHSeXthRC6DRnZ2fGjRvHgQMH8Pf35/nz5+zfv5/x48fTuHFjbYenEVu3bqVz586SbIpv2ubNm5kxYwa1atXi1atXxMfHA2BiYsL69eu1HJ1Ib9LDKYTQaSNHjlT9Py4uDgB9fX06duzI8OHDtRmaxj1+/Bg/Pz/evn2rVl63bl0tRSTEfzZt2sTUqVNxcnJixYoVqvJSpUoxc+ZMLUYmvgZJOIUQOs3Q0JDx48czbNgwvL29USqV2Nra6tT8Rh8fH/r168fDhw9Vq34B1XWrZfWv+BY8e/aM4sWLJyo3NDQkKipKCxGJr0mG1IUQ34WsWbNStGhRihUrplPJJsC0adPIly8fFy5cIEuWLOzfv59NmzZRqlQpNm7cqO3whAAgX758SX75OXPmDIUKFdJCROJrkh5OIYTO6d+/f6rrLl68OB0j+Tpu3LjB+vXrMTMzI1OmTCgUCipWrMjQoUOZOnUqLi4u2g5RCHr16sXkyZOJiYkB4NatW+zbt48VK1YwdepULUcn0psknEIInaOLVxBKSXx8vGrFfc6cOQkMDKRAgQJYW1vj5eWl5eiESNC6dWvi4uKYPXs2UVFRDBs2jFy5cjF27FidWcAnkicJpxBC50yfPl3bIXxVhQsX5sGDB9jY2FC2bFlWrVqFgYEB27dvx8bGRtvhCaHSrl072rVrR0hICEqlEnNzc22HJL4SmcMphPguBAcHc/XqVa5evUpwcLC2w9Govn37qraYGTx4MH5+fnTu3JnTp08zbtw4LUcnRIKuXbsSHh4OgJmZmSrZjIiIoGvXrtoMTXwFcqUhIYROi4iI4I8//uDAgQOqbZH09PRo1KgRv//+u84Ov798+ZIcOXKoVqoLoW3FihXj/PnziXo1g4ODqVmzJnfv3tVSZOJrkCF1IYROGzduHO7u7ixbtgx7e3sgYZHNtGnTGD9+PAsWLNByhOnD1NRU2yEIAYC7u7vq58ePHxMUFKS6Hx8fz9mzZ8mVK5c2QhNfkfRwCiF0Wrly5Vi1ahUVK1ZUK7969Sq9e/fm5s2b2glMgyIjI1mxYgWXLl0iODhYNbz+3vHjx7UUmRAJPZvve9qTSjmyZMnC+PHjadOmzdcOTXxF0sMphNBppqamSQ6bGxkZYWJiooWING/8+PG4urrSvHlzLC0tZRhdfFOOHz+OUqnEycmJf/75R+0SrAYGBpibm6Onp6fFCMXXID2cQgidtm3bNg4dOsTMmTOxsrICICgoiNGjR1OvXj06dOig5QjTrmLFiixfvpwKFSpoOxQhhEiS9HAKIXTali1bePr0KY6OjuTJkwcAf39/DAwMCAkJYdu2baq6u3fv1laYaWJiYiJzNkWG8f/27ickyi0O4/gzQxrSlH9aNSbSgKKzsaBNUVRMKIIMYypE4k5JiQiCqNwoNKRujCBoUZsIN42j48JZSLtaFSKIm0Qy5iUXLTSQKM2cu7gkesfsXq7vHDvz/ezel1k8Ox/P75zzzs3NaWFhQd+/f9/yPhQKGUqEbKBwArDaxYsXTUdw3Y0bN/Tw4UMNDAxY99lO2MNxHF27dk2zs7PyeDwb+zl/bgHZ7rOXsAcjdQD4w0UiEaVSKaXTaR09elT79m1dS/hTV25hl87OTnm9XkWjUYVCIQ0PD2tpaUkDAwO6fft2xsE+2IUVTgA548uXLxmnZH0+n6E0uycUCnFQCHve1NSUnj17ppKSEnm9Xnk8Hp08eVI3b95UNBpVIpEwHREuonACsJrjOLp3757evHmjlZWVjffpdFoej8eKMd7169dNRwB+a319XQcOHJAkFRcX69OnTwoEAiotLdX8/LzhdHAbhROA1W7duiVJun//vg4fPmzVSuDm+w038/l8OnbsmNrb21VbW2sgGZCpoqJC7969U1lZmWpqavT06VPl5eXpxYsXKisrMx0PLmMPJwCrnThxQvF4XIFAwHSUXffy5ctt3y8vL2t6elojIyPq7+9XfX19lpMBmV69eqWvX7+qtrZWjuPo6tWrev/+vYqKivTgwQOdOnXKdES4iMIJwGptbW3q6urS6dOnTUfJuqGhISUSCcViMdNRgG19/vxZhYWFVk0esD0KJwCrpVIp9fT0KBwOq6KiIuMEd1VVlaFk7vvw4YNaWlr09u1b01GQw+7evfuvftfX1+dyEpjEHk4AVltcXFQqldryR+/nHYC2HBr6lW/fvmn//v2mYyDHjY6Oyu/3KxgMbvstdeQGCicAq3V3dysYDGpwcNC6Q0O/E4vFVF1dbToGctzly5eVTCblOI6ampoUDof5MlYOYqQOwGrHjx/X2NiYysvLTUfZdb8aQS4vL2tmZkaO42hoaEjBYDDLyYCtVldXNTExoXg8rqmpKZ07d07Nzc06c+ZMTv0TmMsonACs1tnZqcbGRtXV1ZmOsuva2tq2fe/z+RQIBHTlyhWVlpZmORWws48fP2p0dFSJREJra2saHx/fuJ8T9mKkDsBqFy5cUF9fn2ZnZ1VZWZlxaCgUChlK9v89f/7cdATgP/u5oplOp7W+vm44DbKFFU4AVtvpFLrth4aAvWLzSH1yclLnz59XU1OTzp49K6/XazoesoDCCQAAXNPb26tkMqkjR47o0qVLCofDKi4uNh0LWUbhBGCljo4ODQ4O6uDBg5Kkx48fq7W1VYcOHZIkLS0tqbW1Vclk0mRMwHpVVVXy+/2qrq7e8YDQo0ePspgK2cYeTgBWev36tVZXVzeenzx5ooaGho3C+ePHD83Pz5uKB+SMSCTCSXRQOAHY6Z/DG4Y5gBn9/f2mI2APYKcuAAAAXEXhBGAlj8fDGA8A9ghG6gCslE6ndefOHeXn50v6+1qW3t5eFRQUbDwDALKDwgnASo2NjVuew+Fwxm8ikUiW0gBAbuNaJAAAALiKPZwAAABwFYUTAAAArqJwAgAAwFUUTgAAALiKwgkAAABXUTgBAADgKgonAAAAXEXhBAAAgKv+AqwYjkKtMVtDAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix = df_employee_for_modeling.corr()\n",
    "seaborn.heatmap(matrix, annot=True)\n",
    "plt.title(\"Correlation Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "0691e0aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tentative = df_employee_for_modeling[['Gender_Code', 'Material_Status_Code', 'Employment_Duration', 'Leave_Count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "id": "3e7d63fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Features  VIF Factor\n",
      "0           Gender_Code    3.939213\n",
      "1  Material_Status_Code    2.739031\n",
      "2   Employment_Duration    2.828820\n",
      "3           Leave_Count    2.736451\n"
     ]
    }
   ],
   "source": [
    "df_vif = pd.DataFrame()\n",
    "df_vif[\"Features\"] = X_tentative.columns\n",
    "df_vif[\"VIF Factor\"] = [variance_inflation_factor(X_tentative.values, i) for i in range(X_tentative.shape[1])]\n",
    "print(df_vif)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee7875f",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "86cf4155",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X = scaler.fit_transform(X_tentative.astype(float))\n",
    "y = df_employee_for_modeling['Is_Attrition']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=55)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf3c4fc8",
   "metadata": {},
   "source": [
    "\n",
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "id": "fd021fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()\n",
    "fitted_model = model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "id": "66b2dba7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Leaner Regression: 0.8013245033112583\n",
      "Precision: 0.8333333333333334\n",
      "Recall: 0.9426229508196722\n",
      "F1 score: 0.8846153846153847\n",
      "R2 score: 0.8846153846153847\n",
      "Confusion matrix: [[  6  23]\n",
      " [  7 115]]\n"
     ]
    }
   ],
   "source": [
    "lr_y_predicted = model.predict(X_test)\n",
    "\n",
    "lr_accuracy = accuracy_score(y_test, lr_y_predicted)\n",
    "print(f'Accuracy Leaner Regression: {lr_accuracy}')\n",
    "lr_precision = precision_score(y_test, lr_y_predicted)\n",
    "print(f'Precision: {lr_precision}')\n",
    "lr_recall = recall_score(y_test, lr_y_predicted)\n",
    "print(f'Recall: {lr_recall}')\n",
    "lr_f1 = f1_score(y_test, lr_y_predicted)\n",
    "print(f'F1 score: {lr_f1}')\n",
    "lr_r2 = f1_score(y_test, lr_y_predicted)\n",
    "print(f'R2 score: {lr_r2}')\n",
    "lr_confusion_matrix = confusion_matrix(y_test, lr_y_predicted)\n",
    "print(f'Confusion matrix: {lr_confusion_matrix}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "939ff843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.heatmap(lr_confusion_matrix, annot=True, cmap='Blues', fmt='.0f')\n",
    "plt.title('Confusion matrix - Linear Regression')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d76c6161",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "48cb4536",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum error:- 0.19205298013245034 at K = 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "error_rate = []\n",
    "for i in range(1,21):\n",
    " knn = KNeighborsClassifier(n_neighbors=i)\n",
    " knn.fit(X_train, y_train)\n",
    " knn_y_predicted = knn.predict(X_test)\n",
    " error_rate.append(np.mean(knn_y_predicted != y_test))\n",
    "    \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,21),error_rate,color='blue', linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')\n",
    "print(\"Minimum error:-\",min(error_rate),\"at K =\",error_rate.index(min(error_rate)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "609f950f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of model at K=8 is 0.8079470198675497\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/priyanka/etc/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "#Train Model and Predict using KNN Classifier\n",
    "k = 5\n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train, y_train)\n",
    "knn_Pred_y = neigh.predict(X_test)\n",
    "print(\"Accuracy of model at K=8 is\",metrics.accuracy_score(y_test, knn_Pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "id": "7d893cb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for KNN: 0.8013245033112583\n",
      "Precision: 0.8484848484848485\n",
      "Recall: 0.9180327868852459\n",
      "F1 score: 0.8818897637795275\n",
      "R2 score: -0.28038439796495185\n",
      "Confusion matrix: [[  9  20]\n",
      " [ 10 112]]\n"
     ]
    }
   ],
   "source": [
    "knn_accuracy = accuracy_score(y_test, knn_y_predicted)\n",
    "print(f'Accuracy for KNN: {knn_accuracy}')\n",
    "knn_precision = precision_score(y_test, knn_y_predicted)\n",
    "print(f'Precision: {knn_precision}')\n",
    "knn_recall = recall_score(y_test, knn_y_predicted)\n",
    "print(f'Recall: {knn_recall}')\n",
    "knn_f1 = f1_score(y_test, knn_y_predicted)\n",
    "print(f'F1 score: {knn_f1}')\n",
    "knn_r2 = r2_score(y_test, knn_y_predicted)\n",
    "print(f'R2 score: {knn_r2}')\n",
    "\n",
    "knn_confusion_matrix = confusion_matrix(y_test, knn_y_predicted)\n",
    "print(f'Confusion matrix: {knn_confusion_matrix}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "id": "0be4646b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.heatmap(knn_confusion_matrix, annot=True, cmap='Blues', fmt='.0f')\n",
    "plt.title('Confusion Matrix - KNN')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a7da75e",
   "metadata": {},
   "source": [
    "## Naive Bayesian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "id": "658f913d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8013245033112583\n"
     ]
    }
   ],
   "source": [
    "# Create a Naive Bayes classifier and fit it to the training data\n",
    "nb = GaussianNB()\n",
    "nb.fit(X_train, y_train)\n",
    "\n",
    "# Use the classifier to make predictions on the testing data\n",
    "nb_y_predicted = nb.predict(X_test)\n",
    "\n",
    "# Evaluate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, nb_y_predicted)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "id": "04860f85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Naive Bayes: 0.8013245033112583\n",
      "Precision: 0.859375\n",
      "Recall: 0.9016393442622951\n",
      "F1 score: 0.88\n",
      "R2 score: -0.28038439796495185\n",
      "Confusion matrix: [[ 11  18]\n",
      " [ 12 110]]\n"
     ]
    }
   ],
   "source": [
    "nb_accuracy = accuracy_score(y_test, nb_y_predicted)\n",
    "print(f'Accuracy for Naive Bayes: {nb_accuracy}')\n",
    "nb_precision = precision_score(y_test, nb_y_predicted)\n",
    "print(f'Precision: {nb_precision}')\n",
    "nb_recall = recall_score(y_test, nb_y_predicted)\n",
    "print(f'Recall: {nb_recall}')\n",
    "nb_f1 = f1_score(y_test, nb_y_predicted)\n",
    "print(f'F1 score: {nb_f1}')\n",
    "nb_r2 = r2_score(y_test, nb_y_predicted)\n",
    "print(f'R2 score: {nb_r2}')\n",
    "\n",
    "nb_confusion_matrix = confusion_matrix(y_test, nb_y_predicted)\n",
    "print(f'Confusion matrix: {nb_confusion_matrix}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "760518dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.heatmap(nb_confusion_matrix, annot=True, cmap='Blues', fmt='.0f')\n",
    "plt.title('Confusion Matrix - Naive Bayesian')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9263b2c3",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "id": "0f00b541",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Create the classifier and fit it to the training data\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Use the classifier to make predictions on the testing data\n",
    "dt_y_predicted = clf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "id": "55cde1f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Decision Tree: 0.8410596026490066\n",
      "Precision: 0.8888888888888888\n",
      "Recall: 0.9180327868852459\n",
      "F1 score: 0.9032258064516128\n",
      "R2 score: -0.024307518371961434\n",
      "Confusion matrix: [[ 15  14]\n",
      " [ 10 112]]\n"
     ]
    }
   ],
   "source": [
    "dt_accuracy = accuracy_score(y_test, dt_y_predicted)\n",
    "print(f'Accuracy for Decision Tree: {dt_accuracy}')\n",
    "dt_precision = precision_score(y_test, dt_y_predicted)\n",
    "print(f'Precision: {dt_precision}')\n",
    "dt_recall = recall_score(y_test, dt_y_predicted)\n",
    "print(f'Recall: {dt_recall}')\n",
    "dt_f1 = f1_score(y_test, dt_y_predicted)\n",
    "print(f'F1 score: {dt_f1}')\n",
    "dt_r2 = r2_score(y_test, dt_y_predicted)\n",
    "print(f'R2 score: {dt_r2}')\n",
    "\n",
    "dt_confusion_matrix = confusion_matrix(y_test, dt_y_predicted)\n",
    "print(f'Confusion matrix: {dt_confusion_matrix}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "id": "2115a48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.heatmap(dt_confusion_matrix, annot=True, cmap='Blues', fmt='.0f')\n",
    "plt.title('Confusion Matrix - Decision Tree')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Random Forest"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Rain Forest: 0.8344370860927153\n",
      "Precision: 0.9008264462809917\n",
      "Recall: 0.8934426229508197\n",
      "F1 score: 0.897119341563786\n",
      "R2 score: -0.06698699830412647\n",
      "Confusion matrix: [[  6  23]\n",
      " [  7 115]]\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "rf_y_predicted = rf_model.predict(X_test)\n",
    "\n",
    "rf_accuracy = accuracy_score(y_test, rf_y_predicted)\n",
    "print(f'Accuracy Rain Forest: {rf_accuracy}')\n",
    "rf_precision = precision_score(y_test, rf_y_predicted)\n",
    "print(f'Precision: {rf_precision}')\n",
    "rf_recall = recall_score(y_test, rf_y_predicted)\n",
    "print(f'Recall: {rf_recall}')\n",
    "rf_f1 = f1_score(y_test, rf_y_predicted)\n",
    "print(f'F1 score: {rf_f1}')\n",
    "rf_r2 = r2_score(y_test, rf_y_predicted)\n",
    "print(f'R2 score: {rf_r2}')\n",
    "\n",
    "rf_confusion_matrix = confusion_matrix(y_test, rf_y_predicted)\n",
    "print(f'Confusion matrix: {lr_confusion_matrix}')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.heatmap(rf_confusion_matrix, annot=True, cmap='Blues', fmt='.0f')\n",
    "plt.title('Confusion matrix - Random Forest')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "d8bcfc38",
   "metadata": {},
   "source": [
    "## Polynomial Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "id": "f678be61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7947019867549668\n",
      "Precision: 0.837037037037037\n",
      "Recall: 0.9262295081967213\n",
      "F1 score: 0.8793774319066149\n",
      "R2 score: -0.3230638778971169\n",
      "Confusion matrix: [[  7  22]\n",
      " [  9 113]]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit a Polynomial Logistic Regression model\n",
    "pr_features = PolynomialFeatures(degree=2)\n",
    "X_pr_train = pr_features.fit_transform(X_train)\n",
    "X_pr_test = pr_features.fit_transform(X_test)\n",
    "pr_model = LogisticRegression()\n",
    "pr_model.fit(X_pr_train, y_train)\n",
    "\n",
    "pr_y_predicted = pr_model.predict(X_pr_test)\n",
    "\n",
    "pr_accuracy = accuracy_score(y_test, pr_y_predicted)\n",
    "print(f'Accuracy: {pr_accuracy}')\n",
    "pr_precision = precision_score(y_test, pr_y_predicted)\n",
    "print(f'Precision: {pr_precision}')\n",
    "pr_recall = recall_score(y_test, pr_y_predicted)\n",
    "print(f'Recall: {pr_recall}')\n",
    "pr_f1 = f1_score(y_test, pr_y_predicted)\n",
    "print(f'F1 score: {pr_f1}')\n",
    "pr_r2 = r2_score(y_test, pr_y_predicted)\n",
    "print(f'R2 score: {pr_r2}')\n",
    "\n",
    "pr_confusion_matrix = confusion_matrix(y_test, pr_y_predicted)\n",
    "print(f'Confusion matrix: {pr_confusion_matrix}')\n",
    "\n",
    "seaborn.heatmap(pr_confusion_matrix, annot=True, cmap='Blues', fmt='.0f')\n",
    "plt.title('Confusion Matrix - Polynomial Logistic Regression')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "id": "4150348c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x800 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotdata = pd.DataFrame({\n",
    "    \"Accuracy\":[dt_accuracy, knn_accuracy, lr_accuracy, nb_accuracy, pr_accuracy, rf_accuracy],\n",
    "    \"Precision\":[dt_precision, knn_precision, lr_precision, nb_precision, pr_precision, rf_precision],\n",
    "    \"Recall\":[dt_recall, knn_recall, lr_recall, nb_recall, pr_recall, rf_recall],\n",
    "    \"F1 score\":[dt_f1, knn_f1, lr_f1, nb_f1, pr_f1, rf_f1],\n",
    "    # \"R2 score\":[dt_r2, knn_r2, lr_r2, nb_r2, pr_r2, rf_r2]\n",
    "    },\n",
    "    index=[\"Decision Tree\", \"KNN\", \"Logistic Regression\", \"Naive Bayes\", \"Polynomial Logistic Regression\", \"Random Forest\"]\n",
    ")\n",
    "\n",
    "# Create the bar chart\n",
    "ax = plotdata.plot(kind=\"bar\", figsize=(15, 8), width=0.8)\n",
    "\n",
    "# Set the chart title and labels\n",
    "ax.set_title(\"Results of Attrition Prediction\")\n",
    "ax.set_ylabel(\"Results\")\n",
    "ax.set_xticklabels(plotdata.index, rotation=0)\n",
    "\n",
    "# Add the value of y in the middle of each bar\n",
    "for i in ax.containers:\n",
    "    ax.bar_label(i, label_type=\"center\", labels=[round(j.get_height(),2) for j in i], rotation=90)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "id": "ab39ee16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x800 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_evaluation_metrics = pd.DataFrame({\n",
    "    \"Decision Tree\": [dt_accuracy, dt_precision, dt_recall, dt_f1],\n",
    "    \"KNN\": [knn_accuracy, knn_precision, knn_recall, knn_f1],\n",
    "    \"Logistic Regression\": [lr_accuracy, lr_precision, lr_recall, lr_f1],\n",
    "    \"Naive Bayes\": [nb_accuracy, nb_precision, nb_recall, nb_f1],\n",
    "    \"Polynomial Logistic Regression\": [pr_accuracy, pr_precision, pr_recall, pr_f1],\n",
    "    \"Random Forest\": [rf_accuracy, rf_precision, rf_recall, rf_f1]\n",
    "},\n",
    "    index=[\"Accuracy\", \"Precision\", \"Recall\", \"F1 score\"]\n",
    ")\n",
    "\n",
    "# Create the bar chart\n",
    "ax = model_evaluation_metrics.plot(kind=\"bar\", figsize=(15, 8), width=0.8)\n",
    "\n",
    "# Set the chart title and labels\n",
    "ax.set_title(\"Results of Attrition Prediction\")\n",
    "ax.set_ylabel(\"Results\")\n",
    "ax.set_xticklabels(model_evaluation_metrics.index, rotation=0)\n",
    "\n",
    "# Add the value of y in the middle of each bar\n",
    "for i in ax.containers:\n",
    "    ax.bar_label(i, label_type=\"center\", labels=[round(j.get_height(),2) for j in i], rotation=90)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "id": "86c0bb3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "id": "c31e60c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn = neigh.predict_proba(X_test)[:,1]\n",
    "y_pred_pr = pr_model.predict_proba(X_pr_test)[:,1]\n",
    "y_pred_nb = nb.predict_proba(X_test)[:,1]\n",
    "y_pred_logistic = model.decision_function(X_test)\n",
    "y_pred_random_forest = rf_model.predict_proba(X_test) [:,1]\n",
    "y_pred_decision_tree = clf.predict_proba(X_test)[:,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "id": "5fd8b13c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "logistic_fpr, logistic_tpr, threshold1 = roc_curve(y_test, y_pred_logistic)\n",
    "auc_logistic = auc(logistic_fpr, logistic_tpr)\n",
    "\n",
    "svm_fpr, svm_tpr, threshold1 = roc_curve(y_test, y_pred_random_forest)\n",
    "auc_svm = auc(svm_fpr, svm_tpr)\n",
    "\n",
    "dt_fpr, dt_tpr, threshold1 = roc_curve(y_test, y_pred_decision_tree)\n",
    "auc_dt = auc(dt_fpr, dt_tpr)\n",
    "\n",
    "nb_fpr, nb_tpr, threshold1 = roc_curve(y_test, y_pred_nb)\n",
    "auc_nb = auc(nb_fpr, nb_tpr)\n",
    "\n",
    "knn_fpr, knn_tpr, threshold5 = roc_curve(y_test, y_pred_knn)\n",
    "auc_knn = auc(knn_fpr, knn_tpr)\n",
    "\n",
    "pr_fpr, pr_tpr, threshold6 = roc_curve(y_test, y_pred_pr)\n",
    "auc_pr = auc(pr_fpr, pr_tpr)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6, 6), dpi=100)\n",
    "plt.plot(svm_fpr, svm_tpr, linestyle='dashdot', label='Random Forest (auc = %0.3f)' % auc_svm)\n",
    "plt.plot(knn_fpr, knn_tpr, linestyle='dashed', label='KNN (auc = %0.3f)' % auc_knn)\n",
    "plt.plot(pr_fpr, pr_tpr, linestyle='dashdot', label='Polynomial Logistic Regression (auc = %0.3f)' % auc_pr)\n",
    "plt.plot(logistic_fpr, logistic_tpr, linestyle='dotted', label='Logistic (auc = %0.3f)' % auc_logistic)\n",
    "plt.plot(dt_fpr, dt_tpr, linestyle='dotted', label='Decision Tree(auc = %0.3f)' % auc_dt)\n",
    "plt.plot(nb_fpr, nb_tpr, linestyle='dashdot', label='Naive Bayesian (auc = %0.3f)' % auc_nb)\n",
    "\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.xlabel('False Positive Rate -->')\n",
    "plt.ylabel('True Positive Rate -->')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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