ModelFinal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf24573c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import operator as op\n",
    "import warnings\n",
    "warnings.filterwarnings(action = 'ignore')\n",
    "from IPython.display import display\n",
    "seed = 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3956ff35",
   "metadata": {},
   "outputs": [],
   "source": [
    "UserActivity = pd.read_csv(\"UserActivity.csv\")\n",
    "Users = pd.read_csv(\"Users.csv\")\n",
    "SampleSubmission = pd.read_csv(\"SampleSubmission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eeb5e83c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove all \"$\" and add \"_\" in between spaces to the activities\n",
    "def remove_id(x):\n",
    "    x = x.replace('$', '')\n",
    "    if 'ID' in x:\n",
    "        return \"_\".join(x.split('_')[:-1])\n",
    "    \n",
    "    return x.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "40dc2c8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Viewed_All_Competitions                 58176\n",
       "comp_ID                                 55540\n",
       "Viewed_All_Discussions                  47172\n",
       "Viewed_All_Learning_Pages               22930\n",
       "Downloaded_Competition_Datafile         19578\n",
       "Updated_Profile                         14748\n",
       "Created_Submission                      13649\n",
       "identify                                12339\n",
       "Signed_Up                               10862\n",
       "create_alias                             9346\n",
       "badge_OCZE                               8864\n",
       "Confirmed_Email                          8818\n",
       "Updated_Discussion                       6498\n",
       "Joined_Competition                       6086\n",
       "Viewed_All_Jobs                          4139\n",
       "Signed_In                                4100\n",
       "Updated_Submission                       2929\n",
       "blog_ID                                  2712\n",
       "badge_HYIO                               1760\n",
       "Signed_Out                               1660\n",
       "job_ID                                   1492\n",
       "Viewed_Discussion                        1207\n",
       "Updated_Comment                           656\n",
       "badge_MLPD                                457\n",
       "Joined_Team                               384\n",
       "Invited_Member_To_Team                    344\n",
       "Created_Team                              310\n",
       "Votes_(Up/Down)                           149\n",
       "Applied_To_Job                            126\n",
       "Viewed_FAQ                                104\n",
       "Updated_Team                               42\n",
       "badge_PLDS                                 40\n",
       "Deleted_Team                               28\n",
       "Transferred_Team_Leadership                14\n",
       "Updated_Discussion_Team_Participants       10\n",
       "Accepted_Team_Leadership_Transfer           9\n",
       "Changed_Password                            5\n",
       "Left_Team                                   4\n",
       "Kicked_Member_From_Team                     3\n",
       "Revoked_Team_Leadership_Transfer            1\n",
       "Report_Something                            1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UserActivity.Title.apply(remove_id).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "66096737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['comp_ID', 'job_ID', 'blog_ID', 'create_alias', 'identify',\n",
       "       'Signed_Up', 'Viewed_All_Competitions', 'Viewed_All_Discussions',\n",
       "       'Viewed_All_Learning_Pages', 'Confirmed_Email',\n",
       "       'Joined_Competition', 'Downloaded_Competition_Datafile',\n",
       "       'Signed_In', 'Updated_Discussion', 'Joined_Team',\n",
       "       'Created_Submission', 'Viewed_Discussion', 'Updated_Submission',\n",
       "       'Signed_Out', 'badge_OCZE', 'Updated_Profile', 'Viewed_FAQ',\n",
       "       'Viewed_All_Jobs', 'Created_Team', 'Invited_Member_To_Team',\n",
       "       'Updated_Team', 'Updated_Comment',\n",
       "       'Updated_Discussion_Team_Participants', 'badge_HYIO', 'badge_MLPD',\n",
       "       'Applied_To_Job', 'Votes_(Up/Down)', 'Transferred_Team_Leadership',\n",
       "       'badge_PLDS', 'Changed_Password', 'Deleted_Team',\n",
       "       'Accepted_Team_Leadership_Transfer',\n",
       "       'Revoked_Team_Leadership_Transfer', 'Report_Something',\n",
       "       'Kicked_Member_From_Team', 'Left_Team'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add new column as the type of activity performed\n",
    "UserActivity['main_activities'] = UserActivity.Title.apply(remove_id)\n",
    "UserActivity.main_activities.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4bc3b5b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Viewed_All_Competitions', 'comp_ID', 'Viewed_All_Discussions',\n",
       "       'Viewed_All_Learning_Pages', 'Downloaded_Competition_Datafile',\n",
       "       'Updated_Profile', 'Created_Submission', 'identify', 'Signed_Up',\n",
       "       'create_alias', 'badge_OCZE', 'Confirmed_Email', 'Updated_Discussion',\n",
       "       'Joined_Competition', 'Viewed_All_Jobs', 'Signed_In',\n",
       "       'Updated_Submission', 'blog_ID', 'badge_HYIO', 'Signed_Out', 'job_ID',\n",
       "       'Viewed_Discussion'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "act_vc = UserActivity.main_activities.value_counts()\n",
    "#Ignore activities with <= 1000 count\n",
    "selected_activities = act_vc[act_vc > 1000].keys()\n",
    "selected_activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "760acb03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Title</th>\n",
       "      <th>datetime time</th>\n",
       "      <th>datetime Year</th>\n",
       "      <th>datetime Month</th>\n",
       "      <th>datetime Day_of_month</th>\n",
       "      <th>main_activities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_RT43AK77</td>\n",
       "      <td>comp_ID_IV5D</td>\n",
       "      <td>22:00:38</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>comp_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_RT43AK77</td>\n",
       "      <td>comp_ID_IV5D</td>\n",
       "      <td>00:41:02</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>comp_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_RT43AK77</td>\n",
       "      <td>comp_ID_IV5D</td>\n",
       "      <td>00:53:15</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>comp_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_RT43AK77</td>\n",
       "      <td>comp_ID_IV5D</td>\n",
       "      <td>01:23:05</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>comp_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_RT43AK77</td>\n",
       "      <td>comp_ID_IV5D</td>\n",
       "      <td>01:27:38</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>comp_ID</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314600</th>\n",
       "      <td>ID_Z759BCS3</td>\n",
       "      <td>job_ID_5ONI</td>\n",
       "      <td>07:14:47</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>job_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314601</th>\n",
       "      <td>ID_WNJMADF8</td>\n",
       "      <td>job_ID_5ONI</td>\n",
       "      <td>06:24:46</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>job_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314602</th>\n",
       "      <td>ID_7NBY9MV2</td>\n",
       "      <td>job_ID_5ONI</td>\n",
       "      <td>11:56:35</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>job_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314603</th>\n",
       "      <td>ID_LHPJJSOK</td>\n",
       "      <td>job_ID_5ONI</td>\n",
       "      <td>01:27:13</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>job_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314604</th>\n",
       "      <td>ID_LCFNGARD</td>\n",
       "      <td>job_ID_5ONI</td>\n",
       "      <td>08:40:29</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>job_ID</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>314605 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID         Title datetime time  datetime Year  \\\n",
       "0       ID_RT43AK77  comp_ID_IV5D      22:00:38              1   \n",
       "1       ID_RT43AK77  comp_ID_IV5D      00:41:02              1   \n",
       "2       ID_RT43AK77  comp_ID_IV5D      00:53:15              1   \n",
       "3       ID_RT43AK77  comp_ID_IV5D      01:23:05              1   \n",
       "4       ID_RT43AK77  comp_ID_IV5D      01:27:38              1   \n",
       "...             ...           ...           ...            ...   \n",
       "314600  ID_Z759BCS3   job_ID_5ONI      07:14:47              1   \n",
       "314601  ID_WNJMADF8   job_ID_5ONI      06:24:46              1   \n",
       "314602  ID_7NBY9MV2   job_ID_5ONI      11:56:35              1   \n",
       "314603  ID_LHPJJSOK   job_ID_5ONI      01:27:13              1   \n",
       "314604  ID_LCFNGARD   job_ID_5ONI      08:40:29              1   \n",
       "\n",
       "        datetime Month  datetime Day_of_month main_activities  \n",
       "0                   11                     22         comp_ID  \n",
       "1                   11                     23         comp_ID  \n",
       "2                   11                     23         comp_ID  \n",
       "3                   11                     23         comp_ID  \n",
       "4                   11                     23         comp_ID  \n",
       "...                ...                    ...             ...  \n",
       "314600              11                     14          job_ID  \n",
       "314601              11                     14          job_ID  \n",
       "314602              11                     20          job_ID  \n",
       "314603              11                     14          job_ID  \n",
       "314604              11                     17          job_ID  \n",
       "\n",
       "[314605 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop rows with activities not in the \"selected_activities\" list\n",
    "UserActivity = UserActivity[UserActivity.main_activities.isin(selected_activities)].reset_index(drop=True)\n",
    "UserActivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82e94734",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group by User_ID, Activity Type and Month of activity\n",
    "dataset = UserActivity.groupby(['User_ID', 'main_activities', 'datetime Month']).size().reset_index().\\\n",
    "    rename(columns={0: 'count'}).\\\n",
    "        sort_values(['User_ID', 'datetime Month', 'count'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5ce186d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>main_activities</th>\n",
       "      <th>datetime Month</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111314</th>\n",
       "      <td>ID_ZZVUJ45W</td>\n",
       "      <td>Downloaded_Competition_Datafile</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111318</th>\n",
       "      <td>ID_ZZVUJ45W</td>\n",
       "      <td>Viewed_All_Discussions</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111313</th>\n",
       "      <td>ID_ZZVUJ45W</td>\n",
       "      <td>Confirmed_Email</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111315</th>\n",
       "      <td>ID_ZZVUJ45W</td>\n",
       "      <td>Joined_Competition</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111316</th>\n",
       "      <td>ID_ZZVUJ45W</td>\n",
       "      <td>Signed_Up</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_000H9XOP</td>\n",
       "      <td>Viewed_All_Competitions</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_000H9XOP</td>\n",
       "      <td>Viewed_All_Discussions</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_000H9XOP</td>\n",
       "      <td>comp_ID</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_000H9XOP</td>\n",
       "      <td>create_alias</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ID_000H9XOP</td>\n",
       "      <td>identify</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111323 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID                  main_activities  datetime Month  count\n",
       "111314  ID_ZZVUJ45W  Downloaded_Competition_Datafile               3      3\n",
       "111318  ID_ZZVUJ45W           Viewed_All_Discussions               3      2\n",
       "111313  ID_ZZVUJ45W                  Confirmed_Email               3      1\n",
       "111315  ID_ZZVUJ45W               Joined_Competition               3      1\n",
       "111316  ID_ZZVUJ45W                        Signed_Up               3      1\n",
       "...             ...                              ...             ...    ...\n",
       "1       ID_000H9XOP          Viewed_All_Competitions               5      1\n",
       "2       ID_000H9XOP           Viewed_All_Discussions               5      1\n",
       "3       ID_000H9XOP                          comp_ID               5      1\n",
       "4       ID_000H9XOP                     create_alias               5      1\n",
       "5       ID_000H9XOP                         identify               5      1\n",
       "\n",
       "[111323 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51c4e5ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For graph\n",
    "temp1 = dataset.groupby(\"datetime Month\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2bdb0134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>41985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count\n",
       "datetime Month       \n",
       "1               58044\n",
       "2               60542\n",
       "3               55578\n",
       "4               26615\n",
       "5               56662\n",
       "11              15179\n",
       "12              41985"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f184b0eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar graph for Month-wise activity\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "courses = [\"11\", \"12\", \"1\", \"2\", \"3\", \"4\", \"5\"]\n",
    "values = [15179, 41985, 58044, 60542, 55578, 26615, 56662]\n",
    "plt.bar(courses, values, color ='maroon',\n",
    "        width = 0.4)\n",
    " \n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Activity Count\")\n",
    "plt.title(\"Month-wise Activity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ecf2dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_ZZVUJ45W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_ZZUNZJ2U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_ZZQK0LU6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_ZZMHNLNT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_ZZJKFBR5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10395</th>\n",
       "      <td>ID_00ETFE7J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10396</th>\n",
       "      <td>ID_008D2J6C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10397</th>\n",
       "      <td>ID_004UKSJV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10398</th>\n",
       "      <td>ID_003YJHQA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10399</th>\n",
       "      <td>ID_000H9XOP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10400 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           User_ID\n",
       "0      ID_ZZVUJ45W\n",
       "1      ID_ZZUNZJ2U\n",
       "2      ID_ZZQK0LU6\n",
       "3      ID_ZZMHNLNT\n",
       "4      ID_ZZJKFBR5\n",
       "...            ...\n",
       "10395  ID_00ETFE7J\n",
       "10396  ID_008D2J6C\n",
       "10397  ID_004UKSJV\n",
       "10398  ID_003YJHQA\n",
       "10399  ID_000H9XOP\n",
       "\n",
       "[10400 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All User_IDs\n",
    "Selected_users = dataset[['User_ID']].drop_duplicates().reset_index(drop=True)\n",
    "Selected_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "65406a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a92a0c67",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "820b6d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Label Encode all countries\n",
    "Users.Countries_ID = Users.Countries_ID.fillna('unspecified')\n",
    "Users.Countries_ID = le.fit_transform(Users.Countries_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2c9d42c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove repeated User_IDs\n",
    "user_df = pd.merge(Selected_users, Users, on='User_ID',  how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "daad4ae4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>FeatureX</th>\n",
       "      <th>FeatureY</th>\n",
       "      <th>Countries_ID</th>\n",
       "      <th>Created At Month</th>\n",
       "      <th>Created At Day_of_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_ZZVUJ45W</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_ZZUNZJ2U</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_ZZQK0LU6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>146</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_ZZMHNLNT</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_ZZJKFBR5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>143</td>\n",
       "      <td>12</td>\n",
       "      <td>8</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10395</th>\n",
       "      <td>ID_00ETFE7J</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10396</th>\n",
       "      <td>ID_008D2J6C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>10397</th>\n",
       "      <td>ID_004UKSJV</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10398</th>\n",
       "      <td>ID_003YJHQA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10399</th>\n",
       "      <td>ID_000H9XOP</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>146</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10400 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           User_ID  FeatureX  FeatureY  Countries_ID  Created At Month  \\\n",
       "0      ID_ZZVUJ45W         0         0           146                 3   \n",
       "1      ID_ZZUNZJ2U         0         0           146                 2   \n",
       "2      ID_ZZQK0LU6         0         3           146                 3   \n",
       "3      ID_ZZMHNLNT         0         0           146                 4   \n",
       "4      ID_ZZJKFBR5         0         1           143                12   \n",
       "...            ...       ...       ...           ...               ...   \n",
       "10395  ID_00ETFE7J         0         0            61                 1   \n",
       "10396  ID_008D2J6C         0         0            60                 1   \n",
       "10397  ID_004UKSJV         0         0            69                12   \n",
       "10398  ID_003YJHQA         0         1            60                 4   \n",
       "10399  ID_000H9XOP         0         1           146                 5   \n",
       "\n",
       "       Created At Day_of_month  \n",
       "0                            5  \n",
       "1                           30  \n",
       "2                           29  \n",
       "3                            1  \n",
       "4                            8  \n",
       "...                        ...  \n",
       "10395                        6  \n",
       "10396                        7  \n",
       "10397                       28  \n",
       "10398                       12  \n",
       "10399                       22  \n",
       "\n",
       "[10400 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop 'Created At time'and 'Created At Year'\n",
    "user_df.drop(columns=['Created At time', 'Created At Year'], inplace=True)\n",
    "user_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f3c7c8ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_activities = dataset.main_activities.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c6c4dffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add month number\n",
    "month_cols = [f'datetime Month_{i}' for i in [1, 2, 3, 4, 5, 11, 12]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5e615f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "#User_IDs from sample submission for test data\n",
    "test_ids = SampleSubmission.User_ID_Next_month_Activity.str.replace('_Month_5', '').unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fb1adfb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "activity_df = user_df[['User_ID']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7bbdf17e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Month-wise activity for each user\n",
    "for activity in all_activities:\n",
    "    #Activity-wise dataframe\n",
    "    _df = pd.get_dummies(\n",
    "        dataset[dataset.main_activities == activity].drop(columns='main_activities').reset_index(drop=True),\n",
    "        columns=['datetime Month'])\n",
    "    for col in month_cols:\n",
    "        if col not in _df.columns:\n",
    "            _df[col] = 0\n",
    "        #Multiply count of a particular activity to activity count of that month\n",
    "        _df[col] *= _df['count']\n",
    "    _df = _df.drop(columns='count')\n",
    "    _df = _df.groupby('User_ID').sum().reset_index()\n",
    "    #Merge each month data for an activity\n",
    "    _df = pd.merge(user_df[['User_ID']], _df, on='User_ID', how='left')\n",
    "    _df[month_cols] = _df[month_cols].fillna(0)\n",
    "    #Activity and month based columns\n",
    "    _df.columns = _df.columns.str.replace('datetime Month_', f'{activity}_month_')\n",
    "\n",
    "    activity_df = pd.merge(activity_df, _df, on='User_ID', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "78a65a7e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>User_ID</th>\n",
       "      <th>Downloaded_Competition_Datafile_month_1</th>\n",
       "      <th>Downloaded_Competition_Datafile_month_2</th>\n",
       "      <th>Downloaded_Competition_Datafile_month_3</th>\n",
       "      <th>Downloaded_Competition_Datafile_month_4</th>\n",
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       "      <th>Viewed_All_Discussions_month_2</th>\n",
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       "      <th>Updated_Submission_month_5</th>\n",
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       "      <td>7.0</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>10399</th>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10400 rows × 155 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           User_ID  Downloaded_Competition_Datafile_month_1  \\\n",
       "0      ID_ZZVUJ45W                                      0.0   \n",
       "1      ID_ZZUNZJ2U                                      0.0   \n",
       "2      ID_ZZQK0LU6                                      0.0   \n",
       "3      ID_ZZMHNLNT                                      0.0   \n",
       "4      ID_ZZJKFBR5                                      0.0   \n",
       "...            ...                                      ...   \n",
       "10395  ID_00ETFE7J                                      0.0   \n",
       "10396  ID_008D2J6C                                      0.0   \n",
       "10397  ID_004UKSJV                                      0.0   \n",
       "10398  ID_003YJHQA                                      0.0   \n",
       "10399  ID_000H9XOP                                      0.0   \n",
       "\n",
       "       Downloaded_Competition_Datafile_month_2  \\\n",
       "0                                          0.0   \n",
       "1                                          0.0   \n",
       "2                                          0.0   \n",
       "3                                          0.0   \n",
       "4                                          0.0   \n",
       "...                                        ...   \n",
       "10395                                      0.0   \n",
       "10396                                      0.0   \n",
       "10397                                      0.0   \n",
       "10398                                      0.0   \n",
       "10399                                      0.0   \n",
       "\n",
       "       Downloaded_Competition_Datafile_month_3  \\\n",
       "0                                          3.0   \n",
       "1                                          0.0   \n",
       "2                                          7.0   \n",
       "3                                          0.0   \n",
       "4                                          0.0   \n",
       "...                                        ...   \n",
       "10395                                      0.0   \n",
       "10396                                      0.0   \n",
       "10397                                      0.0   \n",
       "10398                                      0.0   \n",
       "10399                                      0.0   \n",
       "\n",
       "       Downloaded_Competition_Datafile_month_4  \\\n",
       "0                                          0.0   \n",
       "1                                          0.0   \n",
       "2                                          0.0   \n",
       "3                                          0.0   \n",
       "4                                          0.0   \n",
       "...                                        ...   \n",
       "10395                                      0.0   \n",
       "10396                                      0.0   \n",
       "10397                                      0.0   \n",
       "10398                                      0.0   \n",
       "10399                                      0.0   \n",
       "\n",
       "       Downloaded_Competition_Datafile_month_5  \\\n",
       "0                                          0.0   \n",
       "1                                          0.0   \n",
       "2                                          0.0   \n",
       "3                                          0.0   \n",
       "4                                          0.0   \n",
       "...                                        ...   \n",
       "10395                                      0.0   \n",
       "10396                                      0.0   \n",
       "10397                                      0.0   \n",
       "10398                                      0.0   \n",
       "10399                                      0.0   \n",
       "\n",
       "       Downloaded_Competition_Datafile_month_11  \\\n",
       "0                                           0.0   \n",
       "1                                           0.0   \n",
       "2                                           0.0   \n",
       "3                                           0.0   \n",
       "4                                           0.0   \n",
       "...                                         ...   \n",
       "10395                                       0.0   \n",
       "10396                                       0.0   \n",
       "10397                                       0.0   \n",
       "10398                                       0.0   \n",
       "10399                                       0.0   \n",
       "\n",
       "       Downloaded_Competition_Datafile_month_12  \\\n",
       "0                                           0.0   \n",
       "1                                           0.0   \n",
       "2                                           0.0   \n",
       "3                                           0.0   \n",
       "4                                           5.0   \n",
       "...                                         ...   \n",
       "10395                                       0.0   \n",
       "10396                                       0.0   \n",
       "10397                                       0.0   \n",
       "10398                                       0.0   \n",
       "10399                                       0.0   \n",
       "\n",
       "       Viewed_All_Discussions_month_1  Viewed_All_Discussions_month_2  ...  \\\n",
       "0                                 0.0                             0.0  ...   \n",
       "1                                 0.0                             1.0  ...   \n",
       "2                                 0.0                             0.0  ...   \n",
       "3                                 0.0                             0.0  ...   \n",
       "4                                 0.0                             0.0  ...   \n",
       "...                               ...                             ...  ...   \n",
       "10395                             1.0                             0.0  ...   \n",
       "10396                             1.0                             0.0  ...   \n",
       "10397                             0.0                             0.0  ...   \n",
       "10398                             0.0                             0.0  ...   \n",
       "10399                             0.0                             0.0  ...   \n",
       "\n",
       "       Updated_Submission_month_5  Updated_Submission_month_11  \\\n",
       "0                             0.0                          0.0   \n",
       "1                             0.0                          0.0   \n",
       "2                             0.0                          0.0   \n",
       "3                             0.0                          0.0   \n",
       "4                             0.0                          0.0   \n",
       "...                           ...                          ...   \n",
       "10395                         0.0                          0.0   \n",
       "10396                         0.0                          0.0   \n",
       "10397                         0.0                          0.0   \n",
       "10398                         0.0                          0.0   \n",
       "10399                         0.0                          0.0   \n",
       "\n",
       "       Updated_Submission_month_12  badge_HYIO_month_5  badge_HYIO_month_1  \\\n",
       "0                              0.0                 0.0                 0.0   \n",
       "1                              0.0                 0.0                 0.0   \n",
       "2                              0.0                 0.0                 0.0   \n",
       "3                              0.0                 0.0                 0.0   \n",
       "4                              0.0                 0.0                 0.0   \n",
       "...                            ...                 ...                 ...   \n",
       "10395                          0.0                 0.0                 0.0   \n",
       "10396                          0.0                 0.0                 0.0   \n",
       "10397                          0.0                 0.0                 0.0   \n",
       "10398                          0.0                 0.0                 0.0   \n",
       "10399                          0.0                 0.0                 0.0   \n",
       "\n",
       "       badge_HYIO_month_2  badge_HYIO_month_3  badge_HYIO_month_4  \\\n",
       "0                     0.0                 0.0                 0.0   \n",
       "1                     0.0                 0.0                 0.0   \n",
       "2                     0.0                 0.0                 0.0   \n",
       "3                     0.0                 0.0                 0.0   \n",
       "4                     0.0                 0.0                 0.0   \n",
       "...                   ...                 ...                 ...   \n",
       "10395                 0.0                 0.0                 0.0   \n",
       "10396                 0.0                 0.0                 0.0   \n",
       "10397                 0.0                 0.0                 0.0   \n",
       "10398                 0.0                 0.0                 0.0   \n",
       "10399                 0.0                 0.0                 0.0   \n",
       "\n",
       "       badge_HYIO_month_11  badge_HYIO_month_12  \n",
       "0                      0.0                  0.0  \n",
       "1                      0.0                  0.0  \n",
       "2                      0.0                  0.0  \n",
       "3                      0.0                  0.0  \n",
       "4                      0.0                  0.0  \n",
       "...                    ...                  ...  \n",
       "10395                  0.0                  0.0  \n",
       "10396                  0.0                  0.0  \n",
       "10397                  0.0                  0.0  \n",
       "10398                  0.0                  0.0  \n",
       "10399                  0.0                  0.0  \n",
       "\n",
       "[10400 rows x 155 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "23a3a33d",
   "metadata": {},
   "outputs": [],
   "source": [
    "activity_df = pd.merge(activity_df, user_df, on='User_ID', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7ca98935",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train and test dataframe using User_IDs in sample submission\n",
    "train_df = activity_df[~activity_df.User_ID.isin(test_ids)].reset_index(drop=True)\n",
    "test_df = activity_df[activity_df.User_ID.isin(test_ids)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "70db338c",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_train = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a0d6e6ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(train_df)):\n",
    "    user = train_df.loc[i]\n",
    "    created_month = user['Created At Month']\n",
    "    if created_month < 5: #As there is month 11 after 5\n",
    "        #Create dictionary for User_ID, user created month activity and Active\n",
    "        user_dict = {}\n",
    "        user_dict['User_ID'] = user['User_ID']\n",
    "        for activity in all_activities:\n",
    "            user_dict[f'{activity}'] = user[f'{activity}_month_{created_month}']\n",
    "        #Next month of user created month\n",
    "        next_month = [created_month+1, 1][created_month == 12]\n",
    "        #Take Active as 1 if user has any activity in the next month of user created month\n",
    "        user_dict['Active'] = [0, 1][user[[f'{activity}_month_{next_month}' for activity in all_activities]].sum() > 0]\n",
    "\n",
    "        new_train = new_train.append(user_dict, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "debcdeff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Downloaded_Competition_Datafile</th>\n",
       "      <th>Viewed_All_Discussions</th>\n",
       "      <th>Confirmed_Email</th>\n",
       "      <th>Joined_Competition</th>\n",
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       "      <th>Viewed_All_Competitions</th>\n",
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       "      <th>badge_OCZE</th>\n",
       "      <th>create_alias</th>\n",
       "      <th>...</th>\n",
       "      <th>Viewed_All_Jobs</th>\n",
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       "      <th>Created_Submission</th>\n",
       "      <th>Signed_Out</th>\n",
       "      <th>Updated_Profile</th>\n",
       "      <th>blog_ID</th>\n",
       "      <th>Viewed_Discussion</th>\n",
       "      <th>Updated_Submission</th>\n",
       "      <th>badge_HYIO</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_ZZVUJ45W</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>ID_ZZQK0LU6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <th>5231</th>\n",
       "      <td>ID_01BEG5HQ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5232</th>\n",
       "      <td>ID_011YM9VH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5233</th>\n",
       "      <td>ID_00SL0NNP</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5234</th>\n",
       "      <td>ID_00ETFE7J</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5235</th>\n",
       "      <td>ID_008D2J6C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5236 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          User_ID  Downloaded_Competition_Datafile  Viewed_All_Discussions  \\\n",
       "0     ID_ZZVUJ45W                              3.0                     2.0   \n",
       "1     ID_ZZUNZJ2U                              0.0                     1.0   \n",
       "2     ID_ZZQK0LU6                              7.0                     4.0   \n",
       "3     ID_ZZHDXC1Q                              0.0                     1.0   \n",
       "4     ID_ZYVA36JR                              0.0                     3.0   \n",
       "...           ...                              ...                     ...   \n",
       "5231  ID_01BEG5HQ                              0.0                     2.0   \n",
       "5232  ID_011YM9VH                              0.0                    14.0   \n",
       "5233  ID_00SL0NNP                              0.0                     1.0   \n",
       "5234  ID_00ETFE7J                              0.0                     1.0   \n",
       "5235  ID_008D2J6C                              0.0                     1.0   \n",
       "\n",
       "      Confirmed_Email  Joined_Competition  Signed_Up  Viewed_All_Competitions  \\\n",
       "0                 1.0                 1.0        1.0                      1.0   \n",
       "1                 1.0                 0.0        1.0                      3.0   \n",
       "2                 1.0                 2.0        1.0                     14.0   \n",
       "3                 1.0                 0.0        1.0                      1.0   \n",
       "4                 1.0                 1.0        1.0                      4.0   \n",
       "...               ...                 ...        ...                      ...   \n",
       "5231              1.0                 0.0        1.0                      5.0   \n",
       "5232              1.0                 1.0        1.0                     12.0   \n",
       "5233              1.0                 0.0        1.0                      1.0   \n",
       "5234              1.0                 0.0        1.0                      0.0   \n",
       "5235              1.0                 0.0        1.0                      1.0   \n",
       "\n",
       "      Viewed_All_Learning_Pages  badge_OCZE  create_alias  ...  \\\n",
       "0                           1.0         1.0           1.0  ...   \n",
       "1                           3.0         1.0           1.0  ...   \n",
       "2                           1.0         1.0           0.0  ...   \n",
       "3                           1.0         1.0           1.0  ...   \n",
       "4                           3.0         1.0           1.0  ...   \n",
       "...                         ...         ...           ...  ...   \n",
       "5231                        0.0         1.0           1.0  ...   \n",
       "5232                        7.0         1.0           1.0  ...   \n",
       "5233                        1.0         1.0           1.0  ...   \n",
       "5234                        3.0         1.0           1.0  ...   \n",
       "5235                        1.0         1.0           1.0  ...   \n",
       "\n",
       "      Viewed_All_Jobs  job_ID  Created_Submission  Signed_Out  \\\n",
       "0                 0.0     0.0                 0.0         0.0   \n",
       "1                 0.0     0.0                 0.0         0.0   \n",
       "2                 0.0     0.0                 0.0         0.0   \n",
       "3                 0.0     0.0                 0.0         0.0   \n",
       "4                 0.0     0.0                20.0         0.0   \n",
       "...               ...     ...                 ...         ...   \n",
       "5231              0.0     0.0                 0.0         0.0   \n",
       "5232             16.0     0.0                 0.0         0.0   \n",
       "5233              0.0     0.0                 0.0         0.0   \n",
       "5234              0.0     0.0                 0.0         0.0   \n",
       "5235              0.0     0.0                 0.0         0.0   \n",
       "\n",
       "      Updated_Profile  blog_ID  Viewed_Discussion  Updated_Submission  \\\n",
       "0                 0.0      0.0                0.0                 0.0   \n",
       "1                 0.0      0.0                0.0                 0.0   \n",
       "2                 0.0      0.0                0.0                 0.0   \n",
       "3                 0.0      0.0                0.0                 0.0   \n",
       "4                 1.0      0.0                0.0                 0.0   \n",
       "...               ...      ...                ...                 ...   \n",
       "5231              0.0      0.0                1.0                 0.0   \n",
       "5232              4.0      0.0                0.0                 0.0   \n",
       "5233              2.0      0.0                0.0                 0.0   \n",
       "5234              0.0      1.0                0.0                 0.0   \n",
       "5235              2.0      0.0                0.0                 0.0   \n",
       "\n",
       "      badge_HYIO  Active  \n",
       "0            0.0     0.0  \n",
       "1            0.0     0.0  \n",
       "2            0.0     0.0  \n",
       "3            0.0     0.0  \n",
       "4            0.0     1.0  \n",
       "...          ...     ...  \n",
       "5231         0.0     1.0  \n",
       "5232         0.0     1.0  \n",
       "5233         0.0     0.0  \n",
       "5234         0.0     0.0  \n",
       "5235         0.0     0.0  \n",
       "\n",
       "[5236 rows x 24 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a3c2ed7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For graph\n",
    "temp = new_train[all_activities]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c5a09f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Downloaded_Competition_Datafile     7007.0\n",
       "Viewed_All_Discussions             17629.0\n",
       "Confirmed_Email                     4394.0\n",
       "Joined_Competition                  2661.0\n",
       "Signed_Up                           5365.0\n",
       "Viewed_All_Competitions            20606.0\n",
       "Viewed_All_Learning_Pages           9464.0\n",
       "badge_OCZE                          4395.0\n",
       "create_alias                        4588.0\n",
       "identify                            5679.0\n",
       "comp_ID                            14268.0\n",
       "Signed_In                           1370.0\n",
       "Updated_Discussion                  2030.0\n",
       "Viewed_All_Jobs                     1732.0\n",
       "job_ID                               654.0\n",
       "Created_Submission                  4030.0\n",
       "Signed_Out                           517.0\n",
       "Updated_Profile                     5807.0\n",
       "blog_ID                              748.0\n",
       "Viewed_Discussion                    440.0\n",
       "Updated_Submission                  1096.0\n",
       "badge_HYIO                             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = temp.sum()\n",
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "030c8110",
   "metadata": {},
   "outputs": [],
   "source": [
    "count = k.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "62a0f039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AvgKUmtls4Gtxm05390+Z2XjgR0AlsANY5O5bzOw2oC7OcRTwz+6+pLd70Zm7b4rb/p/Aj83sDKIMSoFmYBFRp1LXer3a9Th3fwlYARwVt+3TwFXAPV3rY2bnxfexGPhzfH5Df+rcVQFpCkkDkCZBBiONUUIqe0wLBSSAQtJYvPZCGwUYjuEUEP0DlCIRbzGK4vMdo50kGehWZhIngcd7oT3+Vt6BorhOGYxUXK/udYquncjWKZn94JoPbSogQxPkVZvyJackGZJ51qaByqmhoYGSkhLMjCeffBKAKVOmkE6nGTJkCAA7duygvr4+O6pj6NCh2Wvv3LmT9vZ2du/eHdW1sJD6+nrKysp4+umns+XV19dTUlKSXb+kvb09uzYJRCNJKioq2LVrFwDl5eWkUilaWlqynSalpaWk02ncnfb29uz1zIxkMklzczMQda50tKmpqSlb17KyMlKpVLYOAEVFRQAkEglaWlqyr4uKikgkEt3Oz2QytLW1ZX/w62gTRAvSARQUFJBIJCgoKOh2vrsPaJvS6TT19fV51aZ8yam1tZVUKpVXbcqXnFpbW/OuTfmSU2NjI21tbXnVpnzJKZlM0tramldt2p+celqHrSfW0zdOexxg1uDuQ7tsqwFOBD4ETHX3RWZ2ItGH27cBnwPqgZ8CDwC73P18M3sIuBoYDSwDpgBbgceBf3L3x8zsYeAL8fbVwGlATVz2t919qZmNcPddZpaMy/8M8DLwCnAuUQfMr4Ayd/8bM1sMvOjuPzOzCuApYDrw8bj+HzGzU4g6Vd7u7n/s4T7MI/oQvqCHfacBtxF1ghjwh/je1MR1mQ68ADxN1ElyFdBR3vviDpIF8flDgOeIOmOmEo0M+Xhc7nLg68AWok6DqfH1J3S8N7MriTtE4n3Z92Z2N1GH0u1m9hFgXnz92+Lrvj/Odbm7H9+1nV2v1WlbBbDN3UvNbBjQ5O6peGrMJ9x9YQ/16u24PcqP63aPuy/p9GdjM/Ab4EJ3bzSzLwLF7v6Vnuq8N5OvvcubKcrlVDnISmlD2YRJ2Rw4m2+4GIB0Os2NN95IYWEhn/3sZzEzUqkUN910E4WFhbzjHe9g1apVJJNJPv3pT5PJZHrcd+WVV3LEEUf0WF6H66+/nsrKSq655hrq6+v51re+xVFHHcWpp57Kgw8+yKhRo1i0aNFA3ZK8Vl9f3+8f0uTQUjbhUjbhUjbhUjbd9GtobK5riXQUPpuoEwR330g0OuRtwCrgrHj/vcBQMysDJsSjBACecve/xqMx1gATulxjJvCwu+9w9xTw87hMgL81s2eJOhKmEI0AORF41d1fiUei/KxTWecB18ajEh4GSoBj4vJ+Ftd/HbAux/sxm2h0R2M8iuE3wLvifa+6+/q4nS8AD8T1W9+lzcvcvdnddwIPAWfE9T4vbuezcRsn5VhHgDOJpkhBlNvsTvuWunvG3V8Ejt7Hcjv/YTsC+F8zex74L6J8etLf43rydqLMH48zvQI4ZFNyREQOhg0bNtDU1MSMGTOynRkFBQVceumlFBUV8bvf/Y4hQ4Zw6aWXZr+p6W1fb+X1pLy8nIULF9LS0sLKlSsZNWoU8+fPPyRtFhEREQnJPj/FJl5zIg28Se+9ME8TTdfYRDQFZCTwMaKpNB1aO71O91CXHss2s2OJRhHMdPeaeIRBxwo0vQ2HMWBhp86ZjrL6OqerF4BT47U2uk7o6qs3qnM7M53eZ9izzV3r4XG5X3P3H3Sp94R+1nlvOl+zcz33deL5dKIpSgD/Bjzk7gviej7cyzn9Pa4nBqx098v2sZ49Sutp18FSNuFSNgfe1KlTmTp1arft48eP5xOf+ESP5/S0r2N4am/lAVx33Z7LiE2ePJnJkwdiSa3Bp6BADxAMlbIJl7IJl7IJl7LJzT79hGtmlcAtwHfjURCPApfH+95GNCrjJXdvA14jWjtjNdGIki/Ev/fXH4CzzWxkPJXmMuARYBjQCOw2s6OBC+PjNwLHxmuFEB/f4X7g0xb3iJjZ9Hh75/pPBU7prTLu/mfgj8D1ncqZZGbz43LeZ2Zl8XonC/axrQDzzazEzI4EziHqZLof+IiZDY2vV2XRQrn1QG/jpfra9wTwgfj15URro+yXuHPjm0RrvEA0MuT1+PWVfdSrt+P6YzXwTjM7Pq5DWfznLycZLTcZLGUTLmUTro4RJBIm5RMuZRMuZRMuZRMuZZOb/ty1Uosf8wv8nmgtkOvjfd8Dkma2nmjNjyvdvWMkwipgu7s3xa/Hsg+dBu6+Dfj/iKabrAWedfdl7r6WaMrJC0QLjj4eH98C/D1wr5k9RjTdp8O/AYXAunhKx7/F279PNP1nHfDPRGuT9OWjRIuY/ilu8w+Bre7+LNEaJE8Rdezc6u7P9betsaeIpiOtBv7N3be6+wqiKTFPxtdbApS7ezXR9JLnzewbXcp5CDgpzuz9XfZ9BlgUt/fvgM/uYx07TLT4Mb/AncB3/K0n2Hwd+JqZPQ57PAe0a716O26v3H0HUafKHXFbVhNNP8pJWh/0gqVswqVswqVvjMKmfMKlbMKlbMKlbMKlbHKz10Va5eCLF2ltcPdvDnRdBiMt0houLQQaLmVz4HQs0nqgaFG2sCmfcCmbcCmbcCmbcCmbbg7qIq0iIiIiIiIiInlD4256YGZ/AIq7bP47d19/MK7n7l8+GOXuDzM7mfgJRZ20uvusgaiPiIiIiIiIyMGkKTYy6KXTaU8m92kZFDlE0uk0yiZMyiZcyiZsyidcyiZcyiZcyiZcyqYbTbER6Q91EoZL2YRL2YRL2YRN+YRL2YRL2YRL2YRL2eRGHSQy6LW0tAx0FaQXyiZcyiZcyiZsyidcyiZcyiZcyiZcyiY36iCRQU+9q+FSNuFSNuFSNmFTPuFSNuFSNuFSNuFSNrnRIq0y6J3x1d/rcaWB0qNkw6VswqVswqZ8wqVswqVswtVTNgf68fUih5JGkMig16Z+wmApm3Apm3Apm7Apn3Apm3Apm3Apm3CVlpYOdBUOS/oTLYOeoeFnoVI24VI24VI2YVM+4VI24VI24eorm/vuu48XXniBxsZGJk2axAc/+EHWrFnDsmXLuh372c9+loqKClpaWrjvvvt46aWXyGQyjB49mkWLFlFdXc0999zD9u3bSafTjB07losvvpgRI0YA8Nxzz7Fq1Srq6+sZP3488+bNY9iwYT3Wa+PGjaxYsYK6ujrGjh3L/PnzGT58+IG5IQFJp9MUFOjj/r7SCBIZ9PSfbriUTbiUTbiUTdiUT7iUTbiUTbj2ls2UKVP2eD9+/HgWLlzIwoULWbBgAclkkiFDhlBeXg7AsmXLWL9+PdOnT+eCCy7IdoDU19fj7pxzzjlMmzaNTZs2cffddwOwdetWli9fTnl5OXPmzGHz5s3ce++9PdanoaGBJUuWUFxczNy5c9m2bRtLly7dz7sQJq1Bkht1KcmgV0CG9oGuhPRI2YRL2YRL2YRN+YRL2YRL2YSrr2wuvPBCamtreeqpp7Lbhg8fnh2t8eKLL5JOp5k+fTrJZJKamho2btzIySefzJw5czAzZsyYAcC4ceO48sors+WsX7+eN998E4DNmzcDcNppp3HKKafw/PPP8/LLL9PU1ERZWdkedVq/fj3pdJrZs2czZcoUtm7dyrp169i1a1e2MyZftLe3U1JSMtDVOOwM2hEkZvawmZ3fZdvnzGyTmV17iOpwm5ldspdjKs2s3cw+3mX7ZjMbGb9u6OP8CWbWbGbPmdkGM3vKzK7otH/eoWpvD3X7rZlVDMS1RURERERk4DzzzDOYGaeddhoAO3bsAKIRIYsXL2bx4sWsXLkSgGQymT1v69atNDc3M378eACGDBkCwJYtW9i5cyfV1dUA1NbWdrtmx7aO6Tcdv9fU1Bzg1snhatB2kAB3AB/osu0DwBXufsMA1Kc3lwKrgcv2o4w/u/t0d59M1MZ/MLNFAO6+fKDa6+4XuXvtQFy7s9Sg/msQNmUTLmUTLmUTNuUTLmUTLmUTrlyz2bVrF5s2beL444+noqIiKiuVAqCtrY1LLrmEY445hieeeIJNmzZlz9u5cye//OUvqaio4MILLwSiaTzjxo3jmWee4eabbyadTgP0a/2NjmkoZpZTO0JWWFg40FU4LA3mf22WAH9jZsUQjbQAxgDHm9l3422VZvZrM3s6/vXOePt6M6uwSLWZfTje/lMzm2NmSTP7RnzOuo7RH/Hx3zWzF83sXuCoftTzMuAfgbFmVrW/jXb3TcDngc/EdbqyU3svNbPnzWytmT0ab0ua2TfjNq8zs0/H2zuPYDndzB6OX59tZmviX8+ZWbmZjTazR+Ntz5vZu3oo4/PxvufN7HPxtgnxqJcfmtkLZrbCzErjfZ+J7+M6M/vlft0T8u8fxHyhbMKlbMKlbMKmfMKlbMKlbMKVazbPPPMMAKeffnp2W0dHyfjx45k8eTInnXQSEHWmQDTC5LbbbiORSPDhD384u25JQUEBixYt4uMf/zif+MQnGDt2LAUFBdmpPKlUKttp0nGNuro6IFrbpPP2fJKPnT6HwqBdg8Tdq83sKeACYBnRyIpfwR4rDX0L+C93f8zMjgHuByYDjwPvBP4CbALeBfwEeDvwCeAqYLe7z4w7YB43sxXAdOAE4GTgaOBF4Ee91dHMxgGj3P0pM7sTeD9w4wFo/rPAiT1s/1fgfHd/vdPUl78HjgWmu3vKzPY2Oe8LwCfd/XEzGwq0xGXc7+5fNbMksMdkQDM7DVgEzAIM+IOZPQLUAJOAy9z9Y/E9WAj8DLgWONbdW/d3mk6SDIVE/2imSZDBSGOUkMoe00IBCaCQdHYxqjYKMBzDKSADRL3oHm8tis93jHaSZKBbmUmcBB7vhXaS8TlQFNcpg5GK69W9TtG1E9k6JbP/TeVDm5JkaIa8alM+5VRMe961KR9ySpIhTTqv2pRPOQGU0pZXbcqXnJJkaCeTV23Kl5ySZPKuTfmSUxEp0qT3aFNHp8Orr76andJSU1PDE088wdixYxk1ahRr1qyhvLycUaNGUV9fT0lJCSNGjKCyspI///nPPP7447zwwguYGVVVVWzdupWf/exntLS0cM4557Blyxb+/Oc/c8IJJ5DJZHj88ceprKxk27ZtbNq0idNOO410Ok0mk+GGG27gyCOP5IorruDYY48lmUzy2GOPUV1dzYYNG6iqquKII46gtbWVTCaTHclSVFQEQCKRoKWlJfu6qKiIRCJBU1NT9p6UlZWRyWRoa2sjk4nuaUlJSfZ1W1v0735BQQGJRIKCgoJu57s7LS0t2VEtpaWlpNNp3J329mill8LCQsyMZDJJc3MzEHWElJSUYGZ7lFlUVJR3bSorKyOVSuXUpo4Otb0ZtB0ksY5pNh0dJB8BTum0fw5wUqfet2FmVg6sAs4i6iD5PvD38eiOXe7eYGbnAad0Wl/kCKIP+mcBd7h7GthqZg/upX4fAO6MX/8S+B8OTAdJb92JjwO3xR0Rv4m3zQFucfcUgLvv2kvZjwM3mtnPgd+4+1/N7GngR2ZWCCx19zVdzpkN3OXujQBm9huiTqflwKudjn8GmBC/Xgf83MyWAkv3Uqc+JXCaKeq2veu2NNF/Pj3paXGq/pSZ6nZE52OTPWzrqU7dj4vK7u/54baplLY92pcPberqcG1TklSP14nKPjzblC85ldKWLSdf2tS9Todvmzrns2edDt829V6nw6tNpbTRSmGnbYd/m/pXp/DbVEiaNMm8atNbxx7ebYq+zNpzX8cH0WeffZa//OUvQDQ1ZuXKlcyfP5+6ujqampp497vfvcejeAsLC7nkkku4++67eeihhzjiiCNYsGABo0ePZvPmzdkPzw899FD2nNNPPx13Z8uWLTz77LMUFRUxc+ZMzjvvvD2m2CQSCcrLyykvL2fhwoWsXLmSRx99lKqqKubPn08ymdxjnZOuepqu0vUDdzKZ7HVaS3Fx8V7PBxg6dOge7zva0NNiqz2d33lbfX19n50Ch2ObOurUm/60aW8GewfJUqIP8zOAUnd/1sw6d5AkgDPdvbnzSfH0k08CxwBfAhYAlxB1nEDUAfFpd7+/y3kXsecIlb25DDjazC6P348xs0nu/so+lNGT6cCGrhvd/WozmwVcDKwxs2lEbempzinemqKV/dPt7jfE04cuAlab2Rx3f9TMzorL/amZfcPdf9KprL7Gf7V2ep0GSuPXFxN1OM0D/p+ZTenoxBERERERkYHV+akzXU2dOrXH7UcddRRXXXVVt+0TJkzguuuu6/EcM+Pqq6/u9Vpdz5s8eTKTJ0/u9XgZ3AbzGiS4ewPwMNE0lzt6OGQF8KmON3GHAe7+GjASmBSv6fEY0dSSjg6S+4FPxCMmMLO3mdkQ4FHgA/G6HqOBd/dWNzM7ARji7lXuPsHdJwBfo/vCsvskXmvlm8B3etg30d3/4O7/CuwExhHdg6vNrCA+pmOKzWbgtPj1wi5lrHf3/wD+CJxoZuOBN939h0SjYGZ0ufSjwPvMrCy+Twt461721IYEMM7dHwL+GagAhvZ2/N5oXmu4lE24lE24lE3YlE+4lE24lE24lE24tAZJbgZ1B0nsDuBUoiksXX0GOD1eCPRFoHPX5B+Al+PXq4Aqoo4SgFuJ1hd51syeB35ANFrnLuAVYD3R1JxH+qjXZfHxnf2a3J5mMzFeMHUD0ZSd77j7j3s47hvxYqzPE3VarI3bsgVYZ2ZrgQ/Gx14PfMvMVkE8+THyuY6FXoFm4D7gHKIRKc8RdaZ8q/NF3f1Z4DbgKaL7equ7P9dHe5LAz8xsPfAc0Toxtf27Fd219zJkUQaesgmXsgmXsgmb8gmXsgmXsgmXsglXT1NYZO+sY8EUkcHq2GvvdldfYZCMDMomTMomXMombMonXMomXMomXD1ls/mGiweoNtJZOp3uc72OQahfQ2r0L40MeiV9Lp0lA0nZhEvZhEvZhE35hEvZhEvZhEvZhKvz01+k/wb7Iq1BMLO7iB6l29kXuy7yupcyTgZ+2mVzq7vP2t/6iYiIiIiIiOQ7TbGRQa++vt739fFPcmjs7fFkMnCUTbiUTdiUT7iUTbiUTbiUTbiUTTf9mmKjDhIZ9NLptGt+Xpg0dzJcyiZcyiZsyidcyiZcyiZcyiZcyqYbrUEi0h+plOZOhkrZhEvZhEvZhE35hEvZhEvZhEvZhEvZ5EYdJDLoZTKZga6C9ELZhEvZhEvZhE35hEvZhEvZhEvZhEvZ5EaLtMqgN+P639FM0UBXQ3pQSpuyycGheLyevpUIl7IJm/IJl7IJl7IJl7IJl7LJjUaQiIiIiIiIiMigpxEkMui1o8WLQqVs9l8qleKWW26hurqamTNnctFFF3HrrbeyY8cOMpkMlZWVnH/++YwfPx6A5557jlWrVlFfX8/48eOZN28ew4YN67O8nmzcuJEVK1ZQV1fH2LFjmT9/PsOHDz8kbR7sioo06ipkyidcyiZcyiZcyiZcyiY3GkEiIpLHHnnkEerq6vbYNnbsWC644ALOOuss3njjDZYvXw7A1q1bWb58OeXl5cyZM4fNmzdz77337rW8rhoaGliyZAnFxcXMnTuXbdu2sXTp0gPaLhERERGRA00dJDLo6UHX4VI2+2f79u2sXr2as88+e4/t559/PieccALHHXccBQUFmEVPPdu8eTMAp512GrNmzWL06NG8/PLLNDU19VleV+vXryedTjN79mxmzZrFiSeeyJYtW9i1a9eBb6R0k0jov/aQKZ9wKZtwKZtwKZtwKZvc6K4NMDMbZWa/NLM/m9mLZvZbM3tbDuW8y8xeMLM1ZlZlZksORn17uG5DH/smmFlzXKeOXx8+ANcc09E+MzvHzO7Zn/KKSO9vleQgUTa5c3eWL1/OzJkzqaqq2mNfa2sr3/jGN7j11ltJJpPMmzcPgCFDhgCwZcsWdu7cSXV1NQC1tbV9ltdVbW0tQHZqTsfvNTU1B6x90ruWlpaBroL0QfmES9mES9mES9mES9nkRh0kA8iir23vAh5294nufhLwf4GjcyjucuCb7j7N3V9390t6uN5ArDnz57hOHb9+sr8FuvvWntonIm957rnnqK2t5dRTT81OiWltbaWxsZGioiI+9KEPccEFF5BKpXjooYcAmDJlCuPGjeOZZ57h5ptvJp2OOqgKCgr6LG9v3KOxQB0jVUREREREQqRFWgfWu4F2d7+lY4O7r7HIN4ALiWYZ/Lu7/8rMzgG+DOwEpgLPAB8CrgL+FjjfzOYAXwLucfepZnYlcDFQAgwxs58A7wOScRn/CRQBfwe0Ahe5+y4zmwjcDFQCTcDH3H2jmR0L/ILoz87vcm14PPLkZmAOUEPUMfR14Bjgc+6+3MwmAD8FhsSnfcrdn4i33+PuU3O9fmcZ9KEtVMomd3V1dTQ1NXHLLdl/Xli3bl12xMjEiROZOHEiGzZsYPPmzTQ1NVFWVsaiRYvYvn07iUSC+++/ny1btjB8+HBefPHFPstLpVKYGclkkoqKimwdAOrr6wGy2+Xg0pDasCmfcCmbcCmbcCmbcCmb3KiDZGB1dHJ09X+AacCpwEjgaTN7NN43HZgCbAUeB97p7rea2WyiToMlcQdCZ2cCp8QdH1fG151O1GnyJ+CL7j7dzP4L+DBwE/DfwNXu/oqZzQK+B5wLfAv4vrv/xMw+2Y82TjSzNZ3ef9rdVxF1ejzs7l80s7uAfwfmAicBtwPLgTeBue7eYmaTgDuA0/txzX3klNIGQJoEGYw0RglvPTu8hQISQCFpLF4Zo40CDMdwCsgAkCIRbzGK4vMdo50kGehWZhIngcd733pqi/PW9JIMRiquV/c6RddOZOuUzHYpFMbnH85tSpIhRSKv2nQocqqvr2fChAkMGzaMwsJCdu7cyaOPPsqECRMYMmQIS5cuZcyYMezevZvXXnuNsrIyEokEzc3NPPjggxx55JG88cYbbNq0iTPOOINEIsGECRMoLy8Hoo6PjvJOOukk6uvrufHGGxk5ciQf/ehHmTBhAslkklWrVlFbW8vGjRupqqqisLCQ+vp6ysrKcHdaWlqyo0tKS0tJp9O4O+3t7dG9KSzMdro0NzcD0SiUkpISzCy7NgpAWVkZqVSKTCZDKhXdv47V2xOJRHaYaSKRoKioiEQi0e38TCZDW1sbmUx0T0tKSrKv29qifyMKCgpIJBIUFBR0Oz+UNrW3t5PJZPKqTfmUUzKZzHYa5kub8iWnjnLzqU35klNHefnUpnzJqbW1lUwmk1dtypecCgsLs/nkS5v2J6eOn2P3xjoqK4eemX0GONbd/6HL9v8C1rv7j+L3PwX+F6gDvuTuc+Pt3wced/efmdlt7NlB0nkEydnuvig+50qiTpWPxe+3AGe6++tm9hHgFOBfgB3AS52qVezuk82sGhjl7u1mNgzY6u5De2lfth497GsFStzdzewrQKu7f9XMEsAud68wsyOA7xJ1FqWBt7l7WZf2nQN8wd3/ph+3vEfHXnu3u2abBcnIoGz23eYbLt7z/ebN3H777cycOZNTTz2VZcuWUVNTQzKZZPTo0cyZM4eqqircnR/84Afs3LmToqIipk6dynnnnUdBQUGv5XU85vf666+nsrKSa665BoANGzawcuVK6urqqKqqYv78+YwYMeLQ3IBBLp1Ok0zqEdmhUj7hUjbhUjbhUjbhUjbd9GtoukaQDKwXgJ7W0ugrvNZOr9P0L8OuiwR0LiPT6X0mLi8B1Lr7tF7KOxC9au3+Vu9ctg7unum0Vso/ANuJRtIkgIOy0lAJKZrRc8JDpGwOjAkTJnDddddl33d0YnRlZlx99dX9Ku/zn//8Hj3xncsHmDx5MpMnT86xxrI/mpqa+v0tiRx6yidcyiZcyiZcyiZcyiY3+mp2YD0IFJvZxzo2mNlMojU53m9mSTOrBM4CnjpUlXL3OuBVM7s0rpOZ2anx7seBD8SvLz/IVTkC2ObuGaI1UtQFKiIiIiIiIgeFOkgGUDyCYgEwN37M7wtEi7D+AlgHrCXqRPlnd3/jEFfvcuAqM1tLNNJlfrz9s8Anzexpog6MvZnY5TG/n9mHOnwPuMLMVgNvo/tIGBEREREREZEDQmuQyKCnNUjCpTVIctN1DZKDQfNaw6VswqZ8wqVswqVswqVswqVsuunXGiT65CGDXuKALKkiB4OyCVfHCuYSHmUTNuUTLmUTLmUTLmUTLmWTG3WQyH4zs5O7TKNZY2Z/GOh69VfHY1IlPMomXB2PdpPwKJuwKZ9wKZtwKZtwKZtwKZvc6Ck2st/cfT3Ro3gPS3/80nu0wnOg6uvrlU2g9K1EuJRN2JRPuJRNuJRNuJRNuJRNbjSCREREREREREQGPXWQyKBXUlIy0FWQXiibcCmbcCmbsCmfcCmbcCmbcCmbcCmb3KiDRAY9DT8Ll7IJl7IJl7IJm/IJl7IJl7IJl7IJl7LJjTpIRERERERERGTQ0yKtMuhNu+63NFM00NWQHpTS1ms2m2+4+BDXRjpra2ujuLh4oKshPVA2YVM+4VI24VI24VI24VI2udEIEhEREREREREZ9DSCRAa9tPoJg7W3bKqrq7nnnnvYvn076XSasWPHcvHFFzNixAhaWlq47777eOmll8hkMowePZpFixYB9Lnvvvvu44UXXqCxsZFJkybxwQ9+sNfrb9y4kRUrVlBXV8fYsWOZP38+w4cPP3A3IGAFBfrvI1TKJmzKJ1zKJlzKJlzKJlzKJjf6ZCiDXgYb6CpIL/aWTX19Pe7OOeecw7Rp09i0aRN33303AMuWLWP9+vVMnz6dCy64gBEjRmTP62sfwJQpU/Zat4aGBpYsWUJxcTFz585l27ZtLF26dN8beZhKJPTfR6iUTdiUT7iUTbiUTbiUTbiUTW7UrSSDXlodJMHaWzbjxo3jyiuvzL5fv349b775JjU1NWzcuJGTTz6ZOXPmYGbMmDEDoM99ABdeeCG1tbU89dRTfV57/fr1pNNpZs+ezZQpU9i6dSvr1q1j165d3Tpc8pG+lQiXsgmb8gmXsgmXsgmXsgmXssmNupUOAjNr6GPfGDNbcoCuc46Z3bOXYy40sz+a2QYz22hm3zwQ195XZvY+Mzup0/uvmNmc+PXnzKys077fmlnFoapbCalDdSnZR3vLJplMZl9v3bqV5uZmxo8fz44dO7LbFi9ezOLFi1m5ciVAn/v2RW1tLQDDhg3b4/eampp9Lutw1NTUNNBVkF4om7Apn3Apm3Apm3Apm3Apm9yog+QQc/et7n7JobiWmU0Fvgt8yN0nA1OBTYfi2j14H5DtIHH3f3X338dvPweUddp3kbvXHsrKyeFt586d/PKXv6SiooILL7yQVCrqWGlra+OSSy7hmGOO4YknnmDTpk197tsf7g6AmUYkiYiIiIgcjtRBcpBY5Btm9ryZrTez98fbJ5jZ8/HrK83sN2b2OzN7xcy+3un888zsSTN71sz+18yGxtsviEeCPAb8n71U45+Br7r7RgB3T7n79+JyxpvZA2a2Lv79mHj7bWb2fTN7yMw2mdnZZvajeATKbZ3q12Bm/xnX7wEzq4y3T4zb84yZrTKzE83sHcA84BtmtiY+5jYzu8TMPgOMAR4ys4fiMjab2cj49efje/i8mX2u0z3cYGY/NLMXzGyFmZXuV2By2NqxYwe33XYbiUSCD3/4w5SXl1NRUQHA+PHjmTx5MiedFPXN7dq1q899e5NKpUin0wDZcurq6oBoPZTO20VERERE5PCiiUkHz/8BpgGnAiOBp83s0R6OmwZMB1qBl8zsO0Az8C/AHHdvNLMvAp+PO1B+CJwL/An41V7qMBX4z172fRf4ibvfbmYfAb5NNMoDYHh8jXnA3cA7gY/GbZjm7muAIcCz7v6PZvavwHXAp4D/Bq5291fMbBbwPXc/18yWA/e4+xJ461t2d/+2mX0eeLe77+xcQTM7DVgEzAIM+IOZPQLUAJOAy9z9Y2Z2J7AQ+Nle7kePUhiltAHRU1MyGGlsj+kdLRSQAApJY0QjBdoowHAMp4BMXFYi3mIUxec7RjtJMtCtzCROAo/3QjvJ+BwoIvognsFIxfXqXqfo2olsnZLZVTsK4/MP5zYZGZKdViLp3KbW1lYaGxv58Y9/TEtLC+985zt57bXX2LJlC8cddxwjR47kz3/+M08//TRr1qzBzDjyyCMZOnQolZWVbNq0iaeffppnn30WM2PkyJGkUin+9Kc/8frrrwPRdJk//vGPVFVVUVFRwde//nWOPPJIPvaxj/G2t72NBx54gEcffZTq6mo2bNjA2LFjOeKII7KdJQBlZWVkMhna2trIZKJ7UlJSkn3d1hb92SsoKCCRSFBQULDHkMiysjLcnZaWluwoldLSUtLpNO5Oe3t7dG8KCzEzkskkzc3NQPT3rKSkBDPrVmYqlSKTyWRH1BQVFQHRgl4tLS3Z10VFRSQSiW7nFxYW0tjYmFdtypec0uk09fX1edWmfMqpuLh4UPwbcTi2KZ1O09jYmFdtypeckskk7e3tedWmfMmp4/+cfGpTvuRUXFxMa2trXrVpf3IqLy+nP6yjsnLgWLQGyQ+B9e7+o3jbT4H/BdYRdRRMNbMrgXe6+8fiY+4DvgpUALcBf42LLAKeBL4DfNvdz4qPnwf8vbv/TS/1eBZY5O5re9i3Exjt7u1mVghsc/eR8SiRle7+czM7Drjf3SfF5/wE+I27LzWzNFDs7qn4uN8As4EdwEudLlXs7pPjcjt3kGTfm9lm4PSODpKO98DlwJHu/q/x9n+Ly18e17GjXl8ECt3933sNpQ8Tr73b9ajfMCXJ9Pqo3803XMzmzZu5/fbbu+277rrrePPNN7n77rvZtm0bRxxxBOeccw4nn3wyQJ/7brvtNv7yl7/sUd78+fOZNm0a119/PZWVlVxzzTUAbNiwgZUrV1JXV0dVVRXz588fFAu0QjSaRot/hUnZhE35hEvZhEvZhEvZhEvZdNOvefC6YwdPfxciaO30Ok2UiRF1AFy2R4Fm04B96dF6ATgN6NZB0oPO5XbUKdOlfhl6/zPjRFO2at192j7UsS993cOu9y3nKTaFpHv9EC4Da2/ZTJgwgeuuu67HfUcddRRXXXXVPu/r/FScrrpea/LkyUyePLnX4/NZS0sLQ4cOHehqSA+UTdiUT7iUTbiUTbiUTbiUTW70qfDgeRR4v5kl4/U5zgL6fm7oW1YD7zSz4wHMrMzM3gZsBI41s4nxcZf1VkDsG8D/jc/FzBLxdBaAJ4APxK8vBx7rZ906JICOxWY/CDzm7nXAq2Z2aXw9M7NT42Pqgd7GNfW271HgfXH7hwALgFX7WM+9sn3qc5JDSdmES6MPw6VswqZ8wqVswqVswqVswqVscqMOkgPMzAqIRjfcRTSdZi3wIPDP7v5Gf8pw9x3AlcAdZraOqMPkRHdvAf4euNeiRVr/0nsp4O7riJ4Qc4eZbQCeB0bHuz8DLIrL/zvgs/vQTIBGYIqZPUO0XslX4u2XA1eZ2VqiESzz4+2/BP7JzJ7r1MHT4b+B+zoWae1U/2eJpho9BfwBuNXdn9vHeoqIiIiIiIjsldYgOcDiERM/dPczBrouB5OZNbh7XozZ0hok4drbGiQycDSvNVzKJmzKJ1zKJlzKJlzKJlzKppt+LYGhT4UHkJldDdxB9AQaOUxoGke4lE24Oh53LOFRNmFTPuFSNuFSNuFSNuFSNrlRl9IB5O63ALcc6uua2SK6T5F53N0/ebCumS+jR0AfwkOmbMKl0YfhUjZhUz7hUjbhUjbhUjbhUja50RQbGfTq6+u9v8/FlkOrvr6+388sl0NL2YRL2YRN+YRL2YRL2YRL2YRL2XSjKTYiIiIiIiIiIv2hDhIZ9AoLCwe6CtILZRMuZRMuZRM25RMuZRMuZRMuZRMuZZMbdZDIoGfWr9FWMgCUTbiUTbiUTdiUT7iUTbiUTbiUTbiUTW60SKsMeiddt6LXR8nKwOrrMb8ysPYnGz2i+eBKJpMDXQXpg/IJl7IJl7IJl7IJl7LJjT55yKBXRGqgqyC9UDbhUjbham5uHugqSB+UT7iUTbiUTbiUTbiUTW40gkRERAbETTfdxO7du7Pvjz76aK6++mpuvfVWduzYQSaTobKykvPPP5/x48cD0NLSwn333cdLL71EJpNh9OjRLFq0qM/yerJx40ZWrFhBXV0dY8eOZf78+QwfPvwgtlZEREREQqcOEhn0vH9PfJIBoGzCdaCyGT9+PKeffjoAJSUlAIwdO5bTTjuNhoYGHnroIZYvX86nP/1pAJYtW8ZLL73ErFmzqKys5LXXXttreV01NDSwZMkSKisrmTt3Lg8++CBLly7NdrQc7jTnOGzKJ1zKJlzKJlzKJlzKJjfqIJFBrx3NzwuVsgnXgcqmoqKCSZMmUVxcnN12/vnn09zcTE1NDatWrcr+B19TU8PGjRs5+eSTmTNnDmbGjBkz9lpeV+vXryedTjN79mymTJnC1q1bWbduHbt27WLEiBEHpF0DqbeOIQmD8gmXsgmXsgmXsgmXssmNOkgGOTP7EvBBIA1kgI8DHwNudPcXD+J1vww0uPs3e9n/MPAFd/9j/H4CcI+7Tz3Qdckc6ALlgFE24TpQ2axdu5a1a9dSVlbGe97zHmbMmEFrayvf+MY3gOg/93nz5gGwY8cOALZu3crixYsxM2bNmsXcuXP7LK+r2tpaAIYNG7bH7zU1NXnRQaJvjMKmfMKlbMKlbMKlbMKlbHKjRVoHMTM7E/gbYIa7nwLMAV5z948ezM6R0JRosclgKZtwHYhsZsyYwaWXXsqCBQtIJpPcc8891NTUUFRUxIc+9CEuuOACUqkUDz30EACpVHTNtrY2LrnkEo455hieeOIJNm3a1Gd5e+PuQP78INHU1DTQVZA+KJ9wKZtwKZtwKZtwKZvcqINkcBsN7HT3VgB33+nuW83sYTM7HcDMrjKzl+NtPzSz78bbbzOzb5vZE2a2ycwu6SjUzP7JzJ42s3Vmdn2n7V8ys5fM7PfACblW2syuNLNlZva7uLzrci1LRAbOWWedxUknncQpp5zClClTcHeqq6tJJBJMnDiRWbNmUVVVxebNm2lqaqKiogKI1hmZPHkyJ510EgC7du3qszyIOlfS6TRAtpy6ujoA6uvr99guIiIiIoOTptgMbiuAfzWzl4HfA79y90c6dprZGOD/ATOAeuBBYG2n80cDs4ETgeXAEjM7D5gEnAEYsNzMzgIagQ8A04n+3D0LPLMfdT8DmAo0AU+b2b0d03H2VQFpSmkDIE2CDEYa2+Mb8hYKSACFpDGib5vbKMBwDKcgnnCQIhFvsexjUB2jnSQZ6FZmEieBx3vfWtfBgSKiD3MZjFRcr+51iq6dyNYpmV06szA+/3BuUwntedemfMkpSYZi2nNqUzqdZvv27TzwwANMmDCBTCbDunXrKCgooKamhiVLljBmzBiampp47bXXGDJkCKlUiqFDhzJy5Eg2bdrE008/zbPPPouZceSRR7Jt2zYeeOABjjvuONrb21m7di0FBQVUVFTQ3t7O4sWLOfLII1m0aBEnnngiDzzwAI8++ijV1dVs2LCBcePGUV5eTmNjI5lMdE9KSkqyr9vaon8jCgoKSCQSFBQU7PHNTFlZGe5OS0tLdkRKaWkp6XQad6e9vT26N4WFmBnJZDL7+D0zo6SkBDPrVmYqlSKTyWRHzxQVFQGQSCRoaWnJvi4qKiKRSNDU1ERDQ0P2/EwmQ1tb22Hfps7nH+5tSqfT2U65fGlTvuTU0NCQd23Kl5za2tpob2/PqzblS04d/+fkU5vyJad0Ok1ra2tetWl/ciovL6c/rKOyMjiZWRJ4F/BuovVHrgWuBL4AjAUWuPsV8bGfAd7m7p8ys9uAle7+83hfvbuXm9k3gUuA2vgSQ4GvAeXACHf/1/j4G4GtfaxB8hDwT13WILnb3U82syuBc939w/G+rwC73P2mXO7Bsdfe7a7BVEEyMiibMO1PNptvuJj6+nqWL1/O66+/Tnt7O5WVlZx77rmUlpaybNkyampqSCaTjB49mjlz5lBVVQXAm2++yd133822bds44ogjOOecczj55JN7Le/4448H4Prrr6eyspJrrrkGgA0bNrBy5Urq6uqoqqpi/vz5ebH+CEQ/ECWTWuA4VMonXMomXMomXMomXMqmm37NpdYIkkHO3dPAw8DDZrYeuKLT7r39IWrt4VgDvubuP+h8oJl9DtiX3rhqYHin9yOAnZ3edy0r556+JK6VLgKlbMK1v9mUl5dz+eWX97ivoxOjJ0cddRRXXXXVPpUHcN11e87Emzx5MpMnT+5nbQ8vqVRKPxAFTPmES9mES9mES9mES9nkRl/NDmJmdoKZTeq0aRrwl07vnwLONrPhZlYALOxHsfcDHzGzofE1qszsKOBRYIGZlZpZOfDevZTzMPAhe2vVxCuAhzrtn2tmI8ysFHgf8Hg/6tajRO59K3KQKZtwKZtwdQx9lTApn3Apm3Apm3Apm3Apm9xoBMngNhT4jplVACngT8DfA0sA3P11M1sM/AHYCrwI7O6rQHdfYWaTgSfjvo0G4EPu/qyZ/QpYQ9QJs2ovdftvorVN1pqZA38E/r9O+x8DfgocD/wi1/VHgOwaDBIeZRMuZROujjm5EiblEy5lEy5lEy5lEy5lkxt1kAxi7v4M8I4edp3T6fUv3P2/4xEkdxEt7Iq7X9mlrKGdXn8L+FYP1/sq8NV+1q0N+FQfh7zp7n3tFxEREREREek3TbGRvfmyma0BngdeBZYOaG0Ogo6nd0h4lE24lE24OlZzlzApn3Apm3Apm3Apm3Apm9xoBIn0yd2/cDDLN7ObgXd22fwtd/9xH3W6DbjtIFZLREREREREBhk95lcGvfb2di8sLBzoakgP2tvbUTZhUjbhUjZhUz7hUjbhUjbhUjbhUjbd9Osxv5piI4NeS0vLQFdBeqFswqVswqVswqZ8wqVswqVswqVswqVscqMOEhEREREREREZ9NRBIoNeIqG/BqFSNuFSNuFSNmFTPuFSNuFSNuFSNuFSNrnRGiQy6E28drmn9USOICVJo2zCpGzCpWzCpnzCpWzCpWzCpWzCdSiy2XzDxQe1/ANMa5CI9Eemf39XZAAom3Apm3Apm7Apn3Apm3Apm3Apm3Apm9yog0QGvRJSA10F6YWyCZeyCZeyCZvyCZeyCZeyCZeyCZeyyU3BQFdARERERERERA4/1dXV3HPPPWzfvp10Os3YsWO5+OKLGTFiBFu2bOHee++lurqayspK5s2bx+jRo4HoKTv33XcfL730EplMhtGjR7No0SKAnM/rauPGjaxYsYK6ujrS6fTDwKLrrrvu1b7aoxEkIiIiIiIiIrLP6uvrcXfOOeccpk2bxqZNm7j77rtJpVLceeedtLW1cf7559PY2Midd95JJpMBYNmyZaxfv57p06dzwQUXMGLECICcz+uqoaGBJUuWUFxczNy5cwFOA27fW3vUQSKDXosGUgVL2YRL2YRL2YRN+YRL2YRL2YRL2YTrUGUzbtw4rrzySs444wwuvPBCSktLefPNN3nllVdobGzk9NNPZ+bMmUyfPp3a2lo2b95MTU0NGzduZOrUqcyZM4dp06Yxf/58gJzP62r9+vWk02lmz57NrFmzAO4C3nX99ddP7Ks9ed1BYmYPm9n5XbZ9zsw2mdm1h6gOt5nZJXs5ptLM2s3s4122bzazkfHrhr2U8TYz+62Z/cnMNpjZnWZ29P63YN+Y2TQzu6jT+3kd99rM3mdmJ3Xa9xUzm3Oo69hVAj3JKVTKJlzKJlzKJmzKJ1zKJlzKJlzKJlyHKptk8q0n5WzdupXm5mbGjx9PbW0tAMOGDdvj95qaGnbs2JE9fvHixSxevJiVK1cC5HxeV13LAf4a/35cX+3J6w4S4A7gA122fQC4wt1vGID69OZSYDVwWS4nm1kJcC/wfXc/3t0nA98HKg9cFfttGpDtIHH35Z3u9fuAkzrt+1d3//0hrV0PCsgMdBWkF8omXMomXMombMonXMomXMomXMomXIc6m507d/LLX/6SiooKLrzwwm773aMOGzMjlYoWkG1ra+OSSy7hmGOO4YknnmDTpk0H7LwedDzWp8+eo3wfE7UE+HczK3b3VjObAIwBjjez97v7p8ysErgFOCY+53Pu/riZrQfeBewGdgL/4O4/MbOfEs1degi4ATgHKAZudvcfmJkB3wHOBV6lf89bvgz4R+AXZlbl7q/vYzs/CDzp7nd3bHD3hyDbefJ94HQgBXze3R8ysyuJOiySwFTgP4Ei4O+AVuAid99lZg8Da4AzgGHAR9z9KTMbErfzZKI/R18G7gO+ApSa2Wzga0BpfO1fAPOAs83sX4CFwP8D7nH3JWb2HuCbcVlPA5+IM9tMdL/fCxQCl7r7RjM7G/hWR3OBs9y9fh/vGwBFpLI9rGkSZDDS2B4rP7dQQAIoJI3Fx7ZRgOEYnv0HKCop2loUn+8Y7STJQLcykzgJPN4L7fGzyh0oIg1Ej+hKxfXqXqfo2olsnZLZP3CF8fmHc5tKaCdFIq/alC85ddQ7n9qULzmV0E4Cz6s25VNOSTKU0pZXbcqXnEpoz5abL23Kl5yKSJEikVdtypecymgjgedVm/Ilp+i89EFtU319PQUFBezatYs77riDRCLBwoULgbdGbuzYsYP6+np27twJwJAhQyguLgZgzJgxHHvssezevZtXX32Vbdu2UVpaCkQjQNLpdPa84uLibJlVVVWMHTuWXbt28eqrr/Lmm28ybtw4GhoaMDMKCwuzx27fvp2KigqAqrjqfS7SmtcdJO5ebWZPARcAy4hGj/yKPXuNvgX8l7s/ZmbHAPcDk4HHgXcCfwE2EXWW/AR4O/AJ4Cpgt7vPNLNi4HEzWwFMB04g6jg4GngR+FFvdTSzccCouNPhTuD9wI372NSpwDO97PtkfC9ONrMTgRVm9rZO500HSoA/AV909+lm9l/Ah4Gb4uOGuPs7zOysuC1TgS8BD7r7R8ysAngK+D3wr8Dp7v6puH1Xxtd/wsyWE3eIxPs67kEJcBvwHnd/2cx+QnSPO66/091nmNk1wBeAj8a/fzLuzBoKtOzjPctKkaSZom7bu25LE/2j1pP2Hrb1p8y+Hr7VTLKHbT3VqftxUdn9PT/sNnV+nS9t2rPsw7VNCVop7PH8w7dN+ZNTx+/51KY963T4tslJ512beq/T4demzv+u5Uub9l6n8NuUIkmaZF616a1jD+82Jcl023e4t6knh2ObSmnr8bpvHbv/bSovL2f37t384he/oLm5mXPPPZfa2lpqa2s58cQTGTJkCOvXr6e8vJwXX3yRiooKJk2ahJlx1FFH8dprr/HCCy/w/PPPY2ZMmjSJESNG8OCDD/Lss89SUlKSPW/y5MnZ87Zs2cLLL7/Mhg0bMDOOO+44CgsL+fa3v01lZSXXXHMNp5xyCg899BDPPvss6XQaYAHw2HXXXffnXm8K+T/FBvacZvOB+H1nc4DvmtkaYDkwzMzKgVXAWfGv7wMnm1kVsMvdG4DzgA/H5/0BOBKYFB9/h7un3X0r8OBe6vcB4M749S/JcZpNH2YDPwVw941EHT4dHSQPuXu9u+8gGinTMQJlPTChUxl3xOc/SnR/Kojaf23c/oeJOlmOITcnAK+6+8vx+9uJ7mOH38S/P9OpXo8DN5rZZ4AKd8/5Qd9tffzDIQNL2YRL2YRL2YRN+YRL2YRL2YRL2YTrUGVTU1NDU1MT7s4DDzzAr3/9a379619TUFDApZdeSlFREb/73e8YMmQIl156KYlEAjNj4cKFjBgxgvvuu4/m5mYWLFjAUUcdlfN5XZWXl7Nw4UJaWlo61il5Drhyb+3J6xEksaVEH6RnAKXu/qyZndJpfwI4092bO59kZo8Sjb44hmi0xALgEqKOE4imznza3e/vct5F7GVeUxeXAUeb2eXx+zFmNsndX9mHMl4Azu5lX19TfFo7vc50ep9hzz8bXdvjcbkL3f2lPS5mNmuvtd23OnauZ7qjXu5+g5ndS7TeyWozmxN3AB3wi8vAUTbhUjbhUjZhUz7hUjbhUjbhUjbhOlTZTJgwgeuuu67HfePHj+cTn/hEj/uOOuoorrrqqgN6Xtd6TJ48mcmTJ3e8PavbCT3I+xEk8WiPh4mmhnQdPQKwAvhUxxszmxaf9xowEpjk7puAx4imdXR0kNwPfMLMCuPz3havy/Eo8AEzS5rZaODdvdXNzE4gmr5S5e4T3H0C0bodXReW3ZtfAO8ws4s7lX2BmZ0c1+fyjjoSdfi81GMpvXt/fP5somlFu4na/+l4zRXMbHp8bD1Q3ks5ve3bCEwws+Pj938HPNJXhcxsoruvd/f/AP4InLgP7RERERERERHZQ953kMTuAE4lmsLS1WeA081snZm9CFzdad8fgI5pH6uIFnZ5LH5/K9H6Is+a2fPAD4hGN9wFvEI0TeX79P1B/7L4+M5+zT5Os4lHv/wNUYfFK3E7rgTeBL4HJONFZ38FXOnurb0W1rMaM3uCaDHbju66fyNaNHVd3P5/i7c/BJxkZmvM7P1dyvkl8E9m9pyZZZ8/7e4twCLgf+N6ZuJr9eVzZva8ma0FmokWiM1Jx6JJEh5lEy5lEy5lEzblEy5lEy5lEy5lEy5lkxvreGyOSE/ip9h8wd3/ONB1OVgmX3uX97TIkQy8Utp6XIBKBp6yCZeyCZvyCZeyCZeyCZeyCdehyGbzDRfv/aBw9GvW0WAZQSLSq95Wi5aBp2zCpWzCpWzCpnzCpWzCpWzCpWzCpWxyMxgWaQ2Cmd0FHNtl8xe7LvK6lzJOJn4iTSet7p7Lwqj94u7nHKyyQ5HR8lLBUjbhUjbhUjZhUz7hUjbhUjbhUjbhUja50RQbGfTS6bQnk3pEWYjS6TTKJkzKJlzKJmzKJ1zKJlzKJlzKJlzKphtNsRHpj6ampoGugvRC2YRL2YRL2YRN+YRL2YRL2YRL2YRL2eRGHSQiIiIiIiIiMuipg0REREREREREBj2tQSKD3rHX3u2uvsIgGRmUTZiUTbiUzf47mI8t1JzwcCmbcCmbcCmbcCmbbrQGiUh/6C9BuJRNuJRNuJRN2PTFVLiUTbiUTbiUTbiUTW70mF8Z9ApJ6znhgVI24VI24VI2B85zzz3HqlWrqK+vZ/z48cybN49hw4YBkEqluOWWW6iurmbmzJlcdNFFALS0tHDffffx0ksvkclkGD16NIsWLcqW2dDQwM9+9rNu53W1ceNGVqxYQV1dHWPHjmX+/PkMHz784Dd6EGtpaWHo0KEDXQ3pgbIJl7IJl7LJjX6CkkHPUO9qqJRNuJRNuJTNgbF161aWL19OeXk5c+bMYfPmzdx7773Z/Y888gh1dXXdzlu2bBnr169n+vTpXHDBBYwYMWKP/U8++WSP53XW0NDAkiVLKC4uZu7cuWzbto2lS5cekHZJ7/Rta7iUTbiUTbiUTW7UQSIiIiLSxebNmwE47bTTmDVrFqNHj+bll1+mqamJ7du3s3r1as4+++w9zqmpqWHjxo1MnTqVOXPmMG3aNObPn5/dv337dp555plu53W1fv160uk0s2fPZtasWZx44ols2bKFXbt2HfB2ioiIyFsOqw4SM3vYzM7vsu1zZrbJzK49RHW4zcwu2csxlWbWbmYf77J9s5mNjF839HH+BDN7/sDUeO/MbIyZLTnAZX7ZzF43szVm9ryZzTuQ5R9IbZppFixlEy5lEy5lc2AMGTIEgC1btrBz506qq6sBqK2tZfny5cycOZOqqqo9ztmxYwcQjT5ZvHgxixcvZuXKlUD0Td7y5cs5/fTTu53XVW1tLUB2Ok/H7zU1NQemcdKj0tLSga6C9ELZhEvZhEvZ5Oaw6iAB7gA+0GXbB4Ar3P2GAahPby4FVgOXDXRFOphZrz8xu/tWd++z0ydH/+Xu04jux4/MLMg/bxqOHi5lEy5lEy5lc2BMmTKFcePG8cwzz3DzzTeTTqcB+Otf/0ptbS2nnnpqdqpMa2srjY2NpFIpANra2rjkkks45phjeOKJJ9i0aRPPPfcctbW1TJkypdt5e9MxTNqsXwvwS446MpbwKJtwKZtwKZvcHG5fMy0B/t3Mit291cwmAGOA483s/e7+KTOrBG4BjonP+Zy7P25m64F3AbuBncA/uPtPzOynwO3AQ8ANwDlAMXCzu//Aop9GvgOcC7xK/x4PdBnwj8AvzKzK3V8/EI03s9OAG4GhcRuudPdtZvYx4O+BIuBPwN+5e5OZ3QbsAqYDz5rZkUAdcDowCvhnd18S38d73H2qmV0JzAPKgInAXe7+z/H1rwK+CGwFXgFa3f1Te6u3u28wsxQw0sz+GxgHlADfcvf/7qvsPvI8G/hWxyWAs9y9fh9vKaAPEyFTNuFSNuFSNgdGQUEBixYtYvv27SQSCe6//362bNnC7t27aWpq4pZbbskeu27dOpLJJKeffjoA48ePZ/LkyTQ2NvLqq6+ya9cuGhoaaGpq4kc/+lG38+bNm0cqlcLMSCaTVFRUAGQ7Uurro//eOrbLwaH5+uFSNuFSNuFSNrk5rDpI3L3azJ4CLgCWEY0e+RXs8dPgt4hGLjxmZscA9wOTgceBdwJ/ATYRdZb8BHg78AngKmC3u880s2LgcTNbQdS5cAJwMnA08CLwI3phZuOAUe7+lJndCbyfqFNjv5hZIVFHzXx332Fm7we+CnwE+I27/zA+7t/jtnwnPvVtwBx3T8cdJqOB2cCJwHKiTqeuphG1uxV4ycy+A6SB/wfMAOqBB4G1/az7LCAD7AA+4u67zKwUeNrMfk3UIdVb2b3l+QXgk3FnyVCgpT916UkJ7RSQASBNggxGGqOEVPaYFgpIED0douPDRxsFGI7h2fNTJOItRlF8vmO0kyQD3cpM4iTweC+0k4zPgSKiXt8MRiquV/c6RddOZOuUzPbgFcbnH85tKqGdOiyv2pQvORXHV8ynNuVLTiW000Imr9p0qHNqaWnB3XnooYcYMWIEb7zxBps2bWLWrFmcfPLJ2YVXq6urefLJJznuuOM4+eSTGTp0KCNHjmTTpk384Q9/YN26dZgZRx55JJWVlYwYMYLW1lZqamp48sknmTBhAjNmzKC9vZ3Fixdz5JFHcsUVV3DiiSfywAMPsGrVKqqrq9mwYQNjx45lyJAhpNNpmpqasm0qKyvD3bN1hmhYdTqdxt1pb2+P7k1hYbYDprm5GYhGpJSUlGBm3cpMpVJkMpnsqJiioiIAEokELS0t2ddFRUUkEolu52cyGdra2shkontaUlKSfd3W1gZEnVCJRIKCgoIg2tTQ0MCwYcPyqk35klNjYyPJZDKv2pQvOdXW1jJ06NC8alO+5JROpzGzvGrT/uRUXl5OfxxWHSSxjmk2HR0kHwFO6bR/DnBSp2Gow8ysHFgFnEXUQfJ94O/NrArY5e4NZnYecEqn9UWOACbF59zh7mlgq5k9uJf6fQC4M379S+B/OAAdJESdNFOBlXHbksC2eN/UuGOkgmh0yf2dzvvfuO4dlrp7BnjRzI7u5VoPuPtuADN7ERgPjAQecfdd8fb/Jep86cs/mNmHiDo93u/ubmafMbMF8f5xRPd4VB9l95bn48CNZvZzog6iv+6lLr1KkaSZom7bu25LQ6+PzmzvYVt/ykx1O6Lzscl+1qn7cVHZ/T0/7DZ1fp0vbdqz7MO1TQlaKezx/MO3TfmTU8fv+dSmPet0cNtUUlKCu7NlyxaeffZZioqKmDlzJnPmzKGgoIBRo0YB0UKuTz75JEceeSTHHnssAJdeeil33303K1eu5IgjjmDBggXZfRCNBunoWKmsrGTs2LHZfYlEIvsD3MKFC1m5ciWPPvooVVVVzJ8/n+LiYqDnH/K6PsqxoKAg25auejq/67Zksud7DNEPs/05v6fjgGw79langWhTx9ozvZ1/OLZpb+cfDm1qa2ujsLAwr9rU4XBvUyqV6rbvcG9TTw7HNtXX1/d43Q6HY5s66tSb/rRpbw7HDpKlRB+MZwCl7v6smXXuIEkAZ7p7c+eTzOxR4JNEUzW+BCwALiHqOIFo6syn3f3+LuddxJ4jVPbmMuBoM7s8fj/GzCa5+yv7UEZPDHjB3c/sYd9twPvcfW08ReacTvu6Tm5u7VJmTzofkyb6c5LLxOf/cvdvZi9mdg5Rh8eZ8RSgh4mm2vRVdo95AjeY2b3ARcBqM5vj7htzqCOpw24pnsFD2YRL2YRL2RwYZsbVV1/d5zETJkzguuuu22PbUUcdxVVXXdXrOYWFhT2e1/X95MmTmTx58j7WWvZHbx8CZOApm3Apm3Apm9wcdj9FuXsD8DDRNJc7ejhkBZBdF8PMpsXnvUY0CmKSu28CHiOaptHRQXI/8Il4Kgtm9jYzGwI8CnzAzJJmNhp4d291M7MTgCHuXuXuE9x9AvA1ui8sm4uXgEozOzO+VqGZTYn3lQPb4rpf3lsB++kp4GwzGx4v+LowhzKOAGrizpETiaY37a3sHvM0s4nuvt7d/wP4I9GUoZx4Tn0/cigom3Apm3Apm7BpodVwKZtwKZtwKZtwKZvcHHYdJLE7gFOJprB09RngdDNbF08P6fz1zx+Al+PXq4Aqoo4SgFuJ1hd51qJH7P6AaOTEXUSLhq4nmprzSB/1uiw+vrNfk9vTbE4ws792/ALmE414+Q8zWwusAd4RH/v/4ratBHIaRbE38UKzi+Pr/J7oXu3ex2J+BxSY2Trg34ie9LO3snvL83MWPT54LdAM3Jdz2/RhIljKJlzKJlzKJmx9DQ2WgaVswqVswqVswqVscmNa3Vb6y8yGxuu1dHQc/cjdu3YIBVf23ky+9i7vad68DLxS2npc00AGnrIJl7LZf5tvuPiglV1fX7/P86Hl0FA24VI24VI24VI23fTrG6TDdQSJDIwvm9ka4HmiRx4vPUzKFhEREREREenT4bhIaxDM7C7g2C6bv9h1kde9lHEy8NMum1vdfdb+1u9gcPcvdN1mZl8CLu2y+X/d/av7W/ahouHo4VI24VI24VI2YdOc8HApm3Apm3Apm3Apm9xoio0MeqlUyjseNyVhSaVSKJswKZtwKZuwKZ9wKZtwKZtwKZtwKZtuNMVGpD/UuxouZRMuZRMuZRM25RMuZRMuZRMuZRMuZZMbdZDIoNfU1DTQVZBeKJtwKZtwKZuwKZ9wKZtwKZtwKZtwKZvcqINERERERERERAY9TUqSQe+Mr/5ej8QMlB5XmpuD+YhSEREREZF8pREkMui1qJ8wWMomXGVlZQNdBemFsgmb8gmXsgmXsgmXsgmXssmNPn3IoJfESQ10JaRHymb/pVIpbrnlFqqrq5k5cyYXXXQRt956Kzt27CCTyVBZWcn555/P+PHjAXjuuedYtWoV9fX1jB8/nnnz5jFs2DAAbrrpJnbv3p0t++ijj+bqq6/u8bobN25kxYoV1NXVMXbsWObPn8/w4cMPfoOFVCpFMpkc6GpIL5RPuJRNuJRNuJRNuJRNbjSCRAa9BHrUdaiUzf575JFHqKur22Pb2LFjueCCCzjrrLN44403WL58OQBbt25l+fLllJeXM2fOHDZv3sy99967x7njx49n4cKFvPe972XOnDk9XrOhoYElS5ZQXFzM3Llz2bZtG0uXLj0o7ZPuMpnMQFdB+qB8wqVswqVswqVswqVscqMOEhn0kugfj1Apm/2zfft2Vq9ezdlnn73H9vPPP58TTjiB4447joKCguxj4DZv3gzAaaedxqxZsxg9ejQvv/zyHqugV1RUMGnSJCZNmsTxxx/f43XXr19POp1m9uzZzJo1ixNPPJEtW7awa9eug9NQ2UMqpXFXIVM+4VI24VI24VI24VI2udEUmzxjZhOAe9x96qE8t48yi4CvA+8FMsCLwCfd/a/x/lHATcBMoBXYDHwOmAt8rFNRBcAU4CTgaGAZ8Gqn/V9w998fqHqLHO7cneXLlzNz5kyqqqr22Nfa2so3vvENAEpKSpg3bx4AQ4YMAWDLli2MGTOG6upqAGpra7PzWNeuXcvatWspLS1lzpw5zJgxo9u1a2trAbJTczp+r6mpYcSIEQe4pSIiIiIiB4ZGkMjBthgoB97m7pOApcBvLAbcBTzs7hPd/STg/wJHu/vN7j6t4xewHPi5u2+Iy13Vef/+dI60o7l5oVI2uXvuueeora3l1FNPzU6xaW1tpbGxkaKiIj70oQ9xwQUXkEqleOihhwCYMmUK48aN45lnnuHmm28mnU4DUFAQ9aXPmDGDSy+9lAULFlBQUMA999xDTU3NXuviHk2V6hipIgdXUZGe/BQy5RMuZRMuZRMuZRMuZZMbjSDJTwVmdjswHXgZ+DDwBaJRHKXAE8DH3d3N7DTgR0AT8FhHAWZWBtwGnAhsACYQjfz4o5mdB1wPFAN/Bha5e0PXSsRlLAKOdfc0gLv/2Mw+ApwLONDu7rd0nOPua3oo5yzgb4HuX1WLSI/q6upoamrilluyf71Yt24dyWSSefPmMXHiRCZOnMiGDRvYvHkzTU1NlJWVsWjRIrZv304ikeD+++9ny5Yt2cVVzzrrrGxZf/3rX3n66aeprq5m+PDhpFIpzIxkMklFRUW2DgD19fUA2e0iIiIiIiFSB0l+OgG4yt0fN7MfAdcA33X3rwCY2U+BvwHuBn4MfNrdHzGzb3Qq4xqgxt1PMbOpwJr43JHAvwBz3L3RzL4IfB74Sg/1OB7Y4u51Xbb/kWi6DMAzfTXEzCriOn64SznvMrM1nd4vdPc/91VWb5KkKST6pjxNggxGGqOk0/NTWiggARSSxuKFQ9sowHAMpyBeKyNFIt5iFMXnO0Z7vJpG1zKTOAk8u9ZGx4gJB4riOmUwUnG9utcpunYiW6ckHd/R50ObkmRohrxq06HIqb6+ngkTJjBs2DAKCwvZuXMnjz76KBMmTGDIkCEsXbqUMWPGsHv3bl577TXKyspIJBI0Nzfz4IMPcuSRR/LGG2+wadMmzjjjDBKJBJs2beLxxx9nwoQJFBQU8Pzzz1NQUMCQIUOor6/nxhtvZOTIkXz0ox9lwoQJJJNJVq1aRW1tLRs3bqSqqorCwkLq6+spKyvD3WlpacmOLiktLSWdTuPutLe3R/emsDDb6dLc3AxEo1BKSkowsz3WRikrKyOVSpHJZLJzbju+OUkkErS0tGRfFxUVkUgkup2fyWRoa2vLLmpWUlKSfd3W1gZEo2kSiQQFBQXdzg+lTe3t7bS1teVVm/IpJ3fPdhrmS5vyJaf29nZSqVRetSlfcmpvb8+7NuVLTs3NzbS1teVVm/Ilp8LCQlpbW/OqTfuTU3l5Of1hHZWV/BCvI/Koux8Tvz8X+AzwU+CfgTJgBPAd4PvA+k7HngL8wt2nmtlS4Fvu/lC871ng74FRRCNL/hpfsgh40t2v6qEupwI/dvcZXbbfRDTyxIhGl/xDH+25A3jZ3a/rtO0cojVH/qaft6VPk6+9y5vRELQQldKGstl3m2+4eM/3mzdz++23M3PmTE499VSWLVtGTU0NyWSS0aNHM2fOHKqqqnB3fvCDH7Bz506KioqYOnUq5513HgUFBdTX17N8+XJef/112tvbOfLII5kzZ052odbrr7+eyspKrrnmGgA2bNjAypUrqauro6qqivnz52v9kUOkvr6+3z8EyKGnfMKlbMKlbMKlbMKlbLrp11xvjSDJT117vRz4HnC6u79mZl8GSoj+kPTWQ9bbHyADVrr7Zf2ox5+A8WZW7u71nbbPIBq9AnBJbyeb2RVEU3v+rh/XEpFeTJgwgeuuy/YxZjsxujIzrr766h73lZeXc/nll2ffd/1Pt3P5AJMnT2by5Mn7U20RERERkUNKi7Tmp2PM7Mz49WW8tbbITjMbStwp4e61wG4zmx3vv7xTGY8RrfuBmZ0EnBxvXw2808yOj/eVmdnbeqqEuzcCtwM3mlkyPv7DRKNYHox/FZtZ9mk1ZjbTzM42s+OArwKXu/tBfUZVpn+diTIAlE24Egn99xEqZRM25RMuZRMuZRMuZRMuZZMb3bX8tAG4wszWEU2n+T7wQ2A90VNknu507CLgZjN7EmjutP17QGVcxheBdcBud98BXAncEe9bTbSQa2/+P6AFeNnMXgEuBRZ4DFgAzDWzP5vZC8CXga3xNYcQPfFmTadf74rLfVeX7b2ORNmblP4aBEvZhEsro4dL2YRN+YRL2YRL2YRL2YRL2eRGa5BIj+IRH4Xu3mJmE4EHiB7V2zbAVTvgjr32bnd9EA+SkUHZ7Luua5AcDOl0mmRSj2EOkbIJm/IJl7IJl7IJl7IJl7Lppl9D0/XJQ3pTBjxmZmuBu4BP5GPnCOz5xBIJi7IJV+cVxSUsyiZsyidcyiZcyiZcyiZcyiY3WqRVehQvqnp6f483s7uAY7ts/qK7339AKyYiIiIiIiJyEKiDRA4Id18w0HXI1VNfmqNHYAVKjycTEREREZFDRWuQyKCXTqdd8/PCpLmT4VI24VI2YVM+4VI24VI24VI24VI23WgNEpH+yGQyA10F6YWyCZeyCZeyCZvyCZeyCZeyCZeyCZeyyY06SGTQa2vLy7Vn84KyCZeyCZeyCZvyCZeyCZeyCZeyCZeyyY06SGTQU+9quJRNuJRNuJRN2JRPuJRNuJRNuJRNuJRNbrRIqwx6Z3z19zRTNNDVkB6U0qZsDoLNN1w80FUQEREREQmORpDIoNeGFi8KlbIJV0lJyUBXQXqhbMKmfMKlbMKlbMKlbMKlbHKjESQy6PVrOWMZEMrm4EulUtxyyy1UV1czc+ZMLrroIm666SZ2796dPeboo4/m6quvBmD16tWsXr2ahoYGysvLefvb386sWbP2uq+rjRs3smLFCurq6hg7dizz589n+PDhB7/Bg4CG1IZN+YRL2YRL2YRL2YRL2eRGI0hERAaxRx55hLq6um7bx48fz8KFC1m4cCFz5swBoLq6mvvvvx8z49xzzyWdTvO73/2O3bt377HvvPPO22NfVw0NDSxZsoTi4mLmzp3Ltm3bWLp06cFuqoiIiIhIn9RBIoNeIemBroL0QtkcXNu3b2f16tWcffbZ3fZVVFQwadIkpk6dyvHHHw+AuwMwbNgwqqqqGDp0KMlkkoKCgj32HXfccXvs62r9+vWk02lmz57NrFmzOPHEE9myZQu7du06iK0dPLRqfdiUT7iUTbiUTbiUTbiUTW40xUb2mZmdA7S5+xMHuMwvuPvfmNk84CR3v+FAlS8ie3J3li9fzsyZM6mqquq2f+3ataxdu5aysjLe8573MGPGDEaOHMl73vMeHnjgAW677TbMjPnz5zNkyBCGDBmS3XfzzTfvsa+r2tpaIOpM6fx7TU0NI0aMOHiNFhERERHpg0aQDHJmlksn2TnAOw5wVbLcffmh7BxJ669BsJTNwfPcc89RW1vLqaeemp1i09raSmNjIzNmzODSSy9lwYIFJJNJ7rnnHmpqamhsbOSpp55i1KhRLFiwgKOPPprf/va31NXV7bHv/e9//x779qZj9ImZVp05EHoatSPhUD7hUjbhUjbhUjbhUja50V0bBMzsw8AXAAfWAWlgFzAdeNbMvgfcDFQCTcDH3H2jmb0X+BegCKgGLgdKgauBtJl9CPg0sBG4BTgmvuTn3P3xXupyBnBTXE4zsMjdX+pyzJXA6e7+qZ7q4O7bzexs4FvxKQ6c5e71udyfjJYCDZayOXjq6upoamrilltuyW5bt24dyWSSefPmZbdt27aN1atXU11dTWtrK/X19Zx++umccMIJ1NbW8tBDD/Haa68BZPedeOKJvPnmm9l9U6ZMIZVKYWYkk0kqKiqydeg4D8hul/2TSKhjMWTKJ1zKJlzKJlzKJlzKJjfqIMlzZjYF+BLwTnffaWYjgBuBtwFz3D1tZg8AV7v7K2Y2C/gecC7wGPB2d3cz+yjwz+7+j2Z2C9Dg7t+Mr/EL4L/c/TEzOwa4H5jcS5U2EnVmpMxsDrAYWNhHE7rVAfhHog6fT7r742Y2FGjJ+R6RoTRe6yJNggxGGqOEVPaYFgpIEK2JYUTfdrdRgOEYTgHRKtEpEvEWoyg+3zHaSZKBbmUmcRJ4vBfa48faOlAU1ymDkYrr1b1O0bUT2Tols10KhXnQJiODQ161KYSc0uk0kyZNoqKignQ6TXV1NU8++STHHXccJ510Ej/96U+ZMGECEE21KSgoYMiQIdmRHuvWraO4uJi1a9cCUFpamv2WouP4jn3l5eWk02m++tWvcuSRR3LFFVcwefJkHnjgAR599FGqq6vZsGED48aNY8iQIbS0tNDe3h7dm8LCbKdKc3MzEI0yKSkpwcxoamrKtqmsrIxUKkUmkyGVitpaVFQERD8gtLS0ZF8XFRWRSCS6nZ/JZGhra8uu+l5SUpJ93TGPt6CggEQiQUFBQbfz3Z2WlpbsfSotLSWdTuPuh7RN6XSatra2vGpTPuVkZtlOwXxpU77klE6nSaVSedWmfMkpnU7T3t6eV23Kl5xaW1tpa2vLqzblS07FxcW0trbmVZv2J6fy8nL6wzoqK/nJzD4NjHL3L3XadhvwkLvfHncu7AA6j+IodvfJZnYy8J/AaKIRHK+6+wVm9mX27CB5E9ja6fxK4MSeRnSY2Tjg28Akos+Xhe5+Ypc1SK7krREkvdXhWmAB8HPgN+7+11zv0eRr7/JminI9XQ6iUtpQNgfe5hsu3vP95s3cfvvtzJw5k3e9610sX76c119/nfb2diorKzn33HOzC7U++eSTPPXUU9TX11NeXs6ZZ57JGWecsdd9119/PZWVlVxzzTUAbNiwgZUrV1JXV0dVVRXz58/X+iMHSMf9lzApn3Apm3Apm3Apm3Apm276NTRdI0jynwE99YI1xr8ngFp3n9bDMd8BbnT35XEHxpd7uUYCONPdm/tRn38j6pxZYGYTgIf3cnyPdXD3G8zsXuAiYLWZzXH3jf24voh0MWHCBK677rrs+8svv7zXY88880zOPPPMHv/T7djXk87lA0yePJnJk3sbaCYiIiIicuhpYlL+ewD4WzM7EiCeYpPl7nXAq2Z2abzfzOzUePcRwOvx6ys6nVYPdP5ktAL4VMcbM5vWR306l3llP+rfYx3MbKK7r3f3/wD+CJzYj7JEREREREREeqQOkjzn7i8AXwUeMbO1ROuPdHU5cFW8/wVgfrz9y8D/mtkqYGen4+8GFpjZGjN7F/AZ4HQzW2dmLxIt4tqbrwNfM7PHIV7IoW+91eFzZvZ8XOdm4L5+lNWjFg2kCpayCVdZWdlAV0F6oWzCpnzCpWzCpWzCpWzCpWxyozVIZNCbeO3drsfJhilJRo/6PQi6rkGSi1QqpcfHBUrZhE35hEvZhEvZhEvZhEvZdNOvNUj0yUMGvY6niEh4lE24OlYJl/Aom7Apn3Apm3Apm3Apm3Apm9yoS0kOCjNbBHy2y+bH3f2TA1GfvliPa9hKCJRNuDT6MFzKJmzKJ1zKJlzKJlzKJlzKJjfqIJGDwt1/DPx4oOvRH099aY4egRUoPZ5MREREREQOFa1BIoNeKpVyzc8Lk+ZOhkvZhEvZhE35hEvZhEvZhEvZhEvZdKM1SET6I53WOhehUjbhUjbhUjZhUz7hUjbhUjbhUjbhUja5UQeJDHoaRRUuZRMuZRMuZRM25RMuZRMuZRMuZRMuZZMbjbmRQW/6l++jmaKBrob0oJQ2ZROoQ53NgXg08WDR3t5OSUnJQFdDeqF8wqVswqVswqVswqVscqMRJCIiIiIiIiIy6GkEiQx6KfUTBkvZhGugsrnpppvYvXt39v3RRx/N1VdfTUtLC/fddx8vvfQSmUyG0aNHs2jRItasWcOyZcu6lfPZz36WiooKnnvuOVatWkV9fT3jx49n3rx5DBs2rMdrb9y4kRUrVlBXV8fYsWOZP38+w4cPP2htzVVhYeFAV0H6oHzCpWzCpWzCpWzCpWxyow4SGfS8fwsaywBQNuEayGzGjx/P6aefDpAdOrps2TJeeuklZs2aRWVlJa+99lr22IULFwKQyWRYvnw5JSUllJeXs3XrVpYvX84xxxzDrFmzWLlyJffeey+XXXZZt2s2NDSwZMkSKisrmTt3Lg8++CBLly5l0aJFh6jV/WemvzchUz7hUjbhUjbhUjbhUja5UQeJ5MzMnnD3d/Sw/TbgHndfkkOZ04Ax7v7b+P084CR3v8HMKoF7gCLgM+6+an/q30EfwsOlbMI1kNlUVFQwadIkiouLAaipqWHjxo2cfPLJzJkzBzNjxowZAAwfPjw7yuPFF18knU4zffp0kskkmzdvBuC0007jlFNO4fnnn+fll1+mqamJsrKyPa65fv160uk0s2fPZsqUKWzdupV169axa9cuRowYcega3w/JZHKgqyB9UD7hUjbhUjbhUjbhUja5UQeJ5KynzpEDYBpwOvDb+BrLgeXxvvcAG939igN5wSJSWgg0UMomXAOZzdq1a1m7di1lZWW85z3vYejQoQBs3bqVxYsXY2bMmjWLuXPn7nHeM888g5lx2mmnATBkyBAAtmzZwpgxY6iurgagtra2WwdJbW0tQHb6TcfvNTU1wXWQNDc3U15ePtDVkF4on3Apm3Apm3Apm3Apm9yog0RyZmYN7j7UovFb3wHOBV6Ft75aNrPTgBuBocBO4Ep332ZmDwN/AN4NVABXxe+/ApSa2Wzga0ApUYfJrcDX431rgJXAX939W/F1vgpsd/dvH+Rmi8gAmjFjBiNHjiSVSvH73/+ee+65hzlz5gDQ1tbGJZdcwtNPP80TTzzBxIkTOe644wDYtWsXmzZtYtKkSVRUVAAwZcoUnnnmmeyvoqKow6egYO//NXY8Ok/DV0VERETyhzpI5EBYAJwAnAwcDbwI/MjMCok6Tua7+w4zez/wVeAj8XkF7n6GmV0EXOfuc8zsX4HT3f1TAGZ2JYC7r+m8z8wmAL8BvmVmCeADwBm5VF7TOMKlbMI1UNmcddZZ2dfbtm1j9erVZDIZIFpvZPLkyTQ2NvLqq6+ya9eubAfJM888A5BduwSijpBFixaxfft2EokE999/P1u2bMlOyUmlUpgZyWQy26lSV1cHQH19PUB2e0jUaRM25RMuZRMuZRMuZRMuZZMbdZDIgXAWcIe7p4GtZvZgvP0EYCqwMv4LmgS2dTrvN/HvzwAT9uWC7r7ZzKrNbDpRp8xz7l6dS+UdKKUNgDQJMhhpjBJS2WNaKCABFJLGiL45bqMAwzGcAqIPaCkS8RajKD7fMdpJkoFuZSZxEni8F9pJZutURBqADEYqrlf3OkXXTmTrlMx+bC2Mzz+c25QkQ5JEXrUpX3LKYBTTfsjaVF9fz44dO3j88ceZNGkS7s7atWspKCjguOOOY/369WzatInVq1ezdu1azIxRo0bR2tqKu/Pcc89RXl7OmDFjaG9vJ5FI0NDQwCOPPMJRRx3Fzp072bRpE6eddhotLS20tLRw4403MnLkSD760Y8yYcIEkskkq1atora2lo0bN1JVVUVhYSH19fWUlZXh7rS0tGRHl5SWlpJOp3F32tvbo3tTWJjtdGlubgaiH2BKSkowM5qamrL3qaysjFQqRSaTIZWK7l/HKJdEIkFLS0v2dVFREYlEgqamJlKpVLZOmUyGtra2bCdSSUlJ9nVbW/TvXkFBAYlEgoKCgm7XD6VNnc8/3NvU8Wcmn9qULzmlUikaGxvzqk35kpOZ0d7enldtypec2tvbqa+vz6s25UtORUVFtLa25lWb9ien/k43so7KiuyrTlNsbgLWuvuP4+2/AX4BvAT8t7uf2cO5DwNfcPc/mtlI4I/uPiEeMdJ1BEnHqJGu+94PvAMYBdzesbDrvjr22rvd9TjZIBkZlE2YDnU2m2+4mPr6epYvX87rr79Oe3s7lZWVnHvuuRx//PG8+eab3H333Wzbto0jjjiCc845h5NPPhmA559/nl//+te8+93v3mMEirvzgx/8gJ07d1JUVMTUqVM577zzslNsrr/+eiorK7nmmmsA2LBhAytXrqSuro6qqirmz58f3PojAOl0WguzBUz5hEvZhEvZhEvZhEvZdNOvITXqIJGcdeog+T/Ax4GLgKOIpth8jGhx1ReBv3P3J+MpN29z9xf66CBZCMzrWIh1Lx0kRcB6oBCYFI9g2WeTr73LtRBomEpp0yKtgTrU2Wy+4eJDdq3DXX19vRZlC5jyCZeyCZeyCZeyCZey6aZfHST6alYOhLuAV4g6K74PPALg7m3AJcB/mNlaYA3RiI++PAScZGZr4hEivYrLfwi4M9fOERERERERERHQGiSyH9x9aPy7A5/q5Zg1RGuUdN1+TqfXO4nXIHH3XcDMLoffFu+7reM1QLw469uBS3NrgYiIiIiIiEhEI0jksGRmJwF/Ah5w91f2p6wW9RMGS9mES9mEq6ysbKCrIH1QPuFSNuFSNuFSNuFSNrnRT7hyWHL3F4HjDkRZSbzT8zUkJMomXMomXKlUSouyBUz5hEvZhEvZhEvZhEvZ5EYjSGTQ63hMqYRH2YRL2YSr4/F7EiblEy5lEy5lEy5lEy5lkxuNIJFB75kvnasVngOl1bfDpWzClUppbE/IlE+4lE24lE24lE24lE1uNIJERERERERERAY9dZDIoFdUVDTQVZBeKJtwKZtwKZuwKZ9wKZtwKZtwKZtwKZvcqINERERERERERAY9rUEig95J191PGq3wHKIkaWUTKGUTLmUTNuUTLmWzd5tvuHhArptI6DvdUCmbcCmb3OiuyaBXRHqgqyC9UDbhUjbhUjZhUz7hUjbhamlpGegqSC+UTbiUTW7UQSIiIiIiIiIig56m2Migl8EGugrSC2UTLmUTLmUTNuUTLmXTP/fddx8vvPACjY2NTJo0iQ9+8IPZfalUiltuuYXq6mpmzpzJRRddRHV1Nffccw/bt28nnU4zduxYLr74YkaMGAFE33Lfd999vPTSS2QyGUaPHs2iRYsAWL16NatXr6ahoYHy8nLe/va3M2vWrB7rtXHjRlasWEFdXR1jx45l/vz5DB8+/ODfkEFO0zjCpWxyo7smg15Kfw2CpWzCpWzCpWzCpnzCpWz6b8qUKT1uf+SRR6irq9tjW319Pe7OOeecw7Rp09i0aRN33313dv+yZctYv34906dP54ILLsh2nFRXV3P//fdjZrznPe8hnU7zu9/9jt27d3e7bkNDA0uWLKG4uJi5c+eybds2li5deuAaLL3Sk1LCpWxyoxEkMujpG6NwKZtwKZtwKZuwKZ9wKZv+ufDCC6mtreWpp57aY/v27dtZvXo155xzDr///e+z28eNG8eVV16Zfb9+/XrefPNNAGpqati4cSMnn3wyc+bMwcyYMWMGAO4OwLBhw5g4cSLr16+nqamJgoLuH1/Wr19POp1m9uzZTJkyha1bt7Ju3Tp27dqV7XCRg0OjFMKlbHKjuybBM7PbzOyS+PXDZvaSma0zs41m9l0zq9if8ktIHZB6yoGnbMKlbMKlbMKmfMKlbHLn7ixfvpyZM2dSVVW1x75k8q0nA23dupXm5mbGjx8PwI4dO7LbFy9ezOLFi1m5ciUAI0eO5D3veQ9btmzh+9//Pm+88Qbvfe97GTJkSLfr19bWAlFnSuffa2pqDmxDpZumpqaBroL0QtnkRh0kcji63N1PAU4BWoFlA1wfERERkUHrueeeo7a2llNPPTU7xaa1tZXGxsbsMTt37uSXv/wlFRUVXHjhhUC0ZglAW1sbl1xyCccccwxPPPEEmzZtorGxkaeeeopRo0Yxb948jj76aH772992m8LTk47RJ2YaFSQi+0ZTbKRfzOzDwBcAB9YB/wL8CKgEdgCL3H2Lmd0GNAMnAuOBRcAVwJnAH9z9yri8BuAHwLuBGuAD7r5jX+rk7m1m9s/An8zsVHdfm0vbCkhTShsAaRJkMNLYHt8ktVBAAigkjRH9p9tGAYZjOAVkgGj+ssdbi+LzHaOdJBnoVmYSJ4HHe6GdZHzOW48bzGCk4np1r1N07US2TsnsAOHC+PzDuU0ltOddm/IlpyQZimnPqzblS04ltOddm/IpJ8Oz/+fkS5vyJacS2vOuTQc6p/r6eiBa8wOijoiGhgZ27NhBU1MTt9xyS7b8devWkclkuPjii6muruaOO+4gkUiwcOFCzIxUKpUd6TFmzBjGjh1LfX09r776Ktu3b2fXrl3U19czbdo0jjnmGGpra3n00Ud55ZVXOPHEE0kkEiSTSVpbWyktLQWikSSjRo3KjhwpLCykvb2dTCa6P21t0d+9goICEokEBQUFe3zLXlZWhrvT0tKS7WQpLS0lnU7j7rS3t2fLNTOSySTNzc1A1BlTUlKCmXUrM5VKkclksh1CHWtDJBKJ7KNYE4kERUVFJBKJbudnMhna2tqy7SgpKQmmTR1/FvKpTfmSUzqdprW1Na/atD85lZeX0x/qIJG9MrMpwJeAd7r7TjMbAdwO/MTdbzezjwDfBt4XnzIcOBeYB9wNvBP4KPC0mU1z9zXAEOBZd/9HM/tX4DrgU/taN3dPm9laog6ZnDpIGijGexhM1cyeCxuliX5I6El7D9u6nt/Ttr4G8zaT7GFbT3XqflxUdn/PD7dNLRTskU0+tKmrw7VNjRT1+PcmKvvwbFO+5NT5702+tKl7nQ7fNjVj3f7uHO5t6r1Oh1ebuv6fkw9t6l+d+t+m8vJyXn755ewaIvX19bz88suceuqpjBs3DoA333yTRx55hOOPP54zzzyT1tZWfvGLX9Dc3My5555LbW0ttbW1TJ06laqqKo466ihee+01Xn75ZdatW4eZMXHixOyHnxdffJFhw4bx4osvAlBVVcWQIUO4/vrrqays5JprruG0007jscce48knn6SpqYk//elPHHPMMdk6dSguLu6xTV0NHTp0j/cd656UlJT06/yu2zpPM+qqsLCwX+f3dBwMfJuKioq6te9wb1NPDsc2pdPpvGtTR51605827Y2m2Eh/nAsscfedAO6+i2hEyC/i/T8FZnc6/m6PugnXA9vdfb27Z4AXgAnxMRngV/Hrn3U5f1/t1/jJjm9NJDzKJlzKJlzKJmzKJ1zKpn+eeOIJHnjgASBamPXuu+/mjTfe4KSTTuKkk05iwoQJAAwfPpwxY8ZQU1NDU1MT7s4DDzzAr3/9a379618D0bfECxcuZMSIEdx33300NzezYMECjjrqKMaMGcN5552XfXpNKpXiwgsvZNSoUd3qVF5ezsKFC2lpaWHlypWMGjWK+fPnH7J7Mph1jCaQ8Cib3FjHcBeR3pjZZ4Cj3P1fOm3bCYx293YzKwS2untlPMXmHndfYmYT4tdT43M670sDxe6eMrPjgF+7+/Rert/5vIeBL7j7H+N9SeAV4H3uvi6X9p1w7VJvpeeeURlYxbSjbMKkbMKlbMKmfMKlbPZu8w0XD8h1Gxsbe1ycVQaesgmXsummX1+qawSJ9McDwN+a2ZEA8RSbJ4APxPsvBx7bxzITwCXx6w/mcD5xx8zXgNdy7RyJKqJOwlApm3Apm3Apm7Apn3Apm3Dpm/BwKZtwKZvcaA0S2St3f8HMvgo8Eo/8eA74DPAjM/sn4kVa97HYRmCKmT0D7Abevw/n/tzMWoFi4PeAxlCKiIiIiIjIftEUGxkQZtbg7kP3fuTBN/Ha5d7bAmUysJKke108TgaWsgmXsgmb8gmXstm7gZpi097e3utCkTKwlE24lE03mmIj0h/7tcKrHFTKJlzKJlzKJmzKJ1zKJlyaKhAuZRMuZZMbTbGRAdHT6BEzu5nokcCdfcvdf3xoaiUiIiIiIiKDlabYyKBXX1/v+/p8bDk06uvr9/nZ5XJoKJtwKZuwKZ9wKZtwKZtwKZtwKZtuNMVGRERERERERKQ/1EEig15BgWaahUrZhEvZhEvZhE35hEvZhEvZhEvZhEvZ5EYdJDLoJRL6axAqZRMuZRMuZRM25RMuZRMuZRMuZRMuZZMbdSvJoHfidSvwPO4rHKhH8h0I6vkOl7IJl7IJm/IJl7IJl7IJl7IJl7LJTf5+KhTppxJSA10F6UVTU9NAV0F6oWzCpWzCpnzCpWzCpWzCpWzCpWxyo24lkUHivvvu44UXXqCxsZFJkybxwQ9+EIBbb72VHTt2kMlkqKys5Pzzz2f8+PGsWbOGZcuWdSvns5/9LBUVFWzZsoV7772X6upqKisrmTdvHqNHj+7x2hs3bmTFihXU1dUxduxY5s+fz/Dhww9qe0VERERERPaFRpCIDCJTpkzptm3s2LFccMEFnHXWWbzxxhssX74cgPHjx7Nw4UIWLlzIggULSCaTDBkyhPLyclKpFHfeeSdtbW2cf/75NDY2cuedd5LJZLqV39DQwJIlSyguLmbu3Lls27bt/2/vzuOsLO/7/78+55wZZgYGARkUBgV3RokKgsSEEI0YXCLEos2idUn6TZVs7a/mW1vbIPnWxFabJm3SmtYmmj0EK+IOrhgVUZBFBbXiiBVUGBhmX845n98f555xVhgOM8zFOe/n4zEPzlz3dl3nzczAZ677ulm6dOlAD1VERERERGS/qEAiea8pTyZSXXDBBZx11lnd2ufMmcNJJ53EscceSyKRwCzziPCRI0cyefJkJk+eTCKRIJVKMWXKFOLxOG+88Qb19fVMmzaN6dOnM2XKFKqrq6msrOx2/o0bN5JKpZg5cyYzZsxg0qRJbN26lV27du2zzyUlJQc8bhkYyiZcyiZsyidcyiZcyiZcyiZcyiY7+fE/Q+kzM7sR+CKQAtLAnwH/B/i+u786gNe9Cahz99t62X4ncL+7L+nva8fIDDZfNTc3c+uttwJQVFTE3Llzu+2zZs0azIwzzjgDgOrqagCGDx/e6c/du3d3O3Zv+44aNWqvfXP3/RyNHCzKJlzKJmzKJ1zKJlzKJlzKJlzKJjuaQSLtzOws4DPAVHc/FZgNvOPufzqQxZHBVpDX5REoLCzkiiuu4PzzzyeZTPLEE0902r5r1y62bNnC8ccfz4gRI3o8R9s34LbZJ3uzP/s2NTXtcx8ZHMomXMombMonXMomXMomXMomXMomOyqQSEdjgZ3u3gzg7jvdfZuZPWlm0wDM7Mtm9nrU9p9m9qOo/U4z+xcze9bMtpjZpW0nNbNvmdkLZrbBzBZ1aL/RzF4zs0eBk/raSTOrNLNFZrbWzDaa2aQDGbSR39XVWCzGcccdx4wZMygvL6eysrLTqtdr1qwBYNq0ae1tbYWSmpoaAGprazu1J5NJUqlUn/bdG1W+w6VswqVswqZ8wqVswqVswqVswqVssqNbbKSj5cC3zex14FHgd+7+VNtGMxsH/B0wFagFHgfWdzh+LDATmAQsA5aY2aeBE4AzAQOWmdksoB74PDCFzN/DtcCa/ejrTnefamYLgOuBP93/4WYkSFFMCwApYqQxUlinx/82kSBGZrZJW0GlhQSGYzgJMouTJolFLUZhdLxjtBInDd3OGceJ4dFWaCUeHQOF0cyWNEYy6lf3PmWuHWvvU5y2eRltM2MaGxuJxWK89dZbvPvuu0Dm9pYXX3yRdDrNO++8w9ixY6mtreWdd95h6NChmBm1tbWkUinWrVvH8OHDOeaYY9qLG2PHjmXo0KG88MILuDtr1qxh+PDhHHXUUTQ3N3PLLbdw+OGHc9VVV3HiiSfy2GOP8Yc//IGqqio2bdpEeXk5paWlpFKpTsWYkpIS0uk0LS0tpNNp6urqKCoqal/8taUlk1MikSAWi5FIJLod7+40NTW1/1AoLi4mlUrh7rS2tmbem4ICzIx4PE5jYyOQmdFSVFSEmXU7ZzKZJJ1Ok0xm3v/CwkIgU1xqq87HYjEKCwuJxWJ7HROQE2NqbW2lvr4+p8aUKznV1dXl3JhyKadUKtX+vTRXxpQrOdXV1eXcmHIlp5aWFlpbW3NqTLmSU9vPnFwaU67klEqlaG5uzqkxHUhOpaWl9IWpsiQdmVkc+ARwDpn1R24AriZThBgPXOLuV0X7fgM40d2/Fq0RssLdfxVtq3X3UjO7DbgUqI4uMQz4HlAKjHL3b0f7fx/Y1pc1SMysEvi4u79rZjOAm919drZjPu6G+zyVw5OpKm+5CIA777yTt99+u9O22bNns379enbv3k08Hmfs2LHMnj2b8vJyAF5++WXuvvtuzjnnHGbNmtXp2LfffpsHH3yQnTt3MmbMGC6++GLGjRsHwKJFiygrK2PBggUAbNq0iRUrVlBTU0N5eTnz5s3b5/ojkJmJkkiojhsiZRMuZRM25RMuZRMuZRMuZRMuZdPNvu/vRzNIpAt3TwFPAk+a2Ubgqg6b9/WXqrmHfQ34nrv/pOOOZvbncED3trRdK8UB/j3Ol1tsrr766h7bP/7xj/d6TNtTbHoyYcIErrvuuh63LVy4sNPnFRUVVFRU9K2jHaRSKX1jD5SyCZeyCZvyCZeyCZeyCZeyCZeyyU7u/tpc9puZnWRmJ3RoOh3oOOVgNfBJMxtpZglgfh9O+wjwJTMbFl2j3MzGACuBS8ys2MxKgYv7ZRBZyJcCyaFIM9zCpWzCpWzCpnzCpWzCpWzCpWzCpWyyowKJdDQMuMvMXjWzDcDJwE1tG939XeC7wPNk1ih5FdiztxO6+3Lg18Bz0YyUJUCpu68FfgesA+4Gnu7vwfRV2/ohEp62exMlPMomXMombMonXMomXMomXMomXMomO5pzI+3cfQ3wsR42nd3h9a/d/T+iGST3kFnYFXe/usu5hnV4/UPghz1c72bg5j727eoOryd2eP1il/6JiIiIiIiI7DfNIJH9dZOZrQNeBt4Clg5qb/pBUl8GwSooKBjsLkgvlE24lE3YlE+4lE24lE24lE24lE12NINE9ou7Xz+Q5zezHwNdVw39obv/bKCu6X1b0FgGgZmyCZWyCZeyCZvyCZeyCZeyCZeyCZeyyY4e8yt5L5lMulZ4DpMeTxYuZRMuZRM25RMuZRMuZRMuZRMuZdNNnypGurdA8l5jY+Ngd0F6oWzCpWzCpWzCpnzCpWzCpWzCpWzCpWyyowKJiIiIiIiIiOQ9FUgk7+n+vHApm3Apm3Apm7Apn3Apm3Apm3Apm3Apm+zopiTJe6f9/ROkVCvsN5W3XNRv5yoqKuq3c0n/UjbhUjZhUz7hUjbhUjbhUjbhUjbZ0f8KJe+lB7sD0itVvsOlbMKlbMKmfMKlbMKlbMKlbMKlbLKjGSSS94pI0kjhYHcjZ1RVVXH//ffz/vvvk0qlGD9+PBdddBGjRo1i69atPPDAA1RVVVFWVsbcuXMZO3YsAHfccQc7duwgnU5TVlbGnDlzGDVqFKWlpXs9rqvNmzezfPlyampqGD9+PPPmzWPkyJEH8y3ICw0NDZSWlg52N6QHyiZsyidcyiZcyiZcyiZcyiY7mkEiIv2qtrYWd+fss8/m9NNPZ8uWLdx3330kk0kWL15MS0sLc+bMob6+nsWLF5NOZ+bwjB8/nvPPP59Zs2bx3nvvsWzZMoB9HtdRXV0dS5YsYciQIZx33nls376dpUuXHszhi4iIiIjIIUoFEhHpV0cddRRXX301Z555JhdccAHFxcV88MEHvPHGG9TX1zNt2jSmT5/OlClTqK6uprKyEoA5c+Zw0kknceyxx5JIJNqnBe7ruI42btxIKpVi5syZzJgxg0mTJrF161Z27dp1EN8BERERERE5FKlAcgDMbKKZvdyl7SYzu34/zlFpZqP3sc/fZNG3q83sR3vZfpOZvWtm68zsDTP7bzM7ucP2Ozp+frCY2TQz+5eDec0m3WnWr+LxePvrbdu20djYyIQJE6iurgZg+PDhnf7cvXs3AM3Nzdx6663ccccdxONx5s6dS0lJyT6P62h/9pUDU1JSMthdkF4om7Apn3Apm3Apm3Apm3Apm+yoQHJo2O8CSR/9s7uf7u4nAL8DHjezMgB3/1N3f3WArtsrd3/R3b9xMK8Zxw/m5fLGzp07+e1vf8uIESO44IILum13z7zvbTNFCgsLueKKKzj//PNJJpM88cQTJJPJfR63N/uzr+yfnrKRMCibsCmfcCmbcCmbcCmbcCmb7KhAMkDM7Ekz+4GZPWtmL5vZmVH74Wa23MxeMrOfANbhmKVmtsbMXjGzr0RttwDF0UyPX0VtV5jZ6qjtJ2YWj9qvMbPXzewp4OP70193/x2wHPhih/5PM7O4md0ZjWGjmf1FtP14M3vUzNab2VozO87Mzjaz+zuM50dmdnXbOMzsVTPbYGa3RW2XReddb2Yro7b2c5jZqOg92WBmq8zs1Kj9JjP7adTHLWZ2QAWVmAok/W7Hjh3ceeedxGIxrrzySkpLSxkxYgQANTU1QGatEqC9PRaLcdxxxzFjxgzKy8uprKykrq5un8clk0lSqVSntt72lf7T0xowEgZlEzblEy5lEy5lEy5lEy5lkx3dWzCwhrr7x8xsFvBTYDKwEPiDu3/HzC4CvtJh/y+5+y4zKwZeMLO73f0GM/uau58OYGYVwOeAj7t7q5n9G3C5ma0AFgFnAHuAJ4CX9rO/a4FJXdpOB8rdfXJ0/RFR+6+AW9z9HjMrIlNsO6qnk5rZKOASYJK7e4dzfBuY4+7vdmjraBHwkrt/1sw+Bfw86g9RP88BSoHXzOzf3b11/4abMYRW4tHDflPESGOkMIr4sOraRIIYUEAKiwoqLSQwHMNJRMcniUUtRmF0vGPtV+h6zjhODG+/fivx6BgoJPOf/jRGMupX9z5lrh1r71O8veJWEB1/sMfUVhxpbGzk4x//OJWVlVRWVjJp0iRKSkpYvXo1AGvXruWwww7j8MMPZ+PGjbz22mtMnDiRPXv28M4771BSUkJzczMTJ06kpKSEF154AXdnzZo1HHbYYYwdO5ZUKsXNN9/M4YcfzlVXXUVFRQWPPfYYK1eupKqqik2bNnHUUUcxdOhQmpqaaG3N/BUpKCjAzIjH4zQ2NgKZWSZFRUWYGQ0NDe1jKikpIZlMkk6n2yvxhYWZpx7FYjGampraXxcWFhKLxbodn06naWlpaf9BVVRU1P66paUFgEQiQSwWI5FIdDve3WlqamqfEVNcXEwqlcLdB21MjY2NpNPpnBpTruRUV1dHMpnMqTHlUk7Nzc2dfquXC2PKlZzq6upIp9M5NaZcyamhoYFEIpFTY8qVnKqrq0kmkzk1plzJKZVKEYvFcmpMB5JTX5/oY22dlf1nZhOAB9qKB1HbTUAtcDHwHXd/PGrfCpwKPAn8kbtvidp3ASe6+87o2EuiU00kUzxYZWZ17j4s2v9rZG65+SDarxj4DbAuOu+V0X7fiM77tV76fhNQ5+63dWj7i+iY68zsSeB64E3gReBB4AEys0yGApvcfXyXc54NXO/un4k+/1F07C+BNdHrB4D73b3FzG4HjgMWA//t7lUdz2FmLwHzO7xX75ApMv0F0OruN0ftm4Dz3P1/exrrvlTccI/rMb/958lrT+Guu+7q1r5w4ULefvttHnzwQXbu3MmYMWO4+OKLGTduHO+++y733nsvu3fvJh6PM3bsWGbPns3w4cMpLS3t9TiARYsWUVZWxoIFCwDYtGkTK1asoKamhvLycubNm8eoUaMO6nuQD2pra/XouEApm7Apn3Apm3Apm3Apm3Apm276dM+9ZpAcmCpgZJe2UcBb0euu1Sfvpb2tuDAbOMvdG6ICRVEP1zTgLnf/6y7Hf7an8+6nKWSKGB922H23mZ0GzAG+Cvwx8Oe9HJ+k821bRdE5kpa5xehc4PPA14BPufu1ZjYDuAhYZ2andzlfT3+J28bY3KEtxQH8XW6btSH9Y+LEiSxcuLDHbRMmTOC6667r1l5eXt5e4Oioubl5r8cB3a5VUVFBRUXF/nZb9lNbtV7Co2zCpnzCpWzCpWzCpWzCpWyyozVIDoC71wHbzexcaL+V5HzgD9Eun4vaZwJ73H0PsBK4PGq/gA8LLIcBu6PiyCTgox0u1WpmBdHrx4BLzWxM2zWjmSzPA2dbZo2TAuCy/RmLmc0HPk1mNkrH9tFAzN3vBv4OmOruNcD/RkUZzGyImZUAbwMnR58fRqYggpkNAw5z9wfJFFdOj9qPc/fn3f3bwE6636LT8b06G9gZXVtERERERESkX2kGyYG7Evixmf1T9Pkid3/TMk/N2G1mzwLDgS+1bQd+Y2ZrgaeArVH7w8C1ZrYBeA1Y1eEa/wFsMLO17n65mf0tsNzMYkAr8NXoVpybgOeA7WTWE9nX1Ii/MLMryNwy8zKZWR07uuxTDvwsuhZA28yVPwF+YmbfifpwmbtvMbPFwAbgDT5cA6UUuDdaq8TI3CIDcKuZnRC1PQasBz7Z4do3RdfeADQAV+1jPFnRTWbhisVUww2VsgmXsgmb8gmXsgmXsgmXsgmXssmO1iAZIG1reLj7i/vaVwaX1iDpX5W3XNRv59K9k+FSNuFSNmFTPuFSNuFSNuFSNuFSNt30aQ0SlZVEREREREREJO/pFpsB4u5nD3YfAMzsRrqvR/L7tifASOYxuhImTQ0Ml7IJl7IJm/IJl7IJl7IJl7IJl7LJjm6xkbzX2trqBQUF+95RDrrW1laUTZiUTbiUTdiUT7iUTbiUTbiUTbiUTTe6xUakL1RdDZeyCZeyCZeyCZvyCZeyCZeyCZeyCZeyyY7eNcl7DQ0Ng90F6YWyCZeyCZeyCZvyCZeyCZeyCZeyCZeyyY4KJCIiIiIiIiKS91QgEREREREREZG8p0VaJe8dc8N97qoV9qjylosG9fqpVIp4PD6ofZCeKZtwKZuwKZ9wKZtwKZtwKZtwKZtutEirSF/EUJEwVOl0erC7IL1QNuFSNmFTPuFSNuFSNuFSNuFSNtlJDHYHRAZbgjQpVF3dm4ceeohXXnmF+vp6TjjhBL74xS+ybt067r333m77fvOb32TEiBHccccd7Nixg3Q6TVlZGXPmzGHChAlUVVVx//338/7775NKpRg/fjwXXXQRo0aN6naulpYW3nzzTZYvX05NTQ3jx49n3rx5jBw58mAMW/aipaVFj44LlLIJm/IJl7IJl7IJl7IJl7LJjmaQSN7TDJK+OeWUUzp9PmHCBObPn8/8+fO55JJLiMfjDB06lNLSUgDGjx/P+eefz6xZs3jvvfdYtmwZALW1tbg7Z599Nqeffjpbtmzhvvvu6/GatbW1LFmyhCFDhnDeeeexfft2li5dOqDjlL7RbyXCpWzCpnzCpWzCpWzCpWzCpWyyoxkkIrJPF1xwAdXV1axevbq9beTIke0zOV599VVSqRRTpkxpv9dxzpw5NDY2snv3bp5++mnMMrf9HXXUUVx99dXt59m4cSMffPBBj9fdvHkzqVSKmTNncsopp7Bt2zY2bNjArl27epxxIiIiIiIiki3NIAmMmT1pZnO6tP25mW0xsxsOUh/uNLNL97FPmZm1mtmfdWmvNLPR0eu6vRw/0cxe3sc1rjazH+1P37PRottrDtiaNWswM84444z2tubmZm699VbuuOMO4vE4c+fOBei0WNS2bdtobGxkwoQJPZ63vr4egOHDh3f6c/fu3QMyDum7oqKiwe6C9ELZhE35hEvZhEvZhEvZhEvZZEcFkvD8Bvh8l7bPA1e5+y2D0J/eXAasAr4w2B05UH1azlh6tWvXLrZs2cLxxx/PiBEj2tsLCwu54oorOP/880kmkzzxxBOdjtu5cye//e1vGTFiBBdccEGP5+76lK22z9tmo8jg0bTNcCmbsCmfcCmbcCmbcCmbcCmb7KhAEp4lwGfMbAhkZloA44Dj22ZTRLM37jazF6KPj0ftG81shGVUmdmVUfsvzGy2mcXN7NbomA1tsz+i/X9kZq+a2QPAmD708wvAXwLjzaz8QAZsZkVm9rOo/y+Z2TkdNh9lZg+b2WtmtjDaf6iZPWBm683sZTP73IFcXw7MmjVrAJg2bVqn9lgsxnHHHceMGTMoLy+nsrKShoYGAHbs2MGdd95JLBbjyiuvbF+3BCCZTJJKpQA47LDDAKipqQEya5IAnQoxIiIiIiIi/UFrkATG3avMbDVwPnAvmdkjv4NOK4n+EPhnd/+DmR0NPAJUAM8AHwfeBrYAnwB+DnwUuA74MrDH3adHBZhnzGw5MAU4CfgIcATwKvDT3vpoZkcBR7r7ajNbDHwO+P4BDPur0dg/YmaTgOVmdmK07UxgMtAAvBAVcCYA29z9oqg/hx3AtSmmhWR0m02KGGmMFEYRyfZ9mkgQAwpIYVEULSQwHMNJkKnQJolFLUZhdLxjtBInDd3OGceJ4dFWaI364UAhmSJBGiMZ9at7nzLXjrX3Kd4+I6YgOv5AxlRbW0tBQQFvvvkmVVVVQOb2lmeffbZ9xshLL71EaWkpRx55JKlUimQyyZtvvsmrr77KuHHjaGho4J133mHo0KEkk0m2bdvGL3/5S5qamjj77LP5n//5HwAmTZpESUkJN998M4cffjhXXXUV48aNIx6P8/TTT1NdXc2mTZsoLy+nuLiY5uZmEolEe9EFoKSkBHenqampfbZJcXExqVQKd6e1tTXz3hQUYGbE43EaGxuBzKyUoqIizKzbOZPJJOl0mmQy8/4VFhYCmSJQU1NT++vCwkJisVi349PpNC0tLe2V/KKiovbXLS0tACQSCWKx2CEzpvr6+vZz5MqYciWnuro6hg0bllNjyqWcGhsb2/uZK2PKlZzq6uoYPnx4To0pV3Kqq6vLuTHlSk7V1dUMGzYsp8aUKzm1/cIxl8Z0IDl1/IXs3ljXKewy+MzsCuAid/+Cma0DvgScCkxz96+Z2QfAtg6HlAGTgLnRfm8DTcBXgPnAf7v7DDNbEm1v+1t2GPBnwIXABnf/aXT9/wZ+7e5Leunft4AR7n6jmZ0K/Je7T4+2VUb93Glmde4+rJdzTATud/fJZnYP8K/u/ni07WkyRZOpwKfcvW0mzHeAXcCDZIpCi6NzPN2nN7YXFTfc440UHsgpclblLRcBcOedd/L222932jZv3jwSiQR3330355xzDrNmzWrf9u6773Lvvfeye/du4vE4Y8eOZfbs2e0zSe66665u11q4cCEAixYtoqysjAULFlBbW8v//u//smLFCmpqaigvL2fevHlaoDUAtbW1ff5BIweXsgmb8gmXsgmXsgmXsgmXsummT/foawZJmJYC3zezqUCxu6+NChFtYsBZ7t7Y8SAzW0mmsHA0cCNwCXAp0FZAMODr7v5Il+MuhP161u0XgCPM7PLo83FmdoK7v7Ef5+jUhb1s69ovd/fXzewMMoWd75nZcnf/TpbXJqU7zfap41Nnupo8eXK3tvLychYsWNDj/hMnTmwvhvSk47ZEIkFFRQUVFRV976wcFImEfnyEStmETfmES9mES9mES9mES9lkR/8zDJC71wFPkrnN5Tc97LIc+FrbJ2Z2enTcO8Bo4AR33wL8AbieDwskjwDXmVlBdNyJZjYUWAl8PlqjZCzQcQ2QTszsJGCou5e7+0R3nwh8j+4Ly+6PlcDlbX0iU+B5Ldp2npmNMrNi4LNkbgsaBzS4+y+B28jMNMlaWsu0BisW07eoUCmbcCmbsCmfcCmbcCmbcCmbcCmb7OhdC9dvgNOA3/aw7RvAtGih1VeBaztsex54PXr9NFBOplACcAeZ9UXWWuYRuz8hM4voHuANYCPw78BTe+nXF6L9O7qb/X+aTQJojl7/GxA3s41k1lu52t3btv0B+AWwDrjb3V8ks1bK6uj2oxuBv9/Pa3eSUoEkWKp8h0vZhEvZhE35hEvZhEvZhEvZhEvZZEdrkMigMLN5wOXu/seD3RetQdK7tjVIBovunQyXsgmXsgmb8gmXsgmXsgmXsgmXsulGa5BImKLFVucBVw9yV0REREREREQAFUhkL6KnyxzTpfmvui7yuo9zfITMLTIdNbv7aQfaPxEREREREZH+oltsJO+lUimPx+OD3Q3pQSqVQtmESdmES9mETfmES9mES9mES9mES9l006dbbLRIq+Q9FQnDpWzCpWzCpWzCpnzCpWzCpWzCpWzCpWyyowKJ5L2mpqbB7oL0QtmES9mES9mETfmES9mES9mES9mES9lkRwUSyXuqroZL2YRL2YRL2YRN+YRL2YRL2YRL2YRL2WRHi7RK3jvz5kc5WI/5HezH5oqIiIiIiEjPNINE8l6L6oTBKi4uHuwuSC+UTbiUTdiUT7iUTbiUTbiUTbiUTXb0P0PJe8bBn37W1NTEQw89xGuvvUY6nWbs2LFcc801/OAHP2DPnj3t+x1xxBFce+217Z8nk0luv/12qqqqmD59OhdeeGGP59+8eTPLly+npqaG8ePHM2/ePEaOHDng4+pvqVSKRELfpkKkbMKlbMKmfMKlbMKlbMKlbMKlbLKjd0zy3mAUSO69915ee+01ZsyYQVlZGe+88077tgkTJjBt2jQAioqKOh331FNPUVNTs9dz19XVsWTJEsrKyjjvvPN4/PHHWbp0Kddcc03/D2SA6d7JcCmbcCmbsCmfcCmbcCmbcCmbcCmb7KhAInkvQZrWg3i93bt3s3nzZj7ykY8we/ZszIypU6e2bx8xYgQnnHACQ4YM6XTc+++/z6pVqzj77LN59NFHez3/xo0bSaVSzJw5k1NOOYVt27axYcMGdu3axahRowZsXAOhtbW1W5FIwqBswqVswqZ8wqVswqVswqVswqVssqMCiQwYM3vW3T/Wy7azgevd/TN9PFclMM3dd5pZCtgIFABJ4C7gB+6e7o9+D7QdO3YAsG3bNr773e9iZsyYMYPzzjsPgPXr17N+/XpKSko499xzmTp1Ku7OsmXLmD59OuXl5Xs9f3V1NQDDhw/v9Ofu3bsPuQKJiIiIiIjIwaICiQyY3ooj/aDR3U8HMLMxwK+Bw4CF2ZwseZDXKk4mkwC0tLRw6aWX8sILL/Dss89y3HHHMXXqVEaPHk0ymeTRRx/l/vvv55hjjuGtt96iurqauXPn8v777wPQ3NxMfX09Q4cO3ev12qbXmdnADmwAFBQUDHYXpBfKJlzKJmzKJ1zKJlzKJlzKJlzKJjsqkMiAMbM6oBT4R+ACwIG/d/ffRbsMN7N7gJOAlcCC/Z0F4u4fmNlXgBfM7CbP4mY75+AWDkaMGAFk1hqpqKigvr6et956i127djFr1qz2/bZv386qVauoqqqipqaGhoYGbr/99vbtGzZsIB6PM3fuXJLJJGZGPB5vP3/bWiW1tbWdrnsoORSLOvlC2YRL2YRN+YRL2YRL2YRL2YRL2WRHBRIZaH8EnA6cBowmU8hYGW07EzgZeBt4ONp3yf5ewN23mFkMGAO8v7/Hx0lTQAqAFDHSGCmMIpLt+zSRIAYUkGpf1LWFBIZjOAkydZ0ksajFKIyOd4xW4qTJFCuGDRvG6NGj2bJlC88//zzr16/HzBg1ahS//OUvOe6443B31q9fTyKRoLS0lBNPPJHRo0fT3NxMVVUVzz33HMcddxynn3469fX13HbbbRx++OH82Z/9GSeeeCKPPfYYK1eupKqqik2bNjF+/HgOO+yw9mIJQElJCe5OU1NT+yyT4uJiUqkU7k5ra2ZlloKCgvbiS2NjI5D5hltUVISZ0dDQ0OmcyWSSdDrdPlOmsLAQgFgsRlNTU/vrwsJCYrFYt+PT6TQtLS3t54jFYqTTmfe3paUFgEQiQSwWI5FIdDs+9DFBZvHdXBhTfX19zo0pF3JKJpO0tLTk1JhyKSeg2/fiQ31MuZJTMpkkmUzm1JhyJae2fw/k0phyJaempiZaWlpyaky5klNhYSHNzc05NaYDyam0tJS+MK1uKwMlmkHyn8BGd/9p1PYL4PdADfAdd58VtX8JONXd/7yXc1Xy4Rokde4+rMv2auAkd9/vAknFDfd4I4X7e1hWKm+5CIAPPviA++67j+3bt3PYYYdx9tlnM3HiRJYtW8a7775La2srZWVlfOpTn+L444/vfI7KSu66665Oj/ldtGgRZWVlLFiwAIBNmzaxYsUKampqKC8vZ968eYfk+iO1tbV9/mYmB5eyCZeyCZvyCZeyCZeyCZeyCZey6aZPU2o0g0QG2t7+InatzmVVrTOzY4EU8EE2xw+GMWPG8OUvf7lb++WXX77PYydOnMjChZ2XW+n6eUVFBRUVFQfWSRERERERkTxycFenlHy0EvicmcXNrAyYBayOtp1pZsdEt8d8DvjD/p48OuftwI+yWX8EDv4aJNJ3uncyXMomXMombMonXMomXMomXMomXMomO5pBIgPJgXuAs4D10ef/193fM7NJwHPALcBHyBRS7unjeYvNbB0fPub3F8D3s+1kK/FsD5UBpme3h0vZhEvZhE35hEvZhEvZhEvZhEvZZEcFEhkQZnY4sCua1fGt6KOduz8JPNnX87n7xA6v+7WisV+PzZGDSpXvcCmbcCmbsCmfcCmbcCmbcCmbcCmb7OgWG+l3ZjaOzOyQ2wa7L33R8Wk1EpaOq1ZLWJRNuJRN2JRPuJRNuJRNuJRNuJRNdjSDRPqdu28DTszmWDN7HhjSpflP3H3jAXdMREREREREpBcqkEhQ3H3Gwb7m6htn6xFYIiIiIiIiec6yfPCHSM5IpVIej2uh1hClUimUTZiUTbiUTdiUT7iUTbiUTbiUTbiUTTd9WpRFa5BI3ksmtQZJqJRNuJRNuJRN2JRPuJRNuJRNuJRNuJRNdlQgkbyXTus5NqFSNuFSNuFSNmFTPuFSNuFSNuFSNuFSNtnRGiSS96YuephGCge7G9KDYlryNpvKWy4a7C7slX4rES5lEzblEy5lEy5lEy5lEy5lkx3NIBERERERERGRvKcCieS9VrR4UaiUTbgKC/NzZs+hQNmETfmES9mES9mES9mES9lkR7fYiIgELplMcvvtt1NVVcX06dO58MIL2bp1Kw888ABVVVWUlZUxd+5cxo4dC8Add9zBjh07SKfTlJWVMWfOHCZMmACw1+O62rx5M8uXL6empobx48czb948Ro4cedDGLSIiIiJyMGkGieQ9Peg6XMom46mnnqKmpqb982QyyeLFi2lpaWHOnDnU19ezePHi9sW4xo8fz/nnn8+sWbN47733WLZsWZ+O66iuro4lS5YwZMgQzjvvPLZv387SpUvbt8di+vERKmUTNuUTLmUTLmUTLmUTLmWTHb1rkvcKSQ12F6QXygbef/99Vq1axSc/+cn2tjfeeIP6+nqmTZvG9OnTmTJlCtXV1VRWVgIwZ84cTjrpJI499lgSiQRm1qfjOtq4cSOpVIqZM2cyY8YMJk2axNatW9m1axcATU1NAz52yY6yCZvyCZeyCZeyCZeyCZeyyY4KJAfAzI40s9+a2Ztm9qqZPWhmJx7gOUeY2YIsjrvJzK7fy/aPmtnzZrbOzDaZ2U37ON9EM3t5f/vR4fhxZrYki+PuMLOTs72uSC5xd5YtW8b06dMpLy9vb6+urgZg+PDhnf7cvXs3AM3Nzdx6663ccccdxONx5s6d26fjOtqffUVEREREcoEKJFmyzK9k7wGedPfj3P1k4G+AIzrsk80KkyOA/S6Q9MFdwFfc/XRgMrB4AK7Rzt23ufulWRz3p+7+6kD0qTdp7GBeTvZDvmfz0ksvUV1dzWmnndZ+i01zczOpVOeZNe6Zm5HaZooUFhZyxRVXcP7555NMJnniiSd6PH/X4/am676athkuZRM25RMuZRMuZRMuZRMuZZMdLdKavXOAVne/va3B3deZ2dlm9gSwHTjdzD4C3AKcDQwBfuzuPzGzYcC9wEigAPhbd7832vc4M1sHrHD3b5nZt4A/jo6/x90XApjZjcCVwDvADmDNXvo7JuoT7p4CXo3OcRNQ5+63RZ+/DHwmOiZhZncBU4DXgSvdvcHMKoFfR+9BAfAV4HvA8cCt7n67mU0E7nf3yWZ2CvAzoJBMUW4+sI1MkWY8EAf+n7v/zsyeBK539xfN7Atkik4GPODufxX1sQ74YdTPRmCeu7+/l7Hvg1NMCwApYqQxUhhFfPjs8CYSxIACUli0MkYLCQzHcBJk1nBIEotajMLoeMdoJU4aup0zjhPDo60fPrXF+fD2kjRGMupX9z5lrh1r71O8vaRQEB1/KI8pTpoksZwaU19zqq2tZceOHTQ0NHD77e3fZtiwYQMjRowAYNeuXdTW1rJz504Ahg4dSjKZpLGxkTFjxnDEEUfw6quvUllZyQcffMCQIUOAzOyQ5ubm9ttlhgwZQnNzM8lkkng8TmtrK8XFxe3XGDduXPu+BQUFpFIpzIz6+vr29UuKioraX7e0ZL6eEokEsViMRCJBQ0ND+xhKSkpwd5qamtoLL8XFxaRSKdyd1tbW9muZGfF4nMbGRiBToCkqKsLMup0zmUySTqdJJjPvf9vq7bFYrH2aaSwWo7CwkFgs1u34dDpNS0vLIT+m1tZW0ul0To0pl3KKx+PU1tbm1JhyJae28+bSmHIlp7bz5dKYciWn5uZm0ul0To0pV3IqKChozydXxnQgOZWWltIX1tZZ2T9m9g3gGHf/iy7tZwMPAJPd/S0z+wowxt3/3syGAM8Al5EpapS4e42ZjQZWAScAE4gKC9H5Pg1cCvwZmULBMuAfgXrgTmAGmULXWuD2tkJHD/39NvAXwJPAw8Bd7t60jwLJW8BMd3/GzH4KvOrut0UFkn9w9383s38GzgU+DhQBr7j7mC4Fkn8FVrn7r8yskExB5ELgfHf/P9F1D3P3PW0FEjIFlFXAGcBuYDnwL+6+1MwcmOvu95nZPwI17v73+wytF8fccJ+7JlMFyUiTr9lU3nIRO3bsYMeOHQB88MEHPPXUUxx//PF84hOfYPHixRQUFPCxj32Mp59+mng8zte//nW2bNnCK6+8wlFHHUVNTQ1PP/00xcXF/OVf/iWpVIof/OAHPR4Xi8VYtGgRZWVlLFiwgNraWn74wx8yZswYTjvtNB5//HGOPPJIrrnmGgBSqRTxuB7DHCJlEzblEy5lEy5lEy5lEy5l002fpqbn5/88Bt5qd38rev1p4MpoRsjzwOFkCiEGfNfMNgCPAuV0uD2ng09HHy+RKYJMio7/BJnZJA3uXkOmcNIrd/8OMI1MoeGLZIok+/KOuz8Tvf4lMLPDtrbrbQSed/dad98BNJnZiC7neQ74GzP7K2CCuzdGx802s38ws0+4+54ux0wnc/vSDndPAr8CZkXbWoD7o9drgIl9GEuvOv62X8KS79mUlZVx8sknc/LJJzNx4kQARo4cydFHH81ll11GYWEhDz/8MEOHDuWyyy4jFotRXFzMu+++y0MPPcSqVas4+uij+cIXvoCZkUgkej2uq9LSUubPn09TUxMrVqzgyCOPZN68ee3bO1bzJSzKJmzKJ1zKJlzKJlzKJlzKJju6xSZ7r5CZ2dGT+g6vDfi6uz/ScQczuxooA85w99ZoVkZRD+cy4Hvu/pMux/85+/kUVHd/E/h3M/tPYIeZHQ4k6Vwo69iHrufv+Hlz9Ge6w+u2zzv9vXL3X5vZ88BFwCNm9qfu/riZnUFmJsn3zGx5VMRpH+JehtLqH059SnW9nkgumjhxIgsXLmz/fMKECVx33XXd9isvL2fBgt6XMertOKDT+QEqKiqoqKjIssciIiIiIocWzSDJ3uPAEDP7P20NZjYd+GSX/R4BrjOzgmifE81sKHAY8EFUHDmHzK01ALVAaZfjvxStWYKZlZvZGGAlcImZFZtZKXDx3jprZhfZhysxnkCmsFANVAJTo32mAsd0OOxoMzsrev0F4A97u8Zern0ssMXd/4XMzJNTzWwc0ODuvwRua+tDB88DnzSz0dFit18Ansrm+iIiIiIiIiL7ot+8Z8nd3cwuAX5gZjcATWSKDUu77HoHmVtA1kYFih3AZ8ncMnKfmb0IrAM2R+etMrNnorVAHooWaa0AnovqG3XAFe6+1sx+Fx37NvD0Prr8J8A/m1kDmVkjl7t7yszu5sNbgF4gsxhrm03AVWb2E+AN4N/7/AZ19jngCjNrBd4DvkPmFppbzSwNtAKdfqXt7tvN7K+BJ8jMJnkwWsS23zXpyyBYyiZcJSUlg90F6YWyCZvyCZeyCZeyCZeyCZeyyY4WaZW8d9wNyzyFFjAKUZwU+ZpN5S0XDXYX9qq1tZWCgoLB7ob0QNmETfmES9mES9mES9mES9l0o0VaRfqi7dGvEh5lE662R7tJeJRN2JRPuJRNuJRNuJRNuJRNdjR/PceY2Y/JPHK3ox+6+88Goz+Hgtj+rXUrB5GyCVc6reJVqJRN2JRPuJRNuJRNuJRNuJRNdlQgyTHu/tXB7sOhZvWNsyktLd33jnLQ1dbWKhsRERERETkodIuN5L2iop6eriwhUDbhUjbhUjZhUz7hUjbhUjbhUjbhUjbZUYFE8p6mn4VL2YRL2YRL2YRN+YRL2YRL2YRL2YRL2WRHBRIRERERERERyXtag0Ty3ukLH6SRwn49Z+iPaD1UtLS0MGTIkMHuhvRA2YRL2YRN+YRL2YRL2YRL2YRL2WRHM0hEREREREREJO9pBonkvdQA1gl/8IMfsGfPnvbPjzjiCK699loeeughXnnlFerr6znhhBP44he/CMC6deu49957u53nm9/8JiNGjOjWvnnzZpYvX05NTQ3jx49n3rx5jBw5csDGc7AlEvoWFSplEy5lEzblEy5lEy5lEy5lEy5lkx29a5L30tiAnn/ChAlMmzYN6Lya9CmnnMLq1au77Tt//vxMv9Jpli1bRlFRUY+Puq2rq2PJkiWUlZVx3nnn8fjjj7N06VKuueaaARzNwRWLaZJbqJRNuJRN2JRPuJRNuJRNuJRNuJRNdlQgkbyXGuACyYgRIzjhhBM63QN4wQUXUF1d3a1AMnLkyPYZIK+++iqpVIopU6YQj8e7nXfjxo2kUilmzpzJKaecwrZt29iwYQO7du1i1KhRAzqmg0WV73Apm3Apm7Apn3Apm3Apm3Apm3Apm+yorJRnzOxGM3vFzDaY2Tozm2Fmd5jZyQN83ZvM7Pq9bDcz+1sze8PMXjezJ8zslD6c97MH2vcikgdy+D6tX7+eW265hVtvvZW1a9f2+bg1a9ZgZpxxxhk9bq+urgZg+PDhnf7cvXv3gXU4IA0NDYPdBemFsgmXsgmb8gmXsgmXsgmXsgmXssmOykp5xMzOAj4DTHX3ZjMbDRS6+58OctcAvgp8DDjN3RvM7NPAMjM7xd2b9nLcZ4H7gVcPQh/329SpUxk9ejTJZJJHH32U+++/n2OOOWaf64Ts2rWLLVu2cMIJJ/S49khP3B0As4GdESMiIiIiIpKLNIMkv4wFdrp7M4C773T3bWb2pJlNAzCzL0czOJ40s/80sx9F7Xea2b+Y2bNmtsXMLm07qZl9y8xeiGalLOrQfqOZvWZmjwIn7aNvfwV83d0bor4tB54FLo/OVdfhvJdG/fkYMBe4NZoNc9yBv0X9a9asWZx88smceuqpnHLKKbg7VVVV+zxuzZo1AO1rl7RJJpOkUimA9sJJTU0NALW1tZ3aRUREREREpO80gyS/LAe+bWavA48Cv3P3p9o2mtk44O+AqUAt8DiwvsPxY4GZwCRgGbAkmulxAnAmYGRmfcwC6oHPA1PI/D1bC6zpqVNmNhwY6u5vdtn0ItDrbTbu/qyZLQPud/clfXoHepDEKKYFyDzRJo2RwjrdetNEghhQQAojM1OjhQSGYzgJ0tG5YjhGMplk69atPPPMMxxzzDHE43E2bNhAIpFg6NChrF+/vr2wUV1dzbPPPsv48eM54ogjSKVSrFu3jtLSUo488kjq6+spLCwkFotx8803c/jhh3PVVVdRUVHBY489xtNPP01VVRWbNm1i/PjxDB06lObmZlpaMmNKJBLEYjESiUSnqXYlJSW4O01NTe2zT4qLi0mlUrg7ra2tABQUFGBmxONxGhsbgcwslaKiIsys2zmTySTpdJpkMvP+FRYWApmFopqamtpft42p6/HpdJqWlhbS6TSpVIrW1lbS6cz7mwtjgsxivYf6mAoKCqivr8+pMeVKTqlUitra2pwaUy7lNGTIkPaCdq6MKVdySqVSnX7m5sKYciWneDxOa2trTo0pV3Jq+5mTS2PKlZyGDBlCc3NzTo3pQHLq6aEXPbG2zkp+MLM48AngHODPgBuAq4HrgfHAJe5+VbTvN4AT3f1rZnYnsMLdfxVtq3X3UjO7DbgUqI4uMQz4HlAKjHL3b0f7fx/Y5u639dCn4UClu4/q0v7nwFHu/pdmVufuw6L2S4HPuPvVUb8OqEBy3A33eX8/6rfylouora1l2bJlvPvuu7S2tlJWVsanPvUpjj/+eO68807efvvtTsfMmzeP008/nZdffpm7776bc845h1mzZnXaZ9GiRZSVlbFgwQIANm3axIoVK6ipqaG8vJx58+blzAKtkJkxowWmwqRswqVswqZ8wqVswqVswqVswqVsuunTOgR6x/KMu6eAJ4EnzWwjcFWHzfv6S9Pcw74GfM/df9Jxx6i40afqm7vXmFm9mR3r7ls6bJoKtM1w6XiuIvpRASn6u0ACmSrl5Zdf3uO2q6++utfjJk+ezOTJk3vctnDhwk6fV1RUUFFRkXUfQ9fU1MSwYcMGuxvSA2UTLmUTNuUTLmUTLmUTLmUTLmWTHa1BkkfM7CQzO6FD0+lAx2kMq4FPmtlIM0sA8/tw2keAL5lZ2+yOcjMbA6wELjGzYjMrBS7ex3luBf7FzIqj88wmczvPr6Pt75tZhZnFgEs6HFdLZrZK1qxvdRwZBJrhFi5lEy5lEzblEy5lEy5lEy5lEy5lkx3NIMkvw4B/NbMRQBL4H+ArwBIAd3/XzL4LPA9sI/NkmD17O6G7LzezCuC56OkpdcAV7r7WzH4HrCNThHl6H337V2AksNHMUsB7wDx3b4y230DmaTXvAC9HYwH4LfCf0e1Al/awjomIiIiIiIjIPmkNEunEzIa5e100g+Qe4Kfufs9g92sgDdQaJHLgdO9kuJRNuJRN2JRPuJRNuJRNuJRNuJRNN31ag0S32EhXN5nZOjKzNN4Clg5qbw4C3WITrrZHGkt4lE24lE3YlE+4lE24lE24lE24lE12VFKSTtz9+oE8v5n9GPh4l+YfuvvPBvK6e6MCSbg0wy1cyiZcyiZsyidcyiZcyiZcyiZcyiY7KpDIQeXuXx3sPnS19sZP9fm52HJwtba2UlTUrw8tkn6ibMKlbMKmfMKlbMKlbMKlbMKlbLKjW2xEREREREREJO+pQCJ5r6CgYLC7IL1QNuFSNuFSNmFTPuFSNuFSNuFSNuFSNtlRgUTyXvR4YgmQsgmXsgmXsgmb8gmXsgmXsgmXsgmXssmO1iCRvHfywuX092N+pX/ESSubQIWSjR6p3V08Hh/sLsheKJ9wKZtwKZtwKZtwKZvsDP6/bkUGWSHJwe6C9ELZhEvZhKuxsXGwuyB7oXzCpWzCpWzCpWzCpWyyoxkkIiJySPvBD37Anj172j8/4ogjuPbaa1m1ahWrVq2irq6O0tJSPvrRjzJjxgwAtm7dygMPPEBVVRVlZWXMnTuXsWPHsm7dOu69995u1/jmN7/JiBEjurVv3ryZ5cuXU1NTw/jx45k3bx4jR44csLGKiIiIyMDRDBLJe47uzwuVsglXaNlMmDCB+fPnM3/+fGbPnk1VVRWPPPIIZsanP/1pUqkUDz/8MHv27CGZTLJ48WJaWlqYM2cO9fX1LF68mHQ63ek8l1xyCfF4nKFDh/b4KPC6ujqWLFnCkCFDOO+889i+fTtLly49+IPvQvcch035hEvZhEvZhEvZhEvZZEcFEsl7rej+vFApm3CFls2IESM44YQTmDx5MscffzzuDsDw4cM59thjGTZsGPF4nEQiwRtvvEF9fT3Tpk1j+vTpTJkyherqaiorKxk5ciSTJ09m8uTJJBIJUqkUU6ZM6fE+3o0bN5JKpZg5cyYzZsxg0qRJbN26lV27dh3s4XdSVFQ0qNeXvVM+4VI24VI24VI24VI22VGBJFBmNtHMXu7SdpOZXb8f56g0s9H72Odvsujb1Wb2o71sv8nM3jWzdWb2spnN3c/zf8LMXomOLzezJVH72WZ2//72d1/S/X1C6TfKJlyhZbN+/XpuueUWbr31VtauXcvo0aM599xz2bp1Kz/+8Y957733uPjiixk6dCjV1dVApnjS8c/du3d3OueaNWswM84444wer9nX8xxs+o1R2JRPuJRNuJRNuJRNuJRNdlQgkf0ukPTRP7v76cBlwE/NrNPfNTPb2/o3lwO3ufvp7v6uu186QH0EoEiLTQZL2YQrpGymTp3KZZdd1n5LzP3338+7777L6tWrOfLII/nc5z7HEUccwYMPPkhNTU2349tmm3T8h8SuXbvYsmULxx9/fI9rj/Skp/MMhoaGhkG9vuyd8gmXsgmXsgmXsgmXssmOCiSHIDN70sx+YGbPRjM0zozaDzez5Wb2kpn9BD5cJMDMlprZmmhmxleitluA4mimxq+itivMbHXU9hMzi0ft15jZ62b2FPDxvvbV3TcBSWB01O/vRuf4ppmdG/V1o5n91MyGmNmfAn8MfNvMftXTTJqoP0OjY16IzjEv2/dTRA5ts2bN4uSTT+bUU0/llFNOwd3ZvXs3tbW1VFRUMGnSJCoqKmhpaeGdd95pL3i0FUtqa2sBOhVC1qxZA8C0adM6XSuZTJJKpTrtv7fziIiIiMihQ0+xOXQNdfePmdks4KfAZGAh8Ad3/46ZXQR8pcP+X3L3XWZWDLxgZne7+w1m9rVopgdmVgF8Dvi4u7ea2b8Bl5vZCmARcAawB3gCeKkvnTSzGWRm4++Imka4+yfNrAh4AzjX3V83s58D17n7D8xsJnC/uy8xs4m9nPpG4HF3/5KZjQBWm9mj7l7fl351lCBFMS0ApIiRxkhhnX5D3kSCGFBACiPzW+IWEhiO4SSiGw6SxKIWa38MqmO0EicN3c4Zx4nh0dYP13VwoJDMf8LSGMmoX937lLl2rL1P8faqWEF0/KE8piJac25MuZJTnDRDaB30MW3ZsoVnnnmGE088kWQyyfr160kkEu1rhqxfv57i4mLWr18PQElJCUceeSRDhw5l9erVuDtr1qzhsMMOo7y8nPr6elpbW3nppZcYPnw4Rx99NM3NzbS0ZL5HfP/732f06NFce+21TJw4kXg8zsqVK6mtrWXz5s2Ul5dTUFBAbW0txcXFpFIp3J3W1tbMe1NQgJkRj8fbH79nZhQVFWFmnX7bU1JSQjKZJJ1Ok0xmxl9YWAhALBajqamp/XVhYSGxWIyGhgbq6uraj0+n07S0tJBOZ97ToqKi9tdtY0okEsRiMRKJRLfruztNTU3ts2MGa0wdjz/Ux5RKpdqLabkyplzJqa6uLufGlCs5tbS00NramlNjypWc2n7m5NKYciWnVCpFc3NzTo3pQHLqacH9nlhbZyUsZjYBeMDdJ3douwmoBS4GvuPuj0ftW4FTgSeBP3L3LVH7LuBEd98ZHXtJdKqJwBx3X2Vmde4+LNr/a2Ruufkg2q8Y+A2wLjrvldF+34jO+7Ve+n4T8H/IFEVqgb9x96fN7Elgobs/ZWanAf/q7rOiY84Fvuruf2Rmd9K5QHK/u082s7OB6939M2b2IlAE7f/rGhWNaVOf3+TIMTfc567JVEEy0iibMIWSzcYbZ7Fs2TLeffddWltbKSsr41Of+hTHH388zz33HKtXr6a2tpbS0lLOOusszjzzTADefvttHnzwQXbu3MmYMWO4+OKLGTduHAAvv/wyd999N+eccw6zZs3qdL1FixZRVlbGggULANi0aRMrVqygpqaG8vJy5s2bx6hRow7um9BFKpXqcVFZCYPyCZeyCZeyCZeyCZey6aZP90BrBkm4qoCRXdpGAW9Fr7tWtryXdqLCwmzgLHdviAoVPS1rbMBd7v7XXY7/bE/n3Yd/dvfbemhvm+FxoDfpGzDf3V87wPMQxwNaTUE6UjbhCiWb0tJSLr/88h63nXXWWZx11lk9bpswYQLXXXddj9vanmLTk4ULF3b6vKKigoqKiv3o8cBLJpP6B1HAlE+4lE24lE24lE24lE12Bv/Xf9Ijd68DtkczKzCzUcD5wB+iXT4Xtc8E9rj7HmAlmQVOMbML+LDAchiwOyqOTAI+2uFSrWZWEL1+DLjUzMa0XTOayfI8cHa0xkkBmYVXD9RmYKKZHR99/ifAU/tx/CPA1y1aDdHMpmTbkdh+137kYFE24VI24Wqb+iphUj7hUjbhUjbhUjbhUjbZ0QySsF0J/NjM/in6fJG7vxnVBHab2bPAcOBLbduB35jZWjLFhq1R+8PAtWa2AXgNWNXhGv8BbDCzte5+uZn9LbDcMk+daSVz28uq6LaZ54DtwFrggMqR7t5kZtcAv7fME21eAG7fj1P8P+AHUd8NqAQ+k01f2tZgkPAom3Apm3C13ZMrYVI+4VI24VI24VI24VI22dEaJIeg6BaZ6939xcHuSy6ouOEeb6RwsLshPSimBWUTplCyqbzlosHuQnDa1lyRMCmfcCmbcCmbcCmbcCmbbvq0xINusZG813pgk2FkACmbcCmbcLWt5i5hUj7hUjbhUjbhUjbhUjbZ0S02hyB3P3uw+wBgZjfSfT2S37v7zYPRHxEREREREZFs6RYbyXutra1eUFCw7x3loGttbUXZhEnZhEvZhE35hEvZhEvZhEvZhEvZdKNbbET6oqmpabC7IL1QNuFSNuFSNmFTPuFSNuFSNuFSNuFSNtlRgURERERERERE8p4KJJL3YjF9GYRK2YRL2YRL2YRN+YRL2YRL2YRL2YRL2WRH75rkPa3wHC5lEy5lEy5lEzblEy5lEy5lEy5lEy5lkx09xUby3ol/9zA+SLXCylsuGpTrHipU+Q6XsgmXsgmb8gmXsgmXsgmXsgmXssmO3jXJe0UkB7sL0ouGhobB7oL0QtmES9mETfmES9mES9mES9mES9lkRzNIRAbRunXruPfee7u1f/Ob32TEiBEkk0luv/12qqqqmD59OhdeeCEAq1atYtWqVdTV1VFaWspHP/pRZsyY0eM1Nm/ezPLly6mpqWH8+PHMmzePkSNHDui4REREREREDjWaQSIyiCZMmMD8+fOZP38+l1xyCfF4nKFDh1JaWgrAU089RU1NTadjqqqqeOSRRzAzPv3pT5NKpXj44YfZs2dPt/PX1dWxZMkShgwZwnnnncf27dtZunTpwRiaiIiIiIjIIUUFEsl7TYM4kWrkyJFMnjyZyZMnk0gkSKVSTJkyhXg8zvvvv8+qVav45Cc/2ekYdwdg+PDhHHvssQwbNox4PE4i0X0cGzduJJVKMXPmTGbMmMGkSZPYunUru3btOijjO1AlJSWD3QXphbIJl7IJm/IJl7IJl7IJl7IJl7LJjgokkjUzm2hmL/fQ/qSZTevH69xkZtdHr+80s7fMbL2ZvW5mPzez8gM5fwzvn44eoDVr1mBmnHHGGbg7y5YtY/r06ZSXdx7e6NGjOffcc9m6dSs//vGPee+997j44osZOnRot3NWV1cDmWJKxz937949sIPpJ+l0erC7IL1QNuFSNmFTPuFSNuFSNuFSNuFSNtlRgUQORd9y99OAk4CXgCfMLOvnWCUY/G8eu3btYsuWLRx//PGMGDGCl156ierqak477bT2W2yam5upr6+nvr6e1atXc+SRR/K5z32OI444ggcffLDbrTg9aZt9YmYDOp7+0tLSMthdkF4om3Apm7Apn3Apm3Apm3Apm3Apm+yoQCIHKmFmd5nZBjNbYmad5nKZ2RfMbKOZvWxm/9Ch/cvRDJAnzew/zexH+3thz/hn4D3ggmwHEMIMkjVr1gAwbVpm4k1NTQ0NDQ3cfvvt3HPPPQBs2LCBxx57jMrKSmpra6moqGDSpElUVFTQ0tLCO++8A0AymSSVSgEwYsSI9vMB1NbWdmoPnSrf4VI24VI2YVM+4VI24VI24VI24VI22dFTbORAnQR82d2fMbOfAgvaNpjZOOAfgDOA3cByM/sssBr4O2AqUAs8Dqw/gD6sBSYB3R8H0wcJUhSTqbCmiJHGSGGdHv/bRIIYUEAKiwoqLSQwHMPbZ6EkiUUtRmF0vGO0EicN3c7Z3NxMa2srL730EqWlpRx99NE0Nzdz4oknti/UumvXLp599lmOO+44Tj755PZZIBs2bKC4uJj16zNvXXFxMa2trXz3u99l9OjRXHnllUycOJF4PM7TTz9NbW0tmzZtory8nIKCAlKpFO5OU1NT+zmLi4vb21tbWwEoKCjAzIjH4zQ2NgKZGShFRUWYWadHiJWUlJBMJkmn0ySTmbEWFmYm98RiMZqamtpfFxYWEovFuh2fTqdpaWkhnU5TV1dHUVFR+zf4tkp4IpEgFouRSCS6HR/6mICcGFNrayv19fU5NaZcyamuri7nxpRLOaVSqfZida6MKVdyqqury7kx5UpOLS0ttLa25tSYciWntp85uTSmXMkplUrR3NycU2M6kJza/m+1L9bWWZH9ZWYTgZXufnT0+aeAbwAjgOuBcmC+u18Zbf8ycAqwErjE3a+K2r8BnOjuX+vlOjcBde5+m5ndCdzv7ks6bP8hsM3d/6Gn4/fluBuWeYp4NocesMpbLuLll1/m7rvv5pxzzmHWrFnd96ms5K677ur0mN/nnnuO1atXU1tbS2lpKWeddRZnnnkmAIsWLaKsrIwFCzK1qk2bNrFixQpqamooLy9n3rx5jBo16uAN8gC0trZSUFAw2N2QHiibcCmbsCmfcCmbcCmbcCmbcCmbbvq0xoBmkMiB6lph6/h5b38J+3sBjCnAY9kePNircbQ9xaY3EydOZOHChZ3azjrrLM4666we9++6b0VFBRUVFQfe0UGgqYHhUjbhUjZhUz7hUjbhUjbhUjbhUjbZ0RokcqCONrO2/6l/AfhDh23PA580s9FmFo+2P0XmFptPmtlIM0sA87O5sGV8AxgLPJz1CERERERERCTvqUAiB2oTcJWZbQBGAf/etsHdtwN/DTxBZo2Rte5+r7u/C3yXTAHlUeBVYM9+XPNWM1sPvA5MB85x96yXaS4gle2hMsC0+na4lE24lE3YlE+4lE24lE24lE24lE12dIuNZM3dK4GTe9h0dod9fg38uod9fu3u/xHNILkHWL6X69zU4fXV2fVWREREREREpHeaQSKD5SYzWwe8DLwFLB2sjqT0ZRCsREI13FApm3Apm7Apn3Apm3Apm3Apm3Apm+zoXZNB4e7Xd20zsxuBy7o0/97dbx7IvqQHfZlW6U0spuJVqJRNuJRN2JRPuJRNuJRNuJRNuJRNdvSYX8l7qVTK4/HBecyv7F0qlULZhEnZhEvZhE35hEvZhEvZhEvZhEvZdNOn34qrrCR5r6GhYbC7IL1QNuFSNuFSNmFTPuFSNuFSNuFSNuFSNtlRgURERERERERE8p4KJCIiIiIiIiKS97QGieS9Y264z70faoWVt1zUD72RjnTvZLiUTbiUTdiUT7iUTbiUTbiUTbiUTTdag0SkL/RFEC4VcMOlbMKlbMKmfMKlbMKlbMKlbMKlbLKjx/xK3isgRaqfyyTJZJLbb7+dqqoqpk+fzoUXXrjX9q1bt/LAAw9QVVVFWVkZc+fOZezYsT2ee/PmzSxfvpyamhrGjx/PvHnzGDlyZL/2PxRNTU0MGzZssLshPVA24VI2YVM+4VI24VI24VI24VI22dEvzyXvGf1fXX3qqaeoqanpU3symWTx4sW0tLQwZ84c6uvrWbx4Mel0utvxdXV1LFmyhCFDhnDeeeexfft2li5d2u/9D4Uq3+FSNuFSNmFTPuFSNuFSNuFSNuFSNtlRgUSkn73//vusWrWKT37yk31qf+ONN6ivr2fatGlMnz6dKVOmUF1dTWVlZbdzb9y4kVQqxcyZM5kxYwaTJk1i69at7Nq1ayCHJCIiIiIikvNUIBkAZvakmc3p0vbnZrbFzG44SH2408wu3cv2J83sNTPbYGabzexHZjaiw/ZnD0Y/e+jXtWZ25cG8Zks/3mnm7ixbtozp06dTXl6+z3aA6upqAIYPH97pz927d3c7//7smwuKi4sHuwvSC2UTLmUTNuUTLmUTLmUTLmUTLmWTHRVIBsZvgM93afs8cJW73zII/enN5e5+KnAq0Azc27bB3T82GB1y99vd/ecH85r9eYvNSy+9RHV1Naeddlr7rTTNzc0888wzPbbX19d3O0fbdDizfS+0vD/7HopSqdRgd0F6oWzCpWzCpnzCpWzCpWzCpWzCpWyyowLJwFgCfMbMhgCY2URgHHC8mf0oaiszs7vN7IXo4+NR+0YzG2EZVW2zKczsF2Y228ziZnZrdMwGM/uzaLtFs0BeNbMHgDF97ay7twD/FzjazE6LzlcX/TnWzFaa2Toze9nMPhG1n29ma81svZk9FrXdZGbXt5032n+imQ01sweifV82s89F22+J+rvBzG7reg4zO93MVkXb7zGzkVH7k2b2D2a22sxeb+tTtvqzQFJTU0NDQwO3334799xzDwAbNmxg165dPbY/9thjjBgxov1YgNraWoD29mQy2f4Nbl/75hrdOxkuZRMuZRM25RMuZRMuZRMuZRMuZZMdPcVmALh7lZmtBs4nMyvj88DvoNP/xH8I/LO7/8HMjgYeASqAZ4CPA28DW4BPAD8HPgpcB3wZ2OPu06MCzDNmthyYApwEfAQ4AngV+Ol+9DllZuuBScD6Dpu+CDzi7jebWRwoMbMy4D+BWe7+lpmN2sfpzwe2uftFAGZ2WHTMJcAkd/eOt/d08HPg6+7+lJl9B1gI/Hm0LeHuZ5rZhVH77L6OtasiWkmQWRA1RYw0RgqjiGT7Pk0kiJF54k1bQaWFBIZjOAnS1NbWcvzxxzNy5EhisRjbt2/nueeeY+LEiXzkIx/h2GOPpbm5maqqKp577jmOO+44TjvtNEaNGkVJSQmrV68mFouxdu1aDjvsMA4//HBqa2v5/ve/z+jRo7n22muZOHEi8XiclStXUltby+bNmykvL6egoIDa2lqKioraF3dtaWnJvFGJBLFYjEQiQUNDQ/uYSkpKcHeamprav4EWFxeTSqVwd1pbWwEoKCjAzIjH4zQ2NhJlSFFREWbW7ZzJZJJ0Ok0ymXn/CgsLAYjFYjQ1NbW/LiwsJBaLdTs+nU7T0tJCOp2mrq6O0aNH59SYgJzIqaGhgVQqlVNjypWc6urqGDZsWE6NKZdyampqar9OrowpV3Kqq6tj+PDhOTWmXMmpvr6eeDyeU2PKlZyqq6sZNmxYTo0pV3JKpVKYWU6N6UByKi0tpS9MlaWBYWZXABe5+xfMbB3wJTK3skxz96+Z2QfAtg6HlJEpTsyN9nsbaAK+AswH/tvdZ5jZkmh729+Uw4A/Ay4ENrj7T6Pr/zfwa3df0kv/ngSud/cXO7TdGx3zOzOrc/dhZjaLTKHll8BSd19nZhcDn3f3y7uc8yagzt3bZoO8DHwGKCRTAFoM3O/uT5tZAlgDvAg8ELW3tJ2DTAFmo7sfHZ3rOOD37j416vuN7v6MmR0BPOPux+89kd5V3HCPN1KY7eHtKm+5qPPnlZXcddddnR7n21v722+/zYMPPsjOnTsZM2YMF198MePGjQNg0aJFlJWVsWDBAgA2bdrEihUrqKmpoby8nHnz5jFq1L5qVIem2traPn8zk4NL2YRL2YRN+YRL2YRL2YRL2YRL2XTTpzUJNINk4CwFvm9mU4Fid19rZqd22B4DznL3xo4HmdlK4KvA0cCNZGZZXAo83bYLmVkVj3Q57kLI/l6RaHbIR4BNHdvdfWVUJLkI+IWZ3QpU93KtJJ1v2yqKzvG6mZ1BpojzPTNb7u7fMbMzgXPJzLD5GvCp/ehyc/RnigP8e5wcoDvNJk6cyMKFC/vUPmHCBK677roez9N134qKCioqKvqvowErKCgY7C5IL5RNuJRN2JRPuJRNuJRNuJRNuJRNdrQGyQBx9zrgSTKzL37Twy7LyRQFgMx6G9Fx7wCjgRPcfQvwB+B6PiyQPAJcZ2YF0XEnmtlQYCXwecusUTIWOKevfY3O9T3gHXff0GXbBOADd/9P4L+AqcBzwCfN7Jhon7bpC5XRdqLCUNv2cUCDu/8SuA2YambDgMPc/UEyt82c3vG67r4H2N1hfZE/AZ7q65j2h/etmCiDwHJ08dlcoGzCpWzCpnzCpWzCpWzCpWzCpWyyoxkkA+s3wH/T/Yk2AN8AfmxmG8jksBK4Ntr2PBCPXj9Npnjxh+jzO4CJwFrL/K3fAXwWuIfMDIyNwOv0rZjwKzNrBoYAjwLzetjnbOBbZtZK5taXK919h5l9BfhvM4sBHwDnAXcDV0a3FL0Q9QMyM1NuNbM00EpmLZVS4F4zKyIzK+Yverj2VcDtZlZCZj2Wa/owpv2mAkm44vH4vneSQaFswqVswqZ8wqVswqVswqVswqVssqM1SCTvDdQaJHLgdO9kuJRNuJRN2JRPuJRNuJRNuJRNuJRNN336rbhusRERERERERGRvKdbbHKcmd1DtBZIB3/VdZHXfKZbbMKleyfDpWzCpWzCpnzCpWzCpWzCpWzCpWyyo1tsJO8lk0lPJFQrDFEymUTZhEnZhEvZhE35hEvZhEvZhEvZhEvZdKNbbET6QtXVcCmbcCmbcCmbsCmfcCmbcCmbcCmbcCmb7KhAInmvoaFhsLsgvVA24VI24VI2YVM+4VI24VI24VI24VI22VGBRERERERERETynm5Kkrx35s2P0h+P+T2Y9EhhERERERGR/qUZJJL3mlQnDFZJSclgd0F6oWzCpWzCpnzCpWzCpWzCpWzCpWyyowKJ5L04epJTqJLJ5GB3QXqhbMKlbMKmfMKlbMKlbMKlbMKlbLKjX51L3osdogWShx56iFdeeYX6+npOOOEEvvjFLwKwdetWHnjgAaqqqigrK2Pu3LmMHTsWgJdeeomnn36a2tpaJkyYwNy5cxk+fDgATU1NPPTQQ7z22muk02nGjh3LNddc0+O1N2/ezPLly6mpqWH8+PHMmzePkSNH9vsY0+l0v59T+oeyCZeyCZvyCZeyCZeyCZeyCZeyyY5mkEjei3PofvM45ZRTOn2eTCZZvHgxLS0tzJkzh/r6ehYvXkw6nWbbtm0sW7aM0tJSZs+eTWVlJQ888ED7sffeey8bN25kypQpnH/++YwaNarHa9bV1bFkyRKGDBnCeeedx/bt21m6dOmAjE+V73Apm3Apm7Apn3Apm3Apm3Apm3Apm+xoBonIIeqCCy6gurqa1atXt7e98cYb1NfXM3v2bKZPn05dXR0rV66ksrKS9957D4AzzjiDU089lZdffpnXX3+dhoYGmpub2bx5Mx/5yEeYPXs2ZsbUqVN7vO7GjRtJpVLMnDmTU045hW3btrFhwwZ27drVa1FFREREREQkdJpBsp/MbKKZvdyl7SYzu34/zlFpZqP3sc/fZNG3q83sR3vZfpKZPWlm68xsk5n9Rx/OWbe//ehy/LNZHPMdM5t9INfdH63ED9alBlx1dTVA+20zbX/u3r2boUOHAplbcHbu3ElVVVX7MTt27ABg27ZtfPe73+W73/0uK1as2O9r9LfCwkPr6UL5RNmES9mETfmES9mES9mES9mES9lkRwWScO13gaQP/gX4Z3c/3d0rgH8dgGt04u4fy+KYb7v7owPRn3zjnllfxcw45ZRTOOqoo1izZg0//vGPSaVSACQSifYpeC0tLVx66aUcffTRPPvss2zZsmW/riEiIiIiInKo0i02/cjMngTWAWcCw4EvuftqMzsc+A1QBqwGrMMxS4GjgCLgh+7+H2Z2C1BsZuuAV9z9cjO7AvgGUAg8Dyxw95SZXQP8NbAdeB1o3ksXxwL/2/aJu2+M+nA1MM3dvxZ9fj9wm7s/GX3+T8A5wG7g8+6+IxrrS8AZ0biujPrxEeB37v630bF17j7MzMYCv4velwRwHfAs8F/ANMCBn7r7P5vZncD97r7EzM4FbouOeQG4zt2bzawSuAu4GCgALnP3zXsZe6/ipCggUyxIESONkcIo4sP79ppIEAMKSGHRoq4tJDAcw0lE65gkiUUtRmF0vGO0RiuddD1nHCeGt6+D0jabxYHCqE9pjGTUr7bja2trKSkpobW1NXPdZJLa2lpKS0sB2LVrF7W1tezcuROAoUOHYmZceuml7Ny5EzPj6aef5p133qGgoKC9wnzUUUdx7LHHsmfPHt566y22b9/OuHHjSKVSJBIJWlpaKC4uBjIzRsaNG8euXbsAKCgoIJVKkUwmSafT7UWXtnPHYjGampraXxcWFhKLxWhoaGh/T0pKSkin07S0tJBOp2ltbSUWi7UvMtXS0gJkijqxWIxEItHteHenqampvXBTXFxMKpXC3dvfr4KCAsyMeDxOY2MjkCnwFBUVYWbdztmfYwIoKio65MeUSqWor6/PqTHlSk6tra20tLTk1JhyKSd3p7a2NqfGlCs5tba2kkwmc2pMuZJT278HcmlMuZJTY2MjLS0tOTWmXMmpoKCA5ubmnBrTgeTU9v+kfbG2zkrfmNlEMv95n9yh7SagDvgM8Ia7/x8zmwX8m7tPNrN/AXa6+3fM7CLgfqDM3Xea2Sh332VmxWQKAJ9096q2wkJ0/grgH4E/cvdWM/s3YBWwgkyx5AxgD/AE8FJboaOHvl8D/IBMYWI58DN3r95bgcTMHLjC3X9lZt8Gxrj716ICyfPu/ldm9k3gr6J+7ALeBE7rOA4z+0ugyN1vNrM4UAKcCNzi7udF1x0R9efO6D26H3gDONfdXzeznwNr3f0HUYHkn9z9X81sATDV3f90vwMFKm64xxs5tKagVd5yEa+//joffPABjz32GEcccQRnnnkm5eXl/OIXv6CgoICPfexjPP3008Tjcb7+9a8D8MgjjzB27FjeffddXnzxRT760Y8yZ84c3J3bb7+duro6PvWpT/HSSy+xbds2rr32WsaMGcOiRYsoKytjwYIF1NbW8sMf/pAxY8Zw2mmn8fjjj3PkkUf2+sSbA9Gx6CNhUTbhUjZhUz7hUjbhUjbhUjbhUjbd9Gm6u26x2X+9VZTa2n8D4O4rgeFmNgKYBfwyan+AzEyMNt8ws/VkCh5HASf0cO5zyRQfXohmlZwLHAvMAJ509x3u3kJmhkbvHXf/GVAB/B44G1hlZkP2dgyQ7nDeXwIzO2xbFv25kcxMl+3u3gxsicbS0QvANVEx6SPuXhvtd6yZ/auZnQ/UdDnmJOAtd389+vwuMu9lm/+O/lwDTNzHOHLOs88+y2OPPQbA+++/z3333cf27du57LLLKCws5OGHH2bo0KFcdtllxGIxzIy3336b+++/n1deeYXp06dz7rnnApkq7fz58xk1ahQPPfQQjY2NXHLJJYwZM6bbdUtLS5k/fz5NTU2sWLGCI488knnz5h3UsYuIiIiIiPQ33WKz/6qAkV3aRgFvRa+7FlC8l3bM7GxgNnCWuzdEszKKerimAXe5+193Of6zPZ13b9x9G/BT4KfRYrOTgSSdi2U99aH9FB1et93Ok6bzrT1puvzdcveV0ayai4BfmNmt7v5zMzsNmAN8Ffhj4EsdDttXla/tmqmu19sf6b4VE4Nz9dVX97rtuuuu69ZmZlx77bW9HjNmzBi+/OUv97ht4cKFnT6vqKigoqKibx09ALGYarihUjbhUjZhUz7hUjbhUjbhUjbhUjbZ0bu2n9y9DtgerY2BmY0Czgf+EO3yuah9JrDH3fcAK4HLo/YL+LDAchiwOyqOTAI+2uFSrWZWEL1+DLjUzMa0XdPMJpC5veZsMzs82veyvfXdzM5vO6eZHQkcDrwLVAKnm1nMzI4is4ZKmxhwafT6ix3GuV+i/n7g7v9JZt2RqZZ5kk/M3e8G/g7o+lzZzcBEMzs++vxPgKeyuf7eJPVlECytvh0uZRMuZRM25RMuZRMuZRMuZRMuZZMdzSDJzpXAj6PFSwEWufub0VM8dlvm0bbD+XA2xCLgN2a2lsx/8LdG7Q8D15rZBuA1MrfZtPkPYIOZrY0Waf1bYLmZxYBW4Kvuviq6ZeU5Mou0roW9PrP208APzawp+vxb7v6emb1PZgbMRuDl6Dxt6oFTzGwNmXVOPtfH96irs4FvmVkrmfVargTKgZ9FY4LMIq/t3L0pWjfl92bWtkjr7Vlev1eH6gySfKDKd7iUTbiUTdiUT7iUTbiUTbiUTbiUTXa0SGs/im6Rud7dXxzsvkjfHaqLtOYDLS4VLmUTLmUTNuUTLmUTLmUTLmUTLmXTjRZpFRERERERERHpC91i04/c/ezB7gOAmd1I9/VIfu/uNw9Gf0RERERERERCp1tsJO+lUimPx/e2dIsMllQqhbIJk7IJl7IJm/IJl7IJl7IJl7IJl7LpRrfYiPRFOp0e7C5IL5RNuJRNuJRN2JRPuJRNuJRNuJRNuJRNdlQgkbzX0tIy2F2QXiibcCmbcCmbsCmfcCmbcCmbcCmbcCmb7KhAInlP1dVwKZtwKZtwKZuwKZ9wKZtwKZtwKZtwKZvsqEAiIiIiIiIiInlPBRLJe0VFRYPdBemFsgmXsgmXsgmb8gmXsgmXsgmXsgmXssmOHvMreS+dTrN161YeeOABqqqqKCsrY+7cuYwdO7bbvps3b2b58uXU1NQwfvx45s2bx8iRIweh1/lBUwPDpWzCpWzCpnzCpWzCpWzCpWzCpWyyoxkkkveSySSLFy+mpaWFOXPmUF9fz+LFi7t9U6mrq2PJkiUMGTKE8847j+3bt7N06dLB6bSIiIiIiIj0KxVIJO+99tpr1NfXM23aNKZPn86UKVOorq6msrKy034bN24klUoxc+ZMZsyYwaRJk9i6dSu7du0anI7nAa2+HS5lEy5lEzblEy5lEy5lEy5lEy5lkx0VSHKcmU00s5cP9rF7OeeTZjat6zXMbIyZvWVmR3bY9m9mdoOZnW1m93do/6yZbTCzzWa20cw+eyB9qqmpAWD48OGd/ty9e3en/aqrq/u0n4iIiIiIiBx6VCCRILj7B8A/ALcBmNlUYCbwTx33M7PTon3mufskYC5wm5mdmu21Y7HOXwbu3natffW5T/tJ9hIJLZMUKmUTLmUTNuUTLmUTLmUTLmUTLmWTHRVI8kPCzO6KZl0sMbMSM/u2mb0Qzd74D4v+l29mZ5jZejN7Dvhq2wmiYxZH5/idmT3fNhPEzD5tZs+Z2Voz+72ZDcuyn/8BHGdm5wA/Ar7m7q1d9rke+K67vwUQ/fk94FtZXpMRI0YAH84kqa2tbW9PJpOkUql97icDo2vxSsKhbMKlbMKmfMKlbMKlbMKlbMKlbLKjslJ+OAn4srs/Y2Y/BRYAP3L37wCY2S+AzwD3AT8Dvu7uT5nZrR3OsQDY7e6nmtlkYF107Gjgb4HZ7l5vZn8F/H/Ad/bSn1+ZWWP0uhBIA7h72syuAx4Hlrn7yh6OPYVolkkHL9KhmLO/Jk6cSElJCatXr8bMWLt2LYcddhiHH344N998M4cffjjXXXcdkyZN4rHHHmPlypVUVVWxefNmxo8fT0lJSXuxpKCgADMjHo/T2JgZoplRVFSEmdHQ0NB+3ZKSEpLJJOl0mmQymXkzCguBzDe0pqam9teFhYXEYrFux6fTaVpaWtoXlC0qKmp/3XbfYSKRIBaLkUgkuh3v7jQ1NbXPhikuLiaVSuHutLa2DvqYUqkUsVgsp8aUKzm5O/X19Tk1plzJKZVK0dLSklNjyqWczKz9Z0aujClXckqlUiSTyZwaU67klEqlaG1tzakx5UpOzc3NtLS05NSYciWnIUOG0NzcnFNjOpCcSktL6Qtr66zkJjObCKx096Ojzz8FfAP4BfB/gRJgFPCvwL8DGzvseyrwa3efbGZLgR+6+xPRtrXAV4AjgTuB/40uWQg85+5f7qU/TwLXu/uLHfp3v7tP7rDPymif1dHnZ0eff8bMXgKucvcNHfY/Hfgvdz8jm/eotrbWd+3axYMPPsjOnTsZM2YMF198MePGjWPRokWUlZWxYMECADZt2sSKFSuoqamhvLycefPmMWrUqGwuK31QW1vb529mcnApm3Apm7Apn3Apm3Apm3Apm3Apm276tC6CZpDkh65VMAf+DZjm7u+Y2U1AEZm/NL1VzHr7C2XACnf/Qn90NJKOPnryCjAN2NChbSrw6oFccMKECVx33XXd2hcuXNjp84qKCioqKg7kUiIiIiIiIhIg3ZiUH442s7Oi118A/hC93hmtF3IpgLtXA3vMbGa0/fIO5/gD8McAZnYy8JGofRXwcTM7PtpWYmYnDtRAyNxe89fRzJO2GSh/Q5fFXEVERERERET2h2aQ5IdNwFVm9hPgDTK30owENgKVwAsd9r0G+KmZNQCPdGj/N+AuM9sAvERmBsced99hZlcDvzGzIdG+fwu8PhADcfd10Ton95lZAdAK/F93X5ftOUtKSvqre9LPlE24lE24lE3YlE+4lE24lE24lE24lE12tAaJ9ImZxYECd28ys+OAx4AT3b1lkLt2wJLJpOsxWGFKJpN6RFmglE24lE3YlE+4lE24lE24lE24lE03fVqDRLfYSF+VAH8ws/XAPcB1uVAcAdpXO5bwKJtwKZtwKZuwKZ9wKZtwKZtwKZtwKZvsqKQkfeLutWQWR+0TM7sHOKZL81+5+yM97T+YNIsqXMomXMomXMombMonXMomXMomXMomXMomOyqQyIBw90sGuw8iIiIiIiIifaU1SCTvaQ2ScOneyXApm3Apm7Apn3Apm3Apm3Apm3Apm260BolIX6RSqcHugvRC2YRL2YRL2YRN+YRL2YRL2YRL2YRL2WRHBRLJe5pFFS5lEy5lEy5lEzblEy5lEy5lEy5lEy5lkx0VSCTvtba2DnYXpBfKJlzKJlzKJmzKJ1zKJlzKJlzKJlzKJjsqkIiIiIiIiIhI3lOBRPJeQUHBYHdBeqFswqVswqVswqZ8wqVswqVswqVswqVssqMCieQ9sz4taCyDQNmES9mES9mETfmES9mES9mES9mES9lkRwUSyXvxeHywuyC9UDbhUjbhUjZhUz7hUjbhUjbhUjbhUjbZUYFE8l5jY+Ngd0F6oWzCpWzCpWzCpnzCpWzCpWzCpWzCpWyyowKJiIiIiIiIiOQ9FUgk7+n+vHApm3Apm3Apm7Apn3Apm3Apm3Apm3Apm+yoQCJ5r6ioaLC7IL1QNuFSNuFSNmFTPuFSNuFSNuFSNuFSNtlRgUTynqqr4VI24VI24VI2YVM+4VI24VI24VI24VI22VGBRPJeQ0PDYHdBeqFswqVswqVswqZ8wqVswqVswqVswqVssqMCiYiIiIiIiIjkPRVIRERERERERCTvmbsPdh9EBlUqlfJ4PD7Y3ZAepFIplE2YlE24lE3YlE+4lE24lE24lE24lE03fVqURTNIJO8lk8nB7oL0QtmES9mES9mETfmES9mES9mES9mES9lkRwUSyXvpdHqwuyC9UDbhUjbhUjZhUz7hUjbhUjbhUjbhUjbZUYFE8p6qq+FSNuFSNuFSNmFTPuFSNuFSNuFSNuFSNtlRgURERERERERE8p4WaRURERERERGRvKcZJCIiIiIiIiKS91QgEREREREREZG8pwKJiIiIiIiIiOQ9FUhEREREREREJO+pQCIiIiIiIiIieU8FEhERERERERHJeyqQiIiIiIiIiEjeU4FERERERERERPKeCiSSt8zsfDN7zcz+x8xuGOz+5AMzO8rMnjCzTWb2ipl9M2q/yczeNbN10ceFHY756yij18xsTof2M8xsY7TtX8zMBmNMucTMKqP3dJ2ZvRi1jTKzFWb2RvTnyA77K5uDwMxO6vC1sc7Maszsz/V1M3jM7Kdm9oGZvdyhrd++VsxsiJn9Lmp/3swmHtQBHsJ6yeZWM9tsZhvM7B4zGxG1TzSzxg5fQ7d3OEbZ9LNesum372PKJnu9ZPO7DrlUmtm6qF1fNweR9f5vZ/3MGSjurg995N0HEAfeBI4FCoH1wMmD3a9c/wDGAlOj16XA68DJwE3A9T3sf3KUzRDgmCizeLRtNXAWYMBDwAWDPb5D/QOoBEZ3aftH4Ibo9Q3APyibQc0oDrwHTNDXzaDmMAuYCrzcoa3fvlaABcDt0evPA78b7DEfKh+9ZPNpIBG9/ocO2UzsuF+X8yibg5NNv30fUzb9m02X7f8EfDt6ra+bg5tNb/921s+cAfrQDBLJV2cC/+PuW9y9BfgtMG+Q+5Tz3H27u6+NXtcCm4DyvRwyD/ituze7+1vA/wBnmtlYYLi7P+eZ7+Y/Bz47sL3PW/OAu6LXd/Hh+6xsBse5wJvu/vZe9lE2A8zdVwK7ujT359dKx3MtAc7VbJ++6Skbd1/u7sno01XA+L2dQ9kMjF6+bnqjr5uDaG/ZRO/hHwO/2ds5lM3A2Mu/nfUzZ4CoQCL5qhx4p8Pn/8ve/6Mu/SyavjcFeD5q+lo0/fmnHaYJ9pZTefS6a7scGAeWm9kaM/tK1HaEu2+HzA9pYEzUrmwGx+fp/I9Ufd2Eoz+/VtqPif5jvwc4fMB6nl++ROY3p22OMbOXzOwpM/tE1KZsDq7++j6mbAbGJ4D33f2NDm36uhkEXf7trJ85A0QFEslXPVVF/aD3Ik+Z2TDgbuDP3b0G+HfgOOB0YDuZqZzQe07Kb2B83N2nAhcAXzWzWXvZV9kcZGZWCMwFfh816evm0JBNHspqAJjZjUAS+FXUtB042t2nAP8f8GszG46yOZj68/uYshkYX6BzYV5fN4Ogh38797prD2362tkPKpBIvvpf4KgOn48Htg1SX/KKmRWQ+Qb/K3f/bwB3f9/dU+6eBv6TzC1Q0HtO/0vnKdLKrx+4+7bozw+Ae8jk8H40LbNt+uwH0e7K5uC7AFjr7u+Dvm4C1J9fK+3HmFkCOIy+35ogPTCzq4DPAJdH08uJpqBXRa/XkLlX/0SUzUHTz9/HlE0/i97HPwJ+19amr5uDr6d/O6OfOQNGBRLJVy8AJ5jZMdFvZT8PLBvkPuW86H7G/wI2ufv3O7SP7bDbJUDbKurLgM9Hq2sfA5wArI6mEtaa2Uejc14J3HtQBpGjzGyomZW2vSazqOHLZDK4KtrtKj58n5XNwdfpt3j6uglOf36tdDzXpcDjbf+pl/1nZucDfwXMdfeGDu1lZhaPXh9LJpstyubg6efvY8qm/80GNrt7+60Z+ro5uHr7tzP6mTNwDnSVV33o41D9AC4ksxL0m8CNg92ffPgAZpKZsrcBWBd9XAj8AtgYtS8DxnY45sYoo9fo8MQNYBqZf0i9CfwIsMEe36H8QeaJTuujj1favibI3IP6GPBG9OcoZTMo+ZQAVcBhHdr0dTN4efyGzDTzVjK/eftyf36tAEVkbqX6HzJPHTh2sMd8qHz0ks3/kLm/vu3nTtvTGuZH3+/WA2uBi5XNQc+m376PKZv+zSZqvxO4tsu++ro5uNn09m9n/cwZoI+2N0VEREREREREJG/pFhsRERERERERyXsqkIiIiIiIiIhI3lOBRERERERERETyngokIiIiIiIiIpL3VCARERERERERkbynAomIiIiIiIiI5D0VSEREREREREQk76lAIiIiIiIiIiJ57/8HrBTvq5U/IbYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar graph for activity based count\n",
    "fig, ax = plt.subplots(figsize =(16, 9))\n",
    " \n",
    "ax.barh(all_activities, count)\n",
    " \n",
    "for s in ['top', 'bottom', 'left', 'right']:\n",
    "    ax.spines[s].set_visible(False)\n",
    " \n",
    "ax.xaxis.set_ticks_position('none')\n",
    "ax.yaxis.set_ticks_position('none')\n",
    " \n",
    "ax.xaxis.set_tick_params(pad = 5)\n",
    "ax.yaxis.set_tick_params(pad = 10)\n",
    " \n",
    "ax.grid(b = True, color ='grey',\n",
    "        linestyle ='-.', linewidth = 0.5,\n",
    "        alpha = 0.2)\n",
    " \n",
    "ax.invert_yaxis()\n",
    " \n",
    "for i in ax.patches:\n",
    "    plt.text(i.get_width()+0.2, i.get_y()+0.5,\n",
    "             str(round((i.get_width()), 2)),\n",
    "             fontsize = 10, fontweight ='bold',\n",
    "             color ='grey')\n",
    " \n",
    "ax.set_title('Activites and Their Counts',\n",
    "             loc ='left', )\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0fbda24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_crit = new_train[[\"User_ID\", \"Active\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9e194684",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save criteria in a csv\n",
    "df_crit.to_csv(\"CriteriaData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3724ccf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_test = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f9fb5c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Activity based grouping for all months\n",
    "for i in range(len(test_df)):\n",
    "    user = test_df.loc[i]\n",
    "    created_month = user['Created At Month']\n",
    "    user_dict = {}\n",
    "    user_dict['User_ID'] = user['User_ID']\n",
    "    for activity in all_activities:\n",
    "        user_dict[f'{activity}'] = user[f'{activity}_month_{created_month}']\n",
    "\n",
    "    new_test = new_test.append(user_dict, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f10c4ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Downloaded_Competition_Datafile</th>\n",
       "      <th>Viewed_All_Discussions</th>\n",
       "      <th>Confirmed_Email</th>\n",
       "      <th>Joined_Competition</th>\n",
       "      <th>Signed_Up</th>\n",
       "      <th>Viewed_All_Competitions</th>\n",
       "      <th>Viewed_All_Learning_Pages</th>\n",
       "      <th>badge_OCZE</th>\n",
       "      <th>create_alias</th>\n",
       "      <th>...</th>\n",
       "      <th>Updated_Discussion</th>\n",
       "      <th>Viewed_All_Jobs</th>\n",
       "      <th>job_ID</th>\n",
       "      <th>Created_Submission</th>\n",
       "      <th>Signed_Out</th>\n",
       "      <th>Updated_Profile</th>\n",
       "      <th>blog_ID</th>\n",
       "      <th>Viewed_Discussion</th>\n",
       "      <th>Updated_Submission</th>\n",
       "      <th>badge_HYIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_ZZMHNLNT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_ZWDD4T41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_ZUVTLGY7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_ZT27LY0Q</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_ZQM6PUGO</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>733</th>\n",
       "      <td>ID_04OT901K</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>ID_04GIS1QZ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>ID_02F14MDW</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>ID_028JJTDW</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>ID_003YJHQA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>738 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         User_ID  Downloaded_Competition_Datafile  Viewed_All_Discussions  \\\n",
       "0    ID_ZZMHNLNT                              0.0                     1.0   \n",
       "1    ID_ZWDD4T41                              0.0                     1.0   \n",
       "2    ID_ZUVTLGY7                              4.0                    13.0   \n",
       "3    ID_ZT27LY0Q                              0.0                     1.0   \n",
       "4    ID_ZQM6PUGO                              0.0                     3.0   \n",
       "..           ...                              ...                     ...   \n",
       "733  ID_04OT901K                              2.0                     3.0   \n",
       "734  ID_04GIS1QZ                              0.0                     2.0   \n",
       "735  ID_02F14MDW                              0.0                     1.0   \n",
       "736  ID_028JJTDW                              2.0                     2.0   \n",
       "737  ID_003YJHQA                              0.0                     2.0   \n",
       "\n",
       "     Confirmed_Email  Joined_Competition  Signed_Up  Viewed_All_Competitions  \\\n",
       "0                1.0                 0.0        1.0                      1.0   \n",
       "1                0.0                 0.0        1.0                      1.0   \n",
       "2                1.0                 1.0        1.0                      5.0   \n",
       "3                1.0                 0.0        1.0                      1.0   \n",
       "4                0.0                 0.0        1.0                      2.0   \n",
       "..               ...                 ...        ...                      ...   \n",
       "733              1.0                 1.0        1.0                      2.0   \n",
       "734              1.0                 0.0        1.0                      7.0   \n",
       "735              0.0                 0.0        1.0                      1.0   \n",
       "736              1.0                 0.0        1.0                      1.0   \n",
       "737              1.0                 0.0        1.0                      1.0   \n",
       "\n",
       "     Viewed_All_Learning_Pages  badge_OCZE  create_alias  ...  \\\n",
       "0                          1.0         1.0           1.0  ...   \n",
       "1                          3.0         0.0           1.0  ...   \n",
       "2                          3.0         1.0           0.0  ...   \n",
       "3                          1.0         1.0           1.0  ...   \n",
       "4                          2.0         0.0           1.0  ...   \n",
       "..                         ...         ...           ...  ...   \n",
       "733                        2.0         1.0           1.0  ...   \n",
       "734                        1.0         1.0           0.0  ...   \n",
       "735                        1.0         0.0           1.0  ...   \n",
       "736                        1.0         1.0           1.0  ...   \n",
       "737                        1.0         1.0           1.0  ...   \n",
       "\n",
       "     Updated_Discussion  Viewed_All_Jobs  job_ID  Created_Submission  \\\n",
       "0                   0.0              0.0     0.0                 0.0   \n",
       "1                   0.0              0.0     0.0                 0.0   \n",
       "2                   0.0              5.0     3.0                20.0   \n",
       "3                   0.0              0.0     0.0                 0.0   \n",
       "4                   0.0              3.0     0.0                 0.0   \n",
       "..                  ...              ...     ...                 ...   \n",
       "733                 0.0              0.0     0.0                 1.0   \n",
       "734                 0.0              0.0     0.0                 0.0   \n",
       "735                 0.0              0.0     0.0                 0.0   \n",
       "736                 0.0              0.0     0.0                 2.0   \n",
       "737                 0.0              0.0     0.0                 0.0   \n",
       "\n",
       "     Signed_Out  Updated_Profile  blog_ID  Viewed_Discussion  \\\n",
       "0           0.0              0.0      0.0                0.0   \n",
       "1           0.0              0.0      2.0                0.0   \n",
       "2           0.0              0.0      0.0                0.0   \n",
       "3           0.0              2.0      0.0                0.0   \n",
       "4           0.0              0.0      0.0                0.0   \n",
       "..          ...              ...      ...                ...   \n",
       "733         0.0              0.0      0.0                0.0   \n",
       "734         0.0              0.0      0.0                0.0   \n",
       "735         0.0              3.0      0.0                0.0   \n",
       "736         0.0              0.0      0.0                0.0   \n",
       "737         0.0              2.0      0.0                0.0   \n",
       "\n",
       "     Updated_Submission  badge_HYIO  \n",
       "0                   0.0         0.0  \n",
       "1                   0.0         0.0  \n",
       "2                  12.0         0.0  \n",
       "3                   0.0         0.0  \n",
       "4                   0.0         0.0  \n",
       "..                  ...         ...  \n",
       "733                 0.0         0.0  \n",
       "734                 0.0         0.0  \n",
       "735                 0.0         0.0  \n",
       "736                 0.0         0.0  \n",
       "737                 0.0         0.0  \n",
       "\n",
       "[738 rows x 23 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2edd5043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    4319\n",
       "1.0     917\n",
       "Name: Active, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train.Active.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "702dfa0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = new_train.reset_index(drop=True)\n",
    "test_df = new_test.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b65c13",
   "metadata": {},
   "source": [
    "# MODEL TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a16a9437",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9b2a2bdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_cols = train_df.columns.difference([\n",
    "    'User_ID', 'Active', \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5a7b0fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_df[main_cols]\n",
    "y = train_df['Active']\n",
    "test = test_df[main_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de8cfb12",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "acc9fcb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "358ae1f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(random_state=0, max_iter=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cc4c5618",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "clf = LogisticRegression(random_state=0, max_iter=40).fit(X, y)\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b7611644",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_p = clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a9cdc8c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20239190432382706"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y, y_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b81018dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8344155844155844"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, y_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2fdfdd62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7540464401245117\n"
     ]
    }
   ],
   "source": [
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "37618d2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op.countOf(clf.predict(test), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a12d809",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5130a2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0c311008",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "neigh = KNeighborsClassifier(n_neighbors=10).fit(X, y)\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7611676a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = neigh.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1c51f6ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23669724770642203"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "891110fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.841100076394194"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "facbeffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.004990100860595703\n"
     ]
    }
   ],
   "source": [
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "90d5f044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op.countOf(neigh.predict(test), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0858b01",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9afbdbe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f22f36b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "clf1 = SVC(kernel='rbf', gamma='scale').fit(X, y)\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bb2312d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf1.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "826f3e70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8504583651642476"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "82cf7930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2702702702702703"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "35ca7c94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0584168434143066\n"
     ]
    }
   ],
   "source": [
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "48e11bb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op.countOf(clf1.predict(test), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389aac20",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "47fe8141",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "18b13677",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf2 = RandomForestClassifier(n_estimators=15).fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9b21d961",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8a5b2425",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start = 80, stop = 2000, num = 10)]\n",
    "max_features = ['sqrt', 'log2']\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "min_samples_split = [2, 5, 10]\n",
    "min_samples_leaf = [1, 2, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "49949f34",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9f294d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_random = RandomizedSearchCV(estimator = clf2, param_distributions = random_grid, \n",
    "                               n_iter = 10, cv = 3, verbose=2, random_state=42, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "360ce6c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(n_estimators=15),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110,\n",
       "                                                      None],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                                        &#x27;n_estimators&#x27;: [80, 293, 506, 720, 933,\n",
       "                                                         1146, 1360, 1573, 1786,\n",
       "                                                         2000]},\n",
       "                   random_state=42, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(n_estimators=15),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110,\n",
       "                                                      None],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                                        &#x27;n_estimators&#x27;: [80, 293, 506, 720, 933,\n",
       "                                                         1146, 1360, 1573, 1786,\n",
       "                                                         2000]},\n",
       "                   random_state=42, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=15)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=15)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(n_estimators=15),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110,\n",
       "                                                      None],\n",
       "                                        'max_features': ['sqrt', 'log2'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [80, 293, 506, 720, 933,\n",
       "                                                         1146, 1360, 1573, 1786,\n",
       "                                                         2000]},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cdafa750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 933,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 80}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "372e4c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "clf2 = RandomForestClassifier(n_estimators=80, min_samples_split=2, \n",
    "                              min_samples_leaf=4, max_features='log2', \n",
    "                              max_depth=20).fit(X, y)\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6d8fa850",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf2.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cc23ebf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8672650878533231"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fae33489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40547476475620187"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1da26216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3244004249572754\n"
     ]
    }
   ],
   "source": [
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5c9cbda4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op.countOf(clf2.predict(test), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dcd9210",
   "metadata": {},
   "source": [
    "## LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7dfe3412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: lightgbm in c:\\users\\hp\\anaconda3\\lib\\site-packages (3.3.3)\n",
      "Requirement already satisfied: numpy in c:\\users\\hp\\anaconda3\\lib\\site-packages (from lightgbm) (1.20.3)\n",
      "Requirement already satisfied: wheel in c:\\users\\hp\\anaconda3\\lib\\site-packages (from lightgbm) (0.37.0)\n",
      "Requirement already satisfied: scipy in c:\\users\\hp\\anaconda3\\lib\\site-packages (from lightgbm) (1.7.1)\n",
      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\hp\\appdata\\roaming\\python\\python39\\site-packages (from lightgbm) (1.1.2)\n",
      "Requirement already satisfied: joblib>=1.0.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (1.1.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (2.2.0)\n",
      "\n",
      "[notice] A new release of pip available: 22.1.2 -> 22.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "aa93ecef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "cbf575a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LGBMClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1f88fbf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "boosting_type = [\"gbdt\", \"dart\", \"goss\", \"rf\"]\n",
    "num_leaves = [int(x) for x in np.linspace(start = 10, stop = 1000, num = 10)]\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "learning_rate = [float(x) for x in np.arange(0, 1, 0.01)]\n",
    "n_estimators = [int(x) for x in np.linspace(start = 80, stop = 2000, num = 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3a17a582",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'boosting_type': boosting_type,\n",
    "               'max_depth': max_depth,\n",
    "               'learning_rate': learning_rate,\n",
    "               'num_leaves': num_leaves}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5ce3bc97",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_random = RandomizedSearchCV(estimator = clf, param_distributions = random_grid, \n",
    "                               n_iter = 10, cv = 3, verbose=2, random_state=42, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "807d38d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=LGBMClassifier(), n_jobs=-1,\n",
       "                   param_distributions={&#x27;boosting_type&#x27;: [&#x27;gbdt&#x27;, &#x27;dart&#x27;,\n",
       "                                                          &#x27;goss&#x27;, &#x27;rf&#x27;],\n",
       "                                        &#x27;learning_rate&#x27;: [0.0, 0.01, 0.02, 0.03,\n",
       "                                                          0.04, 0.05, 0.06,\n",
       "                                                          0.07, 0.08, 0.09, 0.1,\n",
       "                                                          0.11, 0.12, 0.13,\n",
       "                                                          0.14, 0.15, 0.16,\n",
       "                                                          0.17, 0.18, 0.19, 0.2,\n",
       "                                                          0.21, 0.22, 0.23,\n",
       "                                                          0.24, 0.25, 0.26,\n",
       "                                                          0.27, 0.28, 0.29, ...],\n",
       "                                        &#x27;max_depth&#x27;: [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110],\n",
       "                                        &#x27;n_estimators&#x27;: [80, 293, 506, 720, 933,\n",
       "                                                         1146, 1360, 1573, 1786,\n",
       "                                                         2000],\n",
       "                                        &#x27;num_leaves&#x27;: [10, 120, 230, 340, 450,\n",
       "                                                       560, 670, 780, 890,\n",
       "                                                       1000]},\n",
       "                   random_state=42, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3, estimator=LGBMClassifier(), n_jobs=-1,\n",
       "                   param_distributions={&#x27;boosting_type&#x27;: [&#x27;gbdt&#x27;, &#x27;dart&#x27;,\n",
       "                                                          &#x27;goss&#x27;, &#x27;rf&#x27;],\n",
       "                                        &#x27;learning_rate&#x27;: [0.0, 0.01, 0.02, 0.03,\n",
       "                                                          0.04, 0.05, 0.06,\n",
       "                                                          0.07, 0.08, 0.09, 0.1,\n",
       "                                                          0.11, 0.12, 0.13,\n",
       "                                                          0.14, 0.15, 0.16,\n",
       "                                                          0.17, 0.18, 0.19, 0.2,\n",
       "                                                          0.21, 0.22, 0.23,\n",
       "                                                          0.24, 0.25, 0.26,\n",
       "                                                          0.27, 0.28, 0.29, ...],\n",
       "                                        &#x27;max_depth&#x27;: [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110],\n",
       "                                        &#x27;n_estimators&#x27;: [80, 293, 506, 720, 933,\n",
       "                                                         1146, 1360, 1573, 1786,\n",
       "                                                         2000],\n",
       "                                        &#x27;num_leaves&#x27;: [10, 120, 230, 340, 450,\n",
       "                                                       560, 670, 780, 890,\n",
       "                                                       1000]},\n",
       "                   random_state=42, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=LGBMClassifier(), n_jobs=-1,\n",
       "                   param_distributions={'boosting_type': ['gbdt', 'dart',\n",
       "                                                          'goss', 'rf'],\n",
       "                                        'learning_rate': [0.0, 0.01, 0.02, 0.03,\n",
       "                                                          0.04, 0.05, 0.06,\n",
       "                                                          0.07, 0.08, 0.09, 0.1,\n",
       "                                                          0.11, 0.12, 0.13,\n",
       "                                                          0.14, 0.15, 0.16,\n",
       "                                                          0.17, 0.18, 0.19, 0.2,\n",
       "                                                          0.21, 0.22, 0.23,\n",
       "                                                          0.24, 0.25, 0.26,\n",
       "                                                          0.27, 0.28, 0.29, ...],\n",
       "                                        'max_depth': [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110],\n",
       "                                        'n_estimators': [80, 293, 506, 720, 933,\n",
       "                                                         1146, 1360, 1573, 1786,\n",
       "                                                         2000],\n",
       "                                        'num_leaves': [10, 120, 230, 340, 450,\n",
       "                                                       560, 670, 780, 890,\n",
       "                                                       1000]},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "db15b78c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'num_leaves': 780,\n",
       " 'n_estimators': 720,\n",
       " 'max_depth': 100,\n",
       " 'learning_rate': 0.24,\n",
       " 'boosting_type': 'dart'}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7f05d300",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "clf = LGBMClassifier(num_leaves=780, n_estimators=720, max_depth=100, learning_rate=0.24, boosting_type=\"dart\").fit(X, y)\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d3068aa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5536b165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9608479755538579"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "849a35af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.875832828588734"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "cf6e8407",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.853044986724854\n"
     ]
    }
   ],
   "source": [
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ffd82bd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op.countOf(clf.predict(test), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea98c840",
   "metadata": {},
   "source": [
    "## CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4a1379e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: catboost in c:\\users\\hp\\anaconda3\\lib\\site-packages (1.1.1)\n",
      "Requirement already satisfied: six in c:\\users\\hp\\anaconda3\\lib\\site-packages (from catboost) (1.16.0)\n",
      "Requirement already satisfied: pandas>=0.24.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from catboost) (1.3.4)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\hp\\anaconda3\\lib\\site-packages (from catboost) (3.4.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\hp\\anaconda3\\lib\\site-packages (from catboost) (1.7.1)\n",
      "Requirement already satisfied: plotly in c:\\users\\hp\\anaconda3\\lib\\site-packages (from catboost) (5.11.0)\n",
      "Requirement already satisfied: numpy>=1.16.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from catboost) (1.20.3)\n",
      "Requirement already satisfied: graphviz in c:\\users\\hp\\anaconda3\\lib\\site-packages (from catboost) (0.20.1)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from pandas>=0.24.0->catboost) (2021.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (0.10.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (8.4.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (1.3.1)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (3.0.4)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from plotly->catboost) (8.1.0)\n",
      "\n",
      "[notice] A new release of pip available: 22.1.2 -> 22.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c3615740",
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier, Pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f3cc2c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = CatBoostClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8da110cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = [float(x) for x in np.arange(0.5, 0.7, 0.001)]\n",
    "depth = [int(x) for x in np.linspace(0, 20, num = 2)]\n",
    "n_estimators = [int(x) for x in np.linspace(start = 80, stop = 2000, num = 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ff58dc5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_grid = {'depth': depth,\n",
    "               'learning_rate': learning_rate,\n",
    "               'n_estimators': n_estimators}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3aa45752",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_random = RandomizedSearchCV(estimator = clf, param_distributions = random_grid, \n",
    "                               n_iter = 10, cv = 3, verbose=2, random_state=42, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c2ca118b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "0:\tlearn: 0.4978615\ttotal: 142ms\tremaining: 11.2s\n",
      "1:\tlearn: 0.4693080\ttotal: 144ms\tremaining: 5.62s\n",
      "2:\tlearn: 0.4647969\ttotal: 146ms\tremaining: 3.74s\n",
      "3:\tlearn: 0.4640411\ttotal: 147ms\tremaining: 2.8s\n",
      "4:\tlearn: 0.4639140\ttotal: 149ms\tremaining: 2.23s\n",
      "5:\tlearn: 0.4638942\ttotal: 151ms\tremaining: 1.86s\n",
      "6:\tlearn: 0.4638923\ttotal: 152ms\tremaining: 1.59s\n",
      "7:\tlearn: 0.4638847\ttotal: 154ms\tremaining: 1.38s\n",
      "8:\tlearn: 0.4638818\ttotal: 155ms\tremaining: 1.22s\n",
      "9:\tlearn: 0.4638820\ttotal: 156ms\tremaining: 1.09s\n",
      "10:\tlearn: 0.4638820\ttotal: 157ms\tremaining: 988ms\n",
      "11:\tlearn: 0.4638820\ttotal: 159ms\tremaining: 900ms\n",
      "12:\tlearn: 0.4638820\ttotal: 160ms\tremaining: 826ms\n",
      "13:\tlearn: 0.4638820\ttotal: 162ms\tremaining: 764ms\n",
      "14:\tlearn: 0.4638820\ttotal: 164ms\tremaining: 709ms\n",
      "15:\tlearn: 0.4638820\ttotal: 165ms\tremaining: 661ms\n",
      "16:\tlearn: 0.4638820\ttotal: 167ms\tremaining: 618ms\n",
      "17:\tlearn: 0.4638820\ttotal: 168ms\tremaining: 580ms\n",
      "18:\tlearn: 0.4638820\ttotal: 170ms\tremaining: 545ms\n",
      "19:\tlearn: 0.4638820\ttotal: 171ms\tremaining: 513ms\n",
      "20:\tlearn: 0.4638820\ttotal: 172ms\tremaining: 484ms\n",
      "21:\tlearn: 0.4638820\ttotal: 173ms\tremaining: 457ms\n",
      "22:\tlearn: 0.4638820\ttotal: 174ms\tremaining: 432ms\n",
      "23:\tlearn: 0.4638820\ttotal: 176ms\tremaining: 410ms\n",
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     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3,\n",
       "                   estimator=&lt;catboost.core.CatBoostClassifier object at 0x000001FAA3C009D0&gt;,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;depth&#x27;: [0, 20],\n",
       "                                        &#x27;learning_rate&#x27;: [0.5, 0.501, 0.502,\n",
       "                                                          0.503, 0.504, 0.505,\n",
       "                                                          0.506, 0.507, 0.508,\n",
       "                                                          0.509, 0.51, 0.511,\n",
       "                                                          0.512, 0.513, 0.514,\n",
       "                                                          0.515, 0.516, 0.517,\n",
       "                                                          0.518, 0.519, 0.52,\n",
       "                                                          0.521, 0.522, 0.523,\n",
       "                                                          0.524, 0.525, 0.526,\n",
       "                                                          0.527, 0.528, 0.529, ...],\n",
       "                                        &#x27;n_estimators&#x27;: [80, 293, 506, 720, 933,\n",
       "                                                         1146, 1360, 1573, 1786,\n",
       "                                                         2000]},\n",
       "                   random_state=42, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3,\n",
       "                   estimator=&lt;catboost.core.CatBoostClassifier object at 0x000001FAA3C009D0&gt;,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;depth&#x27;: [0, 20],\n",
       "                                        &#x27;learning_rate&#x27;: [0.5, 0.501, 0.502,\n",
       "                                                          0.503, 0.504, 0.505,\n",
       "                                                          0.506, 0.507, 0.508,\n",
       "                                                          0.509, 0.51, 0.511,\n",
       "                                                          0.512, 0.513, 0.514,\n",
       "                                                          0.515, 0.516, 0.517,\n",
       "                                                          0.518, 0.519, 0.52,\n",
       "                                                          0.521, 0.522, 0.523,\n",
       "                                                          0.524, 0.525, 0.526,\n",
       "                                                          0.527, 0.528, 0.529, ...],\n",
       "                                        &#x27;n_estimators&#x27;: [80, 293, 506, 720, 933,\n",
       "                                                         1146, 1360, 1573, 1786,\n",
       "                                                         2000]},\n",
       "                   random_state=42, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x000001FAA3C009D0&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x000001FAA3C009D0&gt;</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=3,\n",
       "                   estimator=<catboost.core.CatBoostClassifier object at 0x000001FAA3C009D0>,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'depth': [0, 20],\n",
       "                                        'learning_rate': [0.5, 0.501, 0.502,\n",
       "                                                          0.503, 0.504, 0.505,\n",
       "                                                          0.506, 0.507, 0.508,\n",
       "                                                          0.509, 0.51, 0.511,\n",
       "                                                          0.512, 0.513, 0.514,\n",
       "                                                          0.515, 0.516, 0.517,\n",
       "                                                          0.518, 0.519, 0.52,\n",
       "                                                          0.521, 0.522, 0.523,\n",
       "                                                          0.524, 0.525, 0.526,\n",
       "                                                          0.527, 0.528, 0.529, ...],\n",
       "                                        'n_estimators': [80, 293, 506, 720, 933,\n",
       "                                                         1146, 1360, 1573, 1786,\n",
       "                                                         2000]},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "d283208b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 80, 'learning_rate': 0.5860000000000001, 'depth': 0}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "d0f21617",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "79:\tlearn: 0.2736940\ttotal: 525ms\tremaining: 0us\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "clf = CatBoostClassifier(n_estimators=80, learning_rate=0.5860000000000001).fit(X, y)\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1fd45407",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "85dd90d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.910427807486631"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7b29ec8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6589090909090909"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "ff1c97fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.79571533203125\n"
     ]
    }
   ],
   "source": [
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "87afc4f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op.countOf(clf.predict(test), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed4b450",
   "metadata": {},
   "source": [
    "## LightGBM with K fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1e47ca49",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "a84b542f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LGBMClassifier(num_leaves=780, n_estimators=720, max_depth=100, learning_rate=0.24, boosting_type=\"dart\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "f8ae40a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "skfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)\n",
    "oof = np.zeros(len(train_df))\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c4965fca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1\n",
      "[100]\tvalid_0's binary_logloss: 0.465215\n",
      "[200]\tvalid_0's binary_logloss: 0.511224\n",
      "[300]\tvalid_0's binary_logloss: 0.544766\n",
      "[400]\tvalid_0's binary_logloss: 0.592306\n",
      "[500]\tvalid_0's binary_logloss: 0.634469\n",
      "[600]\tvalid_0's binary_logloss: 0.654798\n",
      "[700]\tvalid_0's binary_logloss: 0.698629\n",
      "Fold 2\n",
      "[100]\tvalid_0's binary_logloss: 0.535379\n",
      "[200]\tvalid_0's binary_logloss: 0.584556\n",
      "[300]\tvalid_0's binary_logloss: 0.662434\n",
      "[400]\tvalid_0's binary_logloss: 0.723836\n",
      "[500]\tvalid_0's binary_logloss: 0.784968\n",
      "[600]\tvalid_0's binary_logloss: 0.806958\n",
      "[700]\tvalid_0's binary_logloss: 0.850501\n",
      "Fold 3\n",
      "[100]\tvalid_0's binary_logloss: 0.526168\n",
      "[200]\tvalid_0's binary_logloss: 0.569283\n",
      "[300]\tvalid_0's binary_logloss: 0.625368\n",
      "[400]\tvalid_0's binary_logloss: 0.679906\n",
      "[500]\tvalid_0's binary_logloss: 0.747781\n",
      "[600]\tvalid_0's binary_logloss: 0.767597\n",
      "[700]\tvalid_0's binary_logloss: 0.822321\n",
      "Fold 4\n",
      "[100]\tvalid_0's binary_logloss: 0.497806\n",
      "[200]\tvalid_0's binary_logloss: 0.534447\n",
      "[300]\tvalid_0's binary_logloss: 0.597395\n",
      "[400]\tvalid_0's binary_logloss: 0.65575\n",
      "[500]\tvalid_0's binary_logloss: 0.719078\n",
      "[600]\tvalid_0's binary_logloss: 0.733082\n",
      "[700]\tvalid_0's binary_logloss: 0.782718\n",
      "Fold 5\n",
      "[100]\tvalid_0's binary_logloss: 0.502099\n",
      "[200]\tvalid_0's binary_logloss: 0.538955\n",
      "[300]\tvalid_0's binary_logloss: 0.590027\n",
      "[400]\tvalid_0's binary_logloss: 0.63711\n",
      "[500]\tvalid_0's binary_logloss: 0.694664\n",
      "[600]\tvalid_0's binary_logloss: 0.712104\n",
      "[700]\tvalid_0's binary_logloss: 0.754739\n"
     ]
    }
   ],
   "source": [
    "for fold, (trn_idx, val_idx) in enumerate(skfold.split(X, y)):\n",
    "    print(f'Fold {fold + 1}')\n",
    "    X_train, y_train = X.iloc[trn_idx], y.iloc[trn_idx]\n",
    "    X_valid, y_valid = X.iloc[val_idx], y.iloc[val_idx]\n",
    "    \n",
    "    model.fit(\n",
    "        X_train, y_train,\n",
    "        eval_set=(X_valid, y_valid),\n",
    "        verbose=100,\n",
    "        early_stopping_rounds=100,\n",
    "    )\n",
    "\n",
    "    oof[val_idx] = model.predict(X_valid)\n",
    "    predictions.append(model.predict_proba(test)[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e9f30aa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = np.mean(predictions, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c3c075c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2878581173260573"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y, oof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "243653b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80061115355233"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, oof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d830a159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(738,)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "5c0ce0d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(predictions.shape[0]):\n",
    "    if predictions[i] > 0.5:\n",
    "        predictions[i] = 1\n",
    "    else:\n",
    "        predictions[i] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d129de9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op.countOf(predictions, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "972629c4",
   "metadata": {},
   "source": [
    "## CatBoost with K fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "855fffe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CatBoostClassifier(\n",
    "    n_estimators=1000,\n",
    "    learning_rate=0.025,\n",
    "    auto_class_weights='Balanced',\n",
    "    # class_weight='balanced'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "040c206b",
   "metadata": {},
   "outputs": [],
   "source": [
    "skfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)\n",
    "oof = np.zeros(len(train_df))\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "3a5e1a40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1\n",
      "0:\tlearn: 0.6898960\ttest: 0.6893895\tbest: 0.6893895 (0)\ttotal: 4.97ms\tremaining: 4.96s\n",
      "100:\tlearn: 0.6047135\ttest: 0.6058285\tbest: 0.6058285 (100)\ttotal: 344ms\tremaining: 3.06s\n",
      "200:\tlearn: 0.5890514\ttest: 0.5992145\tbest: 0.5989889 (188)\ttotal: 664ms\tremaining: 2.64s\n",
      "Stopped by overfitting detector  (100 iterations wait)\n",
      "\n",
      "bestTest = 0.598988935\n",
      "bestIteration = 188\n",
      "\n",
      "Shrink model to first 189 iterations.\n",
      "Fold 2\n",
      "0:\tlearn: 0.6896967\ttest: 0.6899824\tbest: 0.6899824 (0)\ttotal: 3.76ms\tremaining: 3.75s\n",
      "100:\tlearn: 0.5985032\ttest: 0.6233206\tbest: 0.6233144 (97)\ttotal: 354ms\tremaining: 3.15s\n",
      "200:\tlearn: 0.5828116\ttest: 0.6206994\tbest: 0.6206366 (199)\ttotal: 723ms\tremaining: 2.88s\n",
      "300:\tlearn: 0.5714246\ttest: 0.6233542\tbest: 0.6204882 (206)\ttotal: 1.15s\tremaining: 2.67s\n",
      "Stopped by overfitting detector  (100 iterations wait)\n",
      "\n",
      "bestTest = 0.6204881779\n",
      "bestIteration = 206\n",
      "\n",
      "Shrink model to first 207 iterations.\n",
      "Fold 3\n",
      "0:\tlearn: 0.6892458\ttest: 0.6902551\tbest: 0.6902551 (0)\ttotal: 3.37ms\tremaining: 3.36s\n",
      "100:\tlearn: 0.5996075\ttest: 0.6287305\tbest: 0.6287305 (100)\ttotal: 398ms\tremaining: 3.54s\n",
      "200:\tlearn: 0.5843352\ttest: 0.6261590\tbest: 0.6256601 (164)\ttotal: 825ms\tremaining: 3.28s\n",
      "Stopped by overfitting detector  (100 iterations wait)\n",
      "\n",
      "bestTest = 0.6256601313\n",
      "bestIteration = 164\n",
      "\n",
      "Shrink model to first 165 iterations.\n",
      "Fold 4\n",
      "0:\tlearn: 0.6897436\ttest: 0.6899103\tbest: 0.6899103 (0)\ttotal: 3.68ms\tremaining: 3.67s\n",
      "100:\tlearn: 0.6011941\ttest: 0.6182381\tbest: 0.6182381 (100)\ttotal: 413ms\tremaining: 3.68s\n",
      "200:\tlearn: 0.5854190\ttest: 0.6152751\tbest: 0.6152751 (200)\ttotal: 794ms\tremaining: 3.16s\n",
      "300:\tlearn: 0.5749983\ttest: 0.6160427\tbest: 0.6151897 (207)\ttotal: 1.17s\tremaining: 2.72s\n",
      "Stopped by overfitting detector  (100 iterations wait)\n",
      "\n",
      "bestTest = 0.6151897002\n",
      "bestIteration = 207\n",
      "\n",
      "Shrink model to first 208 iterations.\n",
      "Fold 5\n",
      "0:\tlearn: 0.6896322\ttest: 0.6901430\tbest: 0.6901430 (0)\ttotal: 3.46ms\tremaining: 3.45s\n",
      "100:\tlearn: 0.5997546\ttest: 0.6256832\tbest: 0.6256832 (100)\ttotal: 371ms\tremaining: 3.3s\n",
      "200:\tlearn: 0.5841363\ttest: 0.6224163\tbest: 0.6222859 (188)\ttotal: 793ms\tremaining: 3.15s\n",
      "300:\tlearn: 0.5731922\ttest: 0.6215218\tbest: 0.6214719 (295)\ttotal: 1.21s\tremaining: 2.8s\n",
      "400:\tlearn: 0.5535877\ttest: 0.6214000\tbest: 0.6210886 (309)\ttotal: 1.64s\tremaining: 2.45s\n",
      "Stopped by overfitting detector  (100 iterations wait)\n",
      "\n",
      "bestTest = 0.6210885771\n",
      "bestIteration = 309\n",
      "\n",
      "Shrink model to first 310 iterations.\n"
     ]
    }
   ],
   "source": [
    "for fold, (trn_idx, val_idx) in enumerate(skfold.split(X, y)):\n",
    "    print(f'Fold {fold + 1}')\n",
    "    X_train, y_train = X.iloc[trn_idx], y.iloc[trn_idx]\n",
    "    X_valid, y_valid = X.iloc[val_idx], y.iloc[val_idx]\n",
    "    \n",
    "    model.fit(\n",
    "        X_train, y_train,\n",
    "        eval_set=(X_valid, y_valid),\n",
    "        verbose=100,\n",
    "        early_stopping_rounds=100,\n",
    "    )\n",
    "\n",
    "    oof[val_idx] = model.predict(X_valid)\n",
    "    predictions.append(model.predict_proba(test)[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c89ec93d",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = np.mean(predictions, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "34f2db99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4118098159509203"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y, oof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "f0e62132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7070282658517952"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, oof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7e76d481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(738,)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "be468602",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = predictions.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2e97f0f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(temp.shape[0]):\n",
    "    if temp[i] > 0.5:\n",
    "        temp[i] = 1\n",
    "    else:\n",
    "        temp[i] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "1bd53467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op.countOf(temp, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d03cad0e",
   "metadata": {},
   "source": [
    "Hence, Catboost with K fold cross validation is best model as it produces good f1 score as well as good number of active users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e2110411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "8508120f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame({\n",
    "    'User_ID_Next_month_Activity': test_df.User_ID,\n",
    "    'Active': predictions\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b64f0f17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    738.000000\n",
       "mean       0.432786\n",
       "std        0.155333\n",
       "min        0.211876\n",
       "25%        0.306844\n",
       "50%        0.370835\n",
       "75%        0.539946\n",
       "max        0.904540\n",
       "Name: Active, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.Active.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d4cd68f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.310930\n",
       "1      0.310758\n",
       "2      0.853734\n",
       "3      0.306688\n",
       "4      0.343010\n",
       "         ...   \n",
       "733    0.604838\n",
       "734    0.472600\n",
       "735    0.237972\n",
       "736    0.584016\n",
       "737    0.317675\n",
       "Name: Active, Length: 738, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub[\"Active\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "887d4f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID_Next_month_Activity</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_ZZMHNLNT_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_ZWDD4T41_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_ZUVTLGY7_Month_5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_ZT27LY0Q_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_ZQM6PUGO_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>ID_04OT901K_Month_5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>ID_04GIS1QZ_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>ID_02F14MDW_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>ID_028JJTDW_Month_5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>ID_003YJHQA_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>738 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    User_ID_Next_month_Activity  Active\n",
       "0           ID_ZZMHNLNT_Month_5       0\n",
       "1           ID_ZWDD4T41_Month_5       0\n",
       "2           ID_ZUVTLGY7_Month_5       1\n",
       "3           ID_ZT27LY0Q_Month_5       0\n",
       "4           ID_ZQM6PUGO_Month_5       0\n",
       "..                          ...     ...\n",
       "733         ID_04OT901K_Month_5       1\n",
       "734         ID_04GIS1QZ_Month_5       0\n",
       "735         ID_02F14MDW_Month_5       0\n",
       "736         ID_028JJTDW_Month_5       1\n",
       "737         ID_003YJHQA_Month_5       0\n",
       "\n",
       "[738 rows x 2 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.Active = sub.Active.apply(lambda x: 1 if x > 0.5 else 0)\n",
    "sub.User_ID_Next_month_Activity = sub.User_ID_Next_month_Activity + '_Month_5'\n",
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "9dd3b9b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.merge(SampleSubmission.drop(columns='Active'), sub, on='User_ID_Next_month_Activity', how='left').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "74670040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    1098\n",
       "1.0     242\n",
       "Name: Active, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.Active.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "a9257651",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv('submission2.csv', index=False)"
   ]
  }
 ],
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